From e75c2b20365d14d629b88be0b332fe86e551ebd0 Mon Sep 17 00:00:00 2001 From: Aron Date: Tue, 9 May 2023 12:21:04 +0200 Subject: [PATCH 1/6] Add colab setup to tutorials notebooks --- tutorials/demo.ipynb | 37 +- tutorials/kernelshap_geometric_shapes.ipynb | 114 +- tutorials/kernelshap_mnist.ipynb | 108 +- tutorials/lime_images.ipynb | 67 +- tutorials/lime_text.ipynb | 30 + tutorials/lime_timeseries_coffee.ipynb | 36 + tutorials/lime_timeseries_weather.ipynb | 36 + tutorials/rise_imagenet.ipynb | 106 +- tutorials/rise_mnist.ipynb | 78 +- tutorials/rise_text.ipynb | 126 +- tutorials/rise_timeseries_weather.ipynb | 2126 ++++++++++++------- 11 files changed, 1894 insertions(+), 970 deletions(-) diff --git a/tutorials/demo.ipynb b/tutorials/demo.ipynb index bd9c6e10..f8024fd8 100644 --- a/tutorials/demo.ipynb +++ b/tutorials/demo.ipynb @@ -13,6 +13,35 @@ "DIANNA is a Python package that brings explainable AI (XAI) to your research project. It wraps carefully selected XAI methods in a simple, uniform interface. It's built by, with and for (academic) researchers and research software engineers working on machine learning projects." ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Colab Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "running_in_colab = 'google.colab' in str(get_ipython())\n", + "if running_in_colab:\n", + " # install dianna\n", + " !python3 -m pip install dianna\n", + " \n", + " # download data used in this demo\n", + " import os \n", + " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", + " paths_to_download = ['data/binary-mnist.npz', 'models/mnist_model.onnx']\n", + " for path in paths_to_download:\n", + " local_directory = os.path.dirname(path)\n", + " os.makedirs(local_directory, exist_ok=True)\n", + " !wget {base_url + path} -O {path}" + ] + }, { "cell_type": "markdown", "metadata": { @@ -167,7 +196,7 @@ }, { "data": { - 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+ "image/png": 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", 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", 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" ] diff --git a/tutorials/kernelshap_geometric_shapes.ipynb b/tutorials/kernelshap_geometric_shapes.ipynb index 3a96a6a0..0dfda814 100644 --- a/tutorials/kernelshap_geometric_shapes.ipynb +++ b/tutorials/kernelshap_geometric_shapes.ipynb @@ -21,9 +21,45 @@ "More details about this method can be found in the paper https://arxiv.org/abs/1705.07874." ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Colab Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "running_in_colab = 'google.colab' in str(get_ipython())\n", + "if running_in_colab:\n", + " # install dianna\n", + " !python3 -m pip install dianna\n", + " \n", + " # download data used in this demo\n", + " import os \n", + " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", + " paths_to_download = ['data/shapes.npz', 'models/geometric_shapes_model.onnx']\n", + " for path in paths_to_download:\n", + " local_directory = os.path.dirname(path)\n", + " os.makedirs(local_directory, exist_ok=True)\n", + " !wget {base_url + path} -O {path}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Libraries" + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { "pycharm": { "name": "#%%\n" @@ -66,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": { "pycharm": { "name": "#%%\n" @@ -94,13 +130,33 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metal device set to: Apple M1 Pro\n", + "\n", + "systemMemory: 16.00 GB\n", + "maxCacheSize: 5.33 GB\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-09 11:23:38.365494: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", + "2023-05-09 11:23:38.365632: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: )\n" + ] + } + ], "source": [ "# Load saved onnx model\n", "onnx_model_path = Path('models', 'geometric_shapes_model.onnx')\n", @@ -122,13 +178,22 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-09 11:23:44.017361: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n", + "2023-05-09 11:23:44.017471: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n", + "2023-05-09 11:23:44.024469: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:907] Skipping loop optimization for Merge node with control input: assert_equal_1/Assert/AssertGuard/branch_executed/_9\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -139,16 +204,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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" ] @@ -192,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": { "pycharm": { "name": "#%%\n" @@ -200,18 +265,17 @@ }, "outputs": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ab274ef37fbb40d1a50b26da84280267", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/1 [00:00" ] @@ -341,7 +405,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/tutorials/kernelshap_mnist.ipynb b/tutorials/kernelshap_mnist.ipynb index b89a6cdb..7f8ec271 100644 --- a/tutorials/kernelshap_mnist.ipynb +++ b/tutorials/kernelshap_mnist.ipynb @@ -21,9 +21,45 @@ "More details about this method can be found in the paper https://arxiv.org/abs/1705.07874." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Colab Setup" + ] + }, { "cell_type": "code", "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "running_in_colab = 'google.colab' in str(get_ipython())\n", + "if running_in_colab:\n", + " # install dianna\n", + " !python3 -m pip install dianna\n", + " \n", + " # download data used in this demo\n", + " import os \n", + " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", + " paths_to_download = ['data/binary-mnist.npz', 'models/mnist_model_tf.onnx']\n", + " for path in paths_to_download:\n", + " local_directory = os.path.dirname(path)\n", + " os.makedirs(local_directory, exist_ok=True)\n", + " !wget {base_url + path} -O {path}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": { "pycharm": { "name": "#%%\n" @@ -66,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "pycharm": { "name": "#%%\n" @@ -94,13 +130,33 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metal device set to: Apple M1 Pro\n", + "\n", + "systemMemory: 16.00 GB\n", + "maxCacheSize: 5.33 GB\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-09 11:29:59.327587: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", + "2023-05-09 11:29:59.327685: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: )\n" + ] + } + ], "source": [ "# Load saved onnx model\n", "onnx_model_path = Path('models', 'mnist_model_tf.onnx')\n", @@ -122,13 +178,21 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-09 11:30:01.721074: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n", + "2023-05-09 11:30:01.721134: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -139,16 +203,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -192,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "pycharm": { "name": "#%%\n" @@ -200,18 +264,14 @@ }, "outputs": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6063c70e03b84960bb8b72ca95d53062", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/1 [00:00" ] @@ -341,7 +401,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/tutorials/lime_images.ipynb b/tutorials/lime_images.ipynb index 0e84e3b1..c5bd980d 100644 --- a/tutorials/lime_images.ipynb +++ b/tutorials/lime_images.ipynb @@ -16,6 +16,36 @@ "*NOTE*: This tutorial is still work-in-progress, the final results need to be improved by tweaking the LIME parameters" ] }, + { + "cell_type": "markdown", + "id": "aa59a9c4", + "metadata": {}, + "source": [ + "#### Colab Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "405fe607", + "metadata": {}, + "outputs": [], + "source": [ + "running_in_colab = 'google.colab' in str(get_ipython())\n", + "if running_in_colab:\n", + " # install dianna\n", + " !python3 -m pip install dianna\n", + " \n", + " # download data used in this demo\n", + " import os \n", + " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", + " paths_to_download = ['data/leafsnap_example_acer_rubrum.jpg', 'data/leafsnap_classes.csv', 'models/leafsnap_model.onnx']\n", + " for path in paths_to_download:\n", + " local_directory = os.path.dirname(path)\n", + " os.makedirs(local_directory, exist_ok=True)\n", + " !wget {base_url + path} -O {path}" + ] + }, { "cell_type": "markdown", "id": "a5cf6f82-c1c7-4814-ae0f-5a1c0b8578f6", @@ -26,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "34b556d8-5337-44dc-8efe-14d1dff6f011", "metadata": {}, "outputs": [], @@ -42,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "c616916c-78ef-48d0-a744-b25b37b62a3f", "metadata": {}, "outputs": [], @@ -69,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "6597d1d8-b701-4276-9c3b-769ef492817d", "metadata": {}, "outputs": [], @@ -82,13 +112,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "862a0f29-6cac-4ff4-a9c9-6c0a5511408f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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mYUxvY8I44k36iRs56QQhcEJQlFTKHnmmmLzLza1nUATN59nzE+WlaeYjAQ1GhMgYwIJmWM1b+LYQyK38FakSRwlREmOsYBsWTRVtKlk9RTJITEKz3kQUlldWiPLKrF5kabsSRlGEiQxx7ByKCo2i1SJJTPAxmCAzIQTKLcAk2c8u1c+1hyrflA1huSZQtOb5suSVrTyxqrufJEkQyVv7vMJbEPVRf22rv7UZadpCFSJtBw8ZYzBGaLWatNImadZC1SIG4jhydoE4Kq6f2w9ye0JuQ3AjCrZwNsq9DweJPjyKVnzYa85K9OFMCIHAmPD1vJg+tdC8e71sLs45jn2fPW2gVrFZhogCTg13zj8GVYvVjFbawlrFWPWJQKyPFRDSrEmaNn3fXzFGiCJTXMMJDeu6HXmgkbcnZJn1WgeFh6GIEMcJuk/IQTlo6aB1gYMJQuCE4HvwoG2XHNkzm7J1Or1C3iqnWZPMQpalqLVolmI18610BqLeFqCIKJXEuGMz484spmi5W6nFakpmG64bYgxWW1hraLZ2i9Y+ySoYE/ngI5xLcdQWACIGMIXNIjBZghA4KexTWXpqAR1ZvSzWdnYDUAWxbp3mDkFuf9fCClZbCIIRQVEEsJqiNsXaFDGC68VbLBnWpr6Vtqhm3h4g3tqYZ2/qzGzUNlp2Fr+7lQ8awWgEIXBCcFVpvxe+ZA/o0q1zdTwSEFEkATdyLEVFdVqCMxS2Mmftr++0ECMkSVSEEzea2zRaDdKsToRv3WlhrdBsKXEUU6n4SELNyKw6QWJi/4kwJgKFLFMya8ky64yWprOCh8o9PoIQODGIHwUor2q3qOArv1LSsdW1yjYjy4WA18EVS5o2yLKU3e1tGs06u7vb1OtunagpcgtWKhWiOHJ5A1oprbSOSMV3UVKccRGsgmLaIxRWnepvM0BQC1acwLLWKyT7VPZegiAIh+EIQuBE0VtN7lgu5QN0QiD1Hz9maF03QNXSqG/TbDa4du0qN7ducP36NW7evEmr2WRpaZmKzxOwsLBAtVotzpumKcZYEhVUW1gVRBVRxdqSENAIEUVFsaoI1q9zoxK58VCkf5U/dAMGJwiBeaCo+Law6OfuRZDibAFuCLDZ3GXr5k1ubW3xlQtPs7V1k4sXv8JufYednW3StOUiBFM3NJhUkkIIJIl7ndRCUkmoVqosLi35EGLDQm2RlZU1FheXqVVrrK+doVKtsbiw7J2FlMymoIKRxA89msJPYbBbDlpBvwQhcILY89JryTCo+ZBeWQh4Y6A31mVZk3p9h62tTa5dvcpXvvI0N25scuHCszRbTZrNBsa4UzV2XdLQKIoKIVCpxogY1DqLf5wk1Ko1PzwoLCwssXpqndVT6ywtLZMkVRZVqVZrREb8qIB6E4bXTPIbyZf26Qb0vP9AXwQhcEIQEcSHgqhq0TFQMooBRK/mKy0vFLwAIGNnZ4ud3W0uX7rAhQtf4eKFC3zxS0+wtXWTGzc2iSJDUolZXl4k9o4/WWbJsjrNNMNSJ9MKgpBZZ/1HfZ8fIcucq7FIzMb6GVZWVrl5c4v19dOcv/s+lpdPsbCwSBRVyO0D7SjFztTtoZUfL0EInCCcECgPCeZDd22PwHzwPR/+azZ3abYaXLt2ma2tGzz7zFNcunyJK1cuU69vk2XObddEQmQgTZuoNV6LSMmyFmLc0F8Uu+E9qxa1efyBgApZps4WoIZb2zex1nLl6iWstSwvnfLxA0K1YjAmBsmFlnNp7mXvCIJgPAQhcGIQjHcPouOvW3IagPvgBQBkbN26wc2bN/jSl57g2tUrPPGFz7G1dZPt7ZuYCIwRVleXULVkWUqzsUtmM2q1GKsZaVZHibA2Ik7a+QUVWwgBNzKQC4GIra1Ndne3ERG2tm4iEtFKU9Qq6+sJSQzGxKiKdyCajlv5vDJXQqD9Hu1vRe9Fr5l5+tn/sD0Pu+5B1yrP+uN3xkSRH/fHe/w5VR91cQBRFAHGzwXQ4Nb2Tb781JM8d+kiFy88w9atm2xt3aDVbCCivp+urvXXDGszxCixCNa6bkalEvuEIhEiztlIEEykzukv79qL0wjUgjEgRrlx45rLQ5hUWVhYYGV5xTsVRaSp60pEJoYiqrE9z8F+z3FSkYUnmbkSAo6DW5VBX4ADp+sqQnMHP3+vqbIOOlY69isNAbrYQQSKCEFVS7PVYGvrJpcvX+LCV57hytXn2N3dYXd3B3DxA67CqXMW8rEDxhjEuO6FCCRJXAQQIdbftL9raTsgRuL9AFBM5EYCdus7qML1zatsb2/RaNaxNsXa2GcqjpFhp4EMmkPfzKEQOJlYVazP8JtlGXFsEImcoS5rkaZNWmlGlrW4cvUi165e4amnnuTLT3+RK1eecxpAq0GruYMY11qnqfMytNYFBFEIhm6nJKWo9UBhe6A9EJlHMlhVYomIjCFOAJOxW9/m6rVLVGtVarUqS4srLC6uEItg8gEDbwMItoDxE4TAMWRfjSAfArRaaAYGISMPBU5ppS12d3fYunWTa9eusnXzJjs7OzSbDbKshaIuE0GRACTXJihiBcQYsky91V66tJ2yN2Kp24LmSgJiFDEQRU7TaLUabG9vsbl5jRs3NrFWSRKXnDSyMS5UyakE3VOeBUYnCIFjTIcwyBvfzKJZBhrl1Qa11mkDWZNms86tmze5duUKTz/9FNc3r7Czs4XNWi6HgBGiOPK++s7qjxWfUERIIqf+N4pre+++jnLluQmg3J8HwUSGKIIoAmMibGbZrW/x3KWvcOvWLYyJ2VjfwBjD0tIyqhmVZIXIVIrzh8o/XkYSAiKyBrwVeCHujflB4HPAe4F7gS8Br1TV66NcZx4pt3YHpQDvqGDGjd87h6AMa10/3aqbA8AZB118v4lM4aUntC3wUSylgB3rflXvuptHEDqdICpadnpqAm5ZS12DIj8BGda28mID0Gjsola5fv0KgnL6zBmiSKhUYmKziNBONtLdJQhCYTRGTTT6y8B/VNWvBf4u8DjweuBjqno/8DH/PTAk3f7/+3UFcvU/rxB5JiBrs8Lt1hhnHIziiCgXAj5wKE8AkqcON8a4ff1yEeOfCwGJvF+CKQmH3ChZSmPmuwG53SCKxAuoFoXLMkqrVWd7Z4sbN66xeeOaMxQ2dsiypr8H3SP4Dnoegf4ZWhMQkVXg24DXAqhqE2iKyCuAl/rd3ombo/CnRylkoF/a/XSXEkyL8XpTtNhaWPajOM/6E6OagvgU4orvAuQ+++0kpWma+aHApNMOWIQp56ME5e6C+rI542LaapJlShIrxiRUqjVsBtYqu/Utku2IrVvXWFhMyOxKkeQkT0mWZykKGsB4GEUTuA+4DLxdRP5KRN4qIkvAWVW94Pe5CJztdbCIPCwij4nIY9evhd7CQXS3ePtPEuq7EKYUQuxjBsSP7WdZ5v0EWmgxZ4AUPhS5YVGtLVx3y+p+MUV5bqxTpwW4FGT5fiVVnU6XXyegvJaiGWCJIhBDYSRsNHbZ2dmm0dglTRvFHAZ7u0C9n1VgMEYRAjHwYuBXVPVFwDZdqr8W8al7UdVHVPUBVX1gfWN9hGLML52GwbafvRsebG/Kuwm7uzvcuLHJc89d5MqVS2zdvEmapoV/f25YtFnmk38qailCejtTf8XuQ4yqc0IqugGKFw74QKD8HFI4ComxWNvCagvEYoxFjHJr5ybXN69w8eKzXL58keubV2g2G757Y4vkpIcJgiAM+mcUIfAM8IyqPuq/vx8nFJ4TkXMA/v+l0Yo4XxQt7SEvcne/OA+2QdppwQVFbeZadVUajQbb29vcuLHJ1tYtGo2GOwZK9oFuPwDxgT/+Q9s46Cq85OEI5HECuUaQn8t9IO+KOPtDVKzLspTMZi4/Ydqk1Wr6BCa7NBp1Mpv1vPfAeBhaCKjqReBpEfkav+pB4DPAh4CH/LqHgA+OVMJAX1ivYoML7/X1y6vdTgjs7uywtXWTq1evcmNzk3q9TubnHXAVNW/t9xEE5A5IplT5u1v/cpfADx96IaBYMptiIkOSuGFIVUur1STLWljrhzFbdXZ2b7Gze4t6YwdbmuEotPLjZ1Q/gR8F3iUiFeCLwA/gBMv7ROR1wFPAK0e8RqBPVCliBFpeM0hTZ4FXlEazye7uLtu3tmk0GgCulbXq1PHiTPkwo1O9rW1rCUWmYuNsBbnZL9cCcuOk/1N0S8S40OM0bbn8AVGMS20GmU2JTEIUxVQqhiSpUKtVWFiosbhQQ4yEyj9BRhICqvoJ4IEemx4c5bwTp9e7pPtvl72rivUHHXcYvXwABvWIK1xpO1pg300gN+I5b8Fms0G9XqfZbJCmLXL13MUT0B7390Vql61tHJTSqqKC93pC+SHFfuLtFrZYJyJOoShmOBI3YuHnL4zjiDiJ9zyncizFYc+rl+AIowqdzJ3HYK/+j2hpQEv31uX9Zkgq79dxjgHKM+wL2f3y51N4ZVlKvV7HqpvkI65E7OzusHXrOpcuP8tzl5/l1s5VGq1dME3QDMjAFEYB98+H/eZOSHl3IVf5I3XDfUZAJRc4PkTZZoVfAMbmvQKMxtTiiKyZkTUt4hOImFL3w/gypGm6xwjY3VXJtYPyulDBB2eGhMD0frzuK5UHwbTH9uK40gtWzt6z97j97+Wwl/Sg9FkHlwc3g5D104LnSrrgfQZcPoAs9ZOLaL5PlyqTV6a9V3Mtfnsscc8+oi5paMf5cH6D5cvkMQVRlN/H3iuqauHp2KsrUH5OZUEwqBDY+6wHOvzAcx3GrHRvZkgIBEZB1ZKlTTKbFh52fmrgon7lVT7P+uMqD74lbr/EUj6ojO//iI8ILLoNPsKwvVNbpKqUvhWGPesFgfVeh9LWNEoV29qM1Lb2FQSB8RCEwAmj6LqIX45MMcZvRDBiShN9lOIHyi24X6clbz/n3ltSxf3fdiLT/Dg65YcW9kL3VdWnOwOjZeHhrpNZBVKarQb1Rp3d3d1igpTyJ0QTjo8gBE4akrfFef/eFH3u9sdgxMUJuMrZnnrcn6IYw3c12AKR+57HD0hpgjPtnCosD0POBUXHYGOxrSQ8aHcRrJuhhFbaotls0Wg0C9tA+3J7jamB4QlC4ITg4vTdyJ1qHhvoKlxhD8iydlfBewHmnWCXlTg/WalLIKC2rREY4yqd1Xa0IviWveR+XOD3cxmCvBAyiumaLcklJxVaqZvjUOqG3d1tdna2fWaj4CMwKUaNIgzMENJllMu9CDObFRmH0qwzIo/cmFaqlEJJayC3F+S7S+FG3Gvmn3wfxAki7R5tkTw1elvIlM/RMbRZpEgPTJKgCZw4XOXJbIrVlDStU6/vsrvrPo16nVYr9S7GPrpQXO5B169vG/gK3yAfDiwGTOTsCioGyVxY8N7WucuwWJzSd1GMwSjt/Afk17WFgdL4kOc4MUUegmGs/4HDCULgBGEKvU6w3uaWRwXa0if383fxAHl/XvZqB0CuUbQDgITICJgIK2CtSzVW2AB8V6Kr187efkLel3f7urgE45KamKiY43DJT2PWadMIwmCcBCFwQsgrshjfm7eQV2CreY5Bb3jzpnxjIoQ8FVgpBqDI89/2NWi30OK0ASIynANPe0jSlSF3I24Lm7Z5sB2e3BYCzhDphEAcGaI4plarsbi4yNLSMnEcdzgKGbN/LzYYDQcnCIEThcVlCconAXHut3l2IDcsGBOZBBHjsvzYFLVZOyaAXFMo+Q0Ura/LDBRHEbFJSMU4Q56VvV5WXtaoOKEQS9T2XVBnE8iNl05bodBAIhNTXaixuLTEyqlVKj7paPlTjqIMFX00ghA4Iagfy2+3shSVqnt4ED9XkVPb/VwERXcgr1BSnKcsBJx13010YlRdApMOE0C7cirQPmXuBFQ6v0qxj0UxqO+iiI8dSKhUKj4fYugKTIogBE4IapU0a7a/+/kA8XkA1QrWu/WruvkJjbrhP7URar2KLbkXX56YRAu7gQhey3Cpyawa/92du6M85N4HTuRI4WfQVjpcNiKw3kiZiXX+C1YQYqIoIY4rRexCeUSiV/BVYDiCEBiAQV+6XpFvw5y/VxTd3r4vhZqcO9d0OAcZQxwlJEmFamUBqyliwNLCSoaJan5UwUUX2kxLlc5pGGnaotWKUIUs3SncefNr5zaIXAsot9xuLkKnCRiXXgifzwSR2I9QuASmcZSwtLRCrbZIEleJo9hPodb7Oe2nHQz3zOdPywhCoE8O63v2G8Za3vcgobInWOmA725/gzEJqZ8yLH+Z83kCjYmpVGrUaossLa04BxwsLamTZS0qlUVUU3br22RpizRLSSoRzp9IfG6CFsYYsiyj1axTTkISxW49CpkCpW3gchI4IWAwxrXu9WYDEUNcqZBHJxqJSZKam758eY1qdZEkqhCbuLh3l0atd/dg73MZvFLPW3cjCIETi3uRjYlYqC2wtrrB8553LxsbZzh3x50gShRDfXfb5/BrUq/vcunyRbZv3WR3ewuUYkjR9eVzTSOPNrS+T6+FI3BbA8hdl/P8ha57kmXK4kKVSqVKHC8AoBiy1JJlFiFGJMGYCiIx1oczByZHEAInCelccBXRZepZXFxiY/0MS4vLrK+tYyJIkoid7S0ajV3qjW22t29Rr+9i05RmvY5qWlj28hY39/Fv+xZYl7co9wyUfMgv9w3IJwwxPqTYkiRVarVFamK8lgGtZos0TV0K8qRGEle9IHCpiA9q7QOjEYTACSNXwcvht0lSJYoSapUFrNrCmBfHMfXdWzSbdW5uXWNz8xo7Ozu0mk12tm9Rb6Q+7j8i8o5Cbszf5Q1wBsbStOFEbfU8dxu2efgyRCahulDlzjvv4cxtt3N64zafZkyo79ZpNJqkCrVqjec97x7W1tZZqJ0i0rjwOgzJQ8ZPEAInjM5KIj7Cz1fgxPjvbmjPiBDHbo6/SqVGtbpArbZApVIjjioIu9jCDdD5/LvsQeqCCsFnMnbLJg8w9AIAze0AgBonBKo1lpdPsba2wW23nSWOE5CIRr1Bo9GilVmSpMLKyhq16iIiEaLDG/4ChxOEwAmhqGx+0D4fKciseh8+U7gVZ1mLrJWRZSkCzhgX1ahUFllaPMXiwjK12iK7u9tkeR4BDAg+tt8SRfloQNs3AYkw3g9BFbJMi6FDISZOaiwtn+L06bPccfYunnf+PpJKFRPFtFoZaWppNd08CAsLi7QDm0Y39gX2JwiBE4LirObGzw/gWu72GD94Q10pZt9mmc//H5EkNWrVFisrq6ysrLK1fIOtWzfIbOqt8fmEIS6yL+pIOOIw+cxHgM2cm7JIfv4qq6fWuOuuezi9cTsry2uIJKg1tKyi1uU4qFQqRCbyw5iKzSzlaclzQrdgfAQhcFLIg4Xw1nmvwhuTu+z5fMRKR7wA6pyC4tj5DywvrbC0uMziovPZbzYNWdpyswb5lt9qXjGdECi6HuTGw9xbUJ3zj4mpVKosr6xy9vZzrK1usLi4ghBhrZBmuS3DkMQVosgJpTTN0Cxt3w9BEEyCIAROCCKGOK4UuQCgs3tQqPRAHLnxeGNiBDcfoGJJkoylpVMsL6+ysrxKElcR2aGVttyU5sanJkNpTzqqhXsyXdcVibzTzwKnz9zB+bvv4au/+m9x5vTtLCws0Uq9/4AFMTGRj2tw5UqK8rbvMVT2SRCEwAlBRDBS9qrL1/sxfm3PLJxX0CiiSDrq9nVpy5OkQqVSJYpcLkLN6zp5MtDcKdjHK7flTGkI0RBFhmqtxuLSMuvrG5xaXWVxcZEkqRBFMc00K64rXih1uDhbsFagnX80CIIJEITACcEF3ZScedxa13PX3NOuneYnj9ZLbeYmIE1dJF8cVahVF1heXqZWq7GzW8UUQ3+RDxiy4OcayFMFOCOeFgZK56ZcYWNjg/X1M9z//K/h1Kk1p10Q+ZgFZ0SsJBXiOMGYiGbDGR5baTvLcMWEij9JRkovJiL/XEQ+LSKfEpF3i0hNRO4TkUdF5AkRea+4KcoCE6Y9t0A7V7/rr+dZhSO/X1lLMBjxbsViEBP5qcCqVKtuqLCSuCg+fF/fZu5TRBQVxvtS4lHafggLC0usrJxiY+M0p1ZOkSQVTBQj/pqRibwAiDESQz6WIZGfjmyhNPnJ3lGCoBmMztBCQETuAn4MeEBVX4gbOX4V8AvAv1bV5wPXgdeNo6CBgynyCZanFddc1Y7Ipx/XQgrkgUWu4rtKGZEkCdVqlYUF7zNQrfrkI+Kv4QQNlDSAvFtQchwyEhHHMUtLS5xaWeXMmTNOE0gqmDhGosiFI0fu+q7rEfuuh0t4Uq1UWVxY6ggecoTKfxDd6dkPS9A6ancgBhZEpAUsAheAbwf+sd/+TuDNwK8cdqJpZTxtz9lXWleKyhtniOqgL+p++/cTRehmEXPZAVDx2XstrTR1GYUyfGixICYizz/WTjnm7QSmxsLiKmsq3HnXV1FbWMVqRKPhchW2Wg0XgcgOgpJEvpuAob6b4iZGjji1dhvnzp3nq+75Ok6fvh2xNUQqxFEFkyaQGRLcccYqYlOQlIXEaRcmamFsim01XPxz6Q1pPybpWne8/QkOCizrDqEeZxj10EJAVZ8VkX8JfBnYBT4C/AWwqaqp3+0Z4K5ex4vIw8DDAHecu6Nn5ZwU3S9HrymtJnGdQbfn9BNFmCfwEv9H87x/NkMznApP25e/KEPHyIFLDV5JatiFjNXVDawVbty8wfb2FtaKcwBKcU48fujOEPuuBYgxRFGNpcVTrK+dYe3UaU4trbl9iDCSuGNViPLgIi+QBIgKI6CbJVkzJwD68Rg8zl6F3UOf5YpeFgD73c8oQmFoISAi68ArgPuATeC3gZf1e7yqPgI8AvB1L3xByA4xLpxzP8aIj93v6KkD+SxErmIllYSEGNWWG64DagsLVGsJ588/j9OnN1hcrHD12hUuXvwKFy48S7NVR63Bakar1aJSiUjimDO3naVWXWD11G2cPXsnd5y9i/X1DZYWV6hUFvYM+ZXV+mNSV4+Eciq1cmq1cTFKd+A7gCdV9TKAiHwA+BZgTURirw3cDTw7ejED/eJ66O2KVSTlLLsUSyldl19dTkCSr1xeWiaODfX6BplN2d7e4urVKnHdtfzibQtxFFOt1NhYP83S0gq3nT7HxsbtrK9vUKsukCSJS1PuU4sV2kfXsF+nIDj4RT8uLfwwHNSPn0Q2pVGEwJeBl4jIIq478CDwGPBx4PuA9wAPAR8ctZCBwSj3j51bcNkW4hT4ohIVFdEgXmuIYhdnECenWExrRUqxen2Hy5cvUW/sgDT8ZKNKJVlgaWmFu+48z/r6ae668x6WllZZXlqlVl12Br88fZnPS5AbKntWZunu3Xff3/FV+8vkan93fz+nezmPDh33qMgoNoFHReT9wF8CKfBXOPX+/wHeIyJv8eveNo6CBg6hcAFo+wjkGHETk2rH1F9+b6so1mXxxfXpnSOQdRmAMVQqVRYXl1lbW2dj4zSqlms3doiiiJWVFc6cOcv62mnO3XG3iw48cweVZIEkWQAi1EKaZhSZiLpcgPMkJPkog3SVsQhT4OQIgJxuW0+3FtDLYD3u+x1pdEBV3wS8qWv1F4FvGuW8geHQIuNPKWio0AqM9x70++Yvmc8L6PL+ORuC1dIkJYrL+lutsri4xMrKCo1GneubBhMZlhaXObWyzvr6GdbWNlg9tc7y8imXQkxi0tSSWZehyBgoT0Xuytee40DaykpBeyCgv8p/nAVCN700gfKszOMieAyeMNovTp5nsDzu4pOCdA03Oj8CyFtiZ5FvBwdVKhWWFpdora5z5sztqMJTX36CarXG2bPnOX/+Xs7efgcbG2dZqC0iEoMarILNDGpdenOXdCTqae3u7J4EO/E0CULghKIl9dlFFnaud8taUsV91StlBc7nABAjJEk7pqBSqRKZCklcY9n3/ZeXVn1KsARryxOR+QlFIlOcLxdGnQJgryag+ahmD5lw0rUA6N0FmISXZBACJwk/PNjGew0WlrZeTijuk6b5DMDOd18Q4jhBjBDFkGWWWq1BrbpIrbpItbrM0tIqZ07fwenTTgtIYjcMmLac8cqIepffiMhUCtfmTg3g0Jvac18nrbKXOcgfZFJu0kEInBj8bMH+m3MPzlXsPOpP6La75zEG1mblfMHFUGHhFSymiCtYWFji9OnbWT21ytrqGZYWVkiSmo9iNAixH4HIZztyMwq1XZlzbaAorXdyat9LkaegUCj2f/mPq1A4zNDXyxnqoHkYhyUIgROCa3mNDx4qbylXvPa68rZ25KHFJyXq7Cao+oqcC4FFNtbPcOrUKqdW1qnVlknimj9HbmPw6r8XBvnkJHmEY26wLARBr9mR+7jn/dYdp9mJerX4vcg1gVlyFgrMGHkrUYQN9yAfIRDJJx51rX8ce7U9bk8/1kqbLr2XuExCSVxl9dQ6SVyl1VSWlpbZWL+NhdoyUVTFzypKElfItY48qtGYPAlJYFDajlxBCIyNwzyxDgrgOGhb9/GHqalj/TE7rGp+OMlNC9xDrcwFQW4obL9geYudOxUp7eCiOE6o1RYQMWxsZCzUFqhWF31GI++UJJ1hy7mO3zFRavsJ7Ps8OkYwBngMx0kDKNMdvwIHvz/j9CacSyHQzWECoB+OXA31ocTl8ogY7/BDqdWnYx+/FZe+p30O53Ic+cxCGcQxUKFWW0QVzt7+vC5DlZuyvLPKui6Eu35ZCHQ/b3VaRHFo9zM8nn3+wzjIQ7CbXg5E4yIIgT4p/2Dd4+z5un6GrcbxA/ZsIdQnGXV7dLT2rp9d7OjPsbc8Rf0sHI3Kbq2C4kYNRABTjvEveya1hUK7mL0qsXb9P5jjYPwbtIyzorUEITAA+wmCsjfX0XHYC7hf2crr80qsXet9F0Hz/+V9u5f7ufZgAiAwWYIQGICyACi7b+7xejtGHC68pGs5Nyb0O1TVj/AJHCVBCAzIfimbyqrzkQmDIevVHsNhh6Nx+9R5V2Mwk123ptG9rtf34ydMYXbU+0EJQmBIursFbhhsWknS9qOr8gz9Tnar97njke6zvc+zSsngUL7UnDIrQiMIgTEw7qiu0ejlFLTf9x5H512bfGivOGfbkV+6L3Mg3SMS+2wvb5iNujE3BCEwLwid0UPFytK3fQVZ2Umll+FwP7r22//0B6wcTusI9E8QAkPSbQOYHU2gF74y7lESeqjntH373bLfvZgdOD8m23Mc0odgOPQxSdf/2WdW1PphCUJgQHqNBHQLgkllgBmNrhdVOv7RWel6t75t4VDWCLp3OnikYe8jyaVM6ftMPbeTTxACA5C3/nnG10nGeE+PsrFvP3pts0Nea9DrBCZNEAID0MtTMF/ujgM/Nkh5sfOeoG1G6PAnyOvyAXEAwxUkCIGj4KjHtI4NhwmA460N9IuQZwue6jUDE2VmNIGjbD27r91PWfZr+TuOPURjlj394UlieowOFAXpQlHdp/Ad0YoHnqQ3e4rQj7vzSRYER39vMyMEZoX9BMBhvgA9j+tlNzuSH91HE+1z7d5l6hUyXV4flMjxcbSCIPySgcCcE4RAIDDnBCEQCMw5hwoBEfl1EbkkIp8qrdsQkY+KyOf9/3W/XkTk34jIEyLySRF58SQLHwgERqcfTeAd7J1y/PXAx1T1fuBj/jvAdwH3+8/DwK+Mp5iBQGBSHCoEVPUPgWtdq18BvNMvvxP43tL6/0sdf4qbpvzcmMoaCAQmwLA2gbOqesEvXwTO+uW7gKdL+z3j1+1BRB4WkcdE5LHNa9eHLEYgEBiVkQ2DWqSLHfi4R1T1AVV9YG1jfdRiBAKBIRlWCDyXq/n+/yW//lngfGm/u/26QCAwowwrBD4EPOSXHwI+WFr/Gj9K8BLgRqnbEAgEZpBD3YZF5N3AS4EzIvIM8Cbg54H3icjrgKeAV/rdfxd4OfAEsAP8wATKHAgExsihQkBVX73Ppgd77KvAjwxcCj368NuDZoIZaXahYxRV3C+j/FaDT9BRLA19zUEZNhp08OeyX/blSVxrf4LHIAcLgP3WBabJ7D//4d6R4e4rTEN2RAyVUXiG3t2Tn+vguHL0odJBEwgE5pwgBAKBOSd0BwYgGAZHZ7j+7HTV5XmzAQVNIBCYc4IQmCTz1aDMLcddcwhCIBCYc4JNoA9GkvTHu5E4kMEdf4Z9GL1mLpoMx71VH4agCUyS+XufAseQIAQCgTknCIFAYM4JQiAQmHOCYXCS7DN798lgGEvd9B7IsTDwDRshOeZbC0Jggkx3yrHjMqvvrJdxyuUbokKPu4RzLQT6bS2GjsDTo4jem91KdnwCGadVUJ3q1fZjLoVAyBkQCLQJhsFAYM6ZEU1Aj7QlnqhmMLXbGj5V1Ull1rU7N9O7HvkvFjSBE8NRv0qzxawLAJidMs6IJnA0TNw2MPXf+OhTVQUGYxZ+sZkQAsrRS8VJCARBpnhfoTvQi+EyAE/nGRa/2BG/+6E7MEGm29sLlb+baWYAHgY3gd/R/25BCDDBbsGRdAcCcPSta38cvVEQghAIBOaeQ4WAiPy6iFwSkU+V1v3vIvJZEfmkiPx7EVkrbXuDiDwhIp8Tke+cULknhurRDlcGAtOmH03gHcDLutZ9FHihqv4d4G+ANwCIyAuAVwFf74/5dyISja20gUBg7PQzF+Efisi9Xes+Uvr6p8D3+eVXAO9R1QbwpIg8AXwT8CfjKe7JZzQtZDjL9vTShAVmkXHYBH4Q+D2/fBfwdGnbM37dHkTkYRF5TEQe27y+OYZiDM7JU/1P0r0EpsVIQkBE3gikwLsGPVZVH1HVB1T1gbX1tVGKEQgERmBoZyEReS3w3cCD2m5OnwXOl3a726+bKUIUYWCcDPvuDOspOO53dShNQEReBvwL4HtUdae06UPAq0SkKiL3AfcDfzZ6MQOBk8estDuHagIi8m7gpcAZEXkGeBNuNKAKfNQblf5UVX9YVT8tIu8DPoPrJvyIqmb9FCREEY567eHchkWGc20OmlMnwzwPF0U4+Csy7mffz+jAq3usftsB+/8c8HOjFGpazGIA0fDXz48b3NIfRgdGY9jnkR921AFEwWMwEJhzZiKKEI6+dZm1+IHhot8Gv2DoDoyPQZ/JsN2BYa51EEETmEGmGf0WBMB4GO45Tu9aBzEzmsBRMktawGjXnYUUFfPHsE5nuX/nMKnpxykIghDoIrRygWkxrMieCT+BQCBwcghCIBCYc2QW1F8RuQxsA1eOuizAGUI5yoRydHKcy3GPqt7WvXImhACAiDymqg+EcoRyhHJMtxyhOxAIzDlBCAQCc84sCYFHjroAnlCOTkI5Ojlx5ZgZm0AgEDgaZkkTCAQCR0AQAoHAnDMTQkBEXubnKXhCRF4/pWueF5GPi8hnROTTIvLjfv2GiHxURD7v/69PqTyRiPyViHzYf79PRB71z+S9IlKZQhnWROT9fk6Jx0Xkm4/ieYjIP/e/yadE5N0iUpvW89hnno2ez0Ac/8aX6ZMi8uIJl2My833kwQ9H9QEi4AvAVwEV4K+BF0zhuueAF/vlFdz8CS8A/jfg9X7964FfmNJz+Engt4AP++/vA17ll38V+GdTKMM7gR/yyxVgbdrPA5ed+klgofQcXjut5wF8G/Bi4FOldT2fAfByXKZtAV4CPDrhcvxDIPbLv1Aqxwt8vakC9/n6FPV9rUm/WH3c7DcDv1/6/gbgDUdQjg8C/wD4HHDOrzsHfG4K174b+Bjw7cCH/Ut1pfSDdzyjCZVh1Vc+6Vo/1edBO239Bi7A7cPAd07zeQD3dlW+ns8A+D+BV/fabxLl6Nr2j4B3+eWOOgP8PvDN/V5nFroDfc9VMCn85CovAh4FzqrqBb/pInB2CkX4JVziVuu/nwY2VTX136fxTO4DLgNv992St4rIElN+Hqr6LPAvgS8DF4AbwF8w/edRZr9ncJTv7lDzffRiFoTAkSIiy8DvAD+hqjfL29SJ1YmOoYrIdwOXVPUvJnmdPohx6uevqOqLcLEcHfaZKT2PddxMVvcBdwJL7J0G78iYxjM4jFHm++jFLAiBI5urQEQSnAB4l6p+wK9+TkTO+e3ngEsTLsa3AN8jIl8C3oPrEvwysCYieb6HaTyTZ4BnVPVR//39OKEw7efxHcCTqnpZVVvAB3DPaNrPo8x+z2Dq725pvo/v9wJp5HLMghD4c+B+b/2t4CY0/dCkLyouxe7bgMdV9RdLmz4EPOSXH8LZCiaGqr5BVe9W1Xtx9/6fVfX7gY/TnuNxGuW4CDwtIl/jVz2ISx0/1eeB6wa8REQW/W+Ul2Oqz6OL/Z7Bh4DX+FGClwA3St2GsTOx+T4maeQZwADycpx1/gvAG6d0zW/FqXWfBD7hPy/H9cc/Bnwe+E/AxhSfw0tpjw58lf8hnwB+G6hO4frfADzmn8l/ANaP4nkAPwt8FvgU8Bs4q/dUngfwbpwtooXTjl633zPAGXD/rX9v/yvwwITL8QSu75+/r79a2v+NvhyfA75rkGsFt+FAYM6Zhe5AIBA4QoIQCATmnCAEAoE5JwiBQGDOCUIgEJhzghAIBOacIAQCgTnn/wcdmNVZB/YbeAAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -131,25 +161,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "7c0bfd7d-df1d-4981-b714-496bc16b9347", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c093d992af2f412ba82debcd43ed36f6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/5000 [00:00\n" ] }, + { + "cell_type": "markdown", + "id": "90fa6efe", + "metadata": {}, + "source": [ + "### Colab Setup" + ] + }, { "cell_type": "code", "execution_count": 1, + "id": "07dde9d4", + "metadata": {}, + "outputs": [], + "source": [ + "running_in_colab = 'google.colab' in str(get_ipython())\n", + "if running_in_colab:\n", + " # install dianna\n", + " !python3 -m pip install dianna\n", + " \n", + " # download data used in this demo\n", + " import os \n", + " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", + " paths_to_download = ['img/bee.jpg']\n", + " for path in paths_to_download:\n", + " local_directory = os.path.dirname(path)\n", + " os.makedirs(local_directory, exist_ok=True)\n", + " !wget {base_url + path} -O {path}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b361576b", + "metadata": {}, + "source": [ + "### Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "a626f1c8-40e2-44dc-b7ff-5beab9ab29f0", "metadata": {}, "outputs": [], @@ -58,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "96357156-deb5-4016-8998-50da5dcdb288", "metadata": {}, "outputs": [], @@ -75,16 +114,36 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "2fefe8b2", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metal device set to: Apple M1 Pro\n", + "\n", + "systemMemory: 16.00 GB\n", + "maxCacheSize: 5.33 GB\n", + "\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "2022-06-29 11:19:12.493689: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + "2023-05-09 11:34:37.540871: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", + "2023-05-09 11:34:37.540995: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: )\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n", + "102973440/102967424 [==============================] - 33s 0us/step\n", + "102981632/102967424 [==============================] - 33s 0us/step\n" ] } ], @@ -102,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "59d64afd-fab7-46e1-93ce-64e3cb4e1fca", "metadata": {}, "outputs": [], @@ -124,33 +183,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "8b2d285d-b42d-448e-bc32-e4ae7bd63661", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "img, x = load_img(Path('img', 'bee.jpg'))\n", "plt.imshow(img)" @@ -176,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "bacterial-shakespeare", "metadata": {}, "outputs": [ @@ -345,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "b2209c46", "metadata": {}, "outputs": [ @@ -385,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "intimate-operations", "metadata": { "tags": [] @@ -401,7 +437,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -446,7 +482,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/tutorials/rise_mnist.ipynb b/tutorials/rise_mnist.ipynb index 7faf7757..2f838a87 100644 --- a/tutorials/rise_mnist.ipynb +++ b/tutorials/rise_mnist.ipynb @@ -16,9 +16,48 @@ "More details about this method can be found in the paper https://arxiv.org/abs/1806.07421.
