From c0f9ec9bbfec5273961ab4c5c035fde968989641 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 13 Mar 2018 14:59:45 +0300 Subject: [PATCH 001/616] fix: add error when n_classes is zero --- deeppavlov/models/classifiers/intents/intent_model.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deeppavlov/models/classifiers/intents/intent_model.py b/deeppavlov/models/classifiers/intents/intent_model.py index 0c6c9ee9d0..6f49627f5c 100644 --- a/deeppavlov/models/classifiers/intents/intent_model.py +++ b/deeppavlov/models/classifiers/intents/intent_model.py @@ -76,6 +76,8 @@ def __init__(self, else: self.classes = np.sort(np.array(list(vocabs["classes_vocab"].keys()))) self.n_classes = self.classes.shape[0] + if self.n_classes == 0: + ConfigError("Please, provide vocabulary with considered intents.") if 'add_metrics' in self.opt.keys(): self.add_metrics = self.opt['add_metrics'] From dc16dc1ac2dd5bf07a9751c01ff14c0dcaf45e2d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 13 Mar 2018 15:00:41 +0300 Subject: [PATCH 002/616] feat: add fastText model usage instead of fasttext --- deeppavlov/models/embedders/fasttext_embedder.py | 10 +++++++--- requirements.txt | 2 +- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/embedders/fasttext_embedder.py b/deeppavlov/models/embedders/fasttext_embedder.py index e47e22c3c1..c4e7f122a0 100644 --- a/deeppavlov/models/embedders/fasttext_embedder.py +++ b/deeppavlov/models/embedders/fasttext_embedder.py @@ -49,8 +49,8 @@ def load(self, *args, **kwargs): if self.load_path and self.load_path.is_file(): log.info("[loading embeddings from `{}`]".format(self.load_path)) model_file = str(self.load_path) - if self.emb_module == 'fasttext': - import fasttext as Fasttext + if self.emb_module == 'fastText': + import fastText as Fasttext model = Fasttext.load_model(model_file) elif self.emb_module == 'pyfasttext': from pyfasttext import FastText as Fasttext @@ -80,7 +80,11 @@ def _encode(self, sentence: str, mean): emb = self.tok2emb[t] except KeyError: try: - emb = self.model[t][:self.dim] + if self.emb_module == 'fastText': + import fastText as Fasttext + emb = self.model.get_word_vector(t) + elif self.emb_module == 'pyfasttext': + emb = self.model[t][:self.dim] except KeyError: emb = np.zeros(self.dim, dtype=np.float32) self.tok2emb[t] = emb diff --git a/requirements.txt b/requirements.txt index 36200dbe98..1fb7285e93 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,7 +13,7 @@ h5py==2.7.1 keras==2.1.2 pandas==0.21.1 fuzzywuzzy==0.16.0 -fasttext==0.8.3 +git+https://github.com/facebookresearch/fastText.git@3872afadb3a9f30de7c7792ff2ff1bda64242097 pyfasttext==0.4.4 nltk==3.2.5 scikit-learn==0.19.0 From 1ac7950ede3493e4d57f52365a74ca772c68c38d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 13 Mar 2018 15:05:00 +0300 Subject: [PATCH 003/616] fix: emb_module default fastText --- deeppavlov/models/embedders/fasttext_embedder.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/embedders/fasttext_embedder.py b/deeppavlov/models/embedders/fasttext_embedder.py index c4e7f122a0..798b8c7127 100644 --- a/deeppavlov/models/embedders/fasttext_embedder.py +++ b/deeppavlov/models/embedders/fasttext_embedder.py @@ -30,7 +30,7 @@ @register('fasttext') class FasttextEmbedder(Component, Serializable): def __init__(self, load_path, save_path=None, dim=100, - emb_module='fasttext', **kwargs): + emb_module='fastText', **kwargs): super().__init__(save_path=save_path, load_path=load_path) self.tok2emb = {} From 1b4ff81ba9df164e965fe67eee1df541620f065b Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 13 Mar 2018 15:05:23 +0300 Subject: [PATCH 004/616] chore: embedding fixed in configs --- deeppavlov/configs/intents/intents_dstc2.json | 6 +++--- deeppavlov/configs/intents/intents_snips.json | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/deeppavlov/configs/intents/intents_dstc2.json b/deeppavlov/configs/intents/intents_dstc2.json index c17c0fb86b..a291e7d258 100644 --- a/deeppavlov/configs/intents/intents_dstc2.json +++ b/deeppavlov/configs/intents/intents_dstc2.json @@ -63,9 +63,9 @@ }, "embedder": { "name": "fasttext", - "save_path": "embeddings/dstc2_fasttext_model_100.bin", - "load_path": "embeddings/dstc2_fasttext_model_100.bin", - "emb_module": "fasttext", + "save_path": "embeddings/dstc2_fasttext_model.bin", + "load_path": "embeddings/dstc2_fasttext_model.bin", + "emb_module": "fastText", "dim": 100 }, "tokenizer": { diff --git a/deeppavlov/configs/intents/intents_snips.json b/deeppavlov/configs/intents/intents_snips.json index 7d2d5c67ef..85423ed04e 100644 --- a/deeppavlov/configs/intents/intents_snips.json +++ b/deeppavlov/configs/intents/intents_snips.json @@ -63,9 +63,9 @@ }, "embedder": { "name": "fasttext", - "save_path": "embeddings/dstc2_fasttext_model_100.bin", - "load_path": "embeddings/dstc2_fasttext_model_100.bin", - "emb_module": "fasttext", + "save_path": "embeddings/dstc2_fasttext_model.bin", + "load_path": "embeddings/dstc2_fasttext_model.bin", + "emb_module": "fastText", "dim": 100 }, "tokenizer": { From a2eb548c7734e06a167a889fb8cfe4c9b51838cc Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 13 Mar 2018 16:39:29 +0300 Subject: [PATCH 005/616] chore: change new models names --- deeppavlov/configs/intents/intents_dstc2.json | 10 +++++----- deeppavlov/configs/intents/intents_snips.json | 10 +++++----- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/deeppavlov/configs/intents/intents_dstc2.json b/deeppavlov/configs/intents/intents_dstc2.json index a291e7d258..cdb226abcb 100644 --- a/deeppavlov/configs/intents/intents_dstc2.json +++ b/deeppavlov/configs/intents/intents_dstc2.json @@ -39,8 +39,8 @@ "out": ["y_predicted"], "main": true, "name": "intent_model", - "save_path": "intents/intent_cnn", - "load_path": "intents/intent_cnn", + "save_path": "intents/intent_cnn_v2", + "load_path": "intents/intent_cnn_v2", "classes": "#classes_vocab.keys()", "opt": { "kernel_sizes_cnn": [ @@ -63,10 +63,10 @@ }, "embedder": { "name": "fasttext", - "save_path": "embeddings/dstc2_fasttext_model.bin", - "load_path": "embeddings/dstc2_fasttext_model.bin", + "save_path": "embeddings/wiki.en.bin", + "load_path": "embeddings/wiki.en.bin", "emb_module": "fastText", - "dim": 100 + "dim": 300 }, "tokenizer": { "name": "nltk_tokenizer", diff --git a/deeppavlov/configs/intents/intents_snips.json b/deeppavlov/configs/intents/intents_snips.json index 85423ed04e..ba16016f48 100644 --- a/deeppavlov/configs/intents/intents_snips.json +++ b/deeppavlov/configs/intents/intents_snips.json @@ -34,8 +34,8 @@ "out": ["y_predicted"], "main": true, "name": "intent_model", - "save_path": "intents/intent_cnn_snips", - "load_path": "intents/intent_cnn_snips", + "save_path": "intents/intent_cnn_snips_v2", + "load_path": "intents/intent_cnn_snips_v2", "classes": "#classes_vocab.keys()", "opt": { "kernel_sizes_cnn": [ @@ -63,10 +63,10 @@ }, "embedder": { "name": "fasttext", - "save_path": "embeddings/dstc2_fasttext_model.bin", - "load_path": "embeddings/dstc2_fasttext_model.bin", + "save_path": "embeddings/wiki.en.bin", + "load_path": "embeddings/wiki.en.bin", "emb_module": "fastText", - "dim": 100 + "dim": 300 }, "tokenizer": { "name": "nltk_tokenizer", From 40ce2a4a728be86626b0b8b2bd973f9f302e0323 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 14 Mar 2018 14:36:25 +0300 Subject: [PATCH 006/616] feat: change intent embeddings in gobot configs --- deeppavlov/configs/go_bot/gobot_dstc2.json | 12 ++++++------ deeppavlov/configs/go_bot/gobot_dstc2_all.json | 12 ++++++------ 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/deeppavlov/configs/go_bot/gobot_dstc2.json b/deeppavlov/configs/go_bot/gobot_dstc2.json index 92a1506dee..6c59e125e4 100644 --- a/deeppavlov/configs/go_bot/gobot_dstc2.json +++ b/deeppavlov/configs/go_bot/gobot_dstc2.json @@ -89,8 +89,8 @@ }, "intent_classifier": { "name": "intent_model", - "save_path": "intents/intent_cnn", - "load_path": "intents/intent_cnn", + "save_path": "intents/intent_cnn_v2", + "load_path": "intents/intent_cnn_v2", "classes": "#classes_vocab.keys()", "opt": { "train_now": true, @@ -123,10 +123,10 @@ }, "embedder": { "name": "fasttext", - "save_path": "embeddings/dstc2_fasttext_model_100.bin", - "load_path": "embeddings/dstc2_fasttext_model_100.bin", - "emb_module": "fasttext", - "dim": 100 + "save_path": "embeddings/wiki.en.bin", + "load_path": "embeddings/wiki.en.bin", + "emb_module": "fastText", + "dim": 300 }, "tokenizer": { "name": "nltk_tokenizer", diff --git a/deeppavlov/configs/go_bot/gobot_dstc2_all.json b/deeppavlov/configs/go_bot/gobot_dstc2_all.json index cab1c3663d..98f03cd189 100644 --- a/deeppavlov/configs/go_bot/gobot_dstc2_all.json +++ b/deeppavlov/configs/go_bot/gobot_dstc2_all.json @@ -89,8 +89,8 @@ }, "intent_classifier": { "name": "intent_model", - "save_path": "intents/intent_cnn", - "load_path": "intents/intent_cnn", + "save_path": "intents/intent_cnn_v2", + "load_path": "intents/intent_cnn_v2", "classes": "#classes_vocab.keys()", "opt": { "train_now": true, @@ -123,10 +123,10 @@ }, "embedder": { "name": "fasttext", - "save_path": "embeddings/dstc2_fasttext_model_100.bin", - "load_path": "embeddings/dstc2_fasttext_model_100.bin", - "emb_module": "fasttext", - "dim": 100 + "save_path": "embeddings/wiki.en.bin", + "load_path": "embeddings/wiki.en.bin", + "emb_module": "fastText", + "dim": 300 }, "tokenizer": { "name": "nltk_tokenizer", From 6ec3b5b21a7028807bf7e609f18446a04877c526 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 14 Mar 2018 16:24:04 +0300 Subject: [PATCH 007/616] chore: fastText to fasttext, new model, change intents in gobot configs --- deeppavlov/configs/go_bot/gobot_dstc2.json | 8 ++++---- deeppavlov/configs/go_bot/gobot_dstc2_all.json | 8 ++++---- deeppavlov/configs/intents/intents_dstc2.json | 8 ++++---- deeppavlov/configs/intents/intents_snips.json | 8 ++++---- deeppavlov/models/embedders/fasttext_embedder.py | 6 +++--- tests/test_quick_start.py | 3 ++- 6 files changed, 21 insertions(+), 20 deletions(-) diff --git a/deeppavlov/configs/go_bot/gobot_dstc2.json b/deeppavlov/configs/go_bot/gobot_dstc2.json index 6c59e125e4..187dbaf981 100644 --- a/deeppavlov/configs/go_bot/gobot_dstc2.json +++ b/deeppavlov/configs/go_bot/gobot_dstc2.json @@ -123,10 +123,10 @@ }, "embedder": { "name": "fasttext", - "save_path": "embeddings/wiki.en.bin", - "load_path": "embeddings/wiki.en.bin", - "emb_module": "fastText", - "dim": 300 + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "emb_module": "fasttext", + "dim": 100 }, "tokenizer": { "name": "nltk_tokenizer", diff --git a/deeppavlov/configs/go_bot/gobot_dstc2_all.json b/deeppavlov/configs/go_bot/gobot_dstc2_all.json index 98f03cd189..b7ed9d0fa7 100644 --- a/deeppavlov/configs/go_bot/gobot_dstc2_all.json +++ b/deeppavlov/configs/go_bot/gobot_dstc2_all.json @@ -123,10 +123,10 @@ }, "embedder": { "name": "fasttext", - "save_path": "embeddings/wiki.en.bin", - "load_path": "embeddings/wiki.en.bin", - "emb_module": "fastText", - "dim": 300 + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "emb_module": "fasttext", + "dim": 100 }, "tokenizer": { "name": "nltk_tokenizer", diff --git a/deeppavlov/configs/intents/intents_dstc2.json b/deeppavlov/configs/intents/intents_dstc2.json index 77f7c9f3c1..8f98b99dd1 100644 --- a/deeppavlov/configs/intents/intents_dstc2.json +++ b/deeppavlov/configs/intents/intents_dstc2.json @@ -63,10 +63,10 @@ }, "embedder": { "name": "fasttext", - "save_path": "embeddings/wiki.en.bin", - "load_path": "embeddings/wiki.en.bin", - "emb_module": "fastText", - "dim": 300 + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "emb_module": "fasttext", + "dim": 100 }, "tokenizer": { "name": "nltk_tokenizer", diff --git a/deeppavlov/configs/intents/intents_snips.json b/deeppavlov/configs/intents/intents_snips.json index 815e287620..7842d13c62 100644 --- a/deeppavlov/configs/intents/intents_snips.json +++ b/deeppavlov/configs/intents/intents_snips.json @@ -64,10 +64,10 @@ }, "embedder": { "name": "fasttext", - "save_path": "embeddings/wiki.en.bin", - "load_path": "embeddings/wiki.en.bin", - "emb_module": "fastText", - "dim": 300 + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "emb_module": "fasttext", + "dim": 100 }, "tokenizer": { "name": "nltk_tokenizer", diff --git a/deeppavlov/models/embedders/fasttext_embedder.py b/deeppavlov/models/embedders/fasttext_embedder.py index 798b8c7127..47032e417f 100644 --- a/deeppavlov/models/embedders/fasttext_embedder.py +++ b/deeppavlov/models/embedders/fasttext_embedder.py @@ -30,7 +30,7 @@ @register('fasttext') class FasttextEmbedder(Component, Serializable): def __init__(self, load_path, save_path=None, dim=100, - emb_module='fastText', **kwargs): + emb_module='fasttext', **kwargs): super().__init__(save_path=save_path, load_path=load_path) self.tok2emb = {} @@ -49,7 +49,7 @@ def load(self, *args, **kwargs): if self.load_path and self.load_path.is_file(): log.info("[loading embeddings from `{}`]".format(self.load_path)) model_file = str(self.load_path) - if self.emb_module == 'fastText': + if self.emb_module == 'fasttext': import fastText as Fasttext model = Fasttext.load_model(model_file) elif self.emb_module == 'pyfasttext': @@ -80,7 +80,7 @@ def _encode(self, sentence: str, mean): emb = self.tok2emb[t] except KeyError: try: - if self.emb_module == 'fastText': + if self.emb_module == 'fasttext': import fastText as Fasttext emb = self.model.get_word_vector(t) elif self.emb_module == 'pyfasttext': diff --git a/tests/test_quick_start.py b/tests/test_quick_start.py index de2d30b21f..e5fcd2fbd0 100644 --- a/tests/test_quick_start.py +++ b/tests/test_quick_start.py @@ -110,7 +110,8 @@ def interact(conf_file, model_dir, qr_list=None): def test_downloaded_model_existence(self, model, conf_file, model_dir): if not download_path.exists(): - download() + # download() + download(True) assert download_path.joinpath(model_dir).exists(), f"{model_dir} was not downloaded" def test_interacting_pretrained_model(self, model, conf_file, model_dir): From 48b0e85b6d12b1b506d00c7e353a2a0cfcf0bb7c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 14 Mar 2018 16:25:34 +0300 Subject: [PATCH 008/616] chore: new url on new fasttext embeddings --- deeppavlov/core/data/urls.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/core/data/urls.py b/deeppavlov/core/data/urls.py index a86018bc73..05ac4e5fca 100644 --- a/deeppavlov/core/data/urls.py +++ b/deeppavlov/core/data/urls.py @@ -22,7 +22,7 @@ 'http://lnsigo.mipt.ru/export/deeppavlov_data/error_model.tar.gz', 'http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz', 'http://lnsigo.mipt.ru/export/deeppavlov_data/slots.tar.gz', - 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fasttext_model_100.bin', + 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin', 'http://lnsigo.mipt.ru/export/datasets/dstc2.tar.gz' } @@ -34,7 +34,7 @@ EMBEDDING_URLS = { 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/wiki.en.bin', - 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fasttext_model_100.bin' + 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin' } DATA_URLS = { From a3e40d2c7a59e81906b3afe3d434a40ade854447 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 14 Mar 2018 16:28:12 +0300 Subject: [PATCH 009/616] fix: delete dowload all true --- tests/test_quick_start.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/test_quick_start.py b/tests/test_quick_start.py index e5fcd2fbd0..de2d30b21f 100644 --- a/tests/test_quick_start.py +++ b/tests/test_quick_start.py @@ -110,8 +110,7 @@ def interact(conf_file, model_dir, qr_list=None): def test_downloaded_model_existence(self, model, conf_file, model_dir): if not download_path.exists(): - # download() - download(True) + download() assert download_path.joinpath(model_dir).exists(), f"{model_dir} was not downloaded" def test_interacting_pretrained_model(self, model, conf_file, model_dir): From 29f2b31da093633b420971d925fa5a4053a3c845 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 14 Mar 2018 16:31:46 +0300 Subject: [PATCH 010/616] fix: add url of old embedding file --- deeppavlov/core/data/urls.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/deeppavlov/core/data/urls.py b/deeppavlov/core/data/urls.py index 05ac4e5fca..7b0eef5e67 100644 --- a/deeppavlov/core/data/urls.py +++ b/deeppavlov/core/data/urls.py @@ -22,6 +22,7 @@ 'http://lnsigo.mipt.ru/export/deeppavlov_data/error_model.tar.gz', 'http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz', 'http://lnsigo.mipt.ru/export/deeppavlov_data/slots.tar.gz', + 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fasttext_model_100.bin', 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin', 'http://lnsigo.mipt.ru/export/datasets/dstc2.tar.gz' } @@ -34,7 +35,8 @@ EMBEDDING_URLS = { 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/wiki.en.bin', - 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin' + 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fasttext_model_100.bin' + 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin', } DATA_URLS = { From 2bf9d7546f4add6e9bcf0513a2b1a6313af3787b Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 14 Mar 2018 16:50:38 +0300 Subject: [PATCH 011/616] fix: delete comma --- deeppavlov/core/data/urls.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/core/data/urls.py b/deeppavlov/core/data/urls.py index 7b0eef5e67..a29ed30ed8 100644 --- a/deeppavlov/core/data/urls.py +++ b/deeppavlov/core/data/urls.py @@ -35,8 +35,8 @@ EMBEDDING_URLS = { 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/wiki.en.bin', - 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fasttext_model_100.bin' - 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin', + 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fasttext_model_100.bin', + 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin' } DATA_URLS = { From 55d7f844cd83c56bb59f443d80e2e425a30dbee2 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 14 Mar 2018 16:52:34 +0300 Subject: [PATCH 012/616] fix: delete old embedding file from urls --- deeppavlov/core/data/urls.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/deeppavlov/core/data/urls.py b/deeppavlov/core/data/urls.py index a29ed30ed8..05ac4e5fca 100644 --- a/deeppavlov/core/data/urls.py +++ b/deeppavlov/core/data/urls.py @@ -22,7 +22,6 @@ 'http://lnsigo.mipt.ru/export/deeppavlov_data/error_model.tar.gz', 'http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz', 'http://lnsigo.mipt.ru/export/deeppavlov_data/slots.tar.gz', - 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fasttext_model_100.bin', 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin', 'http://lnsigo.mipt.ru/export/datasets/dstc2.tar.gz' } @@ -35,7 +34,6 @@ EMBEDDING_URLS = { 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/wiki.en.bin', - 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fasttext_model_100.bin', 'http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin' } From 8c3157cba4792f7650ae3c628a26d3f8d68a3b95 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 14 Mar 2018 17:08:35 +0300 Subject: [PATCH 013/616] fix: delete pyfasttext from requirements, fasttext_embedder --- deeppavlov/models/embedders/fasttext_embedder.py | 5 ----- requirements.txt | 2 +- 2 files changed, 1 insertion(+), 6 deletions(-) diff --git a/deeppavlov/models/embedders/fasttext_embedder.py b/deeppavlov/models/embedders/fasttext_embedder.py index 47032e417f..1c763e6bc1 100644 --- a/deeppavlov/models/embedders/fasttext_embedder.py +++ b/deeppavlov/models/embedders/fasttext_embedder.py @@ -52,9 +52,6 @@ def load(self, *args, **kwargs): if self.emb_module == 'fasttext': import fastText as Fasttext model = Fasttext.load_model(model_file) - elif self.emb_module == 'pyfasttext': - from pyfasttext import FastText as Fasttext - model = Fasttext(model_file) else: from gensim.models.wrappers.fasttext import FastText as Fasttext model = Fasttext.load_fasttext_format(model_file) @@ -83,8 +80,6 @@ def _encode(self, sentence: str, mean): if self.emb_module == 'fasttext': import fastText as Fasttext emb = self.model.get_word_vector(t) - elif self.emb_module == 'pyfasttext': - emb = self.model[t][:self.dim] except KeyError: emb = np.zeros(self.dim, dtype=np.float32) self.tok2emb[t] = emb diff --git a/requirements.txt b/requirements.txt index 1fb7285e93..819f99f52b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -14,10 +14,10 @@ keras==2.1.2 pandas==0.21.1 fuzzywuzzy==0.16.0 git+https://github.com/facebookresearch/fastText.git@3872afadb3a9f30de7c7792ff2ff1bda64242097 -pyfasttext==0.4.4 nltk==3.2.5 scikit-learn==0.19.0 spacy==2.0.5 pytelegrambotapi==3.5.2 python-Levenshtein==0.12.0 msgpack-python==0.5.4 +sftp://gpu5/home/jesus/Projects/toxic/lm.py \ No newline at end of file From bf64df9f20464a820440a70039cd9e9955e7cf05 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 14 Mar 2018 17:08:57 +0300 Subject: [PATCH 014/616] fix: change pyfasttext embeddings from gobot --- deeppavlov/configs/go_bot/gobot_dstc2_all.json | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/configs/go_bot/gobot_dstc2_all.json b/deeppavlov/configs/go_bot/gobot_dstc2_all.json index b7ed9d0fa7..9e7919c1fc 100644 --- a/deeppavlov/configs/go_bot/gobot_dstc2_all.json +++ b/deeppavlov/configs/go_bot/gobot_dstc2_all.json @@ -135,11 +135,11 @@ }, "embedder": { "name": "fasttext", - "save_path": "embeddings/wiki.en.bin", - "load_path": "embeddings/wiki.en.bin", + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", "mean": true, - "emb_module": "pyfasttext", - "dim": 300 + "emb_module": "fasttext", + "dim": 100 }, "bow_encoder": { "name": "bow" From 0f78d8d089e2d570a7118a74dd04776104652260 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 14 Mar 2018 17:19:57 +0300 Subject: [PATCH 015/616] fix: delete from requirements --- requirements.txt | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 819f99f52b..f24b521fff 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,5 +19,4 @@ scikit-learn==0.19.0 spacy==2.0.5 pytelegrambotapi==3.5.2 python-Levenshtein==0.12.0 -msgpack-python==0.5.4 -sftp://gpu5/home/jesus/Projects/toxic/lm.py \ No newline at end of file +msgpack-python==0.5.4 \ No newline at end of file From 3d11a90c84e60c426d392ab7b77f9ab8023ec89f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 14 Mar 2018 18:23:29 +0300 Subject: [PATCH 016/616] fix: delete gensim from fasttext_embedder --- deeppavlov/models/embedders/fasttext_embedder.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/deeppavlov/models/embedders/fasttext_embedder.py b/deeppavlov/models/embedders/fasttext_embedder.py index 1c763e6bc1..ea6bfc8493 100644 --- a/deeppavlov/models/embedders/fasttext_embedder.py +++ b/deeppavlov/models/embedders/fasttext_embedder.py @@ -52,9 +52,6 @@ def load(self, *args, **kwargs): if self.emb_module == 'fasttext': import fastText as Fasttext model = Fasttext.load_model(model_file) - else: - from gensim.models.wrappers.fasttext import FastText as Fasttext - model = Fasttext.load_fasttext_format(model_file) else: log.error('No pretrained fasttext model provided or provided load_path "{}" is incorrect.' .format(self.load_path)) From bc763baf9ea16e6a01c3803f9d180baface0a943 Mon Sep 17 00:00:00 2001 From: nikolay-bushkov Date: Wed, 14 Mar 2018 18:28:42 +0300 Subject: [PATCH 017/616] fix: simplify requirements --- requirements.txt | 5 ----- setup.py | 13 +++++++------ 2 files changed, 7 insertions(+), 11 deletions(-) diff --git a/requirements.txt b/requirements.txt index f24b521fff..819467a21c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,13 +2,9 @@ Cython==0.27.1 lxml==4.1.1 tqdm==4.19.5 requests==2.18.4 -gensim==2.3.0 -html5lib==0.9999999 tensorflow==1.4.0 overrides==1.9 -tensorflow==1.4.0 kenlm==0.0.0 -six==1.11.0 h5py==2.7.1 keras==2.1.2 pandas==0.21.1 @@ -19,4 +15,3 @@ scikit-learn==0.19.0 spacy==2.0.5 pytelegrambotapi==3.5.2 python-Levenshtein==0.12.0 -msgpack-python==0.5.4 \ No newline at end of file diff --git a/setup.py b/setup.py index 0e40c40f70..fa8ff79d0f 100644 --- a/setup.py +++ b/setup.py @@ -27,16 +27,17 @@ def read_requirements(): # # parses requirements from requirements.txt reqs_path = os.path.join(__location__, 'requirements.txt') install_reqs = parse_requirements(reqs_path, session=PipSession()) - reqs = [str(ir.req) for ir in install_reqs] - - for r in reqs: - pip.main(['install', '-U', r]) + reqs = [] + for ir in install_reqs: + pip.main(['install', str(ir.req or ir.link)]) + if ir.req: + reqs.append(str(ir.req)) return reqs setup(license='Apache License, Version 2.0', - packages=find_packages(exclude=('tests')), - version='0.0.2', + packages=find_packages(exclude=('tests',)), + version='0.0.3', include_package_data=True, install_requires=read_requirements(), name='deeppavlov' From 7e045afdfbe36a3c1c14eda83e2cdf2bedc6d588 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 6 Apr 2018 18:19:03 +0300 Subject: [PATCH 018/616] chore: refactor keras model and keras intent model --- deeppavlov/configs/intents/intents_snips.json | 48 ++++--- deeppavlov/core/models/keras_model.py | 118 ++++++------------ .../classifiers/intents/intent_model.py | 92 ++++++-------- deeppavlov/run_model.py | 4 +- 4 files changed, 95 insertions(+), 167 deletions(-) diff --git a/deeppavlov/configs/intents/intents_snips.json b/deeppavlov/configs/intents/intents_snips.json index 3c34b7fe8f..16807048a6 100644 --- a/deeppavlov/configs/intents/intents_snips.json +++ b/deeppavlov/configs/intents/intents_snips.json @@ -40,30 +40,28 @@ "save_path": "intents/intent_cnn_snips_v2", "load_path": "intents/intent_cnn_snips_v2", "classes": "#classes_vocab.keys()", - "opt": { - "kernel_sizes_cnn": [ - 1, - 2, - 3 - ], - "filters_cnn": 256, - "lear_metrics": [ - "binary_accuracy", - "fmeasure" - ], - "confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": 0.01, - "lear_rate_decay": 0.1, - "loss": "binary_crossentropy", - "text_size": 15, - "coef_reg_cnn": 1e-4, - "coef_reg_den": 1e-4, - "dropout_rate": 0.5, - "epochs": 1000, - "dense_size": 100, - "model_name": "cnn_model" - }, + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": 256, + "lear_metrics": [ + "binary_accuracy", + "fmeasure" + ], + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 0.01, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 15, + "coef_reg_cnn": 1e-4, + "coef_reg_den": 1e-4, + "dropout_rate": 0.5, + "epochs": 1000, + "dense_size": 100, + "model_name": "cnn_model", "embedder": { "name": "fasttext", "save_path": "embeddings/dstc2_fastText_model.bin", @@ -80,7 +78,7 @@ "out": ["y_predicted"] }, "train": { - "epochs": 100, + "epochs": 10, "batch_size": 64, "metrics": [ "sets_accuracy" diff --git a/deeppavlov/core/models/keras_model.py b/deeppavlov/core/models/keras_model.py index 928172c11d..495a860727 100644 --- a/deeppavlov/core/models/keras_model.py +++ b/deeppavlov/core/models/keras_model.py @@ -16,6 +16,7 @@ from abc import abstractmethod from pathlib import Path +from copy import deepcopy import tensorflow as tf import keras.metrics @@ -41,7 +42,7 @@ class KerasModel(NNModel, metaclass=TfModelMeta): Class builds keras model with tensorflow backend """ - def __init__(self, opt: Dict, **kwargs): + def __init__(self, **kwargs): """ Initialize model using parameters from opt Args: @@ -49,10 +50,12 @@ def __init__(self, opt: Dict, **kwargs): *args: **kwargs: """ - self.opt = opt - save_path = kwargs.get('save_path', None) - load_path = kwargs.get('load_path', None) + print(type(kwargs)) + self.opt = deepcopy(kwargs) + save_path = self.opt.get('save_path', None) + load_path = self.opt.get('load_path', None) url = self.opt.get('url', None) + self.model = None super().__init__(save_path=save_path, load_path=load_path, @@ -73,11 +76,7 @@ def _config_session(self): config.gpu_options.visible_device_list = '0' return tf.Session(config=config) - def init_model_from_scratch(self, model_name, optimizer_name, - lr, decay, loss_name, metrics_names=None, add_metrics_file=None, - loss_weights=None, - sample_weight_mode=None, weighted_metrics=None, - target_tensors=None): + def init_model_from_scratch(self, model_name, optimizer_name, loss_name, lear_rate=None, lear_rate_decay=None): """ Initialize model from scratch with given params Args: @@ -86,12 +85,6 @@ def init_model_from_scratch(self, model_name, optimizer_name, lr: learning rate decay: learning rate decay loss_name: loss function name (from keras.losses) - metrics_names: names of metrics (from keras.metrics) as one string - add_metrics_file: file with additional metrics functions - loss_weights: optional parameter as in keras.model.compile - sample_weight_mode: optional parameter as in keras.model.compile - weighted_metrics: optional parameter as in keras.model.compile - target_tensors: optional parameter as in keras.model.compile Returns: compiled model with given network and learning parameters @@ -106,41 +99,29 @@ def init_model_from_scratch(self, model_name, optimizer_name, optimizer_func = getattr(keras.optimizers, optimizer_name, None) if callable(optimizer_func): - optimizer_ = optimizer_func(lr=lr, decay=decay) + if not(lear_rate is None): + if not(lear_rate_decay is None): + optimizer_ = optimizer_func(lr=lear_rate, decay=lear_rate_decay) + else: + optimizer_ = optimizer_func(lr=lear_rate) + elif not(lear_rate_decay is None): + optimizer_ = optimizer_func(decay=lear_rate_decay) + else: + optimizer_ = optimizer_func() else: - raise AttributeError("Optimizer {} is not callable".format(optimizer_name)) + raise AttributeError("Optimizer {} is not defined in `keras.optimizers`".format(optimizer_name)) loss_func = getattr(keras.losses, loss_name, None) if callable(loss_func): loss = loss_func else: - raise AttributeError("Loss {} is not defined".format(loss_name)) + raise AttributeError("Loss {} is not defined in `keras.losses`".format(loss_name)) - metrics_funcs = [] - for i in range(len(metrics_names)): - metrics_func = getattr(keras.metrics, metrics_names[i], None) - if callable(metrics_func): - metrics_funcs.append(metrics_func) - else: - metrics_func = getattr(add_metrics_file, metrics_names[i], None) - if callable(metrics_func): - metrics_funcs.append(metrics_func) - else: - raise AttributeError("Metric {} is not defined".format(metrics_names[i])) - - model.compile(optimizer=optimizer_, - loss=loss, - metrics=metrics_funcs, - loss_weights=loss_weights, # None - sample_weight_mode=sample_weight_mode, # None - weighted_metrics=weighted_metrics, # None - target_tensors=target_tensors) # None + model.compile(optimizer=optimizer_, loss=loss) return model @overrides - def load(self, model_name, optimizer_name, - lr, decay, loss_name, metrics_names=None, add_metrics_file=None, loss_weights=None, - sample_weight_mode=None, weighted_metrics=None, target_tensors=None): + def load(self, model_name, optimizer_name, loss_name, lear_rate=None, lear_rate_decay=None): """ Initialize model from saved params and weights Args: @@ -149,12 +130,6 @@ def load(self, model_name, optimizer_name, lr: learning rate decay: learning rate decay loss_name: loss function name (from keras.losses) - metrics_names: names of metrics (from keras.metrics) as one string - add_metrics_file: file with additional metrics functions - loss_weights: optional parameter as in keras.model.compile - sample_weight_mode: optional parameter as in keras.model.compile - weighted_metrics: optional parameter as in keras.model.compile - target_tensors: optional parameter as in keras.model.compile Returns: model with loaded weights and network parameters from files @@ -184,9 +159,17 @@ def load(self, model_name, optimizer_name, optimizer_func = getattr(keras.optimizers, optimizer_name, None) if callable(optimizer_func): - optimizer_ = optimizer_func(lr=lr, decay=decay) + if not (lear_rate is None): + if not (lear_rate_decay is None): + optimizer_ = optimizer_func(lr=lear_rate, decay=lear_rate_decay) + else: + optimizer_ = optimizer_func(lr=lear_rate) + elif not (lear_rate_decay is None): + optimizer_ = optimizer_func(decay=lear_rate_decay) + else: + optimizer_ = optimizer_func() else: - raise AttributeError("Optimizer {} is not callable".format(optimizer_name)) + raise AttributeError("Optimizer {} is not defined in `keras.optimizers`".format(optimizer_name)) loss_func = getattr(keras.losses, loss_name, None) if callable(loss_func): @@ -194,45 +177,14 @@ def load(self, model_name, optimizer_name, else: raise AttributeError("Loss {} is not defined".format(loss_name)) - metrics_funcs = [] - for i in range(len(metrics_names)): - metrics_func = getattr(keras.metrics, metrics_names[i], None) - if callable(metrics_func): - metrics_funcs.append(metrics_func) - else: - metrics_func = getattr(add_metrics_file, metrics_names[i], None) - if callable(metrics_func): - metrics_funcs.append(metrics_func) - else: - raise AttributeError( - "Metric {} is not defined".format(metrics_names[i])) - model.compile(optimizer=optimizer_, - loss=loss, - metrics=metrics_funcs, - loss_weights=loss_weights, - sample_weight_mode=sample_weight_mode, - weighted_metrics=weighted_metrics, - target_tensors=target_tensors) + loss=loss) return model else: - return self.init_model_from_scratch(model_name, optimizer_name, - lr, decay, loss_name, - metrics_names=metrics_names, - add_metrics_file=add_metrics_file, - loss_weights=loss_weights, - sample_weight_mode=sample_weight_mode, - weighted_metrics=weighted_metrics, - target_tensors=target_tensors) + return self.init_model_from_scratch(model_name, optimizer_name, loss_name, lr, decay) else: log.warning("No `load_path` is provided for {}".format(self.__class__.__name__)) - return self.init_model_from_scratch(model_name, optimizer_name, - lr, decay, loss_name, metrics_names=metrics_names, - add_metrics_file=add_metrics_file, - loss_weights=loss_weights, - sample_weight_mode=sample_weight_mode, - weighted_metrics=weighted_metrics, - target_tensors=target_tensors) + return self.init_model_from_scratch(model_name, optimizer_name, loss_name, lr, decay) @overrides def save(self, fname=None): @@ -256,7 +208,6 @@ def save(self, fname=None): self.model.save_weights(weights_path) save_json(self.opt, opt_path) - return True def mlp(self, opt): @@ -274,6 +225,7 @@ def mlp(self, opt): for i in range(opt['n_layers']): output = Dense(opt['layer_size'], activation='relu')(output) output = Dense(1, activation='softmax')(output) + model = Model(inputs=inp, outputs=output) return model diff --git a/deeppavlov/models/classifiers/intents/intent_model.py b/deeppavlov/models/classifiers/intents/intent_model.py index 6f49627f5c..af54c537fd 100644 --- a/deeppavlov/models/classifiers/intents/intent_model.py +++ b/deeppavlov/models/classifiers/intents/intent_model.py @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. """ -from typing import Dict + import numpy as np from keras.layers import Dense, Input, concatenate, Activation from keras.layers.convolutional import Conv1D @@ -24,7 +24,6 @@ from keras.regularizers import l2 from deeppavlov.core.common.errors import ConfigError -from deeppavlov.core.common.attributes import check_attr_true from deeppavlov.core.common.registry import register from deeppavlov.core.models.keras_model import KerasModel from deeppavlov.models.classifiers.intents import metrics as metrics_file @@ -44,11 +43,10 @@ class KerasIntentModel(KerasModel): Class implements keras model for intent recognition task for multi-class multi-label data """ def __init__(self, - opt: Dict, - embedder: FasttextEmbedder, - tokenizer: NLTKTokenizer, - classes=None, - vocabs=None, + # embedder: FasttextEmbedder, + # tokenizer: NLTKTokenizer, + # classes=None, + # vocabs=None, **kwargs): """ Initialize and train vocabularies, initializes embedder, tokenizer, @@ -67,70 +65,33 @@ def __init__(self, tokenizer: instance of NLTKTokenizer class **kwargs: """ - super().__init__(opt, **kwargs) + super().__init__(**kwargs) # self.opt initialized in here # Tokenizer and vocabulary of classes - self.tokenizer = tokenizer - if classes: - self.classes = np.sort(np.array(list(classes))) + self.tokenizer = self.opt.get('tokenizer') + if self.opt.get('classes'): + self.classes = np.sort(np.array(list(self.opt.get('classes')))) else: - self.classes = np.sort(np.array(list(vocabs["classes_vocab"].keys()))) + self.classes = np.sort(np.array(list(self.opt.get('vocabs')["classes_vocab"].keys()))) self.n_classes = self.classes.shape[0] if self.n_classes == 0: ConfigError("Please, provide vocabulary with considered intents.") - if 'add_metrics' in self.opt.keys(): - self.add_metrics = self.opt['add_metrics'] - else: - self.add_metrics = None - - self.fasttext_model = embedder + self.fasttext_model = self.opt.get('embedder') self.opt['embedding_size'] = self.fasttext_model.dim if self.fasttext_model.load_path: current_fasttext_md5 = md5_hashsum([self.fasttext_model.load_path]) - self.confident_threshold = self.opt['confident_threshold'] - - # List of parameters that could be changed - # when the model is initialized from saved and is going to be trained further - changeable_params = {"lear_metrics": ["binary_accuracy"], - "confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": 0.1, - "lear_rate_decay": 0.1, - "loss": "binary_crossentropy", - "coef_reg_cnn": 1e-4, - "coef_reg_den": 1e-4, - "dropout_rate": 0.5, - "epochs": 1, - "batch_size": 64, - "val_every_n_epochs": 1, - "verbose": True, - "val_patience": 5} - - # Reinitializing of parameters - for param in changeable_params.keys(): - if param in opt.keys(): - self.opt[param] = opt[param] - else: - self.opt[param] = changeable_params[param] - # Parameters required to init model - params = {"model_name": self.opt['model_name'] if 'model_name' in self.opt.keys() else None, - "optimizer_name": self.opt['optimizer'], - "lr": self.opt['lear_rate'], - "decay": self.opt['lear_rate_decay'], - "loss_name": self.opt['loss'], - "metrics_names": self.opt['lear_metrics'], - "add_metrics_file": metrics_file} + params = {"model_name": self.opt.get('model_name'), + "optimizer_name": self.opt.get('optimizer'), + "loss_name": self.opt.get('loss'), + "lear_rate": self.opt.get('lear_rate'), + "lear_rate_decay": self.opt.get('lear_rate_decay')} self.model = self.load(**params) - - # Reinitializing of parameters - for param in changeable_params.keys(): - if param in opt.keys(): - self.opt[param] = opt[param] + self._init_params(self) # Check if md5 hash sum of current loaded fasttext model # is equal to saved @@ -146,6 +107,23 @@ def __init__(self, # Considered metrics including loss self.metrics_names = self.model.metrics_names + def _init_params(self): + + # list of changeable params + changeable_params = {"confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 1e-2, + "lear_rate_decay": 0., + "loss": "binary_crossentropy", + "coef_reg_cnn": 0., + "coef_reg_den": 0., + "dropout_rate": 0.} + + for param in changeable_params.keys(): + self.opt[param] = self.opt.get(param, default=changeable_params[param]) + + return + def texts2vec(self, sentences): """ Convert texts to vector representations using embedder and padding up to self.opt["text_size"] tokens @@ -218,7 +196,7 @@ def __call__(self, data, predict_proba=False, *args): if predict_proba: return preds else: - return proba2labels(preds, confident_threshold=self.confident_threshold, classes=self.classes) + return proba2labels(preds, confident_threshold=self.opt['confident_threshold'], classes=self.classes) def cnn_model(self, params): """ diff --git a/deeppavlov/run_model.py b/deeppavlov/run_model.py index 3b51247206..c04b96965f 100644 --- a/deeppavlov/run_model.py +++ b/deeppavlov/run_model.py @@ -20,7 +20,7 @@ # PIPELINE_CONFIG_PATH = 'configs/intents/intents_dstc2.json' -# PIPELINE_CONFIG_PATH = 'configs/intents/intents_snips.json' +PIPELINE_CONFIG_PATH = 'configs/intents/intents_snips.json' # PIPELINE_CONFIG_PATH = 'configs/ner/ner_dstc2.json' # PIPELINE_CONFIG_PATH = 'configs/ner/ner_dstc2.json' # PIPELINE_CONFIG_PATH = 'configs/ner/slotfill_dstc2.json' @@ -30,7 +30,7 @@ # PIPELINE_CONFIG_PATH = 'configs/go_bot/config_minimal.json' # PIPELINE_CONFIG_PATH = 'configs/go_bot/config_all.json' # PIPELINE_CONFIG_PATH = 'configs/squad/squad.json' -PIPELINE_CONFIG_PATH = 'configs/ranking/insurance_config.json' +# PIPELINE_CONFIG_PATH = 'configs/ranking/insurance_config.json' train_model_from_config(PIPELINE_CONFIG_PATH) interact_model(PIPELINE_CONFIG_PATH) From d9c100c3fb686d3b502702a59531d9bf4a5d2506 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 6 Apr 2018 18:29:22 +0300 Subject: [PATCH 019/616] chore: refactor keras model and keras intent model --- deeppavlov/core/models/keras_model.py | 9 ++++----- .../models/classifiers/intents/intent_model.py | 13 +++++-------- deeppavlov/models/classifiers/intents/utils.py | 8 ++++---- 3 files changed, 13 insertions(+), 17 deletions(-) diff --git a/deeppavlov/core/models/keras_model.py b/deeppavlov/core/models/keras_model.py index 495a860727..1adfc90c3a 100644 --- a/deeppavlov/core/models/keras_model.py +++ b/deeppavlov/core/models/keras_model.py @@ -16,7 +16,7 @@ from abc import abstractmethod from pathlib import Path -from copy import deepcopy +from copy import deepcopy, copy import tensorflow as tf import keras.metrics @@ -50,8 +50,7 @@ def __init__(self, **kwargs): *args: **kwargs: """ - print(type(kwargs)) - self.opt = deepcopy(kwargs) + self.opt = copy(kwargs) save_path = self.opt.get('save_path', None) load_path = self.opt.get('load_path', None) url = self.opt.get('url', None) @@ -181,10 +180,10 @@ def load(self, model_name, optimizer_name, loss_name, lear_rate=None, lear_rate_ loss=loss) return model else: - return self.init_model_from_scratch(model_name, optimizer_name, loss_name, lr, decay) + return self.init_model_from_scratch(model_name, optimizer_name, loss_name, lear_rate, lear_rate_decay) else: log.warning("No `load_path` is provided for {}".format(self.__class__.__name__)) - return self.init_model_from_scratch(model_name, optimizer_name, loss_name, lr, decay) + return self.init_model_from_scratch(model_name, optimizer_name, loss_name, lear_rate, lear_rate_decay) @overrides def save(self, fname=None): diff --git a/deeppavlov/models/classifiers/intents/intent_model.py b/deeppavlov/models/classifiers/intents/intent_model.py index af54c537fd..a65233b526 100644 --- a/deeppavlov/models/classifiers/intents/intent_model.py +++ b/deeppavlov/models/classifiers/intents/intent_model.py @@ -70,10 +70,10 @@ def __init__(self, # Tokenizer and vocabulary of classes self.tokenizer = self.opt.get('tokenizer') if self.opt.get('classes'): - self.classes = np.sort(np.array(list(self.opt.get('classes')))) + self.classes = list(np.sort(np.array(list(self.opt.get('classes'))))) else: - self.classes = np.sort(np.array(list(self.opt.get('vocabs')["classes_vocab"].keys()))) - self.n_classes = self.classes.shape[0] + self.classes = list(np.sort(np.array(list(self.opt.get('vocabs')["classes_vocab"].keys())))) + self.n_classes = len(self.classes) if self.n_classes == 0: ConfigError("Please, provide vocabulary with considered intents.") @@ -91,7 +91,7 @@ def __init__(self, "lear_rate_decay": self.opt.get('lear_rate_decay')} self.model = self.load(**params) - self._init_params(self) + self._init_params() # Check if md5 hash sum of current loaded fasttext model # is equal to saved @@ -104,9 +104,6 @@ def __init__(self, raise ConfigError( "Given fasttext model does NOT match fasttext model used previously to train loaded model") - # Considered metrics including loss - self.metrics_names = self.model.metrics_names - def _init_params(self): # list of changeable params @@ -120,7 +117,7 @@ def _init_params(self): "dropout_rate": 0.} for param in changeable_params.keys(): - self.opt[param] = self.opt.get(param, default=changeable_params[param]) + self.opt[param] = self.opt.get(param, changeable_params[param]) return diff --git a/deeppavlov/models/classifiers/intents/utils.py b/deeppavlov/models/classifiers/intents/utils.py index 720411d4cd..a8620f31ef 100644 --- a/deeppavlov/models/classifiers/intents/utils.py +++ b/deeppavlov/models/classifiers/intents/utils.py @@ -42,9 +42,9 @@ def labels2onehot(labels, classes): for intent in sample: if intent not in classes: log.warning('Unknown intent {} detected'.format(intent)) - curr += eye[np.where(classes == 'unknown')[0]].reshape(-1) + curr += eye[np.where(np.array(classes) == 'unknown')[0]].reshape(-1) else: - curr += eye[np.where(classes == intent)[0]].reshape(-1) + curr += eye[np.where(np.array(classes) == intent)[0]].reshape(-1) y.append(curr) y = np.asarray(y) return y @@ -67,9 +67,9 @@ def proba2labels(proba, confident_threshold, classes): for sample in proba: to_add = np.where(sample > confident_threshold)[0] if len(to_add) > 0: - y.append(classes[to_add]) + y.append(np.array(classes)[to_add]) else: - y.append(np.array([classes[np.argmax(sample)]])) + y.append(np.array([np.array(classes)[np.argmax(sample)]])) y = np.asarray(y) return y From 61ca5a458fd9e76f3d223175f0c1f8226fe95c5a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 9 Apr 2018 18:13:10 +0300 Subject: [PATCH 020/616] feat: keras model config refactored --- deeppavlov/configs/intents/intents_dstc2.json | 42 +++++++++---------- .../configs/intents/intents_sample_csv.json | 5 --- .../configs/intents/intents_sample_json.json | 5 --- deeppavlov/configs/intents/intents_snips.json | 16 +++---- deeppavlov/core/models/keras_model.py | 3 +- .../classifiers/intents/intent_model.py | 20 ++++----- .../models/embedders/fasttext_embedder.py | 1 + deeppavlov/run_model.py | 6 +-- 8 files changed, 41 insertions(+), 57 deletions(-) diff --git a/deeppavlov/configs/intents/intents_dstc2.json b/deeppavlov/configs/intents/intents_dstc2.json index 6bd9bbc9f7..f7456fd577 100644 --- a/deeppavlov/configs/intents/intents_dstc2.json +++ b/deeppavlov/configs/intents/intents_dstc2.json @@ -39,28 +39,26 @@ "out": ["y_predicted"], "main": true, "name": "intent_model", - "save_path": "intents/intent_cnn_v2", - "load_path": "intents/intent_cnn_v2", + "save_path": "intents/intent_cnn_v3", + "load_path": "intents/intent_cnn_v3", "classes": "#classes_vocab.keys()", - "opt": { - "kernel_sizes_cnn": [ - 3, - 3, - 3 - ], - "filters_cnn": 512, - "confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": 0.1, - "lear_rate_decay": 0.1, - "loss": "binary_crossentropy", - "text_size": 15, - "coef_reg_cnn": 1e-4, - "coef_reg_den": 1e-4, - "dropout_rate": 0.5, - "dense_size": 100, - "model_name": "cnn_model" - }, + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": 512, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 0.1, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 15, + "coef_reg_cnn": 1e-4, + "coef_reg_den": 1e-4, + "dropout_rate": 0.5, + "dense_size": 100, + "model_name": "cnn_model", "embedder": { "name": "fasttext", "save_path": "embeddings/dstc2_fastText_model.bin", @@ -77,7 +75,7 @@ "out": ["y_predicted"] }, "train": { - "epochs": 100, + "epochs": 1000, "batch_size": 64, "metrics": [ "sets_accuracy" diff --git a/deeppavlov/configs/intents/intents_sample_csv.json b/deeppavlov/configs/intents/intents_sample_csv.json index 75227cbcc7..9766e737ff 100644 --- a/deeppavlov/configs/intents/intents_sample_csv.json +++ b/deeppavlov/configs/intents/intents_sample_csv.json @@ -50,10 +50,6 @@ 3 ], "filters_cnn": 256, - "lear_metrics": [ - "binary_accuracy", - "fmeasure" - ], "confident_threshold": 0.5, "optimizer": "Adam", "lear_rate": 0.01, @@ -63,7 +59,6 @@ "coef_reg_cnn": 1e-4, "coef_reg_den": 1e-4, "dropout_rate": 0.5, - "epochs": 1000, "dense_size": 100, "model_name": "cnn_model" }, diff --git a/deeppavlov/configs/intents/intents_sample_json.json b/deeppavlov/configs/intents/intents_sample_json.json index 10fae8c4a1..01314b3b30 100644 --- a/deeppavlov/configs/intents/intents_sample_json.json +++ b/deeppavlov/configs/intents/intents_sample_json.json @@ -48,10 +48,6 @@ 3 ], "filters_cnn": 256, - "lear_metrics": [ - "binary_accuracy", - "fmeasure" - ], "confident_threshold": 0.5, "optimizer": "Adam", "lear_rate": 0.01, @@ -61,7 +57,6 @@ "coef_reg_cnn": 1e-4, "coef_reg_den": 1e-4, "dropout_rate": 0.5, - "epochs": 1000, "dense_size": 100, "model_name": "cnn_model" }, diff --git a/deeppavlov/configs/intents/intents_snips.json b/deeppavlov/configs/intents/intents_snips.json index 16807048a6..0fabb8a0d7 100644 --- a/deeppavlov/configs/intents/intents_snips.json +++ b/deeppavlov/configs/intents/intents_snips.json @@ -37,8 +37,8 @@ "out": ["y_predicted"], "main": true, "name": "intent_model", - "save_path": "intents/intent_cnn_snips_v2", - "load_path": "intents/intent_cnn_snips_v2", + "save_path": "intents/intent_cnn_snips_v3", + "load_path": "intents/intent_cnn_snips_v3", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, @@ -46,10 +46,6 @@ 3 ], "filters_cnn": 256, - "lear_metrics": [ - "binary_accuracy", - "fmeasure" - ], "confident_threshold": 0.5, "optimizer": "Adam", "lear_rate": 0.01, @@ -59,14 +55,12 @@ "coef_reg_cnn": 1e-4, "coef_reg_den": 1e-4, "dropout_rate": 0.5, - "epochs": 1000, "dense_size": 100, "model_name": "cnn_model", "embedder": { "name": "fasttext", "save_path": "embeddings/dstc2_fastText_model.bin", "load_path": "embeddings/dstc2_fastText_model.bin", - "emb_module": "fasttext", "dim": 100 }, "tokenizer": { @@ -78,14 +72,14 @@ "out": ["y_predicted"] }, "train": { - "epochs": 10, + "epochs": 1000, "batch_size": 64, "metrics": [ "sets_accuracy" ], "validation_patience": 5, - "val_every_n_epochs": 1, - "log_every_n_epochs": 1, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, "test_best": false diff --git a/deeppavlov/core/models/keras_model.py b/deeppavlov/core/models/keras_model.py index 1adfc90c3a..5a223edd5d 100644 --- a/deeppavlov/core/models/keras_model.py +++ b/deeppavlov/core/models/keras_model.py @@ -89,7 +89,7 @@ def init_model_from_scratch(self, model_name, optimizer_name, loss_name, lear_ra compiled model with given network and learning parameters """ log.info("[initializing `{}` from scratch]".format(self.__class__.__name__)) - + print(model_name) model_func = getattr(self, model_name, None) if callable(model_func): model = model_func(params=self.opt) @@ -206,6 +206,7 @@ def save(self, fname=None): log.info("[saving model to {}]".format(opt_path)) self.model.save_weights(weights_path) + save_json(self.opt, opt_path) return True diff --git a/deeppavlov/models/classifiers/intents/intent_model.py b/deeppavlov/models/classifiers/intents/intent_model.py index a65233b526..7980f944ac 100644 --- a/deeppavlov/models/classifiers/intents/intent_model.py +++ b/deeppavlov/models/classifiers/intents/intent_model.py @@ -42,12 +42,7 @@ class KerasIntentModel(KerasModel): """ Class implements keras model for intent recognition task for multi-class multi-label data """ - def __init__(self, - # embedder: FasttextEmbedder, - # tokenizer: NLTKTokenizer, - # classes=None, - # vocabs=None, - **kwargs): + def __init__(self, **kwargs): """ Initialize and train vocabularies, initializes embedder, tokenizer, and then initialize model using parameters from opt dictionary (from config), @@ -67,17 +62,23 @@ def __init__(self, """ super().__init__(**kwargs) # self.opt initialized in here - # Tokenizer and vocabulary of classes self.tokenizer = self.opt.get('tokenizer') + self.fasttext_model = self.opt.get('embedder') + self.opt.pop("vocabs") + self.opt.pop("embedder") + self.opt.pop("tokenizer") + if self.opt.get('classes'): self.classes = list(np.sort(np.array(list(self.opt.get('classes'))))) + self.opt['classes'] = self.classes else: - self.classes = list(np.sort(np.array(list(self.opt.get('vocabs')["classes_vocab"].keys())))) + # self.classes = list(np.sort(np.array(list(self.opt.get('vocabs')["classes_vocab"].keys())))) + self.classes = list(self.opt.get('vocabs')["classes_vocab"].keys()) + self.opt['classes'] = self.classes self.n_classes = len(self.classes) if self.n_classes == 0: ConfigError("Please, provide vocabulary with considered intents.") - self.fasttext_model = self.opt.get('embedder') self.opt['embedding_size'] = self.fasttext_model.dim if self.fasttext_model.load_path: @@ -118,7 +119,6 @@ def _init_params(self): for param in changeable_params.keys(): self.opt[param] = self.opt.get(param, changeable_params[param]) - return def texts2vec(self, sentences): diff --git a/deeppavlov/models/embedders/fasttext_embedder.py b/deeppavlov/models/embedders/fasttext_embedder.py index 4299bda603..7ceaafadc2 100644 --- a/deeppavlov/models/embedders/fasttext_embedder.py +++ b/deeppavlov/models/embedders/fasttext_embedder.py @@ -34,6 +34,7 @@ def __init__(self, load_path, save_path=None, dim=100, **kwargs): super().__init__(save_path=save_path, load_path=load_path) self.tok2emb = {} self.dim = dim + self.model = self.load() def save(self, *args, **kwargs): diff --git a/deeppavlov/run_model.py b/deeppavlov/run_model.py index c04b96965f..55ba363741 100644 --- a/deeppavlov/run_model.py +++ b/deeppavlov/run_model.py @@ -19,8 +19,8 @@ from deeppavlov.core.commands.utils import set_deeppavlov_root -# PIPELINE_CONFIG_PATH = 'configs/intents/intents_dstc2.json' -PIPELINE_CONFIG_PATH = 'configs/intents/intents_snips.json' +PIPELINE_CONFIG_PATH = 'configs/intents/intents_dstc2.json' +# PIPELINE_CONFIG_PATH = 'configs/intents/intents_snips.json' # PIPELINE_CONFIG_PATH = 'configs/ner/ner_dstc2.json' # PIPELINE_CONFIG_PATH = 'configs/ner/ner_dstc2.json' # PIPELINE_CONFIG_PATH = 'configs/ner/slotfill_dstc2.json' @@ -32,5 +32,5 @@ # PIPELINE_CONFIG_PATH = 'configs/squad/squad.json' # PIPELINE_CONFIG_PATH = 'configs/ranking/insurance_config.json' -train_model_from_config(PIPELINE_CONFIG_PATH) +# train_model_from_config(PIPELINE_CONFIG_PATH) interact_model(PIPELINE_CONFIG_PATH) From 8d6fba6f4547b11b522c53b1c75d28e5cb5efa1a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 12 Apr 2018 10:56:03 +0300 Subject: [PATCH 021/616] fix: fixed configs --- deeppavlov/configs/go_bot/gobot_dstc2.json | 52 +++++++------------ .../configs/go_bot/gobot_dstc2_all.json | 52 +++++++------------ deeppavlov/configs/intents/intents_dstc2.json | 1 - .../configs/intents/intents_sample_csv.json | 41 +++++++-------- .../configs/intents/intents_sample_json.json | 41 +++++++-------- 5 files changed, 78 insertions(+), 109 deletions(-) diff --git a/deeppavlov/configs/go_bot/gobot_dstc2.json b/deeppavlov/configs/go_bot/gobot_dstc2.json index 8ed28260ae..4bcb496170 100644 --- a/deeppavlov/configs/go_bot/gobot_dstc2.json +++ b/deeppavlov/configs/go_bot/gobot_dstc2.json @@ -89,43 +89,31 @@ }, "intent_classifier": { "name": "intent_model", - "save_path": "intents/intent_cnn_v2", - "load_path": "intents/intent_cnn_v2", + "save_path": "intents/intent_cnn_v3", + "load_path": "intents/intent_cnn_v3", "classes": "#classes_vocab.keys()", - "opt": { - "train_now": true, - "kernel_sizes_cnn": [ - 3, - 3, - 3 - ], - "filters_cnn": 512, - "lear_metrics": [ - "binary_accuracy", - "fmeasure" - ], - "confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": 0.1, - "lear_rate_decay": 0.1, - "loss": "binary_crossentropy", - "text_size": 15, - "coef_reg_cnn": 1e-4, - "coef_reg_den": 1e-4, - "dropout_rate": 0.5, - "epochs": 1, - "dense_size": 100, - "model_name": "cnn_model", - "batch_size": 64, - "val_every_n_epochs": 5, - "verbose": true, - "val_patience": 5 - }, + "train_now": true, + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": 512, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 0.1, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 15, + "coef_reg_cnn": 1e-4, + "coef_reg_den": 1e-4, + "dropout_rate": 0.5, + "dense_size": 100, + "model_name": "cnn_model", "embedder": { "name": "fasttext", "save_path": "embeddings/dstc2_fastText_model.bin", "load_path": "embeddings/dstc2_fastText_model.bin", - "emb_module": "fasttext", "dim": 100 }, "tokenizer": { diff --git a/deeppavlov/configs/go_bot/gobot_dstc2_all.json b/deeppavlov/configs/go_bot/gobot_dstc2_all.json index fa2ec424ec..d68b8fb9ec 100644 --- a/deeppavlov/configs/go_bot/gobot_dstc2_all.json +++ b/deeppavlov/configs/go_bot/gobot_dstc2_all.json @@ -89,43 +89,31 @@ }, "intent_classifier": { "name": "intent_model", - "save_path": "intents/intent_cnn_v2", - "load_path": "intents/intent_cnn_v2", + "save_path": "intents/intent_cnn_v3", + "load_path": "intents/intent_cnn_v3", "classes": "#classes_vocab.keys()", - "opt": { - "train_now": true, - "kernel_sizes_cnn": [ - 3, - 3, - 3 - ], - "filters_cnn": 512, - "lear_metrics": [ - "binary_accuracy", - "fmeasure" - ], - "confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": 0.1, - "lear_rate_decay": 0.1, - "loss": "binary_crossentropy", - "text_size": 15, - "coef_reg_cnn": 1e-4, - "coef_reg_den": 1e-4, - "dropout_rate": 0.5, - "epochs": 1, - "dense_size": 100, - "model_name": "cnn_model", - "batch_size": 64, - "val_every_n_epochs": 5, - "verbose": true, - "val_patience": 5 - }, + "train_now": true, + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": 512, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 0.1, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 15, + "coef_reg_cnn": 1e-4, + "coef_reg_den": 1e-4, + "dropout_rate": 0.5, + "dense_size": 100, + "model_name": "cnn_model", "embedder": { "name": "fasttext", "save_path": "embeddings/dstc2_fastText_model.bin", "load_path": "embeddings/dstc2_fastText_model.bin", - "emb_module": "fasttext", "dim": 100 }, "tokenizer": { diff --git a/deeppavlov/configs/intents/intents_dstc2.json b/deeppavlov/configs/intents/intents_dstc2.json index f7456fd577..fc74118889 100644 --- a/deeppavlov/configs/intents/intents_dstc2.json +++ b/deeppavlov/configs/intents/intents_dstc2.json @@ -63,7 +63,6 @@ "name": "fasttext", "save_path": "embeddings/dstc2_fastText_model.bin", "load_path": "embeddings/dstc2_fastText_model.bin", - "emb_module": "fasttext", "dim": 100 }, "tokenizer": { diff --git a/deeppavlov/configs/intents/intents_sample_csv.json b/deeppavlov/configs/intents/intents_sample_csv.json index 9766e737ff..856ff78ab4 100644 --- a/deeppavlov/configs/intents/intents_sample_csv.json +++ b/deeppavlov/configs/intents/intents_sample_csv.json @@ -40,33 +40,30 @@ "out": ["y_predicted"], "main": true, "name": "intent_model", - "save_path": "intents/intent_cnn_snips_v2", - "load_path": "intents/intent_cnn_snips_v2", + "save_path": "intents/intent_cnn_snips_v3", + "load_path": "intents/intent_cnn_snips_v3", "classes": "#classes_vocab.keys()", - "opt": { - "kernel_sizes_cnn": [ - 1, - 2, - 3 - ], - "filters_cnn": 256, - "confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": 0.01, - "lear_rate_decay": 0.1, - "loss": "binary_crossentropy", - "text_size": 15, - "coef_reg_cnn": 1e-4, - "coef_reg_den": 1e-4, - "dropout_rate": 0.5, - "dense_size": 100, - "model_name": "cnn_model" - }, + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": 256, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 0.01, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 15, + "coef_reg_cnn": 1e-4, + "coef_reg_den": 1e-4, + "dropout_rate": 0.5, + "dense_size": 100, + "model_name": "cnn_model", "embedder": { "name": "fasttext", "save_path": "embeddings/dstc2_fastText_model.bin", "load_path": "embeddings/dstc2_fastText_model.bin", - "emb_module": "fasttext", "dim": 100 }, "tokenizer": { diff --git a/deeppavlov/configs/intents/intents_sample_json.json b/deeppavlov/configs/intents/intents_sample_json.json index 01314b3b30..dd3181363b 100644 --- a/deeppavlov/configs/intents/intents_sample_json.json +++ b/deeppavlov/configs/intents/intents_sample_json.json @@ -38,33 +38,30 @@ "out": ["y_predicted"], "main": true, "name": "intent_model", - "save_path": "intents/intent_cnn_snips_v2", - "load_path": "intents/intent_cnn_snips_v2", + "save_path": "intents/intent_cnn_snips_v3", + "load_path": "intents/intent_cnn_snips_v3", "classes": "#classes_vocab.keys()", - "opt": { - "kernel_sizes_cnn": [ - 1, - 2, - 3 - ], - "filters_cnn": 256, - "confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": 0.01, - "lear_rate_decay": 0.1, - "loss": "binary_crossentropy", - "text_size": 15, - "coef_reg_cnn": 1e-4, - "coef_reg_den": 1e-4, - "dropout_rate": 0.5, - "dense_size": 100, - "model_name": "cnn_model" - }, + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": 256, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 0.01, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 15, + "coef_reg_cnn": 1e-4, + "coef_reg_den": 1e-4, + "dropout_rate": 0.5, + "dense_size": 100, + "model_name": "cnn_model", "embedder": { "name": "fasttext", "save_path": "embeddings/dstc2_fastText_model.bin", "load_path": "embeddings/dstc2_fastText_model.bin", - "emb_module": "fasttext", "dim": 100 }, "tokenizer": { From d4832a46872629bd56ad0aade1503f10b50e63ff Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 13 Apr 2018 16:21:52 +0300 Subject: [PATCH 022/616] feat: working on evolution of structure --- .../models/evolution/check_binary_mask.py | 98 +++++++ .../models/evolution/check_matrix.ipynb | 234 +++++++++++++++ deeppavlov/models/evolution/evolution.py | 0 deeppavlov/models/evolution/intent_model.py | 277 ++++++++++++++++++ .../neuroevolution_param_generator.py | 261 +++++++++++++++++ .../evolution/random_param_generator.py | 85 ++++++ .../models/evolution/train_phenotype.py | 0 deeppavlov/models/evolution/utils.py | 128 ++++++++ 8 files changed, 1083 insertions(+) create mode 100644 deeppavlov/models/evolution/check_binary_mask.py create mode 100644 deeppavlov/models/evolution/check_matrix.ipynb create mode 100644 deeppavlov/models/evolution/evolution.py create mode 100644 deeppavlov/models/evolution/intent_model.py create mode 100644 deeppavlov/models/evolution/neuroevolution_param_generator.py create mode 100644 deeppavlov/models/evolution/random_param_generator.py create mode 100644 deeppavlov/models/evolution/train_phenotype.py create mode 100644 deeppavlov/models/evolution/utils.py diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py new file mode 100644 index 0000000000..fe61e3e188 --- /dev/null +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -0,0 +1,98 @@ +import numpy as np +import networkx as nx +import copy + + +def number_to_type_layer(node_id, n_types): + # return node_layer, node_type + return node_id // n_types, node_id % n_types + + +def type_layer_to_number(node_layer, node_type, n_types): + return node_layer * n_types + node_type + + +def find_sources_and_sinks(directed_graph): + sources = [] + sinks = [] + + for i in directed_graph.nodes(): + if directed_graph.in_degree(i) == 0 and directed_graph.out_degree(i) > 0: + sources.append(i) + if directed_graph.in_degree(i) > 0 and directed_graph.out_degree(i) == 0: + sinks.append(i) + + return sources, sinks + + +def get_digraph_from_binary_mask(nodes, binary_mask): + directed_graph = nx.DiGraph() + total_nodes = len(nodes) + + for i in range(total_nodes): + directed_graph.add_node(i) + + for i in range(total_nodes): + for j in range(total_nodes): + if binary_mask[i, j] == 1: + directed_graph.add_edge(i, j) + return directed_graph + + +def get_binary_mask_from_digraph(nodes, directed_graph): + binary_mask = np.zeros((len(nodes), len(nodes))) + for edge in directed_graph.edges(): + binary_mask[edge[0], edge[1]] = 1 + return binary_mask +# +# +# def check_binary_mask(nodes, binary_mask): +# directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) +# sources, sinks = find_sources_and_sinks(directed_graph) +# +# while not nx.is_directed_acyclic_graph(directed_graph): +# cycles = list(nx.simple_cycles(directed_graph)) +# print("Cycles: {}".format(cycles)) +# for cycle_ in cycles: +# cycle = copy.deepcopy(cycle_) + [cycle_[0]] +# for i in range(len(cycle_)): +# new_directed_graph = copy.deepcopy(directed_graph) +# new_directed_graph.remove_edge(cycle[i], cycle[i+1]) +# new_sources, new_sinks = find_sources_and_sinks(new_directed_graph) +# if nx.is_directed_acyclic_graph(new_directed_graph): +# if set(new_sources) == set(sources) and set(new_sinks) == set(sinks): +# directed_graph.remove_edge(cycle[i], cycle[i+1]) +# continue +# binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) +# return True, binary_mask + + +def check_binary_mask(nodes, binary_mask): + directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) + sources, sinks = find_sources_and_sinks(directed_graph) + + while not nx.is_directed_acyclic_graph(directed_graph): + candidates = [] + cycles = list(nx.simple_cycles(directed_graph)) + print("Cycles: {}".format(cycles)) + # number of candidates to be the best new graph + cycles_len = np.array([len(cycle) for cycle in cycles]) + n_candidates = np.prod(cycles_len) + + for i in range(n_candidates): + new_directed_graph = copy.deepcopy(directed_graph) + candidates.append(new_directed_graph) + + for j, cycle_ in enumerate(cycles): + cycle = copy.deepcopy(cycle_) + [cycle_[0]] + for i in range(len(cycle_)): + candidates[].remove_edge(cycle[i], cycle[i + 1]) + new_sources, new_sinks = find_sources_and_sinks(new_directed_graph) + if set(new_sources).issuperset(set(sources)) and set(new_sinks).issuperset(set(sinks)): + directed_graph.remove_edge(cycle[i], cycle[i + 1]) + continue + else: + new_directed_graph.add_edge(cycle[i], cycle[i + 1]) + + binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) + return True, binary_mask diff --git a/deeppavlov/models/evolution/check_matrix.ipynb b/deeppavlov/models/evolution/check_matrix.ipynb new file mode 100644 index 0000000000..4bcf35ace6 --- /dev/null +++ b/deeppavlov/models/evolution/check_matrix.ipynb @@ -0,0 +1,234 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "from check_binary_mask import check_binary_mask\n", + "from check_binary_mask import number_to_type_layer\n", + "from check_binary_mask import type_layer_to_number" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "T = 3\n", + "L = 2\n", + "total_nodes = T * L\n", + "\n", + "nodes = {}\n", + "types = {0: \"Dense\", 1: \"Conv1D\", \n", + " 2: \"LSTM\", 3: \"BiLSTM\", 4: \"GlobMaxPool1D\", \n", + " 5: \"MaxPool1D\", 6: \"Attention\"}\n", + "\n", + "for i in range(0, total_nodes):\n", + " nodes[i] = types[number_to_type_layer(i, T)[1]]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cm = np.zeros((total_nodes, total_nodes)) \n", + "cm[0, 1] = 1\n", + "cm[0, 3] = 1\n", + "cm[3, 1] = 1\n", + "cm[3, 5] = 1\n", + "cm[5, 2] = 1\n", + "\n", + "dg = nx.DiGraph()\n", + "\n", + "for i in range(total_nodes):\n", + " dg.add_node(i)\n", + " \n", + "pos = {}\n", + "\n", + "for i in range(total_nodes):\n", + " for j in range(total_nodes):\n", + " if cm[i,j] == 1:\n", + " dg.add_edge(i, j)\n", + "# pos[i] = 5 * np.array(number_to_type_layer(i, L, T))\n", + " pos[i] = np.array(number_to_type_layer(i, T))[::-1]\n", + "\n", + "plt.figure(figsize=(6, 6))\n", + "nx.draw(dg, pos, node_color='b', node_size=5000, alpha=0.3)\n", + "\n", + "nx.draw_networkx_labels(dg, pos, nodes, font_size=18)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "check_binary_mask(nodes, cm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_graph_and_plot(nodes, cm):\n", + " total_nodes = len(nodes)\n", + " dg = nx.DiGraph()\n", + "\n", + " for i in range(total_nodes):\n", + " dg.add_node(i)\n", + "\n", + " pos = {}\n", + "\n", + " for i in range(total_nodes):\n", + " for j in range(total_nodes):\n", + " if cm[i,j] == 1:\n", + " dg.add_edge(i, j)\n", + " # pos[i] = 5 * np.array(number_to_type_layer(i, L, T))\n", + " pos[i] = np.array(number_to_type_layer(i, T))[::-1]\n", + "\n", + " plt.figure(figsize=(6, 6))\n", + " nx.draw(dg, pos, node_color='b', node_size=5000, alpha=0.3)\n", + "\n", + " nx.draw_networkx_labels(dg, pos, nodes, font_size=18)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cm = np.zeros((total_nodes, total_nodes)) \n", + "cm[0, 1] = 1\n", + "cm[0, 3] = 1\n", + "cm[3, 1] = 1\n", + "cm[3, 5] = 1\n", + "cm[5, 2] = 1\n", + "cm[5, 3] = 1\n", + "\n", + "get_graph_and_plot(nodes, cm)\n", + "_, new_cm = check_binary_mask(nodes, cm)\n", + "get_graph_and_plot(nodes, new_cm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dg = nx.DiGraph()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(6):\n", + " dg.add_node(i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dg.add_edge(0, 1)\n", + "dg.add_edge(0, 3)\n", + "dg.add_edge(3, 1)\n", + "dg.add_edge(5, 2)\n", + "dg.add_edge(3, 5)\n", + "dg.add_edge(5, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dg.edges()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dg.remove_edge(3, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dg.edges()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py36_main_kernel", + "language": "python", + "name": "py36_main" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/deeppavlov/models/evolution/evolution.py b/deeppavlov/models/evolution/evolution.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/deeppavlov/models/evolution/intent_model.py b/deeppavlov/models/evolution/intent_model.py new file mode 100644 index 0000000000..7980f944ac --- /dev/null +++ b/deeppavlov/models/evolution/intent_model.py @@ -0,0 +1,277 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import numpy as np +from keras.layers import Dense, Input, concatenate, Activation +from keras.layers.convolutional import Conv1D +from keras.layers.core import Dropout +from keras.layers.normalization import BatchNormalization +from keras.layers.pooling import GlobalMaxPooling1D, MaxPooling1D +from keras.models import Model +from keras.regularizers import l2 + +from deeppavlov.core.common.errors import ConfigError +from deeppavlov.core.common.registry import register +from deeppavlov.core.models.keras_model import KerasModel +from deeppavlov.models.classifiers.intents import metrics as metrics_file +from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels +from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder +from deeppavlov.models.classifiers.intents.utils import md5_hashsum +from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer +from deeppavlov.core.common.log import get_logger + + +log = get_logger(__name__) + + +@register('intent_model') +class KerasIntentModel(KerasModel): + """ + Class implements keras model for intent recognition task for multi-class multi-label data + """ + def __init__(self, **kwargs): + """ + Initialize and train vocabularies, initializes embedder, tokenizer, + and then initialize model using parameters from opt dictionary (from config), + if model is being initialized from saved + + Args: + vocabs: dictionary of considered vocabularies + opt: model parameters for network and learning + model_path: path to model serialization dir or file. + It is always an empty string and is ignored if it is not set in json config. + model_dir: name of a serialization dir, can be default or set in json config + model_file: name of a serialization file (usually binary model file), + can be default or set in json config + embedder: instance of FasttextEmbedder class + tokenizer: instance of NLTKTokenizer class + **kwargs: + """ + super().__init__(**kwargs) # self.opt initialized in here + + self.tokenizer = self.opt.get('tokenizer') + self.fasttext_model = self.opt.get('embedder') + self.opt.pop("vocabs") + self.opt.pop("embedder") + self.opt.pop("tokenizer") + + if self.opt.get('classes'): + self.classes = list(np.sort(np.array(list(self.opt.get('classes'))))) + self.opt['classes'] = self.classes + else: + # self.classes = list(np.sort(np.array(list(self.opt.get('vocabs')["classes_vocab"].keys())))) + self.classes = list(self.opt.get('vocabs')["classes_vocab"].keys()) + self.opt['classes'] = self.classes + self.n_classes = len(self.classes) + if self.n_classes == 0: + ConfigError("Please, provide vocabulary with considered intents.") + + self.opt['embedding_size'] = self.fasttext_model.dim + + if self.fasttext_model.load_path: + current_fasttext_md5 = md5_hashsum([self.fasttext_model.load_path]) + + # Parameters required to init model + params = {"model_name": self.opt.get('model_name'), + "optimizer_name": self.opt.get('optimizer'), + "loss_name": self.opt.get('loss'), + "lear_rate": self.opt.get('lear_rate'), + "lear_rate_decay": self.opt.get('lear_rate_decay')} + + self.model = self.load(**params) + self._init_params() + + # Check if md5 hash sum of current loaded fasttext model + # is equal to saved + try: + self.opt['fasttext_md5'] + except KeyError: + self.opt['fasttext_md5'] = current_fasttext_md5 + else: + if self.opt['fasttext_md5'] != current_fasttext_md5: + raise ConfigError( + "Given fasttext model does NOT match fasttext model used previously to train loaded model") + + def _init_params(self): + + # list of changeable params + changeable_params = {"confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 1e-2, + "lear_rate_decay": 0., + "loss": "binary_crossentropy", + "coef_reg_cnn": 0., + "coef_reg_den": 0., + "dropout_rate": 0.} + + for param in changeable_params.keys(): + self.opt[param] = self.opt.get(param, changeable_params[param]) + return + + def texts2vec(self, sentences): + """ + Convert texts to vector representations using embedder and padding up to self.opt["text_size"] tokens + Args: + sentences: list of texts + + Returns: + array of embedded texts + """ + pad = np.zeros(self.opt['embedding_size']) + + embeddings_batch = self.fasttext_model([' '.join(sen.split()[:self.opt['text_size']]) for sen in sentences]) + embeddings_batch = [[pad] * (self.opt['text_size'] - len(tokens)) + tokens for tokens in embeddings_batch] + + embeddings_batch = np.asarray(embeddings_batch) + return embeddings_batch + + def train_on_batch(self, texts, labels): + """ + Train the model on the given batch + Args: + batch - list of data where batch[0] is list of texts and batch[1] is list of labels + + Returns: + loss and metrics values on the given batch + """ + texts = self.tokenizer(list(texts)) + features = self.texts2vec(texts) + onehot_labels = labels2onehot(labels, classes=self.classes) + metrics_values = self.model.train_on_batch(features, onehot_labels) + return metrics_values + + def infer_on_batch(self, batch, labels=None): + """ + Infer the model on the given batch + Args: + batch - list of texts + labels - list of labels + + Returns: + loss and metrics values on the given batch, if labels are given + predictions, otherwise + """ + texts = self.tokenizer(batch) + if labels: + features = self.texts2vec(texts) + onehot_labels = labels2onehot(labels, classes=self.classes) + metrics_values = self.model.test_on_batch(features, onehot_labels) + return metrics_values + else: + features = self.texts2vec(texts) + predictions = self.model.predict(features) + return predictions + + def __call__(self, data, predict_proba=False, *args): + """ + Infer on the given data + Args: + data: [list of sentences] + predict_proba: whether to return probabilities distribution or only labels-predictions + *args: + + Returns: + for each sentence: + vector of probabilities to belong with each class + or list of labels sentence belongs with + """ + preds = np.array(self.infer_on_batch(data)) + + if predict_proba: + return preds + else: + return proba2labels(preds, confident_threshold=self.opt['confident_threshold'], classes=self.classes) + + def cnn_model(self, params): + """ + Build un-compiled model of shallow-and-wide CNN + Args: + params: dictionary of parameters for NN + + Returns: + Un-compiled model + """ + + inp = Input(shape=(params['text_size'], params['embedding_size'])) + + outputs = [] + for i in range(len(params['kernel_sizes_cnn'])): + output_i = Conv1D(params['filters_cnn'], kernel_size=params['kernel_sizes_cnn'][i], + activation=None, + kernel_regularizer=l2(params['coef_reg_cnn']), + padding='same')(inp) + output_i = BatchNormalization()(output_i) + output_i = Activation('relu')(output_i) + output_i = GlobalMaxPooling1D()(output_i) + outputs.append(output_i) + + output = concatenate(outputs, axis=1) + + output = Dropout(rate=params['dropout_rate'])(output) + output = Dense(params['dense_size'], activation=None, + kernel_regularizer=l2(params['coef_reg_den']))(output) + output = BatchNormalization()(output) + output = Activation('relu')(output) + output = Dropout(rate=params['dropout_rate'])(output) + output = Dense(self.n_classes, activation=None, + kernel_regularizer=l2(params['coef_reg_den']))(output) + output = BatchNormalization()(output) + act_output = Activation('sigmoid')(output) + model = Model(inputs=inp, outputs=act_output) + return model + + def dcnn_model(self, params): + """ + Build un-compiled model of deep CNN + Args: + params: dictionary of parameters for NN + + Returns: + Un-compiled model + """ + + if type(self.opt['filters_cnn']) is str: + self.opt['filters_cnn'] = list(map(int, self.opt['filters_cnn'].split())) + + inp = Input(shape=(params['text_size'], params['embedding_size'])) + + output = inp + + for i in range(len(params['kernel_sizes_cnn'])): + output = Conv1D(params['filters_cnn'][i], kernel_size=params['kernel_sizes_cnn'][i], + activation=None, + kernel_regularizer=l2(params['coef_reg_cnn']), + padding='same')(output) + output = BatchNormalization()(output) + output = Activation('relu')(output) + output = MaxPooling1D()(output) + + output = GlobalMaxPooling1D()(output) + output = Dropout(rate=params['dropout_rate'])(output) + output = Dense(params['dense_size'], activation=None, + kernel_regularizer=l2(params['coef_reg_den']))(output) + output = BatchNormalization()(output) + output = Activation('relu')(output) + output = Dropout(rate=params['dropout_rate'])(output) + output = Dense(self.n_classes, activation=None, + kernel_regularizer=l2(params['coef_reg_den']))(output) + output = BatchNormalization()(output) + act_output = Activation('sigmoid')(output) + model = Model(inputs=inp, outputs=act_output) + return model + + def reset(self): + pass diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py new file mode 100644 index 0000000000..625e06c1d3 --- /dev/null +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -0,0 +1,261 @@ +import numpy as np +from copy import deepcopy +from pathlib import Path + + +class Evolution: + """ + Class performs full evolutionary process (task scores -> max): + 1. initializes random population + 2. makes replacement to get next generation: + a. selection according to obtained scores + b. crossover (recombination) with given probability p_crossover + c. mutation with given mutation rate p_mutation (probability to mutate) + according to given mutation power sigma + (current mutation power is randomly from -sigma to sigma) + """ + + def __init__(self, population_size, + p_crossover=0.5, crossover_power=0.5, + p_mutation=0.5, mutation_power=0.1, + **kwargs): + """ + Initialize evolution with random population + Args: + population_size: numer of individuums per generation + p_crossover: probability to cross over for current replacement + crossover_power: part of parents parameters to exchange for offsprings + p_mutation: probability of mutation for current replacement + mutation_power: allowed percentage of mutation + **kwargs: basic config with parameters + """ + self.params = deepcopy(kwargs) + self.population_size = population_size + self.p_crossover = p_crossover + self.p_mutation = p_mutation + self.params_names = np.array(list(self.params.keys())) + self.n_params = len(self.params) + self.mutation_power = mutation_power + self.crossover_power = crossover_power + + def first_generation(self, iter=0): + """ + Initialize first generation randomly according to the given constraints is self.params + Returns: + first generation that consists of self.population_size individuums + """ + population = [] + for i in range(self.population_size): + params = {} + params_for_search = {} + + for param_name in self.params.keys(): + if ((type(self.params[param_name]) is str) + or (type(self.params[param_name]) is int) + or (type(self.params[param_name]) is float) + or (type(self.params[param_name]) is bool) + or (type(self.params[param_name]) is list)): + params[param_name] = deepcopy(self.params[param_name]) + else: + if "choice" in self.params[param_name].keys(): + params_for_search[param_name] = list(self.params[param_name]["values"]) + else: + params_for_search[param_name] = deepcopy(self.params[param_name]) + + params_for_search = deepcopy(self.sample_params(**params_for_search)) + if "model_name" in params_for_search.keys(): + params["model_path"] = str(Path(self.params["model_path"]).joinpath( + "population_" + str(iter)).joinpath(params_for_search["model_name"] + "_" + str(i))) + else: + params["model_path"] = str(Path(self.params["model_path"]).joinpath( + "population_" + str(iter)).joinpath(self.params["model_name"] + "_" + str(i))) + + population.append({**params, **params_for_search}) + return population + + def next_generation(self, generation, scores, iter, + p_crossover=None, crossover_power=None, + p_mutation=None, mutation_power=None): + """ + Provide an operation of replacement + Args: + generation: current generation (set of self.population_size configs + scores: corresponding scores that should be maximized + p_crossover: probability to cross over for current replacement + crossover_power: part of parents parameters to exchange for offsprings + p_mutation: probability of mutation for current replacement + mutation_power: allowed percentage of mutation + + Returns: + the next generation according to the given scores of current generation + """ + if not p_crossover: + p_crossover = self.p_crossover + if not crossover_power: + crossover_power = self.crossover_power + if not p_mutation: + p_mutation = self.p_mutation + if not mutation_power: + mutation_power = self.mutation_power + + selected_individuals = self.selection(generation, scores) + offsprings = self.crossover(selected_individuals, p_crossover=p_crossover, crossover_power=crossover_power) + next = self.mutation(offsprings, p_mutation=p_mutation, mutation_power=mutation_power) + for i in range(self.population_size): + next[i]["model_path"] = str(Path(self.params["model_path"]).joinpath( + "population_" + str(iter)).joinpath(next[i]["model_name"] + "_" + str(i))) + + return next + + def selection(self, population, scores): + """ + Select self.population_size individuums (with replacement) from given population. + Probability of i-th individuum to be selected is scores_i / sum_j(scores_j) + Args: + population: self.population_size individuums + scores: corresponding score that should be maximized + + Returns: + selected self.population_size individuums with replacement + """ + scores = np.array(scores, dtype='float') + scores = (scores - 1.1 * min(scores) + 0.1 * max(scores)) / max(scores) + total = np.sum(scores) + probas_to_be_selected = scores / total + intervals = np.array([np.sum(probas_to_be_selected[:i]) for i in range(self.population_size)]) + selected = [] + for i in range(self.population_size): + r = np.random.random() + individuum = population[np.where(r > intervals)[0][-1]] + selected.append(individuum) + return selected + + def crossover(self, population, p_crossover, crossover_power): + """ + Recombine randomly population in pairs and cross over them with given probability. + Cross over from two parents produces two offsprings + each of which contains half of the parameter values from one parent and the other half from the other parent + Args: + population: self.population_size individuums + p_crossover: probability to cross over for current replacement + crossover_power: part of parents parameters to exchange for offsprings + + Returns: + self.population_size offsprings + """ + perm = np.random.permutation(self.population_size) + offsprings = [] + for i in range(self.population_size // 2): + parents = population[perm[2 * i]], population[perm[2 * i + 1]] + if self.decision(p_crossover): + params_perm = np.random.permutation(self.n_params) + curr_offsprings = [{}, {}] + part = int(crossover_power * self.n_params) + for j in range(self.n_params - part): + curr_offsprings[0][self.params_names[params_perm[j]]] = parents[0][ + self.params_names[params_perm[j]]] + curr_offsprings[1][self.params_names[params_perm[j]]] = parents[1][ + self.params_names[params_perm[j]]] + for j in range(self.n_params - part, self.n_params): + curr_offsprings[0][self.params_names[params_perm[j]]] = parents[1][ + self.params_names[params_perm[j]]] + curr_offsprings[1][self.params_names[params_perm[j]]] = parents[0][ + self.params_names[params_perm[j]]] + offsprings.extend(curr_offsprings) + else: + offsprings.extend(parents) + + if self.population_size % 2 == 1: + offsprings.append(population[perm[-1]]) + return offsprings + + def mutation(self, population, p_mutation, mutation_power): + """ + Mutate each parameter of each individuum in population with probability p_mutation + Args: + population: self.population_size individuums + p_mutation: probability to mutate for each parameter + mutation_power: allowed percentage of mutation + + Returns: + mutated population + """ + mutated = [] + for individuum in population: + mutated_individuum = {} + for param in self.params_names: + if self.decision(p_mutation): + if type(self.params[param]) is dict: + if self.params[param].get('discrete', False): + val = round(individuum[param] + + ((2 * np.random.random() - 1.) * mutation_power + * self.sample_params(**{param: self.params[param]})[param])) + val = min(max(self.params[param]["range"][0], val), + self.params[param]["range"][1]) + mutated_individuum[param] = val + elif 'range' in self.params[param].keys(): + val = individuum[param] + \ + ((2 * np.random.random() - 1.) * mutation_power + * self.sample_params(**{param: self.params[param]})[param]) + val = min(max(self.params[param]["range"][0], val), + self.params[param]["range"][1]) + mutated_individuum[param] = val + elif "choice" in self.params[param].keys(): + mutated_individuum[param] = individuum[param] + else: + mutated_individuum[param] = individuum[param] + else: + mutated_individuum[param] = individuum[param] + mutated.append(mutated_individuum) + return mutated + + def decision(self, probability): + """ + Make decision whether to do action or not with given probability + Args: + probability: probability whether + + Returns: + + """ + r = np.random.random() + if r < probability: + return True + else: + return False + + def sample_params(self, **params): + if not params: + params_copy = deepcopy(self.params) + else: + params_copy = deepcopy(params) + params_sample = dict() + for param, param_val in params_copy.items(): + if isinstance(param_val, list): + params_sample[param] = np.random.choice(param_val) + elif isinstance(param_val, dict): + if 'bool' in param_val and param_val['bool']: + sample = np.random.choice([True, False]) + elif 'range' in param_val: + sample = self._sample_from_ranges(param_val) + params_sample[param] = sample + else: + params_sample[param] = params_copy[param] + return params_sample + + def _sample_from_ranges(self, opts): + from_ = opts['range'][0] + to_ = opts['range'][1] + if opts.get('scale', None) == 'log': + sample = self._sample_log(from_, to_) + else: + sample = np.random.uniform(from_, to_) + if opts.get('discrete', False): + sample = int(np.round(sample)) + return sample + + @staticmethod + def _sample_log(from_, to_): + sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) + return float(sample) + diff --git a/deeppavlov/models/evolution/random_param_generator.py b/deeppavlov/models/evolution/random_param_generator.py new file mode 100644 index 0000000000..df81713585 --- /dev/null +++ b/deeppavlov/models/evolution/random_param_generator.py @@ -0,0 +1,85 @@ +import numpy as np +from copy import deepcopy +from pathlib import Path + + +class HyperPar: + def __init__(self, **kwargs): + self.params = kwargs + + def sample_params(self): + params = deepcopy(self.params) + params_sample = dict() + for param, param_val in params.items(): + if isinstance(param_val, list): + params_sample[param] = np.random.choice(param_val) + elif isinstance(param_val, dict): + if 'bool' in param_val and param_val['bool']: + sample = np.random.choice([True, False]) + elif 'range' in param_val: + sample = self._sample_from_ranges(param_val) + params_sample[param] = sample + else: + params_sample[param] = params[param] + return params_sample + + def _sample_from_ranges(self, opts): + from_ = opts['range'][0] + to_ = opts['range'][1] + if opts.get('scale', None) == 'log': + sample = self._sample_log(from_, to_) + else: + sample = np.random.uniform(from_, to_) + if opts.get('discrete', False): + sample = int(np.round(sample)) + return sample + + @staticmethod + def _sample_log(from_, to_): + sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) + return float(sample) + +# net_params = HyperPar(n_filters={'range': [32, 500], 'discrete': True, 'n_samples': n_layers, 'increasing': True}, +# filter_width={'range': [3, 11], 'discrete': True}, +# char_embeddings_dim={'range': [10, 50], 'discrete': True}, +# embeddings_dropout={'bool': True}, +# dense_dropout={'bool': True}, +# net_type=['cnn', 'rnn', 'cnn_highway'], +# use_crf=True, +# use_batch_norm=True, +# token_embeddings_dim=token_emb_dim, +# two_dense_layers=True) +# parms = net_params.sample_params() +# learning_params = HyperPar(dropout_rate={'range': [0.1, 0.9]}, +# epochs={'range': [10, 100], 'discrete': True}, +# learning_rate={'range': [1e-4, 1e-2], 'scale': 'log'}, +# batch_size={'range': [2, 64], 'discrete': True}, +# learning_rate_decay={'range': [0.3, 0.95]}, +# save_path='conll_models/model.ckpt').sample_params() + + +def get_population(basic_params, population_size, population_num): + population = [] + for i in range(population_size): + params = {} + params_for_search = {} + + for param_name in basic_params.keys(): + if ((type(basic_params[param_name]) is str) + or (type(basic_params[param_name]) is int) + or (type(basic_params[param_name]) is float) + or (type(basic_params[param_name]) is bool) + or (type(basic_params[param_name]) is list)): + params[param_name] = basic_params[param_name] + else: + if "values" in basic_params[param_name].keys(): + params_for_search[param_name] = list(basic_params[param_name]["values"]) + else: + params_for_search[param_name] = basic_params[param_name] + + params_for_search = HyperPar(**params_for_search).sample_params() + print() + params["model_path"] = str(Path(basic_params["model_path"]).joinpath( + "population_" + str(population_num)).joinpath(params_for_search["model_name"] + "_" + str(i))) + population.append({**params, **params_for_search}) + return population diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py new file mode 100644 index 0000000000..a8620f31ef --- /dev/null +++ b/deeppavlov/models/evolution/utils.py @@ -0,0 +1,128 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import numpy as np +import sys +import hashlib + +from deeppavlov.core.common.log import get_logger + + +log = get_logger(__name__) + + +def labels2onehot(labels, classes): + """ + Convert labels to one-hot vectors for multi-class multi-label classification + Args: + labels: list of samples where each sample is a list of classes which sample belongs with + classes: array of classes' names + + Returns: + 2d array with one-hot representation of given samples + """ + n_classes = len(classes) + eye = np.eye(n_classes) + y = [] + for sample in labels: + curr = np.zeros(n_classes) + for intent in sample: + if intent not in classes: + log.warning('Unknown intent {} detected'.format(intent)) + curr += eye[np.where(np.array(classes) == 'unknown')[0]].reshape(-1) + else: + curr += eye[np.where(np.array(classes) == intent)[0]].reshape(-1) + y.append(curr) + y = np.asarray(y) + return y + + +def proba2labels(proba, confident_threshold, classes): + """ + Convert vectors of probabilities to labels using confident threshold + (if probability to belong with the class is bigger than confident_threshold, sample belongs with the class; + if no probabilities bigger than confident threshold, sample belongs with the class with the biggest probability) + Args: + proba: list of samples where each sample is a vector of probabilities to belong with given classes + confident_threshold (float): boundary of probability to belong with a class + classes: array of classes' names + + Returns: + array of lists of labels for each sample + """ + y = [] + for sample in proba: + to_add = np.where(sample > confident_threshold)[0] + if len(to_add) > 0: + y.append(np.array(classes)[to_add]) + else: + y.append(np.array([np.array(classes)[np.argmax(sample)]])) + y = np.asarray(y) + return y + + +def proba2onehot(proba, confident_threshold, classes): + """ + Convert vectors of probabilities to one-hot representations using confident threshold + Args: + proba: list of samples where each sample is a vector of probabilities to belong with given classes + confident_threshold: boundary of probability to belong with a class + classes: array of classes' names + + Returns: + 2d array with one-hot representation of given samples + """ + return labels2onehot(proba2labels(proba, confident_threshold, classes), classes) + + +def log_metrics(names, values, updates=None, mode='train'): + """ + Print training and validation data in the following view: + `mode --> updates: 0 names[0]: 0.0 names[1]: 0.0 names[2]: 0.0` + Args: + names: list of names of considered metrics + values: list of values of considered metrics + updates: number of updates + mode: dataset field on which calculation is being doing (i.e "train") + + Returns: + None + """ + sys.stdout.write("\r") # back to previous line + log.info("{} -->\t".format(mode)) + if updates is not None: + log.info("updates: {}\t".format(updates)) + + for id in range(len(names)): + log.info("{}: {}\t".format(names[id], values[id])) + return + + +def md5_hashsum(file_names): + """ + Calculate md5 hash sum of files listed + Args: + file_names: list of file names + + Returns: + hashsum string + """ + hash_md5 = hashlib.md5() + for file_name in file_names: + with open(file_name, "rb") as f: + for chunk in iter(lambda: f.read(4096), b""): + hash_md5.update(chunk) + return hash_md5.hexdigest() From 80fae02d7f5280330e821f3ef0286a0289b2dfd7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 13 Apr 2018 17:28:40 +0300 Subject: [PATCH 023/616] feat: check and correct binary mask done --- .../models/evolution/check_binary_mask.py | 28 +++--- .../models/evolution/check_matrix.ipynb | 89 ++++++++++--------- 2 files changed, 63 insertions(+), 54 deletions(-) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index fe61e3e188..dc07ebbc62 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -67,13 +67,14 @@ def get_binary_mask_from_digraph(nodes, directed_graph): # return True, binary_mask -def check_binary_mask(nodes, binary_mask): +def check_and_correct_binary_mask(nodes, binary_mask): directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) sources, sinks = find_sources_and_sinks(directed_graph) while not nx.is_directed_acyclic_graph(directed_graph): candidates = [] cycles = list(nx.simple_cycles(directed_graph)) + n_cycles = len(cycles) print("Cycles: {}".format(cycles)) # number of candidates to be the best new graph cycles_len = np.array([len(cycle) for cycle in cycles]) @@ -81,18 +82,23 @@ def check_binary_mask(nodes, binary_mask): for i in range(n_candidates): new_directed_graph = copy.deepcopy(directed_graph) + for j in range(n_cycles): + node_id = (i // np.prod(cycles_len[:j])) % cycles_len[j] + new_directed_graph.remove_edge(cycles[j][node_id], cycles[j][(node_id + 1) % cycles_len[j]]) candidates.append(new_directed_graph) - for j, cycle_ in enumerate(cycles): - cycle = copy.deepcopy(cycle_) + [cycle_[0]] - for i in range(len(cycle_)): - candidates[].remove_edge(cycle[i], cycle[i + 1]) - new_sources, new_sinks = find_sources_and_sinks(new_directed_graph) - if set(new_sources).issuperset(set(sources)) and set(new_sinks).issuperset(set(sinks)): - directed_graph.remove_edge(cycle[i], cycle[i + 1]) - continue - else: - new_directed_graph.add_edge(cycle[i], cycle[i + 1]) + best_cand = None + best_diff = 10e10 + for i in range(n_candidates): + new_sources, new_sinks = find_sources_and_sinks(candidates[i]) + if set(new_sources) == set(sources) and set(new_sinks) == set(sinks): + best_cand = candidates[i] + elif (len(set(new_sources).difference(set(sources))) + + len(set(new_sinks).difference(set(sinks))) < best_diff): + best_cand = candidates[i] + best_diff = len(set(new_sources).difference(set(sources))) + len(set(new_sinks).difference(set(sinks))) + + directed_graph = best_cand binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) return True, binary_mask diff --git a/deeppavlov/models/evolution/check_matrix.ipynb b/deeppavlov/models/evolution/check_matrix.ipynb index 4bcf35ace6..cb9f479e64 100644 --- a/deeppavlov/models/evolution/check_matrix.ipynb +++ b/deeppavlov/models/evolution/check_matrix.ipynb @@ -2,21 +2,21 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import networkx as nx\n", - "from check_binary_mask import check_binary_mask\n", + "from check_binary_mask import check_and_correct_binary_mask\n", "from check_binary_mask import number_to_type_layer\n", "from check_binary_mask import type_layer_to_number" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -35,20 +35,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cm = np.zeros((total_nodes, total_nodes)) \n", "cm[0, 1] = 1\n", @@ -84,7 +73,7 @@ "metadata": {}, "outputs": [], "source": [ - "check_binary_mask(nodes, cm)" + "check_and_correct_binary_mask(nodes, cm)" ] }, { @@ -131,24 +120,27 @@ "cm[5, 3] = 1\n", "\n", "get_graph_and_plot(nodes, cm)\n", - "_, new_cm = check_binary_mask(nodes, cm)\n", + "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", "get_graph_and_plot(nodes, new_cm)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "dg = nx.DiGraph()" + "cm = np.zeros((total_nodes, total_nodes)) \n", + "cm[0, 1] = 1\n", + "cm[0, 3] = 1\n", + "cm[3, 1] = 1\n", + "cm[4, 5] = 1\n", + "cm[5, 2] = 1\n", + "cm[2, 4] = 1\n", + "\n", + "get_graph_and_plot(nodes, cm)\n", + "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "get_graph_and_plot(nodes, new_cm)" ] }, { @@ -157,8 +149,18 @@ "metadata": {}, "outputs": [], "source": [ - "for i in range(6):\n", - " dg.add_node(i)" + "cm = np.zeros((total_nodes, total_nodes)) \n", + "cm[0, 1] = 1\n", + "cm[0, 3] = 1\n", + "cm[3, 1] = 1\n", + "cm[4, 5] = 1\n", + "cm[5, 2] = 1\n", + "cm[2, 4] = 1\n", + "cm[3, 4] = 1\n", + "\n", + "get_graph_and_plot(nodes, cm)\n", + "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "get_graph_and_plot(nodes, new_cm)" ] }, { @@ -167,12 +169,19 @@ "metadata": {}, "outputs": [], "source": [ - "dg.add_edge(0, 1)\n", - "dg.add_edge(0, 3)\n", - "dg.add_edge(3, 1)\n", - "dg.add_edge(5, 2)\n", - "dg.add_edge(3, 5)\n", - "dg.add_edge(5, 3)" + "cm = np.zeros((total_nodes, total_nodes)) \n", + "cm[0, 1] = 1\n", + "cm[0, 3] = 1\n", + "cm[3, 1] = 1\n", + "cm[4, 5] = 1\n", + "cm[5, 2] = 1\n", + "cm[2, 4] = 1\n", + "cm[3, 4] = 1\n", + "cm[4, 3] = 1\n", + "\n", + "get_graph_and_plot(nodes, cm)\n", + "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "get_graph_and_plot(nodes, new_cm)" ] }, { @@ -180,27 +189,21 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "dg.edges()" - ] + "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "dg.remove_edge(3, 5)" - ] + "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "dg.edges()" - ] + "source": [] }, { "cell_type": "code", From 4a271be9e807b3031a6c037d05e4110dd6eff0eb Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 13 Apr 2018 18:16:55 +0300 Subject: [PATCH 024/616] feat: add keras additive and multiplicative attention layers --- deeppavlov/core/layers/keras_layers.py | 101 +++++++++++++++++++++++++ 1 file changed, 101 insertions(+) create mode 100644 deeppavlov/core/layers/keras_layers.py diff --git a/deeppavlov/core/layers/keras_layers.py b/deeppavlov/core/layers/keras_layers.py new file mode 100644 index 0000000000..710156df53 --- /dev/null +++ b/deeppavlov/core/layers/keras_layers.py @@ -0,0 +1,101 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from keras import backend as K +from keras.layers import Dense, Reshape, Concatenate, Lambda, Multiply +from keras.activations import softmax + + +def expand_tile(units, axis): + """Expand and tile tensor along given axis + Args: + units: tf tensor with dimensions [batch_size, time_steps, n_input_features] + axis: axis along which expand and tile. Must be 1 or 2 + + """ + assert axis in (1, 2) + n_time_steps = K.int_shape(units)[1] + repetitions = [1, 1, 1, 1] + repetitions[axis] = n_time_steps + if axis == 1: + expanded = Reshape(target_shape=( (1,) + K.int_shape(units)[1:] ))(units) + else: + expanded = Reshape(target_shape=(K.int_shape(units)[1:2] + (1,) + K.int_shape(units)[2:]))(units) + return K.tile(expanded, repetitions) + + +def softvaxaxis2(x): + return softmax(x, axis=2) + + +def additive_self_attention(units, n_hidden=None, n_output_features=None, activation=None): + """ Computes additive self attention for time series of vectors (with batch dimension) + the formula: score(h_i, h_j) = + v is a learnable vector of n_hidden dimensionality, + W_1 and W_2 are learnable [n_hidden, n_input_features] matrices + + Args: + units: tf tensor with dimensionality [batch_size, time_steps, n_input_features] + n_hidden: number of2784131 units in hidden representation of similarity measure + n_output_features: number of features in output dense layer + activation: activation at the output + + Returns: + output: self attended tensor with dimensionality [batch_size, time_steps, n_output_features] + """ + n_input_features = K.int_shape(units)[2] + if n_hidden is None: + n_hidden = n_input_features + if n_output_features is None: + n_output_features = n_input_features + exp1 = Lambda(lambda x: expand_tile(x, axis=1))(units) + exp2 = Lambda(lambda x: expand_tile(x, axis=2))(units) + units_pairs = Concatenate(axis=3)([exp1, exp2]) + query = Dense(n_hidden, activation="tanh")(units_pairs) + attention = Dense(1, activation=softvaxaxis2)(query) + attended_units = Lambda(lambda x: K.sum(attention * x, axis=2))(exp1) + output = Dense(n_output_features, activation=activation)(attended_units) + return output + + +def multiplicative_self_attention(units, n_hidden=None, n_output_features=None, activation=None): + """ Computes multiplicative self attention for time series of vectors (with batch dimension) + the formula: score(h_i, h_j) = , W_1 and W_2 are learnable matrices + with dimensionality [n_hidden, n_input_features] + + Args: + units: tf tensor with dimensionality [batch_size, time_steps, n_input_features] + n_hidden: number of units in hidden representation of similarity measure + n_output_features: number of features in output dense layer + activation: activation at the output + + Returns: + output: self attended tensor with dimensionality [batch_size, time_steps, n_output_features] + """ + n_input_features = K.int_shape(units)[2] + if n_hidden is None: + n_hidden = n_input_features + if n_output_features is None: + n_output_features = n_input_features + exp1 = Lambda(lambda x: expand_tile(x, axis=1))(units) + exp2 = Lambda(lambda x: expand_tile(x, axis=2))(units) + queries = Dense(n_hidden)(exp1) + keys = Dense(n_hidden)(exp2) + scores = Lambda(lambda x: K.sum(queries * x, axis=3, keepdims=True))(keys) + attention = Lambda(lambda x: softvaxaxis2(x))(scores) + mult = Multiply()([attention, exp1]) + attended_units = Lambda(lambda x: K.sum(x, axis=2))(mult) + output = Dense(n_output_features, activation=activation)(attended_units) + return output From 3eb51952e1dd3d0c544aeee9bcc1849a95f49363 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 13 Apr 2018 18:25:09 +0300 Subject: [PATCH 025/616] chore: basic config --- .../evolution/basic_intents_snips.json | 202 ++++++++++++++++++ .../models/evolution/check_binary_mask.py | 46 ++-- .../models/evolution/check_matrix.ipynb | 8 +- deeppavlov/models/evolution/evolution.py | 106 +++++++++ .../neuroevolution_param_generator.py | 4 +- deeppavlov/models/evolution/utils.py | 3 + 6 files changed, 341 insertions(+), 28 deletions(-) create mode 100644 deeppavlov/configs/evolution/basic_intents_snips.json diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json new file mode 100644 index 0000000000..12a6ed7671 --- /dev/null +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -0,0 +1,202 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "intents", + "data_path": "snips", + "url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator", + "seed": 42, + "field_to_split": "train", + "split_fields": [ + "train", + "valid" + ], + "split_proportions": [ + 0.9, + 0.1 + ] + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "vocabs/snips_classes.dict", + "load_path": "vocabs/snips_classes.dict" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_predicted" + ], + "main": true, + "name": "intent_model", + "save_path": "intents/intent_cnn_snips_v3", + "load_path": "intents/intent_cnn_snips_v3", + "classes": "#classes_vocab.keys()", + "layers": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + } + }, + "LSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + }, + "dropout": { + "range": [ + 1e-2, + 7e-1 + ] + }, + "recurrent_dropout": { + "range": [ + 1e-2, + 7e-1 + ] + } + }, + "BiLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + }, + "dropout": { + "range": [ + 1e-2, + 7e-1 + ] + }, + "recurrent_dropout": { + "range": [ + 1e-2, + 7e-1 + ] + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + } + }, + "Attention": { + } + }, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 0.01, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 15, + "model_name": "cnn_model", + "embedder": { + "name": "fasttext", + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "dim": 100 + }, + "tokenizer": { + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + } + } + ], + "out": [ + "y_predicted" + ] + }, + "train": { + "epochs": 1000, + "batch_size": 64, + "metrics": [ + "sets_accuracy" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} \ No newline at end of file diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index dc07ebbc62..1644534291 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -44,27 +44,6 @@ def get_binary_mask_from_digraph(nodes, directed_graph): for edge in directed_graph.edges(): binary_mask[edge[0], edge[1]] = 1 return binary_mask -# -# -# def check_binary_mask(nodes, binary_mask): -# directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) -# sources, sinks = find_sources_and_sinks(directed_graph) -# -# while not nx.is_directed_acyclic_graph(directed_graph): -# cycles = list(nx.simple_cycles(directed_graph)) -# print("Cycles: {}".format(cycles)) -# for cycle_ in cycles: -# cycle = copy.deepcopy(cycle_) + [cycle_[0]] -# for i in range(len(cycle_)): -# new_directed_graph = copy.deepcopy(directed_graph) -# new_directed_graph.remove_edge(cycle[i], cycle[i+1]) -# new_sources, new_sinks = find_sources_and_sinks(new_directed_graph) -# if nx.is_directed_acyclic_graph(new_directed_graph): -# if set(new_sources) == set(sources) and set(new_sinks) == set(sinks): -# directed_graph.remove_edge(cycle[i], cycle[i+1]) -# continue -# binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) -# return True, binary_mask def check_and_correct_binary_mask(nodes, binary_mask): @@ -101,4 +80,27 @@ def check_and_correct_binary_mask(nodes, binary_mask): directed_graph = best_cand binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) - return True, binary_mask + return binary_mask + +# def get_graph_and_plot(nodes, binary_mask, n_types): +# import matplotlib.pyplot as plt +# +# total_nodes = len(nodes) +# dg = nx.DiGraph() +# +# for i in range(total_nodes): +# dg.add_node(i) +# +# pos = {} +# +# for i in range(total_nodes): +# for j in range(total_nodes): +# if binary_mask[i,j] == 1: +# dg.add_edge(i, j) +# pos[i] = np.array(number_to_type_layer(i, n_types))[::-1] +# +# plt.figure(figsize=(6, 6)) +# nx.draw(dg, pos, node_color='b', node_size=5000, alpha=0.3) +# +# nx.draw_networkx_labels(dg, pos, nodes, font_size=18) +# plt.show() diff --git a/deeppavlov/models/evolution/check_matrix.ipynb b/deeppavlov/models/evolution/check_matrix.ipynb index cb9f479e64..898d23aa67 100644 --- a/deeppavlov/models/evolution/check_matrix.ipynb +++ b/deeppavlov/models/evolution/check_matrix.ipynb @@ -120,7 +120,7 @@ "cm[5, 3] = 1\n", "\n", "get_graph_and_plot(nodes, cm)\n", - "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "new_cm = check_and_correct_binary_mask(nodes, cm)\n", "get_graph_and_plot(nodes, new_cm)" ] }, @@ -139,7 +139,7 @@ "cm[2, 4] = 1\n", "\n", "get_graph_and_plot(nodes, cm)\n", - "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "new_cm = check_and_correct_binary_mask(nodes, cm)\n", "get_graph_and_plot(nodes, new_cm)" ] }, @@ -159,7 +159,7 @@ "cm[3, 4] = 1\n", "\n", "get_graph_and_plot(nodes, cm)\n", - "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "new_cm = check_and_correct_binary_mask(nodes, cm)\n", "get_graph_and_plot(nodes, new_cm)" ] }, @@ -180,7 +180,7 @@ "cm[4, 3] = 1\n", "\n", "get_graph_and_plot(nodes, cm)\n", - "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "new_cm = check_and_correct_binary_mask(nodes, cm)\n", "get_graph_and_plot(nodes, new_cm)" ] }, diff --git a/deeppavlov/models/evolution/evolution.py b/deeppavlov/models/evolution/evolution.py index e69de29bb2..adcb6a5e62 100644 --- a/deeppavlov/models/evolution/evolution.py +++ b/deeppavlov/models/evolution/evolution.py @@ -0,0 +1,106 @@ +import json +import numpy as np +import argparse +from pathlib import Path +from subprocess import Popen, PIPE +import pandas as pd + + +from tuning_parameters.neuroevolution_param_generator import Evolution + + +def score_population(population, population_size, result_file): + population_losses = [] + population_fmeasures = [] + population_accuracies = [] + population_roc_auc_scores = [] + + procs = [] + + for i in range(population_size): + f_name = Path(population[i]["model_path"]) + try: + f_name.mkdir(parents=True) + except FileExistsError: + pass + f_name = f_name.joinpath("config.json") + with open(f_name, 'w') as outfile: + json.dump(population[i], outfile) + + procs.append(Popen("CUDA_VISIBLE_DEVICES={} python train_phenotype.py {}" + " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], + str(f_name), + population[i]["model_path"], + population[i]["model_path"]), + shell=True, stdout=PIPE, stderr=PIPE)) + + for i, proc in enumerate(procs): + print(f'wait on {i}th proc') + proc.wait() + + for i in range(population_size): + val_results = np.loadtxt(fname=str(Path(population[i]["model_path"]).joinpath("valid_results.txt"))) + result_table = pd.DataFrame({"loss": [val_results[0]], + "accuracy": [val_results[1]], + "fmeasure": [val_results[2]], + "roc_auc_score": [val_results[3]], + "params": [population[i]]}) + result_table.loc[:, order].to_csv(result_file, index=False, sep='\t', mode='a', header=None) + population_losses.append(val_results[0]) + population_accuracies.append(val_results[1]) + population_fmeasures.append(val_results[2]) + population_roc_auc_scores.append(val_results[3]) + + return population_roc_auc_scores + + +parser = argparse.ArgumentParser() + +parser.add_argument('--config', help='Please, enter model path to config', default='./configs/basic_config.json') +parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) +parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) + +args = parser.parse_args() + +CONFIG_FILE = args.config +POPULATION_SIZE = args.p_size +GPU_NUMBER = len(args.gpus) +gpus = [int(gpu) for gpu in args.gpus.split(",")] + +with open(CONFIG_FILE, "r") as f: + basic_params = json.load(f) + +print("Given basic params: {}\n".format(basic_params)) + +try: + Path(basic_params["model_path"]).mkdir(parents=True) +except FileExistsError: + pass + +# Result table +order = ["loss", "accuracy", "fmeasure", "roc_auc_score", "params"] +result_file = Path(basic_params["model_path"]).joinpath("result_table.csv") +result_table = pd.DataFrame({"loss": [], "accuracy": [], "fmeasure": [], "roc_auc_score": [], "params": []}) +result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') + +# EVOLUTION starts here! +evolution = Evolution(population_size=POPULATION_SIZE, p_crossover=0.1, + p_mutation=0.5, mutation_power=0.1, **basic_params) + +print("\nIteration #{} starts\n".format(0)) +population = evolution.first_generation() +print("Considered population: {}\nScoring...\n".format(population)) +population_roc_auc_scores = score_population(population, POPULATION_SIZE, result_file) + +iters = 1 + +while True: + print("\nIteration #{} starts\n".format(iters)) + + population = evolution.next_generation(population, population_roc_auc_scores, iter=iters) + print("Considered population: {}\nScoring...\n".format(population)) + population_roc_auc_scores = score_population(population, POPULATION_SIZE, result_file) + + print("\nIteration #{} was done\n".format(iters)) + iters += 1 + diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 625e06c1d3..f70cd159f4 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -57,7 +57,7 @@ def first_generation(self, iter=0): or (type(self.params[param_name]) is list)): params[param_name] = deepcopy(self.params[param_name]) else: - if "choice" in self.params[param_name].keys(): + if self.params[param_name].get("choice"): params_for_search[param_name] = list(self.params[param_name]["values"]) else: params_for_search[param_name] = deepcopy(self.params[param_name]) @@ -200,7 +200,7 @@ def mutation(self, population, p_mutation, mutation_power): val = min(max(self.params[param]["range"][0], val), self.params[param]["range"][1]) mutated_individuum[param] = val - elif "choice" in self.params[param].keys(): + elif self.params[param].get("choice"): mutated_individuum[param] = individuum[param] else: mutated_individuum[param] = individuum[param] diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index a8620f31ef..4541df98f1 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -126,3 +126,6 @@ def md5_hashsum(file_names): for chunk in iter(lambda: f.read(4096), b""): hash_md5.update(chunk) return hash_md5.hexdigest() + + +def Attention(): From f5cd91ea352da39b5bd7da766648f9af3062b7e0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 16 Apr 2018 13:29:34 +0300 Subject: [PATCH 026/616] feat: add attention layer --- deeppavlov/models/evolution/utils.py | 59 +++++++++++++++++++++++++++- 1 file changed, 58 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index 4541df98f1..7c8007ec0f 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -18,7 +18,11 @@ import sys import hashlib +from keras.engine.topology import Layer from deeppavlov.core.common.log import get_logger +from keras import initializers, regularizers, constraints +from keras import backend as K +from keras.layers import concatenate, multiply log = get_logger(__name__) @@ -128,4 +132,57 @@ def md5_hashsum(file_names): return hash_md5.hexdigest() -def Attention(): +class Attention(Layer): + def __init__(self, context_length=None, + W_regularizer=None, b_regularizer=None, + W_constraint=None, b_constraint=None, + use_bias=True, **kwargs): + self.supports_masking = True + self.init = initializers.get('glorot_uniform') + self.W_regularizer = regularizers.get(W_regularizer) + self.b_regularizer = regularizers.get(b_regularizer) + self.W_constraint = constraints.get(W_constraint) + self.b_constraint = constraints.get(b_constraint) + self.use_bias = use_bias + self.context_length = context_length + + super(Attention, self).__init__(**kwargs) + + def build(self, input_shape): + assert len(input_shape) == 3 + + if self.context_length is None: + self.context_length = input_shape[-1] + + self.context = self.add_weight(tuple(input_shape[:-1] + (self.context_length,)), + initializer=self.init) + + self.W = self.add_weight((input_shape[-1] + self.context_length, input_shape[-1], ), + initializer=self.init, + regularizer=self.W_regularizer, + constraint=self.W_constraint) + + if self.use_bias: + self.b = self.add_weight((input_shape[-1], ), + initializer='zero', + regularizer=self.b_regularizer, + constraint=self.b_constraint) + else: + self.b = None + + self.built = True + + def call(self, x, mask=None): + x_full = concatenate(inputs=[x, self.context], axis=-1) + + out = K.dot(x_full, self.W) + if self.use_bias: + out = K.bias_add(out, self.b) + + out = K.softmax(out) + out = multiply(inputs=[out, x]) + + return out + + def compute_output_shape(self, input_shape): + return input_shape \ No newline at end of file From 46b94aa184844da019be455b6bfafa66a6ee7fbc Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 17 Apr 2018 17:43:03 +0300 Subject: [PATCH 027/616] feat: moved embedder and tokenizer in pipe --- .../evolution/basic_intents_snips.json | 75 ++++++++++++++----- deeppavlov/configs/intents/intents_snips.json | 52 ++++++++----- 2 files changed, 92 insertions(+), 35 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json index 12a6ed7671..f821ff7b7b 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -37,6 +37,18 @@ "save_path": "vocabs/snips_classes.dict", "load_path": "vocabs/snips_classes.dict" }, + { + "id": "fasttext_embedder", + "name": "fasttext", + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "dim": 100 + }, + { + "id": "nltk_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, { "in": [ "x" @@ -49,10 +61,11 @@ ], "main": true, "name": "intent_model", - "save_path": "intents/intent_cnn_snips_v3", - "load_path": "intents/intent_cnn_snips_v3", + "save_path": "evolution/intents_snips", + "load_path": "evolution/intents_snips", "classes": "#classes_vocab.keys()", - "layers": { + "to_evolve": true, + "basic_layers_params": { "Dense": { "units": { "range": [ @@ -156,25 +169,39 @@ } }, "Attention": { + "context_length": { + "range": [ + 50, + 200 + ], + "discrete": true + } } }, - "confident_threshold": 0.5, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, "optimizer": "Adam", - "lear_rate": 0.01, - "lear_rate_decay": 0.1, + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, "loss": "binary_crossentropy", "text_size": 15, "model_name": "cnn_model", - "embedder": { - "name": "fasttext", - "save_path": "embeddings/dstc2_fastText_model.bin", - "load_path": "embeddings/dstc2_fastText_model.bin", - "dim": 100 - }, - "tokenizer": { - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - } + "embedder": "#fasttext_embedder", + "tokenizer": "#nltk_tokenizer" } ], "out": [ @@ -182,8 +209,20 @@ ] }, "train": { - "epochs": 1000, - "batch_size": 64, + "epochs": { + "range": [ + 10, + 1000 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 200 + ], + "discrete": true + }, "metrics": [ "sets_accuracy" ], diff --git a/deeppavlov/configs/intents/intents_snips.json b/deeppavlov/configs/intents/intents_snips.json index 0fabb8a0d7..4aaceba7e3 100644 --- a/deeppavlov/configs/intents/intents_snips.json +++ b/deeppavlov/configs/intents/intents_snips.json @@ -20,21 +20,45 @@ ] }, "chainer": { - "in": ["x"], - "in_y": ["y"], + "in": [ + "x" + ], + "in_y": [ + "y" + ], "pipe": [ { "id": "classes_vocab", "name": "default_vocab", - "fit_on": ["y"], + "fit_on": [ + "y" + ], "level": "token", "save_path": "vocabs/snips_classes.dict", "load_path": "vocabs/snips_classes.dict" }, { - "in": ["x"], - "in_y": ["y"], - "out": ["y_predicted"], + "id": "fasttext_embedder", + "name": "fasttext", + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "dim": 100 + }, + { + "id": "nltk_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_predicted" + ], "main": true, "name": "intent_model", "save_path": "intents/intent_cnn_snips_v3", @@ -57,19 +81,13 @@ "dropout_rate": 0.5, "dense_size": 100, "model_name": "cnn_model", - "embedder": { - "name": "fasttext", - "save_path": "embeddings/dstc2_fastText_model.bin", - "load_path": "embeddings/dstc2_fastText_model.bin", - "dim": 100 - }, - "tokenizer": { - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - } + "embedder": "#fasttext_embedder", + "tokenizer": "#nltk_tokenizer" } ], - "out": ["y_predicted"] + "out": [ + "y_predicted" + ] }, "train": { "epochs": 1000, From bf34b0a8a939b009be4263751227e2597c73c565 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 17 Apr 2018 17:43:39 +0300 Subject: [PATCH 028/616] feat: initialization of first generation, selection and crossover work --- deeppavlov/models/evolution/debug.py | 28 ++ .../neuroevolution_param_generator.py | 255 +++++++++++++++--- 2 files changed, 243 insertions(+), 40 deletions(-) create mode 100644 deeppavlov/models/evolution/debug.py diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py new file mode 100644 index 0000000000..ffcfca2989 --- /dev/null +++ b/deeppavlov/models/evolution/debug.py @@ -0,0 +1,28 @@ +import pandas as pd +import json +import numpy as np + +from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution + +n_layers = 2 +n_types = 3 +population_size = 3 +config_path = "../../configs/evolution/basic_intents_snips.json" + +with open(config_path) as fin: + config = json.load(fin) + +evolution = NetworkAndParamsEvolution(n_layers, n_types, + population_size, + key_model_to_evolve="to_evolve", + key_basic_layers="basic_layers_params", + **config) + +population = evolution.first_generation() +print(population) +population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ + evolution.model_to_evolve_index]["binary_mask"].tolist() +print(population) + +evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) +print(population) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index f70cd159f4..c5f75dbcf7 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -1,9 +1,18 @@ import numpy as np from copy import deepcopy from pathlib import Path +import json +from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, number_to_type_layer +from deeppavlov.core.common.file import save_json, read_json -class Evolution: +# TODO: +# if structure of config has been changed, +# please, make sure that +# `config["chainer"]["pipe"]` is a list of models one of which is a model to be evolved, +# otherwise, in the whole class change `config["chainer"]["pipe"]` to new path + +class NetworkAndParamsEvolution: """ Class performs full evolutionary process (task scores -> max): 1. initializes random population @@ -15,28 +24,131 @@ class Evolution: (current mutation power is randomly from -sigma to sigma) """ - def __init__(self, population_size, + def __init__(self, n_layers, n_types, + population_size, p_crossover=0.5, crossover_power=0.5, p_mutation=0.5, mutation_power=0.1, + key_model_to_evolve="to_evolve", + key_basic_layers="basic_layers_params", + seed=None, **kwargs): """ Initialize evolution with random population Args: - population_size: numer of individuums per generation + n_layers: number of available layers of each type + n_types: number of different types of network layers + population_size: number of individuums per generation p_crossover: probability to cross over for current replacement - crossover_power: part of parents parameters to exchange for offsprings + crossover_power: part of EVOLVING parents parameters to exchange for offsprings p_mutation: probability of mutation for current replacement mutation_power: allowed percentage of mutation **kwargs: basic config with parameters """ - self.params = deepcopy(kwargs) + self.n_types = n_types + self.n_layers = n_layers + self.total_nodes = self.n_types * self.n_layers + self.binary_mask_template = np.zeros((self.total_nodes, self.total_nodes)) + + self.basic_config = deepcopy(kwargs) + self.model_to_evolve_index = self._find_model_to_evolve_index_in_pipe(self.basic_config["chainer"]["pipe"], + key_model_to_evolve) + + self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) + self.train_params = deepcopy(self.basic_config.get("train")) + self.basic_layers_params = self.params.pop(key_basic_layers, None) + self.node_types = list(self.basic_layers_params.keys()) + + print("___Basic config___: {}".format(self.basic_config)) + print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) + print("___Model params___: {}".format(self.params)) + print("___Train params___: {}".format(self.train_params)) + print("___Basic layers params___: {}".format(self.basic_layers_params)) + + if self.basic_layers_params is None: + print("\n\n___PARAMS EVOLUTION is being started___") + print("___For network evolution one has to provide config file with `basic_layers_params` key___\n\n") + else: + print("\n\n___NETWORK AND PARAMS EVOLUTION is being started___\n\n") + self.population_size = population_size self.p_crossover = p_crossover self.p_mutation = p_mutation - self.params_names = np.array(list(self.params.keys())) - self.n_params = len(self.params) self.mutation_power = mutation_power self.crossover_power = crossover_power + self.evolving_params = [] + self.n_evolving_params = None + self.evolving_train_params = [] + self.n_evolving_train_params = None + + if seed is None: + pass + else: + np.random.seed(seed) + + def _find_model_to_evolve_index_in_pipe(self, pipe, key): + for element_id, element in enumerate(pipe): + if self._check_if_model_to_evolve(element, key): + return element_id + + def _check_if_model_to_evolve(self, model, key): + if key in model.keys(): + return True + else: + return False + + def _insert_dict_into_model_params(self, params, model_index, dict_to_insert): + params_copy = deepcopy(params) + params_copy["chainer"]["pipe"].insert(model_index, dict_to_insert) + return params_copy + + def print_dict(self, dict, string=None): + if string is None: + print(json.dumps(dict, indent=2)) + else: + print(string) + print(json.dumps(dict, indent=2)) + return + + def initialize_params_in_config(self, basic_params): + params = {} + params_for_search = {} + evolving_params = [] + + for param_name in basic_params.keys(): + if type(basic_params[param_name]) is dict: + if basic_params[param_name].get("choice"): + params_for_search[param_name] = list(basic_params[param_name]["values"]) + evolving_params.append(param_name) + elif basic_params[param_name].get("range"): + params_for_search[param_name] = deepcopy(basic_params[param_name]) + evolving_params.append(param_name) + else: + # NOT evolving params + params[param_name] = deepcopy(basic_params[param_name]) + else: + # NOT evolving params + params[param_name] = deepcopy(basic_params[param_name]) + + params_for_search = deepcopy(self.sample_params(**params_for_search)) + + return params, params_for_search, evolving_params + + def initialize_layers_params(self): + all_layers_params = {} + + for node_id in range(self.total_nodes): + node_layer, node_type = number_to_type_layer(node_id, self.n_types) + node_key = "node_{}_{}".format(node_layer, node_type) + layers_params, layers_params_for_search, _ = self.initialize_params_in_config( + self.basic_layers_params[self.node_types[node_type]]) + + all_layers_params[node_key] = {"node_name": self.node_types[node_type], + "node_type": node_type, + "node_layer": node_layer, + **layers_params, + **layers_params_for_search + } + return all_layers_params def first_generation(self, iter=0): """ @@ -46,31 +158,41 @@ def first_generation(self, iter=0): """ population = [] for i in range(self.population_size): - params = {} - params_for_search = {} - - for param_name in self.params.keys(): - if ((type(self.params[param_name]) is str) - or (type(self.params[param_name]) is int) - or (type(self.params[param_name]) is float) - or (type(self.params[param_name]) is bool) - or (type(self.params[param_name]) is list)): - params[param_name] = deepcopy(self.params[param_name]) - else: - if self.params[param_name].get("choice"): - params_for_search[param_name] = list(self.params[param_name]["values"]) - else: - params_for_search[param_name] = deepcopy(self.params[param_name]) + population.append(deepcopy(self.basic_config)) + + # intitializing parameters for model + params, params_for_search, evolving_params = self.initialize_params_in_config(self.params) + self.evolving_params.extend(evolving_params) + # initializing parameters for train + train_params, train_params_for_search, evolving_params = self.initialize_params_in_config(self.train_params) + self.evolving_train_params.extend(evolving_params) - params_for_search = deepcopy(self.sample_params(**params_for_search)) + # intitializing path to save model if "model_name" in params_for_search.keys(): - params["model_path"] = str(Path(self.params["model_path"]).joinpath( + params["save_path"] = str(Path(self.params["save_path"]).joinpath( "population_" + str(iter)).joinpath(params_for_search["model_name"] + "_" + str(i))) else: - params["model_path"] = str(Path(self.params["model_path"]).joinpath( + params["save_path"] = str(Path(self.params["save_path"]).joinpath( "population_" + str(iter)).joinpath(self.params["model_name"] + "_" + str(i))) - population.append({**params, **params_for_search}) + layers_params = self.initialize_layers_params() + + # exchange model and layers params from basic config to sampled model params + population[-1]["chainer"]["pipe"][self.model_to_evolve_index] = {**params, + **params_for_search, + **layers_params} + # add binary_mask intialization + population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = self.sample_binary_mask() + # exchange train params from basic config to sampled train params + population[-1]["train"] = {**train_params, + **train_params_for_search} + + self.evolving_params = list(set(self.evolving_params)) + self.evolving_train_params = list(set(self.evolving_train_params)) + + self.n_evolving_params = len(self.evolving_params) + self.n_evolving_train_params = len(self.evolving_train_params) + return population def next_generation(self, generation, scores, iter, @@ -138,7 +260,7 @@ def crossover(self, population, p_crossover, crossover_power): Args: population: self.population_size individuums p_crossover: probability to cross over for current replacement - crossover_power: part of parents parameters to exchange for offsprings + crossover_power: part of EVOLVING parents parameters to exchange for offsprings Returns: self.population_size offsprings @@ -148,19 +270,69 @@ def crossover(self, population, p_crossover, crossover_power): for i in range(self.population_size // 2): parents = population[perm[2 * i]], population[perm[2 * i + 1]] if self.decision(p_crossover): - params_perm = np.random.permutation(self.n_params) - curr_offsprings = [{}, {}] - part = int(crossover_power * self.n_params) - for j in range(self.n_params - part): - curr_offsprings[0][self.params_names[params_perm[j]]] = parents[0][ - self.params_names[params_perm[j]]] - curr_offsprings[1][self.params_names[params_perm[j]]] = parents[1][ - self.params_names[params_perm[j]]] - for j in range(self.n_params - part, self.n_params): - curr_offsprings[0][self.params_names[params_perm[j]]] = parents[1][ - self.params_names[params_perm[j]]] - curr_offsprings[1][self.params_names[params_perm[j]]] = parents[0][ - self.params_names[params_perm[j]]] + params_perm = np.random.permutation(self.n_evolving_params) + train_params_perm = np.random.permutation(self.n_evolving_train_params) + nodes_perm = np.random.permutation(self.total_nodes) + + curr_offsprings = [deepcopy(parents[0]), + deepcopy(parents[1])] + + part = int(crossover_power * self.n_evolving_params) + train_part = int(crossover_power * self.n_evolving_params) + nodes_part = int(crossover_power * self.total_nodes) + + # exchange of model params (not layers params) + for j in range(self.n_evolving_params - part): + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[0][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[1][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + for j in range(self.n_evolving_params - part, self.n_evolving_params): + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[1][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[0][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + + # exchange of train params + for j in range(self.n_evolving_train_params - train_part): + curr_offsprings[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] + curr_offsprings[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] + for j in range(self.n_evolving_train_params - train_part, self.n_evolving_train_params): + curr_offsprings[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] + curr_offsprings[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] + + # exchange of nodes (each of which is dict -> deepcopy) + for j in range(self.total_nodes - nodes_part): + node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) + node_key = "node_{}_{}".format(node_layer, node_type) + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( + parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( + parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + for j in range(self.total_nodes - nodes_part, self.total_nodes): + node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) + node_key = "node_{}_{}".format(node_layer, node_type) + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( + parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( + parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + offsprings.extend(curr_offsprings) else: offsprings.extend(parents) @@ -259,3 +431,6 @@ def _sample_log(from_, to_): sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) return float(sample) + def sample_binary_mask(self): + return np.random.randint(0, high=2, size=self.binary_mask_template.shape) + From 4a84d524fc0fd8d8c372a34d8a435732b09bbd8b Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 17 Apr 2018 18:25:16 +0300 Subject: [PATCH 029/616] feat: crossover of binary mask --- .../models/evolution/check_binary_mask.py | 8 ++-- deeppavlov/models/evolution/debug.py | 4 +- .../neuroevolution_param_generator.py | 43 +++++++++++++++++-- deeppavlov/run_model.py | 4 +- 4 files changed, 48 insertions(+), 11 deletions(-) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index 1644534291..50d86c2624 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -1,6 +1,6 @@ import numpy as np import networkx as nx -import copy +from copy import copy, deepcopy def number_to_type_layer(node_id, n_types): @@ -46,7 +46,9 @@ def get_binary_mask_from_digraph(nodes, directed_graph): return binary_mask -def check_and_correct_binary_mask(nodes, binary_mask): +def check_and_correct_binary_mask(nodes, binary_mask_): + binary_mask = deepcopy(binary_mask_) + binary_mask = np.array(binary_mask) directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) sources, sinks = find_sources_and_sinks(directed_graph) @@ -54,7 +56,7 @@ def check_and_correct_binary_mask(nodes, binary_mask): candidates = [] cycles = list(nx.simple_cycles(directed_graph)) n_cycles = len(cycles) - print("Cycles: {}".format(cycles)) + # print("Cycles: {}".format(cycles)) # number of candidates to be the best new graph cycles_len = np.array([len(cycle) for cycle in cycles]) n_candidates = np.prod(cycles_len) diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index ffcfca2989..70e9e23986 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -4,9 +4,9 @@ from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution -n_layers = 2 +n_layers = 3 n_types = 3 -population_size = 3 +population_size = 2 config_path = "../../configs/evolution/basic_intents_snips.json" with open(config_path) as fin: diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index c5f75dbcf7..24813e2f56 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -57,6 +57,7 @@ def __init__(self, n_layers, n_types, self.train_params = deepcopy(self.basic_config.get("train")) self.basic_layers_params = self.params.pop(key_basic_layers, None) self.node_types = list(self.basic_layers_params.keys()) + self.nodes = np.arange(self.total_nodes) print("___Basic config___: {}".format(self.basic_config)) print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) @@ -182,7 +183,11 @@ def first_generation(self, iter=0): **params_for_search, **layers_params} # add binary_mask intialization - population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = self.sample_binary_mask() + print(self.sample_binary_mask()) + print(check_and_correct_binary_mask(self.nodes, self.sample_binary_mask())) + + population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, self.sample_binary_mask()) # exchange train params from basic config to sampled train params population[-1]["train"] = {**train_params, **train_params_for_search} @@ -273,13 +278,15 @@ def crossover(self, population, p_crossover, crossover_power): params_perm = np.random.permutation(self.n_evolving_params) train_params_perm = np.random.permutation(self.n_evolving_train_params) nodes_perm = np.random.permutation(self.total_nodes) + binary_mask_perm = np.random_permutation(self.total_nodes * self.total_nodes) curr_offsprings = [deepcopy(parents[0]), deepcopy(parents[1])] part = int(crossover_power * self.n_evolving_params) - train_part = int(crossover_power * self.n_evolving_params) + train_part = int(crossover_power * self.n_evolving_train_params) nodes_part = int(crossover_power * self.total_nodes) + binary_mask_part = int(crossover_power * self.total_nodes * self.total_nodes) # exchange of model params (not layers params) for j in range(self.n_evolving_params - part): @@ -317,10 +324,11 @@ def crossover(self, population, p_crossover, crossover_power): self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ self.evolving_train_params[train_params_perm[j]]] - # exchange of nodes (each of which is dict -> deepcopy) + # exchange of nodes for j in range(self.total_nodes - nodes_part): node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) node_key = "node_{}_{}".format(node_layer, node_type) + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( @@ -328,11 +336,38 @@ def crossover(self, population, p_crossover, crossover_power): for j in range(self.total_nodes - nodes_part, self.total_nodes): node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) node_key = "node_{}_{}".format(node_layer, node_type) + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + # exchange of binary mask elements + for j in range(self.total_nodes * self.total_nodes - binary_mask_part): + node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes + + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ + parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ + parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] + + for j in range(self.total_nodes * self.total_nodes - binary_mask_part, + self.total_nodes * self.total_nodes): + node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes + + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ + parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ + parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] + + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"]) + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"]) offsprings.extend(curr_offsprings) else: offsprings.extend(parents) @@ -432,5 +467,5 @@ def _sample_log(from_, to_): return float(sample) def sample_binary_mask(self): - return np.random.randint(0, high=2, size=self.binary_mask_template.shape) + return np.random.randint(0, high=2, size=self.binary_mask_template.shape).tolist() diff --git a/deeppavlov/run_model.py b/deeppavlov/run_model.py index 55ba363741..0f7eb0ebf3 100644 --- a/deeppavlov/run_model.py +++ b/deeppavlov/run_model.py @@ -19,8 +19,8 @@ from deeppavlov.core.commands.utils import set_deeppavlov_root -PIPELINE_CONFIG_PATH = 'configs/intents/intents_dstc2.json' -# PIPELINE_CONFIG_PATH = 'configs/intents/intents_snips.json' +# PIPELINE_CONFIG_PATH = 'configs/intents/intents_dstc2.json' +PIPELINE_CONFIG_PATH = 'configs/intents/intents_snips.json' # PIPELINE_CONFIG_PATH = 'configs/ner/ner_dstc2.json' # PIPELINE_CONFIG_PATH = 'configs/ner/ner_dstc2.json' # PIPELINE_CONFIG_PATH = 'configs/ner/slotfill_dstc2.json' From b6b9ff2657dc4fb8da0577f51f40c41161b0c400 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 18 Apr 2018 12:11:42 +0300 Subject: [PATCH 030/616] fix: fixed check of binary mask, sample of binary mask and plot graph --- .../models/evolution/check_binary_mask.py | 63 +++++++++++-------- deeppavlov/models/evolution/debug.py | 6 +- .../neuroevolution_param_generator.py | 35 +++++++---- 3 files changed, 64 insertions(+), 40 deletions(-) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index 50d86c2624..7b2e6718a7 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -1,6 +1,10 @@ import numpy as np import networkx as nx from copy import copy, deepcopy +import datetime +import time +from pathlib import Path +import matplotlib.pyplot as plt def number_to_type_layer(node_id, n_types): @@ -59,19 +63,24 @@ def check_and_correct_binary_mask(nodes, binary_mask_): # print("Cycles: {}".format(cycles)) # number of candidates to be the best new graph cycles_len = np.array([len(cycle) for cycle in cycles]) - n_candidates = np.prod(cycles_len) + n_candidates = int(np.prod(cycles_len)) for i in range(n_candidates): - new_directed_graph = copy.deepcopy(directed_graph) + new_directed_graph = deepcopy(directed_graph) for j in range(n_cycles): node_id = (i // np.prod(cycles_len[:j])) % cycles_len[j] - new_directed_graph.remove_edge(cycles[j][node_id], cycles[j][(node_id + 1) % cycles_len[j]]) + try: + new_directed_graph.remove_edge(cycles[j][node_id], cycles[j][(node_id + 1) % cycles_len[j]]) + except: + continue candidates.append(new_directed_graph) + n_candidates = len(candidates) best_cand = None best_diff = 10e10 for i in range(n_candidates): new_sources, new_sinks = find_sources_and_sinks(candidates[i]) + if set(new_sources) == set(sources) and set(new_sinks) == set(sinks): best_cand = candidates[i] elif (len(set(new_sources).difference(set(sources))) + @@ -84,25 +93,29 @@ def check_and_correct_binary_mask(nodes, binary_mask_): binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) return binary_mask -# def get_graph_and_plot(nodes, binary_mask, n_types): -# import matplotlib.pyplot as plt -# -# total_nodes = len(nodes) -# dg = nx.DiGraph() -# -# for i in range(total_nodes): -# dg.add_node(i) -# -# pos = {} -# -# for i in range(total_nodes): -# for j in range(total_nodes): -# if binary_mask[i,j] == 1: -# dg.add_edge(i, j) -# pos[i] = np.array(number_to_type_layer(i, n_types))[::-1] -# -# plt.figure(figsize=(6, 6)) -# nx.draw(dg, pos, node_color='b', node_size=5000, alpha=0.3) -# -# nx.draw_networkx_labels(dg, pos, nodes, font_size=18) -# plt.show() + +def get_graph_and_plot(nodes, binary_mask, n_types, path=None): + total_nodes = len(nodes) + dg = nx.DiGraph() + + for i in range(total_nodes): + dg.add_node(i) + + pos = {} + + for i in range(total_nodes): + for j in range(total_nodes): + if binary_mask[i, j] == 1: + dg.add_edge(i, j) + pos[i] = 2. * np.array(number_to_type_layer(i, n_types))[::-1] + + plt.figure(figsize=(12, 12)) + nx.draw(dg, pos, node_color='b', node_size=7000, alpha=0.3) + + nx.draw_networkx_labels(dg, pos, nodes, font_size=18) + # plt.show() + if path is None: + path = "./" + curr_time = datetime.datetime.now().strftime("%Hh%Mm%Ss_%dd%mm%Yy") + plt.savefig(Path(path).joinpath("pic_" + curr_time + ".png")) + # time.sleep(1) diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 70e9e23986..7d44eda3b6 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -4,8 +4,8 @@ from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution -n_layers = 3 -n_types = 3 +n_layers = 5 +n_types = 7 population_size = 2 config_path = "../../configs/evolution/basic_intents_snips.json" @@ -16,6 +16,7 @@ population_size, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", + seed=42, **config) population = evolution.first_generation() @@ -26,3 +27,4 @@ evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) print(population) + diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 24813e2f56..fe98a68abd 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -3,7 +3,8 @@ from pathlib import Path import json -from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, number_to_type_layer +from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ + number_to_type_layer, get_graph_and_plot from deeppavlov.core.common.file import save_json, read_json # TODO: @@ -57,7 +58,11 @@ def __init__(self, n_layers, n_types, self.train_params = deepcopy(self.basic_config.get("train")) self.basic_layers_params = self.params.pop(key_basic_layers, None) self.node_types = list(self.basic_layers_params.keys()) - self.nodes = np.arange(self.total_nodes) + + self.nodes = {} + for i in range(self.total_nodes): + l, t = number_to_type_layer(i, self.n_types) + self.nodes[i] = "{}_{}_{}".format(l, t, i) print("___Basic config___: {}".format(self.basic_config)) print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) @@ -144,11 +149,11 @@ def initialize_layers_params(self): self.basic_layers_params[self.node_types[node_type]]) all_layers_params[node_key] = {"node_name": self.node_types[node_type], - "node_type": node_type, - "node_layer": node_layer, - **layers_params, - **layers_params_for_search - } + "node_type": node_type, + "node_layer": node_layer, + **layers_params, + **layers_params_for_search + } return all_layers_params def first_generation(self, iter=0): @@ -183,11 +188,10 @@ def first_generation(self, iter=0): **params_for_search, **layers_params} # add binary_mask intialization - print(self.sample_binary_mask()) - print(check_and_correct_binary_mask(self.nodes, self.sample_binary_mask())) - population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ check_and_correct_binary_mask(self.nodes, self.sample_binary_mask()) + get_graph_and_plot(self.nodes, population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"], + self.n_types, path=None) # exchange train params from basic config to sampled train params population[-1]["train"] = {**train_params, **train_params_for_search} @@ -278,7 +282,7 @@ def crossover(self, population, p_crossover, crossover_power): params_perm = np.random.permutation(self.n_evolving_params) train_params_perm = np.random.permutation(self.n_evolving_train_params) nodes_perm = np.random.permutation(self.total_nodes) - binary_mask_perm = np.random_permutation(self.total_nodes * self.total_nodes) + binary_mask_perm = np.random.permutation(self.total_nodes * self.total_nodes) curr_offsprings = [deepcopy(parents[0]), deepcopy(parents[1])] @@ -467,5 +471,10 @@ def _sample_log(from_, to_): return float(sample) def sample_binary_mask(self): - return np.random.randint(0, high=2, size=self.binary_mask_template.shape).tolist() - + # return np.random.randint(0, high=2, size=self.binary_mask_template.shape).tolist() + # return (1 * (np.log(np.random.random(size=self.binary_mask_template.shape)) > -0.2)).tolist() + ones = np.random.choice(self.total_nodes * self.total_nodes, + size=max(1, int(np.random.random() * self.total_nodes))) + mask = np.zeros((self.total_nodes * self.total_nodes)) + mask[ones] = 1 + return mask.reshape((self.total_nodes, self.total_nodes)) From 6d3e4bdc22cc89fb7ecf188ccf0479632012026f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 18 Apr 2018 12:21:07 +0300 Subject: [PATCH 031/616] feat: sources and sinks are of different colors --- .../models/evolution/check_binary_mask.py | 22 +++++++++++-------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index 7b2e6718a7..f291907bef 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -96,24 +96,28 @@ def check_and_correct_binary_mask(nodes, binary_mask_): def get_graph_and_plot(nodes, binary_mask, n_types, path=None): total_nodes = len(nodes) - dg = nx.DiGraph() - - for i in range(total_nodes): - dg.add_node(i) + dg = get_digraph_from_binary_mask(nodes, binary_mask) pos = {} + val_map = {} + sources, sinks = find_sources_and_sinks(dg) for i in range(total_nodes): - for j in range(total_nodes): - if binary_mask[i, j] == 1: - dg.add_edge(i, j) pos[i] = 2. * np.array(number_to_type_layer(i, n_types))[::-1] + if i in sources: + val_map[i] = 1. + elif i in sinks: + val_map[i] = 0.5 + else: + val_map[i] = 0. plt.figure(figsize=(12, 12)) - nx.draw(dg, pos, node_color='b', node_size=7000, alpha=0.3) + values = [val_map.get(node, 0.25) for node in nodes] + + nx.draw(dg, pos, cmap=plt.get_cmap('jet'), node_color=values, node_size=7000, alpha=0.3) nx.draw_networkx_labels(dg, pos, nodes, font_size=18) - # plt.show() + if path is None: path = "./" curr_time = datetime.datetime.now().strftime("%Hh%Mm%Ss_%dd%mm%Yy") From b8803721c3453fd24c2eeeafed3ea4c4a785282f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 18 Apr 2018 12:45:17 +0300 Subject: [PATCH 032/616] chore: pep --- .../models/evolution/check_matrix.ipynb | 24 +++++++++++++++++-- .../neuroevolution_param_generator.py | 8 ++++--- 2 files changed, 27 insertions(+), 5 deletions(-) diff --git a/deeppavlov/models/evolution/check_matrix.ipynb b/deeppavlov/models/evolution/check_matrix.ipynb index 898d23aa67..12ae7348c3 100644 --- a/deeppavlov/models/evolution/check_matrix.ipynb +++ b/deeppavlov/models/evolution/check_matrix.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -33,6 +33,26 @@ " nodes[i] = types[number_to_type_layer(i, T)[1]]" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 'Dense', 1: 'Conv1D', 2: 'LSTM', 3: 'Dense', 4: 'Conv1D', 5: 'LSTM'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nodes" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index fe98a68abd..ae2e55308a 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -90,6 +90,7 @@ def __init__(self, n_layers, n_types, pass else: np.random.seed(seed) + return None def _find_model_to_evolve_index_in_pipe(self, pipe, key): for element_id, element in enumerate(pipe): @@ -113,7 +114,7 @@ def print_dict(self, dict, string=None): else: print(string) print(json.dumps(dict, indent=2)) - return + return None def initialize_params_in_config(self, basic_params): params = {} @@ -190,8 +191,9 @@ def first_generation(self, iter=0): # add binary_mask intialization population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ check_and_correct_binary_mask(self.nodes, self.sample_binary_mask()) - get_graph_and_plot(self.nodes, population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"], - self.n_types, path=None) + # get_graph_and_plot(self.nodes, population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"], + # self.n_types, path=None) + # exchange train params from basic config to sampled train params population[-1]["train"] = {**train_params, **train_params_for_search} From e60a777ca4ad17e6d55269583fd202d64922873b Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 18 Apr 2018 15:56:35 +0300 Subject: [PATCH 033/616] feat: mutation --- .../models/evolution/check_binary_mask.py | 1 + deeppavlov/models/evolution/debug.py | 11 ++- .../neuroevolution_param_generator.py | 96 +++++++++++++------ 3 files changed, 79 insertions(+), 29 deletions(-) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index f291907bef..a4cb3d7646 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -123,3 +123,4 @@ def get_graph_and_plot(nodes, binary_mask, n_types, path=None): curr_time = datetime.datetime.now().strftime("%Hh%Mm%Ss_%dd%mm%Yy") plt.savefig(Path(path).joinpath("pic_" + curr_time + ".png")) # time.sleep(1) + return None diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 7d44eda3b6..660bc7c6ec 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -6,7 +6,7 @@ n_layers = 5 n_types = 7 -population_size = 2 +population_size = 10 config_path = "../../configs/evolution/basic_intents_snips.json" with open(config_path) as fin: @@ -25,6 +25,13 @@ evolution.model_to_evolve_index]["binary_mask"].tolist() print(population) -evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) +population = evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) print(population) +# print(population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) +mutated = evolution.mutation(population, p_mutation=0.5, mutation_power=.5) + +for i in range(population_size): + if (mutated[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] != + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]).any(): + print("{} mask mutated".format(i)) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index ae2e55308a..fb873d554a 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -145,7 +145,7 @@ def initialize_layers_params(self): for node_id in range(self.total_nodes): node_layer, node_type = number_to_type_layer(node_id, self.n_types) - node_key = "node_{}_{}".format(node_layer, node_type) + node_key = self.nodes[node_id] layers_params, layers_params_for_search, _ = self.initialize_params_in_config( self.basic_layers_params[self.node_types[node_type]]) @@ -333,7 +333,7 @@ def crossover(self, population, p_crossover, crossover_power): # exchange of nodes for j in range(self.total_nodes - nodes_part): node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) - node_key = "node_{}_{}".format(node_layer, node_type) + node_key = self.nodes[nodes_perm[j]] curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) @@ -341,7 +341,7 @@ def crossover(self, population, p_crossover, crossover_power): parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) for j in range(self.total_nodes - nodes_part, self.total_nodes): node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) - node_key = "node_{}_{}".format(node_layer, node_type) + node_key = self.nodes[nodes_perm[j]] curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) @@ -394,34 +394,76 @@ def mutation(self, population, p_mutation, mutation_power): mutated population """ mutated = [] + for individuum in population: - mutated_individuum = {} - for param in self.params_names: - if self.decision(p_mutation): - if type(self.params[param]) is dict: - if self.params[param].get('discrete', False): - val = round(individuum[param] + - ((2 * np.random.random() - 1.) * mutation_power - * self.sample_params(**{param: self.params[param]})[param])) - val = min(max(self.params[param]["range"][0], val), - self.params[param]["range"][1]) - mutated_individuum[param] = val - elif 'range' in self.params[param].keys(): - val = individuum[param] + \ - ((2 * np.random.random() - 1.) * mutation_power - * self.sample_params(**{param: self.params[param]})[param]) - val = min(max(self.params[param]["range"][0], val), - self.params[param]["range"][1]) - mutated_individuum[param] = val - elif self.params[param].get("choice"): - mutated_individuum[param] = individuum[param] - else: - mutated_individuum[param] = individuum[param] - else: - mutated_individuum[param] = individuum[param] + mutated_individuum = deepcopy(individuum) + + # mutation of other model params + for param in self.params.keys(): + mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][param] = \ + self.mutation_of_param(param, self.params, + individuum["chainer"]["pipe"][self.model_to_evolve_index][param], + p_mutation, mutation_power) + + # mutation of train params + for param in self.train_params.keys(): + mutated_individuum["train"][param] = \ + self.mutation_of_param(param, self.train_params, + individuum["train"][param], + p_mutation, mutation_power) + + # mutation of binary mask + if self.decision(p_mutation): + mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, + np.minimum(1, + np.maximum(0, + individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] + + np.round((2 * np.random.random() - 1.) * self.sample_binary_mask())))) + + # mutation of each node params + for node_id in range(self.total_nodes): + node_layer, node_type = number_to_type_layer(node_id, self.n_types) + for param in self.basic_layers_params[self.node_types[node_type]]: + mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][self.nodes[node_id]][param] =\ + self.mutation_of_param(param, self.basic_layers_params[self.node_types[node_type]], + individuum["chainer"]["pipe"][self.model_to_evolve_index][ + self.nodes[node_id]][param], + p_mutation, mutation_power) mutated.append(mutated_individuum) + return mutated + def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutation_power): + new_mutated_value = deepcopy(param_value) + + if self.decision(p_mutation): + if type(params_dict[param]) is dict: + if params_dict[param].get('discrete', False): + val = round(param_value + + ((2 * np.random.random() - 1.) * mutation_power + * self.sample_params(**{param: params_dict[param]})[param])) + val = min(max(params_dict[param]["range"][0], val), + params_dict[param]["range"][1]) + new_mutated_value = val + elif 'range' in params_dict[param].keys(): + val = param_value + \ + ((2 * np.random.random() - 1.) * mutation_power + * self.sample_params(**{param: params_dict[param]})[param]) + val = min(max(params_dict[param]["range"][0], val), + params_dict[param]["range"][1]) + new_mutated_value = val + elif params_dict[param].get("choice"): + new_mutated_value = param_value + else: + new_mutated_value = param_value + else: + new_mutated_value = param_value + else: + new_mutated_value = param_value + + return new_mutated_value + def decision(self, probability): """ Make decision whether to do action or not with given probability From 6fc8009e51af7b6f0b59fef517d365d5903ede39 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 18 Apr 2018 18:39:24 +0300 Subject: [PATCH 034/616] chore: constructing model with bugs --- deeppavlov/__init__.py | 1 + .../evolution/basic_intents_snips.json | 4 +- deeppavlov/models/evolution/debug.py | 46 ++- .../evolution/evolution_intent_model.py | 114 +++++++ deeppavlov/models/evolution/intent_model.py | 277 ------------------ .../neuroevolution_param_generator.py | 27 +- deeppavlov/models/evolution/utils.py | 21 +- 7 files changed, 188 insertions(+), 302 deletions(-) create mode 100644 deeppavlov/models/evolution/evolution_intent_model.py delete mode 100644 deeppavlov/models/evolution/intent_model.py diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index bc9d0d20d1..1233513c3e 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -23,6 +23,7 @@ import deeppavlov.dataset_iterators.squad_iterator import deeppavlov.dataset_iterators.sqlite_iterator import deeppavlov.models.classifiers.intents.intent_model +import deeppavlov.models.evolution.evolution_intent_model import deeppavlov.models.commutators.random_commutator import deeppavlov.models.embedders.fasttext_embedder import deeppavlov.models.embedders.dict_embedder diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json index f821ff7b7b..078713c080 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -60,7 +60,7 @@ "y_predicted" ], "main": true, - "name": "intent_model", + "name": "evolution_intent_model", "save_path": "evolution/intents_snips", "load_path": "evolution/intents_snips", "classes": "#classes_vocab.keys()", @@ -199,7 +199,7 @@ }, "loss": "binary_crossentropy", "text_size": 15, - "model_name": "cnn_model", + "model_name": "evolution_model", "embedder": "#fasttext_embedder", "tokenizer": "#nltk_tokenizer" } diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 660bc7c6ec..431e2c552a 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -1,12 +1,19 @@ import pandas as pd import json import numpy as np +from copy import deepcopy from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution +from deeppavlov.models.evolution.evolution_intent_model import KerasEvolutionIntentModel +from deeppavlov.core.commands.train import train_model_from_config +from deeppavlov.core.commands.infer import interact_model +from deeppavlov.core.commands.utils import set_deeppavlov_root +from deeppavlov.core.common.file import save_json, read_json -n_layers = 5 + +n_layers = 2 n_types = 7 -population_size = 10 +population_size = 1 config_path = "../../configs/evolution/basic_intents_snips.json" with open(config_path) as fin: @@ -20,18 +27,41 @@ **config) population = evolution.first_generation() -print(population) population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ evolution.model_to_evolve_index]["binary_mask"].tolist() + +config_path = "./config_init.json" +full_config = deepcopy(population[0]) +save_json(full_config, config_path) + +print(population) print(population) population = evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) print(population) +# population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ +# evolution.model_to_evolve_index]["binary_mask"].tolist() + +config_path = "./config_crossover.json" +full_config = deepcopy(population[0]) +save_json(full_config, config_path) + # print(population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) -mutated = evolution.mutation(population, p_mutation=0.5, mutation_power=.5) +population = evolution.mutation(population, p_mutation=0.5, mutation_power=.5) +# +# for i in range(population_size): +# if (mutated[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] != +# population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]).any(): +# print("{} mask mutated".format(i)) +# population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ +# evolution.model_to_evolve_index]["binary_mask"].tolist() + +config_path = "./config_mutated.json" +full_config = deepcopy(population[0]) +full_config["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = evolution.nodes +full_config["chainer"]["pipe"][evolution.model_to_evolve_index]["total_nodes"] = evolution.total_nodes + +save_json(full_config, config_path) -for i in range(population_size): - if (mutated[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] != - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]).any(): - print("{} mask mutated".format(i)) +train_model_from_config(config_path) \ No newline at end of file diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py new file mode 100644 index 0000000000..352ff1156e --- /dev/null +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -0,0 +1,114 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import numpy as np +from copy import copy, deepcopy +from keras.layers import Dense, Input, concatenate, Activation +from keras.layers.convolutional import Conv1D +from keras.layers.core import Dropout +from keras.layers.normalization import BatchNormalization +from keras.layers.pooling import GlobalMaxPooling1D, MaxPooling1D +from keras.layers.recurrent import LSTM +from keras.layers.wrappers import Bidirectional +from keras.models import Model +from keras.regularizers import l2 +from keras.layers import Concatenate, Reshape +from keras import backend as K + +from deeppavlov.core.common.errors import ConfigError +from deeppavlov.core.common.registry import register +from deeppavlov.core.models.keras_model import KerasModel +from deeppavlov.models.classifiers.intents.intent_model import KerasIntentModel +from deeppavlov.models.classifiers.intents import metrics as metrics_file +from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels +from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder +from deeppavlov.models.classifiers.intents.utils import md5_hashsum +from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer +from deeppavlov.core.common.log import get_logger +from deeppavlov.models.evolution.check_binary_mask import number_to_type_layer, \ + find_sources_and_sinks, get_digraph_from_binary_mask +from deeppavlov.models.evolution.utils import Attention, expand_tile +log = get_logger(__name__) + + +@register('evolution_intent_model') +class KerasEvolutionIntentModel(KerasIntentModel): + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): + if inp is None: + input_nodes = [edge[0] for edge in dg.in_edges(node_id)] + inp_list = [] + for input_node in input_nodes: + if len(K.int_shape(edges_outputs[input_node])) == 3: + inp_list.append(edges_outputs[input_node]) + elif len(K.int_shape(edges_outputs[input_node])) == 2: + inp_list.append(K.expand_dims(edges_outputs[input_node], axis=1)) + else: + raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") + inp = Concatenate()(inp_list) + + node_func = getattr(globals(), params[params["nodes"][node_id]]["node_name"], None) + node_params = deepcopy(params[params["nodes"][node_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + if callable(node_func): + output_of_node = node_func(**node_params)(inp) + else: + raise AttributeError("Node {} is not defined correctly".format(node_id)) + return output_of_node + + def evolution_model(self, params): + """ + Build un-compiled model of shallow-and-wide CNN + Args: + params: dictionary of parameters for NN + + Returns: + Un-compiled model + """ + print(params) + + inp = Input(shape=(params['text_size'], params['embedding_size'])) + + dg = get_digraph_from_binary_mask(params["nodes"], params["binary_mask"]) + sources, sinks = find_sources_and_sinks(dg) + + edges_outputs = {} + + for node_id in range(params["total_nodes"]): + # node_layer, node_type = number_to_type_layer(node_id, params["n_types"]) + if node_id in sources: + edges_outputs[node_id] = self.get_node_output(node_id, dg, params, inp=inp) + else: + edges_outputs[node_id] = self.get_node_output(node_id, dg, params, edges_outputs=edges_outputs) + + if len(sinks) == 1: + output = edges_outputs[sinks[0]] + else: + outputs = [] + for sink in sinks: + outputs.append(edges_outputs[sink]) + output = Concatenate()(outputs) + + #TODO: make 2dimensional input for dense! + output = Dense(self.n_classes, activation=None)(output) + act_output = Activation('sigmoid')(output) + model = Model(inputs=inp, outputs=act_output) + return model diff --git a/deeppavlov/models/evolution/intent_model.py b/deeppavlov/models/evolution/intent_model.py deleted file mode 100644 index 7980f944ac..0000000000 --- a/deeppavlov/models/evolution/intent_model.py +++ /dev/null @@ -1,277 +0,0 @@ -""" -Copyright 2017 Neural Networks and Deep Learning lab, MIPT - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import numpy as np -from keras.layers import Dense, Input, concatenate, Activation -from keras.layers.convolutional import Conv1D -from keras.layers.core import Dropout -from keras.layers.normalization import BatchNormalization -from keras.layers.pooling import GlobalMaxPooling1D, MaxPooling1D -from keras.models import Model -from keras.regularizers import l2 - -from deeppavlov.core.common.errors import ConfigError -from deeppavlov.core.common.registry import register -from deeppavlov.core.models.keras_model import KerasModel -from deeppavlov.models.classifiers.intents import metrics as metrics_file -from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels -from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder -from deeppavlov.models.classifiers.intents.utils import md5_hashsum -from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer -from deeppavlov.core.common.log import get_logger - - -log = get_logger(__name__) - - -@register('intent_model') -class KerasIntentModel(KerasModel): - """ - Class implements keras model for intent recognition task for multi-class multi-label data - """ - def __init__(self, **kwargs): - """ - Initialize and train vocabularies, initializes embedder, tokenizer, - and then initialize model using parameters from opt dictionary (from config), - if model is being initialized from saved - - Args: - vocabs: dictionary of considered vocabularies - opt: model parameters for network and learning - model_path: path to model serialization dir or file. - It is always an empty string and is ignored if it is not set in json config. - model_dir: name of a serialization dir, can be default or set in json config - model_file: name of a serialization file (usually binary model file), - can be default or set in json config - embedder: instance of FasttextEmbedder class - tokenizer: instance of NLTKTokenizer class - **kwargs: - """ - super().__init__(**kwargs) # self.opt initialized in here - - self.tokenizer = self.opt.get('tokenizer') - self.fasttext_model = self.opt.get('embedder') - self.opt.pop("vocabs") - self.opt.pop("embedder") - self.opt.pop("tokenizer") - - if self.opt.get('classes'): - self.classes = list(np.sort(np.array(list(self.opt.get('classes'))))) - self.opt['classes'] = self.classes - else: - # self.classes = list(np.sort(np.array(list(self.opt.get('vocabs')["classes_vocab"].keys())))) - self.classes = list(self.opt.get('vocabs')["classes_vocab"].keys()) - self.opt['classes'] = self.classes - self.n_classes = len(self.classes) - if self.n_classes == 0: - ConfigError("Please, provide vocabulary with considered intents.") - - self.opt['embedding_size'] = self.fasttext_model.dim - - if self.fasttext_model.load_path: - current_fasttext_md5 = md5_hashsum([self.fasttext_model.load_path]) - - # Parameters required to init model - params = {"model_name": self.opt.get('model_name'), - "optimizer_name": self.opt.get('optimizer'), - "loss_name": self.opt.get('loss'), - "lear_rate": self.opt.get('lear_rate'), - "lear_rate_decay": self.opt.get('lear_rate_decay')} - - self.model = self.load(**params) - self._init_params() - - # Check if md5 hash sum of current loaded fasttext model - # is equal to saved - try: - self.opt['fasttext_md5'] - except KeyError: - self.opt['fasttext_md5'] = current_fasttext_md5 - else: - if self.opt['fasttext_md5'] != current_fasttext_md5: - raise ConfigError( - "Given fasttext model does NOT match fasttext model used previously to train loaded model") - - def _init_params(self): - - # list of changeable params - changeable_params = {"confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": 1e-2, - "lear_rate_decay": 0., - "loss": "binary_crossentropy", - "coef_reg_cnn": 0., - "coef_reg_den": 0., - "dropout_rate": 0.} - - for param in changeable_params.keys(): - self.opt[param] = self.opt.get(param, changeable_params[param]) - return - - def texts2vec(self, sentences): - """ - Convert texts to vector representations using embedder and padding up to self.opt["text_size"] tokens - Args: - sentences: list of texts - - Returns: - array of embedded texts - """ - pad = np.zeros(self.opt['embedding_size']) - - embeddings_batch = self.fasttext_model([' '.join(sen.split()[:self.opt['text_size']]) for sen in sentences]) - embeddings_batch = [[pad] * (self.opt['text_size'] - len(tokens)) + tokens for tokens in embeddings_batch] - - embeddings_batch = np.asarray(embeddings_batch) - return embeddings_batch - - def train_on_batch(self, texts, labels): - """ - Train the model on the given batch - Args: - batch - list of data where batch[0] is list of texts and batch[1] is list of labels - - Returns: - loss and metrics values on the given batch - """ - texts = self.tokenizer(list(texts)) - features = self.texts2vec(texts) - onehot_labels = labels2onehot(labels, classes=self.classes) - metrics_values = self.model.train_on_batch(features, onehot_labels) - return metrics_values - - def infer_on_batch(self, batch, labels=None): - """ - Infer the model on the given batch - Args: - batch - list of texts - labels - list of labels - - Returns: - loss and metrics values on the given batch, if labels are given - predictions, otherwise - """ - texts = self.tokenizer(batch) - if labels: - features = self.texts2vec(texts) - onehot_labels = labels2onehot(labels, classes=self.classes) - metrics_values = self.model.test_on_batch(features, onehot_labels) - return metrics_values - else: - features = self.texts2vec(texts) - predictions = self.model.predict(features) - return predictions - - def __call__(self, data, predict_proba=False, *args): - """ - Infer on the given data - Args: - data: [list of sentences] - predict_proba: whether to return probabilities distribution or only labels-predictions - *args: - - Returns: - for each sentence: - vector of probabilities to belong with each class - or list of labels sentence belongs with - """ - preds = np.array(self.infer_on_batch(data)) - - if predict_proba: - return preds - else: - return proba2labels(preds, confident_threshold=self.opt['confident_threshold'], classes=self.classes) - - def cnn_model(self, params): - """ - Build un-compiled model of shallow-and-wide CNN - Args: - params: dictionary of parameters for NN - - Returns: - Un-compiled model - """ - - inp = Input(shape=(params['text_size'], params['embedding_size'])) - - outputs = [] - for i in range(len(params['kernel_sizes_cnn'])): - output_i = Conv1D(params['filters_cnn'], kernel_size=params['kernel_sizes_cnn'][i], - activation=None, - kernel_regularizer=l2(params['coef_reg_cnn']), - padding='same')(inp) - output_i = BatchNormalization()(output_i) - output_i = Activation('relu')(output_i) - output_i = GlobalMaxPooling1D()(output_i) - outputs.append(output_i) - - output = concatenate(outputs, axis=1) - - output = Dropout(rate=params['dropout_rate'])(output) - output = Dense(params['dense_size'], activation=None, - kernel_regularizer=l2(params['coef_reg_den']))(output) - output = BatchNormalization()(output) - output = Activation('relu')(output) - output = Dropout(rate=params['dropout_rate'])(output) - output = Dense(self.n_classes, activation=None, - kernel_regularizer=l2(params['coef_reg_den']))(output) - output = BatchNormalization()(output) - act_output = Activation('sigmoid')(output) - model = Model(inputs=inp, outputs=act_output) - return model - - def dcnn_model(self, params): - """ - Build un-compiled model of deep CNN - Args: - params: dictionary of parameters for NN - - Returns: - Un-compiled model - """ - - if type(self.opt['filters_cnn']) is str: - self.opt['filters_cnn'] = list(map(int, self.opt['filters_cnn'].split())) - - inp = Input(shape=(params['text_size'], params['embedding_size'])) - - output = inp - - for i in range(len(params['kernel_sizes_cnn'])): - output = Conv1D(params['filters_cnn'][i], kernel_size=params['kernel_sizes_cnn'][i], - activation=None, - kernel_regularizer=l2(params['coef_reg_cnn']), - padding='same')(output) - output = BatchNormalization()(output) - output = Activation('relu')(output) - output = MaxPooling1D()(output) - - output = GlobalMaxPooling1D()(output) - output = Dropout(rate=params['dropout_rate'])(output) - output = Dense(params['dense_size'], activation=None, - kernel_regularizer=l2(params['coef_reg_den']))(output) - output = BatchNormalization()(output) - output = Activation('relu')(output) - output = Dropout(rate=params['dropout_rate'])(output) - output = Dense(self.n_classes, activation=None, - kernel_regularizer=l2(params['coef_reg_den']))(output) - output = BatchNormalization()(output) - act_output = Activation('sigmoid')(output) - model = Model(inputs=inp, outputs=act_output) - return model - - def reset(self): - pass diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index fb873d554a..e837fd5c89 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -121,22 +121,23 @@ def initialize_params_in_config(self, basic_params): params_for_search = {} evolving_params = [] - for param_name in basic_params.keys(): - if type(basic_params[param_name]) is dict: - if basic_params[param_name].get("choice"): - params_for_search[param_name] = list(basic_params[param_name]["values"]) - evolving_params.append(param_name) - elif basic_params[param_name].get("range"): - params_for_search[param_name] = deepcopy(basic_params[param_name]) - evolving_params.append(param_name) + for param_name in list(basic_params.keys()): + if basic_params[param_name]: + if type(basic_params[param_name]) is dict: + if basic_params[param_name].get("choice"): + params_for_search[param_name] = list(basic_params[param_name]["values"]) + evolving_params.append(param_name) + elif basic_params[param_name].get("range"): + params_for_search[param_name] = deepcopy(basic_params[param_name]) + evolving_params.append(param_name) + else: + # NOT evolving params + params[param_name] = deepcopy(basic_params[param_name]) else: # NOT evolving params params[param_name] = deepcopy(basic_params[param_name]) - else: - # NOT evolving params - params[param_name] = deepcopy(basic_params[param_name]) - - params_for_search = deepcopy(self.sample_params(**params_for_search)) + if basic_params: + params_for_search = deepcopy(self.sample_params(**params_for_search)) return params, params_for_search, evolving_params diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index 7c8007ec0f..f66d4b3301 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -22,7 +22,7 @@ from deeppavlov.core.common.log import get_logger from keras import initializers, regularizers, constraints from keras import backend as K -from keras.layers import concatenate, multiply +from keras.layers import concatenate, multiply, Reshape log = get_logger(__name__) @@ -185,4 +185,21 @@ def call(self, x, mask=None): return out def compute_output_shape(self, input_shape): - return input_shape \ No newline at end of file + return input_shape + +def expand_tile(units, axis): + """Expand and tile tensor along given axis + Args: + units: tf tensor with dimensions [batch_size, time_steps, n_input_features] + axis: axis along which expand and tile. Must be 1 or 2 + + """ + assert axis in (1, 2) + n_time_steps = K.int_shape(units)[1] + repetitions = [1, 1, 1, 1] + repetitions[axis] = n_time_steps + if axis == 1: + expanded = Reshape(target_shape=( (1,) + K.int_shape(units)[1:] ))(units) + else: # axis=2 + expanded = Reshape(target_shape=(K.int_shape(units)[1:2] + (1,) + K.int_shape(units)[2:]))(units) + return K.tile(expanded, repetitions) From 6f25c6d78c6e15774e42812a344ea3c23f924957 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 18 Apr 2018 18:48:34 +0300 Subject: [PATCH 035/616] chore: constructing model with bugs --- deeppavlov/models/evolution/debug.py | 3 +++ .../evolution/evolution_intent_model.py | 14 +++++++++---- .../neuroevolution_param_generator.py | 21 +++++++++---------- deeppavlov/run_model.py | 3 ++- 4 files changed, 25 insertions(+), 16 deletions(-) diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 431e2c552a..03008229d0 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -56,6 +56,9 @@ # print("{} mask mutated".format(i)) # population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ # evolution.model_to_evolve_index]["binary_mask"].tolist() +population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ + evolution.model_to_evolve_index]["binary_mask"].tolist() + config_path = "./config_mutated.json" full_config = deepcopy(population[0]) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 352ff1156e..1be15e5bfd 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -61,10 +61,16 @@ def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): inp_list.append(K.expand_dims(edges_outputs[input_node], axis=1)) else: raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") - inp = Concatenate()(inp_list) + if len(input_nodes) > 1: + inp = Concatenate()(inp_list) + else: + inp = inp_list[0] - node_func = getattr(globals(), params[params["nodes"][node_id]]["node_name"], None) - node_params = deepcopy(params[params["nodes"][node_id]]) + print(params[params["nodes"][str(node_id)]]["node_name"]) + print(globals()) + # node_func = getattr(globals(), params[params["nodes"][str(node_id)]]["node_name"], None) + node_func = globals().get(params[params["nodes"][str(node_id)]]["node_name"], None) + node_params = deepcopy(params[params["nodes"][str(node_id)]]) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") @@ -87,7 +93,7 @@ def evolution_model(self, params): inp = Input(shape=(params['text_size'], params['embedding_size'])) - dg = get_digraph_from_binary_mask(params["nodes"], params["binary_mask"]) + dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) sources, sinks = find_sources_and_sinks(dg) edges_outputs = {} diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index e837fd5c89..0d902eb2a8 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -122,20 +122,19 @@ def initialize_params_in_config(self, basic_params): evolving_params = [] for param_name in list(basic_params.keys()): - if basic_params[param_name]: - if type(basic_params[param_name]) is dict: - if basic_params[param_name].get("choice"): - params_for_search[param_name] = list(basic_params[param_name]["values"]) - evolving_params.append(param_name) - elif basic_params[param_name].get("range"): - params_for_search[param_name] = deepcopy(basic_params[param_name]) - evolving_params.append(param_name) - else: - # NOT evolving params - params[param_name] = deepcopy(basic_params[param_name]) + if type(basic_params[param_name]) is dict: + if basic_params[param_name].get("choice"): + params_for_search[param_name] = list(basic_params[param_name]["values"]) + evolving_params.append(param_name) + elif basic_params[param_name].get("range"): + params_for_search[param_name] = deepcopy(basic_params[param_name]) + evolving_params.append(param_name) else: # NOT evolving params params[param_name] = deepcopy(basic_params[param_name]) + else: + # NOT evolving params + params[param_name] = deepcopy(basic_params[param_name]) if basic_params: params_for_search = deepcopy(self.sample_params(**params_for_search)) diff --git a/deeppavlov/run_model.py b/deeppavlov/run_model.py index 0f7eb0ebf3..bcac9b8ef1 100644 --- a/deeppavlov/run_model.py +++ b/deeppavlov/run_model.py @@ -20,7 +20,8 @@ # PIPELINE_CONFIG_PATH = 'configs/intents/intents_dstc2.json' -PIPELINE_CONFIG_PATH = 'configs/intents/intents_snips.json' +# PIPELINE_CONFIG_PATH = 'configs/intents/intents_snips.json' +PIPELINE_CONFIG_PATH = 'configs/evolution/basic_intents_snips.json' # PIPELINE_CONFIG_PATH = 'configs/ner/ner_dstc2.json' # PIPELINE_CONFIG_PATH = 'configs/ner/ner_dstc2.json' # PIPELINE_CONFIG_PATH = 'configs/ner/slotfill_dstc2.json' From 688d4ead1098429b45dc53f4fedd303e8413829d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 20 Apr 2018 16:42:40 +0300 Subject: [PATCH 036/616] chore: change shapes within concatenate --- .../evolution/basic_intents_snips.json | 56 ++-------- .../classifiers/intents/intent_model.py | 2 + .../models/evolution/check_binary_mask.py | 10 +- deeppavlov/models/evolution/debug.py | 31 ++++-- .../evolution/evolution_intent_model.py | 105 +++++++++++++++--- .../neuroevolution_param_generator.py | 10 ++ deeppavlov/models/evolution/utils.py | 52 +++++++-- 7 files changed, 181 insertions(+), 85 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json index 078713c080..cf82a03368 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -60,7 +60,7 @@ "y_predicted" ], "main": true, - "name": "evolution_intent_model", + "name": "evolution_classification_model", "save_path": "evolution/intents_snips", "load_path": "evolution/intents_snips", "classes": "#classes_vocab.keys()", @@ -97,9 +97,10 @@ 7 ], "discrete": true - } + }, + "padding": "same" }, - "LSTM": { + "CuDNNLSTM": { "units": { "range": [ 50, @@ -107,28 +108,11 @@ ], "discrete": true }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - }, - "dropout": { - "range": [ - 1e-2, - 7e-1 - ] - }, - "recurrent_dropout": { - "range": [ - 1e-2, - 7e-1 - ] + "return_sequences": { + "bool": true } }, - "BiLSTM": { + "BiCuDNNLSTM": { "units": { "range": [ 50, @@ -136,25 +120,8 @@ ], "discrete": true }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - }, - "dropout": { - "range": [ - 1e-2, - 7e-1 - ] - }, - "recurrent_dropout": { - "range": [ - 1e-2, - 7e-1 - ] + "return_sequences": { + "bool": true } }, "GlobalMaxPooling1D": { @@ -166,7 +133,8 @@ 5 ], "discrete": true - } + }, + "padding": "same" }, "Attention": { "context_length": { @@ -199,7 +167,7 @@ }, "loss": "binary_crossentropy", "text_size": 15, - "model_name": "evolution_model", + "model_name": "evolution_classification_model", "embedder": "#fasttext_embedder", "tokenizer": "#nltk_tokenizer" } diff --git a/deeppavlov/models/classifiers/intents/intent_model.py b/deeppavlov/models/classifiers/intents/intent_model.py index 7980f944ac..31e864c94c 100644 --- a/deeppavlov/models/classifiers/intents/intent_model.py +++ b/deeppavlov/models/classifiers/intents/intent_model.py @@ -105,6 +105,8 @@ def __init__(self, **kwargs): raise ConfigError( "Given fasttext model does NOT match fasttext model used previously to train loaded model") + print(self.model.summary()) + def _init_params(self): # list of changeable params diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index a4cb3d7646..583532f88e 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -19,6 +19,7 @@ def type_layer_to_number(node_layer, node_type, n_types): def find_sources_and_sinks(directed_graph): sources = [] sinks = [] + isolates = nx.isolates(directed_graph) for i in directed_graph.nodes(): if directed_graph.in_degree(i) == 0 and directed_graph.out_degree(i) > 0: @@ -26,7 +27,7 @@ def find_sources_and_sinks(directed_graph): if directed_graph.in_degree(i) > 0 and directed_graph.out_degree(i) == 0: sinks.append(i) - return sources, sinks + return sources, sinks, isolates def get_digraph_from_binary_mask(nodes, binary_mask): @@ -52,9 +53,8 @@ def get_binary_mask_from_digraph(nodes, directed_graph): def check_and_correct_binary_mask(nodes, binary_mask_): binary_mask = deepcopy(binary_mask_) - binary_mask = np.array(binary_mask) directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) - sources, sinks = find_sources_and_sinks(directed_graph) + sources, sinks, _ = find_sources_and_sinks(directed_graph) while not nx.is_directed_acyclic_graph(directed_graph): candidates = [] @@ -79,7 +79,7 @@ def check_and_correct_binary_mask(nodes, binary_mask_): best_cand = None best_diff = 10e10 for i in range(n_candidates): - new_sources, new_sinks = find_sources_and_sinks(candidates[i]) + new_sources, new_sinks, _ = find_sources_and_sinks(candidates[i]) if set(new_sources) == set(sources) and set(new_sinks) == set(sinks): best_cand = candidates[i] @@ -100,7 +100,7 @@ def get_graph_and_plot(nodes, binary_mask, n_types, path=None): pos = {} val_map = {} - sources, sinks = find_sources_and_sinks(dg) + sources, sinks, _ = find_sources_and_sinks(dg) for i in range(total_nodes): pos[i] = 2. * np.array(number_to_type_layer(i, n_types))[::-1] diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 03008229d0..382b0ebd69 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -1,14 +1,17 @@ import pandas as pd import json import numpy as np +import tensorflow as tf from copy import deepcopy from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution -from deeppavlov.models.evolution.evolution_intent_model import KerasEvolutionIntentModel +from deeppavlov.models.evolution.evolution_intent_model import KerasEvolutionClassificationModel from deeppavlov.core.commands.train import train_model_from_config from deeppavlov.core.commands.infer import interact_model from deeppavlov.core.commands.utils import set_deeppavlov_root from deeppavlov.core.common.file import save_json, read_json +from deeppavlov.models.evolution.utils import expand_tile_batch_size +from deeppavlov.models.evolution.check_binary_mask import get_digraph_from_binary_mask n_layers = 2 @@ -32,22 +35,25 @@ config_path = "./config_init.json" full_config = deepcopy(population[0]) +print(population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]) save_json(full_config, config_path) -print(population) -print(population) +population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = np.array(population[0]["chainer"]["pipe"][ + evolution.model_to_evolve_index]["binary_mask"]) population = evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) print(population) -# population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ -# evolution.model_to_evolve_index]["binary_mask"].tolist() +population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ + evolution.model_to_evolve_index]["binary_mask"].tolist() config_path = "./config_crossover.json" full_config = deepcopy(population[0]) save_json(full_config, config_path) -# print(population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) +population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = np.array(population[0]["chainer"]["pipe"][ + evolution.model_to_evolve_index]["binary_mask"]) + population = evolution.mutation(population, p_mutation=0.5, mutation_power=.5) # # for i in range(population_size): @@ -59,7 +65,6 @@ population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ evolution.model_to_evolve_index]["binary_mask"].tolist() - config_path = "./config_mutated.json" full_config = deepcopy(population[0]) full_config["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = evolution.nodes @@ -67,4 +72,14 @@ save_json(full_config, config_path) -train_model_from_config(config_path) \ No newline at end of file +population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = np.array(population[0]["chainer"]["pipe"][ + evolution.model_to_evolve_index]["binary_mask"]) + +dg = get_digraph_from_binary_mask(evolution.nodes, + population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) + +print("Edges: ", dg.edges) +train_model_from_config(config_path) + + + diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 1be15e5bfd..e0bd6d05b3 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -25,7 +25,7 @@ from keras.layers.wrappers import Bidirectional from keras.models import Model from keras.regularizers import l2 -from keras.layers import Concatenate, Reshape +from keras.layers import Concatenate, Reshape, CuDNNLSTM from keras import backend as K from deeppavlov.core.common.errors import ConfigError @@ -44,43 +44,66 @@ log = get_logger(__name__) -@register('evolution_intent_model') -class KerasEvolutionIntentModel(KerasIntentModel): +@register('evolution_classification_model') +class KerasEvolutionClassificationModel(KerasIntentModel): def __init__(self, **kwargs): super().__init__(**kwargs) def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): if inp is None: + print(dg.in_edges(node_id)) input_nodes = [edge[0] for edge in dg.in_edges(node_id)] + print("Input nodes: {}".format(input_nodes)) inp_list = [] for input_node in input_nodes: if len(K.int_shape(edges_outputs[input_node])) == 3: inp_list.append(edges_outputs[input_node]) elif len(K.int_shape(edges_outputs[input_node])) == 2: - inp_list.append(K.expand_dims(edges_outputs[input_node], axis=1)) + inp_list.append(expand_tile(edges_outputs[input_node], axis=1)) else: raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") if len(input_nodes) > 1: - inp = Concatenate()(inp_list) + try: + inp = Concatenate()(inp_list) + except ValueError: + time_steps = [] + features = [] + for i in range(len(inp_list)): + if len(K.int_shape(inp_list[i])) == 2: + inp_list[i] = expand_tile(inp_list[i], axis=1) + time_steps.append(K.int_shape(inp_list[i])[1]) + features.append(K.int_shape(inp_list[i])[2]) + new_feature_shape = max(features) + for i in range(len(inp_list)): + inp_list[i] = Dense(new_feature_shape)(inp_list[i]) + inp = Concatenate(axis=1)(inp_list) else: inp = inp_list[0] print(params[params["nodes"][str(node_id)]]["node_name"]) - print(globals()) + # print(globals()) # node_func = getattr(globals(), params[params["nodes"][str(node_id)]]["node_name"], None) - node_func = globals().get(params[params["nodes"][str(node_id)]]["node_name"], None) - node_params = deepcopy(params[params["nodes"][str(node_id)]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - if callable(node_func): - output_of_node = node_func(**node_params)(inp) + + if params[params["nodes"][str(node_id)]]["node_name"] == "BiCuDNNLSTM": + node_params = deepcopy(params[params["nodes"][str(node_id)]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + output_of_node = Bidirectional(CuDNNLSTM(**node_params))(inp) else: - raise AttributeError("Node {} is not defined correctly".format(node_id)) + node_func = globals().get(params[params["nodes"][str(node_id)]]["node_name"], None) + node_params = deepcopy(params[params["nodes"][str(node_id)]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + if callable(node_func): + output_of_node = node_func(**node_params)(inp) + else: + raise AttributeError("Node {} is not defined correctly".format(node_id)) return output_of_node - def evolution_model(self, params): + def evolution_classification_model(self, params): """ Build un-compiled model of shallow-and-wide CNN Args: @@ -94,14 +117,44 @@ def evolution_model(self, params): inp = Input(shape=(params['text_size'], params['embedding_size'])) dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) - sources, sinks = find_sources_and_sinks(dg) + print(dg.edges) + sources, sinks, isolates = find_sources_and_sinks(dg) edges_outputs = {} - for node_id in range(params["total_nodes"]): + # for node_id in range(params["total_nodes"]): + # # node_layer, node_type = number_to_type_layer(node_id, params["n_types"]) + # if node_id in sources: + # edges_outputs[node_id] = self.get_node_output(node_id, dg, params, inp=inp) + # elif node_id in isolates: + # pass + # else: + # edges_outputs[node_id] = self.get_node_output(node_id, dg, params, edges_outputs=edges_outputs) + + sequence_of_nodes = [] + sequence_of_nodes.append(sources) + + while True: + if set(sinks).issubset(set(sum(sequence_of_nodes, []))): + break + next_nodes = [] + for node_id in sequence_of_nodes[-1]: + out_edges = dg.out_edges(node_id) + for edge in out_edges: + in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] + if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): + next_nodes.append(edge[1]) + sequence_of_nodes.append(next_nodes) + + sequence_of_nodes = sum(sequence_of_nodes, []) + + for node_id in sequence_of_nodes: + print(node_id) # node_layer, node_type = number_to_type_layer(node_id, params["n_types"]) if node_id in sources: edges_outputs[node_id] = self.get_node_output(node_id, dg, params, inp=inp) + elif node_id in isolates: + pass else: edges_outputs[node_id] = self.get_node_output(node_id, dg, params, edges_outputs=edges_outputs) @@ -111,9 +164,25 @@ def evolution_model(self, params): outputs = [] for sink in sinks: outputs.append(edges_outputs[sink]) - output = Concatenate()(outputs) + print("Sinks: {}".format(sinks)) + try: + output = Concatenate()(outputs) + except ValueError: + time_steps = [] + features = [] + for i in range(len(outputs)): + if len(K.int_shape(outputs[i])) == 2: + outputs[i] = expand_tile(outputs[i], axis=1) + time_steps.append(K.int_shape(outputs[i])[1]) + features.append(K.int_shape(outputs[i])[2]) + new_feature_shape = max(features) + for i in range(len(outputs)): + outputs[i] = Dense(new_feature_shape)(outputs[i]) + print("Outputs: {}".format(outputs[i].shape)) + output = Concatenate(axis=1)(outputs) #TODO: make 2dimensional input for dense! + output = GlobalMaxPooling1D()(output) output = Dense(self.n_classes, activation=None)(output) act_output = Activation('sigmoid')(output) model = Model(inputs=inp, outputs=act_output) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 0d902eb2a8..9cedc4acc6 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -43,6 +43,12 @@ def __init__(self, n_layers, n_types, crossover_power: part of EVOLVING parents parameters to exchange for offsprings p_mutation: probability of mutation for current replacement mutation_power: allowed percentage of mutation + key_model_to_evolve: binary flag that should be inserted into the dictionary + with evolving model in the basic config + key_basic_layers: key value of dictionary in basic_config + that contains considered layers with their evolving parameters + seed: random seed for initialization + seed: random seed for initialization **kwargs: basic config with parameters """ self.n_types = n_types @@ -129,6 +135,9 @@ def initialize_params_in_config(self, basic_params): elif basic_params[param_name].get("range"): params_for_search[param_name] = deepcopy(basic_params[param_name]) evolving_params.append(param_name) + elif basic_params[param_name].get("bool"): + params_for_search[param_name] = deepcopy(basic_params[param_name]) + evolving_params.append(param_name) else: # NOT evolving params params[param_name] = deepcopy(basic_params[param_name]) @@ -521,4 +530,5 @@ def sample_binary_mask(self): size=max(1, int(np.random.random() * self.total_nodes))) mask = np.zeros((self.total_nodes * self.total_nodes)) mask[ones] = 1 + # returns NUMPY 2D ARRAY! return mask.reshape((self.total_nodes, self.total_nodes)) diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index f66d4b3301..814367c189 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -22,7 +22,7 @@ from deeppavlov.core.common.log import get_logger from keras import initializers, regularizers, constraints from keras import backend as K -from keras.layers import concatenate, multiply, Reshape +from keras.layers import concatenate, multiply, Reshape, Lambda log = get_logger(__name__) @@ -154,16 +154,19 @@ def build(self, input_shape): if self.context_length is None: self.context_length = input_shape[-1] - self.context = self.add_weight(tuple(input_shape[:-1] + (self.context_length,)), + self.context = self.add_weight(tuple((self.context_length, input_shape[-1])), + name="context", initializer=self.init) - self.W = self.add_weight((input_shape[-1] + self.context_length, input_shape[-1], ), + self.W = self.add_weight((2 * input_shape[-1], 1,), + name="w", initializer=self.init, regularizer=self.W_regularizer, constraint=self.W_constraint) if self.use_bias: - self.b = self.add_weight((input_shape[-1], ), + self.b = self.add_weight((1, ), + name="b", initializer='zero', regularizer=self.b_regularizer, constraint=self.b_constraint) @@ -173,21 +176,29 @@ def build(self, input_shape): self.built = True def call(self, x, mask=None): - x_full = concatenate(inputs=[x, self.context], axis=-1) + + expanded_context_3d = expand_tile_batch_size(memory=x, context=self.context) + expanded_context_4d = expand_tile(expanded_context_3d, axis=1, n_repetitions=K.int_shape(x)[1]) + expanded_x = expand_tile(x, axis=2, n_repetitions=K.int_shape(expanded_context_3d)[1]) + + # now expanded_context_4d and expanded_x are of + # shape (bs, time_steps, context_size, n_features) + x_full = concatenate(inputs=[expanded_x, expanded_context_4d], axis=-1) out = K.dot(x_full, self.W) if self.use_bias: out = K.bias_add(out, self.b) out = K.softmax(out) - out = multiply(inputs=[out, x]) + out = multiply(inputs=[out, expanded_x]) + out = Lambda(lambda x: K.sum(x, axis=1))(out) return out def compute_output_shape(self, input_shape): return input_shape -def expand_tile(units, axis): +def expand_tile(units, axis, n_repetitions=None): """Expand and tile tensor along given axis Args: units: tf tensor with dimensions [batch_size, time_steps, n_input_features] @@ -195,11 +206,32 @@ def expand_tile(units, axis): """ assert axis in (1, 2) - n_time_steps = K.int_shape(units)[1] - repetitions = [1, 1, 1, 1] - repetitions[axis] = n_time_steps + repetitions = [1] * (len(K.int_shape(units)) + 1) + + if n_repetitions is None: + repetitions[axis] = K.int_shape(units)[1] + else: + repetitions[axis] = n_repetitions + if axis == 1: expanded = Reshape(target_shape=( (1,) + K.int_shape(units)[1:] ))(units) else: # axis=2 expanded = Reshape(target_shape=(K.int_shape(units)[1:2] + (1,) + K.int_shape(units)[2:]))(units) return K.tile(expanded, repetitions) + + +def expand_tile_batch_size(memory, context): + """Expand and tile tensor context along 0 axis up to 0-shape of memory + Args: + memory: tf tensor with dimensions [batch_size, time_steps, n_input_features] + context: tf tensor with dimensions [new_time_steps, n_input_features] + + """ + axis = 0 + # batch_size = K.int_shape(memory)[0] + batch_size = K.shape(memory)[0] + repetitions = [1] * len(K.int_shape(memory)) + repetitions[axis] = batch_size + if axis == 0: + expanded = K.reshape(context, shape=((1,) + K.int_shape(context))) + return K.tile(expanded, repetitions) From 2c1310a4e3f09a99e80ab3d1e16c1570fd955044 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 23 Apr 2018 18:46:33 +0300 Subject: [PATCH 037/616] feat: add classification measures, intent_model returns both labels and probas --- deeppavlov/configs/intents/intents_snips.json | 10 +- deeppavlov/metrics/accuracy.py | 8 ++ deeppavlov/metrics/fmeasure_classification.py | 94 +++++++++++++++++++ deeppavlov/metrics/roc_auc_score.py | 81 ++++++++++++++++ .../classifiers/intents/intent_model.py | 3 +- 5 files changed, 190 insertions(+), 6 deletions(-) create mode 100644 deeppavlov/metrics/fmeasure_classification.py create mode 100644 deeppavlov/metrics/roc_auc_score.py diff --git a/deeppavlov/configs/intents/intents_snips.json b/deeppavlov/configs/intents/intents_snips.json index 0fabb8a0d7..ffe842f6d5 100644 --- a/deeppavlov/configs/intents/intents_snips.json +++ b/deeppavlov/configs/intents/intents_snips.json @@ -34,11 +34,11 @@ { "in": ["x"], "in_y": ["y"], - "out": ["y_predicted"], + "out": ["y_labels", "y_proba"], "main": true, "name": "intent_model", - "save_path": "intents/intent_cnn_snips_v3", - "load_path": "intents/intent_cnn_snips_v3", + "save_path": "intents/intent_cnn_snips_v4", + "load_path": "intents/intent_cnn_snips_v4", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, @@ -69,13 +69,13 @@ } } ], - "out": ["y_predicted"] + "out": ["y_labels", "y_proba"] }, "train": { "epochs": 1000, "batch_size": 64, "metrics": [ - "sets_accuracy" + "classification_accuracy" ], "validation_patience": 5, "val_every_n_epochs": 5, diff --git a/deeppavlov/metrics/accuracy.py b/deeppavlov/metrics/accuracy.py index d57a3c0f64..074dc14238 100644 --- a/deeppavlov/metrics/accuracy.py +++ b/deeppavlov/metrics/accuracy.py @@ -17,6 +17,14 @@ def sets_accuracy(y_true, y_predicted): return correct / examples_len if examples_len else 0 +@register_metric('classification_accuracy') +def classification_accuracy(y_true, y_predicted): + y_pred_labels = [y_predicted[i][0] for i in range(len(y_predicted))] + examples_len = len(y_true) + correct = sum([set(y1) == set(y2) for y1, y2 in zip(y_true, y_pred_labels)]) + return correct / examples_len if examples_len else 0 + + @register_metric('slots_accuracy') def slots_accuracy(y_true, y_predicted): y_true = [{tag.split('-')[-1] for tag in s if tag != 'O'} for s in y_true] diff --git a/deeppavlov/metrics/fmeasure_classification.py b/deeppavlov/metrics/fmeasure_classification.py new file mode 100644 index 0000000000..63b64daf35 --- /dev/null +++ b/deeppavlov/metrics/fmeasure_classification.py @@ -0,0 +1,94 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import numpy as np + +from keras import backend as K + +from deeppavlov.core.common.metrics_registry import register_metric + + +def precision_K(y_true, y_pred): + true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) + predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) + precision = true_positives / (predicted_positives + K.epsilon()) + return precision + + +def recall_K(y_true, y_pred): + true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) + possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) + recall = true_positives / (possible_positives + K.epsilon()) + return recall + + +def fbeta_score_K(y_true, y_pred, beta=1): + if beta < 0: + raise ValueError('The lowest choosable beta is zero (only precision).') + + if K.sum(K.round(K.clip(y_true, 0, 1))) == 0: + return 0 + + p = precision_K(y_true, y_pred) + r = recall_K(y_true, y_pred) + bb = beta ** 2 + fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon()) + return fbeta_score + + +def precision_np(y_true, y_pred): + y_true = np.array(y_true) + y_pred = np.array(y_pred) + true_positives = np.sum(np.round(np.clip(y_true * y_pred, 0, 1))) + predicted_positives = np.sum(np.round(np.clip(y_pred, 0, 1))) + precision = true_positives / (predicted_positives + 10e-8) + return precision + + +def recall_np(y_true, y_pred): + y_true = np.array(y_true) + y_pred = np.array(y_pred) + true_positives = np.sum(np.round(np.clip(y_true * y_pred, 0, 1))) + possible_positives = np.sum(np.round(np.clip(y_true, 0, 1))) + recall = true_positives / (possible_positives + 10e-8) + return recall + + +def fbeta_score_np(y_true, y_pred, beta=1): + y_true = np.array(y_true) + y_pred = np.array(y_pred) + if beta < 0: + raise ValueError('The lowest choosable beta is zero (only precision).') + + # If there are no true positives, fix the F score at 0 like sklearn. + if np.sum(np.round(np.clip(y_true, 0, 1))) == 0: + return 0 + + p = precision_np(y_true, y_pred) + r = recall_np(y_true, y_pred) + bb = beta ** 2 + fbeta_score = (1 + bb) * (p * r) / (bb * p + r + 10e-8) + return fbeta_score + + +@register_metric('f1_classification') +def fmeasure(y_true, y_predicted): + y_pred_labels = [y_predicted[i][0] for i in range(len(y_predicted))] + try: + _ = K.is_keras_tensor(y_pred_labels) + return fbeta_score_K(y_true, y_pred_labels, beta=1) + except ValueError: + return fbeta_score_np(y_true, y_pred_labels, beta=1) diff --git a/deeppavlov/metrics/roc_auc_score.py b/deeppavlov/metrics/roc_auc_score.py new file mode 100644 index 0000000000..7769d0e12a --- /dev/null +++ b/deeppavlov/metrics/roc_auc_score.py @@ -0,0 +1,81 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import sklearn.metrics +import numpy as np + +import tensorflow as tf +from keras import backend as K + +from deeppavlov.core.common.metrics_registry import register_metric + + +def roc_auc_score_np(y_true, y_pred): + """Compute Area Under the Curve (AUC) from prediction scores. + + Args: + y_true: true binary labels + y_pred: target scores, can either be probability estimates of the positive class + + Returns: + Area Under the Curve (AUC) from prediction scores + """ + try: + return sklearn.metrics.roc_auc_score(np.array(y_true), np.array(y_pred), average="macro") + except ValueError: + return 0. + + +# AUC for a binary classifier +def auc(y_true, y_pred): + ptas = tf.stack([binary_PTA(y_true,y_pred,k) for k in np.linspace(0, 1, 1000)], axis=0) + pfas = tf.stack([binary_PFA(y_true,y_pred,k) for k in np.linspace(0, 1, 1000)], axis=0) + pfas = tf.concat([tf.ones((1,)), pfas], axis=0) + binSizes = -(pfas[1:]-pfas[:-1]) + s = ptas*binSizes + return K.sum(s, axis=0) + + +# PFA, prob false alert for binary classifier +def binary_PFA(y_true, y_pred, threshold=K.variable(value=0.5)): + y_pred = K.cast(y_pred >= threshold, 'float32') + # N = total number of negative labels + N = K.sum(1 - y_true) + # FP = total number of false alerts, alerts from the negative class labels + FP = K.sum(y_pred - y_pred * y_true) + return FP/N + + +# P_TA prob true alerts for binary classifier +def binary_PTA(y_true, y_pred, threshold=K.variable(value=0.5)): + y_pred = K.cast(y_pred >= threshold, 'float32') + # P = total number of positive labels + P = K.sum(y_true) + # TP = total number of correct alerts, alerts from the positive class labels + TP = K.sum(y_pred * y_true) + return TP/P + + +@register_metric('roc_auc_score') +def roc_auc_score(y_true, y_predicted): + y_pred_labels = [y_predicted[i][0] for i in range(len(y_predicted))] + try: + _ = K.is_keras_tensor(y_pred_labels) + auc_score = auc(y_true, y_pred_labels) + auc_score = tf.where(tf.is_nan(auc_score), 0., auc_score) + except ValueError: + auc_score = roc_auc_score_np(y_true, y_pred_labels) + return auc_score diff --git a/deeppavlov/models/classifiers/intents/intent_model.py b/deeppavlov/models/classifiers/intents/intent_model.py index 7980f944ac..fafa0c82fa 100644 --- a/deeppavlov/models/classifiers/intents/intent_model.py +++ b/deeppavlov/models/classifiers/intents/intent_model.py @@ -193,7 +193,8 @@ def __call__(self, data, predict_proba=False, *args): if predict_proba: return preds else: - return proba2labels(preds, confident_threshold=self.opt['confident_threshold'], classes=self.classes) + labels = proba2labels(preds, confident_threshold=self.opt['confident_threshold'], classes=self.classes) + return labels, preds def cnn_model(self, params): """ From dd940257bf3c01a4e398814954790746da0e9d4d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 10:23:05 +0300 Subject: [PATCH 038/616] feat: registered classification_f1 and classification_roc_auc --- deeppavlov/__init__.py | 2 ++ deeppavlov/configs/intents/intents_snips.json | 4 +++- deeppavlov/metrics/fmeasure_classification.py | 2 +- deeppavlov/metrics/roc_auc_score.py | 2 +- 4 files changed, 7 insertions(+), 3 deletions(-) diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index bc9d0d20d1..93fae7df8e 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -57,5 +57,7 @@ import deeppavlov.metrics.fmeasure import deeppavlov.metrics.bleu import deeppavlov.metrics.squad_metrics +import deeppavlov.metrics.roc_auc_score +import deeppavlov.metrics.fmeasure_classification import deeppavlov.core.common.log diff --git a/deeppavlov/configs/intents/intents_snips.json b/deeppavlov/configs/intents/intents_snips.json index ffe842f6d5..1a9180368e 100644 --- a/deeppavlov/configs/intents/intents_snips.json +++ b/deeppavlov/configs/intents/intents_snips.json @@ -75,7 +75,9 @@ "epochs": 1000, "batch_size": 64, "metrics": [ - "classification_accuracy" + "classification_accuracy", + "classification_f1", + "classification_roc_auc" ], "validation_patience": 5, "val_every_n_epochs": 5, diff --git a/deeppavlov/metrics/fmeasure_classification.py b/deeppavlov/metrics/fmeasure_classification.py index 63b64daf35..4159899b78 100644 --- a/deeppavlov/metrics/fmeasure_classification.py +++ b/deeppavlov/metrics/fmeasure_classification.py @@ -84,7 +84,7 @@ def fbeta_score_np(y_true, y_pred, beta=1): return fbeta_score -@register_metric('f1_classification') +@register_metric('classification_f1') def fmeasure(y_true, y_predicted): y_pred_labels = [y_predicted[i][0] for i in range(len(y_predicted))] try: diff --git a/deeppavlov/metrics/roc_auc_score.py b/deeppavlov/metrics/roc_auc_score.py index 7769d0e12a..19e6d01e95 100644 --- a/deeppavlov/metrics/roc_auc_score.py +++ b/deeppavlov/metrics/roc_auc_score.py @@ -69,7 +69,7 @@ def binary_PTA(y_true, y_pred, threshold=K.variable(value=0.5)): return TP/P -@register_metric('roc_auc_score') +@register_metric('classification_roc_auc') def roc_auc_score(y_true, y_predicted): y_pred_labels = [y_predicted[i][0] for i in range(len(y_predicted))] try: From 0deba09623b5bd70f9c6ed60afa3961e3d254ac3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 11:11:34 +0300 Subject: [PATCH 039/616] feat: working classification_f1 and classification_roc_auc, intent_model returns labels, probas, classes_list --- deeppavlov/configs/intents/intents_snips.json | 4 ++-- deeppavlov/metrics/fmeasure_classification.py | 12 +++++++++--- deeppavlov/metrics/roc_auc_score.py | 12 ++++++++---- .../models/classifiers/intents/intent_model.py | 2 +- 4 files changed, 20 insertions(+), 10 deletions(-) diff --git a/deeppavlov/configs/intents/intents_snips.json b/deeppavlov/configs/intents/intents_snips.json index 1a9180368e..11b92e328c 100644 --- a/deeppavlov/configs/intents/intents_snips.json +++ b/deeppavlov/configs/intents/intents_snips.json @@ -34,7 +34,7 @@ { "in": ["x"], "in_y": ["y"], - "out": ["y_labels", "y_proba"], + "out": ["y_labels", "y_probas", "y_classes"], "main": true, "name": "intent_model", "save_path": "intents/intent_cnn_snips_v4", @@ -69,7 +69,7 @@ } } ], - "out": ["y_labels", "y_proba"] + "out": ["y_labels", "y_probas", "y_classes"] }, "train": { "epochs": 1000, diff --git a/deeppavlov/metrics/fmeasure_classification.py b/deeppavlov/metrics/fmeasure_classification.py index 4159899b78..ce47afe0f8 100644 --- a/deeppavlov/metrics/fmeasure_classification.py +++ b/deeppavlov/metrics/fmeasure_classification.py @@ -19,6 +19,7 @@ from keras import backend as K from deeppavlov.core.common.metrics_registry import register_metric +from deeppavlov.models.classifiers.intents.utils import labels2onehot def precision_K(y_true, y_pred): @@ -86,9 +87,14 @@ def fbeta_score_np(y_true, y_pred, beta=1): @register_metric('classification_f1') def fmeasure(y_true, y_predicted): + classes = y_predicted[0][2] + y_true_one_hot = labels2onehot(y_true, classes) y_pred_labels = [y_predicted[i][0] for i in range(len(y_predicted))] + y_pred_one_hot = labels2onehot(y_pred_labels, classes) + + print() try: - _ = K.is_keras_tensor(y_pred_labels) - return fbeta_score_K(y_true, y_pred_labels, beta=1) + _ = K.is_keras_tensor(y_pred_one_hot) + return fbeta_score_K(y_true_one_hot, y_pred_one_hot, beta=1) except ValueError: - return fbeta_score_np(y_true, y_pred_labels, beta=1) + return fbeta_score_np(y_true_one_hot, y_pred_one_hot, beta=1) diff --git a/deeppavlov/metrics/roc_auc_score.py b/deeppavlov/metrics/roc_auc_score.py index 19e6d01e95..4063acf7b8 100644 --- a/deeppavlov/metrics/roc_auc_score.py +++ b/deeppavlov/metrics/roc_auc_score.py @@ -21,6 +21,7 @@ from keras import backend as K from deeppavlov.core.common.metrics_registry import register_metric +from deeppavlov.models.classifiers.intents.utils import labels2onehot def roc_auc_score_np(y_true, y_pred): @@ -71,11 +72,14 @@ def binary_PTA(y_true, y_pred, threshold=K.variable(value=0.5)): @register_metric('classification_roc_auc') def roc_auc_score(y_true, y_predicted): - y_pred_labels = [y_predicted[i][0] for i in range(len(y_predicted))] + classes = y_predicted[0][2] + y_true_one_hot = labels2onehot(y_true, classes) + y_pred_probas = [y_predicted[i][1] for i in range(len(y_predicted))] + try: - _ = K.is_keras_tensor(y_pred_labels) - auc_score = auc(y_true, y_pred_labels) + _ = K.is_keras_tensor(y_pred_probas) + auc_score = auc(y_true_one_hot, y_pred_probas) auc_score = tf.where(tf.is_nan(auc_score), 0., auc_score) except ValueError: - auc_score = roc_auc_score_np(y_true, y_pred_labels) + auc_score = roc_auc_score_np(y_true_one_hot, y_pred_probas) return auc_score diff --git a/deeppavlov/models/classifiers/intents/intent_model.py b/deeppavlov/models/classifiers/intents/intent_model.py index fafa0c82fa..04f7bfa08e 100644 --- a/deeppavlov/models/classifiers/intents/intent_model.py +++ b/deeppavlov/models/classifiers/intents/intent_model.py @@ -194,7 +194,7 @@ def __call__(self, data, predict_proba=False, *args): return preds else: labels = proba2labels(preds, confident_threshold=self.opt['confident_threshold'], classes=self.classes) - return labels, preds + return labels, preds, [self.classes for _ in range(len(preds))] def cnn_model(self, params): """ From 08593832462e58d52fe59468d45b152097eba0e6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 11:33:15 +0300 Subject: [PATCH 040/616] chore: training phenotype --- .../evolution/basic_intents_snips.json | 22 ++---- deeppavlov/models/evolution/debug.py | 8 +-- .../evolution/evolution_intent_model.py | 71 +++++++++++-------- .../neuroevolution_param_generator.py | 2 +- .../{evolution.py => run_evolution.py} | 49 +++++++++---- .../models/evolution/train_phenotype.py | 37 ++++++++++ deeppavlov/models/evolution/utils.py | 13 ++-- 7 files changed, 132 insertions(+), 70 deletions(-) rename deeppavlov/models/evolution/{evolution.py => run_evolution.py} (53%) diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json index cf82a03368..799036bc63 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -7,17 +7,7 @@ "url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv" }, "dataset_iterator": { - "name": "basic_classification_iterator", - "seed": 42, - "field_to_split": "train", - "split_fields": [ - "train", - "valid" - ], - "split_proportions": [ - 0.9, - 0.1 - ] + "name": "basic_classification_iterator" }, "chainer": { "in": [ @@ -61,8 +51,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "evolution/intents_snips", - "load_path": "evolution/intents_snips", + "save_path": "/home/dilyara/data/models/deeppavlov_evolution/classification/intents_snips", + "load_path": "/home/dilyara/data/models/deeppavlov_evolution/classification/intents_snips", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -180,7 +170,7 @@ "epochs": { "range": [ 10, - 1000 + 11 ], "discrete": true }, @@ -192,7 +182,9 @@ "discrete": true }, "metrics": [ - "sets_accuracy" + "sets_accuracy", + "roc_auc_score", + "f1_classification" ], "validation_patience": 5, "val_every_n_epochs": 5, diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 382b0ebd69..291c7a7df4 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -55,13 +55,7 @@ evolution.model_to_evolve_index]["binary_mask"]) population = evolution.mutation(population, p_mutation=0.5, mutation_power=.5) -# -# for i in range(population_size): -# if (mutated[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] != -# population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]).any(): -# print("{} mask mutated".format(i)) -# population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ -# evolution.model_to_evolve_index]["binary_mask"].tolist() + population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ evolution.model_to_evolve_index]["binary_mask"].tolist() diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index e0bd6d05b3..97ee098b65 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -25,8 +25,10 @@ from keras.layers.wrappers import Bidirectional from keras.models import Model from keras.regularizers import l2 -from keras.layers import Concatenate, Reshape, CuDNNLSTM +from keras.layers import Concatenate, Reshape, CuDNNLSTM, Lambda from keras import backend as K +from overrides import overrides +from pathlib import Path from deeppavlov.core.common.errors import ConfigError from deeppavlov.core.common.registry import register @@ -41,6 +43,9 @@ from deeppavlov.models.evolution.check_binary_mask import number_to_type_layer, \ find_sources_and_sinks, get_digraph_from_binary_mask from deeppavlov.models.evolution.utils import Attention, expand_tile +from deeppavlov.core.common.file import save_json, read_json + + log = get_logger(__name__) @@ -49,18 +54,18 @@ class KerasEvolutionClassificationModel(KerasIntentModel): def __init__(self, **kwargs): super().__init__(**kwargs) + self.opt["binary_mask"] = np.array(self.opt["binary_mask"]) def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): if inp is None: - print(dg.in_edges(node_id)) input_nodes = [edge[0] for edge in dg.in_edges(node_id)] - print("Input nodes: {}".format(input_nodes)) inp_list = [] for input_node in input_nodes: if len(K.int_shape(edges_outputs[input_node])) == 3: inp_list.append(edges_outputs[input_node]) elif len(K.int_shape(edges_outputs[input_node])) == 2: - inp_list.append(expand_tile(edges_outputs[input_node], axis=1)) + input_expanded = Lambda(lambda x: expand_tile(x, axis=1))(edges_outputs[input_node]) + inp_list.append(input_expanded) else: raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") if len(input_nodes) > 1: @@ -71,7 +76,7 @@ def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): features = [] for i in range(len(inp_list)): if len(K.int_shape(inp_list[i])) == 2: - inp_list[i] = expand_tile(inp_list[i], axis=1) + inp_list[i] = Lambda(lambda x: expand_tile(x, axis=1))(inp_list[i]) time_steps.append(K.int_shape(inp_list[i])[1]) features.append(K.int_shape(inp_list[i])[2]) new_feature_shape = max(features) @@ -81,10 +86,6 @@ def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): else: inp = inp_list[0] - print(params[params["nodes"][str(node_id)]]["node_name"]) - # print(globals()) - # node_func = getattr(globals(), params[params["nodes"][str(node_id)]]["node_name"], None) - if params[params["nodes"][str(node_id)]]["node_name"] == "BiCuDNNLSTM": node_params = deepcopy(params[params["nodes"][str(node_id)]]) node_params.pop("node_name") @@ -112,25 +113,13 @@ def evolution_classification_model(self, params): Returns: Un-compiled model """ - print(params) - inp = Input(shape=(params['text_size'], params['embedding_size'])) dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) - print(dg.edges) sources, sinks, isolates = find_sources_and_sinks(dg) edges_outputs = {} - # for node_id in range(params["total_nodes"]): - # # node_layer, node_type = number_to_type_layer(node_id, params["n_types"]) - # if node_id in sources: - # edges_outputs[node_id] = self.get_node_output(node_id, dg, params, inp=inp) - # elif node_id in isolates: - # pass - # else: - # edges_outputs[node_id] = self.get_node_output(node_id, dg, params, edges_outputs=edges_outputs) - sequence_of_nodes = [] sequence_of_nodes.append(sources) @@ -149,8 +138,6 @@ def evolution_classification_model(self, params): sequence_of_nodes = sum(sequence_of_nodes, []) for node_id in sequence_of_nodes: - print(node_id) - # node_layer, node_type = number_to_type_layer(node_id, params["n_types"]) if node_id in sources: edges_outputs[node_id] = self.get_node_output(node_id, dg, params, inp=inp) elif node_id in isolates: @@ -164,7 +151,6 @@ def evolution_classification_model(self, params): outputs = [] for sink in sinks: outputs.append(edges_outputs[sink]) - print("Sinks: {}".format(sinks)) try: output = Concatenate()(outputs) except ValueError: @@ -172,18 +158,47 @@ def evolution_classification_model(self, params): features = [] for i in range(len(outputs)): if len(K.int_shape(outputs[i])) == 2: - outputs[i] = expand_tile(outputs[i], axis=1) + outputs[i] = Lambda(lambda x: expand_tile(x, axis=1))(outputs[i]) time_steps.append(K.int_shape(outputs[i])[1]) features.append(K.int_shape(outputs[i])[2]) new_feature_shape = max(features) for i in range(len(outputs)): outputs[i] = Dense(new_feature_shape)(outputs[i]) - print("Outputs: {}".format(outputs[i].shape)) output = Concatenate(axis=1)(outputs) - #TODO: make 2dimensional input for dense! - output = GlobalMaxPooling1D()(output) + if len(output.shape) == 3: + output = GlobalMaxPooling1D()(output) output = Dense(self.n_classes, activation=None)(output) act_output = Activation('sigmoid')(output) model = Model(inputs=inp, outputs=act_output) return model + + @overrides + def save(self, fname=None): + """ + Save the model parameters into <>_opt.json (or <>_opt.json) + and model weights into <>.h5 (or <>.h5) + Args: + fname: file_path to save model. If not explicitly given seld.opt["ser_file"] will be used + + Returns: + None + """ + + if not self.save_path: + raise ConfigError("No `save_path` is provided for Keras model!") + elif isinstance(self.save_path, Path) and not self.save_path.parent.is_dir(): + raise ConfigError("Provided save path is incorrect!") + else: + opt_path = "{}_opt.json".format(str(self.save_path.resolve())) + weights_path = "{}.h5".format(str(self.save_path.resolve())) + log.info("[saving model to {}]".format(opt_path)) + self.model.save_weights(weights_path) + + if type(self.opt["binary_mask"]) is list: + pass + else: + self.opt["binary_mask"] = self.opt["binary_mask"].tolist() + + save_json(self.opt, opt_path) + return True \ No newline at end of file diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 9cedc4acc6..f897cf0ffb 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -499,7 +499,7 @@ def sample_params(self, **params): params_sample[param] = np.random.choice(param_val) elif isinstance(param_val, dict): if 'bool' in param_val and param_val['bool']: - sample = np.random.choice([True, False]) + sample = bool(np.random.choice([True, False])) elif 'range' in param_val: sample = self._sample_from_ranges(param_val) params_sample[param] = sample diff --git a/deeppavlov/models/evolution/evolution.py b/deeppavlov/models/evolution/run_evolution.py similarity index 53% rename from deeppavlov/models/evolution/evolution.py rename to deeppavlov/models/evolution/run_evolution.py index adcb6a5e62..dfdadf7a38 100644 --- a/deeppavlov/models/evolution/evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -5,11 +5,10 @@ from subprocess import Popen, PIPE import pandas as pd - -from tuning_parameters.neuroevolution_param_generator import Evolution - +from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution def score_population(population, population_size, result_file): + global evolution population_losses = [] population_fmeasures = [] population_accuracies = [] @@ -18,20 +17,32 @@ def score_population(population, population_size, result_file): procs = [] for i in range(population_size): - f_name = Path(population[i]["model_path"]) + f_name = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) + model_name = population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ + str(f_name.joinpath(model_name + "_" + str(i))) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] =\ + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] + + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = \ + evolution.nodes + print(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) try: f_name.mkdir(parents=True) except FileExistsError: pass f_name = f_name.joinpath("config.json") + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] =\ + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"].tolist() with open(f_name, 'w') as outfile: json.dump(population[i], outfile) - procs.append(Popen("CUDA_VISIBLE_DEVICES={} python train_phenotype.py {}" + procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], str(f_name), - population[i]["model_path"], - population[i]["model_path"]), + str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).parent), + str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).parent) + ), shell=True, stdout=PIPE, stderr=PIPE)) for i, proc in enumerate(procs): @@ -39,7 +50,8 @@ def score_population(population, population_size, result_file): proc.wait() for i in range(population_size): - val_results = np.loadtxt(fname=str(Path(population[i]["model_path"]).joinpath("valid_results.txt"))) + val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).joinpath("valid_results.txt"))) result_table = pd.DataFrame({"loss": [val_results[0]], "accuracy": [val_results[1]], "fmeasure": [val_results[2]], @@ -56,9 +68,12 @@ def score_population(population, population_size, result_file): parser = argparse.ArgumentParser() -parser.add_argument('--config', help='Please, enter model path to config', default='./configs/basic_config.json') +parser.add_argument('--config', help='Please, enter model path to config', + default='./configs/evolution/basic_intents_config.json') parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) +parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) +parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) args = parser.parse_args() @@ -66,6 +81,8 @@ def score_population(population, population_size, result_file): POPULATION_SIZE = args.p_size GPU_NUMBER = len(args.gpus) gpus = [int(gpu) for gpu in args.gpus.split(",")] +N_LAYERS = int(args.n_layers) +N_TYPES = int(args.n_types) with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) @@ -73,19 +90,25 @@ def score_population(population, population_size, result_file): print("Given basic params: {}\n".format(basic_params)) try: - Path(basic_params["model_path"]).mkdir(parents=True) + print(basic_params["chainer"]["pipe"][3]) + Path(basic_params["chainer"]["pipe"][3]["save_path"]).mkdir(parents=True) except FileExistsError: pass # Result table order = ["loss", "accuracy", "fmeasure", "roc_auc_score", "params"] -result_file = Path(basic_params["model_path"]).joinpath("result_table.csv") +result_file = Path(basic_params["chainer"]["pipe"][3]["save_path"]).joinpath("result_table.csv") result_table = pd.DataFrame({"loss": [], "accuracy": [], "fmeasure": [], "roc_auc_score": [], "params": []}) result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') # EVOLUTION starts here! -evolution = Evolution(population_size=POPULATION_SIZE, p_crossover=0.1, - p_mutation=0.5, mutation_power=0.1, **basic_params) +evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, + population_size=POPULATION_SIZE, + p_crossover=0.1, crossover_power=0.5, + p_mutation=0.5, mutation_power=0.1, + key_model_to_evolve="to_evolve", + key_basic_layers="basic_layers_params", + seed=None, **basic_params) print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py index e69de29bb2..d9ebadb048 100644 --- a/deeppavlov/models/evolution/train_phenotype.py +++ b/deeppavlov/models/evolution/train_phenotype.py @@ -0,0 +1,37 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import numpy as np +import sys + +from deeppavlov.core.commands.train import train_model_from_config, train_evaluate_model_from_config +from deeppavlov.core.common.file import read_json, save_json + +config_path = sys.argv[1] + +print("TRAIN PHENOTYPE") +report = train_model_from_config(config_path, is_trained=False) + +# train_model_from_config(config_path) + +# config = read_json(config_path) +# +# model = build_model_from_config(config, mode='infer', load_trained=True) +# +# test_model_on_data(config_path, data) +# +# val_metrics_values = np.mean(np.array(val_metrics_values), axis=0) +# +# np.savetxt(fname=Path(path_to_models).joinpath("valid_results.txt"), X=val_metrics_values) diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index 814367c189..479660ecd8 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -22,8 +22,8 @@ from deeppavlov.core.common.log import get_logger from keras import initializers, regularizers, constraints from keras import backend as K -from keras.layers import concatenate, multiply, Reshape, Lambda - +from keras.layers import Reshape, Lambda, Dense, Flatten +from keras.layers import Concatenate, Multiply, Activation, Dot log = get_logger(__name__) @@ -158,7 +158,7 @@ def build(self, input_shape): name="context", initializer=self.init) - self.W = self.add_weight((2 * input_shape[-1], 1,), + self.W = self.add_weight((2 * input_shape[-1], 1, ), name="w", initializer=self.init, regularizer=self.W_regularizer, @@ -183,14 +183,15 @@ def call(self, x, mask=None): # now expanded_context_4d and expanded_x are of # shape (bs, time_steps, context_size, n_features) - x_full = concatenate(inputs=[expanded_x, expanded_context_4d], axis=-1) + + x_full = Concatenate(axis=-1)([expanded_x, expanded_context_4d]) out = K.dot(x_full, self.W) if self.use_bias: out = K.bias_add(out, self.b) - out = K.softmax(out) - out = multiply(inputs=[out, expanded_x]) + out = Activation('softmax')(out) + out = Multiply()([out, expanded_x]) out = Lambda(lambda x: K.sum(x, axis=1))(out) return out From cf0150a8c648ce6e7122b9e8bc5cfa0f7724ac67 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 11:36:50 +0300 Subject: [PATCH 041/616] feat: reports from training with valid and test results --- deeppavlov/core/commands/train.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index 1cf436c297..d209b4eedf 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -152,6 +152,7 @@ def train_model_from_config(config_path: str) -> None: log.warning('Nothing to train') if train_config['validate_best'] or train_config['test_best']: + all_reports = [] # try: # model_config['load_path'] = model_config['save_path'] # except KeyError: @@ -166,6 +167,7 @@ def train_model_from_config(config_path: str) -> None: } print(json.dumps(report, ensure_ascii=False)) + all_reports.append(report) if train_config['test_best']: report = { @@ -174,6 +176,9 @@ def train_model_from_config(config_path: str) -> None: } print(json.dumps(report, ensure_ascii=False)) + all_reports.append(report) + + return all_reports def _test_model(model: Component, metrics_functions: List[Tuple[str, Callable]], From f625cc2f35119ff448174ad004b8de1e76ddb29e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 11:52:17 +0300 Subject: [PATCH 042/616] chore: moved embeder and tokenizer to pipe from model --- deeppavlov/configs/intents/intents_dstc2.json | 64 +++++++++++++------ deeppavlov/configs/intents/intents_snips.json | 58 +++++++++++------ 2 files changed, 84 insertions(+), 38 deletions(-) diff --git a/deeppavlov/configs/intents/intents_dstc2.json b/deeppavlov/configs/intents/intents_dstc2.json index fc74118889..022f4ef621 100644 --- a/deeppavlov/configs/intents/intents_dstc2.json +++ b/deeppavlov/configs/intents/intents_dstc2.json @@ -22,25 +22,51 @@ ] }, "chainer": { - "in": ["x"], - "in_y": ["y"], + "in": [ + "x" + ], + "in_y": [ + "y" + ], "pipe": [ { "id": "classes_vocab", "name": "default_vocab", - "fit_on": ["y"], + "fit_on": [ + "y" + ], "level": "token", "save_path": "vocabs/classes.dict", "load_path": "vocabs/classes.dict" }, { - "in": ["x"], - "in_y": ["y"], - "out": ["y_predicted"], + "id": "my_embedder", + "name": "fasttext", + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ], "main": true, "name": "intent_model", - "save_path": "intents/intent_cnn_v3", - "load_path": "intents/intent_cnn_v3", + "save_path": "intents/intent_cnn_v4", + "load_path": "intents/intent_cnn_v4", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, @@ -59,25 +85,23 @@ "dropout_rate": 0.5, "dense_size": 100, "model_name": "cnn_model", - "embedder": { - "name": "fasttext", - "save_path": "embeddings/dstc2_fastText_model.bin", - "load_path": "embeddings/dstc2_fastText_model.bin", - "dim": 100 - }, - "tokenizer": { - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - } + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" } ], - "out": ["y_predicted"] + "out": [ + "y_labels", + "y_probas", + "y_classes" + ] }, "train": { "epochs": 1000, "batch_size": 64, "metrics": [ - "sets_accuracy" + "classification_accuracy", + "classification_f1", + "classification_roc_auc" ], "validation_patience": 5, "val_every_n_epochs": 5, diff --git a/deeppavlov/configs/intents/intents_snips.json b/deeppavlov/configs/intents/intents_snips.json index 11b92e328c..7743d2808a 100644 --- a/deeppavlov/configs/intents/intents_snips.json +++ b/deeppavlov/configs/intents/intents_snips.json @@ -20,21 +20,47 @@ ] }, "chainer": { - "in": ["x"], - "in_y": ["y"], + "in": [ + "x" + ], + "in_y": [ + "y" + ], "pipe": [ { "id": "classes_vocab", "name": "default_vocab", - "fit_on": ["y"], + "fit_on": [ + "y" + ], "level": "token", "save_path": "vocabs/snips_classes.dict", "load_path": "vocabs/snips_classes.dict" }, { - "in": ["x"], - "in_y": ["y"], - "out": ["y_labels", "y_probas", "y_classes"], + "id": "my_embedder", + "name": "fasttext", + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ], "main": true, "name": "intent_model", "save_path": "intents/intent_cnn_snips_v4", @@ -57,22 +83,18 @@ "dropout_rate": 0.5, "dense_size": 100, "model_name": "cnn_model", - "embedder": { - "name": "fasttext", - "save_path": "embeddings/dstc2_fastText_model.bin", - "load_path": "embeddings/dstc2_fastText_model.bin", - "dim": 100 - }, - "tokenizer": { - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - } + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" } ], - "out": ["y_labels", "y_probas", "y_classes"] + "out": [ + "y_labels", + "y_probas", + "y_classes" + ] }, "train": { - "epochs": 1000, + "epochs": 100, "batch_size": 64, "metrics": [ "classification_accuracy", From 08a5708fd07d0f2ca93efc337d621de836a6cb96 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 12:00:30 +0300 Subject: [PATCH 043/616] chore: moved embeder and tokenizer to pipe from model for all configs --- .../configs/intents/intents_sample_csv.json | 69 +++++++++++++------ .../configs/intents/intents_sample_json.json | 60 +++++++++++----- 2 files changed, 89 insertions(+), 40 deletions(-) diff --git a/deeppavlov/configs/intents/intents_sample_csv.json b/deeppavlov/configs/intents/intents_sample_csv.json index 856ff78ab4..b19cb48f8e 100644 --- a/deeppavlov/configs/intents/intents_sample_csv.json +++ b/deeppavlov/configs/intents/intents_sample_csv.json @@ -4,7 +4,10 @@ "format": "csv", "sep": ",", "header": 0, - "names": ["text", "classes"], + "names": [ + "text", + "classes" + ], "class_sep": ",", "train": "sample.csv", "data_path": "sample", @@ -23,25 +26,51 @@ ] }, "chainer": { - "in": ["x"], - "in_y": ["y"], + "in": [ + "x" + ], + "in_y": [ + "y" + ], "pipe": [ { "id": "classes_vocab", "name": "default_vocab", - "fit_on": ["y"], + "fit_on": [ + "y" + ], "level": "token", "save_path": "vocabs/snips_classes.dict", "load_path": "vocabs/snips_classes.dict" }, { - "in": ["x"], - "in_y": ["y"], - "out": ["y_predicted"], + "id": "my_embedder", + "name": "fasttext", + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ], "main": true, "name": "intent_model", - "save_path": "intents/intent_cnn_snips_v3", - "load_path": "intents/intent_cnn_snips_v3", + "save_path": "intents/intent_cnn_snips_v4", + "load_path": "intents/intent_cnn_snips_v4", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, @@ -60,25 +89,23 @@ "dropout_rate": 0.5, "dense_size": 100, "model_name": "cnn_model", - "embedder": { - "name": "fasttext", - "save_path": "embeddings/dstc2_fastText_model.bin", - "load_path": "embeddings/dstc2_fastText_model.bin", - "dim": 100 - }, - "tokenizer": { - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - } + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" } ], - "out": ["y_predicted"] + "out": [ + "y_labels", + "y_probas", + "y_classes" + ] }, "train": { "epochs": 100, "batch_size": 64, "metrics": [ - "sets_accuracy" + "classification_accuracy", + "classification_f1", + "classification_roc_auc" ], "validation_patience": 5, "val_every_n_epochs": 1, diff --git a/deeppavlov/configs/intents/intents_sample_json.json b/deeppavlov/configs/intents/intents_sample_json.json index dd3181363b..95a415b51a 100644 --- a/deeppavlov/configs/intents/intents_sample_json.json +++ b/deeppavlov/configs/intents/intents_sample_json.json @@ -21,25 +21,51 @@ ] }, "chainer": { - "in": ["x"], - "in_y": ["y"], + "in": [ + "x" + ], + "in_y": [ + "y" + ], "pipe": [ { "id": "classes_vocab", "name": "default_vocab", - "fit_on": ["y"], + "fit_on": [ + "y" + ], "level": "token", "save_path": "vocabs/snips_classes.dict", "load_path": "vocabs/snips_classes.dict" }, { - "in": ["x"], - "in_y": ["y"], - "out": ["y_predicted"], + "id": "my_embedder", + "name": "fasttext", + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ], "main": true, "name": "intent_model", - "save_path": "intents/intent_cnn_snips_v3", - "load_path": "intents/intent_cnn_snips_v3", + "save_path": "intents/intent_cnn_snips_v4", + "load_path": "intents/intent_cnn_snips_v4", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, @@ -58,19 +84,15 @@ "dropout_rate": 0.5, "dense_size": 100, "model_name": "cnn_model", - "embedder": { - "name": "fasttext", - "save_path": "embeddings/dstc2_fastText_model.bin", - "load_path": "embeddings/dstc2_fastText_model.bin", - "dim": 100 - }, - "tokenizer": { - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - } + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" } ], - "out": ["y_predicted"] + "out": [ + "y_labels", + "y_probas", + "y_classes" + ] }, "train": { "epochs": 100, From 83c1a9ccdd3611aaf97722cc6c97ee74497e9892 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 16:38:21 +0300 Subject: [PATCH 044/616] feat: log loss for classification --- deeppavlov/__init__.py | 1 + deeppavlov/core/commands/train.py | 6 +++--- deeppavlov/metrics/log_loss.py | 29 +++++++++++++++++++++++++++++ 3 files changed, 33 insertions(+), 3 deletions(-) create mode 100644 deeppavlov/metrics/log_loss.py diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index 141d66e607..c066d3556d 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -60,5 +60,6 @@ import deeppavlov.metrics.squad_metrics import deeppavlov.metrics.roc_auc_score import deeppavlov.metrics.fmeasure_classification +import deeppavlov.metrics.log_loss import deeppavlov.core.common.log diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index d209b4eedf..1eccb6b71e 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -83,7 +83,7 @@ def fit_chainer(config: dict, iterator: Union[DataLearningIterator, DataFittingI return chainer -def train_model_from_config(config_path: str) -> None: +def train_model_from_config(config_path: str): config = read_json(config_path) set_deeppavlov_root(config) @@ -129,7 +129,6 @@ def train_model_from_config(config_path: str) -> None: train_config = { 'metrics': ['accuracy'], - 'validate_best': True, 'test_best': True } @@ -177,8 +176,9 @@ def train_model_from_config(config_path: str) -> None: print(json.dumps(report, ensure_ascii=False)) all_reports.append(report) + return all_reports - return all_reports + return None def _test_model(model: Component, metrics_functions: List[Tuple[str, Callable]], diff --git a/deeppavlov/metrics/log_loss.py b/deeppavlov/metrics/log_loss.py new file mode 100644 index 0000000000..071b8a53b6 --- /dev/null +++ b/deeppavlov/metrics/log_loss.py @@ -0,0 +1,29 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +from sklearn.metrics import log_loss + +from deeppavlov.core.common.metrics_registry import register_metric +from deeppavlov.models.classifiers.intents.utils import labels2onehot + + +@register_metric('classification_log_loss') +def classification_log_loss(y_true, y_predicted): + classes = y_predicted[0][2] + y_true_one_hot = labels2onehot(y_true, classes) + y_pred_probas = [y_predicted[i][1] for i in range(len(y_predicted))] + + return log_loss(y_true_one_hot, y_pred_probas) From 2f8062a69364a849d48f32a6516064d17c9f7c36 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 16:38:34 +0300 Subject: [PATCH 045/616] feat: working evolution --- .../evolution/basic_intents_snips.json | 28 ++++---- .../classifiers/intents/intent_model.py | 3 +- .../models/evolution/check_binary_mask.py | 5 ++ .../evolution/evolution_intent_model.py | 2 +- .../neuroevolution_param_generator.py | 65 ++++++++++--------- deeppavlov/models/evolution/run_evolution.py | 36 ++++++---- .../models/evolution/train_phenotype.py | 30 +++++---- deeppavlov/models/evolution/utils.py | 13 ++++ 8 files changed, 110 insertions(+), 72 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json index 799036bc63..ec81aa5c78 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -28,14 +28,14 @@ "load_path": "vocabs/snips_classes.dict" }, { - "id": "fasttext_embedder", + "id": "my_embedder", "name": "fasttext", "save_path": "embeddings/dstc2_fastText_model.bin", "load_path": "embeddings/dstc2_fastText_model.bin", "dim": 100 }, { - "id": "nltk_tokenizer", + "id": "my_tokenizer", "name": "nltk_tokenizer", "tokenizer": "wordpunct_tokenize" }, @@ -47,7 +47,9 @@ "y" ], "out": [ - "y_predicted" + "y_labels", + "y_probas", + "y_classes" ], "main": true, "name": "evolution_classification_model", @@ -158,19 +160,21 @@ "loss": "binary_crossentropy", "text_size": 15, "model_name": "evolution_classification_model", - "embedder": "#fasttext_embedder", - "tokenizer": "#nltk_tokenizer" + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" } ], "out": [ - "y_predicted" + "y_labels", + "y_probas", + "y_classes" ] }, "train": { "epochs": { "range": [ - 10, - 11 + 5, + 6 ], "discrete": true }, @@ -181,10 +185,12 @@ ], "discrete": true }, + "metric_optimization": "minimize", "metrics": [ - "sets_accuracy", - "roc_auc_score", - "f1_classification" + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" ], "validation_patience": 5, "val_every_n_epochs": 5, diff --git a/deeppavlov/models/classifiers/intents/intent_model.py b/deeppavlov/models/classifiers/intents/intent_model.py index 64fad7059f..f7c857869e 100644 --- a/deeppavlov/models/classifiers/intents/intent_model.py +++ b/deeppavlov/models/classifiers/intents/intent_model.py @@ -104,8 +104,7 @@ def __init__(self, **kwargs): if self.opt['fasttext_md5'] != current_fasttext_md5: raise ConfigError( "Given fasttext model does NOT match fasttext model used previously to train loaded model") - - print(self.model.summary()) + print("Model was successfully initialized!\nModel summary:\n{}".format(self.model.summary())) def _init_params(self): diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index 583532f88e..f2cf543c54 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -53,6 +53,11 @@ def get_binary_mask_from_digraph(nodes, directed_graph): def check_and_correct_binary_mask(nodes, binary_mask_): binary_mask = deepcopy(binary_mask_) + + # if binary mask if empty, add one dense layer + if np.sum(binary_mask) == 0: + binary_mask[0, 0] = 1 + directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) sources, sinks, _ = find_sources_and_sinks(directed_graph) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 97ee098b65..021af39c50 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -201,4 +201,4 @@ def save(self, fname=None): self.opt["binary_mask"] = self.opt["binary_mask"].tolist() save_json(self.opt, opt_path) - return True \ No newline at end of file + return True diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index f897cf0ffb..72980305b1 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -6,13 +6,14 @@ from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ number_to_type_layer, get_graph_and_plot from deeppavlov.core.common.file import save_json, read_json +from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe + -# TODO: -# if structure of config has been changed, # please, make sure that # `config["chainer"]["pipe"]` is a list of models one of which is a model to be evolved, # otherwise, in the whole class change `config["chainer"]["pipe"]` to new path + class NetworkAndParamsEvolution: """ Class performs full evolutionary process (task scores -> max): @@ -32,6 +33,7 @@ def __init__(self, n_layers, n_types, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", seed=None, + start_with_one_neuron=False, **kwargs): """ Initialize evolution with random population @@ -55,10 +57,11 @@ def __init__(self, n_layers, n_types, self.n_layers = n_layers self.total_nodes = self.n_types * self.n_layers self.binary_mask_template = np.zeros((self.total_nodes, self.total_nodes)) + self.start_with_one_neuron = start_with_one_neuron self.basic_config = deepcopy(kwargs) - self.model_to_evolve_index = self._find_model_to_evolve_index_in_pipe(self.basic_config["chainer"]["pipe"], - key_model_to_evolve) + self.model_to_evolve_index = find_index_of_dict_with_key_in_pipe(self.basic_config["chainer"]["pipe"], + key_model_to_evolve) self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) self.train_params = deepcopy(self.basic_config.get("train")) @@ -98,17 +101,6 @@ def __init__(self, n_layers, n_types, np.random.seed(seed) return None - def _find_model_to_evolve_index_in_pipe(self, pipe, key): - for element_id, element in enumerate(pipe): - if self._check_if_model_to_evolve(element, key): - return element_id - - def _check_if_model_to_evolve(self, model, key): - if key in model.keys(): - return True - else: - return False - def _insert_dict_into_model_params(self, params, model_index, dict_to_insert): params_copy = deepcopy(params) params_copy["chainer"]["pipe"].insert(model_index, dict_to_insert) @@ -198,10 +190,12 @@ def first_generation(self, iter=0): **params_for_search, **layers_params} # add binary_mask intialization - population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, self.sample_binary_mask()) - # get_graph_and_plot(self.nodes, population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"], - # self.n_types, path=None) + if self.start_with_one_neuron: + population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, self.sample_one_neuron_binary_mask()) + else: + population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, self.sample_binary_mask()) # exchange train params from basic config to sampled train params population[-1]["train"] = {**train_params, @@ -244,8 +238,12 @@ def next_generation(self, generation, scores, iter, offsprings = self.crossover(selected_individuals, p_crossover=p_crossover, crossover_power=crossover_power) next = self.mutation(offsprings, p_mutation=p_mutation, mutation_power=mutation_power) for i in range(self.population_size): - next[i]["model_path"] = str(Path(self.params["model_path"]).joinpath( - "population_" + str(iter)).joinpath(next[i]["model_name"] + "_" + str(i))) + next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ + str(Path(self.params["save_path"]).joinpath("population_" + str(iter)).joinpath( + self.params["model_name"] + "_" + str(i))) + next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ + str(Path(self.params["load_path"]).joinpath("population_" + str(iter)).joinpath( + self.params["model_name"] + "_" + str(i))) return next @@ -361,19 +359,19 @@ def crossover(self, population, p_crossover, crossover_power): for j in range(self.total_nodes * self.total_nodes - binary_mask_part): node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ - parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ - parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ + parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ + parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] for j in range(self.total_nodes * self.total_nodes - binary_mask_part, self.total_nodes * self.total_nodes): node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ - parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ - parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ + parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ + parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ check_and_correct_binary_mask(self.nodes, @@ -424,7 +422,8 @@ def mutation(self, population, p_mutation, mutation_power): # mutation of binary mask if self.decision(p_mutation): mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, + check_and_correct_binary_mask( + self.nodes, np.minimum(1, np.maximum(0, individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] + @@ -532,3 +531,9 @@ def sample_binary_mask(self): mask[ones] = 1 # returns NUMPY 2D ARRAY! return mask.reshape((self.total_nodes, self.total_nodes)) + + def sample_one_neuron_binary_mask(self): + mask = np.zeros((self.total_nodes * self.total_nodes)) + mask[0] = 1 # make sure that Dense is the first in the config + + return mask.reshape((self.total_nodes, self.total_nodes)) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index dfdadf7a38..10a2ca6579 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -36,6 +36,8 @@ def score_population(population, population_size, result_file): population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"].tolist() with open(f_name, 'w') as outfile: json.dump(population[i], outfile) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ + np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], @@ -51,11 +53,11 @@ def score_population(population, population_size, result_file): for i in range(population_size): val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).joinpath("valid_results.txt"))) - result_table = pd.DataFrame({"loss": [val_results[0]], - "accuracy": [val_results[1]], - "fmeasure": [val_results[2]], - "roc_auc_score": [val_results[3]], + "save_path"]).parent.joinpath("valid_results.txt"))) + result_table = pd.DataFrame({"classification_log_loss": [val_results[0]], + "classification_accuracy": [val_results[1]], + "classification_f1": [val_results[2]], + "classification_roc_auc": [val_results[3]], "params": [population[i]]}) result_table.loc[:, order].to_csv(result_file, index=False, sep='\t', mode='a', header=None) population_losses.append(val_results[0]) @@ -74,6 +76,7 @@ def score_population(population, population_size, result_file): parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) +parser.add_argument('--one_neuron_init', help='Please, enter number of types of layers', default=0) args = parser.parse_args() @@ -83,32 +86,37 @@ def score_population(population, population_size, result_file): gpus = [int(gpu) for gpu in args.gpus.split(",")] N_LAYERS = int(args.n_layers) N_TYPES = int(args.n_types) +ONE_NEURON_INIT = bool(int(args.one_neuron_init)) with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) print("Given basic params: {}\n".format(basic_params)) -try: - print(basic_params["chainer"]["pipe"][3]) - Path(basic_params["chainer"]["pipe"][3]["save_path"]).mkdir(parents=True) -except FileExistsError: - pass +Path(basic_params["chainer"]["pipe"][3]["save_path"]).mkdir(parents=True, exist_ok=True) + # Result table -order = ["loss", "accuracy", "fmeasure", "roc_auc_score", "params"] +order = ["classification_log_loss", "classification_accuracy", + "classification_f1", "classification_roc_auc", "params"] result_file = Path(basic_params["chainer"]["pipe"][3]["save_path"]).joinpath("result_table.csv") -result_table = pd.DataFrame({"loss": [], "accuracy": [], "fmeasure": [], "roc_auc_score": [], "params": []}) +result_table = pd.DataFrame({"loss": [], + "classification_accuracy": [], + "classification_f1": [], + "classification_roc_auc": [], + "params": []}) result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, - p_crossover=0.1, crossover_power=0.5, + p_crossover=1., crossover_power=0.5, p_mutation=0.5, mutation_power=0.1, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", - seed=None, **basic_params) + seed=None, + start_with_one_neuron=ONE_NEURON_INIT, + **basic_params) print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py index d9ebadb048..b693f04f54 100644 --- a/deeppavlov/models/evolution/train_phenotype.py +++ b/deeppavlov/models/evolution/train_phenotype.py @@ -15,23 +15,25 @@ """ import numpy as np import sys +from pathlib import Path -from deeppavlov.core.commands.train import train_model_from_config, train_evaluate_model_from_config +from deeppavlov.core.commands.train import train_model_from_config from deeppavlov.core.common.file import read_json, save_json +from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe + config_path = sys.argv[1] print("TRAIN PHENOTYPE") -report = train_model_from_config(config_path, is_trained=False) - -# train_model_from_config(config_path) - -# config = read_json(config_path) -# -# model = build_model_from_config(config, mode='infer', load_trained=True) -# -# test_model_on_data(config_path, data) -# -# val_metrics_values = np.mean(np.array(val_metrics_values), axis=0) -# -# np.savetxt(fname=Path(path_to_models).joinpath("valid_results.txt"), X=val_metrics_values) +reports = train_model_from_config(config_path) +print(reports) + +metrics = dict(reports[0]["valid"]["metrics"]) +val_metrics_values = np.array(list(metrics.values())).reshape(-1) + +config = read_json(config_path) +model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], + key="to_evolve") +np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ + "save_path"]).parent.joinpath("valid_results.txt")), + X=val_metrics_values) diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index 479660ecd8..15319b3f4d 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -236,3 +236,16 @@ def expand_tile_batch_size(memory, context): if axis == 0: expanded = K.reshape(context, shape=((1,) + K.int_shape(context))) return K.tile(expanded, repetitions) + + +def find_index_of_dict_with_key_in_pipe(pipe, key): + for element_id, element in enumerate(pipe): + if check_whether_key_in_dict(element, key): + return element_id + + +def check_whether_key_in_dict(model, key): + if key in model.keys(): + return True + else: + return False From 8653a7406856c22d01014fe62d218aa6323f71c9 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 17:01:12 +0300 Subject: [PATCH 046/616] fix: convert binary_mask to list and to array --- deeppavlov/models/evolution/run_evolution.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 10a2ca6579..8fa8ce43ad 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -36,8 +36,6 @@ def score_population(population, population_size, result_file): population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"].tolist() with open(f_name, 'w') as outfile: json.dump(population[i], outfile) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ - np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], @@ -65,6 +63,9 @@ def score_population(population, population_size, result_file): population_fmeasures.append(val_results[2]) population_roc_auc_scores.append(val_results[3]) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ + np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) + return population_roc_auc_scores From d85039862cad7fc8b5f2c5a01608e3f30b9db355 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 18:11:56 +0300 Subject: [PATCH 047/616] feat: basic configs for gpu --- .../evolution/basic_intents_snips.json | 15 +- deeppavlov/configs/evolution/basic_snli.json | 206 ++++++++++++++++++ 2 files changed, 213 insertions(+), 8 deletions(-) create mode 100644 deeppavlov/configs/evolution/basic_snli.json diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json index ec81aa5c78..bcef56021d 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -3,8 +3,7 @@ "name": "basic_classification_reader", "x": "text", "y": "intents", - "data_path": "snips", - "url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv" + "data_path": "/home/dilyara.baymurzina/evolution_data/snips_data" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -24,14 +23,14 @@ "y" ], "level": "token", - "save_path": "vocabs/snips_classes.dict", - "load_path": "vocabs/snips_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict" }, { "id": "my_embedder", "name": "fasttext", - "save_path": "embeddings/dstc2_fastText_model.bin", - "load_path": "embeddings/dstc2_fastText_model.bin", + "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", "dim": 100 }, { @@ -53,8 +52,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara/data/models/deeppavlov_evolution/classification/intents_snips", - "load_path": "/home/dilyara/data/models/deeppavlov_evolution/classification/intents_snips", + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/start_with_one_neuron", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/start_with_one_neuron", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { diff --git a/deeppavlov/configs/evolution/basic_snli.json b/deeppavlov/configs/evolution/basic_snli.json new file mode 100644 index 0000000000..a12251153a --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snli.json @@ -0,0 +1,206 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "sentence1", + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_one_neuron", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_one_neuron", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "Attention": { + "context_length": { + "range": [ + 50, + 200 + ], + "discrete": true + } + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ] + }, + "train": { + "epochs": { + "range": [ + 5, + 6 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} \ No newline at end of file From 0f979ad155810d65bd1bb58ca3e1c0461485b954 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 18:50:51 +0300 Subject: [PATCH 048/616] some changes --- deeppavlov/models/evolution/evolution_intent_model.py | 3 ++- deeppavlov/models/evolution/run_evolution.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 021af39c50..8a6176bcba 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -41,7 +41,7 @@ from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer from deeppavlov.core.common.log import get_logger from deeppavlov.models.evolution.check_binary_mask import number_to_type_layer, \ - find_sources_and_sinks, get_digraph_from_binary_mask + find_sources_and_sinks, get_digraph_from_binary_mask, get_graph_and_plot from deeppavlov.models.evolution.utils import Attention, expand_tile from deeppavlov.core.common.file import save_json, read_json @@ -55,6 +55,7 @@ class KerasEvolutionClassificationModel(KerasIntentModel): def __init__(self, **kwargs): super().__init__(**kwargs) self.opt["binary_mask"] = np.array(self.opt["binary_mask"]) + get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], path=str(self.save_path.resolve())) def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): if inp is None: diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 8fa8ce43ad..7ade9ebc9d 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -95,7 +95,8 @@ def score_population(population, population_size, result_file): print("Given basic params: {}\n".format(basic_params)) Path(basic_params["chainer"]["pipe"][3]["save_path"]).mkdir(parents=True, exist_ok=True) - +basic_params["chainer"]["pipe"][3]["n_types"] = N_TYPES +basic_params["chainer"]["pipe"][3]["n_layers"] = N_LAYERS # Result table order = ["classification_log_loss", "classification_accuracy", From b78ab35657c1f091c3f64b8923b3abbdd5f9f266 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 25 Apr 2018 10:57:26 +0300 Subject: [PATCH 049/616] chore: new configs --- ....json => basic_snips_one_neuron_init.json} | 19 +- .../evolution/basic_snips_random_init.json | 197 ++++++++++++++++++ ...i.json => basic_snli_one_neuron_init.json} | 17 +- .../evolution/basic_snli_random_init.json | 197 ++++++++++++++++++ .../models/evolution/check_binary_mask.py | 12 +- .../evolution/evolution_intent_model.py | 11 +- .../neuroevolution_param_generator.py | 2 +- 7 files changed, 423 insertions(+), 32 deletions(-) rename deeppavlov/configs/evolution/{basic_intents_snips.json => basic_snips_one_neuron_init.json} (93%) create mode 100644 deeppavlov/configs/evolution/basic_snips_random_init.json rename deeppavlov/configs/evolution/{basic_snli.json => basic_snli_one_neuron_init.json} (93%) create mode 100644 deeppavlov/configs/evolution/basic_snli_random_init.json diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json similarity index 93% rename from deeppavlov/configs/evolution/basic_intents_snips.json rename to deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index bcef56021d..7760ac0f6d 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -52,8 +52,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/start_with_one_neuron", - "load_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/start_with_one_neuron", + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/one_neuron_init", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/one_neuron_init", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -126,15 +126,6 @@ "discrete": true }, "padding": "same" - }, - "Attention": { - "context_length": { - "range": [ - 50, - 200 - ], - "discrete": true - } } }, "confident_threshold": { @@ -172,8 +163,8 @@ "train": { "epochs": { "range": [ - 5, - 6 + 100, + 1000 ], "discrete": true }, @@ -203,4 +194,4 @@ "telegram_utils": "IntentModel" } } -} \ No newline at end of file +} diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json new file mode 100644 index 0000000000..a3c21e36dc --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -0,0 +1,197 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "intents", + "data_path": "/home/dilyara.baymurzina/evolution_data/snips_data" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/start_with_random", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/start_with_random", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ] + }, + "train": { + "epochs": { + "range": [ + 100, + 1000 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} diff --git a/deeppavlov/configs/evolution/basic_snli.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json similarity index 93% rename from deeppavlov/configs/evolution/basic_snli.json rename to deeppavlov/configs/evolution/basic_snli_one_neuron_init.json index a12251153a..d3c03b0365 100644 --- a/deeppavlov/configs/evolution/basic_snli.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json @@ -52,8 +52,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_one_neuron", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_one_neuron", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -126,15 +126,6 @@ "discrete": true }, "padding": "same" - }, - "Attention": { - "context_length": { - "range": [ - 50, - 200 - ], - "discrete": true - } } }, "confident_threshold": { @@ -172,8 +163,8 @@ "train": { "epochs": { "range": [ - 5, - 6 + 100, + 1000 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json new file mode 100644 index 0000000000..32c93325cd --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -0,0 +1,197 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "sentence1", + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ] + }, + "train": { + "epochs": { + "range": [ + 100, + 1000 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} \ No newline at end of file diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index f2cf543c54..948f3ffe8d 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -4,8 +4,10 @@ import datetime import time from pathlib import Path -import matplotlib.pyplot as plt +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt def number_to_type_layer(node_id, n_types): # return node_layer, node_type @@ -100,6 +102,10 @@ def check_and_correct_binary_mask(nodes, binary_mask_): def get_graph_and_plot(nodes, binary_mask, n_types, path=None): + nodes_int = {} + for i in range(len(nodes)): + nodes_int[i] = nodes[str(i)] + total_nodes = len(nodes) dg = get_digraph_from_binary_mask(nodes, binary_mask) @@ -117,11 +123,11 @@ def get_graph_and_plot(nodes, binary_mask, n_types, path=None): val_map[i] = 0. plt.figure(figsize=(12, 12)) - values = [val_map.get(node, 0.25) for node in nodes] + values = [val_map.get(node, 0.25) for node in nodes_int] nx.draw(dg, pos, cmap=plt.get_cmap('jet'), node_color=values, node_size=7000, alpha=0.3) - nx.draw_networkx_labels(dg, pos, nodes, font_size=18) + nx.draw_networkx_labels(dg, pos, nodes_int, font_size=18) if path is None: path = "./" diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 8a6176bcba..d0f5bf08fc 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -55,7 +55,8 @@ class KerasEvolutionClassificationModel(KerasIntentModel): def __init__(self, **kwargs): super().__init__(**kwargs) self.opt["binary_mask"] = np.array(self.opt["binary_mask"]) - get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], path=str(self.save_path.resolve())) + get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], + path=str(self.save_path.resolve().parent)) def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): if inp is None: @@ -116,6 +117,14 @@ def evolution_classification_model(self, params): """ inp = Input(shape=(params['text_size'], params['embedding_size'])) + if np.sum(params["binary_mask"]) == 0: + output = Dense(1, activation=None)(inp) + output = GlobalMaxPooling1D()(output) + output = Dense(self.n_classes, activation=None)(output) + act_output = Activation('sigmoid')(output) + model = Model(inputs=inp, outputs=act_output) + return model + dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) sources, sinks, isolates = find_sources_and_sinks(dg) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 72980305b1..0694d0d949 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -534,6 +534,6 @@ def sample_binary_mask(self): def sample_one_neuron_binary_mask(self): mask = np.zeros((self.total_nodes * self.total_nodes)) - mask[0] = 1 # make sure that Dense is the first in the config + # mask[0] = 1 # make sure that Dense is the first in the config return mask.reshape((self.total_nodes, self.total_nodes)) From 3215fc3163477b0365f850af264b3c4b6430b2e7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 25 Apr 2018 15:26:32 +0300 Subject: [PATCH 050/616] chore: change evolution parameters --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 2 +- deeppavlov/models/evolution/run_evolution.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 0694d0d949..ceb67c1381 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -526,7 +526,7 @@ def sample_binary_mask(self): # return np.random.randint(0, high=2, size=self.binary_mask_template.shape).tolist() # return (1 * (np.log(np.random.random(size=self.binary_mask_template.shape)) > -0.2)).tolist() ones = np.random.choice(self.total_nodes * self.total_nodes, - size=max(1, int(np.random.random() * self.total_nodes))) + size=max(1, int(0.5 * np.random.random() * self.total_nodes))) mask = np.zeros((self.total_nodes * self.total_nodes)) mask[ones] = 1 # returns NUMPY 2D ARRAY! diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 7ade9ebc9d..8d50046454 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -112,7 +112,7 @@ def score_population(population, population_size, result_file): # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, - p_crossover=1., crossover_power=0.5, + p_crossover=0.1, crossover_power=0.5, p_mutation=0.5, mutation_power=0.1, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", From 170cbdc72383ae785dcdea893b0a5f8f6dc58b40 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 25 Apr 2018 16:47:10 +0300 Subject: [PATCH 051/616] Merge branch 'feature/network_evolution' of https://github.com/dilyararimovna/deeppavlov_evolution into feature/network_evolution # Conflicts: # README.md # deeppavlov/__init__.py # deeppavlov/configs/go_bot/gobot_dstc2.json # deeppavlov/configs/go_bot/gobot_dstc2_all.json # deeppavlov/configs/intents/intents_dstc2.json # deeppavlov/configs/intents/intents_sample_csv.json # deeppavlov/configs/intents/intents_sample_json.json # deeppavlov/configs/intents/intents_snips.json # deeppavlov/configs/ner/ner_conll2003.json # deeppavlov/configs/ner/ner_ontonotes_emb.json # deeppavlov/configs/ranking/insurance_config.json # deeppavlov/configs/seq2seq_go_bot/bot_kvret.json # deeppavlov/configs/seq2seq_go_bot/bot_kvret_infer.json # deeppavlov/configs/squad/squad.json # deeppavlov/core/commands/train.py # deeppavlov/core/data/data_learning_iterator.py # deeppavlov/core/data/dataset.py # deeppavlov/core/data/dataset_iterator.py # deeppavlov/core/data/urls.py # deeppavlov/dataset_iterators/basic_classification_iterator.py # deeppavlov/dataset_iterators/dialog_iterator.py # deeppavlov/dataset_iterators/dstc2_intents_iterator.py # deeppavlov/dataset_iterators/dstc2_ner_iterator.py # deeppavlov/dataset_iterators/kvret_dialog_iterator.py # deeppavlov/dataset_iterators/ranking_iterator.py # deeppavlov/dataset_iterators/squad_iterator.py # deeppavlov/dataset_iterators/typos_iterator.py # deeppavlov/models/classifiers/intents/intent_model.py # deeppavlov/models/embedders/fasttext_embedder.py # deeppavlov/models/squad/squad.py # deeppavlov/models/tokenizers/spacy_tokenizer.py # deeppavlov/run_model.py # deeppavlov/skills/seq2seq_go_bot/kb.py # requirements.txt # tests/test_quick_start.py # utils/telegram_utils/models_info.json --- .../configs/evolution/basic_config_local.json | 153 ++++++++++++++++++ 1 file changed, 153 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_config_local.json diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json new file mode 100644 index 0000000000..9291e0ceaf --- /dev/null +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -0,0 +1,153 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "intents", + "data_path": "/home/dilyara/data/data_files/snips/snips_dataset" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara/data/data_files/snips/snips_dataset/snips_classes.dict", + "load_path": "/home/dilyara/data/data_files/snips/snips_dataset/snips_classes.dict" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara/data/data_files/embeddings/reddit/wordpunct_tok_reddit_comments_2017_11_100.bin", + "load_path": "/home/dilyara/data/data_files/embeddings/reddit/wordpunct_tok_reddit_comments_2017_11_100.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara/data/models/evolution_data/snips_classification/start_with_random", + "load_path": "/home/dilyara/data/models/evolution_data/snips_classification/start_with_random", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Attention": { + "context_length": { + "range": [ + 50, + 200 + ], + "discrete": true + } + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ] + }, + "train": { + "epochs": { + "range": [ + 100, + 1000 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From 37e74e1b2f506d4bc1e5919341dd73a08e5b324b Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 27 Apr 2018 10:30:38 +0300 Subject: [PATCH 052/616] Merge branch 'dev' of https://github.com/deepmipt/DeepPavlov into feature/network_evolution # Conflicts: # deeppavlov/__init__.py # deeppavlov/configs/go_bot/gobot_dstc2.json # deeppavlov/configs/go_bot/gobot_dstc2_all.json # deeppavlov/configs/intents/intents_dstc2.json # deeppavlov/configs/intents/intents_sample_csv.json # deeppavlov/configs/intents/intents_sample_json.json # deeppavlov/configs/intents/intents_snips.json # deeppavlov/metrics/roc_auc_score.py # deeppavlov/models/classifiers/intents/intent_model.py # deeppavlov/run_model.py # requirements.txt --- .../configs/evolution/basic_config_local.json | 6 ++---- .../evolution/basic_snips_one_neuron_init.json | 6 ++---- .../evolution/basic_snips_random_init.json | 15 ++------------- .../evolution/basic_snli_one_neuron_init.json | 6 ++---- .../configs/evolution/basic_snli_random_init.json | 6 ++---- .../models/evolution/evolution_intent_model.py | 1 - 6 files changed, 10 insertions(+), 30 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 9291e0ceaf..8abdd186c6 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -47,8 +47,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ], "main": true, "name": "evolution_classification_model", @@ -112,8 +111,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ] }, "train": { diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 7760ac0f6d..4a33e7e2d5 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -47,8 +47,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ], "main": true, "name": "evolution_classification_model", @@ -156,8 +155,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ] }, "train": { diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index adb9a47799..c2880d18da 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -47,8 +47,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ], "main": true, "name": "evolution_classification_model", @@ -126,15 +125,6 @@ "discrete": true }, "padding": "same" - }, - "Attention": { - "context_length": { - "range": [ - 50, - 200 - ], - "discrete": true - } } }, "confident_threshold": { @@ -165,8 +155,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ] }, "train": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json index d3c03b0365..cedc82d74e 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json @@ -47,8 +47,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ], "main": true, "name": "evolution_classification_model", @@ -156,8 +155,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ] }, "train": { diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index 32c93325cd..ffd481525b 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -47,8 +47,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ], "main": true, "name": "evolution_classification_model", @@ -156,8 +155,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ] }, "train": { diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 70aebcc6fd..480348a122 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -34,7 +34,6 @@ from deeppavlov.core.common.registry import register from deeppavlov.core.models.keras_model import KerasModel from deeppavlov.models.classifiers.intents.intent_model import KerasIntentModel -from deeppavlov.models.classifiers.intents import metrics as metrics_file from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder from deeppavlov.models.classifiers.intents.utils import md5_hashsum From cef2d1762df2e81901639c8c8ada7c2bf8df661f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 27 Apr 2018 16:01:03 +0300 Subject: [PATCH 053/616] fix: change nodes.keys to str(i) everywhere --- .../configs/evolution/basic_config_local.json | 53 +++++++++++++++++++ .../models/evolution/check_binary_mask.py | 32 +++++------ deeppavlov/models/evolution/debug.py | 3 +- .../evolution/evolution_intent_model.py | 34 +++++++----- .../neuroevolution_param_generator.py | 20 ++++--- 5 files changed, 97 insertions(+), 45 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 8abdd186c6..b575e17072 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -73,6 +73,59 @@ "choice": true } }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, "Attention": { "context_length": { "range": [ diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index 948f3ffe8d..f3f85151cc 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -23,11 +23,11 @@ def find_sources_and_sinks(directed_graph): sinks = [] isolates = nx.isolates(directed_graph) - for i in directed_graph.nodes(): - if directed_graph.in_degree(i) == 0 and directed_graph.out_degree(i) > 0: - sources.append(i) - if directed_graph.in_degree(i) > 0 and directed_graph.out_degree(i) == 0: - sinks.append(i) + for str_id in directed_graph.nodes(): + if directed_graph.in_degree(str_id) == 0 and directed_graph.out_degree(str_id) > 0: + sources.append(str_id) + if directed_graph.in_degree(str_id) > 0 and directed_graph.out_degree(str_id) == 0: + sinks.append(str_id) return sources, sinks, isolates @@ -37,12 +37,12 @@ def get_digraph_from_binary_mask(nodes, binary_mask): total_nodes = len(nodes) for i in range(total_nodes): - directed_graph.add_node(i) + directed_graph.add_node(str(i)) for i in range(total_nodes): for j in range(total_nodes): if binary_mask[i, j] == 1: - directed_graph.add_edge(i, j) + directed_graph.add_edge(str(i), str(j)) return directed_graph @@ -56,10 +56,6 @@ def get_binary_mask_from_digraph(nodes, directed_graph): def check_and_correct_binary_mask(nodes, binary_mask_): binary_mask = deepcopy(binary_mask_) - # if binary mask if empty, add one dense layer - if np.sum(binary_mask) == 0: - binary_mask[0, 0] = 1 - directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) sources, sinks, _ = find_sources_and_sinks(directed_graph) @@ -67,8 +63,6 @@ def check_and_correct_binary_mask(nodes, binary_mask_): candidates = [] cycles = list(nx.simple_cycles(directed_graph)) n_cycles = len(cycles) - # print("Cycles: {}".format(cycles)) - # number of candidates to be the best new graph cycles_len = np.array([len(cycle) for cycle in cycles]) n_candidates = int(np.prod(cycles_len)) @@ -114,13 +108,13 @@ def get_graph_and_plot(nodes, binary_mask, n_types, path=None): sources, sinks, _ = find_sources_and_sinks(dg) for i in range(total_nodes): - pos[i] = 2. * np.array(number_to_type_layer(i, n_types))[::-1] - if i in sources: - val_map[i] = 1. - elif i in sinks: - val_map[i] = 0.5 + pos[str(i)] = 2. * np.array(number_to_type_layer(i, n_types))[::-1] + if str(i) in sources: + val_map[str(i)] = 1. + elif str(i) in sinks: + val_map[str(i)] = 0.5 else: - val_map[i] = 0. + val_map[str(i)] = 0. plt.figure(figsize=(12, 12)) values = [val_map.get(node, 0.25) for node in nodes_int] diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 291c7a7df4..188aad3e55 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -17,7 +17,7 @@ n_layers = 2 n_types = 7 population_size = 1 -config_path = "../../configs/evolution/basic_intents_snips.json" +config_path = "../../configs/evolution/basic_config_local.json" with open(config_path) as fin: config = json.load(fin) @@ -27,6 +27,7 @@ key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", seed=42, + start_with_one_neuron=True, **config) population = evolution.first_generation() diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 480348a122..2b58f062b1 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -43,7 +43,7 @@ find_sources_and_sinks, get_digraph_from_binary_mask, get_graph_and_plot from deeppavlov.models.evolution.utils import Attention, expand_tile from deeppavlov.core.common.file import save_json, read_json - +from deeppavlov.core.layers.keras_layers import multiplicative_self_attention, additive_self_attention log = get_logger(__name__) @@ -57,9 +57,9 @@ def __init__(self, **kwargs): get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], path=str(self.save_path.resolve().parent)) - def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): + def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None): if inp is None: - input_nodes = [edge[0] for edge in dg.in_edges(node_id)] + input_nodes = [edge[0] for edge in dg.in_edges(node_str_id)] inp_list = [] for input_node in input_nodes: if len(K.int_shape(edges_outputs[input_node])) == 3: @@ -91,15 +91,21 @@ def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): else: inp = inp_list[0] - if params[params["nodes"][str(node_id)]]["node_name"] == "BiCuDNNLSTM": - node_params = deepcopy(params[params["nodes"][str(node_id)]]) + if params[params["nodes"][node_str_id]]["node_name"] == "BiCuDNNLSTM": + node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") output_of_node = Bidirectional(CuDNNLSTM(**node_params))(inp) + elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": + node_params = deepcopy(params[params["nodes"][node_str_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + output_of_node = multiplicative_self_attention(inp, **node_params) else: - node_func = globals().get(params[params["nodes"][str(node_id)]]["node_name"], None) - node_params = deepcopy(params[params["nodes"][str(node_id)]]) + node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) + node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") @@ -140,8 +146,8 @@ def evolution_classification_model(self, params): if set(sinks).issubset(set(sum(sequence_of_nodes, []))): break next_nodes = [] - for node_id in sequence_of_nodes[-1]: - out_edges = dg.out_edges(node_id) + for node_str_id in sequence_of_nodes[-1]: + out_edges = dg.out_edges(node_str_id) for edge in out_edges: in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): @@ -150,13 +156,13 @@ def evolution_classification_model(self, params): sequence_of_nodes = sum(sequence_of_nodes, []) - for node_id in sequence_of_nodes: - if node_id in sources: - edges_outputs[node_id] = self.get_node_output(node_id, dg, params, inp=inp) - elif node_id in isolates: + for node_str_id in sequence_of_nodes: + if node_str_id in sources: + edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, inp=inp) + elif node_str_id in isolates: pass else: - edges_outputs[node_id] = self.get_node_output(node_id, dg, params, edges_outputs=edges_outputs) + edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, edges_outputs=edges_outputs) if len(sinks) == 1: output = edges_outputs[sinks[0]] diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 878a6e56e1..0689a5ddeb 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -71,7 +71,7 @@ def __init__(self, n_layers, n_types, self.nodes = {} for i in range(self.total_nodes): l, t = number_to_type_layer(i, self.n_types) - self.nodes[i] = "{}_{}_{}".format(l, t, i) + self.nodes[str(i)] = "{}_{}_{}".format(l, t, i) print("___Basic config___: {}".format(self.basic_config)) print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) @@ -146,7 +146,7 @@ def initialize_layers_params(self): for node_id in range(self.total_nodes): node_layer, node_type = number_to_type_layer(node_id, self.n_types) - node_key = self.nodes[node_id] + node_key = self.nodes[str(node_id)] layers_params, layers_params_for_search, _ = self.initialize_params_in_config( self.basic_layers_params[self.node_types[node_type]]) @@ -339,16 +339,14 @@ def crossover(self, population, p_crossover, crossover_power): # exchange of nodes for j in range(self.total_nodes - nodes_part): - node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) - node_key = self.nodes[nodes_perm[j]] + node_key = self.nodes[str(nodes_perm[j])] curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) for j in range(self.total_nodes - nodes_part, self.total_nodes): - node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) - node_key = self.nodes[nodes_perm[j]] + node_key = self.nodes[str(nodes_perm[j])] curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) @@ -433,11 +431,11 @@ def mutation(self, population, p_mutation, mutation_power): for node_id in range(self.total_nodes): node_layer, node_type = number_to_type_layer(node_id, self.n_types) for param in self.basic_layers_params[self.node_types[node_type]]: - mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][self.nodes[node_id]][param] =\ - self.mutation_of_param(param, self.basic_layers_params[self.node_types[node_type]], - individuum["chainer"]["pipe"][self.model_to_evolve_index][ - self.nodes[node_id]][param], - p_mutation, mutation_power) + mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][self.nodes[str(node_id)]][param] \ + = self.mutation_of_param(param, self.basic_layers_params[self.node_types[node_type]], + individuum["chainer"]["pipe"][self.model_to_evolve_index][ + self.nodes[str(node_id)]][param], + p_mutation, mutation_power) mutated.append(mutated_individuum) return mutated From 9915617b35b56537612289ca45c95037297d1f9d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 27 Apr 2018 16:40:40 +0300 Subject: [PATCH 054/616] fix: no more than 5 nodes in sample of binary mask, evolve_binary_mask flag added --- .../neuroevolution_param_generator.py | 108 +++++++++--------- 1 file changed, 56 insertions(+), 52 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 0689a5ddeb..e99d39a531 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -34,6 +34,7 @@ def __init__(self, n_layers, n_types, key_basic_layers="basic_layers_params", seed=None, start_with_one_neuron=False, + evolve_binary_mask=True, **kwargs): """ Initialize evolution with random population @@ -94,6 +95,7 @@ def __init__(self, n_layers, n_types, self.n_evolving_params = None self.evolving_train_params = [] self.n_evolving_train_params = None + self.evolve_binary_mask = evolve_binary_mask if seed is None: pass @@ -337,48 +339,49 @@ def crossover(self, population, p_crossover, crossover_power): self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ self.evolving_train_params[train_params_perm[j]]] - # exchange of nodes - for j in range(self.total_nodes - nodes_part): - node_key = self.nodes[str(nodes_perm[j])] - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( - parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( - parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) - for j in range(self.total_nodes - nodes_part, self.total_nodes): - node_key = self.nodes[str(nodes_perm[j])] - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( - parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( - parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) - - # exchange of binary mask elements - for j in range(self.total_nodes * self.total_nodes - binary_mask_part): - node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ - parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ - parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] - - for j in range(self.total_nodes * self.total_nodes - binary_mask_part, - self.total_nodes * self.total_nodes): - node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ - parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ - parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"]) - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"]) + if self.evolve_binary_mask: + # exchange of nodes + for j in range(self.total_nodes - nodes_part): + node_key = self.nodes[str(nodes_perm[j])] + + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( + parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( + parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + for j in range(self.total_nodes - nodes_part, self.total_nodes): + node_key = self.nodes[str(nodes_perm[j])] + + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( + parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( + parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + + # exchange of binary mask elements + for j in range(self.total_nodes * self.total_nodes - binary_mask_part): + node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes + + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ + parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ + parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] + + for j in range(self.total_nodes * self.total_nodes - binary_mask_part, + self.total_nodes * self.total_nodes): + node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes + + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ + parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ + parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] + + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"]) + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"]) offsprings.extend(curr_offsprings) else: offsprings.extend(parents) @@ -417,15 +420,16 @@ def mutation(self, population, p_mutation, mutation_power): individuum["train"][param], p_mutation, mutation_power) - # mutation of binary mask - if self.decision(p_mutation): - mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask( - self.nodes, - np.minimum(1, - np.maximum(0, - individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] + - np.round((2 * np.random.random() - 1.) * self.sample_binary_mask())))) + if self.evolve_binary_mask: + # mutation of binary mask + if self.decision(p_mutation): + mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask( + self.nodes, + np.minimum(1, + np.maximum(0, + individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] + + np.round((2 * np.random.random() - 1.) * self.sample_binary_mask())))) # mutation of each node params for node_id in range(self.total_nodes): @@ -524,7 +528,7 @@ def sample_binary_mask(self): # return np.random.randint(0, high=2, size=self.binary_mask_template.shape).tolist() # return (1 * (np.log(np.random.random(size=self.binary_mask_template.shape)) > -0.2)).tolist() ones = np.random.choice(self.total_nodes * self.total_nodes, - size=max(1, int(0.1 * np.random.random() * self.total_nodes))) + size=min(max(1, int(0.1 * np.random.random() * self.total_nodes)), 5)) mask = np.zeros((self.total_nodes * self.total_nodes)) mask[ones] = 1 # returns NUMPY 2D ARRAY! From 8edcfd39ea953f3c6ac20200478da0da3639a16e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 27 Apr 2018 18:21:47 +0300 Subject: [PATCH 055/616] feat: new mult attention add in config --- .../configs/evolution/basic_config_local.json | 17 ++++++++++++- .../basic_snips_one_neuron_init.json | 24 +++++++++++++++++++ .../evolution/basic_snips_random_init.json | 24 +++++++++++++++++++ .../evolution/basic_snli_one_neuron_init.json | 24 +++++++++++++++++++ .../evolution/basic_snli_random_init.json | 24 +++++++++++++++++++ .../evolution/evolution_intent_model.py | 4 ++-- .../neuroevolution_param_generator.py | 3 +-- 7 files changed, 115 insertions(+), 5 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index b575e17072..6c776f4b9f 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -127,12 +127,27 @@ "padding": "same" }, "Attention": { - "context_length": { + "n_hidden": { "range": [ 50, 200 ], "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true } } }, diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 4a33e7e2d5..b0f3acafb7 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -125,6 +125,30 @@ "discrete": true }, "padding": "same" + }, + "Attention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } } }, "confident_threshold": { diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index c2880d18da..945feba6b6 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -125,6 +125,30 @@ "discrete": true }, "padding": "same" + }, + "Attention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } } }, "confident_threshold": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json index cedc82d74e..15763d78b8 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json @@ -125,6 +125,30 @@ "discrete": true }, "padding": "same" + }, + "Attention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } } }, "confident_threshold": { diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index ffd481525b..b4822e829a 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -125,6 +125,30 @@ "discrete": true }, "padding": "same" + }, + "Attention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } } }, "confident_threshold": { diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 2b58f062b1..d209ba78df 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -43,7 +43,7 @@ find_sources_and_sinks, get_digraph_from_binary_mask, get_graph_and_plot from deeppavlov.models.evolution.utils import Attention, expand_tile from deeppavlov.core.common.file import save_json, read_json -from deeppavlov.core.layers.keras_layers import multiplicative_self_attention, additive_self_attention +from deeppavlov.core.layers.keras_layers import multiplicative_self_attention log = get_logger(__name__) @@ -112,7 +112,7 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) if callable(node_func): output_of_node = node_func(**node_params)(inp) else: - raise AttributeError("Node {} is not defined correctly".format(node_id)) + raise AttributeError("Node {} is not defined correctly".format(node_str_id)) return output_of_node def evolution_classification_model(self, params): diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index e99d39a531..8c52f1be33 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -4,8 +4,7 @@ import json from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ - number_to_type_layer, get_graph_and_plot -from deeppavlov.core.common.file import save_json, read_json + number_to_type_layer from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe From 94beb37ca149b0f37d6f900ddf73544817597110 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 28 Apr 2018 01:00:57 +0300 Subject: [PATCH 056/616] fix: check binary mask --- deeppavlov/models/evolution/check_binary_mask.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index f3f85151cc..22b8ccb60b 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -49,7 +49,7 @@ def get_digraph_from_binary_mask(nodes, binary_mask): def get_binary_mask_from_digraph(nodes, directed_graph): binary_mask = np.zeros((len(nodes), len(nodes))) for edge in directed_graph.edges(): - binary_mask[edge[0], edge[1]] = 1 + binary_mask[int(edge[0]), int(edge[1])] = 1 return binary_mask From 1609dea4075d8011b6686201c4a4a95a3d8334a5 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 28 Apr 2018 01:04:41 +0300 Subject: [PATCH 057/616] fix: configs attention --- deeppavlov/configs/evolution/basic_config_local.json | 3 +-- deeppavlov/configs/evolution/basic_snips_one_neuron_init.json | 2 +- deeppavlov/configs/evolution/basic_snips_random_init.json | 2 +- deeppavlov/configs/evolution/basic_snli_one_neuron_init.json | 2 +- deeppavlov/configs/evolution/basic_snli_random_init.json | 2 +- 5 files changed, 5 insertions(+), 6 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 6c776f4b9f..07087e13be 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -126,7 +126,7 @@ }, "padding": "same" }, - "Attention": { + "SelfMultiplicativeAttention": { "n_hidden": { "range": [ 50, @@ -149,7 +149,6 @@ ], "choice": true } - } }, "confident_threshold": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index b0f3acafb7..db9e709b3d 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -126,7 +126,7 @@ }, "padding": "same" }, - "Attention": { + "SelfMultiplicativeAttention": { "n_hidden": { "range": [ 50, diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index 945feba6b6..f44df8e830 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -126,7 +126,7 @@ }, "padding": "same" }, - "Attention": { + "SelfMultiplicativeAttention": { "n_hidden": { "range": [ 50, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json index 15763d78b8..cc6910cefd 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json @@ -126,7 +126,7 @@ }, "padding": "same" }, - "Attention": { + "SelfMultiplicativeAttention": { "n_hidden": { "range": [ 50, diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index b4822e829a..d5e70adb74 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -126,7 +126,7 @@ }, "padding": "same" }, - "Attention": { + "SelfMultiplicativeAttention": { "n_hidden": { "range": [ 50, From c287a3e7b28b71ac171baa8894b83a10e73050f9 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 28 Apr 2018 01:17:52 +0300 Subject: [PATCH 058/616] fix: log_loss --- deeppavlov/metrics/log_loss.py | 4 ++-- deeppavlov/models/evolution/check_binary_mask.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/deeppavlov/metrics/log_loss.py b/deeppavlov/metrics/log_loss.py index 071b8a53b6..398cf99c32 100644 --- a/deeppavlov/metrics/log_loss.py +++ b/deeppavlov/metrics/log_loss.py @@ -22,8 +22,8 @@ @register_metric('classification_log_loss') def classification_log_loss(y_true, y_predicted): - classes = y_predicted[0][2] + classes = np.array(list(y_predicted[0][1].keys())) y_true_one_hot = labels2onehot(y_true, classes) - y_pred_probas = [y_predicted[i][1] for i in range(len(y_predicted))] + y_pred_probas = [list(y_predicted[i][1].values()) for i in range(len(y_predicted))] return log_loss(y_true_one_hot, y_pred_probas) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index 22b8ccb60b..5024cd8720 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -121,7 +121,7 @@ def get_graph_and_plot(nodes, binary_mask, n_types, path=None): nx.draw(dg, pos, cmap=plt.get_cmap('jet'), node_color=values, node_size=7000, alpha=0.3) - nx.draw_networkx_labels(dg, pos, nodes_int, font_size=18) + nx.draw_networkx_labels(dg, pos, nodes, font_size=18) if path is None: path = "./" From 3e24bbc4cd34ef29f4d297bcbf9e73ced67fe370 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 28 Apr 2018 01:25:48 +0300 Subject: [PATCH 059/616] fix: log loss --- deeppavlov/metrics/log_loss.py | 1 + 1 file changed, 1 insertion(+) diff --git a/deeppavlov/metrics/log_loss.py b/deeppavlov/metrics/log_loss.py index 398cf99c32..368357786a 100644 --- a/deeppavlov/metrics/log_loss.py +++ b/deeppavlov/metrics/log_loss.py @@ -15,6 +15,7 @@ """ from sklearn.metrics import log_loss +import numpy as np from deeppavlov.core.common.metrics_registry import register_metric from deeppavlov.models.classifiers.intents.utils import labels2onehot From 0feb9e123a16e1583c159360d7130e327e8671a4 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 1 May 2018 23:13:27 +0300 Subject: [PATCH 060/616] fix: commit for snli, add preprocessors --- .../evolution/basic_snips_one_neuron_init.json | 11 ++++++++++- .../configs/evolution/basic_snips_random_init.json | 11 ++++++++++- .../evolution/basic_snli_one_neuron_init.json | 13 +++++++++++-- .../configs/evolution/basic_snli_random_init.json | 13 +++++++++++-- deeppavlov/models/evolution/run_evolution.py | 8 ++++---- 5 files changed, 46 insertions(+), 10 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index db9e709b3d..e0d4b95e78 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -26,6 +26,15 @@ "save_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict", "load_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict" }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, { "id": "my_embedder", "name": "fasttext", @@ -40,7 +49,7 @@ }, { "in": [ - "x" + "x_lower" ], "in_y": [ "y" diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index f44df8e830..ba66d8d042 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -26,6 +26,15 @@ "save_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict", "load_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict" }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, { "id": "my_embedder", "name": "fasttext", @@ -40,7 +49,7 @@ }, { "in": [ - "x" + "x_lower" ], "in_y": [ "y" diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json index cc6910cefd..fd566b3c64 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json @@ -1,7 +1,7 @@ { "dataset_reader": { "name": "basic_classification_reader", - "x": "sentence1", + "x": "text", "y": "gold_label", "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data" }, @@ -26,6 +26,15 @@ "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, { "id": "my_embedder", "name": "fasttext", @@ -40,7 +49,7 @@ }, { "in": [ - "x" + "x_lower" ], "in_y": [ "y" diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index d5e70adb74..f86582ce1a 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -1,7 +1,7 @@ { "dataset_reader": { "name": "basic_classification_reader", - "x": "sentence1", + "x": "text", "y": "gold_label", "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data" }, @@ -26,6 +26,15 @@ "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, { "id": "my_embedder", "name": "fasttext", @@ -40,7 +49,7 @@ }, { "in": [ - "x" + "x_lower" ], "in_y": [ "y" diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 8d50046454..d30a600906 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -94,14 +94,14 @@ def score_population(population, population_size, result_file): print("Given basic params: {}\n".format(basic_params)) -Path(basic_params["chainer"]["pipe"][3]["save_path"]).mkdir(parents=True, exist_ok=True) -basic_params["chainer"]["pipe"][3]["n_types"] = N_TYPES -basic_params["chainer"]["pipe"][3]["n_layers"] = N_LAYERS +Path(basic_params["chainer"]["pipe"][4]["save_path"]).mkdir(parents=True, exist_ok=True) +basic_params["chainer"]["pipe"][4]["n_types"] = N_TYPES +basic_params["chainer"]["pipe"][4]["n_layers"] = N_LAYERS # Result table order = ["classification_log_loss", "classification_accuracy", "classification_f1", "classification_roc_auc", "params"] -result_file = Path(basic_params["chainer"]["pipe"][3]["save_path"]).joinpath("result_table.csv") +result_file = Path(basic_params["chainer"]["pipe"][4]["save_path"]).joinpath("result_table.csv") result_table = pd.DataFrame({"loss": [], "classification_accuracy": [], "classification_f1": [], From 1e669cd61d90d66a86978893756dd4acde36cc09 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 7 May 2018 18:26:49 +0300 Subject: [PATCH 061/616] chore: part of data --- .../basic_snli_one_neuron_init_part.json | 228 ++++++++++++++++++ 1 file changed, 228 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json new file mode 100644 index 0000000000..1a95a8976c --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -0,0 +1,228 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 100, + 1000 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From 7000d6e30d7b41b7bcbf32ba8e21874026ce109e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 10:51:13 +0300 Subject: [PATCH 062/616] fix: batch size and text size are fixed --- .../evolution/basic_snli_one_neuron_init_part.json | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 1a95a8976c..bc4fd9959a 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -38,9 +38,9 @@ { "id": "my_embedder", "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", - "dim": 100 + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 }, { "id": "my_tokenizer", @@ -180,7 +180,7 @@ ] }, "loss": "binary_crossentropy", - "text_size": 15, + "text_size": 30, "model_name": "evolution_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" @@ -202,7 +202,7 @@ "batch_size": { "range": [ 50, - 200 + 70 ], "discrete": true }, From 5cbf08e0234cb4731c393c3791f01813fbde994b Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 11:21:31 +0300 Subject: [PATCH 063/616] chore: last layer activation --- .../basic_snli_one_neuron_init_part.json | 1 + ...basic_snli_one_neuron_init_part_half.json} | 19 ++++++++++--------- .../evolution/evolution_intent_model.py | 3 ++- 3 files changed, 13 insertions(+), 10 deletions(-) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init.json => basic_snli_one_neuron_init_part_half.json} (93%) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index bc4fd9959a..b330bf4553 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -181,6 +181,7 @@ }, "loss": "binary_crossentropy", "text_size": 30, + "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json similarity index 93% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init.json rename to deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index fd566b3c64..a2dcf28329 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -3,7 +3,7 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data" + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part_half" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -38,9 +38,9 @@ { "id": "my_embedder", "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", - "dim": 100 + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 }, { "id": "my_tokenizer", @@ -60,8 +60,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_half", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_half", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -180,7 +180,8 @@ ] }, "loss": "binary_crossentropy", - "text_size": 15, + "text_size": 30, + "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" @@ -202,7 +203,7 @@ "batch_size": { "range": [ 50, - 200 + 70 ], "discrete": true }, @@ -225,4 +226,4 @@ "telegram_utils": "IntentModel" } } -} \ No newline at end of file +} diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index d209ba78df..55ec256f15 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -188,7 +188,8 @@ def evolution_classification_model(self, params): if len(output.shape) == 3: output = GlobalMaxPooling1D()(output) output = Dense(self.n_classes, activation=None)(output) - act_output = Activation('sigmoid')(output) + activation = params.get("last_layer_activation", "sigmoid") + act_output = Activation(activation)(output) model = Model(inputs=inp, outputs=act_output) return model From ca34dfdf7f26d3d82eb375e3bd9c24316f6224d0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 11:33:21 +0300 Subject: [PATCH 064/616] feat: two texts classification model --- .../evolution/evolution_intent_model.py | 85 +++++++++++++++++++ 1 file changed, 85 insertions(+) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 55ec256f15..f6aa486d8f 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -193,6 +193,91 @@ def evolution_classification_model(self, params): model = Model(inputs=inp, outputs=act_output) return model + def evolution_two_texts_classification_model(self, params): + """ + Build un-compiled model of shallow-and-wide CNN + Args: + params: dictionary of parameters for NN + + Returns: + Un-compiled model + """ + inp1 = Input(shape=(params['text_size'], params['embedding_size'])) + inp2 = Input(shape=(params['text_size'], params['embedding_size'])) + + full_outputs = [] + + for inp_id, inp in enumerate([inp1, inp2]): + if np.sum(params["binary_mask"]) == 0: + output = Dense(1, activation=None)(inp) + output = GlobalMaxPooling1D()(output) + output = Dense(self.n_classes, activation=None)(output) + act_output = Activation('sigmoid')(output) + model = Model(inputs=inp, outputs=act_output) + return model + + dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) + sources, sinks, isolates = find_sources_and_sinks(dg) + + edges_outputs = {} + + sequence_of_nodes = [] + sequence_of_nodes.append(sources) + + while True: + if set(sinks).issubset(set(sum(sequence_of_nodes, []))): + break + next_nodes = [] + for node_str_id in sequence_of_nodes[-1]: + out_edges = dg.out_edges(node_str_id) + for edge in out_edges: + in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] + if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): + next_nodes.append(edge[1]) + sequence_of_nodes.append(next_nodes) + + sequence_of_nodes = sum(sequence_of_nodes, []) + + for node_str_id in sequence_of_nodes: + if node_str_id in sources: + edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, inp=inp) + elif node_str_id in isolates: + pass + else: + edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, edges_outputs=edges_outputs) + + if len(sinks) == 1: + output = edges_outputs[sinks[0]] + else: + outputs = [] + for sink in sinks: + outputs.append(edges_outputs[sink]) + try: + output = Concatenate()(outputs) + except ValueError: + time_steps = [] + features = [] + for i in range(len(outputs)): + if len(K.int_shape(outputs[i])) == 2: + outputs[i] = Lambda(lambda x: expand_tile(x, axis=1))(outputs[i]) + time_steps.append(K.int_shape(outputs[i])[1]) + features.append(K.int_shape(outputs[i])[2]) + new_feature_shape = max(features) + for i in range(len(outputs)): + outputs[i] = Dense(new_feature_shape)(outputs[i]) + output = Concatenate(axis=1)(outputs) + + if len(output.shape) == 3: + output = GlobalMaxPooling1D()(output) + full_outputs.append(output) + + output = Concatenate()(full_outputs) + output = Dense(self.n_classes, activation=None)(output) + activation = params.get("last_layer_activation", "sigmoid") + act_output = Activation(activation)(output) + model = Model(inputs=[inp1, inp2], outputs=act_output) + return model + @overrides def save(self, fname=None): """ From 0016755deb571a5e2743bd02e3a505f5d6f538c2 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 12:05:11 +0300 Subject: [PATCH 065/616] feat: two texts classification model --- ...c_snli_one_neuron_init_part_two_texts.json | 240 ++++++++++++++++++ 1 file changed, 240 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json new file mode 100644 index 0000000000..3a34a7b853 --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json @@ -0,0 +1,240 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": ["sentence1", "sentence2"], + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/two_texts/part" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "sentence1", + "sentence2" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + }, + { + "in": [ + "sentence1" + ], + "out": [ + "sentence1_lower" + ], + "name": "str_lower" + }, + { + "in": [ + "sentence2" + ], + "out": [ + "sentence2_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "sentence1_lower", + "sentence2_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_two_texts", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_two_texts", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "last_layer_activation": "softmax", + "model_name": "evolution_two_texts_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 100, + 1000 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 70 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From 99496e2b871952d501c01ca5e4139f510c16a242 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 12:15:26 +0300 Subject: [PATCH 066/616] fix: delete usage of model index in run_evolution --- .../neuroevolution_param_generator.py | 6 +++++ deeppavlov/models/evolution/run_evolution.py | 27 +++++++++---------- 2 files changed, 18 insertions(+), 15 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 8c52f1be33..e623bc5b32 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -55,6 +55,7 @@ def __init__(self, n_layers, n_types, """ self.n_types = n_types self.n_layers = n_layers + self.total_nodes = self.n_types * self.n_layers self.binary_mask_template = np.zeros((self.total_nodes, self.total_nodes)) self.start_with_one_neuron = start_with_one_neuron @@ -63,6 +64,11 @@ def __init__(self, n_layers, n_types, self.model_to_evolve_index = find_index_of_dict_with_key_in_pipe(self.basic_config["chainer"]["pipe"], key_model_to_evolve) + self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["n_types"] = self.n_types + self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["n_layers"] = self.n_layers + Path(self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).mkdir(parents=True, + exist_ok=True) + self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) self.train_params = deepcopy(self.basic_config.get("train")) self.basic_layers_params = self.params.pop(key_basic_layers, None) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index d30a600906..223e5a42a6 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -94,21 +94,6 @@ def score_population(population, population_size, result_file): print("Given basic params: {}\n".format(basic_params)) -Path(basic_params["chainer"]["pipe"][4]["save_path"]).mkdir(parents=True, exist_ok=True) -basic_params["chainer"]["pipe"][4]["n_types"] = N_TYPES -basic_params["chainer"]["pipe"][4]["n_layers"] = N_LAYERS - -# Result table -order = ["classification_log_loss", "classification_accuracy", - "classification_f1", "classification_roc_auc", "params"] -result_file = Path(basic_params["chainer"]["pipe"][4]["save_path"]).joinpath("result_table.csv") -result_table = pd.DataFrame({"loss": [], - "classification_accuracy": [], - "classification_f1": [], - "classification_roc_auc": [], - "params": []}) -result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') - # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, @@ -120,6 +105,18 @@ def score_population(population, population_size, result_file): start_with_one_neuron=ONE_NEURON_INIT, **basic_params) +# Result table +order = ["classification_log_loss", "classification_accuracy", + "classification_f1", "classification_roc_auc", "params"] +result_file = Path(basic_params["chainer"]["pipe"][ + evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") +result_table = pd.DataFrame({"loss": [], + "classification_accuracy": [], + "classification_f1": [], + "classification_roc_auc": [], + "params": []}) +result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') + print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() print("Considered population: {}\nScoring...\n".format(population)) From 58fe6c2403b7fffe649283401b034698836bb54f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 16:20:04 +0300 Subject: [PATCH 067/616] fix: activation choice for one neuron --- deeppavlov/models/evolution/evolution_intent_model.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index f6aa486d8f..49557aef3b 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -130,7 +130,8 @@ def evolution_classification_model(self, params): output = Dense(1, activation=None)(inp) output = GlobalMaxPooling1D()(output) output = Dense(self.n_classes, activation=None)(output) - act_output = Activation('sigmoid')(output) + activation = params.get("last_layer_activation", "sigmoid") + act_output = Activation(activation)(output) model = Model(inputs=inp, outputs=act_output) return model From 0e0c8f84c76d44109da6bccdf32b72591b625d41 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 18:03:03 +0300 Subject: [PATCH 068/616] fix: n_epochs range changed --- .../configs/evolution/basic_snli_one_neuron_init_part.json | 4 ++-- .../evolution/basic_snli_one_neuron_init_part_half.json | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index b330bf4553..38a843bae9 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -195,8 +195,8 @@ "train": { "epochs": { "range": [ - 100, - 1000 + 50, + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index a2dcf28329..3a6d14f873 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -195,8 +195,8 @@ "train": { "epochs": { "range": [ - 100, - 1000 + 50, + 100 ], "discrete": true }, From 4cb6f7aadc0a94ad74a0fc1f8792a4d9025686f3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 10:57:17 +0300 Subject: [PATCH 069/616] chore: working --- .../basic_snli_one_neuron_init_part.json | 20 +++++++++---------- .../basic_snli_one_neuron_init_part_half.json | 20 +++++++++---------- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 38a843bae9..3f3749271f 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -69,7 +69,7 @@ "units": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -86,7 +86,7 @@ "filters": { "range": [ 50, - 500 + 100 ], "discrete": true }, @@ -103,7 +103,7 @@ "units": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -115,7 +115,7 @@ "units": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -139,14 +139,14 @@ "n_hidden": { "range": [ 50, - 200 + 100 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -169,13 +169,13 @@ "optimizer": "Adam", "lear_rate": { "range": [ - 0.001, + 0.0001, 0.1 ] }, "lear_rate_decay": { "range": [ - 0.00001, + 0.000001, 0.1 ] }, @@ -202,8 +202,8 @@ }, "batch_size": { "range": [ - 50, - 70 + 20, + 50 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index 3a6d14f873..a0bb9b653f 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -69,7 +69,7 @@ "units": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -86,7 +86,7 @@ "filters": { "range": [ 50, - 500 + 100 ], "discrete": true }, @@ -103,7 +103,7 @@ "units": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -115,7 +115,7 @@ "units": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -139,14 +139,14 @@ "n_hidden": { "range": [ 50, - 200 + 100 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -169,13 +169,13 @@ "optimizer": "Adam", "lear_rate": { "range": [ - 0.001, + 0.0001, 0.1 ] }, "lear_rate_decay": { "range": [ - 0.00001, + 0.000001, 0.1 ] }, @@ -202,8 +202,8 @@ }, "batch_size": { "range": [ - 50, - 70 + 20, + 50 ], "discrete": true }, From 9f8fede8042d60ef7c84edd406a017f974c9bbd3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 12:49:11 +0300 Subject: [PATCH 070/616] feat: experiment without attention add --- ...snli_one_neuron_init_part_without_att.json | 205 ++++++++++++++++++ 1 file changed, 205 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json new file mode 100644 index 0000000000..457deac5ab --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json @@ -0,0 +1,205 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_without_att", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_without_att", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.000001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 30, + "last_layer_activation": "softmax", + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 50, + 100 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 20, + 50 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From ee755787f47edf169f226a911f9b38e1545a0bd0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 15:09:57 +0300 Subject: [PATCH 071/616] chore: comments, making sinks outputs 2d --- .../evolution/evolution_intent_model.py | 50 ++++++++++++++----- 1 file changed, 37 insertions(+), 13 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 49557aef3b..8872104b6f 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -140,54 +140,78 @@ def evolution_classification_model(self, params): edges_outputs = {} - sequence_of_nodes = [] - sequence_of_nodes.append(sources) + # sequence_of_nodes is a list of lists. + # each element of sequence_of_nodes is a list that contains nodes (keras layers) + # that could be initialized when all nodes from previous lists are initialized + sequence_of_nodes = [sources] while True: - if set(sinks).issubset(set(sum(sequence_of_nodes, []))): - break + # unreal condition: if some sources are sinks + # if set(sinks).issubset(set(sum(sequence_of_nodes, []))): + # break next_nodes = [] + # want to get list of nodes that can be initialized next for node_str_id in sequence_of_nodes[-1]: + # for each node that were initialized on the previous step + # take output edges out_edges = dg.out_edges(node_str_id) for edge in out_edges: + # for all output edge + # collect nodes that are input nodes + # for considered child of node_str_id (edge[1]) in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] + # if for considered child all parents are already initialized + # then add this node for initialization if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): next_nodes.append(edge[1]) sequence_of_nodes.append(next_nodes) + # make a list of ints from list of lists sequence_of_nodes = sum(sequence_of_nodes, []) + # now all nodes in sequence + # can be initialized consequently for node_str_id in sequence_of_nodes: if node_str_id in sources: + # if considered node is source, + # give embedded texts as input edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, inp=inp) elif node_str_id in isolates: + # unreal condition + # if considered node is isolate, + # nothing to do pass else: + # if considered node is not source and isolate, + # give all previous outputs as input edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, edges_outputs=edges_outputs) if len(sinks) == 1: + # if the only sink, + # output is this sink's output output = edges_outputs[sinks[0]] else: + # if several sinks exist, + # outputs will be concatenated outputs = [] + # collect outputs for sink in sinks: outputs.append(edges_outputs[sink]) try: output = Concatenate()(outputs) except ValueError: - time_steps = [] - features = [] + # outputs are of 2d and 3d shapes + # make them all 2d and concatenate for i in range(len(outputs)): - if len(K.int_shape(outputs[i])) == 2: - outputs[i] = Lambda(lambda x: expand_tile(x, axis=1))(outputs[i]) - time_steps.append(K.int_shape(outputs[i])[1]) - features.append(K.int_shape(outputs[i])[2]) - new_feature_shape = max(features) - for i in range(len(outputs)): - outputs[i] = Dense(new_feature_shape)(outputs[i]) + if len(K.int_shape(outputs[i])) == 3: + outputs[i] = GlobalMaxPooling1D()(outputs[i]) output = Concatenate(axis=1)(outputs) + # if concatenated output is of 3d shape + # make it 2d using global max pooling if len(output.shape) == 3: output = GlobalMaxPooling1D()(output) + output = Dense(self.n_classes, activation=None)(output) activation = params.get("last_layer_activation", "sigmoid") act_output = Activation(activation)(output) From b1ddef2b6dd5cb6e348e5ad526c5c25185acc970 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 15:11:28 +0300 Subject: [PATCH 072/616] fix: add breaking cycle in sequence of nodes --- deeppavlov/models/evolution/evolution_intent_model.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 8872104b6f..c015069235 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -146,9 +146,8 @@ def evolution_classification_model(self, params): sequence_of_nodes = [sources] while True: - # unreal condition: if some sources are sinks - # if set(sinks).issubset(set(sum(sequence_of_nodes, []))): - # break + if set(sinks).issubset(set(sum(sequence_of_nodes, []))): + break next_nodes = [] # want to get list of nodes that can be initialized next for node_str_id in sequence_of_nodes[-1]: @@ -211,7 +210,7 @@ def evolution_classification_model(self, params): # make it 2d using global max pooling if len(output.shape) == 3: output = GlobalMaxPooling1D()(output) - + output = Dense(self.n_classes, activation=None)(output) activation = params.get("last_layer_activation", "sigmoid") act_output = Activation(activation)(output) From a6b29805cf0b610f9b7c185db6ee3f69ccafcd20 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 15:29:18 +0300 Subject: [PATCH 073/616] fix: delete globalmaxpooling --- .../configs/evolution/basic_snli_one_neuron_init_part.json | 2 -- 1 file changed, 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 3f3749271f..5d9f366a59 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -123,8 +123,6 @@ "bool": true } }, - "GlobalMaxPooling1D": { - }, "MaxPooling1D": { "pool_size": { "range": [ From 904ea7e50f4f164235392f3b066fef554676b864 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 15:31:41 +0300 Subject: [PATCH 074/616] fix: delete globalmaxpool from all configs --- deeppavlov/configs/evolution/basic_snips_one_neuron_init.json | 2 -- deeppavlov/configs/evolution/basic_snips_random_init.json | 2 -- .../configs/evolution/basic_snli_one_neuron_init_part_half.json | 2 -- .../evolution/basic_snli_one_neuron_init_part_without_att.json | 2 -- deeppavlov/configs/evolution/basic_snli_random_init.json | 2 -- 5 files changed, 10 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index e0d4b95e78..fe55f2cdaf 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -123,8 +123,6 @@ "bool": true } }, - "GlobalMaxPooling1D": { - }, "MaxPooling1D": { "pool_size": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index ba66d8d042..8ae49f36a0 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -123,8 +123,6 @@ "bool": true } }, - "GlobalMaxPooling1D": { - }, "MaxPooling1D": { "pool_size": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index a0bb9b653f..e320956f04 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -123,8 +123,6 @@ "bool": true } }, - "GlobalMaxPooling1D": { - }, "MaxPooling1D": { "pool_size": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json index 457deac5ab..fe7e3f7c71 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json @@ -123,8 +123,6 @@ "bool": true } }, - "GlobalMaxPooling1D": { - }, "MaxPooling1D": { "pool_size": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index f86582ce1a..0e86405b02 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -123,8 +123,6 @@ "bool": true } }, - "GlobalMaxPooling1D": { - }, "MaxPooling1D": { "pool_size": { "range": [ From 05f828ff9bac3c92bdc37e94129b6733221e4def Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 16:00:31 +0300 Subject: [PATCH 075/616] chore: configs --- .../configs/evolution/basic_config_local.json | 59 ++++++++++++------- .../basic_snips_one_neuron_init.json | 8 +-- .../evolution/basic_snips_random_init.json | 7 +-- .../basic_snli_one_neuron_init_part.json | 20 +++---- .../basic_snli_one_neuron_init_part_half.json | 20 +++---- ...c_snli_one_neuron_init_part_two_texts.json | 16 ++--- ...snli_one_neuron_init_part_without_att.json | 8 +-- .../evolution/basic_snli_random_init.json | 10 +--- 8 files changed, 69 insertions(+), 79 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 07087e13be..580c42e199 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -1,16 +1,20 @@ { "dataset_reader": { "name": "basic_classification_reader", - "x": "text", - "y": "intents", - "data_path": "/home/dilyara/data/data_files/snips/snips_dataset" + "x": [ + "sentence1", + "sentence2" + ], + "y": "gold_label", + "data_path": "/home/dilyara/data/data_files/SNLI/snli_data/two_texts/part" }, "dataset_iterator": { "name": "basic_classification_iterator" }, "chainer": { "in": [ - "x" + "sentence1", + "sentence2" ], "in_y": [ "y" @@ -23,8 +27,26 @@ "y" ], "level": "token", - "save_path": "/home/dilyara/data/data_files/snips/snips_dataset/snips_classes.dict", - "load_path": "/home/dilyara/data/data_files/snips/snips_dataset/snips_classes.dict" + "save_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict", + "load_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict" + }, + { + "in": [ + "sentence1" + ], + "out": [ + "sentence1_lower" + ], + "name": "str_lower" + }, + { + "in": [ + "sentence2" + ], + "out": [ + "sentence2_lower" + ], + "name": "str_lower" }, { "id": "my_embedder", @@ -40,7 +62,8 @@ }, { "in": [ - "x" + "sentence1_lower", + "sentence2_lower" ], "in_y": [ "y" @@ -50,9 +73,9 @@ "y_probas_dict" ], "main": true, - "name": "evolution_classification_model", - "save_path": "/home/dilyara/data/models/evolution_data/snips_classification/start_with_random", - "load_path": "/home/dilyara/data/models/evolution_data/snips_classification/start_with_random", + "name": "evolution_classification_many_texts_model", + "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_texts", + "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_texts", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -98,9 +121,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { @@ -110,11 +131,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } - }, - "GlobalMaxPooling1D": { + "return_sequences": true }, "MaxPooling1D": { "pool_size": { @@ -126,7 +143,7 @@ }, "padding": "same" }, - "SelfMultiplicativeAttention": { + "Attention": { "n_hidden": { "range": [ 50, @@ -149,6 +166,7 @@ ], "choice": true } + } }, "confident_threshold": { "range": [ @@ -171,7 +189,8 @@ }, "loss": "binary_crossentropy", "text_size": 15, - "model_name": "evolution_classification_model", + "last_layer_activation": "softmax", + "model_name": "evolution_many_texts_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" } diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index fe55f2cdaf..660bae50bd 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -107,9 +107,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { @@ -119,9 +117,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index 8ae49f36a0..b476f08415 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -107,9 +107,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { @@ -119,8 +117,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true + "return_sequences": trueool": true } }, "MaxPooling1D": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 5d9f366a59..b20b80cfbd 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -69,7 +69,7 @@ "units": { "range": [ 50, - 100 + 200 ], "discrete": true }, @@ -86,7 +86,7 @@ "filters": { "range": [ 50, - 100 + 200 ], "discrete": true }, @@ -103,25 +103,21 @@ "units": { "range": [ 50, - 100 + 200 ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { "range": [ 50, - 100 + 200 ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "MaxPooling1D": { "pool_size": { @@ -137,14 +133,14 @@ "n_hidden": { "range": [ 50, - 100 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 100 + 200 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index e320956f04..0aab7a2e80 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -69,7 +69,7 @@ "units": { "range": [ 50, - 100 + 200 ], "discrete": true }, @@ -86,7 +86,7 @@ "filters": { "range": [ 50, - 100 + 200 ], "discrete": true }, @@ -103,25 +103,21 @@ "units": { "range": [ 50, - 100 + 200 ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { "range": [ 50, - 100 + 200 ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "MaxPooling1D": { "pool_size": { @@ -137,14 +133,14 @@ "n_hidden": { "range": [ 50, - 100 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 100 + 200 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json index 3a34a7b853..13642b57b5 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json @@ -97,7 +97,7 @@ "filters": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -118,9 +118,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { @@ -130,11 +128,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } - }, - "GlobalMaxPooling1D": { + "return_sequences": true }, "MaxPooling1D": { "pool_size": { @@ -206,8 +200,8 @@ "train": { "epochs": { "range": [ - 100, - 1000 + 50, + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json index fe7e3f7c71..2ec06848e1 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json @@ -107,9 +107,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { @@ -119,9 +117,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index 0e86405b02..4840a1e685 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -86,7 +86,7 @@ "filters": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -107,9 +107,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { @@ -119,9 +117,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "MaxPooling1D": { "pool_size": { From 8cc86ac088325f9224924efad2d7581b1afcbaab Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 18:01:02 +0300 Subject: [PATCH 076/616] feat: many_inputs classification model for evolution --- deeppavlov/__init__.py | 1 + .../configs/evolution/basic_config_local.json | 10 +- ...nli_one_neuron_init_part_many_inputs.json} | 6 +- ...snli_one_neuron_init_part_without_att.json | 199 --------- deeppavlov/core/layers/keras_layers.py | 21 + .../basic_classification_reader.py | 5 +- .../evolution/evolution_intent_model.py | 85 ---- .../evolution/evolution_many_inputs_model.py | 389 ++++++++++++++++++ 8 files changed, 423 insertions(+), 293 deletions(-) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init_part_two_texts.json => basic_snli_one_neuron_init_part_many_inputs.json} (96%) delete mode 100644 deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json create mode 100644 deeppavlov/models/evolution/evolution_many_inputs_model.py diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index 3cf3e09f8f..902ea55bf2 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -37,6 +37,7 @@ import deeppavlov.dataset_iterators.sqlite_iterator import deeppavlov.models.classifiers.intents.intent_model import deeppavlov.models.evolution.evolution_intent_model +import deeppavlov.models.evolution.evolution_many_inputs_model import deeppavlov.models.commutators.random_commutator import deeppavlov.models.embedders.fasttext_embedder import deeppavlov.models.embedders.dict_embedder diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 580c42e199..3e63aca045 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_classification_many_texts_model", - "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_texts", - "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_texts", + "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_inputs", + "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_inputs", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -100,7 +100,7 @@ "filters": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -143,7 +143,7 @@ }, "padding": "same" }, - "Attention": { + "SelfMultiplicativeAttention": { "n_hidden": { "range": [ 50, @@ -190,7 +190,7 @@ "loss": "binary_crossentropy", "text_size": 15, "last_layer_activation": "softmax", - "model_name": "evolution_many_texts_classification_model", + "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" } diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json similarity index 96% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json rename to deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 13642b57b5..df5be8a0a5 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -71,8 +71,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_two_texts", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_two_texts", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -187,7 +187,7 @@ "loss": "binary_crossentropy", "text_size": 15, "last_layer_activation": "softmax", - "model_name": "evolution_two_texts_classification_model", + "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" } diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json deleted file mode 100644 index 2ec06848e1..0000000000 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json +++ /dev/null @@ -1,199 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_without_att", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_without_att", - "classes": "#classes_vocab.keys()", - "to_evolve": true, - "basic_layers_params": { - "Dense": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - }, - "Conv1D": { - "filters": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "kernel_size": { - "range": [ - 2, - 7 - ], - "discrete": true - }, - "padding": "same" - }, - "CuDNNLSTM": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "return_sequences": true - }, - "BiCuDNNLSTM": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "return_sequences": true - }, - "MaxPooling1D": { - "pool_size": { - "range": [ - 2, - 5 - ], - "discrete": true - }, - "padding": "same" - } - }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.0001, - 0.1 - ] - }, - "lear_rate_decay": { - "range": [ - 0.000001, - 0.1 - ] - }, - "loss": "binary_crossentropy", - "text_size": 30, - "last_layer_activation": "softmax", - "model_name": "evolution_classification_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "range": [ - 50, - 100 - ], - "discrete": true - }, - "batch_size": { - "range": [ - 20, - 50 - ], - "discrete": true - }, - "metric_optimization": "minimize", - "metrics": [ - "classification_log_loss", - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, - "show_examples": false, - "validate_best": true, - "test_best": false - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} diff --git a/deeppavlov/core/layers/keras_layers.py b/deeppavlov/core/layers/keras_layers.py index 710156df53..3439f69f51 100644 --- a/deeppavlov/core/layers/keras_layers.py +++ b/deeppavlov/core/layers/keras_layers.py @@ -99,3 +99,24 @@ def multiplicative_self_attention(units, n_hidden=None, n_output_features=None, attended_units = Lambda(lambda x: K.sum(x, axis=2))(mult) output = Dense(n_output_features, activation=activation)(attended_units) return output + + +def multiplicative_self_attention_init(n_hidden, n_output_features, activation): + layers = {} + layers["queries"] = Dense(n_hidden) + layers["keys"] = Dense(n_hidden) + layers["output"] = Dense(n_output_features, activation=activation) + return layers + + +def multiplicative_self_attention_get_output(units, layers): + exp1 = Lambda(lambda x: expand_tile(x, axis=1))(units) + exp2 = Lambda(lambda x: expand_tile(x, axis=2))(units) + queries = layers["queries"](exp1) + keys = layers["keys"](exp2) + scores = Lambda(lambda x: K.sum(queries * x, axis=3, keepdims=True))(keys) + attention = Lambda(lambda x: softvaxaxis2(x))(scores) + mult = Multiply()([attention, exp1]) + attended_units = Lambda(lambda x: K.sum(x, axis=2))(mult) + output = layers["output"](attended_units) + return output diff --git a/deeppavlov/dataset_readers/basic_classification_reader.py b/deeppavlov/dataset_readers/basic_classification_reader.py index 626988ddcd..d8ab55e025 100644 --- a/deeppavlov/dataset_readers/basic_classification_reader.py +++ b/deeppavlov/dataset_readers/basic_classification_reader.py @@ -82,7 +82,10 @@ def read(self, data_path, url=None, *args, **kwargs): x = kwargs.get("x", "text") y = kwargs.get('y', 'labels') class_sep = kwargs.get('class_sep', ',') - data[data_type] = [(row[x], row[y].split(class_sep)) for _, row in df.iterrows()] + if isinstance(x, list): + data[data_type] = [([row[x_] for x_ in x], row[y].split(class_sep)) for _, row in df.iterrows()] + else: + data[data_type] = [(row[x], row[y].split(class_sep)) for _, row in df.iterrows()] else: log.warning("Cannot find {} file".format(file)) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index c015069235..b471f0f38c 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -217,91 +217,6 @@ def evolution_classification_model(self, params): model = Model(inputs=inp, outputs=act_output) return model - def evolution_two_texts_classification_model(self, params): - """ - Build un-compiled model of shallow-and-wide CNN - Args: - params: dictionary of parameters for NN - - Returns: - Un-compiled model - """ - inp1 = Input(shape=(params['text_size'], params['embedding_size'])) - inp2 = Input(shape=(params['text_size'], params['embedding_size'])) - - full_outputs = [] - - for inp_id, inp in enumerate([inp1, inp2]): - if np.sum(params["binary_mask"]) == 0: - output = Dense(1, activation=None)(inp) - output = GlobalMaxPooling1D()(output) - output = Dense(self.n_classes, activation=None)(output) - act_output = Activation('sigmoid')(output) - model = Model(inputs=inp, outputs=act_output) - return model - - dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) - sources, sinks, isolates = find_sources_and_sinks(dg) - - edges_outputs = {} - - sequence_of_nodes = [] - sequence_of_nodes.append(sources) - - while True: - if set(sinks).issubset(set(sum(sequence_of_nodes, []))): - break - next_nodes = [] - for node_str_id in sequence_of_nodes[-1]: - out_edges = dg.out_edges(node_str_id) - for edge in out_edges: - in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] - if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): - next_nodes.append(edge[1]) - sequence_of_nodes.append(next_nodes) - - sequence_of_nodes = sum(sequence_of_nodes, []) - - for node_str_id in sequence_of_nodes: - if node_str_id in sources: - edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, inp=inp) - elif node_str_id in isolates: - pass - else: - edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, edges_outputs=edges_outputs) - - if len(sinks) == 1: - output = edges_outputs[sinks[0]] - else: - outputs = [] - for sink in sinks: - outputs.append(edges_outputs[sink]) - try: - output = Concatenate()(outputs) - except ValueError: - time_steps = [] - features = [] - for i in range(len(outputs)): - if len(K.int_shape(outputs[i])) == 2: - outputs[i] = Lambda(lambda x: expand_tile(x, axis=1))(outputs[i]) - time_steps.append(K.int_shape(outputs[i])[1]) - features.append(K.int_shape(outputs[i])[2]) - new_feature_shape = max(features) - for i in range(len(outputs)): - outputs[i] = Dense(new_feature_shape)(outputs[i]) - output = Concatenate(axis=1)(outputs) - - if len(output.shape) == 3: - output = GlobalMaxPooling1D()(output) - full_outputs.append(output) - - output = Concatenate()(full_outputs) - output = Dense(self.n_classes, activation=None)(output) - activation = params.get("last_layer_activation", "sigmoid") - act_output = Activation(activation)(output) - model = Model(inputs=[inp1, inp2], outputs=act_output) - return model - @overrides def save(self, fname=None): """ diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py new file mode 100644 index 0000000000..c776a9e411 --- /dev/null +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -0,0 +1,389 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import numpy as np +from copy import copy, deepcopy +from keras.layers import Dense, Input, concatenate, Activation +from keras.layers.convolutional import Conv1D +from keras.layers.core import Dropout +from keras.layers.normalization import BatchNormalization +from keras.layers.pooling import GlobalMaxPooling1D, MaxPooling1D +from keras.layers.recurrent import LSTM +from keras.layers.wrappers import Bidirectional +from keras.models import Model +from keras.regularizers import l2 +from keras.layers import Concatenate, Reshape, CuDNNLSTM, Lambda +from keras import backend as K +from overrides import overrides +from pathlib import Path + +from deeppavlov.core.common.errors import ConfigError +from deeppavlov.core.common.registry import register +from deeppavlov.core.models.keras_model import KerasModel +from deeppavlov.models.classifiers.intents.intent_model import KerasIntentModel +from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels +from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder +from deeppavlov.models.classifiers.intents.utils import md5_hashsum +from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer +from deeppavlov.core.common.log import get_logger +from deeppavlov.models.evolution.check_binary_mask import number_to_type_layer, \ + find_sources_and_sinks, get_digraph_from_binary_mask, get_graph_and_plot +from deeppavlov.models.evolution.utils import expand_tile +from deeppavlov.core.common.file import save_json, read_json +from deeppavlov.core.layers.keras_layers import multiplicative_self_attention_init, \ + multiplicative_self_attention_get_output + + +log = get_logger(__name__) + + +@register('evolution_classification_many_texts_model') +class KerasEvolutionClassificationManyInputsModel(KerasIntentModel): + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.opt["binary_mask"] = np.array(self.opt["binary_mask"]) + get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], + path=str(self.save_path.resolve().parent)) + + def texts2vec(self, sentences): + """ + Convert texts to vector representations using embedder and padding up to self.opt["text_size"] tokens + Args: + sentences: list of lists of tokens + + Returns: + array of embedded texts + """ + pad = np.zeros(self.opt['embedding_size']) + + embeddings_batch = self.fasttext_model([sen[:self.opt['text_size']] for sen in sentences]) + embeddings_batch = [[pad] * (self.opt['text_size'] - len(tokens)) + tokens for tokens in embeddings_batch] + + embeddings_batch = np.asarray(embeddings_batch) + return embeddings_batch + + @overrides + def train_on_batch(self, *args, **kwargs): + """ + Train the model on the given batch + Args: + texts - list of texts (or list of lists of text tokens) + labels - list of labels + + Returns: + loss and metrics values on the given batch + """ + if len(args) > len(self.opt["in"]): + labels = args[-1] + texts = args[:-1] + else: + labels = None + texts = args + + features = [] + for i in range(len(self.opt["in"])): + if isinstance(texts[i][0], str): + features.append(self.texts2vec(self.tokenizer(list(texts[i])))) + else: + features.append(self.texts2vec(list(texts[i]))) + + onehot_labels = labels2onehot(labels, classes=self.classes) + metrics_values = self.model.train_on_batch(features, onehot_labels) + return metrics_values + + @overrides + def infer_on_batch(self, *args, **kwargs): + """ + Infer the model on the given batch + Args: + texts - list of texts (or list of lists of text tokens) + labels - list of labels + + Returns: + loss and metrics values on the given batch, if labels are given + predictions, otherwise + """ + if len(args) > 1: + labels = args[-1] + texts = args[:-1] + elif len(args) == 1: + labels = None + texts = args[0] + else: + raise ValueError("Nothing to infer in infer_on_batch") + + features = [] + for i in range(len(self.opt["in"])): + if isinstance(texts[i][0], str): + features.append(self.texts2vec(self.tokenizer(list(texts[i])))) + else: + features.append(self.texts2vec(list(texts[i]))) + + if labels: + onehot_labels = labels2onehot(labels, classes=self.classes) + metrics_values = self.model.test_on_batch(features, onehot_labels) + return metrics_values + else: + predictions = self.model.predict(features) + return predictions + + @overrides + def __call__(self, *args, **kwargs): + """ + Infer on the given data + Args: + data: [list of sentences] + *args: + + Returns: + for each sentence: + vector of probabilities to belong with each class + or list of labels sentence belongs with + """ + assert len(args) == len(self.opt["in"]) + preds = np.array(self.infer_on_batch(args)) + + labels = proba2labels(preds, confident_threshold=self.opt['confident_threshold'], classes=self.classes) + return labels, [dict(zip(self.classes, preds[i])) for i in range(preds.shape[0])] + + def get_node_output(self, model_layers, node_str_id, dg, params, edges_outputs=None, inp=None): + if inp is None: + input_nodes = [edge[0] for edge in dg.in_edges(node_str_id)] + inp_list = [] + for input_node in input_nodes: + if len(K.int_shape(edges_outputs[input_node])) == 3: + inp_list.append(edges_outputs[input_node]) + elif len(K.int_shape(edges_outputs[input_node])) == 2: + input_expanded = Lambda(lambda x: expand_tile(x, axis=1))(edges_outputs[input_node]) + inp_list.append(input_expanded) + else: + raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") + if len(input_nodes) > 1: + try: + inp = Concatenate()(inp_list) + except ValueError: + time_steps = [] + features = [] + for i in range(len(inp_list)): + if len(K.int_shape(inp_list[i])) == 2: + inp_list[i] = Lambda(lambda x: expand_tile(x, axis=1))(inp_list[i]) + time_steps.append(K.int_shape(inp_list[i])[1]) + features.append(K.int_shape(inp_list[i])[2]) + new_feature_shape = max(features) + new_inp_list = [] + for i in range(len(inp_list)): + if K.int_shape(inp_list[i])[2] == new_feature_shape: + new_inp_list.append(inp_list[i]) + else: + new_inp_list.append(Dense(new_feature_shape)(inp_list[i])) + inp = Concatenate(axis=1)(new_inp_list) + else: + inp = inp_list[0] + + if params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": + node_params = deepcopy(params[params["nodes"][node_str_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + output_of_node = multiplicative_self_attention_get_output(inp, + model_layers[params["nodes"][node_str_id]]) + else: + node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) + node_params = deepcopy(params[params["nodes"][node_str_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + output_of_node = model_layers[params["nodes"][node_str_id]](inp) + return output_of_node + + def initialize_all_nodes(self, params): + dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) + sources, sinks, isolates = find_sources_and_sinks(dg) + + model_layers = {} + for node_str_id in list(params["nodes"].keys()): + if not(node_str_id in isolates): + if params[params["nodes"][node_str_id]]["node_name"] == "BiCuDNNLSTM": + node_params = deepcopy(params[params["nodes"][node_str_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + model_layers[params["nodes"][node_str_id]] = Bidirectional(CuDNNLSTM(**node_params)) + elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": + node_params = deepcopy(params[params["nodes"][node_str_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + model_layers[params["nodes"][node_str_id]] = \ + multiplicative_self_attention_init(**node_params) + else: + node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) + node_params = deepcopy(params[params["nodes"][node_str_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + if callable(node_func): + model_layers[params["nodes"][node_str_id]] = node_func(**node_params) + else: + raise AttributeError("Node {} is not defined correctly".format(node_str_id)) + + return model_layers + + def evolution_many_inputs_classification_model(self, params): + """ + Build un-compiled model of shallow-and-wide CNN + Args: + params: dictionary of parameters for NN + + Returns: + Un-compiled model + """ + inputs = [] + for i in range(len(params["in"])): + inputs.append(Input(shape=(params['text_size'], params['embedding_size']))) + + full_outputs = [] + + if np.sum(params["binary_mask"]) == 0: + dense1 = Dense(1, activation=None) + globalmaxpooling = GlobalMaxPooling1D() + for inp in inputs: + output = dense1(inp) + full_outputs.append(globalmaxpooling(output)) + + output = Concatenate()(full_outputs) + output = Dense(self.n_classes, activation=None)(output) + activation = params.get("last_layer_activation", "sigmoid") + act_output = Activation(activation)(output) + model = Model(inputs=inputs, outputs=act_output) + return model + + model_layers = self.initialize_all_nodes(params) + + for inp in inputs: + dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) + sources, sinks, isolates = find_sources_and_sinks(dg) + + edges_outputs = {} + + # sequence_of_nodes is a list of lists. + # each element of sequence_of_nodes is a list that contains nodes (keras layers) + # that could be initialized when all nodes from previous lists are initialized + sequence_of_nodes = [sources] + + while True: + if set(sinks).issubset(set(sum(sequence_of_nodes, []))): + break + next_nodes = [] + # want to get list of nodes that can be initialized next + for node_str_id in sequence_of_nodes[-1]: + # for each node that were initialized on the previous step + # take output edges + out_edges = dg.out_edges(node_str_id) + for edge in out_edges: + # for all output edge + # collect nodes that are input nodes + # for considered child of node_str_id (edge[1]) + in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] + # if for considered child all parents are already initialized + # then add this node for initialization + if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): + next_nodes.append(edge[1]) + sequence_of_nodes.append(next_nodes) + + # make a list of ints from list of lists + sequence_of_nodes = sum(sequence_of_nodes, []) + + # now all nodes in sequence + # can be initialized consequently + for node_str_id in sequence_of_nodes: + if node_str_id in sources: + # if considered node is source, + # give embedded texts as input + edges_outputs[node_str_id] = self.get_node_output(model_layers, node_str_id, dg, params, inp=inp) + elif node_str_id in isolates: + # unreal condition + # if considered node is isolate, + # nothing to do + pass + else: + # if considered node is not source and isolate, + # give all previous outputs as input + edges_outputs[node_str_id] = self.get_node_output(model_layers, node_str_id, dg, params, + edges_outputs=edges_outputs) + + if len(sinks) == 1: + # if the only sink, + # output is this sink's output + output = edges_outputs[sinks[0]] + else: + # if several sinks exist, + # outputs will be concatenated + outputs = [] + # collect outputs + for sink in sinks: + outputs.append(edges_outputs[sink]) + try: + output = Concatenate()(outputs) + except ValueError: + # outputs are of 2d and 3d shapes + # make them all 2d and concatenate + for i in range(len(outputs)): + if len(K.int_shape(outputs[i])) == 3: + outputs[i] = GlobalMaxPooling1D()(outputs[i]) + output = Concatenate(axis=1)(outputs) + + if len(output.shape) == 3: + output = GlobalMaxPooling1D()(output) + full_outputs.append(output) + + output = Concatenate()(full_outputs) + output = Dense(self.n_classes, activation=None)(output) + activation = params.get("last_layer_activation", "sigmoid") + act_output = Activation(activation)(output) + model = Model(inputs=inputs, outputs=act_output) + return model + + @overrides + def save(self, fname=None): + """ + Save the model parameters into <>_opt.json (or <>_opt.json) + and model weights into <>.h5 (or <>.h5) + Args: + fname: file_path to save model. If not explicitly given seld.opt["ser_file"] will be used + + Returns: + None + """ + + if not self.save_path: + raise ConfigError("No `save_path` is provided for Keras model!") + elif isinstance(self.save_path, Path) and not self.save_path.parent.is_dir(): + raise ConfigError("Provided save path is incorrect!") + else: + opt_path = "{}_opt.json".format(str(self.save_path.resolve())) + weights_path = "{}.h5".format(str(self.save_path.resolve())) + log.info("[saving model to {}]".format(opt_path)) + self.model.save_weights(weights_path) + + if type(self.opt["binary_mask"]) is list: + pass + else: + self.opt["binary_mask"] = self.opt["binary_mask"].tolist() + + save_json(self.opt, opt_path) + return True From 70897a3a615227f680b98400de3cad094e32dd79 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 18:14:06 +0300 Subject: [PATCH 077/616] fix: evolution many inputs registered --- deeppavlov/models/evolution/evolution_many_inputs_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index c776a9e411..f392cfcecd 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -50,7 +50,7 @@ log = get_logger(__name__) -@register('evolution_classification_many_texts_model') +@register('evolution_many_inputs_classification_model') class KerasEvolutionClassificationManyInputsModel(KerasIntentModel): def __init__(self, **kwargs): From bc5b5402afc6fab5af37676b0e319fb4349832ba Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 18:18:05 +0300 Subject: [PATCH 078/616] fix: evolution many inputs in configs --- deeppavlov/configs/evolution/basic_config_local.json | 2 +- .../evolution/basic_snli_one_neuron_init_part_many_inputs.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 3e63aca045..20629674b0 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -73,7 +73,7 @@ "y_probas_dict" ], "main": true, - "name": "evolution_classification_many_texts_model", + "name": "evolution_many_inputs_classification_model", "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_inputs", "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_inputs", "classes": "#classes_vocab.keys()", diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index df5be8a0a5..0837e46051 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -70,7 +70,7 @@ "y_probas_dict" ], "main": true, - "name": "evolution_classification_model", + "name": "evolution_many_inputs_classification_model", "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs", "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs", "classes": "#classes_vocab.keys()", From d7d13035abe982ea253dbabd73a5403a1699f1ef Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 21 May 2018 11:59:22 +0300 Subject: [PATCH 079/616] feat: add choice of evolving metric --- deeppavlov/models/evolution/run_evolution.py | 33 ++++++++++++-------- 1 file changed, 20 insertions(+), 13 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 223e5a42a6..7d0ca7b0ec 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -9,10 +9,12 @@ def score_population(population, population_size, result_file): global evolution - population_losses = [] - population_fmeasures = [] - population_accuracies = [] - population_roc_auc_scores = [] + population_metrics = {} + for metric in ["classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc"]: + population_metrics[metric] = [] procs = [] @@ -58,15 +60,15 @@ def score_population(population, population_size, result_file): "classification_roc_auc": [val_results[3]], "params": [population[i]]}) result_table.loc[:, order].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - population_losses.append(val_results[0]) - population_accuracies.append(val_results[1]) - population_fmeasures.append(val_results[2]) - population_roc_auc_scores.append(val_results[3]) + population_metrics["classification_log_loss"].append(val_results[0]) + population_metrics["classification_accuracy"].append(val_results[1]) + population_metrics["classification_f1"].append(val_results[2]) + population_metrics["classification_roc_auc"].append(val_results[3]) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) - return population_roc_auc_scores + return population_metrics parser = argparse.ArgumentParser() @@ -78,6 +80,10 @@ def score_population(population, population_size, result_file): parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) parser.add_argument('--one_neuron_init', help='Please, enter number of types of layers', default=0) +parser.add_argument('--evolve_metric', help='Please, choose target metric out of ["classification_log_loss", ' + '"classification_accuracy",' + ' "classification_f1",' + ' "classification_roc_auc"]', default="classification_roc_auc") args = parser.parse_args() @@ -88,6 +94,7 @@ def score_population(population, population_size, result_file): N_LAYERS = int(args.n_layers) N_TYPES = int(args.n_types) ONE_NEURON_INIT = bool(int(args.one_neuron_init)) +EVOLVE_METRIC = args.evolve_metric with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) @@ -110,7 +117,7 @@ def score_population(population, population_size, result_file): "classification_f1", "classification_roc_auc", "params"] result_file = Path(basic_params["chainer"]["pipe"][ evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") -result_table = pd.DataFrame({"loss": [], +result_table = pd.DataFrame({"classification_log_loss": [], "classification_accuracy": [], "classification_f1": [], "classification_roc_auc": [], @@ -120,16 +127,16 @@ def score_population(population, population_size, result_file): print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() print("Considered population: {}\nScoring...\n".format(population)) -population_roc_auc_scores = score_population(population, POPULATION_SIZE, result_file) +population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] iters = 1 while True: print("\nIteration #{} starts\n".format(iters)) - population = evolution.next_generation(population, population_roc_auc_scores, iter=iters) + population = evolution.next_generation(population, population_scores, iter=iters) print("Considered population: {}\nScoring...\n".format(population)) - population_roc_auc_scores = score_population(population, POPULATION_SIZE, result_file) + population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] print("\nIteration #{} was done\n".format(iters)) iters += 1 From 02f4c042e5069d2ddc0f7b90dd14a61941f479fa Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 21 May 2018 12:42:22 +0300 Subject: [PATCH 080/616] feat: add several text sizes in many_inputs model, add basic config for selqa --- deeppavlov/configs/evolution/basic_selqa.json | 240 ++++++++++++++++++ .../evolution/evolution_many_inputs_model.py | 27 +- 2 files changed, 257 insertions(+), 10 deletions(-) create mode 100644 deeppavlov/configs/evolution/basic_selqa.json diff --git a/deeppavlov/configs/evolution/basic_selqa.json b/deeppavlov/configs/evolution/basic_selqa.json new file mode 100644 index 0000000000..0dd3b717bd --- /dev/null +++ b/deeppavlov/configs/evolution/basic_selqa.json @@ -0,0 +1,240 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": [ + "question", + "answer" + ], + "y": "label", + "data_path": "/home/dilyara.baymurzina/evolution_data/selqa_data" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "question", + "answer" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/selqa_data/selqa_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/selqa_data/selqa_classes.dict" + }, + { + "in": [ + "question" + ], + "out": [ + "question_lower" + ], + "name": "str_lower" + }, + { + "in": [ + "answer" + ], + "out": [ + "answer_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "question_lower", + "answer_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_many_inputs_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/selqa_classification/one_neuron_init_part_many_inputs", + "load_path": "/home/dilyara.baymurzina/evolution_data/selqa_classification/one_neuron_init_part_many_inputs", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": true + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": true + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": [ + 20, + 50 + ], + "last_layer_activation": "softmax", + "model_name": "evolution_many_inputs_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 50, + 100 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 70 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index f392cfcecd..1554792bf7 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -59,7 +59,7 @@ def __init__(self, **kwargs): get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], path=str(self.save_path.resolve().parent)) - def texts2vec(self, sentences): + def texts2vec(self, sentences, i): """ Convert texts to vector representations using embedder and padding up to self.opt["text_size"] tokens Args: @@ -69,9 +69,12 @@ def texts2vec(self, sentences): array of embedded texts """ pad = np.zeros(self.opt['embedding_size']) - - embeddings_batch = self.fasttext_model([sen[:self.opt['text_size']] for sen in sentences]) - embeddings_batch = [[pad] * (self.opt['text_size'] - len(tokens)) + tokens for tokens in embeddings_batch] + if type(self.opt['text_size']) is list: + text_size = self.opt['text_size'][i] + else: + text_size = self.opt['text_size'] + embeddings_batch = self.fasttext_model([sen[:text_size] for sen in sentences]) + embeddings_batch = [[pad] * (text_size - len(tokens)) + tokens for tokens in embeddings_batch] embeddings_batch = np.asarray(embeddings_batch) return embeddings_batch @@ -97,9 +100,9 @@ def train_on_batch(self, *args, **kwargs): features = [] for i in range(len(self.opt["in"])): if isinstance(texts[i][0], str): - features.append(self.texts2vec(self.tokenizer(list(texts[i])))) + features.append(self.texts2vec(self.tokenizer(list(texts[i])), i)) else: - features.append(self.texts2vec(list(texts[i]))) + features.append(self.texts2vec(list(texts[i]), i)) onehot_labels = labels2onehot(labels, classes=self.classes) metrics_values = self.model.train_on_batch(features, onehot_labels) @@ -129,9 +132,9 @@ def infer_on_batch(self, *args, **kwargs): features = [] for i in range(len(self.opt["in"])): if isinstance(texts[i][0], str): - features.append(self.texts2vec(self.tokenizer(list(texts[i])))) + features.append(self.texts2vec(self.tokenizer(list(texts[i])), i)) else: - features.append(self.texts2vec(list(texts[i]))) + features.append(self.texts2vec(list(texts[i]), i)) if labels: onehot_labels = labels2onehot(labels, classes=self.classes) @@ -253,8 +256,12 @@ def evolution_many_inputs_classification_model(self, params): Un-compiled model """ inputs = [] - for i in range(len(params["in"])): - inputs.append(Input(shape=(params['text_size'], params['embedding_size']))) + if type(params['text_size']) is list: + for i in range(len(params["in"])): + inputs.append(Input(shape=(params['text_size'][i], params['embedding_size']))) + else: + for i in range(len(params["in"])): + inputs.append(Input(shape=(params['text_size'], params['embedding_size']))) full_outputs = [] From 41340b54a242a82944f276ad36621fcd386ef921 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 21 May 2018 12:52:40 +0300 Subject: [PATCH 081/616] fix: test best true for selqa --- deeppavlov/configs/evolution/basic_selqa.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_selqa.json b/deeppavlov/configs/evolution/basic_selqa.json index 0dd3b717bd..52a12e83d2 100644 --- a/deeppavlov/configs/evolution/basic_selqa.json +++ b/deeppavlov/configs/evolution/basic_selqa.json @@ -230,7 +230,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { From f407a34327c9cd6fdcdad566cd02c752078352e2 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 21 May 2018 12:54:44 +0300 Subject: [PATCH 082/616] fix: bigger sizes for selqa --- deeppavlov/configs/evolution/basic_selqa.json | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_selqa.json b/deeppavlov/configs/evolution/basic_selqa.json index 52a12e83d2..1eec402e37 100644 --- a/deeppavlov/configs/evolution/basic_selqa.json +++ b/deeppavlov/configs/evolution/basic_selqa.json @@ -83,7 +83,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -100,7 +100,7 @@ "filters": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -117,7 +117,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -127,7 +127,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -147,14 +147,14 @@ "n_hidden": { "range": [ 50, - 200 + 500 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 500 ], "discrete": true }, From b8d05c40ebd8cb3b15df5c1c4803100bc522a7e0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 11:51:49 +0300 Subject: [PATCH 083/616] fix: confident_threshold is equal to 1 for SNLI because we want only max proba label --- .../configs/evolution/basic_config_local.json | 7 +- deeppavlov/configs/evolution/basic_selqa.json | 7 +- .../basic_snips_one_neuron_init.json | 7 +- .../evolution/basic_snips_random_init.json | 10 +- .../basic_snli_one_neuron_init_part.json | 11 +- .../basic_snli_one_neuron_init_part_half.json | 7 +- ...snli_one_neuron_init_part_many_inputs.json | 7 +- .../evolution/basic_snli_random_init.json | 7 +- .../configs/evolution/intents_snli.json | 124 ++++++++++++++++++ 9 files changed, 135 insertions(+), 52 deletions(-) create mode 100644 deeppavlov/configs/evolution/intents_snli.json diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 20629674b0..a1b859edee 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -168,12 +168,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_selqa.json b/deeppavlov/configs/evolution/basic_selqa.json index 1eec402e37..e6cc11465a 100644 --- a/deeppavlov/configs/evolution/basic_selqa.json +++ b/deeppavlov/configs/evolution/basic_selqa.json @@ -168,12 +168,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 660bae50bd..34022c6b80 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -154,12 +154,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index b476f08415..c200379281 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -117,8 +117,7 @@ ], "discrete": true }, - "return_sequences": trueool": true - } + "return_sequences": true }, "MaxPooling1D": { "pool_size": { @@ -155,12 +154,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index b20b80cfbd..ee81a38e99 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -154,12 +154,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ @@ -209,8 +204,8 @@ "classification_roc_auc" ], "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, "show_examples": false, "validate_best": true, "test_best": false diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index 0aab7a2e80..e1568f7d17 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -154,12 +154,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 0837e46051..b2d269cddf 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -165,12 +165,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index 4840a1e685..508c6a98d7 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -154,12 +154,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/intents_snli.json b/deeppavlov/configs/evolution/intents_snli.json new file mode 100644 index 0000000000..7e60b2908c --- /dev/null +++ b/deeppavlov/configs/evolution/intents_snli.json @@ -0,0 +1,124 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "gold_label", + "data_path": "/home/dilyara/data/data_files/SNLI/snli_data" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict", + "load_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", + "load_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "intent_model", + "save_path": "intents/intent_snli_v0", + "load_path": "intents/intent_snli_v0", + "classes": "#classes_vocab.keys()", + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": 256, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 0.01, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 15, + "coef_reg_cnn": 1e-4, + "coef_reg_den": 1e-4, + "dropout_rate": 0.5, + "dense_size": 100, + "model_name": "cnn_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": 100, + "batch_size": 64, + "metrics": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + }, + "download": [ + "http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz", + "http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz", + { + "url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv", + "subdir": "snips" + }, + { + "url": "http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin", + "subdir": "embeddings" + } + ] + } +} From 9bcdc0f48f10875bc4e125cfc32158a01c5399bb Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 12:51:34 +0300 Subject: [PATCH 084/616] feat: mrr classification --- deeppavlov/__init__.py | 1 + deeppavlov/metrics/mrr_classification.py | 97 ++++++++++++++++++++++++ 2 files changed, 98 insertions(+) create mode 100644 deeppavlov/metrics/mrr_classification.py diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index c83028c05e..b3bc41b8b8 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -85,6 +85,7 @@ import deeppavlov.metrics.roc_auc_score import deeppavlov.metrics.fmeasure_classification import deeppavlov.metrics.log_loss +import deeppavlov.metrics.mrr_classification import deeppavlov.core.common.log diff --git a/deeppavlov/metrics/mrr_classification.py b/deeppavlov/metrics/mrr_classification.py new file mode 100644 index 0000000000..41f85be199 --- /dev/null +++ b/deeppavlov/metrics/mrr_classification.py @@ -0,0 +1,97 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import numpy as np +import json +from scipy.stats import rankdata +import tensorflow as tf +from keras import backend as K + +from deeppavlov.core.common.metrics_registry import register_metric +from deeppavlov.models.classifiers.intents.utils import labels2onehot + + +def calc_mrr(rank): + rank = list(map(lambda x: 1./x, rank)) + return np.mean(rank) + + +def mrr_from_json(fname): + data = [] + with open(fname) as f: + for line in f.readlines(): + data += [json.loads(line)] + rank_i = [] + for elem in data: + cand = elem['candidates'] + results = elem['results'] + cand_ranks = (len(results) - rankdata(results, method='max'))[cand] + 1 + rank_i.append( min(cand_ranks)) + mrr = calc_mrr(rank_i) + return mrr + + +def mrr_from_dict(data): + rank_i = [] + for elem in data: + cand = elem['candidates'] + results = elem['results'] + cand_ranks = (len(results) - rankdata(results, method='max'))[cand] + 1 + rank_i.append( min(cand_ranks)) + mrr = calc_mrr(rank_i) + return mrr + + +def make_json_predictions(fname, predictions): + data = [] + with open(fname) as f: + for line in f.readlines(): + data += [json.loads(line)] + + pointer = 0 + for elem_id, elem in enumerate(data): + n = len(elem["sentences"]) + results = [] + for i in range(n): + if elem["sentences"][i] == "": + results.append(0) + else: + results.append(1 * (predictions[pointer])) + pointer += 1 + data[elem_id]["results"] = results + return data + + +@register_metric('classification_mrr') +def mrr_score(y_true, y_predicted): + # there is hard code for selqa dataset! + if len(y_predicted) == 66438: + data_type = "train" + elif len(y_predicted) == 9377: + data_type = "dev" + elif len(y_predicted) == 19435: + data_type = "test" + else: + return 0. + + classes = np.array(list(y_predicted[0][1].keys())) + y_true_one_hot = labels2onehot(y_true, classes) + y_pred_probas = [y_predicted[i][1]["correct"] for i in range(len(y_predicted))] + + score = make_json_predictions("/home/dilyara.baymurzina/evolution_data/selqa_data/SelQA-ass-" + data_type + ".json", + y_pred_probas) + + return score From c68f0283ce20c8f30edbc621f8a645f5107a72ca Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 12:52:26 +0300 Subject: [PATCH 085/616] fix: max to min in mrr classification --- deeppavlov/metrics/mrr_classification.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/metrics/mrr_classification.py b/deeppavlov/metrics/mrr_classification.py index 41f85be199..d495de313f 100644 --- a/deeppavlov/metrics/mrr_classification.py +++ b/deeppavlov/metrics/mrr_classification.py @@ -38,7 +38,7 @@ def mrr_from_json(fname): for elem in data: cand = elem['candidates'] results = elem['results'] - cand_ranks = (len(results) - rankdata(results, method='max'))[cand] + 1 + cand_ranks = (len(results) - rankdata(results, method='min'))[cand] + 1 rank_i.append( min(cand_ranks)) mrr = calc_mrr(rank_i) return mrr @@ -49,7 +49,7 @@ def mrr_from_dict(data): for elem in data: cand = elem['candidates'] results = elem['results'] - cand_ranks = (len(results) - rankdata(results, method='max'))[cand] + 1 + cand_ranks = (len(results) - rankdata(results, method='min'))[cand] + 1 rank_i.append( min(cand_ranks)) mrr = calc_mrr(rank_i) return mrr From d57b269e1dc8093e95cca7ffb626c4d7a00366d3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 15:09:09 +0300 Subject: [PATCH 086/616] feat: add saving givne portion of best models with weights --- .../neuroevolution_param_generator.py | 49 ++++++++++++++----- 1 file changed, 38 insertions(+), 11 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index e623bc5b32..f43cfbf73c 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -2,6 +2,7 @@ from copy import deepcopy from pathlib import Path import json +import shutil from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ number_to_type_layer @@ -34,6 +35,7 @@ def __init__(self, n_layers, n_types, seed=None, start_with_one_neuron=False, evolve_binary_mask=True, + save_best_with_weights_portion=0, **kwargs): """ Initialize evolution with random population @@ -101,6 +103,7 @@ def __init__(self, n_layers, n_types, self.evolving_train_params = [] self.n_evolving_train_params = None self.evolve_binary_mask = evolve_binary_mask + self.n_saved_best_with_weights = int(save_best_with_weights_portion * self.population_size) if seed is None: pass @@ -216,7 +219,7 @@ def first_generation(self, iter=0): return population - def next_generation(self, generation, scores, iter, + def next_generation(self, generation, scores, iteration, p_crossover=None, crossover_power=None, p_mutation=None, mutation_power=None): """ @@ -242,14 +245,31 @@ def next_generation(self, generation, scores, iter, mutation_power = self.mutation_power selected_individuals = self.selection(generation, scores) - offsprings = self.crossover(selected_individuals, p_crossover=p_crossover, crossover_power=crossover_power) - next = self.mutation(offsprings, p_mutation=p_mutation, mutation_power=mutation_power) - for i in range(self.population_size): + + unchangable_individuals = selected_individuals[:self.n_saved_best_with_weights] + changable_individuals = selected_individuals[self.n_saved_best_with_weights:] + + changable_offsprings = self.crossover(changable_individuals, + p_crossover=p_crossover, + crossover_power=crossover_power) + changable_next = self.mutation(changable_offsprings, + p_mutation=p_mutation, + mutation_power=mutation_power) + + next = unchangable_individuals.extend(changable_next) + + for i in range(self.n_saved_best_with_weights): next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ - str(Path(self.params["save_path"]).joinpath("population_" + str(iter)).joinpath( + str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( + self.params["model_name"] + "_" + str(i))) + # load_path does not change to provide loading weights from saved model + + for i in range(self.n_saved_best_with_weights, self.population_size): + next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ + str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(Path(self.params["load_path"]).joinpath("population_" + str(iter)).joinpath( + str(Path(self.params["load_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) return next @@ -271,7 +291,13 @@ def selection(self, population, scores): probas_to_be_selected = scores / total intervals = np.array([np.sum(probas_to_be_selected[:i]) for i in range(self.population_size)]) selected = [] - for i in range(self.population_size): + + for i in range(self.n_saved_best_with_weights): + ind_id = np.argsort(scores)[-(1+i)] + new = deepcopy(population[ind_id]) + selected.append(new) + + for i in range(self.n_saved_best_with_weights, self.population_size): r = np.random.random() individuum = population[np.where(r > intervals)[0][-1]] selected.append(individuum) @@ -288,11 +314,12 @@ def crossover(self, population, p_crossover, crossover_power): crossover_power: part of EVOLVING parents parameters to exchange for offsprings Returns: - self.population_size offsprings + part_of_population offsprings """ - perm = np.random.permutation(self.population_size) + part_of_population = len(population) + perm = np.random.permutation(part_of_population) offsprings = [] - for i in range(self.population_size // 2): + for i in range(part_of_population // 2): parents = population[perm[2 * i]], population[perm[2 * i + 1]] if self.decision(p_crossover): params_perm = np.random.permutation(self.n_evolving_params) @@ -391,7 +418,7 @@ def crossover(self, population, p_crossover, crossover_power): else: offsprings.extend(parents) - if self.population_size % 2 == 1: + if part_of_population % 2 == 1: offsprings.append(population[perm[-1]]) return offsprings From 54eef94adb92c3017facf488562b02bcf1ce3b74 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 15:14:50 +0300 Subject: [PATCH 087/616] feat: add argument save_best_portion --- deeppavlov/models/evolution/run_evolution.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 7d0ca7b0ec..0f532e1287 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -84,6 +84,8 @@ def score_population(population, population_size, result_file): '"classification_accuracy",' ' "classification_f1",' ' "classification_roc_auc"]', default="classification_roc_auc") +parser.add_argument('--save_best_portion', + help='Please, enter portion of population to save for the next generation with weights', default=0.) args = parser.parse_args() @@ -95,6 +97,7 @@ def score_population(population, population_size, result_file): N_TYPES = int(args.n_types) ONE_NEURON_INIT = bool(int(args.one_neuron_init)) EVOLVE_METRIC = args.evolve_metric +SAVE_BEST_PORTION = float(args.save_best_portion) with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) @@ -110,6 +113,7 @@ def score_population(population, population_size, result_file): key_basic_layers="basic_layers_params", seed=None, start_with_one_neuron=ONE_NEURON_INIT, + save_best_with_weights_portion=SAVE_BEST_PORTION, **basic_params) # Result table From 4cf9de00b8fe5e6525e9730143d01619beda7433 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 16:08:35 +0300 Subject: [PATCH 088/616] fix: test_best true in all configs --- deeppavlov/configs/evolution/basic_snips_one_neuron_init.json | 2 +- deeppavlov/configs/evolution/basic_snips_random_init.json | 2 +- .../configs/evolution/basic_snli_one_neuron_init_part.json | 2 +- .../configs/evolution/basic_snli_one_neuron_init_part_half.json | 2 +- .../evolution/basic_snli_one_neuron_init_part_many_inputs.json | 2 +- deeppavlov/configs/evolution/basic_snli_random_init.json | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 34022c6b80..0182c2dba6 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -207,7 +207,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index c200379281..5ca329a9c7 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -207,7 +207,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index ee81a38e99..362b9850d2 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -208,7 +208,7 @@ "log_every_n_epochs": 1, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index e1568f7d17..4b1fe3aa25 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -208,7 +208,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index b2d269cddf..88a71bf005 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -219,7 +219,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index 508c6a98d7..6903ae2f3b 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -207,7 +207,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { From b037ce4b2120015de5142bd7ed7447eaeda246d6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 16:09:14 +0300 Subject: [PATCH 089/616] fix: fix seed for evolution --- deeppavlov/models/evolution/run_evolution.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 0f532e1287..fc7ef31985 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -82,8 +82,8 @@ def score_population(population, population_size, result_file): parser.add_argument('--one_neuron_init', help='Please, enter number of types of layers', default=0) parser.add_argument('--evolve_metric', help='Please, choose target metric out of ["classification_log_loss", ' '"classification_accuracy",' - ' "classification_f1",' - ' "classification_roc_auc"]', default="classification_roc_auc") + '"classification_f1",' + '"classification_roc_auc"]', default="classification_roc_auc") parser.add_argument('--save_best_portion', help='Please, enter portion of population to save for the next generation with weights', default=0.) @@ -111,7 +111,7 @@ def score_population(population, population_size, result_file): p_mutation=0.5, mutation_power=0.1, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", - seed=None, + seed=42, start_with_one_neuron=ONE_NEURON_INIT, save_best_with_weights_portion=SAVE_BEST_PORTION, **basic_params) From a228b6372eeb5e95e47d061a5cb4995b83733736 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 17:03:58 +0300 Subject: [PATCH 090/616] feat: add train partition --- .../basic_snli_one_neuron_init_part.json | 17 ++++++++++------- ...c_snli_one_neuron_init_part_many_inputs.json | 15 +++++++++------ .../evolution/basic_snli_random_init.json | 15 +++++++++------ .../evolution/neuroevolution_param_generator.py | 12 ++++++++++-- deeppavlov/models/evolution/run_evolution.py | 12 ++++++++---- 5 files changed, 46 insertions(+), 25 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 362b9850d2..fff2ed480f 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -3,7 +3,10 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part" + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -23,8 +26,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict" }, { "in": [ @@ -60,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_1", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_1", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -169,7 +172,7 @@ ] }, "loss": "binary_crossentropy", - "text_size": 30, + "text_size": 51, "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", @@ -192,7 +195,7 @@ "batch_size": { "range": [ 20, - 50 + 70 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 88a71bf005..8215c8e195 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -3,7 +3,10 @@ "name": "basic_classification_reader", "x": ["sentence1", "sentence2"], "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/two_texts/part" + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/cutted_many_inputs/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -24,8 +27,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/cutted_many_inputs/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/cutted_many_inputs/snli_classes.dict" }, { "in": [ @@ -71,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_1", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_1", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -180,7 +183,7 @@ ] }, "loss": "binary_crossentropy", - "text_size": 15, + "text_size": [30, 20], "last_layer_activation": "softmax", "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index 6903ae2f3b..a57d2fc672 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -3,7 +3,10 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data" + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -23,8 +26,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict" }, { "in": [ @@ -60,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random_1", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random_1", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -169,7 +172,7 @@ ] }, "loss": "binary_crossentropy", - "text_size": 15, + "text_size": 51, "model_name": "evolution_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index f43cfbf73c..6ef783bc17 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -36,6 +36,7 @@ def __init__(self, n_layers, n_types, start_with_one_neuron=False, evolve_binary_mask=True, save_best_with_weights_portion=0, + train_partition=1, **kwargs): """ Initialize evolution with random population @@ -52,7 +53,10 @@ def __init__(self, n_layers, n_types, key_basic_layers: key value of dictionary in basic_config that contains considered layers with their evolving parameters seed: random seed for initialization - seed: random seed for initialization + start_with_one_neuron: whether to start with one neuron binary mask or random one + evolve_binary_mask: whether to evolve binary mask or evolve only hyper parameters + save_best_with_weights_portion: portion (from interval [0,1]) of population to save with weights + train_partition: integer number of train data parts **kwargs: basic config with parameters """ self.n_types = n_types @@ -104,12 +108,12 @@ def __init__(self, n_layers, n_types, self.n_evolving_train_params = None self.evolve_binary_mask = evolve_binary_mask self.n_saved_best_with_weights = int(save_best_with_weights_portion * self.population_size) + self.train_partition = train_partition if seed is None: pass else: np.random.seed(seed) - return None def _insert_dict_into_model_params(self, params, model_index, dict_to_insert): params_copy = deepcopy(params) @@ -259,12 +263,16 @@ def next_generation(self, generation, scores, iteration, next = unchangable_individuals.extend(changable_next) for i in range(self.n_saved_best_with_weights): + next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0] + "_" + + str(iteration % self.train_partition)) next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) # load_path does not change to provide loading weights from saved model for i in range(self.n_saved_best_with_weights, self.population_size): + next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0] + "_" + + str(iteration % self.train_partition)) next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index fc7ef31985..a6343ee73f 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -75,17 +75,19 @@ def score_population(population, population_size, result_file): parser.add_argument('--config', help='Please, enter model path to config', default='./configs/evolution/basic_intents_config.json') +parser.add_argument('--evolve_metric', help='Please, choose target metric out of ["classification_log_loss", ' + '"classification_accuracy",' + '"classification_f1",' + '"classification_roc_auc"]') parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) parser.add_argument('--one_neuron_init', help='Please, enter number of types of layers', default=0) -parser.add_argument('--evolve_metric', help='Please, choose target metric out of ["classification_log_loss", ' - '"classification_accuracy",' - '"classification_f1",' - '"classification_roc_auc"]', default="classification_roc_auc") parser.add_argument('--save_best_portion', help='Please, enter portion of population to save for the next generation with weights', default=0.) +parser.add_argument('--train_partition', + help='Please, enter partition of splitted train', default=1) args = parser.parse_args() @@ -98,6 +100,7 @@ def score_population(population, population_size, result_file): ONE_NEURON_INIT = bool(int(args.one_neuron_init)) EVOLVE_METRIC = args.evolve_metric SAVE_BEST_PORTION = float(args.save_best_portion) +TRAIN_PARTITION = int(args.train_partition) with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) @@ -114,6 +117,7 @@ def score_population(population, population_size, result_file): seed=42, start_with_one_neuron=ONE_NEURON_INIT, save_best_with_weights_portion=SAVE_BEST_PORTION, + train_partition=TRAIN_PARTITION, **basic_params) # Result table From dd54c32577282d857a5821434da2693ba448bbbd Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 17:22:59 +0300 Subject: [PATCH 091/616] fix: add participation of best models in selection --- .../neuroevolution_param_generator.py | 25 +++++++++++-------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 6ef783bc17..be641b5619 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -250,13 +250,14 @@ def next_generation(self, generation, scores, iteration, selected_individuals = self.selection(generation, scores) - unchangable_individuals = selected_individuals[:self.n_saved_best_with_weights] - changable_individuals = selected_individuals[self.n_saved_best_with_weights:] + offsprings = self.crossover(selected_individuals, + p_crossover=p_crossover, + crossover_power=crossover_power) - changable_offsprings = self.crossover(changable_individuals, - p_crossover=p_crossover, - crossover_power=crossover_power) - changable_next = self.mutation(changable_offsprings, + unchangable_individuals = offsprings[:self.n_saved_best_with_weights] + changable_individuals = offsprings[self.n_saved_best_with_weights:] + + changable_next = self.mutation(changable_individuals, p_mutation=p_mutation, mutation_power=mutation_power) @@ -324,10 +325,9 @@ def crossover(self, population, p_crossover, crossover_power): Returns: part_of_population offsprings """ - part_of_population = len(population) - perm = np.random.permutation(part_of_population) + perm = np.random.permutation(self.population_size) offsprings = [] - for i in range(part_of_population // 2): + for i in range(self.population_size // 2): parents = population[perm[2 * i]], population[perm[2 * i + 1]] if self.decision(p_crossover): params_perm = np.random.permutation(self.n_evolving_params) @@ -422,11 +422,16 @@ def crossover(self, population, p_crossover, crossover_power): check_and_correct_binary_mask(self.nodes, curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ "binary_mask"]) + # if parent is one of the best and will be saved with weights + if perm[2 * i] in range(self.n_saved_best_with_weights): + curr_offsprings[0] = population[perm[2 * i]] + if perm[2 * i + 1] in range(self.n_saved_best_with_weights): + curr_offsprings[1] = population[perm[2 * i + 1]] offsprings.extend(curr_offsprings) else: offsprings.extend(parents) - if part_of_population % 2 == 1: + if self.population_size % 2 == 1: offsprings.append(population[perm[-1]]) return offsprings From 6fa3b2a87edd2811c83385c65f626f711d870f1a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 10:46:29 +0300 Subject: [PATCH 092/616] fix: average in mrr --- deeppavlov/metrics/mrr_classification.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/metrics/mrr_classification.py b/deeppavlov/metrics/mrr_classification.py index d495de313f..6d8d5ea280 100644 --- a/deeppavlov/metrics/mrr_classification.py +++ b/deeppavlov/metrics/mrr_classification.py @@ -38,7 +38,7 @@ def mrr_from_json(fname): for elem in data: cand = elem['candidates'] results = elem['results'] - cand_ranks = (len(results) - rankdata(results, method='min'))[cand] + 1 + cand_ranks = (len(results) - rankdata(results, method='average'))[cand] + 1 rank_i.append( min(cand_ranks)) mrr = calc_mrr(rank_i) return mrr @@ -49,7 +49,7 @@ def mrr_from_dict(data): for elem in data: cand = elem['candidates'] results = elem['results'] - cand_ranks = (len(results) - rankdata(results, method='min'))[cand] + 1 + cand_ranks = (len(results) - rankdata(results, method='average'))[cand] + 1 rank_i.append( min(cand_ranks)) mrr = calc_mrr(rank_i) return mrr From 9b585aa83ee059d3aa7ee583dd8fd3eea5d81c51 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 11:48:10 +0300 Subject: [PATCH 093/616] fix: dataset train path in neuroevolution --- .../models/evolution/neuroevolution_param_generator.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index be641b5619..f93fc6a753 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -264,16 +264,16 @@ def next_generation(self, generation, scores, iteration, next = unchangable_individuals.extend(changable_next) for i in range(self.n_saved_best_with_weights): - next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0] + "_" + - str(iteration % self.train_partition)) + next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ + + "_" + str(iteration % self.train_partition) + ".csv" next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) # load_path does not change to provide loading weights from saved model for i in range(self.n_saved_best_with_weights, self.population_size): - next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0] + "_" + - str(iteration % self.train_partition)) + next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ + + "_" + str(iteration % self.train_partition) + ".csv" next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) From ce3fdbc0f3d0bc04fd9a6cff3653e37c7df18bfa Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 14:17:58 +0300 Subject: [PATCH 094/616] fix: iteration except of iter --- .../models/evolution/neuroevolution_param_generator.py | 6 +++--- deeppavlov/models/evolution/run_evolution.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index f93fc6a753..ac7c06989d 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -172,7 +172,7 @@ def initialize_layers_params(self): } return all_layers_params - def first_generation(self, iter=0): + def first_generation(self, iteration=0): """ Initialize first generation randomly according to the given constraints is self.params Returns: @@ -192,10 +192,10 @@ def first_generation(self, iter=0): # intitializing path to save model if "model_name" in params_for_search.keys(): params["save_path"] = str(Path(self.params["save_path"]).joinpath( - "population_" + str(iter)).joinpath(params_for_search["model_name"] + "_" + str(i))) + "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) else: params["save_path"] = str(Path(self.params["save_path"]).joinpath( - "population_" + str(iter)).joinpath(self.params["model_name"] + "_" + str(i))) + "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) layers_params = self.initialize_layers_params() diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index a6343ee73f..0c89e2a544 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -142,7 +142,7 @@ def score_population(population, population_size, result_file): while True: print("\nIteration #{} starts\n".format(iters)) - population = evolution.next_generation(population, population_scores, iter=iters) + population = evolution.next_generation(population, population_scores, iteration=iters) print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] From 2d2ca1758d5593cfcf27ca65bbef3e007334861a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 14:38:44 +0300 Subject: [PATCH 095/616] fix: saving config.json with save_json from deeppavlov --- deeppavlov/models/evolution/run_evolution.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 0c89e2a544..12188b6156 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -6,6 +6,8 @@ import pandas as pd from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution +from deeppavlov.core.common.file import save_json + def score_population(population, population_size, result_file): global evolution @@ -36,9 +38,7 @@ def score_population(population, population_size, result_file): f_name = f_name.joinpath("config.json") population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] =\ population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"].tolist() - with open(f_name, 'w') as outfile: - json.dump(population[i], outfile) - + save_json(population[i], f_name) procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], str(f_name), From 0a17d2ce46df881e76373d058569f8d066f6eba7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 14:43:48 +0300 Subject: [PATCH 096/616] fix: change train_i only if train_partiotion not equal to 1 --- .../models/evolution/neuroevolution_param_generator.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index ac7c06989d..c9f68243ca 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -264,16 +264,18 @@ def next_generation(self, generation, scores, iteration, next = unchangable_individuals.extend(changable_next) for i in range(self.n_saved_best_with_weights): - next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ - + "_" + str(iteration % self.train_partition) + ".csv" + if self.train_partition != 1: + next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ + + "_" + str(iteration % self.train_partition) + ".csv" next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) # load_path does not change to provide loading weights from saved model for i in range(self.n_saved_best_with_weights, self.population_size): - next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ - + "_" + str(iteration % self.train_partition) + ".csv" + if self.train_partition != 1: + next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ + + "_" + str(iteration % self.train_partition) + ".csv" next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) From 6daf6568acfc320dd30d8cd88841b313158c11ef Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 16:44:55 +0300 Subject: [PATCH 097/616] fix: save path and load path changed --- deeppavlov/models/evolution/run_evolution.py | 29 +++++++++++++------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 12188b6156..2f85fbe108 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -21,29 +21,38 @@ def score_population(population, population_size, result_file): procs = [] for i in range(population_size): - f_name = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) - model_name = population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] + # f_name = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) + # model_name = population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] + # population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ + # str(f_name.joinpath(model_name + "_" + str(i))) + # population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] =\ + # population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] + + save_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) + load_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - str(f_name.joinpath(model_name + "_" + str(i))) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] =\ - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] + str(save_path.joinpath("model")) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ + str(load_path.joinpath("model")) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = \ evolution.nodes print(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) try: - f_name.mkdir(parents=True) + save_path.mkdir(parents=True) except FileExistsError: pass - f_name = f_name.joinpath("config.json") + + f_name = save_path.joinpath("config.json") population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] =\ population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"].tolist() save_json(population[i], f_name) procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], str(f_name), - str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).parent), - str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).parent) + str(save_path), + str(save_path) ), shell=True, stdout=PIPE, stderr=PIPE)) @@ -142,7 +151,7 @@ def score_population(population, population_size, result_file): while True: print("\nIteration #{} starts\n".format(iters)) - population = evolution.next_generation(population, population_scores, iteration=iters) + population = evolution.next_generation(population, population_scores, iters) print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] From 275b1ef087ce3e529d012fef0d0a8c13ee1e62fa Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 16:52:38 +0300 Subject: [PATCH 098/616] fix: asve path comment --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 1 + 1 file changed, 1 insertion(+) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index c9f68243ca..be52285e2f 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -190,6 +190,7 @@ def first_generation(self, iteration=0): self.evolving_train_params.extend(evolving_params) # intitializing path to save model + # save_path = population_iteration/model_name_i/ if "model_name" in params_for_search.keys(): params["save_path"] = str(Path(self.params["save_path"]).joinpath( "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) From 5ddc69248dcb2f959676f4c4f90d6cee309fe1df Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 16:53:25 +0300 Subject: [PATCH 099/616] fix: configs --- .../basic_snli_one_neuron_init_part.json | 4 ++-- deeppavlov/configs/evolution/intents_snli.json | 17 ++++++++++------- 2 files changed, 12 insertions(+), 9 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index fff2ed480f..60b9371c08 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 1, + 2 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/intents_snli.json b/deeppavlov/configs/evolution/intents_snli.json index 7e60b2908c..fb9bf4fa12 100644 --- a/deeppavlov/configs/evolution/intents_snli.json +++ b/deeppavlov/configs/evolution/intents_snli.json @@ -3,7 +3,10 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara/data/data_files/SNLI/snli_data" + "data_path": "/home/dilyara/data/data_files/SNLI/one_input", + "train": "train.csv", + "valid": "valid.csv", + "test": "test.csv" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -23,8 +26,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict", - "load_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict" + "save_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict", + "load_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict" }, { "in": [ @@ -74,7 +77,7 @@ "lear_rate": 0.01, "lear_rate_decay": 0.1, "loss": "binary_crossentropy", - "text_size": 15, + "text_size": 51, "coef_reg_cnn": 1e-4, "coef_reg_den": 1e-4, "dropout_rate": 0.5, @@ -98,11 +101,11 @@ "classification_roc_auc" ], "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { From 0b160457953c5427e9f2453c2a60493bfd06198a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 17:04:16 +0300 Subject: [PATCH 100/616] fix: number of epochs increase, next generation --- .../configs/evolution/basic_snli_one_neuron_init_part.json | 4 ++-- deeppavlov/models/evolution/neuroevolution_param_generator.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 60b9371c08..fff2ed480f 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 1, - 2 + 50, + 100 ], "discrete": true }, diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index be52285e2f..f0f73cba66 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -255,14 +255,14 @@ def next_generation(self, generation, scores, iteration, p_crossover=p_crossover, crossover_power=crossover_power) - unchangable_individuals = offsprings[:self.n_saved_best_with_weights] + next = offsprings[:self.n_saved_best_with_weights] changable_individuals = offsprings[self.n_saved_best_with_weights:] changable_next = self.mutation(changable_individuals, p_mutation=p_mutation, mutation_power=mutation_power) - next = unchangable_individuals.extend(changable_next) + next.extend(changable_next) for i in range(self.n_saved_best_with_weights): if self.train_partition != 1: From fb84508459e604f591f434055db15c251b25ae7a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 17:31:41 +0300 Subject: [PATCH 101/616] feat: add, subtract and multiply to many_inputs model --- .../evolution/evolution_many_inputs_model.py | 24 ++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index 1554792bf7..6a1619bb4c 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -25,7 +25,7 @@ from keras.layers.wrappers import Bidirectional from keras.models import Model from keras.regularizers import l2 -from keras.layers import Concatenate, Reshape, CuDNNLSTM, Lambda +from keras.layers import Concatenate, Reshape, CuDNNLSTM, Lambda, Add, Subtract, Multiply from keras import backend as K from overrides import overrides from pathlib import Path @@ -272,6 +272,17 @@ def evolution_many_inputs_classification_model(self, params): output = dense1(inp) full_outputs.append(globalmaxpooling(output)) + summ = Add()(full_outputs) + mult = Multiply()(full_outputs) + + try: + subt = Subtract()(full_outputs) + full_outputs.append(subt) + except ValueError: + pass + full_outputs.append(summ) + full_outputs.append(mult) + output = Concatenate()(full_outputs) output = Dense(self.n_classes, activation=None)(output) activation = params.get("last_layer_activation", "sigmoid") @@ -358,6 +369,17 @@ def evolution_many_inputs_classification_model(self, params): output = GlobalMaxPooling1D()(output) full_outputs.append(output) + summ = Add()(full_outputs) + mult = Multiply()(full_outputs) + + try: + subt = Subtract()(full_outputs) + full_outputs.append(subt) + except ValueError: + pass + full_outputs.append(summ) + full_outputs.append(mult) + output = Concatenate()(full_outputs) output = Dense(self.n_classes, activation=None)(output) activation = params.get("last_layer_activation", "sigmoid") From 243728ee945f755ecb7af2a0116b44f7a6112fd9 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 18:18:03 +0300 Subject: [PATCH 102/616] fix: error save path to load path --- deeppavlov/core/models/keras_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/core/models/keras_model.py b/deeppavlov/core/models/keras_model.py index f82835986a..ce510bc9a9 100644 --- a/deeppavlov/core/models/keras_model.py +++ b/deeppavlov/core/models/keras_model.py @@ -136,7 +136,7 @@ def load(self, model_name, optimizer_name, loss_name, lear_rate=None, lear_rate_ """ if self.load_path: if isinstance(self.load_path, Path) and not self.load_path.parent.is_dir(): - raise ConfigError("Provided save path is incorrect!") + raise ConfigError("Provided load path is incorrect!") opt_path = Path("{}_opt.json".format(str(self.load_path.resolve()))) weights_path = Path("{}.h5".format(str(self.load_path.resolve()))) From 40c970d47de0825b7bf2e3500d84e4745786aed4 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 18:26:28 +0300 Subject: [PATCH 103/616] fix: load path init and change --- .../models/evolution/neuroevolution_param_generator.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index f0f73cba66..d7e0bacbf5 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -198,6 +198,14 @@ def first_generation(self, iteration=0): params["save_path"] = str(Path(self.params["save_path"]).joinpath( "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) + # load_path = population_iteration/model_name_i/ + if "model_name" in params_for_search.keys(): + params["load_path"] = str(Path(self.params["load_path"]).joinpath( + "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) + else: + params["load_path"] = str(Path(self.params["load_path"]).joinpath( + "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) + layers_params = self.initialize_layers_params() # exchange model and layers params from basic config to sampled model params @@ -271,6 +279,8 @@ def next_generation(self, generation, scores, iteration, next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) + next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ + str(next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"].parent) # load_path does not change to provide loading weights from saved model for i in range(self.n_saved_best_with_weights, self.population_size): From a538d2d09c015b77f38006dbc8b92423951a36f3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 18:28:03 +0300 Subject: [PATCH 104/616] fix: mrr classification to config --- deeppavlov/configs/evolution/basic_selqa.json | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_selqa.json b/deeppavlov/configs/evolution/basic_selqa.json index e6cc11465a..fddae03149 100644 --- a/deeppavlov/configs/evolution/basic_selqa.json +++ b/deeppavlov/configs/evolution/basic_selqa.json @@ -218,7 +218,8 @@ "classification_log_loss", "classification_accuracy", "classification_f1", - "classification_roc_auc" + "classification_roc_auc", + "classification_mrr" ], "validation_patience": 5, "val_every_n_epochs": 5, From db26283036114d74bea6af1dc2678e11989d7d7f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 21:46:35 +0300 Subject: [PATCH 105/616] fix: path parent for load [ath --- ...asic_snli_one_neuron_init_part (copy).json | 223 +++++++++++++++++ ..._one_neuron_init_part_many_inputs_big.json | 234 ++++++++++++++++++ .../configs/evolution/check_config.json | 1 + .../neuroevolution_param_generator.py | 2 +- 4 files changed, 459 insertions(+), 1 deletion(-) create mode 100644 deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json create mode 100644 deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json create mode 100644 deeppavlov/configs/evolution/check_config.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json new file mode 100644 index 0000000000..b20b80cfbd --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json @@ -0,0 +1,223 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": true + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": true + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.000001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 30, + "last_layer_activation": "softmax", + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 50, + 100 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 20, + 50 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json new file mode 100644 index 0000000000..39816b462f --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json @@ -0,0 +1,234 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]], "nodes": {"0": "0_0_0", "1": "0_1_1", "2": "0_2_2", "3": "0_3_3", "4": "0_4_4", "5": "0_5_5", "6": "1_0_6", "7": "1_1_7", "8": "1_2_8", "9": "1_3_9", "10": "1_4_10", "11": "1_5_11", "12": "2_0_12", "13": "2_1_13", "14": "2_2_14", "15": "2_3_15", "16": "2_4_16", "17": "2_5_17", "18": "3_0_18", "19": "3_1_19", "20": "3_2_20", "21": "3_3_21", "22": "3_4_22", "23": "3_5_23", "24": "4_0_24", "25": "4_1_25", "26": "4_2_26", "27": "4_3_27", "28": "4_4_28", "29": "4_5_29"}}], "out": ["y_labels"]}, "train": {"metric_optimization": "minimize", "metrics": ["classification_log_loss", "classification_accuracy", "classification_f1", "classification_roc_auc"], "validation_patience": 5, "val_every_n_epochs": 5, "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, "test_best": true, "epochs": 77, "batch_size": 51}, "metadata": {"labels": {"telegram_utils": "IntentModel"}}} diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index d7e0bacbf5..5b33cb9435 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -280,7 +280,7 @@ def next_generation(self, generation, scores, iteration, str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"].parent) + str(Path(next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"]).parent) # load_path does not change to provide loading weights from saved model for i in range(self.n_saved_best_with_weights, self.population_size): From 0f63d3335c2c78ab4e3e224c68308133663db87a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 24 May 2018 14:19:17 +0300 Subject: [PATCH 106/616] Fix: mrr score was incorrect --- deeppavlov/metrics/mrr_classification.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/deeppavlov/metrics/mrr_classification.py b/deeppavlov/metrics/mrr_classification.py index 6d8d5ea280..b7fd72c493 100644 --- a/deeppavlov/metrics/mrr_classification.py +++ b/deeppavlov/metrics/mrr_classification.py @@ -91,7 +91,9 @@ def mrr_score(y_true, y_predicted): y_true_one_hot = labels2onehot(y_true, classes) y_pred_probas = [y_predicted[i][1]["correct"] for i in range(len(y_predicted))] - score = make_json_predictions("/home/dilyara.baymurzina/evolution_data/selqa_data/SelQA-ass-" + data_type + ".json", - y_pred_probas) + json_with_predictions = make_json_predictions("/home/dilyara.baymurzina/evolution_data/selqa_data/SelQA-ass-" + + data_type + ".json", + y_pred_probas) + score = mrr_from_dict(json_with_predictions) return score From 2dc6715998484f53f6576d4dd98eb4306bd5bafb Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 24 May 2018 18:40:07 +0300 Subject: [PATCH 107/616] fix: add reinit of changable parameters --- .../classifiers/intents/intent_model.py | 50 +++++++++++++------ 1 file changed, 36 insertions(+), 14 deletions(-) diff --git a/deeppavlov/models/classifiers/intents/intent_model.py b/deeppavlov/models/classifiers/intents/intent_model.py index b0ab9dc744..5b82e37825 100644 --- a/deeppavlov/models/classifiers/intents/intent_model.py +++ b/deeppavlov/models/classifiers/intents/intent_model.py @@ -45,6 +45,32 @@ class KerasIntentModel(KerasModel): """ Class implements keras model for intent recognition task for multi-class multi-label data """ + FIXED_PARAMS = [ + "classes", + "model_name", + "embedding_size", + "fasttext_md5", + "kernel_sizes_cnn", + "filters_cnn", + "dense_size", + "units_lstm", + "units_lstm_1", + "units_lstm_2", + "self_att_hid", + "self_att_out" + ] + + CHANGABLE_PARAMS = {"confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 1e-2, + "lear_rate_decay": 0., + "loss": "binary_crossentropy", + "coef_reg_cnn": 0., + "coef_reg_den": 0., + "coef_reg_lstm": 0., + "dropout_rate": 0., + "rec_dropout_rate": 0.} + def __init__(self, **kwargs): """ Initialize and train vocabularies, initializes embedder, tokenizer, @@ -95,7 +121,8 @@ def __init__(self, **kwargs): "lear_rate_decay": self.opt.get('lear_rate_decay')} self.model = self.load(**params) - self._init_params() + self._init_missed_params() + self._change_not_fixed_params(**kwargs) # Check if md5 hash sum of current loaded fasttext model # is equal to saved @@ -108,20 +135,15 @@ def __init__(self, **kwargs): raise ConfigError( "Given fasttext model does NOT match fasttext model used previously to train loaded model") - def _init_params(self): - - # list of changeable params - changeable_params = {"confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": 1e-2, - "lear_rate_decay": 0., - "loss": "binary_crossentropy", - "coef_reg_cnn": 0., - "coef_reg_den": 0., - "dropout_rate": 0.} + def _init_missed_params(self): + for param in list(self.CHANGABLE_PARAMS.keys()): + self.opt[param] = self.opt.get(param, self.CHANGABLE_PARAMS[param]) + return - for param in changeable_params.keys(): - self.opt[param] = self.opt.get(param, changeable_params[param]) + def _change_not_fixed_params(self, **kwargs): + for param in self.opt.keys(): + if param not in self.FIXED_PARAMS: + self.opt[param] = kwargs.get(param) return def texts2vec(self, sentences): From 7c16867be5d602d9b0b8e28b787597c4c1241db4 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 11:33:33 +0300 Subject: [PATCH 108/616] fix: changing load_path for keras model trained from one location and saved to another one --- deeppavlov/core/models/keras_model.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/deeppavlov/core/models/keras_model.py b/deeppavlov/core/models/keras_model.py index f82835986a..33936d0bff 100644 --- a/deeppavlov/core/models/keras_model.py +++ b/deeppavlov/core/models/keras_model.py @@ -209,7 +209,11 @@ def save(self, fname=None): log.info(f"[saving model to {opt_path}]") self.model.save_weights(weights_path) - + # if model was loaded from one path and saved to another one + # then change load_path to save_path for config + if self.opt.get("load_path") and self.opt.get("save_path"): + if self.opt.get("save_path") != self.opt.get("load_path"): + self.opt["load_path"] = str(self.opt["save_path"]) save_json(self.opt, opt_path) return True From 38c962653ed5e2742a6ce40c5238a46a7612855a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 11:54:16 +0300 Subject: [PATCH 109/616] fix: deepcopy to crossover --- .../models/evolution/neuroevolution_param_generator.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 5b33cb9435..6fe5231aeb 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -321,7 +321,7 @@ def selection(self, population, scores): for i in range(self.n_saved_best_with_weights, self.population_size): r = np.random.random() - individuum = population[np.where(r > intervals)[0][-1]] + individuum = deepcopy(population[np.where(r > intervals)[0][-1]]) selected.append(individuum) return selected @@ -437,15 +437,15 @@ def crossover(self, population, p_crossover, crossover_power): "binary_mask"]) # if parent is one of the best and will be saved with weights if perm[2 * i] in range(self.n_saved_best_with_weights): - curr_offsprings[0] = population[perm[2 * i]] + curr_offsprings[0] = deepcopy(parents[0]) if perm[2 * i + 1] in range(self.n_saved_best_with_weights): - curr_offsprings[1] = population[perm[2 * i + 1]] + curr_offsprings[1] = deepcopy(parents[1]) offsprings.extend(curr_offsprings) else: - offsprings.extend(parents) + offsprings.extend(deepcopy(parents)) if self.population_size % 2 == 1: - offsprings.append(population[perm[-1]]) + offsprings.append(deepcopy(population[perm[-1]])) return offsprings def mutation(self, population, p_mutation, mutation_power): From c5df71750b42d7ce74069890e9af796de6bab35e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 12:20:41 +0300 Subject: [PATCH 110/616] feat: no hardcoded metrics in run_evolution --- deeppavlov/models/evolution/run_evolution.py | 51 ++++++++++---------- 1 file changed, 26 insertions(+), 25 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 2f85fbe108..7cc13884bf 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -4,6 +4,7 @@ from pathlib import Path from subprocess import Popen, PIPE import pandas as pd +from copy import deepcopy, copy from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution from deeppavlov.core.common.file import save_json @@ -11,12 +12,10 @@ def score_population(population, population_size, result_file): global evolution + population_metrics = {} - for metric in ["classification_log_loss", - "classification_accuracy", - "classification_f1", - "classification_roc_auc"]: - population_metrics[metric] = [] + for m in CONSIDERED_METRICS: + population_metrics[m] = [] procs = [] @@ -63,16 +62,18 @@ def score_population(population, population_size, result_file): for i in range(population_size): val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ "save_path"]).parent.joinpath("valid_results.txt"))) - result_table = pd.DataFrame({"classification_log_loss": [val_results[0]], - "classification_accuracy": [val_results[1]], - "classification_f1": [val_results[2]], - "classification_roc_auc": [val_results[3]], - "params": [population[i]]}) + result_table_dict = {} + for el in order: + result_table_dict[el] = [] + for m_id, m in enumerate(CONSIDERED_METRICS): + result_table_dict[m].append(val_results[m_id]) + result_table_dict[order[-1]] = [population[i]] + result_table = pd.DataFrame(result_table_dict) + result_table.loc[:, order].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - population_metrics["classification_log_loss"].append(val_results[0]) - population_metrics["classification_accuracy"].append(val_results[1]) - population_metrics["classification_f1"].append(val_results[2]) - population_metrics["classification_roc_auc"].append(val_results[3]) + + for m_id, m in enumerate(CONSIDERED_METRICS): + population_metrics[m].append(val_results[m_id]) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) @@ -84,10 +85,7 @@ def score_population(population, population_size, result_file): parser.add_argument('--config', help='Please, enter model path to config', default='./configs/evolution/basic_intents_config.json') -parser.add_argument('--evolve_metric', help='Please, choose target metric out of ["classification_log_loss", ' - '"classification_accuracy",' - '"classification_f1",' - '"classification_roc_auc"]') +parser.add_argument('--evolve_metric', help='Please, choose target metric out of given in your config.train.metrics') parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) @@ -116,6 +114,9 @@ def score_population(population, population_size, result_file): print("Given basic params: {}\n".format(basic_params)) +# list of names of considered metrics +CONSIDERED_METRICS = basic_params["train"]["metrics"] + # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, @@ -130,15 +131,15 @@ def score_population(population, population_size, result_file): **basic_params) # Result table -order = ["classification_log_loss", "classification_accuracy", - "classification_f1", "classification_roc_auc", "params"] +order = deepcopy(CONSIDERED_METRICS) +order.extend(["params"]) +result_table_dict = {} +for el in order: + result_table_dict[el] = [] + result_file = Path(basic_params["chainer"]["pipe"][ evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") -result_table = pd.DataFrame({"classification_log_loss": [], - "classification_accuracy": [], - "classification_f1": [], - "classification_roc_auc": [], - "params": []}) +result_table = pd.DataFrame(result_table_dict) result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') print("\nIteration #{} starts\n".format(0)) From a29d4b3d9abed59b206860d02ef850a148f1f5f9 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 14:19:47 +0300 Subject: [PATCH 111/616] chore: change save method to super --- .../models/evolution/evolution_intent_model.py | 13 +------------ .../evolution/evolution_many_inputs_model.py | 14 +------------- 2 files changed, 2 insertions(+), 25 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index b471f0f38c..d99f3f28e1 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -228,21 +228,10 @@ def save(self, fname=None): Returns: None """ - - if not self.save_path: - raise ConfigError("No `save_path` is provided for Keras model!") - elif isinstance(self.save_path, Path) and not self.save_path.parent.is_dir(): - raise ConfigError("Provided save path is incorrect!") - else: - opt_path = "{}_opt.json".format(str(self.save_path.resolve())) - weights_path = "{}.h5".format(str(self.save_path.resolve())) - log.info("[saving model to {}]".format(opt_path)) - self.model.save_weights(weights_path) - if type(self.opt["binary_mask"]) is list: pass else: self.opt["binary_mask"] = self.opt["binary_mask"].tolist() - save_json(self.opt, opt_path) + super().save(fname) return True diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index 6a1619bb4c..d078c521af 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -387,7 +387,6 @@ def evolution_many_inputs_classification_model(self, params): model = Model(inputs=inputs, outputs=act_output) return model - @overrides def save(self, fname=None): """ Save the model parameters into <>_opt.json (or <>_opt.json) @@ -398,21 +397,10 @@ def save(self, fname=None): Returns: None """ - - if not self.save_path: - raise ConfigError("No `save_path` is provided for Keras model!") - elif isinstance(self.save_path, Path) and not self.save_path.parent.is_dir(): - raise ConfigError("Provided save path is incorrect!") - else: - opt_path = "{}_opt.json".format(str(self.save_path.resolve())) - weights_path = "{}.h5".format(str(self.save_path.resolve())) - log.info("[saving model to {}]".format(opt_path)) - self.model.save_weights(weights_path) - if type(self.opt["binary_mask"]) is list: pass else: self.opt["binary_mask"] = self.opt["binary_mask"].tolist() - save_json(self.opt, opt_path) + super().save(fname) return True From e61a393b983e8d18d7dec556cedb0c9dc3a0ad8d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 14:46:16 +0300 Subject: [PATCH 112/616] fix: del comment --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 6fe5231aeb..4360351437 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -280,8 +280,7 @@ def next_generation(self, generation, scores, iteration, str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(Path(next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"]).parent) - # load_path does not change to provide loading weights from saved model + str(Path(next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) for i in range(self.n_saved_best_with_weights, self.population_size): if self.train_partition != 1: From fab47ada862d55fbf61b8a0c89853a0ca3ce3795 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 14:46:55 +0300 Subject: [PATCH 113/616] fix: 1 to 2 epochs for snli part to test --- .../configs/evolution/basic_snli_one_neuron_init_part.