{
"info": {
"author": "Mikhail Korobov, Konstantin Lopuhin",
"author_email": "kmike84@gmail.com, kostia.lopuhin@gmail.com",
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"Operating System :: OS Independent",
"Programming Language :: Python",
"Programming Language :: Python :: 2",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.4",
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"description": "====\nELI5\n====\n\n.. image:: https://img.shields.io/pypi/v/eli5.svg\n :target: https://pypi.python.org/pypi/eli5\n :alt: PyPI Version\n\n.. image:: https://travis-ci.org/TeamHG-Memex/eli5.svg?branch=master\n :target: https://travis-ci.org/TeamHG-Memex/eli5\n :alt: Build Status\n\n.. image:: https://codecov.io/github/TeamHG-Memex/eli5/coverage.svg?branch=master\n :target: https://codecov.io/github/TeamHG-Memex/eli5?branch=master\n :alt: Code Coverage\n\n.. image:: https://readthedocs.org/projects/eli5/badge/?version=latest\n :target: https://eli5.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation\n\n\nELI5 is a Python package which helps to debug machine learning\nclassifiers and explain their predictions.\n\n.. image:: https://raw.githubusercontent.com/TeamHG-Memex/eli5/master/docs/source/static/word-highlight.png\n :alt: explain_prediction for text data\n\n.. image:: https://raw.githubusercontent.com/TeamHG-Memex/eli5/master/docs/source/static/gradcam-catdog.png\n :alt: explain_prediction for image data\n\nIt provides support for the following machine learning frameworks and packages:\n\n* scikit-learn_. Currently ELI5 allows to explain weights and predictions\n of scikit-learn linear classifiers and regressors, print decision trees\n as text or as SVG, show feature importances and explain predictions\n of decision trees and tree-based ensembles. ELI5 understands text\n processing utilities from scikit-learn and can highlight text data\n accordingly. Pipeline and FeatureUnion are supported.\n It also allows to debug scikit-learn pipelines which contain\n HashingVectorizer, by undoing hashing.\n\n* Keras_ - explain predictions of image classifiers via Grad-CAM visualizations.\n\n* xgboost_ - show feature importances and explain predictions of XGBClassifier,\n XGBRegressor and xgboost.Booster.\n\n* LightGBM_ - show feature importances and explain predictions of\n LGBMClassifier and LGBMRegressor.\n\n* CatBoost_ - show feature importances of CatBoostClassifier,\n CatBoostRegressor and catboost.CatBoost.\n\n* lightning_ - explain weights and predictions of lightning classifiers and\n regressors.\n\n* sklearn-crfsuite_. ELI5 allows to check weights of sklearn_crfsuite.CRF\n models.\n\n\nELI5 also implements several algorithms for inspecting black-box models\n(see `Inspecting Black-Box Estimators`_):\n\n* TextExplainer_ allows to explain predictions\n of any text classifier using LIME_ algorithm (Ribeiro et al., 2016).\n There are utilities for using LIME with non-text data and arbitrary black-box\n classifiers as well, but this feature is currently experimental.\n* `Permutation importance`_ method can be used to compute feature importances\n for black box estimators.\n\nExplanation and formatting are separated; you can get text-based explanation\nto display in console, HTML version embeddable in an IPython notebook\nor web dashboards, a ``pandas.DataFrame`` object if you want to process\nresults further, or JSON version which allows to implement custom rendering\nand formatting on a client.\n\n.. _lightning: https://github.com/scikit-learn-contrib/lightning\n.. _scikit-learn: https://github.com/scikit-learn/scikit-learn\n.. _sklearn-crfsuite: https://github.com/TeamHG-Memex/sklearn-crfsuite\n.. _LIME: https://eli5.readthedocs.io/en/latest/blackbox/lime.html\n.. _TextExplainer: https://eli5.readthedocs.io/en/latest/tutorials/black-box-text-classifiers.html\n.. _xgboost: https://github.com/dmlc/xgboost\n.. _LightGBM: https://github.com/Microsoft/LightGBM\n.. _Catboost: https://github.com/catboost/catboost\n.. _Keras: https://keras.io/\n.. _Permutation importance: https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html\n.. _Inspecting Black-Box Estimators: https://eli5.readthedocs.io/en/latest/blackbox/index.html\n\nLicense is MIT.\n\nCheck `docs `_ for more.\n\n----\n\n.. image:: https://hyperiongray.s3.amazonaws.com/define-hg.svg\n\t:target: https://www.hyperiongray.com/?pk_campaign=github&pk_kwd=eli5\n\t:alt: define hyperiongray\n\n\nChangelog\n=========\n\n0.10.1 (2019-08-29)\n-------------------\n\n* Don't include typing dependency on Python 3.5+\n to fix installation on Python 3.7\n\n0.10.0 (2019-08-21)\n-------------------\n\n* Keras image classifiers: explaining predictions with Grad-CAM\n (GSoC-2019 project by @teabolt).