" ] }, + { + "cell_type": "markdown", + "id": "bb84d1e9", + "metadata": {}, + "source": [ + "### Colab Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "587488c0", + "metadata": {}, + "outputs": [], + "source": [ + "running_in_colab = 'google.colab' in str(get_ipython())\n", + "if running_in_colab:\n", + " # install dianna\n", + " !python3 -m pip install dianna\n", + " \n", + " # download data used in this demo\n", + " import os \n", + " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", + " paths_to_download = ['data/binary-mnist.npz', 'models/mnist_model.onnx']\n", + " for path in paths_to_download:\n", + " local_directory = os.path.dirname(path)\n", + " os.makedirs(local_directory, exist_ok=True)\n", + " !wget {base_url + path} -O {path}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "4b4d551e", + "metadata": {}, + "source": [ + "### Libraries" + ] + }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 2, "id": "instructional-threshold", "metadata": {}, "outputs": [], @@ -54,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 3, "id": "configured-drill", "metadata": {}, "outputs": [], @@ -76,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 4, "id": "polar-placement", "metadata": {}, "outputs": [], @@ -106,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 5, "id": "normal-wallet", "metadata": {}, "outputs": [ @@ -120,16 +159,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 52, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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" ] @@ -171,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "bacterial-shakespeare", "metadata": {}, "outputs": [ @@ -179,7 +218,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Explaining: 100%|██████████| 50/50 [00:00<00:00, 140.13it/s]\n" + "Explaining: 100%|██████████| 50/50 [00:00<00:00, 143.17it/s]\n" ] } ], @@ -200,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "intimate-operations", "metadata": {}, "outputs": [ @@ -213,15 +252,24 @@ }, { "data": { - "image/png": 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" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -257,7 +305,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/tutorials/rise_text.ipynb b/tutorials/rise_text.ipynb index 20b8907a..7a5b8d74 100644 --- a/tutorials/rise_text.ipynb +++ b/tutorials/rise_text.ipynb @@ -17,6 +17,37 @@ "*NOTE*: This tutorial is still work-in-progress, the final results need to be improved by tweaking the RISE parameters" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "40dc5e32", + "metadata": {}, + "source": [ + "#### Colab Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "236ca562", + "metadata": {}, + "outputs": [], + "source": [ + "running_in_colab = 'google.colab' in str(get_ipython())\n", + "if running_in_colab:\n", + " # install dianna\n", + " !python3 -m pip install dianna\n", + " \n", + " # download data used in this demo\n", + " import os \n", + " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", + " paths_to_download = ['data/movie_reviews_word_vectors.txt', 'models/movie_review_model.onnx']\n", + " for path in paths_to_download:\n", + " local_directory = os.path.dirname(path)\n", + " os.makedirs(local_directory, exist_ok=True)\n", + " !wget {base_url + path} -O {path}" + ] + }, { "cell_type": "markdown", "id": "a5cf6f82-c1c7-4814-ae0f-5a1c0b8578f6", @@ -27,10 +58,19 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "34b556d8-5337-44dc-8efe-14d1dff6f011", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/miniforge/base/envs/dianna/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "import os\n", "import matplotlib.pyplot as plt\n", @@ -48,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "c616916c-78ef-48d0-a744-b25b37b62a3f", "metadata": {}, "outputs": [], @@ -71,12 +111,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "486540bd-2676-4dfa-bbe8-ee8aa289acd3", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting en-core-web-sm==3.2.0\n", + " Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.2.0/en_core_web_sm-3.2.0-py3-none-any.whl (13.9 MB)\n", + " ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.9/13.9 MB 2.2 MB/s eta 0:00:00\n", + "Requirement already satisfied: spacy<3.3.0,>=3.2.0 in 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already satisfied: MarkupSafe>=2.0 in /opt/homebrew/Caskroom/miniforge/base/envs/dianna/lib/python3.9/site-packages (from jinja2->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.1.1)\n", + "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n", + "You can now load the package via spacy.load('en_core_web_sm')\n" + ] + } + ], "source": [ "# ensure the tokenizer for english is available\n", "spacy.cli.download('en_core_web_sm')" @@ -84,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "555842c5-3f82-4f63-93bb-696645d4b447", "metadata": {}, "outputs": [], @@ -126,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "443e8a99-6fa3-4a73-9311-2fbe0251c2b1", "metadata": {}, "outputs": [], @@ -152,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "7fc6ebcb-2328-4c06-ae67-c5590032eb69", "metadata": {}, "outputs": [], @@ -162,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "7c0bfd7d-df1d-4981-b714-496bc16b9347", "metadata": {}, "outputs": [ @@ -177,23 +259,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "Explaining: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:17<00:00, 1.72s/it]\n" + "Explaining: 100%|██████████| 10/10 [00:03<00:00, 2.75it/s]\n" ] }, { "data": { "text/plain": [ - "[('A', 0, 0.7158780014514923),\n", - " ('delectable', 1, 0.913871341049671),\n", - " ('and', 2, 0.6892129376530648),\n", - " ('intriguing', 3, 1.0620161551237106),\n", - " ('thriller', 4, 0.840078490972519),\n", - " ('filled', 5, 0.6051010835170746),\n", - " ('with', 6, 0.6926153092086315),\n", - " ('surprises', 7, 0.6697717276215553)]" + "[('A', 0, 0.5653130280971527),\n", + " ('delectable', 1, 0.8641307824850082),\n", + " ('and', 2, 0.7081780250370502),\n", + " ('intriguing', 3, 1.004394978582859),\n", + " ('thriller', 4, 0.9396217280626297),\n", + " ('filled', 5, 0.6516930902004242),\n", + " ('with', 6, 0.7476113395392894),\n", + " ('surprises', 7, 0.7425235873460769)]" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -217,14 +299,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "0136005d-a22f-43a0-80da-4ec1f283f870", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "A delectable and intriguing thriller filled with surprises" + "A delectable and intriguing thriller filled with surprises" ], "text/plain": [ "" @@ -263,7 +345,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/tutorials/rise_timeseries_weather.ipynb b/tutorials/rise_timeseries_weather.ipynb index 31819073..7298359b 100644 --- a/tutorials/rise_timeseries_weather.ipynb +++ b/tutorials/rise_timeseries_weather.ipynb @@ -1,819 +1,1307 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "\"Logo_ER10\"\n", - "\n", - "### Model Interpretation using RISE for timeseries data\n", - "This notebook shows how to apply the RISE explainability method on a model trained to classify timeseries data. Two examples are included here:\n", - "- Verify RISE for timeseries with a simple \"expert\" model\n", - "- Demonstrate RISE with a pretrained weather forecast (onnx) model\n", - "\n", - "It visualizes the relevance attributions for each segmentation of timeseries by displaying them on top of the timeseries.
\n", - "\n", - "RISE is short for Randomized Input Sampling for Explanation of Black-box Models. It estimates the relevance empirically by probing the model with randomly masked versions of the input image to obtain the corresponding outputs.
\n", - "\n", - "More details about this method can be found in the paper https://arxiv.org/abs/1806.07421.
" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "from dianna import visualization\n", - "from matplotlib import pyplot as plt\n", - "from sklearn.model_selection import train_test_split\n", - "import onnx\n", - "import onnxruntime as ort\n", - "import dianna\n", - "\n", - "np.random.seed(0)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### 1 - Create a mini dataset with extremes for verification\n", - "To demonstrate the skill of RISE for timeseries model explanation, we \"make up\" a weather dataset (timeseries) with extrem hot days and cold days." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# make up a weather dataset with extrems\n", - "cold_with_2_hot_days = np.expand_dims(np.array([30, 29] + list(np.zeros(26))) , axis=1)\n", - "data_extreme = cold_with_2_hot_days\n", - "fig = plt.figure()\n", - "plt.plot(data_extreme)\n", - "plt.xlabel(\"Time index\")\n", - "plt.ylabel(\"Celcius\")\n", - "plt.title(\"Temperature\")\n", - "plt.show()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### 2 - Define an \"expert\" model to verify RISE for timeseries\n", - "We can define an 'expert' model to test RISE. This expert model decides it's summer if the mean temp is above the threshold, and winter in other cases." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "# We define a threshold for the model to make decisions\n", - "# The label is [\"summer\", \"winter\"]\n", - "threshold = 14\n", - "\n", - "def run_expert_model(data):\n", - " is_summer = np.mean(np.mean(data, axis=1), axis=1) > threshold\n", - " number_of_classes = 2\n", - " number_of_instances = data.shape[0]\n", - " result = np.zeros((number_of_instances ,number_of_classes))\n", - " result[is_summer] = [1.0, 0.0]\n", - " result[~is_summer] = [0.0, 1.0]\n", - " return result" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### 3 - Compute and visualize the relevance scores\n", - "In this section we compute the relevance scores for each segment of timeseries using RISE and visualize them on the original timeseries." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "RISE masks random portions of the input timseries based on given segmentations and passes the masked timeseries through the model — the masked portion that decreases accuracy the most is the most “important” portion.
\n", - "\n", - "To call the explainer and generate relevance scores map, the user need to specifiy the number of masks being randomly generated (`n_masks`), the resolution of features in masks (`feature_res`) and for each mask and each feature in the image, the probability of being kept unmasked (`p_keep`).
\n", - "\n", - "Also, we need to define the approach for masking (`mask_type`). Since our data is highly skewed, here we make the masked data to be the \"threshold\" value instead of the mean." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "# we use the threshold to mask the data\n", - "def input_train_mean(_data):\n", - " return threshold" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Explaining: 100%|██████████| 100/100 [00:00<00:00, 24949.76it/s]\n" - ] - } - ], - "source": [ - "# call the explainer\n", - "explanation = dianna.explain_timeseries(run_expert_model, timeseries_data=data_extreme,\n", - " method='rise', labels=[0,1], p_keep=0.1,\n", - " n_masks=10000, mask_type=input_train_mean)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Now we can visualize the relevance scores on top of the displayed timeseries using the visualization tool in dianna." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": "" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Normalize the explanation scores for the purpose of visualization\n", - "def normalize(data):\n", - " \"\"\"Squash all values into [-1,1] range.\"\"\"\n", - " zero_to_one = (data - np.min(data)) / (np.max(data) - np.min(data))\n", - " return 2*zero_to_one -1\n", - "\n", - "heatmap_channel = normalize(explanation[0])\n", - "segments = []\n", - "for i in range(len(heatmap_channel) - 1):\n", - " segments.append({\n", - " 'index': i,\n", - " 'start': i - 0.5,\n", - " 'stop': i + 0.5,\n", - " 'weight': heatmap_channel[i]})\n", - "visualization.plot_timeseries(range(len(heatmap_channel)), data_extreme,\n", - " segments, xlabel=\"Time index\", ylabel=\"Temperature\",\n", - " show_plot=True)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Here we plot the explanation for the classification of summer. The results are consistent with our expectation as it marks all hot days in the timeseries.
\n", - "\n", - "Now let's try out RISE with a weather prediction dataset from real life.
\n", - "Here is the doi to this dataset:
\n", - "10.5281/zenodo.4770936" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### 4 - Loading the weather prediction dataset\n", - "Downloading the weather prediction dataset from zenodo." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": " DATE MONTH BASEL_cloud_cover BASEL_humidity \\\ncount 3.654000e+03 3654.000000 3654.000000 3654.000000 \nmean 2.004568e+07 6.520799 5.418446 0.745107 \nstd 2.874287e+04 3.450083 2.325497 0.107788 \nmin 2.000010e+07 1.000000 0.000000 0.380000 \n25% 2.002070e+07 4.000000 4.000000 0.670000 \n50% 2.004567e+07 7.000000 6.000000 0.760000 \n75% 2.007070e+07 10.000000 7.000000 0.830000 \nmax 2.010010e+07 12.000000 8.000000 0.980000 \n\n BASEL_pressure BASEL_global_radiation BASEL_precipitation \\\ncount 3654.000000 3654.000000 3654.000000 \nmean 1.017876 1.330380 0.234849 \nstd 0.007962 0.935348 0.536267 \nmin 0.985600 0.050000 0.000000 \n25% 1.013300 0.530000 0.000000 \n50% 1.017700 1.110000 0.000000 \n75% 1.022700 2.060000 0.210000 \nmax 1.040800 3.550000 7.570000 \n\n BASEL_sunshine BASEL_temp_mean BASEL_temp_min ... \\\ncount 3654.000000 3654.000000 3654.000000 ... \nmean 4.661193 11.022797 6.989135 ... \nstd 4.330112 7.414754 6.653356 ... \nmin 0.000000 -9.300000 -16.000000 ... \n25% 0.500000 5.300000 2.000000 ... \n50% 3.600000 11.400000 7.300000 ... \n75% 8.000000 16.900000 12.400000 ... \nmax 15.300000 29.000000 20.800000 ... \n\n STOCKHOLM_temp_min STOCKHOLM_temp_max TOURS_wind_speed \\\ncount 3654.000000 3654.000000 3654.000000 \nmean 5.104215 11.470635 3.677258 \nstd 7.250744 8.950217 1.519866 \nmin -19.700000 -14.500000 0.700000 \n25% 0.000000 4.100000 2.600000 \n50% 5.000000 11.000000 3.400000 \n75% 11.200000 19.000000 4.600000 \nmax 21.200000 32.900000 10.800000 \n\n TOURS_humidity TOURS_pressure TOURS_global_radiation \\\ncount 3654.000000 3654.000000 3654.000000 \nmean 0.781872 1.016639 1.369787 \nstd 0.115572 0.018885 0.926472 \nmin 0.330000 0.000300 0.050000 \n25% 0.700000 1.012100 0.550000 \n50% 0.800000 1.017300 1.235000 \n75% 0.870000 1.022200 2.090000 \nmax 1.000000 1.041400 3.560000 \n\n TOURS_precipitation TOURS_temp_mean TOURS_temp_min TOURS_temp_max \ncount 3654.000000 3654.000000 3654.000000 3654.000000 \nmean 0.186100 12.205802 7.860536 16.551779 \nstd 0.422151 6.467155 5.692256 7.714924 \nmin 0.000000 -6.200000 -13.000000 -3.100000 \n25% 0.000000 7.600000 3.700000 10.800000 \n50% 0.000000 12.300000 8.300000 16.600000 \n75% 0.160000 17.200000 12.300000 22.400000 \nmax 6.200000 31.200000 22.600000 39.800000 \n\n[8 rows x 165 columns]", - "text/html": "
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8 rows × 165 columns

\n
" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fname = \"weather_prediction_dataset.csv\"\n", - "if os.path.isfile(fname):\n", - " data = pd.read_csv(fname)\n", - "else:\n", - " data = pd.read_csv(f\"https://zenodo.org/record/5071376/files/{fname}?download=1\")\n", - "data.describe()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Given how the classification model is trained, we prepare the testing data for prediction.
