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Sync meta-learning experiments in public repository
Signed-off-by: m09 <[email protected]>
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[flake8]
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ignore=B008,E121,E123,E126,E203,E226,E24,E704,W503,W504,D100,D105,D200,D202,D301,D402,D
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max-line-length=88
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exclude=
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.git
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import-order-style=appnexus
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application-package-names=formatml
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per-file-ignores=
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**/tests/*:D
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**/test*.py:D

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*__pycache__/
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*.egg-info/

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[MASTER]
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jobs=0
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load-plugins=pylint.extensions.docparams
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[MESSAGES CONTROL]
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disable=all
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enable=missing-param-doc,
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differing-param-doc,
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missing-return-doc

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Diff for: ‎MAINTAINERS

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Hugo Mougard <[email protected]> (@m09)

Diff for: ‎MANIFEST.in

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include README.md LICENSE MAINTAINERS

Diff for: ‎Makefile

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check:
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black --check formatml setup.py
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mypy formatml
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flake8 --count
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pylint formatml setup.py
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bblfsh-start:
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! docker ps | grep formatml_bblfshd # bblfsh server has been run already.
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docker run -d --rm --name formatml_bblfshd --privileged -p 9999\:9432 \
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bblfsh/bblfshd\:v2.12.0 --log-level DEBUG
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docker exec formatml_bblfshd bblfshctl driver install \
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javascript docker://bblfsh/javascript-driver\:v2.7.3
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.PHONY: check bblfsh-start

Diff for: ‎README.md

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# formatml
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Formatting with meta learning experiments.

Diff for: ‎configs/main.jsonnet

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local utils = import 'utils.libjsonnet';
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local embedder_features = [
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{ name: 'roles', vocabulary: 'voc_roles', dimension: 48 },
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{ name: 'internal_type', vocabulary: 'voc_type', dimension: 48 },
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{ name: 'length', vocabulary: 'voc_length', dimension: 32 },
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];
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local encoder_input_dim = utils.sum(utils.pluck(embedder_features, 'dimension'));
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local encoder_message_dim = encoder_input_dim;
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local encoder_output_dim = encoder_input_dim;
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local encoder_edge_types = ['child', 'parent', 'previous_token', 'next_token'];
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local encoder_iterations = 8;
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local output_embedding_dim = 32;
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local decoder_hidden_dim = 128;
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utils.class(
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'main',
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{
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run_dir: utils.resource('run_dir'),
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config: {
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dataset: utils.class(
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'repositories',
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{
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local root_dir = 'rootdir',
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download_dir: std.join('/', [root_dir, 'download']),
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parse_dir: std.join('/', [root_dir, 'parse']),
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tensor_dir: std.join('/', [root_dir, 'tensor']),
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repositories: [
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// ['telescopejs', 'telescope', 'master'],
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['axios', 'axios', 'master'],
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],
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instance: {
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fields: {
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graph: utils.class(
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'typed_dgl_graph',
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{
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edge_types: encoder_edge_types,
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vocabulary: utils.resource('voc_graph'),
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},
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),
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roles: utils.class('roles', { vocabulary: utils.resource('voc_roles') }),
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internal_type: utils.class(
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'internal_type',
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{ vocabulary: utils.resource('voc_type') }
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),
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length: utils.class(
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'length',
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{
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max_length: 128,
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vocabulary: utils.resource('voc_length'),
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},
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),
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label: utils.class('label', { vocabulary: utils.resource('voc_label') }),
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},
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},
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},
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),
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model: utils.class(
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'gnn_rnn',
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{
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graph_embedder: {
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dimensions: utils.pluck(embedder_features, 'dimension'),
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vocabularies: std.map(
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utils.resource,
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utils.pluck(embedder_features, 'vocabulary')
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),
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},
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graph_encoder: utils.class(
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'ggnn',
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{
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iterations: encoder_iterations,
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n_types: std.length(encoder_edge_types),
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x_dim: encoder_input_dim,
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h_dim: encoder_output_dim,
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m_dim: encoder_message_dim,
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},
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),
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output_embedder: {
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dimension: output_embedding_dim,
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vocabulary: utils.resource('voc_label'),
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},
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decoder: utils.class(
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'concat_conditioning_recurrent',
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{
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recurrent: utils.class(
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'lstm',
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{
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input_size: encoder_output_dim + output_embedding_dim,
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hidden_size: decoder_hidden_dim,
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batch_first: true,
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},
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),
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}
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),
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class_projection: {
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in_features: decoder_hidden_dim,
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vocabulary: utils.resource('voc_label'),
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},
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graph_field_name: 'graph',
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feature_field_names: utils.pluck(embedder_features, 'name'),
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label_field_name: 'label',
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},
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),
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optimizer: utils.class('adam', {}),
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trainer: {
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epochs: 100,
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batch_size: 2,
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eval_every: 20,
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train_eval_split: 0.8,
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run_dir: utils.resource('run_dir'),
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metric_names: ['cross_entropy', 'perplexity', 'accuracy'],
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},
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scheduler: utils.class('step_lr', { step_size: 5, gamma: 0.8 },),
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},
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},
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{
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run_dir: utils.class('date_template_path',
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{ date_template: 'runs/%m-%d-%H:%M:%S%z' }),
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voc_graph: utils.class('vocabulary'),
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voc_roles: utils.class('vocabulary', { unknown: '<UNK>' }),
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voc_type: utils.class('vocabulary', { unknown: '<UNK>' }),
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voc_length: utils.class('vocabulary'),
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voc_label: utils.class('vocabulary', { unknown: '<UNK>' }),
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}
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)

Diff for: ‎configs/utils.libjsonnet

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local add(x, y) = x + y;
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{
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sum(x)::std.foldl(add, x, 0),
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pluck(array, field)::[x[field] for x in array],
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resource(name)::{ _resource: name },
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class(type, config = {}, resources = {})::{
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_type: type,
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_config: config,
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_resources_registration: resources,
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},
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}

Diff for: ‎formatml/__init__.py

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"""Formatting with meta-learning experiments."""
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__version__ = "0.1.0"

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