\n\n0.9.0 (2019-07-05)\n------------------\n\n* CatBoost support: show feature importances of CatBoostClassifier,\n CatBoostRegressor and catboost.CatBoost.\n* Test fixes: fixes for scikit-learn 0.21+, use xenial base on Travis\n* Catch exceptions from improperly installed LightGBM\n\n0.8.2 (2019-04-04)\n------------------\n\n* fixed scikit-learn 0.21+ support (randomized linear models are removed\n from scikit-learn);\n* fixed pandas.DataFrame + xgboost support for PermutationImportance;\n* fixed tests with recent numpy;\n* added conda install instructions (conda package is maintained by community);\n* tutorial is updated to use xgboost 0.81;\n* update docs to use pandoc 2.x.\n\n0.8.1 (2018-11-19)\n------------------\n\n* fixed Python 3.7 support;\n* added support for XGBoost > 0.6a2;\n* fixed deprecation warnings in numpy >= 1.14;\n* documentation, type annotation and test improvements.\n\n0.8 (2017-08-25)\n----------------\n\n* **backwards incompatible**: DataFrame objects with explanations no longer\n use indexes and pivot tables, they are now just plain DataFrames;\n* new method for inspection black-box models is added\n (`eli5-permutation-importance`);\n* transfor_feature_names is implemented for sklearn's MinMaxScaler,\n StandardScaler, MaxAbsScaler and RobustScaler;\n* zero and negative feature importances are no longer hidden;\n* fixed compatibility with scikit-learn 0.19;\n* fixed compatibility with LightGBM master (2.0.5 and 2.0.6 are still\n unsupported - there are bugs in LightGBM);\n* documentation, testing and type annotation improvements.\n\n0.7 (2017-07-03)\n----------------\n\n* better pandas.DataFrame integration: `eli5.explain_weights_df`,\n `eli5.explain_weights_dfs`, `eli5.explain_prediction_df`,\n `eli5.explain_prediction_dfs`,\n `eli5.format_as_dataframe `\n and `eli5.format_as_dataframes `\n functions allow to export explanations to pandas.DataFrames;\n* `eli5.explain_prediction` now shows predicted class for binary\n classifiers (previously it was always showing positive class);\n* `eli5.explain_prediction` supports ``targets=[]`` now\n for binary classifiers; e.g. to show result as seen for negative class,\n you can use ``eli5.explain_prediction(..., targets=[False])``;\n* support `eli5.explain_prediction` and `eli5.explain_weights`\n for libsvm-based linear estimators from sklearn.svm: ``SVC(kernel='linear')``\n (only binary classification), ``NuSVC(kernel='linear')`` (only\n binary classification), ``SVR(kernel='linear')``, ``NuSVR(kernel='linear')``,\n ``OneClassSVM(kernel='linear')``;\n* fixed `eli5.explain_weights` for LightGBM_ estimators in Python 2 when\n ``importance_type`` is 'split' or 'weight';\n* testing improvements.\n\n0.6.4 (2017-06-22)\n------------------\n\n* Fixed `eli5.explain_prediction` for recent LightGBM_ versions;\n* fixed Python 3 deprecation warning in formatters.html;\n* testing improvements.\n\n0.6.3 (2017-06-02)\n------------------\n\n* `eli5.explain_weights` and `eli5.explain_prediction`\n works with xgboost.Booster, not only with sklearn-like APIs;\n* `eli5.formatters.as_dict.format_as_dict` is now available as\n ``eli5.format_as_dict``;\n* testing and documentation fixes.\n\n0.6.2 (2017-05-17)\n------------------\n\n* readable `eli5.explain_weights` for XGBoost models trained on\n pandas.DataFrame;\n* readable `eli5.explain_weights` for LightGBM models trained on\n pandas.DataFrame;\n* fixed an issue with `eli5.explain_prediction` for XGBoost\n models trained on pandas.DataFrame when feature names contain dots;\n* testing improvements.\n\n0.6.1 (2017-05-10)\n------------------\n\n* Better pandas support in `eli5.explain_prediction` for\n xgboost, sklearn, LightGBM and lightning.\n\n0.6 (2017-05-03)\n----------------\n\n* Better scikit-learn Pipeline support in `eli5.explain_weights`:\n it is now possible to pass a Pipeline object directly. Curently only\n SelectorMixin-based transformers, FeatureUnion and transformers\n with ``get_feature_names`` are supported, but users can register other\n transformers; built-in list of supported transformers will be expanded\n in future. See `sklearn-pipelines` for more.\n* Inverting of HashingVectorizer is now supported inside FeatureUnion\n via `eli5.sklearn.unhashing.invert_hashing_and_fit`.\n See `sklearn-unhashing`.\n* Fixed compatibility with Jupyter Notebook >= 5.0.0.\n* Fixed `eli5.explain_weights` for Lasso regression with a single\n feature and no intercept.\n* Fixed unhashing support in Python 2.x.\n* Documentation and testing improvements.\n\n\n0.5 (2017-04-27)\n----------------\n\n* LightGBM_ support: `eli5.explain_prediction` and\n `eli5.explain_weights` are now supported for\n ``LGBMClassifier`` and ``LGBMRegressor``\n (see `eli5 LightGBM support `).\n* fixed text formatting if all weights are zero;\n* type checks now use latest mypy;\n* testing setup improvements: Travis CI now uses Ubuntu 14.04.