\n", - "To make it simpler, we only choose one location and make it a binary classification task, to determine whether it is summer or winter." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": " DE_BILT_temp_max\ncount 3654.000000\nmean 14.798604\nstd 7.210740\nmin -4.700000\n25% 9.200000\n50% 14.900000\n75% 20.200000\nmax 35.700000", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
DE_BILT_temp_max
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\n
" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# select only data from one location (De Bilt)\n", - "columns = [col for col in data.columns if col.startswith('DE_BILT') and col.endswith('temp_max')]\n", - "data_debilt = data[columns]\n", - "data_debilt.describe()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(120, 28, 1)\n" - ] - } - ], - "source": [ - "# find where the month changes\n", - "idx = np.where(np.diff(data['MONTH']) != 0)[0]\n", - "# idx contains the index of the last day of each month, except for the last month.\n", - "# of the last month only a single day is recorded, so we discard it.\n", - "\n", - "nmonth = len(idx)\n", - "# add start of first month\n", - "idx = np.insert(idx, 0, 0)\n", - "ncol = len(columns)\n", - "# create single object containing each timeseries\n", - "# for simplicity we truncate each timeseries to the same length, i.e. 28 days\n", - "nday = 28\n", - "data_ts = np.zeros((nmonth, nday, ncol))\n", - "for m in range(nmonth):\n", - " data_ts[m] = data_debilt[idx[m]:idx[m+1]][:28]\n", - " \n", - "print(data_ts.shape)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "We label the data based on the seasons.
\n", - "To simplify the problem, we make it a binary classification task and only select summer and winter.
" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "data": { - "text/plain": "(60, 28, 1)" - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the labels are based on the month of each timeseries, in range 1 to 12\n", - "months = (np.arange(nmonth) + data['MONTH'][0] - 1) % 12 + 1\n", - "\n", - "# one class per meteorological season\n", - "labels = np.zeros_like(months, dtype=int)\n", - "summer = (6 <= months) & (months <= 8) # jun - aug\n", - "winter = (months <= 2) | (months == 12) # dec - feb\n", - "\n", - "labels[summer] = 0\n", - "labels[winter] = 1\n", - "\n", - "target = pd.get_dummies(labels[summer + winter])\n", - "\n", - "classes = ['summer', 'winter']\n", - "nclass = len(classes)\n", - "\n", - "data_ts = data_ts[summer + winter]\n", - "data_ts.shape" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Train/test split" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(45, 28, 1) (7, 28, 1) (8, 28, 1)\n" - ] - } - ], - "source": [ - "data_trainval, data_test, target_trainval, target_test = train_test_split(data_ts, target, stratify=target, random_state=0, test_size=.12)\n", - "data_train, data_val, target_train, target_val = train_test_split(data_trainval, target_trainval, stratify=target_trainval, random_state=0, test_size=.12)\n", - "print(data_train.shape, data_val.shape, data_test.shape)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Load ONNX model and create a ONNX model runner." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [], - "source": [ - "# onnx model available on surf drive\n", - "# path to ONNX model\n", - "onnx_file = 'models/season_prediction_model_temp_max_binary.onnx'\n", - "\n", - "# verify the ONNX model is valid\n", - "onnx_model = onnx.load(onnx_file)\n", - "onnx.checker.check_model(onnx_model)\n", - "\n", - "def run_model(data):\n", - " # model must receive input in the order of [batch, timeseries, channels]\n", - " # data = data.transpose([0,2,1])\n", - " # get ONNX predictions\n", - " sess = ort.InferenceSession(onnx_file)\n", - " input_name = sess.get_inputs()[0].name\n", - " output_name = sess.get_outputs()[0].name\n", - "\n", - " onnx_input = {input_name: data.astype(np.float32)}\n", - " pred_onnx = sess.run([output_name], onnx_input)[0]\n", - " \n", - " return pred_onnx" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Select an instance to explain and check the prediction with the model." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The predicted class is: winter\n", - "The actual class is: winter\n" - ] - } - ], - "source": [ - "idx = 6 # explained instance\n", - "data_instance = data_test[idx][np.newaxis, ...]\n", - "# precheck ONNX predictions\n", - "pred_onnx = run_model(data_instance)\n", - "pred_class = classes[np.argmax(pred_onnx)]\n", - "print(\"The predicted class is:\", pred_class)\n", - "print(\"The actual class is:\", classes[np.argmax(target_test.iloc[idx])])" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### 5 - Compute and visualize the relevance scores\n", - "In this section we compute the relevance scores for each segment of timeseries using RISE and visualize them on the original timeseries." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "# call the explainer\n", - "explanation = dianna.explain_timeseries(run_model, timeseries_data=data_instance[0],\n", - " method='rise', labels=[0,1], p_keep=0.1,\n", - " n_masks=10000)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": true - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Now we can visualize the relevance scores on top of the displayed timeseries using the visualization tool in dianna." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "heatmap_channel = normalize(explanation[np.argmax(pred_onnx)])\n", - "segments = []\n", - "for i in range(len(heatmap_channel) - 1):\n", - " segments.append({\n", - " 'index': i,\n", - " 'start': i - 0.5,\n", - " 'stop': i + 0.5,\n", - " 'weight': heatmap_channel[i]})\n", - "visualization.plot_timeseries(range(len(heatmap_channel)), data_instance[0],\n", - " segments, xlabel=\"Time index\", ylabel=\"Temperature\",\n", - " show_plot=True)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": true - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### 6 - Conclusions\n", - "The relevance scores are generated by passing multiple randomly masked inputs to the black-box model and averaging their segment-wise relevances. The idea behind this is that whenever a mask preserves important parts of the timeseries it gets higher score.
\n", - "\n", - "The first example with a designed timeseries and an expert model demonstrates that RISE is able to identify the important segments for the classification in a simplified case.\n", - "\n", - "The second example shows that the method still runs in a more case with real data and a real model. It is, however, hard to understand the explanation of this case. This could be due to an imperfecty trained model, not really suitable masking strategy or suboptimal masking generation.\n" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": true - } - }, - "outputs": [], - "source": [ - "# onnx model available on surf drive\n", - "# path to ONNX model\n", - "onnx_file = 'models/season_prediction_model_temp_max_binary.onnx'\n", - "\n", - "# verify the ONNX model is valid\n", - "onnx_model = onnx.load(onnx_file)\n", - "onnx.checker.check_model(onnx_model)\n", - "\n", - "def run_model(data):\n", - " # model must receive input in the order of [batch, timeseries, channels]\n", - " # data = data.transpose([0,2,1])\n", - " # get ONNX predictions\n", - " sess = ort.InferenceSession(onnx_file)\n", - " input_name = sess.get_inputs()[0].name\n", - " output_name = sess.get_outputs()[0].name\n", - "\n", - " onnx_input = {input_name: data.astype(np.float32)}\n", - " pred_onnx = sess.run([output_name], onnx_input)[0]\n", - " \n", - " return pred_onnx" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Select an instance to explain and check the prediction with the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": true - } - }, - "outputs": [], - "source": [ - "idx = 6 # explained instance\n", - "data_instance = data_test[idx][np.newaxis, ...]\n", - "# precheck ONNX predictions\n", - "pred_onnx = run_model(data_instance)\n", - "pred_class = classes[np.argmax(pred_onnx)]\n", - "print(\"The predicted class is:\", pred_class)\n", - "print(\"The actual class is:\", classes[np.argmax(target_test.iloc[idx])])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 5 - Compute and visualize the relevance scores\n", - "In this section we compute the relevance scores for each segment of timeseries using RISE and visualize them on the original timeseries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "is_executing": true - } - }, - "outputs": [], - "source": [ - "# call the explainer\n", - "explanation = dianna.explain_timeseries(run_model, timeseries_data=data_instance[0],\n", - " method='rise', labels=[0,1], p_keep=0.1,\n", - " n_masks=10000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can visualize the relevance scores on top of the displayed timeseries using the visualization tool in dianna." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": true - } - }, - "outputs": [], - "source": [ - "heatmap_channel = normalize(explanation[np.argmax(pred_onnx)])\n", - "segments = []\n", - "for i in range(len(heatmap_channel) - 1):\n", - " segments.append({\n", - " 'index': i,\n", - " 'start': i - 0.5,\n", - " 'stop': i + 0.5,\n", - " 'weight': heatmap_channel[i]})\n", - "visualization.plot_timeseries(range(len(heatmap_channel)), data_instance[0],\n", - " segments, xlabel=\"Time index\", ylabel=\"Temperature\",\n", - " show_plot=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "#### 6 - Conclusions\n", - "The relevance scores are generated by passing multiple randomly masked inputs to the black-box model and averaging their segment-wise relevances. The idea behind this is that whenever a mask preserves important parts of the timeseries it gets higher score.
\n", - "\n", - "The first example with a designed timeseries and an expert model demonstrates that RISE is able to identify the important segments for classification in this simplified case.\n", - "\n", - "The second example shows that RISE for timeseries also runs on real timeseries data. The explanation is, however, hard to interpret in this case. This could be due to an suboptimally trained model, unsuitable masking strategy or unsuitable mask generation.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "torch", - "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": "3.10.8" - }, - "vscode": { - "interpreter": { - "hash": "f74811edbe99894b2f930b63702daebe3ce5897f538d47d6f6827e4475af2be0" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "\"Logo_ER10\"\n", + "\n", + "### Model Interpretation using RISE for timeseries data\n", + "This notebook shows how to apply the RISE explainability method on a model trained to classify timeseries data. Two examples are included here:\n", + "- Verify RISE for timeseries with a simple \"expert\" model\n", + "- Demonstrate RISE with a pretrained weather forecast (onnx) model\n", + "\n", + "It visualizes the relevance attributions for each segmentation of timeseries by displaying them on top of the timeseries.
\n", + "\n", + "RISE is short for Randomized Input Sampling for Explanation of Black-box Models. It estimates the relevance empirically by probing the model with randomly masked versions of the input image to obtain the corresponding outputs.
\n", + "\n", + "More details about this method can be found in the paper https://arxiv.org/abs/1806.07421.
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Colab Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "running_in_colab = 'google.colab' in str(get_ipython())\n", + "if running_in_colab:\n", + " # install dianna\n", + " !python3 -m pip install dianna\n", + " \n", + " # download data used in this demo\n", + " import os \n", + " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", + " paths_to_download = ['models/season_prediction_model_temp_max_binary.onnx']\n", + " for path in paths_to_download:\n", + " local_directory = os.path.dirname(path)\n", + " os.makedirs(local_directory, exist_ok=True)\n", + " !wget {base_url + path} -O {path}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "from dianna import visualization\n", + "from matplotlib import pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "import onnx\n", + "import onnxruntime as ort\n", + "import dianna\n", + "\n", + "np.random.seed(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### 1 - Create a mini dataset with extremes for verification\n", + "To demonstrate the skill of RISE for timeseries model explanation, we \"make up\" a weather dataset (timeseries) with extrem hot days and cold days." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# make up a weather dataset with extrems\n", + "cold_with_2_hot_days = np.expand_dims(np.array([30, 29] + list(np.zeros(26))) , axis=1)\n", + "data_extreme = cold_with_2_hot_days\n", + "fig = plt.figure()\n", + "plt.plot(data_extreme)\n", + "plt.xlabel(\"Time index\")\n", + "plt.ylabel(\"Celcius\")\n", + "plt.title(\"Temperature\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### 2 - Define an \"expert\" model to verify RISE for timeseries\n", + "We can define an 'expert' model to test RISE. This expert model decides it's summer if the mean temp is above the threshold, and winter in other cases." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# We define a threshold for the model to make decisions\n", + "# The label is [\"summer\", \"winter\"]\n", + "threshold = 14\n", + "\n", + "def run_expert_model(data):\n", + " is_summer = np.mean(np.mean(data, axis=1), axis=1) > threshold\n", + " number_of_classes = 2\n", + " number_of_instances = data.shape[0]\n", + " result = np.zeros((number_of_instances ,number_of_classes))\n", + " result[is_summer] = [1.0, 0.0]\n", + " result[~is_summer] = [0.0, 1.0]\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### 3 - Compute and visualize the relevance scores\n", + "In this section we compute the relevance scores for each segment of timeseries using RISE and visualize them on the original timeseries." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "RISE masks random portions of the input timseries based on given segmentations and passes the masked timeseries through the model — the masked portion that decreases accuracy the most is the most “important” portion.
\n", + "\n", + "To call the explainer and generate relevance scores map, the user need to specifiy the number of masks being randomly generated (`n_masks`), the resolution of features in masks (`feature_res`) and for each mask and each feature in the image, the probability of being kept unmasked (`p_keep`).