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index fff2ed480f..60b9371c08 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 1, + 2 ], "discrete": true }, From db278b62955f80f9f3d81c34f25e3b38e8e59db9 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 15:14:32 +0300 Subject: [PATCH 114/616] fix: save and load changed by order --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 4360351437..4f64c80b89 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -276,11 +276,11 @@ def next_generation(self, generation, scores, iteration, if self.train_partition != 1: next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ + "_" + str(iteration % self.train_partition) + ".csv" + next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ + str(Path(next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"])) next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) - next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(Path(next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) for i in range(self.n_saved_best_with_weights, self.population_size): if self.train_partition != 1: From a837f1f1f7ce90218e00b7e1267f47fe75e16c25 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 15:25:16 +0300 Subject: [PATCH 115/616] fix: parent from save path --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 4f64c80b89..409dabfef8 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -277,7 +277,7 @@ def next_generation(self, generation, scores, iteration, next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ + "_" + str(iteration % self.train_partition) + ".csv" next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(Path(next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"])) + str(Path(next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) From 21b3a1aaafab96418a08469b722ec652d5a746b8 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 18:32:34 +0300 Subject: [PATCH 116/616] fix: new folder for next experiment --- .../basic_snli_one_neuron_init_part_many_inputs_big.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json index 39816b462f..2403b1b3e0 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json @@ -71,8 +71,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_1", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_1", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { From fbf6907e263ffc4ae8c883cce4be97fac9c642ae Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 18:57:39 +0300 Subject: [PATCH 117/616] fix: configs --- ...> basic_ru_snli_one_neuron_init_part.json} | 25 ++++---- ...nli_one_neuron_init_part_many_inputs.json} | 61 +++++++++++-------- .../basic_snli_one_neuron_init_part.json | 8 +-- ...snli_one_neuron_init_part_many_inputs.json | 4 +- 4 files changed, 55 insertions(+), 43 deletions(-) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init_part_half.json => basic_ru_snli_one_neuron_init_part.json} (83%) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init_part (copy).json => basic_ru_snli_one_neuron_init_part_many_inputs.json} (74%) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json similarity index 83% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json rename to deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json index 4b1fe3aa25..73a9274319 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json @@ -3,7 +3,10 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part_half" + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -23,8 +26,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/one_input/ru_snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/one_input/ru_snli_classes.dict" }, { "in": [ @@ -38,8 +41,8 @@ { "id": "my_embedder", "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "save_path": "/home/dilyara.baymurzina/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.bin", "dim": 300 }, { @@ -60,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_half", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_half", + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_2", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_2", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -169,7 +172,7 @@ ] }, "loss": "binary_crossentropy", - "text_size": 30, + "text_size": 51, "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", @@ -192,7 +195,7 @@ "batch_size": { "range": [ 20, - 50 + 70 ], "discrete": true }, @@ -204,8 +207,8 @@ "classification_roc_auc" ], "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, "show_examples": false, "validate_best": true, "test_best": true diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json similarity index 74% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json rename to deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json index b20b80cfbd..2121ee763e 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json @@ -1,16 +1,20 @@ { "dataset_reader": { "name": "basic_classification_reader", - "x": "text", + "x": ["sentence1", "sentence2"], "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part" + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/cutted_many_inputs/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" }, "dataset_iterator": { "name": "basic_classification_iterator" }, "chainer": { "in": [ - "x" + "sentence1", + "sentence2" ], "in_y": [ "y" @@ -23,23 +27,32 @@ "y" ], "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/cutted_many_inputs/ru_snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/cutted_many_inputs/ru_snli_classes.dict" }, { "in": [ - "x" + "sentence1" ], "out": [ - "x_lower" + "sentence1_lower" + ], + "name": "str_lower" + }, + { + "in": [ + "sentence2" + ], + "out": [ + "sentence2_lower" ], "name": "str_lower" }, { "id": "my_embedder", "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "save_path": "/home/dilyara.baymurzina/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.bin", "dim": 300 }, { @@ -49,7 +62,8 @@ }, { "in": [ - "x_lower" + "sentence1_lower", + "sentence2_lower" ], "in_y": [ "y" @@ -59,9 +73,9 @@ "y_probas_dict" ], "main": true, - "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", + "name": "evolution_many_inputs_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_2", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_2", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -154,29 +168,24 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ - 0.0001, + 0.001, 0.1 ] }, "lear_rate_decay": { "range": [ - 0.000001, + 0.00001, 0.1 ] }, "loss": "binary_crossentropy", - "text_size": 30, + "text_size": [30, 20], "last_layer_activation": "softmax", - "model_name": "evolution_classification_model", + "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" } @@ -196,8 +205,8 @@ }, "batch_size": { "range": [ - 20, - 50 + 50, + 70 ], "discrete": true }, @@ -213,7 +222,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 60b9371c08..d61cc3e695 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_1", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_1", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_2", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_2", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 1, - 2 + 50, + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 8215c8e195..71213c283f 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_1", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_1", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_2", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_2", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { From aab5814f0dcdbfaf9e9f4cfd9cbefbb83f848584 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 19:04:57 +0300 Subject: [PATCH 118/616] fix: configs --- .../evolution/basic_ru_snli_one_neuron_init_part.json | 2 +- .../basic_ru_snli_one_neuron_init_part_many_inputs.json | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json index 73a9274319..727a3c809b 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json @@ -3,7 +3,7 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/parts", + "data_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/one_input/parts", "train": "train_0.csv", "valid": "valid.csv", "test": "test.csv" diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json index 2121ee763e..c1c443e312 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json @@ -3,7 +3,7 @@ "name": "basic_classification_reader", "x": ["sentence1", "sentence2"], "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/cutted_many_inputs/parts", + "data_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/many_inputs/parts", "train": "train_0.csv", "valid": "valid.csv", "test": "test.csv" @@ -27,8 +27,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/cutted_many_inputs/ru_snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/cutted_many_inputs/ru_snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/many_inputs/ru_snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/many_inputs/ru_snli_classes.dict" }, { "in": [ From 4af3072c31657b77d2690bf3d324510d2644bc0d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 28 May 2018 12:41:02 +0300 Subject: [PATCH 119/616] fix: configs --- .../basic_ru_snli_one_neuron_init_part.json | 6 +++--- ...ru_snli_one_neuron_init_part_many_inputs.json | 16 ++++++++-------- .../basic_snli_one_neuron_init_part.json | 6 +++--- ...ic_snli_one_neuron_init_part_many_inputs.json | 16 ++++++++-------- 4 files changed, 22 insertions(+), 22 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json index 727a3c809b..a2bc958885 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json @@ -199,14 +199,14 @@ ], "discrete": true }, - "metric_optimization": "minimize", + "metric_optimization": "maximize", "metrics": [ - "classification_log_loss", "classification_accuracy", + "classification_log_loss", "classification_f1", "classification_roc_auc" ], - "validation_patience": 5, + "validation_patience": 2, "val_every_n_epochs": 1, "log_every_n_epochs": 1, "show_examples": false, diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json index c1c443e312..2061429032 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json @@ -172,7 +172,7 @@ "optimizer": "Adam", "lear_rate": { "range": [ - 0.001, + 0.0001, 0.1 ] }, @@ -198,8 +198,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 2, + 10 ], "discrete": true }, @@ -210,16 +210,16 @@ ], "discrete": true }, - "metric_optimization": "minimize", + "metric_optimization": "maximize", "metrics": [ - "classification_log_loss", "classification_accuracy", + "classification_log_loss", "classification_f1", "classification_roc_auc" ], - "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, + "validation_patience": 2, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, "show_examples": false, "validate_best": true, "test_best": true diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index d61cc3e695..a0e2ca0b40 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -199,14 +199,14 @@ ], "discrete": true }, - "metric_optimization": "minimize", + "metric_optimization": "maximize", "metrics": [ - "classification_log_loss", "classification_accuracy", + "classification_log_loss", "classification_f1", "classification_roc_auc" ], - "validation_patience": 5, + "validation_patience": 2, "val_every_n_epochs": 1, "log_every_n_epochs": 1, "show_examples": false, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 71213c283f..17e769581a 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -172,7 +172,7 @@ "optimizer": "Adam", "lear_rate": { "range": [ - 0.001, + 0.0001, 0.1 ] }, @@ -198,8 +198,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 2, + 10 ], "discrete": true }, @@ -210,16 +210,16 @@ ], "discrete": true }, - "metric_optimization": "minimize", + "metric_optimization": "maximize", "metrics": [ - "classification_log_loss", "classification_accuracy", + "classification_log_loss", "classification_f1", "classification_roc_auc" ], - "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, + "validation_patience": 2, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, "show_examples": false, "validate_best": true, "test_best": true From 435e22d6e263da6fe01733cad57f82aaf0e96fd2 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 28 May 2018 14:48:30 +0300 Subject: [PATCH 120/616] fix: configs --- .../configs/evolution/basic_ru_snli_one_neuron_init_part.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json index a2bc958885..a40735e7af 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 2, + 10 ], "discrete": true }, From fddcec3471432c6266bf31dec8839286c2b66622 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 28 May 2018 18:57:59 +0300 Subject: [PATCH 121/616] fix: change order of offsprings --- .../evolution/neuroevolution_param_generator.py | 14 ++++++++------ deeppavlov/models/evolution/run_evolution.py | 2 +- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 409dabfef8..5e5f965188 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -338,7 +338,7 @@ def crossover(self, population, p_crossover, crossover_power): part_of_population offsprings """ perm = np.random.permutation(self.population_size) - offsprings = [] + offsprings = deepcopy(population) for i in range(self.population_size // 2): parents = population[perm[2 * i]], population[perm[2 * i + 1]] if self.decision(p_crossover): @@ -436,15 +436,17 @@ def crossover(self, population, p_crossover, crossover_power): "binary_mask"]) # if parent is one of the best and will be saved with weights if perm[2 * i] in range(self.n_saved_best_with_weights): - curr_offsprings[0] = deepcopy(parents[0]) + offsprings[perm[2 * i]] = deepcopy(population[perm[2 * i]]) if perm[2 * i + 1] in range(self.n_saved_best_with_weights): - curr_offsprings[1] = deepcopy(parents[1]) - offsprings.extend(curr_offsprings) + offsprings[perm[2 * i + 1]] = deepcopy(population[perm[2 * i + 1]]) + + offsprings[perm[2 * i]] = deepcopy(curr_offsprings[0]) + offsprings[perm[2 * i + 1]] = deepcopy(curr_offsprings[1]) else: - offsprings.extend(deepcopy(parents)) + pass if self.population_size % 2 == 1: - offsprings.append(deepcopy(population[perm[-1]])) + offsprings[-1] = deepcopy(population[perm[-1]]) return offsprings def mutation(self, population, p_mutation, mutation_power): diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 7cc13884bf..d8f7c1e3fa 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -155,7 +155,7 @@ def score_population(population, population_size, result_file): population = evolution.next_generation(population, population_scores, iters) print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - + print("Population scores: {}".foramt(population_scores)) print("\nIteration #{} was done\n".format(iters)) iters += 1 From 98f73fdbdf3aad107c95211d98c218c04c14429f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 28 May 2018 23:51:41 +0300 Subject: [PATCH 122/616] fix: misprint --- deeppavlov/models/evolution/run_evolution.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index d8f7c1e3fa..f5b2794c70 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -155,7 +155,7 @@ def score_population(population, population_size, result_file): population = evolution.next_generation(population, population_scores, iters) print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - print("Population scores: {}".foramt(population_scores)) + print("Population scores: {}".format(population_scores)) print("\nIteration #{} was done\n".format(iters)) iters += 1 From e496028fbc41113b7e8e393b38fd28bd577cd019 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 29 May 2018 01:31:08 +0300 Subject: [PATCH 123/616] fix: crossover --- .../neuroevolution_param_generator.py | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 5e5f965188..5f67c24c85 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -263,37 +263,37 @@ def next_generation(self, generation, scores, iteration, p_crossover=p_crossover, crossover_power=crossover_power) - next = offsprings[:self.n_saved_best_with_weights] + next_population = offsprings[:self.n_saved_best_with_weights] changable_individuals = offsprings[self.n_saved_best_with_weights:] changable_next = self.mutation(changable_individuals, p_mutation=p_mutation, mutation_power=mutation_power) - next.extend(changable_next) + next_population.extend(changable_next) for i in range(self.n_saved_best_with_weights): if self.train_partition != 1: - next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ + next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ + "_" + str(iteration % self.train_partition) + ".csv" - next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(Path(next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) - next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ + str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) for i in range(self.n_saved_best_with_weights, self.population_size): if self.train_partition != 1: - next[i]["dataset_reader"]["train"] = str(Path(next[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ + next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ + "_" + str(iteration % self.train_partition) + ".csv" - next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) - next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ str(Path(self.params["load_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) - return next + return next_population def selection(self, population, scores): """ @@ -434,14 +434,14 @@ def crossover(self, population, p_crossover, crossover_power): check_and_correct_binary_mask(self.nodes, curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ "binary_mask"]) + + offsprings[perm[2 * i]] = deepcopy(curr_offsprings[0]) + offsprings[perm[2 * i + 1]] = deepcopy(curr_offsprings[1]) # if parent is one of the best and will be saved with weights if perm[2 * i] in range(self.n_saved_best_with_weights): offsprings[perm[2 * i]] = deepcopy(population[perm[2 * i]]) if perm[2 * i + 1] in range(self.n_saved_best_with_weights): offsprings[perm[2 * i + 1]] = deepcopy(population[perm[2 * i + 1]]) - - offsprings[perm[2 * i]] = deepcopy(curr_offsprings[0]) - offsprings[perm[2 * i + 1]] = deepcopy(curr_offsprings[1]) else: pass From 9ec46f13b9deab294d511676d9b3000ff3c1556c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 29 May 2018 11:17:00 +0300 Subject: [PATCH 124/616] fix: change config --- .../evolution/basic_ru_snli_one_neuron_init_part.json | 8 ++++---- .../basic_ru_snli_one_neuron_init_part_many_inputs.json | 8 ++++---- .../evolution/basic_snli_one_neuron_init_part.json | 8 ++++---- .../basic_snli_one_neuron_init_part_many_inputs.json | 8 ++++---- 4 files changed, 16 insertions(+), 16 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json index a40735e7af..1e0abaf3ef 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_2", - "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_2", + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_3", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_3", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 2, - 10 + 10, + 50 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json index 2061429032..e72a08c5ca 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_2", - "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_2", + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_3", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_3", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -198,8 +198,8 @@ "train": { "epochs": { "range": [ - 2, - 10 + 10, + 50 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index a0e2ca0b40..aa32c5e142 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_2", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_2", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_3", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_3", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 10, + 50 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 17e769581a..27d98fd86b 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_2", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_2", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_3", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_3", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -198,8 +198,8 @@ "train": { "epochs": { "range": [ - 2, - 10 + 10, + 50 ], "discrete": true }, From 32681812874093f3dbcfc78b7b694a55086abdd7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 29 May 2018 11:53:53 +0300 Subject: [PATCH 125/616] chore --- .../evolution/neuroevolution_param_generator.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 5f67c24c85..968a7f11bb 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -434,16 +434,15 @@ def crossover(self, population, p_crossover, crossover_power): check_and_correct_binary_mask(self.nodes, curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ "binary_mask"]) - - offsprings[perm[2 * i]] = deepcopy(curr_offsprings[0]) - offsprings[perm[2 * i + 1]] = deepcopy(curr_offsprings[1]) - # if parent is one of the best and will be saved with weights + if perm[2 * i] in range(self.n_saved_best_with_weights): offsprings[perm[2 * i]] = deepcopy(population[perm[2 * i]]) + else: + offsprings[perm[2 * i]] = deepcopy(curr_offsprings[0]) if perm[2 * i + 1] in range(self.n_saved_best_with_weights): offsprings[perm[2 * i + 1]] = deepcopy(population[perm[2 * i + 1]]) - else: - pass + else: + offsprings[perm[2 * i + 1]] = deepcopy(curr_offsprings[1]) if self.population_size % 2 == 1: offsprings[-1] = deepcopy(population[perm[-1]]) From aacdaea9dbcda9460f9084d00a5a985521fe3ba6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 29 May 2018 12:48:07 +0300 Subject: [PATCH 126/616] feat: start with given binary mask --- .../neuroevolution_param_generator.py | 11 +++++++--- deeppavlov/models/evolution/run_evolution.py | 22 ++++++++++++++----- 2 files changed, 25 insertions(+), 8 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 968a7f11bb..ab471aebe9 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -37,6 +37,7 @@ def __init__(self, n_layers, n_types, evolve_binary_mask=True, save_best_with_weights_portion=0, train_partition=1, + initial_binary_mask=None, **kwargs): """ Initialize evolution with random population @@ -63,7 +64,7 @@ def __init__(self, n_layers, n_types, self.n_layers = n_layers self.total_nodes = self.n_types * self.n_layers - self.binary_mask_template = np.zeros((self.total_nodes, self.total_nodes)) + self.initial_binary_mask = initial_binary_mask self.start_with_one_neuron = start_with_one_neuron self.basic_config = deepcopy(kwargs) @@ -216,6 +217,9 @@ def first_generation(self, iteration=0): if self.start_with_one_neuron: population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ check_and_correct_binary_mask(self.nodes, self.sample_one_neuron_binary_mask()) + elif self.initial_binary_mask: + population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, self.sample_given_binary_mask(self.initial_binary_mask)) else: population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ check_and_correct_binary_mask(self.nodes, self.sample_binary_mask()) @@ -594,6 +598,7 @@ def sample_binary_mask(self): def sample_one_neuron_binary_mask(self): mask = np.zeros((self.total_nodes * self.total_nodes)) - # mask[0] = 1 # make sure that Dense is the first in the config - return mask.reshape((self.total_nodes, self.total_nodes)) + + def sample_given_binary_mask(self, mask): + return np.array(mask).reshape((self.total_nodes, self.total_nodes)) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index f5b2794c70..07949b3234 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -83,18 +83,20 @@ def score_population(population, population_size, result_file): parser = argparse.ArgumentParser() -parser.add_argument('--config', help='Please, enter model path to config', - default='./configs/evolution/basic_intents_config.json') +parser.add_argument('--config', help='Please, enter model path to config') parser.add_argument('--evolve_metric', help='Please, choose target metric out of given in your config.train.metrics') parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) -parser.add_argument('--one_neuron_init', help='Please, enter number of types of layers', default=0) +parser.add_argument('--one_neuron_init', help='whether to start with zero binary mask (one neuron network)', default=0) +parser.add_argument('--given_mask_init', help='whether to start with given binary mask', default=0) parser.add_argument('--save_best_portion', - help='Please, enter portion of population to save for the next generation with weights', default=0.) + help='Please, enter portion of population to save for the next generation with weights', + default=0.) parser.add_argument('--train_partition', - help='Please, enter partition of splitted train', default=1) + help='Please, enter partition of splitted train', + default=1) args = parser.parse_args() @@ -105,6 +107,7 @@ def score_population(population, population_size, result_file): N_LAYERS = int(args.n_layers) N_TYPES = int(args.n_types) ONE_NEURON_INIT = bool(int(args.one_neuron_init)) +GIVEN_MASK_INIT = bool(int(args.given_mask_init)) EVOLVE_METRIC = args.evolve_metric SAVE_BEST_PORTION = float(args.save_best_portion) TRAIN_PARTITION = int(args.train_partition) @@ -117,6 +120,14 @@ def score_population(population, population_size, result_file): # list of names of considered metrics CONSIDERED_METRICS = basic_params["train"]["metrics"] +if GIVEN_MASK_INIT: + # Embedding -> BiLSTM -> Dense -> Dense -> GlobalMaxPooling -> Dense(#classes) + INITIAL_BINARY_MASK = np.zeros((N_TYPES * N_LAYERS, N_TYPES * N_LAYERS)) + INITIAL_BINARY_MASK[3, 0] = 1 + INITIAL_BINARY_MASK[0, N_TYPES] = 1 +else: + INITIAL_BINARY_MASK = None + # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, @@ -128,6 +139,7 @@ def score_population(population, population_size, result_file): start_with_one_neuron=ONE_NEURON_INIT, save_best_with_weights_portion=SAVE_BEST_PORTION, train_partition=TRAIN_PARTITION, + initial_binary_mask=INITIAL_BINARY_MASK, **basic_params) # Result table From 48392c35c262b8d5cf28f07ea679467250ed2242 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 30 May 2018 11:26:50 +0300 Subject: [PATCH 127/616] fix: config --- .../basic_snli_one_neuron_init_part.json | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index aa32c5e142..66369c01c7 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_3", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_3", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_4", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_4", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -72,7 +72,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -89,7 +89,7 @@ "filters": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -106,7 +106,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -116,7 +116,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -136,14 +136,14 @@ "n_hidden": { "range": [ 50, - 200 + 500 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -187,15 +187,15 @@ "train": { "epochs": { "range": [ - 10, - 50 + 50, + 100 ], "discrete": true }, "batch_size": { "range": [ - 20, - 70 + 50, + 100 ], "discrete": true }, @@ -206,7 +206,7 @@ "classification_f1", "classification_roc_auc" ], - "validation_patience": 2, + "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, "show_examples": false, From b981e1ace26b12206026c2c4995bd04174bd2209 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 30 May 2018 11:33:39 +0300 Subject: [PATCH 128/616] fix: check initial binary mask --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index ab471aebe9..a5c13fa8d2 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -217,7 +217,7 @@ def first_generation(self, iteration=0): if self.start_with_one_neuron: population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ check_and_correct_binary_mask(self.nodes, self.sample_one_neuron_binary_mask()) - elif self.initial_binary_mask: + elif self.initial_binary_mask is None: population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ check_and_correct_binary_mask(self.nodes, self.sample_given_binary_mask(self.initial_binary_mask)) else: From dec618d7e595852628f2f5db3ca796b128ce84c6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 30 May 2018 14:35:24 +0300 Subject: [PATCH 129/616] fix: check initial binary mask --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index a5c13fa8d2..c58b7455af 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -217,7 +217,7 @@ def first_generation(self, iteration=0): if self.start_with_one_neuron: population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ check_and_correct_binary_mask(self.nodes, self.sample_one_neuron_binary_mask()) - elif self.initial_binary_mask is None: + elif not(self.initial_binary_mask is None): population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ check_and_correct_binary_mask(self.nodes, self.sample_given_binary_mask(self.initial_binary_mask)) else: From 02b92a15f1cce7d60cf984cec4c29134a26ec240 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 31 May 2018 15:15:04 +0300 Subject: [PATCH 130/616] feat: second best portion and renovation --- .../evolution/basic_snli_one_neuron_init_part.json | 8 ++++---- .../basic_snli_one_neuron_init_part_many_inputs.json | 8 ++++---- .../models/evolution/neuroevolution_param_generator.py | 8 ++++++++ deeppavlov/models/evolution/run_evolution.py | 6 ++++++ 4 files changed, 22 insertions(+), 8 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 66369c01c7..5f359b269d 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_4", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_4", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_5", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_5", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 1, + 10 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 27d98fd86b..0df28786be 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_3", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_3", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_5", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_5", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -198,8 +198,8 @@ "train": { "epochs": { "range": [ - 10, - 50 + 1, + 10 ], "discrete": true }, diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index c58b7455af..5caa9a8bb9 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -36,6 +36,7 @@ def __init__(self, n_layers, n_types, start_with_one_neuron=False, evolve_binary_mask=True, save_best_with_weights_portion=0, + renovation_frequency=1, train_partition=1, initial_binary_mask=None, **kwargs): @@ -109,6 +110,8 @@ def __init__(self, n_layers, n_types, self.n_evolving_train_params = None self.evolve_binary_mask = evolve_binary_mask self.n_saved_best_with_weights = int(save_best_with_weights_portion * self.population_size) + self.n_saved_best_with_weights_first = self.n_saved_best_with_weights + self.renovation_frequency = renovation_frequency self.train_partition = train_partition if seed is None: @@ -261,6 +264,11 @@ def next_generation(self, generation, scores, iteration, if not mutation_power: mutation_power = self.mutation_power + if iteration % self.renovation_frequency != 0: + self.n_saved_best_with_weights = 2 * self.n_saved_best_with_weights_first + else: + self.n_saved_best_with_weights = self.n_saved_best_with_weights_first + selected_individuals = self.selection(generation, scores) offsprings = self.crossover(selected_individuals, diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 07949b3234..29b816f643 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -94,6 +94,10 @@ def score_population(population, population_size, result_file): parser.add_argument('--save_best_portion', help='Please, enter portion of population to save for the next generation with weights', default=0.) +parser.add_argument('--renovation_frequency', + help='Please, enter frequency of renovation (how often in terms of generations ' + 'to renovate the second best portion)', + default=1) parser.add_argument('--train_partition', help='Please, enter partition of splitted train', default=1) @@ -110,6 +114,7 @@ def score_population(population, population_size, result_file): GIVEN_MASK_INIT = bool(int(args.given_mask_init)) EVOLVE_METRIC = args.evolve_metric SAVE_BEST_PORTION = float(args.save_best_portion) +RENOVATION_FREQUENCY = int(args.renovation_frequency) TRAIN_PARTITION = int(args.train_partition) with open(CONFIG_FILE, "r") as f: @@ -138,6 +143,7 @@ def score_population(population, population_size, result_file): seed=42, start_with_one_neuron=ONE_NEURON_INIT, save_best_with_weights_portion=SAVE_BEST_PORTION, + renovation_frequency=RENOVATION_FREQUENCY, train_partition=TRAIN_PARTITION, initial_binary_mask=INITIAL_BINARY_MASK, **basic_params) From 70ec0d4035c87b4523475cc1fbe40e2c1c020ad8 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 31 May 2018 15:15:32 +0300 Subject: [PATCH 131/616] fix: config --- .../evolution/basic_snli_one_neuron_init_part_many_inputs.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 0df28786be..2c62619894 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -206,7 +206,7 @@ "batch_size": { "range": [ 50, - 70 + 100 ], "discrete": true }, From 84d9e19636201322d35f7ca418c57483681fed07 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 31 May 2018 15:26:32 +0300 Subject: [PATCH 132/616] fix: configs plus ag_news config --- .../configs/evolution/basic_ag_news_part.json | 221 ++++++++++++++++++ ...init_part.json => basic_ru_snli_part.json} | 0 ...on => basic_ru_snli_part_many_inputs.json} | 0 ...on_init_part.json => basic_snli_part.json} | 0 ....json => basic_snli_part_many_inputs.json} | 0 ...n => basic_snli_part_many_inputs_big.json} | 0 6 files changed, 221 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_ag_news_part.json rename deeppavlov/configs/evolution/{basic_ru_snli_one_neuron_init_part.json => basic_ru_snli_part.json} (100%) rename deeppavlov/configs/evolution/{basic_ru_snli_one_neuron_init_part_many_inputs.json => basic_ru_snli_part_many_inputs.json} (100%) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init_part.json => basic_snli_part.json} (100%) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init_part_many_inputs.json => basic_snli_part_many_inputs.json} (100%) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init_part_many_inputs_big.json => basic_snli_part_many_inputs_big.json} (100%) diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json new file mode 100644 index 0000000000..4da359f0b5 --- /dev/null +++ b/deeppavlov/configs/evolution/basic_ag_news_part.json @@ -0,0 +1,221 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "label", + "data_path": "/home/dilyara.baymurzina/evolution_data/ag_news_data/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_data/ag_news_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_data/ag_news_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_5", + "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_5", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.000001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "confident_threshold": 1, + "text_size": 50, + "last_layer_activation": "softmax", + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 1, + 10 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 100 + ], + "discrete": true + }, + "metric_optimization": "maximize", + "metrics": [ + "classification_accuracy", + "classification_log_loss", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "show_examples": false, + "validate_best": true, + "test_best": true + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_ru_snli_part.json similarity index 100% rename from deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json rename to deeppavlov/configs/evolution/basic_ru_snli_part.json diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json similarity index 100% rename from deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json rename to deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_part.json similarity index 100% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json rename to deeppavlov/configs/evolution/basic_snli_part.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json similarity index 100% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json rename to deeppavlov/configs/evolution/basic_snli_part_many_inputs.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json similarity index 100% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json rename to deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json From 49eb3203d760cb113486da4464b2ad8a06f0e946 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 31 May 2018 15:46:18 +0300 Subject: [PATCH 133/616] fix: configs --- deeppavlov/configs/evolution/basic_snli_part.json | 4 ++-- deeppavlov/configs/evolution/basic_snli_part_many_inputs.json | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index 5f359b269d..0f5eb0bfe1 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_5", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_5", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_5", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_5", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index 2c62619894..662ca1ab88 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_5", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_5", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_many_inputs_5", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_many_inputs_5", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { From b0d7d1aad3c6a255ae480f2404a406791c5d6b38 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 4 Jun 2018 12:47:47 +0300 Subject: [PATCH 134/616] feat: save test results --- deeppavlov/models/evolution/run_evolution.py | 31 +++++++++----- .../models/evolution/train_phenotype.py | 41 +++++++++++++++---- 2 files changed, 53 insertions(+), 19 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 29b816f643..abffd83aee 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -20,13 +20,6 @@ def score_population(population, population_size, result_file): procs = [] for i in range(population_size): - # f_name = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) - # model_name = population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] - # population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - # str(f_name.joinpath(model_name + "_" + str(i))) - # population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] =\ - # population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] - save_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) load_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]) @@ -62,11 +55,25 @@ def score_population(population, population_size, result_file): for i in range(population_size): val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ "save_path"]).parent.joinpath("valid_results.txt"))) + try: + test_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).parent.joinpath("test_results.txt"))) + except FileNotFoundError: + pass + result_table_dict = {} for el in order: - result_table_dict[el] = [] + if el == "params": + result_table_dict[el] = [] + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] for m_id, m in enumerate(CONSIDERED_METRICS): - result_table_dict[m].append(val_results[m_id]) + result_table_dict[m + "_valid"].append(val_results[m_id]) + try: + result_table_dict[m + "_test"].append(test_results[m_id]) + except NameError: + result_table_dict[m + "_test"].append(0.) result_table_dict[order[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) @@ -153,7 +160,11 @@ def score_population(population, population_size, result_file): order.extend(["params"]) result_table_dict = {} for el in order: - result_table_dict[el] = [] + if order == "params": + result_table_dict[el] = [] + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] result_file = Path(basic_params["chainer"]["pipe"][ evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py index b693f04f54..0cb26a46eb 100644 --- a/deeppavlov/models/evolution/train_phenotype.py +++ b/deeppavlov/models/evolution/train_phenotype.py @@ -28,12 +28,35 @@ reports = train_model_from_config(config_path) print(reports) -metrics = dict(reports[0]["valid"]["metrics"]) -val_metrics_values = np.array(list(metrics.values())).reshape(-1) - -config = read_json(config_path) -model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], - key="to_evolve") -np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ - "save_path"]).parent.joinpath("valid_results.txt")), - X=val_metrics_values) +if len(reports) == 2: + # valid and test reports + val_metrics = dict(reports[0]["valid"]["metrics"]) + val_metrics_values = np.array(list(val_metrics.values())).reshape(-1) + + config = read_json(config_path) + model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], + key="to_evolve") + np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ + "save_path"]).parent.joinpath("valid_results.txt")), + X=val_metrics_values) + + test_metrics = dict(reports[1]["test"]["metrics"]) + test_metrics_values = np.array(list(test_metrics.values())).reshape(-1) + + config = read_json(config_path) + model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], + key="to_evolve") + np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ + "save_path"]).parent.joinpath("test_results.txt")), + X=test_metrics_values) +else: + # valid report + val_metrics = dict(reports[0]["valid"]["metrics"]) + val_metrics_values = np.array(list(val_metrics.values())).reshape(-1) + + config = read_json(config_path) + model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], + key="to_evolve") + np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ + "save_path"]).parent.joinpath("valid_results.txt")), + X=val_metrics_values) From f02995559362f285f1baf4fecfb9f36ab1492a1e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 4 Jun 2018 14:34:33 +0300 Subject: [PATCH 135/616] feat: config for twitter140 --- .../evolution/basic_twitter_140_part.json | 221 ++++++++++++++++++ 1 file changed, 221 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_twitter_140_part.json diff --git a/deeppavlov/configs/evolution/basic_twitter_140_part.json b/deeppavlov/configs/evolution/basic_twitter_140_part.json new file mode 100644 index 0000000000..35e6f2231f --- /dev/null +++ b/deeppavlov/configs/evolution/basic_twitter_140_part.json @@ -0,0 +1,221 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "label", + "data_path": "/home/dilyara.baymurzina/evolution_data/twitter140_data/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/twitter140_data/twitter140_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/twitter140_data/twitter140_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_5", + "load_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_5", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.000001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "confident_threshold": 1, + "text_size": 30, + "last_layer_activation": "softmax", + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 1, + 10 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 100 + ], + "discrete": true + }, + "metric_optimization": "maximize", + "metrics": [ + "classification_accuracy", + "classification_log_loss", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "show_examples": false, + "validate_best": true, + "test_best": true + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From 4a0ef135d736e22009d23fc733db6ab6280d4347 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 4 Jun 2018 14:40:47 +0300 Subject: [PATCH 136/616] fix:rename config --- .../{basic_twitter_140_part.json => basic_twitter140_part.json} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename deeppavlov/configs/evolution/{basic_twitter_140_part.json => basic_twitter140_part.json} (100%) diff --git a/deeppavlov/configs/evolution/basic_twitter_140_part.json b/deeppavlov/configs/evolution/basic_twitter140_part.json similarity index 100% rename from deeppavlov/configs/evolution/basic_twitter_140_part.json rename to deeppavlov/configs/evolution/basic_twitter140_part.json From 8ee49b9e19f19b51b385015d38c21f2d033e0566 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 4 Jun 2018 15:00:22 +0300 Subject: [PATCH 137/616] fix: order in run evolution --- deeppavlov/models/evolution/run_evolution.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index abffd83aee..7a9eabba8c 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -77,7 +77,7 @@ def score_population(population, population_size, result_file): result_table_dict[order[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) - result_table.loc[:, order].to_csv(result_file, index=False, sep='\t', mode='a', header=None) + result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) for m_id, m in enumerate(CONSIDERED_METRICS): population_metrics[m].append(val_results[m_id]) @@ -158,18 +158,25 @@ def score_population(population, population_size, result_file): # Result table order = deepcopy(CONSIDERED_METRICS) order.extend(["params"]) + +result_table_columns = [] + result_table_dict = {} for el in order: if order == "params": result_table_dict[el] = [] + result_table_columns.extend([el + "_valid"]) else: result_table_dict[el + "_valid"] = [] result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) + +result_table_columns.append("params") result_file = Path(basic_params["chainer"]["pipe"][ evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") result_table = pd.DataFrame(result_table_dict) -result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') +result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() From e14b4c6e829be6b11b340586445356f9f5118f8e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 4 Jun 2018 15:04:20 +0300 Subject: [PATCH 138/616] fix: name of columns --- deeppavlov/configs/evolution/basic_twitter140_part.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_twitter140_part.json b/deeppavlov/configs/evolution/basic_twitter140_part.json index 35e6f2231f..e7c25ccf43 100644 --- a/deeppavlov/configs/evolution/basic_twitter140_part.json +++ b/deeppavlov/configs/evolution/basic_twitter140_part.json @@ -2,7 +2,7 @@ "dataset_reader": { "name": "basic_classification_reader", "x": "text", - "y": "label", + "y": "target", "data_path": "/home/dilyara.baymurzina/evolution_data/twitter140_data/parts", "train": "train_0.csv", "valid": "valid.csv", From 99cf38dd9d4f9e0bdf672ac8d295b21927408667 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 15:28:57 +0300 Subject: [PATCH 139/616] feat: saving epochs_done and final lear rate for keras model --- deeppavlov/core/models/keras_model.py | 39 ++++++++++++++++++++------- 1 file changed, 29 insertions(+), 10 deletions(-) diff --git a/deeppavlov/core/models/keras_model.py b/deeppavlov/core/models/keras_model.py index 33936d0bff..13787ff915 100644 --- a/deeppavlov/core/models/keras_model.py +++ b/deeppavlov/core/models/keras_model.py @@ -100,13 +100,13 @@ def init_model_from_scratch(self, model_name, optimizer_name, loss_name, lear_ra if callable(optimizer_func): if not(lear_rate is None): if not(lear_rate_decay is None): - optimizer_ = optimizer_func(lr=lear_rate, decay=lear_rate_decay) + self.optimizer = optimizer_func(lr=lear_rate, decay=lear_rate_decay) else: - optimizer_ = optimizer_func(lr=lear_rate) + self.optimizer = optimizer_func(lr=lear_rate) elif not(lear_rate_decay is None): - optimizer_ = optimizer_func(decay=lear_rate_decay) + self.optimizer = optimizer_func(decay=lear_rate_decay) else: - optimizer_ = optimizer_func() + self.optimizer = optimizer_func() else: raise AttributeError("Optimizer {} is not defined in `keras.optimizers`".format(optimizer_name)) @@ -116,7 +116,7 @@ def init_model_from_scratch(self, model_name, optimizer_name, loss_name, lear_ra else: raise AttributeError("Loss {} is not defined in `keras.losses`".format(loss_name)) - model.compile(optimizer=optimizer_, loss=loss) + model.compile(optimizer=self.optimizer, loss=loss) return model @overrides @@ -160,13 +160,13 @@ def load(self, model_name, optimizer_name, loss_name, lear_rate=None, lear_rate_ if callable(optimizer_func): if not (lear_rate is None): if not (lear_rate_decay is None): - optimizer_ = optimizer_func(lr=lear_rate, decay=lear_rate_decay) + self.optimizer = optimizer_func(lr=lear_rate, decay=lear_rate_decay) else: - optimizer_ = optimizer_func(lr=lear_rate) + self.optimizer = optimizer_func(lr=lear_rate) elif not (lear_rate_decay is None): - optimizer_ = optimizer_func(decay=lear_rate_decay) + self.optimizer = optimizer_func(decay=lear_rate_decay) else: - optimizer_ = optimizer_func() + self.optimizer = optimizer_func() else: raise AttributeError("Optimizer {} is not defined in `keras.optimizers`".format(optimizer_name)) @@ -176,7 +176,7 @@ def load(self, model_name, optimizer_name, loss_name, lear_rate=None, lear_rate_ else: raise AttributeError("Loss {} is not defined".format(loss_name)) - model.compile(optimizer=optimizer_, + model.compile(optimizer=self.optimizer, loss=loss) return model else: @@ -211,6 +211,9 @@ def save(self, fname=None): # if model was loaded from one path and saved to another one # then change load_path to save_path for config + self.opt["epochs_done"] = self.epochs_done + self.opt["final_lear_rate"] = self.optimizer.lr / (1. + self.optimizer.decay * self.batches_seen) + if self.opt.get("load_path") and self.opt.get("save_path"): if self.opt.get("save_path") != self.opt.get("load_path"): self.opt["load_path"] = str(self.opt["save_path"]) @@ -239,3 +242,19 @@ def mlp(self, opt): @abstractmethod def reset(self): pass + + def process_event(self, event_name, data): + """ + Process event after epoch + Args: + event_name: whether event is send after epoch or batch + data: event data (dictionary) + + Returns: + None + """ + if event_name == "after_epoch": + self.epochs_done = data["epochs_done"] + self.batches_seen = data["batches_seen"] + self.train_examples_seen = data["train_examples_seen"] + return From 97e131f8ddee8c501cae2f8cda4f887a49c07312 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 15:42:46 +0300 Subject: [PATCH 140/616] fix: tensor to float for lear rate --- deeppavlov/core/models/keras_model.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/core/models/keras_model.py b/deeppavlov/core/models/keras_model.py index 13787ff915..9897e4abc1 100644 --- a/deeppavlov/core/models/keras_model.py +++ b/deeppavlov/core/models/keras_model.py @@ -212,7 +212,8 @@ def save(self, fname=None): # if model was loaded from one path and saved to another one # then change load_path to save_path for config self.opt["epochs_done"] = self.epochs_done - self.opt["final_lear_rate"] = self.optimizer.lr / (1. + self.optimizer.decay * self.batches_seen) + self.opt["final_lear_rate"] = K.eval(self.optimizer.lr) / (1. + + K.eval(self.optimizer.decay) * self.batches_seen) if self.opt.get("load_path") and self.opt.get("save_path"): if self.opt.get("save_path") != self.opt.get("load_path"): From 65a69f1b93caef6a7445f8cf5483666dcf95b350 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 16:04:34 +0300 Subject: [PATCH 141/616] feat: exchange initial lear rate to final one --- deeppavlov/configs/evolution/basic_ag_news_part.json | 4 ++-- .../models/evolution/neuroevolution_param_generator.py | 6 ++++++ 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json index 4da359f0b5..41461637b5 100644 --- a/deeppavlov/configs/evolution/basic_ag_news_part.json +++ b/deeppavlov/configs/evolution/basic_ag_news_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_5", - "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_5", + "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 5caa9a8bb9..005dc63d3b 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -113,6 +113,7 @@ def __init__(self, n_layers, n_types, self.n_saved_best_with_weights_first = self.n_saved_best_with_weights self.renovation_frequency = renovation_frequency self.train_partition = train_partition + self.evolution_individuum_id = 0 if seed is None: pass @@ -230,6 +231,8 @@ def first_generation(self, iteration=0): # exchange train params from basic config to sampled train params population[-1]["train"] = {**train_params, **train_params_for_search} + # population[-1]["train"]["evolution_individuum_id"] = self.evolution_individuum_id + # self.evolution_individuum_id += 1 self.evolving_params = list(set(self.evolving_params)) self.evolving_train_params = list(set(self.evolving_train_params)) @@ -294,6 +297,9 @@ def next_generation(self, generation, scores, iteration, str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ + str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["final_lear_rate"]).parent) + for i in range(self.n_saved_best_with_weights, self.population_size): if self.train_partition != 1: next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ From 2f983e831915cbf929f1886e467740a8a51ee639 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 16:21:13 +0300 Subject: [PATCH 142/616] chore: delete division by max scores --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 005dc63d3b..f0cacf6f70 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -325,7 +325,7 @@ def selection(self, population, scores): selected self.population_size individuums with replacement """ scores = np.array(scores, dtype='float') - scores = (scores - 1.1 * min(scores) + 0.1 * max(scores)) / max(scores) + scores = (scores - 1.1 * min(scores) + 0.1 * max(scores)) total = np.sum(scores) probas_to_be_selected = scores / total intervals = np.array([np.sum(probas_to_be_selected[:i]) for i in range(self.population_size)]) From 9b7165e5b0ab6fbdbeffd27daf84f87ee5679731 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 16:39:50 +0300 Subject: [PATCH 143/616] fix: reinit lear rate --- .../models/evolution/neuroevolution_param_generator.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index f0cacf6f70..19cc40eb60 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -2,11 +2,11 @@ from copy import deepcopy from pathlib import Path import json -import shutil from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ number_to_type_layer from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe +from deeppavlov.core.common.file import read_json # please, make sure that @@ -296,9 +296,10 @@ def next_generation(self, generation, scores, iteration, next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) - + # re init learning rate with the final one next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ - str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["final_lear_rate"]).parent) + read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]). + joinpath("model_opt.json")))["chainer"]["pipe"][self.model_to_evolve_index]["final_lear_rate"] for i in range(self.n_saved_best_with_weights, self.population_size): if self.train_partition != 1: From 2dc06e677b990825b00eab8ed76ba1212e53d514 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 16:41:59 +0300 Subject: [PATCH 144/616] chore --- .../models/evolution/neuroevolution_param_generator.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 19cc40eb60..7fa9931143 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -289,7 +289,8 @@ def next_generation(self, generation, scores, iteration, for i in range(self.n_saved_best_with_weights): if self.train_partition != 1: - next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ + next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ + "train"]).stem.split("_")[0]) \ + "_" + str(iteration % self.train_partition) + ".csv" next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) @@ -299,11 +300,13 @@ def next_generation(self, generation, scores, iteration, # re init learning rate with the final one next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]). - joinpath("model_opt.json")))["chainer"]["pipe"][self.model_to_evolve_index]["final_lear_rate"] + joinpath("model_opt.json")))["chainer"]["pipe"][self.model_to_evolve_index][ + "final_lear_rate"] for i in range(self.n_saved_best_with_weights, self.population_size): if self.train_partition != 1: - next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"]["train"]).stem.split("_")[0]) \ + next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ + "train"]).stem.split("_")[0]) \ + "_" + str(iteration % self.train_partition) + ".csv" next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( From 7ef58b73b4aaafa338bd3c2f9565ed15d282234a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 16:46:53 +0300 Subject: [PATCH 145/616] fix: first reinit lr then change paths --- .../evolution/neuroevolution_param_generator.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 7fa9931143..f8f6f139fa 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -292,16 +292,17 @@ def next_generation(self, generation, scores, iteration, next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ "train"]).stem.split("_")[0]) \ + "_" + str(iteration % self.train_partition) + ".csv" - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ - str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( - self.params["model_name"] + "_" + str(i))) # re init learning rate with the final one next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]). joinpath("model_opt.json")))["chainer"]["pipe"][self.model_to_evolve_index][ "final_lear_rate"] + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ + str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ + str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( + self.params["model_name"] + "_" + str(i))) + for i in range(self.n_saved_best_with_weights, self.population_size): if self.train_partition != 1: From be347d051e02d6df761e28b32dcb28e9f2d80cbc Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 16:53:52 +0300 Subject: [PATCH 146/616] fix: first reinit lr then change paths --- .../models/evolution/neuroevolution_param_generator.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index f8f6f139fa..e1977de1cf 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -294,16 +294,15 @@ def next_generation(self, generation, scores, iteration, + "_" + str(iteration % self.train_partition) + ".csv" # re init learning rate with the final one next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ - read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]). - joinpath("model_opt.json")))["chainer"]["pipe"][self.model_to_evolve_index][ - "final_lear_rate"] + read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index][ + "save_path"]).parent.joinpath("model_opt.json")))["chainer"]["pipe"][ + self.model_to_evolve_index]["final_lear_rate"] next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) - for i in range(self.n_saved_best_with_weights, self.population_size): if self.train_partition != 1: next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ From 9c4c7470f512a3e78b52b10e81187cad2b649999 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 16:57:42 +0300 Subject: [PATCH 147/616] feat: evolution model id --- .../models/evolution/neuroevolution_param_generator.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index e1977de1cf..b6302073f0 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -114,6 +114,7 @@ def __init__(self, n_layers, n_types, self.renovation_frequency = renovation_frequency self.train_partition = train_partition self.evolution_individuum_id = 0 + self.evolution_model_id = 0 if seed is None: pass @@ -231,8 +232,8 @@ def first_generation(self, iteration=0): # exchange train params from basic config to sampled train params population[-1]["train"] = {**train_params, **train_params_for_search} - # population[-1]["train"]["evolution_individuum_id"] = self.evolution_individuum_id - # self.evolution_individuum_id += 1 + population[-1]["train"]["evolution_model_id"] = self.evolution_model_id + self.evolution_model_id += 1 self.evolving_params = list(set(self.evolving_params)) self.evolving_train_params = list(set(self.evolving_train_params)) @@ -314,6 +315,8 @@ def next_generation(self, generation, scores, iteration, next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ str(Path(self.params["load_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) + next_population[i]["train"]["evolution_model_id"] = self.evolution_model_id + self.evolution_model_id += 1 return next_population From ad43e940477061635076b67a3c061de3bd513dda Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 17:12:26 +0300 Subject: [PATCH 148/616] fix: lear rate --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index b6302073f0..14270ac55e 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -296,8 +296,7 @@ def next_generation(self, generation, scores, iteration, # re init learning rate with the final one next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index][ - "save_path"]).parent.joinpath("model_opt.json")))["chainer"]["pipe"][ - self.model_to_evolve_index]["final_lear_rate"] + "save_path"]).parent.joinpath("model_opt.json")))["final_lear_rate"] next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ From e557f59b506f0106b1aefcdfb24fd1f9e9af675c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 17:22:45 +0300 Subject: [PATCH 149/616] fix: snli many_inputs config --- .../evolution/basic_snli_part_many_inputs.json | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index 662ca1ab88..9d39c6d3ed 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -83,7 +83,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -100,7 +100,7 @@ "filters": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -117,7 +117,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -127,7 +127,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -206,7 +206,7 @@ "batch_size": { "range": [ 50, - 100 + 200 ], "discrete": true }, @@ -217,7 +217,7 @@ "classification_f1", "classification_roc_auc" ], - "validation_patience": 2, + "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, "show_examples": false, From deba51ffaba7773f37b053645637d3a327807543 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 17:33:28 +0300 Subject: [PATCH 150/616] fix: lear rate init --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 1 + 1 file changed, 1 insertion(+) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 14270ac55e..005ddca253 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -314,6 +314,7 @@ def next_generation(self, generation, scores, iteration, next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ str(Path(self.params["load_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) + next_population[i]["train"]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 From f62ee5a0c639d28540136994b97e0a8b7d5027a4 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 17:37:32 +0300 Subject: [PATCH 151/616] fix: configs --- deeppavlov/configs/evolution/basic_snli_part.json | 2 +- deeppavlov/configs/evolution/basic_snli_part_many_inputs.json | 4 ++-- deeppavlov/models/evolution/neuroevolution_param_generator.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index 0f5eb0bfe1..1315fecfb9 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -195,7 +195,7 @@ "batch_size": { "range": [ 50, - 100 + 200 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index 9d39c6d3ed..ceb5662678 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -147,14 +147,14 @@ "n_hidden": { "range": [ 50, - 200 + 500 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 500 ], "discrete": true }, diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 005ddca253..68656cd901 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -314,7 +314,7 @@ def next_generation(self, generation, scores, iteration, next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ str(Path(self.params["load_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) - + next_population[i]["train"]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 From e6efbe8a101e4a998ea73617229f4adc60db6dd6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 17:44:30 +0300 Subject: [PATCH 152/616] fix: configs --- .../configs/evolution/basic_ru_snli_part.json | 26 +-- .../basic_ru_snli_part_many_inputs.json | 24 +- .../configs/evolution/basic_snli_part.json | 4 +- .../basic_snli_part_many_inputs.json | 4 +- .../basic_snli_part_many_inputs_big.json | 4 +- .../evolution/basic_snli_random_init.json | 220 ------------------ 6 files changed, 31 insertions(+), 251 deletions(-) delete mode 100644 deeppavlov/configs/evolution/basic_snli_random_init.json diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part.json b/deeppavlov/configs/evolution/basic_ru_snli_part.json index 1e0abaf3ef..743ce1768e 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_3", - "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_3", + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -72,7 +72,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -89,7 +89,7 @@ "filters": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -106,7 +106,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -116,7 +116,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -136,14 +136,14 @@ "n_hidden": { "range": [ 50, - 200 + 500 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -187,15 +187,15 @@ "train": { "epochs": { "range": [ - 10, - 50 + 1, + 10 ], "discrete": true }, "batch_size": { "range": [ - 20, - 70 + 50, + 200 ], "discrete": true }, @@ -206,7 +206,7 @@ "classification_f1", "classification_roc_auc" ], - "validation_patience": 2, + "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, "show_examples": false, diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json index e72a08c5ca..3b731f7683 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_3", - "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_3", + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -83,7 +83,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -100,7 +100,7 @@ "filters": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -117,7 +117,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -127,7 +127,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -147,14 +147,14 @@ "n_hidden": { "range": [ 50, - 200 + 500 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -198,15 +198,15 @@ "train": { "epochs": { "range": [ - 10, - 50 + 1, + 10 ], "discrete": true }, "batch_size": { "range": [ 50, - 70 + 200 ], "discrete": true }, @@ -217,7 +217,7 @@ "classification_f1", "classification_roc_auc" ], - "validation_patience": 2, + "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, "show_examples": false, diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index 1315fecfb9..313244da53 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_5", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_5", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index ceb5662678..8508aae6a6 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_many_inputs_5", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_many_inputs_5", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_many_inputs_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_many_inputs_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json index 2403b1b3e0..8fb8a2c6db 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json @@ -71,8 +71,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_1", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_1", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json deleted file mode 100644 index a57d2fc672..0000000000 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ /dev/null @@ -1,220 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input", - "train": "train_0.csv", - "valid": "valid.csv", - "test": "test.csv" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", - "dim": 100 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random_1", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random_1", - "classes": "#classes_vocab.keys()", - "to_evolve": true, - "basic_layers_params": { - "Dense": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - }, - "Conv1D": { - "filters": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "kernel_size": { - "range": [ - 2, - 7 - ], - "discrete": true - }, - "padding": "same" - }, - "CuDNNLSTM": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "return_sequences": true - }, - "BiCuDNNLSTM": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "return_sequences": true - }, - "MaxPooling1D": { - "pool_size": { - "range": [ - 2, - 5 - ], - "discrete": true - }, - "padding": "same" - }, - "SelfMultiplicativeAttention": { - "n_hidden": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "n_output_features": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - } - }, - "confident_threshold": 1, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.001, - 0.1 - ] - }, - "lear_rate_decay": { - "range": [ - 0.00001, - 0.1 - ] - }, - "loss": "binary_crossentropy", - "text_size": 51, - "model_name": "evolution_classification_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "range": [ - 100, - 1000 - ], - "discrete": true - }, - "batch_size": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "metric_optimization": "minimize", - "metrics": [ - "classification_log_loss", - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, - "show_examples": false, - "validate_best": true, - "test_best": true - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} \ No newline at end of file From 12fa543f98d2fba15bc2f1441eadbf94ddfeade8 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 17:49:05 +0300 Subject: [PATCH 153/616] fix: configs --- .../configs/evolution/basic_config_local.json | 232 ----------------- deeppavlov/configs/evolution/basic_selqa.json | 236 ------------------ .../evolution/basic_twitter140_part.json | 6 +- 3 files changed, 3 insertions(+), 471 deletions(-) delete mode 100644 deeppavlov/configs/evolution/basic_config_local.json delete mode 100644 deeppavlov/configs/evolution/basic_selqa.json diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json deleted file mode 100644 index a1b859edee..0000000000 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ /dev/null @@ -1,232 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": [ - "sentence1", - "sentence2" - ], - "y": "gold_label", - "data_path": "/home/dilyara/data/data_files/SNLI/snli_data/two_texts/part" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "sentence1", - "sentence2" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict", - "load_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict" - }, - { - "in": [ - "sentence1" - ], - "out": [ - "sentence1_lower" - ], - "name": "str_lower" - }, - { - "in": [ - "sentence2" - ], - "out": [ - "sentence2_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara/data/data_files/embeddings/reddit/wordpunct_tok_reddit_comments_2017_11_100.bin", - "load_path": "/home/dilyara/data/data_files/embeddings/reddit/wordpunct_tok_reddit_comments_2017_11_100.bin", - "dim": 100 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "sentence1_lower", - "sentence2_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_inputs", - "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_inputs", - "classes": "#classes_vocab.keys()", - "to_evolve": true, - "basic_layers_params": { - "Dense": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - }, - "Conv1D": { - "filters": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "kernel_size": { - "range": [ - 2, - 7 - ], - "discrete": true - }, - "padding": "same" - }, - "CuDNNLSTM": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "return_sequences": true - }, - "BiCuDNNLSTM": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "return_sequences": true - }, - "MaxPooling1D": { - "pool_size": { - "range": [ - 2, - 5 - ], - "discrete": true - }, - "padding": "same" - }, - "SelfMultiplicativeAttention": { - "n_hidden": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "n_output_features": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - } - }, - "confident_threshold": 1, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.001, - 0.1 - ] - }, - "lear_rate_decay": { - "range": [ - 0.00001, - 0.1 - ] - }, - "loss": "binary_crossentropy", - "text_size": 15, - "last_layer_activation": "softmax", - "model_name": "evolution_many_inputs_classification_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "range": [ - 100, - 1000 - ], - "discrete": true - }, - "batch_size": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "metric_optimization": "minimize", - "metrics": [ - "classification_log_loss", - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, - "show_examples": false, - "validate_best": true, - "test_best": false - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} diff --git a/deeppavlov/configs/evolution/basic_selqa.json b/deeppavlov/configs/evolution/basic_selqa.json deleted file mode 100644 index fddae03149..0000000000 --- a/deeppavlov/configs/evolution/basic_selqa.json +++ /dev/null @@ -1,236 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": [ - "question", - "answer" - ], - "y": "label", - "data_path": "/home/dilyara.baymurzina/evolution_data/selqa_data" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "question", - "answer" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/selqa_data/selqa_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/selqa_data/selqa_classes.dict" - }, - { - "in": [ - "question" - ], - "out": [ - "question_lower" - ], - "name": "str_lower" - }, - { - "in": [ - "answer" - ], - "out": [ - "answer_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "question_lower", - "answer_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/selqa_classification/one_neuron_init_part_many_inputs", - "load_path": "/home/dilyara.baymurzina/evolution_data/selqa_classification/one_neuron_init_part_many_inputs", - "classes": "#classes_vocab.keys()", - "to_evolve": true, - "basic_layers_params": { - "Dense": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - }, - "Conv1D": { - "filters": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "kernel_size": { - "range": [ - 2, - 7 - ], - "discrete": true - }, - "padding": "same" - }, - "CuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true - }, - "BiCuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true - }, - "MaxPooling1D": { - "pool_size": { - "range": [ - 2, - 5 - ], - "discrete": true - }, - "padding": "same" - }, - "SelfMultiplicativeAttention": { - "n_hidden": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "n_output_features": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - } - }, - "confident_threshold": 1, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.001, - 0.1 - ] - }, - "lear_rate_decay": { - "range": [ - 0.00001, - 0.1 - ] - }, - "loss": "binary_crossentropy", - "text_size": [ - 20, - 50 - ], - "last_layer_activation": "softmax", - "model_name": "evolution_many_inputs_classification_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "range": [ - 50, - 100 - ], - "discrete": true - }, - "batch_size": { - "range": [ - 50, - 70 - ], - "discrete": true - }, - "metric_optimization": "minimize", - "metrics": [ - "classification_log_loss", - "classification_accuracy", - "classification_f1", - "classification_roc_auc", - "classification_mrr" - ], - "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, - "show_examples": false, - "validate_best": true, - "test_best": true - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} diff --git a/deeppavlov/configs/evolution/basic_twitter140_part.json b/deeppavlov/configs/evolution/basic_twitter140_part.json index e7c25ccf43..ce25c033da 100644 --- a/deeppavlov/configs/evolution/basic_twitter140_part.json +++ b/deeppavlov/configs/evolution/basic_twitter140_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_5", - "load_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_5", + "save_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -195,7 +195,7 @@ "batch_size": { "range": [ 50, - 100 + 200 ], "discrete": true }, From 847212a202b2c83d2dc407191f3d249172b35660 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 7 Jun 2018 11:55:16 +0300 Subject: [PATCH 154/616] fix: mutation and crossover params --- deeppavlov/models/evolution/run_evolution.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 7a9eabba8c..81205386df 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -143,8 +143,8 @@ def score_population(population, population_size, result_file): # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, - p_crossover=0.1, crossover_power=0.5, - p_mutation=0.5, mutation_power=0.1, + p_crossover=0.2, crossover_power=0.2, + p_mutation=1., mutation_power=0.1, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", seed=42, From 1059f91b098219af02ac8f85cd93df7c7ea2535b Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 7 Jun 2018 15:17:49 +0300 Subject: [PATCH 155/616] feat: add dropout --- .../configs/evolution/basic_ru_snli_part.json | 6 ++++++ .../basic_ru_snli_part_many_inputs.json | 6 ++++++ .../evolution/basic_snips_one_neuron_init.json | 6 ++++++ .../evolution/basic_snips_random_init.json | 6 ++++++ .../configs/evolution/basic_snli_part.json | 6 ++++++ .../evolution/basic_snli_part_many_inputs.json | 16 ++++++++++++++-- .../basic_snli_part_many_inputs_big.json | 6 ++++++ .../configs/evolution/basic_twitter140_part.json | 6 ++++++ deeppavlov/configs/evolution/check_config.json | 1 - deeppavlov/configs/evolution/intents_snli.json | 7 ++++++- .../evolution/evolution_many_inputs_model.py | 10 ++++++---- 11 files changed, 68 insertions(+), 8 deletions(-) delete mode 100644 deeppavlov/configs/evolution/check_config.json diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part.json b/deeppavlov/configs/evolution/basic_ru_snli_part.json index 743ce1768e..cbaba4aaa3 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part.json @@ -173,6 +173,12 @@ }, "loss": "binary_crossentropy", "text_size": 51, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json index 3b731f7683..e89eead7fe 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json @@ -184,6 +184,12 @@ }, "loss": "binary_crossentropy", "text_size": [30, 20], + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 0182c2dba6..0f84c322bc 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -170,6 +170,12 @@ }, "loss": "binary_crossentropy", "text_size": 15, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "model_name": "evolution_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index 5ca329a9c7..ada0c083e4 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -170,6 +170,12 @@ }, "loss": "binary_crossentropy", "text_size": 15, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "model_name": "evolution_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index 313244da53..8c3a0024e3 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -173,6 +173,12 @@ }, "loss": "binary_crossentropy", "text_size": 51, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index 8508aae6a6..28563e55e8 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -1,7 +1,10 @@ { "dataset_reader": { "name": "basic_classification_reader", - "x": ["sentence1", "sentence2"], + "x": [ + "sentence1", + "sentence2" + ], "y": "gold_label", "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/cutted_many_inputs/parts", "train": "train_0.csv", @@ -183,7 +186,16 @@ ] }, "loss": "binary_crossentropy", - "text_size": [30, 20], + "text_size": [ + 30, + 20 + ], + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json index 8fb8a2c6db..fc8df5a739 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json @@ -186,6 +186,12 @@ }, "loss": "binary_crossentropy", "text_size": 15, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/basic_twitter140_part.json b/deeppavlov/configs/evolution/basic_twitter140_part.json index ce25c033da..6aa5ddea01 100644 --- a/deeppavlov/configs/evolution/basic_twitter140_part.json +++ b/deeppavlov/configs/evolution/basic_twitter140_part.json @@ -173,6 +173,12 @@ "loss": "binary_crossentropy", "confident_threshold": 1, "text_size": 30, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/check_config.json b/deeppavlov/configs/evolution/check_config.json deleted file mode 100644 index 0157d26a1e..0000000000 --- a/deeppavlov/configs/evolution/check_config.json +++ /dev/null @@ -1 +0,0 @@ -{"dataset_reader": {"name": "basic_classification_reader", "x": ["question", "answer"], "y": "label", "data_path": "/home/dilyara.baymurzina/evolution_data/selqa_data"}, "dataset_iterator": {"name": "basic_classification_iterator"}, "chainer": {"in": ["question", "answer"], "in_y": ["y"], "pipe": [{"id": "classes_vocab", "name": "default_vocab", "fit_on": ["y"], "level": "token", "save_path": "/home/dilyara.baymurzina/evolution_data/selqa_data/selqa_classes.dict", "load_path": "/home/dilyara.baymurzina/evolution_data/selqa_data/selqa_classes.dict"}, {"in": ["question"], "out": ["question_lower"], "name": "str_lower"}, {"in": ["answer"], "out": ["answer_lower"], "name": "str_lower"}, {"id": "my_embedder", "name": "fasttext", "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", "dim": 300}, {"id": "my_tokenizer", "name": "nltk_tokenizer", "tokenizer": "wordpunct_tokenize"}, {"in": ["question_lower", "answer_lower"], "in_y": ["y"], "out": ["y_labels", "y_probas_dict"], "main": true, "name": "evolution_many_inputs_classification_model", "save_path": "/home/dilyara.baymurzina/evolution_data/selqa_classification/one_neuron_init_part_many_inputs/population_3/evolution_many_inputs_classification_model_9/evolution_many_inputs_classification_model_9", "load_path": "/home/dilyara.baymurzina/evolution_data/selqa_classification/one_neuron_init_part_many_inputs/population_3/evolution_many_inputs_classification_model_9/evolution_many_inputs_classification_model_9", "classes": "#classes_vocab.keys()", "to_evolve": true, "optimizer": "Adam", "loss": "binary_crossentropy", "text_size": [20, 50], "last_layer_activation": "softmax", "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer", "n_types": 6, "n_layers": 5, "confident_threshold": 0.4913063945020907, "lear_rate": 0.06101558390361756, "lear_rate_decay": 0.06011880458410778, "0_0_0": {"node_name": "Dense", "node_type": 0, "node_layer": 0, "units": 334, "activation": "sigmoid"}, "0_1_1": {"node_name": "Conv1D", "node_type": 1, "node_layer": 0, "padding": "same", "filters": 70, "kernel_size": 2}, "0_2_2": {"node_name": "CuDNNLSTM", "node_type": 2, "node_layer": 0, "return_sequences": true, "units": 92}, "0_3_3": {"node_name": "BiCuDNNLSTM", "node_type": 3, "node_layer": 0, "return_sequences": true, "units": 280}, "0_4_4": {"node_name": "MaxPooling1D", "node_type": 4, "node_layer": 0, "padding": "same", "pool_size": 5}, "0_5_5": {"node_name": "SelfMultiplicativeAttention", "node_type": 5, "node_layer": 0, "n_hidden": 478, "n_output_features": 184, "activation": "softmax"}, "1_0_6": {"node_name": "Dense", "node_type": 0, "node_layer": 1, "units": 452, "activation": "sigmoid"}, "1_1_7": {"node_name": "Conv1D", "node_type": 1, "node_layer": 1, "padding": "same", "filters": 381, "kernel_size": 4}, "1_2_8": {"node_name": "CuDNNLSTM", "node_type": 2, "node_layer": 1, "return_sequences": true, "units": 203}, "1_3_9": {"node_name": "BiCuDNNLSTM", "node_type": 3, "node_layer": 1, "return_sequences": true, "units": 402}, "1_4_10": {"node_name": "MaxPooling1D", "node_type": 4, "node_layer": 1, "padding": "same", "pool_size": 2}, "1_5_11": {"node_name": "SelfMultiplicativeAttention", "node_type": 5, "node_layer": 1, "n_hidden": 385, "n_output_features": 212, "activation": "sigmoid"}, "2_0_12": {"node_name": "Dense", "node_type": 0, "node_layer": 2, "units": 355, "activation": "relu"}, "2_1_13": {"node_name": "Conv1D", "node_type": 1, "node_layer": 2, "padding": "same", "filters": 413, "kernel_size": 4}, "2_2_14": {"node_name": "CuDNNLSTM", "node_type": 2, "node_layer": 2, "return_sequences": true, "units": 192}, "2_3_15": {"node_name": "BiCuDNNLSTM", "node_type": 3, "node_layer": 2, "return_sequences": true, "units": 427}, "2_4_16": {"node_name": "MaxPooling1D", "node_type": 4, "node_layer": 2, "padding": "same", "pool_size": 4}, "2_5_17": {"node_name": "SelfMultiplicativeAttention", "node_type": 5, "node_layer": 2, "n_hidden": 274, "n_output_features": 465, "activation": "sigmoid"}, "3_0_18": {"node_name": "Dense", "node_type": 0, "node_layer": 3, "units": 489, "activation": "softmax"}, "3_1_19": {"node_name": "Conv1D", "node_type": 1, "node_layer": 3, "padding": "same", "filters": 373, "kernel_size": 4}, "3_2_20": {"node_name": "CuDNNLSTM", "node_type": 2, "node_layer": 3, "return_sequences": true, "units": 463}, "3_3_21": {"node_name": "BiCuDNNLSTM", "node_type": 3, "node_layer": 3, "return_sequences": true, "units": 166}, "3_4_22": {"node_name": "MaxPooling1D", "node_type": 4, "node_layer": 3, "padding": "same", "pool_size": 3}, "3_5_23": {"node_name": "SelfMultiplicativeAttention", "node_type": 5, "node_layer": 3, "n_hidden": 315, "n_output_features": 462, "activation": "sigmoid"}, "4_0_24": {"node_name": "Dense", "node_type": 0, "node_layer": 4, "units": 482, "activation": "softmax"}, "4_1_25": {"node_name": "Conv1D", "node_type": 1, "node_layer": 4, "padding": "same", "filters": 187, "kernel_size": 4}, "4_2_26": {"node_name": "CuDNNLSTM", "node_type": 2, "node_layer": 4, "return_sequences": true, "units": 462}, "4_3_27": {"node_name": "BiCuDNNLSTM", "node_type": 3, "node_layer": 4, "return_sequences": true, "units": 181}, "4_4_28": {"node_name": "MaxPooling1D", "node_type": 4, "node_layer": 4, "padding": "same", "pool_size": 3}, "4_5_29": {"node_name": "SelfMultiplicativeAttention", "node_type": 5, "node_layer": 4, "n_hidden": 469, "n_output_features": 91, "activation": "sigmoid"}, "binary_mask": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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["classification_log_loss", "classification_accuracy", "classification_f1", "classification_roc_auc"], "validation_patience": 5, "val_every_n_epochs": 5, "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, "test_best": true, "epochs": 77, "batch_size": 51}, "metadata": {"labels": {"telegram_utils": "IntentModel"}}} diff --git a/deeppavlov/configs/evolution/intents_snli.json b/deeppavlov/configs/evolution/intents_snli.json index fb9bf4fa12..d056913902 100644 --- a/deeppavlov/configs/evolution/intents_snli.json +++ b/deeppavlov/configs/evolution/intents_snli.json @@ -80,7 +80,12 @@ "text_size": 51, "coef_reg_cnn": 1e-4, "coef_reg_den": 1e-4, - "dropout_rate": 0.5, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "dense_size": 100, "model_name": "cnn_model", "embedder": "#my_embedder", diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index d078c521af..713e4271f2 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -225,14 +225,15 @@ def initialize_all_nodes(self, params): node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") - model_layers[params["nodes"][node_str_id]] = Bidirectional(CuDNNLSTM(**node_params)) + model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( + Bidirectional(CuDNNLSTM(**node_params))) elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") - model_layers[params["nodes"][node_str_id]] = \ - multiplicative_self_attention_init(**node_params) + model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( + multiplicative_self_attention_init(**node_params)) else: node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) node_params = deepcopy(params[params["nodes"][node_str_id]]) @@ -240,7 +241,8 @@ def initialize_all_nodes(self, params): node_params.pop("node_type") node_params.pop("node_layer") if callable(node_func): - model_layers[params["nodes"][node_str_id]] = node_func(**node_params) + model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( + node_func(**node_params)) else: raise AttributeError("Node {} is not defined correctly".format(node_str_id)) From 3291717d05d5236a86a7f782b61b3be281210807 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 7 Jun 2018 15:19:25 +0300 Subject: [PATCH 156/616] feat: add dropout --- deeppavlov/models/evolution/evolution_intent_model.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index d99f3f28e1..0755309765 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -96,13 +96,13 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") - output_of_node = Bidirectional(CuDNNLSTM(**node_params))(inp) + output_of_node = Dropout(rate=params['dropout_rate'])(Bidirectional(CuDNNLSTM(**node_params))(inp)) elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") - output_of_node = multiplicative_self_attention(inp, **node_params) + output_of_node = Dropout(rate=params['dropout_rate'])(multiplicative_self_attention(inp, **node_params)) else: node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) node_params = deepcopy(params[params["nodes"][node_str_id]]) @@ -110,7 +110,7 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) node_params.pop("node_type") node_params.pop("node_layer") if callable(node_func): - output_of_node = node_func(**node_params)(inp) + output_of_node = Dropout(rate=params['dropout_rate'])(node_func(**node_params)(inp)) else: raise AttributeError("Node {} is not defined correctly".format(node_str_id)) return output_of_node @@ -129,6 +129,7 @@ def evolution_classification_model(self, params): if np.sum(params["binary_mask"]) == 0: output = Dense(1, activation=None)(inp) output = GlobalMaxPooling1D()(output) + output = Dropout(rate=params['dropout_rate'])(output) output = Dense(self.n_classes, activation=None)(output) activation = params.get("last_layer_activation", "sigmoid") act_output = Activation(activation)(output) From 79029e0cb06ed8c13abc7742122e9db356c17911 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 7 Jun 2018 15:34:02 +0300 Subject: [PATCH 157/616] feat: add l2 regularization --- .../configs/evolution/basic_ag_news_part.json | 30 ++++++++++++++-- .../configs/evolution/basic_ru_snli_part.json | 30 ++++++++++++++-- .../basic_ru_snli_part_many_inputs.json | 30 ++++++++++++++-- .../basic_snips_one_neuron_init.json | 30 ++++++++++++++-- .../evolution/basic_snips_random_init.json | 30 ++++++++++++++-- .../configs/evolution/basic_snli_part.json | 30 ++++++++++++++-- .../basic_snli_part_many_inputs.json | 30 ++++++++++++++-- .../basic_snli_part_many_inputs_big.json | 35 ++++++++++++++++--- .../evolution/basic_twitter140_part.json | 30 ++++++++++++++-- .../evolution/evolution_intent_model.py | 14 ++++++-- .../evolution/evolution_many_inputs_model.py | 14 ++++++-- 11 files changed, 270 insertions(+), 33 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json index 41461637b5..3511d9e279 100644 --- a/deeppavlov/configs/evolution/basic_ag_news_part.json +++ b/deeppavlov/configs/evolution/basic_ag_news_part.json @@ -83,6 +83,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -100,7 +106,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -110,7 +122,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -120,7 +138,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part.json b/deeppavlov/configs/evolution/basic_ru_snli_part.json index cbaba4aaa3..89948242e7 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part.json @@ -83,6 +83,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -100,7 +106,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -110,7 +122,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -120,7 +138,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json index e89eead7fe..db5efcf723 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json @@ -94,6 +94,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -111,7 +117,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -121,7 +133,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -131,7 +149,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 0f84c322bc..5aae1eb930 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -80,6 +80,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -97,7 +103,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -107,7 +119,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -117,7 +135,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index ada0c083e4..0624d150e6 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -80,6 +80,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -97,7 +103,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -107,7 +119,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -117,7 +135,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index 8c3a0024e3..af00b8b899 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -83,6 +83,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -100,7 +106,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -110,7 +122,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -120,7 +138,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index 28563e55e8..0e276ea7ec 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -97,6 +97,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -114,7 +120,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -124,7 +136,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -134,7 +152,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json index fc8df5a739..7f09f8ad79 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json @@ -1,7 +1,10 @@ { "dataset_reader": { "name": "basic_classification_reader", - "x": ["sentence1", "sentence2"], + "x": [ + "sentence1", + "sentence2" + ], "y": "gold_label", "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/two_texts/part" }, @@ -91,6 +94,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -108,7 +117,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -118,7 +133,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -128,7 +149,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_twitter140_part.json b/deeppavlov/configs/evolution/basic_twitter140_part.json index 6aa5ddea01..a4dc4be135 100644 --- a/deeppavlov/configs/evolution/basic_twitter140_part.json +++ b/deeppavlov/configs/evolution/basic_twitter140_part.json @@ -83,6 +83,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -100,7 +106,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -110,7 +122,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -120,7 +138,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 0755309765..237f4bbfb3 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -96,7 +96,11 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") - output_of_node = Dropout(rate=params['dropout_rate'])(Bidirectional(CuDNNLSTM(**node_params))(inp)) + l2_reg = node_params.get("coef_regul_l2") + node_params.pop("l2_reg") + output_of_node = Dropout(rate=params['dropout_rate'])( + Bidirectional(CuDNNLSTM(**node_params, + kernel_regularizer=l2(l2_reg)))(inp)) elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") @@ -109,8 +113,14 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") + l2_reg = node_params.get("coef_regul_l2") if callable(node_func): - output_of_node = Dropout(rate=params['dropout_rate'])(node_func(**node_params)(inp)) + if l2_reg is None: + output_of_node = Dropout(rate=params['dropout_rate'])(node_func(**node_params)(inp)) + else: + node_params.pop("l2_reg") + output_of_node = Dropout(rate=params['dropout_rate'])( + node_func(**node_params, kernel_regularizer=l2(l2_reg))(inp)) else: raise AttributeError("Node {} is not defined correctly".format(node_str_id)) return output_of_node diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index 713e4271f2..82d5beb3c0 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -225,8 +225,10 @@ def initialize_all_nodes(self, params): node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") + l2_reg = node_params.get("coef_regul_l2") + node_params.pop("l2_reg") model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( - Bidirectional(CuDNNLSTM(**node_params))) + Bidirectional(CuDNNLSTM(**node_params, kernel_regularizer=l2(l2_reg)))) elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") @@ -240,9 +242,15 @@ def initialize_all_nodes(self, params): node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") + l2_reg = node_params.get("coef_regul_l2") if callable(node_func): - model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( - node_func(**node_params)) + if l2_reg is None: + model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( + node_func(**node_params)) + else: + node_params.pop("l2_reg") + model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( + node_func(**node_params, kernel_regularizer=l2(l2_reg))) else: raise AttributeError("Node {} is not defined correctly".format(node_str_id)) From 85cdc6d67bd5a7cfd7af7ef6d029e65ee5ccccfe Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 7 Jun 2018 15:56:39 +0300 Subject: [PATCH 158/616] feat: add l2 regularization --- deeppavlov/models/evolution/evolution_intent_model.py | 4 ++-- deeppavlov/models/evolution/evolution_many_inputs_model.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 237f4bbfb3..5fff0edff1 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -97,7 +97,7 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) node_params.pop("node_type") node_params.pop("node_layer") l2_reg = node_params.get("coef_regul_l2") - node_params.pop("l2_reg") + node_params.pop("coef_regul_l2") output_of_node = Dropout(rate=params['dropout_rate'])( Bidirectional(CuDNNLSTM(**node_params, kernel_regularizer=l2(l2_reg)))(inp)) @@ -118,7 +118,7 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) if l2_reg is None: output_of_node = Dropout(rate=params['dropout_rate'])(node_func(**node_params)(inp)) else: - node_params.pop("l2_reg") + node_params.pop("coef_regul_l2") output_of_node = Dropout(rate=params['dropout_rate'])( node_func(**node_params, kernel_regularizer=l2(l2_reg))(inp)) else: diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index 82d5beb3c0..ff122405a8 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -226,7 +226,7 @@ def initialize_all_nodes(self, params): node_params.pop("node_type") node_params.pop("node_layer") l2_reg = node_params.get("coef_regul_l2") - node_params.pop("l2_reg") + node_params.pop("coef_regul_l2") model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( Bidirectional(CuDNNLSTM(**node_params, kernel_regularizer=l2(l2_reg)))) elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": @@ -248,7 +248,7 @@ def initialize_all_nodes(self, params): model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( node_func(**node_params)) else: - node_params.pop("l2_reg") + node_params.pop("coef_regul_l2") model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( node_func(**node_params, kernel_regularizer=l2(l2_reg))) else: From 4ae085dab8084e125e41fae565e9db98a3271bcc Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 10:50:48 +0300 Subject: [PATCH 159/616] fix: params in config reduced --- deeppavlov/configs/evolution/basic_ag_news_part.json | 4 ++-- deeppavlov/configs/evolution/basic_ru_snli_part.json | 6 +++--- .../configs/evolution/basic_ru_snli_part_many_inputs.json | 6 +++--- .../configs/evolution/basic_snips_one_neuron_init.json | 8 ++++---- deeppavlov/configs/evolution/basic_snips_random_init.json | 2 +- deeppavlov/configs/evolution/basic_snli_part.json | 6 +++--- .../configs/evolution/basic_snli_part_many_inputs.json | 6 +++--- .../evolution/basic_snli_part_many_inputs_big.json | 6 +++--- deeppavlov/configs/evolution/basic_twitter140_part.json | 6 +++--- 9 files changed, 25 insertions(+), 25 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json index 3511d9e279..68ee42a6cc 100644 --- a/deeppavlov/configs/evolution/basic_ag_news_part.json +++ b/deeppavlov/configs/evolution/basic_ag_news_part.json @@ -160,14 +160,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part.json b/deeppavlov/configs/evolution/basic_ru_snli_part.json index 89948242e7..4a3ce204d3 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part.json @@ -160,14 +160,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -225,7 +225,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json index db5efcf723..680b4804a0 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json @@ -171,14 +171,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -236,7 +236,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 5aae1eb930..4b3f8f4718 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -69,7 +69,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -115,7 +115,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -131,7 +131,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -221,7 +221,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index 0624d150e6..573e8841c2 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -221,7 +221,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index af00b8b899..7c5198e947 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -160,14 +160,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -225,7 +225,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index 0e276ea7ec..69a694dc19 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -174,14 +174,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -242,7 +242,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json index 7f09f8ad79..8259544e97 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json @@ -171,14 +171,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -241,7 +241,7 @@ "batch_size": { "range": [ 50, - 70 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_twitter140_part.json b/deeppavlov/configs/evolution/basic_twitter140_part.json index a4dc4be135..7ef90990dd 100644 --- a/deeppavlov/configs/evolution/basic_twitter140_part.json +++ b/deeppavlov/configs/evolution/basic_twitter140_part.json @@ -160,14 +160,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -225,7 +225,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, From 923c35e3328dd44199083eced0306ad764c9abbb Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 11:25:00 +0300 Subject: [PATCH 160/616] fix: params in config reduced --- deeppavlov/configs/evolution/basic_ag_news_part.json | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json index 68ee42a6cc..128146e58e 100644 --- a/deeppavlov/configs/evolution/basic_ag_news_part.json +++ b/deeppavlov/configs/evolution/basic_ag_news_part.json @@ -197,6 +197,12 @@ "loss": "binary_crossentropy", "confident_threshold": 1, "text_size": 50, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", From 46f60a4a335329280f40ad35f18a99b3d6d02256 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 12:10:20 +0300 Subject: [PATCH 161/616] feat: probability based selection and crossover --- .../neuroevolution_param_generator.py | 108 +++++++++--------- 1 file changed, 55 insertions(+), 53 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 68656cd901..c8933d54dc 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -268,35 +268,30 @@ def next_generation(self, generation, scores, iteration, if not mutation_power: mutation_power = self.mutation_power - if iteration % self.renovation_frequency != 0: - self.n_saved_best_with_weights = 2 * self.n_saved_best_with_weights_first - else: - self.n_saved_best_with_weights = self.n_saved_best_with_weights_first - - selected_individuals = self.selection(generation, scores) + # here self.n_saved_best_with_weights = len(next_population) + next_population = self.selection_of_best_with_weights(generation, scores) - offsprings = self.crossover(selected_individuals, + offsprings = self.crossover(generation, p_crossover=p_crossover, crossover_power=crossover_power) - next_population = offsprings[:self.n_saved_best_with_weights] - changable_individuals = offsprings[self.n_saved_best_with_weights:] - - changable_next = self.mutation(changable_individuals, + changable_next = self.mutation(offsprings, p_mutation=p_mutation, mutation_power=mutation_power) next_population.extend(changable_next) for i in range(self.n_saved_best_with_weights): + # if several train files: if self.train_partition != 1: next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ "train"]).stem.split("_")[0]) \ + "_" + str(iteration % self.train_partition) + ".csv" - # re init learning rate with the final one + # re-init learning rate with the final one next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index][ "save_path"]).parent.joinpath("model_opt.json")))["final_lear_rate"] + # paths next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ @@ -304,10 +299,12 @@ def next_generation(self, generation, scores, iteration, self.params["model_name"] + "_" + str(i))) for i in range(self.n_saved_best_with_weights, self.population_size): + # if several train files if self.train_partition != 1: next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ "train"]).stem.split("_")[0]) \ + "_" + str(iteration % self.train_partition) + ".csv" + # paths next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) @@ -320,52 +317,61 @@ def next_generation(self, generation, scores, iteration, return next_population - def selection(self, population, scores): + def selection_of_best_with_weights(self, population, scores): """ - Select self.population_size individuums (with replacement) from given population. - Probability of i-th individuum to be selected is scores_i / sum_j(scores_j) + Select individuums to save with weights for the next generation from given population. + Range is an order of an individuum within sorted scores (1 range = max-score, self.population_size = min-score) + Individuum with the highest score has probability equal to 1 (100%). + Individuum with the lowest score has probability equal to 0.05 (5%). + Probability of i-th individuum to be selected with weights is (a / range_i + b) + where a = 0.95 * self.population_size / (self.population_size - 1), and + b = (0.05 * self.population_size - 1) / (self.population_size - 1). Args: population: self.population_size individuums scores: corresponding score that should be maximized Returns: - selected self.population_size individuums with replacement + selected self.n_saved_best_with_weights (changable) individuums """ scores = np.array(scores, dtype='float') - scores = (scores - 1.1 * min(scores) + 0.1 * max(scores)) - total = np.sum(scores) - probas_to_be_selected = scores / total - intervals = np.array([np.sum(probas_to_be_selected[:i]) for i in range(self.population_size)]) - selected = [] + sorted_ids = np.argsort(scores) + # the same order as scores but ranges + ranges = np.array([self.population_size - np.where(i == sorted_ids)[0][0] + for i in np.arange(self.population_size)]) + # probas = a / ranges + b + a = 0.95 * self.population_size / (self.population_size - 1) + b = (0.05 * self.population_size - 1) / (self.population_size - 1) + probas_to_be_selected = a / ranges + b - for i in range(self.n_saved_best_with_weights): - ind_id = np.argsort(scores)[-(1+i)] - new = deepcopy(population[ind_id]) - selected.append(new) + selected = [] + for i in range(self.population_size): + if self.decision(probas_to_be_selected[i]): + selected.append(deepcopy(population[i])) - for i in range(self.n_saved_best_with_weights, self.population_size): - r = np.random.random() - individuum = deepcopy(population[np.where(r > intervals)[0][-1]]) - selected.append(individuum) + self.n_saved_best_with_weights = len(selected) return selected def crossover(self, population, p_crossover, crossover_power): """ Recombine randomly population in pairs and cross over them with given probability. Cross over from two parents produces two offsprings - each of which contains half of the parameter values from one parent and the other half from the other parent + each of which contains crossover_power portion of the parameter values from one parent, + and the other (1 - crossover_power portion) from the other parent Args: population: self.population_size individuums p_crossover: probability to cross over for current replacement crossover_power: part of EVOLVING parents parameters to exchange for offsprings Returns: - part_of_population offsprings + (self.population_size - self.n_saved_best_with_weights) offsprings """ perm = np.random.permutation(self.population_size) - offsprings = deepcopy(population) - for i in range(self.population_size // 2): - parents = population[perm[2 * i]], population[perm[2 * i + 1]] + offsprings = [] + + for i in range(self.population_size - self.n_saved_best_with_weights): + parent_ids = np.random.choice(self.population_size, size=2) + parents = population[parent_ids[0]], population[parent_ids[1]] + if self.decision(p_crossover): params_perm = np.random.permutation(self.n_evolving_params) train_params_perm = np.random.permutation(self.n_evolving_train_params) @@ -437,21 +443,26 @@ def crossover(self, population, p_crossover, crossover_power): for j in range(self.total_nodes * self.total_nodes - binary_mask_part): node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ - parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ - parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"][node_x, node_y] = parents[0]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"][node_x, node_y] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"][node_x, node_y] = parents[1]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"][node_x, node_y] for j in range(self.total_nodes * self.total_nodes - binary_mask_part, self.total_nodes * self.total_nodes): node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ - parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ - parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"][node_x, node_y] = parents[1]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"][node_x, node_y] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"][node_x, node_y] = parents[0]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"][node_x, node_y] - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"] = \ check_and_correct_binary_mask(self.nodes, curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ "binary_mask"]) @@ -460,17 +471,8 @@ def crossover(self, population, p_crossover, crossover_power): curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ "binary_mask"]) - if perm[2 * i] in range(self.n_saved_best_with_weights): - offsprings[perm[2 * i]] = deepcopy(population[perm[2 * i]]) - else: - offsprings[perm[2 * i]] = deepcopy(curr_offsprings[0]) - if perm[2 * i + 1] in range(self.n_saved_best_with_weights): - offsprings[perm[2 * i + 1]] = deepcopy(population[perm[2 * i + 1]]) - else: - offsprings[perm[2 * i + 1]] = deepcopy(curr_offsprings[1]) + offsprings.append(deepcopy(curr_offsprings[0])) - if self.population_size % 2 == 1: - offsprings[-1] = deepcopy(population[perm[-1]]) return offsprings def mutation(self, population, p_mutation, mutation_power): From 571cc95d190f01dd5df2d507fec9d3860c4f32c7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 12:11:58 +0300 Subject: [PATCH 162/616] feat: probability based selection and crossover --- .../evolution/neuroevolution_param_generator.py | 6 +----- deeppavlov/models/evolution/run_evolution.py | 13 +------------ 2 files changed, 2 insertions(+), 17 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index c8933d54dc..6a30009cc0 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -35,8 +35,6 @@ def __init__(self, n_layers, n_types, seed=None, start_with_one_neuron=False, evolve_binary_mask=True, - save_best_with_weights_portion=0, - renovation_frequency=1, train_partition=1, initial_binary_mask=None, **kwargs): @@ -109,9 +107,7 @@ def __init__(self, n_layers, n_types, self.evolving_train_params = [] self.n_evolving_train_params = None self.evolve_binary_mask = evolve_binary_mask - self.n_saved_best_with_weights = int(save_best_with_weights_portion * self.population_size) - self.n_saved_best_with_weights_first = self.n_saved_best_with_weights - self.renovation_frequency = renovation_frequency + self.n_saved_best_with_weights = 0 self.train_partition = train_partition self.evolution_individuum_id = 0 self.evolution_model_id = 0 diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 81205386df..96cc4c9260 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -98,13 +98,6 @@ def score_population(population, population_size, result_file): parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) parser.add_argument('--one_neuron_init', help='whether to start with zero binary mask (one neuron network)', default=0) parser.add_argument('--given_mask_init', help='whether to start with given binary mask', default=0) -parser.add_argument('--save_best_portion', - help='Please, enter portion of population to save for the next generation with weights', - default=0.) -parser.add_argument('--renovation_frequency', - help='Please, enter frequency of renovation (how often in terms of generations ' - 'to renovate the second best portion)', - default=1) parser.add_argument('--train_partition', help='Please, enter partition of splitted train', default=1) @@ -112,6 +105,7 @@ def score_population(population, population_size, result_file): args = parser.parse_args() CONFIG_FILE = args.config +EVOLVE_METRIC = args.evolve_metric POPULATION_SIZE = args.p_size GPU_NUMBER = len(args.gpus) gpus = [int(gpu) for gpu in args.gpus.split(",")] @@ -119,9 +113,6 @@ def score_population(population, population_size, result_file): N_TYPES = int(args.n_types) ONE_NEURON_INIT = bool(int(args.one_neuron_init)) GIVEN_MASK_INIT = bool(int(args.given_mask_init)) -EVOLVE_METRIC = args.evolve_metric -SAVE_BEST_PORTION = float(args.save_best_portion) -RENOVATION_FREQUENCY = int(args.renovation_frequency) TRAIN_PARTITION = int(args.train_partition) with open(CONFIG_FILE, "r") as f: @@ -149,8 +140,6 @@ def score_population(population, population_size, result_file): key_basic_layers="basic_layers_params", seed=42, start_with_one_neuron=ONE_NEURON_INIT, - save_best_with_weights_portion=SAVE_BEST_PORTION, - renovation_frequency=RENOVATION_FREQUENCY, train_partition=TRAIN_PARTITION, initial_binary_mask=INITIAL_BINARY_MASK, **basic_params) From cd34443cc5e355bf6b0b7d28a6fd530830a3bfa3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 12:25:35 +0300 Subject: [PATCH 163/616] fix: add dropout in another place of network for many inputs --- .../evolution/evolution_many_inputs_model.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index ff122405a8..7fc9e7d155 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -211,6 +211,8 @@ def get_node_output(self, model_layers, node_str_id, dg, params, edges_outputs=N node_params.pop("node_type") node_params.pop("node_layer") output_of_node = model_layers[params["nodes"][node_str_id]](inp) + + output_of_node = Dropout(rate=params['dropout_rate'])(output_of_node) return output_of_node def initialize_all_nodes(self, params): @@ -227,15 +229,14 @@ def initialize_all_nodes(self, params): node_params.pop("node_layer") l2_reg = node_params.get("coef_regul_l2") node_params.pop("coef_regul_l2") - model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( - Bidirectional(CuDNNLSTM(**node_params, kernel_regularizer=l2(l2_reg)))) + model_layers[params["nodes"][node_str_id]] = Bidirectional(CuDNNLSTM(**node_params, + kernel_regularizer=l2(l2_reg))) elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") - model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( - multiplicative_self_attention_init(**node_params)) + model_layers[params["nodes"][node_str_id]] = multiplicative_self_attention_init(**node_params) else: node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) node_params = deepcopy(params[params["nodes"][node_str_id]]) @@ -245,12 +246,11 @@ def initialize_all_nodes(self, params): l2_reg = node_params.get("coef_regul_l2") if callable(node_func): if l2_reg is None: - model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( - node_func(**node_params)) + model_layers[params["nodes"][node_str_id]] = node_func(**node_params) else: node_params.pop("coef_regul_l2") - model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( - node_func(**node_params, kernel_regularizer=l2(l2_reg))) + model_layers[params["nodes"][node_str_id]] = node_func(**node_params, + kernel_regularizer=l2(l2_reg)) else: raise AttributeError("Node {} is not defined correctly".format(node_str_id)) From 8ccdec3849a7320a1eda7e3a276999dfcb13aa11 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 12:49:38 +0300 Subject: [PATCH 164/616] fix: prints --- .../models/evolution/neuroevolution_param_generator.py | 7 ++++++- deeppavlov/models/evolution/run_evolution.py | 4 ++-- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 6a30009cc0..3cf2738957 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -247,6 +247,7 @@ def next_generation(self, generation, scores, iteration, Args: generation: current generation (set of self.population_size configs scores: corresponding scores that should be maximized + iteration: iteration number p_crossover: probability to cross over for current replacement crossover_power: part of parents parameters to exchange for offsprings p_mutation: probability of mutation for current replacement @@ -266,16 +267,20 @@ def next_generation(self, generation, scores, iteration, # here self.n_saved_best_with_weights = len(next_population) next_population = self.selection_of_best_with_weights(generation, scores) - + print("Saved with weights: {} individuums".format(self.n_saved_best_with_weights)) offsprings = self.crossover(generation, p_crossover=p_crossover, crossover_power=crossover_power) + print("Number of offsprings: {} individuums".format(len(offsprings))) + changable_next = self.mutation(offsprings, p_mutation=p_mutation, mutation_power=mutation_power) + print("Number of mutated: {} individuums".format(len(changable_next))) next_population.extend(changable_next) + print("Next population: {} individuums".format(len(next_population))) for i in range(self.n_saved_best_with_weights): # if several train files: diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 96cc4c9260..0d2faab2e2 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -169,7 +169,7 @@ def score_population(population, population_size, result_file): print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() -print("Considered population: {}\nScoring...\n".format(population)) +# print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] iters = 1 @@ -178,7 +178,7 @@ def score_population(population, population_size, result_file): print("\nIteration #{} starts\n".format(iters)) population = evolution.next_generation(population, population_scores, iters) - print("Considered population: {}\nScoring...\n".format(population)) + # print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] print("Population scores: {}".format(population_scores)) print("\nIteration #{} was done\n".format(iters)) From 73335b67068b1b266f1659a7e8dccc5abcecb6ff Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 14:20:44 +0300 Subject: [PATCH 165/616] fix: if no crossover --- .../models/evolution/neuroevolution_param_generator.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 3cf2738957..8a3c4300a2 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -272,15 +272,15 @@ def next_generation(self, generation, scores, iteration, p_crossover=p_crossover, crossover_power=crossover_power) - print("Number of offsprings: {} individuums".format(len(offsprings))) + # print("Number of offsprings: {} individuums".format(len(offsprings))) changable_next = self.mutation(offsprings, p_mutation=p_mutation, mutation_power=mutation_power) - print("Number of mutated: {} individuums".format(len(changable_next))) + # print("Number of mutated: {} individuums".format(len(changable_next))) next_population.extend(changable_next) - print("Next population: {} individuums".format(len(next_population))) + # print("Next population: {} individuums".format(len(next_population))) for i in range(self.n_saved_best_with_weights): # if several train files: @@ -366,7 +366,6 @@ def crossover(self, population, p_crossover, crossover_power): Returns: (self.population_size - self.n_saved_best_with_weights) offsprings """ - perm = np.random.permutation(self.population_size) offsprings = [] for i in range(self.population_size - self.n_saved_best_with_weights): @@ -473,6 +472,8 @@ def crossover(self, population, p_crossover, crossover_power): "binary_mask"]) offsprings.append(deepcopy(curr_offsprings[0])) + else: + offsprings.append(deepcopy(parents[0])) return offsprings From e627e34028bc8c0ae577d7ca5fc07cc800981146 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 14:21:02 +0300 Subject: [PATCH 166/616] fix: if no crossover --- deeppavlov/models/evolution/run_evolution.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 0d2faab2e2..0288ad04d8 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -134,7 +134,7 @@ def score_population(population, population_size, result_file): # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, - p_crossover=0.2, crossover_power=0.2, + p_crossover=0.2, crossover_power=0.1, p_mutation=1., mutation_power=0.1, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", From 5d19f8818ee690f84ff10860e18bffe64ed1e72e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 15:55:40 +0300 Subject: [PATCH 167/616] feat: config for nlu_benchmark --- .../configs/evolution/basic_nlu_part.json | 250 ++++++++++++++++++ 1 file changed, 250 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_nlu_part.json diff --git a/deeppavlov/configs/evolution/basic_nlu_part.json b/deeppavlov/configs/evolution/basic_nlu_part.json new file mode 100644 index 0000000000..727290d280 --- /dev/null +++ b/deeppavlov/configs/evolution/basic_nlu_part.json @@ -0,0 +1,250 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_data/ChatbotCorpus", + "train": "train_ChatbotCorpus_0.csv", + "valid": "valid_ChatbotCorpus_0.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_data/ChatbotCorpus/classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_data/ChatbotCorpus/classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_classification/ChatbotCorpus/one_neuron_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_classification/ChatbotCorpus/one_neuron_init_part_6", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "confident_threshold": 1, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.000001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, + "last_layer_activation": "softmax", + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 1, + 10 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 100 + ], + "discrete": true + }, + "metric_optimization": "maximize", + "metrics": [ + "classification_f1", + "classification_accuracy", + "classification_log_loss", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From dfe87c95c40f111621a0aca47873435a13771375 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 16:12:12 +0300 Subject: [PATCH 168/616] fix: config --- deeppavlov/configs/evolution/basic_nlu_part.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_nlu_part.json b/deeppavlov/configs/evolution/basic_nlu_part.json index 727290d280..3dec69c7cd 100644 --- a/deeppavlov/configs/evolution/basic_nlu_part.json +++ b/deeppavlov/configs/evolution/basic_nlu_part.json @@ -2,7 +2,7 @@ "dataset_reader": { "name": "basic_classification_reader", "x": "text", - "y": "gold_label", + "y": "intent", "data_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_data/ChatbotCorpus", "train": "train_ChatbotCorpus_0.csv", "valid": "valid_ChatbotCorpus_0.csv" From 88a1d770308da852d34aedc84553b7bee0e4991f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 16:31:08 +0300 Subject: [PATCH 169/616] fix: whether to save test metrics --- deeppavlov/models/evolution/run_evolution.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 0288ad04d8..d2c8dbf5da 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -55,11 +55,9 @@ def score_population(population, population_size, result_file): for i in range(population_size): val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ "save_path"]).parent.joinpath("valid_results.txt"))) - try: + if TEST: test_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ "save_path"]).parent.joinpath("test_results.txt"))) - except FileNotFoundError: - pass result_table_dict = {} for el in order: @@ -70,9 +68,9 @@ def score_population(population, population_size, result_file): result_table_dict[el + "_test"] = [] for m_id, m in enumerate(CONSIDERED_METRICS): result_table_dict[m + "_valid"].append(val_results[m_id]) - try: + if TEST: result_table_dict[m + "_test"].append(test_results[m_id]) - except NameError: + else: result_table_dict[m + "_test"].append(0.) result_table_dict[order[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) @@ -122,6 +120,8 @@ def score_population(population, population_size, result_file): # list of names of considered metrics CONSIDERED_METRICS = basic_params["train"]["metrics"] +VALID = basic_params["train"]["valid_best"] +TEST = basic_params["train"]["test_best"] if GIVEN_MASK_INIT: # Embedding -> BiLSTM -> Dense -> Dense -> GlobalMaxPooling -> Dense(#classes) From e8b65553684e0e8a3cad1690e7afefc3bb0e73da Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 16:37:18 +0300 Subject: [PATCH 170/616] fix: whether to save test metrics --- deeppavlov/models/evolution/run_evolution.py | 1 - 1 file changed, 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index d2c8dbf5da..a6c24a3059 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -120,7 +120,6 @@ def score_population(population, population_size, result_file): # list of names of considered metrics CONSIDERED_METRICS = basic_params["train"]["metrics"] -VALID = basic_params["train"]["valid_best"] TEST = basic_params["train"]["test_best"] if GIVEN_MASK_INIT: From 5400c314b56cf396d11368294f1fe3eabf9a6386 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 18:21:20 +0300 Subject: [PATCH 171/616] fix: result table --- deeppavlov/models/evolution/run_evolution.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index a6c24a3059..3a58a31059 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -151,9 +151,9 @@ def score_population(population, population_size, result_file): result_table_dict = {} for el in order: - if order == "params": + if el == "params": result_table_dict[el] = [] - result_table_columns.extend([el + "_valid"]) + result_table_columns.extend([el]) else: result_table_dict[el + "_valid"] = [] result_table_dict[el + "_test"] = [] From cfa906f5c770cfcb61c3394d5d7ad1f0cc258eb1 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 9 Jun 2018 11:08:20 +0300 Subject: [PATCH 172/616] feat: proba to be parent --- .../models/evolution/neuroevolution_param_generator.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 8a3c4300a2..5611e368b6 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -352,7 +352,7 @@ def selection_of_best_with_weights(self, population, scores): self.n_saved_best_with_weights = len(selected) return selected - def crossover(self, population, p_crossover, crossover_power): + def crossover(self, population, scores, p_crossover, crossover_power): """ Recombine randomly population in pairs and cross over them with given probability. Cross over from two parents produces two offsprings @@ -367,10 +367,13 @@ def crossover(self, population, p_crossover, crossover_power): (self.population_size - self.n_saved_best_with_weights) offsprings """ offsprings = [] + scores = np.array(scores, dtype='float') + probas_to_be_parent = scores / np.sum(scores) + intervals = np.array([np.sum(probas_to_be_parent[:i]) for i in range(self.population_size)]) for i in range(self.population_size - self.n_saved_best_with_weights): - parent_ids = np.random.choice(self.population_size, size=2) - parents = population[parent_ids[0]], population[parent_ids[1]] + rs = np.random.random(2) + parents = population[np.where(rs[0] > intervals)[0][-1]], population[np.where(rs[1] > intervals)[0][-1]] if self.decision(p_crossover): params_perm = np.random.permutation(self.n_evolving_params) From e70dbaaabd138d2b6050a440580015eb770ccf41 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 9 Jun 2018 12:11:43 +0300 Subject: [PATCH 173/616] =?UTF-8?q?=D0=B0=D1=83=D1=84=D0=B5=D0=96=20=D1=8B?= =?UTF-8?q?=D0=B8=D1=83=D0=BA=20=D0=B0=D1=84=D0=B9=20=D1=81=D1=89=D1=82?= =?UTF-8?q?=D0=B0=D1=88=D0=BF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../configs/evolution/basic_sber_faq.json | 251 ++++++++++++++++++ 1 file changed, 251 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_sber_faq.json diff --git a/deeppavlov/configs/evolution/basic_sber_faq.json b/deeppavlov/configs/evolution/basic_sber_faq.json new file mode 100644 index 0000000000..6410a1993b --- /dev/null +++ b/deeppavlov/configs/evolution/basic_sber_faq.json @@ -0,0 +1,251 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "label", + "data_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_data", + "train": "train.csv", + "valid": "val.csv", + "test": "test.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_data/classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_data/classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/one_neuron_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/one_neuron_init_part_6", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "confident_threshold": 1, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.000001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 60, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, + "last_layer_activation": "softmax", + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 1, + 10 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 100 + ], + "discrete": true + }, + "metric_optimization": "maximize", + "metrics": [ + "classification_f1", + "classification_accuracy", + "classification_log_loss", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "show_examples": false, + "validate_best": true, + "test_best": true + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From c6aed672dea9b43a4d36ad6758a66180f9f4ee41 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 9 Jun 2018 12:16:53 +0300 Subject: [PATCH 174/616] chore: config sber faq --- deeppavlov/configs/evolution/basic_sber_faq.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_sber_faq.json b/deeppavlov/configs/evolution/basic_sber_faq.json index 6410a1993b..a7dd310b66 100644 --- a/deeppavlov/configs/evolution/basic_sber_faq.json +++ b/deeppavlov/configs/evolution/basic_sber_faq.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/one_neuron_init_part_6", - "load_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/one_neuron_init_part_6", + "save_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/given_mask_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/given_mask_init_part_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { From 3592996024f020232ef63541830b5beb126cec71 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 9 Jun 2018 12:26:57 +0300 Subject: [PATCH 175/616] fix: crossover scores --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 5611e368b6..7c4632e018 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -268,7 +268,7 @@ def next_generation(self, generation, scores, iteration, # here self.n_saved_best_with_weights = len(next_population) next_population = self.selection_of_best_with_weights(generation, scores) print("Saved with weights: {} individuums".format(self.n_saved_best_with_weights)) - offsprings = self.crossover(generation, + offsprings = self.crossover(generation, scores, p_crossover=p_crossover, crossover_power=crossover_power) From 4b2ecf1e1c98fc17be25a496aa41bd9dd77e5db8 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 11:13:11 +0300 Subject: [PATCH 176/616] feat: classification_f1_weighted --- .../configs/evolution/basic_sber_faq.json | 1 + deeppavlov/metrics/fmeasure_classification.py | 25 ++++++++++++++++++- 2 files changed, 25 insertions(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_sber_faq.json b/deeppavlov/configs/evolution/basic_sber_faq.json index a7dd310b66..96ff27addd 100644 --- a/deeppavlov/configs/evolution/basic_sber_faq.json +++ b/deeppavlov/configs/evolution/basic_sber_faq.json @@ -232,6 +232,7 @@ "metric_optimization": "maximize", "metrics": [ "classification_f1", + "classification_f1_weighted", "classification_accuracy", "classification_log_loss", "classification_roc_auc" diff --git a/deeppavlov/metrics/fmeasure_classification.py b/deeppavlov/metrics/fmeasure_classification.py index 83ecc60c6a..502dfbaf73 100644 --- a/deeppavlov/metrics/fmeasure_classification.py +++ b/deeppavlov/metrics/fmeasure_classification.py @@ -25,7 +25,30 @@ @register_metric('classification_f1') def fmeasure(y_true, y_predicted, average="macro"): """ - Calculate F1-measure + Calculate F1-measure macro + Args: + y_true: array of true binary labels + y_predicted: list of predictions. + Each prediction is a tuple of two elements + (predicted_labels, dictionary like {"label_i": probability_i} ) + where probability is float or keras.tensor + average: determines the type of averaging performed on the data + + Returns: + F1-measure + """ + classes = np.array(list(y_predicted[0][1].keys())) + y_true_one_hot = labels2onehot(y_true, classes) + y_pred_labels = [y_predicted[i][0] for i in range(len(y_predicted))] + y_pred_one_hot = labels2onehot(y_pred_labels, classes) + + return f1_score(y_true_one_hot, y_pred_one_hot, average=average) + + +@register_metric('classification_f1_weighted') +def fmeasure(y_true, y_predicted, average="weighted"): + """ + Calculate F1-measure weighted Args: y_true: array of true binary labels y_predicted: list of predictions. From d0ca409b80df1d7df7aa16ca32df85e7677c6cf8 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 11:24:08 +0300 Subject: [PATCH 177/616] Merge branch 'master' of https://github.com/deepmipt/DeepPavlov into feature/network_evolution # Conflicts: # deeppavlov/core/commands/train.py --- deeppavlov/core/commands/train.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index 86598ac652..2c8e2d7f55 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -84,7 +84,7 @@ def fit_chainer(config: dict, iterator: Union[DataLearningIterator, DataFittingI return chainer -def train_model_from_config(config_path: str): +def train_model_from_config(config_path: str) -> None: config = read_json(config_path) set_deeppavlov_root(config) @@ -140,6 +140,7 @@ def train_model_from_config(config_path: str): train_config = { 'metrics': ['accuracy'], + 'validate_best': True, 'test_best': True } @@ -162,7 +163,6 @@ def train_model_from_config(config_path: str): log.warning('Nothing to train') if train_config['validate_best'] or train_config['test_best']: - all_reports = [] # try: # model_config['load_path'] = model_config['save_path'] # except KeyError: @@ -177,7 +177,6 @@ def train_model_from_config(config_path: str): } print(json.dumps(report, ensure_ascii=False)) - all_reports.append(report) if train_config['test_best']: report = { @@ -186,10 +185,6 @@ def train_model_from_config(config_path: str): } print(json.dumps(report, ensure_ascii=False)) - all_reports.append(report) - return all_reports - - return None def _test_model(model: Component, metrics_functions: List[Tuple[str, Callable]], From 75e83a4166cf021b4e440f05022f8e1cd5dbb5f3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 11:26:05 +0300 Subject: [PATCH 178/616] Merge branch 'master' of https://github.com/deepmipt/DeepPavlov into feature/network_evolution # Conflicts: # deeppavlov/core/commands/train.py --- deeppavlov/core/commands/train.py | 64 ++++++++++++++++++------------- 1 file changed, 37 insertions(+), 27 deletions(-) diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index 2c8e2d7f55..345d3d0f22 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -40,6 +40,17 @@ log = get_logger(__name__) +def prettify_metrics(metrics, precision=4): + """ + Prettifies the dictionary of metrics + """ + prettified_metrics = OrderedDict() + for key, value in metrics: + value = round(value, precision) + prettified_metrics[key] = value + return prettified_metrics + + def _fit(model: Estimator, iterator: DataLearningIterator, train_config) -> Estimator: x, y = iterator.get_instances('train') model.fit(x, y) @@ -84,7 +95,7 @@ def fit_chainer(config: dict, iterator: Union[DataLearningIterator, DataFittingI return chainer -def train_model_from_config(config_path: str) -> None: +def train_evaluate_model_from_config(config_path: str, to_train=True, to_validate=True) -> None: config = read_json(config_path) set_deeppavlov_root(config) @@ -127,21 +138,9 @@ def train_model_from_config(config_path: str) -> None: iterator: Union[DataLearningIterator, DataFittingIterator] = from_params(iterator_config, data=data) - if 'chainer' in config: - model = fit_chainer(config, iterator) - else: - vocabs = config.get('vocabs', {}) - for vocab_param_name, vocab_config in vocabs.items(): - v: Estimator = from_params(vocab_config, mode='train') - vocabs[vocab_param_name] = _fit(v, iterator, None) - - model_config = config['model'] - model = from_params(model_config, vocabs=vocabs, mode='train') - train_config = { 'metrics': ['accuracy'], - - 'validate_best': True, + 'validate_best': to_validate, 'test_best': True } @@ -150,17 +149,28 @@ def train_model_from_config(config_path: str) -> None: except KeyError: log.warning('Train config is missing. Populating with default values') - metrics_functions = list(zip(train_config['metrics'], - get_metrics_by_names(train_config['metrics']))) + metrics_functions = list(zip(train_config['metrics'], get_metrics_by_names(train_config['metrics']))) - if callable(getattr(model, 'train_on_batch', None)): - _train_batches(model, iterator, train_config, metrics_functions) - elif callable(getattr(model, 'fit_batches', None)): - _fit_batches(model, iterator, train_config) - elif callable(getattr(model, 'fit', None)): - _fit(model, iterator, train_config) - elif not isinstance(model, Chainer): - log.warning('Nothing to train') + if to_train: + if 'chainer' in config: + model = fit_chainer(config, iterator) + else: + vocabs = config.get('vocabs', {}) + for vocab_param_name, vocab_config in vocabs.items(): + v: Estimator = from_params(vocab_config, mode='train') + vocabs[vocab_param_name] = _fit(v, iterator, None) + + model_config = config['model'] + model = from_params(model_config, vocabs=vocabs, mode='train') + + if callable(getattr(model, 'train_on_batch', None)): + _train_batches(model, iterator, train_config, metrics_functions) + elif callable(getattr(model, 'fit_batches', None)): + _fit_batches(model, iterator, train_config) + elif callable(getattr(model, 'fit', None)): + _fit(model, iterator, train_config) + elif not isinstance(model, Chainer): + log.warning('Nothing to train') if train_config['validate_best'] or train_config['test_best']: # try: @@ -204,7 +214,7 @@ def _test_model(model: Component, metrics_functions: List[Tuple[str, Callable]], report = { 'eval_examples_count': len(val_y_true), - 'metrics': OrderedDict(metrics), + 'metrics': prettify_metrics(metrics), 'time_spent': str(datetime.timedelta(seconds=round(time.time() - start_time + 0.5))) } return report @@ -271,7 +281,7 @@ def improved(score, best): 'epochs_done': epochs, 'batches_seen': i, 'examples_seen': examples, - 'metrics': dict(metrics), + 'metrics': prettify_metrics(metrics), 'time_spent': str(datetime.timedelta(seconds=round(time.time() - start_time + 0.5))) } report = {'train': report} @@ -310,7 +320,7 @@ def improved(score, best): 'epochs_done': epochs, 'batches_seen': i, 'train_examples_seen': examples, - 'metrics': dict(metrics), + 'metrics': prettify_metrics(metrics), 'time_spent': str(datetime.timedelta(seconds=round(time.time() - start_time + 0.5))) } model.process_event(event_name='after_train_log', data=report) From afde4731fc508f80b989b3dae9bad0e88a23a886 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 12:31:33 +0300 Subject: [PATCH 179/616] fix: linear decay of probability to be selected with weights --- .../neuroevolution_param_generator.py | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 7c4632e018..a07d574373 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -323,10 +323,10 @@ def selection_of_best_with_weights(self, population, scores): Select individuums to save with weights for the next generation from given population. Range is an order of an individuum within sorted scores (1 range = max-score, self.population_size = min-score) Individuum with the highest score has probability equal to 1 (100%). - Individuum with the lowest score has probability equal to 0.05 (5%). - Probability of i-th individuum to be selected with weights is (a / range_i + b) - where a = 0.95 * self.population_size / (self.population_size - 1), and - b = (0.05 * self.population_size - 1) / (self.population_size - 1). + Individuum with the lowest score has probability equal to 0 (0%). + Probability of i-th individuum to be selected with weights is (a * range_i + b) + where a = 1. / (1. - self.population_size), and + b = self.population_size / (self.population_size - 1.) Args: population: self.population_size individuums scores: corresponding score that should be maximized @@ -340,9 +340,13 @@ def selection_of_best_with_weights(self, population, scores): ranges = np.array([self.population_size - np.where(i == sorted_ids)[0][0] for i in np.arange(self.population_size)]) # probas = a / ranges + b - a = 0.95 * self.population_size / (self.population_size - 1) - b = (0.05 * self.population_size - 1) / (self.population_size - 1) - probas_to_be_selected = a / ranges + b + # a = 0.95 * self.population_size / (self.population_size - 1) + # b = (0.05 * self.population_size - 1) / (self.population_size - 1) + # probas_to_be_selected = a / ranges + b + + a = 1. / (1. - self.population_size) + b = self.population_size / (self.population_size - 1.) + probas_to_be_selected = a * ranges + b selected = [] for i in range(self.population_size): From 82363a0ed80a4e7fa48d7028ac48ef159231d4cc Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 12:36:21 +0300 Subject: [PATCH 180/616] feat: if did not calculated, scores to zero --- deeppavlov/models/evolution/run_evolution.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 3a58a31059..d808e189d2 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -53,8 +53,15 @@ def score_population(population, population_size, result_file): proc.wait() for i in range(population_size): - val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("valid_results.txt"))) + try: + val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).parent.joinpath("valid_results.txt"))) + except OSError or FileNotFoundError: + for m_id, m in enumerate(CONSIDERED_METRICS): + if "loss" in m: + val_results[m_id] = 1e6 + else: + val_results[m_id] = 0. if TEST: test_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ "save_path"]).parent.joinpath("test_results.txt"))) From 88c6be9af700f4b463a39404618239ae465fa8eb Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 12:40:30 +0300 Subject: [PATCH 181/616] fix: configs for check if works --- deeppavlov/configs/evolution/basic_sber_faq.json | 4 ++-- deeppavlov/configs/evolution/basic_snli_part.json | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_sber_faq.json b/deeppavlov/configs/evolution/basic_sber_faq.json index 96ff27addd..1eed5fd9cd 100644 --- a/deeppavlov/configs/evolution/basic_sber_faq.json +++ b/deeppavlov/configs/evolution/basic_sber_faq.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/given_mask_init_part_6", - "load_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/given_mask_init_part_6", + "save_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/given_mask_init_part_7", + "load_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/given_mask_init_part_7", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index 7c5198e947..a115baa8b5 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_6", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_6", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_7", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_7", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { From 7cb2bd5642a86b21501837428067435b4faf946a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 14:58:40 +0300 Subject: [PATCH 182/616] fix: train evaluate model from config --- deeppavlov/models/evolution/train_phenotype.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py index 0cb26a46eb..45e2686478 100644 --- a/deeppavlov/models/evolution/train_phenotype.py +++ b/deeppavlov/models/evolution/train_phenotype.py @@ -17,7 +17,7 @@ import sys from pathlib import Path -from deeppavlov.core.commands.train import train_model_from_config +from deeppavlov.core.commands.train import train_evaluate_model_from_config from deeppavlov.core.common.file import read_json, save_json from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe @@ -25,7 +25,7 @@ config_path = sys.argv[1] print("TRAIN PHENOTYPE") -reports = train_model_from_config(config_path) +reports = train_evaluate_model_from_config(config_path) print(reports) if len(reports) == 2: From f1d6648a7090f9c5b6cf48537004133511b1971f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 15:03:32 +0300 Subject: [PATCH 183/616] feat: start from given population --- deeppavlov/models/evolution/run_evolution.py | 66 +++++++++++++------- 1 file changed, 44 insertions(+), 22 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index d808e189d2..b3a0ead43b 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -7,7 +7,7 @@ from copy import deepcopy, copy from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution -from deeppavlov.core.common.file import save_json +from deeppavlov.core.common.file import save_json, read_json def score_population(population, population_size, result_file): @@ -106,6 +106,12 @@ def score_population(population, population_size, result_file): parser.add_argument('--train_partition', help='Please, enter partition of splitted train', default=1) +parser.add_argument('--start_from_population', + help='Please, enter the population number to start from. 0 means from scratch', + default=0) +parser.add_argument('--path_to_population', + help='Please, enter the path to population to start from', + default="") args = parser.parse_args() @@ -119,6 +125,9 @@ def score_population(population, population_size, result_file): ONE_NEURON_INIT = bool(int(args.one_neuron_init)) GIVEN_MASK_INIT = bool(int(args.given_mask_init)) TRAIN_PARTITION = int(args.train_partition) +START_FROM_POPULATION = int(args.start_from_population) +PATH_TO_POPULATION = args.path_to_population + with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) @@ -153,36 +162,49 @@ def score_population(population, population_size, result_file): # Result table order = deepcopy(CONSIDERED_METRICS) order.extend(["params"]) +result_file = Path(basic_params["chainer"]["pipe"][ + evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") -result_table_columns = [] +if START_FROM_POPULATION == 0: + result_table_columns = [] -result_table_dict = {} -for el in order: - if el == "params": - result_table_dict[el] = [] - result_table_columns.extend([el]) - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - result_table_columns.extend([el + "_valid", el + "_test"]) + result_table_dict = {} + for el in order: + if el == "params": + result_table_dict[el] = [] + result_table_columns.extend([el]) + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) -result_table_columns.append("params") + result_table_columns.append("params") -result_file = Path(basic_params["chainer"]["pipe"][ - evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") -result_table = pd.DataFrame(result_table_dict) -result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') + result_table = pd.DataFrame(result_table_dict) + result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') -print("\nIteration #{} starts\n".format(0)) -population = evolution.first_generation() -# print("Considered population: {}\nScoring...\n".format(population)) -population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] + print("\nIteration #{} starts\n".format(0)) + population = evolution.first_generation() + population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] -iters = 1 + iters = 1 +else: + iters = START_FROM_POPULATION + print("\nIteration #{} starts\n".format(iters)) + model_name = basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] + population = [] + + for i in range(POPULATION_SIZE): + population.append(read_json(Path(PATH_TO_POPULATION).joinpath( + model_name + "_" + str(i)).joinpath("config.json"))) + + population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] + print("Population scores: {}".format(population_scores)) + print("\nIteration #{} was done\n".format(iters)) + iters += 1 while True: print("\nIteration #{} starts\n".format(iters)) - population = evolution.next_generation(population, population_scores, iters) # print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] From 0d50ebe2360ea0d0740e0066d0c108c09270d8da Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 15:09:04 +0300 Subject: [PATCH 184/616] fix: binary mask to array --- deeppavlov/models/evolution/run_evolution.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index b3a0ead43b..b44b19e28f 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -197,6 +197,8 @@ def score_population(population, population_size, result_file): for i in range(POPULATION_SIZE): population.append(read_json(Path(PATH_TO_POPULATION).joinpath( model_name + "_" + str(i)).joinpath("config.json"))) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ + np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] print("Population scores: {}".format(population_scores)) From feb50270df0dc42a0b033d6514cb5929ff38b96d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 15:27:42 +0300 Subject: [PATCH 185/616] fix: save and load paths --- deeppavlov/models/evolution/run_evolution.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index b44b19e28f..408b6674b2 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -199,6 +199,10 @@ def score_population(population, population_size, result_file): model_name + "_" + str(i)).joinpath("config.json"))) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ + str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).parent) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ + str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]).parent) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] print("Population scores: {}".format(population_scores)) From 746c0b9c05e1d5a6f9f8bc9827f6558cdc7bb833 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 15:30:27 +0300 Subject: [PATCH 186/616] fix: save and load paths --- deeppavlov/models/evolution/run_evolution.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 408b6674b2..2a77a14425 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -200,7 +200,8 @@ def score_population(population, population_size, result_file): population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).parent) + str(Path(basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).joinpath( + "population_" + str(START_FROM_POPULATION)).joinpath(model_name + str(i))) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]).parent) From 0fc8bd3a490f529eb3c40c77a322a652cb2604a6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 15:36:18 +0300 Subject: [PATCH 187/616] fix: save and load paths --- deeppavlov/models/evolution/run_evolution.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 2a77a14425..f1b6ae6ad3 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -201,7 +201,7 @@ def score_population(population, population_size, result_file): np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ str(Path(basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).joinpath( - "population_" + str(START_FROM_POPULATION)).joinpath(model_name + str(i))) + "population_" + str(START_FROM_POPULATION)).joinpath(model_name + "_" + str(i))) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]).parent) From 02573f7b8247e778ce9aa21888fd188fd9f14692 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 15:55:08 +0300 Subject: [PATCH 188/616] feat: class for parameters evolution --- .../evolution/evolution_param_generator.py | 470 ++++++++++++++++++ 1 file changed, 470 insertions(+) create mode 100644 deeppavlov/models/evolution/evolution_param_generator.py diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py new file mode 100644 index 0000000000..695a9b375c --- /dev/null +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -0,0 +1,470 @@ +import numpy as np +from copy import deepcopy +from pathlib import Path +import json + +from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ + number_to_type_layer +from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe +from deeppavlov.core.common.file import read_json + + +# please, make sure that +# `config["chainer"]["pipe"]` is a list of models one of which is a model to be evolved, +# otherwise, in the whole class change `config["chainer"]["pipe"]` to new path + + +class NetworkAndParamsEvolution: + """ + Class performs full evolutionary process (task scores -> max): + 1. initializes random population + 2. makes replacement to get next generation: + a. selection according to obtained scores + b. crossover (recombination) with given probability p_crossover + c. mutation with given mutation rate p_mutation (probability to mutate) + according to given mutation power sigma + (current mutation power is randomly from -sigma to sigma) + """ + + def __init__(self, + population_size, + p_crossover=0.5, crossover_power=0.5, + p_mutation=0.5, mutation_power=0.1, + key_model_to_evolve="to_evolve", + seed=None, + train_partition=1, + **kwargs): + """ + Initialize evolution with random population + Args: + population_size: number of individuums per generation + p_crossover: probability to cross over for current replacement + crossover_power: part of EVOLVING parents parameters to exchange for offsprings + p_mutation: probability of mutation for current replacement + mutation_power: allowed percentage of mutation + key_model_to_evolve: binary flag that should be inserted into the dictionary + with evolving model in the basic config + seed: random seed for initialization + train_partition: integer number of train data parts + **kwargs: basic config with parameters + """ + + self.basic_config = deepcopy(kwargs) + self.model_to_evolve_index = find_index_of_dict_with_key_in_pipe(self.basic_config["chainer"]["pipe"], + key_model_to_evolve) + Path(self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).mkdir(parents=True, + exist_ok=True) + + self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) + self.train_params = deepcopy(self.basic_config.get("train")) + + print("___Basic config___: {}".format(self.basic_config)) + print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) + print("___Model params___: {}".format(self.params)) + print("___Train params___: {}".format(self.train_params)) + + self.population_size = population_size + self.p_crossover = p_crossover + self.p_mutation = p_mutation + self.mutation_power = mutation_power + self.crossover_power = crossover_power + self.evolving_params = [] + self.n_evolving_params = None + self.evolving_train_params = [] + self.n_evolving_train_params = None + self.n_saved_best_with_weights = 0 + self.train_partition = train_partition + self.evolution_individuum_id = 0 + self.evolution_model_id = 0 + + if seed is None: + pass + else: + np.random.seed(seed) + + def _insert_dict_into_model_params(self, params, model_index, dict_to_insert): + params_copy = deepcopy(params) + params_copy["chainer"]["pipe"].insert(model_index, dict_to_insert) + return params_copy + + def print_dict(self, dict, string=None): + if string is None: + print(json.dumps(dict, indent=2)) + else: + print(string) + print(json.dumps(dict, indent=2)) + return None + + def initialize_params_in_config(self, basic_params): + params = {} + params_for_search = {} + evolving_params = [] + + for param_name in list(basic_params.keys()): + if type(basic_params[param_name]) is dict: + if basic_params[param_name].get("choice"): + params_for_search[param_name] = list(basic_params[param_name]["values"]) + evolving_params.append(param_name) + elif basic_params[param_name].get("range"): + params_for_search[param_name] = deepcopy(basic_params[param_name]) + evolving_params.append(param_name) + elif basic_params[param_name].get("bool"): + params_for_search[param_name] = deepcopy(basic_params[param_name]) + evolving_params.append(param_name) + else: + # NOT evolving params + params[param_name] = deepcopy(basic_params[param_name]) + else: + # NOT evolving params + params[param_name] = deepcopy(basic_params[param_name]) + if basic_params: + params_for_search = deepcopy(self.sample_params(**params_for_search)) + + return params, params_for_search, evolving_params + + def first_generation(self, iteration=0): + """ + Initialize first generation randomly according to the given constraints is self.params + Returns: + first generation that consists of self.population_size individuums + """ + population = [] + for i in range(self.population_size): + population.append(deepcopy(self.basic_config)) + + # intitializing parameters for model + params, params_for_search, evolving_params = self.initialize_params_in_config(self.params) + self.evolving_params.extend(evolving_params) + # initializing parameters for train + train_params, train_params_for_search, evolving_params = self.initialize_params_in_config(self.train_params) + self.evolving_train_params.extend(evolving_params) + + # intitializing path to save model + # save_path = population_iteration/model_name_i/ + if "model_name" in params_for_search.keys(): + params["save_path"] = str(Path(self.params["save_path"]).joinpath( + "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) + else: + params["save_path"] = str(Path(self.params["save_path"]).joinpath( + "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) + + # load_path = population_iteration/model_name_i/ + if "model_name" in params_for_search.keys(): + params["load_path"] = str(Path(self.params["load_path"]).joinpath( + "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) + else: + params["load_path"] = str(Path(self.params["load_path"]).joinpath( + "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) + + # exchange model and layers params from basic config to sampled model params + population[-1]["chainer"]["pipe"][self.model_to_evolve_index] = {**params, + **params_for_search} + + # exchange train params from basic config to sampled train params + population[-1]["train"] = {**train_params, + **train_params_for_search} + population[-1]["train"]["evolution_model_id"] = self.evolution_model_id + self.evolution_model_id += 1 + + self.evolving_params = list(set(self.evolving_params)) + self.evolving_train_params = list(set(self.evolving_train_params)) + + self.n_evolving_params = len(self.evolving_params) + self.n_evolving_train_params = len(self.evolving_train_params) + + return population + + def next_generation(self, generation, scores, iteration, + p_crossover=None, crossover_power=None, + p_mutation=None, mutation_power=None): + """ + Provide an operation of replacement + Args: + generation: current generation (set of self.population_size configs + scores: corresponding scores that should be maximized + iteration: iteration number + p_crossover: probability to cross over for current replacement + crossover_power: part of parents parameters to exchange for offsprings + p_mutation: probability of mutation for current replacement + mutation_power: allowed percentage of mutation + + Returns: + the next generation according to the given scores of current generation + """ + if not p_crossover: + p_crossover = self.p_crossover + if not crossover_power: + crossover_power = self.crossover_power + if not p_mutation: + p_mutation = self.p_mutation + if not mutation_power: + mutation_power = self.mutation_power + + next_population = self.selection_of_best_with_weights(generation, scores) + print("Saved with weights: {} individuums".format(self.n_saved_best_with_weights)) + offsprings = self.crossover(generation, scores, + p_crossover=p_crossover, + crossover_power=crossover_power) + + changable_next = self.mutation(offsprings, + p_mutation=p_mutation, + mutation_power=mutation_power) + + next_population.extend(changable_next) + + for i in range(self.n_saved_best_with_weights): + # if several train files: + if self.train_partition != 1: + next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ + "train"]).stem.split("_")[0]) \ + + "_" + str(iteration % self.train_partition) + ".csv" + # re-init learning rate with the final one + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ + read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index][ + "save_path"]).parent.joinpath("model_opt.json")))["final_lear_rate"] + # paths + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ + str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ + str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( + self.params["model_name"] + "_" + str(i))) + + for i in range(self.n_saved_best_with_weights, self.population_size): + # if several train files + if self.train_partition != 1: + next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ + "train"]).stem.split("_")[0]) \ + + "_" + str(iteration % self.train_partition) + ".csv" + # paths + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ + str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( + self.params["model_name"] + "_" + str(i))) + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ + str(Path(self.params["load_path"]).joinpath("population_" + str(iteration)).joinpath( + self.params["model_name"] + "_" + str(i))) + + next_population[i]["train"]["evolution_model_id"] = self.evolution_model_id + self.evolution_model_id += 1 + + return next_population + + def selection_of_best_with_weights(self, population, scores): + """ + Select individuums to save with weights for the next generation from given population. + Range is an order of an individuum within sorted scores (1 range = max-score, self.population_size = min-score) + Individuum with the highest score has probability equal to 1 (100%). + Individuum with the lowest score has probability equal to 0 (0%). + Probability of i-th individuum to be selected with weights is (a * range_i + b) + where a = 1. / (1. - self.population_size), and + b = self.population_size / (self.population_size - 1.) + Args: + population: self.population_size individuums + scores: corresponding score that should be maximized + + Returns: + selected self.n_saved_best_with_weights (changable) individuums + """ + scores = np.array(scores, dtype='float') + sorted_ids = np.argsort(scores) + ranges = np.array([self.population_size - np.where(i == sorted_ids)[0][0] + for i in np.arange(self.population_size)]) + + a = 1. / (1. - self.population_size) + b = self.population_size / (self.population_size - 1.) + probas_to_be_selected = a * ranges + b + + selected = [] + for i in range(self.population_size): + if self.decision(probas_to_be_selected[i]): + selected.append(deepcopy(population[i])) + + self.n_saved_best_with_weights = len(selected) + return selected + + def crossover(self, population, scores, p_crossover, crossover_power): + """ + Recombine randomly population in pairs and cross over them with given probability. + Cross over from two parents produces two offsprings + each of which contains crossover_power portion of the parameter values from one parent, + and the other (1 - crossover_power portion) from the other parent + Args: + population: self.population_size individuums + p_crossover: probability to cross over for current replacement + crossover_power: part of EVOLVING parents parameters to exchange for offsprings + + Returns: + (self.population_size - self.n_saved_best_with_weights) offsprings + """ + offsprings = [] + scores = np.array(scores, dtype='float') + probas_to_be_parent = scores / np.sum(scores) + intervals = np.array([np.sum(probas_to_be_parent[:i]) for i in range(self.population_size)]) + + for i in range(self.population_size - self.n_saved_best_with_weights): + rs = np.random.random(2) + parents = population[np.where(rs[0] > intervals)[0][-1]], population[np.where(rs[1] > intervals)[0][-1]] + + if self.decision(p_crossover): + params_perm = np.random.permutation(self.n_evolving_params) + train_params_perm = np.random.permutation(self.n_evolving_train_params) + + curr_offsprings = [deepcopy(parents[0]), + deepcopy(parents[1])] + + part = int(crossover_power * self.n_evolving_params) + train_part = int(crossover_power * self.n_evolving_train_params) + + # exchange of model params (not layers params) + for j in range(self.n_evolving_params - part): + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[0][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[1][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + for j in range(self.n_evolving_params - part, self.n_evolving_params): + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[1][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[0][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + + # exchange of train params + for j in range(self.n_evolving_train_params - train_part): + curr_offsprings[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] + curr_offsprings[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] + for j in range(self.n_evolving_train_params - train_part, self.n_evolving_train_params): + curr_offsprings[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] + curr_offsprings[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] + + offsprings.append(deepcopy(curr_offsprings[0])) + else: + offsprings.append(deepcopy(parents[0])) + + return offsprings + + def mutation(self, population, p_mutation, mutation_power): + """ + Mutate each parameter of each individuum in population with probability p_mutation + Args: + population: self.population_size individuums + p_mutation: probability to mutate for each parameter + mutation_power: allowed percentage of mutation + + Returns: + mutated population + """ + mutated = [] + + for individuum in population: + mutated_individuum = deepcopy(individuum) + + # mutation of other model params + for param in self.params.keys(): + mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][param] = \ + self.mutation_of_param(param, self.params, + individuum["chainer"]["pipe"][self.model_to_evolve_index][param], + p_mutation, mutation_power) + + # mutation of train params + for param in self.train_params.keys(): + mutated_individuum["train"][param] = \ + self.mutation_of_param(param, self.train_params, + individuum["train"][param], + p_mutation, mutation_power) + + mutated.append(mutated_individuum) + + return mutated + + def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutation_power): + new_mutated_value = deepcopy(param_value) + if self.decision(p_mutation): + if type(params_dict[param]) is dict: + if params_dict[param].get('discrete', False): + val = round(param_value + + ((2 * np.random.random() - 1.) * mutation_power + * self.sample_params(**{param: params_dict[param]})[param])) + val = min(max(params_dict[param]["range"][0], val), + params_dict[param]["range"][1]) + new_mutated_value = val + elif 'range' in params_dict[param].keys(): + val = param_value + \ + ((2 * np.random.random() - 1.) * mutation_power + * self.sample_params(**{param: params_dict[param]})[param]) + val = min(max(params_dict[param]["range"][0], val), + params_dict[param]["range"][1]) + new_mutated_value = val + elif params_dict[param].get("choice"): + new_mutated_value = param_value + else: + new_mutated_value = param_value + else: + new_mutated_value = param_value + else: + new_mutated_value = param_value + + return new_mutated_value + + def decision(self, probability): + """ + Make decision whether to do action or not with given probability + Args: + probability: probability whether + + Returns: + + """ + r = np.random.random() + if r < probability: + return True + else: + return False + + def sample_params(self, **params): + if not params: + params_copy = deepcopy(self.params) + else: + params_copy = deepcopy(params) + params_sample = dict() + for param, param_val in params_copy.items(): + if isinstance(param_val, list): + params_sample[param] = np.random.choice(param_val) + elif isinstance(param_val, dict): + if 'bool' in param_val and param_val['bool']: + sample = bool(np.random.choice([True, False])) + elif 'range' in param_val: + sample = self._sample_from_ranges(param_val) + params_sample[param] = sample + else: + params_sample[param] = params_copy[param] + return params_sample + + def _sample_from_ranges(self, opts): + from_ = opts['range'][0] + to_ = opts['range'][1] + if opts.get('scale', None) == 'log': + sample = self._sample_log(from_, to_) + else: + sample = np.random.uniform(from_, to_) + if opts.get('discrete', False): + sample = int(np.round(sample)) + return sample + + @staticmethod + def _sample_log(from_, to_): + sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) + return float(sample) From 8919611b6074391daf6fe7dffd57e13d03ee9cb3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 16:18:11 +0300 Subject: [PATCH 189/616] fix: reading val results --- deeppavlov/models/evolution/run_evolution.py | 1 + 1 file changed, 1 insertion(+) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index f1b6ae6ad3..339372a0d0 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -57,6 +57,7 @@ def score_population(population, population_size, result_file): val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ "save_path"]).parent.joinpath("valid_results.txt"))) except OSError or FileNotFoundError: + val_results = [None for m in CONSIDERED_METRICS] for m_id, m in enumerate(CONSIDERED_METRICS): if "loss" in m: val_results[m_id] = 1e6 From 56bb666621c0ae03436e4a6c0a1dd12c901a4a32 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 16:57:37 +0300 Subject: [PATCH 190/616] fix; return reports --- deeppavlov/core/commands/train.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index 345d3d0f22..f58f7e215b 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -172,6 +172,7 @@ def train_evaluate_model_from_config(config_path: str, to_train=True, to_validat elif not isinstance(model, Chainer): log.warning('Nothing to train') + reports = [] if train_config['validate_best'] or train_config['test_best']: # try: # model_config['load_path'] = model_config['save_path'] @@ -181,20 +182,22 @@ def train_evaluate_model_from_config(config_path: str, to_train=True, to_validat log.info('Testing the best saved model') if train_config['validate_best']: - report = { + reports.append({ 'valid': _test_model(model, metrics_functions, iterator, train_config.get('batch_size', -1), 'valid') - } + }) - print(json.dumps(report, ensure_ascii=False)) + print(json.dumps(reports[-1], ensure_ascii=False)) if train_config['test_best']: - report = { + reports.append({ 'test': _test_model(model, metrics_functions, iterator, train_config.get('batch_size', -1), 'test') - } + }) + + print(json.dumps(reports[-1], ensure_ascii=False)) - print(json.dumps(report, ensure_ascii=False)) + return reports def _test_model(model: Component, metrics_functions: List[Tuple[str, Callable]], From bcb2e295f229976723ee3dc62f515752a37e9606 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 16:58:47 +0300 Subject: [PATCH 191/616] fix: remove f1 weighted form metrics --- deeppavlov/configs/evolution/basic_sber_faq.json | 1 - 1 file changed, 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_sber_faq.json b/deeppavlov/configs/evolution/basic_sber_faq.json index 1eed5fd9cd..95ec81da41 100644 --- a/deeppavlov/configs/evolution/basic_sber_faq.json +++ b/deeppavlov/configs/evolution/basic_sber_faq.json @@ -232,7 +232,6 @@ "metric_optimization": "maximize", "metrics": [ "classification_f1", - "classification_f1_weighted", "classification_accuracy", "classification_log_loss", "classification_roc_auc" From df12d2ac44360c3312898bb94addaab539af262f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 17:11:41 +0300 Subject: [PATCH 192/616] feat: queue for param evolution --- .../models/evolution/run_param_evolution.py | 198 ++++++++++++++++++ 1 file changed, 198 insertions(+) create mode 100644 deeppavlov/models/evolution/run_param_evolution.py diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py new file mode 100644 index 0000000000..a016a1831d --- /dev/null +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -0,0 +1,198 @@ +import json +import numpy as np +import argparse +from pathlib import Path +from subprocess import Popen, PIPE +import pandas as pd +from copy import deepcopy, copy + +from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution +from deeppavlov.core.common.file import save_json, read_json + + +def score_population(population, population_size, result_file): + global evolution + + population_metrics = {} + for m in CONSIDERED_METRICS: + population_metrics[m] = [] + + for k in range(POPULATION_SIZE // len(gpus) + 1): + procs = [] + for j in range(len(gpus)): + i = k * len(gpus) + j + if i < POPULATION_SIZE: + save_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) + load_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]) + + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ + str(save_path.joinpath("model")) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ + str(load_path.joinpath("model")) + + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = \ + evolution.nodes + print(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) + try: + save_path.mkdir(parents=True) + except FileExistsError: + pass + + f_name = save_path.joinpath("config.json") + save_json(population[i], f_name) + + procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" + " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], + str(f_name), + str(save_path), + str(save_path) + ), + shell=True, stdout=PIPE, stderr=PIPE)) + for j, proc in enumerate(procs): + print(f'wait on {j}th proc') + proc.wait() + + for i in range(population_size): + try: + val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).parent.joinpath("valid_results.txt"))) + except OSError or FileNotFoundError: + val_results = [None for m in CONSIDERED_METRICS] + for m_id, m in enumerate(CONSIDERED_METRICS): + if "loss" in m: + val_results[m_id] = 1e6 + else: + val_results[m_id] = 0. + if TEST: + test_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).parent.joinpath("test_results.txt"))) + + result_table_dict = {} + for el in order: + if el == "params": + result_table_dict[el] = [] + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + for m_id, m in enumerate(CONSIDERED_METRICS): + result_table_dict[m + "_valid"].append(val_results[m_id]) + if TEST: + result_table_dict[m + "_test"].append(test_results[m_id]) + else: + result_table_dict[m + "_test"].append(0.) + result_table_dict[order[-1]] = [population[i]] + result_table = pd.DataFrame(result_table_dict) + + result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) + + for m_id, m in enumerate(CONSIDERED_METRICS): + population_metrics[m].append(val_results[m_id]) + + return population_metrics + + +parser = argparse.ArgumentParser() + +parser.add_argument('--config', help='Please, enter model path to config') +parser.add_argument('--evolve_metric', help='Please, choose target metric out of given in your config.train.metrics') +parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) +parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) +parser.add_argument('--train_partition', + help='Please, enter partition of splitted train', + default=1) +parser.add_argument('--start_from_population', + help='Please, enter the population number to start from. 0 means from scratch', + default=0) +parser.add_argument('--path_to_population', + help='Please, enter the path to population to start from', + default="") + +args = parser.parse_args() + +CONFIG_FILE = args.config +EVOLVE_METRIC = args.evolve_metric +POPULATION_SIZE = args.p_size +GPU_NUMBER = len(args.gpus) +gpus = [int(gpu) for gpu in args.gpus.split(",")] +TRAIN_PARTITION = int(args.train_partition) +START_FROM_POPULATION = int(args.start_from_population) +PATH_TO_POPULATION = args.path_to_population + +with open(CONFIG_FILE, "r") as f: + basic_params = json.load(f) + +print("Given basic params: {}\n".format(basic_params)) + +# list of names of considered metrics +CONSIDERED_METRICS = basic_params["train"]["metrics"] +TEST = basic_params["train"]["test_best"] + + +# EVOLUTION starts here! +evolution = NetworkAndParamsEvolution(population_size=POPULATION_SIZE, + p_crossover=0.2, crossover_power=0.1, + p_mutation=1., mutation_power=0.1, + key_model_to_evolve="to_evolve", + key_basic_layers="basic_layers_params", + seed=42, + train_partition=TRAIN_PARTITION, + **basic_params) + +# Result table +order = deepcopy(CONSIDERED_METRICS) +order.extend(["params"]) +result_file = Path(basic_params["chainer"]["pipe"][ + evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") + +if START_FROM_POPULATION == 0: + result_table_columns = [] + + result_table_dict = {} + for el in order: + if el == "params": + result_table_dict[el] = [] + result_table_columns.extend([el]) + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) + + result_table_columns.append("params") + + result_table = pd.DataFrame(result_table_dict) + result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') + + print("\nIteration #{} starts\n".format(0)) + population = evolution.first_generation() + population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] + + iters = 1 +else: + iters = START_FROM_POPULATION + print("\nIteration #{} starts\n".format(iters)) + model_name = basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] + population = [] + + for i in range(POPULATION_SIZE): + population.append(read_json(Path(PATH_TO_POPULATION).joinpath( + model_name + "_" + str(i)).joinpath("config.json"))) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ + str(Path(basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).joinpath( + "population_" + str(START_FROM_POPULATION)).joinpath(model_name + "_" + str(i))) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ + str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]).parent) + + population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] + print("Population scores: {}".format(population_scores)) + print("\nIteration #{} was done\n".format(iters)) + iters += 1 + +while True: + print("\nIteration #{} starts\n".format(iters)) + population = evolution.next_generation(population, population_scores, iters) + # print("Considered population: {}\nScoring...\n".format(population)) + population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] + print("Population scores: {}".format(population_scores)) + print("\nIteration #{} was done\n".format(iters)) + iters += 1 + From 2d4228c20dba3d912f04682554915b810053a041 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 17:14:39 +0300 Subject: [PATCH 193/616] chhore: intents_snli config --- .../configs/evolution/intents_snli.json | 45 ++++++++++--------- 1 file changed, 23 insertions(+), 22 deletions(-) diff --git a/deeppavlov/configs/evolution/intents_snli.json b/deeppavlov/configs/evolution/intents_snli.json index d056913902..da17d0183e 100644 --- a/deeppavlov/configs/evolution/intents_snli.json +++ b/deeppavlov/configs/evolution/intents_snli.json @@ -63,21 +63,33 @@ ], "main": true, "name": "intent_model", - "save_path": "intents/intent_snli_v0", - "load_path": "intents/intent_snli_v0", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/param_evolution_0", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/param_evolution_0", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, 2, 3 ], - "filters_cnn": 256, + "filters_cnn": { + "range": [ + 50, + 500 + ], + "discrete": true + }, "confident_threshold": 0.5, "optimizer": "Adam", - "lear_rate": 0.01, + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, "lear_rate_decay": 0.1, "loss": "binary_crossentropy", "text_size": 51, + "to_evolve": true, "coef_reg_cnn": 1e-4, "coef_reg_den": 1e-4, "dropout_rate": { @@ -86,7 +98,13 @@ 0.9 ] }, - "dense_size": 100, + "dense_size": { + "range": [ + 50, + 500 + ], + "discrete": true + }, "model_name": "cnn_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" @@ -111,22 +129,5 @@ "show_examples": false, "validate_best": true, "test_best": true - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - }, - "download": [ - "http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz", - "http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz", - { - "url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv", - "subdir": "snips" - }, - { - "url": "http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin", - "subdir": "embeddings" - } - ] } } From 8b55b7107ba3bcf8debe5b0a87cf83bb3a87141b Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 17:19:06 +0300 Subject: [PATCH 194/616] chore: evolve params --- .../evolution/evolution_param_generator.py | 2 +- .../models/evolution/run_param_evolution.py | 18 +++++++++--------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 695a9b375c..9cf3a6a994 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -14,7 +14,7 @@ # otherwise, in the whole class change `config["chainer"]["pipe"]` to new path -class NetworkAndParamsEvolution: +class ParamsEvolution: """ Class performs full evolutionary process (task scores -> max): 1. initializes random population diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index a016a1831d..eb7cadfbd6 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -6,7 +6,7 @@ import pandas as pd from copy import deepcopy, copy -from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution +from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution from deeppavlov.core.common.file import save_json, read_json @@ -129,14 +129,14 @@ def score_population(population, population_size, result_file): # EVOLUTION starts here! -evolution = NetworkAndParamsEvolution(population_size=POPULATION_SIZE, - p_crossover=0.2, crossover_power=0.1, - p_mutation=1., mutation_power=0.1, - key_model_to_evolve="to_evolve", - key_basic_layers="basic_layers_params", - seed=42, - train_partition=TRAIN_PARTITION, - **basic_params) +evolution = ParamsEvolution(population_size=POPULATION_SIZE, + p_crossover=0.2, crossover_power=0.1, + p_mutation=1., mutation_power=0.1, + key_model_to_evolve="to_evolve", + key_basic_layers="basic_layers_params", + seed=42, + train_partition=TRAIN_PARTITION, + **basic_params) # Result table order = deepcopy(CONSIDERED_METRICS) From f8e7521f79ca00cecab5b8b307677ee9e5b3ba29 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 17:20:29 +0300 Subject: [PATCH 195/616] chore: removed nodes from param evolution --- deeppavlov/models/evolution/run_param_evolution.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index eb7cadfbd6..54fdb998e3 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -30,8 +30,6 @@ def score_population(population, population_size, result_file): population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ str(load_path.joinpath("model")) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = \ - evolution.nodes print(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) try: save_path.mkdir(parents=True) From fda008450d392c311b373a4703a35d91fd2e86a6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 17:27:30 +0300 Subject: [PATCH 196/616] chore: removed nodes from param evolution --- deeppavlov/models/evolution/run_evolution.py | 26 +++++++++---------- .../models/evolution/run_param_evolution.py | 26 +++++++++---------- 2 files changed, 25 insertions(+), 27 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 339372a0d0..510157ce8b 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -165,22 +165,20 @@ def score_population(population, population_size, result_file): order.extend(["params"]) result_file = Path(basic_params["chainer"]["pipe"][ evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") +result_table_columns = [] +result_table_dict = {} +for el in order: + if el == "params": + result_table_dict[el] = [] + result_table_columns.extend([el]) + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) + +result_table_columns.append("params") if START_FROM_POPULATION == 0: - result_table_columns = [] - - result_table_dict = {} - for el in order: - if el == "params": - result_table_dict[el] = [] - result_table_columns.extend([el]) - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - result_table_columns.extend([el + "_valid", el + "_test"]) - - result_table_columns.append("params") - result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 54fdb998e3..4c497f5cd1 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -141,27 +141,27 @@ def score_population(population, population_size, result_file): order.extend(["params"]) result_file = Path(basic_params["chainer"]["pipe"][ evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") +result_table_columns = [] -if START_FROM_POPULATION == 0: - result_table_columns = [] - - result_table_dict = {} - for el in order: - if el == "params": - result_table_dict[el] = [] - result_table_columns.extend([el]) - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - result_table_columns.extend([el + "_valid", el + "_test"]) +result_table_dict = {} +for el in order: + if el == "params": + result_table_dict[el] = [] + result_table_columns.extend([el]) + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) - result_table_columns.append("params") +result_table_columns.append("params") +if START_FROM_POPULATION == 0: result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() + print(population) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] iters = 1 From 2ca8c80117cb3b2c21e1615ea6684eac42b4ea56 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 18:08:40 +0300 Subject: [PATCH 197/616] fix: sampling no params --- .../configs/evolution/intents_snli.json | 12 +- .../configs/evolution/intents_snli_local.json | 133 ++++++++++++++++++ .../evolution/evolution_param_generator.py | 2 +- .../neuroevolution_param_generator.py | 2 +- 4 files changed, 141 insertions(+), 8 deletions(-) create mode 100644 deeppavlov/configs/evolution/intents_snli_local.json diff --git a/deeppavlov/configs/evolution/intents_snli.json b/deeppavlov/configs/evolution/intents_snli.json index da17d0183e..2e0afffd0d 100644 --- a/deeppavlov/configs/evolution/intents_snli.json +++ b/deeppavlov/configs/evolution/intents_snli.json @@ -3,8 +3,8 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara/data/data_files/SNLI/one_input", - "train": "train.csv", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/parts", + "train": "train_0.csv", "valid": "valid.csv", "test": "test.csv" }, @@ -26,8 +26,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict", - "load_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict" }, { "in": [ @@ -41,8 +41,8 @@ { "id": "my_embedder", "name": "fasttext", - "save_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", - "load_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", "dim": 300 }, { diff --git a/deeppavlov/configs/evolution/intents_snli_local.json b/deeppavlov/configs/evolution/intents_snli_local.json new file mode 100644 index 0000000000..825371fd0f --- /dev/null +++ b/deeppavlov/configs/evolution/intents_snli_local.json @@ -0,0 +1,133 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "gold_label", + "data_path": "/home/dilyara/data/data_files/SNLI/one_input/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict", + "load_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", + "load_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "intent_model", + "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/param_evolution_0", + "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/param_evolution_0", + "classes": "#classes_vocab.keys()", + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 51, + "to_evolve": true, + "coef_reg_cnn": 1e-4, + "coef_reg_den": 1e-4, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, + "dense_size": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "model_name": "cnn_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": 100, + "batch_size": 64, + "metrics": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "show_examples": false, + "validate_best": true, + "test_best": true + } +} diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 9cf3a6a994..3144d0d465 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -436,7 +436,7 @@ def decision(self, probability): def sample_params(self, **params): if not params: - params_copy = deepcopy(self.params) + return {} else: params_copy = deepcopy(params) params_sample = dict() diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index a07d574373..ac1ff1715b 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -585,7 +585,7 @@ def decision(self, probability): def sample_params(self, **params): if not params: - params_copy = deepcopy(self.params) + return {} else: params_copy = deepcopy(params) params_sample = dict() From 59ace30633325f8bd60ca3df09e21b87a8b87e45 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 18:11:00 +0300 Subject: [PATCH 198/616] fix: test results --- deeppavlov/models/evolution/run_evolution.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 510157ce8b..eb1bafaec5 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -64,8 +64,18 @@ def score_population(population, population_size, result_file): else: val_results[m_id] = 0. if TEST: - test_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("test_results.txt"))) + try: + test_results = np.loadtxt( + fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).parent.joinpath("test_results.txt"))) + except OSError or FileNotFoundError: + test_results = [None for m in CONSIDERED_METRICS] + for m_id, m in enumerate(CONSIDERED_METRICS): + if "loss" in m: + test_results[m_id] = 1e6 + else: + test_results[m_id] = 0. + result_table_dict = {} for el in order: From 686cd08db55ada6f1aee08dce8d313ab73570d84 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 18:11:22 +0300 Subject: [PATCH 199/616] fix: test results --- deeppavlov/models/evolution/run_param_evolution.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 4c497f5cd1..97dfd631ba 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -62,8 +62,17 @@ def score_population(population, population_size, result_file): else: val_results[m_id] = 0. if TEST: - test_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("test_results.txt"))) + try: + test_results = np.loadtxt( + fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).parent.joinpath("test_results.txt"))) + except OSError or FileNotFoundError: + test_results = [None for m in CONSIDERED_METRICS] + for m_id, m in enumerate(CONSIDERED_METRICS): + if "loss" in m: + test_results[m_id] = 1e6 + else: + test_results[m_id] = 0. result_table_dict = {} for el in order: From ff8882c7fc8d3d96db448d5521e589a4aa563a50 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 10:28:58 +0300 Subject: [PATCH 200/616] fix: gpus number --- deeppavlov/models/evolution/run_param_evolution.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 97dfd631ba..7279be2c6f 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -40,7 +40,7 @@ def score_population(population, population_size, result_file): save_json(population[i], f_name) procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" - " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], + " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], str(f_name), str(save_path), str(save_path) From f98349ba409b0cdfb032a1df04f411b50da2ce91 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 10:53:41 +0300 Subject: [PATCH 201/616] fix: first generation for start from population --- deeppavlov/models/evolution/run_evolution.py | 2 ++ deeppavlov/models/evolution/run_param_evolution.py | 2 ++ 2 files changed, 4 insertions(+) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index eb1bafaec5..512ed8d7e4 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -198,6 +198,8 @@ def score_population(population, population_size, result_file): iters = 1 else: + # to define some clue params of evolution + _ = evolution.first_generation() iters = START_FROM_POPULATION print("\nIteration #{} starts\n".format(iters)) model_name = basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 7279be2c6f..3f01113838 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -175,6 +175,8 @@ def score_population(population, population_size, result_file): iters = 1 else: + # to define some clue params of evolution + _ = evolution.first_generation() iters = START_FROM_POPULATION print("\nIteration #{} starts\n".format(iters)) model_name = basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] From a4933957e44c623748251c1170ab471eacb11caf Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 11:56:20 +0300 Subject: [PATCH 202/616] feat: mini tutorial how to use param evolution --- .../evolution/Tutorial_params_evolution.ipynb | 328 ++++++++++++++++++ 1 file changed, 328 insertions(+) create mode 100644 deeppavlov/models/evolution/Tutorial_params_evolution.ipynb diff --git a/deeppavlov/models/evolution/Tutorial_params_evolution.ipynb b/deeppavlov/models/evolution/Tutorial_params_evolution.ipynb new file mode 100644 index 0000000000..f729ce8fec --- /dev/null +++ b/deeppavlov/models/evolution/Tutorial_params_evolution.ipynb @@ -0,0 +1,328 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to use evolution of model parameters in DeepPavlov" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Скопируйте в новый файл свой обычный конфиг, соответствующий рассматриваемой модели.\n", + "\n", + "* Для каждого параметра, который можно варьировать, в конфиге замените значение параметра на словарь, определяющий возможные принимаемые значения. Тренировочные параметры (из `config[\"train\"]`) варьируются автоматически, а для варьирования параметров модели необходимо определить тот подсловарь конфига, в котором находятся варьируемые параметры, добавив в него параметр `\"to_evolve\": true`. Варьируемые параметры должны быть ключами словаря, содержащего ключ `to_evolve`, вложенность пока не поддерживается.\n", + "\n", + "* Запустите эволюцию с необходимыми параметрами:\n", + " - config - путь к файлу конфигу для эволюции\n", + " - evolve_metric - зарегистрированное название метрики из тренировочных параметров конфига, по значениям которой будет происходить эволюция\n", + " - p_size - размер одной популяции\n", + " - gpus - номера gpu, доступных для использования. Если количество gpu меньше размера популяции, то модели будут запускаться группами по len(gpus) штук.\n", + "```\n", + "python ./models/evolution/run_param_evolution.py --config config_file \n", + " --evolve_metric registered_metric_from_config \n", + " --p_size 10\n", + " --gpus 0,1,2\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Примеры словаря возможных значений для различных видов параметров" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import json\n", + "\n", + "def print_json(dictionary):\n", + " print(json.dumps(dictionary, indent=2))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "config = {\"dense_size\": 100, \n", + " \"activation\": \"sigmoid\", \n", + " \"learning_rate\": 0.001, \n", + " \"learning_rate_decay\": 0.00001,\n", + " \"is_main\": True}" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"dense_size\": 100,\n", + " \"activation\": \"sigmoid\",\n", + " \"learning_rate\": 0.001,\n", + " \"learning_rate_decay\": 1e-05,\n", + " \"is_main\": true\n", + "}\n" + ] + } + ], + "source": [ + "print_json(config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Дискретный параметр из промежутка" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + "}\n" + ] + } + ], + "source": [ + "config[\"dense_size\"] = {\"range\": [50, 500], \"discrete\": True}\n", + "print_json(config[\"dense_size\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Дискретный параметр из листа возможных значений" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"values\": [\n", + " \"softmax\",\n", + " \"sigmoid\",\n", + " \"relu\"\n", + " ],\n", + " \"choice\": true\n", + "}\n" + ] + } + ], + "source": [ + "config[\"activation\"] = {\"values\": [\"softmax\", \"sigmoid\", \"relu\"], \"choice\": True}\n", + "print_json(config[\"activation\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Параметр из промежутка" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"range\": [\n", + " 0.001,\n", + " 0.1\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "config[\"learning_rate\"] = {\"range\": [0.001, 0.1]}\n", + "print_json(config[\"learning_rate\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Параметр из промежутка с логарифмической шкалой" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"range\": [\n", + " 1e-05,\n", + " 0.0001\n", + " ],\n", + " \"scale\": \"log\"\n", + "}\n" + ] + } + ], + "source": [ + "config[\"learning_rate_decay\"] = {\"range\": [0.00001, 0.0001], \"scale\": \"log\"}\n", + "print_json(config[\"learning_rate_decay\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Булевый параметр" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"bool\": true\n", + "}\n" + ] + } + ], + "source": [ + "config[\"is_main\"] = {\"bool\": True}\n", + "print_json(config[\"is_main\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Therefore, evolving parameters can be written in DeepPavlov config in the following way" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"dense_size\": {\n", + " \"range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"activation\": {\n", + " \"values\": [\n", + " \"softmax\",\n", + " \"sigmoid\",\n", + " \"relu\"\n", + " ],\n", + " \"choice\": true\n", + " },\n", + " \"learning_rate\": {\n", + " \"range\": [\n", + " 0.001,\n", + " 0.1\n", + " ]\n", + " },\n", + " \"learning_rate_decay\": {\n", + " \"range\": [\n", + " 1e-05,\n", + " 0.0001\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"is_main\": {\n", + " \"bool\": true\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "print_json(config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python-deep36", + "language": "python", + "name": "deep36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From f771b7399a35417fed740d938e8cc58c00929641 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 12:02:49 +0300 Subject: [PATCH 203/616] feat: snips config --- .../configs/evolution/intents_snips.json | 132 ++++++++++++++++++ 1 file changed, 132 insertions(+) create mode 100644 deeppavlov/configs/evolution/intents_snips.json diff --git a/deeppavlov/configs/evolution/intents_snips.json b/deeppavlov/configs/evolution/intents_snips.json new file mode 100644 index 0000000000..494167e921 --- /dev/null +++ b/deeppavlov/configs/evolution/intents_snips.json @@ -0,0 +1,132 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "intents", + "data_path": "/home/dilyara.baymurzina/evolution_data/snips_data", + "train": "train.csv", + "valid": "valid.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "intent_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/param_evolution_0", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/param_evolution_0", + "classes": "#classes_vocab.keys()", + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 51, + "to_evolve": true, + "coef_reg_cnn": 1e-4, + "coef_reg_den": 1e-4, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, + "dense_size": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "model_name": "cnn_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": 100, + "batch_size": 64, + "metrics": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "show_examples": false, + "validate_best": true, + "test_best": false + } +} From cb75ac85cdcc4653bd2bc684575636db9094f582 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 14:54:01 +0300 Subject: [PATCH 204/616] fix: ag news config --- deeppavlov/configs/evolution/basic_ag_news_part.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json index 128146e58e..a6e9459f25 100644 --- a/deeppavlov/configs/evolution/basic_ag_news_part.json +++ b/deeppavlov/configs/evolution/basic_ag_news_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_6", - "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_6", + "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/given_mask_init_part_7", + "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/given_mask_init_part_7", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { From 3bb4b7446a2c751fe5b358c850a8b0ebd647261c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 16:25:04 +0300 Subject: [PATCH 205/616] fix: mutation_power in binary mask mutation --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index ac1ff1715b..fed043b6f7 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -558,6 +558,7 @@ def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutatio params_dict[param]["range"][1]) new_mutated_value = val elif params_dict[param].get("choice"): + # TODO: mutation of this parameters new_mutated_value = param_value else: new_mutated_value = param_value @@ -622,7 +623,7 @@ def sample_binary_mask(self): # return np.random.randint(0, high=2, size=self.binary_mask_template.shape).tolist() # return (1 * (np.log(np.random.random(size=self.binary_mask_template.shape)) > -0.2)).tolist() ones = np.random.choice(self.total_nodes * self.total_nodes, - size=min(max(1, int(0.1 * np.random.random() * self.total_nodes)), 5)) + size=min(max(1, int(self.mutation_power * np.random.random() * self.total_nodes)), 5)) mask = np.zeros((self.total_nodes * self.total_nodes)) mask[ones] = 1 # returns NUMPY 2D ARRAY! From 558dcb7d737e45c8dfae90840fa4ade9f165e6e0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 16:26:20 +0300 Subject: [PATCH 206/616] fix: mutation of choice parameter --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index fed043b6f7..09fc43e9bd 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -559,7 +559,8 @@ def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutatio new_mutated_value = val elif params_dict[param].get("choice"): # TODO: mutation of this parameters - new_mutated_value = param_value + # new_mutated_value = param_value + new_mutated_value = self.sample_params(**{param: params_dict[param]})[param] else: new_mutated_value = param_value else: From 957b38c4fff1118c92fe29f994310db335141e3c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 16:31:03 +0300 Subject: [PATCH 207/616] fix: delete to do --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 1 - 1 file changed, 1 deletion(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 09fc43e9bd..4b39481cf8 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -558,7 +558,6 @@ def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutatio params_dict[param]["range"][1]) new_mutated_value = val elif params_dict[param].get("choice"): - # TODO: mutation of this parameters # new_mutated_value = param_value new_mutated_value = self.sample_params(**{param: params_dict[param]})[param] else: From 12e33efe3a62e6c871a67db070f0c7f752c2aa83 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 17:00:04 +0300 Subject: [PATCH 208/616] feat: dataset iterator evolution --- .../evolution/evolution_param_generator.py | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 3144d0d465..8c0d4a7483 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -55,11 +55,13 @@ def __init__(self, Path(self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).mkdir(parents=True, exist_ok=True) + self.dataset_iterator_params = deepcopy(self.basic_config.get("dataset_iterator")) self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) self.train_params = deepcopy(self.basic_config.get("train")) print("___Basic config___: {}".format(self.basic_config)) print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) + print("___Dataset iterator params___: {}".format(self.dataset_iterator_params)) print("___Model params___: {}".format(self.params)) print("___Train params___: {}".format(self.train_params)) @@ -132,6 +134,10 @@ def first_generation(self, iteration=0): for i in range(self.population_size): population.append(deepcopy(self.basic_config)) + # initializing parameters for dataset iterator + dataset_iterator_params, dataset_iterator_params_for_search, evolving_params = \ + self.initialize_params_in_config(self.dataset_iterator_params) + self.evolving_params.extend(evolving_params) # intitializing parameters for model params, params_for_search, evolving_params = self.initialize_params_in_config(self.params) self.evolving_params.extend(evolving_params) @@ -156,6 +162,9 @@ def first_generation(self, iteration=0): params["load_path"] = str(Path(self.params["load_path"]).joinpath( "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) + # exchange dataset iterator params from basic config to sampled train params + population[-1]["dataset_iterator"] = {**dataset_iterator_params, + **dataset_iterator_params_for_search} # exchange model and layers params from basic config to sampled model params population[-1]["chainer"]["pipe"][self.model_to_evolve_index] = {**params, **params_for_search} @@ -372,6 +381,13 @@ def mutation(self, population, p_mutation, mutation_power): for individuum in population: mutated_individuum = deepcopy(individuum) + # mutation of dataset iterator params + for param in self.dataset_iterator_params.keys(): + mutated_individuum["dataset_iterator"][param] = \ + self.mutation_of_param(param, self.dataset_iterator_params, + individuum["dataset_iterator"][param], + p_mutation, mutation_power) + # mutation of other model params for param in self.params.keys(): mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][param] = \ @@ -409,7 +425,8 @@ def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutatio params_dict[param]["range"][1]) new_mutated_value = val elif params_dict[param].get("choice"): - new_mutated_value = param_value + # new_mutated_value = param_value + new_mutated_value = self.sample_params(**{param: params_dict[param]})[param] else: new_mutated_value = param_value else: From ad5338f61f047ddba72a100ee92f219744f9aa30 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 17:00:58 +0300 Subject: [PATCH 209/616] feat: dataset iterator evolution --- deeppavlov/configs/evolution/intents_snips.json | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/intents_snips.json b/deeppavlov/configs/evolution/intents_snips.json index 494167e921..912ad3c94e 100644 --- a/deeppavlov/configs/evolution/intents_snips.json +++ b/deeppavlov/configs/evolution/intents_snips.json @@ -8,7 +8,14 @@ "valid": "valid.csv" }, "dataset_iterator": { - "name": "basic_classification_iterator" + "name": "basic_classification_iterator", + "seed": { + "range": [ + 50, + 500 + ], + "discrete": true + } }, "chainer": { "in": [ From 9bb904e830aa505fbb58d90ab0f740197a2c3515 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 17:01:58 +0300 Subject: [PATCH 210/616] fix: config for dataset iterator evolution --- deeppavlov/configs/evolution/intents_snli_local.json | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/intents_snli_local.json b/deeppavlov/configs/evolution/intents_snli_local.json index 825371fd0f..3a2fc819a1 100644 --- a/deeppavlov/configs/evolution/intents_snli_local.json +++ b/deeppavlov/configs/evolution/intents_snli_local.json @@ -9,7 +9,14 @@ "test": "test.csv" }, "dataset_iterator": { - "name": "basic_classification_iterator" + "name": "basic_classification_iterator", + "seed": { + "range": [ + 50, + 500 + ], + "discrete": true + } }, "chainer": { "in": [ From 4b4d73d81b1006a0cad93ecf7b02f9baea87a8bd Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 17:04:23 +0300 Subject: [PATCH 211/616] fix: number of printed proc --- deeppavlov/models/evolution/run_param_evolution.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 3f01113838..d1902d4fb4 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -47,7 +47,8 @@ def score_population(population, population_size, result_file): ), shell=True, stdout=PIPE, stderr=PIPE)) for j, proc in enumerate(procs): - print(f'wait on {j}th proc') + i = k * len(gpus) + j + print(f'wait on {i}th proc') proc.wait() for i in range(population_size): From 5ea987d0fcb58a284f5337101269c6e51a6ee096 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 17:41:42 +0300 Subject: [PATCH 212/616] fix: final lear rate try --- .../models/evolution/evolution_param_generator.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 8c0d4a7483..d90b9550cf 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -227,10 +227,13 @@ def next_generation(self, generation, scores, iteration, next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ "train"]).stem.split("_")[0]) \ + "_" + str(iteration % self.train_partition) + ".csv" - # re-init learning rate with the final one - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ - read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index][ - "save_path"]).parent.joinpath("model_opt.json")))["final_lear_rate"] + try: + # re-init learning rate with the final one + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ + read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index][ + "save_path"]).parent.joinpath("model_opt.json")))["final_lear_rate"] + except: + pass # paths next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) From 070d724243acba5925f954009e66234e69e0f307 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 15 Jun 2018 11:23:46 +0300 Subject: [PATCH 213/616] fix: crossover of dataset iterator params --- .../evolution/evolution_param_generator.py | 29 ++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index d90b9550cf..cee8b6aa06 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -70,6 +70,8 @@ def __init__(self, self.p_mutation = p_mutation self.mutation_power = mutation_power self.crossover_power = crossover_power + self.evolving_dataset_iterator_params = [] + self.n_evolving_dataset_iterator_params = None self.evolving_params = [] self.n_evolving_params = None self.evolving_train_params = [] @@ -137,7 +139,7 @@ def first_generation(self, iteration=0): # initializing parameters for dataset iterator dataset_iterator_params, dataset_iterator_params_for_search, evolving_params = \ self.initialize_params_in_config(self.dataset_iterator_params) - self.evolving_params.extend(evolving_params) + self.evolving_dataset_iterator_params.extend(evolving_params) # intitializing parameters for model params, params_for_search, evolving_params = self.initialize_params_in_config(self.params) self.evolving_params.extend(evolving_params) @@ -175,9 +177,11 @@ def first_generation(self, iteration=0): population[-1]["train"]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 + self.evolving_dataset_iterator_params = list(set(self.evolving_dataset_iterator_params)) self.evolving_params = list(set(self.evolving_params)) self.evolving_train_params = list(set(self.evolving_train_params)) + self.n_evolving_dataset_iterator_params = len(self.evolving_dataset_iterator_params) self.n_evolving_params = len(self.evolving_params) self.n_evolving_train_params = len(self.evolving_train_params) @@ -317,15 +321,38 @@ def crossover(self, population, scores, p_crossover, crossover_power): parents = population[np.where(rs[0] > intervals)[0][-1]], population[np.where(rs[1] > intervals)[0][-1]] if self.decision(p_crossover): + dataset_iterator_params_perm = np.random.permutation(self.n_evolving_dataset_iterator_params) params_perm = np.random.permutation(self.n_evolving_params) train_params_perm = np.random.permutation(self.n_evolving_train_params) curr_offsprings = [deepcopy(parents[0]), deepcopy(parents[1])] + dataset_iterator_part = int(crossover_power * self.n_evolving_dataset_iterator_params) part = int(crossover_power * self.n_evolving_params) train_part = int(crossover_power * self.n_evolving_train_params) + # exchange of dataset_iterator params + for j in range(self.n_evolving_dataset_iterator_params - dataset_iterator_part): + curr_offsprings[0]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ + parents[0]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] + curr_offsprings[1]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ + parents[1]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] + for j in range(self.n_evolving_dataset_iterator_params - dataset_iterator_part, + self.n_evolving_dataset_iterator_params): + curr_offsprings[0]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ + parents[1]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] + curr_offsprings[1]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ + parents[0]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] + # exchange of model params (not layers params) for j in range(self.n_evolving_params - part): curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ From b6cb7a0d6aff479313c178327c3f90b3c84450a5 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 15 Jun 2018 14:45:28 +0300 Subject: [PATCH 214/616] fix: evolution --- .../evolution/evolution_param_generator.py | 91 ++++++++++++------- 1 file changed, 60 insertions(+), 31 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index cee8b6aa06..1aa33730d8 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -3,15 +3,12 @@ from pathlib import Path import json -from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ - number_to_type_layer from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe from deeppavlov.core.common.file import read_json +from deeppavlov.core.common.log import get_logger -# please, make sure that -# `config["chainer"]["pipe"]` is a list of models one of which is a model to be evolved, -# otherwise, in the whole class change `config["chainer"]["pipe"]` to new path +log = get_logger(__name__) class ParamsEvolution: @@ -30,7 +27,7 @@ def __init__(self, population_size, p_crossover=0.5, crossover_power=0.5, p_mutation=0.5, mutation_power=0.1, - key_model_to_evolve="to_evolve", + key_main_model="main_model", seed=None, train_partition=1, **kwargs): @@ -43,7 +40,7 @@ def __init__(self, p_mutation: probability of mutation for current replacement mutation_power: allowed percentage of mutation key_model_to_evolve: binary flag that should be inserted into the dictionary - with evolving model in the basic config + with main model in the basic config (to determine save and load paths that will be changed) seed: random seed for initialization train_partition: integer number of train data parts **kwargs: basic config with parameters @@ -51,33 +48,23 @@ def __init__(self, self.basic_config = deepcopy(kwargs) self.model_to_evolve_index = find_index_of_dict_with_key_in_pipe(self.basic_config["chainer"]["pipe"], - key_model_to_evolve) + key_main_model) Path(self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).mkdir(parents=True, exist_ok=True) - self.dataset_iterator_params = deepcopy(self.basic_config.get("dataset_iterator")) - self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) - self.train_params = deepcopy(self.basic_config.get("train")) - - print("___Basic config___: {}".format(self.basic_config)) - print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) - print("___Dataset iterator params___: {}".format(self.dataset_iterator_params)) - print("___Model params___: {}".format(self.params)) - print("___Train params___: {}".format(self.train_params)) + self.print_dict(self.basic_config, string="Basic config:") + log.info("Main model index in pipe: {}".format(self.model_to_evolve_index)) self.population_size = population_size self.p_crossover = p_crossover self.p_mutation = p_mutation self.mutation_power = mutation_power self.crossover_power = crossover_power - self.evolving_dataset_iterator_params = [] - self.n_evolving_dataset_iterator_params = None - self.evolving_params = [] - self.n_evolving_params = None - self.evolving_train_params = [] - self.n_evolving_train_params = None + self.n_saved_best_with_weights = 0 self.train_partition = train_partition + + self.paths_to_evolving_params = [] self.evolution_individuum_id = 0 self.evolution_model_id = 0 @@ -86,17 +73,59 @@ def __init__(self, else: np.random.seed(seed) - def _insert_dict_into_model_params(self, params, model_index, dict_to_insert): - params_copy = deepcopy(params) - params_copy["chainer"]["pipe"].insert(model_index, dict_to_insert) - return params_copy + def _find_main_model(self, config, key_main_model, path=[]): + """ + Find path to the main model in config which paths will be changed + Args: + config: + key_main_model: + + Returns: + path in config -- list of keys (strings and integers) + """ + config_pointer = config + if key_main_model in config_pointer.keys(): + return path + else: + if type(config_pointer) is dict: + for key in list(config_pointer.keys()): + path += key + path_ = self._find_main_model(config_pointer[key], key_main_model, path) + if len(path_) > 0: + path = path_ + elif type(config_pointer) is list: + for i in range(len(config_pointer)): + path += i + path_ = self._find_main_model(config_pointer[i], key_main_model, path) + if len(path_) > 0: + path = path_ + if len(path) > 0: + return path + else: + return [] + + + @staticmethod + def _insert_value_or_dict_into_config(config, path, value): + config_copy = deepcopy(config) + config_pointer = config_copy + for el in path[:-1]: + if type(config_pointer) is dict: + config_pointer = config_pointer.setdefault(el, {}) + elif type(config_pointer) is list: + config_pointer = config_pointer[el] + else: + pass + config_pointer[path[-1]] = value + return config_copy - def print_dict(self, dict, string=None): + @staticmethod + def print_dict(config, string=None): if string is None: - print(json.dumps(dict, indent=2)) + log.info(json.dumps(config, indent=2)) else: - print(string) - print(json.dumps(dict, indent=2)) + log.info(string) + log.info(json.dumps(config, indent=2)) return None def initialize_params_in_config(self, basic_params): From 6652475dd8de55f71f4aca4f27eba7633acc8695 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 11:38:06 +0300 Subject: [PATCH 215/616] feat: find main model (debugged) --- .../evolution/evolution_param_generator.py | 27 ++++++++++--------- 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 1aa33730d8..c5d4a78dcd 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -84,26 +84,29 @@ def _find_main_model(self, config, key_main_model, path=[]): path in config -- list of keys (strings and integers) """ config_pointer = config - if key_main_model in config_pointer.keys(): + if type(config_pointer) is dict and key_main_model in config_pointer.keys(): + # main model is an element of chainer.pipe list + # main model is a dictionary and has key key_main_model return path else: + main_path = [] if type(config_pointer) is dict: + for key in list(config_pointer.keys()): - path += key - path_ = self._find_main_model(config_pointer[key], key_main_model, path) - if len(path_) > 0: - path = path_ + path_ = self._find_main_model(config_pointer[key], key_main_model, path + [key]) + if path_: + main_path = path_ elif type(config_pointer) is list: for i in range(len(config_pointer)): - path += i - path_ = self._find_main_model(config_pointer[i], key_main_model, path) - if len(path_) > 0: - path = path_ - if len(path) > 0: - return path + path_ = self._find_main_model(config_pointer[i], key_main_model, path + [i]) + if path_: + main_path = path_ else: return [] - + if main_path: + return main_path + else: + return [] @staticmethod def _insert_value_or_dict_into_config(config, path, value): From d2e391c8a390102def7bac5c4defbb82c850847f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 15:36:03 +0300 Subject: [PATCH 216/616] feat: initialization of evolving model params from config --- .../configs/evolution/intents_snips.json | 42 ++-- .../evolution/evolution_param_generator.py | 186 ++++++++---------- 2 files changed, 107 insertions(+), 121 deletions(-) diff --git a/deeppavlov/configs/evolution/intents_snips.json b/deeppavlov/configs/evolution/intents_snips.json index 912ad3c94e..58d21fd4ce 100644 --- a/deeppavlov/configs/evolution/intents_snips.json +++ b/deeppavlov/configs/evolution/intents_snips.json @@ -78,7 +78,7 @@ 3 ], "filters_cnn": { - "range": [ + "evolve_range": [ 50, 500 ], @@ -87,7 +87,7 @@ "confident_threshold": 0.5, "optimizer": "Adam", "lear_rate": { - "range": [ + "evolve_range": [ 0.0001, 0.1 ] @@ -99,13 +99,13 @@ "coef_reg_cnn": 1e-4, "coef_reg_den": 1e-4, "dropout_rate": { - "range": [ + "evolve_range": [ 0.1, 0.9 ] }, "dense_size": { - "range": [ + "evolve_range": [ 50, 500 ], @@ -113,7 +113,10 @@ }, "model_name": "cnn_model", "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" + "tokenizer": "#my_tokenizer", + "check_bool": { + "evolve_bool": true + } } ], "out": [ @@ -122,13 +125,28 @@ ] }, "train": { - "epochs": 100, - "batch_size": 64, - "metrics": [ - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], + "epochs": { + "evolve_range": [ + 50, + 500 + ], + "discrete": true + }, + "batch_size": { + "evolve_range": [ + 50, + 500 + ], + "discrete": true + }, + "metrics": { + "evolve_choice": true, + "values": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ] + }, "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index c5d4a78dcd..060065c0db 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -27,9 +27,10 @@ def __init__(self, population_size, p_crossover=0.5, crossover_power=0.5, p_mutation=0.5, mutation_power=0.1, - key_main_model="main_model", + key_main_model="main", seed=None, train_partition=1, + load_pretrained=False, **kwargs): """ Initialize evolution with random population @@ -47,25 +48,28 @@ def __init__(self, """ self.basic_config = deepcopy(kwargs) - self.model_to_evolve_index = find_index_of_dict_with_key_in_pipe(self.basic_config["chainer"]["pipe"], - key_main_model) - Path(self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).mkdir(parents=True, - exist_ok=True) - + self.main_model_path = list(self._find_model_path(self.basic_config, key_main_model))[0] + Path(self._get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, + exist_ok=True) self.print_dict(self.basic_config, string="Basic config:") - log.info("Main model index in pipe: {}".format(self.model_to_evolve_index)) + log.info("Main model path in config: {}".format(self.main_model_path)) self.population_size = population_size self.p_crossover = p_crossover self.p_mutation = p_mutation self.mutation_power = mutation_power self.crossover_power = crossover_power + self.load_pretrained = load_pretrained - self.n_saved_best_with_weights = 0 + self.n_saved_best_pretrained = 0 self.train_partition = train_partition self.paths_to_evolving_params = [] - self.evolution_individuum_id = 0 + for evolve_type in ["evolve_range", "evolve_choice", "evolve_bool"]: + for path_ in self._find_model_path(self.basic_config, evolve_type): + self.paths_to_evolving_params.append(path_) + + self.n_evolving_params = len(self.paths_to_evolving_params) self.evolution_model_id = 0 if seed is None: @@ -73,40 +77,30 @@ def __init__(self, else: np.random.seed(seed) - def _find_main_model(self, config, key_main_model, path=[]): + def _find_model_path(self, config, key_model, path=[]): """ Find path to the main model in config which paths will be changed Args: config: - key_main_model: + key_model: Returns: path in config -- list of keys (strings and integers) """ config_pointer = config - if type(config_pointer) is dict and key_main_model in config_pointer.keys(): + if type(config_pointer) is dict and key_model in config_pointer.keys(): # main model is an element of chainer.pipe list # main model is a dictionary and has key key_main_model - return path + yield path else: - main_path = [] if type(config_pointer) is dict: - for key in list(config_pointer.keys()): - path_ = self._find_main_model(config_pointer[key], key_main_model, path + [key]) - if path_: - main_path = path_ + for path_ in self._find_model_path(config_pointer[key], key_model, path + [key]): + yield path_ elif type(config_pointer) is list: for i in range(len(config_pointer)): - path_ = self._find_main_model(config_pointer[i], key_main_model, path + [i]) - if path_: - main_path = path_ - else: - return [] - if main_path: - return main_path - else: - return [] + for path_ in self._find_model_path(config_pointer[i], key_model, path + [i]): + yield path_ @staticmethod def _insert_value_or_dict_into_config(config, path, value): @@ -122,6 +116,19 @@ def _insert_value_or_dict_into_config(config, path, value): config_pointer[path[-1]] = value return config_copy + @staticmethod + def _get_value_from_config(config, path): + config_copy = deepcopy(config) + config_pointer = config_copy + for el in path[:-1]: + if type(config_pointer) is dict: + config_pointer = config_pointer.setdefault(el, {}) + elif type(config_pointer) is list: + config_pointer = config_pointer[el] + else: + pass + return config_pointer[path[-1]] + @staticmethod def print_dict(config, string=None): if string is None: @@ -131,32 +138,33 @@ def print_dict(config, string=None): log.info(json.dumps(config, indent=2)) return None - def initialize_params_in_config(self, basic_params): - params = {} - params_for_search = {} - evolving_params = [] - - for param_name in list(basic_params.keys()): - if type(basic_params[param_name]) is dict: - if basic_params[param_name].get("choice"): - params_for_search[param_name] = list(basic_params[param_name]["values"]) - evolving_params.append(param_name) - elif basic_params[param_name].get("range"): - params_for_search[param_name] = deepcopy(basic_params[param_name]) - evolving_params.append(param_name) - elif basic_params[param_name].get("bool"): - params_for_search[param_name] = deepcopy(basic_params[param_name]) - evolving_params.append(param_name) - else: - # NOT evolving params - params[param_name] = deepcopy(basic_params[param_name]) - else: - # NOT evolving params - params[param_name] = deepcopy(basic_params[param_name]) - if basic_params: - params_for_search = deepcopy(self.sample_params(**params_for_search)) - - return params, params_for_search, evolving_params + def initialize_params_in_config(self, basic_config, paths): + config = deepcopy(basic_config) + + for path_ in paths: + param_name = path_[-1] + value = self._get_value_from_config(basic_config, path_) + if type(value) is dict: + if value.get("evolve_choice"): + config = self._insert_value_or_dict_into_config(config, + path_, + self.sample_params( + **{param_name: + list(value["values"])})[param_name]) + elif value.get("evolve_range"): + config = self._insert_value_or_dict_into_config(config, + path_, + self.sample_params( + **{param_name: + deepcopy(value)})[param_name]) + elif value.get("evolve_bool"): + config = self._insert_value_or_dict_into_config(config, + path_, + self.sample_params( + **{param_name: + deepcopy(value)})[param_name]) + + return config def first_generation(self, iteration=0): """ @@ -166,57 +174,17 @@ def first_generation(self, iteration=0): """ population = [] for i in range(self.population_size): - population.append(deepcopy(self.basic_config)) - - # initializing parameters for dataset iterator - dataset_iterator_params, dataset_iterator_params_for_search, evolving_params = \ - self.initialize_params_in_config(self.dataset_iterator_params) - self.evolving_dataset_iterator_params.extend(evolving_params) - # intitializing parameters for model - params, params_for_search, evolving_params = self.initialize_params_in_config(self.params) - self.evolving_params.extend(evolving_params) - # initializing parameters for train - train_params, train_params_for_search, evolving_params = self.initialize_params_in_config(self.train_params) - self.evolving_train_params.extend(evolving_params) - - # intitializing path to save model - # save_path = population_iteration/model_name_i/ - if "model_name" in params_for_search.keys(): - params["save_path"] = str(Path(self.params["save_path"]).joinpath( - "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) - else: - params["save_path"] = str(Path(self.params["save_path"]).joinpath( - "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) - - # load_path = population_iteration/model_name_i/ - if "model_name" in params_for_search.keys(): - params["load_path"] = str(Path(self.params["load_path"]).joinpath( - "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) - else: - params["load_path"] = str(Path(self.params["load_path"]).joinpath( - "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) - - # exchange dataset iterator params from basic config to sampled train params - population[-1]["dataset_iterator"] = {**dataset_iterator_params, - **dataset_iterator_params_for_search} - # exchange model and layers params from basic config to sampled model params - population[-1]["chainer"]["pipe"][self.model_to_evolve_index] = {**params, - **params_for_search} - - # exchange train params from basic config to sampled train params - population[-1]["train"] = {**train_params, - **train_params_for_search} - population[-1]["train"]["evolution_model_id"] = self.evolution_model_id + population.append(self.initialize_params_in_config(self.basic_config, self.paths_to_evolving_params)) + for which_path in ["save_path", "load_path"]: + population[-1] = self._insert_value_or_dict_into_config(population[-1], + self.main_model_path + [which_path], + str(Path( + self.basic_config["save_path"]).joinpath( + "population_" + str(iteration)).joinpath( + "model_" + str(i)))) + population[-1]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 - self.evolving_dataset_iterator_params = list(set(self.evolving_dataset_iterator_params)) - self.evolving_params = list(set(self.evolving_params)) - self.evolving_train_params = list(set(self.evolving_train_params)) - - self.n_evolving_dataset_iterator_params = len(self.evolving_dataset_iterator_params) - self.n_evolving_params = len(self.evolving_params) - self.n_evolving_train_params = len(self.evolving_train_params) - return population def next_generation(self, generation, scores, iteration, @@ -246,7 +214,7 @@ def next_generation(self, generation, scores, iteration, mutation_power = self.mutation_power next_population = self.selection_of_best_with_weights(generation, scores) - print("Saved with weights: {} individuums".format(self.n_saved_best_with_weights)) + print("Saved with weights: {} individuums".format(self.n_saved_best_pretrained)) offsprings = self.crossover(generation, scores, p_crossover=p_crossover, crossover_power=crossover_power) @@ -257,7 +225,7 @@ def next_generation(self, generation, scores, iteration, next_population.extend(changable_next) - for i in range(self.n_saved_best_with_weights): + for i in range(self.n_saved_best_pretrained): # if several train files: if self.train_partition != 1: next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ @@ -277,7 +245,7 @@ def next_generation(self, generation, scores, iteration, str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) - for i in range(self.n_saved_best_with_weights, self.population_size): + for i in range(self.n_saved_best_pretrained, self.population_size): # if several train files if self.train_partition != 1: next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ @@ -310,7 +278,7 @@ def selection_of_best_with_weights(self, population, scores): scores: corresponding score that should be maximized Returns: - selected self.n_saved_best_with_weights (changable) individuums + selected self.n_saved_best_pretrained (changable) individuums """ scores = np.array(scores, dtype='float') sorted_ids = np.argsort(scores) @@ -326,7 +294,7 @@ def selection_of_best_with_weights(self, population, scores): if self.decision(probas_to_be_selected[i]): selected.append(deepcopy(population[i])) - self.n_saved_best_with_weights = len(selected) + self.n_saved_best_pretrained = len(selected) return selected def crossover(self, population, scores, p_crossover, crossover_power): @@ -341,14 +309,14 @@ def crossover(self, population, scores, p_crossover, crossover_power): crossover_power: part of EVOLVING parents parameters to exchange for offsprings Returns: - (self.population_size - self.n_saved_best_with_weights) offsprings + (self.population_size - self.n_saved_best_pretained) offsprings """ offsprings = [] scores = np.array(scores, dtype='float') probas_to_be_parent = scores / np.sum(scores) intervals = np.array([np.sum(probas_to_be_parent[:i]) for i in range(self.population_size)]) - for i in range(self.population_size - self.n_saved_best_with_weights): + for i in range(self.population_size - self.n_saved_best_pretrained): rs = np.random.random(2) parents = population[np.where(rs[0] > intervals)[0][-1]], population[np.where(rs[1] > intervals)[0][-1]] From 3ee85bd0045a8fa4bb05740b041e3411da388c76 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 16:08:51 +0300 Subject: [PATCH 217/616] feat: next generation --- .../evolution/evolution_param_generator.py | 61 ++++---- deeppavlov/models/evolution/test.py | 134 ++++++++++++++++++ 2 files changed, 170 insertions(+), 25 deletions(-) create mode 100644 deeppavlov/models/evolution/test.py diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 060065c0db..6601e19b67 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -228,38 +228,49 @@ def next_generation(self, generation, scores, iteration, for i in range(self.n_saved_best_pretrained): # if several train files: if self.train_partition != 1: - next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ - "train"]).stem.split("_")[0]) \ - + "_" + str(iteration % self.train_partition) + ".csv" + next_population[i]["dataset_reader"]["train"] = "_".join(str(Path(next_population[i]["dataset_reader"][ + "train"]).stem.split("_")[:-1])) \ + + "_" + str(iteration % self.train_partition) + ".csv" try: - # re-init learning rate with the final one - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ - read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index][ - "save_path"]).parent.joinpath("model_opt.json")))["final_lear_rate"] + # re-init learning rate with the final one (works for KerasModel) + next_population[i] = self._insert_value_or_dict_into_config( + next_population[i], + self._get_value_from_config(next_population[i], + self.main_model_path + ["lear_rate"]), + read_json(str(Path(self._get_value_from_config(next_population[i], + self.main_model_path + ["save_path"]) + ).parent.joinpath("model_opt.json")))["final_lear_rate"]) except: pass - # paths - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ - str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( - self.params["model_name"] + "_" + str(i))) + next_population[i] = self._insert_value_or_dict_into_config( + next_population[i], + self._get_value_from_config(next_population[i], + self.main_model_path + ["load_path"]), + str(Path(self._get_value_from_config(next_population[i], + self.main_model_path + ["save_path"])).parent)) + + next_population[i] = self._insert_value_or_dict_into_config( + next_population[i], + self._get_value_from_config(next_population[i], + self.main_model_path + ["save_path"]), + str(Path(self._get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) for i in range(self.n_saved_best_pretrained, self.population_size): # if several train files if self.train_partition != 1: - next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ - "train"]).stem.split("_")[0]) \ - + "_" + str(iteration % self.train_partition) + ".csv" - # paths - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ - str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( - self.params["model_name"] + "_" + str(i))) - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(Path(self.params["load_path"]).joinpath("population_" + str(iteration)).joinpath( - self.params["model_name"] + "_" + str(i))) - - next_population[i]["train"]["evolution_model_id"] = self.evolution_model_id + next_population[i]["dataset_reader"]["train"] = "_".join(str(Path(next_population[i]["dataset_reader"][ + "train"]).stem.split("_")[:-1])) \ + + "_" + str(iteration % self.train_partition) + ".csv" + for which_path in ["save_path", "load_path"]: + next_population[i] = self._insert_value_or_dict_into_config( + next_population[i], + self._get_value_from_config(next_population[i], + self.main_model_path + [which_path]), + str(Path(self._get_value_from_config(next_population[i], self.main_model_path + [which_path]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) + + next_population[i]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 return next_population diff --git a/deeppavlov/models/evolution/test.py b/deeppavlov/models/evolution/test.py new file mode 100644 index 0000000000..31da975a78 --- /dev/null +++ b/deeppavlov/models/evolution/test.py @@ -0,0 +1,134 @@ +import numpy as np +from deeppavlov.core.common.file import read_json +from copy import copy, deepcopy +import json + + +def _find_main_model_path(config, key_model, path=[]): + """ + Find path to the main model in config which paths will be changed + Args: + config: + key_model: + + Returns: + path in config -- list of keys (strings and integers) + """ + config_pointer = config + # add_paths = [] + + if type(config_pointer) is dict and key_model in config_pointer.keys(): + # main model is an element of chainer.pipe list + # main model is a dictionary and has key key_main_model + yield path + else: + if type(config_pointer) is dict: + for key in list(config_pointer.keys()): + for path_ in _find_main_model_path(config_pointer[key], key_model, path + [key]): + yield path_ + elif type(config_pointer) is list: + for i in range(len(config_pointer)): + for path_ in _find_main_model_path(config_pointer[i], key_model, path + [i]): + yield path_ + + +def _insert_value_or_dict_into_config(config, path, value): + config_copy = deepcopy(config) + config_pointer = config_copy + for el in path[:-1]: + if type(config_pointer) is dict: + config_pointer = config_pointer.setdefault(el, {}) + elif type(config_pointer) is list: + config_pointer = config_pointer[el] + else: + pass + config_pointer[path[-1]] = value + return config_copy + + +def _get_value_from_config(config, path): + config_copy = deepcopy(config) + config_pointer = config_copy + for el in path[:-1]: + if type(config_pointer) is dict: + config_pointer = config_pointer.setdefault(el, {}) + elif type(config_pointer) is list: + config_pointer = config_pointer[el] + else: + pass + return config_pointer[path[-1]] + + +def initialize_params_in_config(basic_config, paths): + config = deepcopy(basic_config) + + for path_ in paths: + param_name = path_[-1] + value = _get_value_from_config(basic_config, path_) + if type(value) is dict: + if value.get("evolve_choice"): + config = _insert_value_or_dict_into_config(config, + path_, + sample_params( + **{param_name: list(value["values"])})[param_name]) + elif value.get("evolve_range"): + config = _insert_value_or_dict_into_config(config, + path_, + sample_params( + **{param_name: deepcopy(value)})[param_name]) + elif value.get("evolve_bool"): + config = _insert_value_or_dict_into_config(config, + path_, + sample_params( + **{param_name: deepcopy(value)})[param_name]) + + return config + + +def sample_params(**params): + if not params: + return {} + else: + params_copy = deepcopy(params) + params_sample = dict() + for param, param_val in params_copy.items(): + if isinstance(param_val, list): + params_sample[param] = np.random.choice(param_val) + elif isinstance(param_val, dict): + if 'evolve_bool' in param_val and param_val['evolve_bool']: + sample = bool(np.random.choice([True, False])) + elif 'evolve_range' in param_val: + sample = _sample_from_ranges(param_val) + params_sample[param] = sample + else: + params_sample[param] = params_copy[param] + return params_sample + + +def _sample_from_ranges(opts): + from_ = opts['evolve_range'][0] + to_ = opts['evolve_range'][1] + if opts.get('scale', None) == 'log': + sample = _sample_log(from_, to_) + else: + sample = np.random.uniform(from_, to_) + if opts.get('discrete', False): + sample = int(np.round(sample)) + return sample + + +def _sample_log(from_, to_): + sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) + return float(sample) + + +config = read_json("/home/dilyara/Documents/GitHub/deeppavlov_evolution/deeppavlov/configs/evolution/intents_snips.json") +paths = list(_find_main_model_path(config, "evolve_range")) + +print(paths) + +for t in ["evolve_range", "evolve_choice", "evolve_bool"]: + paths = list(_find_main_model_path(config, t)) + config = initialize_params_in_config(config, paths) + +print(json.dumps(config, indent=2)) From 5899cb1d26f84a23a49cf47464dc2febb9dd33a6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 16:17:40 +0300 Subject: [PATCH 218/616] feat: add param elitism with weights or not --- .../evolution/evolution_param_generator.py | 27 +++++++++++++------ 1 file changed, 19 insertions(+), 8 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 6601e19b67..7535e8e61e 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -30,7 +30,7 @@ def __init__(self, key_main_model="main", seed=None, train_partition=1, - load_pretrained=False, + elitism_with_weights=False, **kwargs): """ Initialize evolution with random population @@ -59,7 +59,7 @@ def __init__(self, self.p_mutation = p_mutation self.mutation_power = mutation_power self.crossover_power = crossover_power - self.load_pretrained = load_pretrained + self.elitism_with_weights = elitism_with_weights self.n_saved_best_pretrained = 0 self.train_partition = train_partition @@ -242,12 +242,23 @@ def next_generation(self, generation, scores, iteration, ).parent.joinpath("model_opt.json")))["final_lear_rate"]) except: pass - next_population[i] = self._insert_value_or_dict_into_config( - next_population[i], - self._get_value_from_config(next_population[i], - self.main_model_path + ["load_path"]), - str(Path(self._get_value_from_config(next_population[i], - self.main_model_path + ["save_path"])).parent)) + + if self.elitism_with_weights: + # if elite models are saved with weights + next_population[i] = self._insert_value_or_dict_into_config( + next_population[i], + self._get_value_from_config(next_population[i], + self.main_model_path + ["load_path"]), + str(Path(self._get_value_from_config(next_population[i], + self.main_model_path + ["save_path"])).parent)) + else: + # if elite models are saved only as configurations and trained again + next_population[i] = self._insert_value_or_dict_into_config( + next_population[i], + self._get_value_from_config(next_population[i], + self.main_model_path + ["load_path"]), + str(Path(self._get_value_from_config(next_population[i], self.main_model_path + ["load_path"]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) next_population[i] = self._insert_value_or_dict_into_config( next_population[i], From 9203696a78aea0b3c63ab67e74c7b7c19730491a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 16:26:35 +0300 Subject: [PATCH 219/616] feat: crossover --- .../evolution/evolution_param_generator.py | 77 ++++--------------- 1 file changed, 16 insertions(+), 61 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 7535e8e61e..f0ff3d97ab 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -343,74 +343,29 @@ def crossover(self, population, scores, p_crossover, crossover_power): parents = population[np.where(rs[0] > intervals)[0][-1]], population[np.where(rs[1] > intervals)[0][-1]] if self.decision(p_crossover): - dataset_iterator_params_perm = np.random.permutation(self.n_evolving_dataset_iterator_params) params_perm = np.random.permutation(self.n_evolving_params) - train_params_perm = np.random.permutation(self.n_evolving_train_params) curr_offsprings = [deepcopy(parents[0]), deepcopy(parents[1])] - dataset_iterator_part = int(crossover_power * self.n_evolving_dataset_iterator_params) part = int(crossover_power * self.n_evolving_params) - train_part = int(crossover_power * self.n_evolving_train_params) - - # exchange of dataset_iterator params - for j in range(self.n_evolving_dataset_iterator_params - dataset_iterator_part): - curr_offsprings[0]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ - parents[0]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] - curr_offsprings[1]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ - parents[1]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] - for j in range(self.n_evolving_dataset_iterator_params - dataset_iterator_part, - self.n_evolving_dataset_iterator_params): - curr_offsprings[0]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ - parents[1]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] - curr_offsprings[1]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ - parents[0]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] - - # exchange of model params (not layers params) - for j in range(self.n_evolving_params - part): - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[0][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[1][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - for j in range(self.n_evolving_params - part, self.n_evolving_params): - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[1][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[0][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - - # exchange of train params - for j in range(self.n_evolving_train_params - train_part): - curr_offsprings[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] - curr_offsprings[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] - for j in range(self.n_evolving_train_params - train_part, self.n_evolving_train_params): - curr_offsprings[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] - curr_offsprings[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] + for j in range(self.n_evolving_params - part, self.n_evolving_params): + curr_offsprings[0] = self._insert_value_or_dict_into_config(curr_offsprings[0], + self.paths_to_evolving_params[ + params_perm[j]], + self._get_value_from_config( + parents[1], + self.paths_to_evolving_params[ + params_perm[j]])) + + curr_offsprings[1] = self._insert_value_or_dict_into_config(curr_offsprings[1], + self.paths_to_evolving_params[ + params_perm[j]], + self._get_value_from_config( + parents[0], + self.paths_to_evolving_params[ + params_perm[j]])) offsprings.append(deepcopy(curr_offsprings[0])) else: offsprings.append(deepcopy(parents[0])) From 2e28fe8b4731f14b15bad0dda6e15d47a5e2aab3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 16:27:51 +0300 Subject: [PATCH 220/616] fix: add evolve_ to range bool and choice --- .../evolution/evolution_param_generator.py | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index f0ff3d97ab..a9b4cc8861 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -421,17 +421,17 @@ def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutatio val = round(param_value + ((2 * np.random.random() - 1.) * mutation_power * self.sample_params(**{param: params_dict[param]})[param])) - val = min(max(params_dict[param]["range"][0], val), - params_dict[param]["range"][1]) + val = min(max(params_dict[param]["evolve_range"][0], val), + params_dict[param]["evolve_range"][1]) new_mutated_value = val - elif 'range' in params_dict[param].keys(): + elif 'evolve_range' in params_dict[param].keys(): val = param_value + \ ((2 * np.random.random() - 1.) * mutation_power * self.sample_params(**{param: params_dict[param]})[param]) - val = min(max(params_dict[param]["range"][0], val), - params_dict[param]["range"][1]) + val = min(max(params_dict[param]["evolve_range"][0], val), + params_dict[param]["evolve_range"][1]) new_mutated_value = val - elif params_dict[param].get("choice"): + elif params_dict[param].get("evolve_choice"): # new_mutated_value = param_value new_mutated_value = self.sample_params(**{param: params_dict[param]})[param] else: @@ -468,9 +468,9 @@ def sample_params(self, **params): if isinstance(param_val, list): params_sample[param] = np.random.choice(param_val) elif isinstance(param_val, dict): - if 'bool' in param_val and param_val['bool']: + if 'evolve_bool' in param_val and param_val['evolve_bool']: sample = bool(np.random.choice([True, False])) - elif 'range' in param_val: + elif 'evolve_range' in param_val: sample = self._sample_from_ranges(param_val) params_sample[param] = sample else: @@ -478,8 +478,8 @@ def sample_params(self, **params): return params_sample def _sample_from_ranges(self, opts): - from_ = opts['range'][0] - to_ = opts['range'][1] + from_ = opts['evolve_range'][0] + to_ = opts['evolve_range'][1] if opts.get('scale', None) == 'log': sample = self._sample_log(from_, to_) else: From 1615e50569140285598344a5134fe314a785b011 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 16:51:25 +0300 Subject: [PATCH 221/616] feat: mutation --- .../evolution/evolution_param_generator.py | 88 ++++++------------- 1 file changed, 28 insertions(+), 60 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index a9b4cc8861..8025a031a5 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -187,9 +187,7 @@ def first_generation(self, iteration=0): return population - def next_generation(self, generation, scores, iteration, - p_crossover=None, crossover_power=None, - p_mutation=None, mutation_power=None): + def next_generation(self, generation, scores, iteration): """ Provide an operation of replacement Args: @@ -204,24 +202,12 @@ def next_generation(self, generation, scores, iteration, Returns: the next generation according to the given scores of current generation """ - if not p_crossover: - p_crossover = self.p_crossover - if not crossover_power: - crossover_power = self.crossover_power - if not p_mutation: - p_mutation = self.p_mutation - if not mutation_power: - mutation_power = self.mutation_power next_population = self.selection_of_best_with_weights(generation, scores) print("Saved with weights: {} individuums".format(self.n_saved_best_pretrained)) - offsprings = self.crossover(generation, scores, - p_crossover=p_crossover, - crossover_power=crossover_power) + offsprings = self.crossover(generation, scores) - changable_next = self.mutation(offsprings, - p_mutation=p_mutation, - mutation_power=mutation_power) + changable_next = self.mutation(offsprings) next_population.extend(changable_next) @@ -319,7 +305,7 @@ def selection_of_best_with_weights(self, population, scores): self.n_saved_best_pretrained = len(selected) return selected - def crossover(self, population, scores, p_crossover, crossover_power): + def crossover(self, population, scores): """ Recombine randomly population in pairs and cross over them with given probability. Cross over from two parents produces two offsprings @@ -342,13 +328,13 @@ def crossover(self, population, scores, p_crossover, crossover_power): rs = np.random.random(2) parents = population[np.where(rs[0] > intervals)[0][-1]], population[np.where(rs[1] > intervals)[0][-1]] - if self.decision(p_crossover): + if self.decision(self.p_crossover): params_perm = np.random.permutation(self.n_evolving_params) curr_offsprings = [deepcopy(parents[0]), deepcopy(parents[1])] - part = int(crossover_power * self.n_evolving_params) + part = int(self.crossover_power * self.n_evolving_params) for j in range(self.n_evolving_params - part, self.n_evolving_params): curr_offsprings[0] = self._insert_value_or_dict_into_config(curr_offsprings[0], @@ -372,7 +358,7 @@ def crossover(self, population, scores, p_crossover, crossover_power): return offsprings - def mutation(self, population, p_mutation, mutation_power): + def mutation(self, population): """ Mutate each parameter of each individuum in population with probability p_mutation Args: @@ -387,53 +373,35 @@ def mutation(self, population, p_mutation, mutation_power): for individuum in population: mutated_individuum = deepcopy(individuum) - - # mutation of dataset iterator params - for param in self.dataset_iterator_params.keys(): - mutated_individuum["dataset_iterator"][param] = \ - self.mutation_of_param(param, self.dataset_iterator_params, - individuum["dataset_iterator"][param], - p_mutation, mutation_power) - - # mutation of other model params - for param in self.params.keys(): - mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][param] = \ - self.mutation_of_param(param, self.params, - individuum["chainer"]["pipe"][self.model_to_evolve_index][param], - p_mutation, mutation_power) - - # mutation of train params - for param in self.train_params.keys(): - mutated_individuum["train"][param] = \ - self.mutation_of_param(param, self.train_params, - individuum["train"][param], - p_mutation, mutation_power) - + for path_ in self.paths_to_evolving_params: + mutated_individuum = self._insert_value_or_dict_into_config( + mutated_individuum, path_, + self.mutation_of_param(path_, self._get_value_from_config(individuum, path_))) mutated.append(mutated_individuum) return mutated - def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutation_power): - new_mutated_value = deepcopy(param_value) - if self.decision(p_mutation): - if type(params_dict[param]) is dict: - if params_dict[param].get('discrete', False): + def mutation_of_param(self, param_path, param_value): + if self.decision(self.p_mutation): + basic_value = self._get_value_from_config(self.basic_config, param_path) + param_name = param_path[-1] + if type(basic_value) is dict: + if basic_value.get('discrete', False): val = round(param_value + - ((2 * np.random.random() - 1.) * mutation_power - * self.sample_params(**{param: params_dict[param]})[param])) - val = min(max(params_dict[param]["evolve_range"][0], val), - params_dict[param]["evolve_range"][1]) + ((2 * np.random.random() - 1.) * self.mutation_power + * self.sample_params(**{param_name: basic_value})[param_name])) + val = min(max(basic_value["evolve_range"][0], val), + basic_value["evolve_range"][1]) new_mutated_value = val - elif 'evolve_range' in params_dict[param].keys(): + elif 'evolve_range' in basic_value.keys(): val = param_value + \ - ((2 * np.random.random() - 1.) * mutation_power - * self.sample_params(**{param: params_dict[param]})[param]) - val = min(max(params_dict[param]["evolve_range"][0], val), - params_dict[param]["evolve_range"][1]) + ((2 * np.random.random() - 1.) * self.mutation_power + * self.sample_params(**{param_name: basic_value})[param_name]) + val = min(max(basic_value["evolve_range"][0], val), + basic_value["evolve_range"][1]) new_mutated_value = val - elif params_dict[param].get("evolve_choice"): - # new_mutated_value = param_value - new_mutated_value = self.sample_params(**{param: params_dict[param]})[param] + elif basic_value.get("evolve_choice"): + new_mutated_value = self.sample_params(**{param_name: basic_value})[param_name] else: new_mutated_value = param_value else: From d04f87c7e64902ad2a9e1e337e3abd5fab46aaa0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:08:30 +0300 Subject: [PATCH 222/616] chore --- deeppavlov/core/commands/train.py | 15 +- .../evolution/Tutorial_params_evolution.ipynb | 328 --------- .../models/evolution/check_binary_mask.py | 131 ---- .../models/evolution/check_matrix.ipynb | 257 ------- deeppavlov/models/evolution/debug.py | 80 --- .../evolution/evolution_intent_model.py | 248 ------- .../evolution/evolution_many_inputs_model.py | 416 ------------ .../evolution/evolution_param_generator.py | 71 +- .../neuroevolution_param_generator.py | 637 ------------------ .../evolution/random_param_generator.py | 85 --- deeppavlov/models/evolution/run_evolution.py | 232 ------- .../models/evolution/run_param_evolution.py | 87 +-- deeppavlov/models/evolution/test.py | 134 ---- .../models/evolution/train_phenotype.py | 1 - deeppavlov/models/evolution/utils.py | 12 - 15 files changed, 89 insertions(+), 2645 deletions(-) delete mode 100644 deeppavlov/models/evolution/Tutorial_params_evolution.ipynb delete mode 100644 deeppavlov/models/evolution/check_binary_mask.py delete mode 100644 deeppavlov/models/evolution/check_matrix.ipynb delete mode 100644 deeppavlov/models/evolution/debug.py delete mode 100644 deeppavlov/models/evolution/evolution_intent_model.py delete mode 100644 deeppavlov/models/evolution/evolution_many_inputs_model.py delete mode 100644 deeppavlov/models/evolution/neuroevolution_param_generator.py delete mode 100644 deeppavlov/models/evolution/random_param_generator.py delete mode 100644 deeppavlov/models/evolution/run_evolution.py delete mode 100644 deeppavlov/models/evolution/test.py diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index f58f7e215b..345d3d0f22 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -172,7 +172,6 @@ def train_evaluate_model_from_config(config_path: str, to_train=True, to_validat elif not isinstance(model, Chainer): log.warning('Nothing to train') - reports = [] if train_config['validate_best'] or train_config['test_best']: # try: # model_config['load_path'] = model_config['save_path'] @@ -182,22 +181,20 @@ def train_evaluate_model_from_config(config_path: str, to_train=True, to_validat log.info('Testing the best saved model') if train_config['validate_best']: - reports.append({ + report = { 'valid': _test_model(model, metrics_functions, iterator, train_config.get('batch_size', -1), 'valid') - }) + } - print(json.dumps(reports[-1], ensure_ascii=False)) + print(json.dumps(report, ensure_ascii=False)) if train_config['test_best']: - reports.append({ + report = { 'test': _test_model(model, metrics_functions, iterator, train_config.get('batch_size', -1), 'test') - }) - - print(json.dumps(reports[-1], ensure_ascii=False)) + } - return reports + print(json.dumps(report, ensure_ascii=False)) def _test_model(model: Component, metrics_functions: List[Tuple[str, Callable]], diff --git a/deeppavlov/models/evolution/Tutorial_params_evolution.ipynb b/deeppavlov/models/evolution/Tutorial_params_evolution.ipynb deleted file mode 100644 index f729ce8fec..0000000000 --- a/deeppavlov/models/evolution/Tutorial_params_evolution.ipynb +++ /dev/null @@ -1,328 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to use evolution of model parameters in DeepPavlov" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Скопируйте в новый файл свой обычный конфиг, соответствующий рассматриваемой модели.\n", - "\n", - "* Для каждого параметра, который можно варьировать, в конфиге замените значение параметра на словарь, определяющий возможные принимаемые значения. Тренировочные параметры (из `config[\"train\"]`) варьируются автоматически, а для варьирования параметров модели необходимо определить тот подсловарь конфига, в котором находятся варьируемые параметры, добавив в него параметр `\"to_evolve\": true`. Варьируемые параметры должны быть ключами словаря, содержащего ключ `to_evolve`, вложенность пока не поддерживается.\n", - "\n", - "* Запустите эволюцию с необходимыми параметрами:\n", - " - config - путь к файлу конфигу для эволюции\n", - " - evolve_metric - зарегистрированное название метрики из тренировочных параметров конфига, по значениям которой будет происходить эволюция\n", - " - p_size - размер одной популяции\n", - " - gpus - номера gpu, доступных для использования. Если количество gpu меньше размера популяции, то модели будут запускаться группами по len(gpus) штук.\n", - "```\n", - "python ./models/evolution/run_param_evolution.py --config config_file \n", - " --evolve_metric registered_metric_from_config \n", - " --p_size 10\n", - " --gpus 0,1,2\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Примеры словаря возможных значений для различных видов параметров" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import json\n", - "\n", - "def print_json(dictionary):\n", - " print(json.dumps(dictionary, indent=2))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "config = {\"dense_size\": 100, \n", - " \"activation\": \"sigmoid\", \n", - " \"learning_rate\": 0.001, \n", - " \"learning_rate_decay\": 0.00001,\n", - " \"is_main\": True}" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"dense_size\": 100,\n", - " \"activation\": \"sigmoid\",\n", - " \"learning_rate\": 0.001,\n", - " \"learning_rate_decay\": 1e-05,\n", - " \"is_main\": true\n", - "}\n" - ] - } - ], - "source": [ - "print_json(config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Дискретный параметр из промежутка" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - "}\n" - ] - } - ], - "source": [ - "config[\"dense_size\"] = {\"range\": [50, 500], \"discrete\": True}\n", - "print_json(config[\"dense_size\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Дискретный параметр из листа возможных значений" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"values\": [\n", - " \"softmax\",\n", - " \"sigmoid\",\n", - " \"relu\"\n", - " ],\n", - " \"choice\": true\n", - "}\n" - ] - } - ], - "source": [ - "config[\"activation\"] = {\"values\": [\"softmax\", \"sigmoid\", \"relu\"], \"choice\": True}\n", - "print_json(config[\"activation\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Параметр из промежутка" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"range\": [\n", - " 0.001,\n", - " 0.1\n", - " ]\n", - "}\n" - ] - } - ], - "source": [ - "config[\"learning_rate\"] = {\"range\": [0.001, 0.1]}\n", - "print_json(config[\"learning_rate\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Параметр из промежутка с логарифмической шкалой" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"range\": [\n", - " 1e-05,\n", - " 0.0001\n", - " ],\n", - " \"scale\": \"log\"\n", - "}\n" - ] - } - ], - "source": [ - "config[\"learning_rate_decay\"] = {\"range\": [0.00001, 0.0001], \"scale\": \"log\"}\n", - "print_json(config[\"learning_rate_decay\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Булевый параметр" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"bool\": true\n", - "}\n" - ] - } - ], - "source": [ - "config[\"is_main\"] = {\"bool\": True}\n", - "print_json(config[\"is_main\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Therefore, evolving parameters can be written in DeepPavlov config in the following way" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"dense_size\": {\n", - " \"range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"activation\": {\n", - " \"values\": [\n", - " \"softmax\",\n", - " \"sigmoid\",\n", - " \"relu\"\n", - " ],\n", - " \"choice\": true\n", - " },\n", - " \"learning_rate\": {\n", - " \"range\": [\n", - " 0.001,\n", - " 0.1\n", - " ]\n", - " },\n", - " \"learning_rate_decay\": {\n", - " \"range\": [\n", - " 1e-05,\n", - " 0.0001\n", - " ],\n", - " \"scale\": \"log\"\n", - " },\n", - " \"is_main\": {\n", - " \"bool\": true\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "print_json(config)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python-deep36", - "language": "python", - "name": "deep36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py deleted file mode 100644 index 5024cd8720..0000000000 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ /dev/null @@ -1,131 +0,0 @@ -import numpy as np -import networkx as nx -from copy import copy, deepcopy -import datetime -import time -from pathlib import Path -import matplotlib -matplotlib.use('Agg') - -import matplotlib.pyplot as plt - -def number_to_type_layer(node_id, n_types): - # return node_layer, node_type - return node_id // n_types, node_id % n_types - - -def type_layer_to_number(node_layer, node_type, n_types): - return node_layer * n_types + node_type - - -def find_sources_and_sinks(directed_graph): - sources = [] - sinks = [] - isolates = nx.isolates(directed_graph) - - for str_id in directed_graph.nodes(): - if directed_graph.in_degree(str_id) == 0 and directed_graph.out_degree(str_id) > 0: - sources.append(str_id) - if directed_graph.in_degree(str_id) > 0 and directed_graph.out_degree(str_id) == 0: - sinks.append(str_id) - - return sources, sinks, isolates - - -def get_digraph_from_binary_mask(nodes, binary_mask): - directed_graph = nx.DiGraph() - total_nodes = len(nodes) - - for i in range(total_nodes): - directed_graph.add_node(str(i)) - - for i in range(total_nodes): - for j in range(total_nodes): - if binary_mask[i, j] == 1: - directed_graph.add_edge(str(i), str(j)) - return directed_graph - - -def get_binary_mask_from_digraph(nodes, directed_graph): - binary_mask = np.zeros((len(nodes), len(nodes))) - for edge in directed_graph.edges(): - binary_mask[int(edge[0]), int(edge[1])] = 1 - return binary_mask - - -def check_and_correct_binary_mask(nodes, binary_mask_): - binary_mask = deepcopy(binary_mask_) - - directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) - sources, sinks, _ = find_sources_and_sinks(directed_graph) - - while not nx.is_directed_acyclic_graph(directed_graph): - candidates = [] - cycles = list(nx.simple_cycles(directed_graph)) - n_cycles = len(cycles) - cycles_len = np.array([len(cycle) for cycle in cycles]) - n_candidates = int(np.prod(cycles_len)) - - for i in range(n_candidates): - new_directed_graph = deepcopy(directed_graph) - for j in range(n_cycles): - node_id = (i // np.prod(cycles_len[:j])) % cycles_len[j] - try: - new_directed_graph.remove_edge(cycles[j][node_id], cycles[j][(node_id + 1) % cycles_len[j]]) - except: - continue - candidates.append(new_directed_graph) - - n_candidates = len(candidates) - best_cand = None - best_diff = 10e10 - for i in range(n_candidates): - new_sources, new_sinks, _ = find_sources_and_sinks(candidates[i]) - - if set(new_sources) == set(sources) and set(new_sinks) == set(sinks): - best_cand = candidates[i] - elif (len(set(new_sources).difference(set(sources))) + - len(set(new_sinks).difference(set(sinks))) < best_diff): - best_cand = candidates[i] - best_diff = len(set(new_sources).difference(set(sources))) + len(set(new_sinks).difference(set(sinks))) - - directed_graph = best_cand - - binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) - return binary_mask - - -def get_graph_and_plot(nodes, binary_mask, n_types, path=None): - nodes_int = {} - for i in range(len(nodes)): - nodes_int[i] = nodes[str(i)] - - total_nodes = len(nodes) - dg = get_digraph_from_binary_mask(nodes, binary_mask) - - pos = {} - val_map = {} - sources, sinks, _ = find_sources_and_sinks(dg) - - for i in range(total_nodes): - pos[str(i)] = 2. * np.array(number_to_type_layer(i, n_types))[::-1] - if str(i) in sources: - val_map[str(i)] = 1. - elif str(i) in sinks: - val_map[str(i)] = 0.5 - else: - val_map[str(i)] = 0. - - plt.figure(figsize=(12, 12)) - values = [val_map.get(node, 0.25) for node in nodes_int] - - nx.draw(dg, pos, cmap=plt.get_cmap('jet'), node_color=values, node_size=7000, alpha=0.3) - - nx.draw_networkx_labels(dg, pos, nodes, font_size=18) - - if path is None: - path = "./" - curr_time = datetime.datetime.now().strftime("%Hh%Mm%Ss_%dd%mm%Yy") - plt.savefig(Path(path).joinpath("pic_" + curr_time + ".png")) - # time.sleep(1) - return None diff --git a/deeppavlov/models/evolution/check_matrix.ipynb b/deeppavlov/models/evolution/check_matrix.ipynb deleted file mode 100644 index 12ae7348c3..0000000000 --- a/deeppavlov/models/evolution/check_matrix.ipynb +++ /dev/null @@ -1,257 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import networkx as nx\n", - "from check_binary_mask import check_and_correct_binary_mask\n", - "from check_binary_mask import number_to_type_layer\n", - "from check_binary_mask import type_layer_to_number" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "T = 3\n", - "L = 2\n", - "total_nodes = T * L\n", - "\n", - "nodes = {}\n", - "types = {0: \"Dense\", 1: \"Conv1D\", \n", - " 2: \"LSTM\", 3: \"BiLSTM\", 4: \"GlobMaxPool1D\", \n", - " 5: \"MaxPool1D\", 6: \"Attention\"}\n", - "\n", - "for i in range(0, total_nodes):\n", - " nodes[i] = types[number_to_type_layer(i, T)[1]]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: 'Dense', 1: 'Conv1D', 2: 'LSTM', 3: 'Dense', 4: 'Conv1D', 5: 'LSTM'}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nodes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cm = np.zeros((total_nodes, total_nodes)) \n", - "cm[0, 1] = 1\n", - "cm[0, 3] = 1\n", - "cm[3, 1] = 1\n", - "cm[3, 5] = 1\n", - "cm[5, 2] = 1\n", - "\n", - "dg = nx.DiGraph()\n", - "\n", - "for i in range(total_nodes):\n", - " dg.add_node(i)\n", - " \n", - "pos = {}\n", - "\n", - "for i in range(total_nodes):\n", - " for j in range(total_nodes):\n", - " if cm[i,j] == 1:\n", - " dg.add_edge(i, j)\n", - "# pos[i] = 5 * np.array(number_to_type_layer(i, L, T))\n", - " pos[i] = np.array(number_to_type_layer(i, T))[::-1]\n", - "\n", - "plt.figure(figsize=(6, 6))\n", - "nx.draw(dg, pos, node_color='b', node_size=5000, alpha=0.3)\n", - "\n", - "nx.draw_networkx_labels(dg, pos, nodes, font_size=18)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "check_and_correct_binary_mask(nodes, cm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_graph_and_plot(nodes, cm):\n", - " total_nodes = len(nodes)\n", - " dg = nx.DiGraph()\n", - "\n", - " for i in range(total_nodes):\n", - " dg.add_node(i)\n", - "\n", - " pos = {}\n", - "\n", - " for i in range(total_nodes):\n", - " for j in range(total_nodes):\n", - " if cm[i,j] == 1:\n", - " dg.add_edge(i, j)\n", - " # pos[i] = 5 * np.array(number_to_type_layer(i, L, T))\n", - " pos[i] = np.array(number_to_type_layer(i, T))[::-1]\n", - "\n", - " plt.figure(figsize=(6, 6))\n", - " nx.draw(dg, pos, node_color='b', node_size=5000, alpha=0.3)\n", - "\n", - " nx.draw_networkx_labels(dg, pos, nodes, font_size=18)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cm = np.zeros((total_nodes, total_nodes)) \n", - "cm[0, 1] = 1\n", - "cm[0, 3] = 1\n", - "cm[3, 1] = 1\n", - "cm[3, 5] = 1\n", - "cm[5, 2] = 1\n", - "cm[5, 3] = 1\n", - "\n", - "get_graph_and_plot(nodes, cm)\n", - "new_cm = check_and_correct_binary_mask(nodes, cm)\n", - "get_graph_and_plot(nodes, new_cm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cm = np.zeros((total_nodes, total_nodes)) \n", - "cm[0, 1] = 1\n", - "cm[0, 3] = 1\n", - "cm[3, 1] = 1\n", - "cm[4, 5] = 1\n", - "cm[5, 2] = 1\n", - "cm[2, 4] = 1\n", - "\n", - "get_graph_and_plot(nodes, cm)\n", - "new_cm = check_and_correct_binary_mask(nodes, cm)\n", - "get_graph_and_plot(nodes, new_cm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cm = np.zeros((total_nodes, total_nodes)) \n", - "cm[0, 1] = 1\n", - "cm[0, 3] = 1\n", - "cm[3, 1] = 1\n", - "cm[4, 5] = 1\n", - "cm[5, 2] = 1\n", - "cm[2, 4] = 1\n", - "cm[3, 4] = 1\n", - "\n", - "get_graph_and_plot(nodes, cm)\n", - "new_cm = check_and_correct_binary_mask(nodes, cm)\n", - "get_graph_and_plot(nodes, new_cm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cm = np.zeros((total_nodes, total_nodes)) \n", - "cm[0, 1] = 1\n", - "cm[0, 3] = 1\n", - "cm[3, 1] = 1\n", - "cm[4, 5] = 1\n", - "cm[5, 2] = 1\n", - "cm[2, 4] = 1\n", - "cm[3, 4] = 1\n", - "cm[4, 3] = 1\n", - "\n", - "get_graph_and_plot(nodes, cm)\n", - "new_cm = check_and_correct_binary_mask(nodes, cm)\n", - "get_graph_and_plot(nodes, new_cm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py36_main_kernel", - "language": "python", - "name": "py36_main" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py deleted file mode 100644 index 188aad3e55..0000000000 --- a/deeppavlov/models/evolution/debug.py +++ /dev/null @@ -1,80 +0,0 @@ -import pandas as pd -import json -import numpy as np -import tensorflow as tf -from copy import deepcopy - -from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution -from deeppavlov.models.evolution.evolution_intent_model import KerasEvolutionClassificationModel -from deeppavlov.core.commands.train import train_model_from_config -from deeppavlov.core.commands.infer import interact_model -from deeppavlov.core.commands.utils import set_deeppavlov_root -from deeppavlov.core.common.file import save_json, read_json -from deeppavlov.models.evolution.utils import expand_tile_batch_size -from deeppavlov.models.evolution.check_binary_mask import get_digraph_from_binary_mask - - -n_layers = 2 -n_types = 7 -population_size = 1 -config_path = "../../configs/evolution/basic_config_local.json" - -with open(config_path) as fin: - config = json.load(fin) - -evolution = NetworkAndParamsEvolution(n_layers, n_types, - population_size, - key_model_to_evolve="to_evolve", - key_basic_layers="basic_layers_params", - seed=42, - start_with_one_neuron=True, - **config) - -population = evolution.first_generation() -population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ - evolution.model_to_evolve_index]["binary_mask"].tolist() - -config_path = "./config_init.json" -full_config = deepcopy(population[0]) -print(population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]) -save_json(full_config, config_path) - -population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = np.array(population[0]["chainer"]["pipe"][ - evolution.model_to_evolve_index]["binary_mask"]) - -population = evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) -print(population) - -population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ - evolution.model_to_evolve_index]["binary_mask"].tolist() - -config_path = "./config_crossover.json" -full_config = deepcopy(population[0]) -save_json(full_config, config_path) - -population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = np.array(population[0]["chainer"]["pipe"][ - evolution.model_to_evolve_index]["binary_mask"]) - -population = evolution.mutation(population, p_mutation=0.5, mutation_power=.5) - -population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ - evolution.model_to_evolve_index]["binary_mask"].tolist() - -config_path = "./config_mutated.json" -full_config = deepcopy(population[0]) -full_config["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = evolution.nodes -full_config["chainer"]["pipe"][evolution.model_to_evolve_index]["total_nodes"] = evolution.total_nodes - -save_json(full_config, config_path) - -population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = np.array(population[0]["chainer"]["pipe"][ - evolution.model_to_evolve_index]["binary_mask"]) - -dg = get_digraph_from_binary_mask(evolution.nodes, - population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) - -print("Edges: ", dg.edges) -train_model_from_config(config_path) - - - diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py deleted file mode 100644 index 5fff0edff1..0000000000 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ /dev/null @@ -1,248 +0,0 @@ -""" -Copyright 2017 Neural Networks and Deep Learning lab, MIPT - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import numpy as np -from copy import copy, deepcopy -from keras.layers import Dense, Input, concatenate, Activation -from keras.layers.convolutional import Conv1D -from keras.layers.core import Dropout -from keras.layers.normalization import BatchNormalization -from keras.layers.pooling import GlobalMaxPooling1D, MaxPooling1D -from keras.layers.recurrent import LSTM -from keras.layers.wrappers import Bidirectional -from keras.models import Model -from keras.regularizers import l2 -from keras.layers import Concatenate, Reshape, CuDNNLSTM, Lambda -from keras import backend as K -from overrides import overrides -from pathlib import Path - -from deeppavlov.core.common.errors import ConfigError -from deeppavlov.core.common.registry import register -from deeppavlov.core.models.keras_model import KerasModel -from deeppavlov.models.classifiers.intents.intent_model import KerasIntentModel -from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels -from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder -from deeppavlov.models.classifiers.intents.utils import md5_hashsum -from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer -from deeppavlov.core.common.log import get_logger -from deeppavlov.models.evolution.check_binary_mask import number_to_type_layer, \ - find_sources_and_sinks, get_digraph_from_binary_mask, get_graph_and_plot -from deeppavlov.models.evolution.utils import Attention, expand_tile -from deeppavlov.core.common.file import save_json, read_json -from deeppavlov.core.layers.keras_layers import multiplicative_self_attention - -log = get_logger(__name__) - - -@register('evolution_classification_model') -class KerasEvolutionClassificationModel(KerasIntentModel): - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.opt["binary_mask"] = np.array(self.opt["binary_mask"]) - get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], - path=str(self.save_path.resolve().parent)) - - def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None): - if inp is None: - input_nodes = [edge[0] for edge in dg.in_edges(node_str_id)] - inp_list = [] - for input_node in input_nodes: - if len(K.int_shape(edges_outputs[input_node])) == 3: - inp_list.append(edges_outputs[input_node]) - elif len(K.int_shape(edges_outputs[input_node])) == 2: - input_expanded = Lambda(lambda x: expand_tile(x, axis=1))(edges_outputs[input_node]) - inp_list.append(input_expanded) - else: - raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") - if len(input_nodes) > 1: - try: - inp = Concatenate()(inp_list) - except ValueError: - time_steps = [] - features = [] - for i in range(len(inp_list)): - if len(K.int_shape(inp_list[i])) == 2: - inp_list[i] = Lambda(lambda x: expand_tile(x, axis=1))(inp_list[i]) - time_steps.append(K.int_shape(inp_list[i])[1]) - features.append(K.int_shape(inp_list[i])[2]) - new_feature_shape = max(features) - new_inp_list = [] - for i in range(len(inp_list)): - if K.int_shape(inp_list[i])[2] == new_feature_shape: - new_inp_list.append(inp_list[i]) - else: - new_inp_list.append(Dense(new_feature_shape)(inp_list[i])) - inp = Concatenate(axis=1)(new_inp_list) - else: - inp = inp_list[0] - - if params[params["nodes"][node_str_id]]["node_name"] == "BiCuDNNLSTM": - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - l2_reg = node_params.get("coef_regul_l2") - node_params.pop("coef_regul_l2") - output_of_node = Dropout(rate=params['dropout_rate'])( - Bidirectional(CuDNNLSTM(**node_params, - kernel_regularizer=l2(l2_reg)))(inp)) - elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - output_of_node = Dropout(rate=params['dropout_rate'])(multiplicative_self_attention(inp, **node_params)) - else: - node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - l2_reg = node_params.get("coef_regul_l2") - if callable(node_func): - if l2_reg is None: - output_of_node = Dropout(rate=params['dropout_rate'])(node_func(**node_params)(inp)) - else: - node_params.pop("coef_regul_l2") - output_of_node = Dropout(rate=params['dropout_rate'])( - node_func(**node_params, kernel_regularizer=l2(l2_reg))(inp)) - else: - raise AttributeError("Node {} is not defined correctly".format(node_str_id)) - return output_of_node - - def evolution_classification_model(self, params): - """ - Build un-compiled model of shallow-and-wide CNN - Args: - params: dictionary of parameters for NN - - Returns: - Un-compiled model - """ - inp = Input(shape=(params['text_size'], params['embedding_size'])) - - if np.sum(params["binary_mask"]) == 0: - output = Dense(1, activation=None)(inp) - output = GlobalMaxPooling1D()(output) - output = Dropout(rate=params['dropout_rate'])(output) - output = Dense(self.n_classes, activation=None)(output) - activation = params.get("last_layer_activation", "sigmoid") - act_output = Activation(activation)(output) - model = Model(inputs=inp, outputs=act_output) - return model - - dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) - sources, sinks, isolates = find_sources_and_sinks(dg) - - edges_outputs = {} - - # sequence_of_nodes is a list of lists. - # each element of sequence_of_nodes is a list that contains nodes (keras layers) - # that could be initialized when all nodes from previous lists are initialized - sequence_of_nodes = [sources] - - while True: - if set(sinks).issubset(set(sum(sequence_of_nodes, []))): - break - next_nodes = [] - # want to get list of nodes that can be initialized next - for node_str_id in sequence_of_nodes[-1]: - # for each node that were initialized on the previous step - # take output edges - out_edges = dg.out_edges(node_str_id) - for edge in out_edges: - # for all output edge - # collect nodes that are input nodes - # for considered child of node_str_id (edge[1]) - in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] - # if for considered child all parents are already initialized - # then add this node for initialization - if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): - next_nodes.append(edge[1]) - sequence_of_nodes.append(next_nodes) - - # make a list of ints from list of lists - sequence_of_nodes = sum(sequence_of_nodes, []) - - # now all nodes in sequence - # can be initialized consequently - for node_str_id in sequence_of_nodes: - if node_str_id in sources: - # if considered node is source, - # give embedded texts as input - edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, inp=inp) - elif node_str_id in isolates: - # unreal condition - # if considered node is isolate, - # nothing to do - pass - else: - # if considered node is not source and isolate, - # give all previous outputs as input - edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, edges_outputs=edges_outputs) - - if len(sinks) == 1: - # if the only sink, - # output is this sink's output - output = edges_outputs[sinks[0]] - else: - # if several sinks exist, - # outputs will be concatenated - outputs = [] - # collect outputs - for sink in sinks: - outputs.append(edges_outputs[sink]) - try: - output = Concatenate()(outputs) - except ValueError: - # outputs are of 2d and 3d shapes - # make them all 2d and concatenate - for i in range(len(outputs)): - if len(K.int_shape(outputs[i])) == 3: - outputs[i] = GlobalMaxPooling1D()(outputs[i]) - output = Concatenate(axis=1)(outputs) - - # if concatenated output is of 3d shape - # make it 2d using global max pooling - if len(output.shape) == 3: - output = GlobalMaxPooling1D()(output) - - output = Dense(self.n_classes, activation=None)(output) - activation = params.get("last_layer_activation", "sigmoid") - act_output = Activation(activation)(output) - model = Model(inputs=inp, outputs=act_output) - return model - - @overrides - def save(self, fname=None): - """ - Save the model parameters into <>_opt.json (or <>_opt.json) - and model weights into <>.h5 (or <>.h5) - Args: - fname: file_path to save model. If not explicitly given seld.opt["ser_file"] will be used - - Returns: - None - """ - if type(self.opt["binary_mask"]) is list: - pass - else: - self.opt["binary_mask"] = self.opt["binary_mask"].tolist() - - super().save(fname) - return True diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py deleted file mode 100644 index 7fc9e7d155..0000000000 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ /dev/null @@ -1,416 +0,0 @@ -""" -Copyright 2017 Neural Networks and Deep Learning lab, MIPT - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import numpy as np -from copy import copy, deepcopy -from keras.layers import Dense, Input, concatenate, Activation -from keras.layers.convolutional import Conv1D -from keras.layers.core import Dropout -from keras.layers.normalization import BatchNormalization -from keras.layers.pooling import GlobalMaxPooling1D, MaxPooling1D -from keras.layers.recurrent import LSTM -from keras.layers.wrappers import Bidirectional -from keras.models import Model -from keras.regularizers import l2 -from keras.layers import Concatenate, Reshape, CuDNNLSTM, Lambda, Add, Subtract, Multiply -from keras import backend as K -from overrides import overrides -from pathlib import Path - -from deeppavlov.core.common.errors import ConfigError -from deeppavlov.core.common.registry import register -from deeppavlov.core.models.keras_model import KerasModel -from deeppavlov.models.classifiers.intents.intent_model import KerasIntentModel -from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels -from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder -from deeppavlov.models.classifiers.intents.utils import md5_hashsum -from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer -from deeppavlov.core.common.log import get_logger -from deeppavlov.models.evolution.check_binary_mask import number_to_type_layer, \ - find_sources_and_sinks, get_digraph_from_binary_mask, get_graph_and_plot -from deeppavlov.models.evolution.utils import expand_tile -from deeppavlov.core.common.file import save_json, read_json -from deeppavlov.core.layers.keras_layers import multiplicative_self_attention_init, \ - multiplicative_self_attention_get_output - - -log = get_logger(__name__) - - -@register('evolution_many_inputs_classification_model') -class KerasEvolutionClassificationManyInputsModel(KerasIntentModel): - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.opt["binary_mask"] = np.array(self.opt["binary_mask"]) - get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], - path=str(self.save_path.resolve().parent)) - - def texts2vec(self, sentences, i): - """ - Convert texts to vector representations using embedder and padding up to self.opt["text_size"] tokens - Args: - sentences: list of lists of tokens - - Returns: - array of embedded texts - """ - pad = np.zeros(self.opt['embedding_size']) - if type(self.opt['text_size']) is list: - text_size = self.opt['text_size'][i] - else: - text_size = self.opt['text_size'] - embeddings_batch = self.fasttext_model([sen[:text_size] for sen in sentences]) - embeddings_batch = [[pad] * (text_size - len(tokens)) + tokens for tokens in embeddings_batch] - - embeddings_batch = np.asarray(embeddings_batch) - return embeddings_batch - - @overrides - def train_on_batch(self, *args, **kwargs): - """ - Train the model on the given batch - Args: - texts - list of texts (or list of lists of text tokens) - labels - list of labels - - Returns: - loss and metrics values on the given batch - """ - if len(args) > len(self.opt["in"]): - labels = args[-1] - texts = args[:-1] - else: - labels = None - texts = args - - features = [] - for i in range(len(self.opt["in"])): - if isinstance(texts[i][0], str): - features.append(self.texts2vec(self.tokenizer(list(texts[i])), i)) - else: - features.append(self.texts2vec(list(texts[i]), i)) - - onehot_labels = labels2onehot(labels, classes=self.classes) - metrics_values = self.model.train_on_batch(features, onehot_labels) - return metrics_values - - @overrides - def infer_on_batch(self, *args, **kwargs): - """ - Infer the model on the given batch - Args: - texts - list of texts (or list of lists of text tokens) - labels - list of labels - - Returns: - loss and metrics values on the given batch, if labels are given - predictions, otherwise - """ - if len(args) > 1: - labels = args[-1] - texts = args[:-1] - elif len(args) == 1: - labels = None - texts = args[0] - else: - raise ValueError("Nothing to infer in infer_on_batch") - - features = [] - for i in range(len(self.opt["in"])): - if isinstance(texts[i][0], str): - features.append(self.texts2vec(self.tokenizer(list(texts[i])), i)) - else: - features.append(self.texts2vec(list(texts[i]), i)) - - if labels: - onehot_labels = labels2onehot(labels, classes=self.classes) - metrics_values = self.model.test_on_batch(features, onehot_labels) - return metrics_values - else: - predictions = self.model.predict(features) - return predictions - - @overrides - def __call__(self, *args, **kwargs): - """ - Infer on the given data - Args: - data: [list of sentences] - *args: - - Returns: - for each sentence: - vector of probabilities to belong with each class - or list of labels sentence belongs with - """ - assert len(args) == len(self.opt["in"]) - preds = np.array(self.infer_on_batch(args)) - - labels = proba2labels(preds, confident_threshold=self.opt['confident_threshold'], classes=self.classes) - return labels, [dict(zip(self.classes, preds[i])) for i in range(preds.shape[0])] - - def get_node_output(self, model_layers, node_str_id, dg, params, edges_outputs=None, inp=None): - if inp is None: - input_nodes = [edge[0] for edge in dg.in_edges(node_str_id)] - inp_list = [] - for input_node in input_nodes: - if len(K.int_shape(edges_outputs[input_node])) == 3: - inp_list.append(edges_outputs[input_node]) - elif len(K.int_shape(edges_outputs[input_node])) == 2: - input_expanded = Lambda(lambda x: expand_tile(x, axis=1))(edges_outputs[input_node]) - inp_list.append(input_expanded) - else: - raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") - if len(input_nodes) > 1: - try: - inp = Concatenate()(inp_list) - except ValueError: - time_steps = [] - features = [] - for i in range(len(inp_list)): - if len(K.int_shape(inp_list[i])) == 2: - inp_list[i] = Lambda(lambda x: expand_tile(x, axis=1))(inp_list[i]) - time_steps.append(K.int_shape(inp_list[i])[1]) - features.append(K.int_shape(inp_list[i])[2]) - new_feature_shape = max(features) - new_inp_list = [] - for i in range(len(inp_list)): - if K.int_shape(inp_list[i])[2] == new_feature_shape: - new_inp_list.append(inp_list[i]) - else: - new_inp_list.append(Dense(new_feature_shape)(inp_list[i])) - inp = Concatenate(axis=1)(new_inp_list) - else: - inp = inp_list[0] - - if params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - output_of_node = multiplicative_self_attention_get_output(inp, - model_layers[params["nodes"][node_str_id]]) - else: - node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - output_of_node = model_layers[params["nodes"][node_str_id]](inp) - - output_of_node = Dropout(rate=params['dropout_rate'])(output_of_node) - return output_of_node - - def initialize_all_nodes(self, params): - dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) - sources, sinks, isolates = find_sources_and_sinks(dg) - - model_layers = {} - for node_str_id in list(params["nodes"].keys()): - if not(node_str_id in isolates): - if params[params["nodes"][node_str_id]]["node_name"] == "BiCuDNNLSTM": - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - l2_reg = node_params.get("coef_regul_l2") - node_params.pop("coef_regul_l2") - model_layers[params["nodes"][node_str_id]] = Bidirectional(CuDNNLSTM(**node_params, - kernel_regularizer=l2(l2_reg))) - elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - model_layers[params["nodes"][node_str_id]] = multiplicative_self_attention_init(**node_params) - else: - node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - l2_reg = node_params.get("coef_regul_l2") - if callable(node_func): - if l2_reg is None: - model_layers[params["nodes"][node_str_id]] = node_func(**node_params) - else: - node_params.pop("coef_regul_l2") - model_layers[params["nodes"][node_str_id]] = node_func(**node_params, - kernel_regularizer=l2(l2_reg)) - else: - raise AttributeError("Node {} is not defined correctly".format(node_str_id)) - - return model_layers - - def evolution_many_inputs_classification_model(self, params): - """ - Build un-compiled model of shallow-and-wide CNN - Args: - params: dictionary of parameters for NN - - Returns: - Un-compiled model - """ - inputs = [] - if type(params['text_size']) is list: - for i in range(len(params["in"])): - inputs.append(Input(shape=(params['text_size'][i], params['embedding_size']))) - else: - for i in range(len(params["in"])): - inputs.append(Input(shape=(params['text_size'], params['embedding_size']))) - - full_outputs = [] - - if np.sum(params["binary_mask"]) == 0: - dense1 = Dense(1, activation=None) - globalmaxpooling = GlobalMaxPooling1D() - for inp in inputs: - output = dense1(inp) - full_outputs.append(globalmaxpooling(output)) - - summ = Add()(full_outputs) - mult = Multiply()(full_outputs) - - try: - subt = Subtract()(full_outputs) - full_outputs.append(subt) - except ValueError: - pass - full_outputs.append(summ) - full_outputs.append(mult) - - output = Concatenate()(full_outputs) - output = Dense(self.n_classes, activation=None)(output) - activation = params.get("last_layer_activation", "sigmoid") - act_output = Activation(activation)(output) - model = Model(inputs=inputs, outputs=act_output) - return model - - model_layers = self.initialize_all_nodes(params) - - for inp in inputs: - dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) - sources, sinks, isolates = find_sources_and_sinks(dg) - - edges_outputs = {} - - # sequence_of_nodes is a list of lists. - # each element of sequence_of_nodes is a list that contains nodes (keras layers) - # that could be initialized when all nodes from previous lists are initialized - sequence_of_nodes = [sources] - - while True: - if set(sinks).issubset(set(sum(sequence_of_nodes, []))): - break - next_nodes = [] - # want to get list of nodes that can be initialized next - for node_str_id in sequence_of_nodes[-1]: - # for each node that were initialized on the previous step - # take output edges - out_edges = dg.out_edges(node_str_id) - for edge in out_edges: - # for all output edge - # collect nodes that are input nodes - # for considered child of node_str_id (edge[1]) - in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] - # if for considered child all parents are already initialized - # then add this node for initialization - if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): - next_nodes.append(edge[1]) - sequence_of_nodes.append(next_nodes) - - # make a list of ints from list of lists - sequence_of_nodes = sum(sequence_of_nodes, []) - - # now all nodes in sequence - # can be initialized consequently - for node_str_id in sequence_of_nodes: - if node_str_id in sources: - # if considered node is source, - # give embedded texts as input - edges_outputs[node_str_id] = self.get_node_output(model_layers, node_str_id, dg, params, inp=inp) - elif node_str_id in isolates: - # unreal condition - # if considered node is isolate, - # nothing to do - pass - else: - # if considered node is not source and isolate, - # give all previous outputs as input - edges_outputs[node_str_id] = self.get_node_output(model_layers, node_str_id, dg, params, - edges_outputs=edges_outputs) - - if len(sinks) == 1: - # if the only sink, - # output is this sink's output - output = edges_outputs[sinks[0]] - else: - # if several sinks exist, - # outputs will be concatenated - outputs = [] - # collect outputs - for sink in sinks: - outputs.append(edges_outputs[sink]) - try: - output = Concatenate()(outputs) - except ValueError: - # outputs are of 2d and 3d shapes - # make them all 2d and concatenate - for i in range(len(outputs)): - if len(K.int_shape(outputs[i])) == 3: - outputs[i] = GlobalMaxPooling1D()(outputs[i]) - output = Concatenate(axis=1)(outputs) - - if len(output.shape) == 3: - output = GlobalMaxPooling1D()(output) - full_outputs.append(output) - - summ = Add()(full_outputs) - mult = Multiply()(full_outputs) - - try: - subt = Subtract()(full_outputs) - full_outputs.append(subt) - except ValueError: - pass - full_outputs.append(summ) - full_outputs.append(mult) - - output = Concatenate()(full_outputs) - output = Dense(self.n_classes, activation=None)(output) - activation = params.get("last_layer_activation", "sigmoid") - act_output = Activation(activation)(output) - model = Model(inputs=inputs, outputs=act_output) - return model - - def save(self, fname=None): - """ - Save the model parameters into <>_opt.json (or <>_opt.json) - and model weights into <>.h5 (or <>.h5) - Args: - fname: file_path to save model. If not explicitly given seld.opt["ser_file"] will be used - - Returns: - None - """ - if type(self.opt["binary_mask"]) is list: - pass - else: - self.opt["binary_mask"] = self.opt["binary_mask"].tolist() - - super().save(fname) - return True diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 8025a031a5..30a482403c 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -3,7 +3,6 @@ from pathlib import Path import json -from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe from deeppavlov.core.common.file import read_json from deeppavlov.core.common.log import get_logger @@ -48,8 +47,8 @@ def __init__(self, """ self.basic_config = deepcopy(kwargs) - self.main_model_path = list(self._find_model_path(self.basic_config, key_main_model))[0] - Path(self._get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, + self.main_model_path = list(self.find_model_path(self.basic_config, key_main_model))[0] + Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, exist_ok=True) self.print_dict(self.basic_config, string="Basic config:") log.info("Main model path in config: {}".format(self.main_model_path)) @@ -66,7 +65,7 @@ def __init__(self, self.paths_to_evolving_params = [] for evolve_type in ["evolve_range", "evolve_choice", "evolve_bool"]: - for path_ in self._find_model_path(self.basic_config, evolve_type): + for path_ in self.find_model_path(self.basic_config, evolve_type): self.paths_to_evolving_params.append(path_) self.n_evolving_params = len(self.paths_to_evolving_params) @@ -77,7 +76,7 @@ def __init__(self, else: np.random.seed(seed) - def _find_model_path(self, config, key_model, path=[]): + def find_model_path(self, config, key_model, path=[]): """ Find path to the main model in config which paths will be changed Args: @@ -95,15 +94,15 @@ def _find_model_path(self, config, key_model, path=[]): else: if type(config_pointer) is dict: for key in list(config_pointer.keys()): - for path_ in self._find_model_path(config_pointer[key], key_model, path + [key]): + for path_ in self.find_model_path(config_pointer[key], key_model, path + [key]): yield path_ elif type(config_pointer) is list: for i in range(len(config_pointer)): - for path_ in self._find_model_path(config_pointer[i], key_model, path + [i]): + for path_ in self.find_model_path(config_pointer[i], key_model, path + [i]): yield path_ @staticmethod - def _insert_value_or_dict_into_config(config, path, value): + def insert_value_or_dict_into_config(config, path, value): config_copy = deepcopy(config) config_pointer = config_copy for el in path[:-1]: @@ -117,7 +116,7 @@ def _insert_value_or_dict_into_config(config, path, value): return config_copy @staticmethod - def _get_value_from_config(config, path): + def get_value_from_config(config, path): config_copy = deepcopy(config) config_pointer = config_copy for el in path[:-1]: @@ -143,22 +142,22 @@ def initialize_params_in_config(self, basic_config, paths): for path_ in paths: param_name = path_[-1] - value = self._get_value_from_config(basic_config, path_) + value = self.get_value_from_config(basic_config, path_) if type(value) is dict: if value.get("evolve_choice"): - config = self._insert_value_or_dict_into_config(config, + config = self.insert_value_or_dict_into_config(config, path_, self.sample_params( **{param_name: list(value["values"])})[param_name]) elif value.get("evolve_range"): - config = self._insert_value_or_dict_into_config(config, + config = self.insert_value_or_dict_into_config(config, path_, self.sample_params( **{param_name: deepcopy(value)})[param_name]) elif value.get("evolve_bool"): - config = self._insert_value_or_dict_into_config(config, + config = self.insert_value_or_dict_into_config(config, path_, self.sample_params( **{param_name: @@ -176,7 +175,7 @@ def first_generation(self, iteration=0): for i in range(self.population_size): population.append(self.initialize_params_in_config(self.basic_config, self.paths_to_evolving_params)) for which_path in ["save_path", "load_path"]: - population[-1] = self._insert_value_or_dict_into_config(population[-1], + population[-1] = self.insert_value_or_dict_into_config(population[-1], self.main_model_path + [which_path], str(Path( self.basic_config["save_path"]).joinpath( @@ -219,11 +218,11 @@ def next_generation(self, generation, scores, iteration): + "_" + str(iteration % self.train_partition) + ".csv" try: # re-init learning rate with the final one (works for KerasModel) - next_population[i] = self._insert_value_or_dict_into_config( + next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self._get_value_from_config(next_population[i], + self.get_value_from_config(next_population[i], self.main_model_path + ["lear_rate"]), - read_json(str(Path(self._get_value_from_config(next_population[i], + read_json(str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) ).parent.joinpath("model_opt.json")))["final_lear_rate"]) except: @@ -231,26 +230,26 @@ def next_generation(self, generation, scores, iteration): if self.elitism_with_weights: # if elite models are saved with weights - next_population[i] = self._insert_value_or_dict_into_config( + next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self._get_value_from_config(next_population[i], + self.get_value_from_config(next_population[i], self.main_model_path + ["load_path"]), - str(Path(self._get_value_from_config(next_population[i], + str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"])).parent)) else: # if elite models are saved only as configurations and trained again - next_population[i] = self._insert_value_or_dict_into_config( + next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self._get_value_from_config(next_population[i], + self.get_value_from_config(next_population[i], self.main_model_path + ["load_path"]), - str(Path(self._get_value_from_config(next_population[i], self.main_model_path + ["load_path"]) + str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["load_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) - next_population[i] = self._insert_value_or_dict_into_config( + next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self._get_value_from_config(next_population[i], + self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]), - str(Path(self._get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) + str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) for i in range(self.n_saved_best_pretrained, self.population_size): @@ -260,11 +259,11 @@ def next_generation(self, generation, scores, iteration): "train"]).stem.split("_")[:-1])) \ + "_" + str(iteration % self.train_partition) + ".csv" for which_path in ["save_path", "load_path"]: - next_population[i] = self._insert_value_or_dict_into_config( + next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self._get_value_from_config(next_population[i], + self.get_value_from_config(next_population[i], self.main_model_path + [which_path]), - str(Path(self._get_value_from_config(next_population[i], self.main_model_path + [which_path]) + str(Path(self.get_value_from_config(next_population[i], self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) next_population[i]["evolution_model_id"] = self.evolution_model_id @@ -337,18 +336,18 @@ def crossover(self, population, scores): part = int(self.crossover_power * self.n_evolving_params) for j in range(self.n_evolving_params - part, self.n_evolving_params): - curr_offsprings[0] = self._insert_value_or_dict_into_config(curr_offsprings[0], + curr_offsprings[0] = self.insert_value_or_dict_into_config(curr_offsprings[0], self.paths_to_evolving_params[ params_perm[j]], - self._get_value_from_config( + self.get_value_from_config( parents[1], self.paths_to_evolving_params[ params_perm[j]])) - curr_offsprings[1] = self._insert_value_or_dict_into_config(curr_offsprings[1], + curr_offsprings[1] = self.insert_value_or_dict_into_config(curr_offsprings[1], self.paths_to_evolving_params[ params_perm[j]], - self._get_value_from_config( + self.get_value_from_config( parents[0], self.paths_to_evolving_params[ params_perm[j]])) @@ -374,16 +373,16 @@ def mutation(self, population): for individuum in population: mutated_individuum = deepcopy(individuum) for path_ in self.paths_to_evolving_params: - mutated_individuum = self._insert_value_or_dict_into_config( + mutated_individuum = self.insert_value_or_dict_into_config( mutated_individuum, path_, - self.mutation_of_param(path_, self._get_value_from_config(individuum, path_))) + self.mutation_of_param(path_, self.get_value_from_config(individuum, path_))) mutated.append(mutated_individuum) return mutated def mutation_of_param(self, param_path, param_value): if self.decision(self.p_mutation): - basic_value = self._get_value_from_config(self.basic_config, param_path) + basic_value = self.get_value_from_config(self.basic_config, param_path) param_name = param_path[-1] if type(basic_value) is dict: if basic_value.get('discrete', False): diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py deleted file mode 100644 index 4b39481cf8..0000000000 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ /dev/null @@ -1,637 +0,0 @@ -import numpy as np -from copy import deepcopy -from pathlib import Path -import json - -from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ - number_to_type_layer -from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe -from deeppavlov.core.common.file import read_json - - -# please, make sure that -# `config["chainer"]["pipe"]` is a list of models one of which is a model to be evolved, -# otherwise, in the whole class change `config["chainer"]["pipe"]` to new path - - -class NetworkAndParamsEvolution: - """ - Class performs full evolutionary process (task scores -> max): - 1. initializes random population - 2. makes replacement to get next generation: - a. selection according to obtained scores - b. crossover (recombination) with given probability p_crossover - c. mutation with given mutation rate p_mutation (probability to mutate) - according to given mutation power sigma - (current mutation power is randomly from -sigma to sigma) - """ - - def __init__(self, n_layers, n_types, - population_size, - p_crossover=0.5, crossover_power=0.5, - p_mutation=0.5, mutation_power=0.1, - key_model_to_evolve="to_evolve", - key_basic_layers="basic_layers_params", - seed=None, - start_with_one_neuron=False, - evolve_binary_mask=True, - train_partition=1, - initial_binary_mask=None, - **kwargs): - """ - Initialize evolution with random population - Args: - n_layers: number of available layers of each type - n_types: number of different types of network layers - population_size: number of individuums per generation - p_crossover: probability to cross over for current replacement - crossover_power: part of EVOLVING parents parameters to exchange for offsprings - p_mutation: probability of mutation for current replacement - mutation_power: allowed percentage of mutation - key_model_to_evolve: binary flag that should be inserted into the dictionary - with evolving model in the basic config - key_basic_layers: key value of dictionary in basic_config - that contains considered layers with their evolving parameters - seed: random seed for initialization - start_with_one_neuron: whether to start with one neuron binary mask or random one - evolve_binary_mask: whether to evolve binary mask or evolve only hyper parameters - save_best_with_weights_portion: portion (from interval [0,1]) of population to save with weights - train_partition: integer number of train data parts - **kwargs: basic config with parameters - """ - self.n_types = n_types - self.n_layers = n_layers - - self.total_nodes = self.n_types * self.n_layers - self.initial_binary_mask = initial_binary_mask - self.start_with_one_neuron = start_with_one_neuron - - self.basic_config = deepcopy(kwargs) - self.model_to_evolve_index = find_index_of_dict_with_key_in_pipe(self.basic_config["chainer"]["pipe"], - key_model_to_evolve) - - self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["n_types"] = self.n_types - self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["n_layers"] = self.n_layers - Path(self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).mkdir(parents=True, - exist_ok=True) - - self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) - self.train_params = deepcopy(self.basic_config.get("train")) - self.basic_layers_params = self.params.pop(key_basic_layers, None) - self.node_types = list(self.basic_layers_params.keys()) - - self.nodes = {} - for i in range(self.total_nodes): - l, t = number_to_type_layer(i, self.n_types) - self.nodes[str(i)] = "{}_{}_{}".format(l, t, i) - - print("___Basic config___: {}".format(self.basic_config)) - print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) - print("___Model params___: {}".format(self.params)) - print("___Train params___: {}".format(self.train_params)) - print("___Basic layers params___: {}".format(self.basic_layers_params)) - - if self.basic_layers_params is None: - print("\n\n___PARAMS EVOLUTION is being started___") - print("___For network evolution one has to provide config file with `basic_layers_params` key___\n\n") - else: - print("\n\n___NETWORK AND PARAMS EVOLUTION is being started___\n\n") - - self.population_size = population_size - self.p_crossover = p_crossover - self.p_mutation = p_mutation - self.mutation_power = mutation_power - self.crossover_power = crossover_power - self.evolving_params = [] - self.n_evolving_params = None - self.evolving_train_params = [] - self.n_evolving_train_params = None - self.evolve_binary_mask = evolve_binary_mask - self.n_saved_best_with_weights = 0 - self.train_partition = train_partition - self.evolution_individuum_id = 0 - self.evolution_model_id = 0 - - if seed is None: - pass - else: - np.random.seed(seed) - - def _insert_dict_into_model_params(self, params, model_index, dict_to_insert): - params_copy = deepcopy(params) - params_copy["chainer"]["pipe"].insert(model_index, dict_to_insert) - return params_copy - - def print_dict(self, dict, string=None): - if string is None: - print(json.dumps(dict, indent=2)) - else: - print(string) - print(json.dumps(dict, indent=2)) - return None - - def initialize_params_in_config(self, basic_params): - params = {} - params_for_search = {} - evolving_params = [] - - for param_name in list(basic_params.keys()): - if type(basic_params[param_name]) is dict: - if basic_params[param_name].get("choice"): - params_for_search[param_name] = list(basic_params[param_name]["values"]) - evolving_params.append(param_name) - elif basic_params[param_name].get("range"): - params_for_search[param_name] = deepcopy(basic_params[param_name]) - evolving_params.append(param_name) - elif basic_params[param_name].get("bool"): - params_for_search[param_name] = deepcopy(basic_params[param_name]) - evolving_params.append(param_name) - else: - # NOT evolving params - params[param_name] = deepcopy(basic_params[param_name]) - else: - # NOT evolving params - params[param_name] = deepcopy(basic_params[param_name]) - if basic_params: - params_for_search = deepcopy(self.sample_params(**params_for_search)) - - return params, params_for_search, evolving_params - - def initialize_layers_params(self): - all_layers_params = {} - - for node_id in range(self.total_nodes): - node_layer, node_type = number_to_type_layer(node_id, self.n_types) - node_key = self.nodes[str(node_id)] - layers_params, layers_params_for_search, _ = self.initialize_params_in_config( - self.basic_layers_params[self.node_types[node_type]]) - - all_layers_params[node_key] = {"node_name": self.node_types[node_type], - "node_type": node_type, - "node_layer": node_layer, - **layers_params, - **layers_params_for_search - } - return all_layers_params - - def first_generation(self, iteration=0): - """ - Initialize first generation randomly according to the given constraints is self.params - Returns: - first generation that consists of self.population_size individuums - """ - population = [] - for i in range(self.population_size): - population.append(deepcopy(self.basic_config)) - - # intitializing parameters for model - params, params_for_search, evolving_params = self.initialize_params_in_config(self.params) - self.evolving_params.extend(evolving_params) - # initializing parameters for train - train_params, train_params_for_search, evolving_params = self.initialize_params_in_config(self.train_params) - self.evolving_train_params.extend(evolving_params) - - # intitializing path to save model - # save_path = population_iteration/model_name_i/ - if "model_name" in params_for_search.keys(): - params["save_path"] = str(Path(self.params["save_path"]).joinpath( - "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) - else: - params["save_path"] = str(Path(self.params["save_path"]).joinpath( - "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) - - # load_path = population_iteration/model_name_i/ - if "model_name" in params_for_search.keys(): - params["load_path"] = str(Path(self.params["load_path"]).joinpath( - "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) - else: - params["load_path"] = str(Path(self.params["load_path"]).joinpath( - "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) - - layers_params = self.initialize_layers_params() - - # exchange model and layers params from basic config to sampled model params - population[-1]["chainer"]["pipe"][self.model_to_evolve_index] = {**params, - **params_for_search, - **layers_params} - # add binary_mask intialization - if self.start_with_one_neuron: - population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, self.sample_one_neuron_binary_mask()) - elif not(self.initial_binary_mask is None): - population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, self.sample_given_binary_mask(self.initial_binary_mask)) - else: - population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, self.sample_binary_mask()) - - # exchange train params from basic config to sampled train params - population[-1]["train"] = {**train_params, - **train_params_for_search} - population[-1]["train"]["evolution_model_id"] = self.evolution_model_id - self.evolution_model_id += 1 - - self.evolving_params = list(set(self.evolving_params)) - self.evolving_train_params = list(set(self.evolving_train_params)) - - self.n_evolving_params = len(self.evolving_params) - self.n_evolving_train_params = len(self.evolving_train_params) - - return population - - def next_generation(self, generation, scores, iteration, - p_crossover=None, crossover_power=None, - p_mutation=None, mutation_power=None): - """ - Provide an operation of replacement - Args: - generation: current generation (set of self.population_size configs - scores: corresponding scores that should be maximized - iteration: iteration number - p_crossover: probability to cross over for current replacement - crossover_power: part of parents parameters to exchange for offsprings - p_mutation: probability of mutation for current replacement - mutation_power: allowed percentage of mutation - - Returns: - the next generation according to the given scores of current generation - """ - if not p_crossover: - p_crossover = self.p_crossover - if not crossover_power: - crossover_power = self.crossover_power - if not p_mutation: - p_mutation = self.p_mutation - if not mutation_power: - mutation_power = self.mutation_power - - # here self.n_saved_best_with_weights = len(next_population) - next_population = self.selection_of_best_with_weights(generation, scores) - print("Saved with weights: {} individuums".format(self.n_saved_best_with_weights)) - offsprings = self.crossover(generation, scores, - p_crossover=p_crossover, - crossover_power=crossover_power) - - # print("Number of offsprings: {} individuums".format(len(offsprings))) - - changable_next = self.mutation(offsprings, - p_mutation=p_mutation, - mutation_power=mutation_power) - # print("Number of mutated: {} individuums".format(len(changable_next))) - - next_population.extend(changable_next) - # print("Next population: {} individuums".format(len(next_population))) - - for i in range(self.n_saved_best_with_weights): - # if several train files: - if self.train_partition != 1: - next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ - "train"]).stem.split("_")[0]) \ - + "_" + str(iteration % self.train_partition) + ".csv" - # re-init learning rate with the final one - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ - read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index][ - "save_path"]).parent.joinpath("model_opt.json")))["final_lear_rate"] - # paths - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ - str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( - self.params["model_name"] + "_" + str(i))) - - for i in range(self.n_saved_best_with_weights, self.population_size): - # if several train files - if self.train_partition != 1: - next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ - "train"]).stem.split("_")[0]) \ - + "_" + str(iteration % self.train_partition) + ".csv" - # paths - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ - str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( - self.params["model_name"] + "_" + str(i))) - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(Path(self.params["load_path"]).joinpath("population_" + str(iteration)).joinpath( - self.params["model_name"] + "_" + str(i))) - - next_population[i]["train"]["evolution_model_id"] = self.evolution_model_id - self.evolution_model_id += 1 - - return next_population - - def selection_of_best_with_weights(self, population, scores): - """ - Select individuums to save with weights for the next generation from given population. - Range is an order of an individuum within sorted scores (1 range = max-score, self.population_size = min-score) - Individuum with the highest score has probability equal to 1 (100%). - Individuum with the lowest score has probability equal to 0 (0%). - Probability of i-th individuum to be selected with weights is (a * range_i + b) - where a = 1. / (1. - self.population_size), and - b = self.population_size / (self.population_size - 1.) - Args: - population: self.population_size individuums - scores: corresponding score that should be maximized - - Returns: - selected self.n_saved_best_with_weights (changable) individuums - """ - scores = np.array(scores, dtype='float') - sorted_ids = np.argsort(scores) - # the same order as scores but ranges - ranges = np.array([self.population_size - np.where(i == sorted_ids)[0][0] - for i in np.arange(self.population_size)]) - # probas = a / ranges + b - # a = 0.95 * self.population_size / (self.population_size - 1) - # b = (0.05 * self.population_size - 1) / (self.population_size - 1) - # probas_to_be_selected = a / ranges + b - - a = 1. / (1. - self.population_size) - b = self.population_size / (self.population_size - 1.) - probas_to_be_selected = a * ranges + b - - selected = [] - for i in range(self.population_size): - if self.decision(probas_to_be_selected[i]): - selected.append(deepcopy(population[i])) - - self.n_saved_best_with_weights = len(selected) - return selected - - def crossover(self, population, scores, p_crossover, crossover_power): - """ - Recombine randomly population in pairs and cross over them with given probability. - Cross over from two parents produces two offsprings - each of which contains crossover_power portion of the parameter values from one parent, - and the other (1 - crossover_power portion) from the other parent - Args: - population: self.population_size individuums - p_crossover: probability to cross over for current replacement - crossover_power: part of EVOLVING parents parameters to exchange for offsprings - - Returns: - (self.population_size - self.n_saved_best_with_weights) offsprings - """ - offsprings = [] - scores = np.array(scores, dtype='float') - probas_to_be_parent = scores / np.sum(scores) - intervals = np.array([np.sum(probas_to_be_parent[:i]) for i in range(self.population_size)]) - - for i in range(self.population_size - self.n_saved_best_with_weights): - rs = np.random.random(2) - parents = population[np.where(rs[0] > intervals)[0][-1]], population[np.where(rs[1] > intervals)[0][-1]] - - if self.decision(p_crossover): - params_perm = np.random.permutation(self.n_evolving_params) - train_params_perm = np.random.permutation(self.n_evolving_train_params) - nodes_perm = np.random.permutation(self.total_nodes) - binary_mask_perm = np.random.permutation(self.total_nodes * self.total_nodes) - - curr_offsprings = [deepcopy(parents[0]), - deepcopy(parents[1])] - - part = int(crossover_power * self.n_evolving_params) - train_part = int(crossover_power * self.n_evolving_train_params) - nodes_part = int(crossover_power * self.total_nodes) - binary_mask_part = int(crossover_power * self.total_nodes * self.total_nodes) - - # exchange of model params (not layers params) - for j in range(self.n_evolving_params - part): - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[0][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[1][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - for j in range(self.n_evolving_params - part, self.n_evolving_params): - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[1][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[0][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - - # exchange of train params - for j in range(self.n_evolving_train_params - train_part): - curr_offsprings[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] - curr_offsprings[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] - for j in range(self.n_evolving_train_params - train_part, self.n_evolving_train_params): - curr_offsprings[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] - curr_offsprings[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] - - if self.evolve_binary_mask: - # exchange of nodes - for j in range(self.total_nodes - nodes_part): - node_key = self.nodes[str(nodes_perm[j])] - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( - parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( - parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) - for j in range(self.total_nodes - nodes_part, self.total_nodes): - node_key = self.nodes[str(nodes_perm[j])] - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( - parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( - parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) - - # exchange of binary mask elements - for j in range(self.total_nodes * self.total_nodes - binary_mask_part): - node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"][node_x, node_y] = parents[0]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"][node_x, node_y] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"][node_x, node_y] = parents[1]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"][node_x, node_y] - - for j in range(self.total_nodes * self.total_nodes - binary_mask_part, - self.total_nodes * self.total_nodes): - node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"][node_x, node_y] = parents[1]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"][node_x, node_y] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"][node_x, node_y] = parents[0]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"][node_x, node_y] - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"]) - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"]) - - offsprings.append(deepcopy(curr_offsprings[0])) - else: - offsprings.append(deepcopy(parents[0])) - - return offsprings - - def mutation(self, population, p_mutation, mutation_power): - """ - Mutate each parameter of each individuum in population with probability p_mutation - Args: - population: self.population_size individuums - p_mutation: probability to mutate for each parameter - mutation_power: allowed percentage of mutation - - Returns: - mutated population - """ - mutated = [] - - for individuum in population: - mutated_individuum = deepcopy(individuum) - - # mutation of other model params - for param in self.params.keys(): - mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][param] = \ - self.mutation_of_param(param, self.params, - individuum["chainer"]["pipe"][self.model_to_evolve_index][param], - p_mutation, mutation_power) - - # mutation of train params - for param in self.train_params.keys(): - mutated_individuum["train"][param] = \ - self.mutation_of_param(param, self.train_params, - individuum["train"][param], - p_mutation, mutation_power) - - if self.evolve_binary_mask: - # mutation of binary mask - if self.decision(p_mutation): - mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask( - self.nodes, - np.minimum(1, - np.maximum(0, - individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] + - np.round((2 * np.random.random() - 1.) * self.sample_binary_mask())))) - - # mutation of each node params - for node_id in range(self.total_nodes): - node_layer, node_type = number_to_type_layer(node_id, self.n_types) - for param in self.basic_layers_params[self.node_types[node_type]]: - mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][self.nodes[str(node_id)]][param] \ - = self.mutation_of_param(param, self.basic_layers_params[self.node_types[node_type]], - individuum["chainer"]["pipe"][self.model_to_evolve_index][ - self.nodes[str(node_id)]][param], - p_mutation, mutation_power) - mutated.append(mutated_individuum) - - return mutated - - def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutation_power): - new_mutated_value = deepcopy(param_value) - - if self.decision(p_mutation): - if type(params_dict[param]) is dict: - if params_dict[param].get('discrete', False): - val = round(param_value + - ((2 * np.random.random() - 1.) * mutation_power - * self.sample_params(**{param: params_dict[param]})[param])) - val = min(max(params_dict[param]["range"][0], val), - params_dict[param]["range"][1]) - new_mutated_value = val - elif 'range' in params_dict[param].keys(): - val = param_value + \ - ((2 * np.random.random() - 1.) * mutation_power - * self.sample_params(**{param: params_dict[param]})[param]) - val = min(max(params_dict[param]["range"][0], val), - params_dict[param]["range"][1]) - new_mutated_value = val - elif params_dict[param].get("choice"): - # new_mutated_value = param_value - new_mutated_value = self.sample_params(**{param: params_dict[param]})[param] - else: - new_mutated_value = param_value - else: - new_mutated_value = param_value - else: - new_mutated_value = param_value - - return new_mutated_value - - def decision(self, probability): - """ - Make decision whether to do action or not with given probability - Args: - probability: probability whether - - Returns: - - """ - r = np.random.random() - if r < probability: - return True - else: - return False - - def sample_params(self, **params): - if not params: - return {} - else: - params_copy = deepcopy(params) - params_sample = dict() - for param, param_val in params_copy.items(): - if isinstance(param_val, list): - params_sample[param] = np.random.choice(param_val) - elif isinstance(param_val, dict): - if 'bool' in param_val and param_val['bool']: - sample = bool(np.random.choice([True, False])) - elif 'range' in param_val: - sample = self._sample_from_ranges(param_val) - params_sample[param] = sample - else: - params_sample[param] = params_copy[param] - return params_sample - - def _sample_from_ranges(self, opts): - from_ = opts['range'][0] - to_ = opts['range'][1] - if opts.get('scale', None) == 'log': - sample = self._sample_log(from_, to_) - else: - sample = np.random.uniform(from_, to_) - if opts.get('discrete', False): - sample = int(np.round(sample)) - return sample - - @staticmethod - def _sample_log(from_, to_): - sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) - return float(sample) - - def sample_binary_mask(self): - # return np.random.randint(0, high=2, size=self.binary_mask_template.shape).tolist() - # return (1 * (np.log(np.random.random(size=self.binary_mask_template.shape)) > -0.2)).tolist() - ones = np.random.choice(self.total_nodes * self.total_nodes, - size=min(max(1, int(self.mutation_power * np.random.random() * self.total_nodes)), 5)) - mask = np.zeros((self.total_nodes * self.total_nodes)) - mask[ones] = 1 - # returns NUMPY 2D ARRAY! - return mask.reshape((self.total_nodes, self.total_nodes)) - - def sample_one_neuron_binary_mask(self): - mask = np.zeros((self.total_nodes * self.total_nodes)) - return mask.reshape((self.total_nodes, self.total_nodes)) - - def sample_given_binary_mask(self, mask): - return np.array(mask).reshape((self.total_nodes, self.total_nodes)) diff --git a/deeppavlov/models/evolution/random_param_generator.py b/deeppavlov/models/evolution/random_param_generator.py deleted file mode 100644 index df81713585..0000000000 --- a/deeppavlov/models/evolution/random_param_generator.py +++ /dev/null @@ -1,85 +0,0 @@ -import numpy as np -from copy import deepcopy -from pathlib import Path - - -class HyperPar: - def __init__(self, **kwargs): - self.params = kwargs - - def sample_params(self): - params = deepcopy(self.params) - params_sample = dict() - for param, param_val in params.items(): - if isinstance(param_val, list): - params_sample[param] = np.random.choice(param_val) - elif isinstance(param_val, dict): - if 'bool' in param_val and param_val['bool']: - sample = np.random.choice([True, False]) - elif 'range' in param_val: - sample = self._sample_from_ranges(param_val) - params_sample[param] = sample - else: - params_sample[param] = params[param] - return params_sample - - def _sample_from_ranges(self, opts): - from_ = opts['range'][0] - to_ = opts['range'][1] - if opts.get('scale', None) == 'log': - sample = self._sample_log(from_, to_) - else: - sample = np.random.uniform(from_, to_) - if opts.get('discrete', False): - sample = int(np.round(sample)) - return sample - - @staticmethod - def _sample_log(from_, to_): - sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) - return float(sample) - -# net_params = HyperPar(n_filters={'range': [32, 500], 'discrete': True, 'n_samples': n_layers, 'increasing': True}, -# filter_width={'range': [3, 11], 'discrete': True}, -# char_embeddings_dim={'range': [10, 50], 'discrete': True}, -# embeddings_dropout={'bool': True}, -# dense_dropout={'bool': True}, -# net_type=['cnn', 'rnn', 'cnn_highway'], -# use_crf=True, -# use_batch_norm=True, -# token_embeddings_dim=token_emb_dim, -# two_dense_layers=True) -# parms = net_params.sample_params() -# learning_params = HyperPar(dropout_rate={'range': [0.1, 0.9]}, -# epochs={'range': [10, 100], 'discrete': True}, -# learning_rate={'range': [1e-4, 1e-2], 'scale': 'log'}, -# batch_size={'range': [2, 64], 'discrete': True}, -# learning_rate_decay={'range': [0.3, 0.95]}, -# save_path='conll_models/model.ckpt').sample_params() - - -def get_population(basic_params, population_size, population_num): - population = [] - for i in range(population_size): - params = {} - params_for_search = {} - - for param_name in basic_params.keys(): - if ((type(basic_params[param_name]) is str) - or (type(basic_params[param_name]) is int) - or (type(basic_params[param_name]) is float) - or (type(basic_params[param_name]) is bool) - or (type(basic_params[param_name]) is list)): - params[param_name] = basic_params[param_name] - else: - if "values" in basic_params[param_name].keys(): - params_for_search[param_name] = list(basic_params[param_name]["values"]) - else: - params_for_search[param_name] = basic_params[param_name] - - params_for_search = HyperPar(**params_for_search).sample_params() - print() - params["model_path"] = str(Path(basic_params["model_path"]).joinpath( - "population_" + str(population_num)).joinpath(params_for_search["model_name"] + "_" + str(i))) - population.append({**params, **params_for_search}) - return population diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py deleted file mode 100644 index 512ed8d7e4..0000000000 --- a/deeppavlov/models/evolution/run_evolution.py +++ /dev/null @@ -1,232 +0,0 @@ -import json -import numpy as np -import argparse -from pathlib import Path -from subprocess import Popen, PIPE -import pandas as pd -from copy import deepcopy, copy - -from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution -from deeppavlov.core.common.file import save_json, read_json - - -def score_population(population, population_size, result_file): - global evolution - - population_metrics = {} - for m in CONSIDERED_METRICS: - population_metrics[m] = [] - - procs = [] - - for i in range(population_size): - save_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) - load_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]) - - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - str(save_path.joinpath("model")) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ - str(load_path.joinpath("model")) - - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = \ - evolution.nodes - print(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) - try: - save_path.mkdir(parents=True) - except FileExistsError: - pass - - f_name = save_path.joinpath("config.json") - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] =\ - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"].tolist() - save_json(population[i], f_name) - procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" - " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], - str(f_name), - str(save_path), - str(save_path) - ), - shell=True, stdout=PIPE, stderr=PIPE)) - - for i, proc in enumerate(procs): - print(f'wait on {i}th proc') - proc.wait() - - for i in range(population_size): - try: - val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("valid_results.txt"))) - except OSError or FileNotFoundError: - val_results = [None for m in CONSIDERED_METRICS] - for m_id, m in enumerate(CONSIDERED_METRICS): - if "loss" in m: - val_results[m_id] = 1e6 - else: - val_results[m_id] = 0. - if TEST: - try: - test_results = np.loadtxt( - fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("test_results.txt"))) - except OSError or FileNotFoundError: - test_results = [None for m in CONSIDERED_METRICS] - for m_id, m in enumerate(CONSIDERED_METRICS): - if "loss" in m: - test_results[m_id] = 1e6 - else: - test_results[m_id] = 0. - - - result_table_dict = {} - for el in order: - if el == "params": - result_table_dict[el] = [] - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - for m_id, m in enumerate(CONSIDERED_METRICS): - result_table_dict[m + "_valid"].append(val_results[m_id]) - if TEST: - result_table_dict[m + "_test"].append(test_results[m_id]) - else: - result_table_dict[m + "_test"].append(0.) - result_table_dict[order[-1]] = [population[i]] - result_table = pd.DataFrame(result_table_dict) - - result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - - for m_id, m in enumerate(CONSIDERED_METRICS): - population_metrics[m].append(val_results[m_id]) - - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ - np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) - - return population_metrics - - -parser = argparse.ArgumentParser() - -parser.add_argument('--config', help='Please, enter model path to config') -parser.add_argument('--evolve_metric', help='Please, choose target metric out of given in your config.train.metrics') -parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) -parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) -parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) -parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) -parser.add_argument('--one_neuron_init', help='whether to start with zero binary mask (one neuron network)', default=0) -parser.add_argument('--given_mask_init', help='whether to start with given binary mask', default=0) -parser.add_argument('--train_partition', - help='Please, enter partition of splitted train', - default=1) -parser.add_argument('--start_from_population', - help='Please, enter the population number to start from. 0 means from scratch', - default=0) -parser.add_argument('--path_to_population', - help='Please, enter the path to population to start from', - default="") - -args = parser.parse_args() - -CONFIG_FILE = args.config -EVOLVE_METRIC = args.evolve_metric -POPULATION_SIZE = args.p_size -GPU_NUMBER = len(args.gpus) -gpus = [int(gpu) for gpu in args.gpus.split(",")] -N_LAYERS = int(args.n_layers) -N_TYPES = int(args.n_types) -ONE_NEURON_INIT = bool(int(args.one_neuron_init)) -GIVEN_MASK_INIT = bool(int(args.given_mask_init)) -TRAIN_PARTITION = int(args.train_partition) -START_FROM_POPULATION = int(args.start_from_population) -PATH_TO_POPULATION = args.path_to_population - - -with open(CONFIG_FILE, "r") as f: - basic_params = json.load(f) - -print("Given basic params: {}\n".format(basic_params)) - -# list of names of considered metrics -CONSIDERED_METRICS = basic_params["train"]["metrics"] -TEST = basic_params["train"]["test_best"] - -if GIVEN_MASK_INIT: - # Embedding -> BiLSTM -> Dense -> Dense -> GlobalMaxPooling -> Dense(#classes) - INITIAL_BINARY_MASK = np.zeros((N_TYPES * N_LAYERS, N_TYPES * N_LAYERS)) - INITIAL_BINARY_MASK[3, 0] = 1 - INITIAL_BINARY_MASK[0, N_TYPES] = 1 -else: - INITIAL_BINARY_MASK = None - -# EVOLUTION starts here! -evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, - population_size=POPULATION_SIZE, - p_crossover=0.2, crossover_power=0.1, - p_mutation=1., mutation_power=0.1, - key_model_to_evolve="to_evolve", - key_basic_layers="basic_layers_params", - seed=42, - start_with_one_neuron=ONE_NEURON_INIT, - train_partition=TRAIN_PARTITION, - initial_binary_mask=INITIAL_BINARY_MASK, - **basic_params) - -# Result table -order = deepcopy(CONSIDERED_METRICS) -order.extend(["params"]) -result_file = Path(basic_params["chainer"]["pipe"][ - evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") -result_table_columns = [] -result_table_dict = {} -for el in order: - if el == "params": - result_table_dict[el] = [] - result_table_columns.extend([el]) - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - result_table_columns.extend([el + "_valid", el + "_test"]) - -result_table_columns.append("params") - -if START_FROM_POPULATION == 0: - result_table = pd.DataFrame(result_table_dict) - result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') - - print("\nIteration #{} starts\n".format(0)) - population = evolution.first_generation() - population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - - iters = 1 -else: - # to define some clue params of evolution - _ = evolution.first_generation() - iters = START_FROM_POPULATION - print("\nIteration #{} starts\n".format(iters)) - model_name = basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] - population = [] - - for i in range(POPULATION_SIZE): - population.append(read_json(Path(PATH_TO_POPULATION).joinpath( - model_name + "_" + str(i)).joinpath("config.json"))) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ - np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - str(Path(basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).joinpath( - "population_" + str(START_FROM_POPULATION)).joinpath(model_name + "_" + str(i))) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ - str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]).parent) - - population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - print("Population scores: {}".format(population_scores)) - print("\nIteration #{} was done\n".format(iters)) - iters += 1 - -while True: - print("\nIteration #{} starts\n".format(iters)) - population = evolution.next_generation(population, population_scores, iters) - # print("Considered population: {}\nScoring...\n".format(population)) - population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - print("Population scores: {}".format(population_scores)) - print("\nIteration #{} was done\n".format(iters)) - iters += 1 - diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index d1902d4fb4..e94e6e9003 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -103,17 +103,22 @@ def score_population(population, population_size, result_file): parser.add_argument('--config', help='Please, enter model path to config') parser.add_argument('--evolve_metric', help='Please, choose target metric out of given in your config.train.metrics') + +parser.add_argument('--p_cross', help='Please, enter probability of crossover', type=float, default=0.2) +parser.add_argument('--pow_cross', help='Please, enter crossover power', type=float, default=0.1) +parser.add_argument('--p_mut', help='Please, enter probability of mutation', type=float, default=1.) +parser.add_argument('--pow_mut', help='Please, enter mutation power', type=float, default=0.1) + parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) -parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) +parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default="0") parser.add_argument('--train_partition', - help='Please, enter partition of splitted train', - default=1) + help='Please, enter partition of splitted train', default=1) parser.add_argument('--start_from_population', - help='Please, enter the population number to start from. 0 means from scratch', - default=0) + help='Please, enter the population number to start from. 0 means from scratch', default=0) parser.add_argument('--path_to_population', - help='Please, enter the path to population to start from', - default="") + help='Please, enter the path to population to start from', default="") +parser.add_argument('--elitism_with_weights', + help='Please, enter whether to save elite models with weights or not', default=False) args = parser.parse_args() @@ -125,44 +130,49 @@ def score_population(population, population_size, result_file): TRAIN_PARTITION = int(args.train_partition) START_FROM_POPULATION = int(args.start_from_population) PATH_TO_POPULATION = args.path_to_population +ELITISM_WITH_WEIGHTS = args.elitism_with_weights + +P_CROSSOVER = args.p_cross +POW_CROSSOVER = args.pow_cross +P_MUTATION = args.p_mut +POW_MUTATION = args.pow_mut with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) -print("Given basic params: {}\n".format(basic_params)) - -# list of names of considered metrics -CONSIDERED_METRICS = basic_params["train"]["metrics"] -TEST = basic_params["train"]["test_best"] +print("Given basic params: {}\n".format(json.dumps(basic_params, indent=2))) - -# EVOLUTION starts here! evolution = ParamsEvolution(population_size=POPULATION_SIZE, - p_crossover=0.2, crossover_power=0.1, - p_mutation=1., mutation_power=0.1, + p_crossover=P_CROSSOVER, crossover_power=POW_CROSSOVER, + p_mutation=P_MUTATION, mutation_power=POW_MUTATION, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", seed=42, train_partition=TRAIN_PARTITION, + elitism_with_weights=ELITISM_WITH_WEIGHTS, **basic_params) +CONSIDERED_METRICS = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "metrics"))[0]) +TEST = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "test_best"))[0]) + # Result table order = deepcopy(CONSIDERED_METRICS) -order.extend(["params"]) -result_file = Path(basic_params["chainer"]["pipe"][ - evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") +result_file = Path(evolution.get_value_from_config(evolution.basic_config, + evolution.main_model_path + ["save_path"]) + ).joinpath("result_table.csv") result_table_columns = [] - result_table_dict = {} for el in order: - if el == "params": - result_table_dict[el] = [] - result_table_columns.extend([el]) - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - result_table_columns.extend([el + "_valid", el + "_test"]) + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) +order.extend(["params"]) +result_table_dict["params"] = [] result_table_columns.append("params") if START_FROM_POPULATION == 0: @@ -173,24 +183,24 @@ def score_population(population, population_size, result_file): population = evolution.first_generation() print(population) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - iters = 1 else: - # to define some clue params of evolution - _ = evolution.first_generation() + # _ = evolution.first_generation() iters = START_FROM_POPULATION print("\nIteration #{} starts\n".format(iters)) - model_name = basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] - population = [] + population = [] for i in range(POPULATION_SIZE): population.append(read_json(Path(PATH_TO_POPULATION).joinpath( - model_name + "_" + str(i)).joinpath("config.json"))) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - str(Path(basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).joinpath( - "population_" + str(START_FROM_POPULATION)).joinpath(model_name + "_" + str(i))) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ - str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]).parent) + "model_" + str(i)).joinpath("config.json"))) + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["save_path"], + str(Path(evolution.get_value_from_config(evolution.basic_config, evolution.main_model_path + ["save_path"]) + ).joinpath("population_" + str(START_FROM_POPULATION)).joinpath("model_" + str(i)))) + + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["load_path"], + str(Path(evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"]).parent))) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] print("Population scores: {}".format(population_scores)) @@ -200,7 +210,6 @@ def score_population(population, population_size, result_file): while True: print("\nIteration #{} starts\n".format(iters)) population = evolution.next_generation(population, population_scores, iters) - # print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] print("Population scores: {}".format(population_scores)) print("\nIteration #{} was done\n".format(iters)) diff --git a/deeppavlov/models/evolution/test.py b/deeppavlov/models/evolution/test.py deleted file mode 100644 index 31da975a78..0000000000 --- a/deeppavlov/models/evolution/test.py +++ /dev/null @@ -1,134 +0,0 @@ -import numpy as np -from deeppavlov.core.common.file import read_json -from copy import copy, deepcopy -import json - - -def _find_main_model_path(config, key_model, path=[]): - """ - Find path to the main model in config which paths will be changed - Args: - config: - key_model: - - Returns: - path in config -- list of keys (strings and integers) - """ - config_pointer = config - # add_paths = [] - - if type(config_pointer) is dict and key_model in config_pointer.keys(): - # main model is an element of chainer.pipe list - # main model is a dictionary and has key key_main_model - yield path - else: - if type(config_pointer) is dict: - for key in list(config_pointer.keys()): - for path_ in _find_main_model_path(config_pointer[key], key_model, path + [key]): - yield path_ - elif type(config_pointer) is list: - for i in range(len(config_pointer)): - for path_ in _find_main_model_path(config_pointer[i], key_model, path + [i]): - yield path_ - - -def _insert_value_or_dict_into_config(config, path, value): - config_copy = deepcopy(config) - config_pointer = config_copy - for el in path[:-1]: - if type(config_pointer) is dict: - config_pointer = config_pointer.setdefault(el, {}) - elif type(config_pointer) is list: - config_pointer = config_pointer[el] - else: - pass - config_pointer[path[-1]] = value - return config_copy - - -def _get_value_from_config(config, path): - config_copy = deepcopy(config) - config_pointer = config_copy - for el in path[:-1]: - if type(config_pointer) is dict: - config_pointer = config_pointer.setdefault(el, {}) - elif type(config_pointer) is list: - config_pointer = config_pointer[el] - else: - pass - return config_pointer[path[-1]] - - -def initialize_params_in_config(basic_config, paths): - config = deepcopy(basic_config) - - for path_ in paths: - param_name = path_[-1] - value = _get_value_from_config(basic_config, path_) - if type(value) is dict: - if value.get("evolve_choice"): - config = _insert_value_or_dict_into_config(config, - path_, - sample_params( - **{param_name: list(value["values"])})[param_name]) - elif value.get("evolve_range"): - config = _insert_value_or_dict_into_config(config, - path_, - sample_params( - **{param_name: deepcopy(value)})[param_name]) - elif value.get("evolve_bool"): - config = _insert_value_or_dict_into_config(config, - path_, - sample_params( - **{param_name: deepcopy(value)})[param_name]) - - return config - - -def sample_params(**params): - if not params: - return {} - else: - params_copy = deepcopy(params) - params_sample = dict() - for param, param_val in params_copy.items(): - if isinstance(param_val, list): - params_sample[param] = np.random.choice(param_val) - elif isinstance(param_val, dict): - if 'evolve_bool' in param_val and param_val['evolve_bool']: - sample = bool(np.random.choice([True, False])) - elif 'evolve_range' in param_val: - sample = _sample_from_ranges(param_val) - params_sample[param] = sample - else: - params_sample[param] = params_copy[param] - return params_sample - - -def _sample_from_ranges(opts): - from_ = opts['evolve_range'][0] - to_ = opts['evolve_range'][1] - if opts.get('scale', None) == 'log': - sample = _sample_log(from_, to_) - else: - sample = np.random.uniform(from_, to_) - if opts.get('discrete', False): - sample = int(np.round(sample)) - return sample - - -def _sample_log(from_, to_): - sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) - return float(sample) - - -config = read_json("/home/dilyara/Documents/GitHub/deeppavlov_evolution/deeppavlov/configs/evolution/intents_snips.json") -paths = list(_find_main_model_path(config, "evolve_range")) - -print(paths) - -for t in ["evolve_range", "evolve_choice", "evolve_bool"]: - paths = list(_find_main_model_path(config, t)) - config = initialize_params_in_config(config, paths) - -print(json.dumps(config, indent=2)) diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py index 45e2686478..1f9a61d6bc 100644 --- a/deeppavlov/models/evolution/train_phenotype.py +++ b/deeppavlov/models/evolution/train_phenotype.py @@ -19,7 +19,6 @@ from deeppavlov.core.commands.train import train_evaluate_model_from_config from deeppavlov.core.common.file import read_json, save_json -from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe config_path = sys.argv[1] diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index ce6c652ab7..bd8b7b349c 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -248,15 +248,3 @@ def expand_tile_batch_size(memory, context): expanded = K.reshape(context, shape=((1,) + K.int_shape(context))) return K.tile(expanded, repetitions) - -def find_index_of_dict_with_key_in_pipe(pipe, key): - for element_id, element in enumerate(pipe): - if check_whether_key_in_dict(element, key): - return element_id - - -def check_whether_key_in_dict(model, key): - if key in model.keys(): - return True - else: - return False From 3eebe1b9c64cad5a739cb1e792ebe02f73e3c5dd Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:22:43 +0300 Subject: [PATCH 223/616] chore: train phenotype --- .../models/evolution/run_param_evolution.py | 33 ++++++--------- .../models/evolution/train_phenotype.py | 40 +------------------ 2 files changed, 14 insertions(+), 59 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index e94e6e9003..e1f8f01b14 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -4,7 +4,7 @@ from pathlib import Path from subprocess import Popen, PIPE import pandas as pd -from copy import deepcopy, copy +from copy import deepcopy from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution from deeppavlov.core.common.file import save_json, read_json @@ -22,20 +22,16 @@ def score_population(population, population_size, result_file): for j in range(len(gpus)): i = k * len(gpus) + j if i < POPULATION_SIZE: - save_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) - load_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]) - - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - str(save_path.joinpath("model")) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ - str(load_path.joinpath("model")) - - print(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) - try: - save_path.mkdir(parents=True) - except FileExistsError: - pass - + save_path = Path(evolution.get_value_from_config(population[i], + evolution.main_model_path + ["save_path"])) + load_path = Path(evolution.get_value_from_config(population[i], + evolution.main_model_path + ["load_path"])) + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["save_path"], str(save_path.joinpath("model"))) + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["load_path"], str(load_path.joinpath("model"))) + + save_path.mkdir(parents=True, exist_ok=True) f_name = save_path.joinpath("config.json") save_json(population[i], f_name) @@ -53,8 +49,8 @@ def score_population(population, population_size, result_file): for i in range(population_size): try: - val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("valid_results.txt"))) + val_results = np.loadtxt(fname=str(Path(evolution.get_value_from_config( + population[i], evolution.main_model_path + ["save_path"])).parent.joinpath("valid_results.txt"))) except OSError or FileNotFoundError: val_results = [None for m in CONSIDERED_METRICS] for m_id, m in enumerate(CONSIDERED_METRICS): @@ -90,12 +86,9 @@ def score_population(population, population_size, result_file): result_table_dict[m + "_test"].append(0.) result_table_dict[order[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) - result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - for m_id, m in enumerate(CONSIDERED_METRICS): population_metrics[m].append(val_results[m_id]) - return population_metrics diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py index 1f9a61d6bc..828f798d1c 100644 --- a/deeppavlov/models/evolution/train_phenotype.py +++ b/deeppavlov/models/evolution/train_phenotype.py @@ -13,49 +13,11 @@ See the License for the specific language governing permissions and limitations under the License. """ -import numpy as np import sys -from pathlib import Path from deeppavlov.core.commands.train import train_evaluate_model_from_config -from deeppavlov.core.common.file import read_json, save_json config_path = sys.argv[1] - print("TRAIN PHENOTYPE") -reports = train_evaluate_model_from_config(config_path) -print(reports) - -if len(reports) == 2: - # valid and test reports - val_metrics = dict(reports[0]["valid"]["metrics"]) - val_metrics_values = np.array(list(val_metrics.values())).reshape(-1) - - config = read_json(config_path) - model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], - key="to_evolve") - np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ - "save_path"]).parent.joinpath("valid_results.txt")), - X=val_metrics_values) - - test_metrics = dict(reports[1]["test"]["metrics"]) - test_metrics_values = np.array(list(test_metrics.values())).reshape(-1) - - config = read_json(config_path) - model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], - key="to_evolve") - np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ - "save_path"]).parent.joinpath("test_results.txt")), - X=test_metrics_values) -else: - # valid report - val_metrics = dict(reports[0]["valid"]["metrics"]) - val_metrics_values = np.array(list(val_metrics.values())).reshape(-1) - - config = read_json(config_path) - model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], - key="to_evolve") - np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ - "save_path"]).parent.joinpath("valid_results.txt")), - X=val_metrics_values) +train_evaluate_model_from_config(config_path) From 1a4218587850bcd48da70421051b3c3f2dfd2e7e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:37:58 +0300 Subject: [PATCH 224/616] feat: run param evolution --- .../models/evolution/run_param_evolution.py | 47 ++++++++++--------- 1 file changed, 25 insertions(+), 22 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index e1f8f01b14..8f20734e61 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -48,28 +48,31 @@ def score_population(population, population_size, result_file): proc.wait() for i in range(population_size): - try: - val_results = np.loadtxt(fname=str(Path(evolution.get_value_from_config( - population[i], evolution.main_model_path + ["save_path"])).parent.joinpath("valid_results.txt"))) - except OSError or FileNotFoundError: - val_results = [None for m in CONSIDERED_METRICS] - for m_id, m in enumerate(CONSIDERED_METRICS): + with open(str(Path(evolution.get_value_from_config( + population[i], + evolution.main_model_path + ["save_path"])).parent.joinpath("out.txt")), "r") as fout: + reports_data = fout.read().splitlines()[-2:] + reports = [] + for i in range(2): + try: + reports.append(json.loads(reports_data[i])) + except: + pass + if len(reports) == 2 and "valid" in reports[0].keys() and "test" in reports[1].keys(): + val_results = reports[0] + test_results = reports[1] + elif len(reports) == 1 and "valid" in reports[0].keys(): + val_results = reports[0] + else: + val_results = {} + test_results = {} + for m in CONSIDERED_METRICS: if "loss" in m: - val_results[m_id] = 1e6 + val_results[m] = 1e6 + test_results[m] = 1e6 else: - val_results[m_id] = 0. - if TEST: - try: - test_results = np.loadtxt( - fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("test_results.txt"))) - except OSError or FileNotFoundError: - test_results = [None for m in CONSIDERED_METRICS] - for m_id, m in enumerate(CONSIDERED_METRICS): - if "loss" in m: - test_results[m_id] = 1e6 - else: - test_results[m_id] = 0. + val_results[m] = 0. + test_results[m] = 0. result_table_dict = {} for el in order: @@ -79,9 +82,9 @@ def score_population(population, population_size, result_file): result_table_dict[el + "_valid"] = [] result_table_dict[el + "_test"] = [] for m_id, m in enumerate(CONSIDERED_METRICS): - result_table_dict[m + "_valid"].append(val_results[m_id]) + result_table_dict[m + "_valid"].append(val_results[m]) if TEST: - result_table_dict[m + "_test"].append(test_results[m_id]) + result_table_dict[m + "_test"].append(test_results[m]) else: result_table_dict[m + "_test"].append(0.) result_table_dict[order[-1]] = [population[i]] From 29d91e5794f35586659673131e1b588349e162ce Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:39:01 +0300 Subject: [PATCH 225/616] fix: local config --- .../configs/evolution/intents_snli_local.json | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/intents_snli_local.json b/deeppavlov/configs/evolution/intents_snli_local.json index 3a2fc819a1..2ef1e5725d 100644 --- a/deeppavlov/configs/evolution/intents_snli_local.json +++ b/deeppavlov/configs/evolution/intents_snli_local.json @@ -123,8 +123,20 @@ ] }, "train": { - "epochs": 100, - "batch_size": 64, + "epochs": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 500 + ], + "discrete": true + }, "metrics": [ "classification_accuracy", "classification_f1", From a1c987f8d1d442265a2769240fd1df47e8bb13d3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:42:56 +0300 Subject: [PATCH 226/616] fix: local config --- ...ts_snli_local.json => intents_snips_local.json} | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) rename deeppavlov/configs/evolution/{intents_snli_local.json => intents_snips_local.json} (88%) diff --git a/deeppavlov/configs/evolution/intents_snli_local.json b/deeppavlov/configs/evolution/intents_snips_local.json similarity index 88% rename from deeppavlov/configs/evolution/intents_snli_local.json rename to deeppavlov/configs/evolution/intents_snips_local.json index 2ef1e5725d..7a82708cb9 100644 --- a/deeppavlov/configs/evolution/intents_snli_local.json +++ b/deeppavlov/configs/evolution/intents_snips_local.json @@ -2,9 +2,9 @@ "dataset_reader": { "name": "basic_classification_reader", "x": "text", - "y": "gold_label", - "data_path": "/home/dilyara/data/data_files/SNLI/one_input/parts", - "train": "train_0.csv", + "y": "intents", + "data_path": "/home/dilyara/data/data_files/snips/snips_dataset", + "train": "train.csv", "valid": "valid.csv", "test": "test.csv" }, @@ -33,8 +33,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict", - "load_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict" + "save_path": "/home/dilyara/data/data_files/snips/snips_dataset/classes.dict", + "load_path": "/home/dilyara/data/data_files/snips/snips_dataset/classes.dict" }, { "in": [ @@ -70,8 +70,8 @@ ], "main": true, "name": "intent_model", - "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/param_evolution_0", - "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/param_evolution_0", + "save_path": "/home/dilyara/data/models/evolution_data/snips_classification/param_evolution_0", + "load_path": "/home/dilyara/data/models/evolution_data/snips_classification/param_evolution_0", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, From 751ec8ed5d56031d353f527f3e6b106ca25c7a18 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:44:37 +0300 Subject: [PATCH 227/616] fix: elitism param type --- deeppavlov/models/evolution/run_param_evolution.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 8f20734e61..305d32224c 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -114,7 +114,7 @@ def score_population(population, population_size, result_file): parser.add_argument('--path_to_population', help='Please, enter the path to population to start from', default="") parser.add_argument('--elitism_with_weights', - help='Please, enter whether to save elite models with weights or not', default=False) + help='Please, enter whether to save elite models with weights or not', default=0) args = parser.parse_args() @@ -126,7 +126,7 @@ def score_population(population, population_size, result_file): TRAIN_PARTITION = int(args.train_partition) START_FROM_POPULATION = int(args.start_from_population) PATH_TO_POPULATION = args.path_to_population -ELITISM_WITH_WEIGHTS = args.elitism_with_weights +ELITISM_WITH_WEIGHTS = int(args.elitism_with_weights) P_CROSSOVER = args.p_cross POW_CROSSOVER = args.pow_cross From 280a4c7905d1449206521ccbf6051bf335428159 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:50:30 +0300 Subject: [PATCH 228/616] fix: registered param evolution model --- deeppavlov/__init__.py | 3 +-- deeppavlov/models/evolution/evolution_param_generator.py | 3 ++- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index 2b9cc36ded..1dda84f49f 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -43,8 +43,7 @@ import deeppavlov.dataset_iterators.morphotagger_iterator import deeppavlov.models.classifiers.intents.intent_model -import deeppavlov.models.evolution.evolution_intent_model -import deeppavlov.models.evolution.evolution_many_inputs_model +import deeppavlov.models.evolution.evolution_param_generator import deeppavlov.models.commutators.random_commutator import deeppavlov.models.embedders.fasttext_embedder import deeppavlov.models.embedders.dict_embedder diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 30a482403c..572a0531d2 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -3,13 +3,14 @@ from pathlib import Path import json +from deeppavlov.core.common.registry import register from deeppavlov.core.common.file import read_json from deeppavlov.core.common.log import get_logger log = get_logger(__name__) - +@register('params_evolution') class ParamsEvolution: """ Class performs full evolutionary process (task scores -> max): From 25020cdac1723194c0a3ad4d185b66594e46987d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:53:24 +0300 Subject: [PATCH 229/616] fix: considered metrics --- deeppavlov/models/evolution/run_param_evolution.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 305d32224c..2933f21c11 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -148,9 +148,9 @@ def score_population(population, population_size, result_file): elitism_with_weights=ELITISM_WITH_WEIGHTS, **basic_params) -CONSIDERED_METRICS = evolution.get_value_from_config(evolution.basic_config, - list(evolution.find_model_path( - evolution.basic_config, "metrics"))[0]) +CONSIDERED_METRICS = list(evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "metrics"))[0]).values()) TEST = evolution.get_value_from_config(evolution.basic_config, list(evolution.find_model_path( evolution.basic_config, "test_best"))[0]) From 938e1cb2b84e62f55ccbeb9e0efde65303b9ef1e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 12:44:42 +0300 Subject: [PATCH 230/616] fix: paths --- .../evolution/intents_snips_local.json | 14 +++--- .../evolution/evolution_param_generator.py | 45 ++++++++++--------- .../models/evolution/run_param_evolution.py | 16 +++---- 3 files changed, 39 insertions(+), 36 deletions(-) diff --git a/deeppavlov/configs/evolution/intents_snips_local.json b/deeppavlov/configs/evolution/intents_snips_local.json index 7a82708cb9..47ebd2a995 100644 --- a/deeppavlov/configs/evolution/intents_snips_local.json +++ b/deeppavlov/configs/evolution/intents_snips_local.json @@ -11,7 +11,7 @@ "dataset_iterator": { "name": "basic_classification_iterator", "seed": { - "range": [ + "evolve_range": [ 50, 500 ], @@ -79,7 +79,7 @@ 3 ], "filters_cnn": { - "range": [ + "evolve_range": [ 50, 500 ], @@ -88,7 +88,7 @@ "confident_threshold": 0.5, "optimizer": "Adam", "lear_rate": { - "range": [ + "evolve_range": [ 0.0001, 0.1 ] @@ -100,13 +100,13 @@ "coef_reg_cnn": 1e-4, "coef_reg_den": 1e-4, "dropout_rate": { - "range": [ + "evolve_range": [ 0.1, 0.9 ] }, "dense_size": { - "range": [ + "evolve_range": [ 50, 500 ], @@ -124,14 +124,14 @@ }, "train": { "epochs": { - "range": [ + "evolve_range": [ 50, 500 ], "discrete": true }, "batch_size": { - "range": [ + "evolve_range": [ 50, 500 ], diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 572a0531d2..34ee932847 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -51,7 +51,7 @@ def __init__(self, self.main_model_path = list(self.find_model_path(self.basic_config, key_main_model))[0] Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, exist_ok=True) - self.print_dict(self.basic_config, string="Basic config:") + # self.print_dict(self.basic_config, string="Basic config:") log.info("Main model path in config: {}".format(self.main_model_path)) self.population_size = population_size @@ -71,6 +71,7 @@ def __init__(self, self.n_evolving_params = len(self.paths_to_evolving_params) self.evolution_model_id = 0 + self.eps = 1e-6 if seed is None: pass @@ -176,12 +177,10 @@ def first_generation(self, iteration=0): for i in range(self.population_size): population.append(self.initialize_params_in_config(self.basic_config, self.paths_to_evolving_params)) for which_path in ["save_path", "load_path"]: - population[-1] = self.insert_value_or_dict_into_config(population[-1], - self.main_model_path + [which_path], - str(Path( - self.basic_config["save_path"]).joinpath( - "population_" + str(iteration)).joinpath( - "model_" + str(i)))) + population[-1] = self.insert_value_or_dict_into_config( + population[-1], self.main_model_path + [which_path], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) population[-1]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 @@ -229,28 +228,28 @@ def next_generation(self, generation, scores, iteration): except: pass + save_path_prev = self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) + load_path_prev = self.get_value_from_config(next_population[i], self.main_model_path + ["load_path"]) + if self.elitism_with_weights: # if elite models are saved with weights next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self.get_value_from_config(next_population[i], - self.main_model_path + ["load_path"]), + self.main_model_path + ["load_path"], str(Path(self.get_value_from_config(next_population[i], - self.main_model_path + ["save_path"])).parent)) + self.main_model_path + ["save_path"])).parent)) else: # if elite models are saved only as configurations and trained again next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self.get_value_from_config(next_population[i], - self.main_model_path + ["load_path"]), - str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["load_path"]) + self.main_model_path + ["load_path"], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["load_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self.get_value_from_config(next_population[i], - self.main_model_path + ["save_path"]), - str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) + self.main_model_path + ["save_path"], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) for i in range(self.n_saved_best_pretrained, self.population_size): @@ -262,9 +261,8 @@ def next_generation(self, generation, scores, iteration): for which_path in ["save_path", "load_path"]: next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self.get_value_from_config(next_population[i], - self.main_model_path + [which_path]), - str(Path(self.get_value_from_config(next_population[i], self.main_model_path + [which_path]) + self.main_model_path + [which_path], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) next_population[i]["evolution_model_id"] = self.evolution_model_id @@ -321,6 +319,9 @@ def crossover(self, population, scores): """ offsprings = [] scores = np.array(scores, dtype='float') + if np.sum(scores) < self.eps: + scores = [self.eps for _ in range(self.population_size)] + probas_to_be_parent = scores / np.sum(scores) intervals = np.array([np.sum(probas_to_be_parent[:i]) for i in range(self.population_size)]) @@ -374,17 +375,19 @@ def mutation(self, population): for individuum in population: mutated_individuum = deepcopy(individuum) for path_ in self.paths_to_evolving_params: + param_name = path_[-1] + param_value = self.get_value_from_config(individuum, path_) mutated_individuum = self.insert_value_or_dict_into_config( mutated_individuum, path_, - self.mutation_of_param(path_, self.get_value_from_config(individuum, path_))) + self.mutation_of_param(path_, param_value)) mutated.append(mutated_individuum) return mutated def mutation_of_param(self, param_path, param_value): if self.decision(self.p_mutation): - basic_value = self.get_value_from_config(self.basic_config, param_path) param_name = param_path[-1] + basic_value = self.get_value_from_config(self.basic_config, param_path) if type(basic_value) is dict: if basic_value.get('discrete', False): val = round(param_value + diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 2933f21c11..4af5c3d9e5 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -90,8 +90,8 @@ def score_population(population, population_size, result_file): result_table_dict[order[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - for m_id, m in enumerate(CONSIDERED_METRICS): - population_metrics[m].append(val_results[m_id]) + for m in CONSIDERED_METRICS: + population_metrics[m].append(val_results[m]) return population_metrics @@ -141,19 +141,19 @@ def score_population(population, population_size, result_file): evolution = ParamsEvolution(population_size=POPULATION_SIZE, p_crossover=P_CROSSOVER, crossover_power=POW_CROSSOVER, p_mutation=P_MUTATION, mutation_power=POW_MUTATION, - key_model_to_evolve="to_evolve", - key_basic_layers="basic_layers_params", + key_main_model="main", seed=42, train_partition=TRAIN_PARTITION, elitism_with_weights=ELITISM_WITH_WEIGHTS, **basic_params) -CONSIDERED_METRICS = list(evolution.get_value_from_config(evolution.basic_config, - list(evolution.find_model_path( - evolution.basic_config, "metrics"))[0]).values()) +CONSIDERED_METRICS = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "metrics"))[0] + ["metrics"]) +print(CONSIDERED_METRICS) TEST = evolution.get_value_from_config(evolution.basic_config, list(evolution.find_model_path( - evolution.basic_config, "test_best"))[0]) + evolution.basic_config, "test_best"))[0] + ["test_best"]) # Result table order = deepcopy(CONSIDERED_METRICS) From b2dd5083757d37d57848a6aea84a78b136180b90 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 14:04:17 +0300 Subject: [PATCH 231/616] fix: metrics --- .../configs/evolution/intents_snips_local.json | 13 +++++-------- .../models/evolution/evolution_param_generator.py | 3 --- deeppavlov/models/evolution/run_param_evolution.py | 8 ++++++-- 3 files changed, 11 insertions(+), 13 deletions(-) diff --git a/deeppavlov/configs/evolution/intents_snips_local.json b/deeppavlov/configs/evolution/intents_snips_local.json index 47ebd2a995..baf97ee142 100644 --- a/deeppavlov/configs/evolution/intents_snips_local.json +++ b/deeppavlov/configs/evolution/intents_snips_local.json @@ -114,7 +114,10 @@ }, "model_name": "cnn_model", "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" + "tokenizer": "#my_tokenizer", + "check_bool": { + "evolve_bool": true + } } ], "out": [ @@ -123,13 +126,7 @@ ] }, "train": { - "epochs": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - }, + "epochs": 1, "batch_size": { "evolve_range": [ 50, diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 34ee932847..e1131b465c 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -228,9 +228,6 @@ def next_generation(self, generation, scores, iteration): except: pass - save_path_prev = self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) - load_path_prev = self.get_value_from_config(next_population[i], self.main_model_path + ["load_path"]) - if self.elitism_with_weights: # if elite models are saved with weights next_population[i] = self.insert_value_or_dict_into_config( diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 4af5c3d9e5..28e6ce41f5 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -82,9 +82,13 @@ def score_population(population, population_size, result_file): result_table_dict[el + "_valid"] = [] result_table_dict[el + "_test"] = [] for m_id, m in enumerate(CONSIDERED_METRICS): - result_table_dict[m + "_valid"].append(val_results[m]) + val_metrics_path = evolution.find_model_path(val_results, m) + val_m = evolution.get_value_from_config(val_results, val_metrics_path + [m]) + result_table_dict[m + "_valid"].append(val_m) if TEST: - result_table_dict[m + "_test"].append(test_results[m]) + test_metrics_path = evolution.find_model_path(test_results, m) + test_m = evolution.get_value_from_config(test_results, test_metrics_path + [m]) + result_table_dict[m + "_test"].append(test_m) else: result_table_dict[m + "_test"].append(0.) result_table_dict[order[-1]] = [population[i]] From cda7bc6db8dbfa339ad3b9be33beb0caa30684b8 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 15:54:57 +0300 Subject: [PATCH 232/616] fix: metrics --- deeppavlov/models/evolution/run_param_evolution.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 28e6ce41f5..557a9ce2a9 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -82,11 +82,12 @@ def score_population(population, population_size, result_file): result_table_dict[el + "_valid"] = [] result_table_dict[el + "_test"] = [] for m_id, m in enumerate(CONSIDERED_METRICS): - val_metrics_path = evolution.find_model_path(val_results, m) + val_metrics_path = list(evolution.find_model_path(val_results, m))[0] val_m = evolution.get_value_from_config(val_results, val_metrics_path + [m]) + population_metrics[m].append(val_m) result_table_dict[m + "_valid"].append(val_m) if TEST: - test_metrics_path = evolution.find_model_path(test_results, m) + test_metrics_path = list(evolution.find_model_path(test_results, m))[0] test_m = evolution.get_value_from_config(test_results, test_metrics_path + [m]) result_table_dict[m + "_test"].append(test_m) else: @@ -94,8 +95,7 @@ def score_population(population, population_size, result_file): result_table_dict[order[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - for m in CONSIDERED_METRICS: - population_metrics[m].append(val_results[m]) + return population_metrics From f745466acca085ca53294f3cd4d7bf4185dfc6e3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 16:03:05 +0300 Subject: [PATCH 233/616] feat: param evolution works fine --- .../evolution/intents_snips_local.json | 8 +++---- deeppavlov/models/evolution/test.py | 22 +++++++++++++++++++ 2 files changed, 26 insertions(+), 4 deletions(-) create mode 100644 deeppavlov/models/evolution/test.py diff --git a/deeppavlov/configs/evolution/intents_snips_local.json b/deeppavlov/configs/evolution/intents_snips_local.json index baf97ee142..3fcb331a05 100644 --- a/deeppavlov/configs/evolution/intents_snips_local.json +++ b/deeppavlov/configs/evolution/intents_snips_local.json @@ -80,8 +80,8 @@ ], "filters_cnn": { "evolve_range": [ - 50, - 500 + 5, + 50 ], "discrete": true }, @@ -107,8 +107,8 @@ }, "dense_size": { "evolve_range": [ - 50, - 500 + 5, + 50 ], "discrete": true }, diff --git a/deeppavlov/models/evolution/test.py b/deeppavlov/models/evolution/test.py new file mode 100644 index 0000000000..793b463c5e --- /dev/null +++ b/deeppavlov/models/evolution/test.py @@ -0,0 +1,22 @@ +from copy import deepcopy +import numpy as np +import json + +from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution + + + +CONFIG_FILE = "/home/dilyara/Documents/GitHub/deeppavlov_evolution/deeppavlov/configs/evolution/intents_snips_local.json" + +with open(CONFIG_FILE, "r") as f: + basic_params = json.load(f) + +# print("Given basic params: {}\n".format(json.dumps(basic_params, indent=2))) + +evolution = ParamsEvolution(population_size=10, + **basic_params) + +paths = list(evolution.find_model_path(basic_params, "evolve_range")) +print(paths) + +print(evolution.get_value_from_config(basic_params, paths[0])) From 8c8cf43aac4d178a3600be54581ad19fb514bfaf Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 16:30:11 +0300 Subject: [PATCH 234/616] chore: merge dev --- deeppavlov/models/evolution/run_param_evolution.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 557a9ce2a9..7783de9317 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -35,6 +35,8 @@ def score_population(population, population_size, result_file): f_name = save_path.joinpath("config.json") save_json(population[i], f_name) + # __file__ + procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], str(f_name), From ca4a3e791fb08b6bde34dd7af14c7abb2160a84b Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 17:30:22 +0300 Subject: [PATCH 235/616] feat: to evolve --- deeppavlov/evolve.py | 256 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 256 insertions(+) create mode 100644 deeppavlov/evolve.py diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py new file mode 100644 index 0000000000..dc40f48e11 --- /dev/null +++ b/deeppavlov/evolve.py @@ -0,0 +1,256 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import argparse +from pathlib import Path +import sys +import json +from copy import deepcopy +from subprocess import Popen, PIPE +import pandas as pd + +p = (Path(__file__) / ".." / "..").resolve() +sys.path.append(str(p)) + +from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution +from deeppavlov.core.common.file import read_json, save_json +from deeppavlov.core.common.log import get_logger + + + +log = get_logger(__name__) + +parser = argparse.ArgumentParser() + +parser.add_argument("config_path", help="path to a pipeline json config", type=str) +parser.add_argument('--evolve_metric', help='target metric out of given in your config.train.metrics') +parser.add_argument('--p_cross', help='probability of crossover', type=float, default=0.2) +parser.add_argument('--pow_cross', help='crossover power', type=float, default=0.1) +parser.add_argument('--p_mut', help='probability of mutation', type=float, default=1.) +parser.add_argument('--pow_mut', help='mutation power', type=float, default=0.1) + +parser.add_argument('--p_size', help='population size', type=int, default=10) +parser.add_argument('--gpus', help='visible GPUs divided by comma <<,>>', default="0") +parser.add_argument('--train_partition', + help='partition of splitted train file', default=1) +parser.add_argument('--start_from_population', + help='population number to start from. 0 means from scratch', default=0) +parser.add_argument('--path_to_population', + help='path to population to start from', default="") +parser.add_argument('--elitism_with_weights', + help='whether to save elite models with weights or without', default=0) + + +def find_config(pipeline_config_path: str): + if not Path(pipeline_config_path).is_file(): + configs = [c for c in Path(__file__).parent.glob(f'configs/**/{pipeline_config_path}.json') + if str(c.with_suffix('')).endswith(pipeline_config_path)] # a simple way to not allow * and ? + if configs: + log.info(f"Interpreting '{pipeline_config_path}' as '{configs[0]}'") + pipeline_config_path = str(configs[0]) + return pipeline_config_path + + +def main(): + args = parser.parse_args() + + pipeline_config_path = find_config(args.config_path) + evolve_metric = args.evolve_metric + population_size = args.p_size + gpus = [int(gpu) for gpu in args.gpus.split(",")] + train_partition = int(args.train_partition) + start_from_population = int(args.start_from_population) + path_to_population = args.path_to_population + elitism_with_weights = int(args.elitism_with_weights) + + p_crossover = args.p_cross + pow_crossover = args.pow_cross + p_mutation = args.p_mut + pow_mutation = args.pow_mut + + basic_params = read_json(pipeline_config_path) + log.info("Given basic params: {}\n".format(json.dumps(basic_params, indent=2))) + + evolution = ParamsEvolution(population_size=population_size, + p_crossover=p_crossover, crossover_power=pow_crossover, + p_mutation=p_mutation, mutation_power=pow_mutation, + key_main_model="main", + seed=42, + train_partition=train_partition, + elitism_with_weights=elitism_with_weights, + **basic_params) + + considered_metrics = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "metrics"))[0] + ["metrics"]) + + # Result table + order = deepcopy(considered_metrics) + result_file = Path(evolution.get_value_from_config(evolution.basic_config, + evolution.main_model_path + ["save_path"]) + ).joinpath("result_table.csv") + result_table_columns = [] + result_table_dict = {} + for el in order: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) + + order.extend(["params"]) + result_table_dict["params"] = [] + result_table_columns.append("params") + + if start_from_population == 0: + result_table = pd.DataFrame(result_table_dict) + result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') + + log.info("\nIteration #{} starts\n".format(0)) + population = evolution.first_generation() + log.info(population) + population_scores = score_population(population, population_size, result_file, considered_metrics, + evolution, order, gpus, result_table_columns)[evolve_metric] + iters = 1 + else: + # _ = evolution.first_generation() + iters = start_from_population + log.info("\nIteration #{} starts\n".format(iters)) + + population = [] + for i in range(population_size): + population.append(read_json(Path(path_to_population).joinpath( + "model_" + str(i)).joinpath("config.json"))) + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["save_path"], + str(Path( + evolution.get_value_from_config(evolution.basic_config, evolution.main_model_path + ["save_path"]) + ).joinpath("population_" + str(start_from_population)).joinpath("model_" + str(i)))) + + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["load_path"], + str(Path( + evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"]).parent))) + + population_scores = score_population(population, population_size, result_file, considered_metrics, + evolution, order, gpus, result_table_columns)[evolve_metric] + log.info("Population scores: {}".format(population_scores)) + log.info("\nIteration #{} was done\n".format(iters)) + iters += 1 + + while True: + log.info("\nIteration #{} starts\n".format(iters)) + population = evolution.next_generation(population, population_scores, iters) + population_scores = score_population(population, population_size, result_file, considered_metrics, + evolution, order, gpus, result_table_columns)[evolve_metric] + log.info("Population scores: {}".format(population_scores)) + log.info("\nIteration #{} was done\n".format(iters)) + iters += 1 + + +def score_population(population, population_size, result_file, considered_metrics, + evolution, order, gpus, result_table_columns): + test_best = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "test_best"))[0] + ["test_best"]) + population_metrics = {} + for m in considered_metrics: + population_metrics[m] = [] + + for k in range(population_size // len(gpus) + 1): + procs = [] + for j in range(len(gpus)): + i = k * len(gpus) + j + if i < population_size: + save_path = Path(evolution.get_value_from_config(population[i], + evolution.main_model_path + ["save_path"])) + load_path = Path(evolution.get_value_from_config(population[i], + evolution.main_model_path + ["load_path"])) + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["save_path"], str(save_path.joinpath("model"))) + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["load_path"], str(load_path.joinpath("model"))) + + save_path.mkdir(parents=True, exist_ok=True) + f_name = save_path.joinpath("config.json") + save_json(population[i], f_name) + + # __file__ + + procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" + " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], + str(f_name), + str(save_path), + str(save_path) + ), + shell=True, stdout=PIPE, stderr=PIPE)) + for j, proc in enumerate(procs): + i = k * len(gpus) + j + log.info(f'wait on {i}th proc') + proc.wait() + + for i in range(population_size): + with open(str(Path(evolution.get_value_from_config( + population[i], + evolution.main_model_path + ["save_path"])).parent.joinpath("out.txt")), "r") as fout: + reports_data = fout.read().splitlines()[-2:] + reports = [] + for i in range(2): + try: + reports.append(json.loads(reports_data[i])) + except: + pass + if len(reports) == 2 and "valid" in reports[0].keys() and "test" in reports[1].keys(): + val_results = reports[0] + test_results = reports[1] + elif len(reports) == 1 and "valid" in reports[0].keys(): + val_results = reports[0] + else: + val_results = {} + test_results = {} + for m in considered_metrics: + if "loss" in m: + val_results[m] = 1e6 + test_results[m] = 1e6 + else: + val_results[m] = 0. + test_results[m] = 0. + + result_table_dict = {} + for el in order: + if el == "params": + result_table_dict[el] = [] + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + for m_id, m in enumerate(considered_metrics): + val_metrics_path = list(evolution.find_model_path(val_results, m))[0] + val_m = evolution.get_value_from_config(val_results, val_metrics_path + [m]) + population_metrics[m].append(val_m) + result_table_dict[m + "_valid"].append(val_m) + if test_best: + test_metrics_path = list(evolution.find_model_path(test_results, m))[0] + test_m = evolution.get_value_from_config(test_results, test_metrics_path + [m]) + result_table_dict[m + "_test"].append(test_m) + else: + result_table_dict[m + "_test"].append(0.) + result_table_dict[order[-1]] = [population[i]] + result_table = pd.DataFrame(result_table_dict) + result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) + + return population_metrics + + +if __name__ == "__main__": + main() From 1b94b8b34260c77fdeea1ef1750426bc6071b747 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 17:48:31 +0300 Subject: [PATCH 236/616] feat: to evolve --- deeppavlov/evolve.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index dc40f48e11..d460995c7b 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -17,6 +17,7 @@ import argparse from pathlib import Path import sys +import os import json from copy import deepcopy from subprocess import Popen, PIPE @@ -186,7 +187,7 @@ def score_population(population, population_size, result_file, considered_metric f_name = save_path.joinpath("config.json") save_json(population[i], f_name) - # __file__ + curr_file_path = os.path.dirname(os.path.realpath('__file__')) procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], From 1a95c06fa4c079061cb20b23a6d155fc56fc3c41 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 18:09:12 +0300 Subject: [PATCH 237/616] fix: rnadom choice fixed --- deeppavlov/models/evolution/evolution_param_generator.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index e1131b465c..9f4107aa0c 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -2,6 +2,7 @@ from copy import deepcopy from pathlib import Path import json +import random from deeppavlov.core.common.registry import register from deeppavlov.core.common.file import read_json @@ -77,6 +78,7 @@ def __init__(self, pass else: np.random.seed(seed) + random.seed(seed) def find_model_path(self, config, key_model, path=[]): """ @@ -434,10 +436,10 @@ def sample_params(self, **params): params_sample = dict() for param, param_val in params_copy.items(): if isinstance(param_val, list): - params_sample[param] = np.random.choice(param_val) + params_sample[param] = random.choice(param_val) elif isinstance(param_val, dict): if 'evolve_bool' in param_val and param_val['evolve_bool']: - sample = bool(np.random.choice([True, False])) + sample = bool(random.choice([True, False])) elif 'evolve_range' in param_val: sample = self._sample_from_ranges(param_val) params_sample[param] = sample From bb055da484d1186c7c74a9317d8cbbfc6a2de66d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 18:29:39 +0300 Subject: [PATCH 238/616] fix: run subprocess --- deeppavlov/configs/evolution/intents_snips_local.json | 8 ++++++++ deeppavlov/evolve.py | 10 +++++++--- 2 files changed, 15 insertions(+), 3 deletions(-) diff --git a/deeppavlov/configs/evolution/intents_snips_local.json b/deeppavlov/configs/evolution/intents_snips_local.json index 3fcb331a05..a1a3034ebb 100644 --- a/deeppavlov/configs/evolution/intents_snips_local.json +++ b/deeppavlov/configs/evolution/intents_snips_local.json @@ -117,6 +117,14 @@ "tokenizer": "#my_tokenizer", "check_bool": { "evolve_bool": true + }, + "check_choice": { + "evolve_choice": true, + "values": [ + 1, + 2, + 3 + ] } } ], diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index d460995c7b..fa5a6f043a 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -187,15 +187,19 @@ def score_population(population, population_size, result_file, considered_metric f_name = save_path.joinpath("config.json") save_json(population[i], f_name) - curr_file_path = os.path.dirname(os.path.realpath('__file__')) - - procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" + curr_dir_path = os.path.dirname(os.path.realpath('__file__')) + # TODO: choose current python + # TODO: through deep.py train? + procs.append(Popen("CUDA_VISIBLE_DEVICES={} {} {}/deep.py train {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], + sys.executable, + curr_dir_path, str(f_name), str(save_path), str(save_path) ), shell=True, stdout=PIPE, stderr=PIPE)) + for j, proc in enumerate(procs): i = k * len(gpus) + j log.info(f'wait on {i}th proc') From 9dae099931dd94ddcd30b17cf9e9acaf32f9d4e0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 18:29:49 +0300 Subject: [PATCH 239/616] fix: run subprocess --- deeppavlov/evolve.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index fa5a6f043a..7f48198af0 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -188,8 +188,6 @@ def score_population(population, population_size, result_file, considered_metric save_json(population[i], f_name) curr_dir_path = os.path.dirname(os.path.realpath('__file__')) - # TODO: choose current python - # TODO: through deep.py train? procs.append(Popen("CUDA_VISIBLE_DEVICES={} {} {}/deep.py train {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], sys.executable, From 4babeeb3990878bf12995caa6ae2df584b373f55 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 18:37:17 +0300 Subject: [PATCH 240/616] chore --- deeppavlov/evolve.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 7f48198af0..61b3a12d72 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -30,8 +30,6 @@ from deeppavlov.core.common.file import read_json, save_json from deeppavlov.core.common.log import get_logger - - log = get_logger(__name__) parser = argparse.ArgumentParser() From 7c65475e06010ac28a82839a4c0530d7fc4c0650 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 18:42:44 +0300 Subject: [PATCH 241/616] feat: run on cpu --- deeppavlov/evolve.py | 30 ++++++++++++++++++++---------- 1 file changed, 20 insertions(+), 10 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 61b3a12d72..a9072234e2 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -42,7 +42,7 @@ parser.add_argument('--pow_mut', help='mutation power', type=float, default=0.1) parser.add_argument('--p_size', help='population size', type=int, default=10) -parser.add_argument('--gpus', help='visible GPUs divided by comma <<,>>', default="0") +parser.add_argument('--gpus', help='visible GPUs divided by comma <<,>>', default="-1") parser.add_argument('--train_partition', help='partition of splitted train file', default=1) parser.add_argument('--start_from_population', @@ -186,15 +186,25 @@ def score_population(population, population_size, result_file, considered_metric save_json(population[i], f_name) curr_dir_path = os.path.dirname(os.path.realpath('__file__')) - procs.append(Popen("CUDA_VISIBLE_DEVICES={} {} {}/deep.py train {}" - " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], - sys.executable, - curr_dir_path, - str(f_name), - str(save_path), - str(save_path) - ), - shell=True, stdout=PIPE, stderr=PIPE)) + if len(gpus) == 1 and gpus[0] == -1: + procs.append(Popen("{} {}/deep.py train {}" + " 1>{}/out.txt 2>{}/err.txt".format(sys.executable, + curr_dir_path, + str(f_name), + str(save_path), + str(save_path) + ), + shell=True, stdout=PIPE, stderr=PIPE)) + else: + procs.append(Popen("CUDA_VISIBLE_DEVICES={} {} {}/deep.py train {}" + " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], + sys.executable, + curr_dir_path, + str(f_name), + str(save_path), + str(save_path) + ), + shell=True, stdout=PIPE, stderr=PIPE)) for j, proc in enumerate(procs): i = k * len(gpus) + j From fafdc80be0f57bcdbc8f01bd64ef89e1bbd9ae17 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 10:47:43 +0300 Subject: [PATCH 242/616] chore: results dicts out of scope --- deeppavlov/evolve.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index a9072234e2..7921642c85 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -222,14 +222,15 @@ def score_population(population, population_size, result_file, considered_metric reports.append(json.loads(reports_data[i])) except: pass + + val_results = {} + test_results = {} if len(reports) == 2 and "valid" in reports[0].keys() and "test" in reports[1].keys(): val_results = reports[0] test_results = reports[1] elif len(reports) == 1 and "valid" in reports[0].keys(): val_results = reports[0] else: - val_results = {} - test_results = {} for m in considered_metrics: if "loss" in m: val_results[m] = 1e6 From 89d58cff936536b94f74021635412650b56e22e5 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 11:59:03 +0300 Subject: [PATCH 243/616] fix: evolve_choice fixed --- .../evolution/evolution_param_generator.py | 54 +++++++++++-------- 1 file changed, 32 insertions(+), 22 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 9f4107aa0c..4d859ae220 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -51,7 +51,7 @@ def __init__(self, self.basic_config = deepcopy(kwargs) self.main_model_path = list(self.find_model_path(self.basic_config, key_main_model))[0] Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, - exist_ok=True) + exist_ok=True) # self.print_dict(self.basic_config, string="Basic config:") log.info("Main model path in config: {}".format(self.main_model_path)) @@ -148,24 +148,12 @@ def initialize_params_in_config(self, basic_config, paths): param_name = path_[-1] value = self.get_value_from_config(basic_config, path_) if type(value) is dict: - if value.get("evolve_choice"): + if value.get("evolve_choice") or value.get("evolve_range") or value.get("evolve_bool"): config = self.insert_value_or_dict_into_config(config, - path_, - self.sample_params( - **{param_name: - list(value["values"])})[param_name]) - elif value.get("evolve_range"): - config = self.insert_value_or_dict_into_config(config, - path_, - self.sample_params( - **{param_name: - deepcopy(value)})[param_name]) - elif value.get("evolve_bool"): - config = self.insert_value_or_dict_into_config(config, - path_, - self.sample_params( - **{param_name: - deepcopy(value)})[param_name]) + path_, + self.sample_params( + **{param_name: + deepcopy(value)})[param_name]) return config @@ -417,7 +405,7 @@ def decision(self, probability): """ Make decision whether to do action or not with given probability Args: - probability: probability whether + probability: probability whether to do action or not Returns: @@ -429,25 +417,47 @@ def decision(self, probability): return False def sample_params(self, **params): + """ + Sample parameters according to the given possible values + Args: + **params: dictionary {"param_0": {"evolve_range": [0, 10]}, + "param_1": {"evolve_range": [0, 10], "discrete": true}, + "param_2": {"evolve_range": [0, 1], "scale": "log"}, + "param_3": {"evolve_bool": true}, + "param_4": [0, 1, 2, 3]} + + Returns: + + """ if not params: return {} else: params_copy = deepcopy(params) params_sample = dict() for param, param_val in params_copy.items(): - if isinstance(param_val, list): - params_sample[param] = random.choice(param_val) - elif isinstance(param_val, dict): + if isinstance(param_val, dict): if 'evolve_bool' in param_val and param_val['evolve_bool']: sample = bool(random.choice([True, False])) elif 'evolve_range' in param_val: sample = self._sample_from_ranges(param_val) + elif 'evolve_choice' in param_val: + sample = random.choice(param_val['values']) params_sample[param] = sample else: params_sample[param] = params_copy[param] return params_sample def _sample_from_ranges(self, opts): + """ + Sample parameters from ranges + Args: + opts: dictionary {"param_0": {"evolve_range": [0, 10]}, + "param_1": {"evolve_range": [0, 10], "discrete": true}, + "param_2": {"evolve_range": [0, 1], "scale": "log"}} + + Returns: + value + """ from_ = opts['evolve_range'][0] to_ = opts['evolve_range'][1] if opts.get('scale', None) == 'log': From 1725973c181e5449f0b770cdbe10123eb21b82d8 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 14:47:13 +0300 Subject: [PATCH 244/616] chore: many fixes in evolve --- deeppavlov/evolve.py | 132 ++++++++++-------- .../evolution/evolution_param_generator.py | 7 + 2 files changed, 79 insertions(+), 60 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 7921642c85..1d61757c06 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -26,6 +26,7 @@ p = (Path(__file__) / ".." / "..").resolve() sys.path.append(str(p)) +from deeppavlov.core.common.errors import ConfigError from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution from deeppavlov.core.common.file import read_json, save_json from deeppavlov.core.common.log import get_logger @@ -35,7 +36,7 @@ parser = argparse.ArgumentParser() parser.add_argument("config_path", help="path to a pipeline json config", type=str) -parser.add_argument('--evolve_metric', help='target metric out of given in your config.train.metrics') +parser.add_argument('--key_main_model', help='key inserted in dictionary of main model in pipe', default="main") parser.add_argument('--p_cross', help='probability of crossover', type=float, default=0.2) parser.add_argument('--pow_cross', help='crossover power', type=float, default=0.1) parser.add_argument('--p_mut', help='probability of mutation', type=float, default=1.) @@ -67,7 +68,7 @@ def main(): args = parser.parse_args() pipeline_config_path = find_config(args.config_path) - evolve_metric = args.evolve_metric + key_main_model = args.key_main_model population_size = args.p_size gpus = [int(gpu) for gpu in args.gpus.split(",")] train_partition = int(args.train_partition) @@ -86,7 +87,7 @@ def main(): evolution = ParamsEvolution(population_size=population_size, p_crossover=p_crossover, crossover_power=pow_crossover, p_mutation=p_mutation, mutation_power=pow_mutation, - key_main_model="main", + key_main_model=key_main_model, seed=42, train_partition=train_partition, elitism_with_weights=elitism_with_weights, @@ -95,35 +96,29 @@ def main(): considered_metrics = evolution.get_value_from_config(evolution.basic_config, list(evolution.find_model_path( evolution.basic_config, "metrics"))[0] + ["metrics"]) + evolve_metric = considered_metrics[0] - # Result table - order = deepcopy(considered_metrics) result_file = Path(evolution.get_value_from_config(evolution.basic_config, evolution.main_model_path + ["save_path"]) ).joinpath("result_table.csv") result_table_columns = [] result_table_dict = {} - for el in order: + for el in considered_metrics: result_table_dict[el + "_valid"] = [] result_table_dict[el + "_test"] = [] result_table_columns.extend([el + "_valid", el + "_test"]) - order.extend(["params"]) result_table_dict["params"] = [] result_table_columns.append("params") if start_from_population == 0: + iters = 0 result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') log.info("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() - log.info(population) - population_scores = score_population(population, population_size, result_file, considered_metrics, - evolution, order, gpus, result_table_columns)[evolve_metric] - iters = 1 else: - # _ = evolution.first_generation() iters = start_from_population log.info("\nIteration #{} starts\n".format(iters)) @@ -142,31 +137,38 @@ def main(): str(Path( evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"]).parent))) - population_scores = score_population(population, population_size, result_file, considered_metrics, - evolution, order, gpus, result_table_columns)[evolve_metric] - log.info("Population scores: {}".format(population_scores)) - log.info("\nIteration #{} was done\n".format(iters)) - iters += 1 + run_population(population, evolution, gpus) + population_scores = results_to_table(population, evolution, considered_metrics, + result_file, result_table_columns)[evolve_metric] + log.info("Population scores: {}".format(population_scores)) + log.info("\nIteration #{iters} was done\n") + iters += 1 while True: - log.info("\nIteration #{} starts\n".format(iters)) + log.info("\nIteration #{iters} starts\n") population = evolution.next_generation(population, population_scores, iters) - population_scores = score_population(population, population_size, result_file, considered_metrics, - evolution, order, gpus, result_table_columns)[evolve_metric] + run_population(population, evolution, gpus) + population_scores = results_to_table(population, evolution, considered_metrics, + result_file, result_table_columns)[evolve_metric] log.info("Population scores: {}".format(population_scores)) - log.info("\nIteration #{} was done\n".format(iters)) + log.info("\nIteration #{iters} was done\n") iters += 1 -def score_population(population, population_size, result_file, considered_metrics, - evolution, order, gpus, result_table_columns): - test_best = evolution.get_value_from_config(evolution.basic_config, - list(evolution.find_model_path( - evolution.basic_config, "test_best"))[0] + ["test_best"]) - population_metrics = {} - for m in considered_metrics: - population_metrics[m] = [] - +def run_population(population, evolution, gpus): + """ + Change save and load paths for obtained population, save config.json with model config, + run population via current python executor (with which evolve.py already run) + and on given devices (-1 means CPU, other integeres - visible for evolve.py GPUs) + Args: + population: list of dictionaries - configs of current population + evolution: ParamsEvolution + gpus: list of given devices (list of integers) + + Returns: + None + """ + population_size = len(population) for k in range(population_size // len(gpus) + 1): procs = [] for j in range(len(gpus)): @@ -205,12 +207,31 @@ def score_population(population, population_size, result_file, considered_metric str(save_path) ), shell=True, stdout=PIPE, stderr=PIPE)) - for j, proc in enumerate(procs): i = k * len(gpus) + j log.info(f'wait on {i}th proc') proc.wait() + return None + +def results_to_table(population, evolution, considered_metrics, result_file, result_table_columns): + population_size = len(population) + validate_best = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "validate_best"))[0] + + ["validate_best"]) + test_best = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "test_best"))[0] + + ["test_best"]) + if (not validate_best) and test_best: + log.info("validate_best is set to False. Tuning parameters on test") + elif (not validate_best) and (not test_best): + raise ConfigError("validate_best and test_best are set to False. Can not evolve.") + + population_metrics = {} + for m in considered_metrics: + population_metrics[m] = [] for i in range(population_size): with open(str(Path(evolution.get_value_from_config( population[i], @@ -222,42 +243,33 @@ def score_population(population, population_size, result_file, considered_metric reports.append(json.loads(reports_data[i])) except: pass - + val_results = {} test_results = {} + for m in considered_metrics: + val_results[m] = None + test_results[m] = None if len(reports) == 2 and "valid" in reports[0].keys() and "test" in reports[1].keys(): - val_results = reports[0] + val_results = reports[0]["metrics"] test_results = reports[1] elif len(reports) == 1 and "valid" in reports[0].keys(): - val_results = reports[0] - else: - for m in considered_metrics: - if "loss" in m: - val_results[m] = 1e6 - test_results[m] = 1e6 - else: - val_results[m] = 0. - test_results[m] = 0. + val_results = reports[0]["metrics"] + elif len(reports) == 1 and "test" in reports[0].keys(): + test_results = reports[0]["metrics"] result_table_dict = {} - for el in order: - if el == "params": - result_table_dict[el] = [] - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - for m_id, m in enumerate(considered_metrics): - val_metrics_path = list(evolution.find_model_path(val_results, m))[0] - val_m = evolution.get_value_from_config(val_results, val_metrics_path + [m]) - population_metrics[m].append(val_m) - result_table_dict[m + "_valid"].append(val_m) - if test_best: - test_metrics_path = list(evolution.find_model_path(test_results, m))[0] - test_m = evolution.get_value_from_config(test_results, test_metrics_path + [m]) - result_table_dict[m + "_test"].append(test_m) - else: - result_table_dict[m + "_test"].append(0.) - result_table_dict[order[-1]] = [population[i]] + for el in result_table_columns: + result_table_dict[el] = [] + + for m in considered_metrics: + result_table_dict[m + "_valid"].append(val_results[m]) + result_table_dict[m + "_test"].append(test_results[m]) + if validate_best: + population_metrics[m].append(val_results[m]) + elif test_best: + population_metrics[m].append(test_results[m]) + + result_table_dict[result_table_columns[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 4d859ae220..03fe578b57 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -74,6 +74,13 @@ def __init__(self, self.evolution_model_id = 0 self.eps = 1e-6 + try: + self.evolve_metric_optimization = self.get_value_from_config( + self.basic_config, list(self.find_model_path( + self.basic_config, "metric_optimization"))[0] + ["metric_optimization"]) + except: + self.evolve_metric_optimization = "maximize" + if seed is None: pass else: From 73176f1726856ee4e5674092f89a7582f6f8a16e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 16:27:50 +0300 Subject: [PATCH 245/616] docs: docsrtigns in params evolution --- .../evolution/evolution_param_generator.py | 155 ++++++++++++------ deeppavlov/models/evolution/test.py | 22 --- 2 files changed, 102 insertions(+), 75 deletions(-) delete mode 100644 deeppavlov/models/evolution/test.py diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 03fe578b57..8a68ffec05 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -11,6 +11,7 @@ log = get_logger(__name__) + @register('params_evolution') class ParamsEvolution: """ @@ -45,6 +46,7 @@ def __init__(self, with main model in the basic config (to determine save and load paths that will be changed) seed: random seed for initialization train_partition: integer number of train data parts + elitism_with_weights: whether to save elite models with weigths or without **kwargs: basic config with parameters """ @@ -52,7 +54,6 @@ def __init__(self, self.main_model_path = list(self.find_model_path(self.basic_config, key_main_model))[0] Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, exist_ok=True) - # self.print_dict(self.basic_config, string="Basic config:") log.info("Main model path in config: {}".format(self.main_model_path)) self.population_size = population_size @@ -89,10 +90,11 @@ def __init__(self, def find_model_path(self, config, key_model, path=[]): """ - Find path to the main model in config which paths will be changed + Find path to dictionary in config that contains key 'key_model' Args: - config: - key_model: + config: dictionary + key_model: key of sub-dictionary to be found + path: list of keys and/or integers (for list) with relative path (needed for recursion) Returns: path in config -- list of keys (strings and integers) @@ -114,6 +116,16 @@ def find_model_path(self, config, key_model, path=[]): @staticmethod def insert_value_or_dict_into_config(config, path, value): + """ + Insert value to dictionary determined by path[:-1] in field with key path[-1] + Args: + config: dictionary + path: list of keys and/or integers (for list) + value: value to be inserted + + Returns: + config with inserted value + """ config_copy = deepcopy(config) config_pointer = config_copy for el in path[:-1]: @@ -128,6 +140,15 @@ def insert_value_or_dict_into_config(config, path, value): @staticmethod def get_value_from_config(config, path): + """ + Return value of config element determined by path + Args: + config: dictionary + path: list of keys and/or integers (for list) + + Returns: + value + """ config_copy = deepcopy(config) config_pointer = config_copy for el in path[:-1]: @@ -139,18 +160,18 @@ def get_value_from_config(config, path): pass return config_pointer[path[-1]] - @staticmethod - def print_dict(config, string=None): - if string is None: - log.info(json.dumps(config, indent=2)) - else: - log.info(string) - log.info(json.dumps(config, indent=2)) - return None - def initialize_params_in_config(self, basic_config, paths): - config = deepcopy(basic_config) + """ + Randomly initialize all the changable parameters in config + Args: + basic_config: config where changable parameters are dictionaries with keys + `evolve_range`, `evolve_bool`, `evolve_choice` + paths: paths to changable parameters + Returns: + config + """ + config = deepcopy(basic_config) for path_ in paths: param_name = path_[-1] value = self.get_value_from_config(basic_config, path_) @@ -167,6 +188,9 @@ def initialize_params_in_config(self, basic_config, paths): def first_generation(self, iteration=0): """ Initialize first generation randomly according to the given constraints is self.params + Args: + iteration: number of iteration + Returns: first generation that consists of self.population_size individuums """ @@ -185,22 +209,18 @@ def first_generation(self, iteration=0): def next_generation(self, generation, scores, iteration): """ - Provide an operation of replacement + Provide replacement Args: generation: current generation (set of self.population_size configs scores: corresponding scores that should be maximized iteration: iteration number - p_crossover: probability to cross over for current replacement - crossover_power: part of parents parameters to exchange for offsprings - p_mutation: probability of mutation for current replacement - mutation_power: allowed percentage of mutation Returns: the next generation according to the given scores of current generation """ next_population = self.selection_of_best_with_weights(generation, scores) - print("Saved with weights: {} individuums".format(self.n_saved_best_pretrained)) + log.info("Saved with weights: {} models".format(self.n_saved_best_pretrained)) offsprings = self.crossover(generation, scores) changable_next = self.mutation(offsprings) @@ -268,23 +288,19 @@ def selection_of_best_with_weights(self, population, scores): """ Select individuums to save with weights for the next generation from given population. Range is an order of an individuum within sorted scores (1 range = max-score, self.population_size = min-score) - Individuum with the highest score has probability equal to 1 (100%). - Individuum with the lowest score has probability equal to 0 (0%). + Individuum with the best score has probability equal to 1 (100%). + Individuum with the worst score has probability equal to 0 (0%). Probability of i-th individuum to be selected with weights is (a * range_i + b) where a = 1. / (1. - self.population_size), and b = self.population_size / (self.population_size - 1.) Args: population: self.population_size individuums - scores: corresponding score that should be maximized + scores: list of corresponding scores Returns: selected self.n_saved_best_pretrained (changable) individuums """ - scores = np.array(scores, dtype='float') - sorted_ids = np.argsort(scores) - ranges = np.array([self.population_size - np.where(i == sorted_ids)[0][0] - for i in np.arange(self.population_size)]) - + ranges = self.range_scores(scores) a = 1. / (1. - self.population_size) b = self.population_size / (self.population_size - 1.) probas_to_be_selected = a * ranges + b @@ -297,6 +313,25 @@ def selection_of_best_with_weights(self, population, scores): self.n_saved_best_pretrained = len(selected) return selected + def range_scores(self, scores): + not_none_scores = np.array([x for x in scores if x is not None]) + min_score = np.min(not_none_scores) + max_score = np.max(not_none_scores) + for i in range(self.population_size): + if scores[i] is None: + if self.evolve_metric_optimization == "maximize": + scores[i] = min_score - self.eps + else: + scores[i] = max_score + self.eps + scores = np.array(scores, dtype='float') + + sorted_ids = np.argsort(scores) + if self.evolve_metric_optimization == "minimize": + sorted_ids = sorted_ids[::-1] + ranges = np.array([self.population_size - np.where(i == sorted_ids)[0][0] + for i in np.arange(self.population_size)]) + return ranges + def crossover(self, population, scores): """ Recombine randomly population in pairs and cross over them with given probability. @@ -305,18 +340,17 @@ def crossover(self, population, scores): and the other (1 - crossover_power portion) from the other parent Args: population: self.population_size individuums - p_crossover: probability to cross over for current replacement - crossover_power: part of EVOLVING parents parameters to exchange for offsprings + scores: list of corresponding scores Returns: (self.population_size - self.n_saved_best_pretained) offsprings """ offsprings = [] - scores = np.array(scores, dtype='float') - if np.sum(scores) < self.eps: - scores = [self.eps for _ in range(self.population_size)] - probas_to_be_parent = scores / np.sum(scores) + ranges = self.range_scores(scores) + a = 1. / (1. - self.population_size) + b = self.population_size / (self.population_size - 1.) + probas_to_be_parent = (a * ranges + b) / np.sum(a * ranges + b) intervals = np.array([np.sum(probas_to_be_parent[:i]) for i in range(self.population_size)]) for i in range(self.population_size - self.n_saved_best_pretrained): @@ -333,20 +367,20 @@ def crossover(self, population, scores): for j in range(self.n_evolving_params - part, self.n_evolving_params): curr_offsprings[0] = self.insert_value_or_dict_into_config(curr_offsprings[0], - self.paths_to_evolving_params[ - params_perm[j]], - self.get_value_from_config( - parents[1], - self.paths_to_evolving_params[ - params_perm[j]])) + self.paths_to_evolving_params[ + params_perm[j]], + self.get_value_from_config( + parents[1], + self.paths_to_evolving_params[ + params_perm[j]])) curr_offsprings[1] = self.insert_value_or_dict_into_config(curr_offsprings[1], - self.paths_to_evolving_params[ - params_perm[j]], - self.get_value_from_config( - parents[0], - self.paths_to_evolving_params[ - params_perm[j]])) + self.paths_to_evolving_params[ + params_perm[j]], + self.get_value_from_config( + parents[0], + self.paths_to_evolving_params[ + params_perm[j]])) offsprings.append(deepcopy(curr_offsprings[0])) else: offsprings.append(deepcopy(parents[0])) @@ -355,11 +389,9 @@ def crossover(self, population, scores): def mutation(self, population): """ - Mutate each parameter of each individuum in population with probability p_mutation + Mutate each parameter of each individuum in population Args: population: self.population_size individuums - p_mutation: probability to mutate for each parameter - mutation_power: allowed percentage of mutation Returns: mutated population @@ -369,7 +401,6 @@ def mutation(self, population): for individuum in population: mutated_individuum = deepcopy(individuum) for path_ in self.paths_to_evolving_params: - param_name = path_[-1] param_value = self.get_value_from_config(individuum, path_) mutated_individuum = self.insert_value_or_dict_into_config( mutated_individuum, path_, @@ -379,6 +410,15 @@ def mutation(self, population): return mutated def mutation_of_param(self, param_path, param_value): + """ + Mutate particular parameter separately + Args: + param_path: path to parameter in basic config + param_value: current parameter valuer + + Returns: + mutated parameter value + """ if self.decision(self.p_mutation): param_name = param_path[-1] basic_value = self.get_value_from_config(self.basic_config, param_path) @@ -415,7 +455,7 @@ def decision(self, probability): probability: probability whether to do action or not Returns: - + bool decision """ r = np.random.random() if r < probability: @@ -434,7 +474,7 @@ def sample_params(self, **params): "param_4": [0, 1, 2, 3]} Returns: - + random parameter value """ if not params: return {} @@ -463,7 +503,7 @@ def _sample_from_ranges(self, opts): "param_2": {"evolve_range": [0, 1], "scale": "log"}} Returns: - value + random parameter value from range """ from_ = opts['evolve_range'][0] to_ = opts['evolve_range'][1] @@ -477,5 +517,14 @@ def _sample_from_ranges(self, opts): @staticmethod def _sample_log(from_, to_): + """ + Sample parameters from ranges with log scale + Args: + from_: lower boundary of values + to_: upper boundary of values + + Returns: + random parameters value from range with log scale + """ sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) return float(sample) diff --git a/deeppavlov/models/evolution/test.py b/deeppavlov/models/evolution/test.py deleted file mode 100644 index 793b463c5e..0000000000 --- a/deeppavlov/models/evolution/test.py +++ /dev/null @@ -1,22 +0,0 @@ -from copy import deepcopy -import numpy as np -import json - -from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution - - - -CONFIG_FILE = "/home/dilyara/Documents/GitHub/deeppavlov_evolution/deeppavlov/configs/evolution/intents_snips_local.json" - -with open(CONFIG_FILE, "r") as f: - basic_params = json.load(f) - -# print("Given basic params: {}\n".format(json.dumps(basic_params, indent=2))) - -evolution = ParamsEvolution(population_size=10, - **basic_params) - -paths = list(evolution.find_model_path(basic_params, "evolve_range")) -print(paths) - -print(evolution.get_value_from_config(basic_params, paths[0])) From 68d987e24f34b87f4dd608dc9186b20c1139ed81 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 16:34:57 +0300 Subject: [PATCH 246/616] chore: prefix model add to path in evolution class --- .../models/evolution/evolution_param_generator.