\n", + "\n", + "Also, we need to define the approach for masking (`mask_type`). Since our data is highly skewed, here we make the masked data to be the \"threshold\" value instead of the mean." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# we use the threshold to mask the data\n", + "def input_train_mean(_data):\n", + " return threshold" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Explaining: 100%|██████████| 100/100 [00:00<00:00, 35812.02it/s]\n" + ] + } + ], + "source": [ + "# call the explainer\n", + "explanation = dianna.explain_timeseries(run_expert_model, timeseries_data=data_extreme,\n", + " method='rise', labels=[0,1], p_keep=0.1,\n", + " n_masks=10000, mask_type=input_train_mean)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now we can visualize the relevance scores on top of the displayed timeseries using the visualization tool in dianna." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Normalize the explanation scores for the purpose of visualization\n", + "def normalize(data):\n", + " \"\"\"Squash all values into [-1,1] range.\"\"\"\n", + " zero_to_one = (data - np.min(data)) / (np.max(data) - np.min(data))\n", + " return 2*zero_to_one -1\n", + "\n", + "heatmap_channel = normalize(explanation[0])\n", + "segments = []\n", + "for i in range(len(heatmap_channel) - 1):\n", + " segments.append({\n", + " 'index': i,\n", + " 'start': i - 0.5,\n", + " 'stop': i + 0.5,\n", + " 'weight': heatmap_channel[i]})\n", + "visualization.plot_timeseries(range(len(heatmap_channel)), data_extreme,\n", + " segments, xlabel=\"Time index\", ylabel=\"Temperature\",\n", + " show_plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Here we plot the explanation for the classification of summer. The results are consistent with our expectation as it marks all hot days in the timeseries.
\n", + "\n", + "Now let's try out RISE with a weather prediction dataset from real life.
\n", + "Here is the doi to this dataset:
\n", + "10.5281/zenodo.4770936" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### 4 - Loading the weather prediction dataset\n", + "Downloading the weather prediction dataset from zenodo." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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DATEMONTHBASEL_cloud_coverBASEL_humidityBASEL_pressureBASEL_global_radiationBASEL_precipitationBASEL_sunshineBASEL_temp_meanBASEL_temp_min...STOCKHOLM_temp_minSTOCKHOLM_temp_maxTOURS_wind_speedTOURS_humidityTOURS_pressureTOURS_global_radiationTOURS_precipitationTOURS_temp_meanTOURS_temp_minTOURS_temp_max
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8 rows × 165 columns

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" + ], + "text/plain": [ + " DATE MONTH BASEL_cloud_cover BASEL_humidity \\\n", + "count 3.654000e+03 3654.000000 3654.000000 3654.000000 \n", + "mean 2.004568e+07 6.520799 5.418446 0.745107 \n", + "std 2.874287e+04 3.450083 2.325497 0.107788 \n", + "min 2.000010e+07 1.000000 0.000000 0.380000 \n", + "25% 2.002070e+07 4.000000 4.000000 0.670000 \n", + "50% 2.004567e+07 7.000000 6.000000 0.760000 \n", + "75% 2.007070e+07 10.000000 7.000000 0.830000 \n", + "max 2.010010e+07 12.000000 8.000000 0.980000 \n", + "\n", + " BASEL_pressure BASEL_global_radiation BASEL_precipitation \\\n", + "count 3654.000000 3654.000000 3654.000000 \n", + "mean 1.017876 1.330380 0.234849 \n", + "std 0.007962 0.935348 0.536267 \n", + "min 0.985600 0.050000 0.000000 \n", + "25% 1.013300 0.530000 0.000000 \n", + "50% 1.017700 1.110000 0.000000 \n", + "75% 1.022700 2.060000 0.210000 \n", + "max 1.040800 3.550000 7.570000 \n", + "\n", + " BASEL_sunshine BASEL_temp_mean BASEL_temp_min ... \\\n", + "count 3654.000000 3654.000000 3654.000000 ... \n", + "mean 4.661193 11.022797 6.989135 ... \n", + "std 4.330112 7.414754 6.653356 ... \n", + "min 0.000000 -9.300000 -16.000000 ... \n", + "25% 0.500000 5.300000 2.000000 ... \n", + "50% 3.600000 11.400000 7.300000 ... \n", + "75% 8.000000 16.900000 12.400000 ... \n", + "max 15.300000 29.000000 20.800000 ... \n", + "\n", + " STOCKHOLM_temp_min STOCKHOLM_temp_max TOURS_wind_speed \\\n", + "count 3654.000000 3654.000000 3654.000000 \n", + "mean 5.104215 11.470635 3.677258 \n", + "std 7.250744 8.950217 1.519866 \n", + "min -19.700000 -14.500000 0.700000 \n", + "25% 0.000000 4.100000 2.600000 \n", + "50% 5.000000 11.000000 3.400000 \n", + "75% 11.200000 19.000000 4.600000 \n", + "max 21.200000 32.900000 10.800000 \n", + "\n", + " TOURS_humidity TOURS_pressure TOURS_global_radiation \\\n", + "count 3654.000000 3654.000000 3654.000000 \n", + "mean 0.781872 1.016639 1.369787 \n", + "std 0.115572 0.018885 0.926472 \n", + "min 0.330000 0.000300 0.050000 \n", + "25% 0.700000 1.012100 0.550000 \n", + "50% 0.800000 1.017300 1.235000 \n", + "75% 0.870000 1.022200 2.090000 \n", + "max 1.000000 1.041400 3.560000 \n", + "\n", + " TOURS_precipitation TOURS_temp_mean TOURS_temp_min TOURS_temp_max \n", + "count 3654.000000 3654.000000 3654.000000 3654.000000 \n", + "mean 0.186100 12.205802 7.860536 16.551779 \n", + "std 0.422151 6.467155 5.692256 7.714924 \n", + "min 0.000000 -6.200000 -13.000000 -3.100000 \n", + "25% 0.000000 7.600000 3.700000 10.800000 \n", + "50% 0.000000 12.300000 8.300000 16.600000 \n", + "75% 0.160000 17.200000 12.300000 22.400000 \n", + "max 6.200000 31.200000 22.600000 39.800000 \n", + "\n", + "[8 rows x 165 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fname = \"weather_prediction_dataset.csv\"\n", + "if os.path.isfile(fname):\n", + " data = pd.read_csv(fname)\n", + "else:\n", + " data = pd.read_csv(f\"https://zenodo.org/record/5071376/files/{fname}?download=1\")\n", + "data.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Given how the classification model is trained, we prepare the testing data for prediction.
\n", + "To make it simpler, we only choose one location and make it a binary classification task, to determine whether it is summer or winter." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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DE_BILT_temp_max
count3654.000000
mean14.798604
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" + ], + "text/plain": [ + " DE_BILT_temp_max\n", + "count 3654.000000\n", + "mean 14.798604\n", + "std 7.210740\n", + "min -4.700000\n", + "25% 9.200000\n", + "50% 14.900000\n", + "75% 20.200000\n", + "max 35.700000" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# select only data from one location (De Bilt)\n", + "columns = [col for col in data.columns if col.startswith('DE_BILT') and col.endswith('temp_max')]\n", + "data_debilt = data[columns]\n", + "data_debilt.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(120, 28, 1)\n" + ] + } + ], + "source": [ + "# find where the month changes\n", + "idx = np.where(np.diff(data['MONTH']) != 0)[0]\n", + "# idx contains the index of the last day of each month, except for the last month.\n", + "# of the last month only a single day is recorded, so we discard it.\n", + "\n", + "nmonth = len(idx)\n", + "# add start of first month\n", + "idx = np.insert(idx, 0, 0)\n", + "ncol = len(columns)\n", + "# create single object containing each timeseries\n", + "# for simplicity we truncate each timeseries to the same length, i.e. 28 days\n", + "nday = 28\n", + "data_ts = np.zeros((nmonth, nday, ncol))\n", + "for m in range(nmonth):\n", + " data_ts[m] = data_debilt[idx[m]:idx[m+1]][:28]\n", + " \n", + "print(data_ts.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "We label the data based on the seasons.
\n", + "To simplify the problem, we make it a binary classification task and only select summer and winter.
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(60, 28, 1)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the labels are based on the month of each timeseries, in range 1 to 12\n", + "months = (np.arange(nmonth) + data['MONTH'][0] - 1) % 12 + 1\n", + "\n", + "# one class per meteorological season\n", + "labels = np.zeros_like(months, dtype=int)\n", + "summer = (6 <= months) & (months <= 8) # jun - aug\n", + "winter = (months <= 2) | (months == 12) # dec - feb\n", + "\n", + "labels[summer] = 0\n", + "labels[winter] = 1\n", + "\n", + "target = pd.get_dummies(labels[summer + winter])\n", + "\n", + "classes = ['summer', 'winter']\n", + "nclass = len(classes)\n", + "\n", + "data_ts = data_ts[summer + winter]\n", + "data_ts.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Train/test split" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(45, 28, 1) (7, 28, 1) (8, 28, 1)\n" + ] + } + ], + "source": [ + "data_trainval, data_test, target_trainval, target_test = train_test_split(data_ts, target, stratify=target, random_state=0, test_size=.12)\n", + "data_train, data_val, target_train, target_val = train_test_split(data_trainval, target_trainval, stratify=target_trainval, random_state=0, test_size=.12)\n", + "print(data_train.shape, data_val.shape, data_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Load ONNX model and create a ONNX model runner." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# onnx model available on surf drive\n", + "# path to ONNX model\n", + "onnx_file = 'models/season_prediction_model_temp_max_binary.onnx'\n", + "\n", + "# verify the ONNX model is valid\n", + "onnx_model = onnx.load(onnx_file)\n", + "onnx.checker.check_model(onnx_model)\n", + "\n", + "def run_model(data):\n", + " # model must receive input in the order of [batch, timeseries, channels]\n", + " # data = data.transpose([0,2,1])\n", + " # get ONNX predictions\n", + " sess = ort.InferenceSession(onnx_file)\n", + " input_name = sess.get_inputs()[0].name\n", + " output_name = sess.get_outputs()[0].name\n", + "\n", + " onnx_input = {input_name: data.astype(np.float32)}\n", + " pred_onnx = sess.run([output_name], onnx_input)[0]\n", + " \n", + " return pred_onnx" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Select an instance to explain and check the prediction with the model." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The predicted class is: winter\n", + "The actual class is: winter\n" + ] + } + ], + "source": [ + "idx = 6 # explained instance\n", + "data_instance = data_test[idx][np.newaxis, ...]\n", + "# precheck ONNX predictions\n", + "pred_onnx = run_model(data_instance)\n", + "pred_class = classes[np.argmax(pred_onnx)]\n", + "print(\"The predicted class is:\", pred_class)\n", + "print(\"The actual class is:\", classes[np.argmax(target_test.iloc[idx])])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### 5 - Compute and visualize the relevance scores\n", + "In this section we compute the relevance scores for each segment of timeseries using RISE and visualize them on the original timeseries." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Explaining: 100%|██████████| 100/100 [00:00<00:00, 556.21it/s]\n" + ] + } + ], + "source": [ + "# call the explainer\n", + "explanation = dianna.explain_timeseries(run_model, timeseries_data=data_instance[0],\n", + " method='rise', labels=[0,1], p_keep=0.1,\n", + " n_masks=10000)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now we can visualize the relevance scores on top of the displayed timeseries using the visualization tool in dianna." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "heatmap_channel = normalize(explanation[np.argmax(pred_onnx)])\n", + "segments = []\n", + "for i in range(len(heatmap_channel) - 1):\n", + " segments.append({\n", + " 'index': i,\n", + " 'start': i - 0.5,\n", + " 'stop': i + 0.5,\n", + " 'weight': heatmap_channel[i]})\n", + "visualization.plot_timeseries(range(len(heatmap_channel)), data_instance[0],\n", + " segments, xlabel=\"Time index\", ylabel=\"Temperature\",\n", + " show_plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### 6 - Conclusions\n", + "The relevance scores are generated by passing multiple randomly masked inputs to the black-box model and averaging their segment-wise relevances. The idea behind this is that whenever a mask preserves important parts of the timeseries it gets higher score.
\n", + "\n", + "The first example with a designed timeseries and an expert model demonstrates that RISE is able to identify the important segments for the classification in a simplified case.\n", + "\n", + "The second example shows that the method still runs in a more case with real data and a real model. It is, however, hard to understand the explanation of this case. This could be due to an imperfecty trained model, not really suitable masking strategy or suboptimal masking generation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# onnx model available on surf drive\n", + "# path to ONNX model\n", + "onnx_file = 'models/season_prediction_model_temp_max_binary.onnx'\n", + "\n", + "# verify the ONNX model is valid\n", + "onnx_model = onnx.load(onnx_file)\n", + "onnx.checker.check_model(onnx_model)\n", + "\n", + "def run_model(data):\n", + " # model must receive input in the order of [batch, timeseries, channels]\n", + " # data = data.transpose([0,2,1])\n", + " # get ONNX predictions\n", + " sess = ort.InferenceSession(onnx_file)\n", + " input_name = sess.get_inputs()[0].name\n", + " output_name = sess.get_outputs()[0].name\n", + "\n", + " onnx_input = {input_name: data.astype(np.float32)}\n", + " pred_onnx = sess.run([output_name], onnx_input)[0]\n", + " \n", + " return pred_onnx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Select an instance to explain and check the prediction with the model." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The predicted class is: winter\n", + "The actual class is: winter\n" + ] + } + ], + "source": [ + "idx = 6 # explained instance\n", + "data_instance = data_test[idx][np.newaxis, ...]\n", + "# precheck ONNX predictions\n", + "pred_onnx = run_model(data_instance)\n", + "pred_class = classes[np.argmax(pred_onnx)]\n", + "print(\"The predicted class is:\", pred_class)\n", + "print(\"The actual class is:\", classes[np.argmax(target_test.iloc[idx])])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5 - Compute and visualize the relevance scores\n", + "In this section we compute the relevance scores for each segment of timeseries using RISE and visualize them on the original timeseries." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "pycharm": { + "is_executing": true + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Explaining: 100%|██████████| 100/100 [00:00<00:00, 528.36it/s]\n" + ] + } + ], + "source": [ + "# call the explainer\n", + "explanation = dianna.explain_timeseries(run_model, timeseries_data=data_instance[0],\n", + " method='rise', labels=[0,1], p_keep=0.1,\n", + " n_masks=10000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can visualize the relevance scores on top of the displayed timeseries using the visualization tool in dianna." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "heatmap_channel = normalize(explanation[np.argmax(pred_onnx)])\n", + "segments = []\n", + "for i in range(len(heatmap_channel) - 1):\n", + " segments.append({\n", + " 'index': i,\n", + " 'start': i - 0.5,\n", + " 'stop': i + 0.5,\n", + " 'weight': heatmap_channel[i]})\n", + "visualization.plot_timeseries(range(len(heatmap_channel)), data_instance[0],\n", + " segments, xlabel=\"Time index\", ylabel=\"Temperature\",\n", + " show_plot=True)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### 6 - Conclusions\n", + "The relevance scores are generated by passing multiple randomly masked inputs to the black-box model and averaging their segment-wise relevances. The idea behind this is that whenever a mask preserves important parts of the timeseries it gets higher score.