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 8a68ffec05..2899488172 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -201,7 +201,7 @@ def first_generation(self, iteration=0): population[-1] = self.insert_value_or_dict_into_config( population[-1], self.main_model_path + [which_path], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) - ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) population[-1]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 @@ -240,7 +240,7 @@ def next_generation(self, generation, scores, iteration): self.get_value_from_config(next_population[i], self.main_model_path + ["lear_rate"]), read_json(str(Path(self.get_value_from_config(next_population[i], - self.main_model_path + ["save_path"]) + self.main_model_path + ["save_path"]) ).parent.joinpath("model_opt.json")))["final_lear_rate"]) except: pass @@ -251,20 +251,20 @@ def next_generation(self, generation, scores, iteration): next_population[i], self.main_model_path + ["load_path"], str(Path(self.get_value_from_config(next_population[i], - self.main_model_path + ["save_path"])).parent)) + self.main_model_path + ["save_path"])))) else: # if elite models are saved only as configurations and trained again next_population[i] = self.insert_value_or_dict_into_config( next_population[i], self.main_model_path + ["load_path"], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["load_path"]) - ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) next_population[i] = self.insert_value_or_dict_into_config( next_population[i], self.main_model_path + ["save_path"], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"]) - ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) for i in range(self.n_saved_best_pretrained, self.population_size): # if several train files @@ -277,7 +277,7 @@ def next_generation(self, generation, scores, iteration): next_population[i], self.main_model_path + [which_path], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) - ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) next_population[i]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 From 94ffb4ef82cb47d83330f71b02f046f807ae3920 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 16:35:54 +0300 Subject: [PATCH 247/616] chore: remove path work in evolve --- deeppavlov/evolve.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 1d61757c06..191fabe79d 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -175,13 +175,7 @@ def run_population(population, evolution, gpus): i = k * len(gpus) + j if i < population_size: save_path = Path(evolution.get_value_from_config(population[i], - evolution.main_model_path + ["save_path"])) - load_path = Path(evolution.get_value_from_config(population[i], - evolution.main_model_path + ["load_path"])) - population[i] = evolution.insert_value_or_dict_into_config( - population[i], evolution.main_model_path + ["save_path"], str(save_path.joinpath("model"))) - population[i] = evolution.insert_value_or_dict_into_config( - population[i], evolution.main_model_path + ["load_path"], str(load_path.joinpath("model"))) + evolution.main_model_path + ["save_path"])).parent save_path.mkdir(parents=True, exist_ok=True) f_name = save_path.joinpath("config.json") From d2e58b94c6c6e41ca8d6706bb2da46e8ebb0c3e6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 16:42:32 +0300 Subject: [PATCH 248/616] chore: remove path work in evolve --- deeppavlov/evolve.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 191fabe79d..10a9567329 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -130,12 +130,15 @@ def main(): population[i], evolution.main_model_path + ["save_path"], str(Path( evolution.get_value_from_config(evolution.basic_config, evolution.main_model_path + ["save_path"]) - ).joinpath("population_" + str(start_from_population)).joinpath("model_" + str(i)))) + ).joinpath( + "population_" + str(start_from_population)).joinpath( + "model_" + str(i)).joinpath( + "model"))) population[i] = evolution.insert_value_or_dict_into_config( population[i], evolution.main_model_path + ["load_path"], str(Path( - evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"]).parent))) + evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"])))) run_population(population, evolution, gpus) population_scores = results_to_table(population, evolution, considered_metrics, From db6ddfed7b91b2d47330bf88b406df908d46e615 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 16:48:36 +0300 Subject: [PATCH 249/616] fix: fixes some --- deeppavlov/evolve.py | 8 ++++---- deeppavlov/models/evolution/evolution_param_generator.py | 2 ++ 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 10a9567329..3b289b5f60 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -116,7 +116,7 @@ def main(): result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') - log.info("\nIteration #{} starts\n".format(0)) + log.info("\nIteration #{} starts\n".format(iters)) population = evolution.first_generation() else: iters = start_from_population @@ -144,17 +144,17 @@ def main(): population_scores = results_to_table(population, evolution, considered_metrics, result_file, result_table_columns)[evolve_metric] log.info("Population scores: {}".format(population_scores)) - log.info("\nIteration #{iters} was done\n") + log.info("\nIteration #{} was done\n".format(iters)) iters += 1 while True: - log.info("\nIteration #{iters} starts\n") + log.info("\nIteration #{} starts\n".format(iters)) population = evolution.next_generation(population, population_scores, iters) run_population(population, evolution, gpus) population_scores = results_to_table(population, evolution, considered_metrics, result_file, result_table_columns)[evolve_metric] log.info("Population scores: {}".format(population_scores)) - log.info("\nIteration #{iters} was done\n") + log.info("\nIteration #{} was done\n".format(iters)) iters += 1 diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 2899488172..e8072a910f 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -315,6 +315,8 @@ def selection_of_best_with_weights(self, population, scores): def range_scores(self, scores): not_none_scores = np.array([x for x in scores if x is not None]) + if len(not_none_scores) == 0: + not_none_scores = np.array([0]) min_score = np.min(not_none_scores) max_score = np.max(not_none_scores) for i in range(self.population_size): From 0b18482afeb2e0632f7aa21efed6dea207b3e070 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 16:57:35 +0300 Subject: [PATCH 250/616] fix: metrics in reports --- deeppavlov/evolve.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 3b289b5f60..6771faea85 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -247,12 +247,12 @@ def results_to_table(population, evolution, considered_metrics, result_file, res val_results[m] = None test_results[m] = None if len(reports) == 2 and "valid" in reports[0].keys() and "test" in reports[1].keys(): - val_results = reports[0]["metrics"] - test_results = reports[1] + val_results = reports[0]["valid"]["metrics"] + test_results = reports[1]["test"]["metrics"] elif len(reports) == 1 and "valid" in reports[0].keys(): - val_results = reports[0]["metrics"] + val_results = reports[0]["valid"]["metrics"] elif len(reports) == 1 and "test" in reports[0].keys(): - test_results = reports[0]["metrics"] + test_results = reports[0]["test"]["metrics"] result_table_dict = {} for el in result_table_columns: From 3c37c7e1d7b07723e548a0952cb9e8d5bf1101a4 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 17:21:25 +0300 Subject: [PATCH 251/616] fix: re init lear rate for keras model --- deeppavlov/models/evolution/evolution_param_generator.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index e8072a910f..b4c30a398a 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -237,8 +237,7 @@ def next_generation(self, generation, scores, iteration): # re-init learning rate with the final one (works for KerasModel) next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self.get_value_from_config(next_population[i], - self.main_model_path + ["lear_rate"]), + self.main_model_path + ["lear_rate"], read_json(str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) ).parent.joinpath("model_opt.json")))["final_lear_rate"]) From f752f26ca249cfaa5ced6419abc5addd8411e458 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 17:27:40 +0300 Subject: [PATCH 252/616] fix: delete new lines in logs --- deeppavlov/evolve.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 6771faea85..8ba8e3bf7f 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -116,11 +116,11 @@ def main(): result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') - log.info("\nIteration #{} starts\n".format(iters)) + log.info("Iteration #{} starts".format(iters)) population = evolution.first_generation() else: iters = start_from_population - log.info("\nIteration #{} starts\n".format(iters)) + log.info("Iteration #{} starts".format(iters)) population = [] for i in range(population_size): @@ -144,17 +144,17 @@ def main(): population_scores = results_to_table(population, evolution, considered_metrics, result_file, result_table_columns)[evolve_metric] log.info("Population scores: {}".format(population_scores)) - log.info("\nIteration #{} was done\n".format(iters)) + log.info("Iteration #{} was done".format(iters)) iters += 1 while True: - log.info("\nIteration #{} starts\n".format(iters)) + log.info("Iteration #{} starts".format(iters)) population = evolution.next_generation(population, population_scores, iters) run_population(population, evolution, gpus) population_scores = results_to_table(population, evolution, considered_metrics, result_file, result_table_columns)[evolve_metric] log.info("Population scores: {}".format(population_scores)) - log.info("\nIteration #{} was done\n".format(iters)) + log.info("Iteration #{} was done".format(iters)) iters += 1 From af393564aee0871bbefcac896bfea64aeb37c4d0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 18:28:50 +0300 Subject: [PATCH 253/616] feat: saving fiton parts to model dir in paramsevoltion --- .../evolution/evolution_param_generator.py | 30 +++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index b4c30a398a..73a147b950 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -75,6 +75,11 @@ def __init__(self, self.evolution_model_id = 0 self.eps = 1e-6 + self.paths_to_fiton_dicts = [] + for path_ in self.find_model_path(self.basic_config, "fit_on"): + self.paths_to_fiton_dicts.append(path_) + self.n_fiton_dicts = len(self.paths_to_fiton_dicts) + try: self.evolve_metric_optimization = self.get_value_from_config( self.basic_config, list(self.find_model_path( @@ -202,6 +207,13 @@ def first_generation(self, iteration=0): population[-1], self.main_model_path + [which_path], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) + for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + for which_path in ["save_path", "load_path"]: + population[-1] = self.insert_value_or_dict_into_config( + population[-1], path_ + [which_path], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( + "fitted_model_" + str(path_id)))) population[-1]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 @@ -251,6 +263,11 @@ def next_generation(self, generation, scores, iteration): self.main_model_path + ["load_path"], str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"])))) + for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + next_population[i] = self.insert_value_or_dict_into_config( + next_population[i], path_ + ["load_path"], + str(Path(self.get_value_from_config(next_population[i], + path_ + ["save_path"])))) else: # if elite models are saved only as configurations and trained again next_population[i] = self.insert_value_or_dict_into_config( @@ -264,6 +281,12 @@ def next_generation(self, generation, scores, iteration): self.main_model_path + ["save_path"], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) + for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + next_population[i] = self.insert_value_or_dict_into_config( + next_population[i], path_ + ["save_path"], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( + "fitted_model_" + str(path_id)))) for i in range(self.n_saved_best_pretrained, self.population_size): # if several train files @@ -277,6 +300,13 @@ def next_generation(self, generation, scores, iteration): self.main_model_path + [which_path], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) + for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + for which_path in ["save_path", "load_path"]: + next_population[i] = self.insert_value_or_dict_into_config( + next_population[i], path_ + [which_path], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( + "fitted_model_" + str(path_id)))) next_population[i]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 From 6743e19dbb5eca3c7109eebd113436380cfd7faa Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 18:34:34 +0300 Subject: [PATCH 254/616] feat: saving fiton parts to model dir in paramsevoltion --- deeppavlov/evolve.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 8ba8e3bf7f..a10880a291 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -140,6 +140,20 @@ def main(): str(Path( evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"])))) + for path_id, path_ in enumerate(evolution.paths_to_fiton_dicts): + population[i] = evolution.insert_value_or_dict_into_config( + population[i], path_ + ["save_path"], + str(Path(evolution.get_value_from_config(evolution.basic_config, + evolution.main_model_path + ["save_path"]) + ).joinpath("population_" + str(iters)).joinpath("model_" + str(i)).joinpath( + "fitted_model_" + str(path_id)))) + + for path_id, path_ in enumerate(evolution.paths_to_fiton_dicts): + population[i] = evolution.insert_value_or_dict_into_config( + population[i], path_ + ["load_path"], + str(Path(evolution.get_value_from_config( + population[i], path_ + ["load_path"])))) + run_population(population, evolution, gpus) population_scores = results_to_table(population, evolution, considered_metrics, result_file, result_table_columns)[evolve_metric] From 008e565bc8dd49ec76c965d534035e456e91c6fa Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 22 Jun 2018 17:06:22 +0300 Subject: [PATCH 255/616] docs: docstrings in evolution --- .../configs/evolution/basic_ag_news_part.json | 251 ---------------- .../configs/evolution/basic_nlu_part.json | 250 ---------------- .../configs/evolution/basic_ru_snli_part.json | 251 ---------------- .../basic_ru_snli_part_many_inputs.json | 262 ----------------- .../configs/evolution/basic_sber_faq.json | 251 ---------------- .../basic_snips_one_neuron_init.json | 247 ---------------- .../evolution/basic_snips_random_init.json | 247 ---------------- .../configs/evolution/basic_snli_part.json | 251 ---------------- .../basic_snli_part_many_inputs.json | 268 ------------------ .../basic_snli_part_many_inputs_big.json | 267 ----------------- .../evolution/basic_twitter140_part.json | 251 ---------------- .../configs/evolution/intents_snips.json | 157 ---------- .../evolution/intents_snips_local.json | 8 + .../configs/evolution/intents_snli.json | 133 --------- deeppavlov/evolve.py | 6 + .../evolution/evolution_param_generator.py | 10 + 16 files changed, 24 insertions(+), 3086 deletions(-) delete mode 100644 deeppavlov/configs/evolution/basic_ag_news_part.json delete mode 100644 deeppavlov/configs/evolution/basic_nlu_part.json delete mode 100644 deeppavlov/configs/evolution/basic_ru_snli_part.json delete mode 100644 deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json delete mode 100644 deeppavlov/configs/evolution/basic_sber_faq.json delete mode 100644 deeppavlov/configs/evolution/basic_snips_one_neuron_init.json delete mode 100644 deeppavlov/configs/evolution/basic_snips_random_init.json delete mode 100644 deeppavlov/configs/evolution/basic_snli_part.json delete mode 100644 deeppavlov/configs/evolution/basic_snli_part_many_inputs.json delete mode 100644 deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json delete mode 100644 deeppavlov/configs/evolution/basic_twitter140_part.json delete mode 100644 deeppavlov/configs/evolution/intents_snips.json delete mode 100644 deeppavlov/configs/evolution/intents_snli.json diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json deleted file mode 100644 index a6e9459f25..0000000000 --- a/deeppavlov/configs/evolution/basic_ag_news_part.json +++ /dev/null @@ -1,251 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "label", - "data_path": "/home/dilyara.baymurzina/evolution_data/ag_news_data/parts", - "train": "train_0.csv", - "valid": "valid.csv", - "test": "test.csv" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_data/ag_news_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_data/ag_news_classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/given_mask_init_part_7", - "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/given_mask_init_part_7", - "classes": "#classes_vocab.keys()", - "to_evolve": true, - "basic_layers_params": { - "Dense": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - }, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "Conv1D": { - "filters": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "kernel_size": { - "range": [ - 2, - 7 - ], - "discrete": true - }, - "padding": "same", - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "CuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "BiCuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "MaxPooling1D": { - "pool_size": { - "range": [ - 2, - 5 - ], - "discrete": true - }, - "padding": "same" - }, - "SelfMultiplicativeAttention": { - "n_hidden": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "n_output_features": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - } - }, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.0001, - 0.1 - ] - }, - "lear_rate_decay": { - "range": [ - 0.000001, - 0.1 - ] - }, - "loss": "binary_crossentropy", - "confident_threshold": 1, - "text_size": 50, - "dropout_rate": { - "range": [ - 0.1, - 0.9 - ] - }, - "last_layer_activation": "softmax", - "model_name": "evolution_classification_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "range": [ - 1, - 10 - ], - "discrete": true - }, - "batch_size": { - "range": [ - 50, - 100 - ], - "discrete": true - }, - "metric_optimization": "maximize", - "metrics": [ - "classification_accuracy", - "classification_log_loss", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 1, - "log_every_n_epochs": 1, - "show_examples": false, - "validate_best": true, - "test_best": true - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} diff --git a/deeppavlov/configs/evolution/basic_nlu_part.json b/deeppavlov/configs/evolution/basic_nlu_part.json deleted file mode 100644 index 3dec69c7cd..0000000000 --- a/deeppavlov/configs/evolution/basic_nlu_part.json +++ /dev/null @@ -1,250 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "intent", - "data_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_data/ChatbotCorpus", - "train": "train_ChatbotCorpus_0.csv", - "valid": "valid_ChatbotCorpus_0.csv" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_data/ChatbotCorpus/classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_data/ChatbotCorpus/classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_classification/ChatbotCorpus/one_neuron_init_part_6", - "load_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_classification/ChatbotCorpus/one_neuron_init_part_6", - "classes": "#classes_vocab.keys()", - "to_evolve": true, - "basic_layers_params": { - "Dense": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - }, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "Conv1D": { - "filters": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "kernel_size": { - "range": [ - 2, - 7 - ], - "discrete": true - }, - "padding": "same", - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "CuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "BiCuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "MaxPooling1D": { - "pool_size": { - "range": [ - 2, - 5 - ], - "discrete": true - }, - "padding": "same" - }, - "SelfMultiplicativeAttention": { - "n_hidden": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "n_output_features": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - } - }, - "confident_threshold": 1, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.0001, - 0.1 - ] - }, - "lear_rate_decay": { - "range": [ - 0.000001, - 0.1 - ] - }, - "loss": "binary_crossentropy", - "text_size": 15, - "dropout_rate": { - "range": [ - 0.1, - 0.9 - ] - }, - "last_layer_activation": "softmax", - "model_name": "evolution_classification_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "range": [ - 1, - 10 - ], - "discrete": true - }, - "batch_size": { - "range": [ - 50, - 100 - ], - "discrete": true - }, - "metric_optimization": "maximize", - "metrics": [ - "classification_f1", - "classification_accuracy", - "classification_log_loss", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 1, - "log_every_n_epochs": 1, - "show_examples": false, - "validate_best": true, - "test_best": false - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part.json b/deeppavlov/configs/evolution/basic_ru_snli_part.json deleted file mode 100644 index 4a3ce204d3..0000000000 --- a/deeppavlov/configs/evolution/basic_ru_snli_part.json +++ /dev/null @@ -1,251 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/one_input/parts", - "train": "train_0.csv", - "valid": "valid.csv", - "test": "test.csv" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/one_input/ru_snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/one_input/ru_snli_classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.bin", - "load_path": 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"y_probas_dict" - ] - }, - "train": { - "epochs": { - "range": [ - 1, - 10 - ], - "discrete": true - }, - "batch_size": { - "range": [ - 50, - 100 - ], - "discrete": true - }, - "metric_optimization": "maximize", - "metrics": [ - "classification_accuracy", - "classification_log_loss", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 1, - "log_every_n_epochs": 1, - "show_examples": false, - "validate_best": true, - "test_best": true - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} diff --git a/deeppavlov/configs/evolution/intents_snips.json b/deeppavlov/configs/evolution/intents_snips.json deleted file mode 100644 index 58d21fd4ce..0000000000 --- a/deeppavlov/configs/evolution/intents_snips.json +++ /dev/null @@ -1,157 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "intents", - "data_path": "/home/dilyara.baymurzina/evolution_data/snips_data", - "train": "train.csv", - "valid": "valid.csv" - }, - "dataset_iterator": { - "name": "basic_classification_iterator", - "seed": { - "range": [ - 50, - 500 - ], - "discrete": true - } - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "intent_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/param_evolution_0", - "load_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/param_evolution_0", - "classes": "#classes_vocab.keys()", - "kernel_sizes_cnn": [ - 1, - 2, - 3 - ], - "filters_cnn": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - }, - "confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": { - "evolve_range": [ - 0.0001, - 0.1 - ] - }, - "lear_rate_decay": 0.1, - "loss": "binary_crossentropy", - "text_size": 51, - "to_evolve": true, - "coef_reg_cnn": 1e-4, - "coef_reg_den": 1e-4, - "dropout_rate": { - "evolve_range": [ - 0.1, - 0.9 - ] - }, - "dense_size": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - }, - "model_name": "cnn_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer", - "check_bool": { - "evolve_bool": true - } - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - }, - "batch_size": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - }, - "metrics": { - "evolve_choice": true, - "values": [ - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ] - }, - "validation_patience": 5, - "val_every_n_epochs": 1, - "log_every_n_epochs": 1, - "show_examples": false, - "validate_best": true, - "test_best": false - } -} diff --git a/deeppavlov/configs/evolution/intents_snips_local.json b/deeppavlov/configs/evolution/intents_snips_local.json index a1a3034ebb..3563448704 100644 --- a/deeppavlov/configs/evolution/intents_snips_local.json +++ b/deeppavlov/configs/evolution/intents_snips_local.json @@ -125,6 +125,14 @@ 2, 3 ] + }, + "check_choice_str": { + "evolve_choice": true, + "values": [ + "hello", + "hello, again", + "bye-bye" + ] } } ], diff --git a/deeppavlov/configs/evolution/intents_snli.json b/deeppavlov/configs/evolution/intents_snli.json deleted file mode 100644 index 2e0afffd0d..0000000000 --- a/deeppavlov/configs/evolution/intents_snli.json +++ /dev/null @@ -1,133 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/parts", - "train": "train_0.csv", - "valid": "valid.csv", - "test": "test.csv" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "intent_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/param_evolution_0", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/param_evolution_0", - "classes": "#classes_vocab.keys()", - "kernel_sizes_cnn": [ - 1, - 2, - 3 - ], - "filters_cnn": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.0001, - 0.1 - ] - }, - "lear_rate_decay": 0.1, - "loss": "binary_crossentropy", - "text_size": 51, - "to_evolve": true, - "coef_reg_cnn": 1e-4, - "coef_reg_den": 1e-4, - "dropout_rate": { - "range": [ - 0.1, - 0.9 - ] - }, - "dense_size": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "model_name": "cnn_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": 100, - "batch_size": 64, - "metrics": [ - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 1, - "log_every_n_epochs": 1, - "show_examples": false, - "validate_best": true, - "test_best": true - } -} diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index a10880a291..a51ade3905 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -84,6 +84,7 @@ def main(): basic_params = read_json(pipeline_config_path) log.info("Given basic params: {}\n".format(json.dumps(basic_params, indent=2))) + # Initialize evolution evolution = ParamsEvolution(population_size=population_size, p_crossover=p_crossover, crossover_power=pow_crossover, p_mutation=p_mutation, mutation_power=pow_mutation, @@ -98,6 +99,7 @@ def main(): evolution.basic_config, "metrics"))[0] + ["metrics"]) evolve_metric = considered_metrics[0] + # Create table variable for gathering results result_file = Path(evolution.get_value_from_config(evolution.basic_config, evolution.main_model_path + ["save_path"]) ).joinpath("result_table.csv") @@ -112,13 +114,17 @@ def main(): result_table_columns.append("params") if start_from_population == 0: + # if starting evolution from scratch iters = 0 result_table = pd.DataFrame(result_table_dict) + # write down result table file result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') log.info("Iteration #{} starts".format(iters)) + # randomly generate the first population population = evolution.first_generation() else: + # if starting evolution from already existing population iters = start_from_population log.info("Iteration #{} starts".format(iters)) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 73a147b950..61eded9615 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -343,6 +343,16 @@ def selection_of_best_with_weights(self, population, scores): return selected def range_scores(self, scores): + """ + Ranges scores, + range 1 corresponds to the best score, + range self.population_size corresponds to the worst score. + Args: + scores: list of corresponding scores of population + + Returns: + ranges + """ not_none_scores = np.array([x for x in scores if x is not None]) if len(not_none_scores) == 0: not_none_scores = np.array([0]) From c3fbd37f8ad178bac9792b3ea14745dcae3325d2 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 22 Jun 2018 17:57:39 +0300 Subject: [PATCH 256/616] feat: example config for intents snips --- .../evolution/evolve_intents_snips.json | 184 ++++++++++++++++++ 1 file changed, 184 insertions(+) create mode 100644 deeppavlov/configs/evolution/evolve_intents_snips.json diff --git a/deeppavlov/configs/evolution/evolve_intents_snips.json b/deeppavlov/configs/evolution/evolve_intents_snips.json new file mode 100644 index 0000000000..ec1c0696b0 --- /dev/null +++ b/deeppavlov/configs/evolution/evolve_intents_snips.json @@ -0,0 +1,184 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "intents", + "data_path": "snips" + }, + "dataset_iterator": { + "name": "basic_classification_iterator", + "seed": { + "evolve_range": [ + 50, + 500 + ], + "discrete": true + }, + "field_to_split": "train", + "split_fields": [ + "train", + "valid" + ], + "split_proportions": [ + 0.9, + 0.1 + ] + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "vocabs/snips_classes.dict", + "load_path": "vocabs/snips_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "intent_model", + "save_path": "evolution/classification/intents_snips", + "load_path": "evolution/classification/intents_snips", + "classes": "#classes_vocab.keys()", + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": { + "evolve_range": [ + 5, + 50 + ], + "discrete": true + }, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": { + "evolve_range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 15, + "coef_reg_cnn": { + "evolve_range": [ + 1e-6, + 1e-3 + ] + }, + "coef_reg_den": { + "evolve_range": [ + 1e-6, + 1e-3 + ] + }, + "dropout_rate": { + "evolve_range": [ + 0.1, + 0.9 + ] + }, + "dense_size": { + "evolve_range": [ + 5, + 50 + ], + "discrete": true + }, + "model_name": "cnn_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "evolve_range": [ + 50, + 500 + ], + "discrete": true + }, + "batch_size": { + "evolve_range": [ + 50, + 500 + ], + "discrete": true + }, + "metrics": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel", + "server_utils": "KerasIntentModel" + }, + "download": [ + "http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz", + "http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz", + { + "url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv", + "subdir": "snips" + }, + { + "url": "http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin", + "subdir": "embeddings" + } + ] + } +} From 2e43b41d2cd1bb9e26adf54760ce87fc8301521d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 22 Jun 2018 17:59:16 +0300 Subject: [PATCH 257/616] docs: start make README --- deeppavlov/models/evolution/README.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) create mode 100644 deeppavlov/models/evolution/README.md diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md new file mode 100644 index 0000000000..600699c679 --- /dev/null +++ b/deeppavlov/models/evolution/README.md @@ -0,0 +1,24 @@ +[![License Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](/LICENSE.txt) +![Python 3.6](https://img.shields.io/badge/python-3.6-green.svg) + +# Parameters evolution for DeepPavlov models + +This repository contains implementation of parameters evolution for DeepPavlov models. + + + +If one prefers to run evolution on some provided by DeepPavlov dataset, +firstly, download embeddings and datasets running the following command providing +corresponding name of the config file (see above): + +``` +cd deeppavlov +python deep.py download configs/intents/intents_snips.json +``` + +To evolve model of interest run the following command providing corresponding name of the config file (see above): +``` +cd deeppavlov +python evolve.py interact configs/evolution/evolve_intents_snips.json +``` + From c9db778763317bfa148c4fe9ee429f3c544a12b7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 22 Jun 2018 18:24:48 +0300 Subject: [PATCH 258/616] fix: train partition with file ext --- deeppavlov/models/evolution/evolution_param_generator.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 61eded9615..5fef6d9f1a 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -242,9 +242,10 @@ def next_generation(self, generation, scores, iteration): for i in range(self.n_saved_best_pretrained): # if several train files: if self.train_partition != 1: + file_ext = str(Path(next_population[i]["dataset_reader"]["train"]).suffix) next_population[i]["dataset_reader"]["train"] = "_".join(str(Path(next_population[i]["dataset_reader"][ "train"]).stem.split("_")[:-1])) \ - + "_" + str(iteration % self.train_partition) + ".csv" + + "_" + str(iteration % self.train_partition) + file_ext try: # re-init learning rate with the final one (works for KerasModel) next_population[i] = self.insert_value_or_dict_into_config( @@ -291,9 +292,10 @@ def next_generation(self, generation, scores, iteration): for i in range(self.n_saved_best_pretrained, self.population_size): # if several train files if self.train_partition != 1: + file_ext = str(Path(next_population[i]["dataset_reader"]["train"]).suffix) next_population[i]["dataset_reader"]["train"] = "_".join(str(Path(next_population[i]["dataset_reader"][ "train"]).stem.split("_")[:-1])) \ - + "_" + str(iteration % self.train_partition) + ".csv" + + "_" + str(iteration % self.train_partition) + file_ext for which_path in ["save_path", "load_path"]: next_population[i] = self.insert_value_or_dict_into_config( next_population[i], From 93319a40cc7233537ead0bd440ac12aad7c2dc6c Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Fri, 22 Jun 2018 18:32:07 +0300 Subject: [PATCH 259/616] docs: parameters of evolution described --- deeppavlov/models/evolution/README.md | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index 600699c679..b9c733e9b7 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -5,7 +5,21 @@ This repository contains implementation of parameters evolution for DeepPavlov models. - +Evolution process can be described in the following way: +* Initialize parameters of evolutionary process: + - p_size - number of individuums (models) per population + - key_main_model - key of the dictionary in config containing the model being trained. + - p_cross - probability of crossover for a parent pair + - pow_cross - crossover power - portion of evolving parameters that will be exchanged between parents during crossover + - p_mut - probability of mutation for a parameter + - pow_mut - mutation power - maximal portion of maximal possible value of parameter which can be added or subtracted during mutation + - gpus - available GPUs divided by comma "," (default "-1" means CPU support; "0,3,5,2" means visible 0, 2, 3, 5 GPUs) + - train_partition - if train file is too big to train (recommeded to divide train files if train dataset is more than 100 thousands examples), one can split it in train_partition number of files, save it calling "any_name_{0}.any_extension", ..., "any_name_{train_partition}.any_extension". In dataset_reader "train" field indicate the first one file. Population is trained on the $N_{population} % train\_partition$ part of the dataset. + - start_from_population - the number of population to start from that is needed to restart population, for example (by feault, starts from 0 population). + - path_to_population - path to the directory "population_{start_from_population}". Should be given if start_from_population is not 0. + - elitism_with_weights - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not + +## Example If one prefers to run evolution on some provided by DeepPavlov dataset, firstly, download embeddings and datasets running the following command providing From 4f978da81a59b7815a19d862e4c02c3467b556ba Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Fri, 22 Jun 2018 18:34:42 +0300 Subject: [PATCH 260/616] docs: evolve parameters --- deeppavlov/models/evolution/README.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index b9c733e9b7..07909cc8e0 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -7,17 +7,17 @@ This repository contains implementation of parameters evolution for DeepPavlov m Evolution process can be described in the following way: * Initialize parameters of evolutionary process: - - p_size - number of individuums (models) per population - - key_main_model - key of the dictionary in config containing the model being trained. - - p_cross - probability of crossover for a parent pair - - pow_cross - crossover power - portion of evolving parameters that will be exchanged between parents during crossover - - p_mut - probability of mutation for a parameter - - pow_mut - mutation power - maximal portion of maximal possible value of parameter which can be added or subtracted during mutation - - gpus - available GPUs divided by comma "," (default "-1" means CPU support; "0,3,5,2" means visible 0, 2, 3, 5 GPUs) - - train_partition - if train file is too big to train (recommeded to divide train files if train dataset is more than 100 thousands examples), one can split it in train_partition number of files, save it calling "any_name_{0}.any_extension", ..., "any_name_{train_partition}.any_extension". In dataset_reader "train" field indicate the first one file. Population is trained on the $N_{population} % train\_partition$ part of the dataset. - - start_from_population - the number of population to start from that is needed to restart population, for example (by feault, starts from 0 population). - - path_to_population - path to the directory "population_{start_from_population}". Should be given if start_from_population is not 0. - - elitism_with_weights - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not + - `p_size` - number of individuums (models) per population + - `key_main_model` - key of the dictionary in config containing the model being trained. + - `p_cross` - probability of crossover for a parent pair + - `pow_cross` - crossover power - portion of evolving parameters that will be exchanged between parents during crossover + - `p_mut` - probability of mutation for a parameter + - `pow_mut` - mutation power - maximal portion of maximal possible value of parameter which can be added or subtracted during mutation + - `gpus` - available GPUs divided by comma "," (default "-1" means CPU support; "0,3,5,2" means visible 0, 2, 3, 5 GPUs) + - `train_partition` - if train file is too big to train (recommeded to divide train files if train dataset is more than 100 thousands examples), one can split it in `train_partition` number of files, save it calling "any_name_{0}.any_extension", ..., "any_name_{`train_partition`}.any_extension". In dataset_reader "train" field indicate the first one file. Population is trained on the N_{population} % `train_partition` part of the dataset. + - `start_from_population` - the number of population to start from that is needed to restart population, for example (by feault, starts from 0 population). + - `path_to_population` - path to the directory "population_{`start_from_population`}". Should be given if `start_from_population` is not 0. + - `elitism_with_weights` - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not ## Example From f151517091fc0f168a12ef023f72ea2731b50105 Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Fri, 22 Jun 2018 18:50:25 +0300 Subject: [PATCH 261/616] docs: evolution description --- deeppavlov/models/evolution/README.md | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index 07909cc8e0..b0e22f5b7b 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -8,7 +8,7 @@ This repository contains implementation of parameters evolution for DeepPavlov m Evolution process can be described in the following way: * Initialize parameters of evolutionary process: - `p_size` - number of individuums (models) per population - - `key_main_model` - key of the dictionary in config containing the model being trained. + - `key_main_model` - key of the dictionary in config containing the model being trained (see description below). - `p_cross` - probability of crossover for a parent pair - `pow_cross` - crossover power - portion of evolving parameters that will be exchanged between parents during crossover - `p_mut` - probability of mutation for a parameter @@ -19,6 +19,20 @@ Evolution process can be described in the following way: - `path_to_population` - path to the directory "population_{`start_from_population`}". Should be given if `start_from_population` is not 0. - `elitism_with_weights` - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not +* Current version allows to evolve any parameter of the config that is an item of some dictionary in config file. One can make a copy of a usual DeepPavlov model config, and reassign parameters that can be tuned during evolution. +To evolve some parameter one has to assign it to a dictionary of one of the following type: + - ```{"evolve_range": [min_value, max_value]}``` - values uniformly distributed on the given interval, + - ```{"evolve_range": [min_value, max_value], "scale": "log"}``` - values distributed on the given interval logariphmically, + - ```{"evolve_range": [min_value, max_value], "discrete": true}``` - discrete values uniformly distributed on the following interval, + - ```{"evolve_bool": true}``` - bool values, + - ```{"evolve_choice": true, "values": [value_0, ..., value_n]}``` - values uniformly taking on of the given values. + +* Choose the main model in the pipe being evolved. Find or add extra parameter that determines this model (for example, existing `"main": true`). The dictionary - model containing this parameter as a key will be trained (do not forget to give this parameter's name to `key_main_model`). Change `save_path` and `load_path` of this model to any ABSOLUTE paths (VERY IMPORTANT) to folder where population will be saved. + +* All the models in pipe that contain key `fit_on` will be trained every time separately for each model and saved to the same directory with model and called `fitted_model_{i}`. + +That's all you need to change in the config. Now let's mode on to the example. + ## Example If one prefers to run evolution on some provided by DeepPavlov dataset, From 58b6d405a8315bf576efeb49b1ca67e9f1aa64b7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 11:39:56 +0300 Subject: [PATCH 262/616] fix: possibility of setting relative path --- deeppavlov/evolve.py | 21 ++++++++++++------- .../evolution/evolution_param_generator.py | 2 -- 2 files changed, 14 insertions(+), 9 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index a51ade3905..796089c798 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -30,6 +30,7 @@ from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution from deeppavlov.core.common.file import read_json, save_json from deeppavlov.core.common.log import get_logger +from deeppavlov.core.commands.utils import set_deeppavlov_root, expand_path log = get_logger(__name__) @@ -100,9 +101,15 @@ def main(): evolve_metric = considered_metrics[0] # Create table variable for gathering results - result_file = Path(evolution.get_value_from_config(evolution.basic_config, - evolution.main_model_path + ["save_path"]) - ).joinpath("result_table.csv") + set_deeppavlov_root(evolution.basic_config) + + expand_path(Path(evolution.get_value_from_config( + evolution.basic_config, evolution.main_model_path + ["save_path"]))).mkdir(parents=True, exist_ok=True) + + result_file = expand_path(Path(evolution.get_value_from_config(evolution.basic_config, + evolution.main_model_path + ["save_path"]) + ).joinpath("result_table.csv")) + result_table_columns = [] result_table_dict = {} for el in considered_metrics: @@ -130,8 +137,8 @@ def main(): population = [] for i in range(population_size): - population.append(read_json(Path(path_to_population).joinpath( - "model_" + str(i)).joinpath("config.json"))) + population.append(read_json(expand_path(Path(path_to_population).joinpath( + "model_" + str(i)).joinpath("config.json")))) population[i] = evolution.insert_value_or_dict_into_config( population[i], evolution.main_model_path + ["save_path"], str(Path( @@ -197,8 +204,8 @@ def run_population(population, evolution, gpus): for j in range(len(gpus)): i = k * len(gpus) + j if i < population_size: - save_path = Path(evolution.get_value_from_config(population[i], - evolution.main_model_path + ["save_path"])).parent + save_path = expand_path(Path(evolution.get_value_from_config( + population[i], evolution.main_model_path + ["save_path"])).parent) save_path.mkdir(parents=True, exist_ok=True) f_name = save_path.joinpath("config.json") diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 5fef6d9f1a..07acebf027 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -52,8 +52,6 @@ def __init__(self, self.basic_config = deepcopy(kwargs) self.main_model_path = list(self.find_model_path(self.basic_config, key_main_model))[0] - Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, - exist_ok=True) log.info("Main model path in config: {}".format(self.main_model_path)) self.population_size = population_size From 9cb52b2e37c4dd0c6128aac124119edee0174e75 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 11:42:02 +0300 Subject: [PATCH 263/616] fix: out file path expanded --- deeppavlov/evolve.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 796089c798..25b7c8801b 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -257,9 +257,9 @@ def results_to_table(population, evolution, considered_metrics, result_file, res for m in considered_metrics: population_metrics[m] = [] for i in range(population_size): - with open(str(Path(evolution.get_value_from_config( + with open(str(expand_path(Path(evolution.get_value_from_config( population[i], - evolution.main_model_path + ["save_path"])).parent.joinpath("out.txt")), "r") as fout: + evolution.main_model_path + ["save_path"])).parent.joinpath("out.txt"))), "r") as fout: reports_data = fout.read().splitlines()[-2:] reports = [] for i in range(2): From 95e119c52e3de520055abd0fad3998f0c5d132bf Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 12:21:04 +0300 Subject: [PATCH 264/616] fix: considered metrics --- .../evolution/evolve_intents_snips.json | 34 +++- .../evolution/intents_snips_local.json | 165 ------------------ deeppavlov/evolve.py | 6 + 3 files changed, 31 insertions(+), 174 deletions(-) delete mode 100644 deeppavlov/configs/evolution/intents_snips_local.json diff --git a/deeppavlov/configs/evolution/evolve_intents_snips.json b/deeppavlov/configs/evolution/evolve_intents_snips.json index ec1c0696b0..2728d2523b 100644 --- a/deeppavlov/configs/evolution/evolve_intents_snips.json +++ b/deeppavlov/configs/evolution/evolve_intents_snips.json @@ -56,7 +56,7 @@ "name": "fasttext", "save_path": "embeddings/dstc2_fastText_model.bin", "load_path": "embeddings/dstc2_fastText_model.bin", - "dim": 300 + "dim": 100 }, { "id": "my_tokenizer", @@ -91,15 +91,28 @@ ], "discrete": true }, - "confident_threshold": 0.5, + "confident_threshold": { + "evolve_choice": true, + "values": [ + 0.5, + 1 + ] + }, "optimizer": "Adam", "lear_rate": { "evolve_range": [ 0.0001, 0.1 - ] + ], + "scale": "log" + }, + "lear_rate_decay": { + "evolve_range": [ + 0.0001, + 0.1 + ], + "scale": "log" }, - "lear_rate_decay": 0.1, "loss": "binary_crossentropy", "text_size": 15, "coef_reg_cnn": { @@ -152,11 +165,14 @@ ], "discrete": true }, - "metrics": [ - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], + "metrics": { + "evolve_choice": true, + "values": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ] + }, "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, diff --git a/deeppavlov/configs/evolution/intents_snips_local.json b/deeppavlov/configs/evolution/intents_snips_local.json deleted file mode 100644 index 3563448704..0000000000 --- a/deeppavlov/configs/evolution/intents_snips_local.json +++ /dev/null @@ -1,165 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "intents", - "data_path": "/home/dilyara/data/data_files/snips/snips_dataset", - "train": "train.csv", - "valid": "valid.csv", - "test": "test.csv" - }, - "dataset_iterator": { - "name": "basic_classification_iterator", - "seed": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - } - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara/data/data_files/snips/snips_dataset/classes.dict", - "load_path": "/home/dilyara/data/data_files/snips/snips_dataset/classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", - "load_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "intent_model", - "save_path": "/home/dilyara/data/models/evolution_data/snips_classification/param_evolution_0", - "load_path": "/home/dilyara/data/models/evolution_data/snips_classification/param_evolution_0", - "classes": "#classes_vocab.keys()", - "kernel_sizes_cnn": [ - 1, - 2, - 3 - ], - "filters_cnn": { - "evolve_range": [ - 5, - 50 - ], - "discrete": true - }, - "confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": { - "evolve_range": [ - 0.0001, - 0.1 - ] - }, - "lear_rate_decay": 0.1, - "loss": "binary_crossentropy", - "text_size": 51, - "to_evolve": true, - "coef_reg_cnn": 1e-4, - "coef_reg_den": 1e-4, - "dropout_rate": { - "evolve_range": [ - 0.1, - 0.9 - ] - }, - "dense_size": { - "evolve_range": [ - 5, - 50 - ], - "discrete": true - }, - "model_name": "cnn_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer", - "check_bool": { - "evolve_bool": true - }, - "check_choice": { - "evolve_choice": true, - "values": [ - 1, - 2, - 3 - ] - }, - "check_choice_str": { - "evolve_choice": true, - "values": [ - "hello", - "hello, again", - "bye-bye" - ] - } - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": 1, - "batch_size": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - }, - "metrics": [ - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 1, - "log_every_n_epochs": 1, - "show_examples": false, - "validate_best": true, - "test_best": true - } -} diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 25b7c8801b..ea94f81127 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -98,6 +98,12 @@ def main(): considered_metrics = evolution.get_value_from_config(evolution.basic_config, list(evolution.find_model_path( evolution.basic_config, "metrics"))[0] + ["metrics"]) + if type(considered_metrics) is dict: + considered_metrics = evolution.sample_params(considered_metrics)["metrics"] + if type(considered_metrics) is str: + considered_metrics = [considered_metrics] + + log.info(considered_metrics) evolve_metric = considered_metrics[0] # Create table variable for gathering results From d197de308862f6b5560bc6bf813296d51522b12d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 12:23:05 +0300 Subject: [PATCH 265/616] fix: considered metrics --- deeppavlov/evolve.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index ea94f81127..e88d45a2cb 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -99,7 +99,7 @@ def main(): list(evolution.find_model_path( evolution.basic_config, "metrics"))[0] + ["metrics"]) if type(considered_metrics) is dict: - considered_metrics = evolution.sample_params(considered_metrics)["metrics"] + considered_metrics = evolution.sample_params(**{"metrics": considered_metrics})["metrics"] if type(considered_metrics) is str: considered_metrics = [considered_metrics] From fdc9df797ecdb3173ba2c4e755f7af93bb53995b Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Mon, 25 Jun 2018 12:26:07 +0300 Subject: [PATCH 266/616] docs: example --- deeppavlov/models/evolution/README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index b0e22f5b7b..e05c5255e2 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -7,7 +7,7 @@ This repository contains implementation of parameters evolution for DeepPavlov m Evolution process can be described in the following way: * Initialize parameters of evolutionary process: - - `p_size` - number of individuums (models) per population + - `p_size` - number of individuals (models) per population - `key_main_model` - key of the dictionary in config containing the model being trained (see description below). - `p_cross` - probability of crossover for a parent pair - `pow_cross` - crossover power - portion of evolving parameters that will be exchanged between parents during crossover @@ -35,18 +35,18 @@ That's all you need to change in the config. Now let's mode on to the example. ## Example -If one prefers to run evolution on some provided by DeepPavlov dataset, -firstly, download embeddings and datasets running the following command providing -corresponding name of the config file (see above): +* If one prefers to run evolution on some provided by DeepPavlov dataset, +firstly, download embeddings and datasets. +Consider parameters evolution on SNIPS dataset, download data running the following command providing +corresponding name of the config file: ``` cd deeppavlov python deep.py download configs/intents/intents_snips.json ``` - -To evolve model of interest run the following command providing corresponding name of the config file (see above): +* To evolve the model run the following command providing corresponding name of the config file (see above): ``` cd deeppavlov -python evolve.py interact configs/evolution/evolve_intents_snips.json +python evolve.py configs/evolution/evolve_intents_snips.json ``` - +* Folder `download/evolution/classification/intents_snips` will be created. Each population will be saved in a folder `download/evolution/classification/intents_snips/population_i` each of which contains `population_size` folders `model_i` consisting of saved model files explicitly, saved files of models from pipe that has a key "fit_on", `out.txt` and `err.txt` with logs of `deep.py train` script from training each model separately, and `config.json` with config for this individual. From 17f72ac49c397ff69a8916df9ea0a40b81dde609 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 12:34:03 +0300 Subject: [PATCH 267/616] fix: considered metrics can not be evolved --- .../configs/evolution/evolve_intents_snips.json | 13 +++++-------- deeppavlov/evolve.py | 3 +-- 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/deeppavlov/configs/evolution/evolve_intents_snips.json b/deeppavlov/configs/evolution/evolve_intents_snips.json index 2728d2523b..a06fd09318 100644 --- a/deeppavlov/configs/evolution/evolve_intents_snips.json +++ b/deeppavlov/configs/evolution/evolve_intents_snips.json @@ -165,14 +165,11 @@ ], "discrete": true }, - "metrics": { - "evolve_choice": true, - "values": [ - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ] - }, + "metrics": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index e88d45a2cb..6e5cbf1c4f 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -98,8 +98,7 @@ def main(): considered_metrics = evolution.get_value_from_config(evolution.basic_config, list(evolution.find_model_path( evolution.basic_config, "metrics"))[0] + ["metrics"]) - if type(considered_metrics) is dict: - considered_metrics = evolution.sample_params(**{"metrics": considered_metrics})["metrics"] + if type(considered_metrics) is str: considered_metrics = [considered_metrics] From 37214508a7e377d7fcba4fe85f6b3cc5ed8f1ac5 Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Mon, 25 Jun 2018 12:35:59 +0300 Subject: [PATCH 268/616] fix: metrics can not evolve --- deeppavlov/models/evolution/README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index e05c5255e2..d7159696f7 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -19,6 +19,8 @@ Evolution process can be described in the following way: - `path_to_population` - path to the directory "population_{`start_from_population`}". Should be given if `start_from_population` is not 0. - `elitism_with_weights` - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not +* **Warning**: `metrics` can not be evolved because the main metric determines evolutionary process. + * Current version allows to evolve any parameter of the config that is an item of some dictionary in config file. One can make a copy of a usual DeepPavlov model config, and reassign parameters that can be tuned during evolution. To evolve some parameter one has to assign it to a dictionary of one of the following type: - ```{"evolve_range": [min_value, max_value]}``` - values uniformly distributed on the given interval, @@ -37,7 +39,6 @@ That's all you need to change in the config. Now let's mode on to the example. * If one prefers to run evolution on some provided by DeepPavlov dataset, firstly, download embeddings and datasets. - Consider parameters evolution on SNIPS dataset, download data running the following command providing corresponding name of the config file: ``` From 77df99ce53ae2f3c59abb2592f5979c801f16216 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 12:43:33 +0300 Subject: [PATCH 269/616] feat: new params in config --- deeppavlov/configs/evolution/evolve_intents_snips.json | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/configs/evolution/evolve_intents_snips.json b/deeppavlov/configs/evolution/evolve_intents_snips.json index a06fd09318..9c9f849edf 100644 --- a/deeppavlov/configs/evolution/evolve_intents_snips.json +++ b/deeppavlov/configs/evolution/evolve_intents_snips.json @@ -86,8 +86,8 @@ ], "filters_cnn": { "evolve_range": [ - 5, - 50 + 50, + 100 ], "discrete": true }, @@ -135,8 +135,8 @@ }, "dense_size": { "evolve_range": [ - 5, - 50 + 50, + 100 ], "discrete": true }, From 4df783fbccd3363c30d3ebbd39028ba080833dee Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Mon, 25 Jun 2018 12:45:19 +0300 Subject: [PATCH 270/616] docs: where to see results --- deeppavlov/models/evolution/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index d7159696f7..db753703bf 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -51,3 +51,5 @@ cd deeppavlov python evolve.py configs/evolution/evolve_intents_snips.json ``` * Folder `download/evolution/classification/intents_snips` will be created. Each population will be saved in a folder `download/evolution/classification/intents_snips/population_i` each of which contains `population_size` folders `model_i` consisting of saved model files explicitly, saved files of models from pipe that has a key "fit_on", `out.txt` and `err.txt` with logs of `deep.py train` script from training each model separately, and `config.json` with config for this individual. + +* Now one can open iPython Notebook file `deeppavlov/evolution/Results_analysis.ipynb`, set `CONFIG_FILE` to config file path and run cells to see evolution results. From 7fc10a74ee06f7cf1e6eaa2ba877721a75034533 Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Mon, 25 Jun 2018 12:46:12 +0300 Subject: [PATCH 271/616] docs: where to see results --- deeppavlov/models/evolution/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index db753703bf..7698ea5a93 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -52,4 +52,4 @@ python evolve.py configs/evolution/evolve_intents_snips.json ``` * Folder `download/evolution/classification/intents_snips` will be created. Each population will be saved in a folder `download/evolution/classification/intents_snips/population_i` each of which contains `population_size` folders `model_i` consisting of saved model files explicitly, saved files of models from pipe that has a key "fit_on", `out.txt` and `err.txt` with logs of `deep.py train` script from training each model separately, and `config.json` with config for this individual. -* Now one can open iPython Notebook file `deeppavlov/evolution/Results_analysis.ipynb`, set `CONFIG_FILE` to config file path and run cells to see evolution results. +* Now one can open iPython Notebook file `deeppavlov/models/evolution/Results_analysis.ipynb`, set `CONFIG_FILE` to config file path and run cells to see evolution results. From 56d5ae4036c30878d35ef5618813a8186688c193 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 12:46:52 +0300 Subject: [PATCH 272/616] feat: results analysis --- .../models/evolution/Results_analysis.ipynb | 1050 +++++++++++++++++ 1 file changed, 1050 insertions(+) create mode 100644 deeppavlov/models/evolution/Results_analysis.ipynb diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb new file mode 100644 index 0000000000..2ea149ff27 --- /dev/null +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -0,0 +1,1050 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "import copy\n", + "import json\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of populations: 62\n" + ] + } + ], + "source": [ + "PLOT_TEST = False\n", + "\n", + "TITLE = \"imdb_given_mask_init_part_7\"\n", + "model_index = 4\n", + "POPULATION_SIZE = 10\n", + "\n", + "# TITLE = \"sber_faq_given_mask_init_part_7\"\n", + "# model_index = 4\n", + "# POPULATION_SIZE = 10\n", + "\n", + "# TITLE = \"ag_news_given_mask_init_part_7\"\n", + "# model_index = 4\n", + "# POPULATION_SIZE = 10\n", + "\n", + "# TITLE = \"snli_given_mask_init_part_6\"\n", + "# model_index = 4\n", + "# POPULATION_SIZE = 10\n", + "\n", + "# TITLE = \"snli_given_mask_init_part_many_inputs_6\"\n", + "# model_index = 5\n", + "# POPULATION_SIZE = 10\n", + "\n", + "# TITLE = \"twitter140_one_neuron_init_part_6\"\n", + "# model_index = 4\n", + "# POPULATION_SIZE = 10\n", + "\n", + "data = pd.read_csv(\"result_tables/result_table_\" + TITLE + \".csv\", sep='\\t')\n", + "print(\"Number of populations: {}\".format(int(data.shape[0] / POPULATION_SIZE)))\n", + "# data.dropna(axis=1, how=\"any\", inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "classification_log_loss: best value for VALID \t0 individuum on 0 population\n", + "classification_log_loss: best value for TEST \t0 individuum on 0 population\n", + "classification_accuracy: best value for VALID \t3 individuum on 56 population\n", + "classification_accuracy: best value for TEST \t3 individuum on 55 population\n", + "classification_roc_auc: best value for VALID \t9 individuum on 61 population\n", + "classification_roc_auc: best value for TEST \t9 individuum on 61 population\n", + "classification_f1: best value for VALID \t3 individuum on 56 population\n", + "classification_f1: best value for TEST \t3 individuum on 55 population\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:11: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", + " # This is added back by InteractiveShellApp.init_path()\n", + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:12: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", + " if sys.path[0] == '':\n" + ] + } + ], + "source": [ + "MEASURES = [\"classification_log_loss\", \n", + " \"classification_accuracy\",\n", + " \"classification_roc_auc\", \n", + " \"classification_f1\"]\n", + "for measure in MEASURES:\n", + " if (measure == \"classification_log_loss_test\" \n", + " or measure == \"classification_log_loss_valid\"):\n", + " n_best_valid = data[measure + \"_valid\"].argmin()\n", + " n_best_test = data[measure + \"_test\"].argmin()\n", + " else:\n", + " n_best_valid = data[measure + \"_valid\"].argmax()\n", + " n_best_test = data[measure + \"_test\"].argmax()\n", + " print(\"{}: best value for VALID \\t{} individuum on {} population\".format(measure, \n", + " n_best_valid % POPULATION_SIZE, \n", + " n_best_valid // POPULATION_SIZE))\n", + " print(\"{}: best value for TEST \\t{} individuum on {} population\".format(measure, \n", + " n_best_test % POPULATION_SIZE, \n", + " n_best_test // POPULATION_SIZE))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "cmap = plt.get_cmap('rainbow')\n", + "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", + "color_ids = np.argsort(data.loc[:, \"classification_accuracy_valid\"].values)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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BvwT+b30qlHTnN6GjG9qX5O/Htnd+015tSZIOZjb0aB9HruNHlSEbIKV0DdALHF2PwiRle7ZA2+LqtrbFuV2SJE1sNgTtnwFDwLMj4qjKHRFxHtAF/Gc9CpOULV0LA/uq2wb25XZJkjSxugftlNJu4I+BlcDGiLg0Ij4YEf8PuAr4DvCOetYoLXRPfTn09eSx2amUt309uV2SJE2s7mO0AVJKH4uITcC/AG+v2HUf8JnxQ0rGRMQlwCUA69atK7pMacFafTqc/9vVs448682Oz5Yk6VAipVTvGoiI9wAfAD4BfBLYDpwCfBB4MfChlNJ7DnWOs88+O918881FlypJkqQFLiJuSSmdfbjj6j50JCIuAP4auCyl9AcppQdSSn0ppVuBVwNbgT+MiBPqWackSZJUi7oHbWBslOc143eklPqAm8h1njmTRUmSJElHYjYE7dby9mBT+I21D81ALZIkSdK0mA1B+3vl7SURsaZyR0S8FHguMAD8YKYLkyRJkqZqNsw68h/kebJfCNwdEV8lXwx5KnlYSQD/LaW0q34lSpIkSbWpe9BOKZUi4mLgt4HXky+A7AB2A5cDn0gpXVXHEiVJkqSa1T1oA6SUhoGPlb8kSZKkOW82jNGWJEmS5h2DtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklSAugftiHhLRKTDfI3Wu05JkiSpFk31LgC4HfiLg+x7PvALwBUzV44kSZJ05OoetFNKt5PD9pNExA/L/7x05iqSJEmSjlzdh44cTEScAZwLbAW+VedyJEmSpJrM2qANXFLe/nNKyTHakiRJmlNmZdCOiHbgzcAo8L/rXI4kSZJUs1kZtIFfAZYAV6aUHj7YQRFxSUTcHBE379y5c+aqkyRJkg5jtgbtsWEjnzrUQSmlS1NKZ6eUzj766KNnoCxJkiRpcmZd0I6I04GfB7YAl9e5HEmSJGlKZl3QxosgJUmSNA/MqqAdEW3Ar5IvgvznOpcjSZIkTdmsCtrAa4GlwBWHughSkiRJmu1mW9AeGzbiSpCSJEma02ZN0I6IU4Hn4UWQkiRJmgea6l3AmJTS3UDUuw5JkiRpOsyaHm1JkiRpPjFoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQVoqncBkiRJql3P6C62jT5If+qlPbpY3Xg83Y3L612WKtijLUmSNMf0jO7ivuE7GEqDtLGIoTTIfcN30DO6q96lqYJBW5IkaY7ZNvogTdFKS7QSEbREK03RyrbRB+tdmioYtCVJkuaY/tRLMy1Vbc200J9661SRJuIYbWmB21Xaw6b0MPvpYxEdrI9jWd6wtN5lSVoASvu2kB69FQZ2Q9syYsVZNCxeW++y5oT26GIoDdJC6+NtwwzRHl11rErj2aMtLWC7SnvYkO5mMA3RmdoZTENsSHezq7Sn3qVJmudK+7aQNl9FGu4jtS7N281XUdq3pd6lzQmrG49nJA0ylAZJKTGUBhlJg6xuPL7epamCPdrSArYpPUxLaqE18sePrbRAgk08zHLs1ZZUnPToraSmDqK5Izc0d5AAHr0VFniv9j39g1zZ28+24VFWNzdyUVc7p7S3Vh3T3bick3h61awj65tOcdaRWcagLS1g++mjk/aqthaa2U9fnSqStGAM7IbWcW/om9pz+wJ2T/8gn97dy+KGBlY1NdAzWuLTu3t5+zImDNsG69nNoC0tYIvoYJCh3JNdNsQwi+ioY1WSFoS2ZbB7K7FrOwzsh7ZFpOWrYNmaeldWV1f29rO4oYHuxjy6t7sxHm8fH7Q1+zlGW1rA1sexDMUQg2mIlBKDaYihGGJ9HFvv0iTNc9G0gobNd8LgAWjthMEDNGy+k2haUe/S6mrb8ChdDVHV1tUQbBserVNFOhIGbWkBW96wlDPiVFqjhQPRT2u0cEac6qwjkgrXsOMBYumJOWSPDkBrJ7H0RBp2PFDv0upqdXMjvaVU1dZbSqxubqxTRToSDh2RFrjlDUu98FHSzOvdSSxaQcSqJ9pSCXp31q+mWeCirnY+vTvPhd3VEPSWEvtKJV63pLPOlWkq7NGWJEkzr+toGBp34fVQX25fwE5pb+Xty7robmxg+0iJ7sYG3r6sy/HZc5Q92tI8tbFviCt6+tk6PMqa5kZe2t3OaR0th7+hJM2EE58Dt30t/7ulI4fswQNw2ovqW9cscEp7q8F6nrBHW5qHNvYN8amd++kZLXFMeXqoT+3cz8a+oXqXJknZUcfDma+C1kWwf1fenvmq3C7NE7OqRzsiLgR+B3gOsBTYBWwAPp5SuryetUlzyRU9/XQ3xrjpoUpc0dNvr7ak2eOo4w3WmtdmTdCOiL8B3g1sAS4DHgOOBp4JXAAYtKVJ2jo8yjFN1R9YdTUEW50eSpKkGTMrgnZEvJ0csj8LXJJSGhq3v7kuhUlz1JrmRnpGS48vdAB5eqg1Tg8lSdKMqfsY7YhoBf4KeIgJQjZASml4xguT5rCXdrfTM5roGS1RSnnbM5p4aXf74W8sSZKmxWzo0X4ReYjIx4BSRLwMeCowANyUUvphPYuT5qLTOlp4x9GLqmYdef0yZx2RJGkmzYag/azydgC4jRyyHxcR1wO/nFJa2DPYSzU6raPFYC1JUh3VfegIsKK8fTeQgOcDXcDTgKuA84B/n+iGEXFJRNwcETfv3GkOlyRJqrRndBcbhm/lxuHvsWH4VvaM7qp3SQvKbAjaYzWMAK9IKX0/pbQ/pbQBeDV5FpLzI+I542+YUro0pXR2Sunso49e2CtJSZIkVdozuot7SncyxCAddDDEIPeU7jRsz6DZELT3lre3pZQ2Ve5IKfUB3y5/++yZLEqSJGku21LaTAsttEQrEUFLtNJCC1tKm+td2oIxG4L2T8vbvQfZv6e8dboESZKkSTrAAZqpvlanmRYOcKBOFS08syFof5c8Nvu0iJionrGLIx+cuZIkSZLmtk46GaZ61uRhhuiks04VLTx1D9oppc3AN4B1wO9V7ouIFwMvIfd2Xznz1UmSJM1NaxuOY4ghhtIgKSWG0iBDDLG24bh6l7ZgzIbp/QB+GzgT+Eh5Hu3bgOOBVwGjwG+mlHrqWJ8kSdKcsrRxOafwVLaUNnOAA3TSyQkNT2Fp4/J6l7ZgzIqgnVLaEhHPBP4ceAV5Sr995J7uD6aUbqpnfZIkSXPR0sblBus6mhVBG6C8IM3vlr8kSZKkOW3WBG1JkjQ/3Le1xLV3lNi+B1YthQue3sBJa+p+WZg04wzakuri4dJ+bkm72ZUGWR6tPDOWcWzDoicd1z+4nX199zA80kNzUzeLO06hvXVVHSqWNBn3bS3xhatHWdwOK5ZAbx984epR3vQLGLa14PiKlzTjHi7t58rSNg6kEZbRwoE0wpWlbTxc2l91XP/gdh7ruZHR0X6aGhczOtrPYz030j+4vU6VSzqca+8osbgdujqChgi6OoLF7bldWmgM2pJm3C1pNx000RlNRASd0UQHTdySdlcdt6/vHhobWmlsbCciaGxsp7GhlX1999SpckmHs30PdI5bYq6zPbdLC41BW9KM25UG6aCxqq2DRnalwaq24ZEeGhraqtoaGtoYHnG2T2m2WrUUDvRXtx3oz+3SQmPQljTjlkcrfYxWtfUxyvJorWprbuqmVBqoaiuVBmhu6i68RklTc8HTG9jXD719iVJK9PYl9vXndmmh8VUvacY9M5bRxwgH0ggpJQ6kEfoY4ZmxrOq4xR2nMFoaZHS0n5QSo6P9jJYGWdxxSp0ql3Q4J61p4E2/0EhXR/Do3jxG+02/0OiFkFqQnHVE0ow7tmERF7G6ataR82LFk2YdaW9dxVHd51bNOrK060xnHZFmuZPWOJ2fBAZtSXVybMMijuXJ0/mN1966ymAtSZqTfLspSZIkFcCgLUmSJBXAoC1JkiQVwKAtSZIkFcCgLUmSJBXAoC1JkiQVwKAtSZIkFcCgLUmSJBXAoC1JkiQVwKAtSZIkFcCgLUmSJBXAoC1JkiQVwKAtSZIkFcCgLUmSJBXAoC1JkiQVwKAtSZIkFcCgLUmSJBXAoC1JkiQVwKAtSZIkFcCgLUmSJBXAoC1JkiQVwKAtSZIkFcCgLUmSJBWgabIHRkQj0JpS6hvX/gvAK4E+4NKU0oPTW6IkSZI099TSo/1hYHdEdI81RMTrge8Avwv8MXBTRBw7vSVKkiRJc08tQfs84JqUUk9F23uBvcCvAe8BlgB/MH3lSZIkSXNTLUH7WOC+sW8i4gTgZODvUkr/N6X0YeAK4KLpLVGSJEmae2oJ2ouBfRXfPxdIwJUVbXcBa6ehLkmSJGlOqyVoPwIcX/H9C4F+4JaKtkXAyDTUJUmSJM1pk551BLgReEVEvBwYAH4Z+G5KabjimOOBrdNYnyRJkjQn1dKj/YHy8V8Hvg20AH81tjMi2oDnAz+azgIlSZKkuWjSPdoppQ0RcQ7w6+Wmf0sp/bjikDOBq4EvTmN9kiRJ0pxUy9ARUkobgD86yL4fAq+ejqIkSZKkuW7KS7BHxFIXp5EkSZImVlPQjohFEfG3EbEdeAx4sGLfORFxeUScNd1FSpIkSXPNpIN2een1HwLvArYBdwNRccgG8sWQb5jOAiVJkqS5qJYe7T8DTgfeklI6C/j3yp0ppT7gOuDC6StPkiRJmptqCdq/BHw7pfS5QxyzGVhzZCVJkiRJc18tQXst8JPDHLMf6J56OZIkSdL8UEvQ7gVWHOaY48kXSUqSJEkLWi1B+8fAyyOia6KdEXEMcDHw/ekoTJIkSZrLagnaHweWA5dHxKmVO8rf/zvQBnxi+sqTJEmS5qZalmD/dkT8BfBe4E5gGCAiHgOWkqf6++OU0g+KKFSSJM0N2zfAxq/B3odgyTo47VWw6ox6VyXNvJoWrEkp/QV5+r7LgD3AKJCAy4EXppQ+NO0VSpKkOWP7Brjho9C/B7rX5u0NH83t0kIz6R7tMSmla4BrCqhFKty2jbDhW7BnCyxdC2e8DFafVu+qJGn+2Pg1aFsC7Uvz92PbjV+zV7se9o08xo6R++lPvbRHFyubTmRx01H1LmvBqKlHW5rLtm2Ea/8B+vbCktV5e+0e8sYuAAAgAElEQVQ/5HZJ0vTY+xC0jZvot607t2tm7Rt5jAeHb2M4DdLGIobTIA8O38a+ESeImykGbS0YG74F7d3QsQSiIW/bu3O7JGl6LFkHAz3VbQM9uV0za8fI/TTTSnO0EhE0RyvNtLJj5P56l7ZgTHroSESUyOOxDyUB+4C7ga8An0wpDU69PGn67NmSe7IrtS/O7ZKk6XHaq/KYbMg92QM9MLAXnvnW+tY1JffdDdddCTu2wso1cP5FcNKph7/dLNGfemljUVVbEy30p94pn3Pr3XDHFbBnKyxdA09/KayZOw/JjKulR/t68sqQAZTIy63fVN6Wyu0bgK3AmcDfADdEROd0FixN1dK10L+vuq1/X26XJE2PVWfAc9+Vx2b3bMnb575rDo7Pvu9u+OKl0NsDRx+Tt1+8NLfPEe3RxQhDVW0jDNE+8ZIoh7X1brjmU9DfA0uPydtrPpXbNbFaLoZ8A3AD8CXgv6WUHh9tFRHrgP8FnAM8FzgAfAT4DeA95CkBpbo642V5TDbknuz+ffmPxDlvqm9dklSUfSM7eXT4AQZSL23RxYrmE1jcdHTh97vqjDkYrMe77kro6s5f8MT2uivnTK/2yqYTeXD4Nki5J3uEIYYZZG3T1GYBuOOK8hDM8kMxtr3jCnu1D6aWHu2/BnanlN5YGbIBUkoPpZTeSJ7y769TSr3AO4F7gddMW7XSEVh9GlzwW3ls9t5teXvBbznriKT5ad/ITjYP3s5wGqS1fCHc5sHb2Teys96lzQ07tkLnuJ7fzq7cPkcsbjqK45vPpDlaGWA/zdHK8c1nTnnWkT1boX3cQ9Lelds1sVp6tF8C/MthjvkO8DaAlNJoRFwP2F+oWWP1aQZrSQvDo8MP0BT5QjiAZlofb5+JXu3ZbPtP4O6vPrGgzqmvhlVPG3fQyjV5uEhXxRQqB3pze9F2bYIHb4TendB1NBx/LixfP6VTLW46atqm81u6Jn8S3FHxkPT35nZNrJYe7S5g8WGO6S4fN2Z3zRVJkhaErRvhyg/DF9+Vt1udanNaDaRemmipamuihYEjuBBuPtj+E/jBR6oX1PnBR3J7lfMvykG7twdKpSf+ff5FxRa4axPc8XUY3A+LluftHV/P7XX29JfmoN3XA6mUt/09uV0TqyVo3wO8LiJWT7QzItYCryPPODLmWGDX1MuTJM1HWzfCtf+U/6NeckzeXvtP8ztsb98AV78fvvL2vC16pcS2g1wI1zbFC+Hmi7u/+sSCOtGQt21LcnuVk06FN1ySe7R3PpK3b7ik+PHZD94IrZ3QuigX2Loof//gjcXe7ySsORVe8I48TnvPI3n7gnc4PvtQahk68rfA54FbI+LvyBdG7gBWAs8DfhdYQr4IkohoAl4IfG86C5akI3FP/yBX7u9n2/AIq5ubuGhRO6e0t9a7rAVnw+UTX1S14XJYMw+Hd40tS962pHpZ8iJn41jRfAKbB28HnrgQbiQNsqZlYaeivQ/l56DSQRfUOenUmb/wsXdn7smu1NKR22eBNacarGsx6aCdUvpCRKwB/ifwl+N2BzAC/FlK6QvltiXAnwM/mo5CJelI3dM/yKd399Ld2MAxTY3sGy3x6d29vH0Zhu0Ztmdr7smuNJ8vqqrHsuSLm47mOJ5RNevImpZTF/z47CXr8hudsecAZtmCOl1H5+EirRXzXw/15XbNObX0aJNS+puI+HfyBY7PII/J3gfcBvxrSumBimMfAz41jbVK0hG5cn8/3Y0NdDfmUXPdjfF4u0F7Zi1dk4eLLJSLqmrqRZ1Gi5uOXvDBerxTX53HZEP1gjpnva2+dT3u+HPzmGzIPdlDfTB4AE55YX3r0pTUFLQBUkoPknu1JWlO2TY8wjFNjVVtXQ3BtuGROlW0cJ1xcR6TDbknu7+3PK/9G+pbV1Hq1Yv6KD3cyzb20c9i2nkKq1lB9+FvOI+tehr8/B9Uzzpy1tsmmHWkXpavh6e/snrWkVNeOOVZR1RfNQdtSZqrVjc3sW+09HhPNkBvKbG62T+FM23NaXDBO/OY7LGlnM95w/wcnw31WZb8UXq4iftoo4ku2hhgiJu4j2dzkmH7adMbrO/bWuLa20ts3wOrlsIFz2jgpDW1zDcxzvL1But54qD/u0TEeVM9aUrp+lqOj4hNwHEH2b0jpbRqqrVI0piLFrXz6d15arOuhqC3lOgZLfEr3Z11rmxhWnPa/A3W440tS77xa0/0oj7zrcWunngv22ijibbyFH9j23vZtuCD9nS6b2uJf/3uKF3tsGIJ9PbBv353lDdeyJGFbc0Lh+rGuRZIUzxv4+EPeZIe4GMTtO+fYg2SVOWU9lbevoyqWUd+pbvT8dmaETO9LPk++umiraqtlWb20T9zRSwA195eoqsdujryJ2VdHQCJa28vPTlo3383XH9lXl1y5Ro47yI40Sk85rNDBe2/5MlB+xzgIuB+4PvAdmAVeXq/E4ErgJumWMvelNL7pnhbSZqUU9pbDdZaEBbTzgBDj/dkAwwyzGLa61jV/LN9T+7JrtTZntur3H83fOnSPB/30cfkxW++dCm8/hLD9jx20KA9PvRGxLnAnwC/B/x9SqlUsa+BPI/2/+LJU/9JkqQZ9hRWcxP3Abkne5BhBhjhaayvb2HzzKqlebhI7snODvTn9irXX5lD9tiS7mPb66+cetDetQk23Qj7H4NFR8H6qS/VrmLUMnjo/cB/ppT+rjJkA6SUSimljwNXM/Wg3RoRb46IP42I34uIF0TEVIagSNKMuad/kI/t3Mt7tj3Gx3bu5Z7+wXqXJAGwgm6ezUm00UIvA7TR4oWQBbjgGQ309kNvX6KUEr19id7+3F5lx1boHLcqZ2dXbp+KXZtgw2V56r/O5Xm74bJZsVS7nlDLpfbPBv7uMMfcDvzOFGtZRV55stKDEfHWlNJ1UzynNCXbNsKGb8GeLbB0LZzxMli9QC7a0uS5AI5muxV0G6wLdtKaBt54IVWzjvziz08w68jKNXm4SFfF83GgN7dPxaYboaXziYVtxrabbrRXexapJWgHeRz2oZw0xTr+D3mp9ruAXuAEcmC/BLgiIp6TUrrjSQVFXFI+hnXrZsuSTprrtm2Ea/8hLw+9ZDX07c3fX/Bbhm1VcwEcSZDD9mFnGDnvojwmG3JP9oHeHLxf9rqp3en+x3JPdqWWjtyuWaOWoSM/AF4TES+faGdEvAL4JeCGWotIKf1FSunqlNKOlFJfSunOlNI7gY8A7cD7DnK7S1NKZ6eUzj76aFe+0vTY8K0csjuWQDTkbXt3bpcqbRseoashqtpcAEfShE48NV/42NUNOx/J2yO5EHLRUXnVyEpDfblds0YtPdp/BlwPfD0iriv/ewewEjgfOA/oLx83Xf4J+MPyuaUZsWdL7smu1L44t0uVXABHUk1OPHX6ZhhZf24ekw1PLNU+dABOvnB6zq9pMen/DVJKt0TEi4B/AS4ofyXykBKAnwK/kVK6bRrr21neupqEZszStXm4SEfFdE39+3K7VMkFcCTVzfL1cMYrqmcdOflCx2fPMjV1u6SUfgCcEhE/D5wFdJMXmrm1vG+6nVvePlDAuaUJnfGyPCYbck92/z7o74Fz3lTfujT7uACOpLpyqfZZb0qfb5ZD9bQE64g4FXgopXRgXPt64JPlb//vdNyXNBmrT8sXPlbOOnLOm7wQUhNzARxJKt621Mud7GAPgyyllaeyktXRdfgb1tmUgnZEdAJPARallL53hDW8DvjDiLge2EyedeRE4GVAG3A58OEjvA+pJqtPM1hLkjQbbEu9XMcmOmhmCa30M8J1bOL8tH7Wh+2agnZErAU+Dvwi0Egeo91U3vc84FLgt1JK19Zw2muAk4EzgeeSx2PvJS/x/nng8yml8UvBS5IkaQG4kx100Ew7zQCPb+9kB6uZJ0E7Io4BfkSeZeQyYAXwnIpDflRuex1w7WTPW16MxgVpJM0q9/QPVo29vmhRu0NENCPu6R/kyt5+tg2Psrq5kYu6fO1pYdvDIEuo/h1oo4k9zP6VeGvp0X4vOUi/KKV0TUS8l4qgnVIajojvkXulJWnOcsXH+e2RO+Guy2DvFliyFk5/BRzz1HpXlY299hY3NLCqqYEeX3uaZbZuhA2Xw56tsHQNnHExrCl4qOXS8nCRsZ5sgAFGWMrs/52oJWhfDFyWUrrmEMc8BDz/yEqSpPpyxcf565E74XufgPYl0L0a+vfm75//X2dH2L6yt5/FDRO89nqLfe3tTHv5GdvopZ8u2vk5VnN0LDn8Dee57T+Bu78Kex+CJevg1FfDqqfVu6pi9I7s5NHh+xko9dLW0MWK5hPpaqpeDHDrRrj2n8orJx8DfT35+wveWWzYfioruY5NQO7JHmCEPoZ5FlNcvn4G1bIy5ErgZ4c5ZhjnvJY0x7ni4/x112U5ZLeXV34d+/ddl9W7smzb8OhBXnujhd3nzrSXm/kZAwyxiDYGGOJmfsbOtLew+5wLtv8EfvAR6N8D3Wvz9gcfye1Pcu9G+McPw5//ft7eu3HG6z0SvSM72Tx4G8OlQVpjEcOlQTYP3kbvyM6q4zZcXl45ubu8cnJ3eeXky4utb3V0cT7raaeJvQzSThPnM/svhITagvZu4NjDHPMUYPvUy5Gk+lvd3ERvqfoabFd8nB/2boG2xdVtbYtz+2ywurnxIK+9xsLu82dso5Vm2mghCNpooZVmfsa2wu5zLrj7q9C2BNqXlt+ULc3f3/3VcQfeuxE+94+wrwdWrs7bz/3jnArbjw7fTxOtNDe0EhE0N7TSRCuPDt9fddyerdA+Ltu2d+X2oq2OLl4cJ/G6OJ0Xx0lzImRDbUH7BuAVEbFqop0R8XPAReRZRCRpzrpoUTs9oyV6RkuUUnr83xctaq93aTpCS9bCwL7qtoF9uX02uKirnX2l6tfevlKJi7qKe+310k9rxdhXgFaa6aW/sPucC/Y+BG3d1W1t3bm9yncvh64lsLgbGhrytmtJbp8jBkq9NEVLVVtTtDBQ6q1qW7oG+qub6O/N7ZpYLUH7Q+R5ra+LiJcCHZDn1C5//w2gBPzttFcpSTMor/jYxeLGBh4ZGWVxYwNvX9bl+Ox54PRX5HHZ/XshlZ749+mvqHdl2dhrr7uxge0jJbpn4LXXRTuDDFe1DTJMFwv7jeWSdTDQU9020JPbqzyyBRaN611d1JXb54i2hi5G0lBV20gaoq2h+uc64+K8UnJfT/796evJ359x8UxWO7dM+nPQlNKPIuIdwD8C36zYNdY3MAK8LaV01zTWJ0l14YqP89MxT80XPlbOOnL2r82OCyHHzPRr7+dYzc3lS7BaaWaQYQYZ5gzWz1gNs9Gpr85jsiH3ZA/0wMBeOOtt4w48Zm0eLrK4ovt7f29unyNWNJ/I5sHboJR7skfSECMMsqa5+grHNaflCx8rZx055w3Fzzoyl0Wta8GUh4j8FnAusBzoAW4EPplS+um0VzhJZ599drr55pvrdfeS6uyBh0p8/+bEjsdg5VHwvLODE9bV8qGdtHAtuFlH9myGLT+Gvseg4yhY+yxYetyTDpvUrCNjY7S7luSe7P290LsXfu2/wFPmTgKdzKwjekJE3JJSOvuwx82XRRcN2tLC9cBDJf798sSizkRnBxzog/0HgtdebNiWNM6ezfDTy6G5I38N9+Wvky+eMGxPyr0b85jsR7bknuwLL55TIVu1m2zQ9hJ6SXPe92/OIburM0+L1tUJkPj+zXDC+PGUkha2LT/OAbulPBvx2HbLj6cetJ9ymsFaE7KrR9Kct+Mx6OyobuvsyO2SVKXvsRy0KzV35HZpmhm0Jc15K4/Kw0UqHejL7ZJUpeOoPFSk0nBfbpemmUFb0pz3vLOD/QeC3gOJUkr0HkjsPxA87+w4/I0lLSxrn5WD9dABSClvh/tyuzTNDNqS5rwT1jXw2ouDrs5g56689UJISRNaely+8LGlE/p35e2RXAgpHYIXQ0qaF05Y1+CFj5ImZ+lxBmvNiIN290TE7oh4T8X3fx4R581MWZIkSdLcdqjPVZeQl1wf8z7ggiKLkSRJkuaLQwXtHcDcWT9UkiRJmkUONUb7RuBXI2IUeKTcdkHEYa/iTyml909HcZIkSdJcdaig/W7gKcA7Ktou4PDDRxJg0JYkSdKCdtCgnVK6LyLOAI4H1gDXAp8BPjsjlUmSJElz2CGn90splYD7gfvLQ0Y2pZSum4nCJEmSpLls0vNop5Rc+UGSJEmapCktWBMRa4EzyVMA9gC3ppS2TGdhkiRJ0lxWU9COiOOATwEvmmDfd4B3ppQ2TU9pkiRJ0tw16aAdEauA75MvjNwEXE+e9u8Y4PnAi4HvR8TZKaXt01+qJEmSNHfU0qP9P8gh+4+Bj6SURsd2REQj8C7gb4D/DvzOdBYpSZIkzTW1XOD4MuCqlNKHKkM2QEppNKX0YeAq4OXTWaAkSZI0F9UStFcBtxzmmFvKx0mSJEkLWi1Buwc47jDHrCsfJ0mSJC1otQTt7wO/HBE/P9HOiDgHeG35OEmSJGlBq+ViyL8ij9O+LiK+BFxDnnVkFXAB8AagBHxgmmuUJEmS5pxaVoa8NSJ+Gfgs8CbgjRW7A9gNvC2ldLhx3JIkSdK8V9OCNSmlb0bEOuCVwFlAN3lM9m3A11JKB6a/REmSJGnuqXkJ9nKY/tfylyRJkqQJ1HIxpCRJkqRJMmhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQWYdNCOiKsj4v1FFiNJkiTNF7X0aJ8LNBZViCRJkjSf1BK0fwYcW1QhkiRJ0nxSS9D+38DLykuwS5IkSTqEWpZg/wbwIuCGiPhr4MfAdiCNPzCl9ND0lCdJkiTNTbUE7QfIoTqAjx/iuFTjeSVJkqR5p5ZA/Dkm6L2WJEmS9GSTDtoppbcUWIckSZI0r7hgjSRJklSAKY2ljohTgFOBRSmlz09vSVJxtpT2czu72M0Ay2jjGSxnbcOiKR8nSZJ0MDUF7Yh4BnmavzMrmj9f3nc+cAXwupTSN6atQmkSNvQO8/VHB3looMS6tgZeuaKVM7qaq47ZUtrPf6YtdNDEUlroY5j/ZAsvLK2tCtGTPU6SanH/lhLX35rYsTuxcllw3lnBiWvn2AfLD9wDN1wFj26FFWvguS+GE06pd1V199OBQb5zoI9HRkY4pqmJF3V2cHJba73L0ixQyxLsTwGuBU4mzzpyxbhDrgd2A788XcVJk7Ghd5iPbe5nz3BiTWsDe4YTH9vcz4be4arjbmcXHTTREU1EBB3RRAdN3M6uKR0nSZN1/5YSX7qqRG9f4uil0NuX+NJVJe7fUqp3aZP3wD3w5X+G/T1w1DF5++V/zu0L2E8HBvk/PT3sGx1lZWMj+0ZH+T89Pfx0YLDepWkWqKVH+71AC3B2SmljRLwXeOnYzpRSiogfAs+a5hqlQ/r6o4MsaQqWNOf3jUuaAyjx9UcHq3q1dzPAUlqqbttOI7sZqGqb7HELTd/QDvYM3MvQaA8tjd0sbXsKHS0r612WNCdcf2uiqwO6OgKArg6AxPW3Jk5cW9fSJu+Gq2DRYljUnb8f295w1YLu1f7OgT4WRwOLGxsB8nY0t4/v1d7+E7jnK9DzEHSvg1N+CVY9rR5Va6bUErQvBL6SUtp4iGMeJi9qI82YhwZKrGmt/nBmcVPw0EB1T9Ey2uhjmI6Kl30/oyyjbUrHTdaDm0r88EZ4dCesOBqecy4cv37qHxfvGd3FltJmDnCATjpZ23AcSxuXT/l8k9E3tIPt+2+isaGN5obFjJQG2L7/JlYterZhe5IeuRPuugz2boEla+H0V8AxT613VZopO3YnmhoTt/00caAfOtth3SrYsbveldXg0a25J7tSR1duX8AeGRlhZTlkj1nU0MAjIyNVbdt/Atd9aICR7h5Y1c/+x9rZ8aFuzn93m2F7Hqvlf/ulwJbDHBMwritQKti6tgb2jVRP8b5vJLGurfrl/QyW08cIfWmElBJ9aYQ+RngGy6d03GQ8uKnEV7+e2L8/cdTyvP3q1xMPbprax8V7RndxT+lOhhikgw6GGOSe0p3sGS12WMuegXtpbGijqaGNiKCpoY3Ghjb2DNxb6P3OF4/cCd/7BPTvhe7Vefu9T+R2LQzNTXD7vTA0DB3teXv7vbl9zlixBvp6q9v6enP7AnZMUxP7S9V/0/eXShzTVP3k3vYfffR376RxyRDNDS00Lhmiv3snt/1H30yWqxlWS9DeAZx0mGNOJ/dqSzPmlSta2TuS2DtcopTydu9I4pUrqj+yW9uwiBfGWjqimT0xREc088J48gWOkz1uMn54IyzqhEWLgoaGYNGiYFFnbp+KLaXNtNBCS7QSEbREKy20sKW0eWonnKSh0R4ao/rxbIxWhkZ7Cr3f+eKuy6B9Sf6Khif+fddl9a5MMyYlgvKqb+mJZZZJc2gduOe+GPbvy2OzS6W83b8vty9gL+rsYF8qsW90lFJK7BsdZV8q8aLOjqrjHt3cR+tiaIxGIvK2dXFu1/xVy3vpq4E3RMTJKaWfjt8ZEc8iDy/5++kqbj54+N7Erd+FXY/A8mPgrAvh2KdEvcuaV87oaub3j6Nq1pG3rGl70qwjkEP0Wg4fmCd73OE8uhOOGtcR3tGR28fbXdrN5vQwB9IBOqOT4+JYljUsqzrmAAfooPqPdzMtHODAlGt8YHOJG36cePQxWHEUPPdZwQnHVb8Hb2nsZqQ0QFM8MXxmNA3S0tg95ftdSPZuyT3ZldoW53bNfdtSL3eygz0MspRWnspKVkdX1THDo8HTfi7x8A4eHzpy0trc/iSzdWaPE06B1/xGdW0vee3sqK2OTm5r5a10V8068prOrieNz25et5/RPe00LR19vG10XwvN6/YDR81w1ZoptQTtDwKvBa6PiPcBqwEi4nTgPPLFkr3Ah6e5xjnr4XsT3/5cHsK2bCUc2Aff/hy85NeSYXuandHVPGGwrrcVR8P+/bCoIrP39eX2SrtLu7mrtJFmWumgg8E0xF1pI6dzWlXY7qSTIQZp4Yk/4MMM0UnnlOp7YHOJ/7g80dWROGo57D8A/3E5/PLFpaqwvbTtKWzffxOQe7JH0yCjpQGO7nBg4WQsWZuHi7QveaJtYF9u19y2LfVyHZvooJkltNLPCNexifPT+qqwvXJZ0NsHZ578xN/+3r70+MWRjxub2WPR4uqZPV7zG7Mj0J5wyuyoY4bsHd3FttFN9Kf9tMciVjeuZ8kE18Sc3NZ62On81r/qAHd/tIOgkebuUYZ7GhncG5z61ql3lGj2m/TQkXIv9mvIY7A/Cfwm+ZOvn5B7sVuAX0opPVRAnXPSrd/NIbtzcf64uHNx/v7W79a7Ms2U55ybw+v+/YlSKY/R3n8gt1fanB6mmVZao4WI/8/enQfHdd2Hnv+e2/uOfSPABdxJSdS+m5Kt1dZiW5YdJ1Zsxxk7lXjyXmom9eZlXk2SysvLLM8Vl+3y5MXOKF7ixFFs2bJoi5FE21pIUZQomqK4gAvABcS+9b7de8/8cZpE3wYkAk1AWHg+VawLHN6+fRpoAL/+9e/8jsAnvHjwcUY6K7HajVUUKFCQeaSUFGSeAgXajVVVzW/3GyrIDocEhhCEQ4JIULL7Defb2UFvMy3hm3Ebfop2Arfh1wshZ2HroyrQzk6AtCc/3vroQs9Mu1zvMEgQDwE8CAQBPATx8A6DjvO2X68C7WRGYktJMiNJZtS4Q3lnD8NQx3BUjVehK5fnG6Pj/NngMN8YHV/2LedO9to8ucPkb75n8uQOk5OX0T5xwhrlhPk2BZnHT4iCzHPCfJuJKtfEbLm2hdV/chpXbZ5MrwdXbZ7Vf3KaLde2VD1HbfGb1TIMKeVOIcQa4HPArUA9EAf2Av8opVxK66fn3Wi/ymSXC4bVuHZlWLPa4OMfdXYdue/eqV1H0nJqSYgXD2npzHTUuurZxFWOriOdxoaqu44MjbxLacvI1HOD3mYdWFep9Sr4wH9wdh258bO668hyME6e9tN9NO/dS2B4iGxjE4O33krvamet0Np2g0/fj2PDmofunGbDmjns7NGVy/PkuXNE+8/QkhgjEa3jydZVfKGjY1lupnKy1+aHL1pEgtBUC8kM/PBFi0/fC+uq2BiozzqNBx/e0voULz6Qany6rPal1LjqueE66LumPEO+tqpraUvHrNc7SyknUBvWfG3up7O81LeqcpFQdHIsk1Lj2pVjzWqDNavf+5yQCJGXBXxlTXsKFAmJqSUhA4UIL8TX0lc0afO4+XAsQG2gurk1NaiMe7jsbjIZNa7NrdardGC9HHWc7qP9maeRoSi5+gY8qRQdzzyN+OhjsGar41yjJYf3gTg+u4jX8GC4YlDxApumFapcJFy2/qHKzh7P954levQtookJsEyiE2MwMcLzQrJx3foqHu3i9vJv7Ol7lf/GrirQzsoU/oqyPA9esjJV9RyFlLilhdu2cBsWYikthtWqssT2fl1arr9H/X5MJ9TbxemE+vz6exZ6Ztpis0p0UCRPXhaQUpKXBYrkWSU6HOcdzRb41nCSuGXT4nYRt2y+NZzkaLZQ1f3ecZMgmRGk0urt7FRakswI7rhJryHQtJm4au8hMqEg2XAAaQiy4QCZUJCr9h5ynNdjZni6OExKWjQINylp8XRxmB6zouPEHfdD/znY92t45d/Vsf9cVZ09+s52Ex4tlbB4VVY2PDpI39nuKh7p4jcwphaZlgsF1Hg1AiJMEefv1iIFAqK6hfJxc4Se4kGKMo9fhCjKPD3Fg8TNad5C1JaNWQfaQojPCCF2CSHGhBBm6bhLCPGZ+ZjgUtaxQfDAZ1VGe2xQHR/4rO46ok1VZ9RRz2pOyywHZT+nZZZ6Vk/pOvJcPEvUZRBzGRhCEHMZRF0Gz8WzVd1v5yqDxz+iarNHRtXx8Y9M7Tqiadr0oslJErYAACAASURBVCOjrAi24sEgh4UHgxXBVqIjzjrePVacsHARFi61HqL08Z7KFplSQtFU/f8MoY5Fs6o2gG29J0mFY3BhMxWXi1Q4Rlvvyeoe7CLXUqc6upRLZ9V4NdpcqymSd6yJKZKnzbW6qusNWt14hBdPqT2rR/jwCC+D1vJ84aMpMy4dEUJ4gB8BD6MWQVrAMKonzQeBu4UQnwIel1IW52GuS1LHBkHHhoWehbbYnbNTvCYyhFhBHS6yWLwmMkTsFB1l/bv7iiYtbucOZBFD0Fc0Ky/J6W6bvbthZAgamuDWO2B159QAunOVQWd1ayk1TWtsJZJKEAmXRXPZBDQ6awSH7CINwvknN4jBkF3x5/LlndC6EjZcPTmWjKvxtZtnNbX7TxzkyZvuBcNFuJAj5fWT8Hh5/O3ds7pOVU4dg1d2wmAfNLfBBx6EtfPbrWT7tQY/fNECJKGACrKTGXjo9uoSBzWuetZzjaPryGrXxqprqrN2Cn9FOaAbL1m7+lIUbfGbzbPvz4BHgNdRgbVfStkK+IEPAftQQfj/NteT1LTl7oAcJSRdBIUbIQRB4SYkXRyQzqxYm8dN0nZmtpK2pK1ie7nT3TY/+7EknZLUN6jjz34sOd1d/Qp8TdOmcet9qi4wlSht4pJQn996n+O0JsNDBufPXwabJqOiLengeQg5e3ATiqjxWdrY0MQX9vyCaDbFQDhGNJviC3t+wcaGpllfa1ZOHYOnvq1eIDS2qONT31bj82hdu8Gn73URCQqGxgWRoODT97qqqs++oMZVzxbvDdzgu4st3hsua+FiwAhjVpSimBQIVLEZmrZ0zGYx5GeBk8DdUsqLzxQppQX8WghxN/AO8Hngr+dwjpq27I2SR1oGh+0sKSkJC8Eaw03W7WzF9eFYgG8Nqy2QI4YgaUsSls2n65xZkr27IRRWO1LChT7ekr27YXXn+/GINO0KsXojfPT3YO8LMNyvMtn3fkKNl7ndFePpotqpKohBBpuUtLjfXVHX0LxCBaaRssWQ6aQan61HnmDjk19h474XoFgAjxeitfDIE1PPncsM9Cs7VUvCC4/hwvGVnfOe1V7XblxWYD2fml2d9BQPAiqTbVKgKAu0u2f3ToW2tMwm0G4HvlEeZJeTUuaFEM8AX56TmWnaEhC3Rhgwe8jKJAERocW9hphr9i07pOXiLTNLULgIAXkpecvMcb0IQFmlyOaAly81Rngunr3YdeTTdSE2B7yO640MQX3FNIJBNa5pS03/O87WiFsfXWQdXFZvnBJYV1rjDvIYjeyx4gzZRZoMD/e761jjrug6sv1B+OG31MehiAqyk3F46LdmP6/OTfCFP4VXywLoOx+cuuHMqWMUf/S3FOqL2O0CIzuM90dH8Dz+v1QXGA/2qUx2uVBEjV/BYu4G1rCNQaubrJ0iYIRpd28m5tZtnpaz2QTafcCltt7zlM67LEKIJ4Dvlz79opTyHy73mpo21+LWCN2Fg3iEDz9hijJPd+Egnd5tsw62c5YbIcBAIjAwsBFCjVfaHPBOCawrNTRBepodKef7HWOATH6QeLaLohXH44oRC2wk6NP9t7Xq9L8Dr3xd7aoZa1Mb/bzyddWXvDLY7jsCh34O471Q2w5XPwRtWxZm3tNZ4w5ODawrrd0Mn/6SqskePK8y2Q/91qzrsy+awU6OxV3fJddeBMtAFAxsr60+3/VdPGv/z9nfZ3Pbu2Tl2979NleImLthRoF114DFrqM2fRPQVgP3bDbY2OK65O20xWc2gfY/A58XQvy5lDJR+Z9CiBrgceD/u5wJCSE6UDtPpgBduKQtWgNmT2nVuGqb5Sltiz5g9sw+0EawiQgDIksWiwAuNskQOarrUHPrHfCzHwNIgkEVZKdTcM8DVV1uxjL5QYaTr+My/LiNKJadYzj5Oo3cooNtrSqHf6aC7Avb1184Hv6ZM9DuOwK//n8hEIOaNshMqM/v/qPFFWzPyNrN1QfWVSjQB5Ybw3aBAGG7sEvjU7JrMykx+cCDqiYbJrPyqQR8ZGpWvp8E7zDEBDlq8HMVTbQSnXLeTHXl87yYTtNnmrS53dwbCrHRt7Q25+kasPjuHouoH1pikMjCd/dYfO52dLC9BM2mkOmvgDeBfUKI3xFCtAshPKXjZ1C7Q+4D/mu1kxFCCOAfgVHgf1R7HU17P2RlEjfOzLIbL1mZnPW1moQXl3CzUdRwrahno6jBJdw0iffOXL+b1Z0Gj35CEAoLRkfU8dFPiGm7jsyleLYLl+HHZfgRQlz8OJ7tmtf71ZaviV7wV8Rd/qgaL3fo5yrIDtaAMNQxEFPj2nuzw25EwblQUxRs7HBFLm6mixzXboJPfVFltIcH1PFTX5wSkPeT4GXOkKVIDB9ZirzMGfqZksubka58nu/E4yQsixaXi4Rl8Z14nK780tp2ftdRm6gfogGBIQTRgCDqV+Pa0vOuGW0hhI3q4Dnlv5gs66gcXw9k3+u6l/AfUB1M7i4dNW3RCogIRZm/mMmG0gpyEXmPW03vDneUHxeHQZYtlMLkAXdt1fNb3Wm87wsfi1Yct+GMigzho1jZK1jTZqimXZWLXMhkA+QSarzceK/KZJcLRNW49t4MfwN2th9hCtVz27KQwsLwV9SazWaR49pNl6zvfochArgJlPLmF47vMFRVVvvFdJqoEERLfcOjpcfyYjq9pLLafRMqk10u7Ffj2tLzXgHxy0wfaM8LIcRm4P8CvialfFkIoQNtbVFrca+hu1C5gjxPh2f2i4fWuIN8gkZ2mwmGZIEm4eUBd+2l6zkXGY8rhmXncAn/xTFb5vG4Yu9xK017d1sfVTXZoDLZuYQKvG/8rPO82nZVLhIsC8izCTWuvTfvVY+SO/xD7GQGkS0gA26IxPBufdR54hwvcpwgRwxnAOzHzQS5qq7XZ5q0uJylFWHDoM+cus/AYtZWo8pFomW7XKZyalxbet410JZS3v1+TUII4UZlyc8C//v7db+adjlirgY6vdscXUc6PJuq6joCM1wotcjFAhsZTr4OqEy2LfNYdo660LYFnpm2VLVepRY+lncdufGzUxdCXv2QqskGlcnOJiAbh1uW6Z7Fp87avLpfMjgqaa4X3HmDYO3K6krDPBvuAKBw6kVsM4nhjuBde+/F8YvmeJFjDX6yFC9msgFymNTgf49bvbs2t5uEZV3MaAOkbJs2d7Vvsi+MezYbfHeP2ngn7FdBdiIHH79+cbYt1N6bkFVs6zrnkxDir4D/AtwppXytNPaXwF/wHl1HhBBfAr4EsHLlyhvOnDnz/kx4HvQfgiPPwMQ5qOmALR+F1qsvfTtNW2x01xFtoSz2riNz5dRZm3/baRMOQigI6QykMvDJB42qg+2Z3XGpRjscdS5ynKb+eiYu1GgHcOPHTQ6TLCbbWTWldKTbzLK7GGdIFmkSHu7wxOh0BxznXKjRjgpB2DBI2TYJKfl8LLakSkdAdx1ZCoQQ+6WUN17yvIUOtIUQtwC7gb+VUv6nsvG/5BKBdrkbb7xRvvnmm/M2z/nUfwhe/ZqqQSx/a/TO/6iD7eWg105xgFHGyFOHj+uop13vBKZpWpW++xOLZFoSCU12Jbrw+ec+Ps/B2BxvrT6TriPdZpYf54cJC5djs59P+BqnDbaXetcRbWmYaaA96/dThBCPANeiNrCZrq+2lFL+/gyv5Qa+BxwH/o/ZzmW5OPLM9O2rjjyjA+2lrtdO8YI8TxA3tXjJYPIC57nPXqGDbU3TqjI4Kmms2FAyFFTj824Gixxno5XoJRc+7i7GCQsXYaFeRIRLu3jtLsanBNobfb4ZBdYT1ijn7dNkZIqgCLPCWH1Z26tr2ruZcaAthFgFPAtshfds7iuBGQXaqD7ZG0of51R3vym+LYT4NmqR5J/M8LpLysQ5iFXsruuPqnFtaTvAKEHcBIX6UQviBqnG26/wNvFnrAyv2+MMywKNwsstRi2rXEu7Rl3T3tPJo/BS2UY0dz0I62bfL7u5XpQy2JNj6YwaX46GZJGGinAliMGQLFZ1vQlrlOPmITzCS4AQBZnnuHmIDVxddbA9YY3SZ50mK1MERJg2lw7cNWU2Ge2vA1cBT6Ky0OeBy13Km+fdN7i5HrgOeBXoAl67zPtatGo63qV9VcfCzUmbG2Pkqa3otR3AxRhLq6/rXDtjZfiZOUBYuGjAQ1qa/Mwc4FFadLCtLU8nj8K/fEstJGxsVYsK/+Vb8NtfmnWwfecNgn/bKQHpqNH+8PblGWg3CQ8paV3MZANksGkSl9qsenrn7dN4hBdvabMxb6nzyXn7dFXB8YQ1ygnzbTz48JcC9xPm26znGh1sa7MKtD8E/LuU8n+aqzuXUmaBaa9XqtG+Dvjuct+CfctHVY02OGu0b/jcws5Lu3x1+Mhgqkx2SRaLOq7smsHX7XHCwkWolOkPlb4+r9vjOtDWlqeXdqogu7IH9Us7Zx1or11p8MkHcXQd+fD26ruOLHZ3eGL8OD8M4KjRfsBbd4lbTi8jUwQIOcY8eMnIVFXX67NO48HnDNylGteBtjabQLsIHJqviVzJWq9WCx/Lu47c8Dldn70cXEc9L3AepMpkZ7HIYHIHV3YXjmFZoKFiiUcQF8OysEAz0rTqzahr1OB5lckuF4qo8SqsXWmwdmV1811qOt0Btc9AWdeRB7x1U+qzZyoowhRk/mImG6BIgaCorpwvK1P4pwncs1UG7tryMptAezeqdESbB61X68B6OWo3wtxnr3B0HbmD5it+IWSj8JKW5sVMNkAGi8Yqt5zXtIVS3jUqtkK9G/nq16bpGtW84l16UK+Yck1tqk53oOrAutIKYzXHTZU39OClSIGiLLDGtbGq6wXeJXAPVBm4a+9i5DT0vAbJYYg0wprboGH1Qs/qkmbzPtOfA9uFEJ+er8mUk1L+pZRSLPeyEW35azfCPGKs4nPGBh4xVl3xQTbALUYtKWmRliZSStLSJCUtbjGq33Je0xZCedcoYUx+fOSZihPvelAF2sk42Pbkx3c9uCDzrlavneLn9mm+bx/j5/Zpeu2ll7WtcdWzwX01XuEjSxqv8LHBXf1CyDbXaorkKcg8UkoKMk+RPG2u1XM78SvZyGk4+FPIpyBcr44Hf6rGF7kZZ7SllAeEEPcAPxdC/AHwFhCf/lT5X+dqgpqmLT+rXEEepcXRdeRDrkZdn60tKgPEOcYgcbLECLCJZlqIOc6ZcdeodZvVwsfyriMP/1ZVXUcWSq+dYhe9BHFRi48MRXbRyz12+5JLINS46uesfrrGVc96rnF0HVnt2qjrs+dSz2vgC4Gv9Dy7cOx5bdFntWfT3i8G/A1QB9xV+jcdCehAW9O097TKFdSBtbZoDRBnLz348BAtbRW+lx5uZY0j2J5V16h1m5dUYF3pICMEcREsra+4cDzIyKJpV3qqmOWVYpJBu0iz4eEDnghrPXNTcvJe5jJw16aRHFaZ7HLeoBpf5GZTo/1V4IPAi8D3gT4uv72fpmlXqL7D8M6Oye2yr3oY2rYu9Kw0TTnGID48BErB5IXjMQYdgfaV1DVqjBy1FR2TArgZI7dAM3I6VczyVG6UsHDRKNwkbYuncqN8ivr3JdjW5lGkUZWL+Mpe0BUyanyRm02g/TCwR0p5/3xNRtOWmnN2igNylFHy1OPjOlFPxxJ7C3Uh9B2Gl74JwRjUtKnA5KVvwl1f1sG2tjjEyRLF7xjz4yZO1jG2YF2juo/Bq2Vbod/5IHTO3Y6N06nDT4bixUw2QBaTuoqv00J5pZgkLFxEDNVvOyJcYKtxHWgvcWtuUzXZoDLZhQzk07DpvoWd1wzMJtAOAHvmayKaNp1zdor9coxRmade+LhB1C2aQPacneJ52UdIuqgrba/+PH3cb7ctmjkuVu/sUEH2hbfbLxzf2aEDbW1xiBEgS/FiJhsgh0mMqQHb+941qvsY/Nu3IXxh85uE+vyTX5zXYHsbDeyiF1CZ7CwmGSxuo/USt3x/DNpFGoUzrAkJg0G7uh0ktfdHj5lht5lgSBZoEl7ucEdZ464oK2xYDds+5uw6sum+RV+fDbMLtA8AnfM1EU2rdM5OsdPuI4ibOlQ7uJ2yjwdZHIHsATlKSLqm3V69Y5HUKy5W470qk13OH1XjmrYYbKKZvfQAKpOdwyRPketoX+CZoTLZ4Wk2v3l157wG2u1GmHvsdg4ywhg56vBzG63TLoQcIMFRBoiTI4afzbTQQnTe5gbQbHhI2pbKZJekpU2zUd0Oktr86zEz/Lg4TBg3DagdQH9cHOYTNE4fbC+BwLrSbALt/wrsEELcKaV8db4mpGkX7JdjBHE7dw+UarzaQPbcCcmbv4SxfqhrhRs/BB3rq9u2eJQ8ddNsrz56hW+vPhO17dMvIKtdBDGMpgG0EONW1ji6jlxH+5SuIwtisI+uVRt5vqGNPm+AtkKW+0f62Hima97vut0IX3Lh4wAJ9tCDHzdRfGQpsocebmfNvAbbH/BEeCo3CrbKZKel2kHyI56aS99YWxC7zQRh3IRLL47CuECq8SmB9hI1m0C7FdgB/FII8c/AfqZv74eU8ntzMDftCjcqpwayQVyMyuoC2XMnJDu/D8EI1DVDJgE7vw8P/q6sKtiuf5ft1euv8O3VZ+Kqh1VNNkwuIMvE4aYnFnZemlauhdjiCKwrdK3ZyJMNq4gKQUshR8Ll5cmmVXzBgOq2XJlbRxnAj3vKQtKjDMxroL3WE+BT1Du6jnzEU6PrsxexoWl3CTYYWka7BM8m0P4OqnWfAD5b+icrzhGlMR1oa5etXvim3T2wXlQXyL75SxVkh0q/5y8c3/wldKyf/fWuE/U8T59je/W0sLhDXNnbq89E21a18LG868hNT+j6bE2bieev3U70yFtEkxNgW0QNF0RqeP7a7Ysi0I6TI1qRcFALSee/O8laT0AH1ktIk/CSkpbKZJdksGlaRrsEzybQ/r15m4WmTeMGUcdOqQLZIC4yWGQw2S6aqrreWL/KZJcLhtV4NTqMMPfbbRxgsuvIHaJ5UdSPLwVtW3VgrWnV6ANaiqWMXyndFS4W6FuwGTnFSn3Hpy4kXRzdSbTF4w53lB8Xh0t/5w0y2KQwecC9fHYJns3OkN+dz4loWqUOI8yDtDm6jmwXTVUHsnWtqlwkVPbOZSalxi9njnrho6Zp76e2c6dI1DYQNYyLYynbpu3cKVhXxdtzc2wzLeypWEiaw+R6ptvFR7uSrXEH+QSNjq4jD7hrl019Nswuo61p77u5DGRv/JCqyQaVyc6kIJOE7R+bk8trmqa9L+4/9TZPbrkVrCJhyyTlcpPwenj8yF744IMLPT1aiHI7axxdR66nY967jmhL0xp3cFkF1pV0oK1dMTrWCx78XWfXke0fq77riKZp2kLYGAjwhe63eX7FOvp8AdryWR4/c5SNgcVTm9xCVAfWmsYsAm0hRPcMT5VSyrVVzkfT5lXHelHVwkdN07RF4/b72Pj0k2xMxyffnkvF4bEvLPTMNE2rYFz6FMe5Ypp/tcDq0j/vLK+paZqmadpsrNmkgupwFEYG1PGxL6hxTdMWldkshlz9bv8nhFgHfB0IAQ9c/rQ0TdM0TXtXazbpwFrTloA5yT5LKU8CjwErgL+Yi2tqmqZpmqZp2lI2Z2UeUsoc8ALw23N1TU3TNE3TNE1bqua6ntoEWub4mpqmaZqmaZq25MxZoC2EaAA+Dpybq2tqmqZpmqZp2lI1m/Z+f/4e1+gAPgrEgD+bg3lpmqZpmqZp2pI2mw1r/vIS/58A/lpK+f9UPx1N0zRN0zRNWx5mE2h/8F3GbWAcOCalNC9/SpqmaZqmaZq29M2mj/ZL8zkRTdM0TdM0TVtO9C6OmqZpmqZpmjYP3jOjLYSoKhCXUtrVTUfTNE3TNE3TlodLlY4Uq7imnMF1NU3TNE3TNG1Zu1RAfA4VOM9EGKi/vOlomqZpmqZp2vLwnoG2lHL1pS4ghPAAfwz8l9LQ6cuelaZpmqZpmqYtcZe1GFII8UngKPDfAQH8J2DzHMxL0zRN0zRN05a0qmqphRC3A18BbgFM4OvAX0kpx+dwbpqmaZqmaZq2ZM0q0BZCrAX+b+DjqAz2j4A/k1Kemoe5aZqmaZqmadqSNaNAWwhRB/wF8AeAF3gN+F+llHvncW6apmmapmmatmRdqo+2F/gT4D8DNcAp4D9LKX/8PsxN0zRN0zRN05asS2W0u4CVwBgq4P6mlNKa91lpmqZpmqZp2hJ3qUB7FaqPtgD+FPhTIcSlrimllKvmYG6apmmapmmatmTNpEZbAHWlf5qmaZqmaZqmzcClNqy5rD7bmqZpmqZpmnal0oG0pmmapmmaps0DHWhrmqZpmqZp2jzQgbamaZqmaZqmzQMdaGuapmmapmnaPNCBtqZpmqZpmqbNAx1oa5qmaZqmado80IG2pmmapmmaps0DHWhrmqZpmqZp2jzQgbamaZqmaZqmzQMdaGuapmmapmnaPNCBtqZpmqZpmqbNAx1oa5qmaZqmado8cC/0BDRN0zRNm6UzXbBvFwz3QWMb3HwPrNq40LPSNK2CDrQ1TdO0K0rcGmHA7CErkwREhBb3GmKuhoWe1qRLBdFnumDHdyEUhYYWSCfU5w9/TgfbV5gRe5weekmSIUKQNbTTYNQu9LRmZcSe4FTZY1hLOw1GzUJPa87oQFubU8fzeV7MpOg3LVrdLu4Nhtng8y30tK5Ix/M5dmXT9JsmrW439wRCbPD5F3pa2lwYPwPn3oDMCAQboOMmqF210LOatTNWmn32BCMUaMDLzUYNq1yh6i8YPwv9+yEzCsF6aL0BYiudp1gjdBcO4hE+/IQpyjzdhYN0erctjmD7TBfs/DaEbWiUUDgJO7vhwS9OBtH7djGwookj65uIB93EMnVsOTFEy75d8x9oHz4Ezz4D585BRwc88lHYevX83icwSJzj9BMnQ4wgG2ilmdi83+9iNmKPc1B24cNDmAB5Chyki232Rkew3X3GZs8+ydAwNDXC7TcLOldNUzk80gOnXoPkMEQaYe1t0LBm6nlvvw1PPw1nz8LKlfDYY3DNNVPPG+6Bk3sgMQTRJlh3OzQ6rzdiT3AA52M4QBfX2RunBtuH3oaf/mTyfj/2cbh6mvtdZHSNtjZnjufzfCc+QcKyaXa5SFg234lPcDyfX+ipXXGO53N8LxknYVvqe2FbfC8Z53g+t9BT0y7X+Bk4+nMopCFQr45Hf67Gl5AzVpod1iBpaVIvPaSlyQ5rkDNWeurJ8bNw7Cfw1j+oY/zs9Oec3Fn6utSp48mdU84dMHvwFAt4xroRQwfwjHXjKRYYMHvm6ZHO0r6fQTQHPhcInzpGc2q8ZMCaYPe2VrIeg2jGJOsx2L2tlQFrouq7PW1l+Jd8H1/PneZf8n2ctjJTTzp8iPjX/xunRt/mnZY0p0bfJv71/6aC72qNnoa3/hVe+aY6jp6ecsogcfZxkhwFogTIUWAfJxkkXv39LgM99OLDg094EULgE158eOih9+I53Wdsnv7+GKldr9Ow+2ekdr3O098fo/uM7bzYSA8c+CnkUxCuV8cDP1Xj5d5+G77yFRgfh/Z2dfzKV9R4ueEe2P805FIQaVDH/U+r8TKnLjwGvAgEPtRjOFX2GAAVZH/1b533+9W/VeOLnM5oa3PmxUyKqGEQdbkALh5fzKR0Vvt9tiubJmIIokbpeyFcgMWubFpntZe6c2+AN6T+weTx3BtLKqu9z54ghIuQUH+GQrhBqnFHVjt+lszhf6GYHoZiDjx+PGPHCW79bWe2un8/eIJTvy79+x3nZfMD+Md7weUFdwDsIu7x02RrTaj4NWWmz1MYfxs7P47hq8Vbew3u0Ir5+HJMypyBoJ/JP89ucPvVeMmRq1fjz+YJSHVOoGiDaXLk6tW0VHGXp60Mr5x9ky3H9nPzxBgTNXW8sukGWHkjq13Bi+eNPPM9RiJZLL8XVyZDzu+iX2YpPvM9Grb+99nf8ehpeOdZ9b0K1kM+rT6/6hGoX33xtOP048eDHy/AxeNx+q/orHaSDGECjjEvHpJMvkja8+wA4sxRhtcEyYdX4UvlCfYcZM+zm+n8n9smb3jqNfCFwBdWn184nnrNmdV++mmorVX/YPL49NPOrPbJPeoa/tJ1LhxP7nFktZNkCA8l4GgXxOMQi+HdvJFkU9T5YH/6EzBNOHjw4nmsWKHGF3lWWwfa2pzpN1X2tFzYMOg3rXm/71PFLC8XkgzaRZoND9u9EdZ6Ape+4TLVb5pTvxfCoN80q77mkJzgBP0kyBAlyHpaaRKLp46unwSHGWKCLDUE2EoTrUQvfcOlJjOiMtnlPEE1voSMUKAej2MsiIsRCo6xzKmfUxw7Ax4veANgFSmOnSFz6ucEr//DshNHyQb8pEQ/RYp48BD2RAlkRh3XCyTHKXo8eErBGoYX05AEkuOUP13M9Hly/b8CdwDhrcE2M+T6f4W/9YPzG2wHvVC0wFv2tSlaarwkvrKD6NHD4PWpr0uxgL+QJ755a1V3eaT3ILe89hy2P0ImVk8om+GW157jiOFh9arbLp6XPvMOVq0PVz4PUuKyTCy3QfrMO1RVdHPmdQZronQ1RpjwCmoKXjYOu2g+87oj0I6TIVoRUPrwEGearPsVJEKQPAV8TD43ChSJMPni6MzxQdJbongw8OUtzICX0S1ucscHgbJAOzmsMtnlvEE1Xu7sWUauX0v3lijJmIdIvEjnEQ8Nb51ynpcYUpnscr6gGi9/DINJuruOcyS6krGmMHW5FFuOHadTboTWshN/8xvo7oZAEKJRyObg0CFIT/MO2CKjA21tzrS6VblItCzAS9k2rW7Xe9zq8p0qZvnX3BhhDBqFm6Rt8a+5MX6Luis22G51u0nYVimTraSkTau7uh/5ITnBCyd6Gfh1PbmBZvwteU7f3ct961kUwXY/CV7lDH7cxPCTpcirnOFOz1LGvwAAIABJREFUVi2/YDvYoMoivGVZ32JGjS8WQ91wfPdkbeaGO6Cp03FKA17SmCqTXZLBoqEsaAAojp4AtxfcpXSz2weyNF4mGwxwYKTAvu5tjCSjNEQS3Nx5nOsaAo4QrSWep7veDbaNWwpMISkaLjpG81AWPxfG3wZ3AMOtghbhDmKXxqcE2vFzMPAmZMdU2UrLjRDrqOpLx4rNcPItQFwMojFzsPr6i6fEIi1kNwkC585CJgnBCLnO9cQizVXdZd2R17D8EcyAek4VA+q7UnfkNSgLtBPNIQJDE5jRIAgDkPgmUiSap/kdcOgQPPOTyVruj34crnbWcg/KOHsbwviH+4gm02QjIfY21HHrQJzyRxIjiHvkLK09XfiTE+QiNfSv2UiswVl/D9BrpzjICGPkqMPPNhpoN8JVfV0WUtwaod/sIStTBESY1mkW7K6hnYN0gVSZ7AJF8hTZxOTPmt2Yws7GcHtUCafbtMkXfdiNFWU3kUZVLuIr+1oVMmq8zMiN69lzZy3JmiAFtwtvo8VAg4fbDcP5YivapMpF/GXXy2fUeBmxq4tdN20hbFnUFvMkA0F2hbewdtcReOKeyRMn4mC4IFB6Rzbgh3xejS9yukZ7iTk2aPGNVwr82Y4833ilwLHB+c8Wz9S9wTAJ2yZhWdhSkrAsErbNvcH5/SX3ciFJGIOI4cIQgojhIozBy4XkvN7vYnZPIETSliTs0vfCtkjaknsC1S0023tijJM/aMNKegk1WVhJLyd/0MbeE2NzPPPqHGYIP24CeBAIAnjw4+YwQ5e+8VLTcZMKtAtpkHLy446bFnpmylA32YNPMeQf4HwHDPkHyB58SgXfZW42akhjkZYmUkrS0iSNxc2VC6CKRXBV/KlyGWq8zKFMC9/rupGX6mvZv9nDS/W1fK/rRg5lnMUUMVcDnRMGHtsg57Lx2AadE8aUIMbOjyNczhfqwhXAzo875xI/B9071Ysdf506du9U49XY8gCEa9WCr4O/UcdwrRq/cArN5KIRsluvRd70QbJbryUXjbCF6gLtxvg4Gb+zpCzj99MYdz7W4VvW407n8SSyYEs8iSzudJ7hW9Y7L3joEHztb2FiXL29PzGuPj/krOXuigXw9/Xhz+QQXh/+TA5/Xx9dMefXfctIkfaDryDyKfLhGCKfov3gK2wZcT4Heu0UT/UO8atng+z9bjO/ejbIU71D9Nqpqr4uCyVujXBq+GWK+3fjf14dTw2/TNxyvmvVYNSyTWzEJ7ykRBaf8LJNOBdCtt08RjFlkMt7kRJyeS/FlEHbzRW/u9fepkp38imQtjrm02q8zMFPXs9wnR9p2QRyFtKyGa7zc/CT1zuvt+52GO+HY3vh4EvqON6vxssckH5WDcRpmxgglh6ibWKAVQNxDsiKEsdoFLJpldU+eVwds2k1vsjpQHsJOTZo8eQ+k0RO0hKFRE7y5D5z0QTbG3w+Ph+rIeoyGLQsoi6Dz8dq5r0+e9AuEhLOp3JIGAzaxXe5xfK3wefns5EYUcOlvheGi89GYlXXZx/7tY9A1MYXsREG+CI2gajNsV9P/d6ePWnz9D/YfOuv1fHsSXuaK86tCbL4K96g8+Nmguy83/f7rnYVbH5IZbSzo+q4+aHLq88ePwNv/wj2/g91vIyFldnuX3KuzcWJuiCHIxFO1AU51+Yi2/1Lx3mrXCEedjUTEm5GRZGQcPOwq3lq15FIkwqqpQVIdSwW1XiZn55r4URnCNsPkWIR2w8nOkP89FxF1XLLjcQyBTaOG1w7HmPjuEEsU1BZ6DKGrxZpOZ8/0spi+Cpapw28qUp3PCEQQh09QTVejVQehvLqbfcVHeo4lFfjFx4CUe5gDQE8JMgRwMMdrKGlyndv6mrbMbJpCthIoICNkU1TV9vuOK99PMfhL95Dri6Ef2iCXF2Iw1+8h/bxikXWz/xEBWsnj8JLz6ujtNV4mYl8EZ9tg9sAAbgNfLbNRN75u7u+5x3qfC3gC5MXJvjC1PlaqO95x3HeC70THHuhFivjJlJrY2XcHHuhlhd6q18kuhD6+1/H85sDeMb6Ea40nrF+PL85QH//61PObTBqucm4mg8Zt3CTcfWU1n6d2+q45qq38BtpkqkQfiPNNVe9Ree2uooLrYHrPqYy2qlRdbzuY1O6jvTXG3hrGnBjQD6PG/V5f31FOFmwYCIDtgSPoY4TGTVefr32JhpSg8TiBaJxm1i8QENqkP525883ra1g2ep5Ykt1tGw1vsjp0pEl5IXjFjE/RP0CgKgfQPLCcYtNzfNbnjFTG3y+933hY7PhIWlbRMrKJNLSptnwvMetlr8NPv+cLXwsDgTxNeWBya+xETLJDwQd5509afOLH6j2vvVNkE7CL34AH/mMzcp18/e6voYAWYoEymp+c5jUsExLh2pXzd3CxwtdTLwhZxeTKoP3Xt8YZ0MRDFwEbEne8HA2FEYUxqjIe7LKFbpkOz/P+oco/uafwDLVH1cJGB486x9ynHfIEyVoZ/EXbLBt/LbA9poc8lQEn7EO6HzQWerRsX1KqYe39hpy/b/CRmWypZUFM4u38Vbn9bJjKpNdzh1U45VOd8HrL8JIHzS0wS33wuqKdnxv/RJqWiBYNu9MQo23T34FW4hWHVhXqr3mHjwv/YDebJa430csl6e9aBO+5h7HeRuK9dAzwJHfu5dcxIc/mWfLnmNssCtezLzzNgz2gj8A4Yh6i7+7a0o9bU3vBNnGGvxmBoQF0kXeU0NN7wSUl5snhwmF6wmV5wa99pT64YMHPISCEn9QAlw8HjzggalVJotW9vwR/IUUCAm2DcLAXSiQPX8E2h+69AXKbG7ZRt/t0OxN0tCfxtVq475xDZtbtk05tzu9ij1nV062AWwRdFZUpJkYeARgCHAJMASGgGJl3vbgryHaCsHI5FgmqcZXrLs41BqGRNZD1LTA5QbLIuXy0Fr5RrjLAK8XGhvB51PPqXh86rtdi5AOtJeQvrjKZJcL+9T4lWy7N8K/5sbAVpnstLRJYfOQd+Frh5eLVS1+TifziIiFGwMTm1waVrc4A/k3f62C7FDpd+uF45u/hpXrqMrhdIEd43l68xbtPhcP1/rYGnLW8W6liVdRWVg/bnKY5DC5kXnuELEcnHsDMjno6blY70tza9VdTEZiAQxb4kH9XvLYkiKSkVhgSqDNYKmWOz4EsVItd7OzljvYcj2Za6HYswvSoxCqx7PmHoItzreqZUjiiueR5BGWRLoERlFixaZ5sRnruGQNtTu0An/rB51dRxpvnVqfHSiVi3jKXjCYGTVe7nQXPPud0qvQ0iYzz34HHvm8M9geG4DaimxeIKzGq3HmOLzxIoz0Q0Mr3HQvrNrgPKd1HeG7PsOmd16G8UGobYGrtkNrxQ/tPZ9iw4++wYYf7FW14x4vRGvh8U85z8ulQAoVEIE6ZnNqvMzGQdjb5AJ3DF/eJu8zyAmbawcrHsMM64eLYz78gQT0j0GhAF4vRk0dubHqX5DYiXMw+BbkSi+omq/HiFZZfz9Dgf4+ij6JJ65KdDAEZixAoL9v9hd7Owo/3Ib4wADckUMM+uFbLfDpKJQ16+g+Y/P0Dkk4JGlogFQant4Bjz1sO3puN44bDOdVCYvh9WFjUYyP0JhbBeVrKccHoKbyeRxS42U+lBvi+22rYWSIcDJOKhIj2dDEx5KnnbctFGD7duia7E7Ctm1qfJHTgfYS0hYTJHKylMlWUnk1fiVb6wnwW9Q5uo485K25YhdCzoftH/Qz+k8GeVKYoTwi7SOSCLP9UWfAOzKgMtnlgiE1Xo3D6QLf7M8QcwvavAYTps03+zN8uRVHsN1KlDtZ5eg6ciMrlt9CyPnQfwJO94DXrwK6Qh5OHQMr7/hDPFOJYD3h5BC24QXhAmnhswskKko9GOyGfT8CfwSipT67+34ENz8+bbBNRWBd6dp8D2/k6jBcbtweC7PoImfBTf4eqk1nukMrLt1hpOVGVZMNKpNtZlTg3bHded7rL5ZehZaekxeOr7/oDLTrWlQGuzyjnU2p8XITFzbnKW1a1HoD1FQ8zjPH4RffUdeqb1bB/S++Ax/5/LTB9pTAutLKDfD4H8P+XTA6oF4w3HCPGnd8TRpgZAwyWbVoLZsD01LjZZpvup9rX/4+hz+wmoGWANGJLNe+cprm7b/rvN6a2+DgT9XH3qAKsvNp2HSf47QN7jRHh+IIr8Tj8VJEkh6Ks7nWBcw+8WInzsHp59X31Vervq+nn8defX/1wfbRd+C5Z+H8OVUa9OFHYPNVjlNaD/Vw6uY14PfizhcxfR6KhmDloR742Ozu7tjTUJeOsuL1yedTNq3GW8p+vvfsk4TtCcLnuyGXIuwPQ7STPftq6Cx7vX39T9/iV7fVYUc9mC4QlhvvRJHrX3sLfv+DkyfWtky+cC+/41rn83i918+n7HMcXu+n4IlQVxRsHT3Hem/FC+SODvJj/WTuWIcp87iFj2ASfK3z+6JnLuhAewm5b4OLJ/eZgCTsU0F2PAefuGZxlI0spLWegA6s59HK9YKPP+Fl/6/qGBlQuz7f8KgaL9fQospFQuXvFqbVeDV2jOeJuQU1bpVRqXELwGbHeH5KVruV6IIE1ufsFPvlGKMyT73wcYOoo2MpdTkYGSu9LVvKPnp9YBXUeIX+0aO8kz7KhFGgxvZyVWgzrfWbHefI6GZVy5zPYVsFDJeBDEaQUed5HN9NJhogXmtSdI3jsVzExgMEj++eEmjPxGdO7KB/1QNM5GsppDy4fUXagyN85sS/w/13zfp6MzbDUhRG+lRgWi4YVuPlrv8QPP999XEgrILsTBLuLIuwJs7CyedUFv1Cuc/J52Ddh53B9hsvqiC7Mrh/48WpgfZMrdwwNbCutO1alb3sOQPjE1BbA1s3wQbnexrZzgYM9yauPd2LcSiJXRfB+tAmsisbnEVfDath28egp2zXwk33qfEyD488z0jvZtzmOKHiOGlPLQF3LQ9bbwK/N/vHOviWCrI9pRK5C8fBt6Ay0D52GF7YAX290NYO9z0MmyraLR59B/7+GxCrgdYVEJ9Qn//BHzuC7diZUdYmkvRfv5ZsfYTAaJKVu48QG5+avT2zs4t9T/UxPOqisd7i5k+1serByRdu8bMQdZba449N3fNp6EyChvGD4PGBPwTFAsGBgwzlrwMm675bf3OcD8p1HNlUx0TUS02iwJZjY7QePOm84La74Zc/UB8HQirIzibgtkccpyXWbkFOvMQ1BTfuogdTFjGjJomauxy/zdMP30Xhq3+DNGO4YhHkxASZeBzzs5/mMvaSfV/oQHsJ2dTs4gs3q1rtvrikLSb4xDWuRVOfrS1vK9cLVk5579/pxrtVTTaoTHYmrZJodz3ynjd7V715izavswYv6hL05hfHAuBzdoqddh9B3NThJS1Ndso+HqRt6QTbwge5IRgaVosMPR6Ihqe0C+wfPcoviocZCwfJGWH8tsnZwmE+Mooj2N7qX89LsSQN6R78Voa0K8hIaA13+Z1Pnky+n8EWgTTzYFqYhotcvY/mgX6clf+o3eROvQbJIbUIcu1tU7Zy3jzRz5+6XuS51qvpC8Voy8f5cO8hNk/0V/2lGZYTnOI8STJECLKWFTRO185yBqUoNLQx6slyel0tqaBBOGOz+uQ49Q1tzvPa18P9v6tqsscGVCb7zo856rPV5jzTbFrUv98ZaI/0q0x2uWBYjc+nez4C5/8ObrtJ1WinkpCcUONlEpljuGpbcJUW3LkArCyJzDECvooXJQ2rpwTWldaefYnPD7zCy/67GXS1sDI7wPbcv7HWtqkq0M6NqUx2OXdAjZc7dhie/KYqZ2hpg8SE+vwLX3YG2889q4LsWOk5dOH43LPOrHbRSyxeILbzbfVOgNsFPg8UncmFMzu72PHNIUIBQUOtSTplsOObQzwMF4Pt2ErIjkOg7GHk4s69ngCa8idJyShhT6kU1eMlU/DRlD8JlHU0au+g9dQgraNlOz5PTEB7xfN/xTrYcDvs/GcYHVTPwwd/x1GfDTAUyuM2OvGMDUIhg8cbhLqVDAXyjkB7cIMX8UefIvaL13H3DmK2NzPxmQeQG7zM/mX5+0sH2kvMpmYdWGtz64yV4XV7nGFZoFF4ucWoZZVrSqgzIyvXGXzkMzZv/pqLme+7HqHqhZDtPhcTpl3KZCsJS9Lum/ozcCRT4Ll4lvNFixUeFx+OBdgS9E45by7tl2MEcU/Z3XC/HKODKgLtibPQ+wZkRtVOee03TS0HmGu+Ghg/qhZeuUrdAcZT0OoMKF/Nn6A3FMVnQ9CWFIWb3kCUV9Mn+CSTgXa9mWa9meR4qIOE4SZqm2wwk9SbafBOfk3GGlzY+Tgu4QZcYNtY+SRjDTFnoD3cAwd+oupzww2lraF/Atd93Blsr9rC5p6DbE4Pg8sDVhEyKVgzddHXIHG6GCBOlhgBNtIyZYfBYTnBAY7jw0OYAHkKHOA418kN0wfblzB6x+0cmtiDVxYJZdzkhcmhDQGurrmd+sqT29c7A+tKM920qKFVvdINlYUsmZQan08btsBn/xB2/QL6e6G1HT7+22q8TNGM43Y534UyDD9Fc2pv5PN2koOMME6eWnxso4EVRsR5UirBWplgrbtUZuIGZAJSVb7T5a8jUxxn2GOTw8SPm0bTIFi5+PWFHQyva+HkNS0kI24iyXrWvT1A4ws7nIH2+XMqk10uElXj5TbcyFD6JCdu30CiLkx0LMX6PcdpCjmD1H1P9REKCEJhCVw4Wux7qu9ioL3pMXjtr5PQ34vfPUHOrCFHO9f9vvNrd3vNfp5O3A7pcYLuNBkzRMqq5f6aPTgC7Yc/Ct/8mvo4GoVEQmXmn/ic8zH0dMGeF1U50tpr1fNuz4vQtBLWTGbcczKJz98IKyZLy9xSkpPO9rw5O4nvqs1MXD35HJJSkrcXfxtfHWhr2jKVMEcYNE+RlUkCIkKzey1RtzNLecbK8M8TA8TPGxQn3JyrKXJqxQC/U9NyWcF2tQsfKz1c6+Ob/RnAJuoSJCxJ3JQ80ejsbHMkU+Dvh1PEXIJWt0Hcsvn74RR/0Bie12B7VOapq9hgJYiLUZl/l1u8h4mz0PXzUjlAnSoH6Po5bHxoSrB9os/iV4ds+sehtRY+eLXB+rapLz5ePJ/lh8dyDCagOQqf3uTn3hUVJVamrbYjD4TUxjBmQb3NazrbMnYHDLw2eKXKeHmlRNqC7oDzRdREtotW6aHDcoMF4MaUHiayXQS9k9nVdMyLb8BEuA0V4Fs2LtMk3VLx/Tr1GlgWDJyaXBAXbVDj5YH29sdhbEil64ppMNwQa1XjZQaJ8zrd+PEQxU+OIq/TzS10OoLtU5zHh+firnsXjqc4T2MV9b6nW114QxvwlTaZ8QUj0L6W01HX1ED7Uma6adFN96qabFCZ7ExK1X/f/dis5z9rG7ZMCawredwxLCuLq6xfuW3n8LidL3rO20l+SS8BXNTgJUORX9LLh+x2Z7Adialscj6vOlQUCqqtYKS6bdpHmzeQOb0DSQCfO4A0M4yaWbLtt1C+DHPYGmf/nRvw5SXhlEXO52L/ne3c8Pxxx3ms6FBBaazs+ZNMqPEyQ7/9Kd4cegVfMkVkOEsuGOLNRz/AjU0foHylw/CoiwYxBCcGLy7+DNY3Mzw6eVZLSw+33fUSx/ZtJT7SQKxhgutu/jEtLXcBkz8/nSsyPMYz7Bm6gaFsPU2BUe5f8TKdKyoKM7ZeDV/+j7DjGeg9pzLZT3xOjZd77Xn1Ai9cepFz4fja845A2y8iFGUeD5O/000K+IXzhYDfiFC0c3jEZO22KfP4K19sLUI60NYWtSOjJr/osehN2rRHDD6yxsWWev20vZSEOUJP8QAefPgJU5R5eooHWMN1jmD730fH6D1fwFWTRDSZFLNukmcj/Ls1xpeaqgu059LWkJcvt+LoOvJE49SuI8/Fs8Rcglip1VPMpWq5n4tn5zXQrhc+0nLq7ob1oooWl71vTF8O0PuGI9A+0WfxTy/ZRALQXAOJLPzTSzZP3IUj2H7xfJav7s0R8gkaw+q8r+7Nwa04g+1iHjqvheGzkzu5da5X42WKwofXLlLe4tElTQqG87EWrASeij9+LuGjYCUcY7Y/QKGlDe/4BEYhj+31UWhowK7YOIWhUzDWp2pHfSEw82ohZbGid/PKDfCxP7rkQr0uBvDjwV9qBXnh2MWAI9BOksG08/TKs5gUceOhTtRTMEyqkSJDKNIEWydfbHilJFXNNuKtN6iabFCZ7GJGvbhYVbEAc9UGtfCxvOvI3Y9VX589x6LBTYzE9wIqk23bOSw7T23kOsd5BxkhgItg6Xt14XiQEVZQ9lzbuJVkR4yRVkk+7MKXsmjoF0SCFUXKM3Q46sK1+iZWDHbjzyXI+aOcbb8KK+ri7rLzTt7aiS+Zm3xOFWwoFDl5a6cz0P7wIwz/7B85dW2QZEOIyEiatW+M0PhhZ23diTVBfLFr8J/sBpHAH4nCuk5O1AUdgXaja5j0uTFCHlO9sLBMMufHaOwoWztzcjctm3O0XNc1OZbLwcnd0FT2QrUuSEAM0PKBbvzhQWpSaQI9Sah1dnYBVFBdGVhXGn6XNQnDzjUJTZ5OzuR/A4AbLyYFTJlnhde5pqPRu46zuf1gg1v4MGUekzxtXudC0sVIRyzaonVk1OTvDhap8QnawoJ4XvJ3B4v84TaqDra7zSyvFhMXu5Pc6YnS6V5+iygHzVN48OEpBXwe1LbVg+YpR6B9OBFHtCQQtkAUXeC1EC3jHE5Y0FTdH6e5tjXknRJYVzpftGh1OzOrEUNwvji/tdw3iDp2yj6QKpOdwSKDyXbRdOkbV8qMTm0J5wmq8TK/OqSC7Gig1E8/ACD51SHbEWj/8JgKsqOlp7c6Cn54LOcMtC90B+i8tmwuSYg459IqGul39SEsCwMX9v/P3ptGx3Ge956/qt73Bhr7ToAAuJOiKEqyqcXaLNmSJTmRbY2i2M69lnMT+ya5c8+Zk/l0z0xy7tw5mXEcO54bZ5w4cRLH1p1YqyXLkixZlEVKFCXu+wKA2Nfel+qqmg9vg6jqhkSwBYig9P7O4anuh9WF6ga6+/8+9Tz/B52CA5rtUgK3I0zRyOG0ZJ50M4+7rETA724hnzuBw6fgcDnRnQqg4XeXVVxmU6Cq9hHsuibi5SyhUS9OljB2Me/BSbxsuJGuFxhlDCcqTpzo6IyaY7TozVV9cwbxk6dwMTMOUEAjWFmRfmmiHaLx0eo60nnzomVGh2NdPL3zUS7kDdo8KvfVelgt0sTnaaIucgOJzHG0YhyXM0JN6JqK+uxZ8kTLrhz5cDKLfTGYvHMXF4ZfwGG6cONCC2tcaNVoa91FNXnPOFnC4WYGwwt19CYmibK/lWR/N8G33gGvVzQTF/J4cjmS19kXDJPr2nin6dN4zg4QHJ0lXxflncc+zTXRNtu7KEGGUG0T7Fwo8fFgkihblO1UX+AZ9XZQXPjJklHCpNUAn1JfAH6rdLAJyJqwb78o8wiHobdfOLdYGA062b32GrzxaSKJKbIeH7uvu4ZdY2mqKjSqb4FUYiGTDeKKSr29JyHsrKeTbUxoZ8mZSbxKiFb3esJO++dKyFmPqW7iQO4kmjmJSwmxwbuJkHORhcAqY1UIbUVR/huwA+gD6oAsMAA8AXzXNM3p93m45CPKz8/pRD0KEY8QFBHPQrwaoX22mOXx/BRBHNQrTpKGzuP5KR6i7iMntrNmEm9ZjbATN9myujddyWKioBpCoCmGg6KqoytX10TFVpeDuG6UMtmCpGHS6lrZfoZ2NcjdtNhcR25WGqprhPTH3qMcwF5YMDorMtlWgl4RtzKegPqy0wh6RNzGtlvhpX8Rty+6AyThE/Ys223BPh5PG+jKFDoFwI1bqeO2gF3YRn39TKT2iooUxYNu5jHMHFGfvVY65mgjlX4bHRNdVVF1nXA6RbBsGiEOL5ASTiiqC+YnvjqqG8YUwUcO7WL2ESBPkUjZcKOCMSuGcqCWHMFVwBTxMg4lNZ6cyjOUM2j3qtxf52FzyD4wq0tp5xDHwAQ3LgpoFJQC/UpPVc+DaMcl6/cPpwp8d2TeHlNhrmjw3ZEM32iBTUG7cD1y6jRPnxlkqAjtTrivp4ONvctUA/Y++DxNlY2PZdTgIYN2MZMNkKVIDfarKVNNJg7PRlxnz0MyiSsUgu4+pmrMqoR25D0GYZX/rYSizeSuVUQGOpmAUJj8xg2EovbndYZhPE43nogP3AYenw+c7opypDB+chTwWhYXeTTCZYuyTo5yb5vBmxNbmSw2UO+c4FNtv6GT4ws75YC33xC2iKGQyGa//QbssI9CP1IfwpuZxedwQcCLz9QhE+dIfU11QvvGu+CJvxO358uW0gm487crdg076yuEdTkn8zl+knESUjcRVFVSpsGhjIHbkVu2wWwrxaoQ2sCfAPuBXwITQAC4AfgvwGOKotxgmubQez/86ufwbJGnhzSGMibtfoX72l1sqlktv54rw4WkQUvQbh8Xcot4NezWEgRxEFKF+AopDjBE/KMmtH3vUffmK6t7i6lZxkwPqgoOA3QViqZKk3p1Ce17Ij7+ZjIFGIRUhaRhEtdNvlS78r/XdjVYXeNjOW3XiZpssJcDdN9q2625RpSBhC1PLZUTcSuN4UX2y4u4jdZeuP1/gndfEcMkapqEyG61N+Otcfh5KLCON4pzTJoa9YqLG51R1pTV8vvdjTQEr2cue4KCnsDtCBP1bbXVZwM4khfwebsomCl0M49D8eBWgjiSFyBqMfht6IE5L2TnSoNh/BBsgGh1w4j6aWIvZwGRyc5TJIfGVux1siYFYoablGpQxMSJQsRwY2K3WDuU1PjLoQw1ToVWj8KcZvCXQxn+uN1vE9sxtYbNxnrOM0SKDEH89Cs9xMpGZi8nT88sbo/59EzeJrSPnDq+k6TKAAAgAElEQVTNd04MElVNWl0wp8N3TgzyTfhQxPal2EodLycOw+Agvpk5srVRsh0d3Bi25+bzRgJ3tBmuXciaOk2TvFG+ulwa62hkD+eAhUFYeTSuwb4YXEsrb9dmYOd1eHCRRyOPxqaygVnJzDgNR/ZRm5jBo+XJuzzMTI0wsXkHBBaaJntpZh/CLs96vM2UDZBqbqEzMUhni+X5JebAkoHn+AT4XOAq6QmvUzQLH5+wHWrO7SCSKor/Ky0tvVqRuVCVyYo1/fDA74ma7MkRkcm+87dt9dmXw8vZNCFVJVz6/g6XJkG/nE1Lob1EwqZp5sqDiqL8OfC/An8K/MGHflYfEodni3znWJ6oW6HVB3MFk+8cy/PN9XysxXZbSCWeNy9msgGSBRGvhnFDo16xv54BRWV8PkP2EaLR2cM57R0wF+reNPK0Oe3NSb0BA21UJxV2UvCYOPMKsYROb3N1i5krxQa/m6/XB22uI1+qXXnXkWUl2iEaH62uI923VmQtP7VZ5Z9eNQCToFeI7GQW7t9pf198aZ2Xb72egmyKoJolZfhIm0Eeu2aRRUFrb4WwXow1cxOsObdnwct4zQ0Q66rYz+9urBDW5Zj5WVzuWtzKQsbeNE3MfFnGuH8XR/c/xfMNaxn1hGjOJ7l7dpgN/bsueb6L0UiEHZkgx7QTzCh5wqaHHa5+Gv32hjkfPgoUqDespR4F3GXZzCen8tQ4FaKukph1CTH75FS+IqsNYCoKJmCy8oPGLuQNWtz2nyPsMe3v76fPCJEdLV0RijoATJ4+M7gqhHbrmSFu++XPObB1DbMNtdTMJrnxX35O650R6F34TPOo4UUb5jxqda4jTUS4gTUcZ/yiQ801tNFU5lBTr0S51uzlNMMkyRLCxya6KtxpGk4eo3FqGNPppuD24dQ1GqeG4WQArrltYT8lyg5zLacYJUGGMH4200lDudvNb38Fvv1n4nYwLEo1kin4vT9e2GdoFto6gFkgD3hA7RBxC9GcRtYTwTc3AUYRVCe5aAPR3Af4flzTX7WwLmdUL9Ko2kV/UFEZ1avrmfgwWRUqbjGRXeKnCKF96W+Aq5inhzSiboVo6QMx6l6If5yF9mfWOPh/Dog3ecgtRPZc3uThddW9Jo2qi6Shi0x2ibRp0KhWfhkuJ8tdFz50ymTfyzAzCrXNsOM2aC8bHBN21rGGa2yuI23ODRWuI5+o7STtOkFiOEBh2IM7mifckeYToeX5cPww2eB3X13CejGWUA7Q2+Lgd27B5jpy/85K15E7fBMQe5t/TaxnPBek0ZvmsfA73OG7FsozY0th+jwceFI0JAZjwgHkwJOw9f5FxfalUDw1mMWMGAgyj55FKfMtPhDw8oPendQmZmjKxEn4Avz33p38u4CXSuO+S1NMDxMe3cv1Th+Kw4epZ6G4l2Kz3zYFsk9Zx7vmfgBcuNDQKFJkk2IflzmUM2j1lL//FIZydjE7bcxy0DyOGzeBUr32QY6zxVi3YlntNo/6HvaY9kXZUBFayz4Gww4YWi05iF89R26mkcnnehnPhyh6kuS8GfjVczahXeday4WyhjndzNPsqr4qvYlIhbBejHolekk3mrWnTjJV48F0uHEYJnm3B8WhsPbUSbCXc9OgRGm4lLvNLfeI7f/4IYyOQHOLENnzcYD2duFzbf1cic+JuIWNIyl2h4ugePDiJqco5LIpdiScVX1cXA6nhw1eeddgbBaaauDWbSprW+1/o80OJwlDv5jJBkiZBs2O1a+RVvsZzhcJHryiZ7HCDGVMyh23wi4R/zizIebkP2zF5jry8Dpn1Y2Qu1xhHs9PgSEy2WnTIIXOPa6Vu3S73HXhQ6dMnv+RmGpb2yicup7/Edz9qLmo2C4X1uV0O7u4MwSH+k6TIE4YD5vVfrqdXZd9bpJLsJSR2Uukt8WxqJ2fjcG93BFMc0fs0EIsn4bBvVBbxTfnuT1CZHtKGfH57bk9VQltV+1W8uefgnQcijlwejEDEdxdn7Pt91xynIDXiyfQRgHwAAHd4LnkOFuDLYse+/0ozB4Epw+1JPAVpx+jFLcK7S7XGtDgpHmcLFl8+NikbBFxC+1elTnNKGWyBYmiSbvXLhTOcQE3bjyKxS7QFPEYK/MZdF+th++OVNpjPtpgr21ud4pykajlTyqhi/iKc/oYvPIcjA1DUyvceg+stTtOnBnQeTx/E0FngXp3imTRy+NzN/FQ9hdYK9xDrnrauJYp7TR5I4FHDdPs2kTIVVn/u+RhRMtIwADiRWajLvJOBU/RpCZeFPEqmbnpegZ2tZA20wSUAJ1KO7Y25vvuh+/8pbg973s9NwuP2n2vm98aYld9kiO9XcwF/URTGXYcPEPzZAhurP78LsXpYYMX/+kgW08+w67kBWZDbbx4+F74nS02sX2bL8CPknOAyGSnTIOkYfBAoEqP9A+RVSW0FUX5z0AQiCCaI3chRPb/cSXPa6Vp9yvMFcyLmWyAhCbiH3c2xKoX1uV0O308RJ0tu3yPq2ZF67OXuy5838tCZJdPVN73MrRXed2n29lFN13VPViyNJY6Mns5SU1BoMyh2e0X8WpITopMdvnxkpNVHc6ZL8JsBs1pYDqdKIaBezaDs7mIdabyqG7QUH7JWFUZ1atzlDHysyhuu6BSHD6M8pIVhNjuYk1F3Mr9dR7+cqgkZp0KiaLJbNHky812MZsiTaCsmc2NixTpqp7HUtgUdPONFlGrPe868miDp6IR8r6eDr5zYhAwCTuEyJ4zFB7tXeFhSaePwT//DYQj0NAMybi4/8jXbWJ7t3IjnYXjrM8fIKDPkHbUckzZym7vjZS3koZc9YsKayuXNYzowin7lM7tt73/MKH3o3kdgfMHCBTMBc96LQtd1VybgRljhiPGUVx48OMnbxY4Yh5lIxuoVUtye+Nm+OYfw9NPwtCQyGQ/uojv9YUZmonSHD8DagEMNxTCcGGm8gcPn4ZDry70dGy+pWLiIwCHDsITP4PBQejogAcehM32K0IHnjnEzXv+GiMUJVPTSigb5+Y9f82B6DdY+/WF16XP4+XR6SQvTwwyahg0qyoPNHTQd4lG2tXAqhLawH8GrIV9zwNfMU1z0U9yRVEeAx4D6OhY4Q+EFeS+dhffOSZsisIuIbLnCiaP9lzll8FXId1O35IE7nKVeyx3XfjMqMhkW/EHRVyyilnqyOzlJFgnMtgei2otZES8GkL1CwNjrMcLVWmvNfQmTk8Mp9VlpZCGoTdtGfdmhxhAFHYsZLdShkGzY5FejaGSKJoehVizEEVlK1DVU4NRzKBYSlZMPYtaPmobIWQGzCF7tlC12x5uDrn4Y/8sT54fYagA7W74clcLm8uGpAQJvIe9X9lAEOCgNsq7+hmyZPDhZ5ujhy2u6qY5bgq6K4R1ORt71/JNRK32kCYy2Y/2fjDXkSVNcnzlOSGy51+r+e0rz9mEtu7zsGP8eTRHkLQziltLskN/ntca/0NV57bkYUQXTsELpUuINQ3iEuILP4K7Hq1ObG+6B7IzEJ8R9pReL9S2i3gVDJhDuPBUXCUZMIeotea1l+J73dImSkwilqtE8TkRtzJ8Gl75MfhCEG0QVqCv/Bhufdgutg8dhO/9N6hXoFuF7Clx/w/+F5vYrnvjGfRQBM0vXveCP4oLk7o3ngGL0GbsDB1v/AsPeE0yXif+XJHoGQU++TvQVKVzz4fEqhLapmk2ASiK0gh8ApHJfkdRlHtNs1QsZ9//+8D3AXbs2HHV1llsqnHyzfXYXEce7XF/rOuzryTLWe6x3HXhtc3is758onLtCk9UlnxAljoyeznpuB6OPC1uu/1CFBfS0Hvb+z/uvVhzg6jJth4vn4Z1d1TsOmImOcz4RZG1iUZayhxvSE9WTjJ0+UXcwj2hRv52dhR0Q9h6GQZJ0+RLobJM1tAp+NlfQ3JWZApHz8HAMXjwD21i212zhdypJzEmxlBSGcygHxqacPfebzvckrKFAONn2Xzof7DZGwKvH/IZOLQbfL8NjQue4Gto4yDH7fZ+FFiH3Tf8oDbKG/pBHLjwlhoy39BF9WS1YnspbOxdu2yNj0ue5Dg2LDLZVgIhEbewXd1HuqYLsnko5Cm4QxTCdWxX9wE7L/v8kmQIzqRh4AykkhAM4e7sIVlbtujZX7qE6C994M5v979cndCOdcF1j8DAXkhPQaAOOq+vqvQKIG2m8S9ylSRtll0lGZnPQI9DTaPIQLeU/a7vvg/+9rvidigsbArjcfjCo/b9Dr0Kcxl46wjMzkFNFHrXiLhVaD/5T9BcFJ8VOMCvQ3NGxDf/nxd3a8pcYCbYbDNqTDnDNKUu2J/r0ReY9hYwfUHcOMj7HEyQInb0BQJN1S24Piyqs29YYUzTHDdN82fAXUAM+McrfEorzqYaJ3+6xcf3bvDzp1t8UmRfQazlHqqiEFIdBHGwW7t8i6hdrjApdJKGjmGaJA2dFDq7XNXVle24TSQQ0gkxWTidEPd3VKmdJB8S/jphTWdlsZHZy0ltJ2y8T2S009Niu/G+6uqzQYiBrfeLjHZqWmwXaYQcMZO8ynmyFIniIUuRVznPSJmHO4F6SI3D1HEY3S+2qXERt7A12MLXapqJOBQmDJ2IQ+FrNc2V9dmvPA6TF0ABfEGxnbwg4hackym8b55DzRUxQ17UXBHvm+dwTtoH4FizhYqi4FHcuPAwUO40e/J1pmMR9q8N8lqPyv61QaZjETj5uv3lU2vYoqzDo7hJKxk8ipstSmUj5Lv6GRy4cJd+rltx48DFu/qZ9/rNrDqskxwVFPy48OHgAGULy6ZWSJf9XaSTIm6h2z9GyhWjUNeG2dlDoa6NlCtGt3+sqvMLzWQonD4gRrUHgpDPUzh9gNBM2Xt0Zkz8LVnxBUW8WjQDEjmYyYitVn2BdkAJUMB+dbSARkCxLBhGTsOrPxbe+NF6sX31xyJuZd1G+No3IBwVjZXhqLi/bqN9v+OHYM/bkMlCNCK2e94WcSup8+DyIfK5iti6fCJuoW5DO2oqQV4zMTHJayZqKkHdBnuzZiY+iOkN4MRROpoD0xsgEx+8zFftw2dVqznTNAcURTkKbFMUpc40zRVM/0hWI2e0LL8uJC+WcNzsDtHjWllv5OUs91juuvD2XoW7H7W7jtz8QKXriGSVsdSR2ctNbWf1wnoxYl2XzL4dZhx/Potv7jxoGXwuP0QbOewZp8U6NiTaDoOvg9MLTp/Itmemof26imNuDbZcuvHx7CGRUXaVcmMuD5imiFvZ80ucag3OTJiLg/bUBOz5JXQtuO2kzTT+dA4mhyCXBm8Ad3076YDds3dan+FwTy3uooK/AHkHHO7ys+nkDGXXMIhNzRI7+a6Y1hdugL4ANNiFdpYM3jILQRcustWMar9CLHWSI7feI2qyQWSy00lIxOG+L9l2C3c0s8md4PxciHQGAn7oa0gTbqouw9/z6iFOeeM0vXkc3/gs2cYaRnauo2f4EHz+loUda5vgzCk4cX4he9vfBT1V1miPnYU9PxWL1HCdEL17fgo3fAGaui/9+DI6lXaOmEdtV0k08vRZhyAdelWUefhK77357aFXK7Pa6zZWCutyzo8JT25/6W/U74OiJuJWol4oZMGbBUUH0yGG50Tt75/YFz+H9ud/xYlRGDXCxNQE/bUJYl/8im2/ZCSIL1vA8C18N7tzBZKRIKt9NuSqFtol5j9dV3aWsuR9OZnP82ImxWhRp9np4A5/kD6P59IP/ACc0bL8JDdDEPViCcdPcjN8kdoVFdvLXe6x1LrwpdLeq1Td+ChZOqMkOMIEc2SJ4mMjDTRTZYf7ZYzMvhJk8+PEswtjsCO+dfg87++D/V7M5qeJTpwDh1ssKnQN78RZZhtM8Fq+2JPDEOsVNavzEzEj7SJeTmIIxvZDbhq8MWjaDmF7xgtFmZ+zsYBZiluZHOVYSy/PRVoZcflo0bLc4xpm/cgp226BVJ782HE8pgs8ftAKFEaPE2haj7WMd6CzFndeW6j31QFdY6Cz1i60J87Cr/4eEjOgFcB1FoZPwqe+Cg0LIsuHv+TXvSBUNTR8ZSUCZ4YMXttvMj5t0hhTuGm7Qk979Repp41ZUY9OmgCiHr1a28EaPGTS0/gnFhYp2YZ2asqbc9euZ/yrX+bk5LvEHQUieoy++m00dthdR7j2dqLP/yPb2pWFKYOZJFz7YFXnV//GfkIjg8w2BEk11eCfy7D9n3+Nt6UDPm/ZMdwOv/ohBIMQDYtFwK9et3leXxbHXxMiu1z0Hn+tKqFdq9aykQ22PoI+pcde2jQ7LjLZVrwBEa8KLzgTpb9hF2haSUmWDY1Z0w1n9oHuEkNw9AI4NVhj/90OeDbwevM3WV98mvbMEHF/Oy83f5lPejbYXAXT63YQeONFFED3+nDksii5NOlrKkvXVhtXXGgritIHjJumGS+Lq8D/DjQAvzFNs7IlXPKhcDKf54fxOcKqSqPDQUI3+GF8jq9Eoisqtn9dSBJErXDs+HUhuaJCe5crzPdT40wUIGeAV4UGNzwWrE54SK4+RkmwmwG8OIngJYvGbgbYRecHE9urRFhbyebHmUy+gUP14nSE0Y0sk8k3qOfGqsR2zdwEWbcH3/zXi8NFzqFQMzcB1rLqzDSEGiFsCZqmiFtJDMG5Xwi/bU+tuBJw7hew5tN2sd29GYb2Q60KHgfkdZjIQtd22+GOtfXx/WAL4XyCpsQIcU+Q74dbeUxVsEqAzmMDHGl2Ay7cRSj43Ggo9B0bsNmdpesb8Q+dEd+mThcUNdzFPOn2siayfU/BxJAQ7d6AqCOfGBLxzywMGNnm6OEN/SAFc8G/W0djm2Ph7M4MGfz0FwZBP9TXQjJt8tNfmHzh01SI7TODBrvfXhDku65V6Omw7zNtzHLYPIbbdIt6dAoc5hibjPVVie2t0zleTp4BU8Xn8ZM1i2Qnz3BjLoA1/ThOnDc7DLwd1xLGRQ6NN9HYSZxGq3d1Rx/c/bvw9kswPQaxJnEpr6Pvss8NgNk5vIUCzePTMDImhKCmiKy1lXcOQt9W4dSTTYnGzZYeEb/1M5f/c+MTIpNtxRsQ8SqpPT5C7bPPwIUhaGuHz94PGyxCu6ZRZM59lqtJubSIV8Pa9TASEO/TXAq8QYg0Q0vZZ1ttHczEIJURY9+9HjFUp9b+/PfuhnxdK6c8d4nX2BckH2pl727otKw9mpt2curGDM3Hj+OPz5KJRBi9Ziu9TZdfo/9hc8WFNvAZ4L8qirIbOAdMI5xHbgG6gTHga1fu9CQvZlKEVZWwozT6tLR9MZNaUaF9pSY5FnWFbMGFYRZRFR3DdJAtOCnqyup4x0hWnCNM4MWJD3EVY357hInqhfYqJZ49jkP14lDF4tWh+C7GqxHam8YnebW9AYrgNUxyqkLG6eK6oTKh7Y8tZLLn0TIibmVsvxDZrtJ+89ux/Xahff3N4BwQdbdZDdwOWBODa+3lOc91bUE5eIGR8U4y+RB+T5JQ4yjPbdliE9q1I+NspJ6BOkh7IZCDvimV2hF7JjDgayLf5sAzNXrR5aXQ1EnAV5ZFHDwKHp+9tAVTxC3MNzzaXUfW2xohX9tvEvRDKCCy9aEAgMlr+016LC/JmUGDx5+3C/LHnzd56G5sYnvAHMKdTOMZOQHZJB5fCFraGAgNVeXx3XpgD7c5NQ601TPrdVKTU7hxcJLW4h64Y+FVPskoXlx4S9n7+e1JRu1CG4SoXoqwPnwInnoCLgyKiYifewA2lTluOFQoJgG3WBxpBbHwcTTY9xseguYOULsWYoYh4mXE9SnGiucuDghrcq4h4igT1ZGGxUVvpOznLpWjh+F734ZIFFpahWvI974Nf/BHsKE0pGfzLaImG4Soz6XFOey8t7qfeee98PffE1dh5qdRJuIibsXQINYDuWPg1sDpFPfLvr8nzyWomzsAbrc4Py2Pf+wAk7lrsF46qlNroOlWzjb1kiRNiAC9tIr4Kmc1yIYXgbUIz+z5VzYNnAR+BPyVaZqLGDlKPixGizqNjkV8bIsrW81zpSY5vpRN0+J0sU5duBSWMHReyqbp83jf55Gri2PZPM8nFkaS3x32sd63suU+VwOjR+Dw0zA7BDXtsOk+aC4rS5wjS6TsUqgXJ3NkP8Qz/eBMG7OcN4dIkSGIn65FygG0Yhynw754UBUvWtF2kXHJtChhbhmJc7guzKxLoUYzuW4kQYtStkBpuw5OPCtuW+vWu2+175ebFplsK06/iFsxp2DNFpgau5gZo65JxC2cGlOYG96EW8ngcycp6D5GhzeRbSh7vtFGaqeT1GYsoiibhqh98dGptHPYn4L2dQtuIkqBPqWstMUEKsauL1LughDb7+cwMj5tUl/2kgT8Im5l99uLC/Ldb5v0WBKQ6fQo/tPHhfj3BUHL4z59lHSfAWG77/GSmBunNVJP63lLhth0QNy+SImTIVxWj+7BRXyRevQRM8lBJpklRw1etlBf6WRz+BD81bcgGi3Z1c2K+//xT+xiO+CCUK0Q14U8uEvPO1D23dLaDgNnYXpcCMpgGGKN9lQrQmSfLRzApXjwEkQz85wtHKDbvdUuttfdBL/4Wxgdg0wG/H5oboJPV5lLfPZJIbKjJUE6v332yQWh3bIWbnnY7jqy897K+uyl0r8RvvoH8MtnYHQYmlvhtx4RcSsFBQ6/A8msKC9x5WD2HbjuFttu9YVTpM3Qwkvv8pDJe6gvnALs/Rp1ag11KzTcaSW54kLbNM3DwDeu9HlcLYwdgqNPiPkX0Q7Y8AA0XcIe84PS7BTlImGL2E4ZBs3OS0ymex9Oa1l+nbc0OXpCrC0rB7nZHeInuZmySY4Gny0bNrHcjBaLlQsLRWW0WFzRn7ucHMvm+f5UkohDpdkpfIi/P5XksTo+1mJ79Aj8+jvgi0K0FbJz4v7N37SL7Sg+smgXM9kAOYpEWdlG3OVk2pjlUKkcIFAaynGIY2wuKwdwOSPoRvZiJhvAMHO4nJVjpzP5ceLZE2h6HJcjQsTXj788691+HaGzT9OTTpFzq3gLBqG8Bt332feLdkD/Z+HCW+IytD8mRHZ5eY03JgS4y5L5LmZE3EpuGqItUGNxrDDNCkFePO3BDDhwu73M51DzBQfF02Xviw03wes/LZ1DKROYS8G19pKBmFrDJmM9AyzUN/cpPZUlF63rYeCAqBkvlZiQz0Dn5Q8raYwpJNNmSTgL0hkRt7JUQR4YGibv9y54fLs8FFwKgaFhKO+NO3EUXnoWRi4IMXv7Z6F/g32faCOZ7BRTPpM8Gh5c1OUU/GWLlAh+chQuZrIB8mhEyurRR8wkrzCIFydRPGTQeIVBbjU77GL7qSeE2IyWXvv57VNP2IV2ZxMMj0AuD0YRNESNYGeZZeTGjfD//RPkCmCYoCrgPQOfsf8tjxXP4VI8uBTxN+QqmdWNFc/ZhXY8C8cnhNWd3w2aLu7fkLVf7VkqF4ZEJttKOCziVlrWVi+sF6N/Y6WwLuedU8IWy+EE1QtKUdx/59TCzG/g+vp9PJX4FOQK+D0FMnk3Kd3NbfW/olxoX62sSns/yeKMHYLXvwXZWYi0ie3r3xLxleQOf5CEYZDQhUVdQtdJGAZ3+IOXfvAinNay/CQ7Q9LUqVedJE2dn2RnOK3Zs4U9Lh9f9NYSUh1MmkVCqoMvehdvhDw0V+TPDmf5+ptp/uxwlkNz1YviZqeTETPPESPJW8YcR4wkI2aeZucVX5cumecTWSIOlYhDRVWUi7efT1xdGdnl5vDTQmT7oqCoC7cPP23fbyMN5CiSRcPEJItGjiIbqfIS7xXgvDmE23Tb7OncppvzZfZ0Ed86dCOHbmQxTRPdyKIbOSK+dbb9MvlxJpN70Y0cTjWMbuSYTO4lk7dnKZMhPwPdLWhOB55cDs3pYKC7hWTILp4Ahv0FXut28ouNYV7rdjLsL1Q+kabtQlhraSGctbS432SvvcYbE3Eriwjy2owT3aWTU1RMIKeo6C6d2kzZ+7u5B9bshLPHYO/zYrtmp4iXERucYPvjv+am7z3D9sd/TWxwkZrb6z4HNS0l8V96LjUtIn6Z3LRdYTqd52xqggF9iLOpCabTeW7abhfajTGFdNlLspgg7zwxRsHnJu8UCfa8U9Skd54oc5I4cRT+4XuQmIOmFrH9h++JuIXpDduYyY6KMhTTCdkkM9lRpjdss+3XRzM5NHIUMDHJUSCHRh/2bP5BJvHitNkFenFykLJZdhcGRR21lXBExK3EGsjUeZiLeZkLupiLecnUeSBW9v7e+xqnOzr4wSMP8+f/6T/yg0ce5nRHB+x9zbZb1kziLHNZceImW25p+ctnRF9CzSYIbhbbUKOIV0NbuxipbiWREPErzb7DMKaA6QQPYjumiLiFzrUOPrdhDwFPnqlUgIAnz+c27KFzbfWJvNXG1aMcJBx9ArxR8JUW6fPbo0+sbFa7z+PhK5GozXXk86Fw1fXZv84nCSqLNDnmkxVZ7R6X75KNj4fminz7RJ6oC1p9CnMFg2+fyPNH/bA5evl/4v1uB69kMvhUBT8qKYpMGhq3eysnuK1WhjWdZqd9HR1SFYa1j7d5z+yQyGRb8YZF3EozYXbRaXMd2UHrVVWfnSJDoCwDL0Z+25WXz9NIPTfaXEdqA9dU1GfHsydKtdyipMaheC/GrVntCe0MTncMV4MwjHIBGHkmtDOEnAt1y8OF0xwqvoXLdOFTvBRMjUPFtwBodVuyb+F20fhodR1pv6nSdaRpu2iSBFFaUsyIf+032Xbr74jiPT/ISDhE0uEhpOfpTiTp7CrLpJ8/Ab95EYLt0LBeOF385kWItdlsADl/Ap78ezFFKtYoSgye/Hu4/6v2/Zp74PZ/B0dfg7lxUYKy4aZFhfulHEVqWufYdOc5TrxTT3zaRySWp/+Tx6lpXQOWiYC7rlV4/HkTMAn4hchOZeCem+1CO+aIsenoHM6F8JoAACAASURBVANdEdJ+hUDGpO9UnJij7KrBS88K4RouXVWc3770rC2rfbzZi2PXHbQcPYxnboZ8tJYL196I3uzlk5bDNRJhJ2s5yShxMkTws4XOivrsWXJEJ+bgxElRDxyO4OvvY7ah7OpmW4coF4lariYk4iJuYTqRId3fgKGboBvgUFEdCoFExuYUc3p4iB8/9BAhTaMhmyXZ2sqPu7p4+BcvYM0P+5QQmpm/mMkGKFLAV17aMjoMjWU2lcGwiFfDZ+8XNdkgMtmJhJjm+MiXqzvecqIbkAVGLH9r+VLcyrZP0Tn1z3RunQZfQJRnZZKw7ZEP82xXFCm0ryLmBkUm24o3IuLlHJvSee5MkeGkSWtI4Z4eJ+vrql8h9nk8y9b4OG5o1KvL1+T45AWNqAuibvFFFHUrgMGTF7SqhPYQeTa7PYwVDVKmQVBxstatMlTuAbuKaXU5iOsGEcfCh1zSMGl1Vf83cF7PsEefY9IsUK+4ucERpctRmaVczdS0i3IRn+X7OZcQ8XKaCV9VwrqcYMk9onLkd+XvzOdpvGTjo6bHcarltdweNN1e25wzkngU+9Uup+ImZ9ize2eLR3CZYjALILamiNuENojBHsfHxKCQWg38OSp+NUsU5DffFONfx/NsGj9BoDhN2hkjWdvPzTeVicq9LwrxPD+GdX6790W7gN7zS3ApkL0AiawYyuGKVPhyA0JULyKsrSzFUWTQHKSjTWdt+0L7UsHUGTQHbaO3ezpUHrobm+vIPTdXuo6w/TZiv/gRsXjRbp/36bIGt5ELIpNtJRgWcQsJMoSauxhqXnMxZmKSXKT2upFIZeNjGTUTcTIH9+M3HWJqYS5L9uB+arZcK6wT5vncA6ImG8SCIBEXDYK/+1Xb8Y60eHGkGmgdH8dTLJJX3AzVN6AHvVhbZ1/dto1QNktIE99NoWwWikVe3bbNJrSbnGs4O/kqnB3EORWnWBdB6+6gvd5ej0xza2mhYPkASiVEvBo2bBKNj88+ueA68siXF+qzV4gl2f1u2A4H94pSKZdbNJymU7Dlevt+bb1wxyPw7q9K7+8m+MTnqpu8uUqRQvsqItohykV8lsV6Ll5Z0nhsSudv3ikQ8Sg0ByGeN/mbdwp8/Rr3BxLby0Wj6iJpLl+T41DGoNVnz9CEXQpDmeombo0bGq0ON+3OhWMaprnibifLyd1hH9+fEsImpCokDZO4bvDFmuqy8uf1DE9q4wQUBzFcpMwiT2rj3E/jVSW2N90narJBZLJzCSG8r3v0/R+32hgjwVHGiZMlgo8NNNJUpjy7lHYOccw+8lsp0K+8v9B7L1yOCHpmAkdyUjQuuvwYoXpcfvvldq8aQjPyF+tVAYpmAa9qz+5lzCw+xd5w6lKcZMyy8qYLp+CFH4lR2DUNkEmI+3c9WvllHG6vzHSXsdZ1ni/VvcyvlY2M5Vto8szx2djzrHXdBtZx6FMjwkbOij8o4lZGToM5KyzinF7hF1wYgZHqyrSW4iiy2Oht12KjtxFiu+dSrpLtvfDpR8Vo8elRiDXDTQ9QYdjf0ibKRcqFYos9AxR+j9rr8CKLvKWw5anf8MymNuKJOvITHjzhPJHQFPc+9Rv4msVrcdNm0fhodR353a9WuI7MRTyE024GOheen5nPkYjYxeJYXx8Nx4+BxwsOB+g6gXSasXV2L+jIiTG6f/YqY9c2kW2O4JvO0v6DV4k82A8bLDXa844dYHfs+K0PkL3dsGn5hPXBg/Czn8HgIHR0wIMPwhZ7M+zJfJ4fjl8gnBilMZ8g4Qnzw3AzX2lss4vtL/97+K8XIBMXAtvpgrpWES+nrfcjJazLkUL7KmLDA6ImG0QmOxeH3Bxca1+s89yZIhGPQsQjPqjnPzueO1NcFUL7Zk+In2TLmhxNg896q2tybPerzBWMUiZbkNBM2v3VtSBcKbeT5WS9z8NjddhcR75YE6i6EXKPPkdAcRAs2S0GSx8de/S5q0poN28UjY9W15HrHq10HVnNjJHgdc7hxUW45PH9Ouf4JGtsYjum1rDZWM95FlxH+hdr0lsiES3E5NweUNyoLh+GkUefO0utyy7cG1w9DOTfAUNksotmgSJ5Wl32hjm/4qNgahcz2gCaWcSvlJWK7X9ZiGx/6bnNb/e/XN2X84ndrG3Is7bDMqAml4cTu6HRIrTrWkTzVsCygMmkRNyKqwA5U9iTgRjUoxXAu0i9+RIYnzap9yVgfPjigiYQamV8euE8AkqAglk51MY2evtyae+tFNbl3P5ZUZMNdqH4oF0o9tLMPsSIbw8u8mjk0dhsG0EiGCduKx3po7kiw138TYL08W6MG7M4GgoYky7SL3RTnDlaaf67aXOlnV8Z0axO1u/GmyvZzhWL5P1uoll7aV3Tmj6SM7OE5maFO4nqIN3QRNOaMpvBnz9FJKMT2XMKjJxo/tP88POn7CJ4qY4dV4KDB+H//r9E2U1bG8zOivv/6X+2ie0XJy8QnjpDWFXA7SdczMLUGV5Uoa/N8lmwaTP86X+5tNXixwAptK8imjbDJ//E7jpy7Vcr67OHkybNZX2KIbeIrwbWunx8kVqb68hnvdGK+uylcn+bi2+fyAMGYZdCQjOZ0+DL3dUJ413uMI/npsrcTnTucVcKlDPFLLsLC+PVd7nD9CzjFMgPwnqfZ9kcRibNAjHsr6cfB5NmdWLiStK88eoS1uUcZRwvrgqP76OMV2S1Y2pNVT7Ii+EfOU29ESXu19HUIi481Gb8+EdOQ/01F/cLOetx6us5oR1D02dwqQH6Xett9dkA3c6NoibbFJlszSyiKRrrnPaGOWbGRCbbii8o4tUQH4dwmce1J1BhPcf1d8DTPyw9+VI5RToBt33evl+0BobioOYXLpFrOjRV97o3+uMkRwYJ+cyLkzXTowM0tnRAqYK4Q+ng/NjL1J04iTeRIBcOM9HfR0fzIhMLz52AN16AyRGob4Eb74I1/ZX7LYX+DfDlP7C7jjz4SIXrSIMSZYe5llOMkiBDGD+b6aRBsSdTxonzJqdLi0YfOQq8yWl2stYmtt8ybyU2kCEwbSI8HAzSqQxvBW+lmhFQ/Wk/e7ojMD6DZzZBviZMrrGWbWftVy1vaevmxypw7iyBqSnSdXUk13Rzb0vZFMeRUxDIgOkC1QOmBuoUjFSWyizJsQPg2BF4/mnh2d3aDnffB+tX8IPrZz8DQ4ejhy7WwdPULOIWoT0an6BRVcSCEsDhJkiB0fgEtJVdLVvCoudymCDOSUZIkCWMjz5aaLhE2dFqQArtq4ymzZdufGwNKcTzJtarYMmCiK8W1rp8VQvrcjZHnfxRv6jVHsoYtPtVvtztqqo+G6DH6eMhb51NQN/jrqkQ0GeKWR7PTRHEcXFE/OO5KR7y1q0asb1c1CtuUmbxYiYbIINOveJ+n0dJVoI4WcKLeHzHF/P4nhu02+e1XbfodMolea6nJ/H76/DnLJ8jiglpu/PDeT3DC2gEXP34cRBHZwANr56xXf2Yr8M+WzxCxsziV3ysc26rrM+ubRLlIn7LIiKbEvFqiDRCLgleSylLPi3iVrr64b6viJrsqRGRyb7t85V11229TMXCnKsxSfochLI6a2YV6nzVTd67qWEfPx3sAs0koGqkNT+posJnGvYBnwagdmKOwJtHSXgNMqEAvnyBjW8exXPjdmiy+PmdOwFP/F2pUbNJZKCf+Dt44Pc+mNgut/NbhAYlSgPvf5VyqQNrpuq3EJt9DXJe8Hggn8ev5Ziqv2nR416Kxm23c8Mv/5kT29qZ624nOpNm294hGu+0Z+bXunw83NLNq7EGxnSNJoeLez3Byu+uMGKMsK+UjFBcYnBStS0ex47A978jPLKbW0WD4/e/A499c+XE9sF3YXAAfL5SHXwOjh2FtL0cqTk7S8IbImwulGamnF6asys7vHvi4qLMSQivbVG22sW2FNofQe7pcfI374hMY8gtRHY8b/KlDVdP6cPlsjnqrFpYL0aP03dJsby7kCCIo8I9ZXch8ZET2jc4ojypiYyfHwcZdNKmzh3Ouks8UrLcRN7D4ztS7vE9NygGwrgC4KsVUxhPPCu8qy1ie8me64H6xSc5BuzZ4T36LAHKyoxMES8vM2p1r60U1uVsv03UZIPIZGdLjXq7HrjEK/Ue9O+CvY+L256AENm5FGy9p3Lfrv5KYV3G1KbtHEjtxWM4COIiH9I40KSzNbidat4dPYFzfOFaeO1MC+NJH42hLJ/ZOEJP4NzCTsdfw+OtpX5+wqAPIAnHX4MmS7b1jReEyA6WFN/89o0XqhfaS2XwJOx7CaZGoa4ZdtxeMdlxqQNr6tbVkPZ+gsDQ0YvZ1kzvduq6FrlqMH0ezu2B5CSE6mHNDRDrsu/Tu4FGHqHxV8/B2EloaoU7H4HeygXEkpJCLfVwYAwmJ8XkSFUFrw+617z/496L558WIjtSWqjMb59/euWEdjIhGhe9pUW81yvEdtJuH3gHeX5ohkFxEDR1UoqDhAmfX8Qs4GQ+x8vZNKN6kWaHk9t8gaqHvp1kBC/ORRZlI1JoSz581tc5+Po1bpvryJc2uFZFffZHiXFDw9AU3slpzBVNok6FXq9CxnX1NE0ulS6Hn/tptLmO3OGs+1Dqs5cyyfHjxAYaeT1/GJJTeAsZcm4/uVAd13rKGqIuvCVE9rwwnt9eeMsmtK2e68BFp5rnE1m70G7fCUdLfr/zkxzzaej5lO3HTpkFupPTdEwew5+Lk/FGGKxfz9lQmavHUmnrFY2P+19ecCXY9UD1zVON3XD9Q6ImOz4uMtlb77HXZ89z5jdw6gXIz4EnCr13Qc8nbLuciyl4vD14Jkcgl8LjDUJdC+cCSlVCG38dPc4xem60uLQU0uC2Dj6ZgHDZ0b0BEbcyOUIqVsM00+TQ8OIi5vcTnCxr6LwcBk7Amy8tlKLsvB06y0T74En4+T9CICQsD9MJcf8zv2sT20sdWHPdLfDsSB1suxl/ADJpSCfh1jJTD6bPw4EnxQIqGIN8Stzfev+iYnsxYV0V4Ri4FWFfZ5piAKhbEfFqGB6qdCIJhRcd/b4kjpfKUObLfe6+D9aVfYjGamBmBrLZBZFtGCJuoa9zG1858iIvRroYdQdoLqT5fPw8fRvvsO13Mp/j/01OoCtpVEXjlO7ibDLAv6ehKrGdIEuo7EqeBxeJq2BarxTaH1HW1zmksF5piiqvJzVCDpWwA7KGyetJnZvCH80rB10O/4fe+LjUSY4fJ5rm5vjkhWMcjUWI+/xEcgWuPXWMprY2iFob96ZFJtuKyy/iFpbsuV7bCRvuhaE3RblIoF6I7Fp7g1tPaoa1g69jOH1kPGFcWpbewddROneBp8oM33K7EjR2Ly6srZz5DRz6V1Fz6woLsXvoX8X/WcR2kgxBfx10LWT23ebiVnZLou06OPFzcfviaPqMfTR9pAGySfBZyl9yaRG3kKqPMZ66gBkM4cGJhs54ZgTq26hq3NjACXjmH0SWvK5JCOhn/gHu/bJdbO97SYjscmvEfS/ZhHYfzbxZ1jSZQ2NLWdNkx1qVzz5s8NarMDUOdY1w670ibuPcHiGyPaVnN789t6dSaC8nEylhDxOrB9Uppk0WMiJeDa3tolwkYim9SSZE/HI5fgT+9rsQiUBzacjQ334XvvYNu9jetg38ARi6ICwRo1Ho6xX/rNSvoW/jHfSdeQOmjkCoATbeAfX29/azmSmySpygquDAha7opIw4z2ZU+jxlPsVLYL6Gv9LJZvVfPZZCWyKpkkzehaoWUBUTUFAVE1U1yeQ/mkL7SmCd5AgL28NPf3yFNsP7aCq6aZpVgSKgQtENw/vs9df+2OKlHn57lu2yPNdrOyuEdTk3TJ1jwOnBcHlwoZB2eVAxuGHqHMSureIJXyFOvSBEtvWKQKEUtwjt0Hv4lYeqtLKjphP6P1OqrZ8Cf50Q2TWW133dTbCnbDx8PgXX2MfDH79xE81PnMLESdHvx5vJoqSzHL9zEzuqObc3X1rcW/zNl+xCe2pUZLKt+IMibmGpA2tAiOqOS00RT06KTLYVt1/EV5KZFNT2QXYcillw+sT9mSqF9t33iZpsEJnsZGkQzRer8CF9/mkhshcrQ7EK7Xvvh7/+Nlx7jX34zb33Vx6zfk2FsC7ntJ4kpCo4EZ8jThwEFJ3TevJ9H/de9NGyyKKsyBa6qjreh4kcwS6RVEnWUNjq8uNWVDIYuBWVrS4/WWP1NJ1e7cwOCb9rK4tNcvxYkZ0SmU4rLr+IW2m7TowrL5TGfRfS4n7bdbbd7g77iOsGcd3AMM2Lt+8OV5cpqskn6HLHcKOSRceNSpc7Rk0+cekHrybyc0IwWXH6RNzCGtqEfZ1ZwDRN8maBPBpruPys3UVqOmHzb8P1vy+2NWWLm6ZuuOELIqOdmBLbG75gr88GRtY0MfLAgxSDQTzT0xSDQUYeeJCRNVU2kk6OCMFsxR8UcSt1zcKlxUomJeJlNBLhJtZxL9u5iXWXHF7zvoTqRSbZSiEj4itJcytoKtSug4ZrxFZTqx9Es36jaHyMRIUNYCRafSPkyAUh1q2EKocMsXEz/OEfiUz2yLDY/uEfiXgVBBwaBdO+WC+YDgKO6korG0qLMi9ukuTw4r4qGiFBZrQlkqoRmUCFba6FjGFcN6hzyfXrcnE5kxw/NvjqhGAuz1T7ymp2ox2i8dHqOtJ9a4XryHJ7ruONEdXSRF2WL3ctLSY1Xk14opVXBIpZEbdQp9aw1ejnHBdIkiGEn3V0U1elX/mSaequENblRPExs6aNzJqHL8ZyaESp8qpb/Xt4i9eXeYvvuF3UZIPFGjEJtzxY3c9dKmtuEDXZIDLZhVIfwbo73v9xH5TbPgM/+u/idjAEqSQk4/DAw+//uPdj/cblaXycHzJUXobSsshCcOPmqoV1Odf7HPwiZaAYKj4FsiakTINP+6ovaW0gclUI63KkIpBIquSuoJ+EYc8EJgyDu4JXzwCX1c6m+4TQzs6BaSzc3nTflT6zK0jrjsUz1a2LFANEO2DTb8HOx8R2EWs/EGL7Txqj/EVbjD9pjH4w//Wm7VDMiHMyTbEtZkT8aqL3LjDy4vU1jNI2L+Jl1Kk1XKdu5jb1eq5TN6+8yF4i/TSRK9U9m5gXb/dTZUZ75+1CaKcT4g05f3vn7fb9OvpE42MgDNPjYlvWCLkixLpE46MnCKlpsV2sEXK56d0Aj/6+8J4eHxXbR39/+ZotPwh33wfxuCgDMQyxjcdFfAW5xd3KrmAWt1pkxgC3WmRXMMst7iqz/FcximmujiEmH5QdO3aY+/btu9KnIfmYcTyX54VU5mIm8K6gn3Xe5RkSIxFI15FFmBsUNdnZKZHJbt3xniL6ipAYgrH9kJsWmeym7Zccjb4qWYLryGpnnDgnGGOOLFF89NP0wcozluI6IlldLMV1ZAWYMuY4Y7nS00MbdWp1E6BXI4qivG2a5iXbHaTQlkgkEolEIpFILoOlCm1ZOiKRSCQSiUQikawAUmhLJBKJRCKRSCQrgBTaEolEIpFIJBLJCiCFtkQikUgkEolEsgJIoS2RSCQSiUQikawAUmhLJBKJRCKRSCQrgBTaEolEIpFIJBLJCiCFtkQikUgkEolEsgJIoS2RSCQSiUQikawAUmhLJBKJRCKRSCQrgBTaEolEIpFIJBLJCiCFtkQikUgkEolEsgJIoS2RSCQSiUQikawAUmhLJBKJRCKRSCQrgBTaEolEIpFIJBLJCiCFtkQikUgkEolEsgJIoS2RSCQSiUQikawAUmhLJBKJRCKRSCQrgBTaEolEIpFIJBLJCiCFtkQikUgkEolEsgJIoS2RSCQSiUQikawAUmhLJBKJRCKRSCQrgBTaEolEIpFIJBLJCiCFtkQikUgkEolEsgJIoS2RSCQSiUQikawAUmhLJBKJRCKRSCQrgBTaEolEIpFIJBLJCiCFtkQikUgkEolEsgJIoS2RSCQSiUQikawAUmhLJBKJRCKRSCQrgBTaEolEIpFIJBLJCiCFtkQikUgkEolEsgJIoS2RSCQSiUQikawAUmhLJBKJRCKRSCQrgPNKn4BEsho5r2fYo88yZRaoU9zc4Kihy+G/0qf1kWJWn2bYGCBNigBBWtVOahyxK31aEolEIpEsG1JoV8mF4ybv/hJmRqC2BbbdCW3rlCt9Whc5mND4t7ECgzmDDq/K55vcbAm7rvRpXRWc1zM8VRwjgIMYLtJmkaeKY3yOpqrE9oCe5k1jjikK1OFmpxql0xFYgTOvDj01THH6Xcz8LIqnBmdsG45ga8V+A2cN9u6GyXGob4Trd0Fnd3UXxWb1aU7oh3Hjxk+AAnlO6IfpZ1PVYvtELs8L6QwjxSItTid3Bfz0ez1VHWslyOVGSaeOUtTmcLqiBIIb8Hqbr/RpXRYvphL8LDPDHAWiuHnQX8sdwfCVPi2JRCJZtcjSkSq4cNzkqW/B0Vdg6JDYPvUtEV8NHExo/MW5HLOaQZtHYVYz+ItzOQ4mtCt9alcFe/RZAjgIKk5URSGoOAngYI8+W7FvLjfG1PTLjI3/jKnpl8nlxmz/P6CneUYfJ20WiZlCtD+jjzOgpz+sp/O+6KlhCsMvYRYz4I5iFjMUhl9CTw3b9hs4a/DU45BOQl292D71uIhXw7AxgBs3bsWDoii4FQ9u3AwbA1Ud70Quz9/NxUnoOk0OBwld5+/m4pzI5as63nKTy40Sn30dXc/icEbQ9Szx2dfJ5Uav9KktmRdTCX6QGSVNkTBO0hT5QWaUF1OJK31qEolEsmqRGe0qeO1fYPIc+ELiX7Eg7r/2L/Dw/1bdMZ85VeDvj2iMpU2aAgpf3eji3l53Vcf6t7ECumlyKK0TL5pEnArNbgf/NlaoOqt9OKXx1HSeC3mdNo+Dz8U8bAqungz5qUKOX+VSjOpFmh1OPuUN0uv2VnWsKbNADPtz8+NgyizYYrncGLNzr6OqXhyOMLqeZXbudWqin8TrbQLgTWOOAA4CinirBXCCKeLlWe18tjLj6fGtbMazOP0uitOH4ixl6kvb4vS7tqz23t0QDEIgJO7Pb/fuhs7uy/+5aVL4sT9/F27SpC7/YMAL6cz/z96bB0lSnve6z5dLZWbt1XtPL7MwCwwzw44QIARiMRIILQ5dW9biRZZ9fI9DtkMn4t64ETfuiXvCESfCoZCssC3bsiRzrOX4XB+sBQQCIRYhQAwwzAzDrMzSPb1vtWdm5fLdP7Knu7J6gJ46GoFC9URMZNc7X1Vm19a/fPP3vi9ZRSGrqgAr20dr9XdEVrtWfQ2hmKiqBYCqWgTL8eas9o8rJb5dqjDlBQzqKr+Ty3BHJvc2HXWcf68vYsTeywrIKN7Janfo0KHD+ekI7TY49QqYadCX/37rBkgZxdvhweMN/vLnDdI69FlQdiV/+fNI1LUjtl+p+BypeNi+wA9hXpFMagG1oH2R/eWJGnlNYUNCoeiHfHmixueGUu8IsX284fDN6hIZRaFfUSmHAd+sLvHJdKEtsd0jEtSkT7rp41EnoEfEX4tq7TUmJnvYd2CI+QWTnm6Hq/ZMoOuvrQjted5AtBMX7a49RWnxGYRqrWY8F58h13XzRRXb0l2CRD4eVK0o3sTcTJTJbiaZiuLtkCJNA5cEqyLYo0GKdFuPN+n7DCyL63OkFYVJ32/vAC8AvzaBt7h/xXqjd12Blopbb3yviKrFBbOimPheceX2jysl/mquSFoV9GsqpSDkr+ai/38niO0iDbItfzIsFIot7+UOHTp06LBKR2i3wxs5RM4TP1DyeGDaY8wOGbUUPjqgsycXF17fOOSR1iFrRB7vrBE92DcOeW0J7UknYMGBpAqGAoGEBQcm9eCCHwvg+wsueU0hr0VOo7wmVuLvBKH9hFMloyhkleVsplBX4u0I7RvUAt91TuG5i1h+HVtL4hhd3G5ujq07dTrk0Z9sJ5UK6O5yqdV1fvT4Jdz1viP0LNuMe0hQw48y2cvUCegh/rrWqq9FIvs8Gc+LKbSFUYhsI1qT9zywEUYhtq63P7KLnMtkA9RrUbyVsRMhe5+C+Rno6Yfr3gujW+MutSFlI0eDV0FGmWyPBg0abFa2t/V7bNA0ykGwkskGqIYhG7S1X3FHpwMePxwyWYQNebj9MoUdA+qadevBr03w0qkXeNAZZjzYxIha497SC1yz+fqY2Nb0fGQbWX59AcLQQdNXT3K+XaqQVgU5NXquom3It0uVd4TQzq+8l1dfS5uQPO1deevQoUOHXwc6Hu022LwHnBp4bpTJ9tzo9uY98XUHSh5fOLnslTYjr/QXTjocKMW90tM1SateTetRvB3qLigC5HJtphTR7XqbdtWzbkBWjRd6ZlXBWbc94f6LZirwGWzMs3XpZ+yef5itSz9jsDHPVNBeNnPIWeLOhddIBi6LWpJk4HLnwmsMOfEs7ysHN2FZDqmkjxCQSvpYlsMrBzetrLleyVMjoCZ9pJTUpE+NgOuVeBbZ94ooSvykoDXjeTHQuq9E+jbSryOljLa+jdZ9ZWzdu26GajUS2zKMttVqFG9m7ETIQ9+J/r972cv90HeieDMFtZsk2/ipb/A/fI+f+gZJtrVdCHlXKkk5DCkHAaGUlIOAchhyVypevHp0OuD+ZwPKtmQgJynbkvufDTg63d57ed/EMb5U2cLpUKCrRU6Hgi9VtrBv4lhsXSq9k2nF4wnD53tJeMLwmVY8UumdK2umvICMEv9KzigKU97F/5yd8Gy+Vp3jL0uTfK06xwnPXrPmI8ku3OX3cihDatLHJeAjya6LfnwdOnTo8KtKR2i3wS2fFPQNRz/by5bSvuEo3swD0x55XVDQFRQRbfO64IHpuNAeSAmqLXWKVS+Kt0NKKPRIgVsXLNTArQt6pCAl2nu5hw2VchAX/eVAMmy0lwX8RbPdX2S0vBc9dHCUDHroMFrey3Z/sa3HaxQPMipVcq2k5gAAIABJREFUfrPh80d2jd9s+IxKlUbxYGxdqdzPQt7hhz0+3xmU/LDHZyHvUCqvpnk3qinuVftJCY0F4ZESGveq/Wv82ZqeJwydWKw143kxUNNDJIZujzzajSJCS5IYun1N15GNWxTu+1iU0Z6fi7b3fWxt15G9T0X/l8qAUFZ/3vtUfL+ngzo/CRoklR4uVUZIKj38JGhwOqi39XvsMA3+IJ8jq6pML2e2/yCfW+PPfvxwSNaErCVQhCBrCbJmFG+HfyvqCNUhrXokhEJa9RCqw78V42fOC0aafYVRXFUnE7i4qs6+wigLxqpVZlBXqYTx46iEIYP6xf2cnfBsvlNbpBIG9CkalTDgO7XFNWL7jnSWzyQHSaFRXr5K85nkYMef3aFDhw5vQsc60gZDl8F9/0mw/xFYnICuIbji7ijezJgdZbKbyWmCMTv+x/T3L9eXPdlRZrvqRf/+7Or2bBnbEypPzwjyGugqeAFU63D1SHtC+75ugy9PRF0ysqqgHEiKfsin+623uOcvhxsaZzgiEgiRICGgRgIpAm5onAF2vuX9WwkbS4gWgStUi7ARz2i7Qz4/TWZI0SDrBTiqwk+7M/yGFc+kh+MW3vMW7hx4vRDeAGyK7zOV3klp8RkCokx2GDrIwCaVu+aCj/9CUdND523n18rGLcpbFj7Oz0SZ7GaSqSjezHN+kaRfJ1mfQ/o2Sc1CJnt5Tim23a98h2m8ZeHjZBEGWlwYaTOKt8NxmSZV9hmf6cOtJzCSDQr9CxzPxo/jAPOk1QxJazX7W8fjAPMMEflxfieXWfZkh2QUhUoYUg0kf9KV4WLylFslo6hklq1XmWXr1VNula16/DN+RzrbEdYdOnTocAF0hHabSCGRAqQSWTMim0ZcVI9aCkteSEFfjZd8yagVF7znfNjNXUf+7Orzdx1ZT3/sfjQKukdDSBohaAoUdEF/my/3rrTO54ZSsa4jn+63zuvPPlj1+P68y7gTMGKq3NdjsPsi+7hzYYWE1s+DXooZqdEvfO7Va+TChTVr1zOIRkkUCP36aicOQAY2SiLuW566ZA79dIqEoqHpIdJTaIRRHKK1p06H/Pv3JOkU9HRHdot//x585EMhmzetvg8Ma5Bc183xriO5ay5615FfND1v4OXuafFyzzaKdJXPIBUdNBMZeljlM8xmAWPDRTu+DXmYsj0cq4qDj4mG6aTZkG/TZ+ynOHvCwkwEJCyPwFU4e2KAwp54NngJhzxx8W2hscTqVYxzPuzmriN/0nXxu45MBx59Svy7ISUUpoP224Ge8GyecqtMBx4Dqs57jfQa0d6hQ4cOvw50hHYbnD0iefyrkMxBYRDqZXj8q3D7Z2VsaM1HB3S+cNIBQnKaoORLip7kMyNrhee92xJvWfh4rj92QSPWH/s/bSYmtj0f3tevcbwaUvIkOV2wLa3gnceyfGQ24JETAZNlyYas4O6tKpf2rb1UvSutv2Xh48Gqx1+frVFQFYYMhaIX8tdna/zZcGqN2D4yE/DYsYDJkmRDTnDndpVL+9fut9aYZck9hhuUMdQsBWM7qURfbM1J0c/3vSwZFfYQUsXg+55BztRodo+udxBNIr8bZ+ZJQqJMtgxsCGwSPe+K7beSbLBjI8xOW9h1FSsZsGPAppJc7cLw3POgJl1KqTKzeBgpnYTM8tzzJps3xX9Xwxpcl7Cu+HPMeq/jhBVMJUOffgkZrXfNOteeol45ROAVUfU8yczlF124X/feyJMNUSa7XouE9633xtf11Gepqgbp5ewpik5NKPTUZyFz4Vch1suVlzn87Nk6aSBtapQdmHSqvP/qJHDhJ4T5UoFJQ6JpNqb0aOgJbGGwuRT32xcwqeORbNqHjU+B+Lo7MrlfeuHjgKpTCYOVTDZATYYMqO2dIJ+zomQUNWZF+XiqqyO2O3To8GtHx6PdBvsfgSCE8aOw/8loG4RRvJk9OZ3PbzEp6ApnHUlBV/j8FnNN15H18sB0g4JGzPNd0KJ4M8NpBUtRuKVX54MbEtzSq2MpCsPp+Mt9ZDbgqy96lBzJQAZKjuSrL3ocmW2zO8m8S0FVyC8fX15XKKgK35+PV2EemQn4+gs+ZUcykIWyI/n6Cz5HZuL7rTVmOVt5inLtOK59lnLtOGcrT1FrzMbW/VTdTAaHjGygABnZIIPDT9V4l5D1DqLRU0OY/beiaEmkV0TRkpj9t6K3tGwb0gUi3WD7pRWuuLrI9ksriHSDoaYrGOOzLuXkPD4BCXR8AsrJecZn26tMrfhznHH34YUuhkjjhS5n3H1U/LnYOteeorz4DGFgo2g5wsCmvPgMrr12QMopv8633Cm+aI/xLXeKU357PmmIuotcfROcOQ7PPh5tr75pbdeR6ysz1FSdmhBIoCYENVXn+kqb/QLXiTMwzW03lslYUCypZCy47cYyzsD0mrVjYY0H/HG+6r/OA/44Y+HaIUMF22Jzr0uoKywIi1BX2NzrUrDjgnIPPdj41PGQSOp42Pjsoeei/a7r5b1GmkoYUAmjQtJzP7/XaK/VYrMVRRFi5een3PZ6pHfo0KHDrzKdjHYbjB2GhQnQTTBTUdeRyePgnkef7MmtbefX9n6daNJjMzlNMObEPd/3bFL5u4PRZd9sAsoNKDUkn9gRf7kfORGQNQW5ZR95zlyNny+r/VaMOwGqlLxcDCj5kNPg0rSg0lJI+dixgJwJ2eX9Zk0AyWPHglhWe7b2Ml5jAU0YCMVCSh+vscBs7WU2J+5eWTdNkr7kEEFjDhk4CNWkYPYyTdwSst5BNBCJ7VZh3cpHunr46+k58EPSqqAaSEoh/F7XqnhSeor4tQTmsmbRUHHqKmZPEbjw7N6s9zoaBroS2RB0YUAYxZuz2vXKIRTFRFluJyeWt/XKoVhW+5Rf5wFvjrRQ6REaVRnwgDfHR+lls3bhXumxEyEv/ww2boPLrowy2i//DAZHw5jY3qSm+GBlgRdSeeYUld4w4LZqkU0XeTR9CZvNAyZbBlanGUokJeKFqGNhjYeDKZKodKFTw+fhYIr3M8iosnqMWywHa3GRgayKo6qYQUCqFDDYZUHTQJ4hkeE2OcIB5lnCoYDJDQwyJC6u/3pSVjjI7Mo+d9PHhpZ9btUt3msm+YG/wELo0a3ofDDR3Xb2+WJYUTp06NDhV5WO0G4Duwoo8YE1XmO1A0kzZ49K9j0GC1PQPQhX3QnDO9rrJjJqKpyo+0x5IeVAklUFg7rC1mT8ZdzZrfG/74aHTgecrYYMpxU+sUNjZ3d83WQ5ymQ3kzGi+Jrf2Z2mUjuC55fQtRyZ1KVYxkBsjQ48sRCQ1QRZFewAnloIua2nZb8lCUbAS4uSog95DXYkBdVSXNzXGxOoJBDLk+iE0FClpN6Ijwcf0lVKQYpcevWXKQUhQ2o8i9ojEszIOlVcHAJMVNIY9Iu1grLqzbLgnsANyxhKlm5jK2k9blm5LtfPnwH/vjjPhCcZ0gW/19XDdblVQ/LIDXMc+P4QNgFmMsSpK3g1lZHbJ4C4jaMYLDARnqYuqyRFmiFlE/mWdndOWMEQ8UyjJhI4YSUWC7wiSsuAFKGYBC3tAp8NSqSFumLhSKOuxNsR2s1dR2B1u/cpGN26uk7puZLR8R+z0W+AZsFyi0Fl5I4L3ueFkMPCxsNqOuFy8Mm1nPS8GC6SPM9EzxfDxZjQfl/Pfv5lapi+siSd8Kk2NCquzvt69gPviz3mkMisFD7+MpiUFf6/ySnOHMhSW8qTKngc2TPFxzYQE9ungjpP1Is05nT0qkEjHfJEb5ER3WBzG4Wpv2grSocOHTr8KtMR2m1g5aBWjDLZeiIS2TKI4s2cPSp59BuQykJXf+TlfvQbcNfvy7bE9u6s4NszPhkVshoUPcm4E/KRgbV/wHZ2rxXWrWzICkqOXMlkA1TcKN6M7U6zUHoOVZhoy6PGF0rP0Z17d0xsB4ECQiKEACEQQoKQUbwJxQx5cjEkq0FuWZA/vSS5tSu+XyUMkSL+O0ghUMK4xeSudJKvLUUZyowiqISSchjysVxckG5WDPaGM5gomKjU8VkUDW5Q4kWOVW+WSftFVGGSUDL40mHSfpENXLtGbPel01yV9BmhQQ8J+pT4PrdsUtDvm+Hk8wVK8zq5Ho/Lb59nZFP8OSkGCxzzD6KLBBYpGtLlmH+Q7eyOiW1TyVAJy5RwaeCRQCcnDTJKvBOEqucJA3slkw0gQwe1pZvKbOjR0/IcJ1GYDdvLPq6364iaGYaROwjnX0E6iwizC3Xwxih+EdlJP387NsUz4ymKniCvS24eqfAfR+PVmgs00Ak5QWXlpKwPiwXiV4+2J8f41C7JT8b6mapZDKZsPrxthu3J8TX7fq3e4KElh4lGwFBC5Z6Cyc7kxRv28tjkIgee7CJlQi4f4tQ1DjzZRf7WRX53aFVoP7S0xJkJJbJVmdBwVM6Mw0PhEn/ac+FC+71Gmu/UotaaKaFQkyGVMODe1i/IDh06dPg1oCO022DkMjAsKE6DXQErA70boW9TfN2+xyKRnVzWQOe2+x6D4R0Xvt9jjYCbuhXGapKiB3kd9uQFxxrtearv3qry1RcjQZUxIpFddiS/tSv+tqjUjqAKMza1kCCKNwttN4SbcwmO2wFlX5LVBLtzCdyWFsWuCSIARRUgQJEgAonbMsQxow1S9KPstYJKSEAoG+S1uKXjUtPgM4Usj1brTHgBQ7rKx3JpLm1p9TYt6mxTUixIj7oMSAqNEaEzLeKenwX3RHRSsTxARhMmhFG8WWifCWo8GMysFlfi82Aww72s9snewhDlTUe5YZNDAp0GHi4NthB/A0yEp9FFgoSIjvncaPKJ8HRMaGtaH7PuSRQSaCTwcJmVFQra1tjjJTOXU158Bogy2TJ0CEOHdP7a2Lo+Racqg5VMNkCdkD5l7cnb2HHJS0/A/DT0DMA1t8HotvjJ0Xq7jgAEmoptGQSaiaobWJrKxe7M/tJYgodO5gmEj6b5LPoqD53McwMJ7hldXacjeZ0yJioGCh4hr1PmElrEotnFNnWO7Vet+relVwc9PsTltXqDr0xXyakKg7pCyQ/5ynSVPxlIXzSx/fIBnZQpsZa1spWUK/HfbfoIHS42SKJhLNcWGDpIT+FwsUE7FvKtusXHU12xriP3WrlOIWSHDh1+LekI7Ta48k74/hfBd4Ag2jrVKN7MwlSUyW7GSkfxVg4t+vxgzOdsVTKcFnxwVOPyrvjLc9YN2ZpS2Z5eFTehlJxtVbLAwaLPd6cajNVDRpMKHx5MsDsff7xL+1Q+ey2xriO/tUtb48/2/BKaGs+YKoqJ55disVFT4Xg1AE9BehKkoKrBtpYiTAe4fljh9KKk4kYi//o+pcUlC92Za/GWyri4BHioKFhk6M5cSyuXmsYaYd3KAi4DmAwqTVleKVkgXpjohmUSSvwSvyoM3LAci70QFkmdx17wQlhcEdo9SoErwx2cZIIKNTKkuIzN9LRk0euyiuGF+PYYBDaoFqrVT12PZ5YnNRuVUTRvCSkddGHiJwaY1Gw2Nq0zrEGyXTfHuo6k89eu6Tpyo5rjAS8qpEyiUCekKgPu0uJCcey45OFvRQK6uy8S0w9/C97/CRkT2+vtOtKwpzjy0ivsf2kTSwuXUuiucsU1r3DpNZBoOcaxoMbecIl5XHowuE4pMNqml/vvztQIkKQUDUXqhIqkFkj+7kyNe0ZXz/Q0ITlnoBIIIGrnqYm4rUr0XQ1nHo3WLltghF9HDMVHZj605JBTFXLa8nh1TazEL5bQdpcMrLwHTacvqhVgL8U/J0FJJ5EOoOnKk2KGNErtWz226lZHWHfo0KEDHaHdFkIKFE+CjKbfIUHxongz3cut/5JNGtWuRvFmDi36/M0hj1wCNqSg6Er+5pDHn15OTGwPGwpFPySvre4nmtAYF7IHiz5fPOGQ12HYEiw1Qr54wuEvtprnFdtvVfioazleKwp+PNfHhJNgyGxwR+8sO/Px7N6upMq3xl0yqmiytkg+0hcXEsNpQdGF6zeuHnfRlfS1FHpa5gBW4RpmG69iyzqWSLIxsQvLjHvD10s3BqcnYfxgivKSSrYQMLK7xqahuPAwlCy+dKJM9jKBdDFa7BnzvEFxJfHiyh6lQA9xYd2K6Unc6kl0kQDVgtCjUT2Jmb4Emp6+CjZprYBoEsI6kgprR2avp13gZi3JR+nl2aDEbOjRp+jcpXWt8We/9MT5vdcvPQGj21bXjW5VuOfjIXufiuwiPf2RyG7tOnL8wCkef2QnqbSk0ONi1y0ef2Qnqn6Ky9+1esxjQY2HwilSaHSToIbPQ+EU9zC4Rmyvp53huO+T1EBd7nmvIrBUybgf730ZCMkOMkxJB5sAC5VRkSRoEdpKdphw410w+zI4i2B2IYZuRsnGLTATjYBBvWW8uiqYaPNq1HhY5WW5yAIu3RhcLboYabEtbS+YHKl7iGSAvpyVr9lwaSF+6WhrLcvR9DxCFWiBgq+G2ATsqL35e7ZDhw4dOrw1b7vQFkJ0Ax8B7gF2A0NAAzgIfAP4hpSyvfnIF4n9D0fdRpw6BC4kEtHt/Q/Hp0NedSd8/V99XjU8ilZI3lbY5ev8wZ3xp/0HYz65BOSXhWbeAJD8YMyPCe0Pdhn8zWQdCFcmNJZ8yaf64kLxu1MN8joUEtEf9kJCACHfnWqsEdoHKx7fm3VXBuB8qM9gdyYuHse9y/ja6UVyumTAaFBsSL52uo8/v7yLZsfy4aLk8pTCkYbPdCOyjlxuahwuyuhVPfd7jGr8zSEPkE1dUeBT2+LHthQs8FNvgVecbSwGOl2qx5VigTv0BQotRYLroXuym3970iVlSjK5gGoNXnwyzTW3GTDStM7YyqT9IoRRJjuQLoF06Dd3xR6vZ1n4pZo+RnUCerjwDGVfschJUwNUNCnxVRUPjZFisbl5BRksHDzMpn24eGTa6GByjs1a8i0LH+eno0x2M8lUFG9ldKsSK3w8Hy8/lyGVkiRT/vJj+SA1Xn4uw+VN7cr3hkuk0NZcNdgbLsWEtmtPcbj0cw4aGZbMfgpBg92ln3MZ74qJbUuTeIGgeaq5FwgsLS6guzCo43OZsnoyWZc+yfN8ZSrZYci+ubd8KKFS8sOVTDZAJZAMJdro7hNW+ZGcJCk1upbfgz9ikt8IN8TE9gf3mMw8KbCp41se2DoFJ8kHb4h/X3zokhRffQns0TJ22kOt6hTGevjQNRe3A0yHDh06/DrwTuij/THgq8C7gJ8DXwL+J7AL+Cfgfwgh2mvTcZEYf00ydhAWXofq2Wg7djCKN7M4EPDqe1xcMyRVFrhmyKvvcVkciGexzlYjwdlMNhHFm9mVTnB30uDQuOCBQ5JD44K7kwa70vE7j9VDnEDy1JzH9ycbPDXn4QSSsXr8fOVgxeNLZ2yWPMmQobDkSb50xuZgJW5XeHQmS1+ql3xCAemSTyj0pXp5dCae4X215lFSPTan4LqcwuYUlFSPV2vxx7u8S+NPL9fJG4LJWnSC8aeX62usMs/a4zxayeFInR5V4kidRys5nrXXFpqthyMHDbZZFumUwFEC0inBNsviyMG48EjrfWywrkUTJo2wgiZMNlhrCyGvV/LUCKhJHyklNelTI+B6JV5wuB6y9Sqb7Sy6VHCUEF0qbLazZOvxVjZbGcLFw6GBROLQwMVjK289Qv1/hZ4BqC5V8UvH8RYP4JeOU12q0tPexQWWFguYVtwsZFoOS4vxLOo8LqZXIygew5/fT1A8hunVmG+x+xyvHebJZB5b1SlIia3qPJnMc7x2OLbu1g3gSLADSYjEDiSOjOLNXEU3dXzqy69tXfrU8bmKCz/BA7inYFIKQkp+SCglJT+kFITc05JdXg8vy0WSMjr5EEKQEhpJqfGyXIyt2zak8oe3GuxO5egt9bA7leMPbzXYNqSuWffZa1Lsme5ncO8we6b7+ew1qTXrOnTo0KHDhfO2Z7SBY8B9wEPNmWshxP8FvAD8JvBRIvH9jqA0DbU5MMyol3boR7dLLfMdvjvVYKgbCoPnzmciG8d3pxrsyTVZQtKCoiuXM9kR5UYUb+bQos+PTsEuU+fGbLTmR6dgWzKe+TYUeHrej9rs6QInkDyz4HNLTzxT/b1Zl7wWDZYByOtR5vt7s24sqz1el1iJBEfDXqpKSFoojCo64/X4iYCjeahU6SvMkEy41BsGE0v9OMra87nLu9Z60Fv5qQ1hyeDEhEWtppJKBfQM2fxU97injVka00vQlfFIeSUC6aIKA8PQmF5a60VN631rhHUrG9UU99LPC2GR+eWuI7cpPSv+7AtBGF0k7XkGHBtfumjCwJIWwohXo/WKPNfIbZxgggo2GSx2sYlesVbcnz4V8vyzMDcLvX1ww42waXN759ZXvmuWh/+5QpgBK21Rr0C9MsN7PlAHzlPp+Bb0D2cpzlVJpn2E0JDSx65F8Wa6PJdK9QwpVNAsZOhRq56hK70pNsjxZcUniclyvV+0FQovKw2ar0P8h41ZluQir05plH1BRpNcPejzHzbGPenDSpo7wyH2scAiLl0Y3EQ/w0p7Q1x2JhP8cbrOD2emmPAlQ5rgt/sH2ZmM7/fYVMDjh0KmizCQh9svV9g+GBe8C7h0tVw1SaKuqTWASESvRzCvd92Bssd3m66AfbjPiE2l7dChQ4cOcd52oS2l/MkbxKeFEH8P/CVwK+8goe1VIm/2OUu2FNFtL97KmLF6yLDVMmBGF2syy+u1UqzXYgJNAljK88eJBuAMtfi7s+cZgJMyAvbWXbIJQQoFV0pesl2uS8Z/t67CEoP6OF6gU3cNNNVna99pXG8E2sgEnp3LMHE0iWlAMhnQaCicPJLC3SGgpYXcCc/mabfCTOjRr+jcYmTWFGN1ZWtMl2fJJEElQSh9ZiszDGT7oLWbxDrZqKbaEtatNLIjHHtxkn2v7WKhlKc7V+SqnYfZfu0IrTnPdMNlsz2/0tM8bfVASx3o6VMh33tAkkpDdw9Uq/C9B+BDHw3bEtuDhZd43wc89r3Qz/xUkq6eOje8Z4bBgg584IIf74bb03z/WyMIew7TqOK4aRpBLzfcHn/Nrl48w8OWgUAhKSV1NUGdkPcunoHk6qj2kmaRCz1oalVohj4lLf54m7Ukv7fR5qEN8yxIn26hcY/ec17rTH3G4uxrG5gqQj0PO3Yqra3P101YPkt66Sm29HaR0w26PZf00jFC870rfu5jUwH/7ZmArAn9OajY8N+eCfj0zcTEdjfGeS1L3a1vgl8wB8oeXzxjk9cEw8tXwL54xuYvNtIR2x06dOjwBrwTrCNvxjnPgf+mq37JmCrke0HVwPOibb43ijczmlQoeXFxW/Iko8n4075eK8V6LSZuKLi5W8VSBeVAYKnRbTeMC+NRU+Gw7fBYrcgP7EUeqxU5bDuMmi1FW70OgS8I/Oj+537O9MYv/RfMeVRFIwh1fCkIQh1V0SiY82/8ZL4JjakcJEK0RIAQoCUCSIRRvIkTns2/2otUZECvolGRAf9qL3LCixcI7tlxAsc1sB0DicB2DBzXYM+OE20d3y+SVyd9Ht57O3UnTXe2SN1J8/De23l1Mv7Wt90Z5irPEYR21NM8tJmrPIftxhtVP/8spNKQTgsURZBOC1LpKN4Ofm2C/v4jvP/Dz/GJzz7F+z/8HP39R/BrE2vWnglq/Jt3lr/3TvJv3lnOBGtHl2+8ROG+T1h09Y9Sd3fS1T/KfZ+w2HhJ/L03XJ3j7rpLUkoW1Uhs3113Ga7GR8736T3UCZAyer6k9KkT0KfHrwiMhTUOssSleoI7Exku1RMcZGnNePVjUwH3/yygbEv6c5KyLbn/ZwHHptorXhxb2s/D3f3UNYPuUFLXDB7u7mdsaf/KmscPhWRNyFoCRQiyliBrRvFmrhZd1IUfsyzVhc/Voqt1t79Qvrt8BaygKygi2uY1wXdn12bSO3To0KFDxNue0X4jRDQO8NPLNx95O4+llUt2wf69EARRwjgIwLXh0uvi6z48mOCLJxwgJKcLSl7U//r3N64tlpMCQlUS6JJQXc2WN7Nei8loUmGpAbf0roqWpUbIYCIuYnr1kCOihi4VjFBgE92+TY8fn5LxuX2LxqEZwZINBQuuG5YombgITGkNXE8jZ0pUAYEEX6qktLUjztdD3k1S1sCXDVThE0gNQ0uQd+PZx6fdCmmhkFGiM52MUCGM4s1Z7YH+GT54k+SFw13MFw168i63Xb3IQP9sW8f3i+SlF1JYWZ1EOo9PngRgVVVeeiHFjauJW0r2EVTFRF1uUagKayVuGasWjrnZKJPdTDIZxdvBlw4CsTKlM8oce/gyfrJ1Jqjxw3CaZFNv8R+G03yAgTWZ/42XKGy85M33K8wuRpwqo8Gq2JReHWHGReW7E6P8/RScXZQ0FEiEMNwl+MDG0di69U58fPy1VdELkLUAJI+/Fq6xcqyHF/WQJILU8hWmlJRIIXhRD9m0vGa6CH6mxM+pUFRC8qHCpYkMtWLcFjSipPmNcAMvs9p15D2ib03XkV80Y06IoUierXiUg5CsqnCJoTLmvKNKaDp06NDhHcU7VmgD/5WoIPKHUsofnW+BEOKPgD8CGB0dPd+Si8Lm2wWvPC2jGXEKhB5IL4o3syen8RdbzVg/69/fmIj5swFeXfL58lGXvC4YsqDYkHz5qMvndsCuQlPXkXVaTNYr8J+r2AyiURNRgZglBD0oPFex+WNWvbIDisYZ3SefFKhq1Pc60CXDSny/WVII1aUqBQ0pSQhBToEM7VkrdhY0kjWLWZGgEoYUFIU+obIpG9/vTOjR23IsKaEw0zLd0FCyDA8U2bRhVRz6oYPW0rYP4HjD4QmnylTgM6hq3Gam2Za48MK19VJZyJDtqtP8kTSTLuWFeD/v8/Y0F2t7mvf2RXaRdJP2qtejeDsERhrVq0PggaJFhQlSEhhxcbc3XDqvkN0bLrUjHIcXAAAgAElEQVRlsVF7rmRh5kfMGja2LrA8SV/g091zY2zdmSnJRNkgVEI0IFRgoqxwZkoy2tQQZIEGXedpybjQ0pJxqgg9yRqNygxh4KCoJpbRz1SxPTG7aKYpNFxoGkOe9D0WzdXHC7NFnlYqZIQkFyrYQvK0WuHWLLR6pUaUNCNcXGHdiqXCM+UGOU0howqcUPJspcHN2YtrWenQoUOHX2XekUJbCPE54PPAEeBTb7ROSvmPwD8CXHvttfKN1v2imXgdLrlJUB0HpwRmDtIjUfyKlrWFaZUrf2IxMgndG6BwO2vswN8/65HXBfnEsvc6sRpvFtqRxYTYYJtPbVtbVLg7r3HPgMZXTrlM2pINluBPNhtrWvvNSZ/L1CI71NNkRYWyzHA02MTRIJ5B21hL8oP5RZIopBIK1TDkwHzILWShSQfeZI7yY/cw3VKsTEEMhM9Nxjba4Te2qnz1pZAdhkbGiiZXllzJb2yNZxT7FZ2KDKJM9jI1GdLfMt2wy9jOZP2FWNs+Xzr0GXti6443HL5ZXSKjKPQrKuUw4JvVJT6ZLlw0sT3Ul2a8UiNIO/gCNAmqrTLSFxdTupaj4c4jGyVkaCMUC5HIkWgpmrzhxsiTDZJkMhLZtSrccVd7x6emhwmVBIq9CL4NmkWY7kdNxpX7PA26PJuwPov0bYRmYSX7mG9zeEnVshjr70e1i5gNB083GevvxrCs2MfoobkShqfRKKs0PEjokMgGPDRX4j3Dq89hNwkqzhRGZQwZOAjVxM2M0t3Sb7svVWFxYZKsJZcna3qUFifo694AXLhFo9voo9Y4TTJg5UTFJqDbWH3+at0zKLMZFAVQQxRfRQmh1jfDmqKEdXKo1uDBJZezbsCwoXJvweDyVHsDchKJAKSIyj7kcvmHFFG8Q4cOHTqcl3ec0BZC/Cnw18BrwO1StvSsegewNAHdm6Gn6bK3DKN4M+NHJY/eH41h7xqIhtc8ej/c9buSkR2r2e/xumSoRYdkddZ09YD1des4UPL55pkGngO5UOA58M0zDbZltFg2/TKlxBXafkJpUpFpTFyu0vajyCtpbnx9+ITOdpmj2FWnrvmkfY3hxQyHizp3NrV3e3cmEgM/c8ZwRB1TJrnJ2LoSv1Au61X57DXwoxMBE2XJUFbwv+3SuKw3LrRvMTJ8qzZBrVHCCB1cxcRTc9yTire8SyX62MD1LLrHcMMyhpKlz9hDKhEXi084VTKKQnbZipJdFvBPONWLJrR33yR54V+yOCdUQk+g6BIzF/CBu+PvgaTooursQyGBIgyC0CF0yuTN7bF1mzYrfOij8a4jd9x1/q4jVW+WRfc4blDGULN0GdvWdFyxsruoNJ6G/MbVke6BTSob7y3e3XCoVMeiLiGqiQw8apUxutMboY16ucngFE49z8zpfuq1qHd3/yaXycQpck291KcDD2deQ1dB1yI7V3leodEXv6qxpzTDI944ARJLMagjqdfGuamhQPdqM/WbB1/ja3ODnAUwAnCSGCHcN/gaEJ/6uB6uszbxL7MqZw8q1EsqyVzA8O6QT/Wt7tPWXa7qhfFSkqqrkTZ8tubq2Hp7HuhDtQZ/O1Unpwk2JKJhV387Vec/DtKW2A5UuLVH5XAlpOhBXoer8ypBpwtghw4dOrwh7yihLYT4c+CLwKtEIvvtN8+eh8IQ2CVINqXU7EoUb2bf4xAGMH40+n8rA/m+KD6yY3XdSFJQbMiVTDZA2Yvi7fCPrzscXQhoSAiIBjDP2VH8b65eze59JDPBgXoCXyRIAGUSaGHIRzITwKoxeKIsGcoYjCytiswQyUR57YnAuzO96xLWrj2FXX51ZYqfld113imGl/Wqa4R1Kxtkmff6r7NPZFlQTLppcKP/OhtkBloGuaQSfWuEdStTgU+/Et9nWihMBRevJneMWTTRhS4S+EKgCYkmGowxy9VNNh7qU2TEAI6oE+CiCpOU7IL6FOR2xx5z02aFTZvffL9Vb5bJ2l40xSShZPBDh8naXjakrouJbcMahJ5bYq9ZqnDdmtfs6sUzPJxs6hKiNXUJSe1s3f1bMleqcPrVFIlE5DH3GnDy1QTh7gqXNb/NlnQwGigNH4IQRShgaLAUF5QDEz/jdlWwPz/Iom7Q5bm8e2GCgeAsdF+7ss7qnmb0JoXTR7uplgzSOZfRKxewEueZ0LMOnBmThb2jBIaD3ucTOBoLe00c01jpZDKkC8648cLHmgzZqLf3PfDgkktOE+SXx75HE2VDHlxy2xLawwmVkhJyR//qGVM0hOedXlPfoUOHDm8f7xihLYT4P4h82a8Ad0op22tV8UvgivfDE/8Q/WxlIhFtl+CG346vO3MYpl4Hz40Ed2UJlmaiiZLN3Des8+WjUdYqq0ciu+hJPr2lvUu8P53yqARRP+3EclFiJYjizWw06wiZ5VXHoyZDUkJhl5Vl1Iwf4FBWUHIkuaZkbsWN4u3g2lPsO7aPpydGmK5dwkCqxi1D+7hqO285Mvx8LDnH2KwpbFN8zjWo8UOFJecYycSF93geVDXKYbCSyQaoypBB9eJ9XA4+b9I1EGBtXe1+YVcVDj5v8uEtq+sCbwlTK2A1dZiQUhJ4S23td9E9jqaYaEr04p4bO7/oHl+T1V7PSPeR6hx3y15etgzmVZWeIOA9tstIbe5N7/dGzJ1KY6RdtOXWdXoCRKLB3Kl0zE3RM5bgzPYKnhRonsDXQ4KES8+xuPUmbBQZ0bOMLEyuBmVI6JVj645kcgxTZvtNq5+FuvQ5Qo4dXDiPHwoZsDSy1rkZ9lBWJI8fWi2uvEIZ4KGlBikkmYRPKRBMLmW5r7XVEDApKxxgjiUcCpjsoZcNIu7nP+sGbGgpgM6qgrNue1aPD+RNvjJbBT8aH18JJMUw5OP5N58q2qFDhw6/zrwjhLYQ4v8G/l/gJeCud6JdpJmhy+C2P45Gri9NRJnsG347Pn4doDgL5cXIyygDECqIWiTOm9lV0PjcjsiTPV6XjCQFn96SiPmzL4SyJ1CERFMiIawJ8KSk7MWFsa7l2JS0uSSzWqQWBDaqGjeR37Vd5Wsv+IAkY0Qiu+zAx/aszTQfKHk8MO0xZoeMWgofHdDZk4t7Bg6cOsl3jmwnZ0j6Uw2qDZPvHNmOqp/kup0XLrQbQQm9paBRFQaNoPQG93hzbjPTfLMaCde0UKjKkEoY8qFke72214M7Z2F1u0TXHyK0pIc9F8/Iq3qBMLAR6mpchg6qHp+ouO79BmUSSvwNqQoDNyi/wT3eHGF2MepU2BisirnzdQlZLwsHNtF9w4Go0aeXAL2BprssPL8Drl9dt0vOkzisMDGi4KQkZk1h08mQ7co8zYpcSeSRfh3R1DdbBg5KIl6XUEl2kaxMIBW5MlDHCD0qmfYmcE4Xo97YzaTNKH6Os+O9vNstccYqR11HUNhjZzg7nqNZ3U/KCk8yholGHoM6Hk8yxq1yNCa2hw2V0yWfhXlBxYaMBd09kk259r5XdiYT/Elfmh8WHc42AoYTKh/PJ9mZbC8h0KFDhw6/DrztQlsI8btEIjsAfgp87jwT109LKf/5l3xob8rQZWuFdSvVJZjN+Exc5lPPhSRLCkOHNVJLa5/2XQWtbWHdSi4hmHckviLRhMCXkiCEghl/XrPJS5kvPQ+AopiEoUMQuhQyV8XWXdqn8pnr4dFjq17pj+1RubQvLrQPlDy+cNIhrwuGTcGSF/KFkw6f30JMbP/kVJKsIckYkRjLGAESlZ+cSnJdi7vgeMPhSbfKdOAxoOrcaqzt/pFQc1H3ELEaD6RLQm1PGG9LmHwyXYh1HflQMndRu45s6jU5VW2QSgeoqAQE1OuCzb3xfZq5XVTnngJo8krXSXZdd76HfUsMNXve585Q13ZiWY+XW+m5En/8x9ENzQLfRvp11MEb1zzemddD9j4N8zPQ0w/X3cKaPtoFrZvyS9uxth5GpOaRtST2icsoaPEBSDdecpSpF6/kOtcjaQbUHY2KY3Djta8Aqx9UffAW3NPfA0CoJjJwkIFLYuTu2ON1a13U0gqqs4BcPrEJkwN0a2sncK6HgXw0gCbbdN5UdaL4OaaLsDWXZTs5WHaQhKaMiXGAA8xhopFcNr2f2x5gjg1N1cm7fZ3vTXpkFEnGFJR8ydlJ+GBqrVn+6HTAY0dCpkqSwZzgzksVdgysPZHemUx0hHWHDh06XABvu9AGzrlIVeDP32DNU8A//1KO5hfITNrn2O4GuiOwSoKGJTl2Y4PkQWitDPuHV+rcvz+kbEuyluB3r1D44yvXXpJ98PUGXz/kMVWXDCYFf3C5zr2XxP/w3dKr8ePpBo0QnFCiiciScktv/OW2jAEqwfUcnz5MGBRR1Czbeq9gxBigFSFBCQVqEG3FeXq8PDAddU8pLI90LyyPdH9g2osJ7Vk7D0qDQ0sZyoFKVg3YbFap23ERc7zh8O36Ehmh0KdoVMKAb9eX+B3i3T8K5namqy8Aq91EgtChNxnvJnIhbEuYF1VYt3L7uy3+9bsKDar4yQbUE2RqaW6/M946LZHcgCjsYa72Em44gaGm6cleQyK5oa39dhnbmKztBZo6sYQOfVbc771eL7eaGYaROwjnX0E6iwizC3XwxijexJnXQx7675DKQHcv1Crw0H+He347jIntK961wHf+SaH6zDX4noam+6SzDh//wwWaM9VbRlV+UznMs8c3MVsy6cs53LXnBFuG42LR6I1OIr2ppwkbRZREnsTI3Svxc+yhhxfsEwwsHCPZqFFPpJjs3s4efWtbz/Ptlyvc/0SFoDJHUq1QDzJU6eXD164K44E8lMo1MuHUSseWijLIQD5uf1nCId8yBdJCY4l4T/OzR1Vu8kzOpBtNGfIEZ4+qrDTvJhLZ33jeJ2sI+rNQtiXfeN7n92/gvGK7Q4cOHTqsn7ddaEsp/zPwn9/mw7hgJg7D/kdgcQK6huCKu9dmuMd3+BiuwAoFwoyGaNhuFG8W2v/wSp0vPxega4K0Kag34MvPBUA9JrYffL3Bf3mhQVqDfjNqdfdfXoj6/zaL7c9uM5i0Q+acEDcAQ4VeU+Gz2+J/nI/MBnxtXxdZ8+YVS8jTZyWfVYNYtvroTMD/+VydQwTUkKQWBY8/p/Jf351kR//qujE7ZLgla57TBGN2vMArkczzzLRLOhGSVkLsUPBCMcfNA/Hje9KtkjnPIJon3Xj3j2Sin4H09Sw5x2gEJRJqjt7knrb82RdKyZ9nJjiJHVaxlDT96hZyWs9b37GFzZsVfuvDBs8/ZzA3B729cMOdUbyZqjfLXHgaLTVEZlkYz4Wn0b3eNdnl9ZDW++hVNjFfexE7rJJQ0vSmrl3zWBfi5bZNg4W+LpxQx1QydCeMNR2f9z4diezUOctyZjXePMSmbp8ERhBCQQgQQgHEcrzJEtJ3FZvtR9ncX17JpOPXUPrW9jM0eq9aI6xb6Zk/wXVTP2fBTFJOpEj7Lted+Tk9gz3wFvc9H1sy0/z2lhd5cnwT07U8A6kK9448xZbMtZzr8HPb5nnuf6KINCBlmtRsKLvTfPiKPM2z3wuY1PFWMtkANj4F4ieGU0twSV5nm7v63RBqkqkWO/9jR0LclM8rqQYlJSRnKAzVEjx25PxZ7Q4dOnTosH7edqH9q8jEYXj8H6OuI4VBqJei27f/UVxsqxtDkqcEkqgYUiiQlKBuigvP+/eH6Jrg3BXZZALqCO7fH/LHV66u+/ohj7QGOSMSszkDQPL1Q15MaO/Oa/w/eyy+d9ZjvB4yklT40LC+po/2IycCsqYgtyyOzxU7PnIiLrT/ar/Nc9LHEpAR4ErJc9Lnr/bb/NNdqxJq1FJY8sLlTHZEyZeMWnGxKC0LVVFRZA2EhyJ1VCWJtOKZ+enAo+88g2img3hRZ/Sc9f9ShHUzJX+eU95+dJHAFCk86XLK289mroiJ7YXaYaadfTSCGgk1xYB5Fd2ptb6jzZsVNr9Fl5ALEbzroVGfRCweoE/JIdR+pOcgFw/Q0AqxLPl6vdxVb46zzotoGCREGi90OOu8yDDXktZXhfH8DGg9HodDG5sAC5XBpMX8TPxKz8vPZegd8Ni0dTVbW6uqvPxchiuvXl2nZIZh412Es/vAWQCzG2XopijeBvbsMyRlgoyngxcCOoFMYM8+85Yi/Xz4i/vZ2uexbcNqD1Dpe/iL+9HSkdDeor7EJ3arPDU+ynTVZCDt8MHtZ9iiBsC9K/fbQy9PMgZEmWwbHwefG4hf1RgsQPk8dpXBFjv/q/UGr3fZWFKQlQJHhLyasbEXoa2ejB06dOjQYYWO0G6D/Y9EIvtcbdy57f5H4kL76h0KL1clajUS2ooKQTqKN1O2JemWTLCpR/FmpuqStCoZr8iVTHU+AVMtXUwgEtutwrqVybJkoKUwM2NE8WaeqPmYAqzl4kpLgAwlT9Ti7e4+OqDzhZPLEym1yBNa9CSfGYn/sXYlbBtUOTCXoeZIUoZgz6DAbbGjDKg6lXDtIJoB9eL/8T95JuTZFySzc9DXCzdeL9iyMf66zQQn0UUCXSx3xFi+nD8TnFwR2gu1w5ypPY0qdHTFwg8dztSeBjiv2H4rLqR4MahO4C+8gnSXEEYBrftK1HS8mM8tHkQoFooWqTGhWYR+FG8W2uv1ci80jqNhxE8EwijeLLT1Po/D5RrJDFioeIQcqda4rC8FTbaI0kKOXFeVZsFnWg6lhbX+eyUz3LawbiX0Sgg1/jwLxSD02iuwle4iJFoUrmpF8XNrnEW29RXY3n9qNSYl0omnoDeIDPL0EF8Zs1nwoVuD3xu12LApfry37Va4/yc1HHseU6/heCmcoIcPXR+f0LmYb6B6AnO5TZ+JoOGFLOYb0OZU1w4dOnToENFpgNoGixPgepJDr0hefCbaup5ksWVgzSev1OnbLUltlGR7om3fbsknr4wLxawlcFqStI4XxWPrNMl4FfwQEkq0Ha9G8XbYkBW8MufzzVMuXznh8s1TLq/M+WxoadvnCInesgtdRvFm9uR0Pr/FpKArnHUkBV3h81vMNV1HdCPkVTcg3yPZMgL5HsmrboBuxDP9txppKjKkEgaEUlIJAyoy5Faj/dHTrj3F4tzjzE7+TxbnHse1p9asOXkm5IEHJdWapKcn2j7woOTkmfjx2WEVjXgWXiOBHVZXbk87+1CFjqaYCKGgKSaq0Jl29rV1/IaaJZDxASbnE7xBdYLG/9/enYfJdZV3Hv++tXRVdXX1oqW1tXbLkoW8gWy8gJGxsY2NwWZNGAg4gYx5npCJE7JMJgtJZiZMloEMSSAhExycTEKCYxOCLSexMLINxgi8IoyxVttqLa1u9Vr7PfPHva2uqq62WiXdru727/M897ndp07dOn1PVfVbp859z8sP4kpjEGTaKLz8IOWR6idpuTCARaunHFg0SblQHdwtSGyg5OUoeTmccyd/XlCz6mfOGyZq1VOAopYg5w1XlUXe0A8jMdxwFOeBG47CSMwvr7B0eYaxUcPzijjn8LwiY6PG0uU1nxDPski8A+dVn2fn5YnEG7vA1hILoJytLixn/fLxOskF/pSXSqXspIwt9x3I8rm9JaJeC+vjCaJeC5/bW+K+A9X3XbngKG87/9ukEzn6R9tJJ3K87fxvs3JB9fIE6S6HK0G+6GdIyhfBlfxyERE5MxrRbkAk5fjhk5BK+1sxD889Dedd5ICJIHVLV4xfu7Q6bd/beyan7fvQhRE+82iZQta/2LAcBRd1fOyS6vmRq9sj7B3yKHkQjfqBdtn55Y1ItJZ4pL9MIgrpKIyW4JH+MpevMagIINe0RTgw5GEeRCNQ9iDrYE1m8uNe0DE5nV+tYsYje8jjeNZRCD40tEaMYqY6kN3QkuT9dFVlHbk5UT/7x55ilp2FYY54RZZE4lzVkmF9zbLf+Wwvg/2PYNEU0VgH5XKWwf5H6Fjwhqr80N963NGWdrSl/b5sSwM4vvU4rFs9cbxUpI2iy58cyQYoUSAVmfggUCiPEo/UpOizFgrlURox3YsXS8ef5OCRJXz3mdX09adYtCDLJecfYE3yyapR7WhLF15w4d04V84RrRl9bYt3szx9SVXWke7U+ZOmqyQjGYp1Rr6TNaPw5XWjbH6P4+VHUowdidG6pMTat2Ypr6v+euaKq9u49+9XkMseJ5EcIZ9ro1xcyBVXN7ak+3Slut/A6Ev/Srnkj2Q7Lw9enlT3Wxo6XmzBhRQPPYgDiKb8oLs0Rqz78pN1oosuovhSTcaW8hixmowtf3MwR1vE6Ij67w8dUYAyf3Mwx42rJ87L6Mhu1i0tsmHFxIfJcrnI6MhuksmJ5/trOmK0WoljAzCcg0wSerqNte369yAicqb0TtqAcgvgcTLzhjn/93KdrFfTSdt3UzLFdwey7Mp45KKOpDO2DkS5KVkdTLTGI2xbAU/2eQwXIROHy5ZGaI03Fmjf3+vRnYFSwSiUIB2DjlbH/b0eH6uYG/6LmxLc8USW40VHueQH2+m4X17rB6MFvtaf58V8mZWJKDcvSExahW5vOUdL5wgLykXKxQjRuMdINM7echvUXDY3newfe4pZvpzrp40Ii83PTvLlXD/vY0FVsD06stsPsoMc1NFoinJQXhloHz0Gi2quZ2xt9csrLYmuY1/xKcAfyS5RoOgK9MQmpoS0RNN1plwUaIk29pX8dAPe/Xs9vr5zE+nWEgu7coyOxfn6NzZx01VPs7Hiw0Ki83zGjn4Tr1SR7s7Lkui8lFpt8e5TzgNf2LKBgyMPUygMYeU8LpqAlnaWtr2xup4lGF2fZ8s5E/m2R12JdM1o+Or1EW75yRSPP9zDsSOweAlc+sbJaQDPtvF52Nmjj+AVB4nEO0h1v6Wh+dmAPw97+TWU+p/C5fuxxAJi3ZefnJ8NEG3rgZ5rKVdkbIktu8Ivr9Bb9FhSs1pkJmr0Fqs/qJaKJ4jGqkfgI5EkpWJ1vsAbO5N8rjDCxhVWsRCN48bOmcu6IyKN2b/X47FHoe8oLOqGy66ENes0WWE2UaDdgGIRNrweju6B7BCk2mHDa/zyRjy1Ha73UtwKEATvY55f3rNpol5PJsJg3njfwoluG8y7kxdHnq7eMUdbHE44Rynij5Jn4n55pQ2ZGOe0G/tGHLkyJKOwts3YkKl++vxgtMBnD43RGTNWtEQ4UfL47KExPr6cqmC7VDrBtefuoezi5MsxEtESUSvy1NH1wOln7NhZGKaNydlJdhaGqwLt6QYe3YthZHR8JNs3NuaXV+qILWItF1ZlHemJnVd1IeTS5MX+nGzPH8kuuwJlV6QneTmNas3maT3SB7l+SBZgyapJ16x9d/cG0skc6SBpTbq1BF6J7+7ewMarJ+q1tC6H7jeRP/EM5cIA0ZYuEp2XNpwusKVYpGt4mKGYRzESI172aB8epiVRrGrj62wB290hcNBKlDHKjFHiKpscyK9eH6nKRDJTfpTYwr2JDRx0HqsSEW5JJKiXMHKXd4yH3CEGXYEOa2GbLWdrZPGkerG2FVWBdT3Rtp5JgXWtZfEIfcdL2OEypWyEWMrDLYVlC6tfj7F4Z7AA1cRrwPNyxOLVaTS1EI3I3LR/r8e/3O1It8HCRTA6Av9yN7z9XZ6C7VlEgXYDFi6D0SHYcNlE2egQdEz+3zot/Yf87CWVUhm/vNKNa6N87ik/ms+0wHABTuQdP7mpsW5sjzkODEMqVj3ne3WmOtC+t7fAhV0xti2ZeOEOFDzu7S1wQcUqc1/rz9MZMzqDi6o6Y34e7a/156sC7fUdveTLcYpeHDPIluLEI345nH6e4iNeEdfXwtMvxBgaitDe7rH+HGNsUaGqXizeyUBhmP3OX+2xzSKsMY+umlUBr7jU+Od/BXC0tvpB9siocd3Vkz/QdMQWvWI6v4Xp8zh4sIUd3xnmeF8LCxcV2Pb6DAu7G4scvaEXGXr5fo62RclmjFSxj+6X76edtxJpX3myXv/oCjoTL4AXh0gMvBKpRJH+0cnnt6V1ecOBda38iWdopYM2UhOLrpCddHHlqkiaG1jO91w/x12ehZbgKutmVWR2XHz39FCRTx/I0hkzehIRBoqOTx/IcsdquKB94hPDLu8Y93j7SFqUdouTpcQ9nn8xY71g+1Se7y2z41mP3gE/O8ibt0ROLtE+7tpYkS/sjROPebQkHNlChOLeCD+xpPqTfrptM4MDj1JmYkEq5+VId7xu0uNqIRqRueexRyHdBm1twTTHNgDHY4/CmnVNbZpUUKDdgNdeAw98yf+5tQ3GRmBsGN54a2PHW7AcxoYmspcAZIf98kqbF8a4fqXHF39Q5PCoY2nauO01cTYvbKwb12SCOd8OogYl58/5rp17fXDMo6fmwsyOuHFwrPqr6hfzZZbaGPnho3hejkgkSTrRzYv56oV3lrflOJZNEAkeM2aAi7G8rXrBDfBzfW9/ocyhIcfyduOGcyavSGl9Cb69K0YmCZmMI5czHtsV54qtRsVCefQnz2H/6DdxliQdSeCVc+xzOVz7xVRebrZudYR3vs3jW49zMuvIdVdPzjoyHfv2e3z1njUMDTqKBRg7Bl89ZHSmPNauOf3jDR1/jAOdECNK0jOKMceBzjKrjz9GZ0Wg3b0izfDAelLW688HjqbI2yq6F4cbyJYLA0RqRkzrXVwJfrC9ahpZLQbLffSW9pF1I6SsjWWxtXRET/+bj9Nx71H/Q2Pt4kv3Hs1XBdoPuUMkLUoqeCtNEQPzy7dyeoH2871l7trpkUnBkk4/Nd9dOz0+eBVVwXb20AAXtyXZ47Uy4iK0tXhsSY6QPZQDJr4RSCaXQdeVjI7splQ8QSzeSbrjdVXzs0Vk7uo76o9kV2pt9ctl9lCg3YCV5xrX/5Tj+w/C8V5/hPuNt/rltV78sWPXDujvhQXLYOubYeWG6noX3gAPfsH/OZXxg+yxQbj8vdXH+uGxMg/uK3N+V5Qrlvgj2g/uK7O+s8x5i2nTjdIAABphSURBVE9/YYl0PMrFiz2eOA4nio5k1Lh4sV9eaVVrhIGCR1dLRX7somNVa3WguDwyxrHhl+mMmb88uCtxfORllmdWABPB18bWDCdyY8RooT1q5D2HUWRja/UFc88dLfOFXUXak8bSDAzmHF/YVeSjW6kKtgv70kQSOfzpvYYlHBHnl1ORl/ohWiF9MWvy+0iWh8lFM+xPnMd+WieNo69bHam68LFRX9/uONzraE0Zra3+9KLDvY6vb4efu/30j3c02k+MFuLOP/dxZ2Bxjkb7qQxvL7sCvvKVKMOpNC1po5BtxctGectNZ/43vZJoSxc/OuTxo2ycXEuJZCHGxlSBjcu7Tn3nOgbLfewpPEXcEiTxc5XvKTzF+pYLQw22D+Y8ehLVz++OmHEwV/3hctAVaHMepfIIzpUwi9ESTTJo1fUAns/n2JEdpbdcYlk0xptTac5NTMyD3vGsH2SPZxvy8187djzrVQXaRwaN8xbkeI1NZEXxnOPI4OT3n2RymQJrkXlqUbc/XaSt4tKmsTG/XGYPTeJp0MpzjXd8zPjp3/X3UwXZ2+/yR6sXLPH32+/yyyv1bDKu+Si0tsNAr7+/5qN+eaXte0p0JIyOhBExO/nz9j3V+aynKx53vOQ51i+B1/YY65fAS54jXpPL75ZlLZwo+tNFPOcYKHicKPrlla6N/JghEgy5FpyDIdfCEAmujfy4qt62zAYuWeTIxIuMlDwy8SKXLHJsy1SniqtcUCdi/r49aWx/oVxVLzcc5aJ0koQZo3gkzLgonSQ3XP2B4bBXwkssYX/H5Ty34Dr2d1yOl1jCYa+x8zcdP3oeUiloaQEzf59K+eWNyLbEiZWr//5YuUy2pXqS9qKePi654XskW/MMH8+QbM1zyQ3fY1FPX6N/yrTsG17PU2UoRsokClGKkTJPlf3yRvSW9hG3BHFLYGYnf+4t7Tv1nc/AqmSEwVL162Cw5FiVrH7LbPccY6URnPMwi+Gcx1hphHav+r7P53PcNXyCIa/MkkiUIa/MXcMneD4/8S1O7wC4RJE9boBn3TH2uAFcojhpJcclHY7Rmi9/RnN+uYi8elx2pR9oj4w4PM8xMuIYHfHLZfbQiHaIdu2A1gykgxTH4/tdO2BldUxJzyaruvCxnpeHHctqUkhnWvzyRpTTJSJ9BmX8rIRliHhGOV0deF7QEeOOc5Lc21vg4JjHqtYIt61uqZqfDXBu5DA/2w4PZDt4uRxnRbTIe9ODnBs5XFWvK7qQN6cvYmPqAKOMkKaDFZHVdEUXVtWb7oI6yzqMoWyUi1IT0xCGso6FHdUfVJZGYvUXwImE9zKY6jLVxi5fhVTrSopDLxD37OTc6xJFUq3VY/LH8y/Qs6bEmnVHTpaVvBLH8y80tILkdO0eHCZOG1FXwFEi7mJESLF7cLjuhYSnknUjJGuml8RoIetGprjH2XFLd4JPH8hStfhSyXHbiupMHJeMjvBAawSIkHCQj0QoBOWVKdZ3ZEfJRCK0BxfstgfPwR3Z0ZOj2m2dBZ7LDpNJQZIoRco8nxtiU2eGyitJr31tF196cBDwSCf9IHs4F+HWKxvL8S0ic9OadRHe/q7qrCPXXK+sI7ONAu0Q9ff6I9mVWtv88kasyFiQZWSibLjglzciF3dcusrY2zeRP3fzMr+81gUdsUmBda1ovItNkRNsTlZ8pV3OEolOnjbQFV04KbCutbzdGMy5k0vDAwznmbSgzls2RfjiY/6Hg7akv8z0UN7xrourR7S3JTL8v7F+8Pyl3Eedx7DzuDlRPaf4bNp4Ljz9rGHmiMf9qSNjY8YFWxo73tLWi9nHKIyeIFbMUoonKaWXsbK1Ou1c3ptiBUlv8gqSZ1M2MkrSS2MViwpFnCMbaSxveMqmyFVujS9aNB0XtMe5Y7U/V/tgzmNVMsJtK5JV87MBNmcHcHTynWSEoQi0e7At57E5ewIqnva95RJLItXPxzaL0Fue+FC7eEsfz+5MUwJiSUcpF6OUhcWX9FG5QuPmtd381DXwH98f4MigsaTDceuVHWxeq++LRV5t1qyL6MLHWU6BdogWLPOni6QrFu0bG/HLG3HD+hh/+YSfSWM868hg3vG+zY0tSb4yGeVE1OPyik+/J4oeS+KnP98bINmxhZFj3wTw52h7ObzyGK0LLmnoeDecE+ULu4IsKwk/yB7KOd63pfppu3FplNsug39/zqN30LGsw3jXxVE2Lq3+O85pSfJ+FvBQfpjDXomlkRg3Jzo55xR5us/EjTcYx487Tgwao6PQEocVy/zyRrTHFrG29Q0cadlD1g2TsgwrY+tpr8l8koi0U3J1lkyPtNce8qxKeWmKFIhXDOeWKJLyGrsIc1lsLXsKtbnK86yKn+Lrn7Pggvb4pMC6VizeyZZ8lgtLEyn0yuUs0ZoLQpdFYwx55ZMj2QAjzmNZdOK5nFw2wrarYPezKU6ciNLZWeZ1l2RJLps8er95bbcCaxGROcCcmx/z+rZu3ep27drV7GZUGZ+j3Zqpzk5ywwcnXxA5XT88Vmb7nhIvDztWZIwb1scauhAS4JmRIn/y0ihd0QjtMWOo5Bgoe/yXnjTntzUWvBfGDpEbfJZycYBovItkx5YzSh03nawjs92+/R7ffmwig8nll9FQxpHTMVI8yqHsLqKWPLmCZNnlWJ7aGu7UkQN9fKdvN7FyghhxShQpRfO8ftFmNq9u7OLFZmQdma7K1UZPptArZyetNjo+RzsTidBmfnrJYc/jg5nOk1NHHnB7GKNIa8U0kfHfr7cmJBIXEZEpmdn3nHNbT1lPgXa4ppN1pJmeGSnyL315XsyVWZmM8vZFiYaDbJldRopHOZ5/gbw3RCLSzsLEOaEG2eN2H+jj6SMvko2MkvLSXLBkZcNB9lyQz/ZWp9Br21wVZI87VdaRQ26YhzhAihgpYmQpkaXENlaz3DKTjiciIs2jQFtEZI455IZ5hqMMkKOLJOfTrSBbRGQWmm6grTnaIiKzxHLLsBwF1iIi84VywIiIiIiIhECBtoiIiIhICBRoi4iIiIiEQIG2iIiIiEgIFGiLiIiIiIRAgbaIiIiISAgUaIuIiIiIhECBtoiIiIhICBRoi4iIiIiEQIG2iIiIiEgIFGiLiIiIiIRAgbaIiIiISAgUaIuIiIiIhECBtoiIiIhICBRoi4iIiIiEQIG2iIiIiEgIFGiLiIiIiIRAgbaIiIiISAgUaIuIiIiIhECBtoiIiIhICBRoi4iIiIiEQIG2iIiIiEgIFGiLiIiIiIRAgbaIiIiISAgUaIuIiIiIhECBtoiIiIhICBRoi4iIiIiEQIG2iIiIiEgIFGiLiIiIiIRAgbaIiIiISAgUaIuIiIiIhECBtoiIiIhICBRoi4iIiIiEQIG2iIiIiEgIFGiLiIiIiIRAgbaIiIiISAgUaIuIiIiIhECBtoiIiIhICBRoi4iIiIiEQIG2iIiIiEgIFGiLiIiIiIRAgbaIiIiISAgUaIuIiIiIhECBtoiIiIhICBRoi4iIiIiEQIG2iIiIiEgIFGiLiIiIiIRAgbaIiIiISAgUaIuIiIiIhECBtoiIiIhICBRoi4iIiIiEoOmBtpm928w+a2YPm9mQmTkz+9tmt0tERERE5EzEmt0A4DeAC4ER4CVgU3ObIyIiIiJy5po+og3cAZwLtAMfa3JbRERERETOiqaPaDvnvjH+s5k1sykiIiIiImfNbBjRFhERERGZdxRoi4iIiIiEYE4H2mb2s2a2y8x2HTt2rNnNERERERE5aU4H2s65v3TObXXObV28eHGzmyMiIiIictKcDrRFRERERGarpmcdkdOzu6/MfftKvDzsWJExblwbY/OiaLObJSIiIiI1NKI9h+zuK/P5pwoM5h3L2mAw7/j8UwV295Wb3TQRERERqaFAew65b1+JjoTRkTAiZid/vm9fqdlNExEREZEaTZ86Yma3ALcEvy4N9peb2Z3Bz33OuU/MeMNmoZeH/ZHsSpkWv1xEREREZpemB9rARcCHasrWBRvAAUCBNrAiYwzmHR2JibLhgl8uIiIiIrNL06eOOOc+6ZyzV9jWNLuNs8WNa2MM5h2DeYfn3Mmfb1w7Gz4viYiIiEilpgfaMn2bF0W5/cIWOhJG7wh0JIzbL2xR1hERERGRWUhDoXPM5kVRBdYiIiIic4BGtEVEREREQqBAW0REREQkBAq0RURERERCoEBbRERERCQECrRFREREREKgQFtEREREJAQKtEVEREREQqBAW0REREQkBAq0RURERERCoEBbRERERCQECrRFREREREKgQFtEREREJAQKtEVEREREQqBAW0REREQkBAq0RURERERCoEBbRERERCQECrRFREREREKgQFtEREREJAQKtEVEREREQqBAW0REREQkBAq0RURERERCoEBbRERERCQECrRFREREREKgQFtEREREJAQKtEVEREREQqBAW0REREQkBOaca3YbzgozOwYcaMJDLwL6mvC4Up/6Y/ZQX8we6ovZRf0xe6gvZo+51hernXOLT1Vp3gTazWJmu5xzW5vdDvGpP2YP9cXsob6YXdQfs4f6YvaYr32hqSMiIiIiIiFQoC0iIiIiEgIF2mfuL5vdAKmi/pg91Bezh/pidlF/zB7qi9ljXvaF5miLiIiIiIRAI9oiIiIiIiFQoC0iIiIiEgIF2iIiIiIiIVCg3QAz6zGzvzazQ2aWN7P9ZvYZM+tqdtvmIzN7t5l91sweNrMhM3Nm9renuM8VZnafmfWbWdbMnjazXzCz6Ey1ez4ys4Vm9hEzu8fMXgjO7aCZPWJmP2Nmdd9T1B/hMLP/ZWYPmtmLwXntN7MnzOy3zWzhFPdRX8wQM/tA8H7lzOwjU9R5m5k9FLyORszsO2b2oZlu63wT/F92U2yHp7iPXhshMrNrgv8dh4PY6ZCZPWBmN9apO2/6QhdDniYzWw98C+gGvgo8B1wKXA38CLjSOXe8eS2cf8zsSeBCYAR4CdgE/J1z7gNT1H8HcDeQA74M9AM3AxuBrzjn3jMT7Z6PzOx24HNAL/AN4CCwBHgn0IF/3t/jKt5Y1B/hMbMC8H1gN3AUSAOXAVuBQ8BlzrkXK+qrL2aIma0EngGiQBvwUefcX9XU+Tngs8Bx/P4oAO8GeoA/ds59YkYbPY+Y2X6gE/hMnZtHnHN/VFNfr40QmdkfAL+M/z/8fvwVIBcDrwP+wzn3KxV151dfOOe0ncYGPAA44OM15f87KP98s9s43zb8DzEbAAO2Bef5b6eo244fcOSBrRXlSfwPSA74iWb/TXN1A96M/4YXqSlfih90O+Bd6o8Z64/kFOX/Izi3f66+aEq/GPAfwB7gD4Nz+5GaOmvwA4njwJqK8i7gheA+lzf7b5mrG7Af2D/NunpthNsXHw3O4Z1AS53b4/O5LzR15DQEo9nX4b+A/6zm5t8GRoEPmll6hps2rznnvuGc+7ELXm2n8G78T8n/4JzbVXGMHPAbwa8fC6GZrwrOuR3Oua8557ya8sPA54Nft1XcpP4IUXAe6/nHYL+hokx9MXN+Hv9D6W34/xfq+WkgAfypc27/eKFzbgD4n8Gvt4fYRpmg10ZIzCyB/8H/IPCzzrlCbR3nXLHi13nXF7FmN2COuTrY/1udQGPYzB7FD8QvAx6c6cYJ4P9zA9he57adwBhwhZklnHP5mWvWq8L4m2Wpokz90Rw3B/unK8rUFzPAzM4DPgX8iXNup5m9eYqqr9Qf99fUkcYkzOwDwCr8DzxPAzudc+WaenpthOct+IHzZwDPzG4CtuB/m/O4c+7bNfXnXV8o0D49G4P981Pc/mP8QPtcFGg3y5R95Jwrmdk+4DXAOuCHM9mw+czMYsBPBb9WvkGqP2aAmX0Cfx5wB/787DfgBxWfqqimvghZ8Dq4C3/07tdPUf2V+qPXzEaBHjNrdc6Nnd2Wvmosxe+PSvvM7Dbn3DcryvTaCM8lwT4HPIEfZJ9kZjuBdzvnjgVF864vNHXk9HQE+8Epbh8v75yBtkh96qPm+BT+G+h9zrkHKsrVHzPjE/jT134BP8jeDlxX8c8L1Bcz4beAi4EPO+eyp6g73f7omOJ2eWVfBK7BD7bTwPnAX+DPjb/fzC6sqKvXRni6g/0v48+vfiOQAS4A/g24Cvinivrzri8UaIvIGTGznwd+CT8Dzweb3JxXJefcUuec4QcV78Qf7XnCzF7b3Ja9epjZ6/FHsf+4ztfhMsOcc78TXFNyxDk35px71jl3O37ighTwyea28FVjPM4sAW93zj3inBtxzj0D3IqfheRNZnZ501oYMgXap+dUIwzj5SdmoC1Sn/poBgXpyf4EP73c1c65/poq6o8ZFAQV9+BPYVsIfKniZvVFSIIpI1/C/7r7N6d5t+n2x1Qje9KY8Yu2r6oo02sjPOPn7InKi34BgilR49+AXhrs511fKNA+PT8K9udOcfv4Ff5TzeGW8E3ZR8E/w7X4n6z3zmSj5iMz+wX8HMDP4gfZ9RaBUH80gXPuAP6Hn9eY2aKgWH0Rnjb883oekKtcHAV/Sg/AF4Ky8bzOr9Qfy/CnO7yk+dln3fh0qsrsYHpthGf83E4VGA8E+1RN/XnTFwq0T883gv11tSvgmVkGuBL/itjHZrphctKOYH9DnduuAlqBb82Vq5VnKzP7VeDTwJP4QfbRKaqqP5pnebAfz7CgvghPHvi/U2xPBHUeCX4fn1bySv3x1po6cvZcFuwrAzW9NsLzIP7c7M1TrBw8fnHkvmA///qi2Ym859qGFqxp9vnfxqkXrDnGPEp2P9s2/K/GHbALWHCKuuqP8PrhXKCjTnmEiQVrHlVfNL2fPkn9BWvWogVrwjrn5wHpOuVr8LODOeDXK8r12gi3P74anMM7asqvAzz8Ue2O+doXWoL9NNVZgv2HwOvxc2w/D1zhtAT7WWVmtwC3BL8uBa7HH414OCjrcxVLFQf1v4L/T+wf8JdvfTvB8q3Ae52e+A0xsw/hr+5Vxp82Um/+6H7n3J0V91F/hCCYuvP7+COl+/ADtiXAm/AvhjwMXOOc211xH/XFDDOzT+JPH6m3BPvHgf+DlmA/q4Jz/kv4eZcPAMPAeuAm/IDtPuBWV7F4il4b4TGzHvy4aSX+CPcT+B80b2EicL67ov786otmR/pzccN/snwR6MV/YzyAn4y9q9ltm48bEyNCU23769znSvw30wEgCzwD3AFEm/33zOVtGn3hgIfUHzPSF1uAP8WfvtOHP29xEPhu0E91v21QX8x4P42/Zj4yxe03A9/EDwZHg/77ULPbPZc3/A+bf4+fCekE/mJax4B/x8/3b1PcT6+N8PpkMf7gzIEgbuoD7gEune99oRFtEREREZEQ6GJIEREREZEQKNAWEREREQmBAm0RERERkRAo0BYRERERCYECbRERERGRECjQFhEREREJgQJtEREREZEQKNAWEXkVMrM7zcyZ2ZqQH2e/me0P8zFERGYrBdoiItIwM3vIzLTymYhIHbFmN0BEROa1a5rdABGRZlGgLSIioXHO7Wl2G0REmkVTR0REToOZrQnmNt9pZpvM7F4z6zezUTN7xMyuq3OfhJn9mpk9Y2ZjZjZkZg+b2XvP0vE/Gdxn2ysdb5p/34fN7G4z22tm2aCtj5rZB+odF3hT8Lur2B6qqFd3jvYZnJM1ZvYPZtZnZjkz22Vmb5vO3yYiMtM0oi0i0pi1wLeBZ4C/AJYB7wPuN7P3O+e+DGBmLcAD+AHpc8CfAa3Au4Evm9lFzrlfb/T4Ifgc8ANgJ9ALLARuBO4ys43Oud8M6p0Afgf4MLA6+Hnc/ld6gDM4J6uBx4G9wF3AAvxz8lUzu9Y5943T/WNFRELlnNOmTZs2bdPcgDWAC7Y/rLltK1AEBoD2oOy/BnXvA2IVdbvxA1IHXNHo8YPyTwb1t71Ce++sKb8zKF9TU76+zjFagAeDx15Rc9tD/r+SKc/XfmB/TdmZnJPfrjnW9ePHavZzQ5s2bdpqN00dERFpzCDwu5UFzrldwN8BncCtQfFP4weCv+icK1XUPQr8XvDrR87g+GeVqzOn2jlXwB91jnF2Lm5s9JwcAP57TdseAA4Cl56FdomInFUKtEVEGvN959xwnfKHgv3FZpYBzgEOOeeeq1N3x3jdRo5/Gm2dNjNbZWZ/ZmbPBXOnXTAX++6gyoozPP6ZnJMnnXPlOuUvAl1n0i4RkTBojraISGOOTFF+ONh3BBv4c53rGS/vbPD4Z5WZrcOfA90FPAz8G/7Iehl/+saHgMQZPsyZnJMTU9ynhAaORGQWUqAtItKYJVOULw32g8FWWVZrWUXdRo4/zgv29d7T6wWsU/lF/Isfb3PO3Vl5g5n9JH6gfabO5JyIiMwpGgEQEWnMa4NpELW2Bfsngqkfe4AVZrahTt2rg/33Gzl+RdlAsF9Zp/7WOmVTOSfY313ntjdNcZ8ygJlFp/MAZ3hORETmFAXaIiKN6QB+q7LAzLYC/wl/NPaeoPivAQP+sDIYNbNFwG9W1Gn0+OBP9wC4zcxiFfVX1h7jFPYH+201j3s99S9OBDge7FedxuM0ek5EROYUTR0REWnMTuAjZvZ64FEm8lxHgP/snBsK6v0R8FbgHcBTZnYffs7o9+Cns/sD59wjZ3B8nHPfMbOdwFXA42a2A3/qyc34+arrjXTX8+fAbcA/mdlXgEPAFuAG4B+Dx6/1YPC3/HPwt2WBA865u17hcRo9JyIic4pGtEVEGrMPuAJ/2sbtwHvxpzvc6CoWkwlS470F+G9B0cfx5zr/GHi/c+5Xz+T4Fd4B/BXQEzzGxcCvAFMdfxLn3NP4Uze+BdwEfAxoB94JfH6Ku/0V8Pv4I/C/gp+e72dO8TiNnhMRkTnFnHPNboOIyJxhZmvwg+C/cc59eK4dX0REZo5GtEVEREREQqBAW0REREQkBAq0RURERERCoDnaIiIiIiIh0Ii2iIiIiEgIFGiLiIiIiIRAgbaIiIiISAgUaIuIiIiIhECBtoiIiIhICP4/vz18c7Jh5bkAAAAASUVORK5CYII=\n", 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\n", 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\n", 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v1dTUHD1xYqfmZIuIiIiIlOTJJ59c7e5DS8m7JzUQdhvufjXJmsyTJ0/2mTNndnONRERERGRPZmavlZp3T9ooLffkvq7I+Vx8XTe8RkRERERkt7AnNRBeTtLxRc4fmKTpuQNd+hoz6wWMJdY2f3V7lRURERER6Yn2pAZCbu+E05N9DFoly5xOIVYYeix16r4knZ4tLFnmdDyxGsmrpbwGOIlY2/wRrWAkIiIiIruj3a6BYGa9zWxisu9BK3efB9xDLO32iczLLgdqgBtyeyAkHiBWGjrJzM5JXaMC+N/k8Ke5PRASNwOrgfPNbHLqNX2A/04Of9LJtyciIiIi0q16xEZpZnYecF5yOAI4g3hq/1ASW+3uX0jyjiE2c3nN3cdkytkfeITYTflW4ub/WGKPhDnACe7+euY1xxK9Ar2Jm/+FwKnAZOBh4NRsb0BS35uBTcBNwBrgHGIJ1JuBf/ESP1hNUhYRERGRnc3MnnT3yR3n7DmrGB0BXJSJjUt+IIb5fKGjQtx9XvJU/+vEEKCziB2UfwBc7u5rC7zmcTN7E9HLcDpQm1zv68C3Cw0VcvdbzGwq8J/AO4E+xC7OnwN+WGrjQERERESkp+kRPQhvZOpBEBEREZGdrZwehN1uDoKIiIiIiOw8aiCIiIiIiEgrNRBERERERKSVGggiIiIiItJKDQQREREREWmlBoKIiIiIiLRSA0FERERERFqpgSAiIiIiIq3UQBARERERkVZqIIiIiIiISCs1EEREREREpJUaCCIiIiIi0koNBBERERERaaUGgoiIiIiItFIDQUREREREWqmBICIiIiIirdRAEBERERGRVmogiIiIiIhIKzUQRERERESklRoIIiIiIiLSSg0EERERERFppQaCiIiIiIi0UgNBRERERERaqYEgIiIiIiKt1EAQEREREZFWPaqBYGajzeyXZrbUzDab2QIzu9LMBpVZzjvNbIaZrTezJjObZWZfMrOqAnmvMzPv4OfezGsu7iD/R3f0sxARERER6Q69ursCOWa2P/AIMAy4FXgJOAb4NDDdzKa4++sllPMt4EtAA/BHYA1wIvAt4FQzO9Pdt6ZecguwoEhxFwLjgLuKnL8VeKZAfGZH9RQRERER6Yl6TAMBuIpoHHzK3X+UC5rZFcBngW8C230yb2ZHEY2DdcDR7v5qErek/I8CnwSuyL3G3W8hGgnZsgYC/w5sAa4rcslb3L3YORERERGR3U6PGGKU9B6cTjzJ/7/M6a8BG4ELzaymg6LOS9Kf5xoHAO7uwJeTw0+UWK0Lgb7An9x9dYmvERERERHZrfWIBgJwcpLe4+4t6RPuXg88DPQDjuugnBFJ+mr2hLuvBdYC48xsbAl1+kiSXr2dPEeY2WfM7D/M7EIzG11CuSIiIiIiPVZPGWI0IUnnFDn/CtHDMB64t0gegNyT/rwGQDJkKDfZeQIwv1ghZnY8cCgwx93v3871Pp053mZmPwc+4+6btvM6EREREZEeqaf0INQl6foi53PxgR2Uc0eSfsTMxuSCyRyEb6bydbQq0iVJek2R8/OJuQwTgBpgb+BfiCFSlwK/3F7hZnaJmc00s5mrVq3qoCoiIiIiIrtOT2kgdAl3fxj4BdGQeM7MrjWz7wGPAx8iVkYCaClSBGZWR9zsF52c7O4PuPuP3X2Ouze6+zJ3/wMxVGot8F4zO3w79bza3Se7++ShQ4eW/0ZFRERERHaSntJAyPUQ1BU5n4uvK6GsjxBP8V8mbvQvBTYA04B5SZ6V23n9BcR8h7InJ7v7IuDO5PCkcl4rIiIiItIT9JQ5CC8n6fgi5w9M0mJzFFolKxZdTYHJxWZ2KNF78NR2ishNTv5ZR9cqIjdmqKMVl0REREREepye0oOQmwh8upm1q5OZ1QJTgEbgsc5ewMymAfsCd7h7wbkOZnYscDgxOXlGJy91bJLmraQkIiIiItLT9YgGgrvPA+4BxpC/T8HlxNP4G9x9Yy5oZhPNbGK2LDMbUCC2H/BzYl7BV7ZTldzk5O0tbYqZTS4QqzCzLwHHE6sp3b29MkREREREeqKeMsQI4OPAI8APzexUYDbxNP5kYmjRf2byz05Sy8R/kTQIngLWEEuengP0Bi509+cKXTxpWLwH2Axc30Fd/2lmLwDPAkuIORJTgEOIno73u/uGDsoQEREREelxekwDwd3nJU/mvw5MB84ClgE/AC5PNjorxe1ET8C7gVpgBXAz8G13n72d172f6Km4qYTJyd8FjgFOAQYT8xoWErtAX5HexVlEREREZHdiMadXusvkyZN95syZ3V0NEREREdmDmdmT7p43TL6QHjEHQUREREREegY1EEREREREpJUaCCIiIiIi0koNBBERERERaaUGgoiIiIiItFIDQUREREREWqmBICIiIiIirdRAEBERERGRVmogiIiIiIhIq7IaCGY21MxOMrPaIucHJOeHdE31RERERERkVyq3B+ErwG3AtiLntyXnv7QjlRIRERERke5RbgPhNOBv7t5Y6KS7bwTuAc7Y0YqJiIiIiMiuV24DYR9gXgd5Xk3yiYiIiIjIbqbcBoIDVR3kqQIqO1cdERERERHpTuU2EF5mO8OHzMyS83N3pFIiIiIiItI9ym0g3AxMNLMfm1nf9Ink+MfABOB3XVQ/ERERERHZhXqVmf+HwHuBjwHnmdmDwBJgFHASsDfwLHBlV1ZSRERERER2jbIaCO7eZGbTgKuAfwHOT51uAX4D/Ju7N3VZDUVEREREZJcptwcBd18HvM/MPg28CRgIrAOecPfVXVw/ERERERHZhcpuIOS4+yrgzi6si4iIiIiIdLNyJymLiIiIiMgerKweBDP7ZYlZ3d0/1In6iIiIiIhINyp3iNHFHZx3wJJUDQQRERERkd1MuQ2EsUXiA4kJy18FHgH+Y0cqJSIiIiIi3aPcZU5fK3LqNeBZM/sr8Bzwd+AXO1g3ERERERHZxbp0krK7LwJuAz7dleWKiIiIiMiusTNWMVoBHNiZF5rZaDP7pZktNbPNZrbAzK40s0FllvNOM5thZuvNrMnMZpnZl8ysqkDeMWbm2/m5aTvXucjMnjCzhuRaM8zs7M68dxERERGRnqDT+yAUYmaVwCnA+k68dn9i/sIw4FbgJeAYojdiuplNcffXSyjnW8CXgAbgj8Aa4ETgW8CpZnamu28t8NJngVsKxF8ocp3vAp8HFgPXAFXEztK3mdkn3f3HHdVVRERERKSnKXeZ05O2U84+wAeAI4Cfd6IuVxGNg0+5+49S17wC+CzwTeCjHdTvKKJxsA442t1fTeKWlP9R4JPAFQVe/oy7X1ZKRc3sBKJxMA94k7uvTeLfAZ4Evmtmt7v7glLKExERERHpKcodYjQDuL/Az9+AXwInAQ8BXyyn0KT34HRgAfB/mdNfAzYCF5pZTQdFnZekP881DiA2ZQC+nBx+opy6FZFrqHwz1zhIrrOAqH810VgSEREREdmtlDvE6OvEHgdZLcBa4Al3f6IT9Tg5Se9x95b0CXevN7OHiQbEccC92ylnRJK+mj3h7mvNbC0wzszGuvv8TJa9zexSYC/gdeBRd3+uyHVOSdK7C5y7i1ju9RSicSMiIiIistsod5nTy3ZSPSYk6Zwi518hGgjj2X4DYXWS5u3XYGYDgdxk5wlAtoFwWvKTfs0M4CJ3X5iK1QCjgAZ3X1akriR1FRERERHZreyMVYw6oy5Ji01uzsUHdlDOHUn6ETMbkwsmcxC+mcqXXhWpEfgGcHQSHwRMJYZOTQPuzQxt2uG6mtklZjbTzGauWrVqO29HRERERGTX6ikNhC7h7g8TG7QNBJ4zs2vN7HvA48CHiJWRIIZE5V6z0t3/y92fcvd1yc+DRI/F48ABwIe7uJ5Xu/tkd588dOjQrixaRERERGSHlN1AMLMaM/uimf3dzGab2asFfuaVWWzuqXtdkfO5+LoSyvoIcCnwMvAvyb83EL0BuXqt7KgQd2+mbTWm9OpNXVlXEREREZEepdxlTgcC/wAOIm66BxA3zFVA3yTbUqDQPgPb83KSFhu3n9t4rdgchVbJikVXJz/tmNmhRO/BUyXWKzf+p3WIkbtvNLMlwCgzG1lgHkLJdRURERER6WnK7UH4CtE4+BBt4/i/D/QHTiBuvOcBk8os9/4kPd3M2tXJzGqBKcRcgcfKLDddzjRgX+AOdy91I7fjkjS7KtJ9STq9wGvOzOQREREREdltlNtAOAd40N2vTZ7UA/HU3t0fA84CJgL/WU6h7j4PuAcYQ/4+BZcTT/BvcPeNuaCZTTSzidmyzGxAgdh+xHChLUQjJ33uqGyjJImfSmzQBvDrzOmfJul/mtmg1Gty9d8MXJstU0RERESkpyt3H4R9gNtSxy3EpmBATPg1s7uA84m9AMrxceAR4IfJzfls4Fhij4Q55Dc6ZiepZeK/SBoETwFriCVPzwF6AxcW2NvgCuBAM3sEWJzEDqNtr4Ovuvsj6Re4+yPJDs+fIyZD30wMs3oPMBj4pHZRFhEREZHdUbkNhEZSKwAR8w9GZPKsIPYJKIu7zzOzycRmbNOJ3ohlwA+Ay9M7FnfgduAS4N1AbVKfm4Fvu/vsAvlvAN4OvIkYHtQ7ec3vgR+7+0NF6vt5M3ue6DG4hLa5Dd9x99tLrKuIiIiISI9iqZFCHWc2ewZ4yd3PT44fIhoIE3I7ICe7Ho9093E7ob57nMmTJ/vMmTO7uxoiIiIisgczsyfdfXIpecudg/AAMDXZeAzgd8D+wJ1m9gkz+wMxsffOMssVEREREZEeoNwhRtcTY+1HA4uIybqnAOcRG4sBPExmIrCIiIiIiOweymoguPtTwMdSx83AO8zsaGLH4QXAP3PDjUREREREZPdSbg9CQe7+JPBksfNmNhWY6u5f74rriYiIiIjIzlHuHITOmgZ8bRddS0REREREOmlXNRBERERERGQ3oAaCiIiIiIi0UgNBRERERERaqYEgIiIiIiKt1EAQEREREZFWaiCIiIiIiEirLtkHQUREREREMp5/Hm79MyxaBPvsA+e+HQ49tLtr1SH1IIiIiIiIdLXnn4cfXAHr1sKoUZH+4IqI93C7qgdhPbBwF11LOvKZz8Azz3R3LUREROSNZuNGWL0KNm2GPtUwZCjU1BTOt2oVbN4M1dUwdBfm66r6vfYaNDXC5i1Q1RsOPQRGj4oehR7ei7BLehDc/Up3H7srriUiIiIiPdDGjbB4ETQ3x011c3Mcb9yYn29RJt+iXZSvK+vX0BCxlhaoqoJNm+C55+HZZzv/Ge4iZfUgmNlXgK8B+7n70gLnRwHzga+6+/92TRWly115ZXfXQERERPYks2fB3bfBkkUwah+Y/jaYdHD7PN+4HNauhUGD2mK5469+rS122WXF8112WVvs8u2U97VOlFdq/Uq97ltOgfXroa6uLbZ5c+Tt4crtQXgbMKNQ4wDA3ZcA9wPn7WjFRERERGQ3MHsWXP0jWL8ORo6K9OofRTxt4cL2N8sQxwsX7pn5ageAe/QcpNPaAfR05c5BOAD4dQd5XgQu6Fx1RERERLrJnBfh3jth2WIYORpOPQvGH5Sfb+5smHEXLF8CI0bBtDPhgEn5+V55Ee5P5Tv5TDiwQHmvvAj33QnLlsQN9ilnFc9XSnnl1K+U63bk7tugbmD8QFt6923texH23Tf/yfv69RFP21PyHX5EzEtYvgw2bIABA2DMWDjgQHq6cnsQ+gKNHeTZBNR2rjoiIiIiXWzW8/Dt/4ZPXBrprAKryMx5EX71E9iwHobvHemvfhLxtLmz4cafQf16GDYy0ht/FvG0V14snO+VF/Pz3fDT5LojI73hp4XzlVJeOfUr5bqlWLIo/6l47YCIp533dli3Lm6uW1oiXbcu4mnveEecS+dbuzbiaW9/e6wM1K68tRHvTHml1q+c61ZUwsGHwvSzIq2ozL9uD1RuD8Ji4LgO8hwHLOlcdURERES60Kzn4UdXwsBByVKT6+L4k5+JG7ace++EjVvghX/C2nUwaCCM3S/i6V6EGXfFqjRPPt02vnzf0RFPP6W//y7YuBmeT5U3br+Ip5/S33cnNG6GWTNT19034ul8998FTVvgpafiPQwcCPvtk1/ejLtg2XKSudbbAAAgAElEQVT4231QXw+1tXDIQfn1u+9OWLESHngANjZATX+YOCn/ugB33Qq//jmsXA7DRsAFH4Yzz207P2ofePZJmPtyW1kHTIDDj25fzqGHwdFHwM9+AmvWwODBcOnHIp522GHwpqPgJ//Xlu9jn4h4Nt/kI+GnV7Xl++jHC+c7Ninv9bWw16DC5R16GBx9JFx9VVsPwSUfL1y/z30e/vznGFa0777wwQ8Wvu6558BVV8HSJbD3KPh4gfr1QOU2EO4GPmFm73H332VPmtn5wFTgqq6onIiIiMgOue1W8BZ4ZRbUb4gn20NGRDzdQHj2GXhhDvTrCwProLEJ/vk0NGVWppn1HLw8F/r2jSEjTU3wzHOxnGXac8/CCy+3ldeUK69Avllz2ueb+Ux+vheeg5deievWJfmefjY/3z8egqeeilVzampgUxM88mgs2/nhz7Xle+Qf8Pxzka9fTUye/ecTMU7+0i+05bvrVvju5XHTP2RY9Eh89/I4l2sk1NTAPx+NFX369oPGjXF8wknt63bHLXDTdbD3UBg/Dhrq43jMGHhravrq7bfATdfCPsNg0v5Jvmth7Bg4O5Pvd9fCqKEwISnvd9dGeel8d94CN18P+42Agw+MfDdfH+Wdlcp3x60R32c4TNw/Gjs3X5/UL9UggrjJ7+hG//nn4I7b4fDD4KQTo0F5x+0wfnx+o6OHKXeI0f8C64DfmNmfzOwSM3trkv4ZuBFYA3y7qysqIiIiUrYXnoN5L8HmTdC/NtJ5L0U8bflq6FUZN+pmkfaqjHjaqjVQWRE36maRVlZEPG3Zqnh9Ol+vyohnr1uovOx1V74O27ZA/RpYuiDSbVsinjZ3HlT2gj59oaIi0speEU9bMB8qK+Om3izSysqIp/3659E4qK2L8mrr4vjXP2/Lc/9fYfiIaGhs3Rrp8BERT7v+mnjtgKSsAUlZ11+TyXc19M/k698/4tl8/TL5+hXId8M1hfPdkLnur5P61Q5I3uuA5L1m8pXqlj9HT8+gQVHeoEFxfMufO1feLlRWD4K7LzGzM4A/ECsVpZtTBiwA3u3ui7ushiIiItI95s6GB+6GFUtg+CiYOr3wZNd5s+HBVL6TpsP+BfK9+hL8425YsTTG+b95Ooyb2Pl8816Ch1L5TpwO+2fyNdXDxnpYsxyat0Kv3lBdE/G0Pv2hcRG8vhhatsVY8b4DYpJvWt/+sUrP5k1QVQ1bNrfFs+VtWBfne1fB1i1gHvFsPsuUZ+Tnc4eGddCrCnr3jif+zVtgyPD2+Zq3xc1oc3Pc8G/LHW9rn6/FocLifGs+i3jayuXRc5BWUxvxdJ6hw+I6reW3tM8DsGIZDM3Ut39txNvlS8rLy5cpr1Dd+tfmX3fliiL5VrSPrVoOe2Xfa/+Id8bChTB6dPtYodWOeqCyN0pz95nAeOBdwPeAXyTpu4AJ7v5kl9ZQREREdr25s+G3V8eQkqHJZNffXp0/2XXebLgpk++mqyOe9upL8IdrYpjP0JGR/uGaiHcm37yX4PfXJNcdEenvr4l4WmUzrF4Z8wYqKyNdvTLiaXU1sGlD/Nst0k0bIp426VAYNwGq+0TDo7pPHE/K7Ix78GEwdkLc9G9siHTshIhn840YCY3rYPmrkY4YmZ+vV0XcmPtWaFofaU1txNP22S+efPfqFY2SXr3ieJ/92ucbvW/0BlRWwpbks6mti3jasBGwfjWsXwFrFke6fnXE03kaMg2uhvr2eSAmQxfKN3xkJl+R8oZnyiv1usOGF8mXaawMHRHfVdrGhohnzX4Brvgf+PzHI539Qn6effeNYUVphVY76oE6tZOyu2919z+5+7+7+yVJ+id339rVFRQREZFu8MDdccOYHlpSWxfxtAeL5Hswk+8fd0P/TL7+dRHP5mtpgYVz4MkZkba05Od76G6ocFizAOY+FmmFRzzNN8KIOqgENjZGOqIu4mm2Fip6xfCTEcOS4Si9Ip525tvAKqKH5M2nRWoVEU875zwgyXdiko+KJJ5yxGGwYmHcxI8ZH+mKhRFPq+4NmzfGUKC6wZFu3hjxtIsvga3N0XjYZ2ykW5sjnnbhR2BbM/QfAPuOjXRbc8TTTj8TGtfC5kawykgb10Y8XVZjQ6yE1NISaWNDflkXfSRuuNP5NjZEvF2+S2IX4nS+hoaIZ/MVum42X6n1uyCpX/2GyFe/IY4vyOSb/QL8LLPvw89+lN9IKHVVpB6o3EnK7ZhZLTAQWO/uG7qmSiIiItLtViyJJ/hpNbUR71S+pUXyZfZenTcbls6HTY0x7GXd67BmZRynvfYyrF8ck3Sbm+NJed/VMSk3rW8F9HIY0hcq+sUQGveIp/VuhmMPhrlLYcNGGFADh4yJeNqkQ+DST8JdqV2Dz78w4mmHHAqf+iz85RZYvDCezP/rByKetugVOPoYWLw4blwH1MXeAYteaZ+vAhgyNCYRb94cDYQBdfmPet96Xnz2v782VinqXwcfuqT9JGCAs86N/R5++wtYvTQabBd8OOJpLZvhqONgzkttKxQdelTE02VBjOnPrXT0iS/kl5Wrw/XXxLCi4SPhk1/Ir1tugvH1V8ewouEj4JNfbD/xOJsvd91C+XITkW+4JoYVDRsOn/h8+wnK0DYR+dfXxLCioSPgY5/Pn6B8V5F9H+66rf3vwaGHwWc/F3MOcqsdfeCDPX6CMnSigWBmvYAvAB8Gxqbi84GfA9919+YiL++o7NHA14HpwF7AMuAW4HJ3L3lfajN7J/BJ4EigCniV2ODte+6+JZP3QOAdwBnAgcBwYC3wGHClu99foPyLgWu3U4WPuftPS62viIjILrfgZXjsb7BqWdy4H3cajJnQdn74qBi2U5vaMXZjfcTTho+CJQtg7cq2G8hBw2DUmEy+vWHJawXyZYa+rFsJ69dAdd8YlrNtWxz3ywz12bQG1r8OvdP5Xofqfu3z1VTB4L5QvyWepPfuBbVVEU8bNDTe35TUDfzG+mjEZE06JL9BUMiAapg0BIY1w15D4jhr2RLYbwyMHdcWa2mJeLv6DYT1a+P7qK6ORsLmpoinzZ0NS+bC9LOj7hvr43ju7PbzR+a8CPNfgtPeGuPxG+rjeM6L7Zd1XbEEDj4cDj2yff2yDcCzzs1vEBTy1vPyGwSFnH1e/o3+juQ767z8BkHB+p2b3yDIWrIoeg7SCu37ANEY2A0aBFllDTEysyrgHuCbwBhgEfBEko5J4n9P8pXFzPYHngQ+kJT5feLG/tPAo2a2V4nlfAu4GTga+DPwE2Jzt28Bd5pZpi+ObxCrLg0H7iTmUzwMvBW4z8w+tZ3L3QpcXuBnZil1FRER6RYLXoZbr4WGDbDX8EhvvTbiOVOnRwOhPhmWkfv31Ontyxo3AeY8GzeYfftFOufZiKeNLZJvbCbfpqZYVSfNLL9noHkTVBpUN0PV1kgrLeJpvXpDvyrYuw7GDI20X1XE0854Xwyf2Vgf73djfRyf8b7SPtOshXPg7l9FOYOHR3r3ryKeNnJU4fHx2RvQiYfCxIOhT58436dPHE/M9EiUOjTs3juhdmD7lX1qB0Y8bfioqHtaoYbiG8mofWL4UVr9hojvIcqdg/A5YBpwBzDJ3ce4+/HuPgaYANwGnJjkK9dVwDDgU+5+nrv/h7ufQjQUJhCNj+0ys6OALxFLsR7u7he7++eAY4CfAqcSPQtpdwNHufvB7n6pu3/J3d+R5N0KfMfMMn2irW5x98sK/KiBICIiPddjf4OaATHuvKIi0poBEc85YBK895K4uVy1LNL3XpK/itHCuXDQkfEUunFjpAcdGfFsvvETYJBBrzWRjp+Qn6+qOlaSqayMSbaVlXFclXn6XmEwqF/bSjzp47S+/eMGvaKybXWiwcPzVx2acga897Px1H3965G+97MR74wn74V+yedqFZH2GxDxtFPOioZXenx8/fqIp02dDlW94ZDD4C1nRlrVO7/BtmJJfq9HoSFfyxbHd5XWvzbiaScVaSielLnuG8mZb4t5B+vXxWeS+3d2HspurNwhRu8DXgDOc/eW9Al3n2dm7wCeAd5PGXshJL0HpxPLpP5f5vTXgEuAC83s8+7ZWUXt5PqOfu7ur6bq5mb2ZeCjwCeAK1LnritUkLs/YGYzgNOAE4A/lvp+REREerRVy6LnIK1f/4inHTCp8LKmaSuXwMj9YNTYtlhLS8TTlr4CVU3Jcpi9oWUrbGuKeNr4w2HWP2Hw0LblQRsbYNLh7fMNrIO1DXGjn7v5b94Eg+ra5xtzKMx7CvYeG0uENm+BpoaIZ005o/MNgqzXl0dDJK1f/4inHXgQXPjR2MF42ZLoOTjvvfm7GecabOllZ89+T/73U+rQsJGj2+Y85DTURzxt/0lw/iXtl7F963sKL2O7J5j/Mjx6D6xaCkP3huNPz+/lKnUeym6s3AbCAcCPso2DHHdvMbO7yH9K35GTk/SeAg2PejN7mGhAHAfcm31xSm4dqlezJ9x9rZmtBcaZ2Vh3n5/NU0BuVaZicyqOMLPPAH2AJcD92gNCRKSHWTkf5j4MG1bCgGFwwBQYNrZAvldhTirf+CkwbFx+vhVJvvUroS7JN7xIvpf/EctC1g2HCW8unq8ry1v+Krz0UFt5E0+EEZl8Q0fCykXQtDZZCacG+g6CYZkhEqV8JsNGQcP6mAyb01gf8bTqbbDZY+degMrk5r86sz7/W98Hq5dD/dpoGPSuguH7RDxt+Mi48d22JW76rRKqBuQvmTn9fPj1cti8HjY1QGVvqNs74jvTXiOSOQwD2mKNDRHPOvCg/AZBIaU02KZOj+VooW0OQv36aEyknXoW/Oon8e/cHIT6dfD29+aXuf+kPbdBkDb/Zbjll/Gd7TUiht7d8ks474OFGwl7UIMgq9whRluA/h3kqaHtxrpUuU99TpHzuccL4zsoJ7ftYN5/+c1sIDAoc72izGw/YphRI/BgkWyfJoZA/Q/wK2CBmf3UzPp0VL6IiOwCK+fDzD/GjWHtkEhn/jHi7fK9Ck9k8j3xx4inrXgVnrg5zg/I5bs54tl8j/8BNtXDgKGRPv6Hwvm6srzlr8Jjv49NwAYMifSx30c87cBJsPDFuHms6hvpwhcjXu5nMuX0uJFqSIagNKyP4ymnt8+316CYJLxlc6witGVzHO81qH2+cRPhos/BkSfCmImRXvS5/I3S9hkPEw6DIaOh3+BIJxwW8Wx5F3wGJh4PQ/eP9ILPFN54rSsdfSo0boCNG8BbIm3cEPGdqdShYeMPgn/9WPQgrFga6b9+rP0E5TeaR+8pPPTu0Xu6u2a7XLk9CM8B7zKzy9x9VfakmQ0hNkx7tsxyc48d1hc5n4sPLHI+5w5iDsJHzOwqd1+Q1MtoP4dhUIHXtjKzauBGoBr49wIrKM0neknuARYn9X8z0VC4FBhADMcqVv4lxLAp9t0NNssQEdltzX04dqTN7UqbS+c+3L4XYU6RfHMebv/EfM7D0Ke2cL700/yX/wGbtsBrs9pWwhk+MuLDM+WtWAnzHorx+/1qYP8JhctbtQoWPxYr11T3hdH755f30kOwejUsfQK2NMXN/97jIp7uRWhcCqNHwssvxeTfPn1hwsSIp+vWes3GWB1o9P75n8m4iXDCqXD7jfHkf8gIOPv9+Tfgow6MlXcWvNL2XsccGPGsygro2ytWGurbK46zDjkJFl0DbIVeFunWpojnlWfQpxf07R1ppeXn6Wr7jofp/xpzDl5fHk+kTzov4jtbKT0NEI2B3b1BsOY1WPg4NKyG/kNg32Nh8H4dv66QVUvze3j69Y/4G0y5PQg/BoYCT5jZh8xsnJn1NbOxZvYB4PHk/I+7uqKlcPeHiZ2dBwLPmdm1Zva9pF4fAnLbKxYcIgVgZpXADcAU4HfAdwtc5wF3/7G7z3H3Rndf5u5/IIZKrQXea2aHZ1+Xev3V7j7Z3ScPHTq0c29WREQ6tmFl/rKX1f0i3pl864vkW5/Jt/Cl2NF3y+a4wdiyOY4XZnb5feEJeG5mnO/bL9LnZkY87cV/wtznYkhO7z6Rzn0u4mkvPQnzX0h20E3yzX8h4mmzZ8LqhTCsDsbuE+nqhRFP55mXuea859rngdiP4JkHYcLBcNq5kT7zYMTT6kbDyoUwcGA0DAYOjOO6zJj31+bAndfFE/e9hkd653URT9vSAhuaYl+DXhWRbmiKeLZ+t18f5QwZEent1+fXb2fYdzy8/WPw4csj3RWNgz3FyvnwyK/h7isizfb6QTQOZt0Ww+Rq9op01m0R74yhe8cwsLTGhoi/wZTVQHD33xOTj/cDriaG/jQAc4k9EMYC30nylSPXQ1BX5Hwuvq6Esj5CPMV/GfiX5N8biNWX5iV5VhZ6YdI4+DXwbuD3wAXu7iVcEwB3X0QslQpQ4BGGiIjsUgOGxdPvtM2NEe9Mvroi+eoy+dauiyEKVdWxRGdVdRyvzfxvbOECqOzVPl9lr4inrVgcY+x7V8UqPb2r4nhFZtrbysWxYk67fBURz5ZXURmTds0irciUt3wxkCsrSalI4ilP3BtDaJbNhRcfjtRbIp4292UYdED0umzbHOmgAyKe9s+/F179559/z883aG8YOxnGHRfpoL3z8z1xb5SRLq9mQH79pOcodWjgwsdh61ZYPhdeeTTSrVsj3hnHnx4NyIZkJ+WGZIjY8ad3/No9TNkbpbn7l83sL8QT+SOJm/f1wNPAL9390U7UI/dfh2JN61z/Y7E5Cun6OdF4uTp7zswOJXoPnipwrjcxrOjdwG+Af3X3bdl8JcgNvarZbi4REdn5DpgSNxYQT/o3N8bNxiGZlWrGT4nx9dl8hxXKd3MmX31+vl79oGItbFsDFQ4tFjfsvTK9D83bYvhMbvnNluS4OfO/H/e4kW/ZFg0D3xbHBZ9hWfvyKDScxiOczmdJvNA1KyrihqnQNV97CTasiAZEdT9o3hw3ctmdj1cthSEjwVKTl70lf/jG6iIrLK1e1rl8q5ZGz0E2X6FhI8vmwawHYd0KGDgcDj4JRu6fn092rlKHBq58FdYuiwZuVb+YrL5qQfwOdsbYCTEhOb2K0Wnvyp+g/AZQdgMBwN0fI3Ya7iq53YpPN7OK9EpGZlZLDPdp3JFrmtk0YF/gNndfnzlXRfQYnEtMNv5AsZWaSnBskuatpCQiIrvYsLEw+Z3tVzE65Iz8VYyGjYNj3tl+xZ7DzshfsWf4ODjmXe1XHTrsjPzVhIYMg5aVsG0rbGuOlXP69Y542l57w4bXwZuheWts3lVZDQMye4MOHAn1r8fSoLl8ffpAbTbfCKhfk6zsk+Tr2xdqBxfJtzV+KnvHMKJ0vlyelq1x49WrKrlmpqytSUOgd3VbunVLWzxn6N7xNDa7qk92+MaQkYXzDRnZuXylXnfZPPjHTTHHpG5oTPD+x03w5vN3v0bC4lfgqftgzXIYPAKOOgVGF5jrsWwuvPAgrF0Bg4bH/I2RB+TnWzoXnn+gLd+hU2HvTL4lc+HZGbB2OQwaAYdPg1EFylqSKyvJd+jU/HwbVgKV8T42NUQDYdi+sCXzO9VUH7+bG+vbfkerqyPemesCNC6H5gVQ9XqU2bicEta22eOUu5Pyq2aW3adgh7n7PGLC7xhin4K0y4mn8Tek90Aws4lmlrcEgZkNKBDbjxgCtQX4SuZcNbHj8rnE/IUOGwdmNrlArMLMvgQcT6ymdHfeC0VEZNcbNhZOuACmfy7SQkucQjQG3nwhnPX5SAstcQrRGDjxQjj785EWWmq0rm/0HPTrDQP6RVrhEU875T3Q3Ay9a2DwqEibmyOeduK74kl+VX8YNCrSlm0RT5vyzohX94/yqpN8U96Zn8+3QZ+aGJbTpyaO0/lyZVXVJNesKVxW7YDoXUivTtTSEvG0Y06NG/X0qj4bN0Q87U1vKbz6z5ve0rl8pV531oPROOhbG0OR+tbG8axiCxl2g6Vz4a+/gJu+FenSufl5Fr8C99wQn8WgYZHec0PE05bNhQd/FzfTA5MG0YO/i3j2mg/8tn2+B37b/tpL5sJ9N8bytgOHRXrfjRFPWzIXZvy2fb4Zv83PZ71g/rPRE9CnJtL5z0Y8rbkFGtZGI7dX70gb1ka8M9ed9QA88AvY3BDL/m5uiONZDxT+PvZg5fYgDKX4SkM76uPAI8APzexUYDbxNP5kYmjRf2byz07SbN/pL5IGwVPAGmJexDlAb+BCd38uk/+nwFnETf0S4L8su8U7zHD3Ganjf5rZC8RqTUuIYVZTgEOIno73u/uGbCEiItKFlrwCz8xoeyJ4xLTCK+J0hy3roa4WmhrbntDX9Yt42jGnxQo3998Ea5ZCv1o4+fyIp+WWxnzoZqhfHeOyT784f8nM3PHDf2zL95aL8vMdlc73evREnHpRW7ycsvadEKsqvb6ibcWmkfvC8MwqfftNgLMvirH/ueEbJ7894u3yjYezLo65BKuXRY/AtHdEvFP5SrzuuhXRc5DWpybiPUHuRr1vbfsb9anvbf80/6n74veoX9JAy6VP3de+F+GFB2OTub7Jbsq59IUH2/ciPP9AnMvme/6Btus+OyOGxS2e0/bEv25oxEcVKKtfUka/VFnpfE1bY0ieWYx6s2SIXlNmFf2mrVDRHypboheusjdQnZ+v1Os++ZcYcretCbbWQ0WvOH7yL3DwVN5Iym0gzAJ2Sj9bshPzZODrwHTipn0Z8APg8gJLjRZzO7GE6LuBWmAFcDPwbXefXSB/7lHSEOC/tlPujNS/vwscA5wCDCbmNSwkdoG+Ir2Ls4iI7ARLXoF7f5PcLCVPBO/9DZz6vp7RSNjWFENtalLDcbZsjHja4ldgxTyYfAr0rYGmjXG8+JX8ISFHn1raGvql5jvq1PYNgs6WddQp8YR6zIS44WxqiO/jqFPy8+43If/GvJD9xuff6O9QvhKuO3B43HTnboABNm2MeNbq+TDvUahfBbVDYf/jYUiBnqnVC2B+Kt/Y42HImPx8ry+A+Y+l8h0He2XylXKjDjGsaFBmKFvf/hFPW7siGhppfWoiXm6+xS/D6qXxO19dA1s3x5CtrZnf97XL4++1Xd1qIp62aTMMmwj1y6KM3n3jeFNmbkFzr+g5qKiKm/mWZrAtEe/MdTcsB0vm+VgyN8e3RLyzls+D2Q+1zWuZdCKM6PlD1spd5vSHwNvM7LCdURl3X+TuH3D3ke5e5e77uftnCjUO3N3cPe9Rv7tf7+5T3H2vpIx93P2iIo0D3H1arqzt/FyWec0X3X2qu+/t7n3cvZ+7T3T3f1PjQERkF3hmRtsTQauItG9txHuCPv1jmM22LYBH2tLSNtky55n7k6e9qffRrzbiu4vRB8LpF8aT6rUrIz39wsJj3nuyg0+KCedN9TEUqak+jg/OLEq4ej48fUsMP+m/V6RP3xLxdvkWwLOZfM/eEvG01xfAs7dm8t0a8bS1K+LGPK3QDf3gEdFIS2tqiHjaoOHRAErbtDHi5eZrbCBWvEpW4+pdHcfZJUMHjYhGcLu6bYx49prWG4YfDKMnR2q98+s2dH+oHhE9B9s2RVo9IuKduW6vyvg7rahMei2S416VdMryefBIsnFhbl7LI7+PeA9XbgNhMfB34GEz+66ZvcfMpprZSdmfnVBXERGRsHZ5PAFMK/REsLsMGRtPiiurYmJlZVUcZ58yrynyPrJPe3u60QfCOZfCxV+LdHdrHEBMRH7z+dHQXL8q0kITlOc9Gk/Jq/tHo666fxzPyyziOL9IvvnZfI8VyZdZl6XUG/qjTokenMZkzkXjhsI9OoecFA2HdIOoqSF/o7lDp8ZKQfNnxnucPzOOD00NuelbC7TE5HQ8SVva98bkymqqj/p4S6RN9e3LSudrV7cC+Y6YBs1A9d4w5LBIm5N4Z647aGT0GjRvifk0zVvieFBm4nupZj8UDwXazWvpH/EertwhRjNIRoMBn6Pdemh5OtncEhER6cCgEfE/+X6pG5BCTwS7y5jj4Pm/wNCxsfzilsYYYjTmuPb5Bhd5H9mnvbJrjNy/4xWL6lfFk/60qn4R35n5Dp0acw4geg42bYyb3GPObp8v16OTXsXozeflN9pGHgAnvaf9KkZvemv+KkYVFVDdC7YYbPPYhbq6V8TT16zuA+tWtc1BGDoKhu7TvqxRB8BR0+C522HDGug7GI46O381ob0PiLkV6ZWTjjk7f+WkUQfGsML0XKQT3pY/zHDUATDtve1XMTq2wHX3PSwa86tfa9uJfMSBMCpvTZyYuPzUX6BxDfQbDEedkz9PoafPa9mOchsIX2f7jQIREZGd74hpcPvPYOG6GL5TWQX9B8IJl3Z3zcJeY+DQc2DBY9CwGvoPgQmn5o8rP+Jk+PuN8e/cHITGejjhnF1dYylV7dAYBlSdGi62pTHiOzNfqTfNEDfspfTijDyg8LKmaS89FE/Q907N9Wiqj/iIZAWvI6bFHKBRB7b9HjfV5z/Jf30BrHga9hnf1nBe8TQM2yf/b2PvAwq/t6xRB5Y276iqEgb2hcoaqO0bx1kHnADLX4lJ+7n/rlRWRjxt1gPw4LWxNHDfgbGD84PXxrl0I6GceS09TFkNhOxYfBERkW5RUQFVFTFQtplIqyraP9XsbnuNyb/pyRp9ILzl/THnIPe094Rzds8hOm8U+x8fcw6g7SZ380Y4KLPy1NjjY85BNt/EbL7jYs5BXr7Mcq1Q+k1zV1q/EgYMaR/rUxPxnFKf5C94LJbLzTWGcumCxzr+W9kRq+bD03+O6/Ufkswb+TMc+fbo5ctJPwIv9m+InoPefWIoGLSlT2VWO5p0Ysw5gLZen00NcNRZXfTGdp5ObZRWLjO7CLjI3QssaSAiIlKmWQ/CkNGwz6S2WFN9xHe3Ta1KfdorPcOQsTDuGHjxLmhcC/0GwUFn5s8vGTIGDj+v/SpGE0/LX8VorzFw+LntVzGa+Jade8NcjrphhZ+C12VWBXvzqhAAACAASURBVOrTG4bXQr9NUFsbx1kNq6GmwHCqhtVdX++0eY8mczsyDZN5j7ZvIMx9GOpGwPBUI2xTQ/4Ozo1roucgrapvxNNG7A8HvTmGVK1MhiIddvZusYrRLmkgEBugvbEWkBURkZ1nNx7bK7u51xfA4qdgyLi2J/6Ln4KBo/Jv6oeMKbysaVYpvU3dZeKJ8FjmKfjmBjgy9RQ8txJTdU37lZgOP7f9++o/JHpHssOp+md6KLpa/cr8a1T1i3jahpWx30dadb9kZ+eUfoOT95FaYGBLU8TTVi+AJU9Gr0/ud2XJkzBkn9J+L7pRD+qLFRERKdHAIiu67AZje2U399rjyTCZmlgKs7omjl97vLtrtnOMGAeTpsC6hfDKQ5FOmtI2/wBKX4lpzHExWX9zQ6wmtLmh8OT9rlY7LG7O07Y0RjxtwDDYnMm3uTHiaUedA1s3RSPBWyLduiniaaWuZNUDqYEgIiK7n1LXrBfpahtXx9PgtKp+Ed8Tvb4Alj8DYybC0adHuvyZ9vs01K8q/JlkV2LKTd6vroGNr0d66Dk7v/dk/+OjMZJumGxuiHjaAVNiSNGmJF/u3wdMaZ/v4Klw0gei/k3rIj3pA/mrGJX6ufRAu2qIkYiISNfJrVk/68G2HUonv7Xw/INSdqkVKVXNkALDSxojvidK95hAW/ra421/R6WuxATdM5xq6NiYkDzv0RhWVDssJpUPzcwbGTYWJr8z5hxsWBk9B4ec0X7+Qc7BU/MbBFnlfC49jBoIIiKyeyplzfpSx0aLlGq/Y+GF2+Lf6T0uxu+h67BsXA39CkwsTveYlLMSU3cZOja/QVDIsLGFGwSdUepKVj2QhhiJiMieq9Sx0SKl2msMHPK2+D1qTIbJHPK2PbfBWTOk8Pj9dI9JbiWm6v7Q8HqkaoS3rWTV7nM5r8dPUAb1IIiIyJ6s1F1qRcrRk1cd6mql9pi8kT6TcpS6klUPowaCiMhO0LR5ORsaX2Jr83p696pjQL+J9K0e0d3VeuPZjccAi/QIuR6T1x6PYUU1Q6JxoMbAHk0NBBGRLta0eTmr1z9GZUU1vSoHsG1bE6vXP8aQuuPUSNjVdoex0SI9XU/uHVjzGix8PDZb6z8E9j0WBu/X3bXa7e2qOQjPAL/aRdcSEelWGxpforKimsrKvpgZlZV9qayoZkPjS91dtTcejY0W2XOteQ1m3RaN/pq9Ip11W8Rlh+ySHgR3vxW4dVdcS0Sku21tXk+vygHtYhUVfdjavL6bavQG15OffopI5y0ssgTrwsfVi7CDym4gmNlg4IPAMcAgoLJANnf3U3ewbiIiu6XeverYtq2Jysq+rbGWlk307lXXjbWSLrNuISz+Z6xg028vGP0mGLhvfr71C2HZk235Rh4NdTuSbxEsnwlNa6DvYBgxGer22fnlSc+x9rXkd2819BsSv3uD3sA3wg2ro+cgrapfxGWHlNVAMLOJwAxgKGD/P3t3HifHVR56//d0d3V1zz4jzWi3LMuWbfBu2dh4QRCusclCwpKX9w0mLDcEEgghyc1NCPey3EDC5xJC2JJAwpKQF0gIXJJgwAFjG+NV8r5K1mbtM5Jmn97ruX+cmlFvM1M9mtFsz/fzmU9Nn3rq1KnulqZOnW2KUD2NMhljzKLW1nQBxwfdNJqxWIogyFIKcnS2Xj7PJZsjUW+YB16AQ9shcxzSK2Hd1vpx+++H534IYyehqQvOfxVsvKY2rn8/HCi7Wdowyc3Syf1w4EEY7YPmbthwdf2ni1HyG3gBHv9nyA5CMQ+JA3ByL1zyq5XXMvgCPP8D8JrczXd+1L0+9+bKm/XIcQdgTxiX6oLCmHt9zs2VN/WznZ9ZOPr3w3O3hZ/tCvfZPncbnP/q5VtJaJlk0bqWJbpo3RnU6BiETwA9wMeBcwBPVWN1fuq1KhhjzLKQ9lezsv0a4vE0xdIQ8Xh66Q5QHngBnvueu1kZvyF97nsuvTpu121QGHU3N4VR97o6bv/98PA/uXxSHW778D+59HL9++GZ8fOGN0vPfM+llzu5H57+D7e/aaXbPv0ftX2Uo+a360cwdNT9ngxvSoaOuvRyR3a4G7lkM4i4rdfk0mcSd3S7S/fCOC+MO7p9bvMzdQVDByk9/2+UnvwKpef/jWDo4Nyf9OBD9T/bgw/N/bkXqrNe4v6t5kZB1W3zoy7dnJZGKwg3AN9T1fer6j5VLc1FoYwxZrFL+6tZ1bmN9d2vYVXntqVZOYDwpqW56qalufam5dD2+nGHqm5In/sheGm3PxYL49IuvdyBh9y+8vySzS69Iu5B93SxPM5vdukzye/ELvBSEE+6uHjSvT6xqzJu7IS7eSvnNbn0mcRlTkKiKi7R5NLnMj9TIxg6iO6/HS2MoX6n2+6/fe4rCWPHJ/lsl3F3mq6N8OJw0brRcNG6F/+ijT+YBY2OQRDg6bkoiDHGmEVo7IRrOShX94b0uHsyXx2Xqbq5GTvpWg7KJdIuvSJukvyqb5ZG+1zLQXXcaNVCaVHzU3UVgwri0ss1ha0Q460M4LrxNK2YWVw67AbklcUVx2rf+wbyOxEbY3+7x2hCaS4KGwcLrKjK70TQzz49wAhjtNDE2bKBFbFOljPtfRhNNCHjN+tek+tX3fswtK2fuxOPt4DVfLbLvDtN10arEMyBRlsQdgDnz0VBjDHGLEJNK9xNSrm6N6Qr68elq25umrqgmKlMK2ZcekXcJPlV3yw1d9ePa65aKC1qfl3nuvKU8q5SUMq7113nVsatudIdnw+7PuRH3es1V84sbvVWl14I4wph3OqtNfn1xzI83jrEfSvGeLx1iP5Ypia/E2su4MmOIjkKNBWVHAWe7ChyYs0Fp2KCfp7QZ8hpnmZNk9M8T+gznAj6WdayJ12ltVwi7dLn0vqr6n9X1l81t+c1y1KjFYSPAK8WkW1zUBZjjDGLzfqr3M1qxU3LaO1Ny7qt9ePWVd3gnv8qKGTc/iAI4zIuvdyGq9y+8vzyoy69Iu7qU/2Sx+Nyoy59Jvmd918g3eN+H6/IpHtcern2s9zA4GSz67aTbK4dKNxQ3AY3gNhrcjeiXlPdAcX9LS08s2k9ec+jKZsj73k8s2k9/S0tFXH7mwskm8/CJ4EUc/gkSDafxf7mwkTMPj1AUpP4kkRE8CVJUpPs0wMsa6lJKrGprvrxs6VzoxuQnGyGzAm3Xc4DlM2cEq1uFp0qWOTNwC8CrwG+jmtRGKgXq6q2MFoEW7du1e3bbVCYMWYRWyqzGB18APbeAbkB8Dtg0ytgfZ3BjlHzmwePFx4hT46k+BNpec2RxOcS79QsWj8t3UeTNiFl3aVUlTEZ44b4tQDcVbqfZk3XxIxKhpfF63wey8TEGIREk2s5KGaQ4hiy8SZic9nFyJjTJCI7VHXr9JGNVxAC3BSm5R0wqzMQ3DoINpNRBFZBMMaYBWDoAOz9oRuom2hy/fuLY7DpVdC2MKb9HCwd52hxLxkdJi2trE5soj1e2QXqvsI9NFHnxp8xrvWun0h7uPQ4Oc3jS3Iibfz1FfFLANgxRcyVYcxyFQwdRHsfdq05qS6k5wqrHJgFr5EKQqODlN86g/IYY4xZjAbGF9wKn9CvubL+E/+ooj7xnw9HH3YVg/FBwOPbow8viArCYOk4e/KP4YlPihYKmmNP/jHOSV5aUUloptm1IHCqBaFAnmaaK/LbKBt4kmdAIYlHngJ5ybNFNk/EnC0beKJOzPllMctVrG393A5INmaeNTQGQVW/GvVnJoURkfUi8iUROSwiORHZJyKfEpGGpkwQkdeJyJ0iMigiGRF5SkT+WKTsMUjtMS8VkdtE5GR4zOMi8rsiMmlLiIj8Qtl5RkTkARH59UbKaowxC9LAC/D89yvXBXj++7XrFkQVdZ2B+ZI9UX/az+yJ+vFn2NHiXjzx8cRHRCZ+P1rcWxG3IbbR3chrDlUlrznyFNgQq6yIrYh1cpFciC9JxmQMX5JcJBdWzFC0ItbJxWHMqGTwJcnFVTHGmKWp0RaEOSMim4F7cQuxfRd4FrgaeC9ws4hcp6rT/k8tIh8D/hgYAf4VOIlbv+FjwM+JyC2qWqg65jVhbBb4ZnjMLwJ/CVwHvKHOed4NfAY4AXwNyAOvB74iIher6h80+h4YY8yCcWTHqXUL4NT2yI6ZtSKUrzNQnt+BhxZGK0IqXLytehrR1IrJjzmDMjpMisqBxgmSZHS4Iq0z3sWFvJgDwX5GGaWZZjbHttAZrx1AuyLWyQqmvtmPEmOMWXpmVEEQkSbgtcDlQAcwCDwMfEdVR2dYls/jKge/o6qfKTvXJ4H3AR8F3jlNua7AVQ4GgCtVdU+YLmH+7wTeA3yy7Jg24ItACdimqtvD9P8B3AG8XkTeqKrfKDvmbNyq0ieBraq6L0z/CPAQ8Psi8q+qet8M3wtjjJlfUdcFmK/8ZtvqK9wYBKgcg7DhhvktVygtrRQ0h1fWdahInrS01sR2xrvqVgiMMSaqRqc5RUReDewHvoq7cX8r8Lvh630i8gszyHMzcBOwD/hc1e4PAqPArSLSzNR+Odz+3XjlANyIaeD94cvfrjrm9UA38I3xykF4TBb4QPjyXVXHvA3wgc+OVw7CY/pxLRUwTWXGGGMWtKjrAsxXfrOtbYMbkOw1Q+6k204yQLk4eojMgdsYe/6fyBy4jeLooTkv3urEJgqaoxB2HRr/fXVi05yfe1YNvADPfAd2fNFtZ9plzRgzpxqqIIRP6L+NazX4J9yN8i3h9p/C9G+JyJWTZlLfy8Pt7aoalO9Q1WHgZ0ATMN28aqvD7Z7qHeHNez9wjoiU/4/6inD7gzr53Q2MAS8VKZszbupjvl8VY4wxi8+aK+uvW1C9gFdUUdcZmE9tG2DLa+CSt7ntJJWD3OE70OIYJDvQ4hi5w3fMeSWhPb6Sc5KX4olPlhE88WsGKM+rgRfgqW/D9i+4bb0b/9ke12KMmTONtiD8CW5a0xtU9c2q+hVV/WG4fTNwfbj//VPmUmt8deadk+zfFW63TJPPeFt1zSMVEemAiY6U5atBT3puVS0Ce3Fdsc6JeMwRXIvH+rArljHGLD4dZ8G5t1QuynTuLTOfxahzI1z485X5XfjzC2P8QQMKJx9DEmkk4aYSlUQTkkhTOPnYnJ+7Pb6S8/2ruCz1Cs73r1pYlYNdt7kKZDocy7Hrttob//JxLSJu6zW7dGPMgtLoGIQbgH9R1fvr7VTVB0TkW8Cr6u2fQnu4HZxk/3h6xzT5fA83BuE3ROTzZWMDBDeGYVz5iKuZnDvKMc1h3Fj1ThF5B/AOgLPOOo0pA40xZi51nHV605pW69y46CoE1TTXD8mqP0XxtEtfrg5th1IJhve5bmNek1vg7tD2yu/PQh+HYoyZ0GgLQjsw3RrrLwBtMyvO6VHVnwF/j7uZf1xEviwifwE8ALwdNzMSQDBJFmeEqn5BVbeq6tbu7u75LIoxxpgGiN8JpUxlYinj0per/r0wcABKebeycCnvXvdXTsG64MehGGMmNFpBOIybenQqW4EjDeY7/iS+fZL94+kDEfL6DeA3geeAXw1/HwK2AbvDmN7TPHfUYyZrYTDGGLMIeV2XosUMWhxDVd22mMHrunS+izZ/8mOAQDzpug7Fk+51vqoyMNvjWowxc6bRCsJtwCtE5I+qFxATkZiI/D7wyjCuEc+F28nGGJwXbicbozBBnS+o6lWq2qyqLar6yrBb1MW41oOHo5xbRBK48QxFKgc+T3XMGlz3ooOqWtO9yBhjzOKVaF6Hv/YVSKIJ8gNIogl/7StINK+b76LNH68JCKAULjFUKrjXXtUwvNke12KMmTONjkH4X7ipRD8K/KaI/BTXWrAaN0D5bOAo8KcN5vuTcHuTiMTKZzISkVbcYmVjQN2xD1GIyDbgLODfVbX8yf4dwK8BNwNfrzrsRtzsSXeraq7qmOvCY6rXOrilLMYYY8wSk2heN7sVgqEDcPRht2pzaoVbk6HODEoLVsdGiPuQ7T81BqG5B1pX14md5XEtxpg50VALgqoexd0Y/wjYCLwJ+G/Arbgn7T8Crg9n8mkk393A7bgKRvU6BR/GPZH/x/JF2ETkAhG5oDqvcOGz6rSNwN/hVjv+QNXub+FmP3qjiGwtOybFqYrOX1cd82UgB7w7XDRt/JhOTs3g9De1V2qMMcaUGTrgFmgrjILf5bZ7f+jSF4v1V0EsDu1nwZor3DYWd+lmXgRDBwh2fZfgiS+77WL6PpkFQdwaYjM4UGQdbiXldlxf+0dUdcYTQYeLpd2LW035u8AzwEtwayTsBF6qqifK4hVAVaUqn3/BVV4exq10vAn4JcADblXVb9Y59y/jKgpZ4Bvhcb+Em870W8CvatUbJSLvAT4NnAC+iat8vB5YD/yFqv5BlOveunWrbt++ffpAY4wxS8/O77pKgVe2Duj46y2vmb9yNap/Pxx8yM1I1LTSVQ4W+YxVi1UwdAD23e5WBE+koZhxq4KffROxxdQyZWadiOxQ1a3TRzbexWhCWBmYtZVhVHV3+AT/I7iuO6/GdV/6K+DD4UJnUfwHbgrRNwCtwDHcTf6fq+ozk5z7/4jIy3DrPLwOSAHPA78HfLq6chAe8xkR2Qf8AfBmXGvM08AHVPWrEctqjDFmOcuecC0H5RJNLn0xWQJT2C4Zxx5236HxMSDj22MPL66ua2ZezbiCMBdU9QDw1oixMkn6V4GGb9DDKVJf3eAx/w78e6PnMsYYYwA35qC6BaE45tKNmYnsSaiedjeRdunGRDRlBUFEvkS4MrKqHgtfR6Gq+vbTLp0xxhizWEUZfLz6CjfmANxT3+KY+9lww5kvr1kaUl2nBouPK2ZcujERTTkGQUQCXAXhQlXdGb6OQlU1Pn2YsTEIxhizBI0PPk40Vd74b3pVbSVhsc9iNI8Ko4fIDzxBkO8nluwk2XEx3nKechYbg2AmN5tjEDaF20NVr40xxhgzmaPj/cDDrkPj26N1+oG3bbAKwQwURg+RPXYnxNOI10FQHHOvV21b1pWEWNsGgrNvcmMOsiddy8H6661yYBoyZQVBVfdP9doYY4wxdSyVwccLWH7gCYiniSVcVxpJNBGE6cu5ggCukmCVTnM6GhqkLCL/E7hTVe+eIuYG4OWq+pHTLZwxZuErjhyiePIxNHcS8btIdF1KoqX2j3MwdKDyidaqK+o+0SqNHKJ44lE014/4nSRWXEa8Tn5R46KWrzR8kOD4o2j2JJLqIrbyMuKt62ceN3KQUllcfOVlxFtq44LhgwS9j0x0L4n1XE5sjvMLhg6ivac+C+m5glhbnbwixkUt27Jig4/nXJDvR7yOijSJpwnyUSc9NMZMpqGF0oAPAdumibkR+OBMCmOMWVyKI4coHP4xWhyDZCdaHKNw+McURypnQJ7oE1sYc7NrFMZg3+01i/eURg6RPzSeXwdaHCN/6MeUqvKLGhe1fKXhgxQP/AgNy6eFMYoHfkRp+ODM4kYOUjhYGVc4+CNKI5VxwfBBSvtvRwtjqN+FFsYo7b+dYA7zC4YOohMxLi/dfzvBUFVeEeOilm0pGc33cnD4HnYP3MbB4XsYzffWBq2+gkLuJENjuziZ3cnQ2C4KuZNufIGZFbFkJ1rKVKRpKUMs2TnJEcaYqBqtIEThAVEHMxtjFrHiyccg0YQkmhARJByQWTz5WGVg+bzcIm6baHLp5fmdeBRJpCvyk0Sa4olHZxYXsXzB8UddjBfGee6Y4PijM4orHX8UiVfFxZsoVefX+wgkmiviSDS79DnKT3sfRquuQRNNrqWgTNS4qGVbKkbzvRwZe5BikCUZa6UYZDky9mBNJWE05XO4p4NSPE4yX6QUj3O4p4PRlD9PJV96kh0XQylDUBxDVQmKY1DKuHRjzGmZi3UQrgCOz0G+xpgFRnMnofppXTzt0stFnJdbc/2QrOwy4PLrn2FctPLpJOXT6vLNclz9furpmn7qs5pf1DnSo35mUcu2RPTndpKQFIlYCoCEpCBw6c3Jnom4k7mdBK2rGWk/eyItCLKcrIozM+c1r4NV2ypnMVr5kmU//sCY2TBtBUFE7qhKeouIbKsTGgc2ABuBr59+0YwxC534Xa77TqJsvu1SBqm+SY04L7f4nZPk1znDuGjlk5TrjlNdPqku3yzHuX7q9d6Xyn7qs5pf1DnSo35mUcu2RORKQyRjrRVpcfHJlYYq44IhklInLqiMM6fHa15nFQJj5kCULkbbyn4UOLsqbfznOqAZ+CbwvlksozFmgUp0XQrFMTRs4tdwrvdE16WVgauucAM0C2Og6rbFMZdent+Ky9BipiI/LWZIrLhsZnERyxdbeZmLKYRxBXdMbOVlM4qLr7wMLVXFlcaIV+fXczkURyviKI669DnKT3quQKquQYpjSE/lZxE1LmrZFoNM7ii9J+/kUO936T15J5nc0ZoYP95GSXMVaSXN4cfbKuNik8TFKuOMMWYhmrYFQVUnKhHhQmkfshmKzGKRyR1lePRZCsVBvEQ7rc0XkPZX18Tlxw6THXySUqGfuNdJqv0ikk1ra+JymSNkhp6kVBgg7nWQbrsIP72m9rzZo4yMPkOhOICX6KCl+ULSqTrnzRwhU3bedPtFJOvkl8scYXTkaYqFARJeB80tL6o5b9QFg7LZo4yMPk2xOEgi0U5L84tI1SlblLhEyzryKy4h13sPmh9Eku34PdfXzBIUa9tAfvXllA7dCYMDkOogvm4byapZjOIt62DlJeSO/RTNDSJ+O8lVN9TMThRvWYesuJjCkZ8S5AeIJTvw1tTGjZcvf+ynE+VLrrqhpnzx1vUUui+lcOQuNDeA+B0k1rysZnaieOt6Cj2Xkj9yNwwNgN+Bt+bG2riW9eS6L3PnHT113uqZfWKt6ymsupzCkbth+FR+Xp38iisupXDk7rLrvbFufsWeyygevgtyLr/E2pdVzGIUa1tPbvXlFA7fDcP94Hfirb0Rv2p2oljberJrLqd4+G4YcXGJtTeSaqtXtkvIHbuHYGSQmNeOv+r6urMYRf2+Z7O13/dUqjZuNmVyRzkxeB9xSZGIt1EqZTgxeB8r2q+t+D+j09/CkbEHIXAtAiXNUdQs3f4lFfl1+Vs4XCeupyrOGGMWoilXUq4JFvl14BFVfXzuirS82ErKc6f8D34sliIIspQ0W/MHPz92mJG+u4jFm5BYCg2yBKUxWrpfVlFJyGWOMHz8bmLxdFlchtaVN1bcrGeyRzk5eG/NebvaX1pRSchnjjDSdzdSlp+WMrR031hx05TLHGHw5D1IPD2Rn5YytHddP3HeigWD4mk3s0cpQ6pqwaBs9ij9Az8jFjtVtiDI0tlxXcXNf9S4KGVr9D0eOnkPsVjqVFyQpa1OfmO9dyGxNBJPoaUsGmRo6pnZZxY1v6ifWSPvX5TrLYwdZvTone46ws83KGVoXr0Nr6x8UeJymSMMHfkBsdwgUiqgcY/Ab6dtzc0V58xmjzB09IfExwaIlfIE8SSlpg7aVr+q4mY96ncv+nt3hMH+nyFl750GWdo7r5vTSkLvyTsplTLE4+mJtPHXPV3bKmJH873053aSKw3hx9vo9LfUHVcwmu/lZG4nuWAIP9ZG1yRxxhhzJszmSsoVVPWrMyuSMWfe8OizxCU18Qc/Hk9DyaWXVxCyg08SizcRC+Mk3GYHn6y4OcwMPUksnq6Jyww9WXFjNTL6TN3zjow+U1FByAw+6W6sy/ILwvTyG6bRkaeReLoiv1KYPn7eqAsGjYw+TSxWVbYwvfzGNWpclLI18h6PDT/lbqqr4saGn6rILzfwBBJLE0uEcYk0QdGlz+Qzi5xfxM8s6vsX+Xr7n3DXUfb5jqeXVxCixI0ev4/YyFFi8ZS7qQ8KMHKU0eP34W947UReo8fvIzF0BIn7kEgTCwrI0BFGE/eRWn8qLup3r6Hve9V7N/6dmssKQqE4SKKqm1AslqJQHKyJbU72RLrRjxoXVXH0EPn+xwly/cT8TpKdl5Co00oYNW4hGyr20VvYQ1aHSUkrPd45tCW657tYxiwbM57FSETWA+uAunO2TbWYmjFnQtQ/+KVCP7FE1WI7sRSlQn9V3ACxRHuduIGq8w6QiFfGufNWxkU9b7EwQDxRm1+x7LxRFwwqFgeJ13lPilXvSeS4CGVr5FqjvselfD+xmutNUcrPdX4RP7OI79/plS9dW758P0qcwsjzBKUcsbhPPNmDlsUFw3vDykHSJcSTSJheYXAPEvMr4xR0cA+U9R6K+t2bze/7XPAS7TUtCEGQxasqy1yI0iWxOHqI7JGfQCKNJDsIimNkj/yE1JqXV9z8R41byIaKfezPPUpCfHxaKGiO/blH2chlVkkw5gxpeB0EEblJRJ4C9gP3Aj+Z5MeYeeUl2gmCbEVavT/4ca8TrYrTIEvc66yK65gkrvKmx0t0THLeyrio50149fNLlJ036oJBiUnek0TVexI5LkLZGrnWqO9xPNmJlqriSlniybnOL+JnFvH9a6x8tZ9vdflUEuRH96BBEYn5aFB0r+XUs6BYEKBC1XEuvVwsUFSkKk6IBZXdUqN+92bz+z4XWpsvoKRZSqUMqkqplKGkWVqbL5jT8453SSyVMiTirpJycvBeMtnKAdL5/sddS064pkcs0QSJtEufQdxC1lvYQ0J8PPERETzxSYhPb2HPfBfNmGWjoQqCiFwD/AfQAXwWEOBu4IvAs+HrfwdsELOZd1H/4KfaLyIojRGEcUEpQ1AaI9V+UUVcuu2icF95XIZ0W2VcS/OFdc/b0nxhZX7tF030ER/PT0sZ0lXnbW55EVrKVOSnpQzNLS+aiIm6YFBL84vcmIiyvIIgS0vzi2YUF6VsjbzHTa0vdv31y+OCLE2tL66I8zsuRoMMQTGMK2bQIINfdb1RP7PI+UX8zKK+f5Gvt/Nit6/s8w1KGfzOqgWhBNxkc+U0THeSTRuh8QI1XgAAIABJREFUlCPQAooSaAFKOZdexms+KxyLUQQFDYpoKYvXfFZFXNTvXkPf96r3ToNszXdqtqX91axov5Z4PE2xNEQ8nq4ZrzQXyrskigjxeJq4pBgZfaYiLsj1T3RBGyfxNEHV2h9R4xayrA6TIFmRliBJVofnqUTGLD+NtiD8MZAFrlLV94ZpP1HVdwIXAX8KvBL41uwV0ZiZifoHP9m0lpbulxGLpwmKA8Ti6ZrBswB+eg2tK28M4waJxdM1g10B0qnVdLW/NDzvIPF4umaAMkAyvYaW7hurzntjzawufnoN7V3Xu77YRZdf9SBgr3kdqVXbiCWa0MIAsURTzSBRgFRqNZ0d17m8wvekeuBsI3FRytboe9zWdX3Fe1w9YHc8v6aelxFLpAkKA8QS6ZoBxY18ZlHzi/qZNfL+Rbler2ktzavd5xuEn2/1AGUAtIjXshmJeWiQRWIeXstm0OKpsq26Ds/rci0EpSyxQPG8LlKrrqu8hp7rSXpdiAYEpQyiAUmvi1TP9ZVli/jdi/7eraG987rK79QcD1Ael/ZX09O1jXU9r6Gna9ucVw7AdUmMhYuujavXJTHmT9JSU7X2R9S4hSwlrRTJV6QVyZOqWlfCGDN3Gp3FqBe4XVXfFL4OgI+o6ofKYh4C9qvq62e5rEuSzWI0M5ncUYbGTk1f2tZUf/pSY6Iayx1jMPMchdIgXryd9vT5NPmr5rtYDRk+/EOCYmZisDUw8bp17asm0oqjhyicfAzN9SN+J17XpfUHu44conjyMTR3EvG7SHRdWjNF7HI0m9+VvhM/mXT2pO4VL59IqxhbMD5bVDEz9RiEKeIWsvIxCAmSFMlT1BwbfRuDYMzpmLNZjIB24IWy13nc4mjlfgb8fw3ma0xkmdxRjg/eTzzmT8xXfnzwfla2X2OVBDMjY7lj9A0/QDyWIhFroxRk6Rt+gG5esqgqCX7HxYz13kVQpGK6Vr/j6oq4RPO6SDeLiZZ1ViGoMtvflZbmCzk5eC+UqJgWub25ahHB5nWk1ry8cnai7mtqPseocQtZW6KbjVxWMYvRuuSFC6pyYA+pzFLXaAWhF+iser25KsYD0hgzR4bGniUe82umkBwae9b+gzY1xvLH6M/uJF8aJBlvpzO1haZk5Y3cYOY54rEU8bCrR1xSE+mLqYKQbFoLPS8jN/AEpXw/8WQnfsfVdRf9MzMz29+VdGo1Xby0Yhaj9uYr6i6sGLliFzFuIWtLdC+oCkE5e0hlloNGKwg7qawQ3A/cIiJbVHWniKwGXgfsmq0CGlOtkfnKzfI2lj/G0ZEHicdSeLE2ikGWoyMPsrrl6opKQqE0SCJW9Z0Sn0Jp8X2nkk1rrUIwh+biu5JOra5bITALkz2kMstBoxWEHwB/KiJdqnoS+CvgtcAjIvI0cB7QCvzh7BbTmFPmc75ys7j0Z3eGXUHcU95E+LS3P7uzooLgxdspBdmJp8EAgebw4vadWgqijhkYyx9jIPMc+dIQyXgbHenza1qbvHg72cIJCsEIpSBHPObjxVpIeSvq5jdd65VZfOwhlVkOGp3F6G+BG4ECgKr+DHgDsBc3i9ER4F2q+g+zWUhjyrU1XUApyFVOIxrkaGua2/nKzeKTLw0Sl8q1HOPik6962tuePp9SkKUUZN13Kvy9PX3+mSyumQPjYwZKQbZizMBY7lhlXP4YvSMPUAyyeLFWikGW3pEHGMtXxvmJlYzlD1MMssTwKAZZxvKH8RMra/I7OvJgmN+p1qvq/MziE3WNHWMWs4YqCKo6pKoPqJ6ajFhVv6OqF6lqWlUvVNUvzH4xjTkl7a9mZfs1FdOXWt9PU08y3k5JcxVpJc2RrGoZaPJX0d36EuKxFMVgiHgsRXfr4hqgbOorHzMgIhO/D2aeq4gbyDxHTFxrk4iQiKWISYqBqrhs6Tjp5FoSsRRKgUQsRTq5lmzpeEVceevVeH7xWIr+7M45v2Yzt+whlVkOGu1iZMyCkPZXW4XATKsztYWjIw8CruWgpDlKQZbupktqYpv8VVYhWGQyuWMMZk7NJNOevoB01WcYdcxAvjSEF6ucZ9+1Ng3VxPleFyk51aVIVevEDeJVnbde65VZfMYfUpXPYtTZern9TTJLSkMVBBG5Evh54G9VtaadNByk/A7g31T10dkpojHGzExTchWrW66u6Afe3XSJ9QNfAjK5Y/QN3+ee0sfbKAUZ+obvo5trKyoJUceXJOOuG1CiLM61NrXNMK59kjjrhrIU2EMqs9Q1Ogbh94H/ipvetJ5jwNuB35tJYURkvYh8SUQOi0hORPaJyKdEpKElIEXkehH5bnh8VkReEJHbROTmOrEfEhGd5md31THbpon/85lcvzFm9jUlV7Gu7QY2df4C69pusMrBEjGYeTbsLpQOuw6lw65Dz1bERR1f0pE+n0CzFMO4YpAl0CwdM4zrTG2hFFTGlYIsnaktc/OGGGPMLGq0i9G1wE90kuWXVVVF5A7cQOaGiMhm4F6gB/gu8CxwNfBe4GYRuU5VT0TI513A54FR4DvAQWA9bralW0TkA6r60bJD7pwiu18ErgC+P8n+uyY5/p7pymmMMWbm6s4kI7UzyTT5q+jmJRWzGHU1X1rTnawpuYqelpdUzWJ0aU2FspE4a70yxixWjVYQVuNuuKdyGFgzg7J8Hlc5+B1V/cx4ooh8Engf8FHgnVNlICIe8GdAFrhSVZ8r2/cx4BHgT0TkE6pu5KKq3kmdm3wRieNaQwAmG3h9p6p+KMK1GWOMmUVeop1SkCEuZdMda/2ZZKKOL2lKrop0Az/bccYYs9A02sVoDJhuacNuIDdNTIWw9eAmYB/wuardH8S1BtwqIs3TZNUFtAM7yysHAKr6DG6htzTQEqFYr8a1PNyvqo9HiDdmURgsHee53EM8mr2D53IPMVg1+4oxi0F7+oKwu1A4k0yQCbsO2UwyxhhzuhqtIDwKvEZE6t5gi0gb8JowrhEvD7e3q2pQviOcUvVnQBNwzTT59AJ9wBYROa+qbFtwC7k9GqWrEm6wNUzeegBwroi8W0TeLyJvqz6nMQvNYOk4e/KPUdAcKVooaI49+ceskmAWnbS/iu7Wa4nHwumOY2m6W6+tmcXIGGNM4xrtYvQF4OvAf4rIb5Y/WReRS3ELqa1k6pvqesZHd002QfQuXAvDFuDHk2USjoH4beBrwA4R+Q6uy9M64FeAp4A3TlcYEVkP3AIMAt+cIvTXwp/yY/8V+A1V7Z8i/3cQVkDOOuus6YpjzKw5WtyLJz5euHiYhz+R3h5fOdWhxiw4aX+VVQiMMWYONFRBUNVvisgtwJuBR0TkGHAIdwO+ChDgH1T16w2WY7zT6GQTRI+nd0Qo47+IyGFcRebNZbuOAV8G9kQoz9uBOPA1VR2rs78P+CPge7huUSlgK/Ax4HXAahG5sbo1pKyMXyCsRG3durXugG9j5kJGh0lV9bBLkCRzau1DY4wxxixzjXYxQlXfghss/DRu0PKV4fYp4B3h/nkjIm8CfgT8FLgQ1zXpQlzLw2eBb0xzfIxTg5P/tl6Mqj6lqh9X1SdVdURVj6vqD4BtwF7gOtwMSMYsKGlppUi+Iq1InrS0TnKEMcYYY5abhisI4J6Aq+rFuMG+64EWVb1EVf9uhuUYbyGYbAWZ8fSBqTIJxxl8CVdZuVVVn1XVjKo+C9wK7ADeICLbpsjmFmADbnDyExHLD4CqDgH/f/iy4alejZlrqxObKGiOguZQ1YnfVyc2zXfRjDHGGLNANDoGoULY/aZeF5xGjc84NNkKMuODfycbozDuJsAD7qoz2DkQkbtxLR5XMvn6B+ODk+u2HkTQF26nm3HJmDOuPb6Sc5KXcrS4l4wOk5ZWNngX2PgDY4DhYh+9hd1kg2FSsVZ6vM20JqabuM8YY5ae06ogzKKfhNubRCRWfnMvIq24LjtjwP3T5OOH28n+Rx9Pz9fbKSJrgZ9n+sHJUxmfaSnKWAdjIhnN93Iyt5NcMIQfa6PL30JzsqciZqTQy4nc8xMxK/xzafF6avKKB0qqWICgQCpWIB5TN+KmynChj+OFU/mt9M6l1av9pzVS6ONEftfETdWK5Hm01I3r5WRuF7nSEH68jS7/vLrli3odjeQXLS7adZilabjYx/7cIyTw8aWFQpBjf+4RNnK5VRKMMcvOlF2MRGSPiOwWkU1lr6P87G6kEKq6G7gdOBv47ardH8Y9jf9HVR0tK9sFIlI94fVPw+3rReSSqmu5DHg9oMAdkxRlfHDyP6pqZrLyisjWSdLfBPw/uArIP092vDGNGM33cnjsQYpBlqS0UgyyHB57kNF870TMSKGXw5ntFDVLMtZKUbMczmxnpNBbkddIoY+D2e0UgixJaaEQZDmY3c5Ioa8ibrjQx8HsjjCuNYzbwXBVXNT8Rgq9HB59yF1DLLyG0YfqlC/qdTSQX6S4aNdhlq7ewm4S+HgxHxHBi/kk8OktNPTnzBhjloTpWhBiuBvqyV5PRmZQlt8C7gU+LSI/BzwDvAS3RsJO4E+q4p+pPpeqPigiXwbeCjwUTnO6H1fx+GUgCXxKVZ+qKXDl4OTppmn9logUge24laVTwFXA1UAR+E1V3Tf9JZvlbix/jP7sTvKlQZLxdjpTW2pWXj2Z20lCUiRiKQASkoLApY+3IpzIPU+8TsyJ3PMVT8tP5HeRwK+Ny++qeFp+vPA8cfHxwjgvjDteeL6iFSFqfidzu0jEqsoXpleUL+J1RM0v8nkjXodZurLBMH7VEj8JSZINbIYvY8zyM2UFQVXPnur1bFLV3eGT+Y8AN+NWMj4C/BXw4anWFajyduBu4C3Aq4BWYAi4B/iiqk42i9GrgI1EG5z818ArcV2fVuIqKYeAr+AqII9FLKtZokbzvfTndk50a+ms0yVoLH+MoyMPEo+l8GJtFIMsR0ceZHXL1RWVhFwwRLJqlqG4+OSCocqY2NQx4G6CklU3QXHxa26C6p0zcTr5lSYpX6kyv6jXETm/iHFRr8MsXalYK4UgN7FGCEBR86RiNsOXmXtRu3Qac6ZMWUEQkU8CP1DV28PXZwED4Ww9s05VD+Ce/keJrdtKoaqKu1H/SoPn/j4RWz5U9ePAxxvJ3ywfo/lejow9SEJSE91ajow9yBqurqgk9Gd3Eq/zdLs/u7OiguCHlYfx/QAlzeHH2ipjdOoYGL8Jqo2rvgnyY20UgqxrOQgVTye/+CTXEK/ML+p1RM4vYlzU6zBLV4+3mf25RyBwLQdFzVMkxzrvRfNdNLPEjXfpjItf0aVzPVdaJcHMm+mmOf1dTg26BTfH/+/OXXGMWfz6y7oEiYjr4iIp+nOVk3DlS4PEy55WgntqnS9VrhfY5W+hqFmKQRZVpRhkKWqWLv/UpF8r/HMpVcWUNMsK/9yKvFYkz6NIrjIvcqxInlcRt9I7l5LmKIRxhSBLSXOs9GaWX5d/nttXHhdk6fIr46JeR9T8Ip834nWYpas10c1G/3K8mE9OR/BiPht9G6Bs5l55l043/iVFXHyOF56f76KZZWy6CsIIbqGxcTMZW2DMspIrDdW98a/u1pKMt1PSXEVaSXMk45XLgTQne1jbdDWJWIq8DpOIpVjbVNka0eL1sDa9lYSkyAfDJCTF2vTWmtl6Wrxu1qe24sVS5HUEL5ZifWprTT/7Vq+b9akrw7jhMK72aVbU/Fq8HtY2X+WuIQivofmqOuWLeh0N5BcpLtp1mKWtNdHN5vQ1vLj5v7A5fY1VDswZkQuGSFT9zajXpdOYM2m6QcrPA68NB/seCdM6wq5GU1LVF063cMYsRlG7tXSmtnB05EHAVSBKmqMUZOluqpiAC3CVhOoxDNVavJ6603fWxnVHuvFt9bojNW9HzS8eKMliESkW8LRI3Ks/30H065jtuGjXYYwxsylql05jzqTpWhD+N7AZN7vQ3jDtveHvU/3YGgBm2eqcpEtQp1+5DmBTchWrW1zLQCEYIhFL1QxQXioyuWP0Dd9HKciQiLdRCjL0Dd9HJndsvotmjDHzKmqXTmPOpOlmMfq6iOzFLR62Djcz0OPAo3NfNGMWp+ZkD2u4umIWo27/krotAE3JVUuyQlBtMPMs8ViKeCwNQFzSE+lpf+lfvzHGTKbV62Y9V1bMYrTGu8gGKJt5Ne1Kyqp6P+EKxiLyFuA7qvqROS6XMYtalC5By0mhOEiiqotVTFIUioOTHGGMMctH1C6dxpwp01YQqrwVaz0wxjTIS7RTCjITLQcAgWbxEu1THGWMMcaY+TDdGIQKqvpVWwTMGNOo9vQFlIIspSCDqlIKMpSCLO3pC+a7aMYYY4ypMt1CaTeGvz6oqtmy19NS1btPq2TGmCUj7a+im2sZzDxLoTiIl2inq/lyG39gjDHGLEDTdTG6E1DgQmBn2eso4jMulTFmyUn7q6xCYIwxxiwC01UQPoKrEByvem2MMcYYY4xZgqab5vRDU702xhhjjDHGLC0NDVI2xhhjjDHGLG0NTXMqInHAV9WxqvRXAK8BxoAvqOreescbY4wxxhhjFrZGWxA+AZwUkYnJy0XkjcB/Au8B/jvwoIhsmL0iGmOMMcYYY86URisINwI/UdXy5U8/CAwAbwb+EOgAfm92imeMMcYYY4w5kxqtIGwAnh9/ISLnAOcDn1HVr6nqJ4DvAzfPXhGNMcYYY4wxZ0pDYxCANmCo7PV1uGlPf1CW9hTw8tMslzHGGGOMMYvacLGPvvzzZINhUrFWupPn0pronu9iTavRFoQjwKay168EMsCOsrQWoHia5TLGGGOMMWbRGi728UJ2B4Ugiy8tFIIsL2R3MFzsm++iTavRFoT7gV8SkV8AssDrgR+raqEsZhNwaJbKZ4xZIoaLffQWdk88RenxNi+KpyjGGGNMtaFiH72FPWR1mJS00uOdQ1vV37S+/PMk8PFiKQA8SUHg0hf6379GWxA+Fh7zXeCHQBL46PhOEUkBNwAPzFYBjTGL33Cxj/25RygEufApSo79uUcWxVMUY4wxptxQsY/9uUcpaA6fFgqaY3/uUYaq/qZlg2ES4lekJcQnGwyfyeLOSEMtCKr6hIi8BPj1MOmbqvpQWcjlwB3A12epfMaYJaC3sDt8iuL+o/TEh8ClL/SnKMYYY0y53sIeEuK7v2WAhz+RXt6KkIq1UgiyruUgVNQcqVjrmS3wDDTaxQhVfQL4g0n23Qf8yukWyhiztGSDYXxpqUhLSHJRPEUxxhizcM1H99WsDuNT9TeNJFmt/JvWnTyXF7I7IHAtB0XNUSTH2uRFc1q+2dBoF6O6RMQTkctF5PzZyM8Ys7SkYq0UNV+RVtT8oniKYowxZmGar+6rKWmlSNXfNPKkpPJvWmuim7NSV+LFUuR0BC+W4qzUlYui5byhCoKI/KqI/LOIdJWlbcZNbbodeFpEvi0iDbdMGGOWrh5vM0VyFIIcqkohcE9RerzN8100Y4wxi1R591URwYv5JPDpLeye0/P2eOdQ1BwFDf+maY6i5ujxzqmJbU10c07Ttbyo5SbOabp2UVQOoPEWhLcBF6jqybK0vwDOBX4CPA68BnjrTAojIutF5EsiclhEciKyT0Q+JSKdDeZzvYh8Nzw+KyIviMhtIlJ3ATcR0Sl+7p/iPL8gIneKyKCIjIjIAyLy65PFG7NctSa62ehfjhfzw6coPhv9y0/rP8rhYh97xu7j6ZHb2TN2nw14NsaYZcYNAk5WpJ2J7qttiW42+pfhiU+OETzx2ehfVjOL0WLW6JP+FwH/Of5CRNqAVwP/rKpvFBEPeBRXQfhiIxmHLRH3Aj24WZKeBa4G3gvcLCLXqeqJCPm8C/g8MAp8BzgIrAdeC9wiIh9Q1Y/WOXQ/8JU66QcnOc+7gc8AJ4CvAXnctK9fEZGLVbXuOA1jlqvWRPesPTkZn1s6gV8xt/Riabo1xhhz+twg4NzEYGE4c91X2xLdS6pCUK3RCkI3brG0cdeGeXwDQFULIvKfwP87g7J8Hlc5+B1V/cx4ooh8EngfbjrVd06VQVhB+TPcGg1XqupzZfs+BjwC/ImIfEJVc1WH71PVD0UpqIicDXwCOAlsVdV9YfpHgIeA3xeRfw0HbRtjZtlinlvaGGPM7OjxNrM/90g4CDhJUfMUybHOe9F8F23Ra7SL0TDQXvb6ZYAC95SlZYGGqm5h68FNwD7gc1W7P4hrDbhVRJqnyaorLN/O8soBgKo+A+wE0lA19LxxbwN84LPjlYPwHP24tSJgmsqMMWbmFvPc0sYYY2bHXHRfNU6jLQi7cN10fFzF4FeBx1X1eFnMRqC3wXxfHm5vV9WgfIeqDovIz3AViGuAH0+RTy/QB2wRkfNUddf4DhHZApwHPDpJV6UOEXkbsBoYBHao6mTjD14Rbn9QZ9/3q2KMMbNsMc8tbYwxi81g8TjHSnvIBCOkYy2sip9De2LlfBcLmN3uq+aURlsQvgCcg6soPANsAr5cFXMlblajRoxPj7pzkv3jN/pbpspEVRX4bdx17RCRr4rIn4nIPwA7wnK9YZLDLwX+HteV6bPAfSLyqIhc3Eh5VfUIrsVjvYg0TVVeY8zMdCfPDWdFyoazImUpkqM7ee58F80YY5aUweJx9hYeo6A5UtJMQXPsLTzGYPH49AebRauhCoKqfhX4c6AJ15Xns7iBugCIyEs5NaNRI8a7LQ1Osn88vSNCGf8F9/R+AHgz8EfArbib9i8De+oc9kngOtwYi1bgKuBbuErDHSKyboblba+3U0TeISLbRWR7X5/NvGJMoxbz3NLGGLOYHCvtwZMknoRTiYqPJ0mOlerdTpmlYiYrKb8feP8ku7cDnbib8XkhIm/CzaD0beB/4WYn2gj8D1yF5mW4rlETVPX3q7LZDrxBRL4FvA63cvT7ZquMqvoFXGsMW7du1dnK15jlxJqVjTFm7mWCEVJVQ0ATJMkEI/NUInMmzOqCZqqah6ql5aKZ8ol7WfrAVJmE4wy+hFuP4day8QzPisituK5BbxCRbap6Z4Ry/Q2ugnBjnfKuDMtVbzzDdC0MxphJDBWPc6y4m4wOk5ZWViU207ZA+roaY8xyk461UNAcHmVTiZInHTvd+V7MQtboGIS5Mj7j0GRjDM4Lt5ONURh3E+ABd9UZ7BwAd4cvr4xYrvH+P9WzJ01aXhFZE8YfVNWxiOcxxuAqB3sLj7i+rrSEfV0fYcj6uhpjzLxYFT+HguYrVg0uaJ5V8dpVg83S0XAFQUTWiMjnROR5EcmISKnOT7HBbMfHLNwkIhVlEpFW3PiAMWDSVY1D49XbyfodjKdHbeW4JtxWd7S7I9zWW5n5lqoYY0xEx4q78fAr+7ric6y4e76LZowxy1J7YiWbvEvxxCero3jis8m7dMHMYmTmRkMVhHCw7nbgN3HjDHzgBdwsQyVAgMeAnzaSr6ruBm4HzsbNQlTuw7gn8v+oqhNjG0TkAhG5oCp2/LyvF5FLqsp+GW6lY6Xs5l1ELgkXWKu+1ktwMxqBWym53JeBHPDucNG08WM6OTU+42/qXKoxZgoZHSZBsiItQZKM2voGxhgzX9oTK9niX82l6Vewxb/aKgfLQKNjEP4nbp2AV6nqj0QkAL6sqh8RkfW4wcFnAz83g7L8FnAv8GkR+TncNKovwa2RsBP4k6r4Z8KtjCeo6oMi8mXgrcBDIvId3CDls4FfBpLAp1S1fBrW3wN+UUR+ChzA3fhfgGsdiIfX9PXyE6vqXhH5b8Cnge0i8k1cq8TrgfXAX9gqysY0Li2t9fu6iq1vYIwxxpwpjVYQXgX8QFV/VL1DVQ+KyBuAJ3FP/X+nkYxVdbeIbAU+grs5fzVwBPgr4MPhKsVRvB031uAtYXlbgSHcas9fVNVvVMX/H6ANuAQ3PWoKN/D4+2H8v01S3s+IyD7cDEdvxrXGPA18IJwO1hjToFWJzewtPALqWg6K5CmQY33iRfNdNGOMMWbZELe2WMRgkSzwaVX9w/B1AfiEqv5xWcwXgZtUdeNsF3Yp2rp1q27fvn2+i2HMgmGzGBljjDGzT0R2qOrWKLGNtiAMQUUH4X6gehGxQSYfJGyMWaai3vi3JVZahcAYY4yZR43OYrQf2FD2+jHgFSLSBBDOQHQTcHB2imeMWQps+lJjjDFm8Wi0gvBj4OVls/58FVgL3Csi/xv4GfBi4JuzV0RjzGJn05caY4wxi0ejXYz+HtetaCVwRFW/JiJXAu/BDfIF+Aanpgc1xhgyOkyKylU3bfpSY4wxZmFqqIKgqruAj1elvU9EPgacA+xT1WOzWD5jzDw5HvSzh0MMM0orzZzDOlbGOmvi+nSAXRxmmAytpDmPtXRLR0VMWloZ1CGGyJGnQBKPNnzape1MXY4xxhhjImp4JeV6VLVPVR+wyoExS8PxoJ9HeY4ceVpoIkeeR3mO40HlbMN9OsB2dpElTwspsuTZzi76dKAiLhnvpk+PuzUONEFBc/TpcZJxm8/AGGOMWWhmpYJgjFla9nAInyQ+SQSZ+H0PhyridnEYH49UGJciiY/HLg5XxB1OZPC8DXjig+bcGARvA4cTmTN5WcYYY4yJYMouRiLypRnmq6r69hkea4yZZ8OM0kJTRVoSj2FGq+IytJCqSPPxGCZTG5foRBJdE2mK1sQZY4wxZv5NNwbhLTPMV3ErGhtjFqFWmsmRxy9b9iRPgVaaq+LSZMmTKovLUaCVdJ24wrRxxhhjjJl/01UQNp2RUhhjFpRzWMejPAe4loM8BXLkubDqv4TzWMt2dgGu5SBHgRwFLubsirhzWceOOnEXVcUZY4wxZv5NWUFQ1f1nqiDGmIVjZayTy4LzK2YxupBNNbMYdUsHW/W8ilmMLubsmlmMuqWDK/U8nufQRNxFdeKMMcYYM/8amuZURN4AvAt4k6oerrN/HfAPwOdU9duzU0RjzHxYGetkJbXTmlbrlg66mf5GP2qcMcYYY+ZXowsdq339AAAcrElEQVSl/Vego17lAEBVD4lIexhnFQRjjDHGGNOwoeJxjhV3k9Fh0tLKqsRm2hIr57tYy0aj05xeDGyfJuYhTq2qbIwxxhhjTGRDxePsLTxCQXOkaKGgOfYWHmGoeHy+i7ZsNFpB6AJ6p4k5AVgVzxhjjDHGNOxYcTcePp74iIhbOwefY8Xd8120ZaPRCsJx4LxpYs4DBqaJMcYYY4wxpkZGh0mUTYsNkCBJRofnqUTLT6MVhJ8BvyQiF9TbKSIXAq8Bfnq6BTPGGGOMMctPWlopkq9IK5InLa3zVKLlp9EKwidwA5vvEZHfEZEtItIcbt+LqxjEwzhjjDHGGGMasiqx2a2WozlUlYK61XNWJTbPd9GWjYYqCKr6EPBbQBvwl8AzwFC4/WSY/i5VfWCWy2mMMcYYY5aBtsRKNnmX44lPlhE88dnkXW6zGJ1BjU5ziqp+UUTuwVUUXgJ04MYc3A/8tao+M7tFNMYYY4wxy0lbYqVVCOZRwxUEgLAS8J5ZLosxxhhjjDFmnjU6BsEYY4wxxhizhFkFwRhjjDHGGDPBKgjGGGOMMcaYCVZBMMYYY4wxxkywCoIxxhhjjDFmwoKqIIjIehH5kogcFpGciOwTkU+JSGeD+VwvIt8Nj8+KyAsicpuI3Fwndp2IvEdEvh/G50TkhIj8p4i8dpL8t4mITvHz5zN9D4wxxhhjjJlPM5rmdC6IyGbgXqAH+C7wLHA18F7gZhG5TlVPRMjnXcDngVHgO8BBYD3wWuAWEfmAqn607JD3AP8d2Av8BDgKbAzjXykif6mqvzfJ6e4C7qyTfs905TTGGGPmQp8OsJtDDDNGK01sZh3d0jHfxTLGLCKiqvNdBgBE5IfATcDvqOpnytI/CbwP+FtVfec0eXhAH+ADl6nqc2X7LgQeAQKgU1VzYfprgROqeldVXhfiFn9rA7aq6o6yfdtwlYkPq+qHZnrNAFu3btXt27efThbGGGMM4CoHj7ATH48kHnkK5ChwOVuskmDMMiciO1R1a5TYBdHFKGw9uAnYB3yuavcHca0Bt4pI8zRZdQHtwM7yygFMLO62E0gDLWXp366uHJTFfzN8uS3qtRiz3AyUTvBUYQcP5e/iqcIOBkrTNvQZY+bIbg7h4+GTRBB8kvh47ObQfBfNGLOILIgKAvDycHu7qgblO1R1GPgZ0ARcM00+vbgWhC0icl75DhHZApwHPBqlq1KoEG6Lk+w/V0TeLSLvF5G3VZ/TmKVuoHSCncUnyGuONM3kNcfO4hNWSTBmngwzRhKvIi2JxzBj81QiY8xitFDGIJwfbndOsn8XroVhC/DjyTJRVRWR3wa+BuwQke8Ah4F1wK8ATwFvjFIgEWkDXgcocPskYb8W/pQf96/Ab6hqf5TzGHOmDRaPc6y0h0wwQjrWwqr4ObQnVs4or0PBPjxJkhQfgCT+RHpHfMWslXkxGiyd4HBpLxkdJi2trI1von2Zvydm7rXSRI48PsmJtDwFWmmax1IZYxabhdKC0B5uByfZP54+bQdKVf0X4BXAAPBm4I+AW3HdlL4M7JkuDxER4O+AVcBfh92NyvWF+V4MtALdwC24MQ6vA/5dRCZ9b0XkHSKyXUS29/X1TVccY2bNYPE4ewuPUdAcKWmmoDn2Fh5jsHh8RvmN6Qhe2Y0IgEeSMR2ZjeIuWoOlEzxfeIy85kjRQl5zPF94jEFrWTFzbDPryFEgRx5FyZEnR4HNrJvvohljFpGFUkGYNSLyJuBHwE+BC3Fdky7EtTx8FvhGhGz+AnhDmEfNDEaq+pSqflxVn1TVEVU9rqo/wI1V2Av/t707D5OrqtM4/n1Jr5jQYBIIipIOEkHFuERAcCCIRkVZVMDRASGKiuPooOI+KuiMMuoI7jui6AgIIuPzEFHZwqJgFAguBCUJLiFgQEICnXR38ps/zqnypqjq7nSnuqq73s/z3Oemzj331ql7Ul33d+9ZOBg4stbBI+KrETE/IubPnDlzmz6f2Vjcu3kF7eqgXZ1Iol2dtKuDezcPGzdXtaOmMkD/VmkD9LOjptbYozWs3rySNnXSkc9zhzppUyerN69sdNFskpupnXkmc+mkgw300UmHOyib2TZrliZGpScEPTW2l9IfHOoguZ/BucAy4MRCf4Y7JJ1Iasp0nKQFEXFNjWN8gjRq0hLgpaXRjkYiIh6S9L/AB4BDSMO1mjWNvi0b6Kro699GB31bRnfH//E7zObOwduB9ORggH4Gop/eKU8eZs/JrS/W08XWQVI7HfTF+gaVyFrJTO3MzOEfuJuZ1dQsTxBKIw7NrbG91Pm3Vh+FkoVAO3Btlc7OW0gX/QDPrrazpLOBd5GGMH1JxKjaSZTaDA034pLZuOveYSqDFXf8B+mne4fR3fHfecp05rbtR4c66eNhOtTJ3Lb9Wr7/QbemVX2y0q1pDSqRTQb3so4lLOdH3MoSlnNvzVa5ZmZj0yxPEK7O64WSdihe3EuaRmqy8whpXoKhdOZ1rXY7pfStfrlzn4PPA/8K/BQ4OiL6Rl78rZRGWhpdmw2zOtptyhxWDtwGpCcHg/mO/x5t+476mDtPmd7yAUGlx03p5Y/5PJeerAzGJma37dPgktlEdS/ruIkVdNHOTnSxkQFuYgUHMIfdaj58NzMbnaZ4ghARd5FGCpoNvKVi85mku/HnR8TDpURJ+0iq/LW9Lq+PlfT04gZJzwCOJY1KdFUhXcBXScHBYuCo4YIDSVUnmcj9H15FCkAuGuoYZo3Q0zaD3vZ5tKuTjfEw7eqkt33eqEcxsup6pkznSe3z6FAnG9lAhzp5Uvs8j2Jko7acNXTRThftCJX/vZw1jS6amU1CzfIEAdIF+o3AZyUdDvweOIA0R8KdpHb9RaWRhVRKiIibJX0TWAT8Mg9zejcp8DgG6ADOiYjfFo7zIeAUoA+4FXhvihm2cmtE/LDw+mJJg8BS4C9AF/AcYH/SnAlviohV2/j5zcZFT9sMBwTjoGfKdAcEtt2so4+d6NoqrZM21jHah91mZrU1TYAQEXflO/MfAV4MHAHcA3wGOHMb5hV4PamvwcnAi0jDkD4EXA98LSIqRzHqzetu4H01jvktoBggfAl4Aanp0wxSkPJX4DxSAHLbCMtqZmY2rB662cgAXYVJ0DYxSA/dDSyVmU1WiohGl6GlzZ8/P5YuXdroYpiZWRMr9kHopI1NDLKRAfdBMLMRk/SriKjaTL5SU/RBMDMzs9p2o4cDmEMX7TzERrpod3BgZnXTNE2MzMzMrLbd6HFAYGbjwk8QzMzMzMyszAGCmZmZmZmVOUAwMzMzM7My90EwMzMza0HrNq9lzeBK+mI93ZrGrLZeeqZ4nhzzEwQzMzOzlrNu81pW9N/GQGyii6kMxCZW9N/Gus1rG100awIOEMzMzMxazJrBlbSrk3Z1Iqn87zWDKxtdNGsCDhDMzMzMWkxfrKeNjq3S2uigL9Y3qETWTBwgmJmZmbWYbk1jkP6t0gbpp1vTGlQiayYOEMzMzMxazKy2XgZiEwOxiYgo/3tWW2+ji2ZNwAGCmZmZWYvpmTKDOR3zaFcnG9lAuzqZ0zHPoxgZ4GFOzczMzFpSz5QZDgisKj9BMDMzMzOzMgcIZmZmZmZW5gDBzMzMzMzKHCCYmZmZmVmZAwQzMzMzMytzgGBmZmZmZmUOEMzMzMzMrMwBgpmZmZmZlTlAMDMzMzOzMs+kbGZmZmZjsm7zWu4ZXElfbKBbU9m9rdezNE9gfoJgZmZmZqO2bvNa7uq/jYHYRBePYSA2cVf/bazbvLbRRbNRcoBgZmZmZqN2z+BK2tVJuzqRVP73PYMrG100GyUHCGZmZmY2an2xgTY6tkpro4O+2NCgEtlYNVWAIGkPSedKWi1pk6RVks6RtMs2Hud5ki7L+2+U9CdJl0t68RD7PEXSRZLuy/ssl3SmpO4h9jkoH/cBSX2Slkk6TdKUbSmvmZmZ2UTVrakM0r9V2iD9dGtqg0pkY9U0AYKkvYBfAYuAm4GzgRXAvwM/lzR9hMd5M3AdcHhenw1cCxwKLJb0gSr7HAD8EjgG+BnwGeAh4EPATyV1VtnnaGAJcAhwKfB5oCO/3wUj/dxmZmZmE9nubb0MxCYGYhMRUf737m29jS6ajZIiotFlAEDSFcBC4G0R8blC+qeBtwNfiYhThzlGO/A3oBN4RkQsL2zbF7gF2ALsEhGbcvoU4HZgX+DoiPi/nL4DcBHwSuB9EXFW4Vg7AX8EeoCDI2JpTu8CrgKeC7w6IoYNFObPnx9Lly4dLpuZmZlZ0/IoRs1P0q8iYv6I8jZDgJCfHvwRWAXsFRFbCtumAfcAAnaNiIeHOM5uwBpgWUTMq7J9GbAfMCMi7s9pzweuBJZExKEV+ecAdwF3A72RT5ak1wHfAL4dESdV7FPzeNU4QDAzMzOzetuWAKFZmhgdltc/KQYHABGxHrgB2BE4cJjj3Ed6gjBX0t7FDZLmAnsDt5aCg+z5ef3jyoNFxArgTmBPYM5I9iE1O3oEOKha0yQzMzMzs2bWLAHCk/P6zhrb/5DXc4c6SL7D/xbS5/qVpG9J+rikb5P6N/wWOG47vHfNfSJiEFhJmoRuTuV2MzMzM7Nm1iwzKffk9boa20vpOw93oIj4vqTVwPeA1xY23Qt8k9TxeazvPabySnoj8Mb8coOk5dXy1dkMwDOYNA/XR/NwXTQP10XzcF00F9dH85hIdbHnSDM2S4Cw3Ug6Afga8APgo6T+A3sCHySNNHQocHzDCghExFeBrzayDJKWjrQdmtWf66N5uC6ah+uiebgumovro3lM1rpoliZGpTvuPTW2l9IfHOoguZ/BuaSmRCdGxB0R0RcRdwAnkpoZHSdpwRjfe7uU18zMzMys2TRLgFBqYlOrj0Gpw3GtfgIlC4F24NoqnZ23kDoQAzx7jO9dcx9JbUAvMMijmzOZmZmZmTW1ZgkQrs7rhXn+gbI8zOnBpJGBfjHMcUqjBs2ssb2UXpzu76q8ftQsy3mY07mkZkorRrIPaeK0HYEbS3MtNKmGNnGyR3F9NA/XRfNwXTQP10VzcX00j0lZF00xDwJs+0RpkvYByM2HSmn7AzcBfcCBEbGssO0ZwM9JQcR+EfHbnD7URGkXAsdSfaK0u4CdGONEaWZmZmZmzaSZAoS9gBuBXYHLgN8DB5DmSLgTOKg4f4GkAIgIVRznXGAR6SnBpaS7/7OBY4AO4JyIeHvFPgeQLuzbgYuBPwGHA/NJczAcXvk0QNIxOe9G4ALgAeAo0hCoFwPHR7OcXDMzMzOzEWqaAAFA0hOAj5Ca7kwnzaB8KXBmRPy9Im+tAEHAScDJwDxgGvAQcAvwtVp39SU9BTiTFJBMIwUW3wPOioi+GvscDHyA9MSgizQb9LnAZyNi87Z9ejMzMzOzxmuWPggARMSfI2JRROweER0RsWdEnFYZHOS8qgwOcnpExHkRsSAidomItoh4bEQcPlSTn4j4XUQcFxEzIqIzIuZGxIdrBQd5nxsi4oj8Pt0RsV9EnN2swYGkPSSdK2m1pE2SVkk6R9IujS7bZCTpWEmfk3SdpIckhaTvDLPPQZIul/SApD5JyySdlpvC2ShJmi7pFEmXSvpjPrfrJF0v6fWVfZ8K+7k+6kDSf0u6UtKf83l9QNItkj4saXqNfVwX40TSCfnvVUg6pUael0m6Jn+PNki6SdJJ413WyST/JkeNZU2Nffy9qDNJh+ffjjX52mm1pCskHVEl76Spj6Z6gmD1U6UJ1x3A/qQnJstJfSnur30E21aSbiU9xdoA/AXYB/huRJxQI//RwCWkZmsXkpqtHUluthYRlbOA2whJOhX4Eump5NWkZoS7Aa8gDUt8CXBcsVmg66N+JPUDvwZ+B9wHPAY4kNSsczWpD9mfC/ldF+MkP8m/HZgCTAXeEBFfr8jzb8DngPtJ9dFP6q+3B/A/EXH6uBZ6kpC0ijTB6jlVNm+IiE9V5Pf3os4kfQJ4F+k3fDFpQrSZpNEwfxYR7y7knVz1ERFeWmABrgACeGtF+qdz+pcbXcbJtpCCr70BAQvyef5Ojbw7kS6UNgHzC+ldpMAugH9u9GeaqAvwfNIf6h0q0meRgoUAXun6GLf66KqR/l/53H7RddGQehHwM9IgHJ/M5/aUijyzSRdA9wOzC+m7kJrZBvDcRn+WibgAq4BVI8zr70X96+MN+TyeB3RU2d4+meujqZoYWX3kpwcLSX98vlCx+cPAw8CJkh4zzkWb1CLi6oj4Q+S/EsM4lnRX4oLIo2LlY2wE/iO/fHMditkSIuKqiPhRPHp+lDXAl/PLBYVNro86yuexmovyeu9Cmuti/LyNFEwvIv0uVPM60miAn4+IVaXESE2BP5ZfnlplP9u+/L2oI0mdpBsWfwLeGBH9lXkiYqDwctLVR1ujC2Dj4rC8/kmVC6T1km4gBRAHAleOd+EMSD/KAD+usm0JaR6QgyR1RnPPrzERlf7IDxbSXB+NcWReLyukuS7GgaR9gbOAz0TEEknPr5F1qPpYXJHHtl2npBOAJ5KCtGXAknh030Z/L+rrhaQL/nOALZJeCjyN9PTs5oj4eUX+SVcfDhBaw5PzutZM1H8gBQhzcYDQKDXrKCIGJa0EngrMIQ0BbNuB0sznr80vi3/YXR/jQNLppHbuPaT+B88jXRCdVcjmuqiz/D04n3S39P3DZB+qPu6R9DCwh6QdI+KR7VvSljCLVBdFKyUtiohrC2n+XtTXc/J6I2kUzKcVN0paAhwbEX/LSZOuPtzEqDX05PW6GttL6TuPQ1msOtdRY5xF+sN/eURcUUh3fYyP00nNHE8jBQc/BhYWfnTBdTEePgQ8Ezg5hhi5LxtpffTU2G61fZM0B9MsUsf9/YCvkPp9LJY0r5DX34v62jWv30XqP/BPpCHwnw78BDgE+H4h/6SrDwcIZtaSJL0NeCdpRK8TG1yclhQRsyINVz2LNKLUHOAWSc9qbMlah9JEoe8njT5U2WzCxlFEnJn7S90bEY9ExG8i4lTSYCLdwBmNLWFLKV0fDwJHRcT1EbEhIm4HXk4a1ehQSc9tWAnrzAFCaxjujk4p/cFxKItV5zoaR3mYxs+Qhtk8LCIeqMji+hhH+YLoUlJTx+nAtwubXRd1kpsWfZvULOKDI9xtpPVR606qbbvSQAqHFNL8vaiv0nm7pdgZHyA3nSs9cd4/ryddfThAaA3L83puje2lEUNq9VGw+qtZR/lHvJd0J2PFeBZqMpJ0GmkM99+QgoNqExC5PhogIu4mBW1PlTQjJ7su6mcq6bzuC2wsTsxFavoF8LWcVhqbf6j62J3UNOYv7n+wXZWa3BVHGvT3or5K57fWBX1pAt/uivyTpj4cILSGq/N6YeWMsZKmAQeTetj/YrwLZmVX5fWLq2w7BNgRuHGijH7QrCS9BzgbuJUUHNxXI6vro3Eel9elUVtcF/WzCfhGjeWWnOf6/LrU/Gio+nhJRR7bPg7M6+LFpb8X9XUlqe/BUyqvm7JSp+WVeT356qPREzF4GZ8FT5TW6PO/gOEnSvsbk2iSlWZbSE0oAlgKPHaYvK6P+tXDXKCnSvoO/GOitBtcFw2vpzOoPlFaL54orR7ne1/gMVXSZ5NGGgzg/YV0fy/qXyeX5fP49or0hcAW0lOEnslaH8ofwCa5PFnajaSe+ZeRhtk6gDRHwp3AQRFxf+NKOPlIOgY4Jr+cBbyIdAfoupy2NiJOr8h/MenH9wLSNO1HkadpB44Pf2FHRdJJpNkwN5OaF1VrH70qIs4r7OP6qIPcxOvjpDvTK0kXmrsBh5I6Ka8BDo+I3xX2cV2MM0lnkJoZvSEivl6x7a3AZ0l1dyHQT5ooag9SZ+fTsW2Sz/c7SWPm3w2sB/YCXkq6yLwceHkUJuzy96K+JO1Bum56AumJwi2kAPkY/nHBf0kh/+Sqj0ZHKF7GbyH9J/8mcA/pD/rdpElAdml02Sbjwj/uwNVaVlXZ52DSD8HfgT7gduDtwJRGf56JvIygLgK4xvUxLnXxNODzpGZea0ntctcBv8z1VPXpjuti3Oup9J05pcb2I4FrSReyD+f6O6nR5Z6oCylA/h5pVLUHSRM4/g34KWmuFtXYz9+L+tbLTNJNpbvzddNa4FJg/8leH36CYGZmZmZmZe6kbGZmZmZmZQ4QzMzMzMyszAGCmZmZmZmVOUAwMzMzM7MyBwhmZmZmZlbmAMHMzMzMzMocIJiZmZmZWZkDBDMzmzAknScpJM2u8/uskrSqnu9hZtasHCCYmVnLkXSNJM8UamZWRVujC2BmZtaEDm90AczMGsUBgpmZWYWIuKvRZTAzaxQ3MTIzawGSZue2++dJ2kfSDyU9IOlhSddLWlhln05J75V0u6RHJD0k6TpJx2+n45+R91kw1PFG+PlOlnSJpBWS+nJZb5B0QrXjAofm11FYrinkq9oHYQznZLakCyStlbRR0lJJLxvJZzMzG29+gmBm1lp6gZ8DtwNfAXYHXgUslvSaiLgQQFIHcAXpQvoO4AvAjsCxwIWSnhER7x/t8evgS8BvgSXAPcB04AjgfElPjogP5nwPAmcCJwN75n+XrBrqDcZwTvYEbgZWAOcDjyWdk8skvSAirt7WD2tmVk+KcB8tM7PJLo/6szK//FREvKuwbT7pon4DsGdEPCTpfcDHgMXAURExmPPuSrrY3RM4OCJuHM3xc/oZwIeBwyLimhrl/VZEnFxIPw84CeiNiFWF9L0qmwXlC/rFwCHA7Ij4a2HbNcChEaEa52sVQETMLqSN5ZycERFnFo71IuDHwOKIOKJaGczMGsVNjMzMWss64CPFhIhYCnwX2Bl4eU5+HRDAO0oXwjnvfcBH88tTxnD87apan4GI6Cfd5W9j+3Q6Hu05uRv4z4qyXQH8Cdh/O5TLzGy7coBgZtZafh0R66ukX5PXz5Q0DXgSsDoi7qiS96pS3tEcfxvKOmKSnijpC5LuyH0DIvc1uCRnefwYjz+Wc3JrRGyukv5nYJexlMvMrB7cB8HMrLXcWyN9TV735AVSW/5qSuk7j/L425WkOaQmPrsA1wE/IT3J2AzMJjVJ6hzj24zlnDxYY59BfKPOzJqQAwQzs9ayW430WXm9Li/FtEq7F/KO5vglW/K62m9RtQvtWt5B6pS8KCLOK26Q9GpSgDBWYzknZmYTiu9cmJm1lmfl5jKVFuT1LbmJ0F3A4yXtXSXvYXn969Ecv5D297x+QpX886uk1fKkvL6kyrZDa+yzGUDSlJG8wRjPiZnZhOIAwcystfQAHyom5FGG/oV09/vSnHwuIOCTxYtoSTOADxbyjPb4kJoFASyS1FbI/4TKYwxjVV4vqHjfF1G90zDA/Xn9xG14n9GeEzOzCcVNjMzMWssS4BRJBwA38I95CnYA3lQaghT4FPAS4GjgNkmXk8b8Pw7YFfhERFw/huMTETdJWkIahvRmSVeRmigdSZpvoNqThWq+CCwCvi/pYmA18DTgxcBF+f0rXZk/yw/yZ+sD7o6I84d4n9GeEzOzCcVPEMzMWstK4CBS855TgeNJzWKOKE5ilocIfSHwgZz0VlJb/j8Ar4mI94zl+AVHA18H9sjv8Uzg3UCt4z9KRCwjNfG5EXgp8GZgJ+AVwJdr7PZ14OOkJx7vJg1T+vph3me058TMbELxRGlmZi2g1sRjE+X4ZmY2fvwEwczMzMzMyhwgmJmZmZlZmQMEMzMzMzMrcx8EMzMzMzMr8xMEMzMzMzMrc4BgZmZmZmZlDhDMzMzMzKzMAYKZmZmZmZU5QDAzMzMzszIHCGZmZmZmVvb/aMYg2zKC144AAAAASUVORK5CYII=\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Path(\"./pics/\").joinpath(TITLE).mkdir(exist_ok=True, parents=True)\n", + "\n", + "try:\n", + " y_label = \"Number of edges\"\n", + " plt.figure(figsize=(12, 12))\n", + " for i in range(data.shape[0]):\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", + " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", + " d = json.loads(json_acceptable_string)\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " np.sum(d[\"chainer\"][\"pipe\"][model_index][\"binary_mask\"]) \n", + " + (np.random.random() - 0.5) / 2, \n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + "\n", + " plt.ylabel(y_label, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", + " plt.show()\n", + "except:\n", + " pass\n", + "\n", + "\n", + "# ylims = [(0., 1), (0.85, 1), (0.9, 1.), (0.85, 1.)] #ag_news\n", + "ylims = [(0., 1), (0.8, 0.9), (0.8, 1.), (0.8, 0.9)] #imdb\n", + "# ylims = [(0., 1), (0., 1), (0., 1.), (0., 1.)]\n", + "# ylims = [(0., 1), (0.7, 0.9), (0.9, 1.), (0.6, 0.85)] #sber faq\n", + "\n", + "for metric, ylim in zip(MEASURES, ylims):\n", + " y_label = metric\n", + " plt.figure(figsize=(12,6))\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_valid\"].values[i], \n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5, marker='o')\n", + " if PLOT_TEST:\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_test\"].values[i], \n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5, marker='+', s=200)\n", + "\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_valid\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \n", + " c='r')\n", + "\n", + " plt.ylabel(y_label, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.ylim(ylim[0], ylim[1])\n", + " # plt.ylim(0.85, 0.95)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "params_dictionaries = []\n", + "\n", + "for i in range(data.shape[0]):\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", + " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", + " d = json.loads(json_acceptable_string)\n", + " params_dictionaries.append(d)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model ids" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "models_ids = []\n", + "for pdict in params_dictionaries:\n", + " models_ids.append(pdict[\"train\"][\"evolution_model_id\"])\n", + " \n", + "models_ids = np.array(models_ids)\n", + "\n", + "cmap = plt.get_cmap('rainbow')\n", + "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", + "\n", + "# plt.figure(figsize=(12,6))\n", + "# for i in range(data.shape[0]):\n", + "# try:\n", + "# plt.scatter(i // 10, \n", + "# data.loc[:, \"classification_accuracy_valid\"].values[i], \n", + "# c=colors[models_ids[i]], alpha=0.5, marker='o')\n", + "# except IndexError:\n", + "# print(models_ids[i])\n", + "# print(colors[models_ids[i]-min_mid])\n", + "\n", + "\n", + "try:\n", + " y_label = \"Number of edges\"\n", + " plt.figure(figsize=(12, 12))\n", + " for i in range(data.shape[0]):\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", + " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", + " d = json.loads(json_acceptable_string)\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " np.sum(d[\"chainer\"][\"pipe\"][model_index][\"binary_mask\"]) \n", + " + (np.random.random() - 0.5) / 2, \n", + " c=colors[models_ids[i]], alpha=0.5)\n", + "\n", + " plt.ylabel(y_label, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \"_colored_ids.png\")\n", + " plt.show()\n", + "except:\n", + " pass\n", + "\n", + "# ylims = [(0., 1), (0.85, 1), (0.9, 1.), (0.85, 1.)] #ag_news\n", + "# ylims = [(0., 1), (0., 1), (0., 1.), (0., 1.)]\n", + "# ylims = [(0., 1), (0.7, 0.9), (0.9, 1.), (0.6, 0.85)] #sber faq\n", + "ylims = [(0., 1), (0.8, 0.9), (0.8, 1.), (0.8, 0.9)] #imdb\n", + "\n", + "for metric, ylim in zip(MEASURES, ylims):\n", + " y_label = metric\n", + " plt.figure(figsize=(12,6))\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_valid\"].values[i], \n", + " c=colors[models_ids[i]], alpha=0.5, marker='o')\n", + " if PLOT_TEST:\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_test\"].values[i], \n", + " c=colors[models_ids[i]], alpha=0.5, marker='+', s=200)\n", + "\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_valid\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \n", + " c='r')\n", + "\n", + " plt.ylabel(y_label, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.ylim(ylim[0], ylim[1])\n", + " # plt.ylim(0.85, 0.95)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \"_colored_ids.png\")\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train params" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "params_dictionaries = []\n", + "\n", + "for i in range(data.shape[0]):\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", + " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", + " d = json.loads(json_acceptable_string)\n", + " params_dictionaries.append(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for y_label in [\"batch_size\", \"epochs\"]:\n", + "# y_label = \"batch_size\"\n", + " plt.figure(figsize=(12,12))\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // 10, \n", + " params_dictionaries[i][\"train\"][y_label] + (np.random.random() - 0.5) / 2, #s=3,\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + "\n", + " plt.ylabel(y_label, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model params" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for y_label in [\"lear_rate\", \"lear_rate_decay\"]:\n", + " plt.figure(figsize=(12,12))\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // 10, \n", + " params_dictionaries[i][\"chainer\"][\"pipe\"][model_index][y_label],\n", + "# + (np.random.random() - 0.5) / 2, #s=3,\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + "\n", + " plt.ylabel(y_label, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bm = np.array(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][\"binary_mask\"])\n", + "np.sum(bm[0, :])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Layer params" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/matplotlib/pyplot.py:537: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + " max_open_warning, RuntimeWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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7b07yE0lOraoHrvPLAACADbffVndgsTl4vzrJuZnC+lOWqPa6JA/r7s8uOvYXk/xFkpdX1d909xULdv98kgcnOS3Jvbr7svmYlyV5b5JXVNV7uvvr6/2aAABgo2y7QJ/kCUmOSXL0vL2a7v7TZcpfV1XPTvIDSW6X5PQFu39t3j5jV5ifj/lgVb0hycMzBf5X7+0LAPbOx8+/Mn991jdz5qWdmx1YecBN989tD936/1x94YyrctoHOl87J7nhDZK73LlyiyO35R86AbgG2Vb/J6qqo5I8N8mLu/vUPWzmm/P2ygXtXjvJXZJcmuQflzjmb+ftkl8ggM3z8fOvzEs+fXkuuKLzn66TXHBF5yWfvjwfP//KlQ/eQF8446q86a2diy/p3OCwafumt3a+cMZVW9ovANg2gb6q9kvy2iRfSvL0PWzjx5LcJsmXM82p3+X7kuyb5PPdvVQq+Nd5e6s9OS+wfv76rG/mkP0rhxxQ2aem7SH7V/76rG+ufPAGOu0DnYMP6hx80NSvgw+qHHxQ57QP9Jb2CwC2TaBP8qwkd0xybHd/Y60HV9X1k7xmfvqk7v7Wgt3Xm7cXLnP4rvJDdtP+Y6tqZ1XtPPvss9faPWCVzry08z37f2fZ9+w/lW+lr52THHjgd5YdeOBUDgBbaVsE+qq6c6ZR+Rd29/v34PiDkrwl09z5P+7u/73OXUx3v7y7d3T3jsMPP3y9mwdmNzuwctGiwfiLvjmVb6Ub3iC59NLvLLv00qkcALbSlgf6earNa5J8Jskz9+D4g5K8Lcndkhzf3U9botquEfjrLbFvYfkFaz0/sL4ecNP9c8E3Oxdc0bmqp+0F3+w84Kb7r3zwBrrLnSsXX1K5+JKpXxdf0rn4kspd7ry1XzQAYMsDfZKDM81dPyrJZQtuJtWZ1ppPpiUlu6petPDAqrpupgta75FpZP7Jy5zjc0m+leSW8xeIxX5g3n5mL18LsJdue+h+ecKtr5VDDqh8+RvJIQdUnnDra235Kje3OHKfPOhnp7nz55w7bR/0s1a5AWDrbf06cMnlSV61zL47ZZpX/94kn07y7ek4VXW9JO9I8mNJntPdz1juBN19WVWdluTu8+MfFlW537x9z568AGB93fbQ/bY8wC/lFkfuk1scudW9AIDvtOX/x5wvgH30Uvuq6rhMgf7E7n7lgvJDk7wryY4kz+7u31vFqf48U5j/g6paeGOpH0nykCRnJ3njXrwUAADYdFse6PfQmzKF+c8l2WcO/oud1N0fWfD89UkelOnmUR+uqrcmOSxTmN83yWO6+6IN7TUAAKyzUQP9Lebt9+U/5tkvdkaSbwf67u6qemiS05I8KslvJLksyalJ/qC7T9uw3gIAwAapbjdFWasdO3b0zp07t7obAAB8F6uq07t7x0r1LM8AAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIFt20BfVQ+rqp4fj16075CqempVva6qPlFVV8717r2b9o5b0N5Sj5/a+FcFAADra7+t7sBSqupmSV6a5OIkBy9R5cgkfzz/fFaSc5LcaJXNn5jkjCXKP7umTgIAwDaw7QJ9VVWSVyc5N8mbkjxliWpfTHLvJB/u7vOq6oQkj1jlKU7o7pPXoasAALDltl2gT/KEJMckOXreXk13n5/k3ZvYJwAA2Ja2VaCvqqOSPDfJi7v71KpaMtDvpbtV1Y4k+2aaevPu7j5nA84DAAAbbtsE+qraL8lrk3wpydM38FS/v+j55VX1/CTP6u7eTf8em+SxSXLEEUdsYPcAAGD1ttMqN89Kcsckx3b3Nzag/Y8meVSSWya5TpKbJ3lMkguSPCPJc3Z3cHe/vLt3dPeOww8/fAO6BwAAa7ctRuir6s6ZRuVf2N3v34hzdPebFxV9Kckrq+pDSf4pyVOq6njTbwAAGMmWj9DPU21ek+QzSZ652efv7g8l+eck+yf58c0+PwAA7I0tD/SZ1pm/VZKjkly28GZPSZ4913nFXPaiDerD2fP2oA1qHwAANsR2mHJzeZJXLbPvTpnm1b83yaeTrPt0nKrafz5Pknx+vdsHAICNtOWBfr4A9tFL7auq4zIF+hO7+5V7eo6qum6Sm3T3pxeVH5DkT5IckeRTSXbu6TkAAGArbHmg31NV9YIkN5if3m3ePrWqHjb/fFJ3nzT/fFiST1bVziSfTPKVJIcnuWeSWyQ5J8lDu/uqTek8AACsk2EDfZIHZ1p6cqH7Lvj5jCS7Av15SV6a5EeT/GSS6ye5IsnnkjwvyfHd/bWN7CwAAGyEbR3ou/u4JMcts+/INbRzUZInrEunAABgG9kOq9wAAAB7SKAHAICBCfQAADAwgR4AAAYm0AMAwMAEegAAGJhADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADAwgR4AAAYm0AMAwMAEegAAGJhADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADAwgR4AAAYm0AMAwMAEegAAGJhADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADAwgR4AAAYm0AMAwMAEegAAGJhADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADAwgR4AAAYm0AMAwMAEegAAGJhADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADAwgR4AAAYm0AMAwMAEegAAGJhADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADAwgR4AAAYm0AMAwMAEegAAGJhADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADAwgR4AAAYm0AMAwMAEegAAGJhADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADAwgR4AAAYm0AMAwMAEegAAGJhADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADAwgR4AAAYm0AMAwMAEegAAGJhADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADCwbRvoq+phVdXz49GL9h1SVU+tqtdV1Seq6sq53r1XaHPfqnpSVX2sqr5RVedV1dur6i4b+2oAAGBjbMtAX1U3S/LSJBcvU+XIJH+c5BeSXDfJOatos5K8PsnxSQ6Y239zkp9IcmpVPXCvOw4AAJts2wX6OXi/Osm5SV62TLUvJrl3ksO6+2ZJ3rGKpn8+yYOTnJbkDt391O7+5ST3TPKtJK+oquvubf8BAGAzbbtAn+QJSY5J8sgklyxVobvP7+53d/d5a2j31+btM7r7sgVtfTDJG5IcninwAwDAMLZVoK+qo5I8N8mLu/vUdWz32knukuTSJP+4RJW/nbfHrNc5AQBgM2ybQF9V+yV5bZIvJXn6Ojf/fUn2TfL57r5yif3/Om9vtc7nBQCADbXfVndggWcluWOSu3X3N9a57evN2wuX2b+r/JDlGqiqxyZ5bJIcccQR69czAADYC9tihL6q7pxpVP6F3f3+re7PUrr75d29o7t3HH744VvdHQAASLINAv081eY1ST6T5JkbdJpdI/DXW2b/rvILNuj8AACwIbY80Cc5ONPc9aOSXLbgZlKd5NlznVfMZS/aw3N8LtPSlLecv0As9gPz9jN72D4AAGyJ7TCH/vIkr1pm350yzat/b5JPJ9mj6TjdfVlVnZbk7vPjHxZVud+8fc+etA8AAFtlywP9fAHso5faV1XHZQr0J3b3K/fyVH+eKcz/QVXda9da9FX1I0kekuTsJG/cy3MAAMCm2vJAv6eq6gVJbjA/vdu8fWpVPWz++aTuPmnBIa9P8qBMN4/6cFW9NclhmcL8vkke090XbXzPAQBg/Qwb6DMF85svKrvvgp/PSPLtQN/dXVUPTXJakkcl+Y0klyU5NckfdPdpG9pbAADYANXdW92H4ezYsaN37ty51d0AAOC7WFWd3t07Vqq3HVa5AQAA9pBADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADAwgR4AAAYm0AMAwMAEegAAGJhADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADAwgR4AAAYm0AMAwMAEegAAGJhADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADAwgR4AAAYm0AMAwMAEegAAGJhADwAAAxPoAQBgYAI9AAAMTKAHAICBCfQAADAwgR4AAAYm0AMAwMDWJdBX1aFVddB6tAUAAKzeqgN9Vd2rqv64qg5dUHbDqjolyTlJzquq4zeikwAAwNLWMkL/G0ke1N3nLyh7QZK7J/lcknOTPLGq/vs69g8AANiNtQT62yd5764nVXWdJA9O8nfdfaskt05yZpJfXdceAgAAy1pLoL9hkn9b8PzOSa6d5IQk6e6vJ/mbTMEeAADYBGsJ9Jcnuc6C53dP0klOXVB2UZLrr0O/AACAVVhLoP9CkmMWPP+vSf61u7+8oOxmmS6QBQAANsFaAv2JSW5XVR+oqn9Mcrskf7mozg8l+fR6dQ4AANi9tQT6P0/y+iQ7ktw103z55+3aWVW3zRTyT17H/gEAALux32ordvc3k/xCVf3q9LS/vqjKV5PcMckZ69c9AABgd9ZyY6mfqKojuvuiJcJ8uvucJOdlWt4SAADYBGuZcvMPSY5doc4vzfUAAIBNsJZAX6us03vYFwAAYI3WEuhX4+ZJrjYdBwAA2Bi7vSi2qp61qOjoqiUH6vdNckSSn0/y3vXpGgAAsJKVVrk5bsHPneTo+bGcLyf5H3vVIwAAYNVWCvT3nLeV5D1JTsh0g6nFvpXk3CSf7u6r1q13AADAbu020Hf3Kbt+rqoTk5y0sAwAANhaa7mx1CM3siMAAMDarfcqNwAAwCZadoS+qq5KclWS23T3Z+bnq1ljvrt71SP/AADAnttd8D41U4C/dNFzAABgm1g20Hf30bt7DgAAbD1z6AEAYGACPQAADGxNF69W1f5JHpjkR5McmmTfJap1d//yOvQNAABYwaoDfVXdJMnfJfnBTHeOXU4nEegBAGATrGWE/oVJjkryv5K8IsmZSa7ciE4BAACrs5ZAf98kp3b3L25UZwAAgLVZy0Wx107ygY3qCAAAsHZrCfQfT3LzjeoIAACwdmsJ9M9P8oCqus1GdQYAAFibtcyh/1qStyY5rapenOT0JBcsVbG7T12HvgEAACtYS6A/OdOSlJXkmfPPy1lqfXoAAGCdrSXQ/152H+IBAIBNtupA393HbWA/AACAPbCWi2IBAIBtRqAHAICBrXrKTVW9Z5VVu7vvtYf9AQAA1mAtF8UevcL+XSvguHAWAAA2yaqn3HT3Pks9khya5L5JPpLkDUkO2KC+AgAAi+z1HPruvrC7/z7JfZLcI8mT97pXAADAqqzbRbHdfV6Styd59Hq1CQAA7N56r3JzUZIj1rlNAABgGesW6KvqOkl+JsnX1qtNAABg99aybOUv7aaNmyX5hSTfn+QF69AvAABgFdaybOUJWXpJypq3VyX5iyTP2Ms+AQAAq7SWQP/IZcqvSnJ+kp3d/dW97xIAALBaqw703X3iRnYEAABYu/Ve5QYAANhEAj0AAAxs2wb6qnpYVfX8WPJmVVV1/6o6uaourKqLq+oTbfg3AAAgAElEQVQDVfWIZeoeu6C9pR6/urGvCAAA1t9aLordNFV1syQvTXJxkoOXqfP4JH+a5NxMq+tckeTBSU6oqtt191OWaf4tST6yRPnOve03AABstm0X6Kuqkrw6U1B/U5KrBfOqOjLTevfnJdnR3WfM5b+X5INJnlxVb+zu9y9xipO6+4SN6DsAAGy27Tjl5glJjsm0TOYly9R5VJJrJXnprjCfJN19fpI/nJ+aQgMAwHe9bTVCX1VHJXlukhd396lVdcwyVXeVv2OJfX+7qM5id6iq30xy7SRfTvIP3X3WnvYZAAC20l4H+qo6NMkV3b3caPpq29kvyWuTfCnJ01eofut5+5nFO7r7K1V1SZKbVtWB3X3poipPXPT8W1X1yiS/2d2X7UHXAQBgy6w45aaqfrCqXllVb6mqx1fVPnP5z1bV55Ock+Siqnp/Vf3oXvTlWUnumOTY7v7GCnWvN28vXGb/hYvqJckXkvxGpi8DByW5SZL/nuSMJL+S5P/b3Qmr6rFVtbOqdp599tkrdA8AADbHbkfoq+qIJKclOWQuun+S76uq/5Xk/yTZP8n5Sa6b5M5J3lNVd+ruq42cr3CeO2calX/hMhey7rXuPiXJKQuKLk3yv6vqn5J8NMlDq+p53f3RZY5/eZKXJ8mOHTt6I/oIAABrtdII/ZMzhfnfS/LDSX43ya8n+YMkX0xyh+4+LNPSks9LcmCS315LB+apNq/JNH3mmas8bKkR+IVWGsH/tu4+M8nb56c/scrzAwDAtrDSHPr7JDmlu4+bn3+4qo5Ocq8kP93dH0uS7r4iye9U1T3nfWtxcJJbzT9fNq1aeTWvqKpXZLpY9jeTfDrJDebjvmNEv6punGlKzVlLzJ9fzq45NAetse8AALClVgr0N0vyN4vKdia5R5L3LVH/fUket8Y+XJ7kVcvsu1OmefXvzRTid4X39yS5a5KfyqJAn+R+C+qs1p3n7efXcAwAAGy5lQL9ZZnWe1/ogHl7YKY7uS50nSRXraUD8wWwj15qX1UdlynQn9jdr1yw69WZpvY8vqpeveDGUofmP1bIedmitnZ0985FZfskeVqSH890ce9Sy2ACAMC2tVKg/3ySe+96Mt/F9d5JvpXkAUleuWDfAZlGzL+w/t38Tt39hap6apKXJNlZVW9IckWSBye5aZa+uPaDVfXxTBfAfjnTPPu7Jrltpgtkf7G7L9rovgMAwHpaKdC/JsmfVtU7M104er8kR2W6SPaFVXWtJCcnOTzTyPjNk7xww3q7QHf/aVWdkeQpSX4p0wW+n0jyjO4+cYlDXpDkRzPdcOr6mf6S8KUkf5bk+O423QYAgOFU9/IrMM6j7u9JcpcknaSSvLG7/1tV/VWmEfFdDVSSM5PcqbvP3dBeb7EdO3b0zp07V64IAAB7qKpO7+4dK9Xb7Qh9d19RVfdI8nNJbpnkX7r7bfPuh2caEf+ZTPPs/zHJc77bwzwAAGwnK025SXd/K9NNpBaXX57kuPkBAABsgZVuLLXXquqJVWV+OgAAbIAND/SZ7jR78004DwAAXONsRqAHAAA2iEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwPbbhHOcvAnnAACAa6QND/TdfUqSUzb6PAAAcE20qkBfVXdMcnSSK5O8q7s/vUy9ByZ5YHc/at16CAAALGvFOfRV9YIkO5O8IMmLkvxLVb2kqvZfovodkjxifbsIAAAsZ7eBvqp+LslvJfl6klcm+fMkZyd5XJJ3VdWBG95DAABgWSuN0P9aksuS3Lm7f6W7H5/kVknemOQeSf6mqq69wX0EAACWsVKgv1OSNy2cM9/dX+/u/55p+s3RSd5aVdfauC4CAADLWSnQH5zki0vt6O7fyjSv/l5JTqqqA9a5bwAAwApWWuXmK0m+d7md3f3b88WxT8w0Dedj69g3AABgBSsF+k9mmiu/rO5+0jw6/2tJ7rleHQMAAFa20pSbv01yy6q6++4qdffjkrwqiVVvAABgE600Qv/GJDdJcthKDXX3Y6rqrCRHrkO/AACAVdhtoO/uf0vyO6ttrLt/d697BAAArNqKd4rdW1X17Kq6cqPPAwAA10QbHuhntUnnAQCAa5TNCvQAAMAGEOgBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAY2H6bcI6TkpyxCecBAIBrnA0P9N390SQf3ejzAADANdG6TrmpqudX1efWs00AAGB56z2H/gZJjlznNgEAgGW4KBYAAAa22zn0VfWaNbZ3l73oCwAAsEYrXRT7sCSdpNbQZu95dwAAgLVYKdB/PclZSX59le39jyT33aseAQAAq7ZSoP9oktt39ymraayqjt3rHgEAAKu20kWxH0lycFV932Z0BgAAWJuVRuhPSXL3JDdNspr15d0VFgAANtFuA313vzHJG1fbWHe/Jclb9rZTAADA6liHHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBg2zbQV9XDqqrnx6OXqXP/qjq5qi6sqour6gNV9YgV2n1EVf3zXP/C+fj7b8yrAACAjbUtA31V3SzJS5NcvJs6j0/y1iS3TfIXSV6R5CZJTqiqFyxzzAuSnJDkxnP9v0hyuyRvndsDAIChbLtAX1WV5NVJzk3ysmXqHJnkBUnOS7Kjux/X3U9K8kNJPpfkyVX144uOuUuSJ8/7f6i7n9Tdj0vyw3M7L5jbBQCAYWy7QJ/kCUmOSfLIJJcsU+dRSa6V5KXdfcauwu4+P8kfzk9/ddExu54/Z66365gzkvzZ3N4j97LvAACwqbZVoK+qo5I8N8mLu/vU3VQ9Zt6+Y4l9f7uozt4cAwAA29q2CfRVtV+S1yb5UpKnr1D91vP2M4t3dPdXMo3s37SqDpzbPijJf0py8bx/sX+dt7faTf8eW1U7q2rn2WefvUL3AABgc2ybQJ/kWUnumOTY7v7GCnWvN28vXGb/hYvqrbb+IcudsLtf3t07unvH4YcfvkL3AABgc2yLQF9Vd840Kv/C7n7/VvcHAABGseWBfp5q85pM02eeucrDFo/AL7Z4RH619S9Y5fkBAGBb2PJAn+TgTHPXj0py2YKbSXWSZ891XjGXvWh+/ul5e7U571V14yQHJTmruy9Nku6+JMmXkxw871/sB+bt1ebkAwDAdrbfVncgyeVJXrXMvjtlmlf/3kwhftd0nPckuWuSn1pQtsv9FtRZ6D1JHj4f8+pVHgMAANtadfdW92FZVXVcplH6x3T3KxeU3yLJJzOtZvPDu9air6pDk3wwyfclucvC+fjzjaXel+nGUj+yay36+WZSp2ca1f/BhevaL2fHjh29c+fOvX59AACwnKo6vbt3rFRvO4zQr1l3f6GqnprkJUl2VtUbklyR5MFJbpolLq7t7tOq6vgkv5XkY1X1f5IckOQhSa6f5DdWE+YBAGA7GTLQJ0l3/2lVnZHkKUl+KdP1AJ9I8ozuPnGZY55cVf83yeOSPDbJVUk+lOT53f03m9JxAABYR9t6ys12ZcoNAAAbbbVTbrbDKjcAAMAeEugBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxMoAcAgIEJ9AAAMDCBHgAABibQAwDAwAR6AAAYmEAPAAADE+gBAGBgAj0AAAxs2wT6qnpeVb27qs6sqm9U1XlV9eGqenZVHbZE/etW1XOq6lNVdVlVnV9V76yqey3T/nFV1bt5/NTGv0oAAFhf+211BxZ4UpIPJfm7JF9LclCSH0tyXJLHVtWPdfeZSVJVhyZ5b5LbJPmXJC9LcnCSByb5+6p6dHe/apnznJjkjCXKP7turwQAADbJdgr039Pdly0urKrnJHl6kt9J8utz8XGZwvybkjyku6+c6z49yc4kf1pV7+zus5Y4zwndffL6dx8AADbftplys1SYn/3VvP2BBWU/N2+ftSvMz218LcnxSa6T5FHr3kkAANhmttMI/XJ+dt5+bEHZ987bzy9Rf1fZvZL83hL771ZVO5Lsm2nqzbu7+5x16CcAAGy6bRfoq+opmebDXy/JjiR3yxTmn7ug2jlJbpzkFkk+saiJW87bWy9zit9f9Pzyqnp+ptH+3ouuAwDApts2U24WeEqSZyf5zUxh/h1J7tvdZy+o87Z5+7tVte+uwqo6PNPFtUly6KJ2P5ppGs4tM03JuXmSxyS5IMkzkjxnd52qqsdW1c6q2nn22WfvrioAAGya2q6D0lV1oyR3yTQyf90k9+/uD837bpzkA0luluTjSd6daVWcByb5cpI7JLmsu6+zivPcKck/zU9vsprpNzt27OidO3eu+TUBAMBqVdXp3b1jpXrbcYQ+SdLd/97db05y3ySHJXnNgn1fSfIjSf4sU9j/9SQ/k+QNSf7bXO1rqzzPh5L8c5L9k/z4evUfAAA2w7abQ79Yd3+xqj6R5A5VdYNdI+jd/e9JHj8/vq2qjpl//OAaTrNrDs1Be9tfAADYTNt2hH6Rm8zbb62i7i/N279cTcNVtX+SO81Pl1o1BwAAtq1tEeir6lZVdb0lyveZbyx1wySndff5C8oPXqL+wzMF+tOSnLSg/LpVdbVVb6rqgCQvSnJEkk9luikVAAAMY7tMufnpJH9UVe9N8oUk5ya5UZJ7ZFqV5quZVqTZ5cAk/15Vf5fkc0muSnLXTHPgP5nkv3X3VQvqH5bkk1W1c97/lSSHJ7lnpqUvz0ny0EXHAADAtrddAv3fJ/n+TMtU3jHJIUkuSfKZJK9N8pLuPm9B/cuTvH6uf5+57F+T/M8kL+ruSxe1f16Slyb50SQ/meT6Sa7I9GXgeUmOn+8yCwAAQ9kWgb67P55FF7euUP+bSX55DfUvSvKEPegaAABsa9tiDj0AALBnBHoAABiYQA8AAAMT6AEAYGACPQAADEygBwCAgQn0AAAwMIEeAAAGJtADAMDABHoAABiYQA8AAAMT6AEAYGACPQAADEygBwCAgQn0AAAwMIEeAAAGJtADAMDABHoAABiYQA8AAAMT6AEAYGACPQAADEygBwCAgQn0AAAwMIEeAAAGJtADAMDABHoAABiYQA8AAAMT6AEAYGACPQAADEygBwCAgQn0AAAwMIEeAAAGJtADAMDABHoAABiYQA8AAAMT6AEAYGACPQAADEygBwCAgQn0AAAwMIEeAAAGJtADAMDABHoAABiYQA8AAAMT6AEAYGACPQAADEygBwCAgQn0AAAwMIEeAAAGJtADAMDABHoAABiYQA8AAAMT6AEAYGACPQAADEygBwCAgQn0AAAwMIEeAAAGJtADAMDABHoAABiYQA8AAAMT6AEAYGACPQAADEygBwCAgQn0AAAwMIEeAAAGJtADAMDABHoAABjYflvdAQAArnnOvOrifLjPzbm5PIflWrljHZab7XPwVncr5151fr6Qs3JxLsnBOSi3yE1z2D6HbnW3dssIPQAAm+rMqy7Ou/rfcmlfmev3Abm0r8y7+t9y5lUXb2m/zr3q/HysP5XL+4oc1Afm8r4iH+tP5dyrzt/Sfq1EoAcAYFN9uM/NQb1vDqz9UlU5sPbLQb1vPtzn/v/t3XuYHFWZx/HvLxlmBAIJBgTCJYDcVnl0weAlIqAsKCigLgjiqqB4V1ZdV9d73EXR1WcfRRFUVrOyKigX0QVlDSFGQMWsIBdBEEgEkWsImJBkyOTdP85paJrumeme6a6q9O/zPPXUzKlTp0+9qT7zpvpUdaH9up07GWSQIQ0iiSENMsggt3Nnof0aixN6MzMzM+upB1jLxkx9QtnGTOUB1hbUo2QlqxhkoyeUDbIRK1lVUI/Gxwm9mZmZmfXUTIZYzcgTylYzwkyGCupRMo1NGebRJ5QN8yjT2LSgHo2PE3ozMzMz66m9NZNVGuGRWEdE8EisY5VG2FszC+3XzmzPMMOsjWEigrUxzDDD7Mz2hfZrLE7ozczMzKyndpgyjUM0i000wHINs4kGOESzCn/KzcwpW/As7cmQBlmlRxjSIM/SnqV/yo0fW2lmZmZmPbfDlGnsQPGPqWw0c8oWzKTcCXwjX6E3MzMzM6swJ/RmZmZmZhXmhN7MzMzMrMKc0JuZmZmZVZgTejMzMzOzCnNCb2ZmZmZWYU7ozczMzMwqzAm9mZmZmVmFOaE3MzMzM6uw0iT0kj4n6VJJd0haLWm5pKslfVLSzCb1N5P0aUk3SVoj6UFJl0g6aJTXmCrpfZKurXuNiyXN7e7RmZmZmZl1R2kSeuB9wKbAz4AvAd8B1gHzgGsl7VCrKGkL4FfAR3KdM4DzgH2ABZLe3Ni4JAFnA/8BDAJfAS4A9gcWSzqyWwdmZmZmZtYtA0V3oM7mEbGmsVDSp0mJ+4eBd+biecAzgPOBYyJiXa77EWAJ8GVJl0TEnXVNHQscBVwJHFR7LUlnAJcD35C0MCL+2o2DMzMzMzPrhtJcoW+WzGffz+vd6speldefqCXzuY17SVfgNwbe1NDOO/L6Y/WvFRG/Ac4BtiIl/KVz/cpH+cyylbzz5of4zLKVXL/y0aK7ZGZmZmYlUZqEfhSH5/W1dWXb5PVtTerXyh6bSy/pKcBc4BHgF032+Ulev6TzbnbH9Ssf5dQ/r2LFuvXMGpzCinXrOfXPq5zUm5mZmRlQrik3AEj6ADANmA7MAfYjJfOfrat2P7AtsDPw+4YmdsnrPerKng5MBW6rv6Jf55a83n1Cne+CHz2wlhkDU5gxkP7vNWNAj5XvNW2jIrtmZmZmZiVQuoQe+ACwdd3vPwWOj4j76souAk4EPiXp2IgYAZC0FenmWoAt6upPz+uHWrxmrXxGq05JeivwVoAdd9xxHIcxOe5cO8KswSd+kLL5VHHn2pGe9cHMzMzMyqt0U24iYpuIEGlazatJV9yvlrRPXbVPAHeQ5rxfI+mLkr4B3AAsz3XWT3K/vh4RcyJizlZbbTWZTY9q+6GpPDwSTyh7eCTYfmhqz/pgZmZmZuVVuoS+JiLuiYgLgEOAmcC367b9BdgXOA3YjPT0m5eTbm49Ole7t6652hX46TRXK18xKZ2fREfMHGLFuvWsWLee9RGP/XzEzKGiu2ZmZmZmJVDahL4mIpaR5sk/U9KWdeX3RMS7I2KniBiMiFkR8R6gNh/mN3XN3AqMALtIajbNqPYEnZu7cAgTste0jThpu02ZMTCFu4bXM2NgCidtt6nnz5uZmZkZUM459M3MyuvxTBx/Q15/t1YQEWskXQm8KC+XNexzaF4vnEgnu2WvaRs5gTczMzOzpkpxhV7S7pKeNB1G0pT8xVJPA66MiAfryqc1qf96UkJ/JfDDhs2n5/XJ+TGWtX32BY4B7iN926yZmZmZWWWU5Qr9YcApki4HbgceID3p5gDSTbF3A2+pq78JcI+kn5Gm06wHXgi8ALgRODoiGm+KPZt0k+1RpJtsf0yam38M6ZGWb4mIh7tzeGZmZmZm3VGWhH4BsCvpmfN7kx4fuYo0p/0s4NSIWF5Xfy0pQd8PODiX3QJ8FPhiRDzS+AIREZJeS7p6/ybgPcAaYDFwckRc2YXjMjMzMzPrKkXE2LXsCebMmRNLliwpuhtmZmZmtgGT9H8RMWeseqWYQ29mZmZmZp1xQm9mZmZmVmFO6M3MzMzMKswJvZmZmZlZhTmhNzMzMzOrMCf0ZmZmZmYV5oTezMzMzKzCnNCbmZmZmVWYE3ozMzMzswpzQm9mZmZmVmFO6M3MzMzMKswJvZmZmZlZhTmhNzMzMzOrMCf0ZmZmZmYV5oTezMzMzKzCnNCbmZmZmVWYE3ozMzMzswpzQm9mZmZmVmFO6M3MzMzMKswJvZmZmZlZhTmhNzMzMzOrMEVE0X2oHEn3AcsKeOktgfsLeN2qcrza43iNn2PVHserPY7X+DlW7XG82lOGeM2OiK3GquSEvkIkLYmIOUX3oyocr/Y4XuPnWLXH8WqP4zV+jlV7HK/2VClennJjZmZmZlZhTujNzMzMzCrMCX21fL3oDlSM49Uex2v8HKv2OF7tcbzGz7Fqj+PVnsrEy3PozczMzMwqzFfozczMzMwqzAm9mZmZmVmFOaE3MzMzM6swJ/QFkzRT0omSLpD0R0mrJT0k6XJJb5Y0paH+bpI+JGmhpDskDUu6R9KFkl5c1HH0SrvxatHGmZIiL7v2ot9F6DRWkqbm/RZLejDvd5ukcyTt3uvj6JVO4iVpSNK7JF0l6X5JKyXdKOlUSbOLOI5ekvQ5SZfmsWi1pOWSrpb0SUkzW+wzV9LFue5qSddKeq+kqb3uf6+1Ey+P9e2fWw3798U4X9Phe7Evx3poP15VGOt9U2zBJL0dOB34C3AZ8Cdga+DVwHTgPODoyP9Qks4GjgF+D1wOLAf2AI4ApgL/GBGn9vgweqbdeDXZ/3DgR8BKYBqwW0T8sQdd77lOYiVpGnAh8BLgGuDnwBpgO+BFwLsj4n96eBg908F7cQBYBLwQuAlYAKwF9gX2Bx4C5kbE73t6ID0kaRj4LWk8uhfYFHg+MAe4C3h+RNxRV/9IUhzXAOeQxq/DSWPYuRFxdE8PoMfaiZfH+vbOrYZ9+2acr+ngvdi3Yz20/V6sxlgfEV4KXEhvpsOBKQ3l25ASigD+vq78eGDvJu0cAAyTTrJtiz6ussSroc5WwN3A2aQ3ZwC7Fn1MZYoV8J1c/rYWbW5U9HGVJV7A0blsQZN9PpW3fbPo4+pyzJ7SovzT+fi/Wle2OekP51pgTn0bwJW5/rFFH1OJ4tXvY/24Y9Wwva/G+U7j1c9jfbvxqspY7yk3BYuIhRHx44hY31B+N3BG/vXAuvL5EXF1k3Z+Thq8BoG5XetwwdqNV4Pa82Tf1aXulUq7sZK0D3AccE5EfK1Fm492qbuF6+Dc2iWvL2rch3TlC1JyscGKiDUtNn0/r3erKzuKFI+zI2JJQxsfy7++Y9I7WSLtxMtjfVvnVr2+Gudr2olXv4/10Pb5VYmxfqDoDtioam+odV2qv6FpefySjgdeCbwyIh6Q1Mt+lVGzWB2X19+TNJ10tXoH4AFgYWzgH1mPoVm8bsjrQyV9qWGgf0VeL+h6z8rp8Ly+tq7sJXn90yb1FwOPAHMlDUXE2m52roSaxWs0/TzWt4yVx/mmmsXLY31rzeJVibHeCX1J5Tlbb8i/NvsD2Fh/NnAQ6Y/i4i52rZRGi1eOzZeA/46ICxv37TejxGrfvJ4N3ArU3xgUkk4HToqIke73sjxGiddFwPmkOfbXSVpAmgrxHGA/4MvAaT3samEkfYA0V3k6aQ7qfqQ/iJ+tq7ZHXt/cuH9ErJN0O/BM0tWwG7va4YKNM16t9u2rsX68sfI4n4wzXh7rs3HGqxpjfdFzfrw0X4AvkOZlXTSOukOkm6YC+Oei+16meJGe5LQI+DOwRV35IvpkbmUbsboxl68DzgX2JA10BwG35G3ziu5/WeKVtwmYl2MWdcsC0k1Vhfe/RzG6u+H4fwJs3VDn5tHec8AVefsLij6eMsSrxX59N9aP89zyON9evDzWtxGvXK/0Y33hHfDS5B8FTsonyo3AU8eoO5U05ytINwGp6P6XKV7AP+VthzWU991AP45Y/SFvux6Y2rDt2cAI8DAwWPRxlCReT8nvvb8CbyPdPLs5cGj+ozgMHFn0MfQ4XlsDr8rn0l3APnXbnNC3Ea8mdft6rB/j3PI43168PNa3F69KjPWFB9FLwz8IvDu/0W4Athmj7lTge7n+OcBA0f0vU7yA3UmP4XrS3ef9ONCPdW4Bv87bT2mx/x/z9mcXfSwlide8vP2kJtuenbctLfo4CordbNJTWK6vK/tNjslzWuxzfd7+N0X3vwzxatje92N9q1h5nG8vXrnMY3178arEWO+n3JSIpPeS5mJdD7w40tM1WtXdiDTAHwt8FzguIvrqBqlxxOsZpI+oT6j7gpGQFKRHvwHckste2bue9944z60/5PWKFs08mNcbT3L3Smec8ardDHVZ44aI+B0pXrPH8yU4G5qIWEZ6vvMzJW2Zi2vn15O+sCbfp7Az6ePs23rSyRJpES/AY32jJrHyOD+KMd6LfT/WN2oRr0qM9b4ptiQkfYh0E8Y1wMERcf8odQdJH/8cCXwbOCGe/CilDdo447UU+M8WTbyc9LHZD0gfLS6d/F6WQxvn1gLg9cBeTdoY4vHHeC3tQjdLo414DeX1kx5XluO1Wf51eHFPxRsAAAa8SURBVNI7WQ2z8rp2Y91C4HXAy0gJar39gU2AxdF/T7ipaYyXx/rW6mO1FI/zY2k8tzzWj64xXtUY64v+iMBLAHyc9JHNEsaeMz9EuuM6gDNp+JKDfljaidcobSyiDz6KbfPc2pR0U9kw8NyGbSfndhYWfUwlitdXefymqKGGbafkbVcVfUxdjNXuwPQm5VN4/MtZrqgr3xy4jz79YqkO4tW3Y327sRqlnX4Z59s9t/p6rO8gXpUY65U7ZAWR9EZgPul/gl8mfYVwo6URMT/X/xbpGwTv5/GTrNGiiFg0+b0tXrvxGqWdRaSPYzfYrwTvJFaSDgZqX/d9PmnQfx7p0Vz3AvtFxC3d63VxOngvbgf8CtiedCXrp8Bq0teDPzf/fFBE/LLLXS9EnpZ0CumpK7eTnmG9Nel9tQvp6REHRd3XoecpD+eS5jyfDSwHjiA90vJc4DWxgf5Rajde/TzWd3JutWhnERv4OA8dvxf7eaxv971YjbG+6P9R9PvC4zdbjLYsqqu/aBz15xV9XGWJ1yjt1OK4wV656TRWpJt8ziVdTR0G/gScDswq+pjKFi/SR7BfID0FZ02O1zLgW8CeRR9Tl+O1F/AV0tSk+0nz3x8i3fw6jxafcJD+CF5Mmne6GrgOeB8NT9vY0JZ249XPY32n51aTdjb4cX4i8erjsb7teFVhrPcVejMzMzOzCvNTbszMzMzMKswJvZmZmZlZhTmhNzMzMzOrMCf0ZmZmZmYV5oTezMzMzKzCnNCbmZmZmVWYE3ozMzMzswpzQm9mZl0lab6kkLRTl19nqaSl3XwNM7MyckJvZmaVIGmRJH8boplZg4GiO2BmZjZJDiq6A2ZmRXBCb2ZmG4SIuLXoPpiZFcFTbszMSkrSTnnu+XxJe0r6oaTlklZJulzSIU32GZL0L5Kuk/SIpIcl/ULSayap/Xl5nwNHa2+cx3e8pPMk3SZpde7rFZL+oVm7wAH596hbFtXVazqHfgIx2UnS2ZLul7RG0hJJrxjPsZmZ9ZKv0JuZld/OwC+B64CvAdsCxwA/kXRcRJwDIGkQuISU+N4EnAZsAhwFnCPpbyPiI5223wWnAzcAi4G/ADOBw4CzJO0RER/P9VYAnwKOB2bnn2uWjvYCE4jJbOAq4DbgLOCppJhcKOnvIuKydg/WzKxrIsKLFy9evJRwAXYCIi+fb9g2B3gUeBDYPJd9ONe9GBioq/s0UuIbwNxO28/l83L9A0fp7/yG8vm5fKeG8qc3aWMQuDS/9nYN2xalP1st47UUWNpQNpGYfLKhrZfW2ir63PDixYuX+sVTbszMyu8h4F/rCyJiCfAdYAbwqlz8JlLC+f6IWFdX917g3/KvJ06g/UkVTea8R8Qw6Sr6AJNzk2unMVkGnNzQt0uAPwHPnYR+mZlNGif0Zmbl99uI+GuT8kV5vbekzYBdgbsi4qYmdRfW6nbSfht9HTdJO0o6TdJNeW575Lny5+Uq202w/YnE5JqIGGlSfgewxUT6ZWY22TyH3sys/O5pUX53Xk/PC6S56M3Uymd02P6kkrQLaY76FsAvgP8lfVIwQpr28kZgaIIvM5GYrGixzzp8MczMSsYJvZlZ+W3donybvH4oL/Vljbatq9tJ+zXr87rZ349miXEr7yfdBHtCRMyv3yDptaSEfqImEhMzs8rwVQYzs/LbJ08faXRgXl+dp8zcCmwnabcmdV+c17/tpP26sgfzeocm9ec0KWtl17w+r8m2A1rsMwIgaep4XmCCMTEzqwwn9GZm5Tcd+ER9gaQ5wOtIV5cvyMXfBAR8vj7plbQl8PG6Op22D2maDMAJkgbq6u/Q2MYYlub1gQ2v+1Ka36QK8EBe79jG63QaEzOzyvCUGzOz8lsMnCjpecAVPP6c+CnA2yLi4VzvC8ChwJHA7yRdTHrm+tGkxzT+e0RcPoH2iYhfS1oM7A9cJWkhacrO4aTnvTe7ct/MV4ETgB9IOhe4C9gLeBnw/fz6jS7Nx3J+PrbVwLKIOGuU1+k0JmZmleEr9GZm5Xc7MJc03eXtwGtI00QOi7ovfcqPfDwY+Ggueg9pLvotwHER8aGJtF/nSOBMYPv8GnsDHwRatf8kEXEtacrLlcDLgXcAmwOvBs5osduZwCmkTxQ+SHrs5JvHeJ1OY2JmVhmKiKL7YGZmTUjaiZRs/1dEHF+19s3MrDd8hd7MzMzMrMKc0JuZmZmZVZgTejMzMzOzCvMcejMzMzOzCvMVejMzMzOzCnNCb2ZmZmZWYU7ozczMzMwqzAm9mZmZmVmFOaE3MzMzM6uw/wdeyHBVpoVBfgAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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Hc/CcRERERETmXLzbDZhm0DlXbi00s48Cfw38FfCeaQ/9G3AJ8AfOuX+boV6i5f8vAD4AbAfOd86NheWfAO4CPmlm33HO7ZidpyO95IFCletGK+ysNFifinHZkhRnZpMd69w7WePq/VWeKPuclPZ4/cok5wwmOtYRERER6Yae6amfKdCHvhVuz5gqMLPzgN8GvjlToA+PV2sp+oNw+9GpQB/utwP4FyAFvPPYWy697oFClc/sKTJe91mb9Biv+3xmT5EHCtW2de6drPEPO8qM1XzWpYyxms8/7Chz72TrbSUiIiLSfT0T6ju4LNzeO63st8Pt181syMzeamZ/ZWbvNrPT2xznknB74wyPfbdlH1lArhutMBw3huMengXb4bhx3WilbZ2r91cZicNIIqgzkvAYiQflIiIiIr2ml4bfAGBmfw70A0PAJuBCgkD/99N2Oz/cnkwwnGb6RFpnZp8D3ueca4THzAJrgbxzbu8Mp3043M44uTY8xruBdwOcdNJJx/ispJt2VhqsTTa/fx2MGTsrjbZ1nigHPfTTDcWNJ8r+nLRRRERE5ET0Yk/9nwMfAv6UINDfCLzcOXdw2j4rwu2ngFuBZxNMlv01gpD/HuBvpu0/FG4n2pxzqny4XaOcc593zm1yzm1avnz5UT8Z6b71qRiTDddUNtlwrE/F2tY5Ke0xUW+uM1F3nJTuxT8ZERERWex6LqE451Y55wxYBbwBOBW4JxxHP2Wq3b8A3uSc+4VzLu+cuxn4DcAH/szMOs+ElEXhsiUpxuuO8bqP74LteN1x2ZJU2zqvX5lkrA5jtaDOWM1nrB6Ui4iIiPSangv1U5xz+51zVwMvJxhe8+VpD4+H2+umhthMq/dz4DGCnvtnh8VTPfFDzGyqfLzN4xJhZ2aTvHdNhuG4x+6qz3Dc471rMh1XvzlnMMEHNqQZSXjsqjhGEh4f2JDW6jciIiLSk3puTH0r59zjZrYNeI6ZLXPOHQIeAp5H+xA+tbpNX3iMgpntBtaa2eoZxtVPrazzy1luvvSIM7PJIy5h2eqcwYRCvIiIiERCz/bUt1gTbqd65f8r3J7VuqOZpXgqpO+Y9tAt4faVMxz/VS37iIiIiIhERk+EejN7hpk9bWiMmXnhl0+tALZMW1/+28Ae4E1m9ryWan9DMJzmB865fdPK/zXc/g8zG5l2jg3AHwEV4D9m4emIiIiIiMyrXhl+cynwMTPbTDAe/jCwEngxwUTZfcC7pnYOh9O8A/gO8CMzuwrYDfwqwYo5B4Dfn34C59wWM/sU8GfAvWb2/wFJ4E3AEuC9+jZZEREREYmiXgn1/wWcThDIn0uwtGSBYIz7V4B/ds6NTq/gnPt+2Ev/NwRLWQ4RhP9/BT7inNvTehLn3AfM7D6Cnvl3E6ySczfwCefcd+bouYmIiIiIzClzzh15L2myadMmt3Xr1m43Q0REREQWMDO7yzm36Wj27Ykx9SIiIiIicvwU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIi7e7QaIiIjI/Hi0XmJzbZL9fo2VXoILE4OcGu/rdrNEZBaop15ERGQReLRe4srKIXJ+g+UWJ+c3uLJyiEfrpW43TURmgUK9iIjIIrC5Nkk/MQa8GJ4ZA16MfmJsrk12u2kiMgsU6kVERBaB/X6NrDW/7GfNY79f61KLRGQ2KdSLiIgsAiu9BAXnN5UVnM9KL9GlFonIbFKoFxERWQQuTAySp0HOb+A7R85vkKfBhYnBbjdNRGaBQr2IiMgicGq8jzemljHgxTjo6gx4Md6YWqbVb0QWCC1pKSIiskicGu9TiBdZoNRTLyIiIiIScQr1IiIiIiIRp1AvIiIiIhJxCvUiIiIiIhGnUC8iIiIiEnEK9SIiIiIiEadQLyIiIiIScQr1IiIiIiIRpy+fEhERkba2FatcP1Zmd7XB2mSMV4+k2ZhJdrtZItJCPfUiIiIyo23FKp/bl2ei7rM64TFR9/ncvjzbitVuN01EWijUi4iIyIyuHyszFPMYint4ZgzFPYZiHtePlbvdNBFpoVAvIiIiM9pdbTAQs6aygZixu9roUotEpB2FehEREZnR2mSMXMM1leUajrXJWJdaJCLtKNSLiIjIjF49kmai4TNR9/GdY6LuM9HwefVIuttNE5EWCvUiIiIyo42ZJH+4qp+huMfems9Q3OMPV/Vr9RuRHqQlLUVERKStjZmkQrxIBKinXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTi9I2yIiIiLarFPZQn7qdRGyOWGCE9dBbJzJoj1ttWrHLDeJld1QbrkjEuHU7r21jnwS8rZW4pFdjbqLM6FueSvizPSKW73awn7XE57uUgY5QZIc05LGeNDXS7WQveI9Uyt1Zy7PPrrPLiXJwa4PTkke+LG3eW+Oq2GgdyjhUDxls3Jnjl+r55aPGJUU+9iIjINNXiHvIHf4jfKOHFh/EbJfIHf0i1uKdjvW3FKp87kGei7rMm4TFR9/ncgTzbitV5avni9MtKma/kxpn0G6z0Ykz6Db6SG+eXlXK3mwYEgf5WnqBIjWFSFKlxK0+wx+W63bQF7ZFqma8VR8n5DVZYjJzf4GvFUR6pdr4vbtxZ4pM/rpIrO5b1G7my45M/rnLjztI8tfz4KdSLiIhMU564Hy+WwYv1YWZ4sT68WIbyxP0d690wXmbY8xiKe3hmDMU9hj2PG8Z7I1wuVLeUCgx4HoNeDM+MQS/GgOdxS6nQ7aYBcC8HSRMnQwLDyJAgTZx7Odjtpi1ot1ZyDJjHQHhfDHgxBszj1krnN1Nf3VYjm4KBtOFZsM2mgvJep1AvIiIyTaM2hnnNH9Gbl6ZRG+tYb1e1wUDMmsoGYsauamPW2yhP2duo02/NcabfPPY26l1qUbMxyvS1jHbuI84YerM3l/b5dbIt90XWPPb5ne+LAzlHNtX8d5xNGQdybtbbONsU6kVERKaJJUZwfnPgcn6ZWGKkY711yRi5RvMLf67hWJeMzXob5SmrY3Hyzm8qyzuf1bHemDY4QpoSzUGyRJ0RemfM/0K0yotTaLkvCs5nldf5vlgxYBQqzX/HhUowtr7XKdSLiIhMkx46C79RxG+UcM7hN0r4jSLpobM61rt0OM247zNR9/GdY6LuM+77XDqs8DaXLunLkvN9Jv0GvnNM+g1yvs8lfdluNw2Ac1hOmTpFajgcRWqUqXMOy7vdtAXt4tQAOeeTC++LnN8g53wuTnWeoPzWjQkKFciVHb4LtoVKUN7rzLne/zih12zatMlt3bq1280QEZE5otVvokWr38hMFsLqN2Z2l3Nu01Htq1B/7BTqRURERGSuHUuo1/AbEREREZGIU6gXEREREYk4hXoRERERkYhTqBcRERERibjeWMRVRESkhzzhF9jqj3KYKktJsslbwklebyyRKCIyE/XUi4iITPOEX+C7jb0UXJ0lLkHB1fluYy9P+IVuN01EpC2FehERkWm2+qNkiJG1OGZG1uJkiLHVH+1200RE2uqZUG9mHzezm81sp5mVzGzUzO4xsw+Z2dKjqP9/zcyFP6e32SdmZu83s3unneMGM7tg9p+RiIhE0WGqZIg1lWWIcZhql1okInJkPRPqgfcDWeD7wKeBK4A68GHgXjNb366imV0G/C6Q77CPAd8APgUkgc8CVwMvAm4zs9fOyrMQEZFIW0qSIo2msiINlqJvhhWR3tVLE2UHnXPl1kIz+yjw18BfAe+Z4fHlwBeAbwKrgBe3Of5vAb8BbAFeOnUuM/tXYDPwBTO7xTmXm4XnIiIiEbXJW8J3G3vBBT30RRoUafBib0W3myaLzGONIj+uj3PQ1VhuCV4QH+aUWKZjnUfrJTbXJtnv11jpJbgwMcip8b55arF0U8/01M8U6EPfCrdntHn88+H2j45wij8Mt/9z+rmccz8leEOwnCD0i4jIInaSl+VVsdVkLc6o1chanFfFVmv1G5lXjzWKXFM9QN41WEqCvGtwTfUAjzWKbes8Wi9xZeUQOb/BcouT8xtcWTnEo/XSPLZcuqWXeurbuSzc3tv6gJm9A3gd8Drn3OFghM3TmVkauAAoAj+aYZfvAm8DLgH+48SbLCIiUXaSl1WIl676cX2crMXotyCq9YeR7cf18ba99Ztrk/QTY8AL5oQMWAz8oFy99Qtfz4V6M/tzoB8YAjYBFxIE+r9v2e9kgrH3X3XOXXuEw54GxIBHnXP1GR5/ONw+o0O73g28G+Ckk0468hMREREROU4HXY2lJJrKMsQ46Gpt6+z3ayy35miXNY/9fvs6snD0XKgH/hxYOe3/bwTe4Zw7OFVgZh7wJYKJse87imMOhduJNo9PlQ+3O4Bz7vOEQ302bdrkjuKcIiIiIsdluQVDbvqnRbUiDZZbom2dlV6CnN8IeuhDBeez0mtfRxaOnhlTP8U5t8o5ZwSTXt8AnArcY2bnTdvt/QQTYt/lnBvrQjNFRERE5swL4sMUXIO8q+M7R97VKbgGL4i37X/kwsQgeRrk/Aa+c+T8BnkaXJgYnMeWS7f0Yk89AM65/cDVZnY38Evgy8BZZvYM4KPAfzjnbjjKw031xA+1eXyqfPx427vYbStWuWG8zK5qg3XJGJcOp9mY0fJvMn90D4rIQnJKLMPrkiuaVr95WWJpx9VvTo338UaWNa1+86rEiMbTLxI9G+qnOOceN7NtwHPMbBmwEUgB7zSzd7ap9nA4afb1zrlrgO1AAzjVzOIzjKufWlnnl7P/DBa+bcUqnzuQZ9jzWJPwmKj7fO5Anj9c0a9QJfNC96CILESnxDJHXMKy1anxPoX4RarnQ31oTbhtADuAL7bZ79UEw3auBCbDfXHOlc1sC3BR+PODlnqvCre3zFqLF5EbxssMex5D8WA011DcoB6UK1DJfNA9KCIii11PhPpwSM1+59xES7kHfARYAWwJx8+PAb/X5ji3EoT6v3bOPdLy8OcIAv3fmdn0L586H3gTcBD49qw9qUVkV7XBmkTz9IyBmLGr2mhTQ2R26R4UEZHFridCPXAp8DEz2ww8BhwmWAHnxQQTZfcB7zrBc3yDYOLtbxBMvL0OWEoQ6GMEk24nT/Aci9K6ZIyJuh/0joZyDce6ZKxDLZHZo3tQREQWu15Z/ea/CIbULCcI3n8B/DdgFPhb4Ezn3LYTOYFzzgFvBv4MqAPvDc91G/Cio1jrXtq4dDjNuO8zUffxnWOi7jPu+1w6nO5202SR0D0oIiKLnQVZV47Fpk2b3NatW7vdjJ6ilUek23QPymzaXi+xufrUCiIXJgc5TZMPRWSemdldzrlNR7Nvrwy/kYjbmEkqQElX6R6U2bK9XuLK8iH6ibHc4uT8BleWD/HG9DIFexHpWb0y/EZERKQnbK5O0k+MAS+GZ8aAF6OfGJurmnYlIr1LoV5ERGSa/X6NrDW/PGbNY79f61KLRESOTKFeRERkmpVegoLzm8oKzmell+hSi0REjkxj6kUirFbcQ2XsPhrVMWLJEVIjZ5PIrOlYx5/chTtwN5RHIb0EW3Ee3uC6I54b64gcAAAgAElEQVTr4MSDPFx6gJxVGHApzug7k+VDz+5YZ8e+O9g9+XN8V8SzDGsHz2XDqucf8VyPbX2CLd8/yIFRjxVLfC542XJO2XRSxzp3jI2yOb+bolci4/dxYf9anj+y5Ijnqhd2Ux27F78yhpcaITlyDvHs2s6Vxp+AXT+F4mHILIV158Nw5/YdGN/GL4vbmPQqDPopnpHZyIrhjUdsH3u3w7Yfwfh+GF4JGy+C1ad1rHL7/kmueqjIvglYNQRveGaGF64cPOKp8rUDHK48QsWfJOUNsjR1Ov2JFUdu4wJzYXKQK8uHwA966AvOJ0+DVyVHut00EZG21FMvElG14h4K+27FrxfxEsP49SKFfbdSK+5pW8ef3IV7/Hu4WhGXGgm2j38Pf3JXx3MdnHiQreW7KFOj36UoU2Nr+S4OTjzYts6OfXewa2ILzlXxSONclV0TW9ix746O53ps6xNc9fXD5AuwbKRBvgBXff0wj219om2dO8ZGuan4MBWrkfHTVKzGTcWHuWNstOO56oXdlPf+AL9exJLBNSzv/QH1wu72lcafgIeuh2oB+pYE24euD8rbODC+jTvL91CmzoCfokydO8v3cGD8CCv17t0Ot38LSjkYWh5sb/9WUN7G7fsn+exPikyWYcUgTJbhsz8pcvv+zuPB87UD7Cltpe7KJL0B6q7MntJW8rUDndu4AJ0W7+ON6WUMeDEOujoDXkyTZEWk5ynUi0RUZew+vFgfXjyDmeHFM3ixPipj97Wt4w7cjYtnsERQxxIZXDwT9Nx38HDpAVIuRpokhpEmScrFeLj0QNs6uyd/DiQwS4BZsCURlre35fsH6e/z6c+CZ0Z/Fvr7fLZ8/2DbOpvzu4m7BCmCc6VIEHcJNuc7hHOgOnYvxJuvIfG+oLydXT+FRBaSWTALtolsUN7GL4vbSPsx0hbHDNIWJ+3H+GXxCKF+248g3Q99A2BesE33B+VtXPVQkf40DKYNz4zBtNGfDso7OVx5hJiliXtpzIy4lyZmaQ5XWr+ce3E4Ld7H2zMr+cv+dbw9s1KBXkR6nkK9SEQ1qmNYrDloWKyPRnWsfaXyKLSGk3hfUN5BzipBYJ4mRYKcVdrW8V0RaxnhZ8TxXedweWDUI5Np/v6MTMZxYLT9P1dFr0TKNZ8r5eIUvVLHc/mVma+hX+lwDYuHIZFpLktkgvI2Jr0KqZZrkSLOpNf++gHBkJt0trksnQ3K29g3Af2p5rL+VFDeScWfJGbNFWOWouJrxRcRkShQqBeJqFhyBNdoDq2uUSLWadxvegnUW4JuvRSUdzDgUlRoXvmjQo0Bl2pTAzzL4Kg3t486nmXa1AisWOJTLFpTWbForFjit6kBGb+PijWfq2J1Mn7n3lUvNfM19FIdrmFmKdRa3pjUikF5G4N+ikrLtahQZ9Bvf/2AYAx9udBcVi4E5W2sGoJ8y3uFfCUo7yTlDdJwzRUbrkLKO/JYfBER6T6FepGISo2cjd8o4deLOOfw60X8RonUyNlt69iK87B6MRhL7xyuVsTqRWzFeR3PdUbfmVSsQZkqDkeZKhVrcEbfmW3rrB08F6jhXA2cC7bUwvL2LnjZcvIlj3wBfOfIFyBf8rjgZcvb1rmwfy11qwVvPJyjQo261biwv/OE1+TIOVBvvobUS0F5O+vOh1ohGEvvXLCtFYLyNp6R2UjZa1B2dZyDsqtT9ho8I3OEibIbL4JyPhhL7/xgW84H5W284ZkZ8mWYLDt855gsO/LloLyTpanTabgydb+Mc466X6bhyixNnd65jSIi0hPMOXfkvaTJpk2b3NatW7vdDBGtfjONVr95ila/ERFZGMzsLufcpqPaV6H+2CnUi4iIiMhcO5ZQr+E3IiIiIiIRp1AvIiIiIhJxCvUiIiIiIhGnUC8iIiIiEnEK9SIiIiIiEadQLyIiIiIScQr1IiIiIiIRp1AvIiIiIhJxCvUiIiIiIhGnUC8iIiIiEnEK9SIiIiIiEadQLyIiIiIScQr1IiIiIiIRp1AvIiIiIhJxCvUiIiIiIhGnUC8iIiIiEnEK9SIiIiIiEadQLyIiIiIScQr1IiIiIiIRp1AvIiIiIhJxCvUiIiIiIhGnUC8iIiIiEnEK9SIiIiIiEadQLyIiIiIScQr1IiIiIiIRp1AvIiIiIhJx8W43QKRXPTBa57qddXYWfNZnPS5bH+fMJfqTEemWicZh9jQeo+Ry9NkAa2KnMBRb2u1miYj0BPXUi8zggdE6n9lWZbzqWJsxxquOz2yr8sBovdtNE1mUJhqHeaT2c6quQpp+qq7CI7WfM9E43O2miYj0BIV6kRlct7POcMoYThqeBdvhlHHdToV6kW7Y03iMuKVIWgozI2kp4pZiT+OxbjdNRKQnKNSLzGBnwWcw0Vw2mAjKRWT+lVyOBMmmsgRJSi7XpRaJiPQWhXqRGazPekzWmssma0G5iMy/PhugRrWprEaVPhvoUotERHqLEorIDC5bH2e84hivOnwXbMcrjsvWa6KsSDesiZ1C3VWougrOOaquQt1VWBM7pdtNExHpCQr1IjM4c0mc925MMpw0dhcdw0njvRuTWv1GpEuGYks5PXEuSUtRJk/SUpyeOFer34iIhJRQRNo4c4mWsBTpJUOxpQrxIiJtqKdeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6zAGVReKhc4XuFInvqddbE47w8m+GZ6VS3mxUpvyhVuDFfYk+tzppEnFf29/GsPl1DERGZe4/t8NnyE8eBQ7BiGVzwq8YpG9Q3PZ2uhix4D5Ur/Pv4BJONBqtiMSYbDf59fIKHypVuNy0yflGq8IXRHJMNn9XxGJMNny+M5vhFSddQRETm1mM7fK66zpEvOJYtDbZXXed4bIe+5X06hXpZ8L5XKDLoeQzGYnhmDMZiDHoe3ysUu920yLgxX2Io5jEU8/DMnvzvG/OlbjdNREQWuC0/cfRnHf1ZwzOjP2v0Zx1bfuK63bSeolAvC96eep1+r/lW7/c89tTrXWpR9Oyp1RnwrKlswDP21HQNRURkbh04BJlMc1kmE5TLUxTqZcFbE4+T95s/osv7PmvimlJytNYk4uT85h6RnO9Yk9A1FBGRubViGRRbPlwvFoNyeYpekWXBe3k2w1dyu0glD5OMVag2UpTKS/mN7LpuN+1Jd27P870tFUYPGEtWOF5+QYrnndbf7WY96ZX9fXxhNAcEPfQ53zHR8PnNoWyXW9YdD+1rcPODPnvGYc0wvPTZHs9cFet2s0Qir1LaSyG/jXptnHhimGz/RlJ9q49Yr1raS2nifhq1MWKJEfqGziJ5FPUWosP+GDvcTvIU6SfDBlvPUm9kTs614zGfO7bAwQOwfAU8/wLYcMrs9xdf8KvGVdcBODKZINDnC8bLL7EjVV1U1FMvC97SZJHnDu8nHatTaCRIx+o8d3g/S5O9Mab+zu15vnZVlUIeRpY5Cnn42lVV7tye73bTnvSsvhTvWjLAYMxjb73BYMzjXUsGFuXqNw/ta/ClLQ0mS45VQ47JkuNLWxo8tK/R7aaJRFqltJeJ0c00GiVi8SEajRITo5uplPZ2rFct7SV/8Db8RgkvPozfKJE/eBvVI9RbiA77Y9znHqTiqmRdHxVX5T73IIf9sVk/147HfK69ypHPO5YuC7bXXuXY8djsT149ZYPHGy4LxtIfOhxs33CZVr9ppZ56WfAeZTcrYmnWx5JPllWo8ii7Wcbc9F4ci+9tqdDXD9n+oMch2w/g+N6WSk/11j+rL7UoQ3yrmx/0GUzDYF/w+xrsA3Dc/KCv3nqRE1DIb8NifcRifQDEYn00wvJOvfWlifuxWB9eWM9iffhh+WLrrd/hdpJ0SVIWvN6lSIKDHexk6Sy/3t2xJXi96g9fu/rD1647tsCGU2b1VEAQ7E/ZMPvHXUj0FkcWvBwFkiSaypIkyFHoUouajR6wGScAjR7Qx4q9aM849Keby/rTQbmIHL96bRzPa/7j8rw09VrnP65GbQxrqWdemkZt9nune12e4oyvd3lm/5Ppgwdmnrx68MCsn0qOkkK9LHgDZKlSayqrUmOA3hgPvmSFm3EC0JIVWqqrF60Zhny5uSxfDspF5PjFE8P4fvMfl++XiSc6/3HFEiO4lnrOLxNLdP+T2PnWT2bG17t+Mm1qHL/lK2aevLp8xayfSo6SQr0seKeylgpVKlRxuCf/+1TWdrtpALz8ghSlvFHIO5zvKOQdpbzx8gs01KUXvfTZHpNlmCw5fBeMqZ8sB+Uicvyy/RtxjRKNRgnnXLBtlMj2b+xYr2/oLFyjhB/W88N6fUNnzVPLe8cGW0/VqlRcFeccFVelalU22PpZP9fzL4BCHvJ5h+8HY+oL+aBcusOcU2/gsdq0aZPbunVrt5shx+CQP8aj7CZHgQGynMpals3RagDHo9dXv5Fmvb76zcN7GvzgPp+9Y7B6BF5ytscZa3qnfbI4HPLH2c4uchQZIMNprGOZ17nXXavfnLiFuPrNYmZmdznnNh3Vvgr1x06hXkR61cN7Gnz1hz4DfcFY/3wZciV464sV7GX+HPLHuYeHSJEgSYIqNSrUeC7PPGKwF5GnHEuo19spEZEF5Af3BYF+sC/4OvXBPmOgLygXmS/b2UWKBCmSGEaKJCkSbGdXt5smsmAp1IuILCB7x2ZenWfv4lsIRLoo12YVltwcrMIiIgGFehGRBWT1yMyr86zunSkksggMtFmFZWAOVmERkYBCvYjIAvKSsz1ypebVeXKloFxkvpzGOirUWlYdq3Ea67rdNJEFS98oKyJyjO7L1bj2UIWdZZ/1aY/XLktx9kDiyBXnwRlrYlx0QY1v/bwarEgxDL95QZIz1vRG+xa6xxsFfuqPcYgqy0hyvjfCybHe+E6M+bTMG+a5/jObVr/ZyKmaJCsyhxTqRUSOwX25Gv+0s8hI3FibMsZrPv+0s8ifrs/0RLB/uFrmtoFxzrjI47nmkXc+t7kiJ1eHOSOZPvIB5Lg93ihwg7+PDDGWkqBAnRv8fVzKqkUb7JehEC8yX/R5rIjIMbj2UIWRuDGc8PAs2I7EjWsPVbrdNABuKRUYMI9BLxasfuPFGDCPW0qFbjdtwfupP0aGGFmLY2ZkLU6GGD/1NUtZROaeQr2IyDHYWfYZjFtT2WDc2FnujSUj9zbq9FvzP+395rG3Ue9SixaPQ1TJ0PxdABliHKLapRaJyGKiUC8icgzWpz0m681f2jdZd6xP98Y/p6tjcfKu+Q1G3vmsjmm05VxbRpIijaayIg2WkexSi0RkMemNVyERkYh47bIUY3XHeM3Hd8F2rO547bLUnJxvL5N8n0e4kvv5Po+wl8mO+1/SlyXnfCb9RrD6jd8g53wu6Vt8Y7rn2/neCEUaFFwd5xwFV6dIg/M9rScqInNPoV5E5BicPZDgT9dnGE547K44hhPenE2S3cskt/E4JWoMkaJEjdt4vGOwPyOZ5m39wwx6Mfb7DQa9GG/r1yTZ+XByLMul3iqyxDlMjSxxLvUW5yRZEZl/+jxWROQYnT2QmJeVbu7nAH3E6Qu/mXNqez8HWM1g23pnJNMK8V1yciyrEC8iXaGeehGRHjVOmXRL30uaOOOU29QQEZHFSqFeRKRHDZOmTPOqNWXqDKNeeBERaaZQLyLSo85iBSXqlKjhcJSoUaLOWazodtNERKTHaEy9yCzb6ee5241ymApLSXGeLWG919/tZnVFubyPfGEb9foE8fgQ/dmNpNOrut2syFjNIC/iZO7nAOOUGSbN+aztOJ5eREQWJ4V6kVm0089zk9tDxsVZQpICdW5iD6/w1yy6YF8u72Ns/HY8L00sNkijUWJs/HZGhl+oYH8MVjOoEC8iIkek4Tcis+huN0rGxZu/Jt7FuduNdrtp8y5f2BYG+j7MjFisD89Lky9s63bTREREFhyFepFZdJjKjF8Tf5hKl1rUPfX6BJ7XPKHT89LU6xNdapGIiMjCpVAvMouWkprxa+KXMjffNtrL4vEhfL956UXfLxOPD3WpRSIiIguXxtSLzKLzbAk3sQdc0ENfpEHR6lxki2+1kv7sRsbGbweCHnrfL+P7ZYYGf6XLLWv2wGid63bW2VnwWZ/1uGx9nDOX6J9GERGJFvXUi8yi9V4/r7A1ZC3OqFXJWpxX2OKbJAuQTq9iZPiFxGJ9NBqTxGJ9PTdJ9oHROp/ZVmW86libMcarjs9sq/LAaP3IlUVERHqIuqNEZtl6r5/1LL4QP5N0elVPhfhW1+2sM5wyhpMGwHDyqXL11ouISJSop15EFq2dBZ/BRHPZYCIoFxERiRKFehFZtNZnPSZrzWWTtaBcREQkSvTKJSKL1mXr44xXHONVh++C7XjFcdl6Db0REZFo0SuXRMr2eonN1Un2+zVWegkuTA5yWryv282SiDpzSZz3bqRp9Zu3nZbQeHqRReDHpUNcnR9lb73B6niM1/cv4QV9y45Y7+aD2/n65CT7/BirvAZvHhzkpctP61hn++FHuW3iCfY7n5Xm8aKhkzht6alHPNfjBx7izolHOESdZcR53tDpnLzimZ0r7X4Efn4rjO2DkVVw7sWw9vQjnuuLD+e4YixHLt5goB7jLSMD/O4ZAx3r3L1vP9c8OsbuAqzNwutOHeG8VSs71jlUeJCd5XspUiJDH+vT57As++wjtq+R30Xj0M9w5VEsvYTYsucQ61/XudLkTth3N5QPQ3oprDoPBtcf8VyPb/e580dwcD8sXwnPuwhOPq33+8HNOdftNkTOpk2b3NatW7vdjEVne73EleVD9BMjax4F55OnwRvTyxTsRUTkqP24dIjPjh1kwDP6PY+875PzHX88srxjsL/54Hb+YbxAv/n0A3kg7zw+MJxtG+y3H36Ub47voN8gi0cBn7yDNw1v6BjsHz/wEN+Z/AVZZ2TwKOJTMMdrBp/VPtjvfgRuuQL6BqEvC6UClCbhkrd0DPZffDjH/8mNkfQ9Us6omKPq+bxnYKRtsL97337++efjDCV9BhJGruaYqHq879zhtsH+UOFBflG+gwRx4iSoU6NGnWeln98x2Dfyu6jt+i8sloF4H9RLuEaRxLpfax/sJ3fCYzdBPBP81IvBzymv6BjsH9/u851vQbYfMlkoFqCQh9f8ZneCvZnd5ZzbdDT79v7bDpHQ5uok/cQY8GJ4Zgx4MfqJsbk62e2miYhIhFydH2XAMwZjHp7BYMxjwDOuzo92rPf1yUn6zWfQCOoZ9JvP1yfbvw7dNvEE/QYDFr52WYx+C8o7uXPiEbLOyBLDCLZZZ9w58Uj7Sj+/NQj0mQEwL9j2DQblHVwxliPpe/Q5Dw+jz3kkfY8rxnJt61zz6BhDSZ+hlIfnGUMpj6GkzzWPjrWts7N8LwniJC2JZ0bSkiSIs7N8b8f2NQ79DItlsEQGMwu2sQyNQz9rX2nf3UGYT2TBLNjGM0F5B3f+KAj02fASZgeC/7/zRx2r9QSFeomM/X6NrDXfslnz2O/X2tQQERF5ur31Bv1e8+tJv+ext95oUyOwz489bcHi/rC8nf3OJ9sSt7J47HedV9k6RJ1MS70MHofo8D0aY/uCHvrp+rJBeQe5eIOUs6aylDNy8fbXY3cBBhLNdQYSxu5C+/MUKRGnecmxOAmKlDq2z5VHgx76pop9QXk75cNBiG+qkwnKOzi4P+ihny6TDcp7nUK9RMZKL0Gh5R/BgvNZ6SXa1BAREXm61fEYeb/59STv+6yOtw/nAKu8BvmWsnxY3s5KC4bcTFcgGFvfyTLiFFvqFfFZ1mk65MiqYMjNdKVCUN7BQD1GxZqHY1fMMVBvfz3WZiFXa66TqznWZttUADL0Uae5I65OjQydh9BaegnUW4J/vRSUt5NeGgy3aapTDMo7WL4yGHIzXbEQlPc6hXqJjAuTg+RpkPMb+M6R8xvkaXBhcrDbTRMRkQh5ff8Scr5jsuHjO5hsBGPqX9/fISQCbx4cJO88Jh1BPReMqX/zYPvXoRcNnUTeQc6Fr12uQd4F5Z08b+h0CuYo0MARbAvmeN5Qh0mv514cjKEv5sD5wbY0GZR38JaRAaqeT8l8fBwl86l6Pm8ZaT9R9nWnjjBR9Zio+Pi+Y6LiM1H1eN2pI23rrE+fQ406VVfFd46qq1Kjzvr0OR3bF1v2HFyjiKsVcc4F20aR2LLntK+06rwgxNcK4FywrReD8g6ed1Ewhr4QXsJCLvj/513UsVpP0ETZ46CJst3z3fEc/36wyJ5KgzWpGL+zPMOrhjvPzp9vj9RK3FbJPblCz4tSA5ye6NwLsZdJtrGfccoMk2YjK1mN3qwI7HY57uUQY5QZIc05LGOt9dY9LxJFWv2mmVa/eUovrX5zLBNlFeqPg0J9d9yXr/HpXQVGYh6DcWOy7hhr+PzJuixn9/fGEJxHaiW+WRql37ynVuhxPm/qW9I22O9lks3soI84aeKUqVOizoVsULBf5Ha7HD9gJ33E6SNOKbw3XsJ6BXsRkUVAq9/IgvSfhyqMxDyGEx6eGcMJj5GYx38eqnS7aU+6rZKj37zmFXrM47ZK+xUEtrE/DG0JDKOPBH3E2UYEZuXInLqXQ/QRJxPeG5nw3riXQ91umoiI9JieCfVm9nEzu9nMdppZycxGzeweM/uQmS1t2fcMM/ugmd0S7l81s/1mdq2ZveQI53m7md1pZnkzmzCzW83sNXP77GQ27Cw3GIw3z7QfjBs7y51XK5hPx7NCzzhl0i0Tn9LEGac8J22U6BijTF/LvdFHnDHdGyIi0qJnQj3wfiALfB/4NHAFUAc+DNxrZtMHQX0E+HtgJXAD8A/A7cCrgVvM7H0zncDMPglcDqwGvgB8FTgbuM7M/njWn5HMqvXpGJP15uFik3XH+nTn1Qrm0/Gs0DNMmnLLEmVl6gyTnpM2SnSMkKbUcm+UqDOie0NERFr00nehDzrnntb9ZGYfBf4a+CvgPWHxjcDHnXP3tOz7YoI3BZ8wsyudc3unPXYB8AFgO3C+c24sLP8EcBfwSTP7jnNux6w/M5kVv74sxd89nuO+ao2G54j5xhAJ/vvq3hlb/KLUAN8sjYJP05j6V6eH29bZyEo2swOgaUz9r3CECUDz7P58jf88XGFXpcG6VIxfX5rirB6Zy7BQncMyfsBOgKYx9c9ndZdbFi07GkV+XB/noKuy3JK8ID7MhljmyBWPw2F/jMfdTgoUyJLlZFvPUq/9aiAAB90429lNjiIDZDiNtSy39v9mTLlvvM61u2rsLPqsz3i8dl2Cs4d752V9rDHKTv/xJ6/Feu9kRmKdV5dZqB593Of2nzoOHIIVy+CF5xunntxL/aq974HROtc9UWdX3rGu37jspDhnLumd+70X9MwdNVOgD30r3J4xbd/LWwN9WP5D4FYgCVzQ8vAfhNuPTgX6sM4O4F+AFPDO42m7zI90ssGykQrxGNTqRjwGy0YqpJP/P3vvHSXXdR54/u4LlUMHNIBOCAQJQiTAIJEiKSpADBZFmaQkrz1eW6bt9VqeWdvyjMc7e473zAy1f+zM7toeS7bXlrQOkix5HMQogZBIkCBFIpPIOTY656qurvDqhbt/3ALR1ez3CmiCQDf4fuf0efU+1Id7X7j3fe/WFxaO+82NZpx/FW8hLXRGPYe00AODZAHayfBxVhHHJI9FHHPBBckemrb5en+RnOPREdHIOR5f7y9yaDos/PV+0inSfJpuEpjksEhghkGyl8k5t8Sz1RGmpUMrJtPS4dnqCOfcUmPly2Tcm+SQPIolqyRkAktWOSSPMu75V9gclTn2cgKLKiniWFTZywlGZS6wrYM5h68dt8hVPTrjglzV42vHLQ7mAgoTXUUm3QmOeoepYpEgQRWLo95hJt3giq3XI2d6PP5lk2S6KFnSqrb/sklypie4+FTIRQ5POPz5YZucJelIQs6S/Plhm8MTC+N+Xygshlecx2rb4BrCF7lgZcy+0g/Utpvn0HkR+I+17/zny+pdyFVjqzXNmqTGHemL7jYFT8lviiwcd4QbzXjDFJazaSezoIz42Tw/btFkaDQZah2gqRbb8Py4Fa7Wv890ijSdhEb8fNnu5EgKjZRQj7sUBuCw3cld8dX6HtlLREaIiggAUSIgoYdeWpl7tf40/UQx1Xcv6NTkbfiv1j/XZ9NkQlOkNiYjAvB4rs9eEKv1vV4PEUwiIgpAhChIJf+grda/uVuSTkhSSTVvppIAkjd3ww0rr2nXFg0vnHfIRqApqs5hUxRA8sJ5J1ytn8GCOxNCiD9AVV3OAncBH0cZ9P/1EnRXAg8CJeD1GfIk0AlMz3TJmcHJ2nZtwP/9ZeDLACtWBBeMCHl/GHJtlmr1t2xSaAy54Wrx+02f5dIRqf9hL6ML+qyF8ytJSMhcjMoqrbPK0ifQGZXVK95WkSIJ6l8UIpgUKfpoQIESqVnVNCOYFAj+JaG3pFboZ5IxBb2lhbH6O9e5MIkEnovrlZExWDKriGkioeQhl0bftFqhn0kmouQhF1kw7jcz+APUavm/RRn0m4GfkVKOBikJIaKo4Noo8NRMFxvUCwJA3kf9gtx3WURK+U0p5V1Syrva2toaH0XIFWe5PncQ6nI9XCl+v+mK6ky5s4KUXUlXdOEEKYeEzEWbiFCi/uWzhEtbbTX9SpIkSZX6RYYqNkmSPhqQJjGnTprgXxG6ExpT9qwxaUu6EwvjsZ4kiU39i5NNNfBcXK8sXQKlWe9opZKSh1waXSnB1Kz38KmqkodcZGGM/hlIKZdLKQWwHPgicAOwVwjhW9dXCKED3wXuB/4R+KOr0deQq8vGaIqC9Ch4tVLbnktBemyMpq511657Hm+NknM8co6HJ+U7nx9vjV7rroWEBHKf0VQLWHfwpGRaOhSlx31G40DUy2Wl6KYqqliyipQSS1apiiorhX8FyzV0YmFjUUUisahiYbOGzsC2nugyyZwQUrwAACAASURBVNmQq9bGZNUjZyv5QqBbW0kVm6q0kFJSlRZVbLq1D56/yf13CwolwXRRqnuwKCmUBPffHRqkl8pjKwzyVeVL70lJzpLkq0oecpEFezaklMPAM0KIt4ETwHeA9bO/VzPo/x74eVRQ7Zfku8vkXliJzzI3F+TBkUnXiFJ1mMnKCapunoiepTm2lkQkuAwzqFLMPeWDFEWFpIyxMr6hYSnm1wdP8HJliILpkbY1Hoot55Ptvl5JFxk6A8d+CvkRyC6FdZ+A5Q1KYA+ehsOvQ24YmpbBrZ+Edv9S2zdFYvzSaIGtw2cZkpLlQvDYstXc1HQJmUD6TsK+V2FiCFqWwx2fhq6bgnV6T8Lbr8D4ILS2w4cfgO4GOgDnjsOOl2B0ENra4d6HYVVwWe/R4QOczB2goFuk3Sg3Nd1G27LbGjb12thOtmrj5HWDrOuw0WvlU0vuCdQ50XOCLQNnGfQ82jWNBztWs3Zl8DVenzL5ijbG8yd76atCVwSeXNPN+lQDw6jnBOx+GcYGYUk73P0QNGgLYGvPdvY5fXiGi+bo3GF0sXHlfYE6z/Tu5XlK5A2TrGPzOAm+0H1nw7Y4fBBeeA56e6G7Gx57Am7dEKxz5BBseh76+6CzCx59HG551/RUz4ED8MwzcP48rFgBX/gC3Nb4Gp/tOcz24ROMCpc2qXPfsrWsXnlroE7f8bfZ33+ACcOhxTG4vfM2um72XRd5h/zx7Qz3vklZKxH3Eizrvp/szcHnfaR3HyfG9jKlV8m4EdYuuZOl3Xc0buvcLoaHdlAWJeIywbLl95Jd9dFgpaOHYfML0N8Lnd3wyGPwoeBzsUpP8EhfH8cqx6jGXNoqOp+IrWPVTauC29qzBfa+ANU8RLJw52Nw14OBKq1aM/apabZmKxRjJsmKzf35GK03+me/aRNN3HBsiAPRAYpJg2TR4Targ7Z1wediQ5PBY9ZbfKNHMKAl6fCK/NZyyYam4PH/vSMH+HbOYZQobVj8apPBL98SfB+eP7Oft4aOMG64tDo6H1l+CytuuD1Qp1lv4UNno/QO7qSoV0i6Mda030PzjcH+9FuPH2CTNcJUTCNT8Xg0upSNNzceJ5w+Cq9vhuF+WNYJn3wE1gQ/7zh0EJ5/FvrOQ9cKePzzsL7B2Ac4dhhe+iEM9EFHFzz8s7DO/z68YaXG/3DTcd58to+RvMHSrMNnPt/FDSuDnwsAkz176B3eRVGrkPRidC/7KM0rg4uLHjl4mBePnaHfE3Rqks+uu4FbNgSPEwBOHoFXNsFgP7R3wgOPwk23NNZ59UUY6oflnfDpzzbWOXEEtmyCwT5o74IHH4W1wTq3thj8TuQUL+zsoa+i0xVz+ZV7VnJry7rgtuYxZwDzey4sAMS77d+FhxBiL3AH0CalHJshN1EuNz8PfB94Uko5p5OvEKIP5VffMduvXghxH7ANeENK+YlG/bnrrrvknj175ns4l0WpOszQ9C50LYYuorjSwvUqLE99NNCwHyse5XBlJxEMTExsbKo43Bq7x9ewf33wBE97A0QciHiCqiapGvBFrSPYsB86Azv+CaIpiCWhUgRrGu79BX/DfvA0vPHfIZa+qFMpwMd/0d+w7z8Fr3wP4hmIJ6FchPIUPPDL0Hmjf//6TsLL34NE+qJeqQAP/bK/Yd97En78XaWTSEFpWul85leCDftzx+G5v4Vk5qJecQqe+HVfw350+AB7CruJejpRqWMJF0tzuSt9d6Bh/9rYTp6NTBH1XKJSYgmBpel8vprxNexP9JzgO31nSAtJSgimpaQgBU923RBs2J8/AZu+A8kZ56NYgEefhBU+ej0nYNPfQWLGuShNwaO/FmjYb+3Zzl5xDukKhKchNQ+hS+6Uq3wN+2d69/Id0ybqeUQ9D0vTsDSNJ20z2LA/fBD+7E+hqRkyGZiagtwk/O6/9Z/AjxyCv/o6ZJsgnYHCFORz8K+/4m/YHzgAf/LHqp1sFvJ51c7v//tAw/5sz2GeHT1G0oMEGiU8ihp8vm2dr2Hfd/xttgy/TcIVxKVGWXiUdMmDyz4caNjnj2/nbP9LmJ6BIU0cYWNrDqs7H/Y17Ed697Ert5OYqxGVBpZwqOgeH226J9Cwz5/bxdmRLZieiYGJg42t2axe+qC/YX/0MHzzz9593r/8u4EP6amj2+g5/2MMz3inLUdzWLniM2Q+NDtJWo09W2Dnd0GLgh4F1wLPgnt+JdCw33bkVf6puUq0ahOzXSqmjhUx+YXJCB+7Ze66iOP7fsQBZz+RqkfEhqoJ1YjGbcbttN7xOd+2jux8nb8cHCfrVklLh4IwyOsR/k17K7fc88k5db535AD/d04ngUMSlyI6JQz+Q5Pra9ifP7OfzWOHSLiQkBol4VHS4ZEl64MN+3lcr63HD/B9bYyoLYk6YBlgmYJf8pYEG/anj8J//yaks2qOKhagkIdf/LK/YX/oIHz9v0FTE2SyMJWHXA6+8u+CDftjh+Fv/kKN41QGpqfUeP6fftvfsD9wAP7oj6B5xvifnIQ/+IPA8T/Zs4ejo68R8QxMaWILm6rm8KG2T/ka9kcOHuYbR86QBdKaSiaRB37rlgaG/ckj8N2/UucwlYbp2jn8lX/tb6SfPALf+8a7z/sv/5a/zokj8J2/hHTTjHZy8OS/CTbsDx6Er/3Ju6/X7/0+bPC5XvOcM+b1XHgfEUK8JaUMfpOrseDcb3zoqG3fMdiFEBHgn1EG/XeAX/Ez6Gu8Uts+Mse/fXbWdxYMk5UT6FoMQ4shhMDQYuhajMnKiUC9nvJBIhhEiCAQRIgQwaCnfNBX5+XKEBEHYlJDE4KY1Ig4Sh7IsZ8qgz6eBqGpbTSl5H4cfl0Z9DN1Ymkl92P/VmXQJ2o6ibTa3781uH/7Xq0Z5zP0Emkl9+PtV9R3khmlk6y1+3aDW2THS+q7qQxomtomM0ruw8ncAaKeTgwDIQQxDKKezslccMKnrdo4Uc8lLiUaEJeSqOeyVRv31dkycJa0kGSEhoYgIzTSQrJl4Gzwce3ZoibtmecjmVZyP3a/rAz6unOYUfIA9jl9SFegSR0h1Fa6gn1On6/O85SIeh5xz1PnombcP98g2JAXnlMTd1OTul5NTWr/hef8dTY9rx4S2ZrOhc+bnvfXeeYZ9f82Nyud5ma1/8wzgd3bPnyCpAcpoaMJQUroJD0l92N//wESriCBOn8JdBKuYH9/8P003PsmpmdgEkEIgUkE0zMY7n3TV+fE2F5irkYMs3bvmsRcjRNj78o4XN/W0A5Mz8QUtbZEBNMzGR7a4a+0+YW5z/vmFwLbGjn3UwzPqGvL8AxGzgXMT3tfUAa9EVf3rhFX+3uD23rJnCJatUnYHhqChO0Rrdq8ZE756pzN7yNS9Yg6AiEEUUcQqXqcze8LbOtHvX1k3SpZXDQhyOKSdav8qNd/nHw755DAIS08NCFIC48EDt8OSIP51tAREi4ka/dTEp2Eq+SBzON6bbJGiNqSuKOMk7gDUVuyyRoJbuv1zcqwTGdVWxc+vz5Xsrsazz97cbxr2sV54Plng9t66YfKMM/UjivTpPZf+qG/ztNPqzE/c/w3Nyt5AL3Du4h4tee4qD3HPYPe4V2+Oi8eUwZ9VhfqvtAF2Zo8kFc2qXOWqZ3DTO0cvrLJX+fVF+c+76++6K+zZZMy6OvaaVLyIJ57Zu7r9VzAHDrPOWNez4UFwoIw6oUQa4UQ73KNEUJoteJTS4FtMwpGRYFngCeAvwZ+XUrZKOT/r2rb/10I8c5voUKIVcBvAxbwt+/xUK44VTePLur9lnURper6xfwqiqKCOSvjg4lJUfiXly+YHhGv3scv4gkKZoNTmx9Rq+0ziSWV3I/c8Nw6uWF/nckhtdI+k3hSyYOY8NGbCNAbH1SryzNJpJQ8iFEfvVF/vYJuEZX1AadRqVPQrcCm8rpBdNYvbVEpyev+XnWDnkdK1F/jlBAMeg2u8ZjPcY0FnI/56ACe4SK8+qlJeBqe4f/OnjdMorOOIep55I0G/sW9vWolZiaZjJL70d+nVn1mks4ouR/nz6sH/0yyWSUPYFS4JGZN0wk0RoX/uZgwHOKyXicuNSaM4HzOZa2EIevPlyFNypr/i9GUXiUq6++3qDSY0oMzy5RFCWPW/GRgUhYBL2H9vT7nPeBaARVt7rYqAcdFNa9W6GeiR5U8gFwySsyuvzYx2yWX9I89mU5AZFYCr4it5EH0GzHSsv6apqVDv+Gf3neUKMlZQcNJXEbx79+44ZKYdT8lpMZ4wHhUHbz86zUV04jOuk2jjpIHMtyvFhlmkkwruR9955VBOZNMVsmDGOhTizUzSWWU3I95jv+iVsGcNSZNaVLU/J/j/Z4gPet0pTUlD2SwX62czySVVnI/hnzO+1CAzmCfTzsB5w9qc/Uc1ytwrp7fnDGv58ICYUEY9cCjwJAQ4iUhxDeFEP9FCPE3qFSTfwgMAb854/t/VdMZA/qB/ySEeGrW38aZDUgptwF/AqwBDggh/psQ4i+APUAL8AcLsZpsRM/iynrjzpUWEd0vPECRlDHsWRkVbGyS0n/CT9saVa3eSKxqkrTd4DbJLlXuMzOpFJXcj6Zlc+s0BcQKNC9XrjMzKReVPIgWH72WAL3WduUuMpPStJIH0eaj1+avl3ajWLOMNEu4pN3gINSs62DNMtAtIci6/sZbu6YxPetFYFpK2rUG13iJz3EtCTgf89EBNEdHavUGutQ8NMc/007WsbFmHYOlaWSdBulOu7vVT6szmZpScj86u9TPuDMpTCm5HytWqJ/cZ5LPK3kAbVKnRP25KOHRJv3PRYtjUBb1OmXh0eIEh1DFvQSOqD9fjrCJe/7WZcaNYIn6+80SDhk3OLNMXCZwZs1PDjZxGWDJdnb7nPeAawXEvLnbigUcF5GscrmZiWspeQBNRYuKWX9tKqZOU9H/BT1VUi43M6maSh5Ep1OhIOqvaUEYdDr+Bl8bFkXq+1dEpw3//rU6OqVZ91NJeLQGjEfVwcu/XpmKhzXrNrUMJQ9kWady/ZhJsaDkfnStUC4cM5nKK3kQHV3K5WYm01NK7sc8x3/Si2HPGpO2sEl6/s/xTk1SmHW6Cp6SB9LeqVxhZjJdUHI/lvuc9+UBOu1dPu00qKDe3T339Qqcq+c3Z8zrubBAuGSjXgjRLIS4pbZKPlP+60KI54QQ3xdCNIhy8uVl1Ip7Gyrjzf8K/BwwAXwVuFVKOfO3vtW17RLgP6FSYM7+2zi7ESnlv0dVjR1C5Zx/EjgMPCal/PN59v19pTm2Fter4HgVpJQ4XgXXq9AcCw42XBnfQBWHai2jQpUqVRxWxv39wR6KLadqQEWobAoV4VE1lDyQdZ9QPvTlAkhPba1pJffj1k8qH/qZOpWCkvtx+0blQ1+q6ZQKav/2jcH9u+PT6rsz9UoFJffjww+o7xSnlE6x1u6HH/DXARUUW5xSk7znqW1xSsl9uKnpNizNpYKDlJIKDpbmclNTcHDYRq8VS9MpC4EHlGs+9Ru9Vl+dBztWU5CCKenhIZmSHgUpeLBjta8OoPyIi7POR7EQHDh490PKh77uHE4peQB3GF0IXeIJFynVVuiSOwz/Sf9xEliaRlnT1Lmo+dQ/3iAtII89oXwlczl1vXI5tf/YE/46jz6u/DLzNZ0Lnx993F/nC19Q/+/kpNKZnFT7X/hCYPfuW7aWogbT0q1lbnEpakrux+2dt1HSJSXU+SvhUtIlt3cG30/Luu/H1hxsVOYWmyq25rCs+35fnbVL7qSie1Swa/euTUX3WLskOEB52fJ7sTUbu5YlxpZVbM1m2fJ7/ZUeeWzu8/7IY/46wNJVn8DRnLq2HM1h6aqA+enOx5QPvVNW965TVvt3Brf1sJ3BipiUTA0PScnUsCImD9v+heVWZ++gGtGwDKky5hiSakRjdTY42Phz3V3k9Qh5dDwpyaOT1yN8rtt/nPxqk0EJg4LUVAYxqVHC4FcDilV9ZPktlHQo1u6nIi4lXckDmcf1ejS6FMsUlA3UOK751D8aDVggAhUUW8irP8+7+PmTc3nb1nj88xfHu+ddnAce/3xwWw//rDLIp2rHNZVT+w//rL/OF7+oxvzM8T85qeQBdC/7KFWt9hyXtee45tC9zN/U+uy6G8gDeVdlicm7knxNHsgDj6pzNlU7h1O1c/jAo/46n/7s3Of905/113nwUeVDX9dOTsmDeOILc1+vJwLm0HnOGfN6LiwQLjlQVgjxl8CXgKVSynJN9rvAnwIXlgsrwF2zDPDrjqsZKAth9ps6+k8pH/rJIbVCf/vG4CDZC4TZb95hPtlvABUsu2fLxUw2dz3oHyR7gTD7zUXC7Df1bV2l7DeggmVHzv2UilYi5iVYuuoT/kGyF5hH9htQwbIvmVPkklGaihYP2xnfINkLjO/7EWfz+5hOqBX61dk7AoNkL3Bk5+v8qLePfiNGp1Phc91dvkGyF7ha2W+AeV2v+Wa/Ge7dy4mxfeT1Klk3wtold7Cs0fi/StlvADX+n3764vj/4hcvafyH2W9mcPCg8qG/MFc/8QX/INkLXAfZby4nUPZyjPoDwBkp5ednyHpQBv0vofLKfwf4vpTyf77sXi8irrZRHxISEhISEjI3w+TZxSlimEQxsbCpYPNRbmSZbybrkJDFweUY9ZeTp74TeCfVhRDiFqAb+N+klG/UZD8PBC8ThISEhISEhIRcIU4wqDIvoWI5LmxPMBga9SEfKC4nUDaOcq+5wP2ARPnDX+A0NCiDFxISEhISEhJyhchTIjorw1EUk3yjlLYhIdcZl2PU9wMzS3d9BpgC9s+QNQPlK9CvkJCQkJCQkJCGZElgzcpwZGGTbRQoHxJynXE57jevAr8qhPgd1Ir948APZuWHXwMs/ESeISHvIxPeBD2yl6IskhRJVopuWrTg0ughIYuVAVngICNMUqGZGBtYSodIN9Q77xbZ7U0yhsUSotytNbNCTwbqnKpW2GoVGPIclmsGG6Npboz4p/d7L4x5k5yljwIl0iRYTRdLtObGiguccmWI6eJRbCeHaTSRSn6IeIMMZ9MT+5mY2o3lFYlqSVoyd5NqaRwoW+3fjTXwGq49hW5miHZ8ikjn3cFKY+fg7HYojEK6DVbfB0tWBaqspZ3t09up5gcwqxXsSAw728FtqeAg75Brw7ERl82nXAamJB0ZwSM36qxb2iBF6jw5mHN4rs+mt+TRndB4ostkQ0C2pwscyNs8PWRzvuyxIq7xxeUmt2Ub1D1ZAFzOSv1/AaaBrwHfRBn2T134RyFEBvg4sO0K9i8kZFEx4U1w2DuCJaskSGDJKoe9I0x4E9e6ayEhV5wBWWArPZSwaSJKCZut9DAgC4F6590iP/IGKeLQSoQiDj/yBjnvFn11TlUrfL80QcFzWSp0Cp7L90sTnKr652WfL2PeJPvlcSxZJSXjWLLKfnmcMW/yird1NSlXhpjIb8N1yxh6FtctM5HfRjmgavj0xH4Gcq/ieBYRLYHjWQzkXmV6Yr+vDiiDvtTzPJ5bRjNTeG6ZUs/zVPt3+yuNnYP9z6qUyKlWtd3/rJIH0NxzlPVHdhOxqxTjCSJ2lfVHdtPcczRQL+Tqc2zE5Vt7bPIVyfI05CuSb+2xOTbSoJjZPDiYc/jacYtc1aMzLshVPb523OJgQAVlUAb9H5+pMGl7dMUEk7bHH5+pcCDfoO7JAuCSjXop5VngVuD3gK8A66WUx2d85UbgG8C3r2gPQ0IWET2yF5Mo0VpJ+qiIYBKlR4Y/YIVcfxxkhDgGCUwEggQmcQwOElBNGtjtTZLEICkMhBAkhUESg90BRvNWq0BaaKQ1HU0I0ppOWmhstYJfIObDWfqIYtaN4ygmZ2lQ9XKBM108ii5i6HocIQS6HkcXMaaL/sbvxNRuDEwMLYpAYGhRDEwmpgKMc8AaeA2hRdD0OAJNbbUI1sBr/kpnt0M0CdEUCE1to0klD2qrdyttZZd7+3I8dGqIe/tytJVdrN6tgXohV5/Np1wyMUE2JtCE2mZigs2nrrxR/1yfTZMJTRENTQiaIhpNppIH8fSQTZMpaDaVXrOp0WQKnh5a+Eb95bjfIKUcAuYs0iSlfBt4+0p0KiRksVKURRKz/DgjmBSl/wpkSMhiZZIKTdRXPo5jMEnw6vkYFq3UV51NoDMWUNl0yHNYKup/ok8KjSEveNVtPhQokSJeJ4tgUljkgZe2k8OYVY1c02LYTs5Xx/KKRLT6OU3XIlhe8Jzm2lNoZqpOJvQorj3lo4FyuUnNKp4XSSh5YFsFNLPedUvoEVz7yr/whbw3BqbUCv1M0lElv9L0ltQK/UwypqC3FFyh+HxZrdDPJGsIzpcbVDZeAFxORdkzQoivNPjObwshzrz3boWELE6SIkl1VsBWFZukCPYVDglZjDQTo0y9UV3GoZlgP/clRClRvzJXwmXJrBeEmSzXDIqy/qFalB7Ltctam7ok0iTmHMfpRR54aRpNeF79C5fnVTCNJl+dqJbE9ap1MterEtWC5zTdzCDd+pc06Vropn91XdJtUJ314lQtKXlgW2mkW99H6VbRzcaxHSFXl46MoDDr3b1gKfmVpjuhMWXXvyxM2ZLuRLDpuyKukXfq9fKOZEX8cjzWrw2X08NVgP/IVzQBK+fdm5CQRc5K0Y2NhVUrSW/JKjYWK0X3te5aSMgVZwNLKeNQwkYiKWFTxmEDSwP17taaKeJQlA5SSorSoYjD3QGBqBujaQrSo+C5eFJS8FwK0mNj9MobbqvpwsKuG8cWNqvpuuJtXU1SyQ/hygquW0ZKieuWcWWFVECl8ZbM3TjYOJ6FROJ4Fg42LZnggNdox6eQXhXPLSPx1NarEu34lL/S6vvAKipfeumprVVU8qC2ujfW2qrU2qqotro3BuqFXH0euVFnqiLJVySeVNupiuSRG698oOwTXSY5G3JVD09KclWPnK3kQXxxuUnOlkzaSm/S9sjZki8uX/iBspdTUdYDnpJS/h8B3/mvwO9JKeN+37keCCvKhgSx0LPfHCtbbJ4uM2A7dJgGj6TirIv7r5CGhAQx3+w3p0vn2GmdYxyHVgzuia5iTWJVoM7x6fOcLh7C9abQtQxrkuu5ObWiYVvHnF4OydNUKBIjyXqxhnVG8Iv22NQRzpYPURAV0jLG6vh6lmQuoZT96Fk4vR0KI5BeCmvug7bVgSqnh07y04mzDEuPZULjEy2rWbP8poZNFUf3MTGx42JWmpZ7SbbdEahzPWa/Aaj27MDq3YprF9DNNNHujURW3huoc84tscPNMSqrtIkI9+pNrNIX1q8xJ6wKW8pFBh2HdsPgwXiStdH3J+PT1eLF/jJ/f7TK0LRkeUrwpQ9F+Gzn+2M2Xg/Zby6nomygUS+EmDlbngP+tPY3Gx1YAfx/QFVKeesl93YREhr1IYuVY2WLb00UyOoaaU1Q8CR51+M3W9KhYR9y1aiWBiiNvIbQ4gg9hnQrSK9MYumniCQ65tQpW8OMFrajazE0EcOTFVyvQlv6PuLRZb5tHXN62S33o0sTExMbG1fY3C1u9zfsc+fh1ItgJsFMgF0Cuwg3fhaaAl4iRs/C3mdUgGckoVxHrGm48wu+hv3poZP808RpUlLFCBSlx7SAX2hZE2jYF0f3MTD+MgYGuojgyioODh2tDzU07EOUQf+cPUxS6CTQKeFSlC5PmMsWjGF/wqrwnUKetCZICY1p6VHwJE+ms4vWsD9uWfxdPk9GCFKaxrTnMSUlv5bNcnM0fAbNxeUY9Y3cb84BZ2t/oDLfnJ3j7xTwCipP/bcuv8shISFXg83TZbK6RlZXUf0XPm+eDmvGhVw9rNxBhBZHM1QWFs2II7Q4Vu6gr06+fAxdi6FrtcwtWhxdi5EvHwts65A8jS5NIrVMNhERQZcmh+Rpf6XBt5RBH0mCEGprJpU8iNPbaxlbZmZvSSm5Dz+dOEtKQlroaAjSQicllTyIiYkdGBgYWgwhNAwthoHBxMSO4D6GALDDzZEUOilhoAlBShgkhc4O1z9o+GqzpVwkrQkytYxPGU0nrQm2lBdv4oWXi0UyQpDRa8ek62SE4OXi4j2mhUSj3yC+A0hAAE8CB4B9c3zPBcaBLVLKn1zRHoaEhFwxBmyHdqPedzGtCQbsK59BJCTED7c6iWbWh2gJPYZb9U9paTt5DL0+yFITMWwnH9hWhSLRWZlsTEwqBBgRpTGIz8rCYiaUPIjCCKSW1MsiCSX3YVh6tIn69bWk0BiWwZk2VFaa+uPSReOsNCGKUVmllXp3igQ6o7Lqo3H1GXQclun183VKaAw6i3e+HnAcls8+Jk1jYBEf00Ii0KiXUv7ahc9CiCeBZ4J86kNCQhY2HabBlOuR1S9mGih4kg7zymcQCQnxQ4804zllhHHRKJVuBT3iHyhrGllcr4wuLup4soJpZH11AGIkqWIRmZFC08YmRkD2lsQSqBbVCv07SiUlDyK9VLnbRGekcqyWlNyHZUKjID3SM9J1Fmu+9UFEtSSOrGCIi24YrmyclSZE0SYiTEuH1AwzqIRLm4gEaF1d2g2DKc8lM+PemJYe7cbina87DIMp1yUzw7Cf9jw6FvExLSQup/iUFhr0ISGLm0dScfKuR95VUf0XPj+Suq5j20MWGNGmDUivjOeoLCyeU0Z6ZaJNG3x1svF1uF4F16tlbvHKuF6FbHxdYFvrxRpcYVOtZbKpyiqusFkv1vgrtX9E+dBXiyCl2tpFJQ9izX21jC0zs7dMK7kPn2hZzbSAgnTxkBSky7RQ8iBaWu7FwcHxKkjp4XgVHBxaWoKDQ0MU9+pNFKXLtHTwpGRaOhSly716oyR/V48H40kKnmSqlvFpynMpeJIH44v3xe2hZJIpKZlya8fkukxJyUPJxXtMC4lLzn4TcpEwUPbdlK1h8uVj2E4e08iSja8LDF4DeM0+wxuijzIOcQw+Lrv4Z2cqOAAAIABJREFUlHlDoE6vN81bcoJxadEqonxEtNCtpQJ1ANzpfpzxfUhrEhFtxmi9Az3VGayUOw99u6E0DolW6Lo7OEjuAhM90LsLiqOQbIPuj0JLg0yvo2fh1DaYGoHMUrjxYw0zZsyXxZD95rxXfNd1XhGuQF5XVEsDWLmDuNVJ9Egz0aYNvkGyF5jPPAPzy35D7rzyoS+NqRX69o9c2vhf4NlvFjoFZ5TR6ikqXoGYlqYtciNpIzhPPcCozHGKfgqUSRPnRjppE8EGepj95tpw3LJ4uVhkwHHoMAweSibDINkArkj2GyHE36D86f9QSjlc278UpJTyNy7xu4uS0KivZz5ZKV6zz/ATcQ4dgYmGjYeL5GfkKl/DvtebZrM3QALjnWwFJRwe0ToCDXt3up9q/xb1U78eB7eMdMpEOh/0N+xz5+H4j96d/eLmzwU/2Cd64MgPVWnzC3pWEW75WX/DfvQsvPV0LaguAVYtY8ZHvvi+GfYLmfNe0fc6h4Z9SMj1S8EZ5XzlLQyiGCKKIy0cLFbEPhJo2I/KHG9xkigmUUxVYwCbj3BTQ8M+JGShczlGfZAT06+hjPr/Cxiu7V8KEriujfqQemZmpQDe8XnNl4/5GvVviD50BFGUX10UHQuXN0Qfn2Juo/4tOUECg6RQt20SA6SSd+Nv1Dvj+xBGHGHUVmBqW2d8n79R37f7YvYLuLjt2x1s1PfuUgb9bL3eXf5G/altyqCP1Y7hwvbUtg+kUR90nVcE+UGHhIQsakarpzCIYmpqJdoUMfCUPMioP0U/UUxitbiJC9tT9NPWsGZmSMj1Q5BRf8Ga6J+1HxJSx3yyUpRx3jHoL2Civavk/EzGpUUL9UFMCXTGpeWjoZDWJERmTex6XMn9KI1DfFbBKDOh5EEUR98dTGcmlNyPqRFIz9KJJpT8A8h8r3NISMjipuIViIr6BRpDRKl4hUC9AmVSszIcRTEpEKbqDflg4WvUSyl7gvZDQi4wn6wUcQws3DrD3sYjHvCe2SqiFKWjVm5rlHBpFcG+eCLajHRK76zQA+CWEVH/TBskWn2yX7T664DyoZ9LLxngE5pZCpXpiyv0oFxwMv4ZM65n5nudQ0JCFjcxLY3tVdQKfQ1HWsS04ArFaeJUsN9ZoQewsEkTJgAI+WAR5hAKec9k4+sYLajiKjN96luSd/rqfFx28RNxDgu3zqf+47LLV+cjooXNcgAkdb7WnxTBxq/RegfV/i1qZ4ZPvbnsY/5KXXcrn3qo96m/YWNgW3R/VPnUz9SzirDm0/46N35M+dRDvU/9+p8JbmueDMgCBxlhkgrNxNjAUjpE8EPzajLf6zxfjlcsXiqW3glEeziZ4OZY+ALxQSfvjjHonKUsp4mLFO3GarJ6g5SWIe+JtsiNnK+8BR51PvUdkfWBejfSyVucBKjzqV/Pqveln2VriKnSxYDtTGId8ejyQJ1Dkw7P99n0liTdCcHjXSbrmxeWCXbCsni5NM2g49Ju6DyUSLG2QQBrvyxwgLF3nie3sYTOBfQ8+aBx2dlvhBA6cDPQDLP8J2pIKV9/711buISBsu8mzH4zgwWc/WZAFthKD3EM4hiUcSjjsJGVC8qwv1rZb45XLP42nycjtBklyz1+PZsNDfsPMHl3jNPV/ZgiikEEhyq2tFgTuT007N9nrmb2m/lQtoYYy+9A16JoWgzPq+B6Fkuy9/oa9ocmHb5+3KLJFGRMmLIhZ0u+cnN0wRj2JyyLv8vnyGgz5kLP49eyTb6Gfb8s8Cq973qefJru0LC/glyR7Dc+//F/BP4dEFjtQ0o5p7F/vRAa9SGLlR/L05SwScyopHhh/zNBebuvU/58fPJdhVAu7P9Oa4B7Vsh1zTFrN7a0MGe4fF3YXxe9+xr2LORaMzy5Fdcto+sXXXsu7C9r3jinzv95sEyuKmmKXCz6d2H/DzcsDBeh/3dynCnXm2Mu1Phfmud2O31RnvV9nnxWhGGYV4orlf1m9n/6H4CvAnngu0AvBEQ1hoSELDgmqdBE/apLHINJKteoR9eWOcuwa4u7DHvIe6csp99VcdYgQllOX6MehSwU5kwMoQUnhugtSTpn2e4ZU8kXCoOO6zMXur464fNk4XE5v/v8JioTzoellAGpPEJCQhYqzcTetbJSxqGZxV3MZL60+5QsX8xl2EPeO3GRUivzMwwWhypx0djVL+T6xjSy71qp97zgxBDdCVFbmb8om7KVfKHQbujvWqlXc6G/40X4PFl4aJfx3W7g2dCgDwlZvGxgKWUcSthIJCVsyjhs4IOZaefhZIIp6c0qWe7xcHJhVZUMubq0G6uxpYUtLaSU73xuN0KXgg86mcQ6XM/CdctIKXHdMq5nkUms89V5vMskZ0tyVYkn1TZnSx7vMn11rjYPJVJMebPmQs/joYT/i+xtLJnzeXIbYdzJteJylqOGL/P7ISEhC4wOkWajXFmX/eYeOhdUkOzV5OZYlF8nW5f95ueS6TBI9gNOVl/CmsjtddlvVpjrwiDZ64xxb5JzspdpSqRIsEp006oFx9LEo8tpNlZTyO+k6kxjGimas/cEZr9Z32zwleUDPH9ihN6KoDsmeXLtUtY3N068MD5xiLOF/UyLCikZY3X6dlpbgrMBnRs9wY78aUZxaUPn3uwaVrWtDdRZG43yUKGJH+yqMj4uaG2V/NxHI6xd6j8Xdoo0n5bdddlv7qU9DJK9hlxyoKwQ4v8BvgDcKuUHuwpMGCgbEhISEhKyeBn3JjkojxKRESKYVLGpiiobxIcCDXu30IfT+7KqUG7EwSkjnRJG90PoaZ+UzOPn4NALqn5JJAHVkqpnsv4xaF3l38eJQxwo7iQidSLSpCpsqsLltuQ9vob9udETPJc/QVIKEkKjJD2KQvJEdm2gYX/6vMc/b/ZIJSCZgGIJpkvw849orFlxOU4dIVeaywmUvZwr9Z+BQeBfhAjDmkNCQkJCQkIWJ+dkLxEZISoiCCGIiggRGeGc7A3U88b2IYwEwkwghFBbI4E3ts9fqWenMuijSRBCbSNJJQ/gbGE/EakTpdZHIkSkztnCfl+dHfnTJKUgJXQ01DYpBTvypwPbeuMtSSoB6aRAE4J0UpBKKHnI4uFy3GkOASbQATwqhMgDuTm+J6WUH7zceCEhISEhISGLgmlKJGdVnI1gMk0pUE9WJmB2NXIjruR+FMfeXY08klDyoD6KCkkZgxnxtBFpMi38s8uM4tIq6oNbE0JjFP8sNgDD45K2lnpZMqHkIYuHy1mp11ApLM/X/vKoW232X/g7TUhISEhISMiCJUWCKnadrIpNiuAgeRFrAadcL3TKSu5HcolyualrrKTkQX2UMapiVh+FTUr6Z5dpQ6ckvTpZSXq0zV0r9B2WtQqKs7pYLCl5yOLhkg1wKeUqKeXqS/l7PzscEhISEhISEvJeWCW6qYoqlqwipcSSVaqiyirRHainLbkD6ZSQdgkppdo6JbQld/grrbxH+dBbRZBSbatFJQ9gdfp2qsLFotZHqlSFy+r07b4692bXUBSSaeniobZFIbk3G+xA8fGPCKZLUCiqDD2FomS6pOQhi4cwm01IiA9nnRJvOlOMyCpLRYT7jQyrjTDVYUhISMhip1VrZoP3Ic5xMfvNzWJNw+w3eroLuh/CG9uHrEwgYi3o7R/zD5IFFQy7/jHlQ18cUyv0ax8IDJIFaG1Zz21Ql/1mXequwOw3q9rWco9ms9kZZUIXtLiSR4w2VrUGZ79Zs0Kje43Lcz/xKBYgmYYnfqZxkOyhSYcXem16S5LuhOCxbpP1zaFpea245Ow3IRcJs99c/5x1SvzAHiWFQQKNEh7TOPyc2RYa9iEhISEhC5Jeb5rN3gAJDBLolHAp4fCI1kG35p9z/qUDNn/9Dx6xhCSeFJSLkkpJ8Bv/o8bDt82dT//QpMOfHbVoiggypiqolatKfvdD0dCwv4JcTvabSz7rQognL/W7UsrvXOp3Q0IWIm86U6QwSNUCjlLoIJU8NOpDQkJCQhYib8kJEhgkhTLvkhgglbwbf6P+2VdcYglIppW7jdpKnn3F9TXqX+i1aYoImiJK50LF3Bd67dCov0Zczln/O6DRsr6ofSc06kMWNSOyyhLqJ7IEGiOyeo16FBISEhISEsy4tGghUidLoDPeoLzQ5ChkZwXFxpOCyVF/s6+3JOmsTyBExlTykGvD5Rj1v+4jbwLuBn4R+AHwo/faqZCQa81SEWFaumqFvkYJj6UiEqAVEhISEhJy7WgVUYrSUSv0NUq4tIrgKtnNbVAsyHdW6gHKRUlzm79Od0KQq8p3VuhBueB0J8Lg2mvFJRv1UspvB/27EOJvUQb9199rp0LqKVnD5MvHsd08pp4lG7+ZRHRZQz1vqheG34bKBMRaYNmH0TLBkf0T+cOcLx+kKEokZYIV8Q20ZG9t3MnJHujbDaUxSCyBrruheWWwztAZOPZTyI9Adims+wQsvyFYZ+AUHHwNJoeheRls+BR03Ni4f8Nn4PgbkB+G7DK4+eOwzL+t+40MP8idgbEBEtN5Sqks00s6+ExTg/4B9J2Et1+BiSFoWQ4ffgC6bmqss+/Vizp3fLqxDkBvra3xQWhtV211B+ud3r2bvvMHkFoF4cXoWnEba+6+u2FT+f63GRrfSZkScRIsb72HbOeHA3VyvXvoH9lNSSuT8OJ0Lr2bpu5LcA3sOQ67tsDoALR1wEcfhJU3B+ucOgqvbYbhfljWCZ96BG78UMOmdu59gx+eLzFYTdIeKfKzKxLcc+fHA3X2v3mU114tMTEZp6W5zKc+neD2+4Pb2tezhX7vOJrh4DkGndrN3LHywYb94+QReGUTDPZDeyc88CjcdEuwztljsO0lGBmApR3wsYdh9brGbZ04Als2wWAftHfBg4/C2uC2Kqe2UezZiuMVMLQ0yZUbid34scZtnT4GP90MwwOwrAM+8QisadDHM8fgzZ/ASD8s7YT7fwZuaHxco8d3cWpgDwWjStqJcGPHXbTd/NFAneLJ7Uz2vo4li0RFkubuT5K86b6GbZ09eYBt/ccZ0VyWejof67yZ1TfdFqgzeXoX/QM7KOplkm6czo57aV4T3D+AbXuP8uzhIsOVGMtiFT5/a5KP3Rl8HxbO7mJs8E0sSkRJsKT9ftKrg9s6cWw7r0z2MmiYtDs2DzR3s3Zd43Mxun8Tp6cPUogL0mXJmtQG2m5/NFBn+NCPOZHfTz6ukS17rM3ezrL1n2nYFtt+DFv+EQpjkF4CD/4r+FgDvXPHYcdLMDoIbe1w78OwqsE8Awyf3cvx0b3k9SpZN8LNbXeybPWdgTrH9pxk8/Z+BooaHUmPR+7rZN1dlzDHnzoKW1+EoX5Y3gkbPxs4r31EtLC5cAwG+0jkc5SyTZTau/hkJnicfP4Bnb/+dgVGJoi7U5T1DJVIC7/8hH/6zMe6Tb5/4BgrivtoNvJMOlkK3MFjDcbx9IEXGM/voRL1iFkardm7SN32WPB5AEpHfkRufAdVwybimDS13kvils8F6pw/8PeMe6cRukS6glZtDStu+1LDtraNb+Og2Y9tSExHsMHu5GOtlzCvXWP0p5566or8R0899dTZr371q3cBjzz11FOBLwCLnW9+85tPffnLX74qbZWsYUYLquqcriXxpMW01UNEb8I0/P3jvKleOPcTtRNJq7y6E8eQ8TZENDunzkT+MEcqOwFJjAg2NkNuLyk3Qjy21L+Tkz1wfJP6HM2CXYKRI5BcCvGmuXWGzsCOf1LOWskmqExDz15o6oCUT/aBgVPw2j+oz6mazqm3oLUT0gE5gofPwM5/Vp+TTWBNQ8++wLaaB3to37WZ4USS0UyW5lKRzxx+m9WZdsgEtNV3En7yXfU53QzlAhzbDW1dkGn113n5ezN0puH4HljS6a8DyqD/ca2tTAuUZrSVnVvv9O7d9PbVqhjKKAibqUIv7rSgpbPTt6l8/9ucmXgVkESI4VBlvHKGhBMjlmmfUyfXu4cTY68BkqiMYgub0fJZkk6EWLbD/7h6jsMPa1NItnZch3fBsi5o8snrfOoo/MM31eemVpiegre2QedKaPFfatq59w2+cVIHJC1GhYJrsm0swvJqD13tK+bU2f/mUZ7+F0BI0qkq5YrBwf06Lekxlq+Yu619PVsYEodAgOfqaJpHUQwznSuyPOhF8eQR+O5fqc8trVCYgt1vQtdKaPU5rrPH4Om/AYQ6f8UC7N8B7d3QHJAX+8QR+M5fKr2WJbW23oCuVb5tVU5tI3/uBSQSXYvjSYtK7iiGl8BoCVhAOH0M/ulb6vOF67V3O3SsVG3PxZlj8IO/Vp+zrVCcqh3XisDjGj2+i7dGt4GUJDwTSzj0Vs6TLWskl8x9zxdPbmew90WQEpM4LhZTUyeIOjEirf7HdfbkAZ4ePAJSkvU0isJjf3GU9qpHc+vcCzGTp3dxfFiNrZinxslI6QzJaoR4i/+Y3Lb3KH+5RwKS5kiVacdgW5/Ocm2M7va5r1fh7C76hn6ClBKzNo7zxZPEnBjR5rnbOnFsO98tDAMeLZ5DQdPYbZfpKkzSusT/XIzu38ReeRikJF6VVA1Bnz5GZjhPcvncxuzwoR+zyz4EUpKsgmVAjxijabxAamnA4s22H8MzfwaehHgGKkU4/CakWqHbR+/ccXjub9XnC+Pk4E5Y3u0/z6AM+p2TO1Ufa/dTj9VHU0Un1Tz3XHhsz0m+9dIQIGmNSqZs2H6yxMp4lSUdAXP8qaPwvW8ox+bm2jjZUxv/PvNatucsS7f8kLFMkonmLNlCkU9uf4vubMD8CawZO0J21zOcEd3k3BYyeoFf8p7m4bvTsHTuezc9tIuuwouUhcmol6FZK/JJsZc1kRhm89zZgKYPvECftQuQmLbAMSQ52U9svEhkmf8LVenIjxiZ+ikSieEauJrLtN1DZMrCbJs7s8/5A3/PhHZK7XgCNElFm6A61Ed2mf+L9rbxbbyd6EUKMDxwdRiM5HGmpuhOBC+Mvh989atfHXzqqae+eSnfvdKFok4AlxShG3Jp5MvH0bUYuhZDCPHO53z5eLDi8NtgJMBMqLLUZkLtD7/tq3K+fJCINIiICEJoRESEiDQ4Xz4Y3FbfbvX/R2olsCNJtd+321/n2E8hmoJ4GoSmttGUkvtx8DX1vZk68bSSB3H8DYilIFbTi6XV/vE3Atta7cCXhsf5/dM9fGl4nNUOjdt6+xVIpCGRUW0lMmr/7Vf8dfa9WtOp9e/C532vXlpbyVpbycZt9Z0/ANJAyAgCgZARkIaSBzA0vhNTGphEEIBJBFMaDI37lznvH9mNKQ0itRLnkZpO/0jAfQFqhT6ZqT+uZEbJ/XhtM6Sz6k/TLn5+bXNgUz88XyKtV8gYDpoQZAyHtF7hh+f9q0q+9mqJeKJKIuEiNEEi4RJPVHntVX+dfu84ntSQ0kAItfWkRr/XYBy/skkdR6Z2XJnacb2yyV9n20uQykIqo3RSGbW/7aXgtrZsgnTTrLaalNyHYs9WBGZtftLUFpNiz9bgtn66WfVr5vVKZZTcjzd/cvFYNO3iMb75k8CmTg3sIepoxDARQhDDJOponBrwz2A22fs6hjQwRBQhwBBRDGkw2ft6YFvb+o+T8lRgvSYEKXRSnpL70T+wg4in142TiKfTP7AjsK1nDxdJGVXSposQgrTpkjKqPHu46KszNvgmumdgiihCCEwRRfcMxgbf9NV5ZbKXtGeTkRINQUZK0p7NK5O9gf07PX2QqOURdUAgiDoQtTxOT/s/T07k9xOrusRcTV0rVyNWdTmR3x/YFlv+UT1z4il1b8RTan/LP/rr7HhJzSszx0kyo+QBHB/dq/o1436KuRrHR/f66mze3k8m4pKNCDRNkI0IMhGXzdv7g49r64sXx/yFcZLJKrkfO1+m2xJ8/vQkv/F2L58/PUm3JWDny8Ft/fA5Hk6e5y86n+b7q/6Gv+h8moeT5+GHz/mqFPpfpxWPe8QUn9MGuEdM0YpHod9/nIzn92A4YNSuseFqGI6SB5Eb34HmCgxpKD1poLmC3Lj/OBn3TiM9QGrKNpEa0lPyIA6a/QhPYHgCQW3rCQ6aDa7XAuBKG/W30DiYNuQysN082ixfOE1Esd18sGJlAoxZESxGXMl9KIoS5iyPLBODoggum01pTE2gdYoJJfcjPwKxZL0sllRyPyaH59aZHA7uX34YorP0okkl921rCOKzdOJJJQ9iYkg9UOr0UkoeqDNHW0E6oFxuErPaSqSU3AepVUDOymQgTSUPoEwJY1bwlUGEckBJ9ZJWxpzVlilNSlrZR6PG6MDcxzU64K8z3K8SK88kmVbyAAarSVJ6fcXGlG4zWE36aMDEZJx4zKmTxWMOE5NxHw2Uy41XP916noZmOD4aFzrYD6lZx5VKK7kfIz7nbyTg/IFyuZmzrT5fFccroM2KM9FEBMcrBLc1POBzvQL6ONKvXlpnkkgreQAFo0pU1s9rUWlQMPyD3i1ZRKd+3tWJYkl/gxlgRHNJyPrrnJAaI5rrq1PU5x4nRT14nAxXYqRm3T8pw2G44u8uYfmMYytgHA8aJqlZFUpT0mPQmDsjygUKcUHEqTcHIo6kEPf3t87HNaJO/b9HHUE+3sBUKYxBdNYzKJpQcj9GfebPUf/5EyCvz30/5XX/+2mgqJGe5eycNpQ8kCGfeW0o4J4f8xn/Yw3Gf18vZDL1skxGyX2wvek5x7/tTfvqVKIe+qxrrDuCStTz0VBUDRvd+//Zu/Mouc7zvvPf5966tfa+YyNIgiQoihRJCSQkUhIlyqYlhpZkR7Ysb0qcxMeTZBx5HJ+Zk3ikcU4cZyZOYjvxxOPkTGJHGdmKFZsSbdmyKGsjJVKQSYoUdxIEsTW2Xmu/yzt/3AbY1UJVAWB3Vzf69zkH51Y9uG+/b3VXdT/11vs+t/WquH7i08yEbVqA+S6doV8usTTeQZhx+CuG4ydpfKN73Um9mXlmttvM/jnwPqDDVKtcrMAfJFmxaz1xDQL//EtozmlzKWs6XMq65IqEtP6RCIkouS4lHItj6ZKblobVNN7O4ET6Eely9Uoab2d48vxthrvsLxicTK/gt1yjksbb9jUFtRVtapU03snIVLp8pqVdOY13bHOevjq1gXQNfXVFX9VyGm/DkjysuOw4FqbxDgoUiWj9oxXRpNDhkurFpEC4oq/QQopJ++QXSNfQn+9xjXdYsjO5I/34fLnKYhrvYFu2QjluTU7KccC2bPvkbWS4Rq3e+he6Vs8wMtw+CUuiDJ7X+lfC8xKSqMu2pm07oLzicZUX03g7E22+fxMdvn+QrqE/b1/tL6qT8fpJVlSESlyTjNffpsWSye1tfl4dxjixI12KtVx1MY130B9laVjr77WGRfRH7Te956xETOvv3ZgGOWv/Zg9gIvGpWuvPuWoJEyuSkeVK8flfJ6W48+tkMl+nvOL5U44yTObbv0HPtXkd5zq8jrdFIWVrTRXK5rEtap9MAfTX0iU3yzUz6dr6dgZrCY0ViVMj4xisdU746B+Dxoq/QY1qGm9nvM3vz/H2vz8BBuPzP58G4/bPp+2lhMUV798XozTe0VSb32tTHZ7zY21e/2NdXv87d8HCQmtsYSGNtxF4fed9/Qcd6uHnGx7xip9xnHHkG53T0WwUEK94cxx7Mdmo/ZtLF6dLblp46dr6ToLIiFcMJ/bS+EZ3wUm9mSVmFq/8B4TAy8A/AWaAX1qjsW5Jg4W9xEmdOKnjnDt3e7DQZTPP5JshqqbJtXPpMaqm8TauKNxE0yKarolzCU3XpGkRVxRu6tzXztvSr99cugR2s5Le39lh8+X170jXttcWwSXpsVFO4+3cdFd63vI2tcU03snet6fr7+tL7eqL6f29HTZDnu2rutSmeoF9vfnu9NzqwlK7hfT+m+9u3+aWdy+1WdZXdTGNX0hflaW+Kt372nnFm8AinDVxOJw1waI03sHU6H5Ciwhp4oCQJqFFTI22v8z5jonbCC2iuXSJ8+ZSmx0TXTbl3v6e9LEsf1yVhTTezl3vhcX59F+SvHb7rvd27Oq+K4osxnkWogyJcyxEGRbjPPdd0T7JuevdRWrVLNWqj0sc1apPrZrlrne3b7PD24tnCWYRzqVHzxJ2eF1ex3ffmz6OhaXHtbD0uO7usNnwju+H8ny6/jZJ0mN5Po138p57YXFuRV9zabyN0u534QiXfj8l6ZGQ0u53de7rHe9Nx7X851VeSOPt3HnPa48lSV57jHfe07Gra7bvo5FJqBPinKNOSCOTcM329itFh3e9k8giItfAOYhcg8gihne9s2Nfd+zYS9mDMjGJc5SJKXtpvJ0d299K04tbXidNL2bH9rd27OuDbyxRjrIshj7OORZDn3KU5YNvbP/GY2zbncReROgaOOcIXYPYixjbdmfbNncP72LRC1gwI8GxYMaiF3D3cOe1xXv6bqKR82hkwOFoZKCR89jT1/7vyXWDN1PP+tT9JP1Z+Qn1rM91gzd37Iv3fDj9m1Mrp8+NWjm9/54Pt2/z1u9Pf68sf51UFtJ4B3vHb03Htez5VPcT9o633yj73rftYKHpM990JIljvulYaPq8922d35Dyrve99po/+zpZmE/j7ez/vvP//tz/fZ37uu8DMD8Hc3NpX3Nz6f37PtC2Sf+OdxK7kNjVcSTErk7sQvp3tH+djA7uI8pAtPQzjvyEKJPGOxkafSuJ74gsSttZROI7hkbbv05GvT2YB1iS5iaWYF4a7+SmcAfOc0Sew7F09Bw3hV1+XhvABW+U/ZVf+ZV3A4fO8+8g8Bjwe8Dfc84dWpORbiDruVE2yPSR9YdoxvNEyQIZv4+R0s1dq99YbhBXGIfaaWjMQm4Adr69Y/WbQn6CvjhLOTpD1WoUyHNN4c3dq98UhtJNsZVTUDsD+SG4+l2dq9/0DacbVeenYeEU9I3ALfd2rn7TP5L0SVLdAAAgAElEQVRuip05DnMn0/u339e9+s25vk7Awsm0r5vf17H6DQNLfc0eh7kT6YbV/ffBji59DYymG1VPH0uXBQ2Mwts/2LmSzcBouin2zLI2d7y/e/WbwaW+zhxLl+oMjsE7Ptix+s3Ijh3EZWNx/jR4dczl2LXrLV2r3+QHtlGM8tRq09SpkiPPrtG3d6x+kx/cTinKUq2coObVybs8V43f0b36zdBYuin21DE4fTy9/+4f6lz9ZmQ83RR7/Ei6zGRkHO77cNfqNzu3XcFU8xCHFhtMhyXGgjo/fnWmY/WbqSvGGek/zbEjDWbnCgz0N3nf3wg6Vr+ZGrqa8lyFhWQGPxPj4gzb7Ybu1W9Gx9NNcccOw/QxGBuHD36kc/Wb4bF0U+yJo+lSguExuOdD3avfjI6nm2KPHU4/3h+bgB/68Y7VbzIju8gkRaL5I8RJmYxXov/KH+he/WZkLN0UO30ETh5Pf173frhz9ZvhsXRT7Imj6XNjeBx+4Ee6Vr8pje1gsOaxsDhNOdOklATcOLW/Y/Wb7OguclGe5sJRmlTIWoHxXd/XtfrN8Ogk25oJJxZnOOUnDDufe7a/oWP1m8LIDkrNLNXF41T9OoUkz1Xb3tG1+s2ubeNMeac5eKbByUaekVyTn7ol27H6TW54B/koT6N8jKbVyFqBqW3v7lj9ZnRsFzsXZzlWm2c6k2Usjvng4Pau1W9KU9cycGKehfpJygWPYhNuyN3YsfpN38Q1DJ1ZZL52goWCR1/T8abim7pXv9l1Tbop9vALUD6TFkS492c6V78ZGks3xZ48Cqen0+fX9/3NrtVv+oa3MVT3ma+cZMFv0pcE3Dy6r2P1m7Hto+wuNDlyfJHjVY/xguPDd23vXv1mZOn1f/xwujRtdBze/2Odf68NjcHk0uM6s/T78+4f7l7VZ2ISrrwKXj0ER4+k93/yo/DG9m/CguGdLHg+x7KznOlLKJeK9E/cyeju9m8Ss5N7yZ+p0Kgdp5Fz5EKPqdJtXavfBOPXkV1o0KweI8xEBHHA6PCdHavfDE6+ieb0EWrJ7LmlOKNc07X6za7iLqKFBU7bIlEGgti4pbGzZ9VvLmajrDm38dcIbTT79u1zBw503tQhIiIicrk6wTyP8HK6+ZwMDSLqhOznaibpskRYLpiZfds5d0FFaFZ7o6yIiIiIXOaeYzqt/kOAYeduP0eXIg+yZpTUi4iIiMhFmadGbkXFvBwZ5ulS4UzWjJJ6EREREbkogxRorKiY1yBikC4VzmTNKKkXERERkYuyl6m0+g8hDnfu9l66lGOWNdOlSLKIrIfDSZnH3BnO0GCUHLfaKLs61PoVkQt3KK7ySDLLKddk3LLs94bZ7Xe5/oaIdDTJIPu5mueYZp4agxS4mV3aJNtDSupFeuxwUuYL7hgl5zNClioRX+AY9yTbldiLvE6H4iqfjabpM58xAiou4rPRNO9nSom9yOs0yaCS+A1Ey29Eeuwxd4aS8ylaBjOjaBlKzucxd6bXQxPZ9B5JZukzn9LS66tkGfrM55FkttdDExFZVUrqRXrsDA0KtF5GvoDPmRWXqReRi3fKNSmueH0V8Tm14vL2IiKb3SUl9Wa218z+g5l928yeNrM/NbOfWO3BiWwFo+SoEbfEasSMkuvRiEQuH+OWpbri9VUlZtyyPRqRiMja6Lim3sx+DfgwcK1zLl6KvQ/4IyAP2NKp1wPvNbP3O+c+vIbjFVk30yzwNCfObQC6gUmmGFj1fm61Ub7AMXDpDH2NmIrF3GmTq96XyEbx5FzE/UdDDtcSdhU8PrAj4Kah1d/mtd8b5rNRejGcIj5VYsou5m5/fNX7EhHppW4z9fcA312W0JeA3wcC4N8C7wBuBD4CPAN8yMx+bu2GK7I+plngIQ5SI2SAPDVCHuIg0yysel+7vD7use0ULcOMNSlahntMm2Tl8vXkXMRvvNBgLkzYUTDmwoTfeKHBk3NR98YXabdf5P2ZKUqW4TQhJcvw/ow2yYrI5afbtMhVwFeX3X8vMAr8knPuXy+LP21mfwE8CfwM8DurOkqRdfY0J8gTUCAAOHd8mhNrMlu/y+tjF0riZWu4/2jIcABD2XReaShrQML9R8M1ma3f7ReVxIvIZa/bTH0eWq73uwdwwCdXnuicmwMeAG5YtdGJ9Mg8NfIr3vPmdflrkVVxuJYwEFhLbCAwDteSHo1IRGTz65bUvwrsXXa/unRs9xlpTJr0i2xqgxSor3ia13X5a5FVsavgsRC2/qlYCB27CirIJiJyqbr9Bv08cK+Z7Vm6/yXSzbE/svJEMysC7wOeXtURivTADUxSJ6S2dPnr2tLlr29Am1dFXq8P7AiYDWGumZA4x1wzYTZM4yIicmm6LV78l8DfAr5gZj/nnPtLM/tN4NfNrB/4E2COdLPsPwOuBH517YYrsj6mGOBOrmqpfvMWdq7JenqRreamoQwfu5aW6jcfvXJtqt+IyIV5/njMl55KOD4L24bh7hs9rtvmd2+4Tubj0xyPDlJzZQrWx7bMVQz6Yx3bzMVnOBa/cq7Ndv9KhvzRdRrx+jPnOq+WMbM7gfuBYWAWeBF4C987y2/Af3bO/Z01GOeGsm/fPnfgwIFeD0NERETkdXv+eMx//WpCfwH68lCuw2INfuqdGyOxn49P81LzCQLLkSFLRJPQNdiTvbltYj8Xn+GF6DsE5AjIEtIkpMG1mTdtqsTezL7tnNt3Ied2XcDonHsIeCPw20AC3A74pEm8ka6j/zLww1shoRcRERG5nHzpqTShHygYnhkDBaO/kMY3guPRQQLLEVgOMzt3+3h0sG2bY/ErBOTILrXJWo6AHMfiV9Zv4Ovsgj7rdM6dAH4e+HkzuxKYIH1DMAe85JwL12qAIiIiIrJ2js/C5FBrrC+fxjeCmiuTp9QSy5Cl5soX1Sbo0mazu+gFjM65V4BXVn0kIiIiIrLutg3DQg0GlhV4K9fT+EZQsD5C1yAgdy4W0aRg7a/vUrA+mq5BdlmbsEubzU71w0RERES2sLtv9FiswULNkTjHQs2xWEvjG8G2zFWErkHoGjjnzt3elrmqbZvt/pWENGgutWm6BiENtvtXrt/A19kFzdSbWQa4ibQ+/VOuze5aM3sTcItz7vdXb4giIlvXbHyGI8khKlQoUWKnt5vhTbTJS0Q2vuu2+fzUO2mpfvPB27pvkv3OfMSfHG/yajXhiqLHB7dledNg59TywefrfOrlOtNxzJTv85Gr87znunzHNoP+GP2Za3k2fo6GO03OSlyf2dux+s2QP8q1vKml+s2V/t5NtUn2Yl1I9ZsPAr8LnP0uHAF+wTn3P85z7ieAjzvner9Veg2p+o2IrIfZ+AzPJk+RJXuuekOTJtd7NyqxF5Ge+s58xL99sc5QAIOBMR865kL4hWvybRP7B5+v869fKNNnHv1mLDpH2SX84rV9HRP7o8kiX+IIBXwKZKgRUSPmbnayw+tfq4e4Iaxa9RszuxX4NDBGWsryGWAX8N/N7F+83oGKiEh7R5JDZMm2VG/IkuVIcqjXQxORLe5PjjcZCmA46+GZMZz1GArSeDuferlOn3kMeB5mxoDn0Wcen3q53rGvJzhNAZ8iAYZRJKCAzxOcXu2Htal1Wyz1S6RLdH7CObfXOXcjcAfwEvC/mtn/tdYDFBHZqipUCMi2xAKyVKj0aEQiIqlXqwmDgbXEBgPj1Wr7MpjTcUy/tbbpN2M6jjv2NUuDwooV4wUyzNK4yFFf3rol9e8E/sI596mzAefcN4H9wMPAL2rGXkRkbZQoEdI66xXSpLSiTJuIyHq7ougxH7Yu4Z4PHVcU26eWU77P4opl34vOMeV3XrU9TI4aUUusRsTwsso20j2pHwceXxl0zs0CPwB8jXTG/lfWYGwi0sUxt8gX3Iv8ofsuX3Avcswt9npIPXMkKfNAcojfT57jgeQQR5LNX4t4p7c7XUW/rHpDkyY7vd29HpqIbHEf3JZlLoTZZkLiHLPNhLkwjbfzkavzlF3CQpLgnGMhSSi7hI9c3Xmj7M2MUSOmSojDUSWkRszNtN8ouxV1S+rPAOct6OmcqwL3Ag8Bv2xm/3SVxyYiHRxzi3yFV6gRMbQ0i/EVXtmSif2RpMwX3RGqLmTYZam6kC+6I5s+sR/2R7neu5EsOapUyZLTJlkR2RDeNJjhF67JM5z1OFJzDGe9jptkAd5zXZ5fvLaPAc/jRJIw4HldN8kC7PD6uZudFAmYo0mRYEtskr1YHavfmNnXgaxz7vYO5/QBfwncTrrWfo+q34isvS+4F6kRUSA4F6sRUiDDPXZND0e2/h5IDlF1IUV77Y9J1UUULeA+zWqLiMgmtWrVb4AvAm8xs6vbneCcK5MuxflrYGtlEiI9NEuD/IqNQ/ktunFohjoFWucSCvjM0LmigoiIyOWiW1L/x8CjwPs6neScWwC+H/gK8OrqDE1EOhkmR33FxqH6Ft04NEKeGq3VE2rEjND5I10REZHLRcek3jn3hHPubc653+72hZxzc865dzvn2l+zV0RWzY1MLm0WSjcO1QipEnIjk70e2rq7hVGqRFRdhHOOqouoEnELWnsuIiJbQ+dr+a4CM/so8FHn3N1r3ZfIVrLd+rnLXclTnGCWBsPkuI0dbLett3Fop9fHbXE/3+AIr7omQ5blbexkp3feff4tkoXDcOKvoT4D+RGYfDPewK7OjWYOweFHoXIKSuOw63YY6bJ2/+RBePEhWDgJAxNwzZ0wcQFzIMdfgqe/BnMnYGgSbngHbNvTscmZyjNM1x+jGVfI+iWm8rcyWnpD166eevkpXjj5LLFXx0/yXDtxPTdefWPHNounn+DU3KPUXYW8lRgfup3+sZu79jV74nGOzH6bitUouQI7h9/C8OQtHduUjx5g5sTDNKiQo8TI5B307bigpabyOixGpzgZvkQ9WSTv9TMR7KE/M97rYYnICt2W36yGK4G71qEfkS1nu/Vzj13Dh+2N3GPXbMmEHmAmmWHGHeRG53E3/dzoPGbcQWaSmY7tkoXD8MoXIKxCbjg9vvKFNN62s0Pw9APQrEBxLD0+/UAab+fkQTjwGaiXoX8sPR74TBrv5PhL8NCnobYIg+Pp8aFPp/E2zlSe4VDlq0RJncArECV1DlW+ypnKMx27eurlp3j29GPEFuElOWKLePb0Yzz18lNt2yyefoJXZ/+K0DXIWYHQNXh19q9YPP1Ex75mTzzOs/Nfo0mTosvTpMmz819j9sT3VFA+p3z0AMdO/CURDbIUiGhw7MRfUj6qogVraTE6xaHGY4RJg5z1ESYNDjUeYzE61euhicgK65HUi4isqUPuMAE5cpbFzMhZloAch1yH5BzSGfpMEYIimKXHTDGNt3P4UciVIFtK22RL6f3Dj7Zv8+JDkO9L/5n32u0XH+o8vqe/lp5X6E/bFfrT+09/rW2T6fpj+BaQ8fKYeWS8PL4FTNcf69jVCyefBRfgE2Dm4ROAC9J4G6fmHiVDhsByGB6B5ciQ4dRch+8FcGT222STDNmln1fWsmSTDEdmv922zcyJh8mQIWO59HEt9TVz4uGOfcnrczJ8iQw5Ai+HmRF4OTLkOBm2f2MpIr2hpF5ENr2Kq5BdVtoTIEtAxVU6N6zPQKbQGssU0njbzk6lyf9yQTGNt7NwEnIr2uSKabyTuROQX3H12HwpjbfRjCv41nrxF9+yNOPO34vYq+OtqEbsOZ/Ya19BqO4qZKz1+56xgHqX73vFagQrfl4BARWrtW3ToJK+0VjGJ6BBl5+xvC71ZJHMiudTxrLUk613PQyRjU5JvYhseiUr0SRsiTUJKVmpTYsl+RGIViSSUS2Nt+1sPF2ms1xYTePtDExAY0WbRjWNdzI0CfUVSWu9ksbbyPolYtdsicWuSdbv/L3wkzyJtVYQSizGT9pXEMpbici1ft8jF5Lv8n0vuQLhip9XSEjJFdq0gBwl4hVtYkJydPkZy+uS9/qJVjyfItckr4v+iGw4SupFZNPbbbsIadBwTZxzNFyTkAa7rcuG18k3Q1RNk3Ln0mNUTePt7LodGpV0Lb1z6bFRSePtXHNnuo6+XgaXvHb7mjs7j++Gd6Tn1RbTdrXF9P4N72jbZCp/K7ELiZI6ziVESZ3YhUzlb+3Y1bUT14OFxIQ4l6QJtIVpvI3xoduJiAhdA0dC6BpERIwPdfheADuH30LTi2gu/byarknTi9g5/Ja2bUYm7yAiInKN9HEt9TUyeUfHvuT1mQj2ENEgTBo45wiTBhENJoLOm7VFZP11vKLsqnRg9gng45fTVWZ1RVmRjWcmmeGQO0zFVShZid22ixGvw4z7ElW/eY2q31z+XjgW81dPJhyfhW3D8O6bPK7d3vnPs6rfiPTOxVxRVkn9JVBSLyIim80Lx2I++ZWE/gL05aFch8Ua/ORd3RN7EemNi0nqtfxGRERkC/irJ9OEfqBgeGYMFIz+QhoXkc1PSb2IiMgWcHw2naFfri+fxkVk81uPpP5x4PfXoR8RERFpY9twuuRmuXI9jYvI5pdZ6w6cc/cD9691PyIilyKsHqMx+yRxcxY/O0xu+CaC4vaObQ69lPCtr8LpEzA2Cbe9E3bv0QefsrG9+yaPT34lAVzLmvoP3K7nrsjl4IJeyWY2aWY/ZGY/aGaDHc67y8w+vnrDExFZO2H1GJXpL5NEVbxgiCSqUpn+MmH1WNs2h15K+NM/gMoijI6nxz/9gzQuspFdu93nJ+/yGCjAiTkYKGiTrMjlpOtMvZn9Q+BfAWcvKVc1s0845/7NeU5/F/Bx4J+t2ghFRNZIY/ZJPL+Al0mv9mpLx8bsk21n67/1VSj1p//gteO3vgq7VbpbNrhrt/tK4kUuUx1n6s3sXcBvLZ33ReDzpG8E/pWZ/Tcz02d2IrJpxc1ZzG+9iqn5BeJm+52Dp09AccVFTIulNC4iItIr3WbqPwZEwPc5574GYGa7gf8G/Fh6137CrXWxexGRNeBnh0mi6rkZegAX1/Cz7XcOjk2mS27OztADVCtpXEREpFe6zbS/Ffjs2YQewDl3CLgb+AxpYv97azc8EZG1kxu+iSSukURVnHMkUZUkrpEbvqltm9vemSb1lUVwyWu3b3vnOg5cRERkhW4z9cPAcyuDzrmmmf0Y6Yz9T5pZ5Jz7mbUYoIjIWgmK2ylNvaul+k1hfH/H6je793j8jR9rrX7zrr+h6jcb2QnmeY5p5qkxSIG9TDFJ25oP6+7FsMZXGmWm45ApP+CuXB/XBIXuDUVElumW1J8ARs73H865xMx+AvCBj5pZCBxf5fGJiKypoLi9awnLlXbv8bQpdpM4wTyP8DJ5AgbIUyfkEV5mP1dviMT+xbDGpyoz9Hs+E16GxSTmU5UZPlIaUWIvIhelW1L/AvD2dv+5lNj/OPBHwN8FTq/i2ERERF6X55gmT0CeAODc8TmmN0RS/5VGmX7Pp99LK9L0m38urqReRC5Gt8+LvwDcYGY3tzvBORcBPwL8OTB+qQMxs//TzB40s8NmVjOzGTN7zMw+YWajbdrcYWZ/tnRuzcy+Y2YfM7O29brM7D4z+7KZzZtZ2cweMbOPXuq4RURk45qnRm7F/FWODPPUejSiVtNxSGlFIbmSeUzHYY9GJCKbVbek/jPAp4G2ST2Acy4Efoh00+xXL3EsvwCUgL8EfpN0vX4E/B/Ad8xs1/KTzewDS329E/hj4N+T1tL/t8AfnK+DpZr7nwNuBD4J/EdgO/BfzOzXL3HcIiKyQQ1SoEHUEmsQMcjGmAWf8gMqrvXCZRWXMOUHPRqRiGxWtlGqUZpZ3jlXP0/8V4F/AvwH59zfX4oNAC8Cg8CdzrkDZ78G8CXgbcBHnHN/sOzrXAk8C1SAtzjnXlmKDwPfAvYAdzjnvtFtrPv27XMHDhy45Mcql24mmeFw8ioVypToY5d3BSPeebd9tEgWj5CcfAzqZyA/ijdxK17/zo5tKqcfZ2b2ERpJhZxXYmR4P6WxW7r29cTJr/JwPM2sHzAch9zhT3HzRJfSKCdehucfgvmTMDgB190Jk1d37WshOsXJ8GXqbpG89TMRXM1A5pI/MBO57CxfU58jQ4OIOuGGXFNfMo+KS1hMYq2pXweHXkp49Gtw6gSMT8Lt79CGd0k9fzzmwacTjs/BtiF4zw0e123rzUXbzOzbzrl9F3Lumj97zewfmdnL3c47X0K/5NNLx2uXxT5EutTnD84m9Mu+xi8v3f2fVnydnwFywL8/m9AvtZkF/sXS3Z/rNk7pnZlkhmfi79J0DYquRNM1eCb+LjPJTMd2yeIR4kNfwIVVXG4EF1aJD32BZPFI2zaV049z7MyDRK5B1isSuQbHzjxI5fTjHft64uRX+RwzVM1nKA6pms/nmOGJkx0+wDrxMjz6R1Avw8BYenz0j9J4BwvRKQ41Hid0DXL0EboGhxqPsxCd6thOZCuZZJD9XE2egAXq5Ak2TEIPcE1Q4COlEfo9n5NJRL/nK6FfB4deSnjg02k52rHx9PjAp9O4bG3PH4/5vYdiFmqOyUHHQs3xew/FPH887vXQuuq2UXY1DAG7X0f7H1w6fmdZ7O6l45+f5/yvAlXgDjPLOecaF9Dm8yvOkQ3ocPIqWbJkLQdAlhy4NN5ptj45+RhkSliwdIGhoIhbirebrZ+ZfYSMBWS8tK+M5SBJ451m6x+OpymYT5EEzNJjAg+76fZr2J5/CPL9kO9L7589Pv9Qx9n6k+HLZCxHsPT9CMidi2u2XuQ1kwxumCT+fK4JCkri19mjX4NS32sXkTt7fPRrqLLVFvfg0wkDeRgoGAADBQDHg08nPZutv1DrkdRfFDP7x0Af6dKafaTVd74D/Mtlp+1dOj6/sr1zLjKzg8AbgauBZy6gzXEzqwA7zazonKueZ1w/C/wswBVXXHEJj0xerwplipRaYgFZKpQ7N6yfgdyKpD9TSONtNJIKWa/YEvMtSyOpdOxq1g8YikMwOxcruJjZTutj50+mM/TL5YppvIO6WyRHX0ssQ5a6W+zYTkRkqzt1Ip2hX65YSuOytR2fg8kVcwB9+TS+0W3ExWP/GPgE8DHShP7PgXucc8vXFJz9ds+3+Rpn40OX0Oa80znOud91zu1zzu0bH9csaC+U6COk2RILaVJakdh+j/woRCsqXUS1NN5GzisRu9a+Ytck55XatEgNxyG1FcWXauYz3KmSxeAENFa8j2xU03gHeesnWvH9iGiSt/6O7UREtrrxSaiumKOpVtK4bG3bhqC8YkF4uZ7GN7oNl9Q756accwZMAT9MOtv+mJm9ubcjk17b5V1BkyZN18A5R9M1aNJkl9f5kxNv4laIKumaeudwYRWiShpvY2R4P5ELiZK0ryhpELmQkeH9Hfu6w5+i5vlU8XDOUcWj5vnc4U+1b3TdnVBfTNfSuyQ91hfTeAcTwdVErkG49P0IXYPINZgIum+wFRHZym5/B1TK6Vp6l6THSjmNy9b2nhs8FuqwUHMkLl1Tv1BP4xvdhh2hc+6Ec+6PgXuAUeD3l/13x1n1ZfHlH5ZcaJt2M/nSYyPeCG/w30jWclStQtZyvMF/Y9fqN17/Tvzd92BBEWvMYEERf/c9HavflMZuYfvoe8hYjmZSJWM5to++p2v1m5sn3skPMkLRxcz5AUUX84OMdK5+M3k13P6hdC39wun0ePuHula/GciMszt3C4HlaFAmsBy7c7doPb2ISBe793jc96PpWvrTp9LjfT+q6jcC123z+eidPgMF48S8MVAwPnqnv+HX08M6lLQ0s08AH3fOXfJ3w8weA24Bxp1zp83sk8BPAD/unPvUinMzpIl5Fug7u1HWzL4O3Ml5ylaa2TbgGHDEOddSD/98VNJSRERERNbahippuUq2Lx3P1hP60tLxvec5951AEXh4WeWbbm3et+IcEREREZFNY0Mk9WZ2nZl9z7IYM/OWLj41QZqkzy791x8Bp4EfM7N9y87PA/986e5/WPHl/jPQAP7h0oWozrYZJr24FcDvvP5HIyIiIiKyvjZKSct7gV9bWiJzEDgDTAJ3kW6UnQb+3tmTnXMLZvb3SJP7L5vZHwAzwPtJS1f+EfCHyztwzh00s18Cfgs4YGZ/CDRJL2S1E/jXF3I1WRERERGRjWY9kvovX8A5XwSuIS1heStpKcoKaU35/wr8lnOu5bKhzrk/MbO7gH8K/E0gD7wI/C9L53/PZgHn3L8zs1dIy2b+NOknFU8Dv+yc+71LeXAiIiIiIr32ujfKmtm/Ab7hnPvvqzOkjU8bZUXkYj05F3H/0ZDDtYRdBY8P7Ai4aWijfFiaeuZUzJ+/FHF00bGj33jvngxvGN/4FR9ERC5X671R9mPA96/C1xERuSw9ORfxGy80mAsTdhSMuTDhN15o8ORc1OuhnfPMqZjffazJfMOxrQ/mG47ffazJM6fi7o1FRKTnOk4TmdnPXODX2bv8XOfc//u6RiUichm5/2jIcABD2XQeZShrQML9R8MNM1v/5y9FDOaMwZwBMJh7La7ZehGRja/bX5P/BHRbn+NI18K/HbCl+0rqRUSWHK6lM/TLDQTG4VrSoxF9r6OL6Qz9cv3ZNC4iIhvfhUwRlUlLPVbP838GfBz4a+BzqzguEZHLxq6Cx1yYLM3QpxZCx67ChqgqDMCOfmO+4c7N0AMsNtO4iIhsfN2S+p8G/h3ww8Dfds59feUJZvZx4K+dc7+yBuMTEdn0PrAj4DdeaAAJA4GxEDpmQ/jolUGvh3bOe/dk+N3HmkA6Q7/YTNfVf/iGzmM8FFf4VjLLaZqMkeU2b5jdfmlNxtioHadSfpoonCMTDFHqu4FcYdua9CUistl0nCZyzn0SeBNwiLQe/K+bWa5TGxERaXXTUIaPXZtjKPA4WnMMBR4fuza3YdbTA7xh3Odnb80ymDOOl2EwZ/zsrdmO6+kPxRX+LJmmQsQoARUi/iyZ5tH+J3YAACAASURBVFBcWfXxNWrHmZ/5OnFcw88MEsc15me+TqN2fNX7EhHZjLr+RXHOHQa+z8z+EfBrwL1m9recc4+u+ehERC4TNw1lNlQSfz5vGPcvalPst5JZiviULH1cJTLg0vhqz9ZXyk9jfgHfLwDg+wXipbhm60VELqKkpXPuN4G3kK6tf8jMfs3MNs5nxyIisq5O06RI65uAIj6naa56X1E4h+flW2KelycK51a9LxGRzeiidmk5554B9pPO2P9j4DG6V8cREZHL0BhZqrTWsa8SM0Z21fvKBEMkSb0lliR1MsHQqvclIrIZXXTpBedc7Jz7OGkJy4C0Ao6IiGwxt3nDVImpuAjnHBUXUSXmNm941fsq9d2Ai2vEcQ3nXHqMa5T6blj1vkRENqNLXuDpnHvEzG4A+oDG6g1JREQ2g91+iXuZaql+8y5vfE2q3+QK2xgceXtr9ZvBt2zp9fSV5klmG8/TiBfI+QMM566jlJ3o9bB6otaYZrHyLGE0T5AZpL90PYXcVK+HJbKuXteuLedcDMyv0lhERGST2e2X1qyE5Uq5wrYtncQvV2me5Hj1UTKWJ+v1EyV1jlcfZRu3b7nEvtaY5sz8N/AtT8YfII5rnJn/BqODb1NiL1vKxrnyiYiIiFyQ2cbzZCxPxstjZmS8PBnLM9t4vtdDW3eLlWfxLY/vFzAzfL+Ab3kWK8/2emgi60pJvYiIyCbTiBfwV1w2xrccjXihRyPqnTCaP29lpDDSQgLZWpTUi4iIbDI5f4DYtW5ni12DnD/QoxH1TpAZPG9lpCAz2KMRifSGknoREZFNZjh3HZGrEyV1nHNESZ3I1RnOXdfroa27/tL1xK7eUhkpdnX6S9f3emgi62pjX95QZJV8t9Lkgbk6R5sxO7I+9w3leWNp9WtpX87m4jMcTV6h6soUrY8d3pUM+aMd2yxGpzgZvkQ9WSTv9TMR7KE/M75OIxa5fJWyE2zj9pbqN+O5N22oTbL1+vHWakV9N5DPr/5G50JuitHBt7VUvxkq3dp1k+w08zzLCeapMUiB65lkCs3uy+ZlzunaURdr37597sCBA70ehlyg71aa/N8nKgz6xoBvLMSO+djx9ydLSuwv0Fx8huejJwksS0CWkCaha3Jd5qa2if1idIpDjcfIkCNjWSLXJKLB7tytSuxFLnP1+nHmZx/CvDyelydJ6rikzuDwnWuS2F+saeb5JgfJEZAnQ52IBiFv5Sol9rKhmNm3nXP7LuRcLb+Ry94Dc3UGfWMo4+FZehz0jQfm6t0bCwBHk1cILEvWcpgZWcsRWJajyStt25wMXyJDjsBL2wRejgw5ToYvrd/ARaQnKuWnMa+1Io15eSrlp3s9NACe5QQ5AgoEGEaBgBwBz3Ki10MTuWRK6uWyd7QZM+C3Xvh4wDeONuM2LWSlqisT0PqpRkCWqiu3bVNPFslYa5uMZakni2syRhHZOKJw7rwVaaJwrkcjajVPjfyKFch5MsxT69GIRF4/JfVy2duR9VmIW5eZLcSOHVm/RyPafIrWR0izJRbSpGh9bdvkvX4i19omck3yXv+ajFFENo5MMHTeijSZYKhHI2o1SIE6UUusTsQghR6NSOT1U1Ivl737hvLMx465KCFx6XE+dtw3lO/eWADY4V1J6Jo0XQPnHE3XIHRNdnhXtm0zEexJV6kmaZswaRDRYCLYs34DF5GeKPXdgEtaK9K4pE6p74ZeDw2A65mkQUiNEIejRkiDkOuZ7PXQRC6Zknq57L2xlOXvT5YYzHgcCxMGM542yV6kIX+U6zI3kbUcNSpkLddxkyxAf2ac3blbCbwcDVcm8HLaJCuyReTz2xgcvhPfLxBH8/h+YcNskgWYYpC3chUFAhaoUyDQJlnZ9FT95hKo+o2IiIiIrDVVvxERERER2UKU1IuIiIiIbHJK6kVERERENrlM91NERC7Ny1GNh8J5TrqQCQu4Mxjk6oxKxoksp9fJa+biMxyLX6HmyhSsj+3+lR035IvIazRTLyJr4uWoxmcapyi7mDEylF3MZxqneDnSxV1EztLr5DVz8RleiL5D0zXIU6LpGrwQfYe5+EyvhyayKSipF5E18VA4T5/59JmPZ3bu9kPhfK+HJrJh6HXymmPxKwTkyFoOMyNrOQJyHItf6fXQRDYFJfUisiZOupDiil8xRTxOurBHIxLZePQ6eU3NlQlovX5IQJaaK/doRCKbi5J6EVkTExZQJWmJVUmYsKBHIxLZePQ6eU3B+ghptsRCmhSsr0cjEtlclNSLyJq4Mxik7GLKLiZx7tztOwNdsVHkLL1OXrPdv5KQBk3XwDlH0zUIabDdv7LXQxPZFFT9RmQDOPRSwqNfg1MnYHwSbn8H7N6zud9zX50p8DcZb6nq8QPZkS1b1UPkfPQ6ec2QP8q1vKml+s2V/l5VvxG5QOac6/UYNp19+/a5AwcO9HoYcpk49FLCA5+GUh8US1CtQKUM9/3o5k/sRURE5NKZ2bedc/su5FxlDCI99ujX0oS+1A/mpcdSXxoXERERuRBK6kV67NSJdIZ+uWIpjYuIiIhcCCX1Ij02PpkuuVmuWknjIiIiIhdCG2VFeuz2d8ADn05vL19T/+57ezsukQvx5FzE/UdCDlcTdhU9PrAz4KYh/WkREVlvmqkX6bHdezzu+9F0Lf3pU+lRm2RlM3hyLuI3n2sw10zYUTDmmgm/+VyDJ+eiXg9NRGTL0XSKyAawe4/H7j29HoXIxbn/SMhQAEPZ9A3oUNaAhPuPhJqtFxFZZ5oKFBGRS3K4mjAQWEtsIDAOV5M2LUREZK0oqRcRkUuyq+ixELZe62QhdOwq6k+LiMh6029eERG5JB/YGTAXwlwzIXGOuWbCXJjGRURkfWnRo4iIXJKbhjL8o720VL/56NWqfiOpauME87XnCON5An+QwcJeijnV6hVZK/rNKyIil+ymoYySePke1cYJTi0+gu/lyXgDxEmdU4uPMM5+JfYia0TLb0RERGRVzdeew/fy+F4eMzt3e772XK+HJnLZUlIvIiIiqyqM5/Es1xLzLEcYz/doRCKXPyX1IiIisqoCf5DENVpiiWsQ+IM9GpHI5U9JvYiIiKyqwcJe4qROnNRxzp27PVjY2+uhiVy2tLtJREREVlUxN8k4+1uq34yUbtYm2XUwzTzPcoJ5agxS4HommUKfkGwFSupFRERk1RVzk0ri19k083yTg+QIGCBPjZBvcpC3cpUS+y1Ay29ERERELgPPcoIcAQUCDKNAQI6AZznR66HJOlBSLyIiInIZmKdGfsUijDwZ5qn1aESynpTUi4iIiFwGBilQJ2qJ1YkYpNCjEcl6UlIvIiIichm4nkkahNQIcThqhDQIuR7tbdgKlNSLiIiIXAamGOStXEWBgAXqFAi0SXYLUfUbERERkcvEFINK4rcozdSLiIiIiGxySupFRERERDY5JfUiIiIiIpuc1tSLiFykp2YjPnsk5HDVsatovH9nwI3D+nUqIiK9o5l6EZGL8NRsxG8912Cu6dhRgLmm47eea/DUbNS9sYiIyBpRUi8ichE+eyRkKDCGsoZn6XEoMD57JOz10EREZAtTUi8ichEOVx0DQWtsIEjjIiIivaKkXkTkIuwqGgsrJuUXwjQuIiLSK0rqRUQuwvt3BsyFjrmmI3HpcS50vH9n0L2xiIjIGlG5BhG5IEeSMk9wmhnqjJDnZsbY6fX1eljr7sbhDD+/l5bqNz99dXZDVb9p1I5TKT9NFM6RCYYo9d1ArrCta7uZ8tMcqz5OnSp5imwv3sJI3w2dG829CkcPQO00FMZgxz4YuqJrX88/9SpfeuQUx+c9tg0m3L1/nOtu7Nxu8dhfc/L0I9StQt6VmBjbT//2N3ft62BU5eF4npNJyIQXcIc/yFWZYtd2cnl7MazxlUaZ6Thkyg+4K9fHNUGh18MSuWTmnNaBXqx9+/a5AwcO9HoYIuvmSFLmQY5QxKdAhhoRVWLew84tmdhvZI3aceZnvo75BTwvT5LUcXGNwZG3d0zsZ8pP82L1IQIyZMgS0SQk4prine0T+7lX4YU/g6AEQRHCKoQVuPbejon980+9yn/9ixn6cwl9OUe5YSw2PH7qB0baJvaLx/6aQ2e+RMYFZCxD5CIiC9k9enfHxP5gVOV/hKfoM58iHlUSyi7mh4NxJfZb2IthjU9VZuj3fErmUXEJi0nMR0ojSuxlQzGzbzvn9l3IuVp+IyJdPcFpivgUCTCMIgFFfJ7gdK+HJitUyk9jfgHfL2Bm6dEvUCk/3bHdserjBGQILIeZEViOgAzHqo+3b3T0QJrQZ0tglh6DUhrv4EuPnKI/lzBQAM8zBgrQn0v40iOn2rY5efoRMi4gsCyGR2BZMi7g5OlHOvb1cDxPn/n0mY9ndu72w/F8x3ZyeftKo0y/59Pvpc+Ls7e/0ij3emgil0xJvYh0NUOdworVegUyzFDv0YiknSicw/PyLTHPyxOFcx3b1amSIdsSy5ClTrV9o9rpdIZ+uaCYxjs4Pu/Rl2v9lLgv5zg+3/5PUt0qZKz1OZixDHWrdOzrZBJSXPGnrojHyUQlSLey6TikZK3Pi5J5TMd6XsjmpaReRLoaIU+N1osr1YgYId+mhfRKJhgiSVrfbCVJnUww1LFdniIRzZZYRJM8HZaoFMbSJTfLhdU03sG2wYRyo7VaULlhbBtM2o/PlYhc63MwchF5V+rY14QXUKX161ZJmPC0sXkrm/IDKq71eVFxCVO+nheyeSmpF5GubmaMKjFVQhyOKiFVYm6mc/J2qebj0zzX+BaP17/Ec41vMR9rmc+FKvXdgItrxHEN51x6jGuUumx43V68hZCI0DVwzhG6BiER24u3tG+0Y1+6hr5ZAefSY1hJ4x3cvX+cxYbHQg2SxLFQg8WGx937x9u2mRjbT2QhoWviSAhdk8hCJsb2d+zrDn+Qsospu5jEuXO37/AHO7aTy9tduT4Wk5jFJH1enL19V057hGTz0kbZS6CNsrIVrVf1m/n4NC83nyCw3GsbNl2Dq7M3M+ivzZuIy42q37RS9Rs5H1W/kc3gYjbKKqm/BErqRdbOc41vEboGgeXOxc7e35u7rYcjExERWV+qfiMim1bNLZ53w2bNLfZoRCIiIhufknoR2VAK1n/eDZsF6+/RiERERDY+JfUisqFMZa5KN2ku37DpGkxlrur10ERERDYsJfUisqEM+mNcnb2ZwHLUKRNYTptkRUREush0P0VEZH0N+mNK4kVERC6CZupFRERERDY5JfUiIiIiIpucknoRERERkU1OSb2IiIiIyCanjbKyJUyzwDNMM0+dQfK8gSmmGOjY5jgLfJeTzFFjiAJvZIJtXdqIiIiI9IJm6uWyN80CD3OQGiED5KgR8jAHmWahbZvjLPB1DlEjZJA8NUK+ziGOd2gjIiIi0itK6uWy9wzT5MlQIMAwCgTkyfAM023bfJeT523zXU6u48hFRERELoySernszVMnv2KlWZ4M89Tbtpmjdt42c9TWZIwiIiIir4eSernsDZKnTtQSqxMxSL5tmyEK520zRGFNxigiIiLyemijrGwqryYVDiQznKHJKFn2eSNc4ZU6tnkDUzzMQSCdba8TUSfizexq2+aNTPB1Dn1Pm33sWL0HIyIiIrJKNFMvm8arSYXPx8epuIgRF1BxEZ+Pj/NqUunYbooB7uAqCgQs0KBAwB1c1bH6zTYGeDu7KRAwT50CAW9nt6rfiIiIyIakmXrZNA4kMxTxKVn6tC2RAZfGu83WTzHQtYTlStsYUBIvIiIim4Jm6mXTOEOTIn5LrIjPGZo9GpGIiIjIxqCkXjaNUbJUiVtiVWJGyfZoRCIiIiIbg5J62TT2eSNUiam4COccFRdRJWafN9LroYmIiIj0lJJ62TSu8Eq8z99GyTLMWEjJMrzP39Z1Pb2IiIjI5U4bZWVTucIrKYkXERERWUEz9SIiIiIim9yGSOrNbNTM/q6Z/bGZvWhmNTObN7Ovm9nfMbPvGaeZ5czsH5jZo2Z22szKZvaMmf2Wme3u0NdHl9qUl/r4spndt7aPUERERERk7WyIpB74EeA/AvuBR4DfAD4D3Aj8J+DTZmZnTzazDPAg8O+BfuBTwO8AJ4H/GXjCzG5Y2YmZ/TrwX4BtS/19ErgJ+JyZ/cM1emwiIiIiImvKnHO9HgNmdjdQAv7UOZcsi08BjwK7gA855z6zFP8R4NOkif09K9r8CvBx4D87535mWfwO4CHgJeA259zsUvxK4NtL/V/vnHul23j37dvnDhw48DoesYiIiIhIZ2b2befcvgs5d0NslHXOfalNfNrMfgf4VeBdpLP3AFcvHVveBCy5nzSpH18R/7ml46+eTeiX+njFzH4b+N+Bvw184lIfh8h6qzRPMtN4nkayQM4bYCR3HaXsRMc2Ufko0cwTuMYMlhshM3Izmb4d3Ts78wocegQqp6E0Brv3w+iVnducPggvfQMWT0H/OOx5G4xddWF9Hfzma+2uemv3vkRERLawjbL8ppNw6Rgti3136fi+86y3P7s+/osr4ncvHf/8PH18fsU5IhtepXmSY9VHiZI6WesnSuocqz5KpXmybZuofJTw2IO4qArZYVxUJTz2IFH5aOfOzrwCT30OGhUojqbHpz6Xxts5fRAe+xNolKFvND0+9idpvFtfT9zf2u6J+zv3JSIissVtiJn6dpbWzv/00t3lyfifAv8D+GHgSTP7ItAE3gK8Hfh3wG8v+zolYAdQds4dP09XLywdr1vVByCyhmYaz5OxPBkvD0DG8pCk8Xaz9dHME5ApYpliGsgUcUvxjrP1hx6BbAlyS+VEzx4PPdJ+Bv2lb6Tn5fqW2vS9Fu80W3/wm+dvd/Cbmq0XERFpY0Mn9cC/JN0s+2fOub84G3TOOTP7EOlSmV8Glm+KfRD4/5xzy2f2B5eO8236ORsfajcQM/tZ4GcBrrjiiot5DCJropEskLX+lphvORrJQts2rjED2eHWoF9I451UTqcz9Mtli2m8ncVT6Uz7yjaLpzr3dantREREtrANu/zGzH4e+EXgWeCnVvxfHvjDpf//B6TVbAaBe4HdwFfN7AOrOR7n3O865/Y55/aNj69cri+y/nLeALFrtMRi1yDnDbRtY7kRiGutwbiWxjspjUGz2hprVtN4O/3j52/T3+X1c6ntREREtrANmdQvlZf8TeBp4N3OuZXTiP8baRnMf+qc+3+cc9POuQXn3OeBDwHBUvv/v717j7KsLO88/n1OVXV1dVVf6G5oaG7doIIrrMXAtILIKEKieCE6LAiJY4JEMCRRhqVOnOgQiYkTMxozRh1NNBFjXCNRdLJQBDMCApKozVXitDe6udjcupu+1KWrq+o888feBafLU9VFd1Wds7u+n7X22nXe/b77vOect0//ate79x43fiR+Kc2Nl2+fkRcgzYHl3S9gNHczWt9NZjJa381o7mZ59+SzyDqXnwyjg+ToIJlZzK0fHSzKp3LsabBnoJhLn1ms9wwU5ZM5/iVFveF+yHqxHh4oyqey9vTm7daePnU7SZLmsbYL9RFxJcWc+AcoAv3jTaqNnwx7y8QNmXkf8DRwbESsKMsGgJ8DfRFxRJP9Pb9c//gAuy/Nmd4Fh7F60YvprC1kT+6is7aQ1YtePOXVbzr7jqRr9TnFnPo9TxOdi+hafc6+r36zYg2cdF4x131wa7E+6byp57ivXAunvKGYE9+/tVif8oZ9X/1mxRo4+fV7tzv59c6nlyRpCm01pz4i3k0xj/5e4Fcyc7IJu93l+hf+Hh8R3RQ3pILi5NlxN1NM4zkX+OyEZq9uqCNVRu+Cw/Z5CcuJOvuOnN4lLCdasea5B+uVa6d3CcuZeC5JkuaxtjlSHxFXUQT6u4Bzpgj0ALeX6/eUIb7R1RS/rHw/M3c1lH+qXL83Ip45U7C8+dTvA8P8YtiXJEmS2l5bHKmPiIuB9wNjFIH9ioiYWG1TZl5T/vwB4DzgHGBDRNwIDAEvBV5c/vyfGxtn5p0R8RHgHcD9EfFlYAFwEbAcePt07iYrSZIktZu2CPXA+N/nO4ArJ6nzbeAagMz8eUScCrwbeC3FnWBrwGNlnT/PzA0Td5CZ74yIH1AcmX8rUAfuBj6UmV+bqRcjSZIkzaXIzFb3oXLWrVuX69evb3U3JEmSdBCLiLsyc9106rbNnHpJkiRJ+8dQL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFVcZ6s7IEmS5sbDYwN8v/40WxhmJd28qHYIx3T0trpbkmaAR+olSZoHHh4b4Ov1xxhglBUsYIBRvl5/jIfHBlrdNUkzwFAvSdI88P360/TSSW90EhH0Rie9dPL9+tOt7pqkGWColyRpHtjCMIvo2KtsER1sYbhFPZI0kwz1kiTNAyvpZpCxvcoGGWMl3S3qkaSZZKiXJGkeeFHtEAYYZSBHyUwGcpQBRnlR7ZBWd03SDPDqN5LUxn42MsRte3bxRH2EVbUuXrZgMcd39bS6W6qgYzp6eS1H7HX1m7Nqh3r1G+kgYaiXpDb1s5Ehrt29jT5qHBqd7KqPce3ubVzEcoO99ssxHb2GeOkg5fQbSWpTt+3ZRR81Ftc6qEWwuNZBHzVu27Or1V2TJLUZQ70ktakn6iP0xt5f071R44n6SIt6JElqV4Z6SWpTq2pdDGR9r7KBrLOq1tWiHkmS2pVz6iVpDmzcVOfO7yZPboHDVsIZpwVr10x9XOVlCxZz7e5tUC+O0A9knX7qvHbBsjnqtSSpKjxSL0mzbOOmOl+5PukfSFauKNZfuT7ZuKk+Zbvju3q4aOFyFtc6eCpHWVzr4KKFniQrSfpFHqmXpFl253eTvt6krzcA6OsFSO78LqxdM3Xb47t6DPGSpH3ySL0kzbInt8CiRXuXLVpUlEuSNBMM9ZI0yw5bCYODe5cNDhblkiTNBEO9JM2yM04L+geC/oGknsWc+v6B4IzTotVdkyQdJAz1kjTL1q6pcf55QV9vsGVrsT7/vH1f/UaSpOnyRFlJmgNr19T2eVKsJEn7y8NEkiRJUsUZ6iVJkqSKM9RLkiRJFWeolyRJkirOE2WlCnuk3s89uZWtDLOCbk6JFRxd69tnm7tz2zNtTo3l+2wjSZLam0fqpYp6pN7PN3MzgznK8lzAYI7yzdzMI/X+KdvclJsZKNsM5Cg37aONJElqf4Z6qaLuya30ZgeLopOIYFF00psd3JNbJ21zd25jUXbSW7bpjU4WZSd357Y57LkkSZpphnqporYyTA8de5X10MFWhqdss2hCm0X7aCNJktqfoV6qqBV0M8TYXmVDjLGC7inbDE5oM7iPNpIkqf0Z6qWKOiVWMBBjDOYomclgjjIQY5wSKyZtc2osZzBGGSjbDOQogzHKqbF8DnsuSZJmmqFeqqija328MlazKDrZFntYFJ28MlZPeSWbo2t9vCpW01u26Y1OXrWPNpIkqf15SUupwo6u9XE0zy2Q708bSZLU3jxSL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcd5SVJEmapifYwY94nO0MsYweTuBwVrG01d2SPFIvSZI0HU+wg3/lQYYYYQkLGWKEf+VBnmBHq7smGeolSZKm40c8zkK6WEgXQTzz8494vNVdkwz1kiRJ07GdIbonzFzuppPtDLWoR9KzDPWSJEnTsIwehhndq2yYUZbR06IeSc8y1EuSJE3DCRzObkbYzQhJPvPzCRze6q5JhnpJkqTpWMVSTuc4euhiJ7vpoYvTOc6r36gteElLSZKkaVrFUkO82pJH6iVJkqSKM9RLkiRJFWeolyRJkirOUC9JkiRVnKFekiRJqjhDvSRJklRxhnpJkiSp4gz1kiRJUsUZ6iVJkqSKM9RLkiRJFWeolyRJkirOUC9JkiRVnKFekiRJqjhDvSRJklRxhnpJkiSp4gz1kiRJUsUZ6iVJkqSKM9RLkiRJFWeolyRJkirOUC9JkiRVnKFekiRJqjhDvSRJklRxhnpJkiSp4gz1kiRJUsUZ6iVJkqSKM9RLkiRJFdfZ6g5IkiRVxYOjQ9wxspMn6iOsqnVxZtcSjuvsaXW3JI/US5IkTceDo0N8aXgLu+pjHBqd7KqP8aXhLTw4OtTqrkmGekmSpOm4Y2QnfXSwuNZBLYLFtQ766OCOkZ2t7ppkqJckSZqOJ+oj9Mbe0ak3ajxRH2lRj6RnGeolSZKmYVWti4Gs71U2kHVW1bpa1CPpWYZ6SZKkaTizawn9jLGrPkY9k131MfoZ48yuJa3ummSolyRJmo7jOnu4sHsli2sdPJWjLK51cGH3Sq9+o7bgJS0lSZKm6bjOHkO82pJH6iVJkqSKM9RLkiRJFWeolyRJkirOUC9JkiRVnKFekiRJqjhDvSRJklRxbRHqI2JFRFwaEV+NiJ9GxFBE7IiIOyLiLRHRtJ8R0VG2uy0ini7bPRgR10bECyZpc3FEfC8i+svnuDUiXje7r1CSJEmaPe1ynfoLgU8CjwG3AA8Dq4Dzgc8Ar46ICzMzxxtERB/wT8DZwL3A54DdwJHAfwBeAPy48Uki4sPAO4FHgU8DC4BfB66PiLdn5sdn8TVKkiRJsyIacnLrOhFxNtALfD0z6w3lhwPfA44GLsjM6xq2fQF4I3B5Zv51k312ZeZIw+MzgO8APwNelJlPl+VrgLvK5z8xMzftq7/r1q3L9evXP/cXKkmSJE1TRNyVmeumU7ctpt9k5s2ZeX1joC/LHwc+VT48a7w8Ik6lCPTXNgv0ZduRCUWXl+sPjAf6st4m4BNAN3DJAbwMSZIkqSXaItTvw3g4H20oe2O5/t8RsTQi3hQRfxgRb42I502yn7PLIw6jIwAAEPxJREFU9Y1Ntn1jQh1JkiSpMtplTn1TEdEJ/Fb5sDGMv6hcH0sxnWZFw7aMiE8CV2TmWLmfXoq59v2Z+ViTp/pJuW56cq0kSZLUztr9SP0HgZOAGzLzpobyw8r1R4BbgRcCi4Ffpgj5vwdc1VB/abneMcnzjJcvm6wj5V8B1kfE+qeeeuq5vAZJkiRpVrVtqI+IKyiuVLMB+M0Jm8f7vQG4KDM3ZGZ/Zn4LuACoA++IiAUz1Z/M/JvMXJeZ6w499NCZ2q0kSZJ0wNoy1EfE24CPAj8EXpGZ2yZU2V6urx+fYjMuM+8DNlIcuX9hWTx+JH4pzY2Xb59kuyRJktS22i7UR8SVwMeABygC/eNNqv2oXE8WwsevbtMDkJkDwM+Bvog4okn955frHzfZJkmSJLW1tgr1EfFu4C8pbib1isx8cpKq/7dcn9RkH908G9I3NWy6uVyf22R/r55QR5IkSaqMtgn1EXEVxYmxdwHnZOaWKapfB2wGLoqIF0/YdhXFdJpbJhzlH7/e/Xsj4pCG510D/D4wDHz2QF6DJEmS1AptcUnLiLgYeD8wBtwOXBERE6ttysxroJhOExFvBr4G3B4RX6GYXnMacCbwJPA7jY0z886I+AjwDuD+iPgysAC4CFgOvH06d5OVJEmS2k1bhHpgbbnuAK6cpM63gWvGH2TmP5dH6a+iuJTlUmD8DrR/kpmbJ+4gM98ZET+gODL/Voqr5NwNfCgzvzYzL0WSJEmaW5GZre5D5axbty7Xr1/f6m5IkiTpIBYRd2XmuunUbZs59ZIkSZL2j6FekiRJqjhDvSRJklRxhnpJkiSp4gz1kiRJUsUZ6iVJkqSKM9RLkiRJFWeolyRJkirOUC9JkiRVnKFekiRJqrjOVndAkiS1rx1jW9k8tpGh3EVPLGZ1x1qWdqxodbcqZcPQMDf2D7F5ZJTVXZ2c29fDiT3d+2z3QP8ert82zKPDdY7qrnHe8m5O6lswBz1WFXmkXpIkNbVjbCs/HbmPPTnMQvrYk8P8dOQ+doxtbXXXKmPD0DCf3raLnWN1jujsYOdYnU9v28WGoeEp2z3Qv4ePbx5k+2id1QuC7aN1Pr55kAf698xRz1U1hnpJktTU5rGNdEY3C6KbiGBBdNMZ3Wwe29jqrlXGjf1DLO2osbSjRi3imZ9v7B+ast3124ZZ2hks6yzaLeussbQzuH7b1L8MaP4y1EuSpKaGchdd7D3do4sFDOWuFvWoejaPjLK4FnuVLa4Fm0dGp2z36HCdJR17t1vSETw6XJ/xPurgYKiXJElN9cRiRth7uscIe+iJxS3qUfWs7upkVz33KttVT1Z3TX1a41HdNXaO7d1u51hyVLfRTc05MiRJUlOrO9YymsPsyWEykz05zGgOs7pjbau7Vhnn9vWwY6zOjrE69cxnfj63r2fKduct72bHaLJ9tGi3fbTOjtHkvOX7PsFW85OhXpIkNbW0YwXP6zqZBdHNbvpZEN08r+tkr37zHJzY081lyxezpKPGY6NjLOmocdnyxfu8+s1JfQt42+pFLOussXlPsqyzxttWL/LqN5qUl7SUJEmTWtqxwhB/gE7s6Z7WJSwnOqlvgSFe0+aRekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVF5nZ6j5UTkQ8BTzUgqdeCWxpwfOq/Tk21IzjQpNxbGgyjo32cmxmHjqdiob6ComI9Zm5rtX9UPtxbKgZx4Um49jQZBwb1eX0G0mSJKniDPWSJElSxRnqq+VvWt0BtS3HhppxXGgyjg1NxrFRUc6plyRJkirOI/WSJElSxRnqJUmSpIoz1EuSJEkVZ6hvAxFxQUR8LCJuj4idEZER8Q/7aHNGRNwQEdsiYigi7o+IKyOiY676rdkTESsi4tKI+GpE/LT8jHdExB0R8ZaIaPpv13ExP0TEn0fEtyLikfJz3hYR90TE+yJixSRtHBvzUES8qfw/JSPi0knqvC4ibi2/Y/oj4rsRcfFc91WzJyI2NYyDicvjk7TxO6NiPFG2DUTEvcDJQD/wKHAi8IXMfNMk9V8PXAfsBq4FtgHnAScAX87MC+ei35o9EXE58EngMeAW4GFgFXA+sJTi878wG/4BOy7mj4jYA9wN/BB4EugFTgfWAZuB0zPzkYb6jo15KCKOBn4AdAB9wGWZ+ZkJdd4GfAzYSjE29gAXAEcBf5GZ75rTTmtWRMQmYBnwP5ts7s/MD0+o73dGBRnq20BEvIIizP8UeDlFiGsa6iNiSVlvKfDSzFxfli8EbgZeAvxGZn5xjrqvWRARZ1MEta9nZr2h/HDge8DRwAWZeV1Z7riYRyJiYWbublL+AeA9wCcz8/fKMsfGPBQRAfwzsBb4CvAuJoT6iFgDbAAGgH+fmZvK8kOA7wPHA2dk5r/MZd8188pQT2aumUZdvzMqyuk3bSAzb8nMn+T0fsO6ADgU+OL4P7RyH7uB/1Y+/N1Z6KbmUGbenJnXNwb6svxx4FPlw7MaNjku5pFmgb70j+X6+Q1ljo356QrgbOASitDezG8D3cDHxwM9QGY+Dfz38uHls9hHtSe/Myqqs9Ud0HN2drm+scm224BB4IyI6M7M4bnrlubQSLkebShzXAiKP48D3N9Q5tiYZyLihcAHgY9m5m3lX/6amWpsfGNCHVVfd0S8CTiG4he9+4HbMnNsQj2/MyrKUF89J5TrH0/ckJmjEbER+CXgOOD/zWXHNPsiohP4rfJh4xeu42Ieioh3UcyVXkoxn/5Miv+oP9hQzbExj5TfEZ+nOA/nPfuoPtXYeCwiBoCjImJRZg7ObE/VAodTjI1GGyPiksz8dkOZ3xkVZaivnqXlesck28fLl81BXzT3PgicBNyQmTc1lDsu5qd3UZxAPe5G4M2Z+VRDmWNjfvkj4BTgzMwc2kfd6YyN3rKeob7aPgvcDvwbsIsikL8NeCvwjYh4SWbeV9b1O6OinFMvVUREXAG8k+LEtt9scXfUBjLz8MwMiiNw51P8R31PRJza2p6pFSLiNIqj83/hya1qlJl/XJ6r9URmDmbmA5l5OfARoAe4urU91Eww1FfP+G/ISyfZPl6+fQ76ojlSXnbuoxSXMHxFZm6bUMVxMY+V/1F/FXglsAL4+4bNjo15oJx28/cUUyaummaz6Y6NyY7YqvrGL7zwsoYyvzMqylBfPT8q1y+YuKH8Ul9LcQLlg3PZKc2eiLiS4jrSD1AE+mY3CnFciMx8iOIXv1+KiJVlsWNjfuij+IxfCOxuvLkQ8L6yzqfLsvFrlU81No6gmHrzqPPpD2rjU/V6G8r8zqgoQ3313Fyuz22y7WXAIuBOz0g/OETEu4G/BO6lCPRPTlLVcaFxq8v1+BUtHBvzwzDwt5Ms95R17igfj0/NmWpsvHpCHR2cTi/XjQHd74yqykyXNloorj2ewD9Msn0JxW/Ww8C6hvKFwJ1l219v9etwmZGxcFX5ea4Hlu+jruNiniwUR8+WNimvAR8oP+vvODZcGj7rq8vP+dIJ5Wsp7hi6FVjTUH4Ixc2HEnhJq/vvcsCf/wuB3ibla4CflJ/zexrK/c6o6OLVb9pARLwBeEP58PBy/ZKIuKb8eUuWt+rOzJ0RcRnwZeDWiPgixe2bf5Xy9s0Ut3RWhUXExcD7KY623g5cUdwgci+bMvMacFzMM68B/iwi7gA2UgSyVRR3oz4OeBy4bLyyY0OTycyNEfFfgL8C1kfEtcAeipsPHYUn3B4sLgLeGRG3AQ9RXP3meOC1FEH9BuDD45X9zqiuKH/7UgtFxNU8O+exmYdywq2dI+KlwHspbte8kOKoyt8Bf5W/eCMJVcw0xgTAtzPzrAntHBcHuYg4ieIun2dSBK9lFDeS+THwdYrPeuKJ1I6Neazh++SyzPxMk+3nUVwe9VSKv/j8kOIus5+by35qdkTEyym+M06hOHDYS3GS670U163/fDYJg35nVI+hXpIkSao4T5SVJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSZlVEXBMRGRFrZvl5NkXEptl8DklqV4Z6SVIlRMStEeEdEyWpic5Wd0CSpBlyTqs7IEmtYqiXJB0UMvNnre6DJLWK028kqU1FxJpyLvo1EXFiRPyfiNgWEQMRcUdEvLJJm+6I+K8R8YOIGIyInRFxe0T82gzt/+qyzVlT7W+ar+/NEXFdRDwYEUNlX78TEW9qtl/g5eXjbFhubajXdE79AbwnayLiixGxJSJ2R8T6iHjddF6bJM01j9RLUvtbC/wL8APgr4EjgIuAb0TEGzPzWoCIWADcRBF+NwCfABYBFwDXRsS/y8z37O/+Z8EngX8DbgMeA1YArwE+HxEnZOZVZb3twB8DbwaOLX8et2mqJziA9+RY4HvAg8DngeUU78k/RcQvZ+Ytz/XFStKsykwXFxcXlzZcgDVAlsuHJmxbB4wATwNLyrI/LOveAHQ21D2MIvwmcMb+7r8sv7qsf9YU/b1mQvk1ZfmaCeXHN9nHAuBb5XMfOWHbrcV/W5O+X5uATRPKDuQ9ed+Efb1qfF+tHhsuLi4uExen30hS+9sBvL+xIDPXA18AlgH/sSz+bYrQ+Y7MHG2o+yTwJ+XDSw9g/zMqm8yBz8w9FEfTO5mZE1/39z15CPjTCX27CXgYePEM9EuSZpShXpLa392ZuatJ+a3l+pSIWAw8D9icmRua1L15vO7+7P859HXaIuKYiPhERGwo57pnOXf+urLKkQe4/wN5T+7NzLEm5Y8AhxxIvyRpNjinXpLa3xOTlD9erpeWCxRz05sZL1+2n/ufURFxHMWc9UOA24FvUvzFYIxiCszFQPcBPs2BvCfbJ2kzigfEJLUhQ70ktb9Vk5QfXq53lEtj2URHNNTdn/2Pq5frZv9/NAvHk3kHxYmxl2TmNY0bIuI3KEL9gTqQ90SSKsWjDZLU/k4tp5JMdFa5vqecPvMz4MiIeH6Tuq8o13fvz/4byp4u10c3qb+uSdlknleur2uy7eWTtBkDiIiO6TzBAb4nklQphnpJan9LgT9qLIiIdcB/ojjK/NWy+O+AAD7UGHwjYiVwVUOd/d0/FFNmAC6JiM6G+kdP3Mc+bCrXZ0143lfR/MRVgK3l+pjn8Dz7+55IUqU4/UaS2t9twKURcRrwHZ69jnwN+J3M3FnW+zDwauD1wH0RcQPFNdkvpLiE4//IzDsOYP9k5ncj4jbgZcD3IuJmiuk751FcD77ZEfxm/hdwCfCliPgysBk4CTgX+Mfy+Sf6VvlavlK+tiHgocz8/BTPs7/viSRVikfqJan9bQTOoJj6cjnwaxRTRl6TDTeGKi8H+SvAe8uit1PMTf8J8MbMfPeB7L/B64HPAEeVz3EK8AfAZPv/BZl5P8X0lzuB1wK/CywBzgc+NUmzzwB/RvGXhT+guCTlW/bxPPv7nkhSpURmtroPkqQmImINReD+XGa+uWr7lyTNHY/US5IkSRVnqJckSZIqzlAvSZIkVZxz6iVJkqSK80i9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkV9/8BFpnKGMe/LW0AAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for y_label in list(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][\"nodes\"].values()):\n", + " layer_params = list(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][y_label].keys())\n", + " layer_params.remove(\"node_name\")\n", + " layer_params.remove(\"node_type\")\n", + " layer_params.remove(\"node_layer\")\n", + " for param in layer_params:\n", + " if (type(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][y_label][param]) is float or\n", + " type(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][y_label][param]) is int):\n", + " plt.figure(figsize=(12,12))\n", + " total_dots = 0\n", + " for i in range(data.shape[0]):\n", + " node_num = int(y_label.split(\"_\")[-1])\n", + " bm = np.array(params_dictionaries[i][\"chainer\"][\"pipe\"][model_index][\"binary_mask\"])\n", + " if np.sum(bm[node_num, :]) > 0 or np.sum(bm[:, node_num]) > 0:\n", + " total_dots += 1\n", + " plt.scatter(i // 10, \n", + " params_dictionaries[i][\"chainer\"][\"pipe\"][model_index][y_label][param],\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + " if total_dots == 0:\n", + " plt.close()\n", + " continue\n", + " plt.ylabel(y_label + \" \" + param, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \"_\" + param + \".png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python-deep36", + "language": "python", + "name": "deep36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 970c6c3f62e36fcd894a22905bc42fbb9686c423 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 14:43:45 +0300 Subject: [PATCH 273/616] fix: logs with upper case letters --- deeppavlov/evolve.py | 6 +- .../models/evolution/run_param_evolution.py | 219 --------------- .../models/evolution/train_phenotype.py | 23 -- deeppavlov/models/evolution/utils.py | 250 ------------------ 4 files changed, 3 insertions(+), 495 deletions(-) delete mode 100644 deeppavlov/models/evolution/run_param_evolution.py delete mode 100644 deeppavlov/models/evolution/train_phenotype.py delete mode 100644 deeppavlov/models/evolution/utils.py diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 6e5cbf1c4f..dc23364de3 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -238,7 +238,7 @@ def run_population(population, evolution, gpus): shell=True, stdout=PIPE, stderr=PIPE)) for j, proc in enumerate(procs): i = k * len(gpus) + j - log.info(f'wait on {i}th proc') + log.info(f'Waiting on {i}th proc') proc.wait() return None @@ -254,9 +254,9 @@ def results_to_table(population, evolution, considered_metrics, result_file, res evolution.basic_config, "test_best"))[0] + ["test_best"]) if (not validate_best) and test_best: - log.info("validate_best is set to False. Tuning parameters on test") + log.info("Validate_best is set to False. Tuning parameters on test") elif (not validate_best) and (not test_best): - raise ConfigError("validate_best and test_best are set to False. Can not evolve.") + raise ConfigError("Validate_best and test_best are set to False. Can not evolve.") population_metrics = {} for m in considered_metrics: diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py deleted file mode 100644 index 7783de9317..0000000000 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ /dev/null @@ -1,219 +0,0 @@ -import json -import numpy as np -import argparse -from pathlib import Path -from subprocess import Popen, PIPE -import pandas as pd -from copy import deepcopy - -from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution -from deeppavlov.core.common.file import save_json, read_json - - -def score_population(population, population_size, result_file): - global evolution - - population_metrics = {} - for m in CONSIDERED_METRICS: - population_metrics[m] = [] - - for k in range(POPULATION_SIZE // len(gpus) + 1): - procs = [] - for j in range(len(gpus)): - i = k * len(gpus) + j - if i < POPULATION_SIZE: - save_path = Path(evolution.get_value_from_config(population[i], - evolution.main_model_path + ["save_path"])) - load_path = Path(evolution.get_value_from_config(population[i], - evolution.main_model_path + ["load_path"])) - population[i] = evolution.insert_value_or_dict_into_config( - population[i], evolution.main_model_path + ["save_path"], str(save_path.joinpath("model"))) - population[i] = evolution.insert_value_or_dict_into_config( - population[i], evolution.main_model_path + ["load_path"], str(load_path.joinpath("model"))) - - save_path.mkdir(parents=True, exist_ok=True) - f_name = save_path.joinpath("config.json") - save_json(population[i], f_name) - - # __file__ - - procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" - " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], - str(f_name), - str(save_path), - str(save_path) - ), - shell=True, stdout=PIPE, stderr=PIPE)) - for j, proc in enumerate(procs): - i = k * len(gpus) + j - print(f'wait on {i}th proc') - proc.wait() - - for i in range(population_size): - with open(str(Path(evolution.get_value_from_config( - population[i], - evolution.main_model_path + ["save_path"])).parent.joinpath("out.txt")), "r") as fout: - reports_data = fout.read().splitlines()[-2:] - reports = [] - for i in range(2): - try: - reports.append(json.loads(reports_data[i])) - except: - pass - if len(reports) == 2 and "valid" in reports[0].keys() and "test" in reports[1].keys(): - val_results = reports[0] - test_results = reports[1] - elif len(reports) == 1 and "valid" in reports[0].keys(): - val_results = reports[0] - else: - val_results = {} - test_results = {} - for m in CONSIDERED_METRICS: - if "loss" in m: - val_results[m] = 1e6 - test_results[m] = 1e6 - else: - val_results[m] = 0. - test_results[m] = 0. - - result_table_dict = {} - for el in order: - if el == "params": - result_table_dict[el] = [] - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - for m_id, m in enumerate(CONSIDERED_METRICS): - val_metrics_path = list(evolution.find_model_path(val_results, m))[0] - val_m = evolution.get_value_from_config(val_results, val_metrics_path + [m]) - population_metrics[m].append(val_m) - result_table_dict[m + "_valid"].append(val_m) - if TEST: - test_metrics_path = list(evolution.find_model_path(test_results, m))[0] - test_m = evolution.get_value_from_config(test_results, test_metrics_path + [m]) - result_table_dict[m + "_test"].append(test_m) - else: - result_table_dict[m + "_test"].append(0.) - result_table_dict[order[-1]] = [population[i]] - result_table = pd.DataFrame(result_table_dict) - result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - - return population_metrics - - -parser = argparse.ArgumentParser() - -parser.add_argument('--config', help='Please, enter model path to config') -parser.add_argument('--evolve_metric', help='Please, choose target metric out of given in your config.train.metrics') - -parser.add_argument('--p_cross', help='Please, enter probability of crossover', type=float, default=0.2) -parser.add_argument('--pow_cross', help='Please, enter crossover power', type=float, default=0.1) -parser.add_argument('--p_mut', help='Please, enter probability of mutation', type=float, default=1.) -parser.add_argument('--pow_mut', help='Please, enter mutation power', type=float, default=0.1) - -parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) -parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default="0") -parser.add_argument('--train_partition', - help='Please, enter partition of splitted train', default=1) -parser.add_argument('--start_from_population', - help='Please, enter the population number to start from. 0 means from scratch', default=0) -parser.add_argument('--path_to_population', - help='Please, enter the path to population to start from', default="") -parser.add_argument('--elitism_with_weights', - help='Please, enter whether to save elite models with weights or not', default=0) - -args = parser.parse_args() - -CONFIG_FILE = args.config -EVOLVE_METRIC = args.evolve_metric -POPULATION_SIZE = args.p_size -GPU_NUMBER = len(args.gpus) -gpus = [int(gpu) for gpu in args.gpus.split(",")] -TRAIN_PARTITION = int(args.train_partition) -START_FROM_POPULATION = int(args.start_from_population) -PATH_TO_POPULATION = args.path_to_population -ELITISM_WITH_WEIGHTS = int(args.elitism_with_weights) - -P_CROSSOVER = args.p_cross -POW_CROSSOVER = args.pow_cross -P_MUTATION = args.p_mut -POW_MUTATION = args.pow_mut - -with open(CONFIG_FILE, "r") as f: - basic_params = json.load(f) - -print("Given basic params: {}\n".format(json.dumps(basic_params, indent=2))) - -evolution = ParamsEvolution(population_size=POPULATION_SIZE, - p_crossover=P_CROSSOVER, crossover_power=POW_CROSSOVER, - p_mutation=P_MUTATION, mutation_power=POW_MUTATION, - key_main_model="main", - seed=42, - train_partition=TRAIN_PARTITION, - elitism_with_weights=ELITISM_WITH_WEIGHTS, - **basic_params) - -CONSIDERED_METRICS = evolution.get_value_from_config(evolution.basic_config, - list(evolution.find_model_path( - evolution.basic_config, "metrics"))[0] + ["metrics"]) -print(CONSIDERED_METRICS) -TEST = evolution.get_value_from_config(evolution.basic_config, - list(evolution.find_model_path( - evolution.basic_config, "test_best"))[0] + ["test_best"]) - -# Result table -order = deepcopy(CONSIDERED_METRICS) -result_file = Path(evolution.get_value_from_config(evolution.basic_config, - evolution.main_model_path + ["save_path"]) - ).joinpath("result_table.csv") -result_table_columns = [] -result_table_dict = {} -for el in order: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - result_table_columns.extend([el + "_valid", el + "_test"]) - -order.extend(["params"]) -result_table_dict["params"] = [] -result_table_columns.append("params") - -if START_FROM_POPULATION == 0: - result_table = pd.DataFrame(result_table_dict) - result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') - - print("\nIteration #{} starts\n".format(0)) - population = evolution.first_generation() - print(population) - population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - iters = 1 -else: - # _ = evolution.first_generation() - iters = START_FROM_POPULATION - print("\nIteration #{} starts\n".format(iters)) - - population = [] - for i in range(POPULATION_SIZE): - population.append(read_json(Path(PATH_TO_POPULATION).joinpath( - "model_" + str(i)).joinpath("config.json"))) - population[i] = evolution.insert_value_or_dict_into_config( - population[i], evolution.main_model_path + ["save_path"], - str(Path(evolution.get_value_from_config(evolution.basic_config, evolution.main_model_path + ["save_path"]) - ).joinpath("population_" + str(START_FROM_POPULATION)).joinpath("model_" + str(i)))) - - population[i] = evolution.insert_value_or_dict_into_config( - population[i], evolution.main_model_path + ["load_path"], - str(Path(evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"]).parent))) - - population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - print("Population scores: {}".format(population_scores)) - print("\nIteration #{} was done\n".format(iters)) - iters += 1 - -while True: - print("\nIteration #{} starts\n".format(iters)) - population = evolution.next_generation(population, population_scores, iters) - population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - print("Population scores: {}".format(population_scores)) - print("\nIteration #{} was done\n".format(iters)) - iters += 1 - diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py deleted file mode 100644 index 828f798d1c..0000000000 --- a/deeppavlov/models/evolution/train_phenotype.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -Copyright 2017 Neural Networks and Deep Learning lab, MIPT - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import sys - -from deeppavlov.core.commands.train import train_evaluate_model_from_config - - -config_path = sys.argv[1] -print("TRAIN PHENOTYPE") -train_evaluate_model_from_config(config_path) diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py deleted file mode 100644 index bd8b7b349c..0000000000 --- a/deeppavlov/models/evolution/utils.py +++ /dev/null @@ -1,250 +0,0 @@ -""" -Copyright 2017 Neural Networks and Deep Learning lab, MIPT - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import numpy as np -import sys -import hashlib - -from keras.engine.topology import Layer -from deeppavlov.core.common.log import get_logger -from keras import initializers, regularizers, constraints -from keras import backend as K -from keras.layers import Reshape, Lambda, Dense, Flatten -from keras.layers import Concatenate, Multiply, Activation, Dot - -log = get_logger(__name__) - - -def labels2onehot(labels, classes): - """ - Convert labels to one-hot vectors for multi-class multi-label classification - Args: - labels: list of samples where each sample is a list of classes which sample belongs with - classes: array of classes' names - - Returns: - 2d array with one-hot representation of given samples - """ - n_classes = len(classes) - eye = np.eye(n_classes) - y = [] - for sample in labels: - curr = np.zeros(n_classes) - for intent in sample: - if intent not in classes: - log.warning('Unknown intent {} detected'.format(intent)) - curr += eye[np.where(np.array(classes) == 'unknown')[0]].reshape(-1) - else: - curr += eye[np.where(np.array(classes) == intent)[0]].reshape(-1) - y.append(curr) - y = np.asarray(y) - return y - - -def proba2labels(proba, confident_threshold, classes): - """ - Convert vectors of probabilities to labels using confident threshold - (if probability to belong with the class is bigger than confident_threshold, sample belongs with the class; - if no probabilities bigger than confident threshold, sample belongs with the class with the biggest probability) - Args: - proba: list of samples where each sample is a vector of probabilities to belong with given classes - confident_threshold (float): boundary of probability to belong with a class - classes: array of classes' names - - Returns: - array of lists of labels for each sample - """ - y = [] - for sample in proba: - to_add = np.where(sample > confident_threshold)[0] - if len(to_add) > 0: - y.append(np.array(classes)[to_add]) - else: - y.append(np.array([np.array(classes)[np.argmax(sample)]])) - y = np.asarray(y) - return y - - -def proba2onehot(proba, confident_threshold, classes): - """ - Convert vectors of probabilities to one-hot representations using confident threshold - Args: - proba: list of samples where each sample is a vector of probabilities to belong with given classes - confident_threshold: boundary of probability to belong with a class - classes: array of classes' names - - Returns: - 2d array with one-hot representation of given samples - """ - return labels2onehot(proba2labels(proba, confident_threshold, classes), classes) - - -def log_metrics(names, values, updates=None, mode='train'): - """ - Print training and validation data in the following view: - `mode --> updates: 0 names[0]: 0.0 names[1]: 0.0 names[2]: 0.0` - Args: - names: list of names of considered metrics - values: list of values of considered metrics - updates: number of updates - mode: dataset field on which calculation is being doing (i.e "train") - - Returns: - None - """ - sys.stdout.write("\r") # back to previous line - log.info("{} -->\t".format(mode)) - if updates is not None: - log.info("updates: {}\t".format(updates)) - - for id in range(len(names)): - log.info("{}: {}\t".format(names[id], values[id])) - return - - -def md5_hashsum(file_names): - """ - Calculate md5 hash sum of files listed - Args: - file_names: list of file names - - Returns: - hashsum string - """ - hash_md5 = hashlib.md5() - for file_name in file_names: - with open(file_name, "rb") as f: - for chunk in iter(lambda: f.read(4096), b""): - hash_md5.update(chunk) - return hash_md5.hexdigest() - - -class Attention(Layer): - def __init__(self, context_length=None, - W_regularizer=None, b_regularizer=None, - W_constraint=None, b_constraint=None, - use_bias=True, **kwargs): - self.supports_masking = True - self.init = initializers.get('glorot_uniform') - self.W_regularizer = regularizers.get(W_regularizer) - self.b_regularizer = regularizers.get(b_regularizer) - self.W_constraint = constraints.get(W_constraint) - self.b_constraint = constraints.get(b_constraint) - self.use_bias = use_bias - self.context_length = context_length - - super(Attention, self).__init__(**kwargs) - - def build(self, input_shape): - assert len(input_shape) == 3 - - if self.context_length is None: - self.context_length = input_shape[-1] - - self.context = self.add_weight(tuple((self.context_length, input_shape[-1])), - name="context", - initializer=self.init) - - self.W = self.add_weight((2 * input_shape[-1], 1, ), - name="w", - initializer=self.init, - regularizer=self.W_regularizer, - constraint=self.W_constraint) - - if self.use_bias: - self.b = self.add_weight((1, ), - name="b", - initializer='zero', - regularizer=self.b_regularizer, - constraint=self.b_constraint) - else: - self.b = None - - self.built = True - super(Attention, self).build(input_shape) - - def call(self, x, mask=None): - expanded_context_3d = expand_tile_batch_size(memory=x, context=self.context) - expanded_context_4d = expand_tile(expanded_context_3d, axis=1, n_repetitions=K.int_shape(x)[1]) - expanded_x = expand_tile(x, axis=2, n_repetitions=K.int_shape(expanded_context_3d)[1]) - - # now expanded_context_4d and expanded_x are of - # shape (bs, time_steps, context_size, n_features) - - print("attention") - x_full = Concatenate(axis=-1)([expanded_x, expanded_context_4d]) - - print("attention", x_full.shape) - out = K.dot(x_full, self.W) - - print("attention", out.shape) - if self.use_bias: - out = K.bias_add(out, self.b) - - print("attention", out.shape) - out = Activation('softmax')(out) - - print("attention", out.shape) - out = Multiply()([out, expanded_x]) - - print("attention", out.shape) - out = Lambda(lambda x: K.sum(x, axis=1))(out) - - print("attention", out.shape) - return out - - def compute_output_shape(self, input_shape): - return input_shape[0], self.context_length, input_shape[1] - - -def expand_tile(units, axis, n_repetitions=None): - """Expand and tile tensor along given axis - Args: - units: tf tensor with dimensions [batch_size, time_steps, n_input_features] - axis: axis along which expand and tile. Must be 1 or 2 - - """ - assert axis in (1, 2) - repetitions = [1] * (len(K.int_shape(units)) + 1) - - if n_repetitions is None: - repetitions[axis] = K.int_shape(units)[1] - else: - repetitions[axis] = n_repetitions - - if axis == 1: - expanded = Reshape(target_shape=( (1,) + K.int_shape(units)[1:] ))(units) - else: # axis=2 - expanded = Reshape(target_shape=(K.int_shape(units)[1:2] + (1,) + K.int_shape(units)[2:]))(units) - return K.tile(expanded, repetitions) - - -def expand_tile_batch_size(memory, context): - """Expand and tile tensor context along 0 axis up to 0-shape of memory - Args: - memory: tf tensor with dimensions [batch_size, time_steps, n_input_features] - context: tf tensor with dimensions [new_time_steps, n_input_features] - - """ - axis = 0 - # batch_size = K.int_shape(memory)[0] - batch_size = K.shape(memory)[0] - repetitions = [1] * len(K.int_shape(memory)) - repetitions[axis] = batch_size - if axis == 0: - expanded = K.reshape(context, shape=((1,) + K.int_shape(context))) - return K.tile(expanded, repetitions) - From 5f99724b242fc5b0e0a5ba474f4dc48603e8f5a7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 14:53:48 +0300 Subject: [PATCH 274/616] fix: considered metrics is only list --- deeppavlov/evolve.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index dc23364de3..91a9eb5e55 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -99,9 +99,6 @@ def main(): list(evolution.find_model_path( evolution.basic_config, "metrics"))[0] + ["metrics"]) - if type(considered_metrics) is str: - considered_metrics = [considered_metrics] - log.info(considered_metrics) evolve_metric = considered_metrics[0] From 5e10dd9b0f453d52b7750bbb4602ddb1a0aeaf39 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 15:52:01 +0300 Subject: [PATCH 275/616] feat: results analysis file --- .../models/evolution/Results_analysis.ipynb | 1270 ++++++----------- 1 file changed, 449 insertions(+), 821 deletions(-) diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb index 2ea149ff27..f02b70ae0d 100644 --- a/deeppavlov/models/evolution/Results_analysis.ipynb +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -2,11 +2,17 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2018-06-25 14:31:29.12 DEBUG in 'matplotlib.backends'['__init__'] at line 90: backend module://ipykernel.pylab.backend_inline version unknown\n" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -14,149 +20,367 @@ "import matplotlib.pyplot as plt\n", "import copy\n", "import json\n", - "%matplotlib inline" + "%matplotlib inline\n", + "\n", + "from deeppavlov.core.commands.utils import set_deeppavlov_root, expand_path\n", + "from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution" ] }, { - "cell_type": "code", - "execution_count": 62, + "cell_type": "markdown", "metadata": {}, + "source": [ + "## Set here path to your config file, key main model and population size" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Number of populations: 62\n" + "Considered basic config:\n", + "{\n", + " \"dataset_reader\": {\n", + " \"name\": \"basic_classification_reader\",\n", + " \"x\": \"text\",\n", + " \"y\": \"intents\",\n", + " \"data_path\": \"snips\"\n", + " },\n", + " \"dataset_iterator\": {\n", + " \"name\": \"basic_classification_iterator\",\n", + " \"seed\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"field_to_split\": \"train\",\n", + " \"split_fields\": [\n", + " \"train\",\n", + " \"valid\"\n", + " ],\n", + " \"split_proportions\": [\n", + " 0.9,\n", + " 0.1\n", + " ]\n", + " },\n", + " \"chainer\": {\n", + " \"in\": [\n", + " \"x\"\n", + " ],\n", + " \"in_y\": [\n", + " \"y\"\n", + " ],\n", + " \"pipe\": [\n", + " {\n", + " \"id\": \"classes_vocab\",\n", + " \"name\": \"default_vocab\",\n", + " \"fit_on\": [\n", + " \"y\"\n", + " ],\n", + " \"level\": \"token\",\n", + " \"save_path\": \"vocabs/snips_classes.dict\",\n", + " \"load_path\": \"vocabs/snips_classes.dict\"\n", + " },\n", + " {\n", + " \"in\": [\n", + " \"x\"\n", + " ],\n", + " \"out\": [\n", + " \"x_lower\"\n", + " ],\n", + " \"name\": \"str_lower\"\n", + " },\n", + " {\n", + " \"id\": \"my_embedder\",\n", + " \"name\": \"fasttext\",\n", + " \"save_path\": \"embeddings/dstc2_fastText_model.bin\",\n", + " \"load_path\": \"embeddings/dstc2_fastText_model.bin\",\n", + " \"dim\": 100\n", + " },\n", + " {\n", + " \"id\": \"my_tokenizer\",\n", + " \"name\": \"nltk_tokenizer\",\n", + " \"tokenizer\": \"wordpunct_tokenize\"\n", + " },\n", + " {\n", + " \"in\": [\n", + " \"x_lower\"\n", + " ],\n", + " \"in_y\": [\n", + " \"y\"\n", + " ],\n", + " \"out\": [\n", + " \"y_labels\",\n", + " \"y_probas_dict\"\n", + " ],\n", + " \"main\": true,\n", + " \"name\": \"intent_model\",\n", + " \"save_path\": \"evolution/classification/intents_snips\",\n", + " \"load_path\": \"evolution/classification/intents_snips\",\n", + " \"classes\": \"#classes_vocab.keys()\",\n", + " \"kernel_sizes_cnn\": [\n", + " 1,\n", + " 2,\n", + " 3\n", + " ],\n", + " \"filters_cnn\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 100\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"confident_threshold\": {\n", + " \"evolve_choice\": true,\n", + " \"values\": [\n", + " 0.5,\n", + " 1\n", + " ]\n", + " },\n", + " \"optimizer\": \"Adam\",\n", + " \"lear_rate\": {\n", + " \"evolve_range\": [\n", + " 0.0001,\n", + " 0.1\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"lear_rate_decay\": {\n", + " \"evolve_range\": [\n", + " 0.0001,\n", + " 0.1\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"loss\": \"binary_crossentropy\",\n", + " \"text_size\": 15,\n", + " \"coef_reg_cnn\": {\n", + " \"evolve_range\": [\n", + " 1e-06,\n", + " 0.001\n", + " ]\n", + " },\n", + " \"coef_reg_den\": {\n", + " \"evolve_range\": [\n", + " 1e-06,\n", + " 0.001\n", + " ]\n", + " },\n", + " \"dropout_rate\": {\n", + " \"evolve_range\": [\n", + " 0.1,\n", + " 0.9\n", + " ]\n", + " },\n", + " \"dense_size\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 100\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"model_name\": \"cnn_model\",\n", + " \"embedder\": \"#my_embedder\",\n", + " \"tokenizer\": \"#my_tokenizer\"\n", + " }\n", + " ],\n", + " \"out\": [\n", + " \"y_labels\",\n", + " \"y_probas_dict\"\n", + " ]\n", + " },\n", + " \"train\": {\n", + " \"epochs\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"batch_size\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"metrics\": [\n", + " \"classification_accuracy\",\n", + " \"classification_f1\",\n", + " \"classification_roc_auc\"\n", + " ],\n", + " \"validation_patience\": 5,\n", + " \"val_every_n_epochs\": 1,\n", + " \"log_every_n_epochs\": 1,\n", + " \"validate_best\": true,\n", + " \"test_best\": false\n", + " },\n", + " \"metadata\": {\n", + " \"labels\": {\n", + " \"telegram_utils\": \"IntentModel\",\n", + " \"server_utils\": \"KerasIntentModel\"\n", + " },\n", + " \"download\": [\n", + " \"http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz\",\n", + " \"http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz\",\n", + " {\n", + " \"url\": \"http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv\",\n", + " \"subdir\": \"snips\"\n", + " },\n", + " {\n", + " \"url\": \"http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin\",\n", + " \"subdir\": \"embeddings\"\n", + " }\n", + " ]\n", + " }\n", + "}\n" ] } ], "source": [ - "PLOT_TEST = False\n", - "\n", - "TITLE = \"imdb_given_mask_init_part_7\"\n", - "model_index = 4\n", - "POPULATION_SIZE = 10\n", - "\n", - "# TITLE = \"sber_faq_given_mask_init_part_7\"\n", - "# model_index = 4\n", - "# POPULATION_SIZE = 10\n", - "\n", - "# TITLE = \"ag_news_given_mask_init_part_7\"\n", - "# model_index = 4\n", - "# POPULATION_SIZE = 10\n", + "CONFIG_FILE = \"../../configs/evolution/evolve_intents_snips.json\"\n", + "KEY_MAIN_MODEL = \"main\"\n", + "POPULATION_SIZE = 2\n", + " \n", + "with open(CONFIG_FILE, \"r\") as f:\n", + " basic_params = json.load(f)\n", "\n", - "# TITLE = \"snli_given_mask_init_part_6\"\n", - "# model_index = 4\n", - "# POPULATION_SIZE = 10\n", + "set_deeppavlov_root(basic_params)\n", + "print(\"Considered basic config:\\n{}\".format(json.dumps(basic_params, indent=2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2018-06-25 14:52:07.93 INFO in 'deeppavlov.models.evolution.evolution_param_generator'['evolution_param_generator'] at line 55: Main model path in config: ['chainer', 'pipe', 4]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Title name for the considered evolution is `intents_snips`.\n", + "Number of populations: 2.\n" + ] + } + ], + "source": [ + "evolution = ParamsEvolution(population_size=POPULATION_SIZE,\n", + " key_main_model=KEY_MAIN_MODEL,\n", + " **basic_params)\n", "\n", - "# TITLE = \"snli_given_mask_init_part_many_inputs_6\"\n", - "# model_index = 5\n", - "# POPULATION_SIZE = 10\n", + "validate_best = evolution.get_value_from_config(\n", + " evolution.basic_config, list(evolution.find_model_path(\n", + " evolution.basic_config, \"validate_best\"))[0] + [\"validate_best\"])\n", + "test_best = evolution.get_value_from_config(\n", + " evolution.basic_config, list(evolution.find_model_path(\n", + " evolution.basic_config, \"test_best\"))[0] + [\"test_best\"])\n", "\n", - "# TITLE = \"twitter140_one_neuron_init_part_6\"\n", - "# model_index = 4\n", - "# POPULATION_SIZE = 10\n", + "TITLE = str(Path(evolution.get_value_from_config(\n", + " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).stem)\n", + "print(\"Title name for the considered evolution is `{}`.\".format(TITLE))\n", "\n", - "data = pd.read_csv(\"result_tables/result_table_\" + TITLE + \".csv\", sep='\\t')\n", - "print(\"Number of populations: {}\".format(int(data.shape[0] / POPULATION_SIZE)))\n", - "# data.dropna(axis=1, how=\"any\", inplace=True)" + "data = pd.read_csv(str(expand_path(Path(evolution.get_value_from_config(\n", + " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).joinpath(\n", + " \"result_table.csv\"))), sep='\\t')\n", + "print(\"Number of populations: {}.\".format(int(data.shape[0] / POPULATION_SIZE)))\n", + "data.fillna(0., inplace=True)" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "classification_log_loss: best value for VALID \t0 individuum on 0 population\n", - "classification_log_loss: best value for TEST \t0 individuum on 0 population\n", - "classification_accuracy: best value for VALID \t3 individuum on 56 population\n", - "classification_accuracy: best value for TEST \t3 individuum on 55 population\n", - "classification_roc_auc: best value for VALID \t9 individuum on 61 population\n", - "classification_roc_auc: best value for TEST \t9 individuum on 61 population\n", - "classification_f1: best value for VALID \t3 individuum on 56 population\n", - "classification_f1: best value for TEST \t3 individuum on 55 population\n" + "\n", + "Measure: classification_accuracy\n", + "valid:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t1 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "\n", + "Measure: classification_f1\n", + "valid:\n", + "min for\t0 model on\t0 population\n", + "max for\t1 model on\t1 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "\n", + "Measure: classification_roc_auc\n", + "valid:\n", + "min for\t1 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:11: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", - " # This is added back by InteractiveShellApp.init_path()\n", - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:12: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", - " if sys.path[0] == '':\n" + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: 'argmin' is deprecated. Use 'idxmin' instead. The behavior of 'argmin' will be corrected to return the positional minimum in the future. Use 'series.values.argmin' to get the position of the minimum now.\n", + " if __name__ == '__main__':\n", + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", + " # Remove the CWD from sys.path while we load stuff.\n" ] } ], "source": [ - "MEASURES = [\"classification_log_loss\", \n", - " \"classification_accuracy\",\n", - " \"classification_roc_auc\", \n", - " \"classification_f1\"]\n", + "MEASURES = evolution.get_value_from_config(\n", + " evolution.basic_config, list(evolution.find_model_path(\n", + " evolution.basic_config, \"metrics\"))[0] + [\"metrics\"])\n", + "\n", "for measure in MEASURES:\n", - " if (measure == \"classification_log_loss_test\" \n", - " or measure == \"classification_log_loss_valid\"):\n", - " n_best_valid = data[measure + \"_valid\"].argmin()\n", - " n_best_test = data[measure + \"_test\"].argmin()\n", - " else:\n", - " n_best_valid = data[measure + \"_valid\"].argmax()\n", - " n_best_test = data[measure + \"_test\"].argmax()\n", - " print(\"{}: best value for VALID \\t{} individuum on {} population\".format(measure, \n", - " n_best_valid % POPULATION_SIZE, \n", - " n_best_valid // POPULATION_SIZE))\n", - " print(\"{}: best value for TEST \\t{} individuum on {} population\".format(measure, \n", - " n_best_test % POPULATION_SIZE, \n", - " n_best_test // POPULATION_SIZE))\n", - " " + " print(\"\\nMeasure: {}\".format(measure))\n", + " for data_type in [\"valid\", \"test\"]:\n", + " print(\"{}:\".format(data_type))\n", + " argmin = data[measure + \"_\" + data_type].argmin()\n", + " argmax = data[measure + \"_\" + data_type].argmax()\n", + " print(\"min for\\t{} model on\\t{} population\".format(argmin % POPULATION_SIZE,\n", + " argmin // POPULATION_SIZE))\n", + " print(\"max for\\t{} model on\\t{} population\".format(argmax % POPULATION_SIZE,\n", + " argmax // POPULATION_SIZE))" ] }, { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": true - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "cmap = plt.get_cmap('rainbow')\n", - "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", - "color_ids = np.argsort(data.loc[:, \"classification_accuracy_valid\"].values)" + "## If you want to plot measures depending on population colored by evolved measure value" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 50, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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XXpL9FpHcoObXwCeAackNPN4CJhI33pkB7EUZN73JcM5fkOz3O2HCBH/hhRdaqmkRERERkaLM7I0sce1l683c6HlNieO5+jUZ2roY+CQwk7h5yCeJm9xMAuYkMek7VLbkuUVERERE2o32MrI/MynHlDg+OilLzen/J4/thf452p5mZuOJUf30nQBnAhOSc08tiO9GzP/fSpFFvyIiIiIi7Vl7Gdl/LClPMLO8PiVbbx5B7IrzbKUnSLbYGwHc7+7p+fmPJuVJRZ52FLF39jPaiUdEREREOpp2key7+xzgYWJrsc8UHP42UAXckt5j38z2MrMGq2GTm2QV1u0G/ArYAnyz4PAfiV18zjGzCann9AK+mzz8eZkvSURERESkzbWXaTwAlwLPANea2XHENpfvJfbgnwV8oyA+tw1m4VZkv06S+78Ti3N3B04DugPnufu0dLC7rzOzi4mk/3Ezuy153mnEtpx/BG5vkVcoIiIiIrIdtYuRffjn6P4E4EYiyf8yMAr4MXCou7+Zsan7gHeAs4GvAEcSCfv+7l40aU/uznc0cROtfwE+l7TxJeAcbw+3GRYRERERKZMpj2052npTRERERLYHM5vq7hOaims3I/siIiIiItKylOyLiIiIiHRSSvZFRERERDopJfsiIiIiIp2Ukn0RERERkU5Kyb6IiIiISCelZF9EREREpJNSsi8iIiIi0kkp2RcRERER6aSU7IuIiIiIdFJK9kVEREREOikl+yIiIiIinZSSfRERERGRTkrJvoiIiIhIJ6VkX0RERESkk1KyLyIiIiLSSSnZFxERERHppJTsi4iIiIh0Ukr2RUREREQ6KSX7IiIiIiKdlJJ9EREREZFOSsm+iIiIiEgnpWRfRERERKSTUrIvIiIiItJJKdkXEREREemklOyLiIiIiHRSSvZFRERERDopJfsiIiIiIp2Ukn0RERERkU5Kyb6IiIiISCelZF9EREREpJNSsi8iIiIi0kkp2RcRERER6aSU7IuIiIiIdFJK9kVEREREOikl+yIiIiIinZSSfRERERGRTkrJvoiIiIhIJ6VkX0RERESkk1KyLyIiIiLSSSnZFxERERHppJTsi4iIiIh0UmUl+2Z2rZmNa63OiIiIiIhIyyl3ZP+zwCtm9qSZnWtmPVqjUyIiIiIi0nzlJvtnA5OBI4CbgSVmdqWZjW3xnomIiIiISLOUley7+x3ufgKwJ/A/wBbgS8BrZvaomX3IzLq3Qj9FRERERKRMFS3Qdfd57v41YAT1o/1HA78DFpnZf5nZqJbrpoiIiIiIlKtZu/G4+9bUaP9hwBJgAPBvwEwzu8/MDmqBfoqIiIiISJmavfWmmR1tZr8FngCGAiuBa4CngFOAZ83sw809j4iIiIiIlKdbJU8ys/7ABcAlwBjAgKeBnwN/cPd3krhDgD8BVwC3N7+7IiIiIiKSVbn77E80s1uAxcCPiJH864H93X2iu/82l+gDuPvzwG+ATPP3zWyYmd1gZkvMbLOZzTeza5KLi3L6eaSZ3Z08f5OZLTCzB8zspBLxXZOtRKeY2TIz22hms8zsN2a2TznnFhERERFpL8od2X8iKV8lRvFvdvf1TTxncfLTqGRB7zPAQOBuYAZwCHAZcJKZHeHub2Zo59PAdcAG4E5gETAMOAs42cy+6e7fK3jab4EPJbF/AuqA8cD5wEfN7GR3f7Spc4uIiIiItCfm7tmDY27+z919Sot3xOzPwAnA5939J6n6q4AvAte7+6eaaKM7sWagJ/Aed5+ZOjYOeBHYBvR3981J/cHA88QFzCHuvjH1nAuBG4DH3P3Ypl7DhAkT/IUXXsj4ikVEREREKmNmU919QlNx5e6z/9FWSvRHEYn+fOBnBYe/RYzSn2dmVU00tRNQA8xKJ/oA7j4dmAX0BvqmDu2RlJPTiX7i7qQckOFliIiIiIi0K+XO2R9gZkeZWb8Sx6uT47uU2Y9jkvJhd9+WPuDudcTi3z7AoU20s4IY2R9jZqML+jYGGA28VDAd6NWkPNbMehe094GkfCTTqxARERERaUfK3Xrzm8C9wLsljr+bHP9ame2OTcpZJY6/npRjGmvEY07SZ4jXNdXMbjKzH5jZzcBUIrE/u+A5rwBXA/sCM8zsZ8lNwe4Ffg3cRrxuEREREZEOpdwFuscDfyky3QUAd99gZg8DJwJfLqPdmqRcW+J4rr62qYbc/Q9mtoS4m+/HU4eWEzsDzS3ynC+Z2Uwi6b80dWgqcJO7byh1PjO7hNiClBEjRjTVPRERERGR7abckf3hwJwmYuYmcW3CzD5GTLuZAowjpv+MAyYDPyVG6tPxZmbXEmsF/pPoez9gIuDAg2b2mVLnc/dfuPsEd58wYICm9ouIiIhI+1Fusu9AjyZiegBdy2w3N3JfU+J4rn5NY40k8/JvIKbrnOfuM9z9bXefAZxHjNSfbWaTUk87H/gccK27/5e7L3L39e7+FHAq8DbwX2bWFxERERGRDqTcZH8mMUWnKDOz5PjsCtqF0nPyc4ttS83pzzkB6A48UWSh7zbgyeThQalDuUW4jxU25u7LiP3++1K/rkBEREREpEMoN9n/I7CXmf20cOea5PFPiaT49jLbzSXaJ5hZXp+SnX+OADYCzzbRTs+kLDWfJle/pZnPERERERFp98pN9q8FpgGfBl43s9+a2Q+Tm229ntRPA64pp1F3nwM8DIwkdtNJ+zZQBdySXihrZnuZ2V4Fsbl7AHzQzPZLHzCz9wAfJKYiPVrkOV8ys5qC53yKuPvuMuC1cl6TiIiIiEhbK+sOugBmVgtcB3yI/IuFbcTi18+6e6Nz60u0Owp4BhhI3MxqOvBeYg/+WcDh6f3xzcwB3N0K2rkBuJAYib8TeIO4iDiDWE9wjbt/MRXfl9jHfz9in/57iLUBBwLHEtuJfsjd/9TUa9AddEVERERke8h6B92yk/3UCQYABxPbYa4Bnnf3VRU1Vt/mcGJHnJOAnYGlRML+bXdfXRBbKtk3YtHtBcD+xM4664AXgV+6e95uPMlz+gJfAs4i1gf0IG7O9RRwpbs/n6X/SvZFREREZHto9WRfGlKyLyIiIiLbQ9Zkv9w5+yIiIiIi0kGUewddAMzsYGKLzaHU72aT5u7+ieZ0TEREREREmqesZD+ZD38j8DHAiJ1t0nPmPVWvZF9EREREpA2VO43ns8SdaG8BJhCJ/TXA4cDXgTpiR549WrCPIiIiIiJSgXKn8ZwPzHT3CwBioJ817v4s8KyZ/Zm48dVfgN+0YD9FRERERKRM5Y7s70X+DakgdcHg7i8C9wGXNrNfIiIiIiLSTJXsxrM29e8NwE4Fx18nLgpERERERKQNlZvsLyZ24MmZCxxUEDOauAgQEREREZE2VG6y/zz5yf2DwCFm9h9mto+ZfQY4nZi3LyIiIiIibajcZP8OoKuZ7Z48/h/gDeDbwDTgJ8Aa4N9brIciIiIiIlKRsnbjcfe7gLtSj98yswOAi4FRwHzgZndf2pKdFBERERGR8lV0B900d18LXNkCfRERERERkRZU1jQeM3vXzG5trc6IiIiIiEjLKXfOfh2woDU6IiIiIiIiLavcZP9FYO/W6IiIiIiIiLSscpP9/wZOMbPjW6MzIiIiIiLScspdoDsQeAh40MzuAv4GLAO8MNDdb25+90REREREpFLlJvs3Eom9AWclP5Cf7FvyWMm+iIiIiEgbKjfZv7BVeiEiIiIiIi2u3Jtq3dRaHRERERERkZZV7gJdERERERHpIJTsi4iIiIh0UmVN4zGzuRlD3d1HVdAfERERERFpIeUu0O1CkW02gVqgJvn3EuCd5nRKRERERESar9wFuiNLHTOzPYFrgSrgxOZ1S0REREREmqvF5uy7+2xi3/2hwLdaql0REREREalMiy7QdfdNwF+Aj7RkuyIiIiIiUr7W2I1nKzC4FdoVEREREZEytGiyb2a7AGcCC1uyXRERERERKV+5W29e3kg7w4HTiV15vtbMfomIiIiISDOVu/XmFU0cXwd8193/p7LuiIiIiIhISyk32T+mRP02YDUww923Nq9LIiIiIiLSEsrdZ/+J1uqIiIiIiIi0rNbYjUdERERERNqBchfong18GviYuy8pcnwocDPwM3f/U8t0UUREREQ6rGnT4E9/ggULYMQIOOss2G+/tu7VDqPcOfv/CtQWS/QB3H2xmdUkcUr2RURERHZk06bBlVdC//4wbBisXh2Pv/KVhgn/tGlw5531FwVnnln8oiDrxcPL0+CuVHtnnAnjS7TXkudtZ8zdswebLQHuc/dLGon5OXCquw9rgf51KBMmTPAXXnhh+570C1+Al17avucUERERyWL+fNi6Fbqlxpdzj0eOrK/bsAEWLoz6bt0iZutWGD4cqqoqi1uUxHXtBu8mccOa0V4ubuhQOPTQuHApdtGynZjZVHef0FRcuXP2dwJWNBHzJrBLme2KiIiISEezYQO8MR9mzoxyw4b845s35yf6EI83b86vW7myPuHOxXTrFvWVxK1KxVkqblWF7aXjzOKbiv79Y6S/nSt3Gs8qYHQTMaOBNZV1R8p2zTVt3QMRERHZEb08Da6+CmproaYG1q6FNWvgi1+qny5zxRUxAt6/f/3zco+vuKK+7hOfiGk+XVLj0Nu2waJF8Otf19dddFHpuBtuqK+7uJH2fplqrznnramJKT3tXLkj+08Dp5nZXsUOmtk44i66U5rbMRERERFpx+66MxL9/v0jCe7fPx7fdWd9zFlnRXK/enUk0bl/n3VWflsjRsTFQtratVHfkeLaoXKT/SuJbwOeMrPPm9kYM6tKysuIJL9rEiciIiIindWCBTG6nVY42r3ffjGvvX//GC3v37/4PPczz4Q1BRcFa1ZHfVrWi4czzoxvGfLaWxP1rXnedqisBboAZnYx8DMiqS/0LnCpu/+qBfrW4bTJAl0RERGRtvCdb5eeovMf3yq/Pe3GU5asC3TLTvaTxscBlwLvBWqJOfrPAj939+llN9hJKNkXERGRHUaWOfvSarIm++Uu0AUgSeg/V8lzRURERKQTGL9fJPbp0fMLL1Ki385UlOyLiIiIiDB+PyX37VxZC3TN7Gwze9TMhpQ4PtTMJptZ+1+tICIiIiLSyZW7G8+/ArXuvqTYQXdfDNQkcSIiIiIi0obKTfbHA02tQP0boO9zRERERETaWLnJ/k7AiiZi3gR2qaw7IiIiIiLSUspN9lcBo5uIGU1sxSkiIiIiIm2o3GT/aeA0M9ur2MFk//3TiTvpls3MhpnZDWa2xMw2m9l8M7vGzPo3/ey8do40s7uT528yswVm9oCZndTE8z5oZn82s1Wp591tZodW8npERERERNpSucn+lcR2nU+Z2efNbIyZVSXlZUSS3zWJK4uZjQKmAhcCzwNXA3OBy4C/mtnOGdv5dNKP45LyauAJ4GjgQTP7RpHndDOzW4E/ALsDtwNXAY8AY4CDyn09IiIiIiJtrew76JrZxcDPiKS+0LvApe7+q7I7YvZn4ATg8+7+k1T9VcAXgevd/VNNtNEdWAn0BN7j7jNTx8YBLwLbgP7uvjl17HvA14HvAZe7+7bCdt39naZeg+6gKyIiIiLbQ9Y76Jad7CeNjwMuBd4L1BJz9J8Ffp7cXbfc9kYBs4H5wKh0sm1m/YClgAED3X1DI+0MApYB09x9/yLHpxE7Cu3i7m8mdYOBN4C/u/th5fY9Tcm+iIiIiGwPWZP9iu6gmyT0n6vkuSUck5QPF46qu3udmT1NjPofCkxupJ0VxMj+GDMb7e6v5w6Y2Rhi8fBLuUQ/8UGgB3CbmfUG3g/sCdQBT7n7P5r30kRERERE2kZFyX4rGJuUs0ocf51I9sfQSLLv7m5mnwH+D5hqZncCS4ChwJnAq8A5BU87OCn7ADOAEemDZnYH8HF335j51YiIiIiItAMVJ/tm1pXYT79nsePuvqCM5mqScm2J47n62qYacvc/mNkS4HfAx1OHlgO/IRb9pg1Myu8Quw2dQVx07Av8FPgXYD1wQbHzmdklwCUAI0aMKBYiIiIiIh3dy9PgrjthwQIYMQLOOBPGt//7yJa7Gw9mNt7M7iemuSwB5hX5KUyotxsz+xixi84UYBwxYj8gwM7hAAAgAElEQVSO+Ebgp8BtBU/JvQdvAae6+4vuvsHdnwNOIxL988xsaLHzufsv3H2Cu08YMGBAy78gEREREWlbL0+Dq6+C1ath2LAor74q6tu5skb2k4W5zyQP/wKcCvyDGDU/kBjpfwwoZ1Qf6kfua0ocz9U3erOuZF7+DcA04LzU/P8ZZnYeMV3obDOb5O6PF7Q52d3Xpdtz96Vm9hyxjecEYHHG1yMiIiJt6eWX4e47YeFCGD4cTj8Txo9v615JR3XXnVBbC/2TWz/lyrvubPej++WO7H8T6A4c7u6nJ3V3uvtJxP70vwH2Bi4vs93cFpljShzP3bW31Jz+nBOS/j1RZKHvNuDJ5GF63/zcuUtdSKxOyt5NnFtERETag5dfhh9fBWtWw9ChUf74qqgXqcSCBVBTMCZdUxP17Vy5yf4k4D53T/+1GECyJeYnieT4O2W2+1hSnmBmeX1Ktt48AthIbO/ZmNz6gVLzaXL1W1J1jyTlviWes09Szmvi3CIiItIe3J2Mwtb2hy5doqytjXqRSowYAWsLlpauXRv17Vy5yf4uxM44OVuJOfEAuPtWInE/oZxG3X0O8DAwEvhMweFvA1XALek99s1sLzPbqyB2SlJ+0MzyvlMxs/cQ22w68GjBc14CjjSzMwueczEx3382oA30RUREOoKFC6G6YBS2uibqRSpxxpmwZk3M1d+2Lco1a6K+nSt3N563gL6px6so2KqSGDUvNfe+MZcS6wGuNbPjgOnETbuOIabvfKMgPnfzLstVuPvzZvYb4ELgb8nWm28QFxFnEPvpX+Pur6ae42Z2PvAEcIeZ3Zucbx/gZGADcL67v1vBaxIREZHtbfjwmLpT27++bt3aqBepxPj94AOnwv9eB0uWwJAh8KlL2/18fSh/ZH8OkTjnTAWON7OBAGZWBZxOBVNektH9CcCNRJL/ZWAU8GPg0IIbYTXmE0Sy/1fgxKSd44GngI+4+xeLnHsascD4ZmLf/S8ABwC3Age5+zOFzxEREZF26vRkFHZNMgq7JhmFPb39j8JKO/Xyy/DAfbDffvChD0f5wH0dYh2IuXv2YLPLgX8DBrn7BjN7P3AvsJQYlT8I2A34srtf0wr9bdcmTJjgL7yg2T4iIiKt5tWX4d6763fZOfV02KfILjstvRvPKy/DPXfBogUwbAScdgbsW6S9rHGvvQL33w2LFsKw4fD+02HvIssHy4l74B5YvAiGDoNTTiseN/0VePBeWLwQhg6Hk0+Fcc1oL0v/sn5mr74M96Xa+kAz47J+Fll89z8bfluUe/zNcvelaRlmNtXdJzQZV2ayvytwFLFN5aqk7jLgW8QNrzYC1wLfLNwNZ0egZF9ERKQVvfoy/OSaSLCqq2Hduki4PveF4sleFlmS31dehmuvjkW+1TUxJWjNGvj8F/OTx6xxr70C1/0YamrrX8faNXDpZfmJcjlx/3ttxPWrhrok7lOfz4+b/gpc/5OGcZ/8XP5rztpelv5l/cxefRl+VqStz1xWWVzWzyKrT14cOzt1SU2K2bYNFi+G639ZfnstIGuyX9Y0Hndf6u635xL9pO7HxC43uwL93P3rO2KiLyIiIq3s3rvrd9bp0qV+x517766svVzyu3YN7Do0yut/EvVp99xVfHefe+6qLO7+uyNZTb+OmtqoryTugXuiviaJy/37gXvy4x68t3jcg/dW1l6W/mX9zO4r0dZ9FcZl/SwgPu+rfgBfvjTKws8f4huJdQW78XSQdSBl30G3GHd/192Xe4mvCczsfDN7tNgxERERkUwWLozR3LTq6sp32cma/C5aUHx3n0ULKowr8ToWLWwYt2UzPP80TH4wyi2bG8YtXhQj8Gn9qqM+L25hibgi7W3eBM89DY88GOXmTQ3by/I6sn5m5bwnmeIyfhZZL/g68DqQcnfjqdRI4OjtdC4RERHpjIYPjwSrtra+bt26ykdXFy+MBC+tWPI7bETx3X2GjagwrsTrGFbwOnr1hOefgX79oG+/SLhf+Csccnh+3NBh8MYcWLUc1tdF7C6DYLdRBXHDs8X17AFPPQpbt8K7W2HNm7B0IRx5bMPXMS/X3jroWx3t7Z5qb/hwmD8H3kydc+dBMHJUw7bmF+lbsbg35sLq5bB+PfTtC/0HwW57FMSNgAVzYe1K2FAHVf2gZgCMKIhLX/BBffngvflTm8aPh8u+lL8O5PyLOsRdmVtkZF9ERESk1Z16ev2I6rZt9SOtp55eWXtDh8d89LS6dVGfdtoZxUd1Tzujsrj3nx4jyOnXsXZN1Kd17QrucYcgiNI96tP23gde+XtcWPSpivKVv0d92ri9o74uiatL4sbtnR+3LunPu1uhR88o166J+sLzvpw7b98oXy447777pmKq6mP2LVgXsc++xV/DPgVx4/eF6S/GxVGfqiinvxj1afvvBzP/EZ9n76ooZ/4j6tOyftsBkdh/8/KYo//NyztEog9K9kVERKSj2Gd8LOysrY2FkbW1zVuce/KpkcSuTZLu3L9PPjU/bt/xsbCztj8sWRRlsYWeWeP23jcWsdbWwpLkdRQuuoWYsnPokdCrF9TVRXnokVGfNmcGHHBwTFNZvz7KAw6O+mJx/Wpgw/ooi8W9MReGDIOevWDLliiHDIv6tNdnwEG589ZFedDBUZ8zewZMOCSO1SV9m3BI1KfNngEHHpL/Gg4sEjd3ZvFzzp1Z8BpmwYSDI3FfXxflhIOjPi3rBV8Htr2m8YiIiIg03z7jK0/uC43bN3aiSe/Gc855xbei3Hd8tl1cssbtvW/xrSzThg6Pi4/Dj6qvW7sGagbnxy1ZBLvtnj99Ztu2qE9buhhG7J4/NWbbtqhPM6B3b9hpp/q6t9+Gre+Uf94li2Dk7rBHE33LGrd0cUzFGbln469hyaKY2rN7QVxheyefGnP0IX/noXPOo7NQsi8iIiI7rnH7Fk/u24OTToVfFElEP1yQiA4ZFlNsqlNrANavi/q0XYfG9JjCuMJ1CwccDH+dAmbQsyds3hyj44dNLH7emlR7dQXnzRJTTlzW15D1PSnngq+D0jQeERERke1t5mtw3Q/hm5dFOfO1hjHj9oFLPhcJ8NLFUV7yuahPO/4DsHZtJLfbtiVz7tdGfWHcuoK4dUXizr0oRtiNmBNvxONzL8qPO+nUOE/eNKi1UV9OTDlxWV/Dce8vHnfc+4u8z/vCl74GP7ouyk6U6EOZN9Wq+CRm3wIud/euTQZ3YLqploiISCub+RpMvj+mYwwZFsnb2L0bxs16DSY/AEsXwa7D4LhTYEyRuLYw8zW46bqYb963Okac162F8y8t/lqymPEq/OW++vfl+A/AXvs0jJuZxC1dHKPhx38AxhaJy9rejFfhoXvr4046tWFclphy4rK+hqy/Kx1Uq9xBtxmdUbIvIiIizZM1SZ71Gtz8c+hXG1s4rq+DujXw8U83TPhnT4fHH4Rli2HwUJh0Muw5ruG5X38NHkvFHXMyjC6SOD5yH9x+A6xcBgMGw4cvgvcVjDpf98NY7LpqWWqbycExx/zSr1bWv0fuhdt+AyuWwcDBcM6F8L5TG8ZJp9Eqd9AVERERaTOT749Evzq5CVZ1bTyefH9B3AOR6FfXJHE18XjyA/lxs6fDrdfHFpQDd43y1uujPu3114rHvV4w9eaR++Da78Zc850HRnntd6M+bcYrMP0fsPgNeGtFlNP/EfWV9O+Re+Ga78XxXQZGec33ol52eNsr2X8JuHk7nUtEREQ6oyWLYkQ/rW91kR1bFsVoeV5cv6hPe/zB5EIguSjoVxOPH38wP+6xB+NYOq5fTdSn3X5D7DffrzqJq47Ht9+QH7dqKax9K/7dvWeUa9+K+kr6d9tvoKoqP66qKuplh7ddkn13v9vdL9we5xIREZFOasiwmLqTVnTXmWExPSYvri7q05YtjjurplX1i/pK4lYug6q+BXF9oz5ty9tgXeIGWXiU1iXqKznvimXF41YUnFd2SGVvvWlmOwEXAYcA/YFi8/Dd3Y9rZt9ERESkJc2eDk88BMsXw6ChcPRJxed/t1fHvT/m7EP+nP0zzy2IOyXm7EP+nP0zP5IfN3hoTHnpV1Nft6Eu6iuJGzA4pu6k78i6YX3Up1VVQbfucZOpzZviplX9+0PPHg3Pu3BezO3fsD4uHHYZDMN3z48bOLh4/wYWnBdgznR4MvU7cNRJMKoD/Q5I2coa2TezvYDXgP8GPggcB0wq8SMiIiLtxezp8LtfRFI4IJn//btfNJz/3Z6N3TsW41bXwrIlURbbwWbM3rEYt7oGli+Jstji3Eknx8VC3drYnrFubTyedHJ+3DEnx7F0XN3aqE/78EWwcX0k/Nu2RblxfdSn7XMgsA12HQyjx0TJtqQ+ZY+x8OqLcVHTuyrKV1+M+rRzLoQNG/L7t2FD1KfNmQ63FfwO3PaLqJdOq9yR/SuBgcB/Ab8AFrr7uy3eKxEREWlZTzxUP9cc6ssnHmo4uv/EA3DPLfDmcth5EJx2Hhx9Sn7M3Bnw1EORTA8aAkeeBHvs1fC8WePmzIApqbiJJ8GoInHdukCf7tCvZ5TdSoxbdu0CvbtBVY8ouxaJ23McnPvJ/N1uTj2n4fsxeu+IS+/Gc9o5DXfjye26k96N51+/0HA3nrPOi9H6t1bFtw49e8ZdYc8quFnWvFmw74HR1vp10LcGdh8b9aT2i8/tupPejefiLzTcjefJEr8DTz6k0f1OrKytN81sLfCku2svpyK09aaIiLRb3/9KjOZ2SSW927bByqXw9Svr6554AH79P9C7L/Spgo0b4O318Il/q0/4586AP/wyks+qfjFlZP1aOPvi/EQ+a9ycGfD7X8bUnH/GrYMPXZyf8Ld0XFvKMqUq62eW1X810t6/V9CetKmsW2+WO7JvxDQeERER6UgGlZh3Pqhg3vk9t0Sin9vNJlfec0t9sv/UQ5HAF44QP/VQfhL/1EPw7rswbwZsrIM+/WCngQ3jpjwUiXlhe1Meyk/OWzquLe05run1Elk/s6xauj3pEMrdjWcqMLbJKBEREWlfjj6p+Lzzo0/Kj3tzeYzop/Wpivqc5Uvgnc3wyvPw3KNRvrM56tNmvwYLZsOWzXEBsWVzPJ5dMG64fAm8swVe/Rv87dEo39nSsL3ceV9+Hp6bHGWx8y5fUnx3msK49i7rZ5bVUSXaO6rC9qRDKDfZ/0/gFDOb1Ap9ERERkday5zj4yCUxqrtyaZQfuaTh6PLOg2LqTtrGDVGf06MHvDY1kvc+VVG+NjXq8563HsygR8/8cuP6/LiePWD6C/kXBdNfaLg7TY8e8GpB3KsvNDzvoCExYp22oS7qO5Ksn1lWo8bBOQXtnXOJ5ut3cuVO4xkO3A08bGa/I0b61xQLdHfdREtERKQ9yTJ15LTzYs4+5M/Z/+il9TFdu8be8Lllf0487lqwG3ff6vhGYP0a8HfBukK3Hg2njXTtWt8G1Ldd2F7XLsk5Uyd2Gi6+nXhSzNmH/Dn7p3y48dfeHmX5zMoxatyOk9zPmwl/fRhWLoEBQ+CwE2KB8w6m3GT/RuLPyoDzkp/CFb6W1CnZFxER6Why8/LTu/F89NL83Xje2QL7HQIL58ZuMn37RUL6zpb8tnYeCAteBxy2OXT1yBJ2Hpgft7VYe3tHfdo7m2H/Q2DB3Ejgq5K4dzbnx43aKxbjpnf3OeXD7We+vrS+eTPhrhugqhp2HhwXe3fdAGdctMMl/OUm+7oLroiISHPNnwnP/iWmUgzYFQ49HkYWSUDmz4TnHoFVS2CXIfDe9xWPe/rP8OCt8OaKSKRPPheOOLHy9kaOgkMn1o+IjhyVf3zgEFgyH7p1hR5do9y0EYaMzI/rkoz/delSUFqR9t6ItnolbW5+G4bsVhA3FJbOj+Pv5OI2wq4F54VI7JXc77j++nAk+n2TG5zlyr8+rGS/Me5+U2t1REREZIcwfybc/ZtkxHFQjDje/Rs4/cL8xHv+TLj3xvqRyQ3r4vGpF+THPf1nuOVHcdOl/rvEiPctP4pj6YQ/a3tvzIT7boq4XZK4+26CD5wPuyVxI0fDE/fEOfv0jZhVS+Hw4/Nf65pV0LM7vL01HhvxeM2q/LiRY/LbW78uLoQK29tjDDx6B3QlLhg2roZlC+CIExq+zwtmwQuTo1+77AoTjoMRY0p/LtK5rFwSv+dpffpG/Q6m3AW6IiIiUsqCWXDnz+FX34pywayGMc/+pX7EsUuXZC/46qhPe+6RqK+qButS/+/nHsmPe/DWSJKr+kV7Vf3i8YO3Vtbe85OLxz0/uT5myVzY+4A418YNUe59QNSnvV0HvXrDsJGw26goe/WO+rSlc2Cfgvb2OSDq06b/Dap7Q7du8O62KKt7R33h5/DAzXERsvOgKB+4ufjnIZ3TgCENF4JvXB/1O5hyp/EAYGZ9gLOAA4BaYC3wd+BOd9/Q2HNFREQ6pQWz4KGboU817DQoRtgfuhlO+nj+iPLKpfk720Ay4rg0v25ViZHJVQUjk2+uiBH9tN5VUV9JeyuXxIh+g/4tyY8ZMgKGjqyv820NR02ra2Jrxy2boXuPmNO/zaM+r29Li7e3quA9mTMNanaC7r3q697ZFPVpL0yOC4aqZOpGrnxhskb3dxSHnRBz9CF+fzeuj4u+4z/Ytv1qA2Un+2Z2CnATsBPxhVyOA1eb2YXufl8L9U9ERKRjmDo5Ev3CBHNqQYI5YNeYppKbQwzJiOOu+e3tMiSSk6qCuF0KRiZ3Hli/WDXn7Q0NF8FmbW9Aibj0iGiWGIA9xkHP3rByWX0fh+6Wn9RDTLNZvgDWrYqdf3r3hepdYNCI/Dgz2LQJ1q2OxbvdekCPZKQ/bVWJC6rCi4dyLJgVn+Wby+Ki6SBNC2rXdh8bi3HTu/Ec/8Edbr4+lDmNx8wOBP5EjObfClwEnJyUtyb1fzSzg1q4nyIiIu3bm8sioUzr0zfq0w49PhLl9evixkbr18XjQwvmp7/3fVG/YV2Mcuf+/d735cedfG4k9xvqor0NdfH45HMra++Q44rHHXJceTEAB78PenSD0XvD4cdF2aNb1KcNHw3zX4277PbsE+X8V6M+bfAIWL08dt/p2j3K1cujPm2XXYtP4dil4IIqq9y3Nhvq8r+10bSg9m33sfDRz8FlP4hyB0z0ofw5+98gRvAnuvvH3f1Gd/9zUn4cODI5/vWW7qiIiEi7tvPg4glm4dSZkWNjMW5uD/q+1Q0X5+biTr0gRs/fXBZl4WJaiEW45305Rs1Xr4ryvC833I0na3u7jY3FuFXVsCqJSy/OzRoDsNsYOCV3zuVRnnJB1Kctmwuj9oM+/WDThihH7Rf1af1qoW8NdOkaiX6XrvG4X21+3ITjIiHPuxipi/pKpL+1ya1j6FMd9SLtXLnTeCYCf3D3Z4sddPfnzOyPQJH9vkRERDqxg46L0V6onyO8cR0cdUbD2JFji295WWncEScW32qz0vZ2G9swca8kBiKxL0zuC725FAYPg11TI/S+LerT3n0Hxh8W22/mpvvsOjLq00aMgVM+nr8bz9FnVj7t5s1lMaKfVuxbG5F2qNxkvwZY2ETMAqC6iRgREZHOZcQYeM/R8OjtsGYl1A6AYz+sed1Z7Lxr8TUAOxdMu9l5cIzQ731wfd2GdVBVsEAZ4n1vqfc+d94G/Rtc+jki7US503iWAIc0ETMBaMYKGBERkQ5o4eswbQqMGAuHnhTltClRL4078NiYp5+edrOxLurTDjouvi3Ji1sX9a2prc4r0gLKTfYfAI41s383s67pA2bWxcy+DLwviRMREekcFr0O91wPN347ykVFEvi/PxpzzfPmdfeLemnc8NFw4nnxnr2VzO0/8byGC3RHjImtTKv6JXH9Gm5t2hra6rwiLcDcPXuw2WBgKjCYmK4zhRjFH0wszh0JLAMmuPsON7o/YcIEf+GFF9q6GyIi0pIWvQ4P3xKJe+++MVd8Yx2ccB4MSyWjN1wR87otNY7m2yI5vOiK7d1r6SwWvR4XjG8tg50Gx7cdw0Y3/Tzp9MxsqrtPaCqurDn77r7MzI4ArgeOB3YrCPkL8KkdMdEXEZFOKjdi3yeZr50r//5oftKVdd55R6AEs31IX2j2HxhThx6+peGFpkgjyp3Gg7vPd/cTgeHAacB5STnc3U9093kt3EcREZG289ayGNFP69036tOyzjtv73IJ5sZ1+QlmsalL0rrSF5rWJUpNDZMylX0H3Rx3XwwsbsG+iIiItD87DY6Et09qxP7t9VGflpt3/vdHY8vInXeFiWc0nHfe3mX9JkNa31vL4oIrrdiFpkgjKk72RUREdggHHhsj25A/Z//IIvvnG9DVoFuXKG279rRlKMFsP7JeaLaGxa/DS4/D6mXQfzC8ZxIM7aQXe4teh5ceq5+29p5jOtWFbaPJvpndQHJHXHdfnjzOwt39E83unYiISFsbNjrmSKfnsB95RsNkYNHr8MitqfnVdfH4fed2rMShLRNMyVfOhWZLWvw6TP4t9O4Htcnv8uTfwnEf7XwJf2f5u21EUyP7FxDJ/n8Dy5PHWTigZF9ERLJp76OIw0Y3/T/+lx5Lpr/0i8e58qXHOlbS0FYJ5o5myWx4+QlYvRz6D4LxR8OQPfNjsl5otvR5X3octr0bf5eb1kOvvnGTuJceb/h3uTjXXvK3O/5oGFrQXpZzQvb/DmQ5Zy7uH6n29p/UMK6z/N02oqlkf/ekXFzwWEREpGV0llHEotNfqjre9JfWSDCzWDobXnmyPiHc9yjYtUgC1xksmQ1P/C75nR8Ab9fF46M/Ujzhz/LeZ3n/sp530evw5mLo3gt6VcE7W2DJHNi8Kb+9xbPh8d/l/+0+/juY9JH6pDrrObP+dyDLOXNxj94Kvavr4x69FY49Nz+us/zdNqLRZN/d32jssYiISLO99HjsXrNsTv0oYvUuxUcR27OdBkdCkRsZBHh7Q8ec/pI1wWwpS2fDk7fHNwm5hPDJ2+GoD1ee8C+dA68+CWuWQ+0g2Oco2HVUy/a7Ui8/Eclq7+R3JVe+/ETxEe+mZH3/sp737TqgC3TvEY+794iE/+264q+jcFT85SfqE+qs53zp8eJtFf53IMs5IUb0e1c3jPvH4/lxnenvtoSytt40s8vN7KgmYiaa2eXN65aIiOwwFr8Oy+fC1s0xirh1czxe3MG2enzPMZE0bKyLi5fcv99zTFv3rP175clIVHv3iy0meyc3MHvlycraWzoHnrotktOaJPl96raobw9WL4/f9bReVVFfiazvX9bz9ukLbIN3NoN7lGxL6tPtLYtR8LTeVVFf7jmztNUacTvA3225u/Fckfw09td3FPAt4D8r65KIiOxQNm8ADLr1jMfdesYo4uYNbdqtsg0bHYv60rt6HH5a8RHyrKPOS+fAa1Pq4/aeWDxu2RyYnoobNxEGN6O97W318hiRTmtO8vvqk9CryGjyq0+2j9fbf1BcgPROjSZv2hD1lcj6/mU977Cx0L03rF1Z/23bLsNg0IiC9kqMivdPjYpnPWeWtlojrpy/2w6q7JtqZdAd2NYK7YqISGfUtxq2bYMtySjils3xuG91089tb4aNhg9cAh+/PMpSiX6WUeelc+Dp3+fHPf37hnHL5sAzBXHP/D7qK2mvLfQfFAlgWnOS3zUlRpPXVHjx0NLGHx3v/9vJaHLu3+OPrqy9rO9f1vPuPwm6dYVhY2D8xCi7dY36Yu2lR8UL28t6zvdMKt7Weyo4Z+41vL2uIG5dw9cA2f5uIf6mHrsR7vzvKAv/xtqp1kj2DwRWtUK7IiLSGQ0fEzee6tEzRhF79IzHw8e0dc9aR3rUOTflole/qE97bUqMqObF9Y36tOkl4qZPqay9trDvUbHrT15CuD7qK1FbIvmtrfDioaUN2TMWqPbuB2tWRllscW5WWd+/IXvCvhPhzfkw44ko953Y8LxD94yFrH36wZoVURYubM3FTfpIflzhQtmsr3Xo6FiMm26r2CL9LOcs5zVklfWiuh1qchqPmRXek/kCM5tUJLQrMBzYDfhd87smIiI7hH2OgtW3wcixMfq6aQNsqov6zmjN8kgW0oqNOrdVXFbL5sKMKbB2BdQMhL0mwuA9Kmtr1z1jMWl6N5mD31/54tx9jopvSyD/d2rC+ytrrzUM2bPy5L5Q1vdv2VyY82yct9f+8b7MeRYGjGj42Q3dM1tinCUu62sdOjrbovyW7FtW6YtqqC+nTyk+Za4dyTJnf1Lq3w6MTH4KbQPeBG4HvtjMfomIyI5i11Fw5Dn5c9gnvL99zK1uDbUl5jAXjjq3VVwWy+bCs7+HnsnOSW/XxeNDP9S8hL+lttrc0X6nINv7N2NKfGaFCeuMKZV/bjuKlr5Y3o6aTPbd/Z9TfcxsG3CFu2vxrYiItJxdR2VLxFbMg9lPw7oVUD0Q9jwCBha5BcyKuTArFTfmCBhYJJlZnsTlRqfHHAGDSsTNfArWLoeaQTD2yNJxTbW3z1Hwl1/B4unw7hbo2gP61DYcdd57Ijzya1j0Grz7DnTtDlX94aBT8uPGTYRHb4DFM1JxtXDgKQ3bm/xrWDK9Pq5PbcP2sugISWPW36kdydoVcXGW1qsq6jujrP+9yKIlL5a3s3Ln7F8I3NUaHQEws2FmdoOZLTGzzWY238yuMbP+ZbZzpJndnTx/k5ktMLMHzOykjM//ppl58vO+yl6NiIi0qBXz4IU7Yl5/v12ifOGOqM+LmwvPF8Q9f0fUpy2fC8//MY5X5+L+GPWFcc/9IaaBVA+I8rk/FI/L0l4Xg+5dwIjvy4143MXy48xigmyu2kgeF4uzpN4LHqd0NejeNeo9eV73rlFfrrUrii+A7axJY2dRM7D4WoaagcXjW8qKefDM/8FDV0VZ+DfbWufM8t+LrMZNjDbS6yI2rY/6dq6srTfd/abW6oiZjQKeAQYCdwMzgEOAy4CTzOwIdzAjQlIAACAASURBVH8zQzufBq4DNgB3AouAYcBZwMlm9k13/14jzz8QuBxYD/QtFSciItvZ7Kdjzmyv5D/NuXL20/mjdbNKxM16On90f9bTsTC2WFx6NH7mU0l7yYherpz5VH5cOe3tPASGjq2v21TXsL0ZU6D/EBiSinu7ruHo+aynYaeh+XGb1jc876ynk/OOaRhX7FuPxtQMLD7K2dpJozTPXhNjuhXUr2XYvB4OqODbnaxySXevvvlJ94R/qXyUPYus/73IavAoOPxD+VvcHnhKu5+vD+Xvs/9PZjYMGAr0LHbc3cu9E8Z1RKL/eXf/Seo8VxFrAL4HfKqJPnUHfgBsAg5y95mpY98HXgS+YWZXuvvmIs/vBdwC/A2YA5xX5msQEZHWsm5FJAtpPftEfSVxxaY09OzTcHR67fIY0c+Lq4r67d1esdHzrOfN+r5k0RZJozTf4D1iXUV6YfUBp7Tu1KuWTrqzasnf95zBozpEcl+o7GTfzE4Argb2aiK0axltjgJOAOYDPys4/C3gEuA8M/uyuzd2l5WdgBpgWjrRB3D36WY2CxhPjNg3SPaJC4Xdgf2Bb2Ttv4iIbAfVA+tv8JOzeWPUVxJXUyKucHS6ZlCMvPdKjWJv3hD1rd1eltHzrOfN+r5k0RZJo7SMwXts38+pNZLuLKoHwtJZ8NYC2LIRevSBnUbArp10S99GlDVn38wOBe4DaoGfEjMInwR+SUy7MeBeyr97bu6exA+7e94Nudy9Dnga6AMc2kQ7K4CVwBgzy9u7yczGAKOBl4pNBzKzY4kpQ19z9w52j3YRkR3AnkdEsrppfcyZzf17zyPy48aUiBtTLK6uIK6uYdzYI+uP+bb654w9snXb22tijJan5whvXh/1lZw36/uS1eA9YNL5cPpXo1SiL8VUD4yLyrRKLzLL0a03LH4FtrwN3XpFufiVqN/BlLtA92vEFJmD3f2ypO4xd/8UsC/wXeB9wB/LbDc30XBWieO55LvRyzF3d+AzxOuaamY3mdkPzOxmYCrwKnB24fPMrAa4EZgCXFtm30VEZHsYuHvM8+3VF+pWRVls3u/APeCQgrhD/qXhvPRBe8AhH4zj63JxH2y4e86gPWDM4bB6Acx5KsoxhxePy9ree8+Okf11K6N879kN43Kj5737RXu9+xXf2jLrebO+LyItKetFejlWzoNnfwt/uSbKlUUW3c5/HvruDD16w7Z3o+y7c9TvYMqdxnMYcI+7L0nVdYF/JtqXm9nJwLeBD5bRbk1Sri1xPFdf21RD7v4HM1tC3Njr46lDy4HfAHOLPO0nxBSgScnryMzMLiGmGTFixIhynioiIuUauHu2eb4D98iWxA7ao/gWmmkr58GCv8Vc3R7jY0rAgr/BzsNgQEFfsrRXTlzWKRdZ28v6vuxIVs2DOX+FupXQbwCMOgx2acW55Dua3EV6egvMfU8s/nec5bNYOe//t3ffYZJd5b3vv29V554cpBETJaGIhIQ0KBslGCSBEIhwfbhgJJA5YB8w2Bhf+xqD8MHGNiaYYCNhEc8lGBACI4FAEUU0yllIaHLO093VPd1V7/1j7ZrZVV01vau7qiv07/M8/eypVavXXlVruvvdq969Fjx8fVj6ddq88GnXw9fDK95U+PPYtz0sVWuxeW3PhfIpptJgfyawJvZ4H1C09hZ3A2+fSKcmwszeQUgr+jHw98Bqwq6+HyOkHp0LvC1W/82EG3H/1N1LXQgclLtfA1wDsHz58oouFEREpAm8cG8ILDqjXPf88YV7Rwf70ly2vQgP/yTcID1tbhQ4/gRe8UYF/NWU5CI96Vgk/XmcNjfcC9MZC1P3ZUJ5qXMnueDbtgpejNU7/EyYtyzJO1BXlabxbAFmFz0uvi25Hag0ISo/cz+zzPP58l0HayTKy7+OkK7zTnd/xt0z7v4MIaB/EHirmZ0X1Z8D/AdwC/DvFfZZRESmgr1bws19cR09oVya2wv3hmCwc1qYAe6cFh6/cO/outtWwQPfhVv/LRy3rSrd5vZVsPJ7cNsXw3F7mXpSKOlYJP15POnS8CncUH+Y0R/qD49PurSwXv4iY6iv8CJjW1Fq0LZV8GhRvUd/Uv7/QQOpNNh/jsLg/j7gNVGQjZktAN7MgRz7pPIr55TLyc/fbFsupz9vBeFi444SN/rmCDcTA5waHZcA84ALgVxsIy0H3hXV+VVU9qFkL0VERFrK9ENCkBC3byCUS3Pbu7VM4Li1sCxpoLd9FTx6Q1G9G5oz4J/si5akY5H05/GYc+Ccd4cLhv6d4XjOu0ffCJ/0IuPFMvVeLHFh2GAqTeP5BfC/zWyOu+8AvkDYrOphM3uKEJRPBz5aYbu3RccVZpaKB+pmNh04GxggXFwcTH7N//llns+X74uO24H/LFP3VYTXcxOwAXhijHOLiEitbV8FL94X+xj9DJi7rLbnPPLMkBMMIfjYNxCCuONfU9vzSu1Nnx/GMp8KAlHgWBRGxAM9OHB88d7CNI4X7ytT777a/z+tpvxFSzyl5tEb4KTLavc6ko5FJT+Px5wzOrgvtnfr6NSeUhcZSes1oEpn9r9KCIKHAdz9bsLqNi8SVuPZCLzf3b9VSaPu/gJwM7CMsJpO3NWE+wK+HV9j38yONbPitf5/Ex3fYmYvjz9hZicTbhp24NbovGvd/apSX4TdfAE+G5X9upLXJCIiVVavWdP5h8MRp8Ou1bDqnnA84nTl67eCI88M6R1D0UoxQ33h8ZFnFtZLOuuctF6ji1+0FMxijzXnOgFJx2L+4eFm3M5p0LctHItvzq3E9PllPimYP756DaiimX133wPcX1R2PXB9FfryJ4QA+9/M7ELgaeB0whr8zzF6k6uno6PF+vJbM/s6cCXwgJldT7hBdxnwRqAD+Ly7P1mF/oqIyGSq16zp9lWw7kGYf8SBmcR1D8Lshc01WyujzTs83AAavznz+NeMvjkz6axz0nqNrh6z2EnHAkJgX62L7SPPDDn6EPukoH/0JwWHnxlSt4rrHdv4n/BVvINurbj7C2a2nLAh10XAJYRPCr4AXO3uOxM29R5Cbv4VwGsJaUV7gLuAa939e1XuuoiITIZ6fYy+6j7oKHGRsarJUjOktHmHj73yTtJA7/AzwqdNo+q9uvr9rqV6XbQkGYtanDPJRca8ZXDSGwtX4zn2NU2xGo9Vsqy8mZ0KvA74qrtvLvH8AsKa8z9190eq1ssmsXz5cl+5cmW9uyEi0ppWfm90AJJ/vPwPa3feO74EvXNHr9fdvx3O/V+1O680lqTLLtbjvpJqi+fsxy9aapmzLxUzswfdfflY9Sqd2f8L4BzC+vWlbCbMrL+Uwg2tREREJqZes6bT5kXrdRfNck6bV9vzSmOZtyzZLO7cZc0fEM9dFgL7+EXLsa9u/tc1RY1nB93byu0y6+5uZrcSbuIVERGpnnoFIMvOgMd/Gv6dv8jY1w/HXFjb84rUUytctAhQebC/AFg3Rp0NwGHj646IiMhB1CMAmbsMTnxDyNHv2xZm9I+5UIGQiDSFSoP9AcqvYZ83HxgaX3dEREQakGY5RUrbvqrwQnhZE96j0OIqXWf/EeAyM5tW6kkzmwFcFtUTERERkUayfRU89H34zZfDcSL7VGxfFVLchvrDTexD/eFxM+4Y3MIqDfavIczc/6rEplUnETbGmhfVExEREZFGsX0VPPGzEJT3RMH5Ez8bf3AeX5Y2v/lWR28ol4ZR6aZa3zeziwkr7TxsZpuB9cBC4FDCBlffcvfvVr2nIiIiItW2fRWsvh/6t0HvPFh6euumoay+PwrOe8Pj/HH1/eN7zX3bwox+XEdPKJeGUenMPu5+BfA+4CnCDbunRscngfdGz4uIiIg0tmrPdDe6/m0hGI/r6Anl4zFtXlidKk7L0jacioN9AHe/xt1PBKYBi4Bp7v5yd/9aVXsnIiIiUivxmW6zaBOp3lDeinrLBOe94wzOl50RlqEd6gsbzQ31hcfLzph4X6VqxhXs57n7gLtvcPeBsWuLiIiINJBqz3Q3uqWnR8F5P7iH477+UD4e+WVpO3vDjtKdveFxq6ZBNalKl94UERERaQ29+d2Rew+UTWSmu9HNXQYnXFp4j8LRF0wsONeytA3voMG+mf0ecODV7v5i9DgJd/cjJ9w7ERERkVpZenrI0YfC3ZGPvqC+/aqlRg7Od6yGNfcfWLN/yekwZ2m9e9X0xkrjSRXVSRFW3Bnra0LpQSIiIiI1l5/p7uyFgSgN5YRLGzcYbmU7VsOTPytcs//Jn4VymZCDzuy7+7KDPRYRERFpao080z2VrCmzLOia+zW7P0EHnYE3s8+a2YrY4yXRLrkiIiIiItXRV+Zmaa3ZP2Fjpdt8CIivn/RiVCYiIiIiUh1as79mxgr2+4D4ZZbVsC8iIiIiMhUtKbMs6JJxLgsq+4219ObzwOVmdj2wMSqbZWZLxmrY3ddMtHMiIiIiMgXMWQovu7RwNZ6jLlC+fhWMFez/C/Ad4J5Y2Z9FXwfjCdoWEREREQnmLFVwXwNjrcbzXTN7EXgdsBC4AngMeKT2XRMRERERkYkYc/bd3e8D7gMwsyuA6939kzXul4iIiIhMJdpUqyYq3fzqSuCGWnRERERERKYobapVMxXl1bv7N2vVERERERGZorSpVs0cNNg3s1dF//ytuw/GHo/J3e+cUM9EREREZGro2xZm9OO0qVZVjDWzfzthZZ3jgOdij5NIj7tXIiLSGpSDKyJJTJsXUnfyM/qgTbWqZKxg/5OE4H5b0WMREZGDy+fgdvQW5uC+7FIF/CJSaMnp4fcDhBn9fQNhU62jLqhvv1rAWEtvfuJgj0VERMpSDq6IJKVNtWpGG1+JiEhtVJKDu3M1rH0ABrZBzzxY/EqYXeKP/I7VsPa30L8VeufD4tNKBwPVbk9Eak+batVERUtvmlnazHpKlF9gZl8ws380s8Or1z0REWla0+aFj+LjSuXg7lwNT/88fGTfPTccn/55KI/bsRqe+u/wfM+8cHzqv0cvzVft9kREmlil6+x/BthhZjPzBWb2h8CvgA8AfwX81swWV6+LIiLSlJacHgLooX5wD8d9/aE8bu0DId2noxfMDvx77QNF9X4bUoHi9Tp7Q3kt2xMRaWKVBvuvAm5z992xso8Du4A/Aj4KzAL+vDrdExGRppXPwe3shf7t4Vjq5tyBbdBe9KFxe08oj+vfWrpe/9batici0sQqzdlfDNyTf2BmRwDHAJ909+9EZa8CLgI+XK1OiohIk0qSg5tPoemILbk3PBDK43rnl67XO7+27YmINLFKZ/ZnAHtij88mLMX5i1jZk8CiCfZLRESmisWvDEH3vijdJ//vxa8sqnfagVSgfL2h/lBey/ZERJpYpcH+RiB+A+6rgQzwYKxsGjAywX6JiMhUMXspHPe6MMOe2R6Ox71u9Oo5c5bC8a8Pzw9sC8fjXz/6k4Nqtyci0sQqTeO5D3iDmb0eGATeAtzi7sOxOocD66vUPxERmQpmLy29NGaxpEvzVbs9EZEmVWmw/w/AZcAN0eMc8Kn8k2bWBfwB8MOq9E5ERBpT0nXsRUSkripK43H3x4HTgc9FX2e5+/2xKq8AbgW+W7UeiohIY0m6jr2IiNRdxTvoRgH/R8o8dy/wpol2SkREGlh8HXs4cFz7gGb3W8XO1bAu9snNIn1yU3V6j2WSVHqDbklm1m5mrzCzY6rRnoiINLCk69g3g11r4IkfwW+vCcdda+rdo/rbuRqevbHwk5tnb9QnN9Wk91gmUUXBvpm9zcx+YGZzYmVHEpbbXAk8ZWY/NrOKPzEQEZEm0TMvrEcfV2od+0a3aw08m09HmhMFXD9XwL/ugXDxFt9ZuL0nlEt16D2WSVRpUP5u4CXuviNW9q/ASwm5+nMJN/BeCVxblR6KiMjk2bUmSi3YDj1zQ2rBrCWFdRa/MuToQwhQhgdCoHzkeeNrr17WPQDtJdKR1j3QOH2sh4FtYbY5rlk/uWlUeo9lElWaxnM8sP+y08xmAJcAP3D3VwOnAc8Qgn0REWkmSWe6Zy+FRafC7jWw9p5wXHTq6HzjRp85H9heJh1pe3360yh65kHfZtj6NGx4MBz7NjffJzeNrFU+HZOmUGmwP5+wsVbemYRPB74HEK23/yvgyKr0TkREqmfXGnj6enjw2nAsDrrjM937Uwt6R6cW7FoDmx+Gucvg8HPCcfPD42+vXnrmlgm45pauP1XMXAw7nod9A9DWHY47ng/lUh2LXnngE7H87s3DA6FcpMoqDfb3AjNjj88FHLgrVjYITJ9gv0REpJp2rYHnbyq8IfD5mwoD9KQz3etXlg7i168srNfoM+eLXgnD/UUBV78Crr3rYd5R0NEDI5lwnHdUKJfqmL0UjrmkcJfnYy7RajxSE5Xm7P8OuNjMOglB/tuAx9w9nmS2FNhSpf6JiEg1bHywdH76xgcP5Kf3RBcB+eeg9Ex3pky+caYo3zhpe/Uyawkc87rCewqOOG9q5+tDGMfpC2DGYQfK3EeP76414f9PfunIw07Ve1eJpLs8i0xQpcH+NcDXCUH/MLAM+HBRnVMJq/OIiEijSHJD4KJXhpz6/HPDA2Gm+4jzCr+ve14oLw7iu4vyjZO2V0+zliQLUHfnA9voouCwU2Fmiwa2ScY3/0lRe2/hJ0UvvVgBv0iDqSjYd/dvRmvpvzcq+hLwxfzzZnYWYWWea6rWQxERmbieeWVm2WMBXNKZ7oXL4Xc3hn/Hg/hl5xbWa5WZ891r4PlfhNeav9H4+V/ASy8aHfAnvSjYvRY2rYTMjtDmguWlc+Kr3V4SScY3ySdFItIQzN2r15hZB9AN9Lv7SNUabhLLly/3lStXjl1RRGSyxWdi4wHceGdid60JOfqZbWHGd+Hy1g3ynrl+9IVS/vGxsU3j4xcF+9/jgdEXBbvXwu+jem09MBLVO+KiwgC92u1VYqzxffDaMKNvdqDMPeSfn/rH4zuniFTEzB509+Vj1avq5lfuvg/YV802RUSkCmYtCYF9PMd66avGH6AnTX9pBQPbw2x5XKkbjTc+eGCjJCic7Y4H55tWRgF89Hz+uGllYXBe7fYqMdb4JvmkSEQagna6FRGZKqZSgL5nLWx6CAa3Q9dcWHAKzBhn4Jv0RuOkFwWZHdBVVK+tJ5TXsr1qOuzU8ElRvk/5T4qWvqp25xSRcal06U3M7DAz+7KZPW9mGTPLlviacik8IiLSIPashRd/GYLPzjnh+OIvQ/l4HHZq6TXRDzu1sF7Sdfu754RUm7iRgdGBfbXbq6b8J0XxpSN1c65IQ6oo2DezhcBK4H8C/UAnsIawOk8WMOBR4Dfj6YyZLTKz68xsg5kNmdkqM/u8mc2usJ1zzOyG6PsHzWyNmd1oZheVek1m9gEzuymqP2Rm283sV2Z2+Xheh4iI1NGmh8LMdnu0D0B7b3i86aHxtTdzSciT7+gNs+UdvaVvzk16UbBg+YGZcPdwHB4I5bVsr9pmLYHj3hRy9I97kwJ9kQZV0Q26ZvZV4Crgte7+azPLAZ9w90+a2SLgWsJynGe5+86KOmJ2JHAPcAhwA/AMcBpwPvAscLa7j7kTi5m9H/gK4WLkemAdsAi4HOgB/tbdPxWr/2ngr4AXgTuATYS9Ai4nXMx8zt3/PMlr0A26IiIN4LHrwox+8c2jQzvg5e+u7blbYTUeEWkKSW/QrTTYXwU86e6vix7vD/ajx9OAJ4CfuvsHK+zwL4EVwAfdPb6c52cJa/l/1d3fN0Yb7cBWQpB+srs/G3vuOOBhIAfMdvehqPxyYLu731HU1nHAfcAMYLm7PzjWa1CwLyLSAJ67Icxut8dz7KPHR19Wv36JiFRR0mC/0pz9BRRumJUlLLUJgLv3Ab8CKvptGs3qrwBWAV8uevrjhFn6d5pZLwc3B5gJPBcP9KO+PQ08F/V3Wqz8x8WBfqz+96OH5yV9LSIiUmcLTomWn4yltYwMhPJa27M2XGw8dl04jvc+ARGRKqk02N8DdMQe7wQWFtXZDcyvsN3zo+PN7p6LP+Hue4G7CSk4Z4zRzhbCzP7RZnZU/AkzOxo4CngkSTpQZDg66oZjEZFmMWMxHP7aMJM/tCMcD3/t+FfjSaraNwaLiFRBpUtvrgbivy0fBS4wsx53HzCzFGGGfl2F7R4THZ8r8/zvonaPBm4p14i7u5n9KfAd4EEzux7YQLggeRPhU4k/TNIhM5sBvBlw4OYk3yMiIg1ixuLaB/fF4jcGQ2y9+4cmvy8iIpFKg/1bgPeaWbu7DwPfBL4F3GNmvwLOAV4G/EOF7c6MjrvLPJ8vnzVWQ+7+X2a2Afgu8EexpzYDXwd+P1YbZmbA14BDga9EKT3l6r4XeC/AkiVaiUBEZMoa3B5m9OPaekK5iEidVJrG85/APwHzANz9O8AXgBOAvwBOJ+S5f6pcA7VmZu8Afk1Y/vM4QvrPcYQLlS8B30vQzL8Cb43aOOhKPO5+jbsvd/fl8+dXmr0kIiIto2tu6fXuu+aWri8iMgkqCvbd/Xfu/k/uvjFW9mHgMOBM4DB3f7u7D1bYj/zM/cwyz+fLdx2skSgv/zpCus473f0Zd8+4+zPAO4EHgbea2XkHaeOfCav/3Alckl+1R0RE5KDqeWOwiEgZFe+gW4q7b3X3+9198zibyK+cc3SZ5/M325bL6c9bAbQDd5S40TdHCOABTi3+RgAz+xzwl8BtwMXR6kIiIiJjq9eNwSIiB1Fpzn6t3BYdV5hZKh6om9l04GxggLDu/cF0Rsdy+TT58n3xwihH/0vAnxAtHerumeTdFxERoT43BouIHMRBg30zu26c7bq7v6eCyi+Y2c2Emfk/Bb4Ye/pqoJewqVZ/rG/HRt/7TKzub6LjW8zsM+7+WKz+ycBbCKvr3BorN+Aaws7ANwGXjyMNSURERESk4Rx0B91oh9zxcHdPV9SRsLHWPcAhwA3A04Qbfs8npO+cFV8f38w8OpEVtXMdcCVh9v56wnKhy4A3EvYI+Hx0n0G+/seBTwAZ4PMUzfpHHnH3n4z1GrSDroiIiIhMhqQ76I6VxnN4lfozpmh2fznwSeAi4BJgI2G1n6vdfWfCpt5DyM2/AngtMJ2wGdhdwLXuXrwaT/41dgN/XabNbwJjBvsiItKi9qwN6+UPbg+r6yw4Rek6ItIUDjqzL5XRzL6ISAvK74zb1hO+RgbCl26+FZE6SjqzX9FqPGb2VjO71cxeUub5hWZ2i5ldXkm7IiIiDSu+M65ZOLb1hHIRkQZX6dKbVwGz3H1DqSfdfT1hTfyrJtoxERGRhjC4PQT3cdoZV0SaRKXB/onAWHkqDwAvH193REREGox2xhWRJlbpOvtzgC1j1NkOzBtfd0SkGeT2rIXND8HgDuiaA4eeQkq5y9KqFpwScvahMGd/8R/Ut18iIglUGuxv48ButuUcBewaX3dEpNHl9qyFVTeHoKdzNgwPwKqbyS1boYBfWlN+Z9z4ajyL/0A358qk0OSKTFSlwf7dwBvM7NiizawAMLPjgMuAn1WjcyLSgDbnb1aMcpjzx80PKfiR1qWdcaUONLki1VBpzv5nCBcId5nZB83saDPrjY5/RtjBNh3VE5FWNLgD2roLy9q6Q7mIiFRPfHLFLBzbekK5SEIVzey7+wNm9ifAl4HPRV9xWeD97n5/lfonIo2ma06YXWqPrU4ykgnlIiJNJrdnHb7lQJqMHXIKqRmL6t2tYHBHmNGP0+SKVKjSNB7c/Vozuwv4E+B0YBYhR/8+4N/d/enqdlFEGsqhp4SPlSH80RnJhJsVF50zqmq2bz0j2x/Bh3ZinbNpm3sy6WkLx11vpG89IzsexYd2YJ1zaJtzEm2l2tu7jty2R/DBHVjXHFLzTiY9ffQf78T1+taRjdVLzzuZ9LTR9XJ715Hb8vD+vO7UIa8gNQntJQ1WktRL2jeRVpDbsw5ffTMeT5NZfTO5pSsaI+DX5IpUgXbQrSLtoCvNLmkwneSGsWzfevatvwVr64Z0N2Qz+EiGjoUXFgTySeuN9K1neMMt4SPsqB4jA7S/5MKCPmb3rmNk7a+xtp79FyM+MkDb4lcXBPKJ6/WtY3jdr7F0rF52gPZFry4IgnN715FdfTO09cYugvpJL11REKBXu72CYCWqZyMDWFGwkqRe0r6JtIrs8z/FhwewWDCdf5x+6Rvq2LOgIGc/PrminH2hRjvoikjrygfTPjIAHbPxkQGGN9zCSN/6UXVTMxaTOuoyUideGY4l/uiMbH8Ea+vG2nows3Bs62Zk+yPjq7fjUWjrKahHW08oj8lteyTUaY/qtYfvyW17ZFz1stsewdJF9dI9ZIvb2/IwtPUW1KOtN5TXsD3f8hBe9Dq8rSfM4FdYL2nfRFpGg9+DlJqxGJatCDP7QzvDUYG+VKjiNB4RaU3xYBqAth48Ki81uz8WH9oJHbMKC9PdoXxc9XZAx+wS9Qr/KHuZHFcfrG09BrdD55xR9Yp3Wa12e4lzehPUS9w3kVbRBGkyKa0EJROkYF9EgOTBdFLWGT4doC32RzSbwYqCyeT15pSpV/hH2brm4CX+eFtXbevRNbdM0FC4y2q126NrDtnMFrLZveRyg6RSXaTT02nrPqRkvVx2D54dwtKdpNIzCuol7lsLGelfz/COR/ffL9I+5yTaeiu/uB3PefftfIzc0E5SnbPpmP3ySTmvFLJDToHVN+NQmN62cPQ9SCLNSmk8IgKEYJpsprCwRDCdVNvck/EoD97dw3EkQ9vck8dXb85JMDJQUI+RgVAek5p3cqgzHNUbDt+TmnfyuOql552MZ4vqZQdIF7d3yCtgpL+gHiP9obyG7WWnL2I4s5ZcNgPWSS6bYTizlmzRjby56YsYyawhNzyIWwe54cHwOFYvad9axUj/eoY23Bqlrs3CRwYY2nArI/2jU9eqfd7BjbeRGxnAOmaRGxlgcONtoQKpvAAAG8BJREFUJc870r+egXU30ffC/8fAuptq3repJjVjEbZ0RUhZG9oZjo1yc65IlegG3SrSDbrSzJLeAFsJrcZT+9V4MmtvJDewBRvcGWb+27rxrtmkeg6he/El++sNrilfr2vJgXpTaTWezNob8ZGBA6lrsP9x/L2rtoF1N5EbGSAVO2/+cc+ii/eX5S8KaOvG0t14NgMjGboOO1+fAohI4ht0lcYjIgAhaH7JhYXB9CFnjjvQB0hPW1gyaB9vvbZpCxP1Jz19Ucmgfdz1pi1KFPCmpi8qGdzXsj0f2ol1z8d6Ymk77iXveShdrzBNK2nfWkHS+0WqLTe0Eys6r6W7yRWdd9/Ox6Cte/9FgbX1kIvKWzHYH+5fz75dj5Pbt5NUx2w6Zp1Iewu+TpHJpmBfRPZLGkxL46j2PQ9TKeBK+t5VW6pzdkjhiX+ikM2QKjpv0ouCVjDcv57BzbdDuhtrj1KbNt8Oh57Xsv//RCaLgn1pOfsGNjC4+wmywztJt8+ma+YJdPS8ZFS9ocxGMnueIDu8i3T7LLpnnEBn92Gj6mUGN9HX/zTDI7tob5vFtN7j6O5aMPq8mY1kYuftnnkCHSXaG8pspL/vKUaGd9HWPoveaceXPG/SoGtwcBN9/U8xMrKbtraZTOs9nq6i/iWpU0nfKnmPB/Y+uf897pn+srLtDe16nOy+naQ7ZtM568QJjVnS9pKOWSXvX5LXOzywgaGdsf7NPpH2Ev0b2PEoma13kR3eQ7p9Bt3zz6Gn6B6F9jknMbD252QH1pIjS4o06XQvPYecWVCvbc5JZNb+d8jT9ywpS9OW6qU7Vq+SgCv5ezf6/1RX1+h69dA+5ySGNtwaHsT3eCh676qtY/bLQ84+FKTndMw/o6Be0ouCVrBv1+OQLvEpxq7HFeyLTJBu0JWmkRnaxJYdt7N+yw1s2XE7maFNo+rsG9hA39Y7yGUzpNpmkctm6Nt6B/sGNhTUG8psZO+2O6N6M8llM+zddidDmY2F5xzcxI7d95DNZmhLzySbzbBj9z1kBgvPvS+zkb6tdxad9072FbU3lNnI7h13kc1mSLeF9nbvuGvUefNBV25koCDoGi66OW9wcBM7d90d2kvPIJvNsHPX3QzG+pekTiV9q+Q93rPjroL3eE+Z9ga23EFuJEOqfRa5kQwDW8Y/ZknbSzpmlbx/SV7v8MAG+jeFsU1FY9u/6XaGi/o3sONR9q7/ObmRQSw9ndzIIHvX/5yBon0FsqkUQx3t5FKGuZNLGUMd7WRThb/eR9pSZLo7yVmKlDs5C49H2g7UiwdcZhYCr3R3KB/Xe7eR3TvvLvw/tfNuBgcL69VLW+9COl9yQQim9+3C2nrofMkFE0qRyQxuYuv229iw+Xq2br9t1O+K/Hm7DjufVFsPvm8Xqbaeknn4HbNfDiMZctFN6bmRgXBRMPvl4+5fo8rt24mlC9e7t3Q3uX2t9ymGyGTTzL40hczQJrbvvpe0ddEWBVzbd9/L3Jln0t15YIZ1cPcTpNI9pKI/Gvk/HoO7nyiY2c3seYJUuntUvcyeJwpmYvv6nyZtXaSj59PpbsiG8vjsfmb3E1hRe7moPD7b2d/3FJbuLmgvG5XHz5t0lquv/6loqcVY/6Ly/MxzkjqV9C3pezyw90lSqa5R9Qb2PlnQ3tCux7FUN6loYxtr6yY3EsrHM2aJ20s4Zknfv8Svd+fj4XXExjZfHp/dz2y9i1Sqc//rINUNI6E8Prvf3/cU9MzHpi8J7QFkM6PGrb/vKeieB9MWk8sXRvXyM+25fTux9hJpI0UBV0X/34veu/z/qUaZ3W/rXVi1/Pf85ED4PXVgcmAOZ436NDDJefMXBQVLdM4/oyXz9VMdZT7FKF4OWEQqppl9aQp7+5/ZH3SbGel0N2nrYm//MwX1ssM7sVRXQZmlusgO7yyqt6tMvV0FZcMju0gV1UuluhgeKayX9Lwjw6XbGyk6b9JZrpGR3aXbG9ldUZ1K+lbt9zi7byeWLqqX7iK7r9btJRyzhO9fZf0bPbaj+7cHUp0FZaQ6Q3m8fwnHLUm9VMfskFISUyrgqvb/91YRnxyI/57q63963G229S6kZ9HFTDvy7fQsurglA32AjlknQrboU4xsJpSLyIQo2JemMFwm4BouCrjS7bPx3GBBmecGSbfPLqo3q0y9wlnN9rZZ5Irq5XKDtLcV1kt63rb20u21FZ03adDV1jazdHttMyuqU0nfqv0epztm49mietlB0h21bi/hmCV8/yrr3+ixHd2/GZAbKigjNxTK4/1LOG5J6iUNuKr9/71VJJ0ckNHaexfSdeh5IbVpOEpt0s25IlWhYF+aQnuZgKu9KODqmnkCuewAuWwmBCvZDLnsAF0zTyio1z3jhOi5eL0M3TMK603rPY6sD5KN6mWzGbI+yLTe4wrbm3kCXtSeZzN0F523d9rxeDZT0J5nM/ROO76gXtKga1rv8eRyhf3L5QaZ1nt8RXUq6VvS97hn+svI5QYL6+UG6Zn+soJ6nbNOxHMZciNRvZEMnsvQWfRak45Z4vYSjlnS9y/x6519YnguNra5bIbO2UX9m38OudwQuZEMuVwuOg7RPb9wZ8+k49Y77Xi86HV4brCgXtKAq6L/72Ocs5UknRyQ0tp7F9K78CKmH/4/6F14kQJ9kSrRplpVpE21aiees59KdYXgywdH5eyDVuPRajxTazWe/HmTjFs1V8ZphdV4qi2esx//PTVn5uicfRGRiUq6qZaC/SpSsF9bmaFN7O1/huGR3bS3zWR677GjAn0RkXpKOjkgIjJR2kFXWk535wIF9yKSSL0mB7q7Fii4F5GGopx9ERFpKfm0v7A/xoGlekvtzSEi0uoU7IuISEtJulSviMhUoGBfRERaStKlekVEpgIF+yIi0lKSLtUrIjIVKNgXEZGWMr332JL7Y0zvPbbeXRMRmXQK9kVEpKV0dy5g7swzSae7GcnuIZ3uLrknh4jIVKClN6XuMkOb2DNwYIm8GT1aP19EJkZL9YqIBJrZl7rKDG1i2+77CpbI27b7Pi2RJyIiIlIFCvalrvYMPEM61Vm4RF6qkz0DWiJPREREZKIU7EtdaYk8ERERkdpRzr7UVXvbTLLZDOl09/4yLZEnjUb3lYiISLPSzL7U1YyeY8nmhgqXyMsNMaNHS+RJY9B9JSIi0swU7EtddXcuYN7MMwqWyJs38wzNmkrD0H0lIiLSzJTGI3WnJfKkkQ2P7KYtPaOgTPeViIhIs9DMvojIQbS3zSSXGywo030lIiLSLBTsi4gchO4rERGRZqZgX0TkIHRfiYiINDPl7IuIjEH3lYiISLNSsC8iIiJTlvbRkFanNB4RERGZkrSPhkwFCvZFRERkStI+GjIVKNgXERGRKWl4ZDepVFdBmfbRkFajYF9ERESmJO2jIVOBgn0RERGZkrSPhkwFDRXsm9kiM7vOzDaY2ZCZrTKzz5vZ7ArbOcfMboi+f9DM1pjZjWZ20UG+53gz+4GZbYm+51kzu9rMuif+ykRERKTRaB8NmQoaZulNMzsSuAc4BLgBeAY4Dfgz4CIzO9vdtydo5/3AV4B+4HpgHbAIuBy42Mz+1t0/VfQ9pwO3Au3AD4G1wAXA3wEXmtmF7j5UlRcqIiKjZIY2sztzYPnDmd3H0t15aL27JVOA9tGQVmfuXu8+AGBmvwRWAB909y/Gyj8LfBj4qru/b4w22oGtQCdwsrs/G3vuOOBhIAfMzgfvZpYGHgeOAy5z959G5SngB8Cbgb9290+P9RqWL1/uK1euTP6iRUSEzNBmtu69l3Sqi5R1kfNBsrlB5k8/UwG/iEgZZvaguy8fq15DpPFEs/orgFXAl4ue/jhhlv6dZtY7RlNzgJnAc/FAH8DdnwaeA7qBabGnziUE+nfmA/2ofg74aPTwfWZmlbwmkVaWGdrMpl13sHbbT9m06w4yQ5vr3SVpYrszz5BOdZFORcsfprpJp7rYndHyhyIiE9UQwT5wfnS8OQqy93P3vcDdQA9wxhjtbCHM7B9tZkfFnzCzo4GjgEeK0oEuiI6/KG7M3X9PuEBYChyR7KWItLb8LGw2F21Ck8uwde+9Cvhl3IZHdpOyouUPTcsfiohUQ6ME+8dEx+fKPP+76Hj0wRrxkJP0p4TX9aCZfdPM/tHMvgU8CDwJvLUW5xaZKpphFnZgaDMbd93Jmu0/Y+OuOxnQhUhDa2+bSc6Llj90LX8oIlINjXKDbv43erlpnHz5rLEacvf/MrMNwHeBP4o9tRn4OvD7ap7bzN4LvDd62Gdmz5aqV2PzgG11OK+U1tLjsWjJ7GXDw9l9xeXt7emOdWt2rqpDlwp0dbd3zztk+qHZbC7bt2ewc9qMrqF0OpXetmXv5sHMcKbe/ZvCyv5cdHW3d8+bP+3QXM6z2Wwum06n0qmUpbdt7dOY1U5L/55qMhqLxtFsY7E0SaVGCfarxszeAVwL/Bj4e2A14c34GPAlQo7+26p1Pne/BrimWu2Nh5mtTHKDhkwOjUfjMLOVe3ZnNBYNQD8XjUXj0Tg0Fo2jVceiUdJ48rPn5T6zzZfvOlgjUV7+dYR0nXe6+zPunnH3Z4B3ElJ53mpm51X73CIiIiIijaZRgv186ku5vPj8zbbl8urzVhDWyr+jxI2+OeDO6OGpNTi3iIiIiEhDaZRg/7bouCJa334/M5sOnA0MAPeN0U5ndJxf5vl8eTzf+NboOGp3XTM7gnARsJrRuf6NpK5pRDKKxqNxaCwah8aisWg8GofGonG05Fg07aZaZnYsQJSiky87DbgfyABnuPtjsedOBu4lXBCc6O5PRuUH21Tr+8BbSLiploiIiIhII2mkYP9I4B7gEOAG4GngdMIa/M8BZ8XXxzczB3B3K2rnOuBKwuz99YRZ+WXAG4EO4PPu/uGi7zmdMMPfDvwQWANcCCwnrPF/YX7HXRERERGRZtEwwT6AmS0GPklIqZkLbCQE7Fe7+86iuuWCfQPeBVwBnARMB/YADwPXuvv3ypz7eOBqwsXFdMJFwneBT7u7ln4TERERkabTKDn7ALj7Wne/0t0Pc/cOd1/q7h8qDvSjulYc6Efl7u7fcPfz3H22u7e5+xx3v7BcoB9931Pu/lZ3n+fune5+tLt/vFEDfTNbZGbXmdkGMxsys1Vm9nkzm13vvrUiM3uLmX3RzH5jZnvMzM3sO2N8z1lmdqOZ7TCzjJk9ZmYfilLHZJzMbK6ZXWVm15vZ89F7u9vM7jKz9xTf9xP7Po1HDZjZP5nZLWa2Nnpfd5jZw2b2cTObW+Z7NBaTxMzeEf2+cjO7qkyd15vZ7dHPUZ+Z3W9m75rsvraa6O+yl/naVOZ79LNRQ2Z2YfS3Y1MUO20ws1+a2SUl6rbMWDTUzL4kUyLl6RngNMKnEs8CZ8dTnmTizOwRwidFfcA64Fjg/7j7O8rUvwz4ETBIuPdjB3ApYcfmH7p78U7OkpCZvQ/4d8Inf7cR0u4OBS4nLJX7I+CtHvvlpvGoHTPbBzwEPAVsAXqBMwhpkBsI90+tjdXXWEyS6NPyx4E0MA34Y3f/WlGd/wV8EdhOGI99hHvVFgH/6u4fmdROtxAzW0XYkPPzJZ7uc/fPFNXXz0YNmdk/A39J+Bt+E2HzrPmEFRp/7e4fjdVtrbFwd3012RfwS8CBDxSVfzYq/49697HVvggXUkcBBpwXvc/fKVN3BiHoGQKWx8q7CBdpDvxhvV9Ts34BFxB+6aaKyhcQAn8H3qzxmLTx6CpT/qnovf2KxqIu42LAr4EXgH+J3turiuosIwQz24FlsfLZwPPR95xZ79fSrF/AKmBVwrr62ajtWPxx9B5+A+go8Xx7K49FQ6XxyNiiWf0VhF8iXy56+uNAP/BOM+ud5K61NHe/zd1/59FP/BjeQpgt+J67r4y1MQj8bfTw/TXo5pTg7re6+8989F4am4D/iB6eF3tK41FD0ftYyg+i41GxMo3F5Pkg4cL4SsLfhVLeTVih7kvuvipf6CF19h+ih+8r8X1SffrZqBEz6yRMPqwB3uvu+4rruPtw7GHLjUVbvTsgFTs/Ot5cItjZa2Z3Ey4GzgBumezOCRD+wAL8osRzdxL2jDjLzDpdqzxVW/4X9kisTONRH5dGx8diZRqLSWBmxwGfBr7g7nea2QVlqh5sPG4qqiPj02lm7wCWEC66HgPudPdsUT39bNTOawjB++eBnJm9DjiB8KnWb9393qL6LTcWCvabzzHRsdyOvr8jBPtHo2C/XsqOkbuPmNmLwMuAIwhLzEoVmFkb8EfRw/gvaY3HJDCzjxDywmcS8vXPIQQ28T1KNBY1Fv0cfJswi/k3Y1Q/2HhsNLN+YJGZ9bj7QHV7OmUsIIxH3ItmdqW73xEr089G7bwyOg4SVmY8If6kmd0JvMXdt0ZFLTcWSuNpPjOj4+4yz+fLZ01CX6Q0jVF9fJrwS/xGd/9lrFzjMTk+Qkgl/BAh0P8FsCL2BxQ0FpPh74BXAFf42KvJJR2PmWWel4P7OmHPngWEG9dPBL5KuFfiJjM7KVZXPxu1c0h0/EtCvv0fEJZYfzlwM/Aq4L9i9VtuLBTsi0jTM7MPAn9BWJnqnXXuzpTk7gs8LIe8gLAy0hHAw2Z2Sn17NnVY2CDybwir6BSnJsgkc/ero3uMNrv7gLs/4e7vIyym0Q18or49nDLyse4I8AZ3v8vd+9z9ceBNhNV5zjWzM+vWwxpTsN98xpppyZfvmoS+SGkao0kULR34BcLSj+e7+46iKhqPSRQFNtcT0gnnAt+KPa2xqJEofedbhNSDjyX8tqTjUW6GU8Ynv5DAq2Jl+tmonfx79nD8RnSAKD0t/0nwadGx5cZCwX7zeTY6Hl3m+fzKF+Vy+qX2yo5R9Af5cMIMw+8ns1OtyMw+RFgj/AlCoF9qoxqNRx24+2rCBdjLzGxeVKyxqJ1phPf1OGAwvoETIb0K4NqoLL/u+8HG4zBC6sk65etXXT61Lb5qnn42aif/3pYLzvMbt3YX1W+ZsVCw33xui44rincKNbPpwNmEO8Xvm+yOyX63RseLSjz3KqAHuKdZ7uJvVGb2V8DngEcIgf6WMlU1HvXzkuiYX3lEY1E7Q8B/lvl6OKpzV/Q4n+JzsPG4uKiOVM8Z0TEeLOpno3ZuIeTqH19mh/X8DbsvRsfWG4t6L/Svr8q/0KZa9X7/z2PsTbW20kIbcjTaFyFNwYGVwJwx6mo8ajcORwMzS5SnOLCp1t0ai7qP0ycovanW4WhTrVq958cBvSXKlxFWzXPgb2Ll+tmo7XjcEL2HHy4qXwHkCLP7M1t1LCx6AdJEoo217iHcYX4DYemn0wlr8D8HnOXu2+vXw9ZjZm8E3hg9XAC8ljAr85uobJvHtpWP6v+Q8If0e4Sttt9AtNU28DbXD9+4mNm7CLsgZgkpPKXyiVe5+zdi36PxqIEojeofCTPGLxKCxkOBcwk36G4CLnT3p2Lfo7GYZGb2CUIqzx+7+9eKnvsA8G+Esfs+sI+wqdAiwo2+H0EqFr3nf0FYl301sBc4EngdIWi8EXiTxzZ40s9G7ZjZIkLctJgw0/8w4WL3jRwI3n8Uq99aY1Hvqw19je+L8B/268BGwi/n1YQNI2bXu2+t+MWBmbFyX6tKfM/ZhF/oO4EM8DjwYSBd79fTzF8JxsKB2zUekzIWJwBfIqRSbSPkse4GHojGqeSnLhqLSR+n/M/MVWWevxS4gxCQ9kfj965697uZvwgXvN8lrBC2i7Dh31bgV4T9QKzM9+lno3ZjMp8wQbQ6ipu2AdcDp7X6WGhmX0RERESkRekGXRERERGRFqVgX0RERESkRSnYFxERERFpUQr2RURERERalIJ9EREREZEWpWBfRERERKRFKdgXEREREWlRCvZFRKQuzOwbZuZmtqzG51llZqtqeQ4RkUalYF9ERJqamd1uZtohUkSkhLZ6d0BERKTGLqx3B0RE6kXBvoiItDR3f6HefRARqRel8YiINBkzWxblun/DzI41s5+Y2Q4z6zezu8xsRYnv6TSz/8fMHjezATPbY2a/MbO3Van9T0Tfc97B2kv4+q4wsx+Z2e/NLBP19W4ze0epdoFzo8ce+7o9Vq9kzv4E3pNlZvY9M9tmZoNmttLMXp/ktYmITDbN7IuINK/DgXuBx4GvAocB/xdwk5m93d2/D2BmHcAvCUHxM8CXgR7gLcD3zexkd/+b8bZfA/8OPAncCWwE5gKXAN82s2Pc/WNRvV3A1cAVwNLo33mrDnaCCbwnS4HfAr8Hvg3MIbwnN5jZq939tkpfrIhILZm77mkSEWkm0eo1L0YPP+Pufxl7bjkhQO8Dlrr7HjP7a+AfgJuAN7j7SFT3EELguhQ4293vGU/7UfkngI8D57v77WX6+013vyJW/g3gXcDh7r4qVn5kcepNFJzfBLwKWObu62PP3Q6c6+5W5v1aBeDuy2JlE3lPPuHuV8faei3wC+Amd7+kVB9EROpFaTwiIs1rN/DJeIG7rwT+DzALeFNU/G7AgT/PB7VR3S3A30cPr5pA+1VVKsfe3fcRZt/bqM4Nt+N9T1YD/7uob78E1gCnVaFfIiJVpWBfRKR5PeTue0uU3x4dX2Fm04GXAhvc/ZkSdW/N1x1P+xX0NTEzW2JmXzazZ6Jceo9y838UVVk4wfYn8p484u7ZEuVrgdkT6ZeISC0oZ19EpHltLlO+KTrOjL4g5L6Xki+fNc72q8rMjiCk0cwGfgPcTPiEIQssI6T9dE7wNBN5T3aV+Z4RNIEmIg1Iwb6ISPM6tEz5gui4O/qKlxU7LFZ3PO3n5aJjqb8rpYLmcv6ccEPule7+jfgTZvY/CMH+RE3kPRERaSqahRARaV6nRCkpxc6Ljg9HaTgvAAvN7KgSdc+Pjg+Np/1Y2c7ouLhE/eUlysp5aXT8UYnnzi3zPVkAM0snOcEE3xMRkaaiYF9EpHnNBP4uXhCtlvN/E2alr4+KrwMM+Jd4QGxm84CPxeqMt30IqTcAV5pZW6z+4uI2xrAqOp5XdN7XUvqGWYDt0XFJBecZ73siItJUlMYjItK87gSuMrPTgbs5sA5+Cvif+WUxgc8AFwOXAY+a2Y2ENeXfChwC/LO73zWB9nH3+83sTsLSmL81s1sJaUCXEtazLzXjX8pXgCuB/zKzHwIbgBOAi4AfROcvdkv0Wn4cvbYMsNrdv32Q84z3PRERaSqa2RcRaV4vAmcRUmjeB7yNkHpySXzDq2jZytcA/29U9AFC7vvvgLe7+19NpP2Yy4CvAYuic7wC+ChQrv1R3P0xQhrNPcDrgPcDM4DLgf8o821fA/6R8EnERwlLZ75njPOM9z0REWkq2lRLRKTJlNukqlnaFxGRyaOZfRERERGRFqVgX0RERESkRSnYFxERERFpUcrZFxERERFpUZrZFxERERFpUQr2RURERERalIJ9EREREZEWpWBfRERERKRFKdgXEREREWlRCvZFRERERFrU/w8AKH2Qps0jDgAAAABJRU5ErkJggg==\n", 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ABcAzadZ4vxh416j2F7T79MR3TpLPAIcAP0tyMnAFzQeC/Wnm3x9TVb+eRHySJEnSrEmz9PkkXphsDjyDZonGW4FzquqmKQWTbAO8D9gXeCRwLXAycGRV3TKqbQFUVUaVB3g1cDDwZGBD4Hbg58Cnq2rMVWV6LV68uJYsWTKV05EkSZLGleTcqlo8brvJJu5rOhN3SZIkzYSJJu5d57hLkiRJmgVdn5wKQJJn0Cz7uBUPruTSq6rqL6cSmCRJkqQHdUrc2/njxwOvoLkptOi5ObTnuAATd0mSJGlAuk6VeRPNE0hPBBbTJOnHAM8G3gncAXwJeNwAY5QkSZLWel2nyrwauKiqDgZoBuC5tarOBs5O8h2ahyR9F/jMAOOUJEmS1mpdR9wX8fsPL3og+a+qnwNfp1mTXZIkSdKATGZVmdt6/r0c2HRU/SU0Cb4kSZKkAemauF9Ds5LMiMuBp49qsyNNQi9JkiRpQLom7ufw0ET9W8BuSf4+yR8keSOwH808d0mSJEkD0jVx/09gfpLHtsf/AlwBHAmcD3wUuBV4x8AilCRJktRtVZmq+hrwtZ7j3yV5KvBXwA7AUuCzVXXtIIOUJEmS1naTenJqr6q6DTh6ALFIkiRJGkOnqTJJVib5/HQFI0mSJKm/rnPc7wCunI5AJEmSJI2ta+L+c+CJ0xGIJEmSpLF1Tdz/GXhRkj+cjmAkSZIk9df15tQtgG8D30ryNeBnwHVAjW5YVZ+deniSJEmSoHvifjxNkh7ggHaDhybuaY9N3CVJkqQB6Zq4HzItUUiSJElara4PYDphugKRJEmSNLauN6dKkiRJmgUm7pIkSdIQ6DRVJsnlE2xaVbXDJOKRJEmS1EfXm1Pn0WfpR2BjYGH772XA/VMJSpIkSdJDdb05dfux6pI8HvgIsAB44dTCkiRJktRrYHPcq+pSmnXdtwLeO6h+JUmSJA345tSqugf4LvDng+xXkiRJWttNx6oyK4Atp6FfSZIkaa010MQ9yWbAnwBXDbJfSZIkaW3XdTnI96ymn22A/WhWl/m7KcYlSZIkqUfX5SCPGKf+duCoqvqXyYUjSZIkqZ+uifveY5SvAm4BLqyqFVMLSZIkSdJoXddx/8F0BSJJkiRpbNOxqowkSZKkAeuUuCc5KMnpSR4zRv1WSU5LcsBgwpMkSZIE3UfcXwtsXFXL+lVW1TU0q8q8dqqBSZIkSXpQ18T9ScCScdr8DNh1cuFIkiRJ6qdr4r4pcMM4bW4GNptcOJIkSZL66Zq43wTsOE6bHYFbJxeOJEmSpH66Ju5nAi9JsqhfZZIn0Dw99UdTDUySJEnSg7om7kfTrP3+4ySHJ9kpyYJ2/2aahH1+206SJEnSgHR9ANPPkhwGfBz4ULv1Wgm8oap+OqD4JEmSJNExcQeoqk8n+TFwGPBMYGOaOe1nA5+sqgsGG6IkSZKkzok7QJuc//WAY5EkSZI0hq5z3CVJkiTNgk6Je5KDkpye5DFj1G+V5LQkBwwmPEmSJEnQfcT9tcDGVbWsX2VVXQMsbNtJkiRJGpCuifuTgCXjtPkZsOvkwpEkSZLUT9fEfVPghnHa3AxsNrlwJEmSJPXTNXG/CdhxnDY70iwPKUmSJGlAuibuZwIvSbKoX2WSJwD70TxBtbMkWyc5LsmyJPcmWZrkmCSbTKKvpyX5QpKr276uT/KDJK+aTGySJEnSbOqauB9Ns/b7j5McnmSnJAva/ZtpEvb5bbtOkuwAnAscApxD81TWy4E3Az9J8sgOfb2JZq79PsBpwL8BJ7exvahrbJIkSdJs6/QApqr6WZLDgI/TJNYfGtVkJfCGqvrpJGL5BLAFcHhVfXSkMMkHgbcC7wdeP14nSfYBPgJ8Fziwqu4YVb/uJGKTJEmSZlWqqvuLmikxhwHPBDammdN+NvDJ9qmqXfvbAbgUWArsUFWreuo2BK4FAmxRVcvH6esXwOOBbavq5q6xjFi8eHEtWTLeAjqSJEnS1CQ5t6oWj9eu04j7iDY5/+vJvHYMe7f7U3uT9va97khyJs20l91ppr70lWQXmqUovwb8LsnewNOBAs4DzhjdvyRJkjQMJpW4T4Od2/3FY9RfQpO478RqEnfgGe3+BuD7wJ6j6n+Z5ICqunSScUqSJEmzouvNqQ9IMj/Jo5Js22/r2N3Cdn/bGPUj5RuP088W7f4vge2BF7d97wR8juYBUt9Isl6/Fyc5NMmSJEtuvPHGCYYuSZIkTb/OiXuSJyX5BnAHsAz4bZ/t8kEG2cHI+cwHXlZV36yq26vqEuBVNE993Ql4ab8XV9WxVbW4qhZvvvnmMxOxJEmSNAGdEvf2ptSzaKagfJfmhtHz23/f3B5/HzixYxwjI+oLx6gfKR/vwU4j9ddV1U96K6q5C/eU9nC3jvFJkiRJs6rriPu7gXWBZ1fVfm3ZyVW1L/BY4DPAE4H3dOz3ona/0xj1I09rHWsO/Oh+xkrwb2n3G0wwLkmSJGlO6Jq47wV8vap+2VMWgHaZxtfRJMf/0LHfM9r9PkkeElO7HOQewF00S06uztnAcmD7JAv61O/S7n/bMT5JkiRpVnVN3DejWeFlxArg4SMHVbWCJgnfp0unVXUZcCrNDaVvHFV9JLAAOLF3Dfcki5IsGtXPXcC/Aw8DjkqSnvZPAg5uY/5ql/gkSZKk2dZ1OcjfAY/oOb4JGL2CzH2MPVd9dQ6jmT//kSTPBy6gecDT3jRTZN41qv3Ig54yqvzvaebgvwV4VrsG/KOAA2gS+re0HxQkSZKkodF1xP0ymlHxEecCf5hkC4B2esp+TGIqSptMLwaOp0nY3wbsAHwY2H2iT0GtqtuB5wAfADYF3gT8MfBj4IVV9eGusUmSJEmzreuI+6nA3yZZ0E5b+T80a6X/PMlZNE8p3Y4m6e6sqq4CDplg29Ej7b11d9KM0I8epZckSZKGUtcR90/TPNxoA4Cq+gbw1vb4pTQPQPpn4CMDjFGSJEla63Uaca+qa4Evjyr7cJKP0dy4ekO7XrokSZKkAer85NR+qmplVV0/VtKe5NVJTh/Ee0mSJElro4Ek7hOwPfDcGXovSZIkaY0zU4m7JEmSpCkwcZckSZKGgIm7JEmSNARM3CVJkqQhYOIuSZIkDQETd0mSJGkImLhLkiRJQ8DEXZIkSRoCM5W4nwd8dobeS5IkSVrjrDMTb1JVpwCnzMR7SZIkSWuizol7kk2B1wC7AZsA8/s0q6p6/hRjkyRJktTqlLgnWQR8H9gcyGqa1hRikiRJkjRK1znuRwNbAP8MPA5Yt6rm9dn6jcJLkiRJmqSuU2WeA3yjqt45HcFIkiRJ6q/riHuA30xHIJIkSZLG1jVxPxfYeToCkSRJkjS2ron7+4AXJdlrGmKRJEmSNIauc9y3oVmP/dQkX6QZgb+1X8Oq8oFLkiRJ0oB0TdyPp1nqMcAr22300o9py0zcJUmSpAHpmrgfMi1RSJIkSVqtTol7VZ0wXYFIkiRJGlvXm1MlSZIkzYKuU2UASPJw4ADgqcDGwG3AfwMnV9XywYUnSZIkCSaRuCd5EXACsCnNjagjCvhQkkOq6usDik+SJEkSHRP3JE8DTgLmA58HTgeuBR4NPA/4c+CrSfaoqnMHHKskSZK01uo64v4umpH151TV2aPqjk/yceD7wDuBl049PEmSJEnQ/ebU5wBf6ZO0A1BVPwW+2raTJEmSNCBdE/eFwFXjtLkS2Ghy4UiSJEnqp2vivgzYbZw2i2nmvUuSJEkakK6J+zeB5yV5R5L5vRVJ5iV5G/CCtp0kSZKkAel6c+o/APsD7wdel+RHNKPrWwL/A9geuA44aoAxSpIkSWu9Tol7VV2XZA/gU8AfAtuNavJd4PVV5VQZSZIkaYA6P4CpqpYCL0yyFc2TUxfSPDn151V1zWDDkyRJkgSTSNxHtEm6ibokSZI0A7renCpJkiRpFqx2xD3JcTRPSn1nVV3fHk9EVdVfTjk6SZIkScD4U2UOpknc/xm4vj2eiAJM3CVJkqQBGS9xf2y7v2bUsSRJkqQZtNrEvaquWN2xJEmSpJnR6ebUJO9Jsuc4bZ6T5D1TC0uSJElSr66ryhwB7DVOmz2B904mGEmSJEn9TcdykOsCq6ahX0mSJGmtNR2J+9OAm6ahX0mSJGmtNe6TU5OcPqro4CR79Wk6H9gG2A744tRDkyRJkjRi3MSdh85pL2D7dhttFXAz8GXgrVOMS5IkSVKPcRP3qnpgOk2SVcARVfW+aY1KkiRJ0kN0neN+CPC16QgEIMnWSY5LsizJvUmWJjkmySZT6HPPJCuTVJKjBhmvJEmSNFMmMlXmAVV1wnQFkmQH4CxgC+AU4EJgN+DNwL5J9qiqmzv2uSFwAnAX8IjBRixJkiTNnE6Je68kWwNbAev3q6+qH3bs8hM0SfvhVfXRnvf5IM2c+fcDr+/Y54eBhcA/tq+XJEmShlLnxD3JPsCHgEXjNJ3foc8dgH2ApcDHR1W/FzgUeGWSt1XV8gn2uR/N1J5XMoUPKJIkSdJc0GmOe5Ldga8DGwMfAwL8EPg0zdSWAP8FdL15de92f2pVPeThTVV1B3Am8HBg9wnGuUUb09eq6nMdY5EkSZLmnK43p/4dcA/wjKp6c1t2RlW9HtgFOAp4AfDVjv3u3O4vHqP+kna/0wT7+zTNuXWdWiNJkiTNSV0T92cB/6+qlo3uoxrvAS4AjuzY78J2f9sY9SPlG4/XUZLXAC8BDquq67sEkeTQJEuSLLnxxhu7vFSSJEmaVl0T94XAlT3H9wELRrU5E9hzKkFNVpLtgWOAr1TVf3R9fVUdW1WLq2rx5ptvPujwJEmSpEnrmrjfAGwy6niHUW3WBTbo2O/IiPrCMepHym8dp5/jgLuBwzq+vyRJkjSndU3cL+ahifrZwB8m2QkgyZbAS3lwTvpEXdTux5rDvmPP+6/O02iWlLyxfeBSJSngM239u9qyaXuIlCRJkjQdui6T+G3gqCSbVtXvaNZJPwD4eZLf0CTYGwJ/27HfM9r9Pknm9a4s0z5EaQ+ahyidPU4/n6VZfWa0HWmm75wHnAv8vGN8kiRJ0qzqmrh/imb5x/sBqurMJAcB/0CzqsxS4G+r6rNdOq2qy5KcSrOW+xuBj/ZUH0kzj/5TvWu4J1nUvvbCnn4O79d/koNpEvdvVNW7u8QmSZIkzQWdEvequh346aiyk4GTBxDLYcBZwEeSPJ9mdZpn0qzxfjHwrlHtL2j3GcB7S5IkSXNa1znu06aqLgMWA8fTJOxvo5lP/2Fg96q6efaikyRJkmZXpxH3JE8HXkwzbeX31khvb049lGat9/O6BlNVVwGHTLDthEfaq+p4mg8EkiRJ0lDqOuL+NuC1NMtA9nM98JfA30wlKEmSJEkPNZknp55RVdWvsi0/nWYVGEmSJEkD0jVx3xK4epw2y4BHTy4cSZIkSf10TdzvAjYfp83mwL2TC0eSJElSP10T9/OA/ZI8ol9lko2A/dp2kiRJkgaka+J+LM2I+neT7NpbkeTJwKnAZm07SZIkSQPS9QFMX07yR8CrgJ8nuR64BtgKeBTNw5A+W1VfHHikkiRJ0lqs8wOYqupg4PXAb2huVn16u/81cGhbL0mSJGmAOo24j6iqY4Fjkzwc2Bi4taruGmhkkiRJkh4wqcR9RJusm7BLkiRJ02xKibskSZI0dM4/H046Ca68ErbdFg44AHbddfzXzbLVJu5JLgcKeEFV/bY9noiqqh2mHJ0kSZI0SOefD0cfDZtsAltvDbfc0hy//e1zPnkfb8R9Hk3iPtbxWDLpiNR4y1vgPJfDlyRJGqilS2HFClhnHdhyS9h336b8pJOGO3Gvqu1XdyxJkiQNlXvvhfXXf2jZwoXNtJk5brypMh8Evl1Vp7bH29KsIHP7TAS3trr73uu44z37c/+KvVl3nYVsuGARG6y/5WyHJUmSNPyOOIKL5s3je7vswrIFC3jM8uW84Fe/Yudtt53tyMY13jrubwF27zn+bVumaXL3vddx820/YeXKu1ln/kasXHk3N9/2E+6+97rZDk2SJGnoXXTggRy/yy7cXsWWy5dzexXH77ILFx144GyHNq7xEvc7gYf3HDt3fZrdsfxC5udhzJ+/AUn56TNEAAAZhElEQVSYP38D5udh3LH8wtkOTZIkaeh97zGPYaMnPpGN5s1j3u23s9G8eWz0xCfyvcc8ZrZDG9d4N6deChyQ5GTg2rZs43bKzGpV1dyfKDQH3b/iNtaZv9FDyubNexj3r7htliKSJElacyxbsYItN98cttjigbJHVLFsxYpZjGpixkvc/xX4HHBWT9mb2211agJ9q49111nIypV3M3/+Bg+UrVp1D+uus3AWo5IkSVozPGaddbh95Uo2mj//gbI7V63iMevM/dR1vFVlvpjkt8CLga2Ag4HzAdcpnCYbLljEzbf9BFY2I+2rVt3DyrqHjRc8dbZDkyRJGnovWLCA42+7DVau5BHz5nHnqlXcXsUBCxbMdmjjGvejRVWdDZwNkORg4OSqet80x7XW2mD9LXnkwmdxx/ILuX/Fbay7zkI2XvBUV5WRJEkagJ3XX5+DFy7ke8uXs2zFCh6zzjocsGABO49eInIO6vo3gUNwtH3abbD+libqkiRJ02Tn9dcfikR9tE6Je1WdMF2BSJIkSRrbeA9g2rP95zlVdU/P8biq6odTikySJEnSA8Ybcf8+zQoxTwAu7jmeiPnjN5EkSZI0EeMl7u+jSdRvGnUsSZIkaQaNtxzkEas7liRJkjQz5s12AJIkSZLG12lVmSTzgfWr6q5R5c8D9gPuAo6tqt8OLkRJkiRJXUfcjwZ+l2ThSEGSlwHfBf4a+N/AOUm2GVyIkiRJkrom7nsCZ1TVbT1l7wVuBV4F/C2wMfA3gwlPkiRJEnRP3LcBLh05SPI4YGfgo1X1uao6GvgWsO/gQpQkSZLUNXHfCLi953gPmuUhv91T9mtg6ynGJUmSJKlH18T9WuCxPccvAO4Gzu0pewSwYopxSZIkSerRaVUZ4GzgJUn+GLgHOBA4raru72nzWOCaAcUnSZIkie4j7h9oX3MK8B1gPeD9I5VJHgY8B/jpoAKUJEmS1HHEvap+meSZwKvboi9X1c96mjwVOB344oDikyRJkkT3qTJU1S+Bt49R9xPgT6YalCRJkqSH6jpVpq8k6yZ5apKdB9GfJEmSpIfqlLgn+dMk/5Fk056yHWiWgFwC/CbJSUk6j+RLkiRJGlvXEffXAIuq6nc9Zf8GPB44Azgf2A84ZDDhSZIkSYLuifsTgQduRk2yEfAi4D+q6gXAbsCFmLhLkiRJA9U1cd+c5iFMI55Fc4PrlwDa9dy/C+wwkOgkSZIkAd0T9zuAhT3HzwUK+HFP2T3AhlOMS5IkSVKPrjeRXgL8UZL1aRL2PwXOr6qbetpsB9wwoPgkSZIk0X3E/VjgcTQJ/AXAY4HPjGrzdJpVZiRJkiQNSKfEvapOAP4JeDjNlJmPAR8dqU/ybB5cYUaSJEnSgEzmyanvBN45RvUSYBNg+VSCkiRJkvRQA31QUlXdB9w3yD4lSZIkdZ/jLkmSJGkWdE7ckzw6yceTXJrk7iQr+2wrpiNYSZIkaW3VKXFPshXNPPbX0cxjXx+4kmaVmZVAgF8AP5pMMEm2TnJckmVJ7k2yNMkxSTaZ4OsXJHl5ki8kuTDJ8iR3JFmS5G1J1ptMXJIkSdJs6zri/h5gS2DfqnpyW/aZqlpEs0zkd4ANgAO6BpJkB+Bc4BDgHOBDwOXAm4GfJHnkBLp5DvA54IXAr2hWvPkCsBVwNHBGkod1jU2SJEmabV0T9xcC366q742uqKqrgYNoEvcjJxHLJ4AtgMOrav+qekdVPY8mgd8ZeP8E+rgOeAXw6Ko6sO3jdcBOwH8DzwbeOInYJEmSpFnVNXHfkoc+XGklTaIOQFXdCXwX2K9Lp+1o+z7AUuDjo6rfSzMt55VJFqyun6o6r6o+365u01t+B/Bv7eFeXWKTJEmS5oKuifvtQO888VtopqH0ug3YvGO/e7f7U6tqVW9Fm3SfSfPQp9079tvr/nbvjbOSJEkaOl0T9yuAbXqOfwE8L8nDAZLMoxk5v7pjvzu3+4vHqL+k3e/Usd9er2n3355CH5IkSdKs6Jq4nwbsnWTd9vgE4DHAWUn+lWZk/A+AL3fsd2G7v22M+pHyjTv2C0CSNwH7AucBx62m3aHtCjRLbrzxxsm8lSRJkjQtuj459d9ppsdsBlxbVZ9L8nTgr4Fd2zZfYmI3ks6IJAcAx9DcuPrSqrp/rLZVdSxwLMDixYtrZiKUJEmSxtcpca+qS4B/HlX21iQfoFkOcmlVXT+JOEZG1BeOUT9SfmuXTpPsT/NB4gZg76q6fBKxSZIkSbOu64h7X1V1IzCVuSUXtfux5rDv2O7HmgP/e5IcRLOG+3XA89oPHZIkSdJQ6jrHfbqc0e73aW9wfUCSDYE9gLuAsyfSWZKXA18ElgHPNWmXJEnSsFvtiHuSMW/kHEdV1V92aHxZklNpVqR5I80TT0ccCSwAPlVVy3tiW9S+9sJRMb+a5gbUK2imx1wxyXOQJEmS5oxUjX0PZpJVY1auXlXV/E6BNA9hOovm6amnABcAz6RZ4/1i4NlVdXNP+2rfKD1lewPfo/lLwnHAVX3e6taqOma8eBYvXlxLlizpcgoDc935cOFJcNuVsHBbWHQAbLnr+K+TJEnS8ElyblUtHq/deHPcHzugeMbVjrovBt5Hs3Tji4BrgQ8DR1bVLRPoZjsenP7zmjHaXEGzysycdN358JOj4WGbwEZbw923NMfPervJuyRJ0tpstYn7TE8zqaqrgEMm2DZ9yo4Hjh9sVDPrwpOapP2an8Kd1zVlq1bAFT+Ejbef1dAkSZLWKFs+Bfads8O5v6/TzalJDkpyepLHjFG/VZLT2rXTNQm3XQkPG7Uo5rx1YMW9sxOPJEmS5oauy0G+Fti4qpb1q6yqa5IsbNudNNXg1kYLt22mxzx+3wfL7r4FNtgE9jpi1sKSJEnSLOu6HOSTgPHu2PwZDz5FVR0tOgDuuaVJ1mtVs7/nlqZckiRJa6+uifumNE8hXZ2bgc0mF4623LW5EXWDTeD2q5u9N6ZKkiSp61SZm3jwKaZj2RG4dXLhCJok3URdkiRJvbqOuJ8JvGTk4UejJXkCsB/wo6kGJkmSJOlBXRP3o2lG6X+c5PAkOyVZ0O7fTJOwz2/bSZIkSRqQTlNlqupnSQ4DPg58qN16rQTeUFU/HVB8kiRJkug+x52q+nSSHwOHAc8ENqaZ03428MmqumCwIUqSJEnqnLgDtMn5Xw84FkmSJElj6DrHXZIkSdIsMHGXJEmShoCJuyRJkjQETNwlSZKkIWDiLkmSJA0BE3dJkiRpCJi4S5IkSUPAxF2SJEkaAibukiRJ0hAwcZckSZKGgIm7JEmSNARM3CVJkqQhYOIuSZIkDQETd0mSJGkImLhLkiRJQ8DEXZIkSRoCJu6SJEnSEDBxlyRJkoaAibskSZI0BEzcJUmSpCFg4i5JkiQNARN3SZIkaQiYuEuSJElDwMRdkiRJGgIm7pIkSdIQMHGXJEmShoCJuyRJkjQETNwlSZKkIWDiLkmSJA0BE3dJkiRpCJi4S5IkSUPAxF2SJEkaAibukiRJ0hAwcZckSZKGgIm7JEmSNARM3CVJkqQhYOIuSZIkDQETd0mSJGkImLhLkiRJQ8DEXZIkSRoCcypxT7J1kuOSLEtyb5KlSY5JsknHfjZtX7e07WdZ2+/W0xW7JEmSNJ3Wme0ARiTZATgL2AI4BbgQ2A14M7Bvkj2q6uYJ9PPItp+dgNOBLwGLgEOAFyd5VlVdPj1nIUmSJE2PuTTi/gmapP3wqtq/qt5RVc8DPgTsDLx/gv18gCZp/2BVPb/tZ3+aDwBbtO8jSZIkDZVU1WzHMDLafimwFNihqlb11G0IXAsE2KKqlq+mn0cANwCrgEdX1R09dfOAy4Ht2vdY7aj74sWLa8mSJZM+J0mSJGkikpxbVYvHazdXRtz3bven9ibtAG3yfSbwcGD3cfrZHdgAOLM3aW/7WQV8Z9T7SZIkSUNhriTuO7f7i8eov6Td7zRD/UiSJElzyly5OXVhu79tjPqR8o2ns58khwKHtod3JrlonPebbpsBN81yDJpeXuM1n9d4zeb1XfN5jddsc+X6bjeRRnMlcZ8TqupY4NjZjmNEkiUTme+k4eU1XvN5jddsXt81n9d4zTZs13euTJUZGQlfOEb9SPmtM9SPJEmSNKfMlcR9ZErKWHPPd2z3Y81dH3Q/kiRJ0pwyVxL3M9r9Pu2yjQ9ol4PcA7gLOHucfs4G7gb2aF/X2888YJ9R7zfXzZlpO5o2XuM1n9d4zeb1XfN5jddsQ3V950TiXlWXAacC2wNvHFV9JLAAOLF3Dfcki5IsGtXPncCJbfsjRvXzprb/7wzLk1PbOfdag3mN13xe4zWb13fN5zVesw3b9Z0TD2CCBx7CdBbN001PAS4Ankmz5vrFwLOr6uae9gVQVRnVzyPbfnYCTgfOAZ4A7EfzcKZntx8UJEmSpKExZxJ3gCTbAO8D9gUeSfPE1JOBI6vqllFt+ybubd2mwHuB/YFHAzcD3wLeU1VXT+c5SJIkSdNhTkyVGVFVV1XVIVX16Kpar6q2q6q3jE7a27bpl7S3db+rqje3r1+v7e81s520J9k6yXFJliW5N8nSJMck2aRjP5u2r1va9rOs7Xfr6Ypd45vq9U2yIMnLk3whyYVJlie5I8mSJG9Lst50n4NWb1Dfw6P63DPJyiSV5KhBxqvuBnmNkzyt/X6+uu3r+iQ/SPKq6Yhd4xvg7+H/keSU9vX3JLkyyTeT7DtdsWt8SQ5M8tEkP0pye/tz9XOT7GvgP+8HYU6NuK/J+kwFuhDYjWYq0EXAHr1TgVbTz+ipQD8DFvHgVKBnDcsc/jXJIK5v+wP/W8DvaG6gvhTYBHgJsGXb//Or6p5pOg2txqC+h0f1uSFwPs0DQB4BvL+q3j3IuDVxg7zGSd4EfBi4BfgGcA2wKbALcHVVvWzgJ6DVGuDv4TcAnwCW08wKuBrYGjgAeDjw7qp6/3Scg1YvyXnAk4E7aa7LIuDzVfWKjv0M/Of9wFSV2wxswHeAAv56VPkH2/L/M8F+PtW2/7dR5Ye35d+e7XNdG7dBXF/gKcDLgfVGlW8InNv287bZPte1dRvU9/Co1x5H80HtnW0fR832ea7N2wB/Tu8DrGr727BP/bqzfa5r4zagn9Pr0jwL5m5g51F1TwDuoVkFb/3ZPt+1caNJrHcEAuzVXtfPzcb/lenaHHGfAe0nt0uBpcAOVbWqp25Dmrn8AbaonpVz+vTzCJpR9VXAo6vqjp66ecDlNI/M3aEcdZ8xg7q+47zHXwCfB75eVf9zykGrk+m4xkn2A74GvJLmKdafwRH3WTPIa5zkF8DjgW1rtkbl9BAD/D38KOA64PyqenKf+vOBJwGbee1nV5K9aP563WnEfSZ+p0/FnJrjvgbbu92f2vsfAKBNvs+k+fPa7uP0szuwAXBmb9Le9jMyutP7fpoZg7q+q3N/u18xhT40eQO9xkm2AD4NfK2qJjX/UgM3kGucZBdgV5oljn+XZO8kb2/vU3l+Rj2rRDNmUN/DNwA3Ajsl2bG3IslONKO955m0D7WZ+J0+af4AmRk7t/uxnth6Sbsf64mvg+5HgzUT1+U17f7bU+hDkzfoa/xpmp+/r59KUBqoQV3jZ7T7G4Dv09yL9K/A0cD3gPOSPH7yYWqSBnJ9q5mm8Eaa799zk5yQ5B+TfJZmSuOvgYMGEK9mz5zOtdaZjTddCy1s97eNUT9SvvEM9aPBmtbr0t7kti9wHs2caM28gV3jJK+hueH4z6rq+gHEpsEY1DXeot3/Jc0NqS8Gfgw8CngP8ArgG0meVFX3TT5cdTSw7+Gq+kqSZcAXgd4Vgq6nmfLmVNXhNqdzLUfcpTksyQHAMTRzKl9aVfeP8xLNYUm2p7meX6mq/5jdaDRNRn6vzgdeVlXfrKrbq+oSmiRvCc1I3UtnK0BNTZJX0Pz15Ec0N6Q+vN2fBnwM+NLsRac1nYn7zBj5dLZwjPqR8ltnqB8N1rRclyT70/wCuAHYyxuOZ9WgrvFxNKtRHDaIoDRQg7rGI/XXVdVPeivaaRantIe7dY5QUzGQ69vOYz+OZkrMK6vqwqq6u6oupLnR/FzgoPbGSA2nOZ1rmbjPjIva/VjzoUZucBlrPtWg+9FgDfy6JDkI+ArNn16fW1UXjfMSTa9BXeOn0UyluLF9MEileQr0Z9r6d7VlX5tauJqEQf+cHuuX+sgDBTeYYFwajEFd331oloT8QZ8bF1cBP2wPnz6ZIDUnzOlcyznuM+OMdr9Pknl9lhbag2bd17PH6edsmtG6PZJs2Gc5yH1GvZ9mxqCu78hrXg6cQDM/dm9H2ueEQV3jz9L8WX20HYE9ae5jOBf4+ZQjVleD/Dm9HNg+yYI+y8Xt0u5/O4CYNXGDur7rt/vNx6gfKff+heE10N/pg+aI+wyoqstolgbbnuZu9F5HAguAE3t/wCdZlGTRqH7uBE5s2x8xqp83tf1/x0RvZg3q+rblr6ZJ7q4E9vRazg0D/B4+vKpeO3rjwRH3b7RlH5+2k1FfA7zGdwH/DjwMOCpJeto/CTiYZlnXrw7+LDSWAf6c/lG7PzDJrr0VSZ4CHEjzgJ7TBxe9pkOSddtrvENv+WT+r8wkH8A0Q/o8PvcC4Jk064VeDDy7d93X9s/nVFVG9fPItp+daH4wnENzU8x+NHOhn93+p9MMGsT1TbI3zQ1P82jmUF7V561urapjpuk0tBqD+h4eo++D8QFMs26AP6c3An5A8zTkn9Ks+/wo4ACaKTJvqaoPT/f56KEGeH2PAw6hGVU/GbiCJsnbH1gPOKaq3jrNp6M+2nvD9m8PtwReSLPKz8gHrpuq6u1t2+1p/vJ1RVVtP6qfTv9XZtSgHsHqNqFH6G5D88v5Wppv+CtoVpjYpE/bor2XqU/dpsCH29ff1/Z3HLD1bJ/j2rxN9frSjMTVONvS2T7PtXkb1Pdwn7Yj1/6o2T7HtX0b4M/pRwDvp/klfy/NnPdTgX1m+xzX5m0Q15fmqZkH06zTfwvNX1B+R7OqzMtm+xzX5o1mNsKEfofSfNga8/dql/8rM7k54i5JkiQNAee4S5IkSUPAxF2SJEkaAibukiRJ0hAwcZckSZKGgIm7JEmSNARM3CVJkqQhYOIuSZIkDQETd0nSQCQ5Pkm1TySczvdZmmTpdL6HJM1FJu6SpDklyfdHHjcvSXrQOrMdgCRJHT1/tgOQpNlg4i5JGipVddlsxyBJs8GpMpI0y5Js384NPz7JoiRfS/K7JMuT/DjJPn1es36SdyT5ZZK7ktye5EdJ/nRA/R/Rvmav1fU3wfM7OMl/Jrk8yd1trGcmeUW/foHntsfVs32/p13fOe5T+Jpsn+RLSW5Kck+SJUn+eCLnJkkzyRF3SZo7Hgv8BPgl8Cng0cCfAd9K8hdV9WWAJOsB36FJcC8EPg48HDgQ+HKSp1TVOyfb/zT4JPBr4IfAtcAjgRcBJybZuar+vm13K3AkcDCwXfvvEUtX9wZT+JpsB5wDXA6cCGxK8zU5JckLquqMricrSdOmqtzc3NzcZnEDtgeq3f51VN1i4H7gFmCjtuzv2rbfBNbpabsFTYJbwLMn239bfkTbfq/VxHv8qPLj2/LtR5Xv0KeP9YDT2vfealTd95tfT2N+vZYCS0eVTeVr8t5Rfb1wpK/Z/r/h5ubm1rs5VUaS5o7bgPf1FlTVEuDzwMbAn7TFr6FJLP+mqlb0tL0B+If28LVT6H+gqs+c9Kq6j2ZUfB0Gc7PpZL8mVwBHjYrtO8CVwG4DiEuSBsbEXZLmjv+uqjv6lH+/3T81yYbA44FlVXVhn7anj7SdTP8dYp2wJNsm+XiSC9u559XOZf/PtslWU+x/Kl+T86pqZZ/yq4BNphKXJA2ac9wlae64fozy69r9wnaDZq54PyPlG0+y/4FK8jiaOeSbAD8CTqUZ+V9JM13l1cD6U3ybqXxNbh3jNStwcEvSHGPiLklzx6PGKN+y3d/Wbr1loz26p+1k+h+xqt33+z3RLwEey9/Q3Ix6SFUd31uR5M9pEvepmsrXRJKGhqMJkjR3PK2d9jHaXu3+5+1Ul8uArZLs2Kft3u3+vyfTf0/ZLe1+mz7tF/cpG8vj2/1/9ql77hivWQmQZP5E3mCKXxNJGhom7pI0dywE3tNbkGQx8HKa0eKT2+LjgAD/2pvcJtkM+PueNpPtH5rpLQCHJFmnp/02o/sYx9J2v9eo930h/W8WBbi53W/b4X0m+zWRpKHhVBlJmjt+CLw2yTOBM3lwnfV5wOuq6va23dHAHwH7Ab9I8k2aNcsPoln+8F+q6sdT6J+q+mmSHwJ7AuckOZ1mqs3/pFkvvd9IfD+fAA4BvpLkq8AyYBdg3//f3h3iRBBDYQD+5wKcAMEVsHgMCBIEAkWCAYFdBAfgCog1BIfgBlgOgEUQToF8iFZsIAvJkgAN3ycnmb5p1Z/2pZPkttd/777P5a7P7TXJS1XdfFJn1TUBGIYdd4C/4znJVlqbykmSg7T2jp1a+DlSv0pxO8lFf3SW1iv+lOSwqs6/M/6CvSTzJOu9xmaSWZJl439QVY9prSoPSXaTnCZZS7Kf5GrJa/Mkl2knBLO06xyPv6iz6poADGOqqt/+BoB/bZqmjbRQfV1VR6OND8DPsOMOAAADENwBAGAAgjsAAAxAjzsAAAzAjjsAAAxAcAcAgAEI7gAAMADBHQAABiC4AwDAAAR3AAAYwBsYiDjk8u7T5AAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -186,248 +410,82 @@ } ], "source": [ - "Path(\"./pics/\").joinpath(TITLE).mkdir(exist_ok=True, parents=True)\n", - "\n", - "try:\n", - " y_label = \"Number of edges\"\n", - " plt.figure(figsize=(12, 12))\n", - " for i in range(data.shape[0]):\n", - " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", - " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", - " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", - " d = json.loads(json_acceptable_string)\n", - " plt.scatter(i // POPULATION_SIZE, \n", - " np.sum(d[\"chainer\"][\"pipe\"][model_index][\"binary_mask\"]) \n", - " + (np.random.random() - 0.5) / 2, \n", - " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", - "\n", - " plt.ylabel(y_label, fontsize=20)\n", - " plt.xlabel(\"population\", fontsize=20)\n", - " plt.title(TITLE, fontsize=20)\n", - " plt.xticks(fontsize=20)\n", - " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", - " plt.show()\n", - "except:\n", - " pass\n", + "path_to_pics = expand_path(Path(evolution.get_value_from_config(\n", + " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).joinpath(\"pics\"))\n", + "path_to_pics.mkdir(exist_ok=True, parents=True)\n", "\n", + "if validate_best:\n", + " evolve_metric = MEASURES[0] + \"_valid\"\n", + "elif test_best:\n", + " evolve_metric = MEASURES[0] + \"_test\"\n", + " \n", + "cmap = plt.get_cmap('rainbow')\n", + "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", + "color_ids = np.argsort(data.loc[:, evolve_metric].values)\n", "\n", - "# ylims = [(0., 1), (0.85, 1), (0.9, 1.), (0.85, 1.)] #ag_news\n", - "ylims = [(0., 1), (0.8, 0.9), (0.8, 1.), (0.8, 0.9)] #imdb\n", - "# ylims = [(0., 1), (0., 1), (0., 1.), (0., 1.)]\n", - "# ylims = [(0., 1), (0.7, 0.9), (0.9, 1.), (0.6, 0.85)] #sber faq\n", + "ylims = [(0., 1)] * len(MEASURES)\n", "\n", "for metric, ylim in zip(MEASURES, ylims):\n", - " y_label = metric\n", " plt.figure(figsize=(12,6))\n", - " for i in range(data.shape[0]):\n", - " plt.scatter(i // POPULATION_SIZE, \n", - " data.loc[:, metric + \"_valid\"].values[i], \n", - " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5, marker='o')\n", - " if PLOT_TEST:\n", + " if validate_best:\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_valid\"].values[i], \n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5, marker='o')\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_valid\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \n", + " c=colors[-1])\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_valid\"].min() * np.ones(data.shape[0]//POPULATION_SIZE), \n", + " c=colors[0])\n", + " if test_best:\n", " for i in range(data.shape[0]):\n", " plt.scatter(i // POPULATION_SIZE, \n", " data.loc[:, metric + \"_test\"].values[i], \n", " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5, marker='+', s=200)\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_test\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \"--\",\n", + " c=colors[-1])\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_test\"].min() * np.ones(data.shape[0]//POPULATION_SIZE), \"--\",\n", + " c=colors[0])\n", + " \n", "\n", - " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", - " data.loc[:, metric + \"_valid\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \n", - " c='r')\n", - "\n", - " plt.ylabel(y_label, fontsize=20)\n", + " plt.ylabel(metric, fontsize=20)\n", " plt.xlabel(\"population\", fontsize=20)\n", " plt.title(TITLE, fontsize=20)\n", " plt.ylim(ylim[0], ylim[1])\n", - " # plt.ylim(0.85, 0.95)\n", " plt.xticks(fontsize=20)\n", " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", + " plt.savefig(path_to_pics.joinpath(y_label + \".png\"))\n", " plt.show()" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 67, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], - "source": [ - "params_dictionaries = []\n", - "\n", - "for i in range(data.shape[0]):\n", - " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", - " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", - " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", - " d = json.loads(json_acceptable_string)\n", - " params_dictionaries.append(d)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, "source": [ - "# Model ids" + "## If you want to plot measures depending on population colored by `evolution_model_id`" ] }, { "cell_type": "code", - "execution_count": 68, - "metadata": { - "scrolled": false - }, + "execution_count": 57, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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tZu8jWgpsypQpJyxbtmyMJoqIiIiI7JtVq1btcPdZY5WrlGC/LKINav4LeA/wcLSBxwvAqYSNd54EllHEpjcxrnk10Xq/7e3tvnLlynJVLSIiIiJSkJmti1OuUpbezPSet45wPJPfEaOu9wLvB54ibB7yfsImN6cDz0ZlsneoLOe1RUREREQqRqX07D8VpUtGOH5ElI40pv9FHpYXerG3PZuZHU3o1c/eCfApoD269qq88jWE8f/DFJj0KyIiIiJSySqlZ//3UXqWmeW0KVp682TCqjh/KvUC0RJ7C4Gb3D17fP6tUXp2gdNOI6ydfbdW4hERERGRyaYign13fxZYQVha7LK8w58FpgDfy15j38yWmdles2GjTbLy8w4B/hMYBP4h7/BPCKv4vNXM2rPOaQD+KXr5zSJvSURERERkwlXKMB6AvwLuBr5qZmcSlrl8OWEN/tXAp/LKZ5bBzF+K7L+i4P5+wuTcQ4E3ALXAhe7+cHZhd+8ys/cSgv7bzOyH0XlvICzL+RPgR2W5QxERERGR/agievbhxd79duBaQpD/ceAw4CvAK9x9Z8yqbgSGgLcAfwOcQgjYj3X3gkF7tDvfKwmbaL0J+GBUx8eAt3olbDMsIiIiIlIkUxxbPlp6U0RERET2BzNb5e7tY5WrmJ59EREREREpLwX7IiIiIiJVSsG+iIiIiEiVUrAvIiIiIlKlFOyLiIiIiFQpBfsiIiIiIlVKwb6IiIiISJVSsC8iIiIiUqUU7IuIiIiIVCkF+yIiIiIiVUrBvoiIiIhIlVKwLyIiIiJSpRTsi4iIiIhUKQX7IiIiIiJVSsG+iIiIiEiVUrAvIiIiIlKlFOyLiIiIiFQpBfsiIiIiIlVKwb6IiIiISJVSsC8iIiIiUqUU7IuIiIiIVCkF+yIiIiIiVUrBvoiIiIhIlVKwLyIiIiJSpRTsi4iIiIhUKQX7IiIiIiJVSsG+iIiIiEiVUrAvIiIiIlKlFOyLiIiIiFQpBfsiIiIiIlVKwb6IiIiISJVSsC8iIiIiUqUU7IuIiIiIVCkF+yIiIiIiVUrBvoiIiIhIlVKwLyIiIiJSpRTsi4iIiIhUKQX7IiIiIiJVSsG+iIiIiEiVUrAvIiIiIlKlFOyLiIiIiFQpBfsiIiIiIlVKwb6IiIiISJVSsC8iIiIiUqUU7IuIiIiIVCkF+yIiIiIiVUrBvoiIiIhIlVKwLyIiIiJSpRTsi4iIiIhUKQX7IiIiIiJVqqKCfTObb2bXmNlmMxsws7VmdqWZTSuynlPM7Ibo/H4zW29mN5vZ2SOUT5rZO8zsTjPbama9ZrbazL5jZkeW5+5ERERERPavign2zewwYBXwLuBe4ArgOeDDwB/NbEbMej4A3AmcGaVXALcDrwR+ZWafKnDa/wD/DSwCfgZ8DXgGuBi438zOKPnGREREREQmiLn7RLcBADP7DXAW8CF3/1pW/peBjwLfcvdLx6ijFtgO1APHuftTWceWAw8AaWCauw9E+ScSHi4eA17m7r1Z57wLuAb4vbuPGfC3t7f7ypUrY96xiIiIiEhpzGyVu7ePVa4ievajXv2zgLXA1/MO/yOwG7jQzKaMUdV0oBVYnR3oA7j7E8BqoBFozjq0OEpvyQ70IzdE6awYtyEiIiIiUlEqItgHXhWlK9w9nX3A3buBu4Am4BVj1PM8oWd/iZkdkX3AzJYARwAPuvvOrEOPRekZZtaYV9/rovR3se5CRERERKSC1Ex0AyJLo3T1CMefJvT8LwFuGakSd3czu4ww/n6VmV0PbAbmAW8kBPZvzTvnUTO7gjBU6EkzuxHoBo4EzgZ+CPxDifclIiIiIjJhKiXYb43SzhGOZ/LbxqrI3X9sZpuBHwAXZR3aBnyHMOk3/5yPmdlThMm8f5V1aBXwXXffPdL1zOx9wPsAFi5cOFbzRERERET2m0oZxlM2ZvZOwrCbO4HlhOE/ywnfCFxF6KnPLm9m9lXCXIHPAQuAqcCpgBNW8LlspOu5+9Xu3u7u7bNmaWi/iIiIiFSOSgn2Mz33rSMcz+R3jFZJNC7/GsJwnQvd/Ul373P3J4ELCT31bzGz07NOuxj4IPBVd/8Xd9/o7j3u/gfg9UAf8C9m1oyIiIiIyCRSKcF+ZuWcJSMcz0y2HWlMf8ZZQC1we4GJvmngjujlCVmHMpNwf59fmbtvBZ4krN6zNP+4iIiIiEglq5RgPxNon2VmOW0ys6nAyUAv8Kcx6qmP0pHG02TyB/fxHBERERGRilcRwb67PwusIOxgmz8+/rPAFOB72RNlzWyZmS3LK3tnlL7ZzI7JPmBmxwFvJozDv7XAOR8zs9a8cy4F5gNbgceLvC0RERERkQlVKavxQFgF527gq2Z2JvAE8HLCGvyrgU/llX8iSi2T4e73mtl3gHcB90VLb64jPEScD9QBV7r7Y1n1fAN4B3AMsNrMfkGYG3A8cAaQAi5z91T5blVEREREZPxVTLDv7s+aWTthRZyzgXOBLcBXgM+6+66YVb2HMDb/EuC1hJV1uoA/AN9295zVeNy9x8xOBj4GXAC8nfBQsB34MfAld7933+5ORERERGT/M3ef6DZUjfb2dl+5cuVEN0NEREREqpyZrXL39rHKVcSYfRERERERKT8F+yIiIiIiVUrBvoiIiIhIlVKwLyIiIiJSpRTsi4iIiIhUKQX7IiIiIiJVSsG+iIiIiEiVUrAvIiIiIlKlFOyLiIiIiFQpBfsiIiIiIlVKwb6IiIiISJVSsC8iIiIiUqUU7IuIiIiIVCkF+yIiIiIiVarswb6ZvcfMril3vSIiIiIiUpzx6Nk/Bbh4HOoVEREREZEiaBiPiIiIiEiVqhmrgJm9u8g6jyixLSIiIiIiUkZjBvvAfwJeRJ1WZHkRERERERkHcYL9IWAL8J2YdZ4PHFNyi0REREREpCziBPuPA7Pd/bNxKjSzRSjYFxERERGZcHEm6D4AzDaz2ePdGBERERERKZ84wf5DhHH4x8Ws80ngjpJbJCIiIiIiZREn2P86MA24NU6F7v6v7v6qfWqViIiIiIjss1HH7JtZC9Dv7p37qT0iIiIiIlImY/Xs7wL+LvPCzK4xszeMb5NERERERKQcxgr2nTBeP+MS4o/dFxERERGRCTRWsL8FOHx/NERERERERMprrHX2bwXeYWYzCYE/wPnRWvqjcXd/zz62TURERERE9sFYwf7/BWYDryF8C+CEYTxjDeVxQMG+iIiIiMgEGjXYd/dtwNlmVgvMBdYCVwJfGf+miYiIiIjIvhirZx8Adx8C1pvZOmCtu68b32aJiIiIiMi+ihXsZ7j7oaVcxMwuBi529zNKOV9ERERERIoXZwfdclgEvHI/XUtERERERCiyZ19EREREpCjrV8PKW2DHFpg5F9rPhIVLJrpVBwwF+yIiIiIyPtavhpuvgylTYcZs2N0VXp970d4Bf9yHgkovV2HM3cf/Imb/CHzG3ZPjfrEJ1N7e7itXrty/F/3IR+DBB/fvNUVERETi2LEZUsOQzOpfzryeefCevIE+2LkVkklIJCGdglQKZsyB+sbKKnfYIfCXZ8Pu7sIPLfuJma1y9/axyu2vMfsiIiIiUm0G+kJAv2VtSAf6co8PDYYgOlsiGfKzde8KgXSyBsxCmkyG/Eorh8GUlvBtxcpbinizJoaG8Ux2V1450S0QERGRA1H2EJ2mZujt2bu3+2ffDEN3prTsOS/z+oIP7Mm7+jNhmI9l9UN7GnZug/d9rjLLNTWHIT0VTj37IiIiIlK8lbeEQH9KSwiCC/V2t58ZHgB2d4UgendXeN1+Zm5dM+eGh4VsvT0hfzKVq0AK9kVERESkeDu2hN7tbPm93QuXhJ7+KS2ht3xKS+Fx7nEfCiq9XAXSBN0ympAJuiIiIiITIe4QnbgqfZWdCluNJ+4E3f0V7J8HnO/u7xr3i00gBfsiIiJywIgzZl/GTUWtxuPuN1R7oC8iIiJyQIk7REcmVNGr8ZjZdODdwMuAaUChoTnu7pU/iElERERESrdwiYL7CldUsG9my4DbgFmAjVJ0/McGiYiIiIjIqIodxvMl4CDgX4HFQK27Jwr8VPVEXBERERGRyaDYYTynAje5+yfHozEiIiIiIlI+xfbsG/D4eDRERERERETKq9hgfxWwdDwaIiIiIiIi5VVssP854FwzO30c2oKZzTeza8xss5kNmNlaM7vSzKYVWc8pZnZDdH6/ma03s5vN7Owxznuzmf3GzHZknXeDmb1i3+5MRERERGT/K3bM/gLgBmCFmf2A0NPfUaigu19XTMVmdhhwN2EC8A3Ak4TlPT8MnG1mJ7v7zhj1fAD4BrAbuB7YCMwHLgDOMbN/cPcv5J1TA3wXeDvwNPAjoBOYA5wEnAD8qZj7ERERERGZaEXtoGtmacKymtnLbuZXYIR19otakcfMfgOcBXzI3b+Wlf9l4KPAt9z90jHqqAW2A/XAce7+VNax5cADQBqY5u4DWce+AHwS+ALwGXdP59fr7kNj3YN20BURERGR/SHuDrrFBvsXxy3r7t8tot7DgGeAtcBh2cG2mU0FthAeIg5y992j1DMb2Ao87O7HFjj+MHA0MDPzLYGZzQHWAfe7+0lx21yIgn0RERER2R/iBvtFDeMpJoAv0quidEV+r7q7d5vZXYRe/1cAt4xSz/OEnv0lZnaEuz+dOWBmS4AjgAfzhgO9GagDfmhmjcCfA4cD3cAf3P2hfbs1EREREZGJUeyY/fGSWeFn9QjHnyYE+0sYJdh3dzezy4D/BlaZ2fXAZmAe8EbgMeCteaedGKVNhHkCC7MPmtlPgYvcvTf23YiIiIiIVICSgn0zayJMeH0p0EaYzHo/cP1ow2xG0RqlnSMcz+S3jVWRu//YzDYDPwAuyjq0DfgO8FzeKQdF6eeBu4DzCQ8dRwFXAW8CeoBLCl3PzN4HvA9g4cKFhYqIiIiIyGT3yCNww/WwYQMsWADnvRGOPnqiWzWmYpfexMzOJYxx/y5h4uy7gI9Er9ea2evK2sIimdk7gd8BdwLLCT32ywnfCFwF/DDvlMx78ALwend/wN13u/s9wBsIgf6FZjav0PXc/Wp3b3f39lmzZpX/hkRERERkYj3yCHzly9CxC+bNC+lXvhzyK1xRPftmdjzwMyAJfB+4lTB5di5wBvA24CfRMpmriqg603PfOsLxTH7BZT6z2rcEuAZ4GLgwa/z/k2Z2IWG40FvM7HR3vy2vzlvcvSu7PnffYmb3AGcC7cCmmPcjIiIiEyjdtQG23Q/9L0DDdJh9PImWBRPdLJmsbrge2tqgLdr6KZPecH3F9+4X27P/KcJSm6e6+0Xufq27/yZKLwJOiY5/ssh6M0tkLhnh+BFROtKY/oyzgFrg9gITfdPAHdHLEwpce6QHiV1R2jjGtUVERKQCpLs2wNoVMNQL9dNCunZFyBcpxYYN0JLXJ93SGvIrXLHB/qnAj9294AZT0dCXn0TlivH7KD3LzHLaFC29eTLQy9gbW9VH6UjjaTL5g1l5v4vSo0Y458goXTPGtUVERKQSbLsfapqgtgnMQlrTFPJFSrFgAXTlTS3t6gz5Fa7YYL8VGOsRZj3QUkyl7v4ssAJYBFyWd/izwBTge9mTf81smZktyyt7Z5S+2cyOyT5gZscRltl0wvCj7HMeBE4xszfmnfNewnj/ZwAtoC8iIjIZ9L8ANXlfyNc0hnyRUpz3RujoCGP10+mQdnSE/ApX7Go8m4GXjVGmnTCOv1h/BdwNfNXMzgSeAF5OWIN/NWEIUbYnovTF3Xzd/V4z+w5h0vB90dKb6wgPEecT1tO/0t0fyzrHo83Cbgd+ama/jK53JHAOsBu42N1TJdyTiIiI7G8N08PQndqmPXnDfSFfpBRHHw3nvA6+fhVs2hQm6V721xU/Xh+K79m/GTjDzP7OzJLZB8wsYWYfB14dlStK1LvfDlxLCPI/DhwGfAV4Rd5GWKN5DyHY/yPw2qie1wB/AN7m7h8tcO2HgeOB6wjr7n+EsKzo94ET3P3uYu9HREREJsjs42G4NwT87iEd7g35IqV4+GG44QY4+hj4y7eG9IYbQn6FM3ePX9hsDrAKmEMYrnMnoRd/DmFy7iJgK9Du7qX07k9q7e3vv1l/AAAgAElEQVTtvnKlRvuIiIhMNK3GI2V1+eWwaxdMm7YnL/P68ssnpElmtsrd28cqV9QwHnffamYnA98i9JYfklfkt8ClB2KgLyIiIuMvbhCfaFkAZQzu415X5fYuV8lti239epg/PzevtTXkV7iiN9Vy97Xu/lpgAWHTqQujdIG7v9bdtWqNiIiIlN14LKmZ7tpA+ukbSD/ynZAWqCvudVVu73KV3LYXPfII/NPn4P3vDWmhjbIWLoTOvNV4OjtDfoUrOtjPcPdN7n6ju38/SrXhlIiIiIyfMi+pGTsgjHvd8SjX3QePPwR//F1Iu/v2z3V39sBd98Avfh7SnT2l1TeR712ccnF3xr3ggjBsZ1e0Gk/m3xdcQKUrdjUeERERkYnR/0IIyrPty5Ka2QEh7Em33Z87BCjudctdbusz8OyzWF09NDXD4AD+9OOQHtiz3Wimvt5h2PAQ7O6BKc2w4DBo6t/7unHKrX8K/rgKG+wDT0PXC/i2LXBSCdctd9vKXS7uzrjHHAN/8zfws5+FoTsLF8J73hPyK9yowb6ZXUO0I667b4tex+Hu/p59bp2IiIhIRrmX1IwbdMe9brnLbduO1SShriG8rmvA0sP4tu255XpT8Nh9WMOUPQ8Fj90HR76stHKPPY317ILGJkjUQjqF9ezCH3saziyyvnK3rdzlNmwIPfrZRtoZ95hjJkVwn2+snv1LCMH+vwLbotdxOGEJTBEREZHymH18GHYDISgf7gtLas4/pbT64gbdca9b7nLpOkj2QnoIEjWQHoakhfxsu3rDQ0FNMuw+VJPEapL4rt7Syu3sgLpaSFgol7DwemdH8fWVu23lLrdgQRi605b10DdJdsaNa6wx+4cCi4Hnsl7H+Vk8Ho0VERGRA1eiZQEsOisE5wO7QrrorNJXWIm5Hn/c65a93KzF0Dg39K4P94e0cW7Izy7X1wezluWWm7Us5JdSrq4ealvAkuEBw5JQ2xLyi6yv7G0rc7nJvDNuXKP27Lv7utFei4iIiOxP5VxSM9GygPSis3KXZ5x/yj4t5VnWcstPxe7+X2g6GBqmQP9u6O+B5afmlmubjfV1w4yle/L6uqFtdmnllh6LPXQPNE6BhnoYHIC+3XDs8cXXV+62lbvc0UfDhz8Wxuhv2BB69C9+96TYGTeuoibomtlngNvc/Y5RypwKvMrdP7evjRMREREZT+Vejz+2rc/CE3dCx7YQgC4/FeYclltmzmHwZ3+RW+74c/cut/xUuPt/w7+zHwqOP7e0cqdcAF3bYft22N0NDQ1w6OKQX2x95W5buctBCOyrKLjPV+wOumng8tECeTP7FPA5d0+WoX2TinbQFRERGV/pro3483t64u2g40m0zC+53ITY+mwIRBuacwPRP/uLvQP5Yuoc6+FhospVctsmsXHZQTemWiA9DvWKiIjIASzdtRFftwKvadqzLv66FaQPOSsnkI9bblzsWAtr/gjd22HqLDj0JJi5KLfME3eGQL9xanidSZ+4s/Rg1B2G0zCUDulInblzDot3jXKWm4hrFlOuyo1HsH88sGMc6hUREZEDmD9/P17ThGWti+8Az98PWUF83HJQ5m8KdqwlvfJ/YLgbSEHvFti5hkT723MD/o5tuA/BplUw1Ae1jTDjcGwgb6WYuNfd8gzpW74D6R5IpGHLVtj2LIkz3wVzD4/z1koVG3MHXTO7NfMTZV2SnZf1c7uZPQf8OXDLuLZaREREDjz9L4SlKrONtGlVjHIvfgMw1IvXTwvpuhWkuzaWVu7J30D/dkgkwmZdiQT0bw/52eUYho0PwPBQtPTmEGx8IOSXct1VN8Lg9rDMZE1TSAe3h3w54MXp2T89698OLIp+8qWBncCPgI/uY7tEREREcpV506qyf1Ow85nQS5+sDa+TteH1zmdy25fehbthGCG0MtwN0rtKah/bngpj/7OvWz8l5MsBb8xg391f7P2PM0FXREREZDzYQcfDuhUh4I02o7LhXmzeKSWVi72DbtxyNQlI542VT3vIz2ngIMw7BN/1Agz0Q30DzJod8vOv2zOErXsAerqguQU/5HBo7s8t11AbxulnR3XD6ZCfb8sz8OgdsGsbTJsNR52moT5VbsxhPHneBfx8PBoiIiIi46t3cBubuu5kza4b2dR1J72D2ya6SUVJtMzHDjkLq23CBnaFtMCk27jlaJgeevyzjfRNQYxyfvCyMAZ/aCDapGsAhvpCfram6VCbgIWHwBFLQ1qbCPnZdg+TePje8EAwZSoM9IfXu3OH+/jhx8BAbyjnHtKB3pCfbcszcMePovXmZ4X0jh+FfKlaRU3QdffvjldDREREZPz0Dm5ja8+9JBMN1CZaGE73s7XnXuY0v4ymutyNhjq6H6Wj6z5Sw10ka1poazmRtqlH7VXfrv7VDKY6qUu2Mq1hyV71jEe5/oZadh00jcFUIipXS9NepeKVK/c3BXbYa/D+Dti1IyynWVcPcw/BDntN7oWXnwMrvwf9QF0TDPaGh4fj3pxb3/YeqLEwBp8orbGQn11u+dnhups2Q083NDXC0qXY8rNzr/voHdBYYBWgR+9Q734VK3k1HjObD8wD6gsdH23jLREREdm/dvWvJplooCbRAECNNbyYnx1Ud3Q/yo6dv8US9ViimVSqjx07fwvwYsAf98Gh0sslWuaTPuSsMAY+s9rNvFMKflMQu9yRbxlz9ZzEIS8Pa5Q/8SvofSH06B/3ZhKHvDy3XG8fPms53rs1PAzUNGKzlmO9ud8yJFrmk37pW/F5Y6zas2tb6NHP1jAl5EvVKjrYN7OzgCuAZWMUPeA21RIREalUg6lOahMtOXlJq2cw1ZmT19F1H5aoJ5nMrGbTSCrKzwT7cR8cdvWvxlLDeN9GhlJ9WLIRq2srWC5ufeUsByFQzl+Os5Cylzvk5ZAX3O9l2mysrxubvnRPXl93GGtfynWnzQ7nZ3r0IXwDUaA+qR5Fjdk3s1cANwJtwFWAAXcA3waejF7/EtAEXhERkQpSl2wl5QM5eSkfoC7Zmps33IVZ7pf2ZvWkhrtefD2Y6oShAYa6n2Gw4xGGup+BoYG9Hhz6+zfjvZvx9BAkGvD0EN67mf7+zTnl4tZXTLlk3j0UerCpeEedBn09IUD3dEj7ekJ+JdQnk0KxE3T/njDC7ER3/3CU93t3vxQ4Cvgn4NXAT8rXRBEREdlX0xqWkEr3M5zux90ZTveTSvczrWFJTrlkTQue91DgPkCyZs+3AsmUMbx7TU4QP7x7DcmU5dY11IubYYlaLErdjORQ7uZRseuLWS7ug03Fm3s4nPaXoSe+Y3tIT/vL0sfXl7s+mRSKHcZzEvALd89+JE8AuLsDnzGzc4DPAm8ucL6IiIhMgKa62cxpflnOJNhZTcfsNaylreVEduz8LSlCj777AJ4eoG3ant7fulSKQSME8oTULeRnq6WO1HAH9Hdh6RSeSEJNPbV5QXfc+uKWm9awhK099wKhRz/lA6TS/cxqyludZjKYe3h5g/Fy11fJnnwMfv1L2LwRDp4PZ78elh050a3a74oN9luB9VmvB4EpeWXuAt6+L40SERGR8muqm11whZtsmXH5OavxTDstZzUeSw8xpX4hA6kXSKX7SSYaaKxfiKWHcuqqtSZ8aJA0Ttog4U7N0CC1Dbnr4sStL265uA82UsWefAy+fRW0tsLcg6GrI7x+718fcAF/scH+88C0vNeH5ZWpBfL2qBYREZGMjtRONqXX0us9NFkz8xKLaEvOKLnc1r7VbO1/gKFUN7XJqcxpeClzGpeUXB9Nc9ndcMyL5doSc3MO19S20Tu0i/6aWoZwaqklmR6iqTZ346lk2nGM4USSlCVIulGbDvml1Be3HMR7sJEq9utfhkC/tS28zqS//uUBF+wXO2Z/NbnB/Z+A15jZEgAzmwO8CXi6PM0TERGpLh2pnawefoRBH6CRKQz6AKuHH6EjtbOkclv7VrN+9+0Mp/tJJpoZTvezfvftbO1bPW7XTTfOoyu1lVSqj1qvIZXqoyu1lXTjvJy6htK72V3fSDphJN1JJ4zd9Y0MpXfnlItbX7pxHn29a2l+fg2ztq6n+fk19PWu3ascQKpnEwPrbqJ/9X8zsO4mUj2bxvhkpKps3ghTc1efYmpLyD/AFBvs/xp4pZlltnj7CqEX/wEzu4+wIs8s4MryNVFERGRy6Ejt5LGhVdw3eDuPDa3aK5AG2JReS63VUWf1mBl1Vk+t1bEpvbakclv7HyBhddQkGkiYRWkdW/sfGLfrPl+3m+HWw8PynKl+kslGhlsP5/m63CC+pyZNIlELjQeRnjIXGg8ikailpyadUy5ufbuGNtDcN0TSIW1G0qG5b4hdQxtyyqV6NjG46RZ8uBfq2vDhXgY33aKA/0By8Hzo7srN6+4K+QeYYoP9bwGnAUMA7n4X8BZgDWE1ni3AB9z9unI2UkREpNLF7Tnv9R5qqcvJq6WOXu8pqdxQqptE3jKTCatnKNU9btft9R6S9TNJTV9O6qATSE1fTrJ+5l51dTdPJ5lOY6lBcMdSgyTTabqbp+eUi1tfzQtPQ91UUs1zSTfPI9U8F+qmhvwswzsfxGoasZqmsApQTRNW08jwzgeRA8TZr4fOTujsgHQ6pJ2dIf8AU1Sw7+5d7n6Pu3dn5V3v7ke5e6O7L3f3q8vfTBERkcoWt+e8yZoZYjAnb4hBmqy5pHK1yamk85aZTPsAtcmpOXnlvG7cuuoaD2b39EPxZB023Icn69g9/VDqGg8uqW2Ng4MMJXP37BxKJmkczD3XB3ZBMm/6YLIx5MuBYdmRYTJuSxts2RzSA3ByLpSwg66IiIjsrdd7aMxboK5Qz/m8xCJWDz/y4vEhBhnyQQ5NLi2p3JyGl0Zj9kOPftoHSPsgcxpOGrfrFlVXfTephiNyyi1JLNqr3HM9d1HTs4Oa4QGGa+oZaJ7JvOaTc8o11c9nuO9ZEqkhEukU6UQST9bS1Ji7VojVTwtDeGqyVv1J9WH1e0/kjW3HWljzR+jeDlNnwaEnwcxFY50lE2nZkQdkcJ+v2B10TzCzz5hZwentZjYnOn5ceZonIiIyOcTtnW5LzmBJzdHUWT197KbO6llSc/Req+LELTencQkLp7ySmkQDqXQPNYkGFk555V6r8ZTzuuW+h6bBQeZ27KImnWIwmaQmnWJuxy6a8nrs61sOZ0pfL8nUMCmDZGqYKX291LfkrhtfM+M4fLgPH+7F3UM63EfNjBLDkx1r4aGfw0APNM8I6UM/D/kiFc7CXlgxC5v9D3AKcIgXONHMDFgL3O7uF5WrkZNFe3u7r1y5cqKbISIiEyAzZr/W6nJ7sQsEt5Krc+tvSaf6SGQNvcm8bp3zmtxyvdtI9u2CqOc+1TiNRNPsnHIQJukO73wQH9iF1U+jZsZxJJv3XrUnlvt+EAL8+qwHt8zrE99WWp0i+8jMVrl7+1jlStlB9/eFAn0Iu+ia2a2ESbwiIiIHjLbkDA5OLWDt8KP0s5sGprCo5igF+jGkhnaRqGnLybNEA6mhXXuXazwIb9ozwMDc9yoHkGyeV3pwn697e+jRz1bXFPJFKlyxq/HMAcZaoHQzMHeMMiIiIlWlM7WD7am1tCVmMD9xOG2JGWxPraUztWOim1bxkrXT8HR/Tp6n+0nmb9IVs1zZTZ0Fg725eYO9IV+kwhUb7PcS1tEfzSxgYIwyIiIik0ac9fO3Dq+h1uqpjVbjyfx76/CaCWjx5NLYehSe6iOd6sPdQ5rqo7H1qJLKld2hJ8HA7jB0x9MhHdgd8kUqXLHB/oPAeWZ5s40iZtYCnBeVExERmfTirp/f593U5K1PX0MdfZ673r3sra5xLs2zTiORbCQ93EEi2UjzrNOoa5xbUrmym7kIjj0/jNHv2RnSY8/XajwyKRQ7Zv9q4AfAb83s/e7+cOaAmR1L2HRrZlRORERk0stePx+gjvoX87PH4zfaVIZ8gFr2bHA1zCCNlrvevRRW1zg3VtAet1zZzVw0McH9tufgqT9A5zZonQ1LT4HZi/d/O2TSKirYd/cfmdk5wEXAA2a2DdgEzANmAwZc5+4/KHtLRUREJkDc9fPn1BzKc4MPAaFHf5hBhnyABbXL9ltby6W/fys9ux9neLiTmppWmqe8hIaGORPdrAPPtufgnh9DQzO0zIL+7vD65W9RwC+xFTuMB3e/BLgUeJwwYfeEKH0MeF90XEREpCrEXT+/NTmTxXXHUmv19NNDrdWzuO5YWpMz92dz91l//1Z2ddxFKtVHMtlCKtXHro676O/fOtFNO/A89YcQ6DdMBUuEtKE55IvEVNIOuu5+NXC1mTUBbUCHu/eOcZqIiMikE3e3WADHSFmCFElSlsCx/d3cfdaz+3ESiQaS0Zr3mbRn9+Pq3d/fOreFHv1s9VNC/njb+DTcfyu8sBWmz4Hjz4D5R4z/dSfC44/Czb+ATRth3nw49w3wknGe9L0fFd2zn83de919swJ9ERGpVnF3ge1I7eTp4YcZ9AEaoom8Tw8/XHDlnko2PNxJItGQk5dINDA83DlBLTqAtc4Oq/5kG9gd8sfTxqdhxfegtwumHRTSFd8L+dXm8UfhP74KnR0w9+CQ/sdXQ36VKKlnX0REpJx2pDt4lo1008tUmjiM+cxMtI194n7Slpwx5uZYm1NrqaU+dyKvh/zJtLFWTU1rNIQnazfbdD81Na0T2KoD1NJTwhh9CD36A7uhvweOPWd8r3v/rdA0FZpawutMev+t1de7f/MvoLUt/MCe9OZfVE3v/qjBvpk9BzjwandfE72Ow939sH1unYiIVL0d6Q4e4CnqqaWZRgYY5AGe4qXppRUV8I+lz3toKDCRty9vIm+la57yEnZ13AWEHv10up90up/WlhPG9bqpnk0M73wQH9iF1U+jZsZx5dsBtxLFWWVn9uIwGTe73LHn7Nvk3DjXfWEr1NXBuofCA0b9FJixIOSXUl/cFYUmotymjaFHP9vUlpBfJcYaxpPIK5MgrLgz1s8+DQ8SEZEDx7NsJIWzjS6eYSvb6CKF8+yYG7ZXlsYRJvI2Ft6apmI1NMxhWtvJJJONpFJdJJONTGs7eVzH66d6NjG46RZ8uBfq2vDhXgY33UKqZ1PplXauhyevh/v/M6Sd68vX4H2VWWWnvzt3lZ1tBfpUZy+G0y6C138ipCMF+tuegzuug1/+W0gL1RX3uvX1sPYhGB6E+qaQrn0o5BdbX9xrTlS5efOhuys3r7sr5FeJUXv23X3RaK9FRET21XY6eIFeaklSTw3DpHieLoYZnuimFeXg5CKeHn4YPGsiLwMsKjCRt9I1NMzZr5Nxh3c+iNU0YjVNISNKh3c+WFrvfud6eObXUNsEjdNhcHd4ffjZ0LqwjC0vUfYqO7AnfeoPpfXax12iM+51G2sh7eHH2fPvxtri7yPuNSeq3LlvCGP0IfTod3eFcftvu4hqMWoPvJl92czOynq9MNolV0REpCz6GCYB1JAEjBqSJKL8yaQtOYMjao6hzurpjybyHlFzTMHx+p2pHTw1cB8P9t/KUwP30ZnaMQEtrhw+sAuy5ggAkGwM+aXYsioE+nVTwCyktU0hvxJ0bgtDY7Ltyyo7cZfojHvdZBqOOhHq6qG3J6RHnRjyi60v7jUnqtxLjoJLPxTG6m/ZHNJLP1Q14/Vh7Am6HwE6gBXR6zXAZ4HPjWejRETkwFFDHQMMkCJFggRp0oBTQ91EN61ocSbydqZ28NzgQ9RaPQ00M+QDPDf4UME1+TtTO9g6vIY+76bRpjKn5tCC6/aXu9z+ZvXTwhCeTM8+QKoPq59WWoW9O0OPfrbappBfCVpnh973hqzdlfdllZ24S3TGvW6m3LGv2JOXf17c+oq95v4uByGwjxPcP/YI3HgDbNwA8xfA686DI48e+7wJNtbY+h4g6y9vEi4YLCIiFW1WYhpttJEkyTDDJEnSRhuzEiUGehVu6/Aaaq2eWqvHzF7899bhNTnlMg8FQz6Q81CQ/y1AuctNhJoZx+HDffhwL+4e0uE+amYcV1qFTTNgKG9V8KHekF8Jlp4SVtXp7wZPh7S/J+SXIu4SnUtPgW0bYdXtcPeKkG7buPd147YvTrly1jUe5eJ67BH4+legowMOnhfSr38l5Fe4sYL9Z4ALzOzlZpYZ5NYWDecZ9Wec2y0iIlViCQdDooZpiWksSMxjWmIaJGpCfhXq8+69vrWooY4+787Ji/tQUO5ycQ32baFz6295YcP/0rn1twz2bSmpHoBk8zzq5p0ZxuwPdmA1TdTNO7P01XjmnhCC+8Hd4B7Sod6QXwkyq+w0TIWu7SHNH19fjLiB7VAKugcgDSQTIe0eCPmltC9OuXLWNR7l4rrxhjDEp60NEomQtraF/Apn7j7yQbO3Af+dnUWYqjEWd/cDbg3/9vZ2X7ly5UQ3Q0Rk0nmeTlazmS76aKGRJRzMQVTnuu5PDdzHkA9Qa3tWNsm8Xlp/4ot5D/bfSgPNmO35Ut3d6aeH4xrOGLdycQz2baFn+x1YshFLNODpfjzVR/Os06hrnFtUXeOmc30Yo9+7M/Tozz2hMibnjpc4y0z+4lthg6ymrOmXmddveP/+be9k88H3hx79RFY/eToNmzfB1741IU0ys1Xu3j5WubFW4/mBma0B/hyYB1wCPAw8WI5GioiIABxEa1mD+7ibdE1EuTk1h/LE4H30+E4GSVNHgmbqWF67LKdco02lK91FN/0MMEQ9tUz1BloSLeNaLo6+zkexZCOJaFKtJRtJR/kVE+y3Lqzu4D7f7MVj91q/sDXsiJutsbnw+vnV4PFHw+ZYmzaGpTTPfUPpE2/nLwhDd9qy/p67ukJ+hRtzPXx3/5O7f9rd3x1lXe/u7xrrp5TGmNl8M7vGzDab2YCZrTWzK82sqIGbZnaKmd0Qnd9vZuvN7GYzOzvm+f9gZh79vLqUexERkfLbke7gnvSj/C59L/ekH2VHuqNgmQd4igEGczbpyi87UeWGrIaOmgbSZtSSJm1GR00DQ5bb/9aQmMl2doRef08y5ANsZwcNiZnjWi6O1NAuLNGQk2eJBlJDJa6eI/vH9DnQl7fJW19PyK82jz8altTs7AibZnV2hNePP1pafa87L9TR0RF69Ds6wuvXnVfedo+DYje/ehcwLoOTzOwwYFV0jXuBK4DngA8DfzSzWLNqzOwDwJ3AmVF6BXA78ErgV2b2qTHOPx74DGFysoiIVIi4wfSzbKSeWuqpwzDqqaOe2r026ZrIck3JVlrrFtNSv5TWusU0JVv3Krc1uZuGmoOpsXqcIWqsnoaag9ma3D2u5eJI1k7D0/05eZ7uJ1lbnZOqq8bxZ0Bvdxi64+mQ9naH/PH03JPwva/Cv/9tSJ97cnyvB6FHvzUaV59I7Pn3zb8orb4jj4bLPhx69jdvCullH54Uq/EUNa7e3b87Xg0BvgEcBHzI3b+WyTSzLwMfBb4AXDpaBWZWC/wz0A+c4O5PZR37IvAA8Ckz+5K7DxQ4vwH4HnAf8Cxw4b7elIiIlEd2MA28mD7LRmay56v1bnppJnfN9jpq6SZ3dZZJUS7ZhiX3BNCOj3u5OBpbj6Jn+x2kIWfMfuP0E8c8VybQ/CPgrAvh/lvD0J3pc+CU80P+eHnuSfjpf0FzC8ycCz2d4fWb3gOLl419fqk2bQw9+tmmtoT8Uh159KQI7vONGuyb2WnRP+919/6s12Ny9zvilo169c8C1gJfzzv8j8D7gAvN7OPuPloXxHSgFXg4O9CP2vOEma0Gjgaagb2CfcKDwqHAscCo3wCIiMj+FTdInkoTAwy++DAAMMgQU3NWkla5kcrFUdc4l+ZZp9HX+SipoV0ka6fROP3EyhmvLyObf8T4Bvf57loRAv3maE5OJr1rxfgG+/Pmh2E2rVlj7Lu7Qv4BZqye/dsIq+8sB1ZnvY4jWUQ7XhWlK9w9Z3s2d+82s7sIDwOvAG4ZpZ7nge3AEjM7wt2fzhwwsyXAEcCD7r7XrhpmdgZhyNBH3f3p7NUKRERk4sUNVg9jPg8Q+nvqqGWQIQYY4iUsVrkY5eKqa5yr4F7G9vym0KOfrWlqyB9P574BPv0JWLcG+vugoREOORQ+/2/je90KNFaw/zlCcL8j73W5LY3S1SMcf5oQ7C9hlGDf3d3MLiMsF7rKzK4HNhNWEnoj8Bjw1vzzzKwVuJYwxv+rpd2CiIiMp7jB6sxEGy9NL81ZFeclLN5rVRyVK1xOpKwOmheG7jRnrbbV2x3yx9O652DjOkiloLYupBvXhfxSV+SZpMZaevPy0V6XUeY3oHOE45n8Mf+L5O4/NrPNwA+Ai7IObQO+Q5j0m+9rhCFAp/toGw8UYGbvIwwzYuHCA2iJLxGR/ayYYHVmoi1nHP9odarcAa5jPWy8b896/PNPhDb9/7xsTj4rjNGH0KPf2w09XfDat5Re5+rH4ZabYctGmDsfzjwXlrwkt8x134aZM6El6yGjqzPkn/OG0q89CVXdxldm9k7g28DPgM8D64BDgE8DVxFW5fmLrPJvIkzEvczdCz0IjMrdrwauhrCp1r62X0RERjZRwWpHaiebU2vp8x4arZmDk4toS8ZaJE4qWcd6eOomqJ0CjdPDTrtP3QRL/1wBf7ksXhYm4961IgzdOWheCPQLjdd/5gm47VewdRPMmQennwOHL88ts/pxuO6bMLUNZh8cBfDfhIs+kBvwb90MB+UtKdo8NeTne+yRsBPuxg1h3fzXnVd4Iu7DD8PPfgbr18PChXDBBXDMMcW/J/tZUcG+mSWBenfvzcs/AzgP/n97dx4neVXf+//16a7q6nV6NmZh9gGGVRAdkDgqm3dUoqII3sQrClG5mlyN5prNLEJujDExLokaReOaPFwCGvLjAYqyCAzqzCCbbAPMzgwMs/Xe1bV8fn+cbzFVPd3TVd1VXUu/n49HPQ51vqe+31P1pfcXtTEAACAASURBVHo+31Pn+zkMAte7e6lrb+dG7sdbUSVXf3RC5cJ+rAG+Tlj468q8+f9PmNmVhOlCV5jZBe5+l5nNBb5MmBr0ryX2WUREZoDDmQM8lX6YOAla6WDEkzyVfpiTOFMBf73bvSkE+i0d4Xmu3L3p6GD/4A7YtREGXoCO42DZuTB3xdH7LLbdTLL6lIlvxn36cfiPr4SR+AWLoa8nPP9f/7sw4L/9lhDo50bsc+XttxQG+4uiC4H8kf3+vlCf79FH4IufDzfyHr8k5M//4uePTqv58MPw6U/DnDmwdCkcOhSef/SjNR/wl5pn/9PAwWiOOwBm9jvAT4EPAn8KbDSzUpcTy2XOWTPO9txt4+PN6c9ZD8SBn49xo28WyGUIenlULgfmE3LyZ/MW0nLg3VGbn0Z1Hy7urYiISCPZk9keEn5aAjOjxUIC0D2Z7dXumkzV4AGIj8pGFG8P9fkO7oDHbg4j/+3zQ/nYzaF+Mu3qwY4tcMOX4Mt/FcodE4VgU3TXrSEw7+oOefG7usPzu24tbLd3dxihz9fZFerzvet9Ibjv7QmLYPX2hOfvel9hu5tvCoH+7Cgf/+woH//No5aV+uEPQ6A/Z05ol/vvH/6wPO+/gkqdxvMa4E53z59b/3HCiPsfAosI6Sv/iJAbv1h3RuV6M2vKD9TNrAtYR/jV4JcT7CcRlceNsz1XPxKVB4B/G6ftawgXGbcSbvKd5JJrIiJSLvvoYQt76GWIWbSxhuNZMO6PwuUx5P200lFQF6eFIdfai3WvfV4IyFvyzm9qMNTn27UREmP8ArBrY+GofbHtat2OLXDLN6F9FsxbCAO94fklV8GK8cZlp+i5Z8OIfr6OrlCfb/HSsUfsF49KqZmbl//tr4apO4uOh//z0aPn6+/eFUb0882aFerz7dwZRvTzdXeH+hpXarC/DLgv98TMVhOmxvyNu/97VPca4PWUEOy7+zNmdhthZP4PCDfM5lwHdABfyc+xb2anRK/NX4btnqi8PFo46+G89i8FLidkE7ojeu0u4L1j9cnMvkkI9j/j7j8r9r2IiEhl7KOHjTxNKzG6aGWYETbyNOdyYkUD/jbrZMSTtLw4ngQpRmizzoodU6bJ0nPCHH0II/qpQUgNwOoLCtsNvBBG6vPF20P9ZNrVuk0/C4F+x6zwPFdu+lnlgv1FS8LUna687/JAX6jPd/ElYY4+hBH9/j7oOwxv/d2j9/mGN098M+7SZWHqzuy8e4F6e0N9vuXLw9SdOXmrRPf0hPoaV+o0nllAb97zdYTg+cd5dY8Ck1mx4PcJefL/2cz+y8w+aWZ3EC4atnD0IlePR48XuftGQsadNmCTmX3PzD5lZt8HfgW0Ap9390cn0T8REamiLeyhlRittGAYrbTQSowtjHHDXRkd37ySwWwPL6R3sC+1lRfSOxjM9nB888qKHlemwezl4Wbclg4YOhjKsW7O7TguXAjkSw2G+sm0q3X790L7qIvZ9s5QXykXvCGM2PdF0276esLzC95Q2G7NaeFm3Fnd8PyeUI6+ObcUb7w0LL51+HA47uHD4fkbLy1sd9llIdg/dCi0y/33ZZdN7rjTqNSR/b2EFWZzXgsMAffn1XUC6VI7Eo3uryXk8n89cEl0vM8D17n7oSJ39R7C3PyrgNcBXYQLlHuBr7r790rtm4iIVF8vQ3TRWlCXIE4vQxU9ruHE3Em54zjmTgzHKrLsjEy72csnzryz7Nww9x6O/AKQHIATLpxcu1o3f3GYupMb0QcY7D96caxyOvHUcDNufjaeN/3O0dl4IAT2kw3uRzv9JeFm3PxsPO9899HZeM48M9yMm5+N5z3vqfmbcwGslLTyZvZd4E2EhamGgZuA2939zXltbgZWuvvMWrGAkHpz8+bN1e6GiEhDupfHGWaE1rwVdHPPX8UYAUGZPJncRMqTxC1vGk/0/OTEORU7rtSYmZSNJ3/OfntnCPQHeys7Z19KZmb3u/vaidqVOrL/d4QUm7lblLPAJ/IO2gq8GrihxP2KiIgc0xqOZyNPA2FEP0mKYdKcycqKHnfI+2ilcEpDjBaGvK+ix5UaM3dFcUF7se1q2Yo1IbDf9LMwdWf+YrjgMgX6daqkYN/dHzGzV3AkLeX33X1TXpOzCTe/frdM/RMREQFgAd2cy4kF2XjOZGXFs/G0WVcYyc+7QTfNCG3WdYxXidS5FWsU3DeIklfQdfdHgI+Os+0XwFun2ikREZGxLKC74sH9aItiq9g68hAQRvTTjJDyJMviEywSJCJSA0rNxjMmM4ub2dlmdnI59iciIlIrupvns7rlLOKWYJh+4pZgdctZdDfPn/jFIiJVVtLIvpm9nZCr/v3ufjCqO4Gw8NQJ0fObgLe7e8kZeURERGpRd/N8BffSGHY/Bb++Aw4+B3MXwcsugqUnTX5/O7YUzu0/57Wa/lNjSh3Z/z3glFygH/kn4ETCKrgPE27gvbo83RMRERGRstj9FNz2nZBZZ86CUN72nVA/GbmsPQO9hSvt7thSzl7LFJUa7J8GvHhDrpnNIuTD/4G7vxY4F3gCBfsiIiIiteXXd0B7V0ipaU1Ras2uUD8Z+SvtWlMo22eFeqkZpd6gexxhoauc34r28T0Ad0+Z2U+BMdYsFhEREakxjZAXv1gHnwsj+vnaOkP9ZOzfG0b081V6pV0pWakj+31QkAbhfMAJq9PmDBNWrRURERGpXQd3hBVvRwagfX4oH7s51DeiuYtgqL+wbqg/1E/G/MVhwa18lV5pV0pWarD/FPAGM0uYWQvwduBhd9+f12YFsK9cHRQRERGpiF0bIdEBLR1gFspER6hvRC+7CAb7wlx9z4ZysC/UT8Y5rw37GIj2N9Abnp/z2vL2W6ak1GD/emA1Ieh/HFgFfGNUm5cDj069ayIiIiIVNPACxNsL6+Ltob4RLT0J1l8Z5tUf2hfK9VdOPhtPbqXdjllw4PlQXnKVsvHUmFJX0P1WlEv/mqjqC8C/5Lab2SsJmXmuL1sPRURERCqh47gwdael40hdajDUN6qlJ00t1eZoWmm35k1mBd2PAR8bZ/NmYA4wMJVOiYiIiFTcsnPDHH0II/qpQUgOwAkXVrdfImVUcrB/LO4+AoyUc58iIiIiFTF3BZz2xsJsPCdc2LjZeGrd1idgw22w71lYsATWrYfVp1S7V3WvrMG+iIiISF2Zu0LBfS3Y+gTc+G/QOStk8+nvCc/f9h4F/FNU6g26mNliM/uimT1tZkNmlhnjka5EZ0VERESkAW24LQT6nd3Q1BTKzlmhXqakpJF9M1sCbAQWEjLuJIAdQJKQpScGPAj0lLebIiIiItKw9j17dH7+9q5QL1NS6sj+XwOLgNe7+1lR3Tfc/RRCsP8ToA24rHxdFBEREZGGtmBJyPmfb7Av1MuUlBrsvw74sbv/bPQGd98NXEEI9q8rQ99EREREZCZYtx76e8Nc/Ww2lP29oV6mpNRgfxGFC2ZlCME9AO7eD/wUuHTqXRMRERGRGWH1KeFm3M5u2L83lLo5tyxKDfZ7gZa854eA0b+v9AANvBqFiIiIiJRd1mEkA8lMKLNe7R41hFKD/R3AsrznDwEXmVk7gJk1AeuB3eXpnoiIiIg0vKcfh+9eD309cNziUH73+lAvU1JqsH87cKGZxaPn3wKOB+4zs38ENgCnA98vXxdFREREpKH9/MfQ1R0eTU1H/vvnP652z+peqYtq/Rth6s58YK+7/7uZvRz4IHBm1OZ7wCfK10URERERaWjPPxtG9PN1dIV6mZKSRvbd/Sl3/5S7782r+wiwGPgtYLG7v8Pdh8vcTxERERFpVAuXwMCo1JsDfaFepqTUkf0xufsLwAvl2JeIiDSO5+jhCZ6nhyG6aeMUFrKI7mp3S0RqzfmvD3P0IYzoD/SFeftv/J/V7VcDKHXOvoiISFGeo4dfso0hUsyilSFS/JJtPKdF1kVktBNPhd+9JszTf2FvKH/3mlAvU3LMkX0z+/ok9+vu/p5JvlZERBrAEzxPgjhthJwOufIJnj9qdL/YXwCq1U5EpsGJpyq4r4CJpvFcNcn9OqBgX0RkButhiFm0FtS1EqOHoYK63C8ACeIFvwCcx6qCwLta7URE6tlEwf6qaemFiIg0nG7aGCL14og+wDBpuo8svA4U/wtAtdqJiNSzYwb77r5jujoiIiKN5RQW8ku2AWFEf5g0SVKczdKCdsX+AlCtdiIi9aykG3TN7Aozu8PMjh9n+xIzu93MLitP90REpF4topvzWEUbcXoZpo34mFNkumljmHRB3Vi/AFSrnYhIPSs1G897gdnuvmesje7+LNAdtRMRkRluEd1cwBou5SwuYM2Y02NOYSFJUgyRwnGGSJEkxSksrIl2IiL1rNRg/yXA5gnabOLIaroiIiLHVOwvANVqJyJSz0pdVGsusG+CNgeA+ZPrjoiIzESL6C4qyK5WOxGRelVqsL8fOGmCNicBhyfXHRERqQfKTy8iUh9KncazAXizmZ0y1kYzOxW4FLhnqh0TEZHapJVxRUTqR6nB/qcJvwbca2YfMrM1ZtYRlX9ICPKbo3YiItKA8vPTG0YbcRLEeYLnq901KZPk0F4OvnA7+/bcyMEXbic5tLfaXRKRSSop2Hf3TcDvA7OAzwKPA71R+Zmo/gPu/qsy91NERGpED0O0jpoFqvz0jSM5tJeeg/eSyQzRHOsmkxmi5+C9CvhF6lSpc/Zx96+a2b2EoP8VwGzCHP1fAv/q7o+Xt4siIlJLil0Ztx4czB5kh+9iwAfosA5W2DLmNs2tdreqaqD/May5jebmcD6bm9vIRPWJtsXV7ZyIlKzkYB8gCug/WOa+iIhIHSh2ZdxadzB7kEezjxEnQTvtJH2ER/0xTue0GR3wp1OHaY4V3mzd1NRKOqXcG2X1/FbYsgF69kH3AlizDhaurnavpAGVOmdfRERmuEbJT7/DdxEnQcJaMDMS1kKcBDt8V7W7VlWx+GxSIwcZHNxGX//jDA5uIzVykFh8drW71jie3wobb4Dhfpg1P5Qbbwj1ImU2qZF9ERGpP896Hw+zn0MMM4dWzmQ+S6xrUvtqhPz0Az5AO+0FdS3EGfCBKvWoNjS3LGC490HMElhTgkxmmHS6h7auU6vdtcaxZQO0dkFrZ3ieK7ds0Oi+lJ2CfRGRGeBZ7+NOdtFGjNkkGCTFneziQl9WEPAXe0FQ7nbV0GEdJH2EBC0v1o2QosM6qtir6htJ7yfeugxP95LNDtPc3IrFFjCS3l/trjWOnn1hRD9foj3Ui5SZpvGIiMwAD7OfNmK0R+ky24nTRoyHORLA5S4IBkkVXBA8630F+yp3u2pZYctIkSTpI7g7SR8hRZIVtqzaXauqVPow8cR8Ep0n0DbrdBKdJxBPzCeVLpyzr/ScU9C9AJKDhXXJwVAvUmYK9kVEZoBDDNM26sfcNmIcYvjF58VcEFSiXbXMbZrL6U2nkbAWBhkkYS2c3jSzb84FiMdmk80OF9Rls8PEY0fm7Cs95xStWQfDfWGuvmdDOdwX6kXKTNN4RERmgDm0MkiK9rx0mUOkmUPri88PMcxsEgWvG31BUIl21TS3aS5zmdnB/WidHadysOc+yIQsPNnsMBkfprvjZS+2UXrOKVq4Gs69vDAbz5mv03x9qQgF+yIiM8CZzOdOQpaZNmIMkWaINOdxJDAr5oKgEu3qwUzKx9/Wuoi5vJL+gcfDlJ7YbLo7XkZb66IX2yg9ZxksXK3gXqaFgn0RkRlgiXVxoS8ruFn2PBYX3CxbzAVBJdrVulLy8Rd7UVCtdsVqa11UENyPFovPDlN4mo8spJbNDis9p0gN0px9EZEZYol18QZbxTvsVN5gq47KirPEuriQZbQT5zBJ2olzIcsq3q7WFZuPP3dRkPSRIxcF2cc4mD1YE+3KqaPzNDwzRCYzhLuHMjNER+dpFTumiEyORvZFRORFS6yLJUwcjJe7XbmVc6S72Hz8+RcFQEjp6aE+/76AarUrp0TbYrrnvoqB/sdIpw4Ti8+mo/vlmq8vUoNqamTfzJaa2dfNbI+ZJc1su5l9zszmlLifV5nZTdHrh81sp5ndYmavH6PtEjP7oJndGrVPmtkBM/upmV1WvncnIiLTodwj3R3WwQipgrqx8vEP+AAtefcnwNgXBdVqV26JtsXMPe5iFhz/NuYed7ECfZEaVTPBvpmdANwPXA1sBD4LbAX+EPiFmc0rcj8fAO4BLo7KzwI/B84HbjWzvxj1kg8C/wycDNwJfAb4CfBq4EYz+8zU3pmIiEynYqfdFKvYfPzFXhRUq52IzEw1E+wDXwIWAB9y97e4+5+5+0WEYP1k4BMT7cDM4sAngWHg5e5+pbv/ubtfCawFksBfmFl+LriNwAXuvtrdr47avwM4G+gFPmJmLy/nGxURkcop90h3sfn4i70oqFY7EZmZzN2r3YfcqP7TwHbgBHfP5m3rAvYCBixwH/+vtZktBJ4DHnb3s8bY/jDwEmC+ux8ool/XA+8DPuru/zRR+7Vr1/rmzZsnaiYiIhX0QOYhkj7y4hx24MXnZzcf9U9DWe0eeZodqUcZyfbT0tTJivjpLG058ah2tZ6NR0Rqn5nd7+5rJ2pXKzfoXhiVt+UH+gDu3mdmG4D1wHnA7cfYzz7gBWCNmZ3k7k/lNpjZGuAk4MFiAv1I7nfRdJHtRUSkylbYMh71x8DDiP4IKVIkWWMnVPS4fekXOJzayjxmE2teQNpHOJzaSndTN12x4wraFruYV7nbicjMUyvTeE6Oyi3jbM8F7WuOtRMPP1P8AeF93W9m3zKzT5rZtwn3AzwKXFFMh8xsFvA2wIHbinmNiIhUX7HTbsptX+oZYiSINyUwM+JNCWIk2Jd6pqLHFRE5lloZ2c8tw9czzvZc/YSrdbj7f5rZHuC7wLvyNj0PfINw0+8xmZkBXwMWAl9y98eP0fYa4BqA5cuXT7RrERGZBtUY6R7O9pGwzoK6mLUwnO2b1n6IiOSrlZH9sjGzdwI/I2TiORVoj8rbgS8A3ytiN/9E+AXgHuCPjtXQ3a9397Xuvva44447VlMREWlgrU1dpH2koC7tI7Q21dciYiLSWGol2M+N3HePsz1Xf/hYO4nm5X+dMF3nSnd/wt2H3P0J4ErCVJ4rzOyCY+zjH4CPAHcDl7h7suh3ISIiM9aC+AmkSZLKJnF3UtkkaZIsiFf2XgERkWOplWD/yagcb07+SVE53pz+nPVAHPj5GDf6ZgkBPMCYqTTN7LPAHxPy7b/B3fsnOJ6IiAgAXbHjWJE4m3hTgqT3E29KsCJx9lE354qITKdambN/Z1SuN7OmMVJvrgMGgV9OsJ9c/vzx/rLm6gt+Z43m6H8B+H3gp8Cl7j5UfPdFRERCwK/gXkRqSU2M7Lv7M4SMNysJ2XTyXQd0AN/Jz7FvZqeY2Smj2t4TlZeb2Zn5G8zspcDlhOw6d+TVG3A9IdC/FXizAn0RERERaQS1MrIPIdi+D/hnM7sYeBx4BSEH/xbgL0a1z2XIsVyFu280s28AVwObzOxHwA7CRcRbgBbgc+7+aN5+/hp4LzAEPAj8WYj/Czzo7v811TcoIiIiIjKdaibYd/dnzGwt8DfA64FLCCvnfh64zt0PFbmr9xDm5l8FvA7oAnqBe4GvuvvobDyrorIN+PNx9vktQMG+iMgM1ZM5wJ7MNoa8jzbr4vjmVXQ3z6t2t0REJmRhHSoph7Vr1/rmzZur3Q0RESmjnswBnk49RMwSxGkhxQhpT3Ji/CwF/CJSNWZ2v7uvnahdTczZFxERqVV7MtuIWYIWCyvjtliCmCXYk9lW7a6JiExIwb6IiMgxDHkfcVoK6uK0MORaGVdEap+CfRERkWNosy5ShRmbSTFCm2llXBGpfQr2RUREjuH45lWkPcmIh5VxRzxJ2pMc37xq4heLiFRZzWTjEZH68Uhfipv2Jdk5nGV5axOXLkjwkq54tbslUhHdzfM4kbMKsvGsjJ2im3Nleux9Bh67Bw4/D7MXwmmvhsUnVLtXUkcU7ItISR7pS/G5HUPMjhlLEk0cSjmf2zHEh1eggF8aVnfzPAX3Mv32PgMbfgCtndB9HAz1hefr3q6AX4qmaTwiUpKb9iWZHTNmx5toslDOjhk37UtWu2siIo3lsXtCoN/WBdYUytbOUC9SJAX7IlKSncNZZsUKV5meFTN2Dmer1CMRkQZ1+Hlo7Sisa+0I9SJFUrAvIiVZ3tpEb7pwMb7etLO8VX9ORETKavZCGB4orBseCPUiRdK/ziLyokf6UvztM/1c82gvf/tMP4/0pY5qc+mCBIfTzuFUlqyH8nDauXRBogo9FhFpYKe9Gob7w1x9z4ZyuD/UixTJ3H3iVlKUtWvX+ubNm6vdDZFJyb/xdlbM6E07h9POh1e0HXXjbbHZeNSu/top05JIjVE2HhmHmd3v7msnbKdgv3wU7Es9+9tn+jmUcmbHj/zgdziVZU7c+MsTOkveX7EXD2pXO+1KueATaRiHd8Kzm2FoP7TNhyVrYfbyavdKZELFBvuaxiMiQPlvvC02a4/aTb1dZ/sQzH+OQ/N2wvzn6GwfmlQ7ZVqaPj3p/WxJbuShoTvYktxIT3p/tbs0Mx3eCU/dAqkBaJsXyqduCfUiDULBvogA5b/xttiLB7WbWrsd2QGYu59sU4amTJxsUwbm7g/1JbZTpqXp0ZPez7bUQ6Q8Sat1kPIk21IPjRnw66Kgwp7dDPEOaOkAs1DGO0K9SINQsC8iQPlvvC324kHtptaue04vqXQzTdkYhtGUjZFKN9M9p7fkdjMx09KhzAF+k/o1v0rdzW9Sv+ZQ5kDFj/l8ZitxayFuCcyMuCWIWwvPZ7YWtCvlokAmaWg/xNsL6+LtoV6kQTTuX3ARKclLuuJ8eEUbc+LGs8kwV38qc7WLvXhQu6m1O74zw0jaGM46jjOcdUbSxvGdmZLbzbRMS4cyB3gy8xtGPEm7dzDiSZ7M/KbiAf9Qtp8YLQV1MVoYyvYX1BV7UdAoMv27Gdl+M8knvs3I9pvJ9O+u/EHb5kNqsLAuNRjqRRqEbtAtI92gK1KolrPONEq7/87sYOfICNsHoCftdMeMlR2wvKWFNzevKLndTMrG85vUrxnxJC125GIm9/yM+MsqdtwtyY2kPEk877i552sS575Y99DQHbRaB2ZHpla5O8M+wFltF1Wsf9WQ6d9NavfPsOZ2iLVBegjPDBJf+lqaO5dW7sC5OfvxjjCinxoM8/ZPukQ36UrNK/YG3dh0dEZkOm3dkWXDJmffflgwH9adY6xecfSPWDOpXbX61nGwmRUPtNEWtes4x6DrqGZqN4V2Z9s8to3spuNAjOaeGK3dGTLxNGcn5k2q3eyOJGeuPMASkswjwWybBxwd7O/K9vOAH+BA1O5sm8eypqOzNhXbrhoG6KedwtVJ47QwQP84ryiPhc2r2ZZ6CAgj+mlGSPkIS2OnFrRra+oMFwEcuShIM0JbjXx+5ZTZ/yDW3I7lptREZWb/g5UN9mcvD4F9fjaelecr0JeG0nzttddWuw8N4/rrr7/2mmuuqXY3ZrStO7LccItjON3dMDAIDz4GixfAnNk2I9vVct/UburtDuyMsenOBJlZw9jsEeiPk/rVQs6Y1VFyu13Zfm7zPZjDLOIMkeEJejmOBN12ZNpJudtVy+HsAVKM0GxHxr1SjNBm7SxoXjypffak97Mz9Rt2p56kN/sCMRK0NhXOCW9taqfNZjHkfQz7AImmdpbGTqU7Vjh1JEaCA5k9ADTRXHBRMHqf9S793C+hpavgVwyaYpA8RGz+WZU9eGs3LDgVjn95KFu7K3s8kTK57rrr9l577bXXT9ROI/tSN4oZTd6wyelqdzo7wj8YnR0AzoZNsHrFzGxXy31Tu2O3az6+nwOrD/Fse5LWwQRtW+ewYVPnUe2OS7bTufXICHV/0tmwyUtu94AfoMObaY+C33Zi4PAAB1jGkdHkcrerliVNK3gy8xvwMKKfYoQRRljVtGZS+8vdUBu3loIbaldx1lGBfHds/lF1o3XH5rOKs3g+s5WhbD9tTZ1jXhQ0Amudi6cGC2+WTQ9hrXOr1ymRBqEbdKUu5EY5+wec+fNCecMtztYdhSkB9+2H9lEDXu3toX6mtqvlvqnd+O12ZgY4+JK9pFrStAy1kGpJc/Ale9mZKUypWc7jHiBJG80Fbdpo5gCFefbL3a5a5jTP4+TmM2ixBIM2QIslOLn5DOY0z5v4xWOoxA213bH5rEmcy1ltF7EmcW5DBvoAzfNfimcG8dQg7h7KzCDN819a7a6J1D0F+1IX8kdDm8zo7DC62sOoZL4F82FwVGKFwcFQP1Pb1XLf1G78dn7aQbIDzcRSIVVmLBUjO9CMn3awYsedR4IhCrP4DJFhHoXZeMrdrprmNM/jjPjLeEX8NZwRf9mkA30oPsuOHK25cynxpa8Nc/aTh7B4e+VvzhWZIRTsS10odvRy3TlG36DRP+BkPfwC0DdorDvHZmy7Wu6b2o3fbtbyJJnBGCMjjrszMuJkBmPMWp6c1P6KaXe2zWPAMgx6Gndn0NMMWIaz7eibfcvZrlG0NXWSZqSgrlFvqK2E5s6ltKx8I4lT3kXLyjcq0BcpE6XeLCOl3qyc79yQoX/gyDxn4MXnV15eOE2glrPiVKtdLfdN7cZu99+ZHeztTbHv2RgDg9DRDguWpFk8K16QKrPcxy13lp1azsZTbvlz9vOz7KyKHz1nX0RkqopNvalgv4wU7FdObs5+V7vT3h6mH/QNGpdfMnZQI1LvcplsOryZNpoZIsOAZVhvxzdssNwIetL7C26oXdi8WoG+iFSE8uxLQ1m9oonLL8myYRMvjkq+7gIF+tK4ljV1sj57PA9wZFR8nS1UoF+kg9mD7MruZIB+OuhkWdNy5jZVPrNLMVl2RESmk4J9qRurVzQVpBIUaXTL61+PZwAAFF5JREFUmjprIkVlvTmYPcjjmUdpoYV2OhghyeOZRzmV06cl4BcRqSUaFhURkYayK7uTFlpoiVJgtliCFlrYld1Z7a6JiEw7BfsiItJQBugnPioFZpwWBlAKTBGZeRTsi4hIQ+mgk9SoFJgpRujQlCgRmYEU7IuISENZ1rScEUYY8WRYo8CTjDDCsqbl1e6aiMi0U7AvIiINZW7TXE5tPp0WSzBoA7RYglObdXOuiMxMysYjVVfsgkAiIsWa2zRXwb2ICBrZlyrLLZbVP+DMnxfKG25xtu7IVrtrIiIiInVPwb5U1YZNYVXczg6jyYzODqOr3dmwSSs7i4iIiEyVgn2pqn37ob29sK69PdSLiIiIyNQo2JeqWjAfBgcL6wYHQ72IiIiITI2CfamqdecYfYNG/4CT9TBnv2/QWHeOVbtrIiIiInVPwb5U1eoVTVx+SZirv/9AKC+/RNl4RERERMpBqTel6lavaGL1imr3QkRERKTxaPhURERERKRBKdgXEREREWlQCvZFRERERBqUgn0RERERkQalYF9EREREpEEpG4+IyAS2pQfZkO5ln4+wwFpYF5vFqlj7xC8Ukdp3aAfs3gSD+6F9Piw9B+YoRZw0Do3si4gcw7b0IDemXqDfM8wnTr9nuDH1AtvSgxO/WERq26Ed8OQtMDIAbfNC+eQtoV6kQSjYFxE5hg3pXjqJ0WnNNJnRac10EmNDurfaXRORqdq9CeLt0NIBZqGMt4d6kQahYF9E5Bj2+Qjto/5UttPEPh+pUo9EpGwG94fgPl+8PdSLNAgF+yIix7DAWhgkW1A3SJYF1lKlHolI2bTPh9SoKXmpwVAv0iBqKtg3s6Vm9nUz22NmSTPbbmafM7M5Je7nVWZ2U/T6YTPbaWa3mNnrj/Ga08zsB2a2L3rNk2Z2nZm1Tf2diUi9WhebRT9p+j1D1p1+z9BPmnWxWdXumohM1dJzQnA/MgDuoUwNhnqRBmHuXu0+AGBmJwD3AQuAm4AngHOBC4EngXXufqCI/XwA+BIwAPwI2A0sBS4D2oG/dPdPjHrNK4A7gDhwA7ALuAhYC2wALnb35ETHXrt2rW/evLmYtysidUTZeCpvnx/mKfbSyyCzaOckFrPAZle7WzITKBuP1Ckzu9/d107YroaC/Z8A64EPufu/5NV/BvgI8BV3f/8E+4gDLwAJ4KXu/mTetlOBB4AsMCcXvJtZM/AIcCpwqbv/d1TfBPwAeBvw5+7+9xO9BwX7IiKl2+eH2czTJIiTIE6SFElSrOVEBfwiIuMoNtiviWk80aj+emA78MVRmz9OGKW/0sw6JtjVXKAb2JIf6AO4++PAFqAN6MzbdD4h0L87F+hH7bPAn0RP329mVsp7Emlkjw6M8KndfXzwmcN8ancfjw7oZlWZvKfYS4I4rbRgGK20kCDOU+ytdtdEROpeTQT7hKk6ALdFQfaL3L2PMJWmHThvgv3sI4zsrzGzk/I3mNka4CTgwVHTgS6Kyh+P3pm7byVcIKwAVhf3VkQa26MDI3xx7yCH01mOb2nicDrLF/cOKuCXSetlkATxgroEcXrRWgYiIlNVK8H+yVG5ZZztT0XlmmPtxMOcpD8gvK/7zexbZvZJM/s2cD/wKHBFJY4tMlPcfChJd8yYHWuiyULZHTNuPjThbS0iY5pFO0lSBXVJUsxC90WIiExVrNodiHRHZc8423P1E07edPf/NLM9wHeBd+Vteh74BrC1nMc2s2uAa6Kn/Wb25FjtKmw+oKTAtaOhz0fTkhUrSaWOHsaPx1v+7Nkd26e/R0dramtra57dPTs7nOxuak30ZA73HM4ODQ1Vu18z3Ljfi3h7om3W4jkLs+lMJpvJZpqam5qbYs3NvXsPPZ8aTOq8VUZD/52qMzoXtaPezkVRd5LXSrBfNmb2TuCrwA+B/wfsIHwYfwV8gTBH/+3lOp67Xw9cX679TYaZbS7mBg2ZHjoftcPMNqd1LmqCvhe1Reejduhc1I5GPRe1Mo0nN3rePc72XP3hY+0kmpf/dcJ0nSvd/Ql3H3L3J4ArCVN5rjCzC8p9bBERERGRWlMrwX5u6st48+JzN9uON68+Zz0hV/7Px7jRNwvcHT19eQWOLSIiIiJSU2ol2L8zKtdH+e1fZGZdwDpgEPjlBPtJROVx42zP1efPN74jKo9aXdfMVhMuAnZw9Fz/WlLVaURyFJ2P2qFzUTt0LmqLzkft0LmoHQ15Lup2US0zOwUgmqKTqzsX+BUwBJzn7g/nbXsp8AvCBcFL3P3RqP5Yi2p9H7icIhfVEhERERGpJbUU7J8A3AcsAG4CHgdeQcjBvwV4ZX5+fDNzAHe3Ufv5OnA1YfT+R4RR+ZXAW4AW4HPu/pFRr3kFYYQ/DtwA7AQuBtYScvxfnFtxV0RERESkXtRMsA9gZsuAvyFMqZkH7CUE7Ne5+6FRbccL9g14N3AVcBbQBfQCDwBfdffvjXPs04DrCBcXXYSLhO8Cf+/uSv0mIiIiInWnVubsA+Duu9z9andf7O4t7r7C3T88OtCP2troQD+qd3f/prtf4O5z3D3m7nPd/eLxAv3odY+5+xXuPt/dE+6+xt0/XquBvpktNbOvm9keM0ua2XYz+5yZzal23xqRmV1uZv9iZveYWa+ZuZn9+wSveaWZ3WJmB81syMweNrMPR1PHZJLMbJ6ZvdfMfmRmT0efbY+Z3Wtm7xl930/e63Q+KsDMPmVmt5vZruhzPWhmD5jZx81s3jiv0bmYJmb2zujvlZvZe8dp80Yzuyv6HvWb2a/M7N3T3ddGE/277OM8nhvnNfpuVJCZXRz92/FcFDvtMbOfmNklY7RtmHNRUyP7Upwxpjw9AZxL+FXiSWBd/pQnmToze5DwS1E/sBs4BfgPd3/nOO0vBW4Ehgn3fhwE3kRYsfkGdx+9krMUyczeD/wr4Ze/OwnT7hYClxFS5d4IXOF5f9x0PirHzEaAXwOPAfuADuA8wjTIPYT7p3bltde5mCbRr+WPAM1AJ/A+d//aqDb/B/gX4ADhfIwQ7lVbCvyTu390WjvdQMxsO2FBzs+Nsbnf3T89qr2+GxVkZv8A/DHh3/BbCYtnHUfI0Pgzd/+TvLaNdS7cXY86ewA/ARz44Kj6z0T1X652HxvtQbiQOgkw4ILoc/73cdrOIgQ9SWBtXn0r4SLNgd+p9nuq1wdwEeGPbtOo+kWEwN+Bt+l8TNv5aB2n/hPRZ/slnYuqnBcDfgY8A/xj9Nm+d1SblYRg5gCwMq9+DvB09JrfqvZ7qdcHsB3YXmRbfTcqey7eF32G3wRaxtgeb+RzUVPTeGRi0aj+esIfkS+O2vxxYAC40sw6prlrDc3d73T3pzz6xk/gcsJowffcfXPePoaBv4yefqAC3ZwR3P0Od////Oi1NJ4Dvhw9vSBvk85HBUWf41h+EJUn5dXpXEyfDxEujK8m/Lswlt8jZKj7grtvz1V6mDr7d9HT94/xOik/fTcqxMwShMGHncA17j4yuo27p/KeNty5iFW7A1KyC6PytjGCnT4z20C4GDgPuH26OydA+AcW4MdjbLubsGbEK80s4cryVG65P9jpvDqdj+p4U1Q+nFenczENzOxU4O+Bz7v73WZ20ThNj3U+bh3VRiYnYWbvBJYTLroeBu5298yodvpuVM7/IATvnwOyZvbbwBmEX7U2uvsvRrVvuHOhYL/+nByV463o+xQh2F+Dgv1qGfccuXvazLYBpwOrCSlmpQzMLAa8K3qa/0da52MamNlHCfPCuwnz9V9FCGzy1yjRuaiw6HvwHcIo5scmaH6s87HXzAaApWbW7u6D5e3pjLGIcD7ybTOzq93953l1+m5UzjlROUzIzHhG/kYzuxu43N1fiKoa7lxoGk/96Y7KnnG25+pnT0NfZGw6R9Xx94Q/4re4+0/y6nU+psdHCVMJP0wI9H8MrM/7BxR0LqbDXwNnA1f5xNnkij0f3eNsl2P7BmHNnkWEG9dfAnyFcK/ErWZ2Vl5bfTcqZ0FU/jFhvv2rCSnWzwRuA14D/Gde+4Y7Fwr2RaTumdmHgP9LyEx1ZZW7MyO5+yIP6ZAXETIjrQYeMLOXVbdnM4eFBSI/RsiiM3pqgkwzd78uusfoeXcfdPffuPv7Cck02oBrq9vDGSMX66aBN7v7ve7e7+6PAG8lZOc538x+q2o9rDAF+/VnopGWXP3haeiLjE3naBpFqQM/T0j9eKG7HxzVROdjGkWBzY8I0wnnAd/O26xzUSHR9J1vE6Ye/FWRLyv2fIw3wimTk0sk8Jq8On03Kif3mT2QfyM6QDQ9LfdL8LlR2XDnQsF+/XkyKteMsz2X+WK8Of1SeeOeo+gf5FWEEYat09mpRmRmHybkCP8NIdAfa6EanY8qcPcdhAuw081sflStc1E5nYTP9VRgOH8BJ8L0KoCvRnW5vO/HOh+LCVNPdmu+ftnlprblZ83Td6Nycp/teMF5buHWtlHtG+ZcKNivP3dG5frRK4WaWRewjnCn+C+nu2Pyojui8vVjbHsN0A7cVy938dcqM/tT4LPAg4RAf984TXU+quf4qMxlHtG5qJwk8G/jPB6I2twbPc9N8TnW+XjDqDZSPudFZX6wqO9G5dxOmKt/2jgrrOdu2N0WlY13Lqqd6F+P0h9oUa1qf/4XMPGiWi/QQAty1NqDME3Bgc3A3Ana6nxU7jysAbrHqG/iyKJaG3Quqn6ermXsRbVWoUW1KvWZnwp0jFG/kpA1z4GP5dXru1HZ83FT9Bl+ZFT9eiBLGN3vbtRzYdEbkDoSLax1H+EO85sIqZ9eQcjBvwV4pbsfqF4PG4+ZvQV4S/R0EfA6wqjMPVHdfs9bVj5qfwPhH9LvEZbafjPRUtvA211fvkkxs3cTVkHMEKbwjDWfeLu7fzPvNTofFRBNo/okYcR4GyFoXAicT7hB9zngYnd/LO81OhfTzMyuJUzleZ+7f23Utg8C/0w4d98HRgiLCi0l3Oj7UaRk0Wf+fwl52XcAfcAJwG8TgsZbgLd63gJP+m5UjpktJcRNywgj/Q8QLnbfwpHg/ca89o11Lqp9taHH5B6E/2G/Aewl/HHeQVgwYk61+9aID46MjI332D7Ga9YR/qAfAoaAR4CPAM3Vfj/1/CjiXDhwl87HtJyLM4AvEKZS7SfMY+0BNkXnacxfXXQupv085b4z7x1n+5uAnxMC0oHo/L272v2u5wfhgve7hAxhhwkL/r0A/JSwHoiN8zp9Nyp3To4jDBDtiOKm/cCPgHMb/VxoZF9EREREpEHpBl0RERERkQalYF9EREREpEEp2BcRERERaVAK9kVEREREGpSCfRERERGRBqVgX0RERESkQSnYFxERERFpUAr2RUSkKszsm2bmZraywsfZbmbbK3kMEZFapWBfRETqmpndZWZaIVJEZAyxandARESkwi6udgdERKpFwb6IiDQ0d3+m2n0QEakWTeMREakzZrYymuv+TTM7xcz+y8wOmtmAmd1rZuvHeE3CzP7MzB4xs0Ez6zWze8zs7WXa/7XRay441v6KfH9XmdmNZrbVzIaivm4ws3eOtV/g/Oi55z3uyms35pz9KXwmK83se2a238yGzWyzmb2xmPcmIjLdNLIvIlK/VgG/AB4BvgIsBv4ncKuZvcPdvw9gZi3ATwhB8RPAF4F24HLg+2b2Unf/2GT3XwH/CjwK3A3sBeYBlwDfMbOT3f2vonaHgeuAq4AV0X/nbD/WAabwmawANgJbge8AcwmfyU1m9lp3v7PUNysiUknmrnuaRETqSZS9Zlv09NPu/sd529YSAvR+YIW795rZnwN/B9wKvNnd01HbBYTAdQWwzt3vm8z+o/prgY8DF7r7XeP091vuflVe/TeBdwOr3H17Xv0Jo6feRMH5rcBrgJXu/mzetruA893dxvm8tgO4+8q8uql8Jte6+3V5+3od8GPgVne/ZKw+iIhUi6bxiIjUrx7gb/Ir3H0z8B/AbOCtUfXvAQ78US6ojdruA/5f9PS9U9h/WY01x97dRwij7zHKc8PtZD+THcDfjurbT4CdwLll6JeISFkp2BcRqV+/dve+MervisqzzawLOBHY4+5PjNH2jlzbyey/hL4WzcyWm9kXzeyJaC69R3Pzb4yaLJni/qfymTzo7pkx6ncBc6bSLxGRStCcfRGR+vX8OPXPRWV39IAw930sufrZk9x/WZnZasI0mjnAPcBthF8YMsBKwrSfxBQPM5XP5PA4r0mjATQRqUEK9kVE6tfCceoXRWVP9MivG21xXtvJ7D8nG5Vj/bsyVtA8nj8i3JB7tbt/M3+Dmf0uIdifqql8JiIidUWjECIi9etl0ZSU0S6IygeiaTjPAEvM7KQx2l4Ylb+ezP7z6g5F5bIx2q8do248J0bljWNsO3+c12QAzKy5mANM8TMREakrCvZFROpXN/DX+RVRtpz/RRiV/lFU/XXAgH/MD4jNbD7wV3ltJrt/CFNvAK42s1he+2Wj9zGB7VF5wajjvo6xb5gFOBCVy0s4zmQ/ExGRuqJpPCIi9etu4L1m9gpgA0fy4DcB/zuXFhP4NPAG4FLgITO7hZBT/gpgAfAP7n7vFPaPu//KzO4mpMbcaGZ3EKYBvYmQz36sEf+xfAm4GvhPM7sB2AOcAbwe+EF0/NFuj97LD6P3NgTscPfvHOM4k/1MRETqikb2RUTq1zbglYQpNO8H3k6YenJJ/oJXUdrK/wH8RVT1QcLc96eAd7j7n05l/3kuBb4GLI2OcTbwJ8B4+z+Kuz9MmEZzH/DbwAeAWcBlwJfHednXgE8Sfon4E0LqzPdMcJzJfiYiInVFi2qJiNSZ8Rapqpf9i4jI9NHIvoiIiIhIg1KwLyIiIiLSoBTsi4iIiIg0KM3ZFxERERFpUBrZFxERERFpUAr2RUREREQalIJ9EREREZEGpWBfRERERKRBKdgXEREREWlQCvZFRERERBrU/w/5Np+T7z4XvgAAAABJRU5ErkJggg==\n", "text/plain": [ - "