\n", + "\n", + "The first example with a designed timeseries and an expert model demonstrates that RISE is able to identify the important segments for classification in this simplified case.\n", + "\n", + "The second example shows that RISE for timeseries also runs on real timeseries data. The explanation is, however, hard to interpret in this case. This could be due to an suboptimally trained model, unsuitable masking strategy or unsuitable mask generation.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "torch", + "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": "3.9.12" + }, + "vscode": { + "interpreter": { + "hash": "f74811edbe99894b2f930b63702daebe3ce5897f538d47d6f6827e4475af2be0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 15b577a0df814fa7866a88393ad3fb98954a8b5e Mon Sep 17 00:00:00 2001 From: Aron Date: Tue, 9 May 2023 12:40:19 +0200 Subject: [PATCH 2/6] Fix missing _tf in model name --- tutorials/demo.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/demo.ipynb b/tutorials/demo.ipynb index f8024fd8..7dd36732 100644 --- a/tutorials/demo.ipynb +++ b/tutorials/demo.ipynb @@ -35,7 +35,7 @@ " # download data used in this demo\n", " import os \n", " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", - " paths_to_download = ['data/binary-mnist.npz', 'models/mnist_model.onnx']\n", + " paths_to_download = ['data/binary-mnist.npz', 'models/mnist_model_tf.onnx']\n", " for path in paths_to_download:\n", " local_directory = os.path.dirname(path)\n", " os.makedirs(local_directory, exist_ok=True)\n", From 8ee69fcff1ada7d3ac0db5ac167dadb38dca7a37 Mon Sep 17 00:00:00 2001 From: Aron Jansen Date: Tue, 9 May 2023 12:54:29 +0200 Subject: [PATCH 3/6] Add colab links to all tutorial notebooks --- tutorials/README.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/tutorials/README.md b/tutorials/README.md index 702488c0..3aeec40a 100644 --- a/tutorials/README.md +++ b/tutorials/README.md @@ -27,3 +27,14 @@ The datasets used in the tutorials are represented with their respective logo: | [Stanford sentiment treebank](https://nlp.stanford.edu/sentiment/index.html) | nlp-logo_half_size| The models used in the tutorials are available at [tutorials/models](https://github.com/dianna-ai/dianna/tree/main/tutorials/models). + + +## Colab +The tutorials can also be run directly in Google Colab, by clicking on the buttons below, or for a general demo here: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/demo.ipynb). + +| modality \ method | RISE | LIME | KernelSHAP | +|-------------------|------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------| +| images | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/rise_mnist.ipynb), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/rise_imagenet.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/lime_images.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/kernelshap_mnist.ipynb), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/kernelshap_geometric_shapes.ipynb) | +| text | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/rise_text.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/lime_text.ipynb) | - | +| timeseries | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/rise_timeseries_weather.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/lime_timeseries_weather.ipynb), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/lime_timeseries_coffee.ipynb) | - | + From 83d965c316e8f3de373e09862c6072a5a7514f99 Mon Sep 17 00:00:00 2001 From: Aron Date: Tue, 9 May 2023 14:59:33 +0200 Subject: [PATCH 4/6] Make sure to install notebook dependencies --- tutorials/demo.ipynb | 2 +- tutorials/kernelshap_geometric_shapes.ipynb | 2 +- tutorials/kernelshap_mnist.ipynb | 2 +- tutorials/lime_images.ipynb | 2 +- tutorials/lime_text.ipynb | 92 +++++++++++++++------ tutorials/lime_timeseries_coffee.ipynb | 4 +- tutorials/lime_timeseries_weather.ipynb | 4 +- tutorials/rise_imagenet.ipynb | 2 +- tutorials/rise_mnist.ipynb | 2 +- tutorials/rise_text.ipynb | 2 +- tutorials/rise_timeseries_weather.ipynb | 2 +- 11 files changed, 79 insertions(+), 37 deletions(-) diff --git a/tutorials/demo.ipynb b/tutorials/demo.ipynb index 7dd36732..4a0ec924 100644 --- a/tutorials/demo.ipynb +++ b/tutorials/demo.ipynb @@ -30,7 +30,7 @@ "running_in_colab = 'google.colab' in str(get_ipython())\n", "if running_in_colab:\n", " # install dianna\n", - " !python3 -m pip install dianna\n", + " !python3 -m pip install dianna[notebooks]\n", " \n", " # download data used in this demo\n", " import os \n", diff --git a/tutorials/kernelshap_geometric_shapes.ipynb b/tutorials/kernelshap_geometric_shapes.ipynb index 0dfda814..626c9066 100644 --- a/tutorials/kernelshap_geometric_shapes.ipynb +++ b/tutorials/kernelshap_geometric_shapes.ipynb @@ -38,7 +38,7 @@ "running_in_colab = 'google.colab' in str(get_ipython())\n", "if running_in_colab:\n", " # install dianna\n", - " !python3 -m pip install dianna\n", + " !python3 -m pip install dianna[notebooks]\n", " \n", " # download data used in this demo\n", " import os \n", diff --git a/tutorials/kernelshap_mnist.ipynb b/tutorials/kernelshap_mnist.ipynb index 7f8ec271..6e594629 100644 --- a/tutorials/kernelshap_mnist.ipynb +++ b/tutorials/kernelshap_mnist.ipynb @@ -37,7 +37,7 @@ "running_in_colab = 'google.colab' in str(get_ipython())\n", "if running_in_colab:\n", " # install dianna\n", - " !python3 -m pip install dianna\n", + " !python3 -m pip install dianna[notebooks]\n", " \n", " # download data used in this demo\n", " import os \n", diff --git a/tutorials/lime_images.ipynb b/tutorials/lime_images.ipynb index c5bd980d..9dc230e6 100644 --- a/tutorials/lime_images.ipynb +++ b/tutorials/lime_images.ipynb @@ -34,7 +34,7 @@ "running_in_colab = 'google.colab' in str(get_ipython())\n", "if running_in_colab:\n", " # install dianna\n", - " !python3 -m pip install dianna\n", + " !python3 -m pip install dianna[notebooks]\n", " \n", " # download data used in this demo\n", " import os \n", diff --git a/tutorials/lime_text.ipynb b/tutorials/lime_text.ipynb index ac15190e..aa0f931a 100644 --- a/tutorials/lime_text.ipynb +++ b/tutorials/lime_text.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "471630ff", "metadata": {}, "outputs": [], @@ -35,7 +35,7 @@ "running_in_colab = 'google.colab' in str(get_ipython())\n", "if running_in_colab:\n", " # install dianna\n", - " !python3 -m pip install dianna\n", + " !python3 -m pip install dianna[notebooks]\n", " \n", " # download data used in this demo\n", " import os \n", @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "34b556d8-5337-44dc-8efe-14d1dff6f011", "metadata": { "pycharm": { @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "c616916c-78ef-48d0-a744-b25b37b62a3f", "metadata": { "pycharm": { @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "486540bd-2676-4dfa-bbe8-ee8aa289acd3", "metadata": { "pycharm": { @@ -130,6 +130,39 @@ "name": "stdout", "output_type": "stream", "text": [ + "Collecting en-core-web-sm==3.2.0\n", + " Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.2.0/en_core_web_sm-3.2.0-py3-none-any.whl (13.9 MB)\n", + " ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.9/13.9 MB 3.6 MB/s eta 0:00:00\n", + "Requirement already satisfied: spacy<3.3.0,>=3.2.0 in 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already satisfied: MarkupSafe>=2.0 in /opt/homebrew/Caskroom/miniforge/base/envs/dianna/lib/python3.9/site-packages (from jinja2->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.1.1)\n", "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n", "You can now load the package via spacy.load('en_core_web_sm')\n" ] @@ -142,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "id": "555842c5-3f82-4f63-93bb-696645d4b447", "metadata": { "pycharm": { @@ -189,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "443e8a99-6fa3-4a73-9311-2fbe0251c2b1", "metadata": { "pycharm": { @@ -223,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "id": "7fc6ebcb-2328-4c06-ae67-c5590032eb69", "metadata": { "pycharm": { @@ -237,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "7c0bfd7d-df1d-4981-b714-496bc16b9347", "metadata": { "pycharm": { @@ -246,21 +279,30 @@ }, "outputs": [ { - "data": { - "text/plain": [ - "[('intriguing', 3, 0.15676576731098502),\n", - " ('thriller', 4, 0.06416006115415027),\n", - " ('delectable', 1, 0.06281683308649923),\n", - " ('A', 0, 0.021482701114620962),\n", - " ('and', 2, 0.018633385524976835),\n", - " ('with', 6, 0.010678822730754416),\n", - " ('filled', 5, -0.01067822597152755),\n", - " ('surprises', 7, 0.0047279251801326146)]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" + "ename": "ValueError", + "evalue": "Method LIME does not exist", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Dropbox/eScience/projects/dianna-project/dianna/dianna/__init__.py:109\u001b[0m, in \u001b[0;36m_get_explainer\u001b[0;34m(method, kwargs, modality)\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 109\u001b[0m method_submodule \u001b[39m=\u001b[39m importlib\u001b[39m.\u001b[39;49mimport_module(\u001b[39mf\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mdianna.methods.\u001b[39;49m\u001b[39m{\u001b[39;49;00mmethod\u001b[39m.\u001b[39;49mlower()\u001b[39m}\u001b[39;49;00m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m 110\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mImportError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/dianna/lib/python3.9/importlib/__init__.py:127\u001b[0m, in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m 126\u001b[0m level \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m--> 127\u001b[0m \u001b[39mreturn\u001b[39;00m _bootstrap\u001b[39m.\u001b[39;49m_gcd_import(name[level:], package, level)\n", + "File \u001b[0;32m:1030\u001b[0m, in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n", + "File \u001b[0;32m:1007\u001b[0m, in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n", + "File \u001b[0;32m:986\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n", + "File \u001b[0;32m:680\u001b[0m, in \u001b[0;36m_load_unlocked\u001b[0;34m(spec)\u001b[0m\n", + "File \u001b[0;32m:850\u001b[0m, in \u001b[0;36mexec_module\u001b[0;34m(self, module)\u001b[0m\n", + "File \u001b[0;32m:228\u001b[0m, in \u001b[0;36m_call_with_frames_removed\u001b[0;34m(f, *args, **kwds)\u001b[0m\n", + "File \u001b[0;32m~/Dropbox/eScience/projects/dianna-project/dianna/dianna/methods/lime.py:7\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[39m# To Do: remove this import when the method for different input type is splitted\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mdianna\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmethods\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlime_timeseries\u001b[39;00m \u001b[39mimport\u001b[39;00m LIMETimeseries \u001b[39m# noqa: F401 ignore unused import\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[39mclass\u001b[39;00m \u001b[39mLIMEText\u001b[39;00m:\n", + "File \u001b[0;32m~/Dropbox/eScience/projects/dianna-project/dianna/dianna/methods/lime_timeseries.py:3\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39msklearn\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mfastdtw\u001b[39;00m \u001b[39mimport\u001b[39;00m fastdtw\n\u001b[1;32m 4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mlime\u001b[39;00m \u001b[39mimport\u001b[39;00m explanation\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'fastdtw'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/aronjansen/Dropbox/eScience/projects/dianna-project/dianna/tutorials/lime_text.ipynb Cell 13\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m# We're getting the explanation for the 'positive' class only, but dianna supports explaining for multiple labels in one\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[39m# go. It therefore always outputs a list of saliency maps. We want the first and only saliency map from this list here.\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m explanation_relevance \u001b[39m=\u001b[39m dianna\u001b[39m.\u001b[39;49mexplain_text(model_runner, review, model_runner\u001b[39m.\u001b[39;49mtokenizer, \u001b[39m'\u001b[39;49m\u001b[39mLIME\u001b[39;49m\u001b[39m'\u001b[39;49m, labels\u001b[39m=\u001b[39;49m[labels\u001b[39m.\u001b[39;49mindex(\u001b[39m'\u001b[39;49m\u001b[39mpositive\u001b[39;49m\u001b[39m'\u001b[39;49m)])[\u001b[39m0\u001b[39m]\n\u001b[1;32m 4\u001b[0m explanation_relevance\n", + "File \u001b[0;32m~/Dropbox/eScience/projects/dianna-project/dianna/dianna/__init__.py:97\u001b[0m, in \u001b[0;36mexplain_text\u001b[0;34m(model_or_function, input_text, tokenizer, method, labels, **kwargs)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mexplain_text\u001b[39m(model_or_function, input_text, tokenizer, method, labels, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m 82\u001b[0m \u001b[39m\"\"\"Explain text (input_text) given a model and a chosen method.\u001b[39;00m\n\u001b[1;32m 83\u001b[0m \n\u001b[1;32m 84\u001b[0m \u001b[39m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 95\u001b[0m \n\u001b[1;32m 96\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 97\u001b[0m explainer \u001b[39m=\u001b[39m _get_explainer(method, kwargs, modality\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mText\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m 98\u001b[0m explain_text_kwargs \u001b[39m=\u001b[39m utils\u001b[39m.\u001b[39mget_kwargs_applicable_to_function(explainer\u001b[39m.\u001b[39mexplain, kwargs)\n\u001b[1;32m 99\u001b[0m \u001b[39mreturn\u001b[39;00m explainer\u001b[39m.\u001b[39mexplain(\n\u001b[1;32m 100\u001b[0m model_or_function\u001b[39m=\u001b[39mmodel_or_function,\n\u001b[1;32m 101\u001b[0m input_text\u001b[39m=\u001b[39minput_text,\n\u001b[1;32m 102\u001b[0m labels\u001b[39m=\u001b[39mlabels,\n\u001b[1;32m 103\u001b[0m tokenizer\u001b[39m=\u001b[39mtokenizer,\n\u001b[1;32m 104\u001b[0m \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mexplain_text_kwargs)\n", + "File \u001b[0;32m~/Dropbox/eScience/projects/dianna-project/dianna/dianna/__init__.py:111\u001b[0m, in \u001b[0;36m_get_explainer\u001b[0;34m(method, kwargs, modality)\u001b[0m\n\u001b[1;32m 109\u001b[0m method_submodule \u001b[39m=\u001b[39m importlib\u001b[39m.