" + "array([1, 1, 2, 2])" ] }, + "execution_count": 57, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], - "source": [ - "models_ids = []\n", - "for pdict in params_dictionaries:\n", - " models_ids.append(pdict[\"train\"][\"evolution_model_id\"])\n", - " \n", - "models_ids = np.array(models_ids)\n", - "\n", - "cmap = plt.get_cmap('rainbow')\n", - "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", - "\n", - "# plt.figure(figsize=(12,6))\n", - "# for i in range(data.shape[0]):\n", - "# try:\n", - "# plt.scatter(i // 10, \n", - "# data.loc[:, \"classification_accuracy_valid\"].values[i], \n", - "# c=colors[models_ids[i]], alpha=0.5, marker='o')\n", - "# except IndexError:\n", - "# print(models_ids[i])\n", - "# print(colors[models_ids[i]-min_mid])\n", - "\n", - "\n", - "try:\n", - " y_label = \"Number of edges\"\n", - " plt.figure(figsize=(12, 12))\n", - " for i in range(data.shape[0]):\n", - " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", - " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", - " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", - " d = json.loads(json_acceptable_string)\n", - " plt.scatter(i // POPULATION_SIZE, \n", - " np.sum(d[\"chainer\"][\"pipe\"][model_index][\"binary_mask\"]) \n", - " + (np.random.random() - 0.5) / 2, \n", - " c=colors[models_ids[i]], alpha=0.5)\n", - "\n", - " plt.ylabel(y_label, fontsize=20)\n", - " plt.xlabel(\"population\", fontsize=20)\n", - " plt.title(TITLE, fontsize=20)\n", - " plt.xticks(fontsize=20)\n", - " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \"_colored_ids.png\")\n", - " plt.show()\n", - "except:\n", - " pass\n", - "\n", - "# ylims = [(0., 1), (0.85, 1), (0.9, 1.), (0.85, 1.)] #ag_news\n", - "# ylims = [(0., 1), (0., 1), (0., 1.), (0., 1.)]\n", - "# ylims = [(0., 1), (0.7, 0.9), (0.9, 1.), (0.6, 0.85)] #sber faq\n", - "ylims = [(0., 1), (0.8, 0.9), (0.8, 1.), (0.8, 0.9)] #imdb\n", - "\n", - "for metric, ylim in zip(MEASURES, ylims):\n", - " y_label = metric\n", - " plt.figure(figsize=(12,6))\n", - " for i in range(data.shape[0]):\n", - " plt.scatter(i // POPULATION_SIZE, \n", - " data.loc[:, metric + \"_valid\"].values[i], \n", - " c=colors[models_ids[i]], alpha=0.5, marker='o')\n", - " if PLOT_TEST:\n", - " for i in range(data.shape[0]):\n", - " plt.scatter(i // POPULATION_SIZE, \n", - " data.loc[:, metric + \"_test\"].values[i], \n", - " c=colors[models_ids[i]], alpha=0.5, marker='+', s=200)\n", - "\n", - " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", - " data.loc[:, metric + \"_valid\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \n", - " c='r')\n", - "\n", - " plt.ylabel(y_label, fontsize=20)\n", - " plt.xlabel(\"population\", fontsize=20)\n", - " plt.title(TITLE, fontsize=20)\n", - " plt.ylim(ylim[0], ylim[1])\n", - " # plt.ylim(0.85, 0.95)\n", - " plt.xticks(fontsize=20)\n", - " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \"_colored_ids.png\")\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train params" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": true - }, - "outputs": [], "source": [ "params_dictionaries = []\n", "\n", @@ -436,51 +494,54 @@ " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", " d = json.loads(json_acceptable_string)\n", - " params_dictionaries.append(d)" + " params_dictionaries.append(d)\n", + "\n", + "models_ids = []\n", + "for pdict in params_dictionaries:\n", + " models_ids.append(pdict[\"evolution_model_id\"])\n", + " \n", + "models_ids = np.array(models_ids)\n", + "models_ids" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" + "array([1, 2])" ] }, + "execution_count": 63, "metadata": {}, - "output_type": "display_data" - }, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(models_ids)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "1" ] }, + "execution_count": 71, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "for y_label in [\"batch_size\", \"epochs\"]:\n", - "# y_label = \"batch_size\"\n", - " plt.figure(figsize=(12,12))\n", - " for i in range(data.shape[0]):\n", - " plt.scatter(i // 10, \n", - " params_dictionaries[i][\"train\"][y_label] + (np.random.random() - 0.5) / 2, #s=3,\n", - " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", - "\n", - " plt.ylabel(y_label, fontsize=20)\n", - " plt.xlabel(\"population\", fontsize=20)\n", - " plt.title(TITLE, fontsize=20)\n", - " plt.xticks(fontsize=20)\n", - " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", - " plt.show()\n" + "np.where(models_ids[2] == np.unique(models_ids))[0][0]" ] }, { @@ -493,22 +554,36 @@ "source": [] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model params" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] }, { "cell_type": "code", - "execution_count": 62, - "metadata": {}, + "execution_count": 73, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": {}, @@ -526,496 +601,49 @@ } ], "source": [ - "for y_label in [\"lear_rate\", \"lear_rate_decay\"]:\n", - " plt.figure(figsize=(12,12))\n", - " for i in range(data.shape[0]):\n", - " plt.scatter(i // 10, \n", - " params_dictionaries[i][\"chainer\"][\"pipe\"][model_index][y_label],\n", - "# + (np.random.random() - 0.5) / 2, #s=3,\n", - " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + "cmap = plt.get_cmap('rainbow')\n", + "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", + "\n", + "ylims = [(0., 1)] * len(MEASURES)\n", + "\n", + "for metric, ylim in zip(MEASURES, ylims):\n", + " plt.figure(figsize=(12,6))\n", + " if validate_best:\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_valid\"].values[i], \n", + "# c=colors[models_ids[i]], alpha=0.5, marker='o')\n", + " c=colors[np.where(models_ids[i] == np.unique(models_ids))[0][0]], alpha=0.5, marker='o')\n", + " \n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_valid\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \n", + " c=colors[-1])\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_valid\"].min() * np.ones(data.shape[0]//POPULATION_SIZE), \n", + " c=colors[0])\n", + " if test_best:\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_test\"].values[i], \n", + " c=colors[np.where(models_ids[i] == np.unique(models_ids))[0][0]], alpha=0.5, marker='+', s=200)\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_test\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \"--\",\n", + " c=colors[-1])\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_test\"].min() * np.ones(data.shape[0]//POPULATION_SIZE), \"--\",\n", + " c=colors[0])\n", + " \n", "\n", - " plt.ylabel(y_label, fontsize=20)\n", + " plt.ylabel(metric, fontsize=20)\n", " plt.xlabel(\"population\", fontsize=20)\n", " plt.title(TITLE, fontsize=20)\n", + " plt.ylim(ylim[0], ylim[1])\n", " plt.xticks(fontsize=20)\n", " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", + " plt.savefig(path_to_pics.joinpath(y_label + \"_colored_ids.png\"))\n", " plt.show()\n" ] }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bm = np.array(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][\"binary_mask\"])\n", - "np.sum(bm[0, :])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Layer params" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/matplotlib/pyplot.py:537: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", - " max_open_warning, RuntimeWarning)\n" - ] - }, - { - "data": { - "image/png": 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N7M/M7MNmdvoI9caAi4EU8MsaIT8rbd90PO0WEREREZlMU2b4jZmFgO8Be4A/G+Vuv1v6Gl7P7cBHnHO9w4pPBYLADudcoUY920rbZcfUaBERERGRKWAq9dT/FXAe8EHnXPo1Yg8B/wtYBTTjD515G/As8E7gbjMbfmytpW1ihPqGyttGekIz+6iZrTOzdYcOHXqN5omIiIiITJwpkdSb2YX4vfNfdM796rXinXObnHP/4Jzb6JwbcM4dds7dB6wBdgKXANeeyDY6577hnFvtnFvd0dFxIqsWERERERmTSU/qS8NuvgtsBf5yLHU555LAbaUf3zjsoaGe+FZqGyrvG8vzi4iIiIhMhklP6vHXoV8GnAFkht9ECvhsKeabpbKbR1Hf0NiYxmFlLwNFYGnpIqLS0ATbrcfefBERERGRyTUVJspmgW+N8Nj5+OPsH8W/YdRrDs0B3lDa7hgqcM5lzOxx4LLS10MV+7yttP3FKNssIiIiIjJlTHpSX5oU+3u1HjOzm/CT+u845/5tWPlq59y6GvHvxb9DbA74j4qHb8FP6D9nZsNvPnVBaZ9DwO1jPqBxsH8TbLwbevdC+0JYeS3MO2uyWyUiIiIiU8WkJ/XH6b/MrACsA7qAGHAB8HqgAHzMOberYp8fAjfg32DqWTO7G5iJn9AH8ZfBTE5M80dv/yZ45GsQb4O2Tkj3+T+/8Y+U2IuIiIiIr16T+luAN+OvcjMLMKAbuBW42Tn3fOUOzjlnZu8GHgc+DPwRkAEeAT7nnHt8Ypp+bDbe7Sf08dJim0PbjXcrqRcRERER35RO6p1zNwE31Sj/B+AfjqO+AvDl0ldd6N3r99APF2vxy0VEREREYGqsfiO/RvtCyFQMCsok/XIREREREVBSP+WtvNYfR5/uA+e9+v3KE3prLRERERGpZ0rqp7h5Z/mTYuNt0NftbzVJVkRERESGm9Jj6sU37ywl8SIiIiIyMvXUi4iIiIjUOSX1IiIiIiJ1Tkm9iIiIiEidU1IvIiIiIlLnlNSLiIiIiNQ5JfUiIiIiInVOSb2IiIiISJ1TUi8iIiIiUueU1IuIiIiI1Dkl9SIiIiIidU5JvYiIiIhInQtNdgNERE4GGwfy3HUkS1e2yIJokOtmRlnZFJ7sZomIyDShnnoRkXG2cSDPV7sH6St4zI8E6Ct4fLV7kI0D+clumoiITBNK6kVExtldR7K0hQK0hQIEzF75/q4j2clumoiITBNK6kVExllXtkhL0MrKWoJGV7Y4SS0SEZHpRkm9iMg4WxANkiy6srJk0bEgGpykFomIyHSjpF5EZJxdNzNKX8Gjr+DhOffK99fNjE5200REZJpQUi8iMs5WNoW5sbORtlCAfTmPtlCAGzsbtfqNiIicMFrSUkRkAqxsCiuJFxGRcaOeehERERGROqekXkRERESkzimpFxERERGpc0rqRURERETqnJJ6EREREZE6p9VvRERERGTcbDpS4Ke7C3QPOjobjWsWhThrZu0U9JnDh3gmuYusDRB1TZzfspjzZ3XUjN25y+PxJx0HD8PsWXDxhcaSxSdvf/XJe+QiIiIiMq42HSnwzxvzJLKO+Q2QyDr+eWOeTUcKVbHPHD7E4/0byJMl4hrIk+Xx/g08c/hQVezOXR533O0YGHTMmulv77jbsXOXNxGHNSUpqRcRERGRcfHT3QVaI9AWNQJmtEWN1ohfXumZ5C6CLkLYopgFCFuUoIvwTHJXVezjTzqaGh1NjX69TY1GU6Pj8SfdBBzV1KSkXkRERETGRfegoyVSXtYS8csrZW2AEOU36QsRJmsDVbEHD0NDQ3lZQ4NffrJSUi8iIiIi46Kz0UjmysuSOb+8UtQ1USBfVlYgT9Q1VcXOngWpVHlZKuWXn6yU1IuIiIjIuLhmUYhEDvqyDs85+rKORM4vr3R+y2KKliPvsjjnkXdZipbj/JbFVbEXX2gMDBoDg369A4OOgUHj4gurLxYAsun99B24nyNd/0nfgfvJpvef6EOddErqRURERGRcnDUzxO+vDNMaNfaloDVq/P7KcM3Vb86f1cHFzasIEyVnKcJEubh5Vc3Vb5YsDnDDtf5Y+sNH/O0N19Ze/Sab3k//4UfwimkCoVa8Ypr+w49Mu8ReS1qKiIiIyLg5a+bIS1hWOn9Wx4hLWFZasjjAksWvHZdObiQQjBMIxgGw0jad3Eg0Pm9Uz1UP1FMvIiIiItNWMd+HBWJlZRaIUcz3TVKLxoeSehERERGZtoLhNpyXKStzXoZguG2SWjQ+lNSLiIiIyLQVb1mJV0zjFdM45175Pt6ycrKbdkIpqRcRERGRaSsan0fzrDcSCMbxCgkCwTjNs944rcbTgybKioiIiMg0F43Pm3ZJfCX11IuIiIiI1Dkl9SIiIiIidU5JvYiIiIhInVNSLyIiIiJS55TUi4iIiIjUOSX1IiIiIiJ1Tkm9iIiIiEidU1IvIiIiIlLnlNSLiIiIiNQ53VFWREREROpOKneAvvQWcsUkkWALbfHlNETm1IzND3aT69uAl+slEGkn0raKcGNnzdjNB4rcv7XIvoRjfqtx1bIgK+YEx/NQTgj11IuIiIhIXUnlDnBw4EkKXoZwoJmCl+HgwJOkcgeqYvOD3WQOrMUrpLBwG14hRebAWvKD3VWxmw8U+fenCiQzjrktkMw4/v2pApsPFCfisMZESb2IiIiI1JW+9BYCFiMUiGFmhAIxAhajL72lKjbXtwGCcQKhBsyMQKgBgnG/vML9W4u0xqAlZgTMaIkZrTG/fKpTUi8iIiIidSVXTBK0aFlZ0KLkismqWC/XiwXjZWUWjOPleqti9yUcTeXV0hT1y6c6JfUiIiIiUlciwRaKLltWVnRZIsGWqthApB1XTJeVuWKaQKS9KnZ+qzFQXi0DWb98qlNSLyIiIiJ1pS2+HM9lKHgZnHMUvAyey9AWX14VG2lbBcU0XiGFcw6vkIJi2i+vcNWyIImMP5bec45kxpHI+OVTnZJ6EREREakrDZE5zG66kFAgRt7rJxSIMbvpwpqr34QbO4nNWUMg1IDL9xEINRCbs6bm6jcr5gT58OtDtMSMnqQ/tv7Drw/Vxeo3WtJSREREROpOQ2TOiEtYVgo3do64hGWlFXPqYwnLSuqpFxERERGpc0rqRURERETqnJJ6EREREZE6p6ReRERERKTOKakXEREREalzSupFREREROqcknoRERERkTqnpF5EREREpM4pqRcRERERqXNK6kVERERE6tyUTerN7L1m5kpfvzdCzDVmttbMEmY2YGZPmtkHXqPeD5jZU6X4RGn/a8bnKESk3uwqpvhhrpuvZ3fyw1w3u4qpyW6SiIjIawpNdgNqMbOFwNeBAaBphJg/BL4GHAG+D+SA3wJuNbNVzrnP1NjnC8CngS7gm0AE+B3gbjP7I+fc18fhcESkTuwqprir0EMjQWYSZtAVuKvQw3XMZXGwYbKbJzItvZDMc0dPjj0Zj1NiAW6YG+HslvCY691R2M1mt5m0SxG3BlbYCpaGFp2AFotMTVOup97MDPg2frL+LyPELAa+ABwFVjvn/sA59yngbOBl4NNmdlHFPhfjJ/QvA2c75z7lnPsD4HWler5QqldETlJPFHtpJEiThQiY0WQhGgnyRLF3spsmMi29kMzzhZ0ZevMeC6JGb97jCzszvJDMj6neHYXdPOOtJ+9yxIiTdzme8dazo7D7BLVcZOqZckk9cCPwJuBDwOAIMR8GosDXnXO7hgqdc73A50s//v8V+wz9/LeluKF9dgH/VKrvQ2Nsu4jUscMuRwPBsrIGghx2uUlqkdSbDX0FPrcpzcfWDfK5TWk29BVGjE0Uj/BSbh3PZB/ipdw6EsUjE9jSqeGOnhztIWgPBwiY0R4O0B7yy8dis9tMmDBhi2BmhC1CmDCb3eYT1HKRqWdKJfVmdgbw98BXnHOP/JrQN5W299V47GcVMWPZR0ROIrMsQopiWVmKIrMsMkktknqyoa/Azduy9OU9OuNGX97j5m3Zmol9oniE7fnnybksMZrIuSzb88+fdIn9noxHa8jKylpDxp6MN6Z60y5FiPIhPCHCpJ3myMj0NWWSejMLAd8D9gB/9hrhy0vbrZUPOOf24/fwLzCzhlLdjUAnMFB6vNK20nbZcTRdRKaJNwTbGaTIgCvgOceAKzBIkTcE2ye7aVIH7uzO0x6Gtojf69wWCdAe9ssr7SvuJGRRIhbFzIhYlJBF2VfcOQktnzynxAIkCq6sLFFwnBIbW3oStwYKlJ/3AnniprkxMn1NmaQe+CvgPOCDzrn0a8S2lraJER5PVMSNNr5tpCc0s4+a2TozW3fo0KHXaJ6IHI/9JHmA7fwXG3iA7ewnOaHPvzjYwHWhuTRaiCPkabQQ14U0SVZGZ2/aoyVc3uvcEjb2pqt7ndOunzDlnwCFiZB2/ePaxqnmhrkRegvQm/fwnKM379Fb8MvHYoWtIE+evMvhnCPvcuTJs8JWnKCWi0w9U2L1GzO7EL93/ovOuV9Ndntqcc59A/gGwOrVq91rhIvIMdpPkkfZTYwQrcRIk+dRdnMpi5hHy4S1Y3GwQUm8HJeF8QB9eY+2yKuJfTLvWBiv7j+LWzM5lyVC9JWyPDni1jwhbZ0qzm4J85kllK1+8z8Wjn31m6WhRVCgbPWbVXa2Vr+RaW3Sk/rSsJvv4g+l+ctR7pYAZuH3wNcagFjZM1/Zcz9SfN8on19ETrBNHCRGiHhpHOzQdhMHJzSpFzle13eGuXlbFvB77JN5R28ePrC4OkGdH1zC9vzzgN9DnydHwWVZHDr5epLPbgmfkCUsKy0NLWIpSuLl5DEVht804Y9lPwPIDLvhlAM+W4r5Zqns5tLPW0rbqjHwZjYPaAS6nPNnxDjnBoFuoKn0eKXTS9uqMfoiMjH6SBOr6GeIEaKP1xqNJzI1rGoL8cnTo7SFA3SnHW3hAJ88Pcqqtur+s9bgTE4Ln0PEomQYIGJRTgufQ2tw5iS0XESmg0nvqQeywLdGeOx8/HH2j+In8kNDc34BXAK8dVjZkLcNixnuF8D7Svt8e5T7iMgEaSNOmvwrPfQAGQq0EZ/EVsl4OEiCrewjSZoW4ixjPrNH/CC1vqxqC9VM4mtpDc5UEi8iJ4w5N3WHh5vZTfi99R9xzv3bsPIlwEv4q9y8bmitejNrB54GTgUuHj4+v3Tzqcfwbz51wdBa9aUbTq3H791fMXzd+5GsXr3arVu3bszHJyKvGj6mPkaIDAUyFCZ8TL2Mr4MkeIrtxAgRJUyWPBkKvJ7TpkViv6c4yNNeL4fJMosoFwTaOSXYONnNEpE6ZWbrnXOrRxM7FYbfHDPn3E7gT4AZwDoz+ycz+zLwAn5CXzXh1jn3OPCl0uMvmNmXzeyfgHWlej4zmoReRMbHPFq4lEXECZMgQ5ywEvppaCv7ShduEQwjRoQYIbayb7KbNmZ7ioPc4+1nkAIziTBIgXu8/ewpjnQfRRGRE2cqDL85Ls65r5nZLuAzwPvxL1BeBP7COfedEfb5tJltAP4A+CjgAc8A/+ic++mENFxERjSPFiXx01ySNM3EysqihEmOMHdiP0k2cpA+MrQRYyWzp+x75Gmvl0ZCNJr/r7WREDi/XL31IjLepnRS75y7Cbjp1zx+N3D3MdZ5K3DrGJolIiLHqYU4GXLEhq3RniVPS425E/tJ8gi7iROilShp8jzCbt44RT/BOUyWmRVrzzcQ5DDZSWqRiJxMpnRSLyIyle1z/WzgIL1kaCfGKmYz/yRbZ/xYLWM+T7EdoGxM/dksrordyEHiNZY53ThFlzmdRZRBCn4PfUmKIrOGrUUvMl28kMhzR0+ePWmPU+IBbpgb5uzWsS9NuiWb5YHBQfYVCswPhXhzYyPLo/odGo26HFMvIjLZ9rl+1rKbFHnaiJIiz1p2s+8kuyPosZpNK6/nNGJE6CdDjMiIk2T7yHA4HebugwFu7Q5y98EAh9Nh+shMaJv3uX5+7l7mh24TP3cvj/gaXxBoZ5ACg66Ac45BV2CQAhcE2ie0vSLj7YVEni/uyNCb91gQM3rzHl/ckeGFRH5M9W7JZrk1kSBZLDI3GCRZLHJrIsGWrD7tGg0l9SIix2FDqRe5gTDSRF0XAAAgAElEQVSG0UCYOCE2cHCymzblzaaVSzmDqzmfSzljxFVvBtJx7jtqpIrGjBCkisZ9R42B9MQtc3osF2+nBBt5e2AejYQ4Qo5GQrw9ME/j6WXauaMnT1vYaA8HCJi/bQsbd/SMLal/YHCQFjNagkECpW2LGQ8MarL5aGj4jYjIceglQ1vFsIo4IXonuBd5OuvubyISSBIOOiBAOOgRwdHd38JE3b5g+MUb8Mp2AweZT/VQq1OCjUriZdrbk/Z76IdrDRl70t6Y6t1XKDA3GCwrawoE2FcojKnek4V66kVEjkM7MdKU/6NJU6C9YmUXOX7JfIhlgWbCBMlSJEyQZYFmkvmJ64/qJUO8ov9LF29ysjslHiBRKL/PUaLgOCU+trRyfijEgFd+YTDgecwPqQ96NJTU14H9G+DBz8HtH/O3+zdMdotEZBWzSVMgRR6HI0WeNAVWMXuymzZtzA8HcV6EpbRzJh0spR3nRZgfDr72zieILt5Eqt0wN0xf3tGb9/Ccv+3LO26YO7aJsm9ubCTpHMliEa+0TTrHmxv16ddoKKmf4vZvgEe/Auk+aO30t49+RYm9yGSbb82sYRENhOkjSwNh1rBIq9+cQG9tjpP0PBJFP3FIFD2SnsdbmyduTL0u3kSqnd0a5tNLY7SHA3RlHO3hAJ9eGhvz6jfLo1E+2NpKSzBIT7FISzDIB1tbtfrNKJlz7rWjpMzq1avdunXrJuS5Hvycn8jH214tG/r5yr+YkCaIiEyazeks9/Wn2ZcvMj8c5K3NcVbEJ/YfvJYuFZHJYmbrnXOrRxOrQUpTXN9ev4d+uFiLXy4iMt2tiEcnPImvNN+aa06KFRGZSjT8ZoprWwiZZHlZJumXi4iIiIiAkvop78zr/eE26T5w3qvfn3n9ZLdMRESktsHcQbr6H+Xlvnvp6n+UwZzu3yAy3pTUT3HzVsGln/DH0Ce6/e2ln/DLRUREpprB3EH2p56i4GWIBJopeBn2p55SYi8yzjSmvg7MW6UkXkRE6kNvdishixEK+Mt+hiwGnl/eGNGqQSLjRUm9iByzPd4g67yjHCHHTCKsDszglMDEriO8PZ/mkWw/B7w8cwJh3hht5rTwxC11KCK1ZYtJIoHyicVBi5ItJkfYQ0ROBA2/EZFjsscb5GfF/Qy6AjNcmEFX4GfF/ezxBiesDdvzaX6UPkq/K9IRCNHvivwofZTt+fSEtUFEaosGWyi6bFlZ0WWJBlsmqUUiJwf11IvIMVnnHaWBII3m//loJATOL5+o3vpHsv00WYDmgH9n0WYLgueXq7deZHK1R5exP/UUeH4PfdFlKbgMHdGzJ7tpE+6Q6+NluuknRTMNnEonHdZWM/ZA/2a2ZDaTsCytLsry2ArmNK+oGbv5wG7uO9TNvoLH/FCAt3Z0smLOopqxG3fv4O5d3XTlYUEYrl3cycpFS2s3uHs7PL8WenugfS6cswY6T6sZes/RAW5LJjnkFekIBHlPSwtvn9FUM3b9gQPcsfsoXSlY0AA3LJrB6+bMqRlbGOwm1/sCXraXQLSdSPvZhBo7a8YWB7ooHn4OlzmKxWYQnHUuwaYFtY8tuRd6noHMEYjNhLnnQ8v0WkpQPfUickyOkKOBYFlZA0GOkBtz3TsLKb6f6eFL6T18P9PDzkKqZtwBL0+jlf/5arQAB7z8mNsgImPTGJnNvIbXEwrEyHn9hAIx5jW8/qQbT3/I9fEsW8mSo4k4WXI8y1YOub6q2AP9m3ky+zwZCrS4KBkKPJl9ngP9m6tiNx/YzTf3d5EsOuaFAiSLjm/u72Lzgd1VsRt37+Dr27rpKzrmh6Gv6Pj6tm427t5R3eDu7bz42L18MbqQP158JV+MLuTFx+71E/0K9xwd4Mt9R+n3PGYGAvR7Hl/uO8o9RweqYtcfOMDNm3pJZB3zY5DIOm7e1Mv6AweqYguD3WT2P4RXSGGRNrxCisz+hygMdlfFFge6yHc9gMunINqOy6fIdz1AcaCr+tiSe2HnzyE/CNEZ/nbnz/3yaURJvYgck5lESFEsK0tRZCaRMdW7s5Di9vwhBlyRWYQZcEVuzx+qmdjPCYQZdF5Z2aDzmBMY2y3KReTEaIzMZkHzpZzadjULmi896RJ6gJfpJkqYKBEMI0qEKGFepjpB3ZLZTMwFiVkYMyNmYWIuyJZMdVJ/36FuWg1ag0YAf9tqfnmlu3d10xpwtIUCBMxoCwVoDTju3lUd++Kmdfzr3PNJRBuZ52VJRBv517nn8+KmdVWxtyWTNOB/WhoobRsIcFuyet7EHbuP0hr2aI0GCASM1miA1rDHHbuPVsXmel+AUJxAqAEzIxBqgFDcL69QPPwcFmzAwn6shRuwYAPFw89VxdLzDIQaINwIZv421OCXTyMafjPN7N8Im+6Cvi5oWwBnXQfzVk52q6a2rs2O5+6Ho/tgxnw49ypYsMImu1lT1urADH5W3A/O76FPUSRFkcsDY/un/VghSRMhmsz/FKCJIDi/fEmooSz2jdFmfpQ+Cp7fQz/oPAacx9tjtT/WFpGpa2s2w4PpQfYXCswLhbgy3siyaGyymzVm/aRoonw4YIQw/VR3VCQsS4srv3NylBAJy1bF7it4zAuV98k2B419Ba8qtisP88Pl/89agkZXjQ81f0YjreZodX6nTasrgAX5GY2cWRF7yCsyM1DxaWnAOOQVqdSVgvmx8jY0h42uGh/EetleLFL+d9yCcbxsb1WsyxyFaHt5YSjul1fKHPF76MtiG/zyaUQ99dPI/o3wy6/6N6dqne9vf/lVv1xq69rseODfIZX0hw+mkvDAv/vlUtspgUbeFpxHo4U4ankaLcTbgvPGPJ7+oMvRUPEnqYEAB131sJ7TwnHeFZ9BswU55BVotiDvis/QeHqROrM1m+G7/QmSXpE5wSBJr8h3+xNszWYmu2lj1kwDOcqz5xx5mmmoim11UbIUysqyFGitSPQB5ocC9BfL/0f1Fx3zQ9Up3YIwJCtik0XHghofanY3dtCcL8+0m/Mpuhs7qmI7AkEGvfJ6Bz1HRyBYFbugAfrzFe3NOxZUnwYC0XZcsXzBA1dME6hM3gGLzYBCxeIIhbRfXik2Eyo/9S2k/PJpREn9NLLpLv/mVPE2sMCr32+6a7JbNnU9dz80tEJDi3/OGlr8n5+7f7JbNrWdEmjkhtBCPhI6lRtCC0/IBNnZFiFFeU9TCo/ZVntYz2nhOB9ums3/bunkw02zldCL1KEH04M0B4yWQJCA+dvmgPFgeuJW0xovp9JJljxZcjgcWXJkyXMq1ZM+l8dWkLEiGZfHOUfG5clYkeWx6omyb+3oJOEgUXR4+NuE88srXbu4k4Rn9BU8POfoK3gkPOPaxdWxnbPm+BcL+Sw4f9tfdHTOqp7Q+p6WFlJ49HtFvNI2hcd7WqpXOLph0QwS+QCJrIfnORJZj0Q+wA2LqpPvSPvZUEjjFVI45/AKKSik/fIKwVnn4oopXN6PdfkUrpgiOOvcqljmnu8n8fnB0rEN+j/PPb86to5p+M000tfl99APF2vxy2vZ1l1k7QaPnl6Y2w5rVgU4vbP6Khtgy4Ei92/x2JdwzG81rloeYPmc2rF3/meRh26DzFGIzYAr3gPX/3bt2F+tLfLw9xz9e6F5IVz+PuOiNSPEPlTk4e87BvZC00K4/L3GRVfUjh2to/v8Hvrh4k1+uUysS0It3J4/VBrWEyCFxwAF3hKq7qERkelhf6HAnGD53/EmC7C/UBhhj/rRYW2c55aVrX5zJktqrn4zp3kFF0LZ6jfnRFfWXP1mxZxFfATKVr/5/0ZY/WbloqX8IQxb/cZ436m1V79528JO/tUzONJFc6qP/oY2EjMX8DsL51fFDq1yM3z1m4+1tNVc/eZ1c+bwSRi2+o3xoWXtNVe/CTV2Ept3RfnqNx1vqLn6TbBpASx4c9nqN6F5F9de/aZlISx5S/nqNwsvm3ar35hzGmZwrFavXu3WraueODLZHvi8P+QmPuzvxdDPb/6z8tht3UV+sNajJQ6NcRhMQzINv7umOrHfcqDIt58s0BIzmqIwkIVkxvGhC0NVif2d/1nk3q9CMOYPVyukoJiBq2+sTux/tbbITz/niLRAuBXyCcgl4Zq/qE7sf/VQkZ/+LYRbHZFmyPVDPmFc8+eMKbH/6dccqaTfQz9k6Odr/kjj6ifazkKKxwpJDrocsy3CJaGWqvH0IjJ93NJ3hKRXpGXYsI2hnz/eNr2GRtSDF1M5fpZI050v0hkO8rbWOGc2jG0RBBkbM1vvnFs9mlj11E8jZ13nj6EHv4c+k/ST+tXvr45du8FP6Jsb/MS1uQHAsXaDV5XU37/FoyVmtJQmurTEXi2vTOofus1P6COli/VIE+RK5df/dnkbHv6en9BH2/16/SFzjoe/57hoTUXs9x3hVoi1+bH+fEjHw9+Hi64Yzdmp7dyr/DH04PfQpwcglYCL33n8dcrxWxJqUBIvchK5Mt7Id/sTQJEmCzDgPPo9xzsaaw/pezGV457eDN25Ip2RIG9vjynpPIHObIjofNYxjamfRuathMtu9HvmE/v87WU31l79pqfX76EfrjHul1fal3A0VczVaYr65ZUyR/0e+uFCDX55pf69fg/9cOFWv7zSwF6IlN91nEizXz4WC1YYb/6w3zPf2+Nv3/xhrX4jIjIRlkVjvL+5lZZAkANFv4f+/c2tNVe/eTGV45aeARIFj3nhAImCxy09A7yYGvs9MkSmA/XUTzPzVo5uCcu57dCfGuqh9w2m/fJK81uNZMa90kMP/hCc+a3ViW9sBuQGXu2ph9IE8xqT0ZsXQra3fEWqfMIvr9S0EDJ9Qz30vly/Xz5WC1YYC2rftE9OgB6SvEQPCTK0EuMM5jKXib1d/K4dHk88BocPwqzZ8IZLYPFS9WmITAXLorFRLWF5T2+G1mCA1tIqL60he6VcvcsiSupPWmtWBfjBWg9wZWPqr72wOtG5anmAbz/pT1oaPqb+nedUj2W/4j1w71f9ITfDx9Rf8dHqNlz+PuOnn3OAKxtT/xs3Vl8sXP5e46d/C1A+pv4tfzim0yDjrIckj7OTGCFaiJImz+Ps5GKWTFhiv2uHx123OxqbYOYsGByAu26H697pKbGXupEsHOJgfgcZ10/MmpkdXkpLqHqpQYBEYiP7B54lTYo4DcxrOo/W1hF6e47sgl1PwMBhaJoFi98AMxfXju15GV76JfQdgLY5cMZlMPfU2rGHdsLLv4L+g9A8G069CDqWHPNxD9edKzIvXL0+e3euem10kZOR/qOdpE7vDPK7awI0N8DBPr/HvtYkWYDlc4J86MIQLTGjJwktMas5SRb8ybBX31gaS9/nb2tNkgW4aE2Qa/7CiLbDYLffY19rkiz4k2Gv+XO/pz61z9+OdZKsjL+X6CFGiDhhDCNOmBghXqJnwtrwxGPQ2ARNTUYgYDQ1GY1NfrlIPUgWDrE7+xx5lyVKE3mXZXf2OZKFQ1WxicRGXh54lLzliFmcvOV4eeBREokaNyw5sgs23AXZQWic6W833OWXV+p5GR7/D0j3Q2uHv338P/zySod2wrM/huyAf6GQHfB/PrRzTOehMxKsuT57Z0T/B0RAPfUntdM7gyMuYVlp+ZzgiEtYVrr+t4NVk2JHctGaYNWk2BFjrwiOaVKsTLwEGVoon5ARI0SCibuxzOGDfg/9cA0NfrlIPTiY30HIooTN/10Kl36nDuZ3VPXW7x94lgDgeRmybgCzEAELsn/g2ere+l1P8GJ0DvdGFtJFlAXRLFfbXs7c9UR1b/1Lv4RYE8RLk5uGti/9srq3/uVfQbTJ/4JXty//aky99W9vj3FLzwDg99D3Fx2Josd7OjS5XgTUUy8i46iVGJmKuyRmKNDKxN0CftZsSFXcSDCV8stF6kHG9ROifMx4iAgZ118VO0g/npfBOQ8jiHMenpdhkOrYF1M5bgmfRoIQ88mRIMQt4dNqTzztOwCxihVpYo1+eaX+gxCpSLQjDX75GJzZEOHjc5toDQXYn/doDQX4+NwmjacXKVFPvYiMmzOYy+P4H7nHCJGhQIYC5zNxN/x4wyX+GHpwNDT4Cf3gAFz5lglrglTY0Ffgzu48e9MeC+MBru8Ms6pN/45GErNm8i77Sg89QIEcMWuuig15HnkzgqU+O8Pw8Ah7XlXsvQ2n0lb0J58CtFIEL8+9DadyZmVw2xx/yE182HNmBv3ySs2z/SE30WErJuRSfvkYjdeSi13eAM9yhKNkmUGU85jJgkD1jZREpjL11IvIuJlLCxezhDhhkmSJE57QSbLgr3Jz2Q2D9DYd5oXDR+htOsxlNwxqkuwk2dBX4OZtWfryHp1xoy/vcfO2LBv66v8OouNldngpBZcl77I458i7LAWXZXa4+q6gM4oxigZ5in4sRYrml1fqalpIczEFxRzgoJijuZiiq9ayYmdcxvZAgG+1zeJv5y3mW22z2B4I+JNlK516kZ/UZwfAea9+f+pFJ+BsnHhd3gD3u25SrkC7i5ByBe533XR5A5PdtBFt7C3w+Q1pPv5kis9vSLOxV78/op56ERlnc2mZ8CUshzvk+uhZuo3zl4aJEiZLnh7yHHKn17xdu4yvO7vztIehLeJfVLVFDPC4szuv3voRtIQ6WMS5ZavfdEbOqLn6TWv0FCy3nyOBfrKBIlEvyNxiMy3ReVWxC5rbSASW05rs8nvSIw30ty9hQWP178X2mfP5v+deSfPRfcwe6KO/oYX/e+6VvHvmfE6rDO5YAue9o3z1mzOvGvPqN+PlWY7QQIgG899/DYTA+eULmHq99Rt7C3x1S5a2sNEZh76c46tbsty4HFa263foZKZXX0SmtW3sI0qYWGlM8tB2G/voQEn9RNub9nvoh2sJG3vT1cND5FUtoY4Rl7AcLta6ksKhIzTZQsxiOMvgkSJWY0nLq9ti3JJrgo4zX5l42ud5vLutulf/4ewAzQ1tNDfNBKAZwCvycHaA08Lxqng6lkzZJL7SUbK0V8xZiBPkKNlJatGvd1dXnrawlS6IoS3yarmS+pObPn8WkWmtnzRRwmVlUcL0k56kFp3cFsYDJPPlyxIm846Fcf07OhEiDfNp6ricQDCOV+gjEIzT1HE5kYb5VbFnNkT4+Gx/4um+oYmns2tPPO0p5mm08teo0QL0FPPjdiwTZQZR0pSvdZ+myIyKlbumir0pR0v5nzRawn65nNx0SSci01ozcTLkXumhB8iSp5kavYsy7q7vDHPztizg0RI2knlHbx4+sDj8mvvWg0ThMAeKO0h7A8QDTcwJLqU1NOu1dzyBIg3zaybxtYx24uncYJh+r0izvbq08aDzmBus/9ftPGZyP93g/B76NEVSFLiEGpOAp4CFDUZfzr3SQw+QzPvlcnJT14hMS1v3F7nlgTyf/a88tzyQZ+t+3XHwZHU688mSJ0MOhyNDjix5Tmd0SY+cWKvaQnzy9Cht4QDdaUdbOMAnT49Oi/H0icJhduafJ++yxKyRvMuyM/88icLhyW7amF0ebaLfK9LvFfGce+X7y6NTb8z5sVoQaOIq66TBQvRajgYLcZV1TtnVb65bEKYv7+jLOTznb/vyjusW1P8FloyNOaePa47V6tWr3bp16ya7GTKCrfuLfPfRIi0xaIrBQAaSGXj/pUGWzdOdB09Gh1wf29hHP2maiXM68zVJVk64rdmn/KUn7dVhG0M/L4u+fhJbdmJsz6d5ODtATzHP3GCYy6NNtcfTy7jb2Fvgrq48e1OOhQ3GdQvCGk8/TZnZeufc6tHE6h0g086DmzxaYtBSmozXEgdwPLjJU1J/kuqwNk2KlXGX9gaIWfkNmkJESE/hpRGPxWnhuJL4KWJle0hJvFTR8BuZdnr6/B764ZpifrmIyHiJB5ooUH431gI54lN0GIeITC+6zJNpZ24b9KeHeuh9Axm/fCKlcgfozWwlV0wQCbbSHltGQ6T2xKviQBfFw8/hMkex2AyCs84l2LSgdsW9u6HraUgdhoZZsOACaF9UMzTdvZ7EoUfJe4OEA420dlxKvPN1NWMf7n2KR0OHSYcCxAselxZmcXl77SED27q3svbATnqcY64Za+Ys4fTOZTVjn9i7gcdyu0lHisRzQS6JLOINC1fVjN304g7u3rCPvWljYdxx7ar5nHVm9Q12APZ2beSZI5s5Eigw0wtx/swVLFxQvWwfAN3b4Lm10NsD7XPh3DXQeXrN0N79z9J1dB2DlqbRxVkwYzXt886rXe+erbDuQTi8H2bNg9VXwim1z8Pmndu4b88u9nkwPwBvPWUxK5bUbgO7t8LTD7xa7wVvhkW16z2W2O1bN7F27zZ68JhLgDULT+e0ZWfVrhdg0wa4+07YuxcWLoRrr4ezar92G55/iTs37mJvPsDCsMf1Kxez6pwzatf7wgvw4x/Dnj1wyinwjnfA2WfXDN25exO/OrCVQ1akwwW5aM4yliyq3ebHtz/L/fn9JOIBWtMeV4XncfFptV+75555muf7XiYXLxBJhzin7VTOPf+C2se2YTt3Pr+XvVljYdRx/TkLWbWqanV25gSXsvPow9DVTai3n0J7M/kFnSyYcXnt8/DSJrjvbujeC50L4a3XwhkjHdsz/Cx/kN54iPZ0gbeFZ3PxaefXrnfDC/CTYef3N98Bq2qf38dffpb78z30xQO0pT2uCs/l4lNrn7MjO9axs+dpBoJZmopRlsy9gJlLa48KeHHDi9yzeQfdRaMz6Hj7iqWcuarqPrUAHNi+ji09z9IXzNNWDLN87nnMOW2E0QZbNsH9P4X93TCvE666BpaP8B4+hvO78YWXuOvFXXQVjAUhx3VnLmbl2SO8f19+CR65Dw50w5xOeONb4dQRYjdugLt+Al17YMEpcN1vwsrav0PpLY+R3PkQeTdI2BppWXIF8eWX1Ix98uFN3Pt0msODDcxqTHH1BXEuvLz2sR3ZvZ6dh9YzEMjQ5MVY0vE6Zi6q/X/gWH7n2fYi/OLeV1+LN10Np4/wGu9+li2HniMRzNFajLC841zmLBrh7+rWF+HBe2F/F8xbAFdeDctq18uGDXDnj19t7/XvgFUj/I16Zgt3rt/N3lyAhRGP61+3iFXnL68Z+8IzL3HHs7vZkzNOiThuOG8RZ58/wms8hQRvuummyW5D3fnGN75x00c/+tHJboaMoDUOT+3w54qEQ9BfGlP/jtVBZjZPzIdTqdwBegaeAiAUaKTosvRndxMNthEOlvfaFQe6yHc9AA6INEMhTTGxBYvNIhCpuGlT727Ycq//fbQV8ik4+CI0zoZ4+VVLuns9hw78DJwjGIjheVkGBrcQ8RoJt5RPEn249yn+O34UzyDiQS5obI9kCPUfZXG8syx2W/dWbuvZAQ7aA8aA51g/2Eun85jZMrMs9om9G3iAl3HOESsYuYDHy4GjRJJ5FrSWX+BsenEHX3uqB5yjIwqJPDzSNciSSJ7ZHe1lsXu7NvLzvo2Ao8UFSZnH5uxBOnLQ2lJxK/rubfDgbf73ze2QHoCt62FWJ1S0t3f/s2zu+yU4R4woefIcyO6iqRAh3lxx8549W+He7/rft86AVD+8+DTMXgCt5fVu3rmNb+7aheGYGTT6nePxvgSL8JjVXh7L7q1w760V9T4JsxdC2/HHbt+6idv2bgXnaMcYwLE+eZTOgseMmRXnDPx/7l+7GTDo6IBEAh5ZC0uWwuzy127D8y9x83N7MOfoCDoSHqztSbLU8syZW7G2+gsvwJe+6H8/ezb09cFDv4Clp8Kc8np37t7ETw5tBudoJcggHhvSh5mbd7S3lbf58e3P8p/hgzigIevIhI0XwilaDvezcEb5a/fcM0/zdG4LDkc4F6AQ9ujmMMGDWebOK3+/b9iwnVu27ia6PMvMZTlyrUUe2T7AKUWPOXNmlMXGdu4mfs89pFsjZGY0Eu3PsOCR7bTOXAYzKs7DS5vgG1/zv5/ZAckEPP4ILFoCHZXH9gzfDx3FGTTlPFLhAM+EMrQf7qs6Nja8AP+PvTePsqs6D3x/+wx3nmoeVJpBCAEGjPCAAWOD0wwGGxLHsd2xY7edZGV4nXTyVq/uXnkdt52V5/WSuBP7JWlncOIpbpMHZlZsYwYLYUBCAoSEJDSUVKpBNd15OsN+f+wrVPecU6CiNJSk81ur1r73q6927XvOued85zvf8NW/aN++Tz4Ba9b4tu+W/dv5QWQSiSTZlNRMeDlSIzNV9M07fWArL0+p70bcNWkKm9H6ITINk0RH+/lk1yu7+JtXD4KELg2KLmyezLNK2vT0tW+Hide38vOp58GFpGtQFw6H6iN01AWpTk8y+55X4Zt/rV53dkOpCM9thuWroNtzDC9g++58eTd/9dIwAN06FFx4arzAamHR61kv+3fD97+hXue6oFyEF7fAspX+fbzzFfirr6rX3b1QyMNTTwZ+h2p7nmHqwEOARBcxXNmkMrubiBvH7F7RpvvcU6/yT09oSAmZaINK0+SF/QY94hhDq9o/2/TwNl6eeQaQxN0ITc1mtD5MpmmQyHm27wK+8+zbBd/+W/W6s0vtixeegaGVanvPYWJ4O8/NPg9SknRNGsJmuDFCrqGTynmO37274Ft/o9ZwfB+/sBmGVvnm5ZVX4C9bx3pPT2v7PgGr/eeSV17cw19uGQEkPaakYMNTh8usNiz6BtqrU7384m7+/LkjIF16TUnehidGSqzVm/QNvHWviFPNF7/4xbE//uM//sbJ6IbhNyHnHesGdD59vU46LpgoCNJxccaTZGfre9G1GIYWQwiBocXQtRiz9b0+XWdqB0JPIMwEQgg16gmcqR3+iUdeADMBkSQIoUYzoeQeCpOb0THRtRgCDV2LoWNSmNzs091sTKG7kqirTgpRF3RXstnwV+14cuIgaQlpTUNDkNY00lLJvTzTHEa3BVFXR6ARdXV0W/BMc9in+9Aro/fteMkAACAASURBVOR0l1xEoAnVWCWnuzz0yqhP98Xp10i4giQ6Qqgx4QpenH7Nv812PAnxNCTSIDQ1xtNK7t28M1uJuDoRIggEESJEXJ2RmYDE+K2PQzINyYyaN5lR77c+7lPddPgQWSHJtrZZVtPICsmmw4f8877wE0hk2udNZJR8EbpPHtlHWkrSQkMTgrTQSEvJk0f2+ecF5a3LdUAuB5qmxlyHknt4YOchOoRLTm/tO13QIVwe2Bnw+e6/X83T0aHm7ehQ7++/36f67MReki6khI4mBCmhk3SV3MuPrTGilkvcBk0I4jZELZcfW2M+3Zfy+9EsgWmr49K0dTRL8FJ+v/+zDR+k7x11YjGJqApiMUnfO+o8MOw/3nl6E9lmnHX7Ta580WHdfpNsM668ul42PQTZnPrRtBOvNz3kU33MOkbMdknaEg1I2pKY7fKYdcw/7w/vV/uqbfvmlNy3zcaJNh0StkBDkLAF0abDj61xn+7B8ReIODpRTIQQRDGJODoHx/3nnkdeO0AWSbZ1PGR1QRbJI68d8OnuGd9OzNaIYSCEIIZBzNbYM77d/9l+/DBkspBpbbNMTr3/8cOL2r4P7jpETpNtx29Okzy465B/3qc3QTqrfjTtxOugffzgD098bzTtxPfpwR/6VIsHnzhxvhYnztfFg0/4dB99oUbCbJKK2miaIBW1SZhNHn3B33vj4OQ2Iq5OVLb2mzSJuDoHJ7f517uA7zw/fVR97kxrO2Ra2+Gnj/pU90zuIOZoxFrHTgyTmKOxZzLgGvf4o5DOeebNKbmXB+4P3r4P+I/1B7YNk9MdcmZrH5uCnO7wwDb/tei+7cPkNIeOlm6HKchpDvdt9+suNcLwm5DzknUDJ2/EHzro8vMtMHlMOXDecx2sWr24+92mU8DU2r3suojSdAo+XVmfgWi7JxojruReqlMQ93hrzYSSe7DcCobWntSmaREst+LTrRkaUae9EpbpKrmXcSnp1drrISc1wXhAJS0VctOuG3EFtYi/xOiRmmBZrH2OjKnkXqY1m05Xhzm/SkiNac326TI7Dh6vLvGkknuoiBoJ2Z6QYWJSEQGNqqbGoMvjvUqklNzDqAsDevvnSGuC0aBKqwuYdyG647j04tlvCMaZp5PrkSOwrN1rTSaj5F5VS2OZ0T5PRlNyH4cPw5AntCybVXIPk8KhS7Z/jxNoTAr/hivENdI1V93stohakkJAU6tm3CZSb5cbtqAZ9x8/pUGbjA1YrXktMJAUBwOOtYmj0OPxPCbTSu7l6BEVsjCXdEbJPczGDbL19s8ctyWz8YBL+AK2bz6ukam377eYreReynqDpBNp+85FpEFZ93ddPeoIBrT273JaU3LfGnSLjG22zRuVOnkjoKnV2FHo83iXUxkl9y3i5LfviC0Y1D3nHk3JfSxkH48chkHPvshkldyDJSsYItEm00QES/rP11OVBJ3xKnM3WsK0mKokfLplrU7SibbvN9egrNf9613Ad17tC892SKUD90VBb5LxHDtRaVDQmz5dxkYC9nFayU9qvdngc1RTY1nEc44ylNzL4aZgKNJ+PGQNJV/qhJ76kAuaQwddHrhPUi5LurrV+MB9kkMHF9eyPqJncWT7xc6RDSJ61qcrYp1ge4xGu6bkXhLdKuRmLlZVyT2YWhLXbT9pum4TU0v6dOO2i9f+sjQl99IvBBW3/YRXcVVsvW/epk7Tc3FvapJ403/DtTwuKXqu40VLyb10uQZV0b62qnDpcgOMnI5+qHkujLWKkntIyjgW7YuwsEjKgIof3QNQ9VQ1qZaV3MOgBiXPNiu5ksGgM/AC5l2Ibj8aFTz7DUn/fJeB5cuhWGyXFYtK7lU1XYqeQ6XoKrmPFSvUY/25FApK7qFH6lQ9Nx1VXHqk//jJ1lwaZvsx2DAF2Zp/DZGagW20bwvbkERq/uMnm3Fp1D3z1gXZTMBn61sGlVK7rFJSci/LlqvQgrmUikruoaNmUzPa11AzBB21gBuLBWzfXM2l7vnIdUPJvaScKE3R/v+awibl+LuuLtMlJc8UJVfJfWtwTBqem7SGcMg5ATXXB5apcJe5lIt+4x0WtH2HDBl4/A4Z/vUuaB8PrVBhP20TF5TcgymSuNJzvpZNTOE/X3cnq1St9u1TtUy6k1WfbsqN0fQ4O5qaTcqN+XQX8p1X+8KzHcqlwH2RdSI0PMdOQ9hknYCmZwND88wbkGO2fHnw9g06R0Vcip6vS9FWci8rIio8Zy4FW8mXOqFRH3JB8/MtkExBKiXUY8yUIJlS8sXQEVuH49ax3TpSSmy3juPW6Yj5Exj17quQThVpVZFSqtGpondf5Z946FplxDcrIKUaraqSe8j2XI+DhePWkbg4bh0Hi2zP9T7d6+1uHE3Q0MAFGho4muB623+zcFPfag7IGI/YPfzAXsYjdg8HZIyb+lb7dN8XWYljSBqag8SloTk4huR9EX9i751XDJJ3tPaGKo7GnVf4m0S9s2s9VU1SwUFKNVY1yTu71vu32VU3Qa2k4s2lq8ZaScm9m7dzI03NodlqVNWkSVNzGOoMSNrbeLO6mFeKat5KUb3feLNP9dYVqyhIQcF1cZEUXJeCFNy6YpV/3mtvgWqxfd5qUckXoXvT8ospCUFJuqp5kHQpCcFNy+dJ1r3zI5CfVTHZrqvG/KySe/jI5auYlRp5p7XvHMms1PjI5QGf7+671Tyzs2re2Vn1/u67farv7VtHRYOyVA2PytKhoim5lw+ZAzRMjZoBrpTUDGiYGh8y/Tc4V+bW4poSy1DHpWU4uKbkytxan+4l6RxNE+oOqnmZA01TyX3ceCuUCurHdU+8vvFWv+6td6oY4EJr+x5/feudPtXbzF7qhkbFELhAxRDUDY3bzIBciI/erfZV2/bNK7lvm/XTiOhUDYmLpGpIGhGdD5n+G97V/dfS1B0aWEgpaWDR1B1W9/vPPXesX0MBQaF1PBQcSQHBHev9Se+X9F/NZNpk+2COZ1b2sX0wx2RaJcv6F/xhSpEa+9dZvPpunf3rLEqRmkqWXcT2vWvDKvKuaDt+867grg2r/PPeeCuTMYtnL43yo+s6ePbSKJMxK3gf3/VRHuvq4zPvu5E7bruDz7zvRh7r6lPJsh4yqz9w4nwtT5yvM6s/4NO9/do4VStCuWHgupJyw6BqRbj9Wr/zYXXPNTQ1h4Zo7Tdh0dQcVvcEJMou4DvPB29Xx3axdawXW8f6B2/3qV7ScxV13aXeOnbqWNR1l0t6Aq5xN98Opbxn3rySe/nI3SfW6Lon1v4R/7H+kWtWknd08lZrH1uSvKPzkWv816J7rl5J3tWZbenOWpK8q3PP1cEFKZYSYaLs2yBMlH1zDh5yeWyT5PEnJAcOSlIpSUduaT62euJxSTYLYo6X2TBgalLw7ve+/TWbeorp17t47vtpXnqog6k9nQz2r6Vv0H8R1iIZRKwb2ZiGxiwimsHovy64+k08p5JiK5NQm4ZYDtbcFFj9xswMcmC2g+8Nd/PQsdXsqw2wovcqlq/1n0hXxZdhlGYYFVXqhkbMkXyg2RVY/WZcT/NgyWBCg2pEUNdMZKSH9w8spzfS7kEdyvYRKVqM2kVqEZeYrXOTvjqw+k1vTwerIxaHp0uM1AV9MfjVdw4EVr/JZnrpacJ0dYYZzSErDW7o3BBc/SbTpZJip8cgP6HeX3dnYPWbeHqAlB2hUpugKurEibGm473B1W+yXSopdmoUpsch1w3vvzuw+k13RxcrcTlSLDDmQrcm+PjKearf5LpUouvk0RPz3nRPcEWbBeh2dvWyzHYZK84yISRdQuPO5evmr37T26cS5A4Pw8iISjz71V8LrITR19/DGmExPJVnxNboMyS/9o6VwdVv+vpUUuyhQ+oxeX8/fO5zgdVvOnK99FuSY5UZpoRDBzof6r0ksPrN8s4BMlMlDlslinGNVENyJ/2B1W/6B5ahH2swWc3TjDuYDYN3Ji8OrH7TncpSMqeo1C1KTUjG4eLBOB/svpIkHm9nZ49KmBwfgWOj6v0dHw+ujNLTq5I2Rw7D6Aj09MHHfzWwOsvyzgE6pvIMW2XycYNMw+EXRXdw9Zu+PpUUOzxn+372c4HVb9Q2K3LEKqvwpYbkLvoCq98kOgbJNExK5XEqepOEG2F973sDq9/09PWwStocnpll1BX06PDJDcHVb45lOtiiawinginrOCLCbMfFrBu8jA6t3RtdygmG19hQrRKZLmFnk8zecBnx5ZcT9T6BXMD27e3rYbWwGJ7Oc9QR9Brw6SuCq99MdphsuzgKjSqJqQKNXIoj111Jtv8SkqL9eHgskuCvVg4hHYdMoUQllWDz1VfTNbCci2PtTzjM7hVE3DhW/gi2rGKIOB1r/l1g9ZuhVb30iGMMTzSYribIxRt87H1GYPWbRG4QXIvReJ6ZTnBjJhelr2Bw+Xt8ugv5ztPVo5JiR4/A+Ch098BHPxFY/SaVGyDX0ClUjlHUm6Rckyu7rgmuftPVo5JiR4/A+FGVYHz3J4Or3/T1qaTY4UMn1vuZzwVWv+kb6Ga1YTE8UWCkqdFnSj7z7hWB1W/6BnpYqzc5dKzAkaZGvwn/YeOKs1b9ZiGJsmFH2bdB2FF2fg4ecrn/AUkqCYkEVKtQrsDdHxGsXrX0Hgx9/7su5bIklTphwB9//yufevvrHXlN8vjfQSKrcjJrJagW4OYvwND6M3ODs2vK4W9fapKNCtIRKDWh0JD85pURNnS//aTh/3x4hhcbNTK6wERgISk6kndG43xlRUDIUEjIOc4xCuxllCI1MsRZxyC9+EPpQhbOt2oTlFyHtHbinHT8/afj7fki+2s/x3IbmNqcjr2t92vjAUbqaeBZ+Sp1LGKcCB2p0ySGyXtFu1H9mSNHKUuH1JzPVnYdUkLnn5cHhOucBmadGXa7rxLBxCSCRZMmFpdql9Ghh+frc4Gwo2zIWePZn0MqyRtGcioFIHn257B61Vlc2Dy85zp44D4A+cZNSKUMt/zC4uZ9aZMy6BOt6/7x8aVNMBQQIXI6ePSgTTYqyEbVvshGT8gXY9S/VG+Q1gWR1tONCIK0puQhIecjvWRDI/40MeFa9Ih2UyQpNCZcf6Js3S0RFe0lgQ0Roe6WfLqnixI1UrSHuUQxKeFPpp9ybTq1dudQQgim3IBciNPEEXeYCCYRoS4AEaIglTw06s8/lp7rNOSc5tik8tDPJZFQ8qXIqtUaH7lHxdJPT6nxI/eIRVe/mRlVHvq5xNNKfqY4WpKkPXlI6YiSL4aI4eK47U8bHFcQMQISB0NCQkLehD7NpCLbzx0V6dKn+RNlY1oa25NMassmMS3t0z1dpInT8CTTN7BI449n79YMqp5oiKqUdGtnzp9aoYJJ+4XAJEIFf1WdkHOf0FN/DvD6iMvTO1zGZ6C/E268SuOioWCjc/ekw6b9NkdLkmVpwa1rDS7tCfbKvjpt8/CwzdGKZFlS8OGVBpd1BR8STz5v89hDkvwE5PrgtjsFN73Lr9vbA8NTkiOWQ6mpjMjlpsbKnuCQk6eet3n0QUlhArJ9cPtdgvcHzAuwa9rmkUMOI2WXoZTGHat0Nsyz3l3TNo8edBgpuQylNW5fPb+u0RQkZ6E+BUkTjIAqWwulc1DlKybmOPdqJSU/UyxLCwoN+YaHHlQIzrL04sJ/3pvV+PGkBCGIa1BzoeRIPtQZfEzua9b5aa3CmGMzoBt8MJ7k4khA5YWQ084xmWcfYxSpkiHBxQzQK+ZvtVyb2kF9cjOuVUQzM8R6ricelMANkD/c6nQ8DYkulbyd81f5AGD6EAw/B5UpSHbDyndD16pA1f1je3l6+hAT0qVPaNzYtYq1A8Edc+/dnee+PTXyVcgl4J5L4nzs0uDPV3CmGLMPUpNl4iLFgLGarO5PDA85vVxvZri3MaWaTwmNinQp43Cb2eHT7TXXMtzYDq7y0NuyiU2DZeY83UZPAxexjG2o3g5RTBpYNLC4nFU+3V9JZ/irwjS4ykNflZKqdPl8xv/ZThdJkjRpKA99C4smSfxVdc5FXp2xeeiwzUhZMpQS3LnC4LLOC9e0DRNl3wZnMlH29RGX7/9ElfvqSEO5Bi/ukQx2Czoz7cbZ7kmHb2xXFmlXHIpN2DLisDKj0ZNsN7henbb5653K29AdU7qbx1xWpQW9iXbdJ5+3+d43JBJIthpy7ngW0v2SVcvadSdsh0efdUFCMqoqB47MSG68Cdb0t+s+9bzN9/5W4s6ddwtkBvzz7pq2+ZuHbZqP6cQ365T2Cp4tO6xaDj0Jv+5Xn7AY3ymoHRRMjsHzMw4X9Qmf7sgeyY++qcrnZjqV4f3ac9CzHDLdb9/4TeRgb6uCjhGBWlHF1L/344ubdyHkooJnjqpjJ6KrfVxoSD6x3vRth4UwGDE4IiqULEHREsQMyfqcw2dznXR6vGv7mnW+Xc4D0KnplKTLC80aQ7pBl37hnnjPBsdknq28DkCCKA0sjjBFloQvwQ+UQV85+rCqsmQkkU6dZnE3wshgJjzVUfKHYc8j6nU00+p0/Cqk+iDmCVuZPgQ7Ww2AYlloVmFsJ6T71BdnDvvH9vK/pw+AlOSERlm6bK/NMui4dKbb+zXcuzvPP7xYU+epKNQseOGoTdR0uKyn/fMVnCn2N18CIEIcmyZTzlESWoaY5q/1HXL66NBMBrUIE9LimLTp1Axui3SwxvB7vqNakriWoS6LNGSZqJZgWWQDaePMdflMihg2kgOMM8oMFi7rWcFK4S+CcHEsShc6e5sW065DTjP4D5kObsuduScLEaJMyHFAoqG/EVO/VruYuBZQqvcc4tUZm6+/2rJj4lBows/GXVanBb0BvRbOVRaSKBteVZc4T+9wSScgnVCGYDoBIHl6h+vz1m/aHxxDvWm/7fPWPzxsk40oww8gF1XzPjxs+7z1jz0kiaYg0fLwJtJK97GHJDd5iqNsc20G3wPNAxqNAiSz0PEOl22uyy2ew+3RByWRtH/eRx+UvN8z7/1PuMQeNYhkQHRDpCqQjxrcn3TZ8Evtut9/3qG8SyeRACMNdgPKu3S+n3T4H7e1r2H7j0804YQT4/Yfw5A/Kf6kGVovuPkLkpc2qZCbzkF47y+fuSRZgA3dOr95ZYRHD554cvOJ9eai4ukB1hpxfrujm83JIhOuRZ9mcn2kg7UBF+Gf1iqkhUamlSiWETq4Sh56688s+xgjivlGgt/xcR9j9OL3ZtcnNyO0KNrx/WrEcW0l93nrR14AM6k6HMOJceQFv7d++Dn1+2hL5/g4/JzPW//09CFSEtJCHT9poYN0eHr6kM9bf9+eGlETEpHW+SQCILlvT83nrR+zD2KKKGYrzthseTHH7IOht/4ssMaIBxrxQaSNnjNqxHuZoMA+MUEnOQbooYHFPiboIE1fQN7Fbbn0GTXivXTonVzKZRxxh6lQIUmStdq68yKe/qHDwXbMQ4ftC9Zbf2F+6nOI8Rno9TypS8aV3MvRkmSgPYdo3hjqoxXJoMchlYkouZf8BGQ8TohYSskD5x0CbfmJGElXSo4GhO8VJiAdMG8hYN7JJwTJDGitzydSEGnJ8Rj1r++CeALMls1oxiDeknNbu+70GHR6GnLGU0q+WIbWi5NOij38ussLT8HUBHT3wbXvhxUXLd7TsKFbX7QRH8RaIx5oxHsZc2z6tPb/nxIaY86ZSxQLURSp+uJ+o5gU8TesAXCtorornoseU3Iv1WmIe4wEM6HkXipTKjxnLpGEknuYkC49ov17kBQaE9Kfv5GvQsZzSMZMJfdSk2VinvADgwg1WfYrn4NMubMcZIQSVdIkWM0Q3dqZC/k4nRx2K2yTM0zLBl0iyjWikxUBDfVOF3sZIxZwc7yXsUCjfinQoXeeF0a8l5GyZNCz6zMRJb9QOX+eT5yn9HdCxZNUX6kpuZdlaUHJEw8+Xwz1sqSg6NEtNpXcS64P6p5rXb2s5IuZNzvPvNmAeVN5QdPTuLAZVXIvZlPgeOxYR1dyL10DKuxnLrWykp8pDr/u8si/qL5FXT1qfORflPxcZ0A3KHsMsLJ0GQhDb844GRKBCX4ZgsNNNDMDjqeVvFNXci+Jrnk6HXf5dZPdKuRmLs2qknvoa8VYz6XSiq33kktA3VMwpW4puZe4SGHjSbikSdxTWeVcZMqd5SW5h4ZskpJxGrLJS3IPU+7s2V7aojnsVtjkjlKRNp1EqEibTe4oh90zl/RZoEqU9jDDKCaFeW6OQ04fQ6lge2ModeaeiC81QqN+iXPjVRqlKpSqqrNZqSopVZXcy61rDQoNSaHR6uLXen3rWr8B9eGVBoUm5Fu6+Yak0FRyL7fdKWiUoVpSutWSpFFW8sXMe/tdgqmmy3bN4rmExXbNYqrpcvtd/nnXrxMUiy77ag67ajb7ag7Fosv6dX7dK5ZrTBRcdk44vDTmsHPCYaLgcsVy/za7+kNwdNrlp8MWD4w3+emwxdFpl6s/5FMFYOeszZ++XOO3fl7lT1+usXN2fo/zK3mbL+2u8oVtZb60u8or+WDdF56CZFr9CO3E6xeemnfqc4YPxpOUpEvRVR1Bi65DSbp8MH5+JGktBV4pW/zJoTK/+VqBPzlU5pWyvxQgwMUM0MCi3uqWW6dJA4uLCb6DjfVcj3QbuHYNV0pcu4Z0G8QCOhKrTscVT6fjSmCnY1a+W/2+0dJttP5u5bt9qjd2raIsoCQdXCQl6VAWSu7lnkviNCyotjoSV5uShqXkXgaM1ViygSUbSCnfeD1g+Lsin05eb9b5+9IkXy6M8felSV5v1t/6j96Cg4wQxSQqIgghiIoIUUwOMnIKVnx22SZnSGCQFAZCCJLCIIHBNhnw6Po0kZ3n5jg7z81xyOnjzhXB9sadKxbvNBre73LvP7n89VfUOLz/3HCyhYmyb4MzmSjbmREMdgvGp2FiVtCVEdxxnR5Y/aYnqbEyozFSlIyWoSch+PgGM7D6TW9CY1VacLgsGa1CT1zwqXVmYPWbVcs00v2S4UNQOAbpTrjnk/NUv1nAvLWI5Ecli9k8uHmwIhDbILnrPYYvsXcm7vLUNpvhboeJDpeyITErcMsnNFZ5tsXegsWW1ySWCy6qe7TlwA3XwlWem4tDEZu/1QvszTY43NtgtK/J6IYG119m0hdt3247Z21+4+k8/3bU5uVZh23HLDaP1Xl3X8SXlPNK3uYPn8uzZX+D/aMOu481+Plsg6u6I/TF2nV/tklycOUk96+a5KfLZnk5WyAhLMwjSTbe4L9psStHaUxsxpp8HrsygjASaJEA7ynglkZwRp7CHXsWt3QEzCQiGqzrlI9ijf0M+9jzOOUjCCM577wnS5duMKQbjDo2465Dt27w0WQmjKc/RbxStvjLkQpCQk9Eo2BLniw0WR3T6fN0902KGFkSFKlRokaSGFewct7qN2aiH2FkcOpjSLuEZqZI9N8SXP0mllVJsZVJqM2o92tuCq5+k8ippNjysVbYTg7WfTCw+k1nuotBx2W8VuCYdOkUGnd0rQ6sfnNZT4yo6fB63qZQg3QMPnVFcPWbmJYgoWWoyhJ1KkRFghXm+jMaT/96s873qsoY7RA6ZemyzaqyTDPpXMSTrD3yEHGibV2ydTTKosZqEdCl+hziGXeSDGbbZzMRzNDkndqZCS+JYTKMChXT0Vo3yhbvYCUpb4fhkNNKb1xjdVpwuKJCfHsTgl+92Fx0PP3wfpeHf6BeZ3NQLcOr26FvUJLrPPNPAcKOsqeZsKPsqeGPflTnhUMu6YggYkDThlJTcu0qjS/9QvvJ8dNbimx9RZI+phMtQSMNpV6HjVcIvnVdu+F593crjEy6RMs6oimQEUkj5TDUo3H/p9o9xJ95dZpn6nUSusAELKDqSN4Xi/HPl7WHDtzxyCQ/L+iYSHTAASwE78k6PHJHe+LWpx+f5GdHBHEkpi6xHEENwQ3LJd+6uV33j+6d4NGVFaIuRG1Bw5A0NLh9OMmXPtYei2RXjtIY/SnCiIMeB6eGtGtEBz+IkWzvUOiWRnCGfwRGEow42DWwK+grfwEt3X5xd8pHaR593DdvZNnN6Kkz0/kwZOH8yaEyecslZ564UTz+/r+tOvdDSc5n/r40OW8n1c+n334i6AvuKzRkk6g4UZv8+PtrtSsWteazzf3OESrSJjmnWdXx93fry8/YOiYosJcxClTJkmAdA0s2nj5k4dz7Ty6Vknpifpzj7z/2a2c+wCXsKBtyTrBjVJIyIdoKT4ya6mn8jlH/jea2MRetA+xeh+NBLJol2TYWkAScd4nkJEbXiXAX6Si5lxfqDeKaoJU8TxSQmuCFgO6oLxY0DOQbXxoDkEheLPi/5M9NSGJA1FDrixoSaSu5l5fXzZJoaHSnq5gRG6tpMFVJ8PK6WaDdqLdmXmJXs5uHpgcZaUYZijS4Mz3K5TMv+Y36Y9vBSCLM1mNhM4Fsyb1GvT29A2HEEUZLtzXa0ztCo34Jc6TusCzafvxlDMGRuhOoP+KW2cE0M9TpJMZVdDGkhcb/2WDctekV3qcpGuOL7Da6miFeYg9IiGDSbNVRX8+aRc27FLhGdLJJjoKEBDpVHKrY3BhQTvJ00kc2NOLPYyYnoNtzX51IKvlSJ4ypDzlraA5IzxEoNSX34jZVvPlchKbkXvS065O7TSX3IjUX4RELV8m92Pi/MFpL7pu3LsDwGPCGVHIPlZjN8p5ZDMOh2TAwDIflPbNUYv6Zd+Ytvja1mrxtMGg2ydsGX5tazc58QBx1fVp56NvWEFdy73obs8pDPxc9ruQhS5blMZ2i3X6cFW3J8pg/5G7ELfMTOUJVWnTICFVp8RM5woh7flR8Odfo14zAJOD+RXYb7dY6uFJcQlREKIsaURHhSnHJeVH9ZoWW5FZtkKQwmKFJUhjcqg2e0eo3Iec/PX1Q9eReVytKvtQJjfqQs8Y7ezXKFtQdiZSSuiMpW0ruhGZpXAAAIABJREFUZUVGo2GDLVUTLFtKGraSe9l4hUOzIWjWVTx9sw7NhmDjFf67hUtSgqoLTaeV3+dA1VVyLzlcbFScvmyNdkvuZZ1rU0fQFEq3KaCOYF2AF64/VqWBRizmkk5bxGIuDTT6Y/5qCg9VVpHTGuQMB01AznDIaQ0eqqzy6RLrUiE3c7FrSu5BRDvA8eg6NSUPWbLc1R1l1nHJW65KFLNcZh2Xu7qjPt0dTJPAINFKMky0kgx3EFB2MuS0c1M0TUm6lFpJ5KVWEvlN0cXXNO/WOrhWu4IPau/mWu2K88KgP84KLcnd+nI+b1zE3fry0KAPOeW86waolFXIjXTVWCkr+VInNOpDToqR1yQPf03yrf+ixpHX5s/FOLxPcv83JH/3P9R4eF+w7ic2GiyzNfJHBYeGIX9UsMzW+MRGv6fqdy6PkBYCx4a6LXFsSAvB71we8en+7oYkl7y3jhaV1MsCLSq55L11fneD/+T/O/0pelMOUkgqNkgh6U05/E6/PyTh86scdFQHXAdl1OtIPr/Kf7PwW1fr9M2AtKAq1Ng3o+RebshUqUiDiitwXai4goo0uCHjN+pHxQBpauBagATXIk2NUeGvYKL1Xg12BWlVkVIirSrYFSX3YHRdhbRrSLula1eRdg2jKyApMuS0c8Cu8e3aOH9ePcK3a+Mc8N6ctbgiZfIfh5LkTI2jDRVL/x+HklyRMn26M9SJ0378xdGZYfEVV0IWzkWRGJ9MdJLWdI5JFUv/yUQnF82TRD4p8zwrX+VHcivPyleZlPkzvOKQkAuDlWs1PvzLKoZ+alKNH/5lJV/qhImyb4MLLVF25DXJT/4RElnVmKlWhmoBbvmcv0Pq4X2Sx76rvgSJpHpkVSnBbZ+CFRcL37zf/3uH4bhLxYCkDStrGr/yeT2w8+pDw3W++ZrFRFnSlxJ8dr3JnSuDL4B7Gw1+Ui0zZjsMGDq3JFKsi/q9lwA/KRX4zmyJUctl0NT49x1pbkkHx0t+Zcs43zqkURQaGeny6VUu//m6/kDdR7cd43tbbY5VDXoTNp/caHD7Nf7Yzx3ODl4olHi6FGXaMenSLW5MN7g2m+Yqvd2o/tLuKjOVEml7QnnW9Tglo4/OZJo/utRfUs0tjajY+vo0xLrQeq/2xdMfxykfxZ7egWzMIqIdGF1XhfH0Z4EDdo3/rzFJSugk0KjiUpYOvxjtOemum0E87A5TlRaJOUmGVWmTECYf1laeiqWHnCYmZZ5t7FOlKjFptOLkr+FieuapXhQSEnJ+sJBE2dCofxtcaEb9w1+TVIuQmFNk5vj7D/9uu/F9/zfkvFnjd/96u+4j/7M17xz7uVpQ897xexdO84gZd4Zd7i4iRDAxsbBo0mSDtoFOT5m2V/I2X329Ts6ErCkoWJK8Bb9/UYwrcmHe+/nAt2vjjLl1ZrGo4pBApwOTAS3Gr8aDbyBPhuMx9QkM4ujUWkmGt4ihMFl2ifOsfJU61hvdSwHqNIlh8l5x2VlcWciFzPABl+c2qwTSnj549/Wwcs3S92afayzEqA+3fshbMjOqPPRziaeU3MvUuPLQzyWRVPLAeT3ho/F08LznM51aJxu0DUREhCpVIiISaNADXJEz+P2LYnRENEZqko6IFhr05xkHnBpH3CpN6RKXGk3pcsStcsCb87BAhrQUt4ghEsJkVjRJCDM06M8RStQCu5iWWNwxERLydhk+4PLgvcpp193qhP7gvUoecvYILYGQt6RzEJ+nvlZWci/d/fg89dWKks877xxPfa0UPO/5TqfWSScn1zzlipwRGvHnMXVhg9SItMo9RRA0pVTyRTKkpRgiNOLPNdLEfZ76BhZp3n44VkjIYnhuM6RSJ671x8fnNsPKc7966jlL6KkPeUuu+pAKi6kWVSZ4tajeX/Uhv+41H2hlis/NGi8puZcrb23NW2jN23p95a2n/zOFhCxVsrpKwm5IFyklDenituQhFyYXsazVubSJRFKnSQOLiwhzXkLODpMTwU/lz4Va7uczoVEf8pYMrRfc8jnlqZ8dV2NQkiyoZNjbPqXu2qePqTEoSfb4vDd/oTXvmBpv/kLwvCEhFwpr9QQXGSZRoVFBEhUaFxkma3V/InTIhUGPyHENFxPDpEyNGGaYJBtyVjmXa7mfzyyZZ/hCiK8AG4F1QDdQA4aBHwJfl1JOz9FdBRx8k+n+t5TyV+b5P58BfhvYgKpMuB34Mynlw4v/FOcvQ+sFQ+tPTnfFxYIVF5/6eUMWzs6yxYPTDUYaDkNRnbu6olweUO4wZOnwLi3HMX2CS/TIG10zKzi8SzvzBlyzNkatsBPHmkU3O4hnLycS95dPBTj8usvWJ1X+THc/bLwJVlwU+o1OFT0iRw+hER+yNHj39SqGHk5UuiuX4YO3nd11Xegsmeo3Qogm8CKwCzgGJIH3oAz9UeA9UsojLd1VKKP+JZTR72WnlPJfA/7HnwF/AIwA/wpEgF8BOoHflVJ+/WTWeqFVvwk5N9lZtviroxVyhkZGFxQdSd52+T+WJUPDfokz7FR43s0zRZNuIrxLy7FSP7NNdpq1McqTTyP0OEKLId060qmR6rnRZ9gfft3l0e9CMjOnlG0Rbv9UaNiHhJyvhNVvzgwLqX6zZDz1QEZK6euCIoT4E+C/Av8F+C3Pr3dIKf/4ZCYXQlyHMuj3A9dKKWdb8v8H2Ab8mRDiYSnlobf9CUJClhAPTjfIGRo5Q51kc4Z4Q75Yo/6IW2abnGFaNugSUa4RnSyfp4rKQ2NH+OZUmTFHZ0B3+Gx3ijsHlgdPnD8MIy9AdRoSXTB0LeRWBOvODMOR56EyCckeWP4u6Ayutz42s5tdld3kRYOcjLIheSkDnZe+nY9+RlipJ8+4Ee+lVtiJ0ONoukrGFHoctyX3GvVbn1QGvTdpbuuTsOKiM7bkkJCQM8jKNVqYFLvEWDK3VEEGfYsftMaTDOiYl99sjX9y3KBv/d9DwP8LRIHPLvJ/hIQsGUYaDhm9PT8howtGGv4OuAvhiFtmkztKRdp0EqEibTa5oxxxyz7dh8aO8OWJGgVX0Ks5FFzBlydqPDR2xD9x/jDseQSaFYh3qnHPI0ruZWYYdj2sdBLdatz1sJJ7GJvZzebaDmrYZGWEGjabazsYm9m9qO1wvuNYswitvbmb0GI41qxPdyGlbENCQkJCTg9Lxqh/E+5sjS8H/G5QCPEbQoj/2hrf8SbzfLA1bgr43WMenZCQc56hqE7RaQ+vKzqSoejiyqhskzMkMEgKAyEESWGQwGCbnPHpfnOqTEq4ZDXQhSCrQUq4fHPKfwPAyAtgJiGSBCHUaCaV3MuR5yHq0Y0mldzDrspu4q5OHAOBII5B3NXZVQmN+jdDNzuQbruvRbp1dLPDp9vdH5w0F1TKNiQkJCTk9LCUwm8AEEL8IZACsqh4+utRBv3/HaD+odbP3L9/EviMlPLwHFkSWAaUpZRjAfPsa43rFrv+kIURJtedPu7qivKl4TLb7CZN6RIRGt2GzqdXBte23jPu8Phul9E8DObg5ks1Lun33wBMywadc+plAyTQmZYNn+6Yo9OrOcCJJwYpIRlzAm4sqtPKQz8XM6HkXiqTykPv1a1M+lTzokFWtq83hk5e+Nd7LlK0p5iw91OTJeIiTZ+xlozR/dZ/+BbEs5dTnnwaF9pi6uOd1/p0N94Ej35XvZ4bU//+O32q5yT1+hiV8i5sK49h5kimNhCLBScMh4SEhJwtlqL19IfAfwd+D2XQbwJ+QUo592pdBb4EXAN0tH7eDzwB3AQ83jLkj3O8vVFhnv95XD5vaQEhxK8LIbYKIbZOTvoNh5CFczy5rlKCrl41PvpdJQ9ZPJomMUwHISRSagih3muaPzl+z7jDP29xKNYk/VlJsSb55y0Oe8b9oTpdIkqVdnkVhy4R9ekO6A5l2R4CVJaCAT0gBCjRBVa1XWZVldxLsidYN9njU83JKHXPeus45KR/vecaRXuKg9Z2LNkgRgpLNjhobadoTy167kh8gFTPjWh6HNfOo+nxwCRZUDfit3tK2Z4vSbL1+hiF2WdwnBq6kcVxahRmn6FeD/IPhYSEhJw9lpynXkrZDyCE6AOuQ3notwshPiylfLGlcwz4vzx/+rQQ4heAzcC7gc8Df3kK1/UN4Bugqt+cqnkvZE5nct34K7DrhyocO7cCNnwU+q9Y3JxLhZeLFvdPNDlcd1kR07i7L8I7Mv7E10fzddbEDK5OnTCsCrbLo/k6GxLtnuvHd7tkYpCJKwM8EweQPL7b9XnrrxGdbJKjIHmj5GIVmxtFr28Nn+1O8eWJGrguKSEpS0FZavxed8DTgqFrVQw9KK+7VQWrAmtu8usuf5eKoZ+r26jAWn+Xsw3JS9lc2wGu8tDXcahpDtfEz/0DYsLej0kUs3VDZRIFqeSnwlsfiQ/MW8LSy4qLtPMyKbZS3oXQYuithGFdj+O05KG3PuRU8lqtwaZSjVHLYdDUuTUdZ3383Hc+hJw5lpxRfxwp5QRwvxDiRWAv8C3g8rf4G1sI8fcoo/5GThj1xz3x2cA/PCHPL2rRIQtialx56OfyZsl1u6ccHttvc7QkWZYW3LbW4NJufxjH+CvwzFchloPsENRm1fv3/f7iDft9zTpP1MuMOTYDusEHYikujsTe+g9PES8XLf78UJ0OA4aiglnL5c8P1fmDVfgM+5GmQypis1evU8cmhkEvMUaa/q/9aB76Pd+OVEzJvSzXUtzKYFv1mxtFb2D1G1Xlpr36ze91x4Or3+RWwCV3tFe/WXNTcPWbzpWw4cPt1W/WfiCw+s1A56VcP0Nb9Ztr4lfMW/1mVJbYyQSzNOggyuX0MSjSgbpnm5os4UqDYxyjgUUUk5xMY1MK1P/u+DTfmy0yK106hMYnOzJ8qj/gSUjIG9hWHt1o/3JoWgzbCi8XIaeO12oN/mIqTxELR7gcbmjsajT4T9250LAPOWmWrFF/HCnlsBBiF3CVEKJbSvlWz5WPx8a8EX4jpawIIY4Cy4QQAwFx9ccr6+w9NasOORm6+1XITXKOvTRfct3uKYf/tb1JNioYSEGhIflf25v8xtURn2G/64fKoI+38vmOj7t+uDijfl+zznfKs6Q1jT5Np+g6fKc8y79PdZwxw/7+iSYdBnSYyvveYQrA5f6Jps+oz0Qd9ogSCXSi6Fi47NNKXBLN+OYdzEGxdtxDryjXlTyI5VqK5QSXsPRy58By7jxZh2ZuxfwlLL10rpy3hKWXgc6TK2E5Kks8xSESmOSIUsPmKQ7xfrlqSRr2EpNxxjGIEsHExmGcCQbwf4m+Oz7N16bzxIUgKzSqUvK1aWWYhob9/BhmToXe6Ce+HK5bxzDDRlAhp45/KRQZp0FaE8TQsIRk3G3wL4UiX4z7wwpDQoI4VwIeB1vjydTie09rPOCR/7Q13hrwN7d5dELOABtvUsl0lRJIV42VopJ7eWy/TTYqyEYFmhBvvH5sv+3TzR+GmMfrHMsGV0ZcCE/Uy6Q1jYymowlBRtNJaxpP1AMquZwmDtddskZ7jHrWEByu+/MQ0gkLxxW4rtJ3XYHjCtIJy6d786UaxToUaxJXqpj6Yl3JLyR2MkECkzhmq1KOSQKTnUyc7aUFUjYiaFKiSRdaoyYlZSPi0/3ebJG4ECQ0gSYgoQniQvC92eJZWPm5QzK1AenWcZwaUko1unWSqQ1ne2kh5xGvNBukBESEhhCCiNBICSUPCTlZlsQVWwixTgjhC40RQmit5lO9wJY5DaPeKYTwrV0IcTPw+6233/H8+m9b438TQnTM+ZtVwG8DDeCbi/woIQtgIcl1R0uStMdOSUeU3EtuBdQ9KdH1wsk7gOdjzLFJeQ67lNAYc/w3FqeLFTGNgt3+mQu2ZEXMv81c3eVdiShRTVByJFFN8K5EFFf33wBc0q/zmet0MnHBeEGQiQs+c50eWP3mfGaWBjHPA8wYBrMszQtrXddIRlaiCRNJE02YJCMrqev+42FWusRE+w1hTAhmZZiY/mbEYgNkO96nYuntAroeJ9vxvjCePuSUYugujqeogCMFRsD5OiRkPpZK+M3twJ8KITYDB4FpoA9V0WYNMA58YY7+XwAXCyG2ACMt2Ts4UWf+j6SUW+b+AynlFiHEXwD/CXhZCPGvQAT4ONAJ/G7YTfbMc7LJdcvSgkJDkp0TWlhqKrmXDR9VMfSgPPT1AtTzcM0iW4sN6AZF1yEjThi6ZekyoJ+5r9HdfRH+/FAdUB77gi2ZteFzQ37PbJ9mUjIcrouc2Ggl1yGtBa/3kv6zb8SfbBLw6aKjFXIT58T/rGPTwdKMaU2TpKE3Seir35A1aJLGfzx0tEJuEnMM+7qUdPj9IyEeYrGBs27EH7SrbHEKHHMtejWT6/Qsq41EoO5+u8bmZpEJ16JPM7k+kmGtEVzKdiFMyjz7OUqJKmkSrGUZPSIMQzoVbEwaPFVwEEhiGtRdKLvw/nTw+fq1Yw6bXncYLUoGM4JbL9JZ33thOWFC/CyVs/lPgH8AeoB7gP8T+EVgBvgicJmUctcc/W8D24FrUcb+b6Hi4n8A3Cil/HLQP5FS/gGqa+w48OvAp4FXgTullF8/9R8r5FRx21qDQkNSaKjwkOOvb1vrP+H1X6GSYuMdUBhR46lIkv1ALEXJdSm6jgpRcR1KrssHYicXW34qeEfG5A9WxegwNUYakg5T4w9WxQIN3xvMNGXpUGqtt+Q6lKXDDebSiw2HE0nAs5bblgT8ctEfLnS6uJw+qljUsJBIalhUsbicvjO2hoWwhmU0aNKgiUS+8XoNy3y6n+zIUJOSqitxJVRdSU1KPtnhz7EIWVoctKvcZ01Slg7dwqAsHe6zJjloV326++0a99anKLkOPcKg5DrcW59iv11b1BomZZ7t7KVBkxRxGjTZzl4mZZgw/GYM73e5959c/vorahzeH+x5/6VUB5elJbomKTiga5LL0pJfSvmbvb12zOHvtloU6pL+NBTqkr/bavHascV1Cw859xFShtUZF8rGjRvl1q1bz/YyLjhOtvrN6eRsV79ZKPutGj+zSm947G4w06w1F++xOx18cV+FWct9IwkYeOP9f784+SZ/eWo5l6rfAEy5sxzgKCUqpEmyhmV0a35DAMLqN+cq322MUZYOqbanhOr9p6LtTxD+uTrReiJ3Qvf4+88k3v7N6c/lqzRoEp3zFOj4+/eIy972vOczw/tdHv4BJFNzmrKV4cO/DCvX+n2qB+wam605T1jMDGsCnrD8zy1NCnVJNnbiqdvx9793nf8pXci5jRBim5Ry48noLpXwm5CQt8TIWKQ3lMk5FmndxIimgGCj/pXCCP9WGGfMcRnQNf5dtp8rskOButtfGmbLpjrFMZPMgMV1t8a4+srgqiq91de5pbyVhqwQFUm63I0QCa606pSPYk/vQDZmEdEOjK6r0FN+DyqAWxxBHnsR6jMQ60T0vhMtE7xe8ofbyz4OXTtvwsDayiRrj26F2hTEu2HZxvmTCyYOwN5noHAMsr2w7n3QtyZY9zRwuK489HOZLwn4dDIo0gyydI14L91aB90EG/FePtXfFRrx5yDHXItu0X65TqBxzPU/xZpwLXo8ukmhMRGguxBKVEnRbmBGMCnhf1oQonj+Z8qg9/Zief5nsHKtX3+NEQ804r2MFpWHfi7pqJKHXNgslfCbkJA3ZV+zzveqs5Rch15NPVL+XnWWfc26T/eVwgj/OD1K0XHpExpFx+Ufp0d5pTDi093+0jCP/YNLvaiR7mtSL2o89g8u218a9ukW8jsZKT2JRQNTS2DRYKT0JIX8Tp+uUz5K8+jjSLsKkRzSrtI8+jhO+ahP1y2OIId/hLSqyGiHGod/hFv0r5f8YdWgqVmBeKca9zwSXNonfxj2PaoaOMW71Ljv0WDdiQPw/L9CvQyZbjU+/69KfoZYSBJwyLlNrTHBeP4pjkw9yHj+KWqNpVldaKnQq5lUab+5reLSq/nD7vo0k4on+bkiXfoCdBdCmgRN2m8MmlikCY7rD4HJCXCTDQ4xw2tMcIgZ3GSDyXkO9xedCb5ub+Ur9ma+bm/lRSdYcTAjKHly90sNJQ+5sAmvliHnBE82yqSFRrpVTjKt6aSFxpMNfznJfyuMkxaQ0XU0TZDRddJCyb1s2VQnmraJZ100XRDPukTTNls2+W8WpstbMYSJKaJoCEwRxRAm02V/KJY9vQNhxBFGAiGEGo049vQOn6489iLSSCDMlq6ZQBoJ5bn3MvICmEmIJEEINZpJJfdydGuw7tGA0LG9z0AsDbEUCE2NsbSSnyHu7oswa6uQG1dKZi2XWVvJQ84fao0JJkvP4rg1DD2D49aYLD0bGvZvwnV6lrJUOTGulG+8vk7391O8PpKhjCeXBofrI4vLnVjLMhpYnvwNi7UB+RshilhfnYOVIhYOUQwsHA5WisT6/NeXF50J/k3upYFFgigNLP5N7g007G+9SKdYlxTqrRyzuqRYl9x6UZgoe6ETGvUh5wTjjkXSU6UjKTTGHf8j5THHnaf0pD+MozhmEk21JxdFUw7FMb9XqyEr6KLdwNRFhIas+HRlYxZ0z2NUPa7kXuoz4H3kasSV3Et1GkyPZ8xMKLmX2lSwbi2gf1vhGLsTPXw1tpI/TKzjq7GV7E70qFCcAA4Mu3znXoe/+GuH79zrcGB48SEyC0kCDjl3KdReQ9di6FocIQS6FkfXYhRqr53tpS1ZVhsJ7jF7SAmdKWmTEjr3mD2B1W/WGnE+FusmrelMSpu0pvOxWPeiq9/0iBxXs44oEcrUiBLhataF1W/ehMwNx3DKJm7JBFfglkycsknmBv95dYs8QgSDGCYaghgmEQy2yCM+3fW9Ol/YaJKNCcZLkI0JvrDRDKvfhIQx9SHnBv26qZK95iSKVaRLv+43+AZ0FXKTwVt60n8PmxmwqBd14tkTRmmjrJMZ8N8sREUSSzbQxInyho5sEhX+JE4R7VChN3Mvuk4NEQ2IfY51glVtN8DtmpJ7SXSpkJvInP9pVZXcS7xbhdx4dePdPtXdHav5htlPVsCA26AgDL5hDvLrHRrePqwHhl3ue1iSSkq6u6Fcgfsehns+7LJm5eL8BO/ImKERf55j2QUMvd1rrIkYll2Y5y9CQBn285Ww9LLWiJ+SEpZeekSOHkIj/mQx1xa4+pclh36WojRhkO6zueT2MuZaf8O3CnUSntK5EQz+f/beNMiO6zrQ/G6ub3+v9ioUAGInSIICN5AiSJEUd5Nym5Kmu8OLOmyr290R7v4xS0RPTMwP9Z+ZcExMO6JjHG0r2rK71bJFu0nREkVT3ESCBHeQBLGvVdiqUPtb8+V+50cWgKqXCZFUEWABzC+CcSsPT13cei+Xk+eepUXcqw+RYZ8a8SmdpJ76lCuC+8wCDRku2lJuyJD7zHg5yUfKgzQk1IOAMJTUg4CGjOSdbH80g9PQaNcUwkDSrik4DY3tj8Yr2vQUbsOXHp50CJF40sGXHj2FeFK61nMT0m8jfQspZTT6bbSem2K6ov8WhG9FsfRSIj0L4VuI/lviH8TKbZGh7rZAymj0WpG8k+HbknWH4+t9fvBmyl6LsmehICl7FmWvxfODN8d033w3MugL+ai7byEvKOQlb76bJml9XlSDGfZ5u3jPfY193i6qQcJOzBWKrpUJ5WJDJZQ2uhYPJUlJWQqWM8F4dQcnZ37GeHUH1mUO8SqTpbDeYtvvz3H/v59i2+/PUVhvUSb+wpUng8viRoYuPnmWb3W1lOVHatSnXBFsNDL8Tq6LoqIyGUZbyr+T60osJ3ljeSV/2LOCkqowIUNKqsIf9qxIrH5z89Zr+I3vKmRKIY0Jg0wp5De+qyRWvylXtrCyeB86Jl5ooWOysngf5Uq8+o1aGMYYfgCh5cCtIrQcxvADidVvlNJKxDUPRzH1zlw0XvNwcvWbymq49vHI+96ejcZrH0+uaFNZDRsfi+Lo2zPRuPGxRN0zZgm7axNveCt4rtHHG94K7K5NnDHjcbiTU5DrcBjmcpE8ZelUgxkO+3twpUOWPK50OOzvuWoM+3J2M0FoE4RtpJQEYZsgtClnN3/RS0u5irCcCaYa7xCENppSIghtphrvXFbDfjMDOB19Lxw8Nif0vdguVuHiY+MRIrHxcPHZLlZdtvWmXPmkdep/DdI69SlXG//H6Cw7p30KioKpghNAMwy5q1fj/1qzOAzov/99QLMVeerPce749/5puh28VPZ5u3Clg7EgzOvc8Q36rV/gyj4/2s4EtfZBPL+GrpUpZzeTNZdng6+UK5Px6g6C0EZVLjh+zh0PVe65bOs4S42DTFCjTZksmxlgkORdqQ+CCd6Up2hhkyfDdrGKW9T0uviyk9apT0lJ+Uy4VR2hegg1BARCk4hA4lbj8e3bbxf85U9Cxus+LS0k7ysMhSrf/frl3fhz2uNYjX0EXhVVr5Ar3oCZHfrkX1zmWLJJlsV5GjoGloxXegI4GbbYJWeZkQ49wuRW0c1qJblZ14hvsdOvMyld+oXBXVrpV8ZpH7IdXmxZjPs+Q5rGQ/kc12bMi+p/WrLmQGrEp1xSvKCGCASuc5owtFGUDIrZj6c6n/zLnyODlC9qxHdyizrALcu0e3XKlUEafpOSkoLd1LiVLKZUaEmJKRVuJYvdjL/3t3oCTt3UxjdCzKaCb4ScuqlNq+fytSh32uPUZ98gDNooWpkwaFOffQOnPX7Z1nCpyIkCHu4imYdLTsTzR06GLZ4Px2hJn24MWtLn+XCMk2G8ItOIb/GUN0VTBvSi05QBT3lTjPjJzYMO2Q5/VatRDwIGVJV6EPBXtRqH7MtrFKWk/DooocBtH0dKH6FkkNLHbR9HCdNa7ilXL6mnPiUlhZUFQdXR2WZeKNlZdSV9hfgD8KfTDgPDUFkTwnxDnKoXyW8sLK1yzVnq7F+wVX09AwwSj+u3Gvto6BoTGR/+jp/8AAAgAElEQVRLqZELVQZsDa2x74r31g8razjs7wEiD72Hiydd1qrXxnR3yVlyaOTnO4jm0UBG8tUd3v6dfp0CGoX5ClIFVJCRPMlb/2LLoiQUSmqkX1JVCCL55+GtT0k5R9s+S7N1AM+vomsVCvnryGbihQ0AmrO7ma2/hxO2MJU83aVtFLq3xvQyjkfbs5HeHEIGSKEi9SwZ5yKddadHYeQtaExBsQ/W3gm9axJV3RNv45x6lcBroOpFzFX3YVzz1UTdg7bDC02LM17AsK7ycCHH5vT6+UI46tq86jQ4G/oMKhr3mUU2JOTlQeTUeKFlMeb7rNA0Hv6cdikvNalRn5JyhXGg7fB8vX3+IfFoKct12aXdbH5ztcb/t88DJCUD6i7UXPjOxvgt4pQdMGwu3uQraYJT9tI89Weps5MRMuiUyNDGYycj3MXamGE/G84wUlDQpUI2VHBFyPFcyNrmDAlFQ68oKmoPm7iRM+EolmySEwXWqtdSUeNlS2ekQzeLeyfkUJmRcW/6pHTpRe/QVZiUbkwXYNz3GVAX50gUFIVx30/U3zvn89PTHqcsyaqc4J+s1NnSdXU8Yg64p3kjOM2M9OgROnerK7nOSEhk/4w47XFazf34XhVNr5AvXH/Fv5Seo+VOMuscxgnrmEqJbnMTeaM/pte2zzJbexNVZNDUMkHQZrb2Jt1sjxn2zdndjFV/iYaOoeTwQ4ex6i9ZATHDXrbGMJwGnqYgFQXCEMNqIIOx+GKnR2H3M2DmodADTjM63vpEzLB3T7yNdfynCMVA0fOEgY11/KcAMcP+oO3wl3N1SorCkKZQC0L+cq7Od7tKqWF/mTnq2vyNNUtRKPQLdb4r/Sy/Q3fMsD9kO/ygWqOkKAzO71L+oFrjDyvlZW/Yp+E3KSlXEAfaDt+fblALwvMPie9PNzjQXlpIxA3dGv/2Bp2KKRhrQcUU/NsbdG7ojhtlqzIqdX9xgn3dl6zKLC1Jdj8TZNDJoiMQZNHJoLOfeLWKyZyBHoYYUkEgMKSCHoZM5q6O7rMVtYcb9FvZZtzLDfqtiQY9QI8wsVj8MmUR0CPiD55+YWARduiG9Ivkz2xI02iGi/WbYciQFj8n9s75/KdDDlVXMpyNdnn+0yGHvXPJLwBXEgfc0zzjj0QhTkKnJX2e8Uc44J5e0rxOe5za7BsEQRtVi4zZ2lUSQtZyJxmz3sUPbQxRxA9txqx3abnxpkvN1gFUkUFV55uRqVlUkaHZOhDTna2/h4aOppgIBJpioqEzW4931PbtWVQpyPsaeVcl72uoUuAnNfUbeSsy6M35jtpmIToeeSum6px6NTLo1QwCJRoVA+fUqzHdF5oWJUWhrCooQlBWFUqKwgvN5JC3lEvHq07jIl3pGzHdF1rR91ZSI92SqkbfW2v5f29XhxslJaWD4ydCdr4nmZyG/l64a5tYcmOkz4rlTFBrH8ILauhqmXL2WnIXSQ4M66dg4oOoi2ymGwZuQSnFS5k9X29jBnO03UnqwkOXOqbaz/N1LdlbP3cCTr8H1jTkeqN69l3xcp0AN7gnuaH6etRF1u+Hwa8B62J6/6TX5P85cpop7yyGYuOGGUJ9kH+xMdlzGY6+TXj8JXCrYFRQ1j2Isia+VV2jTXlyBK05gsBDopMtrKXWvzam6xpFGo1TjKsubVUjG/gMBT7FYrLx+97L7/Liu5LZeonuUp2Hbhdse+D2RN0dEy+xI2vR0AyKvss97Rz3DDyYqHtk5y5ee2eWiVaWgXybe+/oZuNdyRVqRk88x1HlOI4JpgMbwnWsueaxRN36x39Lrb2bQJeonqCc3UrpK78d07tVdPP8zAdw8gi5RhWrWMFavZF7euM9Du7SSjw1cxROnSRXncWqdNNctZpHejYkruGhfI7/98A4E8fb2C1BJi8ZWJflf70u7kn+6WkP5USNqb0e4zUDrexibtH5ab4r0Vs/eex9jozvoq66lAKDjUO30r/+IsUdjuyHV56D8TMwNAz3PwYbr0/WHTkIb74Ik2PQvwK2PwRrL1Iq8/B+ePk5GD8NQyvhgcdgU3zeN4LTZD2X/FwVPIe8bkJXhTfEaa4jfs67h3biHHuJIGigqkXM9Q9iXHtXTK/V3E/YrOE2DhDON7UziitoGfsTvfWzh97m1Om3aakO+cBk1cqv0n1tcshHa+/Pmau+g6P7mJ5GV+UO8lseT9Tde+gf+cgYp51TyVoBN7lDbLn2NxJ1P9r3C15W5pjK5uhrWzwQdnHTDY/E1+ocZs+BgBcPFpmxi/RkGjy0uUVmy+GYt97zqxwbUXnjMEy1CvTlm9y9yWf92mpsXids0XAFo7g0VZ1C4LGGgKIRzyEhCCH0kU4j+llVwMyCkuB8aEzh2HO0ggl8AzQX8uoAZia+7xd4DWzh0860CHQV1QvINhUyXjys54wXMOycQQuOIzQH6ZuU1XWcMeOljQGCPa8R7n8e6dcRWgnl+kdRb7w3UffY3h3smBtlwjAYcF3u6VrD+i3JVX1O7P0l79aPMm1q9Do+t5c2cM2WryfqPn/yFX6RDajrBiXP5ZG2yqOr70/Ufe2pEZ55KWDaztCbsXniQZV7vx2/XwOM7hzh7Z9PMDWj0tcT8NXHB1hzV7Iuez6GZ34CJ0/C6tXwxDfhxq8k644egndegukx6F0BdzwIa+KhimdDn/5qFUaOQLMOhRL5tRs5W4k3UxvzfXL1CSbCs6iGR+DqmMogY6Xln8SceupTrjqOnwj5H89Jmi1Jb080/o/nJMdPhJ/8y58Tn6VGclg/BaMvRN1eza5oHH0hkndwvDWNHZwhIECXGgEBdnCG463p+CLmTsCh56KmU9n5TrSHnovknZw9Dm//HbQbUOqNxrf/LpJ3MNzax0P6G+QUh7mwSE5xeEh/g+HWvvjfNvo2wcG/RwYWUi8hA4vg4N8Tjr4d061MjKK0jwA+Eg3wUdpHqEyMxnSdZoPjZgZPUciGPp6icNzM4DTjXpf3Xn6XH79QotU26So2aLVNfvxCifdefjemu2PiJZ4tBdiqRj7wsFWNZ0sBOyZeiuke2bmLH79k03A0+nJtGo7Gj1+yObJzV0x39MRz7M2O4KlguOCpsDc7wuiJ52K69Y//ltngI0JVoniCUJXMBh9R//hvY7qrP3qbR5/+EflWk9nuXvKtJo8+/SNWfxT/fNeOnuDbT/2EQrPJdG8vhWaTbz/1E9aOJpwPQHDoNO4700hbouQVpC1x35kmOBT3UB/cU6XxuiBoq6glj6Ct0nhdcHBP3DCbPPY+70+9hS18ioGBLXzen3qLyWMJZYKP7Icf/jnUazAwFI0//PNI3snIQXj6B9EDu3cwGp/+QSTv5PB++G//eX7eFdH43/5zJO9gxm2Rm5iAwAfdhMAnNzHBjBs3JN1DO7EOP00Y2CjqfGjG4adxD+2M6TqzR7DqI0jpowgDKX2s+gjO7JGY7uyhtzkw/iouHjnfxMXjwPirzB6Kf8+tvT9n3NqJrwQYnoqvBIxbO2nt/XlMd++hf+StrilcQ5BpB7iG4K2uKfYe+seY7kf7fsHfFFyauk5vu01T1/mbgstH+34R031v/xR/8+EmWp5Bt9mg5Rn8zYebeG9/vJnF8VGVpz5YQdMx6M02aToGT32wguOjceO74Soc0EwcoZIPPRyhckAzabhxU8ZoBmhzVUQQIjUVEYRoc1WMZjxM0LGr1LQJAlWiuhCokpo2gWMnvFgIn0aPQqgqKF5IqCo0ehQcEd+VWumcAfUgQvHBN6JRPRjJOwj2vIb/8Y+RgQVaARlY+B//mGDPazHdY3t38KQ1TkNV6HNdGqrCk9Y4x/buiOme2PtLnnVP0FKhx/ZoqfCse4ITe38Z033+5Cs8WdawVI2C72GpGk+WNZ4/+UpM97WnRvj+z0yarka3YdN0Nb7/M5PXnhqJ6Y7uHOEf/usMzZagpzug2RL8w3+dYXRnXJc9H8Of/keYm4OVK6PxT/9jJI9NfAh+9tfQqkPPYDT+7K8jeQeDc3O0Dn4Mjg35Ijg2rYMfMzg3F9PN1yfwjJMoSkDoaihKgGecJF+/vM3Lfh1Soz7lqmPne5JibnHH02JOsvO9y9eTodY+hKpkUJVMtKU8/3OtHb/ZMPEBaDnQcyBENGq5SN5BTp7F0xS0Lh/R56B1+XiaQk6ejc97+r1oLiMfzWvko+PT8a1qDr4ebTlni9H2c7YYHR98PaZ6qvoBm3WLf10c4f8s7+FfF0fYrFucqsbXGx5/CVQToeYQQkGoOVDNSN7B4PhePEXDVw0QAl818BSNwfG9Md1jtoVBiC4lSIEuJQYhx+z49uiL70oypk0+6yIE5LMuGdPmxYQOuDuyFkYYkAkDFCATBhhhwI5sfN7X3pnF7ZPsv6nAK7f1sv+mAm6f5LV34tv7R5XjqL5EDwUCgR4KVF9yVIm/NNXauxEBqIGCgkANFEQQyWO8/hSraxbf3H2Qf/na23xz90FW1yx4/am47mvPs9aV/N6xcf6XXYf4vWPjrHUlvPZ8XBd4btcYq2Sbe5wZHqhNcI8zwyrZ5rld8Zhk9YBLaErUbIgQoGbD6PhAPF7/yPguzEAhg44Qggw6ZqBwZDz+MsQrz0GxDKUyKEo0FsuRvJM3X4RCGQqlSLdQio7ffDGu+/JzUKx0zFuJ5B30zM5hZU1Q53ccVA0ra9IzGzcGnGMvITBQtAxCKNGIgXMsfr579UkUoSCEBgiE0FCEglePh6icOv02RqBiYCCEwMDACFROnY4b9XPVd9B8gRaqUYhKqKL5grnqOzHdj4xxVC/A8IjC2DxQvYCPjHgI0MvKHHnPpeD7CAEF3yfvubysxD+HF/evoGDY5A0PoQjyhkfBsHlx/4qY7hv7yuQMm7zpggJ50yVn2LyxL14GcnezRFb4ZBUPgSSreGSFz+5mPJk+f3A8uve2fMwZG7XlI4QgfzD+t7W8KUQgUUMRnb+hQASSlhd/CbGNEBFIlFAiIBoDiW3EnUabWh/TUk2mCjmmuqKxpZpsasUN1HD/8whhILT5e6WWQwiDcH/8+twxN0rB9yiGMgolCSUF32PH3GhM9936UfKeRz4g+vsDyHse79aPxnR/kQ0wgoBsGCCAbBgd/yIbfxF65qWAnOpSMAIURVAwouNnXorrvv3zCfLZgEKe+Wcy5LMBb/88wUh+5idQqUBXV3RtdnVFx8/8JK77zkuQL0X/CeXCz+/Er7f73nydRqFIo1AgFIJGoUCjUOS+N+PPuFzjNK7U8IQKQuAJFVdq5BpLC7m7HKRGfcpVx+T0RTqeJjizLxVeUEPpiGtWhIkX1OLK9ixoHW3DtWwk7+Da/Dh6JcBTBNIHTxHolYBr8wlxuNZ0ZMQvRM9F8k5qk5DpqG2eyUfyDlrCRu9IuNTRaQk7Pq9bBaWjuoCSieQd6IHF8IyFFoQ4moYWhAzPWOhB3KCeVnOEdZChACUaw3ok72S2XiKXWWxc5jIus/W4IdDQDIxw8UPJCAMaWjzu/JBe5PANeRxdpWAFOLrK4RvyHNKLMV3HBK3jma+FkbyTQJeIYHHVIREIAj3hpbQxDWbH32zmInknE2ciD9VC8sVInsAZS1Ds+DeLuuSMFa+I1GW5+IbACVWkBCdU8Q1BlxU36uuqiykXh+SYUqOuJiTsjp+BQseaC8VI3snkGOQ6yn7mCpE8Nu/pi8wbf2jf/eFR2oZJS1ORQEtTaRsmd38YN4qCoIFQF58rQjUIgvgOknQcUBSkkEgkUsjo2Innx7RUB112XHNSp5VQc93RfdRw8aNdDRUcPe5JbudUdG/xd6x7knYu7iWfyubIe4vnyHs+U9n4NXdirpuCaaMoISBRlJCCaXNirjumO9nKUxABQka+ByGhIAImW/FeC6OyRLVaIAxUdDUkDFSq1QKjMn4tZ8ZrlI85qJ4kyKqonqR8zCEzHr8H+2qI4kYvVyABgeJq+GrcUA90gWoFIEGqAiSoVkCgx68LX2+T0SWKkAShQBGSjC7x9XZMV/p1UDvulWomkncwYRjkg8VrywchE0b8PjVtauQ6cqByvmTajIfF1XWDTMf9LxNGoTixee0MOW3x+ZDTfKbteDWZqRmVXK5jDTnJ1ExCKNTJk1DueKErlyN5bBEXuean49f8hsMH+Z3RExQ9j8lshqLn8TujJ9hwOL6TpwiP/pkALQBXE2gB9M8EKOIilZOWEWlMfcpVR38vNFtQWPBMsKxIfrnQ1XLUvVBcuMGF0kFXE5qQZLqjkJuFBrjfjuQdGHmd1bLJZJDHkhq5MGBYa2HkE0pJ5nqjkBtjwQfhWZG8k3J/FHKTXWDo2K1I3kFeZnDxMBZUXfHwyMuE0mBGBQILFhrboR3JOzB9HTVss3b6wo3TFz5aGP/behyblqaTb154sLVUhR4n/mLRXarTapvksxeMRss26C7FH5ZF38VWtUUPNldRKfpxg7O6QUXYEnM+mdT0QtxAoboh/qAynSjkRl/wHPaVSN6JOh9ywwLDXqpRbH18wb1gNyG74MHmWJG8k4FhaNQiT/c5Wo1InsBwTlJzBOUFLx4NTzCci79crO6tozcCJpUcrUAnr3qskg2GeuMhKqX5kJvMghdDR/iUgoSE3aHhKDSmtGDNzUYk76R/xflY2fNYzUgem3flReaNx8hf18rBe8d444ZhZnImPZbDIx+diOQdqGqRMLAR2oVrQQYuqhp/0TODMq5lQUYglRARKoi2wAji94h8YOKKjmtOeOSD+Fuh6Wn4SoAWXjgPAyXE9OKP+6wVhdwYC2wVTxdkrbi3ta9t0dR1CguqH7V0jb52/KW7jM3YTJmBchNND/E9hYlagTLx63Mg26bhGBTlhX+z4aoMZOOG75DvMa3kcKsXnCB1IRgKE4ytfBeZmRaZhgrnksldG/LxOHnN1wkUH3XBORgKD82P33s0GwINVBuiFwAI1EjeyXSuQtF36F6wPk8Jmc7F739CK82H3iw4rwIbocVfWAbmQ26K4YVrsaUqDLjx+1Sv49PSIg/9OSxN0OvEX/JKnoulamQX3P9sRaXkJcybiUJuCsYFXcvX6M3EP4i+nijkZvEzWdCX1Ntk9eoo5KZrwfdUq0Xy2CJWRCE3+Y5rvjfpmh9mw9gYG5oL7kn1WuK9JGuruJrPyrkLn6+rBhju8u+YnnrqrwDG9sELfwJP/rtoHIuHLp/nzAF47k/hv/9v0XgmXkDgqueubYKGJWi2JKGMYuobluCubZev6Ug5ey1BaBOENlLK8z+Xs/EEHgZuAd+KDG4po9G3InkHwuihrNhcZ8xxuznLdcYcZcVGGAkJoiu3RXO5rWhetxUdr9wW1938taiMW7sBMoxGpxnJO1hVuQVX+Li4SCQuLq7wWVWJr1dZ9yAEThRTL8PooRU4kbyD7q478BWJL3ykjEZfkXR33RHTvTfsw1INWqqClJKWqmCpBveGfTHdh24X2E6GVttASmi1DWwnw0O3x8+He9o5XEXFVlRCogeaq6jc044bcPnVObAV3EBEn0MgwFYieQcbwnUEmsBTIq+sp0gCTbAhjCcil7NbkSoEakiIJFBDpBrJY3zt2+Ba0G5CGEaja0Xy2If2aGTUN2qR7rmf7300rgs8dusKar5CzYEwlNQcqPkKj90af2B+/d4cuifZKOa4szDGRjGH7km+fm/8s9g4dCuOGmLjIaXExsNRQzYOJSQY3/8YcxmHPZsFb2/PsmezYC7jRMmynWx/CEfUmO2eZnJVldnuaRxRi5JlO3ngMXxZxS6fpT08hV0+iy+rUbJsJ3c9zHVHj/OvdrzO//7Ky/yrHa9z3dHjcNfDMVVz/YMcbZb5wbGb+b/3fZ0fHLuZo80y5vr4+V5edQ94Dup0FeP0HOp0lIhbXhVPdly18qu4ahBdc3L+mlMDVq2MJ8p2Ve7A1yS+EiCJRl+TdFXi19FN7hCBruLqROewDoGucpMbT9R9IOyimslyslTmZLnCyVKZaibLA2HcSP696z2qTo5TU2UmJ/KcmipTdXL83vVx4/uRrWWavkHDVZFS0nBVmr7BI1vjLzf3d62ioejUhSBEUheChqJzf1e8qAB3fxs8e/7+F87f/+xI3kF+1b1IVRKI6JwMhIdUJflV8STVSuZGpC4IVImUUfy91AWVzI0x3UZuNar0kUT3NImPKn0aubiBqlz/KFK6SH/+XulbSOmiXB+/Pu/pWkNT02koglBKGoqgqenc07Umpnt7aQMtXaelMn+vhJauc3spniD/SFvFVVXaSrQr1Vai40facWP2iQdVrMCg6aqEoaTpRsdPPBjX/erjA7TaKs0W889kaLVVvvp4QuLpE9+EajUy7MMwGqvVSN7JHQ9GRn2rHn3H536+I6Gwwf2PRfe7+vz9rz5//0u4l2wrr8bXI0NeSomrBvh6JF/uqN/73ve+6DVccXz/+9//3h/90R9dln9rbB+89mfRpmChF+waHH0detZAscOJeuYAvPz96OdiD7TrcPhN6L0GSnFb56qlqyIY6oeJKZiaEXRVBI/ed3mr3+haAUOt4AY1/LCOphbozm9NrH4jzDIy2wftaXDmwCzByrsTq99Mah5SCsLQxsNHQyOvD9BrDrGODs9+tgL5fmhNQXsGMhVYd19y9ZtCF1RWQO0s1Keg0A03PQaDcaMzWxik4Bu07EksYZPFZH3lDroHbor/bZWVCK0MtVMIr4bQi6gbfzOx+o3Rs4FM08Vpj+NqPkag01/eTn5T/KHW37WOvqnTjMkms2aGsufxmNfFDQkvC8PrhulWjnNq0meuUaJcsPite9zE6jfXFNZhzo5yRnVoagb5wOOhlplY/WbEUFG1Nk7DxfJ0slrAyjUZ1q0cZFtmsTFbqWzEmJ6mxhyuCYYHm73k6jfmwI0oU9O47gShLlF9QZdxU2L1G4bWRRf72DFozkC+Ag/+C7jlgbhudx8MXxOFmEyORcff+Oew4bq4LtA32M01GY9Tkw3G2gp9WfjtO4e4fuv6mO7gqj56CtOcOeMwV8tSKro8/qjO1u3xufPdKyg7KvXGBA3VJR/q3Dhwe2L1m7mKwsGNAuw2mak6XleBibu3UBi6jqyy+DN2Mh61rlmk00attwkLGewt16AN34jWERLlmy5eaRLabai2oJgjvGkNYu3NKEaHZ1RYEJ4Epw1WG7IZWDcE626GzGLD89hZ+PGuXqQMKRsNLC/Hx9XNrFk/TE//Yl3dsjH2vI+rBfgZgeZA94wgu+FrUFp8LWd7V1JwNFr1cSzNISsN1g/fnVj9xujfhDln47bHcPUAI9DoK96ZWP2mv3cj5ukxpmhg51Qydsi21kBi9ZtW1zAHWzN4eLiKihFKShS4/Zo76VYXe7Q3bRiit3mcQzMw7efp0m3+zY0tHn80/uI/ODzAoDLNqek2k3aW7ozL/3Rbjpu2xaud9PSuYmVjjrF2jbOaQW8Q8ER5BZs23xnTZeX66J535ihYVciW4P7vwFfjL2PaiuvQGi5+7RSB5qEFOsXh+zFvjRuS5vBW1DNncd1JggxorqBbu5HStu/EdI+rCk6gonoNUDyE1PEza6kUV7OlY/dGGViDEEWYOQF+A6EVULc8kVj9prv/GlbMzXLWmmPSMOj2fR4vrUqsflPpX8vAzBxT9iwzGZ2KF/L13LrE6jcbymvJTB7jpBrQ0A0KvsdvNUmsfrPm+i56g2mOnwiYcTJUTJff/Y0wsfpNZXUXg90Ok6NNpmdVusohD/7TvuTqNwMDsG4dnBiFU6dgcBD+4A+Tq99UemFgFUyegZnx6Pj+byVWv6GnD1ZeA2On4OwY9PbBE7+dWElrqK8fc9Zjot3AzoRkXJXt+TXccv2W+LyXgf/wH/7D+Pe+973vfxpdIeXlSx68Wrjtttvk++8nVGq4BLzwJ9CuRvbZOc4dP/zvF+s+96dg1SC34Nlx7vix//myLDflEjNOnTc4QQaNDBo2PjY+d3MNQwmdV1MiToVNPpQzzODQg8nNoodVSuGTf/FXcNix+S+tCaRhgeJDqCHcHP8yP8AmM7lLYcpnY4/3AS4OxoL8FFc6GJjcqC82EGenXo5qvqsXQjPOHXf3LX7JsU8+h/QtxIJQh3PHmdUdL1r7ngavI4zNbYGehxu+tUj1z/9yF/W2oLQgRabehlJW8m++27ET8fxfgtWA3ALj7tzxo9/9VR/LF8JfW5M0woDigpKQ545/PxcP00uB0cDiH7wJ8kIlh4pFQEsG/JY+wJqE/J+Uz04tmGbcH6Etm2RFgSFtLWU1Oda2GswwFoye112hrrloD5DlhBBil5TyIjV/F5OG3yxz5k5DpsNWy5QieSezZxaHREN0PJucB5dyBTJEibu5hiw6NWyy6KlB/wmcCpu8IMewpE+3NLCkzwtyjFNhc0nzZnWfwYKFqoS4oYKqhAwWLLIJCYmXkpHRkB89GfCnfxbwoycDRkYvX+nWS02LFnpHx1wdgxbxWH3fq6J0JGUrSgbfiydlS2cW1I7kdDUbyTtpXyThvB1PRh6vKRTMxY6ygikZryU8aufOQrYjGTSbj+TLkInQIy8W/x15oTCRFM+eAsAaNcdv6QMUhMYMHgWhpQb950gtmOaYuxtPOmTI40mHY+5uakH82qwGMxzxP8ad13WlwxH/Y6rBzBew8ktHmii7zOlaGffU2/VI3kn3cNxT325E8pSrhyFKqRH/GfhQzpCXKjkR3e5yaCDhQ2ZYxa/vrf9AzjKomKxfUEWiJX0+kLNLmvezMDIa8vTPJIW8pLcnShB/+mfwrd8MWbvmyvfZ5MlHnnoueOo9XPLEK6NoeiXmqQ9DG01PSEo0u5F+Z1JiG2HGk9PJ9sY99Z4VyTsYKocxT33TEQyVE160ugbjnvp2K5IvQwYUPfLMiwue+pYMGVASkvRTzrNGzaVG/CVi3B9BFyb6/E6ePn+fGPdHYt76sWAUHfP8rp+BCWIIEoUAACAASURBVDKSXwne+k/LlX/Xv8rZ8o3IUG9X5/MXq9Hxlm/Edbc+Gv0/qxbpnvt5a3IeXErKl4IZHLIsTt7KojJDQumZzzhvrmPe3Ocw72fhzXcig35hT4ZCXvLmO1dHWOVK5ZooKVQ6UcKadHBxWanE80LyheuRQZsgaEfJjkEbGbTJF+Ixs1r3VvCt+WREGRn4vhXJOxm+LTLqFyWctyJ5B/ff0UfDUai3o+TiehsajsL9dyQkNd14b+R1seaT061GdHyRDqJfNF/TizRlQCMMouTMMKApA76WUML1s3IyaPGUd5q/8I7xlHeak0FCh9h5asEMB9z3+cD5JQfc96ldZZ7WlE9PWzbROnbyNAzaMr4L25bNxF2/JN0rmdSoX+asuAHu/ePIU18di8Z7/ziSdzJ8HTzwR5Gnfm48Gh/4o0iekvJlpQeTNotLp7UJ6CGhSPxnnNfqmNf6HOb9LCyHngyXki61h83KFgxMLCwMTDYrW+hK8KyZ2SHK3XejqlkCv4aqZil3342ZjVdy0QrD6CseiGLq3TmElkNf8QBaIWFbs7IaNj4WxdC3Z6Jx42ORvINNW1bznUe6KWUlEw2FUlbynUe62bQloWrG8Aa477cjT311Mhrv++1IvgxZr2f5Z5keiorKlPQpKir/LNPDej37yb/8KzgZtPh5OE4Lnx4MWvj8PBxPNOxrwQxHvd3zIRQFXOlw1NudGvZfUrKigM/icps+LlkR3ynNigJeh653Ed0rmTRR9tfgcibKpqSkLI1zMfV5qZJFpU1ASwQ8LFYsKVn2VNjkF3KMnNTOJ8FZwueRJc77WfjRkwHNVuSpP8e549/958u/pnJKylPeaVr45MXiMLY8Gt/WF8eZHnDfjxKlOxOnhcl1xqfKI0z5BI77bd7w6kyEHgOKzt16iXWdzRGXCedi6nVhomHg4+JJh/XG1lj4zbmYeh0THQMPFw+HjdpXEsNvxqmznwmq2FTIcD0DX1jYa5oom5KSkjLPKqXAw2IFOaExK1xyQluyQX9u3kfECvLz8+aFdlkNeoDtdwiarcU9GZotwfY7Ll9PhpSUpTB9kTC26YQwtrZsXCSEIt6x97PS8Kc4br3F/uYLHLfeouFPLXnOK43jfpun3VEscZRu7TCWOMrT7ijH/XgjsOVAWe1lvbEVXZjYtNCFmWjQA1TUHjZqX8GY1zWE+SsN+jcYpY1HGZM2Hm8wyjjxhoXLjTRRNiUl5apnlVK4JMmrl2reT8vaNQrf+s2QN9+JQm76e+Hh+8VVkSSb8uWgFzPy1C8wRywCehPC2LKieL6k6TmiEIqlxfU3/ClO2rvQMDFFAS+0OWnvYnXmVoral6fJyw7vDEV1DA0dyKALn6I6xg5PZ522PMPCymrvRUtYdlJRez5VUux+JsiikZ3ven1u3M/Esi9SkRr1KSkpKVcwa9corF3zRa/i0nFGNviYaeaw6SLDV+hleIlGHMChswEvHgwZr0mGyoKHNitcO5iGLF1utild/DwcB8mFWu743KfEjekV6lqOersBzodQ+NJhjbZ5SWuYco+iYaLPl0TVRQbCSP5lMuobIjJmBecqGumoSBpiAlieRv2loIpNueOlMoNGFfsLWtGnJ3XnpKSkpKQsS87IBr/kFBYeFUwsPH7JKc4sMdzi0NmAv3rbp96WDJSg3pb81ds+h84Gn/zLKZ8rq9U8jytD5NGYwSWPxuPKEKvVeNnSstrDBn3rfAhFE0OYbNC3Ul5iSUI7bKCJxUacJkzscOlhPVcSeeHiy8Uvtr5UyQv3Ir9xdVIhg83ifiM2PhWWf1PB1FOfkpKSkrIs+Zhpsmjk5j2H58aPmWaYX99b/+LBkJIpKGWj3INzdeVfPBim3vovgNVqPtGIT6Ks9izZiO8koxTxQjvy0M/jS4eMsvQdoSuJ1WqZA34dpEAXAk9KPHw2qOVE/cPjAa/sDRmfg6EuuH+LwqahK//6uZ4B3mAU4Hzn9jY+t5LQIGiZkRr1KSkpKZeBajDDmXAUSzbJiQLDypXRovyLZA6bSsc2eBaNuSVug4/XIg/9QgqZSJ7y+XCWOgc4Sw2bMhmuY5DBZRqP3Gds4KS9C8LIQ+9LBx+HFcaWL3ppl5WblbW0tb1MBgGWFOSEZFhVuVlZG9M9PB7wwx0hxSwMVKDehh/uCPnOPSQa9m3nLI3WQTy/hq6VKeY3kzWTG601q3uYqb+HLS0yIkdPaRuFyo2Jujsmj/C8O05dhVIAjxpD3NO/MVF3fGY/exsHqCouldBgS/E6hnrifSyGKLEx7OctTlPHpkSGO1nJkLI8z9+FpOE3KSkpKZeYajDDYX8PrnTIzrcoP+zvuepalH/edJGh3bEN3sana4nb4ENlQbPjvaBpR/KUpXOWOm8yQhuP0nz1kDcZ4ewyrR5S1PpYnbkVXcngyCa6kvnSJckC9ChdbFe2cJNW4Su6zk1ahe3KFnqUrpjuK3sjg76UjRrflbKCYjaSd9J2zjJTe4sgaKOpJYKgzUztLdrO2Zhus7qH07VX8aSDIbJ40uF07VWa1T0x3R2TR3gyHKctoOBDW8CT4Tg7Jo/EdMdn9rPD2k1b+JRDg7bw2WHtZnxmf0z3dNjkI1mlR1a4Xg7TIyt8JKucDpd/o6rUU5+SkpJyiTkTjqILY3GL8nl5kre+4U8x6R3DDhtklCL9+vovnYEB8BV6+SWngMhD357fBv8q8YZSn4WHNiv81dvRy0IhExn0dUfy7Zuv/NCBz8qIb/FmUGMy9OhXdLarZdZquU/+xV/BAc6SSagecoCzy9ZbX9T6rtprbCSweMuvMiU9+oTOnVqFtWryd9yjdNFD3IjvZHwu8tAvpJCJ5J00WgdRRQZVjeLcVDULQSTv9NbP1N9DQ0ObT1rWRAZCm5n6ezFv/fPuOIaArBQgICuBQPK8P849LPbW720cICuU6FwU8+dk6LG3cSDmrf+IGXJo5OZ7J+TQQEbylV9gtbNPQ+qpT0lJSbnEWBdpUW4ltChv+FOccD7EC5358noOJ5wPv5R1s4dFka+zihw6VRxy6HydVUuufnPtoMoffFWjlBVM1CNv4x98VfvSxdOP+BZPe1M0ZUCv0GjKgKe9KUZ8a0nz1rDJdPgMM2jUroDqIVcbI4HFM+4kTRnQg05TBjzjTjISLO07Huoiebcr4X3A82soyuLdNUXJ4Pm1mK4tLVSx+F6pCgNbxtdbV8Hs2Bgww0jeSVVxyciOc1JqVJV4EvAsNtmO3glZVGavgPM39dSnpKSkXGJyopBYXzuX0KJ80js2X14v0tWFCWEkv1o8iaPnPYcufcLgTq3Cmot4DodFcUlJsRfj2kH1S2fEd/JmUKMgVAoi+hwK84bMm0FtSd76MhnaeOc99BBVDylfAdVDrjbe8qvkhUph3utcmDf73vKrF/XWfxru36Lwwx0hIM/vdjXa8MS2uK9Y18oEQfu8px4gDG10LZ6AmxE5POlEHvp5AumSEfG1lgJoK/Me+nkcJZJ3UpkPuVl0TgqfSmjEdLvJYOFFHvp52gR0XwHnb2rUp6SkpFxihpU1HPajmNDzLcqly1r12piuHTYwO4x9TRhXTXm90XnPYV4o855Dn2fcSZ4w+i9q2H9abPsszdZ+fL+GppUp5K8nk7lIMt7sbqq1d3HDFoaSp1K+nUL31uSJp0dh5C1oTEGxD9beCb1rElXnJj7i9NwuWqJNXmZZ2XUrXQM3Ja/hzPvMTryJQwuTPN0D2ykMf6pu8J8Lk6FHr1hsBuRQmAy9Jc17HYO8yQhwoXqIjc8trFrSvCmfnSnp0bPAkIWoH8CUXNp3vGlI5Tv3sKj6zRPbkqvfFPObmam9BUHkoQ9Dm0DaVPI3x3R7Sts4XXsVQhtVGATSxcdnsHR3TPdRY4gnw3EIJGYYGfSuCt9U4uF5W4rXscPaDaFHRmrYwqethGzLXRfTvYkeXuI0yMhD3ybAwmc7yfeS5URq1KekpKRcYipqD5u4cVH1m7XqtYnx9FF5PSfy0M/jS/eqKa8XeQ6VDs+hz1t+dUlGvW2fZa66E0XJoM4n481Vd9JVuStm2DdndzM59zKKMNCVHL50mJx7OVpPp2E/PQq7nwEzD4UecJrR8dYnYob93MRHHKy9joFGTmZwcTlYe53NEDPsm2feZ2ziRTQ0DLL4OIxNvMgKuGyGfb8ShWMUFoQaWIT0K/qv+K1PZpAS21m7qPrNLaxatvH0VzN94tx3vLhjb59Y2ncMkWH/aUpYZs1Besp3Lqp+U8nfnFj9plC5kZWwqPrNYOnuxOo39/RvhEl43h+nrkUe+m8qydVvhnqu5x5YVP1mWy65+s1KpcCD4Uo+YoZZbLrJsJ1BVirLO54eUqM+JSUl5bLwaVuU9+vrOeF8OF9ez8CXLj4Ow3r84XMlMiXdi3gOl9bgptnaP2/QL0jGm5d3GvXV2rsowkCbD3HShIkfRvKYUT/yVmTQm/MP9HPjyFsxo/703C4MNIz5mGADA8JI3mnUz068GSUEzr+8aZggI/nlMuq3q2We9qJcjRwKFiFNGfCw1r3kuQcppUb8MuBOrcIz7iSwoGOvDHhIv7zldLPm4EVLWHZSqNx40RKWndzTvzGWFHsxhnquTzTik1ipFJZ9UmwSaaJsSkpKyjKiqPVxjXkzumLOl9czuca8+aqJp+8TBhaLg14jz2E8tvWz4F8kGc9PSMZzw1ZiMp4btuITN6bA6NhBMHKRvIOWaKN3vLDo6LREO6br0ELt0FXRcUhYwyVirZbjW3ofBaEyLX0KQuVbet+Sq9+kLB/WqjmeMPopCJUZPApC5Qmjf0nx9CnLl9RTn5KSkrLMuJrL613wHPoLPIchD+mVT/zdX4V2kWQ8LSEZz1Dy+NI57yWHKBnPUBK6mhb7opAbc4HXzrUieQd5mcXFjTz083h45GU2pmuSx8eJPPTn1oCHyafrrPp5sVbLpUb8Vc5aNZca8V8SUqM+JeUSceRMwKt7Qs7OwWAX3Hejwsbh5PjDfS2XZ+ccTjsBK02Vb3SZ3JBfmucyJWU5smbec7iw+s1D+sWr33xaCvnrmavuBC4k44WhTbl0a0y3Ur6dybmX8UPOJ+OF0qVS+Vp84rV3RjH0EHnoXQucFmx+KKa6sutWDtZehzDy0Ht4uIrPuvKdMd3uge2MTbwIMvLQB3j4+PQPfH1Jn0PKr8cENQ4zTg2LMjk2McQA8RdCgMOOzSvtFuOBz5CqcX82zyZz+VdGSbn6EVKmbbE/K7fddpt8//33v+hlpCxjjpwJ+NGrIaUs5LPQakdttH/3vrhhv6/l8mfjFmVNUFIF9UBS8yV/PJRLDfuUlM9AWv0m5ddhghrvcpQMOiY6Dh42HrezIWbYH3ZsftioUlQUCkKhKUMaYch3ipXUsE+5JAghdkkpP9WNIfXUp6RcAl7dExn0xVzUdr6YA5C8uieMGfXPzjmUNUFFi1JcKpoAQp6dc1KjPiXlM5DJDF7UiO+k0L314kZ8J71rLmrEd9I1cNNFjfjYGoZvS434ZcBhxsmgk5kPmzo3HmY8ZtS/0m5RVBRKSnQfL83X+H+l3UqN+pQvnDRRNiXlEnB2LvLQLySfjeSdnHYCSqpYJCupgtNOQgeNlJSUlJTPlRoWZkfSsolOjXgX0/HApyAWm04FoTAe+Jd0jSkpn4bUU5+ScgkY7IKGdc5DH9FqR/JOVpoqVT+c99BH1APJSvPL3e0yJSUlZSlMySrHOEMDiyI51jNMn4gnZJfJYeOe99ADOHiUied5DKka9TA476EHaMqQITU1p1K+eFJPfUrKJeC+GxXqbWhYklBKGpak3o7knXyjy6TmS6p+SCijseZLvtFlJsyckpKSkvJJTMkqH3IYB5cCWRxcPuQwU7Ia093EEFZQo+Yep+4couYexwpqbCLemfT+bJ5GGFIPA0IpqYcBjTDk/uzlrVqUkpJEatSnpFwCNg6r/O59CsUcTFYjj31SkizADXmDPx7KUdEUxtyQiqakSbIpKSkpS+AYZzDRMTEQCEwMTHSOcSamawY+3X4bRUpcFBQp6fbbmAkhNZvMDN8pVigpKhNhQElR0yTZlGVDul+UknKJ2DisXrSEZSc35I3UiF8mtNxJZp3DOGEdUynRbW4ib/Qn6vrNM/izu5HOLMLsRuveilYYTtQ9PXeI3dYRZhWP7lBna24jK7uuTV7E9AgcW1BtZf2d0Ls2UbU2+zFna7to0yJLnsHyrZS7v5KoOxvOckKeoiVb5EWea8QqupWLdw8N66dg4gOwZyHTDQO3oJRWJSvPnoBT70JrCvJ9sOp26L4mWXdyBI7uhPoklPphw13Qn/z3MX4M9r8O1QmoDMD1X4Oh9YmqDW+Kae/o+e+uV99AUb866/2n/GoaWBRYnNhkoNNIiJMfC0YpUqBnQd8CVzqMBaOJXaA3mZnUiE9ZlqSe+pSUlJR5Wu4kY9a7+KGNIYr4oc2Y9S6t+TbrC/GbZ/DGXkb6FhhdSN/CG3sZvxn3BJ6eO8TL9gEsArpCHYuAl+0DnJ47FF/E9Ah8+EzU8KjQE40fPhPJO6jNfszx2g48HDJk8XA4XttBbfbjmO5sOMu+cD+OdMmRw5Eu+8L9zIaziZ9FWD8Foy+AZ4HZFY2jL0Ty2OQnYP+z4LYg1xuN+5+N5J1MjsD7T4HdhGJvNL7/VCTvZPwY7Pw7aDeg3BeNO/8uknfQ8KY4be/Cm//uvNDmtL2Lhhfv/Jpy9VMkh4u3SObiUUyIk2/LJjqLnSo6Bm3ZvKRrTEn5vEmN+pSUlJR5Zp3DaCKDpmQQQqApGTSRYdY5HNP1Z3eDlkNoOYQQCC0HWi6Sd7DbOkIuFOSEhhDRmAsFu60j8UUcewvMfNTBVCjRaOYjeQdna7vQ0dCFiRAKujDR0Thb2xXTPSFPoWNiCgMhBKYw0DE5IROMdICJD9jnDvAnZ6/j3x3fzJ+cvY597kDkue/k1LvRGo08CBGNZj6Sd3J0J2QK0X9CufDz0Z1x3f2vR/8vW4x0s8XoeP/rMdVp7yiqMNHnvztdyaAKk2nvaPLfl3JVs55hHDwcXCQSBxcHj/XEd9KyooCHu0jm4ZIVhZhuSspyJg2/SUm5wthvufxjrc0ZL2BYV/mNcpbrc2nozueBE9YxRHGRTBUmTliP6UpnFoyOckZqNpJ3MKt4dIWLS+ZlUZlVvJgujanIQ78QIxfJO2jTItMRYqCh06YV023JFrkOL6WBTkvGdQH2zfr82cy1lLWAFbpD1df5s8kN/HGwjxtjk09FHvqF6LlI3kl9MvLQL8TMRfJOqhORh34hmXwk7yDpu9Mu8t0BNL1JZpwLoTo95gYKenKY1aWi4U8x6R3DDhtklCL9+nqKWhou9HnQJyrcLDctqn5zPWsTq9+sUNdwxP8YZOSh93DxcFijXiQ8LiVlmZJ66lNSriD2Wy5/MdWkFoQMaQq1IOQvpprst9xP/uWUT8RUSgTSWSQLpIOplGK6wuyGoL1YGLQjeQfdoU6bxX0H2gR0dxj6QBRD73bE/bpWJO8gSx6/I8TAxyNLvBJHXuQTwxHyIrlqx7Ot9ZQVm4rmowioaD5lxebZVkI8e74vCs9ZiGdF8k5K/eB06DpWJO+kMgB2x0uH3YrkHZhKCb/ju/Mv8t01vUnG2u/jSxtDKeJLm7H2+zS9hBeLS0TDn+KE8yFe6GCKAl7ocML58P9n786jLK3qe/+/v6equ6auqaGhB7qZ5ynRUhkiswQcEHGIWfkpJjdBYyRXhKzclWjMeG9yLwk34o1eTa5odDnEKEoEJDIrgjYmDCIgMjQ0Dd3Q1TVXdVWd/fvjOUVXnTrVVdVdw3m63q+1ztrnfM9+nrOr0e7P2bWf/dAz4nKhubIq2jgljucN8RpOieMrBnqAtpr9OLL2JJZHHYP0sTzqOLL2pIrr6aVqZqiXcuSmrgFaa4LWmgKFyNrWmuCmroHpD9a0VtYdxUgaZKQ4SEqJkeIgI2mQlXVHTepbu/JkGOknjfSTUsrW1o/0Z/UyJzceSX8h0Z9GSClr+wuJkxuPnDyIw0+Fob5sLX0qZu1QX1Yvs7r11QwzwnAaIqUiw2mIYUZY3frqSX0PjvUMM8RQ2klKiaGUzUYeHJUvfH2usI6WGIDiMJCgOExLDPBcocKFwOtfm41xZx+klLVDfVm93BGnZ+voB0s/39jzI06f3Pe412fvDfRkfQd6stfHvX5S1/2XHcFoGmK49N9uuDjIaBpi/2VHTOr78tAT1JQts6qJel4eWrilOluHf0EtdSwr1JWWC9VRSx1bhydfL6D511azH8ctfzWvrjuT45a/2kCvXDLUSzmyeXiU5sLEu882F4LNw959di40LT+AtY2vpbZQz87UQ22hnrWNr624+03tinUsW3tutpZ+ZydR28iytedW3P3moPajObf+WBqpobMwTCM1nFt/bOXdb/Y/FH754mwtfe/LWfvLF1fc/aZ15Ukc1noGy6hjkAGWUcdhrWdU3P1mZWElxxeOoy6W008/dbGc4wvHTbn7zfr2ZrqbDofCMhgZhMIyupsOZ3178+TOKw+G496craXvfylrj3tz5d1vDjgUOt6erY3veSlrO95eefebNYfD6e/K1tJ3bcva099Vcfeb5mWrOKj+1Swr/bdbVqjnoPpXV9z9ZqjYTU1MvA/EVMus5stgsYfamLhsrjaWM1jsWbAxSNq3REppsceQOx0dHWnjxo2LPQwtQX+7pYuu0SKtNbu+j4+9vnJN6yKOTPuan24f4dpHdtJWF7Qsg+5h2DGUuPy45Ry/Mt+XYz3Tew8jaZDawq5tCUeKg9RGPQevOG1BxvCLgXsZLg6xrLDry8XY68MbTlmQMUiqfhFxf0qpYyZ9namXcuTC1ga6RhNdo9ndZ7tGi3SNJi5sbZj+YGkWjl9Zy+XHLadtebC5P9G2PPaJQA+wX90RjJYtsxpNg+xXN3mpznw5YNnhjDDEcHGotFxoiBGGOGBZ5T34JWk6ztTvAWfqtZjc/Ubae+5+IykPZjNTn/8pF2mJOa5xuSFeufbU00XuuS+x9SU4YH847XXBoYcs7C+OVyw7YMFDfLnm2lWGeElzxuU3kqQF89TTRb5xQ6K3L7H/fln7jRsSTz1dXOyhSVKuOVMvSXvo0YEhbu4d4PnhEdYuq+WCFQ0c01A3/YFL2D33JVY0JVY0Zbs4rWgCSNxzHxx6yCIObB+yqdjHxuJ2XmYn+7GcjsJKNhQq349A+77Hhwa5daCPLSMjrKmt5dyGJo6qq5/+QOWOM/WStAceHRjis9t76B4tsqa2hu7RIp/d3sOjA0PTH7yEbX0JGife2JbGxqyuqb1IF3fxGDfwn9zFY7xIV8V+m4p93DS6hb40wsq0jL40wk2jW9hUrHznYO3bHh8a5As9XXQXRzmwpobu4ihf6Oni8aHBxR6a5oGhXpL2wM29A6Wbf42/EViBm3u9EdjuHLA/9JfdULa/P6urshfp4j6eZJBhWqhnkGHu48mKwX5jcTuN1NAUtUQETVFLIzVsLG5fhJFrsd060EdzIWgp1FCIrG0uBLcO+CVvX2Sol6Q98PzwSMUbgT0/PLJII8qH014X9PYFvX2JYsrW1Pf2Bae9LqY/eIl6jBeoZxn1LCOIV54/xguT+r7MThqpmVBrpIaX2blQw1UV2TIywoqYGPVWRIEtI/49tS8y1EvSHli7rJae4sQtgXuKibXLvFRpdw49pMAlbwlWNAUvvZy1l7xl4Xe/yZMuBqgruwSujlq6mPxbof1YTj8T7zDdzyj74Y5ZS9Ga2lp608SL0HtTkTW1/j21L/K/qiTtgQtWNPDZ7T1ANkPfU8xuBvauVi9InM6hhxS8KHYWWmlgkGHqWfZKbYgRWpl807mOwkpuGt0CKZuh72eUfkY5s7C423dqcZzb0MQXerqAUVZEgd5UpKeYeFuTf0/ti5wakaQ9cExDHb+zspmWmgJbRkZpqSnwOyub3f1Gc+5oVjPIMIMMk0ivPD+a1ZP6big0cWHNGpqilu0xTFPUcmHNGne/WaKOqqvnvc2ttBRqeHF0lJZCDe9tbnX3m32UM/WStIeOaagzxGveHUgrr+MwHuMFuhiglQZOZj0H0lqx/4ZCkyFerziqrt4Qv0RUTaiPiL8BOoCjgP2BAeAZ4HrgkymllysccxrwUeAUoAH4OfD/gGtTSqPl/UvHvBm4CvhloAb4KfAPKaXPz/XPJGnftrP/eQa7HmZ0uJOaZe3Ut57A8sa1iz0s7YMOpHXKEC9JUF3Lb64AmoB/B/4e+BIwAvwp8GBErB/fOSLeCtwFnAF8E/gksBy4BvhKpQ+IiA8BNwAnAF8EPgusBa6LiKvn/CeStM/a2f88vdvupDg6QKG2jeLoAL3b7mRn//OLPTRJ0hIUKaXpey2AiKhPKU26G0JE/BXwR8CnUkofLNVagCeAVuD0lNLGsXMAtwGnAr+eUvrKuPMcAjwK9AGvTik9Xaq3Az8GDgdOSyn9cLqxdnR0pI0bN+7xzyop/7q33JIF+ppdFyuOvW5Zc/4ijkyStK+IiPtTSh0z6Vs1y28qBfqSr5GF+iPH1d4BrAK+MBbox84RER8FbgV+l4kz9r8F1AF/MxboS8d0RsR/B/4J+AAwbajXvuXJTUW+vzHx4ktw4P7wKx3BYRsq/xLroZ5hvrV1iE2DRTbUF3jrAXWc2LysYt/Hhob4Xl8fz4+MsLa2lvOamji6rvL6601PFPnxnfDSi7D/gfCaM2HDEdX0izSVGx3upFDbNqEWhXpGhzsXaURzr3d4Ky8PPcFQsZu6Qgv71R3BimXuoiJpdh7sGuH6LTvZ1F9kQ2OBi9cs56TWyhH0ZwM7ualrgOeHR1i7rJYLWxs4tsEtWWciD6nhLaX2wXG1c0rtzRX63wX0A6dFxPgEtbtjbirroyXiyU1F/uXGRE9fYtV+zga0KwAAIABJREFUWfsvNyae3FSc1PehnmH+9zMDdA4n1tUV6BxO/O9nBnioZ3hS38eGhriuq4vu0VFW19TQPTrKdV1dPDY0NKnvpieKfOfL0NcD+63K2u98OauretUsaycVJ85FpOIgNcvaF2lEc6t3eCvPD2xkJA2yvNDMSBrk+YGN9A5vXeyhScqRB7tGuOaJQTp3FjmoIejcWeSaJwZ5sGvyDbB+NrCTz2zroWu0yOraGrpGi3xmWw8/G/DmaTOx16E+Io6JiCsi4v0RsddX8UTEVRHxpxFxTUTcDfwFWaD/63Hdji61j5cfn1IaAZ4i+y3EYTM8ZgvZspyDIqJxb38G5cf3NyZWNCWam4JCBM1NwYqmxPc3Tl6W9q2tQ7TVBm3LChQia9tqg29tnRzUv9fXR0sELTWlW3PX1NASwff6Jt+a+8d3QlNz9ojCruc/vnNefmTNkfrWEyiO9lMcHSClRHF0gOJoP/WtJyz20ObEy0NPUBP11BbqiQhqC/XURD0vDz2x2EOTlCPXb9lJ2zJoX57929m+vEDbsqxe7qauAVpqCrTWZH1bawq01BS4qWvyjdY02YxDfUT8SURsiYiV42rnAf8BXA38A/CTiNhvL8d0FfBx4MPAr5DNrJ+fUto2rs/Yl4euKc4xVh//u/GZHlPxi0lEXBYRGyNi47Zt2yp1UQ69+BI0lX2Na2rM6uU2DRZpqZ14K/uW2mDT4OQZ9edHRlhRKLs1d6HA8xVuzf3Si9BYtvtcY1NWV/Va3riWFavOpFDTQHFkB4WaBlasOnOf2f1mqNhNTUxcLlYTdQwVuxdpRJLyaFN/kdZlE//tbF0WbOqv8G/n8AjNhYl9mwvB88OT/+3UZLOZqb8QeDSltH1c7X8AiSyEfwo4FPivezOglNLqlFIAq4FLyGbb/yMiXrU3591bKaXPpJQ6Ukodq1atWsyhaA4duD/09U+s9fVn9XIb6gt0j0ycwe8eSWyon/x/o7W1tfQWy27NXSyytsKtufc/EPrLJvD7+7K6qtvyxrW0rDmf9g2/Rsua8/eZQA9QV2hhNE38LdRoGqKu0LJII5KURxsaC3QNT/y3s2s4saGxwr+dy2rpKU7s21NMrF1WNZeAVrXZhPpDgJ+NvYiIdcCryfZ4/8uU0ofIdp65eC4GllJ6MaX0TeB8YD/gC+Pe3u2s+rj6jj04ZqqZfO2DfqUj6O0LevoSxZStqe/tC36lIyb1fesBdewYSewYLlJMWbtjJPHWAyZf/HpeUxPdKdE9Okqx1HanxHkVbs39mjOzdfR9PZCKu56/5sx5+ZGlGdmv7ghG0yAjxUFSSowUBxlNg+xXd8RiD21J2lzs4cbiU3yp+Cg3Fp9ic7FnsYckzcjFa5azYxg6d2b/dnbuLLJjOKuXu7C1ge7RIl2jWd+u0SLdo0UubG2ocGaVm02obwfGz9KfTjZL/2/javcDG+ZgXK9IKT0DPAIcHxFj86ePldqjyvtHRC3ZbwxGgCfHvbW7Y9aQ7ZH/XEqpv/x97bsO21DgnW/M1tJvezlr3/nGyrvfnNi8jA8f3ED7smDzUJH2ZcGHD26ouPvN0XV1vK+1lZaaGl4YHaWlpob3tbZW3P1mwxEF3vTr2Tr6l7dl7Zt+3d1vtLhWLDuAtQ0d1EY9O4s91EY9axs63P1mEWwu9nAbz9HPMG0sp59hbuM5g71y4aTWWq44op725QWeG0i0Ly9wxRH1FXe/ObZhOZetaqa1psALI6O01hS4bFWzu9/M0Gx+n7ENWDfu9dnAMHDfuNpy5mdHnbHfaY/dJfY24DeAC4Avl/U9A2gE7kppwu+ObyP7InIBk7etvHBcHy0xh20ocNgMv4qe2Lxsyi0syx1dVzflFpblNhxRYIMToKoyK5YdYIivAg/wEg3U0Ej2d89Y+wAvsY7mxRyaNCMntdZOuYVluWMblhvi99BsAvh/AhdFxAkRcQTwa8D3U0rjL0k+BNgy20FExFGVds6JiELp5lMHAPeklMY2gP468BLw7ojoGNe/HvjL0stPlZ3uc8AQ8KHSjajGjmkn2wcf4NOzHbskSfOpkyEayubgGqilk8k7b0laumYzU/8/gduBB8bV/nbsSUTUkM2E//sejOONwP+IiO+TbUf5MnAgcCbZhbIvAL8z1jml1B0Rv0MW7u+IiK+QLQ26iGzryq8DXx3/ASmlpyLiD4BPABsj4qvATrIbWR0E/O1M7iYrSdJCaqeOfoZfmaEHGGCEdmb2m0BpPjw+NMT3+nvZMjLKmtoazmtcwVFT/HZ6c+rhQV6ik0Haqeck9mdd+FumuTbjmfqU0t3Am4HrgW8C70gp3TSuy2nA5tJ7s/U9sju6riLb8eYPgLeTBfU/A45PKT1SNp7ryUL/XaW+l5MtB/oI8O6U0qSNxlNK15IF/58C7wUuI/vC8L6U0lV7MG5JkubVyezPAKP0M0wi0c8wA4xyMhW26ZIWwONDQ1zXtYPu0SIH1tTQPVrkuq4dPF7hBoubUw+382zpmpDsC+rtPMvm5DUhcy0qZF9No6OjI23cuHGxhyFJWiI2F3t4gJfoZIh26jiZ/VlXcKZTi+MfOl+me7RIS03NK7Xu0VFaagp8sH3i7YpuSk9N+k3T2OsL49AFG3NeRcT9KaWO6XvOYvlNRPw/4PqU0rd30+fNwCUppd+a6Xm1eJ59LPEft8LLz8N+a+GXz4X1R0/eylGStLjWFZq9KFZVY8vIKAeOC/SQ3WBxy8jopL6dDNJWtlQsuyZkcF7HuBTN5kLZ9wG/NE2fk4FL93g0WjDPPpa45fPQ3w0rV2ftLZ/P6pIkSVNZU1tT8QaLa2prJvVtp54BJt4RNrsmpH5ex7gUzfX2k3Xs2nZSVew/boWmFmhsgShkbVNLVpckSZrKeY0r6C4WJ95gsVjkvMYVk/qexP4MMFJ2TcgIJ3lNyJyb7X13p5zGjYg6sj3iX9irEWlBvPx8NkM/XsOKrC5Js/XYC6Pc+rMiz++AtW1w7rEFjl49edZOUv4dVVfH+1rbJux+c0lzS8Xdb9ZFM2en9RN2vzmFNe5+Mw92G+oj4smy0hUR8ZsVutaQ7VxTh3u958J+a7MlN40tu2oDvVldkmbjsRdG+fw9o7TUw+pW6B6Az98zyqWnYbCX9lFH1dVNuYVluXXhNSELYbrlNwUgSo807nn5Yxh4CPgbsu0oVeV++Vzo686CfSpmbV93Vpek2bj1Z0Va6qGlIShE0NIQtNRndUnSwtjtTH1K6ZCx5xFRBK5JKf35fA9K82/90cH5l07c/eb0t7n7jaTZe35HNkM/3or6rC5JWhizWVN/NvD0PI1Di2D90cH6oxd7FJLybm1btuSmpWFXrXcwq0vSbDzziyI/vgteehH2PxBecwYcfPhc7+uyb5rNHWXvTCk9M5+DkSTlz7nHFugehO6BlO2EMZDoHszqkjRTz/yiyHe+An09sN+qrP3OV7K6pjflTH1EvLf09JsppZ5xr6eVUvrCXo9MkpQLR6+u4dLTmLD7zdte5e43kmbnx3dBU3P2gF3tj++Cgw9fvHHlxe6W31xHdnHsvUDPuNe7M3ZBraFeqgIPdg9z/dYhNg0W2VBf4OID6jipZdn0B0qzdPTqGkO8pL3y0ovZDP14jU1ZXdPbXaj/LbKAvqX0utJWlpKq1IPdw1zzzABttcFBdQU6hxPXPDPAFQdjsJckVZ39D8yW3DSN2/2yvy+ra3pThvqU0nVlrz8/76ORNGeu3zpEW23Qvixb19y+LIAi128dMtRLkqrOa87I1tBDNkPf35eF/LPetLjjyguvYpL2UZsGi7TWTtyitLU22DToBUeSpOpz8OEF3vTubKb+5W1Z+6Z3u/vNTM1mS0tJObKhPltyk83QZ7pGEhvq/ctRklSdDj684EWxe2hW/7pHxJkR8W8RsTUihiNitMJjZL4GK2nmLj6gjh0jic7hIsWUtTtGEhcfMLPbekuSpPyY8Ux9RLwJuB6oATYBjwEGeKlKndSyjCsOZsLuN7+5rt719JIk7YNms/zmT4Fh4E0ppVvmZziS5tJJLcsM8ZIkLQGzWX5zAvBVA70kSZJUXWYT6nuB7fM1EEmSJEl7Zjah/lbg1PkaiCRJkqQ9M5tQ/4fA4RHx0YiIaXtLkiRJWhCzuVD248BPgT8Dfisi/hPYUaFfSin9l7kYnCRJkqTpzSbUv2/c80NKj0oSYKiXJEmSFshsQv2h8zYKSZIkSXtsxqE+pfTMfA5EkiRJ0p6ZzYWykiRJkqrQjGfqI2LDTPumlDbt2XAkSZIkzdZs1tQ/TXYR7HTSLM8rSZIkaS/MJnx/gcqhvg34JeBg4A7AtfeSJEnSAprNhbLvm+q9iCgAHwM+AFy698OSJEmSNFNzcqFsSqmYUvozsiU6fz0X55QkSZI0M3O9+809wPlzfE5JkiRJuzHXF7SuBJrm+JyahS0/hYdvgM5noX09nPAWWHP8Yo9KkiRJ82nOZuoj4jzg14CH5+qcmp0tP4W7roWBHdC2LmvvujarS5Ikad81m33qb9vNOdYDY/vY//neDkp75uEboKEte8Cu9uEbnK2XJEnal81m+c1ZU9QT0Al8F7g6pTRV+Nc863w2m6Efr74lq0uSJGnfNZstLef6olrNsfb12ZKbsRl6gMHurC5JkqR9l0F9H3LCW7JQP7ADUnHX8xPestgjkyRJ0nwy1O9D1hwPZ1yezdTv2Jy1Z1zuenpJkqR93VxvaalFtuZ4Q7wkSdJS40y9JEmSlHOGekmSJCnnDPWSJElSzhnqJUmSpJwz1EuSJEk5N+PdbyKiATgFOAoYu73RDuBx4N6U0sDcD08Az/48sfE22L4FVq6BjnNg/ZFRse+Pvpe49WvQuRXaD4Bz3wWvPa9yX0mSJO0bpg31EdEO/BXwHqBxim79EfEF4KMppc45HN+S9+zPEzf/MzQ2w8oDob8bbv5nuOA9aVKw/9H3Ev9yLdQ3Qtv+0N8D/3ItQDLYS5Ik7cN2G+ojog34AXAM0Af8O/BzoKvUpRU4Ejgd+F3g7Ig4NaXUVeF02gMbb8sCfVNL9nqs3XgbrD9yYt9bv5YF+sbm7PVYe+vX4LXnLcx4JUmStPCmm6n/OFmgvwb4eEqpt1KniFgB/DnwYeBPgCvncpBL2fYt2Qz9eI0rsnq5zq3ZDP14DU1ZXZIkSfuu6S6UvRi4LaV05VSBHiCl1JtS+ghwB3DJHI5vyVu5BvrL/uT7e7N6ufYDYKBvYm2gL6tLkiRp3zVdqF8D/GgW57u3dIzmSMc52dr4vm5Ixazt78nq5c59Fwz2Z++nYtYO9md1SZIk7bumC/UvA0fP4nzHlo7RHFl/ZHDBe6CxBba/mLUXvKfy7jevPS945+XZWvodL2XtOy939xtJkqR93XRr6r8LXBoRH0wp/cPuOkbEh4CLgOvmaGwqWX9kTLoodiqvPS+8KFaSJGmJmS7Ufwx4E3BtRFwJ3EK2L/343W+OAs4HDgG2kl0oK0mSJGmB7DbUp5Q2R8SpwKeANwDvB1JZt7G1HbcAH0wpbZ7zUUqSJEma0rQ3n0opPQn8akQcBpxNtsa+tfR2F/AYcHupnyRJkqQFNm2oH1MK7bMO7hHRArSllDbN9lhJkiRJ05tu95u5cAXw1AJ8jiRJkrQkLUSolyRJkjSPDPWSJElSzhnqJUmSpJwz1EuSJEk5Z6iXJEmScs5QL0mSJOWcoV6SJEnKOUO9JEmSlHMLEeqj9JAkSZI0DxYi1H8OOHsBPkeSJElakmpn2jEiGoBTgKOAtlJ5B/A4cG9KaaDScSmlZ4Bnpjn3fsDbgDcBJwLrgJ3AQ2RfCj6XUiqO638I8NRuTvnVlNK7p/isS4HfA44DRoH/AK5OKf3b7sYoSZIkVatpQ31EtAN/BbwHaJyiW39EfAH4aEqpcw/G8U7gU8AW4HZgE3AgcAnwj8CFEfHOlFIqO+4B4PoK53u40odExNXAlcBzwGeB5cC7gRsi4vKU0if3YOySJEnSoorJOXncmxFtwD3AMUAf8APg50BXqUsrcCRwOtAEPAqcmlLqmny23Qwi4pzS8d8pm5FfDfwIWA+8I6X0r6X6IWQz9Z9PKb1vhp9xWmn8vwBeM/blo3Su+0uff0xK6enpztXR0ZE2btw4o59NkiRJ2hMRcX9KqWMmfadbU/9xskB/DbAmpXRBSunylNJHS4/LU0oXAGuA/13q+yezHXBK6baU0g3jA32p/gLw6dLLs2Z73jIfKLV/Nf63CaUQ/3+AOuA39/IzJEmSpAU3Xai/GLgtpXRlSql3qk4ppd6U0keAO8iWzMyl4VI7UuG9tRHx/oj4o1J70m7Oc06pvbnCezeV9ZEkSZJyY7o19WuAL8/ifPcCp+35cCaKiFrgvaWXlcL4G0qP8cfcAVyaUto0rtZEdvFtb0ppS4Xz/LzUHrWbsVwGXAawYcOGGf4EkiRJ0vybbqb+ZeDoWZzv2NIxc+WvgROAG1NK3x1X7wf+Ang10F56nEl2ke1ZwK2lID+mtdROtdZ/rN42xfuklD6TUupIKXWsWrVqtj+HJEmSNG+mC/XfBS6OiA9Od6KI+BBwEZVn1GctIn6fbKeaR8l23nlFSmlrSulPUko/SSntKD3uAs4H7gOOAH57LsYhSZIkVbvplt98jGzv+Gsj4krgFrJ96cfvfnMUWZg+BNjKHlwoW670BeHvgUeAc1NK22dyXEppJCL+EXgdcEbpHJSNt5Kx+o49G7EkSZK0eHYb6lNKmyPiVLI95N8AvB8o3wMzSu0twAdTSpv3ZkAR8WGy3XYeJgv0W2d5im2l9pXlNymlvojYDKyLiDUV1tUfWWof35MxS5IkSYtp2ptPpZSeBH41Ig4DziZbYz9+jfpjwO2lfnslIv6QbB39fwJvSCm9tAenOaXUlo/nNrJlPBeQ3aV2vAvH9ZEkSZJyZdpQP6YU2mcd3COiBWgbvxvNFP0+Bvw52Y2gzt/dkpuIeBXwn+X72kfEucAVpZdfLDvs02Sh/o8j4vqym0/9HjDE5LAvSZIkVb0Zh/q9cAXZOvuaqTpExKVkgX4UuBv4/Ygo7/Z0Sum60vO/A46MiHuA50q1k9i1z/zHUkr3jD84pXRPRPwd8BHgwYj4OrAc+DVgJXD5TO4mK0mSJFWbhQj1M3Foqa0BPjxFnzuB60rP/xl4G/AasqUzy4AXga8Bn0wp3V3pBCmlKyPiIbKZ+cuAIvAT4H+llP5t738MSZIkaeFVRahPKf0p8Kez6P9PwD/t4Wddx64vB5IkSVLuTbdPvSRJkqQqZ6iXJEmScs5QL0mSJOWcoV6SJEnKOUO9JEmSlHOGekmSJCnnFiLUR+khSZIkaR4sRKj/HHD2AnyOJEmStCTN+OZTEdEAnAIcBbSVyjuAx4F7U0oDlY5LKT0DPLOX45QkSZI0hWlDfUS0A38FvAdonKJbf0R8AfhoSqlzDscnSZIkaRq7DfUR0Qb8ADgG6AP+Hfg50FXq0gocCZwO/C5wdkScmlLqqnA6SZIkSfNgupn6j5MF+muAj6eUeit1iogVwJ8DHwb+BLhyLgcpSZIkaWrTXSh7MXBbSunKqQI9QEqpN6X0EeAO4JI5HJ8kSZKkaUwX6tcAP5rF+e4tHSNJkiRpgUwX6l8Gjp7F+Y4tHSNJkiRpgUwX6r8LXBwRH5zuRBHxIeAi4Oa5GJgkSZKkmZnuQtmPAW8Cro2IK4FbyPalH7/7zVHA+cAhwFayC2UlSZIkLZDdhvqU0uaIOBX4FPAG4P1AKusWpfYW4IMppc1zPkpJkiRJU5r25lMppSeBX42Iw4CzydbYt5be7gIeA24v9ZMkSZK0wKYN9WNKod3gLkmSJFWZ6S6UlSRJklTlDPWSJElSzs14+Y32PU8/WeTeH8BLW2H/A+CU0+GQw/yeJ0mSlDcmuCXq6SeLfPtfE329if32z9pv/2vi6SeLiz00SZIkzZKhfom69wfQtAJWrAgKhWDFiqBpRVaXJElSvhjql6iXtkJj48RaY2NWlyRJUr4Y6peo/Q+A/v6Jtf7+rC5JkqR8MdQvUaecDn290NubKBYTvb2Jvt6sLkmSpHwx1C9RhxxW4KK3B00rgpdfytqL3h7ufiNJkpRDbmm5hB1yWIFDDlvsUUiSJGlvOS0rSZIk5ZyhXpIkSco5Q70kSZKUc4Z6SZIkKecM9ZIkSVLOGeolSZKknDPUS5IkSTlnqJckSZJyzlAvSZIk5ZyhXpIkSco5Q70kSZKUc4Z6SZIkKecM9ZIkSVLOGeolSZKknKtd7AFoelt+Cg/fAJ3PQvt6OOEtsOb4xR6VJEmSqoUz9VVuy0/hrmthYAe0rcvau67N6pIkSRIY6qvewzdAQ1v2iMKu5w/fsNgjkyRJUrUw1Fe5zmehvmVirb4lq0uSJElgqK967ethsHtibbA7q0uSJElgqK96J7wlW0c/sANScdfzE96y2COTJElStTDUV7k1x8MZl2fr6HdsztozLnf3G0mSJO3ilpY5sOZ4Q7wkSZKm5ky9JEmSlHOGekmSJCnnDPWSJElSzhnqJUmSpJwz1EuSJEk5Z6iXJEmScs5QL0mSJOWcoV6SJEnKOUO9JEmSlHOGekmSJCnnDPWSJElSzhnqJUmSpJwz1EuSJEk5Z6iXJEmScs5QL0mSJOWcoV6SJEnKuaoI9RGxX0T8dkR8MyKeiIiBiOiKiO9HxH+JiIrjjIjTIuLGiNheOubBiPhwRNTs5rPeHBF3lM7fGxH3RcSl8/fTSZIkSfOrdrEHUPJO4FPAFuB2YBNwIHAJ8I/AhRHxzpRSGjsgIt4K/CswCHwV2A68BbgGOL10zgki4kPAtcDLwBeBncA7gOsi4sSU0lXz9QNKkiRJ8yXG5eTFG0TEOUAT8J2UUnFcfTXwI2A98I6U0r+W6i3AE0ArcHpKaWOpXg/cBpwK/HpK6SvjznUI8CjQB7w6pfR0qd4O/Bg4HDgtpfTD6cbb0dGRNm7cuHc/tCRJkrQbEXF/SqljJn2rYvlNSum2lNIN4wN9qf4C8OnSy7PGvfUOYBXwlbFAX+o/CHy09PJ3yz7mt4A64JNjgb50TCfw30svP7B3P4kkSZK08Koi1E9juNSOjKudU2pvrtD/LqAfOC0i6mZ4zE1lfSRJkqTcqOpQHxG1wHtLL8eH8aNL7ePlx6SURoCnyK4XOGyGx2whW5ZzUEQ0TjGWyyJiY0Rs3LZt26x+DkmSJGk+VXWoB/4aOAG4MaX03XH11lLbNcVxY/W2PTimtdKbKaXPpJQ6Ukodq1at2v2oJUmSpAVULbvfTBIRvw9cSXZx63sWeTiL6qmnitz7Q9i2DVatglNOhUMPrfx97Kmni/zwXti6DQ5YBaeeAoceMkXfWZxXkiRJ1asqE1xp68m/Bx4Bzk4pbS/rsttZ9XH1HXtwzFQz+YviqaeKfOv6RG9vYr/9svZb1yeeeqo4ue/TRb75razP/qW+3/xW4qmnK/SdxXklSZJU3aou1EfEh8n2kn+YLNC/UKHbY6X2qArH1wKHkl1Y++QMj1lDtqXmcyml/j0f/dy794fQ1AQrVgSFQrBiRdDUlNXL/fBeWFHWd0VTVt+b80qSJKm6VVWoj4g/JLt51H+SBfqtU3S9rdReUOG9M4BG4J6U0tAMj7mwrE/V2LYNGssu3W1szOrltk7Rd2uFvrM5ryRJkqpb1YT6iPgY2YWx9wPnppRe2k33rwMvAe+OiFc25C/dfOovSy8/VXbM54Ah4EOlG1GNHdMO/FHp5aepMqtWQX/Z7w76+7N6uQOm6HtAhb6zOa8kSZKqW1VcKBsRlwJ/DowCdwO/HxHl3Z5OKV0HkFLqjojfIQv3d0TEV4DtwEVkW1d+Hfjq+INTSk9FxB8AnwA2RsRXgZ1kN7I6CPjbmdxNdqGdcip863qARGNjFrz7+uC8N0zue+op8M1vTezb2wdvOG/vzitJkqTqFimlxR4DEfGnwMen6XZnSumssuNOB/4YOBWoB54A/h/wiZTS6BSf9RbgKuBVZL+peITsLrOfn+l4Ozo60saNG6fvOEfc/UaSJGnpiYj7U0od0/esklCfNwsd6iVJkrT0zCbUOy0rSZIk5ZyhXpIkSco5Q70kSZKUc4Z6SZIkKecM9ZIkSVLOGeolSZKknDPUS5IkSTlXFXeUVfXb/DN44GbYvhlWroOTL4B1xy72qCRJkgTO1GsGNv8Mbv0M9HdB+5qsvfUzWV2SJEmLz1CvaT1wMzS2Zo8o7Hr+wM2LPTJJkiSBoV4zsH0zNDRPrDU0Z3VJkiQtPkO9prVyHQz0TKwN9GR1SZIkLT5DvaZ18gXZOvr+LkjFXc9PvmCxRyZJkiQw1GsG1h0L516WraPv3JK1517m7jeSJEnVwi0tNSPrjjXES5IkVStn6iVJkqScM9RLkiRJOWeolyRJknLOUC9JkiTlnKFekiRJyjlDvSRJkpRzhnpJkiQp5wz1kiRJUs4Z6iVJkqScM9RLkiRJOWeolyRJknLOUC9JkiTlnKFekiRJyjlDvSRJkpRzhnpJkiQp5wz1kiRJUs4Z6iVJkqScM9RLkiRJOWeolyRJknLOUC9JkiTlnKFekiRJyjlDvSRJkpRzhnpJkiQp5wz1kiRJUs4Z6iVJkqScM9RLkiRJOWeolyRJknLOUC9JkiTlnKFekiRJyjlDvSRJkpRztYs9AOXDCw/Co9+Ark3QugGOuQRWn7TYo5IkSRI4U68ZeOFB+OHVMNAJLQdl7Q+vzuqSJElafIZ6TevRb0B9OzS0QxSytr49q0uSJGnxGeo1ra5NUN86sVbfmtUlSZK0+Az1mlbrBhjsmlgb7MrqkiRJWnyGek3rmEtgsDNbS5+KWTvYmdUlSZK0+Az1mtbqk+DUq7K19N3PZe2pV7n7jSRJUrVwS0vNyOqTDPGSJEnVypl6SZIkKecM9ZIkSVLOGepwmhCbAAAckklEQVQlSZKknDPUS5IkSTlnqJckSZJyzlAvSZIk5ZyhXpIkSco596nXjGx5GH76bdjxHLQdBMdfBGtOWOxRSZIkCZyp1wxseRju/gQM7IDWtVl79yeyuiRJkhafoV7T+um3oaEte0Rh1/OffnuxRyZJkiQw1GsGdjwH9S0Ta/UtWV2SJEmLz1CvabUdBIPdE2uD3VldkiRJi89Qr2kdf1G2jn5gB6TirufHX7TYI5MkSRIY6jUDa06A1/9+to6+6/msff3vu/uNJElStXBLS83ImhMM8ZIkSdWqambqI+IdEXFtRNwdEd0RkSLii1P0PaT0/lSPr+zmcy6NiB9FRG9EdEXEHRHx5vn7ySRJkqT5VU0z9R8FTgZ6geeAY2ZwzAPA9RXqFXdQj4irgStL5/8ssBx4N3BDRFyeUvrkHox73v3i2SJ3/yTx4suJA/cLXv+q4PD1lb+P/eK5Inf9JPHi9sSBK4MzXhUcflDlvvfeXuS2L0P3lkTLmuCcX4dTzq7c975bi9z5Jeh+PtGyNjjzN+B1507R9/Yit4/re/ZvwOumOO/mR+ChG6FzM7SvgxPfCOuOm8EfiiRJkl4RKaXFHgMAEXE2Wdh+AjgTuB34Ukrp/6vQ9xDgKeDzKaX3zfD8pwE/AH4BvCal1DnuXPcDTcAxKaWnpztXR0dH2rhx40w+dq/94tkiX/tukRWN0NQIff3Q2w/v+tXCpGD/i+eKfOWWIs2N0NQAfQPQ0w/vPr8wKdjfe3uRb14Ny5uhvhkGe2BnD7ztqsnB/r5bi3z7r7O+dS0w1J31vei/TQ72991e5Pq/gbrmrP/OHhjqgYv/cHKw3/wI3PFpaGiFhmYY6IGBLjjrAwZ7SZKkiLg/pdQxk75Vs/wmpXR7Sunnaf6+ZXyg1P7VWKAvfe7TwP8B6oDfnKfP3mN3/ySxohGam4JCBM1NwYrGrF7urp8kmhuhubHUtzFobszq5W77cha6G1ugEFm7vDmrl7vzS9l7DW1QKN18anlzVi93+5eyQF/fmvWtb81e316h70M3ZoG+sTW7qVVja/b6oRv35E9KkiRp6aqaUL+H1kbE+yPij0rtSbvpe06pvbnCezeV9akaL76caGqcWGtqzOqT+m5PNDWU9W3I6uW6tyTqmyfW6puz+qS+zyfqym4+VdeS1Sv1XV523uXNlft2bs5m6MdraM7qkiRJmrlqWlO/J95QerwiIu4ALk0pbRpXawLWAb0ppS0VzvPzUnvUVB8UEZcBlwFs2LBh70Y9CwfuF/T0JZqbdtX6+rP6pL4rg57+bLb+lb4DWb1cy5pgoDuboR8z2JPVJ/VdGwzsyGboxwx1Z/VKfQe7shn6MTt7KvdtXwf9XdkM/ZiBnqwuSZKkmcvrTH0/8BfAq4H20mNsHf5ZwK2lID9mLDZ2TXG+sXrbFO+TUvpMSqkjpdSxatWqvRj67Lz+VUFvP/T0JYop0dOX6O3P6uXOeFXQ0w89/aW+/Yme/qxe7pxfz8J2fzcUU9bu7Mnq5c78jey9gR1QLN18amdPVi939m9ka+gHu7K+g13Z67Mr9D3xjdka+v6u7KZW/V3Z6xPfuCd/UpIkSUtXLkN9SmlrSulPUko/SSntKD3uAs4H7gOOAH57cUc5Nw5fX+Bdv1qguSnYtj1bW1/pIlmAww8q8O7zCzQ3Bts6s7X1lS6Shexi2LddBQ0t0PNCoqGl8kWykF0Me9F/y2bqe19INLRVvkgWsothL/7DbKa+94VEfWvli2Qhuxj2rA9kM/U7tmStF8lKkiTNXtXsfjNeRJzFbna/mebY3ybbrvIbKaW3l2pNZFtl9qaUmiscsz+wDdiaUjpwus9YyN1vJEmStDTlcvebObSt1L6y/Cal1AdsBlZExJoKxxxZah+f57FJkiRJc25fDPWnlNony+q3ldoLKhxzYVkfSZIkKTdyGeoj4lURMWnsEXEucEXp5RfL3v50qf3jiGgfd8whwO8BQ8Dn5nywkiRJ0jyrmi0tI+Ji4OLSy9Wl9tSIuK70/KWU0lWl538HHBkR95DdhRbgJHbtM/+xlNI948+fUronIv4O+AjwYER8HVgO/BqwErh8JneTlSRJkqpN1YR64JeAS8tqh5UeAM8AY6H+n4G3Aa8hWzqzDHgR+BrwyZTS3ZU+IKV0ZUQ8RDYzfxlQBH4C/K+U0r/N3Y8iSZIkLZyq3P2m2rn7jSRJkubbUt/9RpIkSVpSDPWSJElSzhnqJUmSpJwz1EuSJEk5Z6iXJEmScs5QL0mSJOWcoV6SJEnKOUO9JEmSlHOGekmSJCnnDPWSJElSzhnqJUmSpJwz1EuSJEk5Z6iXJEmScs5QL0mSJOWcoV6SJEnKOUO9JEmSlHOGekmSJCnnDPWSJElSzhnqJUmSpJwz1EuSJEk5Z6iXJEmScs5QL0mSJOVc7WIPQPuezY/AQzdC52ZoXwcnvhHWHbfYo5IkSdp3OVOvObX5Ebjj09DfBW1rsvaOT2d1SZIkzQ9DvebUQzdCQys0tkIUsrahNatLkiRpfhjqNac6N0ND88RaQ3NWlyRJ0vww1GtOta+DgZ6JtYGerC5JkqT5YajXnDrxjTDQla2lT8WsHejK6pIkSZofhnrNqXXHwVkfyNbS79iStWd9wN1vJEmS5pNbWmrOrTvOEC9JkrSQnKmXJEmScs5QL0mSJOWcoV6SJEnKOUO9JEmSlHOGekmSJCnnDPWSJElSzhnqJUmSpJxzn/ocePbniY23wfYtsHINdJwD64+Min1/9L0id1+X6HkOmg+C178veO15lb+73fnjEa6/BbZ3wsp2uPh8OPM1lf8n8cTmInc8UOSFTljdDmedXOCIdZXPO5u+kiRJ2nsmrSr37M8TN/8z9HfDygOz9uZ/zurlfvS9It/5s8TgDmhaA4M74Dt/lvjR94qT+t754xH+8cuJvr5Ee1vW/uOXE3f+eGRS3yc2F/nSbaP09CcOaEv09Ce+dNsoT2yefN7Z9JUkSdLcMNRXuY23QWMzNLVAFLK2sTmrl7v7usTyVqhvCwo1QX1bsLw1q5e7/hZoqIcVTUEhghVNQUN9Vi93xwNFWhqguTHr29wYtDRk9b3pK0mSpLlhqK9y27dA44qJtcYVWb1cz3OwvHlibXlzVp903k5obCw7b2NWL/dCJzQ1TKw1NWT1vekrSZKkuWGor3Ir10B/78Raf29WL9d8EOzsmVjb2ZPVJ523Hfr7y87bn9XLrW6HvoGJtb6BrL43fSVJkjQ3DPVVruMc6O+Bvm5Ixazt78nq5V7/vmBnFwzuSBRHE4M7Eju7snq5i8+HgUHo7UsUU6K3LzEwmNXLnXVyge4B6OnP+vb0J7oHsvre9JUkSdLciJQmr7fW7nV0dKSNGzcu2Oe5+40kSdLSExH3p5Q6ZtTXUD97Cx3qJUmStPTMJtQ7fSpJkiTlnKFekiRJyjlDvSRJkpRzhnpJkiQp5ypvdSItkBcegkeuhx2boG0DHHcxrD5xir4PwqPfgK5N0LoBjrkEVp+0sOOVJEmqRs7Ua9G88BD84BoY6ITWg7L2B9dk9Ul9H4QfXp31aSn1/eHVWV2SJGmpM9Rr0TxyPdS3QUM7RCFr69uyerlHvwH17WV927O6JEnSUmeo16LZsQnqWyfW6luzermuKfp2VegrSZK01BjqtWjaNsBg18TaYFdWL9c6Rd/WCn0lSZKWGkO9Fs1xF8Pgjmx9fCpm7eCOrF7umEtgsLOsb2dWlyRJWuoM9Vo0q0+E06/I1sd3PZe1p19Refeb1SfBqVdlfbpLfU+9yt1vJEmSwC0ttchWnzj1FpaT+p5kiJckSarEmXpJkiQp5wz1kiRJUs4Z6iVJkqScM9RLkiRJOWeolyRJknLOUC9JkiTlnKFekiRJyjn3qV/CNv8MHrgJOjdD+zo4+UJYd+xij0qSJEmz5Uz9ErX5Z3D7/4WBLmhfk7W3/9+sLkmSpHwx1C9RD9wEDa3Q2ApRyNqG1qwuSZKkfDHUL1Gdm6GheWKtoTmrS5IkKV+qItRHxDsi4tqIuDsiuiMiRcQXpznmtIi4MSK2R8RARDwYER+OiJrdHPPmiLgjIroiojci7ouIS+f+J6p+7etgoGdibaAnq0uSJClfqiLUAx8FPgT8EjDtXHFEvBW4CzgD+CbwSWA5cA3wlSmO+RBwA3AC8EXgs8Ba4LqIuHrvf4R8OfnCbB19fxekYtYOdGV1SZIk5Uu1hPorgKOAFuB3d9cxIlrIAvkocFZK6b+klP6A7AvBD4F3RMS7y445BLga2A50pJR+L6V0BXAS8Avgyog4dU5/oiq37lg4+/3ZOvrOLVl79vvd/UaSJCmPqmJLy5TS7WPPI2K67u8AVgFfSCltHHeOwYj4KHAr2ReD8TP2vwXUAX+TUnp63DGdEfHfgX8CPkD2pWDJWHesIV6SJGlfUC0z9bNxTqm9ucJ7dwH9wGkRUTfDY24q6yNJkiTlSh5D/dGl9vHyN1JKI8BTZL+BOGyGx2wB+oCDIqJxqg+NiMsiYmNEbNy2bduejl2SJEmac3kM9a2ltmuK98fqbXtwTOsU75NS+kxKqSOl1LFq1aoZDVSSJElaCHkM9ZIkSZLGyWOon25Wfay+Yw+OmWomX5IkSapaeQz1j5Xao8rfiIha4FBgBHhyhsesAZqA51JK/XM7VEmSJGn+5THU31ZqL6jw3hlAI3BPSmlohsdcWNZHkiRJypU8hvqvAy8B746IjrFiRNQDf1l6+amyYz4HDAEfKt2IauyYduCPSi8/PU/jlSRJkuZVVdx8KiIuBi4uvVxdak+NiOtKz19KKV0FkFLqjojfIQv3d0TEV8juFHsR2daVXwe+Ov78KaWnIuIPgE8AGyPiq8BOshtZHQT8bUppSd14SpIkSfuOqgj1wC8Bl5bVDmPXXvPPAFeNvZFSuj4izgT+GHg7UA88AXwE+ERKKZV/QErp2oh4unSe95L9luIR4KMppc/P6U8jSZIkLaCokH81jY6OjrRx48bFHoYkSZL2YRFxf0qpY/qe+VxTL0mSJGkcQ70kSZKUc9Wypl67seUheORbsONZaFsPx70V1py42KOSJElStXCmvspteQi+//cwsANa12Xt9/8+q0uSJElgqK96j3wLGtqyRxR2PX/kW4s9MkmSJFULQ32V2/Es1LdMrNW3ZHVJkiQJDPVVr209DHZPrA12Z3VJkiQJDPVV77i3ZuvoB3ZAKu56ftxbF3tkkiRJqhaG+iq35kT4lf+araPv2py1v/Jf3f1GkiRJu7ilZQ6sOdEQL0mSpKk5Uy9JkiTlnKFekiRJyjlDvSRJkpRzhnpJkiQp5wz1kiRJUs4Z6iVJkqScM9RLkiRJOWeolyRJknLOUC9JkiTlnKFekiRJyjlDvSRJkpRzhnpJkiTp/2/v3mNtKcs7jn9/ciJW1AOCiAXkeEWiSYshokDgoK136qVgW4sFFFtMxBol2mIRsBdp1dZLvbSl9CiagJVS0xTElIvcrIYI3igCyj5qAOUiCHITfPrHOzuuLNc6Z7P32XvNsL+f5M2c9c47M++a58xez579rncGzqRekiRJGjiTekmSJGngTOolSZKkgTOplyRJkgbOpF6SJEkaOJN6SZIkaeBM6iVJkqSBM6mXJEmSBs6kXpIkSRo4k3pJkiRp4EzqJUmSpIEzqZckSZIGzqRekiRJGjiTekmSJGngTOolSZKkgTOplyRJkgYuVTXrPgxOkpuAjTM49A7AzTM4rpbGuA2TcRsuYzdMxm2YjNvy2q2qHreQhib1A5Lksqraa9b90INj3IbJuA2XsRsm4zZMxq0/HH4jSZIkDZxJvSRJkjRwJvXD8s+z7oAWxbgNk3EbLmM3TMZtmIxbTzimXpIkSRo479RLkiRJA2dSL0mSJA2cSb0kSZI0cCb1PZdklySnJLk+yb1J5pJ8MMl2s+7bapfk4CQfSXJRkp8mqSSf3sw2+yQ5K8mtSe5O8o0kb02y1Ur1ezVLsn2SI5OcmeTaLga3J7k4yRuSTPyZaNxmL8nfJjk3yQ+6GNya5PIkxyfZfso2xq2Hkhza/bysJEdOafPyJBd01+edSb6S5LCV7utq1eUaNaXcOGUbr7cZ84uyPZbkKcClwI7A54GrgOcABwLfAfatqltm18PVLckVwG8AdwI/BJ4BfKaqDp3S/hXAGcA9wOnArcBBwO7A56rqkJXo92qW5Cjg48ANwPnA94HHA68G1tLic0iN/GA0bv2Q5D7ga8CVwI+BbYDnAnsB1wPPraofjLQ3bj2UZFfgm8BWwKOAN1bVyWNt3gx8BLiFFrv7gIOBXYAPVNUxK9rpVSjJHLAt8MEJq++sqvePtfd664OqsvS0AOcABRw9Vv/3Xf0nZt3H1Vxov1w9DQiwvovJp6e0fQwtEbkX2Guk/hG0X9wK+P1Zv6eHegGeT/ugedhY/U60BL+A3zVu/SvAI6bU/3UXh48Zt36X7mfl/wDfBd7XxeHIsTbraInhLcC6kfrtgGu7bZ436/fyUC/AHDC3wLZebz0pDr/pqe4u/QtpF9ZHx1YfD/wMeF2SbVa4a+pU1flVdU11P70242DgccBpVXXZyD7uAf6ie/mmZeimRlTVeVX1X1X1i7H6G4FPdC/Xj6wybj3RnfNJPtstnzZSZ9z66S20X6yPoH2GTfJ6YGvgH6tqbr6yqn4C/E338qhl7KMePK+3njCp768Du+UXJyQgdwCXAI+k/flZ/ff8bvmFCesuBO4C9kmy9cp1SWN+3i3vH6kzbv13ULf8xkidceuZJHsAJwEfqqoLN9F0U7E7e6yNltfW3fcfjk3yp0kOnDI+3uutJ9bMugOaavduefWU9dfQ7uQ/HTh3RXqkpZgaz6q6P8l1wDOBJwP/t5IdEyRZA/xR93L0g8m49UySY2hjsdfSxtPvR0voTxppZtx6pLu+TqUNcTt2M803FbsbkvwM2CXJI6vqri3bU43ZiRa3UdclOaKqvjRS5/XWEyb1/bW2W94+Zf18/bYr0BctnfHst5OAZwFnVdU5I/XGrX+OoX25ed4XgMOr6qaROuPWL+8G9gT2q6q7N9N2IbHbpmtnUr98/g24CPg2cActIX8z8MfA2UmeV1Vf79p6vfWEw28krWpJ3gK8nTa71Otm3B1tRlXtVFWh3UV8NS3ZuDzJs2fbM02SZG/a3fkPVNWXZ90fLUxVndh9B+lHVXVXVX2rqo6iTdTxa8AJs+2hJjGp76/532zXTlk/X3/bCvRFS2c8e6ibOu9DtGkSD6yqW8eaGLee6pKNM2nDELcHPjWy2rj1QDfs5lO0YRnHLXCzhcZu2l1hLa/5CQX2H6nzeusJk/r++k63fPqU9fMzPUwbc69+mRrP7oPvSbQvaH5vJTu1miV5K20u7G/REvpJD1Qxbj1XVRtpv5Q9M8kOXbVx64dH0WKwB3DP6AOMaLO4AfxLVzc/H/qmYvcE2tCbHzqefmbmh7mNzrzn9dYTJvX9dX63fOH4Uy6TPBrYlzae8H9XumNalPO65YsnrNufNpPRpVV178p1afVK8k7gH4AraAn9j6c0NW7D8Ovd8oFuadz64V7gX6eUy7s2F3ev54fmbCp2Lxlro5U3P+PeaILu9dYXs54o3zK94MOnBlNY2MOnbsKHc8y80IYBFHAZ8NjNtDVuPSi0O4BrJ9Q/jF8+fOoS4zacQhuTPenhU0/Ch0/NOjZ7ANtMqF9Hm3mvgGNH6r3eelLSnXj1UPcAqkuBHYHP06aC2ps2h/3VwD5Vdcvseri6JXkl8Mru5U7Ai2h3Ly7q6m6ukceZd+0/R/vAOo32GO3foXuMNvCa8oJcVkkOAzbQ7uh+hMnjcueqasPINsZtxrqhUu+l3dW9jpbwPR44gPZF2RuBF1TVlSPbGLceS3ICbQjOG6vq5LF1RwMfpsX5dOA+2gOOdqF94fYYtGy62LydNsf8RtrsN08BXkZL1M8CXlVV941s4/XWAyb1PZdkV+A9tD9rbQ/cAJwJnFjtCXuakZEPpWk2VtW6sW32Bd4FPI/2w/Fa4BTgw1X1wK/sQVvUAmIG8KWqWj+2nXGboSTPoj1FdD9aYrct7YmkVwP/TYvD+JecjVuPbSqp79YfRJu+9Nm0v8hcSXvK7CdXsp+rUZIDaNfbnrQbVtvQvuR6BW3e+lMnJeheb7NnUi9JkiQNnF+UlSRJkgbOpF6SJEkaOJN6SZIkaeBM6iVJkqSBM6mXJEmSBs6kXpIkSRo4k3pJkiRp4EzqJUnLKsmGJJVk3TIfZy7J3HIeQ5L6yqRekjQISS5I4hMTJWmCNbPugCRJW8gLZt0BSZoVk3pJ0kNCVX131n2QpFlx+I0k9VSSdd1Y9A1JnpHkP5PcmuRnSS5O8sIJ22yd5M+SfDPJXUl+muSiJK/ZQvs/odtm/ab2t8D3d3iSM5J8L8ndXV8vSXLopP0CB3Sva6RcMNJu4pj6JZyTdUlOS3JzknuSXJbk5Qt5b5K00rxTL0n99yTgy8A3gX8CngD8HnB2ktdW1ekASR4OnENLfq8CPgo8EjgYOD3Jb1bVsYvd/zL4OPBt4ELgBmB74KXAqUl2r6rjuna3AScChwO7df+eN7epAyzhnOwGfBX4HnAq8FjaOfl8kt+qqvMf7JuVpGVVVRaLxWLpYQHWAdWV942t2wv4OfAT4DFd3Z93bc8C1oy03ZGW/Bawz2L339Wf0LVfv4n+bhir39DVrxurf8qEfTwcOLc79s5j6y5oH1tTz9ccMDdWt5RzcvzYvl40v69Z/9+wWCyW8eLwG0nqv9uB94xWVNVlwGeAbYFXddWvpyWdb6uq+0fa/hj4y+7lkUvY/xZVE8bAV9V9tLvpa9gyX3xd7DnZCPzVWN/OAb4PPGcL9EuStiiTeknqv69V1R0T6i/olnsmeTTwVOD6qrpqQtvz5tsuZv8Poq8LluSJST6a5KpurHt1Y+fP6JrsvMT9L+WcXFFVD0yo/wGw3VL6JUnLwTH1ktR/P5pSf2O3XNsVaGPTJ5mv33aR+9+ikjyZNmZ9O+Ai4Iu0vxg8QBsCcxiw9RIPs5RzctuUbe7HG2KSesikXpL67/FT6nfqlrd3ZbRu3BNG2i5m//N+0S0nfX5MSo6neRvti7FHVNWG0RVJ/oCW1C/VUs6JJA2Kdxskqf+e3Q0lGbe+W17eDZ/5LrBzkqdNaHtgt/zaYvY/UveTbrnrhPZ7Taib5qnd8owJ6w6Yss0DAEm2WsgBlnhOJGlQTOolqf/WAu8erUiyF/CHtLvMZ3bVpwAB3jea+CbZAThupM1i9w9tyAzAEUnWjLTfdXwfmzHXLdePHfdFTP7iKsAt3fKJD+I4iz0nkjQoDr+RpP67EDgyyd7AJfxyHvmHAX9SVT/t2r0feAnwCuDrSc6izcl+CG0Kx7+rqouXsH+q6itJLgT2B76a5Dza8J2DaPPBT7qDP8nHgCOAf0/yOeB64FnAi4HPdscfd273Xv6je293Axur6tRNHGex50SSBsU79ZLUf9cB+9CGvhwFvIY2ZOSlNfJgqG46yN8G3tVVHU0bm34N8NqqeudS9j/iFcDJwC7dMfYE3gFM2/+vqKpv0Ia/XAq8DHgT8Bjg1cAnpmx2MvBe2l8W3kGbkvINmznOYs+JJA1KqmrWfZAkTZBkHS3h/mRVHT60/UuSVo536iVJkqSBM6mXJEmSBs6kXpIkSRo4x9RLkiRJA+edekmSJGngTOolSZKkgTOplyRJkgbOpF6SJEkaOJN6SZIkaeD+H38noqnjH9ipAAAAAElFTkSuQmCC\n", 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\n", 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QJEmSVDEgSJIkSap0RECIiDkRkQN8dbfQ3/YR8e2IeDgiVkfE/Ij4akRs3k+bZ0fEDyLi0YhYFRF3RsTJETGpyWN+q6bWXZutVZIkSeokY0e7gNKtwMkNtu0PHAT8vJmOImIX4AZga+Ai4A7gJcBxwKsjYr/MfKKuzUuBq4BxwAXAg+UxPwnMjojZmbm6n2O+HngPsAyY0kydkiRJUifqiICQmbdShIQNRMSvy3+e3mR3X6cIB8dm5mk1/XwFOB74LPD+mvVjgO8AmwCHZ+ZPy/VdwA+AN5ftPt+gvunA/wDnA9sCBzRZpyRJktRxOmKIUSMR8TxgH+Ah4OIm9t8FOASYD/xX3eaTgOXAERExuWb9AcBewHW94QAgM3uAj5bfvj8iosFhe4PLBweqT5IkSep0HR0QgGPK5RmZ2cwchAPL5eXlCX4lM5cC11NcKdinZtNB5fLS+s4y815gHrAjsHP99oiYA7wBeF/9sCVJkiRpY9SxAaGcHPxuoBv4VpPN9iiX8xpsv6tc7j7ENkTEjsCpwHcz86Im65MkSZI6WscGBOCtwDTg0sx8sMk2m5XLxQ22966fNpQ25fyEsygmJR/bZG2ViDgmIuZGxNzHHnus1eaSJEnSiOnkgNA7vOibo1pF346nmLtwdGY+1WrjzDw9M2dm5szp06cPf3WSJEnSIHVkQIiI5wD7AguAS1po2vtp/2YNtveuXzTYNhGxO8WdkL6Tma3UJkmSJHW8jgwItD45uded5XL3Btt3K5e18w1abfNsYAJwVP3D3PjLLU7vKte9oYXaJUmSpFHXEc9BqBURE4EjKCYnn9Fi86vL5SER0VV7J6OI2BTYD1gB3FjT5irgX4FXA5+rq2VniuBwP3BvuXp+P3W9luJZCD8ElpT7SpIkSRuNjgsIwFuAzYH/bTQ5OSLGAbsAazPznt71mXlPRFxO8SyEDwKn1TQ7GZgMfDMzl9esvxa4HXhFRBxW96C0L5T7fCMzszzGrcB7G9R1DUVA+JfMvLulVy1JkiR1gE4MCL3Di/p7cvIzKU7q7wdm1G37AHAD8LWImF3u91KKZyTMo7haUMnM7og4iuJKwgURcQHwADAbmEnx7IRThvB6JEmSpI1GR81BiIi9gJfT+uTkSnlFYSZwJkUwOJHiasOpwD59PdAsM28CXgxcRHH14XiKycmfBl6ZmasHU4skSZK0semoKwiZeTsQTew3v7/9yqFJR7V47NsohjcNWmbOGkp7SZIkabR11BUESZIkSaPLgCBJkiSpYkCQJEmSVDEgSJIkSaoYECRJkiRVDAiSJEmSKgYESZIkSRUDgiRJkqSKAUGSJElSxYAgSZIkqWJAkCRJklQxIEiSJEmqGBAkSZIkVQwIkiRJkioGBEmSJEkVA4IkSZKkigFBkiRJUsWAIEmSJKliQJAkSZJUMSBIkiRJqhgQJEmSJFUMCJIkSZIqBgRJkiRJFQOCJEmSpIoBQZIkSVLFgCBJkiSpYkCQJEmSVDEgSJIkSaoYECRJkiRVDAiSJEmSKgYESZIkSRUDgiRJkqSKAUGSJElSxYAgSZIkqWJAkCRJklQxIEiSJEmqGBAkSZIkVQwIkiRJkioGBEmSJEkVA4IkSZKkigFBkiRJUsWAIEmSJKliQJAkSZJUMSBIkiRJqhgQJEmSJFUMCJIkSZIqBgRJkiRJFQOCJEmSpIoBQZIkSVLFgCBJkiSpYkCQJEmSVDEgSJIkSaoYECRJkiRVDAiSJEmSKgYESZIkSRUDgiRJkqSKAUGSJElSxYAgSZIkqWJAkCRJklQxIEiSJEmqGBAkSZIkVQwIkiRJkioGBEmSJEkVA4IkSZKkigFBkiRJUsWAIEmSJKliQJAkSZJUMSBIkiRJqhgQJEmSJFUMCJIkSZIqBgRJkiRJFQOCJEmSpIoBQZIkSVLFgCBJkiSpYkCQJEmSVDEgSJIkSaoYECRJkiRVDAiSJEmSKgYESZIkSRUDgiRJkqSKAUGSJElSxYAgSZIkqWJAkCRJklQxIEiSJEmqGBAkSZIkVQwIkiRJkioGBEmSJEkVA4IkSZKkigFBkiRJUsWAIEmSJKliQJAkSZJUMSBIkiRJqhgQJEmSJFUMCJIkSZIqBgRJkiRJFQOCJEmSpIoBQZIkSVLFgCBJkiSpYkCQJEmSVDEgSJIkSaoYECRJkiRVDAiSJEmSKgYESZIkSRUDgiRJkqSKAUGSJElSxYAgSZIkqWJAkCRJklQxIEiSJEmqGBAkSZIkVQwIkiRJkioGBEmSJEkVA4IkSZKkigFBkiRJUsWAIEmSJKnSEQEhIuZERA7w1d1Cf9tHxLcj4uGIWB0R8yPiqxGxeT9tnh0RP4iIRyNiVUTcGREnR8SkPvbdLSI+FhFXRcSDEbEmIv4cERdFxIGDfR8kSZKk0TZ2tAso3Qqc3GDb/sBBwM+b6SgidgFuALYGLgLuAF4CHAe8OiL2y8wn6tq8FLgKGAdcADxYHvOTwOyImJ2Zq2uafAZ4G3AbcAnwJLAHcBhwWEQcl5lfa6ZeSZIkqZN0REDIzFspQsIGIuLX5T9Pb7K7r1OEg2Mz87Safr4CHA98Fnh/zfoxwHeATYDDM/On5fou4AfAm8t2n685xqXAFzLzlrpaDwB+AfxHRPwwMxc2WbMkSZLUETpiiFEjEfE8YB/gIeDiJvbfBTgEmA/8V93mk4DlwBERMblm/QHAXsB1veEAIDN7gI+W374/IqJm25n14aBcfy1wDTAe2HegeiVJkqRO09EBATimXJ6Rmc3MQegd/395eYJfycylwPUUVwr2qdl0ULm8tL6zzLwXmAfsCOzcZM1ry+W6JveXJEmSOkbHBoRycvC7gW7gW00226Nczmuw/a5yufsQ2/QpInYEZgMrgOsG2l+SJEnqNB0bEIC3AtOASzPzwSbbbFYuFzfY3rt+2hDbbCAiJgDnAhOAT2XmU/3se0xEzI2IuY899lh/3UqSJElt1ckBoXd40TdHtYomlBOdzwH2A84HvtTf/pl5embOzMyZ06dPb0eJkiRJUlM6MiBExHMoJvkuoLiNaLN6P+3frMH23vWLhtimUoaD7wJvobjr0bszM5uqVpIkSeowHRkQaH1ycq87y2Wj+QK7lcva+QaDaQNARIwDvg+8Hfge8M7MdHKyJEmSNlodFxAiYiJwBMXk5DNabH51uTykfI5Bbb+bUgwBWgHcWLPpqnL56j5q2ZkiONwP3Fu3bTzwQ4orB2cDR7QYZiRJkqSO03EBgeKEe3Pg540mJ0fEuIjYs3zuQSUz7wEuB2YAH6xrdjIwGTgnM5fXrL8WuB14RUQcVnOMLuAL5bffqB02VE5I/glwOEWIOar+tqqSJEnSxqgjnqRcp3d4UX9PTn4mxUn9/RRhoNYHgBuAr0XE7HK/l1I8I2Ee8K+1O2dmd0QcRXEl4YKIuAB4gOJ2pTMpnp1wSt0xvgEcCjxO8RC3T9Y8R63XNZl5TT+vQZIkSeo4HRUQImIv4OW0Pjm5kpn3RMRM4NMUw4YOBRYCpwIn93X70cy8KSJeTHGV4RBgU4rw8Wng85m5uq7JTuVyK+CT/ZRzzWBegyRJkjRaOiogZObtwAYfxfex3/z+9iuHJh3V4rFvoxje1My+s1rpW5IkSdpYdOIcBEmSJEmjxIAgSZIkqWJAkCRJklQxIEiSJEmqGBAkSZIkVQwIkiRJkioGBEmSJEkVA4IkSZKkigFBkiRJUsWAIEmSJKliQJAkSZJUMSBIkiRJqhgQJEmSJFUMCJIkSZIqBgRJkiRJFQOCJEmSpIoBQZIkSVLFgCBJkiSpYkCQJEmSVDEgSJIkSaoYECRJkiRVDAiSJEmSKgYESZIkSRUDgiRJkqSKAUGSJElSxYAgSZIkqWJAkCRJklQxIEiSJEmqGBAkSZIkVQwIkiRJkioGBEmSJEkVA4IkSZKkigFBkiRJUsWAIEmSJKkyttUGEfF84J3AXsDkzDy4XD8DeAnwi8x8ahhrlCRJktQmLQWEiPg08C/85cpD1mzuAr4PfBg4bViqkyRJktRWTQ8xioi3Ax8HfgG8EPhc7fbMvBeYCxw2nAVKkiRJap9W5iAcC9wNHJ6ZvwfW9LHP7cBuw1GYJEmSpPZrJSA8D7gsM/sKBr0eBrYZWkmSJEmSRksrASGAngH22QZYNfhyJEmSJI2mVgLCXcC+jTZGRBfwcuBPQy1KkiRJ0uhoJSD8APibiDixwfZ/AXYFvjfkqiRJkiSNilZuc/pV4C3AFyPirZS3OI2ILwH7AzOBG4HTh7tISZIkSe3RdEDIzJURcSBwKvAuYEy56QSKuQnfBT6UmeuGvUpJkiRJbdHSg9IyczEwJyJOAF4MbAksBn6TmY+NQH2SJEmS2qilgNArM58ELhvmWiRJkiSNslaepPztiDgtIrboZ5/DI+Lbw1OaJEmSpHZr5S5Gc4APADdExM4N9nkhcORQi5IkSZI0OloJCAC3ADsDv46Il41APZIkSZJGUasB4afAocBE4MrydqeSJEmSniZaDQhk5hXAfsBjwPci4mPDXpUkSZKkUdFyQADIzD8CLwV+B/x7RJweEWMGaCZJkiSpww3qNqcAmflIROwPnA+8F9gBuG24CpMkSZLUfoO6gtArM1cAhwP/BRwCHDscRUmSJEkaHa0EhPuBRfUrM7MnM/8ROAGI4SpMkiRJUvs1PcQoM3caYPtXI+L7FHc4kiRJkrQRGvQchL5k5p+Hsz9JkiRJ7dUwIETEDuU/H8rM7prvB5SZDwy5MkmSJElt198VhPlAAnsB82q+H0gO0K8kSZKkDtXfifzZFCf7i+u+lyRJkvQ01TAgZOac/r6XJEmS9PQzpOcgSJIkSXp6GfJcgYjYE3gNsAI4LzMXD9BEkiRJUodq+gpCRHwyIhZGxBY16w4GbgG+BHwd+L+I2HL4y5QkSZLUDq0MMXoNcEdmPlmz7nMUE5dPAv4b2Ak4bvjKkyRJktROrQSEGcDtvd9ExDOBFwFfz8x/y8wPAVcBbxjWCiVJkiS1TSsBYXOg9urBfhRXD/63Zt3NQNMPVJMkSZLUWVoJCI8Bz6z5/kBgLXBTzbrxLfYpSZIkqYO0chejW4HDIuK5wCrgbcCvMnNlzT4zgIXDV54kSZKkdmrl0/4vApsBvwPuLP/95d6NETGGYtjR3OEsUJIkSVL7NH0FITN/GRGvA46mmHtwbmb+vGaXfYGHgJ8Mb4mSJEmS2qWlB6Vl5qXApQ22/RLYu359ROwAzMjM6wZVoSRJkqS2aceE4qOAq9twHEmSJElD5B2HJEmSJFUMCJIkSZIqBgRJkiRJFQOCJEmSpIoBQZIkSVLFgCBJkiSpYkCQJEmSVDEgSJIkSaq0IyBE+SVJkiSpw7UjIJwC7NSG40iSJEkaorGNNkTEDoPtNDMfqPn3YmDxYPuSJEmS1D4NAwIwH8hB9JkD9CtJkiSpQ/V3In82gwsIkiRJkjZSDQNCZs5pYx2SJEmSOoC3OZUkSZJUGdRcgYjYE9gLmJKZ5wxvSZIkSZJGS0tXECLihRExF/gTcAFwZs22AyJiRUS8fnhLlCRJktQuTQeEiNgduAbYAzgV+HndLtcBTwJ/O1zFSZIkSWqvVq4gnASMB16amScAv63dmJkJ/Bp48fCVJ0mSJKmdWgkIs4EfZ+Zt/ezzIPCMoZUkSZIkabS0EhA2BxYMsE9QXGWQJEmStBFqJSD8Gdh1gH2eQ3EVQZIkSdJGqJWAcBXw+ojYo6+NEfFiimFIlw1HYZIkSZLar5WA8DlgHXBdRPwD5VyDiHhO+f3PgKXAl4a9SkmSJElt0fSD0jLzzoh4M/B94D/L1QH8vlwuAt6UmQ8Me5WSJEmS2qKlJyln5qURsRNwJLAPsCWwGLgR+E5mPjn8JUqSJElql5YCAkBmLqJ4UNqpw1+OJEmSpNHUyhwESZIkSU9zLV9BiIitgZkUz0UY09c+mXn2EOuSJEmSNAqaDggRMQ74BvB3NL7yEEACBgRJkiRpI9TKFYTPAEcB9wDnUjwQbd1IFCVJkiRpdLQSEN4JzAP2zsyVI1SPJEmSpFHUyiTlrYFLDAeSJEnS01crAeEBYOpIFSJJkiRp9LUSEM4EXhMRm41QLZIkSZLnvJ2oAAAgAElEQVRGWSsB4fPAr4ArIuLAiPBqgiRJkvQ008ok5bXlMoArACKir/0yM1t+voIkSZKk0dfKifwvKZ5xIEmSJOlpqumAkJmzRrAOSZIkSR2g6TkIEfGKiHjhSBYjSZIkaXS1Mkn5auCYkSpEkiRJ0uhrJSA8DozIQ9IiYk5E5ABf3S30t31EfDsiHo6I1RExPyK+GhGb99Pm2RHxg4h4NCJWRcSdEXFyREzqp82+EXFJRDwZESsj4vcR8eGIGNPqeyBJkiR1glYmKV8D7DtCddwKnNxg2/7AQcDPm+koInYBbqB48vNFwB3AS4DjgFdHxH6Z+URdm5cCVwHjgAuAB8tjfhKYHRGzM3N1XZvDgR8Bq4DzgSeB1wOnAPsBb2mmXkmSJKmTtBIQPg7cFBGfAT6dmWsHatCszLyVIiRsICJ+Xf7z9Ca7+zpFODg2M0+r6ecrwPHAZ4H316wfA3wH2AQ4PDN/Wq7vAn4AvLls9/maNlOB/wG6gVmZObdc/wmKoPG3EfH2zDyvyZolSZKkjhCZzd25NCK+DexK8en4n4HfAY+w4a1PMzPfMyzFRTwP+D3wELBjZvY7zKi8enA3MB/YJTN7arZtCiykeI7D1pm5vFx/EHAlcF1mHlDX387APcD9wE5ZvlkR8ffAGcDZmXlkXZuG/fVl5syZOXfu3IF2GzYP3N3D3Gvg8Udgq21h5izYYddWRprpr9Fda1Zx1crlLOxex3ZjxnLQpMnsNn7iaJclAbCQJfyRR1nEKqYxkeeyNdvhszw7wYM9y7gln+AJVrMlE9g7tuRZXVP6bfNEz1PcxwKWsZwpTGYntmfLroYjhNtm3uoN/w7uPqH/v4OP9yziHhawlBVsyibswvZs1TWtTRWrP/NWr+aKFctYuK6b7caO4eBNprD7hAn9trli0WLOWricBSt72H5SF0duN5mDp23Wb5sr563i+/eu4pHubrYdM4Z37DyR2buPzv8/I+LmzJzZzL6tnBnOAV5OcYK9LfAq4Mhyff3XcOmdFH3GQOGgdGC5vLw2HABk5lLgeoorBfvUbDqoXF5a31lm3gvMA3YEdm6mDXAdsALYNyL6/01rswfu7uGSc2H5Uthy62J5ybnFeqmRu9as4pxli1jS0802XWNY0tPNOcsWcdeaVaNdmsRClnAd97OStWzGBFayluu4n4UsGe3S/uo92LOMy/NhVuQ6tsjxrMh1XJ4P82DPsoZtnuh5it/nHazONUzOTVida/h93sETPU+1sfINzVu9inOW1v0dXLqIeasb/x18vGcRt3Anq1nDFCaxmjXcwp083rOojZWrL/NWr+bMxYtY0t3DNmPGsKS7hzMXL2Le6tUN21yxaDGfuWcpi9Ymz5jYxaK1yWfuWcoVixY3bHPlvFV8+a5lLOnpYZuuLpb09PDlu5Zx5bzO//9nKwFhpya/dm7UQSvKycHvphjG860mm+1RLuc12H5Xudx9pNpk5jrgPorhW8PyXgyXudfA5KkweVOIrmI5eWqxXmrkqpXL2TS6mNo1hq4IpnaNYdPo4qqVy0e7NIk/8iiTGMskxhEEkxjHJMbyRx4d7dL+6t2STzA5x7BJjCUi2CTGMjnHcMv60wDXcx8LGM94JsR4IoIJMZ7xjOc+FrSx8g1dtXI5m3bV/R3s6v/v4D0sYALjmMB4gmAC45nAOO4Z5dciuGLFMqZ2dTF1TPnzHDOGqV1dXLGicXg9a+Fypo4Lpo0LugKmjQumjgvOWtj4d+D7965iSnQxtauLiGBqVxdToovv39v5AaGVB6XdP5KF9OGtwDTg4sx8sMk2vdd5GsW53vW11/fa1aYSEcdQXh3ZYYcdGnQx/B5/pLhyUGuTycV6qZGF3evYpmv9G3NNiS4Wdq8bpYqkv1jEKjZj/Yu1ExnLIjr/f8BPd0+wmi0Yv966SYzhCRp/SruM5Uxmk/XWjWccyxjdDyQG83dwKSuYwvo3QhzPOJayYkRqVPMWrutmmzF1P8+uLhauazxYZcHKHp4xcf3P1aeODRasbDwK45HubrbpWr/NphE80t30jTlHTScPPu8dXvTNUa1iBGTm6Zk5MzNnTp8+vW3H3WpbWFH3N3bF8mK91Mh2Y8aybP0ReyzLHrYb08o9DqSRMY2JrGL9k7RVrGMazpEZbVsygZWsfyK0km62pPHo2ylMZg3r3wNlDWuZwuQRqbFZg/k7uCmb9PlaNq0LQGq/7caOYVlP3c+zp4ftxja+S/32k7pYsm79abdL1iXbT2p8Kr3tmDEsrZvruzSTbcd0/t3wWw4IEfH6iDgvIn4XEXfXrN8rIj4aEc8calER8RyKW6ouAC5poWnvJ/eNZoz0rq8dANiuNqNu5ixYvqSYe5A9xXL5kmK91MhBkyazNHtY0tNNTyZLerpZmj0cNGl0/4ctATyXrVnJOlayliRZyVpWso7nsvXAjTWi9o4tWR7drMh1ZCYrch3Lo5u9Y8uGbXZie9awhtW5hsxkda5hDWvYie3bWPmGDpo0maU9dX8He/r/O7gL27OataxmDUmymjWsZi27jPJrERy8yRSW9PSwpLv8eXZ3s6Snh4M3aTyB/sjtJrNkbbJobdKTsGhtsmRtcuR2jX8H3rHzRJZlD0t6eshMlvT0sCx7eMfOnf8BRtMBIQpnARdS3ON/F4o5B72eAv6dYt7AULU6ObnXneVy9wbbdyuXtXMHhrVNRIyleF/WAff2V2y77bBrF4e+q5h78MSjxfLQd3kXI/Vvt/ETOWLKNKZ2jeHPPd1M7RrDEVOmeRcjdYTtmMor2JFJjGMxq5nEOF7Bjt7FqAM8q2sKh8Qz2CTG8mSsYZMYyyHxjH7vYrRl1+Y8P/ZkQoxneaxgQozn+bHnqN/FaPcJEzli07q/g5tO6/cuRlt1TWNv9mAC41nGSiYwnr3Zw7sYdYDdJ0xgzmbTmDqmiz93dzN1TBdzNpvW712MDp62GZ/YZVOmjQseXtXDtHHBJ3bZtN+7GM3efSIn7jaFqV1d/Lmnh6ldXZy425RRu4tRK1q5zekHgdOAbwMnUjwb4BOZOaZmn2vLPl8x6IIiJgIPA1Mpbi3a7PwDb3MqSZIk9WGkbnP6HopnHxydmYvZ8PkHUNzxZ6c+1rfiLcDmwM8bhYOIGBcRe5aBoJKZ9wCXAzOAD9Y1OxmYDJzTGw5K1wK3A6+IiMNqjtEFfKH89hu5fpK6AHgceHtEzKxpMxH4t/Lb/27itUqSJEkdpZVZhnsA38z+Lzk8Cgx11m3v8KL+npz8TIqT+vspwkCtDwA3AF+LiNnlfi+leEbCPOBfa3fOzO6IOIriCcgXRMQFwAPAbGAmxbMTTqlrsyQijqYICtdExHnAk8BhFO/TBcD5zb9kSZIkqTO0cgVhHQx4W4hnAo1vIjuAiNiL4mFsrU5OrpRXEWYCZ1IEgxMp5kucCuyTueENmDPzJuDFwEXAIRTDpzYDPg28MjM3uCdbZl4IHEDxYLQ3A/8IrAVOAN4+QJCSJEmSOlIrVxBuA2ZFRPR18lsOrzkIuGWwxWTm7RRzBAbab35/+5VDk45q8di3UQxvaqXN9cChrbSRJEmSOlkrVxDOAfYETinH51ciYgzwFeAZFJ/cS5IkSdoItXIF4ZsUY+yPpfikfSlAOWZ/H4pwcFFmnjvcRUqSJElqj6avIJTPI3gdxbj8CRTPAAjgTcAmwGdocYiOJEmSpM7SyhUEMnMd8KmIOJkiIGxJ8VThO1p8oJkkSZKkDtR0QIiICb138yknKd/ZYL8Z5SRiSZIkSRuZViYpDzi3ICKeRfE8AUmSJEkboVYCwpsi4tRGGyNiW4pw8MwhVyVJkiRpVLQSEE4DPhQR/1S/ISK2Bq4GdgL+bphqkyRJktRmrUxS/jCwPfD5iFiQmecBRMQWwBUUk5aPyszzh79MSZIkSe3Qym1OE3gncCNwZkTMiojNgF8AzwX+ITPPHpkyJUmSJLVDq7c5XR0RhwHXAz8B7gX2Bo7PzNNHoD5JkiRJbdTKHAQAMvNJ4FXACuCFwP/LzIaTlyVJkiRtPBpeQYiIbw/Q9n5gPLBH3b6Zme8ZjuIkSZIktVd/Q4zmNNlH/X4JGBAkSZKkjVB/AWGntlUhSZIkqSM0DAiZeX87C5EkSZI0+lqepCxJkiTp6cuAIEmSJKliQJAkSZJUMSBIkiRJqhgQJEmSJFUMCJIkSZIqBgRJkiRJFQOCJEmSpMqwBoSI+FhEXDWcfUqSJElqn+G+grAncMAw9ylJkiSpTRxiJEmSJKkytr+NEfHpFvvbewi1SJIkSRpl/QYE4ONAAtFCnzn4ciRJkiSNpoECwkrgIeCzTfb3XmDfIVUkSZIkadQMFBD+AOyamWc101lEzMKAIEmSJG20BpqkfCuweUQ8qx3FSJIkSRpdAwWE3wJLgL2a7O9XwNlDqkiSJEnSqOk3IGTmGZm5eWZe3kxn5f5HDU9pkiRJktrN5yBIkiRJqox4QIiIkyJi3UgfR5IkSdLQtesKQivPUZAkSZI0ShxiJEmSJKliQJAkSZJUMSBIkiRJqhgQJEmSJFUMCJIkSZIqBgRJkiRJFQOCJEmSpIoBQZIkSVJlbBuOcSEwvw3HkSRJkjREIx4QMvN3wO9G+jiSJEmShm5YhxhFxH9ExD3D2ackSZKk9hnuOQhbATOGuU9JkiRJbeIkZUmSJEmVfucgRMTZLfa37xBqkSRJkjTKBpqk/G4ggWihzxx8OZIkSZJG00ABYSmwAPhAk/39M3DIkCqSJEmSNGoGCgi/A16Qmdc201lEzBlyRZIkSZJGzUCTlG8FpkTELu0oRpIkSdLoGugKwrXA/sD2QDPPN/CpyZIkSdJGrN+AkJk/An7UbGeZeRFw0VCLkiRJkjQ6Rvw5CBExNSJ2GOnjSJIkSRq6djwo7XjgvjYcR5IkSdIQ+SRlSZIkSRUDgiRJkqSKAUGSJElSxYAgSZIkqWJAkCRJklQxIEiSJEmqGBAkSZIkVQwIkiRJkirtCAhRfkmSJEnqcO0ICN8BDmzDcSRJkiQN0dhmd4yIScA+wO7AtHL1ImAecGNmruyrXWbeD9w/xDolSZIktcGAASEiNgc+CxwBbNJgtxURcTbw8cx8ahjrkyRJktRG/QaEiJgGXA/sCSwHfgHcBSwud9kM2A3YD/gH4MCIeFlmLu6jO0mSJEkdbqArCCdRhINTgJMyc1lfO0XEFODTwIeBTwInDmeRkiRJktpjoEnKbwCuyswTG4UDgMxclpknANcAbxrG+iRJkiS10UABYTvgNy30d2PZRpIkSdJGaKCA8ASwRwv97VW2kSRJkrQRGiggXAa8ISI+MFBHEfEh4DDg0uEoTJIkSVL7DTRJ+RPAa4HTIuJE4HKK5x7U3sVod+AQYAbwKMUkZUmSJEkboX4DQmY+FBEvA/4beCXwPiDrdotyeTnwgcx8aNirlCRJktQWAz4oLTPvBV4VETsDB1LMSdis3LwYuBO4utxPkiRJ0kZswIDQqwwAhgBJkiTpaWygScqSJEmS/ooYECRJkiRVDAiSJEmSKgYESZIkSRUDgiRJkqSKAUGSJElSxYAgSZIkqWJAkCRJklQxIEiSJEmqGBAkSZIkVQwIkiRJkioGBEmSJEkVA4IkSZKkigFBkiRJUsWAIEmSJKliQJAkSZJUMSBIkiRJqhgQJEmSJFUMCJIkSZIqBgRJkiRJFQOCJEmSpIoBQZIkSVLFgCBJkiSpYkCQJEmSVDEgSJIkSaoYECRJkiRVDAiSJEmSKgYESZIkSRUDgiRJkqSKAUGSJElSxYAgSZIkqWJAkCRJklQxIEiSJEmqdFxAiIjZEfGTiHgkIlZHxMMRcVlEHNpk+4iIoyPipohYFhHLI2JuRLw/Ivp8vRGxTUScFhH3lcd8rKzhb/o5zvMi4tyIuDsiVkbEQxFxdUS8rdFxJEmSpE43drQLqBURXwQ+AiwAfgo8DkwHXgTMAi5popvvAu8EHgW+D6wAXgn8N7Av8Hd1x5wB3ABsB/wG+HF5zDcBr42I12fmZXVtXl/u11PWeQGwFfBG4DzgYODo5l+5JEmS1BkiM0e7BgAi4mjgdOAs4JjMXFO3fVxmrh2gjzdSnLjfB7wkMx8v148HfgS8DnhzZv64ps1FwGHA14APZ/mGRMTuwFxgGbBbZi6vafMn4NnArMy8tmb9tsDvgK2BHTPzgYFe98yZM3Pu3LkD7SZJkiQNWkTcnJkzm9m3I4bCRMQE4LPAA/QRDgAGCgelN5bLL/eGg7LtGuAT5bcfqjnuROA1FFcCPp41aSkz5wHfpriy8Oa64+wMLKkNB2WbR4Cbym+nN1GvJEmS1FE6IiBQDAGaTjlsJyJeGxEfi4jjIuJlLfSzbbm8t49tvev2L68oAGwBjAMez8yl/bSZXbf+T8DUiHh57cqI2Bp4CbAQuK2FuiVJkqSO0ClzEF5cLlcBtwDPrd0YEdcBf5uZjw3QT+9Vg5362LZzuRxb/vsO4CmgG9gqIqZk5rIGbfaoW3888L/AFeUQpXsp5iC8AVgEvDMzVw5QqyRJktRxOuUKwtbl8iNAAvsDmwLPBy4HXgH8sIl+Li6XJ0TEFr0rI2IccHLNfpsDlCfxV1O8D5+u7SgidgX+vnb/Xpn5S+BlwN3AW4F/Bt4LTAC+A/yhvyIj4pjyzkpzH3tsoMwjSZIktU+nBITeOtYBh2XmrzJzWWb+gWJewQLggCaGG50HXAbsAtwWEd+MiFOBWylCR++k4Z6aNh8GFgPHR8SvI+JLEXFW2eaePvYnIl4J/BJ4iOIOS5PLY36LYi7FlRHR8OpMZp6emTMzc+b06U5VkCRJUufolICwqFzekpnzazdk5gqKk34oxvc3lJndwOspPtF/DDiy/LqL4hanvfMMHq1p8yeKk/yzgR2BY4EDgFOAf6zfv7wycT6wEnhjZv5fZq7IzHsz8wTgwvJY727ytUuSJEkdo1PmINxZLhc12P5UuZw0UEfl3Y6+UH5VyjsW7UYxIfm+ujb3UAQJ6tr0DjH6bc3qfSmGHF1dhpd6V1PMRXjR/2/vzsMtOes6gX9/EggIElYJw9ZhGUBxhqUH2SQgIygxiExQRJDFBHFARDYdkNUFkEURGQQZDOACyqKjJiAPEJMIY2wJm4AgpAMhLAlLMCQBEn7+UXVfz1zOTd90p/vevvfzeZ7zVJ+33qrznnPerlvfU/VWJTl+T+0FAIDNZLMcQXhHprEH37PGXYhXBi2fsWTeej0oyZUy3TxtvR46T/9koezQebrWuUEr5d92qVYAANjsNkVA6O4zk/xVkhsn+cXFeVV17yT3yXR04a1z2RWr6lZVdbPV66qqqy8pu22SF2Q6EvG8VfMOne/DsFhWVfW0THdvfkN3v3dh9nsyjZW469y2xeVulOTn5qfv2MPbBgCATWeznGKUJI9JcrskL66qozJd7vSITKfrXJLk2O4+b657gyQfSXJmkh2r1vP2qrowyYcyjTm4dZKjMo0ZOLq7z15V/xZJTqmqtyfZnem+CPdK8n1JTk3yqMXK3X12Vf1apqsinVhVf53pkqmHJ3lAkqsleUt3n7DXnwQAAGyQTRMQuvusqrpDkmckuV+mS5t+NdORhed292nrXNUbM51O9JBMYxY+k+SV8zrOWlL/80lOyHTZ0qOTfDPTTc4em+QV3X3xkrY+p6ren+TRmcYkHJXkgkyXN33d/HoAAHDQqe7e6DZsazt37uxdu3ZtdDMAANjCquqfunvneupuijEIAADA5iAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAg4AAAAAMAgIAADAICAAAwCAgAAAAw6YLCFV1r6p6S1V9rqq+XlVnV9Xbquq+61y+quq4qvqHqjq/qr5WVbuq6tFVtfT9VtX1quqlVXXG/JrnzG24/R5e6+ZV9QfzchdV1blV9f+q6ol7894BAGCjHbLRDVhUVb+V5MlJzkryf5Ocm+S6Se6Q5B5JTljHav4oyYOTfCHJnya5IMkPJXl5krsk+ZlVr7kjybuTXD/JaUnePL/mA5IcVVVHd/fblrT1AUn+JMk3k/x1kjOSHJbklvOyL1rn2wYAgE1j0wSEqjouUzh4TZJHdfc3Vs2/4jrW8eOZwsEZSe7Y3efO5VdK8qYkD62qv+juNy8s9pJM4eB3kzy+u3te5teT7Eryh1V1i+7+2sLr3CZTOPhwkvt29+cua1sBAGAz2hSnGFXVoUl+I8mnsiQcJEl3f3Mdq/rxefqilXAwL/uNJE+fnz524XWvnORHknwrya+uhIN5mY8leXWm8PA/Vr3Obya5UpKfXh0OLkNbAQBg09ksRxB+KNNpPb+T5FtVdVSS2yS5KMlp3f2eda7n8Hn6ySXzVsp+oKquNIeGayW5YpIvdPe/Xcoy90ry2iSpqqsnOSrJ+7v7I1V1xyR3S3KFJB9J8rfLAg4AABwMNktA+G/z9KIkp2cKB0NVnZzkmO4+Zw/rWTlqcMSSeTedp4fM//5oki8nuSTJdarqat19/hrL3HKh7A6Zjrzsrqo/S/LAVct8qqqO6e5/3ENbAQBg09kUpxgl+e55+uQkneQHknxXkv+S5G+T3D3Jn69jPX8zT59QVddaKZzHBDx7od41k6S7L0zyrkyfw3MWV1RVN0/yyMX6q9p6dKYjCw/OdCRiR5IXJLlxkhOq6jprNbKqHjVfWWnXOefsKfMAAMCBs1kCwko7Lk5yv+4+tbvP7+4PZhpXcFaSI6vqzntYz+uTvC3JzZJ8uKpeUVUvSfK+TKHjU3O9by0s8/gk5yX5pap6T1W9sKpeMy/ziSX1V9p6hSSP6e4/7e4vd/eZ3f2UTFdBuk6S49ZqZHe/srt3dvfO6173unt4SwAAcOBsloDwlXl6enfvXpzR3Rdk2ulPkjte2kq6+5JMv+z/SpJzkjxsfnw80yVOV8YZfGFhmX/OdNrQa5PcJMnjkhyZ5LeT/MLq+gtt7SR/uaQZb1lPWwEAYDPaLGMQ/mWefmWN+V+ep1fZ04rmKwg9f34M8xWLbpHk3O4+Y9Uyn8gUJLJqmZVTjBbHE6y09aL5FKW9bisAAGw2m+UIwjsy/SL/PWvc7Xhl0PIZS+at14MyXZr0Ty/DMg+dp3+yUtDdn8x0daOrVNXNlixzebQVAAA2xKYICN19ZpK/yjTA9xcX51XVvZPcJ9PRhbfOZVesqlst20GfL0O6uuy2mQYQfznJ81bNO3S+D8NiWVXV0zLdvfkN3f3eVav8vXn6/Ko6ZGG5Gyb5pfnp6y/tPQMAwGa0WU4xSpLHJLldkhfP90E4PdPlSu+f6VKkx3b3eXPdG2S658CZma4etOjtVXVhkg9lGnNw60z3LbgwydHdffaq+rdIckpVvT3J7kz3RbhXku9LcmqSRy1p60uT/HCmG6i9r6rekemqS/fPdMWjF3f33132jwAAADbWpgkI3X1WVd0hyTOS3C/TpU2/munIwnO7+7R1ruqNmU4nekimcQCfSfLKeR1nLan/+SQnJLlzpgHO30zy4Ux3XH5Fd1+8pK0XV9XRmY52/EymEHFxkvcneVl3X5bTmAAAYNOo7t7oNmxrO3fu7F27dm10MwAA2MKq6p+6e+d66m6KMQgAAMDmICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAwCAgAAMAgIAADAICAAAACDgAAAAAzV3Rvdhm2tqs5JcuZGt2Obuk6Scze6EWwofWB78/2jD7Cd+sBNuvu666koILBtVdWu7t650e1g4+gD25vvH30AfWA5pxgBAACDgAAAAAwCAtvZKze6AWw4fWB78/2jD6APLGEMAgAAMDiCAAAADAICAAAwCAgAAMAgILClVNUxVfXSqjqlqr5aVV1Vf7SHZRr6chgAAAt0SURBVO5SVSdU1Zeq6sKq+kBVPb6qrnCg2s3lo6quXVXHVtVbqupf5+/zvKo6tap+tqqWbvP0ga2lqp5fVe+oqk/P3+eXqur0qnpmVV17jWX0gS2uqh4y/03oqjp2jTo/WlUnzduN86vqH6rqYQe6rey7qtq98H2vfnxujWVsB2YGKbOlVNX7kvzXJOcnOSvJrZL8cXc/ZI36P5bkTUkuSvKGJF9KcnSSWyZ5Y3c/8EC0m8tHVT06ycuTfDbJu5J8Ksn1kjwgyWGZvusH9sKGTx/YeqrqG0nem+TDSb6Q5KpJ7pRkZ5Kzk9ypuz+9UF8f2OKq6kZJPpjkCkmuluS47n7VqjqPTfLSJF/M1A++keSYJDdM8qLuftIBbTT7pKp2J7lGkt9ZMvv87n7hqvq2AwsEBLaUqrpnpmDwr0mOzLSTuDQgVNXV53qHJblrd++ay6+c5J1J7pzkp7r79Qeo+eyjqvrBTDuDf9Pd31ooPzzJaUlulOSY7n7TXK4PbEFVdeXuvmhJ+W8keWqSl3f3/5zL9IEtrqoqyduTHJHkzUmelFUBoap2JPlokq8luUN3757Lr5nkH5PcLMlduvs9B7Lt7L05IKS7d6yjru3AKk4xYkvp7nd198d7fcn3mCTXTfL6lY3BvI6Lkvzq/PTn90Mz2U+6+53d/VeL4WAu/1yS35+f3mNhlj6wBS0LB7M/m6e3WCjTB7a+xyX5wSSPyBQAlnlkkkOT/N5KOEiS7v5ykt+cnz56P7aRjWU7sMohG90A2EA/OE/fumTeyUkuSHKXqjq0u79+4JrFfvLNeXrxQpk+sL0cPU8/sFCmD2xhVXXrJM9L8pLuPnk+yrjMpfWDE1fV4eBxaFU9JMmNM4XDDyQ5ubsvWVXPdmAVAYHt7Jbz9GOrZ3T3xVV1RpLvTXLTJB85kA3j8lVVhyT5mfnp4h8AfWALq6onZTrf/LBM4w/ulmkH4XkL1fSBLWr+f/+6TGORnrqH6pfWDz5bVV9LcsOq+s7uvuDybSn70eGZ+sCiM6rqEd39dwtltgOrCAhsZ4fN0/PWmL9Sfo0D0Bb2r+cluU2SE7r7bQvl+sDW9qRMg9RXvDXJw7v7nIUyfWDrekaS2yW5W3dfuIe66+kHV53rCQgHhz9MckqSf07yb5l27h+b5FFJTqyqO3f3++e6tgOrGIMAbGlV9bgkT8w0APGhG9wcDqDuPry7K9OviA/ItINwelXdfmNbxv5WVd+f6ajBiwws3p66+9nzuLTPd/cF3f2h7n50khcnuUqSZ21sCzc3AYHtbOUXgcPWmL9S/pUD0Bb2g/myhS/JdLnLe3b3l1ZV0Qe2gXkH4S1J7p3k2kleuzBbH9hi5lOLXpvpdJGnr3Ox9faDtX5h5uCxcsGKuy+U2Q6sIiCwnf3LPP3Pq2fMf2COyDSg9ZMHslFcPqrq8Zmuaf6hTOFg2Y1x9IFtpLvPzBQWv7eqrjMX6wNbz9UyfZ+3TnLR4g2ykjxzrvMHc9nKNfIvrR9cP9PpRWcZf7AlrJxieNWFMtuBVQQEtrN3ztMfXjLv7km+M8m7t8sVC7aSqvrlJL+d5H2ZwsEX1qiqD2w//2merlzFRB/Yer6e5P+s8Th9rnPq/Hzl9KNL6wc/sqoOB7c7zdPFnX3bgdW628NjSz4yXe++k/zRGvOvnumXhK8n2blQfuUk756XfdBGvw+Py/y9P33+7nYludYe6uoDW+yR6RfAw5aUf0eS35i/07/XB7bnI9N5553k2FXlR2S6g+4Xk+xYKL9mphtodZI7b3T7Pdb9Pd86yVWXlO9I8vH5+3zqQrntwKqHqxixpVTV/ZPcf356+Dy9c1UdP//73O5+UpJ091er6rgkb0xyUlW9PtOt1e+X+dbqmW63zkGiqh6W5DmZfh0+Jcnjppuo/n92d/fxiT6wRd03yXOr6tQkZ2Ta4btepjur3zTJ55Ict1JZHyBJuvuMqnpykt9Nsquq3pDkG5luoHXDGOx8sPnJJE+sqpOTnJnpKkY3S3JUpp3+E5K8cKWy7cC3qzkhwZZQVc/Kf5xjusyZveq261V11yRPy3Qr9Stn+rXo1Ul+t7/9ZipsYuv4/pPk77r7HquW0we2iKq6TaY73t4t047dNTLdIOljSf4m03e6erC6PrBNLGwjjuvuVy2Zf3Smy+PePtNRpw9nurvyaw5kO9k3VXVkpu3A7TL9WHjVTAOM35fpvgiv6yU7wLYD/0FAAAAABoOUAQCAQUAAAAAGAQEAABgEBAAAYBAQAACAQUAAAAAGAQEAABgEBAAOGlV1fFV1Ve3Yz6+zu6p278/XANisBAQAtp2qOqmq3CkUYIlDNroBALAJ3WujGwCwUQQEAFiluz+x0W0A2ChOMQLYBqpqx3zu/vFVdauq+ouq+lJVfa2qTq2qey9Z5tCq+pWq+mBVXVBVX62qU6rqJy6n9T9rXuYel7a+db6/h1fVm6rqk1V14dzWv6+qhyxbb5Ij5+e98Dhpod7SMQj78JnsqKrXV9W5VXVRVe2qqh9dz3sDONAcQQDYXo5I8p4kH0zyiiTXT/KTSU6sqgd39xuSpKqulORtmXakP5rkZUm+M8kxSd5QVbft7qfu7fr3g5cn+eckJyf5bJJrJ7lvktdV1S27++lzva8keXaShye5yfzvFbsv7QX24TO5SZLTknwyyeuSXCvTZ/KXVfXfu/tdl/XNAuxP1W2MFsBWN1/154z56Qu7+8kL83Zm2qk/P8lNuvurVfW/kvxmkhOT3K+7L57rfnemnd2bJLlrd797b9Y/lz8ryTOT3LO7T1qjva/p7ocvlB+f5GFJjuju3QvlN1t9WtC8Q39ikrsn2dHdn1mYd1KSI7u71vi8didJd+9YKNuXz+RZ3f3shXXdJ8lbk5zY3fdd1gaAjeIUI4Dt5bwkz1ks6O5dSf44yTWS/Phc/MgkneQJKzvCc90vJPm1+emx+7D+y9WyMQPd/Y1Mv/Ifkstn0PHefiZnJvn1VW17W5JPJbnj5dAugMuVgACwvby3u/9tSflJ8/R2VfVdSW6e5Ozu/uiSuu9cqbs3678MbV23qrpxVb2sqj46jw3oeazBm+YqN9jH9e/LZ/K+7r5kSfmnk1xzX9oFsD8YgwCwvXx+jfLPzdPD5kcyncu/zEr5NfZy/ZerqrppplN8rpnklCR/m+lIxiVJdmQ6JenQfXyZfflMvrLGMhfHD3XAJiQgAGwv11uj/PB5et78WCxb7foLdfdm/Su+NU+X/S1atqO9lidkGpT8iO4+fnFGVf1UpoCwr/blMwE4qPjlAmB7uf18usxq95inp8+nCH0iyQ2q6hZL6t5znr53b9a/UPbleXqjJfV3Lilby83n6ZuWzDtyjWUuSZKqusJ6XmAfPxOAg4qAALC9HJbkGYsF81WGfjrTr99vmYtfnaSSvGBxJ7qqrpPk6Qt19nb9yXRaUJI8oqoOWah/o9Xr2IPd8/Qeq173Plk+aDhJvjhPb3wZXmdvPxOAg4pTjAC2l5OTHFtV35/k7/Mf9yn4jiQ/t3IJ0iQvTPIjSX4syfur6oRM1/x/YJLvTvJb3X3qPqw/3f0PVXVypsuQnlZV78x0itLRme43sOzIwjL/O8kjkvx5Vb0xydlJbpPkh5P82fz6q71jfi9vnt/bhUnO7O7XXcrr7O1nAnBQcQQBYHs5I8ldMp3e8+gkP5HptJj7Lt7EbL5E6A8ledpc9AuZzuX/eJIHd/cv78v6F/xYklclueH8GrdL8pQka63/23T3BzKd4vPuJEcl+fkkV0/ygCS/v8Zir0ry3ExHPJ6S6TKlP7uH19nbzwTgoOJGaQDbwFo3HjtY1g/AgeMIAgAAMAgIAADAICAAAACDMQgAAMDgCAIAADAICAAAwCAgAAAAg4AAAAAMAgIAADD8O1TpZLzwFEp+AAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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7AABbR6hn3Q69XfK1y67d9rXLpnYAALaOUM+6HfGwaRz9FZckvfuar4942N6uDADghkWoZ91ud4/kIcdP4+gv+uK0fMjxZr8BANhqprRkj9zuHkI8AMDeti2u1FfVt1XVL1TVW6vqk1X1taq6pKr+sap+vqr2WdD/rlX1zKp6T1V9vqq+UVVfrqpTq+rBSxzjuKrqZV6/uDVnCwAAG2u7XKl/bJI/T/LFJKcn+VySWyf58SSvSPLwqnpsd/es/3OTPC7Jx5K8I8mFSe6W5BFJHlFVJ3T3i5c41qlJzl6kfecGnQsAAGyp7RLqP5EpkL+9u3fPNVbV7yT5cJKfyBTw3zxbdVqSP+3uf5m/k6p6UJJ/SPK8qvqb7v7iIsc6pbtP2vhTAACAvWNbDL/p7vd099vmB/pZ+5eSvGz29uh57SctDPSz9jOTnJHkRknuv2kFAwDANrJdrtQv55uz5VUb1P/IqjoxyQFJzk9yeneftwf1AQDAXrWtQ31V7ZfkZ2dvT1tF/zsmeUiSK5K8d4luJyx4f3VVvSLJid195XprBQCAvWVbDL9Zxp8kuWeSd3T3u5brWFU3TvK6JDdO8pzuvmhBl88k+bVMN9QemOS2SX4yyblJnpLkr1bY//FVtbOqdu7atWsdpwIAAJujrplQZnupqqcmeVGSf0vygO6+cJm++yb535lm0Tk5yU/1Kk+sqr4jyTlJDklyZHefs9I2O3bs6J07TZYDAMDmqaqzunvHavpuyyv1VfWrmQL9x5I8eBWB/rWZAv0bkzxhtYE+Sbr785mmxUySB667aAAA2Eu2Xaif3cT6kiQfzRTov7RM3/0zXaF/fJLXJ/np7l7tDbXzzY2nOXAd2wIAwF61rUJ9VT0zyQszPRzqwd39H8v0vVGSv8l0hf7VSZ7Y3Vev89A/MFt+ep3bAwDAXrNtQn1VPTvTjbFnJXlId1+wTN8bJ3lrkkcmeWWSJy2c436Rba4zHqmq9qmq305yVJILsooZdgAAYLvZFlNaVtWxSf4wydVJ3pfkqVW1sNu5854E+7IkP5opiJ+f5PcW6X9Gd58x7/0/VdVHM90Ue36Sg5I8INPsOlck+ZnuvnSDTgkAALbMtgj1Se40W+6b5MQl+pyZ5KQF/W+Z5PeW2e8Z875+fpL7JjkmyaFJdif5XJI/S/KC7jb0BgCAIW3bKS23M1NaAgCw2Yaf0hIAAFg9oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBbYtQX1XfVlW/UFVvrapPVtXXquqSqvrHqvr5qlq0zqq6f1W9o6ounG3zkao6sar2XeZYP1ZVZ8z2f3lVfaiqjt28swMAgM21394uYOaxSf48yReTnJ7kc0luneTHk7wiycOr6rHd3XMbVNUjk7w5yZVJTk5yYZL/kuSFSR4w2+e1VNWvJnlJkq8keW2SbyR5TJKTqupe3f30zTpBAADYLDUvJ++9IqqOSXJgkrd39+557bdJ8uEk35HkMd395ln7LZJ8MslBSR7Q3Ttn7QckeU+So5L8VHe/Yd6+Dk/yb0m+muQ+3X3urP2QJP+U5C5J7t/dH1yp3h07dvTOnTv37KQBAGAZVXVWd+9YTd9tMfymu9/T3W+bH+hn7V9K8rLZ26PnrXpMksOSvGEu0M/6X5nkWbO3v7TgMD+X5MZJXjoX6GfbXJTkj2Zvf3HPzgQAALbetgj1K/jmbHnVvLZjZsvTFun/3iRXJLl/Vd14ldu8c0EfAAAYxrYO9VW1X5Kfnb2dH8bvNlt+YuE23X1Vks9kul/gzqvc5ouZhuXcvqpuukQtx1fVzqrauWvXrjWdBwAAbKZtHeqT/EmSeyZ5R3e/a177QbPlJUtsN9d+8Dq2OWixld398u7e0d07DjvssOWrBgCALbRtQ31VPTXJb2S6ufWJe7kcAADYtrZlqJ9NPfmiJB9L8uDuvnBBl2Wvqs9rv3gd2yx1JR8AALalbRfqq+rETHPJfzRToP/SIt3+32z5XYtsv1+SO2W6sfbTq9zm2zNNqXled1+x/uoBAGDrbatQX1XPzPTwqLMzBfr/WKLre2bLhy2y7oFJbprkA9399VVu8/AFfQAAYBjbJtRX1bMz3Rh7VpKHdPcFy3R/U5ILkjy+qr41If/s4VP/dfb2zxds89dJvp7kV2cPoprb5pAkvzN7+7IAAMBg9tvbBSRJVR2b5A+TXJ3kfUmeWlULu53b3SclSXdfWlVPzhTuz6iqNyS5MMkjMk1d+aYkJ8/fuLs/U1XPSPLiJDur6uQk38j0IKvbJ/kfq3maLAAAbDfbItRnGgOfJPsmOXGJPmcmOWnuTXefUlUPSvK7SX4iyQFJPpnk15O8uLt74Q66+yVVdW6Sp2ea/36fTDfjPqu7X7UhZwIAAFusFsm+rGDHjh29c+fOvV0GAADXY1V1VnfvWLnnNhpTDwAArI9QDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAa3bUJ9VT2mql5SVe+rqkurqqvqtUv0PWm2frnXuxdsc9wK/X9xa84UAAA21n57u4B5npXkiCSXJzkvyd2X6XtKknOXWPfEJHdO8s4l1p+a5OxF2neuqkoAANhmtlOof1qmMP/JJA9KcvpSHbv7lEzB/lqq6uAkv5nkG0lOWmLzU7p7qXUAADCcbRPqu/tbIb6q1rubJya5SZI3dPcFG1EXAABsd9sm1G+QJ8+WL1+mz5FVdWKSA5Kcn+T07j5v0ysDAIBNcr0J9VV1VJJ7JfnE/Kv+izhhwfurq+oVSU7s7is3rUAAANgk22b2mw1w/Gz5l0us/0ySX0tytyQHJrltkp/MdMPtU5L81XI7r6rjq2pnVe3ctWvXhhQMAAAbobp7b9dwHVV1dKYbZV/X3U9YRf+Dknwh018ebreW8fRV9R1JzklySJIju/uclbbZsWNH79xpshwAADZPVZ3V3TtW0/f6cqX+CUlumuQta71Btrs/n+Qds7cP3OjCAABgs11fQv3cDbJ/sc7t58bTHLgBtQAAwJYaPtRX1Q9kemjVJ7r7jHXu5gdmy09vSFEAALCFhg/1ueYG2eWmsUxVXWc8UlXtU1W/neSoJBckOW3jywMAgM21baa0rKpHJXnU7O1tZsujquqk2dcXdPfTF2xziySPS/L1JK9a4RD/VFUfzXRT7PlJDkrygCT3THJFkp/p7kv39DwAAGCrbZtQn+TIJMcuaLvz7JUkn03y9AXrfybTOPjVPEH2+Unum+SYJIcm2Z3kc0n+LMkLutvQGwAAhrQtp7Tc7kxpCQDAZrshTmkJAAA3WEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcKsO9VX1vVX1y1V10Ly2A6vqVVV1cVV9oapO2JwyAQCApazlSv0zk/xud18yr+2Pkzxxtp9vS/KCqvqRDawPAABYwVpC/Y4kp8+9qar9kxyb5MNJbpXkTkkuSPLUjSwQAABY3lpC/a2SnDfv/Y4kN0/yF919ZXd/IcmpSe69gfUBAAArWEuo7yT7zXv/g7O2M+e17Upy2AbUBQAArNJaQv3nktxv3vtHJjmvuz89r+22SS7aiMIAAIDVWUuof2OS+1fVm6rqtUmOSvKmBX3ukeRTG1UcAACwsv1W7vItL0zysCQ/Pnt/dpI/nFtZVXdK8v2ZZsQBAAC2yKpDfXdfnuQBVXXPWdPHunv3/C6ZAv/ODawPAABYwapDfVXdIcnF3f3RxdZ397lV9ZUkh2xUcQAAwMrWMqb+M0lOXKHPU2f9AACALbKWUF+bVgUAALBuawn1q3GbJF/d4H0CAADLWHZMfVX97IKmIxdpS5J9k9whyROS/OsG1QYAAKzCSjfKnpRpVpvMlo+cvRaaG5pzRZI/2JDKAACAVVkp1D9ptqwkf5XklCSnLtLv6iRfSfLB7r5448oDAABWsmyo7+5XzX1dVccmOaW7X73pVQEAAKu2lodPPXgzCwEAANZno2e/AQAAttiSV+qr6tOZbo794e7+zOz9anR332VDqgMAAFa03PCbfXLNzDeLvV+Kh1QBAMAWWjLUd/fhy70HAAC2B2PqAQBgcNsm1FfVY6rqJVX1vqq6tKq6ql67RN/DZ+uXer1hmeMcW1UfrqrLq+qSqjqjqn5s884MAAA216qntJxTVTuS3DfJIUn2XaRLd/dz11HLs5IckeTyJOclufsqtjkn0wOxFvroYp2r6vlJfmO2/79McqMkj0/ytqr6te5+6TrqBgCAvWrVob6qbpHkLUkenOVvhu0k6wn1T8sUtj+Z5EFJTl/FNmd393NWs/Oqun+mQP+pJN/f3RfN2p+X5Kwkz6+qv+vuc9deOgAA7D1ruVL/vCTHJHlfkr9O8vkkV21UId39rRBftSkT6PzibPnf5gL97LjnVtWfJXl2kicl+f3NODgAAGyWtYT6Ryb55yQP7u7dm1TPWt22qp6S5NuSfCXJB7v7I0v0PWa2PG2Rde/MFOqPiVAPAMBg1hLqD0rymm0U6JPkobPXt1TVGUmO7e7PzWs7MMntklze3V9cZD//Plt+1ybVCQAAm2Yts9/8e5Jbb1Yha3RFpnH798l0w+4huWYc/tFJ3j0L8nMOmi0vWWJ/c+0HL3XAqjq+qnZW1c5du3btQekAALCx1hLq/yzJf6mq221WMavV3f/R3b/X3f/c3RfPXu9N8iNJPpTkO5P8wgYf8+XdvaO7dxx22GEbuWsAANgjawn170zy90neX1VPqqp7V9UdFnttUq0r6u6rkrxi9vaB81bNXYk/KIuba794M+oCAIDNtJYx9edmmq6yck1wXkyvcb8bbW5szLeQAJAgAAAgAElEQVSG33T3V6vq/CS3q6pvX2Rc/V1ny09sRYEAALCR1hK+X50psG9395stP72g/T1JnpjkYZmm5Jzv4fP6AADAUFYd6rv7uE2sY02q6vsyPXhq94L2h2R6iFWSvHbBZi/LFOp/t6pOmffwqcOT/EqSr+e6YR8AALa9vTlM5lqq6lFJHjV7e5vZ8qiqOmn29QXd/fTZ1y9Icteq+kCmp9Amyb1zzVz0z+7uD8zff3d/oKpekOTXk3ykqt6U5EZJHpfk0CS/5mmyAACMaNuE+iRHJjl2QdudZ68k+WySuVD/miSPTvL9mYbO7J/ky0nemOSl3f2+xQ7Q3b9RVf+a6cr88Ul2Z3qg1vO6++827lQAAGDrVPfqhslX1V+tcp/d3T+//pK2vx07dvTOnTv3dhkAAFyPVdVZ3b1jNX3XcqX+uBXWz82M00mu16EeAAC2k7WE+jst0X5wpmEwz07ygSS/tadFAQAAq7eW2W8+u8SqzyY5p6releQjSf5PklduQG0AAMAqrOWJssvq7s8neVuSEzZqnwAAwMo2LNTPfDnXPJ0VAADYAhsW6qtq30zzxF+yUfsEAABWtuox9VX1wGX28R1JnpRprvlXbEBdAADAKq1l9pszMk1XuZRK8t4kz9iTggAAgLVZS6j/wywe6ncnuSjJh7v7wxtSFQAAsGprmdLyOZtYBwAAsE4bPfsNAACwxYR6AAAYnFAPAACDE+oBAGBwQj0AAAxOqAcAgMEJ9QAAMDihHgAABreqUF9Vt62qZ1XV6VX1har6WlV9tao+U1UnV9UjN7tQAABgcSs+UbaqnpTkJUlukqQWrL7j7PWYqnp/kp/s7i9teJUAAMCSlr1SX1XHJHllkq8k+fUkj0zytCSfS/IvSb4zyUOSvCbJA5L8Q1UdsJkFAwAA17bSlfrfTLIryX26+4K5xqp6fZKPJnlqd5+Y5PSqeneSVyU5IcmfblK9AADAAiuNqb9vklPnB/ok6e5dSU5N8ph5ba9J8sEkj9voIgEAgKWtFOpvkuTyJdZ9NcktF7S9P8ld97QoAABg9VYK9ecm+eGqula/2fuHJPnCGvcHAABssJVC+FuS3DPJG6rqHlV146q6e5L/neR7kvzdgv53SXLexpcJAAAsZaUbZf84yaMyjZ3/iXntlWkGnOd+q6HqwCQPzRT4AQCALbLslfruvjzTVJUvyXQF/qokX0ryiiT3m90wO+eKJLdO8kubUyoAALCYFR8+1d0XZ5qm8oQV+nWmm2cBAIAttOk3tlbVCVX16c0+DgAA3FBtxWw1Bye54xYcBwAAbpBMQQkAAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABjcfltwjDO24BgAAHCDtemhvrvPTHLmZh8HAABuqFYcflNVN6uqp1fVX1TVL1fVjWbtd6iqv62qS6vqwqp6dVXdevNLBgAA5lv2Sn1V3TTJB5N8d5JK0kkeXVWPTPKuJHdLcnGSWyR5QpL7VNV9uvvKTa0aAAD4lpWu1D85yfckeX2SRyX56yTHJPlfmYL8Ud19aJKDk7wmyd2T/NKmVQsAAFzHSqH+p5N8vLuf2N1/292/kOScJE9M8lvd/aEk6e7Lk/xCki8nefRmFgwAAFzbSqH+8CTvWdD2vtny/8xv7O5vJvn7TEN1AACALbJSqL9FkksWtF2YJN39xUX6f2G2DQAAsEVWCvUXJjlsQVvNXos5OMlle1oUAACweiuF+k8lueuCtucmuckS/e+Y6Wo9AACwRVYK9f+c5L5Vte9cQ3df3d1fX9ixqm6c5AGZpsAEAAC2yEqh/tmZprTcvYp93TXTlJd/vadFAQAAq7fsw6e6+7Kscox8d380ydM2oigAAGD1VrpSv8eq6ver6qrNPg4AANxQbXqon1lqthwAAGAPbVWoBwAANolQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOD224JjnJLk3C04DgAA3CBteqjv7nOSnLPZxwEAgBuqDR1+U1XPq6pPbeQ+AQCA5W30mPpbJjl8g/cJAAAsw42yAAAwuGXH1FfVq9e4v/vvQS0AAMA6rHSj7BOSdJJawz57/eUAAABrtVKovyzJeUl+eZX7+60kP7JHFQEAAGuyUqg/J8kR3X3manZWVcftcUUAAMCarHSj7NlJblZVd9mKYgAAgLVbKdSfmeQjSW6/yv2dkuQP11NIVT2mql5SVe+rqkurqqvqtUv0vWtVPbOq3lNVn6+qb1TVl6vq1Kp68BLbHDfb51KvX1xP3QAAsLctO/ymu9+c5M2r3Vl3n5rk1HXW8qwkRyS5PNM4/rsv0/e5SR6X5GNJ3pHkwiR3S/KIJI+oqhO6+8VLbHtqpr9ALLRznXUDAMBetdKY+q30tExh/pNJHpTk9GX6npbkT7v7X+Y3VtWDkvxDkudV1d909xcX2faU7j5pY0oGAIC9b9s8fKq7T+/uf+/uFafE7O6TFgb6WfuZSc5IcqOYMx8AgBuI7XSlfqN8c7a8aon1R1bViUkOSHJ+ktO7+7wtqQwAADbB9SrUV9UdkzwkyRVJ3rtEtxMWvL+6ql6R5MTuvnIz6wMAgM2wbYbf7KmqunGS1yW5cZLndPdFC7p8JsmvZbqh9sAkt03yk0nOTfKUJH+1wv6Pr6qdVbVz165dG1w9AACs3/Ui1FfVvklek+QBSU5O8vyFfbr7zO5+aXd/oruv6O4vdvffJHlwkouS/FRVHbHUMbr75d29o7t3HHbYYZt0JgAAsHbDh/pZoH9tkscmeWOSJ6zmZts53f35TNNiJskDN75CAADYXEOH+qraP8n/TvL4JK9P8tPdvdQNssuZG09z4EbVBgAAW2XYG2Wr6kaZrsw/Msmrkzypu3evc3c/MFt+eiNqAwCArTTklfrZTbFvzRToX5lVBPqq2rFI2z5V9dtJjkpyQaaHWgEAwFC2zZX6qnpUkkfN3t5mtjyqqk6afX1Bdz999vXLkvxopiB+fpLfq6qFuzyju8+Y9/6fquqjSc6ZbXNQphtr75lpCsyf6e5LN+yEAABgi2ybUJ/kyCTHLmi78+yVJJ9NMhfq7zRb3jLJ7y2zzzPmff38JPdNckySQ5PsTvK5JH+W5AXdbegNAABDqjVMFMPMjh07eufOnXu7DAAArseq6qzuvs4Q8sUMOaYeAAC4hlAPAACDE+oBAGBwQj0AAAxOqAcAgMEJ9QAAMDihHgAABifUAwDA4IR6AAAYnFAPAACDE+oBAGBwQj0AAAxOqAcAgMEJ9QAAMDihHgAABifUAwDA4IR6AAAYnFAPAACDE+oBAGBwQj0AAAxOqAcAgMEJ9QAAMDihHgAABifUAwDA4IR6AAAYnFAPAACDE+oBAGBwQj0AAAxOqAcAgMEJ9QAAMDihHgAABifUAwDA4IR6AAAYnFAPAACDE+oBAGBwQj0AAAxOqAcAgMEJ9QAAMDihHgAABifUAwDA4IR6AAAYnFAPAACDE+oBAGBwQj0AAAxOqAcAgMEJ9QAAMDihHgAABifUAwDA4IR6AAAYnFAPAACDE+oBAGBwQj0AAAxOqAcAgMEJ9QAAMDihHgAABifUAwDA4IR6AAAYnFAPAACDE+oBAGBwQj0AAAxOqAcAgMEJ9QAAMDihHgAABifUAwDA4IR6AAAYnFAPAACDE+oBAGBwQj0AAAxOqAcAgMEJ9QAAMDihHgAABifUAwDA4LZFqK+qx1TVS6rqfVV1aVV1Vb12hW3uX1XvqKoLq+prVfWRqjqxqvZdZpsfq6ozquqSqrq8qj5UVcdu/BkBAMDW2W9vFzDzrCRHJLk8yXlJ7r5c56p6ZJI3J7kyyclJLkzyX5K8MMkDkjx2kW1+NclLknwlyWuTfCPJY5KcVFX36u6nb9TJAADAVtoWV+qTPC3JdyW5RZJfWq5jVd0iyV8muTrJ0d398939jCRHJvlgksdU1eMXbHN4kudnCv87uvtXuvtpSe6d5FNJfqOqjtrQMwIAgC2yLUJ9d5/e3f/e3b2K7o9JcliSN3T3znn7uDLTFf/kur8Y/FySGyd5aXefO2+bi5L80eztL66zfAAA2Ku2Rahfo2Nmy9MWWffeJFckuX9V3XiV27xzQR8AABjKiKH+brPlJxau6O6rknwm070Cd17lNl9M8tUkt6+qmy510Ko6vqp2VtXOXbt2rbd2AADYcCOG+oNmy0uWWD/XfvA6tjloifXp7pd3947u3nHYYYetqlAAANgKI4Z6AABgnhFD/UpX1efaL17HNktdyQcAgG1rxFD//2bL71q4oqr2S3KnJFcl+fQqt/n2JAcmOa+7r9jYUgEAYPONGOrfM1s+bJF1D0xy0yQf6O6vr3Kbhy/oAwAAQxkx1L8pyQVJHl9VO+Yaq+qAJP919vbPF2zz10m+nuRXZw+imtvmkCS/M3v7sk2qFwAANtV+e7uAJKmqRyV51OztbWbLo6rqpNnXF3T305Okuy+tqidnCvdnVNUbMj0p9hGZpq58U5KT5++/uz9TVc9I8uIkO6vq5CTfyPQgq9sn+R/d/cHNOj8AANhM2yLUJzkyybEL2u6ca+aa/2ySp8+t6O5TqupBSX43yU8kOSDJJ5P8epIXL/Zk2u5+SVWdO9vPz2b6K8XHkjyru1+1oWcDAABbqBbJv6xgx44dvXPnzr1dBgAA12NVdVZ371i555hj6gEAgHmEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcPvt7QIAANj+PvXNr+W937gsX979zdx6n/3zwBvdPHfZ/yZ7uyxmXKkHAGBZn/rm13LylRfmst1X57DaL5ftvjonX3lhPvXNr+3t0pgR6gEAWNZ7v3FZbpZ9cvN99s0+Vbn5PvvmZtkn7/3GZXu7NGaEegAAlvXl3d/MgXXt2Hhg7ZMv7/7mXqqIhYR6AACWdet99s9Xe/e12r7au3PrffbfSxWxkFAPAMCyHnijm+fy7M5lu6/O7u5ctvvqXJ7deeCNbr63S2NGqAcAYFl32f8medwBh+bm++ybXX1Vbr7PvnncAYea/WYbMaUlAAArusv+NxHitzFX6gEAYHBCPQAADE6oBwCAwQ0b6qvquKrqFV5Xz+t/+Ap937A3zwcAANZr5Btlz07yB0us+6EkxyR55yLrzklyyiLtH92gugAAYEsNG+q7++xMwf46quqDsy9fvsjqs7v7OZtVFwAAbLVhh98sparuleR+Sc5P8va9XA4AAGy6Ya/UL+P42fKV3X31IutvW1VPSfJtSb6S5IPd/ZEtqw4AADbY9SrUV9VNkjwhydVJXrFEt4fOXvO3OyPJsd39uU0tEAAANsH1bfjNTyY5OMlp3f35BeuuSPLcJPdJcsjs9aAkpyc5Osm7q+rApXZcVcdX1c6q2rlr167NqB0AANaluntv17Bhqur9Se6f5BHd/bZVbrNfkn9M8gNJTuzuF620zY4dO3rnzp17VCsAACynqs7q7h2r6Xu9uVJfVd+TKdCfl+Qdq92uu6/KNUN1HrgJpQEAwKa63oT6rHyD7HLmxtMsOfwGAAC2q+tFqK+qA5I8MdMNsq9cxy7uN1t+esOKAgCALXK9CPVJHpvpxtd3LnKDbJKkqr6vqq5zvlX1kCRPm7197eaVCAAAm+P6MqXl3NCbxZ4gO+cFSe5aVR/INO4+Se6d5JjZ18/u7g9sUn0AALBphg/1VXWPJD+YlW+QfU2SRyf5/iQPT7J/ki8neWOSl3b3+za5VAAA2BTDh/ru/niSWkW/V2Z94+0BAGBbu76MqQcAgBssoR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AMD/397dB9lV1gcc//42yYYkTTYhopEEiK+EEaTajCJ0IEjVqFUUwarVKlVBp7Vay/TNUqG1rW2d+lbr6ygtdgYtqEynEHV4MSC0HaryUomvJLwrkOyS5m2T3V//eM6ay+Vuspu9d+89u9/PzJln73POPfe357nP7u+e+5znSDVnUi9JkiTVnEm9JEmSVHMm9ZIkSVLNmdRLkiRJNWdSL0mSJNWcSb0kSZJUcyb1kiRJUs2Z1EuSJEk1Z1IvSZIk1ZxJvSRJklRzJvWSJElSzZnUS5IkSTVnUi9JkiTVnEm9JEmSVHMm9ZIkSVLNmdRLkiRJNWdSL0mSJNXc3G4HoIO77064dQNsvQ8OXwknroeVx3U7KknSZG0beYR7R7ewgx0sYhGr+o5h2Zzl3Q5LmpBNu/awYfsu7t87wpHz5rB+8QLWLJjf7bBU8Ux9j7vvTrjmM7BzCJY9uZTXfKbUS5LqY9vII2wavYNh9rCQhQyzh02jd7Bt5JFuhyYd1KZde/js1u0MjYyyYm4fQyOjfHbrdjbt2tPt0FQxqe9xt26AhQNlib79P9+6oduRSZIm497RLfTTT3/MJyLoj/n008+9o1u6HZp0UBu272JJXx8Dc/roi2BgTh9L+vrYsH1Xt0NTxaS+x229DxYsfmzdgsWlXpJUHzvYwTz6H1M3j352sKNLEUkTd//eERb3xWPqFvcF9+8d6VJEauaY+h53+Moy5GbhwP66XdtLvabXg7fBpq/A0N0wcDSsOQtWPLvbUUmqi0UsYpg99LN/DPJehlnEoi5GJU3MkfPmMDQyysCc/Yn99tHkyHlzuhiVGnmmvseduL4k9TuHIEf3/3zi+m5HNrs8eBvc/CHYtQ2WrCrlzR8q9ZI0Eav6jmGYYYZzD5nJcO5hmGFW9R3T7dCkg1q/eAGPjo4yNDLKaCZDI6M8OjrK+sULuh2aKib1PW7lcXDGeeVM/bYHSnnGec5+M902fQUOWwYLlpVrGxYsK483faXbkUmqi2VzlrOm73j6mc9OdtLPfNb0He/sN6qFNQvm8/bDFzMwp48H940yMKePtx++2NlveojDb2pg5XHtTeLvuxNuvRq23QfLVsKJL/VDwsEM3V3O0Dc6bKDUS9JELZuz3CRetbVmwfxZkcTfPriPK+/dyz07RzlqYR9nrprHCUt7P2X2TP0sc9+dcN2nYVc1ReauofLYKTIPbOBo2D302LrdQ6VekiTNDLcP7uOjP9jD4PAoKxcEg8OjfPQHe7h9cF+3Qzsok/pZ5tarYUHTFJkLBkq9xrfmLNi9rYylz9FS7t5W6iVJ0sxw5b17WToPlvaXqTuX9vexdF6p73Um9bPMtnGmyNzmFJkHtOLZ8IILylj6R+8t5QsucPYbSZJmknt2jrJk3mOn7lwyL7hn52iXIpq43h8gpLZatrIMuWmeInOZU2Qe1Ipnm8RLkjSTHbWwj8HhUZb270/sH92bHLWw98+D936EaqsTX1qS+sYpMncNlXpJkqTZ7MxV8xjcC4PDZerOweFRBveW+l5nUj/LrDwOTj+/jKPf9kApTz/f2W8kSZJOWDqXdx87n6X9fdy3K1na38e7j51fi9lvej9CtV27p8iUJEmaKU5YOrcWSXwzz9RLkiRJNWdSL0mSJNWcSb0kSZJUcyb1kiRJUs2Z1EuSJEk1Z1IvSZIk1ZxJvSRJklRzJvWSJElSzZnUS5IkSTVnUi9JkiTVnEm9JEmSVHMm9ZIkSVLNmdRLkiRJNWdSL0mSJNWcSb0kSZJUcyb1kiRJUs2Z1EuSJEk1V+ukPiI2R0SOszw4znNOjoirImJrROyKiNsi4j0RMWe645ckSZLaYW63A2iDIeAjLer/r7kiIs4ErgB2A18CtgKvAD4MnAKc07kwJUmSpM6YCUn9YGZedLCNImIJ8FlgBFiXmbdU9RcC1wJnR8TrMvOyTgYrSZIktVuth99M0tnAEcBlYwk9QGbuBv6sevjObgQmSZIkTcVMOFM/PyLeCBwN7ABuAzZm5kjTdi+syg0t9rER2AmcHBHzM3NPx6KVJEmS2mwmJPUrgEub6u6KiHMz81sNdcdW5Q+bd5CZ+yLiLuBZwFOBOzsSqSRJktQBdR9+8wXgDEpivwg4Afg0sBq4OiJObNh2oCqHxtnXWP3SVisj4ryIuCUibnnooYemGrckSZLUNrVO6jPz4sy8NjN/lpk7M/OOzHwH8A/AAuCiNr7WZzJzbWauPeKII9q1W0mSJGnKap3UH8CnqvLUhrqxM/EDtDZWP9iRiCRJkqQOmalJ/dj4mEUNdT+oymc2bxwRc4GnAPuAn3Y2NEmSJKm9ZmpSf1JVNibo11bl+hbbnwosBG5y5htJkiTVTWRmt2M4JBFxHHB3Zu5oql8NfBN4OvC+zPzrqn4J8BNgCXBKw82nDqMk/C8AXj+Rm09FxEPAlgmG+gTg4Qluq86wDXqD7dAbbIfeYDt0n23QG2yHAzsmMyd0MWedk/qLgD+gzDG/BdgOPA14OXAYcBXw6swcbnjOq4DLgd3AZcBW4JWU6S4vB16bbT4gEXFLZq5t5z41ObZBb7AdeoPt0Btsh+6zDXqD7dA+dZ6n/jpKMv4c4BTK+PlB4EbKvPWXNifomfm1iDgNeB/wGkry/2PgvcDH2p3QS5IkSdOhtkl9dWOpbx10w8c/79vAy9ofkSRJktQdM/VC2V7ymW4HINugR9gOvcF26A22Q/fZBr3BdmiT2o6plyRJklR4pl6SJEmqOZN6SZIkqeZM6iVJkqSaM6nvgIhYFRGfj4j7I2JPRGyOiI9ExLJuxzZbVMc8x1ke7HZ8M0lEnB0RH4+IGyLi0eoYf/Egzzk5Iq6KiK0RsSsibouI90TEnOmKeyaZTBtExOoD9I2MiIPegE+PFxHLI+JtEfHViPhx9b4eiogbI+KtEdHy/619ob0m2w72h86JiL+NiGsi4p6qHbZGxHcj4v0RsXyc59gfpqC2U1r2qoh4GnAT8ETgSmAT8Dzg3cD6iDglMx/pYoizyRDwkRb1/zfdgcxwfwacSDmu9wJrDrRxRJwJXEG5CdyXKDeBewXwYco9J87pZLAz1KTaoHIr8LUW9Xe0Ma7Z5Bzgk8ADlPuo3A08CTgL+Bzw0og4p/F+KPaFjph0O1TsD+33+8B3gG8CP6fcT+gk4CLgvIg4KTPvGdvY/tAGmenSxgX4OpDAu5rq/6Gq/1S3Y5wNC7AZ2NztOGbDApwOPAMIYF31Pv/iONsuofxx3wOsbag/jPJhOIHXdft3qtsyyTZYXa2/pNtxz6QFeCElAelrql9BSSwTeE1DvX2hN9rB/tC5tjhsnPq/qo75PzXU2R/asDj8po2qs/QvpiSUn2ha/X5gB/CmiFg0zaFJHZOZ12Xmj7L6C3wQZwNHAJdl5i0N+9hNOdsM8M4OhDmjTbIN1AGZeW1m/ntmjjbVPwh8qnq4rmGVfaEDDqEd1CHVe7mVL1flMxrq7A9t4PCb9jq9Kr/R4g/K9oj4NiXpPwm4ZrqDm4XmR8QbgaMpH6huAzZm5kh3w5rVXliVG1qs2wjsBE6OiPmZuWf6wpqVjoyI84HlwCPAzZl5W5djmqn2VuW+hjr7wvRr1Q5j7A/T5xVV2Xh87Q9tYFLfXsdW5Q/HWf8jSlL/TEzqp8MK4NKmursi4tzM/FY3AtL4fSQz90XEXcCzgKcCd05nYLPQi6rlFyLieuDNmXl3VyKagSJiLvBb1cPGhMW+MI0O0A5j7A8dEhEXAL8EDABrgV+lJPQfbNjM/tAGDr9pr4GqHBpn/Vj90mmIZbb7AnAGJbFfBJwAfJoyfvLqiDixe6HNavaR7tsJ/CXwK8CyajmNclHhOuAahwi21QeB44GrMvPrDfX2hek1XjvYHzrvAsoQ5PdQEvoNwIsz86GGbewPbWBSrxkpMy+uxlb+LDN3ZuYdmfkOygXLCyhX30uzTmb+PDP/PDO/k5mD1bKR8i3ifwFPB97W3Shnhoj4PeAPKLOgvanL4cxaB2oH+0PnZeaKzAzKSbazKGfbvxsRz+1uZDOPSX17jX2SHBhn/Vj94DTEotbGLpQ6tatRzF72kR6VmfsoU/6B/WPKIuJ3gY8C3wdOz8ytTZvYF6bBBNqhJftD+1Un2b5K+cC0HPiXhtX2hzYwqW+vH1TlM8dZP3al93hj7tV5Y1/3+XVqd4zbR6oxr0+hXMT20+kMSr9g/2iDiHgP8HHKHOenVzOvNLMvdNgE2+FA7A8dkJlbKB+ynhURT6iq7Q9tYFLfXtdV5Ytb3LVuMeXmCTuB/5zuwPQLJ1Wlfxi649qqXN9i3anAQuAmZzfoGvvHFEXEH1FulvM9SiL583E2tS900CTa4UDsD51zZFWOzUZnf2gDk/o2ysyfAN+gXIz5O02rL6Z82r80M3dMc2izSkQc1+rCpohYDfxj9fCL0xmTfuFy4GHgdRGxdqwyIg4DPlA9/GQ3ApstIuK5zScdqvozKHeABPvHIYmICykXZP4PcEZmPnyAze0LHTKZdrA/dEZEPDMiHjeUJiL6IuKvgCdSkvRt1Sr7QxuE9wf5SBgAAAYNSURBVCppr+oGVDdR3rBXUqZeej5lDvsfAidn5iPdi3Dmi4iLKBdFbQS2ANuBpwEvp9yd7irg1Zk53K0YZ5KIeBXwqurhCuAllDNbN1R1D2fmBU3bX065FfhllFuBv5IypdnlwGu9idLkTKYNqmn6nkH5O3Vvtf7Z7J8n+sLMHPsnqgmKiDcDl1DOPH6c1rN4bM7MSxqeY19os8m2g/2hM6qhT38D3AjcRZn7/0mUmYWeCjxI+cD1/Ybn2B+myKS+AyLiKOAvKF8jLQceAL4KXNzwqVQdEhGnAe8AnsP+KS0HKV/DXkr5tsQ3fptUH6Lef4BNtmTm6qbnnAK8D3gB5YPWj4HPAx/z5mCTN5k2iIi3Aq+mTO/3BGAe8DPgZuAfM/OG8Xai8U2gDQC+lZnrmp5nX2ijybaD/aEzIuJ4yv/hXwVWUaai3EE5ufkflPf34y5atj9MjUm9JEmSVHOOqZckSZJqzqRekiRJqjmTekmSJKnmTOolSZKkmjOplyRJkmrOpF6SJEmqOZN6SZIkqeZM6iVJHRURl0RERsTqDr/O5ojY3MnXkKReZVIvSaqFiLg+IrxjoiS1MLfbAUiS1CZndDsASeoWk3pJ0oyQmT/pdgyS1C0Ov5GkHhURq6ux6JdExJqI+FpEbI2IHRFxY0S8uMVz5kfEH0fE7RGxMyIejYgbIuK1bdr/RdVz1h1ofxP8/d4SEVdExE8jYlcV67cj4o2t9gucVj3OhuX6hu1ajqmfwjFZHRGXRcTDEbE7Im6JiF+fyO8mSdPNM/WS1PueAtwM3A58Gngy8BvA1RHxhsz8EkBE9ANfpyS/m4BPAAuBs4EvRcQvZ+afHur+O+CTwP8CG4EHgOXAy4BLI+LYzLyw2m4QuBh4C3BM9fOYzQd6gSkck2OA/wZ+ClwKHE45JldGxK9l5nWT/WUlqaMy08XFxcWlBxdgNZDV8vdN69YCe4FtwJKq7k+qba8C5jZs+0RK8pvAyYe6/6r+omr7dQeI95Km+kuq+tVN9U9rsY9+4JrqtVc2rbu+/Nsa93htBjY31U3lmLy/aV8vGdtXt98bLi4uLs2Lw28kqfcNAX/RWJGZtwD/CiwFXl1V/zYl6XxvZu5r2PbnwF9WD982hf23VbYYA5+Zw5Sz6XNpz4Wvh3pMtgAfaIrt68DdwPPaEJcktZVJvST1vu9k5vYW9ddX5XMiYjHwdOD+zNzUYttrx7Y9lP1PItYJi4ijI+ITEbGpGuue1dj5K6pNVk5x/1M5Jt/LzJEW9fcAy6YSlyR1gmPqJan3/Wyc+gercqBaoIxNb2Wsfukh7r+tIuKplDHry4AbgG9QvjEYoQyBeTMwf4ovM5VjMjjOc/bhCTFJPcikXpJ635PGqV9RlUPV0ljX7MkN2x7K/seMVmWr/x+tkuPxvJdyYey5mXlJ44qIeD0lqZ+qqRwTSaoVzzZIUu97bjWUpNm6qvxuNXzmJ8DKiHhGi21Pr8rvHMr+G+q2VeVRLbZf26JuPE+vyitarDttnOeMAETEnIm8wBSPiSTVikm9JPW+AeDPGysiYi3wm5SzzF+tqj8PBPD3jYlvRDwBuLBhm0PdP5QhMwDnRsTchu2Pat7HQWyuynVNr/sSWl+4CvBIVR49idc51GMiSbXi8BtJ6n0bgbdFxPOBb7N/Hvk+4PzMfLTa7kPAS4EzgVsj4irKnOznUKZw/LvMvHEK+ycz/ysiNgKnAv8dEddShu+8gjIffKsz+K38E3Au8G8RcTlwP3A8sB74cvX6za6pfpevVL/bLmBLZl56gNc51GMiSbXimXpJ6n13ASdThr68A3gtZcjIy7LhxlDVdJAvAt5XVb2LMjb9R8AbMvOPprL/BmcCnwNWVa/xHOAPgfH2/ziZeRtl+MtNwMuBdwJLgLOAT43ztM8Bf0P5ZuEPKVNSvvUgr3Oox0SSaiUys9sxSJJaiIjVlIT7nzPzLXXbvyRp+nimXpIkSao5k3pJkiSp5kzqJUmSpJpzTL0kSZJUc56plyRJkmrOpF6SJEmqOZN6SZIkqeZM6iVJkqSaM6mXJEmSau7/Af6zFDUF355PAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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4NEbqV7nPXzOev3pvy027WjZ/27D9q/e2fP6a8QWf61M37c3Lrr45N+5tOXH9mty4t+VlV9+cT920d0Hn+c89e3Lh9u3ZsX9/7rJ2bXbs358Lt2/Pf+7Zs+CaAAA4dEL9KvfRrS1HbWg5ekNlTVWO3lA5akPLR7e2BZ/rnV/bk01jlU3r1mRNDdtNY5V3fm1hYfyDu3blmKocs3Zt1oy2x1Tlg7t2LbgmAAAOnVC/yn31hmTDkQe2bThyaF+oa24ZzzFjdUDbMWOVa25Z2Kj/l/bty1FrDnzrHLVmTb60b9/CiwIA4JAJ9avcnY9Ldu0+sG3X7qF9oU46fE127DtwhH/HvpaTDl/Y2+CuY2PZOX7gB4Gd4+O565hLNAAAVoJQv8o9ZEtl567KTbtaxtswp37nrspDttTcB0/xmOPXZ9u+lm17xzPehu22fS2POX5hF7j+wIYN2dFaduzfn/HRdkdr+YENGxZcEwAAh06oX+VOPWlNfuyRw1z6678+bH/skQe3+s39jl6XZ558RI5dV7luz3iOXVd55slHLHj1m3utX58nbdyYY9auzVf2788xa9fmSRs3Wv0GAGCFmC/RgVNPWpNTT1qcc93v6HUHtYTlVPdav16IBwBYJYzUAwBA54R6AADonFAPAACdE+oBAKBzLpTlduPTu2/N+7bfnOv27s+J69bmERuPyH2OPGylywIAWHJG6rld+PTuW/Mn1+/M9v3jOWFsTbbvH8+fXL8zn95960qXBgCw5IR6bhfet/3mbFxb2bh2TdbUsN24tvK+7TevdGkAAEtOqOd24bq9+3P0mgO/ZffoNZXr9u5foYoAAJaPUM/twonr1uam8XZA203jLSeuW7tCFQEALB+hntuFR2w8Itv3t2zfP57xNmy37295xMYjVro0AIAlJ9Rzu3CfIw/LUzYflY1r1+TL+8azce2aPGXzUVa/AQDuECxpye3GfY48TIgHAO6QjNQDAEDnhHoAAOicUA8AAJ0T6gEAoHNCPQAAdE6oBwCAzgn1AADQOaEeAAA6J9QDAEDnhHoAAOicUA8AAJ0T6gEAoHNCPQAAdE6oBwCAzgn1AADQOaEeAAA6J9QDAEDnhHoAAOicUA8AAJ0T6gEAoHNCPQAAdE6oBwCAzgn1AADQOaEeAAA6J9QDAEDnhHoAAOicUA8AAJ0T6gEAoHNjK10ArDZX7Nibi762J9fcMp6TDl+Tc49fn9OPWbfSZQEAzMhIPUxyxY69eenVN+fGvS13W78mN+5teenVN+eKHXtXujQAgBkJ9TDJRV/bk01jlWPXrcmaGrabxioXfW3PSpcGADAjoR4mueaW8WwcqwPaNo5VrrllfIUqAgCYm1APk5x0+Jps39cOaNu+r+Wkw/1TAQBWL0kFJjn3+PXZtq/lxr3jGW/Ddtu+lnOPX7/SpQEAzEioh0lOP2ZdLjj5iBy7rnLtnvEcu65ywclHWP0GAFjVLGkJU5x+zDohHgDoipF6AADonFAPAACdE+oBAKBz5tSzov5t16159417cu2e/bnb+rV59LHr850bDlvpsgAAumKknhXzb7tuzSu/vDvb9o3nroetybZ943nll3fn33bdutKlAQB0Rahnxbz7xj3ZOFbZNLYma2rYbhyrvPvGPStdGgBAV4R6Vsy1e/bnmLV1QNsxayvX7tm/QhUBAPRJqGfF3G392uzY3w5o27G/5W7r165QRQAAfRLqWTGPPnZ9tu9r2bZvPONt2G7f1/LoY9evdGkAAF0R6lkx37nhsDz1hCOzaWxNvnTreDaNrclTTzjS6jcAAAtkSUtW1HduOEyIBwA4REbqAQCgc0I9AAB0TqgHAIDOCfUAANA5oR4AADon1AMAQOeEegAA6JxQDwAAnRPqAQCgc0I9AAB0TqgHAIDOCfUAANA5oR4AADq3akJ9VT2uqv64qj5SVTuqqlXVGxdw/J+OjmlV9e0z9FlbVRdU1RVVdXNVfaOq3ltVD1q8ZwIAAMtr1YT6JM9L8rQkZyS5biEHVtUPJ/nZJDtn6VNJ3pTkJUkOS/KKJO9I8n1JPlxVjzm4sgEAYGWtplB/QZLTkhyT5Bfne1BVbU7ymiRvTnL5LF2fkORxSS5LckZr7TmttZ9NcnaS/UleU1VHH2TtAACwYlZNqG+tXdxau7K11hZ46KtH26fO0W/ig8LzWmu3THrcf8rwgWBzhtAPAABdWTWh/mBU1ZOSnJvkKa21r8/S7/AkD0qyO8lHpunyvtH2nMWuEQAAllq3ob6qTk7y8iRvbK29c47u/yXJ2iSfb63tm2b/laPtaYtYIgAALIsuQ31VrUny+gwXxj59HodsHG23z7B/on3TLI/55KraWlVbr7/++nnXCgAAS63LUJ/hotqHJvn51tqNy/GArbVXt9a2tNa2bN68eTkeEgAA5qW7UF9VpyX5nSSva629d56HTYzEb5xh/0T7tkOpDQAAVkJ3oT7JfZKsT3L+pC+balXVMozeJ8mVo7ZzR/c/l2HZylOramyac95ztP3MklYOAABLYLqAu9pdleS1M+x7VJK7JPmrJDtGfdNau6WqLkvyvaPbxVOOe8Ro+6FFrhUAAJZcd6G+tfaJJD833b6quiRDqP/V1tpnp+z+PxkC/W9X1fdPrFVfVQ9M8vgk1yd521LVDQAAS2XVhPrRVJmJ6TJ3GW3PrKoLRz/f0Fp79iE8xJuSPDbDF0z9S1W9K8m3ZQj0azNcdLvjEM4PAAArYtWE+iRnJDlvStupo1uSXJ3koEN9a61V1Y8nuSzJzyT5pSS3JPlwkt9urV12sOcGAICVVK21la6hO1u2bGlbt25d6TIAALgdq6rLW2tb5tO3x9VvAACASYR6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOrdqQn1VPa6q/riqPlJVO6qqVdUbZ+h796r631X1j1X1laraU1VfGh17flWtm+Vxzquqj1fVzqraXlWXVNWjl+6ZAQDA0lo1oT7J85I8LckZSa6bo+9/SfKTSbYnuSjJHyZ5V5KTk/xZkg9U1djUg6rqxUkuTHJCktckeWOS+yV5V1U9bVGeBQAALLPbBN8VdEGSa5N8NslDk1w8S9/LkhzbWhuf3Dgaof+bJGcneWySt0za96Akz0ryuSQPbK3dOGp/UZLLk7y4qt7dWrtqsZ4QAAAsh1UzUt9au7i1dmVrrc2j761TA/2ofW+GkfskueeU3b8w2v7ORKAfHXNVklcmWZ/k/IOpHQAAVtKqCfWLoarWJnnk6O4VU3afM9q+f5pD3zelDwAAdGM1Tb9ZsKo6LsM8/EqyOcnDknx7kr9srb1rUr8NSU5MsrO19uVpTnXlaHva0lYMAACLr+tQn+S4JM+fdL8leXGSX53Sb+Nou32G80y0b5rpgarqyUmenCQnnXTSggsFAICl0vX0m9baf7TWKsOHk5MzXGz75CQfrqo7LfJjvbq1tqW1tmXz5s2LeWoAADgkXYf6Ca21/a21a1prL0/ylCTfk+Q3J3WZGInfeJuDD2zftkQlAgDAkrldhPopJi56PWuiobW2K8Pa90dV1QnTHDOxUs5nlrY0AABYfLfHUH/iaLtvSvuHRtuHT3PMI6b0AQCAbnQZ6qvqu0bLV05tPyrJy0d33zNl96tG21+rqmMnHXNKkqcm2ZPkdYteLAAALLFVs/pNVZ2b5NzR3buMtmdW1YWjn29orT179POvJ3lwVV2W5Joku5PcPcOI+6YM3zj7wsnnb61dVlUvSfLLSa6oqrcmOSzJ45PcKckv+TZZAAB6tGpCfZIzkpw3pe3U0S1Jrk4yEepfk2Rnkv+aYe78kUluTHJ5krck+bPW2tTpN2mtPauqPpVhZP7JScaT/HOSF7XW3r2YTwYAAJZLtdZWuobubNmypW3dunWlywAA4Hasqi5vrW2ZT98u59QDAADfItQDAEDnhHoAAOicUA8AAJ0T6gEAoHNCPQAAdE6oBwCAzgn1AADQOaEeAAA6J9QDAEDnhHoAAOicUA8AAJ0T6gEAoHNCPQAAdE6oBwCAzgn1AADQOaEeAAA6J9QDAEDnhHoAAOicUA8AAJ0T6gEAoHNCPQAAdE6oBwCAzgn1AADQOaEeAAA6N+9QX1UPqKr/XlUbJ7VtqKrXV9W2qvpSVT1jacoEAABmspCR+ucm+bXW2vZJbS9M8sTReb4tyUuq6gcXsT4AAGAOCwn1W5JcPHGnqtYlOS/Jx5Mcn+QeSW5I8vTFLBAAAJjdQkL98UmunXR/S5Kjk/xJa+2W1tqXkrwzyemLWB8AADCHhYT6lmRs0v2HjNoundR2fZLNi1AXAAAwTwsJ9dck+Z5J9x+T5NrW2ucntd01yY2LURgAADA/Cwn1b0nyoKp6a1W9McmZSd46pc93JPncYhUHAADMbWzuLt/00iQPT/LY0f1PJPnNiZ1VdY8kD8ywIg4AALBM5h3qW2s7kzy4qu47avp0a218cpcMgX/rItYHAADMYd6hvqpOSrKttfav0+1vrV1VVV9PcuxiFQcAAMxtIXPqv5DkmXP0efqoHwAAsEwWEupryaoAAAAO2kJC/XzcJcmuRT4nAAAwi1nn1FfVT09pOmOatiRZm+SkJD+V5FOLVBsAADAPc10oe2GGVW0y2j5mdJtqYmrO7iS/sSiVAQAA8zJXqD9/tK0kf5bkoiTvnKbf/iRfT/Kx1tq2xSsPAACYy6yhvrX2+omfq+q8JBe11t6w5FUBAADztpAvnzp7KQsBAAAOzmKvfgMAACyzGUfqq+rzGS6O/YHW2hdG9+ejtdb+y6JUBwAAzGm26Tdr8q2Vb6a7PxNfUgUAAMtoxlDfWjtltvsAAMDqYE49AAB0TqgHAIDOzXtJywlVtSXJf01ybJK103RprbXfOtTCAACA+Zl3qK+qY5K8PcnZmf1i2JZEqAcAgGWykJH6FyU5J8lHkrwuyReT7FuKogAAgPlbSKh/TJJ/TnJ2a218ieoBAAAWaCEXym5McrFADwAAq8tCQv2VSe68VIUAAAAHZyGh/pVJfriqTlyqYgAAgIVbyJz692W4UPbvq+o3klyeZNt0HVtr1yxCbQAAwDwsJNRflWG5ykryp7P0aws8LwAAcAgWEr7fkCGwAwAAq8i8Q31r7UlLWAcAAHCQFnKhLAAAsAoJ9QAA0Ll5T7+pqj+bZ9fWWvvZg6wHAABYoIVcKPukOfZPrIzTkgj1AACwTBYS6u8xQ/umJA9M8r+SXJbkVw61KAAAYP4WsvrN1TPsujrJJ6vqA0muSPLBJK9dhNoAAIB5WLQLZVtrX0zyriTPWKxzAgAAc1vs1W++muSei3xOAABgFosW6qtqbZJzkmxfrHMCAABzW8iSlt83yznunuT8JGck+dNFqAsAAJinhazHhFPXAAAgAElEQVR+c0mG5SpnUkk+nOQ5h1IQAACwMAsJ9b+Z6UP9eJIbk3y8tfbxRakKAACYt4UsafmCJawDAAA4SIu9+g0AALDMhHoAAOicUA8AAJ0T6gEAoHNCPQAAdE6oBwCAzgn1AADQOaEeAAA6N69QX1VjVfWAqrpfVdUs/U6vqp9evPIAAIC5zBnqq+rcJF9KsjXJJ5JcVVWPnaH7jyZ53eKVBwAAzGXWUF9VD0jyliTHJflskn9Pcvckf1VVv7v05QEAAHOZa6T+OUnGkvxka+1erbX7JnlQks8leW5V/cFSFwgAAMxurlD/fUk+0Fr7vxMNrbV/SPL/JbksybOM2AMAwMqaK9RvzjCP/gCttRuT/FCSj2QYsf+NJagNAACYh7E59n89yVHT7Wit7a6qRyZ5f5LnVdWti10cAAAwt7lC/eczTLWZ1qRg/7dJfjPDXHsAAGAZzTX95oNJvruqTp2pQ2ttZ4apOP+c5NsXsTYAAGAe5gr170jy8SSPmK1Ta21HkocluTTJNYtTGgAAMB+zTr9prX0yyZnzOVFrbVuSsxejKAAAYP7m/EZZAABgdRPqAQCgc4sa6qvquVX1ocU8JwAAMLvFHqm/d5KHLvI5AQCAWZh+AwAAnZt19Zuq+s0Fnu8Bh1ALAABwEOb6RtnnJWlJagHnbAdfDgAAsFBzhfqbk1yX5Hfmeb6fS/KgQ6oIAABYkLlC/aeSfHtr7fXzOVlVnRWhHgAAltVcF8p+IsmxVXX35SgGAABYuLlC/T8l2ZHkO+Z5vo8mecMhVQQAACzIrKG+tfba1tqxrbW/mc/JRv3PP5hCqupxVfXHVfWRqtpRVa2q3jhD33tOfNFVVX2xqm6tqq9W1Tur6uw5Hue8qvp4Ve2squ1VdUlVPfpgagYAgNVgNa1T/7wkT0tyRoaLc2fzW0l+L8mdk7w3yR8m+fskj0ryoap6+nQHVdWLk1yY5IQkr0nyxiT3S/KuqnraoT8FAABYfkse6qvq+VW1bx5dL0hyWpJjkvziHH3fn+S7Wmvf2Vp7Smvtf7bWHpvk+5PsTfKiqjphSh0PSvKsJJ9Lcnpr7YLW2lOTfHeSbyR5cVWdsoCnBgAAq8JyjdTPuc59a+3i1tqVrbU517lvrV3YWvuXadovTXJJksNy21V4fmG0/Z3W2o2TjrkqySuTrE9yUFOHAABgJa2m6TeLZe9oO/WvA+eMtu+f5pj3TekDAADduF2F+qo6OcMUnN1JPjypfUOSE5PsbK19eZpDrxxtT1vyIgEAYJHdbkJ9Va1P8hcZptG8YPIUmyQbR9vtMxw+0b5plvM/uaq2VtXW66+//pDrBQCAxXK7CPVVtTbJnyd5cJI3J3nxYj9Ga+3VrbUtrbUtmzdvXuzTAwDAQes+1I8C/RuT/FiStyT5qWkutp0Yid+Y6U20b1v8CgEAYGl1Heqral2S/5vkCUn+MslPtNZus3xma21XhrXvj5q61OXIPUfbzyxVrQAAsFS6DfVVdViSv8owQv+GJE9sre2f5ZAPjbYPn2bfI6b0AQCAbnQZ6kcXxb4jyWOSvDbJ+a218TkOe9Vo+2tVdeykc52S5KlJ9iR53aIXCwAAS2xsGR7joiRXzdWpqs5Ncu7o7l1G2zOr6sLRzze01p49+vlVSR6Z5IYM02p+veo23291SWvtkok7rbXLquolSX45yRVV9dYMX1L1+CR3SvJLoy+iAgCAriwo1FfVPTKs5T6x9OO2JJ9prX1hpmNaa59M8sl5nP6MJOdNaTt1dEuSq5NMhPp7jLbHJfn1Wc55yZRanlVVn8owMv/kJONJ/jnJi1pr755HjQAAsOrUbReKmdJhuBj1giS/kOTkGbpdneT/JHlZa23vDH1uN7Zs2dK2bt260mUAAHA7VlWXt9a2zKfvrCP1VXV4kr/JsP57ZVgd5socuETkPTOM3v9ekkdX1Q+21vYcZO0AAMACzTX95leSPCTJ25L8j5mm2Yym5fxBkscmeW6S31zMIgEAgJnNtfrNE5L8U2vtx+aYN/+FJP9/ksuT/MQi1gcAAMxhrlB/cua5dvvoW1z/LjPPuwcAAJbAXKF+R5K7LeB8JyW56eDLAQAAFmquUH9pkh+rqkfNdaKq+uEkj0ty8WIUBgAAzM9cF8o+P8mjkvx1VV2a5AMZVsCZvPrNaUkenuT7kuweHQMAACyTWUN9a+3fq+qcJK9LclaSh87QtZL8e5LzW2v/sagVAgAAs5rzG2Vba/9YVffNEOjPSXKvDCP0yTBi/58ZLqa9tLU2vlSFAgAA05sz1CfJKKxfHPPlAQBg1ZnrQtlDVlXPr6p9S/04AABwR7XkoX6klulxAADgDme5Qj0AALBEhHoAAOicUA8AAJ0T6gEAoHNCPQAAdE6oBwCAzgn1AADQOaEeAAA6N7YMj3FRkquW4XEAAOAOaclDfWvtk0k+udSPAwAAd1Tzmn5TVT9aVS+vqj+sqofN0u+8qvrQ4pUHAADMZdaR+qqqJG9O8t+S1Kj5mVX1niQ/3VrbNuWQU5I8dLGLBAAAZjbX9JvzkzwuyReTvCrJ3iTnJXl0ko9W1Tmtta8tbYkAAMBs5hPqtyV54ER4r6qXJvn9JL+c5IOjYH/D0pYJAADMZK459fdL8vbJo/Gttf2ttWcneWaS+2YI9scuYY0AAMAs5gr1hyX56nQ7Wmt/lOTpSU5P8rdVtWmRawMAAOZhrlB/XZKTZtrZWntFhmk435XkA0k2Ll5pAADAfMw1p/5TSc6erUNr7WVVtT7JC5M8YLEKAwAA5meukfr3JrlrVT1qtk6ttd9P8vwszzfUAgAAk8wVwt+eZG2SXXOdqLX2W1V1TYa16gEAgGUya6hvrX0jyZ/M92SttdcfckUAAMCCzDX9BgAAWOWEegAA6JxQDwAAnRPqAQCgc0I9AAB0TqgHAIDOCfUAANA5oR4AADon1AMAQOeEegAA6JxQDwAAnRPqAQCgc0I9AAB0TqgHAIDOCfUAANA5oR4AADon1AMAQOeEegAA6JxQDwAAnRPqAQCgc0I9AAB0TqgHAIDOCfUAANA5oR4AADon1AMAQOeEegAA6JxQDwAAnRPqAQCgc0I9AAB0TqgHAIDOCfUAANA5oR4AADon1AMAQOeEegAA6JxQDwAAnRPqAQCgc0I9AAB0TqgHAIDOCfUAANA5oR4AADon1AMAQOeEegAA6JxQDwAAnRPqAQCgc0I9AAB0TqgHAIDOCfUAANA5oR4AADon1AMAQOeEegAA6JxQDwAAnRPqAQCgc0I9AAB0TqgHAIDOCfUAANA5oR4AADon1AMAQOeEegAA6JxQDwAAnRPqAQCgc0I9AAB0TqgHAIDOCfUAANA5oR4AADon1AMAQOeEegAA6JxQDwAAnRPqAQCgc0I9AAB0TqgHAIDOCfUAANC5VRPqq+pxVfXHVfWRqtpRVa2q3jhD33VV9Yyqel1VfaKqbh31/7l5PM55VfXxqtpZVdur6pKqevTiPyMAAFgeYytdwCTPS3L/JDuTXJvk3rP03ZDkZaOfv5rkK0nuPtcDVNWLkzxrdP7XJDksyROSvKuqfqm19oqDrh4AAFbIqhmpT3JBktOSHJPkF+fouzvJI5PctbV2lyR/NtfJq+pBGQL955Kc3lq7oLX21CTfneQbSV5cVaccdPUAALBCVk2ob61d3Fq7srXW5tH31tba+1prX17AQ/zCaPs7rbUbJ53rqiSvTLI+yfkLqRkAAFaDVRPql8E5o+37p9n3vil9AACgG3eIUF9VG5KcmGTnDKP7V462py1fVQAAsDjuEKE+ycbRdvsM+yfaN810gqp6clVtraqt119//aIWBwAAh+KOEuoPWWvt1a21La21LZs3b17pcgAA4JvuKKF+YiR+4wz7J9q3LUMtAACwqO4Qob61tivJdUmOqqoTpulyz9H2M8tXFQAALI47RKgf+dBo+/Bp9j1iSh8AAOjGHSnUv2q0/bWqOnaicfSFU09NsifJ65a/LAAAODRjK13AhKo6N8m5o7t3GW3PrKoLRz/f0Fp79qT+v5Lk3qO7Z4y251fVQ0Y/f7S19qcT/Vtrl1XVS5L8cpIrquqtSQ5L8vgkd0ryS6MvogIAgK6smlCfIZifN6Xt1NEtSa5O8uxJ+x6e5KFT+j9odJvwp5N3ttaeVVWfyjAy/+Qk40n+OcmLWmvvPqTqAQBghVRrbaVr6M6WLVva1q1bV7oMAABux6rq8tbalvn0vSPNqQcAgNsloR4AADon1AMAQOeEegAA6JxQDwAAnRPqAQCgc0I9AAB0TqgHAIDOCfUAANA5oR4AADon1AMAQOeEegAA6JxQDwAAnRPqAQCgc0I9AAB0TqgHAIDOCfUAANA5oR4AADon1AMAQOeEegAA6JxQDwAAnRPqAQCgc0I9AAB0bmylCwCA26ur9u/Ox/Zty/Xt1myuw3Lm2KacsvbIlS4LmMNn9tySD928K1/evy8nrB3LOUdsyGnrD1/psmZlpB4AlsBV+3fnolu/lp1tX74t67Kz7ctFt34tV+3fvdKlAbP4zJ5b8uc3bcuO8f2585q12TG+P39+07Z8Zs8tK13arIR6AFgCH9u3LRtqTY6qsaypylE1lg21Jh/bt22lSwNm8aGbd+XoNWtyzJq1WVOVY9aszdFr1uRDN+9a6dJmJdQDwBK4vt2aI7P2gLYjszbXt1tXqCJgPr68f1+OqgMj8lG1Jl/ev2+FKpofoR4AlsDmOiy7s/+Att3Zn8112ApVBMzHCWvHsrONH9C2s43nhLWr+1JUoR4AlsCZY5uyq41nZ9uX8days+3LrjaeM8c2rXRpwCzOOWJDbhofz47x/RlvLTvG9+em8fGcc8SGlS5tVkI9ACyBU9YemXMPOz5H1Vi+nr05qsZy7mHHW/0GVrnT1h+eJx69KcesWZuvju/PMWvW5olHb1r1q9+s7r8jAEDHTll7pBAPHTpt/eGrPsRPZaQeAAA6J9QDAEDnhHoAAOicUA8AAJ0T6gEAoHNCPQAAdE6oBwCAzgn1AADQOaEeAAA6J9QDAEDnhHoAAOicUA8AAJ0T6gEAoHNCPQAAdE6oBwCAzgn1AADQubGVLgAAmN117aZckRtyY27JsTk8p+e4nFhHr3RZwCpipB4AVrHr2k25OF/M7uzNpqzP7uzNxflirms3rXRpwCoi1APAKnZFbsgRGcuRWZdK5cisyxEZyxW5YaVLA1YRoR4AVrEbc0uOmDJb9oiM5cbcskIVAauRUA8Aq9ixOTw3Z98BbTdnX47N4StUEbAaCfUAsIqdnuNyc/Zld/ampWV39ubm7MvpOW6lSwNWEaEeAFaxE+vonJ2758isy7bsyZFZl7Nzd6vfAAewpCUArHIn1tE5MUI8MDMj9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0DmhHgAAOifUAwBA54R6AADonFAPAACdE+oBAKBzQj0AAHROqAcAgM4J9QAA0LlVEeqr6nFV9cdV9ZGq2lFVrareOMcxD6qq91bVN6rq5qq6oqqeWVVrZznm0VV1SVVtr6qdVfWPVXXe4j8jAABYPmMrXcDI85LcP8nOJNcmufdsnavqMUneluSWJG9O8o0kP5zkpUkenOTHpjnmaUn+OMnXk7wxya1JHpfkwqq6X2vt2Yv1ZAAAYDmtipH6JBckOS3JMUl+cbaOVXVMktck2Z/krNbaz7bWnpPkjCQfS/K4qnrClGNOSfLiDOF/S2vtqa21C5KcnuRzSZ5VVWcu6jMCAIBlsipCfWvt4tbala21No/uj0uyOcmbWmtbJ53jlgwj/sltPxj8TJL1SV7RWrtq0jE3Jvnd0d1fOMjyAQBgRa2KUL9A54y2759m34eT7E7yoKpaP89j3jelDwAAdKXHUH+v0fYzU3e01vYl+UKGawVOnecxX06yK8ndqurImR60qp5cVVurauv1119/sLUDAMCi6zHUbxxtt8+wf6J900Ecs3GG/Wmtvbq1tqW1tmXz5s3zKhQAAJZDj6EeAACYpMdQP9eo+kT7toM4ZqaRfAAAWLV6DPX/OdqeNnVHVY0luUeSfUk+P89jTkiyIcm1rbXdi1sqAAAsvR5D/YdG24dPs+/7khyZ5LLW2p55HvOIKX0AAKArPYb6tya5IckTqmrLRGNVHZ7kt0d3/8+UY16XZE+Sp42+iGrimGOT/Oro7quWqF4AAFhSYytdQJJU1blJzh3dvctoe2ZVXTj6+YbW2rOTpLW2o6p+PkO4v6Sq3pThm2J/JMPSlW9N8ubJ52+tfaGqnpPkj5Jsrao3J7k1wxdZ3S3JH7bWPrZUzw8AAJbSqgj1+X/t3XuwXVV9wPHvD4IkYJMg2BABuVAQRcYCk6lIKUSsLyrFItjWUgEJI860jKVUW5VHtVVaalvFV1vaiaIzYKHQaQelUyANAQZNAcFSWnlcHkN4k0QgAYK//rHXLcfDOfd1zj3nrpvvZ2bN5qy9zj7r/PLj3t/dZ5294UDgxLa+vXnpWvP3AWeO7cjMKyLiCOCTwPuA+cBdwBnAFzvdmTYzL4iI0XKcD9J8SnEH8KnM/Hpf340kSZI0QNGh/tUEli1blmvXrh32NCRJkjSHRcR/ZuayiUfWuaZekiRJUguLekmSJKlyFvWSJElS5SzqJUmSpMpZ1EuSJEmVs6iXJEmSKmdRL0mSJFXOol6SJEmqnEW9JEmSVDmLekmSJKlyFvWSJElS5SzqJUmSpMpZ1EuSJEmVs6iXJEmSKmdRL0mSJFXOol6SJEmqnEW9JEmSVDmLekmSJKlyFvWSJElS5SzqJUmSpMpZ1EuSJEmVs6iXJEmSKmdRL0mSJFVu3rAnIEn9sI6N3MEjrGczi5nP/ixhKQuHPS1JkgbCM/WSqreOjaxhlE28wCK2ZxMvsIZR1rFx2FOTJGkgLOolVe8OHmEB81jAdgTBArZjAfO4g0eGPTVJkgbCol5S9dazmfltqwnnM4/1bB7SjCRJGiyLeknVW8x8NrPlp/o2s4XFzB/SjCRJGiyLeknV258lbGILm3iBJNnEC2xiC/uzZNhTkyRpICzqJVVvKQs5jBEWsB0beI4FbMdhjHj1G0nSVsNLWkqaE5ay0CJekrTV8ky9JEmSVDmLekmSJKlyFvWSJElS5SzqJUmSpMpZ1EuSJEmVs6iXJEmSKmdRL0mSJFXOol6SJEmqnEW9JEmSVDmLekmSJKlyFvWSJElS5SzqJUmSpMpZ1EuSJEmVs6iXJEmSKmdRL0mSJFXOol6SJEmqnEW9JEmSVDmLekmSJKlyFvWSJElS5SzqJUmSpMpZ1EuSJEmVs6iXJEmSKmdRL0mSJFXOol6SJEmqnEW9JEmSVLnIzGHPoToR8Rhw37DnMUvsAjw+7ElsRYz3YBnvwTLeg2W8B8t4D9ZcifeemfnqyQy0qFdPImJtZi4b9jy2FsZ7sIz3YBnvwTLeg2W8B2trjLfLbyRJkqTKWdRLkiRJlbOoV6/+dtgT2MoY78Ey3oNlvAfLeA+W8R6srS7erqmXJEmSKueZekmSJKlyFvWSJElS5SzqJUmSpMpZ1OtlIuK4iLggIq6LiI0RkRHxzQmec2hEXBkRT0bEpoi4LSI+GhHbDmretZpKvCNipOzv1i4e9PxrExE7R8SKiLg8Iu4q+bohItZExCkR0fHnojk+PVONtznem4j4s4i4OiIeKLF+MiJuiYhzImLnLs8xt6dpKvE2t2dGRJzQEsMVXca8JyJWlZ89T0fETRFx4qDnOtP8oqxeJiJuBX4eeBp4EHg98K3MPKHL+GOAy4DNwCXAk8DRwH7ApZl5/CDmXaupxDsiRoB7gR8AV3Q43A8z89IZm+wcEBGnAV8F1gHXAvcDS4BjgUU0uXx8tvxwNMenb6rxNsd7ExHPAzcDdwCPAjsChwDLgIeAQzLzgZbx5nYPphJvc7v/ImIP4HZgW+CVwKmZeWHbmN8BLgCeoMnx54HjgN2Bz2fmmQOd9EzKTJvtpxrwVmBfIIDlQALf7DJ2Ic0PsueAZS3984EbynN/Y9jvaTa3KcZ7pOxfOex519qAI2mKlm3a+nelKTgTeF9Lvzk+2Hib473Fe36X/j8tcf1KS5+5Pdh4m9v9jX0A/w7cDZxfYruibcwIzR+sTwAjLf07AXeV57xl2O+lX83lN3qZzLw2M3+UJfMncBzwauDizFzbcozNwKfKw4/MwDTnjCnGWz3KzGsy818y8ydt/Q8DXysPl7fsMsd7MI14qwclLzv5dtnu29JnbvdoivFWf51Oc9LgZOCZLmM+BGwPfCkzR8c6M/Mp4LPl4WkzOMeBmjfsCah6R5btdzvsWw08CxwaEdtn5nODm9ac95qI+DCwM80ZiBsz87Yhz2kueKFst7T0meMzp1O8x5jj/XV02bbG0NyeOZ3iPcbc7lFEvAE4D/hCZq6OiCO7DB0vx7/TNqZ6FvXq1X5l+7/tOzJzS0TcC7wR2Bv470FObI57e2n/LyJWASdm5v1DmVHlImIe8MHysPUXgDk+A8aJ9xhzvAcRcSbNGuNFNOu7D6MpMM9rGWZu98kk4z3G3O5B+dlxEc3yvU9MMHy8HF8XEc8Au0fEDpn5bH9nOngW9erVorLd0GX/WP/iAcxla/As8BmaL1ndU/reBJxLszb/6og4MDO7fRSp7s4DDgCuzMyrWvrN8ZnRLd7meH+cSfOF5DHfBU7KzMda+szt/plMvM3t/jgbOAg4LDM3TTB2Mjm+YxlXfVHvmnqpIpn5aGaenZk3Z+b60lYD7wBuAvYBOl7SS91FxOnA7wN3Ar895OnMeePF2xzvj8zcNTOD5gvJx9Kcbb8lIg4e7szmpsnE29zuXUS8mebs/Ocz88Zhz2e2sahXr8b++l3UZf9Y//oBzGWrlZlbgLHLeB0+zLnUplzu7As0l6R7a2Y+2TbEHO+jScS7I3N8ejLzkcy8nKZw3Bn4Rstuc7vPJoh3t+eY25NQlt18g2YpzVmTfNpkc7zbmfyqWNSrV/9Ttq9r31H+B9yL5ktw97TvV9+Nfcy741BnUZGI+CjN9Yt/SFNgPtxhmDneJ5OM93jM8WnKzPto/pB6Y0TsUrrN7RnSJd7jMbcn9kqaXH0DsLn1xl3AOWXM35W+vy6Px8vxpTTxfnAurKcHi3r17pqyfVeHfYcDOwA3eOWEgTikbP0FPAkR8XHgr4BbaQrMR7sMNcf7YArxHo853pvXlO2LZWtuz6z2eI/H3J7Yc8Dfd2m3lDFryuOxpTnj5fi728bUb9gXyrfN7sbkbj71GN68ZFDxPpi2m/iU/rfR3GAjgUOH/T5me6P56DaBtcCrJhhrjg823ub49OP8OmBRh/5teOlmSNe39Jvbg423uT1z/xbn0vnmU3uxFd18yqvf6GUi4r3Ae8vDXcv2LRGxsvz341luq5yZGyPiVOBSYFVEXExzm/FfpdxmnOa2zOpiKvEG/hLYNyJuAB4sfW/ipevsnpWZN8zwlKsWEScCn6Y5e3YdcHpEtA8bzcyVYI73aqrxxhzvxVHA5yJiDXAvTSGzBDiC5oubDwOnjg02t3s2pXhjbg9cZt4bEX8AfBFYGxGXAM/T3Hhtd+baF26H/VeFbfY1XvqLt1sb7fCcXwSuBJ4CNgG3A78HbDvs9zPb21TiDZwC/CswCjxNc4btfppfvL807PdSQ5tEvBNY1eF55vgA4m2O9xTrA4Av0SxxepxmPfwG4Pvl36HjpyTm9mDibW7P6L/F2M+ZFV32Hw38B/BjmrvPfp/mvgBDn3s/W5Q3K0mSJKlSflFWkiRJqpxFvSRJklQ5i3pJkiSpchb1kiRJUuUs6iVJkqTKWdRLkiRJlbOolyRJkipnUS9JmlERsTIiMiJGZvh1RiNidCZfQ5JmK4t6SVIVImJVRHjHREnqYN6wJyBJUp+8bdgTkKRhsaiXJM0JmXn3sOcgScPi8htJmqUiYqSsRV8ZEa+PiCsi4smIeCYi1kTEOzo8Z/uI+MOIuD0ino2IjRFxXUS8v0/HP7c8Z/l4x5vk+zspIi6LiHsiYlOZ6/URcUKn4wJHlMfZ0la1jOu4pr6HmIxExMUR8XhEbI6ItRHxnsm8N0kaNM/US9LstxdwI3A78DfAUuDXge9ExAcy8xKAiHgFcBVN8Xsn8GVgB+A44JKIODAzPzHd48+ArwL/BawG1gE7A0cBF0XEfpl5Vhm3Hvhj4CRgz/LfY0bHe4EeYrIn8D3gHuAi4FU0MfnniPjlzLx2qm9WkmZUZtpsNpttFjZgBMjSzm/btwx4AXgKWFj6/qiMvRKY1zL2Z2mK3wQOne7xS/+5Zfzycea7sq1/Zekfaev/uQ7HeAVwdXnt3dr2rWp+bXWN1ygw2tbXS0zOaTvWO8eONezcsNlstvbm8htJmv02AJ9u7cjMtcC3gMXAr5XuD9EUnWdk5paWsY8CnykPV/Rw/L7KDmvgM/N5mrPp8+jPF1+nG5P7gD9pm9tVwP3AL/RhXpLUVxb1kjT73ZyZP+7Qv6psD4qInwH2AR7KzDs7jL1mbOx0jj+FuU5aRLw2Ir4cEXeWte5Z1s5fVobs1uPxe4nJrZn5Yof+B4CdepmXJM0E19RL0uz3SJf+h8t2UWnQrE3vZKx/8TSP31cRsTfNmvWdgOuAf6P5xOBFmiUwJwLb9/gyvcRkfZfnbD/MwFAAAAIMSURBVMETYpJmIYt6SZr9lnTp37VsN5TW2tduacvY6Rx/zE/KttPvj07FcTdn0Hwx9uTMXNm6IyJ+k6ao71UvMZGkqni2QZJmv4PLUpJ2y8v2lrJ85m5gt4jYt8PYt5btzdM5fkvfU2W7R4fxyzr0dbNP2V7WYd8RXZ7zIkBEbDuZF+gxJpJUFYt6SZr9FgFnt3ZExDLgt2jOMl9euv8BCOD81sI3InYBzmoZM93jQ7NkBuDkiJjXMn6P9mNMYLRsl7e97jvp/MVVgCfK9rVTeJ3pxkSSquLyG0ma/VYDKyLizcD1vHQd+W2AD2fmxjLuL4B3A8cAP4iIK2muyX48zSUc/zwz1/RwfDLzpohYDRwOfC8irqFZvnM0zfXgO53B7+QrwMnAP0bEpcBDwAHAu4Bvl9dvd3V5L/9U3tsm4L7MvGic15luTCSpKp6pl6TZ717gUJqlL6cB76dZMnJUttwYqlwO8u3AJ0vX79KsTf8R8IHM/Hgvx29xDHAhsHt5jYOAjwHdjv8ymXkbzfKXG4BfAT4CLASOBb7W5WkXAp+j+WThYzSXpDxlgteZbkwkqSqRmcOegySpg4gYoSm4v56ZJ9V2fEnS4HimXpIkSaqcRb0kSZJUOYt6SZIkqXKuqZckSZIq55l6SZIkqXIW9ZIkSVLlLOolSZKkylnUS5IkSZWzqJckSZIq938z6z/sjeJaxwAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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Hc/CcRERERETmXLzbDZhm0DlXbi00s48Cfw38FfCeaQ/9G3AJ8AfOuX+boV6i5f8vAD4AbAfOd86NheWfAO4CPmlm33HO7ZidpyO95IFCletGK+ysNFifinHZkhRnZpMd69w7WePq/VWeKPuclPZ4/cok5wwmOtYRERER6Yae6amfKdCHvhVuz5gqMLPzgN8GvjlToA+PV2sp+oNw+9GpQB/utwP4FyAFvPPYWy697oFClc/sKTJe91mb9Biv+3xmT5EHCtW2de6drPEPO8qM1XzWpYyxms8/7Chz72TrbSUiIiLSfT0T6ju4LNzeO63st8Pt181syMzeamZ/ZWbvNrPT2xznknB74wyPfbdlH1lArhutMBw3huMengXb4bhx3WilbZ2r91cZicNIIqgzkvAYiQflIiIiIr2ml4bfAGBmfw70A0PAJuBCgkD/99N2Oz/cnkwwnGb6RFpnZp8D3ueca4THzAJrgbxzbu8Mp3043M44uTY8xruBdwOcdNJJx/ispJt2VhqsTTa/fx2MGTsrjbZ1nigHPfTTDcWNJ8r+nLRRRERE5ET0Yk/9nwMfAv6UINDfCLzcOXdw2j4rwu2ngFuBZxNMlv01gpD/HuBvpu0/FG4n2pxzqny4XaOcc593zm1yzm1avnz5UT8Z6b71qRiTDddUNtlwrE/F2tY5Ke0xUW+uM1F3nJTuxT8ZERERWex6LqE451Y55wxYBbwBOBW4JxxHP2Wq3b8A3uSc+4VzLu+cuxn4DcAH/szMOs+ElEXhsiUpxuuO8bqP74LteN1x2ZJU2zqvX5lkrA5jtaDOWM1nrB6Ui4iIiPSangv1U5xz+51zVwMvJxhe8+VpD4+H2+umhthMq/dz4DGCnvtnh8VTPfFDzGyqfLzN4xJhZ2aTvHdNhuG4x+6qz3Dc471rMh1XvzlnMMEHNqQZSXjsqjhGEh4f2JDW6jciIiLSk3puTH0r59zjZrYNeI6ZLXPOHQIeAp5H+xA+tbpNX3iMgpntBtaa2eoZxtVPrazzy1luvvSIM7PJIy5h2eqcwYRCvIiIiERCz/bUt1gTbqd65f8r3J7VuqOZpXgqpO+Y9tAt4faVMxz/VS37iIiIiIhERk+EejN7hpk9bWiMmXnhl0+tALZMW1/+28Ae4E1m9ryWan9DMJzmB865fdPK/zXc/g8zG5l2jg3AHwEV4D9m4emIiIiIiMyrXhl+cynwMTPbTDAe/jCwEngxwUTZfcC7pnYOh9O8A/gO8CMzuwrYDfwqwYo5B4Dfn34C59wWM/sU8GfAvWb2/wFJ4E3AEuC9+jZZEREREYmiXgn1/wWcThDIn0uwtGSBYIz7V4B/ds6NTq/gnPt+2Ev/NwRLWQ4RhP9/BT7inNvTehLn3AfM7D6Cnvl3E6ySczfwCefcd+bouYmIiIiIzClzzh15L2myadMmt3Xr1m43Q0REREQWMDO7yzm36Wj27Ykx9SIiIiIicvwU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIi7e7QaIiIjI/Hi0XmJzbZL9fo2VXoILE4OcGu/rdrNEZBaop15ERGQReLRe4srKIXJ+g+UWJ+c3uLJyiEfrpW43TURmgUK9iIjIIrC5Nkk/MQa8GJ4ZA16MfmJsrk12u2kiMgsU6kVERBaB/X6NrDW/7GfNY79f61KLRGQ2KdSLiIgsAiu9BAXnN5UVnM9KL9GlFonIbFKoFxERWQQuTAySp0HOb+A7R85vkKfBhYnBbjdNRGaBQr2IiMgicGq8jzemljHgxTjo6gx4Md6YWqbVb0QWCC1pKSIiskicGu9TiBdZoNRTLyIiIiIScQr1IiIiIiIRp1AvIiIiIhJxCvUiIiIiIhGnUC8iIiIiEnEK9SIiIiIiEadQLyIiIiIScQr1IiIiIiIRpy+fEhERkba2FatcP1Zmd7XB2mSMV4+k2ZhJdrtZItJCPfUiIiIyo23FKp/bl2ei7rM64TFR9/ncvjzbitVuN01EWijUi4iIyIyuHyszFPMYint4ZgzFPYZiHtePlbvdNBFpoVAvIiIiM9pdbTAQs6aygZixu9roUotEpB2FehEREZnR2mSMXMM1leUajrXJWJdaJCLtKNSLiIjIjF49kmai4TNR9/GdY6LuM9HwefVIuttNE5EWCvUiIiIyo42ZJH+4qp+huMfems9Q3OMPV/Vr9RuRHqQlLUVERKStjZmkQrxIBKinXkREREQk4hTqRUREREQiTqFeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6hXkREREQk4hTqRUREREQiTqFeRERERCTi9I2yIiIiLarFPZQn7qdRGyOWGCE9dBbJzJoj1ttWrHLDeJld1QbrkjEuHU7r21jnwS8rZW4pFdjbqLM6FueSvizPSKW73awn7XE57uUgY5QZIc05LGeNDXS7WQveI9Uyt1Zy7PPrrPLiXJwa4PTkke+LG3eW+Oq2GgdyjhUDxls3Jnjl+r55aPGJUU+9iIjINNXiHvIHf4jfKOHFh/EbJfIHf0i1uKdjvW3FKp87kGei7rMm4TFR9/ncgTzbitV5avni9MtKma/kxpn0G6z0Ykz6Db6SG+eXlXK3mwYEgf5WnqBIjWFSFKlxK0+wx+W63bQF7ZFqma8VR8n5DVZYjJzf4GvFUR6pdr4vbtxZ4pM/rpIrO5b1G7my45M/rnLjztI8tfz4KdSLiIhMU564Hy+WwYv1YWZ4sT68WIbyxP0d690wXmbY8xiKe3hmDMU9hj2PG8Z7I1wuVLeUCgx4HoNeDM+MQS/GgOdxS6nQ7aYBcC8HSRMnQwLDyJAgTZx7Odjtpi1ot1ZyDJjHQHhfDHgxBszj1krnN1Nf3VYjm4KBtOFZsM2mgvJep1AvIiIyTaM2hnnNH9Gbl6ZRG+tYb1e1wUDMmsoGYsauamPW2yhP2duo02/NcabfPPY26l1qUbMxyvS1jHbuI84YerM3l/b5dbIt90XWPPb5ne+LAzlHNtX8d5xNGQdybtbbONsU6kVERKaJJUZwfnPgcn6ZWGKkY711yRi5RvMLf67hWJeMzXob5SmrY3Hyzm8qyzuf1bHemDY4QpoSzUGyRJ0RemfM/0K0yotTaLkvCs5nldf5vlgxYBQqzX/HhUowtr7XKdSLiIhMkx46C79RxG+UcM7hN0r4jSLpobM61rt0OM247zNR9/GdY6LuM+77XDqs8DaXLunLkvN9Jv0GvnNM+g1yvs8lfdluNw2Ac1hOmTpFajgcRWqUqXMOy7vdtAXt4tQAOeeTC++LnN8g53wuTnWeoPzWjQkKFciVHb4LtoVKUN7rzLne/zih12zatMlt3bq1280QEZE5otVvokWr38hMFsLqN2Z2l3Nu01Htq1B/7BTqRURERGSuHUuo1/AbEREREZGIU6gXEREREYk4hXoRERERkYhTqBcRERERibjeWMRVRESkhzzhF9jqj3KYKktJsslbwklebyyRKCIyE/XUi4iITPOEX+C7jb0UXJ0lLkHB1fluYy9P+IVuN01EpC2FehERkWm2+qNkiJG1OGZG1uJkiLHVH+1200RE2uqZUG9mHzezm81sp5mVzGzUzO4xsw+Z2dKjqP9/zcyFP6e32SdmZu83s3unneMGM7tg9p+RiIhE0WGqZIg1lWWIcZhql1okInJkPRPqgfcDWeD7wKeBK4A68GHgXjNb366imV0G/C6Q77CPAd8APgUkgc8CVwMvAm4zs9fOyrMQEZFIW0qSIo2msiINlqJvhhWR3tVLE2UHnXPl1kIz+yjw18BfAe+Z4fHlwBeAbwKrgBe3Of5vAb8BbAFeOnUuM/tXYDPwBTO7xTmXm4XnIiIiEbXJW8J3G3vBBT30RRoUafBib0W3myaLzGONIj+uj3PQ1VhuCV4QH+aUWKZjnUfrJTbXJtnv11jpJbgwMcip8b55arF0U8/01M8U6EPfCrdntHn88+H2j45wij8Mt/9z+rmccz8leEOwnCD0i4jIInaSl+VVsdVkLc6o1chanFfFVmv1G5lXjzWKXFM9QN41WEqCvGtwTfUAjzWKbes8Wi9xZeUQOb/BcouT8xtcWTnEo/XSPLZcuqWXeurbuSzc3tv6gJm9A3gd8Drn3OFghM3TmVkauAAoAj+aYZfvAm8DLgH+48SbLCIiUXaSl1WIl676cX2crMXotyCq9YeR7cf18ba99Ztrk/QTY8AL5oQMWAz8oFy99Qtfz4V6M/tzoB8YAjYBFxIE+r9v2e9kgrH3X3XOXXuEw54GxIBHnXP1GR5/ONw+o0O73g28G+Ckk0468hMREREROU4HXY2lJJrKMsQ46Gpt6+z3ayy35miXNY/9fvs6snD0XKgH/hxYOe3/bwTe4Zw7OFVgZh7wJYKJse87imMOhduJNo9PlQ+3O4Bz7vOEQ302bdrkjuKcIiIiIsdluQVDbvqnRbUiDZZbom2dlV6CnN8IeuhDBeez0mtfRxaOnhlTP8U5t8o5ZwSTXt8AnArcY2bnTdvt/QQTYt/lnBvrQjNFRERE5swL4sMUXIO8q+M7R97VKbgGL4i37X/kwsQgeRrk/Aa+c+T8BnkaXJgYnMeWS7f0Yk89AM65/cDVZnY38Evgy8BZZvYM4KPAfzjnbjjKw031xA+1eXyqfPx427vYbStWuWG8zK5qg3XJGJcOp9mY0fJvMn90D4rIQnJKLMPrkiuaVr95WWJpx9VvTo338UaWNa1+86rEiMbTLxI9G+qnOOceN7NtwHPMbBmwEUgB7zSzd7ap9nA4afb1zrlrgO1AAzjVzOIzjKufWlnnl7P/DBa+bcUqnzuQZ9jzWJPwmKj7fO5Anj9c0a9QJfNC96CILESnxDJHXMKy1anxPoX4RarnQ31oTbhtADuAL7bZ79UEw3auBCbDfXHOlc1sC3BR+PODlnqvCre3zFqLF5EbxssMex5D8WA011DcoB6UK1DJfNA9KCIii11PhPpwSM1+59xES7kHfARYAWwJx8+PAb/X5ji3EoT6v3bOPdLy8OcIAv3fmdn0L586H3gTcBD49qw9qUVkV7XBmkTz9IyBmLGr2mhTQ2R26R4UEZHFridCPXAp8DEz2ww8BhwmWAHnxQQTZfcB7zrBc3yDYOLtbxBMvL0OWEoQ6GMEk24nT/Aci9K6ZIyJuh/0joZyDce6ZKxDLZHZo3tQREQWu15Z/ea/CIbULCcI3n8B/DdgFPhb4Ezn3LYTOYFzzgFvBv4MqAPvDc91G/Cio1jrXtq4dDjNuO8zUffxnWOi7jPu+1w6nO5202SR0D0oIiKLnQVZV47Fpk2b3NatW7vdjJ6ilUek23QPymzaXi+xufrUCiIXJgc5TZMPRWSemdldzrlNR7Nvrwy/kYjbmEkqQElX6R6U2bK9XuLK8iH6ibHc4uT8BleWD/HG9DIFexHpWb0y/EZERKQnbK5O0k+MAS+GZ8aAF6OfGJurmnYlIr1LoV5ERGSa/X6NrDW/PGbNY79f61KLRESOTKFeRERkmpVegoLzm8oKzmell+hSi0REjkxj6kUirFbcQ2XsPhrVMWLJEVIjZ5PIrOlYx5/chTtwN5RHIb0EW3Ee3uC6I54b64gcAAAgAElEQVTr4MSDPFx6gJxVGHApzug7k+VDz+5YZ8e+O9g9+XN8V8SzDGsHz2XDqucf8VyPbX2CLd8/yIFRjxVLfC542XJO2XRSxzp3jI2yOb+bolci4/dxYf9anj+y5Ijnqhd2Ux27F78yhpcaITlyDvHs2s6Vxp+AXT+F4mHILIV158Nw5/YdGN/GL4vbmPQqDPopnpHZyIrhjUdsH3u3w7Yfwfh+GF4JGy+C1ad1rHL7/kmueqjIvglYNQRveGaGF64cPOKp8rUDHK48QsWfJOUNsjR1Ov2JFUdu4wJzYXKQK8uHwA966AvOJ0+DVyVHut00EZG21FMvElG14h4K+27FrxfxEsP49SKFfbdSK+5pW8ef3IV7/Hu4WhGXGgm2j38Pf3JXx3MdnHiQreW7KFOj36UoU2Nr+S4OTjzYts6OfXewa2ILzlXxSONclV0TW9ix746O53ps6xNc9fXD5AuwbKRBvgBXff0wj219om2dO8ZGuan4MBWrkfHTVKzGTcWHuWNstOO56oXdlPf+AL9exJLBNSzv/QH1wu72lcafgIeuh2oB+pYE24euD8rbODC+jTvL91CmzoCfokydO8v3cGD8CCv17t0Ot38LSjkYWh5sb/9WUN7G7fsn+exPikyWYcUgTJbhsz8pcvv+zuPB87UD7Cltpe7KJL0B6q7MntJW8rUDndu4AJ0W7+ON6WUMeDEOujoDXkyTZEWk5ynUi0RUZew+vFgfXjyDmeHFM3ixPipj97Wt4w7cjYtnsERQxxIZXDwT9Nx38HDpAVIuRpokhpEmScrFeLj0QNs6uyd/DiQwS4BZsCURlre35fsH6e/z6c+CZ0Z/Fvr7fLZ8/2DbOpvzu4m7BCmCc6VIEHcJNuc7hHOgOnYvxJuvIfG+oLydXT+FRBaSWTALtolsUN7GL4vbSPsx0hbHDNIWJ+3H+GXxCKF+248g3Q99A2BesE33B+VtXPVQkf40DKYNz4zBtNGfDso7OVx5hJiliXtpzIy4lyZmaQ5XWr+ce3E4Ld7H2zMr+cv+dbw9s1KBXkR6nkK9SEQ1qmNYrDloWKyPRnWsfaXyKLSGk3hfUN5BzipBYJ4mRYKcVdrW8V0RaxnhZ8TxXedweWDUI5Np/v6MTMZxYLT9P1dFr0TKNZ8r5eIUvVLHc/mVma+hX+lwDYuHIZFpLktkgvI2Jr0KqZZrkSLOpNf++gHBkJt0trksnQ3K29g3Af2p5rL+VFDeScWfJGbNFWOWouJrxRcRkShQqBeJqFhyBNdoDq2uUSLWadxvegnUW4JuvRSUdzDgUlRoXvmjQo0Bl2pTAzzL4Kg3t486nmXa1AisWOJTLFpTWbForFjit6kBGb+PijWfq2J1Mn7n3lUvNfM19FIdrmFmKdRa3pjUikF5G4N+ikrLtahQZ9Bvf/2AYAx9udBcVi4E5W2sGoJ8y3uFfCUo7yTlDdJwzRUbrkLKO/JYfBER6T6FepGISo2cjd8o4deLOOfw60X8RonUyNlt69iK87B6MRhL7xyuVsTqRWzFeR3PdUbfmVSsQZkqDkeZKhVrcEbfmW3rrB08F6jhXA2cC7bUwvL2LnjZcvIlj3wBfOfIFyBf8rjgZcvb1rmwfy11qwVvPJyjQo261biwv/OE1+TIOVBvvobUS0F5O+vOh1ohGEvvXLCtFYLyNp6R2UjZa1B2dZyDsqtT9ho8I3OEibIbL4JyPhhL7/xgW84H5W284ZkZ8mWYLDt855gsO/LloLyTpanTabgydb+Mc466X6bhyixNnd65jSIi0hPMOXfkvaTJpk2b3NatW7vdDBGtfjONVr95ila/ERFZGMzsLufcpqPaV6H+2CnUi4iIiMhcO5ZQr+E3IiIiIiIRp1AvIiIiIhJxCvUiIiIiIhGnUC8iIiIiEnEK9SIiIiIiEadQLyIiIiIScQr1IiIiIiIRp1AvIiIiIhJxCvUiIiIiIhGnUC8iIiIiEnEK9SIiIiIiEadQLyIiIiIScQr1IiIiIiIRp1AvIiIiIhJxCvUiIiIiIhGnUC8iIiIiEnEK9SIiIiIiEadQLyIiIiIScQr1IiIiIiIRp1AvIiIiIhJxCvUiIiIiIhGnUC8iIiIiEnEK9SIiIiIiEadQLyIiIiIScQr1IiIiIiIRp1AvIiIiIhJx8W43QKRXPTBa57qddXYWfNZnPS5bH+fMJfqTEemWicZh9jQeo+Ry9NkAa2KnMBRb2u1miYj0BPXUi8zggdE6n9lWZbzqWJsxxquOz2yr8sBovdtNE1mUJhqHeaT2c6quQpp+qq7CI7WfM9E43O2miYj0BIV6kRlct7POcMoYThqeBdvhlHHdToV6kW7Y03iMuKVIWgozI2kp4pZiT+OxbjdNRKQnKNSLzGBnwWcw0Vw2mAjKRWT+lVyOBMmmsgRJSi7XpRaJiPQWhXqRGazPekzWmssma0G5iMy/PhugRrWprEaVPhvoUotERHqLEorIDC5bH2e84hivOnwXbMcrjsvWa6KsSDesiZ1C3VWougrOOaquQt1VWBM7pdtNExHpCQr1IjM4c0mc925MMpw0dhcdw0njvRuTWv1GpEuGYks5PXEuSUtRJk/SUpyeOFer34iIhJRQRNo4c4mWsBTpJUOxpQrxIiJtqKdeRERERCTiFOpFRERERCJOoV5EREREJOIU6kVEREREIk6zAGVReKhc4XuFInvqddbE47w8m+GZ6VS3mxUpvyhVuDFfYk+tzppEnFf29/GsPl1DERGZe4/t8NnyE8eBQ7BiGVzwq8YpG9Q3PZ2uhix4D5Ur/Pv4BJONBqtiMSYbDf59fIKHypVuNy0yflGq8IXRHJMNn9XxGJMNny+M5vhFSddQRETm1mM7fK66zpEvOJYtDbZXXed4bIe+5X06hXpZ8L5XKDLoeQzGYnhmDMZiDHoe3ysUu920yLgxX2Io5jEU8/DMnvzvG/OlbjdNREQWuC0/cfRnHf1ZwzOjP2v0Zx1bfuK63bSeolAvC96eep1+r/lW7/c89tTrXWpR9Oyp1RnwrKlswDP21HQNRURkbh04BJlMc1kmE5TLUxTqZcFbE4+T95s/osv7PmvimlJytNYk4uT85h6RnO9Yk9A1FBGRubViGRRbPlwvFoNyeYpekWXBe3k2w1dyu0glD5OMVag2UpTKS/mN7LpuN+1Jd27P870tFUYPGEtWOF5+QYrnndbf7WY96ZX9fXxhNAcEPfQ53zHR8PnNoWyXW9YdD+1rcPODPnvGYc0wvPTZHs9cFet2s0Qir1LaSyG/jXptnHhimGz/RlJ9q49Yr1raS2nifhq1MWKJEfqGziJ5FPUWosP+GDvcTvIU6SfDBlvPUm9kTs614zGfO7bAwQOwfAU8/wLYcMrs9xdf8KvGVdcBODKZINDnC8bLL7EjVV1U1FMvC97SZJHnDu8nHatTaCRIx+o8d3g/S5O9Mab+zu15vnZVlUIeRpY5Cnn42lVV7tye73bTnvSsvhTvWjLAYMxjb73BYMzjXUsGFuXqNw/ta/ClLQ0mS45VQ47JkuNLWxo8tK/R7aaJRFqltJeJ0c00GiVi8SEajRITo5uplPZ2rFct7SV/8Db8RgkvPozfKJE/eBvVI9RbiA77Y9znHqTiqmRdHxVX5T73IIf9sVk/147HfK69ypHPO5YuC7bXXuXY8djsT149ZYPHGy4LxtIfOhxs33CZVr9ppZ56WfAeZTcrYmnWx5JPllWo8ii7Wcbc9F4ci+9tqdDXD9n+oMch2w/g+N6WSk/11j+rL7UoQ3yrmx/0GUzDYF/w+xrsA3Dc/KCv3nqRE1DIb8NifcRifQDEYn00wvJOvfWlifuxWB9eWM9iffhh+WLrrd/hdpJ0SVIWvN6lSIKDHexk6Sy/3t2xJXi96g9fu/rD1647tsCGU2b1VEAQ7E/ZMPvHXUj0FkcWvBwFkiSaypIkyFHoUouajR6wGScAjR7Qx4q9aM849Keby/rTQbmIHL96bRzPa/7j8rw09VrnP65GbQxrqWdemkZt9nune12e4oyvd3lm/5Ppgwdmnrx68MCsn0qOkkK9LHgDZKlSayqrUmOA3hgPvmSFm3EC0JIVWqqrF60Zhny5uSxfDspF5PjFE8P4fvMfl++XiSc6/3HFEiO4lnrOLxNLdP+T2PnWT2bG17t+Mm1qHL/lK2aevLp8xayfSo6SQr0seKeylgpVKlRxuCf/+1TWdrtpALz8ghSlvFHIO5zvKOQdpbzx8gs01KUXvfTZHpNlmCw5fBeMqZ8sB+Uicvyy/RtxjRKNRgnnXLBtlMj2b+xYr2/oLFyjhB/W88N6fUNnzVPLe8cGW0/VqlRcFeccFVelalU22PpZP9fzL4BCHvJ5h+8HY+oL+aBcusOcU2/gsdq0aZPbunVrt5shx+CQP8aj7CZHgQGynMpals3RagDHo9dXv5Fmvb76zcN7GvzgPp+9Y7B6BF5ytscZa3qnfbI4HPLH2c4uchQZIMNprGOZ17nXXavfnLiFuPrNYmZmdznnNh3Vvgr1x06hXkR61cN7Gnz1hz4DfcFY/3wZciV464sV7GX+HPLHuYeHSJEgSYIqNSrUeC7PPGKwF5GnHEuo19spEZEF5Af3BYF+sC/4OvXBPmOgLygXmS/b2UWKBCmSGEaKJCkSbGdXt5smsmAp1IuILCB7x2ZenWfv4lsIRLoo12YVltwcrMIiIgGFehGRBWT1yMyr86zunSkksggMtFmFZWAOVmERkYBCvYjIAvKSsz1ypebVeXKloFxkvpzGOirUWlYdq3Ea67rdNJEFS98oKyJyjO7L1bj2UIWdZZ/1aY/XLktx9kDiyBXnwRlrYlx0QY1v/bwarEgxDL95QZIz1vRG+xa6xxsFfuqPcYgqy0hyvjfCybHe+E6M+bTMG+a5/jObVr/ZyKmaJCsyhxTqRUSOwX25Gv+0s8hI3FibMsZrPv+0s8ifrs/0RLB/uFrmtoFxzrjI47nmkXc+t7kiJ1eHOSOZPvIB5Lg93ihwg7+PDDGWkqBAnRv8fVzKqkUb7JehEC8yX/R5rIjIMbj2UIWRuDGc8PAs2I7EjWsPVbrdNABuKRUYMI9BLxasfuPFGDCPW0qFbjdtwfupP0aGGFmLY2ZkLU6GGD/1NUtZROaeQr2IyDHYWfYZjFtT2WDc2FnujSUj9zbq9FvzP+395rG3Ue9SixaPQ1TJ0PxdABliHKLapRaJyGKiUC8icgzWpz0m681f2jdZd6xP98Y/p6tjcfKu+Q1G3vmsjmm05VxbRpIijaayIg2WkexSi0RkMemNVyERkYh47bIUY3XHeM3Hd8F2rO547bLUnJxvL5N8n0e4kvv5Po+wl8mO+1/SlyXnfCb9RrD6jd8g53wu6Vt8Y7rn2/neCEUaFFwd5xwFV6dIg/M9rScqInNPoV5E5BicPZDgT9dnGE547K44hhPenE2S3cskt/E4JWoMkaJEjdt4vGOwPyOZ5m39wwx6Mfb7DQa9GG/r1yTZ+XByLMul3iqyxDlMjSxxLvUW5yRZEZl/+jxWROQYnT2QmJeVbu7nAH3E6Qu/mXNqez8HWM1g23pnJNMK8V1yciyrEC8iXaGeehGRHjVOmXRL30uaOOOU29QQEZHFSqFeRKRHDZOmTPOqNWXqDKNeeBERaaZQLyLSo85iBSXqlKjhcJSoUaLOWazodtNERKTHaEy9yCzb6ee5241ymApLSXGeLWG919/tZnVFubyPfGEb9foE8fgQ/dmNpNOrut2syFjNIC/iZO7nAOOUGSbN+aztOJ5eREQWJ4V6kVm0089zk9tDxsVZQpICdW5iD6/w1yy6YF8u72Ns/HY8L00sNkijUWJs/HZGhl+oYH8MVjOoEC8iIkek4Tcis+huN0rGxZu/Jt7FuduNdrtp8y5f2BYG+j7MjFisD89Lky9s63bTREREFhyFepFZdJjKjF8Tf5hKl1rUPfX6BJ7XPKHT89LU6xNdapGIiMjCpVAvMouWkprxa+KXMjffNtrL4vEhfL956UXfLxOPD3WpRSIiIguXxtSLzKLzbAk3sQdc0ENfpEHR6lxki2+1kv7sRsbGbweCHnrfL+P7ZYYGf6XLLWv2wGid63bW2VnwWZ/1uGx9nDOX6J9GERGJFvXUi8yi9V4/r7A1ZC3OqFXJWpxX2OKbJAuQTq9iZPiFxGJ9NBqTxGJ9PTdJ9oHROp/ZVmW86libMcarjs9sq/LAaP3IlUVERHqIuqNEZtl6r5/1LL4QP5N0elVPhfhW1+2sM5wyhpMGwHDyqXL11ouISJSop15EFq2dBZ/BRHPZYCIoFxERiRKFehFZtNZnPSZrzWWTtaBcREQkSvTKJSKL1mXr44xXHONVh++C7XjFcdl6Db0REZFo0SuXRMr2eonN1Un2+zVWegkuTA5yWryv282SiDpzSZz3bqRp9Zu3nZbQeHqRReDHpUNcnR9lb73B6niM1/cv4QV9y45Y7+aD2/n65CT7/BirvAZvHhzkpctP61hn++FHuW3iCfY7n5Xm8aKhkzht6alHPNfjBx7izolHOESdZcR53tDpnLzimZ0r7X4Efn4rjO2DkVVw7sWw9vQjnuuLD+e4YixHLt5goB7jLSMD/O4ZAx3r3L1vP9c8OsbuAqzNwutOHeG8VSs71jlUeJCd5XspUiJDH+vT57As++wjtq+R30Xj0M9w5VEsvYTYsucQ61/XudLkTth3N5QPQ3oprDoPBtcf8VyPb/e580dwcD8sXwnPuwhOPq33+8HNOdftNkTOpk2b3NatW7vdjEVne73EleVD9BMjax4F55OnwRvTyxTsRUTkqP24dIjPjh1kwDP6PY+875PzHX88srxjsL/54Hb+YbxAv/n0A3kg7zw+MJxtG+y3H36Ub47voN8gi0cBn7yDNw1v6BjsHz/wEN+Z/AVZZ2TwKOJTMMdrBp/VPtjvfgRuuQL6BqEvC6UClCbhkrd0DPZffDjH/8mNkfQ9Us6omKPq+bxnYKRtsL97337++efjDCV9BhJGruaYqHq879zhtsH+UOFBflG+gwRx4iSoU6NGnWeln98x2Dfyu6jt+i8sloF4H9RLuEaRxLpfax/sJ3fCYzdBPBP81IvBzymv6BjsH9/u851vQbYfMlkoFqCQh9f8ZneCvZnd5ZzbdDT79v7bDpHQ5uok/cQY8GJ4Zgx4MfqJsbk62e2miYhIhFydH2XAMwZjHp7BYMxjwDOuzo92rPf1yUn6zWfQCOoZ9JvP1yfbvw7dNvEE/QYDFr52WYx+C8o7uXPiEbLOyBLDCLZZZ9w58Uj7Sj+/NQj0mQEwL9j2DQblHVwxliPpe/Q5Dw+jz3kkfY8rxnJt61zz6BhDSZ+hlIfnGUMpj6GkzzWPjrWts7N8LwniJC2JZ0bSkiSIs7N8b8f2NQ79DItlsEQGMwu2sQyNQz9rX2nf3UGYT2TBLNjGM0F5B3f+KAj02fASZgeC/7/zRx2r9QSFeomM/X6NrDXfslnz2O/X2tQQERF5ur31Bv1e8+tJv+ext95oUyOwz489bcHi/rC8nf3OJ9sSt7J47HedV9k6RJ1MS70MHofo8D0aY/uCHvrp+rJBeQe5eIOUs6aylDNy8fbXY3cBBhLNdQYSxu5C+/MUKRGnecmxOAmKlDq2z5VHgx76pop9QXk75cNBiG+qkwnKOzi4P+ihny6TDcp7nUK9RMZKL0Gh5R/BgvNZ6SXa1BAREXm61fEYeb/59STv+6yOtw/nAKu8BvmWsnxY3s5KC4bcTFcgGFvfyTLiFFvqFfFZ1mk65MiqYMjNdKVCUN7BQD1GxZqHY1fMMVBvfz3WZiFXa66TqznWZttUADL0Uae5I65OjQydh9BaegnUW4J/vRSUt5NeGgy3aapTDMo7WL4yGHIzXbEQlPc6hXqJjAuTg+RpkPMb+M6R8xvkaXBhcrDbTRMRkQh5ff8Scr5jsuHjO5hsBGPqX9/fISQCbx4cJO88Jh1BPReMqX/zYPvXoRcNnUTeQc6Fr12uQd4F5Z08b+h0CuYo0MARbAvmeN5Qh0mv514cjKEv5sD5wbY0GZR38JaRAaqeT8l8fBwl86l6Pm8ZaT9R9nWnjjBR9Zio+Pi+Y6LiM1H1eN2pI23rrE+fQ406VVfFd46qq1Kjzvr0OR3bF1v2HFyjiKsVcc4F20aR2LLntK+06rwgxNcK4FywrReD8g6ed1Ewhr4QXsJCLvj/513UsVpP0ETZ46CJst3z3fEc/36wyJ5KgzWpGL+zPMOrhjvPzp9vj9RK3FbJPblCz4tSA5ye6NwLsZdJtrGfccoMk2YjK1mN3qwI7HY57uUQY5QZIc05LGOt9dY9LxJFWv2mmVa/eUovrX5zLBNlFeqPg0J9d9yXr/HpXQVGYh6DcWOy7hhr+PzJuixn9/fGEJxHaiW+WRql37ynVuhxPm/qW9I22O9lks3soI84aeKUqVOizoVsULBf5Ha7HD9gJ33E6SNOKbw3XsJ6BXsRkUVAq9/IgvSfhyqMxDyGEx6eGcMJj5GYx38eqnS7aU+6rZKj37zmFXrM47ZK+xUEtrE/DG0JDKOPBH3E2UYEZuXInLqXQ/QRJxPeG5nw3riXQ91umoiI9JieCfVm9nEzu9nMdppZycxGzeweM/uQmS1t2fcMM/ugmd0S7l81s/1mdq2ZveQI53m7md1pZnkzmzCzW83sNXP77GQ27Cw3GIw3z7QfjBs7y51XK5hPx7NCzzhl0i0Tn9LEGac8J22U6BijTF/LvdFHnDHdGyIi0qJnQj3wfiALfB/4NHAFUAc+DNxrZtMHQX0E+HtgJXAD8A/A7cCrgVvM7H0zncDMPglcDqwGvgB8FTgbuM7M/njWn5HMqvXpGJP15uFik3XH+nTn1Qrm0/Gs0DNMmnLLEmVl6gyTnpM2SnSMkKbUcm+UqDOie0NERFr00nehDzrnntb9ZGYfBf4a+CvgPWHxjcDHnXP3tOz7YoI3BZ8wsyudc3unPXYB8AFgO3C+c24sLP8EcBfwSTP7jnNux6w/M5kVv74sxd89nuO+ao2G54j5xhAJ/vvq3hlb/KLUAN8sjYJP05j6V6eH29bZyEo2swOgaUz9r3CECUDz7P58jf88XGFXpcG6VIxfX5rirB6Zy7BQncMyfsBOgKYx9c9ndZdbFi07GkV+XB/noKuy3JK8ID7MhljmyBWPw2F/jMfdTgoUyJLlZFvPUq/9aiAAB90429lNjiIDZDiNtSy39v9mTLlvvM61u2rsLPqsz3i8dl2Cs4d752V9rDHKTv/xJ6/Feu9kRmKdV5dZqB593Of2nzoOHIIVy+CF5xunntxL/aq974HROtc9UWdX3rGu37jspDhnLumd+70X9MwdNVOgD30r3J4xbd/LWwN9WP5D4FYgCVzQ8vAfhNuPTgX6sM4O4F+AFPDO42m7zI90ssGykQrxGNTqRjwGy0YqpJP/P3vvHSXXdR54/u4LlUMHNIBOCAQJQiTAIJEiKSpADBZFmaQkrz1eW6bt9VqeWdvyjMc7e473zAy1f+zM7toeS7bXlrQOkix5HMQogZBIkCBFIpPIOTY656qurvDqhbt/3ALR1ez3CmiCQDf4fuf0efU+1Id7X7j3fe/WFxaO+82NZpx/FW8hLXRGPYe00AODZAHayfBxVhHHJI9FHHPBBckemrb5en+RnOPREdHIOR5f7y9yaDos/PV+0inSfJpuEpjksEhghkGyl8k5t8Sz1RGmpUMrJtPS4dnqCOfcUmPly2Tcm+SQPIolqyRkAktWOSSPMu75V9gclTn2cgKLKiniWFTZywlGZS6wrYM5h68dt8hVPTrjglzV42vHLQ7mAgoTXUUm3QmOeoepYpEgQRWLo95hJt3giq3XI2d6PP5lk2S6KFnSqrb/sklypie4+FTIRQ5POPz5YZucJelIQs6S/Plhm8MTC+N+Xygshlecx2rb4BrCF7lgZcy+0g/Utpvn0HkR+I+17/zny+pdyFVjqzXNmqTGHemL7jYFT8lviiwcd4QbzXjDFJazaSezoIz42Tw/btFkaDQZah2gqRbb8Py4Fa7Wv890ijSdhEb8fNnu5EgKjZRQj7sUBuCw3cld8dX6HtlLREaIiggAUSIgoYdeWpl7tf40/UQx1Xcv6NTkbfiv1j/XZ9NkQlOkNiYjAvB4rs9eEKv1vV4PEUwiIgpAhChIJf+grda/uVuSTkhSSTVvppIAkjd3ww0rr2nXFg0vnHfIRqApqs5hUxRA8sJ5J1ytn8GCOxNCiD9AVV3OAncBH0cZ9P/1EnRXAg8CJeD1GfIk0AlMz3TJmcHJ2nZtwP/9ZeDLACtWBBeMCHl/GHJtlmr1t2xSaAy54Wrx+02f5dIRqf9hL6ML+qyF8ytJSMhcjMoqrbPK0ifQGZXVK95WkSIJ6l8UIpgUKfpoQIESqVnVNCOYFAj+JaG3pFboZ5IxBb2lhbH6O9e5MIkEnovrlZExWDKriGkioeQhl0bftFqhn0kmouQhF1kw7jcz+APUavm/RRn0m4GfkVKOBikJIaKo4Noo8NRMFxvUCwJA3kf9gtx3WURK+U0p5V1Syrva2toaH0XIFWe5PncQ6nI9XCl+v+mK6ky5s4KUXUlXdOEEKYeEzEWbiFCi/uWzhEtbbTX9SpIkSZX6RYYqNkmSPhqQJjGnTprgXxG6ExpT9qwxaUu6EwvjsZ4kiU39i5NNNfBcXK8sXQKlWe9opZKSh1waXSnB1Kz38KmqkodcZGGM/hlIKZdLKQWwHPgicAOwVwjhW9dXCKED3wXuB/4R+KOr0deQq8vGaIqC9Ch4tVLbnktBemyMpq511657Hm+NknM8co6HJ+U7nx9vjV7rroWEBHKf0VQLWHfwpGRaOhSlx31G40DUy2Wl6KYqqliyipQSS1apiiorhX8FyzV0YmFjUUUisahiYbOGzsC2nugyyZwQUrwAACAASURBVNmQq9bGZNUjZyv5QqBbW0kVm6q0kFJSlRZVbLq1D56/yf13CwolwXRRqnuwKCmUBPffHRqkl8pjKwzyVeVL70lJzpLkq0oecpEFezaklMPAM0KIt4ETwHeA9bO/VzPo/x74eVRQ7Zfku8vkXliJzzI3F+TBkUnXiFJ1mMnKCapunoiepTm2lkQkuAwzqFLMPeWDFEWFpIyxMr6hYSnm1wdP8HJliILpkbY1Hoot55Ptvl5JFxk6A8d+CvkRyC6FdZ+A5Q1KYA+ehsOvQ24YmpbBrZ+Edv9S2zdFYvzSaIGtw2cZkpLlQvDYstXc1HQJmUD6TsK+V2FiCFqWwx2fhq6bgnV6T8Lbr8D4ILS2w4cfgO4GOgDnjsOOl2B0ENra4d6HYVVwWe/R4QOczB2goFuk3Sg3Nd1G27LbGjb12thOtmrj5HWDrOuw0WvlU0vuCdQ50XOCLQNnGfQ82jWNBztWs3Zl8DVenzL5ijbG8yd76atCVwSeXNPN+lQDw6jnBOx+GcYGYUk73P0QNGgLYGvPdvY5fXiGi+bo3GF0sXHlfYE6z/Tu5XlK5A2TrGPzOAm+0H1nw7Y4fBBeeA56e6G7Gx57Am7dEKxz5BBseh76+6CzCx59HG551/RUz4ED8MwzcP48rFgBX/gC3Nb4Gp/tOcz24ROMCpc2qXPfsrWsXnlroE7f8bfZ33+ACcOhxTG4vfM2um72XRd5h/zx7Qz3vklZKxH3Eizrvp/szcHnfaR3HyfG9jKlV8m4EdYuuZOl3Xc0buvcLoaHdlAWJeIywbLl95Jd9dFgpaOHYfML0N8Lnd3wyGPwoeBzsUpP8EhfH8cqx6jGXNoqOp+IrWPVTauC29qzBfa+ANU8RLJw52Nw14OBKq1aM/apabZmKxRjJsmKzf35GK03+me/aRNN3HBsiAPRAYpJg2TR4Targ7Z1wediQ5PBY9ZbfKNHMKAl6fCK/NZyyYam4PH/vSMH+HbOYZQobVj8apPBL98SfB+eP7Oft4aOMG64tDo6H1l+CytuuD1Qp1lv4UNno/QO7qSoV0i6Mda030PzjcH+9FuPH2CTNcJUTCNT8Xg0upSNNzceJ5w+Cq9vhuF+WNYJn3wE1gQ/7zh0EJ5/FvrOQ9cKePzzsL7B2Ac4dhhe+iEM9EFHFzz8s7DO/z68YaXG/3DTcd58to+RvMHSrMNnPt/FDSuDnwsAkz176B3eRVGrkPRidC/7KM0rg4uLHjl4mBePnaHfE3Rqks+uu4FbNgSPEwBOHoFXNsFgP7R3wgOPwk23NNZ59UUY6oflnfDpzzbWOXEEtmyCwT5o74IHH4W1wTq3thj8TuQUL+zsoa+i0xVz+ZV7VnJry7rgtuYxZwDzey4sAMS77d+FhxBiL3AH0CalHJshN1EuNz8PfB94Uko5p5OvEKIP5VffMduvXghxH7ANeENK+YlG/bnrrrvknj175ns4l0WpOszQ9C50LYYuorjSwvUqLE99NNCwHyse5XBlJxEMTExsbKo43Bq7x9ewf33wBE97A0QciHiCqiapGvBFrSPYsB86Azv+CaIpiCWhUgRrGu79BX/DfvA0vPHfIZa+qFMpwMd/0d+w7z8Fr3wP4hmIJ6FchPIUPPDL0Hmjf//6TsLL34NE+qJeqQAP/bK/Yd97En78XaWTSEFpWul85leCDftzx+G5v4Vk5qJecQqe+HVfw350+AB7CruJejpRqWMJF0tzuSt9d6Bh/9rYTp6NTBH1XKJSYgmBpel8vprxNexP9JzgO31nSAtJSgimpaQgBU923RBs2J8/AZu+A8kZ56NYgEefhBU+ej0nYNPfQWLGuShNwaO/FmjYb+3Zzl5xDukKhKchNQ+hS+6Uq3wN+2d69/Id0ybqeUQ9D0vTsDSNJ20z2LA/fBD+7E+hqRkyGZiagtwk/O6/9Z/AjxyCv/o6ZJsgnYHCFORz8K+/4m/YHzgAf/LHqp1sFvJ51c7v//tAw/5sz2GeHT1G0oMEGiU8ihp8vm2dr2Hfd/xttgy/TcIVxKVGWXiUdMmDyz4caNjnj2/nbP9LmJ6BIU0cYWNrDqs7H/Y17Ed697Ert5OYqxGVBpZwqOgeH226J9Cwz5/bxdmRLZieiYGJg42t2axe+qC/YX/0MHzzz9593r/8u4EP6amj2+g5/2MMz3inLUdzWLniM2Q+NDtJWo09W2Dnd0GLgh4F1wLPgnt+JdCw33bkVf6puUq0ahOzXSqmjhUx+YXJCB+7Ze66iOP7fsQBZz+RqkfEhqoJ1YjGbcbttN7xOd+2jux8nb8cHCfrVklLh4IwyOsR/k17K7fc88k5db535AD/d04ngUMSlyI6JQz+Q5Pra9ifP7OfzWOHSLiQkBol4VHS4ZEl64MN+3lcr63HD/B9bYyoLYk6YBlgmYJf8pYEG/anj8J//yaks2qOKhagkIdf/LK/YX/oIHz9v0FTE2SyMJWHXA6+8u+CDftjh+Fv/kKN41QGpqfUeP6fftvfsD9wAP7oj6B5xvifnIQ/+IPA8T/Zs4ejo68R8QxMaWILm6rm8KG2T/ka9kcOHuYbR86QBdKaSiaRB37rlgaG/ckj8N2/UucwlYbp2jn8lX/tb6SfPALf+8a7z/sv/5a/zokj8J2/hHTTjHZy8OS/CTbsDx6Er/3Ju6/X7/0+bPC5XvOcM+b1XHgfEUK8JaUMfpOrseDcb3zoqG3fMdiFEBHgn1EG/XeAX/Ez6Gu8Uts+Mse/fXbWdxYMk5UT6FoMQ4shhMDQYuhajMnKiUC9nvJBIhhEiCAQRIgQwaCnfNBX5+XKEBEHYlJDE4KY1Ig4Sh7IsZ8qgz6eBqGpbTSl5H4cfl0Z9DN1Ymkl92P/VmXQJ2o6ibTa3781uH/7Xq0Z5zP0Emkl9+PtV9R3khmlk6y1+3aDW2THS+q7qQxomtomM0ruw8ncAaKeTgwDIQQxDKKezslccMKnrdo4Uc8lLiUaEJeSqOeyVRv31dkycJa0kGSEhoYgIzTSQrJl4Gzwce3ZoibtmecjmVZyP3a/rAz6unOYUfIA9jl9SFegSR0h1Fa6gn1On6/O85SIeh5xz1PnombcP98g2JAXnlMTd1OTul5NTWr/hef8dTY9rx4S2ZrOhc+bnvfXeeYZ9f82Nyud5ma1/8wzgd3bPnyCpAcpoaMJQUroJD0l92N//wESriCBOn8JdBKuYH9/8P003PsmpmdgEkEIgUkE0zMY7n3TV+fE2F5irkYMs3bvmsRcjRNj78o4XN/W0A5Mz8QUtbZEBNMzGR7a4a+0+YW5z/vmFwLbGjn3UwzPqGvL8AxGzgXMT3tfUAa9EVf3rhFX+3uD23rJnCJatUnYHhqChO0Rrdq8ZE756pzN7yNS9Yg6AiEEUUcQqXqcze8LbOtHvX1k3SpZXDQhyOKSdav8qNd/nHw755DAIS08NCFIC48EDt8OSIP51tAREi4ka/dTEp2Eq+SBzON6bbJGiNqSuKOMk7gDUVuyyRoJbuv1zcqwTGdVWxc+vz5Xsrsazz97cbxr2sV54Plng9t66YfKMM/UjivTpPZf+qG/ztNPqzE/c/w3Nyt5AL3Du4h4tee4qD3HPYPe4V2+Oi8eUwZ9VhfqvtAF2Zo8kFc2qXOWqZ3DTO0cvrLJX+fVF+c+76++6K+zZZMy6OvaaVLyIJ57Zu7r9VzAHDrPOWNez4UFwoIw6oUQa4UQ73KNEUJoteJTS4FtMwpGRYFngCeAvwZ+XUrZKOT/r2rb/10I8c5voUKIVcBvAxbwt+/xUK44VTePLur9lnURper6xfwqiqKCOSvjg4lJUfiXly+YHhGv3scv4gkKZoNTmx9Rq+0ziSWV3I/c8Nw6uWF/nckhtdI+k3hSyYOY8NGbCNAbH1SryzNJpJQ8iFEfvVF/vYJuEZX1AadRqVPQrcCm8rpBdNYvbVEpyev+XnWDnkdK1F/jlBAMeg2u8ZjPcY0FnI/56ACe4SK8+qlJeBqe4f/OnjdMorOOIep55I0G/sW9vWolZiaZjJL70d+nVn1mks4ouR/nz6sH/0yyWSUPYFS4JGZN0wk0RoX/uZgwHOKyXicuNSaM4HzOZa2EIevPlyFNypr/i9GUXiUq6++3qDSY0oMzy5RFCWPW/GRgUhYBL2H9vT7nPeBaARVt7rYqAcdFNa9W6GeiR5U8gFwySsyuvzYx2yWX9I89mU5AZFYCr4it5EH0GzHSsv6apqVDv+Gf3neUKMlZQcNJXEbx79+44ZKYdT8lpMZ4wHhUHbz86zUV04jOuk2jjpIHMtyvFhlmkkwruR9955VBOZNMVsmDGOhTizUzSWWU3I95jv+iVsGcNSZNaVLU/J/j/Z4gPet0pTUlD2SwX62czySVVnI/hnzO+1CAzmCfTzsB5w9qc/Uc1ytwrp7fnDGv58ICYUEY9cCjwJAQ4iUhxDeFEP9FCPE3qFSTfwgMAb854/t/VdMZA/qB/ySEeGrW38aZDUgptwF/AqwBDggh/psQ4i+APUAL8AcLsZpsRM/iynrjzpUWEd0vPECRlDHsWRkVbGyS0n/CT9saVa3eSKxqkrTd4DbJLlXuMzOpFJXcj6Zlc+s0BcQKNC9XrjMzKReVPIgWH72WAL3WduUuMpPStJIH0eaj1+avl3ajWLOMNEu4pN3gINSs62DNMtAtIci6/sZbu6YxPetFYFpK2rUG13iJz3EtCTgf89EBNEdHavUGutQ8NMc/007WsbFmHYOlaWSdBulOu7vVT6szmZpScj86u9TPuDMpTCm5HytWqJ/cZ5LPK3kAbVKnRP25KOHRJv3PRYtjUBb1OmXh0eIEh1DFvQSOqD9fjrCJe/7WZcaNYIn6+80SDhk3OLNMXCZwZs1PDjZxGWDJdnb7nPeAawXEvLnbigUcF5GscrmZiWspeQBNRYuKWX9tKqZOU9H/BT1VUi43M6maSh5Ep1OhIOqvaUEYdDr+Bl8bFkXq+1dEpw3//rU6OqVZ91NJeLQGjEfVwcu/XpmKhzXrNrUMJQ9kWady/ZhJsaDkfnStUC4cM5nKK3kQHV3K5WYm01NK7sc8x3/Si2HPGpO2sEl6/s/xTk1SmHW6Cp6SB9LeqVxhZjJdUHI/lvuc9+UBOu1dPu00qKDe3T339Qqcq+c3Z8zrubBAuGSjXgjRLIS4pbZKPlP+60KI54QQ3xdCNIhy8uVl1Ip7Gyrjzf8K/BwwAXwVuFVKOfO3vtW17RLgP6FSYM7+2zi7ESnlv0dVjR1C5Zx/EjgMPCal/PN59v19pTm2Fter4HgVpJQ4XgXXq9AcCw42XBnfQBWHai2jQpUqVRxWxv39wR6KLadqQEWobAoV4VE1lDyQdZ9QPvTlAkhPba1pJffj1k8qH/qZOpWCkvtx+0blQ1+q6ZQKav/2jcH9u+PT6rsz9UoFJffjww+o7xSnlE6x1u6HH/DXARUUW5xSk7znqW1xSsl9uKnpNizNpYKDlJIKDpbmclNTcHDYRq8VS9MpC4EHlGs+9Ru9Vl+dBztWU5CCKenhIZmSHgUpeLBjta8OoPyIi7POR7EQHDh490PKh77uHE4peQB3GF0IXeIJFynVVuiSOwz/Sf9xEliaRlnT1Lmo+dQ/3iAtII89oXwlczl1vXI5tf/YE/46jz6u/DLzNZ0Lnx993F/nC19Q/+/kpNKZnFT7X/hCYPfuW7aWogbT0q1lbnEpakrux+2dt1HSJSXU+SvhUtIlt3cG30/Luu/H1hxsVOYWmyq25rCs+35fnbVL7qSie1Swa/euTUX3WLskOEB52fJ7sTUbu5YlxpZVbM1m2fJ7/ZUeeWzu8/7IY/46wNJVn8DRnLq2HM1h6aqA+enOx5QPvVNW965TVvt3Brf1sJ3BipiUTA0PScnUsCImD9v+heVWZ++gGtGwDKky5hiSakRjdTY42Phz3V3k9Qh5dDwpyaOT1yN8rtt/nPxqk0EJg4LUVAYxqVHC4FcDilV9ZPktlHQo1u6nIi4lXckDmcf1ejS6FMsUlA3UOK751D8aDVggAhUUW8irP8+7+PmTc3nb1nj88xfHu+ddnAce/3xwWw//rDLIp2rHNZVT+w//rL/OF7+oxvzM8T85qeQBdC/7KFWt9hyXtee45tC9zN/U+uy6G8gDeVdlicm7knxNHsgDj6pzNlU7h1O1c/jAo/46n/7s3Of905/113nwUeVDX9dOTsmDeOILc1+vJwLm0HnOGfN6LiwQLjlQVgjxl8CXgKVSynJN9rvAnwIXlgsrwF2zDPDrjqsZKAth9ps6+k8pH/rJIbVCf/vG4CDZC4TZb95hPtlvABUsu2fLxUw2dz3oHyR7gTD7zUXC7Df1bV2l7DeggmVHzv2UilYi5iVYuuoT/kGyF5hH9htQwbIvmVPkklGaihYP2xnfINkLjO/7EWfz+5hOqBX61dk7AoNkL3Bk5+v8qLePfiNGp1Phc91dvkGyF7ha2W+AeV2v+Wa/Ge7dy4mxfeT1Klk3wtold7Cs0fi/StlvADX+n3764vj/4hcvafyH2W9mcPCg8qG/MFc/8QX/INkLXAfZby4nUPZyjPoDwBkp5ednyHpQBv0vofLKfwf4vpTyf77sXi8irrZRHxISEhISEjI3w+TZxSlimEQxsbCpYPNRbmSZbybrkJDFweUY9ZeTp74TeCfVhRDiFqAb+N+klG/UZD8PBC8ThISEhISEhIRcIU4wqDIvoWI5LmxPMBga9SEfKC4nUDaOcq+5wP2ARPnDX+A0NCiDFxISEhISEhJyhchTIjorw1EUk3yjlLYhIdcZl2PU9wMzS3d9BpgC9s+QNQPlK9CvkJCQkJCQkJCGZElgzcpwZGGTbRQoHxJynXE57jevAr8qhPgd1Ir948APZuWHXwMs/ESeISHvIxPeBD2yl6IskhRJVopuWrTg0ughIYuVAVngICNMUqGZGBtYSodIN9Q77xbZ7U0yhsUSotytNbNCTwbqnKpW2GoVGPIclmsGG6Npboz4p/d7L4x5k5yljwIl0iRYTRdLtObGiguccmWI6eJRbCeHaTSRSn6IeIMMZ9MT+5mY2o3lFYlqSVoyd5NqaRwoW+3fjTXwGq49hW5miHZ8ikjn3cFKY+fg7HYojEK6DVbfB0tWBaqspZ3t09up5gcwqxXsSAw728FtqeAg75Brw7ERl82nXAamJB0ZwSM36qxb2iBF6jw5mHN4rs+mt+TRndB4ostkQ0C2pwscyNs8PWRzvuyxIq7xxeUmt2Ub1D1ZAFzOSv1/AaaBrwHfRBn2T134RyFEBvg4sO0K9i8kZFEx4U1w2DuCJaskSGDJKoe9I0x4E9e6ayEhV5wBWWArPZSwaSJKCZut9DAgC4F6590iP/IGKeLQSoQiDj/yBjnvFn11TlUrfL80QcFzWSp0Cp7L90sTnKr652WfL2PeJPvlcSxZJSXjWLLKfnmcMW/yird1NSlXhpjIb8N1yxh6FtctM5HfRjmgavj0xH4Gcq/ieBYRLYHjWQzkXmV6Yr+vDiiDvtTzPJ5bRjNTeG6ZUs/zVPt3+yuNnYP9z6qUyKlWtd3/rJIH0NxzlPVHdhOxqxTjCSJ2lfVHdtPcczRQL+Tqc2zE5Vt7bPIVyfI05CuSb+2xOTbSoJjZPDiYc/jacYtc1aMzLshVPb523OJgQAVlUAb9H5+pMGl7dMUEk7bHH5+pcCDfoO7JAuCSjXop5VngVuD3gK8A66WUx2d85UbgG8C3r2gPQ0IWET2yF5Mo0VpJ+qiIYBKlR4Y/YIVcfxxkhDgGCUwEggQmcQwOElBNGtjtTZLEICkMhBAkhUESg90BRvNWq0BaaKQ1HU0I0ppOWmhstYJfIObDWfqIYtaN4ygmZ2lQ9XKBM108ii5i6HocIQS6HkcXMaaL/sbvxNRuDEwMLYpAYGhRDEwmpgKMc8AaeA2hRdD0OAJNbbUI1sBr/kpnt0M0CdEUCE1to0klD2qrdyttZZd7+3I8dGqIe/tytJVdrN6tgXohV5/Np1wyMUE2JtCE2mZigs2nrrxR/1yfTZMJTRENTQiaIhpNppIH8fSQTZMpaDaVXrOp0WQKnh5a+Eb95bjfIKUcAuYs0iSlfBt4+0p0KiRksVKURRKz/DgjmBSl/wpkSMhiZZIKTdRXPo5jMEnw6vkYFq3UV51NoDMWUNl0yHNYKup/ok8KjSEveNVtPhQokSJeJ4tgUljkgZe2k8OYVY1c02LYTs5Xx/KKRLT6OU3XIlhe8Jzm2lNoZqpOJvQorj3lo4FyuUnNKp4XSSh5YFsFNLPedUvoEVz7yr/whbw3BqbUCv1M0lElv9L0ltQK/UwypqC3FFyh+HxZrdDPJGsIzpcbVDZeAFxORdkzQoivNPjObwshzrz3boWELE6SIkl1VsBWFZukCPYVDglZjDQTo0y9UV3GoZlgP/clRClRvzJXwmXJrBeEmSzXDIqy/qFalB7Ltctam7ok0iTmHMfpRR54aRpNeF79C5fnVTCNJl+dqJbE9ap1MterEtWC5zTdzCDd+pc06Vropn91XdJtUJ314lQtKXlgW2mkW99H6VbRzcaxHSFXl46MoDDr3b1gKfmVpjuhMWXXvyxM2ZLuRLDpuyKukXfq9fKOZEX8cjzWrw2X08NVgP/IVzQBK+fdm5CQRc5K0Y2NhVUrSW/JKjYWK0X3te5aSMgVZwNLKeNQwkYiKWFTxmEDSwP17taaKeJQlA5SSorSoYjD3QGBqBujaQrSo+C5eFJS8FwK0mNj9MobbqvpwsKuG8cWNqvpuuJtXU1SyQ/hygquW0ZKieuWcWWFVECl8ZbM3TjYOJ6FROJ4Fg42LZnggNdox6eQXhXPLSPx1NarEu34lL/S6vvAKipfeumprVVU8qC2ujfW2qrU2qqotro3BuqFXH0euVFnqiLJVySeVNupiuSRG698oOwTXSY5G3JVD09KclWPnK3kQXxxuUnOlkzaSm/S9sjZki8uX/iBspdTUdYDnpJS/h8B3/mvwO9JKeN+37keCCvKhgSx0LPfHCtbbJ4uM2A7dJgGj6TirIv7r5CGhAQx3+w3p0vn2GmdYxyHVgzuia5iTWJVoM7x6fOcLh7C9abQtQxrkuu5ObWiYVvHnF4OydNUKBIjyXqxhnVG8Iv22NQRzpYPURAV0jLG6vh6lmQuoZT96Fk4vR0KI5BeCmvug7bVgSqnh07y04mzDEuPZULjEy2rWbP8poZNFUf3MTGx42JWmpZ7SbbdEahzPWa/Aaj27MDq3YprF9DNNNHujURW3huoc84tscPNMSqrtIkI9+pNrNIX1q8xJ6wKW8pFBh2HdsPgwXiStdH3J+PT1eLF/jJ/f7TK0LRkeUrwpQ9F+Gzn+2M2Xg/Zby6nomygUS+EmDlbngP+tPY3Gx1YAfx/QFVKeesl93YREhr1IYuVY2WLb00UyOoaaU1Q8CR51+M3W9KhYR9y1aiWBiiNvIbQ4gg9hnQrSK9MYumniCQ65tQpW8OMFrajazE0EcOTFVyvQlv6PuLRZb5tHXN62S33o0sTExMbG1fY3C1u9zfsc+fh1ItgJsFMgF0Cuwg3fhaaAl4iRs/C3mdUgGckoVxHrGm48wu+hv3poZP808RpUlLFCBSlx7SAX2hZE2jYF0f3MTD+MgYGuojgyioODh2tDzU07EOUQf+cPUxS6CTQKeFSlC5PmMsWjGF/wqrwnUKetCZICY1p6VHwJE+ms4vWsD9uWfxdPk9GCFKaxrTnMSUlv5bNcnM0fAbNxeUY9Y3cb84BZ2t/oDLfnJ3j7xTwCipP/bcuv8shISFXg83TZbK6RlZXUf0XPm+eDmvGhVw9rNxBhBZHM1QWFs2II7Q4Vu6gr06+fAxdi6FrtcwtWhxdi5EvHwts65A8jS5NIrVMNhERQZcmh+Rpf6XBt5RBH0mCEGprJpU8iNPbaxlbZmZvSSm5Dz+dOEtKQlroaAjSQicllTyIiYkdGBgYWgwhNAwthoHBxMSO4D6GALDDzZEUOilhoAlBShgkhc4O1z9o+GqzpVwkrQkytYxPGU0nrQm2lBdv4oWXi0UyQpDRa8ek62SE4OXi4j2mhUSj3yC+A0hAAE8CB4B9c3zPBcaBLVLKn1zRHoaEhFwxBmyHdqPedzGtCQbsK59BJCTED7c6iWbWh2gJPYZb9U9paTt5DL0+yFITMWwnH9hWhSLRWZlsTEwqBBgRpTGIz8rCYiaUPIjCCKSW1MsiCSX3YVh6tIn69bWk0BiWwZk2VFaa+uPSReOsNCGKUVmllXp3igQ6o7Lqo3H1GXQclun183VKaAw6i3e+HnAcls8+Jk1jYBEf00Ii0KiXUv7ahc9CiCeBZ4J86kNCQhY2HabBlOuR1S9mGih4kg7zymcQCQnxQ4804zllhHHRKJVuBT3iHyhrGllcr4wuLup4soJpZH11AGIkqWIRmZFC08YmRkD2lsQSqBbVCv07SiUlDyK9VLnbRGekcqyWlNyHZUKjID3SM9J1Fmu+9UFEtSSOrGCIi24YrmyclSZE0SYiTEuH1AwzqIRLm4gEaF1d2g2DKc8lM+PemJYe7cbina87DIMp1yUzw7Cf9jw6FvExLSQup/iUFhr0ISGLm0dScfKuR95VUf0XPj+Suq5j20MWGNGmDUivjOeoLCyeU0Z6ZaJNG3x1svF1uF4F16tlbvHKuF6FbHxdYFvrxRpcYVOtZbKpyiqusFkv1vgrtX9E+dBXiyCl2tpFJQ9izX21jC0zs7dMK7kPn2hZzbSAgnTxkBSky7RQ8iBaWu7FwcHxKkjp4XgVHBxaWoKDQ0MU9+pNFKXLtHTwpGRaOhSly716oyR/V48H40kKnmSqlvFpynMpeJIH44v3xe2hZJIpKZlya8fkukxJyUPJxXtMC4lLzn4TcpEwUPbdlK1h8uVj2E4e08iSja8LDF4DeM0+wxuijzIOcQw+Lrv4Z2cqOAAAIABJREFUlHlDoE6vN81bcoJxadEqonxEtNCtpQJ1ANzpfpzxfUhrEhFtxmi9Az3VGayUOw99u6E0DolW6Lo7OEjuAhM90LsLiqOQbIPuj0JLg0yvo2fh1DaYGoHMUrjxYw0zZsyXxZD95rxXfNd1XhGuQF5XVEsDWLmDuNVJ9Egz0aYNvkGyF5jPPAPzy35D7rzyoS+NqRX69o9c2vhf4NlvFjoFZ5TR6ikqXoGYlqYtciNpIzhPPcCozHGKfgqUSRPnRjppE8EGepj95tpw3LJ4uVhkwHHoMAweSibDINkArkj2GyHE36D86f9QSjlc278UpJTyNy7xu4uS0KivZz5ZKV6zz/ATcQ4dgYmGjYeL5GfkKl/DvtebZrM3QALjnWwFJRwe0ToCDXt3up9q/xb1U78eB7eMdMpEOh/0N+xz5+H4j96d/eLmzwU/2Cd64MgPVWnzC3pWEW75WX/DfvQsvPV0LaguAVYtY8ZHvvi+GfYLmfNe0fc6h4Z9SMj1S8EZ5XzlLQyiGCKKIy0cLFbEPhJo2I/KHG9xkigmUUxVYwCbj3BTQ8M+JGShczlGfZAT06+hjPr/Cxiu7V8KEriujfqQemZmpQDe8XnNl4/5GvVviD50BFGUX10UHQuXN0Qfn2Juo/4tOUECg6RQt20SA6SSd+Nv1Dvj+xBGHGHUVmBqW2d8n79R37f7YvYLuLjt2x1s1PfuUgb9bL3eXf5G/altyqCP1Y7hwvbUtg+kUR90nVcE+UGHhIQsakarpzCIYmpqJdoUMfCUPMioP0U/UUxitbiJC9tT9NPWsGZmSMj1Q5BRf8Ga6J+1HxJSx3yyUpRx3jHoL2Civavk/EzGpUUL9UFMCXTGpeWjoZDWJERmTex6XMn9KI1DfFbBKDOh5EEUR98dTGcmlNyPqRFIz9KJJpT8A8h8r3NISMjipuIViIr6BRpDRKl4hUC9AmVSszIcRTEpEKbqDflg4WvUSyl7gvZDQi4wn6wUcQws3DrD3sYjHvCe2SqiFKWjVm5rlHBpFcG+eCLajHRK76zQA+CWEVH/TBskWn2yX7T664DyoZ9LLxngE5pZCpXpiyv0oFxwMv4ZM65n5nudQ0JCFjcxLY3tVdQKfQ1HWsS04ArFaeJUsN9ZoQewsEkTJgAI+WAR5hAKec9k4+sYLajiKjN96luSd/rqfFx28RNxDgu3zqf+47LLV+cjooXNcgAkdb7WnxTBxq/RegfV/i1qZ4ZPvbnsY/5KXXcrn3qo96m/YWNgW3R/VPnUz9SzirDm0/46N35M+dRDvU/9+p8JbmueDMgCBxlhkgrNxNjAUjpE8EPzajLf6zxfjlcsXiqW3glEeziZ4OZY+ALxQSfvjjHonKUsp4mLFO3GarJ6g5SWIe+JtsiNnK+8BR51PvUdkfWBejfSyVucBKjzqV/Pqveln2VriKnSxYDtTGId8ejyQJ1Dkw7P99n0liTdCcHjXSbrmxeWCXbCsni5NM2g49Ju6DyUSLG2QQBrvyxwgLF3nie3sYTOBfQ8+aBx2dlvhBA6cDPQDLP8J2pIKV9/711buISBsu8mzH4zgwWc/WZAFthKD3EM4hiUcSjjsJGVC8qwv1rZb45XLP42nycjtBklyz1+PZsNDfsPMHl3jNPV/ZgiikEEhyq2tFgTuT007N9nrmb2m/lQtoYYy+9A16JoWgzPq+B6Fkuy9/oa9ocmHb5+3KLJFGRMmLIhZ0u+cnN0wRj2JyyLv8vnyGgz5kLP49eyTb6Gfb8s8Cq973qefJru0LC/glyR7Dc+//F/BP4dEFjtQ0o5p7F/vRAa9SGLlR/L05SwScyopHhh/zNBebuvU/58fPJdhVAu7P9Oa4B7Vsh1zTFrN7a0MGe4fF3YXxe9+xr2LORaMzy5Fdcto+sXXXsu7C9r3jinzv95sEyuKmmKXCz6d2H/DzcsDBeh/3dynCnXm2Mu1Phfmud2O31RnvV9nnxWhGGYV4orlf1m9n/6H4CvAnngu0AvBEQ1hoSELDgmqdBE/apLHINJKteoR9eWOcuwa4u7DHvIe6csp99VcdYgQllOX6MehSwU5kwMoQUnhugtSTpn2e4ZU8kXCoOO6zMXur464fNk4XE5v/v8JioTzoellAGpPEJCQhYqzcTetbJSxqGZxV3MZL60+5QsX8xl2EPeO3GRUivzMwwWhypx0djVL+T6xjSy71qp97zgxBDdCVFbmb8om7KVfKHQbujvWqlXc6G/40X4PFl4aJfx3W7g2dCgDwlZvGxgKWUcSthIJCVsyjhs4IOZaefhZIIp6c0qWe7xcHJhVZUMubq0G6uxpYUtLaSU73xuN0KXgg86mcQ6XM/CdctIKXHdMq5nkUms89V5vMskZ0tyVYkn1TZnSx7vMn11rjYPJVJMebPmQs/joYT/i+xtLJnzeXIbYdzJteJylqOGL/P7ISEhC4wOkWajXFmX/eYeOhdUkOzV5OZYlF8nW5f95ueS6TBI9gNOVl/CmsjtddlvVpjrwiDZ64xxb5JzspdpSqRIsEp006oFx9LEo8tpNlZTyO+k6kxjGimas/cEZr9Z32zwleUDPH9ihN6KoDsmeXLtUtY3N068MD5xiLOF/UyLCikZY3X6dlpbgrMBnRs9wY78aUZxaUPn3uwaVrWtDdRZG43yUKGJH+yqMj4uaG2V/NxHI6xd6j8Xdoo0n5bdddlv7qU9DJK9hlxyoKwQ4v8BvgDcKuUHuwpMGCgbEhISEhKyeBn3JjkojxKRESKYVLGpiiobxIcCDXu30IfT+7KqUG7EwSkjnRJG90PoaZ+UzOPn4NALqn5JJAHVkqpnsv4xaF3l38eJQxwo7iQidSLSpCpsqsLltuQ9vob9udETPJc/QVIKEkKjJD2KQvJEdm2gYX/6vMc/b/ZIJSCZgGIJpkvw849orFlxOU4dIVeaywmUvZwr9Z+BQeBfhAjDmkNCQkJCQkIWJ+dkLxEZISoiCCGIiggRGeGc7A3U88b2IYwEwkwghFBbI4E3ts9fqWenMuijSRBCbSNJJQ/gbGE/EakTpdZHIkSkztnCfl+dHfnTJKUgJXQ01DYpBTvypwPbeuMtSSoB6aRAE4J0UpBKKHnI4uFy3GkOASbQATwqhMgDuTm+J6WUH7zceCEhISEhISGLgmlKJGdVnI1gMk0pUE9WJmB2NXIjruR+FMfeXY08klDyoD6KCkkZgxnxtBFpMi38s8uM4tIq6oNbE0JjFP8sNgDD45K2lnpZMqHkIYuHy1mp11ApLM/X/vKoW232X/g7TUhISEhISMiCJUWCKnadrIpNiuAgeRFrAadcL3TKSu5HcolyualrrKTkQX2UMapiVh+FTUr6Z5dpQ6ckvTpZSXq0zV0r9B2WtQqKs7pYLCl5yOLhkg1wKeUqKeXqS/l7PzscEhISEhISEvJeWCW6qYoqlqwipcSSVaqiyirRHainLbkD6ZSQdgkppdo6JbQld/grrbxH+dBbRZBSbatFJQ9gdfp2qsLFotZHqlSFy+r07b4692bXUBSSaeniobZFIbk3G+xA8fGPCKZLUCiqDD2FomS6pOQhi4cwm01IiA9nnRJvOlOMyCpLRYT7jQyrjTDVYUhISMhip1VrZoP3Ic5xMfvNzWJNw+w3eroLuh/CG9uHrEwgYi3o7R/zD5IFFQy7/jHlQ18cUyv0ax8IDJIFaG1Zz21Ql/1mXequwOw3q9rWco9ms9kZZUIXtLiSR4w2VrUGZ79Zs0Kje43Lcz/xKBYgmYYnfqZxkOyhSYcXem16S5LuhOCxbpP1zaFpea245Ow3IRcJs99c/5x1SvzAHiWFQQKNEh7TOPyc2RYa9iEhISEhC5Jeb5rN3gAJDBLolHAp4fCI1kG35p9z/qUDNn/9Dx6xhCSeFJSLkkpJ8Bv/o8bDt82dT//QpMOfHbVoiggypiqolatKfvdD0dCwv4JcTvabSz7rQognL/W7UsrvXOp3Q0IWIm86U6QwSNUCjlLoIJU8NOpDQkJCQhYib8kJEhgkhTLvkhgglbwbf6P+2VdcYglIppW7jdpKnn3F9TXqX+i1aYoImiJK50LF3Bd67dCov0Zczln/O6DRsr6ofSc06kMWNSOyyhLqJ7IEGiOyeo16FBISEhISEsy4tGghUidLoDPeoLzQ5ChkZwXFxpOCyVF/s6+3JOmsTyBExlTykGvD5Rj1v+4jbwLuBn4R+AHwo/faqZCQa81SEWFaumqFvkYJj6UiEqAVEhISEhJy7WgVUYrSUSv0NUq4tIrgKtnNbVAsyHdW6gHKRUlzm79Od0KQq8p3VuhBueB0J8Lg2mvFJRv1UspvB/27EOJvUQb9199rp0LqKVnD5MvHsd08pp4lG7+ZRHRZQz1vqheG34bKBMRaYNmH0TLBkf0T+cOcLx+kKEokZYIV8Q20ZG9t3MnJHujbDaUxSCyBrruheWWwztAZOPZTyI9Adims+wQsvyFYZ+AUHHwNJoeheRls+BR03Ni4f8Nn4PgbkB+G7DK4+eOwzL+t+40MP8idgbEBEtN5Sqks00s6+ExTg/4B9J2Et1+BiSFoWQ4ffgC6bmqss+/Vizp3fLqxDkBvra3xQWhtV211B+ud3r2bvvMHkFoF4cXoWnEba+6+u2FT+f63GRrfSZkScRIsb72HbOeHA3VyvXvoH9lNSSuT8OJ0Lr2bpu5LcA3sOQ67tsDoALR1wEcfhJU3B+ucOgqvbYbhfljWCZ96BG78UMOmdu59gx+eLzFYTdIeKfKzKxLcc+fHA3X2v3mU114tMTEZp6W5zKc+neD2+4Pb2tezhX7vOJrh4DkGndrN3LHywYb94+QReGUTDPZDeyc88CjcdEuwztljsO0lGBmApR3wsYdh9brGbZ04Als2wWAftHfBg4/C2uC2Kqe2UezZiuMVMLQ0yZUbid34scZtnT4GP90MwwOwrAM+8QisadDHM8fgzZ/ASD8s7YT7fwZuaHxco8d3cWpgDwWjStqJcGPHXbTd/NFAneLJ7Uz2vo4li0RFkubuT5K86b6GbZ09eYBt/ccZ0VyWejof67yZ1TfdFqgzeXoX/QM7KOplkm6czo57aV4T3D+AbXuP8uzhIsOVGMtiFT5/a5KP3Rl8HxbO7mJs8E0sSkRJsKT9ftKrg9s6cWw7r0z2MmiYtDs2DzR3s3Zd43Mxun8Tp6cPUogL0mXJmtQG2m5/NFBn+NCPOZHfTz6ukS17rM3ezrL1n2nYFtt+DFv+EQpjkF4CD/4r+FgDvXPHYcdLMDoIbe1w78OwqsE8Awyf3cvx0b3k9SpZN8LNbXeybPWdgTrH9pxk8/Z+BooaHUmPR+7rZN1dlzDHnzoKW1+EoX5Y3gkbPxs4r31EtLC5cAwG+0jkc5SyTZTau/hkJnicfP4Bnb/+dgVGJoi7U5T1DJVIC7/8hH/6zMe6Tb5/4BgrivtoNvJMOlkK3MFjDcbx9IEXGM/voRL1iFkardm7SN32WPB5AEpHfkRufAdVwybimDS13kvils8F6pw/8PeMe6cRukS6glZtDStu+1LDtraNb+Og2Y9tSExHsMHu5GOtlzCvXWP0p5566or8R0899dTZr371q3cBjzz11FOBLwCLnW9+85tPffnLX74qbZWsYUYLquqcriXxpMW01UNEb8I0/P3jvKleOPcTtRNJq7y6E8eQ8TZENDunzkT+MEcqOwFJjAg2NkNuLyk3Qjy21L+Tkz1wfJP6HM2CXYKRI5BcCvGmuXWGzsCOf1LOWskmqExDz15o6oCUT/aBgVPw2j+oz6mazqm3oLUT0gE5gofPwM5/Vp+TTWBNQ8++wLaaB3to37WZ4USS0UyW5lKRzxx+m9WZdsgEtNV3En7yXfU53QzlAhzbDW1dkGn113n5ezN0puH4HljS6a8DyqD/ca2tTAuUZrSVnVvv9O7d9PbVqhjKKAibqUIv7rSgpbPTt6l8/9ucmXgVkESI4VBlvHKGhBMjlmmfUyfXu4cTY68BkqiMYgub0fJZkk6EWLbD/7h6jsMPa1NItnZch3fBsi5o8snrfOoo/MM31eemVpiegre2QedKaPFfatq59w2+cVIHJC1GhYJrsm0swvJqD13tK+bU2f/mUZ7+F0BI0qkq5YrBwf06Lekxlq+Yu619PVsYEodAgOfqaJpHUQwznSuyPOhF8eQR+O5fqc8trVCYgt1vQtdKaPU5rrPH4Om/AYQ6f8UC7N8B7d3QHJAX+8QR+M5fKr2WJbW23oCuVb5tVU5tI3/uBSQSXYvjSYtK7iiGl8BoCVhAOH0M/ulb6vOF67V3O3SsVG3PxZlj8IO/Vp+zrVCcqh3XisDjGj2+i7dGt4GUJDwTSzj0Vs6TLWskl8x9zxdPbmew90WQEpM4LhZTUyeIOjEirf7HdfbkAZ4ePAJSkvU0isJjf3GU9qpHc+vcCzGTp3dxfFiNrZinxslI6QzJaoR4i/+Y3Lb3KH+5RwKS5kiVacdgW5/Ocm2M7va5r1fh7C76hn6ClBKzNo7zxZPEnBjR5rnbOnFsO98tDAMeLZ5DQdPYbZfpKkzSusT/XIzu38ReeRikJF6VVA1Bnz5GZjhPcvncxuzwoR+zyz4EUpKsgmVAjxijabxAamnA4s22H8MzfwaehHgGKkU4/CakWqHbR+/ccXjub9XnC+Pk4E5Y3u0/z6AM+p2TO1Ufa/dTj9VHU0Un1Tz3XHhsz0m+9dIQIGmNSqZs2H6yxMp4lSUdAXP8qaPwvW8ox+bm2jjZUxv/PvNatucsS7f8kLFMkonmLNlCkU9uf4vubMD8CawZO0J21zOcEd3k3BYyeoFf8p7m4bvTsHTuezc9tIuuwouUhcmol6FZK/JJsZc1kRhm89zZgKYPvECftQuQmLbAMSQ52U9svEhkmf8LVenIjxiZ+ikSieEauJrLtN1DZMrCbJs7s8/5A3/PhHZK7XgCNElFm6A61Ed2mf+L9rbxbbyd6EUKMDxwdRiM5HGmpuhOBC+Mvh989atfHXzqqae+eSnfvdKFok4AlxShG3Jp5MvH0bUYuhZDCPHO53z5eLDi8NtgJMBMqLLUZkLtD7/tq3K+fJCINIiICEJoRESEiDQ4Xz4Y3FbfbvX/R2olsCNJtd+321/n2E8hmoJ4GoSmttGUkvtx8DX1vZk68bSSB3H8DYilIFbTi6XV/vE3Atta7cCXhsf5/dM9fGl4nNUOjdt6+xVIpCGRUW0lMmr/7Vf8dfa9WtOp9e/C532vXlpbyVpbycZt9Z0/ANJAyAgCgZARkIaSBzA0vhNTGphEEIBJBFMaDI37lznvH9mNKQ0itRLnkZpO/0jAfQFqhT6ZqT+uZEbJ/XhtM6Sz6k/TLn5+bXNgUz88XyKtV8gYDpoQZAyHtF7hh+f9q0q+9mqJeKJKIuEiNEEi4RJPVHntVX+dfu84ntSQ0kAItfWkRr/XYBy/skkdR6Z2XJnacb2yyV9n20uQykIqo3RSGbW/7aXgtrZsgnTTrLaalNyHYs9WBGZtftLUFpNiz9bgtn66WfVr5vVKZZTcjzd/cvFYNO3iMb75k8CmTg3sIepoxDARQhDDJOponBrwz2A22fs6hjQwRBQhwBBRDGkw2ft6YFvb+o+T8lRgvSYEKXRSnpL70T+wg4in142TiKfTP7AjsK1nDxdJGVXSposQgrTpkjKqPHu46KszNvgmumdgiihCCEwRRfcMxgbf9NV5ZbKXtGeTkRINQUZK0p7NK5O9gf07PX2QqOURdUAgiDoQtTxOT/s/T07k9xOrusRcTV0rVyNWdTmR3x/YFlv+UT1z4il1b8RTan/LP/rr7HhJzSszx0kyo+QBHB/dq/o1436KuRrHR/f66mze3k8m4pKNCDRNkI0IMhGXzdv7g49r64sXx/yFcZLJKrkfO1+m2xJ8/vQkv/F2L58/PUm3JWDny8Ft/fA5Hk6e5y86n+b7q/6Gv+h8moeT5+GHz/mqFPpfpxWPe8QUn9MGuEdM0YpHod9/nIzn92A4YNSuseFqGI6SB5Eb34HmCgxpKD1poLmC3Lj/OBn3TiM9QGrKNpEa0lPyIA6a/QhPYHgCQW3rCQ6aDa7XAuBKG/W30DiYNuQysN082ixfOE1Esd18sGJlAoxZESxGXMl9KIoS5iyPLBODoggum01pTE2gdYoJJfcjPwKxZL0sllRyPyaH59aZHA7uX34YorP0okkl921rCOKzdOJJJQ9iYkg9UOr0UkoeqDNHW0E6oFxuErPaSqSU3AepVUDOymQgTSUPoEwJY1bwlUGEckBJ9ZJWxpzVlilNSlrZR6PG6MDcxzU64K8z3K8SK88kmVbyAAarSVJ6fcXGlG4zWE36aMDEZJx4zKmTxWMOE5NxHw2Uy41XP916noZmOD4aFzrYD6lZx5VKK7kfIz7nbyTg/IFyuZmzrT5fFccroM2KM9FEBMcrBLc1POBzvQL6ONKvXlpnkkgreQAFo0pU1s9rUWlQMPyD3i1ZRKd+3tWJYkl/gxlgRHNJyPrrnJAaI5rrq1PU5x4nRT14nAxXYqRm3T8pw2G44u8uYfmMYytgHA8aJqlZFUpT0mPQmDsjygUKcUHEqTcHIo6kEPf3t87HNaJO/b9HHUE+3sBUKYxBdNYzKJpQcj9GfebPUf/5EyCvz30/5XX/+2mgqJGe5eycNpQ8kCGfeW0o4J4f8xn/Yw3Gf18vZDL1skxGyX2wvek5x7/tTfvqVKIe+qxrrDuCStTz0VBUDRvd+//Zu/Mouc7zvvPf5966tfa+YyNIgiQoihRJCSQkUhIlyqYlhpZkR7Ysb0qcxMeTZBx5HJ+Zk3ikcU4cZyZOYjvxxOPkTGJHGdmKFZsSbdmyKGsjJVKQSYoUdxIEsTW2Xmu/yzt/3AbY1UJVAWB3Vzf69zkH51Y9uG+/b3VXdT/11vs+t/WquH7i08yEbVqA+S6doV8usTTeQZhx+CuG4ydpfKN73Um9mXlmttvM/jnwPqDDVKtcrMAfJFmxaz1xDQL//EtozmlzKWs6XMq65IqEtP6RCIkouS4lHItj6ZKblobVNN7O4ET6Eely9Uoab2d48vxthrvsLxicTK/gt1yjksbb9jUFtRVtapU03snIVLp8pqVdOY13bHOevjq1gXQNfXVFX9VyGm/DkjysuOw4FqbxDgoUiWj9oxXRpNDhkurFpEC4oq/QQopJ++QXSNfQn+9xjXdYsjO5I/34fLnKYhrvYFu2QjluTU7KccC2bPvkbWS4Rq3e+he6Vs8wMtw+CUuiDJ7X+lfC8xKSqMu2pm07oLzicZUX03g7E22+fxMdvn+QrqE/b1/tL6qT8fpJVlSESlyTjNffpsWSye1tfl4dxjixI12KtVx1MY130B9laVjr77WGRfRH7Te956xETOvv3ZgGOWv/Zg9gIvGpWuvPuWoJEyuSkeVK8flfJ6W48+tkMl+nvOL5U44yTObbv0HPtXkd5zq8jrdFIWVrTRXK5rEtap9MAfTX0iU3yzUz6dr6dgZrCY0ViVMj4xisdU746B+Dxoq/QY1qGm9nvM3vz/H2vz8BBuPzP58G4/bPp+2lhMUV798XozTe0VSb32tTHZ7zY21e/2NdXv87d8HCQmtsYSGNtxF4fed9/Qcd6uHnGx7xip9xnHHkG53T0WwUEK94cxx7Mdmo/ZtLF6dLblp46dr6ToLIiFcMJ/bS+EZ3wUm9mSVmFq/8B4TAy8A/AWaAX1qjsW5Jg4W9xEmdOKnjnDt3e7DQZTPP5JshqqbJtXPpMaqm8TauKNxE0yKarolzCU3XpGkRVxRu6tzXztvSr99cugR2s5Le39lh8+X170jXttcWwSXpsVFO4+3cdFd63vI2tcU03snet6fr7+tL7eqL6f29HTZDnu2rutSmeoF9vfnu9NzqwlK7hfT+m+9u3+aWdy+1WdZXdTGNX0hflaW+Kt372nnFm8AinDVxOJw1waI03sHU6H5Ciwhp4oCQJqFFTI22v8z5jonbCC2iuXSJ8+ZSmx0TXTbl3v6e9LEsf1yVhTTezl3vhcX59F+SvHb7rvd27Oq+K4osxnkWogyJcyxEGRbjPPdd0T7JuevdRWrVLNWqj0sc1apPrZrlrne3b7PD24tnCWYRzqVHzxJ2eF1ex3ffmz6OhaXHtbD0uO7usNnwju+H8ny6/jZJ0mN5Po138p57YXFuRV9zabyN0u534QiXfj8l6ZGQ0u53de7rHe9Nx7X851VeSOPt3HnPa48lSV57jHfe07Gra7bvo5FJqBPinKNOSCOTcM329itFh3e9k8giItfAOYhcg8gihne9s2Nfd+zYS9mDMjGJc5SJKXtpvJ0d299K04tbXidNL2bH9rd27OuDbyxRjrIshj7OORZDn3KU5YNvbP/GY2zbncReROgaOOcIXYPYixjbdmfbNncP72LRC1gwI8GxYMaiF3D3cOe1xXv6bqKR82hkwOFoZKCR89jT1/7vyXWDN1PP+tT9JP1Z+Qn1rM91gzd37Iv3fDj9m1Mrp8+NWjm9/54Pt2/z1u9Pf68sf51UFtJ4B3vHb03Htez5VPcT9o633yj73rftYKHpM990JIljvulYaPq8922d35Dyrve99po/+zpZmE/j7ez/vvP//tz/fZ37uu8DMD8Hc3NpX3Nz6f37PtC2Sf+OdxK7kNjVcSTErk7sQvp3tH+djA7uI8pAtPQzjvyEKJPGOxkafSuJ74gsSttZROI7hkbbv05GvT2YB1iS5iaWYF4a7+SmcAfOc0Sew7F09Bw3hV1+XhvABW+U/ZVf+ZV3A4fO8+8g8Bjwe8Dfc84dWpORbiDruVE2yPSR9YdoxvNEyQIZv4+R0s1dq99YbhBXGIfaaWjMQm4Adr69Y/WbQn6CvjhLOTpD1WoUyHNN4c3dq98UhtJNsZVTUDsD+SG4+l2dq9/0DacbVeenYeEU9I3ALfd2rn7TP5L0SVLdAAAgAElEQVRuip05DnMn0/u339e9+s25vk7Awsm0r5vf17H6DQNLfc0eh7kT6YbV/ffBji59DYymG1VPH0uXBQ2Mwts/2LmSzcBouin2zLI2d7y/e/WbwaW+zhxLl+oMjsE7Ptix+s3Ijh3EZWNx/jR4dczl2LXrLV2r3+QHtlGM8tRq09SpkiPPrtG3d6x+kx/cTinKUq2coObVybs8V43f0b36zdBYuin21DE4fTy9/+4f6lz9ZmQ83RR7/Ei6zGRkHO77cNfqNzu3XcFU8xCHFhtMhyXGgjo/fnWmY/WbqSvGGek/zbEjDWbnCgz0N3nf3wg6Vr+ZGrqa8lyFhWQGPxPj4gzb7Ybu1W9Gx9NNcccOw/QxGBuHD36kc/Wb4bF0U+yJo+lSguExuOdD3avfjI6nm2KPHU4/3h+bgB/68Y7VbzIju8gkRaL5I8RJmYxXov/KH+he/WZkLN0UO30ETh5Pf173frhz9ZvhsXRT7Imj6XNjeBx+4Ee6Vr8pje1gsOaxsDhNOdOklATcOLW/Y/Wb7OguclGe5sJRmlTIWoHxXd/XtfrN8Ogk25oJJxZnOOUnDDufe7a/oWP1m8LIDkrNLNXF41T9OoUkz1Xb3tG1+s2ubeNMeac5eKbByUaekVyTn7ol27H6TW54B/koT6N8jKbVyFqBqW3v7lj9ZnRsFzsXZzlWm2c6k2Usjvng4Pau1W9KU9cycGKehfpJygWPYhNuyN3YsfpN38Q1DJ1ZZL52goWCR1/T8abim7pXv9l1Tbop9vALUD6TFkS492c6V78ZGks3xZ48Cqen0+fX9/3NrtVv+oa3MVT3ma+cZMFv0pcE3Dy6r2P1m7Hto+wuNDlyfJHjVY/xguPDd23vXv1mZOn1f/xwujRtdBze/2Odf68NjcHk0uM6s/T78+4f7l7VZ2ISrrwKXj0ER4+k93/yo/DG9m/CguGdLHg+x7KznOlLKJeK9E/cyeju9m8Ss5N7yZ+p0Kgdp5Fz5EKPqdJtXavfBOPXkV1o0KweI8xEBHHA6PCdHavfDE6+ieb0EWrJ7LmlOKNc07X6za7iLqKFBU7bIlEGgti4pbGzZ9VvLmajrDm38dcIbTT79u1zBw503tQhIiIicrk6wTyP8HK6+ZwMDSLqhOznaibpskRYLpiZfds5d0FFaFZ7o6yIiIiIXOaeYzqt/kOAYeduP0eXIg+yZpTUi4iIiMhFmadGbkXFvBwZ5ulS4UzWjJJ6EREREbkogxRorKiY1yBikC4VzmTNKKkXERERkYuyl6m0+g8hDnfu9l66lGOWNdOlSLKIrIfDSZnH3BnO0GCUHLfaKLs61PoVkQt3KK7ySDLLKddk3LLs94bZ7Xe5/oaIdDTJIPu5mueYZp4agxS4mV3aJNtDSupFeuxwUuYL7hgl5zNClioRX+AY9yTbldiLvE6H4iqfjabpM58xAiou4rPRNO9nSom9yOs0yaCS+A1Ey29Eeuwxd4aS8ylaBjOjaBlKzucxd6bXQxPZ9B5JZukzn9LS66tkGfrM55FkttdDExFZVUrqRXrsDA0KtF5GvoDPmRWXqReRi3fKNSmueH0V8Tm14vL2IiKb3SUl9Wa218z+g5l928yeNrM/NbOfWO3BiWwFo+SoEbfEasSMkuvRiEQuH+OWpbri9VUlZtyyPRqRiMja6Lim3sx+DfgwcK1zLl6KvQ/4IyAP2NKp1wPvNbP3O+c+vIbjFVk30yzwNCfObQC6gUmmGFj1fm61Ub7AMXDpDH2NmIrF3GmTq96XyEbx5FzE/UdDDtcSdhU8PrAj4Kah1d/mtd8b5rNRejGcIj5VYsou5m5/fNX7EhHppW4z9fcA312W0JeA3wcC4N8C7wBuBD4CPAN8yMx+bu2GK7I+plngIQ5SI2SAPDVCHuIg0yysel+7vD7use0ULcOMNSlahntMm2Tl8vXkXMRvvNBgLkzYUTDmwoTfeKHBk3NR98YXabdf5P2ZKUqW4TQhJcvw/ow2yYrI5afbtMhVwFeX3X8vMAr8knPuXy+LP21mfwE8CfwM8DurOkqRdfY0J8gTUCAAOHd8mhNrMlu/y+tjF0riZWu4/2jIcABD2XReaShrQML9R8M1ma3f7ReVxIvIZa/bTH0eWq73uwdwwCdXnuicmwMeAG5YtdGJ9Mg8NfIr3vPmdflrkVVxuJYwEFhLbCAwDteSHo1IRGTz65bUvwrsXXa/unRs9xlpTJr0i2xqgxSor3ia13X5a5FVsavgsRC2/qlYCB27CirIJiJyqbr9Bv08cK+Z7Vm6/yXSzbE/svJEMysC7wOeXtURivTADUxSJ6S2dPnr2tLlr29Am1dFXq8P7AiYDWGumZA4x1wzYTZM4yIicmm6LV78l8DfAr5gZj/nnPtLM/tN4NfNrB/4E2COdLPsPwOuBH517YYrsj6mGOBOrmqpfvMWdq7JenqRreamoQwfu5aW6jcfvXJtqt+IyIV5/njMl55KOD4L24bh7hs9rtvmd2+4Tubj0xyPDlJzZQrWx7bMVQz6Yx3bzMVnOBa/cq7Ndv9KhvzRdRrx+jPnOq+WMbM7gfuBYWAWeBF4C987y2/Af3bO/Z01GOeGsm/fPnfgwIFeD0NERETkdXv+eMx//WpCfwH68lCuw2INfuqdGyOxn49P81LzCQLLkSFLRJPQNdiTvbltYj8Xn+GF6DsE5AjIEtIkpMG1mTdtqsTezL7tnNt3Ied2XcDonHsIeCPw20AC3A74pEm8ka6j/zLww1shoRcRERG5nHzpqTShHygYnhkDBaO/kMY3guPRQQLLEVgOMzt3+3h0sG2bY/ErBOTILrXJWo6AHMfiV9Zv4Ovsgj7rdM6dAH4e+HkzuxKYIH1DMAe85JwL12qAIiIiIrJ2js/C5FBrrC+fxjeCmiuTp9QSy5Cl5soX1Sbo0mazu+gFjM65V4BXVn0kIiIiIrLutg3DQg0GlhV4K9fT+EZQsD5C1yAgdy4W0aRg7a/vUrA+mq5BdlmbsEubzU71w0RERES2sLtv9FiswULNkTjHQs2xWEvjG8G2zFWErkHoGjjnzt3elrmqbZvt/pWENGgutWm6BiENtvtXrt/A19kFzdSbWQa4ibQ+/VOuze5aM3sTcItz7vdXb4giIlvXbHyGI8khKlQoUWKnt5vhTbTJS0Q2vuu2+fzUO2mpfvPB27pvkv3OfMSfHG/yajXhiqLHB7dledNg59TywefrfOrlOtNxzJTv85Gr87znunzHNoP+GP2Za3k2fo6GO03OSlyf2dux+s2QP8q1vKml+s2V/t5NtUn2Yl1I9ZsPAr8LnP0uHAF+wTn3P85z7ieAjzvner9Veg2p+o2IrIfZ+AzPJk+RJXuuekOTJtd7NyqxF5Ge+s58xL99sc5QAIOBMR865kL4hWvybRP7B5+v869fKNNnHv1mLDpH2SX84rV9HRP7o8kiX+IIBXwKZKgRUSPmbnayw+tfq4e4Iaxa9RszuxX4NDBGWsryGWAX8N/N7F+83oGKiEh7R5JDZMm2VG/IkuVIcqjXQxORLe5PjjcZCmA46+GZMZz1GArSeDuferlOn3kMeB5mxoDn0Wcen3q53rGvJzhNAZ8iAYZRJKCAzxOcXu2Htal1Wyz1S6RLdH7CObfXOXcjcAfwEvC/mtn/tdYDFBHZqipUCMi2xAKyVKj0aEQiIqlXqwmDgbXEBgPj1Wr7MpjTcUy/tbbpN2M6jjv2NUuDwooV4wUyzNK4yFFf3rol9e8E/sI596mzAefcN4H9wMPAL2rGXkRkbZQoEdI66xXSpLSiTJuIyHq7ougxH7Yu4Z4PHVcU26eWU77P4opl34vOMeV3XrU9TI4aUUusRsTwsso20j2pHwceXxl0zs0CPwB8jXTG/lfWYGwi0sUxt8gX3Iv8ofsuX3Avcswt9npIPXMkKfNAcojfT57jgeQQR5LNX4t4p7c7XUW/rHpDkyY7vd29HpqIbHEf3JZlLoTZZkLiHLPNhLkwjbfzkavzlF3CQpLgnGMhSSi7hI9c3Xmj7M2MUSOmSojDUSWkRszNtN8ouxV1S+rPAOct6OmcqwL3Ag8Bv2xm/3SVxyYiHRxzi3yFV6gRMbQ0i/EVXtmSif2RpMwX3RGqLmTYZam6kC+6I5s+sR/2R7neu5EsOapUyZLTJlkR2RDeNJjhF67JM5z1OFJzDGe9jptkAd5zXZ5fvLaPAc/jRJIw4HldN8kC7PD6uZudFAmYo0mRYEtskr1YHavfmNnXgaxz7vYO5/QBfwncTrrWfo+q34isvS+4F6kRUSA4F6sRUiDDPXZND0e2/h5IDlF1IUV77Y9J1UUULeA+zWqLiMgmtWrVb4AvAm8xs6vbneCcK5MuxflrYGtlEiI9NEuD/IqNQ/ktunFohjoFWucSCvjM0LmigoiIyOWiW1L/x8CjwPs6neScWwC+H/gK8OrqDE1EOhkmR33FxqH6Ft04NEKeGq3VE2rEjND5I10REZHLRcek3jn3hHPubc653+72hZxzc865dzvn2l+zV0RWzY1MLm0WSjcO1QipEnIjk70e2rq7hVGqRFRdhHOOqouoEnELWnsuIiJbQ+dr+a4CM/so8FHn3N1r3ZfIVrLd+rnLXclTnGCWBsPkuI0dbLett3Fop9fHbXE/3+AIr7omQ5blbexkp3feff4tkoXDcOKvoT4D+RGYfDPewK7OjWYOweFHoXIKSuOw63YY6bJ2/+RBePEhWDgJAxNwzZ0wcQFzIMdfgqe/BnMnYGgSbngHbNvTscmZyjNM1x+jGVfI+iWm8rcyWnpD166eevkpXjj5LLFXx0/yXDtxPTdefWPHNounn+DU3KPUXYW8lRgfup3+sZu79jV74nGOzH6bitUouQI7h9/C8OQtHduUjx5g5sTDNKiQo8TI5B307bigpabyOixGpzgZvkQ9WSTv9TMR7KE/M97rYYnICt2W36yGK4G71qEfkS1nu/Vzj13Dh+2N3GPXbMmEHmAmmWHGHeRG53E3/dzoPGbcQWaSmY7tkoXD8MoXIKxCbjg9vvKFNN62s0Pw9APQrEBxLD0+/UAab+fkQTjwGaiXoX8sPR74TBrv5PhL8NCnobYIg+Pp8aFPp/E2zlSe4VDlq0RJncArECV1DlW+ypnKMx27eurlp3j29GPEFuElOWKLePb0Yzz18lNt2yyefoJXZ/+K0DXIWYHQNXh19q9YPP1Ex75mTzzOs/Nfo0mTosvTpMmz819j9sT3VFA+p3z0AMdO/CURDbIUiGhw7MRfUj6qogVraTE6xaHGY4RJg5z1ESYNDjUeYzE61euhicgK65HUi4isqUPuMAE5cpbFzMhZloAch1yH5BzSGfpMEYIimKXHTDGNt3P4UciVIFtK22RL6f3Dj7Zv8+JDkO9L/5n32u0XH+o8vqe/lp5X6E/bFfrT+09/rW2T6fpj+BaQ8fKYeWS8PL4FTNcf69jVCyefBRfgE2Dm4ROAC9J4G6fmHiVDhsByGB6B5ciQ4dRch+8FcGT222STDNmln1fWsmSTDEdmv922zcyJh8mQIWO59HEt9TVz4uGOfcnrczJ8iQw5Ai+HmRF4OTLkOBm2f2MpIr2hpF5ENr2Kq5BdVtoTIEtAxVU6N6zPQKbQGssU0njbzk6lyf9yQTGNt7NwEnIr2uSKabyTuROQX3H12HwpjbfRjCv41nrxF9+yNOPO34vYq+OtqEbsOZ/Ya19BqO4qZKz1+56xgHqX73vFagQrfl4BARWrtW3ToJK+0VjGJ6BBl5+xvC71ZJHMiudTxrLUk613PQyRjU5JvYhseiUr0SRsiTUJKVmpTYsl+RGIViSSUS2Nt+1sPF2ms1xYTePtDExAY0WbRjWNdzI0CfUVSWu9ksbbyPolYtdsicWuSdbv/L3wkzyJtVYQSizGT9pXEMpbici1ft8jF5Lv8n0vuQLhip9XSEjJFdq0gBwl4hVtYkJydPkZy+uS9/qJVjyfItckr4v+iGw4SupFZNPbbbsIadBwTZxzNFyTkAa7rcuG18k3Q1RNk3Ln0mNUTePt7LodGpV0Lb1z6bFRSePtXHNnuo6+XgaXvHb7mjs7j++Gd6Tn1RbTdrXF9P4N72jbZCp/K7ELiZI6ziVESZ3YhUzlb+3Y1bUT14OFxIQ4l6QJtIVpvI3xoduJiAhdA0dC6BpERIwPdfheADuH30LTi2gu/byarknTi9g5/Ja2bUYm7yAiInKN9HEt9TUyeUfHvuT1mQj2ENEgTBo45wiTBhENJoLOm7VFZP11vKLsqnRg9gng45fTVWZ1RVmRjWcmmeGQO0zFVShZid22ixGvw4z7ElW/eY2q31z+XjgW81dPJhyfhW3D8O6bPK7d3vnPs6rfiPTOxVxRVkn9JVBSLyIim80Lx2I++ZWE/gL05aFch8Ua/ORd3RN7EemNi0nqtfxGRERkC/irJ9OEfqBgeGYMFIz+QhoXkc1PSb2IiMgWcHw2naFfri+fxkVk81uPpP5x4PfXoR8RERFpY9twuuRmuXI9jYvI5pdZ6w6cc/cD9691PyIilyKsHqMx+yRxcxY/O0xu+CaC4vaObQ69lPCtr8LpEzA2Cbe9E3bv0QefsrG9+yaPT34lAVzLmvoP3K7nrsjl4IJeyWY2aWY/ZGY/aGaDHc67y8w+vnrDExFZO2H1GJXpL5NEVbxgiCSqUpn+MmH1WNs2h15K+NM/gMoijI6nxz/9gzQuspFdu93nJ+/yGCjAiTkYKGiTrMjlpOtMvZn9Q+BfAWcvKVc1s0845/7NeU5/F/Bx4J+t2ghFRNZIY/ZJPL+Al0mv9mpLx8bsk21n67/1VSj1p//gteO3vgq7VbpbNrhrt/tK4kUuUx1n6s3sXcBvLZ33ReDzpG8E/pWZ/Tcz02d2IrJpxc1ZzG+9iqn5BeJm+52Dp09AccVFTIulNC4iItIr3WbqPwZEwPc5574GYGa7gf8G/Fh6137CrXWxexGRNeBnh0mi6rkZegAX1/Cz7XcOjk2mS27OztADVCtpXEREpFe6zbS/Ffjs2YQewDl3CLgb+AxpYv97azc8EZG1kxu+iSSukURVnHMkUZUkrpEbvqltm9vemSb1lUVwyWu3b3vnOg5cRERkhW4z9cPAcyuDzrmmmf0Y6Yz9T5pZ5Jz7mbUYoIjIWgmK2ylNvaul+k1hfH/H6je793j8jR9rrX7zrr+h6jcb2QnmeY5p5qkxSIG9TDFJ25oP6+7FsMZXGmWm45ApP+CuXB/XBIXuDUVElumW1J8ARs73H865xMx+AvCBj5pZCBxf5fGJiKypoLi9awnLlXbv8bQpdpM4wTyP8DJ5AgbIUyfkEV5mP1dviMT+xbDGpyoz9Hs+E16GxSTmU5UZPlIaUWIvIhelW1L/AvD2dv+5lNj/OPBHwN8FTq/i2ERERF6X55gmT0CeAODc8TmmN0RS/5VGmX7Pp99LK9L0m38urqReRC5Gt8+LvwDcYGY3tzvBORcBPwL8OTB+qQMxs//TzB40s8NmVjOzGTN7zMw+YWajbdrcYWZ/tnRuzcy+Y2YfM7O29brM7D4z+7KZzZtZ2cweMbOPXuq4RURk45qnRm7F/FWODPPUejSiVtNxSGlFIbmSeUzHYY9GJCKbVbek/jPAp4G2ST2Acy4Efoh00+xXL3EsvwCUgL8EfpN0vX4E/B/Ad8xs1/KTzewDS329E/hj4N+T1tL/t8AfnK+DpZr7nwNuBD4J/EdgO/BfzOzXL3HcIiKyQQ1SoEHUEmsQMcjGmAWf8gMqrvXCZRWXMOUHPRqRiGxWtlGqUZpZ3jlXP0/8V4F/AvwH59zfX4oNAC8Cg8CdzrkDZ78G8CXgbcBHnHN/sOzrXAk8C1SAtzjnXlmKDwPfAvYAdzjnvtFtrPv27XMHDhy45Mcql24mmeFw8ioVypToY5d3BSPeebd9tEgWj5CcfAzqZyA/ijdxK17/zo5tKqcfZ2b2ERpJhZxXYmR4P6WxW7r29cTJr/JwPM2sHzAch9zhT3HzRJfSKCdehucfgvmTMDgB190Jk1d37WshOsXJ8GXqbpG89TMRXM1A5pI/MBO57CxfU58jQ4OIOuGGXFNfMo+KS1hMYq2pXweHXkp49Gtw6gSMT8Lt79CGd0k9fzzmwacTjs/BtiF4zw0e123rzUXbzOzbzrl9F3Lumj97zewfmdnL3c47X0K/5NNLx2uXxT5EutTnD84m9Mu+xi8v3f2fVnydnwFywL8/m9AvtZkF/sXS3Z/rNk7pnZlkhmfi79J0DYquRNM1eCb+LjPJTMd2yeIR4kNfwIVVXG4EF1aJD32BZPFI2zaV049z7MyDRK5B1isSuQbHzjxI5fTjHft64uRX+RwzVM1nKA6pms/nmOGJkx0+wDrxMjz6R1Avw8BYenz0j9J4BwvRKQ41Hid0DXL0EboGhxqPsxCd6thOZCuZZJD9XE2egAXq5Ak2TEIPcE1Q4COlEfo9n5NJRL/nK6FfB4deSnjg02k52rHx9PjAp9O4bG3PH4/5vYdiFmqOyUHHQs3xew/FPH887vXQuuq2UXY1DAG7X0f7H1w6fmdZ7O6l45+f5/yvAlXgDjPLOecaF9Dm8yvOkQ3ocPIqWbJkLQdAlhy4NN5ptj45+RhkSliwdIGhoIhbirebrZ+ZfYSMBWS8tK+M5SBJ451m6x+OpymYT5EEzNJjAg+76fZr2J5/CPL9kO9L7589Pv9Qx9n6k+HLZCxHsPT9CMidi2u2XuQ1kwxumCT+fK4JCkri19mjX4NS32sXkTt7fPRrqLLVFvfg0wkDeRgoGAADBQDHg08nPZutv1DrkdRfFDP7x0Af6dKafaTVd74D/Mtlp+1dOj6/sr1zLjKzg8AbgauBZy6gzXEzqwA7zazonKueZ1w/C/wswBVXXHEJj0xerwplipRaYgFZKpQ7N6yfgdyKpD9TSONtNJIKWa/YEvMtSyOpdOxq1g8YikMwOxcruJjZTutj50+mM/TL5YppvIO6WyRHX0ssQ5a6W+zYTkRkqzt1Ip2hX65YSuOytR2fg8kVcwB9+TS+0W3ExWP/GPgE8DHShP7PgXucc8vXFJz9ds+3+Rpn40OX0Oa80znOud91zu1zzu0bH9csaC+U6COk2RILaVJakdh+j/woRCsqXUS1NN5GzisRu9a+Ytck55XatEgNxyG1FcWXauYz3KmSxeAENFa8j2xU03gHeesnWvH9iGiSt/6O7UREtrrxSaiumKOpVtK4bG3bhqC8YkF4uZ7GN7oNl9Q756accwZMAT9MOtv+mJm9ubcjk17b5V1BkyZN18A5R9M1aNJkl9f5kxNv4laIKumaeudwYRWiShpvY2R4P5ELiZK0ryhpELmQkeH9Hfu6w5+i5vlU8XDOUcWj5vnc4U+1b3TdnVBfTNfSuyQ91hfTeAcTwdVErkG49P0IXYPINZgIum+wFRHZym5/B1TK6Vp6l6THSjmNy9b2nhs8FuqwUHMkLl1Tv1BP4xvdhh2hc+6Ec+6PgXuAUeD3l/13x1n1ZfHlH5ZcaJt2M/nSYyPeCG/w30jWclStQtZyvMF/Y9fqN17/Tvzd92BBEWvMYEERf/c9HavflMZuYfvoe8hYjmZSJWM5to++p2v1m5sn3skPMkLRxcz5AUUX84OMdK5+M3k13P6hdC39wun0ePuHula/GciMszt3C4HlaFAmsBy7c7doPb2ISBe793jc96PpWvrTp9LjfT+q6jcC123z+eidPgMF48S8MVAwPnqnv+HX08M6lLQ0s08AH3fOXfJ3w8weA24Bxp1zp83sk8BPAD/unPvUinMzpIl5Fug7u1HWzL4O3Ml5ylaa2TbgGHDEOddSD/98VNJSRERERNbahippuUq2Lx3P1hP60tLxvec5951AEXh4WeWbbm3et+IcEREREZFNY0Mk9WZ2nZl9z7IYM/OWLj41QZqkzy791x8Bp4EfM7N9y87PA/986e5/WPHl/jPQAP7h0oWozrYZJr24FcDvvP5HIyIiIiKyvjZKSct7gV9bWiJzEDgDTAJ3kW6UnQb+3tmTnXMLZvb3SJP7L5vZHwAzwPtJS1f+EfCHyztwzh00s18Cfgs4YGZ/CDRJL2S1E/jXF3I1WRERERGRjWY9kvovX8A5XwSuIS1heStpKcoKaU35/wr8lnOu5bKhzrk/MbO7gH8K/E0gD7wI/C9L53/PZgHn3L8zs1dIy2b+NOknFU8Dv+yc+71LeXAiIiIiIr32ujfKmtm/Ab7hnPvvqzOkjU8bZUXkYj05F3H/0ZDDtYRdBY8P7Ai4aWijfFiaeuZUzJ+/FHF00bGj33jvngxvGN/4FR9ERC5X671R9mPA96/C1xERuSw9ORfxGy80mAsTdhSMuTDhN15o8ORc1OuhnfPMqZjffazJfMOxrQ/mG47ffazJM6fi7o1FRKTnOk4TmdnPXODX2bv8XOfc//u6RiUichm5/2jIcABD2XQeZShrQML9R8MNM1v/5y9FDOaMwZwBMJh7La7ZehGRja/bX5P/BHRbn+NI18K/HbCl+0rqRUSWHK6lM/TLDQTG4VrSoxF9r6OL6Qz9cv3ZNC4iIhvfhUwRlUlLPVbP838GfBz4a+BzqzguEZHLxq6Cx1yYLM3QpxZCx67ChqgqDMCOfmO+4c7N0AMsNtO4iIhsfN2S+p8G/h3ww8Dfds59feUJZvZx4K+dc7+yBuMTEdn0PrAj4DdeaAAJA4GxEDpmQ/jolUGvh3bOe/dk+N3HmkA6Q7/YTNfVf/iGzmM8FFf4VjLLaZqMkeU2b5jdfmlNxtioHadSfpoonCMTDFHqu4FcYdua9CUistl0nCZyzn0SeBNwiLQe/K+bWa5TGxERaXXTUIaPXZtjKPA4WnMMBR4fuza3YdbTA7xh3Odnb80ymDOOl2EwZ/zsrdmO6+kPxRX+LJmmQsQoARUi/iyZ5tH+J3YAACAASURBVFBcWfXxNWrHmZ/5OnFcw88MEsc15me+TqN2fNX7EhHZjLr+RXHOHQa+z8z+EfBrwL1m9recc4+u+ehERC4TNw1lNlQSfz5vGPcvalPst5JZiviULH1cJTLg0vhqz9ZXyk9jfgHfLwDg+wXipbhm60VELqKkpXPuN4G3kK6tf8jMfs3MNs5nxyIisq5O06RI65uAIj6naa56X1E4h+flW2KelycK51a9LxGRzeiidmk5554B9pPO2P9j4DG6V8cREZHL0BhZqrTWsa8SM0Z21fvKBEMkSb0lliR1MsHQqvclIrIZXXTpBedc7Jz7OGkJy4C0Ao6IiGwxt3nDVImpuAjnHBUXUSXmNm941fsq9d2Ai2vEcQ3nXHqMa5T6blj1vkRENqNLXuDpnHvEzG4A+oDG6g1JREQ2g91+iXuZaql+8y5vfE2q3+QK2xgceXtr9ZvBt2zp9fSV5klmG8/TiBfI+QMM566jlJ3o9bB6otaYZrHyLGE0T5AZpL90PYXcVK+HJbKuXteuLedcDMyv0lhERGST2e2X1qyE5Uq5wrYtncQvV2me5Hj1UTKWJ+v1EyV1jlcfZRu3b7nEvtaY5sz8N/AtT8YfII5rnJn/BqODb1NiL1vKxrnyiYiIiFyQ2cbzZCxPxstjZmS8PBnLM9t4vtdDW3eLlWfxLY/vFzAzfL+Ab3kWK8/2emgi60pJvYiIyCbTiBfwV1w2xrccjXihRyPqnTCaP29lpDDSQgLZWpTUi4iIbDI5f4DYtW5ni12DnD/QoxH1TpAZPG9lpCAz2KMRifSGknoREZFNZjh3HZGrEyV1nHNESZ3I1RnOXdfroa27/tL1xK7eUhkpdnX6S9f3emgi62pjX95QZJV8t9Lkgbk6R5sxO7I+9w3leWNp9WtpX87m4jMcTV6h6soUrY8d3pUM+aMd2yxGpzgZvkQ9WSTv9TMR7KE/M75OIxa5fJWyE2zj9pbqN+O5N22oTbL1+vHWakV9N5DPr/5G50JuitHBt7VUvxkq3dp1k+w08zzLCeapMUiB65lkCs3uy+ZlzunaURdr37597sCBA70ehlyg71aa/N8nKgz6xoBvLMSO+djx9ydLSuwv0Fx8huejJwksS0CWkCaha3Jd5qa2if1idIpDjcfIkCNjWSLXJKLB7tytSuxFLnP1+nHmZx/CvDyelydJ6rikzuDwnWuS2F+saeb5JgfJEZAnQ52IBiFv5Sol9rKhmNm3nXP7LuRcLb+Ry94Dc3UGfWMo4+FZehz0jQfm6t0bCwBHk1cILEvWcpgZWcsRWJajyStt25wMXyJDjsBL2wRejgw5ToYvrd/ARaQnKuWnMa+1Io15eSrlp3s9NACe5QQ5AgoEGEaBgBwBz3Ki10MTuWRK6uWyd7QZM+C3Xvh4wDeONuM2LWSlqisT0PqpRkCWqiu3bVNPFslYa5uMZakni2syRhHZOKJw7rwVaaJwrkcjajVPjfyKFch5MsxT69GIRF4/JfVy2duR9VmIW5eZLcSOHVm/RyPafIrWR0izJRbSpGh9bdvkvX4i19omck3yXv+ajFFENo5MMHTeijSZYKhHI2o1SIE6UUusTsQghR6NSOT1U1Ivl737hvLMx465KCFx6XE+dtw3lO/eWADY4V1J6Jo0XQPnHE3XIHRNdnhXtm0zEexJV6kmaZswaRDRYCLYs34DF5GeKPXdgEtaK9K4pE6p74ZeDw2A65mkQUiNEIejRkiDkOuZ7PXQRC6Zknq57L2xlOXvT5YYzHgcCxMGM542yV6kIX+U6zI3kbUcNSpkLddxkyxAf2ac3blbCbwcDVcm8HLaJCuyReTz2xgcvhPfLxBH8/h+YcNskgWYYpC3chUFAhaoUyDQJlnZ9FT95hKo+o2IiIiIrDVVvxERERER2UKU1IuIiIiIbHJK6kVERERENrlM91NERC7Ny1GNh8J5TrqQCQu4Mxjk6oxKxoksp9fJa+biMxyLX6HmyhSsj+3+lR035IvIazRTLyJr4uWoxmcapyi7mDEylF3MZxqneDnSxV1EztLr5DVz8RleiL5D0zXIU6LpGrwQfYe5+EyvhyayKSipF5E18VA4T5/59JmPZ3bu9kPhfK+HJrJh6HXymmPxKwTkyFoOMyNrOQJyHItf6fXQRDYFJfUisiZOupDiil8xRTxOurBHIxLZePQ6eU3NlQlovX5IQJaaK/doRCKbi5J6EVkTExZQJWmJVUmYsKBHIxLZePQ6eU3B+ghptsRCmhSsr0cjEtlclNSLyJq4Mxik7GLKLiZx7tztOwNdsVHkLL1OXrPdv5KQBk3XwDlH0zUIabDdv7LXQxPZFFT9RmQDOPRSwqNfg1MnYHwSbn8H7N6zud9zX50p8DcZb6nq8QPZkS1b1UPkfPQ6ec2QP8q1vKml+s2V/l5VvxG5QOac6/UYNp19+/a5AwcO9HoYcpk49FLCA5+GUh8US1CtQKUM9/3o5k/sRURE5NKZ2bedc/su5FxlDCI99ujX0oS+1A/mpcdSXxoXERERuRBK6kV67NSJdIZ+uWIpjYuIiIhcCCX1Ij02PpkuuVmuWknjIiIiIhdCG2VFeuz2d8ADn05vL19T/+57ezsukQvx5FzE/UdCDlcTdhU9PrAz4KYh/WkREVlvmqkX6bHdezzu+9F0Lf3pU+lRm2RlM3hyLuI3n2sw10zYUTDmmgm/+VyDJ+eiXg9NRGTL0XSKyAawe4/H7j29HoXIxbn/SMhQAEPZ9A3oUNaAhPuPhJqtFxFZZ5oKFBGRS3K4mjAQWEtsIDAOV5M2LUREZK0oqRcRkUuyq+ixELZe62QhdOwq6k+LiMh6029eERG5JB/YGTAXwlwzIXGOuWbCXJjGRURkfWnRo4iIXJKbhjL8o720VL/56NWqfiOpauME87XnCON5An+QwcJeijnV6hVZK/rNKyIil+ymoYySePke1cYJTi0+gu/lyXgDxEmdU4uPMM5+JfYia0TLb0RERGRVzdeew/fy+F4eMzt3e772XK+HJnLZUlIvIiIiqyqM5/Es1xLzLEcYz/doRCKXPyX1IiIisqoCf5DENVpiiWsQ+IM9GpHI5U9JvYiIiKyqwcJe4qROnNRxzp27PVjY2+uhiVy2tLtJREREVlUxN8k4+1uq34yUbtYm2XUwzTzPcoJ5agxS4HommUKfkGwFSupFRERk1RVzk0ri19k083yTg+QIGCBPjZBvcpC3cpUS+y1Ay29ERERELgPPcoIcAQUCDKNAQI6AZznR66HJOlBSLyIiInIZmKdGfsUijDwZ5qn1aESynpTUi4iIiFwGBilQJ2qJ1YkYpNCjEcl6UlIvIiIichm4nkkahNQIcThqhDQIuR7tbdgKlNSLiIiIXAamGOStXEWBgAXqFAi0SXYLUfUbERERkcvEFINK4rcozdSLiIiIiGxySupFRERERDY5JfUiIiIiIpuc1tSLiFykp2YjPnsk5HDVsatovH9nwI3D+nUqIiK9o5l6EZGL8NRsxG8912Cu6dhRgLmm47eea/DUbNS9sYiIyBpRUi8ichE+eyRkKDCGsoZn6XEoMD57JOz10EREZAtTUi8ichEOVx0DQWtsIEjjIiIivaKkXkTkIuwqGgsrJuUXwjQuIiLSK0rqRUQuwvt3BsyFjrmmI3HpcS50vH9n0L2xiIjIGlG5BhG5IEeSMk9wmhnqjJDnZsbY6fX1eljr7sbhDD+/l5bqNz99dXZDVb9p1I5TKT9NFM6RCYYo9d1ArrCta7uZ8tMcqz5OnSp5imwv3sJI3w2dG829CkcPQO00FMZgxz4YuqJrX88/9SpfeuQUx+c9tg0m3L1/nOtu7Nxu8dhfc/L0I9StQt6VmBjbT//2N3ft62BU5eF4npNJyIQXcIc/yFWZYtd2cnl7MazxlUaZ6Thkyg+4K9fHNUGh18MSuWTmnNaBXqx9+/a5AwcO9HoYIuvmSFLmQY5QxKdAhhoRVWLew84tmdhvZI3aceZnvo75BTwvT5LUcXGNwZG3d0zsZ8pP82L1IQIyZMgS0SQk4prine0T+7lX4YU/g6AEQRHCKoQVuPbejon980+9yn/9ixn6cwl9OUe5YSw2PH7qB0baJvaLx/6aQ2e+RMYFZCxD5CIiC9k9enfHxP5gVOV/hKfoM58iHlUSyi7mh4NxJfZb2IthjU9VZuj3fErmUXEJi0nMR0ojSuxlQzGzbzvn9l3IuVp+IyJdPcFpivgUCTCMIgFFfJ7gdK+HJitUyk9jfgHfL2Bm6dEvUCk/3bHdserjBGQILIeZEViOgAzHqo+3b3T0QJrQZ0tglh6DUhrv4EuPnKI/lzBQAM8zBgrQn0v40iOn2rY5efoRMi4gsCyGR2BZMi7g5OlHOvb1cDxPn/n0mY9ndu72w/F8x3ZyeftKo0y/59Pvpc+Ls7e/0ij3emgil0xJvYh0NUOdworVegUyzFDv0YiknSicw/PyLTHPyxOFcx3b1amSIdsSy5ClTrV9o9rpdIZ+uaCYxjs4Pu/Rl2v9lLgv5zg+3/5PUt0qZKz1OZixDHWrdOzrZBJSXPGnrojHyUQlSLey6TikZK3Pi5J5TMd6XsjmpaReRLoaIU+N1osr1YgYId+mhfRKJhgiSVrfbCVJnUww1LFdniIRzZZYRJM8HZaoFMbSJTfLhdU03sG2wYRyo7VaULlhbBtM2o/PlYhc63MwchF5V+rY14QXUKX161ZJmPC0sXkrm/IDKq71eVFxCVO+nheyeSmpF5GubmaMKjFVQhyOKiFVYm6mc/J2qebj0zzX+BaP17/Ec41vMR9rmc+FKvXdgItrxHEN51x6jGuUumx43V68hZCI0DVwzhG6BiER24u3tG+0Y1+6hr5ZAefSY1hJ4x3cvX+cxYbHQg2SxLFQg8WGx937x9u2mRjbT2QhoWviSAhdk8hCJsb2d+zrDn+Qsospu5jEuXO37/AHO7aTy9tduT4Wk5jFJH1enL19V057hGTz0kbZS6CNsrIVrVf1m/n4NC83nyCw3GsbNl2Dq7M3M+ivzZuIy42q37RS9Rs5H1W/kc3gYjbKKqm/BErqRdbOc41vEboGgeXOxc7e35u7rYcjExERWV+qfiMim1bNLZ53w2bNLfZoRCIiIhufknoR2VAK1n/eDZsF6+/RiERERDY+JfUisqFMZa5KN2ku37DpGkxlrur10ERERDYsJfUisqEM+mNcnb2ZwHLUKRNYTptkRUREush0P0VEZH0N+mNK4kVERC6CZupFRERERDY5JfUiIiIiIpucknoRERERkU1OSb2IiIiIyCanjbKyJUyzwDNMM0+dQfK8gSmmGOjY5jgLfJeTzFFjiAJvZIJtXdqIiIiI9IJm6uWyN80CD3OQGiED5KgR8jAHmWahbZvjLPB1DlEjZJA8NUK+ziGOd2gjIiIi0itK6uWy9wzT5MlQIMAwCgTkyfAM023bfJeT523zXU6u48hFRERELoySernszVMnv2KlWZ4M89Tbtpmjdt42c9TWZIwiIiIir4eSernsDZKnTtQSqxMxSL5tmyEK520zRGFNxigiIiLyemijrGwqryYVDiQznKHJKFn2eSNc4ZU6tnkDUzzMQSCdba8TUSfizexq2+aNTPB1Dn1Pm33sWL0HIyIiIrJKNFMvm8arSYXPx8epuIgRF1BxEZ+Pj/NqUunYbooB7uAqCgQs0KBAwB1c1bH6zTYGeDu7KRAwT50CAW9nt6rfiIiIyIakmXrZNA4kMxTxKVn6tC2RAZfGu83WTzHQtYTlStsYUBIvIiIim4Jm6mXTOEOTIn5LrIjPGZo9GpGIiIjIxqCkXjaNUbJUiVtiVWJGyfZoRCIiIiIbg5J62TT2eSNUiam4COccFRdRJWafN9LroYmIiIj0lJJ62TSu8Eq8z99GyTLMWEjJMrzP39Z1Pb2IiIjI5U4bZWVTucIrKYkXERERWUEz9SIiIiIim9yGSOrNbNTM/q6Z/bGZvWhmNTObN7Ovm9nfMbPvGaeZ5czsH5jZo2Z22szKZvaMmf2Wme3u0NdHl9qUl/r4spndt7aPUERERERk7WyIpB74EeA/AvuBR4DfAD4D3Aj8J+DTZmZnTzazDPAg8O+BfuBTwO8AJ4H/GXjCzG5Y2YmZ/TrwX4BtS/19ErgJ+JyZ/cM1emwiIiIiImvKnHO9HgNmdjdQAv7UOZcsi08BjwK7gA855z6zFP8R4NOkif09K9r8CvBx4D87535mWfwO4CHgJeA259zsUvxK4NtL/V/vnHul23j37dvnDhw48DoesYiIiIhIZ2b2befcvgs5d0NslHXOfalNfNrMfgf4VeBdpLP3AFcvHVveBCy5nzSpH18R/7ml46+eTeiX+njFzH4b+N+Bvw184lIfh8h6qzRPMtN4nkayQM4bYCR3HaXsRMc2Ufko0cwTuMYMlhshM3Izmb4d3Ts78wocegQqp6E0Brv3w+iVnducPggvfQMWT0H/OOx5G4xddWF9Hfzma+2uemv3vkRERLawjbL8ppNw6Rgti3136fi+86y3P7s+/osr4ncvHf/8PH18fsU5IhtepXmSY9VHiZI6WesnSuocqz5KpXmybZuofJTw2IO4qArZYVxUJTz2IFH5aOfOzrwCT30OGhUojqbHpz6Xxts5fRAe+xNolKFvND0+9idpvFtfT9zf2u6J+zv3JSIissVtiJn6dpbWzv/00t3lyfifAv8D+GHgSTP7ItAE3gK8Hfh3wG8v+zolYAdQds4dP09XLywdr1vVByCyhmYaz5OxPBkvD0DG8pCk8Xaz9dHME5ApYpliGsgUcUvxjrP1hx6BbAlyS+VEzx4PPdJ+Bv2lb6Tn5fqW2vS9Fu80W3/wm+dvd/Cbmq0XERFpY0Mn9cC/JN0s+2fOub84G3TOOTP7EOlSmV8Glm+KfRD4/5xzy2f2B5eO8236ORsfajcQM/tZ4GcBrrjiiot5DCJropEskLX+lphvORrJQts2rjED2eHWoF9I451UTqcz9Mtli2m8ncVT6Uz7yjaLpzr3dantREREtrANu/zGzH4e+EXgWeCnVvxfHvjDpf//B6TVbAaBe4HdwFfN7AOrOR7n3O865/Y55/aNj69cri+y/nLeALFrtMRi1yDnDbRtY7kRiGutwbiWxjspjUGz2hprVtN4O/3j52/T3+X1c6ntREREtrANmdQvlZf8TeBp4N3OuZXTiP8baRnMf+qc+3+cc9POuQXn3OeBDwHBUvv/v717j7KsLO88/n1OVXV1dVVf6G5oaG7doIIrrMXAtILIKEKieCE6LAiJY4JEMCRRhqVOnOgQiYkTMxozRh1NNBFjXCNRdLJQBDMCApKozVXitDe6udjcupu+1KWrq+o888feBafLU9VFd1Wds7u+n7X22nXe/b77vOect0//ate79x43fiR+Kc2Nl2+fkRcgzYHl3S9gNHczWt9NZjJa381o7mZ59+SzyDqXnwyjg+ToIJlZzK0fHSzKp3LsabBnoJhLn1ms9wwU5ZM5/iVFveF+yHqxHh4oyqey9vTm7daePnU7SZLmsbYL9RFxJcWc+AcoAv3jTaqNnwx7y8QNmXkf8DRwbESsKMsGgJ8DfRFxRJP9Pb9c//gAuy/Nmd4Fh7F60YvprC1kT+6is7aQ1YtePOXVbzr7jqRr9TnFnPo9TxOdi+hafc6+r36zYg2cdF4x131wa7E+6byp57ivXAunvKGYE9+/tVif8oZ9X/1mxRo4+fV7tzv59c6nlyRpCm01pz4i3k0xj/5e4Fcyc7IJu93l+hf+Hh8R3RQ3pILi5NlxN1NM4zkX+OyEZq9uqCNVRu+Cw/Z5CcuJOvuOnN4lLCdasea5B+uVa6d3CcuZeC5JkuaxtjlSHxFXUQT6u4Bzpgj0ALeX6/eUIb7R1RS/rHw/M3c1lH+qXL83Ip45U7C8+dTvA8P8YtiXJEmS2l5bHKmPiIuB9wNjFIH9ioiYWG1TZl5T/vwB4DzgHGBDRNwIDAEvBV5c/vyfGxtn5p0R8RHgHcD9EfFlYAFwEbAcePt07iYrSZIktZu2CPXA+N/nO4ArJ6nzbeAagMz8eUScCrwbeC3FnWBrwGNlnT/PzA0Td5CZ74yIH1AcmX8rUAfuBj6UmV+bqRcjSZIkzaXIzFb3oXLWrVuX69evb3U3JEmSdBCLiLsyc9106rbNnHpJkiRJ+8dQL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFVcZ6s7IEmS5sbDYwN8v/40WxhmJd28qHYIx3T0trpbkmaAR+olSZoHHh4b4Ov1xxhglBUsYIBRvl5/jIfHBlrdNUkzwFAvSdI88P360/TSSW90EhH0Rie9dPL9+tOt7pqkGWColyRpHtjCMIvo2KtsER1sYbhFPZI0kwz1kiTNAyvpZpCxvcoGGWMl3S3qkaSZZKiXJGkeeFHtEAYYZSBHyUwGcpQBRnlR7ZBWd03SDPDqN5LUxn42MsRte3bxRH2EVbUuXrZgMcd39bS6W6qgYzp6eS1H7HX1m7Nqh3r1G+kgYaiXpDb1s5Ehrt29jT5qHBqd7KqPce3ubVzEcoO99ssxHb2GeOkg5fQbSWpTt+3ZRR81Ftc6qEWwuNZBHzVu27Or1V2TJLUZQ70ktakn6iP0xt5f071R44n6SIt6JElqV4Z6SWpTq2pdDGR9r7KBrLOq1tWiHkmS2pVz6iVpDmzcVOfO7yZPboHDVsIZpwVr10x9XOVlCxZz7e5tUC+O0A9knX7qvHbBsjnqtSSpKjxSL0mzbOOmOl+5PukfSFauKNZfuT7ZuKk+Zbvju3q4aOFyFtc6eCpHWVzr4KKFniQrSfpFHqmXpFl253eTvt6krzcA6OsFSO78LqxdM3Xb47t6DPGSpH3ySL0kzbInt8CiRXuXLVpUlEuSNBMM9ZI0yw5bCYODe5cNDhblkiTNBEO9JM2yM04L+geC/oGknsWc+v6B4IzTotVdkyQdJAz1kjTL1q6pcf55QV9vsGVrsT7/vH1f/UaSpOnyRFlJmgNr19T2eVKsJEn7y8NEkiRJUsUZ6iVJkqSKM9RLkiRJFWeolyRJkirOE2WlCnuk3s89uZWtDLOCbk6JFRxd69tnm7tz2zNtTo3l+2wjSZLam0fqpYp6pN7PN3MzgznK8lzAYI7yzdzMI/X+KdvclJsZKNsM5Cg37aONJElqf4Z6qaLuya30ZgeLopOIYFF00psd3JNbJ21zd25jUXbSW7bpjU4WZSd357Y57LkkSZpphnqporYyTA8de5X10MFWhqdss2hCm0X7aCNJktqfoV6qqBV0M8TYXmVDjLGC7inbDE5oM7iPNpIkqf0Z6qWKOiVWMBBjDOYomclgjjIQY5wSKyZtc2osZzBGGSjbDOQogzHKqbF8DnsuSZJmmqFeqqija328MlazKDrZFntYFJ28MlZPeSWbo2t9vCpW01u26Y1OXrWPNpIkqf15SUupwo6u9XE0zy2Q708bSZLU3jxSL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcd5SVJEmapifYwY94nO0MsYweTuBwVrG01d2SPFIvSZI0HU+wg3/lQYYYYQkLGWKEf+VBnmBHq7smGeolSZKm40c8zkK6WEgXQTzz8494vNVdkwz1kiRJ07GdIbonzFzuppPtDLWoR9KzDPWSJEnTsIwehhndq2yYUZbR06IeSc8y1EuSJE3DCRzObkbYzQhJPvPzCRze6q5JhnpJkqTpWMVSTuc4euhiJ7vpoYvTOc6r36gteElLSZKkaVrFUkO82pJH6iVJkqSKM9RLkiRJFWeolyRJkirOUC9JkiRVnKFekiRJqjhDvSRJklRxhnpJkiSp4gz1kiRJUsUZ6iVJkqSKM9RLkiRJFWeolyRJkirOUC9JkiRVnKFekiRJqjhDvSRJklRxhnpJkiSp4gz1kiRJUsUZ6iVJkqSKM9RLkiRJFWeolyRJkirOUC9JkiRVnKFekiRJqjhDvSRJklRxhnpJkiSp4gz1kiRJUsUZ6iVJkqSKM9RLkiRJFdfZ6g5IkiRVxYOjQ9wxspMn6iOsqnVxZtcSjuvsaXW3JI/US5IkTceDo0N8aXgLu+pjHBqd7KqP8aXhLTw4OtTqrkmGekmSpOm4Y2QnfXSwuNZBLYLFtQ766OCOkZ2t7ppkqJckSZqOJ+oj9Mbe0ak3ajxRH2lRj6RnGeolSZKmYVWti4Gs71U2kHVW1bpa1CPpWYZ6SZKkaTizawn9jLGrPkY9k131MfoZ48yuJa3ummSolyRJmo7jOnu4sHsli2sdPJWjLK51cGH3Sq9+o7bgJS0lSZKm6bjOHkO82pJH6iVJkqSKM9RLkiRJFWeolyRJkirOUC9JkiRVnKFekiRJqjhDvSRJklRxbRHqI2JFRFwaEV+NiJ9GxFBE7IiIOyLiLRHRtJ8R0VG2uy0ini7bPRgR10bECyZpc3FEfC8i+svnuDUiXje7r1CSJEmaPe1ynfoLgU8CjwG3AA8Dq4Dzgc8Ar46ICzMzxxtERB/wT8DZwL3A54DdwJHAfwBeAPy48Uki4sPAO4FHgU8DC4BfB66PiLdn5sdn8TVKkiRJsyIacnLrOhFxNtALfD0z6w3lhwPfA44GLsjM6xq2fQF4I3B5Zv51k312ZeZIw+MzgO8APwNelJlPl+VrgLvK5z8xMzftq7/r1q3L9evXP/cXKkmSJE1TRNyVmeumU7ctpt9k5s2ZeX1joC/LHwc+VT48a7w8Ik6lCPTXNgv0ZduRCUWXl+sPjAf6st4m4BNAN3DJAbwMSZIkqSXaItTvw3g4H20oe2O5/t8RsTQi3hQRfxgRb42I502yn7PLIw6jIwAAEPxJREFU9Y1Ntn1jQh1JkiSpMtplTn1TEdEJ/Fb5sDGMv6hcH0sxnWZFw7aMiE8CV2TmWLmfXoq59v2Z+ViTp/pJuW56cq0kSZLUztr9SP0HgZOAGzLzpobyw8r1R4BbgRcCi4Ffpgj5vwdc1VB/abneMcnzjJcvm6wj5V8B1kfE+qeeeuq5vAZJkiRpVrVtqI+IKyiuVLMB+M0Jm8f7vQG4KDM3ZGZ/Zn4LuACoA++IiAUz1Z/M/JvMXJeZ6w499NCZ2q0kSZJ0wNoy1EfE24CPAj8EXpGZ2yZU2V6urx+fYjMuM+8DNlIcuX9hWTx+JH4pzY2Xb59kuyRJktS22i7UR8SVwMeABygC/eNNqv2oXE8WwsevbtMDkJkDwM+Bvog4okn955frHzfZJkmSJLW1tgr1EfFu4C8pbib1isx8cpKq/7dcn9RkH908G9I3NWy6uVyf22R/r55QR5IkSaqMtgn1EXEVxYmxdwHnZOaWKapfB2wGLoqIF0/YdhXFdJpbJhzlH7/e/Xsj4pCG510D/D4wDHz2QF6DJEmS1AptcUnLiLgYeD8wBtwOXBERE6ttysxroJhOExFvBr4G3B4RX6GYXnMacCbwJPA7jY0z886I+AjwDuD+iPgysAC4CFgOvH06d5OVJEmS2k1bhHpgbbnuAK6cpM63gWvGH2TmP5dH6a+iuJTlUmD8DrR/kpmbJ+4gM98ZET+gODL/Voqr5NwNfCgzvzYzL0WSJEmaW5GZre5D5axbty7Xr1/f6m5IkiTpIBYRd2XmuunUbZs59ZIkSZL2j6FekiRJqjhDvSRJklRxhnpJkiSp4gz1kiRJUsUZ6iVJkqSKM9RLkiRJFWeolyRJkirOUC9JkiRVnKFekiRJqrjOVndAkiS1rx1jW9k8tpGh3EVPLGZ1x1qWdqxodbcqZcPQMDf2D7F5ZJTVXZ2c29fDiT3d+2z3QP8ert82zKPDdY7qrnHe8m5O6lswBz1WFXmkXpIkNbVjbCs/HbmPPTnMQvrYk8P8dOQ+doxtbXXXKmPD0DCf3raLnWN1jujsYOdYnU9v28WGoeEp2z3Qv4ePbx5k+2id1QuC7aN1Pr55kAf698xRz1U1hnpJktTU5rGNdEY3C6KbiGBBdNMZ3Wwe29jqrlXGjf1DLO2osbSjRi3imZ9v7B+ast3124ZZ2hks6yzaLeussbQzuH7b1L8MaP4y1EuSpKaGchdd7D3do4sFDOWuFvWoejaPjLK4FnuVLa4Fm0dGp2z36HCdJR17t1vSETw6XJ/xPurgYKiXJElN9cRiRth7uscIe+iJxS3qUfWs7upkVz33KttVT1Z3TX1a41HdNXaO7d1u51hyVLfRTc05MiRJUlOrO9YymsPsyWEykz05zGgOs7pjbau7Vhnn9vWwY6zOjrE69cxnfj63r2fKduct72bHaLJ9tGi3fbTOjtHkvOX7PsFW85OhXpIkNbW0YwXP6zqZBdHNbvpZEN08r+tkr37zHJzY081lyxezpKPGY6NjLOmocdnyxfu8+s1JfQt42+pFLOussXlPsqyzxttWL/LqN5qUl7SUJEmTWtqxwhB/gE7s6Z7WJSwnOqlvgSFe0+aRekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVF5nZ6j5UTkQ8BTzUgqdeCWxpwfOq/Tk21IzjQpNxbGgyjo32cmxmHjqdiob6ComI9Zm5rtX9UPtxbKgZx4Um49jQZBwb1eX0G0mSJKniDPWSJElSxRnqq+VvWt0BtS3HhppxXGgyjg1NxrFRUc6plyRJkirOI/WSJElSxRnqJUmSpIoz1EuSJEkVZ6hvAxFxQUR8LCJuj4idEZER8Q/7aHNGRNwQEdsiYigi7o+IKyOiY676rdkTESsi4tKI+GpE/LT8jHdExB0R8ZaIaPpv13ExP0TEn0fEtyLikfJz3hYR90TE+yJixSRtHBvzUES8qfw/JSPi0knqvC4ibi2/Y/oj4rsRcfFc91WzJyI2NYyDicvjk7TxO6NiPFG2DUTEvcDJQD/wKHAi8IXMfNMk9V8PXAfsBq4FtgHnAScAX87MC+ei35o9EXE58EngMeAW4GFgFXA+sJTi878wG/4BOy7mj4jYA9wN/BB4EugFTgfWAZuB0zPzkYb6jo15KCKOBn4AdAB9wGWZ+ZkJdd4GfAzYSjE29gAXAEcBf5GZ75rTTmtWRMQmYBnwP5ts7s/MD0+o73dGBRnq20BEvIIizP8UeDlFiGsa6iNiSVlvKfDSzFxfli8EbgZeAvxGZn5xjrqvWRARZ1MEta9nZr2h/HDge8DRwAWZeV1Z7riYRyJiYWbublL+AeA9wCcz8/fKMsfGPBQRAfwzsBb4CvAuJoT6iFgDbAAGgH+fmZvK8kOA7wPHA2dk5r/MZd8188pQT2aumUZdvzMqyuk3bSAzb8nMn+T0fsO6ADgU+OL4P7RyH7uB/1Y+/N1Z6KbmUGbenJnXNwb6svxx4FPlw7MaNjku5pFmgb70j+X6+Q1ljo356QrgbOASitDezG8D3cDHxwM9QGY+Dfz38uHls9hHtSe/Myqqs9Ud0HN2drm+scm224BB4IyI6M7M4bnrlubQSLkebShzXAiKP48D3N9Q5tiYZyLihcAHgY9m5m3lX/6amWpsfGNCHVVfd0S8CTiG4he9+4HbMnNsQj2/MyrKUF89J5TrH0/ckJmjEbER+CXgOOD/zWXHNPsiohP4rfJh4xeu42Ieioh3UcyVXkoxn/5Miv+oP9hQzbExj5TfEZ+nOA/nPfuoPtXYeCwiBoCjImJRZg7ObE/VAodTjI1GGyPiksz8dkOZ3xkVZaivnqXlesck28fLl81BXzT3PgicBNyQmTc1lDsu5qd3UZxAPe5G4M2Z+VRDmWNjfvkj4BTgzMwc2kfd6YyN3rKeob7aPgvcDvwbsIsikL8NeCvwjYh4SWbeV9b1O6OinFMvVUREXAG8k+LEtt9scXfUBjLz8MwMiiNw51P8R31PRJza2p6pFSLiNIqj83/hya1qlJl/XJ6r9URmDmbmA5l5OfARoAe4urU91Eww1FfP+G/ISyfZPl6+fQ76ojlSXnbuoxSXMHxFZm6bUMVxMY+V/1F/FXglsAL4+4bNjo15oJx28/cUUyaummaz6Y6NyY7YqvrGL7zwsoYyvzMqylBfPT8q1y+YuKH8Ul9LcQLlg3PZKc2eiLiS4jrSD1AE+mY3CnFciMx8iOIXv1+KiJVlsWNjfuij+IxfCOxuvLkQ8L6yzqfLsvFrlU81No6gmHrzqPPpD2rjU/V6G8r8zqgoQ3313Fyuz22y7WXAIuBOz0g/OETEu4G/BO6lCPRPTlLVcaFxq8v1+BUtHBvzwzDwt5Ms95R17igfj0/NmWpsvHpCHR2cTi/XjQHd74yqykyXNloorj2ewD9Msn0JxW/Ww8C6hvKFwJ1l219v9etwmZGxcFX5ea4Hlu+jruNiniwUR8+WNimvAR8oP+vvODZcGj7rq8vP+dIJ5Wsp7hi6FVjTUH4Ixc2HEnhJq/vvcsCf/wuB3ibla4CflJ/zexrK/c6o6OLVb9pARLwBeEP58PBy/ZKIuKb8eUuWt+rOzJ0RcRnwZeDWiPgixe2bf5Xy9s0Ut3RWhUXExcD7KY623g5cUdwgci+bMvMacFzMM68B/iwi7gA2UgSyVRR3oz4OeBy4bLyyY0OTycyNEfFfgL8C1kfEtcAeipsPHYUn3B4sLgLeGRG3AQ9RXP3meOC1FEH9BuDD45X9zqiuKH/7UgtFxNU8O+exmYdywq2dI+KlwHspbte8kOKoyt8Bf5W/eCMJVcw0xgTAtzPzrAntHBcHuYg4ieIun2dSBK9lFDeS+THwdYrPeuKJ1I6Neazh++SyzPxMk+3nUVwe9VSKv/j8kOIus5+by35qdkTEyym+M06hOHDYS3GS670U163/fDYJg35nVI+hXpIkSao4T5SVJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSZlVEXBMRGRFrZvl5NkXEptl8DklqV4Z6SVIlRMStEeEdEyWpic5Wd0CSpBlyTqs7IEmtYqiXJB0UMvNnre6DJLWK028kqU1FxJpyLvo1EXFiRPyfiNgWEQMRcUdEvLJJm+6I+K8R8YOIGIyInRFxe0T82gzt/+qyzVlT7W+ar+/NEXFdRDwYEUNlX78TEW9qtl/g5eXjbFhubajXdE79AbwnayLiixGxJSJ2R8T6iHjddF6bJM01j9RLUvtbC/wL8APgr4EjgIuAb0TEGzPzWoCIWADcRBF+NwCfABYBFwDXRsS/y8z37O/+Z8EngX8DbgMeA1YArwE+HxEnZOZVZb3twB8DbwaOLX8et2mqJziA9+RY4HvAg8DngeUU78k/RcQvZ+Ytz/XFStKsykwXFxcXlzZcgDVAlsuHJmxbB4wATwNLyrI/LOveAHQ21D2MIvwmcMb+7r8sv7qsf9YU/b1mQvk1ZfmaCeXHN9nHAuBb5XMfOWHbrcV/W5O+X5uATRPKDuQ9ed+Efb1qfF+tHhsuLi4uExen30hS+9sBvL+xIDPXA18AlgH/sSz+bYrQ+Y7MHG2o+yTwJ+XDSw9g/zMqm8yBz8w9FEfTO5mZE1/39z15CPjTCX27CXgYePEM9EuSZpShXpLa392ZuatJ+a3l+pSIWAw8D9icmRua1L15vO7+7P859HXaIuKYiPhERGwo57pnOXf+urLKkQe4/wN5T+7NzLEm5Y8AhxxIvyRpNjinXpLa3xOTlD9erpeWCxRz05sZL1+2n/ufURFxHMWc9UOA24FvUvzFYIxiCszFQPcBPs2BvCfbJ2kzigfEJLUhQ70ktb9Vk5QfXq53lEtj2URHNNTdn/2Pq5frZv9/NAvHk3kHxYmxl2TmNY0bIuI3KEL9gTqQ90SSKsWjDZLU/k4tp5JMdFa5vqecPvMz4MiIeH6Tuq8o13fvz/4byp4u10c3qb+uSdlknleur2uy7eWTtBkDiIiO6TzBAb4nklQphnpJan9LgT9qLIiIdcB/ojjK/NWy+O+AAD7UGHwjYiVwVUOd/d0/FFNmAC6JiM6G+kdP3Mc+bCrXZ0143lfR/MRVgK3l+pjn8Dz7+55IUqU4/UaS2t9twKURcRrwHZ69jnwN+J3M3FnW+zDwauD1wH0RcQPFNdkvpLiE4//IzDsOYP9k5ncj4jbgZcD3IuJmiuk751FcD77ZEfxm/hdwCfCliPgysBk4CTgX+Mfy+Sf6VvlavlK+tiHgocz8/BTPs7/viSRVikfqJan9bQTOoJj6cjnwaxRTRl6TDTeGKi8H+SvAe8uit1PMTf8J8MbMfPeB7L/B64HPAEeVz3EK8AfAZPv/BZl5P8X0lzuB1wK/CywBzgc+NUmzzwB/RvGXhT+guCTlW/bxPPv7nkhSpURmtroPkqQmImINReD+XGa+uWr7lyTNHY/US5IkSRVnqJckSZIqzlAvSZIkVZxz6iVJkqSK80i9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkV9/8BFpnKGMe/LW0AAAAASUVORK5CYII=\n", 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\n", 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\n", 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ibl13B9es+xG3rruDhwcWTXRJG4R+m34zPSLeDOxAGVG/A7g2M9c19Tuw2v5Xi3NcCywH9o2I6Zm5qoNjvtfUR5IkSWPs4YFF/CR/zrScxiZsxCpW8xN+zh4Du7PFpNkTXV6t9Vuo3wb4SlPbvRFxdGZe09C2W7W9s/kEmbk2Iu4F/hjYCfh5B8f8LiIeA7aLiI0zc/lo3oQkSZLWd18+wLScxvSYBsB0pkHCfTzAFhjqR6Ofpt+cDbycEuw3AfYA/gOYC3wvIp7b0HdWtV3S5lyD7ZuP4JhZrXZGxHERsSAiFixcuLDde5AkSVIby1jONKY+qW0aU1mG46mj1TehPjNPycwrM/PBzFyemf+bmX8FfBbYCDh5gus7MzPnZea8OXPmTGQpkiRJtTSTjVnNmie1rWYNM9l4giracPRNqB/CGdV2/4a2IUfVG9oXj+CYdiP5kiRJGoW5sT2rYzWrcjWZyapczepYzdzYfqJLq706hPrBuS6bNLT9stru2tw5IqYAOwJrgXs6PGbb6vy/cT69JElSb2wxaTZ7xO5Mj2k8FiuYHtPYI7xIdiz024Wyrbyo2jYG9CuBvwD+FPjPpv77AxtTVs1Z1XTMS6pjmteiP6ihjyRJknpki0mzvSi2B/pipD4ido+ITVq0zwVOr55+tWHXt4CHgCMiYl5D/xnAx6qnX2g63dnAKuBd1XkHj5kNnFA9PQNJkiSpZvplpP5w4AMRcS3wa+BRyh1eXw3MAC4HPj3YOTOXRsSxlHB/dUScDzwCvJaydOW3gAsaXyAz742IvwE+DyyIiAuA1ZQbWW0HfKaTu8lKkiRJ/aZfQv1VlDC+F2WKzCaUi1yvp6xb/5XMzMYDMvOSiHgZ8A/AGyjh/y7g/cDnm/tXx/xrRNwHfBA4kvKXip8BJ2bmub15a5IkSVJv9UWor24sdc2wHdc/7gbg4C6PuQy4rNvXkiRJkvpVX8yplyRJkjRyhnpJkiSp5gz1kiRJUs0Z6iVJkqSaM9RLkiRJNWeolyRJkmrOUC9JkiTVnKFekiRJqjlDvSRJklRzhnpJkiSp5gz1kiRJUs0Z6iVJkqSaM9RLkiRJNWeolyRJkmrOUC9JkiTVnKFekiRJqjlDvSRJklRzhnpJkiSp5gz1kiRJUs0Z6iVJkqSaM9RLkiRJNWeolyRJkmrOUC9JkiTVnKFekiRJqjlDvSRJklRzhnpJkiSp5gz1kiRJUs0Z6iVJkqSaM9RLkiRJNTdlogvQ8H5/B/ziIlhyP8zaAZ51KGyz50RXJUmSpH7hSH2f+/0dcNOnYcUi2Gy7sr3p06VdkiRJAkN93/vFRTBjNmw0G2JS2c6YXdolSZIkMNT3vSX3w4xZT26bMau0S5IkSWCo73uzdoCVS57ctnJJaZckSZLAUN/3nnUorFxU5tLnQNmuXFTaJUmSJDDU971t9oQXf7DMpV/6m7J98Qdd/UaSJElPcEnLGthmT0O8JEmS2nOkXpIkSao5Q70kSZJUc4Z6SZIkqeYM9ZIkSVLNGeolSZKkmjPUS5IkSTVnqJckSZJqzlAvSZIk1ZyhXpIkSao5Q70kSZJUc4Z6SZIkqeYM9ZIkSVLNGeolSZKkmjPUS5IkSTVnqJckSZJqzlAvSZIk1ZyhXpIkSao5Q70kSZJUc4Z6SZIkqeYM9ZIkSVLNGeolSZKkmjPUS5IkSTVnqJckSZJqzlAvSZIk1ZyhXpIkSao5Q70kSZJUc4Z6SZIkqeYM9ZIkSVLNGeolSZKkmjPUS5IkSTVnqJckSZJqrq9DfUS8OSKyehzTYv/WEfGvEXFvRKyKiIURcXFE7D3EOTeKiFMi4pcRsTIi/hAR34iI3Xv7biRJkqTe6NtQHxHbA6cDy9rsnwvcBrwL+EPV93vAK4EfRcSrWhwzHfgB8BFgKXAacAXwemBBRLxwrN+HJEmS1Gt9GeojIoCzgYeBM9p0Ow3YFvg88KLM/EBmHgnsDawEzo6ITZqOeT/wEuBbwAsz80OZ+SbgMGBj4MsR0ZefiSRJktROvwbY9wAHAkcDjzXvjIgZwEHAAHBiZubgvsy8E/gyJfC/oeGYAP6qevq3mTnQcMylwHXAs4GXjfWbkSRJknqp70J9Nbf9k8BpmXltm25PA6YCD2Xmoy3231NtX97QtjOwA3BnZt7b4pjvVdsDu69akiRJmjh9FeojYgrwFeB+4IQhui4C1gFbRsTMFvt3qra7NbQN/vedbc75q2q7a2fVSpIkSf2hr0I95QLWvYCjMnNFu07Vvqso9f9j476IeCbw1urp7IZds6rtkjanHWzfvNXOiDguIhZExIKFCxcO+SYkSZKk8dQ3ob5aeeYE4DOZeVMHh7yPEsSPj4ibIuLTEXEucDtwd9VnoO3RXcrMMzNzXmbOmzNnzlidVpIkSRq1vgj11bSb8yhTYz7cyTGZ+VPg+dVxz6BcXPsy4FTg3VW3PzQcMjgSP4vWBtsXd1y4JEmS1AemTHQBlZk8MZd9ZVmoZj1nRcRZlAto3weQmXcDb2nuGBGD029uaWj+ZbVtN2d+l2rbbs69JEmS1Jf6JdSvAr7UZt/elHn211OCeSdTc/6y2n69oe1uygW4u0bEji1WwDmo2l7ZUcWSJElSn+iLUF9d+HpMq30RcTIl1J+bmV9saJ9eHbuqoS0o8/LnAxdk5o8bXiMj4gzg48A/R8Thg2vVR8QhwH7Az4BrxvTNSZIkST3WF6F+hHYBrouIHwD3UdatfzmwB2VU/7gWx3wWeA3lDrI3R8QPKWvX/zmwHHhr402pJEmSpDroiwtlR+hB4HJgHuXC2LdRgvm7gAMyc2nzAdWo/iuBj1KWrjy+en4JsE9m3jw+pUuSJEljJzJzomuonXnz5uWCBQsmugxJkiRtwCLi1syc10nfOo/US5IkScJQL0mSJNWeoV6SJEmqOUO9JEmSVHOGekmSJKnmDPWSJElSzRnqJUmSpJoz1EuSJEk1Z6iXJEmSas5QL0mSJNWcoV6SJEmqOUO9JEmSVHOGekmSJKnmDPWSJElSzRnqJUmSpJoz1EuSJEk1Z6iXJEmSas5QL0mSJNWcoV6SJEmqOUO9JEmSVHOGekmSJKnmDPWSJElSzRnqJUmSpJoz1EuSJEk1Z6iXJEmSas5QL0mSJNWcoV6SJEmqOUO9JEmSVHOGekmSJKnmDPWSJElSzRnqJUmSpJoz1EuSJEk1Z6iXJEmSas5QL0mSJNWcoV6SJEmqOUO9JEmSVHOGekmSJKnmDPWSJElSzRnqJUmSpJoz1EuSJEk1Z6iXJEmSas5QL0mSJNWcoV6SJEmqOUO9JEmSVHOGekmSJKnmDPWSJElSzRnqJUmSpJoz1EuSJEk1Z6iXJEmSas5QL0mSJNWcoV6SJEmqOUO9JEmSVHOGekmSJKnmDPWSJElSzRnqJUmSpJoz1EuSJEk1Z6iXJEmSas5QL0mSJNWcoV6SJEmqOUO9JEmSVHOGekmSJKnmDPWSJElSzRnqJUmSpJqbMtEFSJKk0bt77QquX72UBwfWsPWkqbx02mbsPGWjiS5L0jhxpF6SpJq7e+0KvrnyIR4dWMecmMKjA+v45sqHuHvtiokuTdI4MdRLklRz169eykwms+mkyUyKYNNJk5nJZK5fvXSiS5M0Tgz1kiTV3IMDa9gknvx/6ZvEJB4cWDNBFUkab4Z6SZJqbutJU3ksB57U9lgOsPWkqRNUkaTx1rehPiLeHBFZPY5psX+ziDghIm6PiMURsSQifhIRH42IOW3OOTkijo+IOyJiRUQ8EhGXR8S+vX9HkiT1xkunbcYy1vHowDoGMnl0YB3LWMdLp2020aVJGid9GeojYnvgdGBZm/2zgFuAfwLWAGcDXwZWAycCP46IrZuOCeB84LPAtOr8FwP7A9dGxCE9eTOSJPXYzlM24s9nbMmmkyazMNey6aTJ/PmMLV39RnoK6bslLavwfTbwMHAR8MEW3Y4DdgXOzsy3Nh1/DvAW4O3APzbsOgI4DLgReHlmrqz6nwFcD5wVEVdm5qNj+oYkSRoHO0/ZyBAvPYX140j9e4ADgaOBx9r02anaXtZi37erbfMUnHdU2xMHAz1AZt4CXFD1P2wkBUuSJEkTqa9CfUTsDnwSOC0zrx2i60+r7atb7HtNtb2i4bwzgH2B5cB1LY75XrU9sKuCJUmSpD7QN9NvImIK8BXgfuCEYbp/EXgj8LaI2AO4oWrfD3g28A+ZeWlD/52BycA9mbm2xfl+VW13HWH5kiRJ0oTpm1APfATYC3hpZg55C7zMXBkRBwKnUebOv6Bh97eAS5oOmVVtl7Q55WD75u1eMyKOo8zlZ4cddhiqPEmSJGlc9cX0m4h4IWV0/jOZeVMH/bcAvg+8jnIB7JbV4wjKaP3NEfGC9mfoXmaemZnzMnPenDktV8yUJEmSJsSEj9RX027OA+4EPtzhYZ8BXgYckpnfbmi/ICJWUkbq/xmYX7UPjsTPorXB9sUdvr4kSZLUN/phpH4mZS777sDKhhtOJXBS1eesqu1z1fPBi2GvanG+wbbnN7TdDawDdqq+RDTbpdreOdI3IUmSJE2UCR+pB1YBX2qzb2/KPPvrgV8Cg1NzplfbOUDzuvKDc2NWDzZUc/BvpEzN2Y/1vwwcVG2v7LZ4SZIkaaJNeKivLoo9ptW+iDiZEurPzcwvNuy6jhLET4qIozNzoOo/GTil6vPDptN9gRLoPxYRjTef2gc4HFgIXDgmb0qSJEm19Ou7B7jlWnjoQdhya9hnf3jGzv0wuWVoEx7qR+hDlHXnjwSeHxGDI+wvpyxp+RDrL4t5PnAo5QZTt0XEZcAWlEA/GTg2M5eOQ+2SJEnqQ7++e4Dvng+bbApbzIHHHoXvng+vPmKg74N9f1fXRmb+hDKC/x/ARpRlLY8DpgGnA8/LzLuajknK2vbvB9YC76aE/GuB/ZvWtZckSdJTzC3XlkC/yaYQk57471uGuiVqn+jrkfrMPBk4uc2+e4G/6vJ8a4FTq4ckSZL0uIceLCP0jTbepLT3u1qO1EuSJEljbcutYfljT25b/lhp73eGekmSJIlyUexjj5ZHDjzx3/vsP9GVDc9QL0mSJFFWuXn1EWUe/cMLy/bVR7j6jSRJklQrz9h5Es/YeaKr6F7/f+2QJEmSNCRDvSRJklRzhnpJkiSp5gz1kiRJUs0Z6iVJkqSaM9RLkiRJNWeolyRJkmrOUC9JkiTVnKFekiRJqjlDvSRJklRzhnpJkiSp5gz1kiRJUs0Z6iVJkqSaM9RLkiRJNWeolyRJkmrOUC9JkiTVnKFekiRJqjlDvSRJklRzhnpJkiSp5gz1kiRJUs0Z6iVJkqSaM9RLkiRJNWeolyRJkmrOUC9JkiTVnKFekiRJqjlDvSRJklRzhnpJkiSp5gz1kiRJUs0Z6iVJkqSaM9RLkiRJNWeolyRJkmrOUC9JkiTVnKFekiRJqjlDvSRJklRzhnpJkiSp5qZ0e0BEbAXMA2YDk1v1yczzRlmXJEmSpA51HOojYipwBnAk7Uf4A0jAUC9JkiSNk25G6j8KHA3cDXwNeABY24uiJEmSJHWum1D/JuBOYK/MXNGjeiRJkiR1qZsLZbcCLjfQS5IkSf2lm1B/P7BZrwqRJEmSNDLdhPpzgIMiYlaPapEkSZI0At2E+k8C1wNXRMQBEeGovSRJktQHurlQdk21DeAKgIho1S8zs+v17yVJkiSNTDfh+zrKGvSSJEmS+kjHoT4z5/ewDkmSJEkj1M2cekmSJEl9aERz3yNiE2BXYGZmXje2JUmSJEnqRlcj9RGxXURcCCwCFgBXNex7aUT8LCLmj22JkiRJkobScaiPiG2Bm4FDgO8AN1FWwhl0M+Wus4ePZYGSJEmShtbNSP1JlND+ysw8FPhB487MXENZIeclY1eeJEmSpOF0M6f+YODbmXnVEH3uB/YbXUmSJEnS6N2xZC2X/G419y8fYIeNJ/G6baex56wN83ZK3YzUbw38apg+a4BNRl6OJEmSNHp3LFnLqXetZNHqAbbbKFi0eoBT71rJHUvWTnRpPdFNqH8E2H6YPrsCvx95OZIkSdLoXfK71Ww+FWZPm8SkCGZPm8TmU0v7hqibUH8D8NqI2KbVzojYBfhFqrEnAAAgAElEQVRTGlbEkSRJkibC/csHmDU1ntQ2a2pw//KBCaqot7oJ9f8CzACuiYiDgI2hrFlfPb8MGAA+M+ZVSpIkSV3YYeNJLFmTT2pbsibZYeMN896rHb+rzLwZeDswl7Kk5QerXUur5zsCb8vMn45xjZIkSVJXXrftNBavgUWrBxjIZNHqARavKe0boq6+qmTml4HnAJ8H/hu4G/gx8O/Anpn5tTGvUJIkSerSnrOmcPwzZzB72iR+syKZPW0Sxz9zxga7+k3X7yozfwUc34NaJEmSpDGz56wpG2yIb7ZhTiqSJEmSnkLafnWJiB1GetLMvH+kx0qSJEnqzlB/j7gPyCH2t5PDnFeSJEnSGBoqfJ/H+qF+R2B/YAlwO+VGU9sAzwNmAdcC9459mZIkSZLaaRvqM/OoxucRsRtwE3AqcEpmLm3YtxlwCnAkcFxPKpUkSZLUUjcXyn4S+ElmfqAx0ANk5tLMPB74adVv1CLizRGR1eOYpn1XN+xr9/hSi3NOjojjI+KOiFgREY9ExOURse9Y1CxJkiRNhG7mvu8PnDFMn+spN6galYjYHjgdWAbMbNHlHODqNoe/G3ga8L2mcwZwPnAY8Mvq/E8DDgeujYg3ZOalo61dkiRJGm/dhPrplPnzQ9m26jdiVfg+G3gYuIgn7lz7uMw8p82xuwEnAQ8CzQH9CEqgvxF4eWaurI45g/Jl5KyIuDIzHx1N/ZIkSdJ462b6zW3AERGxV6udEfF8yqj3j0dZ03uAA4Gjgce6PHZwPv/Zmbmmad87qu2Jg4EeIDNvAS4A5lBCvyRJklQr3YT6Uyij8D+KiC9HxFERcVC1PZsyAj616jciEbE7ZU7+aZl5bZfHTqdcqJvAWU37ZgD7AsuB61ocPjhV58Bua5YkSZImWsfTbzLziog4AvgP4CjgLQ27A1gEHJeZPxxJIRExBfgKcD9wwghOcSiwJfCDzLynad/OwGTgnsxc2+LYX1XbXUfwupIkSdKE6uomUZn5rYj4HnAIsDdlbfollCk3l2Zmt9NlGn0E2At4aWauGMHxg1Nvzmyxb1a1XdLm2MH2zdudPCKOG3yNHXYY8c12JUmSpDHX9Z1fq+D+9eoxJiLihZTR+c9k5k0jOH4XYD6tL5AdE5l5JtUXhnnz5o3kTruSJElST3Qzp74nqmk35wF3Ah8e4WmGukAWnhiJn9ViX2P74hG+viRJkjRh2o7UR8SRIz1pZp7XRfeZPDGXfWVZ0XI9Z0XEWZQLaN/XuCMiplHm9693gWyDu4F1wE4RMaXFvPpdqu2dXdQtSZIk9YWhpt+cQwnKg6LpeSuDfboJ9auA9e7+WtmbMs/+esoNo1pNzXk9ZTnKVhfIApCZKyPiRmC/6nFVU5eDqu2VXdQtSZIk9YWhQv3RLdoOBf4MuIZyR9ffU25IdQDljrPfBi7upoDqothjWu2LiJMpof7czPxim1MMTr35j2Fe6guUQP+xiGi8+dQ+lPX1FwIXdlO7JEmS1A/ahvrMPLfxeUQcDPwpcEhmXtbU/ZSIOAT4BnDGmFfZRkQ8k/KF4kHKF4qhnE/5UnIYcFtEXAZsQQn0k4FjM3NpD8uVJEmSeqKbC2X/Abi4RaAHIDMvBS5h5Be7jsSxlCk/7S6QfVxmJvBG4P3AWuDdlJB/LbB/Vb8kSZJUO1GybgcdI5YBn8vME4fo80/AezJz0zGqry/NmzcvFyxYMNFlSJIkaQMWEbdm5rxO+nYzUr8aeO4wfZ4LDDliLkmSJGlsdRPqfwgcHBHviqZ1J6N4N2UVmSvGskBJkiRJQ+vmjrJ/R7ko9TTgfRFxPeUC1a2BlwI7Ao9U/SRJkiSNk45DfWbeHREvAv4deAWwU1OXHwDvbLdWvCRJkqTe6Gaknsy8C/iTiHg6Zf34WcAS4LbM/G0P6pMkSZI0jK5C/aAqwBviJUmSpD7QzYWykiRJkvpQ25H6iPgykMAJmflg9bwTmZlvG5PqJEmSJA1rqOk3R1FC/acoq9wc1eE5EzDUS5IkSeNkqFC/Y7X9bdNzSZIkSX2kbajPzF8P9VySJElSf/BCWUmSJKnmul7SMiImA7sBs4HJrfpk5rWjrEuSJElSh7oK9RHxYeB4yk2nhtIy7EuSJEkaex2H+oj4W+AUyh1kvwI8AKztUV2SJEmSOtTNSP2xlJVw9s7MhT2qR5IkSVKXurlQdnvgEgO9JEmS1F+6CfUPMoILayVJkiT1Vjeh/hvAKyNieq+KkSRJktS9bkL9ScDvgG9FhHeXlSRJkvpE2+k0EXFPi+apwB8BB0fEEmBxiz6ZmTuPUX2SJEmShjHUHPlJQDa1rQXub3geLY5r1SZJkiSpR9qG+sycO451SJIkSRqhbubUj0hE7BkRR/b6dSRJkqSnqp6HeuD1wNnj8DqSJEnSU9J4hHpJkiRJPWSolyRJkmrOUC9JkiTVnKFekiRJqjlDvSRJklRzhnpJkiSp5gz1kiRJUs0Z6iVJkqSaG49QH9VDkiRJUg/0PNRn5smZ6V8EJEmSpB6Z0u0BEbEVMA+YDUxu1SczzxtlXZIkSZI61HGoj4ipwBnAkbQf4Q8gAUO9JEmSNE66Gan/KHA0cDfwNeABYG0vipIkSZLUuW5C/ZuAO4G9MnNFj+qRJEmS1KVuLmDdCrjcQC9JkiT1l25C/f3AZr0qRJIkSdLIdBPqzwEOiohZPapFkiRJ0gh0E+o/CVwPXBERB0SEo/aSJElSH+jmQtk11TaAKwAiWt4oNjOz6/XvJUmSJI1MN+H7Osoa9JIkSZL6SMehPjPn97AOSZIkSSPUzZx6SZIkSX3IUC9JkiTVnKFekiRJqjlDvSRJklRzhnpJkiSp5gz1kiRJUs0Z6iVJkqSaM9RLkiRJNdfxzaciYnvgQGA3YDYwAPwBuAX4QWau6UmFkiRJkoY0bKiPiO2A04HXANG8G0hgYUR8ODPPGvsSJUmSJA1lyFAfEVsBNwDbA/8D3APsBDwXuA34evXfrwHOiIhnZeYHelqxJEmSpCcZbk79ScB2wBszc6/MfENm7gUcATwPeDAzjwR2BL4DvC8i/qSnFUuSJEl6kuFC/WuA72TmBY2NmfkNSoh/f/V8CSXo/x54dw/qlCRJktTGcKF+G+AXbfb9Ath98ElmrgAuA144NqVJkiRJ6sRwoX4RsGubfbsAy5vaHgY2G21RkiRJkjo3XKi/DviziDiksTEiXgu8Fri5qf+2lGAvSZIkaZwMt6Tlxynh/aKIWEBZ/WZHYB/KUpafauo/n7IqjiRJkqRxMmSoz8zbIuINwBcpQX6fatdi4L2Zec1g34iYCXwGWNCjWiVJkiS1MOzNpzLzOxHxDGBfyoWzDwE3ZObypn7LgH/rSZWSJEmS2ho21ANk5irgqh7XIkmSJGkEhrtQVpIkSVKf6yjUR8SUiNgrIvaIiBii354RceTYlSdJkiRpOMOG+oh4HfB/lAtgbwfui4hD23R/PXD22JUnSZIkaThDhvqI2Av4BrAlcBfwc2B74JsR8fFeFhYRb46IrB7HtOkzPSI+EBG3RMTSiHgsIu6MiHMjYk6L/pMj4viIuCMiVkTEIxFxeUTs28v3IkmSJPXScCP1f0O5mPYvMnO3zHwOZRWcu4EPRcQ/96KoiNgeOB1YNkSfbYBbgE8Dq4CzKKvv/Bh4FbB1U/8Azgc+C0yrzn8xsD9wbfMNtiRJkqS6GG71m/2B72fmfw42ZOaPIuKFwLeBD0TE2sw8YawKqsL32ZQ7014EfLBFn0mUvyDsBrw2My9rcY7mLyxHAIcBNwIvz8yVVd8zgOuBsyLiysx8dKzeiyRJkjQehhupn0OZR/8kmbmIMhp+HWXE/pQxrOk9wIHA0cBjbfq8DtgPOLU50Ff1ZWaua2p+R7U9cTDQV31vAS6gvNfDRlm7JEmSNO6GC/UPAzNb7ahuPnUwcANwYkT8w2iLiYjdgU8Cp2XmtUN0fVO1/c+I2Doi3hYRfx8RR0fE01ucdwZl2tByyheRZt+rtgeOonxJkiRpQgw3/eYe4IXtdmbm8og4GPgB8I+UufYjEhFTgK8A9wPDTefZp9q+APgcsHHDvjUR8Y+Z+bGGtp2BycA9mbm2xfl+VW13HaK+44DjAHbYYYdhypMkSZLGz3Aj9VcAz4+Indp1yMxllKk4PwaeOYpaPgLsBRyVmSuG6btVtf0CcA6wE7A58AZgEfDRiDiqof+sarukzfkG2zdv94KZeWZmzsvMeXPmrLewjiRJkjRhhgv1FwP/DRw0VKfMXAq8EriGMtLelerC2xOAz2TmTR0cMlj3FZn5zsy8NzOXZOZFwODyl3/fbR2SJElSHQ05/SYz/wd4cScnyszFwAHdFlBNuzkPuBP4cIeHLaaM1l/cYt/lwGpg14iYlZlLeGIkflaL/o3tizt8fUmSJKlvDHtH2dGKiLdExJVDdJlJmcu+O7Cy4YZTCZxU9Tmravtc9fyX1Xa9EF6terO0erpRtb0bWAfsVH2JaLZLtb2zozclSZIk9ZHhLpQdC3OBlw2xfxXwpTb79qbMs7+eEuQHp+ZcQVnS8jmU5SgfFxFbU+6Auwx4CCAzV0bEjdUx+wFXNb3O4PSiob58SJIkSX1pPEL9kKqLYo9ptS8iTqaE+nMz84sNu74MfAh4Z0ScnZn3VP0nA/9S9flm00o3X6AE+o9FROPNp/YBDgcWAheO2RuTJEmSxsmEh/qRyMzfRMRfU+48e3tEXAw8AswHnkeZRvO3TYedDxxKucHUbRFxGbAFJdBPBo6tLviVJEmSaqXnc+p7JTPPpdws6kbgtcA7gU0pI/UvzMyHmvon8Ebg/cBa4N2UkH8tsH9mXjp+1UuSJEljp69H6jPzZODkIfZfDVzdxfnWAqdWD0mSJGmDUNuRekmSJEmFoV6SJEmqOUO9JEmSVHOGekmSJKnmxiPU3w6cNw6vI0mSJD0l9Xz1m2qpSJeLlCRJknpkRKE+Ijai3AV2f2AT4B7gq5n5ozGsTZIkSVIHhgz1EfF14FuZeVFD2/bAFcAzgWjo/o6IODEzP9GTSiVJkiS1NNyc+iOA5zS1nQvsAvw3cCzwOuBTwGrgYxHxkrEuUpIkSVJ7XU2/iYg9gPnAlcCrMnNdtevbEXEF8APgncANY1mkJEmSpPa6Xf3mxUACJzcEegAy84eUsL/vGNUmSZIkqQPdhvotqu0dbfbfAWw98nIkSZIkdavb1W8e7qDPmpEUIkmSJLVz56pVXLF8Gb9bu45tp0zmFRvPZNfp04c85rf5KHfwEItYyWxmsCdb8vTYdJwqHl+djNS/LiK+HBFfBg6t2nZq03c74KExqUySJEmiBPpzlixm6boBtp48maXrBjhnyWLuXLWq7TG/zUe5igdYzho2ZzrLWcNVPMBv89FxrHz8dDJS/7zq0eh1lDvFPi4igjKf/sdjU5okSZIEVyxfxmaTJrHZ5MkAj2+vWL6s7Wj9HTzERkxhY6YCPL69g4d4OhveaP1woX7HNu3LW7Q9D/gVcPGoKpIkSZIa/G7tOraugvygmZMm8bu169ocAYtYyeY8OfBvxBQWsbInNU60IUN9Zv660xNl5m3AAaOuSJIkSWqw7ZQy5WazhmC/bGCAbadMbnvMbGawnDWPj9ADrGAts5nR01onSrer33QtIt4bEff0+nUkSZK0YXrFxjNZOjDA0nXrGMhk6bp1LB0Y4BUbz2x7zJ5syQrWspw1JMly1rCCtezJluNY+fjpeagHNgeeMQ6vI0mSpA3QrtOnc9Sszdls8iQeXLeOzSZP4qhZmw+5+s3TY1MOYHs2ZiqLWcXGTOUAtt9gV7/pdklLSZIkadztOn36sEtYNnt6bLpBXhTbyniM1EuSJEnqIUO9JEmSVHOGekmSJKnmDPWSJElSzRnqJUmSpJoz1EuSJEk1Z6iXJEmSam481qm/ehxeQ5IkSXrKGnWoj4jPAjdl5jdb7c/Ma4BrRvs6kiRJklobi+k37wNeOQbnkSRJkjQCQ47UR8RbOzzPbo19M/PLo6pKkiRJUseGm37zRSCH6ZPAS6tHVM8N9ZIkSdI46WRO/TLgDGB5i30BfAT4MXDZGNYlSZIkqUPDhfojgX8FDgWOzszrmztExEeAH2fmKT2oT5IkSdIwhrxQNjO/CuwJ/Bq4OiI+HRHTx6UySZIkSR0ZdvWbzHwgM18BfAD4a+C2iHhBzyuTJEmS1JGOl7TMzNOA51Pm1t8QEZ+IiKk9q0ySJElSR7papz4zfw68EPgE8EHgNoZfHUeSJElSD3V986nMXJeZH6EsYTmVsgKOJEmSpAnSyZKWLWXmzRHxbGAmsGrsSpIkSZLUjRGHeiij9sCSMapFkiRJ0gh0Pf1GkiRJUn8x1EuSJEk1Z6iXJEmSas5QL0mSJNWcoV6SJEmqOUO9JEmSVHOGekmSJKnmDPWSJElSzRnqJUmSpJoz1EuSJEk1Z6iXJEmSas5QL0mSJNWcoV6SJEmqOUO9JEmSVHOGekmSJKnmDPWSJElSzRnqJUmSpJoz1EuSJEk1Z6iXJEmSas5QL0mSJNWcoV6SJEmqOUO9JEmSVHOGekmSJKnmDPWSJElSzRnqJUmSpJoz1EuSJEk1Z6iXJEmSaq5vQ31EvDkisnoc07RvfsO+Vo9Ptjnn5Ig4PiLuiIgVEfFIRFweEfuOz7uSJEmSxt6UiS6glYjYHjgdWAbMHKLrNcDVLdqvb3HOAM4HDgN+WZ3/acDhwLUR8YbMvHR0lUuSJEnjr+9CfRW+zwYeBi4CPjhE96sz8+QOT30EJdDfCLw8M1dWr3cG5UvAWRFxZWY+OtLaJUmSpInQj9Nv3gMcCBwNPDaG531HtT1xMNADZOYtwAXAHErolyRJkmqlr0J9ROwOfBI4LTOv7eCQZ0bEuyLihIh4a0Ts0ua8M4B9geXAdS26fK/aHjiSuiVJkqSJ1DfTbyJiCvAV4H7ghA4P+4vq0XieC4FjM3NRQ/POwGTgnsxc2+I8v6q2u3ZVtCRJktQH+mmk/iPAXsBRmblimL4Lgb8D9gA2pUydOQi4DXgDcFlENL63WdV2SZvzDbZv3u4FI+K4iFgQEQsWLlw4THmSJEnS+OmLUB8RL6SMzn8mM28arn9m/jQzP5WZ/5uZyzLzocz8L2A+cC/wEuDPxrLGzDwzM+dl5rw5c+aM5aklSZKkUZnwUF9NuzkPuBP48GjOlZlLga9XT/dv2DU4Ej+L1gbbF4/m9SVJkqSJMOGhnrIO/a7A7sDKxptIASdVfc6q2j7XwfkG58Zs0tB2N7AO2Kn6EtFs8ALbO7svX5IkSZpY/XCh7CrgS2327U2ZZ3895YZRw07NAV5Ube8ZbMjMlRFxI7Bf9biq6ZiDqu2VHdYsSZIk9Y0JD/XVRbHHtNoXESdTQv25mfnFhvZ5mbmgRf83U+4Quxr4RtPuL1AC/cciovHmU/tUxywELhz1G5IkSZLG2YSH+hH6VkSsBRYAvwFmAPsALwDWAm/PzPuajjkfOJRyg6nbIuIyYAtKoJ9MWQZz6fiUL0mSJI2duob6LwCvoKxysyUQwG+Bc4DPZeb/NB+QmRkRbwRuBN4KvBtYCfz/9u492pKrrhP495eOQHikg5gh8gjhbUZnKdgOEhFCfGAQBmQFUScOBEPEGWX5QJ2JAgFRUUFBcWAgYiC4BhgQHZdEBgmYxDBgKwg+eAgEcBB5SRIg7/zmj6orh8u5nUv37XvP7v581qpVObt2Ve1zzk7d76neVXVRkmd296Xb03QAANha1d073Ybh7Nmzp/fu/ZLRPwAAsGWq6i+7e89m6q7C3W8AAIADINQDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAINb6VBfVadXVc/TmTdRt6rqDQv1j9yg3lFV9fSqek9VXV1VH6+qV1XViQfnXQAAwMG1sqG+qu6c5PlJPrvJVX40yYOTXL2Pbd48yRuSPDXJFUmel+RPk3xPkr1Vdb8DaTMAAOyElQz1VVVJfjfJp5K8cBP1753kV5I8O8k/76PqTyb5liSvTnK/7v7Z7v6BJKcluWWSl1TVSn4mAACwkVUNsE9KckqSM5J8bl8V52E25yf5QJKn7aNeJXni/PJnuvvGtWXd/YdJLk7yb5M86IBaDgAA22zlQv08tv1ZSZ7X3RdtYpWfT3KfJI/r7mv2Ue/uSY5P8t7u/uCS5RfM81O+nPYCAMBOW6lQv3DW/cNJzt5E/W9K8nNJntXde2+i+r3n+Xs3WP6+eX6vTTQVAABWxtI7xOygp2Y66/6A7r5qXxWr6qhMPwD+NskzNrHt3fP88g2Wr5Ufs8H+zkpyVpIcf/zxm9gdAABsj5U5Uz/feebsJM/p7rdsYpVfTXK3JI/t7usOauOSdPeLuntPd+859thjD/buAABg01Yi1M/Dbl6WaWjMUzZR/0FJ/kuSZ3b3X29yN2tn4ndvsHyt/DOb3B4AAKyElQj1SW6daSz7iUmuXniAVOcLd7R58Vz23ExDdCrJ0xfrzvXvMte/bi77hvn1e+b5RmPm7znPNxpzDwAAK2lVxtRfk+R3Nlh230wh/pJMwfwtme5fv1H9x2T6kfCSJD3XTZL3Z7oA915Vddcld8A5dZ5fuD9vAAAAdspKhPr5otgzly2rqnMyhfqXdve5C4v+dIP6354p1P9wd1+/sI+uqhcm+aUkv1pVj1m7V31VPSLJtyb5uyR/duDvCAAAts9KhPpt9OtJHpbpCbJvrao3Zrp3/aOTfD7J4xcfSgUAACNYlTH122J+ONV3JPmFTLeu/In59R8k+abufusONg8AAPZLdfdOt2E4e/bs6b17b+pZVwAAsP+q6i+7e89m6h5WZ+oBAOBQJNQDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAINb2VBfVadXVc/TmeuW/buqOreq3l5Vn6iqa6rqI1X1p1X1qKqqDba5q6p+oqreWVVXVdWnq+p1VXXS9rwrAADYeisZ6qvqzkmen+SzG1T5xiSPTPL/krwqyXOSvCHJ1yd5TZKXLtlmJXlFkl9PcrN5+69N8sAkF1XVI7b2XQAAwPY4cqcbsN4cvn83yaeS/H6SJy+p9j+7+7wl6x6d5P8m+cGqen53v21h8fclOS3JpUm+rbuvntd5YZJLkry4qi7s7iu38v0AAMDBtopn6p+U5JQkZyT53LIK3X3NBuVXJHn9/PKe6xb/yDz/+bVAP6/zF0lemeTYTKEfAACGslKhvqpOTPKsJM/r7ov2Y/1bZvpBkCTvWii/RZKTknw+ycVLVr1gnp+yZBkAAKy0lRl+U1VHJjk/yYeTnL3Jde6R5PQku5LcPsl3J7lDkl/u7ncuVL37XOcD3X39kk29b57fa/9aDwAAO2dlQn2Spya5T5IHdPdVm1znHkmetvD62iQ/nenC2UW75/nlG2xnrfyYjXZUVWclOStJjj/++E02DwAADr6VGH5TVffLdHb+Od39ls2u191/0t2V6W4290jyi0l+Kcn/rqqbbWUbu/tF3b2nu/cce+yxW7lpAAA4IDse6udhNy9L8t4kT9mfbXT3dd39/u5+RqYz/g/LdMHtmrUz8bu/ZOUvLv/M/uwfAAB20o6H+iS3zjSW/cQkVy88cKrzhaE1L57LnruJ7a1d9HryQtn7k9yQ5G7zj4j11u6U894vu/UAALDDVmFM/TVJfmeDZffNNM7+kiTvSbKZoTl3nOf/ekFsd19dVZcm+dZ5etO6dU6d5xduss0AALAydjzUzxfFnrlsWVWdkynUv7S7z10o39Pde5fUPzbTLTGT5I/XLX5BpkD/zKpafPjUNyV5TJJPZHoaLQAAh6m/v+raXHD5VfnoddfnDl9xZE7dfVROPGpLL9U8KHY81O+nc6vqdknelukWmDckOSHJQ5McleQPkrxk3TqvSPKoTA+YentV/VGS22UK9LuSPGF+eBUAAIehv7/q2rzoE1fm6F1H5Lgjd+XyG27Miz5xZc469jYrH+xHDfXPTvLITMNzHpLp7jefzDR85vYVhZMAAA1XSURBVPwkr+ruXlyhu7uqvj/JpUken+THklyd5KIkz+zuS7ev+QAArJoLLr8qR+86Irt3TZed7t5V/1ou1B+A7j4nyTlLyl+e5OX7sb3rk/zGPAEAwL/66HXX57gjd31R2W2OqHz0umXPLl0tq3D3GwAA2HF3+Iojc+WNXzTYI1fe2LnDV6z0efAkQj0AACRJTt19VK644cZcfsONubE7l99wY6644cacuvuonW7aTRLqAQAgyYlH3SxnHXub7N51RD52/Q3ZveuIIS6STVZ8TD0AAGynE4+62RAhfj1n6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwVV373QbhlNVn0jyoZ1uxyHiq5J8cqcbwcrQH1ijL7BIf2DR4dQf7tLdx26molDPjqqqvd29Z6fbwWrQH1ijL7BIf2CR/rCc4TcAADA4oR4AAAYn1LPTXrTTDWCl6A+s0RdYpD+wSH9Ywph6AAAYnDP1AAAwOKEeAAAGJ9QDAMDghHq2XFWdVlW/VVUXV9UVVdVV9fKbWOekqnpdVX26qq6qqndW1Y9X1a7tajdbr6puV1VnVtVrq+of5u/28qq6pKp+qKqWHoP0h0NXVf1KVb2xqj4yf7efrqq3V9XTqup2G6yjPxwmqur0+W9GV9WZG9R5WFW9eT6WfLaq3lpVj93utrK1quqyhe9+/fSxDdZxbFjgQlm2XFW9I8nXJ/lskn9M8jVJfq+7T9+g/iOSvCbJ1UlemeTTSR6e5N5JXt3dj96OdrP1quqJSV6Q5J+SvCnJh5PcPsmjkuzO9L0/uhcORPrDoa2qrk3yV0n+LsnHk9wqyTcn2ZPko0m+ubs/slBffzhMVNWdk7wrya4kt07yhO4+d12dH03yW0k+lak/XJvktCR3SvKc7n7ytjaaLVNVlyU5Jslzlyz+bHc/e119x4Z1hHq2XFU9OFOY/4ckD8oU5paG+qo6eq63O8m3dPfeufwWSS5Mcv8k39/dr9im5rOFquqUTKHtj7v7xoXy45K8Lcmdk5zW3a+Zy/WHQ1xV3aK7r15S/otJzk7ygu7+z3OZ/nCYqKpK8oYkd03y+0menHWhvqpOSPLuJJ9L8o3dfdlcftskf5Hk7klO6u63bGfb2RpzqE93n7CJuo4NSxh+w5br7jd19/t6c78YT0tybJJXrP1POW/j6iQ/P7/8kYPQTLZBd1/Y3X+0GOjn8o8leeH88uSFRfrDIW5ZoJ+9ap7fc6FMfzh8PCnJKUnOyBTal3l8kpsnef5aoE+S7v6XJL80v3ziQWwjq8OxYYkjd7oBHPZOmed/smTZRUk+n+Skqrp5d1+zfc1iG1w3z69fKNMfDl8Pn+fvXCjTHw4DVXVikmcleV53XzT/C98y++oPF6yrw5huXlWnJzk+04+7dya5qLtvWFfPsWEJoZ6ddu95/t71C7r7+qr6YJKvTXK3JH+/nQ3j4KmqI5P8p/nl4kFZfzhMVNWTM42b3p1pPP0DMv0Bf9ZCNf3hEDcfC87PdL3N2TdRfV/94Z+q6nNJ7lRVt+zuz29tS9kmx2XqD4s+WFVndPefLZQ5Niwh1LPTds/zyzdYvlZ+zDa0he3zrCRfl+R13f36hXL94fDx5EwXTa/5kySP6+5PLJTpD4e+pya5T5IHdPdVN1F3M/3hVnM9oX48v5vk4iR/m+TKTIH8R5OcleSCqrp/d//1XNexYQlj6oFtVVVPSvJTmS54+8Edbg47pLuP6+7KdGbuUZn+gL+9qu67sy1ju1TV/TKdnX+Oi1vp7qfP12H9c3d/vrv/prufmOTXkxyV5JydbeHqE+rZaWu/pndvsHyt/DPb0BYOsvl2dM/LdDvDB3f3p9dV0R8OM/Mf8Ncm+c4kt0vysoXF+sMhah5287JMwyeessnVNtsfNjp7y5jWbqrwwIUyx4YlhHp22nvm+b3WL5gP+nfNdCHlB7azUWy9qvrxTPeX/ptMgX7Zw0T0h8NUd38o04+9r62qr5qL9YdD160zfa8nJrl68UFDSZ4213nxXLZ23/J99YevzjT05h+Npz/krA3Ju9VCmWPDEkI9O+3Cef5dS5Y9MMktk1x6OF29fiiqqp9N8htJ3pEp0H98g6r6w+HtDvN87U4X+sOh65okv7PB9Pa5ziXz67WhOfvqD6euq8Oh45vn+WJAd2xYprtNpoM2ZboHeSd5+QbLj870K/yaJHsWym+R5NJ53e/b6fdhOqA+8JT5e9yb5Ctvoq7+cAhPmc6q7V5SfkSSX5y/3z/XHw7vKdPY6U5y5rryu2Z6euinkpywUH7bTA8i6iT33+n2m/brOz8xya2WlJ+Q5H3zd3v2Qrljw5LJ3W/YclX1yCSPnF8eN8/vX1Xnzf/9yZ4f5d3dV1TVE5K8Osmbq+oVmR71/B8yP+o50+OfGVBVPTbJMzKdeb04yZOmB0d+kcu6+7xEfzgMPDTJL1fVJUk+mCmc3T7Tk6fvluRjSZ6wVll/YFF3f7CqfjrJbybZW1WvTHJtpgcR3SkuuB3ZY5L8VFVdlORDme5+c/ck350pqL8uybPXKjs2LFfzLxvYMlV1Tr4wJnKZD/W6x0BX1bck+blMj3a+RaazLi9J8pv9pQ+dYBCb6AtJ8mfdffK69fSHQ1BVfV2mJ34+IFMIOybTA2bem+SPM32/6y+e1h8OMwvHjSd097lLlj880y1R75vpX3n+LtNTZl+6ne1k61TVgzIdG+6T6WTgrTJd5PqOTPetP7+XBFbHhi8m1AMAwOBcKAsAAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoADqqqOq+quqpOOMj7uayqLjuY+wBYVUI9AEOoqjdXlScmAixx5E43AAC2yLftdAMAdopQD8Ahobvfv9NtANgpht8ArKiqOmEei35eVX1NVf1BVX26qj5XVZdU1XcuWefmVfVfq+pdVfX5qrqiqi6uqu/dou2fM69z8r62t8n397iqek1VfaCqrprb+udVdfqy7SZ50Py6F6Y3L9RbOqb+AD6TE6rqFVX1yaq6uqr2VtXDNvPeALabM/UAq++uSd6S5F1J/keSr07ymCQXVNUPdPcrk6Sqbpbk9ZnC77uT/HaSWyY5Lckrq+obuvvs/d3+QfCCJH+b5KIk/5TkdkkemuT8qrp3dz9lrveZJE9P8rgkd5n/e81l+9rBAXwmd0nytiQfSHJ+kq/M9Jn8YVV9e3e/6ct9swAHVXebTCaTaQWnJCck6Xn6tXXL9iS5Lsm/JDl6Lvtvc93XJTlyoe6/yRR+O8lJ+7v9ufycuf7J+2jveevKz5vLT1hXfvcl27hZkjfO+77jumVvnv5sbfh5XZbksnVlB/KZPG3dth6ytq2d7hsmk8m0fjL8BmD1XZ7kGYsF3b03ye8lOSbJ98zFj88UOn+yu69fqPvxJL8wvzzzALa/pXrJGPjuvjbT2fQjszUXvu7vZ/KhJM9c17bXJ/lwkn+/Be0C2FJCPcDq+6vuvnJJ+Zvn+X2q6jZJ7pHko9397iV1L1yruz/b/zLaumlVdXxV/XZVvXse697z2PnXzFXueIDbP5DP5B3dfcOS8o8kue2BtAvgYDCmHmD1/fMG5R+b57vnKZnGpi+zVn7Mfm5/S1XV3TKNWb9tkouT/J9M/2JwQ6YhMI9NcvMD3M2BfCaf2WCd6+OEGLCChHqA1Xf7DcqPm+eXz9Ni2XpfvVB3f7a/5sZ5vuzvx7JwvJGfzHRh7Bndfd7igqr6/kyh/kAdyGcCMBRnGwBW333noSTrnTzP3z4Pn3l/kjtW1T2X1H3wPP+r/dn+Qtm/zPM7L6m/Z0nZRu4xz1+zZNmDNljnhiSpql2b2cEBfiYAQxHqAVbf7iRPXSyoqj1J/mOms8yvnYtfkqSS/Npi8K2qr0rylIU6+7v9ZBoykyRnVNWRC/XvvH4bN+GyeX7yuv0+JMsvXE2ST83z47+M/ezvZwIwFMNvAFbfRUnOrKr7JfnzfOE+8kck+eHuvmKu9+wkpyZ5RJK/rqrXZbon+6Mz3cLxV7v7kgPYfrr7rVV1UZIHJnlbVV2YafjOwzPdD37ZGfxl/nuSM5L8r6p6dZKPJvm6JN+V5FXz/td74/xefn9+b1cl+VB3n7+P/ezvZwIwFGfqAVbfB5OclGnoyxOTfG+mISMP7YUHQ823g/yOJD83F/1YprHp70vyA939swey/QWPSHJukjvN+7hPkp9JstH2v0R3vzPT8JdLk3x3kh9JcnSSRyV54QarnZvklzP9y8LPZLol5Q/dxH729zMBGEp19063AYAlquqETIH7pd39uNG2D8D2caYeAAAGJ9QDAMDghHoAABicMfUAADA4Z+oBAGBwQj0AAAxOqAcAgMEJ9QAAMDihHgAABvf/AaUq29c16ED8AAAAAElFTkSuQmCC\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for y_label in list(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][\"nodes\"].values()):\n", - " layer_params = list(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][y_label].keys())\n", - " layer_params.remove(\"node_name\")\n", - " layer_params.remove(\"node_type\")\n", - " layer_params.remove(\"node_layer\")\n", - " for param in layer_params:\n", - " if (type(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][y_label][param]) is float or\n", - " type(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][y_label][param]) is int):\n", - " plt.figure(figsize=(12,12))\n", - " total_dots = 0\n", - " for i in range(data.shape[0]):\n", - " node_num = int(y_label.split(\"_\")[-1])\n", - " bm = np.array(params_dictionaries[i][\"chainer\"][\"pipe\"][model_index][\"binary_mask\"])\n", - " if np.sum(bm[node_num, :]) > 0 or np.sum(bm[:, node_num]) > 0:\n", - " total_dots += 1\n", - " plt.scatter(i // 10, \n", - " params_dictionaries[i][\"chainer\"][\"pipe\"][model_index][y_label][param],\n", - " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", - " if total_dots == 0:\n", - " plt.close()\n", - " continue\n", - " plt.ylabel(y_label + \" \" + param, fontsize=20)\n", - " plt.xlabel(\"population\", fontsize=20)\n", - " plt.title(TITLE, fontsize=20)\n", - " plt.xticks(fontsize=20)\n", - " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \"_\" + param + \".png\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, From 17dfa1f357f0d0e8ea3da20f3531af428c1e8123 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 16:11:57 +0300 Subject: [PATCH 276/616] feat: add check bool --- .../evolution/evolve_intents_snips.json | 5 +- .../models/evolution/Results_analysis.ipynb | 299 ++++++++++++++---- 2 files changed, 245 insertions(+), 59 deletions(-) diff --git a/deeppavlov/configs/evolution/evolve_intents_snips.json b/deeppavlov/configs/evolution/evolve_intents_snips.json index 9c9f849edf..0f7f35878a 100644 --- a/deeppavlov/configs/evolution/evolve_intents_snips.json +++ b/deeppavlov/configs/evolution/evolve_intents_snips.json @@ -142,7 +142,10 @@ }, "model_name": "cnn_model", "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" + "tokenizer": "#my_tokenizer", + "check_bool": { + "bool": true + } } ], "out": [ diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb index f02b70ae0d..c0fa6812f5 100644 --- a/deeppavlov/models/evolution/Results_analysis.ipynb +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -504,64 +504,6 @@ "models_ids" ] }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2])" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.unique(models_ids)" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.where(models_ids[2] == np.unique(models_ids))[0][0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 73, @@ -644,6 +586,247 @@ " plt.show()\n" ] }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['dataset_iterator', 'seed'] seed\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'filters_cnn'] filters_cnn\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'lear_rate'] lear_rate\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'lear_rate_decay'] lear_rate_decay\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'coef_reg_cnn'] coef_reg_cnn\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'coef_reg_den'] coef_reg_den\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'dropout_rate'] dropout_rate\n" + ] + }, + { + "data": { + "image/png": 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AgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhllXgVFVd62qv66qS6rq2qq6qKpeUVV7zTjO3tPPXTQd55LpuHcdse+q+v6qelpVvWPOPi6vqvdU1WOW8t0BAGA12HVHT2CUqto/yYeS7Jvk1CTnJnlwkmckObaqDu/uyxcxzh2m42xMclqSk5McmOSJSR5RVYd294XL3PfTkjw7yWeTvD/Jl5PcI8ljkjysql7e3c9a0g8CAAB2oHUTGElem8kv+E/v7ldvfbOqXpbkmUn+JMlTFjHOCzOJi5d19+/MGefpSV453c+xy9z3R5Mc2d2nzx2kqu6T5Kwkz6yqN3f32YuYLwAArBrV3Tt6Dss2PYLwmSQXJdm/u2+cs+52Sb6UpJLs293fvJlxbpvkK0luTHLn7v76nHUbklyYyZGG/bcexRi17zmfeUOSJyX53e5+6WK+/6ZNm3rz5s2L2RQAAJakqs7u7k3b2269XINx1HT57rm/4CfJNBLOTHLrJIdsZ5xDkuye5My5cTEd58Yk75q3v5H73urb0+X1i9weAABWjfUSGAdMl1u2sf786XLjCowzat+pqj2SPDZJJ3n39rYHAIDVZr0Exp7T5ZXbWL/1/duvwDhD9l1VleQvk3xfktd196e3s/2Tq2pzVW2+9NJLb25TAAD4nlkvgbEevDTJzyT5YJLt3kGqu9/Q3Zu6e9M+++yz4pMDAIDFWC+BsfUowZ7bWL/1/StWYJxl77uqXpLJ3abOSPKT3X3tduYJAACr0nq5Te150+W2rnO493S5reskljPOsvZdVS9P8tuZPA/jf3X3t7YzRwAAWLXWyxGM90+XD5/eTvY7preKPTzJtzJ5xsTNOSvJ1UkOn35u7jgbkjx83v6WvO+a+PNM4uI9SR4hLgAAWOvWRWB09wWZ3HVpvyRPnbf6hCS3SfKmuc+hqKoDq+rAeeN8I8mbptsfP2+c35qO/665T/Je4r4ryRuS/GaSdyR5ZHdfvdjvCwAAq9W6eNBe8p0H3n0okydqn5rk00kOzuQ5FVuSHNbdl8/ZvpOku2veOHeYjrMxyWmZPHX7PkkelclD+A6bRsVy9v38TALm6iSvSHLdAl/p49399sV8dw/aAwBgpS32QXvr5RqMdPcFVbUpyQuSHJvkJzN5ivYrk5zQ3V9b5DiXV9WhSZ6f5NFJHpLk8iQnJnled39hwL7vOV3unuQ525jKG5MsKjAAAGC1WDdHMHZmjmAAALDSFnsEY11cgwEAAKwOAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADLPrUj9YVQcmuU+S23b3m8ZNCQAAWKtmPoJRVfevqs1J/ivJKUlOmrPuoVX1rar6qXFTBAAA1oqZAqOqNib5QJIDkrwyyTvmbXJGkq8mOW7E5AAAgLVl1iMYz09yyyQHd/ezknxs7sru7iQfTvKgMdMDAADWklkD45gkb+3uT93MNhcnucvSpwQAAKxVswbGXkm+sJ1tKpOjHAAAwE5m1sD47yQ/sJ1tfjCToxgAAMBOZtbAOC3JT1XVAQutrKoHZXIa1buWOzEAAGDtmTUw/jTJ9UnOqKrfyPRai6r6wenrf07y9SR/NnSWAADAmjDTg/a6+7yqemySv0vymunbleST0+UVSR7T3Z8fOksAAGBNmPlJ3t39zqq6Z5LHJzkkyR2SXJnkrCQndvdXx04RAABYK2YOjCTp7isyedDeK8dOBwAAWMtmfZL3EVW1aTvb3L2qjljetAAAgLVo1ou8P5DkI1V1cxdxPzHJ+5c8IwAAYM2aNTCS5Nokz6yqf6yq3UZPCAAAWLuWEhgvTfKmJD+d5ANVtc/YKQEAAGvVUgLj2939hCTHJ3lwkrO29eA9AABg57KUwEiSdPcLkvxSku9P8qGqOnLUpAAAgLVpyYGRJN395iQ/nslD9t5ZVb88ZFYAAMCatKTnYMzV3adX1WFJ/jXJiUkuXPasAACANWlZRzC26u5zkxyc5KNJ9h8xJgAAsPbMegTjqCQXLbSiuy+bXofxjCS7L29aAADAWjRTYHT36dtZf22SlyxrRgAAwJo15BQpAACAZDtHMKrqtCSd5PHd/YXp68Xo7j5m2bMDAADWlO2dInVkJoFx6zmvF6OXOB8AAGANu9nA6O4NN/caAABgLsEAAAAMMywwqmqvqrrNqPEAAIC1Z6bAqKpjquolVbXXnPf2rarTk1yW5KtV9bLRkwQAANaGWY9gPC3JY7r7a3Pe+7MkD0lyQZLLkzyjqn520PwAAIA1ZNbAuF+Sf9/6oqp2T3Jckvd098YkByS5OMlThs0QAABYM2YNjH2TXDLn9cFJdktyUpJ099eT/EsmoQEAAOxkZg2Ma5PsPuf1QzJ55sUZc967Ksney5wXAACwBs0aGJ9NcvSc149Ncn53f3HOe3fL5IJvAABgJzNrYLwxyQ9V1Ueq6oNJfijJ387b5oeTnDdicgAAwNoya2C8LsnJSTYlOTyT6y1evHVlVR2USXR8YND8AACANWTXWTbu7m8n+YWqesrkZX993iZfTvKAJBeNmR4AALCWLOlJ3t191QJxke6+rLs/0d1Xzn2/qp5RVRcudZIAAMDasKTAWILbJ7nH92hfAADADvK9CgwAAGAnIDAAAIBhBAYAADCMwAAAAIYRGAAAwDDrKjCq6q5V9ddVdUlVXVtVF1XVK6pqrxnH2Xv6uYum41wyHfeuI/ddVfetqrdU1Veq6pqqOq+qTqiq3WeZLwAArBYzPWhvNauq/ZN8KMm+SU5Ncm6SByd5RpJjq+rw7r58EePcYTrOxiSnZfLk8gOTPDHJI6rq0O6+cN5nZt53VR08Hf8WSU5JcnGSo5M8L8kxVXVMd1+7lJ8FAADsKOvpCMZrM/kF/+nd/eju/oPuPjrJy5MckORPFjnOCzOJi5d19zHTcR6dSSzsO93PsvZdVbskOTHJrZMc192/0N3PTnJwkn9McniSZ87y5QEAYDWo7l75nVQ9NMmR3X3CCo2/f5LPJLkoyf7dfeOcdbdL8qUklWTf7v7mzYxz2yRfSXJjkjvPfVp5VW1IcmEmDwzcf+tRjKXsu6qOTvK+JGd090PnzeFeSS5I8rkk9+xF/A+0adOm3rx58/Y2WxEXn/PlfGnLubk+V2bX7Jk7bzwwdzvoTjtkLgAA681555yT927ZkkuS3CXJwzZuzAEHHbRD5lJVZ3f3pu1tt6QjGFV1i6o6tqqeWVX/e877u1XVvtNfxr+ju09fqbiYOmq6fPfcX/Cn+/56kjMzOVpwyHbGOSTJ7knOnBsX03FuTPKueftb6r6Pni7fOX8C03DZkknI3Gs7892hLj7ny7l4y4dzQ1+dXbNHbuirc/GWD+fic768o6cGALDmnXfOOTlpy5Zc1Z07JbmqOydt2ZLzzjlnR0/tZs0cGFV1bCb/Wv+vSV6a5Pg5q++fyb/Y/9yAuc3igOlyyzbWnz9dblyBcb5Xn1l1vrTl3FTvll027J5UZZcNu6d6t3xpy7k7emoAAGvee7dsyR7d2WPDhmyoyh4bNmSP7rx3y7Z+hVwdZgqMqtqU5O1JOpNrBP527vruPivJZ5P89KgJLtKe0+WV21i/9f3br8A436vP3ERVPbmqNlfV5ksvvXRbm62o63NldqndbvLeLrVbrt/m1wIAYLEuSXLbqpu8d9uqXLJjprNosx7B+N9JvpVkU3e/Kv/zL+1zfSzJ/ZY7MW5ed7+huzd196Z99tlnh8xh1+yZG/qam7x3Q1+TXb/TTwAALNVdknxj3uW43+jOXXbMdBZt1sA4PMnbu/vmTrK/OMmdlz6lJdn6T+bb+s126/tXrMA436vPrDp33nhguq7JDTdenXTnhhuvTtc1ufPGA3f01AAA1ryHbdyYq6py1Y035sbuXHXjjbmqKg/buKrPop85MG6b5LLtbHPrJYy7XOdNl9v6ad97utzeCWtLGed79ZlV524H3Sl323hodqndc32uyi61e+628VB3kQIAGOCAgw7KEzZuzB5V+XKSParyhB14F6nFmvVBe19M8oPb2eb+mdzO9Xvp/dPlw6tqwwK3ij08k1O7ztrOOGcluTrJ4VV1uwVuU/vweftb6r5PS/JHSY5N8qdzJzC9Te3GTG5T+73+Oc7sbgfdSVAAAKyQAw46aNUHxXyzHml4R5Ifr6ofXWhlVf1EksOS/MtyJzaL7r4gybuT7JfkqfNWn5DkNkneNPcZGFV1YFXd5Fye7v5GkjdNtz9+3ji/NR3/XXOf5L2UfSc5PcmnkxxRVY+cM6cNSV48ffn6xTwDAwAAVpOZHrRXVd+f5BOZnAb16kx+qT4uySOTHJHJL9hXJrlfd2/vVKqhpg+8+1AmT9Q+NZNf4A/O5DkVW5Ic1t2Xz9m+k6S7a944d5iOszGTIw0fTXKfJI/K5CF8h02jYsn7nn7m4On4t0hySpLPJzkmyaZMnp1xTHdfu5jvviMftAcAwM5hsQ/am/lJ3lX1wCRvyU0fAteZPK36giSP6e7/nGnQQarqbklekMmpR3fI5Jkcb0tyQnd/bd62CwbGdN3eSZ6f5NGZXLB+eSZHb57X3V9Y7r7nfOa+mRzlOCrJ7TI5Lervkryou69e7PcWGAAArLQVC4zp4LskeUSSQzP5ZfrKTK4xOLW7r595QJZFYAAAsNIWGxizXuSdJOnuG5L80/QPAABAkkG3k62qW1TVA6rqgBHjAQAAa9NMgVFVP1tVb5leo7D1vf2T/FeSzUk+VVVvraolHRkBAADWtlmPYPxKkgO7+6tz3ntpkh/I5HkQn8zkbktPHDM9AABgLZk1MO6b5GNbX1TVHkl+MslbuvthSR6c5NwIDAAA2CnNGhj7ZHL71a0OzeRC8ZOTpLu/neQ9SfYfMjsAAGBNmTUwvp5kzzmvH5rJMzD+fc5712TyTAcAAGAnM+vF2Ocn+YmqulUmYfGzST4576nd98jkidcAAMBOZtYjGG/I5Ane5yf5dJJ7Jjlx3jY/ksldpQAAgJ3BrN28AAAdpUlEQVTMTIHR3W9M8qIkt87kVKnXJHn11vVVdVj+545SAADATmbm51V09x8m+cNtrN6cZK8k31zOpAAAgLVp6APxuvu6JNeNHBMAAFg7lhwYVXXrTI5W7LLQ+u7+/FLHBgAA1qaZA6OqfinJs5Pc52Y266WMDQAArG0zRUBVPSHJXye5IckHk1yc5Prx0wIAANaiWY8y/G6SryX50e7+9ArMBwAAWMNmfQ7GDyT5B3EBAAAsZNbA+GqSa1diIgAAwNo3a2D8S5Ijq6pWYjIAAMDaNmtgPCfJrZK8vqpuuwLzAQAA1rBZL/L+hyTfSvJrSX6hqs5PcsUC23V3H7PcyQEAAGvLrIFx5Jy/3ybJ/bexXS9pNgAAwJo2U2B096ynVAEAADsRwQAAAAwjMAAAgGFmDoyq2lBVT6uqs6rqyqq6fs66B1TVa6tq49hpAgAAa8FMgVFVt0zyniSvSLJ/kq8nmftMjM8m+ZUkvzhqggAAwNox6xGM30tyVJITknxfkr+cu7K7r0hyRpIfHzI7AABgTZk1MH4xyZnd/YLuvjEL3472s0nuvuyZAQAAa86sgXHPJGdtZ5uvJtl7adMBAADWslkD45okt9/ONnfPwk/3BgAA1rlZA+PjSR4+vdj7u1TVnplcf/HR5U4MAABYe2YNjDckuVuSN1fVHnNXVNXtk5yUZK8krx8yOwAAYE3ZdZaNu/vvqurHkjwhySOTfC1Jqmpzkh9Mcqskf97d/zZ4ngAAwBow84P2uvtXMnnWxaeS7JPJczAemOQzSX61u582dIYAAMCaMdMRjK26+6QkJ1XV7pmcEnVld39z5MQAAIC1Z0mBsVV3X53k6kFzAQAA1ribDYyqunCJ43Z377/EzwIAAGvU9o5gbMh3P637lknuPP37DUkuS3LHJLtM3/tSkutGTRAAAFg7bvYi7+7er7vvufVPkvsl+WImT/M+Kslu3X3nJLslOTrJR5J8IckPr+y0AQCA1WjWu0j9SSZP8j6yu0/v7huSpLtv6O4PZBIde0+3AwAAdjKzBsZPJzm1uxc8Baq7r0lyapLHLHdiAADA2jNrYNwhyS22s80tptsBAAA7mVkD44Ikx1XVngutrKq9khyXZKl3nwIAANawWQPj9UnukuSjVfXLVbVfVe0+XT4+k4u875Tkz0dPFAAAWP1metBed7+mqu6d5GlJTlxgk0ry6u5+7YjJAQAAa8vMT/Lu7mdU1clJfiXJA5LsmeTKJP+R5KTu/tDYKQIAAGvFzIGRJN394SQfHjwXAABgjZv1GgwAAIBtEhgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAwAgMAABhGYAAAAMMIDAAAYBiBAQAADCMwAACAYQQGAAAwjMAAAACGERgAAMAw6yYwquqwqvq3qvpqVV1dVZ+sqt+uql2WMNZ9q+otVfWVqrqmqs6rqhOqavcR+6+q+1fV8VV1ZlV9qaquq6ovVtXfVdUDZ50vAACsFusiMKrqUUnOSHJEkrcleU2SWyZ5eZKTZxzr4CQfS/LoJO9N8sokVyV5XpL3VNWtBuz/9Umen+RWSd463e6cJI9L8pGqeswscwYAgNWiuntHz2FZqmqPJJ9JsmeSw7t78/T93ZKcluTQJD/f3dsNjenRhv9Mcp8kj+ruf5q+vyHJW5I8NslzuvtFy9l/VT0tyTu6+zPz9v+LSf4myeVJ7tLd1y3mZ7Bp06bevHnzYjYFAIAlqaqzu3vT9rZbD0cwjkuyT5KTt/5ynyTdfU2S505f/sYix3poJnFxxta4mI51Y5Lfn758SlXVcvbf3a+eHxfT99+c5Pwkd0jyQ4ucMwAArBrrITCOni7fucC6M5J8K8lhC53aNMtY3X1hki1J7pHkXiu0/yT59nR5/SK3BwCAVWM9BMYB0+WW+Su6+/okn02ya24aBTOPNXX+dLlxJfZfVYckuW+SL2ZyTQYAAKwp6yEw9pwur9zG+q3v336Fxhqy/6raO8n/m758ZnffsJ3tn1xVm6tq86WXXnpzmwIAwPfMqgiMqrqoqnqGP3+zo+c8UlXdJsmpSe6d5CXd/Q/b+0x3v6G7N3X3pn322WfF5wgAAIux646ewNQFSa6ZYftL5vx96xGCPRfacM77Vyxi3KWMtaz9T+PiX5P8aJKXdfezFzFPAABYlVZFYHT3Mcv4+HlJNmVyXcTZc1dU1a5J7pnJBdMXLnKs5KbXWMx17+ly7vUWS95/Vd0uk7h4SCZHLsQFAABr2qo4RWqZTpsuj11g3RFJbp3kQ9197XLGqqp7ZRIRn8tNY2FJ+6+qPZO8O5O4+BNxAQDAerAeAuOUJJcleVxVfefBH9MH3f3x9OXr5n6gqm5dVQdW1d3njXV6kk8nOaKqHjln+w1JXjx9+fq+6dMJl7L/vTJ5SvghSZ7f3c8NAACsA6viFKnl6O6rqupJmfyi/4GqOjnJV5M8MpNbyJ6S5O/nfezBSd6fSVAcOWesG6rqiZkclTilqk5J8vkkx2RyGtSZSV4+YP9vnY53QZINVXX8Al/t7d398cX/JAAAYMdb84GRJN399qp6aJI/SvLYJLsl+UySZyV51bwjDtsb6yNV9aAkJyR5eJLbZXJa1AuSvGihU62WsP97Tpf7J3n+NqZyURKBAQDAmlIz/O7NKrVp06bevHnzjp4GAADrWFWd3d2btrfdergGAwAAWCUEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMMy6CYyqOqyq/q2qvlpVV1fVJ6vqt6tqlyWMdd+qektVfaWqrqmq86rqhKrafSX2XxPvqaqe/tl11jkDAMBqsC4Co6oeleSMJEckeVuS1yS5ZZKXJzl5xrEOTvKxJI9O8t4kr0xyVZLnJXlPVd1qBfb/W0mOSnLNLHMFAIDVZs0HRlXtkeT/JrkhyZHd/avd/XtJ7p/kw0mOq6rHLXKsXZKcmOTWSY7r7l/o7mcnOTjJPyY5PMkzR+6/qg5I8uIkf5bkvxf/zQEAYPVZ84GR5Lgk+yQ5ubs3b32zu69J8tzpy99Y5FgPTXKfJGd09z/NGevGJL8/ffmUqqoR+5+eCvWmJBcmef4i5wgAAKvWegiMo6fLdy6w7owk30py2EKnNs0yVndfmGRLknskudeg/T83yQOSPKG7r13E/AAAYFVbD4FxwHS5Zf6K7r4+yWeT7JqbRsHMY02dP11uXO7+q+pBSf4oyYvmHvkAAIC1bD0Exp7T5ZXbWL/1/duv0Fgzf2Z6N6o3JfmvJC9YxLy+S1U9uao2V9XmSy+9dClDAADAcKsiMKrqojm3aF3Mn7/Z0XNeppdkckTj8d397aUM0N1v6O5N3b1pn332GTs7AABYotXyvIULMtstWi+Z8/etRwj2XGjDOe9fsYhxlzLWTJ+pqocmeWqS47v7E4uYEwAArBmrIjC6+5hlfPy8JJsyuS7i7LkrpndpumeS6zO5U9Nixkpueo3FXPeeLudebzHr/h+QpJKcUFUnbGM/357eqOoB3f3xRcwbAABWhVVxitQynTZdHrvAuiMyeabFhxZ5l6ZtjlVV98okIj6Xm8bKrPs/J8lfbePPN6bb/PX09eWLmDMAAKwa1d07eg7LMn3Q3QVJ9khy+NY7MlXVbpn88n9okp/v7pPnfObWSe6e5Fvd/fk57++S5D8zeRbGo7Y+C6OqNiT5+0yeefGc7n7RcvZ/M9/lokxug3uL6R2oFmXTpk29ebMbUQEAsHKq6uzu3rS97VbFKVLL0d1XVdWTkpyS5ANVdXKSryZ5ZCa3kD0lkziY68FJ3p/k9CRHzhnrhqp6YiZhcEpVnZLk80mOyeQ0qDOTvHzA/gEAYF1aD6dIpbvfnslTuM9I8tgkT0vy7STPSvK4nuEwTXd/JMmDkpya5OFJnpnJhdovSPJjC51qNXL/AACwlq35U6RwihQAACtvsadIrYsjGAAAwOogMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAAYRmAAAADDCAwAAGAYgQEAAAwjMAAAgGEEBgAAMIzAAAAAhhEYAADAMAIDAAD4/9u792BJqvqA498f8ojAytMFSmDX8EwVKBKCBlKwWyQLwSgEQQlgXJCqaKJWosYkvgIJxKpIUlAJRqPZgEiiIgGqwrMiLE+VgCKWlVUEdhF5y/slAr/8cc7FcZjZe+/MmTt77/1+qrp67+me06fntzPdv+lzupsxwZAkSZLUjAmGJEmSpGZMMCRJkiQ1Y4IhSZIkqRkTDEmSJEnNmGBIkiRJasYEQ5IkSVIzJhiSJEmSmjHBkCRJktSMCYYkSZKkZkwwJEmSJDVjgiFJkiSpGRMMSZIkSc2YYEiSJElqxgRDkiRJUjMmGJIkSZKaMcGQJEmS1IwJhiRJkqRmTDAkSZIkNWOCIUmSJKkZEwxJkiRJzZhgSJIkSWrGBEOSJElSMyYYkiRJkpoxwZAkSZLUjAmGJEmSpGZMMCRJkiQ1Y4IhSZIkqRkTDEmSJEnNmGBIkiRJasYEQ5IkSVIzJhiSJEmSmjHBkCRJktSMCYYkSZKkZkwwJEmSJDVjgiFJkiSpGRMMSZIkSc2YYEiSJElqxgRDkiRJUjMmGJIkSZKaMcGQJEmS1IwJhiRJkqRmTDAkSZIkNWOCIUmSJKkZEwxJkiRJzURmjrsNGlJEPAisGXMztgYeGnMbNDrGd+4zxnOfMZ7bjO/cty7EeFFmvnqylUww1ERE3JSZ+4y7HRoN4zv3GeO5zxjPbcZ37ptNMbaLlCRJkqRmTDAkSZIkNWOCoVb+ddwN0EgZ37nPGM99xnhuM75z36yJsWMwJEmSJDXjFQxJkiRJzZhgSJIkSWrGBEOSJElSMyYYepmI2D4iVkTEPRHxs4hYHRGnR8QW06xny/q61bWee2q924+q7ZqaYWMcEZtExLER8R8RsSoinoqIJyLipoj4UERsOOp9UH+tPsNddR4QES9EREbEKS3bq+lrGeOI2Lt+lu+udd0fEVdHxB+Oou2amobH4t+KiIvq65+NiLsi4pKIOGRUbdfaRcSREfFPEXFtRDxev1e/NGBdzb/vW3CQt35JROwE3AAsBC4CVgH7AkuBHwD7Z+ZPp1DPVrWeXYErgf8FdgcOAx4AfjMz7xjFPmjtWsS4HpguBR4GrgJ+BGwBvBXYttZ/UGY+O6LdUB+tPsNddS4AbqU8RXZT4NTM/HjLdmvqWsY4It4HnAE8AlwM/ATYEtgDuDszj26+A5pUw2Pxe4HPAE8BFwB3A9sDRwAbAx/PzFNHsQ/qLyJuAV4PPEmJye7AuZl53DTraf5930xmOjm9NAGXAwm8v6v8H2v5Z6dYz+fq+v/QVf6BWn7ZuPd1vk4tYgzsBRwLbNhVvgC4udbzoXHv63ycWn2Gu167gpJMfrTWccq493M+Tw2/p5cBL9b6FvRYvsG493W+To2+pzcAHgWeAXbrWvZrwLPA08BG497f+TZREoBdgACW1Jh+aRz/T0Y1eQVDL6mZ8I+A1cBOmflix7IFwL2UD8PCzHxqLfVsSrlK8SKwXWY+0bFsPeAOYFHdhlcxZlCrGE+yjWOAc4H/zsy3DN1oTdko4hsRhwEXAu8E1gf+Ha9gjE3LGEfEd4GdgR1zXL9y6mUaHou3Ae4Dbs3M1/dYfiuwJ7C18R+fiFhC6QkwrSsYM3E8H4ZjMNRpaZ1f0fkfFaAmCddTLqm+aZJ63gS8Eri+M7mo9Uz8Wta5Pc2cVjFem5/X+fND1KHBNI1vRCwEPg9cmJkD9Q9Wc01iHBF7AK8DrgAejoilEfHhOobqoPpjkMaj1ef4AeBBYNeI2KVzQUTsSvkF/RaTi1lrJo7nA/MLRJ12q/Mf9ll+W53vOkP1qL2ZiM0JdX7ZEHVoMK3j+3nKceI9wzRKTbWK8W/U+QPASspYuU8DpwH/A9wSETsP3kwNoUmMs3RR+RPKZ/jmiDg7Ij4VEV+kdGX9PnBUg/ZqPNbpc631x7FRrbM2q/PH+iyfKN98hupReyONTR0weghwC6XfvmZWs/hGxAmUQfvvyMz7G7RNbbSK8cI6fzdlYPebgeuAbYBPAscBF0fEnpn53ODN1QCafY4z87yIuAf4T6DzrmD3U7o72k159lqnz7W8giGpiYg4Ajid0uf3bZn580leonVURCymxPK8zPzqeFujEZk4/r8CODozL8nMxzPzNsqJ6E2UXz7fNq4GangRcRzlitS1lIHdG9f514F/Br48vtZpLjPBUKeJbHezPssnyh+doXrU3khiExGHUw5UDwBLHLw/Nq3iu4Jy55k/btEoNdUqxhPL78vMb3QuqF1rLqp/7jvtFmpYTWJcx1msoHSFemdmrsrMZzJzFeWmDTcDR9VBxpp91ulzLRMMdfpBnffrrzcxSKxff7/W9ai95rGJiKOA8yiX3A/MzB9M8hKNTqv47k3pQvNgfQBURkRSulQAfKyWXThcczWA1t/T/U4+HqnzV06xXWqnVYyXUW5Ve3WPQcAvAtfUP399kEZq7Nbpcy3HYKjTVXW+LCLW63HLs/0p98z+5iT1fJPy6+f+EbGgx21ql3VtTzOnVYwnXnMscDalD/dSr1yMXav4fpHSlaLbLsABlDE2NwPfGbrFmq6W39NPAYsjYpMet7Hco87vbNBmTU+rGG9U56/us3yi3DE2s1PT43lrXsHQSzLzdsotCxdT7jzR6WRgE+CczgNRROweEbt31fMkcE5d/6Suet5X67/ck9GZ1yrGtfxdlBPRu4ADjOf4NfwMfyAzT+ye+MUVjItr2Zkj2xn11DDGTwP/BvwKcEpERMf6ewLLKbea/lr7vdDaNPyevrbOj4yI13UuiIi9gCMpD2O7sl3r1VpEbFDju1Nn+SD/T2aSD9rTL+nx2Pn/A95Iud/yD4H9Ou+ZXbtNkJnRVc9WtZ5dKV9eN1IGlh1G6ae/X/1waIa1iHFELKUMHFyP0sf3xz029Whmnj6i3VAfrT7Dfepejg/aG7uG39OvAq4G9gK+Rblv/jbAEZSuUX+amWeMen/0cg1jvAI4nnKV4gJgDeWE9HBgQ+D0zPyzEe+OutRxi4fXP7cFDqbc0WsiKXwoMz9c111MuZK4JjMXd9Uzrf8nM6rVI8Gd5s4E7EA5ibiX8qW0hnJHmS16rJvUMYE9lm0JnFFf/1ytbwWw/bj3cb5Pw8aY8utmTjKtHvd+ztep1We4x7oTcT9l3Ps436eG39ObAqdSTkZ+RhmTcQWwbNz7ON+nFjGmPMl5OeVZJ49Qrko9TLmL1NHj3sf5OlF6d0zp+ElJCPseU6fz/2QmJ69gSJIkSWrGMRiSJEmSmjHBkCRJktSMCYYkSZKkZkwwJEmSJDVjgiFJkiSpGRMMSZIkSc2YYEiSJElqxgRDkjSvRMRZEZH1Cbmj3M7qiFg9ym1I0rrIBEOSpAFExMqI8Gm1ktRl/XE3QJKkOeqgcTdAksbBBEOSpBHIzNvH3QZJGge7SEmSpiQiFtexC2dFxO4RcWFEPBwRT0XEdRGxrMdrNoqIv4yI70XE0xHxeERcGxFvb1T/SfU1S9ZW3xT3b3lEnB8Rd0TEM7Wt10fEcb3qBQ6sf2fHtLJjvZ5jMIZ4TxZHxJcj4qGIeDYiboqI35vKvknSTPIKhiRpul4LfAP4HvA5YDvgHcClEXFMZn4FICI2BC6nnIivAs4ENgaOBL4SEXtl5kcHrX8E/gX4PnANcC+wFXAocE5E7JaZn6jrPQqcDCwHFtV/T1i9tg0M8Z4sAm4E7gDOAbakvCcXRcRvZ+ZV091ZSRqVyHR8miRpcvWuS3fWP0/LzD/vWLYPJSl4EliUmY9HxF8BfwdcCrw1M5+v6y6knCwvAvbPzBsGqb+WnwT8NbA0M1f2ae/Zmbm8o/ws4F3AazNzdUf5Tt3dmmpCcClwALA4M3/SsWwlcGBmRp/3azVAZi7uKBvmPTkpM0/uqOtg4DLg0sw8tFcbJGkc7CIlSZqux4C/6SzIzJuAc4HNgd+vxScACXxw4kS6rvsA8Lf1zxOHqL+pXmMmMvM5ylWG9WkzaHvQ92QNcEpX2y4H7gL2bdAuSWrGBEOSNF3fzswnepSvrPM3RMQCYGfgnsxc1WPdKyfWHaT+abR1yiJix4g4MyJW1bERWcdanF9Xec2Q9Q/zntySmS/0KP8xsMUw7ZKk1hyDIUmarvv7lN9X55vVCcpYhl4myjcfsP6mIuJXKV2UtgCuBa6gXEl5AVhM6VK10ZCbGeY9ebTPa57HHwslrWNMMCRJ07VNn/Jt6/yxOnWWdduuY91B6p/wYp33Op71OlHv54OUQd3HZ+ZZnQsi4g8oCcawhnlPJGnW8FcPSdJ07V27+3RbUuffqV2cbgdeExG79Fh3aZ1/e5D6O8oeqfMdeqy/T4+yfnau8/N7LDuwz2teAIiIV0xlA0O+J5I0a5hgSJKmazPgk50F9S5Px1J+fb+gFq8AAvh050l4RGwNfKJjnUHrh9KtCeD4iFi/Y/0duuuYxOo6X9K13YPpPega4Kd1vuM0tjPoeyJJs4ZdpCRJ03UNcGJEvBG4nl88p2I94I8mbiELnAb8LnAY8N2IuITyzIejgIXA32fmdUPUT2Z+KyKuodxG9saIuJLSxeotlOdN9Lqy0ctngOOB8yLia8A9wB7AIcBX6/a7fb3uy3/VfXsGWJOZ56xlO4O+J5I0a3gFQ5I0XXcC+1G6J70HeDulW8+hnQ/Bq7d4/R3gY7Xo/ZSxDLcBx2TmXwxTf4fDgC8A29dtvAH4CNCv/pfJzFspXZRuAN4MvBd4FXAE8Nk+L/sC8CnKFZePUG4z++5JtjPoeyJJs4YP2pMkTUm/B9fNlvolSTPDKxiSJEmSmjHBkCRJktSMCYYkSZKkZhyDIUmSJKkZr2BIkiRJasYEQ5IkSVIzJhiSJEmSmjHBkCRJktSMCYYkSZKkZv4fPm+QjFbsrC4AAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['train', 'epochs'] epochs\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['train', 'batch_size'] batch_size\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmap = plt.get_cmap('rainbow')\n", + "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", + "color_ids = np.argsort(data.loc[:, evolve_metric].values)\n", + "\n", + "for param_path in evolution.paths_to_evolving_params:\n", + " param_name = param_path[-1]\n", + " print(param_path, param_name)\n", + " \n", + " plt.figure(figsize=(12,12))\n", + " for i in range(data.shape[0]):\n", + " param_dict = evolution.get_value_from_config(evolution.basic_config, param_path)\n", + " if param_dict.get(\"evolve_range\") and param_dict.get(\"discrete\"):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " evolution.get_value_from_config(params_dictionaries[i], param_path),\n", + "# + (np.random.random() - 0.5) / 2,\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + " elif param_dict.get(\"evolve_range\"):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " evolution.get_value_from_config(params_dictionaries[i], param_path),\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + " elif param_dict.get(\"evolve_choice\"):\n", + " values = np.array(param_dict.get(\"values\"))\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " np.where(values == evolution.get_value_from_config(\n", + " params_dictionaries[i], param_path))[0][0],\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + " plt.yticks(np.arange(len(values)), values)\n", + " elif param_dict.get(\"evolve_bool\"):\n", + " values = np.array([False, True])\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " np.where(values == evolution.get_value_from_config(\n", + " params_dictionaries[i], param_path))[0][0],\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + " plt.yticks(np.arange(len(values)), [\"False\", \"True\"])\n", + "\n", + " plt.ylabel(param_name, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(path_to_pics.joinpath(param_name + \".png\"))\n", + " plt.show()\n", + " " + ] + }, { "cell_type": "code", "execution_count": null, From a6b448dbdd59165330a71bfe7c886d4989a56ba3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 16:13:07 +0300 Subject: [PATCH 277/616] feat: add mutation of bool --- deeppavlov/models/evolution/evolution_param_generator.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 07acebf027..7cf7f66091 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -480,6 +480,8 @@ def mutation_of_param(self, param_path, param_value): new_mutated_value = val elif basic_value.get("evolve_choice"): new_mutated_value = self.sample_params(**{param_name: basic_value})[param_name] + elif basic_value.get("evolve_bool"): + new_mutated_value = self.sample_params(**{param_name: basic_value})[param_name] else: new_mutated_value = param_value else: From 2995cf494d22aa9100ab6ad7cfb658f3968116b6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 16:14:17 +0300 Subject: [PATCH 278/616] fix: clear all outputs --- .../models/evolution/Results_analysis.ipynb | 563 +----------------- 1 file changed, 16 insertions(+), 547 deletions(-) diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb index c0fa6812f5..93fbde75f0 100644 --- a/deeppavlov/models/evolution/Results_analysis.ipynb +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -2,17 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2018-06-25 14:31:29.12 DEBUG in 'matplotlib.backends'['__init__'] at line 90: backend module://ipykernel.pylab.backend_inline version unknown\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -35,216 +27,11 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Considered basic config:\n", - "{\n", - " \"dataset_reader\": {\n", - " \"name\": \"basic_classification_reader\",\n", - " \"x\": \"text\",\n", - " \"y\": \"intents\",\n", - " \"data_path\": \"snips\"\n", - " },\n", - " \"dataset_iterator\": {\n", - " \"name\": \"basic_classification_iterator\",\n", - " \"seed\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"field_to_split\": \"train\",\n", - " \"split_fields\": [\n", - " \"train\",\n", - " \"valid\"\n", - " ],\n", - " \"split_proportions\": [\n", - " 0.9,\n", - " 0.1\n", - " ]\n", - " },\n", - " \"chainer\": {\n", - " \"in\": [\n", - " \"x\"\n", - " ],\n", - " \"in_y\": [\n", - " \"y\"\n", - " ],\n", - " \"pipe\": [\n", - " {\n", - " \"id\": \"classes_vocab\",\n", - " \"name\": \"default_vocab\",\n", - " \"fit_on\": [\n", - " \"y\"\n", - " ],\n", - " \"level\": \"token\",\n", - " \"save_path\": \"vocabs/snips_classes.dict\",\n", - " \"load_path\": \"vocabs/snips_classes.dict\"\n", - " },\n", - " {\n", - " \"in\": [\n", - " \"x\"\n", - " ],\n", - " \"out\": [\n", - " \"x_lower\"\n", - " ],\n", - " \"name\": \"str_lower\"\n", - " },\n", - " {\n", - " \"id\": \"my_embedder\",\n", - " \"name\": \"fasttext\",\n", - " \"save_path\": \"embeddings/dstc2_fastText_model.bin\",\n", - " \"load_path\": \"embeddings/dstc2_fastText_model.bin\",\n", - " \"dim\": 100\n", - " },\n", - " {\n", - " \"id\": \"my_tokenizer\",\n", - " \"name\": \"nltk_tokenizer\",\n", - " \"tokenizer\": \"wordpunct_tokenize\"\n", - " },\n", - " {\n", - " \"in\": [\n", - " \"x_lower\"\n", - " ],\n", - " \"in_y\": [\n", - " \"y\"\n", - " ],\n", - " \"out\": [\n", - " \"y_labels\",\n", - " \"y_probas_dict\"\n", - " ],\n", - " \"main\": true,\n", - " \"name\": \"intent_model\",\n", - " \"save_path\": \"evolution/classification/intents_snips\",\n", - " \"load_path\": \"evolution/classification/intents_snips\",\n", - " \"classes\": \"#classes_vocab.keys()\",\n", - " \"kernel_sizes_cnn\": [\n", - " 1,\n", - " 2,\n", - " 3\n", - " ],\n", - " \"filters_cnn\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 100\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"confident_threshold\": {\n", - " \"evolve_choice\": true,\n", - " \"values\": [\n", - " 0.5,\n", - " 1\n", - " ]\n", - " },\n", - " \"optimizer\": \"Adam\",\n", - " \"lear_rate\": {\n", - " \"evolve_range\": [\n", - " 0.0001,\n", - " 0.1\n", - " ],\n", - " \"scale\": \"log\"\n", - " },\n", - " \"lear_rate_decay\": {\n", - " \"evolve_range\": [\n", - " 0.0001,\n", - " 0.1\n", - " ],\n", - " \"scale\": \"log\"\n", - " },\n", - " \"loss\": \"binary_crossentropy\",\n", - " \"text_size\": 15,\n", - " \"coef_reg_cnn\": {\n", - " \"evolve_range\": [\n", - " 1e-06,\n", - " 0.001\n", - " ]\n", - " },\n", - " \"coef_reg_den\": {\n", - " \"evolve_range\": [\n", - " 1e-06,\n", - " 0.001\n", - " ]\n", - " },\n", - " \"dropout_rate\": {\n", - " \"evolve_range\": [\n", - " 0.1,\n", - " 0.9\n", - " ]\n", - " },\n", - " \"dense_size\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 100\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"model_name\": \"cnn_model\",\n", - " \"embedder\": \"#my_embedder\",\n", - " \"tokenizer\": \"#my_tokenizer\"\n", - " }\n", - " ],\n", - " \"out\": [\n", - " \"y_labels\",\n", - " \"y_probas_dict\"\n", - " ]\n", - " },\n", - " \"train\": {\n", - " \"epochs\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"batch_size\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"metrics\": [\n", - " \"classification_accuracy\",\n", - " \"classification_f1\",\n", - " \"classification_roc_auc\"\n", - " ],\n", - " \"validation_patience\": 5,\n", - " \"val_every_n_epochs\": 1,\n", - " \"log_every_n_epochs\": 1,\n", - " \"validate_best\": true,\n", - " \"test_best\": false\n", - " },\n", - " \"metadata\": {\n", - " \"labels\": {\n", - " \"telegram_utils\": \"IntentModel\",\n", - " \"server_utils\": \"KerasIntentModel\"\n", - " },\n", - " \"download\": [\n", - " \"http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz\",\n", - " \"http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz\",\n", - " {\n", - " \"url\": \"http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv\",\n", - " \"subdir\": \"snips\"\n", - " },\n", - " {\n", - " \"url\": \"http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin\",\n", - " \"subdir\": \"embeddings\"\n", - " }\n", - " ]\n", - " }\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "CONFIG_FILE = \"../../configs/evolution/evolve_intents_snips.json\"\n", "KEY_MAIN_MODEL = \"main\"\n", @@ -259,25 +46,9 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2018-06-25 14:52:07.93 INFO in 'deeppavlov.models.evolution.evolution_param_generator'['evolution_param_generator'] at line 55: Main model path in config: ['chainer', 'pipe', 4]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Title name for the considered evolution is `intents_snips`.\n", - "Number of populations: 2.\n" - ] - } - ], + "outputs": [], "source": [ "evolution = ParamsEvolution(population_size=POPULATION_SIZE,\n", " key_main_model=KEY_MAIN_MODEL,\n", @@ -303,50 +74,9 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Measure: classification_accuracy\n", - "valid:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t1 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n", - "\n", - "Measure: classification_f1\n", - "valid:\n", - "min for\t0 model on\t0 population\n", - "max for\t1 model on\t1 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n", - "\n", - "Measure: classification_roc_auc\n", - "valid:\n", - "min for\t1 model on\t0 population\n", - "max for\t0 model on\t0 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: 'argmin' is deprecated. Use 'idxmin' instead. The behavior of 'argmin' will be corrected to return the positional minimum in the future. Use 'series.values.argmin' to get the position of the minimum now.\n", - " if __name__ == '__main__':\n", - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", - " # Remove the CWD from sys.path while we load stuff.\n" - ] - } - ], + "outputs": [], "source": [ "MEASURES = evolution.get_value_from_config(\n", " evolution.basic_config, list(evolution.find_model_path(\n", @@ -373,42 +103,11 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "path_to_pics = expand_path(Path(evolution.get_value_from_config(\n", " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).joinpath(\"pics\"))\n", @@ -472,20 +171,9 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 1, 2, 2])" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "params_dictionaries = []\n", "\n", @@ -506,42 +194,11 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", @@ -588,199 +245,11 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['dataset_iterator', 'seed'] seed\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'filters_cnn'] filters_cnn\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'lear_rate'] lear_rate\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'lear_rate_decay'] lear_rate_decay\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'coef_reg_cnn'] coef_reg_cnn\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'coef_reg_den'] coef_reg_den\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'dropout_rate'] dropout_rate\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['train', 'batch_size'] batch_size\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", From f956215459391416395f1fdccc729a21bad69721 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 16:24:36 +0300 Subject: [PATCH 279/616] fix: config check evolve bool --- deeppavlov/configs/evolution/evolve_intents_snips.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/evolve_intents_snips.json b/deeppavlov/configs/evolution/evolve_intents_snips.json index 0f7f35878a..c34a2a6e5a 100644 --- a/deeppavlov/configs/evolution/evolve_intents_snips.json +++ b/deeppavlov/configs/evolution/evolve_intents_snips.json @@ -144,7 +144,7 @@ "embedder": "#my_embedder", "tokenizer": "#my_tokenizer", "check_bool": { - "bool": true + "evolve_bool": true } } ], From ef217fb4999855c9481c7ff267ee8e22b57849e0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 16:25:00 +0300 Subject: [PATCH 280/616] fix: clear all cells --- .../models/evolution/Results_analysis.ipynb | 601 +++++++++++++++++- 1 file changed, 581 insertions(+), 20 deletions(-) diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb index 93fbde75f0..8ed1df5314 100644 --- a/deeppavlov/models/evolution/Results_analysis.ipynb +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -2,9 +2,35 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", + " return f(*args, **kwds)\n", + "/home/dilyara/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n", + "Using TensorFlow backend.\n", + "[nltk_data] Downloading package punkt to /home/dilyara/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package stopwords to\n", + "[nltk_data] /home/dilyara/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n", + "[nltk_data] Downloading package perluniprops to\n", + "[nltk_data] /home/dilyara/nltk_data...\n", + "[nltk_data] Package perluniprops is already up-to-date!\n", + "[nltk_data] Downloading package nonbreaking_prefixes to\n", + "[nltk_data] /home/dilyara/nltk_data...\n", + "[nltk_data] Package nonbreaking_prefixes is already up-to-date!\n", + "2018-06-25 16:20:16.625 DEBUG in 'gensim.models.doc2vec'['doc2vec'] at line 73: Fast version of gensim.models.doc2vec is being used\n", + "2018-06-25 16:20:16.629 INFO in 'summa.preprocessing.cleaner'['textcleaner'] at line 20: 'pattern' package not found; tag filters are not available for English\n", + "2018-06-25 16:20:17.53 DEBUG in 'matplotlib.backends'['__init__'] at line 90: backend module://ipykernel.pylab.backend_inline version unknown\n" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -27,11 +53,219 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Considered basic config:\n", + "{\n", + " \"dataset_reader\": {\n", + " \"name\": \"basic_classification_reader\",\n", + " \"x\": \"text\",\n", + " \"y\": \"intents\",\n", + " \"data_path\": \"snips\"\n", + " },\n", + " \"dataset_iterator\": {\n", + " \"name\": \"basic_classification_iterator\",\n", + " \"seed\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"field_to_split\": \"train\",\n", + " \"split_fields\": [\n", + " \"train\",\n", + " \"valid\"\n", + " ],\n", + " \"split_proportions\": [\n", + " 0.9,\n", + " 0.1\n", + " ]\n", + " },\n", + " \"chainer\": {\n", + " \"in\": [\n", + " \"x\"\n", + " ],\n", + " \"in_y\": [\n", + " \"y\"\n", + " ],\n", + " \"pipe\": [\n", + " {\n", + " \"id\": \"classes_vocab\",\n", + " \"name\": \"default_vocab\",\n", + " \"fit_on\": [\n", + " \"y\"\n", + " ],\n", + " \"level\": \"token\",\n", + " \"save_path\": \"vocabs/snips_classes.dict\",\n", + " \"load_path\": \"vocabs/snips_classes.dict\"\n", + " },\n", + " {\n", + " \"in\": [\n", + " \"x\"\n", + " ],\n", + " \"out\": [\n", + " \"x_lower\"\n", + " ],\n", + " \"name\": \"str_lower\"\n", + " },\n", + " {\n", + " \"id\": \"my_embedder\",\n", + " \"name\": \"fasttext\",\n", + " \"save_path\": \"embeddings/dstc2_fastText_model.bin\",\n", + " \"load_path\": \"embeddings/dstc2_fastText_model.bin\",\n", + " \"dim\": 100\n", + " },\n", + " {\n", + " \"id\": \"my_tokenizer\",\n", + " \"name\": \"nltk_tokenizer\",\n", + " \"tokenizer\": \"wordpunct_tokenize\"\n", + " },\n", + " {\n", + " \"in\": [\n", + " \"x_lower\"\n", + " ],\n", + " \"in_y\": [\n", + " \"y\"\n", + " ],\n", + " \"out\": [\n", + " \"y_labels\",\n", + " \"y_probas_dict\"\n", + " ],\n", + " \"main\": true,\n", + " \"name\": \"intent_model\",\n", + " \"save_path\": \"evolution/classification/intents_snips\",\n", + " \"load_path\": \"evolution/classification/intents_snips\",\n", + " \"classes\": \"#classes_vocab.keys()\",\n", + " \"kernel_sizes_cnn\": [\n", + " 1,\n", + " 2,\n", + " 3\n", + " ],\n", + " \"filters_cnn\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 100\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"confident_threshold\": {\n", + " \"evolve_choice\": true,\n", + " \"values\": [\n", + " 0.5,\n", + " 1\n", + " ]\n", + " },\n", + " \"optimizer\": \"Adam\",\n", + " \"lear_rate\": {\n", + " \"evolve_range\": [\n", + " 0.0001,\n", + " 0.1\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"lear_rate_decay\": {\n", + " \"evolve_range\": [\n", + " 0.0001,\n", + " 0.1\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"loss\": \"binary_crossentropy\",\n", + " \"text_size\": 15,\n", + " \"coef_reg_cnn\": {\n", + " \"evolve_range\": [\n", + " 1e-06,\n", + " 0.001\n", + " ]\n", + " },\n", + " \"coef_reg_den\": {\n", + " \"evolve_range\": [\n", + " 1e-06,\n", + " 0.001\n", + " ]\n", + " },\n", + " \"dropout_rate\": {\n", + " \"evolve_range\": [\n", + " 0.1,\n", + " 0.9\n", + " ]\n", + " },\n", + " \"dense_size\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 100\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"model_name\": \"cnn_model\",\n", + " \"embedder\": \"#my_embedder\",\n", + " \"tokenizer\": \"#my_tokenizer\",\n", + " \"check_bool\": {\n", + " \"bool\": true\n", + " }\n", + " }\n", + " ],\n", + " \"out\": [\n", + " \"y_labels\",\n", + " \"y_probas_dict\"\n", + " ]\n", + " },\n", + " \"train\": {\n", + " \"epochs\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"batch_size\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"metrics\": [\n", + " \"classification_accuracy\",\n", + " \"classification_f1\",\n", + " \"classification_roc_auc\"\n", + " ],\n", + " \"validation_patience\": 5,\n", + " \"val_every_n_epochs\": 1,\n", + " \"log_every_n_epochs\": 1,\n", + " \"validate_best\": true,\n", + " \"test_best\": false\n", + " },\n", + " \"metadata\": {\n", + " \"labels\": {\n", + " \"telegram_utils\": \"IntentModel\",\n", + " \"server_utils\": \"KerasIntentModel\"\n", + " },\n", + " \"download\": [\n", + " \"http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz\",\n", + " \"http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz\",\n", + " {\n", + " \"url\": \"http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv\",\n", + " \"subdir\": \"snips\"\n", + " },\n", + " {\n", + " \"url\": \"http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin\",\n", + " \"subdir\": \"embeddings\"\n", + " }\n", + " ]\n", + " }\n", + "}\n" + ] + } + ], "source": [ "CONFIG_FILE = \"../../configs/evolution/evolve_intents_snips.json\"\n", "KEY_MAIN_MODEL = \"main\"\n", @@ -46,9 +280,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2018-06-25 16:20:17.65 INFO in 'deeppavlov.models.evolution.evolution_param_generator'['evolution_param_generator'] at line 55: Main model path in config: ['chainer', 'pipe', 4]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Title name for the considered evolution is `intents_snips`.\n", + "Number of populations: 6.\n" + ] + } + ], "source": [ "evolution = ParamsEvolution(population_size=POPULATION_SIZE,\n", " key_main_model=KEY_MAIN_MODEL,\n", @@ -74,9 +324,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Measure: classification_accuracy\n", + "valid:\n", + "min for\t0 model on\t0 population\n", + "max for\t1 model on\t4 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "\n", + "Measure: classification_f1\n", + "valid:\n", + "min for\t0 model on\t5 population\n", + "max for\t1 model on\t4 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "\n", + "Measure: classification_roc_auc\n", + "valid:\n", + "min for\t1 model on\t3 population\n", + "max for\t0 model on\t0 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: 'argmin' is deprecated. Use 'idxmin' instead. The behavior of 'argmin' will be corrected to return the positional minimum in the future. Use 'series.values.argmin' to get the position of the minimum now.\n", + " if __name__ == '__main__':\n", + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", + " # Remove the CWD from sys.path while we load stuff.\n" + ] + } + ], "source": [ "MEASURES = evolution.get_value_from_config(\n", " evolution.basic_config, list(evolution.find_model_path(\n", @@ -103,11 +394,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "path_to_pics = expand_path(Path(evolution.get_value_from_config(\n", " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).joinpath(\"pics\"))\n", @@ -156,7 +478,7 @@ " plt.ylim(ylim[0], ylim[1])\n", " plt.xticks(fontsize=20)\n", " plt.yticks(fontsize=20)\n", - " plt.savefig(path_to_pics.joinpath(y_label + \".png\"))\n", + " plt.savefig(path_to_pics.joinpath(metric + \".png\"))\n", " plt.show()" ] }, @@ -171,9 +493,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "params_dictionaries = []\n", "\n", @@ -194,11 +527,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", @@ -239,17 +603,205 @@ " plt.ylim(ylim[0], ylim[1])\n", " plt.xticks(fontsize=20)\n", " plt.yticks(fontsize=20)\n", - " plt.savefig(path_to_pics.joinpath(y_label + \"_colored_ids.png\"))\n", + " plt.savefig(path_to_pics.joinpath(metric + \"_colored_ids.png\"))\n", " plt.show()\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['dataset_iterator', 'seed'] seed\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'filters_cnn'] filters_cnn\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'lear_rate'] lear_rate\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'lear_rate_decay'] lear_rate_decay\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'coef_reg_cnn'] coef_reg_cnn\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'coef_reg_den'] coef_reg_den\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'dropout_rate'] dropout_rate\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'dense_size'] dense_size\n" + ] + }, + { + "data": { + "image/png": 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6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQAQ6AAAMRKADAMBABDoAAAxEoAMAwEAEOgAADESgAwDAQIYI9Kp6elX1Xn7t2MsxXrtg37us1uwAADBPm9Z6gKkzkrxsN9senuRRSd6zuzdX1eOT/ESSq5Lcau7TAQDAKhki0Lv7jEwi/Uaq6kPT3560m+2bk/xJkrckuW2SR67EjAAAsBqGuMRld6rq3kkenOT8JO/azW67wv1nVmUoAABYQUOcQd+DZ0+Xr+vuG12DXlVPT/J9Sb6vuy+pqtWcDQAA5m7YM+hVdUiSpybZkeS1S2zfkuRVSf6su98x47GfXVVbq2rrRRddNJd5AQBgHoYN9CRPSXJEklO6+9yFG6pqQ5I3ZHJT6HNmPXB3n9Tdx3X3cZs3b57LsAAAMA8jX+Ky6/KW1yyx7XmZ3Az6uO6+bPVGAgCAlTXkGfSqumeShyQ5L8m7F227W5KXJ3l9d797ibcDAMDN1pCBnj3fHHqPJAclecbiDzPK9Y9YPHO67vtWa2AAAJiH4S5xqaqDkzwtk5tDX7fELmfvZn2SPC6TZ6H/VZIrp/sCAMDNxnCBnuTJSY5M8neLbw5N/vtDjZ651Bur6tRMAv1F3f35lRwSAABWwoiXuOy6vGXJTw4FAID92VCBXlXHJnlYlrg5FAAA1oOhLnHp7s8kWfbHgXb38fObBgAAVt9QZ9ABAGC9E+gAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADCQTct9Y1V9S5Jjk9yqu984v5EAAGD9mvkMelXdr6q2JvmPJH+d5OQF2x5ZVV+rqsfPb0QAAFg/Zgr0qrpbklOT3D3Jq5K8Z9Eupye5NMmJ8xgOAADWm1nPoL8kyYFJvq27fz7JRxdu7O5O8qEkD5zPeAAAsL7MGuiPTvK27v70HvY5N8ntlj8SAACsX7MG+pFJztvLPpXJWXYAAGBGswb6fyW5y172uWcmZ9EBAIAZzRro70/y+Kq6+1Ibq+qBmVwG8/f7OhgAAKxHswb6byTZnuT0qvqfmV5rXlX3nL5+Z5JtSX5nrlMCAMA6MdMHFXX356rq+5O8KckfTFdXkk9Ol5cneVJ3f2muUwIAwDox8yeJdvcpVXVMkh9L8uAkRye5IsmHk7y+uy+d74gAALB+zBzoSdLdl2fyQUWvmu84AACwvs36SaKPqKrj9rLPHarqEfs2FgAArE+z3iR6apJ/rao93QT6jCQfWPZEAACwjs0a6ElyTZLnVdVbq+rgeQ8EAADr2XIC/RVJ3pjkiUlOrarN8x0JAADWr+UE+nXd/fQkL03yoCQf3t0HFwEAALNZTqAnSbr7V5I8Lcntk/xLVR0/r6EAAGC9WnagJ0l3/3mS78rkQ4pOqaofnctUAACwTi3rOegLdfdpVfWQJO9K8vokX9znqQAAYJ3apzPou3T3Z5N8W5KPJLnzPI4JAADr0axn0E9IcvZSG7r74ul16M9Ncsi+jQUAAOvTTIHe3aftZfs1SX57nyYCAIB1bC6XuAAAAPOxxzPoVfX+JJ3kx7r7vOnrm6K7+9H7PB0AAKwze7vE5fhMAv0WC17fFL3MeQAAYF3bY6B394Y9vQYAAOZLcAMAwEDmFuhVdWRV3XJexwMAgPVopkCvqkdX1W9X1ZEL1t2mqk5LcnGSS6vqlfMeEgAA1otZz6D/XJIndfdlC9b9TpKHJ/lCkkuSPLeqnjKn+QAAYF2ZNdDvm+Sfdr2oqkOSnJjkH7v7bknunuTcJD81twkBAGAdmTXQb5PkggWvvy3JwUlOTpLu3pbk7zIJdQAAYEazBvo1SQ5Z8PrhmTzz/PQF665MctQ+zgUAAOvSrIF+VpJHLXj9/UnO7O7zF6z75kxuGAUAAGY0a6C/Icm9q+pfq+qDSe6d5C8W7XOfJJ+bx3AAALDezBrof5zkzUmOS/LQTK43/61dG6vqXplE+6lzmg8AANaVTbPs3N3XJfnhqvqpycvetmiXC5PcP8nZ8xkPAADWl2V9kmh3X7lEnKe7L+7uT3T3FQvXV9Vzq+qLyx0SAADWi2UF+jIckWTLKn0tAAC42VqtQAcAAG4CgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAPZtEpf59RV+joAAHCztqxAr6oDkjw6ybFJbtXdvzpdf3CSw5Jc3N07d+3f3aclOW3fxwUAgP3bzJe4VNV3Jzk7ybuSvCLJSxdsvl+SLyf5gTnMBgAA685MgV5VxyX5mySd5HlJ/mLh9u7+cJKzkjxxXgMCAMB6MusZ9Bcn+VqS47r795KcucQ+H01y330dDAAA1qNZA/2hSf6muy/cwz7nJvnG5Y8EAADr16yBfqskF+9ln1ss47gAAEBmD+nzk9xzL/vcL8kXlzcOAACsb7MG+nuSfFdVPWypjVX1PUkekuTv9nUwAABYj2YN9N9IcnmSf6iq30pyjySpqsdNX/9VJo9ZfOVcpwQAgHVipg8q6u7zq+o7k/xlkhcu2PS3SSrJF5I8qbv3dp06AACwhJk/SbS7/62q7p7kcUm+PcnRSa5I8uEk7+ju7fMdEQAA1o+ZAz1JuntHJmfN/3a+4wAAwPo2l8chVtUBVXX/6Zl1AABgmWYK9Kp6SlX9ZVUdtWDdnZP8R5KtST5dVW+rqmWdmQcAgPVu1jPoP57kW7r70gXrXpHkLkk+kOSTSb43yTPmMx4AAKwvswb6PZJ8dNeLqjosyWOT/GV3PybJg5J8NgIdAACWZdZA35zJc853+fZMbjR9c5J093VJ/jHJnecyHQAArDOzBvq2JIcveP3IJJ3knxasuzrJofs4FwAArEuz3sx5ZpLvqaqDMgnzpyT55KIPJtqS5Ctzmg8AANaVWc+gn5TkTpmE+meSHJPk9Yv2+dZMnuoCAADMaKZA7+43JPnNJLfI5FKXP0jy+7u2V9VDcv0TXQAAgBnN/Lzy7n5RkhftZvPWJEcm+eq+DAUAAOvVXD9QqLuvTXLtPI8JAADrybIDvapukcnZ8o1Lbe/uLy332AAAsF7NHOhV9bQkv5Dk2D3s1ss5NgAArHczRXRVPT3J/0uyI8kHk5ybZPv8xwIAgPVp1rPcL0hyWZKHdfdnVmAeAABY12Z9DvpdkvyVOAcAgJUxa6BfmuSaeQ9RVU+vqt7Lrx0L9r9rVf1CVb2/qs6tqmur6r+q6h1VdcK85wMAgNUy6yUuf5fk+Kqq7u45znFGkpftZtvDkzwqyXsWrPvVJD+Q5NNJ3p3JXxzunuQJSZ5QVc/t7t+b43wAALAqZg30X0zyz0leXVXP7+6r5jFEd5+RSaTfSFV9aPrbkxasPiXJb3X3xxft+8gk/5jk/6uqv+ruL89jPgAAWC2zBvpfJflakmcm+eGqOjPJ5Uvs19396H0drqruneTBSc5P8q4FBz95qf27+7SqOjXJdyR5SJK37usMAACwmmYN9OMX/P6WSe63m/3mdfnLs6fL13X3jj3ueb3rpkuPfwQA4GZnpkDv7llvKl22qjokyVMzeeb6a2/ie7YkeXQmZ/lPX7npAABgZaxacC/DU5IckeSU7j53bztX1UFJ/jzJQUle2t2X7WHfZ1fV1qraetFFF81tYAAA2FcjB/quy1tes7cdq2pjkjcmeWiStyT5nT3t390ndfdx3X3c5s2b93lQAACYl5kDvao2VNXPVdWHq+qKqtq+YNv9q+qPqupu+zJUVd0zk5s8z8vkMYp72ndjkj9L8uQkf5nkqXN+BCQAAKyamQK9qg7M5DGGv5vkzkm2JakFu5yV5MeT/Mg+znWTbg6tqgOSvCnJDyb5iyQ/3N1uDgUA4GZr1jPoL0xyQiYfKvQNWXTzZndfnsnNmd+13IGq6uAkT8vk5tDX7WG/AzN57OOTk/xpkqfN8KQXAAAY0qyB/iNJ/rm7f6W7d2bpxymeleQO+zDTk5McmeQ9u7s5dHpD6NuTfG8mEf+M6TwAAHCzNutz0I/Jgg8M2o1Lkxy1vHGSXH95y0l72OfVSR6b5OJMPsTol6tq8T6ndvep+zAHAACsulkD/epMHn24J3fI0p8uuldVdWySh2XvN4ceM13eOskv72G/U5czBwAArJVZA/2MJN9ZVQd297WLN1bV4Zlcf/4vyxmmuz+TG950urv9jl/O8QEAYHSzXoN+UpJvTvLnVXXYwg1VdUSSkzO5fvzVc5kOAADWmZnOoHf3m6rqO5I8PckTklyWJFW1Nck9M/kUzz/s7j0+uxwAAFjazB9U1N0/nsmzzj+dZHMml6Q8IMnnk/xEd//cXCcEAIB1ZNZr0JMk3X1ykpOr6pBMLmm5oru/Os/BAABgPVpWoO/S3V9P8vU5zQIAAOveHgO9qr64zON2d995me8FAIB1a29n0Dfkxp8WemCSb5z+fkcmHxZ06yQbp+u+nORGj2AEAAD2bo83iXb3Hbv7mF2/ktw3k0/u/HCSE5Ic3N3fmOTgJI9K8q+ZfMjQfVZ2bAAA2D/N+hSXl2fySaLHd/dp3b0jSbp7R3efmkm0HzXdDwAAmNGsgf7EJO9Y6lNEk6S7r07yjiRP2tfBAABgPZo10I9OcsBe9jlguh8AADCjWQP9C0lOrKrDl9pYVUcmOTHJcp/+AgAA69qsgf7qJLdL8pGq+tGqumNVHTJd/lgmN4neNskfzntQAABYD2b6oKLu/oOqumuSn0vy+iV2qSS/391/NI/hAABgvZn5k0S7+7lV9eYkP57k/kkOT3JFkn9LcnJ3/8t8RwQAgPVj5kBPku7+UJIPzXkWAABY92a9Bh0AAFhBAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQDGvy+HAAAWXUlEQVRgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABiLQAQBgIAIdAAAGItABAGAgAh0AAAYi0AEAYCACHQAABjJEoFfV06uq9/JrxxLve0hVvbuqLq2qr1fVJ6vqf1XVxrX4PgAAYF9tWusBps5I8rLdbHt4kkclec/ClVX1vUnemuTqJG9JcmmSxyf5v0kemuTJKzUsAACslCECvbvPyCTSb6SqPjT97UkL1h2W5E+S7EhyfHdvna5/cZL3Jzmxqn6wu9+8ooMDAMCcDRHou1NV907y4CTnJ3nXgk0nJtmc5E93xXmSdPfVVfV/krwvyf9MItCBFXHxJZ/KWds+mW11dQ7tg3PMoffJrY++91qPBcACXzjnS7ngnI+nrr0sfeCRud2W++fOW+6w1mPt1RDXoO/Bs6fL13X3wmvQHzVdnrLEe05P8rUkD6mqg1ZyOGB9uviST+UTV30k1+S63KoPyjW5Lp+46iO5+JJPrfVoAEx94Zwv5cL/fG+y/WvZecARyfav5cL/fG++cM6X1nq0vRo20KvqkCRPzeQyltcu2nz36fI/F7+vu7cnOSuT/ztwp5WcEVifztr2yRzUG3NQDkilclAOyEG9MWdt++RajwbA1AXnfDw7Nx6SbLpFqirZdIvs3HhILjjn42s92l4NG+hJnpLkiCSndPe5i7YdPl1esZv37lp/xFIbq+rZVbW1qrZedNFF+z4psK5sq6tz4KIrBA/Mpmyrq9doIgAWq2svS2885AbreuMhqWsvW6OJbrqRA33X5S2vmfeBu/uk7j6uu4/bvHnzvA8P7OcO7YNzbbbfYN212Z5D++A1mgiAxfrAI1M7vn6DdbXj6+kDj1yjiW66IQO9qu6Z5CFJzkvy7iV22XWG/PAlti1cf/mcRwPIMYfeJ9fUjlyT69LpXJPrck3tyDGH3metRwNg6nZb7p8NO76ebP9aujvZ/rVs2PH13G7L/dd6tL0aMtCz+5tDd/ncdHm3xRuqalOSY5JsT/LFlRkPWM9uffS9c99bPSgH5YBcVdfkoByQ+97qQZ7iAjCQO2+5Q257t8ckm26RDdddnmy6RW57t8fcLJ7iMtxjFqvq4CRPy+Tm0NftZrf3J/mRJN+d5E2Ltj0iyS2SnN7d16zUnMD6duuj7y3IAQZ35y13uFkE+WIjnkF/cpIjk7xniZtDd/nrJBcn+cGqOm7Xymnc/9r05R+v6JQAALAChjuDnusvbzlpdzt095VV9axMQv3UqnpzkkuTPCGTRzD+dZK3rPSgAAAwb0OdQa+qY5M8LLu/OfS/dfffJHlkJh9M9P1Jfi7JdUl+PskPdnev7LQAADB/Q51B7+7PJKkZ9v/nJI9duYkAAGB1DXUGHQAA1juBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMJDhAr2qHl1Vb6+qC6vqmqq6oKr+vqoeu2i/g6rqZ6rqI1V1cVVdVVWfqarfq6otazU/AADsi6ECvap+O8l7kxyX5G+TvCLJu5JsTnL8gv02JXlfkj9IcmiSNyV5dZKvJPm5JJ+oqnus5uwAADAPm9Z6gF2q6llJXpjkDUme3d3XLtp+wIKXT0zy0Ewi/Tu7e+eC/V6W5JeTvCDJj6/03AAAME9DBHpVHZTk5Um+lCXiPEm6+7oFL+80Xb5rYZxPvSOTQN+8ErMCAPuH8999Ri79k7dn43lfyo5vukOOetYTc/vH3m+tx4JhLnH5jkyC+m1JdlbV46rqF6rquVX17Uvs/x/T5fdU1eLv4X9Ml+9doVkBgJu58999Rq588StSl1+aHbe7feryS3Pli1+R8999xlqPBmOcQU/ywOny6iQfT3KvhRur6vQkJ3b3RdNV78ok5p+U5FNV9d4k1yb51iQPS/L7Sf5wFeYGAG6GLv2Tt6cOOyI54qhUkhxxVHZM1zuLzlob5Qz6babLFybpJA/P5ObP+yT5hySPSPJXu3bu7k5yYpKXJbl7kudkcs35CUlOT/IX3b19d1+sqp5dVVurautFF120u90AgP3UxvO+lD7s8Bus68MOz8bzvrRGE8H1Rgn0XXNsT/KE7v6n7r6quz+VyQ2h5yV55K7LXarq4CRvSfL8JD+T5BuTHJ7ksUm2JDm9qr53d1+su0/q7uO6+7jNm12qDgDrzY5vukPqyitusK6uvCI7vukOazQRXG+UQL98uvx4d5+9cEN3fy3J309fPmi6/N9Jnpzkl7r7Nd19YXdf2d3vyeTM+gFJXrXyYwMAN0dHPeuJ2Xjl5cnll6Z37kguvzQbr7w8Rz3riWs9GgwT6J+bLi/fzfbLpstDpstdN4J+YPGO3f2J6f5bqurouU0IAOw3bv/Y++WwX31++oijsvGC89NHHJXDfvX5rj9nCKPcJPq+TK49v0dVbVji0Ym7bho9a7o8aLq80fUp00c2Hjp9eaPHNQIAJJNIF+SMaIgz6N19TpJ3JrlDkucu3FZV35nkuzI5u37KdPUHp8sXTYN8oZdm8hePj3b3tpWaGQAAVsIoZ9CTyc2e90/yyqp6XCaPWzwmyfcl2ZHkmd29626Olyd5fJJHJ/lsVZ2S5OuZfLrog6a/f24AAOBmZogz6EnS3edl8hzzP0hy10wC+/hMzqw/tLvfumDf85M8IMkrMnl2+jOS/GyS2yY5OckDuvtDqzg+AADMRU0eKb5+HXfccb1169a1HgMAgP1YVX2su4+7KfsOcwYdAAAQ6AAAMBSBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADAQgQ4AAAMR6AAAMBCBDgAAAxHoAAAwEIEOAAADEegAADCQ6u61nmFNVdVFSc5Zoy9/6yQXr9HXZnX4Ge///Iz3f37G+z8/4/3fCD/jLd29+absuO4DfS1V1dbuPm6t52Dl+Bnv//yM939+xvs/P+P9383tZ+wSFwAAGIhABwCAgQj0tXXSWg/AivMz3v/5Ge///Iz3f37G+7+b1c/YNegAADAQZ9ABAGAgAh0AAAYi0AEAYCACfZVV1TdV1f+rqguq6pqqOruqfreqjlzr2dh3VXViVf1+VX2wqq6sqq6qP1vruZiPqjq6qp5ZVW+vqs9X1der6oqq+qeq+omq8u/U/UBV/VZVva+qzp3+jC+tqo9X1Uuq6ui1no+VUVVPnf47u6vqmWs9D/tm2le9m18XrvV8e+Mm0VVUVXdO8i9JbpPkHUk+m+RBSU5I8rkkD+3uS9ZuQvZVVZ2R5L5JrkpyXpJvSfLn3f3UNR2Muaiqn0ryx0m+nOQDSb6U5BuSPCnJ4UnemuTJ7V+sN2tVdW2Sf0vy6SRfSXLLJA9OclySC5I8uLvPXbsJmbeq+uYkn0qyMcmtkjyru1+7tlOxL6rq7CRHJPndJTZf1d2/s7oTzWbTWg+wzvxRJnH+nO7+/V0rq+qVSZ6X5OVJfmqNZmM+npdJmH8+ySMziTj2H/+Z5AlJ3tXdO3etrKoXJflIku/PJNbfujbjMSeHdffVi1dW1cuTvCjJLyb56VWfihVRVZXk9UkuSfK2JC9Y24mYo8u7+6VrPcRy+N+xq2R69vw7k5yd5A8XbX5Jkq8meVpV3XKVR2OOuvsD3X2mM6j7p+5+f3e/c2GcT9dfmOTV05fHr/pgzNVScT71l9PlXVdrFlbFc5I8KskzMvlvMaw5gb56Tpgu/2GJ/7hvS/LPSW6Ryf9GBW5+rpsut6/pFKykx0+Xn1zTKZibqjo2yW8meVV3n77W8zB3B03vLXhRVT23qk6oqo1rPdRN4RKX1XP36fI/d7P9zEzOsN8tyftWZSJgLqpqU5Ifnb48ZS1nYX6q6gWZXI98eCbXnz8skzj/zbWci/mY/nP7xkzuJXnRGo/DyrhtJj/jhc6qqmd092lrMdBNJdBXz+HT5RW72b5r/RGrMAswX7+Z5F5J3t3df7/WwzA3L8jkJuBdTkny9O6+aI3mYb5+Ocn9kzysu7++1sMwd69P8sEk/5FkW5I7JfnZJM9O8p6q+vbu/sQazrdHLnEB2AdV9Zwkz8/kqUxPW+NxmKPuvm13VyZn4Z6UyX/gP15VD1jbydhXVfVtmZw1f0V3f2it52H+uvtl0/uG/qu7v9bd/97dP5XklUkOSfLStZ1wzwT66tl1hvzw3Wzftf7yVZgFmIOq+tkkr8rkcXwndPelazwSK2D6H/i3Z3IZ4tFJ/nSNR2IfTC9t+dNMLjl98RqPw+rbdUP/I9Z0ir0Q6Kvnc9Pl3XazfddTAXZ3jTowkKr6/9u711g5izqO499facCo3JQIiEAp9RZ9QUkjEROpQQoWDWgUIpq0aI23aAgmoJIqBRMS4RWKSkRzSDERFS9vMBCBWgoqQUTRSDQtp5hwMWgphotKHV/MnLiue9rTs+fsPqd+P8nkOTs7O8/spO3+z/S/MxcAXwJ+Sw3OO3/whYZTStlO/WXsdUkOG/d4NGsvpn4WvxZ4rvcAG+quagBfb3WD9tDWwjaVotbpXfPMQR+dqf2wVyVZ1LeH8oHAm4BngJ+PY3CSZi7JxdS88/uB00opT4x5SBqdl7frrrGOQsP4O/CNaZ47kZqXvoW6sGb6y75nare8bWMdxR4YoI9IKWVrklup/0X6cerK25QN1N/kri2luAer1GFJ1gOXAb8EVpnWsm9J8irg8VLKzr76RcDl1MPm7i6l7BjH+DS89oXQdYOeS3IpNUC/3pNEF662febD/TFVkiXAl9vDG0Y8rL1igD5aHwPuBq5Ocirwe+Ak6h7pfwAuGePYNAeSnA2c3R4e0a5vTDLRfn6ilOIpdQtUkjXU4HwXdXeAT9ZDCP/LZCllYsRD09xZDVyRZAvwEPV0ycOpJwMvBR4DPjS+4UmagXOBTyXZDGyn7uJyPHAm8ALgZuCq8Q1vzwzQR6itoq+gfsCfQf0geJT6JbMNrsjsE04A1vTVLW0F6j8UBugL13Htuh9wwTRtfgpMjGQ0mg8/AZZR9zxfTt369mnqIspG4Gr/10TqvDuo588sp6YQv4i6CccW6t/jjV0/8TsdH58kSZL0f8VdXCRJkqQOMUCXJEmSOsQAXZIkSeoQA3RJkiSpQwzQJUmSpA4xQJckSZI6xABdkiRJ6hADdEnSjCWZSFLakdnzeZ/JJJPzeQ9J6ioDdEnSyCXZlMST8iRpgMXjHoAkSQOcOu4BSNK4GKBLkjqnlLJ13GOQpHExxUWSRiDJkpa7PZHkNUl+mOSvSZ5OsiXJqgGvOSDJp5M8kOSZJE8luTPJOXPU/6XtNSt3198M39/aJDcl2Zbk2TbWu5K8f1C/wCntcekpm3raDcxBH2JOliT5dpInkjyX5N4kb5/Je5OkUXMFXZJG6zjgZ8ADwLXAkcC5wI+TnFdKuREgyf7ALdRA9kHgGuCFwLuBG5OcUEr57Gz7nwdfBX4HbAYeBV4KrAY2Jnl1KWV9a/cksAFYCxzbfp4yubsbDDEnxwL3ANuAjcBLqHPyoyRvLaXcsbdvVpLmVSnFYrFYLPNcgCVAaeXKvudWAP8EdgAHtbrPtLY3A4t72r6MGsgW4OTZ9t/qL23tV+5mvBN99ROtfklf/fED+tgfuK3d+6i+5zbVj6Bp52sSmOyrG2ZOPt/X1+lTfY37z4bFYrH0F1NcJGm0dgKX9VaUUu4FvgUcAryzVX+AGkBeWEp5vqftn4HL28N1Q/Q/p8qAnPFSyj+oq9yLmZsvfc52TrYDX+gb2y3Aw8Ab5mBckjSnDNAlabTuK6X8bUD9pnZdnuRAYBnwSCnlwQFtb59qO5v+92KsM5bkmCTXJHmw5YaXlmt+U2ty1JD9DzMn95dSdg2o/xNw6DDjkqT5YA66JI3W49PUP9auB7cCNZd7kKn6Q2bZ/5xKspSa430ocCdwK3Ulfxc1zWQNcMCQtxlmTp6c5jXP40KVpA4yQJek0Tp8mvoj2nVnK711/Y7saTub/qf8q10HfRYMCnSncyH1S6Hnl1Imep9I8l5qgD6sYeZEkhYUVw4kabRObOka/Va2669aispW4KgkrxzQ9i3tet9s+u+p29GuRw9ov2JA3XSWtetNA547ZZrX7AJIst9MbjDknEjSgmKALkmjdTDwud6KJCuA91FXf3/Qqr8JBLiyN4hNchiwvqfNbPuHmpYCcH6SxT3tj+7vYw8m23Vl331PZ/CXNgH+0q7H7MV9ZjsnkrSgmOIiSaO1GViX5CTgLv6zT/ki4MOllKdau6uAtwFnAb9OcjN1z+/3ULcV/GIpZcsQ/VNK+UWSzcCbgXuS3E5NkXkHdb/xQSvrg3wFOB/4bpLvAY8ArwfOAL7T7t/vtvZevt/e27PA9lLKxt3cZ7ZzIkkLiivokjRaDwEnU9NLPgKcQ03LWF16DhFqWxSeBlzSqj5BzeX+I3BeKeXiYfrvcRZwHfCKdo/lwEXAdP3/j1LKb6gpJncDZwIfBQ4C3gV8bZqXXQdcQV3xv4i6TeIH93Cf2c6JJC0oKaWMewyStM9LsoQaPF9fSlm70PqXJI2OK+iSJElShxigS5IkSR1igC5JkiR1iDnokiRJUoe4gi5JkiR1iAG6JEmS1CEG6JIkSVKHGKBLkiRJHWKALkmSJHXIvwEuGk0Zeha+tgAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['train', 'epochs'] epochs\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['train', 'batch_size'] batch_size\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'confident_threshold'] confident_threshold\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", @@ -277,14 +829,14 @@ " np.where(values == evolution.get_value_from_config(\n", " params_dictionaries[i], param_path))[0][0],\n", " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", - " plt.yticks(np.arange(len(values)), values)\n", + " plt.yticks(np.arange(len(values)), values, fontsize=20)\n", " elif param_dict.get(\"evolve_bool\"):\n", " values = np.array([False, True])\n", " plt.scatter(i // POPULATION_SIZE, \n", " np.where(values == evolution.get_value_from_config(\n", " params_dictionaries[i], param_path))[0][0],\n", " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", - " plt.yticks(np.arange(len(values)), [\"False\", \"True\"])\n", + " plt.yticks(np.arange(len(values)), [\"False\", \"True\"], fontsize=20)\n", "\n", " plt.ylabel(param_name, fontsize=20)\n", " plt.xlabel(\"population\", fontsize=20)\n", @@ -296,6 +848,15 @@ " " ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, From 5f6c399278b04c5681f72eccb20f104ec61742e6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 16:38:01 +0300 Subject: [PATCH 281/616] fix: clear all cells --- .../models/evolution/Results_analysis.ipynb | 597 +----------------- 1 file changed, 28 insertions(+), 569 deletions(-) diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb index 8ed1df5314..3cb6d21dca 100644 --- a/deeppavlov/models/evolution/Results_analysis.ipynb +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -2,35 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", - " return f(*args, **kwds)\n", - "/home/dilyara/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", - " from ._conv import register_converters as _register_converters\n", - "Using TensorFlow backend.\n", - "[nltk_data] Downloading package punkt to /home/dilyara/nltk_data...\n", - "[nltk_data] Package punkt is already up-to-date!\n", - "[nltk_data] Downloading package stopwords to\n", - "[nltk_data] /home/dilyara/nltk_data...\n", - "[nltk_data] Package stopwords is already up-to-date!\n", - "[nltk_data] Downloading package perluniprops to\n", - "[nltk_data] /home/dilyara/nltk_data...\n", - "[nltk_data] Package perluniprops is already up-to-date!\n", - "[nltk_data] Downloading package nonbreaking_prefixes to\n", - "[nltk_data] /home/dilyara/nltk_data...\n", - "[nltk_data] Package nonbreaking_prefixes is already up-to-date!\n", - "2018-06-25 16:20:16.625 DEBUG in 'gensim.models.doc2vec'['doc2vec'] at line 73: Fast version of gensim.models.doc2vec is being used\n", - "2018-06-25 16:20:16.629 INFO in 'summa.preprocessing.cleaner'['textcleaner'] at line 20: 'pattern' package not found; tag filters are not available for English\n", - "2018-06-25 16:20:17.53 DEBUG in 'matplotlib.backends'['__init__'] at line 90: backend module://ipykernel.pylab.backend_inline version unknown\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -53,219 +27,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Considered basic config:\n", - "{\n", - " \"dataset_reader\": {\n", - " \"name\": \"basic_classification_reader\",\n", - " \"x\": \"text\",\n", - " \"y\": \"intents\",\n", - " \"data_path\": \"snips\"\n", - " },\n", - " \"dataset_iterator\": {\n", - " \"name\": \"basic_classification_iterator\",\n", - " \"seed\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"field_to_split\": \"train\",\n", - " \"split_fields\": [\n", - " \"train\",\n", - " \"valid\"\n", - " ],\n", - " \"split_proportions\": [\n", - " 0.9,\n", - " 0.1\n", - " ]\n", - " },\n", - " \"chainer\": {\n", - " \"in\": [\n", - " \"x\"\n", - " ],\n", - " \"in_y\": [\n", - " \"y\"\n", - " ],\n", - " \"pipe\": [\n", - " {\n", - " \"id\": \"classes_vocab\",\n", - " \"name\": \"default_vocab\",\n", - " \"fit_on\": [\n", - " \"y\"\n", - " ],\n", - " \"level\": \"token\",\n", - " \"save_path\": \"vocabs/snips_classes.dict\",\n", - " \"load_path\": \"vocabs/snips_classes.dict\"\n", - " },\n", - " {\n", - " \"in\": [\n", - " \"x\"\n", - " ],\n", - " \"out\": [\n", - " \"x_lower\"\n", - " ],\n", - " \"name\": \"str_lower\"\n", - " },\n", - " {\n", - " \"id\": \"my_embedder\",\n", - " \"name\": \"fasttext\",\n", - " \"save_path\": \"embeddings/dstc2_fastText_model.bin\",\n", - " \"load_path\": \"embeddings/dstc2_fastText_model.bin\",\n", - " \"dim\": 100\n", - " },\n", - " {\n", - " \"id\": \"my_tokenizer\",\n", - " \"name\": \"nltk_tokenizer\",\n", - " \"tokenizer\": \"wordpunct_tokenize\"\n", - " },\n", - " {\n", - " \"in\": [\n", - " \"x_lower\"\n", - " ],\n", - " \"in_y\": [\n", - " \"y\"\n", - " ],\n", - " \"out\": [\n", - " \"y_labels\",\n", - " \"y_probas_dict\"\n", - " ],\n", - " \"main\": true,\n", - " \"name\": \"intent_model\",\n", - " \"save_path\": \"evolution/classification/intents_snips\",\n", - " \"load_path\": \"evolution/classification/intents_snips\",\n", - " \"classes\": \"#classes_vocab.keys()\",\n", - " \"kernel_sizes_cnn\": [\n", - " 1,\n", - " 2,\n", - " 3\n", - " ],\n", - " \"filters_cnn\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 100\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"confident_threshold\": {\n", - " \"evolve_choice\": true,\n", - " \"values\": [\n", - " 0.5,\n", - " 1\n", - " ]\n", - " },\n", - " \"optimizer\": \"Adam\",\n", - " \"lear_rate\": {\n", - " \"evolve_range\": [\n", - " 0.0001,\n", - " 0.1\n", - " ],\n", - " \"scale\": \"log\"\n", - " },\n", - " \"lear_rate_decay\": {\n", - " \"evolve_range\": [\n", - " 0.0001,\n", - " 0.1\n", - " ],\n", - " \"scale\": \"log\"\n", - " },\n", - " \"loss\": \"binary_crossentropy\",\n", - " \"text_size\": 15,\n", - " \"coef_reg_cnn\": {\n", - " \"evolve_range\": [\n", - " 1e-06,\n", - " 0.001\n", - " ]\n", - " },\n", - " \"coef_reg_den\": {\n", - " \"evolve_range\": [\n", - " 1e-06,\n", - " 0.001\n", - " ]\n", - " },\n", - " \"dropout_rate\": {\n", - " \"evolve_range\": [\n", - " 0.1,\n", - " 0.9\n", - " ]\n", - " },\n", - " \"dense_size\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 100\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"model_name\": \"cnn_model\",\n", - " \"embedder\": \"#my_embedder\",\n", - " \"tokenizer\": \"#my_tokenizer\",\n", - " \"check_bool\": {\n", - " \"bool\": true\n", - " }\n", - " }\n", - " ],\n", - " \"out\": [\n", - " \"y_labels\",\n", - " \"y_probas_dict\"\n", - " ]\n", - " },\n", - " \"train\": {\n", - " \"epochs\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"batch_size\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"metrics\": [\n", - " \"classification_accuracy\",\n", - " \"classification_f1\",\n", - " \"classification_roc_auc\"\n", - " ],\n", - " \"validation_patience\": 5,\n", - " \"val_every_n_epochs\": 1,\n", - " \"log_every_n_epochs\": 1,\n", - " \"validate_best\": true,\n", - " \"test_best\": false\n", - " },\n", - " \"metadata\": {\n", - " \"labels\": {\n", - " \"telegram_utils\": \"IntentModel\",\n", - " \"server_utils\": \"KerasIntentModel\"\n", - " },\n", - " \"download\": [\n", - " \"http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz\",\n", - " \"http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz\",\n", - " {\n", - " \"url\": \"http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv\",\n", - " \"subdir\": \"snips\"\n", - " },\n", - " {\n", - " \"url\": \"http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin\",\n", - " \"subdir\": \"embeddings\"\n", - " }\n", - " ]\n", - " }\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "CONFIG_FILE = \"../../configs/evolution/evolve_intents_snips.json\"\n", "KEY_MAIN_MODEL = \"main\"\n", @@ -280,25 +46,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2018-06-25 16:20:17.65 INFO in 'deeppavlov.models.evolution.evolution_param_generator'['evolution_param_generator'] at line 55: Main model path in config: ['chainer', 'pipe', 4]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Title name for the considered evolution is `intents_snips`.\n", - "Number of populations: 6.\n" - ] - } - ], + "outputs": [], "source": [ "evolution = ParamsEvolution(population_size=POPULATION_SIZE,\n", " key_main_model=KEY_MAIN_MODEL,\n", @@ -324,50 +74,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Measure: classification_accuracy\n", - "valid:\n", - "min for\t0 model on\t0 population\n", - "max for\t1 model on\t4 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n", - "\n", - "Measure: classification_f1\n", - "valid:\n", - "min for\t0 model on\t5 population\n", - "max for\t1 model on\t4 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n", - "\n", - "Measure: classification_roc_auc\n", - "valid:\n", - "min for\t1 model on\t3 population\n", - "max for\t0 model on\t0 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: 'argmin' is deprecated. Use 'idxmin' instead. The behavior of 'argmin' will be corrected to return the positional minimum in the future. Use 'series.values.argmin' to get the position of the minimum now.\n", - " if __name__ == '__main__':\n", - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", - " # Remove the CWD from sys.path while we load stuff.\n" - ] - } - ], + "outputs": [], "source": [ "MEASURES = evolution.get_value_from_config(\n", " evolution.basic_config, list(evolution.find_model_path(\n", @@ -394,42 +103,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "path_to_pics = expand_path(Path(evolution.get_value_from_config(\n", " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).joinpath(\"pics\"))\n", @@ -488,25 +166,16 @@ "collapsed": true }, "source": [ - "## If you want to plot measures depending on population colored by `evolution_model_id`" + "## If you want to plot measures depending on population colored by `evolution_model_id`\n", + "\n", + "#### That means model of the same `id` are of the same color." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "params_dictionaries = []\n", "\n", @@ -527,42 +196,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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HrhrMgJn58cw8dFhVSZIkSdrEZte4R8S2wJrMXDlK9UiSJEnqw0Az7iuA9/c+iYjzIuINI1uSJEmSpGYDBfekrG/vdRyDX+suSZIkqU0GCu4PAy8cjUIkSZIk9W+gfdyvAv4kImZQQjzAMdVe7ZuTmfmOYdYmSZIkqTJQcP9rYCfgtZTZ+aQslRlouUwCBndJkiSpTTYb3DPzEeDIiJgI7AwsBc4Gzhn50iRJkiT1GmjGHYDMfAa4PyLuA5Zm5n0jW5YkSZKkRoMK7r0yc+5Q3iQi3ga8LTMPG8rrJUmSpC3dYO6c2g5zgFeP0ntJkiRJY85oBXdJkiRJw2BwlyRJkmrA4C5JkiTVgMFdkiRJqgGDuyRJklQDBndJkiSpBgzukiRJUg0Y3CVJkqQaGK3gfivw5VF6L0mSJGnMmTAab5KZlwGXjcZ7SZIkSWNRy8E9IrYH3g68ApgOjO/jtMzMw4dZmyRJkqRKS8E9IuYB3wdmArGZU3MYNUmSJElq0uoa94XAjsDHgd2BiZk5ro9HX7PwkiRJkoao1aUyrwK+nZkfGIliJEmSJPWt1Rn3AG4fiUIkSZIk9a/V4H4LsPdIFCJJkiSpf60G9zOBoyLikBGohYjYNSLOi4iHImJtRCyNiLMjYvoQxnpZRPxrRDxQjfVIRPwgIv50JGqXJEmSRlKra9xfQNmP/YqI+BplBv6Jvk7MzJZuuBQRewDXUy5+vQy4k7Ll5HuAIyPioMx8fJBjvRs4B1gBfBt4ENge2Ac4Cm8GJUmSpJppNbhfQNnqMYC3Vo/mrR+j6ms1HH+OEtpPzsxPPztYxCeAU4CzgBMHGiQijgA+BXwPeGNmrmo6PrHFuiRJkqSOi8zBb7keEW8b7LmZeWEL4+4B/AJYCuyRmRsbjk0DHqb8QrBjZq4eYKzFwAuB2YOdoe/LggULctGiRUN9uSRJkjQoEXFLZi4Y6LyWZtxbCeMtOrRqr2gM7dV7roqI64AjgAOAK/sbJCL2AeYDlwK/iohDgZdT/gJwK3B18/iSJElSHbS6VGak9O5Uc1c/x++mBPe92ExwB36rah+l3OH14KbjP4mIYzPzF0OsU5IkSeqIIQX3iNgGOBbYD9gOWAn8L3DJQEtZ+tFTtSv7Od7bv90A4+xYte+gXJD6euBaYCfgw8BbgG9HxEsyc13ziyPiBOAEgNmzZw+6eEmSJGmktRzcI+Io4ELKLi3RcCiBT0bE8Zn5X22qr1W921uOB/44M2+onv+62gZyHrAA+APga80vzsxzgXOhrHEf+XIlSZKkwWlpH/eIeBlwMWXm+6vA24HfqdqvVv3/HhEvb7GO3hn1nn6O9/b3ufVkg97jyxpCOwBZrsK9rHr6ihbrkyRJkjqq1Rn3D1Jm1l+VmTc2HbsgIj5LWVv+Acqs9mD9vGr36uf4nlXb3xr45nH6C/grqnbrQdYlSZIkdYVW75z6KuDf+gjtAGTmj4B/r85rxdVVe0REbFJTtR3kQcBTQJ/v2+BGYDUwJyKm9HF8n6pd0mJ9kiRJUke1Gtx7gF8OcM79wLatDJqZ9wBXAHOAdzUdPgOYAlzUeOFrRMyLiHlN4zwF/AuwFfCRiIiG818CHAesp/xyIUmSJNVGq0tlHmLg9eELKDdMatVJwPXApyLicOAOYH/KHu93UZbpNLqjaqOp/0OUbSDfCxxY7QG/E2UXnK2A91a/KEiSJEm10eqM++XAYRHx/ogY33ggIsZFxPuA11TntaQK0wuACyiB/X3AHsA5wAGDvQtqZv6aslTno5Sdb94N/C5lW8jXZeY5rdYmSZIkdVqUzVYGeXLELOAWYBZlScwPKbPrs4Dfpix1WQYsyMyhzLp3jQULFuSiRYs6XYYkSZLGuIi4JTMXDHReS0tlMnNZRBwEfAF4LbBb0ynfA06se2iXJEmSuk3LN2DKzKXA6yJiF8qdU3so+7D/ODMfbG95kiRJkmAIwb1XFdIN6pIkSdIoaPXiVEmSJEkdsNkZ94g4j3Kn1A9k5iPV88HIzHzHsKuTJEmSBAy8VOY4SnD/OPBI9XwwEjC4S5IkSW0yUHCfW7UPNj2XJEmSNIo2G9wz877NPZckSZI0Olq6ODUiPhwRBw9wzqsi4sPDK0uSJElSo1Z3lTkdOGSAcw4GThtKMZIkSZL6NhLbQU4ENo7AuJIkSdIWaySC+8uA5SMwriRJkrTFGvDOqRFxVVPXcRFxSB+njgdeAOwGfG34pUmSJEnqNWBwZ9M17QnMqR7NNgKPA98AThlmXZIkSZIaDBjcM/PZ5TQRsRE4PTPPHNGqJEmSJG1iMDPujY4HfjwShUiSJEnqX0vBPTMvHKlCJEmSJPWv1Rn3Z0XErsAuwOS+jmfmNUMdW5IkSdKmWg7uEXEE8Elg3gCnjh9SRZIkSZKep6V93CPiAOC/gO2AzwABXAN8Ebizev6fgBevSpIkSW3U6g2Y/hZYA/xWZr6n6rs6M08E9gE+ArwG+Pf2lShJkiSp1eB+IPAfmflQ8xhZfBi4AzijTfVJkiRJovXg3gPc3/B8HTCl6ZzrgIOHU5QkSZKkTbUa3B8Fpjc936PpnInA1sMpSpIkSdKmWg3ud7FpUL8ReG1E7AUQEbOAPwDubk95kiRJkqD14P4d4NURsX31/BzK7PqPI+Jmys4yM4Gz21eiJEmSpFaD+xco69efAcjM64A3AUsou8o8DPxFZn65nUVKkiRJW7qWbsCUmb8GftTUdwlwSTuLkiRJkrSpVmfcJUmSJHVAq3dOfXlEfDgidurn+Kzq+EvbU54kSZIkaH3G/X3AOynbQPblEeAdwF8NpyhJkiRJmxrKnVOvzszs62DVfxVw0HALkyRJkvScVoP7LOCBAc55CNh5aOVIkiRJ6kurwf0pyj7tmzMTWDu0ciRJkiT1pdXgfitwdERM7etgRGwLHF2dJ0mSJKlNWg3u51Jm1L8XEfMbD0TEvsAVwIzqPEmSJElt0uoNmL4REb8D/Cnw44h4BHgQ2AXYCQjgy5n5tbZXKkmSJG3BWr4BU2YeB5wI3E65WPXlVfsz4ITquCRJkqQ2amnGvVdmngucGxHbANsBT2TmU22tTJIkSdKzhhTce1Vh3cAuSZIkjbCWl8pIkiRJGn2bnXGPiHuBBF6TmUuq54ORmbnHsKuTJEmSBAy8VGYcJbj397w/MeSKJEmSJD3PZoN7Zs7Z3HNJkiRJo2Oza9wj4hMRcUTD89nV3VElSZIkjaKBLk59L3BAw/MlVZ8kSZKkUTRQcH8S2KbhuWvXJUmSpA4Y6OLUXwDHRsQlwMNV33YRMXuggTPz/uEWJ0mSJKkYKLj/I/AV4PqGvvdUj83JQYwtSZIkaZAG2lXmaxGxBHg9sAtwHHAbcOvIlyZJkiSp14Cz4pl5I3AjQEQcB1ySmWeORDERsStwJnAksANlec6lwBmZuWKIYx4MXE1Zz39WZv5dm8qVJEmSRk2ry1mOZ4Rm2yNiD8qSnB2By4A7gVdQluUcGREHZebjLY45DbgQeAqY2t6KJUmSpNEz0K4ym8jMCzNz8QjV8jlKaD85M4/JzPdn5mHAJ4G9gbOGMOY5QA/wsfaVKUmSJI2+zc64V8tMAG7KzDUNzweUmdcM9txqtv0IYCnw2abDpwEnAG+NiPdl5upBjnk05S8Eb8ULZSVJklRzAwXa71N2iHkRcFfD88EY30Idh1btFZm5sfFAZq6KiOsowf4A4MqBBouIHYEvApdm5leqtfmSJElSbQ0U3M+kBPXlTc/bbe+qvauf43dTgvteDCK4U0L7OODE4ZcmSZIkdd5A20GevrnnbdRTtSv7Od7bv91AA0XE24E3AH+UmY+0UkREnEBZlsPs2QPeY0qSJEkaNS1dnNrtImIOcDbwb5n5zVZfn5nnZuaCzFwwc+bMdpcnSZIkDVlLF21GxHhgcmY+1dR/GHA0ZdvFczNzSYt19M6o9/RzvLf/iQHGOQ94GjipxfeXJEmSulqrM+4LgV9FxLMBOyL+GPge8JfA3wA3RcQLWhz351W7Vz/H96za/tbA93oZZUvJxyIiex/A+dXxD1Z9l7ZYnyRJktRRrW6TeDBwdWY2rkU/jTIT/h5gFmXP9L8CTmlh3Kur9oiIGNe4s0x1E6WDKLP5Nw4wzpeBbfro37Oq/VbgFuDHLdQmSZIkdVyrwf0FlLubAhARu1N2hDkzM79S9R0MHEkLwT0z74mIKyg7x7wL+HTD4TOAKcAXGvdwj4h51WvvbBjn5L7Gr7aDPBj4dmb+3WDrkiRJkrpFq8F9W+DXDc8PomwP+Z2Gvp/x3L7srTiJ8kvBpyLicOAOYP9qrLuADzadf0fVxhDeS5IkSaqVVte4PwzMbXj+GsrFoLc09E0F1rdaSGbeAywALqAE9vcBewDnAAdk5uOtjilJkiSNFa3OuN8IvCEifhdYA7wRuDIzn2k4Zy7w4FCKycxfAscP8txBz7Rn5gWUXwgkSZKkWmp1xv2j1WsuA74LTALO6j0YEVsBrwJ+1K4CJUmSJLU4456ZP4mI/YG3VV3fyMybG07ZD7gK+Fqb6pMkSZJE60tlyMyfAKf2c+wG4PeHW5QkSZKkTbW6VKZPETExIvaLiL3bMZ4kSZKkTbUU3CPiDyPimxGxfUPfHpQtIBcBt0fExRHR8ky+JEmSpP61OuP+dmBeZv6qoe+fgBdS7n56G3A0g9wZRpIkSdLgtBrcXww8ezFqRGwLHAV8MzNfA7wCuBODuyRJktRWrQb3mZSbMPU6kHKB69cBqv3cv0e5cZIkSZKkNmk1uK8CehqevxpI4NqGvjXAtGHWJUmSJKlBqxeR3g38TkRMpgT2PwRuy8zlDefsBjzapvokSZIk0fqM+7nA7pQAfwcwFzi/6ZyXU3aZkSRJktQmrd459cJqr/YTqq7PAJ/uPR4Rr6TsMHNu2yqUJEkaTcuXwpIbYNVjMG0mzD0QZszpdFXSkO6c+gHgA/0cXgRMB1YPpyhJkqSOWL4UFl8Kk6fA1B1g7ZPl+b7HGN7VcW29UVJmrgPWtXPMLc573wu33trpKiRJ2jKtehQ2boBx45/r27gBxp0L03bsXF0aGS99KZx9dqerGLRW17hLkiSNXRuegXFN8WjcuNIvdVjLM+4RsTPwd8DrgF2ASX2clpnZ1tn8LUaNfuuTJGnMuflrZXnM5KnP9fU+/603d64uiRZn3CNiF8o69j+nrGOfDNxP2WVmAxDAYuCH7S1TkiRpFMw9ENauLmE9N5Z27erSL3VYq0tlPgzMAo7MzH2rvvMzcx5lm8jvAlsDx7avREmSusfKDcv5+dqbuXXNVfx87c2s3LB84BepPmbMKReiTp4KTz5eWi9MVZdodTnL64DvZOb/NB/IzAci4k3AT4EzgJPbUJ8kSV1j5Ybl3LtuMRNjMlsxlWdyLfeuW8zuk/alZ/yMTpendpkxx6CurtRqcJ8FfLPh+QbKDDsAmflkRHwPOBqDu6Qt0JINT3HD+id4LJ9hZkzkwAnbMXf8Np0uS22ybP0SJsZkJsZkACYy+dl+g7tUI7fdBhdfDPffD7Nnw7HHwvz5na5qQK0ulfk1m16MuoJygWqjlcDM4RS1pVp2G3z/dLjs7aVddlunK5LUiiUbnuLSdY/yZG5gBybyZG7g0nWPsmTDU50uTW3ydK5iQtOeDBOYxNO5qkMVSWrZbbfBwoWwYgXsumtpFy4s/V2u1eB+H/CChueLgcMiYhuAiBgHHAE80J7ythzLboMbFsLTK2DbXUt7w0LD+1hz+/INLLx5LadctYaFN6/l9uUbOl2S2uiG9U8wJcYzNSYwLoKpMYEpMZ4b1j/R6dLUJlvHNNY33a5kPevYOqZ1qCJJLbv4Ypg+vTzGjXvu64sv7nRlA2o1uF8JHBoRE6vnFwK/AVwfEf8IXAf8JvCN9pW4ZbjzYthqOmw9HWJcabeaXvo1Nty+fAOfX7yOlWuTnafCyrXJ5xevM7yPIY/lM6zbuJGfbFjFj9av5CcbVrFu40YeS/d/HitmTZjLM7mWZ3Itmfns17MmzO10aZIG6/77oadn076entLf5VoN7v8CfByYAZCZXwHOAfYB3gfsTwntZ7Wxxi3Cyvthq6b/DW3VU/o1Nly+ZD09k4NVU6PoAAAWa0lEQVSeycG4iGe/vnzJ+k6XpjaZkPCzXM263MjWBOtyIz/L1UzITlemdukZP4PdJ+3LxJjMGp5kYkz2wlSpbmbPhpUrN+1bubL0d7mWLk7NzLspwb2x75SI+ChlO8ilmflIG+vbYvTMLstjtp7+XN+alaVfY8ODq8pMe6Npk0q/xogIyCx3tCCArJ5HhwtTO/WMn2FQl+rs2GPLmnYoM+0rV5Z17u94R2frGoRWZ9z7lJmPZeaPDO1DN+9YWLOihPfcWNo1K0q/xoZdpgWrNl0ay6p1pV9jw3qSfcZPZVKM42k2MCnGsc/4qazHX84kqWvMnw+nnlrWtT/wQGlPPbUWu8q0uh2kRsis+XDgqWVN+8r7y0z7fu8o/Robjpo7gc8vLsl92qQS2leuTd48b+IAr1RdzIyJPMkGXjLuuQsVn8z1TI/xHaxKkvQ88+fXIqg322xwj4jzhjhuZmb3/72hy8yab1Afy148Yzwn7juJy5es58FVyS7TgjfPm8iLZxjqxooDJ2zHpeseBWAbxvMUG1idG3jtxB06XJkkaSyIzP7/hBsRG4c4bmZmrdPIggULctGiRZ0uQ1LNeAMmSVKrIuKWzFww0HkDLZVxfytJasHc8dsY1CVJI2KzwT0z7xutQiRJkiT1r6VdZSLiTRFxVUT8Rj/Hd4mIKyPCvVAkSZKkNmp1O8h3Attl5kN9HczMB4Ge6jxJkiRJbdJqcH8JMNAVmzcD7o0iSZIktVGrwX174NEBznkc8JZykiRJUhu1GtyXA3sOcM6ewBNDK0eSJElSX1oN7tcBb4iIeX0djIgXAUcDPxxuYZIkSZKe02pwX0jZQvLaiDg5IvaKiClV+x5KYB9fnSdJkiSpTQa6AdMmMvPmiDgJ+CzwyerRaAPwF5n5ozbVJ0mSJIkWgztAZn4xIq4FTgL2B7ajrGm/EfjnzLyjvSVKktQ9Vm5YzrL1S3g6V7F1TGPWhLn0jHdPBkkjr+XgDlCF879scy2SJHW1lRuWc++6xUyMyWzFVJ7Jtdy7bjG7T9rX8C5pxLW6xl2SpC3WsvVLmBiTmRiTiYhnv162fkmnS5O0BTC4S5I0SE/nKiYwaZO+CUzi6VzVoYokbUkM7pIkDdLWMY31rNukbz3r2DqmdagiSVsSg7skSYM0a8Jcnsm1PJNrycxnv541YW6nS5O0BTC4S5I0SD3jZ7D7pH2ZGJNZw5NMjMlemCpp1AxpVxmNjGW3wZ0Xw8r7oWc2zDsWZs3vdFWSpEY942cY1CV1hDPuXWLZbXDDQnh6BWy7a2lvWFj6JUmSJIN7l7jzYthqOmw9HWJcabeaXvolSZIkl8p0iZX3l5n2Rlv1lH5JkjSKli+FJTfAqsdg2kyYeyDMmNPpqiRn3LtFz2xYs3LTvjUrS78kSRoly5fC4kth7ZMwdYfSLr609Esd1lXBPSJ2jYjzIuKhiFgbEUsj4uyImD7I10+JiD+JiH+NiDsjYnVErIqIRRHxvoiYNPAonTHvWFizoqxtz42lXbOi9EuSpFGy5AaYPAUmTy1rVydPLc+X3NDpyqTuCe4RsQdwC3A8cBPwSeBe4D3ADRGxwyCGeRXwFeB1wE+BTwP/CuwCLASujoit2l/98M2aDweeWta2//qB0h54qrvKSJI0qlY9BpO22bRv0jalX+qwblrj/jlgR+DkzPx0b2dEfAI4BTgLOHGAMZYBbwH+LTOfvbVdRJwKfB94JfAu4J/aWnmbzJpvUJckqaOmzSzLYyZPfa5v3VOlX+qwrphxr2bbjwCWAp9tOnwasBp4a0RM2dw4mXlrZn61MbRX/at4Lqwf0o6aJUnSGDT3QFi7uoT33FjatatLv9Rh3TLjfmjVXpGZGxsPZOaqiLiOEuwPAK4c4ns8U7Xrh/h6adhuX76By5es58FVyS7TgqPmTuDFM8Z3uixJUq8Zc2DfYzbdVWbea91VRl2hW4L73lV7Vz/H76YE970YenB/e9V+Z4ivl4bl9uUb+PzidfRMDnaeCivXJp9fvI4T951keJekbjJjjkFdXakrlsoAPVW7sp/jvf3bDWXwiHg3cCRwK3DeZs47odqBZtFjj3kRitrr8iXr6Zkc9EwOxkU8+/XlS/wjkCRJGli3BPcRExHHAmdTLlz9g8x8pr9zM/PczFyQmQtmzvQiFLXXg6uSaU0bkk6bVPolSZIG0i3BvXdGvaef4739T7QyaEQcA3wdeBQ4JDPvHVp50vDtMi1YtW7TvlXrSr8kSdJAumWN+8+rdq9+ju9Ztf2tgX+eiHgTZQ/3ZcBhmXn30MuThu+ouRP4/OKS3KdNKqF95drkzfMmdrgySZK2MLfdBhdfDPffD7Nnw7HHwvzu35O7W2bcr67aIyJik5oiYhpwEPAUcONgBouIPwG+BjwEvNrQrm7w4hnjOXHfSfRMDh5+EnomhxemSpI02m67DRYuhBUrYNddS7twYenvcl0x456Z90TEFZSdY95FueNprzOAKcAXMnN1b2dEzKtee2fjWBHxNsoFqPcBh2bmfSNcflt9572w7NZOV6GRM54ZjGdG9eym6iFJkkbJ0m1h/YdgwkRmzVrGkUdWGw5efHHXz7p3RXCvnARcD3wqIg4H7gD2p+zxfhfwwabz76jaZxcIR8ShlNA+jjKLf3zE89YPP5GZZ7e9ekmSJHW/tWtg8uRN+3p6yrKZLtc1wb2adV8AnEnZuvEo4GHgHOCMzFwxiGF247nlP2/v55z7KLvMdKUju7YySZKkMeD0r5flMdOnP9e3cmVZ697lumWNOwCZ+cvMPD4zd87MSZm5W2a+t6/QnpmRmdHUd0Fv/2Yec0btA0mSJKm7HHtsCe4rVsDGjc99feyxna5sQF0V3CVJkqQRNX8+nHpqmXF/4IHSnnpq169vhy5aKiNJkiSNivnzaxHUmznjLkmSJNWAwV2SJEmqAYO7JEmSVAMGd0mSJKkGDO6SJElSDRjcJUmSpBowuEuSJEk1YHCXJEmSasDgLkmSJNWAwV2SJEmqAYO7JEmSVAMGd0mSJKkGDO6SJElSDRjcJUmSpBowuEuSJEk1YHCXJEmSasDgLkmSJNWAwV2SJEmqAYO7JEmSVAMGd0mSJKkGDO6SJElSDRjcJUmSpBowuEuSJEk1YHCXJEmSasDgLkmSJNWAwV2SJEmqAYO7JEmSVAMGd0mSJKkGDO6SJElSDRjcJUmSpBowuEuSJEk1YHCXJEmSasDgLkmSJNWAwV2SJEmqAYO7JEmSVAMGd0mSJKkGDO6SJElSDRjcJUmSpBowuEuSJEk1YHCXJEmSasDgLkmSJNWAwV2SJEmqAYO7JEmSVAMGd0mSJKkGDO6SJElSDRjcJUmSpBowuEuSJEk1YHCXJEmSaqCrgntE7BoR50XEQxGxNiKWRsTZETG9xXG2r163tBrnoWrcXUeqdkmSJGkkTeh0Ab0iYg/gemBH4DLgTuAVwHuAIyPioMx8fBDj7FCNsxdwFfB1YB5wPPD6iDgwM+8dmU8hSZIkjYxumnH/HCW0n5yZx2Tm+zPzMOCTwN7AWYMc56OU0P6JzDy8GucYyi8AO1bvI0mSJNVKZGana+idbf8FsBTYIzM3NhybBjwMBLBjZq7ezDhTgUeBjcDOmbmq4dg44F5gt+o9NjvrvmDBgly0aNGQP5MkSZI0GBFxS2YuGOi8bplxP7Rqr2gM7QBV+L4O2AY4YIBxDgC2Bq5rDO3VOBuB7za9nyRJklQL3RLc967au/o5fnfV7jVK40iSJEldpVsuTu2p2pX9HO/t324kx4mIE4ATqqdPRsTPB3i/kTIDWN6h99bo8Gc89vkzHvv8GY99/ozHvm75Ge82mJO6Jbh3hcw8Fzi303VExKLBrHNSffkzHvv8GY99/ozHPn/GY1/dfsbdslSmdya8p5/jvf1PjNI4kiRJUlfpluDeuySlv7Xne1Ztf2vX2z2OJEmS1FW6JbhfXbVHVNs2PqvaDvIg4CngxgHGuRF4Gjioel3jOOOAI5rer1t1fLmORpw/47HPn/HY58947PNnPPbV6mfcFcE9M+8BrgDmAO9qOnwGMAW4qHEP94iYFxHzmsZ5ErioOv/0pnHeXY3/3W6/c2q11l5jmD/jsc+f8djnz3js82c89tXtZ9wVN2CCZ2/CdD3l7qaXAXcA+1P2XL8LeGVmPt5wfgJkZjSNs0M1zl7AVcBNwIuAoyk3Z3pl9YuCJEmSVBtdE9wBIuIFwJnAkcAOlDumXgKckZkrms7tM7hXx7YHTgOOAXYGHgf+G/hwZj4wkp9BkiRJGgldFdy3ZBGxK8//peVS+vilRfUTEW8EXg28FNgXmAZ8NTPf0tHC1BbVX/p+H3g98BJgF2Ad8BPgfOD85rtCq34i4uPAAspfdGdQrqm6j/Lf6s80/lVYY0dEvIWyDBfgzzLzS52sR8MTEUvpf8/0RzJz1iiW0zKDexfoY5nQncArKMuEfg4c5D8I9RYRt1IC+5PAA8A8DO5jRkScCPwz5Rfuq4H7gZ2AYynb0H4LeFP6H9xai4h1wP8Ct1OWXk4BDqCE+YeAAzLzl52rUO1WrQT4CTAemIrBvfaq4L4dcHYfh5/MzIWjW1FrvAFTd/gcJbSfnJmf7u2MiE8ApwBnASd2qDa1xymUwP4Lysx7t+9spNbcBbwB+HbjzHpEfIBync0fUEL8tzpTntpk28xc09wZEWcBHwD+Fjhp1KvSiIiIoPzF7HHgYuDUzlakNnoiM0/vdBFD0RW7ymzJqtn2I4ClwGebDp8GrAbeGhFTRrk0tVFmXp2ZdzvjOjZl5lWZ+Z/Ny2Eycxnw+erpIaNemNqqr9Be+WbV7tnPcdXTycBhwPGUf4uljjO4d96hVXtFH//orwKuA7ah/DlWUv08U7XrO1qFRtLvVe1tHa1CbRMRLwL+ATgnM6/pdD1qu8kR8ZaI+EBEvCciDo2I8Z0uajBcKtN5e1dtf3dzvZsyI78XcOWoVCSpLSJiAvCn1dPvdLIWtU9EnEpZ79xDWd/+25TQ/g+drEvtUf3/9iLKtSof6HA5GhmzeO6C415LIuL4zPxBJwoaLIN75/VU7cp+jvf2bzcKtUhqr38A9gEuz8zvdroYtc2plIuPe30HOC4zH+tQPWqvDwP7Ab+dmU93uhi13fnAD4GfAauA3Sk36TwB+O+IODAzF3ewvs1yqYwkjYCIOBl4H2WXqLd2uBy1UWbOqu4hMoty0fHuwI8j4mWdrUzDFRH7U2bZ/ykzb+h0PWq/zDyjui7pkcx8KjN/mpknAp8AtgZO72yFm2dw77zeGfWefo739j8xCrVIaoOIeDdwDmXbwEMz81cdLkkjoPqH/xLKcsYdgC93uCQNQ7VE5suUpasf6nA5Gn29Gwkc3NEqBmBw77yfV+1e/Rzv3aWgvzXwkrpIRLwX+DTwU0poX9bhkjTCMvM+yi9pvxkRMzpdj4ZsKuXf4hcBayIiex+UXd4Avlj19bUHuOqtd6lbV+/i5xr3zuvdz/uIiBjXtAf0NOAg4Cngxk4UJ2nwIuJvKOvabwVem5nLO1ySRs9vVO2Gjlah4VgL/Es/x15GWfd+LWXCzWU0Y0/v7n33drSKARjcOywz74mIKyh/an0XZaau1xmU3/y+kJnuISt1sYj4EHAmcAtwhMtjxpaI2ItyO/SVTf3jgL+n3ETv+sxc0Yn6NHzVhajv7OtYRJxOCe4XeufU+qq2+by/OVNFxBzgM9XTr4xyWS0xuHeHk4DrgU9FxOHAHcD+lD3e7wI+2MHa1AYRcQxwTPV0VtUeGBEXVF8vz0zvyldTEfE2SmjfQNmt4ORy08VNLM3MC0a5NLXPUcDHIuJaYAnlbpo7Ue6EvDuwDPizzpUnaRD+CHhfRFwD3EfZVWYP4PXAVsDlwMLOlTcwg3sXqGbdF1D+4T+S8g/Ew5SL285wBmdMeCnwtqa+3asHlP+AGNzra27Vjgfe2885PwAuGJVqNBL+B3ghZc/2/Shb9K6mTK5cBHzKv7JIXe9qyv1z9qMsRZ5C2fzjWsr/jy/q9jucR5fXJ0mSJAl3lZEkSZJqweAuSZIk1YDBXZIkSaoBg7skSZJUAwZ3SZIkqQYM7pIkSVINGNwlSZKkGjC4S5LaIiIuiIisbh8+ku+zNCKWjuR7SFI3MrhLkrpKRHw/Irw7oCQ1mdDpAiRJatHhnS5AkjrB4C5JqpXMvKfTNUhSJ7hURpI6LCLmVGvDL4iIeRFxaUT8KiJWR8S1EXFEH6+ZHBHvj4ifRMRTEfHriPhhRPxhm8Y/vXrNIZsbb5Cf77iI+FZE3BsRT1e1XhcRb+lrXODV1fNseHy/4bw+17gP43syJyK+HhHLI2JNRCyKiN8dzGeTpNHkjLskdY+5wA3AT4AvADsDfwT8d0T8n8z8BkBETAK+Swm4dwKfBbYB3gh8IyJempkfGOr4I+CfgZ8B1wAPAzsARwEXRcTemfmh6rwngDOA44Ddqq97Ld3cGwzje7IbcBNwL3ARsD3le3JZRLwmM69u9cNK0ojJTB8+fPjw0cEHMAfI6vGPTccWAM8AK4Btq76/rc69HJjQcO6OlICbwCuHOn7Vf3p1/iGbqfeCpv4Lqv45Tf179DHGJODK6r13aTr2/fLPU7/fr6XA0qa+4XxPTmsa63W9Y3X6fxs+fPjw0fhwqYwkdY+VwJmNHZm5CPgqsB3w+1X32ynB8q8yc33DuY8Cf189fecwxm+r7GNNemauo8yKT6A9F5sO9XtyH/CRptq+C9wPvKINdUlS2xjcJal7/G9mruqj//tVu19ETANeCDyUmXf2ce5VvecOZfwWah20iJgdEZ+NiDurtedZrWX/VnXKLsMcfzjfk1szc0Mf/b8Epg+nLklqN9e4S1L3eKSf/mVV21M9oKwV70tv/3ZDHL+tImJ3yhry6cAPgSsoM/8bKMtV3gZMHubbDOd78kQ/r1mPk1uSuozBXZK6x0799M+q2pXVo7Gv2c4N5w5l/F4bq7avfyf6CsD9+SvKxajHZ+YFjQci4s2U4D5cw/meSFJtOJsgSd3jZdWyj2aHVO2Pq6Uu9wC7RMSefZx7aNX+71DGb+hbUbUv6OP8BX309eeFVfutPo69up/XbACIiPGDeYNhfk8kqTYM7pLUPXqADzd2RMQC4E8os8WXVN3nAQH8Y2O4jYgZwIcazhnq+FCWtwAcHxETGs5/QfMYA1hatYc0ve/r6PtiUYDHq3Z2C+8z1O+JJNWGS2UkqXtcA7wzIvYHruO5fdbHAX+emb+uzlsI/A5wNLA4Ii6n7Fn+Jsr2h/8vM68dxvhk5o8i4hrgYOCmiLiKstTm9yj7pfc1E9+XzwHHA/8WEf8OPATsAxwJfLN6/2ZXVp/l4uqzPQ3cl5kXbeZ9hvo9kaTacMZdkrrHEuCVlGUqJwJ/SFnecVQ23Byp2krxtcAHq66/pKwVvxv4P5n5N8MZv8HRwJeAXav32A/4a6C/8Z8nM2+jLFW5Hng98BfAtsCxwOf7edmXgI9R/kLw15TtHN8xwPsM9XsiSbURmdnpGiRpixYRcyih+sLMPK5u40uSRocz7pIkSVINGNwlSZKkGjC4S5IkSTXgGndJkiSpBpxxlyRJkmrA4C5JkiTVgMFdkiRJqgGDuyRJklQDBndJkiSpBgzukiRJUg38f2QLdZZghEJyAAAAAElFTkSuQmCC\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", @@ -609,199 +247,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['dataset_iterator', 'seed'] seed\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'filters_cnn'] filters_cnn\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'lear_rate'] lear_rate\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'lear_rate_decay'] lear_rate_decay\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'coef_reg_cnn'] coef_reg_cnn\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'coef_reg_den'] coef_reg_den\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'dropout_rate'] dropout_rate\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['train', 'epochs'] epochs\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['train', 'batch_size'] batch_size\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", @@ -857,6 +307,15 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, From 0f70e5f4629b038c03dbf1405ac7a06dfcbf8658 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 18:51:17 +0300 Subject: [PATCH 282/616] feat: suffix for fitted on models --- .../models/evolution/Results_analysis.ipynb | 615 +++++++++++++++++- .../evolution/evolution_param_generator.py | 12 +- 2 files changed, 604 insertions(+), 23 deletions(-) diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb index 3cb6d21dca..3271729b7b 100644 --- a/deeppavlov/models/evolution/Results_analysis.ipynb +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -2,9 +2,35 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", + " return f(*args, **kwds)\n", + "/home/dilyara/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n", + "Using TensorFlow backend.\n", + "[nltk_data] Downloading package punkt to /home/dilyara/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package stopwords to\n", + "[nltk_data] /home/dilyara/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n", + "[nltk_data] Downloading package perluniprops to\n", + "[nltk_data] /home/dilyara/nltk_data...\n", + "[nltk_data] Package perluniprops is already up-to-date!\n", + "[nltk_data] Downloading package nonbreaking_prefixes to\n", + "[nltk_data] /home/dilyara/nltk_data...\n", + "[nltk_data] Package nonbreaking_prefixes is already up-to-date!\n", + "2018-06-25 16:47:39.319 DEBUG in 'gensim.models.doc2vec'['doc2vec'] at line 73: Fast version of gensim.models.doc2vec is being used\n", + "2018-06-25 16:47:39.323 INFO in 'summa.preprocessing.cleaner'['textcleaner'] at line 20: 'pattern' package not found; tag filters are not available for English\n", + "2018-06-25 16:47:39.729 DEBUG in 'matplotlib.backends'['__init__'] at line 90: backend module://ipykernel.pylab.backend_inline version unknown\n" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -27,11 +53,219 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Considered basic config:\n", + "{\n", + " \"dataset_reader\": {\n", + " \"name\": \"basic_classification_reader\",\n", + " \"x\": \"text\",\n", + " \"y\": \"intents\",\n", + " \"data_path\": \"snips\"\n", + " },\n", + " \"dataset_iterator\": {\n", + " \"name\": \"basic_classification_iterator\",\n", + " \"seed\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"field_to_split\": \"train\",\n", + " \"split_fields\": [\n", + " \"train\",\n", + " \"valid\"\n", + " ],\n", + " \"split_proportions\": [\n", + " 0.9,\n", + " 0.1\n", + " ]\n", + " },\n", + " \"chainer\": {\n", + " \"in\": [\n", + " \"x\"\n", + " ],\n", + " \"in_y\": [\n", + " \"y\"\n", + " ],\n", + " \"pipe\": [\n", + " {\n", + " \"id\": \"classes_vocab\",\n", + " \"name\": \"default_vocab\",\n", + " \"fit_on\": [\n", + " \"y\"\n", + " ],\n", + " \"level\": \"token\",\n", + " \"save_path\": \"vocabs/snips_classes.dict\",\n", + " \"load_path\": \"vocabs/snips_classes.dict\"\n", + " },\n", + " {\n", + " \"in\": [\n", + " \"x\"\n", + " ],\n", + " \"out\": [\n", + " \"x_lower\"\n", + " ],\n", + " \"name\": \"str_lower\"\n", + " },\n", + " {\n", + " \"id\": \"my_embedder\",\n", + " \"name\": \"fasttext\",\n", + " \"save_path\": \"embeddings/dstc2_fastText_model.bin\",\n", + " \"load_path\": \"embeddings/dstc2_fastText_model.bin\",\n", + " \"dim\": 100\n", + " },\n", + " {\n", + " \"id\": \"my_tokenizer\",\n", + " \"name\": \"nltk_tokenizer\",\n", + " \"tokenizer\": \"wordpunct_tokenize\"\n", + " },\n", + " {\n", + " \"in\": [\n", + " \"x_lower\"\n", + " ],\n", + " \"in_y\": [\n", + " \"y\"\n", + " ],\n", + " \"out\": [\n", + " \"y_labels\",\n", + " \"y_probas_dict\"\n", + " ],\n", + " \"main\": true,\n", + " \"name\": \"intent_model\",\n", + " \"save_path\": \"evolution/classification/intents_snips\",\n", + " \"load_path\": \"evolution/classification/intents_snips\",\n", + " \"classes\": \"#classes_vocab.keys()\",\n", + " \"kernel_sizes_cnn\": [\n", + " 1,\n", + " 2,\n", + " 3\n", + " ],\n", + " \"filters_cnn\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 100\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"confident_threshold\": {\n", + " \"evolve_choice\": true,\n", + " \"values\": [\n", + " 0.5,\n", + " 1\n", + " ]\n", + " },\n", + " \"optimizer\": \"Adam\",\n", + " \"lear_rate\": {\n", + " \"evolve_range\": [\n", + " 0.0001,\n", + " 0.1\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"lear_rate_decay\": {\n", + " \"evolve_range\": [\n", + " 0.0001,\n", + " 0.1\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"loss\": \"binary_crossentropy\",\n", + " \"text_size\": 15,\n", + " \"coef_reg_cnn\": {\n", + " \"evolve_range\": [\n", + " 1e-06,\n", + " 0.001\n", + " ]\n", + " },\n", + " \"coef_reg_den\": {\n", + " \"evolve_range\": [\n", + " 1e-06,\n", + " 0.001\n", + " ]\n", + " },\n", + " \"dropout_rate\": {\n", + " \"evolve_range\": [\n", + " 0.1,\n", + " 0.9\n", + " ]\n", + " },\n", + " \"dense_size\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 100\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"model_name\": \"cnn_model\",\n", + " \"embedder\": \"#my_embedder\",\n", + " \"tokenizer\": \"#my_tokenizer\",\n", + " \"check_bool\": {\n", + " \"evolve_bool\": true\n", + " }\n", + " }\n", + " ],\n", + " \"out\": [\n", + " \"y_labels\",\n", + " \"y_probas_dict\"\n", + " ]\n", + " },\n", + " \"train\": {\n", + " \"epochs\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"batch_size\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"metrics\": [\n", + " \"classification_accuracy\",\n", + " \"classification_f1\",\n", + " \"classification_roc_auc\"\n", + " ],\n", + " \"validation_patience\": 5,\n", + " \"val_every_n_epochs\": 1,\n", + " \"log_every_n_epochs\": 1,\n", + " \"validate_best\": true,\n", + " \"test_best\": false\n", + " },\n", + " \"metadata\": {\n", + " \"labels\": {\n", + " \"telegram_utils\": \"IntentModel\",\n", + " \"server_utils\": \"KerasIntentModel\"\n", + " },\n", + " \"download\": [\n", + " \"http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz\",\n", + " \"http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz\",\n", + " {\n", + " \"url\": \"http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv\",\n", + " \"subdir\": \"snips\"\n", + " },\n", + " {\n", + " \"url\": \"http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin\",\n", + " \"subdir\": \"embeddings\"\n", + " }\n", + " ]\n", + " }\n", + "}\n" + ] + } + ], "source": [ "CONFIG_FILE = \"../../configs/evolution/evolve_intents_snips.json\"\n", "KEY_MAIN_MODEL = \"main\"\n", @@ -46,9 +280,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2018-06-25 16:47:39.741 INFO in 'deeppavlov.models.evolution.evolution_param_generator'['evolution_param_generator'] at line 55: Main model path in config: ['chainer', 'pipe', 4]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Title name for the considered evolution is `intents_snips`.\n", + "Number of populations: 10.\n" + ] + } + ], "source": [ "evolution = ParamsEvolution(population_size=POPULATION_SIZE,\n", " key_main_model=KEY_MAIN_MODEL,\n", @@ -74,9 +324,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Measure: classification_accuracy\n", + "valid:\n", + "min for\t0 model on\t0 population\n", + "max for\t1 model on\t0 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "\n", + "Measure: classification_f1\n", + "valid:\n", + "min for\t1 model on\t6 population\n", + "max for\t1 model on\t0 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "\n", + "Measure: classification_roc_auc\n", + "valid:\n", + "min for\t1 model on\t6 population\n", + "max for\t1 model on\t9 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: 'argmin' is deprecated. Use 'idxmin' instead. The behavior of 'argmin' will be corrected to return the positional minimum in the future. Use 'series.values.argmin' to get the position of the minimum now.\n", + " if __name__ == '__main__':\n", + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", + " # Remove the CWD from sys.path while we load stuff.\n" + ] + } + ], "source": [ "MEASURES = evolution.get_value_from_config(\n", " evolution.basic_config, list(evolution.find_model_path(\n", @@ -103,11 +394,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2018-06-25 16:47:39.818 DEBUG in 'matplotlib.font_manager'['font_manager'] at line 1343: findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=20.0 to DejaVu Sans ('/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "path_to_pics = expand_path(Path(evolution.get_value_from_config(\n", " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).joinpath(\"pics\"))\n", @@ -173,11 +502,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9,\n", + " 9, 10, 10])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "params_dictionaries = []\n", + "models_ids = []\n", "\n", "for i in range(data.shape[0]):\n", " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", @@ -185,22 +527,50 @@ " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", " d = json.loads(json_acceptable_string)\n", " params_dictionaries.append(d)\n", + " models_ids.append(d[\"evolution_model_id\"])\n", "\n", - "models_ids = []\n", - "for pdict in params_dictionaries:\n", - " models_ids.append(pdict[\"evolution_model_id\"])\n", - " \n", "models_ids = np.array(models_ids)\n", "models_ids" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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FQ3MP8F3gPZn55DBiORG4CvhCRBwO3AK8lLLH++3Ah/vcf0tVRkN810bEN4Djgd9ExHnAEsoHgmMp8+/PzMz/HUZ8kiRJUstE2fp8GE+MmAW8hLJF42PAtZn5yIiCidgF+BhwNLAt8CBwHnB6Zq7oc28CZGb0qQ/gzcBxwAuALYHHgRuAr2fmgLvKNJo/f34uWrRoJG9HkiRJGlREXJeZ8we9b7iJ+6bOxF2SJEljYaiJe9057pIkSZJaoO7JqQBExEso2z7uxPqdXBplZr51JIFJkiRJWq9W4l7NHz8H+GvKotCkYXFow3UCJu6SJElSk9SdKvMeygmk3wLmU5L0M4GXAR8CngC+DzyniTFKkiRJE17dqTJvBm7LzOMAygA8j2XmNcA1EXEh5ZCki4FvNDFOSZIkaUKrO+I+lz8+vOiZ5D8zbwAuoOzJLkmSJKlJhrOrzMqGr7uAbfq030FJ8CVJkiQ1Sd3E/QHKTjK97gb27XPPXpSEXpIkSVKT1E3cr2XDRP1/gP0i4h8j4k8i4t3AMZR57pIkSZKapG7i/p/A5IjYo7r+F2AJcDpwE/BF4DHg75sWoSRJkqR6u8pk5o+BHzdcPxoRLwLeDuwJLAa+mZkPNjNISZIkaaIb1smpjTJzJXBGE2KRJEmSNIBaU2UiojsivjNawUiSJEnqX9057k8A945GIJIkSZIGVjdxvwF43mgEIkmSJGlgdRP3TwOvjIhXjEYwkiRJkvpXd3Hq9sDPgP+JiB8DvwEeArLvjZn5zZGHJ0mSJAnqJ+7nUJL0ABZUD9gwcY/q2sRdkiRJapK6ifvxoxKFJEmSpI2qewDTuaMViCRJkqSB1V2cKkmSJKkFTNwlSZKkNlBrqkxE3D3EWzMz9xxGPJIkSZL6UXdx6iT62foR2BrorL5eCjw9kqAkSZIkbaju4tTdB2qLiOcCXwA6gKNGFpYkSZKkRk2b456Zd1L2dd8JOLVZ/UqSJElq8uLUzHwKuBj4y2b2K0mSJE10o7GrzDpgh1HoV5IkSZqwmpq4R8R2wGuB+5rZryRJkjTR1d0O8qMb6WcX4BjK7jL/MMK4JEmSJDWoux3kaYO0Pw58PDP/ZXjhSJIkSepP3cT90AHqe4AVwK2ZuW5kIUmSJEnqq+4+7r8YrUAkSZIkDWw0dpWRJEmS1GS1EveIeENEXBYROw7QvlNEXBoRC5oTniRJkiSoP+L+NmDrzFzaX2NmPkDZVeZtIw1MkiRJ0np1E/fnA4sGuec3wLzhhSNJkiSpP3UT922Ahwe5Zzmw3fDCkSRJktSfuon7I8Beg9yzF/DY8MKRJEmS1J+6ifuVwGsiYm5/jRGxN+X01F+ONDBJkiRJ69VN3M+g7P3+q4g4KSJmR0RHVZ5MSdgnV/dJkiRJapK6BzD9JiJOBL4MfK56NOoG3pWZv25SfJIkSZKombgDZObXI+JXwInAS4GtKXParwG+mpm3NDdESZIkSbUTd4AqOf+bJsciSZIkaQB157hLkiRJaoFaiXtEvCEiLouIHQdo3ykiLo2IBc0JT5IkSRLUH3F/G7B1Zi7trzEzHwA6q/skSZIkNUndxP35wKJB7vkNMG944UiSJEnqT93EfRvg4UHuWQ5sN7xwJEmSJPWnbuL+CLDXIPfsRdkeUpIkSVKT1E3crwReExFz+2uMiL2BYygnqNYWETtHxNkRsTQi1kTE4og4MyJmDqOvF0fEdyPi/qqvP0TELyLiTcOJTZIkSWqluon7GZS9338VESdFxOyI6KjKkykJ++TqvloiYk/gOuB44FrKqax3AycDV0fEtjX6eg9lrv2RwKXAZ4DzqtheWTc2SZIkqdVqHcCUmb+JiBOBL1MS68/1uaUbeFdm/noYsXwF2B44KTO/2FsZEZ8F3gd8AnjnYJ1ExJHAF4CLgddn5hN92qcOIzZJkiSppSIz6z+pTIk5EXgpsDVlTvs1wFerU1Xr9rcncCewGNgzM3sa2rYEHgQC2D4zuwbp67fAc4FdM3N53Vh6zZ8/PxctGmwDHUmSJGlkIuK6zJw/2H21Rtx7Vcn53wznuQM4tCovakzaq9d6IiKupEx72Z8y9aVfEbEPZSvKHwOPRsShwL5AAjcCl/ftX5IkSWoHw0rcR8Gcqrx9gPY7KIn7bDaSuAMvqcqHgZ8DB/dp/11ELMjMO4cZpyRJktQSdRenPiMiJkfEsyJi1/4eNbvrrMqVA7T31m89SD/bV+Vbgd2BV1V9zwa+TTlA6icRMa2/J0fECRGxKCIWLVu2bIihS5IkSaOvduIeEc+PiJ8ATwBLgXv6edzdzCBr6H0/k4G/yMyfZubjmXkH8CbKqa+zgdf19+TMPCsz52fm/FmzZo1NxJIkSdIQ1Ercq0WpV1GmoFxMWTB6U/X18ur658C3asbRO6LeOUB7b/1gBzv1tj+UmVc3NmRZhXt+dblfzfgkSZKklqo74v4RYCrwssw8pqo7LzOPBvYAvgE8D/hozX5vq8rZA7T3ntY60Bz4vv0MlOCvqMrNhxiXJEmSNC7UTdwPAS7IzN811AVAtU3jOyjJ8T/V7PfyqjwyIjaIqdoO8kBgNWXLyY25BugCdo+Ijn7a96nKe2rGJ0mSJLVU3cR9O8oOL73WATN6LzJzHSUJP7JOp5l5F3ARZUHpu/s0nw50AN9q3MM9IuZGxNw+/awG/h3YDPh4RETD/c8Hjqti/lGd+CRJkqRWq7sd5KPAFg3XjwB9d5BZy8Bz1TfmRMr8+S9ExOHALZQDng6lTJH5cJ/7ew96ij71/0iZg/9e4IBqD/hnAQsoCf17qw8KkiRJUtuoO+J+F2VUvNd1wCsiYnuAanrKMQxjKkqVTM8HzqEk7B8A9gQ+D+w/1FNQM/Nx4CDgk8A2wHuAVwO/Ao7KzM/XjU2SJElqtboj7hcBH4yIjmrayv+j7JV+Q0RcRTmldDdK0l1bZt4HHD/Ee/uOtDe2raKM0PcdpZckSZLaUt0R969TDjfaHCAzfwK8r7p+HeUApE8DX2hijJIkSdKEV2vEPTMfBH7Qp+7zEfElysLVh6v90iVJkiQ1Ue2TU/uTmd2Z+YeBkvaIeHNEXNaM15IkSZImoqYk7kOwO/DyMXotSZIkaZMzVom7JEmSpBEwcZckSZLagIm7JEmS1AZM3CVJkqQ2YOIuSZIktQETd0mSJKkNmLhLkiRJbcDEXZIkSWoDY5W43wh8c4xeS5IkSdrkTBmLF8nM84Hzx+K1JEmSpE1R7cQ9IrYB3gLsB8wEJvdzW2bm4SOMTZIkSVKlVuIeEXOBnwOzgNjIrTmCmCRJkiT1UXeO+xnA9sCngecAUzNzUj+P/kbhJUmSJA1T3akyBwE/ycwPjUYwkiRJkvpXd8Q9gJtHIxBJkiRJA6ubuF8HzBmNQCRJkiQNrG7i/jHglRFxyCjEIkmSJGkAdee470LZj/2iiPgeZQT+sf5uzEwPXJIkSZKapG7ifg5lq8cA3lg9+m79GFWdibskSZLUJHUT9+NHJQpJkiRJG1Urcc/Mc0crEEmSJEkDq7s4VZIkSVIL1J0qA0BEzAAWAC8CtgZWAtcD52VmV/PCkyRJkgTDSNwj4pXAucA2lIWovRL4XEQcn5kXNCk+SZIkSdRM3CPixcBCYDLwHeAy4EHg2cBhwF8CP4qIAzPzuibHKkmSJE1YdUfcP0wZWT8oM6/p03ZORHwZ+DnwIeB1Iw9PkiRJEtRfnHoQ8MN+knYAMvPXwI+q+yRJkiQ1Sd3EvRO4b5B77gW2Gl44kiRJkvpTN3FfCuw3yD3zKfPeJUmSJDVJ3cT9p8BhEfH3ETG5sSEiJkXEB4AjqvskSZIkNUndxan/BBwLfAJ4R0T8kjK6vgPwp8DuwEPAx5sYoyRJkjTh1UrcM/OhiDgQ+BrwCmC3PrdcDLwzM50qI0mSJDVR7QOYMnMxcFRE7EQ5ObWTcnLqDZn5QHPDkyRJkgTDSNx7VUm6ibokSZI0BuouTpUkSZLUAhsdcY+IsyknpX4oM/9QXQ9FZuZbRxydJEmSJGDwqTLHURL3TwN/qK6HIgETd0mSJKlJBkvc96jKB/pcS5IkSRpDG03cM3PJxq4lSZIkjY1ai1Mj4qMRcfAg9xwUER8dWViSJEmSGtXdVeY04JBB7jkYOHU4wUiSJEnq32hsBzkV6BmFfiVJkqQJazQS9xcDj4xCv5IkSdKENejJqRFxWZ+q4yLikH5unQzsAuwGfG/koUmSJEnqNWjizoZz2hPYvXr01QMsB34AvG+EcUmSJElqMGjinpnPTKeJiB7gtMz82KhGJUmSJGkDdee4Hw/8eDQCAYiInSPi7IhYGhFrImJxRJwZETNH0OfBEdEdERkRH29mvM320E3w89Pg/LeU8qGbWh2RJEmSxotaiXtmnpuZo5JORsSewHWUDwfXAp8D7gZOBq6OiG2H0eeWwLnA6iaGOioeugmuPgOeXAFb7VzKq88weZckSVIxlDnu/YqInYGdgOn9tWfmFTW7/AqwPXBSZn6x4XU+S5kz/wngnTX7/DzQCfxz9fxx69aFsNlMeP6v38sWD90IQPc6mHwF/a8okCRJ0si88IVw5pmtjmLIaifuEXEkZTR87iC3Tq7R557AkcBi4Mt9mk8FTgDeGBEfyMyuIfZ5DGX0/o2M4APKWFl5bxlpbzRpCqxb05p4JEmSNL7USmgjYn/gAmAZ8CXgb4BfALcBBwF7A/8F3FAzjkOr8qLM3ODwpsx8IiKupCT2+wOXDiHO7YGvAz/OzG9HxHE14xlznbuW6TF3Hr3+U9+TK2DzmXDIaa2LS5IkSeND3cWp/wA8BbwkM0+u6i7PzHcC+wAfB44AflSz3zlVefsA7XdU5ewh9vd1ynurO7WmZeYugKdWlGQ9e0r51IpSL0mSJNVN3A8A/iszl/btI4uPArcAp9fst7MqVw7Q3lu/9WAdRcRbgNcAJ2bmH+oEEREnRMSiiFi0bNmyOk8dsR3mwQGnlBH2x+8v5QGnlHpJkiSp7tzvTuDehuu1QEefe64E/u9IghquiNgdOBP4YWb+R93nZ+ZZwFkA8+fPz6YGNwQ7zDNRlyRJUv/qjrg/DMzsc71nn3umApvX7Ld3RL1zgPbe+scG6eds4EngxJqvL0mSJI1rdUfcb2fDRP0a4M8iYnZm3h4ROwCvY/2c9KG6rSoHmsO+V8Prb8yLKUn+sojor/3DEfFh4PzMPLZmjJI0rtzXs4rr81GWs4Ztmc6LYxt2mbRFq8OSJI2Suon7z4CPR8Q2mfkoZZ/0BcANEXEzJcHeEvhgzX4vr8ojI2JS484y1SFKB1IOUbpmkH6+Cczop34v4GDgRsohT3V3vZGkceW+nlVcmEuZkVPYhml0sY4LWcpRPTuavEvSJqpu4v414ArgaYDMvDIi3gD8E2VXmcXABzPzm3U6zcy7IuIiypaP7wa+2NB8OmUe/dca93CPiLnVc29t6Oek/vqvtoM8GPhJZn6kTmySNB5dn48yI6fQEeXHeAdTIOF6HmUXTNwlaVNUK3HPzMeBX/epOw84rwmxnAhcBXwhIg6n7E7zUsoe77cDH+5z/y1V2e+cGEnalC1nDdswbYO6GUxmOZ7aJmkAS26Day+FZUth1o6w3+Gw25zBn6dxo+7i1FGTmXcB84FzKAn7Byjz6T8P7J+Zy1sXnSSNL9syndV0b1C3mm62ZXqLIpI0ri25DS44F7oeh+12KOUF55Z6tY26J6fuC7yKMm3lj/ZIrxannkDZ6/3GusFk5n3A8UO8d8gj7Zl5DuUDgSRtEl4c23AhSyHLSPtqulkd6zgotm91aJLGo2svhY6tygPWl9de6qh7G6k74v4B4G2UbSD78wfgrcD7RxKUJGnjdpm0BUfFjnTEFB6NtXTEFI4KF6ZKGsCypTCjz8+HGVuUerWNuotTDwAuz8x+DyfKzIyIyygLQSVJo2iXSVu4EFXS0MzasUyP6R1pB1i9qtSrbdQdcd8BuH+Qe5YCzx5eOJIkSWq6/Q4viXvX45A967/e7/BWR6Ya6ibuq4FZg9wzC9zWQJIkadzYbQ68+s1lxP2Rh0r56jc7v73N1J0qcyNwTES8PzNX9W2MiK2AY6r7JEmSNF7sNsdEvc3VHXE/izKzQSqYAAAe2UlEQVSifnFEzGtsiIgXABcB21X3SZI0Jpb3rOD67pv4ZffVXN99E8t7VrQ6JElquroHMP0gIv4MeBNwQ0T8AXgA2Al4FuUwpG9m5veaHqkkSf1Y3rOC3+ctTMtpzGAGa1jL77mFfXr2ZttJM1sdniQ1Te0DmDLzOOCdwM2Uxar7VuX/AidU7ZIkjYkleR/TchrTYxoRwfSYxrScxpK8r9WhSVJT1Z3jDkBmngWcFREzgK2BxzJzdVMjkyRpCLroYgYzNqibxlS66GpRRJLGvZtugoUL4d57YdddYcECmDdv8Oe1WO0R90aZuTozl5q0Sxort61Zw5cffZQPP/wwX370UW5b4yZWE10HHazl6Q3q1vI0HXS0KCJJ49pNN8EZZ8CKFbDzzqU844xSP86NKHGXpLF025o1nLNyJY93d7PD5Mk83t3NOStXTtjk/b6eVZzffS9nd9/B+d33cl/PH232NSHsFruwNtayJteSmazJtayNtewWu7Q6NEnj0cKFMHNmeUyatP7rhQtbHdmgNjpVJiLuBhI4IjPvqa6HIjNzzxFHJ0kNLunqYqsItpo8GaCU3d1c0tXFnOnTWxzd2LqvZxUX5lJm5BS2YRpdrONClnJUz47sMmlinaa67aSZ7NOzN0u4jy666KCD2bGnC1Ml9e/ee8tIe6POzlI/zg02x30SJXEf6HogMeyIJODuJT1cdW3y8DLYfha8bL/gObtNzD8QnX/PGr5++1oefDJ59ubB22dP45g9JlaS2mvpunXsUCXtvbaYNIml69a1KKLWuT4fZUZOoSPKj/EOpkDC9TzKLkysxF2Satl11zI9ZmbDh/uVK0v9OLfRTCgzd8/MPTLznj7Xgz7GJnxtiu5e0sPCC5JVXcl225Vy4QXJ3Ut6Wh3amDv/njWcesMaVq5NnrUZrFybnHrDGs6/Z2JODdlxyhRW9Wz4fbCqp4cdpwxrnX1bW84aZrDhh5gZTGb5BDy4unc7yDW5lhk5gzW5lt/nLe7lLql/CxaUxH3FCujpWf/1ggWtjmxQG03cI+KzEXFkw/Wu1emo0qi56tpki45ki45gUgRbdARbdCRXXTuUP/ZsWr5++1q2nAqd0yYxKSbROW0SW04t9RPRER0dPJ7J493d9FTl45kc0THxFiFuy3RW071B3Wq62ZaJ99cYt4OUhmjJbfDDr8BXPlLKJbe1OqLWmDcPTjmljLjff38pTzmlLXaVGWyY6r3AY5QTUQHuAU4HPjaaQWlie3gZTJ4Cdy9OulZDxwzYaUdYPQH3LnrwyTLS3mjLqaV+IpozfTrHdXZySVcXS9etY8cpU1jQ0THh5rcDvDi24UKWQpaR9tV0szrWcVBs3+rQxpzbQUpDsOQ2uOBc6NgKttsBuh4v169+M+w2p9XRjb1589oiUe9rsMR9FWzw09C56xp1U6cmN90MHZvDjM1h7Vr4/S0w73kTL1l99ubByrVJ57T1dU88XeonqjnTp0/IRL2vXSZtwVE9O3I9j7KcNWzLdA6K7SfcwlQo20GuYS3TWf8/ittBSn1ce2lJ2juqiRO95bWXTszEvU0NlrjfCSyIiPOAB6u6rSNi0Nn7mTn+l+ZqfIuGxwT19tnTOPWGNUAPW04tSfsTT8Mp+0wb9Lna9O0yaQsXolK2g/w9t0CWkfa1PM3aWMvscHMz6RnLlpaR9kYztij1ahuDJe7/CnwbuKqh7uTqsTE5hL6lfj39dPD8vZP7l/LMVJnn7F3qJ5re3WMad5U5ZZ+Ju6uM1B+3g5SGYNaOZXpMR8NSxdWrSr3axkaT68z8XkTcA7wK2Ak4DrgJuHH0Q9NEtf0sWNUF8/5kfaK+qivZZoL+Dj5mj+km6tIgtp00k22ZoD8kpKHY7/Aypx3KSPvqVSWRP/S1rY1LtQw6Kp6Z1wDXAETEccB5meniVI2al+0XLLwAIJkxoyxKXdUVHHnoxBtxlySpKXabUxaiXntpmR4za8eStE/U+e033VROSr333rJ/+4IFbbFYNTKHvuAvIt4M3JiZvx29kMaH+fPn56JFi1odxoTlAUySJGlU3HQTnHFG2Qays7McvrRiRUu3hIyI6zJz/mD31ZqHnpnnDj8kaeies9sknrNbq6OQJLW9R5fAfddC1zLomAW77Afb+AtmQlu4sCTtvSen9pYLF477UfeNJu4RcXD15bWZ+VTD9aAy84oRRSZJkjQSjy6Bmy+A6R0wYztY21Wun/dqk/eJ7N57YeedN6zr7Cz149xgI+4/p+wQszdwe8P1UEwe/BZJkqRRct+1JWmfVu3p31ved62J+0S2665laszMhgXtK1eW+nFusMT9Y5RE/ZE+15IkSeNb17Iy0t5o6oxSr4lrwYIyxx02nOP+1re2Nq4hGGw7yNM2di1JksafJ9c8xBNdt/L0upVMndLJlh1z2Xz6DoM/cVPTMQsefxBWPwpPry5J+4xtYKtntzoytdK8eWUhauOuMm9967if3w4ekiRJ0iblyTUPsXzl1UyOzZgyeSu6u59k+cqr2bbzgImXvG+1M9x1BUzdvDzWroau5bDzS1odmVpt3ry2SNT7qpW4R8RkYHpmru5TfxhwDLAaOCsz72leiJK03m1r1nBJVxdL161jxylTOKKjgznTPaBK6vVE161Mjs2YPHlzgFJ2l/oJl7g/fj/M2mv9iPu0GbD1zqVeakN1R9zPAN4VEc/KzJUAEfEXwHeA3tNx3hYRL87M+5oYpyRx25o1nLNyJVtFsMPkyTze3c05K1dyXGenybtUeXrdSqZM3mqDukmTNuPpdStbFFELdS2DLXfYcGpMpnPc1bbqnmhzMHB5b9JeORV4DHgT8EFga+D9zQlPkta7pKuLrSLYavJkJlXlVhFc0tXV6tCkcWPqlE56ep7aoK6n5ymmTulsUUQt1DGrjLQ3enp1qZfaUN3EfRfgzt6LiHgOMAf4YmZ+OzPPAP4HOLp5IUpSsXTdOraYtOGPrS0mTWLpunUtikgaf7bsmMuadSt4YvUdPN51K0+svoM161awZcfcVoc29nbZD9Z0lf3bM0u5pqvUS22obuK+FfB4w/WBlO0hf9ZQ979An13tJWnkdpwyhVU9PRvUrerpYccprrOXGvXOXc3MDa4nnG12K4ctTeuA1Y+U0sOX1Mbq/rZ7ENij4foI4Enguoa6LQCHvyQ13REdHZyzciV0d7PFpEms6unh8UwWdHS0OjRp3Hii61amTZnJ5tN3fKauu/vJibk4FUqSbqKuTUTdEfdrgNdExKsj4gjg9cBlmfl0wz17AA80K0BJ6jVn+nSO6+xkq8mTeai7m60mT3ZhqtTH0+tWMmnSZhvUTdjFqdImpu6I+ycp2z6eX133AJ/obYyIzYCDgB81JTpJ6mPO9Okm6tJGTJ3SSXf3k89sBwkTeHGqtImplbhn5u8i4qXAm6uqH2TmbxpueRFwGfC9JsUnSZJq2LJjLstXXg3dZaS9p+cpuvMptu54UatDkzRCtVd0ZebvgFMGaLsaeO1Ig5IkScOz+fQd2LbzAJ7oupWn161k6pROtu540cSc3y5tYpqyFUNETAX2AVZn5m3N6FOSJA3P5tN3MFGXNkG1FqdGxJ9HxH9ExDYNdXtStoBcBNwcEQsjwr3ZJEmSpCaqu6vMW4C5mfloQ91ngOcClwM3URavHt+c8CRJkiRB/cT9ecAzi1EjYivglcB/ZOYRwH7ArZi4S5IkSU1VN3GfRTmEqdcBlHny3weo9nO/GNizKdFJkiRJAuon7k8AjRvBvhxI4FcNdU8BW44wLkmSJEkN6i4ivQP4s4iYTknY/xy4KTMfabhnN+DhJsUnSZIkifoj7mcBz6Ek8LcAewDf6HPPvpRdZiRJkiQ1Sa3EPTPPBT4FzKBMmfkS8MXe9oh4Get3mJEkSZLUJMM5OfVDwIcGaF4EzAS6RhKUJEmSpA019aCkzFwLrG1mn5IkSZLqz3GXJEmS1AK1E/eIeHZEfDki7oyIJyOiu5/HutEIVpIkSZqoaiXuEbETZR77Oyjz2KcD91J2mekGAvgt8MvhBBMRO0fE2RGxNCLWRMTiiDgzImYO8fkdEfFXEfHdiLg1Iroi4omIWBQRH4iIacOJS5IkSWq1uiPuHwV2AI7OzBdUdd/IzLmUbSIvBDYHFtQNJCL2BK4DjgeuBT4H3A2cDFwdEdsOoZuDgG8DRwG/p+x4811gJ+AM4PKI2KxubJIkSVKr1U3cjwJ+lpmX9G3IzPuBN1AS99OHEctXgO2BkzLz2Mz8+8w8jJLAzwE+MYQ+HgL+Gnh2Zr6+6uMdwGzgeuBlwLuHEZskSZLUUnUT9x3Y8HClbkqiDkBmrgIuBo6p02k12n4ksBj4cp/mUynTct4YER0b6yczb8zM71S72zTWPwF8pro8pE5skiRJ0nhQN3F/HGicJ76CMg2l0UpgVs1+D63KizKzp7GhSrqvpBz6tH/Nfhs9XZUunJUkSVLbqZu4LwF2abj+LXBYRMwAiIhJlJHz+2v2O6cqbx+g/Y6qnF2z30ZvqcqfjaAPSZIkqSXqJu6XAodGxNTq+lxgR+CqiPhXysj4nwA/qNlvZ1WuHKC9t37rmv0CEBHvAY4GbgTO3sh9J1Q70CxatmzZcF5KkiRJGhV1T079d8r0mO2ABzPz2xGxL/A3wLzqnu8ztIWkYyIiFgBnUhauvi4znx7o3sw8CzgLYP78+Tk2EUqSJEmDq5W4Z+YdwKf71L0vIj5J2Q5ycWb+YRhx9I6odw7Q3lv/WJ1OI+JYygeJh4FDM/PuYcQmSZIktVzdEfd+ZeYyYCRzS26ryoHmsO9VlQPNgf8jEfEGyh7uDwGHVR86JEmSpLZUd477aLm8Ko+sFrg+IyK2BA4EVgPXDKWziPgr4HvAUuDlJu2SJElqdxsdcY+IARdyDiIz8601br4rIi6i7EjzbsqJp71OBzqAr2VmV0Nsc6vn3ton5jdTFqAuoUyPWTLM9yBJkiSNG5E58BrMiOgZsHHjMjMn1wqkHMJ0FeX01POBW4CXUvZ4vx14WWYub7g/qxeKhrpDgUsof0k4G7ivn5d6LDPPHCye+fPn56JFi+q8hRF76Ca4dSGsvBc6d4W5C2CHeYM/T5IkSe0rIq7LzPmD3TfYHPc9mhTPoKpR9/nAxyhbN74SeBD4PHB6Zq4YQje7sX76z1sGuGcJZZeZceWhm+DqM2CzmbDVzvDkinJ9wCkm75IkSRokcR/raSaZeR9w/BDvjX7qzgHOaW5UY+PWhSVpf+DXsOqhUtezDpZcAVvv3tLQJEmSNkk7vBCOHnfDuQOrtTg1It4QEZdFxI4DtO8UEZdWe6erhpX3wmZ9NsOcNAXWrWlNPJIkSRpf6m4H+TZg68xc2l9jZj4QEZ3VfQtHGtxE0rlrmR7z3KPX1z25AjafCYec1rKwJEmSNE7U3Q7y+cBgKzZ/w/pTVDVEcxfAUytKsp49pXxqRamXJEmS6ibu21BOId2Y5cB2wwtn4tphXlmIuvlMePz+UrowVZIkSb3qTpV5hPWnmA5kL+Cx4YUzse0wz0RdkiRJ/as74n4l8Jrew4/6ioi9gWOAX440MEmSJEnr1U3cz6CM0v8qIk6KiNkR0VGVJ1MS9snVfZIkSZKapNZUmcz8TUScCHwZ+Fz1aNQNvCszf92k+CRJkiRRf447mfn1iPgVcCLwUmBrypz2a4CvZuYtzQ1RkiRJUu3EHaBKzv+mybFIkiRJGkDdOe6SJEmSWsDEXZIkSWoDJu6SJElSGzBxlyRJktqAibskSZLUBkzcJUmSpDZg4i5JkiS1ARN3SZIkqQ2YuEuSJEltwMRdkiRJagMm7pIkSVIbMHGXJEmS2oCJuyRJktQGTNwlSZKkNmDiLkmSJLUBE3dJkiSpDZi4S5IkSW3AxF2SJElqAybukiRJUhswcZckSZLagIm7JEmS1AZM3CVJkqQ2YOIuSZIktQETd0mSJKkNmLhLkiRJbcDEXZIkSWoDJu6SJElSGzBxlyRJktqAibskSZLUBkzcJUmSpDZg4i5JkiS1ARN3SZIkqQ2YuEuSJEltwMRdkiRJagMm7pIkSVIbMHGXJEmS2oCJuyRJktQGTNwlSZKkNmDiLkmSJLUBE3dJkiSpDYyrxD0ido6IsyNiaUSsiYjFEXFmRMys2c821fMWV/0srfrdebRilyRJkkbTlFYH0Csi9gSuArYHzgduBfYDTgaOjogDM3P5EPrZtupnNnAZ8H1gLnA88KqIOCAz7x6ddyFJkiSNjvE04v4VStJ+UmYem5l/n5mHAZ8D5gCfGGI/n6Qk7Z/NzMOrfo6lfADYvnodSZIkqa1EZrY6ht7R9juBxcCemdnT0LYl8CAQwPaZ2bWRfrYAHgZ6gGdn5hMNbZOAu4HdqtfY6Kj7/Pnzc9GiRcN+T5IkSdJQRMR1mTl/sPvGy4j7oVV5UWPSDlAl31cCM4D9B+lnf2Bz4MrGpL3qpwe4sM/rSZIkSW1hvCTuc6ry9gHa76jK2WPUjyRJkjSujJfFqZ1VuXKA9t76rUezn4g4ATihulwVEbcN8nqjZTvgkRa9tsYvvy80EL83NBC/N9Qfvy/Gn92GctN4SdzHhcw8Czir1XFExKKhzHPSxOL3hQbi94YG4veG+uP3RfsaL1NlekfCOwdo761/bIz6kSRJksaV8ZK4905JGWju+V5VOdDc9Wb3I0mSJI0r4yVxv7wqj6y2bXxGtR3kgcBq4JpB+rkGeBI4sHpeYz+TgCP7vN541fLpOhqX/L7QQPze0ED83lB//L5oU+Micc/Mu4CLgN2Bd/dpPh3oAL7VuId7RMyNiLl9+lkFfKu6/7Q+/byn6v/C8X5yajXXXtqA3xcaiN8bGojfG+qP3xfta1wcwATPHMJ0FeV00/OBW4CXUvZcvx14WWYub7g/ATIz+vSzbdXPbOAy4Fpgb+AYyuFML6s+KEiSJEltY9wk7gARsQvwMeBoYFvKiannAadn5oo+9/abuFdt2wCnAscCzwaWA/8DfDQz7x/N9yBJkiSNhnGVuE9kEbEzf/yh5cf086FFE0P116PXAq8Cng/sBKwFfgd8A/hG35OGNXFFxF9TpgoCvD0z/62V8ai1IuJwyhTRA4CZlAGs3wGfz8yftjI2tUZEvAo4GXge6/OM64DPZubVrYxNQ2fiPg70M03oVmA/yjSh24ADG6cJaWKIiHcCX6X8cL0cuBd4FrCAsrXpfwJvSP8nnvCqv1b+DpgMbIGJ+4QWEf8C/C1wP+WvzY8As4B9gUsy84MtDE8tEBGfBj5I+QD3Y8r3xHOB11DO9HlTZn67dRFqqEzcx4GIuJCy481JmfnFhvrPAu8DvpaZ72xVfGqNiDiMstD6J40j6xGxA2Xtxi7A6zPzP1sUosaBiAjgYmAPYCFwCibuE1ZEvJ2yY8i5wAmZubZP+9TMfLolwaklqt8ZDwDLgHmZ+XBD26GU9YD3ZOZzWhSiahgXu8pMZNVo+5HAYuDLfZpPBbqAN0ZExxiHphbLzMsy87/7TofJzIeA/1ddHjLmgWm8OQk4DDie8vNCE1RETAc+Qfnr3B8l7QAm7RPSbpR879eNSTtAZl4OPEH5i4zagIl76x1alRf1k6A9AVwJzAD2H+vANK71/vJd19Io1FIRsTfwKcq85StaHY9a7hWUBGwh0BMRr4qIv4uIkyPigBbHpta5g7I+ar+I2K6xISIOBrYELmlFYKpvSqsDEHOqcqDTXO+gjMjPBi4dk4g0rkXEFOBN1eXPWhmLWqf6PvgWZXT1Qy0OR+PDS6ryKeAGYJ/Gxoi4gjK9btlYB6bWycxHI+LvgM8CN0fEjylz3fekzHG/GHhHC0NUDSburddZlSsHaO+t33oMYlF7+BTlF/JPM/PCVgejlvko8CLgTzPzyVYHo3Fh+6r8W+Bm4CDgRsr6hzMog0A/xCl2E05mnhkRi4Gzgbc3NN0JnNN3Co3GL6fKSG0kIk4CPkDZeeiNLQ5HLRIRL6WMsn/GbdzUoPd3+jrgNZn5q8xclZm/o2wtez/wcqfNTDwR8UHgR8A5lJH2DsouQ3cD36l2IlIbMHFvvd4R9c4B2nvrHxuDWDSORcR7gM9TRtIOzcxHWxySWqCaIvNNyvS6f2xxOBpfen9P3JCZixsbMnM10PsXuv3GMii1VkQcAnwa+K/MfH9m3p2ZqzPzesoHugeAD0SEu8q0ARP31rutKmcP0L5XVQ40B14TQES8F/gi8HtK0v5Qi0NS62xB+XmxN/BURGTvg7ITFcDXq7ozWxalWqH398lAAz29h/ltPgaxaPx4dVVe3reh+kB3LSUffNFYBqXhcY576/X+j3RkREzqs1/3lsCBwGrgmlYEp9arFhV9ijJX9RWZ+UiLQ1JrrQH+fYC2F1N++f6K/7+9uw+5u6zjOP7+5HAg5ENFJfPhXhlF9EfmSPAPXWhTp2VFrbLArQY9UcT+MEtW0wIho0CwB5C4YwStXCrRQkFbmxoNsQcJRzG97cGHKOcWZZTz2x+/302n4zm32zn3ds7PvV9w8dt9net3Xde5GDvfXff3XL8miDON5uhyJ1DA6/s/T1rzX1Z9+MhOSxO2tL0OO/Jxvv45x4dq+vgApingA5g0TJKNwLU0j6VeZXqMFpJkE82uuw9gOkoluY3mpJANVfW1nvpVNKdQ7QNmqmrYgQh6gUmyBtgCPAGcVVV/7nntYuDHNBsCp/iU9unnjvt0+DhwL3BDkvOBB4Gzac54/x1w9QTnpglJcgVN0H4A2Al8qnlI5v+Zq6rZIzw1SdPrEzS/dflqkktojoVcDryD5t+S9QbtR52bac5pvwB4MMktwOM06XaXAgGuMmjvBgP3KVBVe5KsoAnSLgJWA4/RfBHxmqrau9D9esFa3l6PAT49pM3PaE4JkCSq6k9JzqI5LvTtwLnAfuBHwHVVtWuS89ORV1XPJllN85+699F8IfU44ElgG3BDVd0xwSnqEJgqI0mSJHWAp8pIkiRJHWDgLkmSJHWAgbskSZLUAQbukiRJUgcYuEuSJEkdYOAuSZIkdYCBuyRJktQBBu6SpEWRZDZJJZk5zOPMJZk7nGNI0jQycJckTZUk25P4dEBJ6rNk0hOQJOkQnT/pCUjSJBi4S5I6par2THoOkjQJpspI0oQlmWlzw2eTvC7JrUmeTPKPJHcnWTXgnqVJrkryQJJ/JtmfZGeSNYvU/6b2npUL9XeQ729tkq1JHkrydDvXe5J8cFC/wHntz9VTtve0G5jjPsaazCT5XpK/JvlXkvuSXHow702SjiR33CVpeiwHfg48AHwLOBl4L/CTJJdX1RaAJMcCt9MEuLuBG4HjgHcDW5K8sao+N2r/h8E3gN8CO4DHgJcCq4HNSV5bVRvbdk8B1wBrgdPbP8+bW2iAMdbkdGAX8BCwGXgJzZrcluSCqvrpob5ZSTpsqspisVgsEyzADFBtub7vtRXAf4C9wPFt3WfbttuAJT1tX04T4BZwzqj9t/Wb2vYrF5jvbF/9bFs/01f/6gF9HAvc2Y69rO+17c3H09D1mgPm+urGWZMv9PV14Xxfk/67YbFYLL3FVBlJmh77gGt7K6rqPuC7wInAO9vqD9EElhuq6pmetn8Bvtj+uH6M/hdVDchJr6p/0+yKL2Fxvmw66po8Anypb263A38A3rwI85KkRWPgLknT4/6q+vuA+u3t9cwkLwbOAB6tqt0D2t4133aU/g9hrgctyWlJbkyyu809rzaXfWvbZNmY/Y+zJr+qqgMD6v8InDTOvCRpsZnjLknT44kh9Y+31xPaAk2u+CDz9SeO2P+iSvIqmhzyk4CdwB00O/8HaNJVrgCWjjnMOGvy1JB7nsHNLUlTxsBdkqbHK4bUv7K97mtLb12/k3vajtL/vGfb66DPiUEB8DAbaL6Muq6qZntfSPJ+msB9XOOsiSR1hrsJkjQ93tSmffRb2V5/2aa67AGWJXnNgLZvaa/3j9J/T93e9nrqgPYrBtQNc0Z73TrgtfOG3HMAIMkxBzPAmGsiSZ1h4C5J0+ME4PO9FUlWAB+g2S2+pa3+NhDg+t7gNsnLgI09bUbtH5r0FoB1SZb0tD+1v4/nMddeV/aNeyGDvywK8Lf2etohjDPqmkhSZ5gqI0nTYwewPsnZwD3875z1FwEfqar9bbuvABcDlwG/TrKN5szy99Acf/jlqrp7jP6pql8k2QGcC+xKchdNqs3baM5LH7QTP8jXgXXAD5LcDDwKvAG4CPh+O36/O9v38sP2vT0NPFJVmxcYZ9Q1kaTOcMddkqbHw8A5NGkqHwXW0KR3rK6ehyO1Rym+Fbi6rfokTa7474HLq+oz4/Tf4zLgJuCUdowzgSuBYf0/R1X9hiZV5V7gEuBjwPHAu4BvDrntJuA6mt8QXElznOOHn2ecUddEkjojVTXpOUjSUS3JDE1Q/Z2qWtu1/iVJR4Y77pIkSVIHGLhLkiRJHWDgLkmSJHWAOe6SJElSB7jjLkmSJHWAgbskSZLUAQbukiRJUgcYuEuSJEkdYOAuSZIkdYCBuyRJktQB/wWLO5mkOzs2lwAAAABJRU5ErkJggg==\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", @@ -247,11 +617,216 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['dataset_iterator', 'seed'] seed\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'filters_cnn'] filters_cnn\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'lear_rate'] lear_rate\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'lear_rate_decay'] lear_rate_decay\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'coef_reg_cnn'] coef_reg_cnn\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'coef_reg_den'] coef_reg_den\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'dropout_rate'] dropout_rate\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'dense_size'] dense_size\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['train', 'epochs'] epochs\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['train', 'batch_size'] batch_size\n" + ] + }, + { + "data": { + "image/png": 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QDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcGsm1FfVqVX1yqr6WFVtqaquqrfuZPyGqnpJVX2xqq6vqiuq6q+r6pE7OWefqnpOVV1QVddV1Xer6q+q6sSVuSoAAFh5aybUJ3lBkmclOT7Jt3c2sKoOT/LJJL+V5KYkr03yZ0nun+T9VfWL85xTSd6e5BVJ9k/yqiR/nuShST5aVY9dtisBAIA9aC2F+uckOS7JIUl+eRdjz0jyI0neleT47v617n5qknsl+WaSV1bVD8w552eTnJrkE9Nzfr27fzHJw5PcnOQNVbVhuS4GAAD2lDUT6rv7Q9395e7uBQx/3LR9UXffNGuOyzK5E3+bJE+Zc87MHwov6O7rZ53z6SRnJTkyk9APAABDWTOhfpGOmrZfm+fYTN/31tZX1YFJTkxybZKPzXPOudP2EctVIAAA7CmjhvrN0/au8xw7dtrec1bf3ZLsk+Rrs+/sz/LlaXvc8pQHAAB7zqih/i+n7W9X1T4znVV1ZCZr85Pk8FnjD522V+1gvpn+w3b0hVX1tKraVFWbLr/88iWUDAAAK2PUUP+iTB6IPTXJZ6rqj6rqDUk+n+S70zHbl/MLu/v13b2xuzceeeSRyzk1AADsliFDfXdfkuQBSV6dZEOSZyZ5TCYPvD5hOuyyWafM3Ik/NPOb6b9yeSsFAICVt+9qF7BU3f2dTPa1f9bs/qqaedj107O6v5rJtpXHVtW+86yrv8e0/dJK1AoAACtpyDv1u/AL0/ZPZzqmW1h+IslBSR4yzzknT9sPrmxpAACw/IYM9VW1rqoOnqf/5zMJ9Z9Icvacw6+Ztr873eJy5pwHJHliksszeSstAAAMZc0sv6mqU5KcMv04sw/9CVV15vT3zd19+vT3g5J8p6r+JpOlNduTPCjJCUn+KckTunvug7JvT/L4TB6u/Yeqek+S22US6PdJ8kvdvWXZLwwAAFbYmgn1SY5PctqcvmPz/X3nL0oyE+pvyCSkPzjJT077vpzk+Un+qLuvnTt5d3dV/Vwmd/GfkuTZSa5P8tEkv9vdn1i+SwEAgD2nunu1axjOxo0be9OmTatdBgAAe7GqOq+7Ny5k7JBr6gEAgO8T6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABjcvqtdALu2+Z0X5OpXvSvrvv2NbL/TD2bDsx6fI06972qXBQDAGuFO/Rq3+Z0X5NrfeHly5RXZfvQPJFdekWt/4+XZ/M4LVrs0AADWCKF+jbv6Ve/KzYccnjrs8NS6danDDs/Nhxyeq1/1rtUuDQCANUKoX+PWffsbySGH3rLzkEMn/QAAEKF+zdt+px9Mtlx1y84tV036AQAgQv2at+FZj88+W65IX3lFevv29JVXZJ8tV2TDsx6/2qUBALBGCPVr3BGn3jcHvfT05LDDs+6SbyWHHZ6DXnq63W8AAPieNbOlZVWdmuSkJMcnuV+SDUn+T3f/xx2MPyDJU5OcluTYJAcm+WaSv0nyB9190Q7OOy3JryT5kSQ3J/mHJC/v7nOW9YKW0RGn3leIBwBgh9bSnfoXJHlWJqH+2zsbWFX7JvlAkldlEv7fluS1SS5L8uwkn62qH5nnvJcnOTPJ0UnekOStSe6T5D1V9azluhAAANiT1syd+iTPSfKtJF/J5I79h3Yy9nFJHpRJsH9Ud2+fOVBVv53kRUlOT/KUWf0nJnlekq8meUB3XzHtf1mS85K8vKrO6e4Ll/GaAABgxa2ZO/Xd/aHu/nJ39wKGHztt/3J2oJ/6i2l75Jz+Z0zbl8wE+un3Xpjk1UkOSPLkxVUNAACrb82E+kX6/LQ9uarmXsNPTdv3z+l/xLR97zzznTtnDAAADGMtLb9ZjL9M8q4kj0/yuap6f5JtSX4syYOTvDKTu+9Jkqpan+ROSbZ29yXzzPflaXvcjr6wqp6W5GlJ8oM/aI94AADWjiFDfXf3dLecF2fygO3sh2I/kORPu/umWX0zr2Sd8xan75npP2wn3/n6JK9Pko0bNy5kiRAAAOwRQy6/qaoDk5yVyYOvv5LJbjaHJvk3Se6S5KNV9djVqxAAAPacIUN9kv+c5AlJnt/dr+vuS7t7S3efm+TUJPsl+eNZ42fuxB+a+c30X7ki1QIAwApacqivqv2q6tFV9ZyqeuGs/gOr6vbzPMC6nGYehv0X215292eTXJHkLlV1u2nfNZnsfX9wVR09z3z3mLZfWoFaAQBgRS0peFfVo5NcmMkDq3+Q5IxZh49PckmSJ+5mbTtzwLSdu23lzJtmN0w/bpt16IPT9tHzzHfynDEAADCMRYf6qtqY5OwknckLo/509vHu/mSSr2fygqiV8rFp+1vTED/bGZk8APzp7r56Vv9rp+3zq+rwmc6qOiaTdfk3JHnTShQLAAAraSm737wwybVJNnb3pVX14nnGfDrJ/RczaVWdkuSU6cejpu0JVXXm9PfN3X369PeXJPm3SR6Z5ItV9d4k12Xyltkfn/7+n2bP392fqKpXJHlukguq6p1J9s/kXxRum+TZ3iYLAMCIlhLqH5Tk7O6+dCdjvpnkMYuc9/gkp83pOzbff3vsRUlOT5Lu/nZV3T/Jb06/58mZ/KvDJUnOTPLfu/uLc7+gu59XVZ/L5M7805JsT3J+kpd19zmLrBcAANaEpYT6g5Ns3sWYg7LIpT3dfUZuuTZ/V+MvzyTkn76rsXPOOzOT4A8AAHuFpTwo++0k99rFmOOTfG0JcwMAAIu0lFB/bpJ/XVUPnu9gVZ2c5MQklrMAAMAesJRQ/98yeUnT+6rqvyf5kSSpqsdMP//fTNa2v2LZqgQAAHZo0Wvqpw+pPirJO5L8+qxD705SSb6a5PHdvat19wAAwDJYyoOy6e7zq+qemew8c0KS2yW5Ksknk/xFd9+0fCUCAAA7s6RQnyTdfXMmd+ffvXzlAAAAi7WUN8r+QlWduosx962qX1h6WQAAwEIt5UHZM5OcVVXvqKoDdjDmcUnetOSqAACABVtKqE+S7yY5NcmHquqIZawHAABYpKWG+lcm+Z0kD0zyd1V13PKVBAAALMZSQ326+8VJnpTkB5N8oqoeslxFAQAAC7fkUJ8k3f2WJI9Osk8mL6P6D8tSFQAAsGBL3tJyRnd/qKpOTPJXSd5SVcfuflkAAMBC7XaoT5Lu/qeq+vEk5yQ5I8k/L8e8AADAru3W8pvZuvvyJCclOTuJHXEAAGAPWcqd+rsmuXK+A919/fTFVKcmOWh3CgMAABZm0aG+uy/axfFO8n+XXBEAALAoy7b8BgAAWB27vFNfVV9L0kl+oru/Pv28EN3dd9ut6gAAgF1ayPKbdZmE+h193pFaUkUAAMCi7DLUd/cxO/sMAACsLmvqAQBgcMsW6qtqv6r60aq653LNCQAA7NqiQ31V/buqekdV3XZW392SfD7JpiRfqKp3VdWyvK0WAADYuaXcqX9Kkh/q7u/O6vuDJHdP8qEkFyR5bJIn7355AADAriwl1P9Ikk/PfKiqQ5L8myTv6O6fSPLjSb4YoR4AAPaIpYT6I5NcMuvzCZnsovP2JOnuG5P8TRJ71AMAwB6wlFB/dZJDZ30+KZN96z8+q+/6JBt2oy4AAGCBlvIw65eTnFxVB2QS5v9dkgu6e/OsMXdJctky1AcAAOzCUu7Uvz7JsZmE+39Kctckb5oz5scy2Q0HAABYYYsO9d395iS/n+SgTJbhvCrJK2eOV9WJ+f5OOAAAwApb0l7y3f1bSX5rB4c3JTk8yTVLLQoAAFi4ZXuj7Izu3tbdV3X3TbP7q+rFVXXTjs4DAACWZtlD/S7UHv4+AADY6+3pUA8AACwzoR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDB7bsHv+vsJBfuwe8DAIBbhT0W6rv7s0k+u6e+DwBgR87+5vV53UU35Ns3bM+dDliXp9/lgJxy5wNXuyxYsiUtv6mqk6rqnKq6rKpurKqb5/m5abmLBQDYXWd/8/q84EvX5sobO0fvV7nyxs4LvnRtzv7m9atdGizZou/UV9VjMllKs0+SbyT5f0kEeABgCK+76IZsWLcuh+1XSZLD1iW5cV1ed9EN7tYzrKUsvzkjyY1JHtPd71vecgAAVta3b9ieo6eBfsYh+0z6YVRLWX5z7yRnCfQAwIjudMC6bLn5ln1bbp70w6iW8v/erUm+u9yFAADsCU+/ywG5evv2XHljZ/v2zpU3dq7evj1Pv8sBq10aLNlSlt98IMkJy10IAMCeMLNufvbuN795l4Osp2doSwn1v5nkU1X1giQv6e5e5poAAFbUKXc+UIhnr7LLUF9V/2ue7s8n+e0kT6mqzyS5cp4x3d2/uJv1AQAAu7CQO/VP2smxY6Y/8+kkQj0AAKywhYT6u654FQAAwJLtMtR390V7ohAAAGBpbMgKAACDW3Sor6onVNUHq+qOOzh+p6r6QFU9fvfLAwAAdmUpd+qfmuSw7r54voPd/e0kh07HAQAAK2wpof4+STbtYsynk9x3CXMDAACLtJRQf9skl+1izD8nOWIJcwMAAIu0lFC/Ock9djHmHpn/hVQAAMAyW0qo/9skP11VPzTfwar64SSPTfKx3SkMAABYmKWE+pdnsr/9x6vqV6vquKpaP23/UyZhfp/pOAAAYIUt5I2yt9Ddn66qZyZ5dZI/nP7MdnOSX+7uv1+G+gAAgF1YdKhPku5+Q1V9PMkzk/yrJIdlsob+k0le093/tHwlAgAAO7OkUJ8k0+D+7GWsBQAAWIKlvFH2RVX10F2MeUhVvWjpZQEAAAu1lAdlz0jysF2MeWiSFy9hbgAAYJGWEuoXYr8k21dobgAAYJaVCvX3z+QlVQAAwApb0IOyVfXBOV1PqqqHzTN0nyR3TnKXJG/bvdIAAICFWOjuNw+b9XsnOWb6M9f2JP+c5Kwkz9mNugAAgAVaUKjv7u8t06mq7UnO6O7/umJVAQAAC7aUfeqfnOQflrsQAABgaRYd6rv7zStRCAAAsDRLfqNsklTVDyS5U5ID5jve3R/dnfkBAIBdW1Kor6pHJfnDJD+0i6H7LGV+AABg4Ra9T31VPTDJOUkOS/KqJJXko0nekOSL08/vSeJBWgAA2AOW8vKp/5Lk+iQP6O7/NO37UHc/I8m9k/xukp9I8s7FTFpVp1bVK6vqY1W1paq6qt66g7FnTo/v7OcDOzj3tKr6VFVtraqrqurDVfVTi6kVAADWkqUsvzkhybu7++JZfeuSpLs7yYuq6uQkv53k1EXM+4Ik90uyNcm3svOlPWcnuXAHx34+ybFJzp17oKpenuR50/nfkGT/JD+b5D1V9ezuftUi6gUAgDVhKaH+0CTfmPV5W5L1c8b8bZJ/v8h5n5NJ2P5KkpOSfGhHA7v77EyC/S1U1WFJfmNaFQukAAAgAElEQVRa05lzjp2YSaD/aib/ynDFtP9lSc5L8vKqOqe7L1xk3QAAsKqWEuovS3L4nM93mzNmvyS3Wcyk3f29EF9VSygryeQu/W2SvL27N8859oxp+5KZQD/93gur6tVJXpjJHvwvXuqXA6wV2y74SG4+923JJRcnR98x+5z8c9n/vietdlkArJClrKn/Um4Z4j+Z5Cer6rgkqaqjkvxMki/vfnmL9kvT9vXzHHvEtH3vPMfOnTMGYFjbLvhItr/xpcmWK5M7HJVsuTLb3/jSbLvgI6tdGgArZCmh/r1JTqqq204//3Emd8f/oao+nckOOEcm+aPlKXFhquqEJPdJ8qXZd/2nx9Znsp/+1u6+ZJ7TZ/4AOW5lqwRYeTef+7b0hg3JIYcl69YlhxyW3rBhcucegL3SUkL965I8NMmNSdLdf5vkCUm+nsnuN5ck+eXufstyFblAT5u2b5jn2KHT9qodnDvTf9iOJq+qp1XVpqradPnlly+xRIA94JKLk4MPuWXfwYdM+gHYKy16TX13b0ny93P6/jzJny9XUYtVVYcm+XeZ5wHZ5dLdr890Wc/GjRt7Jb4DYFkcfcfJ0ptDZt2n2Lpl0g/AXmlJb5RNkqo6OMnjkvxoJnfCr0pyfpKzu3vr8pS3YP8xyUGZ/wHZ5Pt34g+d59js/iuXuzCAPW2fk38u29/40nQyuUO/dUvq6quz7om/vNqlAbBClhTqq+oJSV6byXKV2VvVdJIrq+rp3b2ol0/tppkHZF8338Huvqaqvp3kTlV19Dzr6u8xbb+0UgUC7Cn73/ekbHtqbrH7zbon/rLdbwD2YosO9VX1k0nelmR7krck+XCSS5McleThmexP/7aqurK73798pe6wnn+VyUurvtTdH97J0A9msuXlo5O8ac6xk2eNARje/vc9KRHiAW41lvKg7IuS3JDkgd395O5+c3f/9bR9UpITM3mI9kXLWOfOzDwgO982lrO9dto+v6q+t89+VR2T5Fcyuaa5YR8AANa8pSy/+dEkZ3X3+fMd7O5NVfWOJKcuZtKqOiXJKdOPR03bE6rqzOnvm7v79DnnHJLkiZkE8jfvbP7u/kRVvSLJc5NcUFXvTLL/9PzbJnm2t8kCALdGF3/1M7nik+dm3eZLsv2Io3P4A0/OHe92/GqXxSIsJdTfkMm2lTtz8XTcYhyf5LQ5fcdOf5LkoiSnzzn+H5Ksz44fkL2F7n5eVX0ukzvzT8tkCdH5SV7W3ecssl4AgOFd/NXPZMu735havyHbb3f71DVbsuXdb0x++qmC/UCWsvzmY0ketIsxD0ry0cVM2t1ndHft5OeYec55zfTYzy3ie87s7gd09/ru3tDdJwn0AMCt1RWfPDfb129Irz8kqX3S6w/J9vUbcsUnz13t0liEpYT630xy36r6/embWr+nqtZX1UszeQnVf16OAgEAWDnrNl+SPugWkS590Pqs27yrhRmsJbtcflNV/2ue7guS/HqSp1XV+Um+k+QOSe6fyZ7vH03yG0l+cflKBQBguW0/4ujUNVsmd+qn6tprsv2Io1exKhZrIWvqn7STY4clecQ8/ScleWiEegCANe3wB56cLe9+Y7Zncoe+rr0m6665Ooc88omrXRqLsJBQf9cVrwIAgFVxx7sdn/z0U2+x+80hj3yih2QHs8tQ390X7YlCAABYHXe82/FC/OCW8qAsAACwhgj1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwuH1XuwAAWEnXfvPT2fa5c1NXXZY+9PbZ/z4n56A7P2C1ywJYVu7UA7DXuvabn86NH3tTcv3V6UOOSK6/Ojd+7E259pufXu3SAJaVUA/AXmvb585NH7g+uc2GZN265DYb0geuz7bPnbvapQEsK6EegL1WXXVZcsD6W3YesH7SD7AXEeoB2Gv1obdPbrjmlp03XDPpB9iLCPUA7LX2v8/JqeuvSa67Otm+Pbnu6tT112T/+5y82qUBLCuhHoC91kF3fkD2e8iTkwM3pLZsTg7ckP0e8mS73wB7HVtaArBXO+jODxDigb2eO/UAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMbs2E+qo6tapeWVUfq6otVdVV9dZdnLNPVT21qj5aVVdU1XVV9bWqOquqjtvBOadV1aeqamtVXVVVH66qn1qZqwIAgJW3ll4+9YIk90uyNcm3kvzQzgZX1cFJ/iLJI5J8Jsmbk1yf5E5JHpLkuCRfmnPOy5M8bzr/G5Lsn+Rnk7ynqp7d3a9axusBAIA9Yi2F+udkEra/kuSkJB/axfjXZRLon9Hdr5t7sKr2m/P5xEwC/VeTPKC7r5j2vyzJeUleXlXndPeFu3kdAACwR62Z5Tfd/aHu/nJ3967GVtX9k/z7JGfNF+in8904p+sZ0/YlM4F+Ou7CJK9OckCSJy+ldgAAWE1rJtQv0r+ftm+rqkOr6j9W1X+pqqdV1d13cM4jpu175zl27pwxAAAwjLW0/GYxHjBt75LJcprbzTrWVfWaJL/a3TcnSVWtz2St/dbuvmSe+b48bed9uBYAANayUe/U337aviLJh5P8cJINSX4ik5D/zCQvnDX+0Gl71Q7mm+k/bEdfOP1XgE1Vtenyyy9fYtkAALD8Rg31M3V/MckTu/uL3b21uz+Q5NQk25M8t6r2X64v7O7Xd/fG7t545JFHLte0AACw20YN9VdO2/fMLLGZ0d2fTfL1TO7c//C0e+ZO/KGZ30z/lTs4DgAAa9aoof7/TdsdhfCZ3W1ukyTdfU2Sbyc5uKqOnmf8Pabtl+Y5BgAAa9qoof790/becw9U1QH5fki/cNahD07bR88z38lzxgAAwDBGDfV/luTiJE+sqh+fc+yFmSyn+VB3Xzqr/7XT9vlVdfhMZ1Udk+RXktyQ5E0rVTAAAKyUNbOlZVWdkuSU6cejpu0JVXXm9PfN3X16MllOU1VPSnJOko9V1bsyWV7zr5I8OMllSZ4+e/7u/kRVvSLJc5NcUFXvTLJ/kicmuW2SZ3ubLAAAI1ozoT7J8UlOm9N37PQnSS5KcvrMge7+m+ld+hdmspXloUkuzeSO/O9098Vzv6C7n1dVn8vkzvzTMtkl5/wkL+vuc5b3cgAAYM+o7l7tGoazcePG3rRp02qXAQDAXqyqzuvujQsZO+qaegAAYEqoBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAINbSy+fAgCAVfX1i7+US7/yd9n3ms25af0ROeruJ+SudzxutcvaJXfqAQAgk0C/+TNnp7Zdk5sOum1q2zXZ/Jmz8/WLv7Tape2SUA8AAEku/crf5eb9D072X5+qdcn+63Pz/gfn0q/83WqXtktCPQAAJNn3ms3p/W5zi77e7zbZ95rNq1TRwgn1AACQ5Kb1R6RuvO4WfXXjdblp/RGrVNHCCfUAAJDkqLufkH22bU22XZPu7cm2a7LPtq056u4nrHZpuyTUAwBAkrve8bgccfwp6f3XZ99rv5vef32OOP6UIXa/saUlAABM3fWOxw0R4udypx4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDBCfUAADA4oR4AAAYn1AMAwOCEegAAGJxQDwAAgxPqAQBgcEI9AAAMTqgHAIDB7bvaBQBL9/m+OOfn69ma63JwbpP75665V91xtcsCAPYwoR4G9fm+OB/J57Nf9s1BOSDXZ1s+ks8nHcEeAG5lLL+BQZ2fr2e/7JsDs3/WZV0OzP7ZL/vm/Hx9tUsDAPYwoR4GtTXXZf85/9i2f/bN1ly3ShUBAKtFqIdBHZzbZFtuukXfttyUg3ObVaoIAFgtQj0M6v65a27MTbk+27I923N9tuXG3JT7566rXRoAsId5UBYGda+6Y9K5xe43J+aeHpIFgFshoR4Gdq+6Y+4VIR4Abu0svwEAgMEJ9QAAMDihHgAABifUAwDA4IR6AAAYnFAPAACDE+oBAGBwQj0AAAxOqAcAgMEJ9QAAMDihHgAABifUAwDA4IR6AAAYnFAPAACDE+oBAGBwQj0AAAxOqAcAgMGtmVBfVadW1Sur6mNVtaWquqreuoOxx0yP7+jn7Tv5ntOq6lNVtbWqrqqqD1fVT63clQEAwMrad7ULmOUFSe6XZGuSbyX5oQWc89kkZ8/T/4/zDa6qlyd53nT+NyTZP8nPJnlPVT27u1+1hLoBAGBVraVQ/5xMwvZXkpyU5EMLOOcz3X3GQiavqhMzCfRfTfKA7r5i2v+yJOcleXlVndPdFy6+dAAAWD1rJtR39/dCfFWtxFc8Y9q+ZCbQT7/3wqp6dZIXJnlykhevxJcDwGr7+Mdvzvve1dnyneSQOySPenzlwQ/eZ7XLApbBmllTv0R3rKqnV9VvTdv77mTsI6bte+c5du6cMQCwV/n4x2/OO17due7qZMPtk+uuTt7x6s7HP37zapcGLIM1c6d+iX5y+vM9VfXhJKd19zdm9a1PcqckW7v7knnm+fK0PW6F6gSAVfW+d3UOODg5aMPkX8MP2pAknfe9q/PgB69qacAyGPVO/bVJfifJjyU5fPozsw7/YUk+MA3yMw6dtlftYL6Z/sN29IVV9bSq2lRVmy6//PLdKB0A9rwt30kOPPiWfQcePOkHxjdkqO/uy7r7Rd19fndfOf35aJJHJfn7JHdP8tRl/s7Xd/fG7t545JFHLufUALDiDrlDcv3WW/Zdv3XSD4xvyFC/I919U5I3Tj8+dNahmTvxh2Z+M/1XrkRdALDaHvX4yg1bk2uv7mzvzrVXd27YOukHxjf6mvr5zKyN+d7ym+6+pqq+neROVXX0POvq7zFtv7QnCgSAPW2yy80td7957Gl2v4G9xd4Y6h84bb82p/+DSX4+yaOTvGnOsZNnjQGAvdKDH7yPh2JhLzXk8puqun9V/Yvaq+qRmbzEKkneOufwa6ft86vq8FnnHJPkV5LckH8Z9gEAYM1bM3fqq+qUJKdMPx41bU+oqjOnv2/u7tOnv78iyT2q6hOZvIU2Se6b7+8z/8Lu/sTs+bv7E1X1iiTPTXJBVb0zyf5Jnpjktkme7W2yAACMaM2E+iTHJzltTt+x058kuSjJTKj/30kel+QBmSyd2S/Jd5K8I8mruvtj831Bdz+vqj6XyZ35pyXZnuT8JC/r7nOW71IAAGDPqe5e7RqGs3Hjxt60adNqlwEAwF6sqs7r7o0LGTvkmnoAAOD7hHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABicUA8AAIMT6gEAYHBCPQAADE6oBwCAwQn1AAAwOKEeAAAGJ9QDAMDghHoAABjc/2/vzqP0qus7jr8/kEqbGANCFI8iYbUupwcUsWBlEUUFNzy41gUEXKpSxYpWxd3jWupS64Y2Fu2RAiL1iAVlCwiVolgpiiIYrWA0gRAwARXy7R/3Tvs4Pk+YzCRzn8u8X+fcczO/u33nOTfzfOY3v+d3DfWSJElSzxnqJUmSpJ4z1EuSJEk9Z6iXJEmSes5QL0mSJPWcoV6SJEnqOUO9JEmS1HOGekmSJKnnDPWSJElSzxnqJUmSpJ4z1EuSJEk9Z6iXJEmSes5QL0mSJPWcoV6SJEnqOUO9JEmS1HOGekmSJKnnDPWSJElSzxnqJUmSpJ4z1EuSJEk9N6/rAiRpU7j2qhVceuFqVq7agsXbrWef/bdhl4du33VZkiTNCnvqJfXetVet4IzT1nDr2nDvbddz69pwxmlruPaqFV2XJknSrDDUS+q9Sy9czYL5xcIFsGXCwgWwYH5x6YWruy5NkqRZYaiX1HsrV23B/Pn1e23z5xcrV/kjTpI0N/iOJ6n3Fm+3nnXr8ntt69aFxdut76giSZJml6FeUu/ts/82rF0Xbl0Ld1Zx61pYuy7ss/82XZcmSdKsMNRL6r1dHro9hx2+iIULiptu3IKFC4rDDl/k7DeSpDnDKS0l3S3s8tDtDfGSpDnLnnpJkiSp5wz1kiRJUs8Z6iVJkqSeM9RLkiRJPWeolyRJknrOUC9JkiT1nKFekiRJ6jlDvSRJktRzhnpJkiSp5wz1kiRJUs8Z6iVJkqSeM9RLkiRJPWeolyRJknrOUC9JkiT1nKFekiRJ6jlDvSRJktRzhnpJkiSp58Ym1Cc5PMlHk1yU5JYkleTzG3H8Se0xlWTXEftsmeQ1Sb6X5LYkNyU5K8m+m+47kSRJkmbX2IR64M3AK4E9gOs35sAkTwGOAn69gX0CfBE4EbgH8A/AGcB+wLIkT5te2ZIkSVK3xinUvwbYHbgX8PKpHpRkMfBp4BTg2xvY9TnA4cAlwB5V9bqqOgo4ELgT+HSShdOsXZIkSerM2IT6qjq/qq6pqtrIQz/Vrl9xF/tN/KLw5qq6feC6/0nzC8FimtAvSZIk9crYhPrpSHIE8HTgpVV14wb2+2NgX2AdcNGQXb7Wrh+7qWuUJEmSNrfehvokOwIfBj5fVWfexe67AFsC11XVHUO2X9Oud9+EJUqSJEmzopehPskWwOdoPhh77BQOWdSu14zYPtG+9Qau+ZIklye5fOXKlVOuVZIkSdrcehnqaT5Uuz9wTFWtno0LVtWnqmqvqtpr8eLFs3FJSZIkaUp6F+qT7A68G/inqjpriodN9MQvGrF9ov3mmdQmSZIkdaF3oR54CLAVcOTAw6YqSdH03gNc07Y9vf36WpppK3dOMm/IOXdr1z/arJVLkiRJm8GwgDvulgOfGbHtUGB74FTglnZfqur2JJcAj2mX8ycd96R2fd4mrlWSJEna7LLx08JvfkkOoAneX6iq52/EcRfQ9NbvVlU/nrTtucC/0Dx86qCJueqTPBK4mGaIzq5VdcsUrrMS+OlU69qEtgNWdXBdjT/vDY3ivaFhvC80ivfGeNmxqqb0Yc6x6alvh8pMDJfZvl3vk2Rp++9VVfU3M7jEF4Fn0Dxg6ookXwG2BZ5NM93lMVMJ9ABTfXE3tSSXV9VeXVxb4817Q6N4b2gY7wuN4r3RX2MT6oE9gBdNatu5XaDpGZ92qK+qanvrLwFeDLwKuB1YBryrqi6Z7rklSZKkLo3l8BsN52/PGsV7Q6N4b2gY7wuN4r3RX32c/WYu+1TXBWhseW9oFO8NDeN9oVG8N3rKnnpJkiSp5+yplyRJknrOUC9JkiT1nKFekiRJ6jlD/ZhL8oAkn01yQ5LfJFme5ENJtum6NnUjybZJjk5yRpIfJ7ktyZokFyc5Kon/r/V/kjw/SbXL0V3Xo24lOaj92bGifU+5IcnZSQ7pujZ1J8mhSc5J8vP2PeW6JKcm2afr2jR1flB2jCXZhWZe/fsAZwJXA3sDBwI/BB5dVTd2V6G6kORlwMeBX9A8eflnwH1pHq62CDgdeGb5n3vOS7IDcCXNA/buSfOQvZO6rUpdSfJ+4HXAz4Gv0Tw1dDHwCOAbVXV8h+WpI0neBxwP3Ah8mea+2BV4Ks3zjF5YVZ/vrkJNlaF+jCU5GzgYOLaqPjrQfiLwGuCTVfWyrupTN5I8FlgAfLWq1g+0bw9cBuwAHF5Vp3dUosZAkgBfB3YCvkTz8D5D/RyV5BiaqQo/B7ykqn47afsfVdXvOilOnWnfN64HVgJ/VlW/Gth2IHAe8JOq2nnEKTRG/DP9mGp76Q8GlgMfm7T5rcBa4AVJFsxyaepYVZ1XVV8ZDPRt+wrgE+2XB8x6YRo3xwKPBY6k+XmhOSrJVsC7af6q9weBHsBAP2ftSJMFvzUY6AGq6nzgVpq/5qgHDPXj68B2fc6Q8HYr8E1gPvDns12YxtrEG/MdnVahTiV5MPBe4MNVtazretS5x9MEsy8B69vx069P8teOmZ7zrgF+C+ydZLvBDUn2AxYC3+iiMG28eV0XoJEe1K5/NGL7NTQ9+bsD585KRRprSeYBL2y//Pcua1F32vvgZJpe2Td2XI7GwyPb9e3AFcDDBjcmWUYzZG/lbBemblXVTUleD5wIfD/Jl2nG1u9CM6b+68BLOyxRG8FQP74Wtes1I7ZPtG89C7WoH95L82Z9VlWd3XUx6sxbgD2Bv6iq27ouRmPhPu36dcD3gccA36X5vMUHaTqITsVhe3NSVX0oyXLgs8AxA5t+DCydPCxH48vhN9LdQJJjgdfSzJD0go7LUUeSPIqmd/7vqurSruvR2Jh4r78DeGpVXVxVv66qK4HDaGbD2d+hOHNTkuOB04ClND30C2hmRLoO+EI7a5J6wFA/viZ64heN2D7RfvMs1KIxluSVwIdpeuAOrKqbOi5JHWiH3fwzzZC9EzouR+Nl4n3iiqpaPrihqtYBE3/Z23s2i1L3khwAvA/4t6o6rqquq6p1VfUdml/4rgdem8TZb3rAUD++ftiudx+xfbd2PWrMveaAJK8GPgr8N02gX9FxSerOPWl+XjwYuH3ggVNFM2MWwKfbtg91VqW6MPF+MqoTaHW7/pNZqEXj5cnt+vzJG9pf+C6jyYp7zmZRmh7H1I+vif9gByfZYtJ85AuBRwPrgP/oojh1r/1w03tpxsY+vqpWdVySuvUb4DMjtj2c5k35YpqA59CcueVcoICHTH4/aU18cPYns1uWxsBW7XrUtJUT7X8wDarGjw+fGmM+fEqjJDkBeAfwbeBgh9xoQ5K8jaa33odPzVFJzqSZzeS4qvr7gfaDaWbLWgMsqapRkzPobijJs4BTgF8Cj6iq6we2PQn4Kk2HwQN8gv34s6d+vP0VcAnwkSQHAT8AHkUzh/2PgDd1WJs6kuRFNIH+TuAi4Njm4aG/Z3lVLZ3l0iSNr1fQ/LXmxCSH0kxtuRPwdJqfJUcb6Oek02jmoX8c8IMkZwAraIbxPRkI8AYDfT8Y6sdYVV2bZC+aAPdE4BDgFzQfinx7Va3e0PG629qpXW8JvHrEPhfSzGQgSVTVz5M8gmbK06cC+wG3AF8B3lNVl3VZn7pRVeuTHELzS99zaD4cOx+4CTgL+EhVndNhidoIDr+RJEmSes7ZbyRJkqSeM9RLkiRJPWeolyRJknrOUC9JkiT1nKFekiRJ6jlDvSRJktRzhnpJkiSp5wz1kqTNKsnSJJVkyWa+zvIkyzfnNSRpXBnqJUm9kOSCJD4xUZKGmNd1AZIkbSIHdV2AJHXFUC9Juluoqmu7rkGSuuLwG0kaU0mWtGPRlyb50yRfTnJTkrVJLk5y8JBjtkryhiRXJlmX5JYkFyV51iY6/9vaYw7Y0Pmm+P0dkeT0JNclua2t9ZtJnj/svMD+7dc1sFwwsN/QMfUzeE2WJPliklVJbk9yeZInT+V7k6TZZk+9JI2/nYBLgSuBTwL3A54NfC3J86rqFIAk9wDOpgm/VwMfA+YDhwOnJNmjqt443fNvBh8HrgKWAb8AtgUOAU5O8qCqOqHd72bg7cARwI7tvycs39AFZvCa7AhcBlwHnAzcm+Y1OTPJ46rq/I39ZiVps6oqFxcXF5cxXIAlQLXLByZt2wv4HbAauFfb9rftvmcB8wb2vQ9N+C1g3+mev21/W7v/ARuod+mk9qVt+5JJ7bsMOcc9gHPba99/0rYLmretka/XcmD5pLaZvCZvnXSuJ0ycq+t7w8XFxWXy4vAbSRp/a4B3DDZU1eXAF4CtgcPa5hfThM7jquqOgX1/Bbyz/fLoGZx/k6ohY+Cr6rc0venz2DQffJ3ua/JT4F2Tajsb+Bmw9yaoS5I2KUO9JI2/71TVrUPaL2jXeyZZCOwK3FBVVw/Z97yJfadz/o2odcqSPDDJx5Jc3Y51r3bs/OntLvef4fln8pp8t6ruHNL+P8A2M6lLkjYHx9RL0vj75Yj2Fe16UbtAMzZ9mIn2rad5/k0qyc40Y9a3AS4CzqH5i8GdNENgXgRsNcPLzOQ1uXnEMXdgh5ikMWSol6Txd98R7du36zXtMtg22f0G9p3O+Sesb9fD3j+GheNRjqP5YOyRVbV0cEOS59KE+pmayWsiSb1ib4Mkjb+Ht0NJJjugXV/RDp+5Frh/kt2G7Htgu/7OdM4/0La6Xe8wZP+9hrSNsmu7Pn3Itv1HHHMnQJItp3KBGb4mktQrhnpJGn+LgLcMNiTZC/hLml7mM9rmzwIBPjAYfJNsB5wwsM90zw/NkBmAI5PMG9h/h8nnuAvL2/UBk677BIZ/cBXgxnb9wI24znRfE0nqFYffSNL4WwYcneRRwDf5/3nktwBeWlW3tPt9EHgS8DTgv5KcRTMn+zNppnB8f1VdPIPzU9Uerg4AAAEHSURBVFXfSrIM2A+4LMl5NMN3nkIzH/ywHvxh/hE4Ejg1yWnADcDDgCcC/9pef7Jz2+/lS+33dhvw06o6eQPXme5rIkm9Yk+9JI2/nwD70gx9eRnwLJohI4fUwIOh2ukgHw+8qW16Fc3Y9GuA51XV62dy/gFPA04CHtBeY0/geGDU+f9AVX2PZvjLJcChwMuBewHPAD4x4rCTgPfQ/GXheJopKY+6i+tM9zWRpF5JVXVdgyRpiCRLaAL356rqiL6dX5I0e+yplyRJknrOUC9JkiT1nKFekiRJ6jnH1EuSJEk9Z0+9JEmS1HOGekmSJKnnDPWSJElSzxnqJUmSpJ4z1EuSJEk99788ehkaCsOuWQAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'confident_threshold'] confident_threshold\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'check_bool'] check_bool\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 7cf7f66091..3ca2f0040c 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -206,12 +206,14 @@ def first_generation(self, iteration=0): str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + suffix = Path(self.get_value_from_config(self.basic_config, + path_ + ["save_path"])).suffix for which_path in ["save_path", "load_path"]: population[-1] = self.insert_value_or_dict_into_config( population[-1], path_ + [which_path], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( - "fitted_model_" + str(path_id)))) + "fitted_model_" + str(path_id)).with_suffix(suffix))) population[-1]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 @@ -281,11 +283,13 @@ def next_generation(self, generation, scores, iteration): str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + suffix = Path(self.get_value_from_config(self.basic_config, + path_ + ["save_path"])).suffix next_population[i] = self.insert_value_or_dict_into_config( next_population[i], path_ + ["save_path"], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( - "fitted_model_" + str(path_id)))) + "fitted_model_" + str(path_id)).with_suffix(suffix))) for i in range(self.n_saved_best_pretrained, self.population_size): # if several train files @@ -301,12 +305,14 @@ def next_generation(self, generation, scores, iteration): str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + suffix = Path(self.get_value_from_config(self.basic_config, + path_ + ["save_path"])).suffix for which_path in ["save_path", "load_path"]: next_population[i] = self.insert_value_or_dict_into_config( next_population[i], path_ + [which_path], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( - "fitted_model_" + str(path_id)))) + "fitted_model_" + str(path_id)).with_suffix(suffix))) next_population[i]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 From 5c477abeee97bc872c33842e5d52ae50ec4db476 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 26 Jun 2018 10:56:40 +0300 Subject: [PATCH 283/616] fix: load path for fiton models --- .../models/evolution/evolution_param_generator.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 3ca2f0040c..777959a127 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -257,6 +257,7 @@ def next_generation(self, generation, scores, iteration): except: pass + # load_paths if self.elitism_with_weights: # if elite models are saved with weights next_population[i] = self.insert_value_or_dict_into_config( @@ -276,7 +277,16 @@ def next_generation(self, generation, scores, iteration): self.main_model_path + ["load_path"], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["load_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) + for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + suffix = Path(self.get_value_from_config(self.basic_config, + path_ + ["load_path"])).suffix + next_population[i] = self.insert_value_or_dict_into_config( + next_population[i], path_ + ["load_path"], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["load_path"]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( + "fitted_model_" + str(path_id)).with_suffix(suffix))) + # save_paths next_population[i] = self.insert_value_or_dict_into_config( next_population[i], self.main_model_path + ["save_path"], From 1cb0620b22899a19d16d48d3928a1ef2c74edeb1 Mon Sep 17 00:00:00 2001 From: Mary Vikhreva Date: Tue, 26 Jun 2018 11:01:33 +0300 Subject: [PATCH 284/616] refactor: move seq2seq * gobot to 'models' --- README.md | 4 ++-- deeppavlov/__init__.py | 12 ++++++------ deeppavlov/{skills => models}/go_bot/README.md | 12 ++++++------ deeppavlov/{skills => models}/go_bot/__init__.py | 0 deeppavlov/{skills => models}/go_bot/bot.py | 6 ++---- deeppavlov/{skills => models}/go_bot/diagram.png | Bin deeppavlov/{skills => models}/go_bot/metrics.py | 0 deeppavlov/{skills => models}/go_bot/network.py | 0 deeppavlov/{skills => models}/go_bot/templates.py | 0 deeppavlov/{skills => models}/go_bot/tracker.py | 0 .../{skills => models}/seq2seq_go_bot/README.md | 0 .../{skills => models}/seq2seq_go_bot/__init__.py | 0 deeppavlov/{skills => models}/seq2seq_go_bot/bot.py | 2 +- deeppavlov/{skills => models}/seq2seq_go_bot/kb.py | 0 .../{skills => models}/seq2seq_go_bot/network.py | 0 15 files changed, 17 insertions(+), 19 deletions(-) rename deeppavlov/{skills => models}/go_bot/README.md (98%) rename deeppavlov/{skills => models}/go_bot/__init__.py (100%) rename deeppavlov/{skills => models}/go_bot/bot.py (98%) rename deeppavlov/{skills => models}/go_bot/diagram.png (100%) rename deeppavlov/{skills => models}/go_bot/metrics.py (100%) rename deeppavlov/{skills => models}/go_bot/network.py (100%) rename deeppavlov/{skills => models}/go_bot/templates.py (100%) rename deeppavlov/{skills => models}/go_bot/tracker.py (100%) rename deeppavlov/{skills => models}/seq2seq_go_bot/README.md (100%) rename deeppavlov/{skills => models}/seq2seq_go_bot/__init__.py (100%) rename deeppavlov/{skills => models}/seq2seq_go_bot/bot.py (98%) rename deeppavlov/{skills => models}/seq2seq_go_bot/kb.py (100%) rename deeppavlov/{skills => models}/seq2seq_go_bot/network.py (100%) diff --git a/README.md b/README.md index e87ac4a729..afc9a50c91 100644 --- a/README.md +++ b/README.md @@ -140,13 +140,13 @@ Available model configs are: | [NER component](deeppavlov/models/ner/README.md) | Based on neural Named Entity Recognition network. The NER component reproduces architecture from the paper [Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition](https://arxiv.org/pdf/1709.09686.pdf) which is inspired by Bi-LSTM+CRF architecture from https://arxiv.org/pdf/1603.01360.pdf. | | [Slot filling components](deeppavlov/models/slotfill/README.md) | Based on fuzzy Levenshtein search to extract normalized slot values from text. The components either rely on NER results or perform needle in haystack search.| | [Classification component](deeppavlov/models/classifiers/intents/README.md) | Component for classification tasks (intents, sentiment, etc). Based on shallow-and-wide Convolutional Neural Network architecture from [Kim Y. Convolutional neural networks for sentence classification – 2014](https://arxiv.org/pdf/1408.5882) and others. The model allows multilabel classification of sentences. | +| [Goal-oriented bot](deeppavlov/models/go_bot/README.md) | Based on Hybrid Code Networks (HCNs) architecture from [Jason D. Williams, Kavosh Asadi, Geoffrey Zweig, Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning – 2017](https://arxiv.org/abs/1702.03274). It allows to predict responses in goal-oriented dialog. The model is customizable: embeddings, slot filler and intent classifier can switched on and off on demand. | +| [Seq2seq goal-oriented bot](deeppavlov/models/seq2seq_go_bot/README.md) | Dialogue agent predicts responses in a goal-oriented dialog and is able to handle multiple domains (pretrained bot allows calendar scheduling, weather information retrieval, and point-of-interest navigation). The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers. | | [Automatic spelling correction component](deeppavlov/models/spelling_correction/README.md) | Pipelines that use candidates search in a static dictionary and an ARPA language model to correct spelling errors. | | [Ranking component](deeppavlov/models/ranking/README.md) | Based on [LSTM-based deep learning models for non-factoid answer selection](https://arxiv.org/abs/1511.04108). The model performs ranking of responses or contexts from some database by their relevance for the given context. | | [Question Answering component](deeppavlov/models/squad/README.md) | Based on [R-NET: Machine Reading Comprehension with Self-matching Networks](https://www.microsoft.com/en-us/research/publication/mrc/). The model solves the task of looking for an answer on a question in a given context ([SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) task format). | | [Morphological tagging component](deeppavlov/models/morpho_tagger/README.md) | Based on character-based approach to morphological tagging [Heigold et al., 2017. An extensive empirical evaluation of character-based morphological tagging for 14 languages](http://www.aclweb.org/anthology/E17-1048). A state-of-the-art model for Russian and several other languages. Model assigns morphological tags in UD format to sequences of words.| | **Skills** | | -| [Goal-oriented bot](deeppavlov/skills/go_bot/README.md) | Based on Hybrid Code Networks (HCNs) architecture from [Jason D. Williams, Kavosh Asadi, Geoffrey Zweig, Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning – 2017](https://arxiv.org/abs/1702.03274). It allows to predict responses in goal-oriented dialog. The model is customizable: embeddings, slot filler and intent classifier can switched on and off on demand. | -| [Seq2seq goal-oriented bot](deeppavlov/skills/seq2seq_go_bot/README.md) | Dialogue agent predicts responses in a goal-oriented dialog and is able to handle multiple domains (pretrained bot allows calendar scheduling, weather information retrieval, and point-of-interest navigation). The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers. | |[ODQA](deeppavlov/skills/odqa/README.md) | An open domain question answering skill. The skill accepts free-form questions about the world and outputs an answer based on its Wikipedia knowledge.| | **Embeddings** | | | [Pre-trained embeddings for the Russian language](pretrained-vectors.md) | Word vectors for the Russian language trained on joint [Russian Wikipedia](https://ru.wikipedia.org/wiki/%D0%97%D0%B0%D0%B3%D0%BB%D0%B0%D0%B2%D0%BD%D0%B0%D1%8F_%D1%81%D1%82%D1%80%D0%B0%D0%BD%D0%B8%D1%86%D0%B0) and [Lenta.ru](https://lenta.ru/) corpora. | diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index e14b15d8db..f41575db85 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -42,6 +42,12 @@ import deeppavlov.dataset_iterators.sqlite_iterator import deeppavlov.dataset_iterators.morphotagger_iterator +import deeppavlov.models.go_bot.bot +import deeppavlov.models.go_bot.network +import deeppavlov.models.go_bot.tracker +import deeppavlov.models.seq2seq_go_bot.bot +import deeppavlov.models.seq2seq_go_bot.network +import deeppavlov.models.seq2seq_go_bot.kb import deeppavlov.models.classifiers.intents.intent_model import deeppavlov.models.commutators.random_commutator import deeppavlov.models.embedders.fasttext_embedder @@ -68,12 +74,6 @@ import deeppavlov.models.morpho_tagger.common import deeppavlov.models.api_requester -import deeppavlov.skills.go_bot.bot -import deeppavlov.skills.go_bot.network -import deeppavlov.skills.go_bot.tracker -import deeppavlov.skills.seq2seq_go_bot.bot -import deeppavlov.skills.seq2seq_go_bot.network -import deeppavlov.skills.seq2seq_go_bot.kb import deeppavlov.skills.odqa.tfidf_ranker import deeppavlov.vocabs.typos import deeppavlov.vocabs.wiki_sqlite diff --git a/deeppavlov/skills/go_bot/README.md b/deeppavlov/models/go_bot/README.md similarity index 98% rename from deeppavlov/skills/go_bot/README.md rename to deeppavlov/models/go_bot/README.md index bf782aa36b..74ba3d7909 100644 --- a/deeppavlov/skills/go_bot/README.md +++ b/deeppavlov/models/go_bot/README.md @@ -13,16 +13,16 @@ Here is a simple example of interaction with a trained dialogue bot (can be down ```x:: >> Hello, welcome to the Cambridge restaurant system. You can ask for restaurants by area, price range or food type. How may I help you? x::hi, i want cheap food -2018-05-16 12:28:48.459 INFO in 'deeppavlov.skills.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'cheap'}, got 22 results. +2018-05-16 12:28:48.459 INFO in 'deeppavlov.models.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'cheap'}, got 22 results. >> The lucky star serves chinese food in the cheap price range. x::is there anything with french food? -2018-05-16 12:29:02.795 INFO in 'deeppavlov.skills.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'cheap', 'food': 'french'}, got 0 results. +2018-05-16 12:29:02.795 INFO in 'deeppavlov.models.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'cheap', 'food': 'french'}, got 0 results. >> Sorry there is no french restaurant in the cheap price range. x::ok, maybe there is something in expensive pricerange? -2018-05-16 12:29:23.337 INFO in 'deeppavlov.skills.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'expensive', 'food': 'french'}, got 2 results. +2018-05-16 12:29:23.337 INFO in 'deeppavlov.models.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'expensive', 'food': 'french'}, got 2 results. >> Cote serves french food in the expensive price range. x::in what part of town is it? -2018-05-16 12:29:31.193 INFO in 'deeppavlov.skills.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'expensive', 'food': 'french'}, got 2 results. +2018-05-16 12:29:31.193 INFO in 'deeppavlov.models.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'expensive', 'food': 'french'}, got 2 results. >> Restaurant two two is a nice place in the north of town serving tasty french food. x::ok, give me their phone number >> The phone number of restaurant two two is 01223 351880. @@ -31,10 +31,10 @@ x::address and post code too x::ok, thank you! >> Restaurant two two serves french food in the expensive price range. x::and if i wanted an italian restuarant? -2018-05-16 12:30:15.98 INFO in 'deeppavlov.skills.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'expensive', 'food': 'italian'}, got 4 results. +2018-05-16 12:30:15.98 INFO in 'deeppavlov.models.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'expensive', 'food': 'italian'}, got 4 results. >> Frankie and bennys serves italian food in the expensive price range. x::more cheaply priced? -2018-05-16 12:30:36.560 INFO in 'deeppavlov.skills.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'cheap', 'food': 'italian'}, got 5 results. +2018-05-16 12:30:36.560 INFO in 'deeppavlov.models.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'cheap', 'food': 'italian'}, got 5 results. >> Da vinci pizzeria serves italian food in the cheap price range. x::ok, give me their phone number and address, please >> The phone number of da vinci pizzeria is 01223 351707 and it is on 20 milton road chesterton. diff --git a/deeppavlov/skills/go_bot/__init__.py b/deeppavlov/models/go_bot/__init__.py similarity index 100% rename from deeppavlov/skills/go_bot/__init__.py rename to deeppavlov/models/go_bot/__init__.py diff --git a/deeppavlov/skills/go_bot/bot.py b/deeppavlov/models/go_bot/bot.py similarity index 98% rename from deeppavlov/skills/go_bot/bot.py rename to deeppavlov/models/go_bot/bot.py index 5313ac5de5..8338a7c80b 100644 --- a/deeppavlov/skills/go_bot/bot.py +++ b/deeppavlov/models/go_bot/bot.py @@ -17,15 +17,13 @@ import re import numpy as np -from typing import Type from deeppavlov.core.commands.utils import expand_path from deeppavlov.core.common.registry import register from deeppavlov.core.models.nn_model import NNModel -from deeppavlov.core.common.errors import ConfigError -from deeppavlov.skills.go_bot.network import GoalOrientedBotNetwork -import deeppavlov.skills.go_bot.templates as templ from deeppavlov.core.common.log import get_logger +from deeppavlov.models.go_bot.network import GoalOrientedBotNetwork +import deeppavlov.models.go_bot.templates as templ log = get_logger(__name__) diff --git a/deeppavlov/skills/go_bot/diagram.png b/deeppavlov/models/go_bot/diagram.png similarity index 100% rename from deeppavlov/skills/go_bot/diagram.png rename to deeppavlov/models/go_bot/diagram.png diff --git a/deeppavlov/skills/go_bot/metrics.py b/deeppavlov/models/go_bot/metrics.py similarity index 100% rename from deeppavlov/skills/go_bot/metrics.py rename to deeppavlov/models/go_bot/metrics.py diff --git a/deeppavlov/skills/go_bot/network.py b/deeppavlov/models/go_bot/network.py similarity index 100% rename from deeppavlov/skills/go_bot/network.py rename to deeppavlov/models/go_bot/network.py diff --git a/deeppavlov/skills/go_bot/templates.py b/deeppavlov/models/go_bot/templates.py similarity index 100% rename from deeppavlov/skills/go_bot/templates.py rename to deeppavlov/models/go_bot/templates.py diff --git a/deeppavlov/skills/go_bot/tracker.py b/deeppavlov/models/go_bot/tracker.py similarity index 100% rename from deeppavlov/skills/go_bot/tracker.py rename to deeppavlov/models/go_bot/tracker.py diff --git a/deeppavlov/skills/seq2seq_go_bot/README.md b/deeppavlov/models/seq2seq_go_bot/README.md similarity index 100% rename from deeppavlov/skills/seq2seq_go_bot/README.md rename to deeppavlov/models/seq2seq_go_bot/README.md diff --git a/deeppavlov/skills/seq2seq_go_bot/__init__.py b/deeppavlov/models/seq2seq_go_bot/__init__.py similarity index 100% rename from deeppavlov/skills/seq2seq_go_bot/__init__.py rename to deeppavlov/models/seq2seq_go_bot/__init__.py diff --git a/deeppavlov/skills/seq2seq_go_bot/bot.py b/deeppavlov/models/seq2seq_go_bot/bot.py similarity index 98% rename from deeppavlov/skills/seq2seq_go_bot/bot.py rename to deeppavlov/models/seq2seq_go_bot/bot.py index 952905a36a..9a9da1889d 100644 --- a/deeppavlov/skills/seq2seq_go_bot/bot.py +++ b/deeppavlov/models/seq2seq_go_bot/bot.py @@ -22,7 +22,7 @@ from deeppavlov.core.models.nn_model import NNModel from deeppavlov.core.data.vocab import DefaultVocabulary from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder -from deeppavlov.skills.seq2seq_go_bot.network import Seq2SeqGoalOrientedBotNetwork +from deeppavlov.models.seq2seq_go_bot.network import Seq2SeqGoalOrientedBotNetwork from deeppavlov.core.common.log import get_logger diff --git a/deeppavlov/skills/seq2seq_go_bot/kb.py b/deeppavlov/models/seq2seq_go_bot/kb.py similarity index 100% rename from deeppavlov/skills/seq2seq_go_bot/kb.py rename to deeppavlov/models/seq2seq_go_bot/kb.py diff --git a/deeppavlov/skills/seq2seq_go_bot/network.py b/deeppavlov/models/seq2seq_go_bot/network.py similarity index 100% rename from deeppavlov/skills/seq2seq_go_bot/network.py rename to deeppavlov/models/seq2seq_go_bot/network.py From d11775dff132210defb91b65787959dd993dbb63 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 26 Jun 2018 11:02:35 +0300 Subject: [PATCH 285/616] feat add agent and skill switcher * feat: add simplest vocab intersection classifier * feat: add finalized and working agent concept * refactor: finalize agent.py code * feat: add an example for agent usage * refactor: try to improve readability of agent example * chore: fix grammar in agent example * feat: add HighestConfidenceSelector and set it as default * fix: correct registered name for TokensMatcher * chore: use HighestConfidenceSelector in agent example * fix: no need for *args and **kwargs in skill's __call__ * style: simplify imports in agent example * feat: add a default skills filter class for specification purposes * feat: add pattern matching skill and use it in agent example * style: make an agent example look better in github --- deeppavlov/__init__.py | 2 + deeppavlov/core/agent/__init__.py | 1 + deeppavlov/core/agent/agent.py | 67 ++++++++-- .../classifiers/tokens_matcher/__init__.py | 1 + .../tokens_matcher/tokens_matcher.py | 11 ++ .../skills/pattern_matching_skill/__init__.py | 1 + .../pattern_matching_skill.py | 34 +++++ examples/hello_agent.ipynb | 118 ++++++++++++++++++ 8 files changed, 226 insertions(+), 9 deletions(-) create mode 100644 deeppavlov/models/classifiers/tokens_matcher/__init__.py create mode 100644 deeppavlov/models/classifiers/tokens_matcher/tokens_matcher.py create mode 100644 deeppavlov/skills/pattern_matching_skill/__init__.py create mode 100644 deeppavlov/skills/pattern_matching_skill/pattern_matching_skill.py create mode 100644 examples/hello_agent.ipynb diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index f41575db85..72c6667a91 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -94,6 +94,8 @@ import deeppavlov.models.preprocessors.one_hotter import deeppavlov.dataset_readers.ontonotes_reader +import deeppavlov.models.classifiers.tokens_matcher.tokens_matcher + import deeppavlov.metrics.accuracy import deeppavlov.metrics.fmeasure diff --git a/deeppavlov/core/agent/__init__.py b/deeppavlov/core/agent/__init__.py index e69de29bb2..dfb5373f19 100644 --- a/deeppavlov/core/agent/__init__.py +++ b/deeppavlov/core/agent/__init__.py @@ -0,0 +1 @@ +from .agent import * diff --git a/deeppavlov/core/agent/agent.py b/deeppavlov/core/agent/agent.py index 8510a22690..52f4ae95d8 100644 --- a/deeppavlov/core/agent/agent.py +++ b/deeppavlov/core/agent/agent.py @@ -1,15 +1,64 @@ -from typing import List, Dict +from collections import defaultdict +from typing import List +import random from deeppavlov.core.models.component import Component -# TODO Create this class dynamically? -class Agent(Component): - def __init__(self, skill_configs: List[Dict], commutator_config: Dict, *args, **kwargs): - self.skill_configs = skill_configs - self.commutator_config = commutator_config - self.history = [] - super().__init__(*args, **kwargs) +class RandomSelector(Component): + def __init__(self, *args, **kwargs): + pass + + def __call__(self, utterances, batch_history, *responses): + return [random.choice([t for t, sc in r if t]) for r in zip(*responses)] - def __call__(self, *args, **kwargs): + +class HighestConfidenceSelector(Component): + def __init__(self, *args, **kwargs): pass + + def __call__(self, utterances, batch_history, *responses): + responses, confidences = zip(*[zip(*r) for r in responses]) + indexes = [c.index(max(c)) for c in zip(*confidences)] + return [responses[i] for i, *responses in zip(indexes, *responses)] + + +class TransparentFilter(Component): + def __init__(self, skills_count, *args, **kwargs): + self.size = skills_count + + def __call__(self, utterances, batch_history): + return [[True] * self.size] * len(utterances) + + +class Agent(Component): + def __init__(self, skills: List[Component], skills_selector=None, skills_filter=None, *args, **kwargs): + self.skills = skills + self.skills_filter = skills_filter or TransparentFilter(len(skills)) + self.skills_selector = skills_selector or HighestConfidenceSelector() + self.history = defaultdict(list) + self.states = defaultdict(lambda: [None] * len(self.skills)) + + def __call__(self, utterances, ids=None): + batch_size = len(utterances) + ids = ids or list(range(batch_size)) + batch_history = [self.history[id] for id in ids] + batch_states = [self.states[id] for id in ids] + filtered = self.skills_filter(utterances, batch_history) + responses = [] + for skill_i, (m, skill) in enumerate(zip(zip(*filtered), self.skills)): + m = [i for i, m in enumerate(m) if m] + batch = tuple(zip(*[(utterances[i], batch_history[i], batch_states[i][skill_i]) for i in m])) + res = [(None, 0.)] * batch_size + if batch: + predicted, confidence, *state = skill(*batch) + state = state[0] if state else [None] * len(predicted) + for i, predicted, confidence, state in zip(m, predicted, confidence, state): + res[i] = (predicted, confidence) + batch_states[i][skill_i] = state + responses.append(res) + responses = self.skills_selector(utterances, batch_history, *responses) + for history, utterance, response in zip(batch_history, utterances, responses): + history.append(utterance) + history.append(response) + return responses diff --git a/deeppavlov/models/classifiers/tokens_matcher/__init__.py b/deeppavlov/models/classifiers/tokens_matcher/__init__.py new file mode 100644 index 0000000000..1a7155e3ee --- /dev/null +++ b/deeppavlov/models/classifiers/tokens_matcher/__init__.py @@ -0,0 +1 @@ +from .tokens_matcher import * diff --git a/deeppavlov/models/classifiers/tokens_matcher/tokens_matcher.py b/deeppavlov/models/classifiers/tokens_matcher/tokens_matcher.py new file mode 100644 index 0000000000..6412681d3d --- /dev/null +++ b/deeppavlov/models/classifiers/tokens_matcher/tokens_matcher.py @@ -0,0 +1,11 @@ +from deeppavlov.core.common.registry import register +from deeppavlov.core.models.component import Component + + +@register('tokens_matcher') +class TokensMatcher(Component): + def __init__(self, words, *args, **kwargs): + self.words = set(words) + + def __call__(self, tokens_batch): + return [float(any(word in ' '.join(tokens) for word in self.words)) for tokens in tokens_batch] diff --git a/deeppavlov/skills/pattern_matching_skill/__init__.py b/deeppavlov/skills/pattern_matching_skill/__init__.py new file mode 100644 index 0000000000..837e9ce14e --- /dev/null +++ b/deeppavlov/skills/pattern_matching_skill/__init__.py @@ -0,0 +1 @@ +from .pattern_matching_skill import * diff --git a/deeppavlov/skills/pattern_matching_skill/pattern_matching_skill.py b/deeppavlov/skills/pattern_matching_skill/pattern_matching_skill.py new file mode 100644 index 0000000000..f09f590934 --- /dev/null +++ b/deeppavlov/skills/pattern_matching_skill/pattern_matching_skill.py @@ -0,0 +1,34 @@ +import random +import re + +from deeppavlov.core.models.component import Component + + +class PatternMatchingSkill(Component): + def __init__(self, responses, patterns=None, regex=False, ignore_case=True): + if isinstance(responses, str): + responses = [responses] + self.responses = responses + if isinstance(patterns, str): + patterns = [patterns] + if patterns and regex: + patterns = [re.compile(pattern) for pattern in patterns] + self.patterns = patterns + self.regex = regex + self.ignore_case = ignore_case + + def __call__(self, utterances_batch, history_batch, states_batch): + response = [random.choice(self.responses) for _ in utterances_batch] + if self.patterns is None: + confidence = [0.5] * len(utterances_batch) + else: + if self.ignore_case: + utterances_batch = [utterance.lower() for utterance in utterances_batch] + if self.regex: + confidence = [float(any([pattern.search(utterance) for pattern in self.patterns])) + for utterance in utterances_batch] + else: + confidence = [float(any([pattern in utterance for pattern in self.patterns])) + for utterance in utterances_batch] + + return response, confidence diff --git a/examples/hello_agent.ipynb b/examples/hello_agent.ipynb new file mode 100644 index 0000000000..99865d0678 --- /dev/null +++ b/examples/hello_agent.ipynb @@ -0,0 +1,118 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/yoptar/reps/DeepPavlov/venv/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n", + "Using TensorFlow backend.\n", + "[nltk_data] Downloading package punkt to /home/yoptar/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package stopwords to /home/yoptar/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n", + "[nltk_data] Downloading package perluniprops to\n", + "[nltk_data] /home/yoptar/nltk_data...\n", + "[nltk_data] Package perluniprops is already up-to-date!\n", + "[nltk_data] Downloading package nonbreaking_prefixes to\n", + "[nltk_data] /home/yoptar/nltk_data...\n", + "[nltk_data] Package nonbreaking_prefixes is already up-to-date!\n", + "2018-06-22 11:47:45.69 DEBUG in 'gensim.models.doc2vec'['doc2vec'] at line 73: Fast version of gensim.models.doc2vec is being used\n", + "2018-06-22 11:47:45.73 INFO in 'summa.preprocessing.cleaner'['textcleaner'] at line 20: 'pattern' package not found; tag filters are not available for English\n" + ] + } + ], + "source": [ + "from deeppavlov.skills.pattern_matching_skill import PatternMatchingSkill\n", + "from deeppavlov.core.agent import Agent, HighestConfidenceSelector" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "hello = PatternMatchingSkill(['Hello world!'], patterns=[\"hi\", \"hello\", \"good day\"])\n", + "bye = PatternMatchingSkill(['Goodbye world!', 'See you aroung'],\n", + " patterns=[\"bye\", \"chao\", \"see you\"])\n", + "fallback = PatternMatchingSkill([\"I don't understand, sorry\", 'I can say \"Hello world!\"'])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "agent = Agent([hello, bye, fallback], skills_selector=HighestConfidenceSelector())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Hello world!', 'See you aroung', 'I can say \"Hello world!\"']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent(['Hello', 'Bye', 'Or not'])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Hello world!', 'Goodbye world!', \"I don't understand, sorry\"]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent(['Hello', 'Bye', 'Or not'])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "DeepPavlov", + "language": "python", + "name": "deeppavlov" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 4355816e3637e5f4ea059e16b967ea5805420517 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 26 Jun 2018 11:04:05 +0300 Subject: [PATCH 286/616] fix: reading reports fixed --- deeppavlov/evolve.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 91a9eb5e55..2f9edd4df6 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -264,9 +264,9 @@ def results_to_table(population, evolution, considered_metrics, result_file, res evolution.main_model_path + ["save_path"])).parent.joinpath("out.txt"))), "r") as fout: reports_data = fout.read().splitlines()[-2:] reports = [] - for i in range(2): + for j in range(2): try: - reports.append(json.loads(reports_data[i])) + reports.append(json.loads(reports_data[j])) except: pass From 3995178bfc2086d92e42edc4b38cb2b9a0463d95 Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Tue, 26 Jun 2018 12:01:04 +0300 Subject: [PATCH 287/616] docs: gpus and default values --- deeppavlov/models/evolution/README.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index 7698ea5a93..e990796342 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -6,18 +6,18 @@ This repository contains implementation of parameters evolution for DeepPavlov models. Evolution process can be described in the following way: -* Initialize parameters of evolutionary process: - - `p_size` - number of individuals (models) per population - - `key_main_model` - key of the dictionary in config containing the model being trained (see description below). - - `p_cross` - probability of crossover for a parent pair - - `pow_cross` - crossover power - portion of evolving parameters that will be exchanged between parents during crossover - - `p_mut` - probability of mutation for a parameter - - `pow_mut` - mutation power - maximal portion of maximal possible value of parameter which can be added or subtracted during mutation - - `gpus` - available GPUs divided by comma "," (default "-1" means CPU support; "0,3,5,2" means visible 0, 2, 3, 5 GPUs) - - `train_partition` - if train file is too big to train (recommeded to divide train files if train dataset is more than 100 thousands examples), one can split it in `train_partition` number of files, save it calling "any_name_{0}.any_extension", ..., "any_name_{`train_partition`}.any_extension". In dataset_reader "train" field indicate the first one file. Population is trained on the N_{population} % `train_partition` part of the dataset. - - `start_from_population` - the number of population to start from that is needed to restart population, for example (by feault, starts from 0 population). - - `path_to_population` - path to the directory "population_{`start_from_population`}". Should be given if `start_from_population` is not 0. - - `elitism_with_weights` - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not +* Initialize parameters of evolutionary process setting the following arguments to `evolve.py`: + - `--p_size` - number of individuals (models) per population (*Default: 10*). + - `--key_main_model` - key of the dictionary in config containing the model being trained (see description below) (Default: "main"). + - `--p_cross` - probability of crossover for a parent pair (*Default: 0.2*). + - `--pow_cross` - crossover power - portion of evolving parameters that will be exchanged between parents during crossover (Default: 0.1). + - `--p_mut` - probability of mutation for a parameter (*Default: 1.*). + - `--pow_mut` - mutation power - maximal portion of maximal possible value of parameter which can be added or subtracted during mutation (Default: 0.1). + - `--gpus` - available GPUs divided by comma "," (*Default: -1 means CPU support*). If one runs `evolve.py` with assigned `CUDA_VISIBLE_DEVICES`, gpus are either of the same numeration (e.g. `CUDA_VISIBLE_DEVICES=3,4,5` and `--gpus 4,4,5` mean running models on `4,4,5` original GPUs) or ordinal number of device within those from `CUDA_VISIBLE_DEVICES` (e.g. `CUDA_VISIBLE_DEVICES=3,4,5` and `--gpus 1,1,2` mean running models on `4,4,5` original GPUs). + - `--train_partition` - if train file is too big to train (recommeded to divide train files if train dataset is more than 100 thousands examples), one can split it in `train_partition` number of files, save it calling "any_name_{0}.any_extension", ..., "any_name_{`train_partition`}.any_extension". In dataset_reader "train" field indicate the first one file. Population is trained on the N_{population} % `train_partition` part of the dataset (*Default: 1*). + - `--start_from_population` - the number of population to start from that is needed to restart population (*Default: 0 means starts from 0 population*). + - `--path_to_population` - path to the directory "population_{`start_from_population`}". Should be given if `start_from_population` is not 0 (*Default: ""*). + - `--elitism_with_weights` - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not (*Default: 0 means save elite models without weights*). * **Warning**: `metrics` can not be evolved because the main metric determines evolutionary process. From 57fd800753cfb4d6ce1871c17afcb8b024f0706f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 13 Apr 2018 16:21:52 +0300 Subject: [PATCH 288/616] feat: working on evolution of structure --- .../models/evolution/check_binary_mask.py | 98 +++++++ .../models/evolution/check_matrix.ipynb | 234 +++++++++++++++ deeppavlov/models/evolution/evolution.py | 0 deeppavlov/models/evolution/intent_model.py | 277 ++++++++++++++++++ .../neuroevolution_param_generator.py | 261 +++++++++++++++++ .../evolution/random_param_generator.py | 85 ++++++ .../models/evolution/train_phenotype.py | 0 deeppavlov/models/evolution/utils.py | 128 ++++++++ 8 files changed, 1083 insertions(+) create mode 100644 deeppavlov/models/evolution/check_binary_mask.py create mode 100644 deeppavlov/models/evolution/check_matrix.ipynb create mode 100644 deeppavlov/models/evolution/evolution.py create mode 100644 deeppavlov/models/evolution/intent_model.py create mode 100644 deeppavlov/models/evolution/neuroevolution_param_generator.py create mode 100644 deeppavlov/models/evolution/random_param_generator.py create mode 100644 deeppavlov/models/evolution/train_phenotype.py create mode 100644 deeppavlov/models/evolution/utils.py diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py new file mode 100644 index 0000000000..fe61e3e188 --- /dev/null +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -0,0 +1,98 @@ +import numpy as np +import networkx as nx +import copy + + +def number_to_type_layer(node_id, n_types): + # return node_layer, node_type + return node_id // n_types, node_id % n_types + + +def type_layer_to_number(node_layer, node_type, n_types): + return node_layer * n_types + node_type + + +def find_sources_and_sinks(directed_graph): + sources = [] + sinks = [] + + for i in directed_graph.nodes(): + if directed_graph.in_degree(i) == 0 and directed_graph.out_degree(i) > 0: + sources.append(i) + if directed_graph.in_degree(i) > 0 and directed_graph.out_degree(i) == 0: + sinks.append(i) + + return sources, sinks + + +def get_digraph_from_binary_mask(nodes, binary_mask): + directed_graph = nx.DiGraph() + total_nodes = len(nodes) + + for i in range(total_nodes): + directed_graph.add_node(i) + + for i in range(total_nodes): + for j in range(total_nodes): + if binary_mask[i, j] == 1: + directed_graph.add_edge(i, j) + return directed_graph + + +def get_binary_mask_from_digraph(nodes, directed_graph): + binary_mask = np.zeros((len(nodes), len(nodes))) + for edge in directed_graph.edges(): + binary_mask[edge[0], edge[1]] = 1 + return binary_mask +# +# +# def check_binary_mask(nodes, binary_mask): +# directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) +# sources, sinks = find_sources_and_sinks(directed_graph) +# +# while not nx.is_directed_acyclic_graph(directed_graph): +# cycles = list(nx.simple_cycles(directed_graph)) +# print("Cycles: {}".format(cycles)) +# for cycle_ in cycles: +# cycle = copy.deepcopy(cycle_) + [cycle_[0]] +# for i in range(len(cycle_)): +# new_directed_graph = copy.deepcopy(directed_graph) +# new_directed_graph.remove_edge(cycle[i], cycle[i+1]) +# new_sources, new_sinks = find_sources_and_sinks(new_directed_graph) +# if nx.is_directed_acyclic_graph(new_directed_graph): +# if set(new_sources) == set(sources) and set(new_sinks) == set(sinks): +# directed_graph.remove_edge(cycle[i], cycle[i+1]) +# continue +# binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) +# return True, binary_mask + + +def check_binary_mask(nodes, binary_mask): + directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) + sources, sinks = find_sources_and_sinks(directed_graph) + + while not nx.is_directed_acyclic_graph(directed_graph): + candidates = [] + cycles = list(nx.simple_cycles(directed_graph)) + print("Cycles: {}".format(cycles)) + # number of candidates to be the best new graph + cycles_len = np.array([len(cycle) for cycle in cycles]) + n_candidates = np.prod(cycles_len) + + for i in range(n_candidates): + new_directed_graph = copy.deepcopy(directed_graph) + candidates.append(new_directed_graph) + + for j, cycle_ in enumerate(cycles): + cycle = copy.deepcopy(cycle_) + [cycle_[0]] + for i in range(len(cycle_)): + candidates[].remove_edge(cycle[i], cycle[i + 1]) + new_sources, new_sinks = find_sources_and_sinks(new_directed_graph) + if set(new_sources).issuperset(set(sources)) and set(new_sinks).issuperset(set(sinks)): + directed_graph.remove_edge(cycle[i], cycle[i + 1]) + continue + else: + new_directed_graph.add_edge(cycle[i], cycle[i + 1]) + + binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) + return True, binary_mask diff --git a/deeppavlov/models/evolution/check_matrix.ipynb b/deeppavlov/models/evolution/check_matrix.ipynb new file mode 100644 index 0000000000..4bcf35ace6 --- /dev/null +++ b/deeppavlov/models/evolution/check_matrix.ipynb @@ -0,0 +1,234 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "from check_binary_mask import check_binary_mask\n", + "from check_binary_mask import number_to_type_layer\n", + "from check_binary_mask import type_layer_to_number" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "T = 3\n", + "L = 2\n", + "total_nodes = T * L\n", + "\n", + "nodes = {}\n", + "types = {0: \"Dense\", 1: \"Conv1D\", \n", + " 2: \"LSTM\", 3: \"BiLSTM\", 4: \"GlobMaxPool1D\", \n", + " 5: \"MaxPool1D\", 6: \"Attention\"}\n", + "\n", + "for i in range(0, total_nodes):\n", + " nodes[i] = types[number_to_type_layer(i, T)[1]]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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7x123FvX1wvkA4eFJI34nLEx4s6mrOz30M7PZjJCQuBH39fLSAwDa2hqGfpaaegP8/EJx+PC/cP31ywEAX365HX193bj66tvHrPHChYGhv0e7z3g/B4Q9cwDw9PTBwEA/+1iCent70d/fD29vbwwMDMBoNKK7uxseHs779q2EhMlDRw4bGs7i5Mn92LPnbygsPIinn/43vPjiN0NHEAHg9OnTmDx5slWPER9/FVJTZyEvbwvCwuJRU1OG++7b7LDnIKs9WLXaMYcjzpz5DgAQE5MO8w/dMXXqjbjjjscnvIzk5BQAQGxs3FAAe3t7Q6PRID7eslU3CS+//C0KCw/ixInPcOrUAWzb9hQ+/XQzfve7XTAYrkdPj7BVn5Nzz5ifc3l66i5Z5ug8PICMDCAqasJPgUQSHCwMZk5MnIJTpw7A03NwQoeJAwOFQ7fh4REj+qGlpWzoPpafeXh4QKfzGXFfo7ESABASEjLsZ7m592Dnzk3w8BhAdHQK/vKXXdDrg3DrrT+DVjv6DLJz58J+WFboqD1aVhYKAAgNDRuzh7/4ohYAYDDMQnR0DPtYgvz8/PDdd98hMjISS5cuRUJCAtRqNWprXXPGd3j4JMybtxq5uavwxBNzUVR0CGVlXyIzc47dy77xxnvx2mvCiYQhITGYPv1mu5dpYc31wZIIWEf47LM3AABXX30rAgLC4OsbiJ6edkybduM4v3mRr68vPD290djYiIiIiDHvp9FoMHlyztCFypWV3+GRR2biX//6v3jyyU8QFZUClUqFCxf6rXr80R/Lrl8nF7H08Q033IFTpw5gz56/YfXqZ8b9vYgIIYSrqk5h6tT5w3527lwhAEz489zRzJv3U+zcuQn5+W9jwYL7cPLkPixYsH7McHUE4ZrDd6BWazBjxs24cIF9LEUhISFYtmwZYmNjhx3Rc9R78kSpVCqkpV2LoqJDaG52zBDhrKy78cYbj+Dbb/Nw550bRz1iaStreln0s4g1GvsORwwODuLNN3+FwsIvcPXVi5CZORtqtRrZ2StRWvrl0FmdlxvrUpnY2FjU1NRgcIwD7Zd+nnvxdwzw9NSho6MFAODvH4KZMxfhyJFtKC4+OuL+ZrMZRmPjhJ6fq5udbGPp4wUL1iEmJh3bt78w6mU2AFBe/g127XoVADBt2k3w9vbFxx+/jO7ujqH7dHd34OOPX4ZOp8e0aTfZXFdS0jQkJEzBvn1/R37+OzCZTE47exgA+vp6sHnzGtTVVWDhwvsRHi7s4bKPpcfX1xfx8fEjwsfe9+SxFBR8hsHBkR8T9PX1DJ0TExeX6ZDH8vUNwIMPvobly3+HhQvvd8gyLWS1B+vtPfH7nj59HPn5fweAoW9yOnZsOxoazmL69AV49NGLn3euWvU0iooO4bnn7sKcOXchLe06aLWeaGg4i2++2YXk5JkjziIGhKbz8/NDfX09oqOjR/z8lVfuQ1PTeUyfvgBhYZPQ39+DL774J3p6OpCbe/EyiQce+DOeeGIONm7MQm7uaiQlTYfZbEJdXQWOHduB3NzV455FbDZb9/qQeCzrycvLB7/97cd46qlb8cwzt2P69AWYNu0m+PmFwGhsxPff56Og4H+xdOkGAIBeH4g1a57Da6/9Ox577FrMm7cGALB371bU1pbjwQdfh6+vdSd5XG7evJ/izTcfxbZt/4WYmDQYDNeNuE9Dw1nk578DQNibBoAvv/wITU3nAQC5uauGwtKisPDgD9+KY0ZXlxFVVcI3ORmNjcjJuQfr1m0CwD6WG3vW1bff5o36TUn+/qH45JNX0NHRjGuuWYJJkybDy8sHTU3ncODAu6iuLkVu7mokJFj3eeuVzJu3evw7WcnaXhY9YH19AU9PYGBg/AubDxz4Hxw48D9Qq9Xw9tYjJCQWV12VjQceuBszZy68bLkBeO65Q/jwwz/i0KF/4dixHdBoPBASEovMzDm46aZ1Yz5ObGwsCgsLER4ePuJnOTmrsHfvVuzd+xaMxkb4+PgjLi4TTzzxPm644Y6h+4WFxeGll77BBx/8F44d24F9+/4OT09vhIbG4ZprFg+7xGI0AwPC6+Jr/7X/5AKX9nF0dAo2bSrA7t2v4/DhD/Cvfz2N3t5O6PXBSE29Gg8//BayslYM/e6iRQ8iKCgKH374PN577z8BAImJU7Fx44e47rrb7a4tO3sl3nrrcXR3tw8F++Xq6yvxj38Mv9TsyJFtOHJEuGYxM3POiIC1fK+rWq2BTueH8PAEXH/9UuTmrkZGxg0A2MdyZM178uWOH9+N48d3j7g9JiYd69f/N44d24HCwi9w+PAH6Opqg69vABISpmDp0sdH/d5rKbGll1Vms/jTGnfvFr5E2cqzsZ2qsrISHh4eiIuLE+XxjUYgNBRYuHD8+5I0SLGPxcY+liep9PLx48cxefJkaCUwN8+WXpbEJyNRUUB39/j3c6WYmBg0NDSgv79flMfv7gZGOUJNEibFPhYb+1ie2Msj2dLLkgjY0FDpfdOLp6cnwsLCUF3tmLParGUyASGOuS6aXESKfSw29rE8sZdHsqWXJRGwYh+GGEtUVBRaW1vRK9IXqUr1daHRcX2Njq+L/HCdjc7a10USAXvph+pSotVqERkZ6fK9WJ4YIk9S7WOxsI/li708nK29LImAVamAzEyguVnsSkaKiIhAe3s7ul34gURzs/B6cH6mvEi5j8XAPpYv9vJwtvayJAIWANLTha0E8c9pHk6j0SA6Ogrnzp1zyeOZzcLrkJ7ukocjB5NqH7sa+1j+2MsCe3pZMgEbFATExgLt7WJXMlJYWDh6e3vR0dEx/p3tZDQKr0NQ0Pj3JemRch+7EvtY/tjLAnt6WTIBCwDTpgEuyDCrqdVqxMTEuGQvtqNDeB1IvqTax67EPlYG9rJ9vSypgI2JEb6Gqm9ik75cKiQkBIODg2hra3XaY/T1ATqd8DqQfEm5j12Bfawc7GX7ellSAavRANOnS/ODdZVKhdjYWJw/Xw1nfflVc7Pw/Dl5RN6k3MeuwD5WDvayfb0sqYAFgNRU4clIcYspKCgIarUaLS0tDl92X5/wvFNTHb5oEoGU+9iZ2MfKw162fRmSC1hfXyArC6ivl+bZa8Je7HmYHPg1J2YzUFcHZGfzmkGlkHofOwP7WJnYy7YvR3IBCwhbDElJQOPERqa6lL+/P7y8vNDowOIaG4HkZCAlxWGLJAmQch87A/tYudjLtpFkwKpUwNy5wlaEFA9LxMXFXXEouzX6+oTnOXcuL8hXGqn3sSOxj5WNvWwbSQYsAOj10j0scelQdntcehhCr3dQcSQpUu5jR2Efuwf2svUkG7DAxcMSDQ1iVzJSTEwM6urqcOHCBZuX0dAgHILgITVlk3IfOwL72H2wl60j6YBVqYQtJp0OcMKJu3bR6XQICgpCbW2tTb/f3Cw8Lx5SUz4p97G92Mfuhb1sHUkHLCCcwXXbbcKuu9EodjXD2TqUva1NWIGLF/NsS3ch5T62FfvYPbGXJ07yAQsIM/iWLAH6+6W1Qi1D2Wtqaib8O21twhdHL14M+Ps7sTiSHKn2sS3Yx+6NvTwxsghYAAgOBm6/HbhwQVqHJqKiotDS0jKhoezNzYDJJDyP4GAXFEeSI9U+tgb7mAD28kTIJmAB4QVYuhTw8JDOmWxarRYRERFXHMpuNgv1arXAj3/MNyV3J8U+ngj2MV2OvXxlsgpYQNiFv/124cuXz52TxjVZkZGRYw5l7+sT6oyNFerm4TQCpNnHV8I+prGwl8emMjvrm+udzGwGysuB/fuFD6fDwsQ9i7G+vg5GYzvS0tKG6mtsFP7OzhZO++ZZlnQ5qfXx5djHNFHO6OXjx49j8uTJ0Gq1DqnP1b3s4dzFO49KJVyTFRUFHDwIVFQAERGAl5c49YSFhaO2tg4dHR3w9PRDfb1wvdjcubz4nsYmtT6+VF8f2Mc0YezlkWS7B3upS7ecBgeBkBBxVmp1dSMqKoxIS0vh1j5ZTSp93NcnnPyh0XCvlWzjqF62dw9W7F5WRMBadHUBZWVAQQHQ0wP4+QmnkzvzBbVcC9bRAXh7m2EyfY358ychMTHceQ9KiiZ2H+t0wgzM1FRe30r2sbeXbQlYKfWyogLWYnAQqK4WVmp1tXCmWEiI8LejDAwIW0YDA8KH5dOmCR/yNzbWoaSkBFlZWVBxs5/sIGYfc1g6OZKtvWxNwEqxlxUZsJdqbQVKSoDCQuGiaABQqwEfH+HPRN6sBgaA7m7hj2UMrKcnkJkJpKcDQUHD73/w4EEkJSUhJibGsU+G3JYYfUzkDNb08lgBK5deVnzAWpjNwuEKoxFoagJqa4GaGmEFX2lH02wWVlx0tPDhfWiocIjD13fs32tqasJ3332HnJwcqNWyuxKKJMyVfUzkTBPp5VOnTiEtLW1YwMqpl90mYEdjWcG9vUB//wX89a9vYvnyFfD310OjEbaqvL1tW3FHjx5FVFQUJk2a5JziiX5waR+bTMLhOJNJ6F97+5jIlS7v5c8/z8d1190Anc5Llr0s28t0HEGlEk7X1uuBkpLTOHPmCFpaJiM9/Xq7l20wGPDVV18hNjYWGn6gRU50aR8TydnlvRwS0o+YGGlc6mMLHr8EcOHCBRw9ehTh4eEoKCgY9RuZrBUYGIigoCBUVlY6oEIiIpIbBiyA06dPo7u7GzqdDoBw3N8RDAYDTp8+jYGBAYcsj4iI5IMBC6CiogIA0NzcjMHBQYftder1ekRGRuL06dMOWR4REcmHW38Ga7Fw4UKYzWbs3r0b8+fPd8j3XlqkpaVh//79SExMhJdcP0ggIiKrcQ8WgEqlglqtHvbHUXQ6HeLi4lBaWuqwZRIRkfQxYF0gNTUV1dXVDjl5ioiI5IEB6wKenp5ISkpCSUmJ2KUQEZGLMGBdJCkpCY2NjWhvbxe7FCIicgEGrIt4eHggJSWFe7FERG6CAetCCQkJMBqNaGlpEbsUIiJyMgasC6nVaqSlpaG4uFjsUoiIyMkYsC4WFxeHvr4+NDY2il0KERE5EQPWxVQqFQwGA4qKiuDGg4yIiBSPASuCqKgoqFQq1NbWil0KERE5CQNWJAaDAcXFxTCZTGKXQkRETsCAFUlYWBh0Oh3Onz8vdilEROQEDFgRZWRkoKSkBIODg2KXQkREDsaAFVFgYCACAwNx5swZsUshIiIHY8CKzGAwoLy8nEPZiYgUhgErMj8/P0RERHAoOxGRwjBgJSA9PR1nzpxBX1+f2KUQEZGDMGAlwDKUvaysTOxSiIjIQRiwEpGSkoLz589zKDsRkUIwYCXCy8sLiYmJKC0tFbsUIiJyAAashCQnJ6OhoQEdHR1il0JERHZiwEqIZSg7x9kREckfA1ZiEhIS0NbWhtbWVrFLISIiOzBgJUatViM9PZ17sUREMseAlaC4uDj09PRwKDsRkYwxYCXo0qHsREQkTwxYiYqKigIADmUnIpIpBqxEqVQqZGRkoLi4GGazWexyiIjISgxYCQsLC4OXlxfOnTsndilERGQlBqzEWYaym0wmsUshIiIrMGAlLigoiEPZiYhkiAErA5ah7BcuXBC7FCIimiAGrAz4+fkhLCyMQ9mJiGSEASsT6enpqKys5FB2IiKZYMDKhI+PD2JjY1FeXi52KURENAEMWBlJTU3FuXPn0NPTI3YpREQ0DgasjHh5eSEhIQElJSVil0JERONgwMpMcnIy6uvrOZSdiEjiGLAyo9VqkZKSwr1YIiKJY8DKUEJCAlpbW9HW1iZ2KURENAYGrAxpNBqkpaVxnB0RkYQxYGWKQ9mJiKSNAStTarUaBoMBxcXFYpdCRESjYMDKWFRUFMxmM4eyExFJEANWxlQq1dBeLIeyExFJCwNW5sLDw+Hl5YXz58+LXQoREV2CAasABoOBQ9mJiCSGAasAwcHB8Pf351B2IiIJYcAqBIeyExFJCwNWIfz9/REWFoaKigqxSyEiIjBgFcUylL2/v1/sUoiI3B4DVkF8fHwQHR2NsrIysUshInJ7DFiFSUtL41B2IiIJYMAqjGUoe2lpqdilEBG5NQasAiUnJ6Ourg6dnZ1il0JE5LYYsAqk1WqRnJzMQQBERCJiwCpUYmIih7ITEYmIAatQlqHs3IvH11DdAAAgAElEQVQlIhIHA1bB4uLi0N3djaamJrFLISJyOwxYBVOr1UhPT0dRUZHYpRARuR0GrMJFR0fDZDKhrq5O7FKIiNwKA1bhVCoVMjIyOJSdiMjFGLBuIDw8HFqtlkPZiYhciAHrJjIyMjiUnYjIhRiwbsIylP3s2bNil0JE5BYYsG7EYDCgrKyMQ9mJiFyAAetG/P39ERoayqHsREQuwIB1MxzKTkTkGgxYN+Pr64vo6GiUl5eLXQoRkaIxYN1Qamoqqqqq0NvbK3YpRESKxYB1Q97e3pg0aRJKSkrELoWISLEYsG4qJSUFdXV16OrqErsUIiJFYsC6KQ5lJyJyLgasG0tMTERLSwuMRqPYpRARKQ4D1o1pNBqkpqZynB0RkRMwYN1cfHw8urq60NzcLHYpRESKwoB1c2q1GgaDgXuxREQOxoAlREdHY3BwkEPZiYgciAFLUKlUMBgMHMpORORADFgCAERERECr1aK6ulrsUoiIFIEBS0M4lJ2IyHEYsDQkODgYer2eQ9mJiByAAUvDZGRkcCg7EZEDMGBpGMtQ9srKSrFLISKSNQYsjZCeno6KigoOZScisgMDlkbw9fVFVFQUh7ITEdmBAUujSktL41B2IiI7MGBpVJah7KWlpWKXQkQkSwxYGlNKSgpqa2s5lJ2IyAYMWBqTVqtFUlISSkpKxC6FiEh2GLB0RUlJSWhqauJQdiIiKzFg6YosQ9mLi4vFLoWISFYYsDSuSZMmobOzk0PZiYiswIClcanVaqSnp3MvlojICgxYmpCYmBgMDAygvr5e7FKIiGSBAUsTwqHsRETWYcDShEVGRkKj0aCmpkbsUoiIJI8BS1bJyMhAcXExh7ITEY2DAUtWCQkJga+vL6qqqsQuhYhI0hiwZDXLUPbBwUGxSyEikiwGLFktICAAwcHBqKioELsUIiLJYsCSTQwGAyoqKjAwMCB2KUREksSAJZv4+voiMjKSQ9mJiMbAgCWbpaen4+zZsxzKTkQ0CgYs2czb2xvx8fEoKysTuxQiIslhwJJdUlJSUFNTw6HsRESXYcCSXTw9PZGYmMih7EREl2HAkt0sQ9nb29vFLoWISDIYsGQ3Dw8PDmUnIroMA5YcYtKkSWhvb0dLS4vYpRARSQIDlhzCMpS9qKhI7FKIiCSBAUsOExsbi4GBATQ0NIhdChGR6Biw5DCWoexFRUUcyk5Ebo8BSw7FoexERAIGLDmcwWBASUkJh7ITkVtjwJLDhYaGwsfHB+fOnRO7FCIi0TBgySkMBgNKS0s5lJ2I3BYDlpwiMDAQQUFBqKysFLsUIiJRMGDJaQwGA06fPs2h7ETklhiw5DR6vR6RkZE4ffq02KUQEbkcA5acKi0tDWfOnEFfX5/YpRARuRQDlpxKp9MhLi4OpaWlYpdCRORSDFhyutTUVNTU1KC7u1vsUoiIXIYBS07HoexE5I4YsOQSSUlJaGxs5FB2InIbDFhyCQ8PD6SkpHAoOxG5DQYsuUxCQgKHshOR22DAkstYhrJzL5aI3AEDllwqNjYWfX19HMpORIrHgCWX4lB2InIXDFhyuaioKKjVatTW1opdChGR0zBgSRQZGRkoLi7mUHYiUiwGLIkiNDQUOp2OQ9mJSLEYsCSajIwMDmUnIsViwJJoLEPZz5w5I3YpREQOx4AlURkMBpSXl3MoOxEpDgOWRKXX6xEREcGh7ESkOAxYEl16ejqHshOR4jBgSXSWoexlZWVil0JE5DAMWJKE1NRUnD9/nkPZiUgxGLAkCRzKTkRKw4AlyUhOTkZjYyM6OjrELoWIyG4MWJIMDmUnIiVhwJKkJCQkwGg0orW1VexSiIjswoAlSVGr1UhLS0NRUZHYpRAR2YUBS5ITFxeHvr4+NDY2il0KEZHNGLAkORzKTkRKwIAlSYqKigIADmUnItliwJJkWYaycy+WiOSIAUuSFRYWxqHsRCRbDFiSNIPBgJKSEg5lJyLZYcCSpAUFBSEwMJBD2YlIdhiwJHkGgwGnT5/mUHYikhUGLEmen58fwsPDUVFRIXYpREQTxoAlWUhLS0NlZSWHshORbDBgSRZ8fHwQGxuL8vJysUshIpoQBizJRmpqKs6dO4eenh6xSyEiGhcDlmTDy8sLCQkJHMpORLLAgCVZSU5ORn19PYeyE5HkMWBJVrRaLYeyE5EsMGBJdhISEtDW1sah7EQkaQxYkh2NRoP09HTuxRKRpDFgSZbi4uLQ09PDoexEJFkMWJIly1B27sUSkVQxYEm2oqKiYDabOZSdiCSJAUuypVKpOJSdiCSLAUuyFhYWBi8vLw5lJyLJYcCS7GVkZKC0tBQmk0nsUoiIhjBgSfaCgoIQEBDAoexEJCkMWFIEg8GA8vJyXLhwQexSiIgAMGBJIfz8/BAWFobTp0+LXQoREQAGLClIeno6Kisr0d/fL3YpREQMWFIOy1D2srIysUshImLAkrJwKDsRSQUDlhTFMpS9tLRU7FKIyM0xYElxkpOTUVdXh87OTrFLISI3xoAlxdFqtUhOTuYgACISFQOWFCkxMRGtra1oa2sTuxQiclMMWFIkjUaDtLQ07sUSkWgYsKRYcXFx6O7uRlNTk9ilEJEbYsCSYqnVaqSnp6OoqEjsUojIDTFgSdGio6NhMplQV1cndilE5GYYsKRolqHsRUVFHMpORC7FgCXFCw8Ph6enJ86fPy92KUTkRhiw5BYyMjJQUlLCoexE5DIMWHILwcHB8Pf351B2InIZBiy5DQ5lJyJXYsCS2/D390doaCgqKirELoWI3AADltwKh7ITkaswYMmt+Pr6Ijo6GuXl5WKXQkQKx4Alt5OamoqqqioOZScip2LAktvx9vbGpEmTOJSdiJyKAUtuKSUlhUPZicipGLDklixD2UtKSsQuhYgUigFLbisxMREtLS0wGo1il0JECsSAJbel0WiQmprKcXZE5BQMWHJr8fHx6Orq4lB2InI4Biy5NbVaDYPBgOLiYrFLISKFYcCS24uOjsbg4CCHshORQzFgye2pVKqhvVgOZSciR2HAEgGIiIiAVqtFdXW12KUQkUIwYIl+wKHsRORIDFiiHwQHB0Ov1+Ps2bNil0JECsCAJbpERkYGysrKOJSdiOzGgCW6hGUoe2VlpdilEJHMMWCJLpOeno6KigoOZSciuzBgiS7j6+uLqKgoDmUnIrswYIlGkZaWhqqqKvT29opdChHJFAOWaBQcyk5E9mLAEo0hJSUFtbW16OrqErsUIpIhBizRGLRaLZKSkjgIgIhswoAluoKkpCQOZScimzBgia7AMpSde7FEZC0GLNE44uPj0dnZiebmZrFLISIZYcASjUOtViM9PR1FRUVil0JEMsKAJZqAmJgYXLhwAfX19WKXQkQywYAlmgCVSoWMjAwUFRVxKDsRTQgDlmiCIiIi4OHhwaHsRDQhDFgiK3AoOxFNFAOWyAohISHQ6/WoqqoSuxQikjgGLJGVDAYDysrKMDg4KHYpRCRhDFgiKwUEBCA4OBgVFRVil0JEEsaAJbKBwWBARUUFBgYGxC6FiCSKAUtkAw5lJ6LxMGCJbJSWloazZ89yKDsRjYoBS2Qjb29vxMfHcyg7EY2KAUtkBw5lJ6KxMGCJ7ODp6YmkpCSUlJSIXQoRSQwDlshOiYmJaGpqQnt7u9ilEJGEMGCJ7OTh4YHU1FSOsyOiYRiwRA4wadIkdHZ2oqWlRexSiEgiGLBEDsCh7ER0OQYskYPExMRgYGCAQ9mJCAADlshhVCoVDAYDiouLOZSdiBiwRI4UGRkJjUaDmpoasUshIpExYIkczGAwcCg7ETFgiRwtNDQUPj4+HMpO5OYYsEROkJGRwaHsRG6OAUvkBJah7JWVlWKXQkQiYcASOUl6ejpOnz7NoexEbooBS+Qker0ekZGRHMpO5KYYsEROxKHsRO6LAUvkRDqdDnFxcSgrKxO7FCJyMQYskZOlpqaipqaGQ9mJ3AwDlsjJPD09kZiYyKHsRG6GAUvkAklJSRzKTuRmGLBELuDh4YGUlBQUFxeLXQoRuQgDlshFEhIS0N7ezqHsRG6CAUvkIhzKTuReGLBELhQbG4v+/n40NDSIXQoRORkDlsiFVCoVMjIyUFRUxKHsRArHgCVyscjISKjVag5lJ1I4BiyRCDIyMjiUnUjhGLBEIrAMZT937pzYpRCRkzBgiURiMBhQWlrKoexEl6mrq0NlZeXQ31VVVWKXZBMPsQsgcleBgYEICgpCZWUlUlJSxC6HSDJOnjyJEydOoKqqCo2NjYiNjUVcXBxUKpXYpVmFe7BEIjIYDBzKTnSZadOmwcfHB8HBwdBqtbjmmmtkF64AA5ZIVHq9HhERETh9+rTYpRBJRmhoKJKSktDY2IiAgABMmjRJ7JJswoAlEll6ejrOnDmDzs5OlJSUoK2tTeySiER39dVXAwCuu+46aDQakauxDQOWSGSenp4wmUzYvHkzdu7cye8qJoKwF7ty5UpZn5/g1ic5mc1AVxfQ2wsMDgItLV6orga8vAC1GtBoAG9vwNcXkOHhf5IBk8mEHTt2oLKyEl1dXdDr9VZ/w9PlfWwyCX/UavYxycvlvZyYeAPq6+Xby24TsJYVZzQCTU1AbS1QUwP09ws/V6mAU6fC0dqqgofHxd8BAE9PIDoaiIoCQkOBgAD5rGCSNrVajRkzZqClpQW9vb2or6+/4pdPTKSPLfe79L8B9jFJizv0suIDtrUVKCkBCgsvrji1GvDxAQIDAa324n1ra3sRHY2hgLUYGBAaoKpK2DMAhBWcmQmkpwNBQa55LqRMCQkJWL58OT7//HPs2LEDRqNxxH2s6eOxsI9JCtypl1VmBX7j+OAgUF0NFBQIf2u1QEjI+Cvum2++wdSpU+FxecKOYmAAaG4GLlwAYmKAadOEv2X6WTxJgMlkwt69exEaGopp06bZ3MfWYB+TK7hrLysqYLu6gLIyYSX29gJ+foC//8QPG1gTsBZms3CIo7NT+Gxg+nQgNVU4XEFkC3v72BbsY3IGd+9lRQSs2SysxAMHhC2lkBDhRCVr2RKwl+rrE7agNBogK0tYqVL7TICky1F9bC/2MdmLvSyQfcB2dgIHDwIVFUBEhH0r0d6AtejrA+rrgaQkYO5cQK+3a3HkBhzZx47CPiZbsJcvkm3AXrqFpFIBYWH2b5k4KmAt9TU2Cn9zL4DG4ow+diT2MU0Ue3kkWQZsV5ewEh29heTIgLW4dMspK4ufadFFzupjZ2Af05Wwl0cnu4A1GoGPPwZ6eoDwcMdugTgjYAFhi6mhAdDpgMWLhQ/5yb05s4+dhX1Mo2Evj01WX5XY0gJ8+KFwGnZEhDxWJCDUGREh1L1tm/A8yH2xj0kp2MtXJpuAbWkBtm8XvgQiOFjsamwTHCzUv30735zcFfuYlIK9PD5ZBKzRCOzcKXxTR0CA2NXYJyBAuLh6506gvV3sasiV2MekFOzliZF8wHZ1Ccf3VSr5r0iLwEDh748+Ep4fKR/7mJSCvTxxkg5YsxnYv1/48FyuhyDGEhIiPK8DBy5+gTUpE/uYlIK9bB1JB2xZGVBZKZyZpkTh4cJp7eXlYldCzsQ+JqVgL1tHsgHb2SlsScjpzDRrWc5k279feL6kPOxjUgr2svUkGbBm88VvA5HyBcuO4OUlPM+DB3mITWnYx6QU7GXbSDJgLYchwsLErsQ1wsJ4iE2J2MekFOxl20guYC1fuaXkwxCXu/SwBM/GVAb2sdjVkKOwl21fjuQCtqxMGG+k9MMQl/PyEp53WZnYlZAjsI/FroQchb1s+zIkFbCDg8Jg3pAQsSsRR0iI8PwHB8WuhOzBPmYfKwV72b5ellTAVlcL1yG525aShZeX8Pyrq8WuhOzBPmYfKwV72b5ellTAFhRwQoefH3DihNhVkD3Yx+xjpWAv29fLkgnY1lZhK8HdV2ZAAHD+vPB6kPywjwXsY/ljLwvs6WXJBGxJifCFy+5yltpYVCrhdSgpEbsSsgX7WMA+lj/2ssCeXpZEwJrNQGGh+36QfrmQEOH14AX78sI+Ho59LF/s5eFs7WVJBGxXF9DfL2wlkPA69PfzWkK5YR8Pxz6WL/bycLb2sodzyrGO0Xjln3///T785je5Q/+vVquh0/kjJCQGyckzkZV1N2bMuBkqhR3LMBoBvV7sKmiiLu3jvr5u7N79Fxw58gGqqk6hp6cDen0wUlJmYs6cu5CTcw80Gkn88wMAlJZ+iX37/o7y8m9w5sy36O3twkMPbcH8+WtG3Dcvbys2b1479P8ajQd8fPwRFjYJaWnXICdnFTIzZw/9nH0sP+O9J1/O8h69du3z+PGPfzXm/erqKvD++8/i1KkDaGysglbrhaCgSKSmXoP589dgypRcbNq0Bnv3vjWhx12+/HdYseL32LgxBydP7odG44E33zyHoKDIEff9618fwkcf/TcA4Omn8zF5co51TxLW97Ik/oU3NQHqCexLZ2XdjZkzFwEwo6enA9XVJTh6dDvy89/G1Kk34vHH/x/0+kCn1+sKarXwusTEiF0JTZSlj2tqyvHUU7eiuroUU6feiGXLfg1//1AYjQ04ceJzbN68FlVVhVi79jmxSx7y9de7sGvXnxATY0BCwlQUFx8e93cWL/4lUlJmwWw2obvbiLNnT+LIkW3Yvft1ZGevwC9/uQVqtSf7WIYm+p5sjbKyr7FxYzY8PLTIzV2N+Pir0NfXg9raMhQU7IFO54cpU3KxcOH9mDr1xmG/+9JLqxAba8Cdd/5m2O0JCVOG/tuywZqf/w6WLn1s2P0GBvqxb98/4Onpjf7+Xpvqt+U9WRIBW1sL+PiMf7/k5BnIzb1n2G333vsitm7dgB07XsQLL9yN3//+UydV6Vo+PsLrMnWq2JXQRNXWAh4ePXjyydtQV1eBJ574ADfcsHTYfe6443GUlX2FsrKvRKpydLfc8gCWLn0M3t6+OHTo/QkFbGbmXMyevWzYbevWbcLLL/8M+/e/C53OHytX/pl9LEMTfU+2xnvv/Sf6+rrx3HMnkJg4siFaW+sAAAbD9TAYrh/2s5deWoXAwIgR7/+X0mq9MGXKPOTlbRkRsMeO7UBHRzOys1dg//53barflvdk0T+DNZuBmhrbV6ZGo8HPfvZHZGbOwfHju1FY+MXQz7q6jNi69XGsX5+CpUu9cM89YXj++btRV1cxbBl5eVuxZIkKlZVfYfv2F7F+fTKWLvXCz3+ehry8kYcqvvrqE/z619lYuTIUy5bpcO+98XjmmaWori4ddr+Wllq8+uoDuPfeeCxd6ok1a6Lxyivr0dbWMO7z8vERXheeICIPlj4+fPhvqK4uwe23PzoiXC1SU2dh0aIHh9129Oh2bNgwG3fe6Yu77tJjw4bZOHp0x4jfXbcuARs35uD8+WL84Q+34ic/8cPy5QF49tllQ29QALBr15+xZIkKx47tHLEMk8mEtWtj8dBD04ZuCwqKgLe3r61Pf4iXlw4PPbQVkZFJ2LPnr+jsPMM+lhl735PHUlNTBj+/kFHDFcCoh3WtNX/+Wpw7V4SSkmPDbs/L24LExKlISppu87JteU8WPWAd9WH6TTf9DIAQfsJyjdiw4QZ8+umrmDXrVqxf/zJuvfX/4Lvv9uJXv7oWDQ1nRywjL+9P2L//77j55vuxZs1zUKnU2Lx5DQoLDw3d5+TJ/Xj66SXo6mrDsmW/xvr1r2DBgvvQ0dGM2tqLoxcaG6vwyCNX4/Dh95GdvQL33/8n5OSswsGD7+Hxx2ejq+vKH3LwBBF5sfTx0aPvAwBuvnn9hH93165X8cwzP0ZnZwuWL38SP/nJb9HZ2YJnnrkdu3f/ZcT9m5ursXFjDkJD47FmzfPIylqBI0e24aWXVg/dJytrObRaL+Tnvz3i97/9Ng/NzdWYN++nNjzT8Wm1nsjJWQWTaRDff/+/7GOZcdYJTlFRyejoaMbhw9scu+BLzJp1GwICwvH5528O3dbcXI2Cgj248cZ77Vq2Le/Joh8i7rXtcPgIlmPxNTXCXuS77z6J+voKPP/80WFbTPPnr8EvfjEZ7777Ozz88NZhyxgc7MeLLxZApxM23WbPXob77kvCJ5+8MnTSxrFjO2AymfCHP3yGwMDwod9dvvy3w5b1+uu/wODgADZtKkBoaOzQ7bNn34nHHrsOO3a8hBUrfn/F56RSCa8PTxCRlr179yIoKAgZGRnw9vYGcLGPq6pOwsfHH5GRSRNaVmdnK7Zu3YDIyGS88MIx+PgIV/XfcssDePjh6diy5VHMmXPXsHMLamvLsWHDPzFnzl1Dt6nVauza9SrOny9BbGw69PogzJq1GF999RE6O1uh1wcN3Tc//21oNB7Izl5p70sxJsu/x+rqUkyZwj6WE0e9J1/urrv+AydOfIZnn70D0dGpyMiYg9TUWZg8OQdxcRkOeQwPDy1ycu7B55+/gXXrNsHLS4e8vLegVmuQnb0SeXlb7Fq+te/Jou/BDg465kJmnU54Y+rubofZbMa+ff/AVVdlISQkBu3tTUN/vL19kZ5+HU6c2DNiGVdffSe0Ws+h/w8JiUFMTBpqay+OU/DxCQAAHD78AQYHL4xaS1eXEV9//TGuuWYJPD29hz1+REQCoqJSRn380ZhME34JyEVqa2tx6NAhvPPOOygoKEBvb+9QH3d3t0On85vwsk6c+Ay9vV1YvPiXQ+EKAD4+/rjttl+ip6cT3377+bDfCQ6OHhauADBlyrwfarvYq/Pn/xQDA304ePCfQ7f19HTi6NEPMWPGwmEbiI5meS49Pe0A2Mdy4qj35MsZDNfjxRe/wbx5P0VXlxF5eVvw2msP4t//PRNPPJE14qM7W910073o6jLiyBFhT3nv3q249tp/g7+/Yy7qtaaXRd+DddQ/PMs/ZB8ffxiNjejoaEZBwR7cc8/oE4LVo5wiZzbrcPz48ct+5ommplp8/fXXAICYmNmIjEzHa689iDfffAzx8VORnHw9fvSjm+HrK+wlVFefgslkwmefvYHPPntj1McPCooZWuZYmpu94OvbgODgvvGePrnQiRMn4OnpCZPJhEOHDmHSpElYt+63AITLVXp6Oia8rPr6SgBAfPxVI35mue3yN57R9o79/IQ3j/b25qHbZsxYiICAcOTnv41bbvk5AGHDsLe3C7m5q0csw5G6u4V/j5YNX07WkQ9nbgwlJEweOnLY0HAWJ0/ux549f0Nh4UE8/fS/4cUXvxm2k2OL+PirkJo6C3l5WxAWFo+amjLcd99mB1QvsKaXJRGwjjgB4syZ7wAAMTHpMP+wwKlTb8Qddzw+4WVcd931+NGPZg677YMP9Oju1mL69OmX3O87FBUdxIkTn6Ow8CA+++wlfPHFG/iP//gIBsP18PXtBwBkZ69Ebu6qUR/L01OHjIwrf+BeUwNkZWXyEgeJaWpqQnt7O9RqNRYtWoQpU6ags9MDZjMQH/8jnDp1AHV1FRM+TGwttVpzhZ9e/MckHAZegZ07N6GmphzR0SnIz38ben0Qrr12iVNqs7j03yPAPVg5cdR78njCwydh3rzVyM1dhSeemIuiokMoK/sSmZlz7F72jTfei9deE04kDAmJwfTpN9u9TAtZ7cGq1Y45HGHZU7z66lsREBAGX99A9PS0Y9q0G8f5zUtrUUOjGf7mpVKpoFJh2O0ajQZTp87H1KnzAQCVld/hkUdm4oMP/j88+eQniIlJh0qlwuDgAGbMsH3FajTCB+uaK72fksuFhoYiOTkZU6ZMgZ+fcDi4u1vo4xtuuAOnTh3Anj1/w+rVz4y7rIgIIYSrqk4N9ZPFuXOFAEbfY52oefN+ip07NyE//20sWHAfTp7chwUL1kOrdd78MeGaw3egVmswY8bNuHDB8ddUkvM46j15olQqFdLSrkVR0SE0NztmxmFW1t14441H8O23ebjzzo2jHrG0lTWLEr3t7X3eg4ODePPNX6Gw8AtcffUiZGbOhlqtRnb2SpSWfolDh94f9fcmcqnMaNrbm0bcFhtrgKenDh0dLQAAf/8QzJy5CEeObENx8dER9zebzTAaGyf0eAxX6Vm0aBFmz549FK7AxT5esGAdYmLSsX37C6NeZgMA5eXfYNeuVwEA06bdBG9vX3z88cvo7r54aLm7uwMff/wydDo9pk27yeZak5KmISFhCvbt+zvy89+ByWRy2tnDANDX14PNm9egrq4CCxfej/DwSQDYx3LirI2hgoLPRj1vpa+vZ+iclLi4TIc8lq9vAB588DUsX/47LFx4v0OWaWFNL4u+B6vRTPxwxOnTx5Gf/3cAGPomp2PHtqOh4SymT1+ARx+9eAHxqlVPo6joEJ577i7MmXMX0tKug1briYaGs/jmm11ITp454iziiXjllfvQ1HQe06cvQFjYJPT39+CLL/6Jnp6OYZ9rPfDAn/HEE3OwcWMWcnNXIylpOsxmE+rqKnDs2A7k5q4e9yxigFv+cmHpYy8vH/z2tx/jqaduxTPP3I7p0xdg2rSb4OcXAqOxEd9/n4+Cgv/F0qUbAAB6fSDWrHkOr73273jssWsxb94aAMKJGbW15Xjwwdfh6xtgV23z5v0Ub775KLZt+y/ExKTBYLhuxH0aGs4iP/8dAMLeNAB8+eVHaGo6DwDIzV01FJYWhYUHf/hWHDO6uoyoqhK+yclobEROzj1Yt27T0H3Zx/JhzXvy5b79Nm/Ub0ry9w/FJ5+8go6OZlxzzRJMmjQZXl4+aGo6hwMH3kV1dSlyc1cjIWGyndVfNG+ec84zsKaXRQ/YH65ymJADB/4HBw78D9RqNby99QgJicVVV2XjgQfuxsyZC4fd19c3AM89dwgffvhHHDr0Lxw7tgMajQdCQmKRmTkHN920zqZ6c3JWYe/erdi79y0YjY3w8fFHXFwmnnjifdxwwx1D9wsLi8NLL32DDz74Lxw7tgP79v0dnp7eCA2NwzXXLB5xFsUJ+iYAAAYnSURBVOhozGbrXh8Sz6XrKTo6BZs2FWD37tdx+PAH+Ne/nkZvbyf0+mCkpl6Nhx9+C1lZK4buv2jRgwgKisKHHz6P9977TwBAYuJUbNz4Ia677na7a8vOXom33noc3d3tQ8F+ufr6SvzjH8MvNTtyZNvQmZiZmXNGBKzle13Vag10Oj+Ehyfg+uuXIjd3NTIybhi6H/tYXuxZV8eP78bx47tH3B4Tk4716/8bx47tQGHhFzh8+AN0dbXB1zcACQlTsHTp46N+77XUWNvLKrNZ3O9YMZuBLVuAwEBObrjUwADQ1gasXct5jHLAPh4d+1h+2Mujs6WXRT9wo1IB0dHCSSJ0UXe38LrwTUke2MejYx/LD3t5dLb0sugBCwBRUVyZl7OsTJIP9vFI7GN5Yi+PZEsvSyJgQ0N5ndzlTCYgxDFfPEIuwj4eiX0sT+zlkWzpZUkEbIB9J0kqFl8XeeH6Gh1fF/nhOhudta+LJALW1xfw9BQ+RCbhdfD0FF4Xkg/28XDsY/liLw9nay9LImBVKiAzE2huHv++7qC5WXg9eGKIvLCPh2Mfyxd7eThbe1kSAQsA6enCVoK7D2Y2m4XXIT1d7ErIFuxjAftY/tjLAnt6WTIBGxQExMYC7e1iVyIuo1F4HYKCxr8vSQ/7WMA+lj/2ssCeXpZMwALAtGlAx8QnfSlSR4fwOpB8sY/Zx0rBXravlyUVsDExwtdQ9bnp+NO+PkCnA8fTyRz7mH2sFOxl+3pZUgGr0QDTp7vvB+vNzcLz5+QReWMfs4+Vgr1sXy9LKmABIDVVeDLutsXU1yc879RUsSshR2Afi10JOQp72fZlSC5gfX2BrCygvt59zl4zm4G6OiA7m9cMKgX7WOxqyFHYy7YvR3IBCwhbDElJQOPEZpLLXmMjkJwMpKSIXQk5EvuYlIK9bBtJBqxKBcydK2xFKP2wRF+f8DznzuUF+UrDPialYC/bRpIBCwB6vfIPS1x6GEKvF7sacgb2MSkFe9l6kg1Y4OJhiYYGsStxjoYG4RAED6kpG/uYlIK9bB1JB6xKJWwx6XRAS4vY1ThWc7PwvHhITfnYx6QU7GXrSDpgAeEMrttuE3bdjUaxq3GMtjZhBS5ezLMt3QX7mJSCvTxxkg9YQJjBt2QJ0N8v/xXa1iZ8cfTixYC/v9jVkCuxj0kp2MsTozKb5fNxdUsLsHOnsKURHCx2NdZrbr64lSTH+skx2MekFOzlK5NVwALCZIePPgJ6eoDwcHl87mM2Cx+e63Tc4icB+5iUgr08NtkFLAB0dQEHDgAVFUBEBODlJXZFY+vrE05rT04WPjznZ1VkwT4mpWAvj06WAQsIWyDl5cD+/cIWU1iYtLaczGbh20DMZuGaqpQUadVH0sA+JqVgL48k24C16OwEDh6U1paTZQspKUnYQuLF9zQe9jEpBXv5ItkHLDB8y2lwEAgJEWel9vUJH5prNNzaJ+uxj0kp2MsCRQSsRVcXUFYGFBQIH7j7+QmnkzvzBbVcC9bRIXxgPn268G0n/IyKbMU+JqVw915WVMBaDA4C1dXCSq2uBrRaYQtKq3XcYwwMCFtGAwNAbCwwbZow9Z5DpslR2MekFO7ay4oM2Eu1tgIlJUBhoXBRNACo1YCPj/BnIit4YADo7hb+mEzCbZ6eQGYmkJ4OBAU5r34igH1MyuFOvaz4gLUwm4XDFUYj0NQE1NYCNTXCCr7S4QqzWVhx0dFAVBQQGioc4vD15edS5HrsY1IKd+hltwnY0VhWcG+vsBU0OCj8rVYLhxXUasDbW5orjsiCfUxKobReduuAJSIichZZfNk/ERGR3DBgiYiInIABS0RE5AQMWCIiIidgwBIRETkBA5aIiMgJGLBEREROwIAlIiJyAgYsERGREzBgiYiInIABS0RE5AQMWCIiIidgwBIRETkBA5aIiMgJGLBEREROwIAlIiJyAgYsERGREzBgiYiInIABS0RE5AQMWCIiIidgwBIRETkBA5aIiMgJGLBEREROwIAlIiJyAgYsERGREzBgiYiInIABS0RE5AQMWCIiIidgwBIRETkBA5aIiMgJGLBEREROwIAlIiJyAgYsERGRE/z/3zHTCaM55PAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cm = np.zeros((total_nodes, total_nodes)) \n", + "cm[0, 1] = 1\n", + "cm[0, 3] = 1\n", + "cm[3, 1] = 1\n", + "cm[3, 5] = 1\n", + "cm[5, 2] = 1\n", + "\n", + "dg = nx.DiGraph()\n", + "\n", + "for i in range(total_nodes):\n", + " dg.add_node(i)\n", + " \n", + "pos = {}\n", + "\n", + "for i in range(total_nodes):\n", + " for j in range(total_nodes):\n", + " if cm[i,j] == 1:\n", + " dg.add_edge(i, j)\n", + "# pos[i] = 5 * np.array(number_to_type_layer(i, L, T))\n", + " pos[i] = np.array(number_to_type_layer(i, T))[::-1]\n", + "\n", + "plt.figure(figsize=(6, 6))\n", + "nx.draw(dg, pos, node_color='b', node_size=5000, alpha=0.3)\n", + "\n", + "nx.draw_networkx_labels(dg, pos, nodes, font_size=18)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "check_binary_mask(nodes, cm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_graph_and_plot(nodes, cm):\n", + " total_nodes = len(nodes)\n", + " dg = nx.DiGraph()\n", + "\n", + " for i in range(total_nodes):\n", + " dg.add_node(i)\n", + "\n", + " pos = {}\n", + "\n", + " for i in range(total_nodes):\n", + " for j in range(total_nodes):\n", + " if cm[i,j] == 1:\n", + " dg.add_edge(i, j)\n", + " # pos[i] = 5 * np.array(number_to_type_layer(i, L, T))\n", + " pos[i] = np.array(number_to_type_layer(i, T))[::-1]\n", + "\n", + " plt.figure(figsize=(6, 6))\n", + " nx.draw(dg, pos, node_color='b', node_size=5000, alpha=0.3)\n", + "\n", + " nx.draw_networkx_labels(dg, pos, nodes, font_size=18)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cm = np.zeros((total_nodes, total_nodes)) \n", + "cm[0, 1] = 1\n", + "cm[0, 3] = 1\n", + "cm[3, 1] = 1\n", + "cm[3, 5] = 1\n", + "cm[5, 2] = 1\n", + "cm[5, 3] = 1\n", + "\n", + "get_graph_and_plot(nodes, cm)\n", + "_, new_cm = check_binary_mask(nodes, cm)\n", + "get_graph_and_plot(nodes, new_cm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dg = nx.DiGraph()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(6):\n", + " dg.add_node(i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dg.add_edge(0, 1)\n", + "dg.add_edge(0, 3)\n", + "dg.add_edge(3, 1)\n", + "dg.add_edge(5, 2)\n", + "dg.add_edge(3, 5)\n", + "dg.add_edge(5, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dg.edges()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dg.remove_edge(3, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dg.edges()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py36_main_kernel", + "language": "python", + "name": "py36_main" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/deeppavlov/models/evolution/evolution.py b/deeppavlov/models/evolution/evolution.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/deeppavlov/models/evolution/intent_model.py b/deeppavlov/models/evolution/intent_model.py new file mode 100644 index 0000000000..7980f944ac --- /dev/null +++ b/deeppavlov/models/evolution/intent_model.py @@ -0,0 +1,277 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import numpy as np +from keras.layers import Dense, Input, concatenate, Activation +from keras.layers.convolutional import Conv1D +from keras.layers.core import Dropout +from keras.layers.normalization import BatchNormalization +from keras.layers.pooling import GlobalMaxPooling1D, MaxPooling1D +from keras.models import Model +from keras.regularizers import l2 + +from deeppavlov.core.common.errors import ConfigError +from deeppavlov.core.common.registry import register +from deeppavlov.core.models.keras_model import KerasModel +from deeppavlov.models.classifiers.intents import metrics as metrics_file +from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels +from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder +from deeppavlov.models.classifiers.intents.utils import md5_hashsum +from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer +from deeppavlov.core.common.log import get_logger + + +log = get_logger(__name__) + + +@register('intent_model') +class KerasIntentModel(KerasModel): + """ + Class implements keras model for intent recognition task for multi-class multi-label data + """ + def __init__(self, **kwargs): + """ + Initialize and train vocabularies, initializes embedder, tokenizer, + and then initialize model using parameters from opt dictionary (from config), + if model is being initialized from saved + + Args: + vocabs: dictionary of considered vocabularies + opt: model parameters for network and learning + model_path: path to model serialization dir or file. + It is always an empty string and is ignored if it is not set in json config. + model_dir: name of a serialization dir, can be default or set in json config + model_file: name of a serialization file (usually binary model file), + can be default or set in json config + embedder: instance of FasttextEmbedder class + tokenizer: instance of NLTKTokenizer class + **kwargs: + """ + super().__init__(**kwargs) # self.opt initialized in here + + self.tokenizer = self.opt.get('tokenizer') + self.fasttext_model = self.opt.get('embedder') + self.opt.pop("vocabs") + self.opt.pop("embedder") + self.opt.pop("tokenizer") + + if self.opt.get('classes'): + self.classes = list(np.sort(np.array(list(self.opt.get('classes'))))) + self.opt['classes'] = self.classes + else: + # self.classes = list(np.sort(np.array(list(self.opt.get('vocabs')["classes_vocab"].keys())))) + self.classes = list(self.opt.get('vocabs')["classes_vocab"].keys()) + self.opt['classes'] = self.classes + self.n_classes = len(self.classes) + if self.n_classes == 0: + ConfigError("Please, provide vocabulary with considered intents.") + + self.opt['embedding_size'] = self.fasttext_model.dim + + if self.fasttext_model.load_path: + current_fasttext_md5 = md5_hashsum([self.fasttext_model.load_path]) + + # Parameters required to init model + params = {"model_name": self.opt.get('model_name'), + "optimizer_name": self.opt.get('optimizer'), + "loss_name": self.opt.get('loss'), + "lear_rate": self.opt.get('lear_rate'), + "lear_rate_decay": self.opt.get('lear_rate_decay')} + + self.model = self.load(**params) + self._init_params() + + # Check if md5 hash sum of current loaded fasttext model + # is equal to saved + try: + self.opt['fasttext_md5'] + except KeyError: + self.opt['fasttext_md5'] = current_fasttext_md5 + else: + if self.opt['fasttext_md5'] != current_fasttext_md5: + raise ConfigError( + "Given fasttext model does NOT match fasttext model used previously to train loaded model") + + def _init_params(self): + + # list of changeable params + changeable_params = {"confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 1e-2, + "lear_rate_decay": 0., + "loss": "binary_crossentropy", + "coef_reg_cnn": 0., + "coef_reg_den": 0., + "dropout_rate": 0.} + + for param in changeable_params.keys(): + self.opt[param] = self.opt.get(param, changeable_params[param]) + return + + def texts2vec(self, sentences): + """ + Convert texts to vector representations using embedder and padding up to self.opt["text_size"] tokens + Args: + sentences: list of texts + + Returns: + array of embedded texts + """ + pad = np.zeros(self.opt['embedding_size']) + + embeddings_batch = self.fasttext_model([' '.join(sen.split()[:self.opt['text_size']]) for sen in sentences]) + embeddings_batch = [[pad] * (self.opt['text_size'] - len(tokens)) + tokens for tokens in embeddings_batch] + + embeddings_batch = np.asarray(embeddings_batch) + return embeddings_batch + + def train_on_batch(self, texts, labels): + """ + Train the model on the given batch + Args: + batch - list of data where batch[0] is list of texts and batch[1] is list of labels + + Returns: + loss and metrics values on the given batch + """ + texts = self.tokenizer(list(texts)) + features = self.texts2vec(texts) + onehot_labels = labels2onehot(labels, classes=self.classes) + metrics_values = self.model.train_on_batch(features, onehot_labels) + return metrics_values + + def infer_on_batch(self, batch, labels=None): + """ + Infer the model on the given batch + Args: + batch - list of texts + labels - list of labels + + Returns: + loss and metrics values on the given batch, if labels are given + predictions, otherwise + """ + texts = self.tokenizer(batch) + if labels: + features = self.texts2vec(texts) + onehot_labels = labels2onehot(labels, classes=self.classes) + metrics_values = self.model.test_on_batch(features, onehot_labels) + return metrics_values + else: + features = self.texts2vec(texts) + predictions = self.model.predict(features) + return predictions + + def __call__(self, data, predict_proba=False, *args): + """ + Infer on the given data + Args: + data: [list of sentences] + predict_proba: whether to return probabilities distribution or only labels-predictions + *args: + + Returns: + for each sentence: + vector of probabilities to belong with each class + or list of labels sentence belongs with + """ + preds = np.array(self.infer_on_batch(data)) + + if predict_proba: + return preds + else: + return proba2labels(preds, confident_threshold=self.opt['confident_threshold'], classes=self.classes) + + def cnn_model(self, params): + """ + Build un-compiled model of shallow-and-wide CNN + Args: + params: dictionary of parameters for NN + + Returns: + Un-compiled model + """ + + inp = Input(shape=(params['text_size'], params['embedding_size'])) + + outputs = [] + for i in range(len(params['kernel_sizes_cnn'])): + output_i = Conv1D(params['filters_cnn'], kernel_size=params['kernel_sizes_cnn'][i], + activation=None, + kernel_regularizer=l2(params['coef_reg_cnn']), + padding='same')(inp) + output_i = BatchNormalization()(output_i) + output_i = Activation('relu')(output_i) + output_i = GlobalMaxPooling1D()(output_i) + outputs.append(output_i) + + output = concatenate(outputs, axis=1) + + output = Dropout(rate=params['dropout_rate'])(output) + output = Dense(params['dense_size'], activation=None, + kernel_regularizer=l2(params['coef_reg_den']))(output) + output = BatchNormalization()(output) + output = Activation('relu')(output) + output = Dropout(rate=params['dropout_rate'])(output) + output = Dense(self.n_classes, activation=None, + kernel_regularizer=l2(params['coef_reg_den']))(output) + output = BatchNormalization()(output) + act_output = Activation('sigmoid')(output) + model = Model(inputs=inp, outputs=act_output) + return model + + def dcnn_model(self, params): + """ + Build un-compiled model of deep CNN + Args: + params: dictionary of parameters for NN + + Returns: + Un-compiled model + """ + + if type(self.opt['filters_cnn']) is str: + self.opt['filters_cnn'] = list(map(int, self.opt['filters_cnn'].split())) + + inp = Input(shape=(params['text_size'], params['embedding_size'])) + + output = inp + + for i in range(len(params['kernel_sizes_cnn'])): + output = Conv1D(params['filters_cnn'][i], kernel_size=params['kernel_sizes_cnn'][i], + activation=None, + kernel_regularizer=l2(params['coef_reg_cnn']), + padding='same')(output) + output = BatchNormalization()(output) + output = Activation('relu')(output) + output = MaxPooling1D()(output) + + output = GlobalMaxPooling1D()(output) + output = Dropout(rate=params['dropout_rate'])(output) + output = Dense(params['dense_size'], activation=None, + kernel_regularizer=l2(params['coef_reg_den']))(output) + output = BatchNormalization()(output) + output = Activation('relu')(output) + output = Dropout(rate=params['dropout_rate'])(output) + output = Dense(self.n_classes, activation=None, + kernel_regularizer=l2(params['coef_reg_den']))(output) + output = BatchNormalization()(output) + act_output = Activation('sigmoid')(output) + model = Model(inputs=inp, outputs=act_output) + return model + + def reset(self): + pass diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py new file mode 100644 index 0000000000..625e06c1d3 --- /dev/null +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -0,0 +1,261 @@ +import numpy as np +from copy import deepcopy +from pathlib import Path + + +class Evolution: + """ + Class performs full evolutionary process (task scores -> max): + 1. initializes random population + 2. makes replacement to get next generation: + a. selection according to obtained scores + b. crossover (recombination) with given probability p_crossover + c. mutation with given mutation rate p_mutation (probability to mutate) + according to given mutation power sigma + (current mutation power is randomly from -sigma to sigma) + """ + + def __init__(self, population_size, + p_crossover=0.5, crossover_power=0.5, + p_mutation=0.5, mutation_power=0.1, + **kwargs): + """ + Initialize evolution with random population + Args: + population_size: numer of individuums per generation + p_crossover: probability to cross over for current replacement + crossover_power: part of parents parameters to exchange for offsprings + p_mutation: probability of mutation for current replacement + mutation_power: allowed percentage of mutation + **kwargs: basic config with parameters + """ + self.params = deepcopy(kwargs) + self.population_size = population_size + self.p_crossover = p_crossover + self.p_mutation = p_mutation + self.params_names = np.array(list(self.params.keys())) + self.n_params = len(self.params) + self.mutation_power = mutation_power + self.crossover_power = crossover_power + + def first_generation(self, iter=0): + """ + Initialize first generation randomly according to the given constraints is self.params + Returns: + first generation that consists of self.population_size individuums + """ + population = [] + for i in range(self.population_size): + params = {} + params_for_search = {} + + for param_name in self.params.keys(): + if ((type(self.params[param_name]) is str) + or (type(self.params[param_name]) is int) + or (type(self.params[param_name]) is float) + or (type(self.params[param_name]) is bool) + or (type(self.params[param_name]) is list)): + params[param_name] = deepcopy(self.params[param_name]) + else: + if "choice" in self.params[param_name].keys(): + params_for_search[param_name] = list(self.params[param_name]["values"]) + else: + params_for_search[param_name] = deepcopy(self.params[param_name]) + + params_for_search = deepcopy(self.sample_params(**params_for_search)) + if "model_name" in params_for_search.keys(): + params["model_path"] = str(Path(self.params["model_path"]).joinpath( + "population_" + str(iter)).joinpath(params_for_search["model_name"] + "_" + str(i))) + else: + params["model_path"] = str(Path(self.params["model_path"]).joinpath( + "population_" + str(iter)).joinpath(self.params["model_name"] + "_" + str(i))) + + population.append({**params, **params_for_search}) + return population + + def next_generation(self, generation, scores, iter, + p_crossover=None, crossover_power=None, + p_mutation=None, mutation_power=None): + """ + Provide an operation of replacement + Args: + generation: current generation (set of self.population_size configs + scores: corresponding scores that should be maximized + p_crossover: probability to cross over for current replacement + crossover_power: part of parents parameters to exchange for offsprings + p_mutation: probability of mutation for current replacement + mutation_power: allowed percentage of mutation + + Returns: + the next generation according to the given scores of current generation + """ + if not p_crossover: + p_crossover = self.p_crossover + if not crossover_power: + crossover_power = self.crossover_power + if not p_mutation: + p_mutation = self.p_mutation + if not mutation_power: + mutation_power = self.mutation_power + + selected_individuals = self.selection(generation, scores) + offsprings = self.crossover(selected_individuals, p_crossover=p_crossover, crossover_power=crossover_power) + next = self.mutation(offsprings, p_mutation=p_mutation, mutation_power=mutation_power) + for i in range(self.population_size): + next[i]["model_path"] = str(Path(self.params["model_path"]).joinpath( + "population_" + str(iter)).joinpath(next[i]["model_name"] + "_" + str(i))) + + return next + + def selection(self, population, scores): + """ + Select self.population_size individuums (with replacement) from given population. + Probability of i-th individuum to be selected is scores_i / sum_j(scores_j) + Args: + population: self.population_size individuums + scores: corresponding score that should be maximized + + Returns: + selected self.population_size individuums with replacement + """ + scores = np.array(scores, dtype='float') + scores = (scores - 1.1 * min(scores) + 0.1 * max(scores)) / max(scores) + total = np.sum(scores) + probas_to_be_selected = scores / total + intervals = np.array([np.sum(probas_to_be_selected[:i]) for i in range(self.population_size)]) + selected = [] + for i in range(self.population_size): + r = np.random.random() + individuum = population[np.where(r > intervals)[0][-1]] + selected.append(individuum) + return selected + + def crossover(self, population, p_crossover, crossover_power): + """ + Recombine randomly population in pairs and cross over them with given probability. + Cross over from two parents produces two offsprings + each of which contains half of the parameter values from one parent and the other half from the other parent + Args: + population: self.population_size individuums + p_crossover: probability to cross over for current replacement + crossover_power: part of parents parameters to exchange for offsprings + + Returns: + self.population_size offsprings + """ + perm = np.random.permutation(self.population_size) + offsprings = [] + for i in range(self.population_size // 2): + parents = population[perm[2 * i]], population[perm[2 * i + 1]] + if self.decision(p_crossover): + params_perm = np.random.permutation(self.n_params) + curr_offsprings = [{}, {}] + part = int(crossover_power * self.n_params) + for j in range(self.n_params - part): + curr_offsprings[0][self.params_names[params_perm[j]]] = parents[0][ + self.params_names[params_perm[j]]] + curr_offsprings[1][self.params_names[params_perm[j]]] = parents[1][ + self.params_names[params_perm[j]]] + for j in range(self.n_params - part, self.n_params): + curr_offsprings[0][self.params_names[params_perm[j]]] = parents[1][ + self.params_names[params_perm[j]]] + curr_offsprings[1][self.params_names[params_perm[j]]] = parents[0][ + self.params_names[params_perm[j]]] + offsprings.extend(curr_offsprings) + else: + offsprings.extend(parents) + + if self.population_size % 2 == 1: + offsprings.append(population[perm[-1]]) + return offsprings + + def mutation(self, population, p_mutation, mutation_power): + """ + Mutate each parameter of each individuum in population with probability p_mutation + Args: + population: self.population_size individuums + p_mutation: probability to mutate for each parameter + mutation_power: allowed percentage of mutation + + Returns: + mutated population + """ + mutated = [] + for individuum in population: + mutated_individuum = {} + for param in self.params_names: + if self.decision(p_mutation): + if type(self.params[param]) is dict: + if self.params[param].get('discrete', False): + val = round(individuum[param] + + ((2 * np.random.random() - 1.) * mutation_power + * self.sample_params(**{param: self.params[param]})[param])) + val = min(max(self.params[param]["range"][0], val), + self.params[param]["range"][1]) + mutated_individuum[param] = val + elif 'range' in self.params[param].keys(): + val = individuum[param] + \ + ((2 * np.random.random() - 1.) * mutation_power + * self.sample_params(**{param: self.params[param]})[param]) + val = min(max(self.params[param]["range"][0], val), + self.params[param]["range"][1]) + mutated_individuum[param] = val + elif "choice" in self.params[param].keys(): + mutated_individuum[param] = individuum[param] + else: + mutated_individuum[param] = individuum[param] + else: + mutated_individuum[param] = individuum[param] + mutated.append(mutated_individuum) + return mutated + + def decision(self, probability): + """ + Make decision whether to do action or not with given probability + Args: + probability: probability whether + + Returns: + + """ + r = np.random.random() + if r < probability: + return True + else: + return False + + def sample_params(self, **params): + if not params: + params_copy = deepcopy(self.params) + else: + params_copy = deepcopy(params) + params_sample = dict() + for param, param_val in params_copy.items(): + if isinstance(param_val, list): + params_sample[param] = np.random.choice(param_val) + elif isinstance(param_val, dict): + if 'bool' in param_val and param_val['bool']: + sample = np.random.choice([True, False]) + elif 'range' in param_val: + sample = self._sample_from_ranges(param_val) + params_sample[param] = sample + else: + params_sample[param] = params_copy[param] + return params_sample + + def _sample_from_ranges(self, opts): + from_ = opts['range'][0] + to_ = opts['range'][1] + if opts.get('scale', None) == 'log': + sample = self._sample_log(from_, to_) + else: + sample = np.random.uniform(from_, to_) + if opts.get('discrete', False): + sample = int(np.round(sample)) + return sample + + @staticmethod + def _sample_log(from_, to_): + sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) + return float(sample) + diff --git a/deeppavlov/models/evolution/random_param_generator.py b/deeppavlov/models/evolution/random_param_generator.py new file mode 100644 index 0000000000..df81713585 --- /dev/null +++ b/deeppavlov/models/evolution/random_param_generator.py @@ -0,0 +1,85 @@ +import numpy as np +from copy import deepcopy +from pathlib import Path + + +class HyperPar: + def __init__(self, **kwargs): + self.params = kwargs + + def sample_params(self): + params = deepcopy(self.params) + params_sample = dict() + for param, param_val in params.items(): + if isinstance(param_val, list): + params_sample[param] = np.random.choice(param_val) + elif isinstance(param_val, dict): + if 'bool' in param_val and param_val['bool']: + sample = np.random.choice([True, False]) + elif 'range' in param_val: + sample = self._sample_from_ranges(param_val) + params_sample[param] = sample + else: + params_sample[param] = params[param] + return params_sample + + def _sample_from_ranges(self, opts): + from_ = opts['range'][0] + to_ = opts['range'][1] + if opts.get('scale', None) == 'log': + sample = self._sample_log(from_, to_) + else: + sample = np.random.uniform(from_, to_) + if opts.get('discrete', False): + sample = int(np.round(sample)) + return sample + + @staticmethod + def _sample_log(from_, to_): + sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) + return float(sample) + +# net_params = HyperPar(n_filters={'range': [32, 500], 'discrete': True, 'n_samples': n_layers, 'increasing': True}, +# filter_width={'range': [3, 11], 'discrete': True}, +# char_embeddings_dim={'range': [10, 50], 'discrete': True}, +# embeddings_dropout={'bool': True}, +# dense_dropout={'bool': True}, +# net_type=['cnn', 'rnn', 'cnn_highway'], +# use_crf=True, +# use_batch_norm=True, +# token_embeddings_dim=token_emb_dim, +# two_dense_layers=True) +# parms = net_params.sample_params() +# learning_params = HyperPar(dropout_rate={'range': [0.1, 0.9]}, +# epochs={'range': [10, 100], 'discrete': True}, +# learning_rate={'range': [1e-4, 1e-2], 'scale': 'log'}, +# batch_size={'range': [2, 64], 'discrete': True}, +# learning_rate_decay={'range': [0.3, 0.95]}, +# save_path='conll_models/model.ckpt').sample_params() + + +def get_population(basic_params, population_size, population_num): + population = [] + for i in range(population_size): + params = {} + params_for_search = {} + + for param_name in basic_params.keys(): + if ((type(basic_params[param_name]) is str) + or (type(basic_params[param_name]) is int) + or (type(basic_params[param_name]) is float) + or (type(basic_params[param_name]) is bool) + or (type(basic_params[param_name]) is list)): + params[param_name] = basic_params[param_name] + else: + if "values" in basic_params[param_name].keys(): + params_for_search[param_name] = list(basic_params[param_name]["values"]) + else: + params_for_search[param_name] = basic_params[param_name] + + params_for_search = HyperPar(**params_for_search).sample_params() + print() + params["model_path"] = str(Path(basic_params["model_path"]).joinpath( + "population_" + str(population_num)).joinpath(params_for_search["model_name"] + "_" + str(i))) + population.append({**params, **params_for_search}) + return population diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py new file mode 100644 index 0000000000..a8620f31ef --- /dev/null +++ b/deeppavlov/models/evolution/utils.py @@ -0,0 +1,128 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import numpy as np +import sys +import hashlib + +from deeppavlov.core.common.log import get_logger + + +log = get_logger(__name__) + + +def labels2onehot(labels, classes): + """ + Convert labels to one-hot vectors for multi-class multi-label classification + Args: + labels: list of samples where each sample is a list of classes which sample belongs with + classes: array of classes' names + + Returns: + 2d array with one-hot representation of given samples + """ + n_classes = len(classes) + eye = np.eye(n_classes) + y = [] + for sample in labels: + curr = np.zeros(n_classes) + for intent in sample: + if intent not in classes: + log.warning('Unknown intent {} detected'.format(intent)) + curr += eye[np.where(np.array(classes) == 'unknown')[0]].reshape(-1) + else: + curr += eye[np.where(np.array(classes) == intent)[0]].reshape(-1) + y.append(curr) + y = np.asarray(y) + return y + + +def proba2labels(proba, confident_threshold, classes): + """ + Convert vectors of probabilities to labels using confident threshold + (if probability to belong with the class is bigger than confident_threshold, sample belongs with the class; + if no probabilities bigger than confident threshold, sample belongs with the class with the biggest probability) + Args: + proba: list of samples where each sample is a vector of probabilities to belong with given classes + confident_threshold (float): boundary of probability to belong with a class + classes: array of classes' names + + Returns: + array of lists of labels for each sample + """ + y = [] + for sample in proba: + to_add = np.where(sample > confident_threshold)[0] + if len(to_add) > 0: + y.append(np.array(classes)[to_add]) + else: + y.append(np.array([np.array(classes)[np.argmax(sample)]])) + y = np.asarray(y) + return y + + +def proba2onehot(proba, confident_threshold, classes): + """ + Convert vectors of probabilities to one-hot representations using confident threshold + Args: + proba: list of samples where each sample is a vector of probabilities to belong with given classes + confident_threshold: boundary of probability to belong with a class + classes: array of classes' names + + Returns: + 2d array with one-hot representation of given samples + """ + return labels2onehot(proba2labels(proba, confident_threshold, classes), classes) + + +def log_metrics(names, values, updates=None, mode='train'): + """ + Print training and validation data in the following view: + `mode --> updates: 0 names[0]: 0.0 names[1]: 0.0 names[2]: 0.0` + Args: + names: list of names of considered metrics + values: list of values of considered metrics + updates: number of updates + mode: dataset field on which calculation is being doing (i.e "train") + + Returns: + None + """ + sys.stdout.write("\r") # back to previous line + log.info("{} -->\t".format(mode)) + if updates is not None: + log.info("updates: {}\t".format(updates)) + + for id in range(len(names)): + log.info("{}: {}\t".format(names[id], values[id])) + return + + +def md5_hashsum(file_names): + """ + Calculate md5 hash sum of files listed + Args: + file_names: list of file names + + Returns: + hashsum string + """ + hash_md5 = hashlib.md5() + for file_name in file_names: + with open(file_name, "rb") as f: + for chunk in iter(lambda: f.read(4096), b""): + hash_md5.update(chunk) + return hash_md5.hexdigest() From 8dca86301f301ff21315f9668328a4e7b5ab649e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 13 Apr 2018 17:28:40 +0300 Subject: [PATCH 289/616] feat: check and correct binary mask done --- .../models/evolution/check_binary_mask.py | 28 +++--- .../models/evolution/check_matrix.ipynb | 89 ++++++++++--------- 2 files changed, 63 insertions(+), 54 deletions(-) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index fe61e3e188..dc07ebbc62 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -67,13 +67,14 @@ def get_binary_mask_from_digraph(nodes, directed_graph): # return True, binary_mask -def check_binary_mask(nodes, binary_mask): +def check_and_correct_binary_mask(nodes, binary_mask): directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) sources, sinks = find_sources_and_sinks(directed_graph) while not nx.is_directed_acyclic_graph(directed_graph): candidates = [] cycles = list(nx.simple_cycles(directed_graph)) + n_cycles = len(cycles) print("Cycles: {}".format(cycles)) # number of candidates to be the best new graph cycles_len = np.array([len(cycle) for cycle in cycles]) @@ -81,18 +82,23 @@ def check_binary_mask(nodes, binary_mask): for i in range(n_candidates): new_directed_graph = copy.deepcopy(directed_graph) + for j in range(n_cycles): + node_id = (i // np.prod(cycles_len[:j])) % cycles_len[j] + new_directed_graph.remove_edge(cycles[j][node_id], cycles[j][(node_id + 1) % cycles_len[j]]) candidates.append(new_directed_graph) - for j, cycle_ in enumerate(cycles): - cycle = copy.deepcopy(cycle_) + [cycle_[0]] - for i in range(len(cycle_)): - candidates[].remove_edge(cycle[i], cycle[i + 1]) - new_sources, new_sinks = find_sources_and_sinks(new_directed_graph) - if set(new_sources).issuperset(set(sources)) and set(new_sinks).issuperset(set(sinks)): - directed_graph.remove_edge(cycle[i], cycle[i + 1]) - continue - else: - new_directed_graph.add_edge(cycle[i], cycle[i + 1]) + best_cand = None + best_diff = 10e10 + for i in range(n_candidates): + new_sources, new_sinks = find_sources_and_sinks(candidates[i]) + if set(new_sources) == set(sources) and set(new_sinks) == set(sinks): + best_cand = candidates[i] + elif (len(set(new_sources).difference(set(sources))) + + len(set(new_sinks).difference(set(sinks))) < best_diff): + best_cand = candidates[i] + best_diff = len(set(new_sources).difference(set(sources))) + len(set(new_sinks).difference(set(sinks))) + + directed_graph = best_cand binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) return True, binary_mask diff --git a/deeppavlov/models/evolution/check_matrix.ipynb b/deeppavlov/models/evolution/check_matrix.ipynb index 4bcf35ace6..cb9f479e64 100644 --- a/deeppavlov/models/evolution/check_matrix.ipynb +++ b/deeppavlov/models/evolution/check_matrix.ipynb @@ -2,21 +2,21 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import networkx as nx\n", - "from check_binary_mask import check_binary_mask\n", + "from check_binary_mask import check_and_correct_binary_mask\n", "from check_binary_mask import number_to_type_layer\n", "from check_binary_mask import type_layer_to_number" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -35,20 +35,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cm = np.zeros((total_nodes, total_nodes)) \n", "cm[0, 1] = 1\n", @@ -84,7 +73,7 @@ "metadata": {}, "outputs": [], "source": [ - "check_binary_mask(nodes, cm)" + "check_and_correct_binary_mask(nodes, cm)" ] }, { @@ -131,24 +120,27 @@ "cm[5, 3] = 1\n", "\n", "get_graph_and_plot(nodes, cm)\n", - "_, new_cm = check_binary_mask(nodes, cm)\n", + "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", "get_graph_and_plot(nodes, new_cm)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "dg = nx.DiGraph()" + "cm = np.zeros((total_nodes, total_nodes)) \n", + "cm[0, 1] = 1\n", + "cm[0, 3] = 1\n", + "cm[3, 1] = 1\n", + "cm[4, 5] = 1\n", + "cm[5, 2] = 1\n", + "cm[2, 4] = 1\n", + "\n", + "get_graph_and_plot(nodes, cm)\n", + "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "get_graph_and_plot(nodes, new_cm)" ] }, { @@ -157,8 +149,18 @@ "metadata": {}, "outputs": [], "source": [ - "for i in range(6):\n", - " dg.add_node(i)" + "cm = np.zeros((total_nodes, total_nodes)) \n", + "cm[0, 1] = 1\n", + "cm[0, 3] = 1\n", + "cm[3, 1] = 1\n", + "cm[4, 5] = 1\n", + "cm[5, 2] = 1\n", + "cm[2, 4] = 1\n", + "cm[3, 4] = 1\n", + "\n", + "get_graph_and_plot(nodes, cm)\n", + "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "get_graph_and_plot(nodes, new_cm)" ] }, { @@ -167,12 +169,19 @@ "metadata": {}, "outputs": [], "source": [ - "dg.add_edge(0, 1)\n", - "dg.add_edge(0, 3)\n", - "dg.add_edge(3, 1)\n", - "dg.add_edge(5, 2)\n", - "dg.add_edge(3, 5)\n", - "dg.add_edge(5, 3)" + "cm = np.zeros((total_nodes, total_nodes)) \n", + "cm[0, 1] = 1\n", + "cm[0, 3] = 1\n", + "cm[3, 1] = 1\n", + "cm[4, 5] = 1\n", + "cm[5, 2] = 1\n", + "cm[2, 4] = 1\n", + "cm[3, 4] = 1\n", + "cm[4, 3] = 1\n", + "\n", + "get_graph_and_plot(nodes, cm)\n", + "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "get_graph_and_plot(nodes, new_cm)" ] }, { @@ -180,27 +189,21 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "dg.edges()" - ] + "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "dg.remove_edge(3, 5)" - ] + "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "dg.edges()" - ] + "source": [] }, { "cell_type": "code", From 57aca4e0a497af7837bf4ab62b8416cb1b72c866 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 13 Apr 2018 18:25:09 +0300 Subject: [PATCH 290/616] chore: basic config --- .../evolution/basic_intents_snips.json | 202 ++++++++++++++++++ .../models/evolution/check_binary_mask.py | 46 ++-- .../models/evolution/check_matrix.ipynb | 8 +- deeppavlov/models/evolution/evolution.py | 106 +++++++++ .../neuroevolution_param_generator.py | 4 +- deeppavlov/models/evolution/utils.py | 3 + 6 files changed, 341 insertions(+), 28 deletions(-) create mode 100644 deeppavlov/configs/evolution/basic_intents_snips.json diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json new file mode 100644 index 0000000000..12a6ed7671 --- /dev/null +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -0,0 +1,202 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "intents", + "data_path": "snips", + "url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator", + "seed": 42, + "field_to_split": "train", + "split_fields": [ + "train", + "valid" + ], + "split_proportions": [ + 0.9, + 0.1 + ] + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "vocabs/snips_classes.dict", + "load_path": "vocabs/snips_classes.dict" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_predicted" + ], + "main": true, + "name": "intent_model", + "save_path": "intents/intent_cnn_snips_v3", + "load_path": "intents/intent_cnn_snips_v3", + "classes": "#classes_vocab.keys()", + "layers": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + } + }, + "LSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + }, + "dropout": { + "range": [ + 1e-2, + 7e-1 + ] + }, + "recurrent_dropout": { + "range": [ + 1e-2, + 7e-1 + ] + } + }, + "BiLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + }, + "dropout": { + "range": [ + 1e-2, + 7e-1 + ] + }, + "recurrent_dropout": { + "range": [ + 1e-2, + 7e-1 + ] + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + } + }, + "Attention": { + } + }, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 0.01, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 15, + "model_name": "cnn_model", + "embedder": { + "name": "fasttext", + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "dim": 100 + }, + "tokenizer": { + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + } + } + ], + "out": [ + "y_predicted" + ] + }, + "train": { + "epochs": 1000, + "batch_size": 64, + "metrics": [ + "sets_accuracy" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} \ No newline at end of file diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index dc07ebbc62..1644534291 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -44,27 +44,6 @@ def get_binary_mask_from_digraph(nodes, directed_graph): for edge in directed_graph.edges(): binary_mask[edge[0], edge[1]] = 1 return binary_mask -# -# -# def check_binary_mask(nodes, binary_mask): -# directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) -# sources, sinks = find_sources_and_sinks(directed_graph) -# -# while not nx.is_directed_acyclic_graph(directed_graph): -# cycles = list(nx.simple_cycles(directed_graph)) -# print("Cycles: {}".format(cycles)) -# for cycle_ in cycles: -# cycle = copy.deepcopy(cycle_) + [cycle_[0]] -# for i in range(len(cycle_)): -# new_directed_graph = copy.deepcopy(directed_graph) -# new_directed_graph.remove_edge(cycle[i], cycle[i+1]) -# new_sources, new_sinks = find_sources_and_sinks(new_directed_graph) -# if nx.is_directed_acyclic_graph(new_directed_graph): -# if set(new_sources) == set(sources) and set(new_sinks) == set(sinks): -# directed_graph.remove_edge(cycle[i], cycle[i+1]) -# continue -# binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) -# return True, binary_mask def check_and_correct_binary_mask(nodes, binary_mask): @@ -101,4 +80,27 @@ def check_and_correct_binary_mask(nodes, binary_mask): directed_graph = best_cand binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) - return True, binary_mask + return binary_mask + +# def get_graph_and_plot(nodes, binary_mask, n_types): +# import matplotlib.pyplot as plt +# +# total_nodes = len(nodes) +# dg = nx.DiGraph() +# +# for i in range(total_nodes): +# dg.add_node(i) +# +# pos = {} +# +# for i in range(total_nodes): +# for j in range(total_nodes): +# if binary_mask[i,j] == 1: +# dg.add_edge(i, j) +# pos[i] = np.array(number_to_type_layer(i, n_types))[::-1] +# +# plt.figure(figsize=(6, 6)) +# nx.draw(dg, pos, node_color='b', node_size=5000, alpha=0.3) +# +# nx.draw_networkx_labels(dg, pos, nodes, font_size=18) +# plt.show() diff --git a/deeppavlov/models/evolution/check_matrix.ipynb b/deeppavlov/models/evolution/check_matrix.ipynb index cb9f479e64..898d23aa67 100644 --- a/deeppavlov/models/evolution/check_matrix.ipynb +++ b/deeppavlov/models/evolution/check_matrix.ipynb @@ -120,7 +120,7 @@ "cm[5, 3] = 1\n", "\n", "get_graph_and_plot(nodes, cm)\n", - "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "new_cm = check_and_correct_binary_mask(nodes, cm)\n", "get_graph_and_plot(nodes, new_cm)" ] }, @@ -139,7 +139,7 @@ "cm[2, 4] = 1\n", "\n", "get_graph_and_plot(nodes, cm)\n", - "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "new_cm = check_and_correct_binary_mask(nodes, cm)\n", "get_graph_and_plot(nodes, new_cm)" ] }, @@ -159,7 +159,7 @@ "cm[3, 4] = 1\n", "\n", "get_graph_and_plot(nodes, cm)\n", - "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "new_cm = check_and_correct_binary_mask(nodes, cm)\n", "get_graph_and_plot(nodes, new_cm)" ] }, @@ -180,7 +180,7 @@ "cm[4, 3] = 1\n", "\n", "get_graph_and_plot(nodes, cm)\n", - "_, new_cm = check_and_correct_binary_mask(nodes, cm)\n", + "new_cm = check_and_correct_binary_mask(nodes, cm)\n", "get_graph_and_plot(nodes, new_cm)" ] }, diff --git a/deeppavlov/models/evolution/evolution.py b/deeppavlov/models/evolution/evolution.py index e69de29bb2..adcb6a5e62 100644 --- a/deeppavlov/models/evolution/evolution.py +++ b/deeppavlov/models/evolution/evolution.py @@ -0,0 +1,106 @@ +import json +import numpy as np +import argparse +from pathlib import Path +from subprocess import Popen, PIPE +import pandas as pd + + +from tuning_parameters.neuroevolution_param_generator import Evolution + + +def score_population(population, population_size, result_file): + population_losses = [] + population_fmeasures = [] + population_accuracies = [] + population_roc_auc_scores = [] + + procs = [] + + for i in range(population_size): + f_name = Path(population[i]["model_path"]) + try: + f_name.mkdir(parents=True) + except FileExistsError: + pass + f_name = f_name.joinpath("config.json") + with open(f_name, 'w') as outfile: + json.dump(population[i], outfile) + + procs.append(Popen("CUDA_VISIBLE_DEVICES={} python train_phenotype.py {}" + " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], + str(f_name), + population[i]["model_path"], + population[i]["model_path"]), + shell=True, stdout=PIPE, stderr=PIPE)) + + for i, proc in enumerate(procs): + print(f'wait on {i}th proc') + proc.wait() + + for i in range(population_size): + val_results = np.loadtxt(fname=str(Path(population[i]["model_path"]).joinpath("valid_results.txt"))) + result_table = pd.DataFrame({"loss": [val_results[0]], + "accuracy": [val_results[1]], + "fmeasure": [val_results[2]], + "roc_auc_score": [val_results[3]], + "params": [population[i]]}) + result_table.loc[:, order].to_csv(result_file, index=False, sep='\t', mode='a', header=None) + population_losses.append(val_results[0]) + population_accuracies.append(val_results[1]) + population_fmeasures.append(val_results[2]) + population_roc_auc_scores.append(val_results[3]) + + return population_roc_auc_scores + + +parser = argparse.ArgumentParser() + +parser.add_argument('--config', help='Please, enter model path to config', default='./configs/basic_config.json') +parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) +parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) + +args = parser.parse_args() + +CONFIG_FILE = args.config +POPULATION_SIZE = args.p_size +GPU_NUMBER = len(args.gpus) +gpus = [int(gpu) for gpu in args.gpus.split(",")] + +with open(CONFIG_FILE, "r") as f: + basic_params = json.load(f) + +print("Given basic params: {}\n".format(basic_params)) + +try: + Path(basic_params["model_path"]).mkdir(parents=True) +except FileExistsError: + pass + +# Result table +order = ["loss", "accuracy", "fmeasure", "roc_auc_score", "params"] +result_file = Path(basic_params["model_path"]).joinpath("result_table.csv") +result_table = pd.DataFrame({"loss": [], "accuracy": [], "fmeasure": [], "roc_auc_score": [], "params": []}) +result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') + +# EVOLUTION starts here! +evolution = Evolution(population_size=POPULATION_SIZE, p_crossover=0.1, + p_mutation=0.5, mutation_power=0.1, **basic_params) + +print("\nIteration #{} starts\n".format(0)) +population = evolution.first_generation() +print("Considered population: {}\nScoring...\n".format(population)) +population_roc_auc_scores = score_population(population, POPULATION_SIZE, result_file) + +iters = 1 + +while True: + print("\nIteration #{} starts\n".format(iters)) + + population = evolution.next_generation(population, population_roc_auc_scores, iter=iters) + print("Considered population: {}\nScoring...\n".format(population)) + population_roc_auc_scores = score_population(population, POPULATION_SIZE, result_file) + + print("\nIteration #{} was done\n".format(iters)) + iters += 1 + diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 625e06c1d3..f70cd159f4 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -57,7 +57,7 @@ def first_generation(self, iter=0): or (type(self.params[param_name]) is list)): params[param_name] = deepcopy(self.params[param_name]) else: - if "choice" in self.params[param_name].keys(): + if self.params[param_name].get("choice"): params_for_search[param_name] = list(self.params[param_name]["values"]) else: params_for_search[param_name] = deepcopy(self.params[param_name]) @@ -200,7 +200,7 @@ def mutation(self, population, p_mutation, mutation_power): val = min(max(self.params[param]["range"][0], val), self.params[param]["range"][1]) mutated_individuum[param] = val - elif "choice" in self.params[param].keys(): + elif self.params[param].get("choice"): mutated_individuum[param] = individuum[param] else: mutated_individuum[param] = individuum[param] diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index a8620f31ef..4541df98f1 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -126,3 +126,6 @@ def md5_hashsum(file_names): for chunk in iter(lambda: f.read(4096), b""): hash_md5.update(chunk) return hash_md5.hexdigest() + + +def Attention(): From 7b6dfd4bea8fd8bf5bcb73c65ab044efbcc48967 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 16 Apr 2018 13:29:34 +0300 Subject: [PATCH 291/616] feat: add attention layer --- deeppavlov/models/evolution/utils.py | 59 +++++++++++++++++++++++++++- 1 file changed, 58 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index 4541df98f1..7c8007ec0f 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -18,7 +18,11 @@ import sys import hashlib +from keras.engine.topology import Layer from deeppavlov.core.common.log import get_logger +from keras import initializers, regularizers, constraints +from keras import backend as K +from keras.layers import concatenate, multiply log = get_logger(__name__) @@ -128,4 +132,57 @@ def md5_hashsum(file_names): return hash_md5.hexdigest() -def Attention(): +class Attention(Layer): + def __init__(self, context_length=None, + W_regularizer=None, b_regularizer=None, + W_constraint=None, b_constraint=None, + use_bias=True, **kwargs): + self.supports_masking = True + self.init = initializers.get('glorot_uniform') + self.W_regularizer = regularizers.get(W_regularizer) + self.b_regularizer = regularizers.get(b_regularizer) + self.W_constraint = constraints.get(W_constraint) + self.b_constraint = constraints.get(b_constraint) + self.use_bias = use_bias + self.context_length = context_length + + super(Attention, self).__init__(**kwargs) + + def build(self, input_shape): + assert len(input_shape) == 3 + + if self.context_length is None: + self.context_length = input_shape[-1] + + self.context = self.add_weight(tuple(input_shape[:-1] + (self.context_length,)), + initializer=self.init) + + self.W = self.add_weight((input_shape[-1] + self.context_length, input_shape[-1], ), + initializer=self.init, + regularizer=self.W_regularizer, + constraint=self.W_constraint) + + if self.use_bias: + self.b = self.add_weight((input_shape[-1], ), + initializer='zero', + regularizer=self.b_regularizer, + constraint=self.b_constraint) + else: + self.b = None + + self.built = True + + def call(self, x, mask=None): + x_full = concatenate(inputs=[x, self.context], axis=-1) + + out = K.dot(x_full, self.W) + if self.use_bias: + out = K.bias_add(out, self.b) + + out = K.softmax(out) + out = multiply(inputs=[out, x]) + + return out + + def compute_output_shape(self, input_shape): + return input_shape \ No newline at end of file From 6c15a9ac4e32fddb2151fa0448b4b0c6341e6b31 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 17 Apr 2018 17:43:03 +0300 Subject: [PATCH 292/616] feat: moved embedder and tokenizer in pipe --- .../evolution/basic_intents_snips.json | 75 ++++++++++++++----- 1 file changed, 57 insertions(+), 18 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json index 12a6ed7671..f821ff7b7b 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -37,6 +37,18 @@ "save_path": "vocabs/snips_classes.dict", "load_path": "vocabs/snips_classes.dict" }, + { + "id": "fasttext_embedder", + "name": "fasttext", + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "dim": 100 + }, + { + "id": "nltk_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, { "in": [ "x" @@ -49,10 +61,11 @@ ], "main": true, "name": "intent_model", - "save_path": "intents/intent_cnn_snips_v3", - "load_path": "intents/intent_cnn_snips_v3", + "save_path": "evolution/intents_snips", + "load_path": "evolution/intents_snips", "classes": "#classes_vocab.keys()", - "layers": { + "to_evolve": true, + "basic_layers_params": { "Dense": { "units": { "range": [ @@ -156,25 +169,39 @@ } }, "Attention": { + "context_length": { + "range": [ + 50, + 200 + ], + "discrete": true + } } }, - "confident_threshold": 0.5, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, "optimizer": "Adam", - "lear_rate": 0.01, - "lear_rate_decay": 0.1, + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, "loss": "binary_crossentropy", "text_size": 15, "model_name": "cnn_model", - "embedder": { - "name": "fasttext", - "save_path": "embeddings/dstc2_fastText_model.bin", - "load_path": "embeddings/dstc2_fastText_model.bin", - "dim": 100 - }, - "tokenizer": { - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - } + "embedder": "#fasttext_embedder", + "tokenizer": "#nltk_tokenizer" } ], "out": [ @@ -182,8 +209,20 @@ ] }, "train": { - "epochs": 1000, - "batch_size": 64, + "epochs": { + "range": [ + 10, + 1000 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 200 + ], + "discrete": true + }, "metrics": [ "sets_accuracy" ], From 2e3dae1cdc2c471d03b819387223b4f1528a1125 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 17 Apr 2018 17:43:39 +0300 Subject: [PATCH 293/616] feat: initialization of first generation, selection and crossover work --- deeppavlov/models/evolution/debug.py | 28 ++ .../neuroevolution_param_generator.py | 255 +++++++++++++++--- 2 files changed, 243 insertions(+), 40 deletions(-) create mode 100644 deeppavlov/models/evolution/debug.py diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py new file mode 100644 index 0000000000..ffcfca2989 --- /dev/null +++ b/deeppavlov/models/evolution/debug.py @@ -0,0 +1,28 @@ +import pandas as pd +import json +import numpy as np + +from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution + +n_layers = 2 +n_types = 3 +population_size = 3 +config_path = "../../configs/evolution/basic_intents_snips.json" + +with open(config_path) as fin: + config = json.load(fin) + +evolution = NetworkAndParamsEvolution(n_layers, n_types, + population_size, + key_model_to_evolve="to_evolve", + key_basic_layers="basic_layers_params", + **config) + +population = evolution.first_generation() +print(population) +population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ + evolution.model_to_evolve_index]["binary_mask"].tolist() +print(population) + +evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) +print(population) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index f70cd159f4..c5f75dbcf7 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -1,9 +1,18 @@ import numpy as np from copy import deepcopy from pathlib import Path +import json +from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, number_to_type_layer +from deeppavlov.core.common.file import save_json, read_json -class Evolution: +# TODO: +# if structure of config has been changed, +# please, make sure that +# `config["chainer"]["pipe"]` is a list of models one of which is a model to be evolved, +# otherwise, in the whole class change `config["chainer"]["pipe"]` to new path + +class NetworkAndParamsEvolution: """ Class performs full evolutionary process (task scores -> max): 1. initializes random population @@ -15,28 +24,131 @@ class Evolution: (current mutation power is randomly from -sigma to sigma) """ - def __init__(self, population_size, + def __init__(self, n_layers, n_types, + population_size, p_crossover=0.5, crossover_power=0.5, p_mutation=0.5, mutation_power=0.1, + key_model_to_evolve="to_evolve", + key_basic_layers="basic_layers_params", + seed=None, **kwargs): """ Initialize evolution with random population Args: - population_size: numer of individuums per generation + n_layers: number of available layers of each type + n_types: number of different types of network layers + population_size: number of individuums per generation p_crossover: probability to cross over for current replacement - crossover_power: part of parents parameters to exchange for offsprings + crossover_power: part of EVOLVING parents parameters to exchange for offsprings p_mutation: probability of mutation for current replacement mutation_power: allowed percentage of mutation **kwargs: basic config with parameters """ - self.params = deepcopy(kwargs) + self.n_types = n_types + self.n_layers = n_layers + self.total_nodes = self.n_types * self.n_layers + self.binary_mask_template = np.zeros((self.total_nodes, self.total_nodes)) + + self.basic_config = deepcopy(kwargs) + self.model_to_evolve_index = self._find_model_to_evolve_index_in_pipe(self.basic_config["chainer"]["pipe"], + key_model_to_evolve) + + self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) + self.train_params = deepcopy(self.basic_config.get("train")) + self.basic_layers_params = self.params.pop(key_basic_layers, None) + self.node_types = list(self.basic_layers_params.keys()) + + print("___Basic config___: {}".format(self.basic_config)) + print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) + print("___Model params___: {}".format(self.params)) + print("___Train params___: {}".format(self.train_params)) + print("___Basic layers params___: {}".format(self.basic_layers_params)) + + if self.basic_layers_params is None: + print("\n\n___PARAMS EVOLUTION is being started___") + print("___For network evolution one has to provide config file with `basic_layers_params` key___\n\n") + else: + print("\n\n___NETWORK AND PARAMS EVOLUTION is being started___\n\n") + self.population_size = population_size self.p_crossover = p_crossover self.p_mutation = p_mutation - self.params_names = np.array(list(self.params.keys())) - self.n_params = len(self.params) self.mutation_power = mutation_power self.crossover_power = crossover_power + self.evolving_params = [] + self.n_evolving_params = None + self.evolving_train_params = [] + self.n_evolving_train_params = None + + if seed is None: + pass + else: + np.random.seed(seed) + + def _find_model_to_evolve_index_in_pipe(self, pipe, key): + for element_id, element in enumerate(pipe): + if self._check_if_model_to_evolve(element, key): + return element_id + + def _check_if_model_to_evolve(self, model, key): + if key in model.keys(): + return True + else: + return False + + def _insert_dict_into_model_params(self, params, model_index, dict_to_insert): + params_copy = deepcopy(params) + params_copy["chainer"]["pipe"].insert(model_index, dict_to_insert) + return params_copy + + def print_dict(self, dict, string=None): + if string is None: + print(json.dumps(dict, indent=2)) + else: + print(string) + print(json.dumps(dict, indent=2)) + return + + def initialize_params_in_config(self, basic_params): + params = {} + params_for_search = {} + evolving_params = [] + + for param_name in basic_params.keys(): + if type(basic_params[param_name]) is dict: + if basic_params[param_name].get("choice"): + params_for_search[param_name] = list(basic_params[param_name]["values"]) + evolving_params.append(param_name) + elif basic_params[param_name].get("range"): + params_for_search[param_name] = deepcopy(basic_params[param_name]) + evolving_params.append(param_name) + else: + # NOT evolving params + params[param_name] = deepcopy(basic_params[param_name]) + else: + # NOT evolving params + params[param_name] = deepcopy(basic_params[param_name]) + + params_for_search = deepcopy(self.sample_params(**params_for_search)) + + return params, params_for_search, evolving_params + + def initialize_layers_params(self): + all_layers_params = {} + + for node_id in range(self.total_nodes): + node_layer, node_type = number_to_type_layer(node_id, self.n_types) + node_key = "node_{}_{}".format(node_layer, node_type) + layers_params, layers_params_for_search, _ = self.initialize_params_in_config( + self.basic_layers_params[self.node_types[node_type]]) + + all_layers_params[node_key] = {"node_name": self.node_types[node_type], + "node_type": node_type, + "node_layer": node_layer, + **layers_params, + **layers_params_for_search + } + return all_layers_params def first_generation(self, iter=0): """ @@ -46,31 +158,41 @@ def first_generation(self, iter=0): """ population = [] for i in range(self.population_size): - params = {} - params_for_search = {} - - for param_name in self.params.keys(): - if ((type(self.params[param_name]) is str) - or (type(self.params[param_name]) is int) - or (type(self.params[param_name]) is float) - or (type(self.params[param_name]) is bool) - or (type(self.params[param_name]) is list)): - params[param_name] = deepcopy(self.params[param_name]) - else: - if self.params[param_name].get("choice"): - params_for_search[param_name] = list(self.params[param_name]["values"]) - else: - params_for_search[param_name] = deepcopy(self.params[param_name]) + population.append(deepcopy(self.basic_config)) + + # intitializing parameters for model + params, params_for_search, evolving_params = self.initialize_params_in_config(self.params) + self.evolving_params.extend(evolving_params) + # initializing parameters for train + train_params, train_params_for_search, evolving_params = self.initialize_params_in_config(self.train_params) + self.evolving_train_params.extend(evolving_params) - params_for_search = deepcopy(self.sample_params(**params_for_search)) + # intitializing path to save model if "model_name" in params_for_search.keys(): - params["model_path"] = str(Path(self.params["model_path"]).joinpath( + params["save_path"] = str(Path(self.params["save_path"]).joinpath( "population_" + str(iter)).joinpath(params_for_search["model_name"] + "_" + str(i))) else: - params["model_path"] = str(Path(self.params["model_path"]).joinpath( + params["save_path"] = str(Path(self.params["save_path"]).joinpath( "population_" + str(iter)).joinpath(self.params["model_name"] + "_" + str(i))) - population.append({**params, **params_for_search}) + layers_params = self.initialize_layers_params() + + # exchange model and layers params from basic config to sampled model params + population[-1]["chainer"]["pipe"][self.model_to_evolve_index] = {**params, + **params_for_search, + **layers_params} + # add binary_mask intialization + population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = self.sample_binary_mask() + # exchange train params from basic config to sampled train params + population[-1]["train"] = {**train_params, + **train_params_for_search} + + self.evolving_params = list(set(self.evolving_params)) + self.evolving_train_params = list(set(self.evolving_train_params)) + + self.n_evolving_params = len(self.evolving_params) + self.n_evolving_train_params = len(self.evolving_train_params) + return population def next_generation(self, generation, scores, iter, @@ -138,7 +260,7 @@ def crossover(self, population, p_crossover, crossover_power): Args: population: self.population_size individuums p_crossover: probability to cross over for current replacement - crossover_power: part of parents parameters to exchange for offsprings + crossover_power: part of EVOLVING parents parameters to exchange for offsprings Returns: self.population_size offsprings @@ -148,19 +270,69 @@ def crossover(self, population, p_crossover, crossover_power): for i in range(self.population_size // 2): parents = population[perm[2 * i]], population[perm[2 * i + 1]] if self.decision(p_crossover): - params_perm = np.random.permutation(self.n_params) - curr_offsprings = [{}, {}] - part = int(crossover_power * self.n_params) - for j in range(self.n_params - part): - curr_offsprings[0][self.params_names[params_perm[j]]] = parents[0][ - self.params_names[params_perm[j]]] - curr_offsprings[1][self.params_names[params_perm[j]]] = parents[1][ - self.params_names[params_perm[j]]] - for j in range(self.n_params - part, self.n_params): - curr_offsprings[0][self.params_names[params_perm[j]]] = parents[1][ - self.params_names[params_perm[j]]] - curr_offsprings[1][self.params_names[params_perm[j]]] = parents[0][ - self.params_names[params_perm[j]]] + params_perm = np.random.permutation(self.n_evolving_params) + train_params_perm = np.random.permutation(self.n_evolving_train_params) + nodes_perm = np.random.permutation(self.total_nodes) + + curr_offsprings = [deepcopy(parents[0]), + deepcopy(parents[1])] + + part = int(crossover_power * self.n_evolving_params) + train_part = int(crossover_power * self.n_evolving_params) + nodes_part = int(crossover_power * self.total_nodes) + + # exchange of model params (not layers params) + for j in range(self.n_evolving_params - part): + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[0][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[1][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + for j in range(self.n_evolving_params - part, self.n_evolving_params): + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[1][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[0][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + + # exchange of train params + for j in range(self.n_evolving_train_params - train_part): + curr_offsprings[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] + curr_offsprings[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] + for j in range(self.n_evolving_train_params - train_part, self.n_evolving_train_params): + curr_offsprings[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] + curr_offsprings[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] + + # exchange of nodes (each of which is dict -> deepcopy) + for j in range(self.total_nodes - nodes_part): + node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) + node_key = "node_{}_{}".format(node_layer, node_type) + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( + parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( + parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + for j in range(self.total_nodes - nodes_part, self.total_nodes): + node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) + node_key = "node_{}_{}".format(node_layer, node_type) + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( + parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( + parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + offsprings.extend(curr_offsprings) else: offsprings.extend(parents) @@ -259,3 +431,6 @@ def _sample_log(from_, to_): sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) return float(sample) + def sample_binary_mask(self): + return np.random.randint(0, high=2, size=self.binary_mask_template.shape) + From 5399f83f4d0e5490ec4faf51469327857e129d61 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 17 Apr 2018 18:25:16 +0300 Subject: [PATCH 294/616] feat: crossover of binary mask --- .../models/evolution/check_binary_mask.py | 8 ++-- deeppavlov/models/evolution/debug.py | 4 +- .../neuroevolution_param_generator.py | 43 +++++++++++++++++-- 3 files changed, 46 insertions(+), 9 deletions(-) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index 1644534291..50d86c2624 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -1,6 +1,6 @@ import numpy as np import networkx as nx -import copy +from copy import copy, deepcopy def number_to_type_layer(node_id, n_types): @@ -46,7 +46,9 @@ def get_binary_mask_from_digraph(nodes, directed_graph): return binary_mask -def check_and_correct_binary_mask(nodes, binary_mask): +def check_and_correct_binary_mask(nodes, binary_mask_): + binary_mask = deepcopy(binary_mask_) + binary_mask = np.array(binary_mask) directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) sources, sinks = find_sources_and_sinks(directed_graph) @@ -54,7 +56,7 @@ def check_and_correct_binary_mask(nodes, binary_mask): candidates = [] cycles = list(nx.simple_cycles(directed_graph)) n_cycles = len(cycles) - print("Cycles: {}".format(cycles)) + # print("Cycles: {}".format(cycles)) # number of candidates to be the best new graph cycles_len = np.array([len(cycle) for cycle in cycles]) n_candidates = np.prod(cycles_len) diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index ffcfca2989..70e9e23986 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -4,9 +4,9 @@ from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution -n_layers = 2 +n_layers = 3 n_types = 3 -population_size = 3 +population_size = 2 config_path = "../../configs/evolution/basic_intents_snips.json" with open(config_path) as fin: diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index c5f75dbcf7..24813e2f56 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -57,6 +57,7 @@ def __init__(self, n_layers, n_types, self.train_params = deepcopy(self.basic_config.get("train")) self.basic_layers_params = self.params.pop(key_basic_layers, None) self.node_types = list(self.basic_layers_params.keys()) + self.nodes = np.arange(self.total_nodes) print("___Basic config___: {}".format(self.basic_config)) print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) @@ -182,7 +183,11 @@ def first_generation(self, iter=0): **params_for_search, **layers_params} # add binary_mask intialization - population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = self.sample_binary_mask() + print(self.sample_binary_mask()) + print(check_and_correct_binary_mask(self.nodes, self.sample_binary_mask())) + + population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, self.sample_binary_mask()) # exchange train params from basic config to sampled train params population[-1]["train"] = {**train_params, **train_params_for_search} @@ -273,13 +278,15 @@ def crossover(self, population, p_crossover, crossover_power): params_perm = np.random.permutation(self.n_evolving_params) train_params_perm = np.random.permutation(self.n_evolving_train_params) nodes_perm = np.random.permutation(self.total_nodes) + binary_mask_perm = np.random_permutation(self.total_nodes * self.total_nodes) curr_offsprings = [deepcopy(parents[0]), deepcopy(parents[1])] part = int(crossover_power * self.n_evolving_params) - train_part = int(crossover_power * self.n_evolving_params) + train_part = int(crossover_power * self.n_evolving_train_params) nodes_part = int(crossover_power * self.total_nodes) + binary_mask_part = int(crossover_power * self.total_nodes * self.total_nodes) # exchange of model params (not layers params) for j in range(self.n_evolving_params - part): @@ -317,10 +324,11 @@ def crossover(self, population, p_crossover, crossover_power): self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ self.evolving_train_params[train_params_perm[j]]] - # exchange of nodes (each of which is dict -> deepcopy) + # exchange of nodes for j in range(self.total_nodes - nodes_part): node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) node_key = "node_{}_{}".format(node_layer, node_type) + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( @@ -328,11 +336,38 @@ def crossover(self, population, p_crossover, crossover_power): for j in range(self.total_nodes - nodes_part, self.total_nodes): node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) node_key = "node_{}_{}".format(node_layer, node_type) + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) + # exchange of binary mask elements + for j in range(self.total_nodes * self.total_nodes - binary_mask_part): + node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes + + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ + parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ + parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] + + for j in range(self.total_nodes * self.total_nodes - binary_mask_part, + self.total_nodes * self.total_nodes): + node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes + + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ + parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ + parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] + + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"]) + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ + "binary_mask"]) offsprings.extend(curr_offsprings) else: offsprings.extend(parents) @@ -432,5 +467,5 @@ def _sample_log(from_, to_): return float(sample) def sample_binary_mask(self): - return np.random.randint(0, high=2, size=self.binary_mask_template.shape) + return np.random.randint(0, high=2, size=self.binary_mask_template.shape).tolist() From 4275bdb217655f0c0f54f8af8e848994d76365a3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 18 Apr 2018 12:11:42 +0300 Subject: [PATCH 295/616] fix: fixed check of binary mask, sample of binary mask and plot graph --- .../models/evolution/check_binary_mask.py | 63 +++++++++++-------- deeppavlov/models/evolution/debug.py | 6 +- .../neuroevolution_param_generator.py | 35 +++++++---- 3 files changed, 64 insertions(+), 40 deletions(-) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index 50d86c2624..7b2e6718a7 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -1,6 +1,10 @@ import numpy as np import networkx as nx from copy import copy, deepcopy +import datetime +import time +from pathlib import Path +import matplotlib.pyplot as plt def number_to_type_layer(node_id, n_types): @@ -59,19 +63,24 @@ def check_and_correct_binary_mask(nodes, binary_mask_): # print("Cycles: {}".format(cycles)) # number of candidates to be the best new graph cycles_len = np.array([len(cycle) for cycle in cycles]) - n_candidates = np.prod(cycles_len) + n_candidates = int(np.prod(cycles_len)) for i in range(n_candidates): - new_directed_graph = copy.deepcopy(directed_graph) + new_directed_graph = deepcopy(directed_graph) for j in range(n_cycles): node_id = (i // np.prod(cycles_len[:j])) % cycles_len[j] - new_directed_graph.remove_edge(cycles[j][node_id], cycles[j][(node_id + 1) % cycles_len[j]]) + try: + new_directed_graph.remove_edge(cycles[j][node_id], cycles[j][(node_id + 1) % cycles_len[j]]) + except: + continue candidates.append(new_directed_graph) + n_candidates = len(candidates) best_cand = None best_diff = 10e10 for i in range(n_candidates): new_sources, new_sinks = find_sources_and_sinks(candidates[i]) + if set(new_sources) == set(sources) and set(new_sinks) == set(sinks): best_cand = candidates[i] elif (len(set(new_sources).difference(set(sources))) + @@ -84,25 +93,29 @@ def check_and_correct_binary_mask(nodes, binary_mask_): binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) return binary_mask -# def get_graph_and_plot(nodes, binary_mask, n_types): -# import matplotlib.pyplot as plt -# -# total_nodes = len(nodes) -# dg = nx.DiGraph() -# -# for i in range(total_nodes): -# dg.add_node(i) -# -# pos = {} -# -# for i in range(total_nodes): -# for j in range(total_nodes): -# if binary_mask[i,j] == 1: -# dg.add_edge(i, j) -# pos[i] = np.array(number_to_type_layer(i, n_types))[::-1] -# -# plt.figure(figsize=(6, 6)) -# nx.draw(dg, pos, node_color='b', node_size=5000, alpha=0.3) -# -# nx.draw_networkx_labels(dg, pos, nodes, font_size=18) -# plt.show() + +def get_graph_and_plot(nodes, binary_mask, n_types, path=None): + total_nodes = len(nodes) + dg = nx.DiGraph() + + for i in range(total_nodes): + dg.add_node(i) + + pos = {} + + for i in range(total_nodes): + for j in range(total_nodes): + if binary_mask[i, j] == 1: + dg.add_edge(i, j) + pos[i] = 2. * np.array(number_to_type_layer(i, n_types))[::-1] + + plt.figure(figsize=(12, 12)) + nx.draw(dg, pos, node_color='b', node_size=7000, alpha=0.3) + + nx.draw_networkx_labels(dg, pos, nodes, font_size=18) + # plt.show() + if path is None: + path = "./" + curr_time = datetime.datetime.now().strftime("%Hh%Mm%Ss_%dd%mm%Yy") + plt.savefig(Path(path).joinpath("pic_" + curr_time + ".png")) + # time.sleep(1) diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 70e9e23986..7d44eda3b6 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -4,8 +4,8 @@ from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution -n_layers = 3 -n_types = 3 +n_layers = 5 +n_types = 7 population_size = 2 config_path = "../../configs/evolution/basic_intents_snips.json" @@ -16,6 +16,7 @@ population_size, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", + seed=42, **config) population = evolution.first_generation() @@ -26,3 +27,4 @@ evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) print(population) + diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 24813e2f56..fe98a68abd 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -3,7 +3,8 @@ from pathlib import Path import json -from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, number_to_type_layer +from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ + number_to_type_layer, get_graph_and_plot from deeppavlov.core.common.file import save_json, read_json # TODO: @@ -57,7 +58,11 @@ def __init__(self, n_layers, n_types, self.train_params = deepcopy(self.basic_config.get("train")) self.basic_layers_params = self.params.pop(key_basic_layers, None) self.node_types = list(self.basic_layers_params.keys()) - self.nodes = np.arange(self.total_nodes) + + self.nodes = {} + for i in range(self.total_nodes): + l, t = number_to_type_layer(i, self.n_types) + self.nodes[i] = "{}_{}_{}".format(l, t, i) print("___Basic config___: {}".format(self.basic_config)) print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) @@ -144,11 +149,11 @@ def initialize_layers_params(self): self.basic_layers_params[self.node_types[node_type]]) all_layers_params[node_key] = {"node_name": self.node_types[node_type], - "node_type": node_type, - "node_layer": node_layer, - **layers_params, - **layers_params_for_search - } + "node_type": node_type, + "node_layer": node_layer, + **layers_params, + **layers_params_for_search + } return all_layers_params def first_generation(self, iter=0): @@ -183,11 +188,10 @@ def first_generation(self, iter=0): **params_for_search, **layers_params} # add binary_mask intialization - print(self.sample_binary_mask()) - print(check_and_correct_binary_mask(self.nodes, self.sample_binary_mask())) - population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ check_and_correct_binary_mask(self.nodes, self.sample_binary_mask()) + get_graph_and_plot(self.nodes, population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"], + self.n_types, path=None) # exchange train params from basic config to sampled train params population[-1]["train"] = {**train_params, **train_params_for_search} @@ -278,7 +282,7 @@ def crossover(self, population, p_crossover, crossover_power): params_perm = np.random.permutation(self.n_evolving_params) train_params_perm = np.random.permutation(self.n_evolving_train_params) nodes_perm = np.random.permutation(self.total_nodes) - binary_mask_perm = np.random_permutation(self.total_nodes * self.total_nodes) + binary_mask_perm = np.random.permutation(self.total_nodes * self.total_nodes) curr_offsprings = [deepcopy(parents[0]), deepcopy(parents[1])] @@ -467,5 +471,10 @@ def _sample_log(from_, to_): return float(sample) def sample_binary_mask(self): - return np.random.randint(0, high=2, size=self.binary_mask_template.shape).tolist() - + # return np.random.randint(0, high=2, size=self.binary_mask_template.shape).tolist() + # return (1 * (np.log(np.random.random(size=self.binary_mask_template.shape)) > -0.2)).tolist() + ones = np.random.choice(self.total_nodes * self.total_nodes, + size=max(1, int(np.random.random() * self.total_nodes))) + mask = np.zeros((self.total_nodes * self.total_nodes)) + mask[ones] = 1 + return mask.reshape((self.total_nodes, self.total_nodes)) From 4fca544205a55010e8959bb760aece5be80dc3a5 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 18 Apr 2018 12:21:07 +0300 Subject: [PATCH 296/616] feat: sources and sinks are of different colors --- .../models/evolution/check_binary_mask.py | 22 +++++++++++-------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index 7b2e6718a7..f291907bef 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -96,24 +96,28 @@ def check_and_correct_binary_mask(nodes, binary_mask_): def get_graph_and_plot(nodes, binary_mask, n_types, path=None): total_nodes = len(nodes) - dg = nx.DiGraph() - - for i in range(total_nodes): - dg.add_node(i) + dg = get_digraph_from_binary_mask(nodes, binary_mask) pos = {} + val_map = {} + sources, sinks = find_sources_and_sinks(dg) for i in range(total_nodes): - for j in range(total_nodes): - if binary_mask[i, j] == 1: - dg.add_edge(i, j) pos[i] = 2. * np.array(number_to_type_layer(i, n_types))[::-1] + if i in sources: + val_map[i] = 1. + elif i in sinks: + val_map[i] = 0.5 + else: + val_map[i] = 0. plt.figure(figsize=(12, 12)) - nx.draw(dg, pos, node_color='b', node_size=7000, alpha=0.3) + values = [val_map.get(node, 0.25) for node in nodes] + + nx.draw(dg, pos, cmap=plt.get_cmap('jet'), node_color=values, node_size=7000, alpha=0.3) nx.draw_networkx_labels(dg, pos, nodes, font_size=18) - # plt.show() + if path is None: path = "./" curr_time = datetime.datetime.now().strftime("%Hh%Mm%Ss_%dd%mm%Yy") From d920d24db94279a6ef72b49bdfdf345fb5f9d149 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 18 Apr 2018 12:45:17 +0300 Subject: [PATCH 297/616] chore: pep --- .../models/evolution/check_matrix.ipynb | 24 +++++++++++++++++-- .../neuroevolution_param_generator.py | 8 ++++--- 2 files changed, 27 insertions(+), 5 deletions(-) diff --git a/deeppavlov/models/evolution/check_matrix.ipynb b/deeppavlov/models/evolution/check_matrix.ipynb index 898d23aa67..12ae7348c3 100644 --- a/deeppavlov/models/evolution/check_matrix.ipynb +++ b/deeppavlov/models/evolution/check_matrix.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -33,6 +33,26 @@ " nodes[i] = types[number_to_type_layer(i, T)[1]]" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 'Dense', 1: 'Conv1D', 2: 'LSTM', 3: 'Dense', 4: 'Conv1D', 5: 'LSTM'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nodes" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index fe98a68abd..ae2e55308a 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -90,6 +90,7 @@ def __init__(self, n_layers, n_types, pass else: np.random.seed(seed) + return None def _find_model_to_evolve_index_in_pipe(self, pipe, key): for element_id, element in enumerate(pipe): @@ -113,7 +114,7 @@ def print_dict(self, dict, string=None): else: print(string) print(json.dumps(dict, indent=2)) - return + return None def initialize_params_in_config(self, basic_params): params = {} @@ -190,8 +191,9 @@ def first_generation(self, iter=0): # add binary_mask intialization population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ check_and_correct_binary_mask(self.nodes, self.sample_binary_mask()) - get_graph_and_plot(self.nodes, population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"], - self.n_types, path=None) + # get_graph_and_plot(self.nodes, population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"], + # self.n_types, path=None) + # exchange train params from basic config to sampled train params population[-1]["train"] = {**train_params, **train_params_for_search} From 23667fde5afb16959887791138d1b0a8ac9314fa Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 18 Apr 2018 15:56:35 +0300 Subject: [PATCH 298/616] feat: mutation --- .../models/evolution/check_binary_mask.py | 1 + deeppavlov/models/evolution/debug.py | 11 ++- .../neuroevolution_param_generator.py | 96 +++++++++++++------ 3 files changed, 79 insertions(+), 29 deletions(-) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index f291907bef..a4cb3d7646 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -123,3 +123,4 @@ def get_graph_and_plot(nodes, binary_mask, n_types, path=None): curr_time = datetime.datetime.now().strftime("%Hh%Mm%Ss_%dd%mm%Yy") plt.savefig(Path(path).joinpath("pic_" + curr_time + ".png")) # time.sleep(1) + return None diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 7d44eda3b6..660bc7c6ec 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -6,7 +6,7 @@ n_layers = 5 n_types = 7 -population_size = 2 +population_size = 10 config_path = "../../configs/evolution/basic_intents_snips.json" with open(config_path) as fin: @@ -25,6 +25,13 @@ evolution.model_to_evolve_index]["binary_mask"].tolist() print(population) -evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) +population = evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) print(population) +# print(population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) +mutated = evolution.mutation(population, p_mutation=0.5, mutation_power=.5) + +for i in range(population_size): + if (mutated[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] != + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]).any(): + print("{} mask mutated".format(i)) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index ae2e55308a..fb873d554a 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -145,7 +145,7 @@ def initialize_layers_params(self): for node_id in range(self.total_nodes): node_layer, node_type = number_to_type_layer(node_id, self.n_types) - node_key = "node_{}_{}".format(node_layer, node_type) + node_key = self.nodes[node_id] layers_params, layers_params_for_search, _ = self.initialize_params_in_config( self.basic_layers_params[self.node_types[node_type]]) @@ -333,7 +333,7 @@ def crossover(self, population, p_crossover, crossover_power): # exchange of nodes for j in range(self.total_nodes - nodes_part): node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) - node_key = "node_{}_{}".format(node_layer, node_type) + node_key = self.nodes[nodes_perm[j]] curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) @@ -341,7 +341,7 @@ def crossover(self, population, p_crossover, crossover_power): parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) for j in range(self.total_nodes - nodes_part, self.total_nodes): node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) - node_key = "node_{}_{}".format(node_layer, node_type) + node_key = self.nodes[nodes_perm[j]] curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) @@ -394,34 +394,76 @@ def mutation(self, population, p_mutation, mutation_power): mutated population """ mutated = [] + for individuum in population: - mutated_individuum = {} - for param in self.params_names: - if self.decision(p_mutation): - if type(self.params[param]) is dict: - if self.params[param].get('discrete', False): - val = round(individuum[param] + - ((2 * np.random.random() - 1.) * mutation_power - * self.sample_params(**{param: self.params[param]})[param])) - val = min(max(self.params[param]["range"][0], val), - self.params[param]["range"][1]) - mutated_individuum[param] = val - elif 'range' in self.params[param].keys(): - val = individuum[param] + \ - ((2 * np.random.random() - 1.) * mutation_power - * self.sample_params(**{param: self.params[param]})[param]) - val = min(max(self.params[param]["range"][0], val), - self.params[param]["range"][1]) - mutated_individuum[param] = val - elif self.params[param].get("choice"): - mutated_individuum[param] = individuum[param] - else: - mutated_individuum[param] = individuum[param] - else: - mutated_individuum[param] = individuum[param] + mutated_individuum = deepcopy(individuum) + + # mutation of other model params + for param in self.params.keys(): + mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][param] = \ + self.mutation_of_param(param, self.params, + individuum["chainer"]["pipe"][self.model_to_evolve_index][param], + p_mutation, mutation_power) + + # mutation of train params + for param in self.train_params.keys(): + mutated_individuum["train"][param] = \ + self.mutation_of_param(param, self.train_params, + individuum["train"][param], + p_mutation, mutation_power) + + # mutation of binary mask + if self.decision(p_mutation): + mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, + np.minimum(1, + np.maximum(0, + individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] + + np.round((2 * np.random.random() - 1.) * self.sample_binary_mask())))) + + # mutation of each node params + for node_id in range(self.total_nodes): + node_layer, node_type = number_to_type_layer(node_id, self.n_types) + for param in self.basic_layers_params[self.node_types[node_type]]: + mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][self.nodes[node_id]][param] =\ + self.mutation_of_param(param, self.basic_layers_params[self.node_types[node_type]], + individuum["chainer"]["pipe"][self.model_to_evolve_index][ + self.nodes[node_id]][param], + p_mutation, mutation_power) mutated.append(mutated_individuum) + return mutated + def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutation_power): + new_mutated_value = deepcopy(param_value) + + if self.decision(p_mutation): + if type(params_dict[param]) is dict: + if params_dict[param].get('discrete', False): + val = round(param_value + + ((2 * np.random.random() - 1.) * mutation_power + * self.sample_params(**{param: params_dict[param]})[param])) + val = min(max(params_dict[param]["range"][0], val), + params_dict[param]["range"][1]) + new_mutated_value = val + elif 'range' in params_dict[param].keys(): + val = param_value + \ + ((2 * np.random.random() - 1.) * mutation_power + * self.sample_params(**{param: params_dict[param]})[param]) + val = min(max(params_dict[param]["range"][0], val), + params_dict[param]["range"][1]) + new_mutated_value = val + elif params_dict[param].get("choice"): + new_mutated_value = param_value + else: + new_mutated_value = param_value + else: + new_mutated_value = param_value + else: + new_mutated_value = param_value + + return new_mutated_value + def decision(self, probability): """ Make decision whether to do action or not with given probability From 8b87f16fcb698ae0f2ae3ccdae76605d92d9619e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 18 Apr 2018 18:39:24 +0300 Subject: [PATCH 299/616] chore: constructing model with bugs --- deeppavlov/__init__.py | 1 + .../evolution/basic_intents_snips.json | 4 +- deeppavlov/models/evolution/debug.py | 46 ++- .../evolution/evolution_intent_model.py | 114 +++++++ deeppavlov/models/evolution/intent_model.py | 277 ------------------ .../neuroevolution_param_generator.py | 27 +- deeppavlov/models/evolution/utils.py | 21 +- 7 files changed, 188 insertions(+), 302 deletions(-) create mode 100644 deeppavlov/models/evolution/evolution_intent_model.py delete mode 100644 deeppavlov/models/evolution/intent_model.py diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index 72c6667a91..e4b14682a3 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -49,6 +49,7 @@ import deeppavlov.models.seq2seq_go_bot.network import deeppavlov.models.seq2seq_go_bot.kb import deeppavlov.models.classifiers.intents.intent_model +import deeppavlov.models.evolution.evolution_intent_model import deeppavlov.models.commutators.random_commutator import deeppavlov.models.embedders.fasttext_embedder import deeppavlov.models.embedders.dict_embedder diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json index f821ff7b7b..078713c080 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -60,7 +60,7 @@ "y_predicted" ], "main": true, - "name": "intent_model", + "name": "evolution_intent_model", "save_path": "evolution/intents_snips", "load_path": "evolution/intents_snips", "classes": "#classes_vocab.keys()", @@ -199,7 +199,7 @@ }, "loss": "binary_crossentropy", "text_size": 15, - "model_name": "cnn_model", + "model_name": "evolution_model", "embedder": "#fasttext_embedder", "tokenizer": "#nltk_tokenizer" } diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 660bc7c6ec..431e2c552a 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -1,12 +1,19 @@ import pandas as pd import json import numpy as np +from copy import deepcopy from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution +from deeppavlov.models.evolution.evolution_intent_model import KerasEvolutionIntentModel +from deeppavlov.core.commands.train import train_model_from_config +from deeppavlov.core.commands.infer import interact_model +from deeppavlov.core.commands.utils import set_deeppavlov_root +from deeppavlov.core.common.file import save_json, read_json -n_layers = 5 + +n_layers = 2 n_types = 7 -population_size = 10 +population_size = 1 config_path = "../../configs/evolution/basic_intents_snips.json" with open(config_path) as fin: @@ -20,18 +27,41 @@ **config) population = evolution.first_generation() -print(population) population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ evolution.model_to_evolve_index]["binary_mask"].tolist() + +config_path = "./config_init.json" +full_config = deepcopy(population[0]) +save_json(full_config, config_path) + +print(population) print(population) population = evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) print(population) +# population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ +# evolution.model_to_evolve_index]["binary_mask"].tolist() + +config_path = "./config_crossover.json" +full_config = deepcopy(population[0]) +save_json(full_config, config_path) + # print(population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) -mutated = evolution.mutation(population, p_mutation=0.5, mutation_power=.5) +population = evolution.mutation(population, p_mutation=0.5, mutation_power=.5) +# +# for i in range(population_size): +# if (mutated[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] != +# population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]).any(): +# print("{} mask mutated".format(i)) +# population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ +# evolution.model_to_evolve_index]["binary_mask"].tolist() + +config_path = "./config_mutated.json" +full_config = deepcopy(population[0]) +full_config["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = evolution.nodes +full_config["chainer"]["pipe"][evolution.model_to_evolve_index]["total_nodes"] = evolution.total_nodes + +save_json(full_config, config_path) -for i in range(population_size): - if (mutated[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] != - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]).any(): - print("{} mask mutated".format(i)) +train_model_from_config(config_path) \ No newline at end of file diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py new file mode 100644 index 0000000000..352ff1156e --- /dev/null +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -0,0 +1,114 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import numpy as np +from copy import copy, deepcopy +from keras.layers import Dense, Input, concatenate, Activation +from keras.layers.convolutional import Conv1D +from keras.layers.core import Dropout +from keras.layers.normalization import BatchNormalization +from keras.layers.pooling import GlobalMaxPooling1D, MaxPooling1D +from keras.layers.recurrent import LSTM +from keras.layers.wrappers import Bidirectional +from keras.models import Model +from keras.regularizers import l2 +from keras.layers import Concatenate, Reshape +from keras import backend as K + +from deeppavlov.core.common.errors import ConfigError +from deeppavlov.core.common.registry import register +from deeppavlov.core.models.keras_model import KerasModel +from deeppavlov.models.classifiers.intents.intent_model import KerasIntentModel +from deeppavlov.models.classifiers.intents import metrics as metrics_file +from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels +from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder +from deeppavlov.models.classifiers.intents.utils import md5_hashsum +from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer +from deeppavlov.core.common.log import get_logger +from deeppavlov.models.evolution.check_binary_mask import number_to_type_layer, \ + find_sources_and_sinks, get_digraph_from_binary_mask +from deeppavlov.models.evolution.utils import Attention, expand_tile +log = get_logger(__name__) + + +@register('evolution_intent_model') +class KerasEvolutionIntentModel(KerasIntentModel): + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): + if inp is None: + input_nodes = [edge[0] for edge in dg.in_edges(node_id)] + inp_list = [] + for input_node in input_nodes: + if len(K.int_shape(edges_outputs[input_node])) == 3: + inp_list.append(edges_outputs[input_node]) + elif len(K.int_shape(edges_outputs[input_node])) == 2: + inp_list.append(K.expand_dims(edges_outputs[input_node], axis=1)) + else: + raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") + inp = Concatenate()(inp_list) + + node_func = getattr(globals(), params[params["nodes"][node_id]]["node_name"], None) + node_params = deepcopy(params[params["nodes"][node_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + if callable(node_func): + output_of_node = node_func(**node_params)(inp) + else: + raise AttributeError("Node {} is not defined correctly".format(node_id)) + return output_of_node + + def evolution_model(self, params): + """ + Build un-compiled model of shallow-and-wide CNN + Args: + params: dictionary of parameters for NN + + Returns: + Un-compiled model + """ + print(params) + + inp = Input(shape=(params['text_size'], params['embedding_size'])) + + dg = get_digraph_from_binary_mask(params["nodes"], params["binary_mask"]) + sources, sinks = find_sources_and_sinks(dg) + + edges_outputs = {} + + for node_id in range(params["total_nodes"]): + # node_layer, node_type = number_to_type_layer(node_id, params["n_types"]) + if node_id in sources: + edges_outputs[node_id] = self.get_node_output(node_id, dg, params, inp=inp) + else: + edges_outputs[node_id] = self.get_node_output(node_id, dg, params, edges_outputs=edges_outputs) + + if len(sinks) == 1: + output = edges_outputs[sinks[0]] + else: + outputs = [] + for sink in sinks: + outputs.append(edges_outputs[sink]) + output = Concatenate()(outputs) + + #TODO: make 2dimensional input for dense! + output = Dense(self.n_classes, activation=None)(output) + act_output = Activation('sigmoid')(output) + model = Model(inputs=inp, outputs=act_output) + return model diff --git a/deeppavlov/models/evolution/intent_model.py b/deeppavlov/models/evolution/intent_model.py deleted file mode 100644 index 7980f944ac..0000000000 --- a/deeppavlov/models/evolution/intent_model.py +++ /dev/null @@ -1,277 +0,0 @@ -""" -Copyright 2017 Neural Networks and Deep Learning lab, MIPT - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import numpy as np -from keras.layers import Dense, Input, concatenate, Activation -from keras.layers.convolutional import Conv1D -from keras.layers.core import Dropout -from keras.layers.normalization import BatchNormalization -from keras.layers.pooling import GlobalMaxPooling1D, MaxPooling1D -from keras.models import Model -from keras.regularizers import l2 - -from deeppavlov.core.common.errors import ConfigError -from deeppavlov.core.common.registry import register -from deeppavlov.core.models.keras_model import KerasModel -from deeppavlov.models.classifiers.intents import metrics as metrics_file -from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels -from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder -from deeppavlov.models.classifiers.intents.utils import md5_hashsum -from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer -from deeppavlov.core.common.log import get_logger - - -log = get_logger(__name__) - - -@register('intent_model') -class KerasIntentModel(KerasModel): - """ - Class implements keras model for intent recognition task for multi-class multi-label data - """ - def __init__(self, **kwargs): - """ - Initialize and train vocabularies, initializes embedder, tokenizer, - and then initialize model using parameters from opt dictionary (from config), - if model is being initialized from saved - - Args: - vocabs: dictionary of considered vocabularies - opt: model parameters for network and learning - model_path: path to model serialization dir or file. - It is always an empty string and is ignored if it is not set in json config. - model_dir: name of a serialization dir, can be default or set in json config - model_file: name of a serialization file (usually binary model file), - can be default or set in json config - embedder: instance of FasttextEmbedder class - tokenizer: instance of NLTKTokenizer class - **kwargs: - """ - super().__init__(**kwargs) # self.opt initialized in here - - self.tokenizer = self.opt.get('tokenizer') - self.fasttext_model = self.opt.get('embedder') - self.opt.pop("vocabs") - self.opt.pop("embedder") - self.opt.pop("tokenizer") - - if self.opt.get('classes'): - self.classes = list(np.sort(np.array(list(self.opt.get('classes'))))) - self.opt['classes'] = self.classes - else: - # self.classes = list(np.sort(np.array(list(self.opt.get('vocabs')["classes_vocab"].keys())))) - self.classes = list(self.opt.get('vocabs')["classes_vocab"].keys()) - self.opt['classes'] = self.classes - self.n_classes = len(self.classes) - if self.n_classes == 0: - ConfigError("Please, provide vocabulary with considered intents.") - - self.opt['embedding_size'] = self.fasttext_model.dim - - if self.fasttext_model.load_path: - current_fasttext_md5 = md5_hashsum([self.fasttext_model.load_path]) - - # Parameters required to init model - params = {"model_name": self.opt.get('model_name'), - "optimizer_name": self.opt.get('optimizer'), - "loss_name": self.opt.get('loss'), - "lear_rate": self.opt.get('lear_rate'), - "lear_rate_decay": self.opt.get('lear_rate_decay')} - - self.model = self.load(**params) - self._init_params() - - # Check if md5 hash sum of current loaded fasttext model - # is equal to saved - try: - self.opt['fasttext_md5'] - except KeyError: - self.opt['fasttext_md5'] = current_fasttext_md5 - else: - if self.opt['fasttext_md5'] != current_fasttext_md5: - raise ConfigError( - "Given fasttext model does NOT match fasttext model used previously to train loaded model") - - def _init_params(self): - - # list of changeable params - changeable_params = {"confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": 1e-2, - "lear_rate_decay": 0., - "loss": "binary_crossentropy", - "coef_reg_cnn": 0., - "coef_reg_den": 0., - "dropout_rate": 0.} - - for param in changeable_params.keys(): - self.opt[param] = self.opt.get(param, changeable_params[param]) - return - - def texts2vec(self, sentences): - """ - Convert texts to vector representations using embedder and padding up to self.opt["text_size"] tokens - Args: - sentences: list of texts - - Returns: - array of embedded texts - """ - pad = np.zeros(self.opt['embedding_size']) - - embeddings_batch = self.fasttext_model([' '.join(sen.split()[:self.opt['text_size']]) for sen in sentences]) - embeddings_batch = [[pad] * (self.opt['text_size'] - len(tokens)) + tokens for tokens in embeddings_batch] - - embeddings_batch = np.asarray(embeddings_batch) - return embeddings_batch - - def train_on_batch(self, texts, labels): - """ - Train the model on the given batch - Args: - batch - list of data where batch[0] is list of texts and batch[1] is list of labels - - Returns: - loss and metrics values on the given batch - """ - texts = self.tokenizer(list(texts)) - features = self.texts2vec(texts) - onehot_labels = labels2onehot(labels, classes=self.classes) - metrics_values = self.model.train_on_batch(features, onehot_labels) - return metrics_values - - def infer_on_batch(self, batch, labels=None): - """ - Infer the model on the given batch - Args: - batch - list of texts - labels - list of labels - - Returns: - loss and metrics values on the given batch, if labels are given - predictions, otherwise - """ - texts = self.tokenizer(batch) - if labels: - features = self.texts2vec(texts) - onehot_labels = labels2onehot(labels, classes=self.classes) - metrics_values = self.model.test_on_batch(features, onehot_labels) - return metrics_values - else: - features = self.texts2vec(texts) - predictions = self.model.predict(features) - return predictions - - def __call__(self, data, predict_proba=False, *args): - """ - Infer on the given data - Args: - data: [list of sentences] - predict_proba: whether to return probabilities distribution or only labels-predictions - *args: - - Returns: - for each sentence: - vector of probabilities to belong with each class - or list of labels sentence belongs with - """ - preds = np.array(self.infer_on_batch(data)) - - if predict_proba: - return preds - else: - return proba2labels(preds, confident_threshold=self.opt['confident_threshold'], classes=self.classes) - - def cnn_model(self, params): - """ - Build un-compiled model of shallow-and-wide CNN - Args: - params: dictionary of parameters for NN - - Returns: - Un-compiled model - """ - - inp = Input(shape=(params['text_size'], params['embedding_size'])) - - outputs = [] - for i in range(len(params['kernel_sizes_cnn'])): - output_i = Conv1D(params['filters_cnn'], kernel_size=params['kernel_sizes_cnn'][i], - activation=None, - kernel_regularizer=l2(params['coef_reg_cnn']), - padding='same')(inp) - output_i = BatchNormalization()(output_i) - output_i = Activation('relu')(output_i) - output_i = GlobalMaxPooling1D()(output_i) - outputs.append(output_i) - - output = concatenate(outputs, axis=1) - - output = Dropout(rate=params['dropout_rate'])(output) - output = Dense(params['dense_size'], activation=None, - kernel_regularizer=l2(params['coef_reg_den']))(output) - output = BatchNormalization()(output) - output = Activation('relu')(output) - output = Dropout(rate=params['dropout_rate'])(output) - output = Dense(self.n_classes, activation=None, - kernel_regularizer=l2(params['coef_reg_den']))(output) - output = BatchNormalization()(output) - act_output = Activation('sigmoid')(output) - model = Model(inputs=inp, outputs=act_output) - return model - - def dcnn_model(self, params): - """ - Build un-compiled model of deep CNN - Args: - params: dictionary of parameters for NN - - Returns: - Un-compiled model - """ - - if type(self.opt['filters_cnn']) is str: - self.opt['filters_cnn'] = list(map(int, self.opt['filters_cnn'].split())) - - inp = Input(shape=(params['text_size'], params['embedding_size'])) - - output = inp - - for i in range(len(params['kernel_sizes_cnn'])): - output = Conv1D(params['filters_cnn'][i], kernel_size=params['kernel_sizes_cnn'][i], - activation=None, - kernel_regularizer=l2(params['coef_reg_cnn']), - padding='same')(output) - output = BatchNormalization()(output) - output = Activation('relu')(output) - output = MaxPooling1D()(output) - - output = GlobalMaxPooling1D()(output) - output = Dropout(rate=params['dropout_rate'])(output) - output = Dense(params['dense_size'], activation=None, - kernel_regularizer=l2(params['coef_reg_den']))(output) - output = BatchNormalization()(output) - output = Activation('relu')(output) - output = Dropout(rate=params['dropout_rate'])(output) - output = Dense(self.n_classes, activation=None, - kernel_regularizer=l2(params['coef_reg_den']))(output) - output = BatchNormalization()(output) - act_output = Activation('sigmoid')(output) - model = Model(inputs=inp, outputs=act_output) - return model - - def reset(self): - pass diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index fb873d554a..e837fd5c89 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -121,22 +121,23 @@ def initialize_params_in_config(self, basic_params): params_for_search = {} evolving_params = [] - for param_name in basic_params.keys(): - if type(basic_params[param_name]) is dict: - if basic_params[param_name].get("choice"): - params_for_search[param_name] = list(basic_params[param_name]["values"]) - evolving_params.append(param_name) - elif basic_params[param_name].get("range"): - params_for_search[param_name] = deepcopy(basic_params[param_name]) - evolving_params.append(param_name) + for param_name in list(basic_params.keys()): + if basic_params[param_name]: + if type(basic_params[param_name]) is dict: + if basic_params[param_name].get("choice"): + params_for_search[param_name] = list(basic_params[param_name]["values"]) + evolving_params.append(param_name) + elif basic_params[param_name].get("range"): + params_for_search[param_name] = deepcopy(basic_params[param_name]) + evolving_params.append(param_name) + else: + # NOT evolving params + params[param_name] = deepcopy(basic_params[param_name]) else: # NOT evolving params params[param_name] = deepcopy(basic_params[param_name]) - else: - # NOT evolving params - params[param_name] = deepcopy(basic_params[param_name]) - - params_for_search = deepcopy(self.sample_params(**params_for_search)) + if basic_params: + params_for_search = deepcopy(self.sample_params(**params_for_search)) return params, params_for_search, evolving_params diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index 7c8007ec0f..f66d4b3301 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -22,7 +22,7 @@ from deeppavlov.core.common.log import get_logger from keras import initializers, regularizers, constraints from keras import backend as K -from keras.layers import concatenate, multiply +from keras.layers import concatenate, multiply, Reshape log = get_logger(__name__) @@ -185,4 +185,21 @@ def call(self, x, mask=None): return out def compute_output_shape(self, input_shape): - return input_shape \ No newline at end of file + return input_shape + +def expand_tile(units, axis): + """Expand and tile tensor along given axis + Args: + units: tf tensor with dimensions [batch_size, time_steps, n_input_features] + axis: axis along which expand and tile. Must be 1 or 2 + + """ + assert axis in (1, 2) + n_time_steps = K.int_shape(units)[1] + repetitions = [1, 1, 1, 1] + repetitions[axis] = n_time_steps + if axis == 1: + expanded = Reshape(target_shape=( (1,) + K.int_shape(units)[1:] ))(units) + else: # axis=2 + expanded = Reshape(target_shape=(K.int_shape(units)[1:2] + (1,) + K.int_shape(units)[2:]))(units) + return K.tile(expanded, repetitions) From a07f0cf09f66860a78226a54f26451a90aea2b53 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 18 Apr 2018 18:48:34 +0300 Subject: [PATCH 300/616] chore: constructing model with bugs --- deeppavlov/models/evolution/debug.py | 3 +++ .../evolution/evolution_intent_model.py | 14 +++++++++---- .../neuroevolution_param_generator.py | 21 +++++++++---------- 3 files changed, 23 insertions(+), 15 deletions(-) diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 431e2c552a..03008229d0 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -56,6 +56,9 @@ # print("{} mask mutated".format(i)) # population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ # evolution.model_to_evolve_index]["binary_mask"].tolist() +population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ + evolution.model_to_evolve_index]["binary_mask"].tolist() + config_path = "./config_mutated.json" full_config = deepcopy(population[0]) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 352ff1156e..1be15e5bfd 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -61,10 +61,16 @@ def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): inp_list.append(K.expand_dims(edges_outputs[input_node], axis=1)) else: raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") - inp = Concatenate()(inp_list) + if len(input_nodes) > 1: + inp = Concatenate()(inp_list) + else: + inp = inp_list[0] - node_func = getattr(globals(), params[params["nodes"][node_id]]["node_name"], None) - node_params = deepcopy(params[params["nodes"][node_id]]) + print(params[params["nodes"][str(node_id)]]["node_name"]) + print(globals()) + # node_func = getattr(globals(), params[params["nodes"][str(node_id)]]["node_name"], None) + node_func = globals().get(params[params["nodes"][str(node_id)]]["node_name"], None) + node_params = deepcopy(params[params["nodes"][str(node_id)]]) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") @@ -87,7 +93,7 @@ def evolution_model(self, params): inp = Input(shape=(params['text_size'], params['embedding_size'])) - dg = get_digraph_from_binary_mask(params["nodes"], params["binary_mask"]) + dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) sources, sinks = find_sources_and_sinks(dg) edges_outputs = {} diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index e837fd5c89..0d902eb2a8 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -122,20 +122,19 @@ def initialize_params_in_config(self, basic_params): evolving_params = [] for param_name in list(basic_params.keys()): - if basic_params[param_name]: - if type(basic_params[param_name]) is dict: - if basic_params[param_name].get("choice"): - params_for_search[param_name] = list(basic_params[param_name]["values"]) - evolving_params.append(param_name) - elif basic_params[param_name].get("range"): - params_for_search[param_name] = deepcopy(basic_params[param_name]) - evolving_params.append(param_name) - else: - # NOT evolving params - params[param_name] = deepcopy(basic_params[param_name]) + if type(basic_params[param_name]) is dict: + if basic_params[param_name].get("choice"): + params_for_search[param_name] = list(basic_params[param_name]["values"]) + evolving_params.append(param_name) + elif basic_params[param_name].get("range"): + params_for_search[param_name] = deepcopy(basic_params[param_name]) + evolving_params.append(param_name) else: # NOT evolving params params[param_name] = deepcopy(basic_params[param_name]) + else: + # NOT evolving params + params[param_name] = deepcopy(basic_params[param_name]) if basic_params: params_for_search = deepcopy(self.sample_params(**params_for_search)) From a003d7d85c61bc74b88ad0d3b8d07834fe9636ea Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 20 Apr 2018 16:42:40 +0300 Subject: [PATCH 301/616] chore: change shapes within concatenate --- .../evolution/basic_intents_snips.json | 56 ++-------- .../models/evolution/check_binary_mask.py | 10 +- deeppavlov/models/evolution/debug.py | 31 ++++-- .../evolution/evolution_intent_model.py | 105 +++++++++++++++--- .../neuroevolution_param_generator.py | 10 ++ deeppavlov/models/evolution/utils.py | 52 +++++++-- 6 files changed, 179 insertions(+), 85 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json index 078713c080..cf82a03368 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -60,7 +60,7 @@ "y_predicted" ], "main": true, - "name": "evolution_intent_model", + "name": "evolution_classification_model", "save_path": "evolution/intents_snips", "load_path": "evolution/intents_snips", "classes": "#classes_vocab.keys()", @@ -97,9 +97,10 @@ 7 ], "discrete": true - } + }, + "padding": "same" }, - "LSTM": { + "CuDNNLSTM": { "units": { "range": [ 50, @@ -107,28 +108,11 @@ ], "discrete": true }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - }, - "dropout": { - "range": [ - 1e-2, - 7e-1 - ] - }, - "recurrent_dropout": { - "range": [ - 1e-2, - 7e-1 - ] + "return_sequences": { + "bool": true } }, - "BiLSTM": { + "BiCuDNNLSTM": { "units": { "range": [ 50, @@ -136,25 +120,8 @@ ], "discrete": true }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - }, - "dropout": { - "range": [ - 1e-2, - 7e-1 - ] - }, - "recurrent_dropout": { - "range": [ - 1e-2, - 7e-1 - ] + "return_sequences": { + "bool": true } }, "GlobalMaxPooling1D": { @@ -166,7 +133,8 @@ 5 ], "discrete": true - } + }, + "padding": "same" }, "Attention": { "context_length": { @@ -199,7 +167,7 @@ }, "loss": "binary_crossentropy", "text_size": 15, - "model_name": "evolution_model", + "model_name": "evolution_classification_model", "embedder": "#fasttext_embedder", "tokenizer": "#nltk_tokenizer" } diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index a4cb3d7646..583532f88e 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -19,6 +19,7 @@ def type_layer_to_number(node_layer, node_type, n_types): def find_sources_and_sinks(directed_graph): sources = [] sinks = [] + isolates = nx.isolates(directed_graph) for i in directed_graph.nodes(): if directed_graph.in_degree(i) == 0 and directed_graph.out_degree(i) > 0: @@ -26,7 +27,7 @@ def find_sources_and_sinks(directed_graph): if directed_graph.in_degree(i) > 0 and directed_graph.out_degree(i) == 0: sinks.append(i) - return sources, sinks + return sources, sinks, isolates def get_digraph_from_binary_mask(nodes, binary_mask): @@ -52,9 +53,8 @@ def get_binary_mask_from_digraph(nodes, directed_graph): def check_and_correct_binary_mask(nodes, binary_mask_): binary_mask = deepcopy(binary_mask_) - binary_mask = np.array(binary_mask) directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) - sources, sinks = find_sources_and_sinks(directed_graph) + sources, sinks, _ = find_sources_and_sinks(directed_graph) while not nx.is_directed_acyclic_graph(directed_graph): candidates = [] @@ -79,7 +79,7 @@ def check_and_correct_binary_mask(nodes, binary_mask_): best_cand = None best_diff = 10e10 for i in range(n_candidates): - new_sources, new_sinks = find_sources_and_sinks(candidates[i]) + new_sources, new_sinks, _ = find_sources_and_sinks(candidates[i]) if set(new_sources) == set(sources) and set(new_sinks) == set(sinks): best_cand = candidates[i] @@ -100,7 +100,7 @@ def get_graph_and_plot(nodes, binary_mask, n_types, path=None): pos = {} val_map = {} - sources, sinks = find_sources_and_sinks(dg) + sources, sinks, _ = find_sources_and_sinks(dg) for i in range(total_nodes): pos[i] = 2. * np.array(number_to_type_layer(i, n_types))[::-1] diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 03008229d0..382b0ebd69 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -1,14 +1,17 @@ import pandas as pd import json import numpy as np +import tensorflow as tf from copy import deepcopy from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution -from deeppavlov.models.evolution.evolution_intent_model import KerasEvolutionIntentModel +from deeppavlov.models.evolution.evolution_intent_model import KerasEvolutionClassificationModel from deeppavlov.core.commands.train import train_model_from_config from deeppavlov.core.commands.infer import interact_model from deeppavlov.core.commands.utils import set_deeppavlov_root from deeppavlov.core.common.file import save_json, read_json +from deeppavlov.models.evolution.utils import expand_tile_batch_size +from deeppavlov.models.evolution.check_binary_mask import get_digraph_from_binary_mask n_layers = 2 @@ -32,22 +35,25 @@ config_path = "./config_init.json" full_config = deepcopy(population[0]) +print(population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]) save_json(full_config, config_path) -print(population) -print(population) +population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = np.array(population[0]["chainer"]["pipe"][ + evolution.model_to_evolve_index]["binary_mask"]) population = evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) print(population) -# population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ -# evolution.model_to_evolve_index]["binary_mask"].tolist() +population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ + evolution.model_to_evolve_index]["binary_mask"].tolist() config_path = "./config_crossover.json" full_config = deepcopy(population[0]) save_json(full_config, config_path) -# print(population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) +population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = np.array(population[0]["chainer"]["pipe"][ + evolution.model_to_evolve_index]["binary_mask"]) + population = evolution.mutation(population, p_mutation=0.5, mutation_power=.5) # # for i in range(population_size): @@ -59,7 +65,6 @@ population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ evolution.model_to_evolve_index]["binary_mask"].tolist() - config_path = "./config_mutated.json" full_config = deepcopy(population[0]) full_config["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = evolution.nodes @@ -67,4 +72,14 @@ save_json(full_config, config_path) -train_model_from_config(config_path) \ No newline at end of file +population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = np.array(population[0]["chainer"]["pipe"][ + evolution.model_to_evolve_index]["binary_mask"]) + +dg = get_digraph_from_binary_mask(evolution.nodes, + population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) + +print("Edges: ", dg.edges) +train_model_from_config(config_path) + + + diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 1be15e5bfd..e0bd6d05b3 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -25,7 +25,7 @@ from keras.layers.wrappers import Bidirectional from keras.models import Model from keras.regularizers import l2 -from keras.layers import Concatenate, Reshape +from keras.layers import Concatenate, Reshape, CuDNNLSTM from keras import backend as K from deeppavlov.core.common.errors import ConfigError @@ -44,43 +44,66 @@ log = get_logger(__name__) -@register('evolution_intent_model') -class KerasEvolutionIntentModel(KerasIntentModel): +@register('evolution_classification_model') +class KerasEvolutionClassificationModel(KerasIntentModel): def __init__(self, **kwargs): super().__init__(**kwargs) def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): if inp is None: + print(dg.in_edges(node_id)) input_nodes = [edge[0] for edge in dg.in_edges(node_id)] + print("Input nodes: {}".format(input_nodes)) inp_list = [] for input_node in input_nodes: if len(K.int_shape(edges_outputs[input_node])) == 3: inp_list.append(edges_outputs[input_node]) elif len(K.int_shape(edges_outputs[input_node])) == 2: - inp_list.append(K.expand_dims(edges_outputs[input_node], axis=1)) + inp_list.append(expand_tile(edges_outputs[input_node], axis=1)) else: raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") if len(input_nodes) > 1: - inp = Concatenate()(inp_list) + try: + inp = Concatenate()(inp_list) + except ValueError: + time_steps = [] + features = [] + for i in range(len(inp_list)): + if len(K.int_shape(inp_list[i])) == 2: + inp_list[i] = expand_tile(inp_list[i], axis=1) + time_steps.append(K.int_shape(inp_list[i])[1]) + features.append(K.int_shape(inp_list[i])[2]) + new_feature_shape = max(features) + for i in range(len(inp_list)): + inp_list[i] = Dense(new_feature_shape)(inp_list[i]) + inp = Concatenate(axis=1)(inp_list) else: inp = inp_list[0] print(params[params["nodes"][str(node_id)]]["node_name"]) - print(globals()) + # print(globals()) # node_func = getattr(globals(), params[params["nodes"][str(node_id)]]["node_name"], None) - node_func = globals().get(params[params["nodes"][str(node_id)]]["node_name"], None) - node_params = deepcopy(params[params["nodes"][str(node_id)]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - if callable(node_func): - output_of_node = node_func(**node_params)(inp) + + if params[params["nodes"][str(node_id)]]["node_name"] == "BiCuDNNLSTM": + node_params = deepcopy(params[params["nodes"][str(node_id)]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + output_of_node = Bidirectional(CuDNNLSTM(**node_params))(inp) else: - raise AttributeError("Node {} is not defined correctly".format(node_id)) + node_func = globals().get(params[params["nodes"][str(node_id)]]["node_name"], None) + node_params = deepcopy(params[params["nodes"][str(node_id)]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + if callable(node_func): + output_of_node = node_func(**node_params)(inp) + else: + raise AttributeError("Node {} is not defined correctly".format(node_id)) return output_of_node - def evolution_model(self, params): + def evolution_classification_model(self, params): """ Build un-compiled model of shallow-and-wide CNN Args: @@ -94,14 +117,44 @@ def evolution_model(self, params): inp = Input(shape=(params['text_size'], params['embedding_size'])) dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) - sources, sinks = find_sources_and_sinks(dg) + print(dg.edges) + sources, sinks, isolates = find_sources_and_sinks(dg) edges_outputs = {} - for node_id in range(params["total_nodes"]): + # for node_id in range(params["total_nodes"]): + # # node_layer, node_type = number_to_type_layer(node_id, params["n_types"]) + # if node_id in sources: + # edges_outputs[node_id] = self.get_node_output(node_id, dg, params, inp=inp) + # elif node_id in isolates: + # pass + # else: + # edges_outputs[node_id] = self.get_node_output(node_id, dg, params, edges_outputs=edges_outputs) + + sequence_of_nodes = [] + sequence_of_nodes.append(sources) + + while True: + if set(sinks).issubset(set(sum(sequence_of_nodes, []))): + break + next_nodes = [] + for node_id in sequence_of_nodes[-1]: + out_edges = dg.out_edges(node_id) + for edge in out_edges: + in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] + if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): + next_nodes.append(edge[1]) + sequence_of_nodes.append(next_nodes) + + sequence_of_nodes = sum(sequence_of_nodes, []) + + for node_id in sequence_of_nodes: + print(node_id) # node_layer, node_type = number_to_type_layer(node_id, params["n_types"]) if node_id in sources: edges_outputs[node_id] = self.get_node_output(node_id, dg, params, inp=inp) + elif node_id in isolates: + pass else: edges_outputs[node_id] = self.get_node_output(node_id, dg, params, edges_outputs=edges_outputs) @@ -111,9 +164,25 @@ def evolution_model(self, params): outputs = [] for sink in sinks: outputs.append(edges_outputs[sink]) - output = Concatenate()(outputs) + print("Sinks: {}".format(sinks)) + try: + output = Concatenate()(outputs) + except ValueError: + time_steps = [] + features = [] + for i in range(len(outputs)): + if len(K.int_shape(outputs[i])) == 2: + outputs[i] = expand_tile(outputs[i], axis=1) + time_steps.append(K.int_shape(outputs[i])[1]) + features.append(K.int_shape(outputs[i])[2]) + new_feature_shape = max(features) + for i in range(len(outputs)): + outputs[i] = Dense(new_feature_shape)(outputs[i]) + print("Outputs: {}".format(outputs[i].shape)) + output = Concatenate(axis=1)(outputs) #TODO: make 2dimensional input for dense! + output = GlobalMaxPooling1D()(output) output = Dense(self.n_classes, activation=None)(output) act_output = Activation('sigmoid')(output) model = Model(inputs=inp, outputs=act_output) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 0d902eb2a8..9cedc4acc6 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -43,6 +43,12 @@ def __init__(self, n_layers, n_types, crossover_power: part of EVOLVING parents parameters to exchange for offsprings p_mutation: probability of mutation for current replacement mutation_power: allowed percentage of mutation + key_model_to_evolve: binary flag that should be inserted into the dictionary + with evolving model in the basic config + key_basic_layers: key value of dictionary in basic_config + that contains considered layers with their evolving parameters + seed: random seed for initialization + seed: random seed for initialization **kwargs: basic config with parameters """ self.n_types = n_types @@ -129,6 +135,9 @@ def initialize_params_in_config(self, basic_params): elif basic_params[param_name].get("range"): params_for_search[param_name] = deepcopy(basic_params[param_name]) evolving_params.append(param_name) + elif basic_params[param_name].get("bool"): + params_for_search[param_name] = deepcopy(basic_params[param_name]) + evolving_params.append(param_name) else: # NOT evolving params params[param_name] = deepcopy(basic_params[param_name]) @@ -521,4 +530,5 @@ def sample_binary_mask(self): size=max(1, int(np.random.random() * self.total_nodes))) mask = np.zeros((self.total_nodes * self.total_nodes)) mask[ones] = 1 + # returns NUMPY 2D ARRAY! return mask.reshape((self.total_nodes, self.total_nodes)) diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index f66d4b3301..814367c189 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -22,7 +22,7 @@ from deeppavlov.core.common.log import get_logger from keras import initializers, regularizers, constraints from keras import backend as K -from keras.layers import concatenate, multiply, Reshape +from keras.layers import concatenate, multiply, Reshape, Lambda log = get_logger(__name__) @@ -154,16 +154,19 @@ def build(self, input_shape): if self.context_length is None: self.context_length = input_shape[-1] - self.context = self.add_weight(tuple(input_shape[:-1] + (self.context_length,)), + self.context = self.add_weight(tuple((self.context_length, input_shape[-1])), + name="context", initializer=self.init) - self.W = self.add_weight((input_shape[-1] + self.context_length, input_shape[-1], ), + self.W = self.add_weight((2 * input_shape[-1], 1,), + name="w", initializer=self.init, regularizer=self.W_regularizer, constraint=self.W_constraint) if self.use_bias: - self.b = self.add_weight((input_shape[-1], ), + self.b = self.add_weight((1, ), + name="b", initializer='zero', regularizer=self.b_regularizer, constraint=self.b_constraint) @@ -173,21 +176,29 @@ def build(self, input_shape): self.built = True def call(self, x, mask=None): - x_full = concatenate(inputs=[x, self.context], axis=-1) + + expanded_context_3d = expand_tile_batch_size(memory=x, context=self.context) + expanded_context_4d = expand_tile(expanded_context_3d, axis=1, n_repetitions=K.int_shape(x)[1]) + expanded_x = expand_tile(x, axis=2, n_repetitions=K.int_shape(expanded_context_3d)[1]) + + # now expanded_context_4d and expanded_x are of + # shape (bs, time_steps, context_size, n_features) + x_full = concatenate(inputs=[expanded_x, expanded_context_4d], axis=-1) out = K.dot(x_full, self.W) if self.use_bias: out = K.bias_add(out, self.b) out = K.softmax(out) - out = multiply(inputs=[out, x]) + out = multiply(inputs=[out, expanded_x]) + out = Lambda(lambda x: K.sum(x, axis=1))(out) return out def compute_output_shape(self, input_shape): return input_shape -def expand_tile(units, axis): +def expand_tile(units, axis, n_repetitions=None): """Expand and tile tensor along given axis Args: units: tf tensor with dimensions [batch_size, time_steps, n_input_features] @@ -195,11 +206,32 @@ def expand_tile(units, axis): """ assert axis in (1, 2) - n_time_steps = K.int_shape(units)[1] - repetitions = [1, 1, 1, 1] - repetitions[axis] = n_time_steps + repetitions = [1] * (len(K.int_shape(units)) + 1) + + if n_repetitions is None: + repetitions[axis] = K.int_shape(units)[1] + else: + repetitions[axis] = n_repetitions + if axis == 1: expanded = Reshape(target_shape=( (1,) + K.int_shape(units)[1:] ))(units) else: # axis=2 expanded = Reshape(target_shape=(K.int_shape(units)[1:2] + (1,) + K.int_shape(units)[2:]))(units) return K.tile(expanded, repetitions) + + +def expand_tile_batch_size(memory, context): + """Expand and tile tensor context along 0 axis up to 0-shape of memory + Args: + memory: tf tensor with dimensions [batch_size, time_steps, n_input_features] + context: tf tensor with dimensions [new_time_steps, n_input_features] + + """ + axis = 0 + # batch_size = K.int_shape(memory)[0] + batch_size = K.shape(memory)[0] + repetitions = [1] * len(K.int_shape(memory)) + repetitions[axis] = batch_size + if axis == 0: + expanded = K.reshape(context, shape=((1,) + K.int_shape(context))) + return K.tile(expanded, repetitions) From f687e0f4f35f91721f753e23f8d8708c811970a3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 11:33:15 +0300 Subject: [PATCH 302/616] chore: training phenotype --- .../evolution/basic_intents_snips.json | 22 ++---- deeppavlov/models/evolution/debug.py | 8 +-- .../evolution/evolution_intent_model.py | 71 +++++++++++-------- .../neuroevolution_param_generator.py | 2 +- .../{evolution.py => run_evolution.py} | 49 +++++++++---- .../models/evolution/train_phenotype.py | 37 ++++++++++ deeppavlov/models/evolution/utils.py | 13 ++-- 7 files changed, 132 insertions(+), 70 deletions(-) rename deeppavlov/models/evolution/{evolution.py => run_evolution.py} (53%) diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json index cf82a03368..799036bc63 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -7,17 +7,7 @@ "url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv" }, "dataset_iterator": { - "name": "basic_classification_iterator", - "seed": 42, - "field_to_split": "train", - "split_fields": [ - "train", - "valid" - ], - "split_proportions": [ - 0.9, - 0.1 - ] + "name": "basic_classification_iterator" }, "chainer": { "in": [ @@ -61,8 +51,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "evolution/intents_snips", - "load_path": "evolution/intents_snips", + "save_path": "/home/dilyara/data/models/deeppavlov_evolution/classification/intents_snips", + "load_path": "/home/dilyara/data/models/deeppavlov_evolution/classification/intents_snips", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -180,7 +170,7 @@ "epochs": { "range": [ 10, - 1000 + 11 ], "discrete": true }, @@ -192,7 +182,9 @@ "discrete": true }, "metrics": [ - "sets_accuracy" + "sets_accuracy", + "roc_auc_score", + "f1_classification" ], "validation_patience": 5, "val_every_n_epochs": 5, diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 382b0ebd69..291c7a7df4 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -55,13 +55,7 @@ evolution.model_to_evolve_index]["binary_mask"]) population = evolution.mutation(population, p_mutation=0.5, mutation_power=.5) -# -# for i in range(population_size): -# if (mutated[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] != -# population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]).any(): -# print("{} mask mutated".format(i)) -# population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ -# evolution.model_to_evolve_index]["binary_mask"].tolist() + population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ evolution.model_to_evolve_index]["binary_mask"].tolist() diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index e0bd6d05b3..97ee098b65 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -25,8 +25,10 @@ from keras.layers.wrappers import Bidirectional from keras.models import Model from keras.regularizers import l2 -from keras.layers import Concatenate, Reshape, CuDNNLSTM +from keras.layers import Concatenate, Reshape, CuDNNLSTM, Lambda from keras import backend as K +from overrides import overrides +from pathlib import Path from deeppavlov.core.common.errors import ConfigError from deeppavlov.core.common.registry import register @@ -41,6 +43,9 @@ from deeppavlov.models.evolution.check_binary_mask import number_to_type_layer, \ find_sources_and_sinks, get_digraph_from_binary_mask from deeppavlov.models.evolution.utils import Attention, expand_tile +from deeppavlov.core.common.file import save_json, read_json + + log = get_logger(__name__) @@ -49,18 +54,18 @@ class KerasEvolutionClassificationModel(KerasIntentModel): def __init__(self, **kwargs): super().__init__(**kwargs) + self.opt["binary_mask"] = np.array(self.opt["binary_mask"]) def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): if inp is None: - print(dg.in_edges(node_id)) input_nodes = [edge[0] for edge in dg.in_edges(node_id)] - print("Input nodes: {}".format(input_nodes)) inp_list = [] for input_node in input_nodes: if len(K.int_shape(edges_outputs[input_node])) == 3: inp_list.append(edges_outputs[input_node]) elif len(K.int_shape(edges_outputs[input_node])) == 2: - inp_list.append(expand_tile(edges_outputs[input_node], axis=1)) + input_expanded = Lambda(lambda x: expand_tile(x, axis=1))(edges_outputs[input_node]) + inp_list.append(input_expanded) else: raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") if len(input_nodes) > 1: @@ -71,7 +76,7 @@ def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): features = [] for i in range(len(inp_list)): if len(K.int_shape(inp_list[i])) == 2: - inp_list[i] = expand_tile(inp_list[i], axis=1) + inp_list[i] = Lambda(lambda x: expand_tile(x, axis=1))(inp_list[i]) time_steps.append(K.int_shape(inp_list[i])[1]) features.append(K.int_shape(inp_list[i])[2]) new_feature_shape = max(features) @@ -81,10 +86,6 @@ def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): else: inp = inp_list[0] - print(params[params["nodes"][str(node_id)]]["node_name"]) - # print(globals()) - # node_func = getattr(globals(), params[params["nodes"][str(node_id)]]["node_name"], None) - if params[params["nodes"][str(node_id)]]["node_name"] == "BiCuDNNLSTM": node_params = deepcopy(params[params["nodes"][str(node_id)]]) node_params.pop("node_name") @@ -112,25 +113,13 @@ def evolution_classification_model(self, params): Returns: Un-compiled model """ - print(params) - inp = Input(shape=(params['text_size'], params['embedding_size'])) dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) - print(dg.edges) sources, sinks, isolates = find_sources_and_sinks(dg) edges_outputs = {} - # for node_id in range(params["total_nodes"]): - # # node_layer, node_type = number_to_type_layer(node_id, params["n_types"]) - # if node_id in sources: - # edges_outputs[node_id] = self.get_node_output(node_id, dg, params, inp=inp) - # elif node_id in isolates: - # pass - # else: - # edges_outputs[node_id] = self.get_node_output(node_id, dg, params, edges_outputs=edges_outputs) - sequence_of_nodes = [] sequence_of_nodes.append(sources) @@ -149,8 +138,6 @@ def evolution_classification_model(self, params): sequence_of_nodes = sum(sequence_of_nodes, []) for node_id in sequence_of_nodes: - print(node_id) - # node_layer, node_type = number_to_type_layer(node_id, params["n_types"]) if node_id in sources: edges_outputs[node_id] = self.get_node_output(node_id, dg, params, inp=inp) elif node_id in isolates: @@ -164,7 +151,6 @@ def evolution_classification_model(self, params): outputs = [] for sink in sinks: outputs.append(edges_outputs[sink]) - print("Sinks: {}".format(sinks)) try: output = Concatenate()(outputs) except ValueError: @@ -172,18 +158,47 @@ def evolution_classification_model(self, params): features = [] for i in range(len(outputs)): if len(K.int_shape(outputs[i])) == 2: - outputs[i] = expand_tile(outputs[i], axis=1) + outputs[i] = Lambda(lambda x: expand_tile(x, axis=1))(outputs[i]) time_steps.append(K.int_shape(outputs[i])[1]) features.append(K.int_shape(outputs[i])[2]) new_feature_shape = max(features) for i in range(len(outputs)): outputs[i] = Dense(new_feature_shape)(outputs[i]) - print("Outputs: {}".format(outputs[i].shape)) output = Concatenate(axis=1)(outputs) - #TODO: make 2dimensional input for dense! - output = GlobalMaxPooling1D()(output) + if len(output.shape) == 3: + output = GlobalMaxPooling1D()(output) output = Dense(self.n_classes, activation=None)(output) act_output = Activation('sigmoid')(output) model = Model(inputs=inp, outputs=act_output) return model + + @overrides + def save(self, fname=None): + """ + Save the model parameters into <>_opt.json (or <>_opt.json) + and model weights into <>.h5 (or <>.h5) + Args: + fname: file_path to save model. If not explicitly given seld.opt["ser_file"] will be used + + Returns: + None + """ + + if not self.save_path: + raise ConfigError("No `save_path` is provided for Keras model!") + elif isinstance(self.save_path, Path) and not self.save_path.parent.is_dir(): + raise ConfigError("Provided save path is incorrect!") + else: + opt_path = "{}_opt.json".format(str(self.save_path.resolve())) + weights_path = "{}.h5".format(str(self.save_path.resolve())) + log.info("[saving model to {}]".format(opt_path)) + self.model.save_weights(weights_path) + + if type(self.opt["binary_mask"]) is list: + pass + else: + self.opt["binary_mask"] = self.opt["binary_mask"].tolist() + + save_json(self.opt, opt_path) + return True \ No newline at end of file diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 9cedc4acc6..f897cf0ffb 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -499,7 +499,7 @@ def sample_params(self, **params): params_sample[param] = np.random.choice(param_val) elif isinstance(param_val, dict): if 'bool' in param_val and param_val['bool']: - sample = np.random.choice([True, False]) + sample = bool(np.random.choice([True, False])) elif 'range' in param_val: sample = self._sample_from_ranges(param_val) params_sample[param] = sample diff --git a/deeppavlov/models/evolution/evolution.py b/deeppavlov/models/evolution/run_evolution.py similarity index 53% rename from deeppavlov/models/evolution/evolution.py rename to deeppavlov/models/evolution/run_evolution.py index adcb6a5e62..dfdadf7a38 100644 --- a/deeppavlov/models/evolution/evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -5,11 +5,10 @@ from subprocess import Popen, PIPE import pandas as pd - -from tuning_parameters.neuroevolution_param_generator import Evolution - +from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution def score_population(population, population_size, result_file): + global evolution population_losses = [] population_fmeasures = [] population_accuracies = [] @@ -18,20 +17,32 @@ def score_population(population, population_size, result_file): procs = [] for i in range(population_size): - f_name = Path(population[i]["model_path"]) + f_name = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) + model_name = population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ + str(f_name.joinpath(model_name + "_" + str(i))) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] =\ + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] + + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = \ + evolution.nodes + print(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) try: f_name.mkdir(parents=True) except FileExistsError: pass f_name = f_name.joinpath("config.json") + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] =\ + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"].tolist() with open(f_name, 'w') as outfile: json.dump(population[i], outfile) - procs.append(Popen("CUDA_VISIBLE_DEVICES={} python train_phenotype.py {}" + procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], str(f_name), - population[i]["model_path"], - population[i]["model_path"]), + str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).parent), + str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).parent) + ), shell=True, stdout=PIPE, stderr=PIPE)) for i, proc in enumerate(procs): @@ -39,7 +50,8 @@ def score_population(population, population_size, result_file): proc.wait() for i in range(population_size): - val_results = np.loadtxt(fname=str(Path(population[i]["model_path"]).joinpath("valid_results.txt"))) + val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).joinpath("valid_results.txt"))) result_table = pd.DataFrame({"loss": [val_results[0]], "accuracy": [val_results[1]], "fmeasure": [val_results[2]], @@ -56,9 +68,12 @@ def score_population(population, population_size, result_file): parser = argparse.ArgumentParser() -parser.add_argument('--config', help='Please, enter model path to config', default='./configs/basic_config.json') +parser.add_argument('--config', help='Please, enter model path to config', + default='./configs/evolution/basic_intents_config.json') parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) +parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) +parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) args = parser.parse_args() @@ -66,6 +81,8 @@ def score_population(population, population_size, result_file): POPULATION_SIZE = args.p_size GPU_NUMBER = len(args.gpus) gpus = [int(gpu) for gpu in args.gpus.split(",")] +N_LAYERS = int(args.n_layers) +N_TYPES = int(args.n_types) with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) @@ -73,19 +90,25 @@ def score_population(population, population_size, result_file): print("Given basic params: {}\n".format(basic_params)) try: - Path(basic_params["model_path"]).mkdir(parents=True) + print(basic_params["chainer"]["pipe"][3]) + Path(basic_params["chainer"]["pipe"][3]["save_path"]).mkdir(parents=True) except FileExistsError: pass # Result table order = ["loss", "accuracy", "fmeasure", "roc_auc_score", "params"] -result_file = Path(basic_params["model_path"]).joinpath("result_table.csv") +result_file = Path(basic_params["chainer"]["pipe"][3]["save_path"]).joinpath("result_table.csv") result_table = pd.DataFrame({"loss": [], "accuracy": [], "fmeasure": [], "roc_auc_score": [], "params": []}) result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') # EVOLUTION starts here! -evolution = Evolution(population_size=POPULATION_SIZE, p_crossover=0.1, - p_mutation=0.5, mutation_power=0.1, **basic_params) +evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, + population_size=POPULATION_SIZE, + p_crossover=0.1, crossover_power=0.5, + p_mutation=0.5, mutation_power=0.1, + key_model_to_evolve="to_evolve", + key_basic_layers="basic_layers_params", + seed=None, **basic_params) print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py index e69de29bb2..d9ebadb048 100644 --- a/deeppavlov/models/evolution/train_phenotype.py +++ b/deeppavlov/models/evolution/train_phenotype.py @@ -0,0 +1,37 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import numpy as np +import sys + +from deeppavlov.core.commands.train import train_model_from_config, train_evaluate_model_from_config +from deeppavlov.core.common.file import read_json, save_json + +config_path = sys.argv[1] + +print("TRAIN PHENOTYPE") +report = train_model_from_config(config_path, is_trained=False) + +# train_model_from_config(config_path) + +# config = read_json(config_path) +# +# model = build_model_from_config(config, mode='infer', load_trained=True) +# +# test_model_on_data(config_path, data) +# +# val_metrics_values = np.mean(np.array(val_metrics_values), axis=0) +# +# np.savetxt(fname=Path(path_to_models).joinpath("valid_results.txt"), X=val_metrics_values) diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index 814367c189..479660ecd8 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -22,8 +22,8 @@ from deeppavlov.core.common.log import get_logger from keras import initializers, regularizers, constraints from keras import backend as K -from keras.layers import concatenate, multiply, Reshape, Lambda - +from keras.layers import Reshape, Lambda, Dense, Flatten +from keras.layers import Concatenate, Multiply, Activation, Dot log = get_logger(__name__) @@ -158,7 +158,7 @@ def build(self, input_shape): name="context", initializer=self.init) - self.W = self.add_weight((2 * input_shape[-1], 1,), + self.W = self.add_weight((2 * input_shape[-1], 1, ), name="w", initializer=self.init, regularizer=self.W_regularizer, @@ -183,14 +183,15 @@ def call(self, x, mask=None): # now expanded_context_4d and expanded_x are of # shape (bs, time_steps, context_size, n_features) - x_full = concatenate(inputs=[expanded_x, expanded_context_4d], axis=-1) + + x_full = Concatenate(axis=-1)([expanded_x, expanded_context_4d]) out = K.dot(x_full, self.W) if self.use_bias: out = K.bias_add(out, self.b) - out = K.softmax(out) - out = multiply(inputs=[out, expanded_x]) + out = Activation('softmax')(out) + out = Multiply()([out, expanded_x]) out = Lambda(lambda x: K.sum(x, axis=1))(out) return out From dbb5f970d77535b99ba1069718951961759e852e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 11:36:50 +0300 Subject: [PATCH 303/616] feat: reports from training with valid and test results --- deeppavlov/core/commands/train.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index 6e3c0a0561..10ff55130c 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -166,6 +166,7 @@ def train_evaluate_model_from_config(config: [str, Path, dict], to_train=True, t log.warning('Nothing to train') if train_config['validate_best'] or train_config['test_best']: + all_reports = [] # try: # model_config['load_path'] = model_config['save_path'] # except KeyError: @@ -180,6 +181,7 @@ def train_evaluate_model_from_config(config: [str, Path, dict], to_train=True, t } print(json.dumps(report, ensure_ascii=False)) + all_reports.append(report) if train_config['test_best']: report = { @@ -188,6 +190,9 @@ def train_evaluate_model_from_config(config: [str, Path, dict], to_train=True, t } print(json.dumps(report, ensure_ascii=False)) + all_reports.append(report) + + return all_reports def _test_model(model: Component, metrics_functions: List[Tuple[str, Callable]], From e84f15c51a368a68805cb1f8c09c94d80f92c95e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 16:38:21 +0300 Subject: [PATCH 304/616] feat: log loss for classification --- deeppavlov/__init__.py | 1 + deeppavlov/metrics/log_loss.py | 29 +++++++++++++++++++++++++++++ 2 files changed, 30 insertions(+) create mode 100644 deeppavlov/metrics/log_loss.py diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index e4b14682a3..5a714abdfd 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -104,6 +104,7 @@ import deeppavlov.metrics.squad_metrics import deeppavlov.metrics.roc_auc_score import deeppavlov.metrics.fmeasure_classification +import deeppavlov.metrics.log_loss import deeppavlov.core.common.log diff --git a/deeppavlov/metrics/log_loss.py b/deeppavlov/metrics/log_loss.py new file mode 100644 index 0000000000..071b8a53b6 --- /dev/null +++ b/deeppavlov/metrics/log_loss.py @@ -0,0 +1,29 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +from sklearn.metrics import log_loss + +from deeppavlov.core.common.metrics_registry import register_metric +from deeppavlov.models.classifiers.intents.utils import labels2onehot + + +@register_metric('classification_log_loss') +def classification_log_loss(y_true, y_predicted): + classes = y_predicted[0][2] + y_true_one_hot = labels2onehot(y_true, classes) + y_pred_probas = [y_predicted[i][1] for i in range(len(y_predicted))] + + return log_loss(y_true_one_hot, y_pred_probas) From 054af4fb2263962099f7e73935e9b03024f307c3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 16:38:34 +0300 Subject: [PATCH 305/616] feat: working evolution --- .../evolution/basic_intents_snips.json | 28 ++++---- .../models/evolution/check_binary_mask.py | 5 ++ .../evolution/evolution_intent_model.py | 2 +- .../neuroevolution_param_generator.py | 65 ++++++++++--------- deeppavlov/models/evolution/run_evolution.py | 36 ++++++---- .../models/evolution/train_phenotype.py | 30 +++++---- deeppavlov/models/evolution/utils.py | 13 ++++ 7 files changed, 109 insertions(+), 70 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json index 799036bc63..ec81aa5c78 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -28,14 +28,14 @@ "load_path": "vocabs/snips_classes.dict" }, { - "id": "fasttext_embedder", + "id": "my_embedder", "name": "fasttext", "save_path": "embeddings/dstc2_fastText_model.bin", "load_path": "embeddings/dstc2_fastText_model.bin", "dim": 100 }, { - "id": "nltk_tokenizer", + "id": "my_tokenizer", "name": "nltk_tokenizer", "tokenizer": "wordpunct_tokenize" }, @@ -47,7 +47,9 @@ "y" ], "out": [ - "y_predicted" + "y_labels", + "y_probas", + "y_classes" ], "main": true, "name": "evolution_classification_model", @@ -158,19 +160,21 @@ "loss": "binary_crossentropy", "text_size": 15, "model_name": "evolution_classification_model", - "embedder": "#fasttext_embedder", - "tokenizer": "#nltk_tokenizer" + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" } ], "out": [ - "y_predicted" + "y_labels", + "y_probas", + "y_classes" ] }, "train": { "epochs": { "range": [ - 10, - 11 + 5, + 6 ], "discrete": true }, @@ -181,10 +185,12 @@ ], "discrete": true }, + "metric_optimization": "minimize", "metrics": [ - "sets_accuracy", - "roc_auc_score", - "f1_classification" + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" ], "validation_patience": 5, "val_every_n_epochs": 5, diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index 583532f88e..f2cf543c54 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -53,6 +53,11 @@ def get_binary_mask_from_digraph(nodes, directed_graph): def check_and_correct_binary_mask(nodes, binary_mask_): binary_mask = deepcopy(binary_mask_) + + # if binary mask if empty, add one dense layer + if np.sum(binary_mask) == 0: + binary_mask[0, 0] = 1 + directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) sources, sinks, _ = find_sources_and_sinks(directed_graph) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 97ee098b65..021af39c50 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -201,4 +201,4 @@ def save(self, fname=None): self.opt["binary_mask"] = self.opt["binary_mask"].tolist() save_json(self.opt, opt_path) - return True \ No newline at end of file + return True diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index f897cf0ffb..72980305b1 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -6,13 +6,14 @@ from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ number_to_type_layer, get_graph_and_plot from deeppavlov.core.common.file import save_json, read_json +from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe + -# TODO: -# if structure of config has been changed, # please, make sure that # `config["chainer"]["pipe"]` is a list of models one of which is a model to be evolved, # otherwise, in the whole class change `config["chainer"]["pipe"]` to new path + class NetworkAndParamsEvolution: """ Class performs full evolutionary process (task scores -> max): @@ -32,6 +33,7 @@ def __init__(self, n_layers, n_types, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", seed=None, + start_with_one_neuron=False, **kwargs): """ Initialize evolution with random population @@ -55,10 +57,11 @@ def __init__(self, n_layers, n_types, self.n_layers = n_layers self.total_nodes = self.n_types * self.n_layers self.binary_mask_template = np.zeros((self.total_nodes, self.total_nodes)) + self.start_with_one_neuron = start_with_one_neuron self.basic_config = deepcopy(kwargs) - self.model_to_evolve_index = self._find_model_to_evolve_index_in_pipe(self.basic_config["chainer"]["pipe"], - key_model_to_evolve) + self.model_to_evolve_index = find_index_of_dict_with_key_in_pipe(self.basic_config["chainer"]["pipe"], + key_model_to_evolve) self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) self.train_params = deepcopy(self.basic_config.get("train")) @@ -98,17 +101,6 @@ def __init__(self, n_layers, n_types, np.random.seed(seed) return None - def _find_model_to_evolve_index_in_pipe(self, pipe, key): - for element_id, element in enumerate(pipe): - if self._check_if_model_to_evolve(element, key): - return element_id - - def _check_if_model_to_evolve(self, model, key): - if key in model.keys(): - return True - else: - return False - def _insert_dict_into_model_params(self, params, model_index, dict_to_insert): params_copy = deepcopy(params) params_copy["chainer"]["pipe"].insert(model_index, dict_to_insert) @@ -198,10 +190,12 @@ def first_generation(self, iter=0): **params_for_search, **layers_params} # add binary_mask intialization - population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, self.sample_binary_mask()) - # get_graph_and_plot(self.nodes, population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"], - # self.n_types, path=None) + if self.start_with_one_neuron: + population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, self.sample_one_neuron_binary_mask()) + else: + population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ + check_and_correct_binary_mask(self.nodes, self.sample_binary_mask()) # exchange train params from basic config to sampled train params population[-1]["train"] = {**train_params, @@ -244,8 +238,12 @@ def next_generation(self, generation, scores, iter, offsprings = self.crossover(selected_individuals, p_crossover=p_crossover, crossover_power=crossover_power) next = self.mutation(offsprings, p_mutation=p_mutation, mutation_power=mutation_power) for i in range(self.population_size): - next[i]["model_path"] = str(Path(self.params["model_path"]).joinpath( - "population_" + str(iter)).joinpath(next[i]["model_name"] + "_" + str(i))) + next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ + str(Path(self.params["save_path"]).joinpath("population_" + str(iter)).joinpath( + self.params["model_name"] + "_" + str(i))) + next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ + str(Path(self.params["load_path"]).joinpath("population_" + str(iter)).joinpath( + self.params["model_name"] + "_" + str(i))) return next @@ -361,19 +359,19 @@ def crossover(self, population, p_crossover, crossover_power): for j in range(self.total_nodes * self.total_nodes - binary_mask_part): node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ - parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ - parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ + parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ + parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] for j in range(self.total_nodes * self.total_nodes - binary_mask_part, self.total_nodes * self.total_nodes): node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ - parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] =\ - parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x][node_y] + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ + parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ + parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ check_and_correct_binary_mask(self.nodes, @@ -424,7 +422,8 @@ def mutation(self, population, p_mutation, mutation_power): # mutation of binary mask if self.decision(p_mutation): mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, + check_and_correct_binary_mask( + self.nodes, np.minimum(1, np.maximum(0, individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] + @@ -532,3 +531,9 @@ def sample_binary_mask(self): mask[ones] = 1 # returns NUMPY 2D ARRAY! return mask.reshape((self.total_nodes, self.total_nodes)) + + def sample_one_neuron_binary_mask(self): + mask = np.zeros((self.total_nodes * self.total_nodes)) + mask[0] = 1 # make sure that Dense is the first in the config + + return mask.reshape((self.total_nodes, self.total_nodes)) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index dfdadf7a38..10a2ca6579 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -36,6 +36,8 @@ def score_population(population, population_size, result_file): population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"].tolist() with open(f_name, 'w') as outfile: json.dump(population[i], outfile) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ + np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], @@ -51,11 +53,11 @@ def score_population(population, population_size, result_file): for i in range(population_size): val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).joinpath("valid_results.txt"))) - result_table = pd.DataFrame({"loss": [val_results[0]], - "accuracy": [val_results[1]], - "fmeasure": [val_results[2]], - "roc_auc_score": [val_results[3]], + "save_path"]).parent.joinpath("valid_results.txt"))) + result_table = pd.DataFrame({"classification_log_loss": [val_results[0]], + "classification_accuracy": [val_results[1]], + "classification_f1": [val_results[2]], + "classification_roc_auc": [val_results[3]], "params": [population[i]]}) result_table.loc[:, order].to_csv(result_file, index=False, sep='\t', mode='a', header=None) population_losses.append(val_results[0]) @@ -74,6 +76,7 @@ def score_population(population, population_size, result_file): parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) +parser.add_argument('--one_neuron_init', help='Please, enter number of types of layers', default=0) args = parser.parse_args() @@ -83,32 +86,37 @@ def score_population(population, population_size, result_file): gpus = [int(gpu) for gpu in args.gpus.split(",")] N_LAYERS = int(args.n_layers) N_TYPES = int(args.n_types) +ONE_NEURON_INIT = bool(int(args.one_neuron_init)) with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) print("Given basic params: {}\n".format(basic_params)) -try: - print(basic_params["chainer"]["pipe"][3]) - Path(basic_params["chainer"]["pipe"][3]["save_path"]).mkdir(parents=True) -except FileExistsError: - pass +Path(basic_params["chainer"]["pipe"][3]["save_path"]).mkdir(parents=True, exist_ok=True) + # Result table -order = ["loss", "accuracy", "fmeasure", "roc_auc_score", "params"] +order = ["classification_log_loss", "classification_accuracy", + "classification_f1", "classification_roc_auc", "params"] result_file = Path(basic_params["chainer"]["pipe"][3]["save_path"]).joinpath("result_table.csv") -result_table = pd.DataFrame({"loss": [], "accuracy": [], "fmeasure": [], "roc_auc_score": [], "params": []}) +result_table = pd.DataFrame({"loss": [], + "classification_accuracy": [], + "classification_f1": [], + "classification_roc_auc": [], + "params": []}) result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, - p_crossover=0.1, crossover_power=0.5, + p_crossover=1., crossover_power=0.5, p_mutation=0.5, mutation_power=0.1, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", - seed=None, **basic_params) + seed=None, + start_with_one_neuron=ONE_NEURON_INIT, + **basic_params) print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py index d9ebadb048..b693f04f54 100644 --- a/deeppavlov/models/evolution/train_phenotype.py +++ b/deeppavlov/models/evolution/train_phenotype.py @@ -15,23 +15,25 @@ """ import numpy as np import sys +from pathlib import Path -from deeppavlov.core.commands.train import train_model_from_config, train_evaluate_model_from_config +from deeppavlov.core.commands.train import train_model_from_config from deeppavlov.core.common.file import read_json, save_json +from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe + config_path = sys.argv[1] print("TRAIN PHENOTYPE") -report = train_model_from_config(config_path, is_trained=False) - -# train_model_from_config(config_path) - -# config = read_json(config_path) -# -# model = build_model_from_config(config, mode='infer', load_trained=True) -# -# test_model_on_data(config_path, data) -# -# val_metrics_values = np.mean(np.array(val_metrics_values), axis=0) -# -# np.savetxt(fname=Path(path_to_models).joinpath("valid_results.txt"), X=val_metrics_values) +reports = train_model_from_config(config_path) +print(reports) + +metrics = dict(reports[0]["valid"]["metrics"]) +val_metrics_values = np.array(list(metrics.values())).reshape(-1) + +config = read_json(config_path) +model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], + key="to_evolve") +np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ + "save_path"]).parent.joinpath("valid_results.txt")), + X=val_metrics_values) diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index 479660ecd8..15319b3f4d 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -236,3 +236,16 @@ def expand_tile_batch_size(memory, context): if axis == 0: expanded = K.reshape(context, shape=((1,) + K.int_shape(context))) return K.tile(expanded, repetitions) + + +def find_index_of_dict_with_key_in_pipe(pipe, key): + for element_id, element in enumerate(pipe): + if check_whether_key_in_dict(element, key): + return element_id + + +def check_whether_key_in_dict(model, key): + if key in model.keys(): + return True + else: + return False From 8a12d2f0ae38ee26c539a278775842ff99301a98 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 17:01:12 +0300 Subject: [PATCH 306/616] fix: convert binary_mask to list and to array --- deeppavlov/models/evolution/run_evolution.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 10a2ca6579..8fa8ce43ad 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -36,8 +36,6 @@ def score_population(population, population_size, result_file): population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"].tolist() with open(f_name, 'w') as outfile: json.dump(population[i], outfile) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ - np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], @@ -65,6 +63,9 @@ def score_population(population, population_size, result_file): population_fmeasures.append(val_results[2]) population_roc_auc_scores.append(val_results[3]) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ + np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) + return population_roc_auc_scores From 92fbcb0b85624daf0903d724ea3738e2b7162b04 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 18:11:56 +0300 Subject: [PATCH 307/616] feat: basic configs for gpu --- .../evolution/basic_intents_snips.json | 15 +- deeppavlov/configs/evolution/basic_snli.json | 206 ++++++++++++++++++ 2 files changed, 213 insertions(+), 8 deletions(-) create mode 100644 deeppavlov/configs/evolution/basic_snli.json diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_intents_snips.json index ec81aa5c78..bcef56021d 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_intents_snips.json @@ -3,8 +3,7 @@ "name": "basic_classification_reader", "x": "text", "y": "intents", - "data_path": "snips", - "url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv" + "data_path": "/home/dilyara.baymurzina/evolution_data/snips_data" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -24,14 +23,14 @@ "y" ], "level": "token", - "save_path": "vocabs/snips_classes.dict", - "load_path": "vocabs/snips_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict" }, { "id": "my_embedder", "name": "fasttext", - "save_path": "embeddings/dstc2_fastText_model.bin", - "load_path": "embeddings/dstc2_fastText_model.bin", + "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", "dim": 100 }, { @@ -53,8 +52,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara/data/models/deeppavlov_evolution/classification/intents_snips", - "load_path": "/home/dilyara/data/models/deeppavlov_evolution/classification/intents_snips", + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/start_with_one_neuron", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/start_with_one_neuron", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { diff --git a/deeppavlov/configs/evolution/basic_snli.json b/deeppavlov/configs/evolution/basic_snli.json new file mode 100644 index 0000000000..a12251153a --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snli.json @@ -0,0 +1,206 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "sentence1", + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_one_neuron", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_one_neuron", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "Attention": { + "context_length": { + "range": [ + 50, + 200 + ], + "discrete": true + } + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ] + }, + "train": { + "epochs": { + "range": [ + 5, + 6 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} \ No newline at end of file From 03ce7bf407df13c458f783998be2948e8f1946db Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 24 Apr 2018 18:50:51 +0300 Subject: [PATCH 308/616] some changes --- deeppavlov/models/evolution/evolution_intent_model.py | 3 ++- deeppavlov/models/evolution/run_evolution.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 021af39c50..8a6176bcba 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -41,7 +41,7 @@ from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer from deeppavlov.core.common.log import get_logger from deeppavlov.models.evolution.check_binary_mask import number_to_type_layer, \ - find_sources_and_sinks, get_digraph_from_binary_mask + find_sources_and_sinks, get_digraph_from_binary_mask, get_graph_and_plot from deeppavlov.models.evolution.utils import Attention, expand_tile from deeppavlov.core.common.file import save_json, read_json @@ -55,6 +55,7 @@ class KerasEvolutionClassificationModel(KerasIntentModel): def __init__(self, **kwargs): super().__init__(**kwargs) self.opt["binary_mask"] = np.array(self.opt["binary_mask"]) + get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], path=str(self.save_path.resolve())) def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): if inp is None: diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 8fa8ce43ad..7ade9ebc9d 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -95,7 +95,8 @@ def score_population(population, population_size, result_file): print("Given basic params: {}\n".format(basic_params)) Path(basic_params["chainer"]["pipe"][3]["save_path"]).mkdir(parents=True, exist_ok=True) - +basic_params["chainer"]["pipe"][3]["n_types"] = N_TYPES +basic_params["chainer"]["pipe"][3]["n_layers"] = N_LAYERS # Result table order = ["classification_log_loss", "classification_accuracy", From 39baf6fec5daf3a31a45ab86e28dbf682199a184 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 25 Apr 2018 10:57:26 +0300 Subject: [PATCH 309/616] chore: new configs --- ....json => basic_snips_one_neuron_init.json} | 19 +- .../evolution/basic_snips_random_init.json | 197 ++++++++++++++++++ ...i.json => basic_snli_one_neuron_init.json} | 17 +- .../evolution/basic_snli_random_init.json | 197 ++++++++++++++++++ .../models/evolution/check_binary_mask.py | 12 +- .../evolution/evolution_intent_model.py | 11 +- .../neuroevolution_param_generator.py | 2 +- 7 files changed, 423 insertions(+), 32 deletions(-) rename deeppavlov/configs/evolution/{basic_intents_snips.json => basic_snips_one_neuron_init.json} (93%) create mode 100644 deeppavlov/configs/evolution/basic_snips_random_init.json rename deeppavlov/configs/evolution/{basic_snli.json => basic_snli_one_neuron_init.json} (93%) create mode 100644 deeppavlov/configs/evolution/basic_snli_random_init.json diff --git a/deeppavlov/configs/evolution/basic_intents_snips.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json similarity index 93% rename from deeppavlov/configs/evolution/basic_intents_snips.json rename to deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index bcef56021d..7760ac0f6d 100644 --- a/deeppavlov/configs/evolution/basic_intents_snips.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -52,8 +52,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/start_with_one_neuron", - "load_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/start_with_one_neuron", + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/one_neuron_init", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/one_neuron_init", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -126,15 +126,6 @@ "discrete": true }, "padding": "same" - }, - "Attention": { - "context_length": { - "range": [ - 50, - 200 - ], - "discrete": true - } } }, "confident_threshold": { @@ -172,8 +163,8 @@ "train": { "epochs": { "range": [ - 5, - 6 + 100, + 1000 ], "discrete": true }, @@ -203,4 +194,4 @@ "telegram_utils": "IntentModel" } } -} \ No newline at end of file +} diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json new file mode 100644 index 0000000000..a3c21e36dc --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -0,0 +1,197 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "intents", + "data_path": "/home/dilyara.baymurzina/evolution_data/snips_data" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/start_with_random", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/start_with_random", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ] + }, + "train": { + "epochs": { + "range": [ + 100, + 1000 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} diff --git a/deeppavlov/configs/evolution/basic_snli.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json similarity index 93% rename from deeppavlov/configs/evolution/basic_snli.json rename to deeppavlov/configs/evolution/basic_snli_one_neuron_init.json index a12251153a..d3c03b0365 100644 --- a/deeppavlov/configs/evolution/basic_snli.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json @@ -52,8 +52,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_one_neuron", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_one_neuron", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -126,15 +126,6 @@ "discrete": true }, "padding": "same" - }, - "Attention": { - "context_length": { - "range": [ - 50, - 200 - ], - "discrete": true - } } }, "confident_threshold": { @@ -172,8 +163,8 @@ "train": { "epochs": { "range": [ - 5, - 6 + 100, + 1000 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json new file mode 100644 index 0000000000..32c93325cd --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -0,0 +1,197 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "sentence1", + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ] + }, + "train": { + "epochs": { + "range": [ + 100, + 1000 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} \ No newline at end of file diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index f2cf543c54..948f3ffe8d 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -4,8 +4,10 @@ import datetime import time from pathlib import Path -import matplotlib.pyplot as plt +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt def number_to_type_layer(node_id, n_types): # return node_layer, node_type @@ -100,6 +102,10 @@ def check_and_correct_binary_mask(nodes, binary_mask_): def get_graph_and_plot(nodes, binary_mask, n_types, path=None): + nodes_int = {} + for i in range(len(nodes)): + nodes_int[i] = nodes[str(i)] + total_nodes = len(nodes) dg = get_digraph_from_binary_mask(nodes, binary_mask) @@ -117,11 +123,11 @@ def get_graph_and_plot(nodes, binary_mask, n_types, path=None): val_map[i] = 0. plt.figure(figsize=(12, 12)) - values = [val_map.get(node, 0.25) for node in nodes] + values = [val_map.get(node, 0.25) for node in nodes_int] nx.draw(dg, pos, cmap=plt.get_cmap('jet'), node_color=values, node_size=7000, alpha=0.3) - nx.draw_networkx_labels(dg, pos, nodes, font_size=18) + nx.draw_networkx_labels(dg, pos, nodes_int, font_size=18) if path is None: path = "./" diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 8a6176bcba..d0f5bf08fc 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -55,7 +55,8 @@ class KerasEvolutionClassificationModel(KerasIntentModel): def __init__(self, **kwargs): super().__init__(**kwargs) self.opt["binary_mask"] = np.array(self.opt["binary_mask"]) - get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], path=str(self.save_path.resolve())) + get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], + path=str(self.save_path.resolve().parent)) def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): if inp is None: @@ -116,6 +117,14 @@ def evolution_classification_model(self, params): """ inp = Input(shape=(params['text_size'], params['embedding_size'])) + if np.sum(params["binary_mask"]) == 0: + output = Dense(1, activation=None)(inp) + output = GlobalMaxPooling1D()(output) + output = Dense(self.n_classes, activation=None)(output) + act_output = Activation('sigmoid')(output) + model = Model(inputs=inp, outputs=act_output) + return model + dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) sources, sinks, isolates = find_sources_and_sinks(dg) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 72980305b1..0694d0d949 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -534,6 +534,6 @@ def sample_binary_mask(self): def sample_one_neuron_binary_mask(self): mask = np.zeros((self.total_nodes * self.total_nodes)) - mask[0] = 1 # make sure that Dense is the first in the config + # mask[0] = 1 # make sure that Dense is the first in the config return mask.reshape((self.total_nodes, self.total_nodes)) From dfdb6f83395ae945a2a22beaba743b94fa3e8e8d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 25 Apr 2018 15:26:32 +0300 Subject: [PATCH 310/616] chore: change evolution parameters --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 2 +- deeppavlov/models/evolution/run_evolution.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 0694d0d949..ceb67c1381 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -526,7 +526,7 @@ def sample_binary_mask(self): # return np.random.randint(0, high=2, size=self.binary_mask_template.shape).tolist() # return (1 * (np.log(np.random.random(size=self.binary_mask_template.shape)) > -0.2)).tolist() ones = np.random.choice(self.total_nodes * self.total_nodes, - size=max(1, int(np.random.random() * self.total_nodes))) + size=max(1, int(0.5 * np.random.random() * self.total_nodes))) mask = np.zeros((self.total_nodes * self.total_nodes)) mask[ones] = 1 # returns NUMPY 2D ARRAY! diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 7ade9ebc9d..8d50046454 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -112,7 +112,7 @@ def score_population(population, population_size, result_file): # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, - p_crossover=1., crossover_power=0.5, + p_crossover=0.1, crossover_power=0.5, p_mutation=0.5, mutation_power=0.1, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", From 7bd406b4444694ca637a986aa65577e60d79577d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 25 Apr 2018 16:47:10 +0300 Subject: [PATCH 311/616] Merge branch 'feature/network_evolution' of https://github.com/dilyararimovna/deeppavlov_evolution into feature/network_evolution # Conflicts: # README.md # deeppavlov/__init__.py # deeppavlov/configs/go_bot/gobot_dstc2.json # deeppavlov/configs/go_bot/gobot_dstc2_all.json # deeppavlov/configs/intents/intents_dstc2.json # deeppavlov/configs/intents/intents_sample_csv.json # deeppavlov/configs/intents/intents_sample_json.json # deeppavlov/configs/intents/intents_snips.json # deeppavlov/configs/ner/ner_conll2003.json # deeppavlov/configs/ner/ner_ontonotes_emb.json # deeppavlov/configs/ranking/insurance_config.json # deeppavlov/configs/seq2seq_go_bot/bot_kvret.json # deeppavlov/configs/seq2seq_go_bot/bot_kvret_infer.json # deeppavlov/configs/squad/squad.json # deeppavlov/core/commands/train.py # deeppavlov/core/data/data_learning_iterator.py # deeppavlov/core/data/dataset.py # deeppavlov/core/data/dataset_iterator.py # deeppavlov/core/data/urls.py # deeppavlov/dataset_iterators/basic_classification_iterator.py # deeppavlov/dataset_iterators/dialog_iterator.py # deeppavlov/dataset_iterators/dstc2_intents_iterator.py # deeppavlov/dataset_iterators/dstc2_ner_iterator.py # deeppavlov/dataset_iterators/kvret_dialog_iterator.py # deeppavlov/dataset_iterators/ranking_iterator.py # deeppavlov/dataset_iterators/squad_iterator.py # deeppavlov/dataset_iterators/typos_iterator.py # deeppavlov/models/classifiers/intents/intent_model.py # deeppavlov/models/embedders/fasttext_embedder.py # deeppavlov/models/squad/squad.py # deeppavlov/models/tokenizers/spacy_tokenizer.py # deeppavlov/run_model.py # deeppavlov/skills/seq2seq_go_bot/kb.py # requirements.txt # tests/test_quick_start.py # utils/telegram_utils/models_info.json --- .../configs/evolution/basic_config_local.json | 153 ++++++++++++++++++ 1 file changed, 153 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_config_local.json diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json new file mode 100644 index 0000000000..9291e0ceaf --- /dev/null +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -0,0 +1,153 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "intents", + "data_path": "/home/dilyara/data/data_files/snips/snips_dataset" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara/data/data_files/snips/snips_dataset/snips_classes.dict", + "load_path": "/home/dilyara/data/data_files/snips/snips_dataset/snips_classes.dict" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara/data/data_files/embeddings/reddit/wordpunct_tok_reddit_comments_2017_11_100.bin", + "load_path": "/home/dilyara/data/data_files/embeddings/reddit/wordpunct_tok_reddit_comments_2017_11_100.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara/data/models/evolution_data/snips_classification/start_with_random", + "load_path": "/home/dilyara/data/models/evolution_data/snips_classification/start_with_random", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Attention": { + "context_length": { + "range": [ + 50, + 200 + ], + "discrete": true + } + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas", + "y_classes" + ] + }, + "train": { + "epochs": { + "range": [ + 100, + 1000 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From 4b052957bbabd022e7e3272444be136cfa08c5fc Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 27 Apr 2018 10:30:38 +0300 Subject: [PATCH 312/616] Merge branch 'dev' of https://github.com/deepmipt/DeepPavlov into feature/network_evolution # Conflicts: # deeppavlov/__init__.py # deeppavlov/configs/go_bot/gobot_dstc2.json # deeppavlov/configs/go_bot/gobot_dstc2_all.json # deeppavlov/configs/intents/intents_dstc2.json # deeppavlov/configs/intents/intents_sample_csv.json # deeppavlov/configs/intents/intents_sample_json.json # deeppavlov/configs/intents/intents_snips.json # deeppavlov/metrics/roc_auc_score.py # deeppavlov/models/classifiers/intents/intent_model.py # deeppavlov/run_model.py # requirements.txt --- deeppavlov/configs/evolution/basic_config_local.json | 6 ++---- .../configs/evolution/basic_snips_one_neuron_init.json | 6 ++---- deeppavlov/configs/evolution/basic_snips_random_init.json | 6 ++---- .../configs/evolution/basic_snli_one_neuron_init.json | 6 ++---- deeppavlov/configs/evolution/basic_snli_random_init.json | 6 ++---- deeppavlov/models/evolution/evolution_intent_model.py | 1 - 6 files changed, 10 insertions(+), 21 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 9291e0ceaf..8abdd186c6 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -47,8 +47,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ], "main": true, "name": "evolution_classification_model", @@ -112,8 +111,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ] }, "train": { diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 7760ac0f6d..4a33e7e2d5 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -47,8 +47,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ], "main": true, "name": "evolution_classification_model", @@ -156,8 +155,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ] }, "train": { diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index a3c21e36dc..c2880d18da 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -47,8 +47,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ], "main": true, "name": "evolution_classification_model", @@ -156,8 +155,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ] }, "train": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json index d3c03b0365..cedc82d74e 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json @@ -47,8 +47,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ], "main": true, "name": "evolution_classification_model", @@ -156,8 +155,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ] }, "train": { diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index 32c93325cd..ffd481525b 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -47,8 +47,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ], "main": true, "name": "evolution_classification_model", @@ -156,8 +155,7 @@ ], "out": [ "y_labels", - "y_probas", - "y_classes" + "y_probas_dict" ] }, "train": { diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index d0f5bf08fc..b2f3460e6e 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -34,7 +34,6 @@ from deeppavlov.core.common.registry import register from deeppavlov.core.models.keras_model import KerasModel from deeppavlov.models.classifiers.intents.intent_model import KerasIntentModel -from deeppavlov.models.classifiers.intents import metrics as metrics_file from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder from deeppavlov.models.classifiers.intents.utils import md5_hashsum From 3f328ba02126ca80c9d92444e1759cf275d68048 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 27 Apr 2018 16:01:03 +0300 Subject: [PATCH 313/616] fix: change nodes.keys to str(i) everywhere --- .../configs/evolution/basic_config_local.json | 53 +++++++++++++++++++ .../models/evolution/check_binary_mask.py | 32 +++++------ deeppavlov/models/evolution/debug.py | 3 +- .../evolution/evolution_intent_model.py | 34 +++++++----- .../neuroevolution_param_generator.py | 20 ++++--- 5 files changed, 97 insertions(+), 45 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 8abdd186c6..b575e17072 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -73,6 +73,59 @@ "choice": true } }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, "Attention": { "context_length": { "range": [ diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index 948f3ffe8d..f3f85151cc 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -23,11 +23,11 @@ def find_sources_and_sinks(directed_graph): sinks = [] isolates = nx.isolates(directed_graph) - for i in directed_graph.nodes(): - if directed_graph.in_degree(i) == 0 and directed_graph.out_degree(i) > 0: - sources.append(i) - if directed_graph.in_degree(i) > 0 and directed_graph.out_degree(i) == 0: - sinks.append(i) + for str_id in directed_graph.nodes(): + if directed_graph.in_degree(str_id) == 0 and directed_graph.out_degree(str_id) > 0: + sources.append(str_id) + if directed_graph.in_degree(str_id) > 0 and directed_graph.out_degree(str_id) == 0: + sinks.append(str_id) return sources, sinks, isolates @@ -37,12 +37,12 @@ def get_digraph_from_binary_mask(nodes, binary_mask): total_nodes = len(nodes) for i in range(total_nodes): - directed_graph.add_node(i) + directed_graph.add_node(str(i)) for i in range(total_nodes): for j in range(total_nodes): if binary_mask[i, j] == 1: - directed_graph.add_edge(i, j) + directed_graph.add_edge(str(i), str(j)) return directed_graph @@ -56,10 +56,6 @@ def get_binary_mask_from_digraph(nodes, directed_graph): def check_and_correct_binary_mask(nodes, binary_mask_): binary_mask = deepcopy(binary_mask_) - # if binary mask if empty, add one dense layer - if np.sum(binary_mask) == 0: - binary_mask[0, 0] = 1 - directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) sources, sinks, _ = find_sources_and_sinks(directed_graph) @@ -67,8 +63,6 @@ def check_and_correct_binary_mask(nodes, binary_mask_): candidates = [] cycles = list(nx.simple_cycles(directed_graph)) n_cycles = len(cycles) - # print("Cycles: {}".format(cycles)) - # number of candidates to be the best new graph cycles_len = np.array([len(cycle) for cycle in cycles]) n_candidates = int(np.prod(cycles_len)) @@ -114,13 +108,13 @@ def get_graph_and_plot(nodes, binary_mask, n_types, path=None): sources, sinks, _ = find_sources_and_sinks(dg) for i in range(total_nodes): - pos[i] = 2. * np.array(number_to_type_layer(i, n_types))[::-1] - if i in sources: - val_map[i] = 1. - elif i in sinks: - val_map[i] = 0.5 + pos[str(i)] = 2. * np.array(number_to_type_layer(i, n_types))[::-1] + if str(i) in sources: + val_map[str(i)] = 1. + elif str(i) in sinks: + val_map[str(i)] = 0.5 else: - val_map[i] = 0. + val_map[str(i)] = 0. plt.figure(figsize=(12, 12)) values = [val_map.get(node, 0.25) for node in nodes_int] diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py index 291c7a7df4..188aad3e55 100644 --- a/deeppavlov/models/evolution/debug.py +++ b/deeppavlov/models/evolution/debug.py @@ -17,7 +17,7 @@ n_layers = 2 n_types = 7 population_size = 1 -config_path = "../../configs/evolution/basic_intents_snips.json" +config_path = "../../configs/evolution/basic_config_local.json" with open(config_path) as fin: config = json.load(fin) @@ -27,6 +27,7 @@ key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", seed=42, + start_with_one_neuron=True, **config) population = evolution.first_generation() diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index b2f3460e6e..2ffb6dcfb3 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -43,7 +43,7 @@ find_sources_and_sinks, get_digraph_from_binary_mask, get_graph_and_plot from deeppavlov.models.evolution.utils import Attention, expand_tile from deeppavlov.core.common.file import save_json, read_json - +from deeppavlov.core.layers.keras_layers import multiplicative_self_attention, additive_self_attention log = get_logger(__name__) @@ -57,9 +57,9 @@ def __init__(self, **kwargs): get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], path=str(self.save_path.resolve().parent)) - def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): + def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None): if inp is None: - input_nodes = [edge[0] for edge in dg.in_edges(node_id)] + input_nodes = [edge[0] for edge in dg.in_edges(node_str_id)] inp_list = [] for input_node in input_nodes: if len(K.int_shape(edges_outputs[input_node])) == 3: @@ -87,15 +87,21 @@ def get_node_output(self, node_id, dg, params, edges_outputs=None, inp=None): else: inp = inp_list[0] - if params[params["nodes"][str(node_id)]]["node_name"] == "BiCuDNNLSTM": - node_params = deepcopy(params[params["nodes"][str(node_id)]]) + if params[params["nodes"][node_str_id]]["node_name"] == "BiCuDNNLSTM": + node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") output_of_node = Bidirectional(CuDNNLSTM(**node_params))(inp) + elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": + node_params = deepcopy(params[params["nodes"][node_str_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + output_of_node = multiplicative_self_attention(inp, **node_params) else: - node_func = globals().get(params[params["nodes"][str(node_id)]]["node_name"], None) - node_params = deepcopy(params[params["nodes"][str(node_id)]]) + node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) + node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") @@ -136,8 +142,8 @@ def evolution_classification_model(self, params): if set(sinks).issubset(set(sum(sequence_of_nodes, []))): break next_nodes = [] - for node_id in sequence_of_nodes[-1]: - out_edges = dg.out_edges(node_id) + for node_str_id in sequence_of_nodes[-1]: + out_edges = dg.out_edges(node_str_id) for edge in out_edges: in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): @@ -146,13 +152,13 @@ def evolution_classification_model(self, params): sequence_of_nodes = sum(sequence_of_nodes, []) - for node_id in sequence_of_nodes: - if node_id in sources: - edges_outputs[node_id] = self.get_node_output(node_id, dg, params, inp=inp) - elif node_id in isolates: + for node_str_id in sequence_of_nodes: + if node_str_id in sources: + edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, inp=inp) + elif node_str_id in isolates: pass else: - edges_outputs[node_id] = self.get_node_output(node_id, dg, params, edges_outputs=edges_outputs) + edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, edges_outputs=edges_outputs) if len(sinks) == 1: output = edges_outputs[sinks[0]] diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index ceb67c1381..a3dd617d60 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -71,7 +71,7 @@ def __init__(self, n_layers, n_types, self.nodes = {} for i in range(self.total_nodes): l, t = number_to_type_layer(i, self.n_types) - self.nodes[i] = "{}_{}_{}".format(l, t, i) + self.nodes[str(i)] = "{}_{}_{}".format(l, t, i) print("___Basic config___: {}".format(self.basic_config)) print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) @@ -146,7 +146,7 @@ def initialize_layers_params(self): for node_id in range(self.total_nodes): node_layer, node_type = number_to_type_layer(node_id, self.n_types) - node_key = self.nodes[node_id] + node_key = self.nodes[str(node_id)] layers_params, layers_params_for_search, _ = self.initialize_params_in_config( self.basic_layers_params[self.node_types[node_type]]) @@ -339,16 +339,14 @@ def crossover(self, population, p_crossover, crossover_power): # exchange of nodes for j in range(self.total_nodes - nodes_part): - node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) - node_key = self.nodes[nodes_perm[j]] + node_key = self.nodes[str(nodes_perm[j])] curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) for j in range(self.total_nodes - nodes_part, self.total_nodes): - node_layer, node_type = number_to_type_layer(nodes_perm[j], self.n_types) - node_key = self.nodes[nodes_perm[j]] + node_key = self.nodes[str(nodes_perm[j])] curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) @@ -433,11 +431,11 @@ def mutation(self, population, p_mutation, mutation_power): for node_id in range(self.total_nodes): node_layer, node_type = number_to_type_layer(node_id, self.n_types) for param in self.basic_layers_params[self.node_types[node_type]]: - mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][self.nodes[node_id]][param] =\ - self.mutation_of_param(param, self.basic_layers_params[self.node_types[node_type]], - individuum["chainer"]["pipe"][self.model_to_evolve_index][ - self.nodes[node_id]][param], - p_mutation, mutation_power) + mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][self.nodes[str(node_id)]][param] \ + = self.mutation_of_param(param, self.basic_layers_params[self.node_types[node_type]], + individuum["chainer"]["pipe"][self.model_to_evolve_index][ + self.nodes[str(node_id)]][param], + p_mutation, mutation_power) mutated.append(mutated_individuum) return mutated From a299a33fb78ea697775d89e4f0fa652879aff2c2 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 27 Apr 2018 18:21:47 +0300 Subject: [PATCH 314/616] feat: new mult attention add in config --- .../configs/evolution/basic_config_local.json | 17 ++++++++++++- .../basic_snips_one_neuron_init.json | 24 +++++++++++++++++++ .../evolution/basic_snips_random_init.json | 24 +++++++++++++++++++ .../evolution/basic_snli_one_neuron_init.json | 24 +++++++++++++++++++ .../evolution/basic_snli_random_init.json | 24 +++++++++++++++++++ .../evolution/evolution_intent_model.py | 4 ++-- .../neuroevolution_param_generator.py | 3 +-- 7 files changed, 115 insertions(+), 5 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index b575e17072..6c776f4b9f 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -127,12 +127,27 @@ "padding": "same" }, "Attention": { - "context_length": { + "n_hidden": { "range": [ 50, 200 ], "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true } } }, diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 4a33e7e2d5..b0f3acafb7 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -125,6 +125,30 @@ "discrete": true }, "padding": "same" + }, + "Attention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } } }, "confident_threshold": { diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index c2880d18da..945feba6b6 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -125,6 +125,30 @@ "discrete": true }, "padding": "same" + }, + "Attention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } } }, "confident_threshold": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json index cedc82d74e..15763d78b8 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json @@ -125,6 +125,30 @@ "discrete": true }, "padding": "same" + }, + "Attention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } } }, "confident_threshold": { diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index ffd481525b..b4822e829a 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -125,6 +125,30 @@ "discrete": true }, "padding": "same" + }, + "Attention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } } }, "confident_threshold": { diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 2ffb6dcfb3..0155d5408b 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -43,7 +43,7 @@ find_sources_and_sinks, get_digraph_from_binary_mask, get_graph_and_plot from deeppavlov.models.evolution.utils import Attention, expand_tile from deeppavlov.core.common.file import save_json, read_json -from deeppavlov.core.layers.keras_layers import multiplicative_self_attention, additive_self_attention +from deeppavlov.core.layers.keras_layers import multiplicative_self_attention log = get_logger(__name__) @@ -108,7 +108,7 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) if callable(node_func): output_of_node = node_func(**node_params)(inp) else: - raise AttributeError("Node {} is not defined correctly".format(node_id)) + raise AttributeError("Node {} is not defined correctly".format(node_str_id)) return output_of_node def evolution_classification_model(self, params): diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index a3dd617d60..0bc176a30b 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -4,8 +4,7 @@ import json from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ - number_to_type_layer, get_graph_and_plot -from deeppavlov.core.common.file import save_json, read_json + number_to_type_layer from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe From 3573e6a69fc4a00c7160fae993ba608d8cf9d75d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 28 Apr 2018 01:00:57 +0300 Subject: [PATCH 315/616] fix: check binary mask --- deeppavlov/models/evolution/check_binary_mask.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index f3f85151cc..22b8ccb60b 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -49,7 +49,7 @@ def get_digraph_from_binary_mask(nodes, binary_mask): def get_binary_mask_from_digraph(nodes, directed_graph): binary_mask = np.zeros((len(nodes), len(nodes))) for edge in directed_graph.edges(): - binary_mask[edge[0], edge[1]] = 1 + binary_mask[int(edge[0]), int(edge[1])] = 1 return binary_mask From 61e4c8e00840446f827077fe3d6583d3a5b11641 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 28 Apr 2018 01:04:41 +0300 Subject: [PATCH 316/616] fix: configs attention --- deeppavlov/configs/evolution/basic_config_local.json | 3 +-- deeppavlov/configs/evolution/basic_snips_one_neuron_init.json | 2 +- deeppavlov/configs/evolution/basic_snips_random_init.json | 2 +- deeppavlov/configs/evolution/basic_snli_one_neuron_init.json | 2 +- deeppavlov/configs/evolution/basic_snli_random_init.json | 2 +- 5 files changed, 5 insertions(+), 6 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 6c776f4b9f..07087e13be 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -126,7 +126,7 @@ }, "padding": "same" }, - "Attention": { + "SelfMultiplicativeAttention": { "n_hidden": { "range": [ 50, @@ -149,7 +149,6 @@ ], "choice": true } - } }, "confident_threshold": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index b0f3acafb7..db9e709b3d 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -126,7 +126,7 @@ }, "padding": "same" }, - "Attention": { + "SelfMultiplicativeAttention": { "n_hidden": { "range": [ 50, diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index 945feba6b6..f44df8e830 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -126,7 +126,7 @@ }, "padding": "same" }, - "Attention": { + "SelfMultiplicativeAttention": { "n_hidden": { "range": [ 50, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json index 15763d78b8..cc6910cefd 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json @@ -126,7 +126,7 @@ }, "padding": "same" }, - "Attention": { + "SelfMultiplicativeAttention": { "n_hidden": { "range": [ 50, diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index b4822e829a..d5e70adb74 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -126,7 +126,7 @@ }, "padding": "same" }, - "Attention": { + "SelfMultiplicativeAttention": { "n_hidden": { "range": [ 50, From bcc651025298ca7345872323e0829dba40715082 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 28 Apr 2018 01:17:52 +0300 Subject: [PATCH 317/616] fix: log_loss --- deeppavlov/metrics/log_loss.py | 4 ++-- deeppavlov/models/evolution/check_binary_mask.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/deeppavlov/metrics/log_loss.py b/deeppavlov/metrics/log_loss.py index 071b8a53b6..398cf99c32 100644 --- a/deeppavlov/metrics/log_loss.py +++ b/deeppavlov/metrics/log_loss.py @@ -22,8 +22,8 @@ @register_metric('classification_log_loss') def classification_log_loss(y_true, y_predicted): - classes = y_predicted[0][2] + classes = np.array(list(y_predicted[0][1].keys())) y_true_one_hot = labels2onehot(y_true, classes) - y_pred_probas = [y_predicted[i][1] for i in range(len(y_predicted))] + y_pred_probas = [list(y_predicted[i][1].values()) for i in range(len(y_predicted))] return log_loss(y_true_one_hot, y_pred_probas) diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py index 22b8ccb60b..5024cd8720 100644 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ b/deeppavlov/models/evolution/check_binary_mask.py @@ -121,7 +121,7 @@ def get_graph_and_plot(nodes, binary_mask, n_types, path=None): nx.draw(dg, pos, cmap=plt.get_cmap('jet'), node_color=values, node_size=7000, alpha=0.3) - nx.draw_networkx_labels(dg, pos, nodes_int, font_size=18) + nx.draw_networkx_labels(dg, pos, nodes, font_size=18) if path is None: path = "./" From 45fb0e28439e5601d96ca367cbcf7924c260322a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 28 Apr 2018 01:25:48 +0300 Subject: [PATCH 318/616] fix: log loss --- deeppavlov/metrics/log_loss.py | 1 + 1 file changed, 1 insertion(+) diff --git a/deeppavlov/metrics/log_loss.py b/deeppavlov/metrics/log_loss.py index 398cf99c32..368357786a 100644 --- a/deeppavlov/metrics/log_loss.py +++ b/deeppavlov/metrics/log_loss.py @@ -15,6 +15,7 @@ """ from sklearn.metrics import log_loss +import numpy as np from deeppavlov.core.common.metrics_registry import register_metric from deeppavlov.models.classifiers.intents.utils import labels2onehot From 0e70f91d3ebd47d4cc3da0abd49acef65c077405 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 1 May 2018 23:13:27 +0300 Subject: [PATCH 319/616] fix: commit for snli, add preprocessors --- .../evolution/basic_snips_one_neuron_init.json | 11 ++++++++++- .../configs/evolution/basic_snips_random_init.json | 11 ++++++++++- .../evolution/basic_snli_one_neuron_init.json | 13 +++++++++++-- .../configs/evolution/basic_snli_random_init.json | 13 +++++++++++-- deeppavlov/models/evolution/run_evolution.py | 8 ++++---- 5 files changed, 46 insertions(+), 10 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index db9e709b3d..e0d4b95e78 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -26,6 +26,15 @@ "save_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict", "load_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict" }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, { "id": "my_embedder", "name": "fasttext", @@ -40,7 +49,7 @@ }, { "in": [ - "x" + "x_lower" ], "in_y": [ "y" diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index f44df8e830..ba66d8d042 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -26,6 +26,15 @@ "save_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict", "load_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict" }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, { "id": "my_embedder", "name": "fasttext", @@ -40,7 +49,7 @@ }, { "in": [ - "x" + "x_lower" ], "in_y": [ "y" diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json index cc6910cefd..fd566b3c64 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json @@ -1,7 +1,7 @@ { "dataset_reader": { "name": "basic_classification_reader", - "x": "sentence1", + "x": "text", "y": "gold_label", "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data" }, @@ -26,6 +26,15 @@ "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, { "id": "my_embedder", "name": "fasttext", @@ -40,7 +49,7 @@ }, { "in": [ - "x" + "x_lower" ], "in_y": [ "y" diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index d5e70adb74..f86582ce1a 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -1,7 +1,7 @@ { "dataset_reader": { "name": "basic_classification_reader", - "x": "sentence1", + "x": "text", "y": "gold_label", "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data" }, @@ -26,6 +26,15 @@ "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, { "id": "my_embedder", "name": "fasttext", @@ -40,7 +49,7 @@ }, { "in": [ - "x" + "x_lower" ], "in_y": [ "y" diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 8d50046454..d30a600906 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -94,14 +94,14 @@ def score_population(population, population_size, result_file): print("Given basic params: {}\n".format(basic_params)) -Path(basic_params["chainer"]["pipe"][3]["save_path"]).mkdir(parents=True, exist_ok=True) -basic_params["chainer"]["pipe"][3]["n_types"] = N_TYPES -basic_params["chainer"]["pipe"][3]["n_layers"] = N_LAYERS +Path(basic_params["chainer"]["pipe"][4]["save_path"]).mkdir(parents=True, exist_ok=True) +basic_params["chainer"]["pipe"][4]["n_types"] = N_TYPES +basic_params["chainer"]["pipe"][4]["n_layers"] = N_LAYERS # Result table order = ["classification_log_loss", "classification_accuracy", "classification_f1", "classification_roc_auc", "params"] -result_file = Path(basic_params["chainer"]["pipe"][3]["save_path"]).joinpath("result_table.csv") +result_file = Path(basic_params["chainer"]["pipe"][4]["save_path"]).joinpath("result_table.csv") result_table = pd.DataFrame({"loss": [], "classification_accuracy": [], "classification_f1": [], From 6176e9803dc45ed4d4dfea8668b071a1bd940c77 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 7 May 2018 18:26:49 +0300 Subject: [PATCH 320/616] chore: part of data --- .../basic_snli_one_neuron_init_part.json | 228 ++++++++++++++++++ 1 file changed, 228 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json new file mode 100644 index 0000000000..1a95a8976c --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -0,0 +1,228 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", + "dim": 100 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 100, + 1000 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From 6b7885f953030efdbb73687f6c0d2380a0291909 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 10:51:13 +0300 Subject: [PATCH 321/616] fix: batch size and text size are fixed --- .../evolution/basic_snli_one_neuron_init_part.json | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 1a95a8976c..bc4fd9959a 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -38,9 +38,9 @@ { "id": "my_embedder", "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", - "dim": 100 + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 }, { "id": "my_tokenizer", @@ -180,7 +180,7 @@ ] }, "loss": "binary_crossentropy", - "text_size": 15, + "text_size": 30, "model_name": "evolution_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" @@ -202,7 +202,7 @@ "batch_size": { "range": [ 50, - 200 + 70 ], "discrete": true }, From bd4ea646d570f58fe97f41d96624de55dbea8e70 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 11:21:31 +0300 Subject: [PATCH 322/616] chore: last layer activation --- .../basic_snli_one_neuron_init_part.json | 1 + ...basic_snli_one_neuron_init_part_half.json} | 19 ++++++++++--------- .../evolution/evolution_intent_model.py | 3 ++- 3 files changed, 13 insertions(+), 10 deletions(-) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init.json => basic_snli_one_neuron_init_part_half.json} (93%) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index bc4fd9959a..b330bf4553 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -181,6 +181,7 @@ }, "loss": "binary_crossentropy", "text_size": 30, + "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json similarity index 93% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init.json rename to deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index fd566b3c64..a2dcf28329 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -3,7 +3,7 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data" + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part_half" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -38,9 +38,9 @@ { "id": "my_embedder", "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", - "dim": 100 + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 }, { "id": "my_tokenizer", @@ -60,8 +60,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_half", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_half", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -180,7 +180,8 @@ ] }, "loss": "binary_crossentropy", - "text_size": 15, + "text_size": 30, + "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" @@ -202,7 +203,7 @@ "batch_size": { "range": [ 50, - 200 + 70 ], "discrete": true }, @@ -225,4 +226,4 @@ "telegram_utils": "IntentModel" } } -} \ No newline at end of file +} diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 0155d5408b..f0e5b4b3f2 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -184,7 +184,8 @@ def evolution_classification_model(self, params): if len(output.shape) == 3: output = GlobalMaxPooling1D()(output) output = Dense(self.n_classes, activation=None)(output) - act_output = Activation('sigmoid')(output) + activation = params.get("last_layer_activation", "sigmoid") + act_output = Activation(activation)(output) model = Model(inputs=inp, outputs=act_output) return model From 8b3660e64f15287f0181f360e48be757121bf4c2 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 11:33:21 +0300 Subject: [PATCH 323/616] feat: two texts classification model --- .../evolution/evolution_intent_model.py | 85 +++++++++++++++++++ 1 file changed, 85 insertions(+) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index f0e5b4b3f2..fb01970f57 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -189,6 +189,91 @@ def evolution_classification_model(self, params): model = Model(inputs=inp, outputs=act_output) return model + def evolution_two_texts_classification_model(self, params): + """ + Build un-compiled model of shallow-and-wide CNN + Args: + params: dictionary of parameters for NN + + Returns: + Un-compiled model + """ + inp1 = Input(shape=(params['text_size'], params['embedding_size'])) + inp2 = Input(shape=(params['text_size'], params['embedding_size'])) + + full_outputs = [] + + for inp_id, inp in enumerate([inp1, inp2]): + if np.sum(params["binary_mask"]) == 0: + output = Dense(1, activation=None)(inp) + output = GlobalMaxPooling1D()(output) + output = Dense(self.n_classes, activation=None)(output) + act_output = Activation('sigmoid')(output) + model = Model(inputs=inp, outputs=act_output) + return model + + dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) + sources, sinks, isolates = find_sources_and_sinks(dg) + + edges_outputs = {} + + sequence_of_nodes = [] + sequence_of_nodes.append(sources) + + while True: + if set(sinks).issubset(set(sum(sequence_of_nodes, []))): + break + next_nodes = [] + for node_str_id in sequence_of_nodes[-1]: + out_edges = dg.out_edges(node_str_id) + for edge in out_edges: + in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] + if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): + next_nodes.append(edge[1]) + sequence_of_nodes.append(next_nodes) + + sequence_of_nodes = sum(sequence_of_nodes, []) + + for node_str_id in sequence_of_nodes: + if node_str_id in sources: + edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, inp=inp) + elif node_str_id in isolates: + pass + else: + edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, edges_outputs=edges_outputs) + + if len(sinks) == 1: + output = edges_outputs[sinks[0]] + else: + outputs = [] + for sink in sinks: + outputs.append(edges_outputs[sink]) + try: + output = Concatenate()(outputs) + except ValueError: + time_steps = [] + features = [] + for i in range(len(outputs)): + if len(K.int_shape(outputs[i])) == 2: + outputs[i] = Lambda(lambda x: expand_tile(x, axis=1))(outputs[i]) + time_steps.append(K.int_shape(outputs[i])[1]) + features.append(K.int_shape(outputs[i])[2]) + new_feature_shape = max(features) + for i in range(len(outputs)): + outputs[i] = Dense(new_feature_shape)(outputs[i]) + output = Concatenate(axis=1)(outputs) + + if len(output.shape) == 3: + output = GlobalMaxPooling1D()(output) + full_outputs.append(output) + + output = Concatenate()(full_outputs) + output = Dense(self.n_classes, activation=None)(output) + activation = params.get("last_layer_activation", "sigmoid") + act_output = Activation(activation)(output) + model = Model(inputs=[inp1, inp2], outputs=act_output) + return model + @overrides def save(self, fname=None): """ From 65eb871ddd75d003341ec586d6b105eefd69f68d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 12:05:11 +0300 Subject: [PATCH 324/616] feat: two texts classification model --- ...c_snli_one_neuron_init_part_two_texts.json | 240 ++++++++++++++++++ 1 file changed, 240 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json new file mode 100644 index 0000000000..3a34a7b853 --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json @@ -0,0 +1,240 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": ["sentence1", "sentence2"], + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/two_texts/part" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "sentence1", + "sentence2" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + }, + { + "in": [ + "sentence1" + ], + "out": [ + "sentence1_lower" + ], + "name": "str_lower" + }, + { + "in": [ + "sentence2" + ], + "out": [ + "sentence2_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "sentence1_lower", + "sentence2_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_two_texts", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_two_texts", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "last_layer_activation": "softmax", + "model_name": "evolution_two_texts_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 100, + 1000 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 70 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From b6fe805da11477d6d1f142b2c89baec68cec3c8d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 12:15:26 +0300 Subject: [PATCH 325/616] fix: delete usage of model index in run_evolution --- .../neuroevolution_param_generator.py | 6 +++++ deeppavlov/models/evolution/run_evolution.py | 27 +++++++++---------- 2 files changed, 18 insertions(+), 15 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 0bc176a30b..5027ca3293 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -54,6 +54,7 @@ def __init__(self, n_layers, n_types, """ self.n_types = n_types self.n_layers = n_layers + self.total_nodes = self.n_types * self.n_layers self.binary_mask_template = np.zeros((self.total_nodes, self.total_nodes)) self.start_with_one_neuron = start_with_one_neuron @@ -62,6 +63,11 @@ def __init__(self, n_layers, n_types, self.model_to_evolve_index = find_index_of_dict_with_key_in_pipe(self.basic_config["chainer"]["pipe"], key_model_to_evolve) + self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["n_types"] = self.n_types + self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["n_layers"] = self.n_layers + Path(self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).mkdir(parents=True, + exist_ok=True) + self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) self.train_params = deepcopy(self.basic_config.get("train")) self.basic_layers_params = self.params.pop(key_basic_layers, None) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index d30a600906..223e5a42a6 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -94,21 +94,6 @@ def score_population(population, population_size, result_file): print("Given basic params: {}\n".format(basic_params)) -Path(basic_params["chainer"]["pipe"][4]["save_path"]).mkdir(parents=True, exist_ok=True) -basic_params["chainer"]["pipe"][4]["n_types"] = N_TYPES -basic_params["chainer"]["pipe"][4]["n_layers"] = N_LAYERS - -# Result table -order = ["classification_log_loss", "classification_accuracy", - "classification_f1", "classification_roc_auc", "params"] -result_file = Path(basic_params["chainer"]["pipe"][4]["save_path"]).joinpath("result_table.csv") -result_table = pd.DataFrame({"loss": [], - "classification_accuracy": [], - "classification_f1": [], - "classification_roc_auc": [], - "params": []}) -result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') - # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, @@ -120,6 +105,18 @@ def score_population(population, population_size, result_file): start_with_one_neuron=ONE_NEURON_INIT, **basic_params) +# Result table +order = ["classification_log_loss", "classification_accuracy", + "classification_f1", "classification_roc_auc", "params"] +result_file = Path(basic_params["chainer"]["pipe"][ + evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") +result_table = pd.DataFrame({"loss": [], + "classification_accuracy": [], + "classification_f1": [], + "classification_roc_auc": [], + "params": []}) +result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') + print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() print("Considered population: {}\nScoring...\n".format(population)) From f270f07617008359e32102054673b6df9ef203b6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 16:20:04 +0300 Subject: [PATCH 326/616] fix: activation choice for one neuron --- deeppavlov/models/evolution/evolution_intent_model.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index fb01970f57..f752b6d080 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -126,7 +126,8 @@ def evolution_classification_model(self, params): output = Dense(1, activation=None)(inp) output = GlobalMaxPooling1D()(output) output = Dense(self.n_classes, activation=None)(output) - act_output = Activation('sigmoid')(output) + activation = params.get("last_layer_activation", "sigmoid") + act_output = Activation(activation)(output) model = Model(inputs=inp, outputs=act_output) return model From 5bbbfb677b30002c978b76f9e18e404c28bf07b3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 8 May 2018 18:03:03 +0300 Subject: [PATCH 327/616] fix: n_epochs range changed --- .../configs/evolution/basic_snli_one_neuron_init_part.json | 4 ++-- .../evolution/basic_snli_one_neuron_init_part_half.json | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index b330bf4553..38a843bae9 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -195,8 +195,8 @@ "train": { "epochs": { "range": [ - 100, - 1000 + 50, + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index a2dcf28329..3a6d14f873 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -195,8 +195,8 @@ "train": { "epochs": { "range": [ - 100, - 1000 + 50, + 100 ], "discrete": true }, From fcd7294a5db77de3addad9f0f2675c52b2d091fc Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 10:57:17 +0300 Subject: [PATCH 328/616] chore: working --- .../basic_snli_one_neuron_init_part.json | 20 +++++++++---------- .../basic_snli_one_neuron_init_part_half.json | 20 +++++++++---------- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 38a843bae9..3f3749271f 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -69,7 +69,7 @@ "units": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -86,7 +86,7 @@ "filters": { "range": [ 50, - 500 + 100 ], "discrete": true }, @@ -103,7 +103,7 @@ "units": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -115,7 +115,7 @@ "units": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -139,14 +139,14 @@ "n_hidden": { "range": [ 50, - 200 + 100 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -169,13 +169,13 @@ "optimizer": "Adam", "lear_rate": { "range": [ - 0.001, + 0.0001, 0.1 ] }, "lear_rate_decay": { "range": [ - 0.00001, + 0.000001, 0.1 ] }, @@ -202,8 +202,8 @@ }, "batch_size": { "range": [ - 50, - 70 + 20, + 50 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index 3a6d14f873..a0bb9b653f 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -69,7 +69,7 @@ "units": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -86,7 +86,7 @@ "filters": { "range": [ 50, - 500 + 100 ], "discrete": true }, @@ -103,7 +103,7 @@ "units": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -115,7 +115,7 @@ "units": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -139,14 +139,14 @@ "n_hidden": { "range": [ 50, - 200 + 100 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 100 ], "discrete": true }, @@ -169,13 +169,13 @@ "optimizer": "Adam", "lear_rate": { "range": [ - 0.001, + 0.0001, 0.1 ] }, "lear_rate_decay": { "range": [ - 0.00001, + 0.000001, 0.1 ] }, @@ -202,8 +202,8 @@ }, "batch_size": { "range": [ - 50, - 70 + 20, + 50 ], "discrete": true }, From 1197c1c003ce47cfbcc70b7534bfd6e556c91533 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 12:49:11 +0300 Subject: [PATCH 329/616] feat: experiment without attention add --- ...snli_one_neuron_init_part_without_att.json | 205 ++++++++++++++++++ 1 file changed, 205 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json new file mode 100644 index 0000000000..457deac5ab --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json @@ -0,0 +1,205 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_without_att", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_without_att", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": { + "bool": true + } + }, + "GlobalMaxPooling1D": { + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.000001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 30, + "last_layer_activation": "softmax", + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 50, + 100 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 20, + 50 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From a469e529211739f26371e4ec7cd3913d98f6f8c5 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 15:09:57 +0300 Subject: [PATCH 330/616] chore: comments, making sinks outputs 2d --- .../evolution/evolution_intent_model.py | 50 ++++++++++++++----- 1 file changed, 37 insertions(+), 13 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index f752b6d080..bd4728e73e 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -136,54 +136,78 @@ def evolution_classification_model(self, params): edges_outputs = {} - sequence_of_nodes = [] - sequence_of_nodes.append(sources) + # sequence_of_nodes is a list of lists. + # each element of sequence_of_nodes is a list that contains nodes (keras layers) + # that could be initialized when all nodes from previous lists are initialized + sequence_of_nodes = [sources] while True: - if set(sinks).issubset(set(sum(sequence_of_nodes, []))): - break + # unreal condition: if some sources are sinks + # if set(sinks).issubset(set(sum(sequence_of_nodes, []))): + # break next_nodes = [] + # want to get list of nodes that can be initialized next for node_str_id in sequence_of_nodes[-1]: + # for each node that were initialized on the previous step + # take output edges out_edges = dg.out_edges(node_str_id) for edge in out_edges: + # for all output edge + # collect nodes that are input nodes + # for considered child of node_str_id (edge[1]) in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] + # if for considered child all parents are already initialized + # then add this node for initialization if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): next_nodes.append(edge[1]) sequence_of_nodes.append(next_nodes) + # make a list of ints from list of lists sequence_of_nodes = sum(sequence_of_nodes, []) + # now all nodes in sequence + # can be initialized consequently for node_str_id in sequence_of_nodes: if node_str_id in sources: + # if considered node is source, + # give embedded texts as input edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, inp=inp) elif node_str_id in isolates: + # unreal condition + # if considered node is isolate, + # nothing to do pass else: + # if considered node is not source and isolate, + # give all previous outputs as input edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, edges_outputs=edges_outputs) if len(sinks) == 1: + # if the only sink, + # output is this sink's output output = edges_outputs[sinks[0]] else: + # if several sinks exist, + # outputs will be concatenated outputs = [] + # collect outputs for sink in sinks: outputs.append(edges_outputs[sink]) try: output = Concatenate()(outputs) except ValueError: - time_steps = [] - features = [] + # outputs are of 2d and 3d shapes + # make them all 2d and concatenate for i in range(len(outputs)): - if len(K.int_shape(outputs[i])) == 2: - outputs[i] = Lambda(lambda x: expand_tile(x, axis=1))(outputs[i]) - time_steps.append(K.int_shape(outputs[i])[1]) - features.append(K.int_shape(outputs[i])[2]) - new_feature_shape = max(features) - for i in range(len(outputs)): - outputs[i] = Dense(new_feature_shape)(outputs[i]) + if len(K.int_shape(outputs[i])) == 3: + outputs[i] = GlobalMaxPooling1D()(outputs[i]) output = Concatenate(axis=1)(outputs) + # if concatenated output is of 3d shape + # make it 2d using global max pooling if len(output.shape) == 3: output = GlobalMaxPooling1D()(output) + output = Dense(self.n_classes, activation=None)(output) activation = params.get("last_layer_activation", "sigmoid") act_output = Activation(activation)(output) From 34b6930aee61029d43a78f25b2104527e50f7bb9 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 15:11:28 +0300 Subject: [PATCH 331/616] fix: add breaking cycle in sequence of nodes --- deeppavlov/models/evolution/evolution_intent_model.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index bd4728e73e..9801d00220 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -142,9 +142,8 @@ def evolution_classification_model(self, params): sequence_of_nodes = [sources] while True: - # unreal condition: if some sources are sinks - # if set(sinks).issubset(set(sum(sequence_of_nodes, []))): - # break + if set(sinks).issubset(set(sum(sequence_of_nodes, []))): + break next_nodes = [] # want to get list of nodes that can be initialized next for node_str_id in sequence_of_nodes[-1]: @@ -207,7 +206,7 @@ def evolution_classification_model(self, params): # make it 2d using global max pooling if len(output.shape) == 3: output = GlobalMaxPooling1D()(output) - + output = Dense(self.n_classes, activation=None)(output) activation = params.get("last_layer_activation", "sigmoid") act_output = Activation(activation)(output) From 1e3d42ac7bb116a82bba775bd6929a997f157e59 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 15:29:18 +0300 Subject: [PATCH 332/616] fix: delete globalmaxpooling --- .../configs/evolution/basic_snli_one_neuron_init_part.json | 2 -- 1 file changed, 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 3f3749271f..5d9f366a59 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -123,8 +123,6 @@ "bool": true } }, - "GlobalMaxPooling1D": { - }, "MaxPooling1D": { "pool_size": { "range": [ From 64e16a5a91da5bef356fd34da86e1188487d603a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 15:31:41 +0300 Subject: [PATCH 333/616] fix: delete globalmaxpool from all configs --- deeppavlov/configs/evolution/basic_snips_one_neuron_init.json | 2 -- deeppavlov/configs/evolution/basic_snips_random_init.json | 2 -- .../configs/evolution/basic_snli_one_neuron_init_part_half.json | 2 -- .../evolution/basic_snli_one_neuron_init_part_without_att.json | 2 -- deeppavlov/configs/evolution/basic_snli_random_init.json | 2 -- 5 files changed, 10 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index e0d4b95e78..fe55f2cdaf 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -123,8 +123,6 @@ "bool": true } }, - "GlobalMaxPooling1D": { - }, "MaxPooling1D": { "pool_size": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index ba66d8d042..8ae49f36a0 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -123,8 +123,6 @@ "bool": true } }, - "GlobalMaxPooling1D": { - }, "MaxPooling1D": { "pool_size": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index a0bb9b653f..e320956f04 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -123,8 +123,6 @@ "bool": true } }, - "GlobalMaxPooling1D": { - }, "MaxPooling1D": { "pool_size": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json index 457deac5ab..fe7e3f7c71 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json @@ -123,8 +123,6 @@ "bool": true } }, - "GlobalMaxPooling1D": { - }, "MaxPooling1D": { "pool_size": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index f86582ce1a..0e86405b02 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -123,8 +123,6 @@ "bool": true } }, - "GlobalMaxPooling1D": { - }, "MaxPooling1D": { "pool_size": { "range": [ From 442653145da3df6b3fc9394fc59a3dfcbbc06c1d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 16:00:31 +0300 Subject: [PATCH 334/616] chore: configs --- .../configs/evolution/basic_config_local.json | 59 ++++++++++++------- .../basic_snips_one_neuron_init.json | 8 +-- .../evolution/basic_snips_random_init.json | 7 +-- .../basic_snli_one_neuron_init_part.json | 20 +++---- .../basic_snli_one_neuron_init_part_half.json | 20 +++---- ...c_snli_one_neuron_init_part_two_texts.json | 16 ++--- ...snli_one_neuron_init_part_without_att.json | 8 +-- .../evolution/basic_snli_random_init.json | 10 +--- 8 files changed, 69 insertions(+), 79 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 07087e13be..580c42e199 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -1,16 +1,20 @@ { "dataset_reader": { "name": "basic_classification_reader", - "x": "text", - "y": "intents", - "data_path": "/home/dilyara/data/data_files/snips/snips_dataset" + "x": [ + "sentence1", + "sentence2" + ], + "y": "gold_label", + "data_path": "/home/dilyara/data/data_files/SNLI/snli_data/two_texts/part" }, "dataset_iterator": { "name": "basic_classification_iterator" }, "chainer": { "in": [ - "x" + "sentence1", + "sentence2" ], "in_y": [ "y" @@ -23,8 +27,26 @@ "y" ], "level": "token", - "save_path": "/home/dilyara/data/data_files/snips/snips_dataset/snips_classes.dict", - "load_path": "/home/dilyara/data/data_files/snips/snips_dataset/snips_classes.dict" + "save_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict", + "load_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict" + }, + { + "in": [ + "sentence1" + ], + "out": [ + "sentence1_lower" + ], + "name": "str_lower" + }, + { + "in": [ + "sentence2" + ], + "out": [ + "sentence2_lower" + ], + "name": "str_lower" }, { "id": "my_embedder", @@ -40,7 +62,8 @@ }, { "in": [ - "x" + "sentence1_lower", + "sentence2_lower" ], "in_y": [ "y" @@ -50,9 +73,9 @@ "y_probas_dict" ], "main": true, - "name": "evolution_classification_model", - "save_path": "/home/dilyara/data/models/evolution_data/snips_classification/start_with_random", - "load_path": "/home/dilyara/data/models/evolution_data/snips_classification/start_with_random", + "name": "evolution_classification_many_texts_model", + "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_texts", + "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_texts", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -98,9 +121,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { @@ -110,11 +131,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } - }, - "GlobalMaxPooling1D": { + "return_sequences": true }, "MaxPooling1D": { "pool_size": { @@ -126,7 +143,7 @@ }, "padding": "same" }, - "SelfMultiplicativeAttention": { + "Attention": { "n_hidden": { "range": [ 50, @@ -149,6 +166,7 @@ ], "choice": true } + } }, "confident_threshold": { "range": [ @@ -171,7 +189,8 @@ }, "loss": "binary_crossentropy", "text_size": 15, - "model_name": "evolution_classification_model", + "last_layer_activation": "softmax", + "model_name": "evolution_many_texts_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" } diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index fe55f2cdaf..660bae50bd 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -107,9 +107,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { @@ -119,9 +117,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index 8ae49f36a0..b476f08415 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -107,9 +107,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { @@ -119,8 +117,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true + "return_sequences": trueool": true } }, "MaxPooling1D": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 5d9f366a59..b20b80cfbd 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -69,7 +69,7 @@ "units": { "range": [ 50, - 100 + 200 ], "discrete": true }, @@ -86,7 +86,7 @@ "filters": { "range": [ 50, - 100 + 200 ], "discrete": true }, @@ -103,25 +103,21 @@ "units": { "range": [ 50, - 100 + 200 ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { "range": [ 50, - 100 + 200 ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "MaxPooling1D": { "pool_size": { @@ -137,14 +133,14 @@ "n_hidden": { "range": [ 50, - 100 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 100 + 200 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index e320956f04..0aab7a2e80 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -69,7 +69,7 @@ "units": { "range": [ 50, - 100 + 200 ], "discrete": true }, @@ -86,7 +86,7 @@ "filters": { "range": [ 50, - 100 + 200 ], "discrete": true }, @@ -103,25 +103,21 @@ "units": { "range": [ 50, - 100 + 200 ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { "range": [ 50, - 100 + 200 ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "MaxPooling1D": { "pool_size": { @@ -137,14 +133,14 @@ "n_hidden": { "range": [ 50, - 100 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 100 + 200 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json index 3a34a7b853..13642b57b5 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json @@ -97,7 +97,7 @@ "filters": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -118,9 +118,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { @@ -130,11 +128,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } - }, - "GlobalMaxPooling1D": { + "return_sequences": true }, "MaxPooling1D": { "pool_size": { @@ -206,8 +200,8 @@ "train": { "epochs": { "range": [ - 100, - 1000 + 50, + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json index fe7e3f7c71..2ec06848e1 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json @@ -107,9 +107,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { @@ -119,9 +117,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index 0e86405b02..4840a1e685 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -86,7 +86,7 @@ "filters": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -107,9 +107,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "BiCuDNNLSTM": { "units": { @@ -119,9 +117,7 @@ ], "discrete": true }, - "return_sequences": { - "bool": true - } + "return_sequences": true }, "MaxPooling1D": { "pool_size": { From b1adfc8897457d075eb70fa5562445f39b5901df Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 18:01:02 +0300 Subject: [PATCH 335/616] feat: many_inputs classification model for evolution --- deeppavlov/__init__.py | 1 + .../configs/evolution/basic_config_local.json | 10 +- ...nli_one_neuron_init_part_many_inputs.json} | 6 +- ...snli_one_neuron_init_part_without_att.json | 199 --------- deeppavlov/core/layers/keras_layers.py | 21 + .../evolution/evolution_intent_model.py | 85 ---- .../evolution/evolution_many_inputs_model.py | 389 ++++++++++++++++++ 7 files changed, 419 insertions(+), 292 deletions(-) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init_part_two_texts.json => basic_snli_one_neuron_init_part_many_inputs.json} (96%) delete mode 100644 deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json create mode 100644 deeppavlov/models/evolution/evolution_many_inputs_model.py diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index 5a714abdfd..99482e1451 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -50,6 +50,7 @@ import deeppavlov.models.seq2seq_go_bot.kb import deeppavlov.models.classifiers.intents.intent_model import deeppavlov.models.evolution.evolution_intent_model +import deeppavlov.models.evolution.evolution_many_inputs_model import deeppavlov.models.commutators.random_commutator import deeppavlov.models.embedders.fasttext_embedder import deeppavlov.models.embedders.dict_embedder diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 580c42e199..3e63aca045 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_classification_many_texts_model", - "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_texts", - "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_texts", + "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_inputs", + "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_inputs", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -100,7 +100,7 @@ "filters": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -143,7 +143,7 @@ }, "padding": "same" }, - "Attention": { + "SelfMultiplicativeAttention": { "n_hidden": { "range": [ 50, @@ -190,7 +190,7 @@ "loss": "binary_crossentropy", "text_size": 15, "last_layer_activation": "softmax", - "model_name": "evolution_many_texts_classification_model", + "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" } diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json similarity index 96% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json rename to deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 13642b57b5..df5be8a0a5 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_two_texts.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -71,8 +71,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_two_texts", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_two_texts", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -187,7 +187,7 @@ "loss": "binary_crossentropy", "text_size": 15, "last_layer_activation": "softmax", - "model_name": "evolution_two_texts_classification_model", + "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" } diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json deleted file mode 100644 index 2ec06848e1..0000000000 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_without_att.json +++ /dev/null @@ -1,199 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_without_att", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_without_att", - "classes": "#classes_vocab.keys()", - "to_evolve": true, - "basic_layers_params": { - "Dense": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - }, - "Conv1D": { - "filters": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "kernel_size": { - "range": [ - 2, - 7 - ], - "discrete": true - }, - "padding": "same" - }, - "CuDNNLSTM": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "return_sequences": true - }, - "BiCuDNNLSTM": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "return_sequences": true - }, - "MaxPooling1D": { - "pool_size": { - "range": [ - 2, - 5 - ], - "discrete": true - }, - "padding": "same" - } - }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.0001, - 0.1 - ] - }, - "lear_rate_decay": { - "range": [ - 0.000001, - 0.1 - ] - }, - "loss": "binary_crossentropy", - "text_size": 30, - "last_layer_activation": "softmax", - "model_name": "evolution_classification_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "range": [ - 50, - 100 - ], - "discrete": true - }, - "batch_size": { - "range": [ - 20, - 50 - ], - "discrete": true - }, - "metric_optimization": "minimize", - "metrics": [ - "classification_log_loss", - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, - "show_examples": false, - "validate_best": true, - "test_best": false - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} diff --git a/deeppavlov/core/layers/keras_layers.py b/deeppavlov/core/layers/keras_layers.py index 710156df53..3439f69f51 100644 --- a/deeppavlov/core/layers/keras_layers.py +++ b/deeppavlov/core/layers/keras_layers.py @@ -99,3 +99,24 @@ def multiplicative_self_attention(units, n_hidden=None, n_output_features=None, attended_units = Lambda(lambda x: K.sum(x, axis=2))(mult) output = Dense(n_output_features, activation=activation)(attended_units) return output + + +def multiplicative_self_attention_init(n_hidden, n_output_features, activation): + layers = {} + layers["queries"] = Dense(n_hidden) + layers["keys"] = Dense(n_hidden) + layers["output"] = Dense(n_output_features, activation=activation) + return layers + + +def multiplicative_self_attention_get_output(units, layers): + exp1 = Lambda(lambda x: expand_tile(x, axis=1))(units) + exp2 = Lambda(lambda x: expand_tile(x, axis=2))(units) + queries = layers["queries"](exp1) + keys = layers["keys"](exp2) + scores = Lambda(lambda x: K.sum(queries * x, axis=3, keepdims=True))(keys) + attention = Lambda(lambda x: softvaxaxis2(x))(scores) + mult = Multiply()([attention, exp1]) + attended_units = Lambda(lambda x: K.sum(x, axis=2))(mult) + output = layers["output"](attended_units) + return output diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 9801d00220..3ac7e842d9 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -213,91 +213,6 @@ def evolution_classification_model(self, params): model = Model(inputs=inp, outputs=act_output) return model - def evolution_two_texts_classification_model(self, params): - """ - Build un-compiled model of shallow-and-wide CNN - Args: - params: dictionary of parameters for NN - - Returns: - Un-compiled model - """ - inp1 = Input(shape=(params['text_size'], params['embedding_size'])) - inp2 = Input(shape=(params['text_size'], params['embedding_size'])) - - full_outputs = [] - - for inp_id, inp in enumerate([inp1, inp2]): - if np.sum(params["binary_mask"]) == 0: - output = Dense(1, activation=None)(inp) - output = GlobalMaxPooling1D()(output) - output = Dense(self.n_classes, activation=None)(output) - act_output = Activation('sigmoid')(output) - model = Model(inputs=inp, outputs=act_output) - return model - - dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) - sources, sinks, isolates = find_sources_and_sinks(dg) - - edges_outputs = {} - - sequence_of_nodes = [] - sequence_of_nodes.append(sources) - - while True: - if set(sinks).issubset(set(sum(sequence_of_nodes, []))): - break - next_nodes = [] - for node_str_id in sequence_of_nodes[-1]: - out_edges = dg.out_edges(node_str_id) - for edge in out_edges: - in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] - if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): - next_nodes.append(edge[1]) - sequence_of_nodes.append(next_nodes) - - sequence_of_nodes = sum(sequence_of_nodes, []) - - for node_str_id in sequence_of_nodes: - if node_str_id in sources: - edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, inp=inp) - elif node_str_id in isolates: - pass - else: - edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, edges_outputs=edges_outputs) - - if len(sinks) == 1: - output = edges_outputs[sinks[0]] - else: - outputs = [] - for sink in sinks: - outputs.append(edges_outputs[sink]) - try: - output = Concatenate()(outputs) - except ValueError: - time_steps = [] - features = [] - for i in range(len(outputs)): - if len(K.int_shape(outputs[i])) == 2: - outputs[i] = Lambda(lambda x: expand_tile(x, axis=1))(outputs[i]) - time_steps.append(K.int_shape(outputs[i])[1]) - features.append(K.int_shape(outputs[i])[2]) - new_feature_shape = max(features) - for i in range(len(outputs)): - outputs[i] = Dense(new_feature_shape)(outputs[i]) - output = Concatenate(axis=1)(outputs) - - if len(output.shape) == 3: - output = GlobalMaxPooling1D()(output) - full_outputs.append(output) - - output = Concatenate()(full_outputs) - output = Dense(self.n_classes, activation=None)(output) - activation = params.get("last_layer_activation", "sigmoid") - act_output = Activation(activation)(output) - model = Model(inputs=[inp1, inp2], outputs=act_output) - return model - @overrides def save(self, fname=None): """ diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py new file mode 100644 index 0000000000..c776a9e411 --- /dev/null +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -0,0 +1,389 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import numpy as np +from copy import copy, deepcopy +from keras.layers import Dense, Input, concatenate, Activation +from keras.layers.convolutional import Conv1D +from keras.layers.core import Dropout +from keras.layers.normalization import BatchNormalization +from keras.layers.pooling import GlobalMaxPooling1D, MaxPooling1D +from keras.layers.recurrent import LSTM +from keras.layers.wrappers import Bidirectional +from keras.models import Model +from keras.regularizers import l2 +from keras.layers import Concatenate, Reshape, CuDNNLSTM, Lambda +from keras import backend as K +from overrides import overrides +from pathlib import Path + +from deeppavlov.core.common.errors import ConfigError +from deeppavlov.core.common.registry import register +from deeppavlov.core.models.keras_model import KerasModel +from deeppavlov.models.classifiers.intents.intent_model import KerasIntentModel +from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels +from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder +from deeppavlov.models.classifiers.intents.utils import md5_hashsum +from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer +from deeppavlov.core.common.log import get_logger +from deeppavlov.models.evolution.check_binary_mask import number_to_type_layer, \ + find_sources_and_sinks, get_digraph_from_binary_mask, get_graph_and_plot +from deeppavlov.models.evolution.utils import expand_tile +from deeppavlov.core.common.file import save_json, read_json +from deeppavlov.core.layers.keras_layers import multiplicative_self_attention_init, \ + multiplicative_self_attention_get_output + + +log = get_logger(__name__) + + +@register('evolution_classification_many_texts_model') +class KerasEvolutionClassificationManyInputsModel(KerasIntentModel): + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.opt["binary_mask"] = np.array(self.opt["binary_mask"]) + get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], + path=str(self.save_path.resolve().parent)) + + def texts2vec(self, sentences): + """ + Convert texts to vector representations using embedder and padding up to self.opt["text_size"] tokens + Args: + sentences: list of lists of tokens + + Returns: + array of embedded texts + """ + pad = np.zeros(self.opt['embedding_size']) + + embeddings_batch = self.fasttext_model([sen[:self.opt['text_size']] for sen in sentences]) + embeddings_batch = [[pad] * (self.opt['text_size'] - len(tokens)) + tokens for tokens in embeddings_batch] + + embeddings_batch = np.asarray(embeddings_batch) + return embeddings_batch + + @overrides + def train_on_batch(self, *args, **kwargs): + """ + Train the model on the given batch + Args: + texts - list of texts (or list of lists of text tokens) + labels - list of labels + + Returns: + loss and metrics values on the given batch + """ + if len(args) > len(self.opt["in"]): + labels = args[-1] + texts = args[:-1] + else: + labels = None + texts = args + + features = [] + for i in range(len(self.opt["in"])): + if isinstance(texts[i][0], str): + features.append(self.texts2vec(self.tokenizer(list(texts[i])))) + else: + features.append(self.texts2vec(list(texts[i]))) + + onehot_labels = labels2onehot(labels, classes=self.classes) + metrics_values = self.model.train_on_batch(features, onehot_labels) + return metrics_values + + @overrides + def infer_on_batch(self, *args, **kwargs): + """ + Infer the model on the given batch + Args: + texts - list of texts (or list of lists of text tokens) + labels - list of labels + + Returns: + loss and metrics values on the given batch, if labels are given + predictions, otherwise + """ + if len(args) > 1: + labels = args[-1] + texts = args[:-1] + elif len(args) == 1: + labels = None + texts = args[0] + else: + raise ValueError("Nothing to infer in infer_on_batch") + + features = [] + for i in range(len(self.opt["in"])): + if isinstance(texts[i][0], str): + features.append(self.texts2vec(self.tokenizer(list(texts[i])))) + else: + features.append(self.texts2vec(list(texts[i]))) + + if labels: + onehot_labels = labels2onehot(labels, classes=self.classes) + metrics_values = self.model.test_on_batch(features, onehot_labels) + return metrics_values + else: + predictions = self.model.predict(features) + return predictions + + @overrides + def __call__(self, *args, **kwargs): + """ + Infer on the given data + Args: + data: [list of sentences] + *args: + + Returns: + for each sentence: + vector of probabilities to belong with each class + or list of labels sentence belongs with + """ + assert len(args) == len(self.opt["in"]) + preds = np.array(self.infer_on_batch(args)) + + labels = proba2labels(preds, confident_threshold=self.opt['confident_threshold'], classes=self.classes) + return labels, [dict(zip(self.classes, preds[i])) for i in range(preds.shape[0])] + + def get_node_output(self, model_layers, node_str_id, dg, params, edges_outputs=None, inp=None): + if inp is None: + input_nodes = [edge[0] for edge in dg.in_edges(node_str_id)] + inp_list = [] + for input_node in input_nodes: + if len(K.int_shape(edges_outputs[input_node])) == 3: + inp_list.append(edges_outputs[input_node]) + elif len(K.int_shape(edges_outputs[input_node])) == 2: + input_expanded = Lambda(lambda x: expand_tile(x, axis=1))(edges_outputs[input_node]) + inp_list.append(input_expanded) + else: + raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") + if len(input_nodes) > 1: + try: + inp = Concatenate()(inp_list) + except ValueError: + time_steps = [] + features = [] + for i in range(len(inp_list)): + if len(K.int_shape(inp_list[i])) == 2: + inp_list[i] = Lambda(lambda x: expand_tile(x, axis=1))(inp_list[i]) + time_steps.append(K.int_shape(inp_list[i])[1]) + features.append(K.int_shape(inp_list[i])[2]) + new_feature_shape = max(features) + new_inp_list = [] + for i in range(len(inp_list)): + if K.int_shape(inp_list[i])[2] == new_feature_shape: + new_inp_list.append(inp_list[i]) + else: + new_inp_list.append(Dense(new_feature_shape)(inp_list[i])) + inp = Concatenate(axis=1)(new_inp_list) + else: + inp = inp_list[0] + + if params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": + node_params = deepcopy(params[params["nodes"][node_str_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + output_of_node = multiplicative_self_attention_get_output(inp, + model_layers[params["nodes"][node_str_id]]) + else: + node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) + node_params = deepcopy(params[params["nodes"][node_str_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + output_of_node = model_layers[params["nodes"][node_str_id]](inp) + return output_of_node + + def initialize_all_nodes(self, params): + dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) + sources, sinks, isolates = find_sources_and_sinks(dg) + + model_layers = {} + for node_str_id in list(params["nodes"].keys()): + if not(node_str_id in isolates): + if params[params["nodes"][node_str_id]]["node_name"] == "BiCuDNNLSTM": + node_params = deepcopy(params[params["nodes"][node_str_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + model_layers[params["nodes"][node_str_id]] = Bidirectional(CuDNNLSTM(**node_params)) + elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": + node_params = deepcopy(params[params["nodes"][node_str_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + model_layers[params["nodes"][node_str_id]] = \ + multiplicative_self_attention_init(**node_params) + else: + node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) + node_params = deepcopy(params[params["nodes"][node_str_id]]) + node_params.pop("node_name") + node_params.pop("node_type") + node_params.pop("node_layer") + if callable(node_func): + model_layers[params["nodes"][node_str_id]] = node_func(**node_params) + else: + raise AttributeError("Node {} is not defined correctly".format(node_str_id)) + + return model_layers + + def evolution_many_inputs_classification_model(self, params): + """ + Build un-compiled model of shallow-and-wide CNN + Args: + params: dictionary of parameters for NN + + Returns: + Un-compiled model + """ + inputs = [] + for i in range(len(params["in"])): + inputs.append(Input(shape=(params['text_size'], params['embedding_size']))) + + full_outputs = [] + + if np.sum(params["binary_mask"]) == 0: + dense1 = Dense(1, activation=None) + globalmaxpooling = GlobalMaxPooling1D() + for inp in inputs: + output = dense1(inp) + full_outputs.append(globalmaxpooling(output)) + + output = Concatenate()(full_outputs) + output = Dense(self.n_classes, activation=None)(output) + activation = params.get("last_layer_activation", "sigmoid") + act_output = Activation(activation)(output) + model = Model(inputs=inputs, outputs=act_output) + return model + + model_layers = self.initialize_all_nodes(params) + + for inp in inputs: + dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) + sources, sinks, isolates = find_sources_and_sinks(dg) + + edges_outputs = {} + + # sequence_of_nodes is a list of lists. + # each element of sequence_of_nodes is a list that contains nodes (keras layers) + # that could be initialized when all nodes from previous lists are initialized + sequence_of_nodes = [sources] + + while True: + if set(sinks).issubset(set(sum(sequence_of_nodes, []))): + break + next_nodes = [] + # want to get list of nodes that can be initialized next + for node_str_id in sequence_of_nodes[-1]: + # for each node that were initialized on the previous step + # take output edges + out_edges = dg.out_edges(node_str_id) + for edge in out_edges: + # for all output edge + # collect nodes that are input nodes + # for considered child of node_str_id (edge[1]) + in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] + # if for considered child all parents are already initialized + # then add this node for initialization + if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): + next_nodes.append(edge[1]) + sequence_of_nodes.append(next_nodes) + + # make a list of ints from list of lists + sequence_of_nodes = sum(sequence_of_nodes, []) + + # now all nodes in sequence + # can be initialized consequently + for node_str_id in sequence_of_nodes: + if node_str_id in sources: + # if considered node is source, + # give embedded texts as input + edges_outputs[node_str_id] = self.get_node_output(model_layers, node_str_id, dg, params, inp=inp) + elif node_str_id in isolates: + # unreal condition + # if considered node is isolate, + # nothing to do + pass + else: + # if considered node is not source and isolate, + # give all previous outputs as input + edges_outputs[node_str_id] = self.get_node_output(model_layers, node_str_id, dg, params, + edges_outputs=edges_outputs) + + if len(sinks) == 1: + # if the only sink, + # output is this sink's output + output = edges_outputs[sinks[0]] + else: + # if several sinks exist, + # outputs will be concatenated + outputs = [] + # collect outputs + for sink in sinks: + outputs.append(edges_outputs[sink]) + try: + output = Concatenate()(outputs) + except ValueError: + # outputs are of 2d and 3d shapes + # make them all 2d and concatenate + for i in range(len(outputs)): + if len(K.int_shape(outputs[i])) == 3: + outputs[i] = GlobalMaxPooling1D()(outputs[i]) + output = Concatenate(axis=1)(outputs) + + if len(output.shape) == 3: + output = GlobalMaxPooling1D()(output) + full_outputs.append(output) + + output = Concatenate()(full_outputs) + output = Dense(self.n_classes, activation=None)(output) + activation = params.get("last_layer_activation", "sigmoid") + act_output = Activation(activation)(output) + model = Model(inputs=inputs, outputs=act_output) + return model + + @overrides + def save(self, fname=None): + """ + Save the model parameters into <>_opt.json (or <>_opt.json) + and model weights into <>.h5 (or <>.h5) + Args: + fname: file_path to save model. If not explicitly given seld.opt["ser_file"] will be used + + Returns: + None + """ + + if not self.save_path: + raise ConfigError("No `save_path` is provided for Keras model!") + elif isinstance(self.save_path, Path) and not self.save_path.parent.is_dir(): + raise ConfigError("Provided save path is incorrect!") + else: + opt_path = "{}_opt.json".format(str(self.save_path.resolve())) + weights_path = "{}.h5".format(str(self.save_path.resolve())) + log.info("[saving model to {}]".format(opt_path)) + self.model.save_weights(weights_path) + + if type(self.opt["binary_mask"]) is list: + pass + else: + self.opt["binary_mask"] = self.opt["binary_mask"].tolist() + + save_json(self.opt, opt_path) + return True From 702f40979c7a658640727faaad1e4cd207bc0754 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 18:14:06 +0300 Subject: [PATCH 336/616] fix: evolution many inputs registered --- deeppavlov/models/evolution/evolution_many_inputs_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index c776a9e411..f392cfcecd 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -50,7 +50,7 @@ log = get_logger(__name__) -@register('evolution_classification_many_texts_model') +@register('evolution_many_inputs_classification_model') class KerasEvolutionClassificationManyInputsModel(KerasIntentModel): def __init__(self, **kwargs): From 9f3087112798b0274c3e998c1bc4a8cd4b9750f0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 10 May 2018 18:18:05 +0300 Subject: [PATCH 337/616] fix: evolution many inputs in configs --- deeppavlov/configs/evolution/basic_config_local.json | 2 +- .../evolution/basic_snli_one_neuron_init_part_many_inputs.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 3e63aca045..20629674b0 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -73,7 +73,7 @@ "y_probas_dict" ], "main": true, - "name": "evolution_classification_many_texts_model", + "name": "evolution_many_inputs_classification_model", "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_inputs", "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_inputs", "classes": "#classes_vocab.keys()", diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index df5be8a0a5..0837e46051 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -70,7 +70,7 @@ "y_probas_dict" ], "main": true, - "name": "evolution_classification_model", + "name": "evolution_many_inputs_classification_model", "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs", "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs", "classes": "#classes_vocab.keys()", From 2404ead37b1de85e19cc11d709d6d569d7128c63 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 21 May 2018 11:59:22 +0300 Subject: [PATCH 338/616] feat: add choice of evolving metric --- deeppavlov/models/evolution/run_evolution.py | 33 ++++++++++++-------- 1 file changed, 20 insertions(+), 13 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 223e5a42a6..7d0ca7b0ec 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -9,10 +9,12 @@ def score_population(population, population_size, result_file): global evolution - population_losses = [] - population_fmeasures = [] - population_accuracies = [] - population_roc_auc_scores = [] + population_metrics = {} + for metric in ["classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc"]: + population_metrics[metric] = [] procs = [] @@ -58,15 +60,15 @@ def score_population(population, population_size, result_file): "classification_roc_auc": [val_results[3]], "params": [population[i]]}) result_table.loc[:, order].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - population_losses.append(val_results[0]) - population_accuracies.append(val_results[1]) - population_fmeasures.append(val_results[2]) - population_roc_auc_scores.append(val_results[3]) + population_metrics["classification_log_loss"].append(val_results[0]) + population_metrics["classification_accuracy"].append(val_results[1]) + population_metrics["classification_f1"].append(val_results[2]) + population_metrics["classification_roc_auc"].append(val_results[3]) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) - return population_roc_auc_scores + return population_metrics parser = argparse.ArgumentParser() @@ -78,6 +80,10 @@ def score_population(population, population_size, result_file): parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) parser.add_argument('--one_neuron_init', help='Please, enter number of types of layers', default=0) +parser.add_argument('--evolve_metric', help='Please, choose target metric out of ["classification_log_loss", ' + '"classification_accuracy",' + ' "classification_f1",' + ' "classification_roc_auc"]', default="classification_roc_auc") args = parser.parse_args() @@ -88,6 +94,7 @@ def score_population(population, population_size, result_file): N_LAYERS = int(args.n_layers) N_TYPES = int(args.n_types) ONE_NEURON_INIT = bool(int(args.one_neuron_init)) +EVOLVE_METRIC = args.evolve_metric with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) @@ -110,7 +117,7 @@ def score_population(population, population_size, result_file): "classification_f1", "classification_roc_auc", "params"] result_file = Path(basic_params["chainer"]["pipe"][ evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") -result_table = pd.DataFrame({"loss": [], +result_table = pd.DataFrame({"classification_log_loss": [], "classification_accuracy": [], "classification_f1": [], "classification_roc_auc": [], @@ -120,16 +127,16 @@ def score_population(population, population_size, result_file): print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() print("Considered population: {}\nScoring...\n".format(population)) -population_roc_auc_scores = score_population(population, POPULATION_SIZE, result_file) +population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] iters = 1 while True: print("\nIteration #{} starts\n".format(iters)) - population = evolution.next_generation(population, population_roc_auc_scores, iter=iters) + population = evolution.next_generation(population, population_scores, iter=iters) print("Considered population: {}\nScoring...\n".format(population)) - population_roc_auc_scores = score_population(population, POPULATION_SIZE, result_file) + population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] print("\nIteration #{} was done\n".format(iters)) iters += 1 From ded484728e53f13f9422a57d9fc5b3ac2ec5be8e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 21 May 2018 12:42:22 +0300 Subject: [PATCH 339/616] feat: add several text sizes in many_inputs model, add basic config for selqa --- deeppavlov/configs/evolution/basic_selqa.json | 240 ++++++++++++++++++ .../evolution/evolution_many_inputs_model.py | 27 +- 2 files changed, 257 insertions(+), 10 deletions(-) create mode 100644 deeppavlov/configs/evolution/basic_selqa.json diff --git a/deeppavlov/configs/evolution/basic_selqa.json b/deeppavlov/configs/evolution/basic_selqa.json new file mode 100644 index 0000000000..0dd3b717bd --- /dev/null +++ b/deeppavlov/configs/evolution/basic_selqa.json @@ -0,0 +1,240 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": [ + "question", + "answer" + ], + "y": "label", + "data_path": "/home/dilyara.baymurzina/evolution_data/selqa_data" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "question", + "answer" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/selqa_data/selqa_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/selqa_data/selqa_classes.dict" + }, + { + "in": [ + "question" + ], + "out": [ + "question_lower" + ], + "name": "str_lower" + }, + { + "in": [ + "answer" + ], + "out": [ + "answer_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "question_lower", + "answer_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_many_inputs_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/selqa_classification/one_neuron_init_part_many_inputs", + "load_path": "/home/dilyara.baymurzina/evolution_data/selqa_classification/one_neuron_init_part_many_inputs", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": true + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "return_sequences": true + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "confident_threshold": { + "range": [ + 0.3, + 0.7 + ] + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.00001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": [ + 20, + 50 + ], + "last_layer_activation": "softmax", + "model_name": "evolution_many_inputs_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 50, + 100 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 70 + ], + "discrete": true + }, + "metric_optimization": "minimize", + "metrics": [ + "classification_log_loss", + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index f392cfcecd..1554792bf7 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -59,7 +59,7 @@ def __init__(self, **kwargs): get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], path=str(self.save_path.resolve().parent)) - def texts2vec(self, sentences): + def texts2vec(self, sentences, i): """ Convert texts to vector representations using embedder and padding up to self.opt["text_size"] tokens Args: @@ -69,9 +69,12 @@ def texts2vec(self, sentences): array of embedded texts """ pad = np.zeros(self.opt['embedding_size']) - - embeddings_batch = self.fasttext_model([sen[:self.opt['text_size']] for sen in sentences]) - embeddings_batch = [[pad] * (self.opt['text_size'] - len(tokens)) + tokens for tokens in embeddings_batch] + if type(self.opt['text_size']) is list: + text_size = self.opt['text_size'][i] + else: + text_size = self.opt['text_size'] + embeddings_batch = self.fasttext_model([sen[:text_size] for sen in sentences]) + embeddings_batch = [[pad] * (text_size - len(tokens)) + tokens for tokens in embeddings_batch] embeddings_batch = np.asarray(embeddings_batch) return embeddings_batch @@ -97,9 +100,9 @@ def train_on_batch(self, *args, **kwargs): features = [] for i in range(len(self.opt["in"])): if isinstance(texts[i][0], str): - features.append(self.texts2vec(self.tokenizer(list(texts[i])))) + features.append(self.texts2vec(self.tokenizer(list(texts[i])), i)) else: - features.append(self.texts2vec(list(texts[i]))) + features.append(self.texts2vec(list(texts[i]), i)) onehot_labels = labels2onehot(labels, classes=self.classes) metrics_values = self.model.train_on_batch(features, onehot_labels) @@ -129,9 +132,9 @@ def infer_on_batch(self, *args, **kwargs): features = [] for i in range(len(self.opt["in"])): if isinstance(texts[i][0], str): - features.append(self.texts2vec(self.tokenizer(list(texts[i])))) + features.append(self.texts2vec(self.tokenizer(list(texts[i])), i)) else: - features.append(self.texts2vec(list(texts[i]))) + features.append(self.texts2vec(list(texts[i]), i)) if labels: onehot_labels = labels2onehot(labels, classes=self.classes) @@ -253,8 +256,12 @@ def evolution_many_inputs_classification_model(self, params): Un-compiled model """ inputs = [] - for i in range(len(params["in"])): - inputs.append(Input(shape=(params['text_size'], params['embedding_size']))) + if type(params['text_size']) is list: + for i in range(len(params["in"])): + inputs.append(Input(shape=(params['text_size'][i], params['embedding_size']))) + else: + for i in range(len(params["in"])): + inputs.append(Input(shape=(params['text_size'], params['embedding_size']))) full_outputs = [] From 5979d42cb40663d0b2f37c2e4b0783a091181458 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 21 May 2018 12:52:40 +0300 Subject: [PATCH 340/616] fix: test best true for selqa --- deeppavlov/configs/evolution/basic_selqa.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_selqa.json b/deeppavlov/configs/evolution/basic_selqa.json index 0dd3b717bd..52a12e83d2 100644 --- a/deeppavlov/configs/evolution/basic_selqa.json +++ b/deeppavlov/configs/evolution/basic_selqa.json @@ -230,7 +230,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { From 85c06468c807818f57da52d5fee846f60b6cf944 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 21 May 2018 12:54:44 +0300 Subject: [PATCH 341/616] fix: bigger sizes for selqa --- deeppavlov/configs/evolution/basic_selqa.json | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_selqa.json b/deeppavlov/configs/evolution/basic_selqa.json index 52a12e83d2..1eec402e37 100644 --- a/deeppavlov/configs/evolution/basic_selqa.json +++ b/deeppavlov/configs/evolution/basic_selqa.json @@ -83,7 +83,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -100,7 +100,7 @@ "filters": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -117,7 +117,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -127,7 +127,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -147,14 +147,14 @@ "n_hidden": { "range": [ 50, - 200 + 500 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 500 ], "discrete": true }, From fe0180de92007688a12d663b742b88847eb0626f Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Mon, 21 May 2018 14:28:21 +0300 Subject: [PATCH 342/616] Delete CNAME --- CNAME | 1 - 1 file changed, 1 deletion(-) delete mode 100644 CNAME diff --git a/CNAME b/CNAME deleted file mode 100644 index 822ec1af2e..0000000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -deeppavlov.ai \ No newline at end of file From 6b3ea76f810bddbd9ae67e748889d8c4a7b228c5 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Mon, 21 May 2018 14:28:30 +0300 Subject: [PATCH 343/616] Create CNAME --- CNAME | 1 + 1 file changed, 1 insertion(+) create mode 100644 CNAME diff --git a/CNAME b/CNAME new file mode 100644 index 0000000000..822ec1af2e --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +deeppavlov.ai \ No newline at end of file From 7ccecb6151230c387f54a4b88272ad1da074ac5b Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Mon, 21 May 2018 18:00:58 +0300 Subject: [PATCH 344/616] Delete CNAME --- CNAME | 1 - 1 file changed, 1 deletion(-) delete mode 100644 CNAME diff --git a/CNAME b/CNAME deleted file mode 100644 index 822ec1af2e..0000000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -deeppavlov.ai \ No newline at end of file From 2298ffa54acc6ff7c3494b60e6078115dcd6a92e Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Mon, 21 May 2018 18:01:10 +0300 Subject: [PATCH 345/616] Create CNAME --- CNAME | 1 + 1 file changed, 1 insertion(+) create mode 100644 CNAME diff --git a/CNAME b/CNAME new file mode 100644 index 0000000000..822ec1af2e --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +deeppavlov.ai \ No newline at end of file From d221adb31ddc9484202c0454b028f0f655a5fa47 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 22 May 2018 10:12:37 +0300 Subject: [PATCH 346/616] Delete CNAME --- CNAME | 1 - 1 file changed, 1 deletion(-) delete mode 100644 CNAME diff --git a/CNAME b/CNAME deleted file mode 100644 index 822ec1af2e..0000000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -deeppavlov.ai \ No newline at end of file From 115fef21af01ad4828309c4720bfcb6c734323a6 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 22 May 2018 10:13:00 +0300 Subject: [PATCH 347/616] Create CNAME --- CNAME | 1 + 1 file changed, 1 insertion(+) create mode 100644 CNAME diff --git a/CNAME b/CNAME new file mode 100644 index 0000000000..822ec1af2e --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +deeppavlov.ai \ No newline at end of file From af2352431992d0fadc5068836656774fbb6f869e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 11:51:49 +0300 Subject: [PATCH 348/616] fix: confident_threshold is equal to 1 for SNLI because we want only max proba label --- .../configs/evolution/basic_config_local.json | 7 +- deeppavlov/configs/evolution/basic_selqa.json | 7 +- .../basic_snips_one_neuron_init.json | 7 +- .../evolution/basic_snips_random_init.json | 10 +- .../basic_snli_one_neuron_init_part.json | 11 +- .../basic_snli_one_neuron_init_part_half.json | 7 +- ...snli_one_neuron_init_part_many_inputs.json | 7 +- .../evolution/basic_snli_random_init.json | 7 +- .../configs/evolution/intents_snli.json | 124 ++++++++++++++++++ 9 files changed, 135 insertions(+), 52 deletions(-) create mode 100644 deeppavlov/configs/evolution/intents_snli.json diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json index 20629674b0..a1b859edee 100644 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ b/deeppavlov/configs/evolution/basic_config_local.json @@ -168,12 +168,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_selqa.json b/deeppavlov/configs/evolution/basic_selqa.json index 1eec402e37..e6cc11465a 100644 --- a/deeppavlov/configs/evolution/basic_selqa.json +++ b/deeppavlov/configs/evolution/basic_selqa.json @@ -168,12 +168,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 660bae50bd..34022c6b80 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -154,12 +154,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index b476f08415..c200379281 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -117,8 +117,7 @@ ], "discrete": true }, - "return_sequences": trueool": true - } + "return_sequences": true }, "MaxPooling1D": { "pool_size": { @@ -155,12 +154,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index b20b80cfbd..ee81a38e99 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -154,12 +154,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ @@ -209,8 +204,8 @@ "classification_roc_auc" ], "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, "show_examples": false, "validate_best": true, "test_best": false diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index 0aab7a2e80..e1568f7d17 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -154,12 +154,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 0837e46051..b2d269cddf 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -165,12 +165,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index 4840a1e685..508c6a98d7 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -154,12 +154,7 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ diff --git a/deeppavlov/configs/evolution/intents_snli.json b/deeppavlov/configs/evolution/intents_snli.json new file mode 100644 index 0000000000..7e60b2908c --- /dev/null +++ b/deeppavlov/configs/evolution/intents_snli.json @@ -0,0 +1,124 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "gold_label", + "data_path": "/home/dilyara/data/data_files/SNLI/snli_data" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict", + "load_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", + "load_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "intent_model", + "save_path": "intents/intent_snli_v0", + "load_path": "intents/intent_snli_v0", + "classes": "#classes_vocab.keys()", + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": 256, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": 0.01, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 15, + "coef_reg_cnn": 1e-4, + "coef_reg_den": 1e-4, + "dropout_rate": 0.5, + "dense_size": 100, + "model_name": "cnn_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": 100, + "batch_size": 64, + "metrics": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 5, + "log_every_n_epochs": 5, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + }, + "download": [ + "http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz", + "http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz", + { + "url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv", + "subdir": "snips" + }, + { + "url": "http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin", + "subdir": "embeddings" + } + ] + } +} From dfadd53284e8f37c833538c09c49fa7aeec6429c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 12:51:34 +0300 Subject: [PATCH 349/616] feat: mrr classification --- deeppavlov/__init__.py | 1 + deeppavlov/metrics/mrr_classification.py | 97 ++++++++++++++++++++++++ 2 files changed, 98 insertions(+) create mode 100644 deeppavlov/metrics/mrr_classification.py diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index 99482e1451..3a2523b709 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -106,6 +106,7 @@ import deeppavlov.metrics.roc_auc_score import deeppavlov.metrics.fmeasure_classification import deeppavlov.metrics.log_loss +import deeppavlov.metrics.mrr_classification import deeppavlov.core.common.log diff --git a/deeppavlov/metrics/mrr_classification.py b/deeppavlov/metrics/mrr_classification.py new file mode 100644 index 0000000000..41f85be199 --- /dev/null +++ b/deeppavlov/metrics/mrr_classification.py @@ -0,0 +1,97 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import numpy as np +import json +from scipy.stats import rankdata +import tensorflow as tf +from keras import backend as K + +from deeppavlov.core.common.metrics_registry import register_metric +from deeppavlov.models.classifiers.intents.utils import labels2onehot + + +def calc_mrr(rank): + rank = list(map(lambda x: 1./x, rank)) + return np.mean(rank) + + +def mrr_from_json(fname): + data = [] + with open(fname) as f: + for line in f.readlines(): + data += [json.loads(line)] + rank_i = [] + for elem in data: + cand = elem['candidates'] + results = elem['results'] + cand_ranks = (len(results) - rankdata(results, method='max'))[cand] + 1 + rank_i.append( min(cand_ranks)) + mrr = calc_mrr(rank_i) + return mrr + + +def mrr_from_dict(data): + rank_i = [] + for elem in data: + cand = elem['candidates'] + results = elem['results'] + cand_ranks = (len(results) - rankdata(results, method='max'))[cand] + 1 + rank_i.append( min(cand_ranks)) + mrr = calc_mrr(rank_i) + return mrr + + +def make_json_predictions(fname, predictions): + data = [] + with open(fname) as f: + for line in f.readlines(): + data += [json.loads(line)] + + pointer = 0 + for elem_id, elem in enumerate(data): + n = len(elem["sentences"]) + results = [] + for i in range(n): + if elem["sentences"][i] == "": + results.append(0) + else: + results.append(1 * (predictions[pointer])) + pointer += 1 + data[elem_id]["results"] = results + return data + + +@register_metric('classification_mrr') +def mrr_score(y_true, y_predicted): + # there is hard code for selqa dataset! + if len(y_predicted) == 66438: + data_type = "train" + elif len(y_predicted) == 9377: + data_type = "dev" + elif len(y_predicted) == 19435: + data_type = "test" + else: + return 0. + + classes = np.array(list(y_predicted[0][1].keys())) + y_true_one_hot = labels2onehot(y_true, classes) + y_pred_probas = [y_predicted[i][1]["correct"] for i in range(len(y_predicted))] + + score = make_json_predictions("/home/dilyara.baymurzina/evolution_data/selqa_data/SelQA-ass-" + data_type + ".json", + y_pred_probas) + + return score From 94a9b0d0730778370348214dcd2f701b5b46ac06 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 12:52:26 +0300 Subject: [PATCH 350/616] fix: max to min in mrr classification --- deeppavlov/metrics/mrr_classification.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/metrics/mrr_classification.py b/deeppavlov/metrics/mrr_classification.py index 41f85be199..d495de313f 100644 --- a/deeppavlov/metrics/mrr_classification.py +++ b/deeppavlov/metrics/mrr_classification.py @@ -38,7 +38,7 @@ def mrr_from_json(fname): for elem in data: cand = elem['candidates'] results = elem['results'] - cand_ranks = (len(results) - rankdata(results, method='max'))[cand] + 1 + cand_ranks = (len(results) - rankdata(results, method='min'))[cand] + 1 rank_i.append( min(cand_ranks)) mrr = calc_mrr(rank_i) return mrr @@ -49,7 +49,7 @@ def mrr_from_dict(data): for elem in data: cand = elem['candidates'] results = elem['results'] - cand_ranks = (len(results) - rankdata(results, method='max'))[cand] + 1 + cand_ranks = (len(results) - rankdata(results, method='min'))[cand] + 1 rank_i.append( min(cand_ranks)) mrr = calc_mrr(rank_i) return mrr From 31089ee4630a7ac4d76b145d4d9024e3493f7ca7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 15:14:50 +0300 Subject: [PATCH 351/616] feat: add argument save_best_portion --- deeppavlov/models/evolution/run_evolution.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 7d0ca7b0ec..0f532e1287 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -84,6 +84,8 @@ def score_population(population, population_size, result_file): '"classification_accuracy",' ' "classification_f1",' ' "classification_roc_auc"]', default="classification_roc_auc") +parser.add_argument('--save_best_portion', + help='Please, enter portion of population to save for the next generation with weights', default=0.) args = parser.parse_args() @@ -95,6 +97,7 @@ def score_population(population, population_size, result_file): N_TYPES = int(args.n_types) ONE_NEURON_INIT = bool(int(args.one_neuron_init)) EVOLVE_METRIC = args.evolve_metric +SAVE_BEST_PORTION = float(args.save_best_portion) with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) @@ -110,6 +113,7 @@ def score_population(population, population_size, result_file): key_basic_layers="basic_layers_params", seed=None, start_with_one_neuron=ONE_NEURON_INIT, + save_best_with_weights_portion=SAVE_BEST_PORTION, **basic_params) # Result table From 354a165b3bbe5158745736fd6f44103c90fe5499 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 16:08:35 +0300 Subject: [PATCH 352/616] fix: test_best true in all configs --- deeppavlov/configs/evolution/basic_snips_one_neuron_init.json | 2 +- deeppavlov/configs/evolution/basic_snips_random_init.json | 2 +- .../configs/evolution/basic_snli_one_neuron_init_part.json | 2 +- .../configs/evolution/basic_snli_one_neuron_init_part_half.json | 2 +- .../evolution/basic_snli_one_neuron_init_part_many_inputs.json | 2 +- deeppavlov/configs/evolution/basic_snli_random_init.json | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 34022c6b80..0182c2dba6 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -207,7 +207,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index c200379281..5ca329a9c7 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -207,7 +207,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index ee81a38e99..362b9850d2 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -208,7 +208,7 @@ "log_every_n_epochs": 1, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json index e1568f7d17..4b1fe3aa25 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json @@ -208,7 +208,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index b2d269cddf..88a71bf005 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -219,7 +219,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index 508c6a98d7..6903ae2f3b 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -207,7 +207,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { From 337108d7caa351dbe3da50df004781b0b7ae783c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 16:09:14 +0300 Subject: [PATCH 353/616] fix: fix seed for evolution --- deeppavlov/models/evolution/run_evolution.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 0f532e1287..fc7ef31985 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -82,8 +82,8 @@ def score_population(population, population_size, result_file): parser.add_argument('--one_neuron_init', help='Please, enter number of types of layers', default=0) parser.add_argument('--evolve_metric', help='Please, choose target metric out of ["classification_log_loss", ' '"classification_accuracy",' - ' "classification_f1",' - ' "classification_roc_auc"]', default="classification_roc_auc") + '"classification_f1",' + '"classification_roc_auc"]', default="classification_roc_auc") parser.add_argument('--save_best_portion', help='Please, enter portion of population to save for the next generation with weights', default=0.) @@ -111,7 +111,7 @@ def score_population(population, population_size, result_file): p_mutation=0.5, mutation_power=0.1, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", - seed=None, + seed=42, start_with_one_neuron=ONE_NEURON_INIT, save_best_with_weights_portion=SAVE_BEST_PORTION, **basic_params) From 294fa5f818d33ac542647a5989ac38463e79563e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 22 May 2018 17:03:58 +0300 Subject: [PATCH 354/616] feat: add train partition --- .../basic_snli_one_neuron_init_part.json | 17 ++++++++++------- ...c_snli_one_neuron_init_part_many_inputs.json | 15 +++++++++------ .../evolution/basic_snli_random_init.json | 15 +++++++++------ deeppavlov/models/evolution/run_evolution.py | 12 ++++++++---- 4 files changed, 36 insertions(+), 23 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 362b9850d2..fff2ed480f 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -3,7 +3,10 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part" + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -23,8 +26,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict" }, { "in": [ @@ -60,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_1", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_1", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -169,7 +172,7 @@ ] }, "loss": "binary_crossentropy", - "text_size": 30, + "text_size": 51, "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", @@ -192,7 +195,7 @@ "batch_size": { "range": [ 20, - 50 + 70 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 88a71bf005..8215c8e195 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -3,7 +3,10 @@ "name": "basic_classification_reader", "x": ["sentence1", "sentence2"], "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/two_texts/part" + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/cutted_many_inputs/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -24,8 +27,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/cutted_many_inputs/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/cutted_many_inputs/snli_classes.dict" }, { "in": [ @@ -71,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_1", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_1", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -180,7 +183,7 @@ ] }, "loss": "binary_crossentropy", - "text_size": 15, + "text_size": [30, 20], "last_layer_activation": "softmax", "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json index 6903ae2f3b..a57d2fc672 100644 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ b/deeppavlov/configs/evolution/basic_snli_random_init.json @@ -3,7 +3,10 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data" + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -23,8 +26,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict" }, { "in": [ @@ -60,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random_1", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random_1", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -169,7 +172,7 @@ ] }, "loss": "binary_crossentropy", - "text_size": 15, + "text_size": 51, "model_name": "evolution_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index fc7ef31985..a6343ee73f 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -75,17 +75,19 @@ def score_population(population, population_size, result_file): parser.add_argument('--config', help='Please, enter model path to config', default='./configs/evolution/basic_intents_config.json') +parser.add_argument('--evolve_metric', help='Please, choose target metric out of ["classification_log_loss", ' + '"classification_accuracy",' + '"classification_f1",' + '"classification_roc_auc"]') parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) parser.add_argument('--one_neuron_init', help='Please, enter number of types of layers', default=0) -parser.add_argument('--evolve_metric', help='Please, choose target metric out of ["classification_log_loss", ' - '"classification_accuracy",' - '"classification_f1",' - '"classification_roc_auc"]', default="classification_roc_auc") parser.add_argument('--save_best_portion', help='Please, enter portion of population to save for the next generation with weights', default=0.) +parser.add_argument('--train_partition', + help='Please, enter partition of splitted train', default=1) args = parser.parse_args() @@ -98,6 +100,7 @@ def score_population(population, population_size, result_file): ONE_NEURON_INIT = bool(int(args.one_neuron_init)) EVOLVE_METRIC = args.evolve_metric SAVE_BEST_PORTION = float(args.save_best_portion) +TRAIN_PARTITION = int(args.train_partition) with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) @@ -114,6 +117,7 @@ def score_population(population, population_size, result_file): seed=42, start_with_one_neuron=ONE_NEURON_INIT, save_best_with_weights_portion=SAVE_BEST_PORTION, + train_partition=TRAIN_PARTITION, **basic_params) # Result table From 9816c34fdebc0a514a71609e28f8f9fee791926f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 10:46:29 +0300 Subject: [PATCH 355/616] fix: average in mrr --- deeppavlov/metrics/mrr_classification.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/metrics/mrr_classification.py b/deeppavlov/metrics/mrr_classification.py index d495de313f..6d8d5ea280 100644 --- a/deeppavlov/metrics/mrr_classification.py +++ b/deeppavlov/metrics/mrr_classification.py @@ -38,7 +38,7 @@ def mrr_from_json(fname): for elem in data: cand = elem['candidates'] results = elem['results'] - cand_ranks = (len(results) - rankdata(results, method='min'))[cand] + 1 + cand_ranks = (len(results) - rankdata(results, method='average'))[cand] + 1 rank_i.append( min(cand_ranks)) mrr = calc_mrr(rank_i) return mrr @@ -49,7 +49,7 @@ def mrr_from_dict(data): for elem in data: cand = elem['candidates'] results = elem['results'] - cand_ranks = (len(results) - rankdata(results, method='min'))[cand] + 1 + cand_ranks = (len(results) - rankdata(results, method='average'))[cand] + 1 rank_i.append( min(cand_ranks)) mrr = calc_mrr(rank_i) return mrr From a453096656be7ff17ed8e75375b86c03864ce858 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Wed, 23 May 2018 11:07:34 +0300 Subject: [PATCH 356/616] Delete CNAME --- CNAME | 1 - 1 file changed, 1 deletion(-) delete mode 100644 CNAME diff --git a/CNAME b/CNAME deleted file mode 100644 index 822ec1af2e..0000000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -deeppavlov.ai \ No newline at end of file From 0b8d4e7e32f07fcd4336631175ba3387181fd0b9 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Wed, 23 May 2018 11:07:52 +0300 Subject: [PATCH 357/616] Create CNAME --- CNAME | 1 + 1 file changed, 1 insertion(+) create mode 100644 CNAME diff --git a/CNAME b/CNAME new file mode 100644 index 0000000000..822ec1af2e --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +deeppavlov.ai \ No newline at end of file From 0e84c11ef4eeb41ddad6f3c12d925f04a30b5d23 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 14:17:58 +0300 Subject: [PATCH 358/616] fix: iteration except of iter --- .../models/evolution/neuroevolution_param_generator.py | 6 +++--- deeppavlov/models/evolution/run_evolution.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 5027ca3293..135be2ba25 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -163,7 +163,7 @@ def initialize_layers_params(self): } return all_layers_params - def first_generation(self, iter=0): + def first_generation(self, iteration=0): """ Initialize first generation randomly according to the given constraints is self.params Returns: @@ -183,10 +183,10 @@ def first_generation(self, iter=0): # intitializing path to save model if "model_name" in params_for_search.keys(): params["save_path"] = str(Path(self.params["save_path"]).joinpath( - "population_" + str(iter)).joinpath(params_for_search["model_name"] + "_" + str(i))) + "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) else: params["save_path"] = str(Path(self.params["save_path"]).joinpath( - "population_" + str(iter)).joinpath(self.params["model_name"] + "_" + str(i))) + "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) layers_params = self.initialize_layers_params() diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index a6343ee73f..0c89e2a544 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -142,7 +142,7 @@ def score_population(population, population_size, result_file): while True: print("\nIteration #{} starts\n".format(iters)) - population = evolution.next_generation(population, population_scores, iter=iters) + population = evolution.next_generation(population, population_scores, iteration=iters) print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] From ea346f68949fef895ee6ade7ad5cb5c096300b5b Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 14:38:44 +0300 Subject: [PATCH 359/616] fix: saving config.json with save_json from deeppavlov --- deeppavlov/models/evolution/run_evolution.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 0c89e2a544..12188b6156 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -6,6 +6,8 @@ import pandas as pd from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution +from deeppavlov.core.common.file import save_json + def score_population(population, population_size, result_file): global evolution @@ -36,9 +38,7 @@ def score_population(population, population_size, result_file): f_name = f_name.joinpath("config.json") population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] =\ population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"].tolist() - with open(f_name, 'w') as outfile: - json.dump(population[i], outfile) - + save_json(population[i], f_name) procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], str(f_name), From b94c0e17d43d499e87cbbe6144f79edc70aa9e6c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 16:44:55 +0300 Subject: [PATCH 360/616] fix: save path and load path changed --- deeppavlov/models/evolution/run_evolution.py | 29 +++++++++++++------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 12188b6156..2f85fbe108 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -21,29 +21,38 @@ def score_population(population, population_size, result_file): procs = [] for i in range(population_size): - f_name = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) - model_name = population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] + # f_name = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) + # model_name = population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] + # population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ + # str(f_name.joinpath(model_name + "_" + str(i))) + # population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] =\ + # population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] + + save_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) + load_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - str(f_name.joinpath(model_name + "_" + str(i))) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] =\ - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] + str(save_path.joinpath("model")) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ + str(load_path.joinpath("model")) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = \ evolution.nodes print(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) try: - f_name.mkdir(parents=True) + save_path.mkdir(parents=True) except FileExistsError: pass - f_name = f_name.joinpath("config.json") + + f_name = save_path.joinpath("config.json") population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] =\ population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"].tolist() save_json(population[i], f_name) procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], str(f_name), - str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).parent), - str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).parent) + str(save_path), + str(save_path) ), shell=True, stdout=PIPE, stderr=PIPE)) @@ -142,7 +151,7 @@ def score_population(population, population_size, result_file): while True: print("\nIteration #{} starts\n".format(iters)) - population = evolution.next_generation(population, population_scores, iteration=iters) + population = evolution.next_generation(population, population_scores, iters) print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] From 354ce96da723b44c54af1d846e26669efbf1380e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 16:52:38 +0300 Subject: [PATCH 361/616] fix: asve path comment --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 1 + 1 file changed, 1 insertion(+) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 135be2ba25..218d8e925b 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -181,6 +181,7 @@ def first_generation(self, iteration=0): self.evolving_train_params.extend(evolving_params) # intitializing path to save model + # save_path = population_iteration/model_name_i/ if "model_name" in params_for_search.keys(): params["save_path"] = str(Path(self.params["save_path"]).joinpath( "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) From e62d86359e81636bba95040d72349af7328bdc7e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 16:53:25 +0300 Subject: [PATCH 362/616] fix: configs --- .../basic_snli_one_neuron_init_part.json | 4 ++-- deeppavlov/configs/evolution/intents_snli.json | 17 ++++++++++------- 2 files changed, 12 insertions(+), 9 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index fff2ed480f..60b9371c08 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 1, + 2 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/intents_snli.json b/deeppavlov/configs/evolution/intents_snli.json index 7e60b2908c..fb9bf4fa12 100644 --- a/deeppavlov/configs/evolution/intents_snli.json +++ b/deeppavlov/configs/evolution/intents_snli.json @@ -3,7 +3,10 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara/data/data_files/SNLI/snli_data" + "data_path": "/home/dilyara/data/data_files/SNLI/one_input", + "train": "train.csv", + "valid": "valid.csv", + "test": "test.csv" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -23,8 +26,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict", - "load_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict" + "save_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict", + "load_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict" }, { "in": [ @@ -74,7 +77,7 @@ "lear_rate": 0.01, "lear_rate_decay": 0.1, "loss": "binary_crossentropy", - "text_size": 15, + "text_size": 51, "coef_reg_cnn": 1e-4, "coef_reg_den": 1e-4, "dropout_rate": 0.5, @@ -98,11 +101,11 @@ "classification_roc_auc" ], "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { From 60373adee4369cdc0f0879acb10f1a4b1f416d08 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 17:04:16 +0300 Subject: [PATCH 363/616] fix: number of epochs increase, next generation --- .../configs/evolution/basic_snli_one_neuron_init_part.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 60b9371c08..fff2ed480f 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 1, - 2 + 50, + 100 ], "discrete": true }, From ef807df4c4f4a5b286c421565c78e46e5d404fbb Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 17:31:41 +0300 Subject: [PATCH 364/616] feat: add, subtract and multiply to many_inputs model --- .../evolution/evolution_many_inputs_model.py | 24 ++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index 1554792bf7..6a1619bb4c 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -25,7 +25,7 @@ from keras.layers.wrappers import Bidirectional from keras.models import Model from keras.regularizers import l2 -from keras.layers import Concatenate, Reshape, CuDNNLSTM, Lambda +from keras.layers import Concatenate, Reshape, CuDNNLSTM, Lambda, Add, Subtract, Multiply from keras import backend as K from overrides import overrides from pathlib import Path @@ -272,6 +272,17 @@ def evolution_many_inputs_classification_model(self, params): output = dense1(inp) full_outputs.append(globalmaxpooling(output)) + summ = Add()(full_outputs) + mult = Multiply()(full_outputs) + + try: + subt = Subtract()(full_outputs) + full_outputs.append(subt) + except ValueError: + pass + full_outputs.append(summ) + full_outputs.append(mult) + output = Concatenate()(full_outputs) output = Dense(self.n_classes, activation=None)(output) activation = params.get("last_layer_activation", "sigmoid") @@ -358,6 +369,17 @@ def evolution_many_inputs_classification_model(self, params): output = GlobalMaxPooling1D()(output) full_outputs.append(output) + summ = Add()(full_outputs) + mult = Multiply()(full_outputs) + + try: + subt = Subtract()(full_outputs) + full_outputs.append(subt) + except ValueError: + pass + full_outputs.append(summ) + full_outputs.append(mult) + output = Concatenate()(full_outputs) output = Dense(self.n_classes, activation=None)(output) activation = params.get("last_layer_activation", "sigmoid") From 7584dc4c0142d45ed232b1432cca369e90d76554 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 18:18:03 +0300 Subject: [PATCH 365/616] fix: error save path to load path --- deeppavlov/core/models/keras_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/core/models/keras_model.py b/deeppavlov/core/models/keras_model.py index 33936d0bff..7d62108c3b 100644 --- a/deeppavlov/core/models/keras_model.py +++ b/deeppavlov/core/models/keras_model.py @@ -136,7 +136,7 @@ def load(self, model_name, optimizer_name, loss_name, lear_rate=None, lear_rate_ """ if self.load_path: if isinstance(self.load_path, Path) and not self.load_path.parent.is_dir(): - raise ConfigError("Provided save path is incorrect!") + raise ConfigError("Provided load path is incorrect!") opt_path = Path("{}_opt.json".format(str(self.load_path.resolve()))) weights_path = Path("{}.h5".format(str(self.load_path.resolve()))) From 32ecb14ca87ba67f89598753c77361ce26216c59 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 18:28:03 +0300 Subject: [PATCH 366/616] fix: mrr classification to config --- deeppavlov/configs/evolution/basic_selqa.json | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_selqa.json b/deeppavlov/configs/evolution/basic_selqa.json index e6cc11465a..fddae03149 100644 --- a/deeppavlov/configs/evolution/basic_selqa.json +++ b/deeppavlov/configs/evolution/basic_selqa.json @@ -218,7 +218,8 @@ "classification_log_loss", "classification_accuracy", "classification_f1", - "classification_roc_auc" + "classification_roc_auc", + "classification_mrr" ], "validation_patience": 5, "val_every_n_epochs": 5, From 3f59916f5915d8514c1a76f9dca7c1feacbb37d6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 23 May 2018 21:46:35 +0300 Subject: [PATCH 367/616] fix: path parent for load [ath --- ...asic_snli_one_neuron_init_part (copy).json | 223 +++++++++++++++++ ..._one_neuron_init_part_many_inputs_big.json | 234 ++++++++++++++++++ .../configs/evolution/check_config.json | 1 + 3 files changed, 458 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json create mode 100644 deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json create mode 100644 deeppavlov/configs/evolution/check_config.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json new file mode 100644 index 0000000000..b20b80cfbd --- /dev/null +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json @@ -0,0 +1,223 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + 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["classification_log_loss", "classification_accuracy", "classification_f1", "classification_roc_auc"], "validation_patience": 5, "val_every_n_epochs": 5, "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, "test_best": true, "epochs": 77, "batch_size": 51}, "metadata": {"labels": {"telegram_utils": "IntentModel"}}} From 4ca7a87c5a46ef8796a2f77fd7293d85a4fd6004 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 24 May 2018 14:19:17 +0300 Subject: [PATCH 368/616] Fix: mrr score was incorrect --- deeppavlov/metrics/mrr_classification.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/deeppavlov/metrics/mrr_classification.py b/deeppavlov/metrics/mrr_classification.py index 6d8d5ea280..b7fd72c493 100644 --- a/deeppavlov/metrics/mrr_classification.py +++ b/deeppavlov/metrics/mrr_classification.py @@ -91,7 +91,9 @@ def mrr_score(y_true, y_predicted): y_true_one_hot = labels2onehot(y_true, classes) y_pred_probas = [y_predicted[i][1]["correct"] for i in range(len(y_predicted))] - score = make_json_predictions("/home/dilyara.baymurzina/evolution_data/selqa_data/SelQA-ass-" + data_type + ".json", - y_pred_probas) + json_with_predictions = make_json_predictions("/home/dilyara.baymurzina/evolution_data/selqa_data/SelQA-ass-" + + data_type + ".json", + y_pred_probas) + score = mrr_from_dict(json_with_predictions) return score From cc58812207a31acaa54c35752c387dffb598a46a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 12:20:41 +0300 Subject: [PATCH 369/616] feat: no hardcoded metrics in run_evolution --- deeppavlov/models/evolution/run_evolution.py | 51 ++++++++++---------- 1 file changed, 26 insertions(+), 25 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 2f85fbe108..7cc13884bf 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -4,6 +4,7 @@ from pathlib import Path from subprocess import Popen, PIPE import pandas as pd +from copy import deepcopy, copy from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution from deeppavlov.core.common.file import save_json @@ -11,12 +12,10 @@ def score_population(population, population_size, result_file): global evolution + population_metrics = {} - for metric in ["classification_log_loss", - "classification_accuracy", - "classification_f1", - "classification_roc_auc"]: - population_metrics[metric] = [] + for m in CONSIDERED_METRICS: + population_metrics[m] = [] procs = [] @@ -63,16 +62,18 @@ def score_population(population, population_size, result_file): for i in range(population_size): val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ "save_path"]).parent.joinpath("valid_results.txt"))) - result_table = pd.DataFrame({"classification_log_loss": [val_results[0]], - "classification_accuracy": [val_results[1]], - "classification_f1": [val_results[2]], - "classification_roc_auc": [val_results[3]], - "params": [population[i]]}) + result_table_dict = {} + for el in order: + result_table_dict[el] = [] + for m_id, m in enumerate(CONSIDERED_METRICS): + result_table_dict[m].append(val_results[m_id]) + result_table_dict[order[-1]] = [population[i]] + result_table = pd.DataFrame(result_table_dict) + result_table.loc[:, order].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - population_metrics["classification_log_loss"].append(val_results[0]) - population_metrics["classification_accuracy"].append(val_results[1]) - population_metrics["classification_f1"].append(val_results[2]) - population_metrics["classification_roc_auc"].append(val_results[3]) + + for m_id, m in enumerate(CONSIDERED_METRICS): + population_metrics[m].append(val_results[m_id]) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) @@ -84,10 +85,7 @@ def score_population(population, population_size, result_file): parser.add_argument('--config', help='Please, enter model path to config', default='./configs/evolution/basic_intents_config.json') -parser.add_argument('--evolve_metric', help='Please, choose target metric out of ["classification_log_loss", ' - '"classification_accuracy",' - '"classification_f1",' - '"classification_roc_auc"]') +parser.add_argument('--evolve_metric', help='Please, choose target metric out of given in your config.train.metrics') parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) @@ -116,6 +114,9 @@ def score_population(population, population_size, result_file): print("Given basic params: {}\n".format(basic_params)) +# list of names of considered metrics +CONSIDERED_METRICS = basic_params["train"]["metrics"] + # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, @@ -130,15 +131,15 @@ def score_population(population, population_size, result_file): **basic_params) # Result table -order = ["classification_log_loss", "classification_accuracy", - "classification_f1", "classification_roc_auc", "params"] +order = deepcopy(CONSIDERED_METRICS) +order.extend(["params"]) +result_table_dict = {} +for el in order: + result_table_dict[el] = [] + result_file = Path(basic_params["chainer"]["pipe"][ evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") -result_table = pd.DataFrame({"classification_log_loss": [], - "classification_accuracy": [], - "classification_f1": [], - "classification_roc_auc": [], - "params": []}) +result_table = pd.DataFrame(result_table_dict) result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') print("\nIteration #{} starts\n".format(0)) From b3a4857a297b9a405921d8c4d89dd4c0fdd4b96b Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 14:19:47 +0300 Subject: [PATCH 370/616] chore: change save method to super --- .../models/evolution/evolution_intent_model.py | 13 +------------ .../evolution/evolution_many_inputs_model.py | 14 +------------- 2 files changed, 2 insertions(+), 25 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 3ac7e842d9..aef0adc73a 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -224,21 +224,10 @@ def save(self, fname=None): Returns: None """ - - if not self.save_path: - raise ConfigError("No `save_path` is provided for Keras model!") - elif isinstance(self.save_path, Path) and not self.save_path.parent.is_dir(): - raise ConfigError("Provided save path is incorrect!") - else: - opt_path = "{}_opt.json".format(str(self.save_path.resolve())) - weights_path = "{}.h5".format(str(self.save_path.resolve())) - log.info("[saving model to {}]".format(opt_path)) - self.model.save_weights(weights_path) - if type(self.opt["binary_mask"]) is list: pass else: self.opt["binary_mask"] = self.opt["binary_mask"].tolist() - save_json(self.opt, opt_path) + super().save(fname) return True diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index 6a1619bb4c..d078c521af 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -387,7 +387,6 @@ def evolution_many_inputs_classification_model(self, params): model = Model(inputs=inputs, outputs=act_output) return model - @overrides def save(self, fname=None): """ Save the model parameters into <>_opt.json (or <>_opt.json) @@ -398,21 +397,10 @@ def save(self, fname=None): Returns: None """ - - if not self.save_path: - raise ConfigError("No `save_path` is provided for Keras model!") - elif isinstance(self.save_path, Path) and not self.save_path.parent.is_dir(): - raise ConfigError("Provided save path is incorrect!") - else: - opt_path = "{}_opt.json".format(str(self.save_path.resolve())) - weights_path = "{}.h5".format(str(self.save_path.resolve())) - log.info("[saving model to {}]".format(opt_path)) - self.model.save_weights(weights_path) - if type(self.opt["binary_mask"]) is list: pass else: self.opt["binary_mask"] = self.opt["binary_mask"].tolist() - save_json(self.opt, opt_path) + super().save(fname) return True From 6eef313765b7212bf9f6b00244dc8856637ac635 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 14:46:55 +0300 Subject: [PATCH 371/616] fix: 1 to 2 epochs for snli part to test --- .../configs/evolution/basic_snli_one_neuron_init_part.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index fff2ed480f..60b9371c08 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 1, + 2 ], "discrete": true }, From f62f3d63e40d6899fa1dcee1453c6459ba231a0e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 18:32:34 +0300 Subject: [PATCH 372/616] fix: new folder for next experiment --- .../basic_snli_one_neuron_init_part_many_inputs_big.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json index 39816b462f..2403b1b3e0 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json @@ -71,8 +71,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_1", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_1", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { From 0b098c2dd9b03f4c54a8b1973bc7af02749a8997 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 18:57:39 +0300 Subject: [PATCH 373/616] fix: configs --- ...> basic_ru_snli_one_neuron_init_part.json} | 25 ++++---- ...nli_one_neuron_init_part_many_inputs.json} | 61 +++++++++++-------- .../basic_snli_one_neuron_init_part.json | 8 +-- ...snli_one_neuron_init_part_many_inputs.json | 4 +- 4 files changed, 55 insertions(+), 43 deletions(-) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init_part_half.json => basic_ru_snli_one_neuron_init_part.json} (83%) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init_part (copy).json => basic_ru_snli_one_neuron_init_part_many_inputs.json} (74%) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json similarity index 83% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json rename to deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json index 4b1fe3aa25..73a9274319 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_half.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json @@ -3,7 +3,10 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part_half" + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" }, "dataset_iterator": { "name": "basic_classification_iterator" @@ -23,8 +26,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/one_input/ru_snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/one_input/ru_snli_classes.dict" }, { "in": [ @@ -38,8 +41,8 @@ { "id": "my_embedder", "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "save_path": "/home/dilyara.baymurzina/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.bin", "dim": 300 }, { @@ -60,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_half", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_half", + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_2", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_2", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -169,7 +172,7 @@ ] }, "loss": "binary_crossentropy", - "text_size": 30, + "text_size": 51, "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", @@ -192,7 +195,7 @@ "batch_size": { "range": [ 20, - 50 + 70 ], "discrete": true }, @@ -204,8 +207,8 @@ "classification_roc_auc" ], "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, "show_examples": false, "validate_best": true, "test_best": true diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json similarity index 74% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json rename to deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json index b20b80cfbd..2121ee763e 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part (copy).json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json @@ -1,16 +1,20 @@ { "dataset_reader": { "name": "basic_classification_reader", - "x": "text", + "x": ["sentence1", "sentence2"], "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/part" + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/cutted_many_inputs/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" }, "dataset_iterator": { "name": "basic_classification_iterator" }, "chainer": { "in": [ - "x" + "sentence1", + "sentence2" ], "in_y": [ "y" @@ -23,23 +27,32 @@ "y" ], "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/cutted_many_inputs/ru_snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/cutted_many_inputs/ru_snli_classes.dict" }, { "in": [ - "x" + "sentence1" ], "out": [ - "x_lower" + "sentence1_lower" + ], + "name": "str_lower" + }, + { + "in": [ + "sentence2" + ], + "out": [ + "sentence2_lower" ], "name": "str_lower" }, { "id": "my_embedder", "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "save_path": "/home/dilyara.baymurzina/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.bin", "dim": 300 }, { @@ -49,7 +62,8 @@ }, { "in": [ - "x_lower" + "sentence1_lower", + "sentence2_lower" ], "in_y": [ "y" @@ -59,9 +73,9 @@ "y_probas_dict" ], "main": true, - "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part", + "name": "evolution_many_inputs_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_2", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_2", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -154,29 +168,24 @@ } } }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, + "confident_threshold": 1, "optimizer": "Adam", "lear_rate": { "range": [ - 0.0001, + 0.001, 0.1 ] }, "lear_rate_decay": { "range": [ - 0.000001, + 0.00001, 0.1 ] }, "loss": "binary_crossentropy", - "text_size": 30, + "text_size": [30, 20], "last_layer_activation": "softmax", - "model_name": "evolution_classification_model", + "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" } @@ -196,8 +205,8 @@ }, "batch_size": { "range": [ - 20, - 50 + 50, + 70 ], "discrete": true }, @@ -213,7 +222,7 @@ "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, - "test_best": false + "test_best": true }, "metadata": { "labels": { diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 60b9371c08..d61cc3e695 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_1", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_1", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_2", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_2", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 1, - 2 + 50, + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 8215c8e195..71213c283f 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_1", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_1", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_2", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_2", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { From dc00ca49767605bb65c43a8209c856ef9f1704a4 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 25 May 2018 19:04:57 +0300 Subject: [PATCH 374/616] fix: configs --- .../evolution/basic_ru_snli_one_neuron_init_part.json | 2 +- .../basic_ru_snli_one_neuron_init_part_many_inputs.json | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json index 73a9274319..727a3c809b 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json @@ -3,7 +3,7 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/parts", + "data_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/one_input/parts", "train": "train_0.csv", "valid": "valid.csv", "test": "test.csv" diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json index 2121ee763e..c1c443e312 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json @@ -3,7 +3,7 @@ "name": "basic_classification_reader", "x": ["sentence1", "sentence2"], "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/cutted_many_inputs/parts", + "data_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/many_inputs/parts", "train": "train_0.csv", "valid": "valid.csv", "test": "test.csv" @@ -27,8 +27,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/cutted_many_inputs/ru_snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/cutted_many_inputs/ru_snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/many_inputs/ru_snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_data/many_inputs/ru_snli_classes.dict" }, { "in": [ From ef2a06de127826461524b13e43dce6f739db3acb Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 28 May 2018 12:41:02 +0300 Subject: [PATCH 375/616] fix: configs --- .../basic_ru_snli_one_neuron_init_part.json | 6 +++--- ...ru_snli_one_neuron_init_part_many_inputs.json | 16 ++++++++-------- .../basic_snli_one_neuron_init_part.json | 6 +++--- ...ic_snli_one_neuron_init_part_many_inputs.json | 16 ++++++++-------- 4 files changed, 22 insertions(+), 22 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json index 727a3c809b..a2bc958885 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json @@ -199,14 +199,14 @@ ], "discrete": true }, - "metric_optimization": "minimize", + "metric_optimization": "maximize", "metrics": [ - "classification_log_loss", "classification_accuracy", + "classification_log_loss", "classification_f1", "classification_roc_auc" ], - "validation_patience": 5, + "validation_patience": 2, "val_every_n_epochs": 1, "log_every_n_epochs": 1, "show_examples": false, diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json index c1c443e312..2061429032 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json @@ -172,7 +172,7 @@ "optimizer": "Adam", "lear_rate": { "range": [ - 0.001, + 0.0001, 0.1 ] }, @@ -198,8 +198,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 2, + 10 ], "discrete": true }, @@ -210,16 +210,16 @@ ], "discrete": true }, - "metric_optimization": "minimize", + "metric_optimization": "maximize", "metrics": [ - "classification_log_loss", "classification_accuracy", + "classification_log_loss", "classification_f1", "classification_roc_auc" ], - "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, + "validation_patience": 2, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, "show_examples": false, "validate_best": true, "test_best": true diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index d61cc3e695..a0e2ca0b40 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -199,14 +199,14 @@ ], "discrete": true }, - "metric_optimization": "minimize", + "metric_optimization": "maximize", "metrics": [ - "classification_log_loss", "classification_accuracy", + "classification_log_loss", "classification_f1", "classification_roc_auc" ], - "validation_patience": 5, + "validation_patience": 2, "val_every_n_epochs": 1, "log_every_n_epochs": 1, "show_examples": false, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 71213c283f..17e769581a 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -172,7 +172,7 @@ "optimizer": "Adam", "lear_rate": { "range": [ - 0.001, + 0.0001, 0.1 ] }, @@ -198,8 +198,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 2, + 10 ], "discrete": true }, @@ -210,16 +210,16 @@ ], "discrete": true }, - "metric_optimization": "minimize", + "metric_optimization": "maximize", "metrics": [ - "classification_log_loss", "classification_accuracy", + "classification_log_loss", "classification_f1", "classification_roc_auc" ], - "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, + "validation_patience": 2, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, "show_examples": false, "validate_best": true, "test_best": true From 1457347e05943cc27fc1f380f54f47e4488b868f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 28 May 2018 14:48:30 +0300 Subject: [PATCH 376/616] fix: configs --- .../configs/evolution/basic_ru_snli_one_neuron_init_part.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json index a2bc958885..a40735e7af 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 2, + 10 ], "discrete": true }, From f9b94ab3a77d26083140e4afe7664b11b3ebface Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 28 May 2018 18:57:59 +0300 Subject: [PATCH 377/616] fix: change order of offsprings --- deeppavlov/models/evolution/run_evolution.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 7cc13884bf..d8f7c1e3fa 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -155,7 +155,7 @@ def score_population(population, population_size, result_file): population = evolution.next_generation(population, population_scores, iters) print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - + print("Population scores: {}".foramt(population_scores)) print("\nIteration #{} was done\n".format(iters)) iters += 1 From 60a9242deb6b2e3db9ef5812ec3b84f314a842d5 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 28 May 2018 23:51:41 +0300 Subject: [PATCH 378/616] fix: misprint --- deeppavlov/models/evolution/run_evolution.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index d8f7c1e3fa..f5b2794c70 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -155,7 +155,7 @@ def score_population(population, population_size, result_file): population = evolution.next_generation(population, population_scores, iters) print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - print("Population scores: {}".foramt(population_scores)) + print("Population scores: {}".format(population_scores)) print("\nIteration #{} was done\n".format(iters)) iters += 1 From 3fba01fa3038e189d074926f9a2a76b5014c5914 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 29 May 2018 11:17:00 +0300 Subject: [PATCH 379/616] fix: change config --- .../evolution/basic_ru_snli_one_neuron_init_part.json | 8 ++++---- .../basic_ru_snli_one_neuron_init_part_many_inputs.json | 8 ++++---- .../evolution/basic_snli_one_neuron_init_part.json | 8 ++++---- .../basic_snli_one_neuron_init_part_many_inputs.json | 8 ++++---- 4 files changed, 16 insertions(+), 16 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json index a40735e7af..1e0abaf3ef 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_2", - "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_2", + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_3", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_3", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 2, - 10 + 10, + 50 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json index 2061429032..e72a08c5ca 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_2", - "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_2", + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_3", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_3", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -198,8 +198,8 @@ "train": { "epochs": { "range": [ - 2, - 10 + 10, + 50 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index a0e2ca0b40..aa32c5e142 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_2", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_2", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_3", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_3", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 10, + 50 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 17e769581a..27d98fd86b 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_2", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_2", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_3", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_3", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -198,8 +198,8 @@ "train": { "epochs": { "range": [ - 2, - 10 + 10, + 50 ], "discrete": true }, From 75b383424220ff23c581b8a3a4437457d71a1418 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 29 May 2018 12:48:07 +0300 Subject: [PATCH 380/616] feat: start with given binary mask --- deeppavlov/models/evolution/run_evolution.py | 22 +++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index f5b2794c70..07949b3234 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -83,18 +83,20 @@ def score_population(population, population_size, result_file): parser = argparse.ArgumentParser() -parser.add_argument('--config', help='Please, enter model path to config', - default='./configs/evolution/basic_intents_config.json') +parser.add_argument('--config', help='Please, enter model path to config') parser.add_argument('--evolve_metric', help='Please, choose target metric out of given in your config.train.metrics') parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) -parser.add_argument('--one_neuron_init', help='Please, enter number of types of layers', default=0) +parser.add_argument('--one_neuron_init', help='whether to start with zero binary mask (one neuron network)', default=0) +parser.add_argument('--given_mask_init', help='whether to start with given binary mask', default=0) parser.add_argument('--save_best_portion', - help='Please, enter portion of population to save for the next generation with weights', default=0.) + help='Please, enter portion of population to save for the next generation with weights', + default=0.) parser.add_argument('--train_partition', - help='Please, enter partition of splitted train', default=1) + help='Please, enter partition of splitted train', + default=1) args = parser.parse_args() @@ -105,6 +107,7 @@ def score_population(population, population_size, result_file): N_LAYERS = int(args.n_layers) N_TYPES = int(args.n_types) ONE_NEURON_INIT = bool(int(args.one_neuron_init)) +GIVEN_MASK_INIT = bool(int(args.given_mask_init)) EVOLVE_METRIC = args.evolve_metric SAVE_BEST_PORTION = float(args.save_best_portion) TRAIN_PARTITION = int(args.train_partition) @@ -117,6 +120,14 @@ def score_population(population, population_size, result_file): # list of names of considered metrics CONSIDERED_METRICS = basic_params["train"]["metrics"] +if GIVEN_MASK_INIT: + # Embedding -> BiLSTM -> Dense -> Dense -> GlobalMaxPooling -> Dense(#classes) + INITIAL_BINARY_MASK = np.zeros((N_TYPES * N_LAYERS, N_TYPES * N_LAYERS)) + INITIAL_BINARY_MASK[3, 0] = 1 + INITIAL_BINARY_MASK[0, N_TYPES] = 1 +else: + INITIAL_BINARY_MASK = None + # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, @@ -128,6 +139,7 @@ def score_population(population, population_size, result_file): start_with_one_neuron=ONE_NEURON_INIT, save_best_with_weights_portion=SAVE_BEST_PORTION, train_partition=TRAIN_PARTITION, + initial_binary_mask=INITIAL_BINARY_MASK, **basic_params) # Result table From fe7c27951559b7225b03de3e43415c6de1e7674f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 30 May 2018 11:26:50 +0300 Subject: [PATCH 381/616] fix: config --- .../basic_snli_one_neuron_init_part.json | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index aa32c5e142..66369c01c7 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_3", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_3", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_4", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_4", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -72,7 +72,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -89,7 +89,7 @@ "filters": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -106,7 +106,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -116,7 +116,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -136,14 +136,14 @@ "n_hidden": { "range": [ 50, - 200 + 500 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -187,15 +187,15 @@ "train": { "epochs": { "range": [ - 10, - 50 + 50, + 100 ], "discrete": true }, "batch_size": { "range": [ - 20, - 70 + 50, + 100 ], "discrete": true }, @@ -206,7 +206,7 @@ "classification_f1", "classification_roc_auc" ], - "validation_patience": 2, + "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, "show_examples": false, From 479cabf714a7f0c9ea3ca0c72ba2e9b0af978119 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 31 May 2018 15:15:04 +0300 Subject: [PATCH 382/616] feat: second best portion and renovation --- .../evolution/basic_snli_one_neuron_init_part.json | 8 ++++---- .../basic_snli_one_neuron_init_part_many_inputs.json | 8 ++++---- deeppavlov/models/evolution/run_evolution.py | 6 ++++++ 3 files changed, 14 insertions(+), 8 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json index 66369c01c7..5f359b269d 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_4", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_4", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_5", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_5", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -187,8 +187,8 @@ "train": { "epochs": { "range": [ - 50, - 100 + 1, + 10 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 27d98fd86b..0df28786be 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_3", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_3", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_5", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_5", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -198,8 +198,8 @@ "train": { "epochs": { "range": [ - 10, - 50 + 1, + 10 ], "discrete": true }, diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 07949b3234..29b816f643 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -94,6 +94,10 @@ def score_population(population, population_size, result_file): parser.add_argument('--save_best_portion', help='Please, enter portion of population to save for the next generation with weights', default=0.) +parser.add_argument('--renovation_frequency', + help='Please, enter frequency of renovation (how often in terms of generations ' + 'to renovate the second best portion)', + default=1) parser.add_argument('--train_partition', help='Please, enter partition of splitted train', default=1) @@ -110,6 +114,7 @@ def score_population(population, population_size, result_file): GIVEN_MASK_INIT = bool(int(args.given_mask_init)) EVOLVE_METRIC = args.evolve_metric SAVE_BEST_PORTION = float(args.save_best_portion) +RENOVATION_FREQUENCY = int(args.renovation_frequency) TRAIN_PARTITION = int(args.train_partition) with open(CONFIG_FILE, "r") as f: @@ -138,6 +143,7 @@ def score_population(population, population_size, result_file): seed=42, start_with_one_neuron=ONE_NEURON_INIT, save_best_with_weights_portion=SAVE_BEST_PORTION, + renovation_frequency=RENOVATION_FREQUENCY, train_partition=TRAIN_PARTITION, initial_binary_mask=INITIAL_BINARY_MASK, **basic_params) From 5cacfe52d496667a55dccf9d6ecde2575774ad10 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 31 May 2018 15:15:32 +0300 Subject: [PATCH 383/616] fix: config --- .../evolution/basic_snli_one_neuron_init_part_many_inputs.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json index 0df28786be..2c62619894 100644 --- a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json @@ -206,7 +206,7 @@ "batch_size": { "range": [ 50, - 70 + 100 ], "discrete": true }, From 4b5cd03bd7e9c2a27f015ad80d61a37845c9cc59 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 31 May 2018 15:26:32 +0300 Subject: [PATCH 384/616] fix: configs plus ag_news config --- .../configs/evolution/basic_ag_news_part.json | 221 ++++++++++++++++++ ...init_part.json => basic_ru_snli_part.json} | 0 ...on => basic_ru_snli_part_many_inputs.json} | 0 ...on_init_part.json => basic_snli_part.json} | 0 ....json => basic_snli_part_many_inputs.json} | 0 ...n => basic_snli_part_many_inputs_big.json} | 0 6 files changed, 221 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_ag_news_part.json rename deeppavlov/configs/evolution/{basic_ru_snli_one_neuron_init_part.json => basic_ru_snli_part.json} (100%) rename deeppavlov/configs/evolution/{basic_ru_snli_one_neuron_init_part_many_inputs.json => basic_ru_snli_part_many_inputs.json} (100%) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init_part.json => basic_snli_part.json} (100%) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init_part_many_inputs.json => basic_snli_part_many_inputs.json} (100%) rename deeppavlov/configs/evolution/{basic_snli_one_neuron_init_part_many_inputs_big.json => basic_snli_part_many_inputs_big.json} (100%) diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json new file mode 100644 index 0000000000..4da359f0b5 --- /dev/null +++ b/deeppavlov/configs/evolution/basic_ag_news_part.json @@ -0,0 +1,221 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "label", + "data_path": "/home/dilyara.baymurzina/evolution_data/ag_news_data/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_data/ag_news_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_data/ag_news_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_5", + "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_5", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.000001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "confident_threshold": 1, + "text_size": 50, + "last_layer_activation": "softmax", + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 1, + 10 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 100 + ], + "discrete": true + }, + "metric_optimization": "maximize", + "metrics": [ + "classification_accuracy", + "classification_log_loss", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "show_examples": false, + "validate_best": true, + "test_best": true + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_ru_snli_part.json similarity index 100% rename from deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part.json rename to deeppavlov/configs/evolution/basic_ru_snli_part.json diff --git a/deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json similarity index 100% rename from deeppavlov/configs/evolution/basic_ru_snli_one_neuron_init_part_many_inputs.json rename to deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json b/deeppavlov/configs/evolution/basic_snli_part.json similarity index 100% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init_part.json rename to deeppavlov/configs/evolution/basic_snli_part.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json similarity index 100% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs.json rename to deeppavlov/configs/evolution/basic_snli_part_many_inputs.json diff --git a/deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json similarity index 100% rename from deeppavlov/configs/evolution/basic_snli_one_neuron_init_part_many_inputs_big.json rename to deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json From 9a952b982b61c72a99d9337b2d57412fa1237d63 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 31 May 2018 15:46:18 +0300 Subject: [PATCH 385/616] fix: configs --- deeppavlov/configs/evolution/basic_snli_part.json | 4 ++-- deeppavlov/configs/evolution/basic_snli_part_many_inputs.json | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index 5f359b269d..0f5eb0bfe1 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_5", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_5", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_5", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_5", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index 2c62619894..662ca1ab88 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_5", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_5", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_many_inputs_5", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_many_inputs_5", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { From 84271352ea788cd5e578cdee0ee00cd978d9125b Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 4 Jun 2018 12:47:47 +0300 Subject: [PATCH 386/616] feat: save test results --- deeppavlov/models/evolution/run_evolution.py | 31 +++++++++----- .../models/evolution/train_phenotype.py | 41 +++++++++++++++---- 2 files changed, 53 insertions(+), 19 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 29b816f643..abffd83aee 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -20,13 +20,6 @@ def score_population(population, population_size, result_file): procs = [] for i in range(population_size): - # f_name = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) - # model_name = population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] - # population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - # str(f_name.joinpath(model_name + "_" + str(i))) - # population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] =\ - # population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] - save_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) load_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]) @@ -62,11 +55,25 @@ def score_population(population, population_size, result_file): for i in range(population_size): val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ "save_path"]).parent.joinpath("valid_results.txt"))) + try: + test_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).parent.joinpath("test_results.txt"))) + except FileNotFoundError: + pass + result_table_dict = {} for el in order: - result_table_dict[el] = [] + if el == "params": + result_table_dict[el] = [] + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] for m_id, m in enumerate(CONSIDERED_METRICS): - result_table_dict[m].append(val_results[m_id]) + result_table_dict[m + "_valid"].append(val_results[m_id]) + try: + result_table_dict[m + "_test"].append(test_results[m_id]) + except NameError: + result_table_dict[m + "_test"].append(0.) result_table_dict[order[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) @@ -153,7 +160,11 @@ def score_population(population, population_size, result_file): order.extend(["params"]) result_table_dict = {} for el in order: - result_table_dict[el] = [] + if order == "params": + result_table_dict[el] = [] + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] result_file = Path(basic_params["chainer"]["pipe"][ evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py index b693f04f54..0cb26a46eb 100644 --- a/deeppavlov/models/evolution/train_phenotype.py +++ b/deeppavlov/models/evolution/train_phenotype.py @@ -28,12 +28,35 @@ reports = train_model_from_config(config_path) print(reports) -metrics = dict(reports[0]["valid"]["metrics"]) -val_metrics_values = np.array(list(metrics.values())).reshape(-1) - -config = read_json(config_path) -model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], - key="to_evolve") -np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ - "save_path"]).parent.joinpath("valid_results.txt")), - X=val_metrics_values) +if len(reports) == 2: + # valid and test reports + val_metrics = dict(reports[0]["valid"]["metrics"]) + val_metrics_values = np.array(list(val_metrics.values())).reshape(-1) + + config = read_json(config_path) + model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], + key="to_evolve") + np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ + "save_path"]).parent.joinpath("valid_results.txt")), + X=val_metrics_values) + + test_metrics = dict(reports[1]["test"]["metrics"]) + test_metrics_values = np.array(list(test_metrics.values())).reshape(-1) + + config = read_json(config_path) + model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], + key="to_evolve") + np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ + "save_path"]).parent.joinpath("test_results.txt")), + X=test_metrics_values) +else: + # valid report + val_metrics = dict(reports[0]["valid"]["metrics"]) + val_metrics_values = np.array(list(val_metrics.values())).reshape(-1) + + config = read_json(config_path) + model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], + key="to_evolve") + np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ + "save_path"]).parent.joinpath("valid_results.txt")), + X=val_metrics_values) From a0673aa6923f6266957f54c3b13b6fb8c217bfd0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 4 Jun 2018 14:34:33 +0300 Subject: [PATCH 387/616] feat: config for twitter140 --- .../evolution/basic_twitter_140_part.json | 221 ++++++++++++++++++ 1 file changed, 221 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_twitter_140_part.json diff --git a/deeppavlov/configs/evolution/basic_twitter_140_part.json b/deeppavlov/configs/evolution/basic_twitter_140_part.json new file mode 100644 index 0000000000..35e6f2231f --- /dev/null +++ b/deeppavlov/configs/evolution/basic_twitter_140_part.json @@ -0,0 +1,221 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "label", + "data_path": "/home/dilyara.baymurzina/evolution_data/twitter140_data/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/twitter140_data/twitter140_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/twitter140_data/twitter140_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_5", + "load_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_5", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same" + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.000001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "confident_threshold": 1, + "text_size": 30, + "last_layer_activation": "softmax", + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 1, + 10 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 100 + ], + "discrete": true + }, + "metric_optimization": "maximize", + "metrics": [ + "classification_accuracy", + "classification_log_loss", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "show_examples": false, + "validate_best": true, + "test_best": true + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From 9e61c66ca557333c5517c5cfa917055ef6cbc6e4 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 4 Jun 2018 14:40:47 +0300 Subject: [PATCH 388/616] fix:rename config --- .../{basic_twitter_140_part.json => basic_twitter140_part.json} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename deeppavlov/configs/evolution/{basic_twitter_140_part.json => basic_twitter140_part.json} (100%) diff --git a/deeppavlov/configs/evolution/basic_twitter_140_part.json b/deeppavlov/configs/evolution/basic_twitter140_part.json similarity index 100% rename from deeppavlov/configs/evolution/basic_twitter_140_part.json rename to deeppavlov/configs/evolution/basic_twitter140_part.json From d2081a1e7c8b61ccdd7974f8e8caac6d99e12c58 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 4 Jun 2018 15:00:22 +0300 Subject: [PATCH 389/616] fix: order in run evolution --- deeppavlov/models/evolution/run_evolution.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index abffd83aee..7a9eabba8c 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -77,7 +77,7 @@ def score_population(population, population_size, result_file): result_table_dict[order[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) - result_table.loc[:, order].to_csv(result_file, index=False, sep='\t', mode='a', header=None) + result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) for m_id, m in enumerate(CONSIDERED_METRICS): population_metrics[m].append(val_results[m_id]) @@ -158,18 +158,25 @@ def score_population(population, population_size, result_file): # Result table order = deepcopy(CONSIDERED_METRICS) order.extend(["params"]) + +result_table_columns = [] + result_table_dict = {} for el in order: if order == "params": result_table_dict[el] = [] + result_table_columns.extend([el + "_valid"]) else: result_table_dict[el + "_valid"] = [] result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) + +result_table_columns.append("params") result_file = Path(basic_params["chainer"]["pipe"][ evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") result_table = pd.DataFrame(result_table_dict) -result_table.loc[:, order].to_csv(result_file, index=False, sep='\t') +result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() From 7a6a78c75ef0fc6665d2fb769e282376680302f0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 4 Jun 2018 15:04:20 +0300 Subject: [PATCH 390/616] fix: name of columns --- deeppavlov/configs/evolution/basic_twitter140_part.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_twitter140_part.json b/deeppavlov/configs/evolution/basic_twitter140_part.json index 35e6f2231f..e7c25ccf43 100644 --- a/deeppavlov/configs/evolution/basic_twitter140_part.json +++ b/deeppavlov/configs/evolution/basic_twitter140_part.json @@ -2,7 +2,7 @@ "dataset_reader": { "name": "basic_classification_reader", "x": "text", - "y": "label", + "y": "target", "data_path": "/home/dilyara.baymurzina/evolution_data/twitter140_data/parts", "train": "train_0.csv", "valid": "valid.csv", From 1fb2ea224221b8d06a23507c6e792cd9db88296d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 15:28:57 +0300 Subject: [PATCH 391/616] feat: saving epochs_done and final lear rate for keras model --- deeppavlov/core/models/keras_model.py | 39 ++++++++++++++++++++------- 1 file changed, 29 insertions(+), 10 deletions(-) diff --git a/deeppavlov/core/models/keras_model.py b/deeppavlov/core/models/keras_model.py index 7d62108c3b..5254aef984 100644 --- a/deeppavlov/core/models/keras_model.py +++ b/deeppavlov/core/models/keras_model.py @@ -100,13 +100,13 @@ def init_model_from_scratch(self, model_name, optimizer_name, loss_name, lear_ra if callable(optimizer_func): if not(lear_rate is None): if not(lear_rate_decay is None): - optimizer_ = optimizer_func(lr=lear_rate, decay=lear_rate_decay) + self.optimizer = optimizer_func(lr=lear_rate, decay=lear_rate_decay) else: - optimizer_ = optimizer_func(lr=lear_rate) + self.optimizer = optimizer_func(lr=lear_rate) elif not(lear_rate_decay is None): - optimizer_ = optimizer_func(decay=lear_rate_decay) + self.optimizer = optimizer_func(decay=lear_rate_decay) else: - optimizer_ = optimizer_func() + self.optimizer = optimizer_func() else: raise AttributeError("Optimizer {} is not defined in `keras.optimizers`".format(optimizer_name)) @@ -116,7 +116,7 @@ def init_model_from_scratch(self, model_name, optimizer_name, loss_name, lear_ra else: raise AttributeError("Loss {} is not defined in `keras.losses`".format(loss_name)) - model.compile(optimizer=optimizer_, loss=loss) + model.compile(optimizer=self.optimizer, loss=loss) return model @overrides @@ -160,13 +160,13 @@ def load(self, model_name, optimizer_name, loss_name, lear_rate=None, lear_rate_ if callable(optimizer_func): if not (lear_rate is None): if not (lear_rate_decay is None): - optimizer_ = optimizer_func(lr=lear_rate, decay=lear_rate_decay) + self.optimizer = optimizer_func(lr=lear_rate, decay=lear_rate_decay) else: - optimizer_ = optimizer_func(lr=lear_rate) + self.optimizer = optimizer_func(lr=lear_rate) elif not (lear_rate_decay is None): - optimizer_ = optimizer_func(decay=lear_rate_decay) + self.optimizer = optimizer_func(decay=lear_rate_decay) else: - optimizer_ = optimizer_func() + self.optimizer = optimizer_func() else: raise AttributeError("Optimizer {} is not defined in `keras.optimizers`".format(optimizer_name)) @@ -176,7 +176,7 @@ def load(self, model_name, optimizer_name, loss_name, lear_rate=None, lear_rate_ else: raise AttributeError("Loss {} is not defined".format(loss_name)) - model.compile(optimizer=optimizer_, + model.compile(optimizer=self.optimizer, loss=loss) return model else: @@ -211,6 +211,9 @@ def save(self, fname=None): # if model was loaded from one path and saved to another one # then change load_path to save_path for config + self.opt["epochs_done"] = self.epochs_done + self.opt["final_lear_rate"] = self.optimizer.lr / (1. + self.optimizer.decay * self.batches_seen) + if self.opt.get("load_path") and self.opt.get("save_path"): if self.opt.get("save_path") != self.opt.get("load_path"): self.opt["load_path"] = str(self.opt["save_path"]) @@ -239,3 +242,19 @@ def mlp(self, opt): @abstractmethod def reset(self): pass + + def process_event(self, event_name, data): + """ + Process event after epoch + Args: + event_name: whether event is send after epoch or batch + data: event data (dictionary) + + Returns: + None + """ + if event_name == "after_epoch": + self.epochs_done = data["epochs_done"] + self.batches_seen = data["batches_seen"] + self.train_examples_seen = data["train_examples_seen"] + return From b6223faa32a9ec2e7d594389fb98e4f7f638c216 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 15:42:46 +0300 Subject: [PATCH 392/616] fix: tensor to float for lear rate --- deeppavlov/core/models/keras_model.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/core/models/keras_model.py b/deeppavlov/core/models/keras_model.py index 5254aef984..ff6fea3028 100644 --- a/deeppavlov/core/models/keras_model.py +++ b/deeppavlov/core/models/keras_model.py @@ -212,7 +212,8 @@ def save(self, fname=None): # if model was loaded from one path and saved to another one # then change load_path to save_path for config self.opt["epochs_done"] = self.epochs_done - self.opt["final_lear_rate"] = self.optimizer.lr / (1. + self.optimizer.decay * self.batches_seen) + self.opt["final_lear_rate"] = K.eval(self.optimizer.lr) / (1. + + K.eval(self.optimizer.decay) * self.batches_seen) if self.opt.get("load_path") and self.opt.get("save_path"): if self.opt.get("save_path") != self.opt.get("load_path"): From 7dc152b1d46452b699a9e277ed0e26945ebda8dc Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 16:04:34 +0300 Subject: [PATCH 393/616] feat: exchange initial lear rate to final one --- deeppavlov/configs/evolution/basic_ag_news_part.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json index 4da359f0b5..41461637b5 100644 --- a/deeppavlov/configs/evolution/basic_ag_news_part.json +++ b/deeppavlov/configs/evolution/basic_ag_news_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_5", - "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_5", + "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { From 3c4cd841d7aa9433c9e9d633ceb96149811d2126 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 16:21:13 +0300 Subject: [PATCH 394/616] chore: delete division by max scores --- deeppavlov/models/evolution/neuroevolution_param_generator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 218d8e925b..939544ded2 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -265,7 +265,7 @@ def selection(self, population, scores): selected self.population_size individuums with replacement """ scores = np.array(scores, dtype='float') - scores = (scores - 1.1 * min(scores) + 0.1 * max(scores)) / max(scores) + scores = (scores - 1.1 * min(scores) + 0.1 * max(scores)) total = np.sum(scores) probas_to_be_selected = scores / total intervals = np.array([np.sum(probas_to_be_selected[:i]) for i in range(self.population_size)]) From 0e2b717d8289ddcc28d4bed2afd98f5e532848c7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 17:22:45 +0300 Subject: [PATCH 395/616] fix: snli many_inputs config --- .../evolution/basic_snli_part_many_inputs.json | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index 662ca1ab88..9d39c6d3ed 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -83,7 +83,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -100,7 +100,7 @@ "filters": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -117,7 +117,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -127,7 +127,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -206,7 +206,7 @@ "batch_size": { "range": [ 50, - 100 + 200 ], "discrete": true }, @@ -217,7 +217,7 @@ "classification_f1", "classification_roc_auc" ], - "validation_patience": 2, + "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, "show_examples": false, From 9204894159d8cd004704e124ede98e058599a662 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 17:37:32 +0300 Subject: [PATCH 396/616] fix: configs --- deeppavlov/configs/evolution/basic_snli_part.json | 2 +- deeppavlov/configs/evolution/basic_snli_part_many_inputs.json | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index 0f5eb0bfe1..1315fecfb9 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -195,7 +195,7 @@ "batch_size": { "range": [ 50, - 100 + 200 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index 9d39c6d3ed..ceb5662678 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -147,14 +147,14 @@ "n_hidden": { "range": [ 50, - 200 + 500 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 500 ], "discrete": true }, From 002d74600fb3b2e335d65dbcd6aa8d550873d99f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 17:44:30 +0300 Subject: [PATCH 397/616] fix: configs --- .../configs/evolution/basic_ru_snli_part.json | 26 +-- .../basic_ru_snli_part_many_inputs.json | 24 +- .../configs/evolution/basic_snli_part.json | 4 +- .../basic_snli_part_many_inputs.json | 4 +- .../basic_snli_part_many_inputs_big.json | 4 +- .../evolution/basic_snli_random_init.json | 220 ------------------ 6 files changed, 31 insertions(+), 251 deletions(-) delete mode 100644 deeppavlov/configs/evolution/basic_snli_random_init.json diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part.json b/deeppavlov/configs/evolution/basic_ru_snli_part.json index 1e0abaf3ef..743ce1768e 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_3", - "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_3", + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -72,7 +72,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -89,7 +89,7 @@ "filters": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -106,7 +106,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -116,7 +116,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -136,14 +136,14 @@ "n_hidden": { "range": [ 50, - 200 + 500 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -187,15 +187,15 @@ "train": { "epochs": { "range": [ - 10, - 50 + 1, + 10 ], "discrete": true }, "batch_size": { "range": [ - 20, - 70 + 50, + 200 ], "discrete": true }, @@ -206,7 +206,7 @@ "classification_f1", "classification_roc_auc" ], - "validation_patience": 2, + "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, "show_examples": false, diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json index e72a08c5ca..3b731f7683 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_3", - "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_3", + "save_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/ru_snli_classification/one_neuron_init_part_many_inputs_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -83,7 +83,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -100,7 +100,7 @@ "filters": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -117,7 +117,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -127,7 +127,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -147,14 +147,14 @@ "n_hidden": { "range": [ 50, - 200 + 500 ], "discrete": true }, "n_output_features": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -198,15 +198,15 @@ "train": { "epochs": { "range": [ - 10, - 50 + 1, + 10 ], "discrete": true }, "batch_size": { "range": [ 50, - 70 + 200 ], "discrete": true }, @@ -217,7 +217,7 @@ "classification_f1", "classification_roc_auc" ], - "validation_patience": 2, + "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, "show_examples": false, diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index 1315fecfb9..313244da53 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_5", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_5", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index ceb5662678..8508aae6a6 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -74,8 +74,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_many_inputs_5", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_many_inputs_5", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_many_inputs_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_many_inputs_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json index 2403b1b3e0..8fb8a2c6db 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json @@ -71,8 +71,8 @@ ], "main": true, "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_1", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_1", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { diff --git a/deeppavlov/configs/evolution/basic_snli_random_init.json b/deeppavlov/configs/evolution/basic_snli_random_init.json deleted file mode 100644 index a57d2fc672..0000000000 --- a/deeppavlov/configs/evolution/basic_snli_random_init.json +++ /dev/null @@ -1,220 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input", - "train": "train_0.csv", - "valid": "valid.csv", - "test": "test.csv" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wordpunct_tok_reddit_comments_2017_11_100.bin", - "dim": 100 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random_1", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/start_with_random_1", - "classes": "#classes_vocab.keys()", - "to_evolve": true, - "basic_layers_params": { - "Dense": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - }, - "Conv1D": { - "filters": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "kernel_size": { - "range": [ - 2, - 7 - ], - "discrete": true - }, - "padding": "same" - }, - "CuDNNLSTM": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "return_sequences": true - }, - "BiCuDNNLSTM": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "return_sequences": true - }, - "MaxPooling1D": { - "pool_size": { - "range": [ - 2, - 5 - ], - "discrete": true - }, - "padding": "same" - }, - "SelfMultiplicativeAttention": { - "n_hidden": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "n_output_features": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - } - }, - "confident_threshold": 1, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.001, - 0.1 - ] - }, - "lear_rate_decay": { - "range": [ - 0.00001, - 0.1 - ] - }, - "loss": "binary_crossentropy", - "text_size": 51, - "model_name": "evolution_classification_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "range": [ - 100, - 1000 - ], - "discrete": true - }, - "batch_size": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "metric_optimization": "minimize", - "metrics": [ - "classification_log_loss", - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, - "show_examples": false, - "validate_best": true, - "test_best": true - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} \ No newline at end of file From 6820d217d07bc4512ba8ab9816b42f02e3e1de69 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 6 Jun 2018 17:49:05 +0300 Subject: [PATCH 398/616] fix: configs --- .../configs/evolution/basic_config_local.json | 232 ----------------- deeppavlov/configs/evolution/basic_selqa.json | 236 ------------------ .../evolution/basic_twitter140_part.json | 6 +- 3 files changed, 3 insertions(+), 471 deletions(-) delete mode 100644 deeppavlov/configs/evolution/basic_config_local.json delete mode 100644 deeppavlov/configs/evolution/basic_selqa.json diff --git a/deeppavlov/configs/evolution/basic_config_local.json b/deeppavlov/configs/evolution/basic_config_local.json deleted file mode 100644 index a1b859edee..0000000000 --- a/deeppavlov/configs/evolution/basic_config_local.json +++ /dev/null @@ -1,232 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": [ - "sentence1", - "sentence2" - ], - "y": "gold_label", - "data_path": "/home/dilyara/data/data_files/SNLI/snli_data/two_texts/part" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "sentence1", - "sentence2" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict", - "load_path": "/home/dilyara/data/data_files/SNLI/snli_data/snli_classes.dict" - }, - { - "in": [ - "sentence1" - ], - "out": [ - "sentence1_lower" - ], - "name": "str_lower" - }, - { - "in": [ - "sentence2" - ], - "out": [ - "sentence2_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara/data/data_files/embeddings/reddit/wordpunct_tok_reddit_comments_2017_11_100.bin", - "load_path": "/home/dilyara/data/data_files/embeddings/reddit/wordpunct_tok_reddit_comments_2017_11_100.bin", - "dim": 100 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "sentence1_lower", - "sentence2_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_inputs", - "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/one_neuron_init_part_many_inputs", - "classes": "#classes_vocab.keys()", - "to_evolve": true, - "basic_layers_params": { - "Dense": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - }, - "Conv1D": { - "filters": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "kernel_size": { - "range": [ - 2, - 7 - ], - "discrete": true - }, - "padding": "same" - }, - "CuDNNLSTM": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "return_sequences": true - }, - "BiCuDNNLSTM": { - "units": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "return_sequences": true - }, - "MaxPooling1D": { - "pool_size": { - "range": [ - 2, - 5 - ], - "discrete": true - }, - "padding": "same" - }, - "SelfMultiplicativeAttention": { - "n_hidden": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "n_output_features": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - } - }, - "confident_threshold": 1, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.001, - 0.1 - ] - }, - "lear_rate_decay": { - "range": [ - 0.00001, - 0.1 - ] - }, - "loss": "binary_crossentropy", - "text_size": 15, - "last_layer_activation": "softmax", - "model_name": "evolution_many_inputs_classification_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "range": [ - 100, - 1000 - ], - "discrete": true - }, - "batch_size": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "metric_optimization": "minimize", - "metrics": [ - "classification_log_loss", - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, - "show_examples": false, - "validate_best": true, - "test_best": false - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} diff --git a/deeppavlov/configs/evolution/basic_selqa.json b/deeppavlov/configs/evolution/basic_selqa.json deleted file mode 100644 index fddae03149..0000000000 --- a/deeppavlov/configs/evolution/basic_selqa.json +++ /dev/null @@ -1,236 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": [ - "question", - "answer" - ], - "y": "label", - "data_path": "/home/dilyara.baymurzina/evolution_data/selqa_data" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "question", - "answer" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/selqa_data/selqa_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/selqa_data/selqa_classes.dict" - }, - { - "in": [ - "question" - ], - "out": [ - "question_lower" - ], - "name": "str_lower" - }, - { - "in": [ - "answer" - ], - "out": [ - "answer_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "question_lower", - "answer_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/selqa_classification/one_neuron_init_part_many_inputs", - "load_path": "/home/dilyara.baymurzina/evolution_data/selqa_classification/one_neuron_init_part_many_inputs", - "classes": "#classes_vocab.keys()", - "to_evolve": true, - "basic_layers_params": { - "Dense": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - }, - "Conv1D": { - "filters": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "kernel_size": { - "range": [ - 2, - 7 - ], - "discrete": true - }, - "padding": "same" - }, - "CuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true - }, - "BiCuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true - }, - "MaxPooling1D": { - "pool_size": { - "range": [ - 2, - 5 - ], - "discrete": true - }, - "padding": "same" - }, - "SelfMultiplicativeAttention": { - "n_hidden": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "n_output_features": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - } - }, - "confident_threshold": 1, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.001, - 0.1 - ] - }, - "lear_rate_decay": { - "range": [ - 0.00001, - 0.1 - ] - }, - "loss": "binary_crossentropy", - "text_size": [ - 20, - 50 - ], - "last_layer_activation": "softmax", - "model_name": "evolution_many_inputs_classification_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "range": [ - 50, - 100 - ], - "discrete": true - }, - "batch_size": { - "range": [ - 50, - 70 - ], - "discrete": true - }, - "metric_optimization": "minimize", - "metrics": [ - "classification_log_loss", - "classification_accuracy", - "classification_f1", - "classification_roc_auc", - "classification_mrr" - ], - "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, - "show_examples": false, - "validate_best": true, - "test_best": true - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} diff --git a/deeppavlov/configs/evolution/basic_twitter140_part.json b/deeppavlov/configs/evolution/basic_twitter140_part.json index e7c25ccf43..ce25c033da 100644 --- a/deeppavlov/configs/evolution/basic_twitter140_part.json +++ b/deeppavlov/configs/evolution/basic_twitter140_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_5", - "load_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_5", + "save_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { @@ -195,7 +195,7 @@ "batch_size": { "range": [ 50, - 100 + 200 ], "discrete": true }, From 2f412d36676fa8385f8eb3b703e3742b90c128f7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 7 Jun 2018 11:55:16 +0300 Subject: [PATCH 399/616] fix: mutation and crossover params --- deeppavlov/models/evolution/run_evolution.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 7a9eabba8c..81205386df 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -143,8 +143,8 @@ def score_population(population, population_size, result_file): # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, - p_crossover=0.1, crossover_power=0.5, - p_mutation=0.5, mutation_power=0.1, + p_crossover=0.2, crossover_power=0.2, + p_mutation=1., mutation_power=0.1, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", seed=42, From 37e19f1900e5aeccab2ded15d4be54ba53000d06 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 7 Jun 2018 15:17:49 +0300 Subject: [PATCH 400/616] feat: add dropout --- .../configs/evolution/basic_ru_snli_part.json | 6 ++++++ .../basic_ru_snli_part_many_inputs.json | 6 ++++++ .../evolution/basic_snips_one_neuron_init.json | 6 ++++++ .../evolution/basic_snips_random_init.json | 6 ++++++ .../configs/evolution/basic_snli_part.json | 6 ++++++ .../evolution/basic_snli_part_many_inputs.json | 16 ++++++++++++++-- .../basic_snli_part_many_inputs_big.json | 6 ++++++ .../configs/evolution/basic_twitter140_part.json | 6 ++++++ deeppavlov/configs/evolution/check_config.json | 1 - deeppavlov/configs/evolution/intents_snli.json | 7 ++++++- .../evolution/evolution_many_inputs_model.py | 10 ++++++---- 11 files changed, 68 insertions(+), 8 deletions(-) delete mode 100644 deeppavlov/configs/evolution/check_config.json diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part.json b/deeppavlov/configs/evolution/basic_ru_snli_part.json index 743ce1768e..cbaba4aaa3 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part.json @@ -173,6 +173,12 @@ }, "loss": "binary_crossentropy", "text_size": 51, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json index 3b731f7683..e89eead7fe 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json @@ -184,6 +184,12 @@ }, "loss": "binary_crossentropy", "text_size": [30, 20], + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 0182c2dba6..0f84c322bc 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -170,6 +170,12 @@ }, "loss": "binary_crossentropy", "text_size": 15, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "model_name": "evolution_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index 5ca329a9c7..ada0c083e4 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -170,6 +170,12 @@ }, "loss": "binary_crossentropy", "text_size": 15, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "model_name": "evolution_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index 313244da53..8c3a0024e3 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -173,6 +173,12 @@ }, "loss": "binary_crossentropy", "text_size": 51, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index 8508aae6a6..28563e55e8 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -1,7 +1,10 @@ { "dataset_reader": { "name": "basic_classification_reader", - "x": ["sentence1", "sentence2"], + "x": [ + "sentence1", + "sentence2" + ], "y": "gold_label", "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/cutted_many_inputs/parts", "train": "train_0.csv", @@ -183,7 +186,16 @@ ] }, "loss": "binary_crossentropy", - "text_size": [30, 20], + "text_size": [ + 30, + 20 + ], + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json index 8fb8a2c6db..fc8df5a739 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json @@ -186,6 +186,12 @@ }, "loss": "binary_crossentropy", "text_size": 15, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/basic_twitter140_part.json b/deeppavlov/configs/evolution/basic_twitter140_part.json index ce25c033da..6aa5ddea01 100644 --- a/deeppavlov/configs/evolution/basic_twitter140_part.json +++ b/deeppavlov/configs/evolution/basic_twitter140_part.json @@ -173,6 +173,12 @@ "loss": "binary_crossentropy", "confident_threshold": 1, "text_size": 30, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", diff --git a/deeppavlov/configs/evolution/check_config.json b/deeppavlov/configs/evolution/check_config.json deleted file mode 100644 index 0157d26a1e..0000000000 --- a/deeppavlov/configs/evolution/check_config.json +++ /dev/null @@ -1 +0,0 @@ -{"dataset_reader": {"name": "basic_classification_reader", "x": ["question", "answer"], "y": "label", "data_path": "/home/dilyara.baymurzina/evolution_data/selqa_data"}, "dataset_iterator": {"name": "basic_classification_iterator"}, "chainer": {"in": ["question", "answer"], "in_y": ["y"], "pipe": [{"id": "classes_vocab", "name": "default_vocab", "fit_on": ["y"], "level": "token", "save_path": "/home/dilyara.baymurzina/evolution_data/selqa_data/selqa_classes.dict", "load_path": "/home/dilyara.baymurzina/evolution_data/selqa_data/selqa_classes.dict"}, {"in": ["question"], "out": ["question_lower"], "name": "str_lower"}, {"in": ["answer"], "out": ["answer_lower"], "name": "str_lower"}, {"id": "my_embedder", "name": "fasttext", "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", "dim": 300}, {"id": "my_tokenizer", "name": "nltk_tokenizer", "tokenizer": "wordpunct_tokenize"}, {"in": ["question_lower", "answer_lower"], "in_y": ["y"], "out": ["y_labels", "y_probas_dict"], "main": true, "name": "evolution_many_inputs_classification_model", "save_path": "/home/dilyara.baymurzina/evolution_data/selqa_classification/one_neuron_init_part_many_inputs/population_3/evolution_many_inputs_classification_model_9/evolution_many_inputs_classification_model_9", "load_path": "/home/dilyara.baymurzina/evolution_data/selqa_classification/one_neuron_init_part_many_inputs/population_3/evolution_many_inputs_classification_model_9/evolution_many_inputs_classification_model_9", "classes": "#classes_vocab.keys()", "to_evolve": true, "optimizer": "Adam", "loss": "binary_crossentropy", "text_size": [20, 50], "last_layer_activation": "softmax", "model_name": "evolution_many_inputs_classification_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer", "n_types": 6, "n_layers": 5, "confident_threshold": 0.4913063945020907, "lear_rate": 0.06101558390361756, "lear_rate_decay": 0.06011880458410778, "0_0_0": {"node_name": "Dense", "node_type": 0, "node_layer": 0, "units": 334, "activation": "sigmoid"}, "0_1_1": {"node_name": "Conv1D", "node_type": 1, "node_layer": 0, "padding": "same", "filters": 70, "kernel_size": 2}, "0_2_2": {"node_name": "CuDNNLSTM", "node_type": 2, "node_layer": 0, "return_sequences": true, "units": 92}, "0_3_3": {"node_name": "BiCuDNNLSTM", "node_type": 3, "node_layer": 0, "return_sequences": true, "units": 280}, "0_4_4": {"node_name": "MaxPooling1D", "node_type": 4, "node_layer": 0, "padding": "same", "pool_size": 5}, "0_5_5": {"node_name": "SelfMultiplicativeAttention", "node_type": 5, "node_layer": 0, "n_hidden": 478, "n_output_features": 184, "activation": "softmax"}, "1_0_6": {"node_name": "Dense", "node_type": 0, "node_layer": 1, "units": 452, "activation": "sigmoid"}, "1_1_7": {"node_name": "Conv1D", "node_type": 1, "node_layer": 1, "padding": "same", "filters": 381, "kernel_size": 4}, "1_2_8": {"node_name": "CuDNNLSTM", "node_type": 2, "node_layer": 1, "return_sequences": true, "units": 203}, "1_3_9": {"node_name": "BiCuDNNLSTM", "node_type": 3, "node_layer": 1, "return_sequences": true, "units": 402}, "1_4_10": {"node_name": "MaxPooling1D", "node_type": 4, "node_layer": 1, "padding": "same", "pool_size": 2}, "1_5_11": {"node_name": "SelfMultiplicativeAttention", "node_type": 5, "node_layer": 1, "n_hidden": 385, "n_output_features": 212, "activation": "sigmoid"}, "2_0_12": {"node_name": "Dense", "node_type": 0, "node_layer": 2, "units": 355, "activation": "relu"}, "2_1_13": {"node_name": "Conv1D", "node_type": 1, "node_layer": 2, "padding": "same", "filters": 413, "kernel_size": 4}, "2_2_14": {"node_name": "CuDNNLSTM", "node_type": 2, "node_layer": 2, "return_sequences": true, "units": 192}, "2_3_15": {"node_name": "BiCuDNNLSTM", "node_type": 3, "node_layer": 2, "return_sequences": true, "units": 427}, "2_4_16": {"node_name": "MaxPooling1D", "node_type": 4, "node_layer": 2, "padding": "same", "pool_size": 4}, "2_5_17": {"node_name": "SelfMultiplicativeAttention", "node_type": 5, "node_layer": 2, "n_hidden": 274, "n_output_features": 465, "activation": "sigmoid"}, "3_0_18": {"node_name": "Dense", "node_type": 0, "node_layer": 3, "units": 489, "activation": "softmax"}, "3_1_19": {"node_name": "Conv1D", "node_type": 1, "node_layer": 3, "padding": "same", "filters": 373, "kernel_size": 4}, "3_2_20": {"node_name": "CuDNNLSTM", "node_type": 2, "node_layer": 3, "return_sequences": true, "units": 463}, "3_3_21": {"node_name": "BiCuDNNLSTM", "node_type": 3, "node_layer": 3, "return_sequences": true, "units": 166}, "3_4_22": {"node_name": "MaxPooling1D", "node_type": 4, "node_layer": 3, "padding": "same", "pool_size": 3}, "3_5_23": {"node_name": "SelfMultiplicativeAttention", "node_type": 5, "node_layer": 3, "n_hidden": 315, "n_output_features": 462, "activation": "sigmoid"}, "4_0_24": {"node_name": "Dense", "node_type": 0, "node_layer": 4, "units": 482, "activation": "softmax"}, "4_1_25": {"node_name": "Conv1D", "node_type": 1, "node_layer": 4, "padding": "same", "filters": 187, "kernel_size": 4}, "4_2_26": {"node_name": "CuDNNLSTM", "node_type": 2, "node_layer": 4, "return_sequences": true, "units": 462}, "4_3_27": {"node_name": "BiCuDNNLSTM", "node_type": 3, "node_layer": 4, "return_sequences": true, "units": 181}, "4_4_28": {"node_name": "MaxPooling1D", "node_type": 4, "node_layer": 4, "padding": "same", "pool_size": 3}, "4_5_29": {"node_name": "SelfMultiplicativeAttention", "node_type": 5, "node_layer": 4, "n_hidden": 469, "n_output_features": 91, "activation": "sigmoid"}, "binary_mask": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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["classification_log_loss", "classification_accuracy", "classification_f1", "classification_roc_auc"], "validation_patience": 5, "val_every_n_epochs": 5, "log_every_n_epochs": 5, "show_examples": false, "validate_best": true, "test_best": true, "epochs": 77, "batch_size": 51}, "metadata": {"labels": {"telegram_utils": "IntentModel"}}} diff --git a/deeppavlov/configs/evolution/intents_snli.json b/deeppavlov/configs/evolution/intents_snli.json index fb9bf4fa12..d056913902 100644 --- a/deeppavlov/configs/evolution/intents_snli.json +++ b/deeppavlov/configs/evolution/intents_snli.json @@ -80,7 +80,12 @@ "text_size": 51, "coef_reg_cnn": 1e-4, "coef_reg_den": 1e-4, - "dropout_rate": 0.5, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "dense_size": 100, "model_name": "cnn_model", "embedder": "#my_embedder", diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index d078c521af..713e4271f2 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -225,14 +225,15 @@ def initialize_all_nodes(self, params): node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") - model_layers[params["nodes"][node_str_id]] = Bidirectional(CuDNNLSTM(**node_params)) + model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( + Bidirectional(CuDNNLSTM(**node_params))) elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") - model_layers[params["nodes"][node_str_id]] = \ - multiplicative_self_attention_init(**node_params) + model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( + multiplicative_self_attention_init(**node_params)) else: node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) node_params = deepcopy(params[params["nodes"][node_str_id]]) @@ -240,7 +241,8 @@ def initialize_all_nodes(self, params): node_params.pop("node_type") node_params.pop("node_layer") if callable(node_func): - model_layers[params["nodes"][node_str_id]] = node_func(**node_params) + model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( + node_func(**node_params)) else: raise AttributeError("Node {} is not defined correctly".format(node_str_id)) From 4e0a96e0fd0bf24df3d15ed48b181ef5744c5473 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 7 Jun 2018 15:19:25 +0300 Subject: [PATCH 401/616] feat: add dropout --- deeppavlov/models/evolution/evolution_intent_model.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index aef0adc73a..6d84f79643 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -92,13 +92,13 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") - output_of_node = Bidirectional(CuDNNLSTM(**node_params))(inp) + output_of_node = Dropout(rate=params['dropout_rate'])(Bidirectional(CuDNNLSTM(**node_params))(inp)) elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") - output_of_node = multiplicative_self_attention(inp, **node_params) + output_of_node = Dropout(rate=params['dropout_rate'])(multiplicative_self_attention(inp, **node_params)) else: node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) node_params = deepcopy(params[params["nodes"][node_str_id]]) @@ -106,7 +106,7 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) node_params.pop("node_type") node_params.pop("node_layer") if callable(node_func): - output_of_node = node_func(**node_params)(inp) + output_of_node = Dropout(rate=params['dropout_rate'])(node_func(**node_params)(inp)) else: raise AttributeError("Node {} is not defined correctly".format(node_str_id)) return output_of_node @@ -125,6 +125,7 @@ def evolution_classification_model(self, params): if np.sum(params["binary_mask"]) == 0: output = Dense(1, activation=None)(inp) output = GlobalMaxPooling1D()(output) + output = Dropout(rate=params['dropout_rate'])(output) output = Dense(self.n_classes, activation=None)(output) activation = params.get("last_layer_activation", "sigmoid") act_output = Activation(activation)(output) From 9867224a9522d2e524cac4124a74107b4b4484e7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 7 Jun 2018 15:34:02 +0300 Subject: [PATCH 402/616] feat: add l2 regularization --- .../configs/evolution/basic_ag_news_part.json | 30 ++++++++++++++-- .../configs/evolution/basic_ru_snli_part.json | 30 ++++++++++++++-- .../basic_ru_snli_part_many_inputs.json | 30 ++++++++++++++-- .../basic_snips_one_neuron_init.json | 30 ++++++++++++++-- .../evolution/basic_snips_random_init.json | 30 ++++++++++++++-- .../configs/evolution/basic_snli_part.json | 30 ++++++++++++++-- .../basic_snli_part_many_inputs.json | 30 ++++++++++++++-- .../basic_snli_part_many_inputs_big.json | 35 ++++++++++++++++--- .../evolution/basic_twitter140_part.json | 30 ++++++++++++++-- .../evolution/evolution_intent_model.py | 14 ++++++-- .../evolution/evolution_many_inputs_model.py | 14 ++++++-- 11 files changed, 270 insertions(+), 33 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json index 41461637b5..3511d9e279 100644 --- a/deeppavlov/configs/evolution/basic_ag_news_part.json +++ b/deeppavlov/configs/evolution/basic_ag_news_part.json @@ -83,6 +83,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -100,7 +106,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -110,7 +122,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -120,7 +138,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part.json b/deeppavlov/configs/evolution/basic_ru_snli_part.json index cbaba4aaa3..89948242e7 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part.json @@ -83,6 +83,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -100,7 +106,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -110,7 +122,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -120,7 +138,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json index e89eead7fe..db5efcf723 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json @@ -94,6 +94,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -111,7 +117,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -121,7 +133,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -131,7 +149,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 0f84c322bc..5aae1eb930 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -80,6 +80,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -97,7 +103,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -107,7 +119,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -117,7 +135,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index ada0c083e4..0624d150e6 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -80,6 +80,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -97,7 +103,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -107,7 +119,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -117,7 +135,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index 8c3a0024e3..af00b8b899 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -83,6 +83,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -100,7 +106,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -110,7 +122,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -120,7 +138,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index 28563e55e8..0e276ea7ec 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -97,6 +97,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -114,7 +120,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -124,7 +136,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -134,7 +152,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json index fc8df5a739..7f09f8ad79 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json @@ -1,7 +1,10 @@ { "dataset_reader": { "name": "basic_classification_reader", - "x": ["sentence1", "sentence2"], + "x": [ + "sentence1", + "sentence2" + ], "y": "gold_label", "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/two_texts/part" }, @@ -91,6 +94,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -108,7 +117,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -118,7 +133,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -128,7 +149,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/configs/evolution/basic_twitter140_part.json b/deeppavlov/configs/evolution/basic_twitter140_part.json index 6aa5ddea01..a4dc4be135 100644 --- a/deeppavlov/configs/evolution/basic_twitter140_part.json +++ b/deeppavlov/configs/evolution/basic_twitter140_part.json @@ -83,6 +83,12 @@ "relu" ], "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] } }, "Conv1D": { @@ -100,7 +106,13 @@ ], "discrete": true }, - "padding": "same" + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "CuDNNLSTM": { "units": { @@ -110,7 +122,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "BiCuDNNLSTM": { "units": { @@ -120,7 +138,13 @@ ], "discrete": true }, - "return_sequences": true + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } }, "MaxPooling1D": { "pool_size": { diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 6d84f79643..887ace23a7 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -92,7 +92,11 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") - output_of_node = Dropout(rate=params['dropout_rate'])(Bidirectional(CuDNNLSTM(**node_params))(inp)) + l2_reg = node_params.get("coef_regul_l2") + node_params.pop("l2_reg") + output_of_node = Dropout(rate=params['dropout_rate'])( + Bidirectional(CuDNNLSTM(**node_params, + kernel_regularizer=l2(l2_reg)))(inp)) elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") @@ -105,8 +109,14 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") + l2_reg = node_params.get("coef_regul_l2") if callable(node_func): - output_of_node = Dropout(rate=params['dropout_rate'])(node_func(**node_params)(inp)) + if l2_reg is None: + output_of_node = Dropout(rate=params['dropout_rate'])(node_func(**node_params)(inp)) + else: + node_params.pop("l2_reg") + output_of_node = Dropout(rate=params['dropout_rate'])( + node_func(**node_params, kernel_regularizer=l2(l2_reg))(inp)) else: raise AttributeError("Node {} is not defined correctly".format(node_str_id)) return output_of_node diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index 713e4271f2..82d5beb3c0 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -225,8 +225,10 @@ def initialize_all_nodes(self, params): node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") + l2_reg = node_params.get("coef_regul_l2") + node_params.pop("l2_reg") model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( - Bidirectional(CuDNNLSTM(**node_params))) + Bidirectional(CuDNNLSTM(**node_params, kernel_regularizer=l2(l2_reg)))) elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") @@ -240,9 +242,15 @@ def initialize_all_nodes(self, params): node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") + l2_reg = node_params.get("coef_regul_l2") if callable(node_func): - model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( - node_func(**node_params)) + if l2_reg is None: + model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( + node_func(**node_params)) + else: + node_params.pop("l2_reg") + model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( + node_func(**node_params, kernel_regularizer=l2(l2_reg))) else: raise AttributeError("Node {} is not defined correctly".format(node_str_id)) From 80670c991f41529d586baf668ff52078e74a86b1 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 7 Jun 2018 15:56:39 +0300 Subject: [PATCH 403/616] feat: add l2 regularization --- deeppavlov/models/evolution/evolution_intent_model.py | 4 ++-- deeppavlov/models/evolution/evolution_many_inputs_model.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py index 887ace23a7..8690103fef 100644 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ b/deeppavlov/models/evolution/evolution_intent_model.py @@ -93,7 +93,7 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) node_params.pop("node_type") node_params.pop("node_layer") l2_reg = node_params.get("coef_regul_l2") - node_params.pop("l2_reg") + node_params.pop("coef_regul_l2") output_of_node = Dropout(rate=params['dropout_rate'])( Bidirectional(CuDNNLSTM(**node_params, kernel_regularizer=l2(l2_reg)))(inp)) @@ -114,7 +114,7 @@ def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None) if l2_reg is None: output_of_node = Dropout(rate=params['dropout_rate'])(node_func(**node_params)(inp)) else: - node_params.pop("l2_reg") + node_params.pop("coef_regul_l2") output_of_node = Dropout(rate=params['dropout_rate'])( node_func(**node_params, kernel_regularizer=l2(l2_reg))(inp)) else: diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index 82d5beb3c0..ff122405a8 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -226,7 +226,7 @@ def initialize_all_nodes(self, params): node_params.pop("node_type") node_params.pop("node_layer") l2_reg = node_params.get("coef_regul_l2") - node_params.pop("l2_reg") + node_params.pop("coef_regul_l2") model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( Bidirectional(CuDNNLSTM(**node_params, kernel_regularizer=l2(l2_reg)))) elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": @@ -248,7 +248,7 @@ def initialize_all_nodes(self, params): model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( node_func(**node_params)) else: - node_params.pop("l2_reg") + node_params.pop("coef_regul_l2") model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( node_func(**node_params, kernel_regularizer=l2(l2_reg))) else: From 6b08089842ae5f842fa2205f08575c1f944cdd2d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 10:50:48 +0300 Subject: [PATCH 404/616] fix: params in config reduced --- deeppavlov/configs/evolution/basic_ag_news_part.json | 4 ++-- deeppavlov/configs/evolution/basic_ru_snli_part.json | 6 +++--- .../configs/evolution/basic_ru_snli_part_many_inputs.json | 6 +++--- .../configs/evolution/basic_snips_one_neuron_init.json | 8 ++++---- deeppavlov/configs/evolution/basic_snips_random_init.json | 2 +- deeppavlov/configs/evolution/basic_snli_part.json | 6 +++--- .../configs/evolution/basic_snli_part_many_inputs.json | 6 +++--- .../evolution/basic_snli_part_many_inputs_big.json | 6 +++--- deeppavlov/configs/evolution/basic_twitter140_part.json | 6 +++--- 9 files changed, 25 insertions(+), 25 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json index 3511d9e279..68ee42a6cc 100644 --- a/deeppavlov/configs/evolution/basic_ag_news_part.json +++ b/deeppavlov/configs/evolution/basic_ag_news_part.json @@ -160,14 +160,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part.json b/deeppavlov/configs/evolution/basic_ru_snli_part.json index 89948242e7..4a3ce204d3 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part.json @@ -160,14 +160,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -225,7 +225,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json index db5efcf723..680b4804a0 100644 --- a/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json @@ -171,14 +171,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -236,7 +236,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json index 5aae1eb930..4b3f8f4718 100644 --- a/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json +++ b/deeppavlov/configs/evolution/basic_snips_one_neuron_init.json @@ -69,7 +69,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -115,7 +115,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -131,7 +131,7 @@ "units": { "range": [ 50, - 200 + 500 ], "discrete": true }, @@ -221,7 +221,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snips_random_init.json b/deeppavlov/configs/evolution/basic_snips_random_init.json index 0624d150e6..573e8841c2 100644 --- a/deeppavlov/configs/evolution/basic_snips_random_init.json +++ b/deeppavlov/configs/evolution/basic_snips_random_init.json @@ -221,7 +221,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index af00b8b899..7c5198e947 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -160,14 +160,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -225,7 +225,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json index 0e276ea7ec..69a694dc19 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs.json @@ -174,14 +174,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -242,7 +242,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json index 7f09f8ad79..8259544e97 100644 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json +++ b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json @@ -171,14 +171,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -241,7 +241,7 @@ "batch_size": { "range": [ 50, - 70 + 100 ], "discrete": true }, diff --git a/deeppavlov/configs/evolution/basic_twitter140_part.json b/deeppavlov/configs/evolution/basic_twitter140_part.json index a4dc4be135..7ef90990dd 100644 --- a/deeppavlov/configs/evolution/basic_twitter140_part.json +++ b/deeppavlov/configs/evolution/basic_twitter140_part.json @@ -160,14 +160,14 @@ "n_hidden": { "range": [ 50, - 500 + 200 ], "discrete": true }, "n_output_features": { "range": [ 50, - 500 + 200 ], "discrete": true }, @@ -225,7 +225,7 @@ "batch_size": { "range": [ 50, - 200 + 100 ], "discrete": true }, From 2c398d56cbaa520cb8e173b235238308eeaaaa78 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 11:25:00 +0300 Subject: [PATCH 405/616] fix: params in config reduced --- deeppavlov/configs/evolution/basic_ag_news_part.json | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json index 68ee42a6cc..128146e58e 100644 --- a/deeppavlov/configs/evolution/basic_ag_news_part.json +++ b/deeppavlov/configs/evolution/basic_ag_news_part.json @@ -197,6 +197,12 @@ "loss": "binary_crossentropy", "confident_threshold": 1, "text_size": 50, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, "last_layer_activation": "softmax", "model_name": "evolution_classification_model", "embedder": "#my_embedder", From d9c95fa55a641936862a904f6a620a86e3e5b9ad Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 12:11:58 +0300 Subject: [PATCH 406/616] feat: probability based selection and crossover --- deeppavlov/models/evolution/run_evolution.py | 13 +------------ 1 file changed, 1 insertion(+), 12 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 81205386df..96cc4c9260 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -98,13 +98,6 @@ def score_population(population, population_size, result_file): parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) parser.add_argument('--one_neuron_init', help='whether to start with zero binary mask (one neuron network)', default=0) parser.add_argument('--given_mask_init', help='whether to start with given binary mask', default=0) -parser.add_argument('--save_best_portion', - help='Please, enter portion of population to save for the next generation with weights', - default=0.) -parser.add_argument('--renovation_frequency', - help='Please, enter frequency of renovation (how often in terms of generations ' - 'to renovate the second best portion)', - default=1) parser.add_argument('--train_partition', help='Please, enter partition of splitted train', default=1) @@ -112,6 +105,7 @@ def score_population(population, population_size, result_file): args = parser.parse_args() CONFIG_FILE = args.config +EVOLVE_METRIC = args.evolve_metric POPULATION_SIZE = args.p_size GPU_NUMBER = len(args.gpus) gpus = [int(gpu) for gpu in args.gpus.split(",")] @@ -119,9 +113,6 @@ def score_population(population, population_size, result_file): N_TYPES = int(args.n_types) ONE_NEURON_INIT = bool(int(args.one_neuron_init)) GIVEN_MASK_INIT = bool(int(args.given_mask_init)) -EVOLVE_METRIC = args.evolve_metric -SAVE_BEST_PORTION = float(args.save_best_portion) -RENOVATION_FREQUENCY = int(args.renovation_frequency) TRAIN_PARTITION = int(args.train_partition) with open(CONFIG_FILE, "r") as f: @@ -149,8 +140,6 @@ def score_population(population, population_size, result_file): key_basic_layers="basic_layers_params", seed=42, start_with_one_neuron=ONE_NEURON_INIT, - save_best_with_weights_portion=SAVE_BEST_PORTION, - renovation_frequency=RENOVATION_FREQUENCY, train_partition=TRAIN_PARTITION, initial_binary_mask=INITIAL_BINARY_MASK, **basic_params) From 880c3f3e2ae7c361f7475a3dfcdf352df71239ca Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 12:25:35 +0300 Subject: [PATCH 407/616] fix: add dropout in another place of network for many inputs --- .../evolution/evolution_many_inputs_model.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py index ff122405a8..7fc9e7d155 100644 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ b/deeppavlov/models/evolution/evolution_many_inputs_model.py @@ -211,6 +211,8 @@ def get_node_output(self, model_layers, node_str_id, dg, params, edges_outputs=N node_params.pop("node_type") node_params.pop("node_layer") output_of_node = model_layers[params["nodes"][node_str_id]](inp) + + output_of_node = Dropout(rate=params['dropout_rate'])(output_of_node) return output_of_node def initialize_all_nodes(self, params): @@ -227,15 +229,14 @@ def initialize_all_nodes(self, params): node_params.pop("node_layer") l2_reg = node_params.get("coef_regul_l2") node_params.pop("coef_regul_l2") - model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( - Bidirectional(CuDNNLSTM(**node_params, kernel_regularizer=l2(l2_reg)))) + model_layers[params["nodes"][node_str_id]] = Bidirectional(CuDNNLSTM(**node_params, + kernel_regularizer=l2(l2_reg))) elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": node_params = deepcopy(params[params["nodes"][node_str_id]]) node_params.pop("node_name") node_params.pop("node_type") node_params.pop("node_layer") - model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( - multiplicative_self_attention_init(**node_params)) + model_layers[params["nodes"][node_str_id]] = multiplicative_self_attention_init(**node_params) else: node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) node_params = deepcopy(params[params["nodes"][node_str_id]]) @@ -245,12 +246,11 @@ def initialize_all_nodes(self, params): l2_reg = node_params.get("coef_regul_l2") if callable(node_func): if l2_reg is None: - model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( - node_func(**node_params)) + model_layers[params["nodes"][node_str_id]] = node_func(**node_params) else: node_params.pop("coef_regul_l2") - model_layers[params["nodes"][node_str_id]] = Dropout(rate=params['dropout_rate'])( - node_func(**node_params, kernel_regularizer=l2(l2_reg))) + model_layers[params["nodes"][node_str_id]] = node_func(**node_params, + kernel_regularizer=l2(l2_reg)) else: raise AttributeError("Node {} is not defined correctly".format(node_str_id)) From 525975aec27b48ff8d5a19caebdb8e6db12910dc Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 12:49:38 +0300 Subject: [PATCH 408/616] fix: prints --- deeppavlov/models/evolution/run_evolution.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 96cc4c9260..0d2faab2e2 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -169,7 +169,7 @@ def score_population(population, population_size, result_file): print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() -print("Considered population: {}\nScoring...\n".format(population)) +# print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] iters = 1 @@ -178,7 +178,7 @@ def score_population(population, population_size, result_file): print("\nIteration #{} starts\n".format(iters)) population = evolution.next_generation(population, population_scores, iters) - print("Considered population: {}\nScoring...\n".format(population)) + # print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] print("Population scores: {}".format(population_scores)) print("\nIteration #{} was done\n".format(iters)) From 0f0c0bd9afbc13fb6d2ef82b7ebfa2290136613f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 14:21:02 +0300 Subject: [PATCH 409/616] fix: if no crossover --- deeppavlov/models/evolution/run_evolution.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 0d2faab2e2..0288ad04d8 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -134,7 +134,7 @@ def score_population(population, population_size, result_file): # EVOLUTION starts here! evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, population_size=POPULATION_SIZE, - p_crossover=0.2, crossover_power=0.2, + p_crossover=0.2, crossover_power=0.1, p_mutation=1., mutation_power=0.1, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", From 2183208ada027a129648eb34667ab14039094f94 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 15:55:40 +0300 Subject: [PATCH 410/616] feat: config for nlu_benchmark --- .../configs/evolution/basic_nlu_part.json | 250 ++++++++++++++++++ 1 file changed, 250 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_nlu_part.json diff --git a/deeppavlov/configs/evolution/basic_nlu_part.json b/deeppavlov/configs/evolution/basic_nlu_part.json new file mode 100644 index 0000000000..727290d280 --- /dev/null +++ b/deeppavlov/configs/evolution/basic_nlu_part.json @@ -0,0 +1,250 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "gold_label", + "data_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_data/ChatbotCorpus", + "train": "train_ChatbotCorpus_0.csv", + "valid": "valid_ChatbotCorpus_0.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_data/ChatbotCorpus/classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_data/ChatbotCorpus/classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_classification/ChatbotCorpus/one_neuron_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_classification/ChatbotCorpus/one_neuron_init_part_6", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "confident_threshold": 1, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.000001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 15, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, + "last_layer_activation": "softmax", + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 1, + 10 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 100 + ], + "discrete": true + }, + "metric_optimization": "maximize", + "metrics": [ + "classification_f1", + "classification_accuracy", + "classification_log_loss", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "show_examples": false, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From 56acf7ba4bcd6e31f284d7da19dcbd97cc878895 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 16:12:12 +0300 Subject: [PATCH 411/616] fix: config --- deeppavlov/configs/evolution/basic_nlu_part.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_nlu_part.json b/deeppavlov/configs/evolution/basic_nlu_part.json index 727290d280..3dec69c7cd 100644 --- a/deeppavlov/configs/evolution/basic_nlu_part.json +++ b/deeppavlov/configs/evolution/basic_nlu_part.json @@ -2,7 +2,7 @@ "dataset_reader": { "name": "basic_classification_reader", "x": "text", - "y": "gold_label", + "y": "intent", "data_path": "/home/dilyara.baymurzina/evolution_data/nlu_evaluation_data/ChatbotCorpus", "train": "train_ChatbotCorpus_0.csv", "valid": "valid_ChatbotCorpus_0.csv" From 6bd63cbc3708af659cbce5e02cf301962a20401d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 16:31:08 +0300 Subject: [PATCH 412/616] fix: whether to save test metrics --- deeppavlov/models/evolution/run_evolution.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 0288ad04d8..d2c8dbf5da 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -55,11 +55,9 @@ def score_population(population, population_size, result_file): for i in range(population_size): val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ "save_path"]).parent.joinpath("valid_results.txt"))) - try: + if TEST: test_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ "save_path"]).parent.joinpath("test_results.txt"))) - except FileNotFoundError: - pass result_table_dict = {} for el in order: @@ -70,9 +68,9 @@ def score_population(population, population_size, result_file): result_table_dict[el + "_test"] = [] for m_id, m in enumerate(CONSIDERED_METRICS): result_table_dict[m + "_valid"].append(val_results[m_id]) - try: + if TEST: result_table_dict[m + "_test"].append(test_results[m_id]) - except NameError: + else: result_table_dict[m + "_test"].append(0.) result_table_dict[order[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) @@ -122,6 +120,8 @@ def score_population(population, population_size, result_file): # list of names of considered metrics CONSIDERED_METRICS = basic_params["train"]["metrics"] +VALID = basic_params["train"]["valid_best"] +TEST = basic_params["train"]["test_best"] if GIVEN_MASK_INIT: # Embedding -> BiLSTM -> Dense -> Dense -> GlobalMaxPooling -> Dense(#classes) From cc71d6fe465655ff36959fa495de7c2646dab151 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 16:37:18 +0300 Subject: [PATCH 413/616] fix: whether to save test metrics --- deeppavlov/models/evolution/run_evolution.py | 1 - 1 file changed, 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index d2c8dbf5da..a6c24a3059 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -120,7 +120,6 @@ def score_population(population, population_size, result_file): # list of names of considered metrics CONSIDERED_METRICS = basic_params["train"]["metrics"] -VALID = basic_params["train"]["valid_best"] TEST = basic_params["train"]["test_best"] if GIVEN_MASK_INIT: From fe4b8e2653a2355a553aca390fd02e6711e8d215 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 8 Jun 2018 18:21:20 +0300 Subject: [PATCH 414/616] fix: result table --- deeppavlov/models/evolution/run_evolution.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index a6c24a3059..3a58a31059 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -151,9 +151,9 @@ def score_population(population, population_size, result_file): result_table_dict = {} for el in order: - if order == "params": + if el == "params": result_table_dict[el] = [] - result_table_columns.extend([el + "_valid"]) + result_table_columns.extend([el]) else: result_table_dict[el + "_valid"] = [] result_table_dict[el + "_test"] = [] From b697f879fd8ce5f18537f6d3bb69cdce63eb0535 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 9 Jun 2018 12:11:43 +0300 Subject: [PATCH 415/616] =?UTF-8?q?=D0=B0=D1=83=D1=84=D0=B5=D0=96=20=D1=8B?= =?UTF-8?q?=D0=B8=D1=83=D0=BA=20=D0=B0=D1=84=D0=B9=20=D1=81=D1=89=D1=82?= =?UTF-8?q?=D0=B0=D1=88=D0=BF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../configs/evolution/basic_sber_faq.json | 251 ++++++++++++++++++ 1 file changed, 251 insertions(+) create mode 100644 deeppavlov/configs/evolution/basic_sber_faq.json diff --git a/deeppavlov/configs/evolution/basic_sber_faq.json b/deeppavlov/configs/evolution/basic_sber_faq.json new file mode 100644 index 0000000000..6410a1993b --- /dev/null +++ b/deeppavlov/configs/evolution/basic_sber_faq.json @@ -0,0 +1,251 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "label", + "data_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_data", + "train": "train.csv", + "valid": "val.csv", + "test": "test.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_data/classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_data/classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "evolution_classification_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/one_neuron_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/one_neuron_init_part_6", + "classes": "#classes_vocab.keys()", + "to_evolve": true, + "basic_layers_params": { + "Dense": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + }, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "Conv1D": { + "filters": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "kernel_size": { + "range": [ + 2, + 7 + ], + "discrete": true + }, + "padding": "same", + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "CuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "BiCuDNNLSTM": { + "units": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "return_sequences": true, + "coef_regul_l2": { + "range": [ + 0.000001, + 0.001 + ] + } + }, + "MaxPooling1D": { + "pool_size": { + "range": [ + 2, + 5 + ], + "discrete": true + }, + "padding": "same" + }, + "SelfMultiplicativeAttention": { + "n_hidden": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "n_output_features": { + "range": [ + 50, + 200 + ], + "discrete": true + }, + "activation": { + "values": [ + "softmax", + "sigmoid", + "relu" + ], + "choice": true + } + } + }, + "confident_threshold": 1, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": { + "range": [ + 0.000001, + 0.1 + ] + }, + "loss": "binary_crossentropy", + "text_size": 60, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, + "last_layer_activation": "softmax", + "model_name": "evolution_classification_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "range": [ + 1, + 10 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 100 + ], + "discrete": true + }, + "metric_optimization": "maximize", + "metrics": [ + "classification_f1", + "classification_accuracy", + "classification_log_loss", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "show_examples": false, + "validate_best": true, + "test_best": true + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel" + } + } +} From b4b07a896a85e3d03dd883ed394fcb3ad91ab0af Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Sat, 9 Jun 2018 12:16:53 +0300 Subject: [PATCH 416/616] chore: config sber faq --- deeppavlov/configs/evolution/basic_sber_faq.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_sber_faq.json b/deeppavlov/configs/evolution/basic_sber_faq.json index 6410a1993b..a7dd310b66 100644 --- a/deeppavlov/configs/evolution/basic_sber_faq.json +++ b/deeppavlov/configs/evolution/basic_sber_faq.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/one_neuron_init_part_6", - "load_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/one_neuron_init_part_6", + "save_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/given_mask_init_part_6", + "load_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/given_mask_init_part_6", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { From d79ca4878dabb75250d864779409ca47eb747fe8 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 11:13:11 +0300 Subject: [PATCH 417/616] feat: classification_f1_weighted --- .../configs/evolution/basic_sber_faq.json | 1 + deeppavlov/metrics/fmeasure_classification.py | 25 ++++++++++++++++++- 2 files changed, 25 insertions(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_sber_faq.json b/deeppavlov/configs/evolution/basic_sber_faq.json index a7dd310b66..96ff27addd 100644 --- a/deeppavlov/configs/evolution/basic_sber_faq.json +++ b/deeppavlov/configs/evolution/basic_sber_faq.json @@ -232,6 +232,7 @@ "metric_optimization": "maximize", "metrics": [ "classification_f1", + "classification_f1_weighted", "classification_accuracy", "classification_log_loss", "classification_roc_auc" diff --git a/deeppavlov/metrics/fmeasure_classification.py b/deeppavlov/metrics/fmeasure_classification.py index 83ecc60c6a..502dfbaf73 100644 --- a/deeppavlov/metrics/fmeasure_classification.py +++ b/deeppavlov/metrics/fmeasure_classification.py @@ -25,7 +25,30 @@ @register_metric('classification_f1') def fmeasure(y_true, y_predicted, average="macro"): """ - Calculate F1-measure + Calculate F1-measure macro + Args: + y_true: array of true binary labels + y_predicted: list of predictions. + Each prediction is a tuple of two elements + (predicted_labels, dictionary like {"label_i": probability_i} ) + where probability is float or keras.tensor + average: determines the type of averaging performed on the data + + Returns: + F1-measure + """ + classes = np.array(list(y_predicted[0][1].keys())) + y_true_one_hot = labels2onehot(y_true, classes) + y_pred_labels = [y_predicted[i][0] for i in range(len(y_predicted))] + y_pred_one_hot = labels2onehot(y_pred_labels, classes) + + return f1_score(y_true_one_hot, y_pred_one_hot, average=average) + + +@register_metric('classification_f1_weighted') +def fmeasure(y_true, y_predicted, average="weighted"): + """ + Calculate F1-measure weighted Args: y_true: array of true binary labels y_predicted: list of predictions. From 7446ba83a0e31f2ea785d282903d840a7a1e2435 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 12:36:21 +0300 Subject: [PATCH 418/616] feat: if did not calculated, scores to zero --- deeppavlov/models/evolution/run_evolution.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 3a58a31059..d808e189d2 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -53,8 +53,15 @@ def score_population(population, population_size, result_file): proc.wait() for i in range(population_size): - val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("valid_results.txt"))) + try: + val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).parent.joinpath("valid_results.txt"))) + except OSError or FileNotFoundError: + for m_id, m in enumerate(CONSIDERED_METRICS): + if "loss" in m: + val_results[m_id] = 1e6 + else: + val_results[m_id] = 0. if TEST: test_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ "save_path"]).parent.joinpath("test_results.txt"))) From 24989483a49a3b5cab9390737a5b24961930db4c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 12:40:30 +0300 Subject: [PATCH 419/616] fix: configs for check if works --- deeppavlov/configs/evolution/basic_sber_faq.json | 4 ++-- deeppavlov/configs/evolution/basic_snli_part.json | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_sber_faq.json b/deeppavlov/configs/evolution/basic_sber_faq.json index 96ff27addd..1eed5fd9cd 100644 --- a/deeppavlov/configs/evolution/basic_sber_faq.json +++ b/deeppavlov/configs/evolution/basic_sber_faq.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/given_mask_init_part_6", - "load_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/given_mask_init_part_6", + "save_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/given_mask_init_part_7", + "load_path": "/home/dilyara.baymurzina/evolution_data/sber_faq_classification/given_mask_init_part_7", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { diff --git a/deeppavlov/configs/evolution/basic_snli_part.json b/deeppavlov/configs/evolution/basic_snli_part.json index 7c5198e947..a115baa8b5 100644 --- a/deeppavlov/configs/evolution/basic_snli_part.json +++ b/deeppavlov/configs/evolution/basic_snli_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_6", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_6", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_7", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/given_mask_init_part_7", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { From 2b20e4e2f0ccae90dcb95dcbb97684e028f7f446 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 14:58:40 +0300 Subject: [PATCH 420/616] fix: train evaluate model from config --- deeppavlov/models/evolution/train_phenotype.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py index 0cb26a46eb..45e2686478 100644 --- a/deeppavlov/models/evolution/train_phenotype.py +++ b/deeppavlov/models/evolution/train_phenotype.py @@ -17,7 +17,7 @@ import sys from pathlib import Path -from deeppavlov.core.commands.train import train_model_from_config +from deeppavlov.core.commands.train import train_evaluate_model_from_config from deeppavlov.core.common.file import read_json, save_json from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe @@ -25,7 +25,7 @@ config_path = sys.argv[1] print("TRAIN PHENOTYPE") -reports = train_model_from_config(config_path) +reports = train_evaluate_model_from_config(config_path) print(reports) if len(reports) == 2: From 7bb2eabec23e8ed8b2c9d44591dd8e81239da374 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 15:03:32 +0300 Subject: [PATCH 421/616] feat: start from given population --- deeppavlov/models/evolution/run_evolution.py | 66 +++++++++++++------- 1 file changed, 44 insertions(+), 22 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index d808e189d2..b3a0ead43b 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -7,7 +7,7 @@ from copy import deepcopy, copy from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution -from deeppavlov.core.common.file import save_json +from deeppavlov.core.common.file import save_json, read_json def score_population(population, population_size, result_file): @@ -106,6 +106,12 @@ def score_population(population, population_size, result_file): parser.add_argument('--train_partition', help='Please, enter partition of splitted train', default=1) +parser.add_argument('--start_from_population', + help='Please, enter the population number to start from. 0 means from scratch', + default=0) +parser.add_argument('--path_to_population', + help='Please, enter the path to population to start from', + default="") args = parser.parse_args() @@ -119,6 +125,9 @@ def score_population(population, population_size, result_file): ONE_NEURON_INIT = bool(int(args.one_neuron_init)) GIVEN_MASK_INIT = bool(int(args.given_mask_init)) TRAIN_PARTITION = int(args.train_partition) +START_FROM_POPULATION = int(args.start_from_population) +PATH_TO_POPULATION = args.path_to_population + with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) @@ -153,36 +162,49 @@ def score_population(population, population_size, result_file): # Result table order = deepcopy(CONSIDERED_METRICS) order.extend(["params"]) +result_file = Path(basic_params["chainer"]["pipe"][ + evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") -result_table_columns = [] +if START_FROM_POPULATION == 0: + result_table_columns = [] -result_table_dict = {} -for el in order: - if el == "params": - result_table_dict[el] = [] - result_table_columns.extend([el]) - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - result_table_columns.extend([el + "_valid", el + "_test"]) + result_table_dict = {} + for el in order: + if el == "params": + result_table_dict[el] = [] + result_table_columns.extend([el]) + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) -result_table_columns.append("params") + result_table_columns.append("params") -result_file = Path(basic_params["chainer"]["pipe"][ - evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") -result_table = pd.DataFrame(result_table_dict) -result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') + result_table = pd.DataFrame(result_table_dict) + result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') -print("\nIteration #{} starts\n".format(0)) -population = evolution.first_generation() -# print("Considered population: {}\nScoring...\n".format(population)) -population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] + print("\nIteration #{} starts\n".format(0)) + population = evolution.first_generation() + population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] -iters = 1 + iters = 1 +else: + iters = START_FROM_POPULATION + print("\nIteration #{} starts\n".format(iters)) + model_name = basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] + population = [] + + for i in range(POPULATION_SIZE): + population.append(read_json(Path(PATH_TO_POPULATION).joinpath( + model_name + "_" + str(i)).joinpath("config.json"))) + + population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] + print("Population scores: {}".format(population_scores)) + print("\nIteration #{} was done\n".format(iters)) + iters += 1 while True: print("\nIteration #{} starts\n".format(iters)) - population = evolution.next_generation(population, population_scores, iters) # print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] From 04edf41ef84f0f33f3a1ecfdcd4ba00be02115ed Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 15:09:04 +0300 Subject: [PATCH 422/616] fix: binary mask to array --- deeppavlov/models/evolution/run_evolution.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index b3a0ead43b..b44b19e28f 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -197,6 +197,8 @@ def score_population(population, population_size, result_file): for i in range(POPULATION_SIZE): population.append(read_json(Path(PATH_TO_POPULATION).joinpath( model_name + "_" + str(i)).joinpath("config.json"))) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ + np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] print("Population scores: {}".format(population_scores)) From 870dd813b5018b0dc45f096c4500cb8a5601b020 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 15:27:42 +0300 Subject: [PATCH 423/616] fix: save and load paths --- deeppavlov/models/evolution/run_evolution.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index b44b19e28f..408b6674b2 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -199,6 +199,10 @@ def score_population(population, population_size, result_file): model_name + "_" + str(i)).joinpath("config.json"))) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ + str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).parent) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ + str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]).parent) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] print("Population scores: {}".format(population_scores)) From f2fbe288d1fe89570806d9bd54475573c430b588 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 15:30:27 +0300 Subject: [PATCH 424/616] fix: save and load paths --- deeppavlov/models/evolution/run_evolution.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 408b6674b2..2a77a14425 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -200,7 +200,8 @@ def score_population(population, population_size, result_file): population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).parent) + str(Path(basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).joinpath( + "population_" + str(START_FROM_POPULATION)).joinpath(model_name + str(i))) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]).parent) From 2f28ca8b040704734645923b6b795132b8002ea6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 15:36:18 +0300 Subject: [PATCH 425/616] fix: save and load paths --- deeppavlov/models/evolution/run_evolution.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 2a77a14425..f1b6ae6ad3 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -201,7 +201,7 @@ def score_population(population, population_size, result_file): np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ str(Path(basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).joinpath( - "population_" + str(START_FROM_POPULATION)).joinpath(model_name + str(i))) + "population_" + str(START_FROM_POPULATION)).joinpath(model_name + "_" + str(i))) population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]).parent) From 279fc9127698cf708bfef0ad9838f1eb3fec18db Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 15:55:08 +0300 Subject: [PATCH 426/616] feat: class for parameters evolution --- .../evolution/evolution_param_generator.py | 470 ++++++++++++++++++ 1 file changed, 470 insertions(+) create mode 100644 deeppavlov/models/evolution/evolution_param_generator.py diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py new file mode 100644 index 0000000000..695a9b375c --- /dev/null +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -0,0 +1,470 @@ +import numpy as np +from copy import deepcopy +from pathlib import Path +import json + +from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ + number_to_type_layer +from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe +from deeppavlov.core.common.file import read_json + + +# please, make sure that +# `config["chainer"]["pipe"]` is a list of models one of which is a model to be evolved, +# otherwise, in the whole class change `config["chainer"]["pipe"]` to new path + + +class NetworkAndParamsEvolution: + """ + Class performs full evolutionary process (task scores -> max): + 1. initializes random population + 2. makes replacement to get next generation: + a. selection according to obtained scores + b. crossover (recombination) with given probability p_crossover + c. mutation with given mutation rate p_mutation (probability to mutate) + according to given mutation power sigma + (current mutation power is randomly from -sigma to sigma) + """ + + def __init__(self, + population_size, + p_crossover=0.5, crossover_power=0.5, + p_mutation=0.5, mutation_power=0.1, + key_model_to_evolve="to_evolve", + seed=None, + train_partition=1, + **kwargs): + """ + Initialize evolution with random population + Args: + population_size: number of individuums per generation + p_crossover: probability to cross over for current replacement + crossover_power: part of EVOLVING parents parameters to exchange for offsprings + p_mutation: probability of mutation for current replacement + mutation_power: allowed percentage of mutation + key_model_to_evolve: binary flag that should be inserted into the dictionary + with evolving model in the basic config + seed: random seed for initialization + train_partition: integer number of train data parts + **kwargs: basic config with parameters + """ + + self.basic_config = deepcopy(kwargs) + self.model_to_evolve_index = find_index_of_dict_with_key_in_pipe(self.basic_config["chainer"]["pipe"], + key_model_to_evolve) + Path(self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).mkdir(parents=True, + exist_ok=True) + + self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) + self.train_params = deepcopy(self.basic_config.get("train")) + + print("___Basic config___: {}".format(self.basic_config)) + print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) + print("___Model params___: {}".format(self.params)) + print("___Train params___: {}".format(self.train_params)) + + self.population_size = population_size + self.p_crossover = p_crossover + self.p_mutation = p_mutation + self.mutation_power = mutation_power + self.crossover_power = crossover_power + self.evolving_params = [] + self.n_evolving_params = None + self.evolving_train_params = [] + self.n_evolving_train_params = None + self.n_saved_best_with_weights = 0 + self.train_partition = train_partition + self.evolution_individuum_id = 0 + self.evolution_model_id = 0 + + if seed is None: + pass + else: + np.random.seed(seed) + + def _insert_dict_into_model_params(self, params, model_index, dict_to_insert): + params_copy = deepcopy(params) + params_copy["chainer"]["pipe"].insert(model_index, dict_to_insert) + return params_copy + + def print_dict(self, dict, string=None): + if string is None: + print(json.dumps(dict, indent=2)) + else: + print(string) + print(json.dumps(dict, indent=2)) + return None + + def initialize_params_in_config(self, basic_params): + params = {} + params_for_search = {} + evolving_params = [] + + for param_name in list(basic_params.keys()): + if type(basic_params[param_name]) is dict: + if basic_params[param_name].get("choice"): + params_for_search[param_name] = list(basic_params[param_name]["values"]) + evolving_params.append(param_name) + elif basic_params[param_name].get("range"): + params_for_search[param_name] = deepcopy(basic_params[param_name]) + evolving_params.append(param_name) + elif basic_params[param_name].get("bool"): + params_for_search[param_name] = deepcopy(basic_params[param_name]) + evolving_params.append(param_name) + else: + # NOT evolving params + params[param_name] = deepcopy(basic_params[param_name]) + else: + # NOT evolving params + params[param_name] = deepcopy(basic_params[param_name]) + if basic_params: + params_for_search = deepcopy(self.sample_params(**params_for_search)) + + return params, params_for_search, evolving_params + + def first_generation(self, iteration=0): + """ + Initialize first generation randomly according to the given constraints is self.params + Returns: + first generation that consists of self.population_size individuums + """ + population = [] + for i in range(self.population_size): + population.append(deepcopy(self.basic_config)) + + # intitializing parameters for model + params, params_for_search, evolving_params = self.initialize_params_in_config(self.params) + self.evolving_params.extend(evolving_params) + # initializing parameters for train + train_params, train_params_for_search, evolving_params = self.initialize_params_in_config(self.train_params) + self.evolving_train_params.extend(evolving_params) + + # intitializing path to save model + # save_path = population_iteration/model_name_i/ + if "model_name" in params_for_search.keys(): + params["save_path"] = str(Path(self.params["save_path"]).joinpath( + "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) + else: + params["save_path"] = str(Path(self.params["save_path"]).joinpath( + "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) + + # load_path = population_iteration/model_name_i/ + if "model_name" in params_for_search.keys(): + params["load_path"] = str(Path(self.params["load_path"]).joinpath( + "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) + else: + params["load_path"] = str(Path(self.params["load_path"]).joinpath( + "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) + + # exchange model and layers params from basic config to sampled model params + population[-1]["chainer"]["pipe"][self.model_to_evolve_index] = {**params, + **params_for_search} + + # exchange train params from basic config to sampled train params + population[-1]["train"] = {**train_params, + **train_params_for_search} + population[-1]["train"]["evolution_model_id"] = self.evolution_model_id + self.evolution_model_id += 1 + + self.evolving_params = list(set(self.evolving_params)) + self.evolving_train_params = list(set(self.evolving_train_params)) + + self.n_evolving_params = len(self.evolving_params) + self.n_evolving_train_params = len(self.evolving_train_params) + + return population + + def next_generation(self, generation, scores, iteration, + p_crossover=None, crossover_power=None, + p_mutation=None, mutation_power=None): + """ + Provide an operation of replacement + Args: + generation: current generation (set of self.population_size configs + scores: corresponding scores that should be maximized + iteration: iteration number + p_crossover: probability to cross over for current replacement + crossover_power: part of parents parameters to exchange for offsprings + p_mutation: probability of mutation for current replacement + mutation_power: allowed percentage of mutation + + Returns: + the next generation according to the given scores of current generation + """ + if not p_crossover: + p_crossover = self.p_crossover + if not crossover_power: + crossover_power = self.crossover_power + if not p_mutation: + p_mutation = self.p_mutation + if not mutation_power: + mutation_power = self.mutation_power + + next_population = self.selection_of_best_with_weights(generation, scores) + print("Saved with weights: {} individuums".format(self.n_saved_best_with_weights)) + offsprings = self.crossover(generation, scores, + p_crossover=p_crossover, + crossover_power=crossover_power) + + changable_next = self.mutation(offsprings, + p_mutation=p_mutation, + mutation_power=mutation_power) + + next_population.extend(changable_next) + + for i in range(self.n_saved_best_with_weights): + # if several train files: + if self.train_partition != 1: + next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ + "train"]).stem.split("_")[0]) \ + + "_" + str(iteration % self.train_partition) + ".csv" + # re-init learning rate with the final one + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ + read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index][ + "save_path"]).parent.joinpath("model_opt.json")))["final_lear_rate"] + # paths + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ + str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ + str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( + self.params["model_name"] + "_" + str(i))) + + for i in range(self.n_saved_best_with_weights, self.population_size): + # if several train files + if self.train_partition != 1: + next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ + "train"]).stem.split("_")[0]) \ + + "_" + str(iteration % self.train_partition) + ".csv" + # paths + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ + str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( + self.params["model_name"] + "_" + str(i))) + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ + str(Path(self.params["load_path"]).joinpath("population_" + str(iteration)).joinpath( + self.params["model_name"] + "_" + str(i))) + + next_population[i]["train"]["evolution_model_id"] = self.evolution_model_id + self.evolution_model_id += 1 + + return next_population + + def selection_of_best_with_weights(self, population, scores): + """ + Select individuums to save with weights for the next generation from given population. + Range is an order of an individuum within sorted scores (1 range = max-score, self.population_size = min-score) + Individuum with the highest score has probability equal to 1 (100%). + Individuum with the lowest score has probability equal to 0 (0%). + Probability of i-th individuum to be selected with weights is (a * range_i + b) + where a = 1. / (1. - self.population_size), and + b = self.population_size / (self.population_size - 1.) + Args: + population: self.population_size individuums + scores: corresponding score that should be maximized + + Returns: + selected self.n_saved_best_with_weights (changable) individuums + """ + scores = np.array(scores, dtype='float') + sorted_ids = np.argsort(scores) + ranges = np.array([self.population_size - np.where(i == sorted_ids)[0][0] + for i in np.arange(self.population_size)]) + + a = 1. / (1. - self.population_size) + b = self.population_size / (self.population_size - 1.) + probas_to_be_selected = a * ranges + b + + selected = [] + for i in range(self.population_size): + if self.decision(probas_to_be_selected[i]): + selected.append(deepcopy(population[i])) + + self.n_saved_best_with_weights = len(selected) + return selected + + def crossover(self, population, scores, p_crossover, crossover_power): + """ + Recombine randomly population in pairs and cross over them with given probability. + Cross over from two parents produces two offsprings + each of which contains crossover_power portion of the parameter values from one parent, + and the other (1 - crossover_power portion) from the other parent + Args: + population: self.population_size individuums + p_crossover: probability to cross over for current replacement + crossover_power: part of EVOLVING parents parameters to exchange for offsprings + + Returns: + (self.population_size - self.n_saved_best_with_weights) offsprings + """ + offsprings = [] + scores = np.array(scores, dtype='float') + probas_to_be_parent = scores / np.sum(scores) + intervals = np.array([np.sum(probas_to_be_parent[:i]) for i in range(self.population_size)]) + + for i in range(self.population_size - self.n_saved_best_with_weights): + rs = np.random.random(2) + parents = population[np.where(rs[0] > intervals)[0][-1]], population[np.where(rs[1] > intervals)[0][-1]] + + if self.decision(p_crossover): + params_perm = np.random.permutation(self.n_evolving_params) + train_params_perm = np.random.permutation(self.n_evolving_train_params) + + curr_offsprings = [deepcopy(parents[0]), + deepcopy(parents[1])] + + part = int(crossover_power * self.n_evolving_params) + train_part = int(crossover_power * self.n_evolving_train_params) + + # exchange of model params (not layers params) + for j in range(self.n_evolving_params - part): + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[0][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[1][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + for j in range(self.n_evolving_params - part, self.n_evolving_params): + curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[1][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] = parents[0][ + "chainer"]["pipe"][self.model_to_evolve_index][ + self.evolving_params[params_perm[j]]] + + # exchange of train params + for j in range(self.n_evolving_train_params - train_part): + curr_offsprings[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] + curr_offsprings[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] + for j in range(self.n_evolving_train_params - train_part, self.n_evolving_train_params): + curr_offsprings[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] + curr_offsprings[1]["train"][ + self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ + self.evolving_train_params[train_params_perm[j]]] + + offsprings.append(deepcopy(curr_offsprings[0])) + else: + offsprings.append(deepcopy(parents[0])) + + return offsprings + + def mutation(self, population, p_mutation, mutation_power): + """ + Mutate each parameter of each individuum in population with probability p_mutation + Args: + population: self.population_size individuums + p_mutation: probability to mutate for each parameter + mutation_power: allowed percentage of mutation + + Returns: + mutated population + """ + mutated = [] + + for individuum in population: + mutated_individuum = deepcopy(individuum) + + # mutation of other model params + for param in self.params.keys(): + mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][param] = \ + self.mutation_of_param(param, self.params, + individuum["chainer"]["pipe"][self.model_to_evolve_index][param], + p_mutation, mutation_power) + + # mutation of train params + for param in self.train_params.keys(): + mutated_individuum["train"][param] = \ + self.mutation_of_param(param, self.train_params, + individuum["train"][param], + p_mutation, mutation_power) + + mutated.append(mutated_individuum) + + return mutated + + def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutation_power): + new_mutated_value = deepcopy(param_value) + if self.decision(p_mutation): + if type(params_dict[param]) is dict: + if params_dict[param].get('discrete', False): + val = round(param_value + + ((2 * np.random.random() - 1.) * mutation_power + * self.sample_params(**{param: params_dict[param]})[param])) + val = min(max(params_dict[param]["range"][0], val), + params_dict[param]["range"][1]) + new_mutated_value = val + elif 'range' in params_dict[param].keys(): + val = param_value + \ + ((2 * np.random.random() - 1.) * mutation_power + * self.sample_params(**{param: params_dict[param]})[param]) + val = min(max(params_dict[param]["range"][0], val), + params_dict[param]["range"][1]) + new_mutated_value = val + elif params_dict[param].get("choice"): + new_mutated_value = param_value + else: + new_mutated_value = param_value + else: + new_mutated_value = param_value + else: + new_mutated_value = param_value + + return new_mutated_value + + def decision(self, probability): + """ + Make decision whether to do action or not with given probability + Args: + probability: probability whether + + Returns: + + """ + r = np.random.random() + if r < probability: + return True + else: + return False + + def sample_params(self, **params): + if not params: + params_copy = deepcopy(self.params) + else: + params_copy = deepcopy(params) + params_sample = dict() + for param, param_val in params_copy.items(): + if isinstance(param_val, list): + params_sample[param] = np.random.choice(param_val) + elif isinstance(param_val, dict): + if 'bool' in param_val and param_val['bool']: + sample = bool(np.random.choice([True, False])) + elif 'range' in param_val: + sample = self._sample_from_ranges(param_val) + params_sample[param] = sample + else: + params_sample[param] = params_copy[param] + return params_sample + + def _sample_from_ranges(self, opts): + from_ = opts['range'][0] + to_ = opts['range'][1] + if opts.get('scale', None) == 'log': + sample = self._sample_log(from_, to_) + else: + sample = np.random.uniform(from_, to_) + if opts.get('discrete', False): + sample = int(np.round(sample)) + return sample + + @staticmethod + def _sample_log(from_, to_): + sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) + return float(sample) From 8be88ac93b8711089b1a525b67f4bcd0ad02125c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 16:18:11 +0300 Subject: [PATCH 427/616] fix: reading val results --- deeppavlov/models/evolution/run_evolution.py | 1 + 1 file changed, 1 insertion(+) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index f1b6ae6ad3..339372a0d0 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -57,6 +57,7 @@ def score_population(population, population_size, result_file): val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ "save_path"]).parent.joinpath("valid_results.txt"))) except OSError or FileNotFoundError: + val_results = [None for m in CONSIDERED_METRICS] for m_id, m in enumerate(CONSIDERED_METRICS): if "loss" in m: val_results[m_id] = 1e6 From 054e074f055d981a696826fdc014e0cc0326825f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 16:58:47 +0300 Subject: [PATCH 428/616] fix: remove f1 weighted form metrics --- deeppavlov/configs/evolution/basic_sber_faq.json | 1 - 1 file changed, 1 deletion(-) diff --git a/deeppavlov/configs/evolution/basic_sber_faq.json b/deeppavlov/configs/evolution/basic_sber_faq.json index 1eed5fd9cd..95ec81da41 100644 --- a/deeppavlov/configs/evolution/basic_sber_faq.json +++ b/deeppavlov/configs/evolution/basic_sber_faq.json @@ -232,7 +232,6 @@ "metric_optimization": "maximize", "metrics": [ "classification_f1", - "classification_f1_weighted", "classification_accuracy", "classification_log_loss", "classification_roc_auc" From 2ba2bde621622df9773a81ede370b5f6460f13b1 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 17:11:41 +0300 Subject: [PATCH 429/616] feat: queue for param evolution --- .../models/evolution/run_param_evolution.py | 198 ++++++++++++++++++ 1 file changed, 198 insertions(+) create mode 100644 deeppavlov/models/evolution/run_param_evolution.py diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py new file mode 100644 index 0000000000..a016a1831d --- /dev/null +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -0,0 +1,198 @@ +import json +import numpy as np +import argparse +from pathlib import Path +from subprocess import Popen, PIPE +import pandas as pd +from copy import deepcopy, copy + +from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution +from deeppavlov.core.common.file import save_json, read_json + + +def score_population(population, population_size, result_file): + global evolution + + population_metrics = {} + for m in CONSIDERED_METRICS: + population_metrics[m] = [] + + for k in range(POPULATION_SIZE // len(gpus) + 1): + procs = [] + for j in range(len(gpus)): + i = k * len(gpus) + j + if i < POPULATION_SIZE: + save_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) + load_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]) + + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ + str(save_path.joinpath("model")) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ + str(load_path.joinpath("model")) + + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = \ + evolution.nodes + print(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) + try: + save_path.mkdir(parents=True) + except FileExistsError: + pass + + f_name = save_path.joinpath("config.json") + save_json(population[i], f_name) + + procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" + " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], + str(f_name), + str(save_path), + str(save_path) + ), + shell=True, stdout=PIPE, stderr=PIPE)) + for j, proc in enumerate(procs): + print(f'wait on {j}th proc') + proc.wait() + + for i in range(population_size): + try: + val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).parent.joinpath("valid_results.txt"))) + except OSError or FileNotFoundError: + val_results = [None for m in CONSIDERED_METRICS] + for m_id, m in enumerate(CONSIDERED_METRICS): + if "loss" in m: + val_results[m_id] = 1e6 + else: + val_results[m_id] = 0. + if TEST: + test_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).parent.joinpath("test_results.txt"))) + + result_table_dict = {} + for el in order: + if el == "params": + result_table_dict[el] = [] + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + for m_id, m in enumerate(CONSIDERED_METRICS): + result_table_dict[m + "_valid"].append(val_results[m_id]) + if TEST: + result_table_dict[m + "_test"].append(test_results[m_id]) + else: + result_table_dict[m + "_test"].append(0.) + result_table_dict[order[-1]] = [population[i]] + result_table = pd.DataFrame(result_table_dict) + + result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) + + for m_id, m in enumerate(CONSIDERED_METRICS): + population_metrics[m].append(val_results[m_id]) + + return population_metrics + + +parser = argparse.ArgumentParser() + +parser.add_argument('--config', help='Please, enter model path to config') +parser.add_argument('--evolve_metric', help='Please, choose target metric out of given in your config.train.metrics') +parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) +parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) +parser.add_argument('--train_partition', + help='Please, enter partition of splitted train', + default=1) +parser.add_argument('--start_from_population', + help='Please, enter the population number to start from. 0 means from scratch', + default=0) +parser.add_argument('--path_to_population', + help='Please, enter the path to population to start from', + default="") + +args = parser.parse_args() + +CONFIG_FILE = args.config +EVOLVE_METRIC = args.evolve_metric +POPULATION_SIZE = args.p_size +GPU_NUMBER = len(args.gpus) +gpus = [int(gpu) for gpu in args.gpus.split(",")] +TRAIN_PARTITION = int(args.train_partition) +START_FROM_POPULATION = int(args.start_from_population) +PATH_TO_POPULATION = args.path_to_population + +with open(CONFIG_FILE, "r") as f: + basic_params = json.load(f) + +print("Given basic params: {}\n".format(basic_params)) + +# list of names of considered metrics +CONSIDERED_METRICS = basic_params["train"]["metrics"] +TEST = basic_params["train"]["test_best"] + + +# EVOLUTION starts here! +evolution = NetworkAndParamsEvolution(population_size=POPULATION_SIZE, + p_crossover=0.2, crossover_power=0.1, + p_mutation=1., mutation_power=0.1, + key_model_to_evolve="to_evolve", + key_basic_layers="basic_layers_params", + seed=42, + train_partition=TRAIN_PARTITION, + **basic_params) + +# Result table +order = deepcopy(CONSIDERED_METRICS) +order.extend(["params"]) +result_file = Path(basic_params["chainer"]["pipe"][ + evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") + +if START_FROM_POPULATION == 0: + result_table_columns = [] + + result_table_dict = {} + for el in order: + if el == "params": + result_table_dict[el] = [] + result_table_columns.extend([el]) + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) + + result_table_columns.append("params") + + result_table = pd.DataFrame(result_table_dict) + result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') + + print("\nIteration #{} starts\n".format(0)) + population = evolution.first_generation() + population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] + + iters = 1 +else: + iters = START_FROM_POPULATION + print("\nIteration #{} starts\n".format(iters)) + model_name = basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] + population = [] + + for i in range(POPULATION_SIZE): + population.append(read_json(Path(PATH_TO_POPULATION).joinpath( + model_name + "_" + str(i)).joinpath("config.json"))) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ + str(Path(basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).joinpath( + "population_" + str(START_FROM_POPULATION)).joinpath(model_name + "_" + str(i))) + population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ + str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]).parent) + + population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] + print("Population scores: {}".format(population_scores)) + print("\nIteration #{} was done\n".format(iters)) + iters += 1 + +while True: + print("\nIteration #{} starts\n".format(iters)) + population = evolution.next_generation(population, population_scores, iters) + # print("Considered population: {}\nScoring...