\u001b[39mimport_module(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mdianna.methods.\u001b[39m\u001b[39m{\u001b[39;00mmethod\u001b[39m.\u001b[39mlower()\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[1;32m 110\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mImportError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m--> 111\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mMethod \u001b[39m\u001b[39m{\u001b[39;00mmethod\u001b[39m}\u001b[39;00m\u001b[39m does not exist\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 112\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 113\u001b[0m method_class \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(method_submodule, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mmethod\u001b[39m.\u001b[39mupper()\u001b[39m}\u001b[39;00m\u001b[39m{\u001b[39;00mmodality\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: Method LIME does not exist" + ] } ], "source": [ @@ -286,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "0136005d-a22f-43a0-80da-4ec1f283f870", "metadata": { "pycharm": { diff --git a/tutorials/lime_timeseries_coffee.ipynb b/tutorials/lime_timeseries_coffee.ipynb index ddeb58f5..fd8b1224 100644 --- a/tutorials/lime_timeseries_coffee.ipynb +++ b/tutorials/lime_timeseries_coffee.ipynb @@ -31,7 +31,7 @@ "running_in_colab = 'google.colab' in str(get_ipython())\n", "if running_in_colab:\n", " # install dianna\n", - " !python3 -m pip install dianna\n", + " !python3 -m pip install dianna[notebooks]\n", " \n", " # download data used in this demo\n", " import os \n", @@ -478,7 +478,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.9.12" }, "orig_nbformat": 4 }, diff --git a/tutorials/lime_timeseries_weather.ipynb b/tutorials/lime_timeseries_weather.ipynb index ede4a5f2..64934591 100644 --- a/tutorials/lime_timeseries_weather.ipynb +++ b/tutorials/lime_timeseries_weather.ipynb @@ -31,7 +31,7 @@ "running_in_colab = 'google.colab' in str(get_ipython())\n", "if running_in_colab:\n", " # install dianna\n", - " !python3 -m pip install dianna\n", + " !python3 -m pip install dianna[notebooks]\n", " \n", " # download data used in this demo\n", " import os \n", @@ -1101,7 +1101,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.9.12" }, "orig_nbformat": 4 }, diff --git a/tutorials/rise_imagenet.ipynb b/tutorials/rise_imagenet.ipynb index f8668b01..bad80a80 100644 --- a/tutorials/rise_imagenet.ipynb +++ b/tutorials/rise_imagenet.ipynb @@ -34,7 +34,7 @@ "running_in_colab = 'google.colab' in str(get_ipython())\n", "if running_in_colab:\n", " # install dianna\n", - " !python3 -m pip install dianna\n", + " !python3 -m pip install dianna[notebooks]\n", " \n", " # download data used in this demo\n", " import os \n", diff --git a/tutorials/rise_mnist.ipynb b/tutorials/rise_mnist.ipynb index 2f838a87..7ba6023e 100644 --- a/tutorials/rise_mnist.ipynb +++ b/tutorials/rise_mnist.ipynb @@ -34,7 +34,7 @@ "running_in_colab = 'google.colab' in str(get_ipython())\n", "if running_in_colab:\n", " # install dianna\n", - " !python3 -m pip install dianna\n", + " !python3 -m pip install dianna[notebooks]\n", " \n", " # download data used in this demo\n", " import os \n", diff --git a/tutorials/rise_text.ipynb b/tutorials/rise_text.ipynb index 7a5b8d74..2b5e5d4f 100644 --- a/tutorials/rise_text.ipynb +++ b/tutorials/rise_text.ipynb @@ -36,7 +36,7 @@ "running_in_colab = 'google.colab' in str(get_ipython())\n", "if running_in_colab:\n", " # install dianna\n", - " !python3 -m pip install dianna\n", + " !python3 -m pip install dianna[notebooks]\n", " \n", " # download data used in this demo\n", " import os \n", diff --git a/tutorials/rise_timeseries_weather.ipynb b/tutorials/rise_timeseries_weather.ipynb index 7298359b..0b3d5f2f 100644 --- a/tutorials/rise_timeseries_weather.ipynb +++ b/tutorials/rise_timeseries_weather.ipynb @@ -39,7 +39,7 @@ "running_in_colab = 'google.colab' in str(get_ipython())\n", "if running_in_colab:\n", " # install dianna\n", - " !python3 -m pip install dianna\n", + " !python3 -m pip install dianna[notebooks]\n", " \n", " # download data used in this demo\n", " import os \n", From 14587ca56db16cf9ab18a64e795b548d67844359 Mon Sep 17 00:00:00 2001 From: Aron Jansen Date: Tue, 9 May 2023 15:20:59 +0200 Subject: [PATCH 5/6] Replace buttons with more descriptive links where available --- tutorials/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tutorials/README.md b/tutorials/README.md index 3aeec40a..af3a9a98 100644 --- a/tutorials/README.md +++ b/tutorials/README.md @@ -30,11 +30,11 @@ The models used in the tutorials are available at [tutorials/models](https://git ## Colab -The tutorials can also be run directly in Google Colab, by clicking on the buttons below, or for a general demo here: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/demo.ipynb). +The tutorials can also be run directly in Google Colab, by clicking on the links/buttons below, or for a general demo here: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/demo.ipynb). | modality \ method | RISE | LIME | KernelSHAP | |-------------------|------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------| -| images | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/rise_mnist.ipynb), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/rise_imagenet.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/lime_images.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/kernelshap_mnist.ipynb), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/kernelshap_geometric_shapes.ipynb) | +| images | [mnist](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/rise_mnist.ipynb), [imagenet](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/rise_imagenet.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/lime_images.ipynb) | [mnist](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/kernelshap_mnist.ipynb), [geometric shapes](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/kernelshap_geometric_shapes.ipynb) | | text | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/rise_text.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/lime_text.ipynb) | - | -| timeseries | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/rise_timeseries_weather.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/lime_timeseries_weather.ipynb), [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/lime_timeseries_coffee.ipynb) | - | +| timeseries | [weather](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/rise_timeseries_weather.ipynb) | [weather](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/lime_timeseries_weather.ipynb), [coffee](https://colab.research.google.com/github/dianna-ai/dianna/blob/main/tutorials/lime_timeseries_coffee.ipynb) | - | From e807a5fb9ca8b1a5651a6dd96f0f2737e93ad9a0 Mon Sep 17 00:00:00 2001 From: Aron Date: Tue, 9 May 2023 15:34:33 +0200 Subject: [PATCH 6/6] :-P --- tutorials/demo.ipynb | 4 +--- tutorials/kernelshap_geometric_shapes.ipynb | 4 +--- tutorials/kernelshap_mnist.ipynb | 4 +--- tutorials/lime_images.ipynb | 4 +--- tutorials/lime_text.ipynb | 4 +--- tutorials/lime_timeseries_coffee.ipynb | 4 +--- tutorials/lime_timeseries_weather.ipynb | 4 +--- tutorials/rise_imagenet.ipynb | 4 +--- tutorials/rise_mnist.ipynb | 4 +--- tutorials/rise_text.ipynb | 4 +--- tutorials/rise_timeseries_weather.ipynb | 4 +--- 11 files changed, 11 insertions(+), 33 deletions(-) diff --git a/tutorials/demo.ipynb b/tutorials/demo.ipynb index 4a0ec924..54b1500e 100644 --- a/tutorials/demo.ipynb +++ b/tutorials/demo.ipynb @@ -37,9 +37,7 @@ " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", " paths_to_download = ['data/binary-mnist.npz', 'models/mnist_model_tf.onnx']\n", " for path in paths_to_download:\n", - " local_directory = os.path.dirname(path)\n", - " os.makedirs(local_directory, exist_ok=True)\n", - " !wget {base_url + path} -O {path}" + " !wget {base_url + path} -P {os.path.dirname(path)}" ] }, { diff --git a/tutorials/kernelshap_geometric_shapes.ipynb b/tutorials/kernelshap_geometric_shapes.ipynb index 626c9066..5b1a4abc 100644 --- a/tutorials/kernelshap_geometric_shapes.ipynb +++ b/tutorials/kernelshap_geometric_shapes.ipynb @@ -45,9 +45,7 @@ " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", " paths_to_download = ['data/shapes.npz', 'models/geometric_shapes_model.onnx']\n", " for path in paths_to_download:\n", - " local_directory = os.path.dirname(path)\n", - " os.makedirs(local_directory, exist_ok=True)\n", - " !wget {base_url + path} -O {path}" + " !wget {base_url + path} -P {os.path.dirname(path)}" ] }, { diff --git a/tutorials/kernelshap_mnist.ipynb b/tutorials/kernelshap_mnist.ipynb index 6e594629..07efaf2e 100644 --- a/tutorials/kernelshap_mnist.ipynb +++ b/tutorials/kernelshap_mnist.ipynb @@ -44,9 +44,7 @@ " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", " paths_to_download = ['data/binary-mnist.npz', 'models/mnist_model_tf.onnx']\n", " for path in paths_to_download:\n", - " local_directory = os.path.dirname(path)\n", - " os.makedirs(local_directory, exist_ok=True)\n", - " !wget {base_url + path} -O {path}" + " !wget {base_url + path} -P {os.path.dirname(path)}" ] }, { diff --git a/tutorials/lime_images.ipynb b/tutorials/lime_images.ipynb index 9dc230e6..f8866a9c 100644 --- a/tutorials/lime_images.ipynb +++ b/tutorials/lime_images.ipynb @@ -41,9 +41,7 @@ " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", " paths_to_download = ['data/leafsnap_example_acer_rubrum.jpg', 'data/leafsnap_classes.csv', 'models/leafsnap_model.onnx']\n", " for path in paths_to_download:\n", - " local_directory = os.path.dirname(path)\n", - " os.makedirs(local_directory, exist_ok=True)\n", - " !wget {base_url + path} -O {path}" + " !wget {base_url + path} -P {os.path.dirname(path)}" ] }, { diff --git a/tutorials/lime_text.ipynb b/tutorials/lime_text.ipynb index aa0f931a..1865b0b9 100644 --- a/tutorials/lime_text.ipynb +++ b/tutorials/lime_text.ipynb @@ -42,9 +42,7 @@ " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", " paths_to_download = ['data/movie_reviews_word_vectors.txt', 'models/movie_review_model.onnx']\n", " for path in paths_to_download:\n", - " local_directory = os.path.dirname(path)\n", - " os.makedirs(local_directory, exist_ok=True)\n", - " !wget {base_url + path} -O {path}" + " !wget {base_url + path} -P {os.path.dirname(path)}" ] }, { diff --git a/tutorials/lime_timeseries_coffee.ipynb b/tutorials/lime_timeseries_coffee.ipynb index fd8b1224..a85587e8 100644 --- a/tutorials/lime_timeseries_coffee.ipynb +++ b/tutorials/lime_timeseries_coffee.ipynb @@ -38,9 +38,7 @@ " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", " paths_to_download = ['data/coffee_train.csv', 'data/coffee_test.csv', 'models/coffee.onnx']\n", " for path in paths_to_download:\n", - " local_directory = os.path.dirname(path)\n", - " os.makedirs(local_directory, exist_ok=True)\n", - " !wget {base_url + path} -O {path}" + " !wget {base_url + path} -P {os.path.dirname(path)}" ] }, { diff --git a/tutorials/lime_timeseries_weather.ipynb b/tutorials/lime_timeseries_weather.ipynb index 64934591..dacb5ae5 100644 --- a/tutorials/lime_timeseries_weather.ipynb +++ b/tutorials/lime_timeseries_weather.ipynb @@ -38,9 +38,7 @@ " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", " paths_to_download = ['models/season_prediction_model_temp_max_binary.onnx']\n", " for path in paths_to_download:\n", - " local_directory = os.path.dirname(path)\n", - " os.makedirs(local_directory, exist_ok=True)\n", - " !wget {base_url + path} -O {path}" + " !wget {base_url + path} -P {os.path.dirname(path)}" ] }, { diff --git a/tutorials/rise_imagenet.ipynb b/tutorials/rise_imagenet.ipynb index bad80a80..26699426 100644 --- a/tutorials/rise_imagenet.ipynb +++ b/tutorials/rise_imagenet.ipynb @@ -41,9 +41,7 @@ " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", " paths_to_download = ['img/bee.jpg']\n", " for path in paths_to_download:\n", - " local_directory = os.path.dirname(path)\n", - " os.makedirs(local_directory, exist_ok=True)\n", - " !wget {base_url + path} -O {path}" + " !wget {base_url + path} -P {os.path.dirname(path)}" ] }, { diff --git a/tutorials/rise_mnist.ipynb b/tutorials/rise_mnist.ipynb index 7ba6023e..174114ba 100644 --- a/tutorials/rise_mnist.ipynb +++ b/tutorials/rise_mnist.ipynb @@ -41,9 +41,7 @@ " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", " paths_to_download = ['data/binary-mnist.npz', 'models/mnist_model.onnx']\n", " for path in paths_to_download:\n", - " local_directory = os.path.dirname(path)\n", - " os.makedirs(local_directory, exist_ok=True)\n", - " !wget {base_url + path} -O {path}" + " !wget {base_url + path} -P {os.path.dirname(path)}" ] }, { diff --git a/tutorials/rise_text.ipynb b/tutorials/rise_text.ipynb index 2b5e5d4f..ee413cb0 100644 --- a/tutorials/rise_text.ipynb +++ b/tutorials/rise_text.ipynb @@ -43,9 +43,7 @@ " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", " paths_to_download = ['data/movie_reviews_word_vectors.txt', 'models/movie_review_model.onnx']\n", " for path in paths_to_download:\n", - " local_directory = os.path.dirname(path)\n", - " os.makedirs(local_directory, exist_ok=True)\n", - " !wget {base_url + path} -O {path}" + " !wget {base_url + path} -P {os.path.dirname(path)}" ] }, { diff --git a/tutorials/rise_timeseries_weather.ipynb b/tutorials/rise_timeseries_weather.ipynb index 0b3d5f2f..3866be4d 100644 --- a/tutorials/rise_timeseries_weather.ipynb +++ b/tutorials/rise_timeseries_weather.ipynb @@ -46,9 +46,7 @@ " base_url = 'https://raw.githubusercontent.com/dianna-ai/dianna/main/tutorials/'\n", " paths_to_download = ['models/season_prediction_model_temp_max_binary.onnx']\n", " for path in paths_to_download:\n", - " local_directory = os.path.dirname(path)\n", - " os.makedirs(local_directory, exist_ok=True)\n", - " !wget {base_url + path} -O {path}" + " !wget {base_url + path} -P {os.path.dirname(path)}" ] }, {