\n".format(population)) + population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] + print("Population scores: {}".format(population_scores)) + print("\nIteration #{} was done\n".format(iters)) + iters += 1 + From 980c8b0fb78ee840b28c415a76882dd1e81297bd Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 17:14:39 +0300 Subject: [PATCH 430/616] chhore: intents_snli config --- .../configs/evolution/intents_snli.json | 45 ++++++++++--------- 1 file changed, 23 insertions(+), 22 deletions(-) diff --git a/deeppavlov/configs/evolution/intents_snli.json b/deeppavlov/configs/evolution/intents_snli.json index d056913902..da17d0183e 100644 --- a/deeppavlov/configs/evolution/intents_snli.json +++ b/deeppavlov/configs/evolution/intents_snli.json @@ -63,21 +63,33 @@ ], "main": true, "name": "intent_model", - "save_path": "intents/intent_snli_v0", - "load_path": "intents/intent_snli_v0", + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/param_evolution_0", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/param_evolution_0", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, 2, 3 ], - "filters_cnn": 256, + "filters_cnn": { + "range": [ + 50, + 500 + ], + "discrete": true + }, "confident_threshold": 0.5, "optimizer": "Adam", - "lear_rate": 0.01, + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, "lear_rate_decay": 0.1, "loss": "binary_crossentropy", "text_size": 51, + "to_evolve": true, "coef_reg_cnn": 1e-4, "coef_reg_den": 1e-4, "dropout_rate": { @@ -86,7 +98,13 @@ 0.9 ] }, - "dense_size": 100, + "dense_size": { + "range": [ + 50, + 500 + ], + "discrete": true + }, "model_name": "cnn_model", "embedder": "#my_embedder", "tokenizer": "#my_tokenizer" @@ -111,22 +129,5 @@ "show_examples": false, "validate_best": true, "test_best": true - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - }, - "download": [ - "http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz", - "http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz", - { - "url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv", - "subdir": "snips" - }, - { - "url": "http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin", - "subdir": "embeddings" - } - ] } } From d41eec70cab33bf1bfdaf55a318497124810c3d4 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 17:19:06 +0300 Subject: [PATCH 431/616] chore: evolve params --- .../evolution/evolution_param_generator.py | 2 +- .../models/evolution/run_param_evolution.py | 18 +++++++++--------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 695a9b375c..9cf3a6a994 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -14,7 +14,7 @@ # otherwise, in the whole class change `config["chainer"]["pipe"]` to new path -class NetworkAndParamsEvolution: +class ParamsEvolution: """ Class performs full evolutionary process (task scores -> max): 1. initializes random population diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index a016a1831d..eb7cadfbd6 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -6,7 +6,7 @@ import pandas as pd from copy import deepcopy, copy -from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution +from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution from deeppavlov.core.common.file import save_json, read_json @@ -129,14 +129,14 @@ def score_population(population, population_size, result_file): # EVOLUTION starts here! -evolution = NetworkAndParamsEvolution(population_size=POPULATION_SIZE, - p_crossover=0.2, crossover_power=0.1, - p_mutation=1., mutation_power=0.1, - key_model_to_evolve="to_evolve", - key_basic_layers="basic_layers_params", - seed=42, - train_partition=TRAIN_PARTITION, - **basic_params) +evolution = ParamsEvolution(population_size=POPULATION_SIZE, + p_crossover=0.2, crossover_power=0.1, + p_mutation=1., mutation_power=0.1, + key_model_to_evolve="to_evolve", + key_basic_layers="basic_layers_params", + seed=42, + train_partition=TRAIN_PARTITION, + **basic_params) # Result table order = deepcopy(CONSIDERED_METRICS) From eb0515db3339d3266573d9b843ddffd0277df587 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 17:20:29 +0300 Subject: [PATCH 432/616] chore: removed nodes from param evolution --- deeppavlov/models/evolution/run_param_evolution.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index eb7cadfbd6..54fdb998e3 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -30,8 +30,6 @@ def score_population(population, population_size, result_file): population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ str(load_path.joinpath("model")) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = \ - evolution.nodes print(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) try: save_path.mkdir(parents=True) From cfa41ba8a5e4368cb837832ed435553b11d170a0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 17:27:30 +0300 Subject: [PATCH 433/616] chore: removed nodes from param evolution --- deeppavlov/models/evolution/run_evolution.py | 26 +++++++++---------- .../models/evolution/run_param_evolution.py | 26 +++++++++---------- 2 files changed, 25 insertions(+), 27 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 339372a0d0..510157ce8b 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -165,22 +165,20 @@ def score_population(population, population_size, result_file): order.extend(["params"]) result_file = Path(basic_params["chainer"]["pipe"][ evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") +result_table_columns = [] +result_table_dict = {} +for el in order: + if el == "params": + result_table_dict[el] = [] + result_table_columns.extend([el]) + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) + +result_table_columns.append("params") if START_FROM_POPULATION == 0: - result_table_columns = [] - - result_table_dict = {} - for el in order: - if el == "params": - result_table_dict[el] = [] - result_table_columns.extend([el]) - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - result_table_columns.extend([el + "_valid", el + "_test"]) - - result_table_columns.append("params") - result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 54fdb998e3..4c497f5cd1 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -141,27 +141,27 @@ def score_population(population, population_size, result_file): order.extend(["params"]) result_file = Path(basic_params["chainer"]["pipe"][ evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") +result_table_columns = [] -if START_FROM_POPULATION == 0: - result_table_columns = [] - - result_table_dict = {} - for el in order: - if el == "params": - result_table_dict[el] = [] - result_table_columns.extend([el]) - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - result_table_columns.extend([el + "_valid", el + "_test"]) +result_table_dict = {} +for el in order: + if el == "params": + result_table_dict[el] = [] + result_table_columns.extend([el]) + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) - result_table_columns.append("params") +result_table_columns.append("params") +if START_FROM_POPULATION == 0: result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') print("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() + print(population) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] iters = 1 From b784b6f9b6e4029c31cdf6d6f6582f71af75dee1 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 18:08:40 +0300 Subject: [PATCH 434/616] fix: sampling no params --- .../configs/evolution/intents_snli.json | 12 +- .../configs/evolution/intents_snli_local.json | 133 ++++++++++++++++++ .../evolution/evolution_param_generator.py | 2 +- .../neuroevolution_param_generator.py | 2 +- 4 files changed, 141 insertions(+), 8 deletions(-) create mode 100644 deeppavlov/configs/evolution/intents_snli_local.json diff --git a/deeppavlov/configs/evolution/intents_snli.json b/deeppavlov/configs/evolution/intents_snli.json index da17d0183e..2e0afffd0d 100644 --- a/deeppavlov/configs/evolution/intents_snli.json +++ b/deeppavlov/configs/evolution/intents_snli.json @@ -3,8 +3,8 @@ "name": "basic_classification_reader", "x": "text", "y": "gold_label", - "data_path": "/home/dilyara/data/data_files/SNLI/one_input", - "train": "train.csv", + "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/parts", + "train": "train_0.csv", "valid": "valid.csv", "test": "test.csv" }, @@ -26,8 +26,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict", - "load_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict" + "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict" }, { "in": [ @@ -41,8 +41,8 @@ { "id": "my_embedder", "name": "fasttext", - "save_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", - "load_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", "dim": 300 }, { diff --git a/deeppavlov/configs/evolution/intents_snli_local.json b/deeppavlov/configs/evolution/intents_snli_local.json new file mode 100644 index 0000000000..825371fd0f --- /dev/null +++ b/deeppavlov/configs/evolution/intents_snli_local.json @@ -0,0 +1,133 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "gold_label", + "data_path": "/home/dilyara/data/data_files/SNLI/one_input/parts", + "train": "train_0.csv", + "valid": "valid.csv", + "test": "test.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict", + "load_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", + "load_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "intent_model", + "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/param_evolution_0", + "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/param_evolution_0", + "classes": "#classes_vocab.keys()", + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 51, + "to_evolve": true, + "coef_reg_cnn": 1e-4, + "coef_reg_den": 1e-4, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, + "dense_size": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "model_name": "cnn_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": 100, + "batch_size": 64, + "metrics": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "show_examples": false, + "validate_best": true, + "test_best": true + } +} diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 9cf3a6a994..3144d0d465 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -436,7 +436,7 @@ def decision(self, probability): def sample_params(self, **params): if not params: - params_copy = deepcopy(self.params) + return {} else: params_copy = deepcopy(params) params_sample = dict() diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py index 939544ded2..701ddd98c9 100644 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ b/deeppavlov/models/evolution/neuroevolution_param_generator.py @@ -493,7 +493,7 @@ def decision(self, probability): def sample_params(self, **params): if not params: - params_copy = deepcopy(self.params) + return {} else: params_copy = deepcopy(params) params_sample = dict() From 6395aa98c6037315490ec6c75b3830ae6b5b1ffb Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 18:11:00 +0300 Subject: [PATCH 435/616] fix: test results --- deeppavlov/models/evolution/run_evolution.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index 510157ce8b..eb1bafaec5 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -64,8 +64,18 @@ def score_population(population, population_size, result_file): else: val_results[m_id] = 0. if TEST: - test_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("test_results.txt"))) + try: + test_results = np.loadtxt( + fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).parent.joinpath("test_results.txt"))) + except OSError or FileNotFoundError: + test_results = [None for m in CONSIDERED_METRICS] + for m_id, m in enumerate(CONSIDERED_METRICS): + if "loss" in m: + test_results[m_id] = 1e6 + else: + test_results[m_id] = 0. + result_table_dict = {} for el in order: From cbc6f80aaecbfc4064ee875e503a58e547b180b2 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 13 Jun 2018 18:11:22 +0300 Subject: [PATCH 436/616] fix: test results --- deeppavlov/models/evolution/run_param_evolution.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 4c497f5cd1..97dfd631ba 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -62,8 +62,17 @@ def score_population(population, population_size, result_file): else: val_results[m_id] = 0. if TEST: - test_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("test_results.txt"))) + try: + test_results = np.loadtxt( + fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ + "save_path"]).parent.joinpath("test_results.txt"))) + except OSError or FileNotFoundError: + test_results = [None for m in CONSIDERED_METRICS] + for m_id, m in enumerate(CONSIDERED_METRICS): + if "loss" in m: + test_results[m_id] = 1e6 + else: + test_results[m_id] = 0. result_table_dict = {} for el in order: From 71cd5b682f4918de049c5a0e156335ba755970d5 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 10:28:58 +0300 Subject: [PATCH 437/616] fix: gpus number --- deeppavlov/models/evolution/run_param_evolution.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 97dfd631ba..7279be2c6f 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -40,7 +40,7 @@ def score_population(population, population_size, result_file): save_json(population[i], f_name) procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" - " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], + " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], str(f_name), str(save_path), str(save_path) From e2ef6770e46e6ebd5ea98841200a98c982ace271 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 10:53:41 +0300 Subject: [PATCH 438/616] fix: first generation for start from population --- deeppavlov/models/evolution/run_evolution.py | 2 ++ deeppavlov/models/evolution/run_param_evolution.py | 2 ++ 2 files changed, 4 insertions(+) diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py index eb1bafaec5..512ed8d7e4 100644 --- a/deeppavlov/models/evolution/run_evolution.py +++ b/deeppavlov/models/evolution/run_evolution.py @@ -198,6 +198,8 @@ def score_population(population, population_size, result_file): iters = 1 else: + # to define some clue params of evolution + _ = evolution.first_generation() iters = START_FROM_POPULATION print("\nIteration #{} starts\n".format(iters)) model_name = basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 7279be2c6f..3f01113838 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -175,6 +175,8 @@ def score_population(population, population_size, result_file): iters = 1 else: + # to define some clue params of evolution + _ = evolution.first_generation() iters = START_FROM_POPULATION print("\nIteration #{} starts\n".format(iters)) model_name = basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] From 83e356a266170d079a22cc0cf6af24fb779f7945 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 11:56:20 +0300 Subject: [PATCH 439/616] feat: mini tutorial how to use param evolution --- .../evolution/Tutorial_params_evolution.ipynb | 328 ++++++++++++++++++ 1 file changed, 328 insertions(+) create mode 100644 deeppavlov/models/evolution/Tutorial_params_evolution.ipynb diff --git a/deeppavlov/models/evolution/Tutorial_params_evolution.ipynb b/deeppavlov/models/evolution/Tutorial_params_evolution.ipynb new file mode 100644 index 0000000000..f729ce8fec --- /dev/null +++ b/deeppavlov/models/evolution/Tutorial_params_evolution.ipynb @@ -0,0 +1,328 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to use evolution of model parameters in DeepPavlov" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Скопируйте в новый файл свой обычный конфиг, соответствующий рассматриваемой модели.\n", + "\n", + "* Для каждого параметра, который можно варьировать, в конфиге замените значение параметра на словарь, определяющий возможные принимаемые значения. Тренировочные параметры (из `config[\"train\"]`) варьируются автоматически, а для варьирования параметров модели необходимо определить тот подсловарь конфига, в котором находятся варьируемые параметры, добавив в него параметр `\"to_evolve\": true`. Варьируемые параметры должны быть ключами словаря, содержащего ключ `to_evolve`, вложенность пока не поддерживается.\n", + "\n", + "* Запустите эволюцию с необходимыми параметрами:\n", + " - config - путь к файлу конфигу для эволюции\n", + " - evolve_metric - зарегистрированное название метрики из тренировочных параметров конфига, по значениям которой будет происходить эволюция\n", + " - p_size - размер одной популяции\n", + " - gpus - номера gpu, доступных для использования. Если количество gpu меньше размера популяции, то модели будут запускаться группами по len(gpus) штук.\n", + "```\n", + "python ./models/evolution/run_param_evolution.py --config config_file \n", + " --evolve_metric registered_metric_from_config \n", + " --p_size 10\n", + " --gpus 0,1,2\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Примеры словаря возможных значений для различных видов параметров" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import json\n", + "\n", + "def print_json(dictionary):\n", + " print(json.dumps(dictionary, indent=2))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "config = {\"dense_size\": 100, \n", + " \"activation\": \"sigmoid\", \n", + " \"learning_rate\": 0.001, \n", + " \"learning_rate_decay\": 0.00001,\n", + " \"is_main\": True}" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"dense_size\": 100,\n", + " \"activation\": \"sigmoid\",\n", + " \"learning_rate\": 0.001,\n", + " \"learning_rate_decay\": 1e-05,\n", + " \"is_main\": true\n", + "}\n" + ] + } + ], + "source": [ + "print_json(config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Дискретный параметр из промежутка" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + "}\n" + ] + } + ], + "source": [ + "config[\"dense_size\"] = {\"range\": [50, 500], \"discrete\": True}\n", + "print_json(config[\"dense_size\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Дискретный параметр из листа возможных значений" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"values\": [\n", + " \"softmax\",\n", + " \"sigmoid\",\n", + " \"relu\"\n", + " ],\n", + " \"choice\": true\n", + "}\n" + ] + } + ], + "source": [ + "config[\"activation\"] = {\"values\": [\"softmax\", \"sigmoid\", \"relu\"], \"choice\": True}\n", + "print_json(config[\"activation\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Параметр из промежутка" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"range\": [\n", + " 0.001,\n", + " 0.1\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "config[\"learning_rate\"] = {\"range\": [0.001, 0.1]}\n", + "print_json(config[\"learning_rate\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Параметр из промежутка с логарифмической шкалой" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"range\": [\n", + " 1e-05,\n", + " 0.0001\n", + " ],\n", + " \"scale\": \"log\"\n", + "}\n" + ] + } + ], + "source": [ + "config[\"learning_rate_decay\"] = {\"range\": [0.00001, 0.0001], \"scale\": \"log\"}\n", + "print_json(config[\"learning_rate_decay\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Булевый параметр" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"bool\": true\n", + "}\n" + ] + } + ], + "source": [ + "config[\"is_main\"] = {\"bool\": True}\n", + "print_json(config[\"is_main\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Therefore, evolving parameters can be written in DeepPavlov config in the following way" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"dense_size\": {\n", + " \"range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"activation\": {\n", + " \"values\": [\n", + " \"softmax\",\n", + " \"sigmoid\",\n", + " \"relu\"\n", + " ],\n", + " \"choice\": true\n", + " },\n", + " \"learning_rate\": {\n", + " \"range\": [\n", + " 0.001,\n", + " 0.1\n", + " ]\n", + " },\n", + " \"learning_rate_decay\": {\n", + " \"range\": [\n", + " 1e-05,\n", + " 0.0001\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"is_main\": {\n", + " \"bool\": true\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "print_json(config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python-deep36", + "language": "python", + "name": "deep36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 607c659fdb591273d51f84b26585fb958135824f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 12:02:49 +0300 Subject: [PATCH 440/616] feat: snips config --- .../configs/evolution/intents_snips.json | 132 ++++++++++++++++++ 1 file changed, 132 insertions(+) create mode 100644 deeppavlov/configs/evolution/intents_snips.json diff --git a/deeppavlov/configs/evolution/intents_snips.json b/deeppavlov/configs/evolution/intents_snips.json new file mode 100644 index 0000000000..494167e921 --- /dev/null +++ b/deeppavlov/configs/evolution/intents_snips.json @@ -0,0 +1,132 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "intents", + "data_path": "/home/dilyara.baymurzina/evolution_data/snips_data", + "train": "train.csv", + "valid": "valid.csv" + }, + "dataset_iterator": { + "name": "basic_classification_iterator" + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "intent_model", + "save_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/param_evolution_0", + "load_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/param_evolution_0", + "classes": "#classes_vocab.keys()", + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": { + "range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 51, + "to_evolve": true, + "coef_reg_cnn": 1e-4, + "coef_reg_den": 1e-4, + "dropout_rate": { + "range": [ + 0.1, + 0.9 + ] + }, + "dense_size": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "model_name": "cnn_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": 100, + "batch_size": 64, + "metrics": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "show_examples": false, + "validate_best": true, + "test_best": false + } +} From 6bd262b90ef06819163ff7faa06dd1a26cab383b Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 14:54:01 +0300 Subject: [PATCH 441/616] fix: ag news config --- deeppavlov/configs/evolution/basic_ag_news_part.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json index 128146e58e..a6e9459f25 100644 --- a/deeppavlov/configs/evolution/basic_ag_news_part.json +++ b/deeppavlov/configs/evolution/basic_ag_news_part.json @@ -63,8 +63,8 @@ ], "main": true, "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_6", - "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/one_neuron_init_part_6", + "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/given_mask_init_part_7", + "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/given_mask_init_part_7", "classes": "#classes_vocab.keys()", "to_evolve": true, "basic_layers_params": { From 0c17e7413754ed57cf6aa825bcd1d96500b5e90d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 17:00:04 +0300 Subject: [PATCH 442/616] feat: dataset iterator evolution --- .../evolution/evolution_param_generator.py | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 3144d0d465..8c0d4a7483 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -55,11 +55,13 @@ def __init__(self, Path(self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).mkdir(parents=True, exist_ok=True) + self.dataset_iterator_params = deepcopy(self.basic_config.get("dataset_iterator")) self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) self.train_params = deepcopy(self.basic_config.get("train")) print("___Basic config___: {}".format(self.basic_config)) print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) + print("___Dataset iterator params___: {}".format(self.dataset_iterator_params)) print("___Model params___: {}".format(self.params)) print("___Train params___: {}".format(self.train_params)) @@ -132,6 +134,10 @@ def first_generation(self, iteration=0): for i in range(self.population_size): population.append(deepcopy(self.basic_config)) + # initializing parameters for dataset iterator + dataset_iterator_params, dataset_iterator_params_for_search, evolving_params = \ + self.initialize_params_in_config(self.dataset_iterator_params) + self.evolving_params.extend(evolving_params) # intitializing parameters for model params, params_for_search, evolving_params = self.initialize_params_in_config(self.params) self.evolving_params.extend(evolving_params) @@ -156,6 +162,9 @@ def first_generation(self, iteration=0): params["load_path"] = str(Path(self.params["load_path"]).joinpath( "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) + # exchange dataset iterator params from basic config to sampled train params + population[-1]["dataset_iterator"] = {**dataset_iterator_params, + **dataset_iterator_params_for_search} # exchange model and layers params from basic config to sampled model params population[-1]["chainer"]["pipe"][self.model_to_evolve_index] = {**params, **params_for_search} @@ -372,6 +381,13 @@ def mutation(self, population, p_mutation, mutation_power): for individuum in population: mutated_individuum = deepcopy(individuum) + # mutation of dataset iterator params + for param in self.dataset_iterator_params.keys(): + mutated_individuum["dataset_iterator"][param] = \ + self.mutation_of_param(param, self.dataset_iterator_params, + individuum["dataset_iterator"][param], + p_mutation, mutation_power) + # mutation of other model params for param in self.params.keys(): mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][param] = \ @@ -409,7 +425,8 @@ def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutatio params_dict[param]["range"][1]) new_mutated_value = val elif params_dict[param].get("choice"): - new_mutated_value = param_value + # new_mutated_value = param_value + new_mutated_value = self.sample_params(**{param: params_dict[param]})[param] else: new_mutated_value = param_value else: From 91edd84ee63b497ad23cf4d8c890d34e90ec6d6c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 17:00:58 +0300 Subject: [PATCH 443/616] feat: dataset iterator evolution --- deeppavlov/configs/evolution/intents_snips.json | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/intents_snips.json b/deeppavlov/configs/evolution/intents_snips.json index 494167e921..912ad3c94e 100644 --- a/deeppavlov/configs/evolution/intents_snips.json +++ b/deeppavlov/configs/evolution/intents_snips.json @@ -8,7 +8,14 @@ "valid": "valid.csv" }, "dataset_iterator": { - "name": "basic_classification_iterator" + "name": "basic_classification_iterator", + "seed": { + "range": [ + 50, + 500 + ], + "discrete": true + } }, "chainer": { "in": [ From 248d918a2f282e8bd1ac868020959103b93a3210 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 17:01:58 +0300 Subject: [PATCH 444/616] fix: config for dataset iterator evolution --- deeppavlov/configs/evolution/intents_snli_local.json | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/intents_snli_local.json b/deeppavlov/configs/evolution/intents_snli_local.json index 825371fd0f..3a2fc819a1 100644 --- a/deeppavlov/configs/evolution/intents_snli_local.json +++ b/deeppavlov/configs/evolution/intents_snli_local.json @@ -9,7 +9,14 @@ "test": "test.csv" }, "dataset_iterator": { - "name": "basic_classification_iterator" + "name": "basic_classification_iterator", + "seed": { + "range": [ + 50, + 500 + ], + "discrete": true + } }, "chainer": { "in": [ From 13c349ba64447fb38997b8e16cc29a3ad66d9c5f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 17:04:23 +0300 Subject: [PATCH 445/616] fix: number of printed proc --- deeppavlov/models/evolution/run_param_evolution.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 3f01113838..d1902d4fb4 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -47,7 +47,8 @@ def score_population(population, population_size, result_file): ), shell=True, stdout=PIPE, stderr=PIPE)) for j, proc in enumerate(procs): - print(f'wait on {j}th proc') + i = k * len(gpus) + j + print(f'wait on {i}th proc') proc.wait() for i in range(population_size): From 51ef86ab886de5384a770a4bd35df225fca49bdb Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 14 Jun 2018 17:41:42 +0300 Subject: [PATCH 446/616] fix: final lear rate try --- .../models/evolution/evolution_param_generator.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 8c0d4a7483..d90b9550cf 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -227,10 +227,13 @@ def next_generation(self, generation, scores, iteration, next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ "train"]).stem.split("_")[0]) \ + "_" + str(iteration % self.train_partition) + ".csv" - # re-init learning rate with the final one - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ - read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index][ - "save_path"]).parent.joinpath("model_opt.json")))["final_lear_rate"] + try: + # re-init learning rate with the final one + next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ + read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index][ + "save_path"]).parent.joinpath("model_opt.json")))["final_lear_rate"] + except: + pass # paths next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) From 1b11d9fdddff787961170ba22370e0726f581d45 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 15 Jun 2018 11:23:46 +0300 Subject: [PATCH 447/616] fix: crossover of dataset iterator params --- .../evolution/evolution_param_generator.py | 29 ++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index d90b9550cf..cee8b6aa06 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -70,6 +70,8 @@ def __init__(self, self.p_mutation = p_mutation self.mutation_power = mutation_power self.crossover_power = crossover_power + self.evolving_dataset_iterator_params = [] + self.n_evolving_dataset_iterator_params = None self.evolving_params = [] self.n_evolving_params = None self.evolving_train_params = [] @@ -137,7 +139,7 @@ def first_generation(self, iteration=0): # initializing parameters for dataset iterator dataset_iterator_params, dataset_iterator_params_for_search, evolving_params = \ self.initialize_params_in_config(self.dataset_iterator_params) - self.evolving_params.extend(evolving_params) + self.evolving_dataset_iterator_params.extend(evolving_params) # intitializing parameters for model params, params_for_search, evolving_params = self.initialize_params_in_config(self.params) self.evolving_params.extend(evolving_params) @@ -175,9 +177,11 @@ def first_generation(self, iteration=0): population[-1]["train"]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 + self.evolving_dataset_iterator_params = list(set(self.evolving_dataset_iterator_params)) self.evolving_params = list(set(self.evolving_params)) self.evolving_train_params = list(set(self.evolving_train_params)) + self.n_evolving_dataset_iterator_params = len(self.evolving_dataset_iterator_params) self.n_evolving_params = len(self.evolving_params) self.n_evolving_train_params = len(self.evolving_train_params) @@ -317,15 +321,38 @@ def crossover(self, population, scores, p_crossover, crossover_power): parents = population[np.where(rs[0] > intervals)[0][-1]], population[np.where(rs[1] > intervals)[0][-1]] if self.decision(p_crossover): + dataset_iterator_params_perm = np.random.permutation(self.n_evolving_dataset_iterator_params) params_perm = np.random.permutation(self.n_evolving_params) train_params_perm = np.random.permutation(self.n_evolving_train_params) curr_offsprings = [deepcopy(parents[0]), deepcopy(parents[1])] + dataset_iterator_part = int(crossover_power * self.n_evolving_dataset_iterator_params) part = int(crossover_power * self.n_evolving_params) train_part = int(crossover_power * self.n_evolving_train_params) + # exchange of dataset_iterator params + for j in range(self.n_evolving_dataset_iterator_params - dataset_iterator_part): + curr_offsprings[0]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ + parents[0]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] + curr_offsprings[1]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ + parents[1]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] + for j in range(self.n_evolving_dataset_iterator_params - dataset_iterator_part, + self.n_evolving_dataset_iterator_params): + curr_offsprings[0]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ + parents[1]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] + curr_offsprings[1]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ + parents[0]["dataset_iterator"][ + self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] + # exchange of model params (not layers params) for j in range(self.n_evolving_params - part): curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ From bde998738223291dd761820ab2ca4c787b759ec4 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 15 Jun 2018 14:45:28 +0300 Subject: [PATCH 448/616] fix: evolution --- .../evolution/evolution_param_generator.py | 91 ++++++++++++------- 1 file changed, 60 insertions(+), 31 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index cee8b6aa06..1aa33730d8 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -3,15 +3,12 @@ from pathlib import Path import json -from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ - number_to_type_layer from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe from deeppavlov.core.common.file import read_json +from deeppavlov.core.common.log import get_logger -# please, make sure that -# `config["chainer"]["pipe"]` is a list of models one of which is a model to be evolved, -# otherwise, in the whole class change `config["chainer"]["pipe"]` to new path +log = get_logger(__name__) class ParamsEvolution: @@ -30,7 +27,7 @@ def __init__(self, population_size, p_crossover=0.5, crossover_power=0.5, p_mutation=0.5, mutation_power=0.1, - key_model_to_evolve="to_evolve", + key_main_model="main_model", seed=None, train_partition=1, **kwargs): @@ -43,7 +40,7 @@ def __init__(self, p_mutation: probability of mutation for current replacement mutation_power: allowed percentage of mutation key_model_to_evolve: binary flag that should be inserted into the dictionary - with evolving model in the basic config + with main model in the basic config (to determine save and load paths that will be changed) seed: random seed for initialization train_partition: integer number of train data parts **kwargs: basic config with parameters @@ -51,33 +48,23 @@ def __init__(self, self.basic_config = deepcopy(kwargs) self.model_to_evolve_index = find_index_of_dict_with_key_in_pipe(self.basic_config["chainer"]["pipe"], - key_model_to_evolve) + key_main_model) Path(self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).mkdir(parents=True, exist_ok=True) - self.dataset_iterator_params = deepcopy(self.basic_config.get("dataset_iterator")) - self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) - self.train_params = deepcopy(self.basic_config.get("train")) - - print("___Basic config___: {}".format(self.basic_config)) - print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) - print("___Dataset iterator params___: {}".format(self.dataset_iterator_params)) - print("___Model params___: {}".format(self.params)) - print("___Train params___: {}".format(self.train_params)) + self.print_dict(self.basic_config, string="Basic config:") + log.info("Main model index in pipe: {}".format(self.model_to_evolve_index)) self.population_size = population_size self.p_crossover = p_crossover self.p_mutation = p_mutation self.mutation_power = mutation_power self.crossover_power = crossover_power - self.evolving_dataset_iterator_params = [] - self.n_evolving_dataset_iterator_params = None - self.evolving_params = [] - self.n_evolving_params = None - self.evolving_train_params = [] - self.n_evolving_train_params = None + self.n_saved_best_with_weights = 0 self.train_partition = train_partition + + self.paths_to_evolving_params = [] self.evolution_individuum_id = 0 self.evolution_model_id = 0 @@ -86,17 +73,59 @@ def __init__(self, else: np.random.seed(seed) - def _insert_dict_into_model_params(self, params, model_index, dict_to_insert): - params_copy = deepcopy(params) - params_copy["chainer"]["pipe"].insert(model_index, dict_to_insert) - return params_copy + def _find_main_model(self, config, key_main_model, path=[]): + """ + Find path to the main model in config which paths will be changed + Args: + config: + key_main_model: + + Returns: + path in config -- list of keys (strings and integers) + """ + config_pointer = config + if key_main_model in config_pointer.keys(): + return path + else: + if type(config_pointer) is dict: + for key in list(config_pointer.keys()): + path += key + path_ = self._find_main_model(config_pointer[key], key_main_model, path) + if len(path_) > 0: + path = path_ + elif type(config_pointer) is list: + for i in range(len(config_pointer)): + path += i + path_ = self._find_main_model(config_pointer[i], key_main_model, path) + if len(path_) > 0: + path = path_ + if len(path) > 0: + return path + else: + return [] + + + @staticmethod + def _insert_value_or_dict_into_config(config, path, value): + config_copy = deepcopy(config) + config_pointer = config_copy + for el in path[:-1]: + if type(config_pointer) is dict: + config_pointer = config_pointer.setdefault(el, {}) + elif type(config_pointer) is list: + config_pointer = config_pointer[el] + else: + pass + config_pointer[path[-1]] = value + return config_copy - def print_dict(self, dict, string=None): + @staticmethod + def print_dict(config, string=None): if string is None: - print(json.dumps(dict, indent=2)) + log.info(json.dumps(config, indent=2)) else: - print(string) - print(json.dumps(dict, indent=2)) + log.info(string) + log.info(json.dumps(config, indent=2)) return None def initialize_params_in_config(self, basic_params): From cd5a3b9b4e7539b441c13b9dee5db129c6da82b6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 11:38:06 +0300 Subject: [PATCH 449/616] feat: find main model (debugged) --- .../evolution/evolution_param_generator.py | 27 ++++++++++--------- 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 1aa33730d8..c5d4a78dcd 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -84,26 +84,29 @@ def _find_main_model(self, config, key_main_model, path=[]): path in config -- list of keys (strings and integers) """ config_pointer = config - if key_main_model in config_pointer.keys(): + if type(config_pointer) is dict and key_main_model in config_pointer.keys(): + # main model is an element of chainer.pipe list + # main model is a dictionary and has key key_main_model return path else: + main_path = [] if type(config_pointer) is dict: + for key in list(config_pointer.keys()): - path += key - path_ = self._find_main_model(config_pointer[key], key_main_model, path) - if len(path_) > 0: - path = path_ + path_ = self._find_main_model(config_pointer[key], key_main_model, path + [key]) + if path_: + main_path = path_ elif type(config_pointer) is list: for i in range(len(config_pointer)): - path += i - path_ = self._find_main_model(config_pointer[i], key_main_model, path) - if len(path_) > 0: - path = path_ - if len(path) > 0: - return path + path_ = self._find_main_model(config_pointer[i], key_main_model, path + [i]) + if path_: + main_path = path_ else: return [] - + if main_path: + return main_path + else: + return [] @staticmethod def _insert_value_or_dict_into_config(config, path, value): From de84cb58095a65ce58346eb9bc7f0f0207214840 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 15:36:03 +0300 Subject: [PATCH 450/616] feat: initialization of evolving model params from config --- .../configs/evolution/intents_snips.json | 42 ++-- .../evolution/evolution_param_generator.py | 186 ++++++++---------- 2 files changed, 107 insertions(+), 121 deletions(-) diff --git a/deeppavlov/configs/evolution/intents_snips.json b/deeppavlov/configs/evolution/intents_snips.json index 912ad3c94e..58d21fd4ce 100644 --- a/deeppavlov/configs/evolution/intents_snips.json +++ b/deeppavlov/configs/evolution/intents_snips.json @@ -78,7 +78,7 @@ 3 ], "filters_cnn": { - "range": [ + "evolve_range": [ 50, 500 ], @@ -87,7 +87,7 @@ "confident_threshold": 0.5, "optimizer": "Adam", "lear_rate": { - "range": [ + "evolve_range": [ 0.0001, 0.1 ] @@ -99,13 +99,13 @@ "coef_reg_cnn": 1e-4, "coef_reg_den": 1e-4, "dropout_rate": { - "range": [ + "evolve_range": [ 0.1, 0.9 ] }, "dense_size": { - "range": [ + "evolve_range": [ 50, 500 ], @@ -113,7 +113,10 @@ }, "model_name": "cnn_model", "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" + "tokenizer": "#my_tokenizer", + "check_bool": { + "evolve_bool": true + } } ], "out": [ @@ -122,13 +125,28 @@ ] }, "train": { - "epochs": 100, - "batch_size": 64, - "metrics": [ - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], + "epochs": { + "evolve_range": [ + 50, + 500 + ], + "discrete": true + }, + "batch_size": { + "evolve_range": [ + 50, + 500 + ], + "discrete": true + }, + "metrics": { + "evolve_choice": true, + "values": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ] + }, "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index c5d4a78dcd..060065c0db 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -27,9 +27,10 @@ def __init__(self, population_size, p_crossover=0.5, crossover_power=0.5, p_mutation=0.5, mutation_power=0.1, - key_main_model="main_model", + key_main_model="main", seed=None, train_partition=1, + load_pretrained=False, **kwargs): """ Initialize evolution with random population @@ -47,25 +48,28 @@ def __init__(self, """ self.basic_config = deepcopy(kwargs) - self.model_to_evolve_index = find_index_of_dict_with_key_in_pipe(self.basic_config["chainer"]["pipe"], - key_main_model) - Path(self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).mkdir(parents=True, - exist_ok=True) - + self.main_model_path = list(self._find_model_path(self.basic_config, key_main_model))[0] + Path(self._get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, + exist_ok=True) self.print_dict(self.basic_config, string="Basic config:") - log.info("Main model index in pipe: {}".format(self.model_to_evolve_index)) + log.info("Main model path in config: {}".format(self.main_model_path)) self.population_size = population_size self.p_crossover = p_crossover self.p_mutation = p_mutation self.mutation_power = mutation_power self.crossover_power = crossover_power + self.load_pretrained = load_pretrained - self.n_saved_best_with_weights = 0 + self.n_saved_best_pretrained = 0 self.train_partition = train_partition self.paths_to_evolving_params = [] - self.evolution_individuum_id = 0 + for evolve_type in ["evolve_range", "evolve_choice", "evolve_bool"]: + for path_ in self._find_model_path(self.basic_config, evolve_type): + self.paths_to_evolving_params.append(path_) + + self.n_evolving_params = len(self.paths_to_evolving_params) self.evolution_model_id = 0 if seed is None: @@ -73,40 +77,30 @@ def __init__(self, else: np.random.seed(seed) - def _find_main_model(self, config, key_main_model, path=[]): + def _find_model_path(self, config, key_model, path=[]): """ Find path to the main model in config which paths will be changed Args: config: - key_main_model: + key_model: Returns: path in config -- list of keys (strings and integers) """ config_pointer = config - if type(config_pointer) is dict and key_main_model in config_pointer.keys(): + if type(config_pointer) is dict and key_model in config_pointer.keys(): # main model is an element of chainer.pipe list # main model is a dictionary and has key key_main_model - return path + yield path else: - main_path = [] if type(config_pointer) is dict: - for key in list(config_pointer.keys()): - path_ = self._find_main_model(config_pointer[key], key_main_model, path + [key]) - if path_: - main_path = path_ + for path_ in self._find_model_path(config_pointer[key], key_model, path + [key]): + yield path_ elif type(config_pointer) is list: for i in range(len(config_pointer)): - path_ = self._find_main_model(config_pointer[i], key_main_model, path + [i]) - if path_: - main_path = path_ - else: - return [] - if main_path: - return main_path - else: - return [] + for path_ in self._find_model_path(config_pointer[i], key_model, path + [i]): + yield path_ @staticmethod def _insert_value_or_dict_into_config(config, path, value): @@ -122,6 +116,19 @@ def _insert_value_or_dict_into_config(config, path, value): config_pointer[path[-1]] = value return config_copy + @staticmethod + def _get_value_from_config(config, path): + config_copy = deepcopy(config) + config_pointer = config_copy + for el in path[:-1]: + if type(config_pointer) is dict: + config_pointer = config_pointer.setdefault(el, {}) + elif type(config_pointer) is list: + config_pointer = config_pointer[el] + else: + pass + return config_pointer[path[-1]] + @staticmethod def print_dict(config, string=None): if string is None: @@ -131,32 +138,33 @@ def print_dict(config, string=None): log.info(json.dumps(config, indent=2)) return None - def initialize_params_in_config(self, basic_params): - params = {} - params_for_search = {} - evolving_params = [] - - for param_name in list(basic_params.keys()): - if type(basic_params[param_name]) is dict: - if basic_params[param_name].get("choice"): - params_for_search[param_name] = list(basic_params[param_name]["values"]) - evolving_params.append(param_name) - elif basic_params[param_name].get("range"): - params_for_search[param_name] = deepcopy(basic_params[param_name]) - evolving_params.append(param_name) - elif basic_params[param_name].get("bool"): - params_for_search[param_name] = deepcopy(basic_params[param_name]) - evolving_params.append(param_name) - else: - # NOT evolving params - params[param_name] = deepcopy(basic_params[param_name]) - else: - # NOT evolving params - params[param_name] = deepcopy(basic_params[param_name]) - if basic_params: - params_for_search = deepcopy(self.sample_params(**params_for_search)) - - return params, params_for_search, evolving_params + def initialize_params_in_config(self, basic_config, paths): + config = deepcopy(basic_config) + + for path_ in paths: + param_name = path_[-1] + value = self._get_value_from_config(basic_config, path_) + if type(value) is dict: + if value.get("evolve_choice"): + config = self._insert_value_or_dict_into_config(config, + path_, + self.sample_params( + **{param_name: + list(value["values"])})[param_name]) + elif value.get("evolve_range"): + config = self._insert_value_or_dict_into_config(config, + path_, + self.sample_params( + **{param_name: + deepcopy(value)})[param_name]) + elif value.get("evolve_bool"): + config = self._insert_value_or_dict_into_config(config, + path_, + self.sample_params( + **{param_name: + deepcopy(value)})[param_name]) + + return config def first_generation(self, iteration=0): """ @@ -166,57 +174,17 @@ def first_generation(self, iteration=0): """ population = [] for i in range(self.population_size): - population.append(deepcopy(self.basic_config)) - - # initializing parameters for dataset iterator - dataset_iterator_params, dataset_iterator_params_for_search, evolving_params = \ - self.initialize_params_in_config(self.dataset_iterator_params) - self.evolving_dataset_iterator_params.extend(evolving_params) - # intitializing parameters for model - params, params_for_search, evolving_params = self.initialize_params_in_config(self.params) - self.evolving_params.extend(evolving_params) - # initializing parameters for train - train_params, train_params_for_search, evolving_params = self.initialize_params_in_config(self.train_params) - self.evolving_train_params.extend(evolving_params) - - # intitializing path to save model - # save_path = population_iteration/model_name_i/ - if "model_name" in params_for_search.keys(): - params["save_path"] = str(Path(self.params["save_path"]).joinpath( - "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) - else: - params["save_path"] = str(Path(self.params["save_path"]).joinpath( - "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) - - # load_path = population_iteration/model_name_i/ - if "model_name" in params_for_search.keys(): - params["load_path"] = str(Path(self.params["load_path"]).joinpath( - "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) - else: - params["load_path"] = str(Path(self.params["load_path"]).joinpath( - "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) - - # exchange dataset iterator params from basic config to sampled train params - population[-1]["dataset_iterator"] = {**dataset_iterator_params, - **dataset_iterator_params_for_search} - # exchange model and layers params from basic config to sampled model params - population[-1]["chainer"]["pipe"][self.model_to_evolve_index] = {**params, - **params_for_search} - - # exchange train params from basic config to sampled train params - population[-1]["train"] = {**train_params, - **train_params_for_search} - population[-1]["train"]["evolution_model_id"] = self.evolution_model_id + population.append(self.initialize_params_in_config(self.basic_config, self.paths_to_evolving_params)) + for which_path in ["save_path", "load_path"]: + population[-1] = self._insert_value_or_dict_into_config(population[-1], + self.main_model_path + [which_path], + str(Path( + self.basic_config["save_path"]).joinpath( + "population_" + str(iteration)).joinpath( + "model_" + str(i)))) + population[-1]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 - self.evolving_dataset_iterator_params = list(set(self.evolving_dataset_iterator_params)) - self.evolving_params = list(set(self.evolving_params)) - self.evolving_train_params = list(set(self.evolving_train_params)) - - self.n_evolving_dataset_iterator_params = len(self.evolving_dataset_iterator_params) - self.n_evolving_params = len(self.evolving_params) - self.n_evolving_train_params = len(self.evolving_train_params) - return population def next_generation(self, generation, scores, iteration, @@ -246,7 +214,7 @@ def next_generation(self, generation, scores, iteration, mutation_power = self.mutation_power next_population = self.selection_of_best_with_weights(generation, scores) - print("Saved with weights: {} individuums".format(self.n_saved_best_with_weights)) + print("Saved with weights: {} individuums".format(self.n_saved_best_pretrained)) offsprings = self.crossover(generation, scores, p_crossover=p_crossover, crossover_power=crossover_power) @@ -257,7 +225,7 @@ def next_generation(self, generation, scores, iteration, next_population.extend(changable_next) - for i in range(self.n_saved_best_with_weights): + for i in range(self.n_saved_best_pretrained): # if several train files: if self.train_partition != 1: next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ @@ -277,7 +245,7 @@ def next_generation(self, generation, scores, iteration, str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( self.params["model_name"] + "_" + str(i))) - for i in range(self.n_saved_best_with_weights, self.population_size): + for i in range(self.n_saved_best_pretrained, self.population_size): # if several train files if self.train_partition != 1: next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ @@ -310,7 +278,7 @@ def selection_of_best_with_weights(self, population, scores): scores: corresponding score that should be maximized Returns: - selected self.n_saved_best_with_weights (changable) individuums + selected self.n_saved_best_pretrained (changable) individuums """ scores = np.array(scores, dtype='float') sorted_ids = np.argsort(scores) @@ -326,7 +294,7 @@ def selection_of_best_with_weights(self, population, scores): if self.decision(probas_to_be_selected[i]): selected.append(deepcopy(population[i])) - self.n_saved_best_with_weights = len(selected) + self.n_saved_best_pretrained = len(selected) return selected def crossover(self, population, scores, p_crossover, crossover_power): @@ -341,14 +309,14 @@ def crossover(self, population, scores, p_crossover, crossover_power): crossover_power: part of EVOLVING parents parameters to exchange for offsprings Returns: - (self.population_size - self.n_saved_best_with_weights) offsprings + (self.population_size - self.n_saved_best_pretained) offsprings """ offsprings = [] scores = np.array(scores, dtype='float') probas_to_be_parent = scores / np.sum(scores) intervals = np.array([np.sum(probas_to_be_parent[:i]) for i in range(self.population_size)]) - for i in range(self.population_size - self.n_saved_best_with_weights): + for i in range(self.population_size - self.n_saved_best_pretrained): rs = np.random.random(2) parents = population[np.where(rs[0] > intervals)[0][-1]], population[np.where(rs[1] > intervals)[0][-1]] From eeffa295c950fedfd45131608935b5efb9ccca27 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 16:08:51 +0300 Subject: [PATCH 451/616] feat: next generation --- .../evolution/evolution_param_generator.py | 61 ++++---- deeppavlov/models/evolution/test.py | 134 ++++++++++++++++++ 2 files changed, 170 insertions(+), 25 deletions(-) create mode 100644 deeppavlov/models/evolution/test.py diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 060065c0db..6601e19b67 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -228,38 +228,49 @@ def next_generation(self, generation, scores, iteration, for i in range(self.n_saved_best_pretrained): # if several train files: if self.train_partition != 1: - next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ - "train"]).stem.split("_")[0]) \ - + "_" + str(iteration % self.train_partition) + ".csv" + next_population[i]["dataset_reader"]["train"] = "_".join(str(Path(next_population[i]["dataset_reader"][ + "train"]).stem.split("_")[:-1])) \ + + "_" + str(iteration % self.train_partition) + ".csv" try: - # re-init learning rate with the final one - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["lear_rate"] = \ - read_json(str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index][ - "save_path"]).parent.joinpath("model_opt.json")))["final_lear_rate"] + # re-init learning rate with the final one (works for KerasModel) + next_population[i] = self._insert_value_or_dict_into_config( + next_population[i], + self._get_value_from_config(next_population[i], + self.main_model_path + ["lear_rate"]), + read_json(str(Path(self._get_value_from_config(next_population[i], + self.main_model_path + ["save_path"]) + ).parent.joinpath("model_opt.json")))["final_lear_rate"]) except: pass - # paths - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(Path(next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).parent) - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ - str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( - self.params["model_name"] + "_" + str(i))) + next_population[i] = self._insert_value_or_dict_into_config( + next_population[i], + self._get_value_from_config(next_population[i], + self.main_model_path + ["load_path"]), + str(Path(self._get_value_from_config(next_population[i], + self.main_model_path + ["save_path"])).parent)) + + next_population[i] = self._insert_value_or_dict_into_config( + next_population[i], + self._get_value_from_config(next_population[i], + self.main_model_path + ["save_path"]), + str(Path(self._get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) for i in range(self.n_saved_best_pretrained, self.population_size): # if several train files if self.train_partition != 1: - next_population[i]["dataset_reader"]["train"] = str(Path(next_population[i]["dataset_reader"][ - "train"]).stem.split("_")[0]) \ - + "_" + str(iteration % self.train_partition) + ".csv" - # paths - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ - str(Path(self.params["save_path"]).joinpath("population_" + str(iteration)).joinpath( - self.params["model_name"] + "_" + str(i))) - next_population[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(Path(self.params["load_path"]).joinpath("population_" + str(iteration)).joinpath( - self.params["model_name"] + "_" + str(i))) - - next_population[i]["train"]["evolution_model_id"] = self.evolution_model_id + next_population[i]["dataset_reader"]["train"] = "_".join(str(Path(next_population[i]["dataset_reader"][ + "train"]).stem.split("_")[:-1])) \ + + "_" + str(iteration % self.train_partition) + ".csv" + for which_path in ["save_path", "load_path"]: + next_population[i] = self._insert_value_or_dict_into_config( + next_population[i], + self._get_value_from_config(next_population[i], + self.main_model_path + [which_path]), + str(Path(self._get_value_from_config(next_population[i], self.main_model_path + [which_path]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) + + next_population[i]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 return next_population diff --git a/deeppavlov/models/evolution/test.py b/deeppavlov/models/evolution/test.py new file mode 100644 index 0000000000..31da975a78 --- /dev/null +++ b/deeppavlov/models/evolution/test.py @@ -0,0 +1,134 @@ +import numpy as np +from deeppavlov.core.common.file import read_json +from copy import copy, deepcopy +import json + + +def _find_main_model_path(config, key_model, path=[]): + """ + Find path to the main model in config which paths will be changed + Args: + config: + key_model: + + Returns: + path in config -- list of keys (strings and integers) + """ + config_pointer = config + # add_paths = [] + + if type(config_pointer) is dict and key_model in config_pointer.keys(): + # main model is an element of chainer.pipe list + # main model is a dictionary and has key key_main_model + yield path + else: + if type(config_pointer) is dict: + for key in list(config_pointer.keys()): + for path_ in _find_main_model_path(config_pointer[key], key_model, path + [key]): + yield path_ + elif type(config_pointer) is list: + for i in range(len(config_pointer)): + for path_ in _find_main_model_path(config_pointer[i], key_model, path + [i]): + yield path_ + + +def _insert_value_or_dict_into_config(config, path, value): + config_copy = deepcopy(config) + config_pointer = config_copy + for el in path[:-1]: + if type(config_pointer) is dict: + config_pointer = config_pointer.setdefault(el, {}) + elif type(config_pointer) is list: + config_pointer = config_pointer[el] + else: + pass + config_pointer[path[-1]] = value + return config_copy + + +def _get_value_from_config(config, path): + config_copy = deepcopy(config) + config_pointer = config_copy + for el in path[:-1]: + if type(config_pointer) is dict: + config_pointer = config_pointer.setdefault(el, {}) + elif type(config_pointer) is list: + config_pointer = config_pointer[el] + else: + pass + return config_pointer[path[-1]] + + +def initialize_params_in_config(basic_config, paths): + config = deepcopy(basic_config) + + for path_ in paths: + param_name = path_[-1] + value = _get_value_from_config(basic_config, path_) + if type(value) is dict: + if value.get("evolve_choice"): + config = _insert_value_or_dict_into_config(config, + path_, + sample_params( + **{param_name: list(value["values"])})[param_name]) + elif value.get("evolve_range"): + config = _insert_value_or_dict_into_config(config, + path_, + sample_params( + **{param_name: deepcopy(value)})[param_name]) + elif value.get("evolve_bool"): + config = _insert_value_or_dict_into_config(config, + path_, + sample_params( + **{param_name: deepcopy(value)})[param_name]) + + return config + + +def sample_params(**params): + if not params: + return {} + else: + params_copy = deepcopy(params) + params_sample = dict() + for param, param_val in params_copy.items(): + if isinstance(param_val, list): + params_sample[param] = np.random.choice(param_val) + elif isinstance(param_val, dict): + if 'evolve_bool' in param_val and param_val['evolve_bool']: + sample = bool(np.random.choice([True, False])) + elif 'evolve_range' in param_val: + sample = _sample_from_ranges(param_val) + params_sample[param] = sample + else: + params_sample[param] = params_copy[param] + return params_sample + + +def _sample_from_ranges(opts): + from_ = opts['evolve_range'][0] + to_ = opts['evolve_range'][1] + if opts.get('scale', None) == 'log': + sample = _sample_log(from_, to_) + else: + sample = np.random.uniform(from_, to_) + if opts.get('discrete', False): + sample = int(np.round(sample)) + return sample + + +def _sample_log(from_, to_): + sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) + return float(sample) + + +config = read_json("/home/dilyara/Documents/GitHub/deeppavlov_evolution/deeppavlov/configs/evolution/intents_snips.json") +paths = list(_find_main_model_path(config, "evolve_range")) + +print(paths) + +for t in ["evolve_range", "evolve_choice", "evolve_bool"]: + paths = list(_find_main_model_path(config, t)) + config = initialize_params_in_config(config, paths) + +print(json.dumps(config, indent=2)) From 3cf6067ccddfe63af4a3a23b5694310858d57ec6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 16:17:40 +0300 Subject: [PATCH 452/616] feat: add param elitism with weights or not --- .../evolution/evolution_param_generator.py | 27 +++++++++++++------ 1 file changed, 19 insertions(+), 8 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 6601e19b67..7535e8e61e 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -30,7 +30,7 @@ def __init__(self, key_main_model="main", seed=None, train_partition=1, - load_pretrained=False, + elitism_with_weights=False, **kwargs): """ Initialize evolution with random population @@ -59,7 +59,7 @@ def __init__(self, self.p_mutation = p_mutation self.mutation_power = mutation_power self.crossover_power = crossover_power - self.load_pretrained = load_pretrained + self.elitism_with_weights = elitism_with_weights self.n_saved_best_pretrained = 0 self.train_partition = train_partition @@ -242,12 +242,23 @@ def next_generation(self, generation, scores, iteration, ).parent.joinpath("model_opt.json")))["final_lear_rate"]) except: pass - next_population[i] = self._insert_value_or_dict_into_config( - next_population[i], - self._get_value_from_config(next_population[i], - self.main_model_path + ["load_path"]), - str(Path(self._get_value_from_config(next_population[i], - self.main_model_path + ["save_path"])).parent)) + + if self.elitism_with_weights: + # if elite models are saved with weights + next_population[i] = self._insert_value_or_dict_into_config( + next_population[i], + self._get_value_from_config(next_population[i], + self.main_model_path + ["load_path"]), + str(Path(self._get_value_from_config(next_population[i], + self.main_model_path + ["save_path"])).parent)) + else: + # if elite models are saved only as configurations and trained again + next_population[i] = self._insert_value_or_dict_into_config( + next_population[i], + self._get_value_from_config(next_population[i], + self.main_model_path + ["load_path"]), + str(Path(self._get_value_from_config(next_population[i], self.main_model_path + ["load_path"]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) next_population[i] = self._insert_value_or_dict_into_config( next_population[i], From 89bf0213a0db56c742ad16705b2efd04aee41cfd Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 16:26:35 +0300 Subject: [PATCH 453/616] feat: crossover --- .../evolution/evolution_param_generator.py | 77 ++++--------------- 1 file changed, 16 insertions(+), 61 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 7535e8e61e..f0ff3d97ab 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -343,74 +343,29 @@ def crossover(self, population, scores, p_crossover, crossover_power): parents = population[np.where(rs[0] > intervals)[0][-1]], population[np.where(rs[1] > intervals)[0][-1]] if self.decision(p_crossover): - dataset_iterator_params_perm = np.random.permutation(self.n_evolving_dataset_iterator_params) params_perm = np.random.permutation(self.n_evolving_params) - train_params_perm = np.random.permutation(self.n_evolving_train_params) curr_offsprings = [deepcopy(parents[0]), deepcopy(parents[1])] - dataset_iterator_part = int(crossover_power * self.n_evolving_dataset_iterator_params) part = int(crossover_power * self.n_evolving_params) - train_part = int(crossover_power * self.n_evolving_train_params) - - # exchange of dataset_iterator params - for j in range(self.n_evolving_dataset_iterator_params - dataset_iterator_part): - curr_offsprings[0]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ - parents[0]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] - curr_offsprings[1]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ - parents[1]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] - for j in range(self.n_evolving_dataset_iterator_params - dataset_iterator_part, - self.n_evolving_dataset_iterator_params): - curr_offsprings[0]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ - parents[1]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] - curr_offsprings[1]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] = \ - parents[0]["dataset_iterator"][ - self.evolving_dataset_iterator_params[dataset_iterator_params_perm[j]]] - - # exchange of model params (not layers params) - for j in range(self.n_evolving_params - part): - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[0][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[1][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - for j in range(self.n_evolving_params - part, self.n_evolving_params): - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[1][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[0][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - - # exchange of train params - for j in range(self.n_evolving_train_params - train_part): - curr_offsprings[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] - curr_offsprings[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] - for j in range(self.n_evolving_train_params - train_part, self.n_evolving_train_params): - curr_offsprings[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] - curr_offsprings[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] + for j in range(self.n_evolving_params - part, self.n_evolving_params): + curr_offsprings[0] = self._insert_value_or_dict_into_config(curr_offsprings[0], + self.paths_to_evolving_params[ + params_perm[j]], + self._get_value_from_config( + parents[1], + self.paths_to_evolving_params[ + params_perm[j]])) + + curr_offsprings[1] = self._insert_value_or_dict_into_config(curr_offsprings[1], + self.paths_to_evolving_params[ + params_perm[j]], + self._get_value_from_config( + parents[0], + self.paths_to_evolving_params[ + params_perm[j]])) offsprings.append(deepcopy(curr_offsprings[0])) else: offsprings.append(deepcopy(parents[0])) From 9db6e63a6972d59697b637de49e07eb2757a6535 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 16:27:51 +0300 Subject: [PATCH 454/616] fix: add evolve_ to range bool and choice --- .../evolution/evolution_param_generator.py | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index f0ff3d97ab..a9b4cc8861 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -421,17 +421,17 @@ def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutatio val = round(param_value + ((2 * np.random.random() - 1.) * mutation_power * self.sample_params(**{param: params_dict[param]})[param])) - val = min(max(params_dict[param]["range"][0], val), - params_dict[param]["range"][1]) + val = min(max(params_dict[param]["evolve_range"][0], val), + params_dict[param]["evolve_range"][1]) new_mutated_value = val - elif 'range' in params_dict[param].keys(): + elif 'evolve_range' in params_dict[param].keys(): val = param_value + \ ((2 * np.random.random() - 1.) * mutation_power * self.sample_params(**{param: params_dict[param]})[param]) - val = min(max(params_dict[param]["range"][0], val), - params_dict[param]["range"][1]) + val = min(max(params_dict[param]["evolve_range"][0], val), + params_dict[param]["evolve_range"][1]) new_mutated_value = val - elif params_dict[param].get("choice"): + elif params_dict[param].get("evolve_choice"): # new_mutated_value = param_value new_mutated_value = self.sample_params(**{param: params_dict[param]})[param] else: @@ -468,9 +468,9 @@ def sample_params(self, **params): if isinstance(param_val, list): params_sample[param] = np.random.choice(param_val) elif isinstance(param_val, dict): - if 'bool' in param_val and param_val['bool']: + if 'evolve_bool' in param_val and param_val['evolve_bool']: sample = bool(np.random.choice([True, False])) - elif 'range' in param_val: + elif 'evolve_range' in param_val: sample = self._sample_from_ranges(param_val) params_sample[param] = sample else: @@ -478,8 +478,8 @@ def sample_params(self, **params): return params_sample def _sample_from_ranges(self, opts): - from_ = opts['range'][0] - to_ = opts['range'][1] + from_ = opts['evolve_range'][0] + to_ = opts['evolve_range'][1] if opts.get('scale', None) == 'log': sample = self._sample_log(from_, to_) else: From 662f6835a4021ceb78bc286a95813925e9a6ef07 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 16:51:25 +0300 Subject: [PATCH 455/616] feat: mutation --- .../evolution/evolution_param_generator.py | 88 ++++++------------- 1 file changed, 28 insertions(+), 60 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index a9b4cc8861..8025a031a5 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -187,9 +187,7 @@ def first_generation(self, iteration=0): return population - def next_generation(self, generation, scores, iteration, - p_crossover=None, crossover_power=None, - p_mutation=None, mutation_power=None): + def next_generation(self, generation, scores, iteration): """ Provide an operation of replacement Args: @@ -204,24 +202,12 @@ def next_generation(self, generation, scores, iteration, Returns: the next generation according to the given scores of current generation """ - if not p_crossover: - p_crossover = self.p_crossover - if not crossover_power: - crossover_power = self.crossover_power - if not p_mutation: - p_mutation = self.p_mutation - if not mutation_power: - mutation_power = self.mutation_power next_population = self.selection_of_best_with_weights(generation, scores) print("Saved with weights: {} individuums".format(self.n_saved_best_pretrained)) - offsprings = self.crossover(generation, scores, - p_crossover=p_crossover, - crossover_power=crossover_power) + offsprings = self.crossover(generation, scores) - changable_next = self.mutation(offsprings, - p_mutation=p_mutation, - mutation_power=mutation_power) + changable_next = self.mutation(offsprings) next_population.extend(changable_next) @@ -319,7 +305,7 @@ def selection_of_best_with_weights(self, population, scores): self.n_saved_best_pretrained = len(selected) return selected - def crossover(self, population, scores, p_crossover, crossover_power): + def crossover(self, population, scores): """ Recombine randomly population in pairs and cross over them with given probability. Cross over from two parents produces two offsprings @@ -342,13 +328,13 @@ def crossover(self, population, scores, p_crossover, crossover_power): rs = np.random.random(2) parents = population[np.where(rs[0] > intervals)[0][-1]], population[np.where(rs[1] > intervals)[0][-1]] - if self.decision(p_crossover): + if self.decision(self.p_crossover): params_perm = np.random.permutation(self.n_evolving_params) curr_offsprings = [deepcopy(parents[0]), deepcopy(parents[1])] - part = int(crossover_power * self.n_evolving_params) + part = int(self.crossover_power * self.n_evolving_params) for j in range(self.n_evolving_params - part, self.n_evolving_params): curr_offsprings[0] = self._insert_value_or_dict_into_config(curr_offsprings[0], @@ -372,7 +358,7 @@ def crossover(self, population, scores, p_crossover, crossover_power): return offsprings - def mutation(self, population, p_mutation, mutation_power): + def mutation(self, population): """ Mutate each parameter of each individuum in population with probability p_mutation Args: @@ -387,53 +373,35 @@ def mutation(self, population, p_mutation, mutation_power): for individuum in population: mutated_individuum = deepcopy(individuum) - - # mutation of dataset iterator params - for param in self.dataset_iterator_params.keys(): - mutated_individuum["dataset_iterator"][param] = \ - self.mutation_of_param(param, self.dataset_iterator_params, - individuum["dataset_iterator"][param], - p_mutation, mutation_power) - - # mutation of other model params - for param in self.params.keys(): - mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][param] = \ - self.mutation_of_param(param, self.params, - individuum["chainer"]["pipe"][self.model_to_evolve_index][param], - p_mutation, mutation_power) - - # mutation of train params - for param in self.train_params.keys(): - mutated_individuum["train"][param] = \ - self.mutation_of_param(param, self.train_params, - individuum["train"][param], - p_mutation, mutation_power) - + for path_ in self.paths_to_evolving_params: + mutated_individuum = self._insert_value_or_dict_into_config( + mutated_individuum, path_, + self.mutation_of_param(path_, self._get_value_from_config(individuum, path_))) mutated.append(mutated_individuum) return mutated - def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutation_power): - new_mutated_value = deepcopy(param_value) - if self.decision(p_mutation): - if type(params_dict[param]) is dict: - if params_dict[param].get('discrete', False): + def mutation_of_param(self, param_path, param_value): + if self.decision(self.p_mutation): + basic_value = self._get_value_from_config(self.basic_config, param_path) + param_name = param_path[-1] + if type(basic_value) is dict: + if basic_value.get('discrete', False): val = round(param_value + - ((2 * np.random.random() - 1.) * mutation_power - * self.sample_params(**{param: params_dict[param]})[param])) - val = min(max(params_dict[param]["evolve_range"][0], val), - params_dict[param]["evolve_range"][1]) + ((2 * np.random.random() - 1.) * self.mutation_power + * self.sample_params(**{param_name: basic_value})[param_name])) + val = min(max(basic_value["evolve_range"][0], val), + basic_value["evolve_range"][1]) new_mutated_value = val - elif 'evolve_range' in params_dict[param].keys(): + elif 'evolve_range' in basic_value.keys(): val = param_value + \ - ((2 * np.random.random() - 1.) * mutation_power - * self.sample_params(**{param: params_dict[param]})[param]) - val = min(max(params_dict[param]["evolve_range"][0], val), - params_dict[param]["evolve_range"][1]) + ((2 * np.random.random() - 1.) * self.mutation_power + * self.sample_params(**{param_name: basic_value})[param_name]) + val = min(max(basic_value["evolve_range"][0], val), + basic_value["evolve_range"][1]) new_mutated_value = val - elif params_dict[param].get("evolve_choice"): - # new_mutated_value = param_value - new_mutated_value = self.sample_params(**{param: params_dict[param]})[param] + elif basic_value.get("evolve_choice"): + new_mutated_value = self.sample_params(**{param_name: basic_value})[param_name] else: new_mutated_value = param_value else: From 9aaccf191d75a96b61a29353d9ccc81de8ed926f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:08:30 +0300 Subject: [PATCH 456/616] chore --- .../evolution/Tutorial_params_evolution.ipynb | 328 -------------- .../models/evolution/check_binary_mask.py | 131 ------ .../models/evolution/check_matrix.ipynb | 257 ----------- deeppavlov/models/evolution/debug.py | 80 ---- .../evolution/evolution_many_inputs_model.py | 416 ------------------ .../evolution/evolution_param_generator.py | 71 ++- .../evolution/random_param_generator.py | 85 ---- deeppavlov/models/evolution/run_evolution.py | 232 ---------- .../models/evolution/run_param_evolution.py | 87 ++-- deeppavlov/models/evolution/test.py | 134 ------ .../models/evolution/train_phenotype.py | 1 - deeppavlov/models/evolution/utils.py | 12 - 12 files changed, 83 insertions(+), 1751 deletions(-) delete mode 100644 deeppavlov/models/evolution/Tutorial_params_evolution.ipynb delete mode 100644 deeppavlov/models/evolution/check_binary_mask.py delete mode 100644 deeppavlov/models/evolution/check_matrix.ipynb delete mode 100644 deeppavlov/models/evolution/debug.py delete mode 100644 deeppavlov/models/evolution/evolution_many_inputs_model.py delete mode 100644 deeppavlov/models/evolution/random_param_generator.py delete mode 100644 deeppavlov/models/evolution/run_evolution.py delete mode 100644 deeppavlov/models/evolution/test.py diff --git a/deeppavlov/models/evolution/Tutorial_params_evolution.ipynb b/deeppavlov/models/evolution/Tutorial_params_evolution.ipynb deleted file mode 100644 index f729ce8fec..0000000000 --- a/deeppavlov/models/evolution/Tutorial_params_evolution.ipynb +++ /dev/null @@ -1,328 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to use evolution of model parameters in DeepPavlov" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Скопируйте в новый файл свой обычный конфиг, соответствующий рассматриваемой модели.\n", - "\n", - "* Для каждого параметра, который можно варьировать, в конфиге замените значение параметра на словарь, определяющий возможные принимаемые значения. Тренировочные параметры (из `config[\"train\"]`) варьируются автоматически, а для варьирования параметров модели необходимо определить тот подсловарь конфига, в котором находятся варьируемые параметры, добавив в него параметр `\"to_evolve\": true`. Варьируемые параметры должны быть ключами словаря, содержащего ключ `to_evolve`, вложенность пока не поддерживается.\n", - "\n", - "* Запустите эволюцию с необходимыми параметрами:\n", - " - config - путь к файлу конфигу для эволюции\n", - " - evolve_metric - зарегистрированное название метрики из тренировочных параметров конфига, по значениям которой будет происходить эволюция\n", - " - p_size - размер одной популяции\n", - " - gpus - номера gpu, доступных для использования. Если количество gpu меньше размера популяции, то модели будут запускаться группами по len(gpus) штук.\n", - "```\n", - "python ./models/evolution/run_param_evolution.py --config config_file \n", - " --evolve_metric registered_metric_from_config \n", - " --p_size 10\n", - " --gpus 0,1,2\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Примеры словаря возможных значений для различных видов параметров" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import json\n", - "\n", - "def print_json(dictionary):\n", - " print(json.dumps(dictionary, indent=2))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "config = {\"dense_size\": 100, \n", - " \"activation\": \"sigmoid\", \n", - " \"learning_rate\": 0.001, \n", - " \"learning_rate_decay\": 0.00001,\n", - " \"is_main\": True}" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"dense_size\": 100,\n", - " \"activation\": \"sigmoid\",\n", - " \"learning_rate\": 0.001,\n", - " \"learning_rate_decay\": 1e-05,\n", - " \"is_main\": true\n", - "}\n" - ] - } - ], - "source": [ - "print_json(config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Дискретный параметр из промежутка" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - "}\n" - ] - } - ], - "source": [ - "config[\"dense_size\"] = {\"range\": [50, 500], \"discrete\": True}\n", - "print_json(config[\"dense_size\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Дискретный параметр из листа возможных значений" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"values\": [\n", - " \"softmax\",\n", - " \"sigmoid\",\n", - " \"relu\"\n", - " ],\n", - " \"choice\": true\n", - "}\n" - ] - } - ], - "source": [ - "config[\"activation\"] = {\"values\": [\"softmax\", \"sigmoid\", \"relu\"], \"choice\": True}\n", - "print_json(config[\"activation\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Параметр из промежутка" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"range\": [\n", - " 0.001,\n", - " 0.1\n", - " ]\n", - "}\n" - ] - } - ], - "source": [ - "config[\"learning_rate\"] = {\"range\": [0.001, 0.1]}\n", - "print_json(config[\"learning_rate\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Параметр из промежутка с логарифмической шкалой" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"range\": [\n", - " 1e-05,\n", - " 0.0001\n", - " ],\n", - " \"scale\": \"log\"\n", - "}\n" - ] - } - ], - "source": [ - "config[\"learning_rate_decay\"] = {\"range\": [0.00001, 0.0001], \"scale\": \"log\"}\n", - "print_json(config[\"learning_rate_decay\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Булевый параметр" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"bool\": true\n", - "}\n" - ] - } - ], - "source": [ - "config[\"is_main\"] = {\"bool\": True}\n", - "print_json(config[\"is_main\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Therefore, evolving parameters can be written in DeepPavlov config in the following way" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"dense_size\": {\n", - " \"range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"activation\": {\n", - " \"values\": [\n", - " \"softmax\",\n", - " \"sigmoid\",\n", - " \"relu\"\n", - " ],\n", - " \"choice\": true\n", - " },\n", - " \"learning_rate\": {\n", - " \"range\": [\n", - " 0.001,\n", - " 0.1\n", - " ]\n", - " },\n", - " \"learning_rate_decay\": {\n", - " \"range\": [\n", - " 1e-05,\n", - " 0.0001\n", - " ],\n", - " \"scale\": \"log\"\n", - " },\n", - " \"is_main\": {\n", - " \"bool\": true\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "print_json(config)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python-deep36", - "language": "python", - "name": "deep36" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/deeppavlov/models/evolution/check_binary_mask.py b/deeppavlov/models/evolution/check_binary_mask.py deleted file mode 100644 index 5024cd8720..0000000000 --- a/deeppavlov/models/evolution/check_binary_mask.py +++ /dev/null @@ -1,131 +0,0 @@ -import numpy as np -import networkx as nx -from copy import copy, deepcopy -import datetime -import time -from pathlib import Path -import matplotlib -matplotlib.use('Agg') - -import matplotlib.pyplot as plt - -def number_to_type_layer(node_id, n_types): - # return node_layer, node_type - return node_id // n_types, node_id % n_types - - -def type_layer_to_number(node_layer, node_type, n_types): - return node_layer * n_types + node_type - - -def find_sources_and_sinks(directed_graph): - sources = [] - sinks = [] - isolates = nx.isolates(directed_graph) - - for str_id in directed_graph.nodes(): - if directed_graph.in_degree(str_id) == 0 and directed_graph.out_degree(str_id) > 0: - sources.append(str_id) - if directed_graph.in_degree(str_id) > 0 and directed_graph.out_degree(str_id) == 0: - sinks.append(str_id) - - return sources, sinks, isolates - - -def get_digraph_from_binary_mask(nodes, binary_mask): - directed_graph = nx.DiGraph() - total_nodes = len(nodes) - - for i in range(total_nodes): - directed_graph.add_node(str(i)) - - for i in range(total_nodes): - for j in range(total_nodes): - if binary_mask[i, j] == 1: - directed_graph.add_edge(str(i), str(j)) - return directed_graph - - -def get_binary_mask_from_digraph(nodes, directed_graph): - binary_mask = np.zeros((len(nodes), len(nodes))) - for edge in directed_graph.edges(): - binary_mask[int(edge[0]), int(edge[1])] = 1 - return binary_mask - - -def check_and_correct_binary_mask(nodes, binary_mask_): - binary_mask = deepcopy(binary_mask_) - - directed_graph = get_digraph_from_binary_mask(nodes, binary_mask) - sources, sinks, _ = find_sources_and_sinks(directed_graph) - - while not nx.is_directed_acyclic_graph(directed_graph): - candidates = [] - cycles = list(nx.simple_cycles(directed_graph)) - n_cycles = len(cycles) - cycles_len = np.array([len(cycle) for cycle in cycles]) - n_candidates = int(np.prod(cycles_len)) - - for i in range(n_candidates): - new_directed_graph = deepcopy(directed_graph) - for j in range(n_cycles): - node_id = (i // np.prod(cycles_len[:j])) % cycles_len[j] - try: - new_directed_graph.remove_edge(cycles[j][node_id], cycles[j][(node_id + 1) % cycles_len[j]]) - except: - continue - candidates.append(new_directed_graph) - - n_candidates = len(candidates) - best_cand = None - best_diff = 10e10 - for i in range(n_candidates): - new_sources, new_sinks, _ = find_sources_and_sinks(candidates[i]) - - if set(new_sources) == set(sources) and set(new_sinks) == set(sinks): - best_cand = candidates[i] - elif (len(set(new_sources).difference(set(sources))) + - len(set(new_sinks).difference(set(sinks))) < best_diff): - best_cand = candidates[i] - best_diff = len(set(new_sources).difference(set(sources))) + len(set(new_sinks).difference(set(sinks))) - - directed_graph = best_cand - - binary_mask = get_binary_mask_from_digraph(nodes, directed_graph) - return binary_mask - - -def get_graph_and_plot(nodes, binary_mask, n_types, path=None): - nodes_int = {} - for i in range(len(nodes)): - nodes_int[i] = nodes[str(i)] - - total_nodes = len(nodes) - dg = get_digraph_from_binary_mask(nodes, binary_mask) - - pos = {} - val_map = {} - sources, sinks, _ = find_sources_and_sinks(dg) - - for i in range(total_nodes): - pos[str(i)] = 2. * np.array(number_to_type_layer(i, n_types))[::-1] - if str(i) in sources: - val_map[str(i)] = 1. - elif str(i) in sinks: - val_map[str(i)] = 0.5 - else: - val_map[str(i)] = 0. - - plt.figure(figsize=(12, 12)) - values = [val_map.get(node, 0.25) for node in nodes_int] - - nx.draw(dg, pos, cmap=plt.get_cmap('jet'), node_color=values, node_size=7000, alpha=0.3) - - nx.draw_networkx_labels(dg, pos, nodes, font_size=18) - - if path is None: - path = "./" - curr_time = datetime.datetime.now().strftime("%Hh%Mm%Ss_%dd%mm%Yy") - plt.savefig(Path(path).joinpath("pic_" + curr_time + ".png")) - # time.sleep(1) - return None diff --git a/deeppavlov/models/evolution/check_matrix.ipynb b/deeppavlov/models/evolution/check_matrix.ipynb deleted file mode 100644 index 12ae7348c3..0000000000 --- a/deeppavlov/models/evolution/check_matrix.ipynb +++ /dev/null @@ -1,257 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import networkx as nx\n", - "from check_binary_mask import check_and_correct_binary_mask\n", - "from check_binary_mask import number_to_type_layer\n", - "from check_binary_mask import type_layer_to_number" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "T = 3\n", - "L = 2\n", - "total_nodes = T * L\n", - "\n", - "nodes = {}\n", - "types = {0: \"Dense\", 1: \"Conv1D\", \n", - " 2: \"LSTM\", 3: \"BiLSTM\", 4: \"GlobMaxPool1D\", \n", - " 5: \"MaxPool1D\", 6: \"Attention\"}\n", - "\n", - "for i in range(0, total_nodes):\n", - " nodes[i] = types[number_to_type_layer(i, T)[1]]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: 'Dense', 1: 'Conv1D', 2: 'LSTM', 3: 'Dense', 4: 'Conv1D', 5: 'LSTM'}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nodes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cm = np.zeros((total_nodes, total_nodes)) \n", - "cm[0, 1] = 1\n", - "cm[0, 3] = 1\n", - "cm[3, 1] = 1\n", - "cm[3, 5] = 1\n", - "cm[5, 2] = 1\n", - "\n", - "dg = nx.DiGraph()\n", - "\n", - "for i in range(total_nodes):\n", - " dg.add_node(i)\n", - " \n", - "pos = {}\n", - "\n", - "for i in range(total_nodes):\n", - " for j in range(total_nodes):\n", - " if cm[i,j] == 1:\n", - " dg.add_edge(i, j)\n", - "# pos[i] = 5 * np.array(number_to_type_layer(i, L, T))\n", - " pos[i] = np.array(number_to_type_layer(i, T))[::-1]\n", - "\n", - "plt.figure(figsize=(6, 6))\n", - "nx.draw(dg, pos, node_color='b', node_size=5000, alpha=0.3)\n", - "\n", - "nx.draw_networkx_labels(dg, pos, nodes, font_size=18)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "check_and_correct_binary_mask(nodes, cm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_graph_and_plot(nodes, cm):\n", - " total_nodes = len(nodes)\n", - " dg = nx.DiGraph()\n", - "\n", - " for i in range(total_nodes):\n", - " dg.add_node(i)\n", - "\n", - " pos = {}\n", - "\n", - " for i in range(total_nodes):\n", - " for j in range(total_nodes):\n", - " if cm[i,j] == 1:\n", - " dg.add_edge(i, j)\n", - " # pos[i] = 5 * np.array(number_to_type_layer(i, L, T))\n", - " pos[i] = np.array(number_to_type_layer(i, T))[::-1]\n", - "\n", - " plt.figure(figsize=(6, 6))\n", - " nx.draw(dg, pos, node_color='b', node_size=5000, alpha=0.3)\n", - "\n", - " nx.draw_networkx_labels(dg, pos, nodes, font_size=18)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cm = np.zeros((total_nodes, total_nodes)) \n", - "cm[0, 1] = 1\n", - "cm[0, 3] = 1\n", - "cm[3, 1] = 1\n", - "cm[3, 5] = 1\n", - "cm[5, 2] = 1\n", - "cm[5, 3] = 1\n", - "\n", - "get_graph_and_plot(nodes, cm)\n", - "new_cm = check_and_correct_binary_mask(nodes, cm)\n", - "get_graph_and_plot(nodes, new_cm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cm = np.zeros((total_nodes, total_nodes)) \n", - "cm[0, 1] = 1\n", - "cm[0, 3] = 1\n", - "cm[3, 1] = 1\n", - "cm[4, 5] = 1\n", - "cm[5, 2] = 1\n", - "cm[2, 4] = 1\n", - "\n", - "get_graph_and_plot(nodes, cm)\n", - "new_cm = check_and_correct_binary_mask(nodes, cm)\n", - "get_graph_and_plot(nodes, new_cm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cm = np.zeros((total_nodes, total_nodes)) \n", - "cm[0, 1] = 1\n", - "cm[0, 3] = 1\n", - "cm[3, 1] = 1\n", - "cm[4, 5] = 1\n", - "cm[5, 2] = 1\n", - "cm[2, 4] = 1\n", - "cm[3, 4] = 1\n", - "\n", - "get_graph_and_plot(nodes, cm)\n", - "new_cm = check_and_correct_binary_mask(nodes, cm)\n", - "get_graph_and_plot(nodes, new_cm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cm = np.zeros((total_nodes, total_nodes)) \n", - "cm[0, 1] = 1\n", - "cm[0, 3] = 1\n", - "cm[3, 1] = 1\n", - "cm[4, 5] = 1\n", - "cm[5, 2] = 1\n", - "cm[2, 4] = 1\n", - "cm[3, 4] = 1\n", - "cm[4, 3] = 1\n", - "\n", - "get_graph_and_plot(nodes, cm)\n", - "new_cm = check_and_correct_binary_mask(nodes, cm)\n", - "get_graph_and_plot(nodes, new_cm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py36_main_kernel", - "language": "python", - "name": "py36_main" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/deeppavlov/models/evolution/debug.py b/deeppavlov/models/evolution/debug.py deleted file mode 100644 index 188aad3e55..0000000000 --- a/deeppavlov/models/evolution/debug.py +++ /dev/null @@ -1,80 +0,0 @@ -import pandas as pd -import json -import numpy as np -import tensorflow as tf -from copy import deepcopy - -from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution -from deeppavlov.models.evolution.evolution_intent_model import KerasEvolutionClassificationModel -from deeppavlov.core.commands.train import train_model_from_config -from deeppavlov.core.commands.infer import interact_model -from deeppavlov.core.commands.utils import set_deeppavlov_root -from deeppavlov.core.common.file import save_json, read_json -from deeppavlov.models.evolution.utils import expand_tile_batch_size -from deeppavlov.models.evolution.check_binary_mask import get_digraph_from_binary_mask - - -n_layers = 2 -n_types = 7 -population_size = 1 -config_path = "../../configs/evolution/basic_config_local.json" - -with open(config_path) as fin: - config = json.load(fin) - -evolution = NetworkAndParamsEvolution(n_layers, n_types, - population_size, - key_model_to_evolve="to_evolve", - key_basic_layers="basic_layers_params", - seed=42, - start_with_one_neuron=True, - **config) - -population = evolution.first_generation() -population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ - evolution.model_to_evolve_index]["binary_mask"].tolist() - -config_path = "./config_init.json" -full_config = deepcopy(population[0]) -print(population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]) -save_json(full_config, config_path) - -population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = np.array(population[0]["chainer"]["pipe"][ - evolution.model_to_evolve_index]["binary_mask"]) - -population = evolution.crossover(population, p_crossover=0.9, crossover_power=0.5) -print(population) - -population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ - evolution.model_to_evolve_index]["binary_mask"].tolist() - -config_path = "./config_crossover.json" -full_config = deepcopy(population[0]) -save_json(full_config, config_path) - -population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = np.array(population[0]["chainer"]["pipe"][ - evolution.model_to_evolve_index]["binary_mask"]) - -population = evolution.mutation(population, p_mutation=0.5, mutation_power=.5) - -population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = population[0]["chainer"]["pipe"][ - evolution.model_to_evolve_index]["binary_mask"].tolist() - -config_path = "./config_mutated.json" -full_config = deepcopy(population[0]) -full_config["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = evolution.nodes -full_config["chainer"]["pipe"][evolution.model_to_evolve_index]["total_nodes"] = evolution.total_nodes - -save_json(full_config, config_path) - -population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = np.array(population[0]["chainer"]["pipe"][ - evolution.model_to_evolve_index]["binary_mask"]) - -dg = get_digraph_from_binary_mask(evolution.nodes, - population[0]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) - -print("Edges: ", dg.edges) -train_model_from_config(config_path) - - - diff --git a/deeppavlov/models/evolution/evolution_many_inputs_model.py b/deeppavlov/models/evolution/evolution_many_inputs_model.py deleted file mode 100644 index 7fc9e7d155..0000000000 --- a/deeppavlov/models/evolution/evolution_many_inputs_model.py +++ /dev/null @@ -1,416 +0,0 @@ -""" -Copyright 2017 Neural Networks and Deep Learning lab, MIPT - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import numpy as np -from copy import copy, deepcopy -from keras.layers import Dense, Input, concatenate, Activation -from keras.layers.convolutional import Conv1D -from keras.layers.core import Dropout -from keras.layers.normalization import BatchNormalization -from keras.layers.pooling import GlobalMaxPooling1D, MaxPooling1D -from keras.layers.recurrent import LSTM -from keras.layers.wrappers import Bidirectional -from keras.models import Model -from keras.regularizers import l2 -from keras.layers import Concatenate, Reshape, CuDNNLSTM, Lambda, Add, Subtract, Multiply -from keras import backend as K -from overrides import overrides -from pathlib import Path - -from deeppavlov.core.common.errors import ConfigError -from deeppavlov.core.common.registry import register -from deeppavlov.core.models.keras_model import KerasModel -from deeppavlov.models.classifiers.intents.intent_model import KerasIntentModel -from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels -from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder -from deeppavlov.models.classifiers.intents.utils import md5_hashsum -from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer -from deeppavlov.core.common.log import get_logger -from deeppavlov.models.evolution.check_binary_mask import number_to_type_layer, \ - find_sources_and_sinks, get_digraph_from_binary_mask, get_graph_and_plot -from deeppavlov.models.evolution.utils import expand_tile -from deeppavlov.core.common.file import save_json, read_json -from deeppavlov.core.layers.keras_layers import multiplicative_self_attention_init, \ - multiplicative_self_attention_get_output - - -log = get_logger(__name__) - - -@register('evolution_many_inputs_classification_model') -class KerasEvolutionClassificationManyInputsModel(KerasIntentModel): - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.opt["binary_mask"] = np.array(self.opt["binary_mask"]) - get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], - path=str(self.save_path.resolve().parent)) - - def texts2vec(self, sentences, i): - """ - Convert texts to vector representations using embedder and padding up to self.opt["text_size"] tokens - Args: - sentences: list of lists of tokens - - Returns: - array of embedded texts - """ - pad = np.zeros(self.opt['embedding_size']) - if type(self.opt['text_size']) is list: - text_size = self.opt['text_size'][i] - else: - text_size = self.opt['text_size'] - embeddings_batch = self.fasttext_model([sen[:text_size] for sen in sentences]) - embeddings_batch = [[pad] * (text_size - len(tokens)) + tokens for tokens in embeddings_batch] - - embeddings_batch = np.asarray(embeddings_batch) - return embeddings_batch - - @overrides - def train_on_batch(self, *args, **kwargs): - """ - Train the model on the given batch - Args: - texts - list of texts (or list of lists of text tokens) - labels - list of labels - - Returns: - loss and metrics values on the given batch - """ - if len(args) > len(self.opt["in"]): - labels = args[-1] - texts = args[:-1] - else: - labels = None - texts = args - - features = [] - for i in range(len(self.opt["in"])): - if isinstance(texts[i][0], str): - features.append(self.texts2vec(self.tokenizer(list(texts[i])), i)) - else: - features.append(self.texts2vec(list(texts[i]), i)) - - onehot_labels = labels2onehot(labels, classes=self.classes) - metrics_values = self.model.train_on_batch(features, onehot_labels) - return metrics_values - - @overrides - def infer_on_batch(self, *args, **kwargs): - """ - Infer the model on the given batch - Args: - texts - list of texts (or list of lists of text tokens) - labels - list of labels - - Returns: - loss and metrics values on the given batch, if labels are given - predictions, otherwise - """ - if len(args) > 1: - labels = args[-1] - texts = args[:-1] - elif len(args) == 1: - labels = None - texts = args[0] - else: - raise ValueError("Nothing to infer in infer_on_batch") - - features = [] - for i in range(len(self.opt["in"])): - if isinstance(texts[i][0], str): - features.append(self.texts2vec(self.tokenizer(list(texts[i])), i)) - else: - features.append(self.texts2vec(list(texts[i]), i)) - - if labels: - onehot_labels = labels2onehot(labels, classes=self.classes) - metrics_values = self.model.test_on_batch(features, onehot_labels) - return metrics_values - else: - predictions = self.model.predict(features) - return predictions - - @overrides - def __call__(self, *args, **kwargs): - """ - Infer on the given data - Args: - data: [list of sentences] - *args: - - Returns: - for each sentence: - vector of probabilities to belong with each class - or list of labels sentence belongs with - """ - assert len(args) == len(self.opt["in"]) - preds = np.array(self.infer_on_batch(args)) - - labels = proba2labels(preds, confident_threshold=self.opt['confident_threshold'], classes=self.classes) - return labels, [dict(zip(self.classes, preds[i])) for i in range(preds.shape[0])] - - def get_node_output(self, model_layers, node_str_id, dg, params, edges_outputs=None, inp=None): - if inp is None: - input_nodes = [edge[0] for edge in dg.in_edges(node_str_id)] - inp_list = [] - for input_node in input_nodes: - if len(K.int_shape(edges_outputs[input_node])) == 3: - inp_list.append(edges_outputs[input_node]) - elif len(K.int_shape(edges_outputs[input_node])) == 2: - input_expanded = Lambda(lambda x: expand_tile(x, axis=1))(edges_outputs[input_node]) - inp_list.append(input_expanded) - else: - raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") - if len(input_nodes) > 1: - try: - inp = Concatenate()(inp_list) - except ValueError: - time_steps = [] - features = [] - for i in range(len(inp_list)): - if len(K.int_shape(inp_list[i])) == 2: - inp_list[i] = Lambda(lambda x: expand_tile(x, axis=1))(inp_list[i]) - time_steps.append(K.int_shape(inp_list[i])[1]) - features.append(K.int_shape(inp_list[i])[2]) - new_feature_shape = max(features) - new_inp_list = [] - for i in range(len(inp_list)): - if K.int_shape(inp_list[i])[2] == new_feature_shape: - new_inp_list.append(inp_list[i]) - else: - new_inp_list.append(Dense(new_feature_shape)(inp_list[i])) - inp = Concatenate(axis=1)(new_inp_list) - else: - inp = inp_list[0] - - if params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - output_of_node = multiplicative_self_attention_get_output(inp, - model_layers[params["nodes"][node_str_id]]) - else: - node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - output_of_node = model_layers[params["nodes"][node_str_id]](inp) - - output_of_node = Dropout(rate=params['dropout_rate'])(output_of_node) - return output_of_node - - def initialize_all_nodes(self, params): - dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) - sources, sinks, isolates = find_sources_and_sinks(dg) - - model_layers = {} - for node_str_id in list(params["nodes"].keys()): - if not(node_str_id in isolates): - if params[params["nodes"][node_str_id]]["node_name"] == "BiCuDNNLSTM": - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - l2_reg = node_params.get("coef_regul_l2") - node_params.pop("coef_regul_l2") - model_layers[params["nodes"][node_str_id]] = Bidirectional(CuDNNLSTM(**node_params, - kernel_regularizer=l2(l2_reg))) - elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - model_layers[params["nodes"][node_str_id]] = multiplicative_self_attention_init(**node_params) - else: - node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - l2_reg = node_params.get("coef_regul_l2") - if callable(node_func): - if l2_reg is None: - model_layers[params["nodes"][node_str_id]] = node_func(**node_params) - else: - node_params.pop("coef_regul_l2") - model_layers[params["nodes"][node_str_id]] = node_func(**node_params, - kernel_regularizer=l2(l2_reg)) - else: - raise AttributeError("Node {} is not defined correctly".format(node_str_id)) - - return model_layers - - def evolution_many_inputs_classification_model(self, params): - """ - Build un-compiled model of shallow-and-wide CNN - Args: - params: dictionary of parameters for NN - - Returns: - Un-compiled model - """ - inputs = [] - if type(params['text_size']) is list: - for i in range(len(params["in"])): - inputs.append(Input(shape=(params['text_size'][i], params['embedding_size']))) - else: - for i in range(len(params["in"])): - inputs.append(Input(shape=(params['text_size'], params['embedding_size']))) - - full_outputs = [] - - if np.sum(params["binary_mask"]) == 0: - dense1 = Dense(1, activation=None) - globalmaxpooling = GlobalMaxPooling1D() - for inp in inputs: - output = dense1(inp) - full_outputs.append(globalmaxpooling(output)) - - summ = Add()(full_outputs) - mult = Multiply()(full_outputs) - - try: - subt = Subtract()(full_outputs) - full_outputs.append(subt) - except ValueError: - pass - full_outputs.append(summ) - full_outputs.append(mult) - - output = Concatenate()(full_outputs) - output = Dense(self.n_classes, activation=None)(output) - activation = params.get("last_layer_activation", "sigmoid") - act_output = Activation(activation)(output) - model = Model(inputs=inputs, outputs=act_output) - return model - - model_layers = self.initialize_all_nodes(params) - - for inp in inputs: - dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) - sources, sinks, isolates = find_sources_and_sinks(dg) - - edges_outputs = {} - - # sequence_of_nodes is a list of lists. - # each element of sequence_of_nodes is a list that contains nodes (keras layers) - # that could be initialized when all nodes from previous lists are initialized - sequence_of_nodes = [sources] - - while True: - if set(sinks).issubset(set(sum(sequence_of_nodes, []))): - break - next_nodes = [] - # want to get list of nodes that can be initialized next - for node_str_id in sequence_of_nodes[-1]: - # for each node that were initialized on the previous step - # take output edges - out_edges = dg.out_edges(node_str_id) - for edge in out_edges: - # for all output edge - # collect nodes that are input nodes - # for considered child of node_str_id (edge[1]) - in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] - # if for considered child all parents are already initialized - # then add this node for initialization - if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): - next_nodes.append(edge[1]) - sequence_of_nodes.append(next_nodes) - - # make a list of ints from list of lists - sequence_of_nodes = sum(sequence_of_nodes, []) - - # now all nodes in sequence - # can be initialized consequently - for node_str_id in sequence_of_nodes: - if node_str_id in sources: - # if considered node is source, - # give embedded texts as input - edges_outputs[node_str_id] = self.get_node_output(model_layers, node_str_id, dg, params, inp=inp) - elif node_str_id in isolates: - # unreal condition - # if considered node is isolate, - # nothing to do - pass - else: - # if considered node is not source and isolate, - # give all previous outputs as input - edges_outputs[node_str_id] = self.get_node_output(model_layers, node_str_id, dg, params, - edges_outputs=edges_outputs) - - if len(sinks) == 1: - # if the only sink, - # output is this sink's output - output = edges_outputs[sinks[0]] - else: - # if several sinks exist, - # outputs will be concatenated - outputs = [] - # collect outputs - for sink in sinks: - outputs.append(edges_outputs[sink]) - try: - output = Concatenate()(outputs) - except ValueError: - # outputs are of 2d and 3d shapes - # make them all 2d and concatenate - for i in range(len(outputs)): - if len(K.int_shape(outputs[i])) == 3: - outputs[i] = GlobalMaxPooling1D()(outputs[i]) - output = Concatenate(axis=1)(outputs) - - if len(output.shape) == 3: - output = GlobalMaxPooling1D()(output) - full_outputs.append(output) - - summ = Add()(full_outputs) - mult = Multiply()(full_outputs) - - try: - subt = Subtract()(full_outputs) - full_outputs.append(subt) - except ValueError: - pass - full_outputs.append(summ) - full_outputs.append(mult) - - output = Concatenate()(full_outputs) - output = Dense(self.n_classes, activation=None)(output) - activation = params.get("last_layer_activation", "sigmoid") - act_output = Activation(activation)(output) - model = Model(inputs=inputs, outputs=act_output) - return model - - def save(self, fname=None): - """ - Save the model parameters into <>_opt.json (or <>_opt.json) - and model weights into <>.h5 (or <>.h5) - Args: - fname: file_path to save model. If not explicitly given seld.opt["ser_file"] will be used - - Returns: - None - """ - if type(self.opt["binary_mask"]) is list: - pass - else: - self.opt["binary_mask"] = self.opt["binary_mask"].tolist() - - super().save(fname) - return True diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 8025a031a5..30a482403c 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -3,7 +3,6 @@ from pathlib import Path import json -from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe from deeppavlov.core.common.file import read_json from deeppavlov.core.common.log import get_logger @@ -48,8 +47,8 @@ def __init__(self, """ self.basic_config = deepcopy(kwargs) - self.main_model_path = list(self._find_model_path(self.basic_config, key_main_model))[0] - Path(self._get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, + self.main_model_path = list(self.find_model_path(self.basic_config, key_main_model))[0] + Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, exist_ok=True) self.print_dict(self.basic_config, string="Basic config:") log.info("Main model path in config: {}".format(self.main_model_path)) @@ -66,7 +65,7 @@ def __init__(self, self.paths_to_evolving_params = [] for evolve_type in ["evolve_range", "evolve_choice", "evolve_bool"]: - for path_ in self._find_model_path(self.basic_config, evolve_type): + for path_ in self.find_model_path(self.basic_config, evolve_type): self.paths_to_evolving_params.append(path_) self.n_evolving_params = len(self.paths_to_evolving_params) @@ -77,7 +76,7 @@ def __init__(self, else: np.random.seed(seed) - def _find_model_path(self, config, key_model, path=[]): + def find_model_path(self, config, key_model, path=[]): """ Find path to the main model in config which paths will be changed Args: @@ -95,15 +94,15 @@ def _find_model_path(self, config, key_model, path=[]): else: if type(config_pointer) is dict: for key in list(config_pointer.keys()): - for path_ in self._find_model_path(config_pointer[key], key_model, path + [key]): + for path_ in self.find_model_path(config_pointer[key], key_model, path + [key]): yield path_ elif type(config_pointer) is list: for i in range(len(config_pointer)): - for path_ in self._find_model_path(config_pointer[i], key_model, path + [i]): + for path_ in self.find_model_path(config_pointer[i], key_model, path + [i]): yield path_ @staticmethod - def _insert_value_or_dict_into_config(config, path, value): + def insert_value_or_dict_into_config(config, path, value): config_copy = deepcopy(config) config_pointer = config_copy for el in path[:-1]: @@ -117,7 +116,7 @@ def _insert_value_or_dict_into_config(config, path, value): return config_copy @staticmethod - def _get_value_from_config(config, path): + def get_value_from_config(config, path): config_copy = deepcopy(config) config_pointer = config_copy for el in path[:-1]: @@ -143,22 +142,22 @@ def initialize_params_in_config(self, basic_config, paths): for path_ in paths: param_name = path_[-1] - value = self._get_value_from_config(basic_config, path_) + value = self.get_value_from_config(basic_config, path_) if type(value) is dict: if value.get("evolve_choice"): - config = self._insert_value_or_dict_into_config(config, + config = self.insert_value_or_dict_into_config(config, path_, self.sample_params( **{param_name: list(value["values"])})[param_name]) elif value.get("evolve_range"): - config = self._insert_value_or_dict_into_config(config, + config = self.insert_value_or_dict_into_config(config, path_, self.sample_params( **{param_name: deepcopy(value)})[param_name]) elif value.get("evolve_bool"): - config = self._insert_value_or_dict_into_config(config, + config = self.insert_value_or_dict_into_config(config, path_, self.sample_params( **{param_name: @@ -176,7 +175,7 @@ def first_generation(self, iteration=0): for i in range(self.population_size): population.append(self.initialize_params_in_config(self.basic_config, self.paths_to_evolving_params)) for which_path in ["save_path", "load_path"]: - population[-1] = self._insert_value_or_dict_into_config(population[-1], + population[-1] = self.insert_value_or_dict_into_config(population[-1], self.main_model_path + [which_path], str(Path( self.basic_config["save_path"]).joinpath( @@ -219,11 +218,11 @@ def next_generation(self, generation, scores, iteration): + "_" + str(iteration % self.train_partition) + ".csv" try: # re-init learning rate with the final one (works for KerasModel) - next_population[i] = self._insert_value_or_dict_into_config( + next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self._get_value_from_config(next_population[i], + self.get_value_from_config(next_population[i], self.main_model_path + ["lear_rate"]), - read_json(str(Path(self._get_value_from_config(next_population[i], + read_json(str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) ).parent.joinpath("model_opt.json")))["final_lear_rate"]) except: @@ -231,26 +230,26 @@ def next_generation(self, generation, scores, iteration): if self.elitism_with_weights: # if elite models are saved with weights - next_population[i] = self._insert_value_or_dict_into_config( + next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self._get_value_from_config(next_population[i], + self.get_value_from_config(next_population[i], self.main_model_path + ["load_path"]), - str(Path(self._get_value_from_config(next_population[i], + str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"])).parent)) else: # if elite models are saved only as configurations and trained again - next_population[i] = self._insert_value_or_dict_into_config( + next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self._get_value_from_config(next_population[i], + self.get_value_from_config(next_population[i], self.main_model_path + ["load_path"]), - str(Path(self._get_value_from_config(next_population[i], self.main_model_path + ["load_path"]) + str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["load_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) - next_population[i] = self._insert_value_or_dict_into_config( + next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self._get_value_from_config(next_population[i], + self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]), - str(Path(self._get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) + str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) for i in range(self.n_saved_best_pretrained, self.population_size): @@ -260,11 +259,11 @@ def next_generation(self, generation, scores, iteration): "train"]).stem.split("_")[:-1])) \ + "_" + str(iteration % self.train_partition) + ".csv" for which_path in ["save_path", "load_path"]: - next_population[i] = self._insert_value_or_dict_into_config( + next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self._get_value_from_config(next_population[i], + self.get_value_from_config(next_population[i], self.main_model_path + [which_path]), - str(Path(self._get_value_from_config(next_population[i], self.main_model_path + [which_path]) + str(Path(self.get_value_from_config(next_population[i], self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) next_population[i]["evolution_model_id"] = self.evolution_model_id @@ -337,18 +336,18 @@ def crossover(self, population, scores): part = int(self.crossover_power * self.n_evolving_params) for j in range(self.n_evolving_params - part, self.n_evolving_params): - curr_offsprings[0] = self._insert_value_or_dict_into_config(curr_offsprings[0], + curr_offsprings[0] = self.insert_value_or_dict_into_config(curr_offsprings[0], self.paths_to_evolving_params[ params_perm[j]], - self._get_value_from_config( + self.get_value_from_config( parents[1], self.paths_to_evolving_params[ params_perm[j]])) - curr_offsprings[1] = self._insert_value_or_dict_into_config(curr_offsprings[1], + curr_offsprings[1] = self.insert_value_or_dict_into_config(curr_offsprings[1], self.paths_to_evolving_params[ params_perm[j]], - self._get_value_from_config( + self.get_value_from_config( parents[0], self.paths_to_evolving_params[ params_perm[j]])) @@ -374,16 +373,16 @@ def mutation(self, population): for individuum in population: mutated_individuum = deepcopy(individuum) for path_ in self.paths_to_evolving_params: - mutated_individuum = self._insert_value_or_dict_into_config( + mutated_individuum = self.insert_value_or_dict_into_config( mutated_individuum, path_, - self.mutation_of_param(path_, self._get_value_from_config(individuum, path_))) + self.mutation_of_param(path_, self.get_value_from_config(individuum, path_))) mutated.append(mutated_individuum) return mutated def mutation_of_param(self, param_path, param_value): if self.decision(self.p_mutation): - basic_value = self._get_value_from_config(self.basic_config, param_path) + basic_value = self.get_value_from_config(self.basic_config, param_path) param_name = param_path[-1] if type(basic_value) is dict: if basic_value.get('discrete', False): diff --git a/deeppavlov/models/evolution/random_param_generator.py b/deeppavlov/models/evolution/random_param_generator.py deleted file mode 100644 index df81713585..0000000000 --- a/deeppavlov/models/evolution/random_param_generator.py +++ /dev/null @@ -1,85 +0,0 @@ -import numpy as np -from copy import deepcopy -from pathlib import Path - - -class HyperPar: - def __init__(self, **kwargs): - self.params = kwargs - - def sample_params(self): - params = deepcopy(self.params) - params_sample = dict() - for param, param_val in params.items(): - if isinstance(param_val, list): - params_sample[param] = np.random.choice(param_val) - elif isinstance(param_val, dict): - if 'bool' in param_val and param_val['bool']: - sample = np.random.choice([True, False]) - elif 'range' in param_val: - sample = self._sample_from_ranges(param_val) - params_sample[param] = sample - else: - params_sample[param] = params[param] - return params_sample - - def _sample_from_ranges(self, opts): - from_ = opts['range'][0] - to_ = opts['range'][1] - if opts.get('scale', None) == 'log': - sample = self._sample_log(from_, to_) - else: - sample = np.random.uniform(from_, to_) - if opts.get('discrete', False): - sample = int(np.round(sample)) - return sample - - @staticmethod - def _sample_log(from_, to_): - sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) - return float(sample) - -# net_params = HyperPar(n_filters={'range': [32, 500], 'discrete': True, 'n_samples': n_layers, 'increasing': True}, -# filter_width={'range': [3, 11], 'discrete': True}, -# char_embeddings_dim={'range': [10, 50], 'discrete': True}, -# embeddings_dropout={'bool': True}, -# dense_dropout={'bool': True}, -# net_type=['cnn', 'rnn', 'cnn_highway'], -# use_crf=True, -# use_batch_norm=True, -# token_embeddings_dim=token_emb_dim, -# two_dense_layers=True) -# parms = net_params.sample_params() -# learning_params = HyperPar(dropout_rate={'range': [0.1, 0.9]}, -# epochs={'range': [10, 100], 'discrete': True}, -# learning_rate={'range': [1e-4, 1e-2], 'scale': 'log'}, -# batch_size={'range': [2, 64], 'discrete': True}, -# learning_rate_decay={'range': [0.3, 0.95]}, -# save_path='conll_models/model.ckpt').sample_params() - - -def get_population(basic_params, population_size, population_num): - population = [] - for i in range(population_size): - params = {} - params_for_search = {} - - for param_name in basic_params.keys(): - if ((type(basic_params[param_name]) is str) - or (type(basic_params[param_name]) is int) - or (type(basic_params[param_name]) is float) - or (type(basic_params[param_name]) is bool) - or (type(basic_params[param_name]) is list)): - params[param_name] = basic_params[param_name] - else: - if "values" in basic_params[param_name].keys(): - params_for_search[param_name] = list(basic_params[param_name]["values"]) - else: - params_for_search[param_name] = basic_params[param_name] - - params_for_search = HyperPar(**params_for_search).sample_params() - print() - params["model_path"] = str(Path(basic_params["model_path"]).joinpath( - "population_" + str(population_num)).joinpath(params_for_search["model_name"] + "_" + str(i))) - population.append({**params, **params_for_search}) - return population diff --git a/deeppavlov/models/evolution/run_evolution.py b/deeppavlov/models/evolution/run_evolution.py deleted file mode 100644 index 512ed8d7e4..0000000000 --- a/deeppavlov/models/evolution/run_evolution.py +++ /dev/null @@ -1,232 +0,0 @@ -import json -import numpy as np -import argparse -from pathlib import Path -from subprocess import Popen, PIPE -import pandas as pd -from copy import deepcopy, copy - -from deeppavlov.models.evolution.neuroevolution_param_generator import NetworkAndParamsEvolution -from deeppavlov.core.common.file import save_json, read_json - - -def score_population(population, population_size, result_file): - global evolution - - population_metrics = {} - for m in CONSIDERED_METRICS: - population_metrics[m] = [] - - procs = [] - - for i in range(population_size): - save_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) - load_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]) - - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - str(save_path.joinpath("model")) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ - str(load_path.joinpath("model")) - - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["nodes"] = \ - evolution.nodes - print(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) - try: - save_path.mkdir(parents=True) - except FileExistsError: - pass - - f_name = save_path.joinpath("config.json") - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] =\ - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"].tolist() - save_json(population[i], f_name) - procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" - " 1>{}/out.txt 2>{}/err.txt".format(gpus[i], - str(f_name), - str(save_path), - str(save_path) - ), - shell=True, stdout=PIPE, stderr=PIPE)) - - for i, proc in enumerate(procs): - print(f'wait on {i}th proc') - proc.wait() - - for i in range(population_size): - try: - val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("valid_results.txt"))) - except OSError or FileNotFoundError: - val_results = [None for m in CONSIDERED_METRICS] - for m_id, m in enumerate(CONSIDERED_METRICS): - if "loss" in m: - val_results[m_id] = 1e6 - else: - val_results[m_id] = 0. - if TEST: - try: - test_results = np.loadtxt( - fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("test_results.txt"))) - except OSError or FileNotFoundError: - test_results = [None for m in CONSIDERED_METRICS] - for m_id, m in enumerate(CONSIDERED_METRICS): - if "loss" in m: - test_results[m_id] = 1e6 - else: - test_results[m_id] = 0. - - - result_table_dict = {} - for el in order: - if el == "params": - result_table_dict[el] = [] - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - for m_id, m in enumerate(CONSIDERED_METRICS): - result_table_dict[m + "_valid"].append(val_results[m_id]) - if TEST: - result_table_dict[m + "_test"].append(test_results[m_id]) - else: - result_table_dict[m + "_test"].append(0.) - result_table_dict[order[-1]] = [population[i]] - result_table = pd.DataFrame(result_table_dict) - - result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - - for m_id, m in enumerate(CONSIDERED_METRICS): - population_metrics[m].append(val_results[m_id]) - - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ - np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) - - return population_metrics - - -parser = argparse.ArgumentParser() - -parser.add_argument('--config', help='Please, enter model path to config') -parser.add_argument('--evolve_metric', help='Please, choose target metric out of given in your config.train.metrics') -parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) -parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) -parser.add_argument('--n_layers', help='Please, enter number of each layer type in network', default=2) -parser.add_argument('--n_types', help='Please, enter number of types of layers', default=1) -parser.add_argument('--one_neuron_init', help='whether to start with zero binary mask (one neuron network)', default=0) -parser.add_argument('--given_mask_init', help='whether to start with given binary mask', default=0) -parser.add_argument('--train_partition', - help='Please, enter partition of splitted train', - default=1) -parser.add_argument('--start_from_population', - help='Please, enter the population number to start from. 0 means from scratch', - default=0) -parser.add_argument('--path_to_population', - help='Please, enter the path to population to start from', - default="") - -args = parser.parse_args() - -CONFIG_FILE = args.config -EVOLVE_METRIC = args.evolve_metric -POPULATION_SIZE = args.p_size -GPU_NUMBER = len(args.gpus) -gpus = [int(gpu) for gpu in args.gpus.split(",")] -N_LAYERS = int(args.n_layers) -N_TYPES = int(args.n_types) -ONE_NEURON_INIT = bool(int(args.one_neuron_init)) -GIVEN_MASK_INIT = bool(int(args.given_mask_init)) -TRAIN_PARTITION = int(args.train_partition) -START_FROM_POPULATION = int(args.start_from_population) -PATH_TO_POPULATION = args.path_to_population - - -with open(CONFIG_FILE, "r") as f: - basic_params = json.load(f) - -print("Given basic params: {}\n".format(basic_params)) - -# list of names of considered metrics -CONSIDERED_METRICS = basic_params["train"]["metrics"] -TEST = basic_params["train"]["test_best"] - -if GIVEN_MASK_INIT: - # Embedding -> BiLSTM -> Dense -> Dense -> GlobalMaxPooling -> Dense(#classes) - INITIAL_BINARY_MASK = np.zeros((N_TYPES * N_LAYERS, N_TYPES * N_LAYERS)) - INITIAL_BINARY_MASK[3, 0] = 1 - INITIAL_BINARY_MASK[0, N_TYPES] = 1 -else: - INITIAL_BINARY_MASK = None - -# EVOLUTION starts here! -evolution = NetworkAndParamsEvolution(n_layers=N_LAYERS, n_types=N_TYPES, - population_size=POPULATION_SIZE, - p_crossover=0.2, crossover_power=0.1, - p_mutation=1., mutation_power=0.1, - key_model_to_evolve="to_evolve", - key_basic_layers="basic_layers_params", - seed=42, - start_with_one_neuron=ONE_NEURON_INIT, - train_partition=TRAIN_PARTITION, - initial_binary_mask=INITIAL_BINARY_MASK, - **basic_params) - -# Result table -order = deepcopy(CONSIDERED_METRICS) -order.extend(["params"]) -result_file = Path(basic_params["chainer"]["pipe"][ - evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") -result_table_columns = [] -result_table_dict = {} -for el in order: - if el == "params": - result_table_dict[el] = [] - result_table_columns.extend([el]) - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - result_table_columns.extend([el + "_valid", el + "_test"]) - -result_table_columns.append("params") - -if START_FROM_POPULATION == 0: - result_table = pd.DataFrame(result_table_dict) - result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') - - print("\nIteration #{} starts\n".format(0)) - population = evolution.first_generation() - population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - - iters = 1 -else: - # to define some clue params of evolution - _ = evolution.first_generation() - iters = START_FROM_POPULATION - print("\nIteration #{} starts\n".format(iters)) - model_name = basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] - population = [] - - for i in range(POPULATION_SIZE): - population.append(read_json(Path(PATH_TO_POPULATION).joinpath( - model_name + "_" + str(i)).joinpath("config.json"))) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"] = \ - np.array(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["binary_mask"]) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - str(Path(basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).joinpath( - "population_" + str(START_FROM_POPULATION)).joinpath(model_name + "_" + str(i))) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ - str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]).parent) - - population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - print("Population scores: {}".format(population_scores)) - print("\nIteration #{} was done\n".format(iters)) - iters += 1 - -while True: - print("\nIteration #{} starts\n".format(iters)) - population = evolution.next_generation(population, population_scores, iters) - # print("Considered population: {}\nScoring...\n".format(population)) - population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - print("Population scores: {}".format(population_scores)) - print("\nIteration #{} was done\n".format(iters)) - iters += 1 - diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index d1902d4fb4..e94e6e9003 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -103,17 +103,22 @@ def score_population(population, population_size, result_file): parser.add_argument('--config', help='Please, enter model path to config') parser.add_argument('--evolve_metric', help='Please, choose target metric out of given in your config.train.metrics') + +parser.add_argument('--p_cross', help='Please, enter probability of crossover', type=float, default=0.2) +parser.add_argument('--pow_cross', help='Please, enter crossover power', type=float, default=0.1) +parser.add_argument('--p_mut', help='Please, enter probability of mutation', type=float, default=1.) +parser.add_argument('--pow_mut', help='Please, enter mutation power', type=float, default=0.1) + parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) -parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default=0) +parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default="0") parser.add_argument('--train_partition', - help='Please, enter partition of splitted train', - default=1) + help='Please, enter partition of splitted train', default=1) parser.add_argument('--start_from_population', - help='Please, enter the population number to start from. 0 means from scratch', - default=0) + help='Please, enter the population number to start from. 0 means from scratch', default=0) parser.add_argument('--path_to_population', - help='Please, enter the path to population to start from', - default="") + help='Please, enter the path to population to start from', default="") +parser.add_argument('--elitism_with_weights', + help='Please, enter whether to save elite models with weights or not', default=False) args = parser.parse_args() @@ -125,44 +130,49 @@ def score_population(population, population_size, result_file): TRAIN_PARTITION = int(args.train_partition) START_FROM_POPULATION = int(args.start_from_population) PATH_TO_POPULATION = args.path_to_population +ELITISM_WITH_WEIGHTS = args.elitism_with_weights + +P_CROSSOVER = args.p_cross +POW_CROSSOVER = args.pow_cross +P_MUTATION = args.p_mut +POW_MUTATION = args.pow_mut with open(CONFIG_FILE, "r") as f: basic_params = json.load(f) -print("Given basic params: {}\n".format(basic_params)) - -# list of names of considered metrics -CONSIDERED_METRICS = basic_params["train"]["metrics"] -TEST = basic_params["train"]["test_best"] +print("Given basic params: {}\n".format(json.dumps(basic_params, indent=2))) - -# EVOLUTION starts here! evolution = ParamsEvolution(population_size=POPULATION_SIZE, - p_crossover=0.2, crossover_power=0.1, - p_mutation=1., mutation_power=0.1, + p_crossover=P_CROSSOVER, crossover_power=POW_CROSSOVER, + p_mutation=P_MUTATION, mutation_power=POW_MUTATION, key_model_to_evolve="to_evolve", key_basic_layers="basic_layers_params", seed=42, train_partition=TRAIN_PARTITION, + elitism_with_weights=ELITISM_WITH_WEIGHTS, **basic_params) +CONSIDERED_METRICS = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "metrics"))[0]) +TEST = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "test_best"))[0]) + # Result table order = deepcopy(CONSIDERED_METRICS) -order.extend(["params"]) -result_file = Path(basic_params["chainer"]["pipe"][ - evolution.model_to_evolve_index]["save_path"]).joinpath("result_table.csv") +result_file = Path(evolution.get_value_from_config(evolution.basic_config, + evolution.main_model_path + ["save_path"]) + ).joinpath("result_table.csv") result_table_columns = [] - result_table_dict = {} for el in order: - if el == "params": - result_table_dict[el] = [] - result_table_columns.extend([el]) - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - result_table_columns.extend([el + "_valid", el + "_test"]) + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) +order.extend(["params"]) +result_table_dict["params"] = [] result_table_columns.append("params") if START_FROM_POPULATION == 0: @@ -173,24 +183,24 @@ def score_population(population, population_size, result_file): population = evolution.first_generation() print(population) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - iters = 1 else: - # to define some clue params of evolution - _ = evolution.first_generation() + # _ = evolution.first_generation() iters = START_FROM_POPULATION print("\nIteration #{} starts\n".format(iters)) - model_name = basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["model_name"] - population = [] + population = [] for i in range(POPULATION_SIZE): population.append(read_json(Path(PATH_TO_POPULATION).joinpath( - model_name + "_" + str(i)).joinpath("config.json"))) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - str(Path(basic_params["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]).joinpath( - "population_" + str(START_FROM_POPULATION)).joinpath(model_name + "_" + str(i))) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ - str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]).parent) + "model_" + str(i)).joinpath("config.json"))) + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["save_path"], + str(Path(evolution.get_value_from_config(evolution.basic_config, evolution.main_model_path + ["save_path"]) + ).joinpath("population_" + str(START_FROM_POPULATION)).joinpath("model_" + str(i)))) + + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["load_path"], + str(Path(evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"]).parent))) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] print("Population scores: {}".format(population_scores)) @@ -200,7 +210,6 @@ def score_population(population, population_size, result_file): while True: print("\nIteration #{} starts\n".format(iters)) population = evolution.next_generation(population, population_scores, iters) - # print("Considered population: {}\nScoring...\n".format(population)) population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] print("Population scores: {}".format(population_scores)) print("\nIteration #{} was done\n".format(iters)) diff --git a/deeppavlov/models/evolution/test.py b/deeppavlov/models/evolution/test.py deleted file mode 100644 index 31da975a78..0000000000 --- a/deeppavlov/models/evolution/test.py +++ /dev/null @@ -1,134 +0,0 @@ -import numpy as np -from deeppavlov.core.common.file import read_json -from copy import copy, deepcopy -import json - - -def _find_main_model_path(config, key_model, path=[]): - """ - Find path to the main model in config which paths will be changed - Args: - config: - key_model: - - Returns: - path in config -- list of keys (strings and integers) - """ - config_pointer = config - # add_paths = [] - - if type(config_pointer) is dict and key_model in config_pointer.keys(): - # main model is an element of chainer.pipe list - # main model is a dictionary and has key key_main_model - yield path - else: - if type(config_pointer) is dict: - for key in list(config_pointer.keys()): - for path_ in _find_main_model_path(config_pointer[key], key_model, path + [key]): - yield path_ - elif type(config_pointer) is list: - for i in range(len(config_pointer)): - for path_ in _find_main_model_path(config_pointer[i], key_model, path + [i]): - yield path_ - - -def _insert_value_or_dict_into_config(config, path, value): - config_copy = deepcopy(config) - config_pointer = config_copy - for el in path[:-1]: - if type(config_pointer) is dict: - config_pointer = config_pointer.setdefault(el, {}) - elif type(config_pointer) is list: - config_pointer = config_pointer[el] - else: - pass - config_pointer[path[-1]] = value - return config_copy - - -def _get_value_from_config(config, path): - config_copy = deepcopy(config) - config_pointer = config_copy - for el in path[:-1]: - if type(config_pointer) is dict: - config_pointer = config_pointer.setdefault(el, {}) - elif type(config_pointer) is list: - config_pointer = config_pointer[el] - else: - pass - return config_pointer[path[-1]] - - -def initialize_params_in_config(basic_config, paths): - config = deepcopy(basic_config) - - for path_ in paths: - param_name = path_[-1] - value = _get_value_from_config(basic_config, path_) - if type(value) is dict: - if value.get("evolve_choice"): - config = _insert_value_or_dict_into_config(config, - path_, - sample_params( - **{param_name: list(value["values"])})[param_name]) - elif value.get("evolve_range"): - config = _insert_value_or_dict_into_config(config, - path_, - sample_params( - **{param_name: deepcopy(value)})[param_name]) - elif value.get("evolve_bool"): - config = _insert_value_or_dict_into_config(config, - path_, - sample_params( - **{param_name: deepcopy(value)})[param_name]) - - return config - - -def sample_params(**params): - if not params: - return {} - else: - params_copy = deepcopy(params) - params_sample = dict() - for param, param_val in params_copy.items(): - if isinstance(param_val, list): - params_sample[param] = np.random.choice(param_val) - elif isinstance(param_val, dict): - if 'evolve_bool' in param_val and param_val['evolve_bool']: - sample = bool(np.random.choice([True, False])) - elif 'evolve_range' in param_val: - sample = _sample_from_ranges(param_val) - params_sample[param] = sample - else: - params_sample[param] = params_copy[param] - return params_sample - - -def _sample_from_ranges(opts): - from_ = opts['evolve_range'][0] - to_ = opts['evolve_range'][1] - if opts.get('scale', None) == 'log': - sample = _sample_log(from_, to_) - else: - sample = np.random.uniform(from_, to_) - if opts.get('discrete', False): - sample = int(np.round(sample)) - return sample - - -def _sample_log(from_, to_): - sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) - return float(sample) - - -config = read_json("/home/dilyara/Documents/GitHub/deeppavlov_evolution/deeppavlov/configs/evolution/intents_snips.json") -paths = list(_find_main_model_path(config, "evolve_range")) - -print(paths) - -for t in ["evolve_range", "evolve_choice", "evolve_bool"]: - paths = list(_find_main_model_path(config, t)) - config = initialize_params_in_config(config, paths) - -print(json.dumps(config, indent=2)) diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py index 45e2686478..1f9a61d6bc 100644 --- a/deeppavlov/models/evolution/train_phenotype.py +++ b/deeppavlov/models/evolution/train_phenotype.py @@ -19,7 +19,6 @@ from deeppavlov.core.commands.train import train_evaluate_model_from_config from deeppavlov.core.common.file import read_json, save_json -from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe config_path = sys.argv[1] diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py index 15319b3f4d..ccdf47104c 100644 --- a/deeppavlov/models/evolution/utils.py +++ b/deeppavlov/models/evolution/utils.py @@ -237,15 +237,3 @@ def expand_tile_batch_size(memory, context): expanded = K.reshape(context, shape=((1,) + K.int_shape(context))) return K.tile(expanded, repetitions) - -def find_index_of_dict_with_key_in_pipe(pipe, key): - for element_id, element in enumerate(pipe): - if check_whether_key_in_dict(element, key): - return element_id - - -def check_whether_key_in_dict(model, key): - if key in model.keys(): - return True - else: - return False From 07cd2c6985fa09ae5481e4be3c7dfd6cf14dd66c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:22:43 +0300 Subject: [PATCH 457/616] chore: train phenotype --- .../models/evolution/run_param_evolution.py | 33 ++++++--------- .../models/evolution/train_phenotype.py | 40 +------------------ 2 files changed, 14 insertions(+), 59 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index e94e6e9003..e1f8f01b14 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -4,7 +4,7 @@ from pathlib import Path from subprocess import Popen, PIPE import pandas as pd -from copy import deepcopy, copy +from copy import deepcopy from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution from deeppavlov.core.common.file import save_json, read_json @@ -22,20 +22,16 @@ def score_population(population, population_size, result_file): for j in range(len(gpus)): i = k * len(gpus) + j if i < POPULATION_SIZE: - save_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) - load_path = Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"]) - - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"] = \ - str(save_path.joinpath("model")) - population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["load_path"] = \ - str(load_path.joinpath("model")) - - print(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index]["save_path"]) - try: - save_path.mkdir(parents=True) - except FileExistsError: - pass - + save_path = Path(evolution.get_value_from_config(population[i], + evolution.main_model_path + ["save_path"])) + load_path = Path(evolution.get_value_from_config(population[i], + evolution.main_model_path + ["load_path"])) + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["save_path"], str(save_path.joinpath("model"))) + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["load_path"], str(load_path.joinpath("model"))) + + save_path.mkdir(parents=True, exist_ok=True) f_name = save_path.joinpath("config.json") save_json(population[i], f_name) @@ -53,8 +49,8 @@ def score_population(population, population_size, result_file): for i in range(population_size): try: - val_results = np.loadtxt(fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("valid_results.txt"))) + val_results = np.loadtxt(fname=str(Path(evolution.get_value_from_config( + population[i], evolution.main_model_path + ["save_path"])).parent.joinpath("valid_results.txt"))) except OSError or FileNotFoundError: val_results = [None for m in CONSIDERED_METRICS] for m_id, m in enumerate(CONSIDERED_METRICS): @@ -90,12 +86,9 @@ def score_population(population, population_size, result_file): result_table_dict[m + "_test"].append(0.) result_table_dict[order[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) - result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - for m_id, m in enumerate(CONSIDERED_METRICS): population_metrics[m].append(val_results[m_id]) - return population_metrics diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py index 1f9a61d6bc..828f798d1c 100644 --- a/deeppavlov/models/evolution/train_phenotype.py +++ b/deeppavlov/models/evolution/train_phenotype.py @@ -13,49 +13,11 @@ See the License for the specific language governing permissions and limitations under the License. """ -import numpy as np import sys -from pathlib import Path from deeppavlov.core.commands.train import train_evaluate_model_from_config -from deeppavlov.core.common.file import read_json, save_json config_path = sys.argv[1] - print("TRAIN PHENOTYPE") -reports = train_evaluate_model_from_config(config_path) -print(reports) - -if len(reports) == 2: - # valid and test reports - val_metrics = dict(reports[0]["valid"]["metrics"]) - val_metrics_values = np.array(list(val_metrics.values())).reshape(-1) - - config = read_json(config_path) - model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], - key="to_evolve") - np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ - "save_path"]).parent.joinpath("valid_results.txt")), - X=val_metrics_values) - - test_metrics = dict(reports[1]["test"]["metrics"]) - test_metrics_values = np.array(list(test_metrics.values())).reshape(-1) - - config = read_json(config_path) - model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], - key="to_evolve") - np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ - "save_path"]).parent.joinpath("test_results.txt")), - X=test_metrics_values) -else: - # valid report - val_metrics = dict(reports[0]["valid"]["metrics"]) - val_metrics_values = np.array(list(val_metrics.values())).reshape(-1) - - config = read_json(config_path) - model_index = find_index_of_dict_with_key_in_pipe(pipe=config["chainer"]["pipe"], - key="to_evolve") - np.savetxt(fname=str(Path(config["chainer"]["pipe"][model_index][ - "save_path"]).parent.joinpath("valid_results.txt")), - X=val_metrics_values) +train_evaluate_model_from_config(config_path) From fbef140cc738d4004355f9068b68b71467163f75 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:37:58 +0300 Subject: [PATCH 458/616] feat: run param evolution --- .../models/evolution/run_param_evolution.py | 47 ++++++++++--------- 1 file changed, 25 insertions(+), 22 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index e1f8f01b14..8f20734e61 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -48,28 +48,31 @@ def score_population(population, population_size, result_file): proc.wait() for i in range(population_size): - try: - val_results = np.loadtxt(fname=str(Path(evolution.get_value_from_config( - population[i], evolution.main_model_path + ["save_path"])).parent.joinpath("valid_results.txt"))) - except OSError or FileNotFoundError: - val_results = [None for m in CONSIDERED_METRICS] - for m_id, m in enumerate(CONSIDERED_METRICS): + with open(str(Path(evolution.get_value_from_config( + population[i], + evolution.main_model_path + ["save_path"])).parent.joinpath("out.txt")), "r") as fout: + reports_data = fout.read().splitlines()[-2:] + reports = [] + for i in range(2): + try: + reports.append(json.loads(reports_data[i])) + except: + pass + if len(reports) == 2 and "valid" in reports[0].keys() and "test" in reports[1].keys(): + val_results = reports[0] + test_results = reports[1] + elif len(reports) == 1 and "valid" in reports[0].keys(): + val_results = reports[0] + else: + val_results = {} + test_results = {} + for m in CONSIDERED_METRICS: if "loss" in m: - val_results[m_id] = 1e6 + val_results[m] = 1e6 + test_results[m] = 1e6 else: - val_results[m_id] = 0. - if TEST: - try: - test_results = np.loadtxt( - fname=str(Path(population[i]["chainer"]["pipe"][evolution.model_to_evolve_index][ - "save_path"]).parent.joinpath("test_results.txt"))) - except OSError or FileNotFoundError: - test_results = [None for m in CONSIDERED_METRICS] - for m_id, m in enumerate(CONSIDERED_METRICS): - if "loss" in m: - test_results[m_id] = 1e6 - else: - test_results[m_id] = 0. + val_results[m] = 0. + test_results[m] = 0. result_table_dict = {} for el in order: @@ -79,9 +82,9 @@ def score_population(population, population_size, result_file): result_table_dict[el + "_valid"] = [] result_table_dict[el + "_test"] = [] for m_id, m in enumerate(CONSIDERED_METRICS): - result_table_dict[m + "_valid"].append(val_results[m_id]) + result_table_dict[m + "_valid"].append(val_results[m]) if TEST: - result_table_dict[m + "_test"].append(test_results[m_id]) + result_table_dict[m + "_test"].append(test_results[m]) else: result_table_dict[m + "_test"].append(0.) result_table_dict[order[-1]] = [population[i]] From e4f47097ba4c3819da97fb8dfacbb95f4ecdd8be Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:39:01 +0300 Subject: [PATCH 459/616] fix: local config --- .../configs/evolution/intents_snli_local.json | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/evolution/intents_snli_local.json b/deeppavlov/configs/evolution/intents_snli_local.json index 3a2fc819a1..2ef1e5725d 100644 --- a/deeppavlov/configs/evolution/intents_snli_local.json +++ b/deeppavlov/configs/evolution/intents_snli_local.json @@ -123,8 +123,20 @@ ] }, "train": { - "epochs": 100, - "batch_size": 64, + "epochs": { + "range": [ + 50, + 500 + ], + "discrete": true + }, + "batch_size": { + "range": [ + 50, + 500 + ], + "discrete": true + }, "metrics": [ "classification_accuracy", "classification_f1", From dd317e155b1e2ae0d63d6e534b3512bf5a55a4f7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:42:56 +0300 Subject: [PATCH 460/616] fix: local config --- ...ts_snli_local.json => intents_snips_local.json} | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) rename deeppavlov/configs/evolution/{intents_snli_local.json => intents_snips_local.json} (88%) diff --git a/deeppavlov/configs/evolution/intents_snli_local.json b/deeppavlov/configs/evolution/intents_snips_local.json similarity index 88% rename from deeppavlov/configs/evolution/intents_snli_local.json rename to deeppavlov/configs/evolution/intents_snips_local.json index 2ef1e5725d..7a82708cb9 100644 --- a/deeppavlov/configs/evolution/intents_snli_local.json +++ b/deeppavlov/configs/evolution/intents_snips_local.json @@ -2,9 +2,9 @@ "dataset_reader": { "name": "basic_classification_reader", "x": "text", - "y": "gold_label", - "data_path": "/home/dilyara/data/data_files/SNLI/one_input/parts", - "train": "train_0.csv", + "y": "intents", + "data_path": "/home/dilyara/data/data_files/snips/snips_dataset", + "train": "train.csv", "valid": "valid.csv", "test": "test.csv" }, @@ -33,8 +33,8 @@ "y" ], "level": "token", - "save_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict", - "load_path": "/home/dilyara/data/data_files/SNLI/one_input/snli_classes.dict" + "save_path": "/home/dilyara/data/data_files/snips/snips_dataset/classes.dict", + "load_path": "/home/dilyara/data/data_files/snips/snips_dataset/classes.dict" }, { "in": [ @@ -70,8 +70,8 @@ ], "main": true, "name": "intent_model", - "save_path": "/home/dilyara/data/models/evolution_data/snli_classification/param_evolution_0", - "load_path": "/home/dilyara/data/models/evolution_data/snli_classification/param_evolution_0", + "save_path": "/home/dilyara/data/models/evolution_data/snips_classification/param_evolution_0", + "load_path": "/home/dilyara/data/models/evolution_data/snips_classification/param_evolution_0", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, From 1e0ba30e1d3d7f80aba0b3e205695628c75d5a91 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:44:37 +0300 Subject: [PATCH 461/616] fix: elitism param type --- deeppavlov/models/evolution/run_param_evolution.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 8f20734e61..305d32224c 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -114,7 +114,7 @@ def score_population(population, population_size, result_file): parser.add_argument('--path_to_population', help='Please, enter the path to population to start from', default="") parser.add_argument('--elitism_with_weights', - help='Please, enter whether to save elite models with weights or not', default=False) + help='Please, enter whether to save elite models with weights or not', default=0) args = parser.parse_args() @@ -126,7 +126,7 @@ def score_population(population, population_size, result_file): TRAIN_PARTITION = int(args.train_partition) START_FROM_POPULATION = int(args.start_from_population) PATH_TO_POPULATION = args.path_to_population -ELITISM_WITH_WEIGHTS = args.elitism_with_weights +ELITISM_WITH_WEIGHTS = int(args.elitism_with_weights) P_CROSSOVER = args.p_cross POW_CROSSOVER = args.pow_cross From f47efeb2929a3e6a0dcc73153ada675ab9159c26 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:50:30 +0300 Subject: [PATCH 462/616] fix: registered param evolution model --- deeppavlov/__init__.py | 3 +-- deeppavlov/models/evolution/evolution_param_generator.py | 3 ++- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index 3a2523b709..34ed9984a2 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -49,8 +49,7 @@ import deeppavlov.models.seq2seq_go_bot.network import deeppavlov.models.seq2seq_go_bot.kb import deeppavlov.models.classifiers.intents.intent_model -import deeppavlov.models.evolution.evolution_intent_model -import deeppavlov.models.evolution.evolution_many_inputs_model +import deeppavlov.models.evolution.evolution_param_generator import deeppavlov.models.commutators.random_commutator import deeppavlov.models.embedders.fasttext_embedder import deeppavlov.models.embedders.dict_embedder diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 30a482403c..572a0531d2 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -3,13 +3,14 @@ from pathlib import Path import json +from deeppavlov.core.common.registry import register from deeppavlov.core.common.file import read_json from deeppavlov.core.common.log import get_logger log = get_logger(__name__) - +@register('params_evolution') class ParamsEvolution: """ Class performs full evolutionary process (task scores -> max): From 40ed0f60fe5c6774889a06f0d2c690644a19dd86 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 18 Jun 2018 18:53:24 +0300 Subject: [PATCH 463/616] fix: considered metrics --- deeppavlov/models/evolution/run_param_evolution.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 305d32224c..2933f21c11 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -148,9 +148,9 @@ def score_population(population, population_size, result_file): elitism_with_weights=ELITISM_WITH_WEIGHTS, **basic_params) -CONSIDERED_METRICS = evolution.get_value_from_config(evolution.basic_config, - list(evolution.find_model_path( - evolution.basic_config, "metrics"))[0]) +CONSIDERED_METRICS = list(evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "metrics"))[0]).values()) TEST = evolution.get_value_from_config(evolution.basic_config, list(evolution.find_model_path( evolution.basic_config, "test_best"))[0]) From 98c7bceff0688beb82feae55d895b0d3425aeee5 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 12:44:42 +0300 Subject: [PATCH 464/616] fix: paths --- .../evolution/intents_snips_local.json | 14 +++--- .../evolution/evolution_param_generator.py | 45 ++++++++++--------- .../models/evolution/run_param_evolution.py | 16 +++---- 3 files changed, 39 insertions(+), 36 deletions(-) diff --git a/deeppavlov/configs/evolution/intents_snips_local.json b/deeppavlov/configs/evolution/intents_snips_local.json index 7a82708cb9..47ebd2a995 100644 --- a/deeppavlov/configs/evolution/intents_snips_local.json +++ b/deeppavlov/configs/evolution/intents_snips_local.json @@ -11,7 +11,7 @@ "dataset_iterator": { "name": "basic_classification_iterator", "seed": { - "range": [ + "evolve_range": [ 50, 500 ], @@ -79,7 +79,7 @@ 3 ], "filters_cnn": { - "range": [ + "evolve_range": [ 50, 500 ], @@ -88,7 +88,7 @@ "confident_threshold": 0.5, "optimizer": "Adam", "lear_rate": { - "range": [ + "evolve_range": [ 0.0001, 0.1 ] @@ -100,13 +100,13 @@ "coef_reg_cnn": 1e-4, "coef_reg_den": 1e-4, "dropout_rate": { - "range": [ + "evolve_range": [ 0.1, 0.9 ] }, "dense_size": { - "range": [ + "evolve_range": [ 50, 500 ], @@ -124,14 +124,14 @@ }, "train": { "epochs": { - "range": [ + "evolve_range": [ 50, 500 ], "discrete": true }, "batch_size": { - "range": [ + "evolve_range": [ 50, 500 ], diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 572a0531d2..34ee932847 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -51,7 +51,7 @@ def __init__(self, self.main_model_path = list(self.find_model_path(self.basic_config, key_main_model))[0] Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, exist_ok=True) - self.print_dict(self.basic_config, string="Basic config:") + # self.print_dict(self.basic_config, string="Basic config:") log.info("Main model path in config: {}".format(self.main_model_path)) self.population_size = population_size @@ -71,6 +71,7 @@ def __init__(self, self.n_evolving_params = len(self.paths_to_evolving_params) self.evolution_model_id = 0 + self.eps = 1e-6 if seed is None: pass @@ -176,12 +177,10 @@ def first_generation(self, iteration=0): for i in range(self.population_size): population.append(self.initialize_params_in_config(self.basic_config, self.paths_to_evolving_params)) for which_path in ["save_path", "load_path"]: - population[-1] = self.insert_value_or_dict_into_config(population[-1], - self.main_model_path + [which_path], - str(Path( - self.basic_config["save_path"]).joinpath( - "population_" + str(iteration)).joinpath( - "model_" + str(i)))) + population[-1] = self.insert_value_or_dict_into_config( + population[-1], self.main_model_path + [which_path], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) population[-1]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 @@ -229,28 +228,28 @@ def next_generation(self, generation, scores, iteration): except: pass + save_path_prev = self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) + load_path_prev = self.get_value_from_config(next_population[i], self.main_model_path + ["load_path"]) + if self.elitism_with_weights: # if elite models are saved with weights next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self.get_value_from_config(next_population[i], - self.main_model_path + ["load_path"]), + self.main_model_path + ["load_path"], str(Path(self.get_value_from_config(next_population[i], - self.main_model_path + ["save_path"])).parent)) + self.main_model_path + ["save_path"])).parent)) else: # if elite models are saved only as configurations and trained again next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self.get_value_from_config(next_population[i], - self.main_model_path + ["load_path"]), - str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["load_path"]) + self.main_model_path + ["load_path"], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["load_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self.get_value_from_config(next_population[i], - self.main_model_path + ["save_path"]), - str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) + self.main_model_path + ["save_path"], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) for i in range(self.n_saved_best_pretrained, self.population_size): @@ -262,9 +261,8 @@ def next_generation(self, generation, scores, iteration): for which_path in ["save_path", "load_path"]: next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self.get_value_from_config(next_population[i], - self.main_model_path + [which_path]), - str(Path(self.get_value_from_config(next_population[i], self.main_model_path + [which_path]) + self.main_model_path + [which_path], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) next_population[i]["evolution_model_id"] = self.evolution_model_id @@ -321,6 +319,9 @@ def crossover(self, population, scores): """ offsprings = [] scores = np.array(scores, dtype='float') + if np.sum(scores) < self.eps: + scores = [self.eps for _ in range(self.population_size)] + probas_to_be_parent = scores / np.sum(scores) intervals = np.array([np.sum(probas_to_be_parent[:i]) for i in range(self.population_size)]) @@ -374,17 +375,19 @@ def mutation(self, population): for individuum in population: mutated_individuum = deepcopy(individuum) for path_ in self.paths_to_evolving_params: + param_name = path_[-1] + param_value = self.get_value_from_config(individuum, path_) mutated_individuum = self.insert_value_or_dict_into_config( mutated_individuum, path_, - self.mutation_of_param(path_, self.get_value_from_config(individuum, path_))) + self.mutation_of_param(path_, param_value)) mutated.append(mutated_individuum) return mutated def mutation_of_param(self, param_path, param_value): if self.decision(self.p_mutation): - basic_value = self.get_value_from_config(self.basic_config, param_path) param_name = param_path[-1] + basic_value = self.get_value_from_config(self.basic_config, param_path) if type(basic_value) is dict: if basic_value.get('discrete', False): val = round(param_value + diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 2933f21c11..4af5c3d9e5 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -90,8 +90,8 @@ def score_population(population, population_size, result_file): result_table_dict[order[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - for m_id, m in enumerate(CONSIDERED_METRICS): - population_metrics[m].append(val_results[m_id]) + for m in CONSIDERED_METRICS: + population_metrics[m].append(val_results[m]) return population_metrics @@ -141,19 +141,19 @@ def score_population(population, population_size, result_file): evolution = ParamsEvolution(population_size=POPULATION_SIZE, p_crossover=P_CROSSOVER, crossover_power=POW_CROSSOVER, p_mutation=P_MUTATION, mutation_power=POW_MUTATION, - key_model_to_evolve="to_evolve", - key_basic_layers="basic_layers_params", + key_main_model="main", seed=42, train_partition=TRAIN_PARTITION, elitism_with_weights=ELITISM_WITH_WEIGHTS, **basic_params) -CONSIDERED_METRICS = list(evolution.get_value_from_config(evolution.basic_config, - list(evolution.find_model_path( - evolution.basic_config, "metrics"))[0]).values()) +CONSIDERED_METRICS = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "metrics"))[0] + ["metrics"]) +print(CONSIDERED_METRICS) TEST = evolution.get_value_from_config(evolution.basic_config, list(evolution.find_model_path( - evolution.basic_config, "test_best"))[0]) + evolution.basic_config, "test_best"))[0] + ["test_best"]) # Result table order = deepcopy(CONSIDERED_METRICS) From 3c314d60c7b5e521e62eba89428e504b3d393c87 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 14:04:17 +0300 Subject: [PATCH 465/616] fix: metrics --- .../configs/evolution/intents_snips_local.json | 13 +++++-------- .../models/evolution/evolution_param_generator.py | 3 --- deeppavlov/models/evolution/run_param_evolution.py | 8 ++++++-- 3 files changed, 11 insertions(+), 13 deletions(-) diff --git a/deeppavlov/configs/evolution/intents_snips_local.json b/deeppavlov/configs/evolution/intents_snips_local.json index 47ebd2a995..baf97ee142 100644 --- a/deeppavlov/configs/evolution/intents_snips_local.json +++ b/deeppavlov/configs/evolution/intents_snips_local.json @@ -114,7 +114,10 @@ }, "model_name": "cnn_model", "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" + "tokenizer": "#my_tokenizer", + "check_bool": { + "evolve_bool": true + } } ], "out": [ @@ -123,13 +126,7 @@ ] }, "train": { - "epochs": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - }, + "epochs": 1, "batch_size": { "evolve_range": [ 50, diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 34ee932847..e1131b465c 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -228,9 +228,6 @@ def next_generation(self, generation, scores, iteration): except: pass - save_path_prev = self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) - load_path_prev = self.get_value_from_config(next_population[i], self.main_model_path + ["load_path"]) - if self.elitism_with_weights: # if elite models are saved with weights next_population[i] = self.insert_value_or_dict_into_config( diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 4af5c3d9e5..28e6ce41f5 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -82,9 +82,13 @@ def score_population(population, population_size, result_file): result_table_dict[el + "_valid"] = [] result_table_dict[el + "_test"] = [] for m_id, m in enumerate(CONSIDERED_METRICS): - result_table_dict[m + "_valid"].append(val_results[m]) + val_metrics_path = evolution.find_model_path(val_results, m) + val_m = evolution.get_value_from_config(val_results, val_metrics_path + [m]) + result_table_dict[m + "_valid"].append(val_m) if TEST: - result_table_dict[m + "_test"].append(test_results[m]) + test_metrics_path = evolution.find_model_path(test_results, m) + test_m = evolution.get_value_from_config(test_results, test_metrics_path + [m]) + result_table_dict[m + "_test"].append(test_m) else: result_table_dict[m + "_test"].append(0.) result_table_dict[order[-1]] = [population[i]] From ed8ca1b8ed268741841c7ef9141f050c8653c7d2 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 15:54:57 +0300 Subject: [PATCH 466/616] fix: metrics --- deeppavlov/models/evolution/run_param_evolution.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 28e6ce41f5..557a9ce2a9 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -82,11 +82,12 @@ def score_population(population, population_size, result_file): result_table_dict[el + "_valid"] = [] result_table_dict[el + "_test"] = [] for m_id, m in enumerate(CONSIDERED_METRICS): - val_metrics_path = evolution.find_model_path(val_results, m) + val_metrics_path = list(evolution.find_model_path(val_results, m))[0] val_m = evolution.get_value_from_config(val_results, val_metrics_path + [m]) + population_metrics[m].append(val_m) result_table_dict[m + "_valid"].append(val_m) if TEST: - test_metrics_path = evolution.find_model_path(test_results, m) + test_metrics_path = list(evolution.find_model_path(test_results, m))[0] test_m = evolution.get_value_from_config(test_results, test_metrics_path + [m]) result_table_dict[m + "_test"].append(test_m) else: @@ -94,8 +95,7 @@ def score_population(population, population_size, result_file): result_table_dict[order[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - for m in CONSIDERED_METRICS: - population_metrics[m].append(val_results[m]) + return population_metrics From 789dc0e97315a0f4434af5c7f9e1f0f2f2c8fc6d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 16:03:05 +0300 Subject: [PATCH 467/616] feat: param evolution works fine --- .../evolution/intents_snips_local.json | 8 +++---- deeppavlov/models/evolution/test.py | 22 +++++++++++++++++++ 2 files changed, 26 insertions(+), 4 deletions(-) create mode 100644 deeppavlov/models/evolution/test.py diff --git a/deeppavlov/configs/evolution/intents_snips_local.json b/deeppavlov/configs/evolution/intents_snips_local.json index baf97ee142..3fcb331a05 100644 --- a/deeppavlov/configs/evolution/intents_snips_local.json +++ b/deeppavlov/configs/evolution/intents_snips_local.json @@ -80,8 +80,8 @@ ], "filters_cnn": { "evolve_range": [ - 50, - 500 + 5, + 50 ], "discrete": true }, @@ -107,8 +107,8 @@ }, "dense_size": { "evolve_range": [ - 50, - 500 + 5, + 50 ], "discrete": true }, diff --git a/deeppavlov/models/evolution/test.py b/deeppavlov/models/evolution/test.py new file mode 100644 index 0000000000..793b463c5e --- /dev/null +++ b/deeppavlov/models/evolution/test.py @@ -0,0 +1,22 @@ +from copy import deepcopy +import numpy as np +import json + +from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution + + + +CONFIG_FILE = "/home/dilyara/Documents/GitHub/deeppavlov_evolution/deeppavlov/configs/evolution/intents_snips_local.json" + +with open(CONFIG_FILE, "r") as f: + basic_params = json.load(f) + +# print("Given basic params: {}\n".format(json.dumps(basic_params, indent=2))) + +evolution = ParamsEvolution(population_size=10, + **basic_params) + +paths = list(evolution.find_model_path(basic_params, "evolve_range")) +print(paths) + +print(evolution.get_value_from_config(basic_params, paths[0])) From 1e844a3afada099e11c8182338197d73c873e964 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 16:30:11 +0300 Subject: [PATCH 468/616] chore: merge dev --- deeppavlov/models/evolution/run_param_evolution.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py index 557a9ce2a9..7783de9317 100644 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ b/deeppavlov/models/evolution/run_param_evolution.py @@ -35,6 +35,8 @@ def score_population(population, population_size, result_file): f_name = save_path.joinpath("config.json") save_json(population[i], f_name) + # __file__ + procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], str(f_name), From 2aceccb5e1ad17f30bdf2b102b9843b37ba34d38 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 17:30:22 +0300 Subject: [PATCH 469/616] feat: to evolve --- deeppavlov/evolve.py | 256 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 256 insertions(+) create mode 100644 deeppavlov/evolve.py diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py new file mode 100644 index 0000000000..dc40f48e11 --- /dev/null +++ b/deeppavlov/evolve.py @@ -0,0 +1,256 @@ +""" +Copyright 2017 Neural Networks and Deep Learning lab, MIPT + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import argparse +from pathlib import Path +import sys +import json +from copy import deepcopy +from subprocess import Popen, PIPE +import pandas as pd + +p = (Path(__file__) / ".." / "..").resolve() +sys.path.append(str(p)) + +from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution +from deeppavlov.core.common.file import read_json, save_json +from deeppavlov.core.common.log import get_logger + + + +log = get_logger(__name__) + +parser = argparse.ArgumentParser() + +parser.add_argument("config_path", help="path to a pipeline json config", type=str) +parser.add_argument('--evolve_metric', help='target metric out of given in your config.train.metrics') +parser.add_argument('--p_cross', help='probability of crossover', type=float, default=0.2) +parser.add_argument('--pow_cross', help='crossover power', type=float, default=0.1) +parser.add_argument('--p_mut', help='probability of mutation', type=float, default=1.) +parser.add_argument('--pow_mut', help='mutation power', type=float, default=0.1) + +parser.add_argument('--p_size', help='population size', type=int, default=10) +parser.add_argument('--gpus', help='visible GPUs divided by comma <<,>>', default="0") +parser.add_argument('--train_partition', + help='partition of splitted train file', default=1) +parser.add_argument('--start_from_population', + help='population number to start from. 0 means from scratch', default=0) +parser.add_argument('--path_to_population', + help='path to population to start from', default="") +parser.add_argument('--elitism_with_weights', + help='whether to save elite models with weights or without', default=0) + + +def find_config(pipeline_config_path: str): + if not Path(pipeline_config_path).is_file(): + configs = [c for c in Path(__file__).parent.glob(f'configs/**/{pipeline_config_path}.json') + if str(c.with_suffix('')).endswith(pipeline_config_path)] # a simple way to not allow * and ? + if configs: + log.info(f"Interpreting '{pipeline_config_path}' as '{configs[0]}'") + pipeline_config_path = str(configs[0]) + return pipeline_config_path + + +def main(): + args = parser.parse_args() + + pipeline_config_path = find_config(args.config_path) + evolve_metric = args.evolve_metric + population_size = args.p_size + gpus = [int(gpu) for gpu in args.gpus.split(",")] + train_partition = int(args.train_partition) + start_from_population = int(args.start_from_population) + path_to_population = args.path_to_population + elitism_with_weights = int(args.elitism_with_weights) + + p_crossover = args.p_cross + pow_crossover = args.pow_cross + p_mutation = args.p_mut + pow_mutation = args.pow_mut + + basic_params = read_json(pipeline_config_path) + log.info("Given basic params: {}\n".format(json.dumps(basic_params, indent=2))) + + evolution = ParamsEvolution(population_size=population_size, + p_crossover=p_crossover, crossover_power=pow_crossover, + p_mutation=p_mutation, mutation_power=pow_mutation, + key_main_model="main", + seed=42, + train_partition=train_partition, + elitism_with_weights=elitism_with_weights, + **basic_params) + + considered_metrics = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "metrics"))[0] + ["metrics"]) + + # Result table + order = deepcopy(considered_metrics) + result_file = Path(evolution.get_value_from_config(evolution.basic_config, + evolution.main_model_path + ["save_path"]) + ).joinpath("result_table.csv") + result_table_columns = [] + result_table_dict = {} + for el in order: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + result_table_columns.extend([el + "_valid", el + "_test"]) + + order.extend(["params"]) + result_table_dict["params"] = [] + result_table_columns.append("params") + + if start_from_population == 0: + result_table = pd.DataFrame(result_table_dict) + result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') + + log.info("\nIteration #{} starts\n".format(0)) + population = evolution.first_generation() + log.info(population) + population_scores = score_population(population, population_size, result_file, considered_metrics, + evolution, order, gpus, result_table_columns)[evolve_metric] + iters = 1 + else: + # _ = evolution.first_generation() + iters = start_from_population + log.info("\nIteration #{} starts\n".format(iters)) + + population = [] + for i in range(population_size): + population.append(read_json(Path(path_to_population).joinpath( + "model_" + str(i)).joinpath("config.json"))) + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["save_path"], + str(Path( + evolution.get_value_from_config(evolution.basic_config, evolution.main_model_path + ["save_path"]) + ).joinpath("population_" + str(start_from_population)).joinpath("model_" + str(i)))) + + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["load_path"], + str(Path( + evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"]).parent))) + + population_scores = score_population(population, population_size, result_file, considered_metrics, + evolution, order, gpus, result_table_columns)[evolve_metric] + log.info("Population scores: {}".format(population_scores)) + log.info("\nIteration #{} was done\n".format(iters)) + iters += 1 + + while True: + log.info("\nIteration #{} starts\n".format(iters)) + population = evolution.next_generation(population, population_scores, iters) + population_scores = score_population(population, population_size, result_file, considered_metrics, + evolution, order, gpus, result_table_columns)[evolve_metric] + log.info("Population scores: {}".format(population_scores)) + log.info("\nIteration #{} was done\n".format(iters)) + iters += 1 + + +def score_population(population, population_size, result_file, considered_metrics, + evolution, order, gpus, result_table_columns): + test_best = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "test_best"))[0] + ["test_best"]) + population_metrics = {} + for m in considered_metrics: + population_metrics[m] = [] + + for k in range(population_size // len(gpus) + 1): + procs = [] + for j in range(len(gpus)): + i = k * len(gpus) + j + if i < population_size: + save_path = Path(evolution.get_value_from_config(population[i], + evolution.main_model_path + ["save_path"])) + load_path = Path(evolution.get_value_from_config(population[i], + evolution.main_model_path + ["load_path"])) + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["save_path"], str(save_path.joinpath("model"))) + population[i] = evolution.insert_value_or_dict_into_config( + population[i], evolution.main_model_path + ["load_path"], str(load_path.joinpath("model"))) + + save_path.mkdir(parents=True, exist_ok=True) + f_name = save_path.joinpath("config.json") + save_json(population[i], f_name) + + # __file__ + + procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" + " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], + str(f_name), + str(save_path), + str(save_path) + ), + shell=True, stdout=PIPE, stderr=PIPE)) + for j, proc in enumerate(procs): + i = k * len(gpus) + j + log.info(f'wait on {i}th proc') + proc.wait() + + for i in range(population_size): + with open(str(Path(evolution.get_value_from_config( + population[i], + evolution.main_model_path + ["save_path"])).parent.joinpath("out.txt")), "r") as fout: + reports_data = fout.read().splitlines()[-2:] + reports = [] + for i in range(2): + try: + reports.append(json.loads(reports_data[i])) + except: + pass + if len(reports) == 2 and "valid" in reports[0].keys() and "test" in reports[1].keys(): + val_results = reports[0] + test_results = reports[1] + elif len(reports) == 1 and "valid" in reports[0].keys(): + val_results = reports[0] + else: + val_results = {} + test_results = {} + for m in considered_metrics: + if "loss" in m: + val_results[m] = 1e6 + test_results[m] = 1e6 + else: + val_results[m] = 0. + test_results[m] = 0. + + result_table_dict = {} + for el in order: + if el == "params": + result_table_dict[el] = [] + else: + result_table_dict[el + "_valid"] = [] + result_table_dict[el + "_test"] = [] + for m_id, m in enumerate(considered_metrics): + val_metrics_path = list(evolution.find_model_path(val_results, m))[0] + val_m = evolution.get_value_from_config(val_results, val_metrics_path + [m]) + population_metrics[m].append(val_m) + result_table_dict[m + "_valid"].append(val_m) + if test_best: + test_metrics_path = list(evolution.find_model_path(test_results, m))[0] + test_m = evolution.get_value_from_config(test_results, test_metrics_path + [m]) + result_table_dict[m + "_test"].append(test_m) + else: + result_table_dict[m + "_test"].append(0.) + result_table_dict[order[-1]] = [population[i]] + result_table = pd.DataFrame(result_table_dict) + result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) + + return population_metrics + + +if __name__ == "__main__": + main() From ad168d9700247870fb0cdfc8c60e5143ff97e8b7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 17:48:31 +0300 Subject: [PATCH 470/616] feat: to evolve --- deeppavlov/evolve.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index dc40f48e11..d460995c7b 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -17,6 +17,7 @@ import argparse from pathlib import Path import sys +import os import json from copy import deepcopy from subprocess import Popen, PIPE @@ -186,7 +187,7 @@ def score_population(population, population_size, result_file, considered_metric f_name = save_path.joinpath("config.json") save_json(population[i], f_name) - # __file__ + curr_file_path = os.path.dirname(os.path.realpath('__file__')) procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], From 1096a25db1050a3c618cd22f33aa1eb2cc3c7c22 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 18:09:12 +0300 Subject: [PATCH 471/616] fix: rnadom choice fixed --- deeppavlov/models/evolution/evolution_param_generator.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index e1131b465c..9f4107aa0c 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -2,6 +2,7 @@ from copy import deepcopy from pathlib import Path import json +import random from deeppavlov.core.common.registry import register from deeppavlov.core.common.file import read_json @@ -77,6 +78,7 @@ def __init__(self, pass else: np.random.seed(seed) + random.seed(seed) def find_model_path(self, config, key_model, path=[]): """ @@ -434,10 +436,10 @@ def sample_params(self, **params): params_sample = dict() for param, param_val in params_copy.items(): if isinstance(param_val, list): - params_sample[param] = np.random.choice(param_val) + params_sample[param] = random.choice(param_val) elif isinstance(param_val, dict): if 'evolve_bool' in param_val and param_val['evolve_bool']: - sample = bool(np.random.choice([True, False])) + sample = bool(random.choice([True, False])) elif 'evolve_range' in param_val: sample = self._sample_from_ranges(param_val) params_sample[param] = sample From 906c51ce22b70d30b4063a7af0fcd0b72a9e0308 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 18:29:39 +0300 Subject: [PATCH 472/616] fix: run subprocess --- deeppavlov/configs/evolution/intents_snips_local.json | 8 ++++++++ deeppavlov/evolve.py | 10 +++++++--- 2 files changed, 15 insertions(+), 3 deletions(-) diff --git a/deeppavlov/configs/evolution/intents_snips_local.json b/deeppavlov/configs/evolution/intents_snips_local.json index 3fcb331a05..a1a3034ebb 100644 --- a/deeppavlov/configs/evolution/intents_snips_local.json +++ b/deeppavlov/configs/evolution/intents_snips_local.json @@ -117,6 +117,14 @@ "tokenizer": "#my_tokenizer", "check_bool": { "evolve_bool": true + }, + "check_choice": { + "evolve_choice": true, + "values": [ + 1, + 2, + 3 + ] } } ], diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index d460995c7b..fa5a6f043a 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -187,15 +187,19 @@ def score_population(population, population_size, result_file, considered_metric f_name = save_path.joinpath("config.json") save_json(population[i], f_name) - curr_file_path = os.path.dirname(os.path.realpath('__file__')) - - procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" + curr_dir_path = os.path.dirname(os.path.realpath('__file__')) + # TODO: choose current python + # TODO: through deep.py train? + procs.append(Popen("CUDA_VISIBLE_DEVICES={} {} {}/deep.py train {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], + sys.executable, + curr_dir_path, str(f_name), str(save_path), str(save_path) ), shell=True, stdout=PIPE, stderr=PIPE)) + for j, proc in enumerate(procs): i = k * len(gpus) + j log.info(f'wait on {i}th proc') From 6fc830bcc2ea5ac83e0193c063a09dc1b0b38f89 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 18:29:49 +0300 Subject: [PATCH 473/616] fix: run subprocess --- deeppavlov/evolve.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index fa5a6f043a..7f48198af0 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -188,8 +188,6 @@ def score_population(population, population_size, result_file, considered_metric save_json(population[i], f_name) curr_dir_path = os.path.dirname(os.path.realpath('__file__')) - # TODO: choose current python - # TODO: through deep.py train? procs.append(Popen("CUDA_VISIBLE_DEVICES={} {} {}/deep.py train {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], sys.executable, From 3d6182eb0f19e0537add209cba7276dda8aeb62c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 18:37:17 +0300 Subject: [PATCH 474/616] chore --- deeppavlov/evolve.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 7f48198af0..61b3a12d72 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -30,8 +30,6 @@ from deeppavlov.core.common.file import read_json, save_json from deeppavlov.core.common.log import get_logger - - log = get_logger(__name__) parser = argparse.ArgumentParser() From 6855cadb25b9864ba7c7e94bfe697bc758b2d279 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 19 Jun 2018 18:42:44 +0300 Subject: [PATCH 475/616] feat: run on cpu --- deeppavlov/evolve.py | 30 ++++++++++++++++++++---------- 1 file changed, 20 insertions(+), 10 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 61b3a12d72..a9072234e2 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -42,7 +42,7 @@ parser.add_argument('--pow_mut', help='mutation power', type=float, default=0.1) parser.add_argument('--p_size', help='population size', type=int, default=10) -parser.add_argument('--gpus', help='visible GPUs divided by comma <<,>>', default="0") +parser.add_argument('--gpus', help='visible GPUs divided by comma <<,>>', default="-1") parser.add_argument('--train_partition', help='partition of splitted train file', default=1) parser.add_argument('--start_from_population', @@ -186,15 +186,25 @@ def score_population(population, population_size, result_file, considered_metric save_json(population[i], f_name) curr_dir_path = os.path.dirname(os.path.realpath('__file__')) - procs.append(Popen("CUDA_VISIBLE_DEVICES={} {} {}/deep.py train {}" - " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], - sys.executable, - curr_dir_path, - str(f_name), - str(save_path), - str(save_path) - ), - shell=True, stdout=PIPE, stderr=PIPE)) + if len(gpus) == 1 and gpus[0] == -1: + procs.append(Popen("{} {}/deep.py train {}" + " 1>{}/out.txt 2>{}/err.txt".format(sys.executable, + curr_dir_path, + str(f_name), + str(save_path), + str(save_path) + ), + shell=True, stdout=PIPE, stderr=PIPE)) + else: + procs.append(Popen("CUDA_VISIBLE_DEVICES={} {} {}/deep.py train {}" + " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], + sys.executable, + curr_dir_path, + str(f_name), + str(save_path), + str(save_path) + ), + shell=True, stdout=PIPE, stderr=PIPE)) for j, proc in enumerate(procs): i = k * len(gpus) + j From 8f92c95ba62146a758ad99bda267a09931cdf4ff Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 10:47:43 +0300 Subject: [PATCH 476/616] chore: results dicts out of scope --- deeppavlov/evolve.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index a9072234e2..7921642c85 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -222,14 +222,15 @@ def score_population(population, population_size, result_file, considered_metric reports.append(json.loads(reports_data[i])) except: pass + + val_results = {} + test_results = {} if len(reports) == 2 and "valid" in reports[0].keys() and "test" in reports[1].keys(): val_results = reports[0] test_results = reports[1] elif len(reports) == 1 and "valid" in reports[0].keys(): val_results = reports[0] else: - val_results = {} - test_results = {} for m in considered_metrics: if "loss" in m: val_results[m] = 1e6 From c3f62292951d971607561821d6cdd67bfe77fb27 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 11:59:03 +0300 Subject: [PATCH 477/616] fix: evolve_choice fixed --- .../evolution/evolution_param_generator.py | 54 +++++++++++-------- 1 file changed, 32 insertions(+), 22 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 9f4107aa0c..4d859ae220 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -51,7 +51,7 @@ def __init__(self, self.basic_config = deepcopy(kwargs) self.main_model_path = list(self.find_model_path(self.basic_config, key_main_model))[0] Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, - exist_ok=True) + exist_ok=True) # self.print_dict(self.basic_config, string="Basic config:") log.info("Main model path in config: {}".format(self.main_model_path)) @@ -148,24 +148,12 @@ def initialize_params_in_config(self, basic_config, paths): param_name = path_[-1] value = self.get_value_from_config(basic_config, path_) if type(value) is dict: - if value.get("evolve_choice"): + if value.get("evolve_choice") or value.get("evolve_range") or value.get("evolve_bool"): config = self.insert_value_or_dict_into_config(config, - path_, - self.sample_params( - **{param_name: - list(value["values"])})[param_name]) - elif value.get("evolve_range"): - config = self.insert_value_or_dict_into_config(config, - path_, - self.sample_params( - **{param_name: - deepcopy(value)})[param_name]) - elif value.get("evolve_bool"): - config = self.insert_value_or_dict_into_config(config, - path_, - self.sample_params( - **{param_name: - deepcopy(value)})[param_name]) + path_, + self.sample_params( + **{param_name: + deepcopy(value)})[param_name]) return config @@ -417,7 +405,7 @@ def decision(self, probability): """ Make decision whether to do action or not with given probability Args: - probability: probability whether + probability: probability whether to do action or not Returns: @@ -429,25 +417,47 @@ def decision(self, probability): return False def sample_params(self, **params): + """ + Sample parameters according to the given possible values + Args: + **params: dictionary {"param_0": {"evolve_range": [0, 10]}, + "param_1": {"evolve_range": [0, 10], "discrete": true}, + "param_2": {"evolve_range": [0, 1], "scale": "log"}, + "param_3": {"evolve_bool": true}, + "param_4": [0, 1, 2, 3]} + + Returns: + + """ if not params: return {} else: params_copy = deepcopy(params) params_sample = dict() for param, param_val in params_copy.items(): - if isinstance(param_val, list): - params_sample[param] = random.choice(param_val) - elif isinstance(param_val, dict): + if isinstance(param_val, dict): if 'evolve_bool' in param_val and param_val['evolve_bool']: sample = bool(random.choice([True, False])) elif 'evolve_range' in param_val: sample = self._sample_from_ranges(param_val) + elif 'evolve_choice' in param_val: + sample = random.choice(param_val['values']) params_sample[param] = sample else: params_sample[param] = params_copy[param] return params_sample def _sample_from_ranges(self, opts): + """ + Sample parameters from ranges + Args: + opts: dictionary {"param_0": {"evolve_range": [0, 10]}, + "param_1": {"evolve_range": [0, 10], "discrete": true}, + "param_2": {"evolve_range": [0, 1], "scale": "log"}} + + Returns: + value + """ from_ = opts['evolve_range'][0] to_ = opts['evolve_range'][1] if opts.get('scale', None) == 'log': From e7a2b016415aefdd8eb26e2585bde41dd19ff16d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 14:47:13 +0300 Subject: [PATCH 478/616] chore: many fixes in evolve --- deeppavlov/evolve.py | 132 ++++++++++-------- .../evolution/evolution_param_generator.py | 7 + 2 files changed, 79 insertions(+), 60 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 7921642c85..1d61757c06 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -26,6 +26,7 @@ p = (Path(__file__) / ".." / "..").resolve() sys.path.append(str(p)) +from deeppavlov.core.common.errors import ConfigError from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution from deeppavlov.core.common.file import read_json, save_json from deeppavlov.core.common.log import get_logger @@ -35,7 +36,7 @@ parser = argparse.ArgumentParser() parser.add_argument("config_path", help="path to a pipeline json config", type=str) -parser.add_argument('--evolve_metric', help='target metric out of given in your config.train.metrics') +parser.add_argument('--key_main_model', help='key inserted in dictionary of main model in pipe', default="main") parser.add_argument('--p_cross', help='probability of crossover', type=float, default=0.2) parser.add_argument('--pow_cross', help='crossover power', type=float, default=0.1) parser.add_argument('--p_mut', help='probability of mutation', type=float, default=1.) @@ -67,7 +68,7 @@ def main(): args = parser.parse_args() pipeline_config_path = find_config(args.config_path) - evolve_metric = args.evolve_metric + key_main_model = args.key_main_model population_size = args.p_size gpus = [int(gpu) for gpu in args.gpus.split(",")] train_partition = int(args.train_partition) @@ -86,7 +87,7 @@ def main(): evolution = ParamsEvolution(population_size=population_size, p_crossover=p_crossover, crossover_power=pow_crossover, p_mutation=p_mutation, mutation_power=pow_mutation, - key_main_model="main", + key_main_model=key_main_model, seed=42, train_partition=train_partition, elitism_with_weights=elitism_with_weights, @@ -95,35 +96,29 @@ def main(): considered_metrics = evolution.get_value_from_config(evolution.basic_config, list(evolution.find_model_path( evolution.basic_config, "metrics"))[0] + ["metrics"]) + evolve_metric = considered_metrics[0] - # Result table - order = deepcopy(considered_metrics) result_file = Path(evolution.get_value_from_config(evolution.basic_config, evolution.main_model_path + ["save_path"]) ).joinpath("result_table.csv") result_table_columns = [] result_table_dict = {} - for el in order: + for el in considered_metrics: result_table_dict[el + "_valid"] = [] result_table_dict[el + "_test"] = [] result_table_columns.extend([el + "_valid", el + "_test"]) - order.extend(["params"]) result_table_dict["params"] = [] result_table_columns.append("params") if start_from_population == 0: + iters = 0 result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') log.info("\nIteration #{} starts\n".format(0)) population = evolution.first_generation() - log.info(population) - population_scores = score_population(population, population_size, result_file, considered_metrics, - evolution, order, gpus, result_table_columns)[evolve_metric] - iters = 1 else: - # _ = evolution.first_generation() iters = start_from_population log.info("\nIteration #{} starts\n".format(iters)) @@ -142,31 +137,38 @@ def main(): str(Path( evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"]).parent))) - population_scores = score_population(population, population_size, result_file, considered_metrics, - evolution, order, gpus, result_table_columns)[evolve_metric] - log.info("Population scores: {}".format(population_scores)) - log.info("\nIteration #{} was done\n".format(iters)) - iters += 1 + run_population(population, evolution, gpus) + population_scores = results_to_table(population, evolution, considered_metrics, + result_file, result_table_columns)[evolve_metric] + log.info("Population scores: {}".format(population_scores)) + log.info("\nIteration #{iters} was done\n") + iters += 1 while True: - log.info("\nIteration #{} starts\n".format(iters)) + log.info("\nIteration #{iters} starts\n") population = evolution.next_generation(population, population_scores, iters) - population_scores = score_population(population, population_size, result_file, considered_metrics, - evolution, order, gpus, result_table_columns)[evolve_metric] + run_population(population, evolution, gpus) + population_scores = results_to_table(population, evolution, considered_metrics, + result_file, result_table_columns)[evolve_metric] log.info("Population scores: {}".format(population_scores)) - log.info("\nIteration #{} was done\n".format(iters)) + log.info("\nIteration #{iters} was done\n") iters += 1 -def score_population(population, population_size, result_file, considered_metrics, - evolution, order, gpus, result_table_columns): - test_best = evolution.get_value_from_config(evolution.basic_config, - list(evolution.find_model_path( - evolution.basic_config, "test_best"))[0] + ["test_best"]) - population_metrics = {} - for m in considered_metrics: - population_metrics[m] = [] - +def run_population(population, evolution, gpus): + """ + Change save and load paths for obtained population, save config.json with model config, + run population via current python executor (with which evolve.py already run) + and on given devices (-1 means CPU, other integeres - visible for evolve.py GPUs) + Args: + population: list of dictionaries - configs of current population + evolution: ParamsEvolution + gpus: list of given devices (list of integers) + + Returns: + None + """ + population_size = len(population) for k in range(population_size // len(gpus) + 1): procs = [] for j in range(len(gpus)): @@ -205,12 +207,31 @@ def score_population(population, population_size, result_file, considered_metric str(save_path) ), shell=True, stdout=PIPE, stderr=PIPE)) - for j, proc in enumerate(procs): i = k * len(gpus) + j log.info(f'wait on {i}th proc') proc.wait() + return None + +def results_to_table(population, evolution, considered_metrics, result_file, result_table_columns): + population_size = len(population) + validate_best = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "validate_best"))[0] + + ["validate_best"]) + test_best = evolution.get_value_from_config(evolution.basic_config, + list(evolution.find_model_path( + evolution.basic_config, "test_best"))[0] + + ["test_best"]) + if (not validate_best) and test_best: + log.info("validate_best is set to False. Tuning parameters on test") + elif (not validate_best) and (not test_best): + raise ConfigError("validate_best and test_best are set to False. Can not evolve.") + + population_metrics = {} + for m in considered_metrics: + population_metrics[m] = [] for i in range(population_size): with open(str(Path(evolution.get_value_from_config( population[i], @@ -222,42 +243,33 @@ def score_population(population, population_size, result_file, considered_metric reports.append(json.loads(reports_data[i])) except: pass - + val_results = {} test_results = {} + for m in considered_metrics: + val_results[m] = None + test_results[m] = None if len(reports) == 2 and "valid" in reports[0].keys() and "test" in reports[1].keys(): - val_results = reports[0] + val_results = reports[0]["metrics"] test_results = reports[1] elif len(reports) == 1 and "valid" in reports[0].keys(): - val_results = reports[0] - else: - for m in considered_metrics: - if "loss" in m: - val_results[m] = 1e6 - test_results[m] = 1e6 - else: - val_results[m] = 0. - test_results[m] = 0. + val_results = reports[0]["metrics"] + elif len(reports) == 1 and "test" in reports[0].keys(): + test_results = reports[0]["metrics"] result_table_dict = {} - for el in order: - if el == "params": - result_table_dict[el] = [] - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - for m_id, m in enumerate(considered_metrics): - val_metrics_path = list(evolution.find_model_path(val_results, m))[0] - val_m = evolution.get_value_from_config(val_results, val_metrics_path + [m]) - population_metrics[m].append(val_m) - result_table_dict[m + "_valid"].append(val_m) - if test_best: - test_metrics_path = list(evolution.find_model_path(test_results, m))[0] - test_m = evolution.get_value_from_config(test_results, test_metrics_path + [m]) - result_table_dict[m + "_test"].append(test_m) - else: - result_table_dict[m + "_test"].append(0.) - result_table_dict[order[-1]] = [population[i]] + for el in result_table_columns: + result_table_dict[el] = [] + + for m in considered_metrics: + result_table_dict[m + "_valid"].append(val_results[m]) + result_table_dict[m + "_test"].append(test_results[m]) + if validate_best: + population_metrics[m].append(val_results[m]) + elif test_best: + population_metrics[m].append(test_results[m]) + + result_table_dict[result_table_columns[-1]] = [population[i]] result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 4d859ae220..03fe578b57 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -74,6 +74,13 @@ def __init__(self, self.evolution_model_id = 0 self.eps = 1e-6 + try: + self.evolve_metric_optimization = self.get_value_from_config( + self.basic_config, list(self.find_model_path( + self.basic_config, "metric_optimization"))[0] + ["metric_optimization"]) + except: + self.evolve_metric_optimization = "maximize" + if seed is None: pass else: From 93cbc631abd8671e5fe3d6a9360f49426ffa8e58 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 16:27:50 +0300 Subject: [PATCH 479/616] docs: docsrtigns in params evolution --- .../evolution/evolution_param_generator.py | 155 ++++++++++++------ deeppavlov/models/evolution/test.py | 22 --- 2 files changed, 102 insertions(+), 75 deletions(-) delete mode 100644 deeppavlov/models/evolution/test.py diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 03fe578b57..8a68ffec05 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -11,6 +11,7 @@ log = get_logger(__name__) + @register('params_evolution') class ParamsEvolution: """ @@ -45,6 +46,7 @@ def __init__(self, with main model in the basic config (to determine save and load paths that will be changed) seed: random seed for initialization train_partition: integer number of train data parts + elitism_with_weights: whether to save elite models with weigths or without **kwargs: basic config with parameters """ @@ -52,7 +54,6 @@ def __init__(self, self.main_model_path = list(self.find_model_path(self.basic_config, key_main_model))[0] Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, exist_ok=True) - # self.print_dict(self.basic_config, string="Basic config:") log.info("Main model path in config: {}".format(self.main_model_path)) self.population_size = population_size @@ -89,10 +90,11 @@ def __init__(self, def find_model_path(self, config, key_model, path=[]): """ - Find path to the main model in config which paths will be changed + Find path to dictionary in config that contains key 'key_model' Args: - config: - key_model: + config: dictionary + key_model: key of sub-dictionary to be found + path: list of keys and/or integers (for list) with relative path (needed for recursion) Returns: path in config -- list of keys (strings and integers) @@ -114,6 +116,16 @@ def find_model_path(self, config, key_model, path=[]): @staticmethod def insert_value_or_dict_into_config(config, path, value): + """ + Insert value to dictionary determined by path[:-1] in field with key path[-1] + Args: + config: dictionary + path: list of keys and/or integers (for list) + value: value to be inserted + + Returns: + config with inserted value + """ config_copy = deepcopy(config) config_pointer = config_copy for el in path[:-1]: @@ -128,6 +140,15 @@ def insert_value_or_dict_into_config(config, path, value): @staticmethod def get_value_from_config(config, path): + """ + Return value of config element determined by path + Args: + config: dictionary + path: list of keys and/or integers (for list) + + Returns: + value + """ config_copy = deepcopy(config) config_pointer = config_copy for el in path[:-1]: @@ -139,18 +160,18 @@ def get_value_from_config(config, path): pass return config_pointer[path[-1]] - @staticmethod - def print_dict(config, string=None): - if string is None: - log.info(json.dumps(config, indent=2)) - else: - log.info(string) - log.info(json.dumps(config, indent=2)) - return None - def initialize_params_in_config(self, basic_config, paths): - config = deepcopy(basic_config) + """ + Randomly initialize all the changable parameters in config + Args: + basic_config: config where changable parameters are dictionaries with keys + `evolve_range`, `evolve_bool`, `evolve_choice` + paths: paths to changable parameters + Returns: + config + """ + config = deepcopy(basic_config) for path_ in paths: param_name = path_[-1] value = self.get_value_from_config(basic_config, path_) @@ -167,6 +188,9 @@ def initialize_params_in_config(self, basic_config, paths): def first_generation(self, iteration=0): """ Initialize first generation randomly according to the given constraints is self.params + Args: + iteration: number of iteration + Returns: first generation that consists of self.population_size individuums """ @@ -185,22 +209,18 @@ def first_generation(self, iteration=0): def next_generation(self, generation, scores, iteration): """ - Provide an operation of replacement + Provide replacement Args: generation: current generation (set of self.population_size configs scores: corresponding scores that should be maximized iteration: iteration number - p_crossover: probability to cross over for current replacement - crossover_power: part of parents parameters to exchange for offsprings - p_mutation: probability of mutation for current replacement - mutation_power: allowed percentage of mutation Returns: the next generation according to the given scores of current generation """ next_population = self.selection_of_best_with_weights(generation, scores) - print("Saved with weights: {} individuums".format(self.n_saved_best_pretrained)) + log.info("Saved with weights: {} models".format(self.n_saved_best_pretrained)) offsprings = self.crossover(generation, scores) changable_next = self.mutation(offsprings) @@ -268,23 +288,19 @@ def selection_of_best_with_weights(self, population, scores): """ Select individuums to save with weights for the next generation from given population. Range is an order of an individuum within sorted scores (1 range = max-score, self.population_size = min-score) - Individuum with the highest score has probability equal to 1 (100%). - Individuum with the lowest score has probability equal to 0 (0%). + Individuum with the best score has probability equal to 1 (100%). + Individuum with the worst score has probability equal to 0 (0%). Probability of i-th individuum to be selected with weights is (a * range_i + b) where a = 1. / (1. - self.population_size), and b = self.population_size / (self.population_size - 1.) Args: population: self.population_size individuums - scores: corresponding score that should be maximized + scores: list of corresponding scores Returns: selected self.n_saved_best_pretrained (changable) individuums """ - scores = np.array(scores, dtype='float') - sorted_ids = np.argsort(scores) - ranges = np.array([self.population_size - np.where(i == sorted_ids)[0][0] - for i in np.arange(self.population_size)]) - + ranges = self.range_scores(scores) a = 1. / (1. - self.population_size) b = self.population_size / (self.population_size - 1.) probas_to_be_selected = a * ranges + b @@ -297,6 +313,25 @@ def selection_of_best_with_weights(self, population, scores): self.n_saved_best_pretrained = len(selected) return selected + def range_scores(self, scores): + not_none_scores = np.array([x for x in scores if x is not None]) + min_score = np.min(not_none_scores) + max_score = np.max(not_none_scores) + for i in range(self.population_size): + if scores[i] is None: + if self.evolve_metric_optimization == "maximize": + scores[i] = min_score - self.eps + else: + scores[i] = max_score + self.eps + scores = np.array(scores, dtype='float') + + sorted_ids = np.argsort(scores) + if self.evolve_metric_optimization == "minimize": + sorted_ids = sorted_ids[::-1] + ranges = np.array([self.population_size - np.where(i == sorted_ids)[0][0] + for i in np.arange(self.population_size)]) + return ranges + def crossover(self, population, scores): """ Recombine randomly population in pairs and cross over them with given probability. @@ -305,18 +340,17 @@ def crossover(self, population, scores): and the other (1 - crossover_power portion) from the other parent Args: population: self.population_size individuums - p_crossover: probability to cross over for current replacement - crossover_power: part of EVOLVING parents parameters to exchange for offsprings + scores: list of corresponding scores Returns: (self.population_size - self.n_saved_best_pretained) offsprings """ offsprings = [] - scores = np.array(scores, dtype='float') - if np.sum(scores) < self.eps: - scores = [self.eps for _ in range(self.population_size)] - probas_to_be_parent = scores / np.sum(scores) + ranges = self.range_scores(scores) + a = 1. / (1. - self.population_size) + b = self.population_size / (self.population_size - 1.) + probas_to_be_parent = (a * ranges + b) / np.sum(a * ranges + b) intervals = np.array([np.sum(probas_to_be_parent[:i]) for i in range(self.population_size)]) for i in range(self.population_size - self.n_saved_best_pretrained): @@ -333,20 +367,20 @@ def crossover(self, population, scores): for j in range(self.n_evolving_params - part, self.n_evolving_params): curr_offsprings[0] = self.insert_value_or_dict_into_config(curr_offsprings[0], - self.paths_to_evolving_params[ - params_perm[j]], - self.get_value_from_config( - parents[1], - self.paths_to_evolving_params[ - params_perm[j]])) + self.paths_to_evolving_params[ + params_perm[j]], + self.get_value_from_config( + parents[1], + self.paths_to_evolving_params[ + params_perm[j]])) curr_offsprings[1] = self.insert_value_or_dict_into_config(curr_offsprings[1], - self.paths_to_evolving_params[ - params_perm[j]], - self.get_value_from_config( - parents[0], - self.paths_to_evolving_params[ - params_perm[j]])) + self.paths_to_evolving_params[ + params_perm[j]], + self.get_value_from_config( + parents[0], + self.paths_to_evolving_params[ + params_perm[j]])) offsprings.append(deepcopy(curr_offsprings[0])) else: offsprings.append(deepcopy(parents[0])) @@ -355,11 +389,9 @@ def crossover(self, population, scores): def mutation(self, population): """ - Mutate each parameter of each individuum in population with probability p_mutation + Mutate each parameter of each individuum in population Args: population: self.population_size individuums - p_mutation: probability to mutate for each parameter - mutation_power: allowed percentage of mutation Returns: mutated population @@ -369,7 +401,6 @@ def mutation(self, population): for individuum in population: mutated_individuum = deepcopy(individuum) for path_ in self.paths_to_evolving_params: - param_name = path_[-1] param_value = self.get_value_from_config(individuum, path_) mutated_individuum = self.insert_value_or_dict_into_config( mutated_individuum, path_, @@ -379,6 +410,15 @@ def mutation(self, population): return mutated def mutation_of_param(self, param_path, param_value): + """ + Mutate particular parameter separately + Args: + param_path: path to parameter in basic config + param_value: current parameter valuer + + Returns: + mutated parameter value + """ if self.decision(self.p_mutation): param_name = param_path[-1] basic_value = self.get_value_from_config(self.basic_config, param_path) @@ -415,7 +455,7 @@ def decision(self, probability): probability: probability whether to do action or not Returns: - + bool decision """ r = np.random.random() if r < probability: @@ -434,7 +474,7 @@ def sample_params(self, **params): "param_4": [0, 1, 2, 3]} Returns: - + random parameter value """ if not params: return {} @@ -463,7 +503,7 @@ def _sample_from_ranges(self, opts): "param_2": {"evolve_range": [0, 1], "scale": "log"}} Returns: - value + random parameter value from range """ from_ = opts['evolve_range'][0] to_ = opts['evolve_range'][1] @@ -477,5 +517,14 @@ def _sample_from_ranges(self, opts): @staticmethod def _sample_log(from_, to_): + """ + Sample parameters from ranges with log scale + Args: + from_: lower boundary of values + to_: upper boundary of values + + Returns: + random parameters value from range with log scale + """ sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) return float(sample) diff --git a/deeppavlov/models/evolution/test.py b/deeppavlov/models/evolution/test.py deleted file mode 100644 index 793b463c5e..0000000000 --- a/deeppavlov/models/evolution/test.py +++ /dev/null @@ -1,22 +0,0 @@ -from copy import deepcopy -import numpy as np -import json - -from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution - - - -CONFIG_FILE = "/home/dilyara/Documents/GitHub/deeppavlov_evolution/deeppavlov/configs/evolution/intents_snips_local.json" - -with open(CONFIG_FILE, "r") as f: - basic_params = json.load(f) - -# print("Given basic params: {}\n".format(json.dumps(basic_params, indent=2))) - -evolution = ParamsEvolution(population_size=10, - **basic_params) - -paths = list(evolution.find_model_path(basic_params, "evolve_range")) -print(paths) - -print(evolution.get_value_from_config(basic_params, paths[0])) From 813813c6ced98bad0f05ddb3e50db65998205dc4 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 16:34:57 +0300 Subject: [PATCH 480/616] chore: prefix model add to path in evolution class --- .../models/evolution/evolution_param_generator.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 8a68ffec05..2899488172 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -201,7 +201,7 @@ def first_generation(self, iteration=0): population[-1] = self.insert_value_or_dict_into_config( population[-1], self.main_model_path + [which_path], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) - ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) population[-1]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 @@ -240,7 +240,7 @@ def next_generation(self, generation, scores, iteration): self.get_value_from_config(next_population[i], self.main_model_path + ["lear_rate"]), read_json(str(Path(self.get_value_from_config(next_population[i], - self.main_model_path + ["save_path"]) + self.main_model_path + ["save_path"]) ).parent.joinpath("model_opt.json")))["final_lear_rate"]) except: pass @@ -251,20 +251,20 @@ def next_generation(self, generation, scores, iteration): next_population[i], self.main_model_path + ["load_path"], str(Path(self.get_value_from_config(next_population[i], - self.main_model_path + ["save_path"])).parent)) + self.main_model_path + ["save_path"])))) else: # if elite models are saved only as configurations and trained again next_population[i] = self.insert_value_or_dict_into_config( next_population[i], self.main_model_path + ["load_path"], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["load_path"]) - ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) next_population[i] = self.insert_value_or_dict_into_config( next_population[i], self.main_model_path + ["save_path"], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"]) - ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) for i in range(self.n_saved_best_pretrained, self.population_size): # if several train files @@ -277,7 +277,7 @@ def next_generation(self, generation, scores, iteration): next_population[i], self.main_model_path + [which_path], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) - ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)))) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) next_population[i]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 From 2d17cb7dbe65baa0f9b111ad4223433701d91d7a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 16:35:54 +0300 Subject: [PATCH 481/616] chore: remove path work in evolve --- deeppavlov/evolve.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 1d61757c06..191fabe79d 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -175,13 +175,7 @@ def run_population(population, evolution, gpus): i = k * len(gpus) + j if i < population_size: save_path = Path(evolution.get_value_from_config(population[i], - evolution.main_model_path + ["save_path"])) - load_path = Path(evolution.get_value_from_config(population[i], - evolution.main_model_path + ["load_path"])) - population[i] = evolution.insert_value_or_dict_into_config( - population[i], evolution.main_model_path + ["save_path"], str(save_path.joinpath("model"))) - population[i] = evolution.insert_value_or_dict_into_config( - population[i], evolution.main_model_path + ["load_path"], str(load_path.joinpath("model"))) + evolution.main_model_path + ["save_path"])).parent save_path.mkdir(parents=True, exist_ok=True) f_name = save_path.joinpath("config.json") From 3998d7b1088ad34765c709fa220b56105f133598 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 16:42:32 +0300 Subject: [PATCH 482/616] chore: remove path work in evolve --- deeppavlov/evolve.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 191fabe79d..10a9567329 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -130,12 +130,15 @@ def main(): population[i], evolution.main_model_path + ["save_path"], str(Path( evolution.get_value_from_config(evolution.basic_config, evolution.main_model_path + ["save_path"]) - ).joinpath("population_" + str(start_from_population)).joinpath("model_" + str(i)))) + ).joinpath( + "population_" + str(start_from_population)).joinpath( + "model_" + str(i)).joinpath( + "model"))) population[i] = evolution.insert_value_or_dict_into_config( population[i], evolution.main_model_path + ["load_path"], str(Path( - evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"]).parent))) + evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"])))) run_population(population, evolution, gpus) population_scores = results_to_table(population, evolution, considered_metrics, From ce98b6e03f4d2fca4aa7c66800460aa8d601fe2f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 16:48:36 +0300 Subject: [PATCH 483/616] fix: fixes some --- deeppavlov/evolve.py | 8 ++++---- deeppavlov/models/evolution/evolution_param_generator.py | 2 ++ 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 10a9567329..3b289b5f60 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -116,7 +116,7 @@ def main(): result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') - log.info("\nIteration #{} starts\n".format(0)) + log.info("\nIteration #{} starts\n".format(iters)) population = evolution.first_generation() else: iters = start_from_population @@ -144,17 +144,17 @@ def main(): population_scores = results_to_table(population, evolution, considered_metrics, result_file, result_table_columns)[evolve_metric] log.info("Population scores: {}".format(population_scores)) - log.info("\nIteration #{iters} was done\n") + log.info("\nIteration #{} was done\n".format(iters)) iters += 1 while True: - log.info("\nIteration #{iters} starts\n") + log.info("\nIteration #{} starts\n".format(iters)) population = evolution.next_generation(population, population_scores, iters) run_population(population, evolution, gpus) population_scores = results_to_table(population, evolution, considered_metrics, result_file, result_table_columns)[evolve_metric] log.info("Population scores: {}".format(population_scores)) - log.info("\nIteration #{iters} was done\n") + log.info("\nIteration #{} was done\n".format(iters)) iters += 1 diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 2899488172..e8072a910f 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -315,6 +315,8 @@ def selection_of_best_with_weights(self, population, scores): def range_scores(self, scores): not_none_scores = np.array([x for x in scores if x is not None]) + if len(not_none_scores) == 0: + not_none_scores = np.array([0]) min_score = np.min(not_none_scores) max_score = np.max(not_none_scores) for i in range(self.population_size): From 087e933f6f0d722c50888d45b66b560f7259f9aa Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 16:57:35 +0300 Subject: [PATCH 484/616] fix: metrics in reports --- deeppavlov/evolve.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 3b289b5f60..6771faea85 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -247,12 +247,12 @@ def results_to_table(population, evolution, considered_metrics, result_file, res val_results[m] = None test_results[m] = None if len(reports) == 2 and "valid" in reports[0].keys() and "test" in reports[1].keys(): - val_results = reports[0]["metrics"] - test_results = reports[1] + val_results = reports[0]["valid"]["metrics"] + test_results = reports[1]["test"]["metrics"] elif len(reports) == 1 and "valid" in reports[0].keys(): - val_results = reports[0]["metrics"] + val_results = reports[0]["valid"]["metrics"] elif len(reports) == 1 and "test" in reports[0].keys(): - test_results = reports[0]["metrics"] + test_results = reports[0]["test"]["metrics"] result_table_dict = {} for el in result_table_columns: From b8ff5e6adbb8b1f148a7eca648a04c6a6fb24051 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 17:21:25 +0300 Subject: [PATCH 485/616] fix: re init lear rate for keras model --- deeppavlov/models/evolution/evolution_param_generator.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index e8072a910f..b4c30a398a 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -237,8 +237,7 @@ def next_generation(self, generation, scores, iteration): # re-init learning rate with the final one (works for KerasModel) next_population[i] = self.insert_value_or_dict_into_config( next_population[i], - self.get_value_from_config(next_population[i], - self.main_model_path + ["lear_rate"]), + self.main_model_path + ["lear_rate"], read_json(str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"]) ).parent.joinpath("model_opt.json")))["final_lear_rate"]) From d9bb62cec784496c93188da2fa68438fec51e134 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 17:27:40 +0300 Subject: [PATCH 486/616] fix: delete new lines in logs --- deeppavlov/evolve.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 6771faea85..8ba8e3bf7f 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -116,11 +116,11 @@ def main(): result_table = pd.DataFrame(result_table_dict) result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') - log.info("\nIteration #{} starts\n".format(iters)) + log.info("Iteration #{} starts".format(iters)) population = evolution.first_generation() else: iters = start_from_population - log.info("\nIteration #{} starts\n".format(iters)) + log.info("Iteration #{} starts".format(iters)) population = [] for i in range(population_size): @@ -144,17 +144,17 @@ def main(): population_scores = results_to_table(population, evolution, considered_metrics, result_file, result_table_columns)[evolve_metric] log.info("Population scores: {}".format(population_scores)) - log.info("\nIteration #{} was done\n".format(iters)) + log.info("Iteration #{} was done".format(iters)) iters += 1 while True: - log.info("\nIteration #{} starts\n".format(iters)) + log.info("Iteration #{} starts".format(iters)) population = evolution.next_generation(population, population_scores, iters) run_population(population, evolution, gpus) population_scores = results_to_table(population, evolution, considered_metrics, result_file, result_table_columns)[evolve_metric] log.info("Population scores: {}".format(population_scores)) - log.info("\nIteration #{} was done\n".format(iters)) + log.info("Iteration #{} was done".format(iters)) iters += 1 From c9675d179b8e57817dcf76f3c7d4aa907ec354a7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 18:28:50 +0300 Subject: [PATCH 487/616] feat: saving fiton parts to model dir in paramsevoltion --- .../evolution/evolution_param_generator.py | 30 +++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index b4c30a398a..73a147b950 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -75,6 +75,11 @@ def __init__(self, self.evolution_model_id = 0 self.eps = 1e-6 + self.paths_to_fiton_dicts = [] + for path_ in self.find_model_path(self.basic_config, "fit_on"): + self.paths_to_fiton_dicts.append(path_) + self.n_fiton_dicts = len(self.paths_to_fiton_dicts) + try: self.evolve_metric_optimization = self.get_value_from_config( self.basic_config, list(self.find_model_path( @@ -202,6 +207,13 @@ def first_generation(self, iteration=0): population[-1], self.main_model_path + [which_path], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) + for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + for which_path in ["save_path", "load_path"]: + population[-1] = self.insert_value_or_dict_into_config( + population[-1], path_ + [which_path], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( + "fitted_model_" + str(path_id)))) population[-1]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 @@ -251,6 +263,11 @@ def next_generation(self, generation, scores, iteration): self.main_model_path + ["load_path"], str(Path(self.get_value_from_config(next_population[i], self.main_model_path + ["save_path"])))) + for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + next_population[i] = self.insert_value_or_dict_into_config( + next_population[i], path_ + ["load_path"], + str(Path(self.get_value_from_config(next_population[i], + path_ + ["save_path"])))) else: # if elite models are saved only as configurations and trained again next_population[i] = self.insert_value_or_dict_into_config( @@ -264,6 +281,12 @@ def next_generation(self, generation, scores, iteration): self.main_model_path + ["save_path"], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) + for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + next_population[i] = self.insert_value_or_dict_into_config( + next_population[i], path_ + ["save_path"], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( + "fitted_model_" + str(path_id)))) for i in range(self.n_saved_best_pretrained, self.population_size): # if several train files @@ -277,6 +300,13 @@ def next_generation(self, generation, scores, iteration): self.main_model_path + [which_path], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) + for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + for which_path in ["save_path", "load_path"]: + next_population[i] = self.insert_value_or_dict_into_config( + next_population[i], path_ + [which_path], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( + "fitted_model_" + str(path_id)))) next_population[i]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 From dd3079cbfa95f4b77e200a6c932fa29ce3be8a54 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 20 Jun 2018 18:34:34 +0300 Subject: [PATCH 488/616] feat: saving fiton parts to model dir in paramsevoltion --- deeppavlov/evolve.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 8ba8e3bf7f..a10880a291 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -140,6 +140,20 @@ def main(): str(Path( evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"])))) + for path_id, path_ in enumerate(evolution.paths_to_fiton_dicts): + population[i] = evolution.insert_value_or_dict_into_config( + population[i], path_ + ["save_path"], + str(Path(evolution.get_value_from_config(evolution.basic_config, + evolution.main_model_path + ["save_path"]) + ).joinpath("population_" + str(iters)).joinpath("model_" + str(i)).joinpath( + "fitted_model_" + str(path_id)))) + + for path_id, path_ in enumerate(evolution.paths_to_fiton_dicts): + population[i] = evolution.insert_value_or_dict_into_config( + population[i], path_ + ["load_path"], + str(Path(evolution.get_value_from_config( + population[i], path_ + ["load_path"])))) + run_population(population, evolution, gpus) population_scores = results_to_table(population, evolution, considered_metrics, result_file, result_table_columns)[evolve_metric] From 4fa702581bccf770bf2138fd7f3a055af7823760 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 22 Jun 2018 17:06:22 +0300 Subject: [PATCH 489/616] docs: docstrings in evolution --- .../configs/evolution/basic_ag_news_part.json | 251 ---------------- .../configs/evolution/basic_nlu_part.json | 250 ---------------- .../configs/evolution/basic_ru_snli_part.json | 251 ---------------- .../basic_ru_snli_part_many_inputs.json | 262 ----------------- .../configs/evolution/basic_sber_faq.json | 251 ---------------- .../basic_snips_one_neuron_init.json | 247 ---------------- .../evolution/basic_snips_random_init.json | 247 ---------------- .../configs/evolution/basic_snli_part.json | 251 ---------------- .../basic_snli_part_many_inputs.json | 268 ------------------ .../basic_snli_part_many_inputs_big.json | 267 ----------------- .../evolution/basic_twitter140_part.json | 251 ---------------- .../configs/evolution/intents_snips.json | 157 ---------- .../evolution/intents_snips_local.json | 8 + .../configs/evolution/intents_snli.json | 133 --------- deeppavlov/evolve.py | 6 + .../evolution/evolution_param_generator.py | 10 + 16 files changed, 24 insertions(+), 3086 deletions(-) delete mode 100644 deeppavlov/configs/evolution/basic_ag_news_part.json delete mode 100644 deeppavlov/configs/evolution/basic_nlu_part.json delete mode 100644 deeppavlov/configs/evolution/basic_ru_snli_part.json delete mode 100644 deeppavlov/configs/evolution/basic_ru_snli_part_many_inputs.json delete mode 100644 deeppavlov/configs/evolution/basic_sber_faq.json delete mode 100644 deeppavlov/configs/evolution/basic_snips_one_neuron_init.json delete mode 100644 deeppavlov/configs/evolution/basic_snips_random_init.json delete mode 100644 deeppavlov/configs/evolution/basic_snli_part.json delete mode 100644 deeppavlov/configs/evolution/basic_snli_part_many_inputs.json delete mode 100644 deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json delete mode 100644 deeppavlov/configs/evolution/basic_twitter140_part.json delete mode 100644 deeppavlov/configs/evolution/intents_snips.json delete mode 100644 deeppavlov/configs/evolution/intents_snli.json diff --git a/deeppavlov/configs/evolution/basic_ag_news_part.json b/deeppavlov/configs/evolution/basic_ag_news_part.json deleted file mode 100644 index a6e9459f25..0000000000 --- a/deeppavlov/configs/evolution/basic_ag_news_part.json +++ /dev/null @@ -1,251 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "label", - "data_path": "/home/dilyara.baymurzina/evolution_data/ag_news_data/parts", - "train": "train_0.csv", - "valid": "valid.csv", - "test": "test.csv" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_data/ag_news_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_data/ag_news_classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/given_mask_init_part_7", - "load_path": "/home/dilyara.baymurzina/evolution_data/ag_news_classification/given_mask_init_part_7", - "classes": "#classes_vocab.keys()", - "to_evolve": true, - "basic_layers_params": { - "Dense": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - }, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "Conv1D": { - "filters": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "kernel_size": { - "range": [ - 2, - 7 - ], - "discrete": true - }, - "padding": "same", - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "CuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "BiCuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "MaxPooling1D": { - "pool_size": { - "range": [ - 2, - 5 - ], - "discrete": true - }, - "padding": "same" - }, - "SelfMultiplicativeAttention": { - "n_hidden": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "n_output_features": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - } - }, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.0001, - 0.1 - ] - }, - "lear_rate_decay": { - "range": [ - 0.000001, - 0.1 - ] - }, - "loss": "binary_crossentropy", - "confident_threshold": 1, - "text_size": 50, - "dropout_rate": { - "range": [ - 0.1, - 0.9 - ] - }, - "last_layer_activation": "softmax", - "model_name": "evolution_classification_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - 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true, - "test_best": true - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} diff --git a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json b/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json deleted file mode 100644 index 8259544e97..0000000000 --- a/deeppavlov/configs/evolution/basic_snli_part_many_inputs_big.json +++ /dev/null @@ -1,267 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": [ - "sentence1", - "sentence2" - ], - "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/two_texts/part" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "sentence1", - "sentence2" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/snli_classes.dict" - }, - { - "in": [ - "sentence1" - ], - "out": [ - "sentence1_lower" - ], - "name": "str_lower" - }, - { - "in": [ - "sentence2" - ], - "out": [ - "sentence2_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "sentence1_lower", - "sentence2_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "evolution_many_inputs_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_6", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/one_neuron_init_part_many_inputs_big_6", - "classes": "#classes_vocab.keys()", - "to_evolve": true, - "basic_layers_params": { - "Dense": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - }, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "Conv1D": { - "filters": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "kernel_size": { - "range": [ - 2, - 7 - ], - "discrete": true - }, - "padding": "same", - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "CuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "BiCuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "MaxPooling1D": { - "pool_size": { - "range": [ - 2, - 5 - ], - "discrete": true - }, - "padding": "same" - }, - "SelfMultiplicativeAttention": { - "n_hidden": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "n_output_features": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - } - }, - "confident_threshold": { - "range": [ - 0.3, - 0.7 - ] - }, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.001, - 0.1 - ] - }, - "lear_rate_decay": { - "range": [ - 0.00001, - 0.1 - ] - }, - "loss": "binary_crossentropy", - "text_size": 15, - "dropout_rate": { - "range": [ - 0.1, - 0.9 - ] - }, - "last_layer_activation": "softmax", - "model_name": "evolution_many_inputs_classification_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "range": [ - 50, - 100 - ], - "discrete": true - }, - "batch_size": { - "range": [ - 50, - 100 - ], - "discrete": true - }, - "metric_optimization": "minimize", - "metrics": [ - "classification_log_loss", - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 5, - "log_every_n_epochs": 5, - "show_examples": false, - "validate_best": true, - "test_best": false - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} diff --git a/deeppavlov/configs/evolution/basic_twitter140_part.json b/deeppavlov/configs/evolution/basic_twitter140_part.json deleted file mode 100644 index 7ef90990dd..0000000000 --- a/deeppavlov/configs/evolution/basic_twitter140_part.json +++ /dev/null @@ -1,251 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "target", - "data_path": "/home/dilyara.baymurzina/evolution_data/twitter140_data/parts", - "train": "train_0.csv", - "valid": "valid.csv", - "test": "test.csv" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/twitter140_data/twitter140_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/twitter140_data/twitter140_classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "evolution_classification_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_6", - "load_path": "/home/dilyara.baymurzina/evolution_data/twitter140_classification/one_neuron_init_part_6", - "classes": "#classes_vocab.keys()", - "to_evolve": true, - "basic_layers_params": { - "Dense": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - }, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "Conv1D": { - "filters": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "kernel_size": { - "range": [ - 2, - 7 - ], - "discrete": true - }, - "padding": "same", - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "CuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "BiCuDNNLSTM": { - "units": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "return_sequences": true, - "coef_regul_l2": { - "range": [ - 0.000001, - 0.001 - ] - } - }, - "MaxPooling1D": { - "pool_size": { - "range": [ - 2, - 5 - ], - "discrete": true - }, - "padding": "same" - }, - "SelfMultiplicativeAttention": { - "n_hidden": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "n_output_features": { - "range": [ - 50, - 200 - ], - "discrete": true - }, - "activation": { - "values": [ - "softmax", - "sigmoid", - "relu" - ], - "choice": true - } - } - }, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.0001, - 0.1 - ] - }, - "lear_rate_decay": { - "range": [ - 0.000001, - 0.1 - ] - }, - "loss": "binary_crossentropy", - "confident_threshold": 1, - "text_size": 30, - "dropout_rate": { - "range": [ - 0.1, - 0.9 - ] - }, - "last_layer_activation": "softmax", - "model_name": "evolution_classification_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "range": [ - 1, - 10 - ], - "discrete": true - }, - "batch_size": { - "range": [ - 50, - 100 - ], - "discrete": true - }, - "metric_optimization": "maximize", - "metrics": [ - "classification_accuracy", - "classification_log_loss", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 1, - "log_every_n_epochs": 1, - "show_examples": false, - "validate_best": true, - "test_best": true - }, - "metadata": { - "labels": { - "telegram_utils": "IntentModel" - } - } -} diff --git a/deeppavlov/configs/evolution/intents_snips.json b/deeppavlov/configs/evolution/intents_snips.json deleted file mode 100644 index 58d21fd4ce..0000000000 --- a/deeppavlov/configs/evolution/intents_snips.json +++ /dev/null @@ -1,157 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "intents", - "data_path": "/home/dilyara.baymurzina/evolution_data/snips_data", - "train": "train.csv", - "valid": "valid.csv" - }, - "dataset_iterator": { - "name": "basic_classification_iterator", - "seed": { - "range": [ - 50, - 500 - ], - "discrete": true - } - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snips_data/snips_classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "intent_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/param_evolution_0", - "load_path": "/home/dilyara.baymurzina/evolution_data/snips_classification/param_evolution_0", - "classes": "#classes_vocab.keys()", - "kernel_sizes_cnn": [ - 1, - 2, - 3 - ], - "filters_cnn": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - }, - "confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": { - "evolve_range": [ - 0.0001, - 0.1 - ] - }, - "lear_rate_decay": 0.1, - "loss": "binary_crossentropy", - "text_size": 51, - "to_evolve": true, - "coef_reg_cnn": 1e-4, - "coef_reg_den": 1e-4, - "dropout_rate": { - "evolve_range": [ - 0.1, - 0.9 - ] - }, - "dense_size": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - }, - "model_name": "cnn_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer", - "check_bool": { - "evolve_bool": true - } - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - }, - "batch_size": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - }, - "metrics": { - "evolve_choice": true, - "values": [ - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ] - }, - "validation_patience": 5, - "val_every_n_epochs": 1, - "log_every_n_epochs": 1, - "show_examples": false, - "validate_best": true, - "test_best": false - } -} diff --git a/deeppavlov/configs/evolution/intents_snips_local.json b/deeppavlov/configs/evolution/intents_snips_local.json index a1a3034ebb..3563448704 100644 --- a/deeppavlov/configs/evolution/intents_snips_local.json +++ b/deeppavlov/configs/evolution/intents_snips_local.json @@ -125,6 +125,14 @@ 2, 3 ] + }, + "check_choice_str": { + "evolve_choice": true, + "values": [ + "hello", + "hello, again", + "bye-bye" + ] } } ], diff --git a/deeppavlov/configs/evolution/intents_snli.json b/deeppavlov/configs/evolution/intents_snli.json deleted file mode 100644 index 2e0afffd0d..0000000000 --- a/deeppavlov/configs/evolution/intents_snli.json +++ /dev/null @@ -1,133 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "gold_label", - "data_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/parts", - "train": "train_0.csv", - "valid": "valid.csv", - "test": "test.csv" - }, - "dataset_iterator": { - "name": "basic_classification_iterator" - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_data/one_input/snli_classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "load_path": "/home/dilyara.baymurzina/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "intent_model", - "save_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/param_evolution_0", - "load_path": "/home/dilyara.baymurzina/evolution_data/snli_classification/param_evolution_0", - "classes": "#classes_vocab.keys()", - "kernel_sizes_cnn": [ - 1, - 2, - 3 - ], - "filters_cnn": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": { - "range": [ - 0.0001, - 0.1 - ] - }, - "lear_rate_decay": 0.1, - "loss": "binary_crossentropy", - "text_size": 51, - "to_evolve": true, - "coef_reg_cnn": 1e-4, - "coef_reg_den": 1e-4, - "dropout_rate": { - "range": [ - 0.1, - 0.9 - ] - }, - "dense_size": { - "range": [ - 50, - 500 - ], - "discrete": true - }, - "model_name": "cnn_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": 100, - "batch_size": 64, - "metrics": [ - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 1, - "log_every_n_epochs": 1, - "show_examples": false, - "validate_best": true, - "test_best": true - } -} diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index a10880a291..a51ade3905 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -84,6 +84,7 @@ def main(): basic_params = read_json(pipeline_config_path) log.info("Given basic params: {}\n".format(json.dumps(basic_params, indent=2))) + # Initialize evolution evolution = ParamsEvolution(population_size=population_size, p_crossover=p_crossover, crossover_power=pow_crossover, p_mutation=p_mutation, mutation_power=pow_mutation, @@ -98,6 +99,7 @@ def main(): evolution.basic_config, "metrics"))[0] + ["metrics"]) evolve_metric = considered_metrics[0] + # Create table variable for gathering results result_file = Path(evolution.get_value_from_config(evolution.basic_config, evolution.main_model_path + ["save_path"]) ).joinpath("result_table.csv") @@ -112,13 +114,17 @@ def main(): result_table_columns.append("params") if start_from_population == 0: + # if starting evolution from scratch iters = 0 result_table = pd.DataFrame(result_table_dict) + # write down result table file result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') log.info("Iteration #{} starts".format(iters)) + # randomly generate the first population population = evolution.first_generation() else: + # if starting evolution from already existing population iters = start_from_population log.info("Iteration #{} starts".format(iters)) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 73a147b950..61eded9615 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -343,6 +343,16 @@ def selection_of_best_with_weights(self, population, scores): return selected def range_scores(self, scores): + """ + Ranges scores, + range 1 corresponds to the best score, + range self.population_size corresponds to the worst score. + Args: + scores: list of corresponding scores of population + + Returns: + ranges + """ not_none_scores = np.array([x for x in scores if x is not None]) if len(not_none_scores) == 0: not_none_scores = np.array([0]) From 9804491916f6ef3f5f679967b11ee74a9ce8d7a8 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 22 Jun 2018 17:57:39 +0300 Subject: [PATCH 490/616] feat: example config for intents snips --- .../evolution/evolve_intents_snips.json | 184 ++++++++++++++++++ 1 file changed, 184 insertions(+) create mode 100644 deeppavlov/configs/evolution/evolve_intents_snips.json diff --git a/deeppavlov/configs/evolution/evolve_intents_snips.json b/deeppavlov/configs/evolution/evolve_intents_snips.json new file mode 100644 index 0000000000..ec1c0696b0 --- /dev/null +++ b/deeppavlov/configs/evolution/evolve_intents_snips.json @@ -0,0 +1,184 @@ +{ + "dataset_reader": { + "name": "basic_classification_reader", + "x": "text", + "y": "intents", + "data_path": "snips" + }, + "dataset_iterator": { + "name": "basic_classification_iterator", + "seed": { + "evolve_range": [ + 50, + 500 + ], + "discrete": true + }, + "field_to_split": "train", + "split_fields": [ + "train", + "valid" + ], + "split_proportions": [ + 0.9, + 0.1 + ] + }, + "chainer": { + "in": [ + "x" + ], + "in_y": [ + "y" + ], + "pipe": [ + { + "id": "classes_vocab", + "name": "default_vocab", + "fit_on": [ + "y" + ], + "level": "token", + "save_path": "vocabs/snips_classes.dict", + "load_path": "vocabs/snips_classes.dict" + }, + { + "in": [ + "x" + ], + "out": [ + "x_lower" + ], + "name": "str_lower" + }, + { + "id": "my_embedder", + "name": "fasttext", + "save_path": "embeddings/dstc2_fastText_model.bin", + "load_path": "embeddings/dstc2_fastText_model.bin", + "dim": 300 + }, + { + "id": "my_tokenizer", + "name": "nltk_tokenizer", + "tokenizer": "wordpunct_tokenize" + }, + { + "in": [ + "x_lower" + ], + "in_y": [ + "y" + ], + "out": [ + "y_labels", + "y_probas_dict" + ], + "main": true, + "name": "intent_model", + "save_path": "evolution/classification/intents_snips", + "load_path": "evolution/classification/intents_snips", + "classes": "#classes_vocab.keys()", + "kernel_sizes_cnn": [ + 1, + 2, + 3 + ], + "filters_cnn": { + "evolve_range": [ + 5, + 50 + ], + "discrete": true + }, + "confident_threshold": 0.5, + "optimizer": "Adam", + "lear_rate": { + "evolve_range": [ + 0.0001, + 0.1 + ] + }, + "lear_rate_decay": 0.1, + "loss": "binary_crossentropy", + "text_size": 15, + "coef_reg_cnn": { + "evolve_range": [ + 1e-6, + 1e-3 + ] + }, + "coef_reg_den": { + "evolve_range": [ + 1e-6, + 1e-3 + ] + }, + "dropout_rate": { + "evolve_range": [ + 0.1, + 0.9 + ] + }, + "dense_size": { + "evolve_range": [ + 5, + 50 + ], + "discrete": true + }, + "model_name": "cnn_model", + "embedder": "#my_embedder", + "tokenizer": "#my_tokenizer" + } + ], + "out": [ + "y_labels", + "y_probas_dict" + ] + }, + "train": { + "epochs": { + "evolve_range": [ + 50, + 500 + ], + "discrete": true + }, + "batch_size": { + "evolve_range": [ + 50, + 500 + ], + "discrete": true + }, + "metrics": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], + "validation_patience": 5, + "val_every_n_epochs": 1, + "log_every_n_epochs": 1, + "validate_best": true, + "test_best": false + }, + "metadata": { + "labels": { + "telegram_utils": "IntentModel", + "server_utils": "KerasIntentModel" + }, + "download": [ + "http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz", + "http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz", + { + "url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv", + "subdir": "snips" + }, + { + "url": "http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin", + "subdir": "embeddings" + } + ] + } +} From 3cdb33c712100d2a77427781b225ea59c3ff2c7a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 22 Jun 2018 17:59:16 +0300 Subject: [PATCH 491/616] docs: start make README --- deeppavlov/models/evolution/README.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) create mode 100644 deeppavlov/models/evolution/README.md diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md new file mode 100644 index 0000000000..600699c679 --- /dev/null +++ b/deeppavlov/models/evolution/README.md @@ -0,0 +1,24 @@ +[![License Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](/LICENSE.txt) +![Python 3.6](https://img.shields.io/badge/python-3.6-green.svg) + +# Parameters evolution for DeepPavlov models + +This repository contains implementation of parameters evolution for DeepPavlov models. + + + +If one prefers to run evolution on some provided by DeepPavlov dataset, +firstly, download embeddings and datasets running the following command providing +corresponding name of the config file (see above): + +``` +cd deeppavlov +python deep.py download configs/intents/intents_snips.json +``` + +To evolve model of interest run the following command providing corresponding name of the config file (see above): +``` +cd deeppavlov +python evolve.py interact configs/evolution/evolve_intents_snips.json +``` + From b1a587b5546f46b5f97b43f48fb7d57b34fde0ac Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Fri, 22 Jun 2018 18:24:48 +0300 Subject: [PATCH 492/616] fix: train partition with file ext --- deeppavlov/models/evolution/evolution_param_generator.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 61eded9615..5fef6d9f1a 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -242,9 +242,10 @@ def next_generation(self, generation, scores, iteration): for i in range(self.n_saved_best_pretrained): # if several train files: if self.train_partition != 1: + file_ext = str(Path(next_population[i]["dataset_reader"]["train"]).suffix) next_population[i]["dataset_reader"]["train"] = "_".join(str(Path(next_population[i]["dataset_reader"][ "train"]).stem.split("_")[:-1])) \ - + "_" + str(iteration % self.train_partition) + ".csv" + + "_" + str(iteration % self.train_partition) + file_ext try: # re-init learning rate with the final one (works for KerasModel) next_population[i] = self.insert_value_or_dict_into_config( @@ -291,9 +292,10 @@ def next_generation(self, generation, scores, iteration): for i in range(self.n_saved_best_pretrained, self.population_size): # if several train files if self.train_partition != 1: + file_ext = str(Path(next_population[i]["dataset_reader"]["train"]).suffix) next_population[i]["dataset_reader"]["train"] = "_".join(str(Path(next_population[i]["dataset_reader"][ "train"]).stem.split("_")[:-1])) \ - + "_" + str(iteration % self.train_partition) + ".csv" + + "_" + str(iteration % self.train_partition) + file_ext for which_path in ["save_path", "load_path"]: next_population[i] = self.insert_value_or_dict_into_config( next_population[i], From 416ed2911fcf444b771ed40cf930f7537fb57c6b Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Fri, 22 Jun 2018 18:32:07 +0300 Subject: [PATCH 493/616] docs: parameters of evolution described --- deeppavlov/models/evolution/README.md | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index 600699c679..b9c733e9b7 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -5,7 +5,21 @@ This repository contains implementation of parameters evolution for DeepPavlov models. - +Evolution process can be described in the following way: +* Initialize parameters of evolutionary process: + - p_size - number of individuums (models) per population + - key_main_model - key of the dictionary in config containing the model being trained. + - p_cross - probability of crossover for a parent pair + - pow_cross - crossover power - portion of evolving parameters that will be exchanged between parents during crossover + - p_mut - probability of mutation for a parameter + - pow_mut - mutation power - maximal portion of maximal possible value of parameter which can be added or subtracted during mutation + - gpus - available GPUs divided by comma "," (default "-1" means CPU support; "0,3,5,2" means visible 0, 2, 3, 5 GPUs) + - train_partition - if train file is too big to train (recommeded to divide train files if train dataset is more than 100 thousands examples), one can split it in train_partition number of files, save it calling "any_name_{0}.any_extension", ..., "any_name_{train_partition}.any_extension". In dataset_reader "train" field indicate the first one file. Population is trained on the $N_{population} % train\_partition$ part of the dataset. + - start_from_population - the number of population to start from that is needed to restart population, for example (by feault, starts from 0 population). + - path_to_population - path to the directory "population_{start_from_population}". Should be given if start_from_population is not 0. + - elitism_with_weights - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not + +## Example If one prefers to run evolution on some provided by DeepPavlov dataset, firstly, download embeddings and datasets running the following command providing From 0f8e748c5f2d82ca1fc26281e27fdb037b0b1ceb Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Fri, 22 Jun 2018 18:34:42 +0300 Subject: [PATCH 494/616] docs: evolve parameters --- deeppavlov/models/evolution/README.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index b9c733e9b7..07909cc8e0 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -7,17 +7,17 @@ This repository contains implementation of parameters evolution for DeepPavlov m Evolution process can be described in the following way: * Initialize parameters of evolutionary process: - - p_size - number of individuums (models) per population - - key_main_model - key of the dictionary in config containing the model being trained. - - p_cross - probability of crossover for a parent pair - - pow_cross - crossover power - portion of evolving parameters that will be exchanged between parents during crossover - - p_mut - probability of mutation for a parameter - - pow_mut - mutation power - maximal portion of maximal possible value of parameter which can be added or subtracted during mutation - - gpus - available GPUs divided by comma "," (default "-1" means CPU support; "0,3,5,2" means visible 0, 2, 3, 5 GPUs) - - train_partition - if train file is too big to train (recommeded to divide train files if train dataset is more than 100 thousands examples), one can split it in train_partition number of files, save it calling "any_name_{0}.any_extension", ..., "any_name_{train_partition}.any_extension". In dataset_reader "train" field indicate the first one file. Population is trained on the $N_{population} % train\_partition$ part of the dataset. - - start_from_population - the number of population to start from that is needed to restart population, for example (by feault, starts from 0 population). - - path_to_population - path to the directory "population_{start_from_population}". Should be given if start_from_population is not 0. - - elitism_with_weights - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not + - `p_size` - number of individuums (models) per population + - `key_main_model` - key of the dictionary in config containing the model being trained. + - `p_cross` - probability of crossover for a parent pair + - `pow_cross` - crossover power - portion of evolving parameters that will be exchanged between parents during crossover + - `p_mut` - probability of mutation for a parameter + - `pow_mut` - mutation power - maximal portion of maximal possible value of parameter which can be added or subtracted during mutation + - `gpus` - available GPUs divided by comma "," (default "-1" means CPU support; "0,3,5,2" means visible 0, 2, 3, 5 GPUs) + - `train_partition` - if train file is too big to train (recommeded to divide train files if train dataset is more than 100 thousands examples), one can split it in `train_partition` number of files, save it calling "any_name_{0}.any_extension", ..., "any_name_{`train_partition`}.any_extension". In dataset_reader "train" field indicate the first one file. Population is trained on the N_{population} % `train_partition` part of the dataset. + - `start_from_population` - the number of population to start from that is needed to restart population, for example (by feault, starts from 0 population). + - `path_to_population` - path to the directory "population_{`start_from_population`}". Should be given if `start_from_population` is not 0. + - `elitism_with_weights` - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not ## Example From 69d0dafec38d1b38e95eb5827bd43f74efbd40c6 Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Fri, 22 Jun 2018 18:50:25 +0300 Subject: [PATCH 495/616] docs: evolution description --- deeppavlov/models/evolution/README.md | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index 07909cc8e0..b0e22f5b7b 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -8,7 +8,7 @@ This repository contains implementation of parameters evolution for DeepPavlov m Evolution process can be described in the following way: * Initialize parameters of evolutionary process: - `p_size` - number of individuums (models) per population - - `key_main_model` - key of the dictionary in config containing the model being trained. + - `key_main_model` - key of the dictionary in config containing the model being trained (see description below). - `p_cross` - probability of crossover for a parent pair - `pow_cross` - crossover power - portion of evolving parameters that will be exchanged between parents during crossover - `p_mut` - probability of mutation for a parameter @@ -19,6 +19,20 @@ Evolution process can be described in the following way: - `path_to_population` - path to the directory "population_{`start_from_population`}". Should be given if `start_from_population` is not 0. - `elitism_with_weights` - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not +* Current version allows to evolve any parameter of the config that is an item of some dictionary in config file. One can make a copy of a usual DeepPavlov model config, and reassign parameters that can be tuned during evolution. +To evolve some parameter one has to assign it to a dictionary of one of the following type: + - ```{"evolve_range": [min_value, max_value]}``` - values uniformly distributed on the given interval, + - ```{"evolve_range": [min_value, max_value], "scale": "log"}``` - values distributed on the given interval logariphmically, + - ```{"evolve_range": [min_value, max_value], "discrete": true}``` - discrete values uniformly distributed on the following interval, + - ```{"evolve_bool": true}``` - bool values, + - ```{"evolve_choice": true, "values": [value_0, ..., value_n]}``` - values uniformly taking on of the given values. + +* Choose the main model in the pipe being evolved. Find or add extra parameter that determines this model (for example, existing `"main": true`). The dictionary - model containing this parameter as a key will be trained (do not forget to give this parameter's name to `key_main_model`). Change `save_path` and `load_path` of this model to any ABSOLUTE paths (VERY IMPORTANT) to folder where population will be saved. + +* All the models in pipe that contain key `fit_on` will be trained every time separately for each model and saved to the same directory with model and called `fitted_model_{i}`. + +That's all you need to change in the config. Now let's mode on to the example. + ## Example If one prefers to run evolution on some provided by DeepPavlov dataset, From f8321639a2696f0dfc7d18ca1c982768ad62afdd Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 11:39:56 +0300 Subject: [PATCH 496/616] fix: possibility of setting relative path --- deeppavlov/evolve.py | 21 ++++++++++++------- .../evolution/evolution_param_generator.py | 2 -- 2 files changed, 14 insertions(+), 9 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index a51ade3905..796089c798 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -30,6 +30,7 @@ from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution from deeppavlov.core.common.file import read_json, save_json from deeppavlov.core.common.log import get_logger +from deeppavlov.core.commands.utils import set_deeppavlov_root, expand_path log = get_logger(__name__) @@ -100,9 +101,15 @@ def main(): evolve_metric = considered_metrics[0] # Create table variable for gathering results - result_file = Path(evolution.get_value_from_config(evolution.basic_config, - evolution.main_model_path + ["save_path"]) - ).joinpath("result_table.csv") + set_deeppavlov_root(evolution.basic_config) + + expand_path(Path(evolution.get_value_from_config( + evolution.basic_config, evolution.main_model_path + ["save_path"]))).mkdir(parents=True, exist_ok=True) + + result_file = expand_path(Path(evolution.get_value_from_config(evolution.basic_config, + evolution.main_model_path + ["save_path"]) + ).joinpath("result_table.csv")) + result_table_columns = [] result_table_dict = {} for el in considered_metrics: @@ -130,8 +137,8 @@ def main(): population = [] for i in range(population_size): - population.append(read_json(Path(path_to_population).joinpath( - "model_" + str(i)).joinpath("config.json"))) + population.append(read_json(expand_path(Path(path_to_population).joinpath( + "model_" + str(i)).joinpath("config.json")))) population[i] = evolution.insert_value_or_dict_into_config( population[i], evolution.main_model_path + ["save_path"], str(Path( @@ -197,8 +204,8 @@ def run_population(population, evolution, gpus): for j in range(len(gpus)): i = k * len(gpus) + j if i < population_size: - save_path = Path(evolution.get_value_from_config(population[i], - evolution.main_model_path + ["save_path"])).parent + save_path = expand_path(Path(evolution.get_value_from_config( + population[i], evolution.main_model_path + ["save_path"])).parent) save_path.mkdir(parents=True, exist_ok=True) f_name = save_path.joinpath("config.json") diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 5fef6d9f1a..07acebf027 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -52,8 +52,6 @@ def __init__(self, self.basic_config = deepcopy(kwargs) self.main_model_path = list(self.find_model_path(self.basic_config, key_main_model))[0] - Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"])).mkdir(parents=True, - exist_ok=True) log.info("Main model path in config: {}".format(self.main_model_path)) self.population_size = population_size From a50a37f816a261bd9003d2385171d75b7e0dad08 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 11:42:02 +0300 Subject: [PATCH 497/616] fix: out file path expanded --- deeppavlov/evolve.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 796089c798..25b7c8801b 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -257,9 +257,9 @@ def results_to_table(population, evolution, considered_metrics, result_file, res for m in considered_metrics: population_metrics[m] = [] for i in range(population_size): - with open(str(Path(evolution.get_value_from_config( + with open(str(expand_path(Path(evolution.get_value_from_config( population[i], - evolution.main_model_path + ["save_path"])).parent.joinpath("out.txt")), "r") as fout: + evolution.main_model_path + ["save_path"])).parent.joinpath("out.txt"))), "r") as fout: reports_data = fout.read().splitlines()[-2:] reports = [] for i in range(2): From 0b33c597ed426b43e00da80bccda07151a94eed7 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 12:21:04 +0300 Subject: [PATCH 498/616] fix: considered metrics --- .../evolution/evolve_intents_snips.json | 34 +++- .../evolution/intents_snips_local.json | 165 ------------------ deeppavlov/evolve.py | 6 + 3 files changed, 31 insertions(+), 174 deletions(-) delete mode 100644 deeppavlov/configs/evolution/intents_snips_local.json diff --git a/deeppavlov/configs/evolution/evolve_intents_snips.json b/deeppavlov/configs/evolution/evolve_intents_snips.json index ec1c0696b0..2728d2523b 100644 --- a/deeppavlov/configs/evolution/evolve_intents_snips.json +++ b/deeppavlov/configs/evolution/evolve_intents_snips.json @@ -56,7 +56,7 @@ "name": "fasttext", "save_path": "embeddings/dstc2_fastText_model.bin", "load_path": "embeddings/dstc2_fastText_model.bin", - "dim": 300 + "dim": 100 }, { "id": "my_tokenizer", @@ -91,15 +91,28 @@ ], "discrete": true }, - "confident_threshold": 0.5, + "confident_threshold": { + "evolve_choice": true, + "values": [ + 0.5, + 1 + ] + }, "optimizer": "Adam", "lear_rate": { "evolve_range": [ 0.0001, 0.1 - ] + ], + "scale": "log" + }, + "lear_rate_decay": { + "evolve_range": [ + 0.0001, + 0.1 + ], + "scale": "log" }, - "lear_rate_decay": 0.1, "loss": "binary_crossentropy", "text_size": 15, "coef_reg_cnn": { @@ -152,11 +165,14 @@ ], "discrete": true }, - "metrics": [ - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], + "metrics": { + "evolve_choice": true, + "values": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ] + }, "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, diff --git a/deeppavlov/configs/evolution/intents_snips_local.json b/deeppavlov/configs/evolution/intents_snips_local.json deleted file mode 100644 index 3563448704..0000000000 --- a/deeppavlov/configs/evolution/intents_snips_local.json +++ /dev/null @@ -1,165 +0,0 @@ -{ - "dataset_reader": { - "name": "basic_classification_reader", - "x": "text", - "y": "intents", - "data_path": "/home/dilyara/data/data_files/snips/snips_dataset", - "train": "train.csv", - "valid": "valid.csv", - "test": "test.csv" - }, - "dataset_iterator": { - "name": "basic_classification_iterator", - "seed": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - } - }, - "chainer": { - "in": [ - "x" - ], - "in_y": [ - "y" - ], - "pipe": [ - { - "id": "classes_vocab", - "name": "default_vocab", - "fit_on": [ - "y" - ], - "level": "token", - "save_path": "/home/dilyara/data/data_files/snips/snips_dataset/classes.dict", - "load_path": "/home/dilyara/data/data_files/snips/snips_dataset/classes.dict" - }, - { - "in": [ - "x" - ], - "out": [ - "x_lower" - ], - "name": "str_lower" - }, - { - "id": "my_embedder", - "name": "fasttext", - "save_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", - "load_path": "/home/dilyara/data/data_files/embeddings/wiki.en.bin", - "dim": 300 - }, - { - "id": "my_tokenizer", - "name": "nltk_tokenizer", - "tokenizer": "wordpunct_tokenize" - }, - { - "in": [ - "x_lower" - ], - "in_y": [ - "y" - ], - "out": [ - "y_labels", - "y_probas_dict" - ], - "main": true, - "name": "intent_model", - "save_path": "/home/dilyara/data/models/evolution_data/snips_classification/param_evolution_0", - "load_path": "/home/dilyara/data/models/evolution_data/snips_classification/param_evolution_0", - "classes": "#classes_vocab.keys()", - "kernel_sizes_cnn": [ - 1, - 2, - 3 - ], - "filters_cnn": { - "evolve_range": [ - 5, - 50 - ], - "discrete": true - }, - "confident_threshold": 0.5, - "optimizer": "Adam", - "lear_rate": { - "evolve_range": [ - 0.0001, - 0.1 - ] - }, - "lear_rate_decay": 0.1, - "loss": "binary_crossentropy", - "text_size": 51, - "to_evolve": true, - "coef_reg_cnn": 1e-4, - "coef_reg_den": 1e-4, - "dropout_rate": { - "evolve_range": [ - 0.1, - 0.9 - ] - }, - "dense_size": { - "evolve_range": [ - 5, - 50 - ], - "discrete": true - }, - "model_name": "cnn_model", - "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer", - "check_bool": { - "evolve_bool": true - }, - "check_choice": { - "evolve_choice": true, - "values": [ - 1, - 2, - 3 - ] - }, - "check_choice_str": { - "evolve_choice": true, - "values": [ - "hello", - "hello, again", - "bye-bye" - ] - } - } - ], - "out": [ - "y_labels", - "y_probas_dict" - ] - }, - "train": { - "epochs": 1, - "batch_size": { - "evolve_range": [ - 50, - 500 - ], - "discrete": true - }, - "metrics": [ - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ], - "validation_patience": 5, - "val_every_n_epochs": 1, - "log_every_n_epochs": 1, - "show_examples": false, - "validate_best": true, - "test_best": true - } -} diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 25b7c8801b..ea94f81127 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -98,6 +98,12 @@ def main(): considered_metrics = evolution.get_value_from_config(evolution.basic_config, list(evolution.find_model_path( evolution.basic_config, "metrics"))[0] + ["metrics"]) + if type(considered_metrics) is dict: + considered_metrics = evolution.sample_params(considered_metrics)["metrics"] + if type(considered_metrics) is str: + considered_metrics = [considered_metrics] + + log.info(considered_metrics) evolve_metric = considered_metrics[0] # Create table variable for gathering results From ab98ee90d93fe2001504d2c047c81cedf0dd962a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 12:23:05 +0300 Subject: [PATCH 499/616] fix: considered metrics --- deeppavlov/evolve.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index ea94f81127..e88d45a2cb 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -99,7 +99,7 @@ def main(): list(evolution.find_model_path( evolution.basic_config, "metrics"))[0] + ["metrics"]) if type(considered_metrics) is dict: - considered_metrics = evolution.sample_params(considered_metrics)["metrics"] + considered_metrics = evolution.sample_params(**{"metrics": considered_metrics})["metrics"] if type(considered_metrics) is str: considered_metrics = [considered_metrics] From 58e6153e1987f00e8b072478d8488ff572bcb04d Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Mon, 25 Jun 2018 12:26:07 +0300 Subject: [PATCH 500/616] docs: example --- deeppavlov/models/evolution/README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index b0e22f5b7b..e05c5255e2 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -7,7 +7,7 @@ This repository contains implementation of parameters evolution for DeepPavlov m Evolution process can be described in the following way: * Initialize parameters of evolutionary process: - - `p_size` - number of individuums (models) per population + - `p_size` - number of individuals (models) per population - `key_main_model` - key of the dictionary in config containing the model being trained (see description below). - `p_cross` - probability of crossover for a parent pair - `pow_cross` - crossover power - portion of evolving parameters that will be exchanged between parents during crossover @@ -35,18 +35,18 @@ That's all you need to change in the config. Now let's mode on to the example. ## Example -If one prefers to run evolution on some provided by DeepPavlov dataset, -firstly, download embeddings and datasets running the following command providing -corresponding name of the config file (see above): +* If one prefers to run evolution on some provided by DeepPavlov dataset, +firstly, download embeddings and datasets. +Consider parameters evolution on SNIPS dataset, download data running the following command providing +corresponding name of the config file: ``` cd deeppavlov python deep.py download configs/intents/intents_snips.json ``` - -To evolve model of interest run the following command providing corresponding name of the config file (see above): +* To evolve the model run the following command providing corresponding name of the config file (see above): ``` cd deeppavlov -python evolve.py interact configs/evolution/evolve_intents_snips.json +python evolve.py configs/evolution/evolve_intents_snips.json ``` - +* Folder `download/evolution/classification/intents_snips` will be created. Each population will be saved in a folder `download/evolution/classification/intents_snips/population_i` each of which contains `population_size` folders `model_i` consisting of saved model files explicitly, saved files of models from pipe that has a key "fit_on", `out.txt` and `err.txt` with logs of `deep.py train` script from training each model separately, and `config.json` with config for this individual. From c11bfef31dd30ca7acdc842c7d3ca0642b717f6e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 12:34:03 +0300 Subject: [PATCH 501/616] fix: considered metrics can not be evolved --- .../configs/evolution/evolve_intents_snips.json | 13 +++++-------- deeppavlov/evolve.py | 3 +-- 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/deeppavlov/configs/evolution/evolve_intents_snips.json b/deeppavlov/configs/evolution/evolve_intents_snips.json index 2728d2523b..a06fd09318 100644 --- a/deeppavlov/configs/evolution/evolve_intents_snips.json +++ b/deeppavlov/configs/evolution/evolve_intents_snips.json @@ -165,14 +165,11 @@ ], "discrete": true }, - "metrics": { - "evolve_choice": true, - "values": [ - "classification_accuracy", - "classification_f1", - "classification_roc_auc" - ] - }, + "metrics": [ + "classification_accuracy", + "classification_f1", + "classification_roc_auc" + ], "validation_patience": 5, "val_every_n_epochs": 1, "log_every_n_epochs": 1, diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index e88d45a2cb..6e5cbf1c4f 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -98,8 +98,7 @@ def main(): considered_metrics = evolution.get_value_from_config(evolution.basic_config, list(evolution.find_model_path( evolution.basic_config, "metrics"))[0] + ["metrics"]) - if type(considered_metrics) is dict: - considered_metrics = evolution.sample_params(**{"metrics": considered_metrics})["metrics"] + if type(considered_metrics) is str: considered_metrics = [considered_metrics] From 9c8be202f3a580beff0858d3d60493ed7bed0481 Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Mon, 25 Jun 2018 12:35:59 +0300 Subject: [PATCH 502/616] fix: metrics can not evolve --- deeppavlov/models/evolution/README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index e05c5255e2..d7159696f7 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -19,6 +19,8 @@ Evolution process can be described in the following way: - `path_to_population` - path to the directory "population_{`start_from_population`}". Should be given if `start_from_population` is not 0. - `elitism_with_weights` - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not +* **Warning**: `metrics` can not be evolved because the main metric determines evolutionary process. + * Current version allows to evolve any parameter of the config that is an item of some dictionary in config file. One can make a copy of a usual DeepPavlov model config, and reassign parameters that can be tuned during evolution. To evolve some parameter one has to assign it to a dictionary of one of the following type: - ```{"evolve_range": [min_value, max_value]}``` - values uniformly distributed on the given interval, @@ -37,7 +39,6 @@ That's all you need to change in the config. Now let's mode on to the example. * If one prefers to run evolution on some provided by DeepPavlov dataset, firstly, download embeddings and datasets. - Consider parameters evolution on SNIPS dataset, download data running the following command providing corresponding name of the config file: ``` From 4b8a5e59ebb175946ce080027c688ddad976c274 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 12:43:33 +0300 Subject: [PATCH 503/616] feat: new params in config --- deeppavlov/configs/evolution/evolve_intents_snips.json | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/deeppavlov/configs/evolution/evolve_intents_snips.json b/deeppavlov/configs/evolution/evolve_intents_snips.json index a06fd09318..9c9f849edf 100644 --- a/deeppavlov/configs/evolution/evolve_intents_snips.json +++ b/deeppavlov/configs/evolution/evolve_intents_snips.json @@ -86,8 +86,8 @@ ], "filters_cnn": { "evolve_range": [ - 5, - 50 + 50, + 100 ], "discrete": true }, @@ -135,8 +135,8 @@ }, "dense_size": { "evolve_range": [ - 5, - 50 + 50, + 100 ], "discrete": true }, From 2cf86bc37ca924e99d8a930fad5e4bed49d3ddc3 Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Mon, 25 Jun 2018 12:45:19 +0300 Subject: [PATCH 504/616] docs: where to see results --- deeppavlov/models/evolution/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index d7159696f7..db753703bf 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -51,3 +51,5 @@ cd deeppavlov python evolve.py configs/evolution/evolve_intents_snips.json ``` * Folder `download/evolution/classification/intents_snips` will be created. Each population will be saved in a folder `download/evolution/classification/intents_snips/population_i` each of which contains `population_size` folders `model_i` consisting of saved model files explicitly, saved files of models from pipe that has a key "fit_on", `out.txt` and `err.txt` with logs of `deep.py train` script from training each model separately, and `config.json` with config for this individual. + +* Now one can open iPython Notebook file `deeppavlov/evolution/Results_analysis.ipynb`, set `CONFIG_FILE` to config file path and run cells to see evolution results. From bd42a40644d7bef60048510f747ee56e50f288c0 Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Mon, 25 Jun 2018 12:46:12 +0300 Subject: [PATCH 505/616] docs: where to see results --- deeppavlov/models/evolution/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index db753703bf..7698ea5a93 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -52,4 +52,4 @@ python evolve.py configs/evolution/evolve_intents_snips.json ``` * Folder `download/evolution/classification/intents_snips` will be created. Each population will be saved in a folder `download/evolution/classification/intents_snips/population_i` each of which contains `population_size` folders `model_i` consisting of saved model files explicitly, saved files of models from pipe that has a key "fit_on", `out.txt` and `err.txt` with logs of `deep.py train` script from training each model separately, and `config.json` with config for this individual. -* Now one can open iPython Notebook file `deeppavlov/evolution/Results_analysis.ipynb`, set `CONFIG_FILE` to config file path and run cells to see evolution results. +* Now one can open iPython Notebook file `deeppavlov/models/evolution/Results_analysis.ipynb`, set `CONFIG_FILE` to config file path and run cells to see evolution results. From 9ff8b7427876f0a95b78166907e9fb0ae123264a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 12:46:52 +0300 Subject: [PATCH 506/616] feat: results analysis --- .../models/evolution/Results_analysis.ipynb | 1050 +++++++++++++++++ 1 file changed, 1050 insertions(+) create mode 100644 deeppavlov/models/evolution/Results_analysis.ipynb diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb new file mode 100644 index 0000000000..2ea149ff27 --- /dev/null +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -0,0 +1,1050 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "import copy\n", + "import json\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of populations: 62\n" + ] + } + ], + "source": [ + "PLOT_TEST = False\n", + "\n", + "TITLE = \"imdb_given_mask_init_part_7\"\n", + "model_index = 4\n", + "POPULATION_SIZE = 10\n", + "\n", + "# TITLE = \"sber_faq_given_mask_init_part_7\"\n", + "# model_index = 4\n", + "# POPULATION_SIZE = 10\n", + "\n", + "# TITLE = \"ag_news_given_mask_init_part_7\"\n", + "# model_index = 4\n", + "# POPULATION_SIZE = 10\n", + "\n", + "# TITLE = \"snli_given_mask_init_part_6\"\n", + "# model_index = 4\n", + "# POPULATION_SIZE = 10\n", + "\n", + "# TITLE = \"snli_given_mask_init_part_many_inputs_6\"\n", + "# model_index = 5\n", + "# POPULATION_SIZE = 10\n", + "\n", + "# TITLE = \"twitter140_one_neuron_init_part_6\"\n", + "# model_index = 4\n", + "# POPULATION_SIZE = 10\n", + "\n", + "data = pd.read_csv(\"result_tables/result_table_\" + TITLE + \".csv\", sep='\\t')\n", + "print(\"Number of populations: {}\".format(int(data.shape[0] / POPULATION_SIZE)))\n", + "# data.dropna(axis=1, how=\"any\", inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "classification_log_loss: best value for VALID \t0 individuum on 0 population\n", + "classification_log_loss: best value for TEST \t0 individuum on 0 population\n", + "classification_accuracy: best value for VALID \t3 individuum on 56 population\n", + "classification_accuracy: best value for TEST \t3 individuum on 55 population\n", + "classification_roc_auc: best value for VALID \t9 individuum on 61 population\n", + "classification_roc_auc: best value for TEST \t9 individuum on 61 population\n", + "classification_f1: best value for VALID \t3 individuum on 56 population\n", + "classification_f1: best value for TEST \t3 individuum on 55 population\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:11: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", + " # This is added back by InteractiveShellApp.init_path()\n", + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:12: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", + " if sys.path[0] == '':\n" + ] + } + ], + "source": [ + "MEASURES = [\"classification_log_loss\", \n", + " \"classification_accuracy\",\n", + " \"classification_roc_auc\", \n", + " \"classification_f1\"]\n", + "for measure in MEASURES:\n", + " if (measure == \"classification_log_loss_test\" \n", + " or measure == \"classification_log_loss_valid\"):\n", + " n_best_valid = data[measure + \"_valid\"].argmin()\n", + " n_best_test = data[measure + \"_test\"].argmin()\n", + " else:\n", + " n_best_valid = data[measure + \"_valid\"].argmax()\n", + " n_best_test = data[measure + \"_test\"].argmax()\n", + " print(\"{}: best value for VALID \\t{} individuum on {} population\".format(measure, \n", + " n_best_valid % POPULATION_SIZE, \n", + " n_best_valid // POPULATION_SIZE))\n", + " print(\"{}: best value for TEST \\t{} individuum on {} population\".format(measure, \n", + " n_best_test % POPULATION_SIZE, \n", + " n_best_test // POPULATION_SIZE))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "cmap = plt.get_cmap('rainbow')\n", + "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", + "color_ids = np.argsort(data.loc[:, \"classification_accuracy_valid\"].values)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Path(\"./pics/\").joinpath(TITLE).mkdir(exist_ok=True, parents=True)\n", + "\n", + "try:\n", + " y_label = \"Number of edges\"\n", + " plt.figure(figsize=(12, 12))\n", + " for i in range(data.shape[0]):\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", + " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", + " d = json.loads(json_acceptable_string)\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " np.sum(d[\"chainer\"][\"pipe\"][model_index][\"binary_mask\"]) \n", + " + (np.random.random() - 0.5) / 2, \n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + "\n", + " plt.ylabel(y_label, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", + " plt.show()\n", + "except:\n", + " pass\n", + "\n", + "\n", + "# ylims = [(0., 1), (0.85, 1), (0.9, 1.), (0.85, 1.)] #ag_news\n", + "ylims = [(0., 1), (0.8, 0.9), (0.8, 1.), (0.8, 0.9)] #imdb\n", + "# ylims = [(0., 1), (0., 1), (0., 1.), (0., 1.)]\n", + "# ylims = [(0., 1), (0.7, 0.9), (0.9, 1.), (0.6, 0.85)] #sber faq\n", + "\n", + "for metric, ylim in zip(MEASURES, ylims):\n", + " y_label = metric\n", + " plt.figure(figsize=(12,6))\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_valid\"].values[i], \n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5, marker='o')\n", + " if PLOT_TEST:\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_test\"].values[i], \n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5, marker='+', s=200)\n", + "\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_valid\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \n", + " c='r')\n", + "\n", + " plt.ylabel(y_label, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.ylim(ylim[0], ylim[1])\n", + " # plt.ylim(0.85, 0.95)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "params_dictionaries = []\n", + "\n", + "for i in range(data.shape[0]):\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", + " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", + " d = json.loads(json_acceptable_string)\n", + " params_dictionaries.append(d)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model ids" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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ubWbvJloKbNq0aUevWLFijCaKiIiIiIzPqlWrtrr7nLHKVUuwXxHRBjXfA94JPBBt4LEdOJ6w8c5jwApibHpTwjW/TbTe78qVK/2ee+6pVNUiIiIiIkWZ2TOllKuWpTezveftoxzP5neWUNe7gH8EHidsHvKPhE1uTgKeisrk7lBZyWuLiIiIiFSNaunZfzxKl49yfFmUjjam/wUelhd6obc9l5kdRujVz90J8HFgZXTtVQXl6wjj/1MUmfQrIiIiIlLNqqVn/+YoPc3M8toULb15LGFVnLvKvUC0xN4S4Nfunjs+/6YoPb3IaScQ1s6+UyvxiIiIiMhUUxXBvrs/BdxAWFrsgoLDnwGmAVfmrrFvZivMbJfZsNEmWYV5+wLfBYaBTxYc/hlhFZ83m9nKnHOagP+IXn4j5i2JiIiIiEy6ahnGA/A+4E7gEjM7hbDM5UsJa/CvBj5RUD67DGbhUmTfi4L7ewmTc/cDXgvUA+e6+wO5hd2928zeRQj6/2hmP4nOey1hWc6fAVdV5A5FRERERPagqujZhxd691cClxOC/I8CBwBfAV7m7ttKrOo6YAQ4B/hn4DhCwH6EuxcN2qPd+U4kbKL1t8AHojo+ArzZq2GbYRERERGRmExxbOVo6U0RERER2RPMbJW7rxyrXNX07IuIiIiISGUp2BcRERERqVEK9kVEREREapSCfRERERGRGqVgX0RERESkRinYFxERERGpUQr2RURERERqlIJ9EREREZEapWBfRERERKRGKdgXEREREalRCvZFRERERGqUgn0RERERkRqlYF9EREREpEYp2BcRERERqVEK9kVEREREapSCfRERERGRGqVgX0RERESkRinYFxERERGpUQr2RURERERqlIJ9EREREZEapWBfRERERKRGKdgXEREREalRCvZFRERERGqUgn0RERERkRqlYF9EREREpEYp2BcRERERqVEK9kVEREREapSCfRERERGRGqVgX0RERESkRinYFxERERGpUQr2RURERERqlIJ9EREREZEapWBfRERERKRGKdgXEREREalRCvZFRERERGqUgn0RERERkRqlYF9EREREpEYp2BcRERERqVEK9kVEREREapSCfRERERGRGqVgX0RERESkRinYFxERERGpUbGCfTO7xMwOnqjGiIiIiIhI5cTt2X8/8JCZ3Wpmf29mDRPRKBERERERGb+4wf45wI3AscAVwEYz+5KZHVTxlomIiIiIyLjECvbd/efufhpwIPDfwDDwEeARM7vJzN5oZvUT0E4REREREYmprAm67r7G3T8GLGFnb/+JwI+B9Wb2X2Z2QOWaKSIiIiIicY1rNR53T+X09r8c2AjMAf4VeNzMrjOzoyvQThERERERiWncS2+a2Ylm9iPgFmAh8DxwMXA7cCZwl5m9abzXERERERGReOrKOcnMZgDnA+8GlgMG3AF8A/ipu49E5Y4BrgYuBK4af3NFRERERKRUcdfZP97MrgQ2AP9L6Mn/FnCEux/v7j/KBvoA7n438H2gpPH7ZrbIzC4zs41mNmRma83s4ujhIk47jzOza6PzB83sWTO73sxOH6V8MlpK9DYz22xm/Wa22sy+b2aHxLm2iIiIiEi1iNuzf0uUPkzoxb/C3XvHOGdD9LNb0YTeO4G5wLXAY8AxwIeA083sWHffVkI97wW+DvQB1wDrgUXA2cAZZvZJd/98wWk/At4Ylb0a6AEOA84D3mJmZ7j7TWNdW0RERESkmpi7l144jM3/hrvfVvGGmP0OOA34oLt/NSf/y8CHgW+5+3vGqKOeMGegETjS3R/POXYwcB+QAWa4+1CU/xLgbsIDzDHu3p9zztuBy4Cb3f3kse5h5cqVfs8995R4xyIiIiIi5TGzVe6+cqxycdfZf8sEBfoHEAL9tcDXCg5/mtBLf66ZTRujqplAO7A6N9AHcPdHgdVAM9Cac2j/KL0xN9CPXBulc0q4DRERERGRqhJ3zP4cMzvBzKaPcrwtOj47ZjteGaU3uHsm94C79xAm/7YALxujnucIPfvLzWxZQduWA8uA+wuGAz0cpSebWXNBfa+O0j+UdBciIiIiIlUk7tKbnwR+BaRHOZ6Ojn8sZr0HRenqUY4/EaXLd1eJhzFJFxDua5WZ/cDM/tPMrgBWEQL7cwrOeQi4CDgUeMzMvhZtCvYr4HvATwj3LSIiIiIypcSdoHsq8Psiw10AcPc+M7sBeBXw0Rj1tkdp1yjHs/kdY1Xk7j81s42E3XzflnNoC2FloKeLnPMRM3ucEPS/L+fQKuAH7t432vXM7N2EJUhZsmTJWM0TEREREdlj4vbsLwaeGqPM01G5SWFmbyUMu7kNOJgw/Odg4EbgUkJPfW55M7NLCHMFPkto+3TgeMCB35jZBaNdz92/7e4r3X3lnDka2i8iIiIi1SNusO9AwxhlGoBkzHqzPfftoxzP5nfurpJoXP5lhOE657r7Y+4+4O6PAecSeurPMbOTck47D/gAcIm7/5e7r3f3Xne/HXgNMAD8l5m1IiIiIiIyhcQN9h8nDNEpyswsOv5kGfXC6GPys5NtRxvTn3UaUA/cUmSibwa4NXp5dM6h7CTcmwsrc/fNhPX+W9k5r0BEREREZEqIG+z/DFhhZpcWrlwTvb6UEBRfFbPebKB9mpnltSla+edYoB+4a4x6GqN0tPE02fzhcZ4jIiIiIlL14gb7lwAPAO8FnjCzH5nZ/0SbbT0R5T8AXBynUnd/CrgBWEpYTSfXZ4BpwJW5E2XNbIWZrSgom90D4A1mdnjuATM7EngDYSjSTUXO+YiZtRec8x7C7rubgUfi3JOIiIiIyGSLtYMugJl1AF8H3kj+w0KGMPn1/e6+27H1o9R7AHAnMJewmdWjwEsJa/CvBl6Ruz6+mTmAu1tBPZcBbyf0xF8DPEN4iHgdYT7Bxe7+4ZzyrYR1/A8nrNP/S8LcgKOAkwnLib7R3a8e6x60g66IiIiI7Aml7qAbO9jPucAc4CWE5TA7gbvdfWtZle2sczFhRZzTgVnAJkLA/hl331FQdrRg3wiTbs8HjiCsrNMN3Ad8x93zVuOJzmkFPgKcTZgf0EDYnOt24Evufncp7VewLyIiIiJ7woQH+7IrBfsiIiIisieUGuzHHbMvIiIiIiJTRNwddAEws5cQlthcyM7VbHK5u79zPA0TEREREZHxiRXsR+PhLwfeChhhZZvcMfOek69gX0RERERkEsUdxvN+wk60VwIrCYH9xcArgI8DPYQVefavYBtFRERERKQMcYfxnAc87u7nA4SOfjrd/S7gLjP7HWHjq98D369gO0VEREREJKa4PfsryN+QCnIeGNz9PuA64H3jbJeIiIiIiIxTOavxdOX83gfMLDj+BOGhQEREREREJlHcYH8DYQWerKeBowvKLCM8BIiIiIiIyCSKG+zfTX5w/xvgGDP7dzM7xMwuAM4ijNsXEREREZFJFDfY/zmQNLP9otf/DTwDfAZ4APgq0An8n4q1UEREREREyhJrNR53/wXwi5zX283sxcC7gAOAtcAV7r6pko0UEREREZH4ytpBN5e7dwFfqkBbRERERESkgmIN4zGztJn9cKIaIyIiIiIilRN3zH4P8OxENERERERERCorbrB/H/CiiWiIiIiIiIhUVtxg/4vAmWZ26kQ0RkREREREKifuBN25wG+B35jZL4C/AJsBLyzo7leMv3kiIiIiIlKuuMH+5YTA3oCzox/ID/Yteq1gX0RERERkEsUN9t8+Ia0QEREREZGKi7up1g8mqiEiIiIiIlJZcSfoioiIiIjIFKFgX0RERESkRsUaxmNmT5dY1N39gDLaIyIiIiIiFRJ3gm6CIstsAh1Ae/T7RmBkPI0SEREREZHxiztBd+lox8zsQOASYBrwqvE1S0RERERExqtiY/bd/UnCuvsLgU9Xql4RERERESlPRSfouvsg8Hvg7ypZr4iIiIiIxDcRq/GkgPkTUK+IiIiIiMRQ0WDfzGYDrwfWVbJeERERERGJL+7Sm5/aTT2LgbMIq/J8bJztEhERERGRcYq79OaFYxzvBv7D3f+7vOaIiIiIiEilxA32XzlKfgbYATzm7qnxNUlERERERCoh7jr7t0xUQ0REREREpLImYjUeERERERGpAnEn6J4DvBd4q7tvLHJ8IXAF8DV3v7oyTRQRERGRKevZ1XDPjbB1E8xeACtPgSXLJ7tVe424Y/b/AegoFugDuPsGM2uPyinYFxEREdmbPbsarr8Cpk2HWfOgrzu8PvNtuwb8pT4UVHu5KmPuXnphs43Ade7+7t2U+QbwGndfVIH2TSkrV670e+65Z89e9J/+Ce6/f89eU0RERKQUWzdCOgXJnP7l7OvZ++zMGxqAbZshmYREEjJpSKdh1nxobK6ucgfsC286Hfp6ij+07CFmtsrdV45VLu6Y/ZnAc2OU2QbMjlmviIiIiEw1QwMhoN+0NqRDA/nHR4ZDEJ0rkQz5uXp2hEA6WQdmIU0mQ361lcNgWlv4tuKeG2O8WZMj7jCercCyMcosAzrLa47EdvHFk90CERER2RvlDtFpaYX+3l17u6/+Rhi6M61t53nZ12e/d2fetz8VhvlYTj+0Z2DbFnj3Z6uzXEtrGNJT5eL27N8BvNbMVhQ7aGYHE3bRvW28DRMRERGRKnbPjSHQn9YWguBivd0rTwkPAH3dIYju6w6vV56SX9fsBeFhIVd/b8ifSuWqUNxg/0uEbwNuN7MPmtlyM5sWpR8iBPnJqJyIiIiI1Kqtm0Lvdq7C3u4ly0NP/7S20Fs+ra34OPdSHwqqvVwVijVBF8DM3gV8jRDUF0oD73P371agbVPOpEzQFREREZkMpQ7RKVW1r7JTZavxlDpBN3awH1V+MPA+4KVAB2GM/l3AN9z90dgV1ggF+yIiIrLXKGXMvkyYUoP9uBN0AYgC+g+Uc66IiIiI1IDsEJ3c3u4TX69Av8qUFeyLiIiIiLBkuYL7Khdrgq6ZnWNmN5nZPqMcX2hmN5rZ2ZVpnoiIiIiIlCvuajz/AHS4+8ZiB919A9AelRMRERERkUkUN9g/DBhrBupfgMPLa46IiIiIiFRK3GB/JvDcGGW2AbPLa46IiIiIiFRK3GB/K7BsjDLLCEtxioiIiIjIJIob7N8BvNbMVhQ7GK2/fxZhJ93YzGyRmV1mZhvNbMjM1prZxWY2I2Y9x5nZtdH5g2b2rJldb2anj3HeG8zsd2a2Nee8a83sZeXcj4iIiIjIZIob7H+JsFzn7Wb2QTNbbmbTovRDhCA/GZWLxcwOAFYBbwfuBi4CngY+BPzJzGaVWM97o3acEqUXAbcAJwK/MbNPFDmnzsx+CPwU2A+4Cvgy8AdgOXB03PsREREREZlssXfQNbN3AV8jBPWF0sD73P27sRti9jvgNOCD7v7VnPwvAx8GvuXu7xmjjnrgeaARONLdH885djBwH5ABZrj7UM6xzwMfBz4PfMrdM4X1uvvIWPegHXRFREREZE8odQfd2MF+VPnBwPuAlwIdhDH6dwHfiHbXjVvfAcCTwFrggNxg28ymA5sAA+a6e99u6pkHbAYecPcjihx/gLCi0Gx33xblzQeeAe5195fHbXsuBfsiIiIisieUGuyXtYNuFNB/oJxzR/HKKL2hsFfd3XvM7A5Cr//LgBt3U89zhJ795Wa2zN2fyB4ws+WEycP3ZwP9yBuABuAnZtYM/H/AgUAPcLu7/3V8tyYiIiIiMjnKCvYnwEFRunqU408Qgv3l7CbYd3c3swuA/wusMrNrgI3AQuD1wMPAmwtOe0mUtgCPAUtyD5rZz4G3uXt/yXcjIiIiIlIFyg72zSxJWE+/sdhxd382RnXtUdo1yvFsfsdYFbn7T81sI/Bj4G05h7YA3ydM+s01N0o/R1ht6HWEh45DgUuBvwV6gfOLXc/M3g28G2DJkiXFioiIiIjIVPfgg3DtNbBuHSxeDGe9Hg47bLJbNaa4q/FgZoeZ2a8Jw1w2AmuK/BQG1HuMmb2VsIrObcDBhB77gwnfCFwK/KTglOx7sB14jbvf5+597v5n4LWEQP9cM1tY7Hru/m13X+nuK+fMmVP5GxIRERGRyfXgg/CVL0PnDli4MKRf+XLIr3I3t+OvAAAgAElEQVSxevajibl3Ri9/D7wG+Cuh1/woQk//zUCcXn3Y2XPfPsrxbP5uN+uKxuVfBjwAnJsz/v8xMzuXMFzoHDM7yd3/WFDnje7enVufu28ysz8TlvFcCWwo8X5ERERkEmW618GWe2FwOzTNhHlHkWhbPNnNkqnq2mugowM6oq2fsum111R9737cnv1PAvXAK9z9rCjvGnc/nbA+/feBFwGfillvdonM5aMcz+7aO9qY/qzTovbdUmSibwa4NXqZu25+9tqjPUjsiNLmMa4tIiIiVSDTvQ7W3gAj/dA4I6Rrbwj5IuVYtw7aCvqk29pDfpWLG+yfBFzn7rnfWRhAtCTmPxKC48/FrPfmKD3NzPLaFC29eSzQT1jec3ey8wdGG0+TzR/OyftDlB46yjmHROmaMa4tIiIi1WDLvVDXAvUtYBbSupaQL1KOxYuhu2BqaXdXyK9ycYP92YSVcbJShDHxALh7ihC4nxanUnd/CrgBWApcUHD4M8A04MrcNfbNbIWZrSgoe1uUvsHMDs89YGZHEpbZdOCmgnPuB44zs9cXnPMuwnj/JwEtoC8iIjIVDG6HuoIv5OuaQ75IOc56PXR2hrH6mUxIOztDfpWLuxrPdqA15/VWCpaqJPSajzb2fnfeR5gPcImZnQI8Sti065WE4TufKCif3bzLshnufreZfR94O/CXaOnNZwgPEa8jrKd/sbs/nHOOm9l5wC3Az83sV9H1DgHOAPqA89w9XcY9iYiIyJ7WNDMM3alv2ZmXGgj5IuU47DA449XwtUthw4YwSfeC91f9eH2I37P/FCFwzloFnGpmcwHMbBpwFmUMeYl691cClxOC/I8CBwBfAV5WsBHW7ryTEOz/CXhVVM+pwO3A37n7h4tc+wHCBOMrCOvu/xPwYuCHwNHufmfhOSIiIlKl5h0Fqf4Q8LuHNNUf8kXK8cADcO21cNjh8KY3h/Taa0N+lYvbs38D8K9mNi0aUvNNwo6z95nZnYSJr/sSAuzY3H0dIVAvpayNku+EB4bLY157DaOspS8iIiLVoZRVdhJti8ksPS2/3KLjxrUaT6mr+6jcruWquW0lu/pqmDEj/MDO9Oqr4fDDRz+vCsTt2f8Ooee8GcDdfw18OHr9t4QNqr4IXFLBNoqIiIjEWmUn0baYxLKzSBz29pCOEuRluteReeJaMg9+P6RF6ir1uiq3a7lqblsszz4L7QWj1NvbQ36VixXsu/smd7/K3bfm5H2FsMrNAmC6u3+8cNlLERERkXGr8Co7JQeEpV5X5XYtV81ty3rwQfiPz8I/viukxTbKWrIEugpW4+nqCvlVLvYOusW4e9rdt0RDaHZhZueZ2U3FjomIiIiUpNKr7JQaEJZ63Yko19MLf/0T3Pn7kPb07pnrbuuCP94cNo36483hdTn1TeZ7V0q5UnfGPfts2LEj/GQyO38/+2yqXdwx++VaCpy4h64lIiIitajSq+wMbg89+rmKBYSlXrfS5frT8PBfsKZp0NIKw0P4w3+BQ47Ztb7n1sGm9dDXC9NaYcEimLu4vHKdw3DzH7DUMHgGurfjm9bDK0+NX1+l21bpcqXujHv44fDP/xzG6D/7bOjRf+c7q368PlSoZ19ERERkwlV6lZ2mmSHIzlUs6C71upUut6Mfq0tCXTIsNF6XDK939OeXs9nw2H3YYB/WMg0b7IPH7gv55ZR7/BmsrytEiQ2NkCC8fvyZ+PVVum2VLhdnZ9zDD4cLL4TLLgvpFAj0QcG+iIiITBGJtsWw9LTQIz60I6RLTyt/hZUSg+5Sr1vxcgMDMGcFJOohNRjSOStCfm65dU9gsw6EplZID0FTKzbrQBLrniiv3Ma10DYH6hrAUyFtmxPyY9ZX8bZVuNxU3hm3VHtqGI+IiIjIuCXaFsM4ltAsrKvUJTpLvW5Fy3XMwwZ6YNZBO/MGeqBjXn65zi1Y+zywBTvzPAOdW8orl0xgloSm6TvzhgYhWbC/aCn1VbptlS531uvDGH0IPfrdXWFn3PPeQa1Qz76IiIjstUpdonNSHHw8DPaGAN8zIR3sDfm5OubBYF9+3mDfrg8FpZY76AgY6AsBvntIB/pCftz6Kt22Spc77DD40EfCWP0NG0L6oY9MiZ1xS6VgX0RERGRP2/wU3Hw5XPPFkG5+atcy8w+AV7wRmqdD1/MhfcUbQ36uUh8KSi133Nmw3/6QTEBfT0j32z/kx62v0m2rdDkIgf0nPwXf+k5IayjQB7BRVsus7EXMPg18yt2TE36xSbRy5Uq/5557JrsZIiIiNSvTvR5/buewG5t7FIm2RWWXmxSbn4I7/18YT940LfQ4D/YWD+Tj1PnobWGYSse8ENQWq2syylVz26YwM1vl7ivHKqcx+yIiIjIlZLrX48/cgNe17NwE65kbyOx7Wl4gX2q5F8pW8OEh88yf4dHfQP92aJkJB59BYt+X5hd69DY8AT6wEXoGoK4ZS7Zhj962SzBa8nV7NsNzj0LXVhjeDouWkSgW2M4/oLSAt5LlJuOaccrVOA3jERERkSnBn7sXr2vB6lswM6y+Ba9rCcFwGeVeeCgY6ccbZ4T0mRvIdK8vr9wzf4Z7roShPmieEdJ7rgz5ueWeX4N3rYVtG2D7c7BtA961lszza8q77hN3wI3fhf5emD4rpDd+N+TLXm9PBfv3A1fsoWuJiIhILarw7qmVfnjg0d+E6zRNg4SFtK455OdKdWHd2zF3rK4hpN3bIZW/BGTJ1733l9DYEjbeSlhIG1tCvuz19kiw7+7Xuvvb98S1REREpEaVuglWqeUq/PBA/3ZoaMnPa2gJ+XnnpvAMkPGw2k3Gw+u6VHnX7doaHixyNU0L+bLXiz1m38xmAu8AjgFmAMUm3bq7nzLOtomIiEgF9Q9vYcfgaobTXTQk25nRtJyWhnljn1glbO5R8MwNOISgNzWApfqxhceVVY6mmWE8f31OgD7aw0Mp5VpmhqE7uYH3cH/Iz9XYDAv2wbu6w7KWjU0waxY0Nux63S3PYuvXQW83tLbhixbDvCX55dpnh6E7La078wb7Qn6hTU/CQ7fCji0wYx4cegIsOHDXclIzYvXsm9kK4BHgi8AbgFOAk0b5ERERkSrRP7yFzb13k8oMUp9oI5UZZHPv3fQPbxn75CqRaFuE7XtaGNYytCOkRSbdllrO5h6FpfrDWHh3fKQ/PBTMPaqschx8RngIGOwLvfaDfeH1wWfkl5t1IDQkYOE+cOBBIW1IhPzc6/osEg+uigL56dDfS+LBVZjPyq/vqNfCUH8ol/GQDvWH/FybnoRbr4o25poT0luvCvlSs+L27H8JmAv8F/BtYJ27p3d/ioiIiEy2HYOrSSaaqEs0AVBnTS/kF/bud/Y8RGf3X0inuknWtdHR9hI6ph+aV6bUbwkqXW6wqZ4dc2cwnE5E5epp2aVUaeUSbYvI7Hsa5K52s/C4og8PJZXb96VkIH81niPfsMtqPIkVryJzzzZI9UCmH0hC0xwSK16VX27t4/jsA/F0d3hoaGnFpu+DrX0clh27s9yyY8N17/1lGLrTPhuOewuJnDJA6NFvbg3r9cPO9KFb1btfw+IG+8cDv3b3j09EY0RERGRiDKe7qE+05eUlrZHhdP6k0M6eh9i67fdYohFLtJJOD7B12+8BXgj4s98SJBNNed8SzG89Ji9Ar/ZyEAJ5Slh/v+Ry+74UCpfaLDR7KYmVb4E1f4Ke52H6HNjv5TB7aX65HVuwjnmYLdiZ55kwBKfwusuOzXsAKGrHltCjn6tpWtH6pHbEDfaNMIxHREREppCGZDupzOALPfoAaR+iIdmeV66z+y9YopFkMjsxtJl0lJ8N9kv9lmDH4GosncIH1jOSHsCSzVhDR9FypdZXyXKTavbSXYP7QjPmhaE22R54CEODZpR5D5WuT6aEuKvxrAIOmoiGiIiIyMSZ0bScdGaQVGYQdyeVGSSdGWRG0/K8culUN2aNeXlmjaRT3S+8Hk53wcgQIz1PMtz5ICM9T8LI0C7fEgwObsT7N+KZEUg04ZkRvH8jg4Mb88qVWl+ccsmCeyj2LUbVO/QEGOgNAbpnQjrQG/KroT6ZEuIG+58FzjSzkyagLSIiIjJBWhrmMb/1GOoSTYxkuqlLNBUd1pKsa8N9KC/PfYhk3c4hQMm0kepbkxfEp/rWkExbfl0j/bgZlqgP68Qn6nEzkiP9+eVKra/Ecg3JdtIF91DsW4yqt+BAOOFNoSe+8/mQnvCm8sfXV7o+mRLiDuNZDFwL3GBmPyb09HcWK+ju2kRLRESkirQ0zBtzGEtH20vYuu33pAk9+u5DeGaIjhk7e38b0mmGjRDIE1K3kJ+rngbSqU4Y7MYyaTyRhLpG6guC7lLrK7XcjKblbO69Gwg9+mkfIp0ZZE7L4bHer6qw4MDKBuOVrq+aPfYw/PZXsHE97LMITn8NrDhkslu1x8UN9i8HnDB2/9zoxwvKWJSnYF9ERGSKyY7Lz1uNZ8YJeavxWGaEaY1LGEpvJ50ZJJloorlxCZYZyaur3lrwkWEyOBmDhDt1I8PUN+Wvi1NqfaWWy36Lkbu6z5yWw6tnvL5MvMcehu9cCu3tsGAf6O4Mr9/1/r0u4I8b7GsXXBERkXHqTG9jQ2Yt/d5Li7WyMLGUjuSsssttHljN5sH7GEn3UJ+czvymFzO/eXnZ9dGygL6mw18o15FYkHe4rr6D/pEdDNbVM4JTTz3JzAgt9TPyyiUzjmOkEknSliDpRn0m5JdTX6nloLRvMaSG/fZXIdBv7wivs+lvf6Vgf3fc/QcT1RAREZG9QWd6G6tTD1JvDTQzjWEfYnXqQZZzWF7gXWq5zQOrebbvFhLWQDLRSiozyLN9twDkBfyVvG6meSHdg4+CN1KfaCSdGaA700lTW/5a/COZPvoam6lLD5LMZMgkEvTVN1Gf6csrV2p9meaFDHTdR2v/AHWZNKlEkv6WZprm55cDSPduILXtfnxoB9Y4g7pZR5JsXRj345KpauP60KOfa3pbyN/LxJ2gKyIiIqPoTG/j4ZFV/GX4Fh4eWUVnetsuZTZk1lJvDTRYI2ZGgzVSbw1syKwtq9zmwftIWAN1iSYSZlHawObB+ybsus819JFqPzAsz5keJJlsJtV+IM815AfxvXUZEol6aJ5LZtoCaJ5LIlFPb10mr1yp9e0YWUfrwAhJh4wZSYfWgRF2jKzLK5fu3cDwhhvxVD80dOCpfoY33Ei6d8Mun4fUqH0WQU93fl5Pd8jfy8QdxgOAmbUAZwMvBjqALuBe4Bp379vduSIiIrWo1J7zfu+lmWl559bTQL/35uWVWm4k3UMy0ZqXl7BGRtI9ZdVXSrl+76W5cTbppp0bNCXdd6mrp3UmbdvXQXoYT9RjmRGSmQzdrTN3vWYJ9dVtfwIappOu27lXQCI1GPI7Tn0hL7XtfqyuGauL5gZEaWrb/erd31uc/powRh9Cj35PN3R1wRvPndx2TYLYwb6ZnQn8AJhJmIyb5cBFZvZ2d7+uQu0TERGZEnJ7xAEaaHwhPzfYb7FWhn3oheMAIwzTYvkBe6nl6pPTSWUGSeRslpXxIeqT0/PKVfK6pdbV0LwPfTPraOl9Dhvpx+tb6G9fREPz3F2umR54Lr9c61xaCso1Dw+TMqjvfw7SI5CsZ6S+hebhvGL40A5o6MjPTDaH/HJtXTv2jrdSPVYcEibj5q7G88Zz97rx+hAz2Dezo4CrgSTwQ+AmYBOwADgZ+DvgZ2Z2rLuvqnBbRUREqlapPecLE0tZnXrwheMjDDPiw+yXPKiscvObXsyzfbeQyoQe/YwPkfFh5je9fMKuG6uuxh7STcvyyi1PLM2/h6EWtm1fTSbRSCLZTCY1QNP21cyavRTqd5ZrTsxgsG81nmzAEnV4ZoT6/udpmpY/GdkaZ4QhPHU5q/6kB7DGXSfylmTrWvjrL6BxGrTOgqHe8PqI1yngr2YrDtkrg/tCccfsf4LQg3+8u7/N3S93999F6duA46LjH690Q0VERKpZi7UyQn4Xc7He7o7kLJbXHUaDNTJAHw3WyPK6w3ZZFafUcvObl7Nk2onUJZpIZ3qpSzSxZNqJu6zGU8nrVvoe6nrX01Y3n0RdMyM2QqKumba6+dT15k+mtIZpNHojCTfSnibhRqM3Yg35D1l1s47EUwN4qh93D2lqgLpZR1KWNX8KgX5jK1gipI3TQr5IlYs7jOd44Kfuflexg+7+ZzP7GfCqcbdMRERkCim1txtCEFx0ycsyy81vXl50qc2JvG4l60qP7KCpbgbNtnN0sJuTHskfdpNOZkjMOJjm/s0Q9dxn2uaTTuZP+E22LqRh4Sl5q/HUz3tF+eP1e54PPfq5GlpCvkiVixvstwPrxijzLNA2RhkREZGa0pGcxT7pxaxNPcQgfTQxjaV1h5YUEO/tkvUzyKQHsGTzC3meGSRZuG5//QwyiQG8ecXOcumBsIpPYZ2tCys3GXf6nDB0pzHnW5rh/pAvUuXiDuPZCBwzRpmVhHH8IiIie42u9FaeT6+lIzGLRYkD6UjM4vn0WrrSWye7aVWvuf1QPD1AJj2Au4c0PUBz+6Fllau4/V4OQ30h4PdMSIf6Qr5IlYsb7F8PnGxm/8fMkrkHzCxhZh8F/iYqJyIiUhNKWT9/c2oN9dZIfbQ+ffb3zak1k9DiqaWheQGtc06IJud2kkg20zrnBBqaF5RVruJmLw2TcRtboXdbSDU5V6YIc/exS2ULm80HVgHzCcN1biP04s8nTM5dCmwGVrr7Xte7v3LlSr/nnnsmuxkiIlJBuevn560oUzDR9P7Bm2iiFcsdd+7OIL0c2XTyZDRdasGWp+Hx26FrC7TPg4OOg3n7T3arpAqY2Sp3XzlWuVhj9t19s5kdC3wLOBXYt6DI74H37I2BvoiI1KZS189vtumM+BD1OWvPpxim2fLXu58KBgc309v3CKlUF3V17bROexFNTfMnu1l7ny1Pw59/Ck2t0DYHBnvC65eeo4BfShZ7Uy13Xwu8yswWEnbQbSfsoHufu2sfahERqSmlrp8/v24/nh7+KwB1NJBimBEfYnH9CqaSwcHN7Oi8g0SiiWSyjXR6gB2ddzCj41gF/Hva47eHQL8pemDMpo/frmBfShY72M+KAnsF9yIiUtNK3S22PTmb/RuOYHNqDQPeQ7NNZ3H9CtqTs/d0k8elt++RKNAPK9xk096+RxTs72ldW0KPfq7GaSFfpERlB/siIiJ7gzjr5ztG2hKkSZK2BI7tUqbapVJdJJP5K2gnEk2kUl2T1KK9WPu8MHSnKWco2FBfyJ9o65+Ae2+C7Zth5nw46mRYtGzirzsZHnkIrv8lbFgPCxfBma+FF03wCk970G6DfTO7jGhHXHffEr0uhbv7O8fdOhERkUnWkZzFcg5jQ2Yt/d5Li7WyX/KgXdbP70xv44nUA9TTSBPTGPYhnkg9wDIOn1Jr7dfVtZMuWLs+kxmkrq59Elu1lzrouDBGH0KP/lAfDPbCEWdM7HXXPwE3XAkt02HGXOjvDq9PO7f2Av5HHoJvXgLtHbBgH+jqDK/f88GaCfjH6tk/nxDsfxHYEr0uhQMK9kVEpCRbM508xXp66Gc6LRzAImYnOia7WS8oZRfYjem11NOYP5HXQ/5UCvZbp72IHZ13AKFHP5MZJJMZpL3t6EluWY0pZZWdefuHybi55Y44Y3zj9Uu57r03AQ7PrwkPGI3TYNqskF8Y7JdSX6krCk1Guet/GQL99ui/N9n0+l/uNcH+flG6oeC1iIhIRWzNdHIfj9NIPa00M8Qw9/E4L84cVFUB/1gGvJemIhN5Bwom8la7pqb5zOg4Nm81nva2oyd8vH66dwOpbffjQzuwxhnUzTqycjvgVps4q+zM27+04L7UoLuU6254Anq2QH0jNLZAahi2roWhgfj1lXrNySq3YX3o0c81vS3k14jdBvvu/szuXouIiIzXU6wnjbOFboYYoZF6ptPEU6xnNlMn2G8eZSJvc8FE3qmgqWn+Hp2Mm+7dwPCGG7G6ZmjowFP9DG+4kYaFp5Qf8Hc9C5tWQf82aJkFC46G9iWVbXi5Kr3KTqmBbanXTfWHMRp1DeF1XQMMD4X8uPdR6jUnq9zCRWHoTnvOf2t6ukN+jYi1g66ZfcrMThijzPFm9qnxNUtERPYWz9PJc3STIk0jdaRI8xzdPE/nZDctln2SSxlhiGEfwt0Z9iFGGGKf5NLJblrVS227H6trxupaMLOQ1jWT2nZ/eRV2PQtP/haG+6B5Zkif/G3IrwZdW8LQmFzjWWUnN7C1REibWkN+Oded0QGZTAjw3UOayYT8uPWVes3JKnfma0Ow39UZ7jH7+5mvpVbEXY3nwujn1t2UOQH4NPDZ8pokIiJ7kwFSJIA6khCladIMkJrchsXUkZzFMg5nY3otA95Ls7WytMhEXoCu9Na8JTrn1+1XdInOySq3p/nQDmgoCCSTzSG/HJtWQX0LNETBXjbdtKo6evcrvcpOqUt0lnrdJSugoRm2bIK+Hpg2HRYvhfkFe6mWUl+p15ysci86NEzGzV2N5+/eVjPj9WFilt6sBzITUK+IiNSgOhoYYog0aRIkyJABnDoaJrtpsZUykbcrvZWnh/9KvTXSRCsjPsTTw39l/4Yj8gLvySo3GaxxBp7qh7qWnZnpAaxxRnkV9m8LPfq56ltCfjWo9Co7pQa2pV73oOOgcxMcdEh+uYOOi19fnGtORjkIgX0pwf3DD8J118L6dbBoMbz6LDjksLHPm2SxhvGU6Chg6wTUKyIiNWhOYgYddJAkSYoUSZJ00MGcRJmBXpXbnFpDvTVSb42Y2Qu/b06tqYpyk6Fu1pF4agBP9ePuIU0NUDfryPIqbJkFIwXjy0f6Q341yK6y0zQdup8PabHJuaU66LgQyA72gGdCWiw4n7c/7HsMPP4w3HpdSPc9ZvRVgMZqXynlKlnXRJQr1cMPwte+Ap2dsM/CkH7tKyG/yo3Zs29mNxVknW9mJxUpmgQWA/sCPx5/00REZG+wnH24O9HPDGbQSD1DjDBIiuXsM/bJU9CA99BE/qTdOhoY8J6qKFeq4YFNDHQ9RHpkB8n6GTS3H0pD84Ky6kq2LqRh4Sl5q/HUz3tF+ZNzFxwdxuhD6NEf6Q8/++522uGeVeoqO6XWVcoSneufgFU3w5z9YMlhMNAbXs9atOuSmqW2r5RylaxrIsqV4rprwyTejmi4WTa97tqq790vZRjPSTm/O7A0+imUAbYBVwEfHme7RERkLzGXdo7hQFazkW4GaKOZw1nKXGpzE6dmm86ID1Gfs2pPimGabXpVlCvF8MAmep+/FUs2k6jrIJMeoPf5W2mdc8K4Av6KLbXZvgQOPD1/NZ59T6iO8foTpZTA9t6bwkZZLdEOydm02Pr5km/9utCjn6utLeRXuTGDfXd/YaiPmWWAC91dk29FRKRi5tJeUnBf6uZb1Vxuft1+PDr8F3p9G8NkaCBBKw0cXL+iKsqVYqDroRDoR7vsWrKZTJRfbrBfce1Laju4L8f2zWFH3FzNrSG/Fj3yUP7E2zNfW/7E20WLw9Cdjpx/z93dIb/KxR2z/3bgFxPREAAzW2Rml5nZRjMbMrO1ZnaxmcUauGlmx5nZtdH5g2b2rJldb2anl3j+J83Mo5+/Ke9uRESkkrKbbw0xnLf51tZM55QqN2J1dNY1kTGjngwZMzrrmhixuqooV4r0yA4s0ZSXZ4km0iNlrp4je8bM+WHoTq6B3pA/kZ5+DK68BP7330L69GMTez0Igf43LwnLaC7YJ6TfvCTkl+PVZ4U6OqMlOjujJTpffVZl2z0BYgX77v4Dd39gIhpiZgcAqwgPFHcDFwFPAx8C/mRmJc2qMbP3ArcBp0TpRcAtwInAb8zsE2OcfxTwKWBqbXkoIlLjnmI9jdTTSAOG0UgDjdTzFOunXLmWZDvtDfvT1ngQ7Q3705Jsr5pypUjWz8Azg3l5nhkkWV+bk6prxlEnQ38P9HeHibz93eH1USdP3DWffgx+/j3o7YLZC0L68+9NfMB//S/DGPv2Dkgkdv5+/S/Lq++Qw+CCD4We/Y0bQnrBh6p+vD6MY+lNM1sELIScwX853H13a/EX83VgLvBBd/9qznW+TJgD8HngPWO0qR74T2AQONrdH8859gXgPuATZvYldx8qcn4TcCXwF+Ap4NyY9yAiIhOkh35aac7La6CeHvpVroLlStHcfii9z99KhtCj75lBPD1A88yXxK5L9qBFy+C0c8MY/e2bQ4/+ca+b2PH6d9wArW3QGg3Ty6Z33AD7xx9CVrIN60OPfq7pbSG/XIccNiWC+0Kxg30zO43QWz7WJ5SMUecBwGnAWuBrBYc/DbwbONfMPurufbupaibQDjyQG+gDuPujZrYaOAxoBXYJ9gkPCvsBRwC7/QZARET2rOm0MMQwjTnr7w8zwnRaVK6C5UrR0LyA1jkn5K/GM/Ml1TNeX0a3aNmenYz73IbQo5+rZXrIn0gLF8Ff74UnH4feHmidDgceBEccNbHXrUKxhvGY2cuA64AO4FLACLvpfgd4LHr9K+LvnvvKKL3B3fM25HL3HuAOoAV42Rj1PAc8Dyw3s7y/ZDNbDiwD7nf3XXbVMLOTCUOGPubuT8Rsv4iITLADWMQQIwwxjOMMMcwQIxzAIpWrYLlSNTQvoH3+qcxc/Eba55+qQF+Km7swDBXK1d8T8idSWxvcfSf09UHLtJDefWfI38vEnaD7McIQmZe4+4eivJvd/T3AocB/AH8D/CxmvQdF6epRjmeD7+W7q8TdHbiAcF+r/v/27jxO0rK89//n6u7q6r17enZmZYCBAQWJA6JjBMSMgEaUxZPkuEAwRJOfRhNjcnKigjlZDzEYRSMa9/zcWA4ef6AoqwwiDLLJMAzL7PtMz/S+VFddvz/up2aqerqnq7qruqqrv+/Xq0yemWcAACAASURBVF4PdT93PXVXPVTPVXddz3Wb2TfN7B/N7FuE6wGeA64a+TgzawW+Qcjx//c8xy4iIlNgTlUbZ3MqcWrpoZ84tZzNqcdUuylGv1OS8xlM7GV/4gUGE3s5JTl/Sp63FP1ECmrNWujpCrn6qVTY9nSF9onatAG+dCN86qNhu2nDsX3u/ymccAI0NsLQUNiecEJon2HyTeN5PfAjd9+V0VYFRwLtT5nZJcANwJV5HDddb61zjP3p9nH/Irn7D81sF2Fhr/dl7NoLfJ1w0e9InyekAF0QvY6cmdl1hDQjli5ViS8RkWKaU9XGnPH/KShov8PJg+xPbWYurcSYS4Ih9qc205Zspq06u3ZEKcZXjH4zyuFtsOPxo/X4F58Dbfr3vGBWnAZXXBty9PftDDP6b71q9Hz9l56HB+6GPTthwSK44BI4eVV2n00b4FtfguY2mH8CdHWG++/7EKw8/Wi/Pbtg3oJwcW5aKhXaZ5h8g/1WYFvG/SGgcUSfdcAfTGZQk2Fm7yGkFd0O/B2wlbCq7ycJqUfnA+/O6H8F4ULcP3X30b4IHJe73wLcArB69eq8viiIiEj525XcEmrsWKhHUUscPLSPDPZlmjm8DV74/yDWCPXtMNQb7p/6NgX8hbTitPEvxn3pefivL0NLK8xbCN2d4f5//+PsgP/eu0Kg3xLNE6e3996VHewviL4ItGSs39HTHdpHeu7ZsBLuju2hbv7bLxv9QtxnnoHbb4dt22DpUrj8cjjzzNzegxLKN41nHzBrxP2TRvSJwYjL/ceXnrkfa0WVdPvhMfYDR/Lyv0ZI13mvu290935330gI6J8ArjKzC6L+7cB/APcCX8pzzCIiMgP0ew+xjItaAWLU0u+q0Dzt7Xg8BPq1jWAWtrHG0D5Sx1Z4+ofwyBfDtmPr6MfMtZ9ke+DuEJg3t4bZ+ObWcP+Bu7P77d4RLrbN1NQc2jO9749CcN8VpQ91dYb77/uj7H7PPQs3fy7UzT9hUdje/LnQnumZZ+DGG+HQIVi8OGxvvDG0l7l8g/1NZAf3jwK/EwXZmNkC4AqO5tjnKl05Z6yc/PTFtmPl9KetJXzZeHCUC31ThIuJAV4bbZcCcwg1+VMZC2k58P6oz8+ito/m9lJERKSS1FsTCYay2hIMUW9NJRqRFEzfQYiNqEYUawjtmTq2woYfh5n/hjlhu+HHxwbyufabDrZuglu/CP/xybDdOl4INkl7dkLjiCC+sTm0Z1q4OATtmXq6Q3umS94Bf31D+MKwb0/Y/vUNoT3Tj+8M9ffbonr8bVE9/h/fmd3v9tth1qxwq6o6+t+33z7x1zxF8k3j+Qnwv8ys3d07gM8BlwNPmtkGQlDeDHwiz+PeH23XmllVZqBuZs3AGqCP8OXieNI1/+eOsT/dnv6rfRD4zzH6vonweu4GdgETXHJNREQKZR+dbGIXXfTTQj0rOYF5Y/4oXBgnVC/nxeFnwMOMfoIhEgyyvPrU8R8s5a1hdgjIazMykhN9oT3T9scg3ni0X3q7/TFoX5Z/v3K3dRPc9Q1oaIHZ86G3K9y/9GpYdtxaKRO3YFFI3WnO+Dz3dof2TBddGnL0Iczo93RD92F41+8fe8xL3nFscD/Sju1hRj9TS0toz7RtW5jRz9TaGtrLXL4z+18mBMEJAHdfR6hus5lQjWc38CF3/1Y+B3X3l4F7gOWEajqZbiBcF/DtzBr7ZnaamY1MAPtFtL3SzLKSqMzsNYSLhh24L3re7e7+gdFuwCPRQz8btf08n9ckIiKFtY9OHuMlBhiimToGGOIxXmLfmLUdCqOtejYLbSk9yf3sGd5ET3I/C22p8vUrweJzINEbAn73sE30hvZMvftH/wWgd//E+pW7x38eAv3GFrCqsG1oCe3FcsElIdWmO0q76e4M9y+4JLvfytPDxbgtrbB3V9iOvDg3H4uXQFdXdltXV2jPtHQpdI74W9PZGdrLXF4z++7eBfxqRNsdwB0FGMufEALsfzezi4DngdcRavBv4thFrp6PtpYxlsfM7OvANcDjZnYH4QLd5cA7gVrgJnd/rgDjFRGRKbSJXdRRQ12UP5/ebmJXUWf3O5MH2J/cQlvVbGpYyDBD7E9uoTnZSmv1nKI9r0yBtqXhYtzMajwrLjj24tzGuaP/AtA4d2L9yt2B3WFGP1NDU2gvlpNXhYtxM6vx/O7vHVuNB0JgP9HgfqS3XxZy9CHM6Hd1QedheM/7s/tdfnnI0Ycwo9/ZGfL2r722MOMoorxX0C0Wd3/ZzFYTFuS6GLiU8EvB54Ab3P1Qjoe6lpCbfzXwVkJaURfwMPAVd/9egYcuIiJToIt+mqnLaosTo4v+oj7vnuHNxCxOLKrGE4syRvcMb1awXwnalo5feWfJuSH3HsJMfaIPBnvhpAsn1q/czVkYUncaMxag6us5diXcQjt51ejBfTGd8Wr40z/LrsbznvcfW43nzDPh4x/PrsZz7bXTohqP5VNW3sxeC7wN+LK77x1l/wJCzfkfuftTBRvlNLF69Wpfv359qYchIlKRHuZ5Bhg6MqMPHLn/RooXIDw1cB91NGF25Idk3J0BenhN3ZuL9rxSZjq2htz73v1hpn7JuaPn4efar5xl5uw3NIVAv6+ruDn7kjcze8LdV4/XL9+Z/b8A3kioXz+avYSZ9ZPJXtBKRERkUlZyAo/xEhBm9AdJMMAwZ7K8qM9bb80kfPDIjD7AMEPUW/NxHiUVp31ZbkF7rv3K2bKVIbB//OchdWfOQrjgcgX609REVtC9f6xVZt3dzew+wkW8IiIiBTOPVs7l5KxqPGeyvOjVeBbUnMgrQ08DUEMtwwyR8EGWxMZZJEhkOlu2UsF9hcg32F8A7Binzy6gyEldIiIyE82jtejB/Uit1XNYUXsWe4Y30+/d1FszS2KnKV9fRKaFfIP9PsauYZ82Fxic2HBERETKT2v1HAX3IqPZuik73eect+gXgTKTb539p4DLzEZfNtDMWoDLon4iIiIiUk52vAg/+jJ844aw3fHixI+VvpC3tyt78a1ir7Yreck32L+FMHP/s1EWrTqLsDDWnKifiIiIiJSLHS/CPd8OlXVmzQvbe7498YC/FItvSd7yXVTr+2Z2CaHSzpNmthfYCSwC5hMWuPqWu3+34CMVERERKbRKKJWZq1/fBw3NISCHo9tf3weLT8n/eKVYfEvylu/MPu5+NfBBYAPhgt3XRtvngOui/SIiIiLlrWNrWARrqBca5oTthh+H9krUsQfqR2Ri1zeF9omYszDU4M80FYtvSV7yDvYB3P0Wd3810AQsBprc/Ux3/2pBRyciIiJSLNsfg3gj1DaCWdjGG0N7JWpfAP0jgvP+ntA+Eee8JaQC9XaBp8K2ryu0S9mYULCf5u597r7L3fsKNSARERGRKdG7H2IN2W2xhtBeiX7rzdDXHQJyT4VtX3don4j04luNLXBwb9hqld2yk2/pTREREZHK0Dg3pO7UNh5tS/SF9kq0+BRY+96Qo9+xJ8zov/GdE8vXT9PiW2XvuMG+mb0COPAWd98c3c+Fu/tJkx6diIiISLEsOTfk6EOY0U/0wWAvnHRhacdVTItPmVxwX0yvbIR198C+nTBvEaxZCyu0UvVkjZfGUzWiTxWh4s54t0mlB4mIiIgUXfsyOP3tYWa/70DYnv72yq3GU85e2Qi3/Sf0dIYLfHs6w/1XNpZ6ZNPecWf23X358e6LiIiITGvtyxTcl4N190BTCzS1hvvp7bp7NLs/ScedgTezz5rZ2oz7S6NVckVERERECmPfzrAGQKaG5tAukzJeus1HgfMy7m+O2kRERERECmPeolAZKFNfd2iXSRkv2O8BMmtSWRHHIiIiIiIz0Zq10NMVcvVTqbDt6QrtMinjld58CbjczO4A0msft5nZ0vEO7O7bJjs4EREREZkBVpwGV1ybXY3nrVcpX78AzN3H3mn2+8B3MpsIpTjH4+4+42r4r1692tevX1/qYYiIiIhIhTOzJ9x99Xj9xqvG810z2wy8DVgEXA08AzxViEGKiIiIiEjxjDv77u6PAo8CmNnVwB3u/pkij0tEREREZpKXnocHfwJ7d8L8RXD+xXDyqlKPatrLd/Gra4A7izEQEREREZmhXnoevnsLdHfC3IVh+91bQrtMSl559e7+zWINRERERERmqAd/As2t4QZHtw/+RLP7k3TcYN/M3hT952PuPpBxf1zu/tCkRiYiIiIiM8PenWFGP1Njc2iXSRlvZv8BQvWdVcCmjPu5qJ7wqEREpCLsoZON7KWTflqp5zTms4DWUg9LRMrN/EUhdac54+9Db3dol0kZL9j/DCG4PzDivoiIyHHtoZNH2UycGC3U0U+CR9nMeZyogF9Esp1/ccjRhzCj39sdgv+3/7fSjqsCHLfOvuRHdfZFRI56gE30k6Ce2JG29P0LWFnCkYlIWVI1nrwUpM6+iIjIRHXSTwt1WW111NBJ/zF9c033KVU/EZkCJ69ScF8EeZXeNLNqM2sYpf3NZvY5M/tHMzuxcMMTEZHpqpV6BhjOahtgmFbqs9rS6T79JLLSffbQWRb9RESms3zr7N8IdJjZkWkPM/s94GfAh4G/Ah4zsyWFG6KIiExHpzGfQRL0k8Bx+kkwSILTmJ/VbyN7iROjnhiGUU+MODE2srcs+omITGf5BvtvAu5398xpj08Dh4H3AZ8A2oA/L8zwRERkulpAK+dxIvXE6GKAemKjXpzbST91I7JKR0v3KVU/EZHpLN+c/SXAI+k7ZrYCOBX4jLt/J2p7E3Ax8LFCDVJERKanBbSOmwPfSv0xF/KOlu5Tqn4iItNZvjP7LUBXxv01hFKcP8loew5YPMlxiYjIDJFruk+p+omITGf5Bvu7gcwLcN8C9ANPZLQ1wYgrskRERMaQa7pPqfqJiExn+abxPAq8w8zeDgwAVwL3unsio8+JgNY2FhGRnOWS7lPKfiIi01W+wf4/AJcBd0b3U8Dfp3eaWR3w28CtBRmdiIiUJdWnFxGZHvIK9t39WTN7HfD+qOn77v54RpezgfuA7xZofCIiUmbS9enjxLLq0ysFRkSk/OS9gq67Pwt8fIx9vwTeNdlBiYhI+cqsTw8c2W5kr4L9CjHYv5veng0MJw5TE2ujsel04vULSz2syrL3Fdi0Djr3Qes8WLkG5q8o9aikAuV7ge6ozCxmZmeb2amFOJ6IiJSvSqpP35Hq4Mnk0zw8/AhPJp+mI9VR6iGV3GD/bjo7HiaZ7Ke6ppVksp/OjocZ7N9d6qFVjr2vwGO3wkAPtMwJ28duDe0iBZZXsG9m7zazH5hZe0bbSYRym+uBDWZ2u5nl/YuBiIhMD63UMzCi6Np0rE/fkergudQGBn2IBhoY9CGeS22Y8QF/b88GrLqe6up6zCxsq+vp7dlQ6qFVjk3roK4Z6prAqsK2rjm0ixRYvkH5HwInuHvmX8J/BU4m5OrPJlzAew3wlYKMUEREpsxO7+YZDnCIAWZRx5nMYZE1Z/U5jfk8ymYgzOgPMMwgCc4eZYmVXI5XKlt9OzHixK0WgDi14KG9nfZxHl25hhOHqa7JTseqqqpjOHG4RCOqQJ37wox+pnhDaBcpsHzTeE4HjlyQa2YtwKXAD9z9LcC5wEZCsC8iItPITu/mfrbTR4I24vSR4H62s9O7s/otoJUVPp+d3sNTvoed3sMKP7YaT67HK5Ve76U2Y/VcgFpi9HpviUZUHmpibSSGOujr20x3z/P09W0mMdRBTayt1EOrHK3zYLAvu22wL7SLFFi+wf5cwsJaaa8n/DrwPYCo3v7PgJMKMjoRESmYnd7N3b6Z/9ef527ffEzQ/QwHqKeGBmIYRgMx6qnhGQ4cc5xnOMQsZnEqS5jFLJ7h0ISPVyqN1sgQiay2IRI0WmOJRlQeqmvnMTC4nWRyALM4yeQAA4Pbqa5VIFowK9fAQHfI1fdU2A50h3aRAss32O+GrKmb8wEHHs5oGwDK4zdaEREBcptlP8QA9SOyO+up4RADWW25BvG5Hq9UltkSEgwy6EO4O4M+RIJBltmSUg+tpIaGDxCrW0J1dR34INXVdcTqljA0XB5f0irC/BVw7pUhV7/rQNiee6Wq8UhR5Juz/yJwiZnFCUH+u4Fn3D3zL8AyQElnIiJlJDNAB45sn+EAi6L5mVnU0UfiyD6AfoaZRV3WsQ4xQBvxrLbRgvhcj1cq7VXtnMHpbPXt9HovjdbISjuJ9qqZm68PkBg+TCw+B6ube6TN3UkMZ+fsqzznJM1foeBepkS+wf4twNcJQX8CWA58bESf1xKq84iISJnIJUA/kzncz/Yj+/oZpp9hziM7gMs1iM/1eKXUXtWe08W4HamOrC8Fy2xJxX4piNW0hbKb1UerK6VSA8Rqjubsp8tzWnV9VnnO1vY3KuAXKTP5rqD7zaiW/nVR0xeAz6f3m9kbCJV5binYCEVEZNJyCdAXWTMX+pKs6jnnsfCY6jm5BvG5Hq/cpUt0xogfLdHpGziD048J+HP9UlCqfrloalxFR+cjkAxVeFKpAZI+QGvjbx3pk1meE6C6up5k1K5gX6S8mLsX7mBmtUA90Ovuw+P1rzSrV6/29evXl3oYIiLHSOfs11OTFaBfyJIJBd/lXFKz0J5MPs2gDx0p0QkcuX929VlH2jK/FNQSY4gECQY5oyr7S0Gp+uWjf2APPb3Ph5SemjaaGldRX7fgyP59u26juqYVMzvS5u4khzuZd8IVE3pOEcmPmT3h7qvH61fQxa/cfQgYKuQxRURk8go9y77Imo/k+le6Xu+lgYasttFKdOZat79U/fJRX7cgK7gfqSY2eqqPynOKlB+tdCsiMkPMpAC9kGktjdYYZvI5OrM/WonOXL8UlKpfITU2nU5nx8MkOZrq48l+GltfW7TnFJGJybf0Jma20MxuNrOXzKzfzJKj3GZcCo+IiJSHdFrLoA8dzbFPbaAj1TH+g0eRa4nOXOv2l6pfIcXrF9La/saQqz/cSXV1vS7OFSlTeQX7ZrYIWA/8MdALxIFthOo8ScCAp4FfTGQwZrbYzL5mZrvMbNDMtpjZTWY2K8/jvNHM7oweP2Bm28zsLjO7eLTXZGYfNrO7o/6DZnbQzH5mZpdP5HWIiEjpZKa1mBlxqyVGnK2+fULHa69q54yq04lbLX30EbfaUfPhc/1SUKp+hRavX0j73IuYd8IVtM+9SIG+SJnKd2b/U8AC4GJ3T1+V9HV3Pw1YAfyUcIFu3kGymZ0EPAFcAzwG/BvwCvBnwC/NbHaOx/kQ4cvGRdH234AHCQuA3W1m/3PEQz4M/DtwKnA/8Nnodfw2cJuZfTbf1yIiIqXT673UZlQdgsmntbRXtXN29Vm8seYNnF191qgpQbl+KShVPxGZmfKqxmNmW4Dn3P1t0f0UcL27fya63wT8BviRu38kr4GY/RRYC3zE3TPLeX6WUMv/y+7+wXGOEQP2E35xeI27v5CxbxXwJJACZrn7YNR+OXDQ3R8ccaxVwKNAC7Da3Z8Y7zWoGo+ISOnlWj1HRGQ6y7UaT74z+wvIXjArSZjJB8Dde4CfAZflc9BoVn8tsAW4ecTuTxNSht5rNm4CYjvQCmzKDPSjsT0PbIrG25TRfvvIQD+j//ejuxfk+lpERKS0SpXWAtA9vJ+X+x/lud6f8XL/o3QP7y/6c4qIHE++wX4XZJQjgEPAohF9OoG55OfCaHuPu6cyd7h7N7AOaADOG+c4+wgz+yvN7JTMHWa2EjgFeMrdD+Y4rvQVT7rgWERkmihVWkv38H62Dj5JIjVI3JpIpAbZOvikAn4RKal8S29uBTKnRp4G3mxmDe7eZ2ZVhBn6HXke99Rou2mM/S9Gx10J3DvWQdzdzexPge8AT5jZHcAuwheSdxF+lfi9XAZkZi3AFYAD9+TyGBERKQ/tVe0TrjE/UfsSL1NDnFhVHICYxSEV2ptr8p0DExEpjHyD/XuB68ws5u4J4JvAt4BHzOxnwBuBM4B/yPO4rdG2c4z96fZxV+tw9x+a2S7gu8D7MnbtBb5OuOj3uCwsCfhVYD7wxSilZ6y+1wHXASxdunS8Q4uISIUaSHUTt6asthqrZSDVXaIRiYjkn8bzn8A/A3MA3P07wOeAVwF/AbyOkOf+9wUcY17M7D3AzwmVeFYR0n9WEb6ofAH4Xg6H+VfgqugYf368ju5+i7uvdvfVc+dq5kZEZKaqq2pm2LMXkR/2IeqqZsZCZiJSnvIK9t39RXf/Z3ffndH2MWAh8Hpgobv/gbsP5DmO9Mx96xj70+2Hj3eQKC//a4R0nfe6+0Z373f3jcB7CaU9rzKzC45zjH8hVP95CLg0XbVHRETkeObFTmKYQRKpQdydRGqQYQaZFzup1EMTkRks7xV0R+Pu+939V+6+d4KHSFfOWTnG/vTFtmPl9KetBWLAg6Nc6JsiBPAAo67nbWb/Bvwlod7+JVF1IRERkXE118xlWfxsYlVxBr2HWFWcZfGzla8vIiWVb85+sdwfbdeaWVVmoG5mzcAaoI9Q9/544tF2rL+s6fas31mjHP0vAH9CVDrU3ftzH76IiEgI+BXci0g5OW6wb2Zfm+Bx3d2vzaPzy2Z2D2Fm/k+Bz2fsvgFoJCyqdWT5QzM7LXrsxoy+v4i2V5rZje7+TEb/1wBXEqrr3JfRbsAtwAeAu4HLJ5CGJCIiIiJSdo67gm60Qu5EuLtX5zWQsLDWI8A84E7gecIFvxcS0nfekFkf38w8eiIbcZyvAdcQZu/vIJQLXQ68k7BGwE3RdQbp/p8Grgf6gZsYMesfecrd/894r0Er6IqIiIjIVMh1Bd3x0nhOLNB4xhXN7q8GPgNcDFwK7CZU+7nB3Q/leKhrCbn5VwNvBZoJi4E9DHzF3UdW40m/xnrgf4xxzG8C4wb7IiJSmTqTB9mV3Ey/d1NvzZxQfSKt1bNLPSwRkXEdd2Zf8qOZfRGRytOZPMhLiaepsTgxakkwxLAPcnLsLAX8IlIyuc7s51WNx8yuMrP7zOyEMfYvMrN7zezyfI4rIiJSrnYlN1NjcWotjplRa3FqLM6u5OZSD01EZFz5lt78ANDm7rtG2+nuOwk18T8w2YGJiIiUg37vJkZtVluMWvpdK+OKSPnLN9h/NTBensrjwJkTG46IiEh5qbdmEiNqNyQYot60Mq6IlL986+y3A/vG6XMQmDOx4YjIdPBsd4I79w2ybSDF0roqLpsX59XNsVIPS6QoTqg+kZcSTwNk5ewvrzmtxCMTERlfvsH+AY6uZjuWU4DDExuOiJS7Z7sT3LS1n7YaY1G8ikMJ56at/Xx0GQr4pSK1Vs/mZM7KqsazvOY0XZwrU2P3y7DhF3B4L7TNh9N/GxaeVOpRyTSSb7C/DniHmZ02YjErAMxsFXAZ8H8LMTgRKT937hukrcZoi4UswLaYASnu3DeoYF8qVmv1bAX3MvV2vwzrfgB1TdA6F/q7w/0171bALznLN2f/RsIXhIfN7CNmttLMGqPtnxFWsK2O+olIBdo2kKKlJmstO1pqjG0DE12DT0RERrXhFyHQr28GqwrbuqbQLpKjvGb23f1xM/sT4Gbg36JbpiTwIXf/VYHGJyJlZmldSN0JM/pB17CztC7fuQMRkTJweBvsXA/9B6B+DixaDW1LSz2q4PDeMKOfqa4xtIvkKN80Htz9K2b2MPAnwOuANkKO/qPAl9z9+cIOUUTKyWXz4ty0tR8IM/xdw87hYefqRXXH9M31Ql71K59+uvhaZpTD2+DFuyDWCPWzIdEb7p9yaXkE/G3zQ+pOfUblp4He0C6SI62gW0BaQVemu0IHjekLeTO/FHx0WX1WX/Urn365HkukYjx3ewjwaxuPtg31huD/jDJYHzQzZ7+uMQT6Az3K2RegSCvoikjlSgd6hxKeVWXn2e7EMX1f3Rzjb09q4pYzWvjbk5pGDQQzL+StsrBtqzHu3DeofmXaL9djiVSM/gMQa8huizWE9nKw8KQQ2Nc3Q+f+sFWgL3nKO41HRCpToavsbBtIsSiePZ8w2oW86lc+/XI9lkjFqJ9z7Mx+oi+0l4uFJym4l0lRsC8iQOEDvVwv5FW/yffbb73UtHSRrE5QnYwx3NXC0rrGvPvNxIuvDyUPsjO1lV56aKSJRVXLmDUFJTY7hw+wN/kK/ake6quamF+9gtaaMgowZ4pFq0OOPoQZ/URfCP6Xn1/acYkUUOX+BReRvCytq6JrOPsanskEepfNi3N42DmcSJHysD087Fw2L65+Bez3hvnD9LTsp58klqyhnyQ9Lft5w/zhvPvl+pyV4lDyIC8kf8OQD9LgjQz5IC8kf8Oh5MGiPm/n8AE2J54m4YPUWSMJH2Rz4mk6h49NHekcPsCmwcd4uv8+Ng0+NmofmYS2peFi3Fgj9B8M23K5OFekQHSBbgHpAl2ZzopxcWY5V52plH4/Sm5l29AQW3qhc9hprTGWN8LS2lreUb0s734zqRrPbxK/ZsgHqbWjX2bS918V+62iPe+mwcdI+CCxjOdN318ZP/dIW/pLQcxqqaGWYYZI+BAnxs7SrwAikvMFugr2C0jBvkx3MynQqxRfT26i3WsxO5p64+502BDXVK/Mu99M8qvEQzR44zHvSZ/18rrYm4r2vE/330edHfu8A97LWfVvPtKW65eCSpHs2UHywFP4QAdW1071nNdQ3bS41MMSKVu5BvvK2ReRI17dHFNwP83MJk4fwzRk/DnvJ8ls4hPqtz3Vw5N+kIMMMps4Z9tsllQ1FfdFlEgjTQwxSG3Ge5BgiEaK+3rrq5pC0J7xvMMMUT/ife5P9VBn2dde1FBLf6qnqOMrhWTPDhI7fo5VN0B8Fp7oI7Hj57D4LQr4RSZJwb5UnFe2plj3uLPvAMybA2vOMVYsOzbvfCb1K+exqd/Y/X65s4t7Ozo4XDVIWyrORe3tvH5RS1afs202t/fsGfTx8gAAGO1JREFUoGOPM9BZTV1rkvYFw1zePD/vfttTPdzju2j0atqppY9h7mEXa1MnHBPw5/qloJy/PCyqWsYLyd+AQ4xaEgwxxBAnVhX3l4751SvYnHgaICs9Z3HNqqx+uX4pqATJA09h1Q1YugxmtE0eeErBvsgkVV9//fWlHkPFuOWWW66/7rrrSj2MivXK1hQ//nmKnz/kvLzFaW6EWW12TJ9b73IMp7UVevvgqQ2wcF5235nUr5zHpn5j9/vlzi5+2LmLlDuNXkM/wzwz1EXLUC1LWo4Gfwe31fD4/XGSLQNY2xD0xEj8aj6vamnMOl4u/R5M7cYcGqwGMyNmVZjDQQY5tartyLHSXwrMoYUY/STZSBdzidNqtXn3K5X6qgYaaaaPHvqsl3pr4MTqlZOqxtM5fIBtid+wI/ECXan91BCnriq7jntdVQP11kK/dzPgvcSrGlhcs+qYPPwa4hxM7gKgiuqsLwUjjzndDe95FGqbs1KbqKqBwUPUzDmrdAMTKWM33HDD7uuvv/6W8fppZl+mhXSA1NzgzJkNPb1w611w5aWprBnRdY+HPk2N4R+MpkYAZ93jsOLoNYgzql85j039xu53b0cHcappsJBW1UAVJEN75uz+useduYMNNL1yNN2jZ9BZ97gf87zj9TvIIO1kB+H1VHOQ7EW1nvSDNHo1DVYTja0GHJ7kIEsyUmBy7VdKs6pnF6zUZuYFtZlVdk7k2AtqW2vmjHuRbWvNHE7krKwSnaN9KagEVteOJ/qyF7ga7sfq2ks3KJEKodKbMi1kBkhVZjQ1Gs0NIVDJtO8ANIyY8GpoCO0ztV85j039xu53uGqQOqqz2uqo5nBVduBdyOedTZx+kll9RsvrP8gg9SPGNtqXglz7VYq9yVeIWS0xi0e/jMSJWS17k69M+JitNXNYGT+Xs+rfzMr4uRUZ6ANUz3kNnuzDE324e9gm+6ie85pSD01k2lOwL9NCrgHNvDnQ15fd1tcX2mdqv3Iem/qN3a8tFWdgROA9QJK2VHbgXcjnPdtm02tJ+nw4VKXxYXotydmWPfOd65eCXPtViv5UDzUjfhmp1AtqC626aTGxxW8JOfuDh7BYAzFdnCtSEAr2ZVrINaBZc47R3Wf09Dopd3p6ne4+Y805NmP7lfPY1G/sfhe1tzNYnaTPE6Q8RZ8nGKxOclF7+4SOl0u/JVVNrLUTaLAaOmyIBqthrR17cW6uXwpy7Vcp6quaGGYoq61SL6gthuqmxdQufzvx095H7fK3K9AXKRDV2S8g1dkvnsyc/YaGEOh39xlXXqrKM6rGU7n9cqnGU4znzUUlVOMpNC2CJSJTSYtqlYCC/eIqZKAiIlIMncMHsi6onV+9QoG+iBSFFtWSirNiWVVWdRERkbF0pDrYntpGLz000sSSqqW0VxW/sksuVXZERKaSpkVFRKSidKQ6eD75HEM+SIM3MuSDPJ98jo5UR6mHJiIy5RTsi4hIRdme2kYttdRGJTBrLU4ttWxPbSv10EREppyCfRERqSi99BAbUQIzRi29qASmiMw8CvZFRKSiNNJEYkQJzARDNJbJqr0iIlNJwb6IiFSUJVVLGWKIIR/E3RnyQYYYYknV0lIPTURkyinYFxGRitJe1c6q6jOotTh91kutxVlVfcaUVOMRESk3Kr0pJaf6+SJSaO1V7QruRUTQzL6UWHpl3J5eZ87ssL31LueVralSD01ERERk2lOwLyW17nGnucFpajSqzGhqNJobnHWPa2VnERERkclSsC8lte8ANDRktzU0hHYRERERmRzl7EtJzZsDPb3Q1Hi0ra8vtIuUi83Dfawb7mKfDzHPallT08KJNQ3jP1BERKTENLMvJbXmHKO7z+jpdVIecva7+4w151iphyYChED/tsR+ejzJHGL0eJLbEvvZPNxX6qGJiIiMS8G+lNSKZVVceWnI1T9wMGyvvFTVeKR8rBvuookamqw6XFdi1TRRw7rhrlIPTUREZFxK45GSW7GsihXLSj0KkdHt8yHmEMtqa6CKfT40xiNERETKh6ZPRUSOY57V0kd2Kdg+Usyz2hKNSEREJHcK9kVEjmNNTQs9DNPjyXBdiSfpYZg1NS2lHpqIiMi4FOyLiBzHiTUNXBGbS5NVc4AETVbNFbG5qsYjIiLTgnL2RUTGcWJNg4J7ERGZlhTsi4iIyMx1aCvseBz6DkDDHFh8DsxS1QipHErjERERkZnp0FZ44S4Y6oX62WH7wl2hXaRCKNgXERGRmWnH4xBrgNpGMAvbWENoF6kQCvZFRERkZuo7EIL7TLGG0C5SIRTsi4iIyMzUMAcSfdltib7QLlIhFOyLiIjIzLT4nBDcD/WCe9gm+kK7SIUoq2DfzBab2dfMbJeZDZrZFjO7ycxm5XmcN5rZndHjB8xsm5ndZWYXH+cxp5vZD8xsX/SYF8zsBjOrn/wrExERkbIzaxmcemnI1e8/GLanXqpqPFJRyqb0ppmdBDwCzAPuBDYC5wJ/BlxsZmvc/WAOx/kQ8EWgF7gD2AEsBi4HLjGzv3X3vx/xmNcB9wEx4FZgO/Bm4FPARWZ2kbsPFuSFiojIMfb5YV5kN1300UIDp7CQedZW6mHJTDBrmYJ7qWjm7qUeAwBm9lNgLfARd/98RvtngY8BX3b3D45zjBiwH4gDr3H3FzL2rQKeBFLArHTwbmbVwLPAKuAyd/9R1F4F/AC4Avgf7v5P472G1atX+/r163N/0SIiwj4/zHpeIk6MODEGSTBIgtWcrIBfRGQMZvaEu68er19ZpPFEs/prgS3AzSN2f5owS/9eM2sc51DtQCuwKTPQB3D354FNQD3QlLHrfEKg/1A60I/6p4BPRHc/aGaWz2sSqWTP9Q7xzzu6+fDLh/nnHd081ztU6iHJNPYiu4kTo45aDKOOWuLEeJHdpR6aiMi0VxbBPnBhtL0nCrKPcPduYB3QAJw3znH2EWb2V5rZKZk7zGwlcArw1Ih0oDdH25+MPJi7v0L4grAMWJHbSxGpbM/1DnHz7j4OD6c4obaKw8Mpbt7dp4BfJqyLPuLEstrixOiib4xHiIhIrsolZ//UaLtpjP0vEmb+VwL3jnUQd3cz+1PgO8ATZnYHsAtYBLwLeA74vQk898ro9vLxX4ZI5fvxoUFaa4y2mjBX0FZjQIofHxrkjMba0g4usmlwgHv7e9k9PMzCmhouqm9kZbyu1MOSMbTQwABD1HH0/59BErTQcJxHiYhILsol2G+Ntp1j7E+3j5u86e4/NLNdwHeB92Xs2gt8HXilkM9tZtcB10V3e8zshdH6FdkcQCuAlI+KPh9Vi5YtJ5E4dho/Fqv9651bt0z9iLJV1dfX18ydO99TyWSqqzte1dI8aFXV1cP79+9N9ff3l3p8M9iYn4tYQ7y+ZeGs+anhZDKVTCWrqquqq2qqq7t2H9qb6BvUOSuOiv47Nc3oXJSP6XYucrqyvFyC/YIxs/cAXwFuB/4O2Ep4Mz4JfIGQo//uQj2fu98C3FKo402Ema3P5QINmRo6H+XDzNYPd3bqXJQBfS7Ki85H+dC5KB+Vei7KJWc/PXveOsb+dPvh4x0kysv/GiFd573uvtHd+919I/Be4AngKjO7oNDPLSIiIiJSbsol2E+nvqwcY3/6Ytux8urT1hJq5T84yoW+KeCh6O5ri/DcIiIiIiJlpVyC/fuj7dqovv0RZtYMrAH6gEfHOU482s4dY3+6PTPf+L5oe8zquma2gvAlYCvH5vqXk5KmEckxdD7Kh85F+dC5KC86H+VD56J8VOS5mLaLapnZaQBRik667VzgV0A/cJ67P5Ox7zXALwlfCF7t7s9F7cdbVOv7wJXkuKiWiIiIiEg5Kadg/yTgEWAecCfwPPA6Qg3+TcAbMuvjm5kDuLuNOM7XgGsIs/d3EGbllwPvBGqBm9z9YyMe8zrCDH8MuBXYBlwErCbU+L8oveKuiIiIiMh0UTbBPoCZLQE+Q0ipmQ3sJgTsN7j7oRF9xwr2DXg/cDVwFtAMdAFPAl9x9++N8dynAzcQvlw0E74kfBf4J3dX6TcRERERmXbKJWcfAHff7u7XuPtCd69192Xu/tGRgX7U10YG+lG7u/s33P0Cd5/l7jXu3u7uF40V6EeP2+DuV7n7HHePu/tKd/90uQb6ZrbYzL5mZrvMbNDMtpjZTWY2q9Rjq0RmdqWZfd7MfmFmXWbmZvadcR7zBjO7y8w6zKzfzJ4xs49GqWMyQWY228w+YGZ3mNlL0XvbaWYPm9m1I6/7yXiczkcRmNk/m9m9ZrY9el87zOxJM/u0mc0e4zE6F1PEzN4T/b1yM/vAGH3ebmYPRJ+jHjP7lZm9f6rHWmmif5d9jNueMR6jz0YRmdlF0b8de6LYaZeZ/dTMLh2lb8Wci7Ka2ZfcjJLytBE4l/CrxAvAmsyUJ5k8M3uK8EtRD7ADOA34L3d/zxj9LwNuAwYI1350AL9LWLH5Vne/airGXYnM7IPAlwi//N1PSLubD1xOKJV7G3CVZ/xx0/koHjMbAn4NbAD2AY3AeYQ0yF2E66e2Z/TXuZgi0a/lzwLVQBPwR+7+1RF9/h/g88BBwvkYIlyrthj4V3f/+JQOuoKY2RbCgpw3jbK7x91vHNFfn40iMrN/Af6S8G/43YTFs+YSKjT+3N0/kdG3ss6Fu+s2zW7ATwEHPjyi/bNR+3+UeoyVdiN8kToFMOCC6H3+zhh9WwhBzyCwOqO9jvAlzYHfK/Vrmq434M2EP7pVI9oXEAJ/B67Q+Ziy81E3RvvfR+/tF3UuSnJeDPg58DLwv6P39gMj+iwnBDMHgeUZ7bOAl6LHvL7Ur2W63oAtwJYc++qzUdxz8UfRe/gNoHaU/bFKPhdllcYj44tm9dcS/ojcPGL3p4Fe4L1m1jjFQ6to7n6/u7/o0Sd+HFcSZgu+5+7rM44xAPxtdPdDRRjmjODu97n7//Vj19LYA/xHdPeCjF06H0UUvY+j+UG0PSWjTedi6nyE8MX4GsK/C6P5Q0KFui+4+5Z0o4fU2X+I7n5wlMdJ4emzUSRmFidMPmwDrnP3oZF93D2RcbfizkVNqQcgebsw2t4zSrDTbWbrCF8GzgPunerBCRD+gQX4ySj7HiKsGfEGM4u7qjwVWvoP9nBGm85HafxutH0mo03nYgqY2Srgn4DPuftDZvbmMboe73zcPaKPTEzczN4DLCV86XoGeMjdkyP66bNRPL9DCN5vAlJm9jbgVYRftR5z91+O6F9x50LB/vRzarQda0XfFwnB/koU7JfKmOfI3YfNbDNwBrCCUGJWCsDMaoD3RXcz/0jrfEwBM/s4IS+8lZCv/0ZCYJO5RonORZFFn4NvE2Yx/2ac7sc7H7vNrBdYbGYN7t5X2JHOGAsI5yPTZjO7xt0fzGjTZ6N4zom2A4TKjK/K3GlmDwFXuvv+qKnizoXSeKaf1mjbOcb+dHvbFIxFRqdzVBr/RPgjfpe7/zSjXedjanyckEr4UUKg/xNgbcY/oKBzMRU+BZwNXO3jV5PL9Xy0jrFfju/rhDV7FhAuXH818GXCtRJ3m9lZGX312SieedH2Lwn59r9NKLF+JnAP8Cbghxn9K+5cKNgXkWnPzD4C/AWhMtV7SzycGcndF3goh7yAUBlpBfCkmf1WaUc2c1hYIPJvCFV0RqYmyBRz9xuia4z2unufu//G3T9IKKZRD1xf2hHOGOlYdxh4h7s/7O497v4s8C5CdZ7zzez1JRthkSnYn37Gm2lJtx+egrHI6HSOplBUOvBzhNKPF7p7x4guOh9TKAps7iCkE84GvpWxW+eiSKL0nW8RUg8+mePDcj0fY81wysSkCwm8KaNNn43iSb9nT2ZeiA4Qpaelfwk+N9pW3LlQsD/9vBBtV46xP135Yqycfim+Mc9R9A/yiYQZhlemclCVyMw+SqgR/htCoD/aQjU6HyXg7lsJX8DOMLM5UbPORfE0Ed7XVcBA5gJOhPQqgK9Ebem678c7HwsJqSc7lK9fcOnUtsyqefpsFE/6vR0rOE8v3Fo/on/FnAsF+9PP/dF27ciVQs2sGVhDuFL80akemBxxX7S9eJR9bwIagEemy1X85crM/gr4N+ApQqC/b4yuOh+lc0K0TVce0bkonkHgP8e4PRn1eTi6n07xOd75uGREHymc86JtZrCoz0bx3EvI1T99jBXW0xfsbo62lXcuSl3oX7f8b2hRrVK//xcw/qJa+6mgBTnK7UZIU3BgPdA+Tl+dj+Kdh5VA6yjtVRxdVGudzkXJz9P1jL6o1oloUa1iveergMZR2pcTquY58DcZ7fpsFPd83Bm9hx8b0b4WSBFm91sr9VxY9AJkGokW1nqEcIX5nYTST68j1ODfBLzB3Q+WboSVx8zeCbwzursAeCthVuYXUdsBz1hWPup/K+Ef0u8Rltp+B9FS28C7XR++CTGz9xNWQUwSUnhGyyfe4u7fyHiMzkcRRGlU/0iYMd5MCBrnA+cTLtDdA1zk7hsyHqNzMcXM7HpCKs8fuftXR+z7MPDvhHP3fWCIsKjQYsKFvh9H8ha9539BqMu+FegGTgLeRgga7wLe5RkLPOmzUTxmtpgQNy0hzPQ/Sfiy+06OBu+3ZfSvrHNR6m8buk3sRvgf9uvAbsIf562EBSNmlXpslXjj6MzYWLctozxmDeEP+iGgH3gW+BhQXerXM51vOZwLBx7Q+ZiSc/Eq4AuEVKoDhDzWTuDx6DyN+quLzsWUn6f0Z+YDY+z/XeBBQkDaG52/95d63NP5RvjC+11ChbDDhAX/9gM/I6wHYmM8Tp+N4p2TuYQJoq1R3HQAuAM4t9LPhWb2RUREREQqlC7QFRERERGpUAr2RUREREQqlIJ9EREREZEKpWBfRERERKRCKdgXEREREalQCvZFRERERCqUgn0RERERkQqlYF9ERErCzL5hZm5my4v8PFvMbEsxn0NEpFwp2BcRkWnNzB4wM60QKSIyippSD0BERKTILir1AERESkXBvoiIVDR3f7nUYxARKRWl8YiITDNmtjzKdf+GmZ1mZv/HzDrMrNfMHjaztaM8Jm5mf21mz5pZn5l1mdkvzOzdBTr+9dFjLjje8XJ8fVeb2W1m9oqZ9UdjXWdm7xntuMD50X3PuD2Q0W/UnP1JvCfLzex7ZnbAzAbMbL2ZvT2X1yYiMtU0sy8iMn2dCPwSeBb4MrAQ+G/A3Wb2B+7+fQAzqwV+SgiKNwI3Aw3AlcD3zew17v43Ez1+EXwJeA54CNgNzAYuBb5tZqe6+yejfoeBG4CrgWXRf6dtOd4TTOI9WQY8BrwCfBtoJ7wnd5rZW9z9/nxfrIhIMZm7rmkSEZlOouo1m6O7N7r7X2bsW00I0HuAZe7eZWb/A/gH4G7gHe4+HPWdRwhclwFr3P2RiRw/ar8e+DRwobs/MMZ4v+nuV2e0fwN4P3Ciu2/JaD9pZOpNFJzfDbwJWO7uOzP2PQCc7+42xvu1BcDdl2e0TeY9ud7db8g41luBnwB3u/ulo41BRKRUlMYjIjJ9dQKfyWxw9/XAfwFtwLui5j8EHPjzdFAb9d0H/F109wOTOH5BjZZj7+5DhNn3Ggpzwe1E35OtwP8aMbafAtuAcwswLhGRglKwLyIyff3a3btHaX8g2p5tZs3AycAud984St/70n0ncvw8xpozM1tqZjeb2cYol96j3Pzboi6LJnn8ybwnT7l7cpT27cCsyYxLRKQYlLMvIjJ97R2jfU+0bY1uEHLfR5Nub5vg8QvKzFYQ0mhmAb8A7iH8wpAElhPSfuKTfJrJvCeHx3jMMJpAE5EypGBfRGT6mj9G+4Jo2xndMttGWpjRdyLHT0tF29H+XRktaB7LnxMuyL3G3b+RucPMfp8Q7E/WZN4TEZFpRbMQIiLT129FKSkjXRBtn4zScF4GFpnZKaP0vTDa/noix89oOxRtl4zSf/UobWM5OdreNsq+88d4TBLAzKpzeYJJviciItOKgn0RkemrFfhUZkNULee/E2al74iavwYY8L8zA2IzmwN8MqPPRI8PIfUG4Bozq8nov2TkMcaxJdpeMOJ538roF8wCHIy2S/N4nom+JyIi04rSeEREpq+HgA+Y2euAdRytg18F/HG6LCZwI3AJcBnwtJndRagpfxUwD/gXd394EsfH3X9lZg8RSmM+Zmb3EdKAfpdQz360Gf/RfBG4Bvihmd0K7AJeBVwM/CB6/pHujV7L7dFr6we2uvu3j/M8E31PRESmFc3si4hMX5uBNxBSaD4IvJuQenJp5oJXUdnK3wH+Z9T0YULu+4vAH7j7X03m+BkuA74KLI6e42zgE8BYxz+Guz9DSKN5BHgb8CGgBbgc+I8xHvZV4B8Jv0R8glA689pxnmei74mIyLSiRbVERKaZsRapmi7HFxGRqaOZfRERERGRCqVgX0RERESkQinYFxERERGpUMrZFxERERGpUJrZFxERERGpUAr2RUREREQqlIJ9EREREZEKpWBfRERERKRCKdgXEREREalQCvZFRERERCrU/w8B7KOB1NE2EgAAAABJRU5ErkJggg==\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "models_ids = []\n", + "for pdict in params_dictionaries:\n", + " models_ids.append(pdict[\"train\"][\"evolution_model_id\"])\n", + " \n", + "models_ids = np.array(models_ids)\n", + "\n", + "cmap = plt.get_cmap('rainbow')\n", + "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", + "\n", + "# plt.figure(figsize=(12,6))\n", + "# for i in range(data.shape[0]):\n", + "# try:\n", + "# plt.scatter(i // 10, \n", + "# data.loc[:, \"classification_accuracy_valid\"].values[i], \n", + "# c=colors[models_ids[i]], alpha=0.5, marker='o')\n", + "# except IndexError:\n", + "# print(models_ids[i])\n", + "# print(colors[models_ids[i]-min_mid])\n", + "\n", + "\n", + "try:\n", + " y_label = \"Number of edges\"\n", + " plt.figure(figsize=(12, 12))\n", + " for i in range(data.shape[0]):\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", + " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", + " d = json.loads(json_acceptable_string)\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " np.sum(d[\"chainer\"][\"pipe\"][model_index][\"binary_mask\"]) \n", + " + (np.random.random() - 0.5) / 2, \n", + " c=colors[models_ids[i]], alpha=0.5)\n", + "\n", + " plt.ylabel(y_label, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \"_colored_ids.png\")\n", + " plt.show()\n", + "except:\n", + " pass\n", + "\n", + "# ylims = [(0., 1), (0.85, 1), (0.9, 1.), (0.85, 1.)] #ag_news\n", + "# ylims = [(0., 1), (0., 1), (0., 1.), (0., 1.)]\n", + "# ylims = [(0., 1), (0.7, 0.9), (0.9, 1.), (0.6, 0.85)] #sber faq\n", + "ylims = [(0., 1), (0.8, 0.9), (0.8, 1.), (0.8, 0.9)] #imdb\n", + "\n", + "for metric, ylim in zip(MEASURES, ylims):\n", + " y_label = metric\n", + " plt.figure(figsize=(12,6))\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_valid\"].values[i], \n", + " c=colors[models_ids[i]], alpha=0.5, marker='o')\n", + " if PLOT_TEST:\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_test\"].values[i], \n", + " c=colors[models_ids[i]], alpha=0.5, marker='+', s=200)\n", + "\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_valid\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \n", + " c='r')\n", + "\n", + " plt.ylabel(y_label, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.ylim(ylim[0], ylim[1])\n", + " # plt.ylim(0.85, 0.95)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \"_colored_ids.png\")\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train params" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "params_dictionaries = []\n", + "\n", + "for i in range(data.shape[0]):\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", + " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", + " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", + " d = json.loads(json_acceptable_string)\n", + " params_dictionaries.append(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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Ix0/A3Dlw1ZWwqF2O54QQQgghzhZJWueRjl6bH2wqMJzRNMdgOKP5waYCHb22q+7BQw4/36BJJjWNDWb58w2ag4ecWWi5EGI6OjJZ/qF/kL/sPcE/9A/SkcnOdpOEEEJMQHrOq9SuEzaP7S/QNaJprVHcssTLijmVe7gn64kOh1hQEQsqAGLBU+XlvecvvAjRCESjpm40CqB54UVY1D6tZpw1Xbvg1cdgoAvqW+HiW6B1xWy3SojZ1ZHJ8oNEgpiyaPJ4GLZtfpBI8DHiLAsGZrt5Qgghykg4r0K7Tth8Z0uOeEDREoVEVvOdLTn+8FL/tAL6sYTpMS8VDZjycsdPgArBa0dshrMQC8DieovUicrrPtqh2fIk9B+Dhnlw6Y0wf5k67bZOVdcu+I9v2hz12oz4NTWHFXu/6eF3/sQjAV28Ke3qs/lFyQH8u5Z4WdHo/nx4YjRFTFnEPOa2mMcDtimXcC6EENVHhrVUocf2F4gHFPGAwlLq5O+P7S9Ma73z4opk2dnsZNaUl7PCms0HHTIFqPFDpgCbDzpYYXeQP9qhefxHkBqG+mazfPxHpnymPL7B5jUKZMOamF+RDWteo8DjG9xDdoQ41+3qs/n2lhyJrD55AP/tLTl29bn39+5Cgag1/qM+all0F6b3eSKEEOLskHBehbpGNDX+8WU1flNeyd4um+8+ludL/5bnu4/l2dtVOZDetMxiOKMZzmgcrU/+ftMy924wUmfjscFT/P/tKYDHNuXltjwJkRiEY6Ass4zETPlMee2AQyAKIUuhMMtA1JQL8WbziwkO4H9R4QC+xesl6Yx/HyQdhxavnDgVQohqJJ/OVai1RpHIauIlZ5xHcqa83N4um/uedoiFYG4tjKTgvqcd7lkHF7SOP8W9rMnDbQ3w+M81vd0Qb4Hb3qsqztYyHNQsuVxzYp9FdhgCMViy2mE46G5v/zGwazTbjjqnhsDUWqSPzdywlpF6m2jGguipMn9GMVJvAz5X/R2DBR46mudoSjM/rHjPfB+r6+TtIM4NXSOmx7zURAfwN0XC/CCRANv0mCcdh2HtcHekpuK6dwwWeLDz1Hvj9jZ5bwghxEySnvMqdMsSL4msJpE1Pdxjv9+yxP0P8untJpjXhE0PWk1YEQuZ8nKduzX7NlisrvPwjss9rK7zsG+DRedu9z/01piCGCy9WrPqFs3SqzXEiuVlnLjDK4dsMwQmYIbAvHLIxonPXK914zqH3DA4SdCOWeaGTXm5HYMFvrEry1BO0xqCoZzmG7uy7BiU0/zi3NBaoxjJjS+b6AB+WTDAx+JxYh4PvbZNzOPhY/HKF4PuGCzw9Y7x742vd8h7QwghZpJ0h1ShFXM8/OGl/nGztbxvpa/ixaA9g6bHvFQkZMrLbX0CwnEz7AROLbc+AW3Lx9e9+UIPX342x/HjmqyGgIK5QcWfrSkbbwMcb7dJHdAcDjmkAxDKwpykxfF2DUxvhpnJet/VPr6SycFvPfj7LHKNDiPrbD5xtbu9Dx3NU+tX1PpNkKn1nyqXHkJxLnjXEi/f3mLSeY3fBPNEVvP+le6zRGAC+mQu/nywM0+tz/3eeLBT3hvijR0opHk2P0yvk6fJ8vF2X4zF3tBsN0uIc4582lapFXM8k5qZpbkODg44dGU1Iznzj7o1oFhU7z4pMnAMCjGHrcc0w1lNLKBYGlNkjrnr2h5NJqghB1Ye8EEmqLE97l72Az6bros1Vr8ikIJCGLoutvH5Zm5Yy5qYj8/dAOtX5jiSybMgaPGJZj9rYu6wcjRlegVLxXymXIhzwYpGD5+81D9utpb3r/RVnK1lKuS9IU7XgUKaf8/2EcXDHOVlxLH592wfv0ujBHQhpkjC+Tlu/gLN+p0O4YAiGoRkCrYMOlx7iTsY27UOLx10CNWMzcCieemw5vJFUN7D/fDhAvPrFLXNp9YzlNU8fLjAqobxu81wDrwRTbz+VN1EVjOcm7lwDiagVwrj5eaHFUM5fbJXEGA4b8qFOFesaPRMO4yXk/eGOF3P5odJJL282O+hLwONQYsVDYpnrWEJ50JMkYw5P8e9mnFYugKiYcikzXLpClNe7tgFNoGMwpdVgFkGMopjF7hnYOka1cTKRoTE/Ka8XCyg0NqEfY02S23KK9mWKPA3u1P8wStJ/mZ3im2JmR3P+p75PoZymqGcGdM/9vt75r9xsBfizez2Nh9D+bL3Rl5ze5u8N8Tr2zJcYGOnh9E8NARgNA8bOz1sGZbrFYSYKgnn57iuUU3rXFh1May92ixb51YO0SfqbBreY2NFNHY/WBFNw3tsTlSYHrE1ohguu+BsOGfKy62aa7Gs0SLoVYxkIehVLGu0WDXXvXttSxT46r4MgzmHtpBiMOfw1X2ZGQ3oq+u83NbkpaPHYcNem44eh9uavDKmVpz3Vtd5+fSyALV+RVcaav2KTy8LyHtDvKFD/T58Hk3EB0pBxAc+j+ZQvxzYCTFV8ol7jmuNmGkXa0uu9ZooRLfVWCT8moa7T/WqJ7KatoA7RN+20Ms/PlIg/7IH74CiUK9JX2Zzz63uXeadSz1892WHFXMUNQEYyZr1vnOp+5T7A9056g9a1Gz0Qrci3qKxrivwQCTHmvjM7I67j9s8swsuC/pYFzftfWYXXFBjs3zuzFzAKkS1Wl0nB6pi6jw5Px5vmqy28KPIofF4HTw5GdIiZs+2RJ71PXmOpB0WhCzuavaxJl79B4zSc36Ou22hl0TOjAd3tGYoq0nkTHm5dy/ymNtLpmgcymrevcgdSGt7vax4zo8vo0jVanwZxYrn/NT2ute7Yo6HT1zmIx5UdI9APKj4xGWVZ5c59ipE7/OhhxW6CfSwInqfj2OvnpntMRmP77GJBU07LaWIBxWxoCkXQggxdavDPubrEAGlGMUhoBTzdYjV4eoPQuLNaVsiz5cPZBjMO7QFFYN5hy8fyLAtkZ/tpr2hWe8eUUr9DnAdcAlwMVAD3Ke1/tDr3Odq4L8CVwIhYC/wfeAbWuvzKmGtavDyqdXmAs6uUU1rRHHPhV7XRZsAKxu8/PHF8OhBm84Rh7Yaiw8s97KyQt3tj0LLXIsl8VPHb6mEKW9d6W7HpGeXedZLJqIJjs2XHoOMrWl+1gvvm/zzrqRzt2brE2ZWmvp5cMlN0LbcfQaha1jTUvb9KzUBUz7Rel997NR6L76l8nqFEOJ8dVezn78/6LDAChH3KhIFzaBjyoWYDet7zNSwdT6TY+p8CnBY35Ov+t7zWQ/nmJB9MZAEOoHlr1dZKXUH8DMgA/wUGADeA3wVeBvwu2ezsdVoVUPlMF7JyobKYbzcYBfUtowvC9WY8kp6tsHu9ZA4AvEFsPwuaF7jrrcg4WFb2AZbE/BA1oZMGC5MVA72e4/ZPLXdoXsQWurg+ossLpjnrtu5W/Or75t53OuaITUMv/o+vOP3tStIt8YUiYwmXvJtpyPZyl+w1Llb8+R3i+ttMet98rtw4yfc6xVCiPPVmpiPzy+C9T05jmQcFgQtPj6/8nS2QsyEI2nTY14q7lUcSc/cFySermoI55/FhPJ9mB70pyaqqJSKAd8FbGCd1npzsfyvgF8Dv6OUer/W+v6z3uo3ubpW6D6q6UvAaAoiYWiMQ8t8dyDt2QYv/D0E6yDWBulB8/dVn3cH9EVLPKQOw2sjDqNZTSSgWBWyWLTEHbj3HrP5140ONSFoqoXhNPzrRocPXYcroE/1C5a+99sCoE+OkR/OwO+ucbfh1ceK640X1xs/VV6+XiGEOJ9NdjpbIWbCgpDFYN4p9pgbiYJmQaj6R3TPegu11k9prfdqrSfzLRe/A8wB7h8L5sV1ZDA98AB/fBaaed5pXK3p2AujSQgFzbJjrykvt3u9CeahOlCWWQbrTHm54LU23UegKadYHrNoyim6j5jyck9td9AFzcFDmhdeMUtd0Dy13X3UO3AMQtHxZaGoKS+3fK6Hj1/uHTdG/uOXeyteDDpwDIZ9DhtP5HnoWI6NJ/IM+5yK6xVCiPPZ3mM23/llni/en+c7v8yz99h5NcpUVJm7ms3UsIN5B0eb5VBec1dz9R9AVkPP+VTcUFw+VuG2Z4AUcLVSKqC1zs5cs958jh6B9ktg5BikRyBSA80XmvK1ZXUTR0yPealg3JSX2+y18V6j4FGLbJ9CNWq873bY7NVcVfZFSHs6Nb3dmkBAEQlDNg8HD2tSOfd66+fB8UMweNy0N1QDdXNhbnvl57d8rmdSM7PkGxw2HS0QrIEanyJjazZ1FbhyvpfyL24SQojz1VTOdAoxE9bEffzZYsbN1vLx+efGbC3nWjhfVlzuKb9Ba11QSh0EVgGLgV0z2bAzrXsHvPYgDHVCbRusuh1aVs/c4/f1wNwF0NR+qkw7prxcfIEZyhKqO1WWSZjycp07wL/DwrMSrLBGp8DZYdE514ZrxtdNjwBeCBTfRwGfGaOeHnGvt+UCeHEDBCKmxzw1DIPdcPFNU33m4x28KEegw4PfC4TBn1bolCk31yILIYR4arsJ5rGQGUIQCwGYM50SzsVsWRM/N8J4uVkf1jJFxRG/JCa4fay8dqIVKKX+UCm1WSm1+cSJE2e0cWdK9w74zdchPQTxeWb5m6+b8pnS2Ayp0fFlqVFTXm75XZAZNAFdO2aZGTTl5SKvWRSCGitivqjCikAhqIm85t4Vay2FBrIFjdbaLIvl5Y7tMz394RhkR82y/RJTPh09TTbxuwtYUdD9CisK8bsL9DTJ6VohhBjTPQjR4PiyaNCUCyGm5lzrOZ82rfV3gO8ArF27djLj3Gfcaw9CqNb8wKnlaw/OXO/5ZdfDL+4zv4cjJpiPjsC1t7vrNq8xF3+WztZy6ccrz9bSlLPY43ewChq/F3IFyPmhPecO58vaLIInND1pc9FmTQAW1sDCOe66/d2Ve/r7u09zAxQtCFsMtjnU3XPqG0wHcw4L/Ofaca0QQpw9LXVmKEus5IRiMmPKhRBTc64ljLGe8fgEt4+VD81AW86aoU4IxsaXBWOmfKYsuEDxrnvMWPP+42b5rntMeSW7cgUesvI8GM7xkJVnV65QsV77BRarYhYBryKZhYBXsSpm0X6Be1d8+1qFX3tY2WBxw1KLlQ0Wfu3h7WvdbWhogXRyfFk6acqn484WP0N5E8gdrRnMOQzlTbkQQgjj+ossRtIwnDZfcjec1oykTbkQYmrOtZ7zDsz1iBcCL5feoJTyAouAAnBg5pt25tS2maEsoZLBOZlhUz6TFlygWHDBG9d7anOBf/m+JhCB2BxFagT+5fsaKHD92vG72EW3Qv+3LC6KQygG6WFIJ0x5ucULLH733Q7PbobePmhqhHddp1i8wP1hf+lN8PgPzO+hqAnmo8PwtrtP44mXtrfWy2eXBnmgO8eRlMOCsMXHFvq5qPZce+sIIcTZc8E8Dx+6jnHfS3HH5ZW/l0II8frOtYTxa+Ae4Bbg38puuxYIA8+c6zO1rLrdjDEH02OeGTZhfe2HZ7ddE3n4URPMw8Vv3RxbPvyo5vqyqV3mrYR1n4Ltj8BgJ9S1wRX3mPJKFi+wWFzhwtJybcsUN39Ms+UJM5SlocUE87Zl0/+ioItqvRLGhRBnRC8JOughQZo4IZbRTNOEJ4PPLRfM80gYFycd3u/w0jPQ1wuNTfDWa2HhEjmTMhnnWuL4D+DvgPcrpb5R8iVEQeC/Fev842w17kxpWQ3XfHr8bC1rPzyzs7VMxdBxTWzO+BAcjJrySuatnDiMT0fbMkXbsjeuJ4QQs6GXBJs4QBAfMYJkyLOJA1zB4jdNQBcCTDB/5H4zJLZhjrlm7ZH74db3OxLQJ2HWw7lS6k7gzuKfY3OBXKWU+mHx9z6t9ecBtNbDSqlPYEL600qp+4EB4HbMNIv/Afx0ptp+NrWsrt4wXq52rhnKMtZjDpBJmvKZdOigw4vPw4njMGcuXHk1tC+SDwEhRHXooIcgPoKYqd3Glh30SDgXbyovPWOCeaSYC8aWLz0DC5fMXrvOFbMezoFLgI+UlS0u/gAcBj4/doPW+gGl1HXAXwJ3A0FgH/A54OuT/KZRcQbd9m5VHGNueswzSTOd4X96X+VwvrO/wCOHbDqTDm1Ri1vbPaxsmN6ueOigw4b1mkgUGhrLyJpcAAAgAElEQVQhmYQN6+GOuxwJ6EKIqpAgTYzx8w0G8JIgPUstEuLs6Os1PealwhFTLt7YrIdzrfUXgC9M8T7PAe8+G+0RU2cu+izw8KOaoeOa2rmK//Q+5boYFEww/9b2PHG/Yl5EMZTVfGt7nk9dxLQC+ovPQyQK0ag5IIhGATQvPg/ti057tUKI13HwkMPzmzTH+2BuI1x9hWJRuxwMTyROiAz5kz3mAFkKxOULzcSbTGOTGcoSKTmjnho15eKNzXo4F9Vr8+Oap34CQ71Q2wTXfxDW3ly5N/z6tV7XxZ+VPHLIJu5X1AbMemoDp8qnE85PHDc95qXCYVMuhDjzDh5yWP+QJhrRNDZAchTWPwR3vceRgD6BZTSzqTiZWAAvWQpkyHMx82e5ZUKcWW+91owxh/HflbKuwsxswk3C+Qza1+nwzFaHngForodrL7FY2lad/8Q2P65Z/2UIRCA+B1LDsP7LAHrCgD4ZnUmHeZHx94/5TXklDxzO8M+78/SOQlME/mC5jzsXBl315sw1Q1lMj7mRSplyIcSZ9/wmE8yjxfdzNAKgeX4TLGqfxYZVsSbiXMHicbO1XMx8GW8u3nQWLrG49f3jZ2tZd6vM1jJZEs5nyL5Oh/t/ZVMThrl1MJKC+39l8/53UJUB/amfmGAeLn4Z0tjyqZ/A2ptPf71tUYuhrD7ZYw4wnDPl5R44nOFvNueJ+mFO2NT7m815AFdAv/JqM8YcNOGwCeajSXjHNNoqhJjY8T5obBhfFg6bcjGxJuISxsV5YeESSy7+PE3VlwrfpJ7Z6lAThpqwwlKKmrCiJmzKq9FQr/kyn1KhqCmfjlvbPSRymqGs+Ra5oawmkdPc2u6eG/efd5tgHvODpcwy6jfl5doXWdxxlyIaVfT3meUddym5GFSIs2RuozkILpVKmXIhhBCnT3rOZ0jPgOkxLxUJmfJqVNtkhrKM9ZiD+dbN2mlezLGywcunLmLcbC33LPNWHG/eO2p6zEtFfaa8kvZFllz8KcQMufoKxfqHoPRsVXJUcfMNMzuFqhBCvNlIOJ8hzfVmKEtNSdgcTZvyanT9B8fGmJse83RxesRb/2j6617ZUDmMl2uKmKEsMf+psmTelAshZteidou73uPw/CZOztZy8w0yW4sQQkyXhPMZcu0lFvf/ygY0kZAJ5iMpuPXqmf1HdnSP5pUnT33F/VtuhPkXunu6zEWf42drufWPJp6t5Wz4g+W+k2PMoz4TzJM5+Nwa3xvcUwgxExa1W3LxpxBCnGHqfP7OnrVr1+rNmzfP2OPN9mwtR/dofvlj802e4SikkpAagXd+uHJArwaTna1FCCGEEKJaKaVe1lpPYtJp6TmfUUvbZnfqxFeeNME8UhxHPrZ85UmYf+GsNet13bkwKGFcCCGEEOcNGRx4HunvNj3mpcJRUy6EEEIIIWafhPPzSEOLGcpSKpU05UIIIYQQYvZJOD+PvOVGM8Z8dBi0Y5apEVMuhBBCCCFmn4Tz88j8CxXv/LAZaz7Qa5bVfDGoEEIIIcT5Ri4IPc/Mv1BV7cWfQgghhBDnO+k5F0IIIYQQokpIOBdCCCGEEKJKSDgXQgghhBCiSkg4F0IIIYQQokpIOBdCCCGEEKJKSDgXQgghhBCiSkg4F0IIIYQQokpIOBdCCCGEEKJKSDgXQgghhBCiSkg4F0IIIYQQokpIOBdCCCGEEKJKSDgXQgghhBCiSkg4F0IIIYQQokpIOBdCCCGEEKJKSDgXQgghhBCiSkg4F0IIIYQQokpIOBdCCCGEEKJKSDgXQgghhBCiSkg4F0IIIYQQokpIOBdCCCGEEKJKSDgXQgghhBCiSkg4F0IIIYQQokpIOBdCCCGEEKJKSDgXQgghhBCiSkg4F0IIIYQQokpIOBdCCCGEEKJKSDgXQgghhBCiSkg4F0IIIYQQokpIOBdCCCGEEKJKeGe7AULMhk4nyRb6GSBLPQEupYE2KzrbzRJCCDENx3L7OFjYQcZJEbTCLPKuZp5/6Ww3600jne1hZHQ3+UICnzdOTWQ5oUDzbDfrTUfCuTjvdDpJntBdhPFSh58UBZ6gi5uc1vMuoO8vpHk2N0yvk6fJ8vF2f4wl3tBsN2tW7E5neSyZ5li+wDyfl1uiIZaHArPdrJk3dAQ6X4JUP4QboO2tULtgRpuQTXczmtxJIT+E11dLJLqSQKhl2uvNZNzrDQanv1669sH2jTDYA3XNcNF10CqBcKYdy+1jZ24TXuUloELkdZaduU0A0w7o+/JpNmaT9Nh5mj0+rgtEWeo7vz4r09ke+hMv4FFBvJ4Ytp2mP/ECDfGrJKCfYUprPdttmDVr167Vmzdvnu1miBn2kHOYlC4QVqeOTcf+fo+1cBZbNrF0tpdE+lRvRTy0nFCgqWJdO9lFoX8rOjuICtThbbgET7TVVW9/Ic2/Z/qI4iGiLEa1QxKb3w02nncBfXc6y3eHewmEh/F48ti2j2wqxidiTRUDesLuo6dwkLQeIaRqaPYuIu5pnNE2b0wP8rPMECecAnMsL3cHa7kuVDe9lQ4dgY5HwBcBXxjyKciPwrJbKwf0bdvg5z+HI0dgwQJ473thzZppNSGb7iYx8CzKE8KygjhOBm2nide/fVoBPZPpJjH4HMoKnlqvkyFe97bpBfSuffD0v0GoBkIRSI9CegTWfaByQN+2DdavP7XN7rpr2ttMGM+lHiCvs/jUqffs2N9vC9952uvdl0/zb6MD1FinPitHHJsPROorB/RfPgg/+R4c74G5zfDBj8M7bz/tx68WxweexrbTeDynnvPY33Pr17nvsH0bPFDy+XDne+Giyvu6nezE7tuKzgyggvV4Gi/BE22rWHf7UIENnXmOphzmhy3uaPNxUW3lvubdzlF26P2kSBMmxGq1hOXW/Ck/9zNBKfWy1nrtZOpKz7k47wyQpQ7/uLIQHgbITnvdU/mAmax0tpcTIy/gsYq9FU6aEyMvMIerXAHdTnaR63oS5Q2BvxZdSJHrehJ/642ugP5sbpiYShG1TgAZYgSxnDk8mxs+78L5g+l+fDV9+PCgtA/LY+PU9PFg2svy0LxxdRN2Hwdyr+JTAYJEyessB3Kvsth/ccWAPmT30+UcIqWThFWUVqudWk/DtNq7MT3It1IniCgPDcpDUjt8K3UCoGJAL4x2kRvchpMdxArU4a9bgzfiPmCj8yUTzP0R8/fYsvMldzjftg2+8FeQGIB8Dg7tgVdfgS98aVphczS5E+UJnQwAHk8Iu1g+nXA+mtxJxrIY9IySZYiAx0cdPrzJnZXD+f7d8JvHoPcYNM2Da26BJcvd9bZvNME8XGP+Hltu3+gO59u2wd//PdTVQVsbDA6avz//+crbLHEUejZDegBC9dC8FuLTDxaDdj9dzmFGSRIhSqu1kLpp7pPVIOOkCKjxn11e/GScVOU7dLwGTzwM3V3Q0go33QbLVrmqbcwmqbE81FgeAGqU52S5K5z/8kH4ypcgEoHGuTCSMH/DOR/Q84UEXk9sXJllBckXEu7K27fBV78CtbWn9vWvfgU++zlXQLeTneQ7f4XyhCFQh86nyHf+Ctre4fr/uX2owL0dWWp90BpSDOUc7u3I8plluAL6bucom/Q2vNpHkABZcmxiGzjMWkCfLAnn4rxTT4AUBcIlu38am3qmN4RhKh8wADtTOX6RSNOVt2n1eXhXPMTKsN9VL5HejccK4rGKYaX4zyeR3u0K54X+rShvCOUNm4ListC/1RXO+/UADc5+GElBNg8BH5HIEP3WEqByr/xrozkeHsrQlbNp9Xu4rTbIqoi7zQCjueMMZPeQdYYJWDHqAxcS8c+deAOeDUf3wiu/hv5uaGiBt9wA8y9wVTvu7SesPVjFfUJpL/5iOYwP5z2Fg/hU4GTvnK+43/QUDrrC+ZDdz57CdnzKT4gIOZ1lT2E7F3LRtAL6zzJDRJSHqDLX9EdRJ8vLw3lhtItM91PgDaH8tTiFFJnupwi2XO8O6Kl+wAODOyE3asJ5zTwo9Lsb8d1vQ9dhqIlBNAa5rPn7u9+Gb3zztJ9bIT+ExxsfV2ZZQQr5odNeJ0Ayf4Jubx4vHvx4KWDTbeVoyedwvRL7dzP46x/TuXIOo5cvIjKYoe3XP6aOD7sD+mAP1Jbt16GIKS+3fr0J5nXF12hsuX69O5wnjsKBx8wZjGC9OYtx4DFYfMu0Avqg3U+HvQM/fsJEyJGlw97BMlaf8wE9aIVNT3nJZ3mBHEEr7K7c8Rr84FsQi5uDr+GE+ftjn3IF9B47z1xrfFyKKIseO+9e70++R++KhXRct5pEQ5R4f5JlG3fQ9JPvVQzn+/NpnsmNnBxaeK2/hiVnYrjMoQ7Y9CvoOwaN8+CKd0D7smmt0ueNu3rOHSeDr+z9Cpge89pa977+wM/d4bxvK8oTRvmKr1Nxafdtdf3v3NCZp3Z0gNqjeyA5Qm20BuZfyIbORlc436H3483YBE70QyZDIBiEObXsCO5nORLOhagql9LAY84+koVhfE6WvBVAe2O8zVN5TGIq20si3UHeTuDzxImHlhGuMKTE7ttKRxaetBXHlMU8rbjRAysqfMDsTOX49okkcY+ixWuRsB2+fSLJJ+dEXQE9X0jgTdvQ3wHZUQhEsBoWkA/lXG3Q2UHw144v9IRMeZnGQieH+mx2JBcwYIeo96RZHe2lvbETWOGq/9pojm/1jhL3KOb5LBIFh2/1jvKpJlwBfTR3nGOp3+JVQfyqhoKT4Vjqt8zj8pkL6Ef3wi//xfRk1jfB6LD5+52/5wroIW+BfLqAJ3nC9AL7/OSjcUIh90dkWo8QZPy1CV78pPWIq26Xcwif8uMvBnl/MTR0OYcqh/Pu/fDaMzDUC7VNsOpaaFniqnbCKdBQ7L0bE0Zxwim46uYGt4E3hFU8UFPeME6x3BXOlRd6d4I/ZP5B2jk4vguaVrrbumUztNXBogiELUgF4KDHlE+D11dbMQB4fbWvc683NuBT2KkU/XmHnAK/hlqfxUC4lvLBbIPbHmf3lS34HS/hFOSiQXZf2cLybY9TVx7O65ohNXKqxxzM0Ja6CmNwjxwxvYil4nFTXq5ns3kNfMWzF2PLns3TCuddzmH8lO2T2pRXCuc9DLOLHhJkiBNkBc00E3PVqwaLvKvZlnuRBHlsFB40AQ0X+i5zV37iYRPMY8X9amz5xMOucN7s8THi2Cd7zAFGtUOzx+dabW/YZtPd1xJM5Yj1j5IJB9h091Vc8ZOnXF0e+/Npftq9n+iefcw50cfInEZ+euFS3teyZHoB/VAHO598kF+0rKSr6WJaUwne9eSDrLzx9mkF9JrIcvoTL4DNyaFhts5QG7nUXXkK+7rODECg7IyfN2TKyxztGaB190vgD0IkCtkssZ0vcdS5AlaP/zxLZYcIHu0Frw8CASgU8B3tJrXAgQrHa9VEwrk479QXRlmT6mSPL8yI5afGKXDhaCf14Rbwjw9dqWwvJ0Y2mSElVgzbyXBiZBNzuMIV0HcN9XIvizhhRchqHx1Wnh16lM8MHeTisjb8IpEmXhglPnwM8inivjCE5/GLhMcVzn1ph0LvdizlmERh53B6E/ia3KfBVaAOXUid7DEHwE6jyj/4gPSJEZ7ct4rw3gihYS8jsQJPXlDHB/VrUKEj5OGhDHGPotZremtrvQpweHgo4wrnA9k9eFUQrxUEwKuC4JjyGQvnr/zaBKZIMUiMLV/5tSucL88UeCU1hHLA7/WRczS51BCrVa3rQzykair2zoVUDeVSOkkonYeBPZBLgT+Mr76NVKhCj1v3fnj2fgjWQHyOGbf87P3w9ve7Avocy0tSOyd7zAFSaOZY7o90JzuIKjtgU54QToUDNhyHklUWKxfLyzX4YEUIbAUpDT5l/i5UeG5TEImuJDHwLDaMG3MeiVcIWAB7dsKTj0J3J7S0wY3vhgvdBxMHVJiaXDdKefBpDwqb0ZxNT2Qe5dGisy6NR0XQ/gzZkEbZCo/to7MuheuddNF18PA/QV8fZNIQDEFjI9z2R+62LlhgTu/XlawlkTDl5dIDpse8lDdsyqdhlCRhIuPKfPgZJemq28Mwz3OQIF5iBEiT53kOcjWLKgb0I/YoLzmD9JGlkQBvtepY4Im46p0tjreZfns+Eacbj86hlZ9+TwuOt8KBUneX6TEvFY2Z8jLXBaL826jZ7qVjzm8LuT8oO25eSzAxQjBv3kjBVA7yWTpuXusK588c3Ud06yvUaAWxGDWJYdj8Ms9colmy+CLXug/bo/zWGaKPHI34udyqZWGF7btzyya+3X45cQUthQyJYJRvt1/OJ7dsYmWlcP7adnh4A3Qehbb5cNsdsMr9+KFAMw3xq8bN1lIbubTyxaBT2NdVsB6dT53sMQegkEaV7//A/K7dDAXj1I4dFwUCDFsB5nftBsaH83BvgmwkQMAuzhru9ZIPWIR7E7DI3eRqIvOci/POULqDZu3nhoKXO3KKGwpemrWfoXSHq24i3VEcUhJEKXXy90SFuvc7zRygDrRFDTnQFgeo437H/cHVNZogNXCEjckaHszMZ2OyhtTAEbpG3WP3ag4fpuArYHsUWnuwPYqCr0DN4cOuut6GS9CFNLqQQmttloU03oZLXHU375xHfFMcX9bCiRfwZS3im+Js3jnPVRegK2cT84xPbjGPoitnu+pmnWE8avwwIY8KkHWGK66bzr3w4Lfhh180y869letNRX83hMtm3wlHTXmZ67dsY2lhBJ9PMeLx4PMplhZGuH7LNlfdZu8i8jpLXmfRWp/8vdnr/rQPpwrkT3SAnTe90Xae/IkOwil3DzevPWOCeagGlGWWwRpTXubuYC2j2iapHRytSWqHUW1zd9Ddu2wF6ihkTpAd2U868RrZkf0UMiewKhywYeegYRl4/FBIm2XDMlNe7qqlkMxCOg9os0xmTfk0BEItxOvfbsaaFxJ4PKGJLwbdsxN+/I9mSMLY0IQf/6MpL3M4pXg1Mp/9dXPZ2ziH/XVzeTUyn8Op8qMRGGmJMmBbbE3M57njF7A1MZ8B22KkpcJsTtkC9I2aAxi/1yz7Rk15ubvuMoFlcNDUG/v9rrvcdUP1UCgbK11ImfJpiBAlz/jXM0+OCO7ntosegngJ4UOhCOEjiJdduIfsHLFHecTpZpQCDfgZpcAjTjdH7NFptXcqttCP19+GJ3QVVvg6PKGr8Prb2EKFYVktrZAs+zxKDpvyMkt9IT4QqafG8nDcKVBjeSa8GDTx1rcQGEpCLgNaQy5DYChJ4q1vcdXt7TxMRCsIhUCZZUQrejvdn+2H7VH+NXuMjSMpNg3YbBxJ8a/ZYxyusH1/4akhrjRxJ48FxJ08caX5hcfdgcBr2+Gb98LQEMxrNctv3mvKKxgOhNhd38Ircxeyu76F4cAEPfx3vhecIajtgQV9ZukMmfIynsZL0HYKnS/+38qn0HYKT6P7/9Ydx37LkUgdz9Y1sbGpiWfrmjgSqeOOY7911V39+D4KIS9Zv8IBsn5FIeRl9eP7Kre5ikjPuTjv5OxhfNb4DymPCpCz3cExbyfwWmUXwKgAedsdordaTUR0ngAmrAYo4GjNVss9BMY70sdv8k3UKE3MssloH8/lm7hmpA8Y37MQGO6m3vGTjFrkveArKOJJm0DSHTI90Vb8wWUUjvwanU2gAnF8C26oOFvLYEcr/mAWn99B2aD9kLctBjsqXCgItPo9JApOscfcGLY1rX6Pq27AilFwMqbHvMjWWQJWhdPhnXvh8eLwk7q5kBo2f9/8e9DmHh8+aQ0tHPLYvLiwhb6gn8ZMjisPd9Le4A558w4e4Q6t2T4vz2DYS12qwEXHRph36KirbtzTiGOv4sXUfvJ6EJ+Kcml4VcWLQVsPHWNPvQ/w4rMh7/eSR7Po0DEo/78z1Gt6zEsFI6a8zNi48tLZWj4Sqq94MagKtpDvfxllBcATxCmksXND+OvKz+cAkTlsT1tsCF/MUSfAfCvLHRzlokiFnvNVSyGZhNFRyGTB54VInSmfpsFQhJ2hBSSYQ5wQK4lQcaK2Jx8lsbCR3hVx0hFFaNRP0y4v8ScfdfWeF7J5BhujeGzw2Brbr7BDUNflvlhw1FvPnnQQbwEiniy5goc96RZWRDPuNjz3ONTPg2hJL2oyYcoXlw2BWbPGXPxZOlvLxz9e+WLQ5rVmjDmYHvNCyow7n3/t62+8N9BqLaRj8Fk4cARff4J8Q5zc4gUsqnu7q26CDLGya3GCeEng3g4vOYNE8BIpzoIVwQvalM9U7/mULva/6TYzxhxMj3ly2Bzc3X1PxXUv9YUmNXVivG0ZmbdDcMsWGE1CJEr2iquIt7l7rJs6uxmpjVDTfwwKOfD6GW2YQ1On+7P9kUw/HaOakPIQ8yiytqYjafOI7udTkfHbtys2l5b0EPhOvXY12VG6YhXOWj68AeK1Znw4nFo+vMHVe95Lgk0cIIiPGEEy5NnEAa5gMU3lp1sX1sGdq+BgJ/QloT4Kdy425WU80TZoe8e4yRS8LVdXvFYrtLCeuc3DpDL1ZHI+Ar48c5uHCWn3QevywQA8epgd17SRinoIJ20u+1WnKa9yEs5FVevZDjsfMDO81S6AlXdCs/ts25T4PZWDo9/jDo4+TxzbyeApqevoLD6P+3SmxoelcqAVoAGFpRQ27nGJ2UQeFVKY43lQSqOwyCYqDAkIeAmlHcIlN+mCA4EKb9/eA3i2bcQTrIXAPMikYNtGCM6BpsXjqkZyMYY9A6S1F20plKPx6wKxXOWxvbfVBvlW7yjgEPMohm1Nwtbc0xh01a0PXMix1G/BMQc+ts5S0BnmBiqEkLHhJ+Hi9g+XDD+ZRjg/9NZreXBgNxFt05DJMorDg+0t3F6/nPbyyg3NzOsZYN5ISW/n6DA0uCPhjmSe+45nWBBRRL0ekgXFfcczBMizOjr+ta490c+Fuo6uWk3KpwnnFYuGAtT2VejJq21ie9biwdBijhJiPmluTx/gotrKF+deF6qb1NSJdrYXb3QhOpdA2xlzwbC/GTvrDv3bG67ga/t6qPNoWj1Zhgqar9nz+dO2Zlxvu9YLSK/zkHCGyHttfAUPcauWUP1i13qnoodhXsxvI5wfIO5kKVgBXvT1cqVvjWsoRSJ/nINXtuLLQzAFeb/i4NpaFv2myzUyyxf0EhzJoi1NwWPhzTv4HIUv6H4fdexdSGDhCbypHDptEwhpPGE/HXsXclP5JQDHu6Cx7IAvXGPKK1mzZnKz2cTnm4s/S2drmX/ttGdrqdvbw7IHN9L1lmZGm+NE+tMs+v5G6m5fCsvHjzmPEyRNnlDJZ1iGAnHc7/k+sjSUBeMwHvrOwCxYkzWli/2XrTIXf5bO1nL3PRVna5mKZTTzSks33vgK/IUMOW8QOxxjWYXDy2sPd/LT8Hzw+4loGPX7SeZS3Hq401V3ey5DUFkEreJwGaXQjsX2XIayUUq0NrWQODBIPJ8Fnx/yOUYcTWtThbNPnUdNj3mpWMyUl+mghyA+gsX9YWzZQY87nPe8AnNaYd6Fp8ryo6Y85t6He8JxXl2wikGy1BHgYuJU6iZ6+upruGzfi7wv/DJhb4FUwcu+fB1PX30Nrm6B99zB8m98jeU7B81zGh6GoUH4z39aYc3VRcK5qFo92+G5r0KwFuJtkB40f7/ts9ML6LWhZfy6ezOPJ8Icy4eY50tzczzPDS3unsR4aBknRsyXWFgqgKOz2E6G+oi77upAkFdyGqXArxU5pRnFx1v87n9kKSfK2swQhwIRkpaHqGOzLDtCyqlw2rzxQujaBihzYUshD3YGmiv8g9/znBkKESyuZ2y55zlXOJ/fGuDpYw34chkCTp6s5SfhxFnVWnn2lVURP59qYtxsLfc0Vp6tJeKfyzwuHzdby9zAmsrjzQd6TI95qVDUlE/Di40xQr55+AcOktNZ/CqAU7+IF+Mxdzi/7EZ47Mfm93AUUknTg3+te27kx4Z6uCC+Fy9+bB0m4s1zQXwvjw15WB0t+6dTM5fa4SS12ZLXNZuEGvd22L5kHfd2dFJX0LR60wwVNPd6FvOZJW3uYDwFdm4QT2AOKnjqMbXW2Dn3mPMN+XrqGjzUJrsgn6LWF4baVjbk4642pNsu4FDoKN3BdpJWkKiToSUzSnvDBUxnromd+T3400cZ0B7S2kNIZYgVjrKTIM2+8VME975lHr7RU/Na+/JALkvvW+a5wnmNP0BydBC/7eBzNHlLkfNY1ETcBzjdvfNoxoNn3jBWPIuTCpA7HKOn9/+w995Bdp3nmefv5JtD59xo5EwAJMEsghRJ00xKY0XLHM2u5PXYnvHM1tb+sbVVs7U1G8rlGq/lsUbiztiyRmFkyRJJkYIokiIIUiTBgEDk1Oic+/bNJ3/7x2mEvue0BLAlhjWfKtTtfvHh4txzzznf873f8z5vxEKprTvIlF+ZOa+Vg/hKke39jVgnLsHeJ8mbOvljPrAoiTB12PskbFxKTDfRwS8ZBIKMuYmLicuuCKeLFgyquEHGfBE1PFpW6IJ1LdhJMz9nDESQMa/jUcPltmXcp9iw5arJ+IxY4BxjlKmRJsEaummVwomMjFVnbXGaouxRVzTinsfa4jSZbB2MpVflGrfOZ578KS/editTrS20z8zy4DO/ZM2qcBG4aSsk1KU7WJosqNnhncvf7e/h674EU8OkK/OUU00U2/v4bH/ENdnTy7lpg5ekG5hyMrRrJW4Xb7CmJ/wdF6mTaViYGagUqYff15wDI6JmwgwnJsb8Ms8zShyFHDo1HJ5nlLv9HrobdrntuMNNLWXciknd8dBVl+3ZMq9F1fFs2RYQ8Scfh5ER6O2FLz4aqad/v+FDcv4h3rc4/uOAmF9MDl58Pf7jlZHzt6syfzvXR0qu067WKfo6fzvXR2tG5qYGnpkw2mnlpiVuLU3J6yLdWr7UN8Dk4DBFt0QVgSpkutQUX4oogJEbF/0AACAASURBVOnLN1GYPMuNlSnwPZAVCnKavo6w3ltefTeevYAozgVuLXoMmvqQV98d/nDFaeY6WhhqhqoOSRv65xI0T06Hx252aR5XKBJjwdCJOTLNKLA5Qiu7iC1JfVnrxEYk9barK/5s6giIcOKKrGi9EsRXgGl3AUctcravHVuW0H1Bm1mk7qZAb5ik+tbD/X8Abz4Hc5NBxvwjHw/iDfDVEZRKHX9uDFwXX1VRmpvwjRFoJC1rboGDPwp+1hNBUahVgc33ht73CamVfIdKbn4IzAq5WAqa+nlCyq+InCt6Ht+tXbbXBIRXR9HDpHTE9OlOZpYQzYwQjJhhWcuQOs/5XCeSbRP3bEwlxvlcBkmdJ8INHM4ch+efvpylvPsBWBcu3CxZw5ydznLqfA+lSpxMqs6G1aOs7RiGBnJeX9dH7K23wRCgG2BbqJZJfVdYWtM7PouuOMwZGjVdI2E7dNZN2ouz0HCZdrVmmZ+t4leSgYONcCmZEl2tEZXSt90HP/zPwc+JdEDMKyX4nd+LOgu/PZw4Cj99EsZGoLsXfvdh2LQ1PG58FDobnjPpTBBvQAcZbmVgiVvLLnoji0FvlPM85U+ACDLmNTyquOyRW0Njf1vokVPc63dzkDnmsWjC4DbaV9z5eUYs8HPvNMOeQk2oJCST88pp7lXWhwh6qXaSpJwmc9FtSAGPOqXayXDhpHBZ0z/AmmdfCIols1nYuAFE+Bnc42YY1+aIA7KQ8SUfE48eN7xA2JzQ+cOBbn7a1HTJqvezy1j1ntv9ab76PZORWBuWpGM4NgfNZv70oRiNS4QscU4UBQcmEkzVZNoTPrs7q2zKRizHY80cFBZPa81MSAadwuIBe46dsbAj0GFmiaOQWMzEX3w9zCzdLCXn2xaOUnNdpHgGZAXH97Bdm20LR6E9Qqq3ZdsHgow34kNy/i5icNDn1VdgZgZaW+HmW2Bg4MOa3OWwMBxkzK9ELBvEV4J/mJojpynktGCCiQGq4/MPU3PclA9PJAmjPZKMN2JLUudfr05zwJrHpEqMJLuNNFsiHogfb3b5D+M6CIOsqFEUBgu+zpeaww9lOd0DGz+JP30wyDrEmpHbdgbxBsx1tvF0k8Ehp4N5K0aTbLKjeZIHpLaQl/NYcx3lLov8sQQUVMi7SFtKjDUbEFEc9lvDrrsZOfAj3urPMJdJ0VyqsOtckd5dEYuPa4DnTjOc0FEBDXBlieGEziprGojIlvWtjyTjjWh2pqnMFIgJHxQVPI/6zBzN7RG67NYB2PkJOPcKlKeDjPnme4N4A0ZMjzVNPvlOCU2WcXwJ3fI5VwsX3AKMizJHmKGASZ4Y22mlK8Ixxshvozr5AhC4tAivju/VibfeFBrbG5NZcHxy2hV1Ba6gNxZ+Tk25swjVAF3CkhQkoSJ8nSl3NkzOzxyHb/4HMCzQfZieg2+egkf/TYigj0wZvHZoHfGYSyZlYtkarx1ah7HzdOiyjOd6cHbIaOeHoFyCdAZ30wbiufAid9vhc5y7bTvTTpJKSSel2RjxKve8fCSk/793Z4ZvPi8j27PE1TJ1N40ptXDvzoj7YvVGrI9/gurQC7j+JKqcIdn/CYxGvflvEyeOwte/GmiHO7uhuBD8/od/GiboXT1QWgjGXkS5FMQj0EHmqqwT+5QkD9K5xK1lj9y6rN687M4w7ZzD9MvE5DRt2hrS6sqJfI+couc3/Px6xRvmqKuQQCYNWELmqAsphnlEXUqOr6lRT0d30KRoz57LsXIR0uFF4GeyWR4rFGjLjBBT6phenOlSD5/JRywYCQh6FBlvxLcG+zmdcjBEnYRfw1EMTqdW861BjX9319KxXrGNfzhbJqsJWuI+C7bgH84m+F/WpkMOXweb1vBYaZ60cGkTFiUkHtM6+HKmKeSOVMAiF6oVUClESKJWLwxxRM+hSzK68LEVDVsobF8IF9F+kPEhOX+XMDjo8/iPBckkNDcHdVSP/xg+9nH/Q4K+DHJ9gZTlSlmtWYzuIn4tGDEFXfrSc55RZEZMETm+Zk9RME9je0V0JUs+tp6EHibr8/48ln6GG3UdjRwODhZnmPc1muSl23vbiq/xb/IyP7b7GHbb6VNNvqQPs60YkX0lIOhRZLwR+/rW8MwMpPFplk1qrswzbg/JPgj5QWgWUrdJen0RFA88hUr13S+UGenq5Gf33E1iaoymwgLVdIaf3XM3v5PqjGwTMXTO58B+mJmC1nbYfQf0rwnfQ1XFBUlHFoEboAwgQVVZmdXf6qEzvJXrxHTAEC6WauBqQTzygFsHIsl4I9Yli2SNEyiSgSMSKLJNPnaCddJmGme+cVHmBYaJoZLDoIbDCwyzR/SFCLqW6CLZsQer8HYgcdHzxFtvQkuECezHWgz+cqQG+GRUiZIrKLiCRzvD10VBVjAo4klG4Cgt+ShSkYIUQeT2fhe0UmCVJhkgu+CXgvi6/33J0ENnB0joNRKKiez6qIqMpMc4dHaAxlReu7KawVwZrt+Fio6LjSNsepSw7n1O6efQ8SbS/R7JpINdVTl0pok7lH4az8T6ToVH707x/NEEEwXobIXf2yqzvjMsIbDqExQTY0hbt6PIMTzfpOiNka1PrKijKcBZa4jz9aN4fhlFTrM6vpW1RqMrO/DTJxlc088vt61jOhWjrWJy69tnGPjpk2Fyfv/D8NhfBz+nMwExLxbh019c0bFCQNCvpviz7M4wZB1ExcCQUji+xZB1kH52/kYI+tXitGXyfL3KhOfSqajcHU+y3gjLEI96FglUYtKi3htAyBz1LB5pYFHX1Kjnrt+Fb389+DmZhmo5IOePfDY0tDte4T55nGFTo2jFaVJddjSP0220AO/cwefIWR89rqAbKSCFDggriDdi/4TBGk3g6lVMXHK6SgtJ9k8Y3NXw8Z72PNJ6jIxbAd8lI2ugxnja80LkPL/4DEtcUdtQxyUfIYnKVetsR2ZQz1yShG60S+SqEdKaDzA+JOfvEl59Jejmm0oFN3cqBSB49RUYeJ/7bb5X2PzxQGMOQcbcLIK5ANd/KXr8rF9gkNFLmsABemiRw1v3vTGJguOTky8TupLnR2YHa/YUk5UDKHIMbdGBZLJygI7U7hBBHxbDi809ggyAjg4iiDc1Pjxrs2xLNbNNunA5JsRih8Z3jv1emljGw1HqzMkSqi8T8+Ls95QQOe/KV6g5No6QUD0ZFx81VaNLW17WMusXOM8YZaqkSbKa7shzfC14S8yTSDSRXB1oC5IAwuUtMU9vQwZs6JzPT74f9J5oaQ3msp98Hx76tB8i6Jai0uy6VBUVhyB73uy6WMrKHns7XztM7QGDiaYslqaRdFw6i0V2vnYYwoYXV43d+XHemhco5XkMp05Vi+OlE+xuGqfRwecIM8QWSiQGB6FSIpHKwMAAR3IzdBHOnmuJrkgy3ohtaY0/603w+KzFiBncE492GmxLh4uay/E8iWoBCQ9PUlGFh+x7lCM03EyfgUQc5MX3kbTAWnI6bJlZm0/QlhrFEwqeLKP4PjmpzPR8eHGaVVtoriUZto9iSQ6G0OjTt5KNh51zHu+/i8yp4+TmWSySs5BceHzDXZGyofWdSiQZb0S1chxJiV8iY4oSx1uMr4Scn7WGOF39JTI6qpTC801OV38JECLog06F79x3G1ZKwVclCq7OhZYdfP6pl8N2zhu3wJf/JNCYj48GGfNPfzGkN7+I4zWbpwqX60wezMeWzchOUeQUkyxQJ0ecDXSECwWBaeccwnMxvQK+sJAlA0VJM+2ce9fI+WnL5FvlBdKyTLusUPI9vlVe4IvkQgTdFBqW43OyplP1JJKKoDthYWjh+yKT2Mj5+ZcZqtsUPI284tCvuqxORzTqWbcZvvCH8IufwuRYkEl/5LORcq8Rf4j2ap3ewQuBbCqVwR5YxYg2RF555+TcFmA0uIkqElgRuarhmk9P3EC+Qnfua4LhWpjIT7g+bYoB2uWxSV8w4YbHXkcLzxfehjODxKfnqLc1U183wC358J2pa91knHGu8y62mfLAt9C130CNx/sIH0hyLkmSBPz3i3+2ECTGTgD/L/ANIUTE/vJ7i5mZIGN+JRKJIP4hotGxLSj+vNKt5fovRevNZ/0CvxTHmBMeJhDDZEIqcqu/JUQef6+9mT+/MEvgOiJT8nyKrsRXesJauIJ5erEB0RXNdBbjjeS8KqokGjrWaGhURYTPb6Llcnv0i3BqQXwFmPNVpJSLQgJFBP1hPHzmauFbPRlz6VQEBVOh7kFckWiLuSSXIeezfoFD5psYc1OkqlWsZJJDzZPsiF2/IoI+h0VThMvDXMSW5oH94GsFRv1ZamWPBApZrYUD+/P0N2RVc3KCql+ixXMI8uY+dQS5FepPnWwrwvfosMtILggfhO/hNNogXiPS5iQ7J4Y5HW+lpCfJuCbrJ8ZIJ/pCco5CaZLckcOBzjqZBsskfuQQhR3XQXZlbinb0lokGW9ELJbmPP10mTPEPZO6YjCe7Kc5FuGlHNeYyKc5unU1C5kEuVKNrUfP0ynCkp2N3jAjlTiphIMuufhColIz2OgNQ0Mpb6F0lOnKYdJo5KUkrnCZtg+TIkU+szRjPGI00b1+M4yeC1Z1yTSZ1WsYaSxau0a4zgJKQ1ZUlmO4zsKK3vd8/SgyOooSPHMUYuAF8UZy/rO7bqCUV4i5AsP2cRWJUl7hZ3fdQEQrpICIL0PGr8Txms3XJitkFZnOxa7AX5us8Ecd4U7GUxR59QqbvToOr3KemyNs9irOFI6zgCypSOj4wsV1ZvBxWFE18TXg+XqVtCyTkYMFWGax++fz9WqInCedNIcqNXQECQVMX3CionFbKkGjcc2Qn+dH7hbWi0Fa5ApFkeJH7gb+mZ+PrsVYtzmSjDeiWhojceRo0OkymQbbRDtymOoOD5ojiP9Von2Vx/Q5FUkSyCr4LtiWRPua8DzQl5AZsS1MrY6JSwyVmBOnLxHOcHeqMiVPkLmiq1lVCDrVcBKs+9gF+p95nr17dlLa2kWmUOP+v3ue7vvSsG3phK+tvw/e/DZ2ysNXPWQX9IqGdv197/gcvB/xgSTnwH8FPg9MA98FasC9wNeAW4E/eO8OLRqtrYGUJXXFJFurBfEPsTw6tl1d8edBMciYsFHRiCPj4DMmbA4yyL0NPf1uyrfyPxFoz0fMQE/7lZ7mSL257RXRGry5A0/0sH4wKSWxhR1kzBfh4JCUIrZ5e26EU08HP2uJgJg7NVi959d/2F+BTFxmugLunIxry6i6j9rs05YMPxATqCRVh3zKQ1s8ZzY+iWXcFc5Xj2JMDWKggpHAsGwYH+R8u0FL+o53fMzNy7g8NEccx4XhInOZKQyky9+zOoU5LEPD93yXsop/lM6Cay+60WjYqs5D8qp3fKwAZ+7/KInzB4lNCVB1cG1MVeLM/R9lJbdzfGQcVfe5wy9wsUeMo/toI+PQsGbLnx2klk5etowzYtQNlfzZQbj+1hUcxdWjS86gxlUmjDxV4ZGUFNplhTYp3Bd74vrdPNUbZzaepaYaJNIWQ01pHhyp05hbvifzFt+evpOap6DoLp6topsS97S9BSz1+J6sHgJJwpQdPMlCETKKLzFZPRQi570JmQW1idz2yyezZPv06iuTFapaLlLGoGrRlqRXC88vo0pLV2WKZOD65dDYoQ3dxKYm0JBBUdEsByyfoQ0ryyY+VTDJKjLZRUKVXexx8FTBDJHza7HZE17gky4veqJLqPjCvRQP4fhRePoJGBuF7h544BHYHFHseg2Y8Fza5aU7IylJZsILk9JiIUsqZgMunvDRFQkdlWIhS+NG1d5yHU9tY1zpYHwx5nk+e8t1NsbfuWwweW4YOx2/PL/oMRxdJnlumFBB0TXgC/dofLXg4lVlPBNQQG3y+MI94QX6rR0O/8fLFqlTCfSiQjHrMbbB5DO3he+hB1JtPLYwCZ5PUpKoCkFZCD6bCktCjz37At9p3UnuwDxdlfOUUjm+07qT5mdfYEsDOad9Ndr1X0A79RLMTUG2Ha6/PeRG9kHHB46cS5L0CQJiPgjsFkLMLsZ14IfAFyVJ+rEQ4h/fw8MM4eZbAo05CBKJgJhXq3BP2LTh//c4WrF5ct5i1PLpMWQebjLYmro6B5DlMOIvoEgq+mLTWx0ZgcqIvwARO9M35VsjyXgjdCW7jCd6eKu2T+rjuDgOIsiYOzjY2KyVIpqy5PthwwMw+jrUZoOM+eo9QXwFyJg2F0YNZE2gaD6uK2OPGmT6wx0e+6QkhpApSDZ14RGXFNpEjHYpOnVVLg+TQg0IKYCqo7tBPEJJcdXYJTXxs8pJGB8hsVCklstS6+rljnQ4z2RlppBqOloyyLhqSNg1BSszRSM536UEk8A+fZwCDnk07qTrUvydotzdScoswMIFwAJkjNwqyt3REgarPkGtfAzPWUDRciTSWyLlDh2nZji/owVcUF1wVXBUjd5DMzSKNLe/dYYXPrIZHJ+47VPXZUwtxs0vHodlutz/prGOTg4Xh3ljvIm5uk5z3OaGrjK35cKfbf+tuxmqz2HUTFL1GrauM9TSxf7eZj7dMHZDus7vSy/zUmELk8U0HUaR29uPsT4V1pSWpQqu7CMjoyDhSwJX8fD8cCv6j/Vo/D+nLMAno0mUHMGCA4+u/vW7BL8KydRmivMv4RFkzH3fRHh1ktmVfRGKnA56LLD02aPI4ZtNjRv47e0wVwDLAsPAb86jroAMQtAVuFNbSrzSy3QFXljGZm8hwmZPl3QcTHzhIksKvvAQi/EQjh+F//RXi8WuXUGx63/6K/gf/tWKCHqnolLyvUsZc4CK8OmMkL3NVXX6aKFkVLEVB93TyFhJ5qrh4x13PDoassNpWWLciS7svlr0vjXKiTv6wAHNFji6hK0ZrNk/DLsj/sGxt5daCD78sUjXkrvXGPB78MMDNnNzEs3Ngk/t1oN4Aybny6w7kmYeiXpKELdkOo/EmdxUhtzSOXFnspMvA09XpplwfTpVmc+m2tmZDD8fnpQS9HvDrBmoEU9CvTrNuYkKT0qJqNL9gIhfJRl/yZznGXeaIhZZDO5T27g9trLdsncDHzhyDlzs/foXF4k5gBDCliTpfwUeAv4EeF+R84EBmY99fKlbyz33/tNzazlasfnr8RpZVaJLl1hwff56vMafdLEigm6hYrBUJKcisFZ4iedj65lYeAHfKiK5NkLV8Y0srbk9obFNchOb2cywGKYqqiSlJGultaFi0Mtv3r9iMt4IZ7hOm6ZQEzq2J5MQPgnNxhn2aExR3iA38VMxQS9JElJgfVaTPG5Y5njTxQpWKolxxRxjGxrpYoVQRd01oHd4jN/Z/zPe2riKueY8zcUKdzzxM3rvSMOqpV310jeMUvjZemq6h0h5SBUFta7Q8ZELELFpvEtpZ9dyHscNsGvjmMWjeE4BRcsTy25Fj9BppwtzmP4csVQm0FD7DpY/R7owF6rLsuoTlOZfQpZjyGoW36tTmn+JTEQ7+qzayuoTFSZXpajHIV6H3jNVshEa3C45y54DFziyuZtCSidfsbn54DBdcrRzw28DU8UUL5/pxdeqZGIWdVvn5TO93LE+RVtD0vhsTKFSz3HKkSgLlbTj0o3gbCy8ck717WFg6EnWJGeRJR1f2PjYpPoeDh+EJCEAeXHrXIagP68khYZuy6n86w3w+KjDSM2nNyHz6GqNbbllnhHnTsCLe2FqDNq74SP3w5pNoWFGvJNs0+1UK8dxnQVULUcye/2Ki0FXx7dyev45PHMS2XXwVQ0pFmdt0w2hsWvlJEdjAqmnBw0JB4EpPLbK0cWZc4v1ORWqpEgyQA/NEdK0i12Bs1d0BS4v0xU4R5w6zqWMOYCFSy5Cp5JS25BRMUUNV1iokkFCzpBQI549Tz8REPOL7jIXX59+YkXk/O54km+VA+lRSpKpCJ+y7/PxZLiguTcms2AadHmXP8uCE12n1KUpFD2frHLFOfMFXdqvr1/4VcirLWx6Y5aRTU1UkzLJqs+aw/Pk1Qgp5LG34at/Cbk8dHfDwkLw+5/+2bIEPYqMN+LELzVa8z49qctzrVWROPFLDSK48s5kZyQZb0Slw+CmrllcT8WsS2iGYOuaEq+NL7Pzc+JYUDNx0Tb0/odhU5jGv2TO831vGAOFNBp1HL7vDYPJ+56gfxDJ+UWj0PMRf3cxdockSboQIpwufA8xMCD/ky/+fHLeIqtK5BYzC0EreJ8n560VkfMcbZTFKJLEpSIRVzjk+PUOJ78Khu1SuSC44OTxFBfFU1mleRibXYg43Mr5HOf25ZidgpZ2aLoTmlbezfyqIU7JJK73yNgmqgOuBq4uEG/K0OCc1ycn+d0ZhzcWjjMnuTQLlTtz6+hrj57QV1cTHEp7oKjoHtgKWJLHpmpYxgDA/BAMvwaVWUi1QN9N0BSxGHn15/RaEr3nCsBiYxxLgld/HiLnLastpn9vhOorbXjTMdQ2k9h947S0RHchrNrTSxohNRnrI73X7do4lZl9yEoCWc3he3UqM/tItd4ZIujrjr3J4z29HHU6WPBj5GSTrdokHzv2JtyxVPdYKx8LiPmi5EFafK2Vj4XJ2433kH3678iW/KWNkB4IN0Lizvvp+u436JqrLXV5+NxXIs/DNeEqJQSPjzn0ZCyyTQXQ6uDEKc6rPD4WCxHeiRKcKiWJSx4pDSxf4VhJYYPihLbkY2tuoa75vOmep6Ar5G2P69XVdPbdEjqGpJRingKukFCQ8BD4kk9Sil6kbMupy5PxK3HuBHzvG4GlXWtncG6/9w347FeWJehXS8ZnxAJnGaNMnTRx1i7TzKbv5CCV2SEm+prxdA3F9ug8PkRfyyBct/Q+ulvuYIFh5n2XqvAxJIkeReduOdwrYM4vcEScREcnSQILmyOcZLu/MUTQH8zH+NpksAuRViTKnqDo+Xy+NXzPb6CDVxenYAMVCxcThx0RFkZNxjpM93UySsvlDsK+SZMR0RF4bJTC1j5G1iWophWSZY/eMzL5oyvz1F1vxPgiuSVuLR9PZiLdWq7Fxej+dJy/mp9njBqy7OL7Kqqf4DO5FZLBPb9L/ttfJz/vXL7nS8WgoLQRTz4OaQ3kaSgOgRKHdCKIr8Dz252JoTfbXLkdLSVcnJnwObsWbO8sUhUaiixBQsL1BJbQ2N4ZYT954hh8o8E29Btfha/8aYigP+NOI5c1rBGdWlVCSQrkXptn0tPcvgKHm3cDH0RyfjFbHkVzL67d1MWfT74rR/Qhrhqjlk+XvjSrlVEkRq2V1fDeKvfytG8jiSIKNh4aHm3cKkd31/PKo/izhxDmPFKsCbllB0qEVeHJk2f4wVs3kI4JkoZL1VJ5w5RQ5TNsuWHpqn74rM9T3w2em82LTiJPfRce/JxP39p3Z4dkteMzeUiivhbMJMSqkDku0eFEnN/xs/Tt+wF98TTEkmBWoX4Y7vwcdIVXFC29t7Hj+I8535OjnDRIVy02jS7QsjmCPM4PwbEng4LXZHPQPOnYk7Dl4TBBn5mA5obsdiIVxBugZtuIJafJtU+hOQ6OpmHGDFQ1TLir9jTjtQOoUgxdSuP6JuO1A3SxO0TQzeLRgJg3kGizeDREzueKNodS3XgxSKk2lqtwqNrN7cVzIc255ywgNxQLSnIML6pYsH89PPDP4fVnYXYiaAm/55NBvBFrNwVEfN8Vmd2HPhPEV4JrkBBMUqSrfQh8DdwYKA7Z9iHGx/tprOorzEiouofmWWD7aIqMoxgUZqQQOR/1K+zvaSZBG12o1HHZj4fuV0LNZHKxPuTZOmV3DluT0B1BXm0m07JCv9UX9wbE/KLf9MXXF/dGknPGzsChF6AwCfkO2LEHusNEc0Ys8CZnMNBIEcfE4U3OcL1YFyLo5slnWOWbrJ69bP3p+ybm7DPo1+1ZMrZXTvFJ+jgozTGHRTMGO6VmeiOKnwcZRUfHWJSQGIuuUoOM0twgDduc0PmjjtQSt5bPtyYi3VrayXIzq5e4teygN9KtJaW10ZW8kXnrDJZXwlAytMW3kdLC93Fh+ypObNXRfZlE2cc2ZE5sj7FJXsXKfKICgh5FxhtxLS5G+bjJ1qYZTpRjLDgqOc1jU26GfDwBK+mWunZTQMRfuMLZ5eHPRt/zI6chWQVfBzkGvgPSDIzU3vn/D6xp1XlrzmXek7FsGUP3aVIEu1pXJktdnzJ5XWSIWTaGY2FpBqahc6NUCg/e+2T0TsreJ0PkvFC18U7EkXUJJSHwbQn3hE5hS/1dbeXxTvBBJOdPAZ8D/q0kSd8TQswDSJKkAf/bFeMi71tJkr4CfAWgL6Jz44f47aLHkFlw/cWMeYCSJ+gxVkZe+5UkD7BmsQGGTQs6N8p5+iM8d73yKO7Is0HHRCOPcGq4I89C7z0hgv7iqQzpmE86Fmg50jEXUHjxVIYtDbvLr+8LiHlyURJ68fX1fdD3LmXPP7q7nW/+fJbmgk/KEFQsiZIt89F7I7Y+394H8XTwBy6/vr0vkpzTvpoWPk7LqZegOB4U4mz+eLT2b/g1BtPNvJxrY1pVaXNdbluYZmD4tTA5b+28ZA12CbVKEG+AJ3zWjc4wmUtQN3TitkPvaAmvP/z55q3TqFKD044fxBvJuecUkBuaiQQkOtzifm/TdtrqJbJX+OIXhcTepu0hYY2i5Zi1ZpiljIeNgk4LaVqMZeod+tdHk/EouD7UbChbkLaD31eKa5AQdLXOYLkqxkUZg69h+YKu1hkub3AGSM2XqWdVjKRANsD3fUTVI1UMF98dZpbEMt0CG5vL5EclrAtnSfgaCgYeFp5cIL9qI/x6A4zlMTUWvv6S6SDeiLEzlA98j+neFOaaPLFKhbYD3yO9+7Mhgn6WMQw0YovbbhdfzzJGK0uvP88rIzfIUiRJx/PCBaEQEPRG69EoVKiSbHCV0tGoEOEqxdU3s4GAoEeR8SiktLZIMt6ITU1S/gAAIABJREFUkfuvI3b6ALEYSGkJzRbINRi5f3fkJH+04vDEnMWo5dFjKDzSbLA1tbK6AoDWdI3b01MUqZMlTivthDrvEBTAronLbIkLILi+TeTIwliAk9Mee896jJcEXRmJ+9cqbGxbRgKzdtPVLcCzMJJt5s2dW5jLpmkulrn+4DF61WUKbq8Sq7dpPPPdBH7CRU14uBWF2ZrB6j0rO79tqsSN1THO6O0U43mynsk2e4y2CIkRYyNBxvxKpDNBvAHShTikXORFqivrAl93g/jKSo9+6/ggkvPvAV8Efgc4LknS44AJ3EOgqh0mMAWOnKmEEN8AvgFwww03RHed+RC/NTzcZPDX44vbg4pEyRMUXcEX26IzCqcti2drFSZcj05V4Z5EivVG9Nh+JRlJxhvhzx5CUhNI2uIEtfjqzx4KkfOpWo7WRAWu0FEmNZOpWngbenYqyJhfiUQyiL9b2LCtn0eB5w5MMV5W6Ep7fOLOFjZsi5CTFKYg13DAsWQQXw6OByUTCnWQzOD3CAy6NX7Y3E2qPEdLvUolnuSH2RY+NTcW3vK6+V54/G+Dny/KOaoluOdToffNj45QU+NsqFxUF8vU1DiJ0RHYuFS3Y/kl9IaGPIpkYPnhbIyi5fG9+qWMOYDwTRQtPP2Pt2ygc+IQKBoXvcfSnsN4547Q2CmjhXL1MJKso0g6krBY8Mt4mU0ry/qdPAb/5T8G7b47uoKOj//lP8K/+OOrsshbFmOjDCWu4/VTG5mtZ2mJF7mx7ST9Y4dDQ/uzFodnNYQiMGSwfDA9lfUREqNVlSmcbDPCUrElGUP4xHBZVQn7+s9jhpqPxFGZJ0wsEj/+BUW1mSc6djAuZegSJR6ZPMTqQ7+AzSswnW/vDndqrJaDeAPKp59jaH0aFQ3DBieuM7Q+Tf/p50g3kPMydWqezhueQ0lARoJ1ioyjhIsmFSWN75tIVxSjC2GjKNHV10Ne9aqSEymSWNhBxnwRNg4pfv2z872AmfZI92ZhoQymjWToxFvTlNPhZ8/RisNfnZsmV5mgyymyoGX5q4VO/tWatmiCPnEWjr4YPPPy7bD1I9AZTkxMUuRVBjGW2EQOcjMDdDQQ7gXqLLiCs36ZOh5xFNbKMXJqeCF6ctrjsTccMjGJjjQUTcFjbzh8+QaWJ+hXgZGta9jb307C8mgqlqmqKntv38X9Q1ORfdKuFq9LDt23g3tax16Q0HMCdZfP65LDPSugkzlTxc4muak8gVKx8OIGfjZNrhLxnt29LGg1xrc1U0/LxMs+XW/PkesOf7Lmw62M3zYKNii2gqd7eLpH+8udIZnn+w0fOHIuhPAkSXoY+LfA7wOPEpDzF4BPAT9YHDr9nhzgh/iV2JrS+ZMulri1fLEt2q3ltGXxd8UFMrJMu6JQ8nz+rrjAP8/mliXoVwNhzoPRQI3UeBBvQGd7joWZCpm4c4mIVcwg3oiW9kv2yZdQqwbxleLc/Hn2l0aYwqMdhTsyvaxpiq5W37CtP5qMNyLfDvXy5Yw5BNKW/DIHPHoGnv02JNKQb4NaOfj9ni9Az1IS8nK6hdTcOCmzCr5Pyq6DVefldEuYnK/aAB/7UqAxn5kIMpb3fCqkNwfYfvoCv9ixNnApcX3qqkxdU7n50NlQPaghZyj6ZWYlBxOPGAotQiMrh7MxsexWKheehJkppEoNkUrgt7aTWBUuQuzKd1ESguz8GXDqoMUpt26mKx8uHj0hzaKlemg2i2ieiaPEmEq0MSpFtLi/Fvz8JwExzyxehxdff/6TaHJ+lc4NQ6mdPHVyB8mkT3OsRNWJ8dTpXTy4Uabxiuo3kigtFueLMiVXkFElNud9eowwydscn+Go34EsBLri4boSvq+xOR5u9NBELLJbYFOjoTRwYsbh77vvIzttsZ4SZQz+Xv4IsZmnWJHA5yP3BxpzWKrpf/AzoaHT8RKqSKD5wW6g5gGyynS8FDIxqns6B80CLf4ceSzqGByUm9kVy4dm49jG+6gc/yH4QcZcCBsfh8TGR0LHMORVedqfJIFCMxpVXJ72J3mAjhBBH6CHI5wEEWTM7UVXqY1RFX0AM4Nw7hUoT0O6DdbcsnzH23MnYf9emBqH9i64435YE32lF705xr1B6qJMXErTpQyQVcKegKnqPGbMoN4Tx0OgIBF3fVLV+ZA04YmRKXKFc+QUCfQEOa8GhbM8MSLYuqlBtjhxFvvVb2PlJLxuCcUexXj12+g3fyFE0E8yxexEjePjdeY8hWbFY3NXnJOdUyFyXnThqFdE9jxivoctKxxVLLaRC33He896ZGIS2Vhw7WRjl+MrIedv7rqOhFkhWZ0HyyKJBE1Z3tzVsSJyPlITdPeC3Hc59+kLsVK1DAmjg7g/z3BvClPziTkyffMSiYgeBAsP38WZyefRfJtYWcOWHM7siLOu4y4aZ+XNegbtrR4m189gpyz0ikHP0U7W6REZ+fcZPnDkHEAI4QD/9+KfS5CCFMM6YFYIMfheHNuH+PXYmtKvqvjz2VqF8nGNY28ZVKsSyaSgb5fFs9dVVkTOpVgTwqldypgD4NaRIqq3774+y399VkGyp0mqZapumpraxievD28f33hnoDGHIGNeqwZz+p6H3vGhAgEx/37hDCm7SqtrUVYNvu+ZfBqiCfrkeTi5H4rTkG2DjXdAR8S4bXfCvsUDvqQ5L8PuZQ740C8CYp5YpBwXXw/9IkTOpx2PlnoJJBkUBYRPol5iOraM9/OqDZFkvBHdcoa7jo9xZFU7hYRKvuZy85kJuiMIt2t0M1F9GRmDmKTjCYsJv0QyHiYL+myF1OsXMDs0vHQcpe6SeP0CeqrS2JyT+1NxHrPbIdtJWpYo+0GR3KdTYVcKW1QQehOTxmXSIYTAEWGrv2vC+GiQMb8SqUwQb8Sxt/G/+ZcwEIOeGJQn4Jt/ifxo2Lnh9fTDWP55hrwWyiRIezU6/FleTz8cIufr6WLBOMvuNgkDDYtgEbQ+wron1WWwU5/ipNVC0dPJqjYbk7OkIpqXXEcLzxF8jvii5ryGxy0hR3TY23UHWatEdvFtslhgWeztuiOanM9egMFXoDwD6VYYuAVaVoXHrdkUFH9e6dby4Gci9eZmLolRdYKuo4tQLRczF16kjJk2vd4onqQSdERw6PVGGTOTIaKpX7eHFIH23PPKKEqaxMZHQnpzgNf9AgkUkou+4UlUEEG8kZw3y3m2+xuXuLVsZHWkWwszg3DwR2CkgqJuqxL8vvMTYYJ+7iR8/7HgOmztCBYz338MPv3lEEEvenOcdQ6jSgYxUtjC4qxzmLVcFyLouuczrXgogIKMh09R8ejxwpvjo4UZuhQJlMXvQtHJYDNamIEGcwD7xM8Y7tMYbG2houukbJuBmVn6TvwMvYGcn5ia4ZcjDklJoknxqPkS+0ZsHHmGPe1LZWgn6i6aZqEggSSj4+EJlxN1N2Q5O14SdDTE0kYQXwnmuvpoOncMOjsWu+HaJFyLua6VSXl7ExILtiB3xfRdcoL4SlDs38RYaT8xRyXlxnCFw1jGJZbZFBICjfepaKmt6KfPQ6WEns7A2o2MN6khcr77Dpj+foauhczlObkCuxv9W9+H+ECS81+BzxJ4aHz3vT6QD7E8ztgmL1gVJj2HDkVjj5FinR7Oih08LHFmXwxfFfiGj12XKO6LYWLCnnf+/8stOwKNOYAaB7eOcGsoneHmLeu6FX7/nhQvvJ1gsgAdLfCJbTLrusNZjb61Mg9+zuf1fVxya9nzEMsXgy4ML/qcz0GiOWhMlAs/PPfPnyZllkgjQFZJew7Ui+wXp8PkfPI8E8ef4HhvKwvr15Cr1tl8/Ak6eSRM0LvWBsWfb++7vK27+6FovTnA/GSQMb8S8WQQb0Db1DiVeJKUsEESgEJNjtM2NQ5R7mfzQzByAKozkGyF3t3Rzi477qL72W/TXTJZNMQNMvj3fCE09IzmYSfXkbSmkLw6shKnHu/jjOaFs/dvPMeFVB/PZVYzocfo1Ew+WjnP+jeeg76lk+/GuMGXm2Bvpc6449KlqXw6m4xsMKJLqcAq7gqZhoeNLq2wGqmrJ5CyZK6YjiqlIN4A/+ffh816QFhcCVIybPbxf/595AZyfq7SymBXilhlnpRdxtKTnOrajl0JLzzayLKbtZxmnBJ1MsTZziraInS1C22ddBenGNAqEFPBc7ElwUJbmHD3yCk+6vdwmFnmMWkixi10hopBAcb6t9J55CUQsaBzomWRNk3G1kVIWmYvUD71Y2aaNcwWjZg1T+upH5Pm48sT9KjizwbE8gM41aNoDqBq4Dq42MTy4doB35/EI4YvSYDARwOh4vvhewgCgh5FxkMfDZtmlso2EijMEm1Y1iznQ8WfkTj3SkDMjcVzf/H13Cthcr5/b0DMG4to9+8NkfNxbxBVMtCl4L7QF++PcW8wRM6rmk7a07Ek8PFQUEgIhaoWTvD02PMsaBlyXJa8lOQ4PXZ4V3QsXuRgXycKHqpvU47Lwe9nJ0LPhzOjJromkxAAEgkFHElwZtQMaZdnbY8m20COeUiyj/AVvKrCPGEZTldGomiKSxlzCEpIujIrI7vNyRaqq7eQnBgBswKxFLW+tTQnV9aB+uFeja+eCGRrGS0g5gu24ItrVlYQOpEw0eQBtOIU2HU0PQ7ZXiZiZuhpUhcVYvlOuPlyEkATgnpEwqN/jcxDn/Y5sB9mpqC1He56IIi/3/GBJOeSJGWEEKWG2A7gzwm82P6v9+TAPsSvxRnb5Du1AmlJpk1WKfse36kV+Dz5EEEfe1PHVnw0PVB8+7rAFoKxN/VIcn6savOThcuuAg/lYmxJhh8aSroHeu9Z4taidN4a6dYCAUGPIuNR6FsrX13x58IwR47t50feAMOsp88s84nifrZvuSNE0KdcC0mK8bbaREnSyQibte48NTes7Z0YeYWX1ncRRyFr+9TjMV5a38XtI6/QGZU971q7PBlvRFMHz+gpvtOxninVoN21+Pzkae5rCj/objt7iv98wx4mvRxV1yCpWXQoC/x3b78AH20YPD/EhfPP8WpLO7NtG2mx6tx8/jlW8dEwQe9ZFxDxQ78IFgVNHXDrI6HMPUABi5zWjKNdnpA0BAXC5+20WefvN1xP2nNpt01Kisrfr9rMH5x6k6jyzI1x46q6/W3UN3Kk9CJutYxi2ngxHT+ZZmPmI7/23/5K3PtQoDGHgBBVSlAswqd+PzxWmsROpDAzCp4Oig2xUgy9GCaE8wg0LYbRG0x8BuBVgngU2shGkvFGmIkOdMlClCpg2WBoqJkUZjxs9QcBQW8s/oxCd18TBXkX6cJJhF9Bkg3K+V1094R3wcpjLzLcqaCiYngSji4z3OnTN/Yi6QhyXnRnmfLOU/crxOUU7cpqshGe0m3NNzIk12B6CrVSxU0lcdt66M7fGBobFyaWMFDF5eeJKzziEXp6AE4dh+eeCnZEunrgow/ChnClaws6Z+sup0sq8w40abA+47E2vjLSRHk6yJhfCT0RxBsxNR5kzK9EMh3EG1AXZWJDE3D67WCBHU+ird9GvT+8WCukWmgpVNDREbKG5DtIvs1sNvxdPBKr8jUtTqzVxDAcLEujOhPjD5Rwseup3iYkPDTfRxKg+T6+HMQbybk7Y+HlmhiZyOLWNNSEQ7KziLsQJv1NjklVi5GsXb5nqrJEkxP+ju9fq/DYG4EbT9oIiHnJFHxm68qo2fVSE3uTJqzdRoLFHha4fERamX3g1rzKV9bM8Y9D85wuQE8CvrKmia35ldUr1EWFmNEM7Ze/U3UZwh2Xgp0W/YqEh4NNfJmERz0lmFzjMd4i8LIS9dTK/ObfLXwgyTnwc0mS6sBRoAxsAh4E6sDDQojw0+BDvC/wglUhLcmkF9smpyUF/CDeSM7rVZmg480VWQRNUK+Gb65jVZu/maqSVSS6NJmi6/M3U1X+ZTvLEvTlyPi7gSPnj/MX9hbyKvRIFgWR4C/sLfyP54+zfddSci67gteMTpLCIy1sLEnhgN7BTVb4Mj+edokLg7gXTAxxV4AiczztRogCrg3PbLuLf18yMYWG58jMSxr/vnMbZGLc1zC2ltjE2OkUolfCSDq4VYmxkRS1RDgTeWHqID/s7gHNQ5VqjGkKPzR6+NTUQVZFZc81BbJxIBm8LtPcI49BHZf4FRlFEzdUbAjw3MAW0laNjBQsNDKeC67NcwNbIsn51WLTydNw/iAnNw9gJ2PoNYeNBw+yaXUH7FjB9bdxC8cf/VOePjPIqCfRowgeWDfA5o3h82t3p6jmQEJCdsBXodoEyKmQitvvEUhvS7h1UGLgmSA5QXwlyMvNVOIysrGAwEQihi/nyEdJKa4B93bO898K07SsdkjHLcp1mdLUNPd2JmjsujWjzqMSQ/OD71jzJZA1ZtT5kDa86M4y6BxGk3RiUhJHWAw6hxnguhBBT6ut9OfvZDp1DtMvE5PTdGtrSEc0j0pIaWyqeMjIBK4FkuSQkCKKPE8dh2/+DWSuKPr95t/Ao/8yRNBzZppfTM2SckvkPJOiEuMX9SQ3dORhJUYa6bZAymJcQXzsWhBvRHvXMkW0YZlTfGgS+/Qb6DYQS4Bt4Zx+gzg30njDxWKdFPMKqcoUslvFV5NUsr3EYuFjaN2wlpvNo4z4aUqWQUaxuLlrltZYeLuumE4Tr5WQkEGSkHyB7jsU02GJXMuCxshMFkWTkZMOwpQpnsmyWgs75zxkmvydZoAsEfcFdVmiDnzGDJPzjW0K91xn8/dTVaZcj/a4wh+0J1akN4fAued+unhTzDMnLJolg49IbZHWmteCujVJm/Qqf7zauNQN1/NPUbduJm5EL7SvBnEphSMstCueze4yhLtLWcWZ+qtQnUdzTBwthpNsYlX85tDYk1Mef7l3gfmZCo7jc06TOXY+xZ/dn2Nj+/ubpH9QyfkPCCQsv09gqDtG4MDyfwohIkSXH+L9gknPQSxoHBxSWKhK5JKCdf1QzTmhsUZSoFgKnuLjCVAkSHgKajJMFH6yYJJVws2NfrJgRpLz3xaGzy6Vtdx4Z7Ss5UelBFnZJiVKeL5LSlLx5Qw/KiXY3jC27DWjyB7yosZSxkeRg3gjFrJpshVzif41ZtosZKMdHq5WYgTwdeIUFZW4Y6ELGxeVopri62ghcv5k/0O0nz5K7u058H2QZRa0HE+ufyjUjnlfXOAYLinfR/YlFMmjYgj2xQWrGg9i6jwc+AHE0pBpCbZsD/wAdv+zkKXjVtrZx4XgHKBi4lLD4UbCjhsTXQO0nzkCun5Jo5mybSbWNX4b14g3HmdAs2kduYBryKiWT6pcgTcehx13h8fPXYDBV6/QRN8MzaGzwPGazdeS7eR2ddKlSBQ9wdd8nz+q2SHbO7O/DWl2AtkHFAXJ8vBlD7O/M0TOB9bBpO5jnZeximBkIbnFp6N/Zdvs22nhF3KduNxzSUdu4XIry2yzHzkC//iPMDwMfX3wyU/C9vB30SX/kgc2FRipZpmvpcjFTB7YdIwueZzAH+CK85CMYVhuUAOxCNVxMZPh633KO48m6WiLsouLpGHKOx+ZPU+rrZFkvBE7jM28aR6gLhysRfPHOIIdRoTv43NPca6/j5c2rmMqGaO9anL7yTOsee6pEDk/NF1kY3GCciJGTdfJujY9xRKHZI170uGdjbOTZ9g3M8ik8OmQZO5sHWBtR0QDoDW3BBpzCDLmdi0g65vvDY+94/5AYw6Xi2grJXggXETb9aPnOHtTB7gSmuniZJK4qmDVj56D//lzSw+Bbg7GqrixDZcKWC0cdkbcx6fzMh31blbNT4A3DUoSM9fN6bgccs5LyjpWLI5hWeB7IMs4RpykHJ4vcoXNSNlZErZHrCphaoKKqpArhL+32/ObYPw1ftLUybyq0eQ6fGZ+gtu7wvYgJ+sWz9rTbNIn2S2VKWtpnrU7GKh3RO7MnbZMnqtXmXBdOlWVj8aTy/q0J3DoZYG8VCNFgkRoCXoZQ+d9XnvpsvTjptuhf3V43irVTqLIBsqiq9XF11Lt5IrIeac6wDk7cIRS0XGxcYRFnxauD8pVa6wbG2c8o1HXDeKOx6qRcXLdNcgsnRO/t2+O4TGTtOKT0gS25zM8VuV7+zz+3ad/vY3ne4n3v/AmAkKIPxdCXC+EyAkhDCHEaiHEH/9TJeYXzvt871s+f/0XweuF878Bv+PfEqSizstHFeq2RCbJ/8femwbJdZ73vb+z9r7M9Oz7YGawrwQFgvuqnRRFKbZoSVRkR7ESx3aSm9RdqlJ1q/LlxhXH5Wur4lhxWbY2R9GVxM0iY4mruIA0QBI7sRADzI5Ze+8++/1wBsBMnx4J4JDAQHh/Vawz/fCdg3e6+5zzf5/3WaiYEq8eUZBywRvitpsdbNNDq9rE3Spa1cY2PbbdHIzdGzcdkspyAZFUJMbN+qX+PgwuNCEqFZY3IRo5Hfw8zkoJYu4seC6ggucSc2c5W8eDlo/0MpQrozkOhiKhOQ5DuTL5SNCrnI53UZUdsEzwPLBMqrJDOh700l4IMSq4zrIQo1Nm/S32s4ZLSAZVV0BXUXWFkOzbaxlVG0gObYPGLog1QGMXyaFtjKpBT+l4IkbMqqLYVSSnhGJXiVlVxhN1tkpPvuoL83DcTzYNx/3XJ18NDO2QEtxNHxFUshhEULmbPjrqvMftqQzFoR2ghfysIS1EcWgH7angAuhKqMpFFvqTOKqEYng4qsRCf5KqXCchdO4sHHzCFz/xjH88+IRvr+Gn2SppWSalysiSREqVScsyP80GPzsn04aUafR3GAwDNAUp04iTCT5M798kYyehYa/L4MMuDXtd7KRvXw2dUoJ76SaKRhaDKBr30k1nPY/xoUPwx38MCwvQ1eUf//iPfXsNC94MjbrE7oYy97fPs7uhTKMuseAFq8CE473Yng2OCXjgmNieTTgevI4qbhG1pgWwik7FXV0i76ZQN7vDe2hS4jRLDk1KnN3hPWwKBWtovGeW+OHu7RR0leZylYKu8sPd23nPDIZojGVn6HQdNlcsbi5U2Fyx6HQdxrLB9+H01Cn+bvoMBc+jRZIpeB5/N32G01OnghNu7oemHXDsMLz6jH9s2lG/WsvARj/5M5GCmSn/WCcZFCB14iyD+ybRKzbVVAi9YjO4b5LUibPBKUhpdrGeEDpFKoTQ2cX6ul1Vc5QJRRqhcyv03wKdWwlFGskRLCcy4DZgyRFMFDzXxUTBkiMMuMF7lJpv5+5cExFLIhdxiVgSd+eaUPN19iNTPdzRcQv/aWGGb547zH9amPGFeSqYT/TszBSp7BlSThVZi5JyqqSyZ3h2JhhydtKo8u1Cjrzr+NXLXIdvF3KcNILX/Jy7wGHvOIZnEvMiGJ7JYe84c26wb8O5My5P/tB/XjUtPree/KFvr8Wyc8jy8sWALIex7DqdPIHjFZM/mcrx70fn+JOpHMcr9XMgUkoTA/oONClElRKaFGJA30FKqbN4n3qLtBtjc6mB3dkUm0sNpN0YTL0VGHpwxCAuO4RUkCSJkApx2eHgSP2O0muJ69VzLljk7BmXJ3/kEYtDpsnPRH7yR/CZz7v01Vn5XmuqY2EkvYqsS4CErHtInkd1LExtOYiv7MwzVSoyc6SJallDj1p03zTFV3bGgeVbj526wpxRJOLM4rpVZDlMRWmiM1R/G+9wweKJaYORqktPWObhlvrd3q6EK2lC1BkyyZZ10p7jR+14HgVPpzMUvHl1xdPkpPWsL41D0a8yk0v00xULesQ2JzbyimLB/BThYpFqPE6lsY3d0eAD8kpCjABkx8VzTX++kgye/1r2gh6e7phM1mwgvfFSjGPe9OjWgx7YhORieQ6q5y6e18HCIyHVWWTmpn2P+VJCUd9ehw4pQccv8Rhd4P5IjL+t5PBa08RtjaIao6ArPBJZXSxlsTuDXKmiVE1wHBRFgbBOsTsTLAw4vI/hfCevDQ8xnY/SkixzW/8p+of3BbznY6ZDSjMZoYSBRQiNBiXGmBlc5KrRLpxmHSk+D3bFLxsaaUSNBD1HG9oU/ult8Nxxl4ksdKThkZtkNrStsAU8PQynX4X8NCRbYPB2aKlfZq9TStB5GZ8FP/4xNDT4/8Gl449/HPCem5qK5rjL3EyK62JqwUdbc/pmRihDfha1WsYOR7CTbXSkbw6MjcgrbLOvMiwAfIFeT4zX8squHcRLRf+6BBKWDabBK7t2MFAztstYIBdKklqSdFhQInQZQTH20swwCSAh+29aQpLAdXlpZjjoPT99HP7+aUhkINW36HF4GpId9ZvhDGxcsXTiMhqaSZ09T2pmSX33Uh4a6u8+pCpltmTHcMwFFL2BULoBokFxniJKFfNicycAA4tUTdMlgL7ZGNbM64w1N1OORImWDAbPnqSv+YFAb6HmVigV2nkg0g6L3etLNsRWKpOb6qkrxmuZyM/QLst+zwQARSOBxUR+htoH4nOVEol8luTZ01DMk4wnoW+Q51Q14D0/642ie8EusGcZDSQCv/EKxOPB59Ybr0BvTZqSpqZwspMoE2O+2IjFcTu60NLBRcrxisk33xslOTpMW26OXCrDN7v7+d2BbjbVyYVIKU31xXgt1TmoLbGoRn17DZJlg3ohgGwRRfbtaxwhzq9z9r0KsTjE477oiccBPPa9Cn0rlK29llTLCgNyiKMFk7LnEJVktoRDVMvBh3939Th/sM3l5a1FplyVNtnmLmmO7qoMqeWe4I9Gi/x5doaUAgk5TM7xyJkzPNoAtUL+cMHiT89VSKsSnSGZBcvjT89V+De91BXoJwyDn5dKTNg2HarKA7EYG+qUcrySJkSfiU/xZ3Yf2DmSTom8HCOrp/hK/Gxg7KfSYf7CtKFxIwlFouB4ZF2X30oHBXQ7Se6IbuNYtIUsVdKE2U0r7QTjKKccixZ5+S0gJslMOcEQI4Addp4TUZmeRJGoZlO2VEYKcTaUg17lqofIAAAgAElEQVSIh7pV/vyYv9C4mNVveDw2EHx/d5pZXlLjYJUJ2wZVRaesRrjbrNPiPtVysfrARYyyb18Fg8Ysj82/yXN6O1NqgjanzGfn32QwdgeE3n9suJ1poTgzykRHJ+VwhGi1Qsf8DPFMcL7Dww7fOb6HQsrD6XSZKMV579BNPGa9Tn+NfmzQLE45WZKqTAgVG4f3nCxDdZomRVJbKZov46V6keQwnlsFp0IkVa9sji/QVxTjS5kehv0/8j+LxGKI0f4fwc2fryvQh50yr9tZZjyLZknjVjVNvxIUTYyM+B7zpaRSvr2GcLgLs/geqitd7EPgeCbhcK189UNPetJ3MRM9fTE2vEMfrBuO0qqsY9iq3WY36VLrV28pWtPLWtE3hoYuq/vlL+P84BDNb+/3q9DoITANYobB+V3BxcSnQmX+ohyC7CyJYo5CPEU23cRvRYMe4ynPpaVShYU5fyclFCLWkGEqUic84qVnfQ94bQWWl569vE6VK/HQY/C3f+z/HIlDpehfx4/+XmCoWZ6gPP0SkhxB1tK4doXy9EvQcjd6dHk8+3raeZPTAEvKe1psDxQChcjL+2hMq0y3SJiyiy5JNE6qRI7v88N5lrDnTnj6f/o/Ly3Jd++n3v9bANBhZZkMJymHobIYox6tqnRUg/e+yflZWg8f8L8PsQSYVeKHDzC5bTekl+/wFSkTY3mFJb8LbPD7MHPe95gvJRrz7bUks2Fmx44DGnI06secjx2ngf5AT/Znzpwj+e4RUpIEsQSpchHePcQzksumLXVCqC6XcAasEmhLHCd22bfXsDOaZV+lBSSbEC4GMkVHZW907bfBEeL8Omd22veYLyUa9e1rEV2BA+OQ1EM0K2A4cGzB49Zg+CCOlWVIS7FeuuT98TwJxwreuLrdE3w16fF8tYlJW6VdtXkkNku3Ow81dZefmDZIqxJpbTE+XfPj05+YNgLi/IRh8De5HElJok1RyDsOf5PL8dVUKiDQr6QJ0baUxh8qEzxZbmfUaqZbM/hKdIJtdbrYbY7qfFyN8tcnTaaKHm1xmd9ZH16xpXY7ybpivJY2RaPgOhc9cwAlz6VNqb+D8Jh+iv8v3UTBUynaCiHZYWd6jn9iz0JNpPyWRpU/6J7iqRPTjFYkuiMej21oYUtjUOgOlPPYTHE80k5OT5JyKuzKn2agjreL9bdz9qcvsm9kPbOlNE2xLHt7jtL3qdvrzjlvzzBtnaHqFQhLCVq0dSTriDFv+i00zaI1Oom2GCeqeRbe9FuQfP/ivGjJDHf3IZsWUaOKpekMd/fRXwjmTTwztYPptEcs6hDyPKyYx7Sk8MzUDmoly2B6gcPTKiEkFAUMR6HqyAxmFoDlolCPtBM3uqmcff5ivexI333okfopwiOnXfa/CLNT0NQGN9+zQjnQ068uhhUtLpQuHE+/GhDnw06Zx81pYpLfJKfoOTxuTvNZvSUo0Ht6OLUQ4oXoR5j0krRLee4t/yNDPUFPZHP8JsbcEraRQ3EqOEoIL9RGc/ymun9bolghMT4JlVmINEFnJ4HiyEBKbaJ/IcP57H4qlIkQpSt9M6nmoGevaE0zMflz1OFR9Lk8dibJRP85OtofWJVAb21oprDzZhJnTkEhD4kkpU3baK3jXd5sRfiX7/yIn7btYizZTFd5lt965yU2f+TTgbFtlSqFhVkStuOLftumtDBLW734//PjflOwpcQSvn013LOoap/6DizM+B7zR3/vkn0JRvYwplWibJ3FwUZBJao1o2QPB8R5Kyn2vD7Gyam3yEVlUmWX7W030Xrr7sB5Z5wFjmwZIFRxacl5GLrOkS0dhF5+j9p3uHdA5sE7hnnzifPMzCo0Nznc+3ArvQMrNGO6TG4Km/y/IY245xJ3HPIoTIRkPl2nBGb72VPkoyGSdhGqWZBVitE47WdPwbrlPSLiRFmgSA4DA5sQKilCNNSpgNS8wnOruc5zK7L/LZoIk2/XsHQXzQzTMKkQmXrLz49ZwsTEBG2S5C8mAEIhEobBxMQErEact90Ew//L/1mN+sLcLkP3nYGhj96V5vwzs8w6CQqyRsi16HezPHrXCv021hBCnK9Rhs+6vL4PpmegpRlu3Qv9fcEHZFMLTE7A3JxHuewL80wG2oMJ8msCDw+J5aENEhJenVJtipZeoaV68MKy7CybIik2Ry9tbXmeb69lpOrSGVr+XiZViZFqMIzi56USSUkiuZhEllQUcBx+XioFxPlH7oanvlPGLk4T0QtUzAQVq4V7HgyKTK1xB1uqz7O1sQRKBJwKnl1BawwmCR6bdfjBEZeiLaPIHsWyxA+OuKwLO2xuev8Z5/eE4ny/vACu7zEveS4Fz+WhUP3SeE5ziJvdAufsOAVPISE59KpFnOY6JQWzI2Ry/8CerWk2hHVSVZPM7FuQ/VigVGSkUKZTMdG9KRzZRXFlmismkTrpAmdLfTw5+nli9hgZfZaSnebJ0c/zmVIikDyat2c4Z7yDKoUI4YcpnDPeoZedAYF+zprlx/EW5mdTVIs64bjJcGOOzxenV+qbeFkclxI0yCUkPYyjxwjj4Mkux6U4tQUsTzqtRMPTaI4EsoLm2ER1i5PV4FOyMVrkkZYYb2Q1Zi2ZJs3lvoxFYzQYj8zwCcxXniY7kMRItBIqmCivPI2uZqB/+UN95LTLT78HsSRkWvyH9k+/B5/6khsU6Plp32O+lFDUt9fwup0lJinEF5vkxBcfPa/b2YA4P3XPo3z3h5Mkqg6teo68IfFdey9fvqed2kd6XGuhK3knc8ZpDDdPWE6SCQ3WF8XZETj1U9/jFln0vp36KQx9KthfYPoMqTd/Rioc97NijTKc+BnsSULL8m/E/NirqEeP+jHqiQRqoQqHjjLvRIj3PxKcx2Vyh57kh2kTbrrl4vVZxOGT9bob/vx19GiCxgYXK16iMeSijybg56/D3uXJm3cfPsnfDXWBbRMzLUqRCAVV5cHDJ4MtzVs7V6jAUsebwpUlLLJrK7T95qU+D+31d3LKxTMU7BlkFBQUXFzy1gRe0QoGSf3sKVr/6D/SGk9AIgGFAhSfhv9Dho8u7/Z7+qZeQqXKxRCYsOmBWeH0Tb0Bcc7ZE/S+9S16NyYhGodyEd7KQ/dvX1bztJUo97Zx78w4J+wM84RppMpH1DnKvcEH+P1H3uDb23cCGnELiqpGQYNHDr0B9y1vHBeX0rztjVByFUxPQZeqLEgluqWgo+GWO/wYc7i0K1Aswn2frDPhmQkiTW1EJpfcCzwXZoOVwzpmp8ilG0k5l0JICtEYHbP1a/pfNslu6P+4H2NenfM95t13+vYa1u/q518zzHOvTTFZkGlPuNx/Wxvrd61uUXU1EOJ8DTJ81uUnT3jEY9CU8S+UnzwBjzzsBgR6d5/HSy9AOAyRqL/VNjcLe26rX/ps/DgcfBbmx6GxE3Z8AjpX1ef6yjBdiY90SJzJQtHwiIckNjX59loiya0UZl8GuLgd7zoVYnXqB2tqmuFRhQNHOphdCNPUUGX31gn6u4NCvifsh7L4HnOfvO3REw4ufiZsG3VB5d0zKoWCRCLh0bcOig3BmLWO9kkeuPst3n67n7m5NJlMgdtve4GO9pugprKAGuuEjvuw5g/iGQtIoQb0llt9ew3fetfgVNUhoUNKljBcj1NVh2+9a/Cf76jjXb5MhvQwX6RhWbWWh0KpFau15OIZ2ufP0O7OAi54MtgauTpdSqdm9vNqZ4awp5C0oaLrvNqZ4faZ/bTVCKE5I8R7TSEqMR1HkVEcl7ykI88Gdkr9MK6IQ9yuQNUiHqlAxKkbxjVtnUEtldEmT0KlhBaJQXsX0/KZgDh/TmtibKqJsOMR0y2sisrYVBPPNbirEudj6U684jTpcBlNMrE8nWw5yng6KB6VVrDyjRjhPA4uCgpyNYlSx4OVJIoeNfhC9NJ17sfaBr8PxcPPMrE9heJp6AbYUZ2J7Sk6Dj9LvEac738RZHmGhdI45ys2uqsSljvZ/2JzsH5/coUQo2Twb5vxrLpNcma8YAjVC8YAiV0pku8dhXyeZDIFWzbygtEUEOfgC/TL8lCP7/eFub64HX7hOL4/KM5PrrArcPLVgDg3Jk+io/s3YYBwGKXq24Pdri6fATXCg5rMUeskhlekmTj3aEMMqMGGUKfNMj+4+07ipk3zgkVBb+QHH2nhCz/7RWAROHhyhN/yPF7qamIqFqWtVObBM5MMnhoNTuLuT8DffdP/+UIFlkIOHgxWYDlpVPn26FkSZ96jdW6WfKaJb68b4CvdfUGBnhvh5PAveD7SzmSqg3a7xH3Dv2B9/52BWO2KnUValOYACr6Tp1LH8cJ3/wriCT8MCi4dv/tXAXFeGOglfvAw6O7FsKGQaVDYUafvw76f+SvW+OLC6MJx389WJc4XImEGWtoZzE3CYtdqL9XOQjh4D15vTfOVo6/yXP92JqMp2ss5Hjm5n/VWcEF+yCty1m6kQS4RxsL0NM46KVJakdrZ9q6T2bhzmMefc1jIh2lIVvns/Qq96+p8eZs74NwZGBuDfB6SST8ErTY4HfikmeebXpMfR+9YFBSNvCfxqJkPnhc4Om/z1IjNWNGjKy7xUI/KlsYVJGqyu64Yr8f6Xf3XhRivRYjzNcjr+yAeC8aRv74P+vuWjx0ZlRjc6LEw6694Y3Ho6vPttRv948fhuW9CNAUN7VDO+a/v/92rJ9A7ExI5Q+bWrkvCOGd4pEJBcR6KtEPTXVTyR3CsLIqWJtbwEd9ew8z8Vv7+xSLxiEsmbVAsSfz9i9184dNxmmtC0R5uCfGn5yqAS1KVyNseWdvjq53BG6Ke1XjjgEdMzRFWDcqFEP94IMYtN2u10QNY8wfp7jfoGboUG+vZBtb8wbqieyaW4GhsPVkqpImwhUTdWuT751ziGoQXq9GEFQlP89g/t/qqPEN6eEUxXkvK0ympCiHT8rclJDBUhZQXDK85FjGZLGbYv5DhvBmiVTe4uWGOY5E5amuEvNzSD4l5kq6B7thYsspkKsWk3BgQFrNnc2SyB/2Sh+EYWAbRqYPMVndSK+WrhXG88fdYaAhjNsfRTYfE+EmcToeacExO202EbAdddQEJXbHxbJnT9uo66jU1djFfyGGUIxdLNJYkl6Y64T39e4oceDpCSEmgR12MsoxRldj9QBFqfHlDtLO/JrbWwGJbndjauVgOxY2hOv73R7UBRWUulgtsco+/N0clfg4NCd1VcCSHrHQO4z05MAcGb/djzMH3mBtlX6xv/XhgDs2SH8oSX/LIKePQLAVDqCYXoLWrCbrvuWiLex6TwdzGK6My63vMl6JFfXstV7ArEJrMYWeS/vu6iBMPEZqsX8GCQ4fgJz+5VCbykUfqlonMObOY7rtsVkOotGBjYrrvknNCgcS5l/fuJl5YkjxqWmBUeXnv7sA1REcXg5MLDC5pkkMuW7e7LIObeOdzX+TYwrs4molitbC5YSM768SbP3dumI6T++hLFwk3ulSrec6+O81zrsf69cvHn5w6wndi/SRkiVZs8mqE78T6eWzqCOtrxLmfJy7h+VXhYXH31at3+5ucgNaaO0wi4dtrSCTbqW6XCQ+f9Us+xpMYGzeSSNZZDc9MQqbGHo379lXQQJhyWCEavlSSsYJFQ73i9M0Z1r/3Hutz8/79zzTBqMBAML/imFNFk2JYJLiw/NUkl2NOFWo2Ww8dOcH3p0dJ7/BYr3jkHInvT0u0HjHZvrVGyrf2wY/+x4VENz/cav+bsCe447vp1tv53Sd+yDPrtjORaKCjsMCjZw6x6eHfCIw9Om/zjaMWKR06Yn5+0jeOWvz+FuoK9EnyHGH6Yl7VVlouK5TzekKI8zXI9IzvMV9KNOrba5mZ8ftTdCwRu67rMVNn7MFnfWEeXXQmXDgefHb14vwXr9n85HmX6QK0JOCR+2TuvC349frkgMpfvu3H0yV0KJi+OH90c/0451Ckva4Yr2X/4UYa02F0dRrXrRKPhdFDTew/HGVzjbttW0Lj3/SyrFrLVzvDdZNB3TMOnlIGzUWSVNAcPK+IeyYaaJbhGQug13jqlYhvr2GSPE/bZ5g0oORIxJQiw6EiD6rrAjcZR3VR3eVefcmVcNSrWzJzcG6KN+MRJD1ByAVDBgObHXNTtWH9HPSaeHayjbjq0qyZFGyVpybbsTolam/jpUaNWFWh4CVBVvyaw55NqTH4eTRZpyl5CS6G5mshykaIJus0ULOjMjvJbGsY1VXQbHBUhdnWMM2zk8GFVTaGHi+D4V1QA8hxGTP7/ncmAPY0DPC04sLkKNF8jnIyRam9m3uTwQfqut4c4x+fJ/9ajOqohtZs0fbxEut6g6FLLVKa9U4zx7xTzFAmSpTN0hAtSnCnyMgkGDkf56XSeqasOG1akbtjJ+lpDe4UebER3HIIJepXEVJQMMoKXmyEgDhv6feTP5dWa9n68brJoLeqaR43fWF7oWNhyXP4qBZM4mpvgHwFkksWUMWqb18VkSY/lEVfkkhmlX17LckWmBuD7IyfrBiJQ7oZMkEB25hLMtFkgaajWOBoYGPRkqsjFg4dYvwHf83B2zew8OAQDedz7PjBX9PJ7wQE+qQ9jCaFArXWJ+3hgDg/v2UTza+97nvvQ2EwqsSqVc7ftjyxEYBPPAT//Rv+z4mkL7ByOfjNxwJD35mb4G37LEUzTDWbIBy1eNs+C3Od7Mwsv+jL08fZ1TKPg45ha2i6w8aWed6ePg414vx5V8OSNY7YIfIoJHHokA2ed7VA06+wlMSsLIBVxJVcZE9G0WLo4Trvb3uH/7ekloThFAp14zwH6eRAqgw7b1q2wN1ap346ze0XBfylP7gYjMe/QrbTzHcXJjg8rpMtqaRjNts6Lb5cr2pN93rQwjAxDsWCv0PQvw7agrkYpqcQxmVpGSMZl6oXvJc8fnQENaEx3RLlXEghYjhEp8s8fnQkKM7fPQ4bd/jXRangf3+6B3z7XTX5Aus3M/Tp++g98Q+4+QKykkD/9MdgfbA2/FMjNikd0otOunQIwOOpETsgzifJ8zLniCzG0VeweJlz3EXvr5VAX3u19gS0NEO5Jqm6XPbttTSvMLa5ztj5cYjUBOlFEr59NfziNZv/+oRLoQrNcShU4b8+4fKL14KhH5uaFL6+SycVkpgsQiok8fVdOptWETsNcH4WUokIsWgfifhGYtE+UokI5+s4xcAX6P9hIM43tyT5DwPxFcsoVhby7NKLhCQooRCSYJdepLIQ3JqTQg3gVJYbnYpvr+Ela4KTZRfTVYjLMqarcLLs8pIV9PDs7JApWh7GosPasKBoeezsuLqXb0txgT0FhbALeQXCLuwpKLQUg4uPg8VewqpJQjGRJY+EYhJWTQ4Wg57dmAZmJOMLc8cCWcGMZIjV+Uj2Nh2g5MQoGjquB0VDp+TE2Nt0IDDWsEwkSUbyPMAv2SlJMoYVTLbqLTRjqgp2WMfTothhHVNV6C386sYyv4xeJcaDySFiQzcxt/t2YkM38WByiF4lWKIxVJ7hDuMAmz52nHW/c4JNHzvOHcYBQuXgSnvenWfSG6WFFOvpoYUUk94o826wnfiotp3vze2gYCm0qEUKlsL35nYwqgW9tW27T2GXwhhl3f+ulXXsUpi23XVqYIMvxG/7Mnzif/OPK5RR7FeifFZvIS4pzGERl5T6yaDAvdtkChXIVzxczyNf8ShUfPuq6LzZF+dmyb+QzJL/ujNY/YR0D4we83cCQlH/OHosGP4CxG99iI5XZ1ALVcyIh1qo0vHqDPFbHwqMHX/1H3j+MzdRbkiQzpuUGxI8/5mbGH/1HwJjK94KtdbrtDRvzbRQuvVWCEd8sR2OULr1VlrrVAVi4xb4578PybTvUU6m/dcba9uDwVvD75A7L2FVNDQVrIpG7rzEW8PvBMZuss9RkcPYrgpI2K5KRQ6zyT4XGPuu1sgxO0wVmQQOVWSO2WHe1YLt5VNGFxSyKAWLUNZDKVhQyPr2Wr78NV+45nJ+87Nczn/95a8FhjZLaXYzRBiNIhXCaOxmqG79dPZ+1C/1WMz75y3m/dc18fxXynw2wqETrZimSixqYZoqh060Mp8Nhi6x6U4IabBpK9z5gH8Mab69hn4pQQUXGwfwsHGo4NJfp6/Au5rOTE8SS5UJmw6WKjPTk+RdrU7BgalxfzGw+SPwkfv8Y1uPb6/BLo1T1c/h7tqJdMvHcHftpKqfwy4Fx44VPZI1/1xS9+21HGGaCCoRNCQkImhEUDnCGq2C8T4RnvM1yK17/Rhz8IhGfbFdLMFHHwiO3XsrPPH48rGlEjxQ557R2OmHskSXOBUqBd++Gn7yvB92kYj4q95ExJ/PT553ufO24PhNTcqqxXgtrU1QKMHSvjWlsm9fDW2RLAUrwe7wJdGdr2o0RYLxjlrjDoyJ5/0XS5I89Zag9+qEVUKXNMLSYqiKBB4qJ6xSoN321wZCjFcr5BegWJXQdI+Odo+vDVxeOMoHhRRqoKVaptW+JKg8u/7iw7QzhGIulllGdSxsRSMUjmLaQU9pr5rimJeHeBM6EiYelmcypAa9IH0DCp/R32TfxGZmC3GaEkXu73+bvu7g98mKx4nPVzHjCo4Kig3xeQcrHqxY8OlNbfzNPgWrbxInXkUqhomebefTe1cnzsEX6PXEeC2JkTF0FfZkL323DBVCI2NQkyt3zhtFIxSoY3zOG6WR5QLnjcIuYo1jxIwKkmESC+vYoTBvFAa5v3auHWW0jx1k9O0BCnNxEpkig7cfo6MjWILtSulXovVLJ9Yw1KHw5bvhhcMukwu+x/zhPTJDHau8Z6R7/OTP8f2XqrX03V1XcDN+GpqGwMj6XTHDcb986/hp2Hr38rGbthDn68SffQrGR6GzGx75OmwKit2D7RoRRyJq+I6LaNWGkMLBdi3gr72SluZ3hRL8oNGCO26/lDzquXw6tEJd+Y1b6orxWsxqAcfSURffekUB21Iwq8G29eukCge9CCFFQndcTEXG8CR2SJXA2HmlAcXOE/Y8kBTCnoUlScwrwXtJ9KXjNKsuufUqVkRCq8g0HraJ2sfhpoeXD74QV/7dv/IXHu0d8K/+XSDe/ALNUprmeuV6aunbAA//th9jPjPpe8wf+Pyq4s0Bnhyz6NB10vriZ6xDFo8nxyy2NtTIs7YBuO034fgvIHse0q1w06d8ew0PKG38D8vGoIqNhYRG0ovygBpsPCa1RXAtF831d2I1x6Xq+fYAbSskCLcFRYS5cAjUCLLqX/OSGsVdtNeGeXbFJbKGt+gx98mbvr2WLFVSLC9CEEYlS/3medcrQpyvQfr7ZB55eHm1lo8+UL9aS3+/zMOfddn3uh/i0tzsC/P+/uDYHZ/wY8zB95hXCr5YvzWY23NFTBd8j/lS4iHffrW442aJH/4UwCMW9YV5sSTxybtX13b8vnUlvnMoAZJKXLcpmip5Ax7eEEzCuZIkz5KlEVMdP6lyEV12KFlBd/H2pMb/vQUeXxKG89mWMNuTq2uadKWomZ2Y48/5L5ZWmGkNrsA2hEOMW02Y0TJVbMKoxKwoneFgZZddyjoMDnPedih6EnHJo0dT2KXUScXceQ99c9+nb9scRGJQKflf5J1fDAyNNfRjjhwhsSBdjPc2cYi1B727g10yX93bzMvvZJiah7ZGuGuvzGDX1dudWHdkjHf29IDloVsepiZhaCE2vTkSEOclr0S0JvlTR6PkBb+XM+UobaluDDeG7RmoUog2uZGZcvDhO5DZRV55nZ1dC+ieiinZGLLDQLpOeMSHyFCHsnoxXo90T30xXsvCeUi3g7Tk2vVc316PTVvqivHAaXtaSJ/PXUoeBSK5Egs9QQ/3lbQ0H9QifIFGXjYKnHctWmWNT4fTDGp1BNYVUMiGCUdNXPPSvUaP2BSyQcdApnUd208d4WxDC4VIhESlwoaFaTJDwSosOnEIeZhWGc0xsRQdtKhvr2V8lGh7J9FjS65F1w8Vq8tHH1pRjK+Kvg2rFuO1jJY9Oms+oqTm2+vSNlBXjNfSp0R5lC72OVlmPJNmSWevmqavzuK4uS3F/ESeiuQ7iaoeWI5Hc0edql33fhK+95f+z0sThD/zaGCoaywg1YR5SkoEt06Y50M9Kt84agG+Bz1vQs6Ex4aCEjVNmAoWkSVerCo26WBbt+saIc7XKP19ciD5c8Wx/TL9l5GM3LnJT/5cWq3l1i+sPt68JeGHsiSWxogavv1qsa5H5jc+5fLKfj/EpbUJPnm3xLqe1YmrLQPr+JL5Fi+O9zJVitMWLfJQ3zm2DNSvo6zGOuuK8Vqa7Aw5bZIQEpKn4EkOpuTQZNevOrE9qV11MV6LEu9E77wfe+6di4sPrfU2lHjw732kVee/nHVpQCelSuRsjwUbHukKbpU2yo3sZRvn5FFKXomYFKNX6qZRDm5x0zkE938R3nkRFqagoQ1ue8i319CV2sW7/VWYmkQrFLESccy2PtbFd9X9+wa7rq4Yr6VJybDz0CxnBhspRGUSZZdNx+doUoK7DTEphuGZvsd8EROLmBT00PtJ2BFSS5op5QyPzkRw4drUtYtdwHtzb1NQqiScMJvTe2jqqv+e/drS0Oov+pbGAVZLvn01p23vpzxzgGi16td/NgwqskNDnQXjhZbmk/YwFa9IRIrTo21csYvioBZZtRivRRqJom2rYCNhmwqq7qBqDsZIUOTF++7BlErsOjOKfLaAm0ngrO8n3ntPYOwGPcyoLZGNRCjhEEOhxdXoVuuUZe3s9hNWU0uEXiHv269zuqMSWdMjveS2mLd8+2rpU6J1xXgtm5syhD0Ync6RcyRSisf6liTrahPfAIY2w5e+Di88sxji0ukL86FgHLkcasC1y0jqkp1Wp4JcZ6d1S6PK729hWbWWx4bqV2vZSgsv44dKhVGpYlPB5iP1cgWuYyTPW2GFdgNw8803e/v379jYQqEAACAASURBVL/W07juuRBzHtd8j3nRgKIFv/dw/aTQ6w2rNI6ZPYxrLiDrDejpbWiXIcB/GccrBv89d55oNIemmli2Trmc4p+nWtkUqfOAug45lLf4yXnzorf/kVb9qi8wFpw5xtxzlCgRI0aX3EtDHbG7Jjh3Ap7+W79k24VayqU8PPhPoXe5x27eneeoewyNEDoaJhYWBlvkzYFFzbFZh/920CQVkpYlYf+LHfqq6uT/WjNxGl76O1+ch2O+MK8U4O7fgo46pfYuk3G3wPMLh4mcPEPk/ByV1gyV9eu4r2EbnfJV9GZcJidfPcnfvD5KZlOBWNKglA8xdzzBV2/tZv3ttambUKlOUSwdx7KzaGqaeGwTkXAwlOJY2eQvpoqkFPli1+Oc4/Iv2+LB5mrHj8I3/9wX5xcTWLPwu39wWbsVa5kjCzZ/dsIgrUmXOilbHn+4IRQMa/mQOGVW+W5xgYQsE5dkip5LwXX5crzhsit51cMujVOdfAHUCJISwXMqYFcIt997WQ6sX8b1Wq1FkqQDnufVSXKpM1aIcyHOPwgut1qL4BLHKwbP5iuMWw6dmsInkpFfG2EueJ+cOwFvPgczE35N4T33B4T5Bebdec55l7HbgC/QfzpsM17wPeaf6leFMP9VTJyGwy/5oSwNrbDt7lUJ8wuMuwUOMssCBg2E2EHTmhTmFzj56kmee37sUhOX+7rqCvMr5VjZ5O8XqoybDp26wqcbVu56zPGjsDSm/xMPXffC/AJHFmyeHLMYLXt0RyU+06VdNWF+gVNmlReqRSYdm3ZF5d5wfFXC/AJ2aRxz4RCusYAcakBv2L5qYX49I8T5ZSLEuUAgEAgEAoHgw+ZKxLkopSgQCAQCgUAgEKwRhDgXCAQCgUAgEAjWCEKcCwQCgUAgEAgEawQhzgUCgUAgEAgEgjWCEOcCgUAgEAgEAsEaQYhzgUAgEAgEAoFgjSDEuUAgEAgEAoFAsEYQ4lwgEAgEAoFAIFgjCHEuEAgEAoFAIBCsEYQ4FwgEAoFAIBAI1ghCnAsEAoFAIBAIBGsEIc4FAoFAIBAIBII1ghDnAoFAIBAIBALBGkGIc4FAIBAIBAKBYI0gxLlAIBAIBAKBQLBGEOJcIBAIBAKBQCBYIwhxLhAIBAKBQCAQrBGEOBcIBAKBQCAQCNYIQpwLBAKBQCAQCARrBCHOBQKBQCAQCASCNYIQ5wKBQCAQCAQCwRpBiHOBQCAQCAQCgWCNIMS5QCAQCAQCgUCwRhDiXCAQCAQCgUAgWCMIcS4QCAQCgUAgEKwRhDgXCAQCgUAgEAjWCEKcCwQCgUAgEAgEawQhzgUCgUAgEAgEgjWCEOcCgUAgEAgEAsEaQYhzgUAgEAgEAoFgjaBe6wkIBAKBQCAQCK4NJycdnj/iMrkA7Q1w31aZ9e3KtZ7WDY0Q578GjB+Hg8/Awjg0dMKOT0Lnpms9K4FAIBAIBGuZk5MO33nZJRGB1jTkK/Cdl10euwsh0K8hQpxf54wfhxf+EiIpaGiHSs5/fe/XhUC/UTk6b/PUiM1Y0aMrLvFQj8qWRnGpXw/k7VnO2+9R8QpEpASt6gBJtanu2DG3yDvMMU+VRsLsJEOXHL/KMxYIBFeLOXeBYcYoUiJOjH66yMgNqzrn80d8YZ6MSAAkIwAezx9xhTi/hoiY8+ucg8/4wjyaAkn2j5GUbxfceBydt/nGUYus4dERg6zh8Y2jFkfn7Ws9NcGvIG/PMmy9jeUZhIljeQbD1tvk7dnA2DG3yM+9McqeRYOnU/Ysfu6NMeYWr8HMBYJff+bcBQ44h3jJ2ccB5xBz7sJV//cPee9ieCYxL4rhmRzy3l31PCYXIB5ebouHfbvg2iHcadc5C+O+x3wpkYRvF9x4PDVik9IhHfK9IOkQgMdTI7bwnq9xztvvoRFCk0IAaITA8+213vN3mCOKSlTyP9MoKni+vQvhPRcIPkjm3AUOe8fRPZ0YEQxMDnOcbe6mVXuuL5dhxtCrFUJzw2CUCIVikGljODJGhvc/h/YGOJN1GbFdcrZHSpXoUWXWNQjf7bVEPK2vcxo6/VCWaOqSrVLw7YIbj7Gi7zFfSlL37YK1TcUrEK4R1io6Fa8QGDtPlQb0ZbYICvNUP9Q5rkVOTDn87F2XyZxHe0rioxtlNrSJ7fi1zohb4oA3z5xnkJFC7JYa6ZFjv/oXrwFnvVF0Tyck+ddcCB08OMvoqoTxlVCsnCc2fgZUHfQo2Cb6+GmKnS7Etr3v83b1uvzP0w7RMCRCEsWqx/6qw107PsDJC64YsTS6ztnxSV+cl3Pguf6xkvPtghuPrrhE3lxuy5u+XbC2iUgJbJZ/eDYmESkRGNtImArOMlsFh0bCgbG/zpyYcvjWPpt8xaM1CfmKx7f22ZyYcn71LwuuGSNuiWfdCUqeTSM6Jc/mWXeCEbd0radWlyJldLRlNh2NIuWrNof47HnMsO6Lc0kCVccM68Rnz6/qvPs9h/4tLvGwhFGGeFiif4vLfk9cQ9cS4Tm/zunc5Cd/Lq3WsvfRlZNBT4+7vHjQZWoB2hrgnh0yg51ijfbLuJ48cw/1qHzjqAV4JHVfmOdMeGxIXOprnVZ1gGHrbfB8j7mNiYVBl7o5MHYnGX7OGHi+x7yCQxmb22i7BjO/dvzsXZdkSKpJZvPta/Uavd4omdPMGycx3DwhOUljaD0xvWVV5zzgzRNFJbYYlhVbDMs64M3TQ9B7PutmeY8xCpRJEGWALprk9KrmcCXEiWJg+h7zRUws4kSv2hz6R+c5NNgIDugOmAqYisbG0/PQ+/7PO1Jx6WqRkFsv7a66nsRIxV31nBecOcbdc5QoEiNOp9xLg5JZ9XlvBMQT+9eAzk2XV5nl9LjL9553SEagJQ2FMnzveYcv3YcQ6CtwwTOXDEnLPHO/vZc1+fDf0qjy+1tYVq3lsSFRreV6IKk20c+uZdVautTNdau1dMlxHnC7llVruY22G65ay2TO95gvJR727YLVUzKnmSi/iSqF0aUEtltlovwmHexZlUCf8wwaa8KyoijMeUZg7Kyb5W1OEEIjvhjv/TYn2OVuuGoCvU/q5jDHwfM95iYWpmSyQRpY/cnnzsK5N6A0C7Em6L0FMn2BYRmlke3nygy3RSmGIG7AxrEyGaVxVf98T0RmwXJp0C7truZsj57I6jTBgjPHCecIOjpRYpgYnHCOsIGtdQX68FmX197wmJ6Flia47RaJ/r4bV5eIJ/ZVZOIoHHkaFsagoQu2PggdW67ev//iQZdkBBJR/yJMRAE8XjzoCnG+Ah+mZ27enWfEG6HklYhJMXqkHhrl1d1owRfoQoxfnyTVphVLJ9bSJcdv+OTP9pREvuJdvC4BilXfLlg988ZJVCmMKvvhUqoUBte3r0acZ6QQJc/2PeaLlHHILCZDL+U9xgihXfRaXzi+xxhNXB1xnpEb2OZu4iyjFCkTJ8oGaWD1yaBzZ+HIU6DHIJoBo+S/3vpQUKAP3kbmwI/JlF0IRcEog1GE3Q+sagqfa9P4L2eqgEtKlcjZHlnL4591a7/yd38Z4+45dHT0xc9UX0xwH3fPBcT58FmXHz/lEY95NGWgWIIfPwWfe8i9YQW6eIJfJSaOwjP/j0c2B6YF+lkYPQyf/L+kqybQpxZ8j/lSYhHfLqjPh+WZm3fnOeYeW/QqRDE9k2PeMTaz+QMR6ALBjcBHN8p8a59fJjQe9oV53vD4/K6ru6t12qzyolFgyrVpk1XuCSUY1D+A+P/sCEwegPIsRJugfTeke1Z/3svEcPPoNTkPihTCcPOrOu9uqZFnvQnwfI95eTEs6y4pKPgLlIkTWWbT0Sh8APHeZmWSSu4IjrWAojUQSW1Fj7TXHZuRGz745M9zb/jCPLQYynPheO6NoDhv7ofdn4PTr0F+GpItsPVjvn0VbE9p/Lt18OMpi5GKS09E5p91a2xPrU6clygSrQlR0tApESz3+tobvjCPx/xFdTwG4PHaG9Dft6ppXLcIcX6VePXbHpPjEE5CNAGWCZPjvv03/ijo5Tk97vLiO0tiw3euHBt+5pzLa296TM9ASzPctkdiXW9wbFuDH8qSWBImV6r4dkF9PizP3Ig3suhV8L1A+mL2/4g3QiNCnP8yZrwsp5igQIUEEYbooFm6evGngrXDhjaF397LspyQz+9SrmrI2WmzyvfL8yQkmRZJoeA6fL88zxdpXJ1Az47A6WdAi0EkA2bJfz34yasm0ENyEtut+h7zRRzPICQnf8lv/Wp65BifoGNZtZa7pJa61VoSK8R7J1YZ721WJimOPI00N4NcqeBGIhQzZ4n3PLiiQP/AKc36HvOl6FHfXo/m/lWL8XpsT61ejNcSI46J4XvMF7EwidXZ7ZuehaaatyEa9e03KkKcXyWG34ZQAvTF76keAi/h22s5Pe7yVz92qEyCW4SZOJw+4/C1zwVjw8+cc/nx04vbQU2L20FPw+cedAMC/Z4dMt973gE8YhFfmOcr8NCtN+a20eXwYXnmSl6JaM3DRUOj5K3NagVrhRkvy35OLcafhqlisp9T3OwNCYF+g7Kh7eqK8VpeNAokJJmE7M8hISng+vZVifPJA74w1xcF64Xj5IGrJs4bQ+uZKL8Jru8xdzwD26vSEtq+6nP3FObpmdoPlXmINELbzZAKivMBunibE8CleG8Di82sW9W/Xxl7GWn8LLIShnAMybJwx89SkV9GH/rCqs592cSa/FCW0JK/2yz79joMO2Vet7PMeBbNksatapp+5eolpV4JnXIvJ5wj4PkecwsTE5N+eX1gbMuidokveRvKZd9+oyJU2VXCVUCuSX6WXd9ey1M/d5k/CYolEU1KKJbE/EnfXstrb17aDpIliXhMIh7zeO3NYNjFYKfMl+5TSEQlprMSiajEl+5TRLz5L8H3zKkkIxLn836L49/eq65aDMSkGBbWMpuFRUxam3V+1wqnmCCERhgdCYkwOiE0TjFxracmuEGZcm1i0vJ7aEySmXJX2ZW3PAtajfDSor79KhHTW+iI7kGVw5heAVUO0xFdXTIoALlROPMsWGUIN/rHM8/69hqa5DS72EAInSIVQujsYvXJoM7caSQl5JcmxC9NKCkhnLnTqzrvFdF7i78jYpTA8/yjWfLtNQw7ZR43pyl6Dhk0ip7D4+Y0w87VK+d4JTQoGTYoW9GlEGWphC6F2KDUTwa97RaJYkmiWPJwPY9iyaNYkrjtlhs3d+S69ZxLkvRp4F8Dm4EMMAkcAP7E87zXr+Xc6tF5C4y+4JcnVcLgVMEoQPe9wbEn3oVYCLRFp4sWhpjn22uZnoGmmtVlNOrb6zHYKUonXikfhmeuR+rhmHds0augYS36FQalwbrjjxQtnpwzGDMcukIKn8mE2Br/YLchrwcKVIjX1PIOoVGgco1mJLjRaZNVCq7je8wXKXkubXL9x+u8O8+oO3KxvFy3vEIieLTJF2r6kgW7VfbtV5GY3rJ6MV7L1H5/oaEt/m0XjlP7IdUdGN4kpy87+dOoTFIqHsO2sqhamlh8M6E6YSpK0cCNhpCW+LE8TUEpBivGfGhk+vzkz6XVWtbfV7day+t2lpikEF8sPxlflG+v29k16z1vUDKXVTqxv0/mcw+5vPYGF6u1fOw+Ua3lukOSpD8C/ndgDngcmAUGgYeBz0uS9BXP8757DacY4J4vwePTYIyDnQU5Coktvr0WvQxuzbXmar69lpbmFbaDmj/Y+f+6ce6MyxuvwMx5aG6FW+6A3nVX70bQKDeymc3LqrUMSoN1H9JHihZ/Nl4ircp06DJZ2+XPxkv8YWdsTQv0SfIcZZosFdJE2EIL7awuVjVBhCom4SXxpwYWiZqEMYHganFPKMH3y/Pg+h7zkudS8FweCgXF5Lw7z3Hn6LLycsedo2xiS/Dab9/tx5iDL2StMlgl6L3rKvxVHzKVed9jvhQ16ttXgVGZJDf/CpISQVFTOE6F3PwrpBrvCAj0iNRK0Z3BlSUkT8aTXDzXIiK1rmoOV0ymr64Yr2XGs8jUNEKKojDjWSv8xvVFf598wyZ/1uO6W5ZIktQG/HvgPLDZ87yveZ73f3qe90+AjwMS8B+v5Rzr0T0k8dl/CwOfgszt/vGz/9a317KrXaZoQtXx8DyPquNRNH17LbftWWE7aM+Nux30qzh3xuXJH0KpAE3N/vHJH/r2q0mj3MhOZSe3q7ezU9m5YpWWJ+cM0qpMWpWRJeniz0/OXUUPzxUySZ5XOEcFixRhKli8wjkmWV2VhyE6MLCoYuLhUcXEwGKIjg9o5gLBlTGoh/litJGErDDtOSRkhS9G6yeDjrojF8vLSZKELoXQ0Rl1R4InTvcwu/5O/rFN5/mmIv/YpjO7/s6rWq3lQyPSCHaNt8ku+/ZVUCoe84W5EkGSJP+oRCgVjwXG6ps+TnwaZNPBlW1k0yE+7dvXIs2SRrmmK3AZh2Zp7TpoBO+f69Fz3ou/qHjD87zppf/D87wXJEkqAGvSb9w9JNE99KvHfexhmYX/BmOyS16BqANbbJmPPRwU5+t6ZT73oMtrb3KxWsvH7q1frUXg88YrEI9DbLFC2IXjG69A7+pyjD4UxgyHDn3555lUJMaMtdte+SjThFGJLHp6LhyPMr0q73mzlOZmb2hZtZZt9Ilk0F/B0Xmbp0ZtRksu3TGZh7pFLfxfxeGszRPjFqMVl+6IzMOdGtvS9d+zQT18WcmfV1JebtZd4GAsTyg2QBwNA4uD5NnhLtC02vra15q2m/0Yc/A95nbZ3xnoXt2ugG1lUdTUMpssh7GtbHBwxyD63i+jH34Jps5DQyvsvRs66ocWXmtuVdM8bvqS50L5yZLn8FFNdNz8deR6vDufAkxgjyRJTZ7nXcyOkSTpLiCBH+py3dK5Cb7wL2QOPiszPw6NnbDjEyt3AV3XK7NuFe17bzRmzoPbX+Wd1hL5sE2yqjJ4PkZ5+AOoS/wh0BVSyNouafXSbkje8egKrb0OpRfIUiH1/7N35+F1neW997/3lmRN1uDZjocMTiCJnYQEU4aENAktIYGElqm0QEdI+7YM7SmnA9fbQs95e0pbShlaCml72kLTi1IoZUoCJQlkAALO7AyEzLbjJJ40WJMl7ef9Y21hSZZsb0vae23p+7kuXY/2s9eW73hF8m8/eta9Ju0Nb6KerlnYG74iOllRoZuPzAf37xvh4w8cpLMxWNsSdB1MfPyBg7zrTAzo07iva4SP/GiIJQ2wtjnoGi7ykR8N8dunMW1APxbltJd7fOzmOzHu5jspm18+2/22K61jPZzyqmyP+Vi3lvUXTrnfvBz1DZ2Mjg5QV3dom1uxOEh9wzQ/L044NbdhfLKT61r4mUUrJ3Rr+emGZRXfb37/3hG++uQIO/sSa1uD15xYz6Zl/hyZbTX3N5pS2hcRvw98GHggIv6LbO/5RuBK4L+BX69iibOibhiae6B1PzS3ZY81O9Ipg9x+QjetBG2DdQzWj3L72m5eEsAMe+fOhSuXNfKxnVmLxfa6oGc00TVS5BdX5XefdSfNDDD84xVzgEFG6HRveMV9ZfsInY1B56LszV3nokPzhvOpfWnnMEsaoLP0G6vs767Il3YOzyicry9s4MHR+w9rL7excPivVOfy5ju50LF+xmF8stbFZ9K971ZGyVbMi8VB0ugArR0vnNU/p1pOrmup6sWf9+8d4RPbhulYBCe0QPdQ4hPbhvnNzRjQZ1lN7n1IKX0EeB3Zm4t3AH8AvBHYDvzz5O0u40XEVRGxNSK27t49TUuTKnvmXvjOh2FgP3Ssy8bvfDib18z1b+kj+oJCfx2kbIy+oH9LPnuMb17cwLvXttJZX+Dpg0U66wu5vxh0EysZZIQBhkkkBhhmkBE2MctdH6pkR/EAXys+wWeKD/G14hPsKB6+LSEvtvcVaZ/0v0p7QzavqW0fKNLeMPG6nfaGYPvAzP7OlhaWckbdpgnt5c6om+JiULKb7xyc1G51Nm6+A9n9Ar6X7ue/0w/4Xrqf3WmKbR81qLF5DR1LL6CurpnRkW7q6pqnvBhUx+erT47QsQg6G7PWzZ2NQceibF6zqybf6kTE7wH/B/gY8DfAM8DpwJ8B10TEC1JKvzfVa1NKVwNXA2zZsmVm92CfIw9+EZo6obn0m8ux8cEvwuqZ3/thwRtoH2XzaQWefgr6+qC1FTZvKDDQnt893JsXN+Q6jE+2hnYu4MQJ3Vq2sHbG3VryYEfxADewgxbqWEIj/QxzAzt4RXEd6wqHb0+otvWtBboOph+vmAP0DGfzmtr65gLPFfqIzl6G64dpGGkgdbWxvnnq+xAMDD1L98BDDI9001DfQUfz6TQ3Tt31Y2lh6bQXf493Muu4hx9CmnjzndNnePOd3amLu3i4dCOvZoY4yF08zLnpefPi2o3G5jWG8Tmysy9xwqT3hu2LsnnNrpoL5xFxEfDnwBdTSv9j3FN3RsTPAg8DvxsRn0wpPVaNGmeq66lsxXy8po5sXjO3pq6eno5RNp1zKJz0FIusmaYvsY7PGtrnRRif7B720EIdLaUtO2PjPexh3RR7h6vtivX1fPyBg0C2Yt4zDF1DibdtrJ03e5X20nXD/PPAHpqKdTSN1NOfRhhcsofXNi+CSVtNBoaeZXfvd6krNFFf185ocYDdvd9lBS+dNqAfi+WFJZxTfD6Ps4Ne+mmjhdM5ZcYXgz7KzmwvO+P2spfmvZZDR7K2NegeSnQeumSCnoPZvGZXLS6dvKY03jT5iZRSP/B9sv+ucytZ1Gzq3ACD3RPnBrvnRwetPLi4aTG9xSI9xVGKKdFTHKW3WOTipvwFK+XPPgZpnrSu0Uw9+xisUkVHtmlpPe86cxGdi4Kd/YnORcG7zlzkfvMj6Grp4dzFi2iJenqHoSXqOXfxIrpaDm8F2j3wEHWFJuoKpfZ9hWbqCk10D0xx17gyLS8s4UWFs7ik8GJeVDhrVrq09NJPXzG4e7iP2w72cvdwH33FmD972TVnXnNiPd0Hszf3xZToGkp0H8zmNbtq8W907D3bdO0Sx+YPVqCWOXHGz2Z7zCFbMR/shsEuOO9Xq1vXfHHaoibeungJNw0eYNfoCGvq6nltSwenHUMrNGkpTfQz/OMVc4ABRlhKfv//2bTU1onl2MMQGxoXcWLToRXBlBJ7OPzeAsMj3dTXTfwNUSGaGB7pPuzYPBgZXcQDQ3tZ1N9Py8gQB+sbeaClhU2Ny2ozEahiNi2r5zc3M6Fby1ueZ7eWuVCLf6O3AO8EroqIT6WUdo49ERGXAecDg8B3qlTfjK0+G172P7I95l1PZSvm5/1qvveb73wA7rsW9u+EJWvhrMth7ZnVrmp6py1qMozruJzDcm5gB5CtmA8wQj+jvBT3uc4Xy2mkjxFax/0T2c8oy8e1QBzTUN/BaHGAuhjXvi8N0jCp33Ze7OxOLIouGhJQV09DOghDB9k5uBRsma2j2LTMMF4Jtfg3/Hngm8BPAQ9GxBfJLgg9g2zLSwB/kFLaW70SZ2712fkO4+PtfAC+9Ulo7oDONdDfnT2+6DfyHdCl47GusJhXFNdxD3vYxyBLaeKlrMnlxaA6Pi8qLOFrxV2Qxt3whREuKhz+C9uO5tPZ3ftdIFsxL6ZBRouDLG3N587Kvfu6WFqsY7SzQGooEsMNNO8usrfQZTiXcqLmwnlKqRgRlwO/BbwZ+Fmy5tT7gGuBj6WUvlHFEivukZ1FvnV3kWf2w+olcNELCpy6tnKXE9x3bRbMW0oLRWPjfdcazjX7dtHDAzxLF4N00sSZrKr4hafrCotzefGnZseGulZezRp+UNzPHoZYTiMXFVawoe7wbi3NjatYwUsndGtZ2nrujC4GnUurevfT29JO295DbSF7CwVW9e6vYlWzaNcjsO1m2F+66+fmC2FNfm809NiTRW77QeK5PbByOZz/Iu/wrRoM5wAppWHgI6WPBe2RnUX+7YZR2pphZSf09sO/3TDKL7yCigX0/TuzFfPxmtuyeWk27aKHW3mCZurpoJEBhrmVJ7iAk+ZlZxhVz4a61inD+FSaG1cdcxh/crSf24v72Z0OsiIW8eLCEk6s4I1lXj40zOdaEhTqaC2O0leo4wBFLh+aB3e62/UI3Pzv0LwYOlfAQG/2+MKfy2VAf+zJIp+/NtHWkli+DA70weevhTdcXjSgL3Ce/Rr3rbuLtDVDW0t2U4C2lqCtOZuvlCVrs5+B4w30ZvPSbHqAZ2mmnmYaCIJmGmimngd4ttqlSUf15Gg/Xx55hr40wnIa6EsjfHnkGZ4crVynlI0nb+FNT/2QtqF+djc00DbUz5ue+iEbT95SsRrmzLabs2De3AZRyMbmxdl8Dt32gyyYL27N/v1e3Bq0tSRu+4F9wxe6mlw51yHP7M9WzMdrbc7mK+Wsy7M95pD9LBzohYFuePHPV66GvHhw4CDXdQ/w9PAIJzTUc1lHM2c0Lzr6C3VMuhikY9JFeU3U05XTNobSeLcX97M46miN7J/esQtOby/ur9zq+epT2MhlbHzoFnjiQehYCWdfBqtndnOjXNj/bLZiPl5TazafQ8/tgeWT9vm3tGTz88FDz41y/SOjPN2TOKE9eNWpdZy+sq7aZdUEw3mNW70k28rSNu7net9ANl8pa8/MLv4c363lxT+/8PabPzhwkKt399JeV2B1fR3do0Wu3t3LVSvaDOizpJMmBhimeVwbw0FG6MxxG0NpzO50kOVMvPlTC3XsThXu/Lv6lPkRxidbsipbHWpuOzQ32JfN59DK5dlWlsXjdk/192fzte6h50b5+63DtDcFq9ugezDx91uHeccWDOjHwHBe4y56QYF/u2EUSLQ2Z8G8dwCueFlldyytPXPhhfHJruseoL2uQEdd9nffURc/njecz44zWcWtPAFkK+aDjDDACC9k3ZFfKM2h7tG9PD36OAOpl+Zo44S6k+moO7z1yYpYRF866zvpHwAAIABJREFUvEXjipj5z4e9e+/jyQP30hcDtKZmTlx8NsuWnTXjr8veJ+Dx70HvbmhbASe/BJadNPOvOxc2X5jtMYdsxXywDwYOwIteXd26pnH+i4LPXwuQaGnJgnlvf3DpRbV/x83rHxmlvSnoKN0roKPp0Lzh/Ojcc17jTl1b4BdeUUdbS/BcV7bn/BdeUVfRbi3KPD08Qlth4g/VtkLw9PBIlSqaf9bQzgWcRDMNdDNEMw1eDKqq6h7dyyPD93AwDdHEYg6mIR4Zvofu0cO7+b64sIQDaZS+NEJKib40woE0yotneOfPvXvvY1v/7QxxkJbUxBAH2dZ/O3v33jejr8veJ+CeL8HQAVi8LBvv+VI2X0nPPQ7f+Ve4/sPZ+NzjUx+35tTs4s/mNujanY1VuBh0tHcHw49/lYMPfprhx7/KaO+OKY875cQCb7g822u+Z282vuHy+dGt5emeRNuk2wK0NWbzOjpXzueBU9fOTevER3YW+dY941o0nlPZFo215oSGerpHiz9eMQfoLSZOaPDbbDatod0wrtx4evRx6qORRZElkUWlayKeHn38sNXzE+tauJLVE7q1XFK3Ysb7zZ88cC+LqKOxtALfyCIoHuTJA/fObPX88e9BYys0ltqGjo2Pf69yq+fPPQ5bvwBNi6FtOQweyB5veT2sPPnw49ecWtXOLKO9OxjZ/k2ivgUal5CG+xnZ/k1Y/1PUtR3+G75TTixwyolzUMhzj8HDt0HPc9C+Ep53Pqys3FamE9qD7sH04xVzgN6hbF5HZ2rQlB7ZWeSaG0dpH9ei8ZobR3nLJZVr0VhrLuto5urdWduatkLQW0z0jBZ589Jja8cmqfYMpF6aJvW8b2ARA6l3yuNPrGuZ9Ys/+2KAljTxuotFNNAXAzP7wr27sxXzCV+4JZuf6vDh3ewZfoShYg+NhXaWN5xKW8PhN24qyyO3ZcG8qfR3PDY+ctvU4bzKinvuJupbiIbSOS6NxT13TxnO58Rzj8H3J72h+f4X4CdeX7GA/qpT6/j7rVl7zrbGLJj3DCZ+bvPMY+fQwC4GerYxOtxFXUMnze2baWyeX3doNmVpSt+6p0j7pBaN7c3ZvKZ2RvMirlrRRkddgWdGRumoK3gxqDTPNUcbw0y8oHOYgzRH2zSvmH2tqZmDTOxTfpBhWlPzzL5w2wo4OKnN48H+bH6S3uHd7Bi8g+HiIIuijeHiIDsG76B3eOogf8x6noPGSW9mGluy+RxKg/ugftLfe31zNl8pD497QxOFQ58/fFvFSjh9ZR3v2NJAR1PwTC90NAXv2NIw4/3mQwO76N1zM8XRAQr1HRRHB+jdczNDA7tmqfJ8cOVcU8pDi8ZadEbzIsO4tICcUHcyjwzfA2Qr5sMcZCQNcVL96RWr4cTFZ7Ot/3YoHmQRDRxkmIOFUZ7XcvbMvvDJL8n2mEO2Yn6wH4b64PSfOuzQPcOPUBeNNBSyFfyGaIJiNj+j1fP2ldnKb9O4304M9WfzORRNS0nD/T9eMQdgZIBoWlq5Inqey1bMx6vCG5rTV85+68SBnm0U6pop1GVvgKI0DvRsm1er566ca0qrl2SdX8ardItGScq7jrplnNpwDouikUEOsCgaObXhnCm7tcyVZcvOYnPLi2lkEf0xSCOL2Nzy4pl3a1l2Epzz2myv+YG92XjOa6fcbz5U7KE+Jl4BWB+NDBV7ZlbDqedn4XzwAKTioc9PPX9mX3eOFJa/gDTSTxruJ6WUjSP9FJa/oHJFtK/M3sCMl+M3NOUYHe4iChO3cEWhidHhripVNDdcOdeULjqnwDU3TmzR2DMAV7zU93OSNF5H3bKKhvGpLFt21uy0TjzsC590TBd/NhbaGS4OZivmJSNpiMbCDC/eXnlydvHnI+Mubtx8aS73mwPZvvL1P0Vxz92kwX1E01Lq1ryscvvNIbv48/tfyD5vbMmC+eABOPvSytUwR+oaOimODvx4xRwgFQepa+g8wqtqT6S0cNvabNmyJW3durXaZeSW3VokScdibM95XTRSH42MpCFG0xDrml4484tCVb4qd2uZK2N7zgt1zUShiVQcpDg6QNvyC3O/rSUi7kgpbTmmYw3nhnNJkmZqTrq1SJPUareWcsK521okSdKMtTWsMIxrzjU2r6mJMD4T7lGQJEmScsJwLkmSJOWE4VySJEnKCcO5JEmSlBOGc0mSJCknDOeSJElSThjOJUmSpJwwnEuSJEk5YTiXJEmScsJwLkmSJOWE4VySJEnKCcO5JEmSlBOGc0mSJCknDOeSJElSThjOJUmSpJwwnEuSJEk5YTiXJEmScsJwLkmSJOWE4VySJEnKCcO5JEmSlBOGc0mSJCknDOeSJElSThjOJUmSpJwwnEuSJEk5YTiXJEmScsJwLkmSJOWE4VySJEnKCcO5JEmSlBOGc0mSJCkn6qtdgCrrmXvhof+E7qegYwOc/jpYfXa1q5IkSRIYzheUZ+6F734ImpZA+zoY2J89ful7pw7oj+4ocvOdiWf3JVYtDS48L9i4zl+2SJIkzRWT1gLy0H9mwbx5CUQhG5uWZPOTPbqjyGe/UaS3P7FiCfT2Jz77jSKP7ihWvnBJkqQFwpXzeeDR7UVuuTPx7N7EqmXBy88LNq4//H1X91PZivl4TR3Z/GQ335loa4G2lgCgrQUgcfOdiY3rDj9ekiRJM+fKeY17dHuRz329SG9fYsVS6O1LfO7rRR7dfvgKd8cGGOyeODfYnc1P9uy+RGvzxLnW5mxekiRJc8NwXuNuuTOxuAXaWoNCBG2tweKWbH6y018Hg/uzveapmI2D+7P5yVYtDfoGJs71DWTzkiRJmhuG8xr37N5Ea8vEudaWbH6y1WdnF382L4GeHdk43cWgF54X9PZne82LKdHbn+jtz+YlSZI0N9xzXuNWLQt6+xJtrYfm+vqz+amsPvvYWiduXFfgza9kQreWV18wO91abOcoSZI0NcN5jXv5ecHnvp6AbAW9rx8O9MPlL5/5CvfGdYVZv/iz3HaOkiRJC4nhvIKOtatKOTauL/CmS5nwdS9/+cy/7lwZ384RDo0P/afhXJIkyXBeIWNdVRa3MK6rSuJNlzIrAX3j+lkqdI6V085RkiRpocnn8uo8VE5XlfmsnHaOkiRJC40r5xXy7N6sD/l403VVyYunH4D7vgb7d8CSdXDWq+GEM2f2NU9/XbbHHLIV88HurJ3jub8283olSUf32MgAtw1381waZmU0cH5DB6fUNx/9hZIqwpXzClm1LOjrnzh3pK4qc+Xp++Ebfw7//q5sfPr+aY57AL71Cejvgs4TsvFbn8jmZ6Kcdo6SpNn12MgAXxjazYE0ynLqOZBG+cLQbh4bGTj6iyVVhCvnFTKXXVWO1dP3w7f/Flo6ssA90JU9/snfghM2TTz2vq9Bcwe0dGaPx8b7vjbz1fNjbecoSZpdtw13szjqWBx1ACym7sfzrp5L+eDKeYVkXVUKtLUGu/dle8/fdGmhol1Vtn01C+bNnRCFbGzpyOYn278DmtsnzjW3Z/OSpNr0XBqmZdI//S0UeC4NV6kiSZPV5Mp5RPwy8E9HOayYUqqrQDnHrNpdVfbvyFbMx2uaJnAvWZdtZRlbMQcY6MnmJUm1aWU0cCCN/njFHKCfIiujoYpVSRqvJsM5cDfwJ9M893LgEuC6ypVTG5asy7ayNI8L3IPTBO6zXp3tMYdsxXygBwa64cVvqUytkqTZd35DB18Y2g1kK+b9FDmQRrl00dKjvFJSpdRkOE8p3U0W0A8TEd8tfXp15SqqDZtfk+0xh2zFfLAH+rvhRW89/NgTzoSLfnNit5YXv2Xm+80lSdVzSn0zr2fFhG4tly5a6n5zKUcipfy28itXRJwF3AvsBE5MKY0e6fgtW7akrVu3VqS2vHj6/myP+Vjg3vyawy8GlSRJ0uyJiDtSSluO5diaXDk/gqtK4z8eLZgvVCdsMoxLkjSfPTYywK3DPTxbHGZVoYELGtr97UgNmTfdWiKiGXgrMAr8wxGOuyoitkbE1t27d1esPkmSpLn22MgA/zG0h97iKCuint7iKP8xtMde9jVk3oRz4E1AJ3B9Smn7dAellK5OKW1JKW1ZsWJF5aqTJEmaY7cO97CYOtoKdRQiaCvUsZg6bh3uqXZpOkbzKZyPbWn5VFWrkCRJqpJni8O0xsR41xoFni3ay75WzItwHhGbgJcBO4Brq1yOJElSVawqNNCXihPm+lKRVQV72deKeRHO8UJQSZIkLmho5wCj9BZHKaZEb3GUA4xyQUP70V+sXKj5bi0R0QS8jexC0H+scjmz5tEdRW6+M/HsvsSqpcGF5wUb182X91KSJGkunFLfzBtZPqFby2UNS+zWUkNqPpwDbwSWAF890oWgteTRHUU++40ibS2wYgn09ic++43Em1+JAV2SJB3RKfXNhvEaNh+S3tiWlnlzR9Cb70y0tUBbS2RXWrcEbS3ZvCRJkuavmg7nEXEGcAHz7ELQZ/clWie94W1tzuYlSZI0f9X0tpaU0oNAVLuO2bZqadDbn62ej+kbyOZn6tHtRW65M/Hs3sSqZcHLzws2rp/5e7Rn7oMH/gu6noLODXDmz8Dqs2b8ZSVJkhaUml45n68uPC/o7c/2mhdTorc/0dufzc/Eo9uLfO7rRXr7EiuWQm9f4nNfL/Lo9uLRX3wEz9wHt/01DOyHjnXZeNtfZ/OSJEk6dobzHNq4rsCbX1mgrSXYvT/be/7mVxZmfDHoLXcmFrdAW2tpL3trsLglm5+JB/4LmjqheQlEIRubOrN5SZIkHbua3tYyn21cV2Djutn9ms/uzVbMx2ttyeZnouupbMV8vKaObF6SJEnHzpXzBWTVsqCvf+JcX382PxOdG2Cwe+LcYHc2L0mSpGNnOF9AXn5ecKA/22teTInevsSB/mx+Js78GRjsyvaap2I2DnZl85IkSTp2hvMFZOP6Am+6tEBba7B7X7b3/E2XFmbcrWX1WXD+72R7zbt3ZOP5v2O3FkmSpHK553yB2bi+wMb1s/91V59lGJckSZopV84lSZKknDCcS5IkSTlhOJckSZJywnAuSZIk5YThXJIkScoJw7kkSZKUE4ZzSZIkKScM55IkSVJOzMpNiCLidOAyoB/4bEqpeza+riRJkrSQlLVyHhF/HBG7ImLpuLmfAu4CPgR8ArgzIpbNbpmSJEnS/FfutpbLgIdSSvvGzf0ZkID3A38HnAy8Z3bKkyRJkhaOcsP5ScCDYw8iYi3wQuATKaX/L6X0TuBG4GdmrUJJkiRpgSg3nC8Bxq+an0+2av7VcXN3ABtmWJckSZK04JQbzncDa8c9vhgYBm4fN7foOL6uJEmStOCV263lbuDKiNgMDAI/B9yaUhoYd8xJwK7ZKU+SJElaOMpd4f4LoAO4B/hh6fO/GnsyIurItrpsna0CJUmSpIWirJXzlNItEfEa4B1ke82vSSldN+6QlwE7gS/OXomSJEnSwlD2TYhSStcD10/z3C3AuTMtSpIkSVqIvHBTkiRJyomyV84BImIlsIWstWLdVMeklD49g7okSZKkBaescB4RDcAngV9k+lX3INuPbjiXJEmSylDuyvn/Bn4FeBS4BtgOjMx2UZIkSdJCVG44/wXgYeDcSb3NJUmSJM1QuReErgSuNZhLkiRJs6/ccP4U0D4XhUiSJEkLXbnh/J+ByyKiYw5qkSRJkha0csP5B4FbgW9GxMUR4Sq6JEmSNEuOeEFoRBTJ2iIe9hTwzdIxU700pZSOq4e6JEmStFAdLUDfzNThXJIkSdIsO2I4TyldVKE6JEmSpAWv3D3nkiRJkuZIWeE8IpojYkNELJrm+cbS802zU54kSZK0cJS7cv7HwA+BxdM83wo8BLxvJkVJkiRJC1G54fwy4JsppX1TPVma/ybwmpkWJkmSJC005Ybzk4CHj3LMw6XjJEmSJJWh3HDeABSPckwC3HMuSZIklanccP4Y8JNHOeYi4MnjqkaSJElawMoN518GXhgRvzfVkxHxB8B5wH/NtDBJkiRpoTnaHUIn+xDwFuDPIuJNwDeAncBa4FLgBcBTwF/MZpGSJEnSQlBWOE8p7Y+Ii4B/A15CtkqegCgd8h3grSml/bNZpCRJkrQQlLtyTkrpCeBlEXEeWUDvBLqA76WU7pzd8iRJkqSFo+xwPqYUxA3jkiRJ0iw57nAeEQ3A6WQr593Agyml4dkqTJIkSVpoyu3WQkS0R8Qnybay3A18C7gL6IqIT0ZE5+yWKEmSJC0MZa2cR0Q7cBuwCegFbgF2AWvIOrVcBVwQES9LKfXMcq2SJEnSvFbuyvkfkgXzvwNOTCldlFL6+ZTSRcCJwN8CZ5aOkyRJklSGcsP568i6svxWSqlr/BMppe6U0ruA7wKvn60CJUmSpIWi3HB+Itke8yP5NrD+uKqRJEmSFrByw3kfsPIox6wA+o+vHEmSJGnhKjec/wB4Y0ScNtWTEbEReFPpOEmSJEllKLfP+V8C3wB+EBEfB24i69ayGrgIeBewGPjQLNYoSZIkLQhlrZynlG4AfhNoAt4H/DewDfgm8EdAK/DOlNI3Z7nOKUXEKyLiixHxTEQMRcTTEfH1iLi8En++JEmSNJvKvkNoSulTEXEd8DbgXKCD7A6hdwH/mlJ6cnZLnFpE/AXwP4EdwJeBPWT73V9Itop/bSXqkCRJkmZL2eEcIKX0FPCns1zLMYuId5AF838BrkopHZz0fENVCpMkSZJmoNwLQqsuIhrJ3hg8xRTBHCClNFzxwiRJkqQZOq5wHhFviYgbImJfRIyUxhsi4i2zXeAUfpps+8p/AsWIeHVE/H5EvCciXlqBP1+SJEmaE2VtayltF/k88BoggFFgN7AcuBi4KCLeBLxhDlevX1QaB8n2uW+eVOPNpT9/91QvjoirgKsANmzYMEclSpIkSeUrd+X8D4ErgNvJwnhTSmkNWfeWS4DvkwX335/NIicZuwnS/wQS8HKgDTibrM3jhcB/TPfilNLVKaUtKaUtK1asmMMyJUmSpPKUG85/EXgEuCil9O2U0ihASmk0pfQtsi4pjwG/PIs1TjZW8whwZUrp1pTSgZTSfcDPknVv+Um3uEiSJKnWlBvO1wFfmuoiTICU0hDwJWDtTAs7gq7SeFdK6YlJf34/8PXSw5+YwxokSZKkWVduOH8aOFqbwobScXPlh6Wxa5rn95fG5jmsQZIkSZp15YbzfwPeEBHtUz0ZEZ3AG4BrZlrYEdxAttf8zIiYqv6xC0Qfn8MaJEmSpFlXbjj/X8BW4PsR8QsRsS4iGkrjW4DvkV0U+r9nu9AxpTuQfgXYALxn/HMR8UrgUrJV9evnqgZJkiRpLpR7h9CB0hjAZ6Z4PoDTgMGIGD+fUkrHdTfSafwWcC7w4Yh4NVlLxZOBnyFr7/j2lFL3LP55kiRJ0pwrNzDfQralpKpSSjsi4oXAHwNXkrVP7CFbUf+zlNL3q1mfJEmSdDzKCucppYvmqI6ylW4y9K7ShyRJklTzjnurSUS0As8DFqeUbpm9kiRJkqSFqdwLQild/PkFspaFW4Gbxj13QUQ8EBEXzV6JkiRJ0sJQVjiPiDXA7cBrga8C3yW7CHTM7cBK4Odmq0BJkiRpoSh35fz9ZOH7p1NKrwP+e/yTKaVhsotGz5+d8iRJkqSFo9w955cDX04p3XSEY54CXn78JalcOx+Ee66D/TthyVo45zJYe8bUx+7aBvd/Gbp2QOc62HQlrNk89bGSJEmqrHJXzlcBPzrKMcNA6/GVo3LtfBBu+hQMdMOSNdl406ey+cl2bYNbPgYDXdBxQjbe8rFsXpIkSdVXbjjfB6w/yjHPA545vnJUrnuug+YOaOmAKGRjc0c2P9n9X4bmzuwjCoc+v//Lla9bkiRJhys3nN8GXBkRq6d6MiJOA17FuA4umlv7d0Jz28S55rZsfrKuHdDUPnGuqT2blyRJUvWVG87/EmgCvh0RlwEtkPU8Lz3+ClAE/mpWq9S0lqyFgd6JcwO92fxknetgsGfi3GBPNi9JkqTqKyucp5RuB34dOImsleJ7S0/1lB6fDPxaSun+WaxRR3DOZdk+8/5uSMVsHOjO5ifbdGW2z3ygKzt27PNNV1a+bkmSJB2u7JsQpZT+L7AZ+BjwfeBR4E7gE8DZKaVrZrVCHdHaM+DiX8/2me/flY0X//rU3VrWbIaXvzvbZ979dDa+/N12a5EkScqLSClVu4aq2bJlS9q6dWu1y5AkSdI8FhF3pJS2HMuxZa+cS5IkSZobhnNJkiQpJwznkiRJUk4YziVJkqScMJxLkiRJOWE4lyRJknLCcC5JkiTlhOFckiRJygnDuSRJkpQThnNJkiQpJwznkiRJUk4YziVJkqScMJxLkiRJOWE4lyRJknLCcC5JkiTlhOFckiRJygnDuSRJkpQThnNJkiQpJwznkiRJUk4YziVJkqScMJxLkiRJOWE4lyRJknLCcC5JkiTlhOFckiRJygnDuSRJkpQThnNJkiQpJwznkiRJUk4YziVJkqScMJxLkiRJOWE4lyRJknLCcC5JkiTlhOFckiRJygnDuSRJkpQThnNJkiQpJwznkiRJUk4YziVJkqScMJxLkiRJOWE4lyRJknLCcC5JkiTlhOFckiRJygnDuSRJkpQThnNJkiQpJwznkiRJUk4YziVJkqScMJxLkiRJOWE4lyRJknKiZsN5RDwREWmaj2eqXZ8kSZJUrvpqFzBD3cBHppg/UOlCJEmSpJmq9XDelVL6QLWLkCRJkmZDzW5rkSRJkuabWl85b4yItwIbgD7gXuDmlNJodcuSJEmSylfr4Xw18JlJc49HxK+klL5djYKOZNd98MCXoGs7dK6HM18La86qdlWSJEnKi1re1vJPwCvIAnorcBbwKeAk4LqIOGeqF0XEVRGxNSK27t69u1K1sus+uPWjMNAFHWuz8daPZvOSJEkS1HA4Tyn9SUrpxpTSsyml/pTStpTSbwAfBpqBD0zzuqtTSltSSltWrFhRsXof+BI0d2YfUTj0+QNfqlgJkiRJyrmaDedH8MnSeGFVq5ikazs0tU+ca2rP5iVJkiSYn+F8bK9Ka1WrmKRzPQz2TJwb7MnmJUmSJJif4fwlpfGxqlYxyZmvzfaZD3RBKh76/MzXVrsySZIk5UVNhvOIOCMiDlsZj4iTgL8pPfzXStZ0NGvOggvek+0z796ZjRe8x24tkiRJOqRWWyn+HPC7EXEz8CTQC2wEXg00AdcCH6peeVNbc5ZhXJIkSdOr1XB+E/B84FzgfLL95V3ArWR9zz+TUkrVK0+SJEkqX02G89INhnJ3kyFJkiRpJmpyz7kkSZI0HxnOJUmSpJwwnEuSJEk5YTiXJEmScsJwLkmSJOWE4VySJEnKCcO5JEmSlBOGc0mSJCknDOeSJElSThjOJUmSpJwwnEuSJEk5YTiXJEmScsJwLkmSJOWE4VySJEnKCcO5JEmSlBOGc0mSJCknDOeSJElSThjOJUmSpJwwnEuSJEk5YTiXJEmScsJwLkmSJOWE4VySJEnKCcO5JEmSlBOGc0mSJCknDOeSJElSThjOJUmSpJwwnEuSJEk5YTiXJEmScsJwLkmSJOWE4VySJEnKCcO5JEmSlBOGc0mSJCknDOeSJElSThjOJUmSpJwwnEuSJEk5YTiXJEmScsJwLkmSJOWE4VySJEnKCcO5JEmSlBOGc0mSJCknDOeSJElSThjOJUmSpJwwnEuSJEk5YTiXJEmScsJwLkmSJOWE4VySJEnKCcO5JEmSlBOGc0mSJCknDOeSJElSThjOJUmSpJwwnEuSJEk5YTiXJEmScsJwLkmSJOWE4VySJEnKCcO5JEmSlBOGc0mSJCknDOeSJElSThjOJUmSpJyYN+E8It4aEan08fZq1yNJkiSVa16E84hYD/wNcKDatUiSJEnHq+bDeUQE8E/AXuCTVS5HkiRJOm41H86BdwOXAL8C9FW5FkmSJOm41XQ4j4gzgA8CH00p3VzteiRJkqSZqNlwHhH1wGeAp4D3VbkcSZIkacbqq13ADPwxcC5wQUpp4FhfFBFXAVcBbNiwYY5KkyRJkspXkyvnEfFistXyv0opfbec16aUrk4pbUkpbVmxYsXcFChJkiQdh5oL56XtLJ8GHgb+qMrlSJIkSbOm5sI5sBh4HnAGMDjuxkMJeH/pmL8vzX2kalVKkiRJZarFPedDwD9O89x5ZPvQbwV+CJS15UWSJEmqppoL56WLP98+1XMR8QGycP4vKaV/qGRdkiRJ0kzV4rYWSZIkaV4ynEuSJEk5Ma/CeUrpAymlcEuLJEmSatG8CueSJElSLTOcS5IkSTlhOJckSZJywnAuSZIk5YThXJIkScoJw7kkSZKUE4ZzSZIkKScM55IkSVJOGM4lSZKknDCcS5IkSTlhOJckSZJywnAuSZIk5YThXJIkScoJw7kkSZKUE4ZzSZIkKScM55IkSVJOGM4lSZKknDCcS5IkSTlhOJckSZJywnAuSZIk5YThXJIkScoJw7kkSZKUE4ZzSZIkKScM55IkSVJOGM4lSZKknDCcS5IkSTlhOJckSZJywnAuSZIk5YThXJIkScoJw7kkSZKUE4ZzSZIkKScM55IkSVJOGM4lSZKknDCcS5IkSTlhOJckSZJywnAuSZIk5YThXJIkScoJw7kkSZKUE4ZzSZIkKScM55IkSVJOGM4lSZKknDCcS5IkSTlhOJckSZJywnAuSZIk5YThXJIkScqJ+moXsJBs/1Fi642wbxcsXQNbLoH1p0W1y5IkSVJOuHJeIdt/lLj+M9DfA0tXZeP1n8nmJUmSJDCcV8zWG6GlDVrbIQrZ2NKWzUuSJElgOK+YfbugZfHEuZbF2bwkSZIE7jmvmKVrYPdjcGAHDHZDUwcsXgcrTql2ZZIkScoLV84r5MR18NQd0N8LjW3g+rpQAAAUnklEQVTZ+NQd2bwkSZIEhvOK2X9XcPrGbJ95/2A2nr4xm5ckSZLAbS0V07UdVq8N1ox7O5SK2bwkSZIErpxXTOd6GOyZODfYk81LkiRJYDivmDNfCwNd2UcqHvr8zNdWuzJJkiTlheG8QtacBRe8B5o7oXtnNl7wnmxekiRJAvecV9Sas449jG//UWLrjVkf9KVrYMslsP40Lx6VJEmaz1w5z6HtP0pc/xno74Glq7Lx+s9k85IkSZq/ajacR8SfR8QNEbE9IgYiYl9E3BUR74+IZdWubya23pi1WmxthyhkY0tbNi9JkqT5q2bDOfA7QCvw38BHgWuAEeADwL0RUbN9UPbtgpbFE+daFmfzkiRJmr9qec95e0ppcPJkRPwp8D7gD4HfrHhVs2DpmmwrS2v7obn+A9m8JEmS5q+aXTmfKpiXfK40nlapWmbblkugvxf6erK2i3092eMtl1S7MkmSJM2lmg3nR3BFaby3qlXMwPrTgle9DVraYd+z2fiqt9mtRZIkab6r5W0tAETEe4HFQAewBbiALJh/sJp1zdT604L1Nbv2L0mSpONR8+EceC+watzj64FfTintnurgiLgKuApgw4YNc1/dcXrqR4k7boI9z8Dy1fDCi2GDK+eSJEnzWs1va0kprU4pBbAaeB1wCnBXRJw3zfFXp5S2pJS2rFixopKlHrOnfpS47hro64VlK7PxumuyeUmSJM1fNR/Ox6SUnk0pfRF4JbAM+HSVSzpud9wErW3ZRxQOfX7HTdWuTJIkSXNp3oTzMSmlJ4EHgE0Rsbza9RyPPc9AS+vEuZbWbF6SJEnz13zYcz6VE0rjaFWrOE7LV2dbWVrbDs3192XzU9n+w8RdN8Dep2HZCXDuK2D9892fLkmSVGtqcuU8Ip4XER1TzBdKNyFaCXwnpbS/8tXN3AsvzsJ5X2+pz3np8xdefPix23+Y+Ma/ZDctWro6G7/xL9m8JEmSakutrpxfDvxZRNwKPA7sJevY8pNkF4Q+A7yjeuXNzIbTgsveMrFby4VXTt2t5a4bsjuJtpTuJjo23nUDrH9+BYuWJEnSjNVqOP8mcCpZT/NzgU6gD3gY+AzwsZTSvuqVN3MbTgs2HEOf871PZyvm4zUvzuYlSZJUW2oynKeUtgHvrHYdebDshGwry9iKOcDAgWxekiRJtaUm95zrkHNfAX09WUBPxWzs68nmJUmSVFsM5zVu/fODV/5StnK+75lsfOUv2a1FkiSpFtXktpZatet+2PYV2L8dlqyHzVfAmk0z/7rrnx9e/ClJkjQPGM4rZNf9cPPHobkTOtfCQFf2+MJ3TR3Q5yrIS5IkKb/c1lIh276SBfPmTojCoc+3feXwY8eC/EDXxCC/6/7K1y1JkqTKMZxXyP7t0NQ+ca6pPZufrJwgL0mSpPnDcF4hS9bDYM/EucGebH6ycoK8JEmS5g/DeYVsviLbnjLQlbU8HPt88xWHH1tOkJckSdL8YTivkDWbsos/mzuha2c2TncxaDlBXpIkSfOH3VoqaM2mY+u4Mhbkx3dredHb7NYiSZI03xnOc+pYg7wkSZLmD7e1SJIkSTlhOJckSZJywnAuSZIk5YThXJIkScoJw7kkSZKUE4ZzSZIkKScM55IkSVJOGM4lSZKknDCcS5IkSTlhOJckSZJywnAuSZIk5YThXJIkScoJw7kkSZKUE4ZzSZIkKScM55IkSVJOGM4lSZKknDCcS5IkSTlhOJckSZJywnAuSZIk5YThXJIkScoJw7kkSZKUE4ZzSZIkKScM55IkSVJOGM4lSZKknDCcS5IkSTlhOJckSZJywnAuSZIk5YThXJIkScoJw7kkSZKUE4ZzSZIkKScM55IkSVJOGM4lSZKknDCcS5IkSTlhOJckSZJywnAuSZIk5UR9tQtYSHbdD9u+Avu3w5L1sPkKWLOp2lVJkiQpL1w5r5Bd98PNH4eBLuhcm403fzyblyRJksBwXjHbvgLNndlHFA59vu0r1a5MkiRJeWE4r5D926GpfeJcU3s2L0mSJIHhvGKWrIfBnolzgz3ZvCRJkgSG84rZfEW2z3ygC1Lx0Oebr6h2ZZIkScoLw3mFrNkEF74r22fetTMbL3yX3VokSZJ0iK0UK2jNJsO4JEmSpufKuSRJkpQThnNJkiQpJwznkiRJUk4YziVJkqScqMlwHhHLIuLtEfHFiHgkIgYiojsibo2IX4uImvzvkiRJ0sJWq91a3gj8HbALuAl4ClgFvA74B+CyiHhjSilVr0RJkiSpPLUazh8GrgS+llIqjk1GxPuA7wOvJwvqX6hOeZIkSVL5anL7R0rpxpTSV8YH89L8M8AnSw8vqnhhkiRJ0gzUZDg/iuHSOFLVKiRJkqQyzatwHhH1wC+WHl5fzVokSZKkcs2rcA58ENgMXJtS+vpUB0TEVRGxNSK27t69u7LVSZIkSUcwb8J5RLwb+F3gIeBt0x2XUro6pbQlpbRlxYoVFatPkiRJOpp5Ec4j4p3AR4EHgItTSvuqXJIkSZJUtpoP5xHx28DHgW1kwfyZKpckSZIkHZeaDucR8fvAXwN3kwXz56pckiRJknTcajacR8QfkV0AegfwipTSniqXJEmSJM1ITd4hNCJ+CfhfwChwC/DuiJh82BMppX+ucGmSJEnScavJcA6cXBrrgN+e5phvA/9ckWokSZKkWVCT21pSSh9IKcVRPi6qdp2SJElSOWoynEuSJEnzkeFckiRJygnDuSRJkpQThnNJkiQpJwznkiRJUk4YziVJkqScMJxLkiRJOWE4lyRJknIiUkrVrqFqImI38GQV/ujlwJ4q/LmaGc9b7fLc1SbPW23yvNUmz9vcOjGltOJYDlzQ4bxaImJrSmlLtetQeTxvtctzV5s8b7XJ81abPG/54bYWSZIkKScM55IkSVJOGM6r4+pqF6Dj4nmrXZ672uR5q02et9rkecsJ95xLkiRJOeHKuSRJkpQThnNJkiQpJwznkiRJUk4YziskItZFxP+NiKcjYiginoiIj0TEkmrXttBFxBsi4uMRcUtE9EREioh/PcprXhYR10bEvogYiIh7I+K3I6KuUnUvZBGxLCLeHhFfjIhHSuegOyJujYhfi4gpf7Z53qovIv48Im6IiO2lc7AvIu6KiPdHxLJpXuN5y6GIeGvp52WKiLdPc8xrIuJbpe/PAxFxe0T8UqVrXchKeSNN8/HMNK/xe66KvCC0AiJiI/AdYCXwJeAh4CeAi4EfAuenlPZWr8KFLSLuBs4BDgA7gNOBa1JKb53m+NcCXwAGgX8H9gFXAM8HPp9SemMl6l7IIuI3gL8DdgE3AU8Bq4DXAR1k5+eNadwPOM9bPkTEQeBO4AHgOaAVeAmwBXgaeElKafu44z1vORQR64H7gDpgMfCOlNI/TDrmncDHgb1k5+4g8AZgHfBXKaX3VrToBSoingA6gY9M8fSBlNKHJh3v91y1pZT8mOMP4OtAAt41af7DpflPVrvGhfxB9ibpNCCAi0rn5F+nObadLFAMAVvGzTeRvQFLwJur/d803z+AS8j+sShMml9NFtQT8HrPW/4+gKZp5v+0dB4+4XnL90fpZ+U3gUeBvyydh7dPOuYksnC3Fzhp3PwS4JHSa15a7f+WhfABPAE8cYzH+j2Xgw+3tcyx0qr5K8m+Of520tPvB/qAt0VEa4VLU0lK6aaU0o9S6SfQUbwBWAF8NqW0ddzXGAT+39LD/2cOytQ4KaUbU0pfSSkVJ80/A3yy9PCicU953nKi9Hc+lc+VxtPGzXne8undZG+Qf4Xs37Cp/CrQCPxNSumJscmU0n7g/5Qe/sYc1qjj4/dcDhjO597FpfEbUwSJXuA2oIXs17rKv0tK4/VTPHcz0A+8LCIaK1eSJhkujSPj5jxv+XdFabx33JznLWci4gzgg8BHU0o3H+HQI5276yYdo7nXWLpG4H0R8Z6IuHia/eN+z+VAfbULWACeXxofnub5H5GtrD8PuKEiFWkmpj2fKaWRiHgc2AScAjxYycIEEVEP/GLp4fh/XDxvORMR7yXbq9xBtt/8ArJg/sFxh3necqT0/fUZsq1j7zvK4Uc6d7siog9YFxEtKaX+2a1UU1hNdu7GezwifiWl9O1xc37P5YDhfO51lMbuaZ4fm++sQC2aOc9nvn0Q2Axcm1L6+rh5z1v+vJfsIt4x1wO/nFLaPW7O85YvfwycC1yQUho4yrHHcu5aS8cZzufWPwG3APcDvWTB+p3AVcB1EfHSlNI9pWP9nssBt7VImhci4t3A75J1Q3pblcvRUaSUVqeUgmxF73VkgeGuiDivupVpKhHxYrLV8r9KKX232vXo2KWU/qR0nc6zKaX+lNK2lNJvkDWlaAY+UN0KNZnhfO6NvcvsmOb5sfmuCtSimfN85lCpZdtHydrzXZxS2jfpEM9bTpUCwxfJtvctAz497mnPWw6UtrN8mmyrwx8d48uO9dxNt0KruTd28fyF4+b8nssBw/nc+2FpfN40z491JphuT7ryZdrzWfoH7GSyCxEfq2RRC1lE/DZZL+VtZMF8qptqeN5yLqX0JNmbq00Rsbw07XnLh8Vk5+AMYHD8TWzIuo4B/H1pbqyX9pHO3RqyLS073G9eVWNbyMZ3i/N7LgcM53PvptL4ysl3LYyINuB8sv1236t0YTouN5bGV03x3IVknXe+k1IaqlxJC1dE/D7w18DdZMH8uWkO9bzVhhNK42hp9LzlwxDwj9N83FU65tbS47EtL0c6d5dNOkbVMdYlbnzQ9nvu/2/v/mLlKuoAjn9/2EAikRYw/EkpXASjDzxY0kisxl6DWgURMFojmtAqRn3gBQ0qBC1oQiI+YfBP0pBLGhOqVOSBGkzA2lKMpEGUSBoUuNUEUEH+GMVU6s+HmRtOlrO3t7t3d8+1308yOd05c+acnXO3+8vsnJkumPRE60dCwkWIlkxiYYsQ/Q0XaJh4ovy8nsBe4IRDlPW+dSBReuOWt+QfxauLEO3xvi2dRBmv3LYI0Zm4CNHEE+XXjmNb8qcos8UlcE0j389cB1LURtcI1YWIHgBOAu6iTD90HmUO9MeAtZn53OSu8MgWEZcAl9SXpwDrKT0Ju2ves9lYZrqWv4PyxXM7ZWnjD1OXNgY2pB+skYqIy4EZSg/rd2gftzqbmTONY7xvE1aHIN1I6WV9khK4nQysozwQ+gxwfmY+2jjG+9ZhEbGZMrTls5m5pWfflcDNlPu8DThAWeTmNMqDpV9CI1Xvzxcpc5Tvp8zWchZwISXg3gFcmpkHGsf4mZswg/MxiYhVwA2Un4pOBJ4G7gSuz7Jimiak8eXSz/7MnOo55p3AtcA7KP/B/RG4Fbg5Mw++pgYtqgXcM4BfZuZ0z3HetwmKiHMoq0K+ixKgraCsMPkYcDflPvQ+zOt967D5gvO6/yLKtJnnUn4heZSyauht47zOI1VErKN85lZTOp+OpTzM+TBl3vOtbYG2n7nJMjiXJEmSOsIHQiVJkqSOMDiXJEmSOsLgXJIkSeoIg3NJkiSpIwzOJUmSpI4wOJckSZI6wuBckiRJ6giDc0nSgkTETERkREyN+DyzETE7ynNIUlcZnEuSxioidkaEK+BJUotlk74ASZJ6nD/pC5CkSTE4lyR1SmY+PulrkKRJcViLJI1YREzVsdozEfHWiPhpRPw9Iv4ZEfdHxPtbjjkmIr4SEY9ExL8i4qWI2B0RGxap/s31mOn56lvg+9sYEdsj4omIeLle656I+FRbvcC6+jobaWejXOuY8yHaZCoibo+IZyPi3xGxNyI+tJD3JknjZs+5JI3PmcCvgEeAHwCnAh8HfhYRl2XmNoCIOBq4hxLE7gNuAV4PfBTYFhFvy8xrBq1/BL4H/B7YBTwNnAhcAGyNiLdk5nW13AvA9cBG4Iz67zmz851giDY5A3gQeALYCpxAaZO7IuK9mfmLw32zkjRSmWkymUymESZgCsiaburZtwb4D/A8cFzN+2otuwNY1ih7EiWITWDtoPXX/M21/PQ81zvTkz9T86d68s9qqeNo4N567pU9+3aWr5++7TULzPbkDdMmX++pa/1cXZP+2zCZTKbe5LAWSRqfF4EbmhmZuRf4IbACuLRmf5oSPF6Vma80yv4V+EZ9ecUQ9S+qbBkjnpkHKL3by1icBzwHbZP9wDd7ru0e4E/A2xfhuiRpURmcS9L4PJSZ/2jJ31m3qyPiDcDZwFOZua+l7H1zZQep/zCudcEi4vSIuCUi9tWx4FnHlm+vRVYOWf8wbfJwZh5syf8zcPww1yVJo+CYc0kan7/0yX+mbpfXBGXsdpu5/BUD1r+oIuJNlDHdxwO7gZ9TevAPUoaWXA4cM+RphmmTF/oc8wp2UEnqIINzSRqfk/vkn1K3L9bUzOt1aqPsIPXP+W/dtn0PtAW5/VxFeQB0U2bONHdExCcowfmwhmkTSVpS7DWQpPE5tw7R6DVdt7+pw1IeB1ZGxJtbyr6nbh8apP5G3vN1u6ql/JqWvH7OrtvtLfvW9TnmIEBEvG4hJxiyTSRpSTE4l6TxWQ58rZkREWuAT1J6fe+s2bcCAdzUDGAj4o3AdY0yg9YPZSgKwKaIWNYov6q3jkOYrdvpnvOup/0BTYDn6vb0wzjPoG0iSUuKw1okaXx2AVdExHnAHl6dh/wo4HOZ+VIt923gg8DFwG8jYgdlTu+PUaYO/FZm3j9E/WTmryNiF/Bu4MGIuI8yLOYiynzibT3qbb4LbAJ+HBF3AE8B5wAfAH5Uz9/r3vpeflLf28vA/szcOs95Bm0TSVpS7DmXpPF5ElhLGVLyeWADZSjGBdlYIKhOQ/g+4NqadSVl7PYfgMsy88vD1N9wMbAFOK2eYzVwNdCv/tfIzN9RhpU8AFwIfAE4DvgI8P0+h20BbqT09F9NmQrxM4c4z6BtIklLSmTmpK9Bkv6vRcQUJXC+LTM3LrX6JUnjY8+5JEmS1BEG55IkSVJHGJxLkiRJHeGYc0mSJKkj7DmXJEmSOsLgXJIkSeoIg3NJkiSpIwzOJUmSpI4wOJckSZI64n+n/yDmvlGmHQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for y_label in [\"batch_size\", \"epochs\"]:\n", + "# y_label = \"batch_size\"\n", + " plt.figure(figsize=(12,12))\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // 10, \n", + " params_dictionaries[i][\"train\"][y_label] + (np.random.random() - 0.5) / 2, #s=3,\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + "\n", + " plt.ylabel(y_label, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model params" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for y_label in [\"lear_rate\", \"lear_rate_decay\"]:\n", + " plt.figure(figsize=(12,12))\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // 10, \n", + " params_dictionaries[i][\"chainer\"][\"pipe\"][model_index][y_label],\n", + "# + (np.random.random() - 0.5) / 2, #s=3,\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + "\n", + " plt.ylabel(y_label, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bm = np.array(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][\"binary_mask\"])\n", + "np.sum(bm[0, :])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Layer params" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/matplotlib/pyplot.py:537: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + " max_open_warning, RuntimeWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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YFF0srnVRi2BxrYtF0cW1Izs6XZqkGTDnht+U4fsjwCaKwP66Ft0+DrwkM29p2vb3gY8BF0fEFzJzuGH1i4EXAdcDv5qZg+U2HwS+CXwoIr6Wmf5rJ0mqvPX1EVbE7v/NL4wa6+sjHapI0kyai1fqXwWcBZwP9LfqkJn/2Bzoy/aPA78ADgZOblr9p+XyjeOBvtzme8AngRUUoV+SpMpbVeuhP+u7tfVnnVW1ng5VJGkmzalQHxEnAu8A3puZ10xxN+OXIEYb9jsPOAPYBVzbYpsvlsuzpnhMSZLmlDN7FrMzx9hRH6OeyY76GDtzjDN7Fne6NEkzYM6E+ojoBj4K3AW8YYr7OB04CbiXYoz9uOOALuC2zBxtsekvyuXxe9j3BRGxNiLWbty4cSrlSZI0a47rmc/vzDuYxbUuNuYoi2td/M68g539RtpPzaUx9X8NnAo8LTMnfWt+RCwHLitfXpiZYw2rl5bLbW02H29f1m7/mXkxcDHAmjVrcrL1SZI0247rmW+Ilw4Qc+JKfUQ8meLq/N9l5remsP1C4HPAo4D/m5n/Mc0lSpIkSXNWx0N9OezmMuBm4E1T2H4hcCXwNOA9mfn6Ft3Gr8QvbbGusX3rZI8vSZIkdVrHQz2wiGIs+4nAYMMDp5JiLnooppvMiPiHxg0jYjHFTa5Pp7hC/9o2x7gVGAOOLT9ENHtUubx5H89FkiRJmnVzYUz9EPDhNuueQDHO/pvAz4EHh+ZExFLgS8DpwNsy843tDpCZgxFxPXBm+fX1pi7nlMuvTeUEJEmSpE7qeKgvb4r9o1brIuIiilB/aWb+S0P7QcCXgTXAmzPz/0zgUP+PItD/TUQ0PnzqicC5wEbgU/twKpIkSVJHdDzUT9GnKQL9rUCtDP/NPpuZNza8/gTwQooHTN0QEVdQPKTqXIrpLl+emdtntGpJkiRpBlQ11B9TLo/joXH3ze4AHgz1mZkR8bvA9cDLgFcCg8A1wN9k5vUzVq0kSZI0gyLTKdcna82aNbl27dpOlyFJkqT9WESsy8w1E+k7F2a/kSRJkrQPDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVN2dDfUS8JCKy/PqjNn1+IyKujohtEbEzIr4TES/dy35fGhHfLftvK7f/jZk5C0mSJGnmzclQHxFHAu8Hdu6hz58DVwCPBT4GfAg4HLgkIt7dZpt3A5cAh5X9PwacDFxR7k+SJEmqnDkX6iMigI8Am4APtulzNPBuYDOwJjNfkZkXAo8DbgVeGxFPadrmDOC15frHZeaFmfkK4LRyP+8u9ytJkiRVypwL9cCrgLOA84H+Nn1eBvQB78/MO8YbM3ML8Lflyz9p2mb89dvKfuPb3AF8oNzf+ftYuyRJkjTr5lSoj4gTgXcA783Ma/bQ9axy+aUW677Y1GdftpEkSZLmvDkT6iOiG/gocBfwhr10f3S5vLl5RWbeR3GF/xERsaDc90LgCGBnub7ZL8rl8Xuo74KIWBsRazdu3LiX8iRJkqTZM2dCPfDXwKnAeZk5sJe+S8vltjbrtzX1m2j/Ze0OmJkXZ+aazFyzYsWKvZQnSZIkzZ45Eeoj4skUV+f/LjO/1el6JEmSpCrpeKgvh91cRjGU5k0T3Kz5Snyz5ivzE+2/dYLHlyRJkuaMjod6YBHFWPYTgcGGB04l8Oayz4fKtn8oX/+8XD5sDHxEHAYsBO7JzF0AmdkP3AssKtc3e1S5fNgYfUmSJGmu6+50AcAQ8OE2655AMc7+mxRBfnxozteApwLPbmgbd05Dn0ZfA/6g3OYjE9xGkiRJmvMiMztdQ1sRcRHF1fqXZ+a/NLQfA/yMYpab08bnqo+Ig4DvAccBZzSOzy8fPnUdxcOnnjg+V335wKl1FFf3T2ic976dNWvW5Nq1a/f5/CRJkqR2ImJdZq6ZSN+5cKV+0jLz9oj4C+B9wNqI+CQwDLwIeAQtbrjNzOsj4j3Aa4AfRsR/Ar3AucBy4JUTCfSSJEnSXFPJUA+Qmf8YEXcArwP+J8X9AT8F3piZl7bZ5rUR8SPgFcAFQB34PvCuzPzCrBQuSZIkTbM5PfxmrnL4jSRJkmbaZIbfzIXZbyRJkiTtA0O9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmquO5OFyBJkiZmU30Ld+bd9NPPQhayOo7k4NpBnS5L0hzglXpJkipgU30LP86fMZTDLMgFDOUwP86fsam+pdOlSZoDDPWSJFXAnXk3vdlLX/QSEfRFL73Zy515d6dLkzQHGOolSaqAfvrppWe3tl566Ke/QxVJmksM9ZIkVcBCFjLMyG5tw4ywkIUdqkjSXGKolySpAlbHkQzHMEM5TGYylMMMxzCr48hOlyZpDjDUS5JUAQfXDuKxcSJ90cuu2EVf9PLYONHZbyQBTmkpSVJlHFw7iIMxxEt6OK/US5IkSRVnqJckSZIqzlAvSZIkVZxj6iVJmiab65u5u34X/exkIYs4snYUy2vL22+w9S64bx3segAWHAKHnQbLjmrff+PtcOu3YMcGWLwSjnsKrDimff/7b4ObroVtG2DpSjjhTDj02Lbd+zfeyObN32ao3k9fbSHLl5/OwhWnTODMJXWaV+olSZoGm+ub+dnYTxjOIRbkQoZziJ+N/YTN9c2tN9h6F9zyRRjuh/kHF8tbvli0t7LxdrjhMzC0ExYdUixv+EzR3sr9t8G3L4eBHbDkkGL57cuL9hb6N97ILzd9ldEcpLc2n9Ec5Jebvkr/xhun8G5Imm2GekmSpsHd9bvopZfe6CMi6I0+eunl7nqbkH7fOuhZCL0LIaJY9iws2lu59VvQt6j4itpDf771W63733RtsX7+4qL//MXF65uubdl98+Zv00033bV5RNTors2jm242b/72FN4NSbPNUC9J0jToZyc99O7W1kMv/exsvcGuB6Bnwe5tPQuK9lZ2bIDepv69C4r2VrZtgHlNT5udt7Bob2Go3k9X7F5/V/QyVO9vvX9Jc4qhXpKkabCQRYwwvFvbCMMsZFHrDRYcAiO7dm8b2VW0t7J4JQw39R/eVbS3snQlDDYF8sH+or2FvtpCxnL3+sdymL7awpb9Jc0thnpJkqbBkbWjGGaY4RwiMxnOIYYZ5shamxtfDzsNRvqLsfSZxXKkv2hv5binFOPoh3ZC1h/683FPad3/hDOL9QM7iv4DO4rXJ5zZsvvy5aczyiij9UEy64zWBxlllOXLT5/CuyFpthnqJUmaBstryzmx6zH0Rh+7op/e6OPErse0n/1m2VHwyHOKsfQDm4rlI89pP/vNimPg1BcU4+J3PlAsT31B+9lvDj0WTv+dYiz99geK5em/03b2m4UrTuHwg3+N7pjHcH2A7pjH4Qf/mrPfSBURmdnpGipnzZo1uXbt2k6XIUmSpP1YRKzLzDUT6euVekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmquO5OFyBJkmbGbaMDXDeyjQ05wsro4ak9Szm2e36ny5I0A7xSL0nSfui20QE+NbSRnTnGIXSzM8f41NBGbhsd6HRpkmaAoV6SpP3QdSPbWBRdLIouahEP/vm6kW2dLk3SDDDUS5K0H9qQIyxo+m9+ATU25EiHKpI0kwz1kiTth1ZGD7uo79a2izoro6dDFUmaSYZ6SZL2Q0/tWcrOHGNnjlHPfPDPT+1Z2unSJM0AQ70kSfuhY7vn89t9K1gUXTzAKIuii9/uW+HsN9J+yiktJUnaTx3bPd8QLx0gDPWSJLUwMLSebQM3MTK6jZ7upSydfwLz+1Z1uixJamnODL+JiHdGxFURcXdEDETE5oi4ISLeHBEHt+jfFxGviIjvRsQDEbEzIn4WEe+LiNV7OM5Ly212RsS2iLg6In5jZs9OklQlA0Pr2bjjW4zVB+juWsJYfYCNO77FwND6TpcmSS3NmVAPXAgsBL4CvBf4ODAKXAT8MCKOHO8YEd3AVcD7gcXAvwMfBDYArwR+EBEnNR8gIt4NXAIcBnwI+BhwMnBFRPz5DJ2XJKlitg3cRFdtHl21+UQEXbX5dNXmsW3gpk6XJkktzaXhN0syc7C5MSLeBrwB+Evgz8rmFwBPpQj2Z2dmvaH/W4C/Bl4HvKyh/QzgtcCtwBMzc0vZ/i5gHfDuiPhCZt4x/acmSaqSkdFtdHct2a2tFvMYGfXBTZLmpjlzpb5VoC9dXi4f1dB2bLm8sjHQlz5XLlc0tf9JuXzbeKAvj3sH8AGgDzh/MjVLkvZPPd1LqTf9t1TPQXq6nQ5S0tw0Z0L9HjyvXP6woe0n5fKciGg+h/Hx8V9taj+rXH6pxTG+2NRHknQAWzr/BMbqg4zVB8hMxuoDjNUHWTr/hE6XJkktzaXhNwBExOuARcBSYA3wNIpA/46GblcCnwZeCPwoIr4KDAOnlf3/keLq+/g+FwJHADsz874Wh/1FuTx+D3VdAFwAcNRRR03l1CRJFTG/bxUreMpus98sX3iqs99ImrMiMztdw24i4n6g8V/NLwHnZeb6pn4BvBl4I9DVsOoq4I2Z+e2GvocD9wL3ZuYjWhyzh+JDwXBm9u2txjVr1uTatWsnflKSJEnSJEXEusxcM5G+c274TWYempkBHEpxJf5Y4IaIeMJ4n4iYB3yS4sbXV1DMZrMUeA6wGrgmIp4/27VLkiRJnTDnQv24zFyfmZ8BzgYOBi5rWP2/gf8B/FVm/nNm3p+Z2zPzi8CLgB6KaTHHjU9X0O4Op/H2rdN2ApIkSdIsmbOhflxm3gn8FHhMRBxSNo/fDPv1Fv1/AGwBVo8/tCoz+ymG3yyKiMNaHGZ8Zp2bp7N2SZIkaTbM+VBfOrxcjpXL8XHvzdNWEhF9FA+kgmKc/Livlctnt9j/OU19JEmSpMqYE6E+Io6PiIcNjYmIWvnwqZXA9Q3zy19bLt9QhvhGF1HM6vO9zNzR0P7BcvlXEXFQwzGOphiXPwR8ZB9PRZIkSZp1c2VKy+cAb4+IbwK3A5soZsB5OsWNsvcDL2/o/zaK+et/FbgpIr4EDFA8ZfZJ5Z9f3XiAzLw+It4DvAb4YUT8J9ALnAssB17p02QlSZJURXMl1H8VeCTFHPOnAsuAfoox7h8F3peZm8c7Z+a95Ww4rweeS/Ek2BpwH3AJ8M7MvKn5IJn52oj4EcWV+QuAOvB94F2Z+YUZOztJkiRpBs25eeqrwHnqJUmSNNMqPU+9JEmSpMkx1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEm0lnPzAAAgAElEQVSSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFVcd6cLkCRJM2NjbuVW7mUHu1jMAo7jCFbEsk6XJWkGeKVekqT90Mbcyg3czBDDLGI+QwxzAzezMbd2ujRJM8BQL0nSfuhW7qWPHvroJQj66KWPHm7l3k6XJmkGGOolSdoP7WAXvfTs1tZLDzvY1aGKJM0kQ70kSfuhxSxgmJHd2oYZYTELOlSRpJk0qRtlI6IHeD7wJOAgoKtFt8zMP5yG2iRJ0hQdxxHcwM1AcYV+mBGGGOEkjulwZZJmwoRDfUQcDnwFOAGIPXRNwFAvSVIHrYhlnJrH7zb7zUkc4+w30n5qMlfq/w44Efh34EPA3cDoTBQlSZL23YpYxgoM8dKBYDKh/mzgmsz8/ZkqRpIkSdLkTeZG2XnAd2aqEEmSJElTM5lQ/2Ng9UwVIkmSJGlqJjP85l3AZRFxUmb+dKYKkiRpInaMbmTj8C0M1ncwr7aYFb2PZHH3ik6XJUkdMZlQvwG4Arg+It4LrANaPms6M6+ZhtokSWppx+hG7hpcRzd99MUiRuqD3DW4jqPmnWawl3RAmkyov5piusoA3lT+uZ1W89dLkjQtNg7fQjd99NTmAdAT86BetBvqJR2IJhPq/w97DvKSJM2KwfoO+mLRbm3d0cdgfUeHKpKkzppwqM/Mi2awDkmSJmxebTEj9cHiCn1pNIeYV1vcdpvhXb9kaOuPGBveQlfvQfQtO5neBYfPRrmSNOMmM/vNjIqId0bEVRFxd0QMRMTmiLghIt4cEQe32aYrIv4oIq6JiC3ldrdFxCcj4vg227w0Ir4bETsjYltEXB0RvzGzZydJmk4reh/JKEOM1AfJTEbqg4wyxIreR7bsP7zrl+za8A3qowPUepZRHx1g14ZvMLzrl7NcuSTNjEmH+ojoiYhnR8SFEfGmhvZ5EbEyIqb6QeFCYCHwFeC9wMcpnlh7EfDDiDiyqY5FwJcpnm67GLi03O464MnAw0J9RLwbuAQ4rNzuY8DJwBUR8edTrFuSNMsWd6/gqHmn0VObx1DupKc2b483yQ5t/RFRm0+tez4RUSxr8xna+qNZrlySZsZkxtQTEc8GPgwcSnHDbAJvLVefQhGoXwL8+xRqWZKZgy2O+TbgDcBfAn/WsOqfgbOAP8nMf26xXU/T6zOA1wK3Ak/MzC1l+7soZvJ5d0R8ITPvmELtkqRZtrh7xYRvih0b3kKtZ9lubdE1j7HhLTNRmiTNuglfVY+INcBnKYL8hcC/Na7PzG8DtwMvmEohrQJ96fJy+aiGWp4A/B7wyVaBvtzfSFPTn5TLt40H+rLfHcAHgD7g/MlXLkma67p6DyLHdv9vJscG6eo9qEMVSdL0msxQmTcBu4A1mfk+4Bct+nwPePx0FNbgeeXyhw1tv1cu/z0ilkbESyLiLyPigohoPaCyuKoP8KUW677Y1EeStB/pW3YyWR+gPjpAZhbL+gB9y07udGmSNC0mM/zmqcBnM/P+PfS5G3juvhQUEa8DFgFLgTXA0ygC/Tsauj2xXK6mGE7TeCNtRsT/A16VmWPlPhcCRwA7M/O+Focd/4DS8ubach8XABcAHHXUUZM8K0lSJ/UuOBxWPr1p9psnOfuNpP3GZEL9IuCBvfRZwL7PqPM6YFXD6y8B52Xmxoa2leXyPRRDgt4I3ENxg+wHKcbeb6S4yRaKDwgA29occ7x9WZv1ZObFwMUAa9ascb5+SaqY3gWHG+Il7bcmE8DvBR6zlz6nALdNvRzIzEMzMyhuxn0hcCxwQzmOftx43TcB52bmTZm5MzOvAl4E1IHXRETvvtQiSZIkVcFkQv0XgV+PiKe1WhkR5wBnAF+YjsIyc31mfgY4m2J4zWUNq7eWyyvGh9g0bPcDiht2FwMnls3jV+KX0tp4+9Y26yVJkqQ5azKh/u0UoffLEfFO4CSAiHhu+fo/gPsohsRMm8y8E/gp8JiIOKRs/nm5bBfCx2e3mV/uo5/iNw2LIuKwFv3HZ9a5ed8rliRJkmbXhEN9Zt5LcdX8l8BfAP+DYq76z5ev7wOenZl7G3c/FeODIMevyn+1XD62uWNE9PFQSL+jYdXXyuWzW+z/nKY+kiRJUmVM6qbWzPw+8Gjgt4B3Av9CcWX+fwAnZuaUHs0XEcdHxMOGxkRErXz41Erg+ob55T9F8eHi3Ih4UtNmb6IYTvP1ppl6Plgu/yoiHpyYOCKOBl4BDAEfmUr9kiRJUidN6omyAOUY9s+XX9PlOcDbI+KbFOPhN1HMgPN0ihtl7wde3lBDf0ScRzF+/9qI+DTF8JonU0yBuQH446a6r4+I9wCvAX4YEf8J9ALnAsuBV/o0WUmSJFXRpEP9DPkq8EiKQH4qxdSS/RRj3D8KvC8zNzdukJlfKa/Svwn4NYqr8/dTXJF/a2b+svkgmfnaiPgRxZX5Cyhmyfk+8K7MnJYbfCVJkqTZFpmtp1yPiP851Z1m5mV771Vda9asybVr13a6DEnar/z8/jG+clOd+7Ylhy0NnnVCjUcf2tXpsiSpYyJiXWaumUjfPV2pvwRoTPzR9Lrlscs++3WolyRNr5/fP8ZHvj3Kkr5g1RLYPpB85NujnH86BntJmoA9hfrzW7S9EHge8A3gaorhLocCzwR+hWKc/Wemt0RJ0v7uKzfVWdIXLJkfACyZ/1C7oV6S9q5tqM/MSxtfR8RzKKaDfH5mXtHU/S0R8Xzgch6aZUaSpAm5b1uyasnubYvmFe2SpL2bzJSWfwV8pkWgByAzPwd8luLGVUmSJuywpcHOwd3bdg4W7ZKkvZtMqH88cMte+twCPG7q5UiSDkTPOqHG9qFk+0BSz2K5fSh51gmTepyKJB2wJvOv5TBFsN+TxwMjUy9HknQgevShXZx/ejdL5gfrt8OS+cH5p3c7nl6SJmgy89RfBbwwIv4c+EA2zIUZEQH8OXAOxdNeJUmalEcf2mWIl6Qpmkyo/98Us9y8F/hf5dNf11M8+fVpwDHA5rKfJEmSpFky4VCfmbdGxOnAP1E8wfXYpi5fAV6RmbdNY32SJEmS9mIyV+rJzFuAsyPiCOBUYCmwDbghM++dgfokSZIk7cWkQv24MsAb4iUdsLaNPcD9o7czkDuYH4s5tPsYlnYd0umyJEkHKOcKk6RJ2jb2ALcN/4CRHGIeixjJIW4b/gHbxh7odGmSpANU2yv1EfGvU9xnZuYfTnFbSZrz7h+9nZ7ooyf6AOih78F2r9ZLkjphT8NvzmvTnkCrR/yNtydgqJe03xrIHcxj0W5t3fQykDs6VJEk6UC3p1B/TNPrGvD3wJnA+4CrgfuBQymmunwlcA3wmmmvUpLmkPmxmJEcevAKPcAow8yPxR2sSpJ0IGsb6jPzzsbXEXEhRaB/QtO6nwPfiIhLgXXA84F/mIFaJWlOOLT7GG4b/gFQXKEfZZiRHOLInhM6XJkk6UA1mRtlLwAubw774zLzduA/yn6StN9a2nUIx/Y+np7oY5Cd9EQfx/Y+3vH0kqSOmcyUlkcDW/fSZ0vZT5L2a0u7DjHES5LmjMlcqX8A+PV2KyMiyvWb9rUoSZIkSRM3mVD/H8ApEXF5ROx2E235+pPA48qlJEmSpFkymeE3fw08DXgR8IKIuBdYD6wCjgC6gO8BF01zjZIkSZL2YMJX6jNzJ0WofyNwB3AU8MRyeTvwV8CZZT9JkiRJs2QyV+rJzGHgb4G/jYhFwFJgm0FekiRJ6pxJhfpGZZA3zEuSJEkdNpkbZSVJkiTNQW2v1EfEbUACv5aZt5evJyIz87hpqU6SJEnaByP99zK89UfUh7dQ6z2I3mUn07PwiLb9b1o/xlduHuOX25LDlwbPOr6LE1Z1zWLFU7OnK/W1pvU1ICbw5dV/SZIkddxI/70Mrr+a+uguomcZ9dFdDK6/mpH+e1v2v2n9GP/63VG2DyaHLoHtg8m/fneUm9aPzXLlk9f2Sn1mHr2n15IkSdJcNrz1R9A1n1r3AgCiewH1sr3V1fqv3DzG0nmwZF4AsGQeQPKVm8fm/NV6r6pLkiRpv1Qf3kJ0zd+tLbrmUx/e0rL/L7cli/p2b1vUV7TPdYZ6SZIk7ZdqvQeRYwO7teXYALXeg1r2P3xpsHNo97adQ0X7XDfpKS0jYg3wJOAgiqfINsvMfOu+FiZJkiTti95lJxdj6imu0OfYAIwN0HvIk1v2f9bxXfzrd0eB4or9ziHYNgi//bi5PfQGJhHqI2IJ8GngmRQ3xLaTgKFekiRJHdWz8AhY9YzdZ7855MltZ785YVUXL3sSu81+89uPq8bsN5O5Uv8u4CzgWuAjwN3A6EwUJUmSJE2HnoVH7HEKy2YnrKpGiG82mVD/fOD7wDMzsz5D9UiSJEmapMncKLsU+LqBXpIkSZpbJhPqfwGsmqlCJEmSJE3NZEL9B4DnRcTEByVJkiRJmnFtx9RHxFFNTV+kuFH2uoh4C7AO2Npq28y8a9oqlCRJkrRHe7pR9g6K6SmbBfAve9gu97JfSZIkSdNoT+H7MlqHekmSJElzSNtQn5nnzWIdkiRJkqZoMjfKTklEPD8i/nWmjyNJkiQdqGY81AOnAC+dheNIkiRJB6TZCPWSJEmSZpChXpIkSao4Q70kSZJUcYZ6SZIkqeIM9ZIkSVLFGeolSZKkijPUS5IkSRVnqJckSZIqbjZC/R3ANbNwHEmSJOmANOOhPjMvzcxnzvRxJEmSpANV92Q6R0QP8HzgScBBQFeLbpmZfzgNtUmSJEmagAmH+og4HPgKcAIQe+iagKFekiRJmiWTuVL/d8CJwL8DHwLuBkZnoihJkiRJEzeZUH82cE1m/v5MFSNJkiRp8iZzo+w84DszVYgkSZKkqZlMqP8xsHqmCpEkSZI0NZMZfvMu4LKIOCkzfzpTBUmSJEmdch/b+Snr2cogy5jHSaziMJZ0uqy9msyV+g3AFcD1EfGWiPjNiPiVVl9TKSQi3hkRV0XE3RExEBGbI+KGiHhzRBw8ge3/JSKy/Hpkmz5dEXFhRPyw4Rj/FRFnTKVmSZIk7T/uYzvf5A4GGGEpfQwwwje5g/vY3unS9ioyc2IdI+oU01WOT2fZdsPMbDV//d72Pwx8H/gpxQeIhcDpwBrgl8DpmXl3m22fB3we2AksAh6Vmbc09QngcuBFwM8pPqAsB86luF/gtzPzcxOpdc2aNbl27drJnqIkSZLmsKv4BQOMMJ+eB9vGX/8qj5r1eiJiXWaumUjfyQy/+T/sIchPgyWZOdjcGBFvA94A/CXwZy3Wr6CYYvOTwKHA09vs/8UUgf564FfHjxURHwS+CXwoIr6WmTum4VwkSZJUMVsZZCl9u7XNo5utPCyizjkTDvWZedEM1kGrQF+6nCLUt/t4dHG5fAXwqT0c4k/L5Rsbj5WZ34uITwJ/QBH6PzLhoiVpDrnz1jrfuwYeWA+HrIIn/gqsPm4yoywl6cC2jHkPu1I/yCjLmNfBqiamCv/aP69c/rB5RUScB/wW8MeZuandDiJiHnAGsAu4tkWXL5bLs/apUknqkDtvrXPlJ6B/Bxy8olhe+YmiXZI0MSexigFGGWCEJBlghAFGOYlVnS5tryYz/GZWRMTrKMbFL6UYT/80ikD/jqZ+q4H3Ah+bwFj444Au4LbMbPUU3F+Uy+P3oXRJ6pjvXQMLFxdf8NDye9fA6uM6V5ckVclhLOFpHL3b7Den8YhKzH4z50I98DrY7ePQl4DzMnPjeENE1IBLKW6MfdUE9rm0XG5rs368fVm7HUTEBcAFAEcdddQEDilJs+eB9cUV+kYLFhbt2n/cPrqL68e2saE+wspaD2d0LeWY7gWdLkvarxzGkkqE+GZzbvhNZh6amUFx0+sLgWOBGyLiCQ3dLqS4Ifblmbllluq6ODPXZOaaFStW7H0DSZpFh6yCXf27t+3qL9q1f7h9dBefHtnIzhzjkOhmZ47x6ZGN3D66q9OlSZoD5lyoH5eZ6zPzM8DZwMHAZQARcTzwNuAjmflfE9zd+JX4pW3Wj7dvnWK5ktRRT/yVYhx9/w7I+kN/fuKUnhyiuej6sW0sii4WRRe1iAf/fP1Yu19CSzqQzNlQPy4z76SYu/4xEXEIcBLQB5zf8LCpjIjkoeksf1G2/Vb5+lZgDDg2IloNORqfWefmmTsTSZo5q4+r8dwXF2PpN20sls99sbPf7E821EdY0PTf9gJqbKiPdKgiSXPJXBxT38rh5XIMuAP4cJt+z6UYtvMfwPayL5k5GBHXA2eWX19v2u6ccvm1aatYkmbZ6uNq3hS7H1tZ62FnjrGIh57vuIs6K2s9e9hK0oFiToT6ckjN+szc1tReA94KrASuL8fPbwH+qM1+rqYI9W9ofqIs8P8oAv3fRETjw6eeSPFU2Y3seZ57SZI65oyupXx6pJgzYgE1dlFnZ45xdvfyDlcmaS6YUKiPiFUUN6z+PDM3N7S/EHgGMApcmZlXTbGO5wBvj4hvArcDmyhmwHl6edz7gZdPcd/jPkFx4+2LKG68vYJirP65FNNdvjwzt+/jMSRJmhHHdC/ghazYbfabs7uXO/uNJGACoT4i3kLxRNcaMBQR/yszL46IfwL+GIiy66sj4n2ZeeEU6vgq8EiKOelPpZhasp9ijPtHgfc1fpiYiszMiPhd4HrgZcArgUHgGuBvMvP6fdm/JEkz7ZjuBYZ4SS1FZrZfGXEWReDeQBGGTwcOAc6nCNv/TPE01kdQBP/DgF/PzK/ObNmdtWbNmly7dm2ny9B+ZHN9M3fX76KfnSxkEUfWjmJ5rf2v1Os77qG+4QYY3ATzDqa28lRqix+xl4PcCXd9B3Y+AIsOgaOeDMtXt+0+dP86dq2/lrHRHXR1L2bBqjPpO/S09vtffxvcfB1s2wBLV8LxT4VVx7btvn10IxtGbmMwdzAvFrOy51iWdDtdrDSdtoxt4t76nQ/+23JEbTUHdR3c6bIkTVBErMvMNRPpu7dpEV4N7ABOzcwXAqdQ3ID6AeC9mfmnmfn5zPwniqEyY7QZ7y6ptc31zfxs7CcM5xALciHDOcTPxn7C5nrrX07Vd9zD2J1fJkd2kX3LyZFdjN35Zeo77tnDQe6En1wBQ/2w8OBi+ZMrivYWhu5fx/Z7r6ReH6TWvYh6fZDt917J0P3rWu9//W3w3f+EwZ2w5JBi+d3/LNpb2D66kTuHbmQkh+hjESM5xJ1DN7J9dGPL/pImb8vYJn4+9uPd/m35+diP2TK2qdOlSZoBewv1JwOfz8z7ADJzA3AFsBj4YGPHzLyV4umvp89AndJ+6+76XfTSS2/0ERH0Rh+99HJ3/a6W/esbboDuhUTPAiKC6FkA3QuL9nbu+g70LoS+hRBRLHsXFu0t7Fp/LbVaL7XafIIatdp8arVedq2/tvX+b74O5i2GeYsgasVy3uKivYUNI7fRHX30lOfcE310Rx8bRlp/CJA0effW72z5b8u99dYf5iVV295C/WFAc7IYvxx4e4v+t1Lc4CppgvrZSQ+9u7X10Es/O1tvMLgJuufv3tY9v2hvZ+cD0Ns0Drd3QdHewtjoDqLWt1tb1PoYG93Rev/bNkBf0/77FhTtLQzmDrqbzrmbXgazzf4lTdqk/22RVGl7u1G2H1jY1DYKkJnDLfqPUgzBkTRBC1nEMEP08lCIHmGYhSxqvcG8g2FkF/Q0hOjRgaK9nUWHFENu+hr+Og/vKtpb6OpeTL0+SNQe+vCQ9SG6uhe33v/SlcWQm3kNNQ/tKtpbnUIsZiSH6Gk451GGmRdt9i9p0ib9b4skAO6p7+RGNrGZQZYzj1M4mEfU5v7fm71dqb+P4ibYRl8A/qxN/yMp5nuXNEFH1o5imGGGc4jMZDiHGGaYI2tHtexfW3kqjPYXY+ozyZFdMNpftLdz1JNhuL8I9pnFcri/aG9hwaozqdeHqdcHSOrU6wPU68MsWHVm6/0f/1QY3FEE+6wXy8EdRXsLK3uOZTSHGCnPeSSHGM0hVva0v7FW0uQcUVvd8t+WI2rtb5CXDnT31Hfy1byHXTnCQdnLrhzhq3kP99Tn/m+49hbqbwR2+18/M9dl5j+36X8q8KPpKEw6UCyvLefErsfQG33sin56o48Tux7Tdvab2uJH0LX67GJM/dBmomcBXavP3vPsN8tXw2OeV1yp799ULB/zvLaz3/QdehpLjngutdo86qM7qdXmseSI57af/WbVsfCkFxVX6rc/UCyf9KK2s98s6V7B6r5T6Ik+hthJT/Sxuu8UZ7+RptFBXQfz6K7H7vZvy6O7HuvsN9Ie3MgmFtDNgugmIlgQ3SygmxuZ+zeY7234zcXALRHR22a4zYPKJ7OuBP5xuoqTDhTLa8v3OIVls9riR+x9CsuHHWT1HqewbNZ36Gl7nsKy2apj9ziFZbMl3SsM8dIMO6jrYEO8NAmbGeSgpntR5tPFZgb/f/buPEyO+zzw+/dXR1ffPSfmAMABQBAAL5PiaZISCZGWTMo6bDm7ktfSer1PrE023mSz6yd+kmzW8l6JYyfPk81uYktPslZkrdbyoYO0CEsmBYI3SPAm7pMA5sAMZqbvrq7jlz9qQEwPqnAJwBx4P8+Dp7pfvNX1Tp+/rv7VW4tU0cU776Bea/08kNDu4pzc14DuK1GUEEIIIYQQ11oPaRp4ZOcNkZsE9JBexKouzoWm3wghhBBCCHFduJNeGvg0tI/Wmob2aeBzJ0v/Fy8Z1AshhBBCCAGsMfL8nFpDVtnMqDZZZfNzas2y6H5zoTn1ACilBoAHiVpW7tBalxPyHgEe0Vr/iytXohBCCCHExZkNTjMaHKWpa2RUnmFzHV1yXIG4BGuMPGuWYevXC+6pV0r9JnAU+HPge8AJpdQ/SUjfCvzOlSpOCCGEEOJizQanOeC/Q1u7pMnR1i4H/HeYDZZ+5xIhflrnHdQrpbYC/3Yu72+Ap4n27v++UupbSimZviOEEEKIJWE0OIqNQ0o5KKVIKQcbh9Hg6GKXJsRVd6HpN/+YaMrNz811wkEpNQJ8C/hidFX9qtZaX90yhRBCCCHOr6lrpMl1xGxSNPXSP3GQED+tC+1p/1ngB2cG9ABa62PAo8BfEA3sv3H1yhNCCCGEuDgZlcej87Q6Hm0yavnNjxbiUl1oUN8N7FsYnDsR1ReBPwW+pJT6f69CbUIIIYQQF23YXIeHS1u7aK1paxcPl2Fz3WKXJsRVd6HpNxNA7GkutdahUupXARP4NaWUB4xd4fqEEEIIIS5Kl9nLTfxMR/ebdeZm6X4jrgsXGtQfAD6a9J9zA/u/Q9QZ5z8Hpq5gbUIIIYQQl6TL7JVBvLguXWhQ/yPg3yil7tBavx2XoLX2lVJ/i6jd5ROAHDR7hR3fp3nzGTg9Cr3D8JHHYO1mtdhlCSGEEEKIJeJCc+r/AvgOcMf5krTWHvBLRAfN7rgypQmIBvQ/+gY0KtAzGC1/9I0oLoQQQgghBFxgT73W+iDwKxdzQ3MHz/76lShKnPXmM5ArQrYYXT+zfPMZWLt58eoSQgghhBBLx1U/eZRS6r9RSh2+2ttZqU6PQmZBJ65MPooLIYQQQggB12BQD3QBI9dgOytS7zA0F5wzo1mL4kIIIYQQQsC1GdSLn8JHHoN6JZpLr8NoWa9EcSGEEEIIIUAG9Uve2s2KT/5aNJd+ejxafvLXpPuNEEIIIYQ460ItLcUSsHazkoNihRDip7R/LODZ90LGZmCoGx69zWDTkLnYZQkhxBUhe+qFEEKsePvHAr65I6TShIEuqDThmztC9o8Fi12aEEJcETKoF0IIseI9+15IIQPFjMJQimJGUchEcSGEWAlkUC+EEGLFG5uBfLozlk9HcSGEWAlkTr0QQogVb6g7mnJTzJyN1VpRfDk5FtR5LZxhijZ9pLjX6GbEzC12WUKIJUD21AshhFjxHr3NoNqESlMTak2lqak2o/hycSyo88NwnDo+vdjU8flhOM6xoL7YpQkhloBrsad++zXYhhBCCJFo05DJlx+mo/vNL967vLrfvBbOkMUkp6KP7hwW6Cgue+uFEFd9UK+1fg547mpvRwghhDifTUPmshrELzRFm17sjlgWkynai1SREGIpuaxBvVJqCPgt4GEgBxwGvqm1/tMrWJsQQohL8MHBkNe3w9Q49A3CPVvhho3LZ3qJOL8+UtTxoz30cxoE9JFaxKqEEEvFed/tlVIvKaX+/oLYbcBbwD8G7ga2AJ8C/qNS6v++WoUKIYRI9sHBkB9+C+pV6F0VLX/4rSguVoZ7jW4aBNS1j9aauvZpEHCvscyO9hVCXBUX2oXzs8CaBbFvAv3AXwKfAO4E/ktgBviKUuoXrnSRQgghzu/17ZArQq4AyoiWuWIUFyvDiJnjU8YgOSxO45HD4lPGoMynF0IAlzj9Ril1P3AH8Gda6y/M+693lFIvA7uA3wD+6sqVKIQQ4kKmxqM99PNlc1F8sVS9Saa8g7hhBcco0mdvpGD3L15BK8CImZNBvBAi1qVOtrwb0MDvLfwPrfU7wDbg3itQlxBCiEvQNwiNBZ0NG/Uovhiq3iQnWrvwwhYpVcALW5xo7aLqTS5OQUIIscJd6qC+NLfcm/D/e4Heyy9HCCHE5bhnK9Qr0Vx6HUbLeiWKL4Yp7yCmcrCNNEopbCONqRymvIOLU5AQQqxwlzqoP/NDbjrh/x2gdfnlCCGEuBw3bDT41K9Gc+lPn4qWn/rVxet+44YVLOV0xCzl4IaVRalHCCFWuouZU//3lFJb517LgJIAACAASURBVC53zS03Aa/E5K4FTl2BuoQQQlyiGzYa3LBxsauIOEYRL2xhq7P7gHzt4hjFRaxKCCFWrovZhbMO2Dr3705AAb+8MEkpZQEfA965YtUJIYRYlvrsjQTaxQtbaK3xwhaBdumzl8i3DiGEWGHOu6dea30pv9veDDwJfPenqkgIIcSyV7D7WcPdHd1vhuzbpPuNEEJcJZd1Rtk4Wut3gV+/UrcnhBBieSvY/TKIF0KIa+SKDeqTKKV+B/iftNZXfVtCCCGunr1Nl23VJqNewLBt8nghw5aMc+EVhRBCXHXXqi2CukbbEUIIcRXsbbp8fbpKOQgZtAzKQcjXp6vsbbqLXZoQQgiu3aBeCCHEMrat2qRoGJRMA0MpSqZB0TDYVm0udmlCCCGQQb0QQoiLMOoFFIzOH10LhmLUCxapIiGEEPPJPHchhBAXNGyblIOQknl2YF8NNcO2mbhOqzVOrb4b3y9jWSXyuVtIpwcT82vTbzNb3kk7rJMycnSV7iPfc0dyUVNH4cjLUJ2EQj+sfwD61iWmz0y8xYmZXdRVk5zOsKb7broH7kyu5+TrTE+8hEsdhxw9Aw+SX31Pcj3ABGX2MU6ZJiUybGaQgQ9Pxi7E8rN/LODZ90LGZmCoGx69zWDTUPLrXiwe2VMvhBDigh4vZKiEIeUgJNSachBSCUMeL2Ri81utcWZmXyQImphmkSBoMjP7Iq3WeGx+bfptTs08g69dbCOLr11OzTxDbfrt+IKmjsLb3wO3BvneaPn296J4jJmJt9hbfp42bbI6TZs2e8vPMzPxVnw9J19ndOLH+LikyODjMjrxY2onX0+8jyYo8yqHaeFRJE0Lj1c5zATlxHWEWMr2jwV8c0dIpQkDXVBpwjd3hOwfk1/oliIZ1AshhLigLRmH3+gpUDINxv2QkmnwGz2FxO43tfpuDCONaWZQSmGaGQwjTa2+OzZ/trwTQ6WwDAelFJbhYKgUs+Wd8QUdeRmcHDh5UEa0dHJRPMaJmV2kQouUSqGUIqVSpEKLEzO7YvOnJ17CwsJSDkoZWMrBwmJ64qXE+2gf46SxSWOjUB9e3kf8Fxkhlrpn3wspZKCYURhKUcwoCpkoLpYemX4jhBCX4YOwzi49zWnt0qsc7lY93GDkFrusq2pLxrnoFpa+X8Y0ix0xw0jj+/F7rdthHdvIdsRMlaId1uM3UJ2M9tDPl8pG8Rh11SSr0x0xG5u6ij/Q16VOis5fIUxsXBLqAco0KdK5DQeLMnIw8bU0QZn9jFGmQYksmxg67xSo/W6LZ5t1xgKfIdPi0UyOTU46Mf96MjYT7aGfL5+O4mLpkT31QghxiT4I62wLR6lrnx5S1LXPtnCUD5IGoNchyyoRhq2OWBi2sKz4wVXKyBHodkcs0G1SSV+UCv3QbnTG2o0oHiOnM3h4HTEPj5yOnz7kkCNYkB/g4ZD8xa1EBhe/I+biUyJ+G+LKm6DMTg7Sok2RDC3a7ORg4hSo/W6Lb1ZnqYQBA4ZJJQz4ZnWW/W4rNv96M9QNtQV3Ra0VxcXSI4N6IYS4RLv0NFkscspCKUVOWWSx2KWnF7u0JSOfu4UwbBEETbTWBEGTMGyRz90Sm99Vuo9Qt/FDF601fugS6jZdpfviN7D+AXDr0Vx6HUZLtx7FY6zpvpu24dPWbbTWtHWbtuGzpvvu2PyegQfx8fG1i9Yhvnbx8ekZeDDxb97MIC08Wnho9IeXN5N8cLC4svYzNjftKTU3BSpFGpv9jMXmP9usUzAMioYZTS8xTAqGwbNN+YIO0UGx1SZUmppQaypNTbUZxcXScy0ele8Bf/8abEcIIa6J09olS2f3hywmp7WciOmMdHqQ7q6HMM0MQVDBNDN0dz2U2P0m33MHq7ofw1IOXtjAUg6ruh9L7n7Ttw7u+MVoLn3tdLS84xcTu990D9zJltLHSJGioVqkSLGl9LHE7jf51fcwPPAJLBzaNLFwGB74xHm73wxQ4n42kMamQos0NvezQbrfXENlGjjYHTEHmzKN2PyxwCevOodCeWUwFvix+debTUMmX37YoJiBiVkoZuDLD0v3m6Xqp55Tr5TqAQKtdexvW1rrt4GE9gVCiKVqOpzmmD5OXdfJqRwjai09Rs9il3XRZoPTnAyP0tA1sirPamMdXWZvYn7Vn+SUd4hWWCVtFFhl30jBip/K0asc6tonN+8ttEFAr7q4+ebXi3R68LwtLBfK99xx/haWC/WtO28Ly4W6B+48bwvLc+pZfc8FW1guNEBJBvGLqESWFm3SpD6MuXiUyMbmD5kWlTCgqM4OUms6ZMiUQw7P2DRkyiB+mbjgnnql1Gql1L9TSv21Uup/VUr1zsXvVEq9A0wC00qpHUqpLVe7YCHE1TcdTvN+uBtXt8mSxdVt3g93Mx0uj+kls8Fp9vvv0tYuGXK0tct+/11mg9Ox+VV/kmPum3ihi6PyeKHLMfdNqn78QZd3qx4a+NS1j9aauvZp4HO3Wj5feoRYiTYxNDftqT03BapNC49NDMXmP5rJUQ1DKmEQTS8JA6phyKOZlX3Qu1iZzjuon9sL/wrwD4FPAL8F/Fgp1Q/8FbCJaC/8JPBR4G+UUl0JNyeEWCaO6ePYODhz7f8clcLG4Zg+vtilXZST4VFslSKlnLn2hQ62SnEyPBqbf8o7hIWDPddO0TYcLBxOeYdi828wcjxuDJNTFtO0ySmLx43hFd/9RoilboAS97GRNCkqNEmT4j42Jv56sslJ8+VCF0XDZCIMKBomXy50SfcbsSxd6Pel3wRWA/8G+A7wWeBfAH8MVIEHtNYfACil/jXw3wP/CPiXV6leIcQ1UNd1sgt+rk5hU9fL4+Cxhq6RWdClxCZFQ9di81thFUflO2KWStEKq4nbuMHIccN5OqEIIRbHpU6B2uSkZRAvVoQLTb/5ReA1rfU/01q/o7X+V8ALwOPAb58Z0M/5Z8Bh4NNXp1QhxLWSUznaC9r5tfHIqeUxiM2qPB6d7RE92mQXDNzPSBsF/AXtFH3dJm0UrlqNQgghxJV0oUH9CNH0m/nOnCO747R6WmsNPEc0JeeSKaV+Tyn1jFLquFKqqZSaVkq9qZT6nTPz+Ofl3qSU+m2l1LNz+W2l1IRS6vtKqY9fYDu/ppTaqZSqKaXKSqntSin5IiLEPCNqLR4u7lz7P1e38XAZUWsXu7SLstpYh6fbtLU7177QxdNtVhvrYvNX2Tfi4+LNtVP0Qhcfl1X2jde2cCGEEOIyXWhQn4FzTp9XBtBaxx1BNgGX/Xv0fzu37o+B/wP4FuADXwXeUapjNPEvgf8FGAB+CPxvwIvALwDPKqX+67gNKKX+gGjq0BDwdeBPgNuBJ5VSv3mZdQux4vQYPdxq3IKjUjRo4KgUtxq3LJvuN11mL5us20kphyZ1Usphk3V7YvebgtXPiPMRbMPB1TVsw2HE+Uhi9xshhBBiqbnQnPopYNWCWB04lZDfC8xeZi1FrfU5p3Cbm6v/PxDN1/+Hc+FtwO9prd9ckPsI0ZeC31dK/ZnWemze/z0I/FPgEHCv1npmLv77wC7gD5RST2mtj15m/UKsKD1GDz0sj0F8nC6z97wtLBcqWP0yiBdCCLFsXWhP/X6g4/R/Wus/0FrH94aC9cCJyykkbkA/5ztzy5vm5f7xwgH9XPw5YDuQAhae9u+/mFv+6zMD+rl1jgL/HnCAX7+c2sXyMx1O81bwFi/6L/JW8NayadX40xinwrMc4Lu8w7McYJzKYpckhBBCiCvkQoP6XcDdSqnUBfJQShWJ2lq+cCUKm+czc8t3LjL/zNF9C08H9+jcclvMOk8vyBEr2HQ4ze5wN+25Huxt3Wb3MurBfjnGqfAiR2jiUSRNE48XOSIDeyGEEGKFOO/0G631bwO/fZG3NUA0ReZvfpqClFK/BeSBEnAP0ReFd4jm0F9o3RHgMaAB7JgXzxG15qzNn5Izz4G55WUd5CuWlw/0B6RIkZr7rpoiBTqKL+fpJuezmwnS2GTmTp9+ZrmbCQYpLmZpQgghhLgCrth5kLXWB4gOcP1p/RbRF4QztgF/L+HA3A8ppRyig2sd4L+bP8UGPmxYW05Y/Uw88cRZSqmvAF8BuOGGG85Xilji4nqw28uoB/vlKNOkSGcf5jQWZZqLVNHS02qNU6vvxvfLWFaJfO4W0unBxS5LCCGEuCgXmn5zzWmtB7XWChgEPg9sAN5USt2VtI5SygS+CTwE/CnwB1ehrq9pre/RWt/T3y8H0y1nOZXDW9CD3VtGPdgvR4kMrQUz0lr4lMgsUkVLS6s1zszsiwRBE9MsEgRNZmZfpNUaX+zShBBCiIuy5Ab1Z2itJ7TW3wU+SdRV5/+Ly5sb0P8J8LeIDqr90lzP/PnO7IlPOsXcmfjldu4Ry8gN6gbatGnP9WBv6zZt2tygVu4vMLcwQAuPJh4aTROPFh63dPwodv2q1XdjGGlMM4NSCtPMYBhpavXdi12aEEIIcVGW7KD+DK31MWA3cKtSqm/+/ymlbODbwBeB/wj8Ha31wgNk0VrXgZNAXikV17nnTGed/VeydrE09Rg93GLcQmquB3tKpbhlGfVgvxyDFHmI9WSwqdAig81DrJf59HN8v4xhdE5PMow0vp80Y08IIYRYWq7YnPqrbHhuGZwJzHXk+Q7wOaK9+L+utQ7PcxvPAl8GHgf+w4L/e2JejrgOLPce7JdjkKIM4hNYVmlu6s3Z6Uhh2MKykn7cE+LKqLdPMePuxw0qOGaRbmcTudTC08OIpeTYoZCdz8PkBPQPwH0fg5Ebl/w+UnEdWBLPQqXUJqXUOZ+eSilj7uRTq4CX5p0wygG+SzSg/3+48IAe4A/nlv+jUqp73jbWAf8V4HLuYF8IcR3I524hDFsEQROtNUHQJAxb5HO3XHhlIS5TvX2KscZO/LBFyijghy3GGjupt5PO7ygW27FDIU99B+pV6OuPlk99J4oLsdiWyp76TwH/s1LqBeAIcJqoA84jRAfKjgO/MS//D+fWmSKaVvPPlVILb3O71nr7mSta65eUUv878E+Ad5RSf050kqovAD3AP5KzyQpxfUqnB+nueqij+02peLd0vxFX1Yy7H0ulseamflkqDWEUl731S9PO5yGXh1whun5mufN5GLlx8eoSApbOoP5vgI1EPek/QtRask40x/2bwL/VWs8/M9D6uWUf8M/Pc7vb51/RWv9TpdS7RHvmvwKEwBvA72utn/rp/wwhxHKVTg/KIF5cU25QIWUUOmKmcnADOSncUjU5Ee2hny+bi+JCLLYlMajXWr8H/OYl5G/9Kbb1x8AfX+76QgghRJKTYZW3mWIGl24c7qCP1QsG7mc4ZhE/bEV76OcE2sUx5diXpap/IJpyk5v3kDbqUXylOnY45NUXzh5DcP9HYWTDkpi9LRaQR0UIIYS4Ak6GVZ7lBA08ukjRwONZTnAyrMbmdzub8HULP2yhtcYPW/i6RbcjJzdfqu77GNRr0cBeh9GyXoviK9GxwyE/+LPOYwh+8GdRXCw9MqgXQgghroC3mSKDSRYbhSKLTQaTt5mKzc+lVjGUvQ/LSNMOq1hGmqHsfTKffgkbudHg03872lM/NRktP/23V273m1dfgPzcMQTKiJb5fBQXS8+SmH4jhBBCLHczuHSR6ohlsJjBTVwnl1olg/hlZuRG47o5KFaOIVheZFAvlqSGO0G5uQ8vKGObJUqZzWSd5EmLYeU4TLwBrWlI98DAXRjFtckbmP0AxnZBYwqyfTB0N3Sd54yyM8fgxGtn89fcC90jyfnjh2Hv81A+BaVVsOVjMLghOX/0ILz7HMxMQPcA3P4IDG9Mzp84DPtegPIElAZg80dh4Dy3D3DyzDbGoXsw2sbq82zjxAF441mYHoeeQbjrUVhzU2L6rqm3eT48STkFpTZ8zFjN3X13JOa/fOpFttszVGyLouez1evmgVUPJea/+NazvGbPUs+a5BoB93pdPHTno4n5e4/9iFetcWYdhy7X5X5/kC0jn0zMn939NCerb9BIabJtxerCXXTd8kRiPu9uhzd+AI0ZyHbDXZ+F27cmpk8f28WxU69RN1vkgjQjq+6lZ+Tu5NsHXt75ND8ch7F2kaFUhU8NwgP3Jdf06qsv8f3DPhNugQGnyuc2WNx//4OJ+Qff+T5Hg4MEDpgurDM3svFnPpeYv2/vU7xvf0Ara5Fu+Nzq3cDmLZ9OzHcPvkT92Hb8sIplFMiNbMXZmFzP9P6/ZrTxFi0nJO0aDGfvpGfTzyfmH9n3FCfa+witAMM3WZPazPrNyfWw6xl4+S+hPg25Hnjg83D3Y8n1vPVtZlrvEaRCzLZBd/o2eu78lcT8bhyOzX7AbDBLYGtMT9FldjFyvveWfbvhmb+C0RMwvAYe+wXYfJ5Wqnveg6efhJPHYfVaeOIzcPNtyfl734dtT569/cc/A1tuTUzfv/Mgz+wYY6xqMFQIeezhITbdd573iUN74fltMDEKA8Pwscfhxi2J6eXjrzM6uZOm0SQTZhjuv4/S2nsS8xt7X6R8eDsedWxylDZsJbsl+X0CoHp0J5NjL9FSddI6R//QgxTW3ZeY39z3ItUjP/lwG4X1HyezOXkbR4/v5pXJ/UyqgH5t8rP9m1i3Nvkx27tnL9sOH2Y01Awbisc3bGDLzcn30d7XD7Dt5ZOM1g2GcyGPP7CaLfckv/dycA9sfxrGT8Lgatj6BGy8OTH9+In32HV6H6cNn97Q4u7ezaxdE/8c6h+A+tEpcif2QKUMxRKNNTfTv64vNh+geeQVqid34IU1bCNPYfXDZNb/bHL9wK53fswPWnVOWjlW+3U+m85x9898IjH/wJFt7DWO4joKx9VsCddx0/rHE/MP7nuSvdYHtDIG6WbIFv8GNm7+TGL+/jeP8MxL42dfBw8Osukj6xPzlwrzq1/96mLXsOx87Wtf++pXvvKVxS5jxWq4E0xWXwXANHKE2qXmHiNldmFb+XPyw8pxOPqj6EqqAH4TpveiM/0oJ+bkQbMfwMGno8tOCbwGTO2G7CpIx+TPHIN9P+zMP7Ubcqsg03Vu/vhheOU7oIFcF7RqcOxN6BqGfPe5+aMH4blvR5fzc/kHd0HvaijEnCBr4jC8+mfR5VwXuDU49lby7UM0oN8+t41CNzTnbaMYs40TB+BH35yXX4W9r0H/Gij2npO+a+ptnrRGoz85gJYJu60ahVqD4ey5HWVePvUi389VQUE2CHAtg/cdj9zsBGtz5w6AXnzrWX7SVQUUaTfEsw0O5dqYR0e5YfDcN9q9x37E0/kZQJH3fFqWxZ60S/fkKH1d5+5im939NPu9XaA1jmfgWSGTjJGbrpPuj/kwfXc7vPAfosfYKYDXhKOvQbobBtadkz59bBfvn34erTVp7dDGY7x1lELbJtM1fE4+RAP6r38QPR97rAbV0OHl2Tyrau+xdvW5Nb366kv84e4sAN12k1rg8NJEhqH2YdasOfcL7sF3vs8h+yAaMHxNaClm7GkYm6Rn4NwBx769T/F61yhaQcoN8VIGJ3M1nBNj9PWdOwfcPfgS5SM/QKMxjQyhdmnN7MbSWayec+uZ3v/XHAzfAK1JeQa+GTJljJGZbpDpPXdQeWTfUxwPd6MVGKFBaGoqTBJOz9AdUw+7noEffw20jh6zdgMOvAqZHhg+9wvx9FvfZkq/A2jMwCA0QxrGBGpikszg7efePnD8+F725KbRgOWDbylqtsuNpzTrSzE7Gfbthm/8X9Hlnj6olmHnC7Bm3bm7RyEa0P/R/xld7u2PBlkv7oCR9dAfs7d/7/vw9X8HCuidu/2Xno/y+87N37/zIN946hSEmp5MSLWl2Lm3ydqcS+/qmPeJQ3vhO1+PLnf1Qq0Cb74MwyPR37NA+fjrHDz9XPQQaAcPj6nWEXJeinTp3NdBY++LTB55CrTGVGlC3aZW3ksqyGD3xX9Rqh7dyQfjP46eR6TxdZuZ+kEyfhqna/U5+c19L3L6yJMd26jP7sFO2MbR47v5/tQe0JqSNqgT8m5zikEfukrnPmZ79+zl64cOAyG9hqKiNS/PzDISBPT1n3sf7X39AF//8Tig6XU0FQ9ePtBgJNOmb/jc914O7oFv/VH0GHfPPQavvwhrRqDn3HqOn3iPbbPvA5qiNmmokD3uKVa1oVQ89zmRnzjAu099gAo1ds6hUVfUTszw2FaXrhvPrad55BVOH/8hfPi6b1Mv78MOM9jda86tn2hA/+8DAw30Bi0qRooXsFkzeYjhgXPfrw8c2cbb2WOgwPYgsGHMKWNPTtHbfe57xcF9T/Jm8QQasNsaz1aMZqs4o+P09G0+J3//m0f4xtOToKEno6PXwb4ma/NteocSPmOvot/93d8d++pXv/q1i8ldmZPAxLJWbu7DNNKYRhql1IeXy8198StMvAFWFuwsKBUtrWwUjzO2C+wcpHJRfioXXR/bFZ9/4rXoNjvys1E8zt7nwclDZm4SYqYQXd/7fHz+u89FOfPzM4UoHmffC5DOQ3ouP12Iru87zyTHM9vIzq2TvcA23ng2yskW5/KL0fU34k+6/Hx4EifQZEKFQpEJFU6geT48GZu/3Z7BCUPSgUahSAcaJwzZbs/E5r9mz5Jqa1K+RilFytek2prX7NnY/FetcTJ+QMYPUPDh5Vet8dj8k9U3sD1NKjCj2w9MbE9zsprwHHrjB2Bl5p4TRrS0MlE8xrFTr2EHJo5KoZTCUSnswOTYqYTnEPDDcSiYLYpmG0Mpimabgtnih/F/At8/7JO3XApWG6WgYLXJWy7fP+zH5h8NDoIPZqBQGJiBAn8uHuN9+wNMLyTlgUKR8sD0Qt63P4jNrx/bjlKpudexMbdMUT+2PTZ/tPEWtgd2GD0Gdmhie1E8zon2PrRWmKGBIlpqrTjRTnifePkvwc6AkwPDiJZ2JorHmGm9h/KjLwwQLZUfxZMccI9TKLexA4VvmdiBolBuc8A9Hr/CM38FxRIUu6Kail3R9Wf+Kj7/6Seh1BX9M4yzl59+Mj5/25NQKi3IL0XxuHJ2jFG0A4oZMAxFMQNFO+CZHWPxt//8NsgXoVCKbr9Qiq4/vy02fXRyJ1ZokZp7HaRUCiu0GJ3cGZtfPrwdM7QxDWfus8DBDG3Kh7fH1wNMjr2EFVrYc+vYhoMVWkyOvRSbXz3yE0xtdzxPTW1TPfKT2PxXJveTCyGvTAylyCuTXBjF42w7fJgiISXDwFCKkmFQJGTb4cPx+S+fpJgKKKUUhqEopRTFVMC2l+PfS9n+dPScmf8YFEtRPMau0/vIhooc0essh0k2VOw6Hf+6GXnzO3x27Svk8pqpdolcXvPZta8w8uZ3YvOrJ3dgKhtTpaP3FZXGVDbVkzvi6wd+0KpTCFxK2ovuI+1RCFx+0KrH5u81jmIGGsuPvstYPpiBZq9xND7f+gDT06R8MFCkfDA9zV4r/r3rmZfGKabCztdBKuSZlxLefJcQmX4jlhwvKGMZnS3dDOXgBeX4FVrT4Cz49mxlonicxhRkFuxhsLNR/Erkl09BccEemHQuiseZmYCu/nPzZxImLZYnoLgg38lF8SQz49C1YC9MJhfF40yPQ/fC/HwUjyspBUWvM5YOo3icim1R8H2it+S5PyEIqdjxb0n1rEm2EURfqubYXkg9a8bmzzoOJbfdWY8fMOs4sfmNlCbjGvPLwfYNGo6O/wMaM+f+SmNnonhc/WaLbJDuiKWwqZut+NsnmnIzYFeYX1TecBlrx7c7nHALrErVOmJ5s82EG99OMXDAaOuO2zcCTeCccyI/AFpZC6cZdOTbnqaVjX/M/LCKaeQ6YoZK4Sd0gmk5Ic6Cx8AKDFpOfJeN0Aowgs79UkaoCK0gNp/6dPxjVo9/nwhSIaa38PYNglRy149GRpNtheRbZx/XcC4ea/QEDC7YQ50vRvE4J4/D0IK9zYViFE+6/aHhc/MTbn+sajCQ63yM845mrBr/OmNilAP5W/jJxEbGvDxDdo2P5w9y08Tu2PSm0SQdZjpiNjZNoxmb71HHUtmOmKFSeMQP9gBaqo5D5zqWStFS8etc6jYmVUCv7nxeZDGYVPHPu9FQM2h0vqYKSjEaxj8nRusGg5nO51jBiuKxxk/CqqHOWK4QxWOcNnx6QrPjdZbVBqeN+C//nDjOyPBqRox5O43CEE7E374X1rBiXvdeWIvNBzhp5Rj06x3v74WwzUkrF5vvOopUu/P+M/0oHqeVMUi1Qub/0ZavaWXi79PoddCZH70Olv5+8KVfobju2GaJUHceWBZqF9uMmRoD0Rx6f8GHgt+M4nGyfdEUmvm8RhS/EvmlVbBwD0OrHsXjdA/E53cnHENQGgB3Qb5bj+JJugehuWCdZj2Kx+kZjKbodOTXonhcSW1oLXg3aRlRPE7R83HNzhVc06DoxX+w5BoBnt2Z79kGuUb8B2mX69KyOgciLcuky40/YDHbVnhW5wepZ4Vk2/EfEmS7oyk3HSs0o3hc/UGaNp3fetp45BYM9OcbSlWohZ1fQmqhw1Aq/sREA06VWtD5LaoWpBhw4gfRpguh2fn3habCTDimM93w8ezOfM9WpBvxj5llFAh15xMg1G2shJ7taTeacjOfb0Zz6+MYvklodH6wh4bG8BMGoLme+McsF/8+YbYNQqOzntCI5tYnyTYVntVZk2dpss2E59Hwmmi6xHy1ShSPs3otVBfkVytRPOn24/ITbn+oEFJzO2utuYqhQvwXmQPF2/iTU7dRCVIMWDUqQYo/OXUbB4rx87MzYQZvwevAwyOzYKB/hk0u9jlkEz/YA0jrHP6CdXzdJq3j17nUbfRrkwad90eDkH4d/7wbNhRV3fmcqOpobn1sfi6kuuAlVfWjeKzB1VGfyfnq1Sgeoze0aKgF9auQ3jBhH++atVBZ8ByqVKJ4DNvIx9+fxrlTZ89Y7depGp3vXVUjxWo//ouV42qCfgul9QAAIABJREFUBeUGVhSPk26G+Fbn/e1binQz/j691NfBUiKDerHklDKbCcIWwVzv5jOXS5lz574BMHAX+I1ooK11tPQbUTzO0N3g1aFdj/Lb9ej6UMJBi2vujW6zI78RxeNs+Vg0z70518i4WY2ub0loZHz7I1HO/PxmNYrH2fzRaN59ay6/VY2ub/5ofP78bTTm1mlcYBt3PRrlNCpz+ZXo+l3xB6Z+zFiNayqahkajaRoa11R8zIj/YNnqdeMaBi1TodG0TIVrGGz14gfF93pdtFOKtqXQWtO2FO2U4l4v5pgG4H5/kKZl0rRMNHx4+X4//kvJ6sJdeLaibQbR7ZsBnh0dLBt//3w2+uLYrkf3T7seXb/rs7HpI6vuxTMDXN1Ga42r23hmwMiqhOcQ8KlBqAZpKkGKUGsqQYpqkOZTCd/DPrfBouY7VP0UWkPVT1HzHT63If7Dep25ESwITI0mJDA1WHPxGLd6NxDYBm0bNJq2DYFtcKsXP7c5N7IVrdtzr+NwbtkmN7I1Nn84eyeeDZ4RPQaeEeDZUTzOmtRmlNIERogmWiqlWZNKeJ944PPRIN6tR3sa3Xp0/YHPx6Z3p29DW3w4sA+NEG1F8ST3myN4KYVraULAtTReSnG/mXBQ/WO/EM2Lr8xGNVVmo+uP/UJ8/hOfgfJs9C8Mz15+IuGAv8c/A+XygvxyFI8r5+EhKp5JpQlhqKk0oeKZPPbwUGz+T7o/ScGvUAyqGGiKQZWCX+En3fEHpA/334dv+LTnXgdt3cY3fIb74w9iLW3YSmB4BKE791ngEhgepQ1b4/9eoH/oQXzDx5tbxwtdfMOnfyj+AO3C+o8TKK/jeRooj8L6j8fm/2z/JuoG1HRAqDU1HVA3onicxzdsoIJBOQwJtaYchlQweHxDfGODxx9YTaVtUm5rwlBTbmsqbZPHH4h/L2XrE9FzplqOHuNqObq+Nf6A+rt7N9MwNHWi11mdgIahubs34XXz6c9Fz5vZuefQ7Nxz7tPxB9QXVj9MoD0C3YreV3SLQHsUVj8cf/vAZ9M5qqZDWdnRfaRsqqbDZ9PxX6y2hOsITIVvRYc1+RYEpmJLuC4+37+BwFa0LQjRtC0IbMUWP/6967EHB6m0jc7XQdvgsQeX/hnH5UDZyyAHyl5dtpUnZXbRDsr4YQXLzNOTuyOx+41ySuhMPzSnwJ0BpwhrPprc/SZdig6KbU5B8zSku2Dk4eTuN5mu6KDY+uTZ/A1bk7vf5Lujg1bL41CZhHwP3Pmp5O43hZ7ogNXpMZg9FV2/79PJ3W8+vP0JqJyKbv+OJ87f/aY4t42ZMZidiA52vf/Tyd1vir3RQbFTo9E0oGIvfPQXE7vfDGcHKdQajIZVKinI+/AJndz9Zm3uBnKzE5xQTaq2Rc4PeLxVSux+c8Pgesyjo0zQpJE1ybghDzVKid1v+rpupHtylAkqlB2HguextdWf2P0m3X8Tuek6jdY4TUeT9g3WZ+5O7n4zsC46KPbUYWjOQroI938xsftNpmuYQtumVh+nYbbIaIeb+h84b/ebtatvYlXtPT5oBIx7RfrsBl9c7SZ2v1mzZi1D7cMcKXucahfoSTX40qYgsftNz8AWGJukEkwTOgrTgw06uftNX98mnBNjTDOLm7VwWgF3NtYkdr+xetZi6Sx++QTB3E/yhXU/n9j9JtO7kcx0g0Z7AtcJcTyDkdRHErvfdPdtIpyeoepPE1ohRmCy1ro5ufvN8IbooNixQ3PTp0rwyJcSu99kBm9HTUziepPRVBzfoNe+/bzdbwa715CZrDHul2lmNGlX8bFwhDs33B+/Ql9/dFDs6HEYOxkdvPpLv5rc/aZ/VXSQ6/EPoik0/QPwxS8nd7/pm5d/5va/8OXE7je9q3tYm3MZHa0xXjPpy2l+6RODid1vnnrPoWdVHlWrQKMGmSz2TZsZV71sve3cPdfp0jA5L0WjMU7LaJLWDiN9DyV2v7H7biAVZGjPnsCngaUy9Gz45Hm73zhdq8n4aVq1MVzVwCHD8NDWxO43dt8N2EEGb/b4h9voWv/zid1vukr9DPpwqjHDlBHSjcnP9W9J7H7T19/HSBBwYnaWsRD6DYMv3Jjc/aZvuJeRTJsTY1XGGgb9Gc0XHhlO7n7T0x8dFDt2POpA1NsPn/1iYvebUnEVq9ow1Zhm2ggoaYuHe25O7H7DqgFYtx4+OAYnT0TXv/RrcGv8weJ29xrsMINXPYkf1rGMLF1rfu683W+GB25kzeQhjnktxqwcq4IWX07Zid1vers3Yk9OMa1naTuKVBtuayd3v+np24wzOs4MZdyMgeNqbm+uTex+0zvUzdp8m9HxGuM1I3odPDqwaN1vLuVAWaV1wlw/keiee+7Rr7/++mKXIYQQQiyar/21R6UJxczZqQqVpqaYga/8vL2IlQmxciildmmtk/u+ziPTb4QQQghxyT5+u0G1GQ3kQ62pNDXVZhQXQlx78soTQgghxCW7adjkS48YFDMwMQvFDHzpEYObhhMOVhZCXFXS0lIIIYQQl+WmYVMG8UIsETKoF0IIIYQQy0ajPcFscx/toELKLNKV2Uw2ldzW2W2O0ay8R+DNYtpdZIq34WTiuzotZzL9RgghhBBCLAuN9gSnaq/ihy1so4AftjhVe5VGO/4EjG5zjOrUDsKgiWGVCIMm1akduM2EMyUvYzKoF0IIIYQQy8Jscx+GSmMZaZRSWEYaQ6WZbe6LzW9W3sMwMxhmBqXUh5eblfeuceVXnwzqhRBCCCHEstAOKpiq82zbpnJoB/Fn2w68WZTRefZuZaQJvNmrVuNikTn1QgghhBDXidPhDEf1cWo0yJNlnVpLrxF/Nu+lKGUW8cMWljo7UA+0S8osxuabdhdh0ESZmQ9jOmxh2vFnJF/OZE+9EEIIIcR14HQ4w7t6D65uk9MZXN3mXb2H0+HMYpd20boymwl1Cz9sobXGD1uEukVXZnNsfqZ4G2HQJAyaaK0/vJwpJpxFdxmTQb0QQgghxHXgqD5OSqdwVAqlFI5KkdIpjurji13aRcumBliVvx/LSOOFVSwjzar8/Yndb5zMEIW+hzHMDKFfxjAzFPoeXpHdb2T6jRBiUXiNUdyZdwnaM5ipbpzu27Gzw4n5YeUE+tQb0JqGdA9q1V0YxTWJ+ZPlPRxovk9VuRS0w02ZW+kv3Zxc0OQROPQyVE9BYRXc+AD0r0/OHzsE7++A2QnoGoBbH4ahGy/mTxdixah6k0x5B3HDCo5RpM/eSMHuT16hfBzGX4fmNGR6YPAeKK1Nzj91BA6+CJVTUFwFGx+CVed5XQLu8Z3UTz6HH9SwzDy51Y/grL0veYXRg/DuczAzAd0DcPsjMLzxAn/58lSjQY5MRyyFTY3GIlV0ebKpgfO2sFzIyQytyEH8QrKnXghxzXmNUerj2wn9BobdReg3qI9vx2uMxuaHlRPoYz9Cew200x0tj/2IsHIiNn+yvIfXW7to4ZHXDi08Xm/tYrK8J76gySPw5nfBrUG+L1q++d0oHmfsELzwn6BZhVJ/tHzhP0VxIa4TVW+SE61deGGLlCrghS1OtHZR9SbjVygfh8PbwGtAuidaHt4WxeOcOgKv/wW0alDoi5av/0UUT+Ae30n5+FMEYQvTzBGELcrHn8I9vjN+hdGD8Ny3o9dw19xr+blvR/EVKE+WNl5HrI1HnuwiVSSuJBnUCyGuOXfm3aitmJWNWoxZWQwzgzvzbmy+PvUG2sqi7Chf2Vm0lY323Mc40HwfR5ukSaFQpEnhaJMDzffjCzr0Mjj56J8yzl4+9HJ8/vs7IF2ATCHKzxSi6+/vuJy7Q4hlaco7iKkc7LnWgraRxlQOU17CgHj8dbCzYOdAqWhpZ6N4nIMvQjof/VPG2csHX0ysqX7yORQ2pplBKSNaYlM/+Vz8Cu8+F71+57+WM4UovgKtU2tpqzaubqO1xtVt2qrNOnWeX0vEsiGDeiHENRe0Zzo6EQAoM0PQTjhYqzUNVmc+ViaKx6gqFwe7I+ZgU1Vu/O1XT0FqwZ6qVDaKx5mdgHSuM5bORXEhrhNuWMFa0FrQUg5uGN9akOY0WAteZ1Y2isepnAJnQb6TjeIJ/KCGYXTWZBgOflCLX2Em4bU8szJfy71GN7erm3FUirpq4qgUt6ubl1X3G5FM5tQLIa45M9VN6DdQ8z7gddDETCV8sJz5qd6e9wHvN6N4jMLclJs0qQ9jLh4F7cTmU1gVTblx8mdj7UYUj9M1wJGTNi+N3sypWo5V+ToPDu9h/erkOZ57pgKePuRzsqpZXVA8caPFzX1mYr4QS51jFDnoBrzuFZgMDfqNkHvsKhud+NaCZM68jucNov1GFI9TXBVNuUnPe126jSiewDLzc1Nvzu4ECEMXy8zHr9A9EE25yRTOxlr1KL5C9Rrd9CKD+JVI9tQLIa45p/v2qK2Y34hajPkNwqCJ0317bL5adRfKb0Rz6bVGew2U30Ctuis2/6bMrbgqoEUbjaZFG1cF3JS5Nb6gGx+IBvVuDXR49vKND8SmH+l6jL9851ZqdejL1ajV4S/fuZUjXY/F5u+ZCvijN9uUXc1QHsqu5o/ebLNnKrjwnSXEEjWr1/GDVpZKGNCjQiphwA9aWWb1uvgVBu+JBvVeHbSOll4jisfZ+FA0qG/NvS7PXN74UGJNudWPoPEIgiZah9ESj9zqR+JXuP2RaFDfrEbbOHP59oR8IZYwGdQLIa45OztMbnArhpUl9GYxrCy5wa2J3W+M4hrUyCejOfXuTLQc+WRi95v+0s3ck76bNDY15ZLG5p703cndb/rXw0d+KdpTX5uKlh/5pcTuNy8dWk1+3Qj5HBhug3wO8utGeOnQ6tj8pw/5lBxFyVEYSn14+elD/oXvLCGWqFe8FANWH0XTRCuXomkyYPXxipeKX6G0FjY8Hv3i1pqOlhseT+5+s2o93PPL0Z766lS0vOeXz9v9xll7H6W1n8Y00gRBHdNIU1r76eTuN8Mb4ZFfifbUz05Gy0d+ZcV2vxErm0y/EUIsCjs7fN4WlgsZxTVwnhaWC/WXbj5/C8tzVlh//haW85yagr7+Iqy688NYVmtOTcXnn6xGe+jnK6SiuBDL1ZjvM2BlMdTZ6TSh1oz55/myWlp7/haWC61af8EWlgs5a+87fwvLhYY3yiBerAgyqBdCiEu0qg9qdcjPmxrcaETxOKsLirKrKc2b0l9tR3Ehlqshy6ISBhTV2WNDajpkyFpeQ4t2c4xm+T0CbwbT7iZTuo3UddDTXKw8Mv1GCCEu0YP3K2p1Ra2uCbWmVtfU6ooH748fpD9xo0XZ1ZTdKP/M5SduXF6DHyHmeyyToxpqKmFAqKNlNdQ8lsldeOUlot0coza5gzBoYlhdhEGT2uQO2s2xxS5NiEsmg3ohhLhE69cZfP4zinxOMXU6Wn7+M4r16+LfUm/uM/kHH0lRchRjNSg5in/wkZR0vxHL2iYnzd8tlCgaJhNBQNEw+buFEpuc9GKXdtGa5fdQZgbDzETnzDAzKDNDs/zeYpcmxCWT3URCCHEZ1q8zWL/u4vNv7jNlEC9WnE1OelkN4hcKvBkMq6sjpow0gZdwzgwhljAZ1AshhBACgHEq7GGcMi1KpLmZQQZJ6Du/Aph2N2HQ7DgZng5bmLb0cRfLj0y/EUIIIQTjVHiJIzTxKOLQxOMljjBOwhliV4BM6TZ00CQMmtE5M4ImOmiSKd222KUJcclkUC+EEEII9jBOGosMNgpFBps0FnsYX+zSrppUZoh8/8MYZobQn8UwM+T7H5buN2JZkuk3QgghLsohr8mOdpWJ0GPAsHk4VeBGO3PhFcWyUKZFEacjlsaiTGuRKro2UpkhGcSLFUH21AshhLigQ16TP21NUw0D+pVFNQz409Y0h7zmYpcmrpASaVp0njiqhU+J5XsgrBDXExnUCyGEuKAd7Sp5DAqGiaEUBcMkj8GOdnWxSxNXyM0M0sKniYdG08Sjhc/NDC52aUKIiyCDeiGEEBc0EXrkVOdHRk4ZTITeIlUkrrRBijzIejLYVHDJYPMg61d09xshVhKZUy/EdaLePsW0ux83rOAYRXqcTeRSqxLz32+f5Dn/JKfx6MXmEWs1t6ZWX8OKry23OUaj+j6BN4tpd5Et3Ioj82w/NGDYVMOAgjrba7+uQwYMexGrElfaIEUZxK9w45TZywRlmpTIsIUBBiktdlniCpA99UJcB+rtU4w2duKHLVKqgB+2GG3spN4+FZv/fvskf+4fpU5ADzZ1Av7cP8r77ZPXuPJrw22OUZl+Ye5U8SXCoEll+gVcOVX8hx5OFagRUg0DQq2phgE1Qh5OFRa7NCHERRqnzCsfti1N08TjFY4wTnmxSxNXgAzqhbgOTLv7sVQay0ijlMIy0lgqzbS7Pzb/Of8kWQxymCggh0kWg+f8lTmob1TfxzDSHaeKN4w0jer7i13aknGjneEL6R4Khsmk9ikYJl9I90j3GyGWkb1M4GB3tC11sNnLxGKXJq4AmX4jxHXADSukVOceVVM5uGH8SWVO49FD57SKLCanWZnzpwNvFsPq/Pk5OlX87CJVtDTdaGdkEC/EMlamSXFBN6Oobal0sVoJZFAvxHXAMYr4YQtLnX0zD7SLY8TPne2dm3KT4+z86QYBvazM+dOm3ZVwqviuRaxKCLEUnQ5nOKqPU6NBnizr1Fp6je7E/KB6gnDqLXRrGpXuwei7E7Ow5jwbOArHXoX6FOT6YOR+6F2XmH5k6gAvlw8zSUA/Jg+UNrC+76bY3BIZPqhOc7TWpKyhpGBdPsMNhZ6L/OvFUibTb4S4DvQ4m/B1Cz9sobXGD1v4ukWPsyk2/xFrNQ1C6gRooE5Ag5BHrJV5oGy2cCth2Oo4VXwYtsgWbl3s0oQQS8jpcIZ39R5c3SanM7i6zbt6D6fDmdj8oHoC//jfoL0GON1or4F//G8IqicSNnAU3nsS3Dpke6Ple09G8RhHpg7wvfIBajqkF5OaDvle+QBHpg7E5mdPu+xstqkqKBhQVbCz2SZ72r2Me0MsNTKoF+I6kEutYjh7H5aRpq2rWEaa4ex9id1vbk2t5j+z1pHDZBqP/7+9ew+TrK7vPP7+Vt+7Z3qmGYZhBIbBAYwKbDSNRkRE4ma9RpeFaLImXqKuSdQYZdddjdFk141ZTVwvWU10NySaDRrF+CheNgoIqI8wiA4GcQAZrsNlYHp6+n6p7/5xTofqtprpmZ7uqhrer+ep5zf1rd859as6UzOfPv2r3+mjjQvatx6xq9909Wym/6izy0vF76PS1kP/UWe7+o2keXblXXRmJ13RSUTQFZ10Zie78q66/at7fkC09xIdvURE0bb3Ut3zg/pPcMf3oLMPuvogomg7+4p6Hd/d91P6MlgTbVQo2r4Mvrvvp3X737xnNyfun2AtMNUWrAVO3D/BzXtcFOBI4PQb6TGir/OYR13CcqEndx7ZS1gu1NWz2RAv6VGNMEYf879X0kkHI4zV7Z8TD0PXgqk57T1FvZ7RPcUZ+nlP0FvU63iQWTbULDML0BsVHmS2bv/7s8rGmQqVfY9cObha1tX6PFMvSZK0BGvoZWrBggFTTLOG3rr9o/somFnwJdSZ8aJeT9/RMLXgB4SpsaJex0baGFsQyMeyykba6vbfFBVGF/QfzSqbwjh4JPAoSpIkLcHWOIGpmGIyp8hMJnOKqZhia5xQt3/l6J8nZ8bI6TEys2hnxqgc/fP1n+DEp8PUaDGXPrNop0aLeh3PWPd4RiMZyVmqFO1oJM9Y9/i6/c8eOIkRYH/Zf3/OMlLW1foM9ZIkSUuwoTLA6fFEuqKT0RinKzo5PZ646Oo3bWuPp/2E5xIdvTC5l+jopf2E5y6++s2GrXDai4u59GMPFe1pL1509ZuTjj6Fl647hTVR4SFmWRMVXrrulEVXv3n8Madw4VHbWBsVHswqa6PChUdt4/HH1O+v1hKZ2egxtJzBwcHcvn17o4chSZKkI1hEXJ+Zg0vp65l6SZIkqcUZ6iVJkqQWZ6iXJEmSWpzr1EuSpKb0YA5xG/ewnzHW0ss2jmNjrG/0sKSm5Jl6SZLUdB7MIW5gJ5NMsYYeJpniBnbyYA41emhSUzLUS5KkpnMb99BFB110EgRddNJFB7dxT6OHJjUlQ70kSWo6+xmjk455tU462M/YIltIj22GekmS1HTW0ssU0/NqU0yzlt4GjUhqbk0T6iPiTyPimxFxV0SMR8TDEXFDRLw7IjYsss1ZEfGVsu94ROyIiLdERNujPM+LIuLKiNgXESMR8b2IeOXKvTJJknSwtnEck0wzyRRJMskUk0yzjeMaPTSpKTVNqAd+H+gD/gn4EPB3wAzwHmBHRJxQ2zkiXgJcBZwDfAH4KNAJfBC4pN4TRMQbgS8BpwGfBj4BPA64OCI+cNhfkSRJOiQbYz1P4VS66GSEcbro5Cmc6uo30iIiMxs9BgAiojszJ+rU3wu8A/hYZv5OWesHbgXWAc/MzO1z+wAuB54B/FpmXlKzn63AzcAo8AuZuausDwDXAduAszLzuwca6+DgYG7fvv2QX6skSZJ0IBFxfWYOLqVv05yprxfoS58t21NqahcAG4FL5gJ9zT7+oLz72wv28xqgC/joXKAvt9kL/Pfy7hsOafCSJB2CO6ujXDpzF5+YuY1LZ+7izupoo4d0UHYMT/OenaO8Zsd+3rNzlB3D0wfeSNKKaJpQ/yheXLY7amrnle3X6vS/ChgDzoqIriVu89UFfSRJWlF3Vkf56uxuRnOGo7KD0Zzhq7O7WybY7xie5gO3T7B3usrxXcHe6SofuH3CYC81SNNdUTYiLgLWUEytGQTOpgj076vp9oSy3blw+8yciYjbgScDjwd+vIRtdkfEKHB8RPRmputlSZJW1Pbqw/TSRl8U/xX30Q5Z1LdU+ho8ugO79L4pBtphoKM4PzjQEUCVS++b4oz+jkffWNJh13ShHrgI2FRz/2vAqzLzwZraurLdt8g+5uq136ZZyjZ9Zb+fCfUR8Xrg9QBbtmxZbOySJC3JQ0xx1IJ12Htp4yGmGjSig3PnRHGGvta69uDOieqi2+ycnODy8VF2z86wua2d83r6OLWre6WHKj0mNN30m8w8NjMDOBY4n+Js+w0R8dQGj+uvMnMwMwc3btzYyKFIko4AG+hkjNl5tTFm2UBng0Z0cLZ0V9g3M3+xjX0zyZbu+tFi5+QEn9o/xHB1lk2VNoars3xq/xA7Jxf7Sp2kg9F0oX5OZt6fmV8AfhnYAPxtzcNzZ9vX/cyG8+tDh7DNYmfyJUk6bAYrRzHGLKM5Q2YymjOMMctg5ahGD21Jzj+2k70zsHe6SjWTvdNV9s4U9XouHx9lbaVCf6WNSgT9lTbWVipcPt4a3yGQml3Thvo5mXkHcBPw5Ig4uiz/pGxPXdg/ItqBkyjWuP9pzUOPts1miqk3dzufXpK0GrZU+nh+22b6op2HY5q+aOf5bZtbYj49wBn9HVx0UjcDHRXunkwGOipcdFL3ovPpd8/OsCbmx441UWH37MxqDFc64jXjnPp6Hle2c7+nvBz498DzgL9f0PccoBe4KjMna+qXA88st1m4Fv3za/pIkrQqtlT6WibE13NGf8eSvxS7ua2d4eos/TUXfR/JKpvbWiWKSM2tKc7UR8SpEfEz02IiolJefOoY4DvlmvIAnwP2AC+PiMGa/t3AfyvvfmzB7v4amATeWF6Iam6bAYqLWwF8fPmvRpIkLXReTx/7q1WGq7NUMxmuzrK/WuW8ntb9oUZqJs3y4/ELgD+JiGuA24GHKFbAeTbFF2XvA1431zkzhyPidRTh/sqIuAR4GPgViqUrPwd8pvYJMvP2iPiPwIeB7RHxGWCK4kJWxwN/tpSryUqSpIN3alc3v8H6eavfvLSv39VvpMOkWUL9N4CTKdakfwrFUpSjFGvKfwr4cGY+XLtBZv5jRDwbeCfw74Bu4FbgrWX/+V/JL7b5SETsolg28zcpflNxE/AHmfk3K/PSJEkSFMHeEC+tjKiTfXUAg4ODuX379kYPQ5KW5YEc4hZ2M8wY/fRyCps5JtYfeENJ0qqIiOszc/DAPZtkTr0kaXU9kENs51YmmGItPUwwxXZu5YEcOvDGkqSmY6iXpMegW9hNFx1000kQdNNJFx3cwu5GD02SdAgM9ZL0GDTMGF3MX4qwiw6G8VIdktSKDPWS9BjUTy+TTM+rTTJNP70NGpEkaTkM9ZL0GHQKm5lkmgmmSJIJpphkmlPY3OihSZIOgaFekh6Djon1DHIy3XSyn3G66WSQk139RpJaVLOsUy9JWmXHxHqOYekhfjfD/IgHGGKC9XRzGsewmf4VHKFW241DM3zx7mnuGqtyQm+FlxzfwenrjQpSK/BMvSTpgHYzzFXcwTjTrKOLcaa5ijvYzXCjh6bD5MahGT70k0mGpqoc1xMMTVX50E8muXFoptFDk7QEhnpJ0gH9iAfooZ0eOgiCHjrooZ0f8UCjh6bD5It3T7O+A9Z3VqhEsL6zwvqOoi6p+RnqJUkHNMQE3QtmbHbTzhATDRqRDre7xqr0d8S8Wn9HcNdYtUEjknQwnCgnSTqg9XQzzjQ9NWvbTzDDerobOKrWd9vMONdMDXN/dZpNlQ7O7uxnW3tPQ8ZyQm+Foakq6zsfCfbD08kJvYuf/7tzdpTrqnvZwyRH08WZlQG2tPWtxnAlLeCZeknSAZ3GMYwzwzjTJMk404wzw2kc0+ihtazbZsb5h4k97K/OsjHa2V+d5R8m9nDbzHhDxvOS4zsYmoahqSrVTIamqgxNF/V67pwd5bLqbkaZYQOdjDLDZdXd3Dk7usojlwSGeknSEmymn3M4kR462MckPXRwDie6+s0yXDM1zBraWFtpoxLB2koba2jjmqnGfPn49PXt/N4TuljfWeGe8WR9Z4Xfe0LXoqvfXFfdSx/t9EXZ+jOrAAAek0lEQVQ7EUFftNNHO9dV967yyCWB028kSUu0mX5D/GF0f3WajTH/v+G+qHB/tXFfTD19ffuSl7DcwyQb6JxX66WNPUyuxNAkHYChXpJWwQM5xC3sZpgx+unlFDYf8EJPM6P3MLV3B9XJvVS6BugcOIP2vuMW32DoTrj7Ohh7CHo3wPFnwvoti49p6CZ2jt3EcGWS/moXp/Y+iWPWP2nx/e++DW66Gobuh/Wb4EnPgs3bFu3+7fuHufQnY9y3D45dB+c/oZdnblr8h4KR6Qd4aPJWJqvDdFX62dB1Mms6jtzpPZsqHeyvzrI22v6lNppVNlXqT3dpNkfTxSgz9NVEiTFmOZquBo5Keuxy+o0krbAHcojt3MoEU6ylhwmm2M6tPJBDi24zM3oPE7uvoDozRnSupzozxsTuK5gZvaf+BkN3wk8ug6lR6DmqaH9yWVGvN6ahm7h24gYmmGFttYsJZrh24gYeGLqp/v533wbf/iyM74d1G4v2258t6nV8+/5hPvq9MYYn4Jh+GJ6Aj35vjG/fX39qycj0A9w7vp2ZnKCzspaZnODe8e2MTB+5S2ae3dnPCLPsr85SzWR/dZYRZjm7szV+G3JmZYBRZhjNGTKT0ZxhlBnOrAw0emjSY5KhXpJW2C3sposOuukkCLrppIsObmH3ottM7d0B7T1U2nuJCCrtvdDeU9Trufs66OiDzj6IKNqOvqJex86xm+iuttEd7URAd7TTXW1j59giof6mq6F7DfSshagUbfeaol7HpT8ZY0039HcHlQj6u4M13UW9nocmb6UtummvdBMRtFe6aYtuHpq8ddH3qNVta+/hwu6jWVtp48GcYW2ljQu7j27Y6jcHa0tbHy+sbKaPdh5iij7aeWFls6vfSA3i9BtJWmHDjLGW+UGtiw6GqR9wAaqTe4nO+dNzoq2H6uQiX0Ice6g4Q1+ro7eo1xtTZZK11fnTJLpoZ7iyyHzoofuLM/S1uvuKeh337SvO0Nda01XU65msDtNZWTuv1hZdTFaP7CvWbmvvaZkQX8+Wtj5DvNQkPFMvSSusn14mmf/lx0mm6ad30W0qXQPk7PylDXN2nErXIlMbejfA9IIfEqbHinq9MVW7mGRmwZhm6K8uMh96/SaYWLBU4cRoUa/j2HUwsuDng5HJol5PV6Wf2Zy/wWxO0lVpjakoktRohnpJWmGnsJlJpplgiiSZYIpJpjmFzYtu0zlwBsyMU50ZIzOpzozBzHhRr+f4M2F6tJhLn1m006NFvY5Te5/ERGWWiZwhEyZyhonKLKf2LvJF2Sc9CyZGirn0WS3aiZGiXsf5T+hlZAKGJ5JqJsMTychEUa9nQ9fJzOYEM9UJMpOZ6gSzOcGGrpMXfY8kSY+IzGz0GFrO4OBgbt++vdHDkNRCXP3G1W8k6WBFxPWZObikvob6g2eolyRJ0ko7mFDv9BtJkiSpxRnqJUmSpBZnqJckSZJanOvUS5Ik6bDYsW+Gf9w9xZ1jVbb0Vnjp5k7OWGfcXA2eqZckSdKy7dg3wwdvnWDvVJXje4K9U1U+eOsEO/bNHHhjLZuhXpIkScv2j7unWN8BA50VKhEMdFZY31HUtfIM9ZIkSVq2O8eqrOuIebV1HcGdY9UGjeixxVAvSZKkZdvSW2Hf9PzrH+2bTrb0GjdXg++yJEmSlu2lmzsZmoa9U1WqmeydqjI0XdS18gz1kiRJWrYz1rXz+yd3M9BZ4e7xZKCzwu+f3O3qN6vEd1mSJEmHxRnr2g3xDeKZekmSJKnFGeolSZKkFmeolyRJklqck54kSZLUMHfcVuW6q2DP/XD0JjjzHDhxm+edD5bvmCRJkhrijtuqXHYJjO6HDRuL9rJLiroOjqFekiRJDXHdVdC3trhF5ZE/X3dVo0fWegz1kiRJaog990Nv3/xab19R18Ex1EuSJKkhjt4EY6Pza2OjRV0Hx1AvSZKkhjjznGIe/eh+yOojfz7znEaPrPUY6iVJktQQJ26r8MKXF/PoH3qwaF/4cle/ORQuaSlJkqSGOXFbhRO3NXoUrc9QL0mSJJUequ5lV97FCGOsoZetcQIbKgONHtYB+bsNSZIkiSLQ35g/ZjKn6MseJnOKG/PHPFTd2+ihHZChXpIkSQJ25V10Zidd0UlE0BWddGYnu/KuRg/tgAz1kiRJEjDCGJ10zKt10sEIYw0a0dIZ6iVJkiRgDb1MMT2vNsU0a+ht0IiWzlAvSZIkAVvjBKZiismcIjOZzCmmYoqtcUKjh3ZAhnpJkiQJ2FAZ4PR4Il3RyWiM0xWdnB5PbInVb1zSUtJhccdtVa69Gh68HzZugqc9y4uHSJJaz4bKABto/hC/kP/jSlq2O26r8uXPFpf2Pnpj0X75s0VdkiStPEO9pGW79mroW1Nc3jsqRdu3pqhLkqSVZ6iXtGwP3g+9ffNrvX1FXZIkrTxDvaRl27gJxkbn18ZGi7okSVp5hnpJy/a0Z8HoSDGXPqtFOzpS1CVJ0soz1EtathO3VXjRrxZz6fc8WLQv+lVXv5EkabW4pKWkw+LEbRVO3NboUUiS9NhkqJckSVJdOycn+cbYCLtnZtnc3sZze9dwalfXov3vyf3sYA97mWCAbs7gaI6Ltas44seupvjdeERsiIjXRsQXIuLWiBiPiH0RcU1E/FZE/Mw4I6IrIn43Iq6NiD0RMRIRP46ID0fEiY/yXK8stxkpn+PKiHjRyr5CSZKk1rJzcpKL9w0xPFtlU1sbw7NVLt43xM7Jybr978n9XMFdjDHNeroYY5oruIt7cv8qj/yxqSlCPXAh8Ang6cD3gP8JfB44Dfgk8NmIiLnOEdEOfBP4KLAW+Hvg48ADwJuAH0bEkxY+SUR8ALgY2Fw+36eB04EvRcQbV+i1SZIktZxvjI3QX6nQ39ZGJYL+tjb6KxW+MTZSt/8O9tBDO710EAS9dNBDOzvYs8ojf2xqluk3O4FfAS7LzH+5BGVEvAO4Fvh3wPkUQR/g3wLPpAj2v7xgmz8C/hC4CHhNTf0s4G3AbcCZmbm3rL8fuB74QER8OTN3rdBrlCRJahm7Z2bZ1NY2r7amUmH3zGzd/nuZYD3zp+b00M5eJlZsjHpEU5ypz8zLM/NLteG8rN9HcQYe4Nyahx5ftpct3Ab4YtluXFB/Q9m+dy7Ql8+xC/gLoAt49SG9AEmSpCPM5vY2RqrzY9ZItcrm9ra6/QfoZpyZebVxZhige8XGqEc0Rag/gOmyrf1b8s9l+/w68+3n5sd/Y0H9vLL9Wp3n+OqCPpIkSY9pz+1dw3C1yvDsLNVMhmdnGa5WeW7vmrr9z+BoxplhjGmSZIxpxpnhDI5e5ZE/NjXL9Ju6yrnzv1nerQ3jlwGXUkzJuTEivgFMAb8AnA18hOLs+9x++oDjgJHM3F3nqW4p21MP6wuQJElqUad2dfGqdevnrX5z/tr+RVe/OS7W8pw8Yd7qN7/IZle/WSVNHeqB91F8WfYrmfn1uWJmZkRcALwb+AOg9kux3wT+b2bWntlfV7b7Fnmeufr6xQYSEa8HXg+wZcuWg3kNkiRJLenUrq5HXcJyoeNiLcdhiG+Epp1+ExFvpvhi683Abyx4rBv4TPn471KsZrMOeAFwInBVRLzkcI4nM/8qMwczc3DjxoXT9SVJkqTGacpQXy4v+SHgJuA5mfnwgi7/mWIZzHdm5l9m5n2ZOZyZXwUuADrK7efMnYlfR31z9aHD8gIkSZKkVdR0oT4i3kIxJ/5HFIH+vjrd5r4Me8XCBzLzh8Be4MSI2FDWRoF7gDURsbnO/k4p253LHL4kSZK06ppqTn1EvJ1iHv0PgH+dmYtdrWBuctfPzIOJiC74l8lcUzUPXU4xjed5wF8v2Oz5NX0kSYfBLffOcsWNVXbvhc0D8JzTK5zyuPpL4UmSlqdpztRHxLsoAv31wC89SqAHuLps31GG+Frvofhh5brMedclnlvv/p0RMVDzvFsp5uVP8rNhX5J0CG65d5ZPf6vK8DhsWg/D4/Dpb1W55d76F62RJC1PU5ypj4hXAn8MzFIE9jdHxMJuuzLz4vLP7wVeDPwScHNEfA0Yp7jK7NPKP/9e7caZ+Z2I+HPgrcCOiPgc0Am8DDgKeJNXk5Wkw+OKG6us7YH+nuLf8v4egOSKG6uerZekFdAUoR44qWzbgLcs0udbwMUAmXlPRDwVeDvwQoorwVaA3WWfP83MmxfuIDPfFhE3UpyZfz1QBb4PvD8zv3y4XowkPdbt3lucoa+1pruoS5IOv6YI9Zn5HoppMwezzYPAReXtYLa7mPKHA0nSytg8UEy5Kc7QF0Ymirok6fBrmjn1kqQjx3NOr7B/HIbHs7i8/Hiyf7yoS5IOP/91lSQddqc8ro1XPLtCfw/cP1ScsX/Fs139RpJWSlNMv5EkHXlOeVybIV6SVoln6iVJkqQWZ6iXJEmSWpyhXpIkSWpxhnpJkiSpxRnqJUmSpBZnqJckSZJanKFekiRJanGGekmSJKnFGeolSZKkFmeolyRJklqcoV6SJElqcYZ6SZIkqcUZ6iVJkqQWZ6iXJEmSWpyhXpIkSWpxhnpJkiSpxRnqJUmSpBZnqJckSZJanKFekiRJanGGekmSJKnFGeolSZKkFmeolyRJklqcoV6SJElqcYZ6SZIkqcUZ6iVJkqQWZ6iXJEmSWpyhXpIkSWpxhnpJkiSpxRnqJUmSpBZnqJckSZJanKFekiRJanGGekmSJKnFGeolSZKkFmeolyRJklqcoV6SJElqcYZ6SZIkqcUZ6iVJkqQWZ6iXJEmSWpyhXpIkSWpxhnpJkiSpxRnqJUmSpBZnqJckSZJanKFekiRJanGGekmSJKnFGeolSZKkFtfe6AFIkuqbHruXyb03Mju1l7bOAboGTqej93GNHpYkqQl5pl6SmtD02L2M3ncl1ZkxKh3rqc6MMXrflUyP3dvooUmSmpChXpKa0OTeG6m09VBp7yUiqLT3UmnrYXLvjY0emiSpCRnqJakJzU7tJdp65tWirYfZqb0NGpEkqZkZ6iWpCbV1DpCz4/NqOTtOW+dAg0YkSWpmhnpJakJdA6dTnR2nOjNGZlKdGaM6O07XwOmNHpokqQkZ6iWpCXX0Po6+Y8+l0t5LdXqISnsvfcee6+o3kqS6XNJSkppUR+/jDPGSpCXxTL0kSZLU4gz1kiRJUosz1EuSJEktzlAvSZIktbimCPURsSEiXhsRX4iIWyNiPCL2RcQ1EfFbEVF3nBHRVm53VUTsLbf7aUR8JiJOXWSbV0bEtRExUj7HlRHxopV9hZIkSdLKaZbVby4EPgbsBq4A7gQ2AecDnwSeHxEXZmbObRARa4AvAucBPwD+BpgAjgOeBZwK7Kx9koj4APA24G7gE0An8HLgSxHxpsz86Aq+RkmSJGlFRE1ObtwgIs4D+oDLMrNaUz8WuBY4AbggMz9f89jfAb8OvCEz/7LOPjsyc7rm/lnAt4HbgDMzc29Z3wpcXz7/z2XmrgONd3BwMLdv337wL1SSJElaooi4PjMHl9K3KabfZOblmfml2kBf1u8DPl7ePXeuHhFPpQj0n6kX6MttpxeU3lC2750L9GW/XcBfAF3Aq5fxMiRJkqSGaIpQfwBz4XympvbrZfv3EbEuIl4REf8lIl4fEScvsp/zyvZrdR776oI+kiRJUstoljn1dUVEO/Cb5d3aMH5m2Z5IMZ1mQ81jGREfA96cmbPlfvoo5tqPZObuOk91S9nW/XKtJEmS1Mya/Uz9+4DTgK9k5tdr6seU7Z8DVwJPBNYCz6UI+b8DvKum/7qy3bfI88zV1y82kPK3ANsjYvuDDz54MK9BkiRJWlFNG+oj4s0UK9XcDPzGgofnxn0z8LLMvDkzRzLzm8AFQBV4a0R0Hq7xZOZfZeZgZg5u3LjxcO1WkiRJWramDPUR8UbgQ8BNwHMy8+EFXYbK9ktzU2zmZOYPgdspztw/sSzPnYlfR31z9aFFHpckSZKaVtOF+oh4C/AR4EcUgf6+Ot1+UraLhfC51W16ADJzFLgHWBMRm+v0P6Vsd9Z5TJIkSWpqTRXqI+LtwAcpLib1nMx8YJGu3yjb0+rso4tHQvqumocuL9vn1dnf8xf0kSRJklpG04T6iHgXxRdjrwd+KTP3PEr3zwP3Ai+LiKcteOxdFNNprlhwln9uvft3RsRAzfNuBX4XmAT+ejmvQZIkSWqEpljSMiJeCfwxMAtcDbw5IhZ225WZF0MxnSYiXgV8Gbg6Ii6lmF7zdOBs4AHgP9RunJnfiYg/B94K7IiIzwGdwMuAo4A3LeVqspIkSVKzaYpQD5xUtm3AWxbp8y3g4rk7mflP5Vn6d1EsZbkOmLsC7X/NzHsX7iAz3xYRN1KcmX89xSo53wfen5lfPjwvRZIkSVpdkZmNHkPLGRwczO3btzd6GJIkSTqCRcT1mTm4lL5NM6dekiRJ0qEx1EuSJEktzlAvSZIktThDvSRJktTiDPWSJElSizPUS5IkSS3OUC9JkiS1ONepPwQR8SBwR6PH8RhxNLCn0YPQivIYH/k8xkc+j/GRz2PcGCdm5saldDTUq6lFxPalXnRBrcljfOTzGB/5PMZHPo9x83P6jSRJktTiDPWSJElSizPUq9n9VaMHoBXnMT7yeYyPfB7jI5/HuMk5p16SJElqcZ6plyRJklqcoV6SJElqcYZ6SZIkqcUZ6rXqIuKCiPhIRFwdEcMRkRHx6QNsc1ZEfCUiHo6I8YjYERFviYi21Rq3li4iNkTEayPiCxFxa3nM9kXENRHxWxFR998ej3NriYg/jYhvRsRd5fF6OCJuiIh3R8SGRbbxGLewiHhF+W92RsRrF+nzooi4svzMj0TE9yLilas9Vi1NROyqOaYLb/ctso2f4ybkF2W16iLiB8C/AkaAu4GfA/4uM1+xSP+XAJ8HJoDPAA8DLwaeAHwuMy9cjXFr6SLiDcDHgN3AFcCdwCbgfGAdxfG8MGv+AfI4t56ImAK+D9wEPAD0Ab8IDAL3Ar+YmXfV9PcYt7CIOAG4EWgD1gCvy8xPLujzRuAjwEMUx3gKuAA4HvizzLxoVQetA4qIXcB64H/WeXgkMz+woL+f4yZlqNeqi4jnUIT5W4FnU4S+uqE+IvrLfuuAZ2bm9rLeDVwOPAP4tcy8ZJWGryWIiPMoAt5lmVmtqR8LXAucAFyQmZ8v6x7nFhQR3Zk5Uaf+XuAdwMcy83fKmse4hUVEAP8EnARcClzEglAfEVuBm4FR4Bcyc1dZHwCuA7YBZ2Xmd1dz7Hp0ZagnM7cuoa+f4ybm9Butusy8IjNvyaX9RHkBsBG4ZO4fj3IfE8AflHd/ewWGqWXIzMsz80u1gb6s3wd8vLx7bs1DHucWVC/Qlz5btqfU1DzGre3NwHnAqylCez2vAbqAj84FeoDM3Av89/LuG1ZwjFp5fo6bWHujByAdwHll+7U6j10FjAFnRURXZk6u3rC0DNNlO1NT8zgfWV5ctjtqah7jFhURTwTeB3woM68qfxNXz6Md468u6KPm0hURrwC2UPzQtgO4KjNnF/Tzc9zEDPVqdk8o250LH8jMmYi4HXgy8Hjgx6s5MB28iGgHfrO8W/ufgse5hUXERRRzrNdRzKc/myIUvK+mm8e4BZWf2U9RfC/mHQfo/mjHeHdEjALHR0RvZo4d3pFqmY6lOM61bo+IV2fmt2pqfo6bmKFezW5d2e5b5PG5+vpVGIuW733AacBXMvPrNXWPc2u7iOKL0HO+BrwqMx+sqXmMW9MfAk8Bzs7M8QP0Xcox7iv7Geqbx18DVwP/DOynCORvBF4PfDUinpGZPyz7+jluYs6pl7QqIuLNwNsovkj3Gw0ejg6jzDw2M4PibN/5FKHghoh4amNHpuWIiKdTnJ3/M7/ceuTKzD8qvwd1f2aOZeaPMvMNwJ8DPcB7GjtCLZWhXs1u7qf+dYs8PlcfWoWx6BCVy9x9iGLpw+dk5sMLunicjwBlKPgC8MvABuBvax72GLeQctrN31JMs3jXEjdb6jFe7Cyvmsvcogbn1NT8HDcxQ72a3U/K9tSFD5T/6ZxE8YXLn67moLR0EfEWinWrf0QR6OtdzMTjfATJzDsofoB7ckQcXZY9xq1lDcWxeiIwUXtBIuDdZZ9PlLW59c0f7Rhvpph6c7fz6VvG3PS5vpqan+MmZqhXs7u8bJ9X57FzgF7gO37LvjlFxNuBDwI/oAj0DyzS1eN85Hlc2c6tnuExbi2TwP9e5HZD2eea8v7c1JxHO8bPX9BHze8Xy7Y2oPs5bmaZ6c1bw24Ua5Un8OlFHu+nOFswCQzW1LuB75TbvrzRr8Nb3WP3rvL4bAeOOkBfj3OL3SjO1K2rU68A7y2P2bc9xkfejWKOdQKvXVA/ieIqow8BW2vqAxQXLErgGY0ev7d5x+yJQF+d+lbglvKYvaOm7ue4iW+ufqNVFxEvBV5a3j22bJ8REReXf96T5aXEM3M4Il4HfA64MiIuobgk9a9QXpKa4jLVaiIR8UrgjynO0l4NvLm4IOU8uzLzYvA4t6gXAH8SEdcAt1MEuU0UV4l+PHAf8Lq5zh7jI19m3h4R/xH4MLA9Ij4DTFFcsOh4/MJtM3oZ8LaIuAq4g2L1m23ACymC+leAD8x19nPc3KL8CUtaNRHxHh6Zk1nPHbngctUR8UzgnRSXoO6mOOvzf4AP589eHEMNtoRjDPCtzDx3wXYe5xYREadRXB30bIrAtp7iojU7gcsojtnCL0R7jI8ANZ/v12XmJ+s8/mKKZU6fSvGbm5sorjL7N6s5Th1YRDyb4nP8FIqTbH0UX3L9AcW69Z/KOkHRz3FzMtRLkiRJLc4vykqSJEktzlAvSZIktThDvSRJktTiDPWSJElSizPUS5IkSS3OUC9JkiS1OEO9JEmS1OIM9ZKkFRURF0dERsTWFX6eXRGxayWfQ5KalaFektQSIuLKiPCKiZJUR3ujByBJ0mHyS40egCQ1iqFeknREyMzbGj0GSWoUp99IUpOKiK3lXPSLI+LnIuIfI+LhiBiNiGsi4pfrbNMVEf85Im6MiLGIGI6IqyPiVw/T/t9TbnPuo+1via/vVRHx+Yj4aUSMl2P9dkS8ot5+gWeX97PmdmVNv7pz6pfxnmyNiEsiYk9ETETE9oh40VJemyStNs/US1LzOwn4LnAj8JfAZuBlwFcj4tcz8zMAEdEJfJ0i/N4M/AXQC1wAfCYifj4z33Go+18BHwP+GbgK2A1sAF4AfCoinpCZ7yr7DQF/BLwKOLH885xdj/YEy3hPTgSuBX4KfAo4iuI9+WJEPDczrzjYFytJKyozvXnz5s1bE96ArUCWt/cveGwQmAb2Av1l7b+Ufb8CtNf0PYYi/CZw1qHuv6y/p+x/7qOM9+IF9YvL+tYF9W119tEJfLN87uMWPHZl8d/Wou/XLmDXgtpy3pN3L9jXv5nbV6P/bnjz5s3bwpvTbySp+e0D/ri2kJnbgb8D1gP/tiy/hiJ0vjUzZ2r6PgD81/Lua5ex/8Mq68yBz8wpirPp7RyeL74e6ntyB/DfFozt68CdwNMOw7gk6bAy1EtS8/t+Zu6vU7+ybJ8SEWuBk4F7M/PmOn0vn+t7KPs/iLEuWURsiYi/iIiby7nuWc6d/3zZ5bhl7n8578kPMnO2Tv0uYGA545KkleCceklqfvcvUr+vbNeVNyjmptczV19/iPs/rCLi8RRz1geAq4H/R/Ebg1mKKTCvBLqW+TTLeU+GFtlmBk+ISWpChnpJan6bFqkfW7b7ylttbaHNNX0PZf9zqmVb7/+PeuF4MW+l+GLsqzPz4toHIuLXKEL9ci3nPZGkluLZBklqfk8tp5IsdG7Z3lBOn7kNOC4iTqnT9zll+/1D2X9NbW/ZnlCn/2Cd2mJOLtvP13ns2YtsMwsQEW1LeYJlvieS1FIM9ZLU/NYBf1hbiIhB4N9TnGX+Qln+P0AA768NvhFxNPCumj6Hun8opswAvDoi2mv6n7BwHwewq2zPXfC8/4b6X1wFeKhstxzE8xzqeyJJLcXpN5LU/K4CXhsRTwe+zSPryFeA/5CZw2W/DwDPB14C/DAivkKxJvuFFEs4/o/MvGYZ+yczvxcRVwHnANdGxOUU03deTLEefL0z+PX8L+DVwD9ExOeAe4HTgOcBny2ff6Fvlq/l0vK1jQN3ZOanHuV5DvU9kaSW4pl6SWp+twNnUUx9eQPwqxRTRl6QNReGKpeD/NfAO8vSmyjmpt8C/Hpmvn05+6/xEuCTwPHlczwF+E/AYvv/GZm5g2L6y3eAFwK/DfQD5wMfX2SzTwJ/QvGbhf9EsSTlbx3geQ71PZGklhKZ2egxSJLqiIitFIH7bzLzVa22f0nS6vFMvSRJktTiDPWSJElSizPUS5IkSS3OOfWSJElSi/NMvSRJktTiDPWSJElSizPUS5IkSS3OUC9JkiS1OEO9JEmS1OL+PzKNrnP1blirAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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NUXoVP9xRoKMR2huNwIz2RqOjMUqvdQrqRUREROSMEmb7sUTTlDRLNBFm+6uWOzDotDZMTWttiNJrncbUi4iIiEhNy2T7GBjbTrYwRDrRTmfTWprTPRXzB+ku8uNHyOeH8HAcCxpJJttJNi6pWs/yDmNo3GlvfD5tZCJKr3XqqRcRERGRmpXJ9nF45GHy4TipoI18OM7hkYfJZPsqF2paRi6zFy+MgTXghTFymb3QtKxqXa+/KMHgeDSWPnRnaNwZHI/Sa52CehERERGpWQNj2wmskWTQiJmRDBoJrJGBse0Vy+Ryh6FtFZZowsKJaChO26oovYqLexK851VJ2huNQ0PR2Pr3vCpZF7PfaPiNiIiIiNSsbGGIVNA2JS1hDWQLQxXLFHIDJBqXYE1LT6S5O4XcwLT1XdxTH1NYllJPvYiIiIjUrHSinYJPTEkr+ATpRHvFMolUJx6OT0nzcJxEqnNO2lgLFNSLiIiISM3qbFpL6OPkw3HcnXw4TujjdDatrVimqf1SwsIYYWEMdz/xuKn90nls+fyqiaDezBab2W+a2XfMbKeZjZnZoJk9YGa/YWZBSf4XmdlHzOxeM9tnZlkz6zOzO8zsddPU9U4ze8TMRuI6NpjZG+Z2C0VERETkVDSne1ja+mqSQSO5cJhk0MjS1ldXnf2moWkZbd0/T5BoIswPEiSaaOv+eRqmuVC2ntXKmPq3Ap8BDgL3AXuBHuDNwBeA683sre4+OUnoXwBvA7YC64HjwFrgRuBGM/ugu3+ytBIz+xjw+0Av8HkgDfwKcJeZvd/dPz13mygiIiIip6I53VM1iC+noWnZGR3El7Ln4+QFbITZNUAL8D13D4vSzwEeAc4D3uLu34rT3wU84e6PlazntcAPAQdWufvBotdeAzwIPAu80t374/RVwKa4/ovdfc907V23bp1v3LjxFLdWRERERGR6ZrbJ3dfNJG9NDL9x93vd/a7igD5OPwR8Nn56dVH6baUBfZx+P7CBqAf+NSUv/9d4+ZeTAX1cZg/wj0AD8O7T2hARERERkQVQE0H9NHLxMn+a+a+Jl3eXKfP9kjwiIiIiInWjpoN6M0sC74iflgvGS/OfD1wLZIAfFaW3AOcCI8VDcoo8Ey8vOq0Gi4iIiIgsgJoO6oG/AS4F1rv7D6plNLMG4CtEw2huLh5iA3TEy8EKxSfTK05eambvM7ONZrbxyJEjM2q8iIiIiMh8qNmg3sw+QDRTzTbg7dPkTQBfBq4Evg58bLbb4+63uvs6d1+3ZMmS2V69iIiIiMgpq8mg3sx+F7iFaMrK17n78Sp5E8C/EE2L+a/Ar/sLp/SZ7InvoLzJ9OnvHSwiIiIiUmNqLqg3sw8BnwKeJAroD1XJmwL+L9Fc818Ffs3dX3BBrbuPAvuBVjMrN2Hpi+LljtNsvoiIiIjIvKupoN7MPgJ8AnicKKA/XCVvGvgGUQ/9l4C3u3uhyurvjZfXlXnt+pI8IiIiIiJ1o2aCejP7U6ILYzcB17r70Sp5G4DvAG8C/gl4d+kc92VMznf/x2bWVbSuVcDvABPAP59q+0VEREREFkpyoRsAYGbvBP4nUAB+DHzAzEqz7XH32+LHnwVuAI4SDav5szL5N7j7hskn7v4TM/s48HvAZjP7JtFNqt4GLALeP5O7yYqIiIiI1JqaCOqB1fEyAXyoQp77gdtK8ncDf1ZlvRuKn7j775vZFqKe+fcBIfBT4O/c/bsn3WoRERERkRpgL5woRqazbt0637hx40I3Q0RERETOYGa2yd3XzSRvzYypFxERERGRU6OgXkRERESkzimoFxERERGpcwrqRURERETqnIJ6EREREZE6p6BeRERERKTOKagXEREREalzCupFREREROqcgnoRERERkTqnoF5EREREpM4pqBcRERERqXMK6kVERERE6pyCehERERGROqegXkRERESkzimoFxERERGpcwrqRURERETqnIJ6EREREZE6p6BeRERERKTOKagXEREREalzCupFREREROqcgnoRERERkTqnoF5EREREpM4pqBcRERERqXMK6kVERERE6pyCehERERGROqegXkRERESkzimoFxERERGpcwrqRURERETqnIJ6EREREZE6p6BeRERERKTOKagXEREREalzCupFREREROqcgnoRERERkTqnoF5EREREpM4pqBcRERERqXMK6kVERERE6pyCehERERGROqegXkRERESkzimoFxERERGpcwrqRURERETqnIJ6EREREZE6p6BeRERERKTOKagXEREREalzyYVugNSWLQN5bj+YZW8mZGVzwE3L0lzWqdNEREREpJapp15O2DKQ5xM7x+nPhqxoMvqzIZ/YOc6WgfxCN01EREREqlBQLyfcfjBLZwq60gGBGV3pgM5UlC4iIiIitUtBvZywNxPSkbIpaR0pY28mXKAWiYiIiMhMaLC0nLCyOaA/G9KVfj6wH8w5K5v13U9E5Gy1Nxxlkx/nmE+w2Bp4hS1iZdAybbmj4QDP0sswGdpo5gJW0B10zkOLRc5OitbkhJuWpRnIQX82JHSnPxsykIvSRUTk7LM3HOXu8ACjnmcRaUY9z93hAfaGo1XLHQ0HeIztTJCllSYmyPIY2zkaDsxTy0XOPgrq5YTLOpN8+MJGutIBvWNOVzrgwxc2avYbEZGz1CY/TjNJWiyJmdFiSZpJssmPVy33LL00kKKBNIbRQJoGUjxL7zy1XOTso2hNprisM6kgXkREADjmEyxi6q+1zSQ45hNVyw2ToZWmKWlpUgyTmfU2ikhEPfUiIiJS1mJrIENhSlqGAoutoWq5NprJkpuSliVHG82z3kYRiSioFxERkbJeYYvIkGfU87g7o54nQ55X2KKq5S5gBRPkmCCL40yQZYIcF7BinloucvZRUC8iIiJlrQxauC5YToslOU6WFktyXbB82tlvuoNOXs5aGkgzwhgNpHk5azX7jcgc0uBpERERqWhl0MJKpp/CslR30Ek3CuJF5ot66kVERERE6pyCehERERGROqegXkRERESkzimoFxERERGpcwrqRURERETqnGa/ERERKfFsfowHskP0hTl6ghRXpdu5INk0fUERkQWinnoREZEiz+bH+Mb4UYbDAkssyXBY4BvjR3k2P7bQTRMRqUhBvYiISJEHskO0kqAtSBCY0RYkaCXBA9mhhW6aiEhFCupFRESK9IU5Wmzqx2OLBfSFuQVqkYjI9DSmXqSOHfBhnqSPfiboooFL6WG5tVUts2NinHvGRjmYz7MsmeTaphYuamicpxaL1L6eIMVwWKDNEifSRj2kJ0gtYKukmj4G2cFBBsnQQTMXsYweOha6WSLzSj31InXqgA9zP3sYI08nDYyR5372cMCHK5bZMTHOl4YHGQoL9CQSDIUFvjQ8yI6J8XlsuUhtuyrdzggFhsMCoTvDYYERClyVbl/opkkZfQzyCDsZJ0s7TYyT5RF20sfgQjdNZF4pqBepU0/SRzMpmkhhGE2kaCbFk/RVLHPP2ChtgdEejxVuDxK0BcY9Y6Pz2HKR2nZBsom3NnbTFiQ44nnaggRvbezW7Dc1agcHaSRFI2kMo5E0jaTYwcGFbprIvNLwG5E61c8EnTRMSWskST8TFcsczOfpSSSmpLVawMF8fk7aKFKvLkg2KYivE4NkaGfqsWogxSCZBWqRyMJQT71IneqigXGmBuPj5OkqCfSLLUsmGfFwStqIhyxL6vu9iNSnDpqZYOpFzBPk6KB5gVoksjAU1IvUqUvpIUOOMXI4zhg5MuS4lJ6KZa5tamE4dIbiscJDYYHh0Lm2qWUeWy4iMnsuYhnj5Bgni+OMk2WcHBexbKGbJjKvaiKoN7PFZvabZvYdM9tpZmNmNmhmD5jZb5hZ2Xaa2WvMbL2ZHY/LbDazD5lZolz+uMwbzGxDvP4RM3vYzN45d1snMjeWWxuvZRVNJBlggiaSvJZVVWe/uaihkXe0ddAeJOgrFGgPEryjrUOz34hI3eqhg1dxIY2kGWKMRtK8igs1+42cdWrlN/e3Ap8BDgL3AXuBHuDNwBeA683sre7ukwXM7E3At4Bx4OvAceCNwCeAK+N1TmFmvwt8CjgG/AuQBd4C3GZml7n7H8zVBorMheXWxnKqT2FZ6qKGRgXxInJG6aFDQbyc9awoTl64RphdA7QA33N/fsCvmZ0DPAKcB7zF3b8Vp7cDO4EO4Ep33xinNwL3AlcAv+ruXyta1ypgGzAKvMLd98TpXcCjwAXAa9z9oenau27dOt+4cePpbfQ8OB4eZ6/vZdRHabEWVtpKFgWLqpY5yBBPcZgBxuikiZewlGVMP43b1kyW9QPj9GYLrEgnuKGzkUua07O1KSJyEnrDEZ7gKMcZZxGNvJRuVgStC90sERE5SWa2yd3XzSRvTQy/cfd73f2u4oA+Tj8EfDZ+enXRS28BlgBfmwzo4/zjwJ/ET3+7pJr3AA3ApycD+rhMP/BX8dP/enpbUjuOh8fZGm4l61maaSbrWbaGWzkeHq9Y5iBDPMBzjJGjg0bGyPEAz3GQ6rdG35rJ8pnDIwzmQ5anAgbzIZ85PMLWTHa2N0tEptEbjnAPvWTI0UUDGXLcQy+94chCN01EROZQTQT105i8pL14mo9r4uXdZfL/CMgArzGz4mlAqpX5fkmeurfX95ImTdrSmBlpS5MmzV7fW7HMUxymkeSUec8bSfIUh6vWtX5gnM4goCMZEJjRkQzoDALWD+iGRiLz7QmO0kyC5vh93EyKZhI8wdGFbpqIiMyhmg7qzSwJvCN+WhyMr42XO0rLuHse2E10vcCaGZY5SDQsZ4WZlZ0Dy8zeZ2YbzWzjkSNHTmo7FsKoj5Ji6i3NU6QY9co3GRpgjMaSyywaSTLAWNW6erMF2hI2Ja0tYfRmCyfZahE5XccZp6nkfdxEkuPoS7aIyJmspoN64G+AS4H17v6DovTJq2Eq3QN6Mr3zFMqUvdLG3W9193Xuvm7JkiXVW10DWqyFXMm8vTlytFjlqQs7aSo773kn1W/AsiKdYLgw9dqM4YKzIl1xEiIRmSOLaGSs5H08Rp5F6OJoEZEzWc0G9Wb2AeD3iS5uffsCN6furLSVZMmS9SzuTtazZMmy0lZWLPMSljJOfsq85+PkeQlLq9Z1Q2cjA2HIYD4kdGcwHzIQhtzQqSBCZL69lG4yFMjE7+MMOTIUeCndC900ERGZQzUZ1MdTT94CbAVe5+6lV3dW7VUvSh84hTKVevLryqJgEZcEl5C2NBkypC3NJcElVWe/WUY7V3E+TaQYZJwmUlzF+dPOfnNJc5rfXtpKRzLgQC6kIxnw20tbNfuNyAJYEbRyLStoJkU/EzST4lpWaPYbEZEzXK3MU3+CmX2IaK75J4Fr3b3cVZrbgXXARcCmkvJJYDXRhbW7Ssp0x2UeKimzjGhKzV53z8zOliy8RcEiFlF9CstSy2if0RSWpS5pTiuIF6kRK4JWVqAgXkTkbFJTPfVm9hGigP5xoh76StOu3Bsvryvz2s8DzcBP3H1ihmWuL8kjIiIiIlI3aiaoN7M/JbowdhNRD321+de+CRwFfsXMTkzIH9986n/FTz9TUuafgQngd+MbUU2W6QL+KH76WURERERE6kxNDL8xs3cC/xMoAD8GPmBmpdn2uPttAO4+ZGbvJQruN5jZ14DjwI1EU1d+E/h6cWF3321mfwh8EthoZl8HskQ3sloB/P1M7iYrIiIiIlJraiKoJxoDD5AAPlQhz/3AbZNP3P12M3st8MfAfwYagZ3A7wGfdHcvXYG7f8rM9gB/QDT/fUB0Me6fuPsXZ2VLRERERETmmZWJfWUa69at840bNy50M0RERETkDGZmm9x93fQ5a2hMvYiIiIiInBoF9SIiIiIidU5BvYiIiIhInVNQLyIiIiJS52pl9hsRmSfbxia4e2SMA7k8y1NJrmtt4uKmhoVuloiIiJwG9dSLnEW2jU3w+ePDDBVCliUTDBVCPn98mG1jE9MXFhERkZqloF7kLHL3yBgdiYCOREBgduLx3SNjC900EREROQ0K6kXOIgdyedqCqXdrbguMA7n8ArVIREREZoOCepGzyPJUkuFw6g3nhkNneUqX14iIiNQzBfVK84NVAAAgAElEQVQiZ5HrWpsYLIQMFkJC9xOPr2ttWuimiYiIyGlQUC9yFrm4qYH3LmqjPRFwMF+gPRHw3kVtmv1GRESkzuk3d5GzzMVNDQriRUREzjDqqRcRERERqXPqqRcRkbqwMzvOholhDoV5zgmSXN3QxoXpxoVulohITVBPvYiI1Lyd2XG+mjnOcFhgqSUYDgt8NXOcndnxhW6aiEhNUFAvIiI1b8PEMG0W0BYkCMxoCxK0WcCGieGFbpqISE1QUC8iIjXvUJinxaZ+ZLVYwKFQN04TEQGNqRcRkTpwTpBkOCzQZokTaaMeck4w/cfYnkKGh/IDHPEsSyzNFclOViWa57K5IiLzTj31IiJS865uaGPYQ4bDAqE7w2GBYQ+5uqGtark9hQy3Zw8z4nkWk2LE89yePcyeQmaeWi4iMj8U1IuISM27MN3IrzUvoi1IcNgLtAUJfq150bSz3zyUH6DFAlotSWBGqyVpsYCH8gPz1HIRkfmh4TciIlIXLkw3nvQUlkc8y2JSU9KaSXDEs7PZNBGRBaegXkREzlhLLM2I52kt+rjLUGCJpRewVVMdC/vZTS8jjNJKC6tZweKga07q6mOQ7RxigDE6aWIt59BDx5zUJSLzS8NvRETkjHVFspNRDxnxPKE7I55n1EOuSHYudNOAKKDf7NuY8Cwt3syEZ9ns2zgW9s96XX0M8h/sYowc7TQyRo7/YBd9DM56XSIy/xTUi4jIGWtVopmb0ktptSTHyNFqSW5KL62Z2W9200uaNA2WxsxosDRp0uymd9br2s4hGknRSArDTjzezqFZr0tE5p+G34iIyBltVaK5ZoL4UiOM0sLUtqVJMcLorNc1wBjtTL0moYEkA4zNel0iMv/UUy8iIrJAWmkhS25KWpYcrbTMel2dNDHB1Jt1TZCnk6ZZr0tE5p+CehERkQWymhVkyTLhWdydCc+SJctqVsx6XWs5h3FyjJPD8ROP13LOrNclIvNPQb2IiMgCWRx0cbldTIOlGbUMDZbmcrt4Tma/6aGDn2UNTaQYYpwmUvwsazT7jcgZQmPqRUREFtDioIvFzM0UlqV66FAQL3KGUk+9iIiIiEidU0+9nBWOhv3sYj/DjNJGC2s4l+45urmLiIiIyHxTT72c8Y6G/TzOdibI0kozE2R5nO0cnYObu4iIiIgsBAX1csbbxX4aSNNAGsNOPN7F/oVumoiIiMisUFAvZ7xhRkmTmpKWJsXwHNzcRURERGQhKKiXM15bhZu7tM3BzV1EREREFoKCejnjreFcJsgyQRbHTzxew7kL3TQRERGRWaGgXs543UEXL2MtDaQZIUMDaV7GWs1+IyIiImcMTWkpZ4XuoIvuebq5i4iIiMh8U0+9iIiIiEidU1AvIiIiIlLnFNSLiIiIiNQ5BfUiIiIiInVOQb2IiIiISJ1TUC8iIiIiUucU1IuIiIiI1DkF9SIiIiIidU5BvYiIiIhIndMdZUVEREoMFo5yML+bMR+hyVpZllxNR6J7oZslIlKReupFRESKDBaO8mz2CXI+QSMt5HyCZ7NPMFg4utBNExGpSEG9iIhIkYP53aSsgZQ1YGYnHh/M717opomIVKSgXkREpMiYj5AkPSUtSZoxH1mgFomITE9BvYiISJEmayVPdkpanixN1rpALRIRmZ6CehERkSLLkqvJ+QQ5n8DdTzxelly90E0TEalIQb2IiEiRjkQ3F6RfSsoaGGeUlDVwQfqlmv1GRGqaprQUEREp0ZHoVhAvInVFPfUiIiIiInVOQb2IiIiISJ1TUC8iIiIiUucU1IuIiIiI1DkF9SIiIiIidU5BvYiIiIhInVNQLyIiIiJS5xTUi4iIiIjUOd18SkSkhu0uZHgoP8ARz7HEUlyR7GR1onmhmyUiIjVGPfUiIjVqdyHD7dnDjHiBxaQY8QK3Zw+zu5BZ6KaJiEiNUVAvIlKjHsoP0GIJWi1JYEarJWmxBA/lBxa6aSIiUmMU1IuI1KgjnqOZxJS0ZhIc8dwCtUhERGqVgnoRkRq1xFJkKExJy1BgiaUWqEUiIlKrFNSLiNSoK5KdjHqBEc8TujPieUa9wBXJzoVumoiI1JiaCerN7C1m9ikz+7GZDZmZm9m/VMnfYGa/Y2aPmNlRMxsxs6fN7JNmdn6Vcu+My4yY2aCZbTCzN8zNVomInLrViWZuSi+l1RIcI0erJbgpvVSz34iIyAvU0pSWfwK8FBgBeoGLK2U0syRwD3AlsA34v8AE8Erg/cA7zOw17r61pNzHgN+P1/95IA38CnCXmb3f3T892xslInI6VieaFcSLiMi0aimo/zBRsL0TeC1wX5W8v0wU0N8D/IK7h5MvmNlHgT8D/gB4T1H6a4gC+meBV7p7f5z+d8Am4GNm9l133zOL2yQiIiIiMudqZviNu9/n7s+4u88g+5p4+b3igD52R7xcUpL+X+PlX04G9HG9e4B/BBqAd59cq0VERESq2/VcyJe/WeDvP1vgy98ssOu50tBF5PTVTFB/kp6Kl9ebWek2TI6P//eS9Gvi5d1l1vf9kjwiIiIip23XcyHfXO+MjDrdi6PlN9e7AnuZdbU0/OZkfA/4NvBmYIuZ/TuQBV4BXAV8iqj3HQAzawHOBUbc/WCZ9T0TLy+ay0aLiIjI2eXBR522Zqe1xQBobQFwHnwU1lSc1kPk5NVlUO/ubmZvAf6c6ALbS4pevgf4qrvni9I64uVghVVOplecJ87M3ge8D2DlypWn0mwRERE5yxw+Ct2Lp6Y1N0fpIrOpLoN6M2sEvgRcD/wO0Tj6DNHFs58EfmRmb3X3Oyqv5eS4+63ArQDr1q2bybh/EZEF8Ux2nHvHRjlYyLMskeSaphZelG6ck7q2DOS5ozfHvkzIec0Bb1qR4rLO2vpoea6Q4eGwnyOeZYmleXXQxfmaUUjmydJuGBmd7KGPZDJRushsqtcx9f8DeCvwx+7+OXc/5O5D7v594C1ACrilKP9kT3wH5U2mD8xJa0VE5skz2XG+PDLAUFigJ0gwFBb48sgAz2THZ72uLQN5btk+wUA25NwmYyAbcsv2CbYM5KcvPE+eK2S4M3+IUc/TTYpRz3Nn/hDPFTIL3TQ5S1z5SmM4Y4yMenQTuVFnOGNc+Upb6KbJGaZeg/rJi2FfMO2luz8B9APnm9niOG0U2A+0mtmyMut7UbzcMQdtFRGZN/eOjdJmAe1BgsCM9iBBmwXcOzY663Xd0ZujMwWd6YDAjM50QGcqSq8VD4f9tFqCFktiZrRYklZL8HDYP31hkVmw5vyAt9xgtLYYR49Fy7fcYKw5v15DMKlVtfUb6cw1xMvSaSsxswagLX6aLXrpXuDtwHXAP5cUu74oj4hI3TpYyNMTJKaktVrAwcLs957vy0Q99MXaU8a+TO3M6nHEs3STmpLWTIIjnq1QQmT2rTk/0EWxMufq9Wvij+PlH8VBfLGbib6sPOruw0Xpn42Xf2xmXZOJZraKaFz+BC8M9kVE6sqyRJKRktt3jHjIssTs9+Gc1xwwlJt6idFQzjmvuXY+WpZYmgyFKWkZCiyx9AK1SERkbtRMT72Z3QTcFD89J15eYWa3xY+PuvsfxI//EngjcC2wzczuBsaILpR9Vfz4g8Xrd/efmNnHgd8DNpvZN4E08DZgEfB+3U1WROrdNU0tfHlkAMKoh37EQ4Y95Kam9lmv600rUtyyfQIIaU8ZQzlnIAfvXJOatux8eXXQxZ35Q0DUQ5+hwIgXuCbxgh96RUTqms3sBq5zz8xuJpqispLn3H1VUf4lwEeAXwJWE/3qcJBoCM3fuvu2CvW8i6hn/hIgBH4K/J27f3embV23bp1v3LhxptlFROaVZr+ZSrPfiEi9MrNN7r5uRnlrJaivJwrqRURERGSunUxQXzsDH0VERERE5JTU1m+kIiIiIgLA7kKGh/IDHPEcSyzFFclOVk8zdGxXfowHckP0hTl6ghRXpdpZk2yapxbLQlJPvYiIiEiN2V3IcHv2MCNeYDEpRrzA7dnD7K5y47Rd+TG+MXGU4bDAEksyHBb4xsRRduXH5rHlslAU1IuIiIjUmIfyA7RYglZLEpjRaklaLMFD+YGKZR7IDdFKgrb45nNtQYJWEjyQG5rHlstCUVAvIiIiUmOOeI5mpt5ILrpxWuU7NveFOVpsamjXYgF9Ye3c5VnmjsbUi4iIiNSYJZbiWJgh78PkyJIiTZI2lgSVx9T3BCmGwwJt9vyXgVEP6Qlq594RMnfUUy8iIiJSY15ixkE/zojnSXqKEc9z0I/zErOKZa5KtTNCgeGwQOjOcFhghAJXpWb/5nNSexTUi4iIiNQYs0O8JnBaLclgPKb+NYFjdqhimTXJJt7a0E1bkOCI52kLEry1oVuz35wlNPxGREREpMaMMsp5iWZWFnXMu6cYZbRquTXJJgXxZykF9XUgk+2jf3wH2cIg6UQHXY0X0ZzumbZcYaSXwtHH8fHjWOMiEt0vI9G6onqh/ueg91HIHIXmbljxSug6f9q6xvZvYvDIA+TCUVJBCx1LrqLp3FdULbPt2EYeZQ/DKactZ7ySVVy8uPpN0x44tJW7c4cZSBmdOee61FKuOueSadv31K5nuWv3Pvbl4LwUvHH1ebxkzQVVy3znsT18bucEh3Ipzknl+K0LG/jll6+atq59vU/y02PbOBbkWRwm+ZnFF3PeikurljnSt5lnBjYznJigrdDAizovZ0nP5dPWtblvExvyBziWDFicD7k6uZzLe6rv9wMHt7Clfyv9QY6uMMVlXZewfNll09Z11/adfPH4MH2WpMfzvHNRG29ce2HVMg8eeJLvZo9yNJmgO1/gDelurlxefV8AbNmxkzt37mNfFs5Lw40XnsdlF1Wva+eOp9iw7xkOEXIOAVef9yIuvOgl09bFU1vgrjtg3z447zx445vgJdPsj61Pwvo7YX8vnLsCbrgRLqm+XVseeZo7Nu5hXzbgvHTIm9at4rJXvXj69u3aBg/cDX0HoGc5XHUdrLm4epnNm+Hb34a9e2HlSnjzm+Hy6c+nwe0P0bfvQcaCDE1hMz3nXUnH2iuqlnny4KM85HsZbDA6JpwrbCWXLnvltHXds/1p7hw9ykCj0Tnu3NjSzbVrp9kfTz8Fd98F+/fBuefBdW+EF8/gGJ/C/ji642F273/0xHty9bmvpPuiV09b1b4dj/HY/i0cSxZYnE/w8nMv47yLXl690LZ4uw70wvIV0XZdPP12bd76EE8PPU0hnSORTfHi9hdz+SXVjxdPbYHv3gG9+2DFefCG6c/3x7c/wpbR7eQaCqQmElzWspaXrX3VtO07leP15OanuXPrHnrzxoqkc+Mlq7j08hm8T559Gn50N/Tth55z4eevgwumKffkFrjzdujdCytWwo03waXT/y8c2/4gQ7vvI+ejpKyF9tWvo2ntlVXLHNi+ic0HHqc/WaArn+Dy5S9j+drq/6tbaCE7cIj07j0wMgSt7eRWr6Kl85yq5bZueYrvb9vF/tA4N3Cuv3gNl1w2g/fJM1vh3vVwcD8sOxeuuQFeNM3n6zNb4b7vw6H9cM658Lrrpy+zYyvcsx4O9sKyFXDtDXDRDD7HH97GXQ8/R+94ghWNBd746vN5yaur/y9c/9h2vrB3lAPewHKb4DdXtnDDy9dOW9edj23nM/ueL/fb57Vw4wzKLbTEzTffvNBtqDu33nrrze973/vmpa5Mto9DI48AkAxaKPgEwxPP0ZDoJJVorViuMNJLrvffwYF0G+THKAxuxxq7CdIVxtb1Pwfb10ePGzogl4HDW6FlKTR1VqxrbP8mjvR9H9xJBI2E4QQjo9tJhy2k2peXLbPt2Eb+PbUHcJoLxkTgPJMcpH0kQ3dz+TIPHNrKV+0I7kZL6IwFxmOJDB0jGVa2LqnYvqd2PcunduwDd5akYDAPPzo2xOqgwNKuRWXLfOexPfzZ09HuW5TIMxQm+MGRgGX5AV68rPK+2Nf7JD8YeBJw2j1BxkK2TRxmSRY62peWLXOkbzMbhx8FoDlMMWEF9uV66cwlaWmt/OVtc98mvmF9UV0FJxPAE0GG7pEhelrL78MDB7ewYfAJcKfNk4xRYGf2EItzRltb5bru2r6Tv+kfx83o8pBhS3DfeIGlo0Os7S6/Dx888CT/7P040F4IGQ2MR22CxSPDrGwrvy8gCuhveXofRny8CrDhSHS8ehaXr2vnjqf46r4d4E4XxgjOpqHjnJsPWbS4cl08tQU+9Q+AwZIlMDgIP9oAq9fA0gr7Y+uT8NlPRo8Xd8PQIDx4P5y/GpaUr2vLI0/zDz/Zi7mzJOUM5mHD3iHWBDl6zq187rJrG3zj81H7OhfDyDA8/hNYfj50dZcvs3kzfOxj0eOlS2FgAO69Fy64AHoqH+PB7Q+xe/8PwZ102EDechwb2UlTNk1j93llyzx58FHWN+wDoDkP40l4Oj1Ex8AIS9vOrVjXPduf5rbwOO5OSx7GErAxGKfj+Chruivsj6efgls/FT1evCTa7z/5UdX9fqr74+iOh3mi7wFwpylMkbU8+zPP0T6RoHlx5Y6RfTse49/6HsfcaQ8TjFmBbZlDLJmAjsXLyhfa9hR8/tNgROfT8CD85MfRdnVX3q7NWx/iqYkncCCRTxAmQo74Qbw/R8+S8seLp7bAP94SPe6Oz/cf3w+rVlc83x/f/gg/LTwFOIl8QJgMOWCHseM5zumufIxP5Xg9uflpPvnEc1HzEjAYwv2HBlltOZb2VHmfPPs0fO3W6HHn4igA/ulP4NzzYVGFck9ugU9+It4XS2FwAO7fUP29TxTQH911V7Q/rJHQs4z2P006bCLVvbJsmQPbN7Hh8Ebcoa2QYCwI2Tm2n8XjRlt3+f/VAOne/fQd2AjuBKkmcpYjO9THBeFKmjrLl9u65Sk+t3UXOCwOYCiEB4/0c77nWdJT5X3yzFb48mejx4sWw/AQPPogrDg/On6Vynzlc9Hjrni/b3ww2u+VyuzYCl/6DGCwqDuu5wFYsapyGaKA/tP390LodKedwRz8eM8Iq5NZlq4o/79w/WPbufm5Ag4sJs8QSf5t0Dh37DgvWlbh/ydRQP9H+wrg0G05hjzJ+iFjReY4a6uUmysf/ehHD9588823ziSvxtTXuP7xHSSCRpJBI2ZGMmgkETTSP76jarnC0cexRDOWasbMomWimcLRxysX6n0UUs2QbgGzaJlqjtKrGDzyAAlSJIJGjIBE0EiCFINHHqhY5lH20FBwmjwgMKPJAxoKzqPsqVjm7txhGvPQ7BBgNDs05qP0au7avY9OczqTRoDRmTQ6zblr976KZT63c4LWIE9HokBg0JEo0Brk+dzOiap1/fTYNppDo4UEZtGyOTR+emxbxTLPDGymIUzQSBIzo5EkDWGCZwY2V61rQ/4AzYUCLWEUD7SE0FwosCF/oGKZLf1baQqNZpIY0bIpNLb0b61a1xePD9NCSLuHBEC7h7QQ8sXjwxXLfDd7lKZCSKsTzbHs0FQI+W72aNW67ty5j67A6UzExythdAXOnTsrH68N+56hzZ02i86nNgtoc2fDvmeq1sVdd0BnF3R2QhBEy86uKL2S9XdCR2f0FwTPP15/Z8Uid2zcQ1cQ0pkyAjM6U0ZXEHLHxj3V2/fA3dDaAW0dUV1tHdHzB+6uXObb34aurugvCJ5//O1vV62qb9+DpMIkKdKYGSnSpMIkffserFjmId9LY95pKkTHqqlgNOadh3xv1bruHD1KYzakOYzKNYdGYzbkztEq58bdd5Xf73ffVbWuU9kfu/c/SkMhoIEUZkYDKRoKAbv3V/9f+Nj+LbQUiN5fFr2/WgpRevXt6ijZro5pt+vpoachnyARRv9rEmEC8okovZLv3hGtv/h87+iM0ivYMrqdIG8kCsno/3shSZA3toxur9q+Uzled27dQ+fke9+i935n4Ny5dU/1un50d/TeKH6ftHVE6RUru/3593sQPP9/4M7bq1Y1tPu+5z/v7PnPu6Hd91Uss/nA4zQWApo9OlbNnqCxELD5QJXPY6Drhz/ixU+NkSZFpjUgTYoXPzVG1w9/VLHM97ftogPoiPdhR8LoiNOrund9tM/a433YHu/De9dXLnPf98vv9/u+X7nMPeuhrbOkns4ovYq7Hn6OjqBAZzo+N9JGR1Dgroefq1jmC3tHaSNPR1CITsGgQBt5vrC3+vClz+wbpd3zdFoh+gyyAu2e5zP7qperBQrqa1y2MEjCGqakJayBbGGwajkfPw6lY+qSTVF6JZmjURBfLNUcpVeRC0cJgvSUtCBIkwsrvwGGU05DOPUK/obQGE55xTIDKaPRp77e6M5AqvJMAAD7ctBeMtCsPRmlV3Iol6ItKExJawsKHMpVnxbsWJCn2ae+rZo94FiQr1hmODFBg0+di7jBEwwnqn+BOJYMaA6npjWHUXol/UGOppJ5j5tI0B9Un8O4z5K0+tTKWj2kzyqP4DuaTNAcTj1ezaFzNJmoUCKyLwvtJVnaE1F6JYcIaWHqedCCcYiwQonJyvZBe8kvV+3tUXol+3uhraRMW3uUXqmabFD+HMxO8y+47wC0tE1Na2mL0ivZuzcKCot1dETpVYwFGZI+9fxOeoqxoPLdKwcbjIapbxMaClF6NQONRmPJoWkMo/SK9u+rsN+rHCs4pf0xnJgg7VMPWNqTM3hPFmgqeS83eYJjyUKFEkRDbspt14HK5xNAIZ0jKEw9f4JCQCFd5b3cW+F87628D3MNhbL15EoPfKlTOF69eaO95C3RHkTpVfXtr/A+2V+5TO/eKKCcUllHlF5FzkcJrOTzztLkvPLnXX+yQFM4dcOawoD+aucFwKH9dE00cvnmca54KMPlm8fpmmiMhrpUsD802kr2YVsQpVd1cD+0luzD1rYovUr7yu73Ku3jYG+Feqqf773jCdpLPn7bU1F6JQe8gTZKPscpcMAbKpR4vly7Tf3Mbrf8tOVqgYL6GpdOdFDwqR8kBZ8gneioUCJijYug9LbQ+bEovZLm7mjITbFcJkqvIhW0EIZTI64wzJIKWiqWactFQ26KTQTR2PpKOnPOeMlUXuMWja2v5rwUDJXE1EP5KL2Sc1I5hsOp/yyGwwTnpKoHv4vDJBmbGq1kLGRxWDn4bSs0MGFT//FMWIG2QvV/IIvzIZmSd3AmiNIr6QpTjJX8kxujQFdY/ctKj+cZKbmhycj/Y+/O4yO7yjv/f55bm6QqrS21end3e2nbYGOgwcY2ttkSA3YbDMSQBBwSQkIGmBDIzLwyhCHJZGZ+E//yS0ImC2QCyZAfAwwkXkJMWLwAju3Y2GC8YbsX975pKdVe994zf9zqtiRUt6TuUpfU/X2/Xnpd6dQ9dR5JJdVTp855rnmMuuYvVob9gJI38/dV8oxhP/6JbH0a8rNOyQdRezOr8Cgy83FQxLGq1b+49eshP+tKi/l81N7M2nXRW8bTTeWj9mbDpMO5H4PpFi86RtdAcda7IcWpqL2ZDRuiZRXTTU5G7TG6wx58m/n49q1Od9i8JnZ/1VGd9ZxaTUTtcQYqjsqsX03Fi9qbWrueyUyZJ1/Wxfev6uHJl3UxmSlHa7XjnMDPozfIUJv1pF4zfx5/k9GSm+nKFq2tb2pNk8fTmvj9T4laijAx8/ETJkIStZi/5XVNHu/rmv8MU9XEnOOkZv/iZ1u7vsnfSfOx1iUd+Vl/Evkwao81urbJ30nM8qB1G6IlQTMGm4zaY6QsS+hmPd+5Gilr/nw36EdLbqYreyGDcY8LiNaoz/V9rWr+fa31HFOzfoZTYdQea/XaaHnfdIWpqL2N8bF6XZNx4h/v67oC8rOefvP1qL2ZNVZlatYk1hQJ1lj8i/M1ViU/60V93iVb9lsKlNQvcYNd5xGEFfywgnMOP6wQhBUGu86L7ZcYvgQXlHD1Es656BiUSAxf0rzTuldESXytCM5Fx3opao/RP3IlAXWCsIIjJAgrBNTpH7myaZ9XsJFqwihbSOgcZQupJqLNss1cm1pJJQklgxBHyaI1vNemYtYJEm2KnXDGhO8IcUz4jglnXL+p+ZPLr5yToRAmmQwShA4mgwSFMMmvnBP/pP6yFedT8hxFApyLjiXP8bIVzTfznDtwMVUvoIKPc44KPlUv4NyB+I181yTXUEokKHrR2v+iB6VEgmuSzRO+iwYvpOw5Svg4omPZc1w0GL9J6eahXop45M0jBPLmUcTj5qHepn2uSw9TTngUDELnKBiUEx7XpeNfJG47Zz3joTERNH5fgWM8NLad0/z3dc36c5kyY8pFj6cpFzJlxjXrz40di+tvgInxaJ11GEbHifGovZk3bYvW3042+hz7/E3bmna5YetGxkOPibojdI6JumM89Lhh68b4+K68FgqT0TrrMIyOhcmovZkbb4Tx8egjDF/4/MYbY4caXX8Fdc+nTg3nHHVq1D2f0fXNNwC+yjZQSRrlRPS7KicclaTxKotPjLZlh6mkPUpe1K/kOSppj23Z5o+Nyetfw7OXdFOjRlchoEaNZy/pZvL618SOdSI/j01rX0E1EVKljnOOKnWqiZBNa+P/F7507UUUE0R/Xy76+yomovamrr0+epEx4/E0GbXHuKDvAkgGBF70vybwAkgGUXsz190Q3f/0x/vkRNTexEXZLYRJR5Dwo//vCZ8w6bgo22LT4LXXz/13EvN9bbtwIxPH/vZd9Lc/ERrbLtwYP9ZV10Z/G9P/TqYmo/amg73lhb/3MHzh/8C2t8QO1bfpNS8837kXnu/6NjV/HF685hIqiZCSRb+rkgVUEiEXr4l5Pga45o3RC43p31d+Mmpv4o3nb2YSmGz8DCcDx2SjPdZr3/TC/YfhC+O+9k3N+7zmjXP/3F/TPD5e9yaYmpg1zkTUHuP6S89iMkwwUWs8NmqOyTDB9Zc2L+Txvg1ZpkgyGSaih2CYYIok79vQ/AUYwAfWZ8lbkp1vQmYAACAASURBVAmXiJ6DXIK8JfnA+vh+S4E2yp6AU7lRNpXIkUkMUAsmqYd5UokcIz0Xt6x+46X7sK5hXPUoVMexTB/JVZfHV7/pHog2xRYPQ/kodA3A5mtaVr9J9a0hHWaplffhhyWSXg9DK18XW/1muGcNfYUSh9wkhZQjGxivDuOr32zIjdBfKLErKDKZMnoDx1u91tVvVg4OsckLeD6fZ0/dGE0Z7z4nvvrNBasHWO1P8KOJOgf9FMNJn9/ckmxZ/aa/byUjNThaGmPMC+h3SV49dGFs9ZtsbpSBepJ85TCFRI1smOai/pe1rH4zmlvDcCHPvmCKsaRHf+B4s7c6tvpNb+8oK+rGeOUoE55Pn0tx6cBFLavfbBkeYmUxz1PlCocsyQoX8MGhbGz1mw29K1lRmGKnX+JoMsFAEHJTckXL6jejK6Lf166JPHtq0e/r5i3x1W+GVqxkrR+yPz/OQXOsMI/r15/XuvrNytFoY9zzu2DPnmjj5Lt/Ib4ayMjKaLPf7l2wb2/09bveE1v9ZnTtCJu9OrsOTLCn5jGacvzCpWe1rn4zOBxtij24Gw7tjzaWvfGm+Oo3o6PRJtCdO6NlRKtWwS/9UstqL13D6+mupSnn91FJlMm4Ltatuya2+s3K3rX0TxQ4EE6Szxi5Orw2aF39ZvPwCP1jRbZXS0x2Gb11uCmzIrb6zY7cAVxXN+mxPDY1RaKrB3fOFkqr+xlJxLxzcQI/j54V6+irJpjKH6CQqNETpjl/9ataVr/pX7GakSoczR9mLBnQHya4cvVL4qvfDB97PD0fLXUYXgk3vbtl9ZvRkfW48TpHK0cJ0j4JP8mF2RfHV79ZORptin1+V7RcbOUo/PzNsY/3VcNrsbE6h6tj+JmQZD3BJZkLWle/OfZ3suf5aCnRyGj0fcVUv1k5OsImq7Pr6AR7A2NlEt5z0Tyq3wyNRJszD+yBQ/uir998U3z1m2N/+7t2RUuCVq6C97y3ZfWb1PAG0mE39Ynd+K5E0roZ3PzTsdVveofXsKJijE0dZCIV0BcmuHTVy1pWv2FoJNqoun93tORuxQhseyec0/z7GhldyVnOZ/fRcfaFxogH77xgHtVvVjTG2rcbDuyLNlK/5V3xlWxWNH7u+2bF16rPuo2NcRqP97f+bMvqNyvXDbMpWeP5A5PsrSZYmXG8+4oNsdVvzl09zNryGE/kq+wnw4jV+Y2zMi2r32xZPcy60hiP5avsd1G//7A+07HqNwvZKGvOtXhLRn7C1q1b3UMPPdTpMEREzhjfr95FFzls2hK86J2tAi/LtJitFxFZpszsYedcfL3vBi2/ERGRJa/beqkzcy1znRrd1nwJmIjImUQXnxIRkVNu0j/CwWA75bBAt5djNLGZ/mTzNfVrEpt4tv4DAFKkqVPDd1U2JltciEtE5AyhmXoRETmlJv0j7Kj/gLqr0mVZ6q7KjvoPmPSbl8/tT6zgnNRLSFuGCgXSluGc1EvoT6w4hZGLiCxdmqkXEZFT6mCwnZSlSTWuwZEic7w9bra+P7FCSbyISBOaqRcRkVOqHBZIMvPCA0nSlMNChyISEVn+NFMvIiIAPHk44M7nfPZOOdb2GteeneSCkRYXyDkB3V6Ouqsen6EH8KnR7eXaPpZ0zngwxu5wF0WKZMmy3juLwUTMBRBF5KRopl5ERHjycMCnH6kxWXWszsFk1fHpR2o8ebjFpexPwGhiM3VXo+6q0YWuXJW6qzGaaHGBHFk2xoMxngwfp0aVHnqoUeXJ8HHGg7FOhyZy2lJSLyIi3PmcT3/G6M8Yntnxz+98zm/7WP3JYTalXkLKMlRckZRl2JR6Sex6elledoe7SJMibRnMjLRlSJNid7ir06GJnLa0/EZERNg7Fc3QT9ebjtoXQ39yWEn8aaxIkR56ZrSlSFOk2KGIRE5/SupFRIS1vcZk1dH/wjJ3pmpRu5w+fpiv89UDNZ6vhGzo8rhxVZqL+1JtHydLlhpV0tP2TdSpkSXb9rFEJKLlNyIiwrVnJ5msOiarjtC5459fe7bmfk4XP8zXuWVHhfF6yLqMMV4PuWVHhR/m620fa713FjXq1Br7JmquSo06672z2j6WiESU1IuICBeMJHj/S9P0Z4z9BejPGO9/aXpRqt9IZ3z1QI3BJAymPDwzBlMeg8movd0GE0Nc4L2INBlKlEiT4QLvRap+I7KINAUjIiJAlNgriT99PV+JZuin608az1fCRRlvMDGkJF7kFFJSLx2zKyjxQDjOYVdjxNJc6g1yVqInts9zfpnv1vIcDOuMeimuTPdxdrJ7UeLb4Zf4np/nkKux0tJckexjUzI+PoBn62XurU4dj/GqTC/npOJj3Lk95P7vwZFDMLwSLrsCNm7WG2ki0j4bujzG6yGDqRcS+0nfsaFL/2tETgf6S5aO2BWUuM0/QNH5DJOi6Hxu8w+wKyg17fOcX+bLlSNMhQEjlmQqDPhy5QjP+eW2x7fDL/GV+mEKLmCYFAUX8JX6YXb4zeODKKH/YnmMKRcw4iWZcgFfLI/xbL15jDu3h9z2FUex4FgxHB1v+4pj5/bFmT0TkTPTjavSjPswXg8JnWO8HjLuR+0isvwpqZeOeCAcJ2cJspbEzMhakpwleCAcb9rnu7U8ORL0egk8M3q9BDkSfLeWb3t83/Pz5Ihi8szIWYIcSb7nx491b3WKnHkzYzSPe6tTTfvc/z3I5iCXMzzPyOWMbC5qFxFpl4v7UnxsUxeDKY89VcdgyuNjm7oWpfqNiJx6Wn4jHXHY1Rhm5hNJDwkOu+Ybtg6GdUZs5kM2ax4Hw/ZXbjg0Z3weh2LiOx6jt7AYjxyCFbPKdff0RO0iIu10cV9KSbzIaUoz9dIRI5amxMzLz5cIGLHmbwOPeimKbuaSlKILGfXa/wS10tKUmDlWiZCVMfGdaIzDK6E0a1VPqRS1i4iIiMyHknrpiEu9QQouoOh8nHMUnU/BBVzqDTbtc2W6jwIBU2FA6BxTYUCBgCvTfW2P74pkHwWimELnKLiAAj5XJOPHuirTS8GFM2N0IVdlepv2uewKKBagUHCEoaNQcBQLUbuIiIjIfCipl444K9HDtuQqspbkCHWylmRbclVs9Zuzk928o2uYXi/BYefT6yV4R9fwolS/2ZTs4W2pEXKW4Ah1cpbgbamRltVvzkl1c1P3EL2W4HDo02sJbuoeiq1+s3Gzx7a3GdmccfRIdNz2NlP1GxEREZk3c851OoZlZ+vWre6hhx7qdBgiIiIichozs4edc1vnc+68N8qaWQLIOOdKs9pfC9wAlIBPO+d2LCRYERERERE5OQupfnML8AEzG3XOTQKY2TuBvwOOXcnifWb2Mufc7jbHKXJSnq5U+ediiX2+z5pkkp/K9rClK9PpsEREpEN2BiXuDyaOXwDxssQAG1tcAFFkKVvIot2rgLuOJfQN/wmYAN4D/DtgAPiN9oUncvKerlT564lJ8kHAqkSCfBDw1xOTPF2pdjo0ERHpgJ1BiVvrByk4nxWkKDifW+sH2RlzAUSRpW4hSf164NljX5jZZmAL8Cnn3Oedc7cA/wRc294QRU7OPxdL9HkefYnoglB9iQR9nsc/F/XPW0TkTHR/MEHWEuQs2bjAYJKsJbg/mOh0aCInbCFJfR8w/XKaVwAOuHNa2+PAujbEJdI2+3yfnDfzoZ7zPPb5fociEhGRTjrsavSQmNHW6gKIIkvdQtbU7wc2Tfv69UAZeHhaWw5QpiRLyppkknwQ0Jd44R94IQxZk9QFlUWkvZ6tl7m3OsXBsM6ol+KqTG9sSVvpjBFLU3A+uWlpUKsLIJ6MH4373Lanzu6SY32PsW1dihcP6jlI2mshM/X3A9vM7Dozez3wduDbzrn6tHM2AXvbGaDIyfqpbA/5MCQfRBeEygcB+TDkp7LaECUi7fNsvcwXy2NMuYARL8mUC/hieYxn6+VOhyazXJYYoOgCCs5vXGDQp+gCLksMtH2sH437/MnTVSZqjrXdMFFz/MnTVX40rjlQaa+FJPX/pXH+rcDXgTTw+8duNLMu4NXAA+0MUORkbenK8IsD/fQlEhxozNj/4kC/qt+ISFvdW50iZx69XrR/p9dLkDOPe6tTnQ5NZtmY6OGG1Cg5S3KUOjlLckNqdFGq39y2p85AyhhIG55Fx4GUcdueeuvOIgsw7/d+nHOPmdmlwM2Npi865/512ikvBb4NfKGN8Ym0xZaujJJ4EVlUB8M6I97Mp9WseRwMlbwtRRsTPaekhOXuUjRDP11fKmoXaaeFXHxqA3DYOfexuW53zv0L8NZ2BSYiIrKcjHopplxAr72wf6foQka9VAejkk5b32NM1BwD05br5+tRu0g7LWT5zQ6mLbcRERGRF1yV6aXgQqbCaP/OVBhQcCFXZXo7HZp00LZ1KSbqjomaI3TRcaLu2LZOL/akvRaS1E8ARxcrEBERkeXsnFQ3N3UP0WsJDoc+vZbgpu4hVb85w714MMmHt2QYSBt7yzCQNj68JaPqN9J2C3lE3U+0bl5ERETmcE6qW0m8/IQXDyaVxMuiW8gj7JPAd8zsfc65v1qkeERETsiesMCjHGWMCkN0cQkrWOflWvZ7fMzn9t0+u4sh67Me169P8qIhPfmKiMjyspBnrjcCdwN/aWYfAB4EDhBdVXY655z7vfaEJyLS2p6wwDfdHnpIMkiaEnW+yR5eH66LTewfH/P51BM1BjLG2sZmtk89UeNDF6LEXkRElpWFztQf81KaL8VxgJJ6ETllHuUoPSTpsehfWg9JcFH7Opon9bfv9hnIRHWjgePVKW7f7SupFxGRZWUhz1qvWbQoREROwhgVBpl5efduEoxRie23uxiydlZZub5U1C4iIrKcLOTiU/csZiAiIidqiC5K1KMZ+oYyAUN0xfZbn/Xmrh+dXUhhMBERkc7T+8uyrDxWqHPbkSq7KwHruxJsG85wUW5xav3uJ8/jHGKCMgN08yJWspq+RRlLTs4lrOCb7AEXzdCXCSjhczmrYvtdvz7Jp56oAdEMfb4OE1XHu89W/WgREVleFjwdZWYXm9l/M7Nbzeyb09o3mtnPmNlge0MUiTxWqPPHe4pM1EPWZjwm6iF/vKfIY4X2X4J9P3m+yy7K1OmnizJ1vssu9pNv+1hy8tZ5OV5v6+ixFONWo8dSvN7iN8lCtBn2Qxemo/rRJcdA2vjQhWmtpxcRkWVnQc9cZva7wG/xwouB6ZVvPOALwK8Dn2pLdCLT3HakymDCYyAVPfwGUna8vd2z9Y9ziC6SdBPd77Hj4xzSbP0Stc7LxW6KbeZFQyphKSIiy9+8Z+rN7J3Ax4FvAJcA/3X67c657cBDwLZ2BihyzO5KQF9y1qbGpLG7ErR9rAnKdM16zdtFkgnKbR9LRERE5GQtZHrqw8CzwA3OuZqZvXWOc54ErmlHYCKzre9KMFEPj8/QA+R9x/quRNvHGqCbMvXjM/QAFXwG0JUiRZabvH+YQ/XtVNwUXdbLytRm+pIj8Z0md8OBh6A8Bt1DsGor9K9vPdjRnbDzfigcgdwwbLwMVmyM73PgOXjyOzBxEAZG4YJXw6qzW491eAc89y8wdQh6V8LZr4KRTa37ichpaSFr6i8Cvu6cq8Wcsw8YPbmQROa2bTjDeBAyUQ8JnWOiHjIehGwbzrR9rBexkgo+Zeo4HGXqVPB5ESvbPpaILJ68f5hd1UepuyoZctRdlV3VR8n7h5t3mtwN2++Eegm6hqLj9juj9jhHd8Jjt0G1CNkV0fGx26L2Zg48B/d9CcpT0D8SHe/7UtQe5/AOeOTvoVqIXjxUC9HXh3fE9xOR09ZCknoDWhVvHoUWhaFFTtBFuRT/dl2WgZTH3mrIQMrj367LLkr1m9X0cSVn0U2KSSp0k+JKztJ6epFl5lB9O0nLkLIMZkbKMiQtw6H69uadDjwEqR5IZcEsOqZ6ovY4O++HdBYyOTAvOqazUXszT34HunLQ3Rv16e6Nvn7yO/FjPfcv0f1PHyuTi9pF5Iy0kOU3zwCXN7vRzDzgSuDxkw1KpJmLcqlFK2E522r6lMSLLHMVN0Vm1gbqJGkqbqp5p/JYNEM/o1NP1B6ncCSaoZ8u3RO1NzNxMJqhn64rG7XHmToUzdDPHmvqUHw/ETltLWSm/kvAy8zso01u/y3gHOD/P+moROSMlveP8EzlAX5Y/ibPVB4g78ckRSIxuqwXn5mrRn1qdFlv807dQ+CXZrb5pag9Tm4YarP61Uo/mXxPNzAKleLMtkoxao/Tu3LusXq1RFDkTLWQpP6PgB8A/93MHgDeCGBmtzS+/h3gfuDTbY9SRM4Yef8IO+qPUHdVuhproHfUH1FiLydkZWozvqtSd1Wcc9RdFd9VWZna3LzTqq3ROvp6EZyLjvVS1B5n42VQK0br210YHWvFqL2ZC14NlUK0lt6F0bFSiNrjnP2q6P6nj1UtRO0ickYy51zrs46dbNYP/DHwc8D0kiMh8HfAB52Le0/z9LB161b30EMt1laKyAl5pvIAdVclZS9sgD729bldl3YwMlmuVP1GRJYrM3vYOddiRqFx7kKS+mkDDAGvAFYAk8CDzrmYUgKnFyX1Iovnh+Vv0kUOsxdKlzrnqFDg4u7XdzAyERGRU2shSf0JXUbROTcGfP1E+oqIxOm23mhmnhdm6n1qdMetgRYRETnDLeSKsoGZ/XaLc/6jmfknH5aInKlGk2dTZ+Ya6DpVRpPzWI4gIiJyhlponXpredb8zvnJTmZvN7NPmdl3zCxvZs7MPt+iT8LM3mdm95rZuJmVzWy7mX3RzM5r0udmM3vQzApmNmlmd5vZdScSs4i0X19ymE2pl5KyDBUKpCzDptRL6UvGVBARERE5w53Q8psYg5z4xac+DrwEKAB7gPPjTjazHHAr8FrgUeBvGmOvBV4NnAf8eFafW4CPNu7/M0AaeCdwu5l9yDn3pycYu4i0UV9yWEm8iIjIAsQm9WZ21aymjXO0QVQJZwNRVZynTzCWjxAl288CVwN3tTj/L4kS+l91zv3l7BvNLDXr68uJEvrngFc458Yb7X8APAzcYmZ3OOd2nmD8IiIiIiId0Wqm/m7gWHkcB9zc+JiLEZW2bHZxqljOueNJ/PSqF3MOZPYy4GeBL86V0Dfurz6r6Vcbx98/ltA3zttpZv8D+G3gvcB/Wnj0IiIiIiKd0yqp/12iZN6ATxAl+ffMcV4AHAXucs491c4Am/jZxvELjdr51wPrGzF82zn37Bx9Xts43jnHbf9ElNS/FiX1IiIiIrLMxCb1zrlPHvvczG4G/sE59yeLHdQ8vKJxPItoOc2Kabc5M/tz4MPOuQDAzLJEa+0Lzrn9c9zfM43jnJtrG/fxfuD9ABs2bDi56EVERERE2mje1W+cc5uWSEIPsLJx/EOidw8uAHqB1xMl+b9GNPN+TH/jONnk/o61DzQb0Dn3aefcVufc1pGRFlciFBERERE5hRZS0nIpORb3U8BNzrmnnHMF59y3gLcTre3/DTNLdyxCEREREZFTpOnyGzP79gnep3POve4E+87XRON4+7ElNtMG/4GZ7QDOJprB/wEvzMT3M7dj7RNNbhcRERERWbLi1tRfc4L36VqfctKeBl5J8yT8WHWbbgDnXNHM9gJrzWz1HOvqz20cf4yIiIiIyDLTdPmNc847wY/EKYj7m43ji2ffYGYZXkjSd0676dg7D9fOcX9vnHWOiIiIiMiysVzX1H8F2AfcZGavnHXbbxMtp7nLOXdgWvtfNI7/0cwGjzWa2Ubg3wBV4LOLFbCIiIiIyGJpVaf+lDGztwBvaXy5qnF8lZl9rvH5Eefcx+D4cppfAO4AvmNmXwX2ApcCVwKHgF+Zfv/OufvM7A+B3wB+aGb/B0gDNwFDwId0NVkRERERWY7iNspe1fj0QedcZdrXLTnn7j2BWC7hJ69Wu7nxAbAL+Ni0Mb7RmKX/baJSlv3AAaIZ+d9zzu2bI66PmtljRDPz7yeqkvN94A+cc3ecQMwiIiIiIh1nzs29r9XMQqJNrxc453487euWTtG6+o7ZunWre+ihhzodhoiIiIicxszsYefc1vmcG7f85neJkvgjs74WETmjPTZV59YjVXZXQtZ3edwwnOGi3lSnwxIRkTNY06TeOffJuK9FRM5Ej03V+aPdJQaTxtqMMVEP+aPdJX59fY8SexER6Zim1W/MbMzMfnPa159YyLp6EZHT0a1HqgwmjYGUh2fRcTBp3Hqk2unQRETkDBZX0nKAxsWbGj7JiV+QSkTktLC7EtKXtBltfUljdyXsUEQiIiLxSf1BYN2pCkREZDlY3+WR92duL8r7jvVdy/WyHyIicjqI2yh7P/BuMwuA/Y22a8wspgsAzjn3e+0ITkRkqblhOMMf7S4B0Yx93neM+46bV2c6HZqIiJzB4kpangPcClywwPt0KmkpIqczVb8REZFToS0lLZ1zz5rZRcAmYC1wN/A54G/aEKOIyLJ1UW9KSbyIiCwpcctvcM6FwHPAc41lNzudc/ecisBERERERGR+YpP66Zxz2gUmHbc7LPCwG+Ooq7LCMrzchljv5Vr221d6hidqP2bSqvS7DBemz2NNz7nxnSaehz3/CqWj0LMC1r0CBja0DnJsF+x+EIqHITsC618JQ2fFdtk/9iRPFJ9kwqoMuAwXZi9g9dBCV76JiIjImUqJuiwbu8MCd4b7KDqfIdIUnc+d4T52h4XYfvtKz/C9+mOUqdPrMpSp8736Y+wrPdO808Tz8PQ/Qq0I3UPR8el/jNrjjO2CJ+6Izu8Zjo5P3BG1N7F/7Em+W36UMj79Lk0Zn++WH2X/2JPxY4mIiIg0KKmXZeNhN0YPSbKWxMzIWpIekjzsxmL7PVH7MV3Oo5sUHtBNii7n8UTtx8077flXSGUhnQWz6JjKRu1xdj8ImVn9MtmovVl8xSfpDhN0k8QwuknSHSZ4oqikXkREROZHSb0sG0ddlR5mFlbqIcFRF38lz0mrkmHmpsYMKSYtpl/pKKR6ZraleqL2OMXDc/crHm7aZcKqdM36vrpIMBEXn4iIiMg0Supl2VhhGUoEM9pKBKyw+Prg/S5DlfqMtip1+l1Mv54VUC/NbKuXovY42ZG5+2VHmnYZcBkqs76vCgEDcfGJiIiITKOkXpaNl9sQJXyKzsc5R9H5lPB5uQ3F9rswfR4VCylTJwTK1KlYyIXp85p3WvcKqBejNfHORcd6MWqPs/6VUJ3Vr1qM2pvFl72AshdQxsfhKONT9gIuzGqjrIiIiMyPknpZNtZ7Oa711pC1JGPUyFqSa701LavfrOk5lytSF9FNiimr0k2KK1IXxVe/GdgAW94crYkvj0XHLW9uXf1m6Cy48Lro/NKR6HjhdbHVb1YPXcCV3ZfQTZJJq9FNkiu7L1H1GxEREZm3pleUleZ0RVkRERERWWxtuaLsPAYZAl4FZIHtzjlluSIiIiIiHRCb1JvZTwHPOue2z2r/z8DH4IWSImb2JPAu59xjixGoiIiIiIjMrdWa+n8Cfn56g5n9LvBbgAH3AbcCu4ELgW+Y2fAixCkiIiIiIk20SuptxhdmK4DfBA4AlznnXu2cuxE4B/hzYCXwkcUIVERERERE5rbQ6jevAzLAx51zjxxrdM75wK8DO4A3tS88EZH52eemuNNt5wvuCe5029nnpjodkoiIyCmz0KR+I+CAf559g3OuDtwFnH3yYYmIzN8+N8XdPE+JOgNkKFHnbp5XYi8iImeMhSb1xy57eajJ7YeB9ImHIyKycD/kMF0k6SGFYfSQooskP+Rwp0MTERE5JeZT0nKjmV3V+LyrcVwN7Jrj3NXAWDsCExGZr3EqDJCZ0dZNknEqHYpIRETk1JpPUn9z4wOijbMOuAb4mznOvQDY2Y7ARETma5AuStTpeaHKLmV8Bo/PQ4iIiJzeWiX1v9OkfWJ2g5mdB2wFPnWyQYnIme1IOM529jJFkV6ybGYtw95g0/MvZoQ7wu0cCUtUcHRhDHse13lrWo71dKXKN4ol9vs+q5NJ3pDtYUtXJrbPj8Z9bttTZ3fJsb7H2LYuxYsH4/+d7tgZct8DjkNHYOUwXH6psWnjQldAioiIzC32Wcg51yypn8s+YDNafiMiJ+FIOM6jPE2GNDl6qFLjUZ7mknBL08S+GiQ44ncRemXSFhC6JEfqXVSTidj/ck9Xqnx2cpI+8xhNJMgHAZ+dnOS99DdN7H807vMnT1cZSBlru2Gi5viTp6t8eAtNE/sdO0O+ersjl3UMr4BCEb56O9x4fajEXkRE2mI+y2/mxTlXAArtuj8ROTNtZy8Z0mQae+6PHbezl2HmTuq/5+cZcjlyYf/xtoIL+J6fZ1Oyp+lY3yiW6DOPvkQCIDoGUXuzpP62PXUGUsZAOrqMx0D6hfZmSf19D0QJfS4b9cllARz3PQCbNjYNT0REZN40RSQiS8oURdLT1sYDpEkxRbFpn0OuRs+sf2c9eBxytdix9vs+OW9mv5znsd/3m/bZXXL0zQyPvlTU3jS+I9Az67VFT0/ULiIi0g7zSurNLGlmLzWzi8zMYs672Mze077wRORM00uWGvUZbTXq9JJt2melpSkRzmgrEbLS4ivsrk4mKYQz+xXCkNXJ5m9iru8x8jPDI1+P2pvGNwyl0sy2UilqFxERaYeWSb2ZvYVovfxDwKPATjO7scnpbwU+277wRORMs5m1VKlRpYbDHf98M2ub9rki2UcBn4ILCJ2j4AIK+FyR7Isd6w3ZHvIuJB9E/fJBQN6FvCHbfMnOtnUpJuqOiZojdNFxou7Yti7VtM/llxqFolEoRn0KRUehaFx+afMXAiIiIgsRm9Sb2UuBLwHDwLPAk8B64Mtm9l8WPzwROdMMe4NcwhYypClQIkOaS2i+SRZgU7KHt6VGyFmCI9TJWYK3pUZi19MDbOnK8N7+fvoSCQ4GAX2JXLriXQAAIABJREFUBO/tb75JFqLNsB/ekmEgbewtw0Da+PCWTGz1m00bPW683shljSNHo+ON16v6jYiItE+rjbK/2Tjn55xzXwAws8uAvwX+vZklnXP/bpFjFJEzzLA32HRTbDObkj0tk/i5bOnKtCxhOduLB5MtS1jOtmmjp02xIiKyaFpNE10FfP1YQg/gnLsfuBS4D/ioZuxFRERERDqrVVI/QrSOfgbn3Djw08B3iGbsF1LPXkRERERE2qjV+8dHgdxcNzjnSmb2JuBO4ONmFl87TkREREREFkWrpH470VKbOU1L7L8B/C7wXBtjExERERGReWi1/OabwMvNbHOzExpXkv1p4PvAOW2MTURERERE5qFVUv/3wIPAG+NOcs7lgTcA9wDPtyc0ERERERGZj9jlN865HwCvms8dOecmgNe0IygREREREZm/Rb/yiZndbGbfXuxxRERERETOVAu7esqJ2QhcfQrGEZEz2BOlGl+bqLCnFrAuneBNA11c2JPudFgicprZ7pf5bj3PwbDOqJfiylQfm5PdsX2eq5e5tzZ1vM9V6V7OTsX3AfhxtcK3ykX2+z6rk0le153lvExXu74VOc3oGuUisuw9Uarx54cKTPoha1Iek37Inx8q8ERJlXZFpH22+2W+XD3CVBgwYkmmwoAvV4+w3S837fNcvcwXK2Mz+nyxMsZz9eZ9IEro/3ZqknwYMJpIkA8D/nZqkh9XK+3+tuQ0oaReRJa9r01UGPA8+pMenhn9SY8Bz+NrE3ryE5H2+W49T44EvV4Cz4xeL0GOBN+t55v2ubc2RQ5vVh+Pe2tTsWN9q1yk1zP6Gv36vAS9nvGtcrHd35acJpTUi8iyt6cW0JuwGW29CWNPLehQRCJyOjoY1snazNQpax4Hw3pb+wDs931ys/rlzGO/7y8wajlTKKkXkWVvXTrBVOBmtE0FjnXpRIciEpHT0aiXoujCGW1FFzLqpdraB2B1MklhVr+CC1mdPBXbIWU5UlIvIsvemwa6mAhDJv2Q0Dkm/ZCJMORNA9pQJiLtc2WqjwIBU2FA6BxTYUCBgCtTfU37XJXupUA4q0/IVene2LFe151lKnTkG/3yYcBU6Hhdd7bd35acJpTUi8iyd2FPmg+szNGf9NhXD+lPenxgZU7Vb0SkrTYnu3lHZpheL8Fh59PrJXhHZji2+s3ZqW5u6hqa0eemrqGW1W/Oy3Txnt5++rwEB4OAPi/Be3r7Vf1GmtJ7OCJyWriwJ60kXkQW3eZkd8sSlrOdneqeVwnL2c7LdCmJl3nTTL2IiIiIyDJ3KmbqHwX+9hSMIyIiIjF0MSOR09eiz9Q75251zr13sccRERGR5nQxI5HTW1uTejP7JTP763bep4iIiJw8XcxI5PTW7pn6K4Gb23yfIiIicpJ0MSOR05uq34iIiJwBVieT7A0rTFKjTEA3CfpdmrVJrakXOR3EJvVm9osLvL9zTyIWERERWSRb0gnuKRfpxqPHPAou4LAr8tq0LmYkcjpoNVP/V4Brcc50tsDzRURE5BTYY1UuSnVxIAgohCE5L8E5yTR7rNrp0ESkDVol9XVgP/DZed7fW4CLTyoiERERabuDYZ21yTTrU3a8LXSOg2G9g1GJSLu0SuqfAEadc78znzszs40oqRcREVlyRr0UU2FAryWOtxVdyKiX6mBUItIurarfPAKMmtnoqQhGREREFseVqT4KBEyFAaFzTIUBBQKuTPV1OjQRaYNWSf0PiNbJXzLP+3sKuPekIhIREZG225zs5h2ZYXq9BIedT6+X4B2ZYTYnuzsdmoi0gTnXfF+rmSWBLFByzmnRXcPWrVvdQw891OkwREREROQ0ZmYPO+e2zufc2DX1zjkfmGxLVCIiIiIisijafUXZn2Bm/9bMti/2OCIiIiIiZ6pFT+qBAeCsUzCOiIiIiMgZ6VQk9SIiIiIisoiU1IuIiIiILHOtLj4lclp4vFjjjokKe2sBa9MJrhvo4kXZdKfDEhEREWmLJTNTb2ZvN7NPmdl3zCxvZs7MPr+A/n/V6OPM7Jwm5yTM7CNm9kMzK5vZmJl9zcwub993IkvN48Uaf3awyKQfsiblMemH/NnBIo8Xa50OTURERKQtlkxSD3wc+CDRha72LqSjmV0P/BJQiDnHgP8N/CGQBv4U+HvgKuBeM7vhxMKWpe6OiQr9CWMg6eFZdOxPGHdMVDodmoiIiEhbLKWk/iPAeUAf8IH5djKzEeAzwBeBh2NOfSfwduA+4BLn3G86534JeA0QAJ8xs94TjF2WsL21gL6EzWjrSxh7a0GHIhIRERFpryWT1Dvn7nLOPePiLnE7t083jv+mxXnHXih83Dl3fIrWOfevRC8IRoiSfjnNrE0nyAczH1b5wLE2nehQRCIiIiLttWSS+hNhZr8AvAX4Fefc0ZjzuoDLgRLwnTlO+afG8bXtjlE677qBLiYDx4QfErroOBk4rhvo6nRoIiIiIm1xKqrf3L0Yd2pmZwF/DHzeOXdri9PPBhLAduecP8ftzzSO57UxRFkiXpRN82ujzKh+83PDqn4jIiIip4+TTurN7A+Bf3HOfXmu251z9wD3nOw4s8b0gL8h2hj74Xl06W8cJ5vcfqx9IGbM9wPvB9iwYcP8ApUl40XZtJJ4EREROW21Y6b+14EcMGdSv0g+AlwNvNk5N34qBnTOfZrG+v2tW7cudN2/iIiILCEHmOQpDjJJmX66OZ9RVh2fA2zuIJM8zQEmKDNAN1tYxWiLfk+Vq9xZKLOv7rMmleTaXDfnd2fa9a2IAC2SejP7xXnez5bp5zrn/vqkoophZucBvw981jn3tXl2OzYT3+yv7lj7xMnEJiIiIkvfASa5nx1kSNFHF2Xq3M8OLmNTbGJ/kEnuZztdM/pt5zI2N03snypX+czYFP0Jj9XJBPkg5DNjU/zyEErspa1azdT/FdBqVtoBVzY+rPH1oiX1wIVABnivmb23yTnPRGXpeatz7h+A54jKVm42s+Qc6+rPbRx/vBgBi4iIyNLxFAfJkKKbFMDx41McjE3qn+YAXaToapx/7Pg0B5om9XcWyvQnPPoTUW2S/kaJ5TsLZSX10lbzWX5TAP6CqHLMbAZ8Avg+cHsb44qzE/ifTW57M7CKaClQvnEuzrmKmd0HvLrxcdesfm9sHL/d5lhFRERkiZmkTB8zK6B1kWSScmy/iTn6ZUgyEdNvX91ndXJmCeVez9hXn6tuh8iJa5XUvwf4FHAj8F7n3Hdnn2BmnwC+75z7nUWI7yc45x4F3jfXbWZ2N1FS/1vOuWdn3fznRAn9fzaz1x2rVW9mrwBuAg4DX1msuEVERCTenrDAIxxljCpDZHgpK1jn5do+Tj/dlKkfn6EHqODTT3dsv4FGv65p/ar4DMT0W5NKkg/C4zP0AFOhY03qVBQgXFzH9hcc25cwn/0Fsnhi69Q75z4PXAzsAu42s1vMbFHeKzKzt5jZ58zsc8B/aDS/6libmd1ykkP8b+D/ENWrf8TM/ruZ/U+iWfsE8MvOufxJjiEiIiInYE9Y4BtuLyXnM+jSlJzPN9xe9oSFto91PqNUqVOmjsNRpk6VOuczGttvC6uoUKfS6Hfs8y2satrn2lw3k0HIZBBdK+XY59fm4l9ALHUHmeQBtlOhTh9dVKjzANs52LTQoCy2lhefcs7tds69Hvgo8GtECfErFyGWS4CbGx8/3WjbPK3tpK722rhS7buA3wB84ENE70DcC1w1j1r3IiIiskge4Sg9JOmxJGZGjyXpIckjNL225AlbRT+XsYluUuSp0E2q5SZZgFH6uYzNs/o13yQL0WbYXx7qpS/hsd8P6Et4/PJQ77JfTz99f4Fhxz9/mgOdDu2MNe/3fpxzf2xm/wz8L+B7jZnzT7QrEOfcJ4FPnuR9XNPidh/4/xofIiIiskSMUWWQmdcT6SbBGNVFGW8V/fMqYTnbKP0LXmJyfndm2Sfxs821LyEzj30JsnhaztRP55x7ErgU+K/Ax4BHaF0dR0RERCTWEBnKBDPaygQMcXolw6eLfrqpMnOzb3Ue+xJk8Sx4l4ZzLgA+YWb/CPwtUQUcERERkRP2UlbwDfaCi2boywSU8LmixTr309muoMiD4QRHqDFMmld6A5yVyMb22RmUuD+Y4LCrMWJpLksMsDHR03Ksex70+YdvOY6Mw/AgvOV1xtWvbJ4mbmEVD7AdiGboq/hUqPMS1i/sm5S2WdBM/XTOuQeIasYPAh9uW0QiIiJyxlnn5XiDraXHkoxbjR5L8gZbuyjVb5aDXUGRO4KDFJ3PCpei6HzuCA6yKyg27bMzKHFr/SAF57OCFAXnc2v9IDuDuaqSv+CeB30+/SVHoQhD/VAowqe/5LjnweZlN0fp51I209XYX9BFiktb7C+QxXVS9ZQas/ba5iwiIiInbZ2XYx1nZhI/24PhBFkSZC1K1bIkwUXtzWbr7w8myFqCXKNPrpHm3R9MxM7W/8O3HD1dkGvc7bHjP3zLcXVMaZQT2V8gi+eEZ+pFREREZHEcoUYPMy9a1UOCI9Sa9jns5u5z2DXvA3BkHHpmLYXv6Y7aZflY/lc+EBERWcaerZe5p1rgQFBnVSLF1Zkc56S02fBMN0yaIn40Q99QImB4VoWg6UYsTcH5x2foj/UZseZ9IFpDXyi+MEMPUCpH7bJ8aKZeRESkQ56tl/lCcYypMGCll2QqDPhCcYxn6yoLeKZ7pTdAkYCi83HOUXQ+RQJe6Q007XNZYoCiCyg4n9A5Cs6n6AIuSzTvA9Gm2FIlSuzDMDqWKlG7LB9K6kVERDrknmqBXi9Br5fAMzv++T3V9l9FVZaXsxJZrkuMkrUkR61O1pJclxiNrX6zMdHDDalRcpbkKHVyluSG1GjL6jdXvzLJ+3/GyGVhbDKasX//z8RXv5GlR78tERGRDjkQ1FnpzXwqzprHgaDeoYhkKTkrkW1ZwnK2jYmeeZWwnO3qVyZjN8XK0qekXkREpENWJVJMhQG99sLmxqILWZVIdTAqkfnZuSPk/vvg8CEYWQmXXQ4bN2kRSKfoJy8iItIhV2dyTIUBU2FA6Nzxz6/OqKyjLG07d4Tc+lVHoeBYMRwdb/2qY+eOsNOhnbGU1IuIiHTIOalu3pUdotdLcCj06fUSvCs7pOo3suTdfx9kc5DLGZ5n5HJGNhe1S2do+Y2IiEgHnZPqVhIvy87hQ7BieGZbT0/ULp2hmXoRERERWZCRlVAqzWwrlaJ26Qwl9SIiIiKyIJddDsUCFAqOMIzW1BcLUbt0hpJ6EREREVmQjZs8brgxWkt/9Eh0vOFGU/WbDtKaehERERFZsI2bPDZu6nQUcoxeTomIiIiILHOaqReRJef5sMjDboyjrsoKy/ByG2KDt7CrKoqIiJxJNFMvIkvK82GRO8N9FJ3PEGmKzufOcB/Ph8VOhyYiIrJkKakXkSXlYTdGD0mylsTMyFqSHpI87MY6HZqIiMiSpaReRJaUo65KD4kZbT0kOOqqHYpIRERk6VNSLyJLygrLUCKY0VYiYIVlOhSRiIjI0qekXkSWlJfbECV8is7HOUfR+ZTwebkNdTo0ERGRJUtJvYgsKRu8LNd6a8hakjFqZC3Jtd4aVb8RERGJoZKWIrLkbPCybEBJvIiIyHxppl5EREREZJlTUi8iIiIisswpqRcRERERWeaU1IuIiIiILHPaKCsiIiKyBP2oUOP2sSp7qiHrMh7XD2V4cS7d6bBkidJMvYiIiMgS86NCjT/dV2LCD1mTNib8kD/dV+JHhVqnQ5MlSkm9iIiIyBJz+1iV/qQxkPTwLDr2J43bx6qdDk2WKCX1IiIiIkvMnmpIX8JmtPUljD3VsEMRyVKnpF5ERERkiVmX8cgHbkZbPnCsyyh1k7npkSEiIiKyxFw/lGHSd0z4IaGLjpO+4/qhTKdDkyVKSb2IiIjIEvPiXJoPrulhIOmxr+YYSHp8cE2Pqt9IUyppKSIiIrIEvTiXVhIv86aZehERERGRZU5JvYiIiIjIMqekXkRERERkmVNSLyIiIiKyzCmpFxERERFZ5pTUi4iIiIgsc0rqRURERESWOSX1IiIiIiLLnJJ6EREREZFlTkm9iIiIiMgyp6ReRERERGSZU1IvIiIiIrLMKakXEREREVnmlNSLiIiIiCxzSupFRERERJY5JfUiIiIiIsucknoRERERkWVOSb2IiIiIyDKnpF5EREREZJlTUi8iIiIisswpqRcRERERWeaU1IuIiIiILHNK6kVEREREljkl9SIiIiIiy5ySehERERGRZU5JvYiIiIjIMqekXkRERERkmVNSLyIiIiKyzCmpFxERERFZ5pTUi4iIiIgsc0rqRURERESWOSX1IiIiIiLL3JJJ6s3s7Wb2KTP7jpnlzcyZ2eebnHuumf17M/u2me02s5qZHTSzW83sNS3GudnMHjSzgplNmtndZnbd4nxXIiIiIiKLb8kk9cDHgQ8ClwB7W5z7e8B/A0aBrwH/L/A94M3At83sw3N1MrNbgM8Bq4HPAJ8HLgJuN7MPnvy3ICIiIiJy6iU7HcA0HwH2AM8CVwN3xZx7J/D/OOcemd5oZlcD3wD+wMy+7JzbP+22y4GPAs8Br3DOjTfa/wB4GLjFzO5wzu1s37ckIiIiIrL4lsxMvXPuLufcM845N49zPzc7oW+03wPcDaSBy2fd/KuN4+8fS+gbfXYC/wPIAO89sehFRERERDpnyST1bVRvHP1Z7a9tHO+co88/zTpHRERERGTZOK2SejM7C3gdUALundaeBdYChelLcqZ5pnE8b9GDFBERERFps9MmqTezDPB3RMtoPjl9iQ3Q3zhONul+rH0g5v7fb2YPmdlDhw8fPul4RURERETaZSltlD1hZpYA/hdwBfBF4JZ2j+Gc+zTwaYCtW7e2XPcvIiIiS9dhN8Fz7GWKEr30cDZrGbGmc3vHHZx6iqcrTzFpVfpdhi1d5zPae358p/FdsPtfoXQEeoZh/Stg8KzWQR54Dp78DkwchIFRuODVsOrs+D57n4Uf3A3jB2BwFbzkGlh7Tsuh/mzvJJ+fyFO0kKzz+PmBPn5tbX9sn3L1APnSU9T9SVLJfvp6zqc7syq2j1/cS238h4TVcbzMIOnBi0lm17aMLyjsITjyKK4yhnUNkRi+hERuXXyn/G448H2oHIWuFbDqZdC3vuVYy9Wyn6lvJPSfB94BfAn4+Tk22x6biW/26DzWPtH+CEVERGQpOewmeIQfU6VGjm6q1HiEH3PYxacBB6ee4oHqD6jg0+cyVPB5oPoDDk491bzT+C548h+hVoTuFdHxyX+M2uMceA7u+xKUp6B/JDre96WovZm9z8K3/w5KUzCwMjp++++i9hh/tneSv5icoEpIt/OoEvIXkxP82d5mCxyihP7I5P0EQZlkoo8gKHNk8n7K1QNN+/jFvVT230Xol7D0AKFforL/LvxifCXzoLCH+p5v4uolyAzi6iXqe75JUNjTvFN+N+z4OtSLkBmKjju+/n/bu/sou+r63uPv7zxnJskkQhSJSAJY4MpqfZjWEltAr7dFC0IttdpLK7biQ2u7XJY+XB8qbe2jWlu11arVWPVesCjei0VqLdCgdKEjKkUFQZIUg0gCSYaZyTx/7x97j5w5nTOZJHNmZs+8X2vttXO++/fb53fO7Jx8Zud39i7qK1SlQ31EtAP/B3gJ8L+BX8zM+i/IkplDFNe+XxsRT5xlV08p199u1lglSdLy8B320Ek7nXQQBJ100Ek73znMbXLuHrmLrmylK9qJCLqina5s5e6ROUL9/V+Gjp5iiXjsz/d/ee5BfusW6FoLa9ZBtBTrrrVFvZGv3wxr1kN32ad7XfH46zfP+VQfOzBAe0JntNISQWe00p5FvZGB4btobemktXUNEUFr6xpaWzoZGG78XoztvwPa1tDS1k1E0NLWDW1rivocJvd9jWjtJtqLftHeTbR2M7nva407PXg7tHVDe/m+t/cUjx+8fc7nqrLKhvqI6AD+keIM/T8Av5SZk3N0ubFcnz/LtufXtZEkSSvUowzTQfuMWgftPMrwnP0OxiiddTOXO2njYIw27jS8D9q7Z9bau4v6XA58H7p6Zta6eop6I/sfhDV1fdb0FPU5DMUU7XWRsJ0WhmKqYZ/xiYO0tHTNqLW0dDE+0fjs/tTofqJ1zYxatK5hanR/gx6FHHkE2mb2o21NUW9k5OEixM/o013UV6hKhvryS7HXAhcBfw+8PDMbH3mF95XrN0bExpp9bQF+HRgFPrzgg5UkScvKOroZ+8EVsAtjjLOO7gY9Cr3ZyWjdFbNHmaA3Oxt36j4exut+WRgfLupz2fAEGBmaWRsZKuqNbDwBDtX1OTRU1OfQky2MMzNGjVPMrW+kva2XqamRGbWpqRHa2xrPw2/p3EhOHppRy8lDtHRubNCjEF2Pg4mZ/Zg4VNQb6ToOJure94nhor5CLZtQHxEXR8T2iNgO/F5ZPnu6FhG1X359H/ACYB/FtJrfj4gr65bzavefmbcCfwmcCtwREe+MiL8B+oHHAVd4N1lJkla+U9nMKOOMMkaSjDLGKOOcytxf2Dy96wxGYpKRHCczGclxRmKS07vm+KLsST9azKMfG4LMx/580o/OPcgzfxJGBou59DlVrEcGi3ojP3IeHBoo5tLnVLE+NFDU53DphvWMB4zmJFOZjOYk41HUG1nffQaTU6NMTh4iM5mcPMTk1Cjruxu/Fx0bfxgmDjE1MUxmMjUxDBOHivocWo9/Gjk5TI4X/XJ8mJwcpvX4pzXudMIzihA/Xr7v40PF4xOeMedzVVnM4wauiyIirgTeMkeT3Zm5pWx7M3DuYXb5B5l55SzPcxnFmfn/BkwBtwNvy8zPzHesfX192d/fP9/mkiRpmfHqNzN59ZvlKSK+kpl982q7XEJ9lRjqJUmS1GxHEuqXzfQbSZIkSUfHUC9JkiRVnKFekiRJqjhDvSRJklRxhnpJkiSp4gz1kiRJUsUZ6iVJkqSKM9RLkiRJFWeolyRJkiqubakHIEmSdCz25gHuZQ+Pcoh1rOE0NrMpNszZZ8fOYT69Z5C9jLOJdi7evJZztnYv0oilheeZekmSVFl78wBf4R5GGGctaxhhnK9wD3vzQMM+O3YO83d79jOYkxyfbQzmJH+3Zz87dg4v4silhWWolyRJlXUve+iknS46CIIuOuiknXvZ07DPp/cM0pMtrI1WIoK10UpPtvDpPYOLOHJpYRnqJUlSZT3KITppn1HrpJ1HOdSwz17G6amLQD20sJfxpoxRWgyGekmSVFnrWMNoXRgfZZx1rGnYZxPtDDE1ozbEFJvqfjmQqsRQL0mSKus0NjPKOCOMkSQjjDHKOKexuWGfizevZSimGMxJMpPBnGQoprh489pFHLm0sAz1kiSpsjbFBp7JU+iinUEO0UU7z+Qpc1795pyt3bxq80bWRiv7YoK10cqrNm/06jeqNC9pKUmSKm1TbGATc1/Cst45W7sN8VpRPFMvSZIkVZyhXpIkSao4Q70kSZJUcYZ6SZIkqeIM9ZIkSVLFGeolSZKkijPUS5IkSRVnqJckSZIqzlAvSZIkVZyhXpIkSao4Q70kSZJUcYZ6SZIkqeIM9ZIkSVLFGeolSZKkijPUS5IkSRVnqJckSZIqzlAvSZIkVZyhXpIkSao4Q70kSZJUcYZ6SZIkqeIM9ZIkSVLFGeolSZKkijPUS5IkSRVnqJckSZIqzlAvSZIkVZyhXpIkSaq4tqUegCRJkqrnGw9P8JndE+wZSjb3BBec3MZTj5s7Wt710CQ33DvJAwPJieuD809r5YzHtx72uXbumuLW25KH9sHjj4dtzwq2bvHcdC3fDUmSJB2Rbzw8wd/eOc7B0eTEbjg4mvztneN84+GJhn3uemiSD/SPc3AkOWEdHBxJPtA/zl0PTc75XDt3TfGp65LBoeT444r1p65Ldu6aWuiXVWmGekmSJB2Rz+yeoLcDNnQGLRFs6Ax6O4p6IzfcO8n6rqC3q+jT2xWs7wpuuHfuUH/rbcnanmRtT9FvbU+wtie59bZc6JdVaYZ6SZIkHZE9Q8n6jpm19R1FvZEHBpJ1nTNr6zqL+lwe2gfd3TNr3d1FXY8x1EuSJOmIbO4JBsZm1gbGinojJ64PHh2dWXt0tKjP5fHHw/DwzNrwcFHXYwz1kiRJOiIXnNzGwTE4MJpMZXJgNDk4VtQbOf+0VgZGkoMjRZ+DI8nASHL+aXN/UXbbs4LBoWBwqOg3OJQMDgXbnjX3LwOrjaFekiRJR+Spx7Xxa2e109sZPDAMvZ3Br53VPufVb854fCuX97XT2xU8+Cj0dgWX97Uf9uo3W7e08KILi7n0+x4u1i+60Kvf1POSlpIkSTpiTz3u8JewrHfG4+d3Cct6W7e0sHXLEXdbVfwVR5IkSao4Q70kSZJUcYZ6SZIkqeIM9ZIkSVLFGeolSZKkijPUS5IkSRVnqJckSZIqzlAvSZIkVZyhXpIkSao4Q70kSZJUcYZ6SZIkqeIM9ZIkSVLFGeolSZKkimtb6gFIkiSpeu7cP8F1949z/3ByUndw4UntnLXRaLlUPFMvSZKkI3Ln/gne/a1RDowlm9fAgbHk3d8a5c79E0s9tFXLUC9JkqQjct3942zoCDZ0BC0RP/jzdfePL/XQVi1DvSRJko7I/cPJ+vaZtfXtRV1Lw1AvSZKkI3JSdzBQd1J+YLyoa2ksi1AfEZdExLsj4paIGIiIjIiPHabPtoi4PiIeiYhDEXFHRLwuIlrn6HNBRNwcEQcjYjAibouIly38K5IkSVq5LjypnQNjyYGxZCrzB3++8KT2w3dWUyyLUA+8CXgt8DRgz+EaR8RFwA7gHOBa4D1AB/BO4KoGfV4LXAecBXwM+ABwIrA9It5+7C9BkiRpdThrYxu/cWYnGzqCPYdgQ0fwG2d2evWbJRSZSz8hwiIVAAAUlElEQVT3KSKeA3wXuBc4F7gJ+HhmXjpL2/Vlu17g2ZnZX9a7gBuBs4GXZuZVNX22AHcBQ8AzM3NXWd8IfBk4FdiWmf8+n/H29fVlf3//0bxUSZIkaV4i4iuZ2TeftsviTH1m3pSZ9+T8fsO4BNgEXDUd6Mt9jFCc8Qd4TV2fXwE6gfdMB/qyz37gT8qHrz7K4UuSJElLalmE+iP03HJ9wyzbdgDDwLaI6Jxnn8/WtZEkSZIqpYqh/vRy/e36DZk5AeykuFPuKfPs8z2KaTlPiojuhR2qJEmS1HxVDPW95fpgg+3T9Q1H0ae3wXYi4pUR0R8R/Xv37p3XQCVJkqTFUMVQvyQy8/2Z2ZeZfZs2bVrq4UiSJEk/UMVQf7iz6tP1A0fRp9GZfEmSJGnZqmKov7tc/1D9hohoA7YCE8B98+zzRKAH+G5mDi/sUCVJkqTmq2Kov7Fcnz/LtnOAbuDWzBydZ5/n17WRJEmSKqWKof4aYB/wkoj4wcX4y5tPvbV8+N66Ph8GRoHXljeimu6zEXhD+fB9TRqvJEmS1FTL4l6+EXExcHH58IRyfXZEbC//vC8zrwDIzIGIuJwi3N8cEVcBjwAvpLh05TXA1bX7z8ydEfHbwLuA/oi4GhijuJHVk4B3zPduspIkSdJysyxCPfA04GV1tVN47Frzu4Erpjdk5qcj4lzgjcDPAV3AvcDrgXfNdmfazHx3ROwq9/PLFP9L8U3gTZn5kQV9NZIkSdIiilnyrw6jr68v+/v7l3oYkiRJWsEi4iuZ2Xf4ltWcUy9JkiSpxnKZfiNJkqQV7u7RUT4/NMQDExOc2NbG83p6OL2zc6mHtSJ4pl6SJElNd/foKNsPHmRgcpITWlsZmJxk+8GD3D06evjOOixDvSRJkpru80NDrI9gfWsrLeV6fQSfHxpa6qGtCIZ6SZIkNd0DExOsbZkZPde2tPDAxMQSjWhlMdRLkiSp6U5sa2NwampGbXBqihPb/IrnQjDUS5Ikqeme19PDQCYDk5NMleuBTJ7X07PUQ1sRDPWSJElqutM7O7mst5f1ra08ODnJ+tZWLuvt9eo3C8T/75AkSdKiOL2z0xDfJJ6plyRJkirOUC9JkiRVnKFekiRJqjhDvSRJklRxhnpJkiSp4gz1kiRJUsUZ6iVJkqSKM9RLkiRJFWeolyRJkirOUC9JkiRVnKFekiRJqjhDvSRJklRxhnpJkiSp4gz1kiRJUsUZ6iVJkqSKM9RLkiRJFWeolyRJkiqubakHIEmSJC0Xu78zxZdugb3fh01PgB/7STj51OV/Hnz5j1CSJElaBLu/M8VnPgFDj8Lxm4r1Zz5R1Jc7Q70kSZIEfOkW6FkLPesgWop1z9qivtwZ6iVJkiSKKTfdPTNr3T1Ffbkz1EuSJEkUc+iHh2bWhoeK+nJnqJckSZIovhQ7NFjMpc+pYj00WNSXO0O9JEmSRHGVmwteXMyl37e3WF/w4mpc/cZLWkqSJEmlk09t4eRTl3oUR275/9ohSZIkaU6GekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHGGekmSJKniDPWSJElSxRnqJUmSpIoz1EuSJEkVZ6iXJEmSKq5tqQcgSZKk1WH/5MPsmdrNEIP0sJbNLSezsfW4pR7WiuCZekmSJDXd/smHuXvyTsZylO7sYSxHuXvyTvZPPrzUQ1sRDPWSJElquj1Tu+mgg47oJCLoiE466GDP1O6lHtqKYKiXJElS0w0xSDsdM2rtdDDE4BKNaGUx1EuSJKnpeljLOGMzauOM0cPaJRrRymKolyRJUtNtbjmZMcYYy1Eyk7EcZYwxNrecvNRDWxEM9ZIkSWq6ja3HcXrrWXREJ8MxREd0cnrrWV79ZoF4SUtJkiQtio2txxnim8Qz9ZIkSVLFGeolSZKkijPUS5IkSRVnqJckSZIqzlAvSZIkVZyhXpIkSaq4Sof6iPiZiPhcRHw3Ig5FxH0R8Y8RcXaD9tsi4vqIeKRsf0dEvC4iWhd77JIkSdJCqWyoj4g/Bz4DPAO4Afhr4HbgIuCLEXFpXfuLgB3AOcC1wHuADuCdwFWLN3JJkiRpYUVmLvUYjlhEnADsAfYCP5yZD9Vsew5wI7AzM08pa+uBe4Fe4NmZ2V/Wu8q2ZwMvzcx5hfu+vr7s7+9fwFckSZIkzRQRX8nMvvm0reqZ+pMpxn5bbaAHyMybgEeBTTXlS8rHV00H+rLtCPCm8uFrmjpiSZIkqUmqGurvAcaAH4uI42s3RMQ5wDrg8zXl55brG2bZ1w5gGNgWEZ1NGKskSZLUVJUM9Zn5CPC7wBOAb0bE+yPiTyPiE8DngH8BXlXT5fRy/e1Z9jUB7ATagFOaOnBJkiSpCdqWegBHKzP/KiJ2AR8CLq/ZdC+wvW5aTm+5Pthgd9P1DY2eLyJeCbwS4MlPfvLRDFmSJElqikqeqQeIiN8BrgG2A6cCPcAzgfuAj0fEXyzk82Xm+zOzLzP7Nm3adPgOkiRJ0iKpZKiPiPOAPwf+X2a+PjPvy8zhzLwd+FmKK+P8VkRMT6eZPhPf+1/3NqN+oFljliRJkpqlkqEeuKBc31S/ITOHgS9RvLanl+W7y/UP1bePiDZgKzBBcZZfkiRJqpSqhvrpq9Q0mgczXR8r1zeW6/NnaXsO0A3cmpmjCzM8SZIkafFUNdTfUq5fGRGbazdExPOBZwMjwK1l+RpgH/CSiOiradsFvLV8+N6mjliSJElqkqpe/eYaiuvQPw/4VkRcCzwInEkxNSeA38vMhwEycyAiLi/73RwRVwGPAC+kuNzlNcDVi/4qJEmSpAVQyVCfmVMR8QLg14GXUHw5tpsiqF8PvCszP1fX59MRcS7wRuDngC6Ky1++vmyfi/gSJEmSpAVTyVAPkJnjwF+Vy3z7fBF4QdMGJUmSJC2Bqs6plyRJklQy1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHHh5dmPXETsBXYvwVMfT3FnXKmex4Zm43GhRjw21IjHxvJycmZumk9DQ32FRER/ZvYt9Ti0/HhsaDYeF2rEY0ONeGxUl9NvJEmSpIoz1EuSJEkVZ6ivlvcv9QC0bHlsaDYeF2rEY0ONeGxUlHPqJUmSpIrzTL0kSZJUcYZ6SZIkqeIM9ZIkSVLFGeqXgYi4JCLeHRG3RMRARGREfOwwfbZFxPUR8UhEHIqIOyLidRHRuljjVvNExHER8YqIuDYi7i1/xgcj4gsR8asRMevfXY+L1SEi/jwi/jUi7i9/zo9ExFcj4i0RcVyDPh4bq1BEXFr+m5IR8YoGbS6IiJvLz5jBiLgtIl622GNV80TErprjoH55sEEfPzMqxi/KLgMR8TXgR4BB4LvAGcDHM/PSBu0vAj4JjABXA48AFwKnA9dk5s8vxrjVPBHxauC9wPeAm4D/BJ4AvAjopfj5/3zW/AX2uFg9ImIMuB34JvAQ0AP8ONAHPAD8eGbeX9PeY2MVioiTgP8AWoG1wOWZ+cG6Nq8F3g08THFsjAGXAE8C3pGZVyzqoNUUEbEL2AD81SybBzPz7XXt/cyoIEP9MhARz6EI8/cC51KEuFlDfUSsL9v1As/OzP6y3gXcCJwNvDQzr1qk4asJIuK5FEHtnzJzqqZ+AvAl4CTgksz8ZFn3uFhFIqIrM0dmqf8x8AbgvZn5a2XNY2MViogA/gXYCnwKuIK6UB8RW4C7gCHgmZm5q6xvBL4MnApsy8x/X8yxa+GVoZ7M3DKPtn5mVJTTb5aBzLwpM+/J+f2GdQmwCbhq+i9auY8R4E3lw9c0YZhaRJl5Y2ZeVxvoy/qDwPvKh+fVbPK4WEVmC/SlT5Trp9TUPDZWp98Engu8nCK0z+ZXgE7gPdOBHiAz9wN/Uj58dRPHqOXJz4yKalvqAeiIPbdc3zDLth3AMLAtIjozc3TxhqVFNF6uJ2pqHheC4r/HAe6oqXlsrDIRcSbwZ8BfZ+aO8n/+ZjPXsfHZujaqvs6IuBR4MsUvencAOzJzsq6dnxkVZaivntPL9bfrN2TmRETsBJ4KnAJ8azEHpuaLiDbgl8uHtR+4HherUERcQTFXupdiPv1PUPxD/Wc1zTw2VpHyM+KjFN/DecNhms91bHwvIoaAJ0VEd2YOL+xItQROoDg2au2MiJdn5r/V1PzMqChDffX0luuDDbZP1zcswli0+P4MOAu4PjP/uabucbE6XUHxBeppNwCXZebemprHxury+8DTgZ/IzEOHaTufY6OnbGeor7YPA7cA3wAepQjkrwVeCXw2Is7OzK+Xbf3MqCjn1EsVERG/CfwWxRfbfmmJh6NlIDNPyMygOAP3Iop/qL8aEc9Y2pFpKUTEsyjOzr/DL7eqVmb+Qfldre9n5nBm3pmZrwb+ElgDXLm0I9RCMNRXz/RvyL0Ntk/XDyzCWLRIysvO/TXFJQyfk5mP1DXxuFjFyn+orwV+CjgO+IeazR4bq0A57eYfKKZMvHme3eZ7bDQ6Y6vqm77wwjk1NT8zKspQXz13l+sfqt9QfqhvpfgC5X2LOSg1T0S8juI60ndSBPrZbhTicSEyczfFL35PjYjjy7LHxuqwluJnfCYwUntzIeAtZZsPlLXpa5XPdWw8kWLqzXedT7+iTU/V66mp+ZlRUYb66rmxXJ8/y7ZzgG7gVr+RvjJExO8C7wS+RhHoH2rQ1ONC004s19NXtPDYWB1Ggb9vsHy1bPOF8vH01Jy5jo3n17XRyvTj5bo2oPuZUVWZ6bKMFoprjyfwsQbb11P8Zj0K9NXUu4Bby74vWerX4bIgx8Kby59nP/C4w7T1uFglC8XZs95Z6i3AH5c/6y96bLjU/KyvLH/Or6irb6W4Y+jDwJaa+kaKmw8lcPZSj9/lmH/+ZwI9s9S3APeUP+c31NT9zKjo4tVvloGIuBi4uHx4Qrk+OyK2l3/el+WtujNzICIuB64Bbo6Iqyhu3/xCyts3U9zSWRUWES8D/pDibOstwG8WN4icYVdmbgePi1XmBcCfRsQXgJ0UgewJFHejPgV4ELh8urHHhhrJzJ0R8dvAu4D+iLgaGKO4+dCT8Au3K8UvAL8VETuA3RRXvzkV+BmKoH498Pbpxn5mVFeUv31pCUXElTw253E2u7Pu1s4R8WzgjRS3a+6iOKvyIeBd+V9vJKGKmccxAfBvmXleXT+PixUuIs6iuMvnT1AErw0UN5L5NvBPFD/r+i9Se2ysYjWfJ5dn5gdn2X4hxeVRn0HxPz7fpLjL7EcWc5xqjog4l+Iz4+kUJw57KL7k+jWK69Z/NGcJg35mVI+hXpIkSao4vygrSZIkVZyhXpIkSao4Q70kSZJUcYZ6SZIkqeIM9ZIkSVLFGeolSZKkijPUS5IkSRVnqJckNVVEbI+IjIgtTX6eXRGxq5nPIUnLlaFeklQJEXFzRHjHREmaRdtSD0CSpAXy35d6AJK0VAz1kqQVITO/s9RjkKSl4vQbSVqmImJLORd9e0ScERGfjohHImIoIr4QET81S5/OiPi9iPiPiBiOiIGIuCUiXrxA+7+y7HPeXPub5+u7LCI+GRH3RcShcqxfjIhLZ9svcG75OGuWm2vazTqn/hjeky0RcVVE7IuIkYjoj4gL5vPaJGmxeaZekpa/rcC/A/8B/B3wROAXgM9GxC9m5tUAEdEB/DNF+L0L+BugG7gEuDoinpaZbzja/TfBe4FvADuA7wHHAS8APhoRp2fmm8t2B4A/AC4DTi7/PG3XXE9wDO/JycCXgPuAjwKPo3hP/m9EPC8zbzrSFytJTZWZLi4uLi7LcAG2AFkub6vb1geMA/uB9WXtf5Vtrwfaato+niL8JrDtaPdf1q8s2583x3i319W3l/UtdfVTZ9lHB/Cv5XNvrtt2c/HPVsP3axewq652LO/JW+r29dPT+1rqY8PFxcWlfnH6jSQtfweBP6wtZGY/8HFgA/CzZflXKELn6zNzoqbtQ8AflQ9fcQz7X1A5yxz4zByjOJvexsJ88fVo35PdwFvrxvbPwH8CP7YA45KkBWWol6Tl7/bMfHSW+s3l+ukRsQ44DXggM++ape2N022PZv9HMNZ5i4gnR8TfRMRd5Vz3LOfOf7JssvkY938s78nXMnNylvr9wMZjGZckNYNz6iVp+ft+g/qD5bq3XKCYmz6b6fqGo9z/goqIUyjmrG8EbgE+R/E/BpMUU2BeBnQe49Mcy3tyoEGfCTwhJmkZMtRL0vL3hAb1E8r1wXKprdV7Yk3bo9n/tKlyPdu/H7OF40ZeT/HF2Jdn5vbaDRHxUopQf6yO5T2RpErxbIMkLX/PKKeS1DuvXH+1nD7zHWBzRDxllrbPKde3H83+a2r7y/VJs7Tvm6XWyGnl+pOzbDu3QZ9JgIhonc8THON7IkmVYqiXpOWvF/j92kJE9AH/k+Is87Vl+UNAAG+rDb4RcTzw5po2R7t/KKbMALw8Itpq2p9Uv4/D2FWuz6t73p9m9i+uAjxcrp98BM9ztO+JJFWK028kafnbAbwiIp4FfJHHriPfArwqMwfKdm8Hng9cBHw9Iq6nuCb7z1NcwvEvMvMLx7B/MvO2iNgBnAN8KSJupJi+cyHF9eBnO4M/m78FXg78Y0RcAzwAnAWcD3yifP56/1q+lk+Vr+0QsDszPzrH8xzteyJJleKZekla/nYC2yimvrwaeDHFlJEXZM2NocrLQf4P4I1l6Tco5qbfA/xiZv7usey/xkXAB4Enlc/xdOB3gEb7/y8y8w6K6S+3Aj8DvAZYD7wIeF+Dbh8E/pTifxZ+h+KSlL96mOc52vdEkiolMnOpxyBJmkVEbKEI3B/JzMuqtn9J0uLxTL0kSZJUcYZ6SZIkqeIM9ZIkSVLFOadekiRJqjjP1EuSJEkVZ6iXJEmSKs5QL0mSJFWcoV6SJEmqOEO9JEmSVHH/H2F6xfyL740GAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for y_label in list(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][\"nodes\"].values()):\n", + " layer_params = list(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][y_label].keys())\n", + " layer_params.remove(\"node_name\")\n", + " layer_params.remove(\"node_type\")\n", + " layer_params.remove(\"node_layer\")\n", + " for param in layer_params:\n", + " if (type(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][y_label][param]) is float or\n", + " type(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][y_label][param]) is int):\n", + " plt.figure(figsize=(12,12))\n", + " total_dots = 0\n", + " for i in range(data.shape[0]):\n", + " node_num = int(y_label.split(\"_\")[-1])\n", + " bm = np.array(params_dictionaries[i][\"chainer\"][\"pipe\"][model_index][\"binary_mask\"])\n", + " if np.sum(bm[node_num, :]) > 0 or np.sum(bm[:, node_num]) > 0:\n", + " total_dots += 1\n", + " plt.scatter(i // 10, \n", + " params_dictionaries[i][\"chainer\"][\"pipe\"][model_index][y_label][param],\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + " if total_dots == 0:\n", + " plt.close()\n", + " continue\n", + " plt.ylabel(y_label + \" \" + param, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \"_\" + param + \".png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python-deep36", + "language": "python", + "name": "deep36" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 65f5292b67fae12237a86369a9f1e1a3012484b0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 14:43:45 +0300 Subject: [PATCH 507/616] fix: logs with upper case letters --- deeppavlov/evolve.py | 6 +- .../models/evolution/run_param_evolution.py | 219 ------------------ .../models/evolution/train_phenotype.py | 23 -- 3 files changed, 3 insertions(+), 245 deletions(-) delete mode 100644 deeppavlov/models/evolution/run_param_evolution.py delete mode 100644 deeppavlov/models/evolution/train_phenotype.py diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 6e5cbf1c4f..dc23364de3 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -238,7 +238,7 @@ def run_population(population, evolution, gpus): shell=True, stdout=PIPE, stderr=PIPE)) for j, proc in enumerate(procs): i = k * len(gpus) + j - log.info(f'wait on {i}th proc') + log.info(f'Waiting on {i}th proc') proc.wait() return None @@ -254,9 +254,9 @@ def results_to_table(population, evolution, considered_metrics, result_file, res evolution.basic_config, "test_best"))[0] + ["test_best"]) if (not validate_best) and test_best: - log.info("validate_best is set to False. Tuning parameters on test") + log.info("Validate_best is set to False. Tuning parameters on test") elif (not validate_best) and (not test_best): - raise ConfigError("validate_best and test_best are set to False. Can not evolve.") + raise ConfigError("Validate_best and test_best are set to False. Can not evolve.") population_metrics = {} for m in considered_metrics: diff --git a/deeppavlov/models/evolution/run_param_evolution.py b/deeppavlov/models/evolution/run_param_evolution.py deleted file mode 100644 index 7783de9317..0000000000 --- a/deeppavlov/models/evolution/run_param_evolution.py +++ /dev/null @@ -1,219 +0,0 @@ -import json -import numpy as np -import argparse -from pathlib import Path -from subprocess import Popen, PIPE -import pandas as pd -from copy import deepcopy - -from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution -from deeppavlov.core.common.file import save_json, read_json - - -def score_population(population, population_size, result_file): - global evolution - - population_metrics = {} - for m in CONSIDERED_METRICS: - population_metrics[m] = [] - - for k in range(POPULATION_SIZE // len(gpus) + 1): - procs = [] - for j in range(len(gpus)): - i = k * len(gpus) + j - if i < POPULATION_SIZE: - save_path = Path(evolution.get_value_from_config(population[i], - evolution.main_model_path + ["save_path"])) - load_path = Path(evolution.get_value_from_config(population[i], - evolution.main_model_path + ["load_path"])) - population[i] = evolution.insert_value_or_dict_into_config( - population[i], evolution.main_model_path + ["save_path"], str(save_path.joinpath("model"))) - population[i] = evolution.insert_value_or_dict_into_config( - population[i], evolution.main_model_path + ["load_path"], str(load_path.joinpath("model"))) - - save_path.mkdir(parents=True, exist_ok=True) - f_name = save_path.joinpath("config.json") - save_json(population[i], f_name) - - # __file__ - - procs.append(Popen("CUDA_VISIBLE_DEVICES={} python ./models/evolution/train_phenotype.py {}" - " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], - str(f_name), - str(save_path), - str(save_path) - ), - shell=True, stdout=PIPE, stderr=PIPE)) - for j, proc in enumerate(procs): - i = k * len(gpus) + j - print(f'wait on {i}th proc') - proc.wait() - - for i in range(population_size): - with open(str(Path(evolution.get_value_from_config( - population[i], - evolution.main_model_path + ["save_path"])).parent.joinpath("out.txt")), "r") as fout: - reports_data = fout.read().splitlines()[-2:] - reports = [] - for i in range(2): - try: - reports.append(json.loads(reports_data[i])) - except: - pass - if len(reports) == 2 and "valid" in reports[0].keys() and "test" in reports[1].keys(): - val_results = reports[0] - test_results = reports[1] - elif len(reports) == 1 and "valid" in reports[0].keys(): - val_results = reports[0] - else: - val_results = {} - test_results = {} - for m in CONSIDERED_METRICS: - if "loss" in m: - val_results[m] = 1e6 - test_results[m] = 1e6 - else: - val_results[m] = 0. - test_results[m] = 0. - - result_table_dict = {} - for el in order: - if el == "params": - result_table_dict[el] = [] - else: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - for m_id, m in enumerate(CONSIDERED_METRICS): - val_metrics_path = list(evolution.find_model_path(val_results, m))[0] - val_m = evolution.get_value_from_config(val_results, val_metrics_path + [m]) - population_metrics[m].append(val_m) - result_table_dict[m + "_valid"].append(val_m) - if TEST: - test_metrics_path = list(evolution.find_model_path(test_results, m))[0] - test_m = evolution.get_value_from_config(test_results, test_metrics_path + [m]) - result_table_dict[m + "_test"].append(test_m) - else: - result_table_dict[m + "_test"].append(0.) - result_table_dict[order[-1]] = [population[i]] - result_table = pd.DataFrame(result_table_dict) - result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t', mode='a', header=None) - - return population_metrics - - -parser = argparse.ArgumentParser() - -parser.add_argument('--config', help='Please, enter model path to config') -parser.add_argument('--evolve_metric', help='Please, choose target metric out of given in your config.train.metrics') - -parser.add_argument('--p_cross', help='Please, enter probability of crossover', type=float, default=0.2) -parser.add_argument('--pow_cross', help='Please, enter crossover power', type=float, default=0.1) -parser.add_argument('--p_mut', help='Please, enter probability of mutation', type=float, default=1.) -parser.add_argument('--pow_mut', help='Please, enter mutation power', type=float, default=0.1) - -parser.add_argument('--p_size', help='Please, enter population size', type=int, default=10) -parser.add_argument('--gpus', help='Please, enter the list of visible GPUs', default="0") -parser.add_argument('--train_partition', - help='Please, enter partition of splitted train', default=1) -parser.add_argument('--start_from_population', - help='Please, enter the population number to start from. 0 means from scratch', default=0) -parser.add_argument('--path_to_population', - help='Please, enter the path to population to start from', default="") -parser.add_argument('--elitism_with_weights', - help='Please, enter whether to save elite models with weights or not', default=0) - -args = parser.parse_args() - -CONFIG_FILE = args.config -EVOLVE_METRIC = args.evolve_metric -POPULATION_SIZE = args.p_size -GPU_NUMBER = len(args.gpus) -gpus = [int(gpu) for gpu in args.gpus.split(",")] -TRAIN_PARTITION = int(args.train_partition) -START_FROM_POPULATION = int(args.start_from_population) -PATH_TO_POPULATION = args.path_to_population -ELITISM_WITH_WEIGHTS = int(args.elitism_with_weights) - -P_CROSSOVER = args.p_cross -POW_CROSSOVER = args.pow_cross -P_MUTATION = args.p_mut -POW_MUTATION = args.pow_mut - -with open(CONFIG_FILE, "r") as f: - basic_params = json.load(f) - -print("Given basic params: {}\n".format(json.dumps(basic_params, indent=2))) - -evolution = ParamsEvolution(population_size=POPULATION_SIZE, - p_crossover=P_CROSSOVER, crossover_power=POW_CROSSOVER, - p_mutation=P_MUTATION, mutation_power=POW_MUTATION, - key_main_model="main", - seed=42, - train_partition=TRAIN_PARTITION, - elitism_with_weights=ELITISM_WITH_WEIGHTS, - **basic_params) - -CONSIDERED_METRICS = evolution.get_value_from_config(evolution.basic_config, - list(evolution.find_model_path( - evolution.basic_config, "metrics"))[0] + ["metrics"]) -print(CONSIDERED_METRICS) -TEST = evolution.get_value_from_config(evolution.basic_config, - list(evolution.find_model_path( - evolution.basic_config, "test_best"))[0] + ["test_best"]) - -# Result table -order = deepcopy(CONSIDERED_METRICS) -result_file = Path(evolution.get_value_from_config(evolution.basic_config, - evolution.main_model_path + ["save_path"]) - ).joinpath("result_table.csv") -result_table_columns = [] -result_table_dict = {} -for el in order: - result_table_dict[el + "_valid"] = [] - result_table_dict[el + "_test"] = [] - result_table_columns.extend([el + "_valid", el + "_test"]) - -order.extend(["params"]) -result_table_dict["params"] = [] -result_table_columns.append("params") - -if START_FROM_POPULATION == 0: - result_table = pd.DataFrame(result_table_dict) - result_table.loc[:, result_table_columns].to_csv(result_file, index=False, sep='\t') - - print("\nIteration #{} starts\n".format(0)) - population = evolution.first_generation() - print(population) - population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - iters = 1 -else: - # _ = evolution.first_generation() - iters = START_FROM_POPULATION - print("\nIteration #{} starts\n".format(iters)) - - population = [] - for i in range(POPULATION_SIZE): - population.append(read_json(Path(PATH_TO_POPULATION).joinpath( - "model_" + str(i)).joinpath("config.json"))) - population[i] = evolution.insert_value_or_dict_into_config( - population[i], evolution.main_model_path + ["save_path"], - str(Path(evolution.get_value_from_config(evolution.basic_config, evolution.main_model_path + ["save_path"]) - ).joinpath("population_" + str(START_FROM_POPULATION)).joinpath("model_" + str(i)))) - - population[i] = evolution.insert_value_or_dict_into_config( - population[i], evolution.main_model_path + ["load_path"], - str(Path(evolution.get_value_from_config(population[i], evolution.main_model_path + ["load_path"]).parent))) - - population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - print("Population scores: {}".format(population_scores)) - print("\nIteration #{} was done\n".format(iters)) - iters += 1 - -while True: - print("\nIteration #{} starts\n".format(iters)) - population = evolution.next_generation(population, population_scores, iters) - population_scores = score_population(population, POPULATION_SIZE, result_file)[EVOLVE_METRIC] - print("Population scores: {}".format(population_scores)) - print("\nIteration #{} was done\n".format(iters)) - iters += 1 - diff --git a/deeppavlov/models/evolution/train_phenotype.py b/deeppavlov/models/evolution/train_phenotype.py deleted file mode 100644 index 828f798d1c..0000000000 --- a/deeppavlov/models/evolution/train_phenotype.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -Copyright 2017 Neural Networks and Deep Learning lab, MIPT - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import sys - -from deeppavlov.core.commands.train import train_evaluate_model_from_config - - -config_path = sys.argv[1] -print("TRAIN PHENOTYPE") -train_evaluate_model_from_config(config_path) From 9f3cb0e31b4a3e18c4ec1ec7333d6513f3109cb9 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 14:53:48 +0300 Subject: [PATCH 508/616] fix: considered metrics is only list --- deeppavlov/evolve.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index dc23364de3..91a9eb5e55 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -99,9 +99,6 @@ def main(): list(evolution.find_model_path( evolution.basic_config, "metrics"))[0] + ["metrics"]) - if type(considered_metrics) is str: - considered_metrics = [considered_metrics] - log.info(considered_metrics) evolve_metric = considered_metrics[0] From 649c036efba451129348a134cfc4544580719944 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 15:52:01 +0300 Subject: [PATCH 509/616] feat: results analysis file --- .../models/evolution/Results_analysis.ipynb | 1270 ++++++----------- 1 file changed, 449 insertions(+), 821 deletions(-) diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb index 2ea149ff27..f02b70ae0d 100644 --- a/deeppavlov/models/evolution/Results_analysis.ipynb +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -2,11 +2,17 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2018-06-25 14:31:29.12 DEBUG in 'matplotlib.backends'['__init__'] at line 90: backend module://ipykernel.pylab.backend_inline version unknown\n" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -14,149 +20,367 @@ "import matplotlib.pyplot as plt\n", "import copy\n", "import json\n", - "%matplotlib inline" + "%matplotlib inline\n", + "\n", + "from deeppavlov.core.commands.utils import set_deeppavlov_root, expand_path\n", + "from deeppavlov.models.evolution.evolution_param_generator import ParamsEvolution" ] }, { - "cell_type": "code", - "execution_count": 62, + "cell_type": "markdown", "metadata": {}, + "source": [ + "## Set here path to your config file, key main model and population size" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Number of populations: 62\n" + "Considered basic config:\n", + "{\n", + " \"dataset_reader\": {\n", + " \"name\": \"basic_classification_reader\",\n", + " \"x\": \"text\",\n", + " \"y\": \"intents\",\n", + " \"data_path\": \"snips\"\n", + " },\n", + " \"dataset_iterator\": {\n", + " \"name\": \"basic_classification_iterator\",\n", + " \"seed\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"field_to_split\": \"train\",\n", + " \"split_fields\": [\n", + " \"train\",\n", + " \"valid\"\n", + " ],\n", + " \"split_proportions\": [\n", + " 0.9,\n", + " 0.1\n", + " ]\n", + " },\n", + " \"chainer\": {\n", + " \"in\": [\n", + " \"x\"\n", + " ],\n", + " \"in_y\": [\n", + " \"y\"\n", + " ],\n", + " \"pipe\": [\n", + " {\n", + " \"id\": \"classes_vocab\",\n", + " \"name\": \"default_vocab\",\n", + " \"fit_on\": [\n", + " \"y\"\n", + " ],\n", + " \"level\": \"token\",\n", + " \"save_path\": \"vocabs/snips_classes.dict\",\n", + " \"load_path\": \"vocabs/snips_classes.dict\"\n", + " },\n", + " {\n", + " \"in\": [\n", + " \"x\"\n", + " ],\n", + " \"out\": [\n", + " \"x_lower\"\n", + " ],\n", + " \"name\": \"str_lower\"\n", + " },\n", + " {\n", + " \"id\": \"my_embedder\",\n", + " \"name\": \"fasttext\",\n", + " \"save_path\": \"embeddings/dstc2_fastText_model.bin\",\n", + " \"load_path\": \"embeddings/dstc2_fastText_model.bin\",\n", + " \"dim\": 100\n", + " },\n", + " {\n", + " \"id\": \"my_tokenizer\",\n", + " \"name\": \"nltk_tokenizer\",\n", + " \"tokenizer\": \"wordpunct_tokenize\"\n", + " },\n", + " {\n", + " \"in\": [\n", + " \"x_lower\"\n", + " ],\n", + " \"in_y\": [\n", + " \"y\"\n", + " ],\n", + " \"out\": [\n", + " \"y_labels\",\n", + " \"y_probas_dict\"\n", + " ],\n", + " \"main\": true,\n", + " \"name\": \"intent_model\",\n", + " \"save_path\": \"evolution/classification/intents_snips\",\n", + " \"load_path\": \"evolution/classification/intents_snips\",\n", + " \"classes\": \"#classes_vocab.keys()\",\n", + " \"kernel_sizes_cnn\": [\n", + " 1,\n", + " 2,\n", + " 3\n", + " ],\n", + " \"filters_cnn\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 100\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"confident_threshold\": {\n", + " \"evolve_choice\": true,\n", + " \"values\": [\n", + " 0.5,\n", + " 1\n", + " ]\n", + " },\n", + " \"optimizer\": \"Adam\",\n", + " \"lear_rate\": {\n", + " \"evolve_range\": [\n", + " 0.0001,\n", + " 0.1\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"lear_rate_decay\": {\n", + " \"evolve_range\": [\n", + " 0.0001,\n", + " 0.1\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"loss\": \"binary_crossentropy\",\n", + " \"text_size\": 15,\n", + " \"coef_reg_cnn\": {\n", + " \"evolve_range\": [\n", + " 1e-06,\n", + " 0.001\n", + " ]\n", + " },\n", + " \"coef_reg_den\": {\n", + " \"evolve_range\": [\n", + " 1e-06,\n", + " 0.001\n", + " ]\n", + " },\n", + " \"dropout_rate\": {\n", + " \"evolve_range\": [\n", + " 0.1,\n", + " 0.9\n", + " ]\n", + " },\n", + " \"dense_size\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 100\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"model_name\": \"cnn_model\",\n", + " \"embedder\": \"#my_embedder\",\n", + " \"tokenizer\": \"#my_tokenizer\"\n", + " }\n", + " ],\n", + " \"out\": [\n", + " \"y_labels\",\n", + " \"y_probas_dict\"\n", + " ]\n", + " },\n", + " \"train\": {\n", + " \"epochs\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"batch_size\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"metrics\": [\n", + " \"classification_accuracy\",\n", + " \"classification_f1\",\n", + " \"classification_roc_auc\"\n", + " ],\n", + " \"validation_patience\": 5,\n", + " \"val_every_n_epochs\": 1,\n", + " \"log_every_n_epochs\": 1,\n", + " \"validate_best\": true,\n", + " \"test_best\": false\n", + " },\n", + " \"metadata\": {\n", + " \"labels\": {\n", + " \"telegram_utils\": \"IntentModel\",\n", + " \"server_utils\": \"KerasIntentModel\"\n", + " },\n", + " \"download\": [\n", + " \"http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz\",\n", + " \"http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz\",\n", + " {\n", + " \"url\": \"http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv\",\n", + " \"subdir\": \"snips\"\n", + " },\n", + " {\n", + " \"url\": \"http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin\",\n", + " \"subdir\": \"embeddings\"\n", + " }\n", + " ]\n", + " }\n", + "}\n" ] } ], "source": [ - "PLOT_TEST = False\n", - "\n", - "TITLE = \"imdb_given_mask_init_part_7\"\n", - "model_index = 4\n", - "POPULATION_SIZE = 10\n", - "\n", - "# TITLE = \"sber_faq_given_mask_init_part_7\"\n", - "# model_index = 4\n", - "# POPULATION_SIZE = 10\n", - "\n", - "# TITLE = \"ag_news_given_mask_init_part_7\"\n", - "# model_index = 4\n", - "# POPULATION_SIZE = 10\n", + "CONFIG_FILE = \"../../configs/evolution/evolve_intents_snips.json\"\n", + "KEY_MAIN_MODEL = \"main\"\n", + "POPULATION_SIZE = 2\n", + " \n", + "with open(CONFIG_FILE, \"r\") as f:\n", + " basic_params = json.load(f)\n", "\n", - "# TITLE = \"snli_given_mask_init_part_6\"\n", - "# model_index = 4\n", - "# POPULATION_SIZE = 10\n", + "set_deeppavlov_root(basic_params)\n", + "print(\"Considered basic config:\\n{}\".format(json.dumps(basic_params, indent=2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2018-06-25 14:52:07.93 INFO in 'deeppavlov.models.evolution.evolution_param_generator'['evolution_param_generator'] at line 55: Main model path in config: ['chainer', 'pipe', 4]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Title name for the considered evolution is `intents_snips`.\n", + "Number of populations: 2.\n" + ] + } + ], + "source": [ + "evolution = ParamsEvolution(population_size=POPULATION_SIZE,\n", + " key_main_model=KEY_MAIN_MODEL,\n", + " **basic_params)\n", "\n", - "# TITLE = \"snli_given_mask_init_part_many_inputs_6\"\n", - "# model_index = 5\n", - "# POPULATION_SIZE = 10\n", + "validate_best = evolution.get_value_from_config(\n", + " evolution.basic_config, list(evolution.find_model_path(\n", + " evolution.basic_config, \"validate_best\"))[0] + [\"validate_best\"])\n", + "test_best = evolution.get_value_from_config(\n", + " evolution.basic_config, list(evolution.find_model_path(\n", + " evolution.basic_config, \"test_best\"))[0] + [\"test_best\"])\n", "\n", - "# TITLE = \"twitter140_one_neuron_init_part_6\"\n", - "# model_index = 4\n", - "# POPULATION_SIZE = 10\n", + "TITLE = str(Path(evolution.get_value_from_config(\n", + " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).stem)\n", + "print(\"Title name for the considered evolution is `{}`.\".format(TITLE))\n", "\n", - "data = pd.read_csv(\"result_tables/result_table_\" + TITLE + \".csv\", sep='\\t')\n", - "print(\"Number of populations: {}\".format(int(data.shape[0] / POPULATION_SIZE)))\n", - "# data.dropna(axis=1, how=\"any\", inplace=True)" + "data = pd.read_csv(str(expand_path(Path(evolution.get_value_from_config(\n", + " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).joinpath(\n", + " \"result_table.csv\"))), sep='\\t')\n", + "print(\"Number of populations: {}.\".format(int(data.shape[0] / POPULATION_SIZE)))\n", + "data.fillna(0., inplace=True)" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "classification_log_loss: best value for VALID \t0 individuum on 0 population\n", - "classification_log_loss: best value for TEST \t0 individuum on 0 population\n", - "classification_accuracy: best value for VALID \t3 individuum on 56 population\n", - "classification_accuracy: best value for TEST \t3 individuum on 55 population\n", - "classification_roc_auc: best value for VALID \t9 individuum on 61 population\n", - "classification_roc_auc: best value for TEST \t9 individuum on 61 population\n", - "classification_f1: best value for VALID \t3 individuum on 56 population\n", - "classification_f1: best value for TEST \t3 individuum on 55 population\n" + "\n", + "Measure: classification_accuracy\n", + "valid:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t1 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "\n", + "Measure: classification_f1\n", + "valid:\n", + "min for\t0 model on\t0 population\n", + "max for\t1 model on\t1 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "\n", + "Measure: classification_roc_auc\n", + "valid:\n", + "min for\t1 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:11: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", - " # This is added back by InteractiveShellApp.init_path()\n", - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:12: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", - " if sys.path[0] == '':\n" + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: 'argmin' is deprecated. Use 'idxmin' instead. The behavior of 'argmin' will be corrected to return the positional minimum in the future. Use 'series.values.argmin' to get the position of the minimum now.\n", + " if __name__ == '__main__':\n", + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", + " # Remove the CWD from sys.path while we load stuff.\n" ] } ], "source": [ - "MEASURES = [\"classification_log_loss\", \n", - " \"classification_accuracy\",\n", - " \"classification_roc_auc\", \n", - " \"classification_f1\"]\n", + "MEASURES = evolution.get_value_from_config(\n", + " evolution.basic_config, list(evolution.find_model_path(\n", + " evolution.basic_config, \"metrics\"))[0] + [\"metrics\"])\n", + "\n", "for measure in MEASURES:\n", - " if (measure == \"classification_log_loss_test\" \n", - " or measure == \"classification_log_loss_valid\"):\n", - " n_best_valid = data[measure + \"_valid\"].argmin()\n", - " n_best_test = data[measure + \"_test\"].argmin()\n", - " else:\n", - " n_best_valid = data[measure + \"_valid\"].argmax()\n", - " n_best_test = data[measure + \"_test\"].argmax()\n", - " print(\"{}: best value for VALID \\t{} individuum on {} population\".format(measure, \n", - " n_best_valid % POPULATION_SIZE, \n", - " n_best_valid // POPULATION_SIZE))\n", - " print(\"{}: best value for TEST \\t{} individuum on {} population\".format(measure, \n", - " n_best_test % POPULATION_SIZE, \n", - " n_best_test // POPULATION_SIZE))\n", - " " + " print(\"\\nMeasure: {}\".format(measure))\n", + " for data_type in [\"valid\", \"test\"]:\n", + " print(\"{}:\".format(data_type))\n", + " argmin = data[measure + \"_\" + data_type].argmin()\n", + " argmax = data[measure + \"_\" + data_type].argmax()\n", + " print(\"min for\\t{} model on\\t{} population\".format(argmin % POPULATION_SIZE,\n", + " argmin // POPULATION_SIZE))\n", + " print(\"max for\\t{} model on\\t{} population\".format(argmax % POPULATION_SIZE,\n", + " argmax // POPULATION_SIZE))" ] }, { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": true - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "cmap = plt.get_cmap('rainbow')\n", - "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", - "color_ids = np.argsort(data.loc[:, \"classification_accuracy_valid\"].values)" + "## If you want to plot measures depending on population colored by evolved measure value" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 50, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -186,248 +410,82 @@ } ], "source": [ - "Path(\"./pics/\").joinpath(TITLE).mkdir(exist_ok=True, parents=True)\n", - "\n", - "try:\n", - " y_label = \"Number of edges\"\n", - " plt.figure(figsize=(12, 12))\n", - " for i in range(data.shape[0]):\n", - " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", - " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", - " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", - " d = json.loads(json_acceptable_string)\n", - " plt.scatter(i // POPULATION_SIZE, \n", - " np.sum(d[\"chainer\"][\"pipe\"][model_index][\"binary_mask\"]) \n", - " + (np.random.random() - 0.5) / 2, \n", - " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", - "\n", - " plt.ylabel(y_label, fontsize=20)\n", - " plt.xlabel(\"population\", fontsize=20)\n", - " plt.title(TITLE, fontsize=20)\n", - " plt.xticks(fontsize=20)\n", - " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", - " plt.show()\n", - "except:\n", - " pass\n", + "path_to_pics = expand_path(Path(evolution.get_value_from_config(\n", + " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).joinpath(\"pics\"))\n", + "path_to_pics.mkdir(exist_ok=True, parents=True)\n", "\n", + "if validate_best:\n", + " evolve_metric = MEASURES[0] + \"_valid\"\n", + "elif test_best:\n", + " evolve_metric = MEASURES[0] + \"_test\"\n", + " \n", + "cmap = plt.get_cmap('rainbow')\n", + "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", + "color_ids = np.argsort(data.loc[:, evolve_metric].values)\n", "\n", - "# ylims = [(0., 1), (0.85, 1), (0.9, 1.), (0.85, 1.)] #ag_news\n", - "ylims = [(0., 1), (0.8, 0.9), (0.8, 1.), (0.8, 0.9)] #imdb\n", - "# ylims = [(0., 1), (0., 1), (0., 1.), (0., 1.)]\n", - "# ylims = [(0., 1), (0.7, 0.9), (0.9, 1.), (0.6, 0.85)] #sber faq\n", + "ylims = [(0., 1)] * len(MEASURES)\n", "\n", "for metric, ylim in zip(MEASURES, ylims):\n", - " y_label = metric\n", " plt.figure(figsize=(12,6))\n", - " for i in range(data.shape[0]):\n", - " plt.scatter(i // POPULATION_SIZE, \n", - " data.loc[:, metric + \"_valid\"].values[i], \n", - " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5, marker='o')\n", - " if PLOT_TEST:\n", + " if validate_best:\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_valid\"].values[i], \n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5, marker='o')\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_valid\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \n", + " c=colors[-1])\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_valid\"].min() * np.ones(data.shape[0]//POPULATION_SIZE), \n", + " c=colors[0])\n", + " if test_best:\n", " for i in range(data.shape[0]):\n", " plt.scatter(i // POPULATION_SIZE, \n", " data.loc[:, metric + \"_test\"].values[i], \n", " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5, marker='+', s=200)\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_test\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \"--\",\n", + " c=colors[-1])\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_test\"].min() * np.ones(data.shape[0]//POPULATION_SIZE), \"--\",\n", + " c=colors[0])\n", + " \n", "\n", - " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", - " data.loc[:, metric + \"_valid\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \n", - " c='r')\n", - "\n", - " plt.ylabel(y_label, fontsize=20)\n", + " plt.ylabel(metric, fontsize=20)\n", " plt.xlabel(\"population\", fontsize=20)\n", " plt.title(TITLE, fontsize=20)\n", " plt.ylim(ylim[0], ylim[1])\n", - " # plt.ylim(0.85, 0.95)\n", " plt.xticks(fontsize=20)\n", " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", + " plt.savefig(path_to_pics.joinpath(y_label + \".png\"))\n", " plt.show()" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 67, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], - "source": [ - "params_dictionaries = []\n", - "\n", - "for i in range(data.shape[0]):\n", - " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", - " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", - " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", - " d = json.loads(json_acceptable_string)\n", - " params_dictionaries.append(d)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, "source": [ - "# Model ids" + "## If you want to plot measures depending on population colored by `evolution_model_id`" ] }, { "cell_type": "code", - "execution_count": 68, - "metadata": { - "scrolled": false - }, + "execution_count": 57, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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KkiRJc7YQWkeIiD8EPgdcQp7J7gGeAtwJfDYi/mqmz0spfSyltDGltHHNGtu4JUmStHC0PGhHxDnAXwJfSin9XkrpzpTSUErpu8BLga3A2yPi5FbWKUmSJDWi5UEbeGFte+30HSmlIeDb5DrPOJxFSZIkSYdiIQTtztp2tt6PyfGxw1CLJEmS1BQLIWjfUNu+OSLW1++IiAuAZwIjwLcOd2GSJEnSfC2EVUc+R14n+/nATyPiP4DtwOPIbSUB/M+U0oOtK1GSJElqTMuDdkqpGhEXAr8FvIp8AWQ3sBu4HPiblNLXWliiJEmS1LCWB22AlNI48KHahyRJkrToLYigLWnhu21klKsGh9g2McG6tjbO6+nm1K7Og3+iJElHqIVwMaSkBe62kVH+sb+ffZUKa8tl9lUq/GN/P7eNjLa6NEmSFiyDtqSDumpwiBVRYkW5TCmCFeUyK6LEVYNDrS5NkqQFy6At6aC2TUzQW5r6z0VvqcS2iYkWVSRJ0sJn0JZ0UOva2thfrU4Z21+tsq7NyzwkSZqNQVvSQZ3X082+VGVfpUI1JfZVKuxLVc7r6W51aZIkLVgGbUkHdWpXJ2/o62NFucyOSoUV5TJv6Otz1RFJkg7A930lzcmpXZ0Ga0mSGuCMtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklQAg7YkSZJUAIO2JEmSVACDtiRJklSAlgftiHh9RKSDfFRaXackSZLUiLZWFwB8H3j3LPueDTwPuOLwlSNJkiQdupYH7ZTS98lh+xEi4j9rf/zY4atIkiRJOnQtbx2ZTUQ8ATgL2Apc1uJyJEmSpIYs2KANvLm2/URKyR5tSZIkLSoLMmhHxDLgV4EK8H9bXI4kSZLUsAUZtIFfBlYCV6aU7p3toIh4c0RsiohNO3fuPHzVSZIkSQexUIP2ZNvIxQc6KKX0sZTSxpTSxjVr1hyGsiRJkqS5WXBBOyIeDzwDuA+4vMXlSJIkSfOy4II2XgQpSZKkJWBBBe2I6AJ+jXwR5CdaXI4kSZI0bwsqaAOvAFYBVxzoIkhJkiRpoVtoQXuybcQ7QUqSJGlRWzBBOyIeBzwLL4KUJEnSEtDW6gImpZR+CkSr65AkSZKaYcHMaEuSJElLiUFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqgEFbkiRJKoBBW5IkSSqAQVuSJEkqQFurC5AkSVKde26HW66GB7fD0cfCU86FDae0uirNgzPakiRJC8U9t8OVn4bBAThqbd5e+ek8rkXHoC1JkrRQ3HI1dK+AnhUQpbztXpHHtejYOiKpJX4+Psz1YwPsqI6zttTO2R3LeXT7slaXJUmt9eD2PJNdr7s3j2vRcUZb0mH38/FhLh3ZzUC1wppoY6Ba4dKR3fx8fLjVpUlSax19LAztnzo2tD+Pa9ExaEs67K4fG6CXEstLZUoRLC+V6aXE9WMDrS5NklrrKefC0D4Y3AepmrdD+/K4Fp0FFbQj4tyI+I+I2B4RoxFxf0R8NSIubHVtkppnR3Wcnpj6z09PlNhRHW9RRZK0QGw4Bc5/LfQsh9078vb817rqyCK1YHq0I+KvgD8A7gO+BOwC1gBPAc4BLm9ZcZKaam2pnYFqheVRfmhsMFVZW2pvYVWStEBsOMVgvUQsiKAdEW8ih+xPAW9OKY1N2+//vtIScnbHci4d2Q3VPJM9mKrsp8pFHStbXZokSU3T8taRiOgE/hy4hxlCNkBKyfeTpSXk0e3LeGXXUSwvldmZJlheKvPKrqNcdUSStKQshBnt88gtIh8CqhFxEfBfgBHg2yml/2xlcZKK8ej2ZQZrSdKSthCC9pm17QjwPXLIfkhEXA+8PKW083AXJkmSJM3XQgjax9S2fwBsBp4NfB94FPB+4AXAv5EviJwiIt4MvBlgw4YNh6FUaem5dXiUK/cPc//4BMe1t3F+7zJOW9bZ6rIkSVr0IqXU2gIiLiaH5VHgtJTSlrp93cBtwPHAMw7URrJx48a0adOmgquVFo/NQ2NctmeErWMV1neUuWhVF6d3d0w55tbhUT6+e4C+conlpWCgmuivVHnTUcsN25IkzSIibkkpbTzYcS2/GBLYW9t+rz5kA6SUhoCv1h4+9XAWJS1mm4fG+Pvt++mfqLKuvUT/RJW/376fzUNTrzW+cv8wfeUSfeUSpYiH/nzlfu/QKEnSoVoIQfu22nbvLPv31LZeNSXN0WV7RnJobqsF6LYcoC/bMzLluPvHJ1heiiljy0vB/eMTh7NcSZKWpIXQo301kIDTI6KUUqpO2z95ceRdh7csafHaOlZhXfvU36OXl4OtY5UpY8e1t7GvUqWv/HDYHqgmjmtfCP80SFq09t4D226BoV3QvRrWPQVWei2Vjjwtn9FOKd0NfBnYAPxO/b6IeAHwC+TZ7isPf3XS4rS+o8xAZer1FwOVxPqO8pSx83uX0V+p0l+pUk3poT+f3+sbSJLmae89cMcVMDYIy47O2zuuyOPSEablQbvmt4B7gQ9GxNcj4n0R8TnybdcrwH9LKfW3tEJpEbloVVcOzRO1AD2RA/RFq7qmHHfask7edNRyVpRLbJuosKJc8kJISYdm2y3Q3gMdPRCRt+09eVw6wiyI94dTSvdFxFOAPwVeDJwN7CPPdP9FSunbraxPWmxO7+7gN47tnbLqyK+s6X7EqiOQw7bBWlLTDO3KM9n12rvzuHSEWRBBG6B2Q5q31T4kHaLTuztmDNaSVKju1bldpKPn4bHxoTw+3d57YOsmGN4Fy1bD+o32cmtJWSitI5I0o7smhvinke18cPge/mlkO3dNDLW6JEkHsu4pMD6Yw3ZKeTs+mMfr7b0HfnZ53rfs6Lz92eX2cmtJWTAz2pI03V0TQ3x+fCe9tLGadvanCp8f38nLWMOj2rpbXZ6kmazcAI+5YOqqIyee/ciZ6q2boFKB/XfD+DC0L4Nlq/K4s9rNs+U2uOkq2LkN1qyDs86Dk05tdVVHDIO2pAXrxol99NJGb+TVUnopQ8rjBm1pAVu54eBhee8W2L8Tyu3Q1gWVMei/Dyqjh6XEI8KW2+CL/wg9K+DotbB/X378kjcYtg8TW0ckLVgPpDG6p/0z1U2JB9LYLJ8hadEYGwICyh15dZJyR348ZntY09x0VQ7ZvSugVMrbnhV5XIeFQVvSgnVMdDDE1HtYDVHlmPAiT2nRa+8GqlAZz48r4/lxu+9WNc3ObdDdO3WsuzeP67AwaEtasJ7ZtoL9TLA/VaimxP5UYT8TPLNtRatLk3SoVp4IK07IrSPjQ3m74oQ8ruZYsw6G9k8dG9qfx3VYGLQlLViPauvmZe1r6I0yuxinN8q8rN0LIaUl4fgzcztD3wZY9+S8LZXyuJrjrPNgcF/uza5W83ZwXx7XYREppYMftQhs3Lgxbdq0qdVlSJKkudp7D9z3HRh6ELqPziHbFUeay1VHChERt6SUNh7sOFcdkSRJrTGX1Uk0u7mE6JNONVi3kK0jkiRJi83k0n37901dum/Lba2uTHUM2pIkSYuNS/ctCgZtSZKkxcal+xYFg7YkSdJi49J9i4JBW5IkabFx6b5FwaAtSZK02Jx0KrzkDbk3+8EdefuSN7jCyALj8n6SJEmLkUv3LXjOaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFMGhLkiRJBTBoS5IkSQUwaEuSJEkFaJvrgRFRBjpTSkPTxp8HvAQYAj6WUrqruSVKkiRJi08jM9rvB3ZHRN/kQES8CrgKeBvwR8C3I+KE5pYoSZIkLT6NBO2zgWtTSv11Y+8E9gKvBf4QWAn8XvPKkyRJkhanRoL2CcAdkw8i4mTgVOAjKaV/Sim9H7gCOL+5JUqSJEmLTyNBewWwr+7xM4EEXFk39hPg+CbUJUmSJC1qjQTtbcCj6h4/HxgGbqkb6wUmmlCXJEmStKjNedUR4CbgxRHxQmAEeDlwdUppvO6YRwFbm1ifJEmStCg1MqP93trxXwS+CnQAfz65MyK6gGcDNzezQEmSJGkxmvOMdkrpRxHxNOB1taFLU0rfqTvkDOAa4J+bWJ8kSZK0KDXSOkJK6UfA78+y7z+BlzajKEmSJGmxm/ct2CNilTenkSRJkmbWUNCOiN6I+EBEbAd2AXfV7XtaRFweEU9udpGSJEnSYjPnoF279fp/Ar8L3A/8FIi6Q35Evhjy1c0sUJIkSVqMGpnR/hPg8cDrU0pPBv6tfmdKaQj4BnBu88qTJEmSFqdGgvYvAV9NKX36AMfcDaw/tJIkSZKkxa+RoH088MODHLMf6Jt/OZIkSdLS0MjyfgPAMQc55lHkiyQlSdKRqv9e2L4JhnfDsqPg2I3Q50JlOvI0MqP9HeCFEbF8pp0RsQ64EPhmMwqTJEmLUP+9cOeVMD4EXUfl7Z1X5nHpCNPIjPaHgSuAyyPizfU7IuJxwMeBLuBvmleeJElaVLZvgvZuaO/Jjye32zc5q72Q/WwzXHsFbN8Kx66H514Ajz291VUtenOe0U4pfRV4N/BM4MfAOwAiYlft8TOAd6SUvlVAnZIkaTEY3g1t3VPH2rrzuBamn22Gz14MA/1wzLq8/ezFeVyHpKEb1qSU3k1evu9LwB6gAiTgcuD5KaX3Nb1CSZK0eCw7CiaGpo5NDOVxLUzXXgHL+/JHqfTwn6+9otWVLXqNtI4AkFK6Fri2gFokSdJid+zG3JMNeSZ7Yij3aZ9wdmvr0uy2b80z2fV6ludxHZKGg7Z0JPjJ7gm+fO8E9w5WOaGnxItOaOPxR/nXRZIOqu8EOPn8qauOnHC2/dkL2bHrc7vI8roVmgcH8rgOiclBmuYnuyf4yOYxVnYG67uDvWOJj2we422nY9hewLZUhripspedaYw10cFZ5ZWcVO4++CdKar6+EwxrPgGbAAAgAElEQVTWi8lzL8g92ZBnsgcHcvB+8ataW9cSMOce7YioRkTlIB8TEbE7Im6MiLdHRGeRxUtF+PK9E6zsDFZ2BKXI25WdwZfvnWh1aZrFlsoQXxzfwf40wdG0sz9N8MXxHWypDB38kyXpSPfY0+E1b8kz2g9sy9vXvMVVR5qgkem568l3fXwS+SLIe4EdwFrgBKBMvnNkG3AGcBbw6oh4TkppsJlFS0W6d7DK+u6YMraiPY9rYbqpspeeKNMb+Z+03to/bTdV9hY+q31PZZDvVPewi1FW08mZpVVsKPcU+pySFphtd8CPr4c9O2DVWvgvZ8O6x7S6qsY89nSDdQEaWXXk1eSg/S/Ao1NKJ6eUnp5SOhl4dG18BXAeOXx/Engy8IfNLVkq1gk9JfaNTx3bN57HtTDtTGN0U54y1k2ZnWms0Oe9pzLIZdVtDDLB0XQwyASXVbdxT8W5BemIse0OuP5SGB6AlWvy9vpL87iOeI0kh78EdqeUfiWldE/9jpTSPSmlXyEv+feXKaUB4K3A7cDLmlatdBi86IQ29o4m9o4lqilv944mXnSC/dkL1ZroYIjKlLEhKqyJjkKf9zvVPfTQRk+0ERH0RBs9tPGd6p5Cn1fSAvLj62FZLyxbDlHK22W9eVxHvEaSwy+QZ6kP5CrgjQAppUpEXA+8Zp61SS3x+KPaeNvpTFl15Nce3e6FkAvYWeWVfHF8B5BnsoeoMJgqPL9tdaHPu4tRjmZqmO+mzC5GC31eSQvInh05YN9zOwzvzyF77QYYOYRrRO6+Hb7zddi1DVavgzOfDyee0ryaddg0khyWk1tDDqSvdtwkbwOlRenxR7mc32JyUrmbl7B2yqojz29bXXh/9mo6GWSCnrp/SoeosBqvA5+LPZXd3Fu9m0EG6aGHE0onsqrsTU2aaWJwK2N7fkh1dA+lzlV0rHoibT0u2dZUpTa4/buwrAe6emB8FH72fTjlyfM73923w+WXQPcKOHotDO7Ljy98vWF7EWqkdeRW4JURcdxMOyPieOCVwE/rhk8AHpx/eZI0NyeVu3lVx3G8rfMkXtVx3GFZ2u/M0ioGmWAwTZBSYjBNMMgEZ5ZWFf7ci92eym5+Wv0JY4zSTTdjjPLT6k/YU3F+plkmBrcysu1aqhNDRMdKqhNDjGy7lolBb0LSVNUEMW0sauPz8Z2v55DdsyLPlPesyI+/8/VDrVQt0MiU3QeAzwDfjYiPADfy8KojzwLeBqwEPggQEW3A84EbmlmwJC0UG8o9XMS6KauOnFNa46ojc3Bv9W46aKejtgpsB52Q8riz2s0xtueH0LaMUlv+pTPauqnWxp3VbqLxMTj5v8LOex5uHTnusXl8PnZtyzPZ9bp78/h83HkrfPNK2HE/rD0OnnU+nHza/M6lhs05aKeUPhsR64H/DfzZtN0BTAB/klL6bG1sJfCnwM3NKFSSDqe5Ltu3odxjsJ6HQQbpZuq7Du10MMjSXbFlaHQH/cO3MV7pp73cR9+yU+nuXHvwT5yn6ugeomPllLEoL6M6+siLdW0xOQSrjoWhATj5jIfHhgZg+dHzO9/qdbldpKeuW3dofx5v1J23wr99HHr7YM06GNiXH7/iTYbtw6Sh9cpSSn8FnAq8E/gP4BrgC+RAfVpK6f/UHbsrpXRxSun7TaxXkgrnsn3F66GHcabO+I0zRg9L85eWodEd7By4mUp1hLbSCirVEXYO3MzQ6I7CnrPUuYpUGZ4ylirDlDqntjbZYnKInnQODO/L4TpV83Z4Xx6fjzOfD0P7cthO1bwd2pfHG/XNK3PIXt4HpVLe9vblcR0WDS8MnFK6K6X0v1NKL08pnZdSelnt8Z3zLSIitkREmuVj+3zPK0nz4bJ9xTuhdCJjjDOWRkkpMZZGGWOcE0ontrq0QvQP30a51EW51EVEPPTn/uHbCnvOjlVPhIlhqhNDpJSoTgzBxHAer1PfYhIRudWkbVke18Gtfww87zXQvRz2PpC3z3tNHp+PE0/JFz72rIAHd+TtfC+E3HF/vqV6vZ7leVyHxUJaVqEf+NAM4/sPdyGSjmwu21e8VeWjeByPn7LqyKNLpyzZ/uzxSj9tpakLd5Wik/FKf2HP2daznq51z53aErLmrEe0hDTSYqJZrH/M/IP1TE48pTkrjKw9LreLLO97eGxwII/rsJg1aEfE2fM9aUppPqu0700pvWu+zylJczGX3muX7Ts8VpWPWrLBerr2ch+V6gjl6HporJpGaS/3HeCzDl1bz/qD9lqXOlfltpG2h3vmZ2ox0SL0rPNzTzbkmezBAdjfDxf8cmvrOoIcaEb7OmCea9NMuxeyJC0Ak73XPbRN6b2+iHVTwvaZpVVcVt0Gqe4GOExwTmlNC6vXYta37FR2DuS1AUrRSTWNUqmOcFTPk1pcWW4xGdl2LVXyTHaqDOcWkzVntbo0HaqTT8sXPtavOnLBL3sh5GF0oKD9ZzwyaD8NOB/4OfBNYDtwLHl5v0cDVwDfnmctnRHxq8AGYBD4IXB9Sqly4E+T5m7zgxNctqXCffurHN9b4qKTypx+9ELqoFKR6nuvgTxjnfJ4fdB22T41W3fnWtbwtCmrjhzV86RCVx2Zq7m2mGiROvk0g3ULzZowprdxRMRZwDuA3wH+NqVUrdtXIq+j/X945NJ/c3UseZ3uendFxBtSSt+Y5zmlh2x+cIK/+9E4fR3BcT3B3tHE3/1onN98AobtI0Qjvdcu26dm6+5cuyCC9Uzm0mIiqXGNrDryHuDrKaWP1IdsgJRSNaX0YfJyf/MJ2v8InEsO2z3AE4CLgZOAKyJixvfWIuLNEbEpIjbt3LlzHk+rI8llWyr0dQQrO4NS5G1fR3DZFt80OVKsppMhpn6/7b2WJBWlkaD9VOBga2J/H2i4qSul9O6U0jUppR0ppaGU0o9TSm8l32VyGfCuWT7vYymljSmljWvW2DupA7tvf5UVUyczWdGRx3Vk8JbpkqTDqZGgHeQ+7ANp4to2APxDbTvvFVCkScf3ltg37Y64+8byuI4MG8o9XFRaRw9tPMgYPbRxUWmdLSJaUEZGtvHgrqvZse3zPLjrakZG5nnrbUkt10hj6reAl0XEC1NKX5m+MyJeDPwScFWzigMm+0H8X1CH7KKTyvzdj8aBPJO9bwz6xxKvOdX+7COJvddayEZGttG/50ai1EW5rY9KZZj+PTfCqmfS1TWPW3BrZtvvhFtvgP4HoO8YOO3ZcOzJra5KS1AjU3l/AowDX4yIayLiXRHxG7XtteRbso/WjmuWyTaUed91Upp0+tFt/OYT2lnZGdw/mFjZGfzmE9q9EFLSgjG4f3MO2eVl+Q6S5WVEqYvB/ZtbXdrSsf1OuOlfYXgAVqzO25v+NY9LTTbnhJFSuiUizgM+CZxT+0jklhKA24BfTyl9r5ECIuJxwD0ppcFp4ycBH609/KdGzinN5vSj2wzWkhasifG9lNum3sSmVOpiYnxviypagm69ATp7YVnt1uST21tvcFZ7Lm7fDFdfDtvug3XHw7kXwimnt7qqBauhxJFS+hZwWkQ8A3gy0Ee+dfp3a/vm45XA2yPieuBuYIDcC34R0AVcDrx/nueWJGnRaGtfSaUyTLm87KGxanWEtvaVB/gsNaT/gTyTXa+rJ4/rwG7fDJ/+e1i+Mt/8Zl9/fvza3zBsz2JeU3u1UD3fYD3dtcCpwBnAM8n92HvJN8T5DPCZlNJ871ApSdKi0dN7Ov17bqRCnsmuVkdI1RF6+p7S6tKWjr5jcrvI5Ew2wMhgHp9u289h8w2wdwesXAunPxvWHWxdiCXs6stzyF5Re9dlcnv15QbtWcxruYWI6ImIMyLi2YdaQErpGymlV6eUTksprUwptaeU1qSUzkspfdqQLUk6UnR1raNv1TMpl5dRmeinXF5GnxdCNtdpz4bR/Tlsp2reju7P4/W2/RxurPVy963J2xv/NY8fqbbdB73Lp471Ls/jmlFDM9oRcTzwYeBFQJnco91W2/cs4GPAb6aUrmtumZIkHRm6utYZrIt07Mlw1i9PXXXkjAsf2Z+9+QbomqGXe/MNR+6s9rrjc7vIirrrCPYP5HHNaM5BOyLWATcDa4EvAccAT6875Oba2CuB65pXoiRJUhMde/LBL3zcuyPPZNfr6snjC8Xdt8N3vg67tsHqdXDm8+HEU4p7vnMvhL95L+zcBaMj0NkFa1bDf//j4p5zkWukdeSd5CB9XkrpEetlp5TGgRvIfdaSJEmL18q1sGsr3PFd+PENebtrax5fCO6+HS6/BAb3wdFr8/byS/J4UaoJJhKklNecS7XHVbt8Z9NI68iFwJdSStce4Jh7gEPu25YkSWqpNSfBLVdCZ3f+GNmfW01OXyAx5ztfh+4V0LMiP57cfufrxc1qX/UVOGEDPP6JD4/t25vHT3t8Mc+5yDUStNcCPzvIMeN4F0dJ0gK2f3wnD479jJHqAF2l5Rzd8Vh629cc/BN1ZNlxJxx/OgzsyquSdPXm8L3jTvKtRFps17Y8k12vuzePF+X+++DY46aO9a7I45pRI0F7N3DCQY45Bdg+/3IkSSrO/vGd3DeyiTY66Yhexqsj3DeyiePZaNjWVHt2wOrjYE3dhX6pmscXgtXrcrvI5Ew2wND+PF6U447PM9gr6tZ1378vj2tGjfRo3wi8OCKOnWlnRDwWOJ+8LrYkSQvOg2M/o41O2kpdRARtpS7a6OTBsYO9Yasjzqq1eSa73shgHl8Iznw+DO3LYTtV83ZoXx4vynkvhP7+HLar1bzt78/jRfvhD+Hd74Zf//W8/eEPi3/OJmgkaL+PfKfGb0TEBUA3PLSm9gXAl4Eq8IGmVylJUhOMVAcoR+eUsXJ0MlIdaFFFWrD+y9kwPG297eH9eXy+tt0BV30S/vUv8nbbHfM/14mnwIWvzzPaD+7I2wtfX+yqI6c9Ht74W3lGe/v9efvG3yq+P/uHP4QPfgD27IHjj8/bD35gUYTtaOR+MBHxRuDvmbnlZAJ4Y0rps02qrSEbN25MmzZtasVTS5IWibsHv8V4dYS2UtdDYxPVEdpLXZzY84wWVqYFadsd8OPrc7vIqrU5ZK97zPzPdf2lsKw3LxM4MpiD+9mvnP85jxTvfncO16tWPTw2+fid72xJSRFxS0pp48GOa+iGNSmlT0bEDcBvAmcBRwP9wE3AR1NKt82nWEmSDoejOx7LfSOboJpnsitplAlGObbjCa0urRATg1sZ3/0D0ugeonMV7Uc9ibae9a0uq/V23Am33/jwDWtOeSasnWFd7XWPaV4I/vH1OWRPvwHOj683aB/MPffkmex6fX15fIFr+BbsKaWfpZR+N6X09JTSKSmlM1NKbzNkS5IWut72NRzftZH2UhdjaT/tpS6O71qaF0JODG5l9P5rSBND0LGSNDHE6P3XMDG4tdWltdaOO+Hbn8vL9a1Ynbff/lxtNZEC7dmRZ7LrdfUsnIsrF7ING3IveL3+/jy+wDU0oy1J0mLX275mSQbr6cZ3/4BoW0a0deeB2nZ89w+O7Fnt22+EruV5uT54eHv7jTPPajfLqrW5z3tyJhsW1sWVC9lLX5p7siHPZPf3w9498MY3trauOWh4RluSJC18aXQPlJdNHSwvy+NHsv4H8g1o6nV25/EiFXFx5ZHiiU+E33t77sm+7768/b235/EFzhltSZKWoOhcldtG2upCZWWY6Fw1+ycdCfqOye0ikzPZAKNDebxI6x6TL3ysv7jyzIvsz56rJz5xUQTr6QzakiQtQe1HPYnR+6/JD8rLoDJMmhim45int7awVjvlmbknG/JM9ugQjAzAE3+h+Odu5sWVWhRsHZEkaQlq61lP53HPyz3aY3uJtm46j3vekd2fDbkP+6kvzzPa+3bl7VNfXmx/to5YzmhLkrREtfWsN1jPZO3JBmsdFrPOaEfE7oj4w7rHfxoRduxLkiRJc3Cg1pGV5FuuT3oXcE6RxUiSJElLxYGC9g7g+APslyRJkjSLA/Vo3wT8WkRUgG21sXMi4mDnTCml9zSjOEmSJGmxOlDQ/gPgFOAtdWPncPD2kQQYtCVJknREmzVop5TuiIgnAI8C1gPXAZcAnzoslUmSJEmL2AGX90spVYGfAz+vtYxsSSl943AUJkmSJC1mc15HO6XkzW0kSZKkOZrXDWsi4njgDPISgP3Ad1NK9zWzMEmSJGkxayhoR8SJwMXAeTPsuwp4a0ppS3NKkyRJkhavOQftiDgW+Cb5wsgtwPXkZf/WAc8GXgB8MyI2ppS2N79USZIkafFoZEb7f5FD9h8BH0wpVSZ3REQZ+F3gr4D/D/jtZhYpSZIkLTaNXOB4EfC1lNL76kM2QEqpklJ6P/A14IXNLFCSJElajBoJ2scCtxzkmFtqx0mSJElHtEaCdj9w4kGO2VA7TpIkSTqiNRK0vwm8PCKeMdPOiHga8IracZIkSdIRrZGLIf+c3Kf9jYj4F+Ba8qojxwLnAK8GqsB7m1yjJEmStOg0cmfI70bEy4FPAa8BfqVudwC7gTemlA7Wxy1JkiQteQ3dsCal9JWI2AC8BHgy0Efuyf4e8IWU0mDzS5QkSZIWn4ZvwV4L0/+v9iFJkiRpBo1cDClJkiRpjgzakiRJUgEM2pIkSVIBDNqSJElSAQzakiRJUgEM2pIkSVIB5hy0I+KaiHhPkcVIkiRJS0UjM9pnAeWiCpEkSZKWkkaC9s+AE4oqRJIkSVpKGgna/xe4qHYLdkmSJEkH0Mgt2L8MnAfcGBF/CXwH2A6k6QemlO5pTnmSJEnS4tRI0L6THKoD+PABjksNnleSJElachoJxJ9mhtlrSZIkSY8056CdUnp9gXVIkiRJS4o3rJEkSZIKMK9e6og4DXgc0JtS+kxzS5IkSZIWv4ZmtCPiv0bEJuAnwOeAS+r2PScihiLiRc0tUZIkSVp8GrkF+ynAdcCp5FVHrph2yPXAbuDlzSpOkiRJWqwaaR15J9ABbEwpbY6IdwIXTO5MKaWI+E/gzCbXKGkBuH10hKuHB9k2McG6tjbOXdbDKZ1drS5L0mLWfy9s3wTDu2HZUXDsRujzJtRaOhoJ2ucC/55S2nyAY+4l39RG0hJy++gInx7oZ3kpWFsus69a4dMD/bwWDNtSCwyN7WDPyO2MVfrpKPexqusUujvWPuK4saH7Gd37Iypjeyh3rKJz5RPo6D6uBRXPoP9euPNKaO+GrqNgfCg/Pvl8w7aWjEZ6tFcB9x3kmCDPektaQq4eHmR5KVhRKlOKvF1eCq4eHmx1adIRZ2hsB9v3f5uJ6gjtpRVMVEfYvv/bDI3tmHLc2ND9DD3wDaoTw5TaV1KdGGbogW8wNnR/iyqfZvumHLLbeyAib9u787i0RDQStHcAjznIMY8nz2pLWkK2TUzQG1P/ueiNEtsmJlpUkXTk2jNyO+VSF22lLiKCtlIX5VIXe0Zun3Lc6N4fEaVllNqWERF5W1rG6N4ftajyaYZ3Q1v31LG27jwuLRGNtI5cA7w6Ik5NKd02fWdEnEluL/nbZhV3JNm6GX50OezZCqvWwxMuhPWnt7oqKVvX1sa+aoUVUX5obH+qsq5tXiuESjoEY5V+2ksrpoyVo5OxSv+UscrYHkrtK6eMRbmLytiewmuck2W1dpH2nofHJobyuLRENDKj/RfABHB9RPwGcBxARDy+9vjLwADw/qZXucRt3QzX/QMM9cPKdXl73T/kcWkhOHdZDwPVxL5qhWrK24Fq4txlPQf/ZEkADI9uZ8ee67hv5xfZsec6hke3z+s8HeU+Kml0ylgljdJR7psyVu5YRaqMTBlLlRHKHavm9bxNd+zGHLTHByGlvB0fyuPSEtHILdhvi4iXAf8MfLQ2HMAPa9u9wC+llO5pepVL3I8uh2V90F37N3Jy+6PLndU+kvx0V4Urfj7B1oHE+uXBBY9u43Grywf/xMPglM4uXgtTVh15ac+hrTpyx/gw148OsKM6ztpSO2d3Lucx7cuaV7S0gAyPbmdX/02US520lVdQqQyzq/8mVvedxbLOYxs616quU9jx4NWkob2UKmNUyx1Uu1ey6uhzpxzXufIJtR7tPJOdKiOk6jCdK5/azC9t/vpOyBc+1q86csLZXgipJaWh931TSldGxKOA1wFnAUcD/cBNwD+mlGysmoc9W/NMdr1ly/O4jgw/3VXh4u+N0dcZrOuF/tHExd8b4y1ndCyosN2sFUbuGB/m0uHd9EaJNaU2BlKFS4d380qOMmxrSdo3dCvlUiflcv75ntzuG7q14aDdPlGhd99eJsYeJFXHKZfa6ZyA9r7KlOUIOrqPg2OeM23VkafOuOrIXFcxabq+EwzWWtIabrBMKe0l37Dmw80v58i0an1uF+mue9dveCCP68hwxc8n6OsM+joDgL7Oh8cXStBuputHB+iNEstL+WtbHmWo5nGDthabuYTU8Yl+SCVGxrZQqY5SLnXS0XY01dQ/y1kP8HwPfAvG9tFeXkG0t5GqE6SxfQw98C36Tpp6z7iO7uMOupzf5Com5VLXlFVMju196uEJ29IS1tAt2FWMJ1wIw/05bKdq3g7353EtbD/dVeGDN4/y9q+P8MGbR/nprsq8zrN1ILF82sKYyzvy+FK0ozpOz7RVTHqixI7qeIsqkuZnrkvtQZmh0XuopglKpU6qaYKh0XuAxn+RHh+8G0pdRCnPlUWpDUpdeXwe5rqKiaTGNTyjHRGvAd4InAGsAPYB3wM+mVL6bHPLOzKsPx3OeevUVUee9mr7sxtx6wMVvnZ7ha37EutXBC84pcxpxxQ7E9zMdo/1y4P+0fTQTDbAwFgeL9qtI6N8bf8QW8crrG8v84Lebk7r6jz4Jx6CtaV2BlIlz2TXDKYqa0vthT6v1Gz1IRWgLboeGp91Njgd2i/QibzsdL2I+Z92rquYLAoPboG7boKBnbB8DTzqLDj6pFZXpSPYnIN2RLQDnwNeSL74sQLsBFYDzwXOiYhfBl6eUnJaqkHrTzdYz9etD1T4xLcnWNEF65ZD/0jiE9+e4NefSqFhu5ntHhc8uo2LvzcG5JnsgbEc3F91erHB89aRUT6xZx8rSiXWtZXor1T5xJ59/PqqFfMO23O5VfvZncv5+P4H2JMqjJNoJ1gVZd7Ue0wzvizpsJl7SK2wrHMDYxO7qaYRStHFss615P9KG9Pes4HxgTvzg2iDNEGqjNK+/OR5fQ0d5T4mqiMP/ZIAM69isuA9uAV+8EXo7IHeo2F0f378pJcYttUyjbSOvAN4EXAzOVh3pZTWAV3A84Bvk0P4HzW7SOlAvnZ7hRVd0NcVlCLo6wpWdOXxIjWz3eNxq8u85YwO+jqDbfuhrzMOy4WQX9s/xIpSib5yKb925RIrSiW+tn9oXuebvFX7vmplyq3abx8deeTBAZBq03Cp9lhaXOa61F57Wx/lUhu9/z97bx4e13Xeab7nLrVvWAgQG3dxM0ktlkVFki06duRYiSLbiR2707aTdrfT6U466bRnnk7P00kmSW8z7k4ce8ZJujN2uyfxtJMokZXIlmzFsi3ZlkSLEimR4iYuAEhsBFALar33nvnjFIi6BVAolKpIADzv8/C5vB9O3TqF9Xe/+r7fF95MIrKLWHgzpmFhWysXs5GeezFC3YAArwQIjFA3kZ57m3oNHaGduF4RxysipcTxirhekY7Qzqaud8M49wMlsoMxEIY6BqMqrtHcIFZSOvIx4AxwSEpZng9KKV3gaSHEIeAV4OeB32vhHjWaN2Q0I+mL+2PxoIrX00oLvVaXe+zpNq974+NoxaXP8t9vxw3BaKW5m5TaUe1AdcCNy1OFOV9W+zulLP1mgF01jY9Zz9XNkJo1R0doJ2O55wGVyXZlCdcrsiFywLcuEdnNVFoJPsMI4XlFXK9ER/z2FT9nINJPfOC9dW4i+5dterwWkUAvG2N3+Ro6N0QOrL1GyOykymTXEoiouEZzg1iJ0B4EPlsrsmuRUpaEEI8C/7wlO9NoGmQgIUgXJcma6oRsScVrabWFXqvLPY5mKvzNRImLRY9NIYP39QQ5kGhv6ciAbZJ2PZLmwucq60kG7OYE/2XHodf0P3apUe3jXoUNhv/Xj26G1KxFGhWp4eBGupN3k8m/RsVJY1tJOuK3r9jab55G3ERWQiTQu/aEdT3xDapcJBhbiJXzKq7R3CBWIrQvAcv91ber6zSa68YDO03+9HkHkMSDSmRnivDBA37B12oLvflyj9oM+Yf32k1d62imwu9fKJCyBINBg5mK5PcvFPiXm2mr2H4gFuFPZzKAymSr6Y8eH0zGlnnk0jQ6ql03Q2rWE42K1HBwY9PCup5i8TJzueM4lVksO0U0tpdQqG/Ruhvmj30j2Hq3qskGlcku56E0B7vffWP3pbmpWUmN9p8DPyOESCz1QSFECvgZQDuPaK4ru3tMPnGXRTIkuJxVtdqfuMta1AjZDgu9Pd0mv34wyH9+d4hfPxhsuvTjbyZKpCxBh61qpTtsg5Ql+JuJ0vIPfhPsDgX5REeCpGlw2fFImsabaoRsdFT7O4JxctIjW12X9Vxy0uMdwfg1rqzRaOYpFi+TnnkW1y1gWklct0B65lmKxcu+dY1bD64TuraoxsdgDHJX1FE3QmpuMCvJaP8OsA94XgjxO8B3gHGgF7gf+LeohsjfbfUmNZrl2N2zvJ3fjbTQW46LRY/BoP++N2kJLha9tj/37lCwZXZ+jY5q32GH+Vk6fSPYfyKU0vXZGk0DzOWOI4yQb8qkW43XZrWbsh5c63RtaUxYT5yDM89CZgISPbDjXujZ2u7daW5Crim0hRAeyq5z0YeA/3GN+C1A4Y2u2whCiH9Y8xz/REr5397M9TQauHEWeo2wKaTKRTrsBdGfdiSbQmtvplSjo9p32GEtrDWaJnAqs5h1biWGEcKpzPpi68ofu5VMnIPDfwWhGMS7oZhT5w+NiVIAACAASURBVHf+tBbbmpbzRoL4OywttNuKEGII+ByQA5orFNVolqCVNdWt5n09QX7/QgHwSFqCtCOZdSS/MLC8YNVoNDcXlp1SZSPmwo2q5xWx7JRv3brxx241Z55VIjtUlRjzxzPPaqGtaTnXFNpSykPXcR8ACCEE8AXgCvAI8KnrvQfN+uZGWOg1woGEzb/cjM915BcGQm13HdFoNG+eufIEM6VTlNwMQTNBR3An0UD7hi9FY3tVjTYLVoHSKxJNvtW3rlHrwZuOzITKZNcSjKi4RtNi3lSJRxv4F6jhN4eqR43mpuFAwtbCWqNZY8yVJ7icfx5LhAgYcRyvyOX88/RxV9vEdijUBx33+l1Hkm9d5DqybvyxW02iR5WLhGreNC/lVbxZRs/AsW/DzBh0bIT998PAjje/V82aZ9UIbSHEHuA/Ap+RUn5HCKGFtkaj0WiYda9wyT1PQeYIixj95hZSZtfyD7wOzJROYYm6hkNPxduZ1Q6F+pa086tnXfhjt5od96qabFCZ7FJeCe9972nueqNn4OkvQzgOqR7IZ9X5oY9osa1ZudAWQjwE3IYaYLNU+k1KKT+xwmtaqObHi8C/WcHjPgl8EmDTpk0reUqNZs3ySq7MY9MlRkrKqeShziD7YoHlH6jRrEFm3Sucdo5iEyRElLIscdo5yi0cWBViu+RmCBh+W0pTBCm5maaveVN5X98Ieraqxsda15F972m+PvvYt5XIjlS/D+aPx769WGiPnIaXvgXTY9C5EW57Jwze0vxr0ax6GhbaQojNwGPAW1AOI9dCAisS2sBvArcD90kpC40+SEr5J8CfANx5553XvXFTo7nevJIr87lLeZKWoD8gmHU8Pncpzy/3o8W2Zl1yyT2PTZCAUBaUAYIgVXw1CO2gmViy4TBoLjlyYlnmva9NI+Tzvt4Yu2tViO1GB+XcMCbPwZnv1dj23QMblhDQPVsbE9aN2ADOjKlMdi3hqIrXMnIavvlnSoh3VDPf3/wzePfPabG9jllJRvsPUT7a/w/wJWAUcN7wEQ0ghDiIymL/Zynl99/s9TSaVnBiyvW5k7x3u9X2Jspj2QqPTpUYLnoMhQwe7g6yP+5/0+ix6RJJS5CylO1fyhKAx2PTJS20NeuSgswRwj/wyCZAQeZu0I78dAR3cjn/PHgLDYeOLLIh2FzD4Wr2vp4flCOMkG9QDh33rg6xPXkOfviIGlQzb9v3w0fgrR9YWmwvR6M2gB0blWiO1LyzUZhT8Vpe+pZaU5/5fulbWmivY1YitH8UeEJK+Y9b9eTVkpEvAadQA280mhvOiSmXPz5SJhkU9MWU1/YfHynzi7cHmhLbjYj2Y9kKfzCcp8MSDAQFsxWPPxjO82tDEZ/YHil59Af8byglTMFIqf2DbTSaG0FYxCjLkspkV6lQJixWh/trNNBDH3f5XEc2BA8sWZ9dKI2TLrxGxUljW0mS4d2Eg37xvJq9rxsdlANAehjGDkNhGsKdsPFOSA61d4NnvqdE9iLbvu81J7QbtQHcf7+qyQaVyS7MQSELB3/Sf73pMZXJriUcVXHNumUl0zAqwLEWP38M2AnsAYpCCDn/D/it6pr/Wo39QYufW6NZkq+ddUgGBcmgwBDi6v+/dnblb+DMi/Z0SfpE+4kp17fu0akSHZYgVR3BnrINOizBo1P+EeyDQYOM66+Syrhy0VRJjWa90G9uoUKJsiwhpaQsS1Qo0W9uudFbu0o00MNg/D62px5kMH7fNUX2ZPb7uF4By0zgegUms9+nUPKPQw+YSVzp/7lfLd7XTmUWw/B7+y81KIf0MLz+dajkIdSpjq9/XcXbSWZCNTfW8mZs+xq93sAO1fgYicPshDou1QjZuVGJ8FoKcyquWbesJKP9LKp0pJWUgD+9xsfuQNVtPwOcBNZkWcnocTj2OMyMQscA7H8QBvbe6F1p3ojRrBLFtcQDKr5SakU7cHX8+9fOOr6s9nDRYyBYl6m2BMN1I9gf6gzyuUt5wCNhCjKuJO1IPtrTmhHqGs1qI2V2cQsHfK4jW8xdq6I+e55GmhfThdcwjRCmUc0Gi/DVeG1WuyO0k0sz38KtZBFeBWnYeHacDR3vvH4v6Bo0OiiHscNgR8CulvzMH8cOtzer3WrbvpVcb2DH8g4jt71T1WTDQuY7n4V7fqq5/WnWBCsR2r8JfFcI8WEp5f/XiievNj4uWYoihPhtlND+72t1BPvocXj6jyCchFQf5NPq/NA/1WK7UU6Ou3zjpMeltKQ/KfixXQa7ettbKz0QF6RL8qooBjWufSD+Rj3AS9OoaB8KGcxWPFI1I9gzjmSobgT7vliAX+7H5zry0R7tOqJZ36TMrlUlrGtptHmx4qSx6hokDRGi4vhLQgzPI1xxKEuJC5hSEq44GN6NLw9rdFAOhWmVya7Fiqh4O9lxj6rJhgXbvlIO9j3Q5PVabAM4eItqfKx1Hbnnp3R99jqnYaEtpTwihHgX8HdCiF8EXgSWKhqTUsrfbdUG1zLHHlciO1J9x2/+eOxxLbQb4eS4yxeec0iEBBsTkClKvvCcwy8cpK1i+73bLf74SBlQojhbViUfH9678mEyjYr2h7uD/MFwNVNtCTKOZMaRfLxvcaZ6XyyghbVGs0potHnRtpIUK1eoeDlcr4RpBLGNGCHbfwMxlzuObacImQs1z65bWLoOugFa6RLS6KAcwtVyEbumidXJq3g72bBVNT7Wuo7se6C5+mxYmQ1gI+4koES1FtbNcfQoPPIIXLwImzbBBz4AB1b/lNOV2PslgX8PdAL3V/8thQS00EaVi6Tqf//EVVyzPN846ZEICRIhJUoToYV4O4X2nm6TX7w94Gtg/PBeu6lGyEZF+/64za8NRXyuIx/vW+w6otFoVheNNi8GrA1cmTuGaQQQBHG9ImUnQyK827fOqcxiWv567CXroBugHS4hDQ3K2XinqskGlcl28kp4D72jqedcERu2Ni+sl6IRG8BG3Uk0zXP0KHz609DRAYODMDOjzj/1qVUvtldSOvL7wDuBb6KGy1yiBfZ+10JK+dvAb7fr+teDjgFVLhKp+Z1ZyKq4ZnkupSUb66xoY0EVbzd7us2W2PmtRLTvj9sNCetGbAA1Gs31IWAml/TRrm9eLHhXCAUH8dwMnldSpSZ2DwXvim9dw3XQDbAil5AG8bIjeBNHoHgFQl0YPbdjxAf9i5JDsO3H/a4jQ+9ov+vIjaJRdxLN0hx/Bf7uURgZhsEh+ImHYW9dS+AjjyiR3dGhzuePjzyyroT2TwLfk1I2Wex087H/QVWTDSqTXchCIQ0HP3Jj97VW6E8KMkV5NZMNkCupeLMcv+Lw+DmXkazHYNzgwa0me7tWPCB1RbRKtEPjNoAazc1I2pli3H2dgpcjbMToNbeRtLrb+pwdoZ2M5Z4HFny0Xa/Ihoj/j3/ZTRO0OxGBhVIRKeWizHfDddAN0MrsOCiR7V54EqwoBFV5iHvhSdj8wNJie70K63oyEyqTXcubcTu5mTj+Cvzfn4FkCvoHYHZWnf+zX/WL7YsXVSa7lmRSxVc5K1EYYeB77drIemRgr2p8rHUdOfgRXZ/dKD+2y+ALz6k3TWJBJbIzRclP39qcaD1+xeHzL1dIBQX9MVU7/fmXK/zSrbRdbLeKWhtAoNo86fHoVEkL7VXOJZnlKJPMUKSDEAfYQL+IL/9ATUOknSnOVV7GFgFCIkpFljhXeZmt3NpWsR0J9LIxdpfPdWRD5MAi15FGM98N10E3QCuz44DKZFtRhF21vLMjyGq8XmjfVGPkV+JOMnoaXnpaTY3s2Ai3HYKBJWq2L56Cw0/B1GXo7oM73wWbdrbrFdw4/u5RJbJT1e/J+ePfPeoX2ps2qXKR+Uw2QDqt4quclaiLI8C2dm1kvTKwVwvrZtnVa/ILB/G5jvz0rWbT9dmPn3NJLWG19/g5d80I7UZtADWri0syy9NcJIRFiiB5KjzNRQ7JTVpst4hx93VsEcCujmq3qwNuxt3XmxbaucoE06XTVwfRdAZvIWYvFk+RQO+yIrLRzDc0WAfdAK3MjgOqXCRY7yYSVvEa2jJG/sp5OPcDyE5CfANsvRu6tixe1+gI9lbSqDvJ6Gl46s/VW9yp6gj2p/4c3vUP/GL74il4/EsQjUNXL8xl1PmDH1t/YntkWGWya0kkVLyWD3xA1WSDymSn00p4f+IT12efb4KVTLn4XeAhIcR97dqMRlPPrl6TX36Hzb9/KMAvv8N+U02QI1mPeJ1ZRzyg4s1ybNbh914p8IvPz/F7rxQ4Ntu2tgVA2QBmnLqBNUvYAGpWF0eZJIRFBBuBIIJNCIujTN7ora0bCl4OC/8PuEWAgtfcqPZcZYJLcy/geEUCRhzHK3Jp7gVylebKAeYz35YRouJlsIzQmxOeDRAK9ZHsuFfVZjtpTDNM8s2MSw91gVPwx5yCitdQ68QihMAyQphGiJniqeae98p5ePlRZdUX61LHlx9V8VrmR7AXc/4R7JPnmnveRpl3JwnFIDuljks1Qr70tBLZkTgIQx3DcRWv5fBTSmRHE2pdNKHODz/V3tdxIxgcgkzGH8tkVLyWAwdU42NHB4yMqOMaaISElWW0+4C/Bf5eCPHnwA9Z2t4PKeWXWrA3jaalDMaNJa32BuPNidRjsw6fOVkiZcNAWDBb9vjMyRK/ugv2p9qTIV+JDeDxfJnHZ4uMlF0GAyYPpkLsjWhbwBvBDEVS+L9GYSxmKN6gHa0/wkaMiixdzWQDOJQJG82Nap8uncZawrZvunR6yax2IzSS+W41rcqOAxg9t+NeeBIJKpPtFMCZwxi417eu5WPkz/0AglE1Xh0Wjud+4M9qt3oE+0poxJ1kZkxlsmsJR1W8lqnLKpNdSySm4uuNn3hY1WSDymRnMpCehZ/7+OK1Bw6sCWFdz0oUxheB96HE+ceAzwBfqPv3xepRo1l1PLjVZLYkSZcknlTH2ZLkwa3NZckfHamQsiEVqI5NDxikbBVvF/M2gCnbYLQkSdnGko2Qx/NlPj+RI+149NsGacfj8xM5jufLbdub5tp0EKJQZ9JUwKGD0DUeoVkpveY2KrJMpTqqvSJLVGSZXrO5iseSm8EU/psjUwQpuZlrPGL9Y8QHqfS/lZw3SS57lJw3SaX/rYvqs1s+Rj47CYG6UeiBiIrX0uoR7K2m4xoj2DvqRrB390G+7p2YfE7F1xt796nGx1QKLo2qY30j5BpnJWm3X2jbLjSa68DeLotfuhWf68hHdltN12cP5z0GwnX10rZgON/eeulGbAAfny2SMgySlrqXTloCHBXXWe3rzwE28DSqOz6MRQGHIg5303+Dd7Z+SFrdbOVWn+vIoLWn6frsoJlYsnkxWDfdcb3g5kZwp15CFqcRoU7M7tswY4sbHMe4iNm/q6bO/CIbyxt9mfqV1KM3RHyDKhcJ1rw7Uc6reC2tHsHeam47pGqyYWEEeyEL9zzkX3fnu1RNNqhMdj4Hc1m4//3XdbvXjb371pWwrmclkyH/ezs3otFcD/Z2NS+s6xmKGMyWPVKBmrHpFclQ5MbXS4+UXfpt/z7ipmCk7N6gHd3c9Is4h+Qmn+vI3fTrRsgWk7S6W+Yw0hm8hUtzLwALYtHxivSE97fk+qsJNzdC8fxjODKPJ12MyiRWbpjQlod8YrvRKZiNOrE0zNa7VU02qEx2OQ+lOdj9bv+6Vo9gbzUDt6jGx1rXkXseWuw6smmnanysdR25//3rrxHyJmFtWC1oNKuQhwdtPnOyBHgkbEGmIpmtwMe33XibvcGASdrxVCa7StaVDAbaN1FT88b0izj9aGG9VojZPfRH3+ZzHekJ72+6Pns1Uxp7hrIzDVYYYYTwpEPZmUaMPUNkx4evrltJ7XVL69G7tsCtD/tdR3a/e7HrSKtHsLeDgVuWtvOrZ9NOLazXCVpoazRNsj9l8au7VE32cN5jKGLw8W32okbIo5kKj4yVuVj02BQy+MDGAAcS7RXjD6ZCfH4iB47KZGddyazn8ZFUZPkHazQaQInt9Sis66nMXQAzhGGo30tC2HimVPEaGvUCbwtdW5a286un1SPYW83EOTUx8qr94L16euQ6p2GhLYR4vcGlUkq5vcn9aDRriv0p6w0dRo5mKnz6XJEOCwaDgpmKx6fPFfnUVtoqtvdGAvxST8znOvKRVETXZ2s0mkW4hoEh/bahQkpcw19+1vLa65uNiXPKbzsUW7AfPPxXS1sBatYNK8loG4BcIp4C5m9nLwHts1zQaNYYj4yV6bCgo1ov3VGd5PjIWLntWe29kYAW1hqNZnlS22DqNRACDBu8Crgl6N7tW9by2uubjTPPKpG9yH7wWS201zEraYbccq2PCSF2AH8IRIH3XGudRnOzcbHoMVg3yTFpCS7qSY4ajWaVENlwD1kngzU3g1Ep4Fk2Tqqf+IZ7Fq+9AV7g64bMhMpk17Ka7Ac1baEl9ghSyjPAB4AB4LdacU2NZj2wKWSQrpvkmHYkm/QkR41Gs0oIhfqI9/043sZbKfbuwNt4K/G+H2/ZoBtNlUSPckKpZTXZD2raQsv+2kspi8A3gI+06poazVrnAxsDzDgwU/HwpGSm4jHjqLhGo9GsFkKhPrq630Vv30/T1f0uLbLbwY57VV12MQfSW/j/jnuXf6xmzdLqtJoDbFx2lUZzk3AgYfOprSE6bIORkqTDNvjU1lDb67M1Go1Gs8ro2aoaH0MxyE6po26EXPe0zN5PCNENvB8YbtU1NZr1wIGErYW1RqPRaJSo1sL6pmIl9n6/+QbXGAIeRrmP/EYL9qXRaDQajUaj0axpVpLR/u1lPp4Bfk9K+X80vx2NRqPRaDQajWZ9sBKh/c5rxD1gBnhNSum8+S1pNBqNRqPRaDRrn5X4aH+7nRvRaDQajUaj0WjWE9rMV6PRaDQajUajaQNvmNEWQjQlxKWUeuydRqPRaDQajeamZrnSkUoT15QNXFej0Wg0Go1Go1nXLCeIh1HCuRFiQNeb245Go9FoNBqNRrM+eEOhLaXcstwFhBA28CvA/1YNnX/Tu9JoNBqNRqPRaNY4b6oZUgjxQeAE8H8CAvhfgT0t2JdGo9FoNBqNRrOmaaqWWghxD/Bp4CDgAH8I/I6UcqaFe9NoNBqNRqPRaNYsKxLaQojtwH8C3o/KYP8l8BtSyrNt2JtGo9FoNBqNRrNmaUhoCyE6gd8CfhEIAN8H/pWU8gdt3JtGo9FoNBqNRrNmWc5HOwD8GvCvgRRwFvjXUsq/ug5702g0Go1Go9Fo1izLZbRPApuAaZTg/r+klG7bd6XRaDQajUaj0axxlhPam1E+2gL4FPApIcRy15RSys0t2JtGo9FoNBqNRrNmaaRGWwCd1X8ajUaj0Wg0Go2mAZYbWPOmfLY1Go1Go9FoNJqbFS2kNRqNRqPRaDSaNqCFtkaj0Wg0Go1G0wa00NZoNBqNRqPRaNqAFtoajUaj0Wg0Gk0b0EJbo9FoNBqNRqNpA1poazQajUaj0Wg0bUALbY1Go9FoNBqNpg1ooa3RaDQajUaj0bQBLbQ1Go1Go9FoNJo2oIW2RqPRaDQajUbTBrTQ1mg0Go1Go9Fo2oAW2hqNRqPRaDQaTRvQQluj0Wg0Go1Go2kDWmhrNBqNRqPRaDRtQAttjUaj0Wg0Go2mDWihrdFoNBqNRqPRtAEttDUajUaj0Wg0mjaghbZGo9FoNBqNRtMGrBu9AY1Go3kjXncKPFPJMO5V6DVs7rMTbLPCN3pb64opb5azjJAlT5wI2xmk20g1fb0JOctpLpMhT4IIt9BHj2j+equdXGWCK6UzlLwMQSNBV3AHMbvnRm9Lo1GcPQHf+TqMj0LvALzjx2H7nhu9q5sGIaW80XtoCXfeeac8fPjwjd6GRqNpIa87Bf6iNEUMk6gwmJMeOVw+GOzWYrtFTHmzHOEkQWwC2JSpUKLC7exqSmxPyFkOc4YgNkFsStXr3cmOVSO2W3ljkatMcKlwGFOEMEUQV5ZwZZH+8J1tFduz7hVGvfPkZY6IiDFgbCFldrXt+VaKlxlBTrwIxWkIdSJ67sBIDDZ/wekLcPE5yE1BrBs2HYTOza3b8CrDywzD+MLnj947MBJDK7/Q2RPwXz8Ns1NQLkMgAKlu+Cefal5sj78OJ5+B9Dgke2HXfdC7rblrrWGEED+UUt653Dqd0dZoNKuWZyoZYpjEDROAuDDBU3EttFvDWUaqojgAcPV4lhG68YvPK94M5xghxxwxomxlkC6jw7fmNJfBc8iQoUIFG5sgEU4bl+mhOTGbdqe47JyjIHOERYw+aytJs3vRuowzxbhzloLMEhZxeq3tJCz/uilvliPOD7EraYJemYIR4Ig9zu3WW5sS21dKZzBFCMsIAWCJEHgqXi+0s84kk+UzFL0sISPOhsAO4taGFT/nrHuFU84xbBEgTJSyLHHKOcZO9q8Kse1lRpCnH4FSBjwHjBFk+jzeLR9YLLZnLsDIC5Cfgkg3DL4NOuoE9PQFePUxCEQh2gWlOXX+lofWpdj2MsNw/kmwIhDsgEoezj+Jt+WBlYvtv/l/YfQ8ROMQiyuxPXpexf/Vv/OvbURAj78O3/pTyM1ApQLj5+DSSXjnJ25Ksd0IukZbo2kzr8w4/IejBf7ZD/L8h6MFXplxbvSW1gzjXoWo8P+aigqDca9yg3a0/siSJ4DtiwWwyZL3xa54MxyVr1GSZaIyQkmWOSpf44o341s35U2TZQYXFwsLF5csM0x5003tL+1Ocbb8MhVZIkSUiixxtvwyaXfKty7jTHGucqS6LkZFljhXOULG8a875ZzALo1jeh6IEKbnYZfGOeWcaGp/JS+DKYK+mCmClLyML5Z1JrlY/CEVr0hQxKh4RS4Wf0jWmVzxc45657FFgIAIIoQgIILYIsCod76p19Bq5MVvIXLjCAnCDKtjbhx58Vv+hTMX4OTjUJ6DcJc6nnxcxWu5+JwS2cEoCKGOgaiKrxZGz8DX/xS+/O/UcfRM89caf1GJbDuiXq8dUefjL678WsePQCQGwWD1cxdU58eP1D3n6/DcX0AxC4kN6vjcX6h4LYe/ClOjIIFQVB2nRlW8nlPH4fOfht/8NXU8dXzl+18H6Iy2RvMmeGXG4bHhCsN5yVBE8NCQzb4Oy/fxz54okQoIBsIwW5Z89kSJX9mDb92N5EShzNfSBS5VHPpti/cmw+wJB270tgDoNWyynqsy2VXmpEevYb/Bo96YC+4cL3gzTFGmmwBvMzrYbEZbsd01SZwIJcpXM9kAZSrEifjWnWOEAAGCoibzLVW8i4WstkcJicBEfc1MTBxcPEpN7e+ycw5bBLGrYtYmeDVem9Ued86qYpXadVLFa7PahcooJoKCKOLiYgqTgDQpVEahiW/7oJFgxh1nljSurGAKmxRJOsxe37rJ8hksgtjVzLddzXxPls+sOKudlznC+L9nbQLkZW7lL6ANiNnXwQqDWf2EVo9itk60jbygRGSg+lrmjyMv+LPauSmVya4lEFHx1cDoGXj6yxCOQ6oH8ll1fugjMLBj5dcrTqtMdi1WWMXrGTsLJ74Ls+OQ6oU9b4eN21f+nCefgVAMQnF1Pn88+Yw/Uz18XH3u7erNpa1+zhiuE9GnjsOXPg/xFPT2Qyatzj/2S7Bzr3/tsWPw6F/D8DAMDcHD74f9+1f+GlYpOqOt0TTJvIieLUufiK7NWD82XCEVEKQCAkOIq/9/bHh1ZGRPFMr8yWSWtOux0TJJux5/MpnlRKF8o7cGwH12ghwuWc/Fk5Ks55LD5T470dT1LrhzPO6NMYdDFzZzODzujXHBnWvxztcO2xms1lGXkUhKlClRYTv+t/hzzC2Z+c7h/9yFsfAABxdZPXrVeD0z7jRHK0f4fuUZjlaOMOMuFhIFmcOqU8AWAQp1orIgs9dYl/XFTLdE3ptDunkMt4h08+S9OUy3uRuBshlm2h3D88qY0sTzyky7Y5RNf2lT0ctSRjIqJzgnRxmVE5SRFL3sNa58bSIiRgX/z2iFMhERa+o1tJ5r9X7VxfNTSmjXYkdUvJZYN5T977BQzqv4auDYt5XIjsRBGOoYjqt4M4Q6wSn4Y05BxWsZOwt/+0fwzN/DC4fV8W//SMXnecvtkM9BqQhSqmM+p+K1pMfVOwW1BKMqXo9Y5hzgqceVyE4kwTDUMZ5S8VqOHYPP/BeYnYGBAXX8zH9R8XWCFtoazRK8Ou3wH18u8s+/l+c/vlzk1enF5R6NiOjhvCRRl3xN2Cq+GvhauoCLx6lKiacLeU5VSrh4fC1dWP7B14FtVpgPBruJGyaT0iFumG+qEfIFb4YIJlFhIYQgKiwimLxQV/5wM9FtpLidXQQJkKNAkMCSjZAxosyS4yKTnOYyF5lklhyxusxqt+hgAwksTMpUsDDZQIJu4c/QzbjTnPBepUyJCBHKlDjhvbpIbIdFDKdOVDqUCdeJyrCIX2NdvO4VC0xZQok+Acjq+VJqYXkuiks4gRSGEUDgYRgBnECKi+KSb50nbMaZwMHFxsbBZZwJPLHyd2cGjC1UZJmyLCGlpCxLVGSZAWNLU69hJXiZEdwzX8V95Yu4Z76KlxlZvCi5HdwieBVAqqNbVPFaIt2q/riWSl7Fa9l0UJWVlOaqYnFOnW862NLX1jQzYxCuE6nhqIo3Q+8d4OTV50JKdXTyKl7Ld/4KTp4G14NYQh1PnlbxeR7+KAxuUf/PVW/qBreoeC3JXvV5raU0p+K1DOyBUh6cstqbU1bnA3WNlZdHVE14LbG4itfy6F9DKgWpDiXIUx3q/NG/Xvx5OXYMfu934Bf/iTquETGuhbZGU8er0w6fPV5WmeqIUJnq4+VFYrsRET0UEWTqkteZioqvBk4Ui5xxypSkR0wIStLjjFPmRLF4o7d2lW1WmI+Fe/lfooN8LNz7ppogpygTwfTFIphMsToy+DeKbiPFwyi30AAAIABJREFUQWMf7zbu4qCxb8mmwCQpxpimJMsEpElJlhljmmRdg+NWBrEQ9JBkO330kMRCsLUuQz7sXSCA7aszDmAz7Pnrc/usrVRkiUpVVM7/v8/a6lvXa21X/ia16yjRa/nFXcUQWNJCSJBSIiRY0qJiNPczWZZzGGYML7gBN9yHF9yAYcYoS79oydghTOlhSg+kvPr/jB1a8XOmzC52WvsJiCAF5giIIDut9jdCepkR5IUnkZU8MtihjheeXCS2jc2HINJTFYkFdYz0qHgtg29TIrJcFdDlOXU++Db/us7NqvExGIW5K+r4Zhshx1+H7/4P+Nv/rI71tcgroWMjFOpEamFOxZvASAxBZDccfxme+Tt1jOxe3Aj56hGIRiEYqtZfh9T5qzX119t3491/CE/k8eYm1fH+Q7B9t/9au+6DYk7VZktPHYs5Fa/lbT8FnQPq61Wsft06B1S8lr7BBWE/Ty6r4rUMD6tsdy2JpIrXsoYz36ujSFSzbjg77PHdFyXjVyS9XYK33yHYPrS27uceG3ZIBVV2GiAVWIi/pXPhR2aoKsJTNe9W14voh4ZsPntCvSWdsNXHZ8uSj25fHTXQOSRCCoKG+hoFEZQ8j9w13/pd23QTYA6HaM2vvjwu3c0U564BRmWWo0wxQ5EOQhygm4FFGd7GGDdydHrdlMhToUKQAAlSjBs5bqlZ12V0cMDb7XMn2c22Re4kc8wRqasDtwkwV1eKkjS72R641ec6ssnevch1JGF1s5Xbfa4jg9beRa4jpmnjySQBWQTpgLBwRAjTbO7PYUBEcWQJg4WGSJcKAeHPcOYtkzBb8CqTSFlEiBCBQD95y6y/ZEOEy2X6crM4lVks2yEcK8MS96CVuVHKs8fwyjMYgQ4Cqf3Y0YGmnlNOvIi0Ioj5cg87on5TTLwItW4iiSGMne+HsReheAVCXbDxDqgXih2bYdeDfteRbYcWu46AEtWNCOvJc3D2+5CdgHgPbP8R2OC/KWP8dXj+L1UdcqJbCcrn/xLu+pmlXTaWc+LYf7+qyQaVyS7MQSELB39y+f0uxdhZjKPfgcgAdO5UgvbodyC20V9/XXEhVPd9axhQXMjueMeehu88ghjohZ07YC6L/M4jeANDGPsPLTyudxsc/KD/td763sWvdeN2+NF/tHxd+LseVDXZoDLZuSxkZ+H9H/GvGxpSojlV8/shk1bxWh79a3BdeOUYZDKQSEBfv4qv8npuLbQ1LePssMdXnvCIRWBDJ2TnJF95QvKh99C02D496vL0MY+xGdjYAYf2G9wy0NwfpkYZnvMYqMs4J2wVr6UREb2vw+JX9uBrmPzo9sCqaYRMWoJZR1L0JEEBJQmelCSt1ZFxbzVvMzp43BsDqTLZeVzyuBwyVm6xttoZlVm+xTBhLFIEyVPhWwzzTjnUlNjOUCBhJBAsZJ8kkgyLy4y6jA5fg+RSRIlSpkSgRqBWKBNlcWNq0uxe0s6vnoTVvUhY19Mpepgwx3BFChMTFxdPlukWzXle7zD38qrzPEgwsXGp4FJht+mvgY0TISdmca0ADqp+3BTeoqbTRigVLpOefgZhhjGtJK5bID39DMnO+wiG+66uq8yNUhx/Gswwwk7hOXl13nuoObG9kia9xNBiYb0UHZuXFtbNMHkOjvw1BGOqfruUU+e3v98vtk89q0R2qFp+NH889axfWM47cYRifieOgx/0rxvYoRofj31blYt0bFQiu5lGSFAiNhRTdd6wcDzxXb+g3bYfzh5R2WzLBqeiRP72mu+9v/8KIhKFeLWvJZ5QBVN//xWoFdqgXlMjFn0bty/fcLlzr2p8fOpxVS7SN6hEdn0j5MPvV5lpUJnsTBpmZ+Hj/8i/7ujLcOE8hMMQj0OxCMdfhbnV31+zOv7aa9YF331REotAPKpEWjwKIPnui5LtTfjsnx51+bOnPRJh6ElBNg9/9rTHzx2irWJ7KGosnamO+m8WGhXR+zqsVSOs69kbChAWgrGKR9b1iJsGW4I2W4PNu3qsZjabUR5ko8915JCxYdW4jrQyA32UKcJYRKoNjPPHo0wxwMqvmSBMkTKhmux/iQqJpdKoDTBkbOaE9ypIlcmuUKZMhe3Gzqau1yhb7b0UyzkKlCjLMrYwiIoYW+29yz94CTbbSsCdcY9TlnMERJTd5u1X4/P0uDHGvNcwsbAIUKaE682xjcEVF3HO5Y4rkV1tuDTNMG41Xiu0y7PHkNLDzV9CukWEGcIMJCnPHmtOaIc6VWlHbQPjUk16ALMXYfQwFKYg3A0Dd0Jq08qfc57M8PIZ8rPfVyI7WBXO88ez3/cL7fSEymTXEoyoeC2NOnGAEtXNCut6ZschWXfzH4qqeC3v/hBMjUExDZU5MCxI9qn4PJPj0FV3ExmNq3i78SSUHCg66ugt8U7p/v3wq7/udx35+D9anKXOpFW2PlQttQqFoFRS8VXO6vzrr1mTjF+RbKj7fRuNqHgzPH1Miex4NbscjwBInj7mtVVoPzRk8dnjqmb3aqa6JPno9sXiczWL6ON5Zds3WnEZsE3emwyzN+IvkXggFuG/FGYpSU85TkiPOU/wQGzlWba1wmYzumqEdS2tzkDPUCSF3985jMUMzdXf76Sf51HewPMTH4s4HGBLU9frMDvZwCZOeKcpkCdMhD3GLXSYi0XbtDfNBTnMnJwjKqJsFkN0GkuIuwZImt0MmTu5UHkFZJGgiDJk71wyYz5ReI2x4suUvRwBI8bG0K30hHcvWrfZ3rpIWNeT5wob6CInipSoECRITCbJcwVW+Dl0KrOYlr+u1TBCOJVZ/7r8KJXiFQzDRpghpFehnL+M7TXXkyB67oDTjyDL2eogGgsRiCNu+YB/4exFOP042FHlj12ZU+e3PNic2M4Mw7knqgNcOtX1zj0BW9/jF9vZicVOJIGIiteS7FHlIqGahtpSXsVrSY8jg0G48pq6obDCEN2IWMqJo0EamviY6lWlJ+Gan/vinIrXsmUX3k9+CL79l3BlHLo64P6fwdiya2HNhl7IZhYy2gBzWRVvJ6+9Cp/595CfVc2w4+fg5DH41X8Du9/iX7t///LlHx0dMDMDhYIS2cUieJ6Kr3LWVvGsZlXT2yWYq2sgn8ureDOMzUC0LlkWDat4O3lLp8Wv7A2QCghG85JUQPArewO++uzVzvF8mT+ezJF2Pfosg7Tr8ceTOY7n/X9gpQTHVf8X1S+T46q45vpSm4EWCCLYhLE4SnNewR2EKOBv4C3g0MHKm+8AekhyFzsIESBLkRAB7mIHPSSXf/ASjJHmVXMG2+6jx96FbffxqjnDGP4M1bQ3zavecUqyTAQ1KOdV7zjTSwzAyTiTnCk8xyv5b3Km8ByZJYbBpJ0pJt0LJM0u+q0dJM0uJt0LpOsG20wUXuPc3LfJudOUvDI5d5pzc99movBaU683L3MkRJwB0cM2McCA6CEh4kt6X5cKl5mZ+CZTo3/JzMQ3KRUu+z5u2Sk8z3/D5HlFLNvfmOo5BQQCUfWdF4b63vLqreMaRjWPqn8L/19k2zd6WImgzEUYO6KOnqfizTA2P8ClOrDGjqrzsboBLvGepW0A43UCeue9C81+0ltoAtx5r2+ZFwrB1GtKKFphdZx6TcWbwMsMw+E/h+98E/7+u+p4+M9VvJY9b1d7KlQbEwvVve55++LreWfgnffAh/+BOnpn/Nf70Q8h83NKbHsSshl1/qMfoq185YswNQJm1e7QNNT5V77Y3PVuvRX27VOlI5mMOu7bp+KrnLWjHDSrnrffIfjKExKQRCNKZOfy8ODbmxPaGztUuUi8Jrk6V1DxdvOWTmtNCet6vpYukDQFSVPdSydNAVXbvtqs9tczBTYFbPabC5nPtOvx9UyBPeFg/WU1baTVGegDdPMthq9ep4BDAYe76Vvmkdemh2RDwnqMNK8xTpoCScLsppeNdY97jXGC2ISrJS3zx9cY9629IIexCS4alHNBDtPJQlY740xyqvQcOcqUkQRkhtnSJDs5SKJmIMy4+zq2CCwagDPuvk6ypr57pHCYiiwjhUAKqQSqdBgpHF4yq70cERGjLBfXpNd7X5cKl5m68k3mRJmKkNiVNNErl+nuevfVspBobC/p6WdwUZlszysi3QLR5Fv9T2qGEW4B6VVAWCAddUNtNuncM/4i0g4hZUndkVsBsEOI8Rf9meX0BZibVB+3wuBWIDMCTXqVU7yiMtm1WBEVr2X7j6iabFCZ7HJe1Wnv/TH/ut5tqvHx1LOqXCTZAwfes7gcJBVBjpahUgRDgidAGpBq8h2/Y1+Dl15BRGKqHrlURL70CgS/Bvd+cmHdxu1wz4f8DYd3PLi4Lnr8RbiSgzPHID0LyRTs2Arhha+Hsf8QHtWa7Mlxlcl++B/7GyHnef01ePZJmBiFngG49wHYtvLvdUA5n8TiEKh+vweC6rzWEWUlPPQwfPYP4LbbVCNkJqOaKB96uLnrXUfWrpLQrDq2Dxl86D34XEcefHvzriOH9hv82dMeIImGlcjOFOChgzf3GzGv5Mo8Nl1ipOQxGDR4qDPIvpi/JGS04mJJj+OFMjlPEjME2wImuboaudGKS5/l/3zGDcFoxW3769D46SBEnsrVWmp4cxnoARHnnXLIV/N9N31N13w3yhhpfsA5gtgkCFGgwg84x91s9QnoNAUSda8thEW6rrlyTi52JwlgM1dnn3e+fJxp5jBFkAAGLh7Tco7z5eMcsO6/uq7g5RAe5OQkrixhiiBhkcIx/D6cBW8WFxeJgUQJbYFHwfOXZzTKgLGFU46yIpuvSa/IMlvNXb5105nDZMsTWE6ekOciDZOsFcHIHKYv/BAAwXAfyc77mMsdr7qOpIgm3+qrzwawo4MUhKRUmVQi1wwSDGwgHKmzWKMxdxKZHcYrTCHMgBLrbgUvdwnDrStFcfIq82xWv5dNW4ltpy7b3CihLkhfguyUKvEIRiDeDcl+/7oNW1XjY63ryN4fW+w6AnjhALIvCR0uhJKIcGDxW/xGAZkIIC5PQqkMwQCybwPCWOIdgUtnqs2Q49DRq5xI+utqtk8cQYRjEKre6ITUeHp54gjcW3e9RhoOL56EH76CCIWVcC8UkC/8ELwStVZAxv5Dixsf63n9NfirP1V+3N19kEur85/+RHNiO2gqX+9aXE/Fm+Et++FXfg0ee3ShlvujH1fxVY4W2pqWsn3IaKrxcSluGTC5b5/H//yBx2QGNiTgZ+9uv+vIauaVXJnPXcqTtAT9AcGs4/G5S3l+uR+f2A4gea5QJmYYxAwoSckLhTIHI/6M6YCtpkGqjLci60kG7MWf41OlEt/M57jsuPRZJu+OxNgZ1FnvVtGODPSAiDfV+PhmaDRTnSRMgcrVjwMUcUjWNVdGRZS0l0W1L1YIYhOTYZKG/3VdkRMYBLCqPukWJpIAV6S/PteQgll3FEsEMAjgSYe0d4mU8ItKB9W3gABRHWYjpcShTjw0SMrsYif7GfXOk5c5IiLGVnPXIu/rXP40dikNhgnCREgPu5QmJ0771gXDfYuEdT2lxBCZwnEIJxFGCOkVKblZjMQQtXn0Rt1JpKtKUWoFtHArSLdOeNpR5YntlsGwFwbX2E32RoQ3wpGvQrGgSlAMQ4nVez65eO2GrUsK61qu+oFbEeWiUsnDhSfxNj+AUWNTKGfGETNpVd/cYYHjIGbSyERdjfalM/Dt+RHsG1Spx7e/DPd/xC+282WI1L2bYJuQb7KU5/VLiKCtyigAwmGEdJCvX4J3rfBazz6pRHas+jM6f3z2yeaE9l0H4alvqRuu+ZrqbBbe9c6VX6sWgSp6XkPGWFpoa1Ytpy67PH1Kcku/4PZtkCvC06ckm3pcdvbdnGL7sekSSUuQqmahU5YqCXlsuuQT2kII5NXfRGr6nUQghP+3048nwvzJlBoqEDcEWU+Sdj1+tsP/B/FUqcQX07MkDINe0yTjenwxPcvPJ1NabLeIlWSgGynNUOsynGCMNEWShNjDRjbS3Pj6Rmk0U72bXn7AuasfL+JQosLtdYNtEjLFKc5hSRsbixJl5phjSPrv6CvCxJZ+EWziURH+3xWGXKgoVj8Z6p9RV2ZcNEOEnDmk511dKarxZkmZXcsPlank1Bjvq/s2VSF0ZXEt93KMhQp4G3YRzYxjlPN4gQhznVsohgrU7qI8ewzMMIal3jkQVgSvGq8V2p4dxVhCQHv1ArpjM5gBKM6ogTV2GKK9kGjypvH0dxfEqBDgoc5PfxeGVj4dslE/cDE9C5YFlqG+BSwDpKXitcyPYK+34zv2bb/Q7tsNwy9CpQKOo65t2zBUN/GxUcom2Ib6OhiWalC1DRVfKROjKpNdSySu4s3wU/8Qpifg/Kiy64tG4Pa3qHgzvHoMfve3YGxCjZE/9ioceRH+7f++6rPaWmivEkZOSo58A65chq4+uP3HYHDXGrplawNPveqRCEEirD4PiTCA5KlXvZtWaI+UPPoDdR7fpmCk5BcYFSQHIwFeLztkXEnCFOwN2VTqmpb2hIN8slvVas+7k/xsR3RRffY38zkShkHCNKvPaV6Na6HdOhrJQDdamjFGhu9xjhAWCYIUqPA9znEPW9sqthvNVG8kyT63g9e8U1xhjhBR9hk72Wj6bximjCyd3gZKzFUH5dgkSDFlZKl9Yz1q9VGoXEBIgfrT5uBSIWr5PZqlcOk0+pljFkeWsUSATtGNFP5yqXKoE4ouAbeMgcRDUDKDKt5Wqj/fckHg++IrIC9zhMM9FCILDhNCykUNmF55BlHXSCnMMF7Z33kukpvwzABGjYD2or2IWN0ExL63wtzXIbVZWQFWquPE++pqyBvl0qtgBVXN9zxOWcWboVE/cNcAgjA1DZUy2AFIdlbjNcyMq0x2LaGoitdg7LwT79VnEIZUor1URhYqGDvvbOplGH07kKMnYWYEygUIhCExiNGMzWDPgCoXidX8/OWzKl5HQ84pm3bCz/86HH4Kpi4rEX/nu1S8nuOvwONfhdERGBiEB38K9u7zr/lvfwynzkAiDsmkypCfOqPiv/+5lb/e64gW2quAkZOSJ78A0QR09kI+A09+AR74BXlTi+2xWeitS9LFQip+szIYNJh1vGomW5FxJYNB/y/+ftsi7Xr8SGwh+6ZKRBbXt+8JB5dtfLzsuPSa/pubmGFw2dG13NebRkszTjBGCGvRuhOMtVVoN5qpnnGvMOWep58YNp1UKDPlnqeLGB01Wd8seYLSQHgOtixjCkHAMMjir/fdZe3hRQq4TgZDlvCEjWf1sMva41sXEnEqRomumlKRiixdbY6cJ2UNMRtycD0HlUI1cA2LlNWi2rhrYAe6KZWnMAEhPaQwcIVJMLD8wJ56Gm3ANAIdeE4eYS3Uwku3gBHwi1Gj+zacwjfxkpuVMHUKSCeP2X2b/4mTm2DHj8PlH0L+CkS6YPM7VLyemQswXDMZcuhtiwfYeK7KoNciqpncZgh1wsQw4vKIsrqLxpF9g9BT97UN98Clw4hAUJVnuBXk1CRsrxPGHdew4+uos9BLD2PsvRPGL6vn7YgjevsgXec60ihbdyFefEJNo0x0quccPw/3/dSyD13EvQ+ommxQmex8FnIZeM8Hfcu8zDCcf7JqtVgtuzn/JN6WB5YW20sJ61qOvwJ/9IeqkbOvXzV1/tEfwj/9F36x/fxzalBNTZkMshpf5awKoS2E+E/AncBOoBsoABeAvwE+J6W88gYPX/Mc+YYS2ZHq377545FvwOCuaz9uvbMxBdnCfCZbkSuq+M3KQ51BPncpD3gkTEHGlaQdyUd7/CLhvckwfzLpLwnJuB4f7myuRrLPUuUiiRqxnfM8+pocH30z0qphNI2WZqQpkqhzMVHrmnMxaZSNJLmbrb7SltsZXFTaMupdIECAQFXgBgiCVPFaoR10PbKOv6Y654wSrxv00m2kuMO6g7PWCFnyxImwnUG6Df8vjB57GxdKLwFqOqNDGUeWGAj4BfkecxcvUEB6OajOcrSMGHvM9v5S7uy4j6nJr+EIiScMDOlhS0Fnx30rvlajDZiB1H6K40/joTLZ0i2AWyDQ7S/LMOODeF0HcC49DeU0BJJY/Ycw44ubK0luWlpY1zJzAU78HQSqftvlOXW+5yf8YjvWC5kxMIQqqZGuymgnNi66pDf8HJx+EgozEO6AWx7AqCsvEXQjTjyqmirDMSjkME68CD3+aZ6GGUEGoqp0RzqqZj4QRZh1riP771c12aAy2cXqCPa76kawp8chEoV4EGwHQkF13qwvd3YE9t0Bly+pEeexOGzfpeK1XDy1fGZ52268ffvhr/47TE9DZyf89Mcx6uuzx1+E0Vl48fswOa3GQN+xx+d0cpWxs8uPan/8q0pkJ6s/p/PHx7/qF9qSxW/q1L7hs4pZFUIb+JfAi8A3gAkgCtwN/DbwSSHE3VLKJm/5Vj9XLqtMdi3hmIqvVxoZrf6utxh86RkXkMRCSmRnivC+O29e15F9sQC/3I/PdeSjPYtdR/aEA3xyQ5yvpQtcqjj02xYf7oyyJxy4xpXfmHdHYnwxrd5KiBkGOc8j43l8IN7eet/1QiuH0TRampGslpUsXre4xrjRmu9G2Uhy2cfPkSNSN27dJsAc/rKGiOcwI10cmWM+swwmEc/vEQ5KbHfzxnfiCWsDm7mNicrrFGWWkIgzENjjswCcv9bbuI2z5hsL91aTSKp608zsc7hOBtPqIJE6eDW+ElJmF9sKvUxnX8CrpAnYSTrjbyMV9NeJ29EB6D3kdx3pPrjIdcTNjeLMvoJIDIG5E9wCzuwrGNFezFgTkyaHX1AiO1D9Ppg/Dr/gF9q3vg/ve/8NUcoDLmAirSDGre/zXc4bfg5e+jLCDkEoBeU55EtfVt81NWLbGD6F7NqBdDNqEE04hoj1I4ZPwY57Fi7olBGb74DpESjNQTAKnYNK5NfSvwPvrT8KP3wUxibVVMe3PoxR7zpi2HDuJSXwgxGolOD8Udha945Ao6THoW8T9G9ZiEnPL9wvnoLHv6SmQXb1wlxGnT/4MZ/Y9l76FjzxPxEbN8CObZDLIp/4n3h9Qxi31TQwnnoVnngeEYtAdwfM5ZFf+y68pwS31FjtjZ2F731FDQVKVhtEv/cVZVtYK7ZHR1Qmu5Z4QsVredtBePIJKJcX6tsDAXjgPc197q4jq0VoJ6SUi9IsQoh/B/wb4DeAf3bdd3Wd6OpT5SKRGs1SyKn4eqTR0eo7+0w+dp+q1R6bVZns991pLFmf/dq4yzdOuVxKS/qTgh/babK7d31mW/fFAouE9VLsCQeaFtb17AwG+flkyuc68oF4YlXVZ5938/zAnWFKlukWAe42O9hSn3m6QbRyHHqjpRl72Mj36tYVcbgDf9ap0ZpvgClvhnMsCM+tDNJtLDa2v+LNcF4OkyNPjAhbxBBddeuixMjIWQqyQIUyNgHChEkIv5CtyBwBKakIOS+xsKWkssSgl0ZJWBsWCeulaES4t4NEcn9TwrqeUuEyzByl20whAhuRXhFv5iglM7WEFeDAsmPZnSsvIazwQolJ9ehceWmR0HZzozhXXkKWZhDBDqyu2xaL8fyUymT7NhJR8Rq8DZuRA9th8iKUSxAIIjdswtuw2W/Jd/pJhGki3SJUsmDY6vz0k/6mydlxRLIXIWo+B9JbPOJ8fkLj5gMLsUIW4v46fS8zgiydRN52x9WSGlE6iZfZ7HMxuTqCfL4pff641GjyRrLQyV41aCdU8zukNKfi8xx+SonsaFVgzB8PP+W/3hNfRsTiCxMk4wmVMH7iy1ArtF96/f9n782jJKvuO8/Pfe/FvuW+VtYKVVRBsVUBYhECIzASQqKRJdutkSzJLXmsdrd9xm6fnjNnjruPz/SMp6fbstWtmYOOZdmyJDfSkQABQiAkJIoqlgJUVEHtS1ZW7nvsEW+588eNzIwXkUVFBZVUFtwPp87N+HHjxZbL9/3e7/f9ISIB1dwIEIsq55lfn4D7qp7bwefh4CnY9RrMzkNrCm67Hlqf9wvt/jVw4pjKyqfTyiO7tw821pykfOhOePxxJbKhsgoVX+WsCqG9nMiu8DBKaF9+lv//nuC6u1VNNixexSKXhls/eXGf10pxPqPVN/ea52x8PDTu8s2XHVJh6ElCuij55ssOX7yRpsX2gVmHHw/ZDOUlA1HB/QOBVTtq/d1icyi0qoR1NafcPI85Y8QwaSdATjo85ozxcXpWhdi+kMNoGi3N6CHJFXSwl1NkKRInzE7W19VnN1rzPeXNsk8eVvZ6RChRZh+Hucbb4hPb094s++VBgjJIrLJvPwfZ7m31ie0ELRyXh/Hw8JAY5MmSpr9mHHlZlgkIi6hY+hxtypRlc2PEAebcaZ/NXr+x/txuIJcghfQBDDOCURlQIyprIX2gTmg72WGcmX3I0gwi1IbVdg1WjTCWpVnKVoi0O7p4cpQUKYIlf9Okmx2mPPwswopAsAXp5CkPP0uw/y6/2I52qHKRYNWVDTuv4tWPO/EasvcyWLskeKWdr3MJITuJpOLAYQRAuki3BNmaWu5GR5xv/aDKwsJSSUgxq4bH1D6/BlxMcMrqNcwMqWOFY7B2U32GvMEsNFtug5e+r74OxZTILmbhmo8s7ZkaVceoJhpX8WomxsCWsO8gFIoQCcOGtVCo8T7PCghbyhd9wXkmbKl4Nb98Dn6yS5XHtCQgn4fHnlVZ/Ds/X/UatsLD31VlL4kEzM+rbPZH7vcf780DcOsHYXQY5tOQSkJvv4o/uLrF0mq/Br/wTr9xUZ/FCrNmi+CeL6iM9sy4Wu/5wnvXdeRCj1Z/5ohLKgzJsMAQgmRYkAqreDMcmHX42sESc2VJfwTmypKvHSxxYLb+UrVmdfCiO0sMk7iwMIQgLiximLzoNvlNdYG50OPQJQIHAxsDB6PKynGJceY5zhi9JLiKPnpJcJwxxmtGnM9TIFx2pHX7AAAgAElEQVSTc1mu5vskZwgRICSCCCEIiSAhApzEf4n3lBwiKIO+fUEZ5FRN9d84o0gMBAZGZZUYjOMXAKYIARJXOkipVpCVuJ8z5WO8kHuUn+e+ywu5RzlTPla3Z86d5oizn7IsESFGWZY44uxnzr20WoHKhVHmx55hZuhh5seeoVyorzV07TmE4f8eE0YY1/Z3lDvZYUqDj2PPvoWTGcKefYvS4OM4Wb+1WykQZtY+g4tDgAAuDrP2GUoB/2NUZ76FEGq1IjjTv/Y/wYEblNAu50DKpa8HbvDvK86oTHE1y7mEmIbKDi9YIwpT3a5tAm9wxDk9m+Dym2H4KPz6WbVefnN9nXFxRlkeTh+C8dfV6pbrn19LN1gBWHcNbLlFrVagXuBXZ6GFodZYQsWr6d4IN31KZbTTk2q96VP+CZcdvZCvufqTz9Zb+TnA/oNQtpU4Ltvqds2fPWPtFrC6K0OICmq1ulW8mpfehGAAolH1GqJRdfulGqeYQ2/BjhuVk0g2q9YdN6p4NadPw6aNcPuH4P771bppo4qvclZVik4I8WdAHEihmiNvQ4ns/+tiPq93gzVbxPum8fFCj1YfmZf01JQKx0Mq3gw/HrJpCQpaKjZ6LcGl+Ps9q71amZJl2vG7EkQxmXoHWc8hL8urcoZpWaJdhNgh2hgw4ue+4zJcyGE0o6TZxSBhrMU67F0Mchvr6K3KVh9hlDABwqhv4IX1CKN0NzE4JkOeeE0sSKDO/SNLHtsrMMo0Dg4WFinasWvGfk/JSUIigiWWfqYc6TAlJ337WswOMliUZQ6HMhZBIiJFwvSXdJwpH+Ot8m4cJFJCUczxVnk3AGuCS5ehh71TCM+m7M5Q8EqYRgjDTDDsnbpkstrlwijZyV8hzAiG1YLnFshO/op45+0EqzLVZkD9P1H13kuviFlj5Vceex6vOKEy0FYEpINXnKA89jzWZb+zuG+8NUVs5BRCuHimScB1ka7LeGvKdz1FlmYhWFNyY0ZUvJrWdarxsdp1ZNMd9a4j4TaV6Q5U/dFwCipe/bgtPYjx4ypDbFrgOuC5yJaapslGR5yPHodDL0DPZbD+GpWFPvQCdAxAb/VeE2YPq2mZZlhle2ePQGvNH/WtH4Qf/lcYHoRyEYJh6F8HD/4v/n2NZqFBiera0fHV7LxLZcMXjpHPKseTD/0L/75MqTIMRvjXTMm/766PIv7x/4VEr8pUZzOQmYO7/Fl+ciUIVry9DVM5xwQtFa/mzBCs3wAbq95Pz1PxatauhSNH4MywynqnUrCmHzafw9VkFbDaMtp/BvwF8Ccokf0UcI+UNb95KwghviyE2CuE2Ds5uewWzSrkju0G6QJk8hJPSjJ5Sbqg4s3QlxJka352syUVb4ahvCRZ4ySVDKi4ZnXSIYLk8V/ByOPSIZqrUR/ysjzljZCTDm0EyUmHp7wRhrzm6oL7RYI7GSBKgDlKRAlwJ+ffCAnwJhOLtn0CQYQAYSzexD8BcZ48oZqTjxAB5muE8RV0U8KmgI1EUsCmhM0V+P/QJ4hSxn8JvoxNomY8uuMWmWQUFxcDExeXSUZx3PoymeVMBGrpN9ZjCJOE2UGXuZ6E2YEhTPqN9b59x+xfY0sHIQxMw0IIA1s6HLP9WdS0M0G5PIknHQyhXEzK5UnSjv/9W80U5g8okW1GEEKo1YxQmD/g2xdJXoXnFvDcAlLKxa8jSb9HsZc5pQSiEVB1w0YAzLCKVzEfDZLtvRrPCmLaOTxL3Z6P+n/ORKhVZTurcQsqXkvrOrj6t+AD/7Naa0U2ILquRzh5pJ1HSom08wgnj+iqGfTSvQ2vd6vy27aLYAXV7e5tdcf0okG8Na14l3WpNbrM74o3f6WyxJGEyspGEur2m7/yP7/KP/WFqEwTXeb7efgUDJ1SJwALJwJDp1S8mo5emBqGwX1weLdap5YZKNMIazerkpNYEqbH1VpbggIYhSKs3aCaDMu2WtduUPFqNm+Dz/2hGvk+PqLWz/2hilfTv1bV8BsWOJVyHiuq4tWsGVC12dWk0ypezbYrYc+LavhNIqHWPS+q+CpnVaXnpJQ9AEKIbuAWVCb7dSHEx6SUry2z/yHgIYCdO3dqFVTFyZMeL+6ByUno7IQP3AwbNqyO86rL+00+cwc+15H7b2p+tPrdm02++bK6nBwPKZE9X4RPXt3c8QaigrmyXMxkA6RtFdesTj5gtvKYMwZSZbLzuORwucs8d8PbcrwqZ4hiEatkW2NYIFXcP7y6cS7UOPQ5CnXOIWEs5mpKPVJEKVJezGQDlLBJ1QjjHlJspoPXqmq5r2d9Xc33Btawj8MgVSa7vCjI/dm0AlnU0HKjMrrcALxKfIk2uphiFCQYmHi4ONh01GT5Gx1dnpcZwMKo5I8EBh5WJb6E4RZwBRiVz1Zg4QoHo1YYrgDz7hRjzkkKMkNEJOixNpAyz98f27VnMayaATNGGNf2Z4xDkV7ouJ1C+gCuPYcZaCHWesM5R7efjaiIk48FcOJLArcsS0Rrynis9mspD1fKHMwIuAWkUyDQfQvNYCTX4K27R9U8V4akiP7b/I2GKEFOYQqvpW+pKXEZQe6lh2Df/4CJcTX4JByGrqN41/y23wt6blw5ZlQTjtU3TUoH0bIZmR9TmXYrgmgZUJaA1fzy+xBrUT7VC+QzKr6jamb6hi2w4I8djqk902Nw44fP961TNOJn3d2LSM/Dpiqby/Q8dC9zlWfztnphXcvnvgR/9R9UyUtbl8p8FzIqXs19n4Cv/436OplUInt+Dj7ze/59+w/ATR+AkUpGu6VFiez9B+CTv/X2z+Uis6qE9gJSynHgR0KI14AjwD8CV739vd4fnD7msfc5mBqDjh7YeQesvcwvoE+e9Hj0EUksBu3tquzp0UfgEw94q0psNyusa7mi2+SLN+JzHfnk1c27jtw/EOBrB1WKPBlQInuuLPnspgvj4KE5P046eXa780x4Nl1GgFvMFBssv1hcb0b5OD0+15G7zM6mGyGnZYk2/J93FJNpWTrLPd49WogwTZYsRUo4hLCIE6a95gRgM728jKpRDhGghE0Rm6vxZwwnmOcE4/SQIEQbJWxOME4HCbqqxHaH0co13haf68gVbKxzHXEoEydGmTIuHiYGcWI4+Mt4NltXMG/PkSODi4OJRYwEm60a314aHF0uTIT051sEVfW6FUIyQIkirnQWBb5Xia8k8+4UJ8r7CIgQYeLYssSJ8j42Bq85b7FtBlrPUhJSnzEORXrPKayNxHrc9DGkECAsJRC9EkbS7/zQqC+3Ge8n2H+Xz3Uk0H1LcxaAC88xucbfWHiWPR4diH1PLDU7XnlfnSDn8NNw8hgiFFMOBHYZefIYhJ+GG35/aV+jTZOV0hbRVvU+2HkI1Fhczk1AquazjsRUvJpG/bEvJA/+Hvz1f4DxMVUvL4Syz/v8v23ueB+pWP394zfUMbt74I/+dCm+wLar4Ct/DE88qspF1gwokV07GfL0aehoAWcO4lL5j3e06Brtd4qUclAI8RZwrRCiQ0o5dc47vYc5fczjye+oKz/tXarM6snvwEc/4/nE9ot7IBaDeFxlYONxAMmLe2DDhovz3FeaK7ovnJ3fVa0W/2YrPteRz24K6vrsi8BJJ88P7UniwqRDWGSlyw/tSR6kc1mxfaEcRtpFiJx0VCa7Qh6X9mUa8N5teoiynzMEMQlhUcQmTZEr6fLt6ybFjVzGEUaZJ0+KKFezzlefDXCEEcJYy9Ryj/iENiix3cHbN1NERJSyLBOvmj5YlmUiov6zMUWIEBKvUmZi0vz7mzD6SMtBXEmlsdJDCoek4T+xSJoduI5L3p2hLG1MESBmtJFcRuxmnEkmy8coehnCRoLO4GUkGrAFXI4x5yQBEVqcQBmovNYx5+R5C+1I6iqyk79SA2aMMNIrIt0CkbYbznnf5Qj2fJBSaRbp5JBuEWFYiFA7wR5/c2CL2c6WTAf5yRegOAfhFqKdt5JqqT8JMuP970hYN8WxFzD2PQ6BCLS0QTkP+x5XFoKX3bq079QBJbKDle+3YEhZ2Z06ANVv4ZW3w65/Vl8vuo5kYGe1jx3Qfb2algiLmXScPKypGTLU0qWy09UZ7UJOxatpxB/7fHj5GfjJd5cydB/5l3Dj3f49fevVePnTJ6FUglAIOnv8z+F8WbcJdtyyNFp93abl90mpmlYX/sllChRSMdj3knIbicSUzeP+V+Cam+r3rjIuBeWw4GT+vp/1vPe5peZjWFr3PgdrqxIPk5Mqk11NNKrimsa4qtXSwnoVsNudJy5M4pWsZBxzMV4rtC8kO0QbT8kRXylKHofbRde577zCTJKlXUYZJ88sJUJYdBNlUtTXj3eTqhPWtaQpUJYlTjKMjUsAk05aKYvmfuVuEVt4Xb4GEgJY2Dg42GwXfl/oQTlE3EjSLpZEZkmWGZRDtOFvcmuk5OLK0PW8WirgeHNI6SCERcDo4sqQv2wgarQz6h4kSBBTxHClTdGdIRr0XwrPOJMcL+ymJIs40sHyZkm7E2yK3NKU2C7IDOGaqw4WQQo1pS2NEIz0Eu+8ncL8AVx7FjPQSqTtBl8j5Plgxfth3cfPae/nZc4QPfMyUasFIr1KUJ55Gc9swVhuOmQjzAzC0MuQm4RYJwzcCG31ddoNceBJJbJDlfd5YT3wpF9olx0lJKsxBZRqSj16N8Ftv6NqsheaJnfeV9MICUZyAG/9PWpqYqW0hTW31Y8k/9Cn4JGvqa8jMSWySzn4yBf9+xrxx26Ul5+Bf/h/VAa4vQuy8+o2+MX2D78NxQJs3AyBINhlNYL9h9+GP/9P5/+4jY5Wf3M//OVfKIFSLMJbb8Hrr8H//h/hyqrfGQNJeMWBsquUa9lVn+PA6h+adtGVhBBiMzAupZyviRvAXwJdwG4p5erw6bqITI2pn5NqojEVr6azU5WLxKt+p+fzKq7RXEpMeDYdwv9rKorBhGef5R4XhgEjzr30+VxHbhddTbuOXEhGZYYZysQIkSKCg8cMZSyZWb6b8ByUZZkzTGIiMDFwcDnDJGtkV1PHW29tAAcOy8MUZJ6IiLJdbFfxKnIyR7SmXjxIgJzM+WKNlly0GW1sNbcy6L1JWWYJijjrzK20GX7RnvemSVm9lLwsjixhGSFiRjt5z2/vd6b0BlkvjSWCBEQID5esl+ZM6Q22WndxvkREAluWFjPZoMpsIk00xIIS280K6+Ww4v11wroWb+J1sGJ1ftHexOvNCe2ZQbx9/wNhZ1S5SnYUOXMS45rfrhfb06fg1IuQnYJ4B6z/ALSv9+/JTUO05opLMKri1XRthsmDEBJLXtDlPHRtpY7eTXXCejmM5ED9CPJaFuqwf/l9VS7S0qVE9o6a76dG/LEb5SffVUIhXjnhXlh/8l2/0D7wmsreVWX5iSVUvBkaHa3+dw/B8eOQTCgnkVJR3f67h+C/fm1pX9yEf3EvvLQPpmagow1+42YVX+VcdKENfBT4P4UQu4CTwDTQDXwI2AiMAV86+93fP3T0qHKRWHUfRU7Fq/nAzaomGyTRqBLZuRx8uOZKkeads3/O4dFhm6GCx0DE4BP9Aba3rIYfq4vHkVLJN0Hyw9F404NuuowAWekuZrIB8nh0GStbTwtKbDfb+LiS5HAxgUDlPQlUnD1yy1z0G2feVzqymd66DHcWJWyNyn8ALu5ivJopb47jnHsk+XprA+t5+zq1mIhRkmVCVbXwZWxiwj+WvdGSi4wzyZx9gnZasMwuHFlmzj5Bykj5MtBFL0PESBGtsgeUUlL0/JnleXcckwBm5UTPxEJKybxbf/l+0D7BUe8QJZkjJGJcblzBuoC/SbTH2sCJ8j5AZbIdytiyxECgvia9UBojkzuE7cwTsFIkYlcQCfXU7XvXKU5DyH/iovysm/Mg9449gyhOKNs+IwqejShO4B17BuPGf7W0cfoU7H9MDbaJtSvhuf8x2P5xv9iOtUMpu5TJBiWgY/5LvMbV9+HtmkQ4ORB5kCYy1IFxdU1JyEqw4656YV3Lgj/24V2qXCTVrUT2cjZ+Jw/DnqdhcgQ6++Dme1Qz5QLLZehiifoMnaC+ZEPKpk62gcZHq7/yknIRCVf6DcIRkJV4NR29EEnD73xsKZZLL026XMWshs64nwF/B3QCDwL/DvgkMAP8R+BKKeVbZ7/7+4edd6jvq1zFYz+XUbd33uHft2GDwSceEMTjgulptX7iAbFqGiHfK+yfc/jq0RJztkd/RDBne3z1aIn9c+/fwTZHSiW+NT9H2vXoNk3Srse35uc4UmquifAWM0VWumSliyfl4te3mG9fDvFexqwIUwcXicSpCGyzpnlznHle5hhFyiSJUKTMyxyrG1hTxiFFFAMDBw8DgxRRyjWTKqa8OV7nMCXKi5MhX+cwU55/+EmjrBMD2JQoyTJSSkpS+ZisE/6sYEFmsGpe23IlFxP2cSxCBIwQQggCRgiLEBP2cd++sJHAqWlqdWSJsOHPLHssbz/o1cQG7RO84e7FlmWCRLBlmTfcvQzaJ3z7UmYHG4PXEBAhimQJiNCyjZCF0hjT83tw3QKWmcR1C0zP76FQqhFGF4NwuyoXqcYpqHgzzBxXQn3hxNkIqNsz/s+MUy8qkR2KK5u9UFzdPvWif99VHwW7oMS29NRqF1S8mu6NGNs+jLBBTM8hbDC2ffjt/ajfbbo3wu2fg/v/nVrPJrIf+aYq8WjvUesj31TxBRYydNXkMvUZuiuvV5m7UlEJ7FJR3b6yxkKxUfrXQKbGti+TVvFqzuYXVxvfedeS6JHekhjaef5Xl95tLnrqTUp5APiji/08LgXWXmbw0c/4XUc+dH+96wgosf1ebXxcjqPDrs8u8I7tzdsFNsqjwzatAWgJqvdfDbjxeHTYfk9mtQ8VSjyVKTBiu/QFTO5NRLgi4s9U/yyfJWkYJE313i+sP8tnm8pqb7CiPEinz3XkHqttReuzVzu9pJjGoECREjYhAiSI0V5jHdjowJo4YUqUfbZ/Skz7LQSPL0yGrBxnYT3OGTqoz2qfizajjSvZxqAcIidzxESMzWJTXalHRCSYl2nSlBZfb5IQKeHPZBW9DEiDtDONjSrRiIkUrud3O+kMXsbp4qvggSVCOLKEQ4m+oN/lIGH1kraHEFIgMJG4uJRJWv4TgaPeISyCBCue7UGCIFV8XY31YcrsOGfjYyZ3COE4eOUzeG4RzDAi2EImd+iiZ7WNrutwB59WGmix6S+H0X/rue56lgMKcEpQTlcGm1hghJTHdDXZqbqsNMGoilezUId94ElVLhJrhx2f9tdnA0ycgOMvqUE0666GUl7dbl8DXTWCdvQ4vFU12GbbBxsqJXlX2PO0yujGKz8LC+uep5ey2h/5l0s12bGEEqf5HHzqD/3HevCzMDkKs9NqTzAEA+tVvBk++nFVkw0qk52p2Pb97uf8+264EXY9r1xOQiHViJnJwG01UzoX/MD3PqsG93T0qqE757ItXAW899TAe5y1lxm+xkeNEtnfec4jGYGuFjV18jvPeXzmDlZUbA8VVCa7mmRAMFSozXld+hwqlPjGTIakYdBjGcy7Ht+YyfClNnxie9Rx6Tb973ncMBh1mu9l3mBF39fCuhY1ZbJAGy2+KZNX4xdw8+RJ1kxyXG5gzTVsZBdvAmUCBLCxKeNyY41IbHQy5PnQZrTVNT7WEjQ7mLBPYhKoWMqVmJBZOi3/8xOYTLsjWEYQiyAuDjPuKO2W//J1wupkbXiHz02kL3hVXYPj2tB2Dniz5Lx53Io7SchMsTbkb+osyRzBmvfFwqIk60tvGqFUGEEUp5TNnhFS9cOFUUpyZfsSGsFIrIF196ha7eI0hNsx+m9tvhEy1Ys8sw9hBpW4tm2km4M11/j3xTtUuUhtSUh8mZOWy26tF9a1HHkBwnH1D5bWIy/4hfbocXjhYfX/U53K6u+Fh+HWT68OsT05ojLZ1UTjKr7AQh12tevIp/6w3nXksq3wB38Ov3wKxoehux8+dK+KN8O2q1Tj45OPLbmO/O7n6m37fv8P4NgRNbinmIdwVAn83/+D+mM24ge+CtFC+xLjzGHJ68/A9Ci098J1d6vx7SvN8SGP51+TjE9LutsFH7xesGlgdZSiPLdfiexEZaCMGu0ueW6/Vye0D066PHXcYTgj6U8I7t1ksbWzycE2EYM521sc1Q6QtiUDkdXxvlxInsoUSBoGKVO9tpQpFuPVQrvXUuUiySqxnfU8eq3V37ByqdAvEtwpB3iDKWYp0kqYD9BbN2Wy0YE1W8QASNjHicWBNTeyUcWrSBClRH1Nde1kyAvNiFkkSD+GO48nSwRECNPqYsQs+qrAXWGoaXyVS84Lk/lcUf/zmLA6z+kc4gqDciCB9FRRjcSibMTqjhcSsUrZyNL74uAQqqk1B8jaE0yXjlHy0oSMJO2hy4gH/PWzhp3DA4yFcgoRwHMdDLs54d4oc+60byhQv7F+We9yI7GmeWFdSzSFCEcgl12clihicWS0pjRs/QdUTTaoTHY5D+UcbGmybCA9AYkakR6Kqng1bz2vRPaCj/bC+tbzq0Nod/apcpF41dWdfFbFq7nx7nphvRyXbW1MWB9+C559AkbOQN8auOs+2LLMAJttV9UL61qSIeUcMiLAlcr9ZSCp4u8RtNC+hDhzWPL036srRW3dkE/D038P93xBrqjYPj7k8fBPPeJR6GyDTE7y8E8ln/5NVoXYHptVmexqYhEVr+bgpMtDr5dJhQS9cZgvSR56vcyXrwvWie03px0eH3QYzkn6Y4KPrbO4st3/4/KJ/gBfPVoCPJIBQdqWzNrwe+tXvlHv3WbEdumx/J91whCM2P5M9Yejcb41r2p244ZB1vNIex4PJlZ/w8qlRCNTJhsdWANKbG/h7R0TNrGGPewnxywOLhYmMYLcTPN1rY00V6bJkzBbEeZS5lsiSddk0l3DI2n1U/RmlZuICJE0O3GN5q4wDcohImYLIWtJCC9nP3i5cQVvuHtBqky2g4NDmW3G1b7jZe0JRgp7MUWYoJHAkUVGCnvpY6dPbFtGiJJbWLQolNJBVuLNUC6M+m0AU1fVuZXMudMccfYTEEEixCjLEkec/Wxme53YbuR4jWLkZpBeQGUxkYAAL4CRm/FvbF8PAzsqJSEzEGtTdde1riONkuxSLh7hqgx5Ka/i1TQ6GRJg/IS/eXHLbStf833zPaomG1QmO59Vtct3r+CkxMNvwT98XY1e7+mD9Jy6/XtfWV5sn4sffRuODcGGtRCNQL6gbv/o2/DHf3nhn/9FQAvtS4jXn1EiO1rRLAvr68/Ami1nv9875fnXJPEoJGKVjHEMQPL8a5JN53AzejfoaVXlIomqxFquoOLVPHXcIRUSpELqdaRCS/Fqof3mtMPXD9ikgtAXVYL86wdsvnIVPrG9vcXiTy7H5zrye+vfm64jfQGT03aJGWxyeMQwaCPA2oD/j//mUIjPp1p8riMPJpJNu45cCgx5WZ8N4A7RtipsALtJsYluXmWQDEUShNmxzMCaRvEEOFKdbC2ccjkYeMuc4zcioBeaK0MEfM2V13lbfHuTZ8nMJ2sy6RGRwDZLpKpqqG1ZItTkkKGczBGyHezyafAKYEQwg53kAv4SjnWBjZSceU47+/G8IgEjzCZre53ryHTpGKYIYxmq9t0SYfBUvFpoh0K9CBHAdrN4XgnDCBEOthAMnv+o9nJhlOzkrxBmBMNqwXMLZCd/Rbzzdp84HvZOERBBgpX3Klhxdhn2TvmEdqPHa5h8BmEFIVT1y7qUU0Ndqpk6BadeUcK6d5vKaJ96BVL90LH+/B93863w/D/A+Dy4NpgBCKfg6pqx341Ohhw/oez4wnFIdioP7Je+r5xDmhXbLz8DT38PZibUCPN7frc+K71hCzzwRb/ryN2/5XcdudA8+4S6bDR8VAn7aOU1P/tEc0J71241ej1W+XleWHfthj++cE/7YvLeUwTvYaZHVSa7mkhcxVeS8WlJZ00ZZSyq4quBO7YbfOc5D5DEIkpkpwtw/03+DOxwRtJbo38SQRWv5vFBh1QQWiqCvCUEIHl80KnLam9vsVatsD5YKPFUusCw7dIfMLk3GWFrpDnRsTVs8NNikagQxIQgIz3GZZHfTETq9m4Ohd7TwrqaIS/LU94IUSzaCJKTDk/JEe6l76KL7THmOcIUHbSwBosiDkeYoo0EPU2I7aOMkhRxwlXZ3CJljjJKV1UzZKMCutHmysvpZW9NZr6EzfaazHyPtYHDpZcpUMCVLqYwiRBhS+jG836tAGHHxS6exBAhMMIgbdziScJio+8vZ8aZpFg6SbcNSAsEFAMnyQTW+MpTSl6aYI2ziSlClDy/M0MkeRXO1K+IBLsXpz56boFI0n8JvpwfoViVWQ6nriIY9ZcMFOYPKFFcGdUuzAheJV4tjPMySwR/qUuAIHnpH4LU6PEaxgiDyCmxu+BnLSrxak7uUX7StYNoTu5pTmgbBgRNWDCfEajbRs0V2m0fVDXZUDUZMgs7alxMDu+q1HxXPt+F9fCu5oT2y8/AP/4VeJUrhiMn1W1YXmyfS1hfyGz70Tdh+ASkM2pgTNCC5JhqsKxl11Pw/W/ByDD09cOnPg+33evfU7AhXvN3xDIg25xT1Wrk4l/31zRMey8Uaoa/FbIqvpJ0twtyNf1OubyKrwYu7zf5zB0GiShMzKnM9mfuqHcd6U8IMn4DAjJlFa9mOCdJ+t3ESAZV/FLhYKHEQ1MZ5l2P3krz4kNTGQ4WmvvlddKzuTYcJGGY5CQkDJNrw0FOrvDgmNXOq3KGKBYxYSGEICYsoli8KmfOfecV5hDjhAgQIYBAECFAiACHaG6Mc5o8Ni6nmOYwY5xiGhu3roSjWkALBCGChAhwHL9/bqZyvCGmOMYoQ0xh49Y1V3aJFnZyGWGCZCgQJshOLqNL+DPkEoEtVGW2UYbr1XAAACAASURBVFltIZBNGgEny0U8BF6l9tsTBh6CZLno2zeafx1ZmkJIiRAhhJTI0hSj+dd9+0JGErfGVtCVJUKGv6wqFOkl0XE7hhnBc+YxzAiJjtsJVQnZcn6E7OQv8dxCVWb5l5TzI/7j27OIGtEqjDCu7a+ri4o4XnGS0OQBwiMvE5o8gFecJCr8J4uNHq9hWtdDvF9llN2CWuP9Kl5NZhIpXeT0YeT4r9UqXcgsM+546hS88j34+d+qdepU/Z6Te6C1Fy6/CbbeptbWXhWvpneTanyMJGB+Uq3LNULOj6sTgWpCseZHpj/2TWWvZ1oQqLiwlIoqfr4sZNuLGX+2ffzEue+7HLPjMDquRqWHQ2odHVfxanY9BX/9l8pppKdXrX/9lypezfYdymWkXFK2guWK68j2HfWPffhN+G9/Bf/bv1Xr4Tebew3vMqszFadZluvuVjXZoDLZhUo51q2fXNnH/eD1god/KgFJLKpEdjYPH/3g6hDaoMT2uRxG7t1k8dDrSmkngkpkz5ckv73NX1PdHxPMl2Qlk61Il1X8UuGpdIGUuUzzYrrQVFZ71HUYCARYV9X46UnJqPv+9QwHmJYl2mo8nqOYTMuLn42Zp0CyxqIvjMU8hbPc4+0xMBhkuiKiLRxcTjPDOvw1vI26kxgYDDFJiCDByvHOMMUA9U2KXaLFlzVfjmHvFFEzRVAslWGUZamu/KFRDGnTYfQwLzLYskxABGkzejBq3D8KpWFMEURUNS+anqRQGvbtaw9dxkhhL3gqk+3KEq4s0h2ubxYLRXp9wrqW4vwBPOlhF0eRXhFhhDGtBMX5A76sthloxXMLCHPp85BeETPgr6vrKUWZnjmCZ4QqAr9AeOYI7R3roerXY6PHA/DSQ/6R5N3X148kH7gR3nocEgNqaI2dV6UjA/6rEF4whJg6pBohrbDKfE8fxuu4wp8tnDoF+x5RIjdeGV6z7xG45gF/5jszqf5/NcHo8sK9kcmQF3JkOsDEGWV1t9BUbprq9sSZ+r0nD8HuZ2BiBLr64Ja7YUPVEKQLnW0v5JWXeTXCUPFqvv8tNYimdjLk97/lz2r/T1+Ef/8mvHlcjYAPR2DdBhWv5vCb8PeV2vDuPkjPq9tf+ApsufL8X8e7iM5oX0Ks2SK45wuqNntmXK33fGHlXUc2DRh8+jcNEjHB5Iyq1f70bxqrohHyfNjaafLl64KkQoLRLKRCYtlGyI+ts5gvw1xJ4knJXEkyX1bxS4Vh2yVh+L8vEoZg2G7OZq/XtMhKf1NZVnr01vrdXkROuXm+Vxrhb4un+F5phFNu87ZzjdIuQuRrJjLmcWlvsi74QpIiQrFm6EwRhxT15T6N4GIs5oYXru2ISryaBFEyXppJb4QRb5BJb4SMl65zJ1HH83+PCkTd8QBmvBn2Ob9mt7OLfc6vmfHqrxjkZZZAzUnPcuUPjRIykgQx6BXdrDUG6BXdBDHqMtCmlHg1r8NDYNZM2YsHuuiL7MQSYcpeBkuE6YvsrHMdaYRyYYRyaRwpbYQRQkqbcmmccsGf0Y6krkK6BTy3gJRSrW6BSMov7q3sGZJWD4YVwRY2hhUhafVgZc80dTwvPQSnnlbCOdSq1lNPq3g1betg28fU8Jn8lFq3fax+/Ho0iPQ8cD31zed66na05tJjdYnJwmCbUKw+U53oVHXevjc1r+LNsOU2VVJSrEyTK2bU7S23NXe8QAC8miZez1Pxak4egh9WBtZ0VAbW/PCbKr7A+WTbx47DL74FP/ortY4dr99jhWBNn3oupZJa1/SpeDUjw8o/u5pEUsV9r6vyfFq71OX51i51u7aH+ZnHlchOtqgSn2SLuv3M4/XPcZWxev5KahpizRaxoo2PZ2PTgLEqGh/fKVs7zXPa+V3ZbvGVq/C5jnxmc73ryGqmP2Ay73qLmWyAjCfpDzRns/cbkRjfzlTcRIRBVnpkPI8HVsn421NunkfKE8SEQTsBstLhkfIEDwS7WG+unP3cDtHGU3IEpMpk53HJ43C7OH/xdKG5gm5e5CSgMtlFHErYXEdz1my2gD7ZwRxZytgECdBBCrvmPL/DS3BKHsMiQACLEjY5JtnorfGldhwhaXMTzDFNCRuTAG2045h+gTrjzfCG/SqOzONJl7yYZlZMcnVgh2+4TVTEKcvSYiMfgE25rvyhUdpCmxnJv+zLQDuySFfI7yYSD/QxZ59GSIEhTDzp4lEiGVhbd8x4oKspYV2L6xXBtfHc4qI7CZi4pr+sJRjpJd55u98lpO2Gunpq154lbLUSEUsfphSyriSk0eMx/hpYUZWlhqV1/DWoyWp7lomMhcCMQjiEsMz6U60AsO4a5OSgqpMOx6Dvcl+2HWg8U73hZpXpXvj/5Uom/YoGLPCWo3sjbPoAvPIIpKcg2QE3PNB8HfTWHfDrF9TXVhCcsiqpuLbGH3z3MxBP1Q+s2f3MUla70Wz72HHYXeMZvvthuOXT0FOV0d92Hbz0HJTKYLsgypDNwk13+I/X16/KRVJVV6IyaRWv5tEfwfr1cO11S7G5WRXfXuVZPzqsMtnVxJMqvsq5dJSDRvMucmX7pSWsa7k3GeGhKdW5nzAEGU8y73r8dmu9t28jbA6F+Swt/LyQY9R16DUtHogl2RwKn/vO7wJ7nDliwiAu1GcWxwIc9jhzKyq0B4w499Lncx25XXRd9EZIgB5SfIANHGKceQqkiHAda5pqhASVIS8Im36WhGIBm1SN2kmLOVplglnSFMgRIEArSdJiDqoaGIMeTDFLlBAGUTxccszS4fVC1fngMecgRW8OCzWIxpMuRTnHMecgNwaXhEe/sZ4jzn6AymCbMrYss8FsLjMRC3bRx43MlI4s+l53ha4mFvQL5a7YddjpNCWvgC1LyvbQaKErdt1ZjnxuCsUxsrmD2M4cAauFeGwrkfDSYBJPCDXYRVggTKTngCziifrymmCk95yNimagFS8/jlGYBScPVhQv0ooZrS99aOR4FGf8TiKgJkkW/VcivPQZ5ODTSCu6lPkefBpv3T0YyaoTwnAbmHnYVDUO3M4vCfgFEp2qXKR2sE1tprpjvSonOblHifBEpxLZzTRWghKph3erYTdrt6uTgcO71aTJnib8tj/xZUjPwOigGuISCsHGrSpezcRI/Sj1aFzFF9hym6rJBpUpLlUaOq/5iP9+B8/iGX7wef9r2HEL/PRJkK7yvC7ZUPZUvJpPfR7vv/wFojQPkQAUbGTRw/hXf+LfNzQE/TXiO5lS8Wp6+1W5SLLqezybVvFVzqWrJDQazVnZGgnx5Q58riO/3Rpbtj67kdHqoMT2ahHWtUzKMu01gi+KyaQsn+UeF44BI84AF19YL0cPqaaFdS2NZshn5Aw58kQJYxLFxSNHHrOmQTSIi4dAVhoXJQJPCoI1pTiz3gQmAczKSZSJhZSSWc8/XKTFbGcz231DVzaYW5qqz14gFuyqE9a1RIPd9Cc/xFzhMGU3TdBM0hLZQjTYXH1uoTjGzPxuZQVopnDdAjPzu2njlkWxLQQQSCA8G6QaXS6NCKLJKsJQoJPS3B6kEUKYYaSdxyjNEkrV1483RLitXgg7BRWvQk68hrSiiKrMtwSYeA2qhXb39aoUBapGv+dhTU1pxvlkqjvWNy+sa2lUpDbKwOXw2X8Pr/18aTrd9b+h4tV0nWVgTVdV5rd7o7IZrHYdueYj9dn2Rj3DDx2EHTfDxKjKUCeS0NWr4vd+YnGbd/WV8Mnbkc/thal56EjBfTvxrr7Sf8ViYABOHofJsaXX0tkDG2ret7s/pmqyQe3JppXw/uRnzvl2Xmy00NZo3qNsjYTO2fjY6Gj11U6nCJKVTiWTrcjj0imCb3MvzfnQaIa8JG0EArOSljYxcXGXGSHu0k8Hs2QpYRMiQCcpqBHaZ+sEWS7eYra/I2HdLNFgd9PCupZs7iCmCGNWGg5NMwKuii8IbcOM4JoFJVCFBdJBevai7d75IrLDWNF1uG4a6arjmmYPIjsM7dee/wEbFcYNZr6N5ADe+nv8zZVrbqtvrrzQmepGOZ/BNo0ycHm9sK7llrtVTTYsDazJzsM9NQ4J3RvPXcbSqGf4yBlYux7WVx3P81S8mvHX4Prr4aaqz9zO15cPXbMdfvjPqnEykVTlJmeG4IEH/MfbcqVqfHzmcVUu0tuvRPYqb4QELbQ1mvc1jY5WX+3cbLXwSHkCcBZrpXPS4+7A2ztVaM6PRjLkQQKUKeHiYmDgoTzugzVXHGLEKFH2uYyoEe9+sdgmOpiQYwgEBiYeLg42XaLmkvl7BNuZwzL977FhhLGducXbwbAaauM6aTyviGGECYbaCITOf6gNgCzNYobbscTS/aWUyFJztn0NC+MGM98Lx6yt716WC5mpbpRGReqFZsMV8OAX/a4j93zS7zrSKFs/qGqywe8Zfn2NZ3jfGjUNsq72uqb3o8GTKAaPwAdvgZNDMDcHLS1w7XYVr2XLlZeEsK5FC22N5n1Mo6PVVzvrzSgPBLvY48wxKct0iiB3B1pWtD5bszxtRjumZ1GggE2ZAEHixEkZfvG4TgxwgIMgF8S5TVmU2Sz8l4w3Wdso2nkKskiZMhYmSZFkk9XEFLpLgIDVgusWFjPagJo4aS0Jm4WhNsFw79sOtWkUEWpFVmqzF3ELiFqhdB40IoxF1/Uw+LQqF6lkvoWTR/Q36dZxsWhUpK4EG65oTljX0rNJNT4efF5l4lu61fOvLX259374xn9TXyeSSmTPz8OnP+vf1+hJ1OgwbLoMLt+8FPO8S6LJsVG00NZo3sf0ncWdpK9Jd5KLyXozqoX1KmCdGOCAkaVVtvkE9DrhF13tRitXeVsZZIgcOWLE2Cw20W74xV2L2c6V7GTEPUVBZomIOH3m+otSIvJuEI9tZWZ+N7gqk+15RVxZJBVbagQMRXqh43YK6QO49hxmoIVY6w1v6739dljt11IeflbdMCPgFpBOgUD3LW9/x3eIkVyDt+4eVZNdyXyL/tv8jZCXAo2K1NVOz6ZzP+crroQv/RE89WNVLtK3RonsK2oyzY2WD13CTY6NIqS8dKbdvR07d+6Ue/fuvdhPQ6O5pKiu0V5wJ0l7Hl9qS1xSpSOa1cW0N8ugXBLQ68RAnYDWnJ1zuY6sBG52GGf618jSLCLUitV+LWb8vSN2NO8+DQ0tqh5EU93keAkMohFCvCql3HnOfVpoazTvbxp1HdFoNBqN5oJz+E1/k+PdH1v1IhsaF9q6dESjeZ9zRSSkhbVGo9FoLg6XaJNjo1xaM7Q1Go1Go9FoNJpLBC20NRqNRqPRaDSaFUALbY1Go9FoNBqNZgXQQluj0Wg0Go1Go1kBtNDWaDQajUaj0WhWAC20NRqNRqPRaDSaFUALbY1Go9FoNBqNZgXQQluj0Wg0Go1Go1kBtNDWaDQajUaj0WhWAC20NRqNRqPRaDSaFUALbY1Go9FoNBqNZgXQQluj0Wg0Go1Go1kBtNDWaDQajUaj0WhWAC20NRqNRqPRaDSaFUALbY1Go9FoNBqNZgXQQluj0Wg0Go1Go1kBtNDWaDQajUaj0WhWAC20NRqNRqPRaDSaFUALbY1Go9FoNBqNZgXQQluj0Wg0Go1Go1kBtNDWaDQajUaj0WhWAC20NRqNRqPRaDSaFUALbY1Go9FoNBqNZgXQQluj0Wg0Go1Go1kBtNDWaDQajUaj0WhWAC20NRqNRqPRaDSaFUALbY1Go9FoNBqNZgXQQluj0Wg0Go1Go1kBtNDWaDQajUaj0WhWAC20NRqNRqPRaDSaFUALbY1Go9FoNBqNZgXQQluj0Wg0Go1Go1kBtNDWaDQajUaj0WhWAC20NRqNRqPRaDSaFUALbY1Go9FoNBqNZgXQQluj0Wg0Go1Go1kBrIv9BDQXl5G3YP8TMHsGWtfA9vugb9vFflYrx4lBjxdekUxMQVcH3HqDYOO61XG+uT9j8+hEidNFj7Vhg090hdieCFzsp3XeHC0X+UUxy6jr0Gta3BmOc3kwfLGf1kVnyMvyupxmmhLthLhOtDNgxH17fpwfYg9nKBtlgl6Qm1nD/dGBi/SMNRqNRvNOEVLKi/0cLgg7d+6Ue/fufdceb7UL1NNHJa/+AqbGoKMHdtwJay8Xvj0jb8FzX4dICiJJKKShMA93fKX+tZw47bFrr2R8Cro74Ladgo1rmxeoR0dcfrHfY3QWelvhzu0Gl/eZTR+vEU4MevzgSUkiKolGIZ+HTF7wWx+9+GJ7f8bmq4MFWixB0hKkHcmcI/mTdZGmxfbFEO5Hy0W+NjfNWEGSdyBqQU9E8G9a2t/XYnvIy/Jt5yTDXpmy9AgKg34jyGetDYti+8f5IX5lHsfwTAxp4gkXz3C53d2kxbZGo1meU4fhpZ/B1Ah09MFNH4b1Wy72s3pfIIR4VUq581z7Vkcq7xJjQaDm56ClT63PfV3FVwOnj0p+8h3IZaC9S60/+Y6KV7P/CciHPPblXJ4ZdNmXc8mHPPY/4T/eidMe339SkslJOtvV+v0nJSdOe009v6MjLv/0S490AbpbIF2Af/qlx9ERt9mX3BAvvKJEdjwmMIQgHhMkopIXXlnZk83HTpS476cZrn00zX0/zfDYiVLdnkcnSrRYgpaAgSHU2mIJHp2o39sIC8J91pb0hwxmbclXBwvsz9jv9OW8Lf88n+ZIxsN1DZKmgesaHMl4/PN8um7vMbvA32Un+T/mR/i77CTH7MKKPrcFfjGY51+/OMGn94zyr1+c4BeD+RV/zEfsYU64BVwJIWHiSjjhFnjEHl7cs4czGJ6JiYUQAhMLwzPZw5kVf36Tco7d8i1+Kl9lt3yLSTm34o+p0WjeIacOw4+/Bbk0tPeo9cffUnHNqkGXjjTB/idUFjjaom4vrPufWB1Z7Vd/AbGE+gdL66u/gLWXL+07fsLjKJKQKYkHoOjAmyUon/CoPgfbtVcSj0kSMZURT8QAJLv2wsa15//8frHfQzqSUychl4NYDNo6VHwls9oTU9DR7o9Foyq+Ujx2osSf7s9TNCUyANM2/Ol+B4CPbwwt7jtd9OgP+c97k5bgdLG5k5lq4Q7QEhCAx6MTpRXNar+eLxEVBmFTfa+ETZCuwet5/wnDMbvA93IzJAyTLsMi47l8LzfD78bauCwQWbHn94vBPP99dI6oFLQLg4x0+e+jSlTeuS7q29tIqUej+w7JDCYGAaE+j4AQSCk5JDOLe8pGmYAXgKoLT4Y0KRvlC/Xyl2VSzrGXo4QIECdMkTJ7OcpOeTmdomVFH1uj0bwDXvoZxJLqHyytL/1MZ7VXEVpoN8HsGZXJriaSVPFaRg/Am4/B3BloWQNXfhx6r1rZ5zc1pjLZ1URjKl7NREhi5iThUEUUWeCUJBMx/77xKeisEaixqIrXcmzY47lfe4zNQk8r3HGtwWX9fgF55IxkckQSCgqiUSiXYfCUJL+yeoKuDsjmIF71+vJ5FV8p/vPhAnOmR8wQWIBjwBwe//lwwSe014ZV1lkJYkXakawNN3fR6XTRI2R6vJgvk/E8EobBxoDJ6WL98Z59zeHhPR4TGUlXQvDpmw3uur65Xw1l2yBi+a8QmIakYPsf95elLAnDJGGoE6uEMBfjKym0fzCaJSoFCWFVHtcA6fCD0axPaA95WZ6WI8SkSRtB8jg8zQj3eH0+ET3kZXnEGySNTRmPEfIMyiwPsM63rywh6K/cwhIqvkDQC+IKB7Pq17InXIJe8AK/C36OMkKIAGHU4yysRxmhEy20NZpVy9SIymRXE42ruGbVoEtHmqB1japnrqaQVvFqRg/A838LhTlI9an1+b9V8ZWkowfyOX8sn1PxaqxuMGywi4BUq2GreDXdHTA6Bm8ckOx5WfLGAcnomIpXc2zY47vPumTykq4WSSYv+e6zLseG/VnZQhowIRgEIdSKWf+eXmhuvUGQyQuyOYknJdmcJJMX3HqDOPedm+So4xE1VAZTCEFACKKGilfzia4Qc45kzvbwpFrnHMknukJnOfLbE7E8Xi6UKXmSuBCUPMnLhTIRy/+4z77m8Dc/dckUPDpikkzB429+6vLsa05Tj7vViJCXEhsPiVrzUrLV8IvnMdcmJvy/fmLCYMytL205Mejx7R+4/Jf/z+XbP3A5Mbh8lv+4U+Af8uP839kz/EN+nONOfSnKpHSXfdxJ6S9bel1OE5MmUaHKOKLCIiZNXpfTvn3PeaOMUUACUSwkMEaB57xR376EF8UVDtIoIM080ijgCoeEtyTub2YNnuHi4iClxMXBM1xupuYXywUmQ4EQ/qscIQJkeHdKeTQaTZN09EE+64/lsyquWTVood0E2+9TTYP5OZCeWgvzKl7Nm49BpEX9E8bS128+Vn/M4YPw5F/DP/2ZWocPLv/YZw5Jnviq5Nt/rtYzh+rri3fcCWOjkldfU8L41dckY6OSHXf6962/DLq2QyAMxYxau7areDUb+iSHjkI2D5GIWg8dVfFqnvu1RyICiaiqgU5EBYmIilfThsATUHQkUkqKjsQTKr6SbFxn8FsfVbXZU9NqXelGyIAFbs1H5EoVr2Z7IsB9bUHemHd5eKTMG/Mu97UFmy7zsAISKUECCLVKqeLVPLzHIx6QJCIGhmGQiBjEA5KH9zRXsvK5zgSpQgTPERRw8RxBqhDhc50J374eM0BO+h8jJz16TP/rXWhgzeYkHe1q/cGTsk5sH3cKfL84RcZz6RSqFOX7xak6sd0pTOaCJWY700z1zTLbmWYuWKJT+EuWpikRwR+LYDKNvwTmBFnCmAQxEEAQgzAmJ/D/8bvDSmEID099EnhIDOFxh5Va3HN/dICr3QFcISkHCrhCcrU7sGwj5Bhpfs5RfsQb/JyjjNH8WWqCCCX8JzglbBKs3JUFjUZzAbjpw6ouO5dWYmTh65s+fLGfmaYKXTrSBH3blDNHtevITZ+pr8+eOwOGBcMvQCkNoSR0bYJSTYnJ8EF49iGIpqC1F/Lz6vZdX4b+rUv7zhySPPuNqn1pePYbcNeXJGuuWBKp0pTMu5KJOSiXIBgCEQFpQnUB6C03Cn44AT3XLrlwZHOCW270C97Tg4JtGyST85ArQDwKG3pVnA8s7Rubha6aK82xiIpXs2WNQXhKMlKEbAniIdgQh3UdK3/et3GdwcZ1K/4wi9zWZvHMhI1AEhJQklD04O4O/4/e/jmHx0cctoctbksI5m3J4yMOl0cdtrec/4+pLeDWVICjeZd5R5KyBFfHA9g15zITGUlHTalQLKTizbA9EeB/HUgpt5Nsxe1koN7t5EOhON/LzajHEwY56ZHxXD4WSfn2VTewwkLZj+SFV/B9jrvKabKyxFFRoCRdQpj0ygi7ymk2WUuC8fY1Fv/k5Qi4BkHboGg42B1lHqipqW4nRB6HaNWvyAIu7fivMEjAlh7zuNh4BDCIIjBqsuZWoMx6N8IZCjhILCFYQwTLWqqXGnTzTFgluo0YUjgIYTHhlRh086wzlzLfY6R5gZOECZAkTAGbFzjJrWygh+Tbf0DLcDl97OIA45Tw8DAwCBNiOytc46bRaN4Z67fA/Z/3u478xoO6PnuVoYV2k/RtO3fjoxmEEy8ogR1KgFOEk3tg463+ffueUuI5WtEYC+u+p/xC++32rbliad9TP5bMZ6CtCwIBsG2Yz6j4H/zx0r6N6wwe/JjH7pdhYhK6OuGeO+szvBNT0NcN/z97bxolx3Xdef7ei9zX2vcFO0BsJEhwBUVxESmSEilqodotiRYleT+2x27PTE/3GXu63V/6TE+P3G1127I8smRJfeymTVKkRMmUuJo7ARIgSBAbsRRqr0JVZeWeGfHefHhZVRmZRSKRIgTait85OIG451VmRC5V/7hx7/8O9KyoNKV1XRNhTyscOqU5NanJFo1gW9MDW9e41d11Vwkmf2Cxo/W9Bf7F5K05m0dHbEYzmoGY4K4hH9vazv/r8m8uCTNVVpxYVKQdTdgS7Gy1+DeXuLOFD0+UsHE4WNCkspqkJei1BA9PlJoS2v0Bi5StuKFtpb53wVYkfe73tisuSOc18arDyRZNvFl2xP3nzMRv8If5l9E2nilmmHTK9Fh+Ph5O1tVnN9rA+qaTYkxk8SEJICmjOCHSFB0FrNRC2Z1F1uY00yKDtjQBRzCgw9gRd6Z6l2jnccZBm0x2HoescNgj3HVVnTrEIb2AX5jKahvFWe2wtaa2eZQMZVlmkCD+yvGVKDNalfl+So1TtBYJ4MOHH0coitYiT6lx7rdWbjMdYooQfsKVco+l7SGmmhLawHLuXtfse3h4fMBZs9kT1h9wPKHdJBNvwZuPwvwZaB2E7XdB7zb3Gi1X/nAtxyrxaubGTIa6mnDcxF3rxt9lXU3fw9HDpsQjUNFYgYApGzh6uP48GsnwNtpEGAkqDpwwZRHhAORKcOAE7N7svs3fqMC/EJw4rXjhFb38vNddVf+8b83ZfO2tMskA9EVhoaj52ltlfnsbdWL7jZTNwxMlRnKKoYjknt4AO5Mra3Ym/PzJzigPTpaW/aw/1RNgZ8ItRPcvljltO4SlICGhoDSHbEW2ScfDj7eE+O9TWUCRsASLjrnL8fkOt5f1Z6+V/Jd/0IAiGjQiO1MWfOXm5t+Lo8UiP81lmLAden0WH4nE2BSsrzXXWuAoga0kjhBoXS/uG/3szYkiErHi6oFAac2ccAvoQyrFYiiH5QgcLbB8sGjlOKQksPLlGpQxblN9vM6Km8ge0V3nJhIkSAg/Go2DRiII4SdYk/nO4dQcn8TWihwrb/C4WCCAha8ic81WMy7cVnsp8iRwv48hfKSarKl+hzGSROmidTlWpMQ7jHnNkB4eHh4/I57QboKJt+DZPzX11i39psnx2T+FG37HLbbtIqzdA7PHoLAIoQT07jDxatr6TblIpOqueT5t4q51faZcpG5dbd/DuyUjm0xSXne14Ft/o1lIa0o2BHzQnpiBbQAAIABJREFUEof7f8n9gM8fFCTDmnQBFkrgtyAZNvHP1ZSMvd8lHIenHH5y1GE8pelLCm7dZLGl252XO3Fa8ZcPOUxqRR4Ij8KhMcmvfBKX2H50xCYZgJaKG0tLEEDz6IjtEtpvpGz+6FCe2byi6GgOLwhem3f4463hOrFdK6xrWUQjNIRkxQFGQMHRLNZdqjXGtmiA3+qGHywUGCs59AcsPt8RYlvU7WCx5C5S7TrylZubdx05WizyrdQCCSnptiwWHcW3Ugvcn2xxie1jpQLfzcwTl5JuabGoHL6bmecLsVbXYJs9Vwr+7jEA95Chj97o/uwJqUGBozUWAqfyugnpfv2mdYG8AktLLGEuQPOOw7Qs1J2L0pKC8pHTiqjwoWT9xUdRC7bQyjQFCtiE8NFFiGLNRUMIixxlyih8CGw0uhJfwidM4YbrGBD4hPtCNUmYPOXlTDZAAZtkkzXVaXLEan42gJ80F95f3MPDw+OfO57QboI3HwVlw9R+KKQglITYgIlXC+3WQSPC131oJZZfgESN+8elt5uabDAZ6nzaCO9r/8Uq676xyrrPutdt2gJvHjCOHkulI7kcbL+0ufPVYqm+m+UUvbZMvJrxs5p8AXDAqmiDXMHEa3lnVPHsa5qpOU13m+CGywXrB+qFzNEJhycOKSYWoLcFbtkq2dTrFtCHpxy++YpNMgQ9CVgsaL75is2Xr8Ilth9+xuE12yYTUpQkBBSMFyQPPwP/6pdXnns0o+mrqVtOBEy8mq+fLHAi7ZD0CZIBQdGBE2mHr58s8N8uq/dbfi+SAcFcEfKOJiRNHbdSJl5Lo2Pkt0UDdcJ6NW653Mctl5/X4b4rP81lSEhJwjKv+9L2p7mMS2g/VcgQl5JExd4vUWlGfKqQcQlt08CqeP5Vls/3ozfWn2+3CDInS5Q0lLXGLwRhYdEm3OdfcBRCgBAaEAihEZV4NaedHH9dGGc8B+myIO7PcySS55dDfa566U4RIKttNlZlfrPYJIX7V+sACQJYLFKkgEMIi3bCdLHyQVsn4rytUsadBkEZTVHbXCLddetb6eZ5TgImk13ApkCZK5p0J4kToUiJICuvVYkycSLv8VMeHh4eHo3guY40wfgbMP0GlPOm/rqcN/vjb7jXbb/LCOt8xZ1k6f/b73Kv67/END5GkjA/Yba1jZAAA1sEt/wqRBKVdQm45VdxNUICfPTjgr5KNjxbsfnr6zfxZnj+VU1vF+zeZeqod+8S9HZRN1HRcSBf6e2q6CvyJROv5p1Rxd88rkjnNJ2tkM5p/uZxxTujbrFzdMLh2887LOY13UnNYl7z7ecdjk64H/AnRx2SIUiEjNtJIiRIhky8mmembabDDkpCGFASpsMOz0y7rewGYoLFGk/vxZKJV/PKWQcLzWRRcTSjmCwqLDSvnD3/eo/LEhYDUTirbY6Ubc5qm4GoiVfTqAvHxWLCdojVZH5jUjJhu1+TCccmVtMwGBOSCafeVvB0Z5pXbjnD3ntP8sotZzjdma5bc6fVgcL4YvdLP3EhUZV4Narsw68sQFRyygK/slBltzB+JH+Wtxc1JdsiYUlKtsXbi5pH8m57v6tlKxntkNXGki+rbTLa4WrZ6lp3Ge1IJH06zjbdTp+OI5FcxkoB+h7ZT78MIdBkcRBo+mWIPdJ9a6uHBHtYSxg/ixQI43/XRsgpUjzLER5hP89yhClSdWvW00+RMkVKaDRFShQps57+urUzeoEX9Vs8rvfyon7LmyDp4eHhcQ68jHYTlM4CFiz1bfnDUC5V4lX0bjPlJNW13FfeV1/LDUZU1wrr1RjYIlyNj6uxZp3kX96veOl5mJ2Gji64Zo+J13JiRPHcXs3UrPHFvn63YN1QfTNkSMHxAyslMF0bYLqmJHQpX6m0uYJT2h1f4tnXNPGIsQEEiEcANM++pllflZR74pAiEYJE2KxLhM26Jw4pV1Z7PKXpqdEYsaCJu84jqNBasyihDPgBv9JMB90i9a4hH197qwxoEgEjslMluG+j++tSsDWzZU1ICoISbA2TJU2H//zLPbbFLP521qElIBi0BGlHc8J2uD/mFtqNunBcLHp9plxkKZMNkFGKXp/7PHotHyPlIrPaIasVUSHpEBZDfven5alciq/npwkjaEOSUTZfz08DcFNVDdWegBGsjzmzzOkybcLPvVbHcnyJnnw7U4lp/EogtIUWCltoevLudQcLBUJYNRMuLQ4WClB1s2LYinA3Pbys5pnRJTpFgJutTlfWG2BAxviIGmA/Z5mjQBshrqOHgaqa714S3CHX8SbTLFCghRDb6aJ3FQHdQ+KcjY9TpHiJEy53kpc4wTWso5uV165TtLBLb+IdxkiTI06Eraytmwo5oxfYtzxBMkyBMvs4xhXeBEkPDw+Pd8UT2k0QiZpa6XIBfMGVmutItH5t77bVhfWFRgBSg1CV7SprTowoHnjMjFfvbId0Fh54DO69U7nEdgg4+oqZBhmKm/M++gpsusb9eMmgQErNQh5KCgISOqIQ97uffWpOI32Ko8cFmQLEQjDUpcnNuQX+xAJYluLw6ZV1a9o02QW3aOtLChYLmkRVf1imaOKu1ySmSWUhoDU+YYRxDgjH3MJ4W5uPe1o13z5gM57X9IUFX7y03nUkLmHMhnxZoxRICdIP8SZmzLxdcNgYtjiccxgtKeKWYEvE4u2Cwyer1l2MMfLnw0ciMb6VMlnOmJRklGJRKT4Vd4vC9ZafH+cXCQtBFEFaO0xrm5tC7pKbB4vzhBHEpHntY0hQNg8W511CG4zY3kPNi1PD/Yku/u9ZhWpZAJ8Ntg+x0ML9He5RqvmiRcSvqL7p57cUuWK9H8ewFakT1qsxIGMM8N4lRVoLHCQ2EgeJRjTdW3GESUL4CVVquZe2R5h0CW0wYvtcjY/HGVt1guRxr2nSw8PD413xhHYT9O8EfxRS05DPQDgGHYPGI7tZxt6GAz+C+TFo7YdL72gsw70ap08oHnkAYjHo6IRsGh55AO6+VzFcldV+bq8R2fFKdjReyY4+txfWDa08XnJK4PjB8Wl8gOMDxy9ITrkVwPZh2HcC1rabhsmSDdmCiVfj82leOwHRoCYahGIZ9p+Ay9e5Ba/fp3j1NMSCmmjArNs3AlcOuzPQt26y+K9Plnl7XmMXwBeCRCt8+mb3xzsRE5wtaaQjUI4Rxn6/JlFTEnLitOLgM5Ibon4iCVPffvAZwca4ctUGB8sSnVOmfl2a8iCdg2DAfcFwLmcSgDcyZcZKDt0ByZCEooKxkkMg4x4k0tUBx+wyx9tKzFuKVkeyYS7Axo4LO6a7UTYFg9yfbHG5jnwqnqhzHTmhymz3h5hWNhmliEuL9dLHCVXmlqp1s8omgmZaFymj8SOIIZlVzU2u3BH387/Tw/enW5ddYD7RVe/zPWgnGAtNEQhkzNWqlpRKfgbz3e/yyD874zrN04wQwkcLQXKUeZoRbtRD9In4uR+ghoVV3EmC+Fho0p0kTb6uadKbIOnh4eHx3nhCuwm2fArm/x/jXR1KmobIwryJN8PY2/DU1yFcGUSTT5n9m369ObH98nNGZEcrf5uXti8/B8PrVtZNzUJnTQIwGjHxanxzko3tNq8vaDI2xHywqx18c+7s3t03W8zMO8zlNJkCBC0YahXcfbN7nbaAJa28pHFVVcPl0johWO6+FLXxFQIFQXxWkkHhBCDgQHxWEii41/VEBemyoFDUOI6pIw8FBT1R97oXXjEXILXlGS+84i7PyE1L2rXEDgjKQuPXAl9Jk5teEdpvpGy+erxAix8GwoL5kuKrxwv8/oaQS2ynbOOWUV2qUFSClO2++Oi61OYbx/LEHUESQcpRPJ3Is2ej5IPifrwpGFzVzq+aSadMuNKUKKVGCE1YUDeCPSoEU7pEEGku8tBMU6ZbNDeaHhrz+f5Iu4+/yjtk80uV3BpLOHyk/cL9ynyDGUL4iFQyz0vbN5ihj/MX2i0Vd5JQlTtJEZuWJt1J4pVykVBV06Q3QdLDw8PjvfGEdhP07IRr/1c4/CCkRiA5BLu+YuK1jL1tBsrMjRm7vktvrxfPB35kRHbdIJofNSe0Z6ZMJruaSNTEq+nuMOUi8aqSl2zOxKsptyuOTEBnRDBQybYeSUFbr/vW+rphyVc+zTkdMYqO4LJNmpFJQSaviYUFGwc1RccteEuO4IphwalZlktHNveYeDXPv6pZk5Bsj1bVBWc1z7+qXcJ4e6dFxA+TOc1iyTiJ9EQE61rcAnV6BlRUsX8KFsuahF+wIQ65Gfe62Jwk3SmIOxK/NtMYiz5FbGblfB+eKNHih9ZKlrs1IADFwxMll9BOSJgXxj97aYKkEiZezf5wie0DkoVJQTYHyYhguEezP1zio3XV8B9cAsDr5QJRaUpHSlpxoFxgV8At2gYtPxN2EUcbj2oHjarEaznp5HjRXmBGl+kUfq71tbC2gZKO1RgppohNhFiQgqJPE7QFMaUZ6U9BrLkyiXO5xcxToKXmPQzjY55668FG2EwPL3ECMJnsYsWd5DLqR7o3wgb62cexyuP5Kw2UZbazpqnH8/Dw8PhFwBPaTdKzc3VhXU2jo9Xn32VgzXzNwJpG6ew25SLRqiRYLmvi1Vy/W/DNBzSHU5pSGQJ+SCbhjnvdQjbVo7FGwPIJRBCsMlhlTaqnvumvEX/snlbjhXzZZsFSqjqdg7YaTdTXAot5wZXrVo5nMa/prEmgNVq3fOdaH39+QLGtXRIPQLoEqaLmzrU1X4OY4qVJTSwIcZ/xs35pCq7rUVRnjTcEfQTnNGeTmqyliTqC3pSPweDK443kFANh9+uZ9AtGcu7yl53xANFsmQlbs2hrEj7B2oBkfdQtKEeLDoOtkuE295TO0WKTk20uEsL1v6U9XVeOHLYku3WUw6pATisiQrJThghb7iuQk06Oh0vTRIVFO34y2uHh0jT3BLqaEtvHFoq02X56AwIqVSpFW3Nsocg5ysBXZcktJh7RdLSbITx/9xh85s6VcqRWQuQoL2eyAfLYtNaUfzRKN0k20sU+TpGmQJwQV7Cmrj4bYFbNc4Ix0mSJE2Ud/XTUOKd0ihau0Bs5zhhp8sQJs501XiOkh4eHx3tw0e39hBDtQohfEUI8JIQ4LoTICyFSQojnhBBfEUJc9GNsluqR6UKu/P/Aj93rWvuNJ3Y1+bSJN8PV10MmY8S2VmabyZi4i0qzJLCsdYSibpxlTmq6tmnGURxaVIyj6Nqmycnzd9cAuPEySTpvbP2U1qRzmnTexKu55RLJ+Kzm5YOK519VvHxQMT6rueWSmjHiHaaO2nXMq0wP3Nph8RuXBkgGBRMZ07z5G5cG2NrhzlTn4xrhgFTmdZEKhGPi1Xz2Wokv52P9rI9rF/ysn/Xhy/n47LUrxzcUkaTK7p9LlTVDEfc53N0RRFqC7REft7cF2B7xIS3B3R3uDOdA0GLRcT/eoqMZCF74spETpxXf+TuH//znDt/5O+dnshQsCdjlDxEUgqxWBIVglz9EqUZpd0s/nT4/d4Za+Ey4jTtDLXT6/HRL9wXIi/YCUWEREz6kEMSEj6iweNFuzn7Ong0gI+6LFxlxsGebq4WvdouRQhCLCuIR7bLI3EknBWxylNFocpQpYLOTzvd45HdnihT71TgpR5J1IqQcyX41XmfxN6vm2c8RipSIVTy193OEWTVf95idooVrxTZuE7u5VmzzRLaHh4fHOfggZLTvBf4MmACeAkaAbuBTwF8Cdwgh7tVaN6fqLhCTb8DbD8HCCLQMwSWfrM9wz42B5YdjLxrhHI6bhsna0eqX3mFqsmFlEE0+Bdf8Uv3znjmm2fskzE1AWy/svhkGN7rVyfA6yd33Kl5+zpSLdHbDzXfgaoQE88e/rws2rV35+dVKLnx+zeuTUA6YITU5PxyahCvbmntLNvRLPncLPL1fMTlvMtx3XSfZ0O8+Pn9REJmXpJXC8Zva68i8xF90n2+j0wPBiO1aYV1LHsHOzZqxcVOeEY3AzmFNvibfesvlPk4Wbf7iaJmzStOeEPzaJr9rquI9vQG+erwAKJJ+QaqsWSjDl4bdgm1HzM//MhDlkdkiZwoOgyGLX+4NsyPmFpQfbw3y3yZyuEar25ovdDZfNnKkUOTxbI5x26bP5+O2aITNoZrmxQYyskscKxV4qpBhwrHptXzcFIq5htAA9EgfaRwu961km9PKoV2635vrAwkeKMyCgqiQZLUig8MdAXe2dUaXacf9WkWwmNHumu9GGU61cbJ1HPzgK1vYfoeCsFmb6jr3D69CI3dd+kScG/UQbzDDPAVaCXENfU01QgK8rM4wqkoEhY8okjKKUVXiZc5wd9UQnBOMESSwPLBmaXuCMTpoXfWxPTw8PDwa44MgtI8CdwM/1Fovp8iEEP8WeAX4NEZ0//3FObx6Jt+AF/5fCLVAcgDy82b/un/lFtu+ILz5D1BKg1M2onvmBGz/qPvx+i8xjY/VriPX/FJ9ffaZY5offwcicWjrNhaDP/4O3H6fXlVsVzc+rkajJRepHCxmIOgDf8BMmlwsmHizbOivF9a1PP+qZrhFsC268jFd7UKg0emBjbJUsrJzW3XJCrTXlKwcXLB5wW9z/WWCpN9krl8o21y9YLOjxRzzzqSP398QcrmOfGm43nUEjNiuFda1bIsGuC1h843JPBNlRa9f8qs94YYmQK7GkUKRby6kSEhJj2Wx6Dh8cyHFl1uSLrHdqH93o6PVbwzG+R+5OZeATmvFXUF3hnS9L8y1vjgPF88yqx06hMU9wXbW+9xvRqcw5SKxql9pORw6xXu/nu/G7ZtjfPfFPkqb5igmisjFIPGjXdx+7flN/Fyiq8NcnMSq+iFWu+vSJ+JNNT6uxmmdIiAsApUblwEkWlic1u6MdpossZopkGYEe/Z9OQ4PDw+PX2QuelmG1vpJrfWj1SK7Ep8E/ryye+PP/cDeg7cfMiI73GpKQsKtZv/th9zr5t6BzCyUi2acdrlo9ufeqX/MyVOaw/s0Rw6Y7eSp+mzx3ieNyI4mzPNGE2Z/75PNnUejJRfjU9DdAoEAlG2z7W4x8QvJ9KwR/tW8l2e0qJTCiJ/x3sctl0gWC6YeXGkzkXKxQF3JSnWjoxSC1oCkxW/i1cQWJGvfDHLJK2HWvhkkttD81+5gusxfjxVJl8CvJOkS/PVYkYPp5jK3j2dzyyPTpRAkLIuElDyedX8wGn0vnipk0FnBycOSl/bBycMSnRU8Vci41m0IhPhcpI24tJjWDnFp8blIGxtqMt/v2HletNOst8Lc7E+w3grzop3mHdttKXetr4XJjM3eY2Ve2Oew91iZyYzNtb7mShvWDUtu/ZAg4BcUimZ764eav3jbc6UgnRNksuYzlclq0jnBniubNMlugKK2sGrqwCw0xRp7nzhRSrg/P2YE+yqDATw8PDw8zosPQkb7vVj67d+cae4FYmHEZLKrCSVNvJrxN0A6gGVKLoQ2tb61o9r3/Ujz8H+CYAySnZBLw8P/CUBzxR0rf4jnJkwmu5pIzMSbYc+Vgm/9T01qoaoZsgXu/2zNoBcHgkFIViXaCiWTpb+QNJoFPHFa8eAPjCVfR+VnHvwBfOrj9WUNjbC5x+KL18ETbyvGF0yG+5OXSzb3uAVKI42O51Ny0Qh/MZrjRNEh4RMkpHEnOVF0+IvRHH96SX2T27kYt216rBo3FSkZt91fua4OODWnGC2vOLEM+GFNTRnO0YUy08ctgn5TxlMqw6ljktyGMrWDDDcEQnXCupbnSovEsIhXSkriwgJl4tVZbT0aYvLVVkY3LFDqLBLI+hl4uhV9ZQiamJh5RmV4s3uC9d0+IgTJ4fAmEwwowaA8/6z2+33XpRG6dDuzYtIMr8JC4VDGpkv3uI+NfvZzBDCZ7FJlHPslrL1gx+bh4eHxi8IHVmgLIXzAL1d2f/wua34N+DWAoaGh1ZZcEFqGTLlIuKp8sZAy8WrKGbCMmxtag6gYLJTdyT2e+a4R2ZGKkF3aPvNduOKOlXVtvaZcJFolWHIZE2+GJeFvdiobpz4jvG0tvHbcHP/SIJpcAS7fUP+Y74wqnn1NMzWn6W4T3HC5YP1A81nARmqvG/W9Ph8291h1wrqWoYhkvqQqln2G2kbH93tk+qtpG5/UnHUcShibvLCUvJpu7lq0z+dj0XHqRqb3+dy/GvrWaR44pon5jRtLOq95dVFw3W73hyU/bkHYISDMzwf8UPTZJt7EV3RKlekU7mOJCsmUcl/l/c2+Mm/4BcmxVmJakBeaN/wm/m+Hz79+fZ+eI4KPaOW5o/hAm/hgzXTHRmrSoTFHnkY57eRcY9+vlq110yk/LPt50CnhyAxKlNHah1BtfNhyd1l3yFYuU5tdriOXsLbOdcTDw8PD4/z5wApt4D8C24HHtNb/sNoCrfVfAH8BsHv37p9bs+Qln4Sf/hFk9oJdAl8AYh3wkS+71/kE5ByQYul4QWmI1Oi3hSmTya4mHDXxanbfbGqywWSycxmT/b7hnubO44WXV2+GfOFlzdo1K+vuusVi9qzDXJ6qQTQmXs07o4q/fMghtWDKZEaCmqOn4Vc+SVNiu9Es4PQMdNRkuSMRE7+QNNLo+H6PTC9oxYJ2CAqBH3OrZ1Y5tKxSgXAoV+KH8wXGSg79AYuPtYbYGnHXct8WjfDNBVOzWz0y/TMJd53wkZRm21qYOyvJ5iAegeEBzZGUdk1ybH0nRmrrAkXbIeBISpZC+TSth2Jwzfmfb7f0k1aOyWRXyGpV5zryXLlEIgARbT4bES3QluK5Qgma8Bc/q4u04X6tIlic1UVXrNGa9PeT006OR+xJYsKiAz9ZbfOIPcnd9LjE9rAV4VOsNYJcVQS5VS/IwYjtRhofG7EB9PDw8PBY4aLXaK+GEOJ3gT8ADgP3XeTDqUMLM8VQCyOeq/erSfaB5VSSxcJsLcfEq2nphnxN31E+a+LVDG4U3H4fRBIwN2W2t99X7zrSKI3W3a4blnzpsxbXXiLZ2i259hKzXyt4H35KMTFhMuLRqNlOTJh4s6wbltz3GYs/+A2L+z5T/5wAXZ3vUmvenCtaw+xoMY2OrQHJaF7TGpD8/obQciMkVEamF2y+Lwr8lcjxfVHgWMGuK39plGTAXKwBlVskZj9Z0wt5KFfizyYzpGzTMJmyFX82meFQzl0/vjkU5MstSRKWxWQls13bCAkwvgCiw2FiS4HTl+eZ2FJAdDiM17jnbQ4G2fJOkqAjyQQcgo5kyztJNp9jUuS7cX0gQQaHtHKMFaRyyOBwfcBdh1KIKXw13oC+kqAQa+6z1y5MuUg1ORzaayZSPlXIEJeShKzUuEuLuJR1NenvJy+reWLCIip8CCGICh8xYfHyKnZ8UihC0iZilQlJGyma/y6ejw2gh4eHh4fhA5fRFkL8NvBfgEPALVrruYt8SHUc+j4E41BKgSpAIGj2D30fenesrOvYCKUM5BfBscHyQbjDxKv58BeWarJNJjufhWIG7vjN+uce3CgY3Fgfr+XUScVLL8DMNHR2wTXXwZq19f7TjdRAQ2O3vQ+/o4kETbMkmK3WJn4hue4qwYM/gOoSk0xWcNtNF67RbIkdLT6XsK6lY5vD114rEy9Dix8WyoqfSMWebc2NTO8PWyzaipyCoqPxSWj3CfrD7sf64XyBpCVJ+sx7nvSJ5XhtVntzKFgnrOtI2PzULpGwBC1ADs1PnRK3JQRU2ertuVIw8ViI7dmgq9xnz53NvRfrfWHuDXXwXGmRKVWmW/q5I9Ba5zpyWa/FvnccWkoCv98446QczRW9zfmLXyHa+LEeB20y2TkcctjcINz2fhOOTTAjOTCml60gB/sFE7EL11Yyo0t0rGpl6L6IOqMyPK7HiWqLNgLksHmccW5TfXV15jN6wTWIZgP9dR7Zng2gh4eHx/nzgRLaQojfA74KvIkR2dMX+ZBWZeIATL8F5TwoxziJpMahVJOV7t8BgTCkx00NdygJ8T7orKltNg2Pmme+a8pFWrqNyK5uhDwfTp1UfO/bmsV5U8IxegreOQaf/6Jyie3rrhY8+CjUCdSbm3te4YCucVPTAsQFbmVdNyzZda3NAy8opiY03XHBvddJ1g1f+CEu52IvNjuGBAvTxpO7JQJr+jV7sbmD87fk2xn1E5EwXtQs2oqET9IXFGwIu1/4sZJDr999YRW3BGOl5iZIZjps5KjAQoBl7sxIGzI97jf3QjT9rfeF64R1LV/aGGS0nCM7qyhlBTKq6e4w8WYYlDFup499eo6zuki7CHKD6KoTqKG0xf7TDjHkcvPngVOKy4YtLpT27BQBsto2deMVjJWh+/P0uj5LVFtEKnXmkUqd+eucddWZz+gF9nGMIH5ihClQZh/HuEJvdIltzwbQw8PD4/z5wAhtIcS/xtRl7wdu1Vo3WcV64VkchexZCESNV7ZyzP7iqHvdjo/B0//deGuHEyaznU+ZeC1d62DDHjg7Du19Zr9ZfvwDzZmTxhVE2SB9kFow8d/4nZV1a9dIPnWX4oWXV0TRbTcL1q5pThSdT9Pk+8mRSYcnTinWrIPtIUGmAE+cUgz0OedsaLzQjOQUg+2C4Y7qkenUjWBvlLvagvzpuMPOmFgeWLNga+5qcwvK/oBFylbLmWyAtKPpDzT3euR8mksGbY6WSxRQhJBc4g+Qs+p9qstBTbpdMW9BuAXKwQtfobYj7ucPt0b4/nSRkYJiKGTxia4gO+LN+WiDEdu1jY+1+N+MojsXjImeI9EhhUbhfzPRVPNnI1wtW3nEngRWsu0Z7XCz5a6VOkt9nXkYi7O468yPM0YQP6HK2qXtccboZEVox4lSpLScyQbPBtDDw8PjXHwghLYQ4g+BPwb2Abd9EMtFqiktGvFajfSZeDV9W+HG34KDP4T5UWgdgKs/b+LVnDmiefzbxk2krcc4izz+bbjti5rBze7s8shxxd6nYXbclftsAAAgAElEQVQSOnpg940wtMEtZN46APmMKd3wB8BxzP5bB+rPZe0a6Wp8/FlotGny/eaJtxWJECQqVnuJMIDmibfVRRfajTiTnA/bogF+pw8enStypugwGLS4rytYN7DmY60h/mzS1AnHLUHa0aQcxec66xvhGiEecnhDFeiMSvxYlNGMqgI7pfs8jkw6fG1/jsWuHOVumzMFH2/vj/Dbl0Wafi9O2HmeL6eY1mW6hJ89/iTrVslw74j7fyZh3Qx6JMiVOsmJ9izpgEO8ZHHJ2Tj6TPOTOs/FsBXhOmL8I2OcoUiCIB+iv67JsZ0gOWyTya6Qx6G9pjk0TZ4Y7tcziJ80bq9yzwbQw8PD4/y56EJbCPFFjMh2gH8EfleIutKFU1rrb/2cD+1dCYSNsC7mwSmCFYR4HHyr/I3v21ovrGt5/YnK8JlKf9fS9vUnYHDzyrqR44rHvmfWtndBNg2PfQ/u/Lxyie1c2myLeSOyLQuEtRK/UJimSWO3Nz1jmhGvu2r1soETpxXPv6qXM+l7rmy+vGB8AaRfcXBCs1iGhB/WJwSZhYtfOnJPb4A/fDvLVLpMUUNQQLcl+Q/DzTtSbIsGzjkJcmskwG/2xFyuI5/rjNTVZzdKNOJQSDsUtIMWGqEFaBOv5oETeSYHUkSFJOpYlIMOkwMpHjgh+D97zt9/+oSd5++LMxWHDR8Z7fD3xRk+TeeqYvvnTVcHZGaDXJNfeT8zWU1slT6HUZXhALPMUaCNEJfSwUBNKcoZleF1fZazFGknyC7RXleuMsEix60p1hEiRIwCNseZoo8QvVVm5btEO49j6szDWORxyAqHPcLdZR2vlIuEqjLVRcrEa8S3ZwPo4eHhcf5cdKENy+kQC/i9d1nzDPCtn8vRNMDANXDyaUh0gC8EdgEKizDwoeYe7+y4yWRXE46ZeDV7nwZHwanjpmEyHIW2DhMfqirPCEqTUbak+Wc7poykvWZgCMDJU4oXX2JZGF97DU2XjgCEspLOU+AbhdYshLbVr3m/h7j4AooXJhXxAMR9UHDgpSnNdT0XvhnyXGipTd26AhwNPoH2m/iFZmsk0LSwrmWOEvEgZB1NSWkCEqJ+wRzuBry3fFkiSALKXOQElIUWJk4T/tPPl1MUy4KjOYeUbZP0CYYjgudlqmmhPaKy7FVznKVEOwF2yzaGZHPlD416vY+qDE8wSgSLVoLkKPMEo9yiBpbFdqPNi28yjcJhkhxFbIL4iBPkTaZdQntQxrhN9fE6K8J9j+iuE+4b6GcfxwCTyS5SpkiZ7aypO99GbQA9PDw8PAwXXWhrrf8d8O8u8mGcF1f9OiyOQXbaNDn6QtC+0cSbob3PlItEqoRwPmPi1Zw6CtOTpiQkHDGNjmdOmsx1NS1RQWZRo6S5TSAl+JSJV3PylOKh72tiUeP1nMnAQ9+HT35CNSW2xw+ZmvRwElr6ILdg9m/8LXdW//lXNaKgmTwmKCxCKAGxweaHuNgBbYbvaOOhaP6vsQPNi9n3K+P+0FSJtRHJ5cmVr9p8WfHQVImdiebKHA4u2Dw8UWIkpxiKSO7pDbyn88n7QVZrIhK6/SvPk3Ycstr9Gsu4Tb6smYuUKUlFQEnCOQt/3L3uWKnAn0/Nk5oUlNOSM3GbIz3z/Ea323/6SKnI4UVNWEoSPkFBwf5Fh0JSQRM6e0Rl+ZEzQQSLNvxksfmRM8Ed9DYlttcNS4Zun+OxwiyFYIlQMcCdoQ7WDbe51h1glggWkYpbyNL2ALMMVC5AGm1eHGeRFDksJAEsyjjMkKFEfd1/I3XmnaKFK/RGl+vIdtbUuY54eHh4eJw/F11o/1OkZwd85I/h0MNm7HrLEGy9x8RrGT/krtHe8bH6UpJdt5iabDCZ7HwGsouw55PuddlspdGwUmIZCBobs2xN0397O+SygmLZNGpKC4IRE6/mxZdMs+SpdyCXhUglQ/7iSzRVt33wh0ZkRyp/n5e2B3/oPudTx2HxCPjDEIpDuQDTb0ChWP+Yx8cUTx9QTM5DTyvceKlkQ79b8BaV4IohwelZSBcgHoJNPYKiai6jfeK04uuP2EygyAsIT8Cbj0h+/W7feYvtkYJiIFgzpt0nGCnUi6JGxP3BBZuvHi/Q4oeBsGC+pPjq8UKdf/f7TVxKUo5DUSkCQlDSGlWJVzOYlLyUyxPUEr8y69KREtdE3Kr4wck046cFMSTRCJQLkvFTigd1mn89tCK0p7KCgKUIVcrJQhIcoZnKSjj/ifPsVXNEsOomPu5Vc00J7acyCzzRcpqY3yYpwVF5nihn6c5IboqtCNU5CrTW1EaH8TFHYXm/0ebFImVA4K/YQ/qxcNCVeHN0ihZX46OHh4eHx/vDB3JgzT8FtABlgfKbbe2wGljJ8OYW3Bne8UPudYObBbd9sTKIZtJsb/sidY2Q0ThoBaWS8aculcx+1D3Ej7UbYeNm6OuFtlaz3bjZxKs5cUJz7IgZKjM3b7bHjph4M8yPGneVasIJE6/GnoLJkMNrfptnlc1rfpvJkINdMwnz+Jjie086pHOarhZNOqf53pMOx8fcIrU/Icja4PjA9mscH2RtE2+GB//R5rDjoCxI+Mz7e9hxePAfz9+ncCgkSdnu1zNla4ZC7q/eUjlNJqvpaDfbv3tMc+K0+1wfnijR4ofWgEQKQWtA0uI38QvJlmCATYEAQSnJaE1QSjYFAmwJuoVhMiRIhsGSmpJttsmwiVfz1qyNkpqTYZsD/jInw2b/rVn3a1xKh5GWxhYOGrOVlqaUbq5s5CwlIjX+5REsztLc6/doaRR/sIRPCoSW+KTAHyzxaMn9oW8jxJlSiWfnSzw6U+TZ+RJnSiXaWLmoaCdIvmZIzmrNi2F8KDR2JYNto1Bowl7exMPDw+MDh/ebuQkm3oR//K/GuizZB/kFs/+h34Xe7SvrGs3wghHV1Y2PqzG8EYIhmJ8x49cjMejuh55B97rdN5omyTXrTJY6lzUZ8t03utednYF0GoJB8PtBKbN/dpXR5Y3UcrcOwMiU4oxeaUocFIKhmvHruZLDab/58AWAEnDaDz0lh+ohLk8fUGhbc/KUIJs10ybb2jVPH1CurPamNvjbgwpbmQuQmazm9AJ8dH1zzZD7ZhSxAISsShbVAu3X7Js5f0u+T3YH+M+nKmPafYKUrZm34csDboH6/KumZj1WKe8xQ4Tqy2lGcoqBcE2G3C+atgsEOJguV9niyVVt8T4SifGt8gKbAwHXqPaPRNxlCUUEV4XCjATKZLUiKiyGpJ8i7mPOpSWjyRJBRxLWUBaa01GHgZT7ddnoCzOeEZQjOYrSIagswtkYfb7mmknbCZCl3oO6vQlPc4BsIEdAS8RSzkILLDTZgHtUaSSXZH/pDAEsYj4fOcrszzlssbuWS9cbbV7sI0EAixRFCtiE8NFOmA7PZs/Dw8PjA4eX0W6Ctx4xIjvcAkKu/P+tR9zrGs3wNsqVHzZNkOWCGQJTLpj9Kz/sXje0QXLn502m++y02d75+XobwGLGHH81Qpp4NUu13JlMJdua0Tz0fc3JU25xl9yt2DenyeQ0ccts981pkrvd60aiEJUQEKZHMCDM/kiNTjh2RnP6NJRKmkhEUyqZ/WNn3BniF84oLGEaP0VlawkTbwY7ALJmrot0TPx82Znw8wdrQrT6JaNFTatf8gdrQnX12dOzEKlx3otETLyaoYgkVa7JkP8MdoEH02X+5HSe+bKmPyiZL2v+5HSeg2l3GcKmYJD7ky0kLMmU45CwJPcnW9hUM1q91/IRkBaXB6J8KBjn8kCUgLTotdzX9KWSD4nCkhqNyXxLFKWSe90nuoIUCwESi62sz3SSWGylWAjwia7m7PN2yzZmdJEDKsWrap4DKsWMLrJbtp37h1chKAVOzQ0gR5t4Nc9P+QkvdBPGD74yYcz+81Mrn4NBGWOXSDItUrwhJpgWKXaJZF3z4la6kUj6iLOVTvqII5FsxS3IPTw8PDwuPl5GuwkWRk0mu5pQwsSraR0w5SKRqtLH/KKJ1zLxFrz5KMyfgdZB2H4X9NY4dgglsOe0mUJZNDXa4Uq8lqEN0uVEshrBILRZkC+b4TIBH7SFwFfzqXjxJZNhjcUq2dYYgK6r5T46r1l3qSYzYpocYwno2aI5Og/XVD1eMQShIliWwLKMBaHjaIo1Scp8GpAQqHhQBwJQtLWJV/H6hKI9AuGqSYj5suL1ieaE9o4hwetHQQi9PM47UxbsWttcKcrOhP+cjY9dHcZ9JVZ1sZHLmXg19/QG+OrxSobcL0iVNQtl+NJw/VXAkUmHJ95WjC9AXwvccoms87L+/nSRFp+gpfLatfgFoPj+dLEuq70pGKwT1rXcHI7yncwCKIgJSUYr0lpxT80VZzTpJ3smiuosocIOIm8RngkR7XY/5464n98bxpVxv78/1LRfttZgI9CAmcda2W+yb3aTjHOQBVACHwIbjSMdtkp3vfNIQdEfjCIXVkSz0pqR4spndFynOSxmGBQRNpEgj81hZujREfrESn1YLwmuZw2HmGKBAi2EuIIBl+PIEhMsutZtpXvVdY0yq+Y5yShpcsSJsJYBz97Pw8PD4z3whHYTtAyYcpFw1d/SwqKJV7M0GRLckyGv/rx73cRb8Pf/h2Z6HIplCPrhyPPw6f8oXGL7mb+B/LigJwb+duM6kh838S/80fmfx+Yt8OYBaIuzLChzWdi83b1uegakX3P0qCabh2gYhrogN+MWnlOz0DXgLmVR2sSr6e2AOR+QAbtopmtaLdBW04vVImFCK6ayYGM+rFFgUNaXhNRK4J/F2O+zV/mYzJRZnIdszrjK9PaZ+IWiUZu4HS0+fn9DyOU68qXheteRI5MO337BIRGCniQs5uHbLzh88TpcYtsIQHc2PPEuzZqNsDEQ4r5YC0/ms8u2ffeEE3W2fdvaJVERIDUVJJuDaASSPZo1bfWZ+UYH0TRiF/iKWqCLEGvFiuDNaptX1ALD1vmXXtwTGCBbLjGmi5RwCCAZtiLc43f/MhgKmbsF5kLGsFhTq3+QacL46pxJDjJNH+5GDIHGQuNDYaErlw5uJljkOU4RxkeSIHnKPMcprmdNU2J7Vs1zQB9ZHtVepMQBjnCp2lwntufUHKf1GbI6S1REGRaDtDV518DDw8PjnzKe0G6CbXebmmwwmezCohHeu3/Zva7RyZD/8FXNyDtGcAbDYJdg5B0Tv/8vV/4wn9wPoSj4K9rBHzIZupP7mzuP2z8mODurSS0Yge0PQG+/iVfjCyj2H4NIGKIhKJXhjeNw2UZFdfVRdweksxCv0ivZnIlXc++HJF/7gUOkQ9MShGwRckUTr6al3VgXagdwQFtQtEy8mst6JC+PKhCaoAVFBzIlwdUD9aLt8JTDT446jKc0fUnBrZsstnS7hfuWbovfvZlzrns/WTcs+cydiudfZdl15KM3rm4puKPFd06HkSfeVuiS5uSEWBaybe310zIbEYDny8ZAqE7g1vKJriB/ks8ztMEI+0Vbs2DTdEnIsVKB72bmiUtJt7RYVA7fzczzhZjbLnCWEu24RXsEi9kmmyEHZIzP+9ex3392eRDNZbTXDaL5RFeQPzmdB1TV+Wru7185tnkKtKzS+Dhf5UwCMEmKJ9QxUpQpogmSY4xFbpEb6amyYjnEFGF8plwFlreHmGpKaJ9klCB+gsLcPQkSAG3i1d7ac2qOt9Qh/ASJEKGoS7ylD7GNrZ7Y9vDw+IXDE9pN0LvdND6+9YgpF2kZMCK7d3v92kYmQx7bZ0R2tW3fUrwa4VD/jkkQq7h6NWIruGat5MZbHX74Q0gVoD0CN95q4tU4lYEropI0E9rsOzVJxut3Cx6oZGWjESOyM1nBHR92C/ebd/kYn1f87QuakRQkw/Av9ghu3uU+uUJcExaCzuhKxj1TNPFq7rvUz2S6yNkCpEsQsGA4aeLVHJ5y+OYrNskQ9CRgsaD55is2X76KVcX2hRTWq7FuWDblI74aR8c002MQDGgiYeNQc/o05GscUBoRgBeC97sk5KlChriUJCp3OxLCWo5XC+2Od2mG7FilGXJEZdmn5ziri7SLIFeI1QfbDMjYshf2z3K+rYTIUV7OZAPksWnF/V68rM4wSYEgPiJYlFFMUuBldYZPyBWhvUCBZI1wD+FjoUa4A0yR4giTpMiTJMxmeuiu8U9Mk0MgmNEzFCkTxE8LMUq4Gz9P6zP4CdYJ8tP6DG14QtvDw+MXC09oN0nv9tWFdTM4Tv34dinBrhHQG7fBa3uhjHEIkRL8wOW73esaHRxz8pTiyadMCUrIZ7ZPPgV9/e6BNSVbsH2bZmyMZfePtWtNvJp1Q5J771Q8t9eUi3R3wB0fFqwbcgv3oxMOR2YEH9opiIXMFMsjMya+qXdF3Ba0YGDI4dC4IJOHWAC2DmkK2v2x3dJl8b9dH+THxx3GFzV9CcHtGyy2dLmF8k+OOiRDkKhYzSVCAJqfHHV+7qL6QpNPAZYmEDCvfSAARUeRT9WUorzPgvd8aLQkpBEmHJvumpKimJBMOG67wKtkCz9wpkCbTHYOhywON0n3bZcRleUB+wwztiKvISxKnPBludc32PQUyXOd7w66eMQ5yYxdIKshKqDTJ7nb6netO0OKABb+yt0kPxKNxRlSrnUthMhTXs5kAxSwaakR7lOkeJkThPCTIESBMi9zgqtZ5xLbUkvGmCZAgAA+bBzGmaFfd7keL6uzRHB39gbwk9U1hv8eHh4evwB4QrtJ3ngYXvgGLE5Cogeu+1XYeU9zj9U+CLOnQAqQPjNEplyAjjXudRtugFdeNSJba1P/bEsTr6ZRW8Ef/UgzMWrKCqIxKJdgYtTEf+s3V9YtlYTs2L4i0tJZTdsqemPdkGTd0Huf75NvKuJhSFRs6hJhAM2TbyqX0Pb5FEcWoKVV02VByYEjC3BVvL5+eEtXvbCuZTyl6am5Yx4LmvgHgUaaFxtd1+EIFqSg4GiCEooKHCnocOqr199PwXux6LV8LCpnOZMNkNGqzu1k2Irycbp5RS0wS4kOAtwkO+rqs39iT3GqZBMSFrHKgJ5TJZufMMVXAusuyDlky35GczGsYIawLGMrP6OZGNmIn+qEu42sy79bQKnGRGor3TzHKcBksgvY5LG5Anf9+BEmCeEnVBHkS9sjTLqEtkIgarofBAJVE4uKKEVdMpnsCiXKRIVnP+jh4fGLh2fv1wRvPAyP/XuTNYx3me1j/97Em+EjvwWBGNgOlPJmG4iZeDUjI7DpSugfhI4Ws910pYlX06it4NHDpu46EFyZOBkJm3g11+8WZLKCdFajtCad1WSygut3N9dyODEPsZrKhFjIxKvRFsYWQgtAmK2uxJugLynI1EyfzBRN/GKz1Ly4mNf0JDWLec23X3A4Muk0tW5Ln2R7TBCyBGnbeIJvjwm29P3z/MrfFIqRVopF5aC0ZlE5pJXiplB9ScewFeVefz+/6V/Lvf7+VZsgDzt5gkISFBKBWP7/YSd/wc7hqUKGFqL0l/voLQ7TX+6jhShPFdx+mz26lSI2DmaIj4NDEZse7W5IXHInCeMnRZEw/lUbIVPkCdbkXIL4SOE+V0dquunEwqKMjYVFN5040n2hOiwGKVOkqEtorSnqEmWKDIsaw38PDw+PXwC8jHYTvPANCEZN1hhWti98o7ms9q6PGaOxZ78NqSlIdsMNX1yKrzA7CZ1D0LVmJaaViVfTsK2gAmpFq6jEq2i0JKRReluNC0aiarhfpmDi1ZQcwRVrakar9wpKq2RlG+HWTRbffMUGNLGgEdmpAnx658UvG3nibUUiVJ/lr21ebHTdnisFE49ZXBZzu5jsufLiX1RcCDYGQnwh1upyHflEJHnOpsx3o+xIgtL9RbCEpuhcuAuVRstf9ogBfqBLOORwsBFYhEiyR9T7hvaSOGfjY5IwBcrLmWyAIjZJ3NM340QoyhJ9VX7dRdyZa4A22cY2trpcRzaJ9V4jpIeHxy8kntBugsVJk8muJhQ38WbZ9THBro+995qOHsim3SPXc1kTr6ZRW8EtG+GNt002e9neLw87L6l/7kZKQhrl5u2S7zyrAL1co53Owz1XukVMb1KwmJfsrvKvXsxrOsPNicUt3RZfvsrtJvLpnT//psfVGF8wNnzVxEIm3sy683Ex+edCI24njTKk45xmHoGDhcRBUUAxrJPn/uEmabT8ZVDG+Lhax+v6LGcp0k6QXaK9brBNo2ymh5c5AZhMdhGbAmUuxZ2BXk8/r3MUMDXXJk9dZitr6x6zTbZ5jY8eHh4eeEK7KRI9RriGq/7mFtImfiG54ib40ffM/5dHq6fhhrvd6xq1Fbzj48bebyFjHEICPujvNPELyaZei/tuMLXaE/Mmk33PldJVnw1w6xbJX71ksnlLgnyxqPn0ruaF8cVwE2mEvpbVs/x9Lc2tg/fXxeQXjbuCHfx/ORvHn8cRNmiLcDnOXZGOc/9wk9wUivHdjKmfWh72oxSfiNSL+0EZY/AcTieN0k2Sq1nnch25lME615FO0cIuvYl3GFseWLOVtXSKVT58Hh4eHh4ACN3sSLQPGLt379Z79+79uTzXUo12MGoy2YU0FLNw5//VfENko4wc0+x7ypSLdPQY8T20sXlhfPqE4uXnYGYKOrvh6utheN0HJ+t5ZNLhJ4cVEylNb1Jw65bVGwT/qVM9YGb5oqIAX7zOcp1vo+s8fnYaGYDzz+E5PTw8PDzOH/H/t3fv0XJW5R3Hvw+kouESEBGoROMtarVVaYqILQSp4A1Fira1KKBoca3q8oLaWi/Ym9ZLq/Vuraagq6JS6upaYKwBDKDVpsSCVcQLJ+ICNNwJ4SLw9I+9DwzTmSRnztlzy/ez1rvezJ497+zzPpk5v3nPnveN+O/MXLHVfgbtwSzkWUckWNizjkiSpHYM2pIkSVID2xq0x2eOgCRJkjRFDNqSJElSAwZtSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ0YtCVJkqQGDNqSJElSAwZtSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ0sGvUANDdXXJZctAauvQr23Bf2PwyWLo9RD0uSJEldPKI9Qa64LFl9KtxyEzxw77JefWpplyRJ0ngxaE+Qi9bA4l1h590gdijrxbuWdkmSJI0Xg/YEufYqWLzLfdsW71LaJUmSNF4M2hNkz31h86b7tm3eVNolSZI0XgzaE2T/w2DzzWVudt5d1ptvLu2SJEkaLwbtCbJ0eXDES8vc7Ot+XtZHvNSzjkiSJI2jkZ/eLyKOAQ4BngQ8EdgV+FxmHjvSgY2ppcuDpctHPQpJkiRtzciDNvBWSsDeBPwMeOxohyNJkiTN3zhMHXkdsBzYDXjViMciSZIkLYiRH9HOzHNn/x3hXGNJkiRNh3E4oi1JkiRNHYO2JEmS1MBEB+2IeGVErIuIdRs3bhz1cCRJkqR7THTQzsxPZuaKzFyx1157jXo4kiRJ0j0mOmhLkiRJ48qgLUmSJDVg0JYkSZIaMGhLkiRJDYz8gjURcRRwVL25T10/NSJW1X9fk5knD31gkiRJ0jyMPGgDTwKO62p7RF0ANgAGbUmSJE2UkU8dycxTMjO2sCwb9RglSZKkuRp50JYkSZKmkUFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGojMHPUYFkREbAQ2jOCpHwRcM4LnVW/WY3xYi/FhLcaL9Rgf1mJ8TFotHpaZe22t09QE7VGJiHWZuWLU41BhPcaHtRgf1mK8WI/xYS3Gx7TWwqkjkiRJUgMGbUmSJKkBg/b8fXLUA9B9WI/xYS3Gh7UYL9ZjfFiL8TGVtXCOtiRJktSAR7QlSZKkBgzakiRJUgMGbUmSJKkBg/YAImK/iPh0RFwZEbdHxExEfCAi9hj12KZRRBwTER+KiPMj4qaIyIj47FYec1BEnBUR10XErRFxcUS8NiJ2HNa4p1FE7BkRJ0bEmRHxo7pvb4yICyLi5RHR8z3FerQREX8bEWsi4oq6X6+LiPUR8Y6I2LPPY6zFkETEsfX9KiPixD59nhsR59XX0aaI+FZEHDfssU6b+ns5+yxX93mMr42GIuKw+rvj6pqdroyI1RHx7B59p6YWfhlyjiLikcA3gAcDXwYuBQ4ADgV+ADwtM68d3QinT0R8B3gisAn4GfBY4HOZeWyf/s8HzgBuA04HrgOOBB4DfCkzXziMcU+jiDgJ+BhwFXAu8FNgb+BoYAllv78wO95YrEc7EXEHcBHwPeAXwM7AgcAK4ErgwMy8oqO/tRiSiFgKXALsCOwCvCIzP9XV50+ADwHXUupxB3AMsB/w/sw8eaiDniIRMQPsDnygx92bMvN9Xf19bTQUEe8B3kj5HX425QqQewG/CXwtM9/U0Xe6apGZLnNYgNVAAq/uav+72v7xUY9x2hbKh5hHAwGsrPv5s3367kYJHLcDKzra70/5gJTAH4z6Z5rUBXg65Q1vh672fSihO4Hfsx5Dq8f9+7T/dd23H7UWI6lLAF8Dfgy8t+7bE7v6LKMEiWuBZR3tewA/qo956qh/lkldgBlgZhv7+hFh0F4AAApSSURBVNpoW4tX1H24Crhfj/t/ZZpr4dSROahHsw+nvIA/0nX3O4BbgJdExM5DHtpUy8xzM/OHWV9tW3EM5VPy5zNzXcc2bgPeWm++qsEwtwuZeU5m/ntm3t3VfjXw8XpzZcdd1qOhuh97+UJdP7qjzVoMz2soH0pPoPxe6OVlwE7AhzNzZrYxM68H/qbePKnhGHUvXxuNRMROlA/+PwVemZl3dPfJzF923Jy6Wiwa9QAmzKF1/dUeQePmiLiQEsQPBNYMe3ACyi83gK/0uG8tsBk4KCJ2yszbhzes7cLsm+WdHW3WYzSOrOuLO9qsxRBExOOAdwMfzMy1EfH0Pl23VI+zu/poMDtFxLHAQykfeC4G1mbmXV39fG208wxKcP4AcHdEPAd4AuWvOd/OzG929Z+6Whi05+YxdX1Zn/t/SAnayzFoj0rfGmXmnRFxOfB44BHA94c5sGkWEYuAl9abnW+Q1mMIIuJkyjzgJZT52b9NCRXv7uhmLRqrr4PTKEfv3rKV7luqx1URcQuwX0QszszNCzvS7cY+lHp0ujwiTsjMr3e0+dpo57fq+jZgPSVk3yMi1gLHZObG2jR1tXDqyNwsqesb+9w/2777EMai3qzRaLyb8gZ6Vmau7mi3HsNxMmX62mspIfsrwOEdv7zAWgzD24EnA8dn5q1b6but9VjS535t2WeAwyhhe2fg14FPUObGnx0RT+zo62ujnQfX9Rsp86t/B9gV+A3gq8DBwBc7+k9dLQzakuYlIl4DvIFyBp6XjHg426XM3CczgxIqjqYc7VkfEfuPdmTbj4h4CuUo9vt7/DlcQ5aZ76zfKfl5Zm7OzO9m5kmUExc8ADhltCPcbszmzDuB52XmBZm5KTMvAV5AOQvJIRHx1JGNsDGD9txs7QjDbPsNQxiLerNGQ1RPT/ZByunlDs3M67q6WI8hqqHiTMoUtj2BUzvuthaN1Ckjp1L+3P22bXzYttaj35E9DWb2S9sHd7T52mhndp+t7/zSL0CdEjX7F9AD6nrqamHQnpsf1PXyPvfPfsO/3xxutde3RvWX4cMpn6x/MsxBTaOIeC3lHMDfpYTsXheBsB4jkJkbKB9+Hh8RD6rN1qKdXSj79XHAbZ0XR6FM6QH4x9o2e17nLdVjX8p0h585P3vBzU6n6jw7mK+Ndmb3bb9gfH1dP6Cr/9TUwqA9N+fW9eHdV8CLiF2Bp1G+Efufwx6Y7nFOXT+zx30HA4uBb0zKt5XHVUS8Gfh74DuUkP2LPl2tx+j8al3PnmHBWrRzO/BPfZb1tc8F9fbstJIt1eNZXX20cA6s686g5mujnTWUudm/1ufKwbNfjry8rqevFqM+kfekLXjBmlHv/5Vs/YI1G5mik92P20L503gC64AHbqWv9WhXh+XAkh7tO3DvBWsutBYjr9Mp9L5gzcPxgjWt9vnjgJ17tC+jnB0sgbd0tPvaaFuPL9d9+Lqu9sOBuylHtZdMay28BPsc9bgE+/eBp1DOsX0ZcFB6CfYFFRFHAUfVm/sAR1CORpxf267JjksV1/5fovwS+zzl8q3Po16+FXhR+h9/IBFxHOXqXndRpo30mj86k5mrOh5jPRqoU3feRTlSejklsO0NHEL5MuTVwGGZ+b2Ox1iLIYuIUyjTR3pdgv3VwD/gJdgXVN3nb6Ccd3kDcDPwSOA5lMB2FvCC7Lh4iq+NdiJiP0puWko5wr2e8kHzKO4Nzmd09J+uWow66U/iQvnP8hngKsob4wbKydj3GPXYpnHh3iNC/ZaZHo95GuXN9HrgVuAS4HXAjqP+eSZ52YZaJHCe9RhKLZ4AfJgyfecayrzFG4H/qnXq+dcGazH0Os2+Zk7sc/+RwNcpYfCWWr/jRj3uSV4oHzb/hXImpBsoF9PaCPwH5Xz/0edxvjba1WQvysGZDTU3XQOcCRww7bXwiLYkSZLUgF+GlCRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUnboYhYFREZEcsaP89MRMy0fA5JGlcGbUnSwCLivIjwymeS1MOiUQ9AkjTVDhv1ACRpVAzakqRmMvPHox6DJI2KU0ckaQ4iYlmd27wqIh4bEf8WEddFxC0RcUFEHN7jMTtFxJ9GxCURsTkiboqI8yPiRQu0/VPqY1ZuaXvb+PMdHxFnRMRPIuLWOtYLI+LYXtsFDqm3s2M5r6Nfzzna89gnyyLi8xFxTUTcFhHrIuK52/KzSdKweURbkgbzcOCbwCXAJ4B9gd8Hzo6IF2fm6QARcT9gNSWQXgp8BFgMHAOcHhFPysy3DLr9Bj4G/C+wFrgK2BN4NnBaRDwmM99W+90AvBM4HnhY/fesmS09wTz2ycOAbwM/AU4DHkjZJ1+OiN/NzHPn+sNKUlOZ6eLi4uKyjQuwDMi6vLfrvhXAL4Hrgd1q25/VvmcBizr6PpgSSBM4aNDt1/ZTav+VWxjvqq72VbV9WVf7I3ts437AmvrcD+m677zyq6Tv/poBZrra5rNP3tG1rSNmtzXq/xsuLi4u3YtTRyRpMDcCf9HZkJnrgM8BuwMvqM0vowTB12fmnR19fwH8Zb154jy2v6Cyx5zqzLyDctR5EQvz5cZB98kG4K+6xrYa+ClwwAKMS5IWlEFbkgZzUWbe3KP9vLp+ckTsCjwKuDIzL+3R95zZvoNsfw5j3WYR8dCI+EhEXFrnTmedi31G7fKQeW5/PvvkO5l5V4/2K4A95jMuSWrBOdqSNJif92m/uq6X1AXKXOdeZtt3H3D7CyoiHkGZA70HcD7wVcqR9bso0zeOA3aa59PMZ5/c0Ocxd+KBI0ljyKAtSYPZu0/7PnV9Y10627rt29F3kO3Puruue72n9wqs/bye8uXHEzJzVecdEfGHlKA9X/PZJ5I0UTwCIEmD2b9Og+i2sq7X16kfPwYeEhGP7tH30Lq+aJDtd7RdX9dLe/Rf0aOtn0fV9Rk97jukz2PuAoiIHbflCea5TyRpohi0JWkwS4C3dzZExArgjyhHY8+szZ8GAnhvZxiNiAcBb+voM+j2oUz3ADghIhZ19F/avY2tmKnrlV3PewS9v5wIcG1dP3QOzzPoPpGkieLUEUkazFrgxIh4CnAh957negfgjzPzptrvfcCzgOcD/xMRZ1HOGf1Cyuns3pOZF8xj+2TmtyJiLXAw8O2IOIcy9eRIyvmqex3p7uWjwAnAFyPiS8CVwBOAZwJfqM/fbU39Wf61/my3Ahsy87QtPM+g+0SSJopHtCVpMJcDB1GmbZwEvIgy3eHZ2XExmXpqvGcAf16bXk2Z6/xD4MWZ+eb5bL/D84FPAfvV53gy8Cag3/b/n8y8mDJ14xvAc4BXAbsBRwMf7/OwTwHvohyBfxPl9Hwv38rzDLpPJGmiRGaOegySNDEiYhklBP9zZh4/aduXJA2PR7QlSZKkBgzakiRJUgMGbUmSJKkB52hLkiRJDXhEW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ0YtCVJkqQG/g9x3S8AebKMkAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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tZu8jWgpsypQpJyxbtmyMJoqIiIiI7JtVq1btcPdZY5WrlGC/LKINav4LeA/wcLSBxwvAqYSNd54EllHEpjcxrnk10Xq/7e3tvnLlynJVLSIiIiJSkJmti1OuUpbezPSet45wPJPfEaOu9wLvB54ibB7yfsImN6cDz0ZlsneoLOe1RUREREQqRqX07D8VpUtGOH5ElI40pv9FHpYXerG3PZuZHU3o1c/eCfApoD269qq88jWE8f/DFJj0KyIiIiJSySqlZ//3UXqWmeW0KVp682TCqjh/KvUC0RJ7C4Gb3D17fP6tUXp2gdNOI6ydfbdW4hERERGRyaYign13fxZYQVha7LK8w58FpgDfy15j38yWmdles2GjTbLy8w4B/hMYBP4h7/BPCKv4vNXM2rPOaQD+KXr5zSJvSURERERkwlXKMB6AvwLuBr5qZmcSlrl8OWEN/tXAp/LKZ5bBzF+K7L+i4P5+wuTcQ4E3ALXAhe7+cHZhd+8ys/cSgv7bzOyH0XlvICzL+RPgR2W5QxERERGR/agievbhxd79duBaQpD/ceAw4CvAK9x9Z8yqbgSGgLcAfwOcQgjYj3X3gkF7tDvfKwmbaL0J+GBUx8eAt3olbDMsIiIiIlIkUxxbPlp6U0RERET2BzNb5e7tY5WrmJ59EREREREpLwX7IiIiIiJVSsG+iIiIiEiVUrAvIiIiIlKlFOyLiIiIiFQpBfsiIiIiIlVKwb6IiIiISJVSsC8iIiIiUqUU7IuIiIiIVCkF+yIiIiIiVUrBvoiIiIhIlVKwLyIiIiJSpRTsi4iIiIhUKQX7IiIiIiJVSsG+iIiIiEiVUrAvIiIiIlKlFOyLiIiIiFQpBfsiIiIiIlVKwb6IiIiISJVSsC8iIiIiUqUU7IuIiIiIVCkF+yIiIiIiVUrBvoiIiIhIlVKwLyIiIiJSpRTsi4iIiIhUKQX7IiIiIiJVSsG+iIiIiEiVUrAvIiIiIlKlFOyLiIiIiFQpBfsiIiIiIlVKwb6IiIiISJVSsC8iIiIiUqUU7IuIiIiIVCkF+yIiIiIiVUrBvoiIiIhIlVKwLyIiIiJSpRTsi4iIiIhUKQX7IiIiIiJVSsG+iIiIiEiVUrAvIiIiIlKlFOyLiIiIiFQpBfsiIiIiIlVKwb6IiIiISJVSsC8iIiIiUqUU7IuIiIiIVCkF+yIiIiIiVUrBvoiIiIhIlVKwLyIiIiJSpRTsi4iIiIhUKQX7IiIiIiJVqqKCfTObb2bXmNlmMxsws7VmdqWZTSuynlPM7Ibo/H4zW29mN5vZ2SOUT5rZO8zsTjPbama9ZrbazL5jZkeW5+5ERERERPavign2zewwYBXwLuBe4ArgOeDDwB/NbEbMej4A3AmcGaVXALcDrwR+ZWafKnDa/wD/DSwCfgZ8DXgGuBi438zOKPnGREREREQmiLn7RLcBADP7DXAW8CF3/1pW/peBjwLfcvdLx6ijFtgO1APHuftTWceWAw8AaWCauw9E+ScSHi4eA17m7r1Z57wLuAb4vbuPGfC3t7f7ypUrY96xiIiIiEhpzGyVu7ePVa4ievajXv2zgLXA1/MO/yOwG7jQzKaMUdV0oBVYnR3oA7j7E8BqoBFozjq0OEpvyQ70IzdE6awYtyEiIiIiUlEqItgHXhWlK9w9nX3A3buBu4Am4BVj1PM8oWd/iZkdkX3AzJYARwAPuvvOrEOPRekZZtaYV9/rovR3se5CRERERKSC1Ex0AyJLo3T1CMefJvT8LwFuGakSd3czu4ww/n6VmV0PbAbmAW8kBPZvzTvnUTO7gjBU6EkzuxHoBo4EzgZ+CPxDifclIiIiIjJhKiXYb43SzhGOZ/LbxqrI3X9sZpuBHwAXZR3aBnyHMOk3/5yPmdlThMm8f5V1aBXwXXffPdL1zOx9wPsAFi5cOFbzRERERET2m0oZxlM2ZvZOwrCbO4HlhOE/ywnfCFxF6KnPLm9m9lXCXIHPAQuAqcCpgBNW8LlspOu5+9Xu3u7u7bNmaWi/iIiIiFSOSgn2Mz33rSMcz+R3jFZJNC7/GsJwnQvd/Ul373P3J4ELCT31bzGz07NOuxj4IPBVd/8Xd9/o7j3u/gfg9UAf8C9m1oyIiIiIyCRSKcF+ZuWcJSMcz0y2HWlMf8ZZQC1we4GJvmngjujlCVmHMpNwf59fmbtvBZ4krN6zNP+4iIiIiEglq5RgPxNon2VmOW0ys6nAyUAv8Kcx6qmP0pHG02TyB/fxHBERERGRilcRwb67PwusIOxgmz8+/rPAFOB72RNlzWyZmS3LK3tnlL7ZzI7JPmBmxwFvJozDv7XAOR8zs9a8cy4F5gNbgceLvC0RERERkQlVKavxQFgF527gq2Z2JvAE8HLCGvyrgU/llX8iSi2T4e73mtl3gHcB90VLb64jPEScD9QBV7r7Y1n1fAN4B3AMsNrMfkGYG3A8cAaQAi5z91T5blVEREREZPxVTLDv7s+aWTthRZyzgXOBLcBXgM+6+66YVb2HMDb/EuC1hJV1uoA/AN9295zVeNy9x8xOBj4GXAC8nfBQsB34MfAld7933+5ORERERGT/M3ef6DZUjfb2dl+5cuVEN0NEREREqpyZrXL39rHKVcSYfRERERERKT8F+yIiIiIiVUrBvoiIiIhIlVKwLyIiIiJSpRTsi4iIiIhUKQX7IiIiIiJVSsG+iIiIiEiVUrAvIiIiIlKlFOyLiIiIiFQpBfsiIiIiIlVKwb6IiIiISJVSsC8iIiIiUqUU7IuIiIiIVCkF+yIiIiIiVarswb6ZvcfMril3vSIiIiIiUpzx6Nk/Bbh4HOoVEREREZEiaBiPiIiIiEiVqhmrgJm9u8g6jyixLSIiIiIiUkZjBvvAfwJeRJ1WZHkRERERERkHcYL9IWAL8J2YdZ4PHFNyi0REREREpCziBPuPA7Pd/bNxKjSzRSjYFxERERGZcHEm6D4AzDaz2ePdGBERERERKZ84wf5DhHH4x8Ws80ngjpJbJCIiIiIiZREn2P86MA24NU6F7v6v7v6qfWqViIiIiIjss1HH7JtZC9Dv7p37qT0iIiIiIlImY/Xs7wL+LvPCzK4xszeMb5NERERERKQcxgr2nTBeP+MS4o/dFxERERGRCTRWsL8FOHx/NERERERERMprrHX2bwXeYWYzCYE/wPnRWvqjcXd/zz62TURERERE9sFYwf7/BWYDryF8C+CEYTxjDeVxQMG+iIiIiMgEGjXYd/dtwNlmVgvMBdYCVwJfGf+miYiIiIjIvhirZx8Adx8C1pvZOmCtu68b32aJiIiIiMi+ihXsZ7j7oaVcxMwuBi529zNKOV9ERERERIoXZwfdclgEvHI/XUtERERERCiyZ19EREREpCjrV8PKW2DHFpg5F9rPhIVLJrpVBwwF+yIiIiIyPtavhpuvgylTYcZs2N0VXp970d4Bf9yHgkovV2HM3cf/Imb/CHzG3ZPjfrEJ1N7e7itXrty/F/3IR+DBB/fvNUVERETi2LEZUsOQzOpfzryeefCevIE+2LkVkklIJCGdglQKZsyB+sbKKnfYIfCXZ8Pu7sIPLfuJma1y9/axyu2vMfsiIiIiUm0G+kJAv2VtSAf6co8PDYYgOlsiGfKzde8KgXSyBsxCmkyG/Eorh8GUlvBtxcpbinizJoaG8Ux2V1450S0QERGRA1H2EJ2mZujt2bu3+2ffDEN3prTsOS/z+oIP7Mm7+jNhmI9l9UN7GnZug/d9rjLLNTWHIT0VTj37IiIiIlK8lbeEQH9KSwiCC/V2t58ZHgB2d4UgendXeN1+Zm5dM+eGh4VsvT0hfzKVq0AK9kVERESkeDu2hN7tbPm93QuXhJ7+KS2ht3xKS+Fx7nEfCiq9XAXSBN0ympAJuiIiIiITIe4QnbgqfZWdCluNJ+4E3f0V7J8HnO/u7xr3i00gBfsiIiJywIgzZl/GTUWtxuPuN1R7oC8iIiJyQIk7REcmVNGr8ZjZdODdwMuAaUChoTnu7pU/iElERERESrdwiYL7CldUsG9my4DbgFmAjVJ0/McGiYiIiIjIqIodxvMl4CDgX4HFQK27Jwr8VPVEXBERERGRyaDYYTynAje5+yfHozEiIiIiIlI+xfbsG/D4eDRERERERETKq9hgfxWwdDwaIiIiIiIi5VVssP854FwzO30c2oKZzTeza8xss5kNmNlaM7vSzKYVWc8pZnZDdH6/ma03s5vN7Owxznuzmf3GzHZknXeDmb1i3+5MRERERGT/K3bM/gLgBmCFmf2A0NPfUaigu19XTMVmdhhwN2EC8A3Ak4TlPT8MnG1mJ7v7zhj1fAD4BrAbuB7YCMwHLgDOMbN/cPcv5J1TA3wXeDvwNPAjoBOYA5wEnAD8qZj7ERERERGZaEXtoGtmacKymtnLbuZXYIR19otakcfMfgOcBXzI3b+Wlf9l4KPAt9z90jHqqAW2A/XAce7+VNax5cADQBqY5u4DWce+AHwS+ALwGXdP59fr7kNj3YN20BURERGR/SHuDrrFBvsXxy3r7t8tot7DgGeAtcBh2cG2mU0FthAeIg5y992j1DMb2Ao87O7HFjj+MHA0MDPzLYGZzQHWAfe7+0lx21yIgn0RERER2R/iBvtFDeMpJoAv0quidEV+r7q7d5vZXYRe/1cAt4xSz/OEnv0lZnaEuz+dOWBmS4AjgAfzhgO9GagDfmhmjcCfA4cD3cAf3P2hfbs1EREREZGJUeyY/fGSWeFn9QjHnyYE+0sYJdh3dzezy4D/BlaZ2fXAZmAe8EbgMeCteaedGKVNhHkCC7MPmtlPgYvcvTf23YiIiIiIVICSgn0zayJMeH0p0EaYzHo/cP1ow2xG0RqlnSMcz+S3jVWRu//YzDYDPwAuyjq0DfgO8FzeKQdF6eeBu4DzCQ8dRwFXAW8CeoBLCl3PzN4HvA9g4cKFhYqIiIiIyGT3yCNww/WwYQMsWADnvRGOPnqiWzWmYpfexMzOJYxx/y5h4uy7gI9Er9ea2evK2sIimdk7gd8BdwLLCT32ywnfCFwF/DDvlMx78ALwend/wN13u/s9wBsIgf6FZjav0PXc/Wp3b3f39lmzZpX/hkRERERkYj3yCHzly9CxC+bNC+lXvhzyK1xRPftmdjzwMyAJfB+4lTB5di5wBvA24CfRMpmriqg603PfOsLxTH7BZT6z2rcEuAZ4GLgwa/z/k2Z2IWG40FvM7HR3vy2vzlvcvSu7PnffYmb3AGcC7cCmmPcjIiIiEyjdtQG23Q/9L0DDdJh9PImWBRPdLJmsbrge2tqgLdr6KZPecH3F9+4X27P/KcJSm6e6+0Xufq27/yZKLwJOiY5/ssh6M0tkLhnh+BFROtKY/oyzgFrg9gITfdPAHdHLEwpce6QHiV1R2jjGtUVERKQCpLs2wNoVMNQL9dNCunZFyBcpxYYN0JLXJ93SGvIrXLHB/qnAj9294AZT0dCXn0TlivH7KD3LzHLaFC29eTLQy9gbW9VH6UjjaTL5g1l5v4vSo0Y458goXTPGtUVERKQSbLsfapqgtgnMQlrTFPJFSrFgAXTlTS3t6gz5Fa7YYL8VGOsRZj3QUkyl7v4ssAJYBFyWd/izwBTge9mTf81smZktyyt7Z5S+2cyOyT5gZscRltl0wvCj7HMeBE4xszfmnfNewnj/ZwAtoC8iIjIZ9L8ANXlfyNc0hnyRUpz3RujoCGP10+mQdnSE/ApX7Go8m4GXjVGmnTCOv1h/BdwNfNXMzgSeAF5OWIN/NWEIUbYnovTF3Xzd/V4z+w5h0vB90dKb6wgPEecT1tO/0t0fyzrHo83Cbgd+ama/jK53JHAOsBu42N1TJdyTiIiI7G8N08PQndqmPXnDfSFfpBRHHw3nvA6+fhVs2hQm6V721xU/Xh+K79m/GTjDzP7OzJLZB8wsYWYfB14dlStK1LvfDlxLCPI/DhwGfAV4Rd5GWKN5DyHY/yPw2qie1wB/AN7m7h8tcO2HgeOB6wjr7n+EsKzo94ET3P3uYu9HREREJsjs42G4NwT87iEd7g35IqV4+GG44QY4+hj4y7eG9IYbQn6FM3ePX9hsDrAKmEMYrnMnoRd/DmFy7iJgK9Du7qX07k9q7e3vv1l/AAAgAElEQVTtvnKlRvuIiIhMNK3GI2V1+eWwaxdMm7YnL/P68ssnpElmtsrd28cqV9QwHnffamYnA98i9JYfklfkt8ClB2KgLyIiIuMvbhCfaFkAZQzu415X5fYuV8lti239epg/PzevtTXkV7iiN9Vy97Xu/lpgAWHTqQujdIG7v9bdtWqNiIiIlN14LKmZ7tpA+ukbSD/ynZAWqCvudVVu73KV3LYXPfII/NPn4P3vDWmhjbIWLoTOvNV4OjtDfoUrOtjPcPdN7n6ju38/SrXhlIiIiIyfMi+pGTsgjHvd8SjX3QePPwR//F1Iu/v2z3V39sBd98Avfh7SnT2l1TeR712ccnF3xr3ggjBsZ1e0Gk/m3xdcQKUrdjUeERERkYnR/0IIyrPty5Ka2QEh7Em33Z87BCjudctdbusz8OyzWF09NDXD4AD+9OOQHtiz3Wimvt5h2PAQ7O6BKc2w4DBo6t/7unHKrX8K/rgKG+wDT0PXC/i2LXBSCdctd9vKXS7uzrjHHAN/8zfws5+FoTsLF8J73hPyK9yowb6ZXUO0I667b4tex+Hu/p59bp2IiIhIRrmX1IwbdMe9brnLbduO1SShriG8rmvA0sP4tu255XpT8Nh9WMOUPQ8Fj90HR76stHKPPY317ILGJkjUQjqF9ezCH3saziyyvnK3rdzlNmwIPfrZRtoZ95hjJkVwn2+snv1LCMH+vwLbotdxOGEJTBEREZHymH18GHYDISgf7gtLas4/pbT64gbdca9b7nLpOkj2QnoIEjWQHoakhfxsu3rDQ0FNMuw+VJPEapL4rt7Syu3sgLpaSFgol7DwemdH8fWVu23lLrdgQRi605b10DdJdsaNa6wx+4cCi4Hnsl7H+Vk8Ho0VERGRA1eiZQEsOisE5wO7QrrorNJXWIm5Hn/c65a93KzF0Dg39K4P94e0cW7Izy7X1wezluWWm7Us5JdSrq4ealvAkuEBw5JQ2xLyi6yv7G0rc7nJvDNuXKP27Lv7utFei4iIiOxP5VxSM9GygPSis3KXZ5x/yj4t5VnWcstPxe7+X2g6GBqmQP9u6O+B5afmlmubjfV1w4yle/L6uqFtdmnllh6LPXQPNE6BhnoYHIC+3XDs8cXXV+62lbvc0UfDhz8Wxuhv2BB69C9+96TYGTeuoibomtlngNvc/Y5RypwKvMrdP7evjRMREREZT+Vejz+2rc/CE3dCx7YQgC4/FeYclltmzmHwZ3+RW+74c/cut/xUuPt/w7+zHwqOP7e0cqdcAF3bYft22N0NDQ1w6OKQX2x95W5buctBCOyrKLjPV+wOumng8tECeTP7FPA5d0+WoX2TinbQFRERGV/pro3483t64u2g40m0zC+53ITY+mwIRBuacwPRP/uLvQP5Yuoc6+FhospVctsmsXHZQTemWiA9DvWKiIjIASzdtRFftwKvadqzLv66FaQPOSsnkI9bblzsWAtr/gjd22HqLDj0JJi5KLfME3eGQL9xanidSZ+4s/Rg1B2G0zCUDulInblzDot3jXKWm4hrFlOuyo1HsH88sGMc6hUREZEDmD9/P17ThGWti+8Az98PWUF83HJQ5m8KdqwlvfJ/YLgbSEHvFti5hkT723MD/o5tuA/BplUw1Ae1jTDjcGwgb6WYuNfd8gzpW74D6R5IpGHLVtj2LIkz3wVzD4/z1koVG3MHXTO7NfMTZV2SnZf1c7uZPQf8OXDLuLZaREREDjz9L4SlKrONtGlVjHIvfgMw1IvXTwvpuhWkuzaWVu7J30D/dkgkwmZdiQT0bw/52eUYho0PwPBQtPTmEGx8IOSXct1VN8Lg9rDMZE1TSAe3h3w54MXp2T89698OLIp+8qWBncCPgI/uY7tEREREcpV506qyf1Ow85nQS5+sDa+TteH1zmdy25fehbthGCG0MtwN0rtKah/bngpj/7OvWz8l5MsBb8xg391f7P2PM0FXREREZDzYQcfDuhUh4I02o7LhXmzeKSWVi72DbtxyNQlI542VT3vIz2ngIMw7BN/1Agz0Q30DzJod8vOv2zOErXsAerqguQU/5HBo7s8t11AbxulnR3XD6ZCfb8sz8OgdsGsbTJsNR52moT5VbsxhPHneBfx8PBoiIiIi46t3cBubuu5kza4b2dR1J72D2ya6SUVJtMzHDjkLq23CBnaFtMCk27jlaJgeevyzjfRNQYxyfvCyMAZ/aCDapGsAhvpCfram6VCbgIWHwBFLQ1qbCPnZdg+TePje8EAwZSoM9IfXu3OH+/jhx8BAbyjnHtKB3pCfbcszcMePovXmZ4X0jh+FfKlaRU3QdffvjldDREREZPz0Dm5ja8+9JBMN1CZaGE73s7XnXuY0v4ymutyNhjq6H6Wj6z5Sw10ka1poazmRtqlH7VXfrv7VDKY6qUu2Mq1hyV71jEe5/oZadh00jcFUIipXS9NepeKVK/c3BXbYa/D+Dti1IyynWVcPcw/BDntN7oWXnwMrvwf9QF0TDPaGh4fj3pxb3/YeqLEwBp8orbGQn11u+dnhups2Q083NDXC0qXY8rNzr/voHdBYYBWgR+9Q734VK3k1HjObD8wD6gsdH23jLREREdm/dvWvJplooCbRAECNNbyYnx1Ud3Q/yo6dv8US9ViimVSqjx07fwvwYsAf98Gh0sslWuaTPuSsMAY+s9rNvFMKflMQu9yRbxlz9ZzEIS8Pa5Q/8SvofSH06B/3ZhKHvDy3XG8fPms53rs1PAzUNGKzlmO9ud8yJFrmk37pW/F5Y6zas2tb6NHP1jAl5EvVKjrYN7OzgCuAZWMUPeA21RIREalUg6lOahMtOXlJq2cw1ZmT19F1H5aoJ5nMrGbTSCrKzwT7cR8cdvWvxlLDeN9GhlJ9WLIRq2srWC5ufeUsByFQzl+Os5Cylzvk5ZAX3O9l2mysrxubvnRPXl93GGtfynWnzQ7nZ3r0IXwDUaA+qR5Fjdk3s1cANwJtwFWAAXcA3waejF7/EtAEXhERkQpSl2wl5QM5eSkfoC7Zmps33IVZ7pf2ZvWkhrtefD2Y6oShAYa6n2Gw4xGGup+BoYG9Hhz6+zfjvZvx9BAkGvD0EN67mf7+zTnl4tZXTLlk3j0UerCpeEedBn09IUD3dEj7ekJ+JdQnk0KxE3T/njDC7ER3/3CU93t3vxQ4Cvgn4NXAT8rXRBEREdlX0xqWkEr3M5zux90ZTveTSvczrWFJTrlkTQue91DgPkCyZs+3AsmUMbx7TU4QP7x7DcmU5dY11IubYYlaLErdjORQ7uZRseuLWS7ug03Fm3s4nPaXoSe+Y3tIT/vL0sfXl7s+mRSKHcZzEvALd89+JE8AuLsDnzGzc4DPAm8ucL6IiIhMgKa62cxpflnOJNhZTcfsNaylreVEduz8LSlCj777AJ4eoG3ant7fulSKQSME8oTULeRnq6WO1HAH9Hdh6RSeSEJNPbV5QXfc+uKWm9awhK099wKhRz/lA6TS/cxqyludZjKYe3h5g/Fy11fJnnwMfv1L2LwRDp4PZ78elh050a3a74oN9luB9VmvB4EpeWXuAt6+L40SERGR8muqm11whZtsmXH5OavxTDstZzUeSw8xpX4hA6kXSKX7SSYaaKxfiKWHcuqqtSZ8aJA0Ttog4U7N0CC1Dbnr4sStL265uA82UsWefAy+fRW0tsLcg6GrI7x+718fcAF/scH+88C0vNeH5ZWpBfL2qBYREZGMjtRONqXX0us9NFkz8xKLaEvOKLnc1r7VbO1/gKFUN7XJqcxpeClzGpeUXB9Nc9ndcMyL5doSc3MO19S20Tu0i/6aWoZwaqklmR6iqTZ346lk2nGM4USSlCVIulGbDvml1Be3HMR7sJEq9utfhkC/tS28zqS//uUBF+wXO2Z/NbnB/Z+A15jZEgAzmwO8CXi6PM0TERGpLh2pnawefoRBH6CRKQz6AKuHH6EjtbOkclv7VrN+9+0Mp/tJJpoZTvezfvftbO1bPW7XTTfOoyu1lVSqj1qvIZXqoyu1lXTjvJy6htK72V3fSDphJN1JJ4zd9Y0MpXfnlItbX7pxHn29a2l+fg2ztq6n+fk19PWu3ascQKpnEwPrbqJ/9X8zsO4mUj2bxvhkpKps3ghTc1efYmpLyD/AFBvs/xp4pZlltnj7CqEX/wEzu4+wIs8s4MryNVFERGRy6Ejt5LGhVdw3eDuPDa3aK5AG2JReS63VUWf1mBl1Vk+t1bEpvbakclv7HyBhddQkGkiYRWkdW/sfGLfrPl+3m+HWw8PynKl+kslGhlsP5/m63CC+pyZNIlELjQeRnjIXGg8ikailpyadUy5ufbuGNtDcN0TSIW1G0qG5b4hdQxtyyqV6NjG46RZ8uBfq2vDhXgY33aKA/0By8Hzo7srN6+4K+QeYYoP9bwGnAUMA7n4X8BZgDWE1ni3AB9z9unI2UkREpNLF7Tnv9R5qqcvJq6WOXu8pqdxQqptE3jKTCatnKNU9btft9R6S9TNJTV9O6qATSE1fTrJ+5l51dTdPJ5lOY6lBcMdSgyTTabqbp+eUi1tfzQtPQ91UUs1zSTfPI9U8F+qmhvwswzsfxGoasZqmsApQTRNW08jwzgeRA8TZr4fOTujsgHQ6pJ2dIf8AU1Sw7+5d7n6Pu3dn5V3v7ke5e6O7L3f3q8vfTBERkcoWt+e8yZoZYjAnb4hBmqy5pHK1yamk85aZTPsAtcmpOXnlvG7cuuoaD2b39EPxZB023Icn69g9/VDqGg8uqW2Ng4MMJXP37BxKJmkczD3XB3ZBMm/6YLIx5MuBYdmRYTJuSxts2RzSA3ByLpSwg66IiIjsrdd7aMxboK5Qz/m8xCJWDz/y4vEhBhnyQQ5NLi2p3JyGl0Zj9kOPftoHSPsgcxpOGrfrFlVXfTephiNyyi1JLNqr3HM9d1HTs4Oa4QGGa+oZaJ7JvOaTc8o11c9nuO9ZEqkhEukU6UQST9bS1Ji7VojVTwtDeGqyVv1J9WH1e0/kjW3HWljzR+jeDlNnwaEnwcxFY50lE2nZkQdkcJ+v2B10TzCzz5hZwentZjYnOn5ceZonIiIyOcTtnW5LzmBJzdHUWT197KbO6llSc/Req+LELTencQkLp7ySmkQDqXQPNYkGFk555V6r8ZTzuuW+h6bBQeZ27KImnWIwmaQmnWJuxy6a8nrs61sOZ0pfL8nUMCmDZGqYKX291LfkrhtfM+M4fLgPH+7F3UM63EfNjBLDkx1r4aGfw0APNM8I6UM/D/kiFc7CXlgxC5v9D3AKcIgXONHMDFgL3O7uF5WrkZNFe3u7r1y5cqKbISIiEyAzZr/W6nJ7sQsEt5Krc+tvSaf6SGQNvcm8bp3zmtxyvdtI9u2CqOc+1TiNRNPsnHIQJukO73wQH9iF1U+jZsZxJJv3XrUnlvt+EAL8+qwHt8zrE99WWp0i+8jMVrl7+1jlStlB9/eFAn0Iu+ia2a2ESbwiIiIHjLbkDA5OLWDt8KP0s5sGprCo5igF+jGkhnaRqGnLybNEA6mhXXuXazwIb9ozwMDc9yoHkGyeV3pwn697e+jRz1bXFPJFKlyxq/HMAcZaoHQzMHeMMiIiIlWlM7WD7am1tCVmMD9xOG2JGWxPraUztWOim1bxkrXT8HR/Tp6n+0nmb9IVs1zZTZ0Fg725eYO9IV+kwhUb7PcS1tEfzSxgYIwyIiIik0ac9fO3Dq+h1uqpjVbjyfx76/CaCWjx5NLYehSe6iOd6sPdQ5rqo7H1qJLKld2hJ8HA7jB0x9MhHdgd8kUqXLHB/oPAeWZ5s40iZtYCnBeVExERmfTirp/f593U5K1PX0MdfZ673r3sra5xLs2zTiORbCQ93EEi2UjzrNOoa5xbUrmym7kIjj0/jNHv2RnSY8/XajwyKRQ7Zv9q4AfAb83s/e7+cOaAmR1L2HRrZlRORERk0stePx+gjvoX87PH4zfaVIZ8gFr2bHA1zCCNlrvevRRW1zg3VtAet1zZzVw0McH9tufgqT9A5zZonQ1LT4HZi/d/O2TSKirYd/cfmdk5wEXAA2a2DdgEzANmAwZc5+4/KHtLRUREJkDc9fPn1BzKc4MPAaFHf5hBhnyABbXL9ltby6W/fys9ux9neLiTmppWmqe8hIaGORPdrAPPtufgnh9DQzO0zIL+7vD65W9RwC+xFTuMB3e/BLgUeJwwYfeEKH0MeF90XEREpCrEXT+/NTmTxXXHUmv19NNDrdWzuO5YWpMz92dz91l//1Z2ddxFKtVHMtlCKtXHro676O/fOtFNO/A89YcQ6DdMBUuEtKE55IvEVNIOuu5+NXC1mTUBbUCHu/eOcZqIiMikE3e3WADHSFmCFElSlsCx/d3cfdaz+3ESiQaS0Zr3mbRn9+Pq3d/fOreFHv1s9VNC/njb+DTcfyu8sBWmz4Hjz4D5R4z/dSfC44/Czb+ATRth3nw49w3wknGe9L0fFd2zn83de919swJ9ERGpVnF3ge1I7eTp4YcZ9AEaoom8Tw8/XHDlnko2PNxJItGQk5dINDA83DlBLTqAtc4Oq/5kG9gd8sfTxqdhxfegtwumHRTSFd8L+dXm8UfhP74KnR0w9+CQ/sdXQ36VKKlnX0REpJx2pDt4lo1008tUmjiM+cxMtI194n7Slpwx5uZYm1NrqaU+dyKvh/zJtLFWTU1rNIQnazfbdD81Na0T2KoD1NJTwhh9CD36A7uhvweOPWd8r3v/rdA0FZpawutMev+t1de7f/MvoLUt/MCe9OZfVE3v/qjBvpk9BzjwandfE72Ow939sH1unYiIVL0d6Q4e4CnqqaWZRgYY5AGe4qXppRUV8I+lz3toKDCRty9vIm+la57yEnZ13AWEHv10up90up/WlhPG9bqpnk0M73wQH9iF1U+jZsZx5dsBtxLFWWVn9uIwGTe73LHn7Nvk3DjXfWEr1NXBuofCA0b9FJixIOSXUl/cFYUmotymjaFHP9vUlpBfJcYaxpPIK5MgrLgz1s8+DQ8SEZEDx7NsJIWzjS6eYSvb6CKF8+yYG7ZXlsYRJvI2Ft6apmI1NMxhWtvJJJONpFJdJJONTGs7eVzH66d6NjG46RZ8uBfq2vDhXgY33UKqZ1PplXauhyevh/v/M6Sd68vX4H2VWWWnvzt3lZ1tBfpUZy+G0y6C138ipCMF+tuegzuug1/+W0gL1RX3uvX1sPYhGB6E+qaQrn0o5BdbX9xrTlS5efOhuys3r7sr5FeJUXv23X3RaK9FRET21XY6eIFeaklSTw3DpHieLoYZnuimFeXg5CKeHn4YPGsiLwMsKjCRt9I1NMzZr5Nxh3c+iNU0YjVNISNKh3c+WFrvfud6eObXUNsEjdNhcHd4ffjZ0LqwjC0vUfYqO7AnfeoPpfXax12iM+51G2sh7eHH2fPvxtri7yPuNSeq3LlvCGP0IfTod3eFcftvu4hqMWoPvJl92czOynq9MNolV0REpCz6GCYB1JAEjBqSJKL8yaQtOYMjao6hzurpjybyHlFzTMHx+p2pHTw1cB8P9t/KUwP30ZnaMQEtrhw+sAuy5ggAkGwM+aXYsioE+nVTwCyktU0hvxJ0bgtDY7Ltyyo7cZfojHvdZBqOOhHq6qG3J6RHnRjyi60v7jUnqtxLjoJLPxTG6m/ZHNJLP1Q14/Vh7Am6HwE6gBXR6zXAZ4HPjWejRETkwFFDHQMMkCJFggRp0oBTQ91EN61ocSbydqZ28NzgQ9RaPQ00M+QDPDf4UME1+TtTO9g6vIY+76bRpjKn5tCC6/aXu9z+ZvXTwhCeTM8+QKoPq59WWoW9O0OPfrbappBfCVpnh973hqzdlfdllZ24S3TGvW6m3LGv2JOXf17c+oq95v4uByGwjxPcP/YI3HgDbNwA8xfA686DI48e+7wJNtbY+h4g6y9vEi4YLCIiFW1WYhpttJEkyTDDJEnSRhuzEiUGehVu6/Aaaq2eWqvHzF7899bhNTnlMg8FQz6Q81CQ/y1AuctNhJoZx+HDffhwL+4e0uE+amYcV1qFTTNgKG9V8KHekF8Jlp4SVtXp7wZPh7S/J+SXIu4SnUtPgW0bYdXtcPeKkG7buPd147YvTrly1jUe5eJ67BH4+legowMOnhfSr38l5Fe4sYL9Z4ALzOzlZpYZ5NYWDecZ9Wec2y0iIlViCQdDooZpiWksSMxjWmIaJGpCfhXq8+69vrWooY4+787Ji/tQUO5ycQ32baFz6295YcP/0rn1twz2bSmpHoBk8zzq5p0ZxuwPdmA1TdTNO7P01XjmnhCC+8Hd4B7Sod6QXwkyq+w0TIWu7SHNH19fjLiB7VAKugcgDSQTIe0eCPmltC9OuXLWNR7l4rrxhjDEp60NEomQtraF/Apn7j7yQbO3Af+dnUWYqjEWd/cDbg3/9vZ2X7ly5UQ3Q0Rk0nmeTlazmS76aKGRJRzMQVTnuu5PDdzHkA9Qa3tWNsm8Xlp/4ot5D/bfSgPNmO35Ut3d6aeH4xrOGLdycQz2baFn+x1YshFLNODpfjzVR/Os06hrnFtUXeOmc30Yo9+7M/Tozz2hMibnjpc4y0z+4lthg6ymrOmXmddveP/+be9k88H3hx79RFY/eToNmzfB1741IU0ys1Xu3j5WubFW4/mBma0B/hyYB1wCPAw8WI5GioiIABxEa1mD+7ibdE1EuTk1h/LE4H30+E4GSVNHgmbqWF67LKdco02lK91FN/0MMEQ9tUz1BloSLeNaLo6+zkexZCOJaFKtJRtJR/kVE+y3Lqzu4D7f7MVj91q/sDXsiJutsbnw+vnV4PFHw+ZYmzaGpTTPfUPpE2/nLwhDd9qy/p67ukJ+hRtzPXx3/5O7f9rd3x1lXe/u7xrrp5TGmNl8M7vGzDab2YCZrTWzK82sqIGbZnaKmd0Qnd9vZuvN7GYzOzvm+f9gZh79vLqUexERkfLbke7gnvSj/C59L/ekH2VHuqNgmQd4igEGczbpyi87UeWGrIaOmgbSZtSSJm1GR00DQ5bb/9aQmMl2doRef08y5ANsZwcNiZnjWi6O1NAuLNGQk2eJBlJDJa6eI/vH9DnQl7fJW19PyK82jz8altTs7AibZnV2hNePP1pafa87L9TR0RF69Ds6wuvXnVfedo+DYje/ehcwLoOTzOwwYFV0jXuBK4DngA8DfzSzWLNqzOwDwJ3AmVF6BXA78ErgV2b2qTHOPx74DGFysoiIVIi4wfSzbKSeWuqpwzDqqaOe2r026ZrIck3JVlrrFtNSv5TWusU0JVv3Krc1uZuGmoOpsXqcIWqsnoaag9ma3D2u5eJI1k7D0/05eZ7uJ1lbnZOqq8bxZ0Bvdxi64+mQ9naH/PH03JPwva/Cv/9tSJ97cnyvB6FHvzUaV59I7Pn3zb8orb4jj4bLPhx69jdvCullH54Uq/EUNa7e3b87Xg0BvgEcBHzI3b+WyTSzLwMfBb4AXDpaBWZWC/wz0A+c4O5PZR37IvAA8Ckz+5K7DxQ4vwH4HnAf8Cxw4b7elIiIlEd2MA28mD7LRmay56v1bnppJnfN9jpq6SZ3dZZJUS7ZhiX3BNCOj3u5OBpbj6Jn+x2kIWfMfuP0E8c8VybQ/CPgrAvh/lvD0J3pc+CU80P+eHnuSfjpf0FzC8ycCz2d4fWb3gOLl419fqk2bQw9+tmmtoT8Uh159KQI7vONGuyb2WnRP+919/6s12Ny9zvilo169c8C1gJfzzv8j8D7gAvN7OPuPloXxHSgFXg4O9CP2vOEma0Gjgaagb2CfcKDwqHAscCo3wCIiMj+FTdInkoTAwy++DAAMMgQU3NWkla5kcrFUdc4l+ZZp9HX+SipoV0ka6fROP3EyhmvLyObf8T4Bvf57loRAv3maE5OJr1rxfgG+/Pmh2E2rVlj7Lu7Qv4BZqye/dsIq+8sB1ZnvY4jWUQ7XhWlK9w9Z3s2d+82s7sIDwOvAG4ZpZ7nge3AEjM7wt2fzhwwsyXAEcCD7r7XrhpmdgZhyNBH3f3p7NUKRERk4sUNVg9jPg8Q+nvqqGWQIQYY4iUsVrkY5eKqa5yr4F7G9vym0KOfrWlqyB9P574BPv0JWLcG+vugoREOORQ+/2/je90KNFaw/zlCcL8j73W5LY3S1SMcf5oQ7C9hlGDf3d3MLiMsF7rKzK4HNhNWEnoj8Bjw1vzzzKwVuJYwxv+rpd2CiIiMp7jB6sxEGy9NL81ZFeclLN5rVRyVK1xOpKwOmheG7jRnrbbV2x3yx9O652DjOkiloLYupBvXhfxSV+SZpMZaevPy0V6XUeY3oHOE45n8Mf+L5O4/NrPNwA+Ai7IObQO+Q5j0m+9rhCFAp/toGw8UYGbvIwwzYuHCA2iJLxGR/ayYYHVmoi1nHP9odarcAa5jPWy8b896/PNPhDb9/7xsTj4rjNGH0KPf2w09XfDat5Re5+rH4ZabYctGmDsfzjwXlrwkt8x134aZM6El6yGjqzPkn/OG0q89CVXdxldm9k7g28DPgM8D64BDgE8DVxFW5fmLrPJvIkzEvczdCz0IjMrdrwauhrCp1r62X0RERjZRwWpHaiebU2vp8x4arZmDk4toS8ZaJE4qWcd6eOomqJ0CjdPDTrtP3QRL/1wBf7ksXhYm4961IgzdOWheCPQLjdd/5gm47VewdRPMmQennwOHL88ts/pxuO6bMLUNZh8cBfDfhIs+kBvwb90MB+UtKdo8NeTne+yRsBPuxg1h3fzXnVd4Iu7DD8PPfgbr18PChXDBBXDMMcW/J/tZUcG+mSWBenfvzcs/AzgP/n97dx4neVXf+//16a7q6nV6NmZh9gGGVRAdkDgqm3dUoqII3sQrClG5mlyN5prNLEJujDExLokaReOaPFwCGvLjAYqyCAzqzCCbbAPMzgwMs/Xe1bV8fn+cbzFVPd3TVd1VXUu/n49HPQ51vqe+31P1pfcXtTEAACAASURBVHo+31Pn+zkMAte7e6lrb+dG7sdbUSVXf3RC5cJ+rAG+Tlj468q8+f9PmNmVhOlCV5jZBe5+l5nNBb5MmBr0ryX2WUREZoDDmQM8lX6YOAla6WDEkzyVfpiTOFMBf73bvSkE+i0d4Xmu3L3p6GD/4A7YtREGXoCO42DZuTB3xdH7LLbdTLL6lIlvxn36cfiPr4SR+AWLoa8nPP9f/7sw4L/9lhDo50bsc+XttxQG+4uiC4H8kf3+vlCf79FH4IufDzfyHr8k5M//4uePTqv58MPw6U/DnDmwdCkcOhSef/SjNR/wl5pn/9PAwWiOOwBm9jvAT4EPAn8KbDSzUpcTy2XOWTPO9txt4+PN6c9ZD8SBn49xo28WyGUIenlULgfmE3LyZ/MW0nLg3VGbn0Z1Hy7urYiISCPZk9keEn5aAjOjxUIC0D2Z7dXumkzV4AGIj8pGFG8P9fkO7oDHbg4j/+3zQ/nYzaF+Mu3qwY4tcMOX4Mt/FcodE4VgU3TXrSEw7+oOefG7usPzu24tbLd3dxihz9fZFerzvet9Ibjv7QmLYPX2hOfvel9hu5tvCoH+7Cgf/+woH//No5aV+uEPQ6A/Z05ol/vvH/6wPO+/gkqdxvMa4E53z59b/3HCiPsfAosI6Sv/iJAbv1h3RuV6M2vKD9TNrAtYR/jV4JcT7CcRlceNsz1XPxKVB4B/G6ftawgXGbcSbvKd5JJrIiJSLvvoYQt76GWIWbSxhuNZMO6PwuUx5P200lFQF6eFIdfai3WvfV4IyFvyzm9qMNTn27UREmP8ArBrY+GofbHtat2OLXDLN6F9FsxbCAO94fklV8GK8cZlp+i5Z8OIfr6OrlCfb/HSsUfsF49KqZmbl//tr4apO4uOh//z0aPn6+/eFUb0882aFerz7dwZRvTzdXeH+hpXarC/DLgv98TMVhOmxvyNu/97VPca4PWUEOy7+zNmdhthZP4PCDfM5lwHdABfyc+xb2anRK/NX4btnqi8PFo46+G89i8FLidkE7ojeu0u4L1j9cnMvkkI9j/j7j8r9r2IiEhl7KOHjTxNKzG6aGWYETbyNOdyYkUD/jbrZMSTtLw4ngQpRmizzoodU6bJ0nPCHH0II/qpQUgNwOoLCtsNvBBG6vPF20P9ZNrVuk0/C4F+x6zwPFdu+lnlgv1FS8LUna687/JAX6jPd/ElYY4+hBH9/j7oOwxv/d2j9/mGN098M+7SZWHqzuy8e4F6e0N9vuXLw9SdOXmrRPf0hPoaV+o0nllAb97zdYTg+cd5dY8Ck1mx4PcJefL/2cz+y8w+aWZ3EC4atnD0IlePR48XuftGQsadNmCTmX3PzD5lZt8HfgW0Ap9390cn0T8REamiLeyhlRittGAYrbTQSowtjHHDXRkd37ySwWwPL6R3sC+1lRfSOxjM9nB888qKHlemwezl4Wbclg4YOhjKsW7O7TguXAjkSw2G+sm0q3X790L7qIvZ9s5QXykXvCGM2PdF0276esLzC95Q2G7NaeFm3Fnd8PyeUI6+ObcUb7w0LL51+HA47uHD4fkbLy1sd9llIdg/dCi0y/33ZZdN7rjTqNSR/b2EFWZzXgsMAffn1XUC6VI7Eo3uryXk8n89cEl0vM8D17n7oSJ39R7C3PyrgNcBXYQLlHuBr7r790rtm4iIVF8vQ3TRWlCXIE4vQxU9ruHE3Em54zjmTgzHKrLsjEy72csnzryz7Nww9x6O/AKQHIATLpxcu1o3f3GYupMb0QcY7D96caxyOvHUcDNufjaeN/3O0dl4IAT2kw3uRzv9JeFm3PxsPO9899HZeM48M9yMm5+N5z3vqfmbcwGslLTyZvZd4E2EhamGgZuA2939zXltbgZWuvvMWrGAkHpz8+bN1e6GiEhDupfHGWaE1rwVdHPPX8UYAUGZPJncRMqTxC1vGk/0/OTEORU7rtSYmZSNJ3/OfntnCPQHeys7Z19KZmb3u/vaidqVOrL/d4QUm7lblLPAJ/IO2gq8GrihxP2KiIgc0xqOZyNPA2FEP0mKYdKcycqKHnfI+2ilcEpDjBaGvK+ix5UaM3dFcUF7se1q2Yo1IbDf9LMwdWf+YrjgMgX6daqkYN/dHzGzV3AkLeX33X1TXpOzCTe/frdM/RMREQFgAd2cy4kF2XjOZGXFs/G0WVcYyc+7QTfNCG3WdYxXidS5FWsU3DeIklfQdfdHgI+Os+0XwFun2ikREZGxLKC74sH9aItiq9g68hAQRvTTjJDyJMviEywSJCJSA0rNxjMmM4ub2dlmdnI59iciIlIrupvns7rlLOKWYJh+4pZgdctZdDfPn/jFIiJVVtLIvpm9nZCr/v3ufjCqO4Gw8NQJ0fObgLe7e8kZeURERGpRd/N8BffSGHY/Bb++Aw4+B3MXwcsugqUnTX5/O7YUzu0/57Wa/lNjSh3Z/z3glFygH/kn4ETCKrgPE27gvbo83RMRERGRstj9FNz2nZBZZ86CUN72nVA/GbmsPQO9hSvt7thSzl7LFJUa7J8GvHhDrpnNIuTD/4G7vxY4F3gCBfsiIiIiteXXd0B7V0ipaU1Ras2uUD8Z+SvtWlMo22eFeqkZpd6gexxhoauc34r28T0Ad0+Z2U+BMdYsFhEREakxjZAXv1gHnwsj+vnaOkP9ZOzfG0b081V6pV0pWakj+31QkAbhfMAJq9PmDBNWrRURERGpXQd3hBVvRwagfX4oH7s51DeiuYtgqL+wbqg/1E/G/MVhwa18lV5pV0pWarD/FPAGM0uYWQvwduBhd9+f12YFsK9cHRQRERGpiF0bIdEBLR1gFspER6hvRC+7CAb7wlx9z4ZysC/UT8Y5rw37GIj2N9Abnp/z2vL2W6ak1GD/emA1Ieh/HFgFfGNUm5cDj069ayIiIiIVNPACxNsL6+Ltob4RLT0J1l8Z5tUf2hfK9VdOPhtPbqXdjllw4PlQXnKVsvHUmFJX0P1WlEv/mqjqC8C/5Lab2SsJmXmuL1sPRURERCqh47gwdael40hdajDUN6qlJ00t1eZoWmm35k1mBd2PAR8bZ/NmYA4wMJVOiYiIiFTcsnPDHH0II/qpQUgOwAkXVrdfImVUcrB/LO4+AoyUc58iIiIiFTF3BZz2xsJsPCdc2LjZeGrd1idgw22w71lYsATWrYfVp1S7V3WvrMG+iIiISF2Zu0LBfS3Y+gTc+G/QOStk8+nvCc/f9h4F/FNU6g26mNliM/uimT1tZkNmlhnjka5EZ0VERESkAW24LQT6nd3Q1BTKzlmhXqakpJF9M1sCbAQWEjLuJIAdQJKQpScGPAj0lLebIiIiItKw9j17dH7+9q5QL1NS6sj+XwOLgNe7+1lR3Tfc/RRCsP8ToA24rHxdFBEREZGGtmBJyPmfb7Av1MuUlBrsvw74sbv/bPQGd98NXEEI9q8rQ99EREREZCZYtx76e8Nc/Ww2lP29oV6mpNRgfxGFC2ZlCME9AO7eD/wUuHTqXRMRERGRGWH1KeFm3M5u2L83lLo5tyxKDfZ7gZa854eA0b+v9AANvBqFiIiIiJRd1mEkA8lMKLNe7R41hFKD/R3AsrznDwEXmVk7gJk1AeuB3eXpnoiIiIg0vKcfh+9eD309cNziUH73+lAvU1JqsH87cKGZxaPn3wKOB+4zs38ENgCnA98vXxdFREREpKH9/MfQ1R0eTU1H/vvnP652z+peqYtq/Rth6s58YK+7/7uZvRz4IHBm1OZ7wCfK10URERERaWjPPxtG9PN1dIV6mZKSRvbd/Sl3/5S7782r+wiwGPgtYLG7v8Pdh8vcTxERERFpVAuXwMCo1JsDfaFepqTUkf0xufsLwAvl2JeIiDSO5+jhCZ6nhyG6aeMUFrKI7mp3S0RqzfmvD3P0IYzoD/SFeftv/J/V7VcDKHXOvoiISFGeo4dfso0hUsyilSFS/JJtPKdF1kVktBNPhd+9JszTf2FvKH/3mlAvU3LMkX0z+/ok9+vu/p5JvlZERBrAEzxPgjhthJwOufIJnj9qdL/YXwCq1U5EpsGJpyq4r4CJpvFcNcn9OqBgX0RkButhiFm0FtS1EqOHoYK63C8ACeIFvwCcx6qCwLta7URE6tlEwf6qaemFiIg0nG7aGCL14og+wDBpuo8svA4U/wtAtdqJiNSzYwb77r5jujoiIiKN5RQW8ku2AWFEf5g0SVKczdKCdsX+AlCtdiIi9aykG3TN7Aozu8PMjh9n+xIzu93MLitP90REpF4topvzWEUbcXoZpo34mFNkumljmHRB3Vi/AFSrnYhIPSs1G897gdnuvmesje7+LNAdtRMRkRluEd1cwBou5SwuYM2Y02NOYSFJUgyRwnGGSJEkxSksrIl2IiL1rNRg/yXA5gnabOLIaroiIiLHVOwvANVqJyJSz0pdVGsusG+CNgeA+ZPrjoiIzESL6C4qyK5WOxGRelVqsL8fOGmCNicBhyfXHRERqQfKTy8iUh9KncazAXizmZ0y1kYzOxW4FLhnqh0TEZHapJVxRUTqR6nB/qcJvwbca2YfMrM1ZtYRlX9ICPKbo3YiItKA8vPTG0YbcRLEeYLnq901KZPk0F4OvnA7+/bcyMEXbic5tLfaXRKRSSop2Hf3TcDvA7OAzwKPA71R+Zmo/gPu/qsy91NERGpED0O0jpoFqvz0jSM5tJeeg/eSyQzRHOsmkxmi5+C9CvhF6lSpc/Zx96+a2b2EoP8VwGzCHP1fAv/q7o+Xt4siIlJLil0Ztx4czB5kh+9iwAfosA5W2DLmNs2tdreqaqD/May5jebmcD6bm9vIRPWJtsXV7ZyIlKzkYB8gCug/WOa+iIhIHSh2ZdxadzB7kEezjxEnQTvtJH2ER/0xTue0GR3wp1OHaY4V3mzd1NRKOqXcG2X1/FbYsgF69kH3AlizDhaurnavpAGVOmdfRERmuEbJT7/DdxEnQcJaMDMS1kKcBDt8V7W7VlWx+GxSIwcZHNxGX//jDA5uIzVykFh8drW71jie3wobb4Dhfpg1P5Qbbwj1ImU2qZF9ERGpP896Hw+zn0MMM4dWzmQ+S6xrUvtqhPz0Az5AO+0FdS3EGfCBKvWoNjS3LGC490HMElhTgkxmmHS6h7auU6vdtcaxZQO0dkFrZ3ieK7ds0Oi+lJ2CfRGRGeBZ7+NOdtFGjNkkGCTFneziQl9WEPAXe0FQ7nbV0GEdJH2EBC0v1o2QosM6qtir6htJ7yfeugxP95LNDtPc3IrFFjCS3l/trjWOnn1hRD9foj3Ui5SZpvGIiMwAD7OfNmK0R+ky24nTRoyHORLA5S4IBkkVXBA8630F+yp3u2pZYctIkSTpI7g7SR8hRZIVtqzaXauqVPow8cR8Ep0n0DbrdBKdJxBPzCeVLpyzr/ScU9C9AJKDhXXJwVAvUmYK9kVEZoBDDNM26sfcNmIcYvjF58VcEFSiXbXMbZrL6U2nkbAWBhkkYS2c3jSzb84FiMdmk80OF9Rls8PEY0fm7Cs95xStWQfDfWGuvmdDOdwX6kXKTNN4RERmgDm0MkiK9rx0mUOkmUPri88PMcxsEgWvG31BUIl21TS3aS5zmdnB/WidHadysOc+yIQsPNnsMBkfprvjZS+2UXrOKVq4Gs69vDAbz5mv03x9qQgF+yIiM8CZzOdOQpaZNmIMkWaINOdxJDAr5oKgEu3qwUzKx9/Wuoi5vJL+gcfDlJ7YbLo7XkZb66IX2yg9ZxksXK3gXqaFgn0RkRlgiXVxoS8ruFn2PBYX3CxbzAVBJdrVulLy8Rd7UVCtdsVqa11UENyPFovPDlN4mo8spJbNDis9p0gN0px9EZEZYol18QZbxTvsVN5gq47KirPEuriQZbQT5zBJ2olzIcsq3q7WFZuPP3dRkPSRIxcF2cc4mD1YE+3KqaPzNDwzRCYzhLuHMjNER+dpFTumiEyORvZFRORFS6yLJUwcjJe7XbmVc6S72Hz8+RcFQEjp6aE+/76AarUrp0TbYrrnvoqB/sdIpw4Ti8+mo/vlmq8vUoNqamTfzJaa2dfNbI+ZJc1su5l9zszmlLifV5nZTdHrh81sp5ndYmavH6PtEjP7oJndGrVPmtkBM/upmV1WvncnIiLTodwj3R3WwQipgrqx8vEP+AAtefcnwNgXBdVqV26JtsXMPe5iFhz/NuYed7ECfZEaVTPBvpmdANwPXA1sBD4LbAX+EPiFmc0rcj8fAO4BLo7KzwI/B84HbjWzvxj1kg8C/wycDNwJfAb4CfBq4EYz+8zU3pmIiEynYqfdFKvYfPzFXhRUq52IzEw1E+wDXwIWAB9y97e4+5+5+0WEYP1k4BMT7cDM4sAngWHg5e5+pbv/ubtfCawFksBfmFl+LriNwAXuvtrdr47avwM4G+gFPmJmLy/nGxURkcop90h3sfn4i70oqFY7EZmZzN2r3YfcqP7TwHbgBHfP5m3rAvYCBixwH/+vtZktBJ4DHnb3s8bY/jDwEmC+ux8ool/XA+8DPuru/zRR+7Vr1/rmzZsnaiYiIhX0QOYhkj7y4hx24MXnZzcf9U9DWe0eeZodqUcZyfbT0tTJivjpLG058ah2tZ6NR0Rqn5nd7+5rJ2pXKzfoXhiVt+UH+gDu3mdmG4D1wHnA7cfYzz7gBWCNmZ3k7k/lNpjZGuAk4MFiAv1I7nfRdJHtRUSkylbYMh71x8DDiP4IKVIkWWMnVPS4fekXOJzayjxmE2teQNpHOJzaSndTN12x4wraFruYV7nbicjMUyvTeE6Oyi3jbM8F7WuOtRMPP1P8AeF93W9m3zKzT5rZtwn3AzwKXFFMh8xsFvA2wIHbinmNiIhUX7HTbsptX+oZYiSINyUwM+JNCWIk2Jd6pqLHFRE5lloZ2c8tw9czzvZc/YSrdbj7f5rZHuC7wLvyNj0PfINw0+8xmZkBXwMWAl9y98eP0fYa4BqA5cuXT7RrERGZBtUY6R7O9pGwzoK6mLUwnO2b1n6IiOSrlZH9sjGzdwI/I2TiORVoj8rbgS8A3ytiN/9E+AXgHuCPjtXQ3a9397Xuvva44447VlMREWlgrU1dpH2koC7tI7Q21dciYiLSWGol2M+N3HePsz1Xf/hYO4nm5X+dMF3nSnd/wt2H3P0J4ErCVJ4rzOyCY+zjH4CPAHcDl7h7suh3ISIiM9aC+AmkSZLKJnF3UtkkaZIsiFf2XgERkWOplWD/yagcb07+SVE53pz+nPVAHPj5GDf6ZgkBPMCYqTTN7LPAHxPy7b/B3fsnOJ6IiAgAXbHjWJE4m3hTgqT3E29KsCJx9lE354qITKdambN/Z1SuN7OmMVJvrgMGgV9OsJ9c/vzx/rLm6gt+Z43m6H8B+H3gp8Cl7j5UfPdFRERCwK/gXkRqSU2M7Lv7M4SMNysJ2XTyXQd0AN/Jz7FvZqeY2Smj2t4TlZeb2Zn5G8zspcDlhOw6d+TVG3A9IdC/FXizAn0RERERaQS1MrIPIdi+D/hnM7sYeBx4BSEH/xbgL0a1z2XIsVyFu280s28AVwObzOxHwA7CRcRbgBbgc+7+aN5+/hp4LzAEPAj8WYj/Czzo7v811TcoIiIiIjKdaibYd/dnzGwt8DfA64FLCCvnfh64zt0PFbmr9xDm5l8FvA7oAnqBe4GvuvvobDyrorIN+PNx9vktQMG+iMgM1ZM5wJ7MNoa8jzbr4vjmVXQ3z6t2t0REJmRhHSoph7Vr1/rmzZur3Q0RESmjnswBnk49RMwSxGkhxQhpT3Ji/CwF/CJSNWZ2v7uvnahdTczZFxERqVV7MtuIWYIWCyvjtliCmCXYk9lW7a6JiExIwb6IiMgxDHkfcVoK6uK0MORaGVdEap+CfRERkWNosy5ShRmbSTFCm2llXBGpfQr2RUREjuH45lWkPcmIh5VxRzxJ2pMc37xq4heLiFRZzWTjEZH68Uhfipv2Jdk5nGV5axOXLkjwkq54tbslUhHdzfM4kbMKsvGsjJ2im3Nleux9Bh67Bw4/D7MXwmmvhsUnVLtXUkcU7ItISR7pS/G5HUPMjhlLEk0cSjmf2zHEh1eggF8aVnfzPAX3Mv32PgMbfgCtndB9HAz1hefr3q6AX4qmaTwiUpKb9iWZHTNmx5toslDOjhk37UtWu2siIo3lsXtCoN/WBdYUytbOUC9SJAX7IlKSncNZZsUKV5meFTN2Dmer1CMRkQZ1+Hlo7Sisa+0I9SJFUrAvIiVZ3tpEb7pwMb7etLO8VX9ORETKavZCGB4orBseCPUiRdK/ziLyokf6UvztM/1c82gvf/tMP4/0pY5qc+mCBIfTzuFUlqyH8nDauXRBogo9FhFpYKe9Gob7w1x9z4ZyuD/UixTJ3H3iVlKUtWvX+ubNm6vdDZFJyb/xdlbM6E07h9POh1e0HXXjbbHZeNSu/top05JIjVE2HhmHmd3v7msnbKdgv3wU7Es9+9tn+jmUcmbHj/zgdziVZU7c+MsTOkveX7EXD2pXO+1KueATaRiHd8Kzm2FoP7TNhyVrYfbyavdKZELFBvuaxiMiQPlvvC02a4/aTb1dZ/sQzH+OQ/N2wvzn6GwfmlQ7ZVqaPj3p/WxJbuShoTvYktxIT3p/tbs0Mx3eCU/dAqkBaJsXyqduCfUiDULBvogA5b/xttiLB7WbWrsd2QGYu59sU4amTJxsUwbm7g/1JbZTpqXp0ZPez7bUQ6Q8Sat1kPIk21IPjRnw66Kgwp7dDPEOaOkAs1DGO0K9SINQsC8iQPlvvC324kHtptaue04vqXQzTdkYhtGUjZFKN9M9p7fkdjMx09KhzAF+k/o1v0rdzW9Sv+ZQ5kDFj/l8ZitxayFuCcyMuCWIWwvPZ7YWtCvlokAmaWg/xNsL6+LtoV6kQTTuX3ARKclLuuJ8eEUbc+LGs8kwV38qc7WLvXhQu6m1O74zw0jaGM46jjOcdUbSxvGdmZLbzbRMS4cyB3gy8xtGPEm7dzDiSZ7M/KbiAf9Qtp8YLQV1MVoYyvYX1BV7UdAoMv27Gdl+M8knvs3I9pvJ9O+u/EHb5kNqsLAuNRjqRRqEbtAtI92gK1KolrPONEq7/87sYOfICNsHoCftdMeMlR2wvKWFNzevKLndTMrG85vUrxnxJC125GIm9/yM+MsqdtwtyY2kPEk877i552sS575Y99DQHbRaB2ZHpla5O8M+wFltF1Wsf9WQ6d9NavfPsOZ2iLVBegjPDBJf+lqaO5dW7sC5OfvxjjCinxoM8/ZPukQ36UrNK/YG3dh0dEZkOm3dkWXDJmffflgwH9adY6xecfSPWDOpXbX61nGwmRUPtNEWtes4x6DrqGZqN4V2Z9s8to3spuNAjOaeGK3dGTLxNGcn5k2q3eyOJGeuPMASkswjwWybBxwd7O/K9vOAH+BA1O5sm8eypqOzNhXbrhoG6KedwtVJ47QwQP84ryiPhc2r2ZZ6CAgj+mlGSPkIS2OnFrRra+oMFwEcuShIM0JbjXx+5ZTZ/yDW3I7lptREZWb/g5UN9mcvD4F9fjaelecr0JeG0nzttddWuw8N4/rrr7/2mmuuqXY3ZrStO7LccItjON3dMDAIDz4GixfAnNk2I9vVct/UburtDuyMsenOBJlZw9jsEeiPk/rVQs6Y1VFyu13Zfm7zPZjDLOIMkeEJejmOBN12ZNpJudtVy+HsAVKM0GxHxr1SjNBm7SxoXjypffak97Mz9Rt2p56kN/sCMRK0NhXOCW9taqfNZjHkfQz7AImmdpbGTqU7Vjh1JEaCA5k9ADTRXHBRMHqf9S793C+hpavgVwyaYpA8RGz+WZU9eGs3LDgVjn95KFu7K3s8kTK57rrr9l577bXXT9ROI/tSN4oZTd6wyelqdzo7wj8YnR0AzoZNsHrFzGxXy31Tu2O3az6+nwOrD/Fse5LWwQRtW+ewYVPnUe2OS7bTufXICHV/0tmwyUtu94AfoMObaY+C33Zi4PAAB1jGkdHkcrerliVNK3gy8xvwMKKfYoQRRljVtGZS+8vdUBu3loIbaldx1lGBfHds/lF1o3XH5rOKs3g+s5WhbD9tTZ1jXhQ0Amudi6cGC2+WTQ9hrXOr1ymRBqEbdKUu5EY5+wec+fNCecMtztYdhSkB9+2H9lEDXu3toX6mtqvlvqnd+O12ZgY4+JK9pFrStAy1kGpJc/Ale9mZKUypWc7jHiBJG80Fbdpo5gCFefbL3a5a5jTP4+TmM2ixBIM2QIslOLn5DOY0z5v4xWOoxA213bH5rEmcy1ltF7EmcW5DBvoAzfNfimcG8dQg7h7KzCDN819a7a6J1D0F+1IX8kdDm8zo7DC62sOoZL4F82FwVGKFwcFQP1Pb1XLf1G78dn7aQbIDzcRSIVVmLBUjO9CMn3awYsedR4IhCrP4DJFhHoXZeMrdrprmNM/jjPjLeEX8NZwRf9mkA30oPsuOHK25cynxpa8Nc/aTh7B4e+VvzhWZIRTsS10odvRy3TlG36DRP+BkPfwC0DdorDvHZmy7Wu6b2o3fbtbyJJnBGCMjjrszMuJkBmPMWp6c1P6KaXe2zWPAMgx6Gndn0NMMWIaz7eibfcvZrlG0NXWSZqSgrlFvqK2E5s6ltKx8I4lT3kXLyjcq0BcpE6XeLCOl3qyc79yQoX/gyDxn4MXnV15eOE2glrPiVKtdLfdN7cZu99+ZHeztTbHv2RgDg9DRDguWpFk8K16QKrPcxy13lp1azsZTbvlz9vOz7KyKHz1nX0RkqopNvalgv4wU7FdObs5+V7vT3h6mH/QNGpdfMnZQI1LvcplsOryZNpoZIsOAZVhvxzdssNwIetL7C26oXdi8WoG+iFSE8uxLQ1m9oonLL8myYRMvjkq+7gIF+tK4ljV1sj57PA9wZFR8nS1UoF+kg9mD7MruZIB+OuhkWdNy5jZVPrNLMVl2RESmk4J9qRurVzQVpBIUaXTL61+PZwAAFF5JREFUmjprIkVlvTmYPcjjmUdpoYV2OhghyeOZRzmV06cl4BcRqSUaFhURkYayK7uTFlpoiVJgtliCFlrYld1Z7a6JiEw7BfsiItJQBugnPioFZpwWBlAKTBGZeRTsi4hIQ+mgk9SoFJgpRujQlCgRmYEU7IuISENZ1rScEUYY8WRYo8CTjDDCsqbl1e6aiMi0U7AvIiINZW7TXE5tPp0WSzBoA7RYglObdXOuiMxMysYjVVfsgkAiIsWa2zRXwb2ICBrZlyrLLZbVP+DMnxfKG25xtu7IVrtrIiIiInVPwb5U1YZNYVXczg6jyYzODqOr3dmwSSs7i4iIiEyVgn2pqn37ob29sK69PdSLiIiIyNQo2JeqWjAfBgcL6wYHQ72IiIiITI2CfamqdecYfYNG/4CT9TBnv2/QWHeOVbtrIiIiInVPwb5U1eoVTVx+SZirv/9AKC+/RNl4RERERMpBqTel6lavaGL1imr3QkRERKTxaPhURERERKRBKdgXEREREWlQCvZFRERERBqUgn0RERERkQalYF9EREREpEEpG4+IyAS2pQfZkO5ln4+wwFpYF5vFqlj7xC8Ukdp3aAfs3gSD+6F9Piw9B+YoRZw0Do3si4gcw7b0IDemXqDfM8wnTr9nuDH1AtvSgxO/WERq26Ed8OQtMDIAbfNC+eQtoV6kQSjYFxE5hg3pXjqJ0WnNNJnRac10EmNDurfaXRORqdq9CeLt0NIBZqGMt4d6kQahYF9E5Bj2+Qjto/5UttPEPh+pUo9EpGwG94fgPl+8PdSLNAgF+yIix7DAWhgkW1A3SJYF1lKlHolI2bTPh9SoKXmpwVAv0iBqKtg3s6Vm9nUz22NmSTPbbmafM7M5Je7nVWZ2U/T6YTPbaWa3mNnrj/Ga08zsB2a2L3rNk2Z2nZm1Tf2diUi9WhebRT9p+j1D1p1+z9BPmnWxWdXumohM1dJzQnA/MgDuoUwNhnqRBmHuXu0+AGBmJwD3AQuAm4AngHOBC4EngXXufqCI/XwA+BIwAPwI2A0sBS4D2oG/dPdPjHrNK4A7gDhwA7ALuAhYC2wALnb35ETHXrt2rW/evLmYtysidUTZeCpvnx/mKfbSyyCzaOckFrPAZle7WzITKBuP1Ckzu9/d107YroaC/Z8A64EPufu/5NV/BvgI8BV3f/8E+4gDLwAJ4KXu/mTetlOBB4AsMCcXvJtZM/AIcCpwqbv/d1TfBPwAeBvw5+7+9xO9BwX7IiKl2+eH2czTJIiTIE6SFElSrOVEBfwiIuMoNtiviWk80aj+emA78MVRmz9OGKW/0sw6JtjVXKAb2JIf6AO4++PAFqAN6MzbdD4h0L87F+hH7bPAn0RP329mVsp7Emlkjw6M8KndfXzwmcN8ancfjw7oZlWZvKfYS4I4rbRgGK20kCDOU+ytdtdEROpeTQT7hKk6ALdFQfaL3L2PMJWmHThvgv3sI4zsrzGzk/I3mNka4CTgwVHTgS6Kyh+P3pm7byVcIKwAVhf3VkQa26MDI3xx7yCH01mOb2nicDrLF/cOKuCXSetlkATxgroEcXrRWgYiIlNVK8H+yVG5ZZztT0XlmmPtxMOcpD8gvK/7zexbZvZJM/s2cD/wKHBFJY4tMlPcfChJd8yYHWuiyULZHTNuPjThbS0iY5pFO0lSBXVJUsxC90WIiExVrNodiHRHZc8423P1E07edPf/NLM9wHeBd+Vteh74BrC1nMc2s2uAa6Kn/Wb25FjtKmw+oKTAtaOhz0fTkhUrSaWOHsaPx1v+7Nkd26e/R0dramtra57dPTs7nOxuak30ZA73HM4ODQ1Vu18z3Ljfi3h7om3W4jkLs+lMJpvJZpqam5qbYs3NvXsPPZ8aTOq8VUZD/52qMzoXtaPezkVRd5LXSrBfNmb2TuCrwA+B/wfsIHwYfwV8gTBH/+3lOp67Xw9cX679TYaZbS7mBg2ZHjoftcPMNqd1LmqCvhe1Reejduhc1I5GPRe1Mo0nN3rePc72XP3hY+0kmpf/dcJ0nSvd/Ql3H3L3J4ArCVN5rjCzC8p9bBERERGRWlMrwX5u6st48+JzN9uON68+Zz0hV/7Px7jRNwvcHT19eQWOLSIiIiJSU2ol2L8zKtdH+e1fZGZdwDpgEPjlBPtJROVx42zP1efPN74jKo9aXdfMVhMuAnZw9Fz/WlLVaURyFJ2P2qFzUTt0LmqLzkft0LmoHQ15Lup2US0zOwUgmqKTqzsX+BUwBJzn7g/nbXsp8AvCBcFL3P3RqP5Yi2p9H7icIhfVEhERERGpJbUU7J8A3AcsAG4CHgdeQcjBvwV4ZX5+fDNzAHe3Ufv5OnA1YfT+R4RR+ZXAW4AW4HPu/pFRr3kFYYQ/DtwA7AQuBtYScvxfnFtxV0RERESkXtRMsA9gZsuAvyFMqZkH7CUE7Ne5+6FRbccL9g14N3AVcBbQBfQCDwBfdffvjXPs04DrCBcXXYSLhO8Cf+/uSv0mIiIiInWnVubsA+Duu9z9andf7O4t7r7C3T88OtCP2troQD+qd3f/prtf4O5z3D3m7nPd/eLxAv3odY+5+xXuPt/dE+6+xt0/XquBvpktNbOvm9keM0ua2XYz+5yZzal23xqRmV1uZv9iZveYWa+ZuZn9+wSveaWZ3WJmB81syMweNrMPR1PHZJLMbJ6ZvdfMfmRmT0efbY+Z3Wtm7xl930/e63Q+KsDMPmVmt5vZruhzPWhmD5jZx81s3jiv0bmYJmb2zujvlZvZe8dp80Yzuyv6HvWb2a/M7N3T3ddGE/277OM8nhvnNfpuVJCZXRz92/FcFDvtMbOfmNklY7RtmHNRUyP7Upwxpjw9AZxL+FXiSWBd/pQnmToze5DwS1E/sBs4BfgPd3/nOO0vBW4Ehgn3fhwE3kRYsfkGdx+9krMUyczeD/wr4Ze/OwnT7hYClxFS5d4IXOF5f9x0PirHzEaAXwOPAfuADuA8wjTIPYT7p3bltde5mCbRr+WPAM1AJ/A+d//aqDb/B/gX4ADhfIwQ7lVbCvyTu390WjvdQMxsO2FBzs+Nsbnf3T89qr2+GxVkZv8A/DHh3/BbCYtnHUfI0Pgzd/+TvLaNdS7cXY86ewA/ARz44Kj6z0T1X652HxvtQbiQOgkw4ILoc/73cdrOIgQ9SWBtXn0r4SLNgd+p9nuq1wdwEeGPbtOo+kWEwN+Bt+l8TNv5aB2n/hPRZ/slnYuqnBcDfgY8A/xj9Nm+d1SblYRg5gCwMq9+DvB09JrfqvZ7qdcHsB3YXmRbfTcqey7eF32G3wRaxtgeb+RzUVPTeGRi0aj+esIfkS+O2vxxYAC40sw6prlrDc3d73T3pzz6xk/gcsJowffcfXPePoaBv4yefqAC3ZwR3P0Od////Oi1NJ4Dvhw9vSBvk85HBUWf41h+EJUn5dXpXEyfDxEujK8m/Lswlt8jZKj7grtvz1V6mDr7d9HT94/xOik/fTcqxMwShMGHncA17j4yuo27p/KeNty5iFW7A1KyC6PytjGCnT4z20C4GDgPuH26OydA+AcW4MdjbLubsGbEK80s4cryVG65P9jpvDqdj+p4U1Q+nFenczENzOxU4O+Bz7v73WZ20ThNj3U+bh3VRiYnYWbvBJYTLroeBu5298yodvpuVM7/IATvnwOyZvbbwBmEX7U2uvsvRrVvuHOhYL/+nByV463o+xQh2F+Dgv1qGfccuXvazLYBpwOrCSlmpQzMLAa8K3qa/0da52MamNlHCfPCuwnz9V9FCGzy1yjRuaiw6HvwHcIo5scmaH6s87HXzAaApWbW7u6D5e3pjLGIcD7ybTOzq93953l1+m5UzjlROUzIzHhG/kYzuxu43N1fiKoa7lxoGk/96Y7KnnG25+pnT0NfZGw6R9Xx94Q/4re4+0/y6nU+psdHCVMJP0wI9H8MrM/7BxR0LqbDXwNnA1f5xNnkij0f3eNsl2P7BmHNnkWEG9dfAnyFcK/ErWZ2Vl5bfTcqZ0FU/jFhvv2rCSnWzwRuA14D/Gde+4Y7Fwr2RaTumdmHgP9LyEx1ZZW7MyO5+yIP6ZAXETIjrQYeMLOXVbdnM4eFBSI/RsiiM3pqgkwzd78uusfoeXcfdPffuPv7Cck02oBrq9vDGSMX66aBN7v7ve7e7+6PAG8lZOc538x+q2o9rDAF+/VnopGWXP3haeiLjE3naBpFqQM/T0j9eKG7HxzVROdjGkWBzY8I0wnnAd/O26xzUSHR9J1vE6Ye/FWRLyv2fIw3wimTk0sk8Jq8On03Kif3mT2QfyM6QDQ9LfdL8LlR2XDnQsF+/XkyKteMsz2X+WK8Of1SeeOeo+gf5FWEEYat09mpRmRmHybkCP8NIdAfa6EanY8qcPcdhAuw081sflStc1E5nYTP9VRgOH8BJ8L0KoCvRnW5vO/HOh+LCVNPdmu+ftnlprblZ83Td6Nycp/teMF5buHWtlHtG+ZcKNivP3dG5frRK4WaWRewjnCn+C+nu2Pyojui8vVjbHsN0A7cVy938dcqM/tT4LPAg4RAf984TXU+quf4qMxlHtG5qJwk8G/jPB6I2twbPc9N8TnW+XjDqDZSPudFZX6wqO9G5dxOmKt/2jgrrOdu2N0WlY13Lqqd6F+P0h9oUa1qf/4XMPGiWi/QQAty1NqDME3Bgc3A3Ana6nxU7jysAbrHqG/iyKJaG3Quqn6ermXsRbVWoUW1KvWZnwp0jFG/kpA1z4GP5dXru1HZ83FT9Bl+ZFT9eiBLGN3vbtRzYdEbkDoSLax1H+EO85sIqZ9eQcjBvwV4pbsfqF4PG4+ZvQV4S/R0EfA6wqjMPVHdfs9bVj5qfwPhH9LvEZbafjPRUtvA211fvkkxs3cTVkHMEKbwjDWfeLu7fzPvNTofFRBNo/okYcR4GyFoXAicT7hB9zngYnd/LO81OhfTzMyuJUzleZ+7f23Utg8C/0w4d98HRgiLCi0l3Oj7UaRk0Wf+fwl52XcAfcAJwG8TgsZbgLd63gJP+m5UjpktJcRNywgj/Q8QLnbfwpHg/ca89o11Lqp9taHH5B6E/2G/Aewl/HHeQVgwYk61+9aID46MjI332D7Ga9YR/qAfAoaAR4CPAM3Vfj/1/CjiXDhwl87HtJyLM4AvEKZS7SfMY+0BNkXnacxfXXQupv085b4z7x1n+5uAnxMC0oHo/L272v2u5wfhgve7hAxhhwkL/r0A/JSwHoiN8zp9Nyp3To4jDBDtiOKm/cCPgHMb/VxoZF9EREREpEHpBl0RERERkQalYF9EREREpEEp2BcRERERaVAK9kVEREREGpSCfRERERGRBqVgX0RERESkQSnYFxERERFpUAr2RUSkKszsm2bmZraywsfZbmbbK3kMEZFapWBfRETqmpndZWZaIVJEZAyxandARESkwi6udgdERKpFwb6IiDQ0d3+m2n0QEakWTeMREakzZrYymuv+TTM7xcz+y8wOmtmAmd1rZuvHeE3CzP7MzB4xs0Ez6zWze8zs7WXa/7XRay441v6KfH9XmdmNZrbVzIaivm4ws3eOtV/g/Oi55z3uyms35pz9KXwmK83se2a238yGzWyzmb2xmPcmIjLdNLIvIlK/VgG/AB4BvgIsBv4ncKuZvcPdvw9gZi3ATwhB8RPAF4F24HLg+2b2Unf/2GT3XwH/CjwK3A3sBeYBlwDfMbOT3f2vonaHgeuAq4AV0X/nbD/WAabwmawANgJbge8AcwmfyU1m9lp3v7PUNysiUknmrnuaRETqSZS9Zlv09NPu/sd529YSAvR+YIW795rZnwN/B9wKvNnd01HbBYTAdQWwzt3vm8z+o/prgY8DF7r7XeP091vuflVe/TeBdwOr3H17Xv0Jo6feRMH5rcBrgJXu/mzetruA893dxvm8tgO4+8q8uql8Jte6+3V5+3od8GPgVne/ZKw+iIhUi6bxiIjUrx7gb/Ir3H0z8B/AbOCtUfXvAQ78US6ojdruA/5f9PS9U9h/WY01x97dRwij7zHKc8PtZD+THcDfjurbT4CdwLll6JeISFkp2BcRqV+/dve+MervisqzzawLOBHY4+5PjNH2jlzbyey/hL4WzcyWm9kXzeyJaC69R3Pzb4yaLJni/qfymTzo7pkx6ncBc6bSLxGRStCcfRGR+vX8OPXPRWV39IAw930sufrZk9x/WZnZasI0mjnAPcBthF8YMsBKwrSfxBQPM5XP5PA4r0mjATQRqUEK9kVE6tfCceoXRWVP9MivG21xXtvJ7D8nG5Vj/bsyVtA8nj8i3JB7tbt/M3+Dmf0uIdifqql8JiIidUWjECIi9etl0ZSU0S6IygeiaTjPAEvM7KQx2l4Ylb+ezP7z6g5F5bIx2q8do248J0bljWNsO3+c12QAzKy5mANM8TMREakrCvZFROpXN/DX+RVRtpz/RRiV/lFU/XXAgH/MD4jNbD7wV3ltJrt/CFNvAK42s1he+2Wj9zGB7VF5wajjvo6xb5gFOBCVy0s4zmQ/ExGRuqJpPCIi9etu4L1m9gpgA0fy4DcB/zuXFhP4NPAG4FLgITO7hZBT/gpgAfAP7n7vFPaPu//KzO4mpMbcaGZ3EKYBvYmQz36sEf+xfAm4GvhPM7sB2AOcAbwe+EF0/NFuj97LD6P3NgTscPfvHOM4k/1MRETqikb2RUTq1zbglYQpNO8H3k6YenJJ/oJXUdrK/wH8RVT1QcLc96eAd7j7n05l/3kuBb4GLI2OcTbwJ8B4+z+Kuz9MmEZzH/DbwAeAWcBlwJfHednXgE8Sfon4E0LqzPdMcJzJfiYiInVFi2qJiNSZ8Rapqpf9i4jI9NHIvoiIiIhIg1KwLyIiIiLSoBTsi4iIiIg0KM3ZFxERERFpUBrZFxERERFpUAr2RUREREQalIJ9EREREZEGpWBfRERERKRBKdgXEREREWlQCvZFRERERBrU/w/5Np+T7z4XvgAAAABJRU5ErkJggg==\n", "text/plain": [ - "
" + "array([1, 1, 2, 2])" ] }, + "execution_count": 57, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], - "source": [ - "models_ids = []\n", - "for pdict in params_dictionaries:\n", - " models_ids.append(pdict[\"train\"][\"evolution_model_id\"])\n", - " \n", - "models_ids = np.array(models_ids)\n", - "\n", - "cmap = plt.get_cmap('rainbow')\n", - "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", - "\n", - "# plt.figure(figsize=(12,6))\n", - "# for i in range(data.shape[0]):\n", - "# try:\n", - "# plt.scatter(i // 10, \n", - "# data.loc[:, \"classification_accuracy_valid\"].values[i], \n", - "# c=colors[models_ids[i]], alpha=0.5, marker='o')\n", - "# except IndexError:\n", - "# print(models_ids[i])\n", - "# print(colors[models_ids[i]-min_mid])\n", - "\n", - "\n", - "try:\n", - " y_label = \"Number of edges\"\n", - " plt.figure(figsize=(12, 12))\n", - " for i in range(data.shape[0]):\n", - " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", - " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", - " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", - " d = json.loads(json_acceptable_string)\n", - " plt.scatter(i // POPULATION_SIZE, \n", - " np.sum(d[\"chainer\"][\"pipe\"][model_index][\"binary_mask\"]) \n", - " + (np.random.random() - 0.5) / 2, \n", - " c=colors[models_ids[i]], alpha=0.5)\n", - "\n", - " plt.ylabel(y_label, fontsize=20)\n", - " plt.xlabel(\"population\", fontsize=20)\n", - " plt.title(TITLE, fontsize=20)\n", - " plt.xticks(fontsize=20)\n", - " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \"_colored_ids.png\")\n", - " plt.show()\n", - "except:\n", - " pass\n", - "\n", - "# ylims = [(0., 1), (0.85, 1), (0.9, 1.), (0.85, 1.)] #ag_news\n", - "# ylims = [(0., 1), (0., 1), (0., 1.), (0., 1.)]\n", - "# ylims = [(0., 1), (0.7, 0.9), (0.9, 1.), (0.6, 0.85)] #sber faq\n", - "ylims = [(0., 1), (0.8, 0.9), (0.8, 1.), (0.8, 0.9)] #imdb\n", - "\n", - "for metric, ylim in zip(MEASURES, ylims):\n", - " y_label = metric\n", - " plt.figure(figsize=(12,6))\n", - " for i in range(data.shape[0]):\n", - " plt.scatter(i // POPULATION_SIZE, \n", - " data.loc[:, metric + \"_valid\"].values[i], \n", - " c=colors[models_ids[i]], alpha=0.5, marker='o')\n", - " if PLOT_TEST:\n", - " for i in range(data.shape[0]):\n", - " plt.scatter(i // POPULATION_SIZE, \n", - " data.loc[:, metric + \"_test\"].values[i], \n", - " c=colors[models_ids[i]], alpha=0.5, marker='+', s=200)\n", - "\n", - " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", - " data.loc[:, metric + \"_valid\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \n", - " c='r')\n", - "\n", - " plt.ylabel(y_label, fontsize=20)\n", - " plt.xlabel(\"population\", fontsize=20)\n", - " plt.title(TITLE, fontsize=20)\n", - " plt.ylim(ylim[0], ylim[1])\n", - " # plt.ylim(0.85, 0.95)\n", - " plt.xticks(fontsize=20)\n", - " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \"_colored_ids.png\")\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train params" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": true - }, - "outputs": [], "source": [ "params_dictionaries = []\n", "\n", @@ -436,51 +494,54 @@ " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"True\", \"true\")\n", " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", " d = json.loads(json_acceptable_string)\n", - " params_dictionaries.append(d)" + " params_dictionaries.append(d)\n", + "\n", + "models_ids = []\n", + "for pdict in params_dictionaries:\n", + " models_ids.append(pdict[\"evolution_model_id\"])\n", + " \n", + "models_ids = np.array(models_ids)\n", + "models_ids" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" + "array([1, 2])" ] }, + "execution_count": 63, "metadata": {}, - "output_type": "display_data" - }, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(models_ids)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "1" ] }, + "execution_count": 71, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "for y_label in [\"batch_size\", \"epochs\"]:\n", - "# y_label = \"batch_size\"\n", - " plt.figure(figsize=(12,12))\n", - " for i in range(data.shape[0]):\n", - " plt.scatter(i // 10, \n", - " params_dictionaries[i][\"train\"][y_label] + (np.random.random() - 0.5) / 2, #s=3,\n", - " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", - "\n", - " plt.ylabel(y_label, fontsize=20)\n", - " plt.xlabel(\"population\", fontsize=20)\n", - " plt.title(TITLE, fontsize=20)\n", - " plt.xticks(fontsize=20)\n", - " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", - " plt.show()\n" + "np.where(models_ids[2] == np.unique(models_ids))[0][0]" ] }, { @@ -493,22 +554,36 @@ "source": [] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model params" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] }, { "cell_type": "code", - "execution_count": 62, - "metadata": {}, + "execution_count": 73, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { - "image/png": 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HqAL77cB4YG4v/cwFxgG3Nwb9qp9OYHHT8QAeAn4NHBMRezbuExFvACYB/973U5EkSZLaQ7uE/UOq8sEetj9UlQcPdj+Z+TTwMWAv4L6IuDIiPh0RXwNuBL4HfKCX40qSJEltp13m7E+pyg09bO+qnzoU/WTmZRGxAvgS8P6GTQ8DVzXO428WEecA5wDst99+vQxPkiRJGj7tcmV/REXEnwP/H3AVcCAwATiaMsf/KxHx2Z72zcwrM7MjMzumT58+HMOVJEmS+qRdwn7XFfcpPWzvql8/2P1ExEnAZyg36F6Qmcszc2tm/ifwbuBx4MLuluyUJEmS2lm7hP0HqrKnOfkHVWVPc/EH0s/bq/KW5saZuZWyRv8Y4Kheji1JkiS1lXYJ+11B+7RqicznVUtvzgO2UpbI3JWlwDZgXrVfYz9jgNOajgfwsqrsaQ5OV/2vezm2JEmS1FbaIuxn5iOUlW/mAOc2bb6EMod+YeMa+xFxaEQc2tTPZmBh1f7ipn4+XPW/uGuN/coPq/KciNi3cYeIeAvlg8Z2YEl/z0uSJEkaSW3xUC14/sFaSyhP0V0E3A8cS1kT/0Hg+Mxc29A+ATIzmvrZo+rnYMrTb+8ADgPOoDxw6/jqw0VX+zGU9fffCGwCrgWeqPZ5O+VhXn+amQt6OwcfqiVJkqThMNoeqtV1db+DsiLOscCFlJVxFgBzG4N+L/2sBY4DLgdeUfVzLPBl4OjGoF+17wTeCpwP3Ee5KfdCygO6vgO8uS9BX5IkSWo3bXNlvw68si9JkqTh0Ncr++3yUC0Ng9X3wj3Xw7qVMG02HPEOmHn4SI9KkiRJQ6VtpvFoaK2+F35wBWxbD1P3LeUPrij1kiRJqifD/ovEPdfDuKnlFWNe+Pqe60d6ZJIkSRoqhv0XiXUrYezkHevGTi71kiRJqifD/ovEtNmwfeOOdds3lnpJkiTVk2H/ReKId5R5+tvWQ3a+8PUR7xjpkUmSJGmoGPZfJGYeDm/4SJmnv/7xUr7hI67GI0mSVGcuvfkiMvNww70kSdKLiVf2JUmSpJoy7EuSJEk1ZdiXJEmSasqwL0mSJNWUYV+SJEmqKcO+JEmSVFOGfUmSJKmmDPuSJElSTRn2JUmSpJryCbovIqvvgXu/Cesfg6mz4PB3wswjRnpUkiRJGipe2X+RWH0P/PBy2LYepuxTyh9eXuolSZJUT4b9F4l7vwnjppZXjHnh63u/OdIjkyRJ0lAx7L9IrH8Mxk7esW7s5FIvSZKkejLsv0hMnQXbN+5Yt31jqZckSVI9GfZfJA5/Z5mnv209ZOcLXx/+zpEemSRJkoaKYf9FYuYRcML8Mk9/w6pSnjDf1XgkSZLqzKU3X0RmHmG4lyRJejHxyr4kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTXlajyj3MqHkmU3w9OrYfeZ0HEKzD4oBt72geSum2DtKthjHzjqVJh9SPdtJUmS1J68sj+KrXwouWEhbN0Iu+9VyhsWlvoBtX0gufHqqu3epbzx6lIvSZKk0cOwP4otuxnGT4IJkyHGlHL8pFI/kLZ33VRtr9qOn1y+v+umoT8nSZIkDR7D/ij29GoYP3HHuvETS/1A2q5dBeOa2o6bWOolSZI0ehj2R7HdZ8LWzTvWbd1c6gfSdo99YFtT222bS70kSZJGD8P+KNZxCmzdBFs2QnaWcuumUj+QtkedWm2v2m7dWL4/6tShPydJkiQNHsP+KDb7oOD0M8uc+qd/VcrTz+x+hZ1+tT0kOO19VdsnSnna+1yNR5IkabRx6c1RbvZBweyDhqDtIcHsQ1oflyRJkkaeV/YlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU20V9iNiVkR8KSJWRcQzEbEiIi6LiGn97Gf3ar8VVT+rqn5nddP2rIjIXl7PDd5ZSpIkScNjt5EeQJeIOBBYAswAFgE/B44BzgNOj4h5mbm2D/3sUfVzMHAz8FXgUOBs4G0RcVxmLm/Y5W7gkh66OwE4BfhuSyclSZIkjaC2CfvA5ylBf35mXtFVGRGXAucDnwQ+2Id+PkUJ+pdm5oUN/cwHFlTHOb2rPjPvpgT+nUTEj6ovr+zXmUiSJEltIDJzpMfQdVX/YWAFcGBmdjZsmwSsBgKYkZlbdtHPROBJoBOYmZmbGraNAZYD+1fHWN59L8+3fxXwU+BxYP/M7HUqT0dHRy5btqy3ZpIkSdKARMSdmdnRW7t2mbN/clXe2Bj0AarAfjswHpjbSz9zgXHA7Y1Bv+qnE1jcdLxdOacqv9iXoC9JkiS1m3YJ+4dU5YM9bH+oKg8ejn4iYhzwXuA54J96OaYkSZLUltol7E+pyg09bO+qnzpM/fx21eaGzFy5q4YRcU5ELIuIZWvWrOmlW0mSJGn4tEvYbzddU3i+0FvDzLwyMzsys2P69OlDPCxJkiSp79ol7HddcZ/Sw/au+vVD3U9EHA4cDzwGfKeX40mSJEltq13C/gNV2dNc+oOqsqe5+IPZjzfmSpIkqRbaJezfUpWnVUtkPq9aenMesBVY2ks/S4FtwLxqv8Z+xgCnNR2PpjZjgTMpN+Z+sT8nIEmSJLWbtgj7mfkIcCMwBzi3afMlwARgYeMa+xFxaEQc2tTPZmBh1f7ipn4+XPW/eBdr7P8WMA34bm835kqSJEntrp2eoPshYAlweUScCtwPHEtZE/9B4ONN7e+vymiqvwg4CbggIl4D3AEcBpxBeeBW84eJRl1TeHxiriRJkka9triyD89f3e8ArqKE/AuBA4EFwNzMXNvHftYCxwGXA6+o+jkW+DJwdHWcnUTEYcDr8cZcSZIk1UQ7Xdmnmjpzdh/bNl/Rb9z2NHBe9errse9n578SSJIkSaNW21zZlyRJkjS4DPuSJElSTRn2JUmSpJoy7EuSJEk1ZdiXJEmSaqqtVuNR/63+Gdy3CNavhKmz4ZVnwMxXjfSoJEmS1A68sj+Krf4Z3LYAtq2HKfuW8rYFpV6SJEky7I9i9y2CcVPLK8a88PV9i0Z6ZJIkSWoHhv1RbP1KGDt5x7qxk0u9JEmS5Jz9UWzqbHjqEdiwGrZvLEF/ykzY88CRHpkkSZLagVf2R7EZR8AvfwzbN8DLJpbylz8u9ZIkSZJhfxR74ucw61gYOwV+vamUs44t9ZIkSZLTeEaxdSthjwNhz4NeqMvOUi9JkiR5ZX8Umza7zNVvtH1jqZckSZIM+6PYEe8oa+tvW1+u6Hd9fcQ7RnpkkiRJageG/VFs5uHwho+UtfXXP17KN3yk1EuSJEnO2R/lZh5uuJckSVL3vLIvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaqp3UZ6ABqY1ffCPdfDupUwbTYc8Q6YefhIj0qSJEntwCv7o9jqe+EHV8C29TB131L+4IpSL0mSJBn2R7F7rodxU8srxrzw9T3Xj/TIJEmS1A4M+6PYupUwdvKOdWMnl3pJkiTJsD+KTZsN2zfuWLd9Y6mXJEmSDPuj2BHvKPP0t62H7Hzh6yPeMdIjkyRJUjsw7I9iMw+HN3ykzNNf/3gp3/ARV+ORJElS4dKbo9zMww33kiRJ6p5X9iVJkqSaaquwHxGzIuJLEbEqIp6JiBURcVlETOtnP7tX+62o+llV9Turl/1OjYhrI+KJhv0WR8RbB3ZmkiRJ0vBrm2k8EXEgsASYASwCfg4cA5wHnB4R8zJzbR/62aPq52DgZuCrwKHA2cDbIuK4zFzezX6fBf4MeAz4JvAUMB04GjgJ+M4AT1GSJEkaVm0T9oHPU4L+/My8oqsyIi4Fzgc+CXywD/18ihL0L83MCxv6mQ8sqI5zeuMOEfF+StC/GjgnM3/dtP2lrZyQJEmSNJIiM0d6DF1X9R8GVgAHZmZnw7ZJwGoggBmZuWUX/UwEngQ6gZmZualh2xhgObB/dYzlVf3LgJXANuCg5qDfHx0dHbls2bJWd5ckSZL6JCLuzMyO3tq1y5z9k6vyxsagD1AF9tuB8cDcXvqZC4wDbm8M+lU/ncDipuMBvIkyXecbQGdEvC0iPhYR50XEcS2djSRJktQG2mUazyFV+WAP2x8CTqNMz7lpgP1Q9dPldVW5HbgLOKJxh4j4AfCbmblmF8eVJEmS2k67XNmfUpUbetjeVT91CPqZUZV/BiRwAjAJOBK4EXgD8G89HTAizomIZRGxbM0aPw9IkiSpfbRL2B9JXe/Bs8A7M/O2zNycmT8D3k1ZnefEnqb0ZOaVmdmRmR3Tp08fpiFLkiRJvWuXsN91xX1KD9u76tcPQT9dX9+VmSsaG2fmVl6Y539ML8eWJEmS2kq7hP0HqvLgHrYfVJU9zcUfSD9d+/T0QWJdVY7r5diSJElSW2mXsH9LVZ5WLZH5vGrpzXnAVmBpL/0spSyhOa/ar7GfMZSbfBuPB+WG3wRe2XzsStcNu4/2dhKSJElSO2mLsJ+Zj1Buhp0DnNu0+RJgArCwcY39iDg0Ig5t6mczsLBqf3FTPx+u+l/c+ATdzPwFcD2wH+Vpvc+LiNOAN1Ou+t/Q0slJkiRJI6QtHqoFzz9YawlldZxFwP3AsZQ18R8Ejs/MtQ3tEyAzo6mfPap+DgZuBu4ADgPOoDxw6/jqw0XjPrOqfWZTrvTfBRwAvIty1f93M/PrvZ2DD9WSJEnScBhtD9XqurrfAVxFCfkXAgcCC4C5jUG/l37WAscBlwOvqPo5FvgycHRz0K/2eQw4GvgcZV7/ecBJlCv+8/oS9CVJkqR20zZX9uvAK/uSJEkaDqPuyr4kSZKkwWXYlyRJkmrKsC9JkiTVlGFfkiRJqinDviRJklRThn1JkiSppgz7kiRJUk3tNtID0MCsuhfu+RasewymzYIj3g77HN592yd+CvdfC+t/CVP3g8PeDXsfObzjlSRJ0vDxyv4otupeuPXvYdt6mLpPKW/9+1Lf7ImfwpJLYds6mDKrlEsuLfWSJEmqJ8P+KHbPt2D8FBg3FWJMKcdPKfXN7r8Wxk6FcdOqttPK9/dfO/zjliRJ0vAw7I9i6x6DsZN3rBs7udQ3W/9LGDulqe2UUi9JkqR6MuyPYtNmwfaNO9Zt31jqm03dD7ZvaGq7odRLkiSpngz7o9gRb4etG8pc/ews5dYNpb7ZYe+G7evLXP3sLOX29aVekiRJ9WTYH8X2ORxOPLfM1V+/qpQnntv9ajx7HwnHX1Dm6m94rJTHX+BqPJIkSXXm0puj3D6H97zUZrO9jzTcS5IkvZh4ZV+SJEmqKcO+JEmSVFOGfUmSJKmmDPuSJElSTRn2JUmSpJoy7EuSJEk1ZdiXJEmSasqwL0mSJNWUYV+SJEmqKcO+JEmSVFOGfUmSJKmmDPuSJElSTRn2JUmSpJoy7EuSJEk1ZdiXJEmSasqwL0mSJNWUYV+SJEmqKcO+JEmSVFOGfUmSJKmmDPuSJElSTRn2JUmSpJoy7EuSJEk1ZdiXJEmSasqwL0mSJNWUYV+SJEmqKcO+JEmSVFOGfUmSJKmmDPuSJElSTRn2JUmSpJoy7EuSJEk1ZdiXJEmSasqwL0mSJNWUYV+SJEmqKcO+JEmSVFOGfUmSJKmmDPuSJElSTRn2JUmSpJoy7EuSJEk1ZdiXJEmSasqwL0mSJNWUYV+SJEmqqd1GegAamNU/g/sWwfqVMHWCCmVSAAAgAElEQVQ2vPIMmPmqkR6VJEmS2oFX9kex1T+D2xbAtvUwZd9S3rag1EuSJEmG/VHsvkUwbmp5xZgXvr5v0UiPTJIkSe3AsD+KrV8JYyfvWDd2cqmXJEmS2irsR8SsiPhSRKyKiGciYkVEXBYR0/rZz+7VfiuqflZV/c7qof2KiMgeXk8MztkNvqmzYfvGHeu2byz1kiRJUtvcoBsRBwJLgBnAIuDnwDHAecDpETEvM9f2oZ89qn4OBm4GvgocCpwNvC0ijsvM5d3sugG4rJv6zS2czrB45Rlljj6UK/rbN5Z5+0e/b2THJUmSpPbQNmEf+Dwl6M/PzCu6KiPiUuB84JPAB/vQz6coQf/SzLywoZ/5wILqOKd3s9/6zLy45dGPgJmvgteft+NqPEe/z9V4JEmSVERmjvQYuq7qPwysAA7MzM6GbZOA1UAAMzJzyy76mQg8CXQCMzNzU8O2McByYP/qGMsbtq0AyMw5AzmPjo6OXLZs2UC6kCRJknoVEXdmZkdv7dplzv7JVXljY9AHqAL77cB4YG4v/cwFxgG3Nwb9qp9OYHHT8Rq9LCLeGxEXRcR5EXFyRLykvyciSZIktYt2mcZzSFU+2MP2h4DTKNNzbhpgP1T9NNsbWNhU92hEnJ2Zt+7imJIkSVJbapcr+1OqckMP27vqpw5RP18GTqUE/gnAq4AvAHOA70bEq3s6YEScExHLImLZmjVrehmeJEmSNHzaJeyPqMy8JDNvzsxfZebWzLwnMz8IXEqZFnTxLva9MjM7MrNj+vTpwzVkSZIkqVftEva7rrhP6WF7V/36Yeqnyz9U5Rv62F6SJElqG+0S9h+oyu7m0gMcVJU9zcUf7H66dM3LmdDH9pIkSVLbaJewf0tVnlYtkfm8aunNecBWYGkv/SwFtgHzqv0a+xlDucm38Xi96Vr9p7uHcEmSJEltrS3CfmY+AtxIuSH23KbNl1CurC9sXGM/Ig6NiEOb+tlMWVFnAjvPs/9w1f/ipjX2D4uIna7cR8Qc4HPVt//cz1OSJEmSRly7LL0J8CFgCXB5RJwK3A8cS1kT/0Hg403t76/KaKq/CDgJuCAiXgPcARwGnEF54Fbzh4nfAS6MiB8AvwA2AQcCbwPGAt8B/naA5yZJkiQNu7YJ+5n5SER0AH8FnA68lfLk3AXAJZm5ro/9rI2I44BPAO8CTgDWUpbX/MvMfKxpl1so6/MfRZkuNIFyA+9tlL8SLMx2eMywJEmS1E9hjh08HR0duWzZspEehiRJkmouIu7MzI7e2rXFnH1JkiRJg8+wL0mSJNWUYV+SJEmqKcO+JEmSVFOGfUmSJKmmDPuSJElSTRn2JUmSpJoy7EuSJEk1ZdiXJEmSasqwL0mSJNWUYV+SJEmqKcO+JEmSVFOGfUmSJKmmDPuSJElSTRn2JUmSpJoy7EuSJEk1ZdiXJEmSasqwL0mSJNWUYV+SJEmqKcO+JEmSVFOGfUmSJKmmDPuSJElSTRn2JUmSpJoy7EuSJEk1ZdiXJEmSamq3kR6ABubRRztZ+iNYswamT4e5x8EBB/gZTpIkSV7ZH9UefbSTRdclmzcne+xRykXXJY8+2jnSQ5MkSVIbMOyPYkt/BBMmwMSJwZgxwcSJwYQJpV6SJEky7I9ia9bA+PE71o0fX+olSZIkw/4oNn06bN26Y93WraVekiRJMuyPYnOPgy1bYPPmpLOzzNnfsqXUS5IkSS2H/YiYHhEfjIgFEfFPTfXHRMS4wRmienLAAWM4411lrv7ataU8413hajySJEkCWlx6MyL+GLgcGAsEkMCfVJv3An4EnAN8cRDGqF044IAxHHDASI9CkiRJ7ajfl4Aj4k3AlcCDwLuB/7dxe2beA9wLvGswBihJkiSpNa1c2f8YsBo4MTM3RsRR3bT5KeDMcUmSJGkEtTK5uwP4VmZu3EWbx4C9WxuSJEmSpMHQStj/DWBLL22mAs+10LckSZKkQdJK2F8BHN1Lm2OBB1roW5IkSdIgaSXsLwJOiIjf6m5jRJwNHAl8fSADkyRJkjQwrdyg+1ngd4H/GxG/CUwBiIgPAycA7wEeAq4YrEFKkiRJ6r9+h/3MXBcRJwLXAI1X9y+vyh8Cv5+Zvc3rlyRJkjSEWnqoVmb+EjgpIo6kLLG5B7ABWJqZdw7i+CRJkiS1qKWw3yUzf0pZU1+SJElSm2nlCbrLI2J+L23OjYjlrQ9LkiRJ0kC1shrPHMo6+rsyFdi/hb4lSZIkDZJWwn5fTAJ+PUR9S5IkSeqDPs3Zj4j9mqqmdlMH8BJgP+C/AU7jkSRJkkZQX2/QXQFkw/fnVa+eBHBBi2OSJEmSNAj6GvavoYT9AP6QsgLP3d20ew5YC9yUmTcOygglSZIktaRPYT8zz+r6OiL+ELg2M/9qqAYlSZIkaeBaeYLuUN3UK0mSJGkQGdwlSZKkmmr5CboR8TrgzcC+wMu6aZKZ+cet9i9JkiRpYPod9iMigKuA91Ju2O26cbdLNtQb9iVJkqQR0so0ng8DZwILgQ5KsL8MOB64CNgEfBV4+SCNUZIkSVILWpnG8z7gga4VesqFftZn5lJgaUQsBpYC3wO+PEjjlCRJktRPrVzZPxS4uanu+Q8NmXkX8C3gQwMYlyRJkqQBanU1ng0NX28Bdm/a/hDlQ4EkSZKkEdJK2H+csgJPl+XA0U1tDqJ8CJAkSZI0QloJ+3ewY7j/LnBMRPxFRBweEecCZ1Dm7UuSJEkaIa2E/a8DL4mIA6rvPwv8ArgE+ClwBbAe+O/97TgiZkXElyJiVUQ8ExErIuKyiJjWz352r/ZbUfWzqup3Vh/3f29EZPX6k/6ehyRJktQO+r0aT2ZeB1zX8P3TEXEU8H7gQGAFcE1mru5PvxFxILAEmAEsAn4OHAOcB5weEfMyc20f+tmj6udgyo3EX6XcP3A28LaIOC4zl+9i/9nA54DNwMT+nIMkSZLUTlp+gm6jzNwA/O0Au/k8JejPz8wruioj4lLgfOCTwAf70M+nKEH/0sy8sKGf+cCC6jind7dj9cCwLwNrgW8AH23pTCRJkqQ20O9pPBHxXER8ZTAHUV3VP43yV4G/b9r8CcrNvmdGxIRe+plIeeDXFuDips2fo0w3enNE9PTAr/nAKZS/AniDsSRJkka1VubsbwJ+OcjjOLkqb8zMzsYNmbkJuB0YD8ztpZ+5wDjg9mq/xn46gcVNx3teRBwG/A2wIDN/0O8zkCRJktpMK2H/LuCVgzyOQ6rywR62P1SVBw9FPxGxG7CQ8iHmol6OIUmSJI0KrYT9zwBvjYg3DeI4plTlhh62d9VPHaJ+/hI4CjgrM7f1cowdRMQ5EbEsIpatWbOmP7tKkiRJQ6qVG3RnADcA342I64AfA08A2dwwM68Z2PCGXkQcS7ma/38y80f93T8zrwSuBOjo6NjpPZAkSZJGSith/ypKsA/gPdULdgz7UX3f17DfdcV9Sg/bu+rXD2Y/1fSdayjTfv6i92FKkiRJo0crYf/sQR8FPFCVPc3JP6gqe5qL32o/Exvabi8rb+7kHyPiHyk37v5pL8eXJEmS2kYrD9W6egjGcUtVnhYRYxpX5ImIScA8YCuwtJd+lgLbgHkRMalxRZ6IGENZ3rPxeM8AX+yhr9dS5vHfRvkQ0e8pPpIkSdJIGpSHavVFRJwHnJeZO61xn5mPRMSNlDB+LnBFw+ZLgAnAFzLz+bXvI+LQat+fN/SzOSIWAudQ1tm/sKGfDwNzgMVdT9Ctbsb9kx7GezEl7F+dmf/Uz9OVJEmSRtywhX3KCjj772L7h4AlwOURcSpwP3AsZU38B4GPN7W/vyqb595cBJwEXBARrwHuAA4DzgCepHyYkCRJkmqvlaU3h0RmPgJ0UG4APpZyVf5AYAEwNzPX9rGftcBxwOXAK6p+jgW+DBxdHUeSJEmqveG8st+rzFxJH28Azsxu76attj0NnFe9Wh3LxZSpQJIkSdKo1DZX9iVJkiQNLsO+JEmSVFOGfUmSJKmmDPuSJElSTRn2JUmSpJoy7EuSJEk11e+lNyPiL4FHM3NhP3f9fn+Ppd6tfChZdjM8vRp2nwkdp8Dsg7pflXTJDzv55uJOntwEMybBO988huNP6P7z3uP3w0++C+seh2n7wqvfAvseNpRnIkmSpMHWypX9/wm8qr87ZeatmXlJC8dTD1Y+lNywELZuhN33KuUNC0t9syU/7OQLX3uOjduSPScmG7clX/jacyz5YedObR+/H275AmzbANNmlvKWL5R6SZIkjR6thP3HgcmDPRD137KbYfwkmDAZYkwpx08q9c2+ubiTCb8Bk8YHMSaYND6Y8BulvtlPvgvjpsD4KaXf8VPK9z/57jCclCRJkgZNK2H/WuCNETFusAej/nl6NYyfuGPd+ImlvtmTm2Di2B3rJo4t9c3WPQ7jJu1YN25SqZckSdLo0UrY/wSwDrguIo4Y5PGoH3afCVs371i3dXOpbzZjEmzevmPd5u2lvtm0fWFb04eAbZtKvSRJkkaPVsL+T4CZwBuBn0TEloh4NCKWN70eGdyhqlnHKbB1E2zZCNlZyq2bSn2zd755DFt+DZu2JtmZbNqabPl1qW/26reUefpbN5R+t24o37/6LcNwUpIkSRo0rYT9McB/Ab+sXk9W9dH0clnPITb7oOD0M2H8ZHj6V6U8/czuV+M5/oQxfOC3X8LkccFTm4PJ44IP/PZLul2NZ9/D4OQPlHn661aX8uQPuBqPJEnSaNPvpTczc84QjEMtmn1QMPugvrU9/oSel9ps9sz4Tp6enTw5FnI6PDPez2+SJEmjjelNO1n+i06+8a1k85Zkzz1L+Y1vJct/sfPKPZIkSWpfhn3tZMkdycQJycQJwZgIJk4IJk5Iltyx8/r9kiRJal/9nsbTJSJeBrwO2Bd4WXdtMvOaVvvXyHlyDey5545148eXekmSJI0eLYX9iPgj4LPAtJ6aAAkY9kehGdNh8xaYOOGFuq1bS70kSZJGj35P44mI04F/AlYDH6UE+0XAx4HvVd//G/BHgzdMDafjjwk2bwk2b0k6s8zZ37wlOP6YnVf5kSRJUvtqZc7+hcBa4PjM/Luq7u7M/JvMPB14P/AewHX2R6mX7z+G97y9zNV/6qlSvuftwcv39xYPSZKk0aSVaTyvBRZlZuMzVp9PgZn5xYg4k3Kl38cwjVIv338ML99/pEchSZKkgWjlUu0EyhSeLtuByU1tlgHHtjooSZIkSQPXSth/Ami8VXM1cEhTmynAS1odlCRJkqSBayXs38uO4f6HwKkRcQJARBwB/HbVTpIkSdIIaSXsfxeYFxH7VN9/FngO+H5ErAF+AkwC/tfgDFGSJElSK1oJ+1+gPEjrKYDMvA84lfIh4CngRuAtmfmdwRqkJEmSpP7r92o8mflfwK+a6pYCbx+sQUmSJEkaOBdOlyRJkmqqlXX2AYiII4HfBw4DJmTmG6v6OcAxwPcyc90gjFGSJElSC1oK+xHxV8BFvPCXgWzYPAb4v8CfAlcMaHSSJEmSWtbvaTwR8bvA/wS+B7wG+HTj9sxcTnmo1jsHY4CSJEmSWtPKnP35wMPAGZn5U+DX3bS5HzhoIAOTJEmSNDCthP1XAYszs7uQ32UVsFdrQ5IkSZI0GFoJ+wF09tJmL2B7C31LkiRJGiSthP2HgON72hgRY4DXA/e2OihJkiRJA9dK2P8a8NqIuLCH7RcBrwD+peVRSZIkSRqwVpbevAz4LeCzEfHbVMtuRsTfAicAHcBS4MrBGqQkSZKk/ut32M/MbRFxMrAA+APgJdWmCyhz+f8Z+HBmPjtoo5QkSZLUby09VCszNwBnRcQFwOuAPYANwB2ZuWYQxydJkiSpRS2F/S6Z+TSweJDGIkmSJGkQ9Rr2I+JLLfadmfnHLe4rSZIkaYD6cmX/rBb7TsCwL0mSJI2QvoT9A4Z8FJIkSZIGXa9hPzN/MRwDkSRJkjS4WnmoliRJkqRRwLAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTXVVmE/ImZFxJciYlVEPBMRKyLisoiY1s9+dq/2W1H1s6rqd1YP7T8TETdFxMqI2BYRT0fEXRHxiYjYY3DOTpIkSRpekZkjPQYAIuJAYAkwA1gE/Bw4BjgZeACYl5lr+9DPHlU/BwM3Az8GDgXOAJ4EjsvM5U37/Br4T+C+qs0EYC7QAawC5mbmyt6O3dHRkcuWLevL6UqSJEkti4g7M7Ojt3a7Dcdg+ujzlKA/PzOv6KqMiEuB84FPAh/sQz+fogT9SzPzwoZ+5gMLquOc3rTP5Mzc3txRRHwSuAj4H8CH+nU2kiRJ0ghriyv71VX9h4EVwIGZ2dmwbRKwGghgRmZu2UU/EylX5juBmZm5qWHbGGA5sH91jOXd97JDf68G7gb+PTPf1Ft7r+xLkiRpOPT1yn67zNk/uSpvbAz6AFVgvx0YT5lasytzgXHA7Y1Bv+qnE1jcdLzevKMqf9rH9pIkSVLbaJdpPIdU5YM9bH8IOI0yPeemAfZD1c9OIuKjwERgCmW+/uspQf9vejpgRJwDnAOw33777WJokiRJ0vBql7A/pSo39LC9q37qEPfzUWCvhu9vAM7KzDU9HTAzrwSuhDKNp5fxSZIkScOmXabxtIXM3DszA9gbeA/wcuCuiHjtyI5MkiRJ6r92CftdV9yn9LC9q379cPSTmb/KzGspU4f2AK7p5biSJElS22mXsP9AVXY7lx44qCp7mos/2P0AkJm/oKy9f3hE7NmXfSRJkqR20S5h/5aqPK1aIvN51dKb84CtwNJe+lkKbAPmVfs19jOGcqW+8Xh9sU9VPtePfSRJkqQR1xZhPzMfAW4E5gDnNm2+hPJE24WNa+xHxKERcWhTP5uBhVX7i5v6+XDV/+LGNfYj4uCI2GnaT0SMqR6qNQNYkpnrWjo5SZIkaYS0y2o8UJ5QuwS4PCJOBe4HjqWsif8g8PGm9vdXZTTVXwScBFwQEa8B7gAOA86gPHCr+cPEW4FPR8RtwKPAWsqKPCdSbtB9Anj/AM9NkiRJGnZtE/Yz85GI6AD+CjidEsJXAwuAS/p6ZT0z10bEccAngHcBJ1AC/JeBv8zMx5p2+XfgFZQ19Y+iLMu5hfIBYyFweWY+PcDTkyRJkoZdZLo0/GDp6OjIZcuWjfQwJEmSVHMRcWdmdvTWri3m7EuSJEkafIZ9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmjLsS5IkSTVl2JckSZJqyrAvSZIk1ZRhX5IkSaopw74kSZJUU4Z9SZIkqaYM+5IkSVJNGfYlSZKkmmqrsB8RsyLiSxGxKiKeiYgVEXFZREzrZz+7V/utqPpZVfU7q5u2e0TEn0TEtRHxcERsi4gNEXFbxP/P3p1GyXFdB57/v1hy32rBVqgiVhLcRVHUQnNIUYtFapclt9rdp2W37dMau9vjZTxzZk7P0tRMd5/+4m5LVttqasbyjG1Z0rRkkZS4SaIo0tzBFSCIwg5WAagCass9M5b35kMkQWRGQgSFLatwf+fwRPHVrUBkZmTEjRfv3VC/rZQaqPdICCGEEEKIM+Vc7A14g1JqC/AksBq4B9gNvAf4A+BOpdQtxpj5M1jPSGc9VwCPAN8CrgR+E/i4UupmY8yBU/7kHwF/ARwDfgq8DqwBPgv8X8BHlVL/yBhjzskLFUIIIYQQ4gIZmGQf+HOiRP/3jTF/9kajUuo/An8E/Dvgd85gPf+eKNH/j8aYPz5lPb8PfLnz79x5Svwe4FPAD40x+pT4fw08C3yOKPH/7i/2soQQQgghhLg4BmKISqdX/yPAIeA/9/z63wB14AtKqexbrCcHfKETf1fPr78KHAbuUEptfqPRGPOIMea+UxP9TvsM8LXO/97+Nl6OEEIIIYQQA2Egkn3gA53lw32S7irwBJAB3vcW63kfkAae6PzdqevRwEM9/95b8TvL4AzjhRBCCCGEGBiDkuxv6yz3nOb3ezvLKy7QelBKOcCvd/73wZ8T90Wl1Hal1PYTJ0681WqFEEIIIYS4YAYl2S92luXT/P6N9tIFWg/AfwCuBe43xjx0uiBjzN3GmJuMMTetWrXqDFYrhBBCCCHEhTEoyf5A6Uzm/WOiikBfuMibI4QQQgghxC9kUJL9N3rci6f5/RvtS+d7PUqp3yOq2rML+IAxZuEt/k0hhBBCCCEG0qAk+5Od5enG0l/eWZ5uLP45WY9S6g+BPwN2EiX6M2/x7wkhhBBCCDGwBiXZ/2ln+ZHeJ9YqpfLALUADePot1vM00ARu6fzdqeuxiMp7nvrvnfr7/wn4T8BLRIn+8bf7IoQQQgghhBgkA5HsG2P2Aw8DG4F/1fPrLwFZ4K+NMfU3GpVSVyqlruxZTw346078XT3r+b3O+h/qeYIuSqn/jWhC7vPAh4wxc2f3ioQQQgghhLj4lDHmYm8DcPLBWk8SPUX3HuA14L1ENfH3AL9kjJk/Jd4AGGNUz3pGOuu5AniE6Cm4VwGfBo531rP/lPjfAP4KCImG8PSr5HPIGPNXb/UabrrpJrN9+/Yzer1CCCGEEEL8opRSzxtjbnqrOOdCbMyZMMbsV0rdBPwfwJ3Ax4BjRJNlv2SMWTzD9cwrpW4mevLuZ4BbgXngG8D/boyZ7vmTTZ2lDfzhaVb7M6ILAiGEEEIIIZaNgenZXwmkZ18IIYQQQlwIZ9qzPxBj9oUQQgghhBDnniT7QgghhBBCrFCS7AshhBBCCLFCSbIvhBBCCCHECiXJvhBCCCGEECuUJPtCCCGEEEKsUJLsCyGEEEIIsUJJsi+EEEIIIcQKJcm+EEIIIYQQK5Qk+0IIIYQQQqxQkuwLIYQQQgixQkmyL4QQQgghxAolyb4QQgghhBArlCT7QgghhBBCrFCS7AshhBBCCLFCSbIvhBBCCCHECuVc7A0QZ2f/lObxFwyz84Y1I4pbb1RsmZBrOCGEEEIIIT37y9r+Kc13HtJU64ZVw1CtG77zkGb/lL7YmyaEEEIIIQaAJPvL2OMvGHIZyGcVllLks4pcJmoXQgghhBBCkv1lbHbekM10t2UzUbsQQgghhBCS7C9ja0YU9UZ3W70RtQshhBBCCCHJ/jJ2642KWiMaq6+NoVo31BpRuxBCCCGEEJLsL2NbJiw+f4dFPqs4sRCN3f/8HZZU4xFCCCGEEICU3lz2tkxYbJm42FshhBBCCCEGkXQBCyGEEEIIsUJJsi+EEEIIIcQKJcm+EEIIIYQQK5Qk+0IIIYQQQqxQkuwLIYQQQgixQkmyL4QQQgghxAolyb4QQgghhBArlCT7QgghhBBCrFCS7AshhBBCCLFCSbIvhBBCCCHECiXJvhBCCCGEECuUJPtCCCGEEEKsUJLsCyGEEEIIsUJJsi+EEEIIIcQKJcm+EEIIIYQQK5Qk+0IIIYQQQqxQkuwLIYQQQgixQkmyL4QQQgghxAolyb4QQgghhBArlCT7QgghhBBCrFCS7AshhBBCCLFCSbIvhBBCCCHECiXJvhBCCCGEECuUJPtCCCGEEEKsUJLsCyGEEEIIsUJJsi+EEEIIIcQKJcm+EEIIIYQQK5Qk+0IIIYQQQqxQkuwLIYQQQgixQkmyL4QQQgghxAolyb4QQgghhBArlCT7QgghhBBCrFCS7AshhBBCCLFCSbIvhBBCCCHECiXJvhBCCCGEECvUQCX7SqlxpdRfKqWOKqXaSqlDSqk/VUoNvc31DHf+7lBnPUc76x0/TfyvKqX+TCn1uFKqopQySqm/OTevSgghhBBCiIvDudgb8Aal1BbgSWA1cA+wG3gP8AfAnUqpW4wx82ewnpHOeq4AHgG+BVwJ/CbwcaXUzcaYAz1/9r8C7wBqwHQnXgghhBBCiGVtkHr2/5wo0f99Y8xnjDH/szHmg8B/ArYB/+4M1/PviRL9/2iM+VBnPZ8humhY3fl3ev1R528KwO+e5esQQgghhBBiIAxEst/p1f8IcAj4zz2//jdAHfiCUir7FuvJAV/oxN/V8+uvAoeBO5RSm0/9hTHmp8aYvcYY84u+BiGEEEIIIQbNQCT7wAc6y4eNMfrUXxhjqsATQAZ431us531AGnii83enrkcDD/X8e0IIIYQQQqxYgzJmf1tnuec0v99L1PN/BfCTs1wPnfUIcUnbNRdy/8GAI1XD+rziY5scrh61L/ZmCSGEEOIcGpSe/WJnWT7N799oL12g9ZwxpdQXlVLblVLbT5w4ca5WK8R5tWsu5Gsve5TbhnU5KLcNX3vZY9dceLE3TQghhBDn0KD07C9bxpi7gbsBbrrpJhnzL5aF+w8GFJOKYlIBUEy+2S69+xfezprPvfNtptsh40mbT40kuTbnXuzNEkIIsQIMSrL/Ro978TS/f6N96QKtR4gV7Ug16nrWMjwAACAASURBVNE/VT4RtYsLa2fN5ytH6pQci7GExVKg+cqROr+/PisJv1hxdiwF3DPtM9XQTGQsPj3ucl1pUFIRIVamQRnGM9lZnm4s/eWd5enG4p/r9Qixoq3PK6ped1vVi9rFhXXvfJuSY1FyLCylTv5873z7Ym+aEOfUjqWAL0+2WfI069OKJU/z5ck2O5aCi71pQqxog5Ls/7Sz/IhSqmublFJ54BagATz9Fut5GmgCt3T+7tT1WESTfE/994S4JH1sk0O5bSi3DdqYkz9/bJP0sF1o0+2Qgt19kVWwFdNtmT8hVpZ7pn1KLpQSnQvbhEXJjdqFEOfPQCT7xpj9wMPARuBf9fz6S0AW+GtjTP2NRqXUlUqprifdGmNqwF934u/qWc/vddb/UJ8n6ApxSbl61OZ33pGgmFQcq0ExqfiddyRkvP5FMJ60qYTdw6cqoWE8KZ+FWFmmGpqC23Nh6yqmGvo0f3Hm9rRbfG1pnn8zP8vXlubZ026d9TqFWCkGqRvvXwJPAl9RSn0IeA14L1FN/D3A/9IT/1pn2Tvu4F8DtwP/vVLqBuBZ4Crg08Bx4hcTKKU+A3ym879rO8ublVJ/1fl5zhjzP/xCr0qIAXX1qC3J/QD41EiSrxyJ+jEKtqISGpYCza+vSV/kLRPi3JrIWCx5mlLizdN2xTdMZM6u33FPu8WfLS1woq1pasOk5fNqu81/VxrmimTqbDdbiGVvIHr24WTv/k3AXxEl+X8MbAG+DLzPGDN/huuZB24GvgJs7aznvcA3gHd1/p1eNwC/0fnvjk7b5lPafvUXelFCCPEWrs25/P76LCXH4qinKTmWTM4VK9Knx12WfFjyNNoYljzNkh+1n41vVyocaAZooyhYFtooDjQDvl2pnKMtF2J5U8ZI9Y1z5aabbjLbt2+/2JshhBBCDKTzUY3nc1NTWFqRtt7sv2xqjbYM352YONtNFmJgKaWeN8bc9FZxgzSMRwghhBAr2HUl55yX2tShwrG6Oy4tZQjCC1tdbE+7xSPNOsfCgHW2wwfTWRlGJAbCwAzjEUIIIYR4u66y0zSMwTMa01k2jOEq+8LNe9nTbvHX1SUqOmSNZVPRIX9dXZKJwmIgSLIvhBBCiGXrn43kGQ2TaAN1NNrAaJjkn43k3/qPz5FHmnXylkXBsrGUomDZ5C2LR5r1t/5jIc4zGcYjhBBCiGXr6kyC/3HtED9cbHHEC1mfsPn4qhRXZxIXbBuOhQFrrO7qZjllcSyUB4aJi0+S/WVu37TmsZc0Mwuwdhhuu8Fi67jcsBHn1qsLAfe9HjBdM4znFJ+8zOGaYTl8CCEGw9WZxAVN7nutsx0qOqSg3kz4a0azzpbjpLj4JCtcxvZNa77145Bqw7B6yFBtGL7145B902f/gBIh3vDqQsBXX/VZahvGsrDUNnz1VZ9XF6THSgghAD6YzlLVmooO0cZQ0SFVrflgOnuxN00ISfaXs8de0uQzkM8oLKXIZxT5TNQuxLly3+sBxQSUktF+VkoqiomoXQghBFyRTPGFfImCZTOrQwqWzRfyJanGIwaC3F9axmYWYPVQd1s2HbUL8VZ21nzunW8z3Q4ZT9p8aiTZ90FO07WoR/9UhUTULoQQInJFMiXJvRhI0rO/jK0dhnqzu63ejNqF+Hl21ny+cqTOUqAZS1gsBZqvHKmzs+bHYsdziorX3VbxonYhhBBCDDZJ9pex226wqDag2jBoE43ZrzaidiF+nnvn25Qci5JjRUNzOj/fO9+OxX7yMoeyF43V18aw1DaUvahdCCGEEINNztbL2NZxi1/7MF3VeD7+S1KNR7y16XbIWKJ7PynYiul2GIu9Ztjh966hqxrPFy6XajzLwY6az71zbaZaIRMpm0+NJrmuz1AtIc7Gfr/J436VWe2zxnK51c2zxb1wD7QSQvx8crZe5raOS3Iv3r7xpM1SoCk5bw7FqYSG8aTdN/6aYUnul5sdNZ8vT9cZsi3WJy2WfM2Xp+v8wXhWEn5xzuz3m3ynNU9O2axSDlUd8p3WPJ9nRBJ+IQaEnL2FuAR9aiTJV45ET3Ys2IpKaFgKNL++Rk7OK8W9c22GbIuSG3UGlFx1sl2SfXGuPO5XySmbfOeBUnllg47a+yX7uxpe98Ovhi7sw6+EuBRJl7AQl6Brcy53FpO8XAn4zmyblysBdxb7V+MRy9NUK6TgdE+iLjiKqVZ8qJYQv6hZ7ZNV3alEVlnM6vhk/10Nj7+YqVEONOtci3Kg+YuZGrsaXixWCHHuSLIvxCXolYrPfSd8rk65fHY4xdUpl/tO+LxSiZ+gxfI0kbKpBN3lUSuBYSLVf6iWEL+INZZL3XQ/26VuNGuseMfBDxdbFG2LYqcwQNGxKNoWP1xsXajNFeKSJMm+EJeg7x9vU3IUQ2500h1yLUqO4vvH49V4xPL0qdEki6FmyddRFSVfsxhqPjWavNibJlaQW908NRNS7Tw5tqpDaibkVjcfiz3iheTt7rtNeVtxxJO7TWJ5eKXsc9dkg996qcZdkw1eKS+PDjJJ9oW4BL3e0hR7hngUHcXrLXn68kpxXc7lD8azlFyLI21NybVkcq4457a4aT6fGiFv2ZwwAXnL5vOp/pNz1ydsqmH33aZqaFifkLtNP88sZR5jkvt4iceYZJbyxd6kS9IrZZ8/OdBi0deMpxSLvuZPDrSWRcIvE3SFuARdlrJY9A1D7psJfzkwXJaS6/+V5LqcK8m9OO+2uOkzqrzz8aEUfzFTA6Ie/WpoKIeaf7oqc743cdmapcwzHCCFS4EULXye4QDvZTNrKF7szbukfG/Gp+RGd8KBzvlT870Zn+uLg32clTO7EJegz6xOshQYFjtDPBZ9zVJg+MxqGeIhhICDQYO/bR/jPzVf52/bxzgYNM56nVdnEvzu2hxFx+KYryk6Fr+7NifVeH6OSWZI4ZLCRaFO/jzJzMXetEvO683T3BFvDv4dcenZF+ISdH3B5Y82RGP3X29pLktZ/Ob6FNcXBrt3Qghx/h0MGnzPP0FO2Ywqh5oJ+Z5/gs+yik3O2fXCX51JSHL/NpRpUiDV1ZbEoUzzIm3RpeuytMWir+N3xNOD328uyb4Ql6jrC64k90KImCfDMjllk1PRWPoc9sn2s032xdtTJE0LnxRvHqvbBBSRZ6JcaJ9d6/InB1pA1MNfDgxLvuG3Jwb/PDr4lyNCCCGEuGCOa59MT3qQweJ4n9r54vzaxlpa+LTwMZiTP29j7cXetEvO9UWXP96cYsi1mG4ZhlyLP96cGvjx+iA9+0KIi+jVuscPlt58muYnSimuycotfiEuptWWS82EJ3v0ARpoVvepnS/OrzUUeS+bmWSGMk2KpHkHEzI59yK5vugui+S+lyT7QoiL4tW6x5/P1inairHO0zT/fLbOv1yDJPxCXES/ZBf5nn8CiHr0G2hqJuQjzvBF3rJL0xqKktyLsyLJvhDiovjBUouirSg50XCBkhOVMfvBUmtgk/0pXeNFM888bUZI8k41woSVO23sC2bhZOyNavi0sUIMkk1Ohs+yiifDMse1z2rL5SPOsIzXF2KZkmRfiEvUrobH/Ustpr2Q8YTNx0qpC1ol44gXMuZ2jwsuDPDTNKd0jYfNUbLGZpgEDQIe5igf0WOxJH5K13jIHCVjHIZJUCfgIY5yR59YIQbRJicjyT1Rnfs9HKNMgyIZrmCd9LJfJJPtNj+u1zkaBIw5Dh/OZtmW7F8uuuHNstScxAsrJOwCpfQ2Mok1fWPbzWM0KzsJ/SVst0S6cC3J9Lrz+VIuOEn2hbgE7Wp4/MXxGiXLOjmE5i+O1/jd1Reu5vX6hE050J0e/UjlIjxN80x7618082SNTUZFh80MDhh4kXkm6I5/wSyQMQ7ZTmy2E/sCC7FYgGld42XmWKDFMCnewSjjclHwc+33mzzmVZnVPmssl9sS+TN6sNNKc0RXeZk5FmkzRJJ3MMp6K9839nVdZ7teYB6PERLcZA1zmZW9wFu8fMxS5ln2dR5olaaFx7Ps4z1slYT/Aptst/mrcpmCUqy1bSphyF+Vy/zzYjGW8De8WY7XnsFSKVwrT6BbHK89w+rce2MJf7t5jFerj3I4A75tcMM5NlQf5Rpu75vwv6RneIppKrQokOJmxrnBGvzJ0lKNR4hL0P1LLUqWRdGxsJSi6FiULIv7l1oXbBs+UUpRDg1LQfRgr6VAUw4Nnyil3vqPz5E3eusbJmDYJGiYgIfNUaZ0LRY7T5s03RciaWzmafeNzfTEZk4TO61r/IRpGvgMkaSBz0+YZrrPNixHO6o+//Zgjf/2tQr/9mCNHdWzr+iy32/y7dYCVR2ySjlUdci3Wwvs9y+t2uNHdJVHOvtOiQQNfB5hmiO6Got9Xdd5IDxG3QQMG5e6CXggPMbrun4Rtnx52MOxzkOsEp0HWiVI4bKHYxd70y45P67XKShFwbaxOsuCUvy4Ht9/l5qTWCqFY6VQSuFYKSyVYqk5GYvd3dzO3lxIaCkcYxNair25kN3N7bHYl/QMD7KXFh45ErTweJC9vKQH/wFnkuwLcQma9kLydveTAPO2YvoCDqG5JpvgjkKCnfWA78632FkPuKOQOO14/VcqPl/aW+e3d1T50t46r1TOPmk8tbdeKUVGOWSNzYtmPhY7QpIm3e9Pk5AR4reRR0jS6IltnCb2ZebIYJPpPCEzg0sGm5eZO8tXd/HtqPr86VSDJV+zPqlY8jV/OtU464T/Ma9KDou8FZ3485ZNDovHvHiSu5K9zBzpnn0nfZp9Z7teIINNtrOvZ5VDBpvteqHvug+Hdf6rP83X/AP8V3+aw+Gld1FQpkGS7sorSVzKnP3ThMXbczQIyFndKWvOsjgaBLFYL6xgq+5jra2SeGElFnvArWIbBwcLC4WDhW0cDrjxY8lTTJPEJqVcLKVIKZckNk8xfZav7vyTZF+IS9B4wqYamq62amgYv4BDaF6te9y/6HN12uVXhtJcnXa5f9Hn1boXi32l4vMnh1os+prxpGLR1/zJodZZJ/xvp7f+nWqEugppmABjDA0TUFch71Qjsdgb1TANFVDvxNZNQEMF3Kji1UwWaLHQcrh/Dv72GNw/BwsthwUu3F2W8+WeuTZDjqLkRneQSq7FkKO4Zy7+/r4ds9onq7pPX1llMXuJ1YFfpE26ZzRuGofFvnebvNPcbYp/3w6Hde7XM9QJGMGlTsD9euaSS/iLZGjTvU+18Sly9nMZDoUNvuUd5c/ah/iWd5RDoVxA/DxjjkNN6662mtaMOfHR6Am7QGi6vwOhaZOwC7FY3wbLdJ8LLWPw+5wKK7RI9HzfEjhUlsGxWpJ9IS5BHyulWNKacmcITTnQLGnNxy7gEJr7FtqUnKgaj6WiZclR3LcQT1T+ftZjyIGhTtI45FoMOVH72Xg7vfUTVo6PqDEyymFBeWSUw0dU/wm3E1aOO9QY2U5sVjnccZrYZivFjxYNjRBKDjRC+NGiodm6cJ/F+TLV0hSc7jtIBUcx1dKn+Yszs8ZyqZvuddSNZs0lVgd+iCRNuns2mwQM9b3blDjN3ab4nbTn9GLfuwDP6cVz+wIG3BWs6zzEyus80Mqjhc8VnN3kzUNhg3v8WWomupiqmYB7/FlJ+H+OD2ezVIyhEobozrJiDB/OxueclNLb0KZFoFsYYwh0C21alNLbYrEpq0hIiOkcT4zRhISkrPicjAIpvJ7vm0dAgcE/VssEXSEuQVdnEvzu6lxXNZ5/Usqck8m5O2o+9861mWqFTKRsPjWa5LpcPAmbaoek7ZAXfZ86miwWE8plqh3vUnm9FfXon6roKF4/y6TxnWqEhzkKJurRbxJSVyG3qP5VGyasXN8JtmcTO1vL4FgVXBsUFq6tcdDM1nKcgw7Ei2oiZbHka0ruKZOwA8NE6uz6mW5L5Pl2awF01KNfN5oamo8nSme7yTS8WRZbe/DCMgm7yFDqitNW8bjY3sEoj3SGEKRxaBLQJOTmPsnoTdYwD4THwEQ9+g1CGoS831odi53DY6Rn+EoGm7k+dwFWsjUUeQ9bu6rxXM+Gs56c+3S4RFbZ5DoT+HOdVOzpcImN9jL/0p8n25JJ/nmx2FWN57OnqcaTSaxhde69PdV43tH3e3y1ey0vmmcJdYhlfLSywUpytXttLPZmxnmQvWCiHn2PgDYhH2D8vLzmc0mSfSEuUVdnEue88s6Oms+Xp+sM2Rbrk1Gi9+XpOn8wno0l/FlH82LQIqsUGRRtNC/rFu904hVVLktZLPqaoVOSxnJguOwsk8YJK8dH9Bgv8mY1nlvUmgtaHrMaWFxl5zlOkyYBaRyusrJUg+V/4/XTo0n+dKoBRD38lcCwGBh+Y13/cnlnaoub5h8z3FWN5+OJ0llX42l4s8zUnsW2UrhWgUC3mKk9y9rcewYy4V9v5fmgHu+qxnMz6/pW47nMyvJR1nVV43m/tbpvNZ7RTrnY7CkpQoOQ0T53Ad6uGSq8xgxlWhRJcRVrWUt8eAXAY+0F7vfnWDI+JeXyMXeU25IX9sFe5+OBVidM/4upE+bSuph6u9ZbFT6R3EvLrZKy8oxYlwOr+sZuD1we1GuoWCUKOsmdgcttfXbfrfYGSMDucA8tUyelslxpXxG197jBWguarmo8H1gm1Xgk2RdCnDP3zrUZsi1Knfr5b/To3jvXjiX7yaQm9MGgwFIYDaExJJPx3vpfWZPgTw61AE3RUZQDw2IAvzV+9snH2+mtPx/GXIdKaHGF9WYCXNY69gyC5ei6vMsfTmS4Z67NVEszkbL4jXVJrsuf/XCbLW76nJfaXGztwbaiKh4AjkqdbB/EZB+ihH89/Utt9rrMyp5Rqc13W0Pcr2didwFut/onVmdqhgpPcpAUDgWSNPF5koP8EptiCf9j7QX+xjtKCouCcmiYgL/xjgJc8IT/XFulErzutzkWGKpak7cs1jmKy9yzuwheyWr+CaZb23FIklA5fN1iurWdcW4i53bvl481Fvi2N0VC2eRwaSqfb3tTANyWie87W+0NfZP7fm6w1nIDg5/c95JkX4gB91rT44Fyk6N+wJjr8NFimqvSA/qE2VbI+mTPg7IcxVQrXuUnsAy/lE2wtxVSCQ0FW3FdKkFgmVjs9QWXP94YjdF/vaW5LGXxW+MJri8M7hjt3c02D9be/NzuzKW5Mh0/md+ZS/P1hajyQ95SVLWhHGo+X7yw9c/3eS0ebVeZ0QFrLYfbk3m2Js5+LOp1efecJPcXgheWca3upDOq4lG+SFt0cWyws3yMtTynF5nDY5QEt1ur2GCf3T75GjOkcEh3erXfWL7GTCzZv9+fI4X15nMtlAMm4H5/btkn+2M6zb3tKhllkUVR1SGzbc377LMfhrZSzXt7cUh2X4jrqL032X+wfYKEsk/Zz6yT7f2S/UuBJPtCDLDXmh53n6hSsC3WOjblUHP3iSpfXJUfyIR/ImWfZox2fBz+mONQUSH/TfHNBLgShhTs/hWBri+4A53cn2p3s83XF6oUbYt1jk0l1Hx9ocq/GCaW8F+ZTvIvhum6MPh8Mdv3wuB82ee1+GZjgbyyWK1sqjrkm40F/inD5yThXy4SdpFAt0726MMbVTwuvQcobbCzZ53c9yrTotAzeTiFQ7lPNZMl41NQ3SlKStksmeVfcWlvEHCllWVRedRNSF7ZXGbS7A0CbrvYGzegWrpKQnXfgbVVklafZ0pUVJtcrGSqTUWdXRWw5UySfSEG2APlJgXbomhHPRPFTm38B8rNgUz2PzWa5MvTUXm+k2O0Q82vr4sPt/hINsNfLkU9pjnLoqY1Fa351cKZDUkYZA/Wmhij2eN71LQmZ1msVg4P1pp9k/gr08kLmtz3erRdJa+iuvUAeWWDjtr7JfuTrTY/qjc4FgSscxx+OZthW2pwhyBU/RPM+fto6wpJq8Cou5W8Gx+SMpS6gpnas0CUSISmTahbrMpcf6E3eVk5EDR5wi9z3PisVi63uEU295l7UyRFE/9kjytAi4Bin2omJeXSMMHJnn2AlgkpqeVxwf/zzIQ+6+wE60+pBa+NYSZc/hcy50vKyuP3uRBP9ZmfUjBJmso/2aMP0CakYAb3GHW+Lf9BoUKsYEf9gLzV8/ArS3HUjz9IZBBcl3P5g/EsJdfiSFtTcq2+k3MBtqWS/FapSMG2men06P9WqTjQSeOZ2t1qsy9o0zaGrLJoG8O+oM3u1mD2LM3ooG/d+hkd388mW22+US5TCUPWdB5b/41ymckBfW1V/wTTrefxdYuEynfG+j5P1T8Ri80k1rA29x4cK4WvKzhWamAn5w6KA0GT77ZPUDMhozjUTMh32yc4EMSfZnwVa2kR0MTHYGji0yLgqj5joD/mjtJC0zABmui5Fi00H3NHL8TLOq/W2v1Lx661l/+FzPkykricgHZXOc2ANiOJy2OxdyZX4RHSxEejaeLjEXJn8uzmnCxn0rMvxAAbcx3KoT7Zow9Q1YYxd3C/utfl3L7JfT/bUskVkdz3qhECiqSKPrckijaq0z541loOVR0NJ3hD3WjWWvH97Ef1BgVlnRxuVbBtCKP2Qfws5/x92CqJ2xnr63bG+s75+/r27mcSayS5fxue8MvklE2us+/kOg/uesIvx3r311Lgl9jUVY3nRib6VuN5Y1z+qdV4fvUiVOM5H96fzPF39ejJxW+Ujq3qkE+kL73hYmcq565inJuY9/bS0lE1nrWJ62Lj9eHNSbgPtk9QUW0KJsmvJMcu2fH6IMm+EAPto8U0d5/onrxZCTW/NnxhJ2+Kt2fIgaUAWtqQVNA2oE3UPohuT+b5ZqO7bn3VaD6ZjE8YPBYErOmZV5GzLI71eWz9IGjrCgnVfavfUUnaunLW6z5qquzgOIu0GCLFdaxmTC3/YWhvx3HjM9qTSmSwOH6asfVrKZy21Gav25LDKyK577XVTfNPssP8rF1jJvRZa7t8Il1k6zmuLrXS5NxVfZP7fm7LDF/SyX2vAT31CCEArkon+OKqfFc1nl8bzg7keH3xpqvTSdKWx1HPnKw0tDllsSk5mJ/b1kSKf8pwVzWeTyZLfcfrr3Oc2ETqmtas6/PY+kGQtAr4uhX16HcEpk3SOrOE83SOmiqPcpg0DiWSNPB5lMPcbjZcUgn/auVSM+HJHn2ABprVK2Bs/fm01U1Lci8umME8OgshTroqnZDkfpn5UDrL/xsEXJdV5JRFzWiq2vCh9ODekdmaSJ1R5Z1fzmb4RrkM4SkTq43mc9nBTHBH3a1Mt54HHfXoB6ZNaNqs6/OEzLdjB8dJ45DpTDZ9Y7mD44ydYd37leAWt8h329H8hwwWDTQ1E3JHQnpVhRgUkuwLsYK81mzzYKXJET9kvWtzZyHNVRexysul6opkil8HftKsn6xY8yvZLFckl38Zy22pJL9Jsasaz+ey+YEcrw+Qd1cxzru6qvGsc6/tO14foBzOcSw4SNPUSKsc65xNFO34pNBFWpR6ykimcVjsU0ZyJdvspPkcq7qq8dyRGO5bjUcIcXFIsi8G1msnQh7aF3KkYlhfUNyx1eaqVf1rsO/1Wjx6yvjH25M5Lj9NL+WO8jQPlWc4FmrW2RZ3FNdyXXG8b+yR1l72Brup0SRHmsudK1mfis/+Bygv7WS+tp22qZNUWUZyN1Es9e89rC3tYKn8DH5Yw7VzlIrvJVe6rm+sProdM/UItJcgWUJNfBBr7KZY3GvNNl+d2gfhcRKqzT6T5Kvl1fzexNa+Cf/+qed4vHKIWcthjQ64tbCRLRPv7rsNr+18igePTXFEJVhvPO5cN8FV197cN3Zh9iWmll6grlpkTYqJ0o0Mr7mh/2s79DT6wI/BW4JECWvzh7E2vq9v7OFDP+Sw3ovvGNxAscG6nA0bP9439oVD9/FKeoFm0iHdDri+OcyNGz/ZN5ZXHoXn/h5qC5Abhnf/Clx/e9/QHbOP8IJzjErCoeAF3Bis47o1H+wbu3nqeS7b/2OMX0G5BZwtH4att/SNLe99iJnKdpquJu1brC3cRPHyO/rGLu16gCPVF2gkDBlPsT5/I6WrP9o3dvLAg+y2DtNOKpJtw5V6A9s239k3duHw8xw+/hx1u0U2TLFh9bsZ3vCuvrHrZ1/mk8eeoE2dJFlG1t0CG97TN5aDk/DUw3DiKKwag5s/Apu29Q09vn87e489T8X2KIQJLl/3LlZvie/rAM/uf5Hvl09w1Eowpj0+U1zFe7a8s29s4vBeiocfJdBVHCtPYoOBrfFkvxzOse/Ej7DmpnG8Fs1Ein2j+9i66pdjCf8QKWYW99L25zCWQWlF0h1l7VD/4wPP/wSe+h7UFyA7DDd/Ft71ob6hCy/9HYutnYQJje1ZDKWuZfiGf9I39qUDz/BMeJhG2pBpKt5rb+CGze/tG7t7/xPsa+wicH0c32Vr5mqu3NJ/n+S1nfDAfXBkCtZPwEc/CVf1P55t3neAzQ/eB0enYWwc7vwkXHlN//Xuew1+9iDMHoE16+H9d8LWq/rH7t8Njz8Is0dhzRjceidsubJ/7KFJeObHMHcURsfgvR+Gjf33s6kDP2UymKSWglwLtjnbmNj8gb6xR6ce5aD/Kq1ESMqz2eRew9jE7X1jm5NPUD34U3zquGTJb/oA6W39398dk3u5Z88UU55iImH49BUTXLftNPvO5C74yQ/ffH8/9HHYdnX/2L274KcPwMwRWLsePvBRuPw0sfteg0dPib39o6f9LCZnn2an3kcjocl4FtdaW9m2pv+xmld3wA/ugekpGJ+AT3warul/fmsefJrqkcfwdQ3XypFffxvpTadZ7/M/gRd/AF4FEgV45ydO+x0qTz7IscoLNBMhac9mXeFGitv6H/t47kfw+HehNg+5Ebj1c/DuX+4bOn/0JQ4tvkDNapHTKTYO3cjIWP/z2yCx77rrrou9DSvG3XfffdcXv/jFi70ZK8JrJ0K+/nw0wWskZC8o3gAAIABJREFUA5U2PDkVcllRsSrbXSJwr9fim41FAIYsm5rRPO83WW+5jNjd17M7ytP85fxRMIZhZVHVmmcaVdZbhjWp7jG8R1p7eSF8ATCkSeDhM22OkNNJCs5IV2x5aSePHnuJ/zqzmQdmt7KrnsOEu1nnWqRSq7tia0s7mDzxM3Y2suxsjbDgG8L2JAU7TSLVXQVEH93O7oOP8HfJy/l+5mpeU1mKJ55nxEmi8mNdsV8//CoL+jg5fFxjYauQJg1mag1uHe4ubbd/6jm+U5sGYygZTQ3Fi36NsVaN4eL67s9i51PcPXMcjGHE+FSwebLeYkNjkVWrJ7piF2Zf4rXyEwCkSOLjM9s+TC5IkM51b4M+9DTh7v8PMOBkQTcxJ15BOUVUqfvi6/ChH7LP3gPK4ISK0DbM2wvYiwuUSld0xb5w6D6eGapggGSg8R2LqayHPXOYdaWek/8rj8IjXwdjIFWAdgP2PwOZEVizsSt0x+wjPJBdZNbNUlYZyk6C190ahcUZ1uQ2dcUG+57A3/3d6H+cDIRt9IkdKCuHNXxZV2x570McaD0LxpAILAJbM6+PkKm0SI1s7Ypd2vUAL7CTA6URprIjLGZSVIPXGTpRIbWqO1GYPPAgL2cOA4aEbwgcOJYs4xyfY3Soe70Lh5/n1fnHMcaQMkk8fGZah8h7LulS935WO/wsR2ceBgMuSUJ8KvW9JMMUiVL3vsPBSeYe/yY7thXYfd1aTmQ1qeefIlMYg6Hu5Pn4/u28tPg4qUyTfDrAJNq8Xp8m33TJDndvw7P7X+SrtWiC7RAhNWXzuO+zvr7I+uF1XbHtfU9SPngvBoNtpdGmTWtxF47J4Ax3778Hjt6PntuLG2iU5WKHPqa5QCOssarQnTAtHnqKyXQ0ed7W4NsWdctj20yF9UPd+yTP/wR+dHe0nyXz4DVg7zOQHoaxzd2fxUt/x5x5BTDYoYW2NQ1rFjV7gvTa7oTppQPP8Egq+oyTnsJ3DfuTZdInaqwd6v4O7d7/BLuDlzEYnNAhtEPmmIGyx2jPPslrO+G//Fn088gqqJThicdgwyZY1X08Y/erLN77f7P/HXkOvmctS/kQ99HHSY+Mw2hP7L7X4O/ujn4ujUCtAs8/Ces3wHDPxdf+3fCdr3fHvvgUjG2A4Z47LYcmKf/D33JwS4KpK4dYSnskXnqWVGEMSt2xUwd+ynOJyWhf9xRtxzCVmCc3V6U41P09Pjr1KLusVwBIBDaBrZm1Z0mVm+SLG7tim5NPMH/wPjAGW6XQxqO+9BpumMYd7X5/d0zu5U9fnUYZWOVAOYRHj1fZbAWsGe0+tzC5C/6fP49+Hh6Fahme/QcY3wijPe/Z3l3wt/8l+nmo855tfyJ6f0d6Yve9FsWqntjx+GcxOfs0zzp7omNqaOPbhilnnlS1yWiup6Ps1R3wn78c/Ty6CsplePxnsHETrO4+vzUPPs381P1w8rvpUS9P4uo0bs/+y/M/gWf+JvoOORkIWzD9Ilj52HeoPPkg+73nuo6pcxwls9QkNdp97OO5H8EDX4sqKKQK0K7DnqchMwzrt3SFzh99iR3lJ8EY0p3j5DHvdfK+SyYfLx97IXzpS186dtddd939VnFSZ18MpIf2hRSTimJKYaloWUwqHtoXL134aLt28oFAllLkLZu8sni0XYuvtzxDXkXlAi1LUbBt8ipq77U32E3COCRVAqUskipBwjjsDXbHYp+a2cO3jlxD3U+zNtmi7qf51pFreGpmTyx2z9wLvNwcpqrT5G1NVad5uTnMnrkXYrG7jzzPX+ZupOJkWWuaVJwsf5m7kd1Hno/F7vMr5HWAjQIFNoq8Dtjnx6uOPF45RE6H5AELRR7I6ZDHK4disQ8em6KofYpKR5+F0hS1z4PHpmKxU0svkDAOCRIoFAmi92xqKf7a9IEfg51E2RmUslB2Buxk1N7jsN6LpQ22tkApbG1hacNhvTcW+0p6ATvQJEKDAhKhwQ40r6QXYrE89/fgpiGVA8uKlm46au/xWGKOeTsDWKRMCFjM2xkeS8zFYoP9PwYriXLS0Wtz0mAlo/YeM5XtuAG42kYphatt3CBq77XT28VstshqXeUqb4bVuspstshOb1csdrd1GDuILo5QCidU2IFht3U4/v4efw43tDv7uiKpErihzeHjz8Vi5489ga1dnM73wlEJbO0yf+yJWOzcrkd48T3jtHMpck1NO5fixfeMM7frkVjs/rmnKWU8HEthtIVjKUoZj/1zT8div18+QUEHFNDYQAFNQQd8vxyvnV8//ChKJbCtFEpZnWWC+uFHY7GN2qHo/bJcUNHSCRWN2qFYbKu2my0Lx0mGmpbrkgw1WxaO06rFjw889T28Uo7qliJLlyepbinilXJRT3+PxdZOQmXRzKWpDKVo5tKEymKxtTMW+0x4GNczJAOFBSQDhesZngnjn/G+xi5UqHC0g6KzDBX7GvF9hwfug2Ip+s+y3vz5gfvi2/vs/Ux+cByvkCZTN3iFNJMfHGfx2fvj6/3Zg5AvRv9Z1ps//+zBeOzjD0Ku0B2bK0TtPcq7fsy+G0bxMilSLYWXSbHvhlHKu+Lft8lgkoRnSIUKS0EqVCQ8w2QwGYs96L+KEyhcbaEUuNrCCRQH/VdjsdWDP8U2btd+ZhuX6sGfxmLv2TPFkDKUnOj8VnIUQ8pwz574MZWf/BAKRSh0PotCKfr/n/wwHvvTB9g/cRnfePe7+A83Xc833v0u9k9cFvX093r0gWg9p76/hWLU3mOn3ocTKhLaio6p2sIJFTv1vvh6f3BPtK+UOttb6uw7P7gn/p4deQxbudgqhcLCVils5VI98lh8vS/+AKwkOGlQVrS0klF7j2OVF3D9nmOqH7XHPP7daF2nngOcdNTe49DiCyS0TbJzfkuSIKFtDi32We+AkWE8YiAdqRjW9cxxyyej9l4zoc/qnnrgWWX1fRrhsVCzpufhQTllcSzUsdgaTbI9Y3ITONSIPyzm4blRCk5IwY3KDxZcH4PDw3Oj9N44fN0LaZOlYdl4OCQsQ0YneN1rcWPveq0RfBx2mBxVXPLGZ4waD1sj9N6YLVl1joUFFlUSz1gklGbItFlrx5P9WcthlQ6IunUiWQyzfeqqH1EJ1pl2V2yekCMqPjSorlpkTPfwKReXuuozjtlbArenIoqVitp7vDF055RNwA4VvhPfH5pJh5TXfVHohoZmss/hrrYAmaHutkQmau8xk8zgaoNjon/TMQato/Zexq8QpDP4yTba1lihhdt2cJrxz6LpalKe1fXanNCimYjvk3PFBBP+IsZY+FgkTMiEv8hcMb4Nbwzd6VpvELX3qtstMmH355bApW7HP7c2dRJ0j8e2cWhTj8XuH/ZI6iTJIHrPkr4Bx2b/cIPeUfBBpoETWpzsgzIKFRqCTCO23qNWgjU9pR2zaI5a8Ynsga5iW90Toy2VINDVWGyy3iBIpnBP2X0C1yVZj2+DdkKGmm2Gm7Mn2wwG7cQ/N09VaGwYRoVgeQbtQGNDDg4s0LvFXhqaqSSWASs0aAXNdBJU/KFljbTBD5IczmZp2Q6pMGB1u06QjscGro8TdH8HbG0TuH1KZB6ZYv7ajRy+PEs975CtBmzYazOy81A8dLSNlxjmeDFFK2mTaoeUyi2OjC4w1Bs8ewRWdd95IZuP2mOxR2HV2j6xR2OhRws1HJMn0an+mggAx+FooUpv5fpaCjKt7mNJIlDU+oz6bCVCkp4d+262EvFOJ586KpGinfEwtkGFCrvh4Pvx78WUp0imXZ5J56lYLgXts6lZZarZ57M4Os2hK6/gqY1rOJFNsare4uZDs2zcHe9I2u81+Pb73ksu8FnVbFFNOHz7Hdfxj59+hi29wTNHYHWfz2Im/lk0Epq03z2E1tUWjT7vA9NTMNZzh69QiNp7+LqG0+e76et4Rx1eBdxcd5udjNp7NBPhaY6pfba3Nn+ac8B8PNRqkdXx42TNGvx5OtKzLwbS+oKi2nO+qraj9l5v52mE6+yoMsqpaiYau98rRxqP7trhHgE54hPPTrSLZHoSo4zd4kQ7/pCUw/4QLUsTokhgCFG0LM1hP3ZqZJe7jl2mRAubHD4tbHaZErvcdbHYCeq8rnK0tI1rNC1t87rKMdEnCVujA+p0v5d1FGv6PDF1vfGo0n2gr2Kz3nix2KxJ4dN9wvLxyZo+Z9JEib1umq8PX8P/uebdfH34Gva6aUjEa7u7QTR051Sh3bkA6JFuB/h2d7tvK9LtPnXgc8PRkIpTeY2ovUdobCw0oIEQ0FhoQhOfRxKks7TSLULbA+MR2h6tdIugTzWetG9RzVhMrcqwfyzP1KoM1YxF2o/vkxkdYgydR3MZQqK72hkdP4kl24bANhD6EHgQ+gS2iS4AemTDFF7P5+bhkw3jn1uSLGHP9yIkIEn8tVVXFUk0ur/IiUab6qr49yJtKQK6ty3AkLbin/GY9qj3nL7qWIzp+D7pWHl0z76qjYdjxSvmrJnT+I7BdxQG8J3ognLNXDyBtwIbbXVvr7YMVhDfH9rrh1FegBVG+YcVgvIC2uvj+5mXSGAZg9W5TrMMWMbgJfpcyPhJDuSK+JZNUof4ls2BXJHAj1+IO75LaHXvJ6EV4vjx4+T89ZvZeX2GdsoiUwtppyx2Xp9h/vrNsdjjm0c5NprCty2SbY1vWxwbTXF8c58n3a5ZD/Wei6x6NWqPxY6dJnYsFtocyeM2uj9jt+HRHIl/xrkWeD2dBJ5jyPXJ11JeNHTnVIGtSXnxz9hy03h5L5q/ESqMZfDyHlaf8pqpTIJn0sO0lE1eB7SUzTPpYVKZ+Gd86OptfH/bOmpJh5FGi1rS4fvb1nHo6vh8hMeuv55co07eD7CAvB+Qa9R57Prr4y9u7Wk+i7XxzyLjWfhW9/vgW9HY/ZjxCaj0JOCVStTew7Vyfb+brpWLxZIoQNiTFITtqL1H+jSfW7rP50Zu5DTngJF4qO5/nMzpwS+8IMm+GEh3bLUptw3llkGbaFluG+7YGv+y3p7MUe08gVAbQ1WHVI3m9mT8gHFHcS1VA5UwRGtDJQypmqi91+XOlXgqoG08jNG0jYenAi534hPENudLVEOb0AQYDKEJqIY2m/PxxPUVfTkZ5ZGlBRiytMgoj1d0fHLWorsai5CU8VAGUsbDImTRXR2Lfbq5jXfkj7FpdZn16+psWl3mHfljPN2MnxRuLWykZtlUAY2hCtQsm1sLG2Oxd66boGy5lI0VfRbGomy53LkufvCeKN2IpwI8PAwGj+g9myj13rOA/Zvv4JtDV1FRilV+nYpSfHPoKvZvjk9M3WBdjrYUoaXBGEJLo61okm6v65vDhI6FZ0cJm2crQsfi+mafUoDv/hXwm9CqgdbR0m9G7T3G2i6BpQgVGCBUEFiKsXY8WQpWreZ4Js0/jG3iB5uv4x/GNnE8kyboHe8MJHNbmR3N4TsK1w/wHcXsaI5kbmssNmssAluB6iQryhDYiqyJH8qvrJYIHUXgWEC0DB3FldX4Prlh9bvx7bCzrxvaxsO3Qzasjk/YHll3C77TxncraLeC71bwnXY0SbdHfngTngrAa0dXJV4bTwXkhzfFYofS42jLEBCCiZbaMgyl45PnP1NcRcVyqGARAhUsKpbDZ4rxSbfZDbdjjEeoWxijO0uP7IbbY7HD4x9k44ETOG2PdtLCaXtsPHCC4fH4JOzxxDaUivZFQ7RUyjCeiH/fwomNKM+DwI/eh8BHeR7hxMZYrJ9dhxXdI8B0lhYGPxu/wHfS67GUwVJREm+pEEsZnHQ8YduauRpjGwIrOkYFVoCxDVsz8cmbh++8gUSlQbLcRBlDstwkUWlw+M74RMTa2DqsIMT1fBQG1/OxgpDaWHx7ef+d0Zjzajn6vr3x8/v7TJy89c5oHPmpsbVK1N4jPbIZX/ld+5mvfNIj8YuTbc42vISiZRu0gZZt8BKKbU78c9vkXkPgGHxLY0yU4AaOYZMbn3zsrhnDGIPRIQaipTG4fS5OMpetQ4cGE4RAtNShIXNZ/D176v23kK3WyVVrWMaQq9bIVus89f749212y+Vka1Vod96Hdptsrcrslj4Tf2//aDQX49T3t1KO2ntca20lsA2epaNjqqUJbMO1VvwYxSc+DeUlWFqK1ru0FP3/Jz4dC82vv43Q+ISmhUETmhah8cmvvy2+3nd+AnQbgiYYHS11O2rvsa5wI74LvhV9Br4V4rtRe8ytn4vWdeo5IGhG7T02Dt2IZ4W0O+e3Nh6eFbJxqM96B4xM0D2HZILuubMqa3FZUTFdMRytwqqs4vPXOn2r8YzYDustl2M6YFYHjNgOn0wX+1bjWZMqoBcsHj9q82oljVdN8enc/8/emwdZdtV3np9z7vr293JfKmuvzFpU2hESEkgYjIwwyLjthm5jvPU4ZsaBPe2ZcM/EhKc9HTET7e5oPJixo5sw2IyxkSBkEGAWISQB2impUJWqKmtTVlVWZlaub3/vrufMH7ckVb77qgOFwCMGff+5+X71q/vuu8u539/v/H7fM8wd4+mXY9EcJK8c6qpKG48sLteYB/uq8Qxn8zy5YqKUjxQe3ThLJCb5V/vHGclsJmJf68KL1SJDZoMBo00tzvL99gGygyN8oLI5O/qPNU1bCtAhhgoJDBtll6mYZT44uLl048+WPcYHO1TMDkXpkZExgZHjZHOU3xnbnEkdKE0y4bW45NdYkSYDWnFPYWtfNZ7hkSm2dapcbNVZlA7DRHxofKKvGk8mP8Zy1+FZFXLGtVnWBbZmrmN3H7WCB4QLSIreBlL5ONJElHdwqbyNm93Nv61cnsaobtBQVUIzmZLdyXRfNZ7x8gzGpfOsGR08x8QNY25qlvur8YxuT5pxV85CuwrZEtzxa33VePK1lzitNbGhUFKCBsc3eJ9vMFzYTCrONZ7lmZEtoDW5yMM3bc5XxijHHsODm1VrjkezqCDAUAHaADPWGLGLn80xld9MxPzFQ6xJAyEUUmpiJKFy2OIbDI5s3u/Q0R9grq6zUTTxMxa2H3HNqSYzbQd2blatyZQnKAQWrfYlOoZHRjvsGb6trxqPKRVG9TiR9NAywhSaATIUJt+GcDbfZ65T5mLBB6+L0WwSFHP423ewv3gTWbH5+cxkR4i8i4RBF1/G2AgGcoNsmbwby9wcuE8OjDPZrjLntbgkbYZ0xEeLA33VeMyBKUydJapfJL5cNlDYfjfO7relfOXINpzApXLmJMNLKww2DHK778E8kCYflaFp1EaVZrSBMhUyNpgy97FjJk0+Qhpoy0S06hB6YDnobfswxvfiFDYTpnpG43l1hO4iJSgpidwRcuM3M2xsJo4/MEMG/SZZu0bZbZMTEaNxHlma4K3m5qBuaGArrK1R0xtErsKMNdNiJ3un04HMmcwyrmUj2ssQ1cARGDum6YwOsE1uDvLPmGuERoAIO8igS2xLVLmMkxtlWvY0/g4M0x6IWC5dYHW8RXsYzBvfgb2vj2LNwBDd8SI1e556qY43kkG+7R6s3elrbLsVjmbWOTRY5IXBEvOVHEalxDUDb8eVm8eSUmUHaukw0qriml1c4TPZzbF7T5rcFUrbcS8t0tCr+I7CCSKmg+1M7EwT4qBzCsvXxH4NJQOMSJFzxjHLA7g9qmzfjmN2mIpaN6AWCQom3DKWQw8U+fnC5uN91PYo5UqIZgNaTcjmMPfsY22wnLrGpw1Nq1TGadah3YJsltbegwxUhrjB7pl5GxhOmnGX5pPSqMFh+MCH+6rxDOW34Da7rKsqXVvhRpIb9J7+ajwjo0kz7oXzsHAx+fyR3+irxmNVtmCpDGFzgUi1MWWW8pZ391fjmdjJCTnI35pDPDAwzYuFLZRn3sHwTXelXN2h3WRrXTreMp6tcCKDrdmb+6vxTO5KmnEXzyZKWdkyvOujfdV4soUxCqFFy1uhLT2y2mGmfMv/p2o8P2qD7ps1+2+iL869pHj6CVhbSQQVbr0dtu/8p50I2jd8danNXuyx3atKbV6J04sxzzw7xHWZIfIutDx4Zh72ZGL2TKS/a9LdwyRXkUO7AtdUTH5lX5nPnHdZ7GgmcoLf3uZyTSX9iF1fNjklRniiOk61BRUXdo7GTJfS378/45D1y6w7eVpakReSSW2yvc9KrNMjdRphjnbw6gsgFiHTI/W+x7xr6i3sor/UZi/2XXPbVaU2r8SxdsBfxaOY5RzSDFGRxZF2nmw74EBu8zEvxRED5RLtkZ3EwsfQDm5QYinqU24D2NvfTsAMNbqUyWCTzpK/jN1bbqMQnaWrm2TcAqODqYrVV3HtXVeV2rwSk94av8wgz+sca1IypBQ3Bj6TXrpB91S+SAZJxsqBlSNLUpJxKl+k91XaFD55axDxchW7kdR+N/vUaE+tdWFYsmjnaWBR1CFbgw5Ta+nyFWrLzJSGmZl7+bl1QGehvpz2BQa23XRVqc0rESx+D21DQQ4iMNDEaOUTLH4Pt/hrm3yHZJkb1BbOlms0K5KCzrBfbWFIpmcXMs4oUxO/SLE7SxjVscwSpcxeMs5oyhfgll03cBWxzxSc3W/rS+77oT2zn+XdLl3VIiPzjBo7U3XfLyM383YEu2jTpUiGHOksLoBTPkgn2EANTyEMFx17aNXF6SO3OxAXWS1nMNmNKSwiHRIRMBWnyxUGhU9Y6GCKMmCCFRG5Haw+9w61C+xtXWCvVQKyEHYgvAC1C1DeTMpznYAVXWdx1zQt0yYfBUwEdUY6g/SuFzbYAavawstmCQt5rFCRq7YoClK+7dUfsmjNYo4NYQubSAcsMsvE6hZyw5sJU9e7xEZhCePgdZjSJVYeG3qJAe8SGXfzTOyl5nmOWRmk1GTRBIbDMSXY0zxPqby5HGPx4tcw1TyDXZk0emoFzLN48WtMbOkJ1NbOMTF/ngmnlNRxBx3wz8PAORjavsnVCDSitoJjFEBYEIeo2gqykL5/Jy2D+lCJnx99tXSzHitKfcpJh4VNa2CA/OCr929HRwyL9LvlHXaB+0shXH8LOSFpa0ULxfvsqyzytnvf1WVPezAzeiszXEUSsxcHDl5VarMXmR23Xl1q8wqc6AZ8ang/xbFrGJOCutJ8Klb8bjfou+hkaeYXKKU65q6Ct/z8VaU2ezE4cf1PhdRmL94s43kTKZx7SfGVBzTtlmZwKNl+5QHNuZfSdas/bXj0qKKQgWImUUEoZgSFTGJ/PTgdeDxjNrh1T8RHr9fcuifiGbPB6SBdCPruXI5sTvO2qZgPzSTbbE7z7ly65vkXKw5CG+ySLm+3c+ySLkIb/GIlXZN7bTmmHRr4SielGErTDg2uLfdpSvoJ4YuNGpQ2sEyFpS0sU0FpI7H3YNDyqTtLKBEhtY0SEXVniUErTVSWaPA45+kSUsKlS8jjnGeJdHNWI1pjLjxMqH1c8oTaZy48TCNKk/LXAsOuMBW0+KDX5b/ptPmg12UqaGHY6V6LZqaCHSflX1qD1go7Dmhm0r4F7eD31IH6hBR0+hq77gRTNZsDHZ+3dGsc6PhM1Wxctw/JLI+C19Ov4bUT++uAFywghIMUJkKIy1sHL+jTZLlxnqEXH+WtFzu8+1KGt17sMPTio7CRVosByNQ7jB2fZ+rQGcaOz5OppxtjX8Zc3OHv/UU+4Z3n7/1F5uKr+/6oqEdrzIUvJPeOyF2+d16g3ufeWaHOs5zBI6CAi0fAs5xhhXRwbWcnyI7ciTQzqLCGNDNkR+7Ezqavm/aXGFMVLGETEmMJmzFVQftLKd8tskGIQXi5p+blv7fI9HPB0nNg5cDOJUpDdhKIspRW9pLzK5wplPAtg5yK8S2DM4UScn4l5btr9iUwLEqeyURVUvJMMKzE3oONjacxMTEvK9aY0sXEZGMjrbjUap9I1FmMDEIIDCODIVxa7RMp36eiefKRZjiASiAYDiAfaZ6K0k2hzeYPAYkQJuLyFuRlew/mngInB04+CQycfPJ57qmUq3NxA22bKMtAQ7K1TZyL6Wb/9+SzNJSiHqukNDJWNJTiPfl0o/1tZjkh7TpCaU1LR7S14jYzHTDvsjJ8yB2gIA1WdURBGnzIHWBXn76B14p51eLL8QU+HZ3my/EF5vs10f4E8Y16l6IhKRkyUYUzJEVD8o16WjDjJ4mTns8n16v8L8urfHK9ykmvT2D9BsSbZP9NpPD0E5DLQz4vkFKQzwty+cT+046lKuR7JgDybmJ/PXjUa1GQkuJl+c+iNChIyaNeekCccRx+s1SiaBhcimOKhsFvlkrMOGlydyBn83vjWcqmZDFQlE3J741nU1lygGszed4xKHGkoBWDIwXvGJRcm+nT7PQTwoJZx9UGhjYQCAxt4GqDBTNNgCYyLSItCVUyDIVKEmnJRCZ9zo6xgotJBguBIIOFi8kx0uRjOTqLhYMlnER2TThYOCxHZ1/Xb3MqB1FxFxV10Fqjog4q7uJU0hmsIWcLscwgNGgihIZYZhhy0vXnezIH8EWMd7kO1CPAFzF7Mn0WJdp1G8JvYwQeZhRhBB7Cb8OuPrMuB94BXhO6zSR72W0mn/uUpLwWxIZEqM3BsVCKuE9WkvlnE3J0JcF0com9FytzcOiBpGa2MJRsDz2Q2HswF3f4crBCS8cMYtHSMV8OVl434V+OX8IS9uZ7R9gsx2nieopFXEzcyzJ8LjYuJqdIq8VAQvgLE3dT3v5hChN39yX6AGFUp0CJKTXMLjXOlBqmQIkwSj9DlvDZI0vYSLoobCR7ZKl/Zr+zBlYPmbSyib0Hz0cOup7HiQXCVDixQNfzPB+lx6jhxWVuWNQ4EbQcgRPBDYua4cX0DJKv2hhi89hlCBtfpUUEwqiGlJsHayldwiidONgwBL00OXvZnkaYEPcrIWRi70VzNcnoXwk7m9h7YK81yAYjSG2gZITUBtlgBHstHXjtdR1+p1KkZEiWoiSj/zuVInv7rEJjrBaaAAAgAElEQVS93cjyS/YIeWGyTkhemPySPcJ2Ix0YQEL4fys3wv9cmOS3ciM/NqL/TbVIW0cMYNPWEd9Ui1cl/Kd8n7+srvPHqyv8ZXWdU/7VCfGResS/m+3wr55v8e9mOxyp95/ZXQwjLNFlRS2xoM6zopawRJfFsL//a0HYXqC98E2ac5+nvfBNwnafxAUJ0f9MrU4jjhkzDBpxzGdq9Z8Kwv9mGc+bSGFtBQZ7hBSy2cT+047xCjS6ULxi/Gt5if31YCmOGJWby3ASSc/+A9GM4/Ql9/1wIGf3JfcpP0aoZs+zK2viYuIR4RFx4L9S7vLjRsaOCSMD84p3aagkmT6SZ4YZcVDkOBdGr5QozVg5DCN9zmp0KbH5xe9iUusjg9rVTVw2BzgmNl2dllp8LbCyE+TG7sKvHiUOqhh2hczwW7H6kLZr3d08KkOMzjpu0MVzMgTZQa610w1tw6V93Ayc7h6jKXwK2uGgez3DpfT0up93aWwbRjY2kH4X5WRobBugmHdJ3U3ju+COD8Ox70FtOcno3/y+xP46YOS3o+qnEC/r0asQjY+Rn047t1ch2zOYWNnE3oszTyQa1+7la/fy9swTMLK5ofepqEau0yK/Og9em7ybg+EpnpI2O/qQIM+7RKt9nCiqY5ol8rn9uG66Kb+rWrhi8wybiU23D6lp0MVWimq8QqR9TOGQMSo05OubSbPMErHqYohXBymlPSwzXUyUI48lfK65Qlko0D42fQL87BB1GiwVJV0jJhMbjDcUpd7rAyznKgx32uC/ut9MHLCc6zNQlkYYXm8y3LmiVKTbhFKfZnSZI9Ie5hX9GrEOcGR6VtMyy8RxF8O44jwoD6tPRnsg1rQFm/SgOpftaVigo82EX6vE3ovCMPitJKP/MoJOYu9FZZQ1u8HJrYO0HEneV8ycbzNR6T+TNuh0ucVZpEmHAlkGmYT0UwyAFxgsN7Mshg7KMvAKBn1E4X5ieE5vEGk4o3zaOiYnDEakwXN6g6mee+2U7/M39RpFKRk1DBqx4m/qNX6zVGa65513pB7xZ2c8yhZsyQiqgeLPznj8690u15Y209MBM+B8uEHREFjYxMTMxxtss9KqOa8FYXsBb/kxMDIIq4yKOsnn0buwcpt7+R5qdyhKSdFI3vUvbx9qd5jpE6i9kfAm2X8TKQyNJL09+Sue4U4nvRjiTyPeeVDyue8qQL9Ss9/swr23vL5JrnHDpKFiiuJVwp9IevZ/xDrBMlXvFEFcxzZKVNxpsnb/l8Ja+wTnu0dpC4+cdtmWOchQLk0ExylyR93mmDdLTYSUtcXN7l7GS+laX4DuwnPUVx8nVG0smaM0fAeZyf4127Prh/gB52hamkIoeAvb2Tt4c8rvgO3yhFenFvokEpUS03K4PZsmKiVcNtpzjERrlA2wYxDmEKViWqmlTIbuxjkyl85C5IHp4o3tojywPeWbEQU2qi+xGtToGJJsrBi2ywxU0socAMcvPMSz1iUarkXRC7klHGP/1vf09Z1rzXPcWKFbiMlEK+xvzTPdh+xPiALv3MhwtNqgKkMqKuTWyhYmehePuIzhusHwoVVYW4KhcbjZoF+heKd5jLY2WDBdOpZNVksmtYHZPIaTSSt5nB8SzN0+hK8zOCLHDlOwre8RwKdb53ksWicUMZY2uMsc5Hfyae/syO00owbKqyPiDtqw0e4Y2ZE+TZa5YQjaSUb/ZYSdxN6LxkqS0b8STjax92C1vcHg/AkwbXCzEAVk50+wunUfOJuvh+ddolp7AildDKNIHHep1p6gUr49RfgzMin7sq4gXREBmT5SgE6sWYsXcbAwsIl1xHq0yJAxCT9aq1FflDJ7WW0mZSJSuCjtESuPgVy6MXVKbuVEfOzyasY2YaJ/xa4+KlX18T2cbT2OpU3c2CLUPmcLEbvyN6VutdHiKM2VMxTiAIyk/rytFaPFPmPU3rfD019I/nZzSamY34Ib7km5DgzcyuL6w6A8DGET64CIiJGBdM12PrePjfqTECcZfaU8Yu1RyqWVT24zp/gqFyHWZEmIftcQvJv0TFqhcD3N5rPolwm/TuR0C4U+ddg7boMXvpz8/UrNfhv2puu7F6/ZwyFxAjtS5HyNLzWH9mS5We9JdXKs6hqHOYWDRZ4MPgGHOcUNepphsTmYme36/PnGBpHsIM2IWmxyaiPL7w8MsDfzT0Mwz6kOF+MQB0kWSaAVZ+MIH5W61x/utPoS4oc7rRTZ//JSQNmCip28fyu2ABRfXgpSZP9gboXZDQdLCLJS01UmXaU5mFsB+ig//YgIakfByCDNJEkgzCzqsr2X7C9GEWNGT1JPShav0mf2RsKbZP9nCCdWY755NmKhqZksCH5hV391m1tvh8/9LayfU3gaXAGDWcFH0oqIP3XYM2HwkTuTGv2lapLRv/cW2bc597XgnW6ez7WSWqC8SLT8m0pxbx+S2wmWudR6FkO6WLJIpDwutZ5lLH9LivCvtU9wzHsGW5hkcQhEwDHvGQ5AmvBXzzN+8hHGrWySPQ1rED4CMy5UNpO27sJzrC5/AwMLU2aIlcfq8jcYhhThn10/xMPWOZxYkw8FntQ8bJyDdVKE/4ZWkyNGjbpyCGID24gpGDVuaMlUJmpkaZ5jhSq2IZNVSA1JQ1bZu2TC+OYs8YG5izyu58DQuNrBMzRebY6b6ybs2Jwt10tV5jM1DAMyUUxoCuZFjcpSFXriiOMXHuLbxQ3s0CDvRXRNg29nNuDCQynCf2rlGZ4zjmMgcCJJIGOeM47DCkyPvHXzji+cYuLr9zORK0A2D50WtI/CPQ5snU751p78PAu7K3SumSDbCJh88vOU+Rcp33r9HGfDJhknpiQVkZKcDUJ21UMqPcH4+fAlTnS/j9HuYnVDwkyNE7mkfnibtTnw+XTrPA9HlyBSmGFMZBk8zCVokSL8dmacQmcX3ZMPE6smhiyQmbkRu0+wwdQt8NTfwvwSNFpQzMPUONz262nf4ghcPAdnz0O9DqUS7NoGW7anXIeX52kJSX59BYIAbJtOeYDh5XkY2Kx80mof56VOwCEjpuEEFP2Qm+OYGet4iuyPGjuZW30MzpzHXK0RDZcJd29jy/BdqWOoBG2WdURcqyG7XeJMhricpxK3+2ZdT158ihPBLP7lhZr22XuZ2dJH0coZpXt+mLPNo8RWiBFa7CocJDOUJtoDcgBnPuCUcYbY0hihYDreysC2tMzsUqZL2JR01AZaglBgyTJLmW6K7L+jPMX91RVYPkMubNO2crRGd/O+clpul7GdnBnYzXebl7iEYEzBnQO72T2WDq5zw9dTOfcDlu0LhBkDy4sZDbaS25sm2hl3jLmVLCfcNUJLYvmKff4Qk31mZGbKN9I9fZKnSjHLdoZS0OXdGwYze9KBwcSWX+TMkQvUMusIA3RsUO6OpJtzAYa2cyFfZtGcR9kSaSgmrCm29jTnApzM11Bdi3UrIrBM7DAiJ0xOZmopsn+WBaJalY36Cr5UOEpSLI1wtrzAMJvJ/gPNGmZwhgPqAgXdpSkynJRbeaA5zf+aSd8Tiyef48jiD6maMZXI4NqJ65mY6Z/EmVt4nJPxLF1bkQkkM8ZedkzekfKrx4p6PeLSmqIVGeTNmLEhSaFkpCZElqKY0UYV5k5DswmFAvkde1gqpmeFLnQUZX+F82uX8ESMqw0qmTEuROnM4kimzq+YMU9eiliJTEbMiPeMmYxkrvLuPnoUHvwSzM/D1BTc+0E4mC65VEGVej3gXOM8LQF5DduLA5RK6dKcCdOksbpC8eRssnZAsUhrZi8TfSSV32h4s2b/ZwQnVmM+dTig7mvG81D3NZ86HHBitc9KgI6mOaDQhsaMNNpIPodOvynRnz7smTD43bst/u2HLX73but1E31I1IA+kq9QlAbLKqYoDT6Sr/RVCKp6pzCke7lJTWBKF0O6VL30iojnu0exMbEv1wXb2NiYnO8eTR/ExR8kJH9TA142sfegvvo4BpeXdufy0u5Y1FcfT/n+gIToZ3TSGJXREifW/IBzKd/F9bNcU6+xX7SYcZvsFy2uqddYXE/Xy9fbs2yr1nEjhW+YuJFiW7VOvT2b8h1/7iHuOHmeTCyo51wyseCOk+cZf+6hlO+J+kWimoEZCYQpMCNBVDM4Ub+Y8n3WuoQdxmRinfy2WGOHMc9al1K+x9UpjFhgKwNJsjViwXGVvm4c+k6yGmWumGQPc8Xk86HvpFxrs9/h1HWDBFmHjCcIsg6nrhukNpv2XQzbFC0PU2i0kphCU7Q8Fvus0jnX/gFGvYnlx0jLwvJjjHqTuXb6fnjMX0FEMaZQCEck2yjmMb9P7d6xo8Rf+3tUpoUeMVGZFvHX/h6O9bknLzXh2TNgtGFcJNtnzyT2XuhhVtbP8vidU3z9o7fy+J1TrKyfBZ2eBbht9gVWdcCJLQMcm57gxJYBVnXAbbMvpHxP1hd5qJRlrZwlyNmslbM8VMpysp6urS/NLjHy1cdpjwQs31KmPRIw8tXHKc2mm2Pd2jIzL8xiB026OYkdNJl5YRa3lq5VP3nxKV4wXiQuhFgZRVwIecF4kZMX042eZ04e42vLF2iaBsK0aJoGX1u+wJmTx9L7PfM4c9FpnFZAcS3CaQXMRac5eSb9HG+sv4iva2hBsuiTAF/X2Fh/MeW7+7nH+dAjX6DQDVjNjFDoBnzokS+w+7n0fs8cepTPxz5NK8eIF9G0cnw+9jlz6NGUb/v5B6l1TpFb8xg81yW35lHrnKL9/IMp38cvfJ+HSyHLRoGGn2XZKPBwKeTxC99P+TaPfx2zfYp3XjjDr5w8xjsvnMFsn6J5/Osp3+oL/8hKsA6rGnMBWNWsBOtUX/jHlO+Fww9w0b2IEgLZ1SghuOhe5MLhB1K+62GNdVMQGQZWpIkMg3VTsB6mewxW6vMsN5eIREL0I6FYbi6xUk83FF9qneEtahZXB7RwcXXAW9Qsl1pnUr6LJ5/jsZVDdISiHBp0hOKxlUMsnkw3Yc8tPM5h8ziBoXACSWAoDpvHmVvoc++sRJxYNehqSU4qulpyYtVgYyWd0R6vV2mdOJpo/efz4Pu0ThxlvJ5ujBsIVjjdXSFC4WiDCMXp7goDQXrcya132fLSM3ykdYQ/VC/ykdYRtrz0DLn1Pg26R4/CJz4OtSpMTibbT3w8sfeg1gg52VrBB3IafOBka4VaI93D8Z7lRRqzszSUQhUKNJSiMTvLe/qs6vxGw5uZ/Z8RfPNsRMkRlJykYankvGrvze4/dlQxOgmFPa82NzU7mseOKvZMvn5i/P9X/Kjyn0Fcx5KbS2sM4RDE6Qa8tvDI9tRxWli0RZ/lHjtrkOmpX7xKA16o2phyc/pRSpuwT6Nc00oy+lcuuOsoQdNKB3/rImIsNBmvvbofjWZdpF8KLVsz4IcM+rVNvq1+7QlelXFdYvzEFSouSoGfPmfrjsFAF5T36r3qas26m753G65F3ouS4Ojl3xYpGm66frdrxjjR5vyIqSRds0+N9toSDPZk3bL5xN6DhWIbS+exL58iOwJMk4Vii97q5DXHYiqCCIVGIrTCBJac9PH6fhUrBl4uJTNMjDjE99Mv3UjGmEol50EDAgwUUZ/68+7T99O+NYvQEgJQGUn71iw8fT+ZXrm9b98PExYIF7zL6eQJL7Hv35yBXzn+NM++4zrcMKDQbONlXZ59x3Xc8uTTjNy+eVrRrS4xOD3Ikqjga4OMiBm0WrjV9Pl90s0QuSZWrJFxshhb6Jo86WfoLTxqPPtFGjeXGKj7GGsesSVo3Fwi/+wXKV6z+be5L53FEG0OXOyS5M0UoVRYL51NVRUcVycwXIWIFUQaIQWGJTkenmCGzdn9p5aPsGu4howNosgkYyh2DdV4avkIu2c2N23P+ieQhsCKk3vbiiEkZjY+wQybM7QqWEOZAjNOnmWhk1WpVdBHpeqpL7E7bLP7+BUKNUEbnvoS3Lq5hOW7qy9RMCSFy/dPQSkIA767+hK7eecm3+rqU5gmmJePN9nGVFefIsfmRZeedNYxI0HS7vPyVvGks05v7nmlcQjT1Fech2S/K94hCmwuJ5qvP49tauw4eZbtWECgmO88T4XN63YshifBBBlLEMnKxwrFYnSSnhUE8MMAYRuYl3vXTQWxAX6QlsXtVFchI4ktSWSCiAwIVWLveej3MkdH28SXe8J8LCIl2SvmoOcOPrL4Q1xTk7U9kDFZZUBscWTxh6ns/sl4lvNWidnsCF1hkdEhe8MVsvEsO3rOcPVCm2zOJsyZ1E2JGSmy7ZDqhYDeE/HuZ77L30xsAwH5KKSVy9PQ8MvPfBd2b164bJ96nhfinXSFSU6EdLWLpyX71PPA5pmhyceOcXI6A9rECjRhwSUQJjseOwYf7ik7ePBLUC5D+fJswsvbB7+Uyu4/tSrY5kYgIdQmeREBEU+tCnrnsWa+8iV+W0ge2nuAxWyOiU6bX5k9xszsUdjfR1DhDYQ3yf7PCBaamnJdsHREElYFVkVTvFaxUEoTtktVGOkZcHKZxP4mXj9so0SkepvUfGyjTwOedglEgM2rDDgkJKf7BBXZof710X0a8CyZI1YexhVqF0oFWH0a5QqhoGoaNA0XX0ocpSjEHpUwTQQHtcmSJVnJ5GkZJvk4YqTbYjxMDzX5QOBLjXOFsEsgE3sKbgWiDly5QE7sJfbeY/Bj2qYgd8XhdUzBoJ8+3qIX0jUNMlc08vmmpOilszqZyCCQMbZ6NWiIpCIT9QmAh8ah3Ugy+q8cRCux96AzUCDT9MF6NaizugGdgXR9v2E7LFFiMO5g6YhQmCwZBQw7XbvrtALCjM2VfD22TZxWAD1xiBlHKEMiX74WGpQUmH0azL3hBkJZCQECiATKSOyp6pVoAYws6MvnSBtgOIm9B6d2m7iBxvUMIIvbBbTm1G4z1WL+4s4xJppVdq2tXA5QNF3H4sWdY6nq3ZVsjrwKkBo0AkMrhErsvdjY5mNGLmYIIC5vE3tv58vwqQtcuG4UQjBDRWSZRBZMvHChl4MRZ5JVZYUWgEjaWXRM3KfcR5RaiEijAg+0QgmJsE1EKd0kHDgKlEGtYBOZAjPSZNoBQZ8ANNvtUC8WiKVGKo2SAi0k2W6fWRavCm7PeGRlEnsPLjk2I76/KWDOxTGX+ggQ+G6M7W8OmI1Y4rvp4+24EtdXXJllMGNNx00XJHhOkp2+EmYs8RyV8m1nIOttHmOsWNDucy2UrZHBpkNARom9F3atgz9WIjI0RqyJDQFSYteaqedNeR5qOAsxiBiUCcoxUNW0mtS4aHBOljG1whSaSAs8abFdpGcMqk5I27R51NzOhswyoDpcFy3iG+mA40S2wHPWFoTWSDQdYfGcswXkxZQ6fdTp4g1lsaMYxw+JDImXMYlW08mW6dMn+E2leHhsC0uZDOPdLr986SLTZ0+mfIcy63wYeNybYDnOMGp0ea+7yFBmPeVbOX6BGTXFwg6Ddk6Sayt2zEVUZtOzIczPJxn9K1EsJfYenNI5lLeFSXudrPToKJeFYIwzOj0+MD/PzOQkMyeumCFQChb6q/e8kfAm2f8ZQaUhWXhYksmDWYa4I1h82GDy59OD4VgFmh24ciG/djexv4mr42gz5MEVnwueYqsruXfE4WAhnXGtuNNcaiXSg4ZwiLVPrDyGs9emfLdlDnLMewZIMvohIQERezJ9ajC3vAVOXp62tl5eNKcDO+9KuZaG72B1+Rugkoy+UgExIQPD7075bpVbmXU2MJTGUgpfQsfMcJ1K1wWPVHbzqFzFiTXZOKItYbZY4DqVLsXYZR/gMC9CpLFVQvR9E/bTJ0NyzXvh0N8lfxtuQvQjD65Pr3p5Y24P34rngJhspOmYgo5l8HY33fh7SzjG13IN1qSFEhKpFY4KubObbmreL6d5zjhOQIypJJFUxIZmP31UaG5+F3z9/0n+fqVmvwl3fjDlmh3YSdA8ih0Clg1hQEhEdqBPE7Y9whm9gmckIqQhmkgqdtvpmtEdixYnphUIiRFBbEIsFdOLFvQI8uxpV5ktDqGERuqkXEEj2NOuQk+sqIo2dGI2SS75KrH3opKBdgjuFa+abpjYe9AYKRJ7itPjI3Rsm2wQsGV9jWAkfS1qAyVkELEwNkrXscn4ASNrdfyB9CvN1IJOaJGTEVJolBZ0QhNTpINKfyCL3QjAfPW5NboR/kBa4acQKrYeX2d1ewUva+J2IiZOVymE6TFVakUQm3h+hkgZmDLGdbrYfWZOymZAx4+S8F5IQOOHIeV+ZZTKpFUyECoh+koKWiWTfD392zIdBbpJJ5slsgzMMKbQ7JDp9lljxK1A2Js46PYNrsf8gKZhJBn9y2gbBmN+mmA6nkFkxq9k9gFiQ+F46YA56ykC8+WMfoLIEGQ9lWped31JaKpXMvuJr8L104FBrguBqZOM/ss/zdDkuulzJgOBMvWmgFmZib0XY6tdrFjRHswTOCa2H1FabjC44cPmhDaOYxE3Q5RjogyBjDVmJ8LpM0PnBhFTmTqrcR5fGTgyZtyo43bTgXhbOnzL3UNRhVS0R0c6fMud5u7O6ZTvcWMUgxiJAPTluSnFcSPdB+DkNcV2l9g2CQ0DK45xuwFOvs89OT7J9KUFpjtXBKeNOvRZpb4U2zhOld9wXvX1CHHjPmPJ+BYq8zUq9SsufqMO4+kmbKamktKdcmWz71S652RMC5Z0jtYVylNNrRijT9JpagpqtWTW4JX9Nvru942GN1TNvhBiixDiM0KIRSGEL4Q4J4T4v4QQr4lmCiEGLv+/c5f3s3h5v33uih/vd79RMTxv4MWa6gqsnYHqCnixZng+PcjedVDS6CalO0prmh1No5vYf9ZwrB3wpwsNfn+uyp8uNDjW7rNSKQnR/6PTNe5ba/FIo819ay3+6HSNo810hjhrjzKWvwVTuoSqgSndvs25kDThHnDfiq1tOvjY2uaA+9a+ajxUtsHMPckLuruebGfuSTXnQtKEOzz6XgzpEqkuhnQZHn1vXzWe5fwkw0EZR2mUoXCUZjgos5xPD96nC8NMikFyMQQiJhfDpBjkdB+putGtd3ED1+AoQdtOSoNu4BpGt96V/m173w43/xqY2aR0x8wmn/e+PeU6tedO7jZ2kIs1G65BLtbcbexgas+dKd/S1G3kZBkBqGSmnpwsU5pKN05Oj7yVm+L92MrANxW2Mrgp3p9uzoWksfaejyaZ/fXlZHvPR9PNucBk5UbCnbsJMg660ybIOIQ7dzNZSTcX7hm8lW2ZArbQdESMLTTbMgX2DKbVTLZtfRf7DlWx/JjQBsuP2Xeoyrat70r5vitb5MDKIlIpIkMileLAyiLvyqaJtswPJ+nIKE5KfqIkPSnzfRR2dlyTBGWeB1on28hL7D1QuQFmJ8cIpCQTBARSMjs5hsqlg0ojW+bstjFCy8GNNKHlcHbbGEY2Lcs4rTIEpkEzdmgHDs3YITANplU64HDcAWJDQxQmxxuFxIbGcdPHwLYDFNZq7Hxhmf2HVtn5wjKFtRpsSwer9iVBoB20AENGaAGBdrAvpQnFlnZAbBsEMlmcKZAGsW2wpc/YY1a2IFFIHQEaqSMkCrOSftUNWNfghBEDtQajl2oM1BrJZyt9Lbjtg8xX8nzlxhn++udu4Ss3zjBfycNt6WD1zuGdrBVznJkaZG73EGemBlkr5rhzON2gWxm+jciEyIjRaCIjJjITey/e5g8SmZLAAIUmMCAyJW/z01KLI8WbiUxBeHm/oRETmYKRYloxbKp0I4EtCAyFRhMYisAWTJX6NPNaM2CAMlQyy2Ik6jMT1kzKd7p4EMMLGZlbY9fRZUbm1jC8kOliuik0VxlCBDFuPaC4EeLWA0QQk6ukZ2F3VpvkA58pWWOHU2VK1sgHPjur6RmZS3IUW4ZYRgAyxjICbBlySabfLasqh43CIAnSDBQ2ilWVzmhvKfoIP6LYbLOlXqXYbCP8iC3FPvryP3cPpy2TT23dxv9+8Do+tXUbpy0Tfi6tzjQzfD2eofAI0VrjEeIZipnhPspI77oHmrWEtCuVbJu1xN6Lez+YkPJaNfGtVZPP96bv37u2TdMUCcFXWtPUiqZI7Cm8/95X96XUq9/x/nvTvm8wvGEy+0KIXcCTwAjwIDAL3AL8AfALQojbtdbpuZ30fgYv72caeAS4D9gL/BbwPiHEbVrrl3r+z4/lu9/IaC8Kcr5BR2liQ2MqQdaXtPv0leyZNPi1u5La/UvVJKP//rfKN3S9/pF6xJeXAi50FFuzkl8at1PSXa8Vx9oBf7ncpmQIJixJPVL85XKb/36UlO79f7rQ4Hg3ImtC3hD4WnO8G/GfLjT4mwPpl1PWHr2q1GYvhnL7+pP7fqhs60vu+yEzedNVpTavxKoOGHe3IMTl7IUJWmtWdZp8LKsQwx5g3SzQUoq8lGyTFsuqz4I1JIR/lLt+pOOd230TT2zfw4oOGBE2t5vFXnGdV+Bvv4V1f1/yvdLCd/pLXh5hjbHcVnZeISnRIeQIa0yS/j/TI29lmj7kvh+2Tvcl970oG4NMF29nIX+Ojm6RFXl2yO2UjfR9UzCH2V95Jyv5s3iqiSsLjFi7KJh9iPa2abbxq2z7wcOwtpCUEL3lV6HPS2zP0HXUrUPcOncKZ72GP1jG37GVPaXrUr7u9rtp8w+oRhvaAeRMdLGMu72PXNcN74OgDvMLUG9AKQ9T2xN7D9YG9mGqc5ixB2GMaQmUa7GWTctIxmM7EeESBB7EMUiBMC3isd5Kanjv4PV06oe5iIdvChyl2a5d3juYJhQDY3exaH4Lqi2Mlkecd4kqeUaG7kr/tpveD90NWFmGThOyGZjcldh7sHh8H/LaJaxcgJAKrSRBw2Hx+HbomdDbvdgm2plhUZg0TIuiDplQEbsX042I5vAoxVVNM1zh5embojWCOZweW0av/VU4AhvRiyhTISPJoN7czGAAACAASURBVHVNYu/B/C238dB2i9zFMwxUN+jkijz0/l/lPSM3p+qYKzdez/6VJzhZC2mEJkUjZGZ7lspI+vzmbryX8eeT2n3fjXE8g+HKbeRuTJOlO7a+nYXnDvH18y6NdoZirss92zzuuClN4Av772HbcVjxDuE5SUZ/Mnszhf1pIli57n3sewHmO8/TzkCuK9iVu5HKdel7cusN/wwOP8BidPJySY9gQswk9tT5fS+3HIFTnaPUs5JSV3Ft9gCj17435Ttc3IqpBPXG6itqPIP5YSrFdIZ4TxDAmuB8IYMyBTLSbGt2E3sPGnaJmcYiq7k8XdMkE0XMtNdo9JEHDiKLVZ2lYnhYUhEqSTXKEMTp2YUDrsCJ65ztuFS1Q0X4HMy22N2npOr0tp187oMfonDhHKNrKzQqg3zugx/iI+Pb6X2SR7fdwFuBk6s/pG4ElGKb6yrXM7otLTPL9H746H8H3/k6LF1MMvof/BeJvRcHD8If/OFmNZ7f+O2+ajy7Zw7wL4HHzp/iktCMacH7t06n+mOSE3EQPvY/wFcffHW/v/4bif0NjjcM2Qf+koRs/77W+pMvG4UQHwf+NfB/AP/tj7Cf/5OE6H9ca/0/XrGf3wc+cfl7ekvSflzf/YZF00sahmwtECFYlsBUib0fDj+vOHQ/BKuCi8Oa0oeu3pz7lftjHrkPulXIVODnPgwf+FB/349/LOa5IxAaSTPZTdfCH36yv+/nPhHx1BcEcROMAtz2zzUf+YP0LXukHvGxJ9osrkAcgGHHfGck5JO35/oS/j//usdXntR0fUHG0XzgbYLfvyddA/+1msf63xssPuuilEBKjXOLx9d+y0uR/acaIUZX0Koa1COBYWqsSsxTsj/J/eRnqzz+jw66YSCKMXe8z+djv9F/EunJb6zx7Qc01Q2LykDIz/8zwdvem84AARxeWuRL51eZ92DKhQ9uG+aG8f4rdarmRdTKYfDWwR1EjtyALKQzgsPC5kKtwdIln6YvKDia8TGHreV01tfUgmfPhfDDAmrDpDMQsXJ9i1t29Fmw5jVgLurw6dYKl5qCti/IOT6zhRV+Jz/CDnNzicWZsMv93Q3yQjIsTZo65v7uBh9igN09q0lW8dj4bpmTDxbprBtkB2Nm7m0wcGe6Hva14sxFxfd+qLi0AWMD8I7rJbu39J8dKxuDfcl9PxTM4f7kvg/m9G6ejHexEsNIDG/Tom+ANCLKDOgbeXxolM5Il6zKcIeeZESkM+WZoev53pLBhfgEWbdFJ86z1dnH3UN9XnjlrfzFxd/liW+4xC0DIx9z+z0ev3d3OlPesAdYP+OwFLcwMhHxmsl4NY+xO51pDHMltnVgXa/hZRPJvjF7iLCP1O0YRUYWr+Pxk11qHShn4caZDGN91p8oVg7yxEWT48E5zLxHFLjs97azt9In2B7aztHrfosHT19iviuYymju3TPGwaF0wLHgX4P7bIy/W2MWQqKmhXNG4Il0Vj1vjbJ3vs7IuMC3Q5xAMLCgyVtpAl8igze8hcErrqpHiNtvgSgSwj9Kmtz34rBex6tMcDozQSPWFA3BlJvYexdRmuMi24bHmR55dUz0dcAcFxkkPaYtjP8i36new9IajJfhXeOyXyEcpxdjnjh2kE6s8KWmUxc8cUxy43jcV0GtsP+eVDPu1VC57n2pZtyrobHnAzyy7nPRj9lSMfjA4NW17UevfS+jpMl9L7aLKZaMFu3WCN2qRVQJcSoh20Wa7IvSTnZXT7InVq8uYhd2EJX07MKE9ni6PcWp5Ql8ZeHIkOn8Irdm0sILUw24ULZYCcxXejiUFGxtkOoxuLW0i+X4GLdH62S9Dh0nS9stcGspTYgf9Voc/u4Ia0e2oiKBNDVD13pM/lKrr4DFZy+M8cUXPkArNMhbMb96XcAfXSVf9fDaMPd1/yWXHIuxbsiH1yzefZV8yrf1Pv6hOM3GKAwU4Ze1QXp1hATd0RkuhTuZ7yjISrqjV39ffYsZ/m78f2K5oBnNC34NyU+DKvkboi7jcmb9PcA54C96/vnfAm3g14UQfTomNu0nD/z6Zf8/6fnn/xs4D9wthNh5xf/5sXz3Gx1+VlOrayJPYxoQeclnP5uuufvCV0O+/h8F3kuSuJVsv/4fBV/4apq4fuX+mIf+DdiPCCqHBPYjgof+TWLvxcc/FvP0sWRRchEm26ePJfZefO4TEd//z5JwTaB8Qbgm+P5/lnzuE+k6xT/5QYeF4xLntEH5hIFz2mDhuORPfpBudvrzr3vc9xD4qwJrNdne91Bi78Wzfw3dp7NolahXaCXoPp3l2b9On99uW9NeNVGxQBoaFQvaqybddvr8fvKzVb7/d1lUV0JeobqS7/9dlk9+Nt389uQ31rj/v5h02galSkinbXD/fzF58htpBY3DS4v82Yk1aiFMOlAL4c9OrHF4KT19o5oXic8/hA47aGcAHXaIzz+EaqblKYeXYo6tenSEJmcrOkJzbNVjeCl93aoXTLyHS6iOxKjEqI7Ee7hE9cLryyt8uVnj1DpEoaRoCaJQcmo9sffie36TvJAUpIEUgoI0yAvJ9/z0tHf1uxWe+6sKflviDij8tuS5v6pQ/e7rq947c1Fx38MxzY5mpJKUwt33cMyZi33qo39CmDun+IevalptzdBgsv2Hr2rmzqWP4cRazH2HMjTn9pCdv5bm3B7uO5ThxFr6Gn/1rM8fv7iDz9fv4f7uP+fz9Xv44xd38NWz6Sn9v/hcwPfuLxIHNkbeIA5svnd/kb/4XJ9ZoVNZvvTcBEdfnGb28H6OvjjNl56bYPlUul6+gouRHWB75QB7y9eyvXIAIztAhTSZeOjFkE8/5BIslRnplgmWynz6IZeHXkyPZ9+YDfj3z2zlkbPv4Mmz7+GRs+/g3z+zlW/Mpo/3aC3iEyuD1IYOMLnzALWhA3xiZZCjtfQYZQwUeLRzE0cO7+HYozs4cngPj3ZuwujThM31d5Ffb7P1bMSecy5bz0bk19tw/V0p1xnGktIHQjT6lb9nSGvRvxacjbocacd4SlOQAk9pjrRjzkbp2YUWbeye4MLGokWaYJ5aivnsEzGNrma0pGl0NZ99IuZUn7HkvkMRs0FMZGlKFkSWZjaIue/QP90CRi+2Qv58oU0tUkzYklqk+POFNi+2+idxflS05kpc+OIMYcvGHe4StmwufHGG1lw6WJXb34l0R5KysrADWiPdEeT2d6Z8u+dcfhhvJTANLBkSmAY/jLfSPZd+Lj62cIbiQoBQmtiUCKUpLgR8bCEt6bk9kNx79Dj5wGe9UCAf+Nx79DjbgzSFfPBBwcrzGVQsEAaoOPn84IPpkrX/8P0lPnMogxdJspbCiySfOZThP3w/rar18JOrfPxRaASSETeiEUg+/mhi78W3j0R86n6f1kaDortCa6PBp+73+faR9L1ztBbxiRc3qK2cZbLxArWVs3zixY2+z/G3jgX86TdjluY0chGW5jR/+s2Ybx3rX977RsIbguzDK/pcD2mtN72JtNZN4AkgC6QLUzfjVpLlTJ64/P+u3I8CvtXzfT/O735DI3DB3qIxXdA+mJc/B31EXb75adC+QJsgLNBm8vmbn077fvt/g8KaQGqBlsm2sCb4f9u793i5yvre45/fmuu+ZueeNIkJCZcEg1xMCSFcRVHUFEUFBC9A8fayilqOtfZQsecc6zm157T12FqrlZZetC310FNB6g0FaUWKgK1YNRIukkDu+zKz954969c/nrVhstZsCNnJ3jPD9/16zWvt/Ztn1jwzz6yZ3zzzrOf56m9my37vfsJqiMkMf1Ec/v9edmpsvnWDTS7A+vQlTuIpD34vonuXUaoa5MK2e5fx4PeyL++//yrkhyFfMygY+ZqRHw7xtPjuMphDlEw0EQHmIZ5S3pUnzjvkPAz8zjlx3invyia5d365hJecqMuxCKIux0vOnV/O9hh99San3BXT3RsTRUZ3b0y5K+arN2W/RHzp4Z0MFJyBghGZMVAwBgrOlx7OvhnGT34f8j1YoRszwwrdkO8J8ZQf/OswSx/K0x0btS7ojo2lD+X5wb9mZwfZcW+Z+T05Cj1O3ZxCjzO/J8eOe599StJn8kBljC6LKOcMwyjnjC6LeKCSTTCfiGv02IFt32NR06FEj948j6i7Tr63ThR52HbXefTmJmO0n4Nv3xfT1w193aEt+rqNvu4Qnyl3fdfp7XF6e0IdenuM3h7nru9mXzu3NkzNG5k99fetW7MfeJ/74QS9BehPyvaXjN5CiKd952aDUni/sShsKSXxlB/8qItiDkr5cAiV8lDMhXjai1hAlQkqSZJboUaVCV6UPpsY+Ou76/QUnf5SRGQR/aWInqLz13dnE8w/v2/ysT1dtrcQ4mk3P1ZjoAADxbD+xEAxYqAQ4mmDA3VyViTqXQQLVxL1LiJnRQYHmkzbuuwYOO8y6O6DfU+G7XmXhXjKYuawkdWUKTDIKGUKbGQ1i5stv/wcPFGNKEVOOTLMoBwZpch5opp9T+2lh3EOfMzj1Ogl2z/29R/G9Jehvyt57XQZ/eUQT7t3R53uEnRF4ZjviozuUojPlH/YPcZAPmIgn7Rx8vc/7G4yVv05uPsO6B6dS/9/nED3dzeG7ehc7s4uIQBzXkC07iJyC9aT611BbsF6onUXwZzsL0hf++lx9G3NU6wb3p2jWDf6tub52k+zvwKs37uV6/Y+wuYfVzjqwTqbf1zhur2PsH5vdk0U7r+dVXRz6V7jPY9VuXSvsYpuuP/2TNG993WFaUpzyed8DrAknvK39xcpmFMuOJFBueAUzPnb+7Mn6H7hX2r05ur0F0PZ/qLTm6vzhX/JHm83fa1CKTdId3mcKJenuzxOKTfITV/LdgDevHUXA5VtDFiFqNDFgFUYqGzj5q3ZDrXPfyuG3WGERL5kFN1gdxJvca0yjGfyldhkdRoAfkLofT8WyK4089z2Q7Kfw33fLa0018kNQ2HuUxN+UBsP8bR4e4QXwZJfSi0HXgzxtK4dhltYoAXC+XqY0ZVdk4h6DmyCp6cxszDtdr3JqzAaAY8aygJuIZ4xZuTGPZQn3C43DrWxbEJRS4YzNc4EaPUQz5iI8IKHKiRzj7sBtezzsHAHVJZDPcwCiBmUPMTTfDCH9R745mAlxwezP03v3VNgztzaAU9EubvO3j3ZnxkfHQ09+o368yGeMbobSqmENt8V4inbB43FfTmirU/XL3bYPpR9fnN78xQG6vRb6akqj3XFxHun91YzVsnTVa6Hs2gnq1uIqVay+10cFRjyOn32dH1HPGZxlH3OKrvzLJhnVKgxQUyeiAXdBSq7p3d+yo49sCj140BPV4jPlCd3wYLUyKDu7hBP+/lQWGyvUV8xxNN2jDiLUp3tvcUQT6sPQS6V8+WKIZ62e8xYXsox5HVqOAWM5cUcu5scx8usj3N9BQ+wi72MMpcyp7GUZZbtKX9iGBam6tBbDPHsY2OKx5Yt+2glZlnXgXXrL1gYCpAy1BVz/Hpj+6NQHYauXlh5tDPUNcVChcuOaZrcN7OYOdNO7tNGhnspzNlD3etEHhFbTD6KGdnfm5kH/iiW8wA/Ag89+uPUGGectWRP0N2+DxanqtpbDvG08bLTNWHQcCjmJ6BanrnFHR8bq/MLxQPf7/tzxmNNpvF9LnY+AQtSI/G6e0K8qTkvaJrcp+2fKNM3XCH6SQGSWXbi2NkfN+lsGVjM+uoO1jcO8akOwUCTc8n27oCB1IxfXT0hnlKfiIgKPnn34XM+B/Umn5vDtRzdhQOPl2LeGa5l3393VAssKh/4pbu34OyoZt/X9+ycYE5fnMxmBVhEVzlmz87sl/ZH9+5lWR6Iki8YUZH+/DiP7t0LqV/IdjwJ3fkDli6h7CHe6lqlZ3/y8M9O2npgPDuAdPr7mdZ9m9nbzeweM7tn585sD2qrWHOUsWwNFIswOhK2y9aEeFq94FjqvczqIZ4W1Xkq0Z/kyeIjmbITPJWQP1U2CvE0DzPOpYLZ2wP07INaqtOg1hXiacVqmDqtUZwP8bTcBOTHwiJDnrxx5ceMXJP6HmPO0dth3hh0edgevT3E06y/jqcSGB8zrD/7pM2dV2O0cuAb32glx9x52d6MFWUYTNVtcCLEM8rzIf2T/EQ1xFOW9jvDqY6s4bEQT1u3KE+lCuOezHThMZVqiE/H6lof1WSRJ8eZiOpUiVldyyZ3Z5X6GPaYobgeZleI6wx7zFlNTtKdOx/qlYh5lFlEN/MoU69EzD244fNTWjIvTFfbaKQa4jNl0QKopDqyKpUQT1vWZwylfokeGg/xtCU9xnCq7PB4iKfl+sJ5NI3q4yGeqUPJGJuIWBQVWRaVWBQVGZuIWFZqMgUeIeG/wI7iMlvHBXZU00QfYHEvTeu7uDdbdklP87JLmgziXNEdMVg78BgYrDkrurNvUiu6IqzPOf4kePEZcPxJYH3Oiq5W+Qg+0Mqol8LQfHJxjologlycozA0n5VR9kmbH83lRbaWkhUZsQolK/IiW8v8KDsUbukADKc6H4ZHQzzt2GNgZBRqY8kIlrHw/7EH9x3osFheyjFYT7Vx3Vleml5nwMLFUEl9gayMhPh0zOmOGLNuIAIPP42PWTdzmrwmWXcmjA6HBN/jsB0dDvG0uUugmqpwdSTEUyLAJ55enA8P/zd7pfcW6oxPHHh8j08YvYXsZ+GSrhrDtQPLDteMJV3Zz8K5/cOMjh/468DoeJG5/dlv+CtsL+kVQgbpYoVlh9V217J9fbUoxFtda77TtBF3/4y7b3D3DQsXHtxJc7PhrFPCz+kr1jonn+GsWOtQSuIpXcc5Nk5YNzoOWxsP8bQ4H0a6NDLPJtQA9WKM5yBOer9jCz3r9WK2J6xe8KeG+ZBso7j5F441389RL8B4ycMUbSWnXgjxtIEJiHNQjxzHqUdOnAvxtL71tfDFZcwoVI1oLPyC0bc+e2RfsilHeTjH2seMM3/urH3MKA/nuGRTtg5nvGoMGzPiquExxFXDxowzXpX9afhlrzNGqxGV4Yg4dirDEaPViJe9Lttur125kH01Y18tTJm6r+bsqxmvXZl9XUaLToaJkTBm3x2vVWBiJMTTdThxHkOjxuBo6NEfHIWhUeNlJ2Yz1y2bCrxgrAzVHMNxDNUcLxgrs2XT9E7QfeOSXkpPDDBRixjLTTBRiyg9McAbl2STj6MLXVzSNY8+y7EznqDPclzSlT05F+AVr4HRCowMhx6wkWFntBLi03HWSRFDlQOnrx2qhPhMOX2jMTxiDI+EOgyPOMMjxukbs6+dC9bk2T/m7B8LZSf/vmBN9kD+5ePzDNdgMCk7OOYM10I8bfOFYf35ibA+FBOjwFgST7lqfYGhOge8fofqIT4dbzw1x8i4MTgWE3vM4FjMyLjxxlOzx+ZbTpp8bE+XHa6FeNqFywvsq8G+8TBl377xmH21EM+UXVZgb6rs3lqIt6JXDpSpjJcoDC5i8eByCoOLqIyXeOVA8+F486O5bIhO4JzoNDZEJzRN9AHOOz5icBQGq8lrp+oMjoZ42pUvLNK7NmYiH1MdcSbyMb1rY658YbMlto+MX5pfYt9EzL6JpN2Sv5/pJN2DceqZMJIsveFx2I4Mh/h0XL4hx1gcUbUycaGHqpUZiyMu39Dky8mSNXD6xdDVB/t3hu3pF4d42onnQHUwzDrlcdhWB0M8ZcMJ4G7Eteipi7uxocn5+284cZyaG6M1I3YYrRk1N95wYnYM/KWnFRiu5xgcD2UHx43heo5LT8seQ6/5xcepViMq1TyxQ6Wap1qNeM0vZs9fu3D+SDiO66Hsvno+HMfzsz/nbVoYMVqHaj28fqt1Z7Qe4q2uVWo42Xs+1W+Rk/FnmyLjUPZzuO67pa1ZHnHp+RF93cbOvWEs8aXnR6xpMjvIm6+OmFgdhzH71TBmf2J1zJuvzpadt6WOuYeE30Oib+7M25L9Zr7x/U7sMVEc9hnFEHvMxvdnP/hPeZ0zkXM8ciwO24mcc8rrsmXP25zjpFvylCrGeK9Rqhgn3ZLnvM3ZN7g3vNlY9EgY5jORN3LjsOiREE97//V58seGlTfrOYgc8seO8/7rsx/8F5w6n2vOc/pKMbuqOfpKMdec51xwaraL+D1vncuZl1eIumIYjoi6Ys68vNJ0Np7TL1jAJe+YoLunzv69Bbp76lzyjomms/GcvPQXeP+6BQwU4OdjMFCA969b0HQ2nqhvObmV54cx+2N7sEI3uZXnN52NZ93qJVxx9lz6y/DEUBhje8XZc1m3Oturs3plxBUX5tk0t8zxw71smlvmigvzrF45vbeaF87Lc+3qftYOLaTnkcWsHVrItav7eWGTRZQgJPxX9S7i1/uXcVXvoqaJPsCmsyMuf5vR02Ps3xO2l7/N2HT29Op79PKIS1+ao6/beHJvGLN/6UtzU87GcyQctSrioi1hrP6u3WF70RbjqFXZOqxbkOMdJxeZUzK2D8OckvGOk4usW5A9hrasKXHdqQX6S8aTlTB2/7pTC2xZk02A3v2mIme9JSZXduojTq7snPWWmHe/KZuwveL4Ih95cZGBvLF9DAbyxkdeXOQVx08vuTt/fYEPnJejv2TsHAnnGHzgvBznN/kSccHaIh86Y/KxhbIfOqPABWuzdThhIM81x5UYKEb8vOoMFCOuOa7ECQPZ1+QJA3ned0yJgUJSthDxvmOal20Fx3cXedeiXubkIx6vxczJR7xrUS/Hd0+vLY5dmuOtm3P0dxlP7A9j9t+6OcexS7Ovsxf1F7juxSVO3AjzTqtz4ka47sUlXtQ/c1+Q1vcWeO+yHgbyEY+PxwzkI967rIf1vdOrw8o1Ea++GHr6YNfOsH31xSE+HVdtLvHuM3N0FYyhcaOrYLz7zBxXbZ7iy8mSNXDuFfDaXwvbZok+wLKj4SWXH3geyUsuD/GUT15d5tQTHMPxGAzn1BOcT16d/aL4wTOXctWGKuV8TKUWUc7HXLWhygfPzK40/tLTF/KBc6G/GPPkaJ7+YswHzg3xtPNPW85VZ99Ld3mM/ftLdJfHuOrsezn/tOzn2wmr13LNwIMMWIWf14oMWIVrBh7khNVrM2XfsiXPaVGOYmwMxUYxNk6LcrxlS2sex43MfebGv01ZCbOrgT8BPuPu72hy/W2EcfMvdfcpx82b2UuBrxJOts3MhmRmfwy8Hbja3T93OO8bYMOGDX7PPfc8U5G2ccc/1/nHr8Du3c78+carXwFnNumlBvj4m2vs+XIuLCOeh3mvqvOhG5u/GX7iH8e55c6Y8SoUu+CVZ0Rc++rmHyCfur7GgzdFeBWsC9a9Lubd1zff72c+XGPr53NEwxD3wpor67z9Y83L/tVtY3zjr5z69ojc0piXXGZc9vLmb4YPDNb4+x3jT62Ke9GS4ox+2IiIiMhzUx9+jPqu+/DRPVh5HrkFJ5HrnWJd1cFHYce9T01DzZJToMmaBwAPb425+45wfsXCxeHXmOl+SZsOM/tXd88uPpEu1yLJ/hrgp4TpL9c0zopjZn3AdsLor0Xu3uwUzcmyvcCThIEfSxtn5DGzCNgKrEru42eH876hs5J9EREREWldB5vst8QwHnffCvwTIRF/d+rqjwI9wI2NybaZrTWzA35ncfdh4Mak/PWp/fxKsv/bGlfQPZT7FhERERFpBy3Rsw9P9bDfRVjJ9mbgQWAjYR78HwOnu/vuhvIO4H7gXDBmNj/Zz7HAN4C7gXXAhYRe/9OTBP+Q73sq6tkXERERkZnQVj378FQP+wbgBkKi/avAGuD3gdMOJtlO9rMb2AT8AXB0sp+NwOeBF6cT/cN53yIiIiIiraRlevY7gXr2RURERGQmtF3PvoiIiIiIHF5K9kVEREREOpSSfRERERGRDqVkX0RERESkQynZFxERERHpUEr2RUREREQ6lJJ9EREREZEOpWRfRERERKRDKdkXEREREelQSvZFRERERDqUkn0RERERkQ6lZF9EREREpEMp2RcRERER6VBK9kVEREREOpSSfRERERGRDqVkX0RERESkQynZFxERERHpUEr2RUREREQ6lJJ9EREREZEOpWRfRERERKRDKdkXEREREelQSvZFRERERDqUkn0RERERkQ6lZF9EREREpEOZu892HTqGme0EHp6lu18A7Jql+5ZDp3ZrT2q39qR2a09qt/akdjvyVrr7wmcrpGS/Q5jZPe6+YbbrIc+N2q09qd3ak9qtPand2pParXVoGI+IiIiISIdSsi8iIiIi0qGU7HeOz8x2BeSQqN3ak9qtPand2pParT2p3VqExuyLiIiIiHQo9eyLiIiIiHQoJfsiIiIiIh1Kyb6IiIiISIdSst+mzGy5mf2pmT1uZmNmts3Mfs/M5s523Z7vzOz1ZvZJM7vDzAbNzM3sL57lNqeb2S1mtsfMqmb2gJm9z8xyM1Xv5zMzm29mV5vZl8zsp0kb7DezO83sl82s6Xul2q01mNn/NLOvm9mjSTvsMbPvm9lHzGz+FLdR27UYM3tT8n7pZnb1FGVebWa3J8fnsJl918zeOtN1fT5L8g2f4rJjitvoeJtFOkG3DZnZGuAuYBFwM/Aj4FTgXOA/gM3uvnv2avj8Zmb3AScCw8BjwFrgL939TVOUvxC4CRgFvgjsAbYAxwF/5+5vmIl6P5+Z2TuBPwK2A98EHgEWAxcBcwjt8wZveMNUu7UOMxsH7gV+CDwJ9ACnARuAx4HT3P3RhvJquxZjZiuAHwA5oBd4m7t/NlXmV4BPArsJ7TYOvB5YDvyuu187o5V+njKzbcAA8HtNrh5290+kyut4m23urkubXYDbAAfek4r/7yT+6dmu4/P5QvjSdQxgwDlJm/zFFGX7CcnJGLChIV4mfKFz4NLZfkydfgFeQvjwiVLxJYTE34HXqd1a8wKUp4j/j6Qt/lBt17qX5L3ya8BW4HeSNrg6VWYV7VgzXQAACfpJREFUIVncDaxqiM8FfprcZtNsP5bnwwXYBmw7yLI63lrgomE8bSbp1T+fcLB9KnX1R4AR4M1m1jPDVZOEu3/T3X/iyTvas3g9sBD4grvf07CPUeC/Jv++6whUUxq4+zfc/f+7e5yK7wA+nfx7TsNVarcWkjzvzfxNsj2mIaa2az3vJXzhvpLwGdbMVUAJ+L/uvm0y6O57gY8l/77zCNZRDo2OtxagZL/9nJts/6lJYjIEfAfoJvyELa3vJcn2K02u+zZQAU43s9LMVUlSasl2oiGmdmsPW5LtAw0xtV0LMbN1wMeB33f3bz9D0Wdqt1tTZeTIKyXnWHzYzK4xs3OnGH+v460F5Ge7AvKcHZdsfzzF9T8h9PwfC3x9Rmok0zFle7r7hJk9BLwQWA08OJMVEzCzPPCW5N/GDyu1Wwsys2sJ473nEMbrn0FI9D/eUExt1yKS4+tGwlC5Dz9L8Wdqt+1mNgIsN7Nud68c3ppKE0sIbdfoITO70t2/1RDT8dYClOy3nznJdv8U10/GB2agLjJ9as/W9nFgPXCLu9/WEFe7taZrCSdWT/oKcIW772yIqe1ax28CJwNnuHv1WcoeTLv1JOWU7B9ZnwfuAP4dGCIk6r8CvB241cw2ufv9SVkdby1Aw3hERJows/cCv0qY7erNs1wdOQjuvsTdjdDreBEhCfm+mZ0yuzWTNDPbSOjN/113/+fZro8cPHf/aHKe0xPuXnH3f3P3dxImCekCrp/dGkqakv32M/kteM4U10/G981AXWT61J4tKJni7/cJUzme6+57UkXUbi0sSUK+RBjSOB/484ar1XazLBm+8+eEoR3XHeTNDrbdpupBliNvcjKDsxpiOt5agJL99vMfyfbYKa6fnHViqjH90lqmbM/kA/EowomhP5vJSj2fmdn7CHN5/xsh0W+2SIzarQ24+8OEL2wvNLMFSVhtN/t6Cc//OmC0cVEmwqxyAH+SxCbncn+mdltKGMLzmMbrz6rJ4XKNswHqeGsBSvbbzzeT7fnpVT3NrA/YTBiv+C8zXTE5JN9Itq9oct1ZhJmV7nL3sZmr0vOXmf0a8H+A+wiJ/pNTFFW7tY9fSLb1ZKu2m31jwOemuHw/KXNn8v/kEJ9narcLUmVkdkzOAtiYuOt4awWzPdG/Ls/9ghbVapsLB7eo1k604MisXwjDCRy4B5j3LGXVbi1yIfQYzmkSj3h6Ua3vqO3a40IY791sUa2j0KJas34h/BrT0yS+ijAboAMfbojreGuBiyVPurSRZGGtu4BFwM2E6ao2Eubg/zFwurvvnr0aPr+Z2WuA1yT/LgFeTujpuCOJ7fKGZd2T8n9H+CD7AmEp8V8iWUocuNh1oB5RZvZW4AZC7+8naT7ud5u739BwG7VbC0iGXf02oSf4IUIyuBg4m3CC7g7gPHf/YcNt1HYtysyuJwzleZu7fzZ13XuAPyC08ReBccKiTcsJJ/peixxRSfv8KmGO/IcJs/GsAV5FSOBvAV7r7uMNt9HxNsuU7LcpM1sB/Bbhp7H5wHbgS8BHPawoKLOk4cNqKg+7+6rUbTYDvwFsIrxh/hT4U+AP3L2e2YMcVgfRZgDfcvdzUrdTu80yM1tPWDn1DELSN0BYhfXHwJcJbZE+wVpt16KeKdlPrt9CmGL1FMKvNz8krKr7ZzNZz+crMzubcLydTOjM6iGcXHsfYd79G5sl7jreZpeSfRERERGRDqUTdEVEREREOpSSfRERERGRDqVkX0RERESkQynZFxERERHpUEr2RUREREQ6lJJ9EREREZEOpWRfRERERKRDKdkXEZFZYWY3mJmb2aojfD/bzGzbkbwPEZFWpWRfRETampndbmZaIVJEpIn8bFdARETkCDtvtisgIjJblOyLiEhHc/ets10HEZHZomE8IiJtxsxWJWPdbzCztWb2/8xsj5mNmNmdZnZ+k9uUzOxDZvYDM6uY2aCZ3WFmFx+m/V+f3OacZ9rfQT6+K8zsJjP7mZlVk7p+x8ze1Gy/wNnJ/95wub2hXNMx+9N4TlaZ2RfMbJeZjZrZPWb26oN5bCIiM009+yIi7eso4J+BHwB/DCwFLgFuNbPL3P2LAGZWBG4jJMU/Aj4FdAOvB75oZie5+4cPdf9HwB8B/w58G9gOzAdeCdxoZse5+3VJuX3AR4ErgJXJ35O2PdMdTOM5WQncDfwMuBGYR3hObjazl7r7N5/rgxUROZLMXec0iYi0k2T2moeSfz/h7v+l4boNhAR9GFjp7oNm9uvAx4BbgV9y94mk7CJC4roS2Ozudx3K/pP49cBHgHPd/fYp6vtn7n5FQ/wG4K3AUe6+rSG+Jj30JknObwXOAla5+88brrsdONvdbYrnaxuAu69qiE3nObne3T/asK+XA18BbnX3Vzarg4jIbNEwHhGR9rUf+K3GgLvfA/wlMAC8NglfBTjwgcmkNin7JPDfkn+vnsb+D6tmY+zdfZzQ+57n8Jxwe6jPycPAf0/V7TbgEeDUw1AvEZHDSsm+iEj7utfdh5rEb0+2J5tZH3A08Li7/6hJ2W9Mlj2U/T+Huh40M3uBmX3KzH6UjKX3ZGz+TUmRZdPc/3Sek/vcvd4k/igwdzr1EhE5EjRmX0SkfT0xRXxHsp2TXCCMfW9mMj5wiPs/rMxsNWEYzVzgDuCfCL8w1IFVhGE/pWnezXSek31T3GYCdaCJSAtSsi8i0r4WTxFfkmz3J5fGWNrShrKHsv9JcbJt9rnSLGmeygcIJ+Re6e43NF5hZm8kJPvTNZ3nRESkragXQkSkfZ2SDElJOyfZfj8ZhrMVWGZmxzQpe26yvfdQ9t8Q25tsVzQpv6FJbCpHJ9ubmlx39hS3qQOYWe5g7mCaz4mISFtRsi8i0r7mAL/ZGEhmy7mc0Cv9pST8p4ABv9OYEJvZAuC6hjKHun8IQ28ArjSzfEP5Fel9PIttyfac1P2+nOYnzALsTrYveA73c6jPiYhIW9EwHhGR9vVt4Goz2wh8h6fnwY+Ad0xOiwl8ArgAuBC438xuIcwp/wZgEfC/3P3Oaewfd/+umX2bMDXm3Wb2DcIwoC2E+eyb9fg384fAlcDfmtnfAY8D64FXAH+T3H/a15PH8vfJY6sCD7v7jc9wP4f6nIiItBX17IuItK+HgNMJQ2jeCVxMGHryysYFr5JpK18G/EYSeg9h7PtPgMvc/dems/8GFwKfBZYn93Ey8EFgqv1nuPsDhGE0dwGvAt4F9AMXAZ+e4mafBX6b8EvEBwlTZ/7ys9zPoT4nIiJtRYtqiYi0makWqWqX/YuIyMxRz76IiIiISIdSsi8iIiIi0qGU7IuIiIiIdCiN2RcRERER6VDq2RcRERER6VBK9kVEREREOpSSfRERERGRDqVkX0RERESkQynZFxERERHpUP8JbJ3wA729BJAAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": {}, @@ -526,496 +601,49 @@ } ], "source": [ - "for y_label in [\"lear_rate\", \"lear_rate_decay\"]:\n", - " plt.figure(figsize=(12,12))\n", - " for i in range(data.shape[0]):\n", - " plt.scatter(i // 10, \n", - " params_dictionaries[i][\"chainer\"][\"pipe\"][model_index][y_label],\n", - "# + (np.random.random() - 0.5) / 2, #s=3,\n", - " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + "cmap = plt.get_cmap('rainbow')\n", + "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", + "\n", + "ylims = [(0., 1)] * len(MEASURES)\n", + "\n", + "for metric, ylim in zip(MEASURES, ylims):\n", + " plt.figure(figsize=(12,6))\n", + " if validate_best:\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_valid\"].values[i], \n", + "# c=colors[models_ids[i]], alpha=0.5, marker='o')\n", + " c=colors[np.where(models_ids[i] == np.unique(models_ids))[0][0]], alpha=0.5, marker='o')\n", + " \n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_valid\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \n", + " c=colors[-1])\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_valid\"].min() * np.ones(data.shape[0]//POPULATION_SIZE), \n", + " c=colors[0])\n", + " if test_best:\n", + " for i in range(data.shape[0]):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " data.loc[:, metric + \"_test\"].values[i], \n", + " c=colors[np.where(models_ids[i] == np.unique(models_ids))[0][0]], alpha=0.5, marker='+', s=200)\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_test\"].max() * np.ones(data.shape[0]//POPULATION_SIZE), \"--\",\n", + " c=colors[-1])\n", + " plt.plot(np.arange(data.shape[0]//POPULATION_SIZE), \n", + " data.loc[:, metric + \"_test\"].min() * np.ones(data.shape[0]//POPULATION_SIZE), \"--\",\n", + " c=colors[0])\n", + " \n", "\n", - " plt.ylabel(y_label, fontsize=20)\n", + " plt.ylabel(metric, fontsize=20)\n", " plt.xlabel(\"population\", fontsize=20)\n", " plt.title(TITLE, fontsize=20)\n", + " plt.ylim(ylim[0], ylim[1])\n", " plt.xticks(fontsize=20)\n", " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \".png\")\n", + " plt.savefig(path_to_pics.joinpath(y_label + \"_colored_ids.png\"))\n", " plt.show()\n" ] }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bm = np.array(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][\"binary_mask\"])\n", - "np.sum(bm[0, :])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Layer params" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/matplotlib/pyplot.py:537: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", - " max_open_warning, RuntimeWarning)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for y_label in list(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][\"nodes\"].values()):\n", - " layer_params = list(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][y_label].keys())\n", - " layer_params.remove(\"node_name\")\n", - " layer_params.remove(\"node_type\")\n", - " layer_params.remove(\"node_layer\")\n", - " for param in layer_params:\n", - " if (type(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][y_label][param]) is float or\n", - " type(params_dictionaries[0][\"chainer\"][\"pipe\"][model_index][y_label][param]) is int):\n", - " plt.figure(figsize=(12,12))\n", - " total_dots = 0\n", - " for i in range(data.shape[0]):\n", - " node_num = int(y_label.split(\"_\")[-1])\n", - " bm = np.array(params_dictionaries[i][\"chainer\"][\"pipe\"][model_index][\"binary_mask\"])\n", - " if np.sum(bm[node_num, :]) > 0 or np.sum(bm[:, node_num]) > 0:\n", - " total_dots += 1\n", - " plt.scatter(i // 10, \n", - " params_dictionaries[i][\"chainer\"][\"pipe\"][model_index][y_label][param],\n", - " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", - " if total_dots == 0:\n", - " plt.close()\n", - " continue\n", - " plt.ylabel(y_label + \" \" + param, fontsize=20)\n", - " plt.xlabel(\"population\", fontsize=20)\n", - " plt.title(TITLE, fontsize=20)\n", - " plt.xticks(fontsize=20)\n", - " plt.yticks(fontsize=20)\n", - " plt.savefig(\"./pics/\" + TITLE + \"/\" + TITLE + \"_\" + y_label + \"_\" + param + \".png\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, From 83e6c2e954cab1b52297562cddd777237944b1e3 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 16:11:57 +0300 Subject: [PATCH 510/616] feat: add check bool --- .../evolution/evolve_intents_snips.json | 5 +- .../models/evolution/Results_analysis.ipynb | 299 ++++++++++++++---- 2 files changed, 245 insertions(+), 59 deletions(-) diff --git a/deeppavlov/configs/evolution/evolve_intents_snips.json b/deeppavlov/configs/evolution/evolve_intents_snips.json index 9c9f849edf..0f7f35878a 100644 --- a/deeppavlov/configs/evolution/evolve_intents_snips.json +++ b/deeppavlov/configs/evolution/evolve_intents_snips.json @@ -142,7 +142,10 @@ }, "model_name": "cnn_model", "embedder": "#my_embedder", - "tokenizer": "#my_tokenizer" + "tokenizer": "#my_tokenizer", + "check_bool": { + "bool": true + } } ], "out": [ diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb index f02b70ae0d..c0fa6812f5 100644 --- a/deeppavlov/models/evolution/Results_analysis.ipynb +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -504,64 +504,6 @@ "models_ids" ] }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2])" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.unique(models_ids)" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.where(models_ids[2] == np.unique(models_ids))[0][0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 73, @@ -644,6 +586,247 @@ " plt.show()\n" ] }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['dataset_iterator', 'seed'] seed\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'filters_cnn'] filters_cnn\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'lear_rate'] lear_rate\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'lear_rate_decay'] lear_rate_decay\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'coef_reg_cnn'] coef_reg_cnn\n" + ] + }, + { + "data": { + "image/png": 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YAQMAABhGwAAAAIYRMAAAgGEEDAAAYBgBAwAAGEbAAAAAhhEwAACAYQQMAABgGAEDAAAYRsAAAACGETAAAIBhBAwAAGAYAQMAABhGwAAAAIYRMAAAgGEEDAAAYBgBAwAAGEbAAAAAhhEwAACAYQQMAABgGAEDAAAYRsAAAACGETAAAIBhBAwAAGAYAQMAABhGwAAAAIYRMAAAgGEEDAAAYBgBAwAAGEbAAAAAhhEwAACAYQQMAABgmG0VMKrqjlX16qq6oKq+XlXnVdVLquqWc/Zzq+m+86Z+Lpj6veOIsavqDlX1tKp6x6IxLqmq91TVo9by3QEAYDPYb28XMEpVHZLkw0lum+TUJJ9Kcr8kP5/k+Ko6qrsvWUU/t576OTTJ+5OckuTwJI9P8vCqOrK7z13n2E9L8qwk/5rkA0m+mOTOSR6V5CFV9eLufuaafhEAALAXbZuAkeQVmf2B//Tu/v1dJ6vqRUmekeS3kzxpFf08P7Nw8aLu/sVF/Tw9yUuncY5f59gfTXJMd39wcSdVdfckZyR5RlX9RXefuYp6AQBg06ju3ts1rNv0BOGzSc5Lckh371x07eZJLkxSSW7b3f+5h35uluRLSXYmuV13f2XRtX2SnJvZk4ZDdj3FGDX2onteleQJSX6pu39vNd9/YWGhd+zYsZqmAACwJlV1ZncvrNRuu8zBOHY6vnvxH/hJMoWE05PcJMkRK/RzRJL9k5y+OFxM/exM8q4l440ce5dvTMerV9keAAA2je0SMA6bjmfv5vpnpuOhG9DPqLFTVQckeXSSTvLuFdo+sap2VNWOiy66aKWuAQDgOrFdAsaB0/Gy3Vzfdf4WG9DPkLGrqpL8SZJvS/LK7v7kntp396u6e6G7Fw466KA9NQUAgOvMdgkY28HvJfmRJB9KYgUpAAC2pO0SMHY9JThwN9d3nb90A/pZ99hV9buZrTZ1WpIf6u6vr1AnAABsSttlmdpPT8fdzXO423Tc3TyJ9fSzrrGr6sVJfiGz/TD+W3d/bYUaAQBg09ouTzA+MB0fNi0n+03TUrFHJflaZntM7MkZSa5IctR03+J+9knysCXjrXnsmvmDzMLFe5I8XLgAAGCr2xYBo7vPyWzVpYOTPGXJ5ZOT3DTJ6xfvQ1FVh1fV4Uv6+WqS10/tT1rSz1On/t+1eCfvNY5dSV6V5MlJ3pHkEd19xWq/LwAAbFbbYqO95Jsb3n04sx21T03yyST3z2yfirOTPKC7L1nUvpOku2tJP7ee+jk0yfsz23X77klOyGwTvgdMoWI9Y5+YWYC5IslLkly1zFf6x+5+62q+u432AADYaKvdaG+7zMFId59TVQtJnpfk+CQ/lNku2i9NcnJ3/8cq+7mkqo5McmKSRyZ5YJJLkrwmyXO7+98GjH2X6bh/kmfvppTXJVlVwAAAgM1i2zzBuD7zBAMAgI222icY22IOBgAAsDkIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAw+63lpqraN8lhSW6ZZN/l2nT3aeuoCwAA2ILmDhhV9RtJnpHkwBWaLhs8AACA7WuugFFVv5Lk5CSXJXl9kvOTXL0BdQEAAFvQvE8wnpDkC0m+t7sv2oB6AACALWzeSd7fkeStwgUAALCceQPGv2eNE8MBAIDtb96A8cYkD62qG21EMQAAwNY2b8A4McmFSd5UVXfZgHoAAIAtbN7Xnc5KcoMkt0/yQ1V1WZJLl2nX3X3IeosDAAC2lnkDxj6ZLUv7+UXnapl2y50DAAC2ubkCRncfvEF1AAAA28C8czAAAAB2S8AAAACGWdOeFlW1kOR+SW6ZZN9lmnR3/+Z6CgMAALaeuQJGVR2Q5M1Jjs2eJ3J3EgEDAACuZ+Z9gvH/J3lwkg8leU2S8zNbVQoAAGDugHFCkn9Icmx379yAegAAgC1s3kneByb5gHABAAAsZ96A8Zkk37YRhQAAAFvfvAHjD5L896q6w0YUAwAAbG3zzsF4R2aTvE+vqpOTnJnk0uUadvfn11kbAACwxcwbMM7LbAnaSvIne2jXa+gbAADY4uYNAX+WWXgAAAD4FnMFjO5+3AbVAQAAbAPzTvIGAADYrbkCRlUdVFVHV9XNd3P9gOn6bcaUBwAAbCXzPsF4TpK/TnLNbq5fM11/9nqKAgAAtqZ5A8ZDk7ynu7+23MXu/s8k707yA+stDAAA2HrmDRjfkeScFdqcO7UDAACuZ+YNGJ3khiu0uWGSfddWDgAAsJXNGzA+nT28/lRVNV3/7HqKAgAAtqZ5A8abkhxeVS+vqv0XX5g+vzzJYUneMKg+AABgC5l3J++XJfmxJD+X5JFVdVqSLyS5Q5Kjk9w+yceTvGRkkQAAwNYw707eV1TVMUlekeT/SfLYRZd3JvnLJE/t7iuGVQgAAGwZ8z7BSHdfmuTHq+rnk9w3yS2SXJrko9198eD6AACALWTugLFLd1+U5G9W07aqTkhyQnf/1FrHAwAANr95J3mv1b2T/OR1NBYAALCXXFcBAwAAuB4QMAAAgGEEDAAAYBgBAwAAGEbAAAAAhhEwAACAYQQMAABgGAEDAAAY5roKGOclOe06GgsAANhLrpOA0d2v6+5jr4uxAACAvWe/eRpX1XNX0WxnksuTfDLJB7v7qrUUBgAAbD1zBYwkJyXpRZ9r0b+Xnu8kl1TV07r7DWsrDwAA2ErmfUXq2CSnJvlGkj9N8rgkPzgdXz2df2uSH0nygiQ3TvLnVfXAMeUCAACb2bxPMO6c5KFJ7tvd/7zk2p9V1cuTnJ7kLd3961V1SpIzk/xSkg+tu1oAAGBTm/cJxjOSvHGZcJEk6e6PJ/mrJM+cPv9zkrcnOXI9RQIAAFvDvAHjsCRfXKHNBVO7XT6T5BZzjgMAAGxB8waMr2TlpxEPSPLVRZ9vOt0HAABsc/MGjL9J8qCqen5V3XTxhaq6aVX9TpKjp3a73DOzjfYAAIBtbt5J3s/ObCWpZyV5UlX9U5J/T/JtSb4ns1ehPp/k15Kkqm6X5DuT/OGoggEAgM1rroDR3V+sqvtmtgTtYzN7WrHLFUlem+RXu/tLU/sLk9xhTKkAAMBmN+8TjHT3RUl+uqqelNlk7gMz27n7U939jcH1AQAAW8jcAWOXKUycNbAWAABgkU+fdVbee/bZuSDJ7ZM85NBDc9g977m3y9qjNQWMqrpBkuOS3D3Jzbr7N6fzN05yQJKLu3vnsCrZtM4/64u58OxP5epclv1yYG536OH5jnt++94uCwBgy/v0WWfltWefnQO68+1Vubw7rz377Dwu2dQhY95VpFJVx2e2KtTbk/xekpMWXb53kguT/OiA2tjkzj/rizn/7L/PNX1F9ssBuaavyPln/33OP2ulrVIAAFjJe6dwccA++2Sfqhywzz45oDvvPfvsvV3aHs0VMKpqIclbk3Rmu3r/5eLr3X1Gkn9N8sOjCmTzuvDsT6X6xtl3n/2Tquy7z/6pvnEuPPtTe7s0AIAt74IkN6u61rmbVeWCvVPOqs37BOM3knwtyUJ3vyyzXbqX+liSe623MDa/q3NZ9q0bX+vcvnXjXJ3L9lJFAADbx+2TfLX7Wue+2p3b751yVm3egHFUkrd2957egTk/ye3WXhJbxX45MNf0ldc6d01fmf1y4F6qCABg+3jIoYfm8qpcvnNndnbn8p07c3lVHnLooXu7tD2aN2DcLMnFK7S5yRr6ZQu63aGHp+vKXLPziqQ71+y8Il1X5naHHr63SwMA2PIOu+c987hDD80BVflikgOq8rhtuIrUF5J81wpt7p3k3LWVw1YyWy3qyP9aRaoOzO0OvY9VpAAABjnsnvfc9IFiqXkDxjuSPKmqvr+7/27pxar6wSQPyGynb64HvuOe3y5QAADwTfO+yvQ7SS5N8u6qemGSeyRJVT18+vxXmS1T+6KhVQIAAFvCXAGju7+Q5GGZrZr1y0l+JEkledv0+cIkx3f3SvM0NkRV3bGqXl1VF1TV16vqvKp6SVXdcs5+bjXdd97UzwVTv3ccOXZV3aOq3lhVX6qqK6vq01V1clXtP0+9AACwWVQvWfpqVTdV7Zvk4UmOTHLrJJclOSPJqd199dAKV1/TIUk+nOS2SU5N8qkk90tybJJPJzmquy9ZRT+3nvo5NMn7M1t29/AkJyT5UpIju/vcJffMPXZV3X/q/wZJ3pTZ6lsPTrKQ5PQkx3X311fz3RcWFnrHjh2raQoAAGtSVWd298JK7eaag1FVd0py1bRM7dumn83iFZn9gf/07v79XSer6kWZbQr420metIp+np9ZuHhRd//ion6enuSl0zjHr2fsKaC9JrMVt07o7rdN5/dJ8sYkj57uM5cFAIAtZa4nGFV1TZLXdfdPbVxJ85ueIHw2yXlJDununYuu3TyzV7cqyW27+z/30M/NMntKsTPJ7br7K4uu7ZPZ6lh3nsY4d61jV9WDk7wvyWnd/aAlNdw1yTlJPpfkLr2K/0CeYAAAsNFW+wRj3knel2blfTD2hmOn47sX/4GfJFNIOD2zpwVHrNDPEUn2T3L64nAx9bMzybuWjLfWsR88Hd+5tIApuJydWZC56wr1AgDApjJvwDgjyX02opB1Omw6nr2b65+Zjitte7iWfq6re66lqp5YVTuqasdFF120u2YAAHCdmjdgnJTkgVX1MxtQy3ocOB0v2831XedvsQH9XFf3XEt3v6q7F7p74aCDDtpdMwAAuE7Nu9HeDyb52yR/VFU/l+SjSb6YZOk8ge7u31x/eQAAwFYyb8A4adG/75Pdvy7VSa7LgLHr//gfuJvru85fugH9XFf3AADApjdvwDh25SZ7xaen4+7mLNxtOu5uzsN6+rmu7gEAgE1vroDR3R/cqELW6QPT8WFVtc8yS8UeleRrmU1S35MzklyR5Kiquvkyy9Q+bMl4ax37/Ul+PbP9NH5ncQHTMrWHZrZM7bU29AMAgM1u3knea1JVJ1bVhu3w3d3nJHl3koOTPGXJ5ZOT3DTJ6xfvgVFVh1fV4Uv6+WqS10/tT1rSz1On/t+1eCfvtYyd5INJPpnk6Kp6xKKa9knywunjH65mDwwAANhM5tpob82DVJ2Y5Lndve8GjnFIkg9ntqP2qZn9AX//zF7rOjvJA7r7kkXtO0m6u5b0c+upn0Mze9Lw0SR3T3JCZpvwPWAKFWsee7rn/lP/N0jypiSfT3JckoXM9s44rru/vprvbqM9AAA22kZttLdpTX/0LyR5bWZ/3P9ikkOSvDTJEUv/wN9DP5ckOTLJy5J859TP/ZO8Jsn3LQ0Xax27uz+S5L6ZBZKHJXlGZpO7n5fkoasNFwAAsJnMO8l7U+vu85M8fpVtaw/Xvpzk56ef4WMvuucTSX5knnsAAGAz2zZPMAAAgL1PwAAAAIYRMAAAgGEEDAAAYBgBAwAAGGaPAaOqDqiqG15XxQAAAFvbSk8w/iPJs3Z9qKpXL955eg5vTfJTa7gPAADYQlYKGL2kzeOS3HveQbr74939unnvAwAAtpaVAsaFme1mDQAAsKKVdvJ+f5KfqKrbZBY2kuSRVXXwCvd1d//0OmsDAAC2mJUCxq8k+bYkD83saUdn9orUSq9JdRIBAwAArmf2GDC6+9+THF9VN0hyuyTnJXlJkpdufGkAAMBWs9ITjCRJd38jyeer6nNJzuvuz21sWQAAwFa0qoCxS3ffZaMKAQAAtr65AsYu0ytTxyW5e5KbdfdvTudvnOSAJBd3985hVQIAAFvCSsvUfouqOj6zuRhvT/J7SU5adPnema029aMDagMAALaYuQJGVS1ktit3J3lGkr9cfL27z0jyr0l+eFSdSlk4AAAc30lEQVSBAADA1jHvE4zfSPK1JAvd/bIkn1mmzceS3Gu9hQEAAFvPvAHjqCRv7e4v7qHN+ZktaQsAAFzPzBswbpbk4hXa3GQN/QIAANvAvEHgC0m+a4U2905y7trKAQAAtrJ5A8Y7kvxAVX3/cher6geTPCDJ/15vYQAAwNYzb8D4nSSXJnl3Vb0wyT2SpKoePn3+q8yWqX3R0CoBAIAtYd6dvL9QVQ9L8sYkv7zo0tuSVJJzkjyqu1eapwEAAGxDc+/k3d3/UFWHJXl4kiOT3DrJZUnOSHJqd189tkQAAGCrmDtgJEl3X5PZU4u3jS0HAADYytYUMHapqpsnuUWSy7r78jElAQAAW9Xc+1VU1X5V9atV9dnMJnyfl+Q/quqz0/l1hRYAAGDrmisMVNUNk7wzyYOSdGa7dl+Y2c7dByf57STHV9XDuvuqsaUCAACb3bxPMJ6Z5Jgkb09y9+4+uLuP7O6DkxyW5K+TPHBqBwAAXM/MGzB+PMlZSR7Z3Z9ZfKG7z0nyqCT/kuQnxpQHAABsJfMGjO9M8o7u3rncxen8O5Icst7CAACArWfegHFVkput0OamSb6xtnIAAICtbN6A8U9JHlNVBy13sapuk+QxST6+3sIAAICtZ96A8fIkByX5aFX9dFXdtar2r6q7VNXjk3xkuv7y0YUCAACb31zL1Hb3G6vq3kl+NcmrlmlSSX63u984ojgAAGBrmXtTvO7+tap6W5KfTnKfJAcmuSzJ/0ny6u7++7ElAgAAW8Wadt3u7jOSnDG4FgAAYIubaw5GVf1IVb2/qm6/m+t3qKr3VdWjxpQHAABsJfNO8v6ZJLfo7guWu9jdX8jslamfWW9hAADA1jNvwPjuJDtWaPOxJN+ztnIAAICtbN6AcaskX1qhzSVJbrO2cgAAgK1s3oBxcZK7rdDmbkkuXVs5AADAVjZvwDg9ySOq6vDlLlbV3ZOckORD6y0MAADYeuYNGP8js6Vt/66qnl5Vh1bVTafjz2cWLPad2gEAANcz8+7k/bGqenKSP0jy4ulnsWuS/Fx3f2RQfQAAwBaylp28/7iq/i7Jk5PcP8ktMptzcUaSV3b3J8eWCAAAbBVr3cn7k0meNrgWAABgi5t3DgYAAMBuCRgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDDbJmBU1QOq6m+q6stVdUVV/VNV/UJV7buGvu5RVW+sqi9V1ZVV9emqOrmq9h8xflXdu6pOqqrTq+rCqrqqqr5QVf+zqr533noBAGCz2BYBo6pOSHJakqOTvCXJy5PcMMmLk5wyZ1/3T/KxJI9M8t4kL01yeZLnJnlPVd1owPh/mOTEJDdK8uap3VlJHpvkI1X1qHlqBgCAzaK6e2/XsC5VdUCSzyY5MMlR3b1jOn/jJO9PcmSSH+vuFYPG9LThn5PcPckJ3f226fw+Sd6Y5NFJnt3dL1jP+FX1tCTv6O7PLhn/J5L8eZJLkty+u69aze9gYWGhd+zYsZqmAACwJlV1ZncvrNRuOzzBeEySg5KcsuuP+yTp7iuTPGf6+HOr7OtBmYWL03aFi6mvnUl+Zfr4pKqq9Yzf3b+/NFxM5/8iyWeS3DrJd6+yZgAA2DS2Q8B48HR85zLXTkvytSQPWO7Vpnn66u5zk5yd5M5J7rpB4yfJN6bj1atsDwAAm8Z2CBiHTcezl17o7quT/GuS/XLtUDB3X5PPTMdDN2L8qjoiyT2SfCGzORl7avvEqtpRVTsuuuiilboGAIDrxHYIGAdOx8t2c33X+VtsUF9Dxq+qWyX5s+njM7r7mj217+5XdfdCdy8cdNBBe2oKAADXmU0RMKrqvKrqOX7+fG/XPFJV3TTJqUnuluR3u/uv9nJJAACwJvvt7QIm5yS5co72Fyz6964nBAcu13DR+UtX0e9a+lrX+FO4eHuS70/you5+1irqBACATWlTBIzuPm4dt386yUJm8yLOXHyhqvZLcpfMJkyfu8q+kmvPsVjsbtNx8XyLNY9fVTfPLFw8MLMnF8IFAABb2qZ4RWqd3j8dj1/m2tFJbpLkw9399fX0VVV3zSxEfC7XDgtrGr+qDkzy7szCxW8LFwAAbAfbIWC8KcnFSR5bVd/c+GPa6O63po+vXHxDVd2kqg6vqjst6euDST6Z5OiqesSi9vskeeH08Q/72rsTrmX8W2a2S/gRSU7s7ucEAAC2gU3xitR6dPflVfWEzP7Q/9uqOiXJl5M8IrMlZN+U5A1Lbrtfkg9kFiiOWdTXNVX1+MyeSrypqt6U5PNJjsvsNajTk7x4wPhvnvo7J8k+VXXSMl/trd39j6v/TQAAwN635QNGknT3W6vqQUl+Pcmjk9w4yWeTPDPJy5Y8cVipr49U1X2TnJzkYUluntlrUc9L8oLlXrVaw/h3mY6HJDlxN6Wcl0TAAABgS6k5/vZmk1pYWOgdO3bs7TIAANjGqurM7l5Yqd12mIMBAABsEgIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADDMtgkYVfWAqvqbqvpyVV1RVf9UVb9QVfuuoa97VNUbq+pLVXVlVX26qk6uqv03YvyaeU9V9fSz37w1AwDAZrAtAkZVnZDktCRHJ3lLkpcnuWGSFyc5Zc6+7p/kY0kemeS9SV6a5PIkz03ynqq60QaM/9Qkxya5cp5aAQBgs9nyAaOqDkjyx0muSXJMd/90d/9yknsn+fskj6mqx66yr32TvCbJTZI8prt/vLufleT+Sf5XkqOSPGPk+FV1WJIXJvkfSf599d8cAAA2ny0fMJI8JslBSU7p7h27Tnb3lUmeM338uVX29aAkd09yWne/bVFfO5P8yvTxSVVVI8afXoV6fZJzk5y4yhoBAGDT2g4B48HT8Z3LXDstydeSPGC5V5vm6au7z01ydpI7J7nroPGfk+Q+SR7X3V9fRX0AALCpbYeAcdh0PHvphe6+Osm/Jtkv1w4Fc/c1+cx0PHS941fVfZP8epIXLH7ysVpV9cSq2lFVOy666KJ5bwcAgA2xHQLGgdPxst1c33X+FhvU19z3TKtRvT7JvyR53irq+hbd/aruXujuhYMOOmgtXQAAwHCbImBU1XmLlmhdzc+f7+2a1+l3M3ui8ZPd/Y29XQwAAIyyWfZbOCfzLdF6waJ/73pCcOByDRedv3QV/a6lr7nuqaoHJXlKkpO6++OrqAkAALaMTREwuvu4ddz+6SQLmc2LOHPxhWmVprskuTqzlZpW01dy7TkWi91tOi6ebzHv+PdJUklOrqqTdzPON6aFqu7T3f+4iroBAGBT2BSvSK3T+6fj8ctcOzqzPS0+vMpVmnbbV1XdNbMQ8blcO6zMO/5ZSf50Nz9fndq8evp8ySpqBgCATaO6e2/XsC7TRnfnJDkgyVG7VmSqqhtn9sf/kUl+rLtPWXTPTZLcKcnXuvvzi87vm+SfM9sL44Rde2FU1T5J3pDZnhfP7u4XrGf8PXyX8zJbBvcG0wpUq7KwsNA7dsy9EBUAAKxaVZ3Z3QsrtdsUr0itR3dfXlVPSPKmJH9bVack+XKSR2S2hOybMgsHi90vyQeSfDDJMYv6uqaqHp9ZMHhTVb0pyeeTHJfZa1CnJ3nxgPEBAGBb2g6vSKW735rZLtynJXl0kqcl+UaSZyZ5bM/xmKa7P5LkvklOTfKwJM/IbKL285I8dLlXrUaODwAAW9mWf0UKr0gBALDxVvuK1LZ4ggEAAGwOAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGMD/be/egyWp6gOOf3/IIwIrTxcogV3DM1WgSAgaSMFukSwEoxAEJYBxQaqiiVqJGpP4CiQQqyJJQSUYjWYDIomKBKgKz4qwPFUCilhWVhHYReQt75cI/PLHORfHYWbvvTNn7uy99/up6uq9p3tOn57fznT/ps/pliRJasYEQ5IkSVIzJhiSJEmSmjHBkCRJktSMCYYkSZKkZkwwJEmSJDVjgiFJkiSpGRMMSZIkSc2YYEiSJElqxgRDkiRJUjMmGJIkSZKaMcGQJEmS1IwJhiRJkqRmTDAkSZIkNWOCIUmSJKkZEwxJkiRJzZhgSJIkSWrGBEOSJElSMyYYkiRJkpoxwZAkSZLUjAmGJEmSpGZMMCRJkiQ1Y4IhSZIkqRkTDEmSJEnNmGBIkiRJasYEQ5IkSVIzJhiSJEmSmjHBkCRJktSMCYYkSZKkZkwwJEmSJDVjgiFJkiSpmcjMcbdBQ4qIB4E1Y27G1sBDY26DRsf4zn3GeO4zxnOb8Z371oUYL8rMV0+2kgmGmoiImzJzn3G3Q6NhfOc+Yzz3GeO5zfjOfbMpxnaRkiRJktSMCYYkSZKkZkww1Mq/jrsBGinjO/cZ47nPGM9txnfumzUxdgyGJEmSpGa8giFJkiSpGRMMSZIkSc2YYEiSJElqxgRDLxMR20fEioi4JyJ+FhGrI+L0iNhimvVsWV+3utZzT613+1G1XVMzbIwjYpOIODYi/iMiVkXEUxHxRETcFBEfiogNR70P6q/VZ7irzgMi4oWIyIg4pWV7NX0tYxwRe9fP8t21rvsj4uqI+MNRtF1T0/BY/FsRcVF9/bMRcVdEXBIRh4yq7Vq7iDgyIv4pIq6NiMfr9+qXBqyr+fd9Cw7y1i+JiJ2AG4CFwEXAKmBfYCnwA2D/zPzpFOrZqtazK3Al8L/A7sBhwAPAb2bmHaPYB61dixjXA9OlwMPAVcCPgC2AtwLb1voPysxnR7Qb6qPVZ7irzgXArZSnyG4KnJqZH2/Zbk1dyxhHxPuAM4BHgIuBnwBbAnsAd2fm0c13QJNqeCx+L/AZ4CngAuBuYHvgCGBj4OOZeeoo9kH9RcQtwOuBJykx2R04NzOPm2Y9zb/vm8lMJ6eXJuByIIH3d5X/Yy3/7BTr+Vxd/x+6yj9Qyy8b977O16lFjIG9gGOBDbvKFwA313o+NO59nY9Tq89w12tXUJLJj9Y6Thn3fs7nqeH39DLgxVrfgh7LNxj3vs7XqdH39AbAo8AzwG5dy34NeBZ4Gtho3Ps73yZKArALEMCSGtMvjeP/yagmr2DoJTUT/hGwGtgpM1/sWLYAuJfyYViYmU+tpZ5NKVcpXgS2y8wnOpatB9wBLKrb8CrGDGoV40m2cQxwLvDfmfmWoRutKRtFfCPiMOBC4J3A+sC/4xWMsWkZ44j4LrAzsGOO61dOvUzDY/E2wH3ArZn5+h7LbwX2BLY2/uMTEUsoPQGmdQVjJo7nw3AMhjotrfMrOv+jAtQk4XrKJdU3TVLPm4BXAtd3Jhe1nolfyzq3p5nTKsZr8/M6f36IOjSYpvGNiIXA54ELM3Og/sFqrkmMI2IP4HXAFcDDEbE0Ij5cx1AdVH8M0ni0+hw/ADwI7BoRu3QuiIhdKb+g32JyMWvNxPF8YH6BqNNudf7DPstvq/NdZ6getTcTsTmhzi8bog4NpnV8P085TrxnmEapqVYx/o06fwBYSRkr92ngNOB/gFsiYufBm6khNIlxli4qf0L5DN8cEWdHxKci4ouUrqzfB45q0F6Nxzp9rrX+ODaqddZmdf5Yn+UT5ZvPUD1qb6SxqQNGDwFuofTb18xqFt+IOIEyaP8dmXl/g7apjVYxXljn76YM7H4zcB2wDfBJ4Djg4ojYMzOfG7y5GkCzz3FmnhcR9wD/CXTeFex+SndHuynPXuv0uZZXMCQ1ERFHAKdT+vy+LTN/PslLtI6KiMWUWJ6XmV8db2s0IhPH/1cAR2fmJZn5eGbeRjkRvYnyy+fbxtVADS8ijqNckbqWMrB74zr/OvDPwJfH1zrNZSYY6jSR7W7WZ/lE+aMzVI/aG0lsIuJwyoHqAWCJg/fHplV8V1DuPPPHLRqlplrFeGL5fZn5jc4FtWvNRfXPfafdQg2rSYzrOIsVlK5Q78zMVZn5TGauoty04WbgqDrIWLPPOn2uZYKhTj+o83799SYGifXr79e6HrXXPDYRcRRwHuWS+4GZ+YNJXqLRaRXfvSldaB6sD4DKiEhKlwqAj9WyC4drrgbQ+nu638nHI3X+yim2S+20ivEyyq1qr+4xCPhF4Jr6568P0kiN3Tp9ruUYDHW6qs6XRcR6PW55tj/lntnfnKSeb1J+/dw/Ihb0uE3tsq7taea0ivHEa44Fzqb04V7qlYuxaxXfL1K6UnTbBTiAMsbmZuA7Q7dY09Xye/opYHFEbNLjNpZ71PmdDdqs6WkV443q/NV9lk+UO8Zmdmp6PG/NKxh6SWbeTrll4WLKnSc6nQxsApzTeSCKiN0jYveuep4Ezqnrn9RVz/tq/Zd7MjrzWsW4lr+LciJ6F3CA8Ry/hp/hD2Tmid0Tv7iCcXEtO3NkO6OeGsb4aeDfgF8BTomI6Fh/T2A55VbTX2u/F1qbht/T19b5kRHxus4FEbEXcCTlYWxXtmu9WouIDWp8d+osH+T/yUzyQXv6JT0eO/9/wBsp91v+IbBf5z2za7cJMjO66tmq1rMr5cvrRsrAssMo/fT3qx8OzbAWMY6IpZSBg+tR+vj+uMemHs3M00e0G+qj1We4T93L8UF7Y9fwe/pVwNXAXsC3KPfN3wY4gtI16k8z84xR749ermGMVwDHU65SXACsoZyQHg5sCJyemX824t1Rlzpu8fD657bAwZQ7ek0khQ9l5ofruospVxLXZObirnqm9f9kRrV6JLjT3JmAHSgnEfdSvpTWUO4os0WPdZM6JrDHsi2BM+rrn6v1rQC2H/c+zvdp2BhTft3MSabV497P+Tq1+gz3WHci7qeMex/n+9Twe3pT4FTKycjPKGMyrgCWjXsf5/vUIsaUJzkvpzzr5BHKVamHKXeROnrc+zhfJ0rvjikdPykJYd9j6nT+n8zk5BUMSZIkSc04BkOSJElSMyYYkiRJkpoxwZAkSZLUjAmGJEmSpGZMMCRJkiQ1Y4IhSZIkqRkTDEmSJEnNmGBIkuaViDgrIrI+IXeU21kdEatHuQ1JWheZYEiSNICIWBkRPq1WkrqsP+4GSJI0Rx007gZI0jiYYEiSNAKZefu42yBJ42AXKUnSlETE4jp24ayI2D0iLoyIhyPiqYi4LiKW9XjNRhHxlxHxvYh4OiIej4hrI+Ltjeo/qb5mydrqm+L+LY+I8yPijoh4prb1+og4rle9wIH17+yYVnas13MMxhDvyeKI+HJEPBQRz0bETRHxe1PZN0maSV7BkCRN12uBbwDfAz4HbAe8A7g0Io7JzK8ARMSGwOWUE/FVwJnAxsCRwFciYq/M/Oig9Y/AvwDfB64B7gW2Ag4FzomI3TLzE3W9R4GTgeXAovrvCavXtoEh3pNFwI3AHcA5wJaU9+SiiPjtzLxqujsrSaMSmY5PkyRNrt516c7652mZ+ecdy/ahJAVPAosy8/GI+Cvg74BLgbdm5vN13YWUk+VFwP6ZecMg9dfyk4C/BpZm5so+7T07M5d3lJ8FvAt4bWau7ijfqbtbU00ILgUOABZn5k86lq0EDszM6PN+rQbIzMUdZcO8Jydl5skddR0MXAZcmpmH9mqDJI2DXaQkSdP1GPA3nQWZeRNwLrA58Pu1+AQggQ9OnEjXdR8A/rb+eeIQ9TfVa8xEZj5HucqwPm0GbQ/6nqwBTulq2+XAXcC+DdolSc2YYEiSpuvbmflEj/KVdf6GiFgA7Azck5mreqx75cS6g9Q/jbZOWUTsGBFnRsSqOjYi61iL8+sqrxmy/mHek1sy84Ue5T8GthimXZLUmmMwJEnTdX+f8vvqfLM6QRnL0MtE+eYD1t9URPwqpYvSFsC1wBWUKykvAIspXao2GnIzw7wnj/Z5zfP4Y6GkdYwJhiRpurbpU75tnT9Wp86ybtt1rDtI/RNerPNex7NeJ+r9fJAyqPv4zDyrc0FE/AElwRjWMO+JJM0a/uohSZquvWt3n25L6vw7tYvT7cBrImKXHusurfNvD1J/R9kjdb5Dj/X36VHWz851fn6PZQf2ec0LABHxiqlsYMj3RJJmDRMMSdJ0bQZ8srOg3uXpWMqv7xfU4hVAAJ/uPAmPiK2BT3SsM2j9ULo1ARwfEet3rL9Ddx2TWF3nS7q2ezC9B10D/LTOd5zGdgZ9TyRp1rCLlCRpuq4BToyINwLX84vnVKwH/NHELWSB04DfBQ4DvhsRl1Ce+XAUsBD4+8y8boj6ycxvRcQ1lNvI3hgRV1K6WL2F8ryJXlc2evkMcDxwXkR8DbgH2AM4BPhq3X63r9d9+a+6b88AazLznLVsZ9D3RJJmDa9gSJKm605gP0r3pPcAb6d06zm08yF49RavvwN8rBa9nzKW4TbgmMz8i2Hq73AY8AVg+7qNNwAfAfrV/zKZeSuli9INwJuB9wKvAo4APtvnZV8APkW54vIRym1m3z3JdgZ9TyRp1vBBe5KkKen34LrZUr8kaWZ4BUOSJElSMyYYkiRJkpoxwZAkSZLUjGMwJEmSJDXjFQxJkiRJzZhgSJIkSWrGBEOSJElSMyYYkiRJkpoxwZAkSZLUzP8D9B5EIX8QxugAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'coef_reg_den'] coef_reg_den\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'dropout_rate'] dropout_rate\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['train', 'epochs'] epochs\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['train', 'batch_size'] batch_size\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmap = plt.get_cmap('rainbow')\n", + "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", + "color_ids = np.argsort(data.loc[:, evolve_metric].values)\n", + "\n", + "for param_path in evolution.paths_to_evolving_params:\n", + " param_name = param_path[-1]\n", + " print(param_path, param_name)\n", + " \n", + " plt.figure(figsize=(12,12))\n", + " for i in range(data.shape[0]):\n", + " param_dict = evolution.get_value_from_config(evolution.basic_config, param_path)\n", + " if param_dict.get(\"evolve_range\") and param_dict.get(\"discrete\"):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " evolution.get_value_from_config(params_dictionaries[i], param_path),\n", + "# + (np.random.random() - 0.5) / 2,\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + " elif param_dict.get(\"evolve_range\"):\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " evolution.get_value_from_config(params_dictionaries[i], param_path),\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + " elif param_dict.get(\"evolve_choice\"):\n", + " values = np.array(param_dict.get(\"values\"))\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " np.where(values == evolution.get_value_from_config(\n", + " params_dictionaries[i], param_path))[0][0],\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + " plt.yticks(np.arange(len(values)), values)\n", + " elif param_dict.get(\"evolve_bool\"):\n", + " values = np.array([False, True])\n", + " plt.scatter(i // POPULATION_SIZE, \n", + " np.where(values == evolution.get_value_from_config(\n", + " params_dictionaries[i], param_path))[0][0],\n", + " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", + " plt.yticks(np.arange(len(values)), [\"False\", \"True\"])\n", + "\n", + " plt.ylabel(param_name, fontsize=20)\n", + " plt.xlabel(\"population\", fontsize=20)\n", + " plt.title(TITLE, fontsize=20)\n", + " plt.xticks(fontsize=20)\n", + " plt.yticks(fontsize=20)\n", + " plt.savefig(path_to_pics.joinpath(param_name + \".png\"))\n", + " plt.show()\n", + " " + ] + }, { "cell_type": "code", "execution_count": null, From 41e61d77eaea2f3d6d0f0a10c141f3702f8090f5 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 16:13:07 +0300 Subject: [PATCH 511/616] feat: add mutation of bool --- deeppavlov/models/evolution/evolution_param_generator.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 07acebf027..7cf7f66091 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -480,6 +480,8 @@ def mutation_of_param(self, param_path, param_value): new_mutated_value = val elif basic_value.get("evolve_choice"): new_mutated_value = self.sample_params(**{param_name: basic_value})[param_name] + elif basic_value.get("evolve_bool"): + new_mutated_value = self.sample_params(**{param_name: basic_value})[param_name] else: new_mutated_value = param_value else: From 3c081dd754bf0ba404f5cf021e1055f64a32e896 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 16:14:17 +0300 Subject: [PATCH 512/616] fix: clear all outputs --- .../models/evolution/Results_analysis.ipynb | 563 +----------------- 1 file changed, 16 insertions(+), 547 deletions(-) diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb index c0fa6812f5..93fbde75f0 100644 --- a/deeppavlov/models/evolution/Results_analysis.ipynb +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -2,17 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2018-06-25 14:31:29.12 DEBUG in 'matplotlib.backends'['__init__'] at line 90: backend module://ipykernel.pylab.backend_inline version unknown\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -35,216 +27,11 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Considered basic config:\n", - "{\n", - " \"dataset_reader\": {\n", - " \"name\": \"basic_classification_reader\",\n", - " \"x\": \"text\",\n", - " \"y\": \"intents\",\n", - " \"data_path\": \"snips\"\n", - " },\n", - " \"dataset_iterator\": {\n", - " \"name\": \"basic_classification_iterator\",\n", - " \"seed\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"field_to_split\": \"train\",\n", - " \"split_fields\": [\n", - " \"train\",\n", - " \"valid\"\n", - " ],\n", - " \"split_proportions\": [\n", - " 0.9,\n", - " 0.1\n", - " ]\n", - " },\n", - " \"chainer\": {\n", - " \"in\": [\n", - " \"x\"\n", - " ],\n", - " \"in_y\": [\n", - " \"y\"\n", - " ],\n", - " \"pipe\": [\n", - " {\n", - " \"id\": \"classes_vocab\",\n", - " \"name\": \"default_vocab\",\n", - " \"fit_on\": [\n", - " \"y\"\n", - " ],\n", - " \"level\": \"token\",\n", - " \"save_path\": \"vocabs/snips_classes.dict\",\n", - " \"load_path\": \"vocabs/snips_classes.dict\"\n", - " },\n", - " {\n", - " \"in\": [\n", - " \"x\"\n", - " ],\n", - " \"out\": [\n", - " \"x_lower\"\n", - " ],\n", - " \"name\": \"str_lower\"\n", - " },\n", - " {\n", - " \"id\": \"my_embedder\",\n", - " \"name\": \"fasttext\",\n", - " \"save_path\": \"embeddings/dstc2_fastText_model.bin\",\n", - " \"load_path\": \"embeddings/dstc2_fastText_model.bin\",\n", - " \"dim\": 100\n", - " },\n", - " {\n", - " \"id\": \"my_tokenizer\",\n", - " \"name\": \"nltk_tokenizer\",\n", - " \"tokenizer\": \"wordpunct_tokenize\"\n", - " },\n", - " {\n", - " \"in\": [\n", - " \"x_lower\"\n", - " ],\n", - " \"in_y\": [\n", - " \"y\"\n", - " ],\n", - " \"out\": [\n", - " \"y_labels\",\n", - " \"y_probas_dict\"\n", - " ],\n", - " \"main\": true,\n", - " \"name\": \"intent_model\",\n", - " \"save_path\": \"evolution/classification/intents_snips\",\n", - " \"load_path\": \"evolution/classification/intents_snips\",\n", - " \"classes\": \"#classes_vocab.keys()\",\n", - " \"kernel_sizes_cnn\": [\n", - " 1,\n", - " 2,\n", - " 3\n", - " ],\n", - " \"filters_cnn\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 100\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"confident_threshold\": {\n", - " \"evolve_choice\": true,\n", - " \"values\": [\n", - " 0.5,\n", - " 1\n", - " ]\n", - " },\n", - " \"optimizer\": \"Adam\",\n", - " \"lear_rate\": {\n", - " \"evolve_range\": [\n", - " 0.0001,\n", - " 0.1\n", - " ],\n", - " \"scale\": \"log\"\n", - " },\n", - " \"lear_rate_decay\": {\n", - " \"evolve_range\": [\n", - " 0.0001,\n", - " 0.1\n", - " ],\n", - " \"scale\": \"log\"\n", - " },\n", - " \"loss\": \"binary_crossentropy\",\n", - " \"text_size\": 15,\n", - " \"coef_reg_cnn\": {\n", - " \"evolve_range\": [\n", - " 1e-06,\n", - " 0.001\n", - " ]\n", - " },\n", - " \"coef_reg_den\": {\n", - " \"evolve_range\": [\n", - " 1e-06,\n", - " 0.001\n", - " ]\n", - " },\n", - " \"dropout_rate\": {\n", - " \"evolve_range\": [\n", - " 0.1,\n", - " 0.9\n", - " ]\n", - " },\n", - " \"dense_size\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 100\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"model_name\": \"cnn_model\",\n", - " \"embedder\": \"#my_embedder\",\n", - " \"tokenizer\": \"#my_tokenizer\"\n", - " }\n", - " ],\n", - " \"out\": [\n", - " \"y_labels\",\n", - " \"y_probas_dict\"\n", - " ]\n", - " },\n", - " \"train\": {\n", - " \"epochs\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"batch_size\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"metrics\": [\n", - " \"classification_accuracy\",\n", - " \"classification_f1\",\n", - " \"classification_roc_auc\"\n", - " ],\n", - " \"validation_patience\": 5,\n", - " \"val_every_n_epochs\": 1,\n", - " \"log_every_n_epochs\": 1,\n", - " \"validate_best\": true,\n", - " \"test_best\": false\n", - " },\n", - " \"metadata\": {\n", - " \"labels\": {\n", - " \"telegram_utils\": \"IntentModel\",\n", - " \"server_utils\": \"KerasIntentModel\"\n", - " },\n", - " \"download\": [\n", - " \"http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz\",\n", - " \"http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz\",\n", - " {\n", - " \"url\": \"http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv\",\n", - " \"subdir\": \"snips\"\n", - " },\n", - " {\n", - " \"url\": \"http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin\",\n", - " \"subdir\": \"embeddings\"\n", - " }\n", - " ]\n", - " }\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "CONFIG_FILE = \"../../configs/evolution/evolve_intents_snips.json\"\n", "KEY_MAIN_MODEL = \"main\"\n", @@ -259,25 +46,9 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2018-06-25 14:52:07.93 INFO in 'deeppavlov.models.evolution.evolution_param_generator'['evolution_param_generator'] at line 55: Main model path in config: ['chainer', 'pipe', 4]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Title name for the considered evolution is `intents_snips`.\n", - "Number of populations: 2.\n" - ] - } - ], + "outputs": [], "source": [ "evolution = ParamsEvolution(population_size=POPULATION_SIZE,\n", " key_main_model=KEY_MAIN_MODEL,\n", @@ -303,50 +74,9 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Measure: classification_accuracy\n", - "valid:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t1 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n", - "\n", - "Measure: classification_f1\n", - "valid:\n", - "min for\t0 model on\t0 population\n", - "max for\t1 model on\t1 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n", - "\n", - "Measure: classification_roc_auc\n", - "valid:\n", - "min for\t1 model on\t0 population\n", - "max for\t0 model on\t0 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: 'argmin' is deprecated. Use 'idxmin' instead. The behavior of 'argmin' will be corrected to return the positional minimum in the future. Use 'series.values.argmin' to get the position of the minimum now.\n", - " if __name__ == '__main__':\n", - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", - " # Remove the CWD from sys.path while we load stuff.\n" - ] - } - ], + "outputs": [], "source": [ "MEASURES = evolution.get_value_from_config(\n", " evolution.basic_config, list(evolution.find_model_path(\n", @@ -373,42 +103,11 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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8OANm5uczc69RRSVJkiRpJUOucY+I9YAlmblojOKRJEmS1EK7GfeFwKcGnkTEWRHxzu6GJEmSJKlZu8Q9Kda3DziY4a91lyRJklSRdon7g8CrxiIQSZIkSYNrt4/75cB7ImIjiiQeYP9yr/ahZGZ+cJSxSZIkSSq1S9z/AdgU+DOK2fmkWCrTbrlMAibukiRJUkWGTNwz82FgdkSsBmwGzAdOBU7rfmiSJEmSBrSbcQcgM18A7o2Ie4D5mXlPd8OSJEmS1GhYifuAzHzFSN4kIj4AfCAz3zqS10uSJEmruuHcObUK04E9xui9JEmSpHFnrBJ3SZIkSaNg4i5JkiTVgIm7JEmSVAMm7pIkSVINmLhLkiRJNWDiLkmSJNWAibskSZJUAybukiRJUg2MVeJ+C/CtMXovSZIkadyZNBZvkpkXAxePxXtJkiRJ41HHiXtEbAAcCuwCTAUmtuiWmbn3KGOTJEmSVOoocY+IGcCVwMZADNE1RxGTJEmSpCadrnE/BdgE+DzwSmC1zJzQ4tFqFl6SJEnSCHW6VOYtwI8z89PdCEaSJElSa53OuAfw+24EIkmSJGlwnSbuNwHbdyMQSZIkSYPrNHE/Edg3IvbsQixExBYRcVZELIiIpRExPyJOjYipIxjr9RHx3Yi4vxzr4Yi4KiLe343YJUmSpG7qdI37lhT7sV8aEedRzMA/2apjZnZ0w6WI2Aa4juLi14uBeRRbTh4FzI6I3TLz8WGO9VHgNGAh8GPgAWADYAdgX7wZlCRJkmqm08R9DsVWjwG8r3w0b/0YZV2nyfEZFEn7kZn5lRcHi/gi8HHgJODwdoNExCzgy8DPgQMz8+mm9tU6jEuSJEnqucgc/pbrEfGB4fbNzHM6GHcb4C5gPrBNZq5oaFsXeJDiA8Emmbm4zVi/AV4FbDXcGfpWZs6cmXPnzh3pyyVJkqRhiYibMnNmu34dzbh3kox3aK+yvLQxaS/f8+mIuBaYBewKXDbYIBGxA7AjcBHwRETsBexM8ReAW4ArmseXJEmS6qDTpTLdMrBTzR2DtN9JkbhvxxCJO/CGsnyE4g6vuze1/zYiDsjMu0YYpyRJktQTI0rcI2Jt4ADgdcD6wCLgv4AL2y1lGcSUslw0SPtA/fptxtmkLD9IcUHqO4BfApsCxwLvBX4cEa/JzOebXxwRhwGHAWy11VbDDl6SJEnqto4T94jYFziHYpeWaGhK4EsRcUhm/qii+Do1sL3lRODdmXl9+fypchvIGcBM4F3Aec0vzswzgTOhWOPe/XAlSZKk4eloH/eIeD1wAcXM93eAQ4G3l+V3yvofRsTOHcYxMKM+ZZD2gfqWW082GGh/qCFpByCLq3AvLp/u0mF8kiRJUk91OuN+DMXM+lsy84amtjkRcTrF2vJPU8xqD9ftZbndIO3bluVga+CbxxkswV9YlmsNMy5JkiSpL3R659S3AD9okbQDkJm/An5Y9uvEFWU5KyJWiqncDnI34Fmg5fs2uAFYDEyPiMkt2ncoyz92GJ8kSZLUU50m7lOA+9r0uRdYr5NBM/Nu4FJgOvCRpuYTgMnAuY0XvkbEjIiY0TTOs8A3gTWBz0ZENPR/DXAwsIziw4UkSZJUG50ulVlA+/XhMylumNSpI4DrgC9HxN7AbcAbKfZ4v4NimU6j28oymur/mWIbyI8Bbyr3gN+UYhecNYGPlR8UJEmSpNrodMb9J8BbI+JTETGxsSEiJkTEJ4F9yn4dKZPpmcAcioT9k8A2wGnArsO9C2pmPkWxVOdkip1vPgr8OcW2kG/LzNM6jU2SJEnqtSg2Wxlm54hpwE3ANIolMddQzK5PA/6UYqnLQ8DMzBzJrHvfmDlzZs6dO7fXYUiSJGmci4ibMnNmu34dLZXJzIciYjfga8CfAVs3dfk5cHjdk3ZJkiSp33R8A6bMnA+8LSI2p7hz6hSKfdhvzswHqg1PkiRJEowgcR9QJukm6pIkSdIY6PTiVEmSJEk9MOSMe0ScRXGn1E9n5sPl8+HIzPzgqKOTJEmSBLRfKnMwReL+eeDh8vlwJGDiLkmSJFWkXeL+irJ8oOm5JEmSpDE0ZOKemfcM9VySJEnS2Ojo4tSIODYidm/T5y0RcezowpIkSZLUqNNdZY4H9mzTZ3fguJEEI0mSJKm1bmwHuRqwogvjSpIkSausbiTurwce68K4kiRJ0iqr7Z1TI+LypqqDI2LPFl0nAlsCWwPnjT40SZIkSQPaJu6svKY9genlo9kK4HHge8DHRxmXJEmSpAZtE/fMfHE5TUSsAI7PzBO7GpUkSZKklQxnxr3RIcDN3QhEkiRJ0uA6Stwz85xuBSJJkiRpcJ3OuL8oIrYANgfWaNWemVePdGxJkiRJK+s4cY+IWcCXgBltuk4cUUSSJEmSXqajfdwjYlfgR8D6wFeBAK4Gvg7MK5//J+DFq5IkSVKFOr0B0z8BS4A3ZOZRZd0VmXk4sAPwWWAf4IfVhShJkiSp08T9TcB/ZOaC5jGycCxwG3BCRfFJkiRJovPEfQpwb8Pz54HJTX2uBXYfTVCSJEmSVtZp4v4IMLXp+TZNfVYD1hpNUJIkSZJW1mnifgcrJ+o3AH8WEdsBRMQ04F3AndWEJ0mSJAk6T9x/BuwRERuUz0+jmF2/OSJ+TbGzzMbAqdWFKEmSJKnTxP1rFOvXXwDIzGuBg4A/Uuwq8yDw4cz8VpVBSpIkSau6jm7AlJlPAb9qqrsQuLDKoCRJkiStrNMZd0mSJEk90OmdU3eOiGMjYtNB2qeV7a+tJjxJkiRJ0PmM+yeBD1FsA9nKw8AHgU+MJihJkiRJKxvJnVOvyMxs1VjWXw7sNtrAJEmSJL2k08R9GnB/mz4LgM1GFo4kSZKkVjpN3J+l2Kd9KBsDS0cWjiRJkqRWOk3cbwH2i4h1WjVGxHrAfmU/SZIkSRXpNHE/k2JG/ecRsWNjQ0TsBFwKbFT2kyRJklSRTm/A9L2IeDvwfuDmiHgYeADYHNgUCOBbmXle5ZFKkiRJq7COb8CUmQcDhwO/p7hYdeey/B1wWNkuSZIkqUIdzbgPyMwzgTMjYm1gfeDJzHy20sgkSZIkvWhEifuAMlk3YZckSZK6rOOlMpIkSZLG3pAz7hHxByCBfTLzj+Xz4cjM3GbU0UmSJEkC2i+VmUCRuA/2fDAx4ogkSZIkvcyQiXtmTh/quSRJkqSxMeQa94j4YkTMani+VXl3VEmSJEljqN3FqR8Ddm14/seyTpIkSdIYape4PwOs3fDcteuSJElSD7S7OPUu4ICIuBB4sKxbPyK2ajdwZt472uAkSZIkFdol7l8Avg1c11B3VPkYSg5jbEmSJEnD1G5XmfMi4o/AO4DNgYOBW4Fbuh+aJEmSpAFtZ8Uz8wbgBoCIOBi4MDNP7EYwEbEFcCIwG9iQYnnORcAJmblwhGPuDlxBsZ7/pMz8TEXhSpIkSWOm0+Ush9Cl2faI2IZiSc4mwMXAPGAXimU5syNit8x8vMMx1wXOAZ4F1qk2YkmSJGnstNtVZiWZeU5m/qZLsZxBkbQfmZn7Z+anMvOtwJeA7YGTRjDmacAU4HPVhSlJkiSNvSFn3MtlJgA3ZuaShudtZebVw+1bzrbPAuYDpzc1HwccBrwvIj6ZmYuHOeZ+FH8heB9eKCtJkqSaa5fQXkmxQ8yrgTsang/HxA7i2KssL83MFY0Nmfl0RFxLkdjvClzWbrCI2AT4OnBRZn67XJsvSZIk1Va7xP1EikT9sabnVdu+LO8YpP1OisR9O4aRuFMk7ROAw0cfmiRJktR77baDPH6o5xWaUpaLBmkfqF+/3UARcSjwTuCvM/PhToKIiMMoluWw1VZt7zElSZIkjZmOLk7tdxExHTgV+EFmfr/T12fmmZk5MzNnbrzxxlWHJ0mSJI1YRxdtRsREYI3MfLap/q3AfhTbLp6ZmX/sMI6BGfUpg7QP1D/ZZpyzgOeAIzp8f0mSJKmvdTrjfgrwRES8mGBHxLuBnwN/D/wjcGNEbNnhuLeX5XaDtG9bloOtgR/weootJR+NiBx4AGeX7ceUdRd1GJ8kSZLUU51uk7g7cEVmNq5FP45iJvwoYBrFnumfAD7ewbhXlOWsiJjQuLNMeROl3Shm829oM863gLVb1G9bxn4LcBNwcwexSZIkST3XaeK+JcXdTQGIiFdS7AhzYmZ+u6zbHZhNB4l7Zt4dEZdS7BzzEeArDc0nAJOBrzXu4R4RM8rXzmsY58hW45fbQe4O/DgzPzPcuCRJkqR+0Wnivh7wVMPz3Si2h/xZQ93veGlf9k4cQfGh4MsRsTdwG/DGcqw7gGOa+t9WljGC95IkSZJqpdM17g8Cr2h4vg/FxaA3NdStAyzrNJDMvBuYCcyhSNg/CWwDnAbsmpmPdzqmJEmSNF50OuN+A/DOiPhzYAlwIHBZZr7Q0OcVwAMjCSYz7wMOGWbfYc+0Z+Ycig8EkiRJUi11OuN+cvmai4FLgNWBkwYaI2JN4C3Ar6oKUJIkSVKHM+6Z+duIeCPwgbLqe5n564YurwMuB86rKD5JkiRJdL5Uhsz8LXD0IG3XA3852qAkSZIkrazTpTItRcRqEfG6iNi+ivEkSZIkrayjxD0i/ioivh8RGzTUbUOxBeRc4PcRcUFEdDyTL0mSJGlwnc64HwrMyMwnGur+FXgVxd1PbwX2Y5g7w0iSJEkank4T9/8BvHgxakSsB+wLfD8z9wF2AeZh4i5JkiRVqtPEfWOKmzANeBPFBa7nA5T7uf+c4sZJkiRJkirSaeL+NDCl4fkeQAK/bKhbAqw7yrgkSZIkNej0ItI7gbdHxBoUCftfAbdm5mMNfbYGHqkoPkmSJEl0PuN+JvBKigT+NuAVwNlNfXam2GVGkiRJUkU6Stwz8xzgX4C1KZbMfBX4ykB7RLyZl3aYkSRJklSRkdw59dPApwdpngtMBRaPJihJkiRJK6v0RkmZ+TzwfJVjSpIkSep8jbskSZKkHug4cY+IzSLi9Ii4KyKei4jlLR7LuhGsJEmStKrqaKlMRGwO3AhsSrFzzBrAPcBSit1mJgG3AIuqDVOSJElatXU6434sMA2YnZk7lXVnZ+YMisT9EmAt4IDqQpQkSZLUaeL+NuBnmfmL5obMvB84iCJxP6GC2CRJkiSVOk3cp7HyzZWWUyTqAGTmM8DPgf1GH5okSZKkAZ0m7k8Bqzc8Xwhs3tRnEbDxaIKSJEmStLJOE/d7gC0bnv8GeGtErA0QEROAWcD91YQnSZIkCTpP3C8D9oqI1crn5wB/AlwXEV8ArgX+J/C96kKUJEmS1OmdU79JsTxmI+DBzPx2ROwM/D2wY9nnfOCk6kKUJEmS1FHinpl3Ap9vqvt4RJxMsR3k/Mx8uML4JEmSJNH5jHtLmfko8GgVY0mSJEl6uUoSd0mSJKkubl+6lF8sXsyCZcv4k0mT2GfyZLZfY41eh9XWkIl7RJw1wnEzMz84wtdKkiRJXXH70qXMWbSI9SKYNnEiTy1fzpxFizh4ypS+T97bzbgfPMJxEzBxH42PfQxuuaXXUUiSJI0r6yxbxuGZTIrgsR124JcnnwzLl/OLxYtrn7i/YkyikCRJksbA0syV7iYKsM6ECSxYtqwn8XRiyMQ9M+8Zq0DU5NRTex2BJEnSuPPTJ57gqeXLWW/ixBfrnlmxgj+Z1P+XfnZ0A6aIOCgiLo+IPxmkffOIuCwiDqgmPEmSJKk6+0yezFOZPLV8OSvK8qlM9pk8udehtdXpnVM/BKyfmQtaNWbmA8CUsp8kSZLUV7ZfYw0OnjKF9SZO5KFy5r0OF6ZC59tBvgb4UZs+vwb+YmThSJIkSd21/e23s/0FF8C998JWW8EBB8COO/Y6rLY6nXHfAHikTZ/HgY1GFo4Anlv6EI88cSUPPHIxjzxxJc8tfajXIUmSJI0Pt94Kp5wCCxfCFlsU5SmnFPV9rtPE/TFg2zZ9tgWeHFk4em7pQzy+6HqWL3+OSRPXY/ny53h80fUm75IkSVW44AKYOrV4TJjw0r8vuKDXkbXVaeJ+LfDOiJjRqjEiXg3sB1wz2sBWVU8vnsfEWJOJE9ciIpg4cS0mxpo8vXher0OTJEmqv3vvhSlTVq6bMqWo73OdJu6nUKxoJ3acAAAUvElEQVSL/2VEHBkR20XE5LI8iiJhn1j20wi8sGwREyasuVLdhAlr8sKyRT2KSJIkaRzZaitY1JRXLVpU1Pe5jhL3zPw1cASwHvAl4DbgqbL8Yln/4cz8VcVxrjJWmzSFFSuWrFS3YsUSVps0ZZBXSJIkadgOOKBY175wIaxY8dK/D+j/3cw7nXEnM78O7AScAdwE3F2WpwM7ZeY3Ko1wFbPu5BkszyUsX/4cmcny5c+xPJew7uSWq5MkSZLUiR13hKOPLta1339/UR59dC12lYnM7HUMfWnmzJk5d+7cnrz3c0sf4unF83hh2SJWmzSFdSfPYK01pvUkFkmSJHVXRNyUmTPb9ev/e7uugtZaY5qJuiRJklbS8VIZSZIkSWPPGfc+9NCtMO8CWHQvTNkKZhwA0/p/2ZUkSZK6yBn3PvPQrXD9KfDcQlhvi6K8/pSiXpIkSasuZ9z7zLwLYM2p8MCv4JnyZqkrlsE9V8P603samiRJ0rgy7bUw+9ReRzF8zrj3mUX3wppNW7ZPmATLlvYmHkmSJPUHZ9z7zJStiuUxr5r9Ut1zC2GtqbDn8T0LS5IkST3mjHufmXEALFlYJOu5oiiXLCzqJUmStOoyce8z03aENx1dzLA/dX9Rvulod5WRJEla1blUpg9N29FEXZIkSSvrqxn3iNgiIs6KiAURsTQi5kfEqRExdZivnxwR74mI70bEvIhYHBFPR8TciPhkRKze7WOQJEmSuqFvZtwjYhvgOmAT4GJgHrALcBQwOyJ2y8zH2wzzFuDbwBPAFcBFwFTgncApwAERsXdmLunOUUiSJEnd0TeJO3AGRdJ+ZGZ+ZaAyIr4IfBw4CTi8zRgPAe8FfpCZzzeMcTRwJfBm4CPAv1YauSRJktRlfbFUppxtnwXMB05vaj4OWAy8LyImDzVOZt6Smd9pTNrL+qd5KVnfs4qYJUmSpLHUF4k7sFdZXpqZKxobyqT7WmBtYNdRvMcLZblsFGNIkiRJPdEvifv2ZXnHIO13luV2o3iPQ8vyZ6MYQ5IkSeqJfkncp5TlokHaB+rXH8ngEfFRYDZwC3DWEP0OK3egmfvoo4+O5K0kSZKkruiXxL1rIuIA4FSKC1fflZkvDNY3M8/MzJmZOXPjjTcesxglSZKkdvolcR+YUZ8ySPtA/ZOdDBoR+wPnA48Ae2bmH0YWniRJktRb/ZK4316Wg61h37YsB1sD/zIRcRDwA+BhYI/MvL3NSyRJkqS+1S+J+xVlOSsiVoopItYFdgOeBW4YzmAR8R7gPGABRdJ+Z5uXSJIkSX2tLxL3zLwbuBSYTnGDpEYnAJOBczNz8UBlRMyIiBnNY0XEB4BvAfcCu7s8RpIkSeNBP9059QjgOuDLEbE3cBvwRoo93u8Ajmnqf1tZxkBFROxFsWvMBIpZ/EMioullPJmZp1YevSRJktRFfZO4Z+bdETETOJFi68Z9gQeB04ATMnPhMIbZmpf+inDoIH3uodhlRpIkSaqNvkncATLzPuCQYfZ92VR6Zs4B5lQblSRJktR7fbHGXZIkSdLQTNwlSZKkGjBxlyRJkmrAxF2SJEmqARN3SZIkqQZM3CVJkqQaMHGXJEmSasDEXZIkSaoBE3dJkiSpBkzcJUmSpBowcZckSZJqwMRdkiRJqgETd0mSJKkGTNwlSZKkGjBxlyRJkmrAxF2SJEmqARN3SZIkqQZM3CVJkqQaMHGXJEmSasDEXZIkSaoBE3dJkiSpBkzcJUmSpBowcZckSZJqwMRdkiRJqgETd0mSJKkGTNwlSZKkGjBxlyRJkmrAxF2SJEmqARN3SZIkqQZM3CVJkqQaMHGXJEmSasDEXZIkSaoBE3dJkiSpBkzcJUmSpBowcZckSZJqwMRdkiRJqgETd0mSJKkGTNwlSZKkGjBxlyRJkmrAxF2SJEmqARN3SZIkqQZM3CVJkqQaMHGXJEmSasDEXZIkSaoBE3dJkiSpBkzcJUmSpBowcZckSZJqwMRdkiRJqgETd0mSJKkGTNwlSZKkGuirxD0itoiIsyJiQUQsjYj5EXFqREztcJwNytfNL8dZUI67RbdilyRJkrppUq8DGBAR2wDXAZsAFwPzgF2Ao4DZEbFbZj4+jHE2LMfZDrgcOB+YARwCvCMi3pSZf+jOUUiSJEnd0U8z7mdQJO1HZub+mfmpzHwr8CVge+CkYY5zMkXS/sXM3LscZ3+KDwCblO8jSZIk1UpkZq9jGJhtvwuYD2yTmSsa2tYFHgQC2CQzFw8xzjrAI8AKYLPMfLqhbQLwB2Dr8j2GnHWfOXNmzp07d8THJEmSJA1HRNyUmTPb9euXGfe9yvLSxqQdoEy+rwXWBnZtM86uwFrAtY1JeznOCuCSpveTJEmSaqFfEvfty/KOQdrvLMvtxmgcSZIkqa/0y8WpU8py0SDtA/Xrd3OciDgMOKx8+kxE3N7m/bptI+CxHseg7vIcj3+e4/HN8zv+eY7Ht345v1sPp1O/JO59ITPPBM7sdRwDImLucNY7qb48x+Of53h88/yOf57j8a1u57dflsoMzIRPGaR9oP7JMRpHkiRJ6iv9krgPLEkZbO35tmU52Nr1qseRJEmS+kq/JO5XlOWsctvGF5XbQe4GPAvc0GacG4DngN3K1zWOMwGY1fR+/a5vlu2oazzH45/neHzz/I5/nuPxrVbnty8S98y8G7gUmA58pKn5BGAycG7jHu4RMSMiZjSN8wxwbtn/+KZxPlqOf0ld7pxarrnXOOY5Hv88x+Ob53f88xyPb3U7v31xAyZ48SZM11Hc3fRi4DbgjRR7rt8BvDkzH2/onwCZGU3jbFiOsx1wOXAj8GpgP4qbM725/KAgSZIk1UbfJO4AEbElcCIwG9iQ4o6pFwInZObCpr4tE/eybQPgOGB/YDPgceCnwLGZeX83j0GSJEnqhr5YKjMgM+/LzEMyc7PMXD0zt87MjzUn7WXfaJW0l21PZOZR5etXL8c7tNdJe0RsERFnRcSCiFgaEfMj4tSImNrhOBuUr5tfjrOgHHeLbsWu9kZ7fiNickS8JyK+GxHzImJxRDwdEXMj4pMRsXq3j0FDq+p7uGnM3SNieURkRHy2ynjVuSrPcUS8vvx+vr8c6+GIuCoi3t+N2NVehb+H/zQiLi5fvyQi7o2In0TE7G7FrvYi4sCI+EpEXBMRT5U/V789wrEq/3lfhb6acR/PWiwFmgfsQrEU6HZgt8alQEOM07wU6NfADF5aCvSmuqzhH0+qOL/lD/yfAk9QXEB9FzAVeCcwrRx/78xc0qXD0BCq+h5uGnNd4FaKG4CsA5yUmZ+pMm4NX5XnOCI+CpwGLAR+DDwAbADsANyfme+u/AA0pAp/D38YOANYTLEq4H5gC+AAYG3gM5l5UjeOQUOLiFuAnYBnKM7LDOA7mfneDsep/Od9ZTLTxxg8gEuABP6+qf6LZf3/G+Y4Xyv7/2tT/ZFl/c96fayr4qOK8wu8FngPsHpT/brATeU4n+z1sa6qj6q+h5teexbFB7VPl2N8ttfHuSo/Kvw5PQtYUY63bov21Xp9rKvio6Kf06tR3AvmOWD7prZXA0sodsFbo9fHuyo+KBLrbYEA9izP67d78X+lWw9n3MdA+cntLmA+sE1mrmhoW5diLX8Am2TDzjktxlmHYlZ9BbBZZj7d0DYB+APFLXO3SWfdx0xV57fNe/wN8B3gR5n5F6MOWh3pxjmOiP2Ai4D3UdzF+mycce+ZKs9xRPwGeBWwVfZqVk4rqfD38KbAQ8CtmblTi/ZbgdcAG3nueysi9qT463VHM+5j8Tt9NPpqjfs4tldZXtr4HwCgTL6vpfjz2q5txtkVWAu4tjFpL8cZmN1pfD+NjarO71BeKMtloxhDI1fpOY6ITYCvAxdl5ojWX6pylZzjiNgB2JFii+MnImKviDi6vE5l72i6V4nGTFXfw48AjwLbRcS2jQ0RsR3FbO8tJu21Nha/00fMHyBjY/uyHOyOrXeW5WB3fK16HFVrLM7LoWX5s1GMoZGr+hx/neLn7+GjCUqVquocv6EsHwGupLgW6QvAKcAvgFsi4lUjD1MjVMn5zWKZwkcovn9viohzIuJzEfEtiiWNvwMOqiBe9U5f51qTevGmq6ApZblokPaB+vXHaBxVq6vnpbzIbTZwC8WaaI29ys5xRBxKccHxX2fmwxXEpmpUdY43KcsPUlyQ+g7gl8CmwLHAe4EfR8RrMvP5kYerDlX2PZyZP4iIBcB5QOMOQQ9TLHlzqWq99XWu5Yy71Mci4gDgVIo1le/KzBfavER9LCKmU5zPH2Tm93sbjbpk4PfqRODdmfmTzHwqM++kSPLmUszUvatXAWp0IuK9FH89uYbigtS1y/Iy4KvA+b2LTuOdifvYGPh0NmWQ9oH6J8doHFWrK+clIvan+AXwCLCnFxz3VFXn+CyK3SiOqCIoVaqqczzQ/lBmXt/YUC6zuLh8ukvHEWo0Kjm/5Tr2syiWxLwvM+dl5nOZOY/iQvObgIPKCyNVT32da5m4j43by3Kw9VADF7gMtp6q6nFUrcrPS0QcBPyA4k+ve2Tm7W1eou6q6hy/nmIpxaPljUEyirtAn122H1PWXTS6cDUCVf+cHuyX+sANBdcaZlyqRlXndxbFlpBXtbhwcQVwdfl055EEqb7Q17mWa9zHxhVlOSsiJrTYWmg3in1fb2gzzg0Us3W7RcS6LbaDnNX0fhobVZ3fgde8BziHYn3sXs6094WqzvG3KP6s3mxbYHeK6xhuAm4edcTqVJU/pxcD0yNicovt4nYoyz9WELOGr6rzu0ZZbjxI+0C91y/UV6W/06vmjPsYyMy7KbYGm05xNXqjE4DJwLmNP+AjYkZEzGga5xng3LL/8U3jfLQc/xITvbFV1fkt6z9AkdzdC+zuuewPFX4PH5mZH2p+8NKM+4/LutO7djBqqcJz/CzwTWBN4LMREQ39XwMcTLGt6w+rPwoNpsKf09eU5YERsWNjQ0S8FjiQ4gY9l1cXvbohIlYrz/E2jfUj+b8ylrwB0xhpcfvc24A3UuwXegfw5sZ9X8s/n5OZ0TTOhuU421H8YLiR4qKY/SjWQr+5/E+nMVTF+Y2IvSgueJpAsYbyvhZv9WRmntqlw9AQqvoeHmTsg/EGTD1X4c/p9YCrKO6G/CuKfZ83BQ6gWCLzscw8rdvHo5VVeH7PAg6hmFW/ELiHIsnbH1gdODUzP97lw1EL5bVh+5dPpwFvo9jlZ+AD12OZeXTZdzrFX77uyczpTeN09H9lTFV1C1Yfw7qF7pYUv5wfpPiGv4dih4mpLfom5bVMLdo2AE4rX/98Od5ZwBa9PsZV+THa80sxE5dtHvN7fZyr8qOq7+EWfQfO/Wd7fYyr+qPCn9PrACdR/JJfSrHm/VJgVq+PcVV+VHF+Ke6aeTDFPv0LKf6C8gTFrjLv7vUxrsoPitUIw/odSvFha9Dfq538XxnLhzPukiRJUg24xl2SJEmqARN3SZIkqQZM3CVJkqQaMHGXJEmSasDEXZIkSaoBE3dJkiSpBkzcJUmSpBowcZckVSIi5kRElnck7Ob7zI+I+d18D0nqRybukqS+EhFXDtxuXpL0kkm9DkCSpA7t3esAJKkXTNwlSbWSmXf3OgZJ6gWXykhSj0XE9HJt+JyImBERF0XEExGxOCJ+GRGzWrxmjYj4VET8NiKejYinIuKaiPirisY/vnzNnkONN8zjOzgi/j0i/hARz5WxXhsR7201LrBH+TwbHlc29Gu5xn0UX5PpEXF+RDwWEUsiYm5E/Plwjk2SxpIz7pLUP14BXA/8FvgasBnw18BPI+JvMvN7ABGxOnAJRYI7DzgdWBs4EPheRLw2Mz890vG74N+A3wFXAw8CGwL7AudGxPaZ+c9lvyeBE4CDga3Lfw+YP9QbjOJrsjVwI/AH4FxgA4qvycURsU9mXtHpwUpS12SmDx8+fPjo4QOYDmT5+EJT20zgBWAhsF5Z909l358Akxr6bkKR4Cbw5pGOX9YfX/bfc4h45zTVzynrpzfVb9NijNWBy8r33ryp7cri19OgX6/5wPymutF8TY5rGuttA2P1+v+GDx8+fDQ+XCojSf1jEXBiY0VmzgW+A6wP/GVZfShFYvmJzFzW0PcR4P+UTz80ivErlS3WpGfm8xSz4pOo5mLTkX5N7gE+2xTbJcC9wC4VxCVJlTFxl6T+8V+Z+XSL+ivL8nURsS7wKmBBZs5r0ffygb4jGb+DWIctIraKiNMjYl659jzLtez/XnbZfJTjj+ZrcktmLm9Rfx8wdTRxSVLVXOMuSf3j4UHqHyrLKeUDirXirQzUrz/C8SsVEa+kWEM+FbgGuJRi5n85xXKVDwBrjPJtRvM1eXKQ1yzDyS1JfcbEXZL6x6aD1E8ry0Xlo7Gu2WYNfUcy/oAVZdnq90SrBHgwn6C4GPWQzJzT2BAR/4sicR+t0XxNJKk2nE2QpP7x+nLZR7M9y/LmcqnL3cDmEbFti757leV/jWT8hrqFZblli/4zW9QN5lVl+e8t2vYY5DXLASJi4nDeYJRfE0mqDRN3SeofU4BjGysiYibwHorZ4gvL6rOAAL7QmNxGxEbAPzf0Gen4UCxvATgkIiY19N+yeYw25pflnk3v+zZaXywK8HhZbtXB+4z0ayJJteFSGUnqH1cDH4qINwLX8tI+6xOAv8vMp8p+pwBvB/YDfhMRP6HYs/wgiu0P/29m/nIU45OZv4qIq4HdgRsj4nKKpTZ/QbFfequZ+FbOAA4BfhARPwQWADsAs4Hvl+/f7LLyWC4oj+054J7MPHeI9xnp10SSasMZd0nqH38E3kyxTOVw4K8olnfsmw03Ryq3Uvwz4Jiy6u8p1orfCfxNZv7jaMZvsB/wDWCL8j1eB/wDMNj4L5OZt1IsVbkOeAfwYWA94ADg/w3ysm8An6P4C8E/UGzn+ME27zPSr4kk1UZkZq9jkKRVWkRMp0iqz8nMg+s2viRpbDjjLkmSJNWAibskSZJUAybukiRJUg24xl2SJEmqAWfcJUmSpBowcZckSZJqwMRdkiRJqgETd0mSJKkGTNwlSZKkGjBxlyRJkmrg/wMIkZy8W455TgAAAABJRU5ErkJggg==\n", 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xcASwN/Bs4CrgKODwqrphgkMdBJxC8yHjWcAGwE3AT4FPVNWYu8rMBVvsBE99c7PWffllzQz84w9q6iVJkrT6SnPPogENlmwNnAO8uao+NbCBZ8HixYtryZIlsx2GJEmSVnFJzqqqxeP1G+jFqVV1OfAt4HWDHFeSJEla3U16O8iVuIb7t2+UJEmS5pTz77yTH956K8vuuYeHrbkmz5w/nx3XWWf8F86ygSbuSeYBz+D+GypJkiRJc8b5d97JsUuXsuFFF7HFtddy02abcewjH8kBCxfO+eS963aQu65knK1pdnN5HPDJKca1Wrv9zqu5+dbzuPue5ay15gI2mL+IddfZYrbDkiRJGno/vOQSNvzlL9kQYMMN2XD5cjjrLH64YgU7PupRsx3eSnWdcT+ZZqvHsYRmR5d/nGxAq7vb77ya65efzrw8iDXnbci9997O9ctPZ9MFTzV5lyRJmqJll13GFgDrrttUrLsu699+O8suuwxWscT9CEZP3FcANwBnVtWZU45qNXbzrecxLw/i9Hc8huv/d30Aqu4lmcda03FFgiRJ0mpkvat34oa11mZerWDNhctZ/6Bfc8uGG/KwZctmO7RxdUoFq+qwaYpDrbvvWc6a8zbsq12DFXXPrMQjSZK0Ktn4nnu5Zi0gazAPuGnttbmpiv2Wz/1LNJ3DnWPWWnMB9957O3/6novuq7v33tuZN29dNt9k99kLTJIkaVVwzh84/zOf4YdPeALLNt2Uh12/nP1+8Qt2fOlLZzuycZm4zzEbzF/E9ctPh3thjTUexIoVd3Bv3cFG8x8/26FJkiQNv512YseXvpQdv/51uOwy2GYbeOlLYae5f5v6zol7kvnAq4FnAVsCo+2bU1W13RRjWy2tu84WbLrgqQ/YVWaj+Y/3wlRJkqRB2WmnoUjU+3XdDnIj4KfAnwA3ARvS7Nm+NtBemssy4O4BxrjaWXedLUzUJUmS9ABrdOz/dpqk/SBg47bug8D6wNOAXwAXA3N7Lx1JkiRpyHRN3J8LnFJVn66q+7aFrMYZwLOBRcDbBhijJEmStNrrmrhvDZzV83wFPWvcq+r3wHeBF0w9NEmSJEkjuibut9Ek6yOWA/2Lsa+huWhVkiRJ0oB0Tdwvp5l1H/FbYNckveP8KXD1VAOTJEmSdL+uifuPgd2SpH3+ZWA74DtJXpPkq8DOwHcGGKMkSZK02uu6j/txNFs/bkUz+/6fwDOAfYG92j6n0uw+I0mSJGlAOiXuVfUL4FU9z+8B9kvyROCRwFLg51W1YvQRJEmSJE1G5zunjqaqzuKBu808QJLdgN2q6ohBvJ8kSZK0uum6xn2ydgcOnaH3kiRJklY5M5W4S5IkSZoCE3dJkiRpCJi4S5IkSUPAxF2SJEkaAibukiRJ0hAwcZckSZKGgIm7JEmSNARM3CVJkqQhMFOJ+3Lgshl6L0mSJGmVMyOJe1UdWVUPn4n3kiRJklZFnRL3JG9PcneSh43RvmWSu5L802DCkyRJkgTdZ9z/Eji5qpaN1lhVVwInAftONTBJkiRJ9+uauD8S+O04fX7b9pMkSZI0IF0T93WB28bpcwewweTCkSRJkjSaron7FcDO4/TZGbhycuFIkiRJGk3XxP17wK5J/ma0xiQvAHYDvjvVwCRJkiTdb82O/f8NeBHwhTZ5/x7N7PqWwJ8DzwX+APzrIIOUJEmSVnedEvequjLJs4Cv0uwcs09Pc4ClwPOr6oqBRShJkiSp84w7VbUkyQ40W0PuDGwE3AicAXyrqu4ebIiSJEmSOifuAG1y/vX2IUmSJGmaTSpxH5FkA5oZ9+VVddNgQpIkSZLUr+uuMiRZM8lbklxEs0RmKXBDkova+il9GJAkSZL0xzol2UnWptlJZjeggMuBq4CHAguBdwN7J9mrqu4abKiSJEnS6qvrjPsbgd2BbwOPqqqFVfXUqloI7Ah8C3h620+SJEnSgHRN3F8I/C+wb1Vd2NtQVRcD+wG/odnrXZIkSdKAdE3cHwl8t6pWjNbY1n8X2G6qgUmSJEm6X9fE/S5g/XH6zAfcy12SJEkaoK6J+znA/kk2G60xyYOB/YFfTTUwSZIkSffrmrh/BNgMODPJQUkekWTdJA9PciDws7b9I4MOVJIkSVqddUrcq+orwL8C2wJHAxcCtwAXAZ8EHg68r+3XWZKtkhyTZFmSO5MsTXJkko0nMdYTknwhyRXtWNck+XGSl04mNkmSJGk2db5ZUlW9Ncl/AwcBjwcWAMuBXwLHVNXpkwkkyXbAacDmwPHAecCTgdfR7A2/S1VdP8GxXgscBdxAs3XllcAmwKOBZwOfmUyMkiRJ0myZ1F1Oq+oM4IwBx/IxmqT9kKr68Ehlkg8Ab6C5udMrxxskyV7Ah4AfAPtX1c197WsNMmhJkiRpJnRaKpPkkiQfHXQQ7Wz7XsBSoH/8Q4FbgZckmT+B4d4H3A68sD9pB6gqd7yRJEnS0Ok6474ZzbKYQdujLU/o3yO+qm5OcipNYr8z8KOxBknyaGAn4JvAH5LsATwRKOBs4KSx9qCXJEmS5rKuiftvmJ6bK+3YlheM0X4hTeK+AytJ3IEnteXvgZOBXfvaf51kv6q6aJJxSpIkSbOi63aQHwL+MslOA45jQVuONZs/Ur/ROONs3pYHAQuB57Rj7wB8DngM8O0ka4/24iQHJ1mSZMm11147wdAlSZKk6dd1xv0K4IfAqUk+DvwcuJpmKcoDVNUpUw+vs5EPIvOAF/TscHNTuw3kImAx8Dzgi/0vrqqjaba5ZPHixX90TJIkSdJs6Zq4n0yTpAd4I6Mk7D3mdRh3ZEZ9wRjtI/U3jjPOSPvV/dtSVlUlOZ4mcX8yoyTukiRJ0lzVNXE/gpUn65N1flvuMEb79m051hr4/nHGSvBvaMt1JxiXJEmSNCd0Styr6rBpiuOkttwryRq9O78k2QDYBbiN8feOP4Nm68iFSeZX1a197Y9uy98NIGZJkiRpxnS9OHVSkrwsyYljtVfVxcAJNBeUvqav+XBgPvDZ3kQ8yaIki/rGuQ34FPAg4F1J0tP/McABwD3A16ZyPJIkSdJMm9SdUydhIbDbOH1eDZwGfCjJnsC5wFNo9ni/AHhbX/9z2zJ99e+g2Qby9cBT2z3gHwLsR5PQv779oCBJkiQNjRmZcZ+INpleDBxLk7C/iWbP+KOAnavq+gmOcxPwdOA9wCbAa4G/AH4KPKuqjhp48JIkSdI0m6kZ9wmpqsuBAyfYt3+mvbftFpoZ+v5ZekmSJGkozZkZd0mSJEljM3GXJEmShoCJuyRJkjQETNwlSZKkIWDiLkmSJA0BE3dJkiRpCMxU4n428JkZei9JkiRplTMj+7hX1fHA8TPxXpIkSdKqqHPinmQT4OXAk4GNgXmjdKuq2nOKsUmSJElqdUrckywCTgY2A8a8cylQU4hJkiRJUp+ua9zfD2wO/BvwCGCtqlpjlMdos/CSJEmSJqnrUpmnA9+uqrdORzCSJEmSRtd1xj3Ab6cjEEmSJElj65q4nwXsOB2BSJIkSRpb18T9CODZSXafhlgkSZIkjaHrGvetafZjPyHJF2lm4G8crWNVecMlSZIkaUC6Ju7H0mz1GOAl7aN/68e0dSbukiRJ0oB0TdwPnJYoJEmSJK1Up8S9qo6brkAkSZIkja3rxamSJEmSZkHXpTIAJFkP2A94PLARsBz4BfCNqrp1cOFJkiRJgkkk7kmeDRwHbEJzIeqIAj6Y5MCq+p8BxSdJkiSJjol7kicAXwfmAZ8HTgSuAh4KPAP4W+BrSXapqrMGHKskSZK02uo64/42mpn1p1fVGX1txyb5KHAy8FbgeVMPT5IkSRJ0vzj16cBXR0naAaiqnwFfa/tJkiRJGpCuifsC4PJx+lwGbDi5cCRJkiSNpmvivgx48jh9FtOse5ckSZI0IF0T9+8Az0jyliTzehuSrJHkTcAz236SJEmSBqTrxan/AuwLvBv4+yQ/oZld3wL4U2AhcDXwrgHGKEmSJK32OiXuVXV1kl2AjwN/Bmzb1+UHwCuryqUykiRJ0gB1vgFTVS0FnpVkS5o7py6guXPqL6vqysGGJ0mSJAkmkbiPaJN0E3VJkiRpBnS9OFWSJEnSLFjpjHuSY2julPrWqrqmfT4RVVUHTTk6SZIkScD4S2UOoEnc/w24pn0+EQWYuEuSJEkDMl7i/vC2vLLvuSRJkqQZtNLEvaouXdlzSZIkSTOj08WpSd6ZZNdx+jw9yTunFpYkSZKkXl13lTkM2H2cPrsCh04mGEmSJEmjm47tINcCVkzDuJIkSdJqazoS9ycA103DuJIkSdJqa9w7pyY5sa/qgCS7j9J1HrA1sC3wxamHJkmSJGnEuIk7D1zTXsDC9tFvBXA98GXgDVOMS5IkSVKPcRP3qrpvOU2SFcBhVXXEtEYlSZIk6QEmMuPe60Dgl9MRiCRJkqSxdUrcq+q46QpEkiRJ0ti6zrjfJ8lWwJbAOqO1V9Upkx1bkiRJ0gN1TtyT7AV8EFg0Ttd5k4pIkiRJ0h/ptI97kp2B/wE2Aj4CBDgF+ARwXvv8W4AXr0qSJEkD1PUGTP8M3AE8qape19adVFWvBB4NvAt4JvC1wYUoSZIkqWvi/lTgv6tqWf8Y1XgncC5w+GSCSbJVkmOSLEtyZ5KlSY5MsvFkxmvH3DXJvUkqybsmO44kSZI0m7om7guAy3qe3wXM7+tzKrBr10CSbAecRbPl5Jk06+gvAV4HnJ5k00mMuQFwHHBb19dKkiRJc0nXxP33wMZ9z7fr67MWsO4kYvkYsDlwSFXtW1Vvqapn0CTwOwLvnsSYR9F82HjvJF4rSZIkzRldE/cLeGCifgbwZ0l2AEiyBfA84MIug7az7XsBS4GP9jUfCtwKvCRJ/+z+ysbch2b2/hBg2TjdJUmSpDmta+L+PWC3JJu0z4+imV3/ZZKf0+wssxlwZMdx92jLE6pqRW9DVd1Ms/xmPWDniQyWZHOanW6+WVWf6xiLJEmSNOd0Tdw/TrN+/W6AqjoVeD7wO5pdZa4CXlVVn+k47o5tecEY7SMz+DtMcLxP0BzbKzvGIUmSJM1JnW7AVFU3AT/rq/sG8I0pxrGgLZeP0T5Sv9F4AyV5OfBc4G+q6pouQSQ5GDgYYJtttunyUkmSJGladZ1xn9OSLKRZpvPVqvpK19dX1dFVtbiqFm+22WaDDk+SJEmatK53Tn1ikncmecgY7Vu07Y/rGMfIjPqCMdpH6m8cZ5xjgNuBV3d8f0mSJGlO6zrj/ibgFTTbQI7mGuAg4I0dxz2/Lcdaw759W461Bn7EE2i2lLy2veFSJSng023729q6b3aMT5IkSZpVnda409w59aSqqtEaq6qSnEj3GzCd1JZ7JVmjd2eZ9iZKu9DcROmMccb5DM3uM/22b2M6m+YmT7/sGJ8kSZI0q7om7lsAV4zTZxnw0C6DVtXFSU6g2cv9NcCHe5oPp7k768er6taRyiSL2tee1zPOIaONn+QAmsT921X19i6xSZIkSXNB18T9Npp92ldmM+DOScTyauA04ENJ9gTOBZ5Cs8f7BcDb+vqf25aZxHtJkiRJQ6XrGvezgX2SrD9aY5INgX3afp1U1cXAYuBYmoT9TTR3aT0K2Lmqru86piRJkrSq6DrjfjTwReAHSf6+qs4ZaUjyWJobND247ddZVV0OHDjBvhOeaa+qY2k+EEiSJElDqesNmL6c5M+BlwK/THINcCWwJfAQmmUrn6mqLw48UkmSJGk11vkGTFV1APBK4Lc0F6s+sS1/AxzctkuSJEkaoK5LZYDmDqPA0UnWAzYCbqyq2wYamSRJkqT7TCpxH9Em6ybskiRJ0jTrvFRGkiRJ0sxb6Yx7kkuAAp5ZVb9rn09EVdV2U45OkiRJEjD+Upk1aBL3sZ6PxZsiSZIkSQO00sS9qhau7LkkSZKkmbHSNe5JPpBkr57n27R3R5UkSZI0g8a7OPX1wM49z3/X1kmSJEmaQeMl7rcA6/U8d+26JEmSNAvGuzj1ImC/JN8ArmrrNkqyzXgDV9VlUw1OkiRJUmO8xP19wOeA03rqXtc+VqYmMLYkSZKkCRpvV5kvJvkd8BxgS+AA4Bzg7OkPTZIkSdKIcWfFq+rqn+jUAAAXlElEQVQM4AyAJAcA36iqI6Y5LkmSJEk9ui5nORBn2yVJkqQZ1ylxr6rjpisQSZIkSWNbaeKeZNf2n2dW1R09z8dVVadMKTJJkiRJ9xlvxv1kmh1iHgVc0PN8IuZNOipJkiRJDzBe4n4ETaJ+Xd9zSZIkSTNovO0gD1vZc0mSJEkzY43ZDkCSJEnS+DrtKpNkHrBOVd3WV/8MYB/gNuDoqvrd4EKUJEmS1HXG/f3AH5IsGKlI8gLgB8A/AP8EnJlk68GFKEmSJKlr4r4rcFJVLe+pOxS4EXgp8H+BjYA3DiY8SZIkSdA9cd8auGjkSZJHADsCH66qz1XV+4HvAnsPLkRJkiRJXRP3DYGbep7vQrM95Pd66n4DbDXFuCRJkiT16Jq4XwU8vOf5M4HbgbN66tYH7pliXJIkSZJ6dNpVBjgDeG6SvwDuAPYHflRVd/f0eThw5YDikyRJkkT3Gff3tK85Hvg+sDbw7pHGJA8Cng78bFABSpIkSeo4415Vv07yFOBlbdWXq+rnPV0eD5wIfHFA8UmSJEmi+1IZqurXwJvHaDsd+KupBiVJkiTpgboulRlVkrWSPD7JjoMYT5IkSdIDdUrck/x1kq8k2aSnbjuaLSCXAL9N8vUknWfyJUmSJI2t64z7y4FFVfWHnrp/Bx4JnAScA+wDHDiY8CRJkiRB98T9T4D7LkZNsiHwbOArVfVM4MnAeZi4S5IkSQPVNXHfjOYmTCOeSnOB65cA2v3cfwBsN5DoJEmSJAHdE/ebgQU9z3cDCvhpT90dwAZTjEuSJElSj64XkV4I/HmSdWgS9r8Gzqmq63r6bAv8fkDxSZIkSaL7jPvRwCNoEvhzgYcDn+7r80SaXWYkSZIkDUinxL2qjgP+FViPZsnMR4APj7QneRr37zAjSZIkaUAmc+fUtwJvHaN5CbAxcOtUgpIkSZL0QAO9UVJV3QXcNcgxJUmSJHVf4y5JkiRpFnRO3JM8NMlHk1yU5PYk947yuGc6gpUkSZJWV52WyiTZEjgTeAjNzjHrAJcCd9LsNrMmcDawfLBhSpIkSau3rjPu7wS2APauqse2dZ+uqkU0ifv3gXWB/QYXoiRJkqSuifuzgO9V1Q/7G6rqCuD5NIn74QOITZIkSVKra+K+BQ+8udK9NIk6AFV1C/ADYJ+phyZJkiRpRNfE/SZg7Z7nNwBb9vVZDmw2laAkSZIkPVDXxP1SYOue578CnpFkPYAkawB7AVcMJjxJkiRJ0D1x/xGwR5K12ufHAQ8DTkvyPuBU4P8AX55MMEm2SnJMkmVJ7kyyNMmRSTae4OvnJ3lRki8kOS/JrUluTrIkyZuSrD3+KJIkSdLc0/XOqZ+iWR7zYOCqqvpckicC/wDs1Pb5EvDuroEk2Q44DdgcOB44D3gy8Dpg7yS7VNX14wzzdOBzwB+Ak4BvAhsDzwXeD+yXZM+quqNrfJIkSdJsSlVNfZBkM5rtIJdW1TWTHOP7NMtsDqmqD/fUfwB4A/DxqnrlOGM8jmbG/6tVdVdP/QbAycATgDdX1b+PF8/ixYtryZIlkzkUSZIkacKSnFVVi8fr1/nOqaOpqmur6mdTSNq3o0nalwIf7Ws+FLgVeEmS+ePEcXZVfb43aW/rbwZGkvXdJxOjJEmSNJsGkrgPwB5teUJVrehtaJPuU4H1gJ2n8B53t+U9UxhDkiRJmhUrXeOe5JhJjltVdVCH/ju25QVjtF9IMyO/A80FspPx8rb83iRfL0mSJM2a8S5OPWCS4xbQJXFf0JbLx2gfqd9oMsEkeS2wN3A2MOaHkSQHAwcDbLPNNpN5K0mSJGlajJe4P3xGophGSfYDjgSuBp5XVXeP1beqjgaOhubi1JmJUJIkSRrfShP3qrp0huIYmVFfMEb7SP2NXQZNsi/N9pS/B/aoqksmF54kSZI0uzpdnJrk+UlOTPKwMdq3TPKjdpa7i/Pbcocx2rdvy7HWwI8Wy/OBrwLXALtV1fnjvESSJEmas7ruKvMKYKOqWjZaY1VdSTM7/oqO457UlnsleUBM7R7suwC3AWdMZLAkLwK+CCyjSdov7BiPJEmSNKd0TdwfA4x3V6Kfc/9dVCekqi4GTgAWAq/paz4cmA98tqpuHalMsijJov6xkrwM+AxwGbCry2MkSZK0Khjv4tR+m9CsF1+Z64EHTyKWVwOnAR9KsidwLvAUmj3eLwDe1tf/3LbMSEWSPWh2jVmDZhb/wCR9L+PGqjpyEvFJkiRJs6Zr4n4d9683H8v2dLyIFJpZ9ySLgSNotm58NnAVcBRweFXdMIFhtuX+vyK8fIw+l9LsMiNJkiQNja6J+6nAc5Msqqrz+huTPArYB/jWZIKpqsuBAyfY94+m0qvqWODYyby3JEmSNJd1XeP+fppk/6dJDkmyQ5L5bfk64CfAvLafJEmSpAHpNONeVT9P8mrgo8AH20eve4FXVdXPBhSfJEmSJLovlaGqPpHkpzQXkz4F2IhmTfsZwH9U1bkre70kSZKk7jon7gBtcv4PA45FkiRJ0hi6rnGXJEmSNAtM3CVJkqQhYOIuSZIkDQETd0mSJGkImLhLkiRJQ8DEXZIkSRoCJu6SJEnSEDBxlyRJkoaAibskSZI0BEzcJUmSpCFg4i5JkiQNARN3SZIkaQiYuEuSJElDwMRdkiRJGgIm7pIkSdIQMHGXJEmShoCJuyRJkjQETNwlSZKkIWDiLkmSJA0BE3dJkiRpCJi4S5IkSUPAxF2SJEkaAibukiRJ0hAwcZckSZKGgIm7JEmSNARM3CVJkqQhYOIuSZIkDQETd0mSJGkImLhLkiRJQ8DEXZIkSRoCJu6SJEnSEDBxlyRJkoaAibskSZI0BEzcJUmSpCFg4i5JkiQNARN3SZIkaQiYuEuSJElDwMRdkiRJGgIm7pIkSdIQMHGXJEmShoCJuyRJkjQETNwlSZKkIWDiLkmSJA0BE3dJkiRpCJi4S5IkSUPAxF2SJEkaAnMqcU+yVZJjkixLcmeSpUmOTLJxx3E2aV+3tB1nWTvuVtMVuyRJkjSd1pztAEYk2Q44DdgcOB44D3gy8Dpg7yS7VNX1Exhn03acHYATgS8Bi4ADgeckeWpVXTI9RyFJkiRNj7k04/4xmqT9kKrat6reUlXPAD4I7Ai8e4LjvIcmaf9AVe3ZjrMvzQeAzdv3kSRJkoZKqmq2YxiZbb8IWApsV1Ureto2AK4CAmxeVbeuZJz1gd8DK4CHVtXNPW1rAJcA27bvsdJZ98WLF9eSJUsmfUySJEnSRCQ5q6oWj9dvrsy479GWJ/Qm7QBt8n0qsB6w8zjj7AysC5zam7S346wAvt/3fpIkSdJQmCuJ+45tecEY7Re25Q4zNI4kSZI0p8yVi1MXtOXyMdpH6jeaznGSHAwc3D69Jcn547zfdHswcN0sx6Dp5Tle9XmOV22e31Wf53jVNlfO77YT6TRXEvc5oaqOBo6e7ThGJFkykfVOGl6e41Wf53jV5vld9XmOV23Ddn7nylKZkZnwBWO0j9TfOEPjSJIkSXPKXEncR5akjLX2fPu2HGvt+qDHkSRJkuaUuZK4n9SWe7XbNt6n3Q5yF+A24IxxxjkDuB3YpX1d7zhrAHv1vd9cN2eW7WjaeI5XfZ7jVZvnd9XnOV61DdX5nROJe1VdDJwALARe09d8ODAf+GzvHu5JFiVZ1DfOLcBn2/6H9Y3z2nb87w/LnVPbNfdahXmOV32e41Wb53fV5zletQ3b+Z0TN2CC+27CdBrN3U2PB84FnkKz5/oFwNOq6vqe/gVQVekbZ9N2nB2AE4EzgUcB+9DcnOlp7QcFSZIkaWjMmcQdIMnWwBHA3sCmNHdM/QZweFXd0Nd31MS9bdsEOBTYF3gocD3wXeCdVXXFdB6DJEmSNB3mxFKZEVV1eVUdWFUPraq1q2rbqnp9f9Le9s1oSXvb9oeqel37+rXb8V4+20l7kq2SHJNkWZI7kyxNcmSSjTuOs0n7uqXtOMvacbeartg1vqme3yTzk7woyReSnJfk1iQ3J1mS5E1J1p7uY9DKDep7uG/MXZPcm6SSvGuQ8aq7QZ7jJE9ov5+vaMe6JsmPk7x0OmLX+Ab4e/hPkxzfvv6OJJcl+U6Svacrdo0vyf5JPpzkJ0luan+ufm6SYw385/0gzKkZ91XZKEuBzgOeTLMU6Hxgl96lQCsZp38p0M+BRdy/FOipw7KGf1UyiPPb/sD/LvAHmguoLwI2Bp4LbNGOv2dV3TFNh6GVGNT3cN+YGwDn0NwAZH3g3VX19kHGrYkb5DlO8lrgKOAG4NvAlcAmwKOBK6rqBQM/AK3UAH8Pvwr4GHArzaqAK4CtgP2A9YC3V9W7p+MYtHJJzgYeC9xCc14WAZ+vqhd3HGfgP+8Hpqp8zMAD+D5QwD/01X+grf/PCY7z8bb/v/fVH9LWf2+2j3V1fAzi/AKPA14ErN1XvwFwVjvOm2b7WFfXx6C+h/teewzNB7W3tmO8a7aPc3V+DPDn9F7Aina8DUZpX2u2j3V1fAzo5/RaNPeCuR3Ysa/tUcAdNLvgrTPbx7s6PmgS6+2BALu35/Vzs/F/ZboezrjPgPaT20XAUmC7qlrR07YBzVr+AJtXz845o4yzPs2s+grgoVV1c0/bGsAlNLfM3a6cdZ8xgzq/47zHC4HPA/9TVX855aDVyXSc4yT7AN8EXkJzF+tP44z7rBnkOU7yK+CRwDY1W7NyeoAB/h5+CHA1cE5VPXaU9nOAxwAP9tzPriS70/z1utOM+0z8Tp+KObXGfRW2R1ue0PsfAKBNvk+l+fPazuOMszOwLnBqb9LejjMyu9P7fpoZgzq/K3N3W94zhTE0eQM9x0k2Bz4BfLOqJrX+UgM3kHOc5NHATjRbHP8hyR5J3txep7Jn+u5VohkzqO/h3wPXAjsk2b63IckONLO9Z5u0D7WZ+J0+af4AmRk7tuVYd2y9sC3HuuProMfRYM3EeXl5W35vCmNo8gZ9jj9B8/P3lVMJSgM1qHP8pLb8PXAyzbVI7wPeD/wQODvJIycfpiZpIOe3mmUKr6H5/j0ryXFJ3pvkMzRLGn8DPH8A8Wr2zOlca83ZeNPV0IK2XD5G+0j9RjM0jgZrWs9Le5Hb3sDZNGuiNfMGdo6TvJzmguO/qaprBhCbBmNQ53jztjyI5oLU5wA/BR4CvBN4MfDtJI+pqrsmH646Gtj3cFV9Ncky4ItA7w5B19AseXOp6nCb07mWM+7SHJZkP+BImjWVz6uqu8d5ieawJAtpzudXq+orsxuNpsnI79V5wAuq6jtVdVNVXUiT5C2hmal73mwFqKlJ8mKav578hOaC1PXa8kfAR4AvzV50WtWZuM+MkU9nC8ZoH6m/cYbG0WBNy3lJsi/NL4DfA7t7wfGsGtQ5PoZmN4pXDyIoDdSgzvFI+9VVdXpvQ7vM4vj26ZM7R6ipGMj5bdexH0OzJOYlVXVeVd1eVefRXGh+FvD89sJIDac5nWuZuM+M89tyrPVQIxe4jLWeatDjaLAGfl6SPB/4Ks2fXnerqvPHeYmm16DO8RNollJc294YpNLcBfrTbfvb2rpvTi1cTcKgf06P9Ut95IaC604wLg3GoM7vXjRbQv54lAsXVwCntE+fOJkgNSfM6VzLNe4z46S23CvJGqNsLbQLzb6vZ4wzzhk0s3W7JNlglO0g9+p7P82MQZ3fkde8CDiOZn3sHs60zwmDOsefofmzer/tgV1prmM4C/jllCNWV4P8OX0rsDDJ/FG2i3t0W/5uADFr4gZ1ftdpy83GaB+p9/qF4TXQ3+mD5oz7DKiqi2m2BltIczV6r8OB+cBne3/AJ1mUZFHfOLcAn237H9Y3zmvb8b9vojezBnV+2/qX0SR3lwG7ei7nhgF+Dx9SVa/of3D/jPu327qPTtvBaFQDPMe3AZ8CHgS8K0l6+j8GOIBmW9evDf4oNJYB/pz+SVvun2Sn3oYkjwP2p7lBz4mDi17TIcla7Tnerrd+Mv9XZpI3YJoho9w+91zgKTT7hV4APK1339f2z+dUVfrG2bQdZweaHwxn0lwUsw/NWuintf/pNIMGcX6T7EFzwdMaNGsoLx/lrW6sqiOn6TC0EoP6Hh5j7APwBkyzboA/pzcEfkxzN+Sf0ez7/BBgP5olMq+vqqOm+3j0QAM8v8cAB9LMqn8DuJQmydsXWBs4sqreMM2Ho1G014bt2z7dAngWzS4/Ix+4rquqN7d9F9L85evSqlrYN06n/yszalC3YPUxoVvobk3zy/kqmm/4S2l2mNh4lL5Fey3TKG2bAEe1r7+rHe8YYKvZPsbV+THV80szE1fjPJbO9nGuzo9BfQ+P0nfk3L9rto9xdX8M8Of0+sC7aX7J30mz5v0EYK/ZPsbV+TGI80tz18wDaPbpv4HmLyh/oNlV5gWzfYyr84NmNcKEfofSfNga8/dql/8rM/lwxl2SJEkaAq5xlyRJkoaAibskSZI0BEzcJUmSpCFg4i5JkiQNARN3SZIkaQiYuEuSJElDwMRdkiRJGgIm7pKkgUhybJJq70g4ne+zNMnS6XwPSZqLTNwlSXNKkpNHbjcvSbrfmrMdgCRJHe052wFI0mwwcZckDZWquni2Y5Ck2eBSGUmaZUkWtmvDj02yKMk3k/whya1Jfppkr1Fes06StyT5dZLbktyU5CdJ/npA4x/Wvmb3lY03weM7IMl/Jbkkye1trKcmefFo4wK7tc+r53FyT79R17hP4WuyMMmXklyX5I4kS5L8xUSOTZJmkjPukjR3PBw4Hfg18HHgocDfAN9N8sKq+jJAkrWB79MkuOcBHwXWA/YHvpzkcVX11smOPw3+A/gNcApwFbAp8Gzgs0l2rKp3tP1uBA4HDgC2bf89YunK3mAKX5NtgTOBS4DPApvQfE2OT/LMqjqp68FK0rSpKh8+fPjwMYsPYCFQ7eN9fW2LgbuBG4AN27p/bvt+B1izp+/mNAluAU+b7Pht/WFt/91XEu+xffXHtvUL++q3G2WMtYEfte+9ZV/byc2vpzG/XkuBpX11U/maHNo31rNGxprt/xs+fPjw0ftwqYwkzR3LgSN6K6pqCfB5YCPgr9rql9Mklm+sqnt6+v4e+Jf26SumMP5A1Shr0qvqLppZ8TUZzMWmk/2aXAq8qy+27wOXAU8eQFySNDAm7pI0d/yiqm4epf7ktnx8kg2ARwLLquq8UfqeONJ3MuN3iHXCkmyT5KNJzmvXnle7lv2/2i5bTnH8qXxNzq6qe0epvxzYeCpxSdKgucZdkuaOa8aov7otF7QPaNaKj2akfqNJjj9QSR5Bs4Z8Y+AnwAk0M//30ixXeRmwzhTfZipfkxvHeM09OLklaY4xcZekueMhY9Rv0ZbL20dvXb+H9vSdzPgjVrTlaL8nRkuAx/JGmotRD6yqY3sbkvwtTeI+VVP5mkjS0HA2QZLmjie0yz767d6Wv2yXulwMbJlk+1H67tGWv5jM+D11N7Tl1qP0XzxK3Vge2Zb/NUrbbmO85l6AJPMm8gZT/JpI0tAwcZekuWMB8M7eiiSLgRfRzBZ/o60+Bgjwvt7kNsmDgXf09Jns+NAsbwE4MMmaPf237h9jHEvbcve+930Wo18sCnB9W27T4X0m+zWRpKHhUhlJmjtOAV6R5CnAqdy/z/oawN9X1U1tv/cDfw7sA/wqyXdo9ix/Ps32h/+vqn46hfGpqp8lOQXYFTgzyYk0S23+kma/9NFm4kfzMeBA4KtJvgYsAx4N7A18pX3/fj9qj+Xr7bHdDlxaVZ9dyftM9msiSUPDGXdJmjt+BzyNZpnKK4G/plne8ezquTlSu5XinwFva6v+gWat+IXAC6vqn6Yyfo99gE8CW7Xv8Xjg/wJjjf9HquocmqUqpwHPAV4FbAjsB/znGC/7JPBemr8Q/F+a7RwPGud9Jvs1kaShkaqa7RgkabWWZCFNUn1cVR0wbONLkmaGM+6SJEnSEDBxlyRJkoaAibskSZI0BFzjLkmSJA0BZ9wlSZKkIWDiLkmSJA0BE3dJkiRpCJi4S5IkSUPAxF2SJEkaAibukiRJ0hD4/3mlhKdRvVTwAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "path_to_pics = expand_path(Path(evolution.get_value_from_config(\n", " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).joinpath(\"pics\"))\n", @@ -472,20 +171,9 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 1, 2, 2])" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "params_dictionaries = []\n", "\n", @@ -506,42 +194,11 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", @@ -588,199 +245,11 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['dataset_iterator', 'seed'] seed\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'filters_cnn'] filters_cnn\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'lear_rate'] lear_rate\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'lear_rate_decay'] lear_rate_decay\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'coef_reg_cnn'] coef_reg_cnn\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'coef_reg_den'] coef_reg_den\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'dropout_rate'] dropout_rate\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['train', 'batch_size'] batch_size\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", From 0a6d1160e32427be5129ad737d285f3938655416 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 16:24:36 +0300 Subject: [PATCH 513/616] fix: config check evolve bool --- deeppavlov/configs/evolution/evolve_intents_snips.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/configs/evolution/evolve_intents_snips.json b/deeppavlov/configs/evolution/evolve_intents_snips.json index 0f7f35878a..c34a2a6e5a 100644 --- a/deeppavlov/configs/evolution/evolve_intents_snips.json +++ b/deeppavlov/configs/evolution/evolve_intents_snips.json @@ -144,7 +144,7 @@ "embedder": "#my_embedder", "tokenizer": "#my_tokenizer", "check_bool": { - "bool": true + "evolve_bool": true } } ], From c2490c671e4f0ad149deeb3d0d401c261484ac40 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 16:25:00 +0300 Subject: [PATCH 514/616] fix: clear all cells --- .../models/evolution/Results_analysis.ipynb | 601 +++++++++++++++++- 1 file changed, 581 insertions(+), 20 deletions(-) diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb index 93fbde75f0..8ed1df5314 100644 --- a/deeppavlov/models/evolution/Results_analysis.ipynb +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -2,9 +2,35 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", + " return f(*args, **kwds)\n", + "/home/dilyara/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n", + "Using TensorFlow backend.\n", + "[nltk_data] Downloading package punkt to /home/dilyara/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package stopwords to\n", + "[nltk_data] /home/dilyara/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n", + "[nltk_data] Downloading package perluniprops to\n", + "[nltk_data] /home/dilyara/nltk_data...\n", + "[nltk_data] Package perluniprops is already up-to-date!\n", + "[nltk_data] Downloading package nonbreaking_prefixes to\n", + "[nltk_data] /home/dilyara/nltk_data...\n", + "[nltk_data] Package nonbreaking_prefixes is already up-to-date!\n", + "2018-06-25 16:20:16.625 DEBUG in 'gensim.models.doc2vec'['doc2vec'] at line 73: Fast version of gensim.models.doc2vec is being used\n", + "2018-06-25 16:20:16.629 INFO in 'summa.preprocessing.cleaner'['textcleaner'] at line 20: 'pattern' package not found; tag filters are not available for English\n", + "2018-06-25 16:20:17.53 DEBUG in 'matplotlib.backends'['__init__'] at line 90: backend module://ipykernel.pylab.backend_inline version unknown\n" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -27,11 +53,219 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Considered basic config:\n", + "{\n", + " \"dataset_reader\": {\n", + " \"name\": \"basic_classification_reader\",\n", + " \"x\": \"text\",\n", + " \"y\": \"intents\",\n", + " \"data_path\": \"snips\"\n", + " },\n", + " \"dataset_iterator\": {\n", + " \"name\": \"basic_classification_iterator\",\n", + " \"seed\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"field_to_split\": \"train\",\n", + " \"split_fields\": [\n", + " \"train\",\n", + " \"valid\"\n", + " ],\n", + " \"split_proportions\": [\n", + " 0.9,\n", + " 0.1\n", + " ]\n", + " },\n", + " \"chainer\": {\n", + " \"in\": [\n", + " \"x\"\n", + " ],\n", + " \"in_y\": [\n", + " \"y\"\n", + " ],\n", + " \"pipe\": [\n", + " {\n", + " \"id\": \"classes_vocab\",\n", + " \"name\": \"default_vocab\",\n", + " \"fit_on\": [\n", + " \"y\"\n", + " ],\n", + " \"level\": \"token\",\n", + " \"save_path\": \"vocabs/snips_classes.dict\",\n", + " \"load_path\": \"vocabs/snips_classes.dict\"\n", + " },\n", + " {\n", + " \"in\": [\n", + " \"x\"\n", + " ],\n", + " \"out\": [\n", + " \"x_lower\"\n", + " ],\n", + " \"name\": \"str_lower\"\n", + " },\n", + " {\n", + " \"id\": \"my_embedder\",\n", + " \"name\": \"fasttext\",\n", + " \"save_path\": \"embeddings/dstc2_fastText_model.bin\",\n", + " \"load_path\": \"embeddings/dstc2_fastText_model.bin\",\n", + " \"dim\": 100\n", + " },\n", + " {\n", + " \"id\": \"my_tokenizer\",\n", + " \"name\": \"nltk_tokenizer\",\n", + " \"tokenizer\": \"wordpunct_tokenize\"\n", + " },\n", + " {\n", + " \"in\": [\n", + " \"x_lower\"\n", + " ],\n", + " \"in_y\": [\n", + " \"y\"\n", + " ],\n", + " \"out\": [\n", + " \"y_labels\",\n", + " \"y_probas_dict\"\n", + " ],\n", + " \"main\": true,\n", + " \"name\": \"intent_model\",\n", + " \"save_path\": \"evolution/classification/intents_snips\",\n", + " \"load_path\": \"evolution/classification/intents_snips\",\n", + " \"classes\": \"#classes_vocab.keys()\",\n", + " \"kernel_sizes_cnn\": [\n", + " 1,\n", + " 2,\n", + " 3\n", + " ],\n", + " \"filters_cnn\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 100\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"confident_threshold\": {\n", + " \"evolve_choice\": true,\n", + " \"values\": [\n", + " 0.5,\n", + " 1\n", + " ]\n", + " },\n", + " \"optimizer\": \"Adam\",\n", + " \"lear_rate\": {\n", + " \"evolve_range\": [\n", + " 0.0001,\n", + " 0.1\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"lear_rate_decay\": {\n", + " \"evolve_range\": [\n", + " 0.0001,\n", + " 0.1\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"loss\": \"binary_crossentropy\",\n", + " \"text_size\": 15,\n", + " \"coef_reg_cnn\": {\n", + " \"evolve_range\": [\n", + " 1e-06,\n", + " 0.001\n", + " ]\n", + " },\n", + " \"coef_reg_den\": {\n", + " \"evolve_range\": [\n", + " 1e-06,\n", + " 0.001\n", + " ]\n", + " },\n", + " \"dropout_rate\": {\n", + " \"evolve_range\": [\n", + " 0.1,\n", + " 0.9\n", + " ]\n", + " },\n", + " \"dense_size\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 100\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"model_name\": \"cnn_model\",\n", + " \"embedder\": \"#my_embedder\",\n", + " \"tokenizer\": \"#my_tokenizer\",\n", + " \"check_bool\": {\n", + " \"bool\": true\n", + " }\n", + " }\n", + " ],\n", + " \"out\": [\n", + " \"y_labels\",\n", + " \"y_probas_dict\"\n", + " ]\n", + " },\n", + " \"train\": {\n", + " \"epochs\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"batch_size\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"metrics\": [\n", + " \"classification_accuracy\",\n", + " \"classification_f1\",\n", + " \"classification_roc_auc\"\n", + " ],\n", + " \"validation_patience\": 5,\n", + " \"val_every_n_epochs\": 1,\n", + " \"log_every_n_epochs\": 1,\n", + " \"validate_best\": true,\n", + " \"test_best\": false\n", + " },\n", + " \"metadata\": {\n", + " \"labels\": {\n", + " \"telegram_utils\": \"IntentModel\",\n", + " \"server_utils\": \"KerasIntentModel\"\n", + " },\n", + " \"download\": [\n", + " \"http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz\",\n", + " \"http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz\",\n", + " {\n", + " \"url\": \"http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv\",\n", + " \"subdir\": \"snips\"\n", + " },\n", + " {\n", + " \"url\": \"http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin\",\n", + " \"subdir\": \"embeddings\"\n", + " }\n", + " ]\n", + " }\n", + "}\n" + ] + } + ], "source": [ "CONFIG_FILE = \"../../configs/evolution/evolve_intents_snips.json\"\n", "KEY_MAIN_MODEL = \"main\"\n", @@ -46,9 +280,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2018-06-25 16:20:17.65 INFO in 'deeppavlov.models.evolution.evolution_param_generator'['evolution_param_generator'] at line 55: Main model path in config: ['chainer', 'pipe', 4]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Title name for the considered evolution is `intents_snips`.\n", + "Number of populations: 6.\n" + ] + } + ], "source": [ "evolution = ParamsEvolution(population_size=POPULATION_SIZE,\n", " key_main_model=KEY_MAIN_MODEL,\n", @@ -74,9 +324,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Measure: classification_accuracy\n", + "valid:\n", + "min for\t0 model on\t0 population\n", + "max for\t1 model on\t4 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "\n", + "Measure: classification_f1\n", + "valid:\n", + "min for\t0 model on\t5 population\n", + "max for\t1 model on\t4 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "\n", + "Measure: classification_roc_auc\n", + "valid:\n", + "min for\t1 model on\t3 population\n", + "max for\t0 model on\t0 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: 'argmin' is deprecated. Use 'idxmin' instead. The behavior of 'argmin' will be corrected to return the positional minimum in the future. Use 'series.values.argmin' to get the position of the minimum now.\n", + " if __name__ == '__main__':\n", + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", + " # Remove the CWD from sys.path while we load stuff.\n" + ] + } + ], "source": [ "MEASURES = evolution.get_value_from_config(\n", " evolution.basic_config, list(evolution.find_model_path(\n", @@ -103,11 +394,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "path_to_pics = expand_path(Path(evolution.get_value_from_config(\n", " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).joinpath(\"pics\"))\n", @@ -156,7 +478,7 @@ " plt.ylim(ylim[0], ylim[1])\n", " plt.xticks(fontsize=20)\n", " plt.yticks(fontsize=20)\n", - " plt.savefig(path_to_pics.joinpath(y_label + \".png\"))\n", + " plt.savefig(path_to_pics.joinpath(metric + \".png\"))\n", " plt.show()" ] }, @@ -171,9 +493,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "params_dictionaries = []\n", "\n", @@ -194,11 +527,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", @@ -239,17 +603,205 @@ " plt.ylim(ylim[0], ylim[1])\n", " plt.xticks(fontsize=20)\n", " plt.yticks(fontsize=20)\n", - " plt.savefig(path_to_pics.joinpath(y_label + \"_colored_ids.png\"))\n", + " plt.savefig(path_to_pics.joinpath(metric + \"_colored_ids.png\"))\n", " plt.show()\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['dataset_iterator', 'seed'] seed\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'filters_cnn'] filters_cnn\n" + ] + }, + { + "data": { + "image/png": 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cqAcAgMGJegAAGJyoBwCAwYl6AAAYnKgHAIDBiXoAABicqAcAgMGJegAAGJyoBwCAwYl6AAAYnKgHAIDBiXoAABicqAcAgMGJegAAGJyoBwCAwYl6AAAYnKgHAIDBiXoAABicqAcAgMGJegAAGJyoBwCAwYl6AAAYnKgHAIDBiXoAABicqAcAgMGJegAAGJyoBwCAwYl6AAAYnKgHAIDBiXoAABicqAcAgMGJegAAGJyoBwCAwYl6AAAYnKgHAIDBiXoAABicqAcAgMGJegAAGJyoBwCAwYl6AAAYnKgHAIDBiXoAABicqAcAgMGJegAAGJyoBwCAwYl6AAAYnKgHAIDBiXoAABicqAcAgMGJegAAGJyoBwCAwYl6AAAYnKgHAIDBiXoAABicqAcAgMGJegAAGJyoBwCAwYl6AAAYnKgHAIDBiXoAABicqAcAgMGJegAAGJyoBwCAwYl6AAAY3NBRXxNPraoPV9XNVfWFqtpaVU+vqn/32qpqc1U9p6o+VlU3VtVNVfXxqvqFqjp5Hq8BAAAO1tBRn+Q1Sc5PclqS1yZ5RZI7JHl5klctPrCqjkvy90l+KcnOJK9M8gdJdiR5bpL/W1V3Wae5AQBg1Wya9wArVVVnJ/n+JBcneVh3Xzvdf2SSNyZ5clW9ubsvmD7kaUlOT/LK7v6RvZ7rVUmekuTHk7xgfV4BAACsjmGjPsnZ0+1LFoI+Sbp7R1U9L8m3J3lGkoWov9d0+9YlnutPM4n6Q/YSnEsvvDqXfvai7KhtObI359R73Sen3v/O8x4LmMG11308F2//x2yvW3NsH517Hvug3OmkB857LAAOAyNffnPKdPvZJe5b2Hfm9Mx9kvzTdPu4JY7/9un2Xas026q69MKrc9HFf59dfWuO3HNsdvWtuejiv8+lF14979GAA3TtdR/PP9z8d7ktO/MVfVRuy878w81/l2uv+/i8RwPgMDBy1C+cnb/nEvctnJXftOjPr0jyN0l+dPrG2pdOv/4+yfcm+Z/d/ZY1nXiFLv3sRdmw5+hsqqOTqmyqo7Nhz9G59LMXzXs04ABdvP0fc1RvzFE5IpXKUTkiR/XGXLz9H+c9GgCHgZGj/m3T7bOq6sSFnVV1RJLnLzruhCTp7luTPCbJ7yZ5WJJnTr+2JHl7kjfv75tV1dOmK+tsveaaa1btRRyIHbUtm3LU7fZtylHZUdvWdQ5g5bbXrTlyrysej8ymbK9b5zQRAIeTkaP+dUn+Ism9k1xYVb9bVb+Z5GNJzkxy2fS4PUlSVSdNj39CknOS3Gn6dc70+A9X1cP29c26+/zu3tLdW04+eX0vvT+yN2dXbrvdvl25LUf25nWdA1i5Y/vo7Miu2+3bkV05to+e00QAHE6Gjfru3p3k8Ul+Lsk1mbzR9SlJLkryiCTbp4cuXHj+kiSPSvK07n59d183/Xp9JqvefEWSX13Hl3DATr3XfbJnw63Z1bcm3dnVt2bPhltz6r3uM+/RgAN0z2MflNtqd27LznQ6t2VnbqvdueexD5r3aAAcBqq75z3Dqquqo5PclGRbd5883XdtkpOSbO7u7Xsdv3l6/M3dfexyz79ly5beunXr6g++H1a/gfFZ/QaAWVTVR7p7y4EcO/KSlvtzTpIjM/lAqgULF6WfnC+dxc+ifcnkg6gOSafe/84iHgZ3p5MeKOIBWBPDXn6T/NsZ9r33nZHkxUluSPLCRXd9cLo9t6o2LDp+Y770xtp3r9GoAACwZkY/U//OqrolyScyOft+v0zWob8lyeO7+8pFx/5sJtfa/2CS/1RV75nu/8Yk989kicznrNfgAACwWoY+U5/kDUmOTfKkJM9K8qAk5ye5f3e/f/GB3f3xJA/JZEnLYzJ5c+zTMrlM57eTnNHdn16/0QEAYHUMfaa+u1+cyaU2B3r8xUmevnYTAQDA+hv9TD0AAHzZE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMLiZo76qHlVVf1ZVV1fVzqravcTXrrUYFgAA+Pc2zXJwVT0uyZuTbExyWZJPJhHwAAAwRzNFfZLzkuxM8rju/svVHwcAAJjVrJffPCDJ6wU9AAAcOmaN+puTXL8WgwAAACsza9S/O8nXrcUgAADAyswa9T+b5N5V9dyqqrUYCAAAmM2sb5Q9N8k/JXl+kh+pqo8luXGJ47q7f/RghwMAAJY3a9T/0KI/nzb9WkonEfUAALAOZo36e67JFAAAwIrNFPXdfelaDbIS0+v6f2z69R+TVJJ/TvKKJOd3954lHnNUkmckOSfJfTP5IK0rkvxtkmd39zXrMz0AAKyOWc/UH2pek+T7k1yd5LVJvpjkm5K8PMkjkvzg4oOr6pQkf5nkgUn+OsnvJdmd5B5JvjnJi5OIegAAhrKiqK+qjZmc5T4hkzPd/053f+Ag5jqQGc7OJOgvTvKw7r52uv/IJG9M8uSqenN3XzDdvyHJ/57O/R3d/da9nq8y+2pAAAAwdzNHfVU9L8kzkxy3zKFLxv4qOnu6fclC0CdJd++YzvjtmVxmc8H0rickOTPJi/YO+unjOpOz9gAAMJSZor6q/nsmy1nelOSPknwuya41mOtAnDLdfnaJ+xb2nVlVR3b3jkzO6ifJa6vqLplE/52TXJXkL7v7ijWdFgAA1sisZ+qfmsmbSh96CLyhdOHs/FIr8txrut00/fO/JPma6b6HJfmNJHdYdPzOqnpBd//ivr5ZVT0tydOS5B73uMdBjA0AAKtr1mvIvyrJmw+BoE+St023z6qqExd2VtURmfxrwoITpts7T7cvT/KqTGL/+CTfneSGJL9QVT+0r2/W3ed395bu3nLyySevygsAAIDVMOuZ+n9dwWPWyuuSPDmTVWsurKq3JLk1yWOTfGWSyzJZ1WZhWcuFX2De1d0/ueh5LqiqnUn+NMn/yCT4AQBgGLOeqf/fSb5putb7XHX37iSPT/JzmSxD+ZTp10WZLGe5fXro1dPtjdPtm5Z4urcn2ZHk9Kpa7g3AAABwSJk16s9N8vkkb6iquX+6bHfv7O4XdfcDu/vo7j6+u5+Q5JIk90lybXdfPD38k9PtjUs8z+4k26Y3j1nruQEAYDXNeinNJ5IckeSuSb6tqm7KEpGcyQqR9z7Y4Q7COUmOzOQDqRa8K5MlLR+Q5PWLD56uhnOnJDfnS2/ABQCAIcx6pn5DJktYXjb9uilJLfG1Lh/iVFWbl9h3RiafDHtDkhcuuusPMvnE2Z+sqnstOn7j9Pgk+T/dPa8lOgEAYEVmOlPf3aet0Rwr9c6quiWTf0HYnuR+SR6X5JYkj+/uKxcO7O7Lq+onkrwyyceq6k1Jrk9yVpIzknwqyX9f3/EBAODgrcsZ9TX0hiTHJnlSkmcleVCS85Pcv7vfv/fB3f3qJI9J8jdJviPJT04f/+IkX7v4k2kBAGAUh8rylCvS3S/Oly6dOdDHvC/J+9ZiHgAAmIeZztRX1XOramdV3XUf99+tqnZU1c+uzngAAMByZr385vFJ3rf4WvXFuvuKJO9N8oSDHQwAADgws0b9Vye5cJljLpweBwAArINZo/6YTJaF3J9bM3nzKQAAsA5mjfrLkzx8mWMenuSKlY0DAADMataof0eSR1bV9y51Z1Wdk+RRSf78YAcDAAAOzKxLWr4oyQ8k+ZNp2L8jk7Pyd0vyrZms/X59bv9JrgAAwBqa9RNlr6iqb07yfzJZ4eY7F91dSS5J8j3dffmqTQgAAOzXzB8+1d1bq+r0TJa3fHiS45PcmORDSd7a3TtXd0QAAGB/VvSJstNwv2D6tayqelCSM7r7D1fy/QAAgH2b9Y2yK3V2kleu0/cCAIAvK+sV9QAAwBoR9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMLj1ivqafgEAAKts1aK+qk6oqjsudV93n9fd/lUAAADWwEyhXVXfWFW/WlUnLNp356p6f5Jrk1xfVS9d7SEBAIB9m/Xs+X9J8l3dfcOifb+W5Mwkn0lyXZL/WlX/zyrNBwAALGPWqH9wkr9auFFVxyR5YpJ3dvfpSe6b5HNJnr5qEwIAAPs1a9TfOcmVi25/bZKjk7wqSbp7e5I/yyTuAQCAdTBr1N+W5JhFt89M0kk+sGjftiQnHuRcAADAAZo16i9O8phFt787yUXdfcWifV+VyZtmAQCAdTBr1L86yQOr6sNV9cEkD0zyJ3sd86Akn1yN4QAAgOXNGvUvT/K6JFuSfH0m18+/aOHOqnpAJqH/vlWaDwAAWMamWQ7u7p1Jvr+qnj652dv3OuSqJA9JcsnqjAcAACxnpqivqj9I8vHu/vWl7u/ua+N6egAAWFezXn7z/ZksawkAABwiZo36SyLqAQDgkDJr1P9Jkm+tqhPWYhgAAGB2s0b9ryTZmuS9VfXtVXWXNZgJAACYwUxvlE1y63RbSd6SJFW11HHd3bM+NwAAsAKzhvcHk/RaDAIAAKzMrOvUn7VGcwAAACs06zX1AADAIWbF171X1R2TnJ7kK7r7g6s3EgAAMIuZz9RX1d2r6o1Jbsh0JZxF931DVV1YVWet3ogAAMD+zBT1VfWVST6c5DuT/FmSv81kJZwFH87kw6m+d7UGBAAA9m/WM/XnZhLt39Td35XknYvv7O6dmayQ8/WrMx4AALCcWaP+25L8aXe/dz/HXJbkrisfCQAAmMWsUX+XJBctc8zOJHdc2TgAAMCsZo3665N81TLHnJ7kqpWNAwAAzGrWqP/rJN9RVacsdWdV3SfJt2TRijgAAMDamjXqX5zk6CTvr6pvTXKHZLJm/fT2W5PsSfKSVZ0SAADYp5k+fKq7P1xVP57k5Zksablg23S7K8mPdPc/rdJ8AADAMmb+RNnu/oOq+mCSn0jy8CQnJbkpyYeS/HZ3f3J1RwQAAPZn5qhPku6+KMkzV3kWAABgBWb9RNmfr6pHLnPMmVX18wc3FgAAcKBmfaPseUnOWuaYR2byybMAAMA6mDXqD8QRmayAAwAArIO1iPqHJrl2DZ4XAABYwrJvlK2q9+y164eq6qwlDt2YyafNnprktQc/GgAAcCAOZPWbsxb9uZOcNv3a254k1yV5fayMAwAA62bZqO/uf7tEp6r2JDmvu1+wplMBAAAHbNZ16n84yUfXYhAAAGBlZor67n71Wg0CAACszH6jfrkPmtqf7v7ASh8LAAAcuOXO1L8vkzfHrsTGFT4OAACYwXJR/4KsPOoBAIB1sN+o7+7z1mkOAABghdbiE2UBAIB1JOoBAGBwy61+855Mrql/SndfPr19ILq7v/GgpwMAAJa13Btlz8ok6u+w6PaB8OZaAABYJ8tF/TOTfKi7P5Uk3e1yHQAAOMQsF+kvTfKfF25U1e6qet7ajgQAAMxiuai/Lcm5zFMjAAAWc0lEQVTRi27X9AsAADhELBf1Fyf55qq6y6J9rpcHAIBDyHJR/7tJHprkyqraPd133vQynP197VrbsQEAgAXLfaLsb1XV1Ukel+SuSR6d5LIkl6z9aAAAwIFYbvWbdPfrkrwuSapqT5JXdvcL1nowAADgwMy6ROXzk7xvDeYAAABWaNkz9Yt19/PXahAAAGBlfJgUAAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgho76mnhqVX24qm6uqi9U1daqenpV7fe1TR/7zqrq6dem9ZobAABW09BRn+Q1Sc5PclqS1yZ5RZI7JHl5klct89hnJHl0klvXbjwAAFh7w56drqqzk3x/kouTPKy7r53uPzLJG5M8uare3N0XLPHY+yZ5UZJfS3JOklPXbXAAAFhlI5+pP3u6fclC0CdJd+9I8rzpzWfs/aDpZTZ/lOSzSc5d6yEBAGCtDXumPskp0+1nl7hvYd+ZVXXkNPQXPDfJQ5J8XXffVlVrOSMAAKy5kc/UL5ydv+cS991rut206M+pqq9J8j+TvLC7t67teAAAsD5Gjvq3TbfPqqoTF3ZW1RFJnr/ouBOm+4/J5LKbf0ryglm/WVU9bbqyztZrrrlm5VMDAMAqG/nym9cleXKSb05yYVW9JZOVbB6b5CuTXJbkHkn2TI//1UzO2n9Nd++c9Zt19/mZrLSTLVu29EFPDwAAq2TYM/XdvTvJ45P8XJJrkjxl+nVRkkck2T499OqqelSSn0zyi939D3MYFwAA1sywUZ8k3b2zu1/U3Q/s7qO7+/jufkKSS5LcJ8m13X1xJm+MrSTPX/RhU11VnS8tZ7lzuu+MubwYAABYoZEvv9mfc5IcmckHUiXJJ5L8/j6O/d4kX5HkD5J0kuvWfDoAAFhFQ0d9VW3u7m177TsjyYuT3JDkhUnS3e9K8q59PMdjM4n6H+/uXWs7MQAArL6hoz7JO6vqlkzOxG9Pcr8kj0tyS5LHd/eV8xwOAADWw+hR/4ZMLrV5UpJjklyRyQo1v9Ldl89zMAAAWC9DR313vziTS20O5jlOW51pAABgPoZe/QYAABD1AAAwPFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwuKGjviaeWlUfrqqbq+oLVbW1qp5eVRv2OvaMqjqvqv66qj5fVTuq6oqqem1VPXRerwEAAA7WpnkPcJBek+T7k1yd5LVJvpjkm5K8PMkjkvzgomP/V5KvTfKRJBckuTnJGUnOSfLEqvre7r5g/UYHAIDVMWzUV9XZmQT9xUke1t3XTvcfmeSNSZ5cVW9eFOp/nORJ3f3pvZ7nBzL55eD8qvqz7t6xbi8CAABWwciX35w93b5kIeiTZBrlz5vefMai/S/bO+in+/84yUVJTkrywLUbFwAA1sbIUX/KdPvZJe5b2Hfm9Mz9cnZOt7sOeioAAFhnI0f9wtn5ey5x372m202L/rykqnp4kvsnuSLJJ1ZtOgAAWCcjR/3bpttnVdWJCzur6ogkz1903An7eoLp4/5wevOZ3b17P8c+bbqyztZrrrnmIMYGAIDVNXLUvy7JXyS5d5ILq+p3q+o3k3wsyZlJLpset2epB1fVHZO8Jcl9kvxqd/+f/X2z7j6/u7d095aTTz55tV4DAAActGGjfnpW/fFJfi7JNUmeMv26KJPlLLdPD71678dOg/5tSb4hyUu7+2fXY2YAAFgLwy5pmSTdvTPJi6Zf/6aqjs7kDPy13X3xXvcdm0nQn5nJGXpBDwDA0IY9U7+Mc5IcmckHUv2bqjouyV9mEvS/JOgBADgcDB31VbV5iX1nJHlxkhuSvHDR/hOSvCvJw5Oc293PXa85AQBgLQ19+U2Sd1bVLZksRbk9yf2SPC7JLUke391XLjr2giRbknwmyYaqOm+J53tzd39sbUcGAIDVNXrUvyGTS22elOSYTNaaPz/Jr3T35Xsdu7Ce/b2TnLuP57skk9VzAABgGENHfXe/OJNLbQ7k2NPWdhoAAJiPoa+pBwAARD0AAAxP1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4EQ9AAAMTtQDAMDgRD0AAAxO1AMAwOBEPQAADE7UAwDA4DbNewBg4l3bbsofXbc9V9y2J3c7akOefNKxeezm4+Y9FgAwAFEPh4B3bbspv3TFTdm8MfnKIzbkxp178ktX3JQkwh4AWJbLb+AQ8EfXbc/mjcnxR2zIhg2T7eaNk/0AAMsR9XAIuOK2Pdm88fb/c9y8cUOuuG3PnCYCAEYi6uEQcLejNmTb7tsH/Lbdk2vrAQCWoxjgEPDkk47Ntt3JjTv3ZM+eyXbb7sl+AIDleKMsHAIW3gy7ePWb/3KK1W8AgAMj6uEQ8djNx4l4AGBFXH4DAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIMT9QAAMDhRDwAAgxP1AAAwOFEPAACDE/UAADA4UQ8AAIOr7p73DMOpqmuSXDqnb3+nJNfO6XuzPvyMD39+xoc/P+PDn5/x4e9Q+Bmf2t0nH8iBon4wVbW1u7fMew7Wjp/x4c/P+PDnZ3z48zM+/I32M3b5DQAADE7UAwDA4ET9eM6f9wCsOT/jw5+f8eHPz/jw52d8+BvqZ+yaegAAGJwz9QAAMDhRDwAAgxP1AAAwOFE/gKq6e1X9QVVdWVW3VdUlVfUbVXXCvGfj4FXVE6vqZVX1waraVlVdVa+Z91ysjqo6qap+rKreVFWfrqpbquqmqvqrqvrRqvLf4cNAVb2oqt5dVZ+b/oyvr6qPVtW5VXXSvOdj9VXVk6b/ve6q+rF5z8PBm/ZV7+PrqnnPtxxvlD3EVdW9k/xNkjsneUuSf0nysCSPTvLJJF/f3dfNb0IOVlV9LMmDk9yc5PIk/yHJH3f3k+Y6GKuiqp6e5OVJPp/kvUkuS3KXJN+V5Lgkb0zyPe0/xkOrqh1J/m+SC5NcneSOSR6eZEuSK5M8vLs/N78JWU1V9VVJPp5kY5KvSPLU7n7FfKfiYFXVJUmOT/IbS9x9c3f/2vpONJtN8x6AZf1OJkH/U939soWdVfXSJM9M8ktJnj6n2Vgdz8wk5j+d5FGZhB+Hj08l+Y4kb+vuPQs7q+o5Sf4uyXdnEvhvnM94rJLN3X3r3jur6peSPCfJ/0jyE+s+FauuqirJK5Ncl+SCJM+e70Ssshu7+7x5D7ES/tn3EDY9S/+fk1yS5P/b6+5zk3whyZOr6o7rPBqrqLvf290XOVN7eOru93T3WxcH/XT/VUn+1/TmWes+GKtqqaCf+t/T7X3WaxbW3E8leUySH87k/4fhkCDqD22Pnm7/cokg2J7kr5PcIZN/4gXGs3O63TXXKVhLj59u/3GuU7Aqqup+SV6Y5De7+wPznoc1cdT0/RLPqar/WlWPrqqN8x7qQLj85tB23+n2U/u4/6JMzuSfnuTd6zIRsCqqalOSH5zefMc8Z2H1VNWzM7nG+rhMrqf/hkyC/oXznIuDN/3f7B9l8r6Y58x5HNbOKZn8nBe7uKp+uLvfP4+BDpSoP7QdN93etI/7F/Yfvw6zAKvrhUkekOTt3f0X8x6GVfPsTN4IveAdSX6ou6+Z0zysnp9P8pAk39Ddt8x7GNbEK5N8MMk/Jdme5F5JnpHkaUn+vKq+rrv/YY7z7ZfLbwDWWVX9VJKfzmQ1qyfPeRxWUXef0t2Vydm+78okCj5aVQ+d72QcjKr62kzOzr+ku/923vOwNrr7+dP3Qf1rd3+xuz/R3U9P8tIkxyQ5b74T7p+oP7QtnIk/bh/3L+y/cR1mAVZBVT0jyW9msvTho7v7+jmPxBqYRsGbMrlE8qQkfzjnkVih6WU3f5jJpbDPm/M4zMfCogaPnOsUyxD1h7ZPTren7+P+hdUU9nXNPfD/t3evsXIWdRzHv7/SgFG5KREQgcPFW/QFJY1ETKQGuVg0oFGIaNJWa7xFQzABlVQpmJAIr1BUIppDqomo9fIGAxGopaASRBSNjYZyigkXU4ViuKjU8cXMieu6p5fdc7r7NN9PMnnOzjM7z+yTnu7/zP53ZoIkuQj4EvA7akA/8ZuZaDSllK3UP+Bel+SwcY9HQ3kx9X34tcBzvRsSUVeiA/h6qxu0vrm6bzZ9bqJXGzSnfrLNrld+ZpJFfWtcHwi8CXgG+MU4Bidp9yW5lJpHfz9wRill25iHpL3n5e24Y6yj0LD+AXxjjnMnU/PsN1En4kzN2TfNrjK4Zayj2AWD+glWSnkwya3Uj28/Tp3hm7WW+hfj9aUU18mVJliSNcAVwK+AM0252bckeRXweClle1/9IuBK6gaCd5dSnhjH+DSa9qXY1YPOJbmcGtTf6I6y3daWK324P6ZKMgV8uT381l4e1h4xqJ98HwPuBq5NcjrwB+AU6hr2fwQuG+PYNA+SnAec1x4e0Y5vTDLdft5WSnHHwo5KsoIa0O+grqrwyboh5f+YKaVM7+Whaf4sB65Ksgl4iLrT6OHUHaKPBx4DPjS+4UnaDRcAn0qyEdhKXf3mBOAc4AXAzcA14xverhnUT7g2W7+UGhScTX3zeJT6Rbu1zvzsE04CVvTVHd8K1P9cDOq767h23A+4aI42PwOm98potBB+CpxIXZN+CXWZ4aepEy/rgGv9dEaaeHdQ9wdaQk1vfhF1IZJN1N/jdZO+83smfHySJEmSdsHVbyRJkqSOM6iXJEmSOs6gXpIkSeo4g3pJkiSp4wzqJUmSpI4zqJckSZI6zqBekiRJ6jiDeknSgkoynaS07dYX8jozSWYW8hqSNKkM6iVJnZBkQxJ3TJSkARaPewCSJM2T08c9AEkaF4N6SdI+oZTy4LjHIEnjYvqNJE2oJFMtF306yWuS/CjJ35I8nWRTkjMHPOeAJJ9O8kCSZ5I8leTOJOfPU/+Xt+cs21l/u/n6ViZZn2RLkmfbWO9K8v5B/QKntcelp2zoaTcwp36EezKV5DtJtiV5Lsm9Sd6+O69NkvY2Z+olafIdB/wceAC4HjgSuAD4SZILSyk3ASTZH7iFGvxuBq4DXgi8G7gpyUmllM8O2/8C+Crwe2Aj8CjwUmA5sC7Jq0spa1q7J4G1wErg2PbzrJmdXWCEe3IscA+wBVgHvIR6T36c5K2llDv29MVK0oIqpVgsFotlAgswBZRWru47txT4F/AEcFCr+0xrezOwuKfty6jBbwFOHbb/Vn95a79sJ+Od7qufbvVTffUnDOhjf+C2du2j+s5tqG9bc96vGWCmr26Ue/L5vr7Omu1r3P82LBaLpb+YfiNJk287cEVvRSnlXuDbwCHAO1v1B6hB58WllOd72v4FuLI9XD1C//OqDMiBL6X8kzqbvpj5+eLrsPdkK/CFvrHdAjwMvGEexiVJ88qgXpIm332llL8PqN/QjkuSHAicCDxSStk8oO3ts22H6X8PxrrbkhyT5Lokm1uue2m58+tbk6NG7H+Ue3J/KWXHgPo/A4eOMi5JWgjm1EvS5Ht8jvrH2vHgVqDmpg8yW3/IkP3PqyTHU3PWDwXuBG6lfmKwg5oCswI4YMTLjHJPnpzjOc/jhJikCWRQL0mT7/A56o9ox+2t9Nb1O7Kn7TD9z/p3Ow56/xgUHM/lYuoXY1eVUqZ7TyR5LzWoH9Uo90SSOsXZBkmafCe3VJJ+y9rx1y195kHgqCSvHND2Le143zD999Q90Y5HD2i/dEDdXE5sx/UDzp02x3N2ACTZb3cuMOI9kaROMaiXpMl3MPC53ookS4H3UWeZf9iqvwkEuLo38E1yGLCmp82w/UNNmQFYlWRxT/uj+/vYhZl2XNZ33bMY/MVVgL+24zF7cJ1h74kkdYrpN5I0+TYCq5OcAtzFf9eRXwR8uJTyVGt3DfA24FzgN0lupq7J/h7qEo5fLKVsGqF/Sim/TLIReDNwT5Lbqek776CuBz9oBn+QrwCrgO8l+T7wCPB64Gzgu+36/W5rr+UH7bU9C2wtpazbyXWGvSeS1CnO1EvS5HsIOJWa+vIR4Hxqysjy0rMxVFsO8gzgslb1CWpu+p+AC0spl47Sf49zgRuAV7RrLAEuAebq//+UUn5LTX+5GzgH+ChwEPAu4GtzPO0G4CrqJwuXUJek/OAurjPsPZGkTkkpZdxjkCQNkGSKGnDfWEpZ2bX+JUl7jzP1kiRJUscZ1EuSJEkdZ1AvSZIkdZw59ZIkSVLHOVMvSZIkdZxBvSRJktRxBvWSJElSxxnUS5IkSR1nUC9JkiR13H8Aqj51kNIlW/gAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'lear_rate'] lear_rate\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'lear_rate_decay'] lear_rate_decay\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'coef_reg_cnn'] coef_reg_cnn\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'coef_reg_den'] coef_reg_den\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'dropout_rate'] dropout_rate\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'dense_size'] dense_size\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['train', 'epochs'] epochs\n" + ] + }, + { + "data": { + "image/png": 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UmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABjU3GO+qo6oqmdX1RlV9fmqurWqbqyqD1XVz1XVuiXjT6+q3s7X+1Y41jOr6vyqumV6jA9U1ZP2zDsFAIDVtWHeE0jy1CSvT3JVkvcnuTzJNyd5SpI3JTm5qp7a3T0d//dJLl1hX09P8qAk7166oapeneSFSb6c5I1J9k3y40nOrKrnd/frVusNAQDAnlDfaOQ5TaDqpCQHJHlnd29dtP6+Sc5P8i1JTunut21nP4cmuTLJ+iRHdfe1i7Y9LsmHk3whyWO7+/rp+mOSXDA9/kO7+9LtzXfjxo29adOmGd4hAADMpqou6O6N2xs398tsuvvs7j5zcchP11+d5I+m356wA7t6epJ7Jfm7xSE/9QvT5SsWQn56jEuT/EGS/ZI8a/bZAwDA/Mw95rfjzuly8w6Mfc50+YZltp00Xb5nmW3vXjIGAACGsGZjvqo2JHnG9NvlInzx2H+T5JFJPtfd71+y7YAkRyW5pbuvWubll0yXx29j/6dW1aaq2nTNNdfs6FsAAIDdas3GfJJXJnlEknd193u3M/bU6fKNy2w7ZLq8cYXXLqw/dKWdd/cbuntjd2888sgjtzMVAADYM9ZkzFfVaZk8eebiTK6F39bYQ5L8WJI7kpy+2ycHAABrxJqL+ap6XpLfT3JRkhO7+7rtvOSnk3xTlr/xNfnGmfdDltm2eP0Ns84VAADmaU3FfFW9IMlrk3wmk5C/egdetnDj6x8vt7G7v5bkiiQHVtX9lhly3HT5uRmnCwAAc7VmYr6qXpTkd5NcmEnIf2UHXvOdSb4tkxtfP7CNoWdPl09cZtvJS8YAAMAQ1kTMV9VLMrnh9YIkj1/hcpnlLNz4utzjKBdbeF79r1XVYYuOe0yS5ya5Pcmbd3S+AACwFmyY9wSq6plJXpZkS5Jzk5xWVUuHXdrdpy953cFJnpZJiP/Zto7R3edV1WuS/HKST1XV3ybZd/r6w5M8f0c+/RUAANaSucd8kmOny/VJXrDCmHNy9yfV/FSSA5K8ZUfO5Hf3C6vq05mciT81ydYkH0/yqu5+x07MGwAA5qq6e95zGMrGjRt706ZN854GAAB7saq6oLs3bm/cmrhmHgAAmJ2YBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAY1IZ5TwAAANaCL1x2ea687BOpO65P73tY7n/0o/Lgox8472ltkzPzAADc433hsstz9efOSjZ/PVv3OTTZ/PVc/bmz8oXLLp/31LZJzAMAcI935WWfyNb190o2fFOqKtnwTdm6/l658rJPzHtq2yTmAQC4x6s7rk+vv9dd1vX6e6XuuH5OM9oxYh4AgHu83vew1JZb77Kuttya3vewOc1ox4h5AADu8e5/9KOybsutyeavp7uTzV/Pui235v5HP2reU9smMQ8AwD3eg49+YO57/BOSDd+UdXfekGz4ptz3+Ces+afZeDQlAABkEvRrPd6XcmYeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUBtWYydV9dAkJyf5epK3dPeNq7FfAABgZTOdma+qX6+qq6rq8EXrnpDkE0leneQPk3y8qo5Y3WkCAABLzXqZzclJLu7u6xat+y9JOslLk7w+ybFJ/uPqTA8AAFjJrDF/TJLPLnxTVUcleUySP+zu3+ru5yU5O8mPrNoMAQCAZc0a84clWXxW/rszOSv/jkXrLkjywF2cFwAAsB2zxvw1SY5a9P2JSe5M8tFF6/bdif0CAAAzmvVpNhcm+aGqekSS25I8LcmHuvvWRWOOSXLV6kwPAABYyaxn0P9bkkOSfDLJP03//DsLG6tqfSaX3mxarQkCAADLm+nMfHefW1VPSvKcTK6V/8vufveiIY9LckWSM1ZvigAAwHJm/tCo7n5PkvessO3cJI/a1UkBAADb50ZVAAAY1Mxn5pOkqu6TZGMmj6pcv9yY7v4fuzAvAABgO2aK+araJ8kfJXlGVj6rX5lcTy/mAQBgN5r1zPzLkzwryReS/GWSf06yebUnBQAAbN+sMf+TST6X5FFLni0PAADsYbPeAHufJO8S8gAAMH+zxvzlSQ7eHRMBAABmM2vMn57k5Ko6ZDfMBQAAmMGsMf/KJB9KclZVnVhVztIDAMCcbPMG2KramsljJu+2KclZ0zHLvbS7e6eeYQ8AAOyY7QX3B7N8zAMAAHO2zZjv7hP20DwAAIAZzXrNPAAAsEbMFPNVda+qemBV7bvC9v2m2/dfnekBAAArmfXM/K8n+ackB66w/YAkFyd58a5MCgAA2L5ZY/7kJGd193XLbZyuPyvJk3Z1YgAAwLbNGvPHJPncdsZ8bjoOAADYjWaN+X2SbN3OmE7imnkAANjNZo35Lyb5vu2MOSHJZTs1GwAAYIfNGvP/kOQxVfX/Lbexqn4lyaOT/P2uTgwAANi27X0C7FKvTvJTSf5LVf1Ykv+Z5IokRyX5D0m+PcnlSf7bak4SAAC4u5livruvr6oTkvxVku/K5Cx8J6npkPOS/HR3X7+akwQAAO5u1jPz6e5Lkzyuqh6dSdAfmuSGJB/p7o+v7vQAAICVzBzzC6bhvsvxXlVHJHlykh9I8shMLtm5I8mnk7w5yZu7+25P0Kmq9UmeleQZ09ftn+SqJB9L8pLuvtsjNKvqmUmem+ThSbYk+USSV3f3O3b1fQAAwJ620zFfVfskeWgmZ+ZvTPLZ7r5zJ3b11CSvzyTE35/JNfffnOQpSd6U5OSqemp396JjH5jk7UlOSnJhkj9Lclsmvwh8T5Ljs+R5+FX16iQvTPLlJG9Msm+SH09yZlU9v7tftxNzBwCAualFjbxjL6g6OJMbXJ+euz5P/rYkf57kV7r7hhn2d1KSA5K8c/EZ+Kq6b5Lzk3xLklO6+22Ltv1lkp9M8gvd/cfL7HOfxb9YVNXjknw4yReSPHbhmv6qOibJBdPjP3R6CdE2bdy4sTdt2rSjbw8AAGZWVRd098btjZvp0ZTTkP9wklOTbE5ybpK/mS7vnK7/0HTcDunus7v7zKWX0nT31Un+aPrtCYvm8OhMQv6ty4X89LVL/w/BL0yXr1h8c+403v8gyX6ZXLIDAADDmPU587+a5F9nclnM0d19Qnf/RHefkOToTML44dNxq2EhyjcvWveT0+VfV9UhVfXTVfWrVXVqVT1khf2cNF2+Z5lt714yBgAAhjDrNfNPyeSpNc9duqG7b0zy/OmZ8x/NLgZ9VW3I5ObW5K4R/tjp8uhMLps5YvE0qur1SU7r7i3T/RyQybX0t3T3Vcsc6pLp8vhdmS8AAOxps56ZPzrJB7Yz5pxMrnPfVa9M8ogk7+ru9y5af5/p8jXTuTwsyUFJnpBJ3P9ikpcsGn/IdHnjCsdZWH/oShOZnvXfVFWbrrnmmlneAwAA7DazxvzX8o2YXsmRSb6+c9OZqKrTMnnyzMWZ3Gi72MKcL07ytO6+uLtv6e73JTklydYkv1xV++7KHBbr7jd098bu3njkkUeu1m4BAGCXzBrzH0vy1Ko6brmNVfXgJD82HbdTqup5SX4/yUVJTuzu65YMWXhSzpkLl9Is6O5PJvlSJmfqHzZdvXDm/ZAsb2H9Dj+BBwAA1oJZY/5VSQ5M8rGqenlVnVRVD6uqE6vqNzOJ+AOTvHpnJlNVL0jy2iSfySTkr15m2D9NlyvF98LTau6VJN39tSRXJDmwqu63zPiFX0zu9iFTAACwls0U89NLWX4xk+fLvzjJP2YS3mdlcp36AUme191nzTqRqnpRkt/N5EOgTuzur6wwdGHfj1hmH/vlG3F+6aJNZ0+XT1xmfycvGQMAAEOY9cx8ps92Pz7Jryc5I5MIPiOTmD++u18/6z6r6iWZ3PB6QZLHd/e12xj+tiRXJnlaVX3Hkm0vyeSymfcvOau/8Lz6X6uqwxYd95gkz01ye5I3zzpvAACYp1kfTZkk6e7Lk7xiNSZQVc9M8rIkWzL58KnTqmrpsEu7+/Tpsb9WVT+T5B1Jzq2qv8vkMprvTPJvk3wlyc8vme95VfWaJL+c5FNV9bdJ9k3ytCSHJ3n+jnz6KwAArCU7FfOr7Njpcn2SF6ww5pwkpy98093/OD0r/5JMHkl5SJKFT4x9eXdfuXQH3f3Cqvp0JmfiT83kqTcfT/Kq7n7H6rwVAADYc6q7Z39R1U8l+dkkj0pycJKbknwiyZ9291+u6gzXmI0bN/amTZvmPQ0AAPZiVXVBd2/c3riZzsxX1T5J/jbJk5JUJpfGXJPk3klOTHJCVf1YklO6+86ZZw0AAOywWW+A/dUkP5jko5nE+/7dfb9Mnm5zUpLzMwn9F63mJAEAgLub9Zr5ZyT5fJITuvuOhZXTD2/6QFWdkMmjKn8myW+t0hwBhnftVz+dL938qdxct+Wg3j/HHvStufcRj5z3tAAY3Kxn5h+Q5O2LQ36x7r49yduTHLWrEwPYW1z71U/nk7ecn9tzZw7s/XJ77swnbzk/13710/OeGgCDmzXmr0yyz3bG7DMdB0CSL938qezX67Nf9kmlsl/2yX69Pl+6+VPznhoAg5s15v8qySlVdfByG6vq0CSnJNmrn2gDMIub67bsu+Sqxn2zITfXbXOaEQB7i1lj/mVJNiU5v6p+sqoeUFX7TJc/leQjmdwE+/LVnijAqA7q/XNHNt9l3R3ZnIN6/znNCIC9xaw3wN46XVaSP19meyU5LsltSz7Ftbt7LXxAFcAed+xB35pP3nJ+0pMz8ndkc26vLXnogY+Z99QAGNysgX1uktk/ZQrgHuzeRzwy35bc5Wk2Dz3wMZ5mA8Aumynmu/uE3TQPgL3avY94pHgHYNXt9KUvVXVAkuOTHNjd567elAAAgB0x6w2wmd7s+rYk12dyM+z7F237t1V10fTDowAAgN1oppivqvsl+WiSH07yjiT/K5ObXhd8NMl9kjxttSYIAAAsb9Yz8y/NJNb/XXc/Jck/Lt7Y3XdmcpPsd6/O9AAAgJXMGvPfn+Qfuvv92xhzeZL77/yUAACAHTFrzH9zkku2M+bOJAfs3HQAAIAdNWvMX5fkW7Yz5vgkV+/cdAAAgB01a8x/OMkPVdV9l9tYVccleWIWPeEGAADYPWaN+Vcl2T/JOVV1cpJvSibPnJ9+f2aSrUl+Z1VnCQAA3M2snwD70apTDiiBAAAZbElEQVT6+SSvz+TRlAtumi43J/nZ7v7fqzQ/AABgBTN/Amx3/2lVnZvkF5N8V5IjktyY5CNJXtfd/7S6UwQAAJYzc8wnSXdfkuSXVnkuAADADGa9Zh4AAFgjxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADGruMV9VR1TVs6vqjKr6fFXdWlU3VtWHqurnqmrdkvHHVFVv4+st2zjWM6vq/Kq6ZXqMD1TVk3b/uwQAgNW3Yd4TSPLUJK9PclWS9ye5PMk3J3lKkjclObmqntrdveR1n0zy98vs7zPLHaSqXp3khUm+nOSNSfZN8uNJzqyq53f361bhvQAAwB6zFmL+c0l+KMk7u3vrwsqqenGS85P8aCZh/7Ylr7uwu39jRw5QVY/LJOS/kOSx3X39dP2rklyQ5NVV9Y7uvnTX3goAAOw5c7/MprvP7u4zF4f8dP3VSf5o+u0Ju3iYX5guX7EQ8tNjXJrkD5Lsl+RZu3gMAADYo+Ye89tx53S5eZlt96+qn6+qF0+X37qN/Zw0Xb5nmW3vXjIGAACGsBYus1lWVW1I8ozpt8tF+L+bfi1+zQeSPLO7L1+07oAkRyW5pbuvWmY/l0yXx+/qnAEAYE9ay2fmX5nkEUne1d3vXbT+60lenuQxSQ6bfn1fJjfPnpDkfdOAX3DIdHnjCsdZWH/oShOpqlOralNVbbrmmmtmfR8AALBbrMmYr6rTMrlh9eIkT1+8rbu/0t2/3t0f7+4bpl8fTPLvk3w0yUOSPHs159Pdb+jujd298cgjj1zNXQMAwE5bczFfVc9L8vtJLkpyYndftyOv6+7NmTzKMkm+d9GmhTPvh2R5C+tvmHGqAAAwV2sq5qvqBUlem8mz4k+cPtFmFgvXwPzLZTbd/bUkVyQ5sKrut8xrjpsuPzfjsQAAYK7WTMxX1YuS/G6SCzMJ+a/sxG6+a7r84pL1Z0+XT1zmNScvGQMAAENYEzFfVS/J5IbXC5I8vruv3cbYR1fV3eZdVY9P8kvTb/9iyeaF59X/WlUdtug1xyR5bpLbk7x5Z+cPAADzMPdHU1bVM5O8LMmWJOcmOa2qlg67tLtPn/75NUmOq6rzknx5uu5b843nxL+ku89b/OLuPq+qXpPkl5N8qqr+Nsm+SZ6W5PAkz/fprwAAjGbuMZ/k2OlyfZIXrDDmnCSnT//850menOSxmVwis0+S/5Pkb5K8rrvPXW4H3f3Cqvp0JmfiT02yNcnHk7yqu9+x628DAAD2rOruec9hKBs3buxNmzbNexoAAOzFquqC7t64vXFr4pp5AABgdmIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGJeYBAGBQYh4AAAYl5gEAYFBiHgAABiXmAQBgUGIeAAAGtWHeE2D7LrvoK7nsi5fkjrop+/bBOfpBx+Xoh99n3tMCAGDOnJlf4y676Cu55Esfy+a+LftuPSib+7Zc8qWP5bKLvjLvqQEAMGdifo277IuXZN3W/bOh9k+qsqH2z7qt++eyL14y76kBADBnYn6Nu6Nuyobsd5d1G7Jf7qib5jQjAADWCjG/xu3bB2dzbr/Lus25Pfv2wXOaEQAAa4WYX+OOftBx2brutmzu25LubO7bsnXdbTn6QcfNe2oAAMyZmF/jjn74fXLcsY/Nhto/d6y7ORtq/xx37GM9zQYAAI+mHMHRD7+PeAcA4G6cmQcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAYlJgHAIBBiXkAABiUmAcAgEGJeQAAGJSYBwCAQYl5AAAY1NxjvqqOqKpnV9UZVfX5qrq1qm6sqg9V1c9V1XbnWFVvqqqefj1khTHrq+qXqupT02NcV1XvqqrHrf67AgCA3W/uMZ/kqUnemOQ7k3w0ye8leVuSRyR5U5K/qapa6cVV9YNJfi7JLdsYU0nekuQ1SfZN8rokZyT53iQfrKofXpV3AgAAe9CGeU8gyeeS/FCSd3b31oWVVfXiJOcn+dEkT8kk8O+iqo7M5BeBtya5b5LvW+EYP57klCTnJXl8d982ff0fJflQkjdW1dndffNqvSkAANjd5h7z3X32Cuuvnsb2K5KckGViPskbpsvnrrB9wf8zXf7nhZCfHuNjVfXWJE/PJPbfPNvsAWDi81/6bM658gu5Oltz36zL993/wXnIsQ+b97RYRVe868Jc98Yzsv7Ll2fLAx6Yw5/z5Bz1/d8+72lxD7cWLrPZljuny81LN1TVzyT5kSQ/391fXWkHVbV/kscl+XqSc5cZ8u7p8qRdmikA91if/9Jn89dXXpKbszX3SeXmbM1fX3lJPv+lz857aqySK951YW56ye+kbrguW+5/VOqG63LTS34nV7zrwnlPjXu4NRvzVbUhyTOm375nybajk/x+kr/o7rdvZ1cPTrI+yRe7+26/FCS5ZLo8fhemC8A92DlXfiEHJTmo1mVdVQ6qdTloup69w3VvPCNbDj40OfTw1Lr1yaGHZ8vBh+a6N54x76lxD7dmYz7JKzO5CfZd3f3ehZXTp9v8WSY3vJ62A/s5ZLq8cYXtC+sPXWkHVXVqVW2qqk3XXHPNDhwSgHuSq7M1B+Suz2o4IJWrs3WFVzCa9V++PH3wIXdZ1wcfkvVfvnxOM4KJNRnzVXVakhcmuTiT69kX+6VMbnR9Tndfvyfm091v6O6N3b3xyCOP3BOHBGAg9826fC19l3VfS+e+a/M/s+yELQ94YOqmu54XrJtuzJYHPHBOM4KJNfdvmap6XiaX0FyU5MTuvm7RtuMzuSH2zd39rh3c5cI/eYessH1h/Q07MV0AyPfd/8G5OcnNvTVbu3Nzb83N0/XsHQ5/zpOz/qYbkhuuS2/dktxwXdbfdEMOf86T5z017uHWVMxX1QuSvDbJZzIJ+auXDHl4kv2SPGvRh0R1VXW+8VjKS6brfmT6/ReSbEnyoOl1+EsdN11+blXfDAD3GA859mH5ifsfl4OyLl9J56Csy0/c/zhPs9mLHPX9356DX/7C9KGHZ/2VV6QPPTwHv/yFnmbD3M390ZQLqupFmVwnf2GSf9fd1y4z7NIkf7LCLn4gk2fN//9JbpqOTXffVlXnJfme6df7l7zu5Oly2UdkAsCOeMixDxPve7mjvv/bxTtrzpqI+ap6SZKXJbkgyb9ffGnNYt19YZJnr7CPD2QS8y/u7s8v2fz6TEL+t6pq8YdGPTbJ05Jck20/px4AANacucd8VT0zk5Dfkslz4E+rqqXDLu3u03fhMG/J5FNkT0nyiao6M8kRmYT8+kxupr1pF/YPAAB73NxjPsmx0+X6JC9YYcw5SU7f2QN0d1fVTyQ5L8nPJnl+ktuSfDDJb3X3eTu7bwAAmJfq7u2P4l9s3LixN23aNO9pAACwF6uqC7p74/bGramn2QAAADtOzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgxDwAAAxKzAMAwKDEPAAADErMAwDAoMQ8AAAMSswDAMCgqrvnPYehVNU1SS6b0+HvneTaOR2bPcPPeO/nZ7z38zPe+/kZ7/3Wws/46O4+cnuDxPxAqmpTd2+c9zzYffyM935+xns/P+O9n5/x3m+kn7HLbAAAYFBiHgAABiXmx/KGeU+A3c7PeO/nZ7z38zPe+/kZ7/2G+Rm7Zh4AAAblzDwAAAxKzAMAwKDEPAAADErMr2FV9YCq+tOqurKqbq+qS6vq96rqsHnPjV1XVadU1Wur6tyquqmquqr+Yt7zYvVU1RFV9eyqOqOqPl9Vt1bVjVX1oar6uary7+C9QFX916p6X1X98/RnfF1VfaKqXlpVR8x7fqy+qvrp6b+zu6qePe/5sOumjdUrfF097/ltixtg16iqenCS85LcJ8nbk1yc5P+2d+cxc1VlHMe/P9qAsrUIkSICpeyRREoa9kArUhBEEAUigrQKWhErW0BKCi1IIIJEQLaI8kIlEbSgMZYlQkspoATZURaBt2DYZGnZBcrjH+dMHMeZl7ez3Xub3ye5OZ1z75z7zG37zvOeeebc7YBJwGPAzhHxSnERWqck3Q98FngT+CewJXB1RBxaaGDWNZKmAZcAzwPzgWeAdYEDgFHAXODA8A/iSpP0HnAv8DfgJWA1YAdgAvAcsENEPFtchNZNkjYAHgJGAKsDR0bE5cVGZZ2SNAiMBn7aZPebEXFufyMavpFFB2AtXUxK5KdHxIW1TknnAccCZwLTCorNuuNYUhL/D2A3UrJnK5bHgS8Bf4yID2udkmYAdwNfISX2c4sJz7pkzYh4t7FT0pnADOBk4Ki+R2VdJ0nAFcArwHXACcVGZF22JCJmFR3E8vJHvCWUZ+UnA4PARQ27TwPeAg6TtFqfQ7Muioj5EfGEZ2VXXBFxa0T8oT6Rz/0vAJfmhxP7Hph1VbNEPrs2t5v1KxbruenA54CppPdis8I5mS+nSbm9uUkS8AZwB7Aq6WNcM6um93P7QaFRWC/tm9sHC43CukLSVsDZwPkRsbDoeKwnVsnfh5gh6QeSJkkaUXRQH8VlNuW0RW4fb7H/CdLM/ebALX2JyMy6RtJI4Bv54Y1FxmLdI+kEUg31KFK9/C6kRP7sIuOyzuX/s3NI33uZUXA41jtjSH/P9Z6WNDUibisioOFwMl9Oo3K7tMX+Wv/oPsRiZt13NrA1MC8ibio6GOuaE0hfcK65EZgSEf8qKB7rnlOB8cAuEfFO0cFYT1wB3A48ArwBjAOOBr4N3CBpx4h4oMD4WnKZjZlZH0maDhxPWqHqsILDsS6KiDERIdLs3gGkZOA+SdsWG5l1QtL2pNn4n0TEXUXHY70REbPz95xejIi3I+LhiJgGnAd8HJhVbIStOZkvp9rM+6gW+2v9S/oQi5l1iaSjgfNJSxhOiohXCw7JeiAnA9eTyiHXBq4qOCRrUy6vuYpU9jqz4HCsGLXFCnYtNIohOJkvp8dyu3mL/bWVEVrV1JtZyUg6BrgQeJiUyJf6JiTWuYhYTPrF7TOS1ik6HmvL6qT34q2Ad+tvJERaXQ7g57mv2frkVn21MrnSriDomvlyqq03PlnSSg3rU68B7Ay8Dfy5iODMbPlIOolUJ38/sEdEvFxwSNY/n8rtskKjsHb9G/hFi33bkuroF5Em4VyCs2KqrRz4VKFRDMHJfAlFxJOSbiZ9RPs90mxezWzSb4eXRYTXuDUrOUkzgdOBvwKTXVqzYpG0OfBiRCxt6F8JOIN08787I+K1IuKzzuQvux7RbJ+kWaRk/krfAbba8rKjzzTmVZLGAj/LD3/V57CGzcl8eR0F3AlcIGl34O/A9qQ16B8HTikwNusCSfsD++eHY3K7o6SB/OeXI8J3F6wwSYeTEvllpFUSpqcbSP6PwYgY6HNo1j17A2dJWgQ8Tboz6LqkuzqPA14AjiwuPDMbhoOB4yUtBBaTVrPZBNgH+BgwDzi3uPCG5mS+pPLs/ARSIrAX6Q3jedKX52Z7lmeFsA1weEPfuLxB+oHiZL7aNs7tCOCYFsfcBgz0JRrrhT8Bm5LWlB9PWjL4LdKkyxzgAn8aY1Z680n3+BlPKmVejbTIyCLS/+M5Zb5bu0ocm5mZmZmZDcGr2ZiZmZmZVZSTeTMzMzOzinIyb2ZmZmZWUU7mzczMzMwqysm8mZmZmVlFOZk3MzMzM6soJ/NmZmZmZhXlZN7MzHpG0oCkyLdF7+V5BiUN9vIcZmZl5GTezMxKT9ICSb7LoZlZg5FFB2BmZtYFuxcdgJlZEZzMm5lZ5UXEk0XHYGZWBJfZmJmVkKSxudZ8QNKWkn4n6VVJb0laJGlyk+esIumHkh6S9Lak1yXdLumgLo0/Kz9n4lDjDfP1TZE0V9JTkt7Jsd4h6dBm4wK75cdRty2oO65pzXwH12SspF9LelnSu5LukfTF4bw2M7N+8sy8mVm5bQzcBTwEXAasBxwM3CDpkIi4BkDSysBNpKT3UeAiYFXgq8A1kraJiBntjt8DlwCPAAuB54G1gb2BOZK2iIiZ+bglwGxgCrBR/nPN4FAn6OCabATcDTwFzAE+Qbomv5f0+YiYv7wv1sysZyLCmzdv3ryVbAPGApG3cxr2TQDeB14D1sx9J+dj5wEj6479JCnpDWCndsfP/bPy8ROHiHegoX8g949t6N+kyRgrA7fkc6/fsG9Bestqeb0GgcGGvk6uyWkNY+1ZG6vofxvevHnzVr+5zMbMrNyWAqfXd0TEPcDVwGjgy7n7m6Rk87iI+KDu2JeAM/LDIzoYv6uiSY17RLxHmj0fSXe+0NruNVkM/KghtpuAZ4DtuhCXmVnXOJk3Myu3eyPijSb9C3I7XtIawKbAcxHxaJNjb60d2874yxHrsEnaUNJFkh7NteyRa+Pn5kPW73D8Tq7J/RGxrEn/s8BancRlZtZtrpk3Myu3F1v0v5DbUXmDVHveTK1/dJvjd5WkcaSa9LWA24GbSZ8QLCOVuhwOrNLhaTq5JktaPOcDPAlmZiXjZN7MrNzWbdE/JrdL81bf12i9umPbGb/mw9w2e+9olhS3chzpC69TI2Kgfoekr5GS+U51ck3MzCrDMwxmZuW2bS4ZaTQxt/flMpkngfUlbdbk2Em5vbed8ev6XsvtBk2On9Ckr5VNczu3yb7dWjxnGYCkEcM5QYfXxMysMpzMm5mV2yjg1PoOSROAr5Nmla/P3b8EBJxTn/BKWgeYWXdMu+NDKo0BmCppZN3xGzSO8REGczux4bx70vwLqQCv5HbD5ThPu9fEzKwyXGZjZlZuC4EjJG0P3MF/14FfCfhORLyejzsX+AKwH/CApHmkNdUPJC3F+OOIWNTB+ETEXyQtBHYF7pZ0K6lMZ1/Seu7NZuybuRiYCvxG0m+B54Ctgb2Aa/P5G92SX8t1+bW9AyyOiDlDnKfda2JmVhmemTczK7engZ1IJS7TgINIpSF7R90NnfKyjnsAp+Su75Nqz58ADomIkzoZv85+wOXAp/M5xgMnAq3G/z8R8SCpzOVOYB/gu8CawAHApS2edjlwFumThBNJS0t+6yPO0+41MTOrDEVE0TGYmVkDSWNJifaVETGlauObmVl/eGbezMzMzKyinMybmZmZmVWUk3kzMzMzs4pyzbyZmZmZWUV5Zt7MzMzMrKKczJuZmZmZVZSTeTMzMzOzinIyb2ZmZmZWUU7mzczMzMwq6j+Av7ddHH0xzgAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['train', 'batch_size'] batch_size\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'confident_threshold'] confident_threshold\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", @@ -277,14 +829,14 @@ " np.where(values == evolution.get_value_from_config(\n", " params_dictionaries[i], param_path))[0][0],\n", " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", - " plt.yticks(np.arange(len(values)), values)\n", + " plt.yticks(np.arange(len(values)), values, fontsize=20)\n", " elif param_dict.get(\"evolve_bool\"):\n", " values = np.array([False, True])\n", " plt.scatter(i // POPULATION_SIZE, \n", " np.where(values == evolution.get_value_from_config(\n", " params_dictionaries[i], param_path))[0][0],\n", " c=colors[np.where(color_ids == i)[0][0]], alpha=0.5)\n", - " plt.yticks(np.arange(len(values)), [\"False\", \"True\"])\n", + " plt.yticks(np.arange(len(values)), [\"False\", \"True\"], fontsize=20)\n", "\n", " plt.ylabel(param_name, fontsize=20)\n", " plt.xlabel(\"population\", fontsize=20)\n", @@ -296,6 +848,15 @@ " " ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, From 1e4f12c94da1ff22005beed8b825d59dae41ab2f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 16:38:01 +0300 Subject: [PATCH 515/616] fix: clear all cells --- .../models/evolution/Results_analysis.ipynb | 597 +----------------- 1 file changed, 28 insertions(+), 569 deletions(-) diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb index 8ed1df5314..3cb6d21dca 100644 --- a/deeppavlov/models/evolution/Results_analysis.ipynb +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -2,35 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", - " return f(*args, **kwds)\n", - "/home/dilyara/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", - " from ._conv import register_converters as _register_converters\n", - "Using TensorFlow backend.\n", - "[nltk_data] Downloading package punkt to /home/dilyara/nltk_data...\n", - "[nltk_data] Package punkt is already up-to-date!\n", - "[nltk_data] Downloading package stopwords to\n", - "[nltk_data] /home/dilyara/nltk_data...\n", - "[nltk_data] Package stopwords is already up-to-date!\n", - "[nltk_data] Downloading package perluniprops to\n", - "[nltk_data] /home/dilyara/nltk_data...\n", - "[nltk_data] Package perluniprops is already up-to-date!\n", - "[nltk_data] Downloading package nonbreaking_prefixes to\n", - "[nltk_data] /home/dilyara/nltk_data...\n", - "[nltk_data] Package nonbreaking_prefixes is already up-to-date!\n", - "2018-06-25 16:20:16.625 DEBUG in 'gensim.models.doc2vec'['doc2vec'] at line 73: Fast version of gensim.models.doc2vec is being used\n", - "2018-06-25 16:20:16.629 INFO in 'summa.preprocessing.cleaner'['textcleaner'] at line 20: 'pattern' package not found; tag filters are not available for English\n", - "2018-06-25 16:20:17.53 DEBUG in 'matplotlib.backends'['__init__'] at line 90: backend module://ipykernel.pylab.backend_inline version unknown\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -53,219 +27,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Considered basic config:\n", - "{\n", - " \"dataset_reader\": {\n", - " \"name\": \"basic_classification_reader\",\n", - " \"x\": \"text\",\n", - " \"y\": \"intents\",\n", - " \"data_path\": \"snips\"\n", - " },\n", - " \"dataset_iterator\": {\n", - " \"name\": \"basic_classification_iterator\",\n", - " \"seed\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"field_to_split\": \"train\",\n", - " \"split_fields\": [\n", - " \"train\",\n", - " \"valid\"\n", - " ],\n", - " \"split_proportions\": [\n", - " 0.9,\n", - " 0.1\n", - " ]\n", - " },\n", - " \"chainer\": {\n", - " \"in\": [\n", - " \"x\"\n", - " ],\n", - " \"in_y\": [\n", - " \"y\"\n", - " ],\n", - " \"pipe\": [\n", - " {\n", - " \"id\": \"classes_vocab\",\n", - " \"name\": \"default_vocab\",\n", - " \"fit_on\": [\n", - " \"y\"\n", - " ],\n", - " \"level\": \"token\",\n", - " \"save_path\": \"vocabs/snips_classes.dict\",\n", - " \"load_path\": \"vocabs/snips_classes.dict\"\n", - " },\n", - " {\n", - " \"in\": [\n", - " \"x\"\n", - " ],\n", - " \"out\": [\n", - " \"x_lower\"\n", - " ],\n", - " \"name\": \"str_lower\"\n", - " },\n", - " {\n", - " \"id\": \"my_embedder\",\n", - " \"name\": \"fasttext\",\n", - " \"save_path\": \"embeddings/dstc2_fastText_model.bin\",\n", - " \"load_path\": \"embeddings/dstc2_fastText_model.bin\",\n", - " \"dim\": 100\n", - " },\n", - " {\n", - " \"id\": \"my_tokenizer\",\n", - " \"name\": \"nltk_tokenizer\",\n", - " \"tokenizer\": \"wordpunct_tokenize\"\n", - " },\n", - " {\n", - " \"in\": [\n", - " \"x_lower\"\n", - " ],\n", - " \"in_y\": [\n", - " \"y\"\n", - " ],\n", - " \"out\": [\n", - " \"y_labels\",\n", - " \"y_probas_dict\"\n", - " ],\n", - " \"main\": true,\n", - " \"name\": \"intent_model\",\n", - " \"save_path\": \"evolution/classification/intents_snips\",\n", - " \"load_path\": \"evolution/classification/intents_snips\",\n", - " \"classes\": \"#classes_vocab.keys()\",\n", - " \"kernel_sizes_cnn\": [\n", - " 1,\n", - " 2,\n", - " 3\n", - " ],\n", - " \"filters_cnn\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 100\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"confident_threshold\": {\n", - " \"evolve_choice\": true,\n", - " \"values\": [\n", - " 0.5,\n", - " 1\n", - " ]\n", - " },\n", - " \"optimizer\": \"Adam\",\n", - " \"lear_rate\": {\n", - " \"evolve_range\": [\n", - " 0.0001,\n", - " 0.1\n", - " ],\n", - " \"scale\": \"log\"\n", - " },\n", - " \"lear_rate_decay\": {\n", - " \"evolve_range\": [\n", - " 0.0001,\n", - " 0.1\n", - " ],\n", - " \"scale\": \"log\"\n", - " },\n", - " \"loss\": \"binary_crossentropy\",\n", - " \"text_size\": 15,\n", - " \"coef_reg_cnn\": {\n", - " \"evolve_range\": [\n", - " 1e-06,\n", - " 0.001\n", - " ]\n", - " },\n", - " \"coef_reg_den\": {\n", - " \"evolve_range\": [\n", - " 1e-06,\n", - " 0.001\n", - " ]\n", - " },\n", - " \"dropout_rate\": {\n", - " \"evolve_range\": [\n", - " 0.1,\n", - " 0.9\n", - " ]\n", - " },\n", - " \"dense_size\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 100\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"model_name\": \"cnn_model\",\n", - " \"embedder\": \"#my_embedder\",\n", - " \"tokenizer\": \"#my_tokenizer\",\n", - " \"check_bool\": {\n", - " \"bool\": true\n", - " }\n", - " }\n", - " ],\n", - " \"out\": [\n", - " \"y_labels\",\n", - " \"y_probas_dict\"\n", - " ]\n", - " },\n", - " \"train\": {\n", - " \"epochs\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"batch_size\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"metrics\": [\n", - " \"classification_accuracy\",\n", - " \"classification_f1\",\n", - " \"classification_roc_auc\"\n", - " ],\n", - " \"validation_patience\": 5,\n", - " \"val_every_n_epochs\": 1,\n", - " \"log_every_n_epochs\": 1,\n", - " \"validate_best\": true,\n", - " \"test_best\": false\n", - " },\n", - " \"metadata\": {\n", - " \"labels\": {\n", - " \"telegram_utils\": \"IntentModel\",\n", - " \"server_utils\": \"KerasIntentModel\"\n", - " },\n", - " \"download\": [\n", - " \"http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz\",\n", - " \"http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz\",\n", - " {\n", - " \"url\": \"http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv\",\n", - " \"subdir\": \"snips\"\n", - " },\n", - " {\n", - " \"url\": \"http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin\",\n", - " \"subdir\": \"embeddings\"\n", - " }\n", - " ]\n", - " }\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "CONFIG_FILE = \"../../configs/evolution/evolve_intents_snips.json\"\n", "KEY_MAIN_MODEL = \"main\"\n", @@ -280,25 +46,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2018-06-25 16:20:17.65 INFO in 'deeppavlov.models.evolution.evolution_param_generator'['evolution_param_generator'] at line 55: Main model path in config: ['chainer', 'pipe', 4]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Title name for the considered evolution is `intents_snips`.\n", - "Number of populations: 6.\n" - ] - } - ], + "outputs": [], "source": [ "evolution = ParamsEvolution(population_size=POPULATION_SIZE,\n", " key_main_model=KEY_MAIN_MODEL,\n", @@ -324,50 +74,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Measure: classification_accuracy\n", - "valid:\n", - "min for\t0 model on\t0 population\n", - "max for\t1 model on\t4 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n", - "\n", - "Measure: classification_f1\n", - "valid:\n", - "min for\t0 model on\t5 population\n", - "max for\t1 model on\t4 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n", - "\n", - "Measure: classification_roc_auc\n", - "valid:\n", - "min for\t1 model on\t3 population\n", - "max for\t0 model on\t0 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: 'argmin' is deprecated. Use 'idxmin' instead. The behavior of 'argmin' will be corrected to return the positional minimum in the future. Use 'series.values.argmin' to get the position of the minimum now.\n", - " if __name__ == '__main__':\n", - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", - " # Remove the CWD from sys.path while we load stuff.\n" - ] - } - ], + "outputs": [], "source": [ "MEASURES = evolution.get_value_from_config(\n", " evolution.basic_config, list(evolution.find_model_path(\n", @@ -394,42 +103,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "path_to_pics = expand_path(Path(evolution.get_value_from_config(\n", " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).joinpath(\"pics\"))\n", @@ -488,25 +166,16 @@ "collapsed": true }, "source": [ - "## If you want to plot measures depending on population colored by `evolution_model_id`" + "## If you want to plot measures depending on population colored by `evolution_model_id`\n", + "\n", + "#### That means model of the same `id` are of the same color." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "params_dictionaries = []\n", "\n", @@ -527,42 +196,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", @@ -609,199 +247,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['dataset_iterator', 'seed'] seed\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'filters_cnn'] filters_cnn\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'lear_rate'] lear_rate\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'lear_rate_decay'] lear_rate_decay\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'coef_reg_cnn'] coef_reg_cnn\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'coef_reg_den'] coef_reg_den\n" - ] - }, - { - "data": { - "image/png": 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wAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwzIYKGFV1j6r6k6q6vKpuqqpLquq3q+ouM/Zz8PS+S6b9XD7t9x4jxq6qw6vqp6rqbXPGuKqq3lVVj1/KdwcAgLVgz9UuYJSqOjrJB5McluScJP+W5EFJfjrJmVV1SndftYh+Dpn2c58k703y+iT3TfLEJI+uqpO6++Jljv1TSX4xyWeT/H2SK5IckeTxSR5RVS/t7p9d0g8CAABW0YYJGEl+N5Nf8J/e3a/YcbKqXpLkGUlekOTHF9HPCzMJFy/p7mfO6efpSV42HefMZY794SSndff75nZSVcclOS/JM6rqz7v7/EXUCwAAa0Z192rXsGzTGYQLk1yS5Oju3j7n2uYkX0xSSQ7r7v/YTT/7J/lSku1J7tbd1825tinJxZnMNBy9YxZj1Nhz7nllkicn+bnufvFivv+WLVt669ati2kKAABLUlXnd/eWhdptlDUYp0+P75z7C36STEPCB5LcKcmJC/RzYpL9knxgbriY9rM9yTt2Gm/k2DvcMj1uW2R7AABYMzZKwDh2evzMLq5fMD3eZwX6GTV2quqAJN+dpJO8c6H2AACw1myUgHHg9HjNLq7vOH/QCvQzZOyqqiR/lOQbkvxed39qgfZPqaqtVbX1y1/+8u6aAgDA7WajBIyN4MVJvifJ+5Ms+Aap7n5ld2/p7i2HHnroihcHAACLsVECxo5ZggN3cX3H+atXoJ9lj11Vv5XJ26bOTfKo7r5pgToBAGBN2iivqf309LirdQ7HTI+7WiexnH6WNXZVvTTJz2SyH8Z3dff1C9QIAABr1kaZwfj76fGM6etkv276qthTklyfyR4Tu3NekhuSnDK9b24/m5KcsdN4Sx67Jv53JuHiXUkeLVwAALDebYiA0d0XZfLWpSOTPG2ny89Lcuckr527D0VV3beq7rtTP19L8tpp+7N36ucnp/2/Y+5O3kscu5K8MslTk7wtyWO6+4bFfl8AAFirNsRGe8nXN7z7YCY7ap+T5FNJHpzJPhWfSXJyd181p30nSXfXTv0cMu3nPknem8mu28clOSuTTfhOnoaK5Yz93EwCzA1JfjvJzfN8pY9295sW891ttAcAwEpb7EZ7G2UNRrr7oqrakuTXkpyZ5FGZ7KL9siTP6+6vLrKfq6rqpCTPTfLYJA9JclWSVyV5Tnd/YcDYR02P+yX55V2U8pokiwoYAACwVmyYGYw7MjMYAACstMXOYGyINRgAAMDaIGAAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAw+w56w1VtVeSs5I8KMldkuwxT7Pu7ictszYAAGCdmSlgVNXdk7wryX2T1G6adhIBAwAA7mBmncF4cZLjkrwuyR8m+XySbaOLAgAA1qdZA8YZSc7t7h9YiWIAAID1bdZF3vsm+dBKFAIAAKx/swaMTyQ5YiUKAQAA1r9ZA8b/n+QxVXW/lSgGAABY32Zdg/GlJH+b5INV9bIk5ye5er6G3X3uMmsDAADWmVkDxj9k8graSvLs6d93Zb79MQAAgA1s1oDxa9l9qAAAAO7AZgoY3X32CtUBAABsALMu8gYAANilWR+RSpJU1V5JHp7Jrt77d/fzp+f3TXJAkiu7e/uwKgEAgHVh5hmMqjozySVJ3pLkxUnOnnP5hCRfTPK9A2oDAADWmZkCRlVtSfKmTBZ6PyPJX8y93t3nJflskseNKhAAAFg/Zp3BeHaS65Ns6e6XJ7lgnjYfSXL8cgsDAADWn1kDxilJ3tTdV+ymzeeT3G3pJQEAAOvVrAFj/yRXLtDmTkvoFwAA2ABmDQKXJfmmBdqckOTipZUDAACsZ7MGjLcl+c6q+vb5LlbVI5OcnOTvllsYAACw/swaMH4jydVJ3llVv5nkfklSVY+efv6rTF5T+5KhVQIAAOvCTBvtdfdlVXVGkjck+fk5l96cpJJclOTx3b3QOg0AAGADmnkn7+7+56o6Nsmjk5yU5JAk1yQ5L8k53b1tbIkAAMB6MXPASJLuvjWTWYs3jy0HAABYz7xOFgAAGGa3MxhV9T+X2nF3/+lS7wUAANanhR6RenWSnvO5dvo8nx1tBAwAALiDWShgPHGec49P8t+TvC/JPyS5Isk3Jjk9yamZrMt447gSAQCA9WK3AaO7XzP3c1U9KsmZSc7q7r/dqfnzquqsTF5h+/tDqwQAANaFWRd5PyvJG+cJF0mS7j4nyZuSPHu5hQEAAOvPrAHj+CQXLtDmwiT3X1o5AADAejZrwLg5k5CxO8cnuWVp5QAAAOvZrAHjPUkeVVU/WVU190JN/FSSRyZ596gCAQCA9WPWnbx/KZO3Rb0syc9U1T8m+fck35Dk25McleQr03YAAMAdzEwBo7svqqoTk/xukkckuddOTd6V5GndffGg+gAAgHVk1hmMdPeFSc6oqsOTPCDJgUmuSfIv3X3Z4PoAAIB1ZOaAscM0TCwqUEz3xziru39kqeMBAABr36yLvJfqhCQ/dDuNBQAArJLbK2AAAAB3AAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMPcXgHjkiTn3k5jAQAAq+R2CRjd/ZruPv32GAsAAFg9e87SuKqes4hm25Ncm+RTSd7X3TcvpTAAAGD9mSlgJDk7Sc/5XHP+vvP5TnJVVf1Ud//l0soDAADWk1kfkTo9yTlJbknyx0l+OMkjp8c/mZ5/U5LvSfKiJPsm+bOqesiYcgEAgLVs1hmMI5J8R5IHdvfHd7r2p1X1O0k+kOSN3f2sqnp9kvOT/FyS9y+7WgAAYE2bdQbjGUneME+4SJJ098eS/FWSn51+/niStyQ5aTlFAgAA68OsAePYJFcs0ObyabsdLkhy0IzjAAAA69CsAeO6LDwbcXKSr835fOfpfQAAwAY3a8B4a5KHVtULq+rOcy9U1Z2r6jeSnDptt8M3Z7LRHgAAsMHNusj7lzN5k9QvJvnxqvrXJP+e5BuS3D+TR6E+l+RXkqSq7pbk3kl+f1TBAMDiXPjZT+V9l1+UK7I935hNeejdj869jzputcsCNriZAkZ3X1FVD8zkFbTfl8lsxQ43JHl1kl/q7i9N238xyeFjSgUAFuvCz34qr7v8gmxOclgq12V7Xnf5Bfn+RMgAVtSsj0ilu7/c3U/KZLbi/kkekuT4JAd194/sCBcAwOp53+UXZXOSzbUpm6qyuTZl8/Q8wEqa9RGpr+vuW5J8YmAtrCOXfvJLufTiC3JzXZu9+4Acca9jcsT9Dlvtshjo3ddek9dedV0uu2l7Dt9nU37wkM15xAEHrnZZwCJdke05LHWbc3dO5YpsX6WKgDuKJQWMqtorycOTHJdk/+5+/vT8vkkOSHJld/s/2AZ16Se/lAs++5Fs6n2zd2/OttyYCz77kSQPFDI2iHdfe01ecNk1OWCP5G57bcrVt2zPCy67JkmEDFgnvjGbcl22Z/OckPEf6Xzj7A8vAMxk5oBRVWcm+eMk35ikknSS508vn5DJTt7/X5LXDaqRNebSiy/Ipt43e9a+SZI9s2+2bZ+cFzA2htdedV0O2CM5aK/JLyIHbdqUZHtee9V1AsYGcuVVH89nr/vXXFc3ZnPvm6M23z93PeRbVrssBnno3Y/O6y6/IOntuXMq/5HOdUm+6+5Hr3ZpDHTZWz+ar/zhG7PHFz6XW+9xzxz85Mfl8EedsNplcQc30z9jVNWWJG/KJFQ8I8lfzL3e3ecl+WySx40qkLXn5ro2e2af25zbM/vk5rp2lSpitMtu2p4D9rjt/x4O2GNTLrvJxORGceVVH8/Hvvbh3JRbsn/vk5tySz72tQ/nyqs+vtqlMci9jzou33/3Y7I5m/KldDZnU77/7sdY4L2BXPbWj+baZ784dfVXcuvdD09d/ZVc++wX57K3fnS1S2Ogiy79XN5/7jn5x3e/Ou8/95xcdOnnVrukBc06g/HsJNcn2TJ9o9Rz52nzkSTfuuzKWLP27gOyLTdmz+z79XPbclP27gNWsSpGOnyfyWNRk5mLiWtvnazFYGP47HX/mn2yR/bJXkkyOfbkvFmMjePeRx0nUGxgX/nDN6YOOCg56ODJg3AHHZxbp+fNYmwMF136uVzxmXcne+yX7XsdlNp2/eRzHpGjj7jnape3S7P+tnBKkjd19xW7afP5JHdbekmsdUfc65hs33RjtvWNSXe29Y3ZvunGHHGvY1a7NAb5wUM259pbk6tv2Z7t2yfHa2+dnGdjuK5uzN47/RvT3tkz19WNq1QRMKs9vvC59E6PrfYBB2aPL6z9f+FmcS6/9F+yfY/9kj3vlKpK9rxTtu+xXy6/9F9Wu7TdmjVg7J/kygXa3GkJ/bKOHHG/w3LMUQ/MnrVvbt50XfasfXPMURZ4bySPOODAPOvwA3PQXpvyxVu256C9NuVZhx9o/cUGsrn3zc3ZdptzN2dbNve+u7gDWGtuvcc9U9dec5tzde01ufUea/dftplN3fzV9B773eZc77Ff6uavrlJFizPrI1KXJfmmBdqckOTipZXDenHE/Q4TKDa4RxwgUGxkR22+fz72tQ8nPZm5uDnbclPdmvvu/22rXRqwSAc/+XG59tkvzq2ZzFzUtddkj2uvzgE//6TVLo1Beu+7pLZdn+x5p6+fq1tvSO99l1WsamGzzjS8Lcl3VtW3z3exqh6Z5OQkf7fcwgBYOXc95Fty/P4Pyj7ZK1+rm7JP9srx+z/I+gtYRw5/1Ak54PnPTB90cPa4/LL0QQfngOc/0/qLDeTuRzwgm269Idl2fbo72XZ9Nt16Q+5+xANWu7Tdqu5efOOqw5N8LJPHoF6R5MgkT0jymCSnJnlakmuSHN/dCz1KNVxV3SPJryU5M8khSb6YyVuvntfdi55LqqqDkzwnyWMzWU9yVZK3J3lOd39h1NhVdb8kZyc5LZP9Qy5N8vokL+ruGxZb75YtW3rr1q2LbQ4AwDpx0aWfy+WX/svkcam975K7H/GAVVvgXVXnd/eWBdvNEjCmHX9rkjckudec053JnhgXJXl8d9/u7zmsqqOTfDDJYUnOSfJvSR6U5PQkn05ySndftYh+Dpn2c58k783krVj3TXJWki8lOam7L97pnpnHrqoHT/vfK8lfZ7I4/mFJtmSyl8jDu/umxXx3AQMAgJW22IAx80Z73f3PVXVskkcnOSmTf62/Jsl5Sc7p7m27u38F/W4mv+A/vbtfseNkVb0kkz07XpDkxxfRzwszCRcv6e5nzunn6UleNh3nzOWMXVV7JHlVJjNBZ3X3m6fnN2US3r57et+LFvPFAQBgrZj1Eal7Jrl5gdfU3u6mMwgXJrkkydHdvX3Otc2ZPK5USQ7r7v/YTT/7ZzJLsT3J3br7ujnXNmWyeP2I6RgXL3XsqnpYkvckObe7H7pTDffKZCbo0iRH9SL+A5nBAABgpS12BmPWRd6fzeRf+Nea06fHd879BT9JpiHhA5nMFpy4QD8nJtkvyQfmhotpP9uTvGOn8ZY69sOmx7fvXMA0uHwmkyBzr52vAwDAWjZrwLg6C++DsRqOnR4/s4vrF0yP91mBfm6vewAAYM2bNWCcl2Qtvhdrx8v6r9mbB+wzAAAeY0lEQVTF9R3nD1qBfm6ve26jqp5SVVurauuXv/zlXTUDAIDb1awB4+wkD6mqH12BWphBd7+yu7d095ZDDz10tcsBAIAks79F6pFJ/iHJH1TVTyT5cJIrMnlN7Vzd3c9ffnmLtuNf/He17fCO81evQD+31z0AALDmzRowzp7z9wdk149LdZLbM2B8enrc1ZqFY6bHXa15WE4/t9c9AACw5s0aME5fuMmq+Pvp8Yyq2jTPq2JPSXJ9JmtIdue8JDckOaWqNs/zmtozdhpvqWO/N8mzMtlP4zfmFjB9Te19MnlN7W029AMAgLVupoDR3e9bqUKWo7svqqp3ZhIAnpbkFXMuPy/JnZP8wdw9MKrqvtN7/21OP1+rqtcmeUomszXPnNPPTyY5Msk75u7kvZSxk7wvyaeSnFpVj9lpo73fnLb5/cXsgQEAAGvJTBvtLXmQqucmeXZ3z7xz+AxjHJ3kg5nsqH1OJr/APziTWZfPJDm5u6+a076TpLtrp34OmfZzn0xmGj6c5LgkZ2WyCd/J3X3Rcsae3vPgaf97JfnrJJ9L8vAkWzLZO+Ph3X3TYr67jfYAAFhpK7XR3nLUwk2WbvpL/5Ykr87kl/tnJjk6ycuSnLjzL/i76eeqJCcleXmSe0/7eXCSVyX5tp3DxVLH7u4PJXlgJoHkjCTPyGRx968l+Y7FhgsAAFhLVmxGYTV09+eTPHGRbXcZeLr7K0l+evpn+Nhz7vlkku+Z5R4AAFjLbs8ZDAAAYIMTMAAAgGEEDAAAYBgBAwAAGEbAAAAAhhEwAACAYXYbMKrqgKra+/YqBgAAWN8WmsH4apJf3PGhqv6kqh6zhHHelORHlnAfAACwjiwUMHqnNj+c5IRZB+nuj3X3a2a9DwAAWF8WChhfTHLv26MQAABg/dtzgevvTfIDVXXXTMJGkjy2qo5c4L7u7ictszYAAGCdWShg/EKSb0jyHZnMdnQmj0gt9JhUJxEwAADgDma3AaO7/z3JmVW1V5K7JbkkyW8nednKlwYAAKw3C81gJEm6+5Ykn6uqS5Nc0t2XrmxZAADAerSogLFDdx+1UoUAAADr30wBY4fpI1MPT3Jckv27+/nT8/smOSDJld29fViVAADAurDQa2r/i6o6M5O1GG9J8uIkZ8+5fEImb5v63gG1AQAA68xMAaOqtmSyK3cneUaSv5h7vbvPS/LZJI8bVSAAALB+zDqD8ewk1yfZ0t0vT3LBPG0+kuT45RYGAACsP7MGjFOSvKm7r9hNm89n8kpbAADgDmbWgLF/kisXaHOnJfQLAABsALMGgcuSfNMCbU5IcvHSygEAANazWQPG25J8Z1V9+3wXq+qRSU5O8nfLLQwAAFh/Zg0Yv5Hk6iTvrKrfTHK/JKmqR08//1Umr6l9ydAqAQCAdWHWnbwvq6ozkrwhyc/PufTmJJXkoiSP7+6F1mkAAAAb0Mw7eXf3P1fVsUkeneSkJIckuSbJeUnO6e5tY0sEAADWi5kDRpJ0962ZzFq8eWw5AADAerakgLFDVW1OclCSa7r72jElAQAA69XM+1VU1Z5V9UtVdWEmC74vSfLVqrpwen5ZoQUAAFi/ZgoDVbV3krcneWiSzmTX7i9msnP3kUlekOTMqjqju28eWyoAALDWzTqD8bNJTkvyliTHdfeR3X1Sdx+Z5Ngkf5vkIdN2AADAHcysAeN/JPlEksd29wVzL3T3RUken+T/JvmBMeUBAADryawB495J3tbd2+e7OD3/tiRHL7cwAABg/Zk1YNycZP8F2tw5yS1LKwcAAFjPZg0Y/5rkCVV16HwXq+quSZ6Q5GPLLQwAAFh/Zg0Yv5Pk0CQfrqonVdW9qmq/qjqqqp6Y5EPT678zulAAAGDtm+k1td39hqo6IckvJXnlPE0qyW919xtGFAcAAKwvM2+K192/UlVvTvKkJA9IcmCSa5L8S5I/6e5/GlsiAACwXixp1+3uPi/JeYNrAQAA1rmZ1mBU1fdU1Xur6u67uH54Vb2nqh4/pjwAAGA9mXWR948mOai7L5/vYndflskjUz+63MIAAID1Z9aA8S1Jti7Q5iNJ7r+0cgAAgPVs1oBxcJIvLdDmqiR3XVo5AADAejZrwLgyyTELtDkmydVLKwcAAFjPZg0YH0jymKq673wXq+q4JGclef9yCwMAANafWQPG/8rk1bb/WFVPr6r7VNWdp8efziRY7DFtBwAA3MHMupP3R6rqqUn+d5KXTv/MdWuSn+juDw2qDwAAWEeWspP3H1bVPyZ5apIHJzkokzUX5yX5ve7+1NgSAQCA9WKpO3l/KslPDa4FAABY52ZdgwEAALBLAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDAbJmBU1clV9daq+kpV3VBV/1pVP1NVeyyhr/tV1Ruq6ktVdWNVfbqqnldV+40Yv6pOqKqzq+oDVfXFqrq5qi6rqtdV1bfOWi8AAKwVGyJgVNVZSc5NcmqSNyb5nSR7J3lpktfP2NeDk3wkyWOTvDvJy5Jcm+Q5Sd5VVfsMGP/3kzw3yT5J/mba7hNJvi/Jh6rq8bPUDAAAa0V192rXsCxVdUCSC5McmOSU7t46Pb9vkvcmOSnJ93f3gkFjOtvw8STHJTmru988Pb8pyRuSfHeSX+7uFy1n/Kr6qSRv6+4Ldxr/B5L8WZKrkty9u29ezM9gy5YtvXXr1sU0BQCAJamq87t7y0LtNsIMxhOSHJrk9Tt+uU+S7r4xya9OP/7EIvt6aCbh4twd4WLa1/YkvzD9+ONVVcsZv7tfsXO4mJ7/8yQXJDkkybcssmYAAFgzNkLAeNj0+PZ5rp2b5PokJ8/3aNMsfXX3xUk+k+SIJPdaofGT5Jbpcdsi2wMAwJqxEQLGsdPjZ3a+0N3bknw2yZ65bSiYua+pC6bH+6zE+FV1YpL7JbkskzUZu2v7lKraWlVbv/zlLy/UNQAA3C42QsA4cHq8ZhfXd5w/aIX6GjJ+VR2c5E+nH5/R3bfurn13v7K7t3T3lkMPPXR3TQEA4HazJgJGVV1SVT3Dnz9b7ZpHqqo7JzknyTFJfqu7/2qVSwIAgCXZc7ULmLooyY0ztL98zt93zBAcOF/DOeevXkS/S+lrWeNPw8Vbknx7kpd09y8uok4AAFiT1kTA6O6HL+P2TyfZksm6iPPnXqiqPZMclcmC6YsX2Vdy2zUWcx0zPc5db7Hk8atqcybh4iGZzFwIFwAArGtr4hGpZXrv9HjmPNdOTXKnJB/s7puW01dV3SuTEHFpbhsWljR+VR2Y5J2ZhIsXCBcAAGwEGyFg/HWSK5N8X1V9feOP6UZ3vz79+Htzb6iqO1XVfavqnjv19b4kn0pyalU9Zk77TUl+c/rx9/u2uxMuZfy7ZLJL+IlJntvdvxoAANgA1sQjUsvR3ddW1ZMz+UX/H6rq9Um+kuQxmbxC9q+T/OVOtz0oyd9nEihOm9PXrVX1xExmJf66qv46yeeSPDyTx6A+kOSlA8b/m2l/FyXZVFVnz/PV3tTdH138TwIAAFbfug8YSdLdb6qqhyZ5VpLvTrJvkguT/GySl+8047BQXx+qqgcmeV6SM5JszuSxqF9L8qL5HrVawvhHTY9HJ3nuLkq5JImAAQDAulIz/O7NGrVly5beunXrapcBAMAGVlXnd/eWhdpthDUYAADAGiFgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDCBgAAMAwAgYAADCMgAEAAAwjYAAAAMMIGAAAwDACBgAAMIyAAQAADCNgAAAAwwgYAADAMAIGAAAwjIABAAAMI2AAAADDbJiAUVUnV9Vbq+orVXVDVf1rVf1MVe2xhL7uV1VvqKovVdWNVfXpqnpeVe23EuPXxLuqqqd/9py1ZgAAWAs2RMCoqrOSnJvk1CRvTPI7SfZO8tIkr5+xrwcn+UiSxyZ5d5KXJbk2yXOSvKuq9lmB8X8yyelJbpylVgAAWGvWfcCoqgOS/GGSW5Oc1t1P6u6fT3JCkn9K8oSq+r5F9rVHklcluVOSJ3T3/+juX0zy4CT/J8kpSZ4xcvyqOjbJbyb5X0n+ffHfHAAA1p51HzCSPCHJoUle391bd5zs7huT/Or0408ssq+HJjkuybnd/eY5fW1P8gvTjz9eVTVi/OmjUK9NcnGS5y6yRgAAWLM2QsB42PT49nmunZvk+iQnz/do0yx9dffFST6T5Igk9xo0/q8meUCSH+7um/5fe3ceZFlZ3nH8+wMSjdtgEMW4MKCiVqyKY41bsARCQINxiWs0GiCiIS7ELRqx0BmMSgwxLnGNy+holRpwqVRErQjIZrRGRdEEQWTABFEWARFUwCd/nNOVS3t7uLfv23262++n6tbb9z3nvufpc2qmz3PPu0wQnyRJkrSirYUE4759ed78DVV1I3AhsAs3Twqmbqt3fl/uM+vxkzwYeBVw3OiTj0kleW6SbUm2XXbZZdN+XJIkSVoSayHBWNeXVy+wfa5+1yVqa+rP9LNRbQW+DRw7QVy/oqreU1Ubq2rj7rvvvpgmJEmSpOZWRIKRZPvIFK2TvD48dMwzeiPdE41Dq+qGoYORJEmSWlkp6y1cwHRTtF4y8vPcE4J143Ycqb9qgnYX09ZUn0myH/B8YFNVfWOCmCRJkqRVY0UkGFV14Awf/w6wkW5cxFdHN/SzNO0F3Eg3U9MkbcHNx1iMuk9fjo63mPb4G4AAm5NsXuA4N/QTVW2oqrMniFuSJElaEVZEF6kZndyXjx6z7ZF0a1qcNeEsTQu2lWRvuiTiIm6erEx7/G8B71vgdW2/z/v791dMELMkSZK0YqSqho5hJv1CdxcAdwD2nZuRKcmt6W7+Hw48vao+OvKZ2wD3BK6rqotH6ncGzqFbC+Pxc2thJNkJ+BjdmhevrKrjZjn+Dn6X7XTT4P5GPwPVRDZu3Fjbtk09EZUkSZI0sSRfraqNt7TfiugiNYuquibJc4ATgFOTfBS4Engc3RSyJ9AlB6MeApwCfBHYf6Stm5IcTpcYnJDkBOBi4EC6blBnAv/U4PiSJEnSmrQWukhRVZ+iW4X7NOBJwAuBG4CXAH9aUzymqaovAw8GPg0cDLyYbqD2scBB47patTy+JEmStJqt+i5SsouUJEmSlt6kXaTWxBMMSZIkSSuDCYYkSZKkZkwwJEmSJDVjgiFJkiSpGRMMSZIkSc2YYEiSJElqxgRDkiRJUjMmGJIkSZKaMcGQJEmS1IwJhiRJkqRmTDAkSZIkNWOCIUmSJKkZEwxJkiRJzZhgSJIkSWrGBEOSJElSMyYYkiRJkpoxwZAkSZLUjAmGJEmSpGZMMCRJkiQ1Y4IhSZIkqRkTDEmSJEnNmGBIkiRJasYEQ5IkSVIzJhiSJEmSmjHBkCRJktSMCYYkSZKkZkwwJEmSJDVjgiFJkiSpGRMMSZIkSc2YYEiSJElqxgRDkiRJUjMmGJIkSZKaMcGQJEmS1IwJhiRJkqRmTDAkSZIkNWOCIUmSJKkZEwxJkiRJzZhgSJIkSWrGBEOSJElSMyYYkiRJkpoxwZAkSZLUjAmGJEmSpGZMMCRJkiQ1Y4IhSZIkqRkTDEmSJEnNmGBIkiRJasYEQ5IkSVIzqaqhY9CMklwGXDTQ4e8EXD7QsbU8vMZrn9d47fMar31e47VvJVzjPatq91vayQRDM0myrao2Dh2Hlo7XeO3zGq99XuO1z2u89q2ma2wXKUmSJEnNmGBIkiRJasYEQ7N6z9ABaMl5jdc+r/Ha5zVe+7zGa9+qucaOwZAkSZLUjE8wJEmSJDVjgiFJkiSpGRMMSZIkSc2YYGgqSe6e5P1JLkny8yTbk7w5yR2Hjk2zS/LkJG9LcnqSa5JUkg8PHZfaSbJbkiOSfDLJd5Ncn+TqJGckeXYS/y6sAUn+PskXkny/v8ZXJvl6ktck2W3o+NRekmf2/2dXkiOGjkez6++xaoHXpUPHtyMO8tbEktwLOAu4M/Bp4FzgIcABwHeAfavqiuEi1KySnA38HnAt8D/A/YCPVNUzBw1MzSQ5Engn8APgFOBi4C7AE4F1wInAU8o/Dqtakl8AXwP+C/gRcFvgYcBG4BLgYVX1/eEiVEtJ7gGcA+wM3A54TlW9d9ioNKsk24FdgTeP2XxtVR2/vBFNbpehA9Cq8g665OKoqnrbXGWSNwEvBl4HHDlQbGrjxXSJxXeB/ehuQLW2nAc8Dvj3qvrlXGWSo4GvAE+iSzZOHCY8NXKHqvrZ/MokrwOOBl4JPG/Zo1JzSQJ8ALgC+ATwsmEjUmNXVdWmoYOYlo/CNZH+6cXBwHbg7fM2vwb4KfCsJLdd5tDUUFWdUlXn++312lVVJ1fVv40mF339pcC7+rf7L3tgampcctH7eF/eZ7li0ZI7CvgD4HC6v8XS4EwwNKkD+vLzY25MfgKcCdyG7hG8pNXphr68cdAotJQe25ffHDQKNZHk/sBxwFuq6rSh49GSuFU/vuboJH+d5IAkOw8d1C2xi5Qmdd++PG+B7efTPeHYB/jCskQkqZkkuwB/3r/97JCxqJ0kL6Prk7+ObvzFI+iSi+OGjEuz6//NbqUbR3X0wOFo6exBd51HXZjk8Kr64hABTcIEQ5Na15dXL7B9rn7XZYhFUnvHAQ8APlNVnxs6GDXzMrpB/HM+CxxWVZcNFI/aeTWwAXhEVV0/dDBaEh8ATge+DfwE2Bt4AfBc4KQkD6+qbwwY34LsIiVJv+aSHAW8lG5muGcNHI4aqqo9qip034I+ke4G5etJHjRsZJpFkofSPbX4x6r60tDxaGlU1eZ+3NwPq+q6qvpWVR0JvAn4LWDTsBEuzARDk5p7QrFuge1z9VctQyySGknyAuAtdNOZHlBVVw4ckpZAf4PySbqurLsBHxo4JC1S3zXqQ3Rdlo8ZOBwNY25CjkcOGsUOmGBoUt/py30W2D43I8lCYzQkrTBJXgS8DfgWXXKxohdu0uyq6iK6ZPJ3k9xp6Hi0KLej+1t8f+Bno4uv0c3qCPAvfd249RO0+s11cVyxM3c6BkOTmlsP4eAkO82bP//2wL7AdcB/DhGcpOkkeQXduIuzgYOq6vKBQ9Ly+Z2+vGnQKLRYPwfet8C2B9GNyziD7otBu0+tTXMzdn5v0Ch2wARDE6mqC5J8nu7x+vPpvvWcs5kui353VTkHt7TCJTkGOBb4KnCw3aLWliT7AD+sqqvn1e8EvJZuwdSzqurHQ8Sn2fQDuo8Yty3JJroE44Ou5L269VMQXzz/virJeuCf+7cfXuawJmaCoWk8DzgLeGuSA4H/Bh5Kt0bGecCrBoxNDSR5AvCE/u0effnwJFv6ny+vKleJXcWSHEqXXNxENzvJUd1CwDezvaq2LHNoaucQ4A1JzgAupFvh+S7AfnSDvC8FnjNceJIm8DTgpUlOAy6im0XqXsBjgFsDnwGOHy68HTPB0MT6pxgb6W5OHk33R+wHdANEN/tt2JrwQODQeXV79y/o/pMzwVjd9urLnYEXLbDPF4EtyxKNlsJ/APemW/NiA9304T+l+yJoK/BWn1pJK94pdGuQbaDrhn5buol0zqD7d7y1qmq48HYsKzg2SZIkSauMs0hJkiRJasYEQ5IkSVIzJhiSJEmSmjHBkCRJktSMCYYkSZKkZkwwJEmSJDVjgiFJkiSpGRMMSdKvlSRbklSS9Ut8nO1Jti/lMSRpJTLBkCRpEZKcmsTVaiVpnl2GDkCSpDXqwKEDkKQhmGBIkrQEquqCoWOQpCHYRUqSNJEk6/uxC1uS3C/Jp5JcmeSnSc5IcvCYz9wqyd8mOSfJdUmuSXJ6kqc2an9T/5n9d9TehL/fYUlOTPK9JNf3sZ6Z5Jnj2gX269/XyOvUkf3GjsGY4ZysT/LRJJcn+VmSbUn+eJLfTZKWk08wJEnT2gv4EnAO8G7grsDTgJOSPKOqPgaQ5DeBz9HdiJ8LvB24DfBk4GNJHlhVRy+2/SXwTuDbwGnAD4DdgEOArUnuW1XH9PtdBWwGDgP27H+es31HB5jhnOwJfAX4HrAV+G26c/LpJH9YVadM+8tK0lJJlePTJEm3rJ916cL+7fFV9Tcj2zbSJQXXAntW1TVJXgm8HjgJeFxV3djve2e6m+U9gX2r6qzFtN/XbwJeAxxQVacuEO8Hq+qwkfotwKHAXlW1faT+XvO7NfUJwUnAI4H1VfW/I9tOBfarqixwvrYDVNX6kbpZzsmmqto80tajgM8CJ1XVIeNikKQh2EVKkjStq4FjRyuqahvwEWBX4E/66r8ACnjJ3I10v++PgNf2b4+Yof2mxo2ZqKpf0D1l2IU2g7YXe04uAv5uXmyfAy4GHtIgLklqxgRDkjStr1XVT8bUn9qXG5LcHrg3cElVnTtm35Pn9l1M+1PEOrEk90zy9iTn9mMjqh9rcWK/y91mbH+Wc3J2Vd00pv77wB1niUuSWnMMhiRpWj9coP7SvlzXv6AbyzDOXP2ui2y/qSR703VRuiNwOvB5uicpNwHr6bpU3WrGw8xyTq5a4DM34peFklYYEwxJ0rTuskD9Hn15df8arZvvriP7Lqb9Ob/sy3F/z8bdqC/kJXSDug+vqi2jG5I8nS7BmNUs50SSVg2/9ZAkTetBfXef+fbvy6/3XZwuAO6W5D5j9j2gL7+2mPZH6n7cl/cYs//GMXULuXdfnjhm234LfOYmgCQ7T3KAGc+JJK0aJhiSpGmtA149WtHP8vRndN++f7Kvfj8Q4B9Gb8KT3Ak4ZmSfxbYPXbcmgMOT7DKy/z3mt3ELtvfl/vOO+yjGD7oGuKIv7znFcRZ7TiRp1bCLlCRpWqcBRyR5KHAm/79OxU7AX85NIQscD/wR8HjgG0k+Q7fmw1OAOwNvrKozZmifqvpyktPoppH9SpKT6bpYPZZuvYlxTzbGeQdwOPCvSU4ALgEeADwa+Hh//Pm+0P8un+h/t+uBi6pq6w6Os9hzIkmrhk8wJEnTuhD4fbruSUcCT6Xr1nPI6CJ4/RSvBwGv6qteSDeW4XzgGVX1ilnaH/F44L3A3ftjbABeDizU/q+oqm/SdVE6C3gM8FfAHYAnAu9a4GPvBd5A98Tl5XTTzD77Fo6z2HMiSauGC+1Jkiay0MJ1q6V9SdLy8AmGJEmSpGZMMCRJkiQ1Y4IhSZIkqRnHYEiSJElqxicYkiRJkpoxwZAkSZLUjAmGJEmSpGZMMCRJkiQ1Y4IhSZIkqZn/AzaH0ZaTKry3AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'dropout_rate'] dropout_rate\n" - ] - }, - { - "data": { - "image/png": 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HAICREvYBAGCkhH0AABgpYR8AAEZK2AcAgJES9gEAYKSEfQAAGClhHwAARkrYBwCAkRL2AQBgpIR9AAAYKWEfAABGStgHAICRWlNhv6r2q6p3VtUlVXVzVV1UVW+oqj2XMObhVbWxqrqqfmcL/R5dVR+tqiur6saq+lJV/WpVbb/YcwMAwGpaM2G/qg5OcnaSZyf5fJLXJ/lGkhcnOaOq7raIMXdN8u4kN2yl388l+WySw5N8OMkfJ9lxUsP7pz0vAACsBWsm7Cd5S5J9khzX3T/f3S/r7sdkCNz3TXL8IsZ8Y5Ldk/ze5jpU1W5J3p5kY5IN3f2c7n5pkh9LckaSo6vqyYs4NwAArKo1EfYnd/WPTHJRkjfP2f2qJNcneXpV7TLFmD+X4a8ExyW5ZAtdj06yd5L3d/dZMxu7+6Ykr5h8/H8Xel4AAFgr1kTYT3LEpD25uzfN3tHd1yY5Lcmdkxy6kMGqap8Md+s/0t1/sZXuj5m0H59n32czTAF6dFXttJBzAwDAWrFWwv59J+3XN7P/vEl7nwWO9/YM1/aCpZy7u29LcmGSdUnuPd/BVfW8qjqrqs763ve+t8DyAABg5a2VsL/7pL16M/tntu+xtYGq6leS/GySY7v7spU+d3e/rbvXd/f6vffeewGnAwCAO8ZaCfvLoqoOTPKGJH/V3R9c3WoAAGB1rZWwP3P3fPfN7J/ZftVWxnlnkhuTHLsK5wYAgDVlrYT9r03azc3JP2TSbm5O/4yHZ1i+83uTl2h1VXWSd032v3yy7SMLOXdVrUtyUJLbMqz5DwAA24x1q13AxKcn7ZFVtd3sFXkmL8Y6LMOqOGduZZw/z7Bqz1yHZHhh1hczvLjrnFn7PpXkaUmOSvK+OccdPhnvs91988IuBQAA1oY1Efa7+4KqOjnDWvsvTHLCrN2vSbJLkj/t7utnNlbV/SbHfnXWOMfNN35VPStDcP/77n7FnN3/N8kfJHlyVZ0ws9Z+Ve2c5Hcmff5k8VcHAACrY02E/Yljk5ye5E1V9dgk5yZ5VIY1+L+e5OVz+p87aWspJ+3ua6rqmAyh/zNV9f4kV2ZY0ee+k+0fWMo5AABgNayVOfvp7guSrE9yYoaQ/+tJDk7yxiSHdvcVK3jujyT5yQwv0fqFJC9KcmuSX0vy5O7ulTo3AACslJJjl8/69ev7rLPOWu0yAAAYsao6u7vXL6TvmrmzDwAALC9hHwAARkrYBwCAkRL2AQBgpIR9AAAYKWEfAABGStgHAICREvYBAGCkhH0AABgpYR8AAEZK2AcAgJES9gEAYKSEfQAAGClhHwAARkrYBwCAkRL2AQBgpIR9AAAYKWEfAABGStgHAICREvYBAGCkhH0AABgpYR8AAEZK2AcAgJES9gEAYKSEfQAAGClhHwAARkrYBwCAkRL2AQBgpIR9AAAYKWEfAABGStgHAICREvYBAGCkhH0AABgpYR8AAEZK2AcAgJES9gEAYKSEfQAAGClhHwAARkrYBwCAkRL2AQBgpIR9AAAYKWEfAABGStgHAICREvYBAGCkhH0AABgpYR8AAEZK2AcAgJES9gEAYKSEfQAAGClhHwAARkrYBwCAkRL2AQBgpIR9AAAYKWEfAABGStgHAICREvYBAGCkhH0AABipdatdAMAPu8uv+HIuvPZLubZuyq69cw7a9SHZ624PXu2yABgBd/YBVtHlV3w5/3Ld53Nzbs1deqfcnFvzL9d9Ppdf8eXVLg2AERD2AVbRhdd+KTv19tkpO6RS2Sk7ZKfePhde+6XVLg2AERD2AVbRtXVTdpwzo3LHrMu1ddMqVQTAmAj7AKto1945t+S22227Jbdl1955lSoCYEyEfYBVdNCuD8nNtTE359Z0Ojfn1txcG3PQrg9Z7dIAGAFhH2AV7XW3B+ehd3lkdsoOua5uzk7ZIQ+9yyOtxgPAsrD0JsAq2+tuDxbuAVgR7uwDAMBICfsAADBSwj4AAIyUsA8AACMl7AMAwEgJ+wAAMFLCPgAAjJSwDwAAIyXsAwDASAn7AAAwUsI+AACMlLAPAAAjJewDAMBICfsAADBSwj4AAIyUsA8AACMl7AMAwEgJ+wAAMFLCPgAAjJSwDwAAIyXsAwDASAn7AAAwUsI+AACMlLAPAAAjJewDAMBICfsAADBSayrsV9V+VfXOqrqkqm6uqouq6g1VtecUY7y0qj46Ofa6qrqmqr5cVa+rqv02c8z2VfW0qvpcVV1aVTdU1der6l1V9cDlu0IAALjjrFvtAmZU1cFJTk+yT5KTknw1ySOTvDjJUVV1WHdfsYChnp/kuiSnJrksyQ5JHpbkJUmeU1UbuvucOcf8ZZJfSvLtJB9Kcm2SByd5ZpKnVtV/7+5PLfESAQDgDrVmwn6St2QI+sd19wkzG6vqdRmC+vFJXrCAcR7U3TfN3VhVxyR522ScJ8za/ogMQf/fkjyyu2+Yte/ZSd6Z5BVJhH0AALYpa2Iaz+Su/pFJLkry5jm7X5Xk+iRPr6pdtjbWfEF/4oOT9pA52+89aU+ZHfQnTpq0e2/tvAAAsNasibCf5IhJe3J3b5q9o7uvTXJakjsnOXQJ5/iZSfulOdv/bdI+pqruNGffT0/aTy7hvAAAsCrWyjSe+07ar29m/3kZ7vzfJ8kpCxmwqp6bZL8kd8kw//5xSS5O8rLZ/br7X6vq9RmmCn21qv4uw5z9ByY5Ksn7M0zjAQCAbcrUYb+qtkvywiRPS3L/JLt097rJvoclOSbJG7p7c8F9PrtP2qs3s39m+x5TjPncJI+a9fkLSZ7a3efP7djdv1ZVX0vy+iTHztp1dpJ3d/f1mztJVT0vyfOSZP/995+iPAAAWFlTTeOpqh2TfCLJG5IcnOEOeM3qcmGSX8nwRWBVdfeh3V1J9srwV4EkObuqHj+7Xw3elOFZgdcmuVeSXZP8RJJO8rGqeuEWzvO27l7f3ev33tvUfgAA1o5p5+y/NMP8+tck+ZEkfzZ7Z3dfleSzSR7/g4du0cyd+903s39m+1VTjpvuvqK7P5Eh8N+Y5D1z5uY/M8mLkrypu3+/u7/d3dd19z9mmOd/Y5Lfr6q7THtuAABYTdOG/aclOa27Xzt5kLbn6XNhkmnns3xt0t5nM/tnVtCZZmrQ7Uy+iJyRYWWd2S/KmnkI99PzHHNphvX+75L/fK4AAAC2CdOG/YOSnLmVPlcmueuU484E7SMnzwT8h6raNclhSW5YwLm35p6T9rZZ23aatJubgzOz/ZYlnhsAAO5Q04b9m7L1h2T3z5TTbbr7giQnJzkww8O/s70myS5J3jP7Qdmqul9V3W92x6rav6p+ZL5zVNXzkzwiybeSfHnWrs9N2l+rqt3nHPOCDCv6XJrkK9NcEwAArLZpV+P5Yoa77zt29w/c6Z6E5ccnOX0RtRw7Oe5NVfXYJOdmWE3niAzTd14+p/+5M6edte3hSf6qqs5Icn6Sy5LcLcP6/A9Ocl2Sp3f3xlnHvCXD9KSHJPl6Vf1Nhi8rD0/ymCQbk7xwzjEAALDmTXtn/20ZVqt5b1XtNntHVe2R5MQkeyZ567SFTO7ur5+M8agkv55hxZ83Jjm0u69YwDD/POm/U5KfSvIbSZ6S4dmCP0rygO4+dc55r8swTehVSb6b5KlJfjXDsqJ/leTR3f2haa8HAABWW3XP94ztFg6oemeSZyW5Ncn3M8xpPyfDQ687JXlzd79oecvcNqxfv77POuus1S4DAIARq6qzu3v9QvpOe2c/3f0rGdbS/0qGoF8Zprycn+Q5P6xBHwAA1pqp36CbJN19YpITJ+vV75nk6i29ZRYAALjjTfsG3cOr6j/W0O/uG7v7kjmr5Nyrqg5fziIBAIDpTTuN59MZ5utvyTMyzwuqAACAO9a0Yb+23iWV+d+sCwAA3IGmfkB3AQ5Icu0KjAsAAExhqw/oVtUr52zaUDXvDf7tM7w998lJ/nHppQEAAEuxkNV4Xj3r351kw+Rnc76T5GWLrggAAFgWCwn7R0zaSvKpDG+4ffc8/TYmuSLJ17p707JUBwAALNpWw353nzrz76p6d5KPzN4GAACsTVO9VKu7n71ShQAAAMtrJVbjAQAA1oCpw35V3b2q3lxV51fVjVW1cZ6f21aiWAAAYOGmmsZTVfdM8vkkP5Lk35LslOTiJDcnufdkvC8muXp5ywQAAKY17Z39VybZN8lR3f3QybZ3dff9MoT9f0hypyRPWr4SAQCAxZg27D8+yce7+5Nzd3T3t5P8Yoaw/5plqA0AAFiCacP+vhmm78zYmCHcJ0m6+7okn0jyc0svDQAAWIppw/41SXac9fn7Se45p8/VSfZeSlEAAMDSTRv2L05yr1mf/yXJY6rqzklSVdslOTLJt5enPAAAYLGmDfunJDmiqnaYfH53knskOb2q/jDJaUkemOQDy1ciAACwGFMtvZnkHRmm7uyV5Lvd/RdV9eNJXpTkIZM+709y/PKVCAAALMZUYb+7z0vyB3O2vaSqfjfD0psXdfdly1gfAACwSNO+VOsZSS7r7n+Yvb27v5fke8tZGAAAsDTTztl/Z5KjVqIQAABgeU0b9i9dxDEAAMAqmDa4fzzDajwCPwAArHHThvaXJ9k1yTuqaq8VqAcAAFgm0y69+b4Mb8h9RpInV9VFGab29Jx+3d2PXXp5AADAYk0b9jfM+vdOSe47+ZlrbvgHAADuYNOus2+uPgAAbCPukPBeVftX1eF3xLkAAIDBHXWn/tlJPn0HnQsAAIg18wEAYLSEfQAAGClhHwAARkrYBwCAkRL2AQBgpIR9AAAYKWEfAABGStgHAICRuqPCfk1+AACAO8hUYb+qXllVh2+lz09U1SvnbH59koOmLQ4AAFi8ae/svzrJhq30OTzJq2Zv6O6ru/viKc8FAAAswUpM49khyaYVGBcAAJjCSoT9hye5fAXGBQAAprBuax2q6lNzNj2rqjbM03X7JPdKckCS9y29NAAAYCm2GvZz+zn6neTAyc9cm5JckeQDSV6yxLoAAIAl2mrY7+7/mOpTVZuSvLq7X7uiVQEAAEu2kDv7sz07yTkrUQgAALC8pgr73f3ulSoEAABYXlOF/a29UGu27v7s9OUAAADLZdppPJ/J8JDuQmw/5dgAAMAymjbsvzbzh/09kjwiyaOT/G2Sf15iXQAAwBJNO2f/1VvaX1XPSnJCkpcvviQAAGA5LOsbdLv7xCRnJvnd5RwXAACY3rKG/YkvJlnwg7wAAMDKWImwf69M/ywAAACwzJYt7FfV9lX13CRHJzlrucYFAAAWZ9p19r+xhXF+ZNLekuR/LbEuAABgiaadbrNd5l9689YkX07y+SQndPe5Sy0MAABYmmmX3jxwheoAAACW2Uo8oAsAAKwBi141p6p2SHK/DG/PvTrJud1963IVBgAALM3Ud/arareqemuSqzKsqf+ZJOckuaqq3lpVeyxviQAAwGJMuxrPbklOS/LAJNcm+VyS7ya5e5IfS/K8JP+1qh7d3dcsc60AAMAUpr2z/1sZgv6fJDmguzd091O6e0OSA5K8OckDJv0AAIBVNG3Yf1KSM7v7hd191ewd3X11d78oyRlJfmG5CgQAABZn2rB/QIY5+ltyapJ7LaoaAABg2Uwb9q9Pss9W+uyd5IbFlQMAACyXacP+F5L8YlUdMt/Oqjo4yS9N+gEAAKto2nX2/zDJyUm+UFUnJPl0htV49k2yIcmLktwlyf9ZxhoBAIBFmCrsd/cpVXVskjcm+V+TnxmV5NYk/6O7P7l8JQIAAIsx9Rt0u/tPq+pjSZ6e5GFJds/wBt1zkvxFd1+8vCUCAACLMXXYT5Lu/maS45e5FgAAYBlN+4AuAACwjVhU2K+qp1XVKVV1ZVXdNmlPqaqnLXeBAADA4kw1jaeqdkjyf5P8dIYHcjcm+V6SvZIckWRDVf1SkqO7+9ZlrhUAAJjCtHf2fyvJzyT5pwzhfufuvnuSnZM8JsnnM3wR+M3lLBIAAJjetGH/GUnOT7Khu0/t7o1J0t0bu/szGdba/0aSZy1jjQAAwCJMG/b3S3JSd98y387uvjnJSUnuudTCAACApZk27F+SZIet9Nlh0g8AAFhF04b9v0xydFXtNt/OqtojydFJ3rvUwgCqfV6JAAAgAElEQVQAgKWZNuy/NslZST5fVU+tqv2qaodJ+7QkZ2Z4SPe3l7tQAABgOtO+QffGSVtJ3jPP/kpySJKbqmr29u7uRb2tFwAAWJxpA/jnkvRKFAIAACyvqcJ+d29YoToAAIBlNu2cfQAAYBux6Hn0VbVDkvsl2SPJ1UnO7e5bl6swAABgaaa+s19Vu1XVW5NcleSLST6T5JwkV1XVWyfLbwIAAKtsqjv7k/X1T0vywCTXZnhg97tJ7p7kx5I8L8l/rapHd/c1y1wrAAAwhWnv7P9WhqD/J0kO6O4N3f2UyYO7ByR5c5IHTPoBAACraNqw/6QkZ3b3C7v7qtk7uvvq7n5RkjOS/MJiipm8nOudVXVJVd1cVRdV1Ruqas8pxnhpVX10cux1VXVNVX25ql5XVftt5dijq+ofquryqrqpqr5ZVSdV1aGLuR4AAFhN0z6ge0CSv95Kn1OTvGTaQqrq4CSnJ9knyUlJvprkkUlenOSoqjqsu69YwFDPT3LdpI7LkuyQ5GGTmp5TVRu6+5w5516X5N1JnprkvCQfyPDQ8b5J/kuSH8/wdmAAANhmTBv2r88Qxrdk7yQ3LKKWt0zGPq67T5jZWFWvyxDUj0/yggWM86Duvmnuxqo6JsnbJuM8Yc7u12QI+scneWV3b5pz7A5TXAcAAKwJ007j+UKSX6yqQ+bbObk7/0uTfgs2Oe7IJBdlmPc/26syfMl4elXtsrWx5gv6Ex+ctLervar2TfIbGaYnvWJu0J+MaUlRAAC2OdPe2f/DJCcn+UJVnZDk0xlW49k3yYYkL0pylyT/Z8pxj5i0J88N2919bVWdluHLwKFJTply7Bk/M2m/NGf70Ul2TPL+qrpTkp9K8qMZVhv6x+7+l0WeDwAAVtVUYb+7T6mqY5O8Mcn/mvzMqCS3Jvkf3f3JKeu476T9+mb2n5ch7N8nCwz7VfXcJPtl+PLx4CSPS3JxkpfN6fqISXvnDM8J7D9nnL9O8ozuXszUJAAAWDVTv0G3u/+0qj6W5OkZHnzdPcPDrOck+YvuvngRdew+aa/ezP6Z7dO8sOu5SR416/MXkjy1u8+f02/mGYTfzvAOgZ/P8KXjQUn+OMPKQtcledZ8J6mq52V4v0D233//+boAACP3nY9+MVe+/cPZ/tvfzMb99s9dj3li7vmEH1vtsmD6N+gmSXd/s7uP7+6ju/u/TdrjFxn0V0R3H9rdlWSvDH8VSJKzq+rxc7rO/A6uTPIz3X1Od1/f3f+U5GczBP2nV9U9N3Oet3X3+u5ev/fee6/AlQAAa9l3PvrFXPO//yh11ZXZeI97pq66Mtf87z/Kdz76xdUuDaYL+1W1sareuwJ1zNy5330z+2e2X7WZ/ZvV3Vd09ycyBP4bk7xnMjd/xsyYp8x96293fzfJP2X4Pa2f9twAwPhd+fYPZ+NueyR73DW13fbJHnfNxt32yJVv//BqlwZT39m/Nsk3V6COr03a+2xm/8wKOpub079Vk5eAnZFhadAHznPuzX2R+P6kvdNm9gMAP8S2//Y307vd/n5l77Z7tv/2SkQmmM60Yf+cJA9YgTo+PWmPrKrb1VRVuyY5LMPa/Ut9sdXMVJzbZm2beZj4QZs5ZuaLwYVLPDcAMEIb99s/dc3tHzusa67Oxv08y8fqmzbs/0GSJ1TVf1vOIrr7ggxLeh6Y5IVzdr8myS5J3tPd189srKr7VdX9Znesqv2r6kfmO0dVPT/DyjvfSvLlWbs+l+SLSf5rVT1xzjHHJLl/kvOTnDX9lQFAcv6F5+Ydp/1djj/tb/KO0/4u51947mqXxDK66zFPzPbXXJVcdWV608bkqiuz/TVX5a7HPHHrB8MKm3Y1nn2SfDzJx6rqIxlWuLk0Sc/t2N1/PuXYxyY5PcmbquqxSc7NsJrOERmm77x8Tv+Z/1PWrG0PT/JXVXVGhoB+WZK7ZVif/8GZPGzb3Rtn1dlV9cwkpyb566r628n5Hpjkv2d4odczZx8DAAt1/oXn5n2XnJddk+yTyrXZlPddcl6ekuRHD7r/apfHMhhW3fn1263Gs9tLn2M1HtaE6v6BnL75zlWbMgT7mrNr9iCVIUNvP3UxVfdK8tokR2UI6d9N8uEkr+nu78/p2xlOVLO27Z/kuCQ/keGvBHdNclOSbyT5RJI3dve3NnPugzK8rffIDPP6L8+wpv9vd/fX5jtmrvXr1/dZZ/kDAAD/6R2n/V2uzabsOmuW6rW9KbtmuzznsJ9excqAbVVVnd3dC1o8Zto7+89eRD0LNgniCzrH7JA/a9s3k/zGIs99YTazlj4ALNal2ZR95twj2yWVS7NpM0cALJ9p36D77pUqBADGaN9sN9zZnxX4r09n38W96gZgKv5PAwAr6CfvcXCuzTB1Z1N3ru1NuXayHWClbfHOflV9Y5Hjdnf7vxgAP/R+9KD75ylJTr3kglyaTdk32+Wn73Gwh3OBO8TWpvFslx9caWfHJHef/HtjhgdZ90oy80Dud5PcslwFAsC27kcPur9wD6yKLU7j6e4Du/ugmZ8kD03ynQwvtzoiyc7dffckOyd5TJJ/SvLtJA9Z2bIBAICtmXbO/vFJ9kiyobtPnVl7vrs3dvdnMnwBuOukHwAAsIqmDftPTHJSd887Tae7b0pyUpInLbUwAABgaaYN+3dLssNW+uww6QcAAKyiacP+BUmOrqrd59tZVXsmOTrDG2sBAIBVNG3Yf2uSeyT5fFU9o6oOrKo7TdpnZnhAd98kb17uQgEAgOlM+wbdP66qQ5K8KMm75ulSSU7o7rcsR3EAAMDiTRX2k6S7X1xV70/yK0kelmT3JFcn+eckJ3b36ctbIgAAsBhTh/0k6e4zkpyxzLUAAADLaNo5+wAAwDZC2AcAgJES9gEAYKSEfQAAGClhHwAARkrYBwCAkRL2AQBgpIR9AAAYKWEfAABGStgHAICREvYBAGCkhH0AABgpYR8AAEZK2AcAgJES9gEAYKSEfQAAGClhHwAARkrYBwCAkRL2AQBgpIR9AAAYKWEfAABGStgHAICREvYBAGCkhH0AABgpYR8AAEZK2AcAgJES9gEAYKSEfQAAGClhHwAARkrYBwCAkRL2AQBgpIR9AAAYKWEfAABGStgHAICREvYBAGCkhH0AABgpYR8AAEZK2AcAgJES9gEAYKSEfQAAGClhHwAARkrYBwCAkRL2AQBgpIR9AAAYKWEfAABGStgHAICREvYBAGCkhH0AABgpYR8AAEZK2AcAgJES9gEAYKSEfQAAGClhHwAARkrYBwCAkRL2AQBgpIR9AAAYKWEfAABGStgHAICREvYBAGCkhH0AABgpYR8AAEZK2AcAgJES9gEAYKSEfQAAGKl1q10AS3PxV/49F3/jvNxS12TH3i0H3PuQHPCAfVa7LAAA1gB39rdhF3/l33PehV/IbX1Tdty0a27rm3LehV/IxV/599UuDQCANUDY34Zd/I3zst2mnbOudk6qsq52znabds7F3zhvtUsDAGANEPa3YbfUNVmXnW63bV12yi11zSpVBADAWiLsb8N27N1yW26+3bbbcnN27N1WqSIAANYSYX8bdsC9D8mm7W7KbX1T0p3b+qZs2u6mHHDvQ1a7NAAA1gBhfxt2wAP2ySEHPSLraufcst21WVc755CDHmE1HgAAklh6c5t3wAP2Ee4BAJiXO/sAADBSwj4AAIyUsA8AACMl7AMAwEgJ+wAAMFLCPgAAjJSwDwAAI7Wmwn5V7VdV76yqS6rq5qq6qKreUFV7TjHGS6vqo5Njr6uqa6rqy1X1uqrab4FjvKKqevLzuMVfEQAArJ4181Ktqjo4yelJ9klyUpKvJnlkkhcnOaqqDuvuKxYw1POTXJfk1CSXJdkhycOSvCTJc6pqQ3efs4U6Hp7klZMx7rL4KwIAgNW1ZsJ+krdkCPrHdfcJMxur6nUZgvrxSV6wgHEe1N03zd1YVcckedtknCfMd2BV7ZzkPUm+kOSCJE+f8hoAAGDNWBPTeCZ39Y9MclGSN8/Z/aok1yd5elXtsrWx5gv6Ex+ctIds4fDfS3JQkmcl2bS1cwEAwFq2JsJ+kiMm7cndfbuQ3d3XJjktyZ2THLqEc/zMpP3SfDur6jEZpgz9Vneft4TzAADAmrBWpvHcd9J+fTP7z8tw5/8+SU5ZyIBV9dwk+2WYd//gJI9LcnGSl83Td/ckJyb5XJI3TVE3AACsWWsl7O8+aa/ezP6Z7XtMMeZzkzxq1ucvJHlqd58/T98Tktw1yYbu7inOkap6XpLnJcn+++8/zaEAALCi1so0nmXX3Yd2dyXZK8NfBZLk7Kp6/Ox+VfULGR7E/Z/d/Y1FnOdt3b2+u9fvvffeS64bAACWy1oJ+zN37nffzP6Z7VdNO3B3X9Hdn8gQ+G9M8p6qulOSVNVdk7w1w9SgP5l2bAAAWMvWStj/2qS9z2b2z6ygs7k5/VvV3VclOSPJ3kkeONm8f4Y7/49NsmnWi7Q6yTMnfT4x2fariz03AACshrUyZ//Tk/bIqtpu9oo8VbVrksOS3JDkzCWe556T9rZJe0WSd2ym7+EZvmR8LMklSf51iecGAIA71JoI+919QVWdnGGqzQszPDA74zVJdknyp919/czGqrrf5Nivztq2f5Kbu/uyueeoqucneUSSbyX58uTYb2V4kPcHVNWJGcL+67r7k0u5PgAAWA1rIuxPHJvk9CRvqqrHJjk3w2o6R2SYvvPyOf3PnbQ1a9vDk/xVVZ2R5PwklyW5W4b1+R+c5LokT+/ujSt1EQAAsFaslTn76e4LkqzPsN79o5L8epKDk7wxyaHdfcUChvnnSf+dkvxUkt9I8pQkneSPkjygu09d9uIBAGANqimXlWcL1q9f32edddZqlwEAwIhV1dndvX4hfdfMnX0AAGB5CfsAADBSwj4AAIyUsA8AACMl7AMAwEgJ+wAAMFLCPgAAjJSwDwAAIyXsAwDASAn7AAAwUsI+AACMlLAPAAAjJewDAMBICfsAADBSwj4AAIyUsA8AACMl7AMAwEgJ+wAAMFLCPgAAjJSwDwAAIyXsAwDASAn7AAAwUsI+AACMlLAPAAAjJewDAMBICfsAADBSwj4AAIyUsA8AACMl7AMAwEgJ+wAAMFLCPgAAjJSwDwAAIyXsAwDASAn7AAAwUsI+AACMlLAPAAAjJewDAMBICfsAADBSwj4AAIyUsA8AACMl7AMAwEgJ+wAAMFLCPgAAjJSwDwAAIyXsAwDASAn7AAAwUsI+AACMlLAPAAAjJewDAMBICfsAADBSwj4AAIyUsA8AACMl7AMAwEgJ+wAAMFLCPgAAjJSwDwAAIyXsAwDASAn7AAAwUsI+AACMlLAPAAAjJewDAMBICfsAADBSwj4AAIyUsA8AACMl7AMAwEgJ+wAAMFLCPgAAjJSwDwAAIyXsAwDASAn7AAAwUsI+AACMlLAPAAAjJewDAMBICfsAADBSwj4AAIyUsA8AACMl7AMAwEgJ+wAAMFLCPgAAjJSwDwAAIyXsAwDASAn7AAAwUsI+AACMlLAPAAAjJewDAMBICfsAADBSwj4AAIyUsA8AACMl7AMAwEgJ+wAAMFLCPgAAjNSaCvtVtV9VvbOqLqmqm6vqoqp6Q1XtOcUYL62qj06Ova6qrqmqL1fV66pqv3n637OqXlRVH5scc3NVXVFVn6iqJy3vFQIAwB1n3WoXMKOqDk5yepJ9kpyU5KtJHpnkxUmOqqrDuvuKBQz1/CTXJTk1yWVJdkjysCQvSfKcqtrQ3efM6v+iJL+Z5MIkn05yaZIDkjwpyeOq6vXd/WvLcIkAAHCHWjNhP8lbMgT947r7hJmNVfW6DEH9+CQvWMA4D+rum+ZurKpjkrxtMs4TZu36fJIN3X3qnP73T3JmkpdU1Xu7++wprwcAAFbVmpjGM7mrf2SSi5K8ec7uVyW5PsnTq2qXrY01X9Cf+OCkPWRO/w/NDfqT7ecm+cDk44atnRcAANaaNRH2kxwxaU/u7k2zd3T3tcn/396dB2lS13ccf392MUSRXXRBMCiXilhqRaiNYDDCRkSFeMSzYiRCXJV4EO8DC2U1RGPQiAavEEVXq6IGj0pFFEWQMzGIKBqRc5FwKejuciwIu9/80T2VYTKzOzvPM9PP0/V+VU399vl1P93f6a6Z+Ww/v/415wMPAA4YYB/Patsfb8V77mnbewfYryRJktSJURnG8+i2vXyG5VfQXPnfGzhzNhtMshJ4GPBA4PHAIcC1wNtn+f4lwPOBAs6YzXskSZKkUTIqYX9p266bYflE/w5bsc2VwP6TXv8X8JKqunJLb0wS4BRgZ+Bj7ZCemdZ9JfBKgN12220rypMkSZLm16gM4xm6qjqgqgLsSPOpAMAPkjx9Fm//IPBC4FxgszPxVNWnqmp5VS3faaedBqpZkiRJGqZRCfsTV+6XzrB8on/t1m64qm6tqm/TBP4NwOok959p/SQfoJn95xzgsKq6e2v3KUmSJI2CUQn7P2/bvWdYPjGDzkxj+reoqtYCFwI7AY+dbp0k/wC8hWa+/WdW1e1z3Z8kSZLUtVEJ+2e17aFJ7lNTku2BA4E7aea9H8SubXuf2XXSOBl4PfBt4PCqunPAfUmSJEmdGomwX1VX0cx4swfwmimLVwHbAaur6o6JziT7JNln8opJdkuy83T7SPIq4A+A64BLJ/WH5mFbrwZOB55dVRsG/Z4kSZKkro3KbDzQhO0LgI8keSrwM5rZdFbQDN9555T1J2bIyaS+/YAvJ7kQuBK4GVhGMz//44HbgSOqauOk97yLZuaeDcAlwNub/H8fl1TV1wb67iRJkqQFNjJhv6quSrIceA/wDOAw4EbgJGBVVf1mFpu5uF3/j4DDgQcDdwFX08ywc1JVXTflPXu27f2Bd8yw3c8Chn1JkiSNlVRV1zX0xvLly+uiiy7qugxJkiT1WJIfVNXy2aw7EmP2JUmSJA2fYV+SJEnqKcO+JEmS1FOGfUmSJKmnDPuSJElSTxn2JUmSpJ4y7EuSJEk9ZdiXJEmSesqwL0mSJPWUYV+SJEnqKcO+JEmS1FOGfUmSJKmnDPuSJElSTxn2JUmSpJ4y7EuSJEk9ZdiXJEmSesqwL0mSJPWUYV+SJEnqKcO+JEmS1FOGfUmSJKmnDPuSJElSTxn2JUmSpJ4y7EuSJEk9ZdiXJEmSesqwL0mSJPXUNl0XIGnzvrN+HatvvY3r797Ertsu4ohl23PIkqVdlyVJksaAYV8aYd9Zv44Trl/HksXw0PstYu09mzjh+nUABn5JkrRFDuORRtjqW29jyWLY4X6LWLSoaZcsbvolSZK2xLAvjbDr797EksX3/TFdsngR19+9qaOKJEnSODHsSyNs120XsX7jfYP9+o3N2H1JkqQtMTFII+yIZduzfiOsvWcTmzY17fqNTb8kSdKWeIOuNMImbsKdPBvP63ZxNh5JkjQ7hn1pxB2yZKnhXpIkzYnDeCRJkqSeMuxLkiRJPWXYlyRJknrKsC9JkiT1lGFfkiRJ6inDviRJktRThn1JkiSppwz7kiRJUk8Z9iVJkqSeMuxLkiRJPWXYlyRJknrKsC9JkiT1lGFfkiRJ6inDviRJktRThn1JkiSppwz7kiRJUk8Z9iVJkqSeMuxLkiRJPWXYlyRJknrKsC9JkiT1lGFfkiRJ6inDviRJktRThn1JkiSppwz7kiRJUk8Z9iVJkqSeMuxLkiRJPZWq6rqG3kjyK+Dajna/I3BLR/vWwvAc95/nuP88x/3nOe6/UTjHu1fVTrNZ0bDfE0kuqqrlXdeh+eM57j/Pcf95jvvPc9x/43aOHcYjSZIk9ZRhX5IkSeopw35/fKrrAjTvPMf95znuP89x/3mO+2+szrFj9iVJkqSe8sq+JEmS1FOGfUmSJKmnDPuSJElSTxn2x1iShyX5dJIbktydZE2SDyd5UNe1aXBJXpDko0nOTbI+SSX5fNd1aTiSLEuyMslXk1yZZEOSdUnOS/LyJP5+7oEkf5fkzCTXtef410l+mOTdSZZ1XZ/mR5KXtr+zK8nKruvRYNp8VTN83dR1fVviDbpjKskjgAuAhwBfBy4DngisAH4OHFhVt3ZXoQaV5BLg94Hbgf8B9gG+UFUv7bQwDUWSo4GPAzcCZwG/AHYGngcsBU4DXlj+kh5rSX4LXAz8N/BLYDvgAGA5cANwQFVd112FGrYkDwcuBRYDDwReUVWndFuVBpFkDbAD8OFpFt9eVScubEVbZ5uuC9CcfYwm6B9TVR+d6EzyIeANwAnA0R3VpuF4A03IvxI4iCYQqj8uB54N/HtVbZroTHIs8H3g+TTB/7RuytOQLKmqu6Z2JjkBOBZ4B/DqBa9K8yJJgM8AtwJfAd7cbUUaorVVdXzXRcyFHxOPofaq/qHAGuDkKYvfDdwBHJFkuwUuTUNUVWdV1RVe2e2nqvpuVf3b5KDf9t8EfKJ9efCCF6ahmi7ot77Uto9aqFq0II4B/hg4iuZvsdQ5w/54WtG2Z0wTFG4DzgceQPNRsaTxc0/b3ttpFZpPz2rbH3dahYYmyWOA9wMnVdU5Xdejodu2vRfj2CR/nWRFksVdFzUbDuMZT49u28tnWH4FzZX/vYEzF6QiSUORZBvgL9qX3+yyFg1PkjfTjN9eSjNe/8k0Qf/9Xdal4Wh/blfT3HtzbMflaH7sQnOOJ7smyVFV9b0uCpotw/54Wtq262ZYPtG/wwLUImm43g88DvhGVX2r62I0NG+muQF7wjeBI6vqVx3Vo+F6F7Av8OSq2tB1MRq6zwDnAj8FbgP2Al4LvBI4PcmTqupHHda3WQ7jkaQRkeQY4E00s2sd0XE5GqKq2qWqQnN18Hk0YeGHSfbrtjINKsn+NFfzP1hVF3Zdj4avqla191ndXFV3VtVPqupo4EPA/YHju61w8wz742niyv3SGZZP9K9dgFokDUGS1wIn0UzRuKKqft1xSZoHbVj4Ks1Qy2XA5zouSQNoh+98jmZY7XEdl6OFNzGZwlM6rWILDPvj6edtu/cMyydmd5hpTL+kEZLk9cBHgZ/QBP2Rf0iLBlNV19L8x+6xSXbsuh7N2QNp/hY/Brhr8sOWaGbHA/intm+6Odo13iaG4Y307IeO2R9PE/OtH5pk0ZQ5urcHDgTuBP6ji+IkzV6St9GM078EeFpV3dJxSVo4v9e2GzutQoO4G/jnGZbtRzOO/zyai3QO8emfiVkPr+60ii0w7I+hqroqyRk0HwO/huaK4IRVNP/D/GRVOcevNMKSHAe8B/gBcKhDd/olyd7AzVW1bkr/IuC9NA9GvKCqftNFfRpcezPuyumWJTmeJux/1ifojq92StVfTM1USfYA/rF9+fkFLmurGPbH16uBC4CPJHkq8DNgf5o5+C8H3tlhbRqCJM8Fntu+3KVtn5Tk1Pbft1SVT2ccU0leRhP0N9LM8nBM8/DN+1hTVacucGkansOA9yU5D7iG5qmqO9M8EXsv4CbgFd2VJ2kWXgy8Kck5wLU0s/E8Ajgc+F3gG8CJ3ZW3ZYb9MdVe3V9OExaeQfNH5UaaG/xWeaWoF54AvGxK317tFzS/dAz742vPtl0MvH6Gdb4HnLog1Wg+fAd4JM2c+vvSTId8B80FmdXAR/w0Rxp5Z9E832hfmmHS29FMgHIezc/x6lF/0n1GvD5JkiRJc+RsPJIkSVJPGfYlSZKknjLsS5IkST1l2JckSZJ6yrAvSZIk9ZRhX5IkSeopw74kSZLUU4Z9SVInkpyapNrHzs/nftYkWTOf+5CkUWXYlySNtSRnJ/EJkZI0jW26LkCSpHn21K4LkKSuGPYlSb1WVVd1XYMkdcVhPJI0ZpLs0Y51PzXJPkm+luTXSe5Icl6SQ6d5z7ZJ3p7k0iR3Jlmf5NwkLxrS9o9v33Pw5rY3y+/vyCSnJbk6yYa21vOTvHS67QIHta9r0tfZk9abdsz+AMdkjyT/kuSWJHcluSjJn8zme5OkheaVfUkaX3sCFwKXAp8EHgq8GDg9yUuq6osASX4H+BZNKL4MOBl4APAC4ItJnlBVx851+/Pg48BPgXOAG4FlwGHA6iSPrqrj2vXWAquAI4Hd239PWLO5HQxwTHYHvg9cDawGHkxzTL6e5JCqOmtrv1lJmk+p8p4mSRon7ew117QvT6yqt0xatpwmoN8O7F5V65O8A/hb4HTg2VV1b7vuQ2iC6+7AgVV1wVy23/YfD7wbWFFVZ89Q72er6shJ/acCLwP2rKo1k/ofMXXoTRvOTweeAuxRVddPWnY2cFBVZYbjtQagqvaY1DfIMTm+qlZN2tbTgW8Cp1fVYdPVIEldcRiPJI2vdcB7JndU1UXAF4AdgD9tu/8SKOCNE6G2XfeXwHvblysH2P5QTTfGvqp+S3P1fRuGc8PtXI/JtcDfTKntW8AvgCcOoS5JGirDviSNr4ur6rZp+s9u232TbA88Erihqi6bZt3vTqw7l+1vRa2zlmS3JCcnuawdS1/t2PzT2lV2HXD7gxyTS6pq4zT91wEPGqQuSZoPjtmXpPF18wz9N7Xt0vYLmrHv05no32GO2x+qJHvRDKN5EHAucAbNJwwbgT1ohv1sO+BuBjkma2d4z714AU3SCDLsS9L42nmG/l3adl37NblvqodOWncu25+wqW2n+7syXWieyRtpbsg9qqpOnbwgyZ/RhP1BDXJMJGmseBVCksbXfu2QlKkObtsftsNwrgJ2TfKoadZd0bYXz2X7k/p+07YPn2b95dP0zeSRbXvaNMsOmuE9GwGSLJ7NDgY8JpI0Vgz7kjS+lgLvmtzRzpbz5zRXpb/adn8aCPD3kwNxkh2B4yatM9ftQzP0BuCoJNtMWv/hU7exBWva9uAp+306098wC3Br2+62FfuZ6zGRpLHiMB5JGl/nACuT7A+cz//Ng78IeNXEtJjAicAzgecAP0ryDZo55V8IPLCHRoMAAAELSURBVAT4QFWdN8D2qar/THIOzdSY30/yXZphQM+imc9+uiv+0/kYcBTw5ST/CtwAPA54BvCldv9Tndl+L19pv7cNwLVVtXoz+5nrMZGkseKVfUkaX9cAf0gzhOZo4EU0Q08Om/zAq3bayqcB72y7Xkcz9v0K4CVV9bZBtj/Jc4BTgIe1+9gXeCsw0/b/n6r6Mc0wmguAw4G/ApYAzwM+McPbTgHeR/NJxFtpps58+Rb2M9djIkljxYdqSdKYmekhVeOyfUnSwvHKviRJktRThn1JkiSppwz7kiRJUk85Zl+SJEnqKa/sS5IkST1l2JckSZJ6yrAvSZIk9ZRhX5IkSeopw74kSZLUU/8Lzdaqrd3vIOEAAAAASUVORK5CYII=\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['train', 'epochs'] epochs\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['train', 'batch_size'] batch_size\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", @@ -857,6 +307,15 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, From eabe3614b26dbee6d9cca4c4a8241b729e40192d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 25 Jun 2018 18:51:17 +0300 Subject: [PATCH 516/616] feat: suffix for fitted on models --- .../models/evolution/Results_analysis.ipynb | 615 +++++++++++++++++- .../evolution/evolution_param_generator.py | 12 +- 2 files changed, 604 insertions(+), 23 deletions(-) diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb index 3cb6d21dca..3271729b7b 100644 --- a/deeppavlov/models/evolution/Results_analysis.ipynb +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -2,9 +2,35 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", + " return f(*args, **kwds)\n", + "/home/dilyara/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n", + "Using TensorFlow backend.\n", + "[nltk_data] Downloading package punkt to /home/dilyara/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package stopwords to\n", + "[nltk_data] /home/dilyara/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n", + "[nltk_data] Downloading package perluniprops to\n", + "[nltk_data] /home/dilyara/nltk_data...\n", + "[nltk_data] Package perluniprops is already up-to-date!\n", + "[nltk_data] Downloading package nonbreaking_prefixes to\n", + "[nltk_data] /home/dilyara/nltk_data...\n", + "[nltk_data] Package nonbreaking_prefixes is already up-to-date!\n", + "2018-06-25 16:47:39.319 DEBUG in 'gensim.models.doc2vec'['doc2vec'] at line 73: Fast version of gensim.models.doc2vec is being used\n", + "2018-06-25 16:47:39.323 INFO in 'summa.preprocessing.cleaner'['textcleaner'] at line 20: 'pattern' package not found; tag filters are not available for English\n", + "2018-06-25 16:47:39.729 DEBUG in 'matplotlib.backends'['__init__'] at line 90: backend module://ipykernel.pylab.backend_inline version unknown\n" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -27,11 +53,219 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Considered basic config:\n", + "{\n", + " \"dataset_reader\": {\n", + " \"name\": \"basic_classification_reader\",\n", + " \"x\": \"text\",\n", + " \"y\": \"intents\",\n", + " \"data_path\": \"snips\"\n", + " },\n", + " \"dataset_iterator\": {\n", + " \"name\": \"basic_classification_iterator\",\n", + " \"seed\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"field_to_split\": \"train\",\n", + " \"split_fields\": [\n", + " \"train\",\n", + " \"valid\"\n", + " ],\n", + " \"split_proportions\": [\n", + " 0.9,\n", + " 0.1\n", + " ]\n", + " },\n", + " \"chainer\": {\n", + " \"in\": [\n", + " \"x\"\n", + " ],\n", + " \"in_y\": [\n", + " \"y\"\n", + " ],\n", + " \"pipe\": [\n", + " {\n", + " \"id\": \"classes_vocab\",\n", + " \"name\": \"default_vocab\",\n", + " \"fit_on\": [\n", + " \"y\"\n", + " ],\n", + " \"level\": \"token\",\n", + " \"save_path\": \"vocabs/snips_classes.dict\",\n", + " \"load_path\": \"vocabs/snips_classes.dict\"\n", + " },\n", + " {\n", + " \"in\": [\n", + " \"x\"\n", + " ],\n", + " \"out\": [\n", + " \"x_lower\"\n", + " ],\n", + " \"name\": \"str_lower\"\n", + " },\n", + " {\n", + " \"id\": \"my_embedder\",\n", + " \"name\": \"fasttext\",\n", + " \"save_path\": \"embeddings/dstc2_fastText_model.bin\",\n", + " \"load_path\": \"embeddings/dstc2_fastText_model.bin\",\n", + " \"dim\": 100\n", + " },\n", + " {\n", + " \"id\": \"my_tokenizer\",\n", + " \"name\": \"nltk_tokenizer\",\n", + " \"tokenizer\": \"wordpunct_tokenize\"\n", + " },\n", + " {\n", + " \"in\": [\n", + " \"x_lower\"\n", + " ],\n", + " \"in_y\": [\n", + " \"y\"\n", + " ],\n", + " \"out\": [\n", + " \"y_labels\",\n", + " \"y_probas_dict\"\n", + " ],\n", + " \"main\": true,\n", + " \"name\": \"intent_model\",\n", + " \"save_path\": \"evolution/classification/intents_snips\",\n", + " \"load_path\": \"evolution/classification/intents_snips\",\n", + " \"classes\": \"#classes_vocab.keys()\",\n", + " \"kernel_sizes_cnn\": [\n", + " 1,\n", + " 2,\n", + " 3\n", + " ],\n", + " \"filters_cnn\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 100\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"confident_threshold\": {\n", + " \"evolve_choice\": true,\n", + " \"values\": [\n", + " 0.5,\n", + " 1\n", + " ]\n", + " },\n", + " \"optimizer\": \"Adam\",\n", + " \"lear_rate\": {\n", + " \"evolve_range\": [\n", + " 0.0001,\n", + " 0.1\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"lear_rate_decay\": {\n", + " \"evolve_range\": [\n", + " 0.0001,\n", + " 0.1\n", + " ],\n", + " \"scale\": \"log\"\n", + " },\n", + " \"loss\": \"binary_crossentropy\",\n", + " \"text_size\": 15,\n", + " \"coef_reg_cnn\": {\n", + " \"evolve_range\": [\n", + " 1e-06,\n", + " 0.001\n", + " ]\n", + " },\n", + " \"coef_reg_den\": {\n", + " \"evolve_range\": [\n", + " 1e-06,\n", + " 0.001\n", + " ]\n", + " },\n", + " \"dropout_rate\": {\n", + " \"evolve_range\": [\n", + " 0.1,\n", + " 0.9\n", + " ]\n", + " },\n", + " \"dense_size\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 100\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"model_name\": \"cnn_model\",\n", + " \"embedder\": \"#my_embedder\",\n", + " \"tokenizer\": \"#my_tokenizer\",\n", + " \"check_bool\": {\n", + " \"evolve_bool\": true\n", + " }\n", + " }\n", + " ],\n", + " \"out\": [\n", + " \"y_labels\",\n", + " \"y_probas_dict\"\n", + " ]\n", + " },\n", + " \"train\": {\n", + " \"epochs\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"batch_size\": {\n", + " \"evolve_range\": [\n", + " 50,\n", + " 500\n", + " ],\n", + " \"discrete\": true\n", + " },\n", + " \"metrics\": [\n", + " \"classification_accuracy\",\n", + " \"classification_f1\",\n", + " \"classification_roc_auc\"\n", + " ],\n", + " \"validation_patience\": 5,\n", + " \"val_every_n_epochs\": 1,\n", + " \"log_every_n_epochs\": 1,\n", + " \"validate_best\": true,\n", + " \"test_best\": false\n", + " },\n", + " \"metadata\": {\n", + " \"labels\": {\n", + " \"telegram_utils\": \"IntentModel\",\n", + " \"server_utils\": \"KerasIntentModel\"\n", + " },\n", + " \"download\": [\n", + " \"http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz\",\n", + " \"http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz\",\n", + " {\n", + " \"url\": \"http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv\",\n", + " \"subdir\": \"snips\"\n", + " },\n", + " {\n", + " \"url\": \"http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin\",\n", + " \"subdir\": \"embeddings\"\n", + " }\n", + " ]\n", + " }\n", + "}\n" + ] + } + ], "source": [ "CONFIG_FILE = \"../../configs/evolution/evolve_intents_snips.json\"\n", "KEY_MAIN_MODEL = \"main\"\n", @@ -46,9 +280,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2018-06-25 16:47:39.741 INFO in 'deeppavlov.models.evolution.evolution_param_generator'['evolution_param_generator'] at line 55: Main model path in config: ['chainer', 'pipe', 4]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Title name for the considered evolution is `intents_snips`.\n", + "Number of populations: 10.\n" + ] + } + ], "source": [ "evolution = ParamsEvolution(population_size=POPULATION_SIZE,\n", " key_main_model=KEY_MAIN_MODEL,\n", @@ -74,9 +324,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Measure: classification_accuracy\n", + "valid:\n", + "min for\t0 model on\t0 population\n", + "max for\t1 model on\t0 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "\n", + "Measure: classification_f1\n", + "valid:\n", + "min for\t1 model on\t6 population\n", + "max for\t1 model on\t0 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n", + "\n", + "Measure: classification_roc_auc\n", + "valid:\n", + "min for\t1 model on\t6 population\n", + "max for\t1 model on\t9 population\n", + "test:\n", + "min for\t0 model on\t0 population\n", + "max for\t0 model on\t0 population\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: 'argmin' is deprecated. Use 'idxmin' instead. The behavior of 'argmin' will be corrected to return the positional minimum in the future. Use 'series.values.argmin' to get the position of the minimum now.\n", + " if __name__ == '__main__':\n", + "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", + " # Remove the CWD from sys.path while we load stuff.\n" + ] + } + ], "source": [ "MEASURES = evolution.get_value_from_config(\n", " evolution.basic_config, list(evolution.find_model_path(\n", @@ -103,11 +394,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2018-06-25 16:47:39.818 DEBUG in 'matplotlib.font_manager'['font_manager'] at line 1343: findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=20.0 to DejaVu Sans ('/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000\n" + ] + }, + { + "data": { + "image/png": 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dCXwHeHdmrhtALacDNwJfiIhXAHcCL6HY4/0e4MPdrr+zPEZNfTdFxAXAqcDNEXEp8BDFDwQnUay/Py8zbx9AfZIkSVLDRLH1+QBeGDELeDHFFo3LgZsyc8mgionYA/gYcDwwE3gcuBQ4JzOXdbs2ATIzurUH8LfAKcDzgSnASuBW4OuZ2euuMrXa29tz4cKFg/k4kiRJUp8i4pbMbO/zuoEG9+2dwV2SJEnDob/Bveoad0mSJEkNUPXJqQBExIsptn3cjS07udTKzPy7wRQmSZIkaYtKwb1cP34h8DcUN4UmNTeH1pwnYHCXJEmS6qTqUpl3UzyB9JtAO0VIPw94KfAhYBXwPeA5daxRkiRJGvGqLpX5W+DuzDwFoJiAZ3lmLgAWRMTPKR6SdCVwQR3rlCRJkka0qjPu8/nThxc9G/4z81bgcoo92SVJkiTVyUB2lVlR8+c1wIxu/fdSBHxJkiRJdVI1uD9GsZNMlweAg7tdsx9FoJckSZJUJ1WD+01sHdT/FzgkIv5vRBwQEe8CTqRY5y5JkiSpTqoG9/8GRkXE3uX5vwEPAecAtwFfBJYDH6xbhZIkSZKq7SqTmT8CflRz/kxEvBB4G7APsAi4ODMfr2eRkiRJ0kg3oCen1srMFcC5dahFkiRJUi8qLZWJiI6I+PZQFSNJkiSpZ1XXuK8CHh6KQiRJkiT1rmpwvxV47lAUIkmSJKl3VYP7p4FXR8SxQ1GMJEmSpJ5VvTl1NvAz4H8j4kfAzcATQHa/MDMvHnx5kiRJkqB6cL+QIqQHcHL5BVsH9yjPDe6SJElSnVQN7qcOSRWSJEmStqnqA5guGqpCJEmSJPWu6s2pkiRJkhrA4C5JkiS1gEpLZSLigX5empm5zwDqkSRJktSDqjenttHD1o/AdGBa+efFwKbBFCVJkiRpa1VvTp3TW19E7At8AZgEvGpwZUmSJEmqVbc17pl5H8W+7rsBZ9VrXEmSJEl1vjk1M9cDVwJ/Vc9xJUmSpJFuKHaV2QzsPATjSpIkSSNWXYN7ROwIvA54pJ7jSpIkSSNd1e0gP7qNcfYATqTYXeafB1mXJEmSpBpVt4M8u4/+lcDHM/PfBlaOJEmSpJ5UDe5H99LeCSwD7srMzYMrSZIkSVJ3Vfdxv3aoCpEkSZLUu6HYVUaSJElSnVUK7hHxxoi4OiJ27aV/t4i4KiJOrk95kiRJkqD6jPtbgemZubinzsx8jGJXmbcOtjBJkiRJW1QN7s8DFvZxzc3AQQMrR5IkSVJPqgb3GcBTfVyzFNhxYOVIkiRJ6knV4L4E2K+Pa/YDlg+sHEmSJEk9qRrcbwBeGxHze+qMiP0pnp76q8EWJkmSJGmLqsH9XIq936+PiDMiYm5ETCqPZ1IE9lHldZIkSZLqpOoDmG6OiNOBLwOfK79qdQDvzMzf1Kk+SZIkSVQM7gCZ+fWIuB44HXgJMJ1iTfsC4KuZeWd9S5QkSZJUObgDlOH87+tciyRJkqReVF3jLkmSJKkBKgX3iHhjRFwdEbv20r9bRFwVESfXpzxJkiRJUH3G/a3A9Mxc3FNnZj4GTCuvkyRJklQnVYP784CFfVxzM3DQwMqRJEmS1JOqwX0G8FQf1ywFdhxYOZIkSZJ6UjW4LwH26+Oa/Si2h5QkSZJUJ1WD+w3AayNifk+dEbE/cCLFE1Qri4jdI+IbEbE4IjZExKKIOC8idhjAWC+KiO9ExKPlWE9GxLUR8eaB1CZJkiQ1UtXgfi7F3u/XR8QZETE3IiaVxzMpAvuo8rpKImIf4BbgVOAmiqeyPgCcCfw6ImZWGOvdFGvtjwOuAj4DXFrW9uqqtUmSJEmNVukBTJl5c0ScDnyZIlh/rtslHcA7M/M3A6jlK8Bs4IzM/GJXY0R8Fngv8AngHX0NEhHHAV8ArgTekJmruvWPGUBtkiRJUkNFZlZ/UbEk5nTgJcB0ijXtC4Cvlk9VrTrePsB9wCJgn8zsrOmbAjwOBDA7M9f0MdbvgX2BPTNzadVaurS3t+fChX1toCNJkiQNTkTckpntfV1Xaca9SxnO/34gr+3F0eXxitrQXr7Xqoi4gWLZy6EUS196FBEHUmxF+SPgmYg4GjgYSOB3wDXdx5ckSZJawYCC+xCYVx7v6aX/XorgPpdtBHfgxeXxKeCXwJHd+v8QESdn5n0DrFOSJElqiKo3pz4rIkZFxE4RsWdPXxWHm1YeV/TS39U+vY9xZpfHvwPmACeUY88FvkXxAKmfRMTYnl4cEadFxMKIWPj000/3s3RJkiRp6FUO7hHxvIj4CbAKWAw82MPXA/UssoKuzzMK+MvM/GlmrszMe4E3Uzz1dS7w+p5enJnnZ2Z7ZrbPmjVreCqWJEmS+qFScC9vSr2RYgnKlRQ3jN5W/nlpef5L4JsV6+iaUZ/WS39Xe18PdurqfyIzf13bkcVduJeVp4dUrE+SJElqqKoz7h8BxgAvzcwTy7ZLM/N4YG/gAuC5wEcrjnt3eZzbS3/X01p7WwPffZzeAv6y8jihn3VJkiRJTaFqcD8KuDwz/1DTFgDlNo1vpwjH/1Jx3GvK43ERsVVN5XaQhwNrKbac3JYFwBpgTkRM6qH/wPL4YMX6JEmSpIaqGtx3pNjhpctmYGLXSWZupgjhx1UZNDPvB66guKH0Xd26zwEmAd+s3cM9IuZHxPxu46wF/hMYD3w8IqLm+ucBp5Q1/7BKfZIkSVKjVd0O8hlgcs35EqD7DjIb6X2t+racTrF+/gsR8QrgTooHPB1NsUTmw92u73rQU3Rr/78Ua/DfAxxW7gG/E3AyRaB/T/mDgiRJktQyqs64308xK97lFuDYiJgNUC5POZEBLEUpw3Q7cCFFYH8fsA/weeDQ/j4FNTNXAkcAnwRmAO8GXgNcD7wqMz9ftTZJkiSp0arOuF8BfCAiJpXLVv4fxV7pt0bEjRRPKd2LInRXlpmPAKf289ruM+21faspZui7z9JLkiRJLanqjPvXKR5uNAEgM38CvLc8fz3FA5A+DXyhjjVKkiRJI16lGffMfBz4fre2z0fElyhuXH2q3C9dkiRJUh1VfnJqTzKzIzOf7C20R8TfRsTV9XgvSZIkaSSqS3DvhznAy4fpvSRJkqTtznAFd0mSJEmDYHCXJEmSWoDBXZIkSWoBBndJkiSpBRjcJUmSpBZgcJckSZJagMFdkiRJagEGd0mSJKkFDFdw/x1w8TC9lyRJkrTdGT0cb5KZlwGXDcd7SZIkSdujysE9ImYAbwEOAXYARvVwWWbmKwZZmyRJkqRSpeAeEfOBXwKzgNjGpTmImiRJkiR1U3WN+7nAbODTwHOAMZnZ1sNXT7PwkiRJkgao6lKZI4CfZOaHhqIYSZIkST2rOuMewB1DUYgkSZKk3lUN7rcA84aiEEmSJEm9qxrcPwa8OiKOGoJaJEmSJPWi6hr3PSj2Y78iIr5LMQO/vKcLM9MHLkmSJEl1UjW4X0ix1WMAbyq/um/9GGWbwV2SJEmqk6rB/dQhqUKSJEnSNlUK7pl50VAVIkmSJKl3VW9OlSRJktQAVZfKABARE4GTgRcC04EVwG+BSzNzTf3KkyRJkgQDCO4R8WrgImAGxY2oXRL4XEScmpmX16k+SZIkSVQM7hHxIuASYBTwbeBq4HFgF+AY4K+AH0bE4Zl5S51rlSRJkkasqjPuH6aYWT8iMxd067swIr4M/BL4EPD6wZcnSZIkCarfnHoE8IMeQjsAmfkb4IfldZIkSZLqpGpwnwY80sc1DwNTB1aOJEmSpJ5UDe6LgUP6uKadYt27JEmSpDqpGtx/ChwTER+MiFG1HRHRFhHvA15ZXidJkiSpTqrenPovwEnAJ4C3R8SvKGbXdwZeBswBngA+XscaJUmSpBGvUnDPzCci4nDga8CxwF7dLrkSeEdmulRGkiRJqqPKD2DKzEXAqyJiN4onp06jeHLqrZn5WH3LkyRJkgQDCO5dypBuUJckSZKGQdWbUyVJkiQ1wDZn3CPiGxRPSv1QZj5ZnvdHZubfDbo6SZIkSUDfS2VOoQjunwaeLM/7IwGDuyRJklQnfQX3vcvjY93OJUmSJA2jbQb3zHxoW+eSJEmShkelm1Mj4qMRcWQf1xwRER8dXFmSJEmSalXdVeZs4Kg+rjkSOGsgxUiSJEnq2VBsBzkG6ByCcSVJkqQRayiC+4uAJUMwriRJkjRi9fnk1Ii4ulvTKRFxVA+XjgL2APYCvjv40iRJkiR16TO4s/Wa9gTmlF/ddQJLge8D7x1kXZIkSZJq9BncM/PZ5TQR0QmcnZkfG9KqJEmSJG2l6hr3U4EfDUUhABGxe0R8IyIWR8SGiFgUEedFxA6DGPPIiOiIiIyIj9ez3rq77TY4+2x4y1uK4223NboiSZIkNYlKwT0zL8rMIUmTEbEPcAvFDwc3AZ8DHgDOBH4dETMHMOYU4CJgbR1LHRq33QbnngvLlsHuuxfHc881vEuSJAno3xr3HkXE7sBuwLie+jPzuopDfgWYDZyRmV+seZ/PUqyZ/wTwjopjfh6YBvxr+frmdcklsMMO8JvfwBNPFG2bN8N118GcOQ0tTZIkabv0ghfAeec1uop+qxzcI+I4itnw+X1cOqrCmPsAxwGLgC936z4LOA14U0S8LzPX9HPMEylm79/EIH5AGTYPP1zMtNcaPRo2bGhMPZIkSWoqlQJtRBwKXA48DXwJ+HvgWuBu4Ahgf+DHwK0V6zi6PF6RmVs9vCkzV0XEDRTB/lDgqn7UORv4OvCjzPxWRJxSsZ7ht+eexfKY44/f0rZsWTELf/bZDStLkiRJzaHqzan/DKwHXpyZZ5Zt12TmO4ADgY8DrwR+WHHceeXxnl767y2Pc/s53tcpPlvVpTWNc/LJRVBftgw6O7f8+eSTG12ZJEmSmkDV4H4Y8OPMXNx9jCx8FLgTOKfiuNPK44pe+rvap/c1UES8BXgtcHpmPlmliIg4LSIWRsTCp59+uspLB++gg+D97y9m2B99tDi+//1FuyRJkka8qmu/pwEP15xvBCZ1u+YG4P8MpqiBiog5wHnADzLzv6q+PjPPB84HaG9vz7oW1x8HHWRQlyRJUo+qzrg/BezQ7XyfbteMASZUHLdrRn1aL/1d7cv7GOcbwDrg9IrvL0mSJDW1qjPu97B1UF8A/FlEzM3MeyJiZ+D1bFmT3l93l8fe1rDvV/P+2/IiipD/dET01P/hiPgwcFlmnlSxRklqLnffDldeDo8/BrvsBse+BuYd0OiqJElDpGpw/xnw8YiYkZnPUOyTfjJwa0TcQRGwpwAfqDjuNeXxuIhoq91ZpnyI0uEUD1Fa0Mc4FwMTe2jfDzgS+B3FQ56q7nojSc3l7tvhgq/A1Gmw066wckVxfurphndJ2k5VDe5fA64DNgFk5g0R8UbgXyh2lVkEfCAzL64yaGbeHxFXUGz5+C7gizXd51Cso/9a7R7uETG/fO1dNeOc0dP45XaQRwI/ycyPVKlNkprSlZcXoX1qec9+1/HKyw3ukrSdqhTcM3Ml8JtubZcCl9ahltOBG4EvRMQrKHaneQnFHu/3AB/udv2d5bHHNTGStF17/LFipr3W5KlFuyT1ZNHdsOBKePpxmLULHHoszJnX9+vUNKrenDpkMvN+oB24kCKwv49iPf3ngUMzc2njqpOkJrPLbrB65dZtq1cW7ZLU3aK74bILiv9OzNypOF52QdGullEpuEfEwRHx0YjYqZf+ncv+FwykmMx8JDNPzcxdMnNsZu6Vme/JzGU9XBuZ2a/Z9sy8sLzeZTKStg/HvqZY175yefHQtpXLi/NjX9PoyiQ1owVXwqSpxW/m2tqK46SpRbtaRtUZ9/cBb6XYBrInTwJ/B/zDYIqSJPVh3gHFjahTp8GTi4ujN6ZK6s3Tj8PEyVu3TZxctKtlVL059TDgmszs8eFEmZkRcTXFjaCSpKE07wCDuqT+mbVLsTxm8tQtbWtXF+1qGVVn3HcGHu3jmsWA3wWSJEnN4tBjYc3KIrx3dhbHNSuLdrWMqsF9LTCrj2tmARsGVo4kSZLqbs48OPHUYsZ96ZPF8cRT3VWmxVRdKvM74MSI+IfMXN29MyKmAieW10mSJKlZzJlnUG9xVYP7+cB3gSsj4u2ZeVvLmLrBAAAflUlEQVRXR0Q8n+IBTTuW10mSNDwevx9uvw6WPwnTd4IDjoRd9ml0VZJUV5WWymTm94GLKfZZvzUiFkfEzRGxGPgtcAjwzcz8bv1LlSSpB4/fD9d/D9atgmmziuP13yvaJWk7UvkBTJl5CvAO4A6Km1UPLo+3A6eV/ZIkDY/br4PxU2DCFIi24jh+StEuSduRqktlAMjM84HzI2IiMB1Ynplr61qZJEn9sfzJYqa91vhJRbsk9WTpIli0AFYvgck7wpxDYeacRlfVpwEF9y5lWDewSxo2nasepfOpW2H9Uhg/k7bZL6Rtyu6NLkuNNH2nYnnMhClb2tavKdolqbuli+APP4axk2DSTNiwpjh/3mubPrxXXiojSY3SuepROh66gty0lhw3g9y0lo6HrqBzVV+Pl9g+bVj3OMue+gVLHvshy576BRvWjdAnIB5wJKxfVYT37CyO61cV7ZLU3aIFRWgfN7lYXjducnG+aEGjK+vTNmfcI+IBIIFXZuaD5Xl/ZGZ6O7+kuup86lYYPYkYM7FoGDORLNtH2qz7hnWPs/KZ62lrG0/b6Gl0dqxj5TPXM3XGyxg3YYQ9A2+XfeBlf7n1rjLtJ7irjKSerV5SzLTXGjuxaG9yfS2VaaMI7r2d9yYGXJEELOtYymOdD7GG1UxiMru17cUOo2b2/cLt0O25mN/yIKtZx2Qm8CL25oDYtdFlNcb6pTBuxtZtoycU7SPM2lW3F6F91AQAojyuXXX7yAvuwOodp7D00Hls6NyFcW1TmTluCpMbXZSk5jR5x2J5zLia/0psXFu0N7ltBvfMnLOtc2koLOtYyt0df2QsY5nIJDaygbs7/sg8Dhxx4f32XMy13M4YRjORcaxnI9dyOyQjM7yPnwmb1kLXjDvA5nVF+wjTsWk5baOnbdUWbePp2LS8QRU1zupNT7F43UJGxXjGtk1hc65n8bqF7Eo7k8fMbnR5kprNnEOLNe1QzLRvXAsb18C8VzS2rn7Y5hr3iPhsRBxXc75n+XRUacg81vkQYxnL2BhHRDA2xjGWsTzW+VCjSxt2v+VBxjCa8YyljTbGM5YxjOa3PNjo0hqibfYLYfOaYo17JrlpLWxeU7SPMKPGTCc712/Vlp3rGTVmeoMqapylG+5jVIxndNt4IoLRbeMZFeNZuuG+RpcmNZWHOtbwg02P8dVND/KDTY/xUMeaRpfUGDPnFDeijpsEa5YWxxa4MRX6XirzHmA5cEV5/iBwDvCxoSxKI9saVvPM5sn8fvMonslgRiTPHz2OGWNWN7q0YbeadUxk3FZtYxnNatY1qKLGapuyO+x13Na7yux2+Ihb3w4wccoBrHzmeqCYac/O9XR2rmfy9PYGVzb8NnSuZGzblK3aRsU4NnSubFBFUvN5qGMNl3c8ySRGMZMxrGEzl3c8yWvYib1GTWp0ecNv5pyWCOrd9RXcVwM1v5N27bqG3rLNU7hyYzA5gh2AtRlcubGN42IKjGl0dcNrMhNYz0bGM/bZto1sZjITGlhVY7VN2X1EBvXuxk3YhakzXsbaVbfTsWk5o8ZMZ/L09hG5vn1c21Q253pGx/hn2zpyA+Pa/AWx1OWmzuVMYhSTooh+kxgNWbSPyODeovoK7vcBJ0fEpUDXPmPTI2LPvgbOzIcHW5xGpgc2z2BcPM24aCMYxTg62EQnD2ye1feLtzMvYu9iTTvFTPtGNrOJzbyUeQ2uTM1g3IRdRmRQ727muH1ZvG4hdBYz7R25gY5cz07jD2x0aVLTWMJGZnab/ZrIKJawsUEVaSD6Cu7/DnwLuLGm7czya1uyH2NLPVqRo9ktdmQ1K9nERsYwlt1iKity5H1LHRC7QrLVrjIvZd7IvDFV6sXkMbPZlXaWbriPDZ0rGdc2lZ3GH+iNqVKNHRnLGjYXM+2ltXSwY81vdNX8+tpV5rsR8SBwArAbcApwG/C7oS9NI9VObWNYlW3Mbtv52bZVnR3sFKMaWFXjHBC7cgAGdWlbJo+ZbVCXtuGQtulc3vEkZDHTvpYO1tDB0W3NvwWituhzCjMzFwALACLiFODSzPTmVA2ZI8dN4fvrnoFOmBRtrMlOVmcnJ4wfebtlSJJUD3uNmsRr2ImbOpezhI3syFiObttxxK5vv2vdBn62ah2LN3Ww65hRHD9lAvMnjOv7hQ1Wde3BqTjbriG275gJ/AUzuG7DKp7s3MRObWM4Yfx09h0zcm/IlCRpsPYaNWnEBvVad63bwNefWcXUtjZ2Ht3Gio5Ovv7MKt42g6YP75WCe2ZeNFSFSLX2HTPBoC5JGrQ/rNrEZU9t4OH1new5vo0TZ4/jeVNG2BZl2srPVq1jalsb00YVjzOaNiqebW/p4B4RR5Z/vCkz19ec9ykzrxtUZZIkSYPwh1WbOO+hdUwfHew2ro1lm5LzHlrHe/bC8D6CLd7Uwc6jt34G6ZS2YPGmjgZV1H99zbj/kmKHmP2Be2rO+2Nk3kkoSZKawmVPbWD66GD6mCKkTR8TQCeXPbXB4D6C7TpmFCs6Op+daQdY1ZnsOqb5o2tfwf1jFEF9SbdzSZKkpvbw+k52G7f1zOrU0cHD6zsbVJGawfFTJvD1Z1YBxUz7qs5kZWcnfzG9+df/97Ud5NnbOpckSc3nnsc7uOr2Tp5YDjtPh1cc0MbcXZp/NrHe9hzfxn2rOnhyNazcmEwdG+w0GfadMvL+LrTF/AnjeNsMttpV5i+mT2r69e3gQ5IkSdqu3PN4Bxdf38HU8bDTNFi1Di6+voM3v4wRF94PHDua7y3dyJS2NqaOhuWbkkeWdnLSzOYPaBpa8yeMa4mg3l2l4B4Ro4Bxmbm2W/sxwInAWuD8zHywfiVK0hYPLurk1wvgqadh9iw47FDYe05b3y+URoirbu9k6niYOqFYvzt1AkBy1e2dIy643/tUcNjYsTwaHSzvTKaPCQ7Msdz7VMAeja5Oqq7qjPu5wDsjYqfMXAEQEX8JfBvoWuH/1oh4UWY+Usc6JYkHF3Vy6WXJ5Emw40xYvRouvQxed2Kn4V0qPbG8mGmvNXl80T7SPLq6k30njWZubLkRtTOTR1e7xl2tqeq/dEcC13SF9tJZwHLgzcAHgOnAP9SnPEna4tcLYPIkmDw5aGsLJk8OJk8q2iUVdp4Oq9dv3bZ6fdE+0uw+uY2VG7duW7mxaJdaUdXv3D2A+7pOIuI5wDzgi5n5rcw8F/hf4Pj6lShJhaeehokTt26bOLFol1R4xQFtLF0ED/44WPSt4MEfB0sXFe0jzQlzRrFiY7J8Q9KZxXHFxuSEOSNryZC2H1X/XzwVWFlzfjjF9pA/q2m7Hdh9kHVJ0p+YPQvWrt26be3aol1SYeLKNna5bxSjNwYbJgSjNwa73DeKiStHXnB/7szRnP68MUwfFyxek0wfF5z+vDE8d6Z7c6g1Vf3OfRzYu+b8lcA64JaatsnA5kHWJUl/4rBDizXtkEycWIT21Wvg2Fc2ujKpedx6JeyyUxv7TN3StnZl0b77vMbV1SjPnTnaoK7tRtUfvxcAr42I10TEK4E3AFdn5qaaa/YGHqtXgZLUZe85bbzuxGJt+5KlxfF1J4Y3pko1lj4OEyZv3TZhctEuqbVV/RH0kxTbPl5WnncCn+jqjIjxwBHAD+tSnSR1s/ecNvae0+gqpOY1c5dihn1izYz7utVFu6TWVmmaKjP/ALwE+Fz59dLM/E3NJS8Erga+W7cKJUlSv73wWFizsgjv2Vkc16ws2iW1tsjMRtfQlNrb23PhwoWNLkOSpMoevTu59cpieczMXYrQvvu86PuFkhoiIm7JzPa+rqvL3RoRMQY4EFibmXfXY0xJkjQwu8+LEXkjqrS9q7RUJiL+PCL+KyJm1LTtQ7EF5ELgjoi4JCK8fVuSJEmqo6pbMbwFmJ+Zz9S0fQbYF7gGuI3i5tVT61OeJEmSJKge3J8L3Nx1EhFTgVcD/5WZrwQOAe7C4C5JkiTVVdXgPoviIUxdDqNYJ/89gHI/9yuBfepSnSRJkiSgenBfBUyrOX85kMD1NW3rgSmDrEuSJElSjao3kd4L/FlEjKMI7H8O3JaZS2qu2Qt4qk71SZIkSaL6jPv5wHMoAvydwN7ABd2uOZhilxlJkiRJdVL1yakXAZ8CJlIsmfkS8MWu/oh4KVt2mJEkSZJUJ5X3W8/MDwEf6qV7IbADsGYwRUmSJEnaWl0flJSZG4GN9RxTkiRJUvU17pIkSZIaoHJwj4hdIuLLEXFfRKyLiI4evjYPRbGSJEnSSFUpuEfEbhTr2N9OsY59HPAwxS4zHUAAvwd+NZBiImL3iPhGRCyOiA0RsSgizouIHfr5+kkR8dcR8Z2IuCsi1kTEqohYGBHvi4ixA6lLkiRJarSqM+4fBXYGjs/M55dtF2TmfIptIn8OTABOrlpIROwD3AKcCtwEfA54ADgT+HVEzOzHMEcA3wJeBfyRYseb7wC7AecC10TE+Kq1SZIkSY1WNbi/CvhZZv6ie0dmPgq8kSK4nzOAWr4CzAbOyMyTMvODmXkMRYCfB3yiH2M8AfwNsEtmvqEc4+3AXOC3wEuBdw2gNkmSJKmhqgb3ndn64UodFEEdgMxcDVwJnFhl0HK2/ThgEfDlbt1nUSzLeVNETNrWOJn5u8z8drm7TW37KuAz5elRVWqTJEmSmkHV4L4SqF0nvoxiGUqtFcCsiuMeXR6vyMzO2o4ydN9A8dCnQyuOW2tTefTGWUmSJLWcqsH9IWCPmvPfA8dExESAiGijmDl/tOK488rjPb3031se51Yct9ZbyuPPBjGGJEmS1BBVg/tVwNERMaY8vwjYFbgxIv6dYmb8AOD7FcedVh5X9NLf1T694rgARMS7geOB3wHf2MZ1p5U70Cx8+umnB/JWkiRJ0pCo+uTU/6RYHrMj8HhmfisiDgb+HjiovOZ79O9G0mEREScD51HcuPr6zNzU27WZeT5wPkB7e3sOT4WSJElS3yoF98y8F/h0t7b3RsQnKbaDXJSZTw6gjq4Z9Wm99He1L68yaEScRPGDxFPA0Zn5wABqkyRJkhqu6ox7jzLzaWAwa0vuLo+9rWHfrzz2tgb+T0TEGyn2cH8COKb8oUOSJElqSVXXuA+Va8rjceUNrs+KiCnA4cBaYEF/BouIvwa+CywGXm5olyRJUqvb5ox7RPR6I2cfMjP/rsLF90fEFRQ70ryL4omnXc4BJgFfy8w1NbXNL197V7ea/5biBtSHKJbHPDTAzyBJkiQ1jcjs/R7MiOjstXPbMjNHVSqkeAjTjRRPT70MuBN4CcUe7/cAL83MpTXXZ/lGUdN2NPALit8kfAN4pIe3Wp6Z5/VVT3t7ey5cuLDKRxi0J26Duy6BFQ/DtD1h/smw80F9v06SJEmtKyJuycz2vq7ra4373nWqp0/lrHs78DGKrRtfDTwOfB44JzOX9WOYvdiy/OctvVzzEMUuM03lidvg1+fC+B1g6u6wbllxftj7De+SJEnqI7gP9zKTzHwEOLWf10YPbRcCF9a3quFx1yVFaH/sN7D6iaKtczM8dB1Mn9PQ0iRJkrZLO78Ajm+66dzeVbo5NSLeGBFXR8SuvfTvFhFXlXunq4IVD8P4bpthto2GzRsaU48kSZKaS9XtIN8KTM/MxT11ZuZjETGtvO6SwRY3kkzbs1ges+/xW9rWLYMJO8BRZzesLEmSJDWJqttBPg/o647Nm9nyFFX10/yTYf2yIqxnZ3Fcv6xolyRJkqoG9xkUTyHdlqXAjgMrZ+Ta+aDiRtQJO8DKR4ujN6ZKkiSpS9WlMkvY8hTT3uwHLB9YOSPbzgcZ1CVJktSzqjPuNwCv7Xr4UXcRsT9wIvCrwRYmSZIkaYuqwf1ciln66yPijIiYGxGTyuOZFIF9VHmdJEmSpDqptFQmM2+OiNOBLwOfK79qdQDvzMzf1Kk+SZIkSVRf405mfj0irgdOB14CTKdY074A+Gpm3lnfEiVJkiRVDu4AZTj/+zrXIkmSJKkXVde4S5IkSWoAg7skSZLUAgzukiRJUgswuEuSJEktwOAuSZIktQCDuyRJktQCDO6SJElSCzC4S5IkSS3A4C5JkiS1AIO7JEmS1AIM7pIkSVILMLhLkiRJLcDgLkmSJLUAg7skSZLUAgzukiRJUgswuEuSJEktwOAuSZIktQCDuyRJktQCDO6SJElSCzC4S5IkSS3A4C5JkiS1AIO7JEmS1AIM7pIkSVILMLhLkiRJLcDgLkmSJLUAg7skSZLUAgzukiRJUgswuEuSJEktwOAuSZIktQCDuyRJktQCDO6SJElSCzC4S5IkSS3A4C5JkiS1AIO7JEmS1AIM7pIkSVILMLhLkiRJLcDgLkmSJLUAg7skSZLUAgzukiRJUgswuEuSJEktoKmCe0TsHhHfiIjFEbEhIhZFxHkRsUPFcWaUr1tUjrO4HHf3oapdkiRJGkqjG11Al4jYB7gRmA1cBtwFHAKcCRwfEYdn5tJ+jDOzHGcucDXwPWA+cCpwQkQclpkPDM2nkCRJkoZGM824f4UitJ+RmSdl5gcz8xjgc8A84BP9HOeTFKH9s5n5inKckyh+AJhdvo8kSZLUUiIzG11D12z7fcAiYJ/M7KzpmwI8DgQwOzPXbGOcycBTQCewS2auqulrAx4A9irfY5uz7u3t7blw4cIBfyZJkiSpPyLilsxs7+u6ZplxP7o8XlEb2gHK8H0DMBE4tI9xDgUmADfUhvZynE7g593eT5IkSWoJzRLc55XHe3rpv7c8zh2mcSRJkqSm0iw3p04rjyt66e9qnz6U40TEacBp5enqiLi7j/cbKjsCSxr03mpefl+oN35vqDd+b6gnfl80n736c1GzBPemkJnnA+c3uo6IWNifdU4aWfy+UG/83lBv/N5QT/y+aF3NslSmayZ8Wi/9Xe3Lh2kcSZIkqak0S3DvWpLS29rz/cpjb2vX6z2OJEmS1FSaJbhfUx6PK7dtfFa5HeThwFpgQR/jLADWAYeXr6sdpw04rtv7NauGL9dRU/L7Qr3xe0O98XtDPfH7okU1RXDPzPuBK4A5wLu6dZ8DTAK+WbuHe0TMj4j53cZZDXyzvP7sbuO8uxz/583+5NRyrb20Fb8v1Bu/N9QbvzfUE78vWldTPIAJnn0I040UTze9DLgTeAnFnuv3AC/NzKU11ydAZka3cWaW48wFrgZuAvYHTqR4ONNLyx8UJEmSpJbRNMEdICL2AD4GHA/MpHhi6qXAOZm5rNu1PQb3sm8GcBZwErALsBT4X+CjmfnoUH4GSZIkaSg0VXAfySJid/70h5Yf0cMPLRoZyt8evQ44AXgesBuwEfgDcAFwQfcnDWvkioi/oVgqCPC2zPyPRtajxoqIV1AsET0M2IFiAusPwOcz86eNrE2NEREnAGcCz2VLzrgF+Gxm/rqRtan/DO5NoIdlQncBh1AsE7obOLx2mZBGhoh4B/BViv+4XgM8DOwEnEyxtel/A29M/0884pW/rfwDMAqYjMF9RIuIfwP+EXiU4rfNS4BZwMHALzLzAw0sTw0QEZ8GPkDxA9yPKL4n9gVeS/FMnzdn5rcaV6H6y+DeBCLi5xQ73pyRmV+saf8s8F7ga5n5jkbVp8aIiGMobrT+Se3MekTsTHHvxh7AGzLzvxtUoppARARwJbA3cAnwfgzuI1ZEvI1ix5CLgNMyc2O3/jGZuakhxakhyn8zHgOeBg7KzKdq+o6muB/wwcx8ToNKVAVNsavMSFbOth8HLAK+3K37LGAN8KaImDTMpanBMvPqzPyf7sthMvMJ4P+Vp0cNe2FqNmcAxwCnUvz3QiNURIwDPkHx27k/Ce0AhvYRaS+KvPeb2tAOkJnXAKsofiOjFmBwb7yjy+MVPQS0VcANwETg0OEuTE2t6x/fzQ2tQg0VEfsDn6JYt3xdo+tRwx1LEcAuAToj4oSI+KeIODMiDmtwbWqceynujzokInas7YiII4EpwC8aUZiqG93oAsS88tjb01zvpZiRnwtcNSwVqalFxGjgzeXpzxpZixqn/D74JsXs6ocaXI6aw4vL43rgVuDA2s6IuI5ied3Tw12YGiczn4mIfwI+C9wRET+iWOu+D8Ua9yuBtzewRFVgcG+8aeVxRS/9Xe3Th6EWtYZPUfyD/NPM/Hmji1HDfBR4IfCyzFzX6GLUFGaXx38E7gCOAH5Hcf/DuRSTQD/AJXYjTmaeFxGLgG8Ab6vpug+4sPsSGjUvl8pILSQizgDeR7Hz0JsaXI4aJCJeQjHL/hm3cVONrn/TNwP/f3v3HiNXVQdw/PuDBiLKS41KymNR8BX/sNBAggmUAAUKghqtiia02sS3ITWpKKkCmqBifGDwEdGsEhKrrYhGDCRApQW1IUWtBtRQFuVRDNIWtaBSfv5xzsRxmCntzHbvXPr9JCene+6Zc8/cdHd+e/Z3zz0nM9dm5j8ycwNla9n7gZNMm9nzRMQyYCUwSVlpfy5ll6GNwDV1JyK1gIF78zor6gcOON5p3zIDc9EYi4gPAl+mrKSdnJmPNjwlNaCmyHyXkl63vOHpaLx0PifuzMyp7gOZuQ3o/IXuuJmclJoVEfOAzwI/zsylmbkxM7dl5nrKL3QPAB+JCHeVaQED9+b9odYvH3D86FoPyoHXHiAiLgC+AvyOErRvanhKas7zKD8vXgU8ERHZKZSdqAC+Wdu+1Ngs1YTO58mghZ7Ow/yeMwNz0fg4u9a39B6ov9Cto8SDc2ZyUhqOOe7N63wjzY+IvXr2694feB2wDfhlE5NT8+pNRZ+h5KqelpmPNDwlNetfwLcGHDuG8uG7lhLEmUazZ7kJSODVvZ8nVedm1Xtndlpq2L61HrTlY6f9aduHavz4AKYx4AOYNEhELAcupTyWer7pMdqRiLiYsuruA5j2UBFxHWWnkKWZ+cWu9vmUXai2AhOZOWhDBD3LRMRCYAXwMHBsZj7QdexM4KeUBYFDfUr7+HPFfTy8H7gduCIiTgHuAo6n7PH+R+CiBuemhkTE+ZSgfTuwBvhweUjm/5nKzMkZnpqk8fUByl9dvhARZ1G2hTwSeAPlZ8kSg/Y9zkrKPu2nAndFxLXAJkq63dlAABcatLeDgfsYyMx7ImIuJUg7A1gAPES5EfGSzNy8o9frWevIWu8NXDCgz88puwRIEpl5f0QcS9ku9BzgROAx4CfAZZm5rsn5aeZl5lMRsYDyS93bKDek7gc8ClwPXJGZNzY4Re0CU2UkSZKkFnBXGUmSJKkFDNwlSZKkFjBwlyRJklrAwF2SJElqAQN3SZIkqQUM3CVJkqQWMHCXJEmSWsDAXZI0LSJiMiIyIiZ283mmImJqd55DksaRgbskaaxExOqI8OmAktRjVtMTkCRpF53S9AQkqQkG7pKkVsnMe5qegyQ1wVQZSWpYREzU3PDJiHhlRPwoIh6NiH9GxNqImN/nNftGxIURsSEitkXEYxGxJiIWTtP4F9fXzNvReDv5/hZFxKqI2BgRj9e53hYR7+w3LnBS/Tq7yuqufn1z3Ee4JhMR8b2IeCQinoiIOyLi7J15b5I0k1xxl6TxcSTwC2AD8A3gEOCtwM8i4rzMXAEQEfsAN1AC3LuBK4H9gDcDKyLitZn58WHH3w2+BvweuBV4CHgBsAC4OiJekZnLa78twCXAIuCI+u+OqR2dYIRrcgSwDtgIXA08n3JNrouIUzPzll19s5K022SmxWKxWBoswASQtVzec2wu8B9gM3BAbftY7Xs9MKur74soAW4CJww7fm2/uPaft4P5Tva0T9b2iZ72l/UZYx/gpnru2T3HVpePp4HXawqY6mkb5Zp8smes0ztjNf1/w2KxWLqLqTKSND62Apd2N2TmHcA1wEHAG2vzuyiB5dLMfLKr71+BT9Uvl4ww/rTKPjnpmflvyqr4LKbnZtNhr8l9wKd75nYD8GfguGmYlyRNGwN3SRof6zPz733aV9d6TkTsDxwFPJiZd/fpe3On7zDj78Jcd1pEHB4RV0bE3TX3PGsu+6raZfaI449yTX6dmdv7tP8FOHiUeUnSdDPHXZLGx8MD2jfV+sBaoOSK99NpP2jI8adVRLyUkkN+MLAGuJGy8r+dkq5yPrDviKcZ5ZpsGfCaJ3FxS9KYMXCXpPHx4gHtL6n11lq623od0tV3mPE7nqp1v8+JfgHwIEspN6MuzszJ7gMR8XZK4D6qUa6JJLWGqwmSND6OqWkfvebV+s6a6nIPMDsiju7T9+Rarx9m/K62zbU+rE//uX3aBjmq1qv6HDtpwGu2A0TE3jtzghGviSS1hoG7JI2PA4FPdDdExFzgHZTV4mtr87eBAC7vDm4j4oXA8q4+w44PJb0FYHFEzOrqf1jvGM9gqtbzes57Ov1vFgX4W60P34XzDHtNJKk1TJWRpPFxK7AkIo4HbuN/+6zvBbwnMx+r/T4PnAmcC/wmIq6n7Fn+Fsr2h5/LzLUjjE9m/ioibgVOBNZFxM2UVJvXU/ZL77cS389XgcXADyJiJfAg8BrgDOD79fy9bqrv5Yf1vT0O3JeZV+/gPMNeE0lqDVfcJWl83AucQElTeS+wkJLesSC7Ho5Ut1I8DbioNn2Ikiv+J+C8zPzoKON3ORe4Cji0nmMOsAwYNP7TZOZvKakqtwNnAe8DDgDeBHx9wMuuAi6j/IVgGWU7x3c/w3mGvSaS1BqRmU3PQZL2aBExQQmqv5OZi9o2viRpZrjiLkmSJLWAgbskSZLUAgbukiRJUguY4y5JkiS1gCvukiRJUgsYuEuSJEktYOAuSZIktYCBuyRJktQCBu6SJElSCxi4S5IkSS3wX+xrbT4/dNMtAAAAAElFTkSuQmCC\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "path_to_pics = expand_path(Path(evolution.get_value_from_config(\n", " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).joinpath(\"pics\"))\n", @@ -173,11 +502,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9,\n", + " 9, 10, 10])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "params_dictionaries = []\n", + "models_ids = []\n", "\n", "for i in range(data.shape[0]):\n", " data.loc[i, \"params\"] = data.loc[i, \"params\"].replace(\"False\", \"false\")\n", @@ -185,22 +527,50 @@ " json_acceptable_string = data.loc[i, \"params\"].replace(\"'\", \"\\\"\")\n", " d = json.loads(json_acceptable_string)\n", " params_dictionaries.append(d)\n", + " models_ids.append(d[\"evolution_model_id\"])\n", "\n", - "models_ids = []\n", - "for pdict in params_dictionaries:\n", - " models_ids.append(pdict[\"evolution_model_id\"])\n", - " \n", "models_ids = np.array(models_ids)\n", "models_ids" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", @@ -247,11 +617,216 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['dataset_iterator', 'seed'] seed\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'filters_cnn'] filters_cnn\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'lear_rate'] lear_rate\n" + ] + }, + { + "data": { + "image/png": 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9td1D0xAYECRJkjQslsy5nJ6Jk8mJUyDGkBOn0DNxMkvmXN7uoWkIDAiSJEkaFl2LFpATJq7VlhMm0rVoQZtGpFYYECRJkjQsemZuS6xYvlZbrFhOz8xt2zQitcKAIEmSpGExfe/D6Vq+jFi+FHINsXwpXcuXMX3vw9s9NA2BAUGSJEnDYrtdXs6Ut7yHnDiFrsWPkBOnMOUt73EVo1HGZU4lSZI0bLbb5eUGglHOJwiSJEmSCgOCJEmSpMKAIEmSJKkwIEiSJEkqDAiSJEmSCgOCJEmSpMKAIEmSJKkwIEiSJEkqDAiSJEmSCgOCJEmSpMKAIEmSJKkwIEiSJEkqDAiSJEmSCgOCJEmSpMKAIEmSJKkwIEiSJEkqDAiSJEmSCgOCJEmSpMKAIEmSJKkwIEiSJEkqDAiSJEmSCgOCJEmSpMKAIEmSJKkwIEiSJEkqDAiSJEmSCgOCJEmSpMKAIEmSJKkwIEiSJEkqDAiSJEmSCgOCJEmSpMKAIEmSJKkwIEiSJEkqDAiSJEmSCgOCJEmSpMKAIEmSJKkwIEiSJEkqDAiSJEmSCgOCJEmSpMKAIEmSJKkwIEiSJEkqDAiSJEmSCgOCJEmSpMKAIEmSJKkwIEiSJEkqDAiSJEmSCgOCJEmSpMKAIEmSJKkwIEiSJEkqOiogRMT2EfGtiJgfEU9FxLyI+FJETB9inS3r6+bVdebXdbfvp/8XIuKaiHgwIlZGxGMRcUtEnBoRMwZ5z3MjIut/dh3KeCVJkqROMbbdA+gVEbsANwJbAZcBdwJ7AR8EDouI/TJz8SDqzKjrzAauBS4CdgOOAd4UEftk5n1Nl30Y+A3wU+ARYCKwN3AacGxE7J2ZD67jnm8G3g08CUwa7GeWJGm0uv76NVx1cbL0zzBlazj0qGD//ce0e1iShkHHBATga1Th4ITMPLu3MSLOpPoC/xng/YOo81mqcHBmZp7YUOcE4Mv1fQ5rumZKZq5qLhQRnwFOBj4GfKCvm0XELODfge8B2wAHDmKMkiSNWtdfv4bvfzUZPwkmbwUrl8H3v5rAGkOCtBHoiFeM6qcHhwLzgK82nT4VWA68IyImDlBnEvCOuv9pTae/AjwAvCEiXtB4oq9wUPt+fXzhOm57Tn08bl1jkyRpY3HVxVU4mDA56IpgwuRg/KSqXdLo1xEBATi4Pl6VmT2NJzJzGXADMIHqtZ912RvYArihvq6xTg9wZdP9BvLm+nhbXycj4l3AkcD7BvP6kyRJG4Olf4bNm16o3XxS1S5p9OuUV4xeVB/v6uf83VRPGGYD16xnHeo6zxERJ1HNIZgKdAP7U4WDz/fRd0eqV5b+IzMvW8eYJEnaqEzZunqtaMLkZ9tWPVm1Sxr9OiUgTK2PT/Rzvrd92gaucxLQ+D9vVwDvysxHGztFRBfwbapJyScMMKbniIhjgWMBdthhh6FeLklSWx16VNRzDpLNJ1Xh4Kkn4Yh/iHYPTdIw6JRXjDpCZm6TmUE12fgo4AXALRGxZ1PXD1NNRn5vZi5p4T7nZGZ3ZnbPmjVrvcctSdJI2n//Mfz1ccEWk2HZI7DFZPjr41zFSNpYdMoThN6/7E/t53xv++MjUScz/wxcEhG/oXpd6QLgJQARMZtqRaXzMvMnA4xHkqSN0v77j2H//ds9CkkbQqc8QfhjfexzbgDPriLU39yC4a4DQGY+ANwB7B4RM+vmvwDGA8c0bIyWEZE8u8Tp3XXbkYO5jyRJktQpOuUJws/q46ER0dW4klFETAb2A1YAcwaoMwdYCewXEZMbVzKq5w0c2nS/wdiuPq6pj/OAb/bT901Uryf9F7C07itJkiSNGh0REDLz3oi4iuoL/HHA2Q2nT6fa2fgbmbm8tzEidquvvbOhzpMRcSHVBODTgBMb6hwP7ARc2biTcv3K0J8zc62JzXWg+Beqzdtu7J1rkJm3Au/p63NExM+pAsLJmXnP4H8DkiRJUmfoiIBQ+wBwI3BWRBwC/AF4NdWeBXcBH2/q/4f62LxkwsnAQcBHIuLlwE3Ai4EjgEd47oZmbwQ+FxHXA/cDi6lWMjqQapLyQuC96/nZJEmSpFGhYwJC/RShG/gUcBjVF/cFVHsNnD7Y1YIyc3FE7EO1A/ORwAFUX/rPA07JzIeaLrka2JVqz4NXUC2BupwqlFwInJWZj63nx5MkSZJGhch0W/R26u7uzrlz57Z7GJIkSdqIRcTNmdk9mL6dsoqRJEmSpA7QMa8YSZIkjUaXPriKbzzwFA8/1cPzxnfxvh3Hc+TzN2/3sKSWGRAkSeu0+rbrWHP5d2HBfNh2O8Yc/reM2+PAgS/cCK148Nes/t3lxBOPkFO3YtxLD2fC81/V7mGpjS59cBWfuGsFk7u62Haz4PGnk0/ctQLAkKBRy1eMJEn9Wn3bdfScewYsfRy23gaWPk7PuWew+rbr2j20EbfiwV/z9C/Pg1XLyCkzYdUynv7leax48NftHpra6BsPPMXkri6mbRZ0dQXTNgsmd3XxjQeeavfQpJYZECRJ/Vpz+XfJyZNhyjTo6oIp08jJk6snCpuY1b+7nNx8ImwxufpdbDGZ3Hwiq393ebuHpjZ6+KkepoxZu23KmKpdGq0MCJKk/i2YD5OmrN02aUrVvomJJx6B8RPXbhw/sWrXJut547tYumbttqVrqnZptPK/vZKk/m27HTy5dO22J5dW7ZuYnLoVPLV87canllft2mS9b8fxLOvp4fGnk56e5PGnk2U9Pbxvx/HtHprUMgOCJKlfYw7/W2LZsmoOQk8PLH2cWLaMMYf/bbuHNuLGvfRwYtVyWLms+l2sXEasWs64lx7e7qGpjY58/uZ8evYEpm0WLHg6mbZZ8OnZE5ygrFHNjdLazI3SJHU6VzF6lqsYSRqthrJRmsucSpLWadweB8ImGgiaTXj+qwwEkjZ6vmIkSZIkqTAgSJIkSSoMCJIkSZIKA4IkSZKkwoAgSZIkqTAgSJIkSSoMCJIkSZIKA4IkSZKkwo3SJAFwe87nN9zPk6xkEluwJzuze2zX7mFJkqQRZkCQxO05n+u4nc0YywTGs4rVXMftkGySIeHe2xfyq+uW8OiiLmbN7GGfA6ezy+7btHtYkiSNCF8xksRvuJ/NGMvmjKOLLjZnHJsxlt9wf7uHNuLuvX0hl/zgCZYtD7ac0cOy5cElP3iCe29f2O6hSZI0IgwIkniSlYxreqA4jrE8yco2jah9fnXdEiZOSCZPhDERTJ4IEyckv7puSbuHJknSiDAgSGISW7CaZ9ZqW80zTGKLNo2ofR5d1MWECblW24QJyaOL/J9LSdKmwf/Hk8Se7MzTPMMqVtNDD6tYzdM8w57s3O6hjbhZM3tYsSLWaluxIpg1s6dNI5IkaWQZECSxe2zHgezO5oxjBU+xOeM4kN03yQnK+xw4neUrgmXLYU0my5bD8hXBPgdOb/fQJEkaEa5iJAmoQsLubHqBoNkuu2/DW2GtVYwOfeM0VzGSJG0yDAiS1GSX3bcxEEiSNlm+YiRJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqSiowJCRGwfEd+KiPkR8VREzIuIL0XE9CHW2bK+bl5dZ35dd/t++n8hIq6JiAcjYmVEPBYRt0TEqRExo4/+L4yIj0bEtfU1qyPizxFxWUQc3OrnlyRJktotMrPdYwAgInYBbgS2Ai4D7gT2Ag4G/gjsl5mLB1FnRl1nNnAt8GtgN+AI4BFgn8y8r+ma1cBvgDvqPhOBvYFuYD6wd2Y+2ND/IuBv6v7XA48BLwLeAowBPpiZZw3mc3d3d+fcuXMH01WSJElqSUTcnJndg+k7dkMPZgi+RhUOTsjMs3sbI+JM4MPAZ4D3D6LOZ6nCwZmZeWJDnROAL9f3OazpmimZuaq5UER8BjgZ+BjwgYZTVwBfyMxbmvofCPwU+P8i4r8yc8EgxitJkiR1jI54glA/PbgHmAfskpk9DecmAwuAALbKzOXrqDOJ6glAD7BtZi5rONcF3AfsWN/jvr6rrFXvZcCtwNWZ+fpBfpargNcDR2fmDwfq7xMESZIkbWhDeYLQKXMQet/bv6oxHADUX/JvACZQvfazLnsDWwA3NIaDuk4PcGXT/Qby5vp42yD7AzxdH58ZwjWSJElSR+iUV4xeVB/v6uf83cChVK8OXbOedajrPEdEnARMAqZSzT/YnyocfH4d92y8fkfgEGAF8IvBXCNJkiR1kk4JCFPr4xP9nO9tn7aB65wEbN3w8xXAuzLz0QHuS0SMB/4TGA/8U2YuWUffY4FjAXbYYYeBSkuSJEkjplNeMeoImblNZgawDXAU8ALglojYc13XRcQY4EJgP+B7wBcHuM85mdmdmd2zZs0ansFLkiRJw6BTAkLvX/an9nO+t/3xkaiTmX/OzEuoXmuaAVzQX986HPwH8Dbg+8DfZyfM/JYkSZJa0CkB4Y/1sc+5AcAL62N/cwuGuw4AmfkA1V4Hu0fEzObzEbEZ8F3g7cB3gL/LTCcnS5IkadTqlIDws/p4aL0caVEvc7of1cTfOQPUmQOsBParr2us00X1RKDxfoOxXX1c01RvHPBfVE8OLgDekZlrkCRJkkaxjggImXkvcBWwE3Bc0+nTqXY2vrBxD4SI2C0idmuq8yTVXICJwGlNdY6v61/ZuAdCRMyOiOe8khQRXfVGaVsBNzZOOq4nJF9CtTvzN4FjmpdnlSRJkkajTlnFCKqdim8EzoqIQ4A/AK+m2rPgLuDjTf3/UB+jqf1k4CDgIxHxcuAm4MVUX+Yf4bkB5I3A5yLieuB+YDHVSkYHUk1SXgi8t+maf6uvWwQ8DJwS0TwMfp6ZPx/gM0uSJEkdpWMCQmbeGxHdwKeAw6i+gC8Avgycvq5lQ5vqLI6IfYBTgSOBA6i+9J8HnJKZDzVdcjWwK9WeB6+gWgJ1OVUouRA4KzMfa7pm5/o4EzhlHcP5+WDGLEmSJHWKcMGd9uru7s65c+e2exiSJEnaiEXEzZnZPZi+HTEHQZIkSVJnMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkgoDgiRJkqTCgCBJkiSpMCBIkiRJKgwIkiRJkoqx7R6ARt6iH9zGsq9cTNfDf6LneTsw+fijmHn0Hu0eliRJkjqATxA2MYt+cBsr/umL8PgSerbdHh5fwop/+iKLfnBbu4cmSZKkDmBA2MQs+8rFrJkynZg2nejqIqZNZ82U6Sz7ysXtHpokSZI6gAFhE9P18J9gytS1G6dMrdolSZK0yTMgbGJ6nrcDLH1i7calT1TtkiRJ2uQZEDYxk48/ijFLl5CPLyF7esjHlzBm6RImH39Uu4cmSZKkDtByQIiIWRHx/oj4ckSc29S+V0RsMTxD1HCaefQeTDjjJJg2na4FD8G06Uw44yRXMZIkSRLQ4jKnEfFu4CxgcyCABN5Tn94a+BVwLPDNYRijhtnMo/cwEEiSJKlPQ36CEBE9JA5YAAAgAElEQVSvB84B7gLeCny98Xxm/h64HThyOAYoSZIkaeS08gTho8AC4MDMXBoRr+ijz23APus1MkmSJEkjrpU5CN3A/2Tm0nX0eQjYprUhSZIkSWqXVgLCOGD5AH2mAWtaqC1JkiSpjVoJCPOAVw7Q59XAH1uoLUmSJKmNWgkIlwEHRMTb+joZEccAewA/XJ+BSZIkSRp5rUxSPgN4O/DdiDgamAoQEccDBwBHAXcDZw/XICVJkiSNjCEHhMxcEhEHAhcAjU8RzqqPvwT+LjMHmqcgSZIkqcO0tFFaZv4JOCgi9qBaznQG8AQwJzNvHsbxSZIkSRpBLQWEXpl5G9WeB5IkSZI2Aq3spHxfRJwwQJ/jIuK+1oclSZIkqR1aWcVoJ6p9DtZlGrBjC7UlSZIktVErAWEwJgOrN1BtSZIkSRvIoOYgRMQOTU3T+mgDGAPsAPwV4CtGkiRJ0igz2EnK84Bs+PmD9T/9CeAjLY5JkiRJUpsMNiBcQBUQAngn1cpFt/bRbw2wGLgmM68alhFKkiRJGjGDCgiZ+a7ef4+IdwKXZOanNtSgJEmSJLVHKzspb6iJzZIkSZLazC/7kiRJkoqWd1KOiFcBbwCeB4zvo0tm5rtbrS9JkiRp5A05IEREAOcDf081abl38nKvbGg3IEiSJEmjSCuvGB0PvAO4EOimCgNfAvYFTgaWARcBLximMUqSJEkaIa28YvQPwB97VzaqHijweGbOAeZExJXAHOCnwHnDNE5JkiRJI6CVJwi7Adc2tZWgkZm3AP8DfGA9xiVJkiSpDVpdxeiJhn9fDmzZdP5uqiAhSZIkaRRpJSA8TLVyUa/7gFc29XkhVXCQJEmSNIq0EhBuYu1AcDmwV0R8MiJ2j4jjgCOo5iFIkiRJGkVaCQg/BMZExM71z2cADwCnA7cBZwOPA/88LCOUJEmSNGKGvIpRZl4KXNrw82MR8QrgvcAuwDzggsxcMFyDlCRJkjQyWt5JuVFmPgF8cThqSZIkSWqfIb9iFBFrIuI/N8RgJEmSJLVXK3MQlgF/Gu6BSJIkSWq/VgLCLcBfDPdAJEmSJLVfKwHhC8AbI+L1wz0YSZIkSe3VyiTlrYArgMsj4lLg18BCIJs7ZuYF6zc8SZIkSSOplYBwPlUYCOCo+h9YOyBE/bMBQZIkSRpFWgkIxwz7KCRJkiR1hFY2Svv2hhiIJEmSpPZrZZJySyLigxFx30jdT5IkSdLQjVhAAKYBO47g/SRJkiQN0UgGBEmSJEkdzoAgSZIkqTAgSJIkSSoMCJIkSZIKA4IkSZKkwoAgSZIkqTAgSJIkSSoMCJIkSZKKsUO9ICJOAe7PzAuHeOnPh3ovSZIkSSOrlScInwBeOtSLMvO6zDy9hftJkiRJGiGtBISHgSnDPRBJkiRJ7ddKQLgEeF1EbDHcg5EkSZLUXq0EhFOBJcClEfGSYR6PJEmSpDYa8iRl4LfAOGBP4LcRsQp4BMimfpmZu6zn+CRJkiSNoFYCQhfwNPCnpvYY4GdJkiRJHW7IASEzd9oA45AkSZLUAdwoTZIkSVJhQJAkSZJUtDIHAYCIGA+8CngeML6vPpl5Qav1JUmSJI28lgJCRPwjcAYwvb8uVKsaGRAkSZKkUWTIrxhFxGHAucAC4CSqMHAZ8HHgp/XP/wX84/ANU5IkSdJIaGUOwonAYmDfzPzXuu3WzPx8Zh4GvBc4Crh3mMYoSZIkaYS0EhD2BH6Umcv6qpOZ3wRuoHqiIEmSJGkUaSUgTKR6vajXKmBKU5+5wKtbHZQkSZKk9mglICwEZjX8vAB4UVOfqcCYVgclSZIkqT1aCQi3s3Yg+CVwSEQcABARLwH+uu4nSZIkaRRpJSBcDuwXEdvVP58BrAF+HhGPAr8FJgOfHp4hSpIkSRoprQSEb1BtjrYIIDPvAA6hCg6LgKuAwzPzJ8M1SEmSJEkjY8gBITOfzsw/Z+bqhrY5mfmXmfnizDw8M69sZTARsX1EfCsi5kfEUxExLyK+FBH9bcjWX50t6+vm1XXm13W376f/FyLimoh4MCJWRsRjEXFLRJwaETPWcZ99I+Indf+VEXFbRHwoIpx/IUmSpFEpMrPdYwAgInYBbgS2otp47U5gL+Bg4I/Afpm5eBB1ZtR1ZgPXAr8GdgOOAB4B9snM+5quWQ38Brij7jMR2BvoBuYDe2fmg03XHAH8kGoVp+8BjwFvppqf8YPMfNtgPnd3d3fOnTt3MF0lSZKklkTEzZnZPZi+Y9fjJnsAfwe8GJiYma+r23ei+mL/08xcMoSSX6MKBydk5tkN9zkT+DDwGeD9g6jzWapwcGZmnthQ5wTgy/V9Dmu6ZkpmrmouFBGfAU4GPgZ8oKF9CvDvVHMvDsrMuXX7J6lCydER8fbMvGgQ45UkSZI6RitzEIiIT1H9xf2fqP5qfnBTze8Cfz+EersAhwLzgK82nT4VWA68IyImDlBnEvCOuv9pTae/AjwAvCEiXtB4oq9wUPt+fXxhU/vRVEu9XtQbDhrqfKL+8X+ta6ySJElSJxpyQIiIt1N9Cf4p8HLgc43n69d35gJvGULZ3oBxVWb2NNVbRrUz8wSq137WZW9gC+CGpp2eqev2zo04uPnCfry5Pt7W1P7a+nhFH9f8AlgB7BsR4wd5H0mSJKkjtPKK0QnAPcARmbk6It7aR58/AAcNoWbvvgp39XP+bqonDLOBa9azDnWd54iIk4BJVBu9dQP7U4WDzw/2Ppn5TETcD+wOvIDqdyFJkiSNCq0EhJcC5zeuYtSH+cDWQ6g5tT4+0c/53vZpG7jOSaw97iuAd2Xmo8N5n4g4FjgWYIcdduinhCRJkjTyWpmDEEDPAH22plrdZ1TJzG0yM4BtgKOongDcEhF7DvN9zsnM7szsnjVr1nCWliRJktZLKwHhbmDf/k5GRBfVqzm3D6Fm71/cp/Zzvrf98ZGoU+/zcAnVa00zgAs2xH0kSZKkTtNKQPg+sGdEnNjP+ZOBXYHvDKHmH+tjn3MDeHYVof7mFgx3HQAy8wGqvRF2j4iZg7lPRIwFdgaeAe5rPi9JkiR1slYCwpeA3wJnRMT/BQ4HiIgv1j+fDswBzhlCzZ/Vx0PrJxBFREwG9qNaGWjOAHXmACuB/errGut0UT0RaLzfYGxXH9c0tF1bH5v3UwB4DdWKSzdm5lNDuI8kSZLUdkMOCJm5kmqZ0AuBPak2RQvgI8Argf8ADsvMZ4ZQ817gKmAn4Lim06dT7Wx8YWYu722MiN0iYremOk/W45rIc/dBOL6uf2XjTsoRMTsinvOqUER01RulbUX1Zb9x07cfAIuAt0dEd8M1mwOfrn/8+ro/tSRJktR5IjNbvzhiS+BVVO/pPwHc1MeKP4OttQtwI9UX8suolgd9NVUYuQvYNzMXN/RPgHpScWOdGXWd2VR/6b+JarfnI4BH6jr3NvT/ENVeDtcD9wOLqSZZH0g1SXkhcEhm3tF0nyOpgsIq4CLgMaq9H15Ut/91DuKX293dnXPnzh2omyRJktSyiLg5M7sH7rmeAWG4RcTzgU9RvbozA1gAXAKc3vQX/H4DQn1uS6odmI8EtqX60n85cEpmPtTU9yXA+6kmVm9PtTTpcqpQ8mPgrMx8rJ/x7gd8HNgH2Jxqf4hv1des6euaZgYESZIkbWjDGhAi4lstjiMz890tXrvJMCBIkiRpQxtKQBjMRmnvanEcCRgQJEmSpFFkMAFh5w0+CkmSJEkdYcCAUO8FIEmSJGkT0Mo+CJIkSZI2UgYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFQYESZIkSYUBQZIkSVJhQJAkSZJUGBAkSZIkFR0VECJi+4j4VkTMj4inImJeRHwpIqYPsc6W9XXz6jrz67rb99F3RkS8JyIuiYh7ImJlRDwREddHxLsjos/fUUSMj4jjIuKmiFgUEU9GxB8i4qyI2LHV34EkSZLUTmPbPYBeEbELcCOwFXAZcCewF/BB4LCI2C8zFw+izoy6zmzgWuAiYDfgGOBNEbFPZt7XcMnbgK8DC4CfAX8CtgaOAs4FDo+It2VmNtxjLHANsF89zu8CTwGvAv438M6I2Dcz72jx1yFJkiS1RccEBOBrVOHghMw8u7cxIs4EPgx8Bnj/IOp8liocnJmZJzbUOQH4cn2fwxr63wW8BfhxZvY09D8ZuAn4K6qw8MOGa95KFQ6uAQ5tuu504BTgJOAfBzFeSZIkqWN0xCtG9dODQ4F5wFebTp8KLAfeERETB6gzCXhH3f+0ptNfAR4A3hARL+htzMxrM/NHjV/y6/aFwL/VPx7UVKv3+h83X0f19ANg1rrGKkmSJHWijggIwMH18ao+vqgvA24AJgB7D1Bnb2AL4Ib6usY6PcCVTfcbyNP18Zmm9tvr4+F9zFH4y/p49SDvIUmSJHWMTnnF6EX18a5+zt9N9YRhNtVrPetTh7rOOtXzDN5Z/3hF0+kfAxdTvXr0u4i4GlgNvBLYHzib5z4Jaax9LHAswA477DDQUCRJkqQR0ykBYWp9fKKf873t00aoDsDngZcAP8nMKxtPZGZGxNFUrz99AviLhtPXAN/JzOanDo3XnwOcA9Dd3Z399ZMkSZJGWqe8YtRR6gnNJ1KtUPSOPs5vDnyv7nMcsC1VOHkjsCPwi4g4YsQGLEmSJA2TTgkIvX/Zn9rP+d72xzd0nYg4nmq1ozuAgzPzsT66/TPV8qgfz8xvZObCzFyamZcDRwOb1TUkSZKkUaVTAsIf62N/cwNeWB/7m1swLHUi4kNU8wd+TxUOFvZTp3ci8s+aT2Tmb4ElwI71ngySJEnSqNEpAaH3i/ahzasCRcRkqj0HVgBzBqgzB1gJ7Fdf11ini2qic+P9Gs9/FPhX4FaqcPDIOu4zvj4+ZynTiBgP9N579QDjlSRJkjpKRwSEzLwXuArYieqd/kanAxOBCzNzeW9jROwWEbs11XkSuLDuf1pTnePr+lc27aRMRHySalLyzcAhmblogCH/sj6eXAeCRqdRTf7+dfNSq5IkSVKni8zOWESn3iztRqrdlC8D/gC8mmrPgruAfTNzcUP/BMjMaKozo64zG7iWajfkFwNHAI/Ude5t6P8PwPnAGqrXi/paAWleZp7fcM3zqJ5WbE+1udsV1E8ugL3qfz8kM3810Ofu7u7OuXPnDtRNkiRJallE3JyZ3YPp2ynLnJKZ90ZEN/Ap4DCqFYEWUE32PT0zlwyyzuKI2IdqCdIjgQOAxcB5wCmZ+VDTJTvXxzHAh/opex1ViOi9x8MRsSfwUeBNwDFUT2MW1P2+kJl3Dma8kiRJUifpmCcImyqfIEiSJGlDG8oThI6YgyBJkiSpMxgQJEmSJBUGBEmSJEmFAUGSJElSYUCQJEmSVBgQJEmSJBUGBEmSJEmFAUGSJElSYUCQJEmSVBgQJEmSJBUGBEmSJEmFAUGSJElSYUCQJEmSVBgQJEmSJBUGBEmSJEmFAUGSJElSYUCQJEmSVBgQJEmSJBUGBEmSJEmFAUGSJElSYUCQJEmSVBgQJEmSJBUGBEmSJEmFAUGSJElSYUCQJEmSVBgQJEmSJBUGBEmSJEmFAUGSJElSYUCQJEmSVBgQJEmSJBUGBEmSJEmFAUGSJElSYUCQJP2/9u496rK6ruP4+wNTkwKNhgy6xABRlBarlTjhBS+MlxHRsMxLmTcSlVLJMLFsoWBZVIa3TC2zUWoVKSqZIKNcQiBjIVqWIsgwmowDCIjcxGC+/bH38+t4eM7cHubZZzzv11qzfjx77/Pb32evw3P25+zf/m1JkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpMSBIkiRJagwIkiRJkhoDgiRJkqTGgCBJkiSpmaqAkGSvJB9Msj7JHUnWJXlHkvtuZT8/1b9uXd/P+r7fvebZdvckRyX5eJKvJ7k9yU1JLkjysiQTj1GSnfvXnp/kxv61a5OcmmT/bTkGkiRJ0pCWDF3AnCT7ARcBy4HTgcuAg4HfAg5LckhVXb8F/eze97M/cA7wj8DDgSOBZyR5TFWtHXnJc4H3At8GzgW+CewJPBv4APD0JM+tqhrbz659nU8CvgR8CPg+8EDg8f3+L9/6IyFJkiQNZ2oCAvCXdOHgmKp699zCJCcDvw28FTh6C/r5I7qT85Or6nUj/RwDvLPfz2Ej218OHAF8qqo2jmz/RuBi4JfpwsJpY/t5P104OLqq3j9eRJIf24JaJUmSpKmSsS/Ghymiu3rwdWAdsN/YifpudN/uB1heVbduop9dgWuBjcADqurmkXU7AWuBvft9rJ2/lx/q7410weQvquo1I8sPAr4AnFpVv7IVv+rdrFixoi655JKFdCFJkiRtUpIvVNWKLdl2Wu5BWNm3a0bDAUB/kn8hcG/g0Zvp59HAvYALR8NB389G4Kyx/W3O//btnWPLX9C3/5BkWZIXJvm9JK9I8pAt7FuSJEmaOtMyxOhhfTtpzP4VwCq6oUNnL7Af+n42KckS4MX9j58eW/3zfbs3cCWw+8i6SvJeuqFSd21uP5IkSdI0mZYrCMv69qYJ6+eW32eR+gE4CTgQOKOqzhpbt7xvTwbOAw4AdgOeQhcYfhM4flLH/ZWGS5Jcct11121BKZIkSdLimJaAMFX6G5pfRzeT0ovm2WTuuF0GPL+qLquqW6rqbOA5dPdAHJvkx+frv6r+qqpWVNWKPfbYYzv8BpIkSdK2mZaAMPfN/rIJ6+eWf3d795Pk1XSzHX0FWFlVN8yz2dzrPzk+jKiq/gO4iu6KwgGbqVeSJEmaKtMSEL7Wt5PuDXho327uuQIL6ifJa4F3A/9FFw42bGY/k4LGjX17r8mlSpIkSdNnWgLCuX27avzJxf00p4cAtwGf30w/nwduBw7pXzfaz050NzqP7m90/RuAt9M99GxlVV27if18tm8PnKefpfx/EFm3mXolSZKkqTIVAaGqrgTWAPsArxpbfSKwC3DK6DMQkjw8ycPH+rkFOKXf/oSxfl7d93/W+DMQkhxPd1PyF4AnV9V3NlPyacB64PlJDh5bdzzdUKZzN3EFQpIkSZpKU/GgNGgPS7uIboag04GvAo+ie2bB5cBjq+r6ke0LoKoy1s/ufT/7A+fQPQ35AOBZdA9Re2wfSOa2fwmwGriLbnjRfDMgrauq1WP7eSrwL/2PHwOu7ut9XL+fx1XVFWyGD0qTJEnS9rY1D0qblucgUFVXJlkBvAU4DDic7gnK7wROrKobN/X6kX6uT/IY4M3ALwKPB64H/hZ4U1V9a+wl+/btzsBrJ3T7r3QhYnQ/n+mvHhxPN73pMmAD8D7gD6pq/ZbUK0mSJE2TqbmCMKu8giBJkqTtbWuuIEzFPQiSJEmSpoMBQZIkSVJjQJAkSZLUGBAkSZIkNQYESZIkSY0BQZIkSVJjQJAkSZLUGBAkSZIkNQYESZIkSY0BQZIkSVJjQJAkSZLUGBAkSZIkNQYESZIkSY0BQZIkSVJjQJAkSZLUGBAkSZIkNQYESZIkSY0BQZIkSVJjQJAkSZLUGBAkSZIkNQYESZIkSY0BQZIkSVJjQJAkSZLUGBAkSZIkNQYESZIkSc2SoQuQhnTpHRtYc8c1XFt3sjxLWLV0Tw5aev+hy5IkSRqMAUEz69I7NvDh29ezS8IeLOHm2siHb18PYEiQJEkzyyFGmllr7riGXRJ2y86kb3dJWHPHNUOXJkmSNBgDgmbWtXUnu479L7ArO3Ft3TlQRZIkScMzIGhmLc8SbmHjDy27hY0sjyPvJEnS7DIgaGatWront1Zxc91F9e2tVaxauufQpUmSJA3Gr0o1s+ZuRB6dxeiXf8JZjCRJ0mwzIGimHbT0/gYCSZKkEQ4xkiRJktQYECRJkiQ1BgRJkiRJjQFBkiRJUmNAkCRJktQYECRJkiQ1BgRJkiRJjQFBkiRJUmNAkCRJktQYECRJkiQ1BgRJkiRJjQFBkiRJUmNAkCRJktQYECRJkiQ1BgRJkiRJjQFBkiRJUmNAkCRJktQYECRJkiQ1BgRJkiRJjQFBkiRJUmNAkCRJktQYECRJkiQ1BgRJkiRJjQFBkiRJUmNAkCRJktQYECRJkiQ1BgRJkiRJjQFBkiRJUpOqGrqGmZbkOuAbA+3+fsB3Btq3ppfvC03ie0Pz8X2hSXxvTJe9q2qPLdnQgDDDklxSVSuGrkPTxfeFJvG9ofn4vtAkvjd2XA4xkiRJktQYECRJkiQ1BoTZ9ldDF6Cp5PtCk/je0Hx8X2gS3xs7KO9BkCRJktR4BUGSJElSY0CQJEmS1BgQJEmSJDUGhBmTZK8kH0yyPskdSdYleUeS+w5dmxZfkt2THJXk40m+nuT2JDcluSDJy5L4N0JNkhcmqf7fUUPXo2EleXL/t2ND/3myPslZSQ4fujYNJ8kzkqxJ8q3+M2Vtko8keczQtWnLeZPyDEmyH3ARsBw4HbgMOBhYCXwNOKSqrh+uQi22JEcD7wW+DZwLfBPYE3g2sAw4DXhu+Ydi5iV5EPBlYGdgV+DlVfWBYavSUJL8KfB64FvAmXRPy90DeCTw2ao6bsDyNJAkfwIcB1wPfILuffEQ4AhgCfDiqvq74SrUljIgzJAkZwGrgGOq6t0jy08Gfht4f1UdPVR9WnxJngTsAnyqqjaOLL8/cDHwIOA5VXXaQCVqCiQJ8BlgX+BjwO9gQJhZSV5ON33lh4BXVNUPxtb/WFX97yDFaTD958bVwHXAz1bVtSPrVgLnAFdV1YMHKlFbweEDM6K/erAKWAe8Z2z1m4FbgRcl2WWRS9OAquqcqvrkaDjol28A3tf/eOiiF6ZpcwzwJOBIur8VmlFJlgJvpbvaeLdwAGA4mFl7051X/vtoOACoqnOBm+muMmkHYECYHSv7ds08J4M3AxcC9wYevdiFaWrNfcjfOWgVGlSSA4CTgHdW1flD16PBPZXuJO9jwMZ+vPkbkvyWY8xn3hXAD4CDk9xvdEWSJwC7AZ8dojBtvSVDF6BF87C+vXzC+ivorjDsD5y9KBVpaiVZAry4//HTQ9ai4fTvg1Povi1+48DlaDr8fN9+H/gicODoyiTn0w1LvG6xC9OwquqGJG8ATga+kuQTdPci7Ed3D8JngFcOWKK2ggFhdizr25smrJ9bfp9FqEXT7yS6D/4zquqsoYvRYN4EPAJ4XFXdPnQxmgrL+/b1wFeAxwNfors/5W10XzR9BIcmzqSqekeSdcAHgZePrPo6sHp86JGml0OMJP2QJMcAr6Ob5epFA5ejgSR5FN1Vgz+vqn8buh5NjbnzhjuBI6rqgqq6paq+DPwS3axGT3S40WxKchzwUWA13ZWDXehmtloL/H0/+5V2AAaE2TF3hWDZhPVzy7+7CLVoSiV5NfBOum8GV1bVDQOXpAH0Q4s+TDck8fiBy9F0mfuM+GJVrRtdUVW3AXNXHA9ezKI0vCSHAn8C/HNVHVtVa6vqtqq6lC48Xg28LomzGO0ADAiz42t9u/+E9Q/t20n3KOhHXJLXAu8G/osuHGwYuCQNZ1e6vxUHAN8feTha0c16BvDX/bJ3DFalhjD3WTLpy6Qb+/Zei1CLpssz+/bc8RV9eLyY7rzzEYtZlLaN9yDMjrn/YVcl2WlszvvdgEOA24DPD1GchtXfWHYS3Vjip1bVdwYuScO6A/ibCesOovuAv4DuZNHhR7PlbKCAnxn/LOnN3bR81eKWpSmwtNAu9fwAAAX0SURBVG8nTWU6t/xuU+Nq+vigtBnig9I0nyTHA28BvgCscliRNiXJCXRXEXxQ2oxKcjrdrDTHVtXbR5avopv17CZgn6qaNCmGfgQleR5wKnAN8Miqunpk3dOBT9F9+bBXVV0/TJXaUl5BmC2/CVwEvCvJk4GvAo+ie0bC5cDvD1ibBpDkJXTh4C7gc8Ax3UNzf8i6qlq9yKVJml6voruKdHKSZ9BNd7ov8It0f0uOMhzMpI/SPefgKcBXk3wc2EA3VPGZQIDfNRzsGAwIM6Sqrkyygu6E8DDgcODbdDelnlhVN27q9fqRtG/f7gy8dsI2/0o3I4UkUVXfSvJIumlwjwCeAHwP+CTwx1V18ZD1aRhVtTHJ4XQB8lfobky+N3ADcAbwrqpaM2CJ2goOMZIkSZLUOIuRJEmSpMaAIEmSJKkxIEiSJElqDAiSJEmSGgOCJEmSpMaAIEmSJKkxIEiSJElqDAiSpB1GktVJKsk+23k/65Ks2577kKRpZUCQJM2cJOcl8UmhkjSPJUMXIEnSFHry0AVI0lAMCJIkjamqK4euQZKG4hAjSZoBSfbpx+6vTvLwJJ9IckOSW5NckGTVPK9ZmuR3k3w5yW1Jvpfkc0medw/1f0L/mkM31d8W/n4vTXJakrVJbu9rvTDJC+frF3hi/3ON/DtvZLt570FYwDHZJ8k/JvlOku8nuSTJM7fkd5OkxeYVBEmaLfsC/wZ8GXg/8ADg+cCZSV5QVacCJPlx4Cy6E+nLgPcA9waeA5ya5Oeq6o3b2v928F7gv4HzgW8DuwOHA6ckeVhVHd9v913gROClwN79f89Zt6kdLOCY7A1cDKwFTgF+iu6YnJ7kKVV17tb+spK0PaXKe7Qk6UddP+vPVf2Pb6uq14+sW0F3Un8LsHdVfS/J7wF/BJwJHFFVd/bbLqc72d0bOKSqLtqW/vvlJwBvBlZW1XkT6v1QVb10ZPlq4CXAvlW1bmT5fuPDgvoT+jOBJwD7VNXVI+vOA55YVZlwvNYBVNU+I8sWckxOqKoTR/p6GvBp4MyqOny+GiRpKA4xkqTZchPwltEFVXUJ8PfAfYBf6hf/OlDAsXMnwv221wJ/0P941AL6v0fNd89AVf2A7lv+JdwzNx1v6zH5BvCHY7WdBXwTOPgeqEuS7lEGBEmaLZdW1c3zLD+vbx+RZDfgIcD6qrpsnm3Pmdt2W/rfilq3WJKfTvKeJJf19wZUf6/Baf0mD1xg/ws5Jl+qqrvmWf4/wH0XUpckbQ/egyBJs+WaCcs39O2y/h90Y/nnM7f8PtvY/z0qyYPphvjcF/gcsIbuSsZdwD50Q5KWLnA3Czkm353wmjvxizpJU8iAIEmzZc8Jy+/ftzf1/0aXjXvAyLbb0v+cjX0732fRfCfakxxLd1PykVW1enRFkl+lCwgLtZBjIkk7FL+5kKTZclA/XGbcoX37xX6I0JXAA5M8dJ5tV/btpdvS/8iyG/v2QfNsv2KeZZM8pG9Pm2fdEye85i6AJDtvyQ4WeEwkaYdiQJCk2bIMeNPogn6WoV+j+/b74/3iDwIB/mz0JDrJ/YDjR7bZ1v6hGxYEcGSSJSPbP2i8j81Y17eHju33acx/0zDA9X3701uxn209JpK0Q3GIkSTNlvOBo5I8CriQ/39OwU7AK+emIAXeBjwdeBbwH0nOoJvz/7nAcuBPq+qCBfRPVf17kvPppiG9OMk5dEOUfoHueQPzXVmYz18CRwIfSfJRYD1wIHAY8E/9/sed3f8uH+t/t9uBb1TVKZvYz7YeE0naoXgFQZJmy1XAY+mG9xwNPI9uWMzhow8x66cIfSrw+/2i19CN5b8CeEFVvWEh/Y94FvABYK9+H48AjgMm9X83VfWfdEN8LgKeAfwG8JPAs4H3TXjZB4A/prvicRzdNKUv28x+tvWYSNIOxQelSdIMmPTgsR2lf0nS4vEKgiRJkqTGgCBJkiSpMSBIkiRJarwHQZIkSVLjFQRJkiRJjQFBkiRJUmNAkCRJktQYECRJkiQ1BgRJkiRJzf8BAfz7Kr9JPGsAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'lear_rate_decay'] lear_rate_decay\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'coef_reg_cnn'] coef_reg_cnn\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'coef_reg_den'] coef_reg_den\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'dropout_rate'] dropout_rate\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'dense_size'] dense_size\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['train', 'epochs'] epochs\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['train', 'batch_size'] batch_size\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'confident_threshold'] confident_threshold\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['chainer', 'pipe', 4, 'check_bool'] check_bool\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 7cf7f66091..3ca2f0040c 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -206,12 +206,14 @@ def first_generation(self, iteration=0): str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + suffix = Path(self.get_value_from_config(self.basic_config, + path_ + ["save_path"])).suffix for which_path in ["save_path", "load_path"]: population[-1] = self.insert_value_or_dict_into_config( population[-1], path_ + [which_path], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( - "fitted_model_" + str(path_id)))) + "fitted_model_" + str(path_id)).with_suffix(suffix))) population[-1]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 @@ -281,11 +283,13 @@ def next_generation(self, generation, scores, iteration): str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + suffix = Path(self.get_value_from_config(self.basic_config, + path_ + ["save_path"])).suffix next_population[i] = self.insert_value_or_dict_into_config( next_population[i], path_ + ["save_path"], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["save_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( - "fitted_model_" + str(path_id)))) + "fitted_model_" + str(path_id)).with_suffix(suffix))) for i in range(self.n_saved_best_pretrained, self.population_size): # if several train files @@ -301,12 +305,14 @@ def next_generation(self, generation, scores, iteration): str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + suffix = Path(self.get_value_from_config(self.basic_config, + path_ + ["save_path"])).suffix for which_path in ["save_path", "load_path"]: next_population[i] = self.insert_value_or_dict_into_config( next_population[i], path_ + [which_path], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + [which_path]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( - "fitted_model_" + str(path_id)))) + "fitted_model_" + str(path_id)).with_suffix(suffix))) next_population[i]["evolution_model_id"] = self.evolution_model_id self.evolution_model_id += 1 From b244b73756ddb3b94d7c2a491d46025e97ad93bd Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 26 Jun 2018 10:56:40 +0300 Subject: [PATCH 517/616] fix: load path for fiton models --- .../models/evolution/evolution_param_generator.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/deeppavlov/models/evolution/evolution_param_generator.py b/deeppavlov/models/evolution/evolution_param_generator.py index 3ca2f0040c..777959a127 100644 --- a/deeppavlov/models/evolution/evolution_param_generator.py +++ b/deeppavlov/models/evolution/evolution_param_generator.py @@ -257,6 +257,7 @@ def next_generation(self, generation, scores, iteration): except: pass + # load_paths if self.elitism_with_weights: # if elite models are saved with weights next_population[i] = self.insert_value_or_dict_into_config( @@ -276,7 +277,16 @@ def next_generation(self, generation, scores, iteration): self.main_model_path + ["load_path"], str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["load_path"]) ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath("model"))) + for path_id, path_ in enumerate(self.paths_to_fiton_dicts): + suffix = Path(self.get_value_from_config(self.basic_config, + path_ + ["load_path"])).suffix + next_population[i] = self.insert_value_or_dict_into_config( + next_population[i], path_ + ["load_path"], + str(Path(self.get_value_from_config(self.basic_config, self.main_model_path + ["load_path"]) + ).joinpath("population_" + str(iteration)).joinpath("model_" + str(i)).joinpath( + "fitted_model_" + str(path_id)).with_suffix(suffix))) + # save_paths next_population[i] = self.insert_value_or_dict_into_config( next_population[i], self.main_model_path + ["save_path"], From 61d956257f608097faed3c5d53ead96e96b20c8d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 26 Jun 2018 11:04:05 +0300 Subject: [PATCH 518/616] fix: reading reports fixed --- deeppavlov/evolve.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 91a9eb5e55..2f9edd4df6 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -264,9 +264,9 @@ def results_to_table(population, evolution, considered_metrics, result_file, res evolution.main_model_path + ["save_path"])).parent.joinpath("out.txt"))), "r") as fout: reports_data = fout.read().splitlines()[-2:] reports = [] - for i in range(2): + for j in range(2): try: - reports.append(json.loads(reports_data[i])) + reports.append(json.loads(reports_data[j])) except: pass From ca4b9bced514ec354f91db2d24c03bdc2fa130f6 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 26 Jun 2018 11:23:36 +0300 Subject: [PATCH 519/616] fix: clear all outputs - everything works in analysis --- .../models/evolution/Results_analysis.ipynb | 609 +----------------- 1 file changed, 16 insertions(+), 593 deletions(-) diff --git a/deeppavlov/models/evolution/Results_analysis.ipynb b/deeppavlov/models/evolution/Results_analysis.ipynb index 3271729b7b..cd5b839053 100644 --- a/deeppavlov/models/evolution/Results_analysis.ipynb +++ b/deeppavlov/models/evolution/Results_analysis.ipynb @@ -2,35 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", - " return f(*args, **kwds)\n", - "/home/dilyara/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", - " from ._conv import register_converters as _register_converters\n", - "Using TensorFlow backend.\n", - "[nltk_data] Downloading package punkt to /home/dilyara/nltk_data...\n", - "[nltk_data] Package punkt is already up-to-date!\n", - "[nltk_data] Downloading package stopwords to\n", - "[nltk_data] /home/dilyara/nltk_data...\n", - "[nltk_data] Package stopwords is already up-to-date!\n", - "[nltk_data] Downloading package perluniprops to\n", - "[nltk_data] /home/dilyara/nltk_data...\n", - "[nltk_data] Package perluniprops is already up-to-date!\n", - "[nltk_data] Downloading package nonbreaking_prefixes to\n", - "[nltk_data] /home/dilyara/nltk_data...\n", - "[nltk_data] Package nonbreaking_prefixes is already up-to-date!\n", - "2018-06-25 16:47:39.319 DEBUG in 'gensim.models.doc2vec'['doc2vec'] at line 73: Fast version of gensim.models.doc2vec is being used\n", - "2018-06-25 16:47:39.323 INFO in 'summa.preprocessing.cleaner'['textcleaner'] at line 20: 'pattern' package not found; tag filters are not available for English\n", - "2018-06-25 16:47:39.729 DEBUG in 'matplotlib.backends'['__init__'] at line 90: backend module://ipykernel.pylab.backend_inline version unknown\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -53,219 +27,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Considered basic config:\n", - "{\n", - " \"dataset_reader\": {\n", - " \"name\": \"basic_classification_reader\",\n", - " \"x\": \"text\",\n", - " \"y\": \"intents\",\n", - " \"data_path\": \"snips\"\n", - " },\n", - " \"dataset_iterator\": {\n", - " \"name\": \"basic_classification_iterator\",\n", - " \"seed\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"field_to_split\": \"train\",\n", - " \"split_fields\": [\n", - " \"train\",\n", - " \"valid\"\n", - " ],\n", - " \"split_proportions\": [\n", - " 0.9,\n", - " 0.1\n", - " ]\n", - " },\n", - " \"chainer\": {\n", - " \"in\": [\n", - " \"x\"\n", - " ],\n", - " \"in_y\": [\n", - " \"y\"\n", - " ],\n", - " \"pipe\": [\n", - " {\n", - " \"id\": \"classes_vocab\",\n", - " \"name\": \"default_vocab\",\n", - " \"fit_on\": [\n", - " \"y\"\n", - " ],\n", - " \"level\": \"token\",\n", - " \"save_path\": \"vocabs/snips_classes.dict\",\n", - " \"load_path\": \"vocabs/snips_classes.dict\"\n", - " },\n", - " {\n", - " \"in\": [\n", - " \"x\"\n", - " ],\n", - " \"out\": [\n", - " \"x_lower\"\n", - " ],\n", - " \"name\": \"str_lower\"\n", - " },\n", - " {\n", - " \"id\": \"my_embedder\",\n", - " \"name\": \"fasttext\",\n", - " \"save_path\": \"embeddings/dstc2_fastText_model.bin\",\n", - " \"load_path\": \"embeddings/dstc2_fastText_model.bin\",\n", - " \"dim\": 100\n", - " },\n", - " {\n", - " \"id\": \"my_tokenizer\",\n", - " \"name\": \"nltk_tokenizer\",\n", - " \"tokenizer\": \"wordpunct_tokenize\"\n", - " },\n", - " {\n", - " \"in\": [\n", - " \"x_lower\"\n", - " ],\n", - " \"in_y\": [\n", - " \"y\"\n", - " ],\n", - " \"out\": [\n", - " \"y_labels\",\n", - " \"y_probas_dict\"\n", - " ],\n", - " \"main\": true,\n", - " \"name\": \"intent_model\",\n", - " \"save_path\": \"evolution/classification/intents_snips\",\n", - " \"load_path\": \"evolution/classification/intents_snips\",\n", - " \"classes\": \"#classes_vocab.keys()\",\n", - " \"kernel_sizes_cnn\": [\n", - " 1,\n", - " 2,\n", - " 3\n", - " ],\n", - " \"filters_cnn\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 100\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"confident_threshold\": {\n", - " \"evolve_choice\": true,\n", - " \"values\": [\n", - " 0.5,\n", - " 1\n", - " ]\n", - " },\n", - " \"optimizer\": \"Adam\",\n", - " \"lear_rate\": {\n", - " \"evolve_range\": [\n", - " 0.0001,\n", - " 0.1\n", - " ],\n", - " \"scale\": \"log\"\n", - " },\n", - " \"lear_rate_decay\": {\n", - " \"evolve_range\": [\n", - " 0.0001,\n", - " 0.1\n", - " ],\n", - " \"scale\": \"log\"\n", - " },\n", - " \"loss\": \"binary_crossentropy\",\n", - " \"text_size\": 15,\n", - " \"coef_reg_cnn\": {\n", - " \"evolve_range\": [\n", - " 1e-06,\n", - " 0.001\n", - " ]\n", - " },\n", - " \"coef_reg_den\": {\n", - " \"evolve_range\": [\n", - " 1e-06,\n", - " 0.001\n", - " ]\n", - " },\n", - " \"dropout_rate\": {\n", - " \"evolve_range\": [\n", - " 0.1,\n", - " 0.9\n", - " ]\n", - " },\n", - " \"dense_size\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 100\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"model_name\": \"cnn_model\",\n", - " \"embedder\": \"#my_embedder\",\n", - " \"tokenizer\": \"#my_tokenizer\",\n", - " \"check_bool\": {\n", - " \"evolve_bool\": true\n", - " }\n", - " }\n", - " ],\n", - " \"out\": [\n", - " \"y_labels\",\n", - " \"y_probas_dict\"\n", - " ]\n", - " },\n", - " \"train\": {\n", - " \"epochs\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"batch_size\": {\n", - " \"evolve_range\": [\n", - " 50,\n", - " 500\n", - " ],\n", - " \"discrete\": true\n", - " },\n", - " \"metrics\": [\n", - " \"classification_accuracy\",\n", - " \"classification_f1\",\n", - " \"classification_roc_auc\"\n", - " ],\n", - " \"validation_patience\": 5,\n", - " \"val_every_n_epochs\": 1,\n", - " \"log_every_n_epochs\": 1,\n", - " \"validate_best\": true,\n", - " \"test_best\": false\n", - " },\n", - " \"metadata\": {\n", - " \"labels\": {\n", - " \"telegram_utils\": \"IntentModel\",\n", - " \"server_utils\": \"KerasIntentModel\"\n", - " },\n", - " \"download\": [\n", - " \"http://lnsigo.mipt.ru/export/deeppavlov_data/intents.tar.gz\",\n", - " \"http://lnsigo.mipt.ru/export/deeppavlov_data/vocabs.tar.gz\",\n", - " {\n", - " \"url\": \"http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv\",\n", - " \"subdir\": \"snips\"\n", - " },\n", - " {\n", - " \"url\": \"http://lnsigo.mipt.ru/export/deeppavlov_data/embeddings/dstc2_fastText_model.bin\",\n", - " \"subdir\": \"embeddings\"\n", - " }\n", - " ]\n", - " }\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "CONFIG_FILE = \"../../configs/evolution/evolve_intents_snips.json\"\n", "KEY_MAIN_MODEL = \"main\"\n", @@ -280,25 +46,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2018-06-25 16:47:39.741 INFO in 'deeppavlov.models.evolution.evolution_param_generator'['evolution_param_generator'] at line 55: Main model path in config: ['chainer', 'pipe', 4]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Title name for the considered evolution is `intents_snips`.\n", - "Number of populations: 10.\n" - ] - } - ], + "outputs": [], "source": [ "evolution = ParamsEvolution(population_size=POPULATION_SIZE,\n", " key_main_model=KEY_MAIN_MODEL,\n", @@ -324,50 +74,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Measure: classification_accuracy\n", - "valid:\n", - "min for\t0 model on\t0 population\n", - "max for\t1 model on\t0 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n", - "\n", - "Measure: classification_f1\n", - "valid:\n", - "min for\t1 model on\t6 population\n", - "max for\t1 model on\t0 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n", - "\n", - "Measure: classification_roc_auc\n", - "valid:\n", - "min for\t1 model on\t6 population\n", - "max for\t1 model on\t9 population\n", - "test:\n", - "min for\t0 model on\t0 population\n", - "max for\t0 model on\t0 population\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: 'argmin' is deprecated. Use 'idxmin' instead. The behavior of 'argmin' will be corrected to return the positional minimum in the future. Use 'series.values.argmin' to get the position of the minimum now.\n", - " if __name__ == '__main__':\n", - "/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n", - " # Remove the CWD from sys.path while we load stuff.\n" - ] - } - ], + "outputs": [], "source": [ "MEASURES = evolution.get_value_from_config(\n", " evolution.basic_config, list(evolution.find_model_path(\n", @@ -394,49 +103,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2018-06-25 16:47:39.818 DEBUG in 'matplotlib.font_manager'['font_manager'] at line 1343: findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=20.0 to DejaVu Sans ('/home/dilyara/anaconda3/envs/deep36/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000\n" - ] - }, - { - "data": { - "image/png": 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\n", 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SswwkuH8B2A24biADZuanMvOYnapKkiRJ0nZ2uMY9IqYBmzNzzQjVI0mSJKkX/c24rwI+3P0kIr4cEW8c3pIkSZIk9dRfcE/K+vZupzDwte6SJEmShkh/wX058PyRKESSJElS3/rbx/064E8jYg9KiAc4qdqrfUcyM9+1k7VJkiRJqvQX3P8GmAMcT5mdT8pSmf6WyyRgcJckSZKGyA6De2auABZFxFhgL2ApcB5w/vCXJkmSJKlbfzPuAGRmB/BQRCwDlmbmsuEtS5IkSVKtAQX3bpm5/2DeJCLeAbwjM48dzOslSZKk0W4gd04dCnOB14zQe0mSJEm7nJEK7pIkSZJ2gsFdkiRJagEGd0mSJKkFGNwlSZKkFmBwlyRJklqAwV2SJElqAQZ3SZIkqQUY3CVJkqQWMFLB/XbgKyP0XpIkSdIuZ8xIvElmXglcORLvJUmSJO2K6g7uEbE78E7gMGA3oL2X0zIzj9vJ2iRJkiRV6gruETEf+DEwC4gdnJo7UZMkSZKkHupd4/5pYDbwKeB5wNjMbOvl0dssvCRJkqRBqnepzKuB72fm3w9HMZIkSZJ6V++MewC/GY5CJEmSJPWt3uB+KzBvOAqRJEmS1Ld6g/s5wOsj4uhhqIWI2CcivhwRj0bElohYGhHnRcRugxjr5RHxHxHxcDXWioj4SUS8fThqlyRJkoZTvWvcn0vZj/3qiPgGZQZ+dW8nZmZdN1yKiAOAmykXv14J3E3ZcvL9wKKIODIzVw5wrPcB5wOrgO8DjwC7AwcDr8ebQUmSJKnF1BvcL6Zs9RjA26pHz60fo+qrNxxfQAntZ2Tm554eLOIzwAeBjwHv6W+QiFgIfBa4BnhTZq7rcXxsnXVJkiRJDReZA99yPSLeMdBzM/OSOsY9ALgfWAockJldNcemAsspXwhmZ+aGfsb6JfB8YN+BztD3ZsGCBblkyZLBvlySJEkakIi4NTMX9HdeXTPu9YTxOh1TtVfXhvbqPddFxE3AQuBw4Nq+BomIg4FDgCuApyLiGOBQym8Abgeu7zm+JEmS1ArqXSozXLp3qrm3j+P3UYL7QewguAO/V7WPU+7welSP43dExMmZef8g65QkSZIaYlDBPSImAScDLwNmAGuA/wEu728pSx+mV+2aPo5398/oZ5zZVfsuygWpJwA3AnOAjwJvBb4fES/OzK09XxwRpwGnAey7774DLl6SJEkabnUH94h4PXAJZZeWqDmUwLkRcWpmfm+I6qtX9/aW7cCfZObPqudrq20g5wMLgD8CvtHzxZl5IXAhlDXuw1+uJEmSNDB17eMeES8HLqPMfH8deCfw+1X79ar/2xFxaJ11dM+oT+/jeHd/r1tP1ug+/lhNaAcgy1W4V1ZPD6uzPkmSJKmh6p1x/whlZv3Vmbm4x7GLI+ILlLXlf0+Z1R6oe6r2oD6OH1i1fa2B7zlOXwF/VdVOHGBdkiRJUlOo986prwa+1UtoByAzfw58uzqvHtdX7cKI2K6majvII4GNQK/vW2MxsAGYGxGTezl+cNU+WGd9kiRJUkPVG9ynA7/r55yHgGn1DJqZDwBXA3OB9/Y4fDYwGfhq7YWvETE/Iub3GGcj8O/ABOCfIiJqzn8xcAqwjfLlQpIkSWoZ9S6VeZT+14cvoNwwqV6nAzcDn42I44C7gFdQ9ni/l7JMp9ZdVRs9+v+Rsg3kB4Ajqj3g51B2wZkAfKD6oiBJkiS1jHpn3H8AHBsRH46I9toDEdEWER8CXludV5cqTC8ALqYE9g8BBwDnA4cP9C6ombmWslTn45Sdb94HvIGyLeTrMvP8emuTJEmSGi3KZisDPDliT+BWYE/KkpifUmbX9wReRVnq8hiwIDMHM+veNBYsWJBLlixpdBmSJEnaxUXErZm5oL/z6loqk5mPRcSRwBeB44H9epxyDfCeVg/tkiRJUrOp+wZMmbkUeF1E7E25c+p0yj7st2XmI0NbniRJkiQYRHDvVoV0g7okSZI0Auq9OFWSJElSA+xwxj0ivky5U+rfZ+aK6vlAZGa+a6erkyRJkgT0v1TmFEpw/xSwono+EAkY3CVJkqQh0l9w379qH+nxXJIkSdII2mFwz8xlO3ouSZIkaWTUdXFqRHw0Io7q55xXR8RHd64sSZIkSbXq3VXmLODofs45CjhzMMVIkiRJ6t1wbAc5FugahnElSZKkUWs4gvvLgSeHYVxJkiRp1Or3zqkRcV2PrlMi4uheTm0HngvsB3xj50uTJEmS1K3f4M72a9oTmFs9euoCVgKXAh/cybokSZIk1eg3uGfm08tpIqILOCszzxnWqiRJkiRtZyAz7rVOBW4bjkIkSZIk9a2u4J6ZlwxXIZIkSZL6Vu+M+9MiYh9gb2B8b8cz84bBji1JkiRpe3UH94hYCJwLzO/n1PZBVSRJkiTpWeraxz0iDge+B8wAPg8EcAPwJeDu6vl3AS9elSRJkoZQvTdg+jtgM/B7mfn+qu/6zHwPcDDwT8BrgW8PXYmSJEmS6g3uRwDfycxHe46RxUeBu4Czh6g+SZIkSdQf3KcDD9U83wpM7nHOTcBRO1OUJEmSpO3VG9wfB3br8fyAHueMBSbuTFGSJEmStldvcL+X7YP6YuD4iDgIICL2BP4IuG9oypMkSZIE9Qf3q4DXRMTu1fPzKbPrt0XELyg7y8wCzhu6EiVJkiTVG9y/SFm/3gGQmTcBbwYepOwqsxz4i8z8ylAWKUmSJI12dd2AKTPXAj/v0Xc5cPlQFiVJkiRpe/XOuEuSJElqgHrvnHpoRHw0Iub0cXzP6vhLh6Y8SZIkSVD/jPuHgD+jbAPZmxXAu4C/3JmiJEmSJG1vMHdOvT4zs7eDVf91wJE7W5gkSZKkZ9Qb3PcEHu7nnEeBvQZXjiRJkqTe1BvcN1L2ad+RWcCWwZUjSZIkqTf1BvfbgRMjYkpvByNiGnBidZ4kSZKkIVJvcL+QMqN+TUQcUnsgIl4CXA3sUZ0nSZIkaYjUewOmSyPi94G3A7dFxArgEWBvYA4QwFcy8xtDXqkkSZI0itV9A6bMPAV4D/AbysWqh1btncBp1XFJkiRJQ6iuGfdumXkhcGFETAJmAKszc+OQViZJkiTpaYMK7t2qsG5glyRJkoZZ3UtlJEmSJI28Hc64R8RvgQRem5kPVs8HIjPzgJ2uTpIkSRLQ/1KZNkpw7+t5X2LQFUmSJEl6lh0G98ycu6PnkiRJkkbGDte4R8RnImJhzfN9q7ujSpIkSRpB/V2c+gHg8JrnD1Z9kiRJkkZQf8F9PTCp5rlr1yVJkqQG6O/i1PuBkyPicmB51TcjIvbtb+DMfGhni5MkSZJU9Bfc/xn4GnBzTd/7q8eO5ADGliRJkjRA/e0q842IeBA4AdgbOAX4FXD78JcmSZIkqVu/s+KZuRhYDBARpwCXZ+Y5w1FMROwDnAMsAmZSludcAZydmasGOeZRwPWU9fwfy8x/GKJyJUmSpBFT73KWUxmm2faIOICyJGc2cCVwN3AYZVnOoog4MjNX1jnmVOASYCMwZWgrliRJkkZOf7vKbCczL8nMXw5TLRdQQvsZmXlSZn44M48FzgXmAR8bxJjnA9OBTwxdmZIkSdLI2+GMe7XMBOCWzNxc87xfmXnDQM+tZtsXAkuBL/Q4fCZwGvC2iPhQZm4Y4JgnUn5D8Da8UFaSJEktrr9A+2PKDjEvAO6teT4Q7XXUcUzVXp2ZXbUHMnNdRNxECfaHA9f2N1hEzAa+BFyRmV+r1uZLkiRJLau/4H4OJag/2eP5UJtXtff2cfw+SnA/iAEEd0pobwPes/OlSZIkSY3X33aQZ+3o+RCaXrVr+jje3T+jv4Ei4p3AG4G3ZOaKeoqIiNMoy3LYd99+7zElSZIkjZi6Lk5tdhExFzgP+FZm/me9r8/MCzNzQWYumDVr1lCXJ0mSJA1aXRdtRkQ7MD4zN/boPxY4kbLt4oWZ+WCddXTPqE/v43h3/+p+xvkysAk4vc73lyRJkppavTPunwaeioinA3ZE/AlwDfB/gL8FbomI59Y57j1Ve1Afxw+s2r7WwHd7OWVLySciIrsfwEXV8Y9UfVfUWZ8kSZLUUPVuk3gUcH1m1q5FP5MyE/5+YE/Knul/CXywjnGvr9qFEdFWu7NMdROlIymz+Yv7GecrwKRe+g+sar8duBW4rY7aJEmSpIarN7g/l3J3UwAi4nmUHWHOycyvVX1HAYuoI7hn5gMRcTVl55j3Ap+rOXw2MBn4Yu0e7hExv3rt3TXjnNHb+NV2kEcB38/MfxhoXSPuV7+Cyy6Dhx6CffeFk0+GQw5pdFWSJElqAvUulZkGrK15fiRle8iravruBPYZRC2nA48Dn42IKyLiExFxHeULwL3AR3qcf1f12DX86lfw6U/DqlWwzz6l/fSnS78kSZJGvXpn3JcD+9c8fy3lYtBba/qmANvqLaSadV9A2St+EfD66v3OB87OzFX1jtlSLrsMdtsNfv5zeOyx0rdtG9xwA8yd29DSJEmSdkkvfSmcd16jqxiweoP7YuCNEfEGYDPwJuDazOyoOWd/4JHBFJOZvwNOHeC5Uce4FwMXD6amEfPQQ2WmvdaYMbBlS2PqkSRJUlOpN7h/nLLt45XV8y7gY90HI2IC8Grg20NS3Wiy775lecyiRc/0rVpVZuHPOqthZUmSJKk51LXGPTPvAF4BnFs9XpmZP6855WXAdcA3hqzC0eLkk0tQX7UKurqe+e+TT250ZZIkSWoCkZmNrqEpLViwIJcsWTKyb+quMpIkSaNORNyamQv6O6/epTJ9vdlY4GBgY2be09/56sMhhxjUJUmS1Ku6lspExB9HxH9GxO41fQdQtoBcAvwmIi6LiCH5QiBJkiSpqDdgvxN4TmY+VdP3L8DzKWvbZ1IuXj0V+NKQVChJ6t09d8I134Plj8Bee8Pxb4B5L2p0VZKkYVLvDZheCPyi+0lETKPst/6fmfla4DDgbga4paMkaZDuuRMuugDWroE5zyntRReUfknSLqne4D6LclOkbkdQZu2/CVDt534NcMCQVCdJ6t0134Np02HaDGhrK+206aVfkrRLqje4rwOm1zx/DZDAjTV9m4GpO1mXJGlHlj8CU6Zt3zdlWumXJO2S6g3u9wG/HxHjI2Ic8MfArzLzyZpz9gMeH6oCJUm92GtvWL92+771a0u/JGmXVG9wvxB4HiXA3wXsD1zU45xDKbvMSJKGy/FvKOva164uN21bu7o8P/4Nja5MkjRM6r1z6iXAJ4FJlCUznwc+1308Il5J2WHm+iGsUZLU07wXwamnl3XtKx4t7amnu6uMJO3ChvTOqdXymYnAhszcNmQDN0BD7pwqSZKkUWdE75zaLTO3AluHckxJkiRJ9a9xlyRJktQAdQf3iNgrIr4QEfdHxKaI6Ozl0dLLZCRJkqRmU9dSmYjYG7gFmEPZOWY8sAzYQtltZgxwO7BmaMuUJEmSRrd6Z9w/CuwJLMrMl1R9F2XmfEpw/yHl4tSTh65ESZIkSfVenPo64KrM/FHPA5n5cES8Gfg1cDZwxhDUJ0mSpKGw9B5YfA08sRxm7QWHHw9z5zW6KtWh3hn3Pdn+5kqdlBl2ADJzPXANcOLOlyZJkqQhsfQeuPKicoflmXNKe+VFpX80WrkUbv0m/OTzpV25tNEVDUi9wX0tMK7m+Sqg5/211wCzdqYoSZIkDaHF18DkaTBlGrS1lXbytNI/2qxcCnd8B7ZsgMkzS3vHd1oivNe7VGYZ8Nya578Ejo2ISZm5MSLagIXAw0NVoCRJ/Vr+ANx5A6xeATPmwIuOgr0OaHRVUvN4YnmZaa81aUrpH22WLoZxk2H8lPK8u126GGbObVhZA1HvjPu1wDERMbZ6fgnwHODmiPhn4CbgRcClQ1eiJEk7sPwBuPGbsGkdTJ9V2hu/WfolFbP2go3rt+/buL70jzbrn4Rxk7bvGzep9De5eoP7vwOfAvYAyMyvAecDBwMfAl5BCe0fG8IaJUnq2503wISpMHEqRFtpJ0wt/ZKKw4+HDWvL2vaurtJuWFv6R5spe8DWjdv3bd1Y+ptcXcE9M+/LzE9l5vKavg8CewFHAHtl5v/OzM1DXKckSb1bvQImTN6+b8Lk0i+pmDvAAOh/AAAbQklEQVQPTjy1rG1fuaK0J546OneVmXs4bN0AW9ZDdpV264bS3+TqXePeq8x8AnhiKMaSJKkuM+aU5TETpz7Tt3lD6Zf0jLnzRmdQ72nmXHjxG8ua9vVPlpn2ecc1/fp2GKLgLg21VZ0reaRrGRtYz2SmsHfbfuzWPrPRZakJdK17mK7Hb4PNK2HCTNpmv4y2qfs0uiw10ouOKmvaocy0b94Am9fBghMaW5ek5jVzbksE9Z52GNwj4suDHDcz812DfK1GuVWdK7mn89eMYxyTmMxWtnBP56+Zx8GG91Gua93DdC67GsZMhvG7Q8fG8ny/hYb30WyvA+BVf7L9rjILTnBXGUm7nP5m3E8Z5LgJGNw1KI90LWMc4xgX4wEYx3jI0m9wH926Hr8Nxkwmxla7AYydRFb9ozG4b9m0nI3r7qSzYzXtY2cwaeqLGD9xFO4QASWkG9Ql7eL6C+77j0gVUo0NrGcS219oNpZxbGB9H6/QqLF5ZZlprzVmYukfZbZsWs7ap26krW0CbWOm09W5ibVP3ci03V81esO7JO3idhjcM3PZSBUidZvMFLaypcy0VzrYymSmNLAqNYUJM6FjI4yt2X9326bSP8psXHdnCe3tEwGIqt247k6DuyTtouraDjIi3hwR10XEc/o4vndEXBsRJw9NeRqN9m7bj61sZWtuITPZmlvYylb2btuv0aWpwdpmvwy2bSA7NpKZZMdG2Lah9I8ynR2ribYJ2/VF2wQ6O1Y3qKLGWt/xOMvW38y9a69i2fqbWd/xeKNLkprOss4NfKvjEf6140G+1fEIyzo3NLok1aneGzD9GTAjMx/t7WBmPgJMr86TBmW39pnMaz+YcTGejbGBcTGeee1emCpom7oP7fstJMZOIrY8RYydRPsovTC1fewMsmv7W2Zk12bax85oUEWNs77jcR7dtIRtuZlxbVPZlpt5dNMSw7tUY1nnBr7XuYINuY2ZOZYNuY3vda4wvLeYereDfDHwvX7O+QXwB4MrRyp2a59pUFev2qbuMyqDek+Tpr6ItU/dCJSZ9uzaTFfXZqbMWNDgykbeyi330x4TGFP9BmJMTICu0j9l7OwGVyc1h1u6VjOZdiZHiX6TGQNZ+vdrn9zPq9Us6g3uuwP9TWGsBJr/nrFqavd3bOKGLetY0dXBnLaxHDV+Ks8fO7HRZTXEo7mOO3icVWxmNybwYmbznJja/wu1Sxs/cS+m7f6q7XaVmTJjwahc376lay3j2rb//0R7jGdL19oGVSQ1nyfZykzGbtc3iXaeZGuDKtJg1BvcnwQO7OecA4HRuchSQ+L+jk1cuukppkQbs9rGsC47uXTTU7yF3UddeH801/FjljGRMcxgPBvp4Mcs4+jcz/Auxk/ca1QG9Z7Gt01jW24uM+2VztzC+LZpDaxKai57MI4NbCsz7ZWNdLIH4xpYlepV7xr3m4A3RsT83g5GxAuAE4Gf7mxhGr1u2LKOKdHG1LZ22iKY2tbOlGjjhi3rGl3aiLuDx2mjg/Ws5FF+x3pW0kYHd/T7iy9p9Jg5/vl05ma2dW0mM9nWtZnO3MzM8c9vdGlS0zisbQYb6GRDbiMz2ZDb2EAnh7WNvutiWlm9wf3TlFn6GyPijIg4KCImV+37KYG9vTpPGpQVXR1Mju1/NCdHGyu6OhpUUeOsYDUbWEUnnYxhLJ10soFVrPCXWtLTpoydzXMmLmBMTGBr1zrGxASeM3GB69ulGvu1T+YN7XOYHGNYGR1MjjG8oX2O69tbTF1LZTLzFxFxOvAF4NzqUasT+IvM/PkQ1adRaE7bWNZlJ1Oj/em+DdnFnLaxO3jVrinYTBfBOMqfRTvtdNBJsLmfV0qjy5Sxsw3qUj/2a59sUG9x9c64k5lfAl4CXADcCjxQtV8AXpKZ/zakFWrUOWr8VNZnF+u6OunKZF1XJ+uzi6PGj7413VNopxPooJMk6aCTzqpfkiSNLvVenApAZt4F/J8hrkUC4PljJ/IWdt9uV5kTJswYdRemAsxhGmNoZzVb2cQ2JjKGWUxkJs6YSJI02gwquEvD7fljJ47KoN7TgTyHNdzH3kxmPGPZQgdb6OBAer15sSRJ2oXVvVRG0siZFTNYwIFMYBzr2cwExrGAA5kV7gIgSdJo44x7E3nsV3D3ZbDmIZi+L8w/GfY8pNFVqdFmxQxmYVCXJGm0M7g3icd+BT/7NEzYDabtA5tWledH/JXhXZIkaSjdvWkLV63bxKMdnTxnbDuLpk5k/sTxjS6rXy6VaRJ3X1ZC+8TdINpKO2G30i9JkqShcfemLXzpqXWs6exizzFtrOns4ktPrePuTVsaXVq/DO5NYs1DMGH69n0Tppd+SZIkDY2r1m1iWlsb09vbaItgensb09rauGrdpkaX1i+De5OYvi9sXrN93+Y1pV+SJElD49GOTqa2xXZ9U9uCRzs6G1TRwBncm8T8k2HzqrK2PbtKu3lV6ZckSdLQeM7YdtZ15XZ967qS54xt/psbGtybxJ6HlAtRJ+4Gax8urRemSpIkDa1FUyeytquLNZ1ddGWyprOLtV1dLJra/PePcVeZJrLnIQZ1SZKk4TR/4njevTvb7SrzlhmTW2JXmaYK7hGxD3AOsAiYCSwHrgDOzsxVA3j9ZOAk4ATg5cBzgS7gHuAbwOcyc+vwVC9JkqRWMH/i+JYI6j01TXCPiAOAm4HZwJXA3cBhwPuBRRFxZGau7GeYVwNfA54CrqeE/t2ANwKfBk6OiOMyc/PwfApJkhrv3uWdXHtnF4+thj1nwHEvauOgvZp//a6kHWumNe4XUEL7GZl5UmZ+ODOPBc4F5gEfG8AYjwFvBfbKzDdVY/w5cBDwP8ArgfcOT/mSJDXevcs7+cqNnazblMyZnqzblHzlxk7uXd78O2ZI2rGmCO7VbPtCYCnwhR6HzwQ2AG+rlsL0KTNvz8yv91wOk5nrgH+pnh49FDVLktSMrr2zi2kTYNrEoC2CaRODaRNKv6TW1ixLZY6p2qszc7u/WTJzXUTcRAn2hwPXDvI9Oqp22yBfL6kJPLi0i58thsefgNmz4IjDYf+5TTEHITWFx1bDnB439JsyofRLam3N8q/dvKq9t4/j91XtQTvxHu+s2qt2YgxJDfTg0i4uvzJZvz7ZY2ZpL78yeXCpM4lStz1nwPoeV3Kt31z6JbW2Zplx754bWNPH8e7+Qf21ExHvo+xUczvw5R2cdxpwGsC++3rLUqnZ/GwxTJkMU6aUO95NmQKQ/Gwx7D+3gYVJTeS4F7XxlRs7gWTKhBLa126GkxY0y1zdyLpjXQdXPr6FhzZ3se+ENk6cPZ4XTx3b6LKkQdnl/18cEScD51EuXP2jzOzo69zMvDAzF2TmglmzZo1YjZIG5vEnYNKk7fsmTSr9koqD9mrn7a9qZ+rEYMWaYOrE4O2vah+Vu8rcsa6D85ZtYlVHsvf4NlZ1JOct28Qd6/qMAlJTa5YZ9+4Z9el9HO/ur2uFXkScBHwTeBw4JjN/O7jyJDWD2bNg/frumfZi48bSL+kZB+01OoN6T1c+voUZY4IZY8s85YyxAXRx5eNbnHVXS2qWGfd7qravNewHVm1fa+CfJSLeDHwLWAG8JjPv6eclkprcEYfD+g2wfn3S1VXWuK/fUPolqaeHNnexpSO5efk2rlrWwc3Lt7GlI3los9fFqDU1S3C/vmoXRsR2NUXEVOBIYCOweCCDRcSfUu6U+igltN/Xz0sktYD957bxhycGU6YET64s7R+eGO4qI6lXE7qCm1d0snkbTB0bbN4GN6/oZEJXNLo0aVCaYqlMZj4QEVdTtnx8L/C5msNnA5OBL2bmhu7OiJhfvfbu2rEi4h2UC1CXUZbHLBvm8iWNoP3ntnkhqqQBGbuxjWyHbEsAsg2yvfRLragpgnvldOBm4LMRcRxwF/AKyh7v9wIf6XH+XVX79NfmiDiGEtrbKLP4p0Y861v16sw8b8irlyRJTWXb5jaOnjiWu7d1srormdEWvGzcGLZtNrirNTVNcK9m3RcA51C2bnw9sBw4Hzg7M1cNYJj9eGb5zzv7OGcZZZcZSZK0C9tnShurtwTHTnzmQtTVW5IZU1wqo9bUNMEdIDN/B5w6wHOf9f+6zLwYuHhoq5IkSa3ohLntXHBH2fpx2jhYuxXWbE3+dF5TxR9pwPxdkSRJ2iW9cOYYTn/xWGaMDx7dkMwYH5z+4rG8cKbBXa3Jn1xJkrTLeuHMMQZ17TKccZckSZJagF9BJUnaxTx8T3LbNbByOczcC152POwzzwsypVbnjLskSbuQh+9Jrr4INq6F3eeU9uqLSr+k1uaMe5O56gPw2O2NrkKS1KpWPgKdnbCq/Zm+zk649L9g5t6Nq0tqRnu+FBa10CbhzrhLkrQL6dgK7e3b97W3l35Jrc0Z9ybTSt/6JEnN57ufL8tjJk17pq/7+R+8r3F1Sdp5zrhLkrQLednxsGFtCevZVdoNa0u/pNZmcJckaReyz7xg4allhv2pFaVdeKq7yki7ApfKSJK0i9lnXrDPvEZXIWmoOeMuSZIktQCDuyRJktQCDO6SJElSCzC4S5IkSS3A4C5JkiS1AIO7JEmS1AIM7pIkSVILMLhLkiRJLcDgLkmSJLUAg7skSZLUAgzukiRJUgswuEuSJEktwOAuSZIktQCDuyRJktQCDO6SJElSCzC4S5IkSS3A4C5JkiS1AIO7JEmS1AIM7pIkSVILMLhLkiRJLcDgLkmSJLUAg7skSZLUAgzukiRJUgswuEuSJEktwOAuSZIktQCDuyRJktQCDO6SJElSCzC4S5IkSS3A4C5JkiS1AIO7JEmS1AIM7pIkSVILMLhLkiRJLcDgLkmSJLUAg7skSZLUAgzukiRJUgswuEuSJEktwOAuSZIktQCDuyRJktQCDO6SJElSC2iq4B4R+0TElyPi0YjYEhFLI+K8iNitznF2r163tBrn0WrcfYardkmSJGk4jWl0Ad0i4gDgZmA2cCVwN3AY8H5gUUQcmZkrBzDOzGqcg4DrgG8C84FTgRMi4ojM/O3wfApJkiRpeDTTjPsFlNB+RmaelJkfzsxjgXOBecDHBjjOxymh/TOZeVw1zkmULwCzq/eRJEmSWkpkZqNr6J5tvx9YChyQmV01x6YCy4EAZmfmhh2MMwV4HOgC9srMdTXH2oDfAvtV77HDWfcFCxbkkiVLBv2ZJEmSpIGIiFszc0F/5zXLjPsxVXt1bWgHqML3TcAk4PB+xjkcmAjcVBvaq3G6gB/2eD9JkiSpJTRLcJ9Xtff2cfy+qj1ohMaRJEmSmkqzXJw6vWrX9HG8u3/GcI4TEacBp1VP10fEPf2833DZA3iyQe+t5uXPhfriz4b64s+GeuPPRfPZbyAnNUtwbwqZeSFwYaPriIglA1nnpNHFnwv1xZ8N9cWfDfXGn4vW1SxLZbpnwqf3cby7f/UIjSNJkiQ1lWYJ7t1LUvpae35g1fa1dn2ox5EkSZKaSrME9+urdmG1bePTqu0gjwQ2Aov7GWcxsAk4snpd7ThtwMIe79esGr5cR03Jnwv1xZ8N9cWfDfXGn4sW1RTBPTMfAK4G5gLv7XH4bGAy8NXaPdwjYn5EzO8xznrgq9X5Z/UY533V+D9s9junVmvtpe34c6G++LOhvvizod74c9G6muIGTPD0TZhuptzd9ErgLuAVlD3X7wVemZkra85PgMyMHuPMrMY5CLgOuAV4AXAi5eZMr6y+KEiSJEkto2mCO0BEPBc4B1gEzKTcMfVy4OzMXNXj3F6De3Vsd+BM4CRgL2Al8N/ARzPz4eH8DJIkSdJwaKrgPppFxD48+0vLFfTypUWjQ/Xboz8ETgBeDOwNbAXuAC4CLup5p2GNXhHxVspSQYB3Z+a/NbIeNVZEHEdZInoEsBtlAusO4PzM/EEja1NjRMQJwPuBF/JMzrgV+Exm/qyRtWngDO5NoJdlQncDh1GWCd0DHFm7TEijQ0S8B/hXyl+u1wMPAXOAkylbm/4X8Ob0/8SjXvXbyjuAdmAKBvdRLSL+L/DXwMOU3zY/CcwCDgV+lJl/08Dy1AAR8Sngbyhf4K6g/Ew8H3gj5Z4+b8/MrzWuQg2Uwb0JRMQPKTvenJGZn6vp/wzwQeCLmfmeRtWnxoiIYykXWn+/dmY9IvakXLvxXOBNmflfDSpRTSAiArgG2B+4DPgrDO6jVkS8m7JjyCXAaZm5tcfxsZnZ0ZDi1BDVvxmPAE8Ah2Tm4zXHjqFcD/hgZj6vQSWqDk2xq8xoVs22LwSWAl/ocfhMYAPwtoiYPMKlqcEy87rM/G7P5TCZ+Rjw/6qnR494YWo2ZwDHAqdS/r7QKBUR44GPUX4796zQDmBoH5X2o+S9n9eGdoDMvB5YR/mNjFqAwb3xjqnaq3sJaOuAm4BJwOEjXZiaWvc/vtsaWoUaKiJeAHySsm75hkbXo4Y7nhLALgO6IuKEiPjbiHh/RBzR4NrUOPdRro86LCL2qD0QEUcBU4EfNaIw1W9MowsQ86q2r7u53keZkT8IuHZEKlJTi4gxwNurp1c1shY1TvVz8FXK7OrfN7gcNYffq9rNwG3AwbUHI+IGyvK6J0a6MDVOZj4VEX8LfAb4TURcQVnrfgBljfs1wJ83sETVweDeeNOrdk0fx7v7Z4xALWoNn6T8g/yDzPxho4tRw3wUeBnwqszc1Ohi1BRmV+1fA78BXg3cTrn+4dOUSaBv4RK7USczz4uIpcCXgXfXHLofuLjnEho1L5fKSC0kIs4APkTZeehtDS5HDRIRr6DMsv+L27ipRve/6duAN2bmjZm5PjPvoGwt+zDwGpfNjD4R8TfAt4GLKTPtkym7DP0W+Hq1E5FagMG98bpn1Kf3cby7f/UI1KImFhHvA86nzKQdk5lPNbgkNUC1ROYrlOV1/9jgctRcuv+duC0zl9YeyMyNQPdv6A4byaLUWBFxNPAp4DuZ+ZeZ+dvM3JiZ/0P5QvcI8KGIcFeZFmBwb7x7qvagPo4fWLV9rYHXKBARHwA+B/yaEtofa3BJapwplL8vXgBsjojsflB2ogL4UtV3XsOqVCN0/3vS10RP9838Jo5ALWoeb6ja63seqL7Q3ULJgy8byaI0OK5xb7zu/yMtjIi2Hvt1TwWOBDYCixtRnBqvuqjok5S1qsdn5pMNLkmNtQX49z6OvZzyj++NlBDnMprR5VoggRf2/Pek0n2x6oMjW5YabHzV9rXlY3f/s7YPVfPxBkxNwBswqS8R8Y/AOZTbUi90eYx2JCLOosy6ewOmUSoirqTsFPKXmXluTf9Cyi5Ua4C5mdnXhgjaxUTEHwOXAiuAQzPzkZpjvw98nzIhsI93aW9+zrg3h9OBm4HPRsRxwF3AKyh7vN8LfKSBtalBIuIdlNDeCfwUOKPcJHM7SzPz4hEuTVLzei/lty6fiYgTKNtC7g+cRPm75M8M7aPOtyn7tL8WuCsiLgceoyy3ewMQwIcN7a3B4N4EMvOBiFhACWmLgNcDyykXIp6dmat29Hrtsvav2nbgA32c8xPKLgGSRGY+HBGHUrYLfSNwFLAW+C7wicy8pZH1aeRlZldEvJ7ype5PKBekTgKeAn4AfDYzr25giaqDS2UkSZKkFuCuMpIkSVILMLhLkiRJLcDgLkmSJLUAg7skSZLUAgzukiRJUgswuEuSJEktwOAuSZIktQCDuyRpSETExRGRETF3mN9naUQsHc73kKRmZHCXJDWViPhxRHh3QEnqYUyjC5AkqU7HNboASWoEg7skqaVk5gONrkGSGsGlMpLUYBExt1obfnFEzI+IKyLiqYjYEBE3RsTCXl4zPiI+HBF3RMTGiFgbET+NiD8eovHPql5z9I7GG+DnOyUi/isifhsRm6pab4qIt/Y2LvCa6nnWPH5cc16va9x34s9kbkR8MyKejIjNEbEkIt4wkM8mSSPJGXdJah77Az8D7gC+COwFvAX474j435l5KUBEjAN+SAm4dwNfACYBbwIujYiXZubfD3b8YfCvwJ3ADcByYCbweuCrETEvM/+xOm81cDZwCrBf9d/dlu7oDXbiz2Q/4Bbgt8BXgd0pfyZXRsRrM/P6ej+sJA2bzPThw4cPHw18AHOBrB7/3OPYAqADWAVMq/r+rjr3B8CYmnNnUwJuAq8c7PhV/1nV+UfvoN6Le/RfXPXP7dF/QC9jjAOurd577x7Hflz+eerzz2spsLRH3878mZzZY6zXdY/V6J8NHz58+Kh9uFRGkv7/9u4n1KoqCsD4t0poVCoEFqZI2KyJIgUN0gZRFiEFBtagXgrVoIkDHYgOKgi0adFApIgmpTULCpTQCoqwPyNBpCyQEqSsgSC+VoO9Hx2u56aec6lz6PvBZfPW3Wftc/fkLfZb97zhOA+82Axk5lfAO8AS4NEafoZSWG7PzEuNuWeBl+qP23rkn6ls6UnPzIuUU/FFzObLpl335DTw8sS9fQT8CNw1g/uSpJmxcJek4TiemX+0xD+p45qIuBFYDZzJzBMtc48szO2S/xru9apFxMqIeC0iTtTe86y97IfqlOU98/fZk28yc74l/hOwtM99SdKs2eMuScPxy5T4z3VcXF9QesXbLMSXdMw/UxFxO6WHfClwDPiYcvI/T2lXeQq4oecyffbktynXXMLDLUkDY+EuScOxbEr8ljqer69mbNKtjbld8i/4s45tvyfaCuBptlO+jDqXmW8234iILZTCva8+eyJJo+FpgiQNx9ra9jFpQx2/rq0up4DlEXFHy9z76ni8S/5G7Nc6rmiZv64lNs3qOh5qeW/9lGvmASLi+qtZoOeeSNJoWLhL0nAsBvY0AxGxDniSclr8QQ0fAALY1yxuI+JmYHdjTtf8UNpbAOYiYlFj/orJHFfwQx03TKz7AO1fFgU4V8eV17BO1z2RpNGwVUaShuMosC0i7gY+4+/nrF8HPJuZv9d5rwIbgU3AtxHxIeWZ5Zspjz/cm5mf9shPZn4REUeBe4EvI+IIpdXmEcrz0ttO4tu8DswB70XEQeAMcCfwIPBuXX/S4fpZ3q+f7QJwOjPf/od1uu6JJI2GJ+6SNBzfA/dQ2lSeAx6ntHc8lI1/jlQfpXg/sKuGXqD0ip8EnsjMnX3yN2wC9gO31TXWADuAafkvk5nfUVpVPgceBp4HbgIeA96Yctl+4BXKXwh2UB7nuPUK63TdE0kajcjM//oeJOl/LSJWUYrqtzLz6bHllyT9OzxxlyRJkkbAwl2SJEkaAQt3SZIkaQTscZckSZJGwBN3SZIkaQQs3CVJkqQRsHCXJEmSRsDCXZIkSRoBC3dJkiRpBCzcJUmSpBH4C0oeKHiNVtFIAAAAAElFTkSuQmCC\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "path_to_pics = expand_path(Path(evolution.get_value_from_config(\n", " evolution.basic_config, evolution.main_model_path + [\"save_path\"])).joinpath(\"pics\"))\n", @@ -502,21 +173,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9,\n", - " 9, 10, 10])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "params_dictionaries = []\n", "models_ids = []\n", @@ -535,42 +194,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(models_ids)))]\n", @@ -617,216 +245,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['dataset_iterator', 'seed'] seed\n" - ] - }, - { - "data": { - "image/png": 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zAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE50FedV9eSqen9V3VpVW6rq5qr6SFU9bcI5x1ZVu49fF4zyawAAgN21eNQDjKuq1yV5WZKbknwgydokq5I8KslpST406UP+NcnFUzzVl+ZuSgAAmDtdxHlVPS+DMH93knNba1snHd9/ig+7prX26nkYDwAA5sXIb2upqiVJ/iDJtzJFmCdJa+2eeR8MAADmWQ9Xzp+Swe0rb0oyVlVPT/KwJJuTfKa1dtU0H3dUVT0/yeFJvpfkqtbatfMxMAAAzIUe4vzRw+3mJF/IIMy/r6ouT3J2a+22SR/3lOGvied+MskvtNa+NTejAgDA3Bn5bS1JjhhuX5akJXlikmVJTkzy0SSnJPmHCedvSvL7GbxQ9LDhr1OTfCKDF45+vKqWTvfJqurcqlpTVWtuu21y7wMAwOhUa220A1S9Pcm5SbYk+S+ttesnHDs4yVeT/GCSx93HLS6pqsVJrkjyY0le1Fp7884+9+rVq9uaNWv27AsAAID7UFWfa62t3pVze7hyvm64/cLEME+S1tqmJB8ZPnzMfT1Ja21bkvOHD0+ZzQEBAGA+9BDnXx1u101z/I7h9qBdeK7x+1Smva0FAAB61UOcfzyDe81/pKqmmmf8BaLX7cJznTzcfnM2BgMAgPk08jhvrd2Q5INJjknymxOPVdUZSX48g6vqHx7uO2mqiK+qJyf5reHDv57LmQEAYC70sJRikvx6kkcmecNwnfMvJDkuyTOSbE9yTmvtzuG5b0jy4Kq6MslNw30nJjl9+PtXttaunLfJAQBglnQR5621m6rqUUl+J8lZGbygc30GV9T/d2vtMxNOf0+SZ2awPvqZSfZP8p0kf5/kra21T83n7AAAMFtGvpTiKFlKEQCAuba3LaUIAABEnAMAQDfEOQAAdEKcAwBAJ8Q5AAB0QpwDAEAnxDkAAHRCnAMAQCfEOQAAdEKcAwBAJ8Q5AAB0QpwDAEAnxDkAAHRCnAMAQCfEOQAAdEKcAwBAJ8Q5AAB0QpwDAEAnxDkAAHRCnAMAQCfEOQAAdEKcAwBAJ8Q5AAB0QpwDAEAnxDkAAHRCnAMAQCfEOQAAdEKcAwBAJ8Q5AAB0QpwDAEAnxDkAAHRCnAMAQCfEOQAAdEKcAwBAJ8Q5AAB0QpwDAEAnxDkAAHRCnAMAQCfEOQAAdEKcAwBAJ8Q5AAB0YvGoBwAAZuaKK7bnoxe1rP9Osvz+yRnPqjzhCfuNeixgFrhyDgB7kSuu2J6//7OWuzcky45I7t6Q/P2ftVxxxfZRjwbMAnEOAHuRj17UsuSQ5OBllUVVOXhZZckhg/3A3k+cA8BeZP13kgMP2XHfgYcM9gN7P3EOAHuR5fdPNt+1477Ndw32A3s/cQ4Ae5EznlXZcleyaUPLWGvZtKFly12D/cDez2otALAXGazKsuNqLT/5C1ZrgX2FOAeAvcwTnrBfnvCEUU8BzAW3tQAAQCfEOQAAdEKcAwBAJ8Q5AAB0QpwDAEAnxDkAAHRCnAMAQCfEOQAAdEKcAwBAJ8Q5AAB0QpwDAEAnxDkAAHRCnAMAQCfEOQAAdEKcAwBAJ8Q5AAB0QpwDAEAnxDkAAHRCnAMAQCfEOQAAdEKcAwBAJ8Q5AAB0QpwDAEAnxDkAAHRCnAMAQCfEOQAAdEKcAwBAJ8Q5AAB0QpwDAEAnxDkAAHRi8X0drKqxJG03nre11u7zuQEAgB3tLKAvz73j/LAkJyYZS3JjkluTHJnk6AyuxF+b5I7ZHRMAAPZ99xnnrbXTJj6uqgckuTLJRUle1lq7bsKx45K8Pskjkzx11icFAIB93EzvOf+jJHe01s6eGOZJMnx8dpI7h+cBAAAzMNM4//EkH5nuYGutDY+7cg4AADM00zhflmTFTs5ZMTwPAACYgZnG+b8leXZVHT3Vwap6YJJnJ/nKng4GAAALzUyXO/zjJH+b5AtV9ZYMVnP5TpL7Jzk1yQszuHL+x7M5JAAALAQzivPW2gXDFVtem+RVkw5XknuSvLS19t5Zmg8AABaMGb9RUGvtjVV1UZKfzWDZxBUZrNDy+SR/01q7YXZHBACAhWG33sVzGOB/MMuzAADAgjbTF4QCAABzZMZxXlWLquqFVXV1Vd1ZVdsmHHtkVf15VR0/u2MCAMC+b0ZxXlUHJPnnJG9K8qAkGzJ4Iei465L8UpL/OVsDAgDAQjHTK+cvS/KkJL+bwfKJ50882Fpbl8Hyij8+K9MBAMACMtM4/59JPt1a+73W2liSNsU51yU5Zo8nAwCABWamcX5ckqt3cs7tSe63e+MAAMDCNdM435zk0J2cc0ySdbs3DgAALFwzjfNrkpwxfGHovVTVigzuN//Mng4GAAALzUzj/LwkRyf5m6paPvFAVR2a5F1JDkvyF7MyHQAALCAzeofQ1trfVdVTkvxikrOS3JEkVbUmyQlJliT5s9bah2Z5TgAA2OfN+E2IWmu/lMFa5l9JsiqDdc5PSvL1JL/cWnvhrE4IAAALxIyunI9rrb0rybuq6qAMbmO5s7W2cTYHAwCAhWa34nxca+3uJHfP0iwAALCg7VacV9WqJD+V5KFJlrbWzpmw/7gkXxyGOwAAsItmHOdV9ctJ3pLkwAzuN29Jzhkevn+Sq5Kcm+QdszQjAAAsCDN6QehwpZbzknwtyTOTvG3i8dbal5J8OckzZmtAAABYKGZ65fy3k9yS5NTW2vqqeuQU51yb5LF7PBkAACwwM11KcXWS/9taW38f59yU5MjdHwkAABammcb5AUl2tmTioUm27944AACwcM00zq9P8qidnPNjSb66W9MAAMACNtM4/8ckT6yqn57qYFU9N8mJSd63p4MBAMBCM9MXhL4uyXOS/F1VnZ1kRZJU1QuSPDHJs5L8R5I/nc0hAQBgIZhRnLfW7qiqU5P8VZKJV8/fMtx+Ksn/aK3t7L50AABgkhm/CVFr7VtJTquqEzNYMvHwJHcmubq19rlZng8AABaMGcf5uNbatRmsaZ6qOjzJKVW1MsnHWmtWawEAgBma6TuE/mpV/UtV3W/Cvkcl+fckFyb5UJIrq2rp7I4JAAD7vpmu1vLsJK21dvuEfX+c5LAk78wgzh+d5FdmZzwAAFg4ZhrnD87wVpYkGd7GcmqSd7TWzmmt/USSzyb5H7M3IgAALAwzjfPDk3x3wuPHD7fvn7DvU0keuCdDAQDAQjTTOL89ycoJj09NMpbkygn7WpIDd2eYqnpyVb2/qm6tqi1VdXNVfaSqnjbFuY+rqg9V1e1VdXdVXVtVL6qq/XbncwMAwKjNNM7/LclPVNXhVXVoBm9I9NnW2voJ5xyb5NaZDlJVr0vysSSrk3wgyZ8k+ackq5KcNuncn0xyeZJTMrhq/9YkByR5Y5ILZvq5AQCgBzNdSvHNSS5OclOSbUkOTvLySeecnOQzM3nSqnpekpcleXeSc1trWycd33/C75cn+csk25Oc1lpbM9z/yiSXJjm7qp7TWhPpAADsVWZ05by19oEMVmL5cpKvJnlpa+2vx49X1WlJDknykV19zqpakuQPknwrU4T58PPeM+Hh2RlcTb9gPMyH52xO8orhw1/d1c8PAAC92J13CD0vyXnTHPtkBssqzsRTMojtNyUZq6qnJ3lYks1JPtNau2rS+acPtx+e4rkuT7IpyeOqaklrbcsMZwEAgJHZ7XcInUWPHm43J/lCBmH+fVV1eZKzW2u3DXc9ZLj92uQnaq1tq6rrkpyQ5IcyuEd+B1V1bpJzk+SYY46ZjfkBAGBWzPQFoXPhiOH2ZRms9PLEJMuSnJjkoxm86PMfJpy/Yri9c5rnG99/6FQHW2vntdZWt9ZWr1q1ak/mBgCAWdVDnI/PsC3JWa21K1prd7XWvpjkmRm8+PTUqnrsyCYEAIB50EOcrxtuv9Bau37igdbapvzni0sfM9yOXxlfkamN7183zXEAAOhSD3H+1eF2upi+Y7g9aNL5x08+saoWJzkug6vw35ytAQEAYD70EOcfz+Be8x+pqqnmGX+B6HXD7aXD7VOnOPeUDNZev9JKLQAA7G1GHuettRuSfDDJMUl+c+KxqjojyY9ncFV9fOnEC5OsTfKcqlo94dwDk7xm+PBtczw2AADMuh6WUkySX0/yyCRvGK5z/oUMbk95RgbvBHpOa+3OJGmtrR++o+iFST5ZVRckuT3JWRkss3hhkvfO/5cAAAB7ZuRXzpOktXZTkkcleWuSB2dwBf20DK6oP7619r5J51+c5NQM3nTop5K8MMk9SV6c5DmttTZvwwMAwCzp5cp5hm8y9MLhr105/9NJnjanQwEAwDzq4so5AAAgzgEAoBviHAAAOiHOAQCgE+IcAAA6Ic4BAKAT4hwAADohzgEAoBPiHAAAOiHOAQCgE+IcAAA6Ic4BAKAT4hwAADohzgEAoBOLRz3AQrP2wmuz4a0XZdG3v5WxHzgmy17wrKw8+8RRjwUA7ANu/sY1uePqS7Jo7S0ZW/mAHHbymTnqQT866rGYAVfO59HaC6/Nppe/Pll3R8Ye8IPJujuy6eWvz9oLrx31aADAXu7mb1yT9R84P7VxfcYOPyK1cX3Wf+D83PyNa0Y9GjMgzufRhrdelO3LD0sdelhq0aLUoYdl+/LDsuGtF416NABgL3fH1ZdkbOmytKXLk9ovbenyjC1dljuuvmTUozED4nweLfr2t5LlK3bcuXzFYD8AwB5YtPaWtIOX7rCvHbw0i9beMqKJ2B3ifB6N/cAxyfo7d9y5/s7BfgCAPTC28gGpTRt32FebNmZs5QNGNBG7Q5zPo2UveFb2W39H2ro70sbG0tbdkf3W35FlL3jWqEcDAPZyh518ZhZt3JDauD5p21Mb12fRxg057OQzRz0aMyDO59HKs0/Mwa97aXLoYVl0y03JoYfl4Ne91GotAMAeO+pBP5rlZ52TtnR5Fn3vu2lLl2f5WedYrWUvU621Uc8wMqtXr25r1qwZ9RgAAOzDqupzrbXVu3KuK+cAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACdEOcAANAJcQ4AAJ0Q5wAA0AlxDgAAnRDnAADQCXEOAACd6CLOq+r6qmrT/Lp10rnH3se5raouGNUREtDJAAAWCUlEQVTXAQAAe2LxqAeY4M4kb5pi/13TnP+vSS6eYv+XZm0iAACYRz3F+brW2qtncP41MzwfAAC61sVtLQAAQF9XzpdU1c8mOSbJxiTXJrm8tbZ9mvOPqqrnJzk8yfeSXNVau3Z+RgUAgNnXU5wfmeQ9k/ZdV1XPba1dNsX5Txn++r6q+mSSX2itfWtuRgQAgLnTy20t70zy5AwCfWmShyd5e5Jjk1xSVY+YcO6mJL+f5FFJDhv+OjXJJ5KcluTjVbV0uk9UVedW1ZqqWnPbbbfN/lcCAAC7qVpro55hWlX1+iQvSXJxa+2ZOzl3cZIrkvxYkhe11t68s+dfvXp1W7NmzazMCgAAU6mqz7XWVu/Kub1cOZ/OXwy3p+zsxNbatiTn7+r5AADQm97jfPy+k2lvU9nD8wEAoBu9x/nJw+035+h8AADoxsjjvKoeOtULOKvq2CRvHT786wn7T6qqe81dVU9O8luTzwcAgL1FD0spPjvJS6rq8iQ3JNmQ5EFJnp7kwCQfSvL6Cee/IcmDq+rKJDcN952Y5PTh71/ZWrtyPgYHAIDZ1EOcfyLJQ5I8MsnjM7hffF0GK6+8J8l72o5LyrwnyTOTPDrJmUn2T/KdJH+f5K2ttU/N3+gAADB7Rh7nwzcYmupNhqY7/x1J3jF3EwEAwGiM/J5zAABgQJwDAEAnxDkAAHRCnAMAQCfEOQAAdEKcAwBAJ8Q5AAB0QpwDAEAnxDkAAHRCnAMAQCfEOQAAdEKcAwBAJ8Q5AAB0QpwDAEAnxDkAAHRi8agHAJIvt5vz+VyXu3J3DslBOSnH5YQ6atRjQVc23fjZbP3iJak7v5u24ogc8PAzc/DRjx71WACzypVzGLEvt5tzWb6czdmag7Mkm7M1l+XL+XK7edSjQTc23fjZ3POpdyabN6QtX5ls3pB7PvXObLrxs6MeDWBWiXMYsc/nuuyfxTkwB2RRFuXAHJD9szifz3WjHg26sfWLl6QduDQ5aFmyaFFy0LK0A5dm6xcvGfVoALNKnMOI3ZW7c8CkO8wOyOLclbtHNBH0p+78brJk6Y47lywd7AfYh4hzGLFDclC2ZtsO+7ZmWw7JQSOaCPrTVhyRbNm4484tGwf7AfYh4hxG7KQcl3uyLZuzNWMZy+ZszT3ZlpNy3KhHg24c8PAzU5s3JndvSMbGkrs3pDZvzAEPP3PUowHMKqu1wIidUEclLTus1vK4PMRqLTDBwUc/OpuemB1Wa9n/0f/dai3APkecQwdOqKNyQsQ43JeDj360GAf2eW5rAQCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6IQ4BwCATohzAADohDgHAIBOiHMAAOiEOAcAgE6IcwAA6EQXcV5V11dVm+bXrdN8zOOq6kNVdXtV3V1V11bVi6pqv/meHwAAZsPiUQ8wwZ1J3jTF/rsm76iqn0zyviSbk7w3ye1JfiLJG5M8PslPz92YAAAwN3qK83WttVfv7KSqWp7kL5NsT3Jaa23NcP8rk1ya5Oyqek5r7YK5HJY99/ktt+ajW76T77ZtOaIW54wl989JS44c9ViM2De+fGuuuuyO3LZ2UVatHMtjTz0sDzrB9wUwtYtv3Jy337Al394ylh9YsijPf+CSPOPoA0c9Fuy2Lm5rmaGzk6xKcsF4mCdJa21zklcMH/7qKAZj131+y635q7tvzoY2llVZnA1tLH919835/JYp72JigfjGl2/N+y+8Mxs2Vu53+Fg2bKy8/8I7840v+74A7u3iGzfnFV/blHX3tDxg/8q6e1pe8bVNufjGzaMeDXZbT3G+pKp+tqr+V1X9ZlU9aZr7x08fbj88xbHLk2xK8riqWjJnk7LHPrrlO1lalWW1X2q4XVqVj275zqhHY4SuuuyOLD24ZdnSZL+qLFuaLD245arL7hj1aECH3n7DlixbtCiH7l9ZtKhy6P6VZYsW5e03bBn1aLDberqt5cgk75m077qqem5r7bIJ+x4y3H5t8hO01rZV1XVJTkjyQ0n+bfI5VXVuknOT5JhjjpmNudkN323bsmrSt98hWZTvtm0jmoge3LZ2Ue53+FiS+v6+gw9uuW1tT9cRgF58e8tYHrB/7bBv+X6D/bC36uUn3juTPDmDQF+a5OFJ3p7k2CSXVNUjJpy7Yri9c5rnGt9/6FQHW2vntdZWt9ZWr1q1ak/nZjcdUYtzV3b8j+ddGcsR1dPfF5lvq1aOZdOmHX/QbtpUWbXSD1rg3n5gyaKs377jvvXbB/thb9XFd29r7Xdba5e21r7TWtvUWvtSa+1XkrwhyUFJXj3aCZltZyy5fza2lg1te9pwu7G1nLHk/qMejRF67KmHZeOmyoaNyfbWsmFjsnFT5bGnHjbq0YAOPf+BS7JhbCzr7mkZG2tZd0/LhrGxPP+B7mxl79VFnN+HvxhuT5mwb/zK+IpMbXz/ujmZiFlx0pIj8/MHHZVltSi3ZVuW1aL8/EFHWa1lgXvQCUfmmWevyLKlLbd/b1GWLW155tkrrNYCTOkZRx+Y1xx/cA7dv3LLPS2H7l95zfEHW62FvVrv9xDcNtwunbDvq0lWJzk+yecmnlxVi5Mcl2Rbkm/Ox4DsvpOWHCnGuZcHnXCkGAd22TOOPlCMs0/p/cr5ycPtxNC+dLh96hTnn5Lk4CRXtta8VBsAgL3KyOO8qh5aVUun2H9skrcOH/71hEMXJlmb5DlVtXrC+Qcmec3w4dvmZFgAAJhDPdzW8uwkL6mqy5PckGRDkgcleXqSA5N8KMnrx09ura2vqudlEOmfrKoLktye5KwMllm8MMl75/UrAACAWdBDnH8ig6h+ZJLHZ3B/+bokV2Sw7vl7Wmtt4ge01i6uqlOT/H9JfiqDiP96khcnecvk8wEAYG8w8jgfvsHQZTs98d4f9+kkT5v9iQAAYDRGfs85AAAwIM4BAKAT4hwAADohzgEAoBPiHAAAOiHOAQCgE+IcAAA6Ic4BAKAT4hwAADohzgEAoBPiHAAAOiHOAQCgE+IcAAA6Ic4BAKAT4hwAADohzgEAoBPiHAAAOiHOAQCgE+IcAAA6Ic4BAKAT4hwAADohzgEAoBPiHAAAOlGttVHPMDJVdVuSG0b06VcmWTuiz02/fF8wHd8bTMX3BdPxvdGXB7bWVu3KiQs6zkepqta01laPeg764vuC6fjeYCq+L5iO7429l9taAACgE+IcAAA6Ic5H57xRD0CXfF8wHd8bTMX3BdPxvbGXcs85AAB0wpVzAADohDgHAIBOiHMAAOiEOJ9HVfWDVfV/qurmqtpSVddX1Zuq6rBRz8b8q6rDq+qcqnp/VX29qu6uqjur6oqq+uWq8v9Pvq+qfraq2vDXOaOeh9GqqicP/9tx6/Dnyc1V9ZGqetqoZ2N0qurpVfXRqrpp+DPlm1X1D1X12FHPxq7zgtB5UlUPSnJlkiOS/GOSf0/ymCRPSvLVJI9vrX1vdBMy36rqV5K8LcktST6R5FtJ7p/kWUlWJHlfkp9u/k+64FXV0Um+mGS/JIckeV5r7fzRTsWoVNXrkrwsyU1JLsngXSBXJXlUko+11l4+wvEYkar6oyQvT/K9JBdn8H3xw0nOSrI4yc+31v56dBOyq8T5PKmqjyQ5I8lvtNb+dML+NyT5rSRvb639yqjmY/5V1elJlib5p9ba2IT9Ryb5TJKjk5zdWnvfiEakA1VVSf45yXFJLkry0ojzBauqnpfBEnnvTnJua23rpOP7t9buGclwjMzw58a3k9yW5MTW2ncnHHtSkkuTXNda+6ERjcgM+GfzeTC8an5GkuuT/Nmkw69KsjHJz1XV0nkejRFqrV3aWvvgxDAf7r81yV8MH54274PRm99IcnqS52bw3woWqKpakuQPMvhXtnuFeZII8wXrgRk03b9MDPMkaa19IsmGDP51hb2AOJ8fTxpuPzpFiG1I8ukkByc5eb4Ho1vjP2C3jXQKRqqqHprktUne3Fq7fNTzMHJPySCwLkoyNry/+Ler6jfdU7zg/UeSrUkeU1UrJx6oqlOSLEvysVEMxswtHvUAC8RDhtuvTXP8PzK4sn58ko/Py0R0q6oWJ/n54cMPj3IWRmf4ffCeDK6S/q8Rj0MfHj3cbk7yhSQPm3iwqi7P4Fa42+Z7MEartXZ7Vf12kjck+UpVXZzBvecPyuCe839O8vwRjsgMiPP5sWK4vXOa4+P7D52HWejfazP4ofuh1tpHRj0MI/M7SR6Z5AmttbtHPQxdOGK4fVmSryR5YpJrMng9wuszuMjzD3E73ILUWntTVV2f5P8ked6EQ19P8q7Jt7vQL7e1QEeq6jeSvCSD1Xx+bsTjMCJV9WMZXC3/k9baVaOeh26M/8zeluSs1toVrbW7WmtfTPLMDFZvOdUtLgtTVb08yYVJ3pXBFfOlGazg880kfzNc5Ye9gDifH+NXxldMc3x8/7p5mIVOVdULkrw5gytiT2qt3T7ikRiB4e0sf5XBbXCvHPE49GX8Z8QXWmvXTzzQWtuUZPxf2h4zn0MxelV1WpI/SvKB1tqLW2vfbK1taq19PoO/uH07yUuqymotewFxPj++OtweP83xBw+3092Tzj6uql6U5E+TfCmDML91xCMxOodk8N+KhybZPOGNh1oGqzslyV8O971pZFMyCuM/S6a7kHPHcHvQPMxCX/7bcPuJyQeGf3H7TAbN98j5HIrd457z+TH+f5YzqmrRpDWtlyV5fJJNSa4exXCM1vBFPK/N4N7Rp7TW1o54JEZrS5J3THPspAx+uF6RQai55WVh+XiSluRHJv8sGRp/geh18zsWHVgy3E63XOL4/nstv0l/vAnRPPEmREylql6Z5PeSfC7JGW5l4b5U1aszuHruTYgWqKr6xwxW33hxa+2NE/afkcHqTncmOba1Nt0CBOyDqur/b+9eQy2t6jiOf385KAh5qahkvJwpo170InNI8IVOaKmjZUVNZYEzNdCNInxhllhagZDRC8EuIHFiCJrSLKQJBW2aUaNB7CLhUIwdu3iJcnSijHL89+JZQ7vdPuPM3ufs/Rz7fmDxnLP2etZaezGc8z9r/s/aG4CtwGPA6VX1x4HXLgB+QPeH/4l+Gnn/uXM+PR8G7gGuT3IO8ABwBt0Z6L8Grpzh3DQDSS6lC8z3AzuBj3UfBvlfFqpqfspTk9RfH6H735MvJbmQ7kjFNcBb6H6WbDYw/790E9055ucCDyS5BXiULj3uIiDAFQbmK4PB+ZRU1Z4ka+mCsfOB9cAjdA8AXlNVew92v56T1rTrEcDHF2nzY7on7yWJqvpDktPpjtp8M3AWsA+4Fbi2qnbNcn6ajap6Jsl6uj/e3kX3EOjRwOPANuD6qrp9hlPUYTCtRZIkSeoJT2uRJEmSesLgXJIkSeoJg3NJkiSpJwzOJUmSpJ4wOJckSZJ6wuBckiRJ6gmDc0mSJKknDM4lSYckyXySSjK3zOMsJFlYzjEkqa8MziVJU5VkexI/AU+SRlg16wlIkjTknFlPQJJmxeBcktQrVbVn1nOQpFkxrUWSllmSuZarPZ/kVUm+l+TxJH9LcleSN46456gkVyS5P8nfk+xLsjPJhiXq/+p2z7qD9XeI729jkpuTPJjkqTbXu5O8d1S/wNnt+xoo2wfajcw5n2BN5pJ8K8mfk/wjyb1JLjqU9yZJ0+bOuSRNzxrgJ8D9wNeAE4B3Aj9McklVbQVIciRwG10Quxu4ATgaeDuwNclrqupT4/a/DL4C/ArYATwCvBBYD2xJ8sqquqq1ewK4BtgInNK+PmDhYANMsCanALuAB4EtwAvo1uT7Sc6tqh8d7puVpGVVVRaLxWJZxgLMAdXKdUOvrQX+BewFjml1n2xttwGrBtq+mC6ILeDMcftv9Ve39usOMt/5ofr5Vj83VP/yEX0cCdzRxl499Nr27tfPouu1ACwM1U2yJp8Z6uu8A33N+t+GxWKxDBfTWiRpep4EPjtYUVX3At8EjgPe2qrfRxc8XlZVTw+0/RPwufbt5gn6X1I1Ike8qv5Jt7u9iqV5wHPcNXkI+PzQ3G4Dfge8bgnmJUlLyuBckqbnvqr664j67e16WpLnA6cCD1fV7hFt7zzQdpz+D2OuhyzJyUluSLK75YJXyy2/uTVZPWH/k6zJz6tq/4j63wPHTzIvSVoO5pxL0vQ8tkj9o+16bCvQ5W6PcqD+uDH7X1JJXkaX0308sBO4nW4Hfz9dasmlwFETDjPJmjyxyD1P4waVpB4yOJek6XnJIvUvbdcnWxmsG3bCQNtx+j/gmXYd9XtgVJC7mMvoHgDdVFXzgy8keTddcD6pSdZEklYUdw0kaXpe21I0hq1r15+1tJQ9wOokrxjR9vXtet84/Q/U7W3Xk0a0XzuibjGntuvNI147e5F79gMkOeJQBphwTSRpRTE4l6TpORb49GBFkrXAe+h2fW9p1V8HAlw3GMAmeRFw1UCbcfuHLhUFYFOSVQPtTxru41kstOu6oXHPY/QDmgB/adeTD2OccddEklYU01okaXp2AJuTnAHczX/OIX8e8IGq2tfafRG4ALgY+EWSbXRner+D7ujAL1TVXRP0T1X9NMkO4CxgV5I76dJi3kR3nvioHfVRvgxsAr6T5CbgYeDVwPnAt9v4w+5o7+W77b09BTxUVVsOMs64ayJJK4o755I0Pb8FzqRLKfkgsIEuFWN9DXxAUDuG8A3Ala3qo3S5278BLqmqT0zS/4CLgRuBE9sYpwGXA4v1/z+q6pd0aSX3ABcCHwKOAd4GfHWR224ErqXb6b+c7ijE9z/LOOOuiSStKKmqWc9Bkp7TkszRBc7fqKqNK61/SdL0uHMuSZIk9YTBuSRJktQTBueSJElST5hzLkmSJPWEO+eSJElSTxicS5IkST1hcC5JkiT1hMG5JEmS1BMG55IkSVJP/BvcZmfGXrc09wAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'filters_cnn'] filters_cnn\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'lear_rate'] lear_rate\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'lear_rate_decay'] lear_rate_decay\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'coef_reg_cnn'] coef_reg_cnn\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'coef_reg_den'] coef_reg_den\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'dropout_rate'] dropout_rate\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['train', 'epochs'] epochs\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['chainer', 'pipe', 4, 'check_bool'] check_bool\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cmap = plt.get_cmap('rainbow')\n", "colors = [cmap(i) for i in np.linspace(0, 1, data.shape[0])]\n", From e23fecfa3df8495a020c04bfa866c12a23c5bcf8 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 26 Jun 2018 11:47:28 +0300 Subject: [PATCH 520/616] feat: gpus --- deeppavlov/evolve.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 2f9edd4df6..b8e5621726 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -82,6 +82,20 @@ def main(): p_mutation = args.p_mut pow_mutation = args.pow_mut + if os.environ.get("CUDA_VISIBLE_DEVICES") is None: + pass + else: + cvd = [int(gpu) for gpu in os.environ.get("CUDA_VISIBLE_DEVICES").split(",")] + if set(gpus).issubset(set(cvd)): + pass + else: + try: + gpus = [cvd[gpu] for gpu in gpus] + except: + raise ConfigError("Can not use gpus `{}` with CUDA_VISIBLE_DEVICES='{}'".format( + ",".join(gpus), ",".join(cvd) + )) + basic_params = read_json(pipeline_config_path) log.info("Given basic params: {}\n".format(json.dumps(basic_params, indent=2))) From c06e6afd715179135e407fbec9d816cbec449d32 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 26 Jun 2018 12:11:25 +0300 Subject: [PATCH 521/616] chore: rebase dev, run -m deeppavlov --- deeppavlov/evolve.py | 7 +- .../evolution/evolution_intent_model.py | 244 -------- .../neuroevolution_param_generator.py | 543 ------------------ deeppavlov/models/evolution/utils.py | 239 -------- tests/test_quick_start.py | 3 +- 5 files changed, 4 insertions(+), 1032 deletions(-) delete mode 100644 deeppavlov/models/evolution/evolution_intent_model.py delete mode 100644 deeppavlov/models/evolution/neuroevolution_param_generator.py delete mode 100644 deeppavlov/models/evolution/utils.py diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index b8e5621726..45ea421f62 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -227,21 +227,18 @@ def run_population(population, evolution, gpus): f_name = save_path.joinpath("config.json") save_json(population[i], f_name) - curr_dir_path = os.path.dirname(os.path.realpath('__file__')) if len(gpus) == 1 and gpus[0] == -1: - procs.append(Popen("{} {}/deep.py train {}" + procs.append(Popen("{} -m deeppavlov train {}" " 1>{}/out.txt 2>{}/err.txt".format(sys.executable, - curr_dir_path, str(f_name), str(save_path), str(save_path) ), shell=True, stdout=PIPE, stderr=PIPE)) else: - procs.append(Popen("CUDA_VISIBLE_DEVICES={} {} {}/deep.py train {}" + procs.append(Popen("CUDA_VISIBLE_DEVICES={} {} -m deeppavlov train {}" " 1>{}/out.txt 2>{}/err.txt".format(gpus[j], sys.executable, - curr_dir_path, str(f_name), str(save_path), str(save_path) diff --git a/deeppavlov/models/evolution/evolution_intent_model.py b/deeppavlov/models/evolution/evolution_intent_model.py deleted file mode 100644 index 8690103fef..0000000000 --- a/deeppavlov/models/evolution/evolution_intent_model.py +++ /dev/null @@ -1,244 +0,0 @@ -""" -Copyright 2017 Neural Networks and Deep Learning lab, MIPT - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import numpy as np -from copy import copy, deepcopy -from keras.layers import Dense, Input, concatenate, Activation -from keras.layers.convolutional import Conv1D -from keras.layers.core import Dropout -from keras.layers.normalization import BatchNormalization -from keras.layers.pooling import GlobalMaxPooling1D, MaxPooling1D -from keras.layers.recurrent import LSTM -from keras.layers.wrappers import Bidirectional -from keras.models import Model -from keras.regularizers import l2 -from keras.layers import Concatenate, Reshape, CuDNNLSTM, Lambda -from keras import backend as K -from overrides import overrides -from pathlib import Path - -from deeppavlov.core.common.errors import ConfigError -from deeppavlov.core.common.registry import register -from deeppavlov.core.models.keras_model import KerasModel -from deeppavlov.models.classifiers.intents.intent_model import KerasIntentModel -from deeppavlov.models.classifiers.intents.utils import labels2onehot, log_metrics, proba2labels -from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder -from deeppavlov.models.classifiers.intents.utils import md5_hashsum -from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer -from deeppavlov.core.common.log import get_logger -from deeppavlov.models.evolution.check_binary_mask import number_to_type_layer, \ - find_sources_and_sinks, get_digraph_from_binary_mask, get_graph_and_plot -from deeppavlov.models.evolution.utils import Attention, expand_tile -from deeppavlov.core.common.file import save_json, read_json -from deeppavlov.core.layers.keras_layers import multiplicative_self_attention - -log = get_logger(__name__) - - -@register('evolution_classification_model') -class KerasEvolutionClassificationModel(KerasIntentModel): - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.opt["binary_mask"] = np.array(self.opt["binary_mask"]) - get_graph_and_plot(self.opt["nodes"], self.opt["binary_mask"], self.opt["n_types"], - path=str(self.save_path.resolve().parent)) - - def get_node_output(self, node_str_id, dg, params, edges_outputs=None, inp=None): - if inp is None: - input_nodes = [edge[0] for edge in dg.in_edges(node_str_id)] - inp_list = [] - for input_node in input_nodes: - if len(K.int_shape(edges_outputs[input_node])) == 3: - inp_list.append(edges_outputs[input_node]) - elif len(K.int_shape(edges_outputs[input_node])) == 2: - input_expanded = Lambda(lambda x: expand_tile(x, axis=1))(edges_outputs[input_node]) - inp_list.append(input_expanded) - else: - raise ValueError("All the layers should take in and take out 2 and 3 dimensional tensors!") - if len(input_nodes) > 1: - try: - inp = Concatenate()(inp_list) - except ValueError: - time_steps = [] - features = [] - for i in range(len(inp_list)): - if len(K.int_shape(inp_list[i])) == 2: - inp_list[i] = Lambda(lambda x: expand_tile(x, axis=1))(inp_list[i]) - time_steps.append(K.int_shape(inp_list[i])[1]) - features.append(K.int_shape(inp_list[i])[2]) - new_feature_shape = max(features) - for i in range(len(inp_list)): - inp_list[i] = Dense(new_feature_shape)(inp_list[i]) - inp = Concatenate(axis=1)(inp_list) - else: - inp = inp_list[0] - - if params[params["nodes"][node_str_id]]["node_name"] == "BiCuDNNLSTM": - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - l2_reg = node_params.get("coef_regul_l2") - node_params.pop("coef_regul_l2") - output_of_node = Dropout(rate=params['dropout_rate'])( - Bidirectional(CuDNNLSTM(**node_params, - kernel_regularizer=l2(l2_reg)))(inp)) - elif params[params["nodes"][node_str_id]]["node_name"] == "SelfMultiplicativeAttention": - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - output_of_node = Dropout(rate=params['dropout_rate'])(multiplicative_self_attention(inp, **node_params)) - else: - node_func = globals().get(params[params["nodes"][node_str_id]]["node_name"], None) - node_params = deepcopy(params[params["nodes"][node_str_id]]) - node_params.pop("node_name") - node_params.pop("node_type") - node_params.pop("node_layer") - l2_reg = node_params.get("coef_regul_l2") - if callable(node_func): - if l2_reg is None: - output_of_node = Dropout(rate=params['dropout_rate'])(node_func(**node_params)(inp)) - else: - node_params.pop("coef_regul_l2") - output_of_node = Dropout(rate=params['dropout_rate'])( - node_func(**node_params, kernel_regularizer=l2(l2_reg))(inp)) - else: - raise AttributeError("Node {} is not defined correctly".format(node_str_id)) - return output_of_node - - def evolution_classification_model(self, params): - """ - Build un-compiled model of shallow-and-wide CNN - Args: - params: dictionary of parameters for NN - - Returns: - Un-compiled model - """ - inp = Input(shape=(params['text_size'], params['embedding_size'])) - - if np.sum(params["binary_mask"]) == 0: - output = Dense(1, activation=None)(inp) - output = GlobalMaxPooling1D()(output) - output = Dropout(rate=params['dropout_rate'])(output) - output = Dense(self.n_classes, activation=None)(output) - activation = params.get("last_layer_activation", "sigmoid") - act_output = Activation(activation)(output) - model = Model(inputs=inp, outputs=act_output) - return model - - dg = get_digraph_from_binary_mask(params["nodes"], np.array(params["binary_mask"])) - sources, sinks, isolates = find_sources_and_sinks(dg) - - edges_outputs = {} - - # sequence_of_nodes is a list of lists. - # each element of sequence_of_nodes is a list that contains nodes (keras layers) - # that could be initialized when all nodes from previous lists are initialized - sequence_of_nodes = [sources] - - while True: - if set(sinks).issubset(set(sum(sequence_of_nodes, []))): - break - next_nodes = [] - # want to get list of nodes that can be initialized next - for node_str_id in sequence_of_nodes[-1]: - # for each node that were initialized on the previous step - # take output edges - out_edges = dg.out_edges(node_str_id) - for edge in out_edges: - # for all output edge - # collect nodes that are input nodes - # for considered child of node_str_id (edge[1]) - in_nodes_to_edge = [in_edge[0] for in_edge in dg.in_edges(edge[1])] - # if for considered child all parents are already initialized - # then add this node for initialization - if set(in_nodes_to_edge).issubset(set(sum(sequence_of_nodes, []))): - next_nodes.append(edge[1]) - sequence_of_nodes.append(next_nodes) - - # make a list of ints from list of lists - sequence_of_nodes = sum(sequence_of_nodes, []) - - # now all nodes in sequence - # can be initialized consequently - for node_str_id in sequence_of_nodes: - if node_str_id in sources: - # if considered node is source, - # give embedded texts as input - edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, inp=inp) - elif node_str_id in isolates: - # unreal condition - # if considered node is isolate, - # nothing to do - pass - else: - # if considered node is not source and isolate, - # give all previous outputs as input - edges_outputs[node_str_id] = self.get_node_output(node_str_id, dg, params, edges_outputs=edges_outputs) - - if len(sinks) == 1: - # if the only sink, - # output is this sink's output - output = edges_outputs[sinks[0]] - else: - # if several sinks exist, - # outputs will be concatenated - outputs = [] - # collect outputs - for sink in sinks: - outputs.append(edges_outputs[sink]) - try: - output = Concatenate()(outputs) - except ValueError: - # outputs are of 2d and 3d shapes - # make them all 2d and concatenate - for i in range(len(outputs)): - if len(K.int_shape(outputs[i])) == 3: - outputs[i] = GlobalMaxPooling1D()(outputs[i]) - output = Concatenate(axis=1)(outputs) - - # if concatenated output is of 3d shape - # make it 2d using global max pooling - if len(output.shape) == 3: - output = GlobalMaxPooling1D()(output) - - output = Dense(self.n_classes, activation=None)(output) - activation = params.get("last_layer_activation", "sigmoid") - act_output = Activation(activation)(output) - model = Model(inputs=inp, outputs=act_output) - return model - - @overrides - def save(self, fname=None): - """ - Save the model parameters into <>_opt.json (or <>_opt.json) - and model weights into <>.h5 (or <>.h5) - Args: - fname: file_path to save model. If not explicitly given seld.opt["ser_file"] will be used - - Returns: - None - """ - if type(self.opt["binary_mask"]) is list: - pass - else: - self.opt["binary_mask"] = self.opt["binary_mask"].tolist() - - super().save(fname) - return True diff --git a/deeppavlov/models/evolution/neuroevolution_param_generator.py b/deeppavlov/models/evolution/neuroevolution_param_generator.py deleted file mode 100644 index 701ddd98c9..0000000000 --- a/deeppavlov/models/evolution/neuroevolution_param_generator.py +++ /dev/null @@ -1,543 +0,0 @@ -import numpy as np -from copy import deepcopy -from pathlib import Path -import json - -from deeppavlov.models.evolution.check_binary_mask import check_and_correct_binary_mask, \ - number_to_type_layer -from deeppavlov.models.evolution.utils import find_index_of_dict_with_key_in_pipe - - -# please, make sure that -# `config["chainer"]["pipe"]` is a list of models one of which is a model to be evolved, -# otherwise, in the whole class change `config["chainer"]["pipe"]` to new path - - -class NetworkAndParamsEvolution: - """ - Class performs full evolutionary process (task scores -> max): - 1. initializes random population - 2. makes replacement to get next generation: - a. selection according to obtained scores - b. crossover (recombination) with given probability p_crossover - c. mutation with given mutation rate p_mutation (probability to mutate) - according to given mutation power sigma - (current mutation power is randomly from -sigma to sigma) - """ - - def __init__(self, n_layers, n_types, - population_size, - p_crossover=0.5, crossover_power=0.5, - p_mutation=0.5, mutation_power=0.1, - key_model_to_evolve="to_evolve", - key_basic_layers="basic_layers_params", - seed=None, - start_with_one_neuron=False, - **kwargs): - """ - Initialize evolution with random population - Args: - n_layers: number of available layers of each type - n_types: number of different types of network layers - population_size: number of individuums per generation - p_crossover: probability to cross over for current replacement - crossover_power: part of EVOLVING parents parameters to exchange for offsprings - p_mutation: probability of mutation for current replacement - mutation_power: allowed percentage of mutation - key_model_to_evolve: binary flag that should be inserted into the dictionary - with evolving model in the basic config - key_basic_layers: key value of dictionary in basic_config - that contains considered layers with their evolving parameters - seed: random seed for initialization - seed: random seed for initialization - **kwargs: basic config with parameters - """ - self.n_types = n_types - self.n_layers = n_layers - - self.total_nodes = self.n_types * self.n_layers - self.binary_mask_template = np.zeros((self.total_nodes, self.total_nodes)) - self.start_with_one_neuron = start_with_one_neuron - - self.basic_config = deepcopy(kwargs) - self.model_to_evolve_index = find_index_of_dict_with_key_in_pipe(self.basic_config["chainer"]["pipe"], - key_model_to_evolve) - - self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["n_types"] = self.n_types - self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["n_layers"] = self.n_layers - Path(self.basic_config["chainer"]["pipe"][self.model_to_evolve_index]["save_path"]).mkdir(parents=True, - exist_ok=True) - - self.params = deepcopy(self.basic_config.get("chainer").get("pipe")[self.model_to_evolve_index]) - self.train_params = deepcopy(self.basic_config.get("train")) - self.basic_layers_params = self.params.pop(key_basic_layers, None) - self.node_types = list(self.basic_layers_params.keys()) - - self.nodes = {} - for i in range(self.total_nodes): - l, t = number_to_type_layer(i, self.n_types) - self.nodes[str(i)] = "{}_{}_{}".format(l, t, i) - - print("___Basic config___: {}".format(self.basic_config)) - print("___Model to evolve index in pipe___: {}".format(self.model_to_evolve_index)) - print("___Model params___: {}".format(self.params)) - print("___Train params___: {}".format(self.train_params)) - print("___Basic layers params___: {}".format(self.basic_layers_params)) - - if self.basic_layers_params is None: - print("\n\n___PARAMS EVOLUTION is being started___") - print("___For network evolution one has to provide config file with `basic_layers_params` key___\n\n") - else: - print("\n\n___NETWORK AND PARAMS EVOLUTION is being started___\n\n") - - self.population_size = population_size - self.p_crossover = p_crossover - self.p_mutation = p_mutation - self.mutation_power = mutation_power - self.crossover_power = crossover_power - self.evolving_params = [] - self.n_evolving_params = None - self.evolving_train_params = [] - self.n_evolving_train_params = None - - if seed is None: - pass - else: - np.random.seed(seed) - return None - - def _insert_dict_into_model_params(self, params, model_index, dict_to_insert): - params_copy = deepcopy(params) - params_copy["chainer"]["pipe"].insert(model_index, dict_to_insert) - return params_copy - - def print_dict(self, dict, string=None): - if string is None: - print(json.dumps(dict, indent=2)) - else: - print(string) - print(json.dumps(dict, indent=2)) - return None - - def initialize_params_in_config(self, basic_params): - params = {} - params_for_search = {} - evolving_params = [] - - for param_name in list(basic_params.keys()): - if type(basic_params[param_name]) is dict: - if basic_params[param_name].get("choice"): - params_for_search[param_name] = list(basic_params[param_name]["values"]) - evolving_params.append(param_name) - elif basic_params[param_name].get("range"): - params_for_search[param_name] = deepcopy(basic_params[param_name]) - evolving_params.append(param_name) - elif basic_params[param_name].get("bool"): - params_for_search[param_name] = deepcopy(basic_params[param_name]) - evolving_params.append(param_name) - else: - # NOT evolving params - params[param_name] = deepcopy(basic_params[param_name]) - else: - # NOT evolving params - params[param_name] = deepcopy(basic_params[param_name]) - if basic_params: - params_for_search = deepcopy(self.sample_params(**params_for_search)) - - return params, params_for_search, evolving_params - - def initialize_layers_params(self): - all_layers_params = {} - - for node_id in range(self.total_nodes): - node_layer, node_type = number_to_type_layer(node_id, self.n_types) - node_key = self.nodes[str(node_id)] - layers_params, layers_params_for_search, _ = self.initialize_params_in_config( - self.basic_layers_params[self.node_types[node_type]]) - - all_layers_params[node_key] = {"node_name": self.node_types[node_type], - "node_type": node_type, - "node_layer": node_layer, - **layers_params, - **layers_params_for_search - } - return all_layers_params - - def first_generation(self, iteration=0): - """ - Initialize first generation randomly according to the given constraints is self.params - Returns: - first generation that consists of self.population_size individuums - """ - population = [] - for i in range(self.population_size): - population.append(deepcopy(self.basic_config)) - - # intitializing parameters for model - params, params_for_search, evolving_params = self.initialize_params_in_config(self.params) - self.evolving_params.extend(evolving_params) - # initializing parameters for train - train_params, train_params_for_search, evolving_params = self.initialize_params_in_config(self.train_params) - self.evolving_train_params.extend(evolving_params) - - # intitializing path to save model - # save_path = population_iteration/model_name_i/ - if "model_name" in params_for_search.keys(): - params["save_path"] = str(Path(self.params["save_path"]).joinpath( - "population_" + str(iteration)).joinpath(params_for_search["model_name"] + "_" + str(i))) - else: - params["save_path"] = str(Path(self.params["save_path"]).joinpath( - "population_" + str(iteration)).joinpath(self.params["model_name"] + "_" + str(i))) - - layers_params = self.initialize_layers_params() - - # exchange model and layers params from basic config to sampled model params - population[-1]["chainer"]["pipe"][self.model_to_evolve_index] = {**params, - **params_for_search, - **layers_params} - # add binary_mask intialization - if self.start_with_one_neuron: - population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, self.sample_one_neuron_binary_mask()) - else: - population[-1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, self.sample_binary_mask()) - - # exchange train params from basic config to sampled train params - population[-1]["train"] = {**train_params, - **train_params_for_search} - - self.evolving_params = list(set(self.evolving_params)) - self.evolving_train_params = list(set(self.evolving_train_params)) - - self.n_evolving_params = len(self.evolving_params) - self.n_evolving_train_params = len(self.evolving_train_params) - - return population - - def next_generation(self, generation, scores, iter, - p_crossover=None, crossover_power=None, - p_mutation=None, mutation_power=None): - """ - Provide an operation of replacement - Args: - generation: current generation (set of self.population_size configs - scores: corresponding scores that should be maximized - p_crossover: probability to cross over for current replacement - crossover_power: part of parents parameters to exchange for offsprings - p_mutation: probability of mutation for current replacement - mutation_power: allowed percentage of mutation - - Returns: - the next generation according to the given scores of current generation - """ - if not p_crossover: - p_crossover = self.p_crossover - if not crossover_power: - crossover_power = self.crossover_power - if not p_mutation: - p_mutation = self.p_mutation - if not mutation_power: - mutation_power = self.mutation_power - - selected_individuals = self.selection(generation, scores) - offsprings = self.crossover(selected_individuals, p_crossover=p_crossover, crossover_power=crossover_power) - next = self.mutation(offsprings, p_mutation=p_mutation, mutation_power=mutation_power) - for i in range(self.population_size): - next[i]["chainer"]["pipe"][self.model_to_evolve_index]["save_path"] = \ - str(Path(self.params["save_path"]).joinpath("population_" + str(iter)).joinpath( - self.params["model_name"] + "_" + str(i))) - next[i]["chainer"]["pipe"][self.model_to_evolve_index]["load_path"] = \ - str(Path(self.params["load_path"]).joinpath("population_" + str(iter)).joinpath( - self.params["model_name"] + "_" + str(i))) - - return next - - def selection(self, population, scores): - """ - Select self.population_size individuums (with replacement) from given population. - Probability of i-th individuum to be selected is scores_i / sum_j(scores_j) - Args: - population: self.population_size individuums - scores: corresponding score that should be maximized - - Returns: - selected self.population_size individuums with replacement - """ - scores = np.array(scores, dtype='float') - scores = (scores - 1.1 * min(scores) + 0.1 * max(scores)) - total = np.sum(scores) - probas_to_be_selected = scores / total - intervals = np.array([np.sum(probas_to_be_selected[:i]) for i in range(self.population_size)]) - selected = [] - for i in range(self.population_size): - r = np.random.random() - individuum = population[np.where(r > intervals)[0][-1]] - selected.append(individuum) - return selected - - def crossover(self, population, p_crossover, crossover_power): - """ - Recombine randomly population in pairs and cross over them with given probability. - Cross over from two parents produces two offsprings - each of which contains half of the parameter values from one parent and the other half from the other parent - Args: - population: self.population_size individuums - p_crossover: probability to cross over for current replacement - crossover_power: part of EVOLVING parents parameters to exchange for offsprings - - Returns: - self.population_size offsprings - """ - perm = np.random.permutation(self.population_size) - offsprings = [] - for i in range(self.population_size // 2): - parents = population[perm[2 * i]], population[perm[2 * i + 1]] - if self.decision(p_crossover): - params_perm = np.random.permutation(self.n_evolving_params) - train_params_perm = np.random.permutation(self.n_evolving_train_params) - nodes_perm = np.random.permutation(self.total_nodes) - binary_mask_perm = np.random.permutation(self.total_nodes * self.total_nodes) - - curr_offsprings = [deepcopy(parents[0]), - deepcopy(parents[1])] - - part = int(crossover_power * self.n_evolving_params) - train_part = int(crossover_power * self.n_evolving_train_params) - nodes_part = int(crossover_power * self.total_nodes) - binary_mask_part = int(crossover_power * self.total_nodes * self.total_nodes) - - # exchange of model params (not layers params) - for j in range(self.n_evolving_params - part): - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[0][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[1][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - for j in range(self.n_evolving_params - part, self.n_evolving_params): - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[1][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] = parents[0][ - "chainer"]["pipe"][self.model_to_evolve_index][ - self.evolving_params[params_perm[j]]] - - # exchange of train params - for j in range(self.n_evolving_train_params - train_part): - curr_offsprings[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] - curr_offsprings[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] - for j in range(self.n_evolving_train_params - train_part, self.n_evolving_train_params): - curr_offsprings[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] - curr_offsprings[1]["train"][ - self.evolving_train_params[train_params_perm[j]]] = parents[0]["train"][ - self.evolving_train_params[train_params_perm[j]]] - - # exchange of nodes - for j in range(self.total_nodes - nodes_part): - node_key = self.nodes[str(nodes_perm[j])] - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( - parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( - parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) - for j in range(self.total_nodes - nodes_part, self.total_nodes): - node_key = self.nodes[str(nodes_perm[j])] - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( - parents[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][node_key] = deepcopy( - parents[0]["chainer"]["pipe"][self.model_to_evolve_index][node_key]) - - # exchange of binary mask elements - for j in range(self.total_nodes * self.total_nodes - binary_mask_part): - node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ - parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ - parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] - - for j in range(self.total_nodes * self.total_nodes - binary_mask_part, - self.total_nodes * self.total_nodes): - node_x, node_y = binary_mask_perm[j] // self.total_nodes, binary_mask_perm[j] % self.total_nodes - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ - parents[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] =\ - parents[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"][node_x, node_y] - - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, - curr_offsprings[0]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"]) - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask(self.nodes, - curr_offsprings[1]["chainer"]["pipe"][self.model_to_evolve_index][ - "binary_mask"]) - offsprings.extend(curr_offsprings) - else: - offsprings.extend(parents) - - if self.population_size % 2 == 1: - offsprings.append(population[perm[-1]]) - return offsprings - - def mutation(self, population, p_mutation, mutation_power): - """ - Mutate each parameter of each individuum in population with probability p_mutation - Args: - population: self.population_size individuums - p_mutation: probability to mutate for each parameter - mutation_power: allowed percentage of mutation - - Returns: - mutated population - """ - mutated = [] - - for individuum in population: - mutated_individuum = deepcopy(individuum) - - # mutation of other model params - for param in self.params.keys(): - mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][param] = \ - self.mutation_of_param(param, self.params, - individuum["chainer"]["pipe"][self.model_to_evolve_index][param], - p_mutation, mutation_power) - - # mutation of train params - for param in self.train_params.keys(): - mutated_individuum["train"][param] = \ - self.mutation_of_param(param, self.train_params, - individuum["train"][param], - p_mutation, mutation_power) - - # mutation of binary mask - if self.decision(p_mutation): - mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] = \ - check_and_correct_binary_mask( - self.nodes, - np.minimum(1, - np.maximum(0, - individuum["chainer"]["pipe"][self.model_to_evolve_index]["binary_mask"] + - np.round((2 * np.random.random() - 1.) * self.sample_binary_mask())))) - - # mutation of each node params - for node_id in range(self.total_nodes): - node_layer, node_type = number_to_type_layer(node_id, self.n_types) - for param in self.basic_layers_params[self.node_types[node_type]]: - mutated_individuum["chainer"]["pipe"][self.model_to_evolve_index][self.nodes[str(node_id)]][param] \ - = self.mutation_of_param(param, self.basic_layers_params[self.node_types[node_type]], - individuum["chainer"]["pipe"][self.model_to_evolve_index][ - self.nodes[str(node_id)]][param], - p_mutation, mutation_power) - mutated.append(mutated_individuum) - - return mutated - - def mutation_of_param(self, param, params_dict, param_value, p_mutation, mutation_power): - new_mutated_value = deepcopy(param_value) - - if self.decision(p_mutation): - if type(params_dict[param]) is dict: - if params_dict[param].get('discrete', False): - val = round(param_value + - ((2 * np.random.random() - 1.) * mutation_power - * self.sample_params(**{param: params_dict[param]})[param])) - val = min(max(params_dict[param]["range"][0], val), - params_dict[param]["range"][1]) - new_mutated_value = val - elif 'range' in params_dict[param].keys(): - val = param_value + \ - ((2 * np.random.random() - 1.) * mutation_power - * self.sample_params(**{param: params_dict[param]})[param]) - val = min(max(params_dict[param]["range"][0], val), - params_dict[param]["range"][1]) - new_mutated_value = val - elif params_dict[param].get("choice"): - new_mutated_value = param_value - else: - new_mutated_value = param_value - else: - new_mutated_value = param_value - else: - new_mutated_value = param_value - - return new_mutated_value - - def decision(self, probability): - """ - Make decision whether to do action or not with given probability - Args: - probability: probability whether - - Returns: - - """ - r = np.random.random() - if r < probability: - return True - else: - return False - - def sample_params(self, **params): - if not params: - return {} - else: - params_copy = deepcopy(params) - params_sample = dict() - for param, param_val in params_copy.items(): - if isinstance(param_val, list): - params_sample[param] = np.random.choice(param_val) - elif isinstance(param_val, dict): - if 'bool' in param_val and param_val['bool']: - sample = bool(np.random.choice([True, False])) - elif 'range' in param_val: - sample = self._sample_from_ranges(param_val) - params_sample[param] = sample - else: - params_sample[param] = params_copy[param] - return params_sample - - def _sample_from_ranges(self, opts): - from_ = opts['range'][0] - to_ = opts['range'][1] - if opts.get('scale', None) == 'log': - sample = self._sample_log(from_, to_) - else: - sample = np.random.uniform(from_, to_) - if opts.get('discrete', False): - sample = int(np.round(sample)) - return sample - - @staticmethod - def _sample_log(from_, to_): - sample = np.exp(np.random.uniform(np.log(from_), np.log(to_))) - return float(sample) - - def sample_binary_mask(self): - # return np.random.randint(0, high=2, size=self.binary_mask_template.shape).tolist() - # return (1 * (np.log(np.random.random(size=self.binary_mask_template.shape)) > -0.2)).tolist() - ones = np.random.choice(self.total_nodes * self.total_nodes, - size=max(1, int(0.5 * np.random.random() * self.total_nodes))) - mask = np.zeros((self.total_nodes * self.total_nodes)) - mask[ones] = 1 - # returns NUMPY 2D ARRAY! - return mask.reshape((self.total_nodes, self.total_nodes)) - - def sample_one_neuron_binary_mask(self): - mask = np.zeros((self.total_nodes * self.total_nodes)) - # mask[0] = 1 # make sure that Dense is the first in the config - - return mask.reshape((self.total_nodes, self.total_nodes)) diff --git a/deeppavlov/models/evolution/utils.py b/deeppavlov/models/evolution/utils.py deleted file mode 100644 index ccdf47104c..0000000000 --- a/deeppavlov/models/evolution/utils.py +++ /dev/null @@ -1,239 +0,0 @@ -""" -Copyright 2017 Neural Networks and Deep Learning lab, MIPT - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import numpy as np -import sys -import hashlib - -from keras.engine.topology import Layer -from deeppavlov.core.common.log import get_logger -from keras import initializers, regularizers, constraints -from keras import backend as K -from keras.layers import Reshape, Lambda, Dense, Flatten -from keras.layers import Concatenate, Multiply, Activation, Dot - -log = get_logger(__name__) - - -def labels2onehot(labels, classes): - """ - Convert labels to one-hot vectors for multi-class multi-label classification - Args: - labels: list of samples where each sample is a list of classes which sample belongs with - classes: array of classes' names - - Returns: - 2d array with one-hot representation of given samples - """ - n_classes = len(classes) - eye = np.eye(n_classes) - y = [] - for sample in labels: - curr = np.zeros(n_classes) - for intent in sample: - if intent not in classes: - log.warning('Unknown intent {} detected'.format(intent)) - curr += eye[np.where(np.array(classes) == 'unknown')[0]].reshape(-1) - else: - curr += eye[np.where(np.array(classes) == intent)[0]].reshape(-1) - y.append(curr) - y = np.asarray(y) - return y - - -def proba2labels(proba, confident_threshold, classes): - """ - Convert vectors of probabilities to labels using confident threshold - (if probability to belong with the class is bigger than confident_threshold, sample belongs with the class; - if no probabilities bigger than confident threshold, sample belongs with the class with the biggest probability) - Args: - proba: list of samples where each sample is a vector of probabilities to belong with given classes - confident_threshold (float): boundary of probability to belong with a class - classes: array of classes' names - - Returns: - array of lists of labels for each sample - """ - y = [] - for sample in proba: - to_add = np.where(sample > confident_threshold)[0] - if len(to_add) > 0: - y.append(np.array(classes)[to_add]) - else: - y.append(np.array([np.array(classes)[np.argmax(sample)]])) - y = np.asarray(y) - return y - - -def proba2onehot(proba, confident_threshold, classes): - """ - Convert vectors of probabilities to one-hot representations using confident threshold - Args: - proba: list of samples where each sample is a vector of probabilities to belong with given classes - confident_threshold: boundary of probability to belong with a class - classes: array of classes' names - - Returns: - 2d array with one-hot representation of given samples - """ - return labels2onehot(proba2labels(proba, confident_threshold, classes), classes) - - -def log_metrics(names, values, updates=None, mode='train'): - """ - Print training and validation data in the following view: - `mode --> updates: 0 names[0]: 0.0 names[1]: 0.0 names[2]: 0.0` - Args: - names: list of names of considered metrics - values: list of values of considered metrics - updates: number of updates - mode: dataset field on which calculation is being doing (i.e "train") - - Returns: - None - """ - sys.stdout.write("\r") # back to previous line - log.info("{} -->\t".format(mode)) - if updates is not None: - log.info("updates: {}\t".format(updates)) - - for id in range(len(names)): - log.info("{}: {}\t".format(names[id], values[id])) - return - - -def md5_hashsum(file_names): - """ - Calculate md5 hash sum of files listed - Args: - file_names: list of file names - - Returns: - hashsum string - """ - hash_md5 = hashlib.md5() - for file_name in file_names: - with open(file_name, "rb") as f: - for chunk in iter(lambda: f.read(4096), b""): - hash_md5.update(chunk) - return hash_md5.hexdigest() - - -class Attention(Layer): - def __init__(self, context_length=None, - W_regularizer=None, b_regularizer=None, - W_constraint=None, b_constraint=None, - use_bias=True, **kwargs): - self.supports_masking = True - self.init = initializers.get('glorot_uniform') - self.W_regularizer = regularizers.get(W_regularizer) - self.b_regularizer = regularizers.get(b_regularizer) - self.W_constraint = constraints.get(W_constraint) - self.b_constraint = constraints.get(b_constraint) - self.use_bias = use_bias - self.context_length = context_length - - super(Attention, self).__init__(**kwargs) - - def build(self, input_shape): - assert len(input_shape) == 3 - - if self.context_length is None: - self.context_length = input_shape[-1] - - self.context = self.add_weight(tuple((self.context_length, input_shape[-1])), - name="context", - initializer=self.init) - - self.W = self.add_weight((2 * input_shape[-1], 1, ), - name="w", - initializer=self.init, - regularizer=self.W_regularizer, - constraint=self.W_constraint) - - if self.use_bias: - self.b = self.add_weight((1, ), - name="b", - initializer='zero', - regularizer=self.b_regularizer, - constraint=self.b_constraint) - else: - self.b = None - - self.built = True - - def call(self, x, mask=None): - - expanded_context_3d = expand_tile_batch_size(memory=x, context=self.context) - expanded_context_4d = expand_tile(expanded_context_3d, axis=1, n_repetitions=K.int_shape(x)[1]) - expanded_x = expand_tile(x, axis=2, n_repetitions=K.int_shape(expanded_context_3d)[1]) - - # now expanded_context_4d and expanded_x are of - # shape (bs, time_steps, context_size, n_features) - - x_full = Concatenate(axis=-1)([expanded_x, expanded_context_4d]) - - out = K.dot(x_full, self.W) - if self.use_bias: - out = K.bias_add(out, self.b) - - out = Activation('softmax')(out) - out = Multiply()([out, expanded_x]) - - out = Lambda(lambda x: K.sum(x, axis=1))(out) - return out - - def compute_output_shape(self, input_shape): - return input_shape - -def expand_tile(units, axis, n_repetitions=None): - """Expand and tile tensor along given axis - Args: - units: tf tensor with dimensions [batch_size, time_steps, n_input_features] - axis: axis along which expand and tile. Must be 1 or 2 - - """ - assert axis in (1, 2) - repetitions = [1] * (len(K.int_shape(units)) + 1) - - if n_repetitions is None: - repetitions[axis] = K.int_shape(units)[1] - else: - repetitions[axis] = n_repetitions - - if axis == 1: - expanded = Reshape(target_shape=( (1,) + K.int_shape(units)[1:] ))(units) - else: # axis=2 - expanded = Reshape(target_shape=(K.int_shape(units)[1:2] + (1,) + K.int_shape(units)[2:]))(units) - return K.tile(expanded, repetitions) - - -def expand_tile_batch_size(memory, context): - """Expand and tile tensor context along 0 axis up to 0-shape of memory - Args: - memory: tf tensor with dimensions [batch_size, time_steps, n_input_features] - context: tf tensor with dimensions [new_time_steps, n_input_features] - - """ - axis = 0 - # batch_size = K.int_shape(memory)[0] - batch_size = K.shape(memory)[0] - repetitions = [1] * len(K.int_shape(memory)) - repetitions[axis] = batch_size - if axis == 0: - expanded = K.reshape(context, shape=((1,) + K.int_shape(context))) - return K.tile(expanded, repetitions) - diff --git a/tests/test_quick_start.py b/tests/test_quick_start.py index 63c6f6edce..a323c8f0e5 100644 --- a/tests/test_quick_start.py +++ b/tests/test_quick_start.py @@ -59,7 +59,8 @@ ("intents/intents_dstc2.json", "intents", ALL_MODES): [ONE_ARGUMENT_INFER_CHECK], ("intents/intents_dstc2_big.json", "intents", ('TI',)): [ONE_ARGUMENT_INFER_CHECK] }, - "snips": {("intents/intents_snips.json", "intents", ('TI',)): [ONE_ARGUMENT_INFER_CHECK], + "snips": { + ("intents/intents_snips.json", "intents", ('TI',)): [ONE_ARGUMENT_INFER_CHECK], ("intents/intents_snips_bigru.json", "intents", ('TI')): [ONE_ARGUMENT_INFER_CHECK], ("intents/intents_snips_bilstm.json", "intents", ('TI',)): [ONE_ARGUMENT_INFER_CHECK], ("intents/intents_snips_bilstm_bilstm.json", "intents", ('TI',)): [ONE_ARGUMENT_INFER_CHECK], From c02ed3f3e3b1a21ce035b93236a2532a417d011c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 26 Jun 2018 12:32:43 +0300 Subject: [PATCH 522/616] feat: tests --- tests/test_quick_start.py | 28 +++++++++++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/tests/test_quick_start.py b/tests/test_quick_start.py index a323c8f0e5..7ec5d73f48 100644 --- a/tests/test_quick_start.py +++ b/tests/test_quick_start.py @@ -21,7 +21,8 @@ download_path = tests_dir / "download" TEST_MODES = ['IP', # test_interacting_pretrained_model - 'TI' # test_consecutive_training_and_interacting + 'TI', # test_consecutive_training_and_interacting + 'E' # test_evolving ] ALL_MODES = ('IP', 'TI') @@ -74,6 +75,9 @@ ("sentiment/sentiment_twitter.json", "sentiment", ALL_MODES): [ONE_ARGUMENT_INFER_CHECK], ("sentiment/sentiment_ag_news.json", "sentiment", ALL_MODES): [ONE_ARGUMENT_INFER_CHECK] }, + "evolution": { + ("evolution/evolve_intents_snips.json", "evolution", ('E',)): [ONE_ARGUMENT_INFER_CHECK] + }, "sample": { ("intents/intents_sample_csv.json", "intents", ('TI',)): [ONE_ARGUMENT_INFER_CHECK], ("intents/intents_sample_json.json", "intents", ('TI',)): [ONE_ARGUMENT_INFER_CHECK] @@ -274,3 +278,25 @@ def test_consecutive_training_and_interacting(self, model, conf_file, model_dir, shutil.rmtree(str(download_path), ignore_errors=True) else: pytest.skip("Unsupported mode: {}".format(mode)) + + def test_evolving(self, model, conf_file, model_dir, mode): + if 'E' in mode: + c = test_configs_path / conf_file + model_path = download_path / model_dir + + if 'IP' not in mode: + config_path = str(test_configs_path.joinpath(conf_file)) + deep_download(['-test', '-c', config_path]) + shutil.rmtree(str(model_path), ignore_errors=True) + + logfile = io.BytesIO(b'') + _, exitstatus = pexpect.run(sys.executable + " -m deeppavlov.evolve " + str(c), timeout=None, withexitstatus=True, + logfile=logfile) + if exitstatus != 0: + logfile.seek(0) + raise RuntimeError('Training process of {} returned non-zero exit code: \n{}' + .format(model_dir, ''.join((line.decode() for line in logfile.readlines())))) + + shutil.rmtree(str(download_path), ignore_errors=True) + else: + pytest.skip("Unsupported mode: {}".format(mode)) From 6125e41b810aaaf4a44177c44f948321eab7e7d8 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Tue, 26 Jun 2018 14:24:20 +0300 Subject: [PATCH 523/616] fix: tests --- tests/test_quick_start.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_quick_start.py b/tests/test_quick_start.py index 7ec5d73f48..0e992fcc75 100644 --- a/tests/test_quick_start.py +++ b/tests/test_quick_start.py @@ -76,7 +76,7 @@ ("sentiment/sentiment_ag_news.json", "sentiment", ALL_MODES): [ONE_ARGUMENT_INFER_CHECK] }, "evolution": { - ("evolution/evolve_intents_snips.json", "evolution", ('E',)): [ONE_ARGUMENT_INFER_CHECK] + ("evolution/evolve_intents_snips.json", "evolution", ('E',)): None }, "sample": { ("intents/intents_sample_csv.json", "intents", ('TI',)): [ONE_ARGUMENT_INFER_CHECK], @@ -284,7 +284,7 @@ def test_evolving(self, model, conf_file, model_dir, mode): c = test_configs_path / conf_file model_path = download_path / model_dir - if 'IP' not in mode: + if 'IP' not in mode and 'TI' not in mode: config_path = str(test_configs_path.joinpath(conf_file)) deep_download(['-test', '-c', config_path]) shutil.rmtree(str(model_path), ignore_errors=True) From 84d707a993f1619f4227f8e0d84cf8829e529ca8 Mon Sep 17 00:00:00 2001 From: Valentin Malykh Date: Tue, 26 Jun 2018 14:34:40 +0300 Subject: [PATCH 524/616] fix: typo --- examples/hello_agent.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/hello_agent.ipynb b/examples/hello_agent.ipynb index 99865d0678..7510ef6f6e 100644 --- a/examples/hello_agent.ipynb +++ b/examples/hello_agent.ipynb @@ -39,7 +39,7 @@ "outputs": [], "source": [ "hello = PatternMatchingSkill(['Hello world!'], patterns=[\"hi\", \"hello\", \"good day\"])\n", - "bye = PatternMatchingSkill(['Goodbye world!', 'See you aroung'],\n", + "bye = PatternMatchingSkill(['Goodbye world!', 'See you around'],\n", " patterns=[\"bye\", \"chao\", \"see you\"])\n", "fallback = PatternMatchingSkill([\"I don't understand, sorry\", 'I can say \"Hello world!\"'])" ] @@ -61,7 +61,7 @@ { "data": { "text/plain": [ - "['Hello world!', 'See you aroung', 'I can say \"Hello world!\"']" + "['Hello world!', 'See you around', 'I can say \"Hello world!\"']" ] }, "execution_count": 4, From 3cc6b8c423797be828e11eaff11ecc48474b249a Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 27 Jun 2018 11:15:22 +0300 Subject: [PATCH 525/616] feat: max num of iterations for evolution --- deeppavlov/evolve.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 45ea421f62..7fe53aed63 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -53,6 +53,7 @@ help='path to population to start from', default="") parser.add_argument('--elitism_with_weights', help='whether to save elite models with weights or without', default=0) +parser.add_argument('--iterations', help='Number of iterations', type=int, default=1) def find_config(pipeline_config_path: str): @@ -76,6 +77,7 @@ def main(): start_from_population = int(args.start_from_population) path_to_population = args.path_to_population elitism_with_weights = int(args.elitism_with_weights) + iterations = int(args.iterations) p_crossover = args.p_cross pow_crossover = args.pow_cross @@ -191,6 +193,9 @@ def main(): iters += 1 while True: + if iters >= iterations: + log.info("End of evolution on iteration #{}".format(iters)) + break log.info("Iteration #{} starts".format(iters)) population = evolution.next_generation(population, population_scores, iters) run_population(population, evolution, gpus) From 5b92d8ee85f825046430589dbe3e60e344306086 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 27 Jun 2018 11:19:11 +0300 Subject: [PATCH 526/616] feat: max num of iterations for evolution --- deeppavlov/evolve.py | 4 ++-- tests/test_quick_start.py | 3 ++- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 7fe53aed63..211ca7f6ae 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -53,7 +53,7 @@ help='path to population to start from', default="") parser.add_argument('--elitism_with_weights', help='whether to save elite models with weights or without', default=0) -parser.add_argument('--iterations', help='Number of iterations', type=int, default=1) +parser.add_argument('--iterations', help='Number of iterations', type=int, default=-1) def find_config(pipeline_config_path: str): @@ -193,7 +193,7 @@ def main(): iters += 1 while True: - if iters >= iterations: + if iters == iterations: log.info("End of evolution on iteration #{}".format(iters)) break log.info("Iteration #{} starts".format(iters)) diff --git a/tests/test_quick_start.py b/tests/test_quick_start.py index 0e992fcc75..7e350b1d9b 100644 --- a/tests/test_quick_start.py +++ b/tests/test_quick_start.py @@ -290,7 +290,8 @@ def test_evolving(self, model, conf_file, model_dir, mode): shutil.rmtree(str(model_path), ignore_errors=True) logfile = io.BytesIO(b'') - _, exitstatus = pexpect.run(sys.executable + " -m deeppavlov.evolve " + str(c), timeout=None, withexitstatus=True, + _, exitstatus = pexpect.run(sys.executable + " -m deeppavlov.evolve " + str(c) + " --iterations 1", + timeout=None, withexitstatus=True, logfile=logfile) if exitstatus != 0: logfile.seek(0) From f09bc60c20eaf9d1949a305453d51eea53629707 Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Wed, 27 Jun 2018 11:22:06 +0300 Subject: [PATCH 527/616] docs: add info about iterations param --- deeppavlov/models/evolution/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index e990796342..682a202069 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -18,6 +18,7 @@ Evolution process can be described in the following way: - `--start_from_population` - the number of population to start from that is needed to restart population (*Default: 0 means starts from 0 population*). - `--path_to_population` - path to the directory "population_{`start_from_population`}". Should be given if `start_from_population` is not 0 (*Default: ""*). - `--elitism_with_weights` - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not (*Default: 0 means save elite models without weights*). + - `--iterations` - number of iterations to conduct (*Default: -1 means infinite number of iterations (while loop)*). * **Warning**: `metrics` can not be evolved because the main metric determines evolutionary process. From 9df2930194271c771c118410456ffb8032c23f28 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 27 Jun 2018 11:45:01 +0300 Subject: [PATCH 528/616] fix: p_size of 1 model for tests --- tests/test_quick_start.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_quick_start.py b/tests/test_quick_start.py index 7e350b1d9b..ea377c7f68 100644 --- a/tests/test_quick_start.py +++ b/tests/test_quick_start.py @@ -290,7 +290,7 @@ def test_evolving(self, model, conf_file, model_dir, mode): shutil.rmtree(str(model_path), ignore_errors=True) logfile = io.BytesIO(b'') - _, exitstatus = pexpect.run(sys.executable + " -m deeppavlov.evolve " + str(c) + " --iterations 1", + _, exitstatus = pexpect.run(sys.executable + " -m deeppavlov.evolve " + str(c) + " --iterations 1 --p_size 1", timeout=None, withexitstatus=True, logfile=logfile) if exitstatus != 0: From a3e6c956ade1893c23a09de801390d63be560761 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 27 Jun 2018 12:32:45 +0300 Subject: [PATCH 529/616] fix: iterations --- deeppavlov/evolve.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 211ca7f6ae..d017eaf47b 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -193,7 +193,7 @@ def main(): iters += 1 while True: - if iters == iterations: + if iterations != -1 and start_from_population + iterations == iters: log.info("End of evolution on iteration #{}".format(iters)) break log.info("Iteration #{} starts".format(iters)) From 6ff0436baea839a41f6ffdd81cdfe70c39e3d533 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 27 Jun 2018 12:39:56 +0300 Subject: [PATCH 530/616] fix: train_evaluate and reports --- deeppavlov/core/commands/train.py | 32 ++++++++++++++----------------- 1 file changed, 14 insertions(+), 18 deletions(-) diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index 10ff55130c..345d3d0f22 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -19,7 +19,6 @@ import json import time from collections import OrderedDict -from pathlib import Path from typing import List, Callable, Tuple, Dict, Union from deeppavlov.core.commands.utils import expand_path, set_deeppavlov_root @@ -70,7 +69,7 @@ def fit_chainer(config: dict, iterator: Union[DataLearningIterator, DataFittingI chainer_config: dict = config['chainer'] chainer = Chainer(chainer_config['in'], chainer_config['out'], chainer_config.get('in_y')) for component_config in chainer_config['pipe']: - component = from_params(component_config, mode='train') + component = from_params(component_config, vocabs=[], mode='train') if 'fit_on' in component_config: component: Estimator @@ -96,9 +95,8 @@ def fit_chainer(config: dict, iterator: Union[DataLearningIterator, DataFittingI return chainer -def train_evaluate_model_from_config(config: [str, Path, dict], to_train=True, to_validate=True) -> None: - if isinstance(config, (str, Path)): - config = read_json(config) +def train_evaluate_model_from_config(config_path: str, to_train=True, to_validate=True) -> None: + config = read_json(config_path) set_deeppavlov_root(config) dataset_config = config.get('dataset', None) @@ -154,7 +152,16 @@ def train_evaluate_model_from_config(config: [str, Path, dict], to_train=True, t metrics_functions = list(zip(train_config['metrics'], get_metrics_by_names(train_config['metrics']))) if to_train: - model = fit_chainer(config, iterator) + if 'chainer' in config: + model = fit_chainer(config, iterator) + else: + vocabs = config.get('vocabs', {}) + for vocab_param_name, vocab_config in vocabs.items(): + v: Estimator = from_params(vocab_config, mode='train') + vocabs[vocab_param_name] = _fit(v, iterator, None) + + model_config = config['model'] + model = from_params(model_config, vocabs=vocabs, mode='train') if callable(getattr(model, 'train_on_batch', None)): _train_batches(model, iterator, train_config, metrics_functions) @@ -166,7 +173,6 @@ def train_evaluate_model_from_config(config: [str, Path, dict], to_train=True, t log.warning('Nothing to train') if train_config['validate_best'] or train_config['test_best']: - all_reports = [] # try: # model_config['load_path'] = model_config['save_path'] # except KeyError: @@ -181,7 +187,6 @@ def train_evaluate_model_from_config(config: [str, Path, dict], to_train=True, t } print(json.dumps(report, ensure_ascii=False)) - all_reports.append(report) if train_config['test_best']: report = { @@ -190,9 +195,6 @@ def train_evaluate_model_from_config(config: [str, Path, dict], to_train=True, t } print(json.dumps(report, ensure_ascii=False)) - all_reports.append(report) - - return all_reports def _test_model(model: Component, metrics_functions: List[Tuple[str, Callable]], @@ -260,7 +262,6 @@ def improved(score, best): log_on = train_config['log_every_n_batches'] > 0 or train_config['log_every_n_epochs'] > 0 train_y_true = [] train_y_predicted = [] - losses = [] start_time = time.time() break_flag = False try: @@ -270,9 +271,7 @@ def improved(score, best): y_predicted = list(model(list(x))) train_y_true += y_true train_y_predicted += y_predicted - loss = model.train_on_batch(x, y_true) - if loss is not None: - losses.append(loss) + model.train_on_batch(x, y_true) i += 1 examples += len(x) @@ -285,9 +284,6 @@ def improved(score, best): 'metrics': prettify_metrics(metrics), 'time_spent': str(datetime.timedelta(seconds=round(time.time() - start_time + 0.5))) } - if losses: - report['loss'] = sum(losses)/len(losses) - losses = [] report = {'train': report} print(json.dumps(report, ensure_ascii=False)) train_y_true.clear() From 047ecba6d9e8a72569c0a51ca5934f7e6d9774d9 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 27 Jun 2018 12:41:33 +0300 Subject: [PATCH 531/616] fix: elitism with weights store true --- deeppavlov/evolve.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index d017eaf47b..5d5cc3ee67 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -52,7 +52,7 @@ parser.add_argument('--path_to_population', help='path to population to start from', default="") parser.add_argument('--elitism_with_weights', - help='whether to save elite models with weights or without', default=0) + help='whether to save elite models with weights or without', action='store_true') parser.add_argument('--iterations', help='Number of iterations', type=int, default=-1) @@ -76,7 +76,7 @@ def main(): train_partition = int(args.train_partition) start_from_population = int(args.start_from_population) path_to_population = args.path_to_population - elitism_with_weights = int(args.elitism_with_weights) + elitism_with_weights = args.elitism_with_weights iterations = int(args.iterations) p_crossover = args.p_cross From d0af78413619ec6ea72234655642358011667bbe Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Wed, 27 Jun 2018 12:43:56 +0300 Subject: [PATCH 532/616] docs: for elitism with weights --- deeppavlov/models/evolution/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index 682a202069..b2e46328b4 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -17,7 +17,7 @@ Evolution process can be described in the following way: - `--train_partition` - if train file is too big to train (recommeded to divide train files if train dataset is more than 100 thousands examples), one can split it in `train_partition` number of files, save it calling "any_name_{0}.any_extension", ..., "any_name_{`train_partition`}.any_extension". In dataset_reader "train" field indicate the first one file. Population is trained on the N_{population} % `train_partition` part of the dataset (*Default: 1*). - `--start_from_population` - the number of population to start from that is needed to restart population (*Default: 0 means starts from 0 population*). - `--path_to_population` - path to the directory "population_{`start_from_population`}". Should be given if `start_from_population` is not 0 (*Default: ""*). - - `--elitism_with_weights` - binary value (set of values: "0", "1") - whether to initialize elite models with pre-trained weights from previous population or not (*Default: 0 means save elite models without weights*). + - `--elitism_with_weights` - whether to initialize elite models with pre-trained weights from previous population or not (*Default: False means save elite models without weights. If parameter is given, then save elite models with weights*). - `--iterations` - number of iterations to conduct (*Default: -1 means infinite number of iterations (while loop)*). * **Warning**: `metrics` can not be evolved because the main metric determines evolutionary process. From 2976977e1fb019538e7117e88f039d07f4399816 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 27 Jun 2018 12:48:44 +0300 Subject: [PATCH 533/616] fix: cpu and cuda visible devices --- deeppavlov/evolve.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index 5d5cc3ee67..d263d3ad11 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -90,6 +90,10 @@ def main(): cvd = [int(gpu) for gpu in os.environ.get("CUDA_VISIBLE_DEVICES").split(",")] if set(gpus).issubset(set(cvd)): pass + elif gpus == [-1]: + raise ConfigError("Unclear to compute on CPU or CUDA_VISIBLE_DEVICES='{}'".format( + ",".join(cvd) + )) else: try: gpus = [cvd[gpu] for gpu in gpus] From c11165e607ae9759858bcef279bf7fc8dc3621e0 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 27 Jun 2018 12:55:42 +0300 Subject: [PATCH 534/616] fix: setting zeros if class is unknown --- deeppavlov/models/classifiers/intents/utils.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/deeppavlov/models/classifiers/intents/utils.py b/deeppavlov/models/classifiers/intents/utils.py index a8620f31ef..225b345702 100644 --- a/deeppavlov/models/classifiers/intents/utils.py +++ b/deeppavlov/models/classifiers/intents/utils.py @@ -41,8 +41,7 @@ def labels2onehot(labels, classes): curr = np.zeros(n_classes) for intent in sample: if intent not in classes: - log.warning('Unknown intent {} detected'.format(intent)) - curr += eye[np.where(np.array(classes) == 'unknown')[0]].reshape(-1) + log.warning('Unknown intent {} detected. Setting no class for this sample'.format(intent)) else: curr += eye[np.where(np.array(classes) == intent)[0]].reshape(-1) y.append(curr) From cb6b706a6c881341960a80c52e5033fedb96147d Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 27 Jun 2018 14:14:29 +0300 Subject: [PATCH 535/616] fix: gpus and cuda visible devices --- deeppavlov/evolve.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/deeppavlov/evolve.py b/deeppavlov/evolve.py index d263d3ad11..8deff2d21f 100644 --- a/deeppavlov/evolve.py +++ b/deeppavlov/evolve.py @@ -88,12 +88,8 @@ def main(): pass else: cvd = [int(gpu) for gpu in os.environ.get("CUDA_VISIBLE_DEVICES").split(",")] - if set(gpus).issubset(set(cvd)): - pass - elif gpus == [-1]: - raise ConfigError("Unclear to compute on CPU or CUDA_VISIBLE_DEVICES='{}'".format( - ",".join(cvd) - )) + if gpus == [-1]: + gpus = cvd else: try: gpus = [cvd[gpu] for gpu in gpus] From 3ddc5c6ff8571d939763ac20bf976323dd2963c1 Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Wed, 27 Jun 2018 14:16:50 +0300 Subject: [PATCH 536/616] docs: gpus and cuda visible devices --- deeppavlov/models/evolution/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/evolution/README.md b/deeppavlov/models/evolution/README.md index b2e46328b4..2dd013abbc 100644 --- a/deeppavlov/models/evolution/README.md +++ b/deeppavlov/models/evolution/README.md @@ -13,7 +13,7 @@ Evolution process can be described in the following way: - `--pow_cross` - crossover power - portion of evolving parameters that will be exchanged between parents during crossover (Default: 0.1). - `--p_mut` - probability of mutation for a parameter (*Default: 1.*). - `--pow_mut` - mutation power - maximal portion of maximal possible value of parameter which can be added or subtracted during mutation (Default: 0.1). - - `--gpus` - available GPUs divided by comma "," (*Default: -1 means CPU support*). If one runs `evolve.py` with assigned `CUDA_VISIBLE_DEVICES`, gpus are either of the same numeration (e.g. `CUDA_VISIBLE_DEVICES=3,4,5` and `--gpus 4,4,5` mean running models on `4,4,5` original GPUs) or ordinal number of device within those from `CUDA_VISIBLE_DEVICES` (e.g. `CUDA_VISIBLE_DEVICES=3,4,5` and `--gpus 1,1,2` mean running models on `4,4,5` original GPUs). + - `--gpus` - available GPUs divided by comma "," (*Default: -1 means CPU support*). If one runs `evolve.py` with assigned `CUDA_VISIBLE_DEVICES`, gpus are either ordinal numbers of device within those from `CUDA_VISIBLE_DEVICES` (e.g. `CUDA_VISIBLE_DEVICES=3,4,5` and `--gpus 1,2` mean running models on `4,5` original GPUs) or all devices from `CUDA_VISIBLE_DEVICES` if gpus is not given. - `--train_partition` - if train file is too big to train (recommeded to divide train files if train dataset is more than 100 thousands examples), one can split it in `train_partition` number of files, save it calling "any_name_{0}.any_extension", ..., "any_name_{`train_partition`}.any_extension". In dataset_reader "train" field indicate the first one file. Population is trained on the N_{population} % `train_partition` part of the dataset (*Default: 1*). - `--start_from_population` - the number of population to start from that is needed to restart population (*Default: 0 means starts from 0 population*). - `--path_to_population` - path to the directory "population_{`start_from_population`}". Should be given if `start_from_population` is not 0 (*Default: ""*). From f0a40f74049de1caad3edfc3ac5e7346d4d3267c Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 27 Jun 2018 16:49:26 +0300 Subject: [PATCH 537/616] fix: epochs done, btaches seen and train examples seen --- deeppavlov/core/models/keras_model.py | 45 ++++++++++++++++++++------- 1 file changed, 34 insertions(+), 11 deletions(-) diff --git a/deeppavlov/core/models/keras_model.py b/deeppavlov/core/models/keras_model.py index 33936d0bff..a0462e4627 100644 --- a/deeppavlov/core/models/keras_model.py +++ b/deeppavlov/core/models/keras_model.py @@ -55,6 +55,9 @@ def __init__(self, **kwargs): load_path = self.opt.get('load_path', None) url = self.opt.get('url', None) self.model = None + self.epochs_done = 0 + self.batches_seen = 0 + self.train_examples_seen = 0 super().__init__(save_path=save_path, load_path=load_path, @@ -100,13 +103,13 @@ def init_model_from_scratch(self, model_name, optimizer_name, loss_name, lear_ra if callable(optimizer_func): if not(lear_rate is None): if not(lear_rate_decay is None): - optimizer_ = optimizer_func(lr=lear_rate, decay=lear_rate_decay) + self.optimizer = optimizer_func(lr=lear_rate, decay=lear_rate_decay) else: - optimizer_ = optimizer_func(lr=lear_rate) + self.optimizer = optimizer_func(lr=lear_rate) elif not(lear_rate_decay is None): - optimizer_ = optimizer_func(decay=lear_rate_decay) + self.optimizer = optimizer_func(decay=lear_rate_decay) else: - optimizer_ = optimizer_func() + self.optimizer = optimizer_func() else: raise AttributeError("Optimizer {} is not defined in `keras.optimizers`".format(optimizer_name)) @@ -116,7 +119,7 @@ def init_model_from_scratch(self, model_name, optimizer_name, loss_name, lear_ra else: raise AttributeError("Loss {} is not defined in `keras.losses`".format(loss_name)) - model.compile(optimizer=optimizer_, loss=loss) + model.compile(optimizer=self.optimizer, loss=loss) return model @overrides @@ -136,7 +139,7 @@ def load(self, model_name, optimizer_name, loss_name, lear_rate=None, lear_rate_ """ if self.load_path: if isinstance(self.load_path, Path) and not self.load_path.parent.is_dir(): - raise ConfigError("Provided save path is incorrect!") + raise ConfigError("Provided load path is incorrect!") opt_path = Path("{}_opt.json".format(str(self.load_path.resolve()))) weights_path = Path("{}.h5".format(str(self.load_path.resolve()))) @@ -160,13 +163,13 @@ def load(self, model_name, optimizer_name, loss_name, lear_rate=None, lear_rate_ if callable(optimizer_func): if not (lear_rate is None): if not (lear_rate_decay is None): - optimizer_ = optimizer_func(lr=lear_rate, decay=lear_rate_decay) + self.optimizer = optimizer_func(lr=lear_rate, decay=lear_rate_decay) else: - optimizer_ = optimizer_func(lr=lear_rate) + self.optimizer = optimizer_func(lr=lear_rate) elif not (lear_rate_decay is None): - optimizer_ = optimizer_func(decay=lear_rate_decay) + self.optimizer = optimizer_func(decay=lear_rate_decay) else: - optimizer_ = optimizer_func() + self.optimizer = optimizer_func() else: raise AttributeError("Optimizer {} is not defined in `keras.optimizers`".format(optimizer_name)) @@ -176,7 +179,7 @@ def load(self, model_name, optimizer_name, loss_name, lear_rate=None, lear_rate_ else: raise AttributeError("Loss {} is not defined".format(loss_name)) - model.compile(optimizer=optimizer_, + model.compile(optimizer=self.optimizer, loss=loss) return model else: @@ -211,6 +214,10 @@ def save(self, fname=None): # if model was loaded from one path and saved to another one # then change load_path to save_path for config + self.opt["epochs_done"] = self.epochs_done + self.opt["final_lear_rate"] = K.eval(self.optimizer.lr) / (1. + + K.eval(self.optimizer.decay) * self.batches_seen) + if self.opt.get("load_path") and self.opt.get("save_path"): if self.opt.get("save_path") != self.opt.get("load_path"): self.opt["load_path"] = str(self.opt["save_path"]) @@ -239,3 +246,19 @@ def mlp(self, opt): @abstractmethod def reset(self): pass + + def process_event(self, event_name, data): + """ + Process event after epoch + Args: + event_name: whether event is send after epoch or batch + data: event data (dictionary) + + Returns: + None + """ + if event_name == "after_epoch": + self.epochs_done = data["epochs_done"] + self.batches_seen = data["batches_seen"] + self.train_examples_seen = data["train_examples_seen"] + return From c1278deb5344365be2262a1b823148611a0a76a2 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Wed, 27 Jun 2018 16:50:35 +0300 Subject: [PATCH 538/616] fix: labels to one hot --- deeppavlov/models/classifiers/intents/utils.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/classifiers/intents/utils.py b/deeppavlov/models/classifiers/intents/utils.py index a8620f31ef..7642efbd93 100644 --- a/deeppavlov/models/classifiers/intents/utils.py +++ b/deeppavlov/models/classifiers/intents/utils.py @@ -41,10 +41,9 @@ def labels2onehot(labels, classes): curr = np.zeros(n_classes) for intent in sample: if intent not in classes: - log.warning('Unknown intent {} detected'.format(intent)) - curr += eye[np.where(np.array(classes) == 'unknown')[0]].reshape(-1) + log.warning('Unknown intent {} detected. Assigning no class'.format(intent)) else: - curr += eye[np.where(np.array(classes) == intent)[0]].reshape(-1) + curr[np.where(np.array(classes) == intent)[0]] = 1 y.append(curr) y = np.asarray(y) return y From 5a05d7c1989197c90889d4ab12be7d7be37fe5bc Mon Sep 17 00:00:00 2001 From: Olga Date: Wed, 27 Jun 2018 19:22:52 +0300 Subject: [PATCH 539/616] feat: perform on batch asynchronously --- .../models/api_requester/api_requester.py | 30 +++++++++++++++++-- 1 file changed, 27 insertions(+), 3 deletions(-) diff --git a/deeppavlov/models/api_requester/api_requester.py b/deeppavlov/models/api_requester/api_requester.py index 88bff9d9e2..1120728fc4 100644 --- a/deeppavlov/models/api_requester/api_requester.py +++ b/deeppavlov/models/api_requester/api_requester.py @@ -1,4 +1,5 @@ import requests +import asyncio from deeppavlov.core.common.registry import register from deeppavlov.core.models.component import Component @@ -6,7 +7,8 @@ @register('api_requester') class ApiRequester(Component): - def __init__(self, url: str, out: [int, list], param_names=(), debatchify=False, *args, **kwargs): + def __init__(self, url: str, out: [int, list], param_names=(), debatchify=False, *args, + **kwargs): self.url = url self.param_names = param_names self.out_count = out if isinstance(out, int) else len(out) @@ -20,8 +22,15 @@ def __call__(self, *args, **kwargs): for v in data.values(): batch_size = len(v) break - response = [requests.post(self.url, json={k: v[i] for k, v in data.items()}).json() - for i in range(batch_size)] + + assert batch_size > 0 + + async def collect(): + return [j async for j in self.get_async_response(data, batch_size)] + + loop = asyncio.get_event_loop() + response = loop.run_until_complete(collect()) + else: response = requests.post(self.url, json=data).json() @@ -29,3 +38,18 @@ def __call__(self, *args, **kwargs): response = list(zip(*response)) return response + + async def get_async_response(self, data, batch_size): + loop = asyncio.get_event_loop() + futures = [ + loop.run_in_executor( + None, + requests.post, + self.url, + None, + {k: v[i] for k, v in data.items()} + ) + for i in range(batch_size) + ] + for r in await asyncio.gather(*futures): + yield r.json() From 0fb7c90937a0014b887b72d677ee143733291d43 Mon Sep 17 00:00:00 2001 From: Olga Date: Wed, 27 Jun 2018 19:25:09 +0300 Subject: [PATCH 540/616] feat: parallel call for api requesters --- deeppavlov/__init__.py | 1 + deeppavlov/models/api_requester/api_router.py | 28 +++++++++++++++++++ 2 files changed, 29 insertions(+) create mode 100644 deeppavlov/models/api_requester/api_router.py diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index 72c6667a91..1de1ae6410 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -73,6 +73,7 @@ import deeppavlov.models.morpho_tagger.tagger import deeppavlov.models.morpho_tagger.common import deeppavlov.models.api_requester +import deeppavlov.models.api_requester.api_router import deeppavlov.skills.odqa.tfidf_ranker import deeppavlov.vocabs.typos diff --git a/deeppavlov/models/api_requester/api_router.py b/deeppavlov/models/api_requester/api_router.py new file mode 100644 index 0000000000..15da3acf5e --- /dev/null +++ b/deeppavlov/models/api_requester/api_router.py @@ -0,0 +1,28 @@ +from concurrent.futures import ProcessPoolExecutor +import concurrent + +from deeppavlov.core.common.registry import register +from deeppavlov.core.common.log import get_logger +from deeppavlov.core.models.component import Component + +logger = get_logger(__name__) + + +@register("api_router") +class ApiRouter(Component): + + def __init__(self, api_requesters, n_workers=1, *args, **kwargs): + self.api_requesters = api_requesters + self.n_workers = n_workers + + def __call__(self, *args, **kwargs): + with ProcessPoolExecutor(self.n_workers) as executor: + futures = [executor.submit(api_requester, *args) for api_requester + in + self.api_requesters] + + results = [] + for future in concurrent.futures.as_completed(futures): + results.append(future.result()) + + return resultsgit \ No newline at end of file From d7a10aed6b28af0c4b6651b958291fbabf1d6927 Mon Sep 17 00:00:00 2001 From: Olga Date: Wed, 27 Jun 2018 19:31:22 +0300 Subject: [PATCH 541/616] fix: typo --- deeppavlov/models/api_requester/api_router.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/models/api_requester/api_router.py b/deeppavlov/models/api_requester/api_router.py index 15da3acf5e..344b077008 100644 --- a/deeppavlov/models/api_requester/api_router.py +++ b/deeppavlov/models/api_requester/api_router.py @@ -25,4 +25,4 @@ def __call__(self, *args, **kwargs): for future in concurrent.futures.as_completed(futures): results.append(future.result()) - return resultsgit \ No newline at end of file + return results \ No newline at end of file From 3301ef12a2c9e0df3735ccb3cc9a3ce288b6bc26 Mon Sep 17 00:00:00 2001 From: Olga Date: Thu, 28 Jun 2018 11:53:27 +0300 Subject: [PATCH 542/616] fix: append multiple outputs correct --- deeppavlov/models/api_requester/api_router.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/api_requester/api_router.py b/deeppavlov/models/api_requester/api_router.py index 344b077008..2d8d236c34 100644 --- a/deeppavlov/models/api_requester/api_router.py +++ b/deeppavlov/models/api_requester/api_router.py @@ -23,6 +23,10 @@ def __call__(self, *args, **kwargs): results = [] for future in concurrent.futures.as_completed(futures): - results.append(future.result()) + result = future.result() + if len(result) > 1: + results += result + else: + results.append(result) - return results \ No newline at end of file + return results From 15bda320bc57179f7a012db2fd3a30daad259c1f Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 28 Jun 2018 12:01:10 +0300 Subject: [PATCH 543/616] chore: rename classifiers/intents/intent_model.py/KerasIntentModel to classifiers/keras_classification_model.py/KerasClassificationModel --- deeppavlov/__init__.py | 2 +- deeppavlov/configs/intents/intents_dstc2.json | 2 +- .../configs/intents/intents_dstc2_big.json | 2 +- .../configs/intents/intents_sample_csv.json | 2 +- .../configs/intents/intents_sample_json.json | 2 +- deeppavlov/configs/intents/intents_snips.json | 2 +- deeppavlov/configs/sentiment/insults_kaggle.json | 2 +- .../configs/sentiment/sentiment_ag_news.json | 2 +- .../configs/sentiment/sentiment_twitter.json | 2 +- .../models/classifiers/{intents => }/README.md | 16 ++++++++-------- .../models/classifiers/intents/__init__.py | 0 ...nt_model.py => keras_classification_model.py} | 8 ++++---- .../models/classifiers/{intents => }/utils.py | 1 - requirements.txt | 4 ++-- 14 files changed, 23 insertions(+), 24 deletions(-) rename deeppavlov/models/classifiers/{intents => }/README.md (93%) delete mode 100644 deeppavlov/models/classifiers/intents/__init__.py rename deeppavlov/models/classifiers/{intents/intent_model.py => keras_classification_model.py} (99%) rename deeppavlov/models/classifiers/{intents => }/utils.py (99%) diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index 72c6667a91..00e19bd8e0 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -48,7 +48,7 @@ import deeppavlov.models.seq2seq_go_bot.bot import deeppavlov.models.seq2seq_go_bot.network import deeppavlov.models.seq2seq_go_bot.kb -import deeppavlov.models.classifiers.intents.intent_model +import deeppavlov.models.classifiers.keras_classification_model import deeppavlov.models.commutators.random_commutator import deeppavlov.models.embedders.fasttext_embedder import deeppavlov.models.embedders.dict_embedder diff --git a/deeppavlov/configs/intents/intents_dstc2.json b/deeppavlov/configs/intents/intents_dstc2.json index 519d0bb8b2..cadacf2e72 100644 --- a/deeppavlov/configs/intents/intents_dstc2.json +++ b/deeppavlov/configs/intents/intents_dstc2.json @@ -63,7 +63,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "intents/intent_cnn_v4", "load_path": "intents/intent_cnn_v4", "classes": "#classes_vocab.keys()", diff --git a/deeppavlov/configs/intents/intents_dstc2_big.json b/deeppavlov/configs/intents/intents_dstc2_big.json index 3fcc7488eb..10ddffc4a4 100644 --- a/deeppavlov/configs/intents/intents_dstc2_big.json +++ b/deeppavlov/configs/intents/intents_dstc2_big.json @@ -63,7 +63,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "intents/intent_cnn_v5", "load_path": "intents/intent_cnn_v5", "classes": "#classes_vocab.keys()", diff --git a/deeppavlov/configs/intents/intents_sample_csv.json b/deeppavlov/configs/intents/intents_sample_csv.json index defdc73d9e..6864a4503d 100644 --- a/deeppavlov/configs/intents/intents_sample_csv.json +++ b/deeppavlov/configs/intents/intents_sample_csv.json @@ -67,7 +67,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "intents/intent_cnn_snips_v4", "load_path": "intents/intent_cnn_snips_v4", "classes": "#classes_vocab.keys()", diff --git a/deeppavlov/configs/intents/intents_sample_json.json b/deeppavlov/configs/intents/intents_sample_json.json index 5c3e732a2c..7ef6f583f5 100644 --- a/deeppavlov/configs/intents/intents_sample_json.json +++ b/deeppavlov/configs/intents/intents_sample_json.json @@ -62,7 +62,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "intents/intent_cnn_snips_v4", "load_path": "intents/intent_cnn_snips_v4", "classes": "#classes_vocab.keys()", diff --git a/deeppavlov/configs/intents/intents_snips.json b/deeppavlov/configs/intents/intents_snips.json index 573b5aca17..8197f0301e 100644 --- a/deeppavlov/configs/intents/intents_snips.json +++ b/deeppavlov/configs/intents/intents_snips.json @@ -60,7 +60,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "intents/intent_cnn_snips_v4", "load_path": "intents/intent_cnn_snips_v4", "classes": "#classes_vocab.keys()", diff --git a/deeppavlov/configs/sentiment/insults_kaggle.json b/deeppavlov/configs/sentiment/insults_kaggle.json index 82eaf6bc36..e01fced69a 100644 --- a/deeppavlov/configs/sentiment/insults_kaggle.json +++ b/deeppavlov/configs/sentiment/insults_kaggle.json @@ -60,7 +60,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "sentiment/insults_kaggle_v0", "load_path": "sentiment/insults_kaggle_v0", "classes": "#classes_vocab.keys()", diff --git a/deeppavlov/configs/sentiment/sentiment_ag_news.json b/deeppavlov/configs/sentiment/sentiment_ag_news.json index 897111dba7..fb6deba5a6 100644 --- a/deeppavlov/configs/sentiment/sentiment_ag_news.json +++ b/deeppavlov/configs/sentiment/sentiment_ag_news.json @@ -59,7 +59,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "sentiment/sentiment_ag_news_v0", "load_path": "sentiment/sentiment_ag_news_v0", "classes": "#classes_vocab.keys()", diff --git a/deeppavlov/configs/sentiment/sentiment_twitter.json b/deeppavlov/configs/sentiment/sentiment_twitter.json index df36bf3b38..ecc37f2526 100644 --- a/deeppavlov/configs/sentiment/sentiment_twitter.json +++ b/deeppavlov/configs/sentiment/sentiment_twitter.json @@ -60,7 +60,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "sentiment/sentiment_twitter_v1", "load_path": "sentiment/sentiment_twitter_v1", "classes": "#classes_vocab.keys()", diff --git a/deeppavlov/models/classifiers/intents/README.md b/deeppavlov/models/classifiers/README.md similarity index 93% rename from deeppavlov/models/classifiers/intents/README.md rename to deeppavlov/models/classifiers/README.md index 470966cc61..b48056ba9c 100644 --- a/deeppavlov/models/classifiers/intents/README.md +++ b/deeppavlov/models/classifiers/README.md @@ -10,7 +10,7 @@ The model can be used for binary, multi-class or multi-label classification. We also provide with **pre-trained models** for classification on DSTC 2 dataset, SNIPS dataset, "AG News" dataset, "Detecting Insults in Social Commentary", Twitter sentiment in Russian dataset. **DSTC 2 dataset** (http://camdial.org/~mh521/dstc/) does not initially contain information about **intents**, -therefore, `IntentDataset` (`deeppavlov/datasets/intent_dataset.py`) instance extracts +therefore, `Dstc2IntentsDatasetIterator` (`deeppavlov/dataset_iterators/dstc2_intents_interator.py`) instance extracts artificial intents for each user reply using information from acts and slots. Below we give several examples of intent construction: @@ -47,7 +47,7 @@ This message contains two intents `(thankyou, bye)`. Train, valid and test divis * SearchScreeningEvent * SearchCreativeWork -Initially, intent model on SNIPS dataset was trained only as an example of usage that is why we provide pre-trained model for SNIPS with embeddings trained on DSTC-2 dataset that is not the best choice for this task. Train set is divided to train and validation sets to illustrate `basic_classification_iterator` work. +Initially, classification model on SNIPS dataset was trained only as an example of usage that is why we provide pre-trained model for SNIPS with embeddings trained on DSTC-2 dataset that is not the best choice for this task. Train set is divided to train and validation sets to illustrate `basic_classification_iterator` work. **AG News** dataset (https://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html) contains **sentiment classification** task for 5 classes (range from 0 to 4 points scale). Test set is initial one from web-site, valid is a Stratified division 1/5 from the train set from web-site with 42 seed, and the train set is the rest. @@ -144,10 +144,10 @@ Some clue parameters for [intents_dstc2.json](../../../configs/intents/intents_d | Parameter | Description | |---------------------|-------------------------------------------------------------------| | **dataset_reader** | **an object that reads datasets from files** | -| name | registered name of the dataset reader
*SetOfValues*: "dstc2_datasetreader", "classification_datasetreader" | +| name | registered name of the dataset reader
*SetOfValues*: "dstc2_reader", "basic_classification_reader" | | data_path | directory where data files are stored | | **dataset_iterator** | **an object that provides models with data in the standard form (each example is a tuple (x, y) where x and y could be numbers, booleans, lists or strings)** | -| name | registered name of the dataset
*SetOfValues*: "intent_dataset", classification_dataset" | +| name | registered name of the dataset
*SetOfValues*: "dstc2_intents_iterator", basic_classification_iterator" | | seed | seed for the batch generator | | fields_to_merge | list of fields to merge
*SetOfValues*: list of fields, i.e ["train", "valid", "test"]| | merged_field | name of the field where the merged fields should be saved
*SetOfValues*: field, i.e "train", "valid", "test" | @@ -185,7 +185,7 @@ Some clue parameters for [intents_dstc2.json](../../../configs/intents/intents_d | load_path | path to file from which model files will be loaded | | save_path | path to file where model files will be saved | | classes | list of class names. In this case they could be simply obtained from vocab `classes_vocab.keys()` method. To make reference one has to set value to "#classes_vocab.keys()" | -| model_name | method of the class KerasIntentModel that corresponds to the model
*SetOfValues*: `cnn_model`, `dcnn_model`, `cnn_model_max_and_aver_pool`, `bilstm_model`, `bilstm_bilstm_model`, `bilstm_cnn_model`, `cnn_bilstm_model`, `bilstm_self_add_attention_model`, `bilstm_self_mult_attention_model`, `bigru_model` | +| model_name | method of the class KerasClassificationModel that corresponds to the model
*SetOfValues*: `cnn_model`, `dcnn_model`, `cnn_model_max_and_aver_pool`, `bilstm_model`, `bilstm_bilstm_model`, `bilstm_cnn_model`, `cnn_bilstm_model`, `bilstm_self_add_attention_model`, `bilstm_self_mult_attention_model`, `bigru_model` | | text_size | length of each sample in words | | confident_threshold | probability threshold for an instance belonging to a class
*SetOfValues*: \[0., 1.\] | | lear_rate | learning rate for training | @@ -220,9 +220,9 @@ python deep.py train configs/intents/intents_dstc2.json ### Train on other datasets -Constructing intents from DSTC 2 makes `IntentDataset` difficult to use. -Therefore, we also provide another dataset reader `ClassificationDatasetReader` and dataset `ClassificationDataset` -to work with `.csv` files. These classes are described in `deeppavlov/dataset_readers` and `deeppavlov/datasets`. +Constructing intents from DSTC 2 makes `Dstc2IntentsDatasetIterator` difficult to use. +Therefore, we also provide another dataset reader `BasicClassificationDatasetReader` and dataset `BasicClassificationDatasetIterator` +to work with `.csv` files. These classes are described in `deeppavlov/dataset_readers/basic_classification_reader.py` and `deeppavlov/dataset_iterators/basic_classification_dataset_iterator.py`. Training data file `train.csv` (and `valid.csv`, if exists) should be in the following format: diff --git a/deeppavlov/models/classifiers/intents/__init__.py b/deeppavlov/models/classifiers/intents/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/deeppavlov/models/classifiers/intents/intent_model.py b/deeppavlov/models/classifiers/keras_classification_model.py similarity index 99% rename from deeppavlov/models/classifiers/intents/intent_model.py rename to deeppavlov/models/classifiers/keras_classification_model.py index 36f821110d..2f58d2dd9f 100644 --- a/deeppavlov/models/classifiers/intents/intent_model.py +++ b/deeppavlov/models/classifiers/keras_classification_model.py @@ -29,9 +29,9 @@ from deeppavlov.core.common.errors import ConfigError from deeppavlov.core.common.registry import register from deeppavlov.core.models.keras_model import KerasModel -from deeppavlov.models.classifiers.intents.utils import labels2onehot, proba2labels +from deeppavlov.models.classifiers.utils import labels2onehot, proba2labels +from deeppavlov.models.classifiers.utils import md5_hashsum from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder -from deeppavlov.models.classifiers.intents.utils import md5_hashsum from deeppavlov.models.tokenizers.nltk_tokenizer import NLTKTokenizer from deeppavlov.core.common.log import get_logger from deeppavlov.core.layers.keras_layers import additive_self_attention, multiplicative_self_attention @@ -40,8 +40,8 @@ log = get_logger(__name__) -@register('intent_model') -class KerasIntentModel(KerasModel): +@register('keras_classification_model') +class KerasClassificationModel(KerasModel): """ Class implements keras model for intent recognition task for multi-class multi-label data """ diff --git a/deeppavlov/models/classifiers/intents/utils.py b/deeppavlov/models/classifiers/utils.py similarity index 99% rename from deeppavlov/models/classifiers/intents/utils.py rename to deeppavlov/models/classifiers/utils.py index 7642efbd93..36c9f4fccd 100644 --- a/deeppavlov/models/classifiers/intents/utils.py +++ b/deeppavlov/models/classifiers/utils.py @@ -35,7 +35,6 @@ def labels2onehot(labels, classes): 2d array with one-hot representation of given samples """ n_classes = len(classes) - eye = np.eye(n_classes) y = [] for sample in labels: curr = np.zeros(n_classes) diff --git a/requirements.txt b/requirements.txt index 7b9d79f001..04107d345c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,7 +3,7 @@ numpy==1.14.3 lxml==4.1.1 tqdm==4.19.5 requests==2.18.4 -tensorflow==1.8.0 +tensorflow-gpu==1.8.0 overrides==1.9 kenlm==0.0.0 h5py==2.7.1 @@ -23,4 +23,4 @@ flask_cors==3.0.3 scipy==1.0.0 pymorphy2==0.8 pymorphy2-dicts-ru -sortedcontainers==2.0.2 \ No newline at end of file +sortedcontainers==2.0.2 From 6749a1874d4c3d6f01a95144d8e7ea3c78008794 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 28 Jun 2018 12:07:21 +0300 Subject: [PATCH 544/616] fix: classification metrics --- deeppavlov/metrics/fmeasure_classification.py | 2 +- deeppavlov/metrics/roc_auc_score.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/metrics/fmeasure_classification.py b/deeppavlov/metrics/fmeasure_classification.py index 83ecc60c6a..497dd7eb65 100644 --- a/deeppavlov/metrics/fmeasure_classification.py +++ b/deeppavlov/metrics/fmeasure_classification.py @@ -19,7 +19,7 @@ from sklearn.metrics import f1_score from deeppavlov.core.common.metrics_registry import register_metric -from deeppavlov.models.classifiers.intents.utils import labels2onehot +from deeppavlov.models.classifiers.utils import labels2onehot @register_metric('classification_f1') diff --git a/deeppavlov/metrics/roc_auc_score.py b/deeppavlov/metrics/roc_auc_score.py index 568a8d680c..fb44eb7e6c 100644 --- a/deeppavlov/metrics/roc_auc_score.py +++ b/deeppavlov/metrics/roc_auc_score.py @@ -18,7 +18,7 @@ import numpy as np from deeppavlov.core.common.metrics_registry import register_metric -from deeppavlov.models.classifiers.intents.utils import labels2onehot +from deeppavlov.models.classifiers.utils import labels2onehot def roc_auc_score_np(y_true, y_pred): From 62e8c3450c19a4d388571b67a207153f70e93cf8 Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Thu, 28 Jun 2018 12:15:30 +0300 Subject: [PATCH 545/616] chore: renamed pre-trained models --- deeppavlov/configs/intents/intents_dstc2.json | 4 ++-- deeppavlov/configs/intents/intents_dstc2_big.json | 4 ++-- deeppavlov/configs/intents/intents_sample_csv.json | 4 ++-- deeppavlov/configs/intents/intents_sample_json.json | 4 ++-- deeppavlov/configs/intents/intents_snips.json | 4 ++-- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/deeppavlov/configs/intents/intents_dstc2.json b/deeppavlov/configs/intents/intents_dstc2.json index cadacf2e72..60cd41f6f0 100644 --- a/deeppavlov/configs/intents/intents_dstc2.json +++ b/deeppavlov/configs/intents/intents_dstc2.json @@ -64,8 +64,8 @@ ], "main": true, "name": "keras_classification_model", - "save_path": "intents/intent_cnn_v4", - "load_path": "intents/intent_cnn_v4", + "save_path": "intents/intents_dstc2_v4", + "load_path": "intents/intents_dstc2_v4", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, diff --git a/deeppavlov/configs/intents/intents_dstc2_big.json b/deeppavlov/configs/intents/intents_dstc2_big.json index 10ddffc4a4..49ed02ca42 100644 --- a/deeppavlov/configs/intents/intents_dstc2_big.json +++ b/deeppavlov/configs/intents/intents_dstc2_big.json @@ -64,8 +64,8 @@ ], "main": true, "name": "keras_classification_model", - "save_path": "intents/intent_cnn_v5", - "load_path": "intents/intent_cnn_v5", + "save_path": "intents/intents_dstc2_v5", + "load_path": "intents/intents_dstc2_v5", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, diff --git a/deeppavlov/configs/intents/intents_sample_csv.json b/deeppavlov/configs/intents/intents_sample_csv.json index 6864a4503d..9667549ad8 100644 --- a/deeppavlov/configs/intents/intents_sample_csv.json +++ b/deeppavlov/configs/intents/intents_sample_csv.json @@ -68,8 +68,8 @@ ], "main": true, "name": "keras_classification_model", - "save_path": "intents/intent_cnn_snips_v4", - "load_path": "intents/intent_cnn_snips_v4", + "save_path": "intents/intents_snips_v4", + "load_path": "intents/intents_snips_v4", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, diff --git a/deeppavlov/configs/intents/intents_sample_json.json b/deeppavlov/configs/intents/intents_sample_json.json index 7ef6f583f5..558adb7e71 100644 --- a/deeppavlov/configs/intents/intents_sample_json.json +++ b/deeppavlov/configs/intents/intents_sample_json.json @@ -63,8 +63,8 @@ ], "main": true, "name": "keras_classification_model", - "save_path": "intents/intent_cnn_snips_v4", - "load_path": "intents/intent_cnn_snips_v4", + "save_path": "intents/intents_snips_v4", + "load_path": "intents/intents_snips_v4", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, diff --git a/deeppavlov/configs/intents/intents_snips.json b/deeppavlov/configs/intents/intents_snips.json index 8197f0301e..c3885d63ec 100644 --- a/deeppavlov/configs/intents/intents_snips.json +++ b/deeppavlov/configs/intents/intents_snips.json @@ -61,8 +61,8 @@ ], "main": true, "name": "keras_classification_model", - "save_path": "intents/intent_cnn_snips_v4", - "load_path": "intents/intent_cnn_snips_v4", + "save_path": "intents/intents_snips_v4", + "load_path": "intents/intents_snips_v4", "classes": "#classes_vocab.keys()", "kernel_sizes_cnn": [ 1, From 0e47213636e6d229df270715e36c8f036c042baf Mon Sep 17 00:00:00 2001 From: Olga Date: Thu, 28 Jun 2018 13:05:52 +0300 Subject: [PATCH 546/616] fix: preserve order --- deeppavlov/models/api_requester/api_router.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/models/api_requester/api_router.py b/deeppavlov/models/api_requester/api_router.py index 2d8d236c34..14625e573e 100644 --- a/deeppavlov/models/api_requester/api_router.py +++ b/deeppavlov/models/api_requester/api_router.py @@ -21,8 +21,9 @@ def __call__(self, *args, **kwargs): in self.api_requesters] + concurrent.futures.wait(futures) results = [] - for future in concurrent.futures.as_completed(futures): + for future in futures: result = future.result() if len(result) > 1: results += result From 594d40770fb0a926aa5d443a493457d55b604b66 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Thu, 28 Jun 2018 13:14:23 +0300 Subject: [PATCH 547/616] fix: correct outputs composition for ApiRouter --- deeppavlov/models/api_requester/api_router.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeppavlov/models/api_requester/api_router.py b/deeppavlov/models/api_requester/api_router.py index 14625e573e..61cb4678c5 100644 --- a/deeppavlov/models/api_requester/api_router.py +++ b/deeppavlov/models/api_requester/api_router.py @@ -23,9 +23,9 @@ def __call__(self, *args, **kwargs): concurrent.futures.wait(futures) results = [] - for future in futures: + for future, api_requester in zip(futures, self.api_requesters): result = future.result() - if len(result) > 1: + if api_requester.out_count > 1: results += result else: results.append(result) From 80f4ed5b31632c94dc50b1683208bc55157dcea7 Mon Sep 17 00:00:00 2001 From: yurakuratov Date: Fri, 29 Jun 2018 15:24:00 +0300 Subject: [PATCH 548/616] feat: add tensorboard logging --- deeppavlov/core/commands/train.py | 37 ++++++++++++++++++++++++++++++- 1 file changed, 36 insertions(+), 1 deletion(-) diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index 6e3c0a0561..0e73b15a6b 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -231,7 +231,8 @@ def _train_batches(model: NNModel, iterator: DataLearningIterator, train_config: # 'show_examples': False, 'validate_best': True, - 'test_best': True + 'test_best': True, + 'tensorboard_log_dir': None, } train_config = dict(default_train_config, **train_config) @@ -258,6 +259,14 @@ def improved(score, best): losses = [] start_time = time.time() break_flag = False + + if train_config['tensorboard_log_dir'] is not None: + import tensorflow as tf + tb_log_dir = expand_path(train_config['tensorboard_log_dir']) + + tb_train_writer = tf.summary.FileWriter(str(tb_log_dir / 'train_log')) + tb_valid_writer = tf.summary.FileWriter(str(tb_log_dir / 'valid_log')) + try: while True: for x, y_true in iterator.gen_batches(train_config['batch_size']): @@ -280,10 +289,23 @@ def improved(score, best): 'metrics': prettify_metrics(metrics), 'time_spent': str(datetime.timedelta(seconds=round(time.time() - start_time + 0.5))) } + if losses: report['loss'] = sum(losses)/len(losses) losses = [] report = {'train': report} + + if train_config['tensorboard_log_dir'] is not None: + for name, score in metrics: + metric_sum = tf.Summary(value=[tf.Summary.Value(tag='every_n_batches/' + name, + simple_value=score), ]) + tb_train_writer.add_summary(metric_sum, i) + + if losses: + loss_sum = tf.Summary(value=[tf.Summary.Value(tag='every_n_batches/' + 'loss', + simple_value=report['loss']), ]) + tb_train_writer.add_summary(loss_sum, i) + print(json.dumps(report, ensure_ascii=False)) train_y_true.clear() train_y_predicted.clear() @@ -322,6 +344,13 @@ def improved(score, best): 'metrics': prettify_metrics(metrics), 'time_spent': str(datetime.timedelta(seconds=round(time.time() - start_time + 0.5))) } + + if train_config['tensorboard_log_dir'] is not None: + for name, score in metrics: + metric_sum = tf.Summary(value=[tf.Summary.Value(tag='every_n_epochs/' + name, + simple_value=score), ]) + tb_train_writer.add_summary(metric_sum, epochs) + model.process_event(event_name='after_train_log', data=report) report = {'train': report} print(json.dumps(report, ensure_ascii=False)) @@ -337,6 +366,12 @@ def improved(score, best): metrics = list(report['metrics'].items()) + if train_config['tensorboard_log_dir'] is not None: + for name, score in metrics: + metric_sum = tf.Summary(value=[tf.Summary.Value(tag='every_n_epochs/' + name, + simple_value=score), ]) + tb_valid_writer.add_summary(metric_sum, epochs) + m_name, score = metrics[0] if improved(score, best): patience = 0 From ad4e201d348a55e5135deed9fd03bf84546084ee Mon Sep 17 00:00:00 2001 From: yurakuratov Date: Fri, 29 Jun 2018 15:24:34 +0300 Subject: [PATCH 549/616] fear: upd ner_rus config with tensorboard logging --- deeppavlov/configs/ner/ner_rus.json | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/configs/ner/ner_rus.json b/deeppavlov/configs/ner/ner_rus.json index 4afc3d7c2f..c5596de33c 100644 --- a/deeppavlov/configs/ner/ner_rus.json +++ b/deeppavlov/configs/ner/ner_rus.json @@ -138,7 +138,8 @@ "val_every_n_epochs": 1, "log_every_n_epochs": 1, - "show_examples": false + "show_examples": false, + "tensorboard_log_dir": "ner_rus/logs" }, "metadata": { "labels": { From 89ced074ab39ff8494da47176f4b38d4ef380b7e Mon Sep 17 00:00:00 2001 From: dilyararimovna Date: Mon, 2 Jul 2018 11:10:40 +0300 Subject: [PATCH 550/616] fix: change names in test configs --- tests/test_configs/intents/intents_snips_bigru.json | 2 +- tests/test_configs/intents/intents_snips_bilstm.json | 2 +- tests/test_configs/intents/intents_snips_bilstm_bilstm.json | 2 +- tests/test_configs/intents/intents_snips_bilstm_cnn.json | 2 +- .../intents/intents_snips_bilstm_self_add_attention.json | 2 +- .../intents/intents_snips_bilstm_self_mult_attention.json | 2 +- tests/test_configs/intents/intents_snips_cnn_bilstm.json | 2 +- 7 files changed, 7 insertions(+), 7 deletions(-) diff --git a/tests/test_configs/intents/intents_snips_bigru.json b/tests/test_configs/intents/intents_snips_bigru.json index 9f05093c61..328dad7e1b 100644 --- a/tests/test_configs/intents/intents_snips_bigru.json +++ b/tests/test_configs/intents/intents_snips_bigru.json @@ -61,7 +61,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "intents/intent_cnn_snips_bigru", "load_path": "intents/intent_cnn_snips_bigru", "classes": "#classes_vocab.keys()", diff --git a/tests/test_configs/intents/intents_snips_bilstm.json b/tests/test_configs/intents/intents_snips_bilstm.json index 5496685fde..2c4e1490c8 100644 --- a/tests/test_configs/intents/intents_snips_bilstm.json +++ b/tests/test_configs/intents/intents_snips_bilstm.json @@ -61,7 +61,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "intents/intent_cnn_snips_bistlm", "load_path": "intents/intent_cnn_snips_bilstm", "classes": "#classes_vocab.keys()", diff --git a/tests/test_configs/intents/intents_snips_bilstm_bilstm.json b/tests/test_configs/intents/intents_snips_bilstm_bilstm.json index e40bbb0775..1d8e6feaf2 100644 --- a/tests/test_configs/intents/intents_snips_bilstm_bilstm.json +++ b/tests/test_configs/intents/intents_snips_bilstm_bilstm.json @@ -61,7 +61,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "intents/intent_cnn_snips_bistlm_bilstm", "load_path": "intents/intent_cnn_snips_bilstm_bilstm", "classes": "#classes_vocab.keys()", diff --git a/tests/test_configs/intents/intents_snips_bilstm_cnn.json b/tests/test_configs/intents/intents_snips_bilstm_cnn.json index 82c13c89fe..49234f2d5f 100644 --- a/tests/test_configs/intents/intents_snips_bilstm_cnn.json +++ b/tests/test_configs/intents/intents_snips_bilstm_cnn.json @@ -61,7 +61,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "intents/intent_cnn_snips_bistlm_cnn", "load_path": "intents/intent_cnn_snips_bilstm_cnn", "classes": "#classes_vocab.keys()", diff --git a/tests/test_configs/intents/intents_snips_bilstm_self_add_attention.json b/tests/test_configs/intents/intents_snips_bilstm_self_add_attention.json index 6e0b5660d1..9491ee6bce 100644 --- a/tests/test_configs/intents/intents_snips_bilstm_self_add_attention.json +++ b/tests/test_configs/intents/intents_snips_bilstm_self_add_attention.json @@ -61,7 +61,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "intents/intent_cnn_snips_bilstm_self_add_attention", "load_path": "intents/intent_cnn_snips_bilstm_self_add_attention", "classes": "#classes_vocab.keys()", diff --git a/tests/test_configs/intents/intents_snips_bilstm_self_mult_attention.json b/tests/test_configs/intents/intents_snips_bilstm_self_mult_attention.json index e707677f12..174d98f09d 100644 --- a/tests/test_configs/intents/intents_snips_bilstm_self_mult_attention.json +++ b/tests/test_configs/intents/intents_snips_bilstm_self_mult_attention.json @@ -61,7 +61,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "intents/intent_cnn_snips_bilstm_self_mult_attention", "load_path": "intents/intent_cnn_snips_bilstm_self_mult_attention", "classes": "#classes_vocab.keys()", diff --git a/tests/test_configs/intents/intents_snips_cnn_bilstm.json b/tests/test_configs/intents/intents_snips_cnn_bilstm.json index affdef2f07..c428c83f60 100644 --- a/tests/test_configs/intents/intents_snips_cnn_bilstm.json +++ b/tests/test_configs/intents/intents_snips_cnn_bilstm.json @@ -61,7 +61,7 @@ "y_probas_dict" ], "main": true, - "name": "intent_model", + "name": "keras_classification_model", "save_path": "intents/intent_cnn_snips_cnn_bistlm", "load_path": "intents/intent_cnn_snips_cnn_bilstm", "classes": "#classes_vocab.keys()", From 0df938fa94b8dcafd95a49690ab3a11205308616 Mon Sep 17 00:00:00 2001 From: Marat Zaynutdinov Date: Mon, 2 Jul 2018 14:00:32 +0300 Subject: [PATCH 551/616] Sentence tokenizer for Russian Language --- .../models/tokenizers/ru_sent_tokenizer.py | 200 ++++++++++++++++++ 1 file changed, 200 insertions(+) create mode 100644 deeppavlov/models/tokenizers/ru_sent_tokenizer.py diff --git a/deeppavlov/models/tokenizers/ru_sent_tokenizer.py b/deeppavlov/models/tokenizers/ru_sent_tokenizer.py new file mode 100644 index 0000000000..11473cbc7f --- /dev/null +++ b/deeppavlov/models/tokenizers/ru_sent_tokenizer.py @@ -0,0 +1,200 @@ +import re +import logging +from typing import Set, Tuple, List + +from deeppavlov.core.models.component import Component +from deeppavlov.core.common.registry import register + + +_SENT_RE = re.compile(r'[^\.?!…]+[\.?!…]*["»“ ]*') + +_LAST_WORD = re.compile(r'(?:\b|\d)([a-zа-я]+)\.$', re.IGNORECASE) +_FIRST_WORD = re.compile(r'^\W*(\w+)') +_ENDS_WITH_ONE_LETTER_LAT_AND_DOT = re.compile(r'(\d|\W|\b)([a-zA-Z])\.$') +_HAS_DOT_INSIDE = re.compile(r'[\w]+\.[\w]+\.$', re.IGNORECASE) +_INITIALS = re.compile(r'(\W|\b)([A-ZА-Я]{1})\.$') +_ONLY_RUS_CONSONANTS = re.compile(r'^[бвгджзйклмнпрстфхцчшщ]{1,4}$', re.IGNORECASE) +_STARTS_WITH_EMPTYNESS = re.compile(r'^\s+') +_ENDS_WITH_EMOTION = re.compile(r'[!?…]|\.{2,}\s?[)"«»,“]?$') +_STARTS_WITH_LOWER = re.compile(r'^\s*[–-—-("«]?\s*[a-zа-я]') +_STARTS_WITH_DIGIT = re.compile(r'^\s*\d') +_NUMERATION = re.compile(r'^\W*[IVXMCL\d]+\.$') +_PAIRED_SHORTENING_IN_THE_END = re.compile(r'\b(\w+)\. (\w+)\.\W*$') + +_JOIN = 0 +_MAYBE = 1 +_SPLIT = 2 + +JOINING_SHORTENINGS = {'mr', 'mrs', 'ms', 'dr', 'vs', 'англ', 'итал', 'греч', 'евр', 'араб', 'яп', 'слав', 'кит', + 'тел', 'св', 'ул', 'устар', 'им', 'г', 'см', 'д', 'стр', 'корп', 'пл', 'пер', 'сокр', 'рис'} +SHORTENINGS = {'co', 'corp', 'inc', 'авт', 'адм', 'барр', 'внутр', 'га', 'дифф', 'дол', 'долл', 'зав', 'зам', 'искл', + 'коп', 'корп', 'куб', 'лат', 'мин', 'о', 'обл', 'обр', 'прим', 'проц', 'р', 'ред', 'руб', 'рус', 'русск', + 'сан', 'сек', 'тыс', 'эт', 'яз', 'гос', 'мн', 'жен', 'муж', 'накл', 'повел', 'букв', 'шутл', 'ед'} + +PAIRED_SHORTENINGS = {('и', 'о'), ('т', 'е'), ('т', 'п'), ('у', 'е'), ('н', 'э')} + + +def _regex_split_separators(text: str) -> [str]: + return [x.strip() for x in _SENT_RE.findall(text)] + + +def _is_sentence_end(left: str, right: str, + shortenings: Set[str], + joining_shortenings: Set[str], + paired_shortenings: Set[Tuple[str, str]]) -> int: + if not _STARTS_WITH_EMPTYNESS.match(right): + return _JOIN + + if _HAS_DOT_INSIDE.search(left): + return _JOIN + + left_last_word = _LAST_WORD.search(left) + lw = ' ' + if left_last_word: + lw = left_last_word.group(1) + + if lw.lower() in joining_shortenings: + return _JOIN + + if _ONLY_RUS_CONSONANTS.search(lw) and lw[-1].islower(): + return _MAYBE + + pse = _PAIRED_SHORTENING_IN_THE_END.search(left) + if pse: + s1, s2 = pse.groups() + if (s1, s2) in paired_shortenings: + return _MAYBE + + right_first_word = _FIRST_WORD.match(right) + if right_first_word: + rw = right_first_word.group(1) + if (lw, rw) in paired_shortenings: + return _MAYBE + + if _ENDS_WITH_EMOTION.search(left) and _STARTS_WITH_LOWER.match(right): + return _JOIN + + initials = _INITIALS.search(left) + if initials: + border, _ = initials.groups() + if (border or ' ') not in "°'": + return _JOIN + + if lw.lower() in shortenings: + return _MAYBE + + last_letter = _ENDS_WITH_ONE_LETTER_LAT_AND_DOT.search(left) + if last_letter: + border, _ = last_letter.groups() + if (border or ' ') not in "°'": + return _MAYBE + if _NUMERATION.match(left): + return _JOIN + return _SPLIT + + +def ru_sent_tokenize(text: str, + shortenings: Set[str] = SHORTENINGS, + joining_shortenings: Set[str] = JOINING_SHORTENINGS, + paired_shortenings: Set[Tuple[str, str]] = PAIRED_SHORTENINGS) -> List[str]: + sentences = [] + sents = _regex_split_separators(text) + si = 0 + processed_index = 0 + sent_start = 0 + while si < len(sents): + s = sents[si] + span_start = text[processed_index:].index(s) + processed_index + span_end = span_start + len(s) + processed_index += len(s) + + si += 1 + + send = _is_sentence_end(text[sent_start: span_end], text[span_end:], + shortenings, joining_shortenings, paired_shortenings) + if send == _JOIN: + continue + + if send == _MAYBE: + if _STARTS_WITH_LOWER.match(text[span_end:]): + continue + if _STARTS_WITH_DIGIT.match(text[span_end:]): + continue + + if not text[sent_start: span_end].strip(): + logging.warning("Something went wrong while tokenizing") + sentences.append(text[sent_start: span_end].strip()) + sent_start = span_end + processed_index = span_end + + if sent_start != len(text): + if text[sent_start:].strip(): + sentences.append(text[sent_start:].strip()) + return sentences + + +@register("ru_sent_tokenizer") +class RuSentTokenizer(Component): + """ + Rule-base sentence tokenizer for Russian language + """ + def __init__(self, shortenings: Set[str] = SHORTENINGS, + joining_shortenings: Set[str] = JOINING_SHORTENINGS, + paired_shortenings: Set[Tuple[str, str]] = PAIRED_SHORTENINGS): + """ + Args: + shortenings: list of known shortenings. Use default value if working on news or fiction texts + joining_shortenings: list of shortenings after that sentence split is not possible (i.e. "ул"). + Use default value if working on news or fiction texts + paired_shortenings: list of known paired shotenings (i.e. "т. е."). + Use default value if working on news or fiction texts + + """ + self.shortenings = shortenings + self.joining_shortenings = joining_shortenings + self.paired_shortenings = paired_shortenings + + def __call__(self, batch: [str]) -> [[str]]: + return [ru_sent_tokenize(x, self.shortenings, self.joining_shortenings, self.paired_shortenings) for x in batch] + + +if __name__ == '__main__': + assert ru_sent_tokenize('купил за 5 руб. и остался доволен.') == ['купил за 5 руб. и остался доволен.'] + assert ru_sent_tokenize('Я ему сказал и т.к. он не послушался за 500р.') == ['Я ему сказал и т.к. он не послушался за 500р.'] + assert ru_sent_tokenize('Ура. Ура. 500р.') == ['Ура.', 'Ура.', '500р.'] + assert ru_sent_tokenize('Среди других её представителей — Л. Р. Зиндер, Л. В. Бондарко, М. И. Матусевич.') == \ + ['Среди других её представителей — Л. Р. Зиндер, Л. В. Бондарко, М. И. Матусевич.'] + assert ru_sent_tokenize('И. П. Павлов.') == ['И. П. Павлов.'] + assert ru_sent_tokenize('Павлов И. П., Сеченов И. М.') == ['Павлов И. П., Сеченов И. М.'] + assert ru_sent_tokenize('Основателем школы является Л. В. Щерба.') == ['Основателем школы является Л. В. Щерба.'] + assert ru_sent_tokenize('Я ему сказале: "Чтобы ничего не трогале." Но он не послушался.') == \ + ['Я ему сказале: "Чтобы ничего не трогале."', 'Но он не послушался.'] + assert ru_sent_tokenize('Нефть за $27/барр. не снится.') == ['Нефть за $27/барр. не снится.'] + assert ru_sent_tokenize('Сказала я. Он оглянулся.') == ['Сказала я.', 'Он оглянулся.'] + assert ru_sent_tokenize( + 'Летописец Нестор относит их возникновение к I столетию н.э., когда св. Андрей, проповедуя в Киеве ' + 'евангельское слово, отправился потом в Новгород, где он увидел чудо – парившихся в бане.') == \ + ['Летописец Нестор относит их возникновение к I столетию н.э., когда св. Андрей, проповедуя в Киеве евангельское слово, отправился потом в Новгород, где он увидел чудо – парившихся в бане.'] + assert ru_sent_tokenize( + '- Ну, хорошо, хочешь, я тебе тоннели покажу? - спрашивает наконец Мариам и сворачивает с одной ничем не примечательной улицы, застроенной обычными городскими многоэтажками, на другую точно такую же.') == ['- Ну, хорошо, хочешь, я тебе тоннели покажу? - спрашивает наконец Мариам и сворачивает с одной ничем не примечательной улицы, застроенной обычными городскими многоэтажками, на другую точно такую же.'] + assert ru_sent_tokenize('Где они были эти …адцать лет?') == ['Где они были эти …адцать лет?'] + assert ru_sent_tokenize('Православие... более всего подходит на роль такой идеи...') == ['Православие... более всего подходит на роль такой идеи...'] + assert ru_sent_tokenize('Yolka стоит 2400р. без трех копеек сто долларов, между прочим.') == ['Yolka стоит 2400р. без трех копеек сто долларов, между прочим.'] + assert ru_sent_tokenize( + 'А если лень читать всё - общее количество ответов: 8272! - то можно почитать книжку избранных мест.') == ['А если лень читать всё - общее количество ответов: 8272! - то можно почитать книжку избранных мест.'] + assert ru_sent_tokenize('Это стоило 50 т. к. вчера') == ['Это стоило 50 т. к. вчера'] + assert ru_sent_tokenize( + "Официально закрытие фастфудов назначено на предстоящее воскресенье, причём менеджеры не планируют снова открывать в этой стране рестораны McDonald's. Причин закрытия исландских McDonald's несколько.") == \ + ["Официально закрытие фастфудов назначено на предстоящее воскресенье, причём менеджеры не планируют снова открывать в этой стране рестораны McDonald's.", + "Причин закрытия исландских McDonald's несколько."] + assert ru_sent_tokenize( + '12 января ожидается понижение до минус 44 — минус 48°C. В школах региона отменены занятия в начальных классах.') == \ + ['12 января ожидается понижение до минус 44 — минус 48°C.', + 'В школах региона отменены занятия в начальных классах.'] + assert ru_sent_tokenize( + 'У государственных людей тоже есть дети, и если для них ночные заведения работать-таки будут… (а вы попробуйте им отказать) ну, в общем, не хотелось бы думать о волне народного возмущения.') == \ + ['У государственных людей тоже есть дети, и если для них ночные заведения работать-таки будут… (а вы попробуйте им отказать) ну, в общем, не хотелось бы думать о волне народного возмущения.'] + assert ru_sent_tokenize( + 'По сравнению с 2009 годом Россия опустилась в рейтинге на 9 позиций (т. е. ситуация в ней улучшилась).') == \ + ['По сравнению с 2009 годом Россия опустилась в рейтинге на 9 позиций (т. е. ситуация в ней улучшилась).'] + logging.info('all tests passed!') \ No newline at end of file From b6b2c09a60071f9fb510200144e1f813ffbde36c Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 3 Jul 2018 12:08:01 +0300 Subject: [PATCH 552/616] feat: add loss to epochs reports --- deeppavlov/core/commands/train.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index 0e73b15a6b..4968ee6f0f 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -344,6 +344,9 @@ def improved(score, best): 'metrics': prettify_metrics(metrics), 'time_spent': str(datetime.timedelta(seconds=round(time.time() - start_time + 0.5))) } + if losses: + report['loss'] = sum(losses)/len(losses) + losses = [] if train_config['tensorboard_log_dir'] is not None: for name, score in metrics: @@ -351,6 +354,11 @@ def improved(score, best): simple_value=score), ]) tb_train_writer.add_summary(metric_sum, epochs) + if losses: + loss_sum = tf.Summary(value=[tf.Summary.Value(tag='every_n_batches/' + 'loss', + simple_value=report['loss']), ]) + tb_train_writer.add_summary(loss_sum, i) + model.process_event(event_name='after_train_log', data=report) report = {'train': report} print(json.dumps(report, ensure_ascii=False)) From 4541b3309cf77611249c134a59bc44ff1b233421 Mon Sep 17 00:00:00 2001 From: Mary Vikhreva Date: Tue, 26 Jun 2018 11:01:33 +0300 Subject: [PATCH 553/616] refactor: move seq2seq * gobot to 'models' --- README.md | 4 ++-- deeppavlov/__init__.py | 12 ++++++------ deeppavlov/{skills => models}/go_bot/README.md | 12 ++++++------ deeppavlov/{skills => models}/go_bot/__init__.py | 0 deeppavlov/{skills => models}/go_bot/bot.py | 6 ++---- deeppavlov/{skills => models}/go_bot/diagram.png | Bin deeppavlov/{skills => models}/go_bot/metrics.py | 0 deeppavlov/{skills => models}/go_bot/network.py | 0 deeppavlov/{skills => models}/go_bot/templates.py | 0 deeppavlov/{skills => models}/go_bot/tracker.py | 0 .../{skills => models}/seq2seq_go_bot/README.md | 0 .../{skills => models}/seq2seq_go_bot/__init__.py | 0 deeppavlov/{skills => models}/seq2seq_go_bot/bot.py | 2 +- deeppavlov/{skills => models}/seq2seq_go_bot/kb.py | 0 .../{skills => models}/seq2seq_go_bot/network.py | 0 15 files changed, 17 insertions(+), 19 deletions(-) rename deeppavlov/{skills => models}/go_bot/README.md (98%) rename deeppavlov/{skills => models}/go_bot/__init__.py (100%) rename deeppavlov/{skills => models}/go_bot/bot.py (98%) rename deeppavlov/{skills => models}/go_bot/diagram.png (100%) rename deeppavlov/{skills => models}/go_bot/metrics.py (100%) rename deeppavlov/{skills => models}/go_bot/network.py (100%) rename deeppavlov/{skills => models}/go_bot/templates.py (100%) rename deeppavlov/{skills => models}/go_bot/tracker.py (100%) rename deeppavlov/{skills => models}/seq2seq_go_bot/README.md (100%) rename deeppavlov/{skills => models}/seq2seq_go_bot/__init__.py (100%) rename deeppavlov/{skills => models}/seq2seq_go_bot/bot.py (98%) rename deeppavlov/{skills => models}/seq2seq_go_bot/kb.py (100%) rename deeppavlov/{skills => models}/seq2seq_go_bot/network.py (100%) diff --git a/README.md b/README.md index e87ac4a729..afc9a50c91 100644 --- a/README.md +++ b/README.md @@ -140,13 +140,13 @@ Available model configs are: | [NER component](deeppavlov/models/ner/README.md) | Based on neural Named Entity Recognition network. The NER component reproduces architecture from the paper [Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition](https://arxiv.org/pdf/1709.09686.pdf) which is inspired by Bi-LSTM+CRF architecture from https://arxiv.org/pdf/1603.01360.pdf. | | [Slot filling components](deeppavlov/models/slotfill/README.md) | Based on fuzzy Levenshtein search to extract normalized slot values from text. The components either rely on NER results or perform needle in haystack search.| | [Classification component](deeppavlov/models/classifiers/intents/README.md) | Component for classification tasks (intents, sentiment, etc). Based on shallow-and-wide Convolutional Neural Network architecture from [Kim Y. Convolutional neural networks for sentence classification – 2014](https://arxiv.org/pdf/1408.5882) and others. The model allows multilabel classification of sentences. | +| [Goal-oriented bot](deeppavlov/models/go_bot/README.md) | Based on Hybrid Code Networks (HCNs) architecture from [Jason D. Williams, Kavosh Asadi, Geoffrey Zweig, Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning – 2017](https://arxiv.org/abs/1702.03274). It allows to predict responses in goal-oriented dialog. The model is customizable: embeddings, slot filler and intent classifier can switched on and off on demand. | +| [Seq2seq goal-oriented bot](deeppavlov/models/seq2seq_go_bot/README.md) | Dialogue agent predicts responses in a goal-oriented dialog and is able to handle multiple domains (pretrained bot allows calendar scheduling, weather information retrieval, and point-of-interest navigation). The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers. | | [Automatic spelling correction component](deeppavlov/models/spelling_correction/README.md) | Pipelines that use candidates search in a static dictionary and an ARPA language model to correct spelling errors. | | [Ranking component](deeppavlov/models/ranking/README.md) | Based on [LSTM-based deep learning models for non-factoid answer selection](https://arxiv.org/abs/1511.04108). The model performs ranking of responses or contexts from some database by their relevance for the given context. | | [Question Answering component](deeppavlov/models/squad/README.md) | Based on [R-NET: Machine Reading Comprehension with Self-matching Networks](https://www.microsoft.com/en-us/research/publication/mrc/). The model solves the task of looking for an answer on a question in a given context ([SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) task format). | | [Morphological tagging component](deeppavlov/models/morpho_tagger/README.md) | Based on character-based approach to morphological tagging [Heigold et al., 2017. An extensive empirical evaluation of character-based morphological tagging for 14 languages](http://www.aclweb.org/anthology/E17-1048). A state-of-the-art model for Russian and several other languages. Model assigns morphological tags in UD format to sequences of words.| | **Skills** | | -| [Goal-oriented bot](deeppavlov/skills/go_bot/README.md) | Based on Hybrid Code Networks (HCNs) architecture from [Jason D. Williams, Kavosh Asadi, Geoffrey Zweig, Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning – 2017](https://arxiv.org/abs/1702.03274). It allows to predict responses in goal-oriented dialog. The model is customizable: embeddings, slot filler and intent classifier can switched on and off on demand. | -| [Seq2seq goal-oriented bot](deeppavlov/skills/seq2seq_go_bot/README.md) | Dialogue agent predicts responses in a goal-oriented dialog and is able to handle multiple domains (pretrained bot allows calendar scheduling, weather information retrieval, and point-of-interest navigation). The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers. | |[ODQA](deeppavlov/skills/odqa/README.md) | An open domain question answering skill. The skill accepts free-form questions about the world and outputs an answer based on its Wikipedia knowledge.| | **Embeddings** | | | [Pre-trained embeddings for the Russian language](pretrained-vectors.md) | Word vectors for the Russian language trained on joint [Russian Wikipedia](https://ru.wikipedia.org/wiki/%D0%97%D0%B0%D0%B3%D0%BB%D0%B0%D0%B2%D0%BD%D0%B0%D1%8F_%D1%81%D1%82%D1%80%D0%B0%D0%BD%D0%B8%D1%86%D0%B0) and [Lenta.ru](https://lenta.ru/) corpora. | diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index f7e3052462..72c6667a91 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -42,6 +42,12 @@ import deeppavlov.dataset_iterators.sqlite_iterator import deeppavlov.dataset_iterators.morphotagger_iterator +import deeppavlov.models.go_bot.bot +import deeppavlov.models.go_bot.network +import deeppavlov.models.go_bot.tracker +import deeppavlov.models.seq2seq_go_bot.bot +import deeppavlov.models.seq2seq_go_bot.network +import deeppavlov.models.seq2seq_go_bot.kb import deeppavlov.models.classifiers.intents.intent_model import deeppavlov.models.commutators.random_commutator import deeppavlov.models.embedders.fasttext_embedder @@ -68,12 +74,6 @@ import deeppavlov.models.morpho_tagger.common import deeppavlov.models.api_requester -import deeppavlov.skills.go_bot.bot -import deeppavlov.skills.go_bot.network -import deeppavlov.skills.go_bot.tracker -import deeppavlov.skills.seq2seq_go_bot.bot -import deeppavlov.skills.seq2seq_go_bot.network -import deeppavlov.skills.seq2seq_go_bot.kb import deeppavlov.skills.odqa.tfidf_ranker import deeppavlov.vocabs.typos import deeppavlov.vocabs.wiki_sqlite diff --git a/deeppavlov/skills/go_bot/README.md b/deeppavlov/models/go_bot/README.md similarity index 98% rename from deeppavlov/skills/go_bot/README.md rename to deeppavlov/models/go_bot/README.md index bf782aa36b..74ba3d7909 100644 --- a/deeppavlov/skills/go_bot/README.md +++ b/deeppavlov/models/go_bot/README.md @@ -13,16 +13,16 @@ Here is a simple example of interaction with a trained dialogue bot (can be down ```x:: >> Hello, welcome to the Cambridge restaurant system. You can ask for restaurants by area, price range or food type. How may I help you? x::hi, i want cheap food -2018-05-16 12:28:48.459 INFO in 'deeppavlov.skills.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'cheap'}, got 22 results. +2018-05-16 12:28:48.459 INFO in 'deeppavlov.models.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'cheap'}, got 22 results. >> The lucky star serves chinese food in the cheap price range. x::is there anything with french food? -2018-05-16 12:29:02.795 INFO in 'deeppavlov.skills.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'cheap', 'food': 'french'}, got 0 results. +2018-05-16 12:29:02.795 INFO in 'deeppavlov.models.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'cheap', 'food': 'french'}, got 0 results. >> Sorry there is no french restaurant in the cheap price range. x::ok, maybe there is something in expensive pricerange? -2018-05-16 12:29:23.337 INFO in 'deeppavlov.skills.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'expensive', 'food': 'french'}, got 2 results. +2018-05-16 12:29:23.337 INFO in 'deeppavlov.models.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'expensive', 'food': 'french'}, got 2 results. >> Cote serves french food in the expensive price range. x::in what part of town is it? -2018-05-16 12:29:31.193 INFO in 'deeppavlov.skills.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'expensive', 'food': 'french'}, got 2 results. +2018-05-16 12:29:31.193 INFO in 'deeppavlov.models.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'expensive', 'food': 'french'}, got 2 results. >> Restaurant two two is a nice place in the north of town serving tasty french food. x::ok, give me their phone number >> The phone number of restaurant two two is 01223 351880. @@ -31,10 +31,10 @@ x::address and post code too x::ok, thank you! >> Restaurant two two serves french food in the expensive price range. x::and if i wanted an italian restuarant? -2018-05-16 12:30:15.98 INFO in 'deeppavlov.skills.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'expensive', 'food': 'italian'}, got 4 results. +2018-05-16 12:30:15.98 INFO in 'deeppavlov.models.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'expensive', 'food': 'italian'}, got 4 results. >> Frankie and bennys serves italian food in the expensive price range. x::more cheaply priced? -2018-05-16 12:30:36.560 INFO in 'deeppavlov.skills.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'cheap', 'food': 'italian'}, got 5 results. +2018-05-16 12:30:36.560 INFO in 'deeppavlov.models.go_bot.bot'['bot'] at line 346: Made api_call with {'pricerange': 'cheap', 'food': 'italian'}, got 5 results. >> Da vinci pizzeria serves italian food in the cheap price range. x::ok, give me their phone number and address, please >> The phone number of da vinci pizzeria is 01223 351707 and it is on 20 milton road chesterton. diff --git a/deeppavlov/skills/go_bot/__init__.py b/deeppavlov/models/go_bot/__init__.py similarity index 100% rename from deeppavlov/skills/go_bot/__init__.py rename to deeppavlov/models/go_bot/__init__.py diff --git a/deeppavlov/skills/go_bot/bot.py b/deeppavlov/models/go_bot/bot.py similarity index 98% rename from deeppavlov/skills/go_bot/bot.py rename to deeppavlov/models/go_bot/bot.py index 5313ac5de5..8338a7c80b 100644 --- a/deeppavlov/skills/go_bot/bot.py +++ b/deeppavlov/models/go_bot/bot.py @@ -17,15 +17,13 @@ import re import numpy as np -from typing import Type from deeppavlov.core.commands.utils import expand_path from deeppavlov.core.common.registry import register from deeppavlov.core.models.nn_model import NNModel -from deeppavlov.core.common.errors import ConfigError -from deeppavlov.skills.go_bot.network import GoalOrientedBotNetwork -import deeppavlov.skills.go_bot.templates as templ from deeppavlov.core.common.log import get_logger +from deeppavlov.models.go_bot.network import GoalOrientedBotNetwork +import deeppavlov.models.go_bot.templates as templ log = get_logger(__name__) diff --git a/deeppavlov/skills/go_bot/diagram.png b/deeppavlov/models/go_bot/diagram.png similarity index 100% rename from deeppavlov/skills/go_bot/diagram.png rename to deeppavlov/models/go_bot/diagram.png diff --git a/deeppavlov/skills/go_bot/metrics.py b/deeppavlov/models/go_bot/metrics.py similarity index 100% rename from deeppavlov/skills/go_bot/metrics.py rename to deeppavlov/models/go_bot/metrics.py diff --git a/deeppavlov/skills/go_bot/network.py b/deeppavlov/models/go_bot/network.py similarity index 100% rename from deeppavlov/skills/go_bot/network.py rename to deeppavlov/models/go_bot/network.py diff --git a/deeppavlov/skills/go_bot/templates.py b/deeppavlov/models/go_bot/templates.py similarity index 100% rename from deeppavlov/skills/go_bot/templates.py rename to deeppavlov/models/go_bot/templates.py diff --git a/deeppavlov/skills/go_bot/tracker.py b/deeppavlov/models/go_bot/tracker.py similarity index 100% rename from deeppavlov/skills/go_bot/tracker.py rename to deeppavlov/models/go_bot/tracker.py diff --git a/deeppavlov/skills/seq2seq_go_bot/README.md b/deeppavlov/models/seq2seq_go_bot/README.md similarity index 100% rename from deeppavlov/skills/seq2seq_go_bot/README.md rename to deeppavlov/models/seq2seq_go_bot/README.md diff --git a/deeppavlov/skills/seq2seq_go_bot/__init__.py b/deeppavlov/models/seq2seq_go_bot/__init__.py similarity index 100% rename from deeppavlov/skills/seq2seq_go_bot/__init__.py rename to deeppavlov/models/seq2seq_go_bot/__init__.py diff --git a/deeppavlov/skills/seq2seq_go_bot/bot.py b/deeppavlov/models/seq2seq_go_bot/bot.py similarity index 98% rename from deeppavlov/skills/seq2seq_go_bot/bot.py rename to deeppavlov/models/seq2seq_go_bot/bot.py index 952905a36a..9a9da1889d 100644 --- a/deeppavlov/skills/seq2seq_go_bot/bot.py +++ b/deeppavlov/models/seq2seq_go_bot/bot.py @@ -22,7 +22,7 @@ from deeppavlov.core.models.nn_model import NNModel from deeppavlov.core.data.vocab import DefaultVocabulary from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder -from deeppavlov.skills.seq2seq_go_bot.network import Seq2SeqGoalOrientedBotNetwork +from deeppavlov.models.seq2seq_go_bot.network import Seq2SeqGoalOrientedBotNetwork from deeppavlov.core.common.log import get_logger diff --git a/deeppavlov/skills/seq2seq_go_bot/kb.py b/deeppavlov/models/seq2seq_go_bot/kb.py similarity index 100% rename from deeppavlov/skills/seq2seq_go_bot/kb.py rename to deeppavlov/models/seq2seq_go_bot/kb.py diff --git a/deeppavlov/skills/seq2seq_go_bot/network.py b/deeppavlov/models/seq2seq_go_bot/network.py similarity index 100% rename from deeppavlov/skills/seq2seq_go_bot/network.py rename to deeppavlov/models/seq2seq_go_bot/network.py From e258ef3ea167b86d6680bfcc513dd207586b90f4 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Mon, 25 Jun 2018 18:30:56 +0300 Subject: [PATCH 554/616] feat: new pip wrapper --- requirements.txt | 2 +- setup.py | 32 ++++++++++++++++---------------- utils/pip_wrapper/__init__.py | 1 + utils/pip_wrapper/pip_wrapper.py | 6 ++++++ 4 files changed, 24 insertions(+), 17 deletions(-) create mode 100644 utils/pip_wrapper/__init__.py create mode 100644 utils/pip_wrapper/pip_wrapper.py diff --git a/requirements.txt b/requirements.txt index 7b9d79f001..09e32cfac5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,7 +11,7 @@ keras==2.1.2 gensim==2.3.0 pandas==0.21.1 fuzzywuzzy==0.16.0 -git+https://github.com/facebookresearch/fastText.git@3872afadb3a9f30de7c7792ff2ff1bda64242097 +git+https://github.com/facebookresearch/fastText.git@25d0bb04bf43d8b674fe9ae5722ef65a0856f5d6#egg=fastText nltk==3.2.5 scikit-learn==0.19.0 spacy==2.0.5 diff --git a/setup.py b/setup.py index b5a65643f7..10dcf6ed54 100644 --- a/setup.py +++ b/setup.py @@ -15,14 +15,7 @@ import os import re -try: # for pip>=10.0.0 - from pip._internal.req import parse_requirements - from pip._internal.download import PipSession - from pip._internal import main as pip_main -except ImportError: # for pip<=9.0.3 - from pip.req import parse_requirements - from pip.download import PipSession - from pip import main as pip_main +from utils.pip_wrapper import install __location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__))) @@ -30,13 +23,20 @@ def read_requirements(): # # parses requirements from requirements.txt reqs_path = os.path.join(__location__, 'requirements.txt') - install_reqs = parse_requirements(reqs_path, session=PipSession()) - reqs = [] - for ir in install_reqs: - pip_main(['install', str(ir.req or ir.link)]) - if ir.req: - reqs.append(str(ir.req)) - return reqs + with open(reqs_path) as f: + reqs = [line.strip() for line in f] + + for req in reqs: + install(req) + + names = [] + links = [] + for req in reqs: + if '://' in req: + links.append(req) + else: + names.append(req) + return {'install_requires': names, 'dependency_links': links} def readme(): @@ -63,5 +63,5 @@ def readme(): download_url='https://github.com/deepmipt/DeepPavlov/archive/' + meta['__version__'] + '.tar.gz', keywords=['NLP', 'NER', 'SQUAD', 'Intents', 'Chatbot'], include_package_data=True, - install_requires=read_requirements() + **read_requirements() ) diff --git a/utils/pip_wrapper/__init__.py b/utils/pip_wrapper/__init__.py new file mode 100644 index 0000000000..24cb413c4d --- /dev/null +++ b/utils/pip_wrapper/__init__.py @@ -0,0 +1 @@ +from .pip_wrapper import * \ No newline at end of file diff --git a/utils/pip_wrapper/pip_wrapper.py b/utils/pip_wrapper/pip_wrapper.py new file mode 100644 index 0000000000..c5b820bb1a --- /dev/null +++ b/utils/pip_wrapper/pip_wrapper.py @@ -0,0 +1,6 @@ +import subprocess +import sys + + +def install(package): + return subprocess.check_call([sys.executable, '-m', 'pip', 'install', package]) From 81d1d5cf9b5b562e0f471dbf9a328bff103a1e04 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 26 Jun 2018 11:44:14 +0300 Subject: [PATCH 555/616] feat: use shell=True for pip install to allow complex parameters in requirements --- utils/pip_wrapper/pip_wrapper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils/pip_wrapper/pip_wrapper.py b/utils/pip_wrapper/pip_wrapper.py index c5b820bb1a..6f2b4ac2bc 100644 --- a/utils/pip_wrapper/pip_wrapper.py +++ b/utils/pip_wrapper/pip_wrapper.py @@ -3,4 +3,4 @@ def install(package): - return subprocess.check_call([sys.executable, '-m', 'pip', 'install', package]) + return subprocess.check_call(f'{sys.executable} -m pip install {package}', shell=True) From f88fc1bde2039dc5d0086fef5a3183049e09181f Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 26 Jun 2018 15:21:42 +0300 Subject: [PATCH 556/616] fix: accept special characters in pip wrapper install --- requirements.txt | 1 + utils/pip_wrapper/pip_wrapper.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 09e32cfac5..8a3ddd31a5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,6 +11,7 @@ keras==2.1.2 gensim==2.3.0 pandas==0.21.1 fuzzywuzzy==0.16.0 +pybind11==2.2.3 git+https://github.com/facebookresearch/fastText.git@25d0bb04bf43d8b674fe9ae5722ef65a0856f5d6#egg=fastText nltk==3.2.5 scikit-learn==0.19.0 diff --git a/utils/pip_wrapper/pip_wrapper.py b/utils/pip_wrapper/pip_wrapper.py index 6f2b4ac2bc..2284d32e08 100644 --- a/utils/pip_wrapper/pip_wrapper.py +++ b/utils/pip_wrapper/pip_wrapper.py @@ -3,4 +3,4 @@ def install(package): - return subprocess.check_call(f'{sys.executable} -m pip install {package}', shell=True) + return subprocess.check_call([sys.executable, '-m', 'pip', 'install', package.replace(' ', '')]) From 5f14e405e11e54186fa073f7b8da343cb362bcf4 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 26 Jun 2018 16:57:55 +0300 Subject: [PATCH 557/616] feat: ignore comments in requirements files --- setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 10dcf6ed54..d8190dbe4f 100644 --- a/setup.py +++ b/setup.py @@ -24,7 +24,7 @@ def read_requirements(): # # parses requirements from requirements.txt reqs_path = os.path.join(__location__, 'requirements.txt') with open(reqs_path) as f: - reqs = [line.strip() for line in f] + reqs = [line.strip() for line in f if not line.strip().startswith('#')] for req in reqs: install(req) @@ -46,7 +46,7 @@ def readme(): meta = {} -with open('deeppavlov/package_meta.py') as f: +with open(os.path.join(__location__, 'deeppavlov/package_meta.py')) as f: exec(f.read(), meta) setup( From 5a94c995bb2dc93558d23448fed45a682edc5b62 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Wed, 27 Jun 2018 15:59:03 +0300 Subject: [PATCH 558/616] feat: allow installation of multiple packages at once with pip wrapper --- utils/pip_wrapper/pip_wrapper.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/utils/pip_wrapper/pip_wrapper.py b/utils/pip_wrapper/pip_wrapper.py index 2284d32e08..271646a553 100644 --- a/utils/pip_wrapper/pip_wrapper.py +++ b/utils/pip_wrapper/pip_wrapper.py @@ -1,6 +1,8 @@ +import re import subprocess import sys -def install(package): - return subprocess.check_call([sys.executable, '-m', 'pip', 'install', package.replace(' ', '')]) +def install(*packages): + return subprocess.check_call([sys.executable, '-m', 'pip', 'install', + *[re.sub(r'\s', '', package) for package in packages]]) From f829614aea97290c14ef754edf0d468e984f7429 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Thu, 28 Jun 2018 15:29:33 +0300 Subject: [PATCH 559/616] feat: use a registry file to import only those modules that are needed for a chosen config --- deeppavlov/__init__.py | 89 ---------------------- deeppavlov/core/commands/infer.py | 1 - deeppavlov/core/commands/train.py | 2 +- deeppavlov/core/common/params.py | 19 +---- deeppavlov/core/common/registry.json | 90 +++++++++++++++++++++++ deeppavlov/core/common/registry.py | 57 +++++++------- deeppavlov/models/morpho_tagger/common.py | 3 +- deeppavlov/run_model.py | 6 +- utils/prepare/__init__.py | 0 utils/prepare/registry.py | 14 ++++ 10 files changed, 144 insertions(+), 137 deletions(-) create mode 100644 deeppavlov/core/common/registry.json create mode 100644 utils/prepare/__init__.py create mode 100644 utils/prepare/registry.py diff --git a/deeppavlov/__init__.py b/deeppavlov/__init__.py index 72c6667a91..df3403b16b 100644 --- a/deeppavlov/__init__.py +++ b/deeppavlov/__init__.py @@ -18,92 +18,3 @@ # check version import sys assert sys.hexversion >= 0x3060000, 'Does not work in python3.5 or lower' - -import deeppavlov.core.models.keras_model -import deeppavlov.core.data.vocab -import deeppavlov.core.data.simple_vocab -import deeppavlov.core.data.sqlite_database -import deeppavlov.dataset_readers.babi_reader -import deeppavlov.dataset_readers.dstc2_reader -import deeppavlov.dataset_readers.kvret_reader -import deeppavlov.dataset_readers.conll2003_reader -import deeppavlov.dataset_readers.typos_reader -import deeppavlov.dataset_readers.basic_classification_reader -import deeppavlov.dataset_readers.squad_dataset_reader -import deeppavlov.dataset_readers.morphotagging_dataset_reader - -import deeppavlov.dataset_iterators.dialog_iterator -import deeppavlov.dataset_iterators.kvret_dialog_iterator -import deeppavlov.dataset_iterators.dstc2_ner_iterator -import deeppavlov.dataset_iterators.dstc2_intents_iterator -import deeppavlov.dataset_iterators.typos_iterator -import deeppavlov.dataset_iterators.basic_classification_iterator -import deeppavlov.dataset_iterators.squad_iterator -import deeppavlov.dataset_iterators.sqlite_iterator -import deeppavlov.dataset_iterators.morphotagger_iterator - -import deeppavlov.models.go_bot.bot -import deeppavlov.models.go_bot.network -import deeppavlov.models.go_bot.tracker -import deeppavlov.models.seq2seq_go_bot.bot -import deeppavlov.models.seq2seq_go_bot.network -import deeppavlov.models.seq2seq_go_bot.kb -import deeppavlov.models.classifiers.intents.intent_model -import deeppavlov.models.commutators.random_commutator -import deeppavlov.models.embedders.fasttext_embedder -import deeppavlov.models.embedders.dict_embedder -import deeppavlov.models.embedders.glove_embedder -import deeppavlov.models.embedders.bow_embedder -import deeppavlov.models.spelling_correction.brillmoore.error_model -import deeppavlov.models.spelling_correction.levenstein.searcher_component -import deeppavlov.models.spelling_correction.electors.kenlm_elector -import deeppavlov.models.spelling_correction.electors.top1_elector -import deeppavlov.models.trackers.hcn_at -import deeppavlov.models.trackers.hcn_et -import deeppavlov.models.preprocessors.str_lower -import deeppavlov.models.preprocessors.squad_preprocessor -import deeppavlov.models.preprocessors.capitalization -import deeppavlov.models.preprocessors.dirty_comments_preprocessor -import deeppavlov.models.tokenizers.nltk_tokenizer -import deeppavlov.models.tokenizers.nltk_moses_tokenizer -import deeppavlov.models.tokenizers.spacy_tokenizer -import deeppavlov.models.tokenizers.split_tokenizer -import deeppavlov.models.tokenizers.ru_tokenizer -import deeppavlov.models.squad.squad -import deeppavlov.models.morpho_tagger.tagger -import deeppavlov.models.morpho_tagger.common -import deeppavlov.models.api_requester - -import deeppavlov.skills.odqa.tfidf_ranker -import deeppavlov.vocabs.typos -import deeppavlov.vocabs.wiki_sqlite -import deeppavlov.dataset_readers.insurance_reader -import deeppavlov.dataset_iterators.ranking_iterator -import deeppavlov.models.ner.network -import deeppavlov.models.ranking.ranking_model -import deeppavlov.models.ranking.metrics -import deeppavlov.models.preprocessors.char_splitter -import deeppavlov.models.preprocessors.mask -import deeppavlov.models.preprocessors.assemble_embeddins_matrix -import deeppavlov.models.preprocessors.capitalization -import deeppavlov.models.preprocessors.field_getter -import deeppavlov.models.preprocessors.sanitizer -import deeppavlov.models.preprocessors.lazy_tokenizer -import deeppavlov.models.slotfill.slotfill_raw -import deeppavlov.models.slotfill.slotfill -import deeppavlov.models.preprocessors.one_hotter -import deeppavlov.dataset_readers.ontonotes_reader - -import deeppavlov.models.classifiers.tokens_matcher.tokens_matcher - - -import deeppavlov.metrics.accuracy -import deeppavlov.metrics.fmeasure -import deeppavlov.metrics.bleu -import deeppavlov.metrics.squad_metrics -import deeppavlov.metrics.roc_auc_score -import deeppavlov.metrics.fmeasure_classification - -import deeppavlov.core.common.log - -import deeppavlov.download diff --git a/deeppavlov/core/commands/infer.py b/deeppavlov/core/commands/infer.py index bc035b4296..911ff3bb1c 100644 --- a/deeppavlov/core/commands/infer.py +++ b/deeppavlov/core/commands/infer.py @@ -16,7 +16,6 @@ from deeppavlov.core.commands.utils import set_deeppavlov_root from deeppavlov.core.common.chainer import Chainer from deeppavlov.core.common.file import read_json -from deeppavlov.core.common.registry import REGISTRY from deeppavlov.core.agent.agent import Agent from deeppavlov.core.common.params import from_params diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index 6e3c0a0561..42eee31b34 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -27,7 +27,7 @@ from deeppavlov.core.common.chainer import Chainer from deeppavlov.core.common.errors import ConfigError from deeppavlov.core.common.file import read_json -from deeppavlov.core.common.registry import model as get_model +from deeppavlov.core.common.registry import get_model from deeppavlov.core.common.metrics_registry import get_metrics_by_names from deeppavlov.core.common.params import from_params from deeppavlov.core.data.data_learning_iterator import DataLearningIterator diff --git a/deeppavlov/core/common/params.py b/deeppavlov/core/common/params.py index faac8a9c02..6b3e2e3ce1 100644 --- a/deeppavlov/core/common/params.py +++ b/deeppavlov/core/common/params.py @@ -19,7 +19,7 @@ from deeppavlov.core.commands.utils import expand_path, get_deeppavlov_root, set_deeppavlov_root from deeppavlov.core.common.file import read_json -from deeppavlov.core.common.registry import REGISTRY +from deeppavlov.core.common.registry import get_model, cls_from_str from deeppavlov.core.common.errors import ConfigError from deeppavlov.core.common.log import get_logger from deeppavlov.core.models.component import Component @@ -84,27 +84,14 @@ def from_params(params: Dict, mode='infer', **kwargs) -> Component: return model elif 'class' in config_params: - c = config_params.pop('class') - try: - module_name, cls_name = c.split(':') - cls = getattr(importlib.import_module(module_name), cls_name) - except ValueError: - e = ConfigError('Expected class description in a `module.submodules:ClassName` form, but got `{}`' - .format(c)) - log.exception(e) - raise e + cls = cls_from_str(config_params.pop('class')) else: cls_name = config_params.pop('name', None) if not cls_name: e = ConfigError('Component config has no `name` nor `ref` or `class` fields') log.exception(e) raise e - try: - cls = REGISTRY[cls_name] - except KeyError: - e = ConfigError('Class {} is not registered.'.format(cls_name)) - log.exception(e) - raise e + cls = get_model(cls_name) # find the submodels params recursively config_params = {k: _init_param(v, mode) for k, v in config_params.items()} diff --git a/deeppavlov/core/common/registry.json b/deeppavlov/core/common/registry.json new file mode 100644 index 0000000000..b70806c2b0 --- /dev/null +++ b/deeppavlov/core/common/registry.json @@ -0,0 +1,90 @@ +{ + "api_requester": "deeppavlov.models.api_requester.api_requester:ApiRequester", + "babi_reader": "deeppavlov.dataset_readers.babi_reader:BabiDatasetReader", + "basic_classification_iterator": "deeppavlov.dataset_iterators.basic_classification_iterator:BasicClassificationDatasetIterator", + "basic_classification_reader": "deeppavlov.dataset_readers.basic_classification_reader:BasicClassificationDatasetReader", + "bow": "deeppavlov.models.embedders.bow_embedder:BoWEmbedder", + "capitalization_featurizer": "deeppavlov.models.preprocessors.capitalization:CapitalizationPreprocessor", + "char_splitter": "deeppavlov.models.preprocessors.char_splitter:CharSplitter", + "char_vocab": "deeppavlov.core.data.simple_vocab:CharacterVocab", + "conll2003_reader": "deeppavlov.dataset_readers.conll2003_reader:Conll2003DatasetReader", + "data_fitting_iterator": "deeppavlov.core.data.data_fitting_iterator:DataFittingIterator", + "data_learning_iterator": "deeppavlov.core.data.data_learning_iterator:DataLearningIterator", + "default_tracker": "deeppavlov.models.trackers.default_tracker:DefaultTracker", + "default_vocab": "deeppavlov.core.data.vocab:DefaultVocabulary", + "dialog_db_result_iterator": "deeppavlov.dataset_iterators.dialog_iterator:DialogDBResultDatasetIterator", + "dialog_iterator": "deeppavlov.dataset_iterators.dialog_iterator:DialogDatasetIterator", + "dialog_vocab": "deeppavlov.core.data.simple_vocab:DialogVocab", + "dict_emb": "deeppavlov.models.embedders.dict_embedder:DictEmbedder", + "dirty_comments_preprocessor": "deeppavlov.models.preprocessors.dirty_comments_preprocessor:DirtyCommentsPreprocessor", + "dstc2_intents_iterator": "deeppavlov.dataset_iterators.dstc2_intents_iterator:Dstc2IntentsDatasetIterator", + "dstc2_ner_iterator": "deeppavlov.dataset_iterators.dstc2_ner_iterator:Dstc2NerDatasetIterator", + "dstc2_reader": "deeppavlov.dataset_readers.dstc2_reader:DSTC2DatasetReader", + "dstc2_v2_reader": "deeppavlov.dataset_readers.dstc2_reader:DSTC2Version2DatasetReader", + "dstc_slotfilling": "deeppavlov.models.slotfill.slotfill:DstcSlotFillingNetwork", + "emb_mat_assembler": "deeppavlov.models.preprocessors.assemble_embeddins_matrix:EmbeddingsMatrixAssembler", + "fasttext": "deeppavlov.models.embedders.fasttext_embedder:FasttextEmbedder", + "featurized_tracker": "deeppavlov.models.go_bot.tracker:FeaturizedTracker", + "field_getter": "deeppavlov.models.preprocessors.field_getter:FieldGetter", + "glove": "deeppavlov.models.embedders.glove_embedder:GloVeEmbedder", + "go_bot": "deeppavlov.models.go_bot.bot:GoalOrientedBot", + "go_bot_rnn": "deeppavlov.models.go_bot.network:GoalOrientedBotNetwork", + "hashing_tfidf_vectorizer": "deeppavlov.models.vectorizers.hashing_tfidf_vectorizer:HashingTfIdfVectorizer", + "hcn_at": "deeppavlov.models.trackers.hcn_at:ActionTracker", + "hcn_et": "deeppavlov.models.trackers.hcn_et:EntityTracker", + "insurance_reader": "deeppavlov.dataset_readers.insurance_reader:InsuranceReader", + "intent_model": "deeppavlov.models.classifiers.intents.intent_model:KerasIntentModel", + "kenlm_elector": "deeppavlov.models.spelling_correction.electors.kenlm_elector:KenlmElector", + "knowledge_base": "deeppavlov.models.seq2seq_go_bot.kb:KnowledgeBase", + "knowledge_base_entity_normalizer": "deeppavlov.models.seq2seq_go_bot.kb:KnowledgeBaseEntityNormalizer", + "kvret_dialog_iterator": "deeppavlov.dataset_iterators.kvret_dialog_iterator:KvretDialogDatasetIterator", + "kvret_reader": "deeppavlov.dataset_readers.kvret_reader:KvretDatasetReader", + "lazy_tokenizer": "deeppavlov.models.preprocessors.lazy_tokenizer:LazyTokenizer", + "lowercase_preprocessor": "deeppavlov.models.preprocessors.capitalization:LowercasePreprocessor", + "mask": "deeppavlov.models.preprocessors.mask:Mask", + "morpho_tagger": "deeppavlov.models.morpho_tagger.tagger:MorphoTaggerWrapper", + "morphotagger_dataset": "deeppavlov.dataset_iterators.morphotagger_iterator:MorphoTaggerDatasetIterator", + "morphotagger_dataset_reader": "deeppavlov.dataset_readers.morphotagging_dataset_reader:MorphotaggerDatasetReader", + "ner": "deeppavlov.models.ner.network:NerNetwork", + "nltk_moses_tokenizer": "deeppavlov.models.tokenizers.nltk_moses_tokenizer:NLTKTokenizer", + "nltk_tokenizer": "deeppavlov.models.tokenizers.nltk_tokenizer:NLTKTokenizer", + "one_hotter": "deeppavlov.models.preprocessors.one_hotter:OneHotter", + "ontonotes_reader": "deeppavlov.dataset_readers.ontonotes_reader:OntonotesReader", + "pymorphy_russian_lemmatizer": "deeppavlov.models.preprocessors.russian_lemmatizer:PymorphyRussianLemmatizer", + "random": "deeppavlov.models.commutators.random_commutator:RandomCommutator", + "random_emb_mat": "deeppavlov.models.preprocessors.assemble_embeddins_matrix:RandomEmbeddingsMatrix", + "ranking_iterator": "deeppavlov.dataset_iterators.ranking_iterator:RankingIterator", + "ranking_model": "deeppavlov.models.ranking.ranking_model:RankingModel", + "ru_tokenizer": "deeppavlov.models.tokenizers.ru_tokenizer:RussianTokenizer", + "russian_words_vocab": "deeppavlov.vocabs.typos:RussianWordsVocab", + "sanitizer": "deeppavlov.models.preprocessors.sanitizer:Sanitizer", + "seq2seq_go_bot": "deeppavlov.models.seq2seq_go_bot.bot:Seq2SeqGoalOrientedBot", + "seq2seq_go_bot_nn": "deeppavlov.models.seq2seq_go_bot.network:Seq2SeqGoalOrientedBotNetwork", + "simple_vocab": "deeppavlov.core.data.simple_vocab:SimpleVocabulary", + "slotfill_raw": "deeppavlov.models.slotfill.slotfill_raw:SlotFillingComponent", + "spelling_error_model": "deeppavlov.models.spelling_correction.brillmoore.error_model:ErrorModel", + "spelling_levenstein": "deeppavlov.models.spelling_correction.levenstein.searcher_component:LevensteinSearcherComponent", + "split_tokenizer": "deeppavlov.models.tokenizers.split_tokenizer:SplitTokenizer", + "sqlite_database": "deeppavlov.core.data.sqlite_database:Sqlite3Database", + "sqlite_iterator": "deeppavlov.dataset_iterators.sqlite_iterator:SQLiteDataIterator", + "squad_ans_postprocessor": "deeppavlov.models.preprocessors.squad_preprocessor:SquadAnsPostprocessor", + "squad_ans_preprocessor": "deeppavlov.models.preprocessors.squad_preprocessor:SquadAnsPreprocessor", + "squad_dataset_reader": "deeppavlov.dataset_readers.squad_dataset_reader:SquadDatasetReader", + "squad_iterator": "deeppavlov.dataset_iterators.squad_iterator:SquadIterator", + "squad_model": "deeppavlov.models.squad.squad:SquadModel", + "squad_preprocessor": "deeppavlov.models.preprocessors.squad_preprocessor:SquadPreprocessor", + "squad_vocab_embedder": "deeppavlov.models.preprocessors.squad_preprocessor:SquadVocabEmbedder", + "static_dictionary": "deeppavlov.vocabs.typos:StaticDictionary", + "str_lower": "deeppavlov.models.preprocessors.str_lower:StrLower", + "stream_spacy_tokenizer": "deeppavlov.models.tokenizers.spacy_tokenizer:StreamSpacyTokenizer", + "tag_output_prettifier": "deeppavlov.models.morpho_tagger.common:TagOutputPrettifier", + "tfidf_ranker": "deeppavlov.skills.odqa.tfidf_ranker:TfidfRanker", + "tokens_matcher": "deeppavlov.models.classifiers.tokens_matcher.tokens_matcher:TokensMatcher", + "top1_elector": "deeppavlov.models.spelling_correction.electors.top1_elector:TopOneElector", + "typos_custom_reader": "deeppavlov.dataset_readers.typos_reader:TyposCustom", + "typos_iterator": "deeppavlov.dataset_iterators.typos_iterator:TyposDatasetIterator", + "typos_kartaslov_reader": "deeppavlov.dataset_readers.typos_reader:TyposKartaslov", + "typos_wikipedia_reader": "deeppavlov.dataset_readers.typos_reader:TyposWikipedia", + "wiki_sqlite_vocab": "deeppavlov.vocabs.wiki_sqlite:WikiSQLiteVocab", + "wikitionary_100K_vocab": "deeppavlov.vocabs.typos:Wiki100KDictionary" +} \ No newline at end of file diff --git a/deeppavlov/core/common/registry.py b/deeppavlov/core/common/registry.py index 028ee42ae0..296d439553 100644 --- a/deeppavlov/core/common/registry.py +++ b/deeppavlov/core/common/registry.py @@ -13,53 +13,58 @@ See the License for the specific language governing permissions and limitations under the License. """ +import importlib +import json +from pathlib import Path -"""Registry for models. Create your models by subclassing one of the abstract model classes (RBModel -, SModel, TModel) and register it. You can assign a code name to the model in the decorator function -parentheses or leave them blank, in the last case the class name will be assigned automatically. -The name should repeat itself in your pipeline json configuration. +from deeppavlov.core.common.log import get_logger +from deeppavlov.core.common.errors import ConfigError -Example: - @registry.register_model('my_model') - class MyModel(TModel) -Note that you should import _REGISTRY variable and all your custom models in the entry point of -your training/inference script. -""" +logger = get_logger(__name__) -from typing import Type, List +_registry_path = Path(__file__).parent / 'registry.json' +if _registry_path.exists(): + with _registry_path.open(encoding='utf-8') as f: + _REGISTRY = json.load(f) +else: + _REGISTRY = {} -from deeppavlov.core.common.log import get_logger -from deeppavlov.core.common.errors import ConfigError -logger = get_logger(__name__) +def cls_from_str(name: str) -> type: + try: + module_name, cls_name = name.split(':') + except ValueError: + raise ConfigError('Expected class description in a `module.submodules:ClassName` form, but got `{}`' + .format(name)) -REGISTRY = {} + return getattr(importlib.import_module(module_name), cls_name) -def register(name: str = None) -> Type: +def register(name: str = None) -> type: """Register model. If name is not passed, the model class name is converted to snake-case.""" - def decorate(model_cls: Type, reg_name: str = None) -> Type: + def decorate(model_cls: type, reg_name: str = None) -> type: model_name = reg_name or short_name(model_cls) - global REGISTRY - if model_name in REGISTRY: + global _REGISTRY + cls_name = model_cls.__module__ + ':' + model_cls.__name__ + if model_name in _REGISTRY and _REGISTRY[model_name] != cls_name: logger.warning('Registry name "{}" has been already registered and will be overwritten.'.format(model_name)) - REGISTRY[model_name] = model_cls + _REGISTRY[model_name] = cls_name return model_cls return lambda model_cls_name: decorate(model_cls_name, name) -def short_name(cls: Type) -> str: +def short_name(cls: type) -> str: return cls.__name__.split('.')[-1] -def model(name: str) -> type: - if name not in REGISTRY: +def get_model(name: str) -> type: + if name not in _REGISTRY: raise ConfigError("Model {} is not registered.".format(name)) - return REGISTRY[name] + return cls_from_str(_REGISTRY[name]) -def list_models() -> List: - return list(REGISTRY) +def list_models() -> list: + return list(_REGISTRY) diff --git a/deeppavlov/models/morpho_tagger/common.py b/deeppavlov/models/morpho_tagger/common.py index 4a1fdb331c..ce4dac6872 100644 --- a/deeppavlov/models/morpho_tagger/common.py +++ b/deeppavlov/models/morpho_tagger/common.py @@ -5,7 +5,7 @@ from deeppavlov.core.commands.utils import set_deeppavlov_root, expand_path from deeppavlov.core.common.file import read_json from deeppavlov.core.common.params import from_params -from deeppavlov.core.common.registry import model as get_model +from deeppavlov.core.common.registry import get_model from deeppavlov.core.common.registry import register from deeppavlov.core.models.component import Component @@ -13,7 +13,6 @@ from deeppavlov.models.morpho_tagger.common_tagger import make_pos_and_tag - def predict_with_model(config_path): config = read_json(config_path) set_deeppavlov_root(config) diff --git a/deeppavlov/run_model.py b/deeppavlov/run_model.py index ab885fe764..4db87b0f0b 100644 --- a/deeppavlov/run_model.py +++ b/deeppavlov/run_model.py @@ -38,5 +38,7 @@ # PIPELINE_CONFIG_PATH = 'configs/odqa/ru_odqa_infer_prod.json' # PIPELINE_CONFIG_PATH = 'configs/odqa/ranker_test.json' -train_evaluate_model_from_config(PIPELINE_CONFIG_PATH) -# interact_model(PIPELINE_CONFIG_PATH) + +if __name__ == '__main__': + train_evaluate_model_from_config(PIPELINE_CONFIG_PATH) + # interact_model(PIPELINE_CONFIG_PATH) diff --git a/utils/prepare/__init__.py b/utils/prepare/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/utils/prepare/registry.py b/utils/prepare/registry.py new file mode 100644 index 0000000000..8b2afa6367 --- /dev/null +++ b/utils/prepare/registry.py @@ -0,0 +1,14 @@ +import pkgutil +import json + +import deeppavlov +from deeppavlov.core.common.registry import _registry_path, _REGISTRY + + +if __name__ == '__main__': + _REGISTRY.clear() + for _, pkg_name, _ in pkgutil.walk_packages(deeppavlov.__path__, deeppavlov.__name__+'.'): + __import__(pkg_name) + + with _registry_path.open('w', encoding='utf-8') as f: + json.dump(dict(sorted(_REGISTRY.items())), f, indent=2) From 9e245ad321a4cf1204ada2a8e61e018a4aabd2f5 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Thu, 28 Jun 2018 18:44:05 +0300 Subject: [PATCH 560/616] feat: working install command for configs --- Jenkinsfile | 2 +- .../levenstein_corrector_ru.json | 3 ++ deeppavlov/deep.py | 6 +++- requirements.txt | 23 ++---------- requirements/fasttext.txt | 2 ++ requirements/other.txt | 11 ++++++ requirements/spelling.txt | 4 +++ requirements/tf.txt | 1 + utils/pip_wrapper/pip_wrapper.py | 35 +++++++++++++++++++ 9 files changed, 64 insertions(+), 23 deletions(-) create mode 100644 requirements/fasttext.txt create mode 100644 requirements/other.txt create mode 100644 requirements/spelling.txt create mode 100644 requirements/tf.txt diff --git a/Jenkinsfile b/Jenkinsfile index 8144e8f6ed..a686818580 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -11,7 +11,7 @@ node('gpu') { sh """ virtualenv --python=python3 ".venv-$BUILD_NUMBER" . .venv-$BUILD_NUMBER/bin/activate - sed -ri 's/^ *tensorflow *(=|<|>|\$)/tensorflow-gpu\\1/g' requirements.txt + sed -ri 's/^\\s*tensorflow\\s*(=|<|>|\$)/tensorflow-gpu\\1/g' requirements.txt sed -i "s/stream=True/stream=False/g" deeppavlov/core/data/utils.py python setup.py develop pip install http://lnsigo.mipt.ru/export/en_core_web_sm-2.0.0.tar.gz diff --git a/deeppavlov/configs/spelling_correction/levenstein_corrector_ru.json b/deeppavlov/configs/spelling_correction/levenstein_corrector_ru.json index fe03fd1584..73b562cdcb 100644 --- a/deeppavlov/configs/spelling_correction/levenstein_corrector_ru.json +++ b/deeppavlov/configs/spelling_correction/levenstein_corrector_ru.json @@ -41,6 +41,9 @@ "out": ["y_predicted"] }, "metadata": { + "requirements": [ + "../requirements/spelling.txt" + ], "labels": { "telegram_utils": "ErrorModel", "server_utils": "ErrorModel" diff --git a/deeppavlov/deep.py b/deeppavlov/deep.py index 826125cbb2..ab8fd267c7 100644 --- a/deeppavlov/deep.py +++ b/deeppavlov/deep.py @@ -28,6 +28,7 @@ from deeppavlov.download import deep_download from utils.telegram_utils.telegram_ui import interact_model_by_telegram from utils.server_utils.server import start_model_server +from utils.pip_wrapper import install_from_config log = get_logger(__name__) @@ -35,7 +36,8 @@ parser = argparse.ArgumentParser() parser.add_argument("mode", help="select a mode, train or interact", type=str, - choices={'train', 'evaluate', 'interact', 'predict', 'interactbot', 'riseapi', 'download'}) + choices={'train', 'evaluate', 'interact', 'predict', 'interactbot', 'riseapi', 'download', + 'install'}) parser.add_argument("config_path", help="path to a pipeline json config", type=str) parser.add_argument("-t", "--token", help="telegram bot token", type=str) parser.add_argument("-b", "--batch-size", dest="batch_size", default=1, help="inference batch size", type=int) @@ -75,6 +77,8 @@ def main(): start_model_server(pipeline_config_path) elif args.mode == 'predict': predict_on_stream(pipeline_config_path, args.batch_size, args.file_path) + elif args.mode == 'install': + install_from_config(pipeline_config_path) if __name__ == "__main__": diff --git a/requirements.txt b/requirements.txt index 8a3ddd31a5..e6bdaa6d11 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,27 +1,8 @@ -Cython==0.27.1 numpy==1.14.3 -lxml==4.1.1 +nltk==3.2.5 tqdm==4.19.5 requests==2.18.4 -tensorflow==1.8.0 -overrides==1.9 -kenlm==0.0.0 -h5py==2.7.1 -keras==2.1.2 -gensim==2.3.0 -pandas==0.21.1 -fuzzywuzzy==0.16.0 -pybind11==2.2.3 -git+https://github.com/facebookresearch/fastText.git@25d0bb04bf43d8b674fe9ae5722ef65a0856f5d6#egg=fastText -nltk==3.2.5 -scikit-learn==0.19.0 -spacy==2.0.5 pytelegrambotapi==3.5.2 -python-Levenshtein==0.12.0 flask==0.12.2 flasgger==0.6.6 -flask_cors==3.0.3 -scipy==1.0.0 -pymorphy2==0.8 -pymorphy2-dicts-ru -sortedcontainers==2.0.2 \ No newline at end of file +flask_cors==3.0.3 \ No newline at end of file diff --git a/requirements/fasttext.txt b/requirements/fasttext.txt new file mode 100644 index 0000000000..327253433f --- /dev/null +++ b/requirements/fasttext.txt @@ -0,0 +1,2 @@ +pybind11==2.2.3 +git+https://github.com/facebookresearch/fastText.git@25d0bb04bf43d8b674fe9ae5722ef65a0856f5d6#egg=fastText \ No newline at end of file diff --git a/requirements/other.txt b/requirements/other.txt new file mode 100644 index 0000000000..f8779e30fe --- /dev/null +++ b/requirements/other.txt @@ -0,0 +1,11 @@ +Cython==0.27.1 +h5py==2.7.1 +keras==2.1.2 +gensim==2.3.0 +pandas==0.21.1 +fuzzywuzzy==0.16.0 +scikit-learn==0.19.0 +spacy==2.0.5 +scipy==1.0.0 +pymorphy2==0.8 +pymorphy2-dicts-ru \ No newline at end of file diff --git a/requirements/spelling.txt b/requirements/spelling.txt new file mode 100644 index 0000000000..ed48b2405c --- /dev/null +++ b/requirements/spelling.txt @@ -0,0 +1,4 @@ +lxml==4.1.1 +python-Levenshtein==0.12.0 +kenlm==0.0.0 +sortedcontainers==2.0.2 \ No newline at end of file diff --git a/requirements/tf.txt b/requirements/tf.txt new file mode 100644 index 0000000000..3980ace68c --- /dev/null +++ b/requirements/tf.txt @@ -0,0 +1 @@ +tensorflow==1.8.0 \ No newline at end of file diff --git a/utils/pip_wrapper/pip_wrapper.py b/utils/pip_wrapper/pip_wrapper.py index 271646a553..186868aad7 100644 --- a/utils/pip_wrapper/pip_wrapper.py +++ b/utils/pip_wrapper/pip_wrapper.py @@ -1,8 +1,43 @@ import re import subprocess import sys +from pathlib import Path + +from deeppavlov.core.commands.utils import expand_path +from deeppavlov.core.common.file import read_json +from deeppavlov.core.common.log import get_logger + + +log = get_logger(__name__) + +_tf_re = re.compile(r'\s*tensorflow\s*([<=>$])') def install(*packages): + if any(_tf_re.match(package) for package in packages)\ + and b'tensorflow-gpu' in subprocess.check_output([sys.executable, '-m', 'pip', 'freeze']): + log.warn('found tensorflow-gpu installed, so upgrading it instead of tensorflow') + packages = [_tf_re.sub(r'tensorflow-gpu\1', package) for package in packages] return subprocess.check_call([sys.executable, '-m', 'pip', 'install', *[re.sub(r'\s', '', package) for package in packages]]) + + +def install_from_config(config: [str, Path, dict]): + if isinstance(config, (str, Path)): + config: dict = read_json(config) + requirements_files = config.get('metadata', {}).get('requirements', []) + + if not requirements_files: + log.warn('No requirements found in config') + return + + requirements = [] + for rf in requirements_files: + with expand_path(rf).open() as f: + for line in f: + line = re.sub(r'\s', '', line.strip()) + if line and not line.startswith('#') and line not in requirements: + requirements.append(line) + + for r in requirements: + install(r) From 43d8a5e764f4819c2efbc3b81bf5888f4b0abd09 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Fri, 29 Jun 2018 11:53:18 +0300 Subject: [PATCH 561/616] fix: correctly catch an end of string in tensorflow regex --- Jenkinsfile | 2 +- utils/pip_wrapper/pip_wrapper.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index a686818580..86ca20f9d7 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -11,7 +11,7 @@ node('gpu') { sh """ virtualenv --python=python3 ".venv-$BUILD_NUMBER" . .venv-$BUILD_NUMBER/bin/activate - sed -ri 's/^\\s*tensorflow\\s*(=|<|>|\$)/tensorflow-gpu\\1/g' requirements.txt + sed -ri 's/^\\s*tensorflow\\s*(=|<|>|;|\$)/tensorflow-gpu\\1/g' requirements.txt sed -i "s/stream=True/stream=False/g" deeppavlov/core/data/utils.py python setup.py develop pip install http://lnsigo.mipt.ru/export/en_core_web_sm-2.0.0.tar.gz diff --git a/utils/pip_wrapper/pip_wrapper.py b/utils/pip_wrapper/pip_wrapper.py index 186868aad7..b98dd999ef 100644 --- a/utils/pip_wrapper/pip_wrapper.py +++ b/utils/pip_wrapper/pip_wrapper.py @@ -10,7 +10,7 @@ log = get_logger(__name__) -_tf_re = re.compile(r'\s*tensorflow\s*([<=>$])') +_tf_re = re.compile(r'\s*tensorflow\s*(<|=|>|;|$)') def install(*packages): From 1a6a9002915be24e2d97af618bf48f488ea880e9 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Fri, 29 Jun 2018 12:18:40 +0300 Subject: [PATCH 562/616] feat: forward environment variables to pip --- utils/pip_wrapper/pip_wrapper.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/utils/pip_wrapper/pip_wrapper.py b/utils/pip_wrapper/pip_wrapper.py index b98dd999ef..323c1a368b 100644 --- a/utils/pip_wrapper/pip_wrapper.py +++ b/utils/pip_wrapper/pip_wrapper.py @@ -2,6 +2,7 @@ import subprocess import sys from pathlib import Path +import os from deeppavlov.core.commands.utils import expand_path from deeppavlov.core.common.file import read_json @@ -15,11 +16,13 @@ def install(*packages): if any(_tf_re.match(package) for package in packages)\ - and b'tensorflow-gpu' in subprocess.check_output([sys.executable, '-m', 'pip', 'freeze']): + and b'tensorflow-gpu' in subprocess.check_output([sys.executable, '-m', 'pip', 'freeze'], + env=os.environ.copy()): log.warn('found tensorflow-gpu installed, so upgrading it instead of tensorflow') packages = [_tf_re.sub(r'tensorflow-gpu\1', package) for package in packages] return subprocess.check_call([sys.executable, '-m', 'pip', 'install', - *[re.sub(r'\s', '', package) for package in packages]]) + *[re.sub(r'\s', '', package) for package in packages]], + env=os.environ.copy()) def install_from_config(config: [str, Path, dict]): From 1216e6bb1f8b52fa1dd7a06d2f9c50a2d3dda766 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Fri, 29 Jun 2018 14:37:27 +0300 Subject: [PATCH 563/616] style: make tensorflow regex prettier --- utils/pip_wrapper/pip_wrapper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils/pip_wrapper/pip_wrapper.py b/utils/pip_wrapper/pip_wrapper.py index 323c1a368b..40044a00e3 100644 --- a/utils/pip_wrapper/pip_wrapper.py +++ b/utils/pip_wrapper/pip_wrapper.py @@ -11,7 +11,7 @@ log = get_logger(__name__) -_tf_re = re.compile(r'\s*tensorflow\s*(<|=|>|;|$)') +_tf_re = re.compile(r'\s*tensorflow\s*([<=>;]|$)') def install(*packages): From 807f1515e110ce6c025c99e37d23302181135845 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Fri, 29 Jun 2018 14:38:26 +0300 Subject: [PATCH 564/616] feat: get kenlm from github to get a newer version potentially makes it easier to install on Windows --- requirements/spelling.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/spelling.txt b/requirements/spelling.txt index ed48b2405c..bc6605c003 100644 --- a/requirements/spelling.txt +++ b/requirements/spelling.txt @@ -1,4 +1,4 @@ lxml==4.1.1 python-Levenshtein==0.12.0 -kenlm==0.0.0 +git+https://github.com/kpu/kenlm.git@328cc2995202e84d29e3773203d29cdd6cc07132#egg=kenlm sortedcontainers==2.0.2 \ No newline at end of file From 3e6674a107484d9b66e779402227c2d6fb294371 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 3 Jul 2018 13:37:51 +0300 Subject: [PATCH 565/616] feat: allow to define required imports in config's `meta` --- deeppavlov/core/commands/infer.py | 5 ++++- deeppavlov/core/commands/train.py | 4 +++- deeppavlov/core/commands/utils.py | 5 +++++ 3 files changed, 12 insertions(+), 2 deletions(-) diff --git a/deeppavlov/core/commands/infer.py b/deeppavlov/core/commands/infer.py index 911ff3bb1c..53b5b956d2 100644 --- a/deeppavlov/core/commands/infer.py +++ b/deeppavlov/core/commands/infer.py @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. """ -from deeppavlov.core.commands.utils import set_deeppavlov_root +from deeppavlov.core.commands.utils import set_deeppavlov_root, import_packages from deeppavlov.core.common.chainer import Chainer from deeppavlov.core.common.file import read_json @@ -27,6 +27,9 @@ def build_model_from_config(config, mode='infer', load_trained=False, as_component=False): set_deeppavlov_root(config) + + import_packages(config.get('metadata', {}).get('imports', [])) + model_config = config['chainer'] model = Chainer(model_config['in'], model_config['out'], model_config.get('in_y'), as_component=as_component) diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index 42eee31b34..8767159a79 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -22,7 +22,7 @@ from pathlib import Path from typing import List, Callable, Tuple, Dict, Union -from deeppavlov.core.commands.utils import expand_path, set_deeppavlov_root +from deeppavlov.core.commands.utils import expand_path, set_deeppavlov_root, import_packages from deeppavlov.core.commands.infer import build_model_from_config from deeppavlov.core.common.chainer import Chainer from deeppavlov.core.common.errors import ConfigError @@ -101,6 +101,8 @@ def train_evaluate_model_from_config(config: [str, Path, dict], to_train=True, t config = read_json(config) set_deeppavlov_root(config) + import_packages(config.get('metadata', {}).get('imports', [])) + dataset_config = config.get('dataset', None) if dataset_config: diff --git a/deeppavlov/core/commands/utils.py b/deeppavlov/core/commands/utils.py index e0c40bf18d..d7550f6fae 100644 --- a/deeppavlov/core/commands/utils.py +++ b/deeppavlov/core/commands/utils.py @@ -50,3 +50,8 @@ def is_empty(d: Path) -> bool: Check if directory is empty. """ return not bool(list(d.iterdir())) + + +def import_packages(packages: list): + for package in packages: + __import__(package) From e13971dfe6073837e385d377c1cf84aaf35863ab Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 3 Jul 2018 14:51:07 +0300 Subject: [PATCH 566/616] feat: add metrics registry file --- deeppavlov/core/common/metrics_registry.json | 22 ++++++++++++++ deeppavlov/core/common/metrics_registry.py | 32 ++++++++++++++++---- utils/prepare/registry.py | 14 ++++++--- 3 files changed, 58 insertions(+), 10 deletions(-) create mode 100644 deeppavlov/core/common/metrics_registry.json diff --git a/deeppavlov/core/common/metrics_registry.json b/deeppavlov/core/common/metrics_registry.json new file mode 100644 index 0000000000..1c6160e113 --- /dev/null +++ b/deeppavlov/core/common/metrics_registry.json @@ -0,0 +1,22 @@ +{ + "accuracy": "deeppavlov.metrics.accuracy:accuracy", + "bleu": "deeppavlov.metrics.bleu:bleu", + "classification_accuracy": "deeppavlov.metrics.accuracy:classification_accuracy", + "classification_f1": "deeppavlov.metrics.fmeasure_classification:fmeasure", + "classification_roc_auc": "deeppavlov.metrics.roc_auc_score:roc_auc_score", + "exact_match": "deeppavlov.metrics.squad_metrics:exact_match", + "loss": "deeppavlov.models.ranking.metrics:triplet_loss", + "ner_f1": "deeppavlov.metrics.fmeasure:ner_f1", + "per_item_accuracy": "deeppavlov.metrics.accuracy:per_item_accuracy", + "per_item_bleu": "deeppavlov.metrics.bleu:per_item_bleu", + "per_item_dialog_accuracy": "deeppavlov.metrics.accuracy:per_item_dialog_accuracy", + "per_item_dialog_bleu": "deeppavlov.metrics.bleu:per_item_dialog_bleu", + "per_token_accuracy": "deeppavlov.metrics.accuracy:per_token_accuracy", + "r@1": "deeppavlov.models.ranking.metrics:r_at_1", + "r@2": "deeppavlov.models.ranking.metrics:r_at_2", + "r@5": "deeppavlov.models.ranking.metrics:r_at_5", + "rank_response": "deeppavlov.models.ranking.metrics:rank_response", + "sets_accuracy": "deeppavlov.metrics.accuracy:sets_accuracy", + "slots_accuracy": "deeppavlov.metrics.accuracy:slots_accuracy", + "squad_f1": "deeppavlov.metrics.squad_metrics:squad_f1" +} \ No newline at end of file diff --git a/deeppavlov/core/common/metrics_registry.py b/deeppavlov/core/common/metrics_registry.py index c9b8253d99..add6be0c42 100644 --- a/deeppavlov/core/common/metrics_registry.py +++ b/deeppavlov/core/common/metrics_registry.py @@ -1,19 +1,39 @@ +import importlib +from pathlib import Path +import json + from deeppavlov.core.common.errors import ConfigError from deeppavlov.core.common.log import get_logger log = get_logger(__name__) -_REGISTRY = {} +_registry_path = Path(__file__).parent / 'metrics_registry.json' +if _registry_path.exists(): + with _registry_path.open(encoding='utf-8') as f: + _REGISTRY = json.load(f) +else: + _REGISTRY = {} + + +def fn_from_str(name: str) -> type: + try: + module_name, fn_name = name.split(':') + except ValueError: + raise ConfigError('Expected function description in a `module.submodules:function_name` form, but got `{}`' + .format(name)) + + return getattr(importlib.import_module(module_name), fn_name) def register_metric(metric_name): - def decorate(f): - if metric_name in _REGISTRY: + def decorate(fn): + fn_name = fn.__module__ + ':' + fn.__name__ + if metric_name in _REGISTRY and _REGISTRY[metric_name] != fn_name: log.warning('"{}" is already registered as a metric name, the old function will be ignored' .format(metric_name)) - _REGISTRY[metric_name] = f - return f + _REGISTRY[metric_name] = fn_name + return fn return decorate @@ -21,4 +41,4 @@ def get_metrics_by_names(names: list): not_found = [name for name in names if name not in _REGISTRY] if not_found: raise ConfigError('Names {} are not registered as metrics'.format(not_found)) - return [_REGISTRY[name] for name in names] + return [fn_from_str(_REGISTRY[name]) for name in names] diff --git a/utils/prepare/registry.py b/utils/prepare/registry.py index 8b2afa6367..017a86acf4 100644 --- a/utils/prepare/registry.py +++ b/utils/prepare/registry.py @@ -2,13 +2,19 @@ import json import deeppavlov -from deeppavlov.core.common.registry import _registry_path, _REGISTRY +from deeppavlov.core.common.registry import _registry_path as c_registry_path, _REGISTRY as C_REGISTRY +from deeppavlov.core.common.metrics_registry import _registry_path as m_registry_path, _REGISTRY as M_REGISTRY if __name__ == '__main__': - _REGISTRY.clear() + C_REGISTRY.clear() + M_REGISTRY.clear() + for _, pkg_name, _ in pkgutil.walk_packages(deeppavlov.__path__, deeppavlov.__name__+'.'): __import__(pkg_name) - with _registry_path.open('w', encoding='utf-8') as f: - json.dump(dict(sorted(_REGISTRY.items())), f, indent=2) + with c_registry_path.open('w', encoding='utf-8') as f: + json.dump(dict(sorted(C_REGISTRY.items())), f, indent=2) + + with m_registry_path.open('w', encoding='utf-8') as f: + json.dump(dict(sorted(M_REGISTRY.items())), f, indent=2) From 020d50de255c1971b54bc5854c71169935a0d2c7 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 3 Jul 2018 14:52:18 +0300 Subject: [PATCH 567/616] chore: move requirements between txt files --- MANIFEST.in | 1 + requirements.txt | 16 +++++++++++++--- requirements/gensim.txt | 1 + requirements/other.txt | 11 ----------- requirements/spacy.txt | 1 + requirements/tf-gpu.txt | 1 + 6 files changed, 17 insertions(+), 14 deletions(-) create mode 100644 requirements/gensim.txt delete mode 100644 requirements/other.txt create mode 100644 requirements/spacy.txt create mode 100644 requirements/tf-gpu.txt diff --git a/MANIFEST.in b/MANIFEST.in index 1f23f03438..f21bad08ff 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,6 +1,7 @@ include README.MD include LICENSE include requirements.txt +recursive-include requirements *.txt recursive-include deeppavlov *.json recursive-include deeppavlov *.md recursive-include utils *.json \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index e6bdaa6d11..f6bd8dbcfe 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,17 @@ -numpy==1.14.3 +Cython==0.27.1 +overrides==1.9 +numpy==1.14.5 +pandas==0.23.1 nltk==3.2.5 -tqdm==4.19.5 -requests==2.18.4 +tqdm==4.23.4 +scipy==1.1.0 +h5py==2.8.0 +keras==2.2.0 +scikit-learn==0.19.1 +fuzzywuzzy==0.16.0 +pymorphy2==0.8 +pymorphy2-dicts-ru +requests==2.19.1 pytelegrambotapi==3.5.2 flask==0.12.2 flasgger==0.6.6 diff --git a/requirements/gensim.txt b/requirements/gensim.txt new file mode 100644 index 0000000000..ce61965790 --- /dev/null +++ b/requirements/gensim.txt @@ -0,0 +1 @@ +gensim==2.3.0 \ No newline at end of file diff --git a/requirements/other.txt b/requirements/other.txt deleted file mode 100644 index f8779e30fe..0000000000 --- a/requirements/other.txt +++ /dev/null @@ -1,11 +0,0 @@ -Cython==0.27.1 -h5py==2.7.1 -keras==2.1.2 -gensim==2.3.0 -pandas==0.21.1 -fuzzywuzzy==0.16.0 -scikit-learn==0.19.0 -spacy==2.0.5 -scipy==1.0.0 -pymorphy2==0.8 -pymorphy2-dicts-ru \ No newline at end of file diff --git a/requirements/spacy.txt b/requirements/spacy.txt new file mode 100644 index 0000000000..4b7eccd8d9 --- /dev/null +++ b/requirements/spacy.txt @@ -0,0 +1 @@ +spacy==2.0.5 \ No newline at end of file diff --git a/requirements/tf-gpu.txt b/requirements/tf-gpu.txt new file mode 100644 index 0000000000..3980ace68c --- /dev/null +++ b/requirements/tf-gpu.txt @@ -0,0 +1 @@ +tensorflow==1.8.0 \ No newline at end of file From 917333ef1f1ca29ea76a829fa2452be2ac0a24d3 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 3 Jul 2018 15:13:52 +0300 Subject: [PATCH 568/616] feat: add appropriate requirements to gobot and spelling configs --- deeppavlov/configs/go_bot/gobot_dstc2.json | 5 +++++ deeppavlov/configs/go_bot/gobot_dstc2_all.json | 5 +++++ deeppavlov/configs/go_bot/gobot_dstc2_best.json | 5 +++++ deeppavlov/configs/go_bot/gobot_dstc2_minimal.json | 5 +++++ .../configs/spelling_correction/brillmoore_kartaslov_ru.json | 3 +++ .../brillmoore_kartaslov_ru_custom_vocab.json | 3 +++ .../spelling_correction/brillmoore_kartaslov_ru_nolm.json | 3 +++ .../configs/spelling_correction/brillmoore_wikitypos_en.json | 3 +++ 8 files changed, 32 insertions(+) diff --git a/deeppavlov/configs/go_bot/gobot_dstc2.json b/deeppavlov/configs/go_bot/gobot_dstc2.json index 24b5aa599f..43ebea8710 100644 --- a/deeppavlov/configs/go_bot/gobot_dstc2.json +++ b/deeppavlov/configs/go_bot/gobot_dstc2.json @@ -83,6 +83,11 @@ "show_examples": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt", + "../requirements/spacy.txt" + ], "labels": { "telegram_utils": "GoalOrientedBot", "server_utils": "GoalOrientedBot" diff --git a/deeppavlov/configs/go_bot/gobot_dstc2_all.json b/deeppavlov/configs/go_bot/gobot_dstc2_all.json index ec4b86e59d..98078b1b20 100644 --- a/deeppavlov/configs/go_bot/gobot_dstc2_all.json +++ b/deeppavlov/configs/go_bot/gobot_dstc2_all.json @@ -88,6 +88,11 @@ "show_examples": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt", + "../requirements/spacy.txt" + ], "labels": { "telegram_utils": "GoalOrientedBot", "server_utils": "GoalOrientedBot" diff --git a/deeppavlov/configs/go_bot/gobot_dstc2_best.json b/deeppavlov/configs/go_bot/gobot_dstc2_best.json index 49baffb351..c4f1218208 100644 --- a/deeppavlov/configs/go_bot/gobot_dstc2_best.json +++ b/deeppavlov/configs/go_bot/gobot_dstc2_best.json @@ -95,6 +95,11 @@ "show_examples": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt", + "../requirements/spacy.txt" + ], "labels": { "telegram_utils": "GoalOrientedBot", "server_utils": "GoalOrientedBot" diff --git a/deeppavlov/configs/go_bot/gobot_dstc2_minimal.json b/deeppavlov/configs/go_bot/gobot_dstc2_minimal.json index dae0c2b08d..14452a720c 100644 --- a/deeppavlov/configs/go_bot/gobot_dstc2_minimal.json +++ b/deeppavlov/configs/go_bot/gobot_dstc2_minimal.json @@ -67,6 +67,11 @@ ] }, "train": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt", + "../requirements/spacy.txt" + ], "epochs": 200, "batch_size": 4, diff --git a/deeppavlov/configs/spelling_correction/brillmoore_kartaslov_ru.json b/deeppavlov/configs/spelling_correction/brillmoore_kartaslov_ru.json index da62bff8a1..f92ba7d22a 100644 --- a/deeppavlov/configs/spelling_correction/brillmoore_kartaslov_ru.json +++ b/deeppavlov/configs/spelling_correction/brillmoore_kartaslov_ru.json @@ -59,6 +59,9 @@ "test_best": true }, "metadata": { + "requirements": [ + "../requirements/spelling.txt" + ], "labels": { "telegram_utils": "ErrorModel", "server_utils": "ErrorModel" diff --git a/deeppavlov/configs/spelling_correction/brillmoore_kartaslov_ru_custom_vocab.json b/deeppavlov/configs/spelling_correction/brillmoore_kartaslov_ru_custom_vocab.json index cc69ec0aa9..884800987e 100644 --- a/deeppavlov/configs/spelling_correction/brillmoore_kartaslov_ru_custom_vocab.json +++ b/deeppavlov/configs/spelling_correction/brillmoore_kartaslov_ru_custom_vocab.json @@ -61,6 +61,9 @@ "test_best": true }, "metadata": { + "requirements": [ + "../requirements/spelling.txt" + ], "labels": { "telegram_utils": "ErrorModel", "server_utils": "ErrorModel" diff --git a/deeppavlov/configs/spelling_correction/brillmoore_kartaslov_ru_nolm.json b/deeppavlov/configs/spelling_correction/brillmoore_kartaslov_ru_nolm.json index b722be5021..22b2c7b1c8 100644 --- a/deeppavlov/configs/spelling_correction/brillmoore_kartaslov_ru_nolm.json +++ b/deeppavlov/configs/spelling_correction/brillmoore_kartaslov_ru_nolm.json @@ -58,6 +58,9 @@ "test_best": true }, "metadata": { + "requirements": [ + "../requirements/spelling.txt" + ], "labels": { "telegram_utils": "ErrorModel", "server_utils": "ErrorModel" diff --git a/deeppavlov/configs/spelling_correction/brillmoore_wikitypos_en.json b/deeppavlov/configs/spelling_correction/brillmoore_wikitypos_en.json index 16ebbad906..5b801ea844 100644 --- a/deeppavlov/configs/spelling_correction/brillmoore_wikitypos_en.json +++ b/deeppavlov/configs/spelling_correction/brillmoore_wikitypos_en.json @@ -58,6 +58,9 @@ "test_best": true }, "metadata": { + "requirements": [ + "../requirements/spelling.txt" + ], "labels": { "telegram_utils": "ErrorModel", "server_utils": "ErrorModel" From 367160936d9d04fb4423075427e8d6cf3726a919 Mon Sep 17 00:00:00 2001 From: leonid Date: Tue, 3 Jul 2018 17:18:18 +0300 Subject: [PATCH 569/616] feat: add datalearningiterator --- deeppavlov/dataset_iterators/ranking_iterator.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/deeppavlov/dataset_iterators/ranking_iterator.py b/deeppavlov/dataset_iterators/ranking_iterator.py index 35d4178aa3..e5e01610ac 100644 --- a/deeppavlov/dataset_iterators/ranking_iterator.py +++ b/deeppavlov/dataset_iterators/ranking_iterator.py @@ -1,15 +1,16 @@ from deeppavlov.core.common.registry import register +from deeppavlov.core.data.data_learning_iterator import DataLearningIterator import numpy as np @register('ranking_iterator') -class RankingIterator: +class RankingIterator(DataLearningIterator): def __init__(self, data, len_vocab, sample_candidates, sample_candidates_valid, sample_candidates_test, num_negative_samples, num_ranking_samples_valid, num_ranking_samples_test, - seed=None): + shuffle=False, seed=None): self.len_vocab = len_vocab self.sample_candidates = sample_candidates self.sample_candidates_valid = sample_candidates_valid @@ -29,6 +30,9 @@ def __init__(self, data, len_vocab, 'all': self.train + self.test + self.valid } + super().__init__(data, seed=seed, shuffle=shuffle) + + def gen_batches(self, batch_size, data_type="train", shuffle=True): y = batch_size * [np.ones(2)] data = self.data[data_type] From 482927e807707552ceaa7a63d1935f7df53294a9 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 3 Jul 2018 18:09:30 +0300 Subject: [PATCH 570/616] feat: add appropriate requirements to ner configs --- deeppavlov/configs/ner/ner_conll2003.json | 4 ++++ deeppavlov/configs/ner/ner_conll2003_pos.json | 4 ++++ deeppavlov/configs/ner/ner_dstc2.json | 3 +++ deeppavlov/configs/ner/ner_ontonotes.json | 4 ++++ deeppavlov/configs/ner/ner_rus.json | 4 ++++ deeppavlov/configs/ner/slotfill_dstc2.json | 3 +++ deeppavlov/configs/ner/slotfill_dstc2_raw.json | 3 +++ requirements/tf-gpu.txt | 2 +- 8 files changed, 26 insertions(+), 1 deletion(-) diff --git a/deeppavlov/configs/ner/ner_conll2003.json b/deeppavlov/configs/ner/ner_conll2003.json index 6739d49177..93474a85d2 100644 --- a/deeppavlov/configs/ner/ner_conll2003.json +++ b/deeppavlov/configs/ner/ner_conll2003.json @@ -144,6 +144,10 @@ "show_examples": false }, "metadata": { + "requirements": [ + "../requirements/gensim.txt", + "../requirements/tf-gpu.txt" + ], "labels": { "telegram_utils": "NERCoNLL2003Model", "server_utils": "NER" diff --git a/deeppavlov/configs/ner/ner_conll2003_pos.json b/deeppavlov/configs/ner/ner_conll2003_pos.json index 89e60faf9d..3bbd8f5c05 100644 --- a/deeppavlov/configs/ner/ner_conll2003_pos.json +++ b/deeppavlov/configs/ner/ner_conll2003_pos.json @@ -162,6 +162,10 @@ "show_examples": false }, "metadata": { + "requirements": [ + "../requirements/gensim.txt", + "../requirements/tf-gpu.txt" + ], "labels": { "telegram_utils": "NERCoNLL2003Model", "server_utils": "NER" diff --git a/deeppavlov/configs/ner/ner_dstc2.json b/deeppavlov/configs/ner/ner_dstc2.json index 3c15142bfa..9da047a5dc 100644 --- a/deeppavlov/configs/ner/ner_dstc2.json +++ b/deeppavlov/configs/ner/ner_dstc2.json @@ -91,6 +91,9 @@ "show_examples": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt" + ], "labels": { "telegram_utils": "NERModel", "server_utils": "NER" diff --git a/deeppavlov/configs/ner/ner_ontonotes.json b/deeppavlov/configs/ner/ner_ontonotes.json index e7f102cf19..166b5719ae 100644 --- a/deeppavlov/configs/ner/ner_ontonotes.json +++ b/deeppavlov/configs/ner/ner_ontonotes.json @@ -128,6 +128,10 @@ "show_examples": false }, "metadata": { + "requirements": [ + "../requirements/gensim.txt", + "../requirements/tf-gpu.txt" + ], "labels": { "telegram_utils": "NERCoNLL2003Model", "server_utils": "NER" diff --git a/deeppavlov/configs/ner/ner_rus.json b/deeppavlov/configs/ner/ner_rus.json index 4afc3d7c2f..799046523a 100644 --- a/deeppavlov/configs/ner/ner_rus.json +++ b/deeppavlov/configs/ner/ner_rus.json @@ -141,6 +141,10 @@ "show_examples": false }, "metadata": { + "requirements": [ + "../requirements/fasttext.txt", + "../requirements/tf-gpu.txt" + ], "labels": { "telegram_utils": "NERCoNLL2003Model", "server_utils": "NER" diff --git a/deeppavlov/configs/ner/slotfill_dstc2.json b/deeppavlov/configs/ner/slotfill_dstc2.json index 3bc13288a1..3dc7ee4535 100644 --- a/deeppavlov/configs/ner/slotfill_dstc2.json +++ b/deeppavlov/configs/ner/slotfill_dstc2.json @@ -37,6 +37,9 @@ "metrics": ["slots_accuracy"] }, "metadata": { + "requirements": [ + "../requirements/tf.txt" + ], "labels": { "telegram_utils": "NERModel", "server_utils": "DstcSlotFillingNetwork" diff --git a/deeppavlov/configs/ner/slotfill_dstc2_raw.json b/deeppavlov/configs/ner/slotfill_dstc2_raw.json index f0197f1679..80089b9a02 100644 --- a/deeppavlov/configs/ner/slotfill_dstc2_raw.json +++ b/deeppavlov/configs/ner/slotfill_dstc2_raw.json @@ -23,6 +23,9 @@ "out": ["slots"] }, "metadata": { + "requirements": [ + "../requirements/tf.txt" + ], "labels": { "telegram_utils": "NERModel" }, diff --git a/requirements/tf-gpu.txt b/requirements/tf-gpu.txt index 3980ace68c..effcf7e687 100644 --- a/requirements/tf-gpu.txt +++ b/requirements/tf-gpu.txt @@ -1 +1 @@ -tensorflow==1.8.0 \ No newline at end of file +tensorflow-gpu==1.8.0 \ No newline at end of file From dc3b4e0a29b16330dbc915ceb55c47f95c859170 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 3 Jul 2018 18:17:03 +0300 Subject: [PATCH 571/616] feat: allow to define required imports in intents and morphotaggers config's `meta` --- deeppavlov/configs/intents/intents_dstc2.json | 4 ++++ deeppavlov/configs/intents/intents_dstc2_big.json | 4 ++++ deeppavlov/configs/intents/intents_sample_csv.json | 4 ++++ deeppavlov/configs/intents/intents_sample_json.json | 4 ++++ deeppavlov/configs/intents/intents_snips.json | 4 ++++ .../configs/morpho_tagger/UD2.0/hu/morpho_hu_predict.json | 3 +++ .../configs/morpho_tagger/UD2.0/hu/morpho_hu_train.json | 3 +++ .../UD2.0/ru_syntagrus/morpho_ru_syntagrus_predict.json | 3 +++ .../UD2.0/ru_syntagrus/morpho_ru_syntagrus_train.json | 3 +++ 9 files changed, 32 insertions(+) diff --git a/deeppavlov/configs/intents/intents_dstc2.json b/deeppavlov/configs/intents/intents_dstc2.json index 519d0bb8b2..c942196c97 100644 --- a/deeppavlov/configs/intents/intents_dstc2.json +++ b/deeppavlov/configs/intents/intents_dstc2.json @@ -107,6 +107,10 @@ "show_examples": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel", "server_utils": "KerasIntentModel" diff --git a/deeppavlov/configs/intents/intents_dstc2_big.json b/deeppavlov/configs/intents/intents_dstc2_big.json index 3fcc7488eb..d1f769fb68 100644 --- a/deeppavlov/configs/intents/intents_dstc2_big.json +++ b/deeppavlov/configs/intents/intents_dstc2_big.json @@ -107,6 +107,10 @@ "show_examples": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel" }, diff --git a/deeppavlov/configs/intents/intents_sample_csv.json b/deeppavlov/configs/intents/intents_sample_csv.json index defdc73d9e..ba823a2260 100644 --- a/deeppavlov/configs/intents/intents_sample_csv.json +++ b/deeppavlov/configs/intents/intents_sample_csv.json @@ -113,6 +113,10 @@ "test_best": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel", "server_utils": "KerasIntentModel" diff --git a/deeppavlov/configs/intents/intents_sample_json.json b/deeppavlov/configs/intents/intents_sample_json.json index 5c3e732a2c..67c68012bb 100644 --- a/deeppavlov/configs/intents/intents_sample_json.json +++ b/deeppavlov/configs/intents/intents_sample_json.json @@ -108,6 +108,10 @@ "test_best": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel", "server_utils": "KerasIntentModel" diff --git a/deeppavlov/configs/intents/intents_snips.json b/deeppavlov/configs/intents/intents_snips.json index 573b5aca17..720261baa5 100644 --- a/deeppavlov/configs/intents/intents_snips.json +++ b/deeppavlov/configs/intents/intents_snips.json @@ -106,6 +106,10 @@ "test_best": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel", "server_utils": "KerasIntentModel" diff --git a/deeppavlov/configs/morpho_tagger/UD2.0/hu/morpho_hu_predict.json b/deeppavlov/configs/morpho_tagger/UD2.0/hu/morpho_hu_predict.json index 02b0384f3d..75d6ca1d09 100644 --- a/deeppavlov/configs/morpho_tagger/UD2.0/hu/morpho_hu_predict.json +++ b/deeppavlov/configs/morpho_tagger/UD2.0/hu/morpho_hu_predict.json @@ -64,6 +64,9 @@ "outfile": "results/ud_hu_test.res" }, "metadata": { + "requirements": [ + "../requirements/tf.txt" + ], "download": [ "http://lnsigo.mipt.ru/export/deeppavlov_data/morpho_tagger.tar.gz", { diff --git a/deeppavlov/configs/morpho_tagger/UD2.0/hu/morpho_hu_train.json b/deeppavlov/configs/morpho_tagger/UD2.0/hu/morpho_hu_train.json index 142c637f34..c9de1b64ca 100644 --- a/deeppavlov/configs/morpho_tagger/UD2.0/hu/morpho_hu_train.json +++ b/deeppavlov/configs/morpho_tagger/UD2.0/hu/morpho_hu_train.json @@ -66,6 +66,9 @@ "log_every_n_epochs": 1 }, "metadata": { + "requirements": [ + "../requirements/tf.txt" + ], "download": [ "http://lnsigo.mipt.ru/export/deeppavlov_data/morpho_tagger.tar.gz", { diff --git a/deeppavlov/configs/morpho_tagger/UD2.0/ru_syntagrus/morpho_ru_syntagrus_predict.json b/deeppavlov/configs/morpho_tagger/UD2.0/ru_syntagrus/morpho_ru_syntagrus_predict.json index 92c8ff3187..36b575881e 100644 --- a/deeppavlov/configs/morpho_tagger/UD2.0/ru_syntagrus/morpho_ru_syntagrus_predict.json +++ b/deeppavlov/configs/morpho_tagger/UD2.0/ru_syntagrus/morpho_ru_syntagrus_predict.json @@ -65,6 +65,9 @@ "outfile": "results/ud_ru_syntagrus_test.res" }, "metadata": { + "requirements": [ + "../requirements/tf.txt" + ], "download": [ "http://lnsigo.mipt.ru/export/deeppavlov_data/morpho_tagger.tar.gz", { diff --git a/deeppavlov/configs/morpho_tagger/UD2.0/ru_syntagrus/morpho_ru_syntagrus_train.json b/deeppavlov/configs/morpho_tagger/UD2.0/ru_syntagrus/morpho_ru_syntagrus_train.json index 28bb090215..5ee015a4b7 100644 --- a/deeppavlov/configs/morpho_tagger/UD2.0/ru_syntagrus/morpho_ru_syntagrus_train.json +++ b/deeppavlov/configs/morpho_tagger/UD2.0/ru_syntagrus/morpho_ru_syntagrus_train.json @@ -66,6 +66,9 @@ "log_every_n_epochs": 1 }, "metadata": { + "requirements": [ + "../requirements/tf.txt" + ], "download": [ "http://lnsigo.mipt.ru/export/deeppavlov_data/morpho_tagger.tar.gz", { From 36fdc64ed1988b8ca2e9d70b44672a9c4743b639 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 3 Jul 2018 18:20:37 +0300 Subject: [PATCH 572/616] feat: allow to define required imports in squad config's `meta` --- deeppavlov/configs/squad/squad.json | 3 +++ deeppavlov/configs/squad/squad_ru.json | 3 +++ 2 files changed, 6 insertions(+) diff --git a/deeppavlov/configs/squad/squad.json b/deeppavlov/configs/squad/squad.json index 87b3d5f4e3..96c2f78a44 100644 --- a/deeppavlov/configs/squad/squad.json +++ b/deeppavlov/configs/squad/squad.json @@ -104,6 +104,9 @@ "metrics": ["squad_f1", "exact_match"] }, "metadata": { + "requirements": [ + "../requirements/tf-gpu.txt" + ], "labels": { "telegram_utils": "SquadModel", "server_utils": "SquadModel" diff --git a/deeppavlov/configs/squad/squad_ru.json b/deeppavlov/configs/squad/squad_ru.json index d97230e8a9..4501ab9846 100644 --- a/deeppavlov/configs/squad/squad_ru.json +++ b/deeppavlov/configs/squad/squad_ru.json @@ -105,6 +105,9 @@ "metrics": ["squad_f1", "exact_match"] }, "metadata": { + "requirements": [ + "../requirements/tf-gpu.txt" + ], "labels": { "telegram_utils": "SquadModel", "server_utils": "SquadModel" From 880abe2f67330fa16330281d9795346f8cf38367 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 3 Jul 2018 18:37:37 +0300 Subject: [PATCH 573/616] feat: allow to define required imports in odqa config's `meta` --- deeppavlov/configs/odqa/en_odqa_infer_prod.json | 4 ++++ deeppavlov/configs/odqa/en_ranker_prod.json | 3 +++ deeppavlov/configs/odqa/odqa_infer_test.json | 4 ++++ deeppavlov/configs/odqa/ranker_test.json | 3 +++ deeppavlov/configs/odqa/ru_odqa_infer_prod.json | 3 +++ deeppavlov/configs/odqa/ru_ranker_prod.json | 1 + .../models/vectorizers/hashing_tfidf_vectorizer.py | 3 +-- tests/test_quick_start.py | 12 ++++++++++++ 8 files changed, 31 insertions(+), 2 deletions(-) diff --git a/deeppavlov/configs/odqa/en_odqa_infer_prod.json b/deeppavlov/configs/odqa/en_odqa_infer_prod.json index 719c2fe7f2..8d9b3e0d6a 100644 --- a/deeppavlov/configs/odqa/en_odqa_infer_prod.json +++ b/deeppavlov/configs/odqa/en_odqa_infer_prod.json @@ -46,6 +46,10 @@ ] }, "metadata": { + "requirements": [ + "../requirements/tf-gpu.txt", + "../requirements/spacy.txt" + ], "labels": { "server_utils": "ODQA" }, diff --git a/deeppavlov/configs/odqa/en_ranker_prod.json b/deeppavlov/configs/odqa/en_ranker_prod.json index b12fd56eee..c4604dd02c 100644 --- a/deeppavlov/configs/odqa/en_ranker_prod.json +++ b/deeppavlov/configs/odqa/en_ranker_prod.json @@ -51,6 +51,9 @@ "batch_size": 10000 }, "metadata": { + "requirements": [ + "../requirements/spacy.txt" + ], "labels": { "server_utils": "Ranker" }, diff --git a/deeppavlov/configs/odqa/odqa_infer_test.json b/deeppavlov/configs/odqa/odqa_infer_test.json index c1e4c2a9c4..e1e8d70399 100644 --- a/deeppavlov/configs/odqa/odqa_infer_test.json +++ b/deeppavlov/configs/odqa/odqa_infer_test.json @@ -66,6 +66,10 @@ ] }, "metadata": { + "requirements": [ + "../requirements/tf-gpu.txt", + "../requirements/spacy.txt" + ], "labels": { "server_utils": "ODQA" }, diff --git a/deeppavlov/configs/odqa/ranker_test.json b/deeppavlov/configs/odqa/ranker_test.json index bd15e4c4b2..a23a22aa33 100644 --- a/deeppavlov/configs/odqa/ranker_test.json +++ b/deeppavlov/configs/odqa/ranker_test.json @@ -51,6 +51,9 @@ "batch_size": 2 }, "metadata": { + "requirements": [ + "../requirements/spacy.txt" + ], "labels": { "server_utils": "Ranker" }, diff --git a/deeppavlov/configs/odqa/ru_odqa_infer_prod.json b/deeppavlov/configs/odqa/ru_odqa_infer_prod.json index 0dd568c206..ddc2fa946a 100644 --- a/deeppavlov/configs/odqa/ru_odqa_infer_prod.json +++ b/deeppavlov/configs/odqa/ru_odqa_infer_prod.json @@ -46,6 +46,9 @@ ] }, "metadata": { + "requirements": [ + "../requirements/tf-gpu.txt" + ], "labels": { "server_utils": "ODQA" }, diff --git a/deeppavlov/configs/odqa/ru_ranker_prod.json b/deeppavlov/configs/odqa/ru_ranker_prod.json index 3b380bc5eb..9db8c31d0f 100644 --- a/deeppavlov/configs/odqa/ru_ranker_prod.json +++ b/deeppavlov/configs/odqa/ru_ranker_prod.json @@ -51,6 +51,7 @@ "batch_size": 10000 }, "metadata": { + "requirements": [], "labels": { "server_utils": "Ranker" }, diff --git a/deeppavlov/models/vectorizers/hashing_tfidf_vectorizer.py b/deeppavlov/models/vectorizers/hashing_tfidf_vectorizer.py index de29bad89e..4a0a464792 100644 --- a/deeppavlov/models/vectorizers/hashing_tfidf_vectorizer.py +++ b/deeppavlov/models/vectorizers/hashing_tfidf_vectorizer.py @@ -22,7 +22,6 @@ import numpy as np from sklearn.utils import murmurhash3_32 -from deeppavlov.models.tokenizers.spacy_tokenizer import StreamSpacyTokenizer from deeppavlov.core.models.component import Component from deeppavlov.core.models.serializable import Serializable from deeppavlov.core.common.log import get_logger @@ -42,7 +41,7 @@ class HashingTfIdfVectorizer(Component, Serializable): Create a tfidf matrix from collection of documents. """ - def __init__(self, hash_size=2 ** 24, tokenizer: Type = StreamSpacyTokenizer, doc_index: dict =None, + def __init__(self, tokenizer, hash_size=2 ** 24, doc_index: dict =None, save_path: str = None, load_path: str = None, **kwargs): """ diff --git a/tests/test_quick_start.py b/tests/test_quick_start.py index 63c6f6edce..3811aefba5 100644 --- a/tests/test_quick_start.py +++ b/tests/test_quick_start.py @@ -166,6 +166,16 @@ def teardown_module(): @pytest.mark.parametrize("model,conf_file,model_dir,mode", TEST_GRID, scope='class') class TestQuickStart(object): + @staticmethod + def install(conf_file): + logfile = io.BytesIO(b'') + _, exitstatus = pexpect.run(sys.executable + " -m deeppavlov install " + str(conf_file), timeout=None, + withexitstatus=True, + logfile=logfile) + if exitstatus != 0: + logfile.seek(0) + raise RuntimeError('Installing process of {} returned non-zero exit code: \n{}' + .format(conf_file, ''.join((line.decode() for line in logfile.readlines())))) @staticmethod def interact(conf_file, model_dir, qr_list=None): @@ -236,6 +246,7 @@ def interact_api(conf_file): def test_interacting_pretrained_model(self, model, conf_file, model_dir, mode): if 'IP' in mode: config_file_path = str(test_configs_path.joinpath(conf_file)) + self.install(config_file_path) deep_download(['-test', '-c', config_file_path]) self.interact(test_configs_path / conf_file, model_dir, PARAMS[model][(conf_file, model_dir, mode)]) @@ -258,6 +269,7 @@ def test_consecutive_training_and_interacting(self, model, conf_file, model_dir, if 'IP' not in mode: config_path = str(test_configs_path.joinpath(conf_file)) + self.install(config_path) deep_download(['-test', '-c', config_path]) shutil.rmtree(str(model_path), ignore_errors=True) From 61cfbee453995f9e12d322c460851fcfc30c5921 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Tue, 3 Jul 2018 18:55:47 +0300 Subject: [PATCH 574/616] feat: allow to define required imports in ranking, sentiment and seq2seq config's `meta` --- deeppavlov/configs/ranking/ranking_insurance.json | 4 ++++ deeppavlov/configs/sentiment/insults_kaggle.json | 4 ++++ deeppavlov/configs/sentiment/sentiment_ag_news.json | 4 ++++ deeppavlov/configs/sentiment/sentiment_twitter.json | 4 ++++ deeppavlov/configs/seq2seq_go_bot/bot_kvret.json | 4 ++++ deeppavlov/configs/seq2seq_go_bot/bot_kvret_infer.json | 4 ++++ deeppavlov/models/seq2seq_go_bot/bot.py | 2 -- deeppavlov/models/seq2seq_go_bot/network.py | 1 - tests/test_configs/intents/intents_snips_bigru.json | 4 ++++ tests/test_configs/intents/intents_snips_bilstm.json | 4 ++++ tests/test_configs/intents/intents_snips_bilstm_bilstm.json | 4 ++++ tests/test_configs/intents/intents_snips_bilstm_cnn.json | 4 ++++ .../intents/intents_snips_bilstm_self_add_attention.json | 4 ++++ .../intents/intents_snips_bilstm_self_mult_attention.json | 4 ++++ tests/test_configs/intents/intents_snips_cnn_bilstm.json | 4 ++++ 15 files changed, 52 insertions(+), 3 deletions(-) diff --git a/deeppavlov/configs/ranking/ranking_insurance.json b/deeppavlov/configs/ranking/ranking_insurance.json index d1de282c03..2b4c6e40b7 100644 --- a/deeppavlov/configs/ranking/ranking_insurance.json +++ b/deeppavlov/configs/ranking/ranking_insurance.json @@ -56,6 +56,10 @@ "log_every_n_batches": 10 }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/gensim.txt" + ], "labels": { "telegram_utils": "RankingModel", "server_utils": "Ranker" diff --git a/deeppavlov/configs/sentiment/insults_kaggle.json b/deeppavlov/configs/sentiment/insults_kaggle.json index 82eaf6bc36..0356360d98 100644 --- a/deeppavlov/configs/sentiment/insults_kaggle.json +++ b/deeppavlov/configs/sentiment/insults_kaggle.json @@ -107,6 +107,10 @@ "test_best": true }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel", "server_utils": "KerasIntentModel" diff --git a/deeppavlov/configs/sentiment/sentiment_ag_news.json b/deeppavlov/configs/sentiment/sentiment_ag_news.json index 897111dba7..669b33ec91 100644 --- a/deeppavlov/configs/sentiment/sentiment_ag_news.json +++ b/deeppavlov/configs/sentiment/sentiment_ag_news.json @@ -106,6 +106,10 @@ "test_best": true }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel", "server_utils": "KerasIntentModel" diff --git a/deeppavlov/configs/sentiment/sentiment_twitter.json b/deeppavlov/configs/sentiment/sentiment_twitter.json index df36bf3b38..a7a4d3f120 100644 --- a/deeppavlov/configs/sentiment/sentiment_twitter.json +++ b/deeppavlov/configs/sentiment/sentiment_twitter.json @@ -107,6 +107,10 @@ "test_best": true }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel", "server_utils": "KerasIntentModel" diff --git a/deeppavlov/configs/seq2seq_go_bot/bot_kvret.json b/deeppavlov/configs/seq2seq_go_bot/bot_kvret.json index c61470632b..69e9d27e03 100644 --- a/deeppavlov/configs/seq2seq_go_bot/bot_kvret.json +++ b/deeppavlov/configs/seq2seq_go_bot/bot_kvret.json @@ -107,6 +107,10 @@ "show_examples": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/spacy.txt" + ], "labels": { "telegram_utils": "Seq2SeqGoalOrientedBot", "server_utils": "GoalOrientedBot" diff --git a/deeppavlov/configs/seq2seq_go_bot/bot_kvret_infer.json b/deeppavlov/configs/seq2seq_go_bot/bot_kvret_infer.json index 269a3fefeb..a03f33644a 100644 --- a/deeppavlov/configs/seq2seq_go_bot/bot_kvret_infer.json +++ b/deeppavlov/configs/seq2seq_go_bot/bot_kvret_infer.json @@ -82,6 +82,10 @@ "show_examples": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/spacy.txt" + ], "labels": { "telegram_utils": "Seq2SeqGoalOrientedBot", "server_utils": "GoalOrientedBot" diff --git a/deeppavlov/models/seq2seq_go_bot/bot.py b/deeppavlov/models/seq2seq_go_bot/bot.py index 9a9da1889d..f4527ea45b 100644 --- a/deeppavlov/models/seq2seq_go_bot/bot.py +++ b/deeppavlov/models/seq2seq_go_bot/bot.py @@ -15,13 +15,11 @@ """ import itertools -import numpy as np from typing import Type from deeppavlov.core.common.registry import register from deeppavlov.core.models.nn_model import NNModel from deeppavlov.core.data.vocab import DefaultVocabulary -from deeppavlov.models.embedders.fasttext_embedder import FasttextEmbedder from deeppavlov.models.seq2seq_go_bot.network import Seq2SeqGoalOrientedBotNetwork from deeppavlov.core.common.log import get_logger diff --git a/deeppavlov/models/seq2seq_go_bot/network.py b/deeppavlov/models/seq2seq_go_bot/network.py index 1456c28f00..30deaea262 100644 --- a/deeppavlov/models/seq2seq_go_bot/network.py +++ b/deeppavlov/models/seq2seq_go_bot/network.py @@ -16,7 +16,6 @@ import json import tensorflow as tf -from tensorflow.contrib.layers import xavier_initializer from deeppavlov.core.common.registry import register from deeppavlov.core.common.errors import ConfigError diff --git a/tests/test_configs/intents/intents_snips_bigru.json b/tests/test_configs/intents/intents_snips_bigru.json index 9f05093c61..945b5a04c7 100644 --- a/tests/test_configs/intents/intents_snips_bigru.json +++ b/tests/test_configs/intents/intents_snips_bigru.json @@ -103,6 +103,10 @@ "test_best": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel" }, diff --git a/tests/test_configs/intents/intents_snips_bilstm.json b/tests/test_configs/intents/intents_snips_bilstm.json index 5496685fde..879bdea6a8 100644 --- a/tests/test_configs/intents/intents_snips_bilstm.json +++ b/tests/test_configs/intents/intents_snips_bilstm.json @@ -103,6 +103,10 @@ "test_best": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel" }, diff --git a/tests/test_configs/intents/intents_snips_bilstm_bilstm.json b/tests/test_configs/intents/intents_snips_bilstm_bilstm.json index e40bbb0775..6d264761ea 100644 --- a/tests/test_configs/intents/intents_snips_bilstm_bilstm.json +++ b/tests/test_configs/intents/intents_snips_bilstm_bilstm.json @@ -104,6 +104,10 @@ "test_best": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel" }, diff --git a/tests/test_configs/intents/intents_snips_bilstm_cnn.json b/tests/test_configs/intents/intents_snips_bilstm_cnn.json index 82c13c89fe..bf65fc44e9 100644 --- a/tests/test_configs/intents/intents_snips_bilstm_cnn.json +++ b/tests/test_configs/intents/intents_snips_bilstm_cnn.json @@ -110,6 +110,10 @@ "test_best": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel" }, diff --git a/tests/test_configs/intents/intents_snips_bilstm_self_add_attention.json b/tests/test_configs/intents/intents_snips_bilstm_self_add_attention.json index 6e0b5660d1..5b964b92bc 100644 --- a/tests/test_configs/intents/intents_snips_bilstm_self_add_attention.json +++ b/tests/test_configs/intents/intents_snips_bilstm_self_add_attention.json @@ -105,6 +105,10 @@ "test_best": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel" }, diff --git a/tests/test_configs/intents/intents_snips_bilstm_self_mult_attention.json b/tests/test_configs/intents/intents_snips_bilstm_self_mult_attention.json index e707677f12..fb3d5e4b0f 100644 --- a/tests/test_configs/intents/intents_snips_bilstm_self_mult_attention.json +++ b/tests/test_configs/intents/intents_snips_bilstm_self_mult_attention.json @@ -105,6 +105,10 @@ "test_best": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel" }, diff --git a/tests/test_configs/intents/intents_snips_cnn_bilstm.json b/tests/test_configs/intents/intents_snips_cnn_bilstm.json index affdef2f07..8bfaeee143 100644 --- a/tests/test_configs/intents/intents_snips_cnn_bilstm.json +++ b/tests/test_configs/intents/intents_snips_cnn_bilstm.json @@ -110,6 +110,10 @@ "test_best": false }, "metadata": { + "requirements": [ + "../requirements/tf.txt", + "../requirements/fasttext.txt" + ], "labels": { "telegram_utils": "IntentModel" }, From b562e0ba81eada2c5af2958e3ab0e5410dd91d40 Mon Sep 17 00:00:00 2001 From: DeepPavlov Date: Wed, 4 Jul 2018 07:31:23 +0300 Subject: [PATCH 575/616] Add files via upload "Hello World!" in Python. --- examples/hello_bot.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 examples/hello_bot.py diff --git a/examples/hello_bot.py b/examples/hello_bot.py new file mode 100644 index 0000000000..90c3630188 --- /dev/null +++ b/examples/hello_bot.py @@ -0,0 +1,17 @@ +# This is "Hello world!" example of simple bot implemented in DeepPavlov. +# +# Imports key components to build HelloBot. +from deeppavlov.core.agent import Agent, HighestConfidenceSelector +from deeppavlov.skills.pattern_matching_skill import PatternMatchingSkill + +# Creates skills as pre-defined responses for a user's input containing specific keywords. +# Every skill returns response and confidence. +hello = PatternMatchingSkill(responses=['Hello world! :)'], patterns=["hi", "hello", "good day"]) +bye = PatternMatchingSkill(['Goodbye world! :(', 'See you around.'], ["bye", "chao", "see you"]) +fallback = PatternMatchingSkill(["I don't understand, sorry :/", 'I can say "Hello world!" 8)']) + +# Agent executes skills and then takes response from the skill with the highest confidence. +HelloBot = Agent([hello, bye, fallback], skills_selector=HighestConfidenceSelector()) + +# Give the floor to the HelloBot! +print(HelloBot(['Hello!', 'Boo...', 'Bye.'])) \ No newline at end of file From 1caec07515b784241fc3ca956abd200c8daec1cd Mon Sep 17 00:00:00 2001 From: DeepPavlov Date: Wed, 4 Jul 2018 07:32:12 +0300 Subject: [PATCH 576/616] Rename hello_agent.ipynb to hello_bot.ipynb --- examples/{hello_agent.ipynb => hello_bot.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename examples/{hello_agent.ipynb => hello_bot.ipynb} (100%) diff --git a/examples/hello_agent.ipynb b/examples/hello_bot.ipynb similarity index 100% rename from examples/hello_agent.ipynb rename to examples/hello_bot.ipynb From 8de35910583b73eb74cd2f8fd45cf41700aa4d0d Mon Sep 17 00:00:00 2001 From: DeepPavlov Date: Wed, 4 Jul 2018 07:40:41 +0300 Subject: [PATCH 577/616] Create README.md --- examples/README.md | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 examples/README.md diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 0000000000..651d820b12 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,26 @@ +# This is "Hello world!" example of simple bot implemented in DeepPavlov + +Import key components to build HelloBot. +```python +from deeppavlov.core.agent import Agent, HighestConfidenceSelector +from deeppavlov.skills.pattern_matching_skill import PatternMatchingSkill +``` + +Create skills as pre-defined responses for a user's input containing specific keywords. Every skill returns response and confidence. +```python +hello = PatternMatchingSkill(responses=['Hello world! :)'], patterns=["hi", "hello", "good day"]) +bye = PatternMatchingSkill(['Goodbye world! :(', 'See you around.'], ["bye", "chao", "see you"]) +fallback = PatternMatchingSkill(["I don't understand, sorry :/", 'I can say "Hello world!" 8)']) +``` + +Agent executes skills and then takes response from the skill with the highest confidence. +```python +HelloBot = Agent([hello, bye, fallback], skills_selector=HighestConfidenceSelector()) +``` + +Give the floor to the HelloBot! +```python +print(HelloBot(['Hello!', 'Boo...', 'Bye.'])) +``` + +[Jupyther notebook with HelloBot example.](hello_bot.ipynb) From 3154811ad879ad90aac9066766de60828a25a566 Mon Sep 17 00:00:00 2001 From: leonid Date: Wed, 4 Jul 2018 11:18:47 +0300 Subject: [PATCH 578/616] chore: add datalearningiterator --- deeppavlov/dataset_iterators/ranking_iterator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/dataset_iterators/ranking_iterator.py b/deeppavlov/dataset_iterators/ranking_iterator.py index e5e01610ac..2b05bb081f 100644 --- a/deeppavlov/dataset_iterators/ranking_iterator.py +++ b/deeppavlov/dataset_iterators/ranking_iterator.py @@ -30,7 +30,7 @@ def __init__(self, data, len_vocab, 'all': self.train + self.test + self.valid } - super().__init__(data, seed=seed, shuffle=shuffle) + super().__init__(self.data, seed=seed, shuffle=shuffle) def gen_batches(self, batch_size, data_type="train", shuffle=True): From 0406e0cdd961a38616bf6c88389f2ff8a01734f0 Mon Sep 17 00:00:00 2001 From: Marat Zaynutdinov Date: Wed, 4 Jul 2018 16:04:22 +0300 Subject: [PATCH 579/616] use ru_sent_tokenize definition from its repo --- .../models/tokenizers/ru_sent_tokenizer.py | 177 +----------------- requirements.txt | 3 +- 2 files changed, 6 insertions(+), 174 deletions(-) diff --git a/deeppavlov/models/tokenizers/ru_sent_tokenizer.py b/deeppavlov/models/tokenizers/ru_sent_tokenizer.py index 11473cbc7f..3c3e733b14 100644 --- a/deeppavlov/models/tokenizers/ru_sent_tokenizer.py +++ b/deeppavlov/models/tokenizers/ru_sent_tokenizer.py @@ -1,142 +1,15 @@ -import re -import logging -from typing import Set, Tuple, List +from typing import Set, Tuple from deeppavlov.core.models.component import Component from deeppavlov.core.common.registry import register - - -_SENT_RE = re.compile(r'[^\.?!…]+[\.?!…]*["»“ ]*') - -_LAST_WORD = re.compile(r'(?:\b|\d)([a-zа-я]+)\.$', re.IGNORECASE) -_FIRST_WORD = re.compile(r'^\W*(\w+)') -_ENDS_WITH_ONE_LETTER_LAT_AND_DOT = re.compile(r'(\d|\W|\b)([a-zA-Z])\.$') -_HAS_DOT_INSIDE = re.compile(r'[\w]+\.[\w]+\.$', re.IGNORECASE) -_INITIALS = re.compile(r'(\W|\b)([A-ZА-Я]{1})\.$') -_ONLY_RUS_CONSONANTS = re.compile(r'^[бвгджзйклмнпрстфхцчшщ]{1,4}$', re.IGNORECASE) -_STARTS_WITH_EMPTYNESS = re.compile(r'^\s+') -_ENDS_WITH_EMOTION = re.compile(r'[!?…]|\.{2,}\s?[)"«»,“]?$') -_STARTS_WITH_LOWER = re.compile(r'^\s*[–-—-("«]?\s*[a-zа-я]') -_STARTS_WITH_DIGIT = re.compile(r'^\s*\d') -_NUMERATION = re.compile(r'^\W*[IVXMCL\d]+\.$') -_PAIRED_SHORTENING_IN_THE_END = re.compile(r'\b(\w+)\. (\w+)\.\W*$') - -_JOIN = 0 -_MAYBE = 1 -_SPLIT = 2 - -JOINING_SHORTENINGS = {'mr', 'mrs', 'ms', 'dr', 'vs', 'англ', 'итал', 'греч', 'евр', 'араб', 'яп', 'слав', 'кит', - 'тел', 'св', 'ул', 'устар', 'им', 'г', 'см', 'д', 'стр', 'корп', 'пл', 'пер', 'сокр', 'рис'} -SHORTENINGS = {'co', 'corp', 'inc', 'авт', 'адм', 'барр', 'внутр', 'га', 'дифф', 'дол', 'долл', 'зав', 'зам', 'искл', - 'коп', 'корп', 'куб', 'лат', 'мин', 'о', 'обл', 'обр', 'прим', 'проц', 'р', 'ред', 'руб', 'рус', 'русск', - 'сан', 'сек', 'тыс', 'эт', 'яз', 'гос', 'мн', 'жен', 'муж', 'накл', 'повел', 'букв', 'шутл', 'ед'} - -PAIRED_SHORTENINGS = {('и', 'о'), ('т', 'е'), ('т', 'п'), ('у', 'е'), ('н', 'э')} - - -def _regex_split_separators(text: str) -> [str]: - return [x.strip() for x in _SENT_RE.findall(text)] - - -def _is_sentence_end(left: str, right: str, - shortenings: Set[str], - joining_shortenings: Set[str], - paired_shortenings: Set[Tuple[str, str]]) -> int: - if not _STARTS_WITH_EMPTYNESS.match(right): - return _JOIN - - if _HAS_DOT_INSIDE.search(left): - return _JOIN - - left_last_word = _LAST_WORD.search(left) - lw = ' ' - if left_last_word: - lw = left_last_word.group(1) - - if lw.lower() in joining_shortenings: - return _JOIN - - if _ONLY_RUS_CONSONANTS.search(lw) and lw[-1].islower(): - return _MAYBE - - pse = _PAIRED_SHORTENING_IN_THE_END.search(left) - if pse: - s1, s2 = pse.groups() - if (s1, s2) in paired_shortenings: - return _MAYBE - - right_first_word = _FIRST_WORD.match(right) - if right_first_word: - rw = right_first_word.group(1) - if (lw, rw) in paired_shortenings: - return _MAYBE - - if _ENDS_WITH_EMOTION.search(left) and _STARTS_WITH_LOWER.match(right): - return _JOIN - - initials = _INITIALS.search(left) - if initials: - border, _ = initials.groups() - if (border or ' ') not in "°'": - return _JOIN - - if lw.lower() in shortenings: - return _MAYBE - - last_letter = _ENDS_WITH_ONE_LETTER_LAT_AND_DOT.search(left) - if last_letter: - border, _ = last_letter.groups() - if (border or ' ') not in "°'": - return _MAYBE - if _NUMERATION.match(left): - return _JOIN - return _SPLIT - - -def ru_sent_tokenize(text: str, - shortenings: Set[str] = SHORTENINGS, - joining_shortenings: Set[str] = JOINING_SHORTENINGS, - paired_shortenings: Set[Tuple[str, str]] = PAIRED_SHORTENINGS) -> List[str]: - sentences = [] - sents = _regex_split_separators(text) - si = 0 - processed_index = 0 - sent_start = 0 - while si < len(sents): - s = sents[si] - span_start = text[processed_index:].index(s) + processed_index - span_end = span_start + len(s) - processed_index += len(s) - - si += 1 - - send = _is_sentence_end(text[sent_start: span_end], text[span_end:], - shortenings, joining_shortenings, paired_shortenings) - if send == _JOIN: - continue - - if send == _MAYBE: - if _STARTS_WITH_LOWER.match(text[span_end:]): - continue - if _STARTS_WITH_DIGIT.match(text[span_end:]): - continue - - if not text[sent_start: span_end].strip(): - logging.warning("Something went wrong while tokenizing") - sentences.append(text[sent_start: span_end].strip()) - sent_start = span_end - processed_index = span_end - - if sent_start != len(text): - if text[sent_start:].strip(): - sentences.append(text[sent_start:].strip()) - return sentences +from rusenttokenize import ru_sent_tokenize, SHORTENINGS, JOINING_SHORTENINGS, PAIRED_SHORTENINGS @register("ru_sent_tokenizer") class RuSentTokenizer(Component): """ - Rule-base sentence tokenizer for Russian language + Rule-base sentence tokenizer for Russian language. + https://github.com/deepmipt/ru_sentence_tokenizer """ def __init__(self, shortenings: Set[str] = SHORTENINGS, joining_shortenings: Set[str] = JOINING_SHORTENINGS, @@ -156,45 +29,3 @@ def __init__(self, shortenings: Set[str] = SHORTENINGS, def __call__(self, batch: [str]) -> [[str]]: return [ru_sent_tokenize(x, self.shortenings, self.joining_shortenings, self.paired_shortenings) for x in batch] - - -if __name__ == '__main__': - assert ru_sent_tokenize('купил за 5 руб. и остался доволен.') == ['купил за 5 руб. и остался доволен.'] - assert ru_sent_tokenize('Я ему сказал и т.к. он не послушался за 500р.') == ['Я ему сказал и т.к. он не послушался за 500р.'] - assert ru_sent_tokenize('Ура. Ура. 500р.') == ['Ура.', 'Ура.', '500р.'] - assert ru_sent_tokenize('Среди других её представителей — Л. Р. Зиндер, Л. В. Бондарко, М. И. Матусевич.') == \ - ['Среди других её представителей — Л. Р. Зиндер, Л. В. Бондарко, М. И. Матусевич.'] - assert ru_sent_tokenize('И. П. Павлов.') == ['И. П. Павлов.'] - assert ru_sent_tokenize('Павлов И. П., Сеченов И. М.') == ['Павлов И. П., Сеченов И. М.'] - assert ru_sent_tokenize('Основателем школы является Л. В. Щерба.') == ['Основателем школы является Л. В. Щерба.'] - assert ru_sent_tokenize('Я ему сказале: "Чтобы ничего не трогале." Но он не послушался.') == \ - ['Я ему сказале: "Чтобы ничего не трогале."', 'Но он не послушался.'] - assert ru_sent_tokenize('Нефть за $27/барр. не снится.') == ['Нефть за $27/барр. не снится.'] - assert ru_sent_tokenize('Сказала я. Он оглянулся.') == ['Сказала я.', 'Он оглянулся.'] - assert ru_sent_tokenize( - 'Летописец Нестор относит их возникновение к I столетию н.э., когда св. Андрей, проповедуя в Киеве ' - 'евангельское слово, отправился потом в Новгород, где он увидел чудо – парившихся в бане.') == \ - ['Летописец Нестор относит их возникновение к I столетию н.э., когда св. Андрей, проповедуя в Киеве евангельское слово, отправился потом в Новгород, где он увидел чудо – парившихся в бане.'] - assert ru_sent_tokenize( - '- Ну, хорошо, хочешь, я тебе тоннели покажу? - спрашивает наконец Мариам и сворачивает с одной ничем не примечательной улицы, застроенной обычными городскими многоэтажками, на другую точно такую же.') == ['- Ну, хорошо, хочешь, я тебе тоннели покажу? - спрашивает наконец Мариам и сворачивает с одной ничем не примечательной улицы, застроенной обычными городскими многоэтажками, на другую точно такую же.'] - assert ru_sent_tokenize('Где они были эти …адцать лет?') == ['Где они были эти …адцать лет?'] - assert ru_sent_tokenize('Православие... более всего подходит на роль такой идеи...') == ['Православие... более всего подходит на роль такой идеи...'] - assert ru_sent_tokenize('Yolka стоит 2400р. без трех копеек сто долларов, между прочим.') == ['Yolka стоит 2400р. без трех копеек сто долларов, между прочим.'] - assert ru_sent_tokenize( - 'А если лень читать всё - общее количество ответов: 8272! - то можно почитать книжку избранных мест.') == ['А если лень читать всё - общее количество ответов: 8272! - то можно почитать книжку избранных мест.'] - assert ru_sent_tokenize('Это стоило 50 т. к. вчера') == ['Это стоило 50 т. к. вчера'] - assert ru_sent_tokenize( - "Официально закрытие фастфудов назначено на предстоящее воскресенье, причём менеджеры не планируют снова открывать в этой стране рестораны McDonald's. Причин закрытия исландских McDonald's несколько.") == \ - ["Официально закрытие фастфудов назначено на предстоящее воскресенье, причём менеджеры не планируют снова открывать в этой стране рестораны McDonald's.", - "Причин закрытия исландских McDonald's несколько."] - assert ru_sent_tokenize( - '12 января ожидается понижение до минус 44 — минус 48°C. В школах региона отменены занятия в начальных классах.') == \ - ['12 января ожидается понижение до минус 44 — минус 48°C.', - 'В школах региона отменены занятия в начальных классах.'] - assert ru_sent_tokenize( - 'У государственных людей тоже есть дети, и если для них ночные заведения работать-таки будут… (а вы попробуйте им отказать) ну, в общем, не хотелось бы думать о волне народного возмущения.') == \ - ['У государственных людей тоже есть дети, и если для них ночные заведения работать-таки будут… (а вы попробуйте им отказать) ну, в общем, не хотелось бы думать о волне народного возмущения.'] - assert ru_sent_tokenize( - 'По сравнению с 2009 годом Россия опустилась в рейтинге на 9 позиций (т. е. ситуация в ней улучшилась).') == \ - ['По сравнению с 2009 годом Россия опустилась в рейтинге на 9 позиций (т. е. ситуация в ней улучшилась).'] - logging.info('all tests passed!') \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 7b9d79f001..1156da84ef 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,4 +23,5 @@ flask_cors==3.0.3 scipy==1.0.0 pymorphy2==0.8 pymorphy2-dicts-ru -sortedcontainers==2.0.2 \ No newline at end of file +sortedcontainers==2.0.2 +rusenttokenize==0.0.4 \ No newline at end of file From eb4872e9245ad57d5149f5f24884f779aab79ce9 Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Thu, 5 Jul 2018 09:47:40 +0300 Subject: [PATCH 580/616] chore: update registry to include ru_sent_tokenizer --- deeppavlov/core/common/registry.json | 1 + 1 file changed, 1 insertion(+) diff --git a/deeppavlov/core/common/registry.json b/deeppavlov/core/common/registry.json index b70806c2b0..90c38dd1a5 100644 --- a/deeppavlov/core/common/registry.json +++ b/deeppavlov/core/common/registry.json @@ -55,6 +55,7 @@ "random_emb_mat": "deeppavlov.models.preprocessors.assemble_embeddins_matrix:RandomEmbeddingsMatrix", "ranking_iterator": "deeppavlov.dataset_iterators.ranking_iterator:RankingIterator", "ranking_model": "deeppavlov.models.ranking.ranking_model:RankingModel", + "ru_sent_tokenizer": "deeppavlov.models.tokenizers.ru_sent_tokenizer:RuSentTokenizer", "ru_tokenizer": "deeppavlov.models.tokenizers.ru_tokenizer:RussianTokenizer", "russian_words_vocab": "deeppavlov.vocabs.typos:RussianWordsVocab", "sanitizer": "deeppavlov.models.preprocessors.sanitizer:Sanitizer", From 7fbb5a8dd2446758b4be1cc27fdf9d2c84c78b8d Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Thu, 5 Jul 2018 11:59:15 +0300 Subject: [PATCH 581/616] chore: update registry for new models --- deeppavlov/core/common/registry.json | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deeppavlov/core/common/registry.json b/deeppavlov/core/common/registry.json index 90c38dd1a5..0366376219 100644 --- a/deeppavlov/core/common/registry.json +++ b/deeppavlov/core/common/registry.json @@ -1,5 +1,6 @@ { "api_requester": "deeppavlov.models.api_requester.api_requester:ApiRequester", + "api_router": "deeppavlov.models.api_requester.api_router:ApiRouter", "babi_reader": "deeppavlov.dataset_readers.babi_reader:BabiDatasetReader", "basic_classification_iterator": "deeppavlov.dataset_iterators.basic_classification_iterator:BasicClassificationDatasetIterator", "basic_classification_reader": "deeppavlov.dataset_readers.basic_classification_reader:BasicClassificationDatasetReader", @@ -33,8 +34,8 @@ "hcn_at": "deeppavlov.models.trackers.hcn_at:ActionTracker", "hcn_et": "deeppavlov.models.trackers.hcn_et:EntityTracker", "insurance_reader": "deeppavlov.dataset_readers.insurance_reader:InsuranceReader", - "intent_model": "deeppavlov.models.classifiers.intents.intent_model:KerasIntentModel", "kenlm_elector": "deeppavlov.models.spelling_correction.electors.kenlm_elector:KenlmElector", + "keras_classification_model": "deeppavlov.models.classifiers.keras_classification_model:KerasClassificationModel", "knowledge_base": "deeppavlov.models.seq2seq_go_bot.kb:KnowledgeBase", "knowledge_base_entity_normalizer": "deeppavlov.models.seq2seq_go_bot.kb:KnowledgeBaseEntityNormalizer", "kvret_dialog_iterator": "deeppavlov.dataset_iterators.kvret_dialog_iterator:KvretDialogDatasetIterator", From f96d4b92794bf396165fd1ab78a1d7bef366fba9 Mon Sep 17 00:00:00 2001 From: yurakuratov Date: Thu, 5 Jul 2018 13:49:46 +0300 Subject: [PATCH 582/616] fix: loss logging tag name --- deeppavlov/core/commands/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index 4968ee6f0f..53a003e8f2 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -355,7 +355,7 @@ def improved(score, best): tb_train_writer.add_summary(metric_sum, epochs) if losses: - loss_sum = tf.Summary(value=[tf.Summary.Value(tag='every_n_batches/' + 'loss', + loss_sum = tf.Summary(value=[tf.Summary.Value(tag='every_n_epochs/' + 'loss', simple_value=report['loss']), ]) tb_train_writer.add_summary(loss_sum, i) From 29ead6dd48c9e7cee19672c9eb51b1f63b54cb6f Mon Sep 17 00:00:00 2001 From: yurakuratov Date: Thu, 5 Jul 2018 13:53:32 +0300 Subject: [PATCH 583/616] fix: use number of epochs instead of number of iterations for loss logging --- deeppavlov/core/commands/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeppavlov/core/commands/train.py b/deeppavlov/core/commands/train.py index 53a003e8f2..27d735f339 100644 --- a/deeppavlov/core/commands/train.py +++ b/deeppavlov/core/commands/train.py @@ -357,7 +357,7 @@ def improved(score, best): if losses: loss_sum = tf.Summary(value=[tf.Summary.Value(tag='every_n_epochs/' + 'loss', simple_value=report['loss']), ]) - tb_train_writer.add_summary(loss_sum, i) + tb_train_writer.add_summary(loss_sum, epochs) model.process_event(event_name='after_train_log', data=report) report = {'train': report} From b1657404bb10a49a4ac0a299e2c3a9f91c4fdf13 Mon Sep 17 00:00:00 2001 From: Dilyara Baymurzina Date: Thu, 5 Jul 2018 14:57:37 +0300 Subject: [PATCH 584/616] docs: info about evolution in main readme --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index afc9a50c91..75458f1aaf 100644 --- a/README.md +++ b/README.md @@ -150,6 +150,8 @@ Available model configs are: |[ODQA](deeppavlov/skills/odqa/README.md) | An open domain question answering skill. The skill accepts free-form questions about the world and outputs an answer based on its Wikipedia knowledge.| | **Embeddings** | | | [Pre-trained embeddings for the Russian language](pretrained-vectors.md) | Word vectors for the Russian language trained on joint [Russian Wikipedia](https://ru.wikipedia.org/wiki/%D0%97%D0%B0%D0%B3%D0%BB%D0%B0%D0%B2%D0%BD%D0%B0%D1%8F_%D1%81%D1%82%D1%80%D0%B0%D0%BD%D0%B8%D1%86%D0%B0) and [Lenta.ru](https://lenta.ru/) corpora. | +| **Parameters Evolution** | | +| [Parameters evolution for DeepPavlov Models](deeppavlov/models/evolution/README.md) | Implementation of parameters evolution for DeepPavlov models that requires only some small changes in a config file. | # Basic examples From 4e33f0c5e121c60ad163afe943728aaefea45014 Mon Sep 17 00:00:00 2001 From: Aleksandr Seliverstov Date: Thu, 5 Jul 2018 15:04:32 +0300 Subject: [PATCH 585/616] doc: Update docs --- README.md | 27 +++------------------------ 1 file changed, 3 insertions(+), 24 deletions(-) diff --git a/README.md b/README.md index 75458f1aaf..924e986269 100644 --- a/README.md +++ b/README.md @@ -111,28 +111,6 @@ Every line of input text will be used as a pipeline input parameter, so one exam as many input parameters your pipeline expects. You can also specify batch size with `-b` or `--batch-size` parameter. -Available model configs are: - -- ```deeppavlov/configs/go_bot/*.json``` - -- ```deeppavlov/configs/intents/*.json``` - -- ```deeppavlov/configs/morpho_tagger/*.json``` - -- ```deeppavlov/configs/ner/*.json``` - -- ```deeppavlov/configs/odqa/*.json``` - -- ```deeppavlov/configs/ranking/*.json``` - -- ```deeppavlov/configs/sentiment/*.json``` - -- ```deeppavlov/configs/seq2seq_go_bot/*.json``` - -- ```deeppavlov/configs/spelling_correction/*.json``` - -- ```deeppavlov/configs/squad/*.json``` - # Features | Component | Description | @@ -148,10 +126,11 @@ Available model configs are: | [Morphological tagging component](deeppavlov/models/morpho_tagger/README.md) | Based on character-based approach to morphological tagging [Heigold et al., 2017. An extensive empirical evaluation of character-based morphological tagging for 14 languages](http://www.aclweb.org/anthology/E17-1048). A state-of-the-art model for Russian and several other languages. Model assigns morphological tags in UD format to sequences of words.| | **Skills** | | |[ODQA](deeppavlov/skills/odqa/README.md) | An open domain question answering skill. The skill accepts free-form questions about the world and outputs an answer based on its Wikipedia knowledge.| +| **Parameters Evolution** | | +| [Parameters evolution for models](deeppavlov/models/evolution/README.md) | Implementation of parameters evolution for DeepPavlov models that requires only some small changes in a config file. | | **Embeddings** | | | [Pre-trained embeddings for the Russian language](pretrained-vectors.md) | Word vectors for the Russian language trained on joint [Russian Wikipedia](https://ru.wikipedia.org/wiki/%D0%97%D0%B0%D0%B3%D0%BB%D0%B0%D0%B2%D0%BD%D0%B0%D1%8F_%D1%81%D1%82%D1%80%D0%B0%D0%BD%D0%B8%D1%86%D0%B0) and [Lenta.ru](https://lenta.ru/) corpora. | -| **Parameters Evolution** | | -| [Parameters evolution for DeepPavlov Models](deeppavlov/models/evolution/README.md) | Implementation of parameters evolution for DeepPavlov models that requires only some small changes in a config file. | + # Basic examples From 7433efce1b5e59b3ec4efbbdd64387e38d70f80a Mon Sep 17 00:00:00 2001 From: Aleksey Lymar Date: Thu, 5 Jul 2018 18:26:19 +0300 Subject: [PATCH 586/616] chore: update registry for evolution --- deeppavlov/core/common/metrics_registry.json | 3 +++ deeppavlov/core/common/registry.json | 1 + deeppavlov/metrics/log_loss.py | 2 +- deeppavlov/metrics/mrr_classification.py | 4 +--- 4 files changed, 6 insertions(+), 4 deletions(-) diff --git a/deeppavlov/core/common/metrics_registry.json b/deeppavlov/core/common/metrics_registry.json index 1c6160e113..7936c400e9 100644 --- a/deeppavlov/core/common/metrics_registry.json +++ b/deeppavlov/core/common/metrics_registry.json @@ -3,6 +3,9 @@ "bleu": "deeppavlov.metrics.bleu:bleu", "classification_accuracy": "deeppavlov.metrics.accuracy:classification_accuracy", "classification_f1": "deeppavlov.metrics.fmeasure_classification:fmeasure", + "classification_f1_weighted": "deeppavlov.metrics.fmeasure_classification:fmeasure", + "classification_log_loss": "deeppavlov.metrics.log_loss:classification_log_loss", + "classification_mrr": "deeppavlov.metrics.mrr_classification:mrr_score", "classification_roc_auc": "deeppavlov.metrics.roc_auc_score:roc_auc_score", "exact_match": "deeppavlov.metrics.squad_metrics:exact_match", "loss": "deeppavlov.models.ranking.metrics:triplet_loss", diff --git a/deeppavlov/core/common/registry.json b/deeppavlov/core/common/registry.json index 0366376219..c55be1fe7b 100644 --- a/deeppavlov/core/common/registry.json +++ b/deeppavlov/core/common/registry.json @@ -51,6 +51,7 @@ "nltk_tokenizer": "deeppavlov.models.tokenizers.nltk_tokenizer:NLTKTokenizer", "one_hotter": "deeppavlov.models.preprocessors.one_hotter:OneHotter", "ontonotes_reader": "deeppavlov.dataset_readers.ontonotes_reader:OntonotesReader", + "params_evolution": "deeppavlov.models.evolution.evolution_param_generator:ParamsEvolution", "pymorphy_russian_lemmatizer": "deeppavlov.models.preprocessors.russian_lemmatizer:PymorphyRussianLemmatizer", "random": "deeppavlov.models.commutators.random_commutator:RandomCommutator", "random_emb_mat": "deeppavlov.models.preprocessors.assemble_embeddins_matrix:RandomEmbeddingsMatrix", diff --git a/deeppavlov/metrics/log_loss.py b/deeppavlov/metrics/log_loss.py index 368357786a..ec42196391 100644 --- a/deeppavlov/metrics/log_loss.py +++ b/deeppavlov/metrics/log_loss.py @@ -18,7 +18,7 @@ import numpy as np from deeppavlov.core.common.metrics_registry import register_metric -from deeppavlov.models.classifiers.intents.utils import labels2onehot +from deeppavlov.models.classifiers.utils import labels2onehot @register_metric('classification_log_loss') diff --git a/deeppavlov/metrics/mrr_classification.py b/deeppavlov/metrics/mrr_classification.py index b7fd72c493..438b9fbbd7 100644 --- a/deeppavlov/metrics/mrr_classification.py +++ b/deeppavlov/metrics/mrr_classification.py @@ -17,11 +17,9 @@ import numpy as np import json from scipy.stats import rankdata -import tensorflow as tf -from keras import backend as K from deeppavlov.core.common.metrics_registry import register_metric -from deeppavlov.models.classifiers.intents.utils import labels2onehot +from deeppavlov.models.classifiers.utils import labels2onehot def calc_mrr(rank): From 6fff9dead5e4c6596bdda1a8d1f449678d998edc Mon Sep 17 00:00:00 2001 From: DeepPavlov Date: Sat, 7 Jul 2018 23:15:56 +0300 Subject: [PATCH 587/616] Create README.md --- examples/tutorials/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 examples/tutorials/README.md diff --git a/examples/tutorials/README.md b/examples/tutorials/README.md new file mode 100644 index 0000000000..a44d665326 --- /dev/null +++ b/examples/tutorials/README.md @@ -0,0 +1 @@ +# DeepPavlov tutorials From 3cf3e54abe12db1eea5287674615ceb81978b7e1 Mon Sep 17 00:00:00 2001 From: DeepPavlov Date: Sat, 7 Jul 2018 23:29:56 +0300 Subject: [PATCH 588/616] Update README.md --- README.md | 118 +++++++++++++++++++++++++++++++----------------------- 1 file changed, 68 insertions(+), 50 deletions(-) diff --git a/README.md b/README.md index 924e986269..445ea8c090 100644 --- a/README.md +++ b/README.md @@ -1,62 +1,42 @@ [![License Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/deepmipt/DeepPavlov/blob/master/LICENSE) ![Python 3.6](https://img.shields.io/badge/python-3.6-green.svg) -**We are in a really early Alpha release. You should be ready for hard adventures. -In version 0.0.5 we updraded to TensorFlow 1.8, please re-download our pre-trained models.** +_We are still in a really early Alpha release._ DeepPavlov is an open-source conversational AI library built on [TensorFlow](https://www.tensorflow.org/) and [Keras](https://keras.io/). It is designed for * development of production ready chat-bots and complex conversational systems, * NLP and dialog systems research. - -Our goal is to enable AI-application developers and researchers with: - * set of pre-trained NLP models, pre-defined dialog system components (ML/DL/Rule-based) and pipeline templates; - * a framework for implementing and testing their own dialog models; - * tools for application integration with adjacent infrastructure (messengers, helpdesk software etc.); - * benchmarking environment for conversational models and uniform access to relevant datasets. -# Demo - -Demo of selected features is available at [demo.ipavlov.ai](https://demo.ipavlov.ai/) - -# Conceptual overview +# Hello Bot in DeepPavlov - - - - -

- -

- -## Key Concepts - * `Agent` is a conversational agent communicating with users in natural language (text). - * `Skill` fulfills user’s goal in some domain. Typically, this is accomplished by presenting information or completing transaction (e.g. answer question by FAQ, booking tickets etc.). However, for some tasks a success of interaction is defined as continuous engagement (e.g. chit-chat). - * `Model` is a reusable functional component of `Skill`. - * `Rule-based Models` cannot be trained. - * `Machine Learning Models` can be trained only stand alone. - * `Deep Learning Models` can be trained independently and in an end-to-end mode being joined in a chain. - * `Skill Manager` performs selection of the `Skill` to generate response. - * ` Chainer` builds an agent/component pipeline from heterogeneous components (rule-based/ml/dl). It allows to train and infer models in a pipeline as a whole. +Import key components to build HelloBot. +```python +from deeppavlov.core.agent import Agent, HighestConfidenceSelector +from deeppavlov.skills.pattern_matching_skill import PatternMatchingSkill +``` -The smallest building block of the library is `Model`. `Model` stands for any kind of function in an NLP pipeline. It can be implemented as a neural network, a non-neural ML model or a rule-based system. Besides that, `Model` can have nested structure, i.e. a `Model` can include other `Model`'(s). +Create skills as pre-defined responses for a user's input containing specific keywords. Every skill returns response and confidence. +```python +hello = PatternMatchingSkill(responses=['Hello world! :)'], patterns=["hi", "hello", "good day"]) +bye = PatternMatchingSkill(['Goodbye world! :(', 'See you around.'], ["bye", "chao", "see you"]) +fallback = PatternMatchingSkill(["I don't understand, sorry :/", 'I can say "Hello world!" 8)']) +``` -`Model`s can be joined into a `Skill`. `Skill` solves a larger NLP task compared to `Model`. However, in terms of implementation `Skill`s are not different from `Model`s. The only restriction of `Skill`s is that their input and output should both be strings. Therefore, `Skill`s are usually associated with dialogue tasks. +Agent executes skills and then takes response from the skill with the highest confidence. +```python +HelloBot = Agent([hello, bye, fallback], skills_selector=HighestConfidenceSelector()) +``` -`Agent` is supposed to be a multi-purpose dialogue system that comprises several `Skill`s and can switch between them. It can be a dialogue system that contains a goal-oriented and chatbot skills and chooses which one to use for generating the answer depending on user input. +Give the floor to the HelloBot! +```python +print(HelloBot(['Hello!', 'Boo...', 'Bye.'])) +``` -DeepPavlov is built on top of machine learning frameworks [TensorFlow](https://www.tensorflow.org/) and [Keras](https://keras.io/). Other external libraries can be used to build basic components. +[Jupyther notebook with HelloBot example.](examples/hello_bot.ipynb) ---- # Installation + 0. Currently we support only `Linux` platform and `Python 3.6` (**`Python 3.5` is not supported!**) 1. Create a virtual environment with `Python 3.6` @@ -81,6 +61,42 @@ DeepPavlov is built on top of machine learning frameworks [TensorFlow](https://w python -m spacy download en ``` +# Demo + +Demo of selected features is available at [demo.ipavlov.ai](https://demo.ipavlov.ai/) + +# Conceptual overview + +Our goal is to enable AI-application developers and researchers with: + * set of pre-trained NLP models, pre-defined dialog system components (ML/DL/Rule-based) and pipeline templates; + * a framework for implementing and testing their own dialog models; + * tools for application integration with adjacent infrastructure (messengers, helpdesk software etc.); + * benchmarking environment for conversational models and uniform access to relevant datasets. + +

+ +

+ +## Key Concepts + * `Agent` is a conversational agent communicating with users in natural language (text). + * `Skill` fulfills user’s goal in some domain. Typically, this is accomplished by presenting information or completing transaction (e.g. answer question by FAQ, booking tickets etc.). However, for some tasks a success of interaction is defined as continuous engagement (e.g. chit-chat). + * `Model` is a reusable functional component of `Skill`. + * `Rule-based Models` cannot be trained. + * `Machine Learning Models` can be trained only stand alone. + * `Deep Learning Models` can be trained independently and in an end-to-end mode being joined in a chain. + * `Skill Manager` performs selection of the `Skill` to generate response. + * ` Chainer` builds an agent/component pipeline from heterogeneous components (rule-based/ml/dl). It allows to train and infer models in a pipeline as a whole. + +The smallest building block of the library is `Model`. `Model` stands for any kind of function in an NLP pipeline. It can be implemented as a neural network, a non-neural ML model or a rule-based system. Besides that, `Model` can have nested structure, i.e. a `Model` can include other `Model`'(s). + +`Model`s can be joined into a `Skill`. `Skill` solves a larger NLP task compared to `Model`. However, in terms of implementation `Skill`s are not different from `Model`s. The only restriction of `Skill`s is that their input and output should both be strings. Therefore, `Skill`s are usually associated with dialogue tasks. + +`Agent` is supposed to be a multi-purpose dialogue system that comprises several `Skill`s and can switch between them. It can be a dialogue system that contains a goal-oriented and chatbot skills and chooses which one to use for generating the answer depending on user input. + +DeepPavlov is built on top of machine learning frameworks [TensorFlow](https://www.tensorflow.org/) and [Keras](https://keras.io/). Other external libraries can be used to build basic components. + +--- + # Quick start To use our pre-trained models, you should first download them: @@ -132,12 +148,8 @@ You can also specify batch size with `-b` or `--batch-size` parameter. | [Pre-trained embeddings for the Russian language](pretrained-vectors.md) | Word vectors for the Russian language trained on joint [Russian Wikipedia](https://ru.wikipedia.org/wiki/%D0%97%D0%B0%D0%B3%D0%BB%D0%B0%D0%B2%D0%BD%D0%B0%D1%8F_%D1%81%D1%82%D1%80%D0%B0%D0%BD%D0%B8%D1%86%D0%B0) and [Lenta.ru](https://lenta.ru/) corpora. | -# Basic examples - -View video demo of deployment of a goal-oriented bot and a slot-filling model with Telegram UI - -[![Alt text for your video](https://img.youtube.com/vi/yzoiCa_sMuY/0.jpg)](https://youtu.be/yzoiCa_sMuY) - +# Examples of some components + * Run goal-oriented bot with Telegram interface: ``` python -m deeppavlov interactbot deeppavlov/configs/go_bot/gobot_dstc2.json -d -t @@ -166,6 +178,12 @@ View video demo of deployment of a goal-oriented bot and a slot-filling model wi ``` python -m deeppavlov predict deeppavlov/configs/intents/intents_snips.json -d --batch-size 15 < /data/in.txt > /data/out.txt ``` + + View [video demo](https://youtu.be/yzoiCa_sMuY) of deployment of a goal-oriented bot and a slot-filling model with Telegram UI + +# Tutorials + +Jupyter notebooks and videos explaining how to use DeepPalov for different tasks can be found in [/examples/tutorials/](examples/tutorials/) --- @@ -220,7 +238,7 @@ View video demo of deployment of a goal-oriented bot and a slot-filling model wi -## Config +## Config of component An NLP pipeline config is a JSON file that contains one required element `chainer`: From 0379500110a8e249ab96dadb7a1c245ce749aa6b Mon Sep 17 00:00:00 2001 From: DeepPavlov Date: Sat, 7 Jul 2018 23:47:48 +0300 Subject: [PATCH 589/616] Update README.md --- examples/tutorials/README.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/examples/tutorials/README.md b/examples/tutorials/README.md index a44d665326..38785a8f3f 100644 --- a/examples/tutorials/README.md +++ b/examples/tutorials/README.md @@ -1 +1,17 @@ # DeepPavlov tutorials + +## Introduction to DeepPavlov + +[video](https://youtu.be/ElO7_wbtO6g) + +## Named Entity Recognition with DeepPavlov + +[video](https://youtu.be/6HlL87PWxXU) + +## Task-oriented bot with DeepPavlov + +[video](https://youtu.be/uvH1zB7qahI) + +## Chit-chat bot with DeepPavlov + +[video](https://youtu.be/G1TkCkoghC8) From 893e229aaa9fa7d1c8d21fa8eddffd7983651822 Mon Sep 17 00:00:00 2001 From: DeepPavlov Date: Mon, 9 Jul 2018 08:06:25 +0300 Subject: [PATCH 590/616] Add files via upload --- examples/tutorials/deeppavlov_intro.ipynb | 674 ++++++++++++++++++++++ examples/tutorials/deeppavlov_intro.pdf | Bin 0 -> 1479277 bytes 2 files changed, 674 insertions(+) create mode 100644 examples/tutorials/deeppavlov_intro.ipynb create mode 100644 examples/tutorials/deeppavlov_intro.pdf diff --git a/examples/tutorials/deeppavlov_intro.ipynb b/examples/tutorials/deeppavlov_intro.ipynb new file mode 100644 index 0000000000..5672212649 --- /dev/null +++ b/examples/tutorials/deeppavlov_intro.ipynb @@ -0,0 +1,674 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DeepPavlov basics \n", + "In this tutorial we will construct elementary components needed for working with different NLP tasks. We will go through typical data preprocessing pipeline which will be used in the next tutorials. This part is mostly about low-level elements of the library. In the end will construct a simple bot based on pattern matching and the library abstactions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tutorial plan\n", + "\n", + "1. [Install the library](#Install-the-library):\n", + " - [on Linux](#Install-the-library)\n", + " - [on Windows](#Install-the-library-on-Windows-using-Docker)\n", + "2. [Hello bot](#Hello-bot)\n", + "3. [Data](#Data):\n", + " - [Parsing text data](#Parsing-text-data-into-a-machine-readable-dataset)\n", + " - [Preparation of a dictionary](#Prepare-dictionaries)\n", + " - [Dataset iterator](#Dataset-Iterator)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Libraries\n", + "\n", + "For this task you will need the following libraries:\n", + " - [Tensorflow](https://www.tensorflow.org) — an open-source software library for Machine Intelligence.\n", + " - [Numpy](http://www.numpy.org) — a package for scientific computing.\n", + " - [DeepPavlov](https://github.com/deepmipt/deeppavlov) - open source library for Natural Language Processing\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Install the library\n", + "\n", + "Currently only Linux platform and Python 3.6 is supported\n", + "\n", + "- Create a virtual environment with Python 3.6\n", + "\n", + " `virtualenv -p python3.6 env`\n", + "\n", + "- Activate the environment.\n", + "\n", + " `source ./env/bin/activate`\n", + "\n", + "- Clone the repo and cd to project root\n", + "\n", + " `git clone https://github.com/deepmipt/DeepPavlov.git`\n", + " \n", + " `cd DeepPavlov`\n", + "\n", + "- Install the requirements:\n", + "\n", + " `python setup.py develop`\n", + "\n", + "- Install spacy dependencies:\n", + "\n", + " `python -m spacy download en`\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Install the library on Windows using Docker\n", + "\n", + "First, install the Docker following these instructions:\n", + "\n", + "https://docs.docker.com/docker-for-windows/install\n", + "\n", + "Then go to console and get the container with the following command:\n", + "\n", + "`docker pull altinsky/convai:deeppavlov`\n", + "\n", + "Run the container:\n", + "\n", + "`docker run -p 8888:8888 altinsky/convai:deeppavlov`\n", + "\n", + "Navigate to http://127.0.0.1:8888/ in your browser.\n", + "\n", + "To STOP the container run:\n", + "\n", + "`docker stop`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hello bot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this part we will construct a simple bot that relies on pattern matching to perform a conversation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from deeppavlov.skills.pattern_matching_skill import PatternMatchingSkill\n", + "from deeppavlov.core.agent import Agent, HighestConfidenceSelector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A pattern matching skill is the simplest example of Natural Language Understanding component. It will search defined patterns through the text. Let's define some simple patterns:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hello = PatternMatchingSkill(['Hello world!'], patterns=[\"hi\", \"hello\", \"good day\"])\n", + "bye = PatternMatchingSkill(['Goodbye world!', 'See you around'],\n", + " patterns=[\"bye\", \"chao\", \"see you\"])\n", + "fallback = PatternMatchingSkill([\"I don't understand, sorry\", 'I can say \"Hello world!\"'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you provide some patterns to the PatternMatchingSkill it will return confidence = 1 when the skill finds the pattern in given text. If no patterns is provided then confidence 0.5 will be returned in any case." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The skills are used in the `Agent` which can be treated as a Dialog Manager. The agent must be provided with skills and the selector of skills. A simple skill selector is the HighestConfidenceSelector which will pick the skill with highest confidence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "HelloBot = Agent([hello, bye, fallback], skills_selector=HighestConfidenceSelector())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since all processing in the library is performed on batches, we can pass a batch of requests to the bot. Let's try it out:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "HelloBot(['Hello', 'Bye', 'Or not'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise** \n", + "- create a WhatIsYourName skill\n", + "- create new agent with this skill\n", + "- check that all works fine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "name = # YOUR_CODE_HERE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "Deeppavlov library has functionality to download and decompress the data. For this purpose the `download_decompress` from `data.utils` is used. \n", + "The following cell will download the CoNLL-2003 data for the Named Entity Recognition (NER) task and put it to the folder `data/`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import deeppavlov\n", + "from deeppavlov.core.data.utils import download_decompress\n", + "download_decompress('http://lnsigo.mipt.ru/export/deeppavlov_data/conll2003_v2.tar.gz', 'data/')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parsing text data into a machine-readable dataset \n", + "\n", + "We will work with a corpus which contains tweets with NE tags. A typical file with NER data contains lines with pairs of tokens (word or punctuation symbol) and tags separated by a whitespace. In many cases additional information such as POS-tags is included. \n", + "\n", + "Different documents are separated by lines **started** with **-DOCSTART-** token. Different sentences are separated by an empty line. Example:\n", + "\n", + " -DOCSTART- -X- -X- O\n", + "\n", + " EU NNP B-NP B-ORG\n", + " rejects VBZ B-VP O\n", + " German JJ B-NP B-MISC\n", + " call NN I-NP O\n", + " to TO B-VP O\n", + " boycott VB I-VP O\n", + " British JJ B-NP B-MISC\n", + " lamb NN I-NP O\n", + " . . O O\n", + "\n", + " Peter NNP B-NP B-PER\n", + " Blackburn NNP I-NP I-PER\n", + "\n", + "In this tutorial we will focus only on tokens and tags (first and last elements of the line) and drop POS information located between them.\n", + "\n", + "We start by building a class *NerDatasetReader* that provides functionality for reading the dataset. It returns a dictionary with fields *train*, *test*, and *valid*. Each field stores a list of samples. Each sample is a tuple of tokens and tags. Both tokens and tags are lists. The following example depicts the structure that should be returned by *read* method:\n", + "\n", + " {'train': [(['Mr.', 'Dwag', 'are', 'derping', 'around'], ['B-PER', 'I-PER', 'O', 'O', 'O']), ....],\n", + " 'valid': [...],\n", + " 'test': [...]}\n", + "\n", + "There are three separate parts in the dataset:\n", + " - *train* data for training the model;\n", + " - *validation* data for evaluation and hyperparameters tuning;\n", + " - *test* data for final evaluation of the model.\n", + " \n", + "\n", + "Each of these parts is stored in a separate txt file.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "class NerDatasetReader:\n", + " def read(self, data_path):\n", + " data_parts = ['train', 'valid', 'test']\n", + " extension = '.txt'\n", + " dataset = {}\n", + " for data_part in data_parts:\n", + " file_path = Path(data_path) / Path(data_part + extension)\n", + " dataset[data_part] = self.read_file(str(file_path))\n", + " return dataset\n", + " \n", + " @staticmethod\n", + " def read_file(file_path):\n", + " \n", + " # Use utf-8 encoding when open the file\n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " ######################################\n", + " return samples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_reader = NerDatasetReader()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = dataset_reader.read('data/')\n", + "assert len(dataset) == 3, 'The dataset must be a dict with three fields: train, test, and valid'\n", + "assert len(set(dataset) & {'train', 'test', 'valid'}) == 3, 'The dataset keys must be exactly train, test, and valid'\n", + "assert isinstance(dataset['train'][0][0][0], str) and isinstance(dataset['train'][0][0][1], str), 'Both tokens and tags must be strings'\n", + "assert len(dataset['train']) == 14041, 'there must be exactly 14041 samples in train'\n", + "assert len(dataset['valid']) == 3250, 'there must be exactly 3250 samples in train'\n", + "assert len(dataset['test']) == 3453, 'there must be exactly 3453 samples in test'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should always understand what kind of data you deal with. For this purpose, you can print the data by running the code in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "for sample in dataset['train'][:2]:\n", + " for token, tag in zip(*sample):\n", + " print('%s\\t%s' % (token, tag))\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can find an implementation of the dataset reader that implemets the same interfaces in the library: [Conll2003DatasetReader](https://github.com/deepmipt/DeepPavlov/blob/dev/deeppavlov/dataset_readers/conll2003_reader.py). The functionality of the presented code is wider and the `register` wrapper allows to use this component as a part of config file (will be discussed later)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare dictionaries\n", + "\n", + "To train a neural network, we will use two mappings: \n", + "- {token}$\\to${token id}: address the row in embeddings matrix for the current token;\n", + "- {tag}$\\to${tag id}: one-hot ground truth probability distribution vectors for computing the loss at the output of the network.\n", + "\n", + "Token indices will be used to address a row in embeddings matrix. The mapping for tags will be used to create one-hot ground-truth probability distribution vectors to compute the loss at the output of the network.\n", + "\n", + "Now you need to implement the *Vocab* class which will return {token or tag}$\\to${index} and vice versa. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from collections import defaultdict, Counter\n", + "from itertools import chain\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Vocab:\n", + " def __init__(self,\n", + " special_tokens=tuple()):\n", + " self.special_tokens = special_tokens\n", + " self._t2i = defaultdict(lambda: 1)\n", + " self._i2t = []\n", + " \n", + " def fit(self, tokens):\n", + " count = 0\n", + " self.freqs = Counter(chain(*tokens))\n", + " # The first special token will be the default token\n", + " for special_token in self.special_tokens:\n", + " self._t2i[special_token] = count\n", + " self._i2t.append(special_token)\n", + " count += 1\n", + " for token, freq in self.freqs.most_common():\n", + " if token not in self._t2i:\n", + " self._t2i[token] = count\n", + " self._i2t.append(token)\n", + " count += 1\n", + "\n", + " def __call__(self, batch, **kwargs):\n", + " # Implement the vocab() method. The input could be a batch of tokens\n", + " # or a batch of indices. A batch is a list of utterances where each\n", + " # utterance is a list of tokens\n", + " pass\n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " ######################################\n", + "\n", + " def __getitem__(self, key):\n", + " # Implement the vocab[] method. The input could be a token\n", + " # (string) or an index. You have to detect what type of data\n", + " # is key and return. \n", + " pass\n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " ######################################\n", + " \n", + " def __len__(self):\n", + " return len(self._i2t)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After implementing the function *build_dict* you can make dictionaries for tokens and tags. Special tokens in our case will be:\n", + " - `` token for out of vocabulary tokens\n", + " - `'O'` for the tag vocab to place out of label tag to the first place with index 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "special_tokens = ['']\n", + "special_tags = ['O']\n", + "\n", + "token_vocab = Vocab(special_tokens)\n", + "tag_vocab = Vocab(special_tags)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will fit the vocabularies on the *train* part of the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "all_tokens_by_sentenses = [tokens for tokens, tags in dataset['train']]\n", + "all_tags_by_sentenses = [tags for tokens, tags in dataset['train']]\n", + "\n", + "token_vocab.fit(all_tokens_by_sentenses)\n", + "tag_vocab.fit(all_tags_by_sentenses)\n", + "\n", + "assert len(token_vocab) == 23624, 'There must be exactly 23624 in the token vocab!'\n", + "assert len(tag_vocab) == 9, 'There must be exactly 9 in the tag vocab!'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try to get the indices. Keep in mind that we are working with batches of the following structure:\n", + " \n", + " [['utt0_tok0', 'utt1_tok1', ...], ['utt1_tok0', 'utt1_tok1', ...], ...]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indices_batch = token_vocab([['How', 'to', 'cook', 'a', 'turnip', '?']])\n", + "\n", + "assert len(indices_batch) == 1, 'the batch length must be 1'\n", + "assert isinstance(indices_batch[0][0], int), 'The batch must contain lists of ints!'\n", + "\n", + "print(indices_batch)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tag_indices_batch = tag_vocab([['O', 'O', 'O'], ['B-PER']])\n", + "\n", + "assert len(tag_indices_batch) == 2, 'the batch length must be 2'\n", + "assert isinstance(tag_indices_batch[0][0], int), 'The batch must contain lists of ints!'\n", + "\n", + "print(tag_indices_batch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will try converting from indices to tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token_vocab([np.random.randint(0, 512, size=10)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A similar vocabulary is already implemented in the [library](https://github.com/deepmipt/DeepPavlov/blob/dev/deeppavlov/core/data/simple_vocab.py). It has extended functionality:\n", + "- token cutoff by frequency\n", + "- limitation of the vocabulary size\n", + "- saving and loading\n", + "- dict like dunders (\\_\\_contain\\_\\_, \\_\\_len\\_\\_, etc.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset Iterator\n", + "\n", + "Neural Networks are usually trained with batches. It means that weight updates of the network are based on several sequences at every single time. You have to iterate over the dataset and generate `x` and `y` batch by batch. The batch of `x`-s is a list of sentences of tokens like\n", + "\n", + " [['Yan', 'is', 'a', 'good', 'fellow],\n", + " ['For', 'instance']]\n", + "\n", + "and the tag sequence should be:\n", + "\n", + " [['B-PER', 'O', 'O', 'O', 'O'],\n", + " ['O', 'O']]\n", + "\n", + "An important concept in the batch generation is shuffling. Shuffling is taking sample from the dataset at random order. It is important to train on the shuffled data because large number consequetive samples of the same class may result in pure quality of the model.\n", + " \n", + "The idea behind the iterator is to perform computation in the lazy way. Use yield generator expression to do so. An example of using yield for generator creation is provided below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def iterator():\n", + " data = [1, 2, 3]\n", + " for d in data:\n", + " yield d\n", + " \n", + "print(iterator)\n", + " \n", + "for i in iterator():\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create the `DatasetIterator`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class DatasetIterator:\n", + " def __init__(self, data):\n", + " self.data = {\n", + " 'train': data['train'],\n", + " 'valid': data['valid'],\n", + " 'test': data['test']\n", + " }\n", + "\n", + " def gen_batches(self, batch_size, data_type='train', shuffle=True):\n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " ######################################\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the dataset iterator from the loaded dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_iterator = DatasetIterator(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try it out:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x, y = next(data_iterator.gen_batches(2))\n", + "\n", + "assert len(x) == 2, 'There must be two examples in the batch!'\n", + "assert len(y) == 2, 'There must be two examples in the batch!'\n", + "assert len(x[0]) == len(y[0]), 'The numbers of tokens and tags are different!'\n", + "assert isinstance(x[0][0], str), 'Token must be a string!'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a typical part of the data preprocessing pipeline. This parts will be used in the following tutorials. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/tutorials/deeppavlov_intro.pdf b/examples/tutorials/deeppavlov_intro.pdf new file mode 100644 index 0000000000000000000000000000000000000000..8e2c7b5761958403d8c38bdbf937177a898ecb86 GIT binary patch literal 1479277 zcmbTd1yo$!vM$;L2!R9$1b4T_-3buff;A4oY1~~B2oM?z9taZL-8}?%YZ`ag;4ZI| zfA4+Ixp%yA&wHzTj%8D-YR>vrRjpdI@83zYaIo^BqtVvFH*_=(3U&$y6KixdVPQ56 zFGq7WNn=-ITL(+F_r{jyE)<;b+iGlz#`cyB&TcGfnv4`|>aNairmh;!=H_Y+4z3g& 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z>Sd%wJu-~-J`YDOR#8X?OFCC%b8OiYwO#3~tYN1O*c_>s3Nyl@>?&=-!;pF_zZTe> zY^l^g<@+#fUukUyxl}rvHeqw5e%hj}rmVN6xBoh4byeh)^V%tyw&m70^QS*7&iJj} zcCxYC=$=gUuk&~EW>-x7x~9u%W%#uf_H(vu^xUiUJ7L=9z+dmFZIjoH!I)1jJ5)=?Q0?pdu+^R;yb;Vk(<2M5=;WGh5GT0hKy& zoYW}^;{>rh(iU#^0%> Date: Mon, 9 Jul 2018 08:22:00 +0300 Subject: [PATCH 596/616] Update README.md --- examples/tutorials/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/tutorials/README.md b/examples/tutorials/README.md index b75c4737b1..f3a4f1f962 100644 --- a/examples/tutorials/README.md +++ b/examples/tutorials/README.md @@ -6,7 +6,7 @@ ## Named Entity Recognition with DeepPavlov -[video](https://youtu.be/6HlL87PWxXU) +[Jupyter notebook](01_deeppavlov_ner.ipynb) | [video](https://youtu.be/6HlL87PWxXU) ## Task-oriented bot with DeepPavlov From cda16a3e637d122c04a670582ad9a5548f8e3bcf Mon Sep 17 00:00:00 2001 From: DeepPavlov Date: Mon, 9 Jul 2018 08:27:09 +0300 Subject: [PATCH 597/616] Add files via upload --- examples/tutorials/03_deeppavlov_to_bot.ipynb | 2162 +++++++++++++++++ examples/tutorials/scheme000.png | Bin 0 -> 92485 bytes examples/tutorials/scheme001.png | Bin 0 -> 103578 bytes examples/tutorials/scheme002.png | Bin 0 -> 106380 bytes examples/tutorials/scheme002_1.png | Bin 0 -> 107916 bytes examples/tutorials/scheme003.png | Bin 0 -> 114592 bytes 6 files changed, 2162 insertions(+) create mode 100644 examples/tutorials/03_deeppavlov_to_bot.ipynb create mode 100644 examples/tutorials/scheme000.png create mode 100644 examples/tutorials/scheme001.png create mode 100644 examples/tutorials/scheme002.png create mode 100644 examples/tutorials/scheme002_1.png create mode 100644 examples/tutorials/scheme003.png diff --git a/examples/tutorials/03_deeppavlov_to_bot.ipynb b/examples/tutorials/03_deeppavlov_to_bot.ipynb new file mode 100644 index 0000000000..8f662c9a61 --- /dev/null +++ b/examples/tutorials/03_deeppavlov_to_bot.ipynb @@ -0,0 +1,2162 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import copy" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# pretty prints\n", + "from pprint import pprint" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# creating directory for json configs\n", + "import os\n", + "\n", + "if not os.path.isdir(\"gobot\"):\n", + " os.mkdir(\"gobot\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"teacher_forcing\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import deeppavlov" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

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zFjgP#r4taB=^#5+9~8#=2ZgcvpfFY+6voC~3IjSg&x07}IfVfooabZ*ba0-N9nisf zPIf>C=Q%yv4C;gPoa}%O&U3N@IyleC4(Q-KCp(~n`{(rBAke{iPIf>C=Q-H{9h~Q6 z2Xt_rlO52({d0Ob59r`LCp(~n^PKE}4$gD313Ea*$qwk?Jf|nNfDX=cvI9Ce&&dwx z;5;Wgpo8(8JAWR$j5FGMi-&nI*1oW&mwwdMHltmT{7R!?OC@B3rPtGqyFLBIX|yu7@Zy` zc6(t_$n7HcPQovCGp836O(~pCe@*SWU;dzg_`m-v0%!kBK*98xXJ4+Rx7idemn9}# zaYgQccw)lO@7cx@6aJqvm@{yC;jAt{~Hjc4B<^F#GZF6yu literal 0 HcmV?d00001 From 115ceae213ffb5965d8e40b642efb8c4607131c8 Mon Sep 17 00:00:00 2001 From: DeepPavlov Date: Mon, 9 Jul 2018 08:15:57 +0300 Subject: [PATCH 594/616] Update README.md --- examples/tutorials/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/tutorials/README.md b/examples/tutorials/README.md index 38785a8f3f..b75c4737b1 100644 --- a/examples/tutorials/README.md +++ b/examples/tutorials/README.md @@ -2,7 +2,7 @@ ## Introduction to DeepPavlov -[video](https://youtu.be/ElO7_wbtO6g) +[Jupyter notebook](00_deeppavlov_intro.ipynb) | [slides](00_deeppavlov_intro.pdf) | [video](https://youtu.be/ElO7_wbtO6g) ## Named Entity Recognition with DeepPavlov From 9d0b72a1687e740fc7fbc5fc10c158a28491fcf0 Mon Sep 17 00:00:00 2001 From: DeepPavlov Date: Mon, 9 Jul 2018 08:19:46 +0300 Subject: [PATCH 595/616] Add files via upload --- examples/tutorials/01_deeppavlov_ner.ipynb | 914 +++++++++++++++++++++ examples/tutorials/conv.png | Bin 0 -> 151252 bytes 2 files changed, 914 insertions(+) create mode 100644 examples/tutorials/01_deeppavlov_ner.ipynb create mode 100644 examples/tutorials/conv.png diff --git a/examples/tutorials/01_deeppavlov_ner.ipynb b/examples/tutorials/01_deeppavlov_ner.ipynb new file mode 100644 index 0000000000..533443f8b3 --- /dev/null +++ b/examples/tutorials/01_deeppavlov_ner.ipynb @@ -0,0 +1,914 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Recognize named entities on news data with CNN\n", + "\n", + "In this tutorial, you will use a convolutional neural network to solve Named Entity Recognition (NER) problem. NER is a common task in natural language processing systems. It serves for extraction of entities from text such as persons, organizations, locations, etc. In this task you will experiment with recognition of named entities in different news texts from CoNLL-2003 dataset.\n", + "\n", + "For example, we want to extract person and organization names from the text. Then for the input text:\n", + "\n", + " Ian Goodfellow works for Google Brain\n", + "\n", + "a NER model needs to provide the following sequence of tags:\n", + "\n", + " B-PER I-PER O O B-ORG I-ORG\n", + "\n", + "Where *B-* and *I-* prefixes stand for the beginning and inside of the entity, while *O* stands for out of tag or no tag. Markup with the prefix scheme is called **BIO markup**. This markup is introduced for distinguishing of consequent entities with similar types.\n", + "\n", + "A solution of the task will be based on neural networks, particularly, on Convolutional Neural Networks." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data\n", + "\n", + "The following cell will download all data required for this assignment into the folder `/data`. The download util from the library is used to download and extract the archive." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import deeppavlov\n", + "from deeppavlov.core.data.utils import download_decompress\n", + "download_decompress('http://lnsigo.mipt.ru/export/deeppavlov_data/conll2003_v2.tar.gz', 'data/')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the CoNLL-2003 Named Entity Recognition corpus\n", + "\n", + "We will work with a corpus which contains tweets with NE tags. A typical file with NER data contains lines with pairs of tokens (word or punctuation symbol) and tags separated by a whitespace. In many cases additional information such as POS tags is included. Different documents are separated with lines **started** with **-DOCSTART-** token. Different sentences are separated with an empty line. Example:\n", + "\n", + " -DOCSTART- -X- -X- O\n", + "\n", + " EU NNP B-NP B-ORG\n", + " rejects VBZ B-VP O\n", + " German JJ B-NP B-MISC\n", + " call NN I-NP O\n", + " to TO B-VP O\n", + " boycott VB I-VP O\n", + " British JJ B-NP B-MISC\n", + " lamb NN I-NP O\n", + " . . O O\n", + "\n", + " Peter NNP B-NP B-PER\n", + " Blackburn NNP I-NP I-PER\n", + "\n", + "In this tutorial we will focus only on tokens and tags (first and last elements of the line) and drop POS information located in between.\n", + "\n", + "We start with using the *Conll2003DatasetReader* class that provides functionality for reading the dataset. It returns a dictionary with fields *train*, *test*, and *valid*. At each field a list of samples is stored. Each sample is a tuple of tokens and tags. Both tokens and tags are lists. The following example depicts the structure that should be returned by *read* method:\n", + "\n", + " {'train': [(['Mr.', 'Dwag', 'are', 'derping', 'around'], ['B-PER', 'I-PER', 'O', 'O', 'O']), ....],\n", + " 'valid': [...],\n", + " 'test': [...]}\n", + "\n", + "There are three separate parts of the dataset:\n", + " - *train* data for training the model;\n", + " - *validation* data for evaluation and hyperparameters tuning;\n", + " - *test* data for final evaluation of the model.\n", + " \n", + "\n", + "Each of these parts is stored in a separate txt file.\n", + "\n", + "We will use [Conll2003DatasetReader](https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/dataset_readers/conll2003_reader.py) from the library to read the data from text files to the format described above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from deeppavlov.dataset_readers.conll2003_reader import Conll2003DatasetReader\n", + "dataset = Conll2003DatasetReader().read('data/')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should always understand what kind of data you deal with. For this purpose, you can print the data running the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for sample in dataset['train'][:4]:\n", + " for token, tag in zip(*sample):\n", + " print('%s\\t%s' % (token, tag))\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare dictionaries\n", + "\n", + "To train a neural network, we will use two mappings: \n", + "- {token}$\\to${token id}: index of the row in embeddings matrix for the current token;\n", + "- {tag}$\\to${tag id}: one-hot ground truth probability distribution vectors for computing the loss at the output of the network.\n", + "\n", + "Token indices will be used to find the corresponding rows in embedding matrix. The mapping for tags will be used to create one-hot ground-truth probability distribution vectors to compute the loss at the output of the network.\n", + "\n", + "The [SimpleVocabulary](https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/core/data/simple_vocab.py) implemented in the library will be used to perform those mappings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from deeppavlov.core.data.simple_vocab import SimpleVocabulary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to build dictionaries for tokens and tags. Sometimes there are special tokens in vocabularies, for instance an unknown word token, which is used every time we encounter an out-of-vocabulary word. In our case the only special token will be`` for out-of-vocabulary words." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "special_tokens = ['']\n", + "\n", + "token_vocab = SimpleVocabulary(special_tokens, save_path='model/token.dict')\n", + "tag_vocab = SimpleVocabulary(save_path='model/tag.dict')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fit the vocabularies on the train part of the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "all_tokens_by_sentences = [tokens for tokens, tags in dataset['train']]\n", + "all_tags_by_sentences = [tags for tokens, tags in dataset['train']]\n", + "\n", + "token_vocab.fit(all_tokens_by_sentences)\n", + "tag_vocab.fit(all_tags_by_sentences)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try to get the indices. Keep in mind that we are working with batches of the following structure:\n", + " \n", + " [['utt0_tok0', 'utt1_tok1', ...], ['utt1_tok0', 'utt1_tok1', ...], ...]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token_vocab([['How', 'to', 'do', 'a', 'barrel', 'roll', '?']])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tag_vocab([['O', 'O', 'O'], ['B-ORG', 'I-ORG']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will try converting from indices to tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "token_vocab([np.random.randint(0, 512, size=10)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset Iterator\n", + "\n", + "Neural Networks are usually trained on batches of examples. It means that weight updates of the network are based on several sequences at every step. The tricky part is that all sequences within a batch need to have the same length. So we will pad them with a special `` token. Likewise, token tags must also be padded. It is also a good practice to provide RNN with sequence lengths, so that it can skip computations for padding parts. We provide the batching function *batches_generator* readily available for you to save time. \n", + "\n", + "An important concept in the batch generation is shuffling. Shuffling is taking sample from the dataset in random order. It is important to train on shuffled data because large number of consequetive samples of the same class may distort the performance of the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from deeppavlov.core.data.data_learning_iterator import DataLearningIterator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the dataset iterator for the loaded dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_iterator = DataLearningIterator(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try it out:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "next(data_iterator.gen_batches(2, shuffle=True))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Masking" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last thing about generating training data. We need to produce a binary mask which is the one where tokens present and zero elsewhere. This mask will stop backpropagation through paddings. An instance of such mask:\n", + "\n", + " [[1, 1, 0, 0, 0],\n", + " [1, 1, 1, 1, 1]]\n", + " For the sentences in batch:\n", + "\n", + " [['The', 'roof'],\n", + " ['This', 'is', 'my', 'domain', '!']]\n", + "\n", + "The Mask preprocessing component from the library will be used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from deeppavlov.models.preprocessors.mask import Mask\n", + "get_mask = Mask()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try it out:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "get_mask([['Try', 'to', 'get', 'the', 'mask'], ['Check', 'paddings']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build a Convolutional Neural Network\n", + "\n", + "This is the most important part of the assignment. Here we will specify the network architecture based on `TensorFlow` building blocks. It's fun and easy as a lego constructor! We will create an Convolutional Neural Network (CNN) which will produce the probability distribution over tags for each token in a sentence. To take into account both right and left contexts of the token, we will use CNN. Dense layer will be used on top to perform tag classification." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "\n", + "np.random.seed(42)\n", + "tf.set_random_seed(42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An essential part of almost every network in NLP domain is embeddings of the words. We pass the text to the network as a series of tokens. Each token is represented by its index. For every token (index) we have a vector. In total the vectors form an embedding matrix. This matrix can be either pretrained using some common algorithm like Skip-Gram or CBOW or it can be initialized by random values and trained along with other parameters of the network. In this tutorial we will follow the second alternative.\n", + "\n", + "We need to build a function that takes the tensor of token indices with shape [batch_size, num_tokens] and for each index in this matrix it retrieves a vector from the embedding matrix, corresponding to that index. That results in a new tensor with sahpe [batch_size, num_tokens, emb_dim]." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_embeddings(indices, vocabulary_size, emb_dim):\n", + " # Initialize the random gaussian matrix with dimensions [vocabulary_size, embedding_dimension]\n", + " # The **VARIANCE** of the random samples must be 1 / embedding_dimension\n", + " \n", + " # YOUR CODE HERE\n", + " \n", + " emb_mat = tf.Variable(emb_mat, trainable=True, dtype=tf.float32)\n", + " emb = tf.nn.embedding_lookup(emb_mat, indices)\n", + " return emb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check whether it works:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indices = [[0, 1, 2]] # batch of indices of tokens\n", + "vocab_size = 5\n", + "emb_dim = 100\n", + "\n", + "emb = get_embeddings(indices, vocab_size, emb_dim)\n", + "emb_shape = emb.get_shape().as_list()\n", + "assert emb_shape[0] == 1\n", + "assert emb_shape[1] == 3\n", + "assert emb_shape[2] == emb_dim\n", + "print('Embeddings are ready to deploy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The body of the network is the convolutional layers. The basic idea behind convolutions is to apply the same dense layer to every n consecutive samples (tokens in our case). A simplified case is depicted below.\n", + "\n", + "\n", + "\n", + "Here number of input and output features equals to 1.\n", + "\n", + "Let's try it on a toy example:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a tensor with shape [batch_size, number_of_tokens, number_of_features]\n", + "x = tf.random_normal(shape=[2, 10, 100])\n", + "y = tf.layers.conv1d(x, filters=200, kernel_size=8)\n", + "print(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, due to the abscence of zero padding (zeros on in the beginning and in the end of input) the size of resulting tensor along the token dimension is reduced. To use padding and preserve the dimensionality along the convolution dimension pass padding='same' parameter to the function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_with_padding = tf.layers.conv1d(x, filters=200, kernel_size=8, padding='same')\n", + "print(y_with_padding)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now stack a number of layers with dimensionality given in n_hidden_list (list of numbers of hidden units in each layer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def conv_net(units, n_hidden_list, cnn_filter_width, activation=tf.nn.relu):\n", + " # Use activation(units) to apply activation to units\n", + " \n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " ######################################\n", + "\n", + " return units\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check the convnet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_hidden_list = [10, 20]\n", + "x = tf.Variable(np.random.randn(2, 10, 32), tf.float32)# tensor with dimensions [batch_size, number_of_tokens, number_of_features]\n", + "cnn_filter_width = 3\n", + "y = conv_net(x, n_hidden_list, cnn_filter_width)\n", + "output_shape = y.get_shape().as_list()\n", + "assert output_shape[0] == 2\n", + "assert output_shape[1] == 10\n", + "assert output_shape[2] == n_hidden_list[-1]\n", + "print('ConvNet is ready to deploy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A common loss for the classification task is cross-entropy. Why classification? Because for each token the network must decide which tag to predict. The cross-entropy has the following form:\n", + "\n", + "$$ H(P, Q) = -E_{x \\sim P} log Q(x) $$\n", + "\n", + "It measures the dissimilarity between the ground truth distribution over the classes and predicted distribution. In the most of the cases ground truth distribution is one-hot. Luckily this loss is already [implemented](https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits_v2) in TensorFlow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The logits\n", + "l = tf.random_normal([1, 4, 3]) # shape [batch_size, number_of_tokens, number of classes]\n", + "indices = tf.placeholder(tf.int32, [1, 4])\n", + "\n", + "# Make one-hot distribution from indices for 3 types of tag\n", + "p = tf.one_hot(indices, depth=3)\n", + "loss_tensor = tf.nn.softmax_cross_entropy_with_logits_v2(labels=p, logits=l)\n", + "print(loss_tensor)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All sentences in the batch have same length and we pad the each sentence to the maximal lendth. So there are paddings at the end and pushing the network to predict those paddings usually results in deteriorated quallity. Then we need to multiply the loss tensor by binary mask to prevent gradient flow from the paddings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mask = tf.placeholder(tf.float32, shape=[1, 4])\n", + "loss_tensor *= mask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last step to do is to compute the mean value of the loss tensor:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "loss = tf.reduce_mean(loss_tensor)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now define your own function that returns a scalar masked cross-entropy loss" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def masked_cross_entropy(logits, label_indices, number_of_tags, mask):\n", + " \n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " ######################################\n", + " \n", + " return loss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check that all works fine:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "logits = tf.placeholder(tf.float32, shape=[2, 3, 10])\n", + "label_indices = tf.placeholder(tf.int32, shape=[2, 3])\n", + "number_of_tags = 10\n", + "mask = tf.placeholder(tf.float32, shape=[2, 3])\n", + "\n", + "loss = masked_cross_entropy(logits, label_indices, number_of_tags, mask)\n", + "\n", + "assert len(loss.get_shape().as_list()) == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Put everything into a class:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "\n", + "class NerNetwork:\n", + " def __init__(self,\n", + " n_tokens,\n", + " n_tags,\n", + " token_emb_dim=100,\n", + " n_hidden_list=(128,),\n", + " cnn_filter_width=7,\n", + " use_batch_norm=False,\n", + " embeddings_dropout=False,\n", + " top_dropout=False,\n", + " **kwargs):\n", + " \n", + " # ================ Building inputs =================\n", + " \n", + " self.learning_rate_ph = tf.placeholder(tf.float32, [])\n", + " self.dropout_keep_ph = tf.placeholder(tf.float32, [])\n", + " self.token_ph = tf.placeholder(tf.int32, [None, None], name='token_ind_ph')\n", + " self.mask_ph = tf.placeholder(tf.float32, [None, None], name='Mask_ph')\n", + " self.y_ph = tf.placeholder(tf.int32, [None, None], name='y_ph')\n", + " \n", + " # ================== Building the network ==================\n", + " \n", + " # Now embedd the indices of tokens using token_emb_dim function\n", + " # this should be like\n", + " \n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " emb = \n", + " ######################################\n", + "\n", + " emb = tf.nn.dropout(emb, self.dropout_keep_ph, (tf.shape(emb)[0], 1, tf.shape(emb)[2]))\n", + " \n", + " # Build a multilayer CNN on top of the embeddings.\n", + " # The number of units in the each layer must match\n", + " # corresponding number from n_hidden_list.\n", + " # Use ReLU activation \n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " units = \n", + " ######################################\n", + " units = tf.nn.dropout(units, self.dropout_keep_ph, (tf.shape(units)[0], 1, tf.shape(units)[2]))\n", + " logits = tf.layers.dense(units, n_tags, activation=None)\n", + " self.predictions = tf.argmax(logits, 2)\n", + " \n", + " # ================= Loss and train ops =================\n", + " # Use cross-entropy loss. \n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " self.loss = \n", + " ######################################\n", + "\n", + " # Create a training operation to update the network parameters.\n", + " # We purpose to use the Adam optimizer as it work fine for the\n", + " # most of the cases. Check tf.train to find an implementation.\n", + " # Put the train operation to the attribute self.train_op\n", + " \n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " self.train_op = \n", + " ######################################\n", + "\n", + " # ================= Initialize the session =================\n", + " \n", + " self.sess = tf.Session()\n", + " self.sess.run(tf.global_variables_initializer())\n", + "\n", + " def __call__(self, tok_batch, mask_batch):\n", + " feed_dict = {self.token_ph: tok_batch,\n", + " self.mask_ph: mask_batch,\n", + " self.dropout_keep_ph: 1.0}\n", + " return self.sess.run(self.predictions, feed_dict)\n", + "\n", + " def train_on_batch(self, tok_batch, tag_batch, mask_batch, dropout_keep_prob, learning_rate):\n", + " feed_dict = {self.token_ph: tok_batch,\n", + " self.y_ph: tag_batch,\n", + " self.mask_ph: mask_batch,\n", + " self.dropout_keep_ph: dropout_keep_prob,\n", + " self.learning_rate_ph: learning_rate}\n", + " self.sess.run(self.train_op, feed_dict)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create an instance of the NerNetwork class:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nernet = NerNetwork(len(token_vocab),\n", + " len(tag_vocab),\n", + " n_hidden_list=[100, 100])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We often want to check the score on validation part of the dataset every epoch. In most of the cases of NER tasks the classes are imbalanced. And the accuracy is not the best measure of performance. If we have 95% of 'O' tags, then a silly classifier that always predicts '0' gets 95% accuracy. To tackle this issue the F1-score is used. The $F_1$-score can be defined as:\n", + "\n", + "$$ F_1 = \\frac{2 P R}{P + R}$$ \n", + "\n", + "where P is precision and R is recall.\n", + "\n", + "Let's write the evaluation function. We need to get all predictions for the given part of the dataset and compute $F_1$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from deeppavlov.models.ner.evaluation import precision_recall_f1\n", + "# The function precision_recall_f1 takes two lists: y_true and y_predicted\n", + "# the tag sequences for each sentences should be merged into one big list \n", + "from deeppavlov.core.data.utils import zero_pad\n", + "# zero_pad takes a batch of lists of token indices, pad it with zeros to the\n", + "# maximal length and convert it to numpy matrix\n", + "from itertools import chain\n", + "\n", + "\n", + "def eval_valid(network, batch_generator):\n", + " total_true = []\n", + " total_pred = []\n", + " for x, y_true in batch_generator:\n", + "\n", + " # Prepare token indices from tokens batch\n", + " x_inds = # YOUR CODE HERE\n", + "\n", + " # Pad the indices batch with zeros\n", + " x_batch = # YOUR CODE HERE\n", + "\n", + " # Get the mask using get_mask\n", + " mask = # YOUR CODE HERE\n", + " \n", + " # We call the instance of the NerNetwork because we have defined __call__ method\n", + " y_inds = network(x_batch, mask)\n", + "\n", + " # For every sentence in the batch extract all tags up to paddings (use length of x element)\n", + " y_inds = # YOUR CODE HERE\n", + " y_pred = tag_vocab(y_inds)\n", + "\n", + " # Add fresh predictions \n", + " total_true.extend(chain(*y_true))\n", + " total_pred.extend(chain(*y_pred))\n", + " res = precision_recall_f1(total_true, total_pred, print_results=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's check " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eval_valid(nernet, data_iterator.gen_batches(16, data_type='valid'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set hyperparameters for the training procedure. You might want to start with the following recommended values:\n", + "- *batch_size*: 32;\n", + "- n_epochs: 10;\n", + "- starting value of *learning_rate*: 0.001\n", + "- *learning_rate_decay*: a square root of 2;\n", + "- *dropout_keep_probability* equal to 0.7 for training (typical values for dropout probability are ranging from 0.3 to 0.9).\n", + "\n", + "A very efficient technique for the learning rate managment is dropping learning rate after convergence. It is common to use dividers 2, 3, and 10 to drop the learning rate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = # YOUR HYPERPARAMETER HERE\n", + "n_epochs = # YOUR HYPERPARAMETER HERE\n", + "learning_rate = # YOUR HYPERPARAMETER HERE\n", + "dropout_keep_prob = # YOUR HYPERPARAMETER HERE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we iterate through the dataset batch by batch and pass the data to the train op" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for epoch in range(n_epochs):\n", + " for x, y in data_iterator.gen_batches(batch_size, 'train'):\n", + " # Convert tokens to indices via Vocab\n", + " x_inds = # YOUR CODE \n", + " # Convert tags to indices via Vocab\n", + " y_inds = # YOUR CODE \n", + " \n", + " # Pad every sample with zeros to the maximal length\n", + " x_batch = zero_pad(x_inds)\n", + " y_batch = zero_pad(y_inds)\n", + "\n", + " mask = get_mask(x)\n", + " nernet.train_on_batch(x_batch, y_batch, mask, dropout_keep_prob, learning_rate)\n", + " print('Evaluating the model on valid part of the dataset')\n", + " eval_valid(nernet, data_iterator.gen_batches(batch_size, 'valid'))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Eval the model on test part now" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eval_valid(nernet, data_iterator.gen_batches(batch_size, 'test'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try to infer the model on our sentence:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sentence = 'Petr stole my vodka'\n", + "x = [sentence.split()]\n", + "\n", + "x_inds = token_vocab(x)\n", + "x_batch = zero_pad(x_inds)\n", + "mask = get_mask(x)\n", + "y_inds = nernet(x_batch, mask)\n", + "print(x[0])\n", + "print(tag_vocab(y_inds)[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/tutorials/conv.png b/examples/tutorials/conv.png new file mode 100644 index 0000000000000000000000000000000000000000..d48358ae47bae9b8f3000442a7dbcee12facf624 GIT binary patch literal 151252 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zFjgP#r4taB=^#5+9~8#=2ZgcvpfFY+6voC~3IjSg&x07}IfVfooabZ*ba0-N9nisf zPIf>C=Q%yv4C;gPoa}%O&U3N@IyleC4(Q-KCp(~n`{(rBAke{iPIf>C=Q-H{9h~Q6 z2Xt_rlO52({d0Ob59r`LCp(~n^PKE}4$gD313Ea*$qwk?Jf|nNfDX=cvI9Ce&&dwx z;5;Wgpo8(8JAWR$j5FGMi-&nI*1oW&mwwdMHltmT{7R!?OC@B3rPtGqyFLBIX|yu7@Zy` zc6(t_$n7HcPQovCGp836O(~pCe@*SWU;dzg_`m-v0%!kBK*98xXJ4+Rx7idemn9}# zaYgQccw)lO@7cx@6aJqvm@{yC;jAt{~Hjc4B<^F#GZF6yu From cc8a3895ce126e1532c15528807968ae2b7dc88e Mon Sep 17 00:00:00 2001 From: DeepPavlov Date: Mon, 9 Jul 2018 08:12:00 +0300 Subject: [PATCH 592/616] Delete deeppavlov_intro.ipynb --- examples/tutorials/deeppavlov_intro.ipynb | 674 ---------------------- 1 file changed, 674 deletions(-) delete mode 100644 examples/tutorials/deeppavlov_intro.ipynb diff --git a/examples/tutorials/deeppavlov_intro.ipynb b/examples/tutorials/deeppavlov_intro.ipynb deleted file mode 100644 index 5672212649..0000000000 --- a/examples/tutorials/deeppavlov_intro.ipynb +++ /dev/null @@ -1,674 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# DeepPavlov basics \n", - "In this tutorial we will construct elementary components needed for working with different NLP tasks. We will go through typical data preprocessing pipeline which will be used in the next tutorials. This part is mostly about low-level elements of the library. In the end will construct a simple bot based on pattern matching and the library abstactions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tutorial plan\n", - "\n", - "1. [Install the library](#Install-the-library):\n", - " - [on Linux](#Install-the-library)\n", - " - [on Windows](#Install-the-library-on-Windows-using-Docker)\n", - "2. [Hello bot](#Hello-bot)\n", - "3. [Data](#Data):\n", - " - [Parsing text data](#Parsing-text-data-into-a-machine-readable-dataset)\n", - " - [Preparation of a dictionary](#Prepare-dictionaries)\n", - " - [Dataset iterator](#Dataset-Iterator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Libraries\n", - "\n", - "For this task you will need the following libraries:\n", - " - [Tensorflow](https://www.tensorflow.org) — an open-source software library for Machine Intelligence.\n", - " - [Numpy](http://www.numpy.org) — a package for scientific computing.\n", - " - [DeepPavlov](https://github.com/deepmipt/deeppavlov) - open source library for Natural Language Processing\n", - " \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Install the library\n", - "\n", - "Currently only Linux platform and Python 3.6 is supported\n", - "\n", - "- Create a virtual environment with Python 3.6\n", - "\n", - " `virtualenv -p python3.6 env`\n", - "\n", - "- Activate the environment.\n", - "\n", - " `source ./env/bin/activate`\n", - "\n", - "- Clone the repo and cd to project root\n", - "\n", - " `git clone https://github.com/deepmipt/DeepPavlov.git`\n", - " \n", - " `cd DeepPavlov`\n", - "\n", - "- Install the requirements:\n", - "\n", - " `python setup.py develop`\n", - "\n", - "- Install spacy dependencies:\n", - "\n", - " `python -m spacy download en`\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Install the library on Windows using Docker\n", - "\n", - "First, install the Docker following these instructions:\n", - "\n", - "https://docs.docker.com/docker-for-windows/install\n", - "\n", - "Then go to console and get the container with the following command:\n", - "\n", - "`docker pull altinsky/convai:deeppavlov`\n", - "\n", - "Run the container:\n", - "\n", - "`docker run -p 8888:8888 altinsky/convai:deeppavlov`\n", - "\n", - "Navigate to http://127.0.0.1:8888/ in your browser.\n", - "\n", - "To STOP the container run:\n", - "\n", - "`docker stop`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hello bot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this part we will construct a simple bot that relies on pattern matching to perform a conversation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from deeppavlov.skills.pattern_matching_skill import PatternMatchingSkill\n", - "from deeppavlov.core.agent import Agent, HighestConfidenceSelector" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A pattern matching skill is the simplest example of Natural Language Understanding component. It will search defined patterns through the text. Let's define some simple patterns:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hello = PatternMatchingSkill(['Hello world!'], patterns=[\"hi\", \"hello\", \"good day\"])\n", - "bye = PatternMatchingSkill(['Goodbye world!', 'See you around'],\n", - " patterns=[\"bye\", \"chao\", \"see you\"])\n", - "fallback = PatternMatchingSkill([\"I don't understand, sorry\", 'I can say \"Hello world!\"'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you provide some patterns to the PatternMatchingSkill it will return confidence = 1 when the skill finds the pattern in given text. If no patterns is provided then confidence 0.5 will be returned in any case." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The skills are used in the `Agent` which can be treated as a Dialog Manager. The agent must be provided with skills and the selector of skills. A simple skill selector is the HighestConfidenceSelector which will pick the skill with highest confidence." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "HelloBot = Agent([hello, bye, fallback], skills_selector=HighestConfidenceSelector())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since all processing in the library is performed on batches, we can pass a batch of requests to the bot. Let's try it out:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "HelloBot(['Hello', 'Bye', 'Or not'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Exercise** \n", - "- create a WhatIsYourName skill\n", - "- create new agent with this skill\n", - "- check that all works fine" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "name = # YOUR_CODE_HERE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data\n", - "Deeppavlov library has functionality to download and decompress the data. For this purpose the `download_decompress` from `data.utils` is used. \n", - "The following cell will download the CoNLL-2003 data for the Named Entity Recognition (NER) task and put it to the folder `data/`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import deeppavlov\n", - "from deeppavlov.core.data.utils import download_decompress\n", - "download_decompress('http://lnsigo.mipt.ru/export/deeppavlov_data/conll2003_v2.tar.gz', 'data/')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Parsing text data into a machine-readable dataset \n", - "\n", - "We will work with a corpus which contains tweets with NE tags. A typical file with NER data contains lines with pairs of tokens (word or punctuation symbol) and tags separated by a whitespace. In many cases additional information such as POS-tags is included. \n", - "\n", - "Different documents are separated by lines **started** with **-DOCSTART-** token. Different sentences are separated by an empty line. Example:\n", - "\n", - " -DOCSTART- -X- -X- O\n", - "\n", - " EU NNP B-NP B-ORG\n", - " rejects VBZ B-VP O\n", - " German JJ B-NP B-MISC\n", - " call NN I-NP O\n", - " to TO B-VP O\n", - " boycott VB I-VP O\n", - " British JJ B-NP B-MISC\n", - " lamb NN I-NP O\n", - " . . O O\n", - "\n", - " Peter NNP B-NP B-PER\n", - " Blackburn NNP I-NP I-PER\n", - "\n", - "In this tutorial we will focus only on tokens and tags (first and last elements of the line) and drop POS information located between them.\n", - "\n", - "We start by building a class *NerDatasetReader* that provides functionality for reading the dataset. It returns a dictionary with fields *train*, *test*, and *valid*. Each field stores a list of samples. Each sample is a tuple of tokens and tags. Both tokens and tags are lists. The following example depicts the structure that should be returned by *read* method:\n", - "\n", - " {'train': [(['Mr.', 'Dwag', 'are', 'derping', 'around'], ['B-PER', 'I-PER', 'O', 'O', 'O']), ....],\n", - " 'valid': [...],\n", - " 'test': [...]}\n", - "\n", - "There are three separate parts in the dataset:\n", - " - *train* data for training the model;\n", - " - *validation* data for evaluation and hyperparameters tuning;\n", - " - *test* data for final evaluation of the model.\n", - " \n", - "\n", - "Each of these parts is stored in a separate txt file.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "class NerDatasetReader:\n", - " def read(self, data_path):\n", - " data_parts = ['train', 'valid', 'test']\n", - " extension = '.txt'\n", - " dataset = {}\n", - " for data_part in data_parts:\n", - " file_path = Path(data_path) / Path(data_part + extension)\n", - " dataset[data_part] = self.read_file(str(file_path))\n", - " return dataset\n", - " \n", - " @staticmethod\n", - " def read_file(file_path):\n", - " \n", - " # Use utf-8 encoding when open the file\n", - " ######################################\n", - " ########## YOUR CODE HERE ############\n", - " ######################################\n", - " return samples" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset_reader = NerDatasetReader()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = dataset_reader.read('data/')\n", - "assert len(dataset) == 3, 'The dataset must be a dict with three fields: train, test, and valid'\n", - "assert len(set(dataset) & {'train', 'test', 'valid'}) == 3, 'The dataset keys must be exactly train, test, and valid'\n", - "assert isinstance(dataset['train'][0][0][0], str) and isinstance(dataset['train'][0][0][1], str), 'Both tokens and tags must be strings'\n", - "assert len(dataset['train']) == 14041, 'there must be exactly 14041 samples in train'\n", - "assert len(dataset['valid']) == 3250, 'there must be exactly 3250 samples in train'\n", - "assert len(dataset['test']) == 3453, 'there must be exactly 3453 samples in test'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You should always understand what kind of data you deal with. For this purpose, you can print the data by running the code in the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "for sample in dataset['train'][:2]:\n", - " for token, tag in zip(*sample):\n", - " print('%s\\t%s' % (token, tag))\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can find an implementation of the dataset reader that implemets the same interfaces in the library: [Conll2003DatasetReader](https://github.com/deepmipt/DeepPavlov/blob/dev/deeppavlov/dataset_readers/conll2003_reader.py). The functionality of the presented code is wider and the `register` wrapper allows to use this component as a part of config file (will be discussed later)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prepare dictionaries\n", - "\n", - "To train a neural network, we will use two mappings: \n", - "- {token}$\\to${token id}: address the row in embeddings matrix for the current token;\n", - "- {tag}$\\to${tag id}: one-hot ground truth probability distribution vectors for computing the loss at the output of the network.\n", - "\n", - "Token indices will be used to address a row in embeddings matrix. The mapping for tags will be used to create one-hot ground-truth probability distribution vectors to compute the loss at the output of the network.\n", - "\n", - "Now you need to implement the *Vocab* class which will return {token or tag}$\\to${index} and vice versa. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from collections import defaultdict, Counter\n", - "from itertools import chain\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "class Vocab:\n", - " def __init__(self,\n", - " special_tokens=tuple()):\n", - " self.special_tokens = special_tokens\n", - " self._t2i = defaultdict(lambda: 1)\n", - " self._i2t = []\n", - " \n", - " def fit(self, tokens):\n", - " count = 0\n", - " self.freqs = Counter(chain(*tokens))\n", - " # The first special token will be the default token\n", - " for special_token in self.special_tokens:\n", - " self._t2i[special_token] = count\n", - " self._i2t.append(special_token)\n", - " count += 1\n", - " for token, freq in self.freqs.most_common():\n", - " if token not in self._t2i:\n", - " self._t2i[token] = count\n", - " self._i2t.append(token)\n", - " count += 1\n", - "\n", - " def __call__(self, batch, **kwargs):\n", - " # Implement the vocab() method. The input could be a batch of tokens\n", - " # or a batch of indices. A batch is a list of utterances where each\n", - " # utterance is a list of tokens\n", - " pass\n", - " ######################################\n", - " ########## YOUR CODE HERE ############\n", - " ######################################\n", - "\n", - " def __getitem__(self, key):\n", - " # Implement the vocab[] method. The input could be a token\n", - " # (string) or an index. You have to detect what type of data\n", - " # is key and return. \n", - " pass\n", - " ######################################\n", - " ########## YOUR CODE HERE ############\n", - " ######################################\n", - " \n", - " def __len__(self):\n", - " return len(self._i2t)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After implementing the function *build_dict* you can make dictionaries for tokens and tags. Special tokens in our case will be:\n", - " - `` token for out of vocabulary tokens\n", - " - `'O'` for the tag vocab to place out of label tag to the first place with index 0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "special_tokens = ['']\n", - "special_tags = ['O']\n", - "\n", - "token_vocab = Vocab(special_tokens)\n", - "tag_vocab = Vocab(special_tags)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will fit the vocabularies on the *train* part of the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "all_tokens_by_sentenses = [tokens for tokens, tags in dataset['train']]\n", - "all_tags_by_sentenses = [tags for tokens, tags in dataset['train']]\n", - "\n", - "token_vocab.fit(all_tokens_by_sentenses)\n", - "tag_vocab.fit(all_tags_by_sentenses)\n", - "\n", - "assert len(token_vocab) == 23624, 'There must be exactly 23624 in the token vocab!'\n", - "assert len(tag_vocab) == 9, 'There must be exactly 9 in the tag vocab!'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try to get the indices. Keep in mind that we are working with batches of the following structure:\n", - " \n", - " [['utt0_tok0', 'utt1_tok1', ...], ['utt1_tok0', 'utt1_tok1', ...], ...]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "indices_batch = token_vocab([['How', 'to', 'cook', 'a', 'turnip', '?']])\n", - "\n", - "assert len(indices_batch) == 1, 'the batch length must be 1'\n", - "assert isinstance(indices_batch[0][0], int), 'The batch must contain lists of ints!'\n", - "\n", - "print(indices_batch)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tag_indices_batch = tag_vocab([['O', 'O', 'O'], ['B-PER']])\n", - "\n", - "assert len(tag_indices_batch) == 2, 'the batch length must be 2'\n", - "assert isinstance(tag_indices_batch[0][0], int), 'The batch must contain lists of ints!'\n", - "\n", - "print(tag_indices_batch)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will try converting from indices to tokens." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "token_vocab([np.random.randint(0, 512, size=10)])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A similar vocabulary is already implemented in the [library](https://github.com/deepmipt/DeepPavlov/blob/dev/deeppavlov/core/data/simple_vocab.py). It has extended functionality:\n", - "- token cutoff by frequency\n", - "- limitation of the vocabulary size\n", - "- saving and loading\n", - "- dict like dunders (\\_\\_contain\\_\\_, \\_\\_len\\_\\_, etc.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset Iterator\n", - "\n", - "Neural Networks are usually trained with batches. It means that weight updates of the network are based on several sequences at every single time. You have to iterate over the dataset and generate `x` and `y` batch by batch. The batch of `x`-s is a list of sentences of tokens like\n", - "\n", - " [['Yan', 'is', 'a', 'good', 'fellow],\n", - " ['For', 'instance']]\n", - "\n", - "and the tag sequence should be:\n", - "\n", - " [['B-PER', 'O', 'O', 'O', 'O'],\n", - " ['O', 'O']]\n", - "\n", - "An important concept in the batch generation is shuffling. Shuffling is taking sample from the dataset at random order. It is important to train on the shuffled data because large number consequetive samples of the same class may result in pure quality of the model.\n", - " \n", - "The idea behind the iterator is to perform computation in the lazy way. Use yield generator expression to do so. An example of using yield for generator creation is provided below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def iterator():\n", - " data = [1, 2, 3]\n", - " for d in data:\n", - " yield d\n", - " \n", - "print(iterator)\n", - " \n", - "for i in iterator():\n", - " print(i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now create the `DatasetIterator`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "class DatasetIterator:\n", - " def __init__(self, data):\n", - " self.data = {\n", - " 'train': data['train'],\n", - " 'valid': data['valid'],\n", - " 'test': data['test']\n", - " }\n", - "\n", - " def gen_batches(self, batch_size, data_type='train', shuffle=True):\n", - " ######################################\n", - " ########## YOUR CODE HERE ############\n", - " ######################################\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create the dataset iterator from the loaded dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_iterator = DatasetIterator(dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try it out:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x, y = next(data_iterator.gen_batches(2))\n", - "\n", - "assert len(x) == 2, 'There must be two examples in the batch!'\n", - "assert len(y) == 2, 'There must be two examples in the batch!'\n", - "assert len(x[0]) == len(y[0]), 'The numbers of tokens and tags are different!'\n", - "assert isinstance(x[0][0], str), 'Token must be a string!'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a typical part of the data preprocessing pipeline. This parts will be used in the following tutorials. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 649caf66970231b3ff6ddca6ab9b9c528655f6c1 Mon Sep 17 00:00:00 2001 From: DeepPavlov Date: Mon, 9 Jul 2018 08:13:02 +0300 Subject: [PATCH 593/616] Add files via upload --- examples/tutorials/00_deeppavlov_intro.ipynb | 674 +++++++++++++++++++ examples/tutorials/00_deeppavlov_intro.pdf | Bin 0 -> 1479277 bytes 2 files changed, 674 insertions(+) create mode 100644 examples/tutorials/00_deeppavlov_intro.ipynb create mode 100644 examples/tutorials/00_deeppavlov_intro.pdf diff --git a/examples/tutorials/00_deeppavlov_intro.ipynb b/examples/tutorials/00_deeppavlov_intro.ipynb new file mode 100644 index 0000000000..5672212649 --- /dev/null +++ b/examples/tutorials/00_deeppavlov_intro.ipynb @@ -0,0 +1,674 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DeepPavlov basics \n", + "In this tutorial we will construct elementary components needed for working with different NLP tasks. We will go through typical data preprocessing pipeline which will be used in the next tutorials. This part is mostly about low-level elements of the library. In the end will construct a simple bot based on pattern matching and the library abstactions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tutorial plan\n", + "\n", + "1. [Install the library](#Install-the-library):\n", + " - [on Linux](#Install-the-library)\n", + " - [on Windows](#Install-the-library-on-Windows-using-Docker)\n", + "2. [Hello bot](#Hello-bot)\n", + "3. [Data](#Data):\n", + " - [Parsing text data](#Parsing-text-data-into-a-machine-readable-dataset)\n", + " - [Preparation of a dictionary](#Prepare-dictionaries)\n", + " - [Dataset iterator](#Dataset-Iterator)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Libraries\n", + "\n", + "For this task you will need the following libraries:\n", + " - [Tensorflow](https://www.tensorflow.org) — an open-source software library for Machine Intelligence.\n", + " - [Numpy](http://www.numpy.org) — a package for scientific computing.\n", + " - [DeepPavlov](https://github.com/deepmipt/deeppavlov) - open source library for Natural Language Processing\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Install the library\n", + "\n", + "Currently only Linux platform and Python 3.6 is supported\n", + "\n", + "- Create a virtual environment with Python 3.6\n", + "\n", + " `virtualenv -p python3.6 env`\n", + "\n", + "- Activate the environment.\n", + "\n", + " `source ./env/bin/activate`\n", + "\n", + "- Clone the repo and cd to project root\n", + "\n", + " `git clone https://github.com/deepmipt/DeepPavlov.git`\n", + " \n", + " `cd DeepPavlov`\n", + "\n", + "- Install the requirements:\n", + "\n", + " `python setup.py develop`\n", + "\n", + "- Install spacy dependencies:\n", + "\n", + " `python -m spacy download en`\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Install the library on Windows using Docker\n", + "\n", + "First, install the Docker following these instructions:\n", + "\n", + "https://docs.docker.com/docker-for-windows/install\n", + "\n", + "Then go to console and get the container with the following command:\n", + "\n", + "`docker pull altinsky/convai:deeppavlov`\n", + "\n", + "Run the container:\n", + "\n", + "`docker run -p 8888:8888 altinsky/convai:deeppavlov`\n", + "\n", + "Navigate to http://127.0.0.1:8888/ in your browser.\n", + "\n", + "To STOP the container run:\n", + "\n", + "`docker stop`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hello bot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this part we will construct a simple bot that relies on pattern matching to perform a conversation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from deeppavlov.skills.pattern_matching_skill import PatternMatchingSkill\n", + "from deeppavlov.core.agent import Agent, HighestConfidenceSelector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A pattern matching skill is the simplest example of Natural Language Understanding component. It will search defined patterns through the text. Let's define some simple patterns:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hello = PatternMatchingSkill(['Hello world!'], patterns=[\"hi\", \"hello\", \"good day\"])\n", + "bye = PatternMatchingSkill(['Goodbye world!', 'See you around'],\n", + " patterns=[\"bye\", \"chao\", \"see you\"])\n", + "fallback = PatternMatchingSkill([\"I don't understand, sorry\", 'I can say \"Hello world!\"'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you provide some patterns to the PatternMatchingSkill it will return confidence = 1 when the skill finds the pattern in given text. If no patterns is provided then confidence 0.5 will be returned in any case." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The skills are used in the `Agent` which can be treated as a Dialog Manager. The agent must be provided with skills and the selector of skills. A simple skill selector is the HighestConfidenceSelector which will pick the skill with highest confidence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "HelloBot = Agent([hello, bye, fallback], skills_selector=HighestConfidenceSelector())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since all processing in the library is performed on batches, we can pass a batch of requests to the bot. Let's try it out:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "HelloBot(['Hello', 'Bye', 'Or not'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise** \n", + "- create a WhatIsYourName skill\n", + "- create new agent with this skill\n", + "- check that all works fine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "name = # YOUR_CODE_HERE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "Deeppavlov library has functionality to download and decompress the data. For this purpose the `download_decompress` from `data.utils` is used. \n", + "The following cell will download the CoNLL-2003 data for the Named Entity Recognition (NER) task and put it to the folder `data/`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import deeppavlov\n", + "from deeppavlov.core.data.utils import download_decompress\n", + "download_decompress('http://lnsigo.mipt.ru/export/deeppavlov_data/conll2003_v2.tar.gz', 'data/')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parsing text data into a machine-readable dataset \n", + "\n", + "We will work with a corpus which contains tweets with NE tags. A typical file with NER data contains lines with pairs of tokens (word or punctuation symbol) and tags separated by a whitespace. In many cases additional information such as POS-tags is included. \n", + "\n", + "Different documents are separated by lines **started** with **-DOCSTART-** token. Different sentences are separated by an empty line. Example:\n", + "\n", + " -DOCSTART- -X- -X- O\n", + "\n", + " EU NNP B-NP B-ORG\n", + " rejects VBZ B-VP O\n", + " German JJ B-NP B-MISC\n", + " call NN I-NP O\n", + " to TO B-VP O\n", + " boycott VB I-VP O\n", + " British JJ B-NP B-MISC\n", + " lamb NN I-NP O\n", + " . . O O\n", + "\n", + " Peter NNP B-NP B-PER\n", + " Blackburn NNP I-NP I-PER\n", + "\n", + "In this tutorial we will focus only on tokens and tags (first and last elements of the line) and drop POS information located between them.\n", + "\n", + "We start by building a class *NerDatasetReader* that provides functionality for reading the dataset. It returns a dictionary with fields *train*, *test*, and *valid*. Each field stores a list of samples. Each sample is a tuple of tokens and tags. Both tokens and tags are lists. The following example depicts the structure that should be returned by *read* method:\n", + "\n", + " {'train': [(['Mr.', 'Dwag', 'are', 'derping', 'around'], ['B-PER', 'I-PER', 'O', 'O', 'O']), ....],\n", + " 'valid': [...],\n", + " 'test': [...]}\n", + "\n", + "There are three separate parts in the dataset:\n", + " - *train* data for training the model;\n", + " - *validation* data for evaluation and hyperparameters tuning;\n", + " - *test* data for final evaluation of the model.\n", + " \n", + "\n", + "Each of these parts is stored in a separate txt file.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "class NerDatasetReader:\n", + " def read(self, data_path):\n", + " data_parts = ['train', 'valid', 'test']\n", + " extension = '.txt'\n", + " dataset = {}\n", + " for data_part in data_parts:\n", + " file_path = Path(data_path) / Path(data_part + extension)\n", + " dataset[data_part] = self.read_file(str(file_path))\n", + " return dataset\n", + " \n", + " @staticmethod\n", + " def read_file(file_path):\n", + " \n", + " # Use utf-8 encoding when open the file\n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " ######################################\n", + " return samples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_reader = NerDatasetReader()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = dataset_reader.read('data/')\n", + "assert len(dataset) == 3, 'The dataset must be a dict with three fields: train, test, and valid'\n", + "assert len(set(dataset) & {'train', 'test', 'valid'}) == 3, 'The dataset keys must be exactly train, test, and valid'\n", + "assert isinstance(dataset['train'][0][0][0], str) and isinstance(dataset['train'][0][0][1], str), 'Both tokens and tags must be strings'\n", + "assert len(dataset['train']) == 14041, 'there must be exactly 14041 samples in train'\n", + "assert len(dataset['valid']) == 3250, 'there must be exactly 3250 samples in train'\n", + "assert len(dataset['test']) == 3453, 'there must be exactly 3453 samples in test'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should always understand what kind of data you deal with. For this purpose, you can print the data by running the code in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "for sample in dataset['train'][:2]:\n", + " for token, tag in zip(*sample):\n", + " print('%s\\t%s' % (token, tag))\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can find an implementation of the dataset reader that implemets the same interfaces in the library: [Conll2003DatasetReader](https://github.com/deepmipt/DeepPavlov/blob/dev/deeppavlov/dataset_readers/conll2003_reader.py). The functionality of the presented code is wider and the `register` wrapper allows to use this component as a part of config file (will be discussed later)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare dictionaries\n", + "\n", + "To train a neural network, we will use two mappings: \n", + "- {token}$\\to${token id}: address the row in embeddings matrix for the current token;\n", + "- {tag}$\\to${tag id}: one-hot ground truth probability distribution vectors for computing the loss at the output of the network.\n", + "\n", + "Token indices will be used to address a row in embeddings matrix. The mapping for tags will be used to create one-hot ground-truth probability distribution vectors to compute the loss at the output of the network.\n", + "\n", + "Now you need to implement the *Vocab* class which will return {token or tag}$\\to${index} and vice versa. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from collections import defaultdict, Counter\n", + "from itertools import chain\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Vocab:\n", + " def __init__(self,\n", + " special_tokens=tuple()):\n", + " self.special_tokens = special_tokens\n", + " self._t2i = defaultdict(lambda: 1)\n", + " self._i2t = []\n", + " \n", + " def fit(self, tokens):\n", + " count = 0\n", + " self.freqs = Counter(chain(*tokens))\n", + " # The first special token will be the default token\n", + " for special_token in self.special_tokens:\n", + " self._t2i[special_token] = count\n", + " self._i2t.append(special_token)\n", + " count += 1\n", + " for token, freq in self.freqs.most_common():\n", + " if token not in self._t2i:\n", + " self._t2i[token] = count\n", + " self._i2t.append(token)\n", + " count += 1\n", + "\n", + " def __call__(self, batch, **kwargs):\n", + " # Implement the vocab() method. The input could be a batch of tokens\n", + " # or a batch of indices. A batch is a list of utterances where each\n", + " # utterance is a list of tokens\n", + " pass\n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " ######################################\n", + "\n", + " def __getitem__(self, key):\n", + " # Implement the vocab[] method. The input could be a token\n", + " # (string) or an index. You have to detect what type of data\n", + " # is key and return. \n", + " pass\n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " ######################################\n", + " \n", + " def __len__(self):\n", + " return len(self._i2t)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After implementing the function *build_dict* you can make dictionaries for tokens and tags. Special tokens in our case will be:\n", + " - `` token for out of vocabulary tokens\n", + " - `'O'` for the tag vocab to place out of label tag to the first place with index 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "special_tokens = ['']\n", + "special_tags = ['O']\n", + "\n", + "token_vocab = Vocab(special_tokens)\n", + "tag_vocab = Vocab(special_tags)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will fit the vocabularies on the *train* part of the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "all_tokens_by_sentenses = [tokens for tokens, tags in dataset['train']]\n", + "all_tags_by_sentenses = [tags for tokens, tags in dataset['train']]\n", + "\n", + "token_vocab.fit(all_tokens_by_sentenses)\n", + "tag_vocab.fit(all_tags_by_sentenses)\n", + "\n", + "assert len(token_vocab) == 23624, 'There must be exactly 23624 in the token vocab!'\n", + "assert len(tag_vocab) == 9, 'There must be exactly 9 in the tag vocab!'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try to get the indices. Keep in mind that we are working with batches of the following structure:\n", + " \n", + " [['utt0_tok0', 'utt1_tok1', ...], ['utt1_tok0', 'utt1_tok1', ...], ...]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indices_batch = token_vocab([['How', 'to', 'cook', 'a', 'turnip', '?']])\n", + "\n", + "assert len(indices_batch) == 1, 'the batch length must be 1'\n", + "assert isinstance(indices_batch[0][0], int), 'The batch must contain lists of ints!'\n", + "\n", + "print(indices_batch)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tag_indices_batch = tag_vocab([['O', 'O', 'O'], ['B-PER']])\n", + "\n", + "assert len(tag_indices_batch) == 2, 'the batch length must be 2'\n", + "assert isinstance(tag_indices_batch[0][0], int), 'The batch must contain lists of ints!'\n", + "\n", + "print(tag_indices_batch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will try converting from indices to tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token_vocab([np.random.randint(0, 512, size=10)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A similar vocabulary is already implemented in the [library](https://github.com/deepmipt/DeepPavlov/blob/dev/deeppavlov/core/data/simple_vocab.py). It has extended functionality:\n", + "- token cutoff by frequency\n", + "- limitation of the vocabulary size\n", + "- saving and loading\n", + "- dict like dunders (\\_\\_contain\\_\\_, \\_\\_len\\_\\_, etc.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset Iterator\n", + "\n", + "Neural Networks are usually trained with batches. It means that weight updates of the network are based on several sequences at every single time. You have to iterate over the dataset and generate `x` and `y` batch by batch. The batch of `x`-s is a list of sentences of tokens like\n", + "\n", + " [['Yan', 'is', 'a', 'good', 'fellow],\n", + " ['For', 'instance']]\n", + "\n", + "and the tag sequence should be:\n", + "\n", + " [['B-PER', 'O', 'O', 'O', 'O'],\n", + " ['O', 'O']]\n", + "\n", + "An important concept in the batch generation is shuffling. Shuffling is taking sample from the dataset at random order. It is important to train on the shuffled data because large number consequetive samples of the same class may result in pure quality of the model.\n", + " \n", + "The idea behind the iterator is to perform computation in the lazy way. Use yield generator expression to do so. An example of using yield for generator creation is provided below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def iterator():\n", + " data = [1, 2, 3]\n", + " for d in data:\n", + " yield d\n", + " \n", + "print(iterator)\n", + " \n", + "for i in iterator():\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create the `DatasetIterator`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class DatasetIterator:\n", + " def __init__(self, data):\n", + " self.data = {\n", + " 'train': data['train'],\n", + " 'valid': data['valid'],\n", + " 'test': data['test']\n", + " }\n", + "\n", + " def gen_batches(self, batch_size, data_type='train', shuffle=True):\n", + " ######################################\n", + " ########## YOUR CODE HERE ############\n", + " ######################################\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the dataset iterator from the loaded dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_iterator = DatasetIterator(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try it out:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x, y = next(data_iterator.gen_batches(2))\n", + "\n", + "assert len(x) == 2, 'There must be two examples in the batch!'\n", + "assert len(y) == 2, 'There must be two examples in the batch!'\n", + "assert len(x[0]) == len(y[0]), 'The numbers of tokens and tags are different!'\n", + "assert isinstance(x[0][0], str), 'Token must be a string!'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a typical part of the data preprocessing pipeline. This parts will be used in the following tutorials. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/tutorials/00_deeppavlov_intro.pdf b/examples/tutorials/00_deeppavlov_intro.pdf new file mode 100644 index 0000000000000000000000000000000000000000..8e2c7b5761958403d8c38bdbf937177a898ecb86 GIT binary patch literal 1479277 zcmbTd1yo$!vM$;L2!R9$1b4T_-3buff;A4oY1~~B2oM?z9taZL-8}?%YZ`ag;4ZI| zfA4+Ixp%yA&wHzTj%8D-YR>vrRjpdI@83zYaIo^BqtVvFH*_=(3U&$y6KixdVPQ56 zFGq7WNn=-ITL(+F_r{jyE)<;b+iGlz#`cyB&TcGfnv4`|>aNairmh;!=H_Y+4z3g& 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