\n\n.. _LightGBM: https://github.com/Microsoft/LightGBM\n\n0.4.2 (2017-03-03)\n------------------\n\n* bug fix: eli5 should remain importable if xgboost is available, but\n not installed correctly.\n\n0.4.1 (2017-01-25)\n------------------\n\n* feature contribution calculation fixed\n for `eli5.xgboost.explain_prediction_xgboost`\n\n\n0.4 (2017-01-20)\n----------------\n\n* `eli5.explain_prediction`: new 'top_targets' argument allows\n to display only predictions with highest or lowest scores;\n* `eli5.explain_weights` allows to customize the way feature importances\n are computed for XGBClassifier and XGBRegressor using ``importance_type``\n argument (see docs for the `eli5 XGBoost support `);\n* `eli5.explain_weights` uses gain for XGBClassifier and XGBRegressor\n feature importances by default; this method is a better indication of\n what's going, and it makes results more compatible with feature importances\n displayed for scikit-learn gradient boosting methods.\n\n0.3.1 (2017-01-16)\n------------------\n\n* packaging fix: scikit-learn is added to install_requires in setup.py.\n\n0.3 (2017-01-13)\n----------------\n\n* `eli5.explain_prediction` works for XGBClassifier, XGBRegressor\n from XGBoost and for ExtraTreesClassifier, ExtraTreesRegressor,\n GradientBoostingClassifier, GradientBoostingRegressor,\n RandomForestClassifier, RandomForestRegressor, DecisionTreeClassifier\n and DecisionTreeRegressor from scikit-learn.\n Explanation method is based on\n http://blog.datadive.net/interpreting-random-forests/ .\n* `eli5.explain_weights` now supports tree-based regressors from\n scikit-learn: DecisionTreeRegressor, AdaBoostRegressor,\n GradientBoostingRegressor, RandomForestRegressor and ExtraTreesRegressor.\n* `eli5.explain_weights` works for XGBRegressor;\n* new `TextExplainer ` class allows to explain predictions\n of black-box text classification pipelines using LIME algorithm;\n many improvements in `eli5.lime `.\n* better ``sklearn.pipeline.FeatureUnion`` support in\n `eli5.explain_prediction`;\n* rendering performance is improved;\n* a number of remaining feature importances is shown when the feature\n importance table is truncated;\n* styling of feature importances tables is fixed;\n* `eli5.explain_weights` and `eli5.explain_prediction` support\n more linear estimators from scikit-learn: HuberRegressor, LarsCV, LassoCV,\n LassoLars, LassoLarsCV, LassoLarsIC, OrthogonalMatchingPursuit,\n OrthogonalMatchingPursuitCV, PassiveAggressiveRegressor,\n RidgeClassifier, RidgeClassifierCV, TheilSenRegressor.\n* text-based formatting of decision trees is changed: for binary\n classification trees only a probability of \"true\" class is printed,\n not both probabilities as it was before.\n* `eli5.explain_weights` supports ``feature_filter`` in addition\n to ``feature_re`` for filtering features, and `eli5.explain_prediction`\n now also supports both of these arguments;\n* 'Weight' column is renamed to 'Contribution' in the output of\n `eli5.explain_prediction`;\n* new ``show_feature_values=True`` formatter argument allows to display\n input feature values;\n* fixed an issue with analyzer='char_wb' highlighting at the start of the\n text.\n\n0.2 (2016-12-03)\n----------------\n\n* XGBClassifier support (from `XGBoost `__\n package);\n* `eli5.explain_weights` support for sklearn OneVsRestClassifier;\n* std deviation of feature importances is no longer printed as zero\n if it is not available.\n\n0.1.1 (2016-11-25)\n------------------\n\n* packaging fixes: require attrs > 16.0.0, fixed README rendering\n\n0.1 (2016-11-24)\n----------------\n\n* HTML output;\n* IPython integration;\n* JSON output;\n* visualization of scikit-learn text vectorizers;\n* `sklearn-crfsuite `__\n support;\n* `lightning `__ support;\n* `eli5.show_weights` and `eli5.show_prediction` functions;\n* `eli5.explain_weights` and `eli5.explain_prediction`\n functions;\n* `eli5.lime ` improvements: samplers for non-text data,\n bug fixes, docs;\n* HashingVectorizer is supported for regression tasks;\n* performance improvements - feature names are lazy;\n* sklearn ElasticNetCV and RidgeCV support;\n* it is now possible to customize formatting output - show/hide sections,\n change layout;\n* sklearn OneVsRestClassifier support;\n* sklearn DecisionTreeClassifier visualization (text-based or svg-based);\n* dropped support for scikit-learn < 0.18;\n* basic mypy type annotations;\n* ``feature_re`` argument allows to show only a subset of features;\n* ``target_names`` argument allows to change display names of targets/classes;\n* ``targets`` argument allows to show a subset of targets/classes and\n change their display order;\n* documentation, more examples.\n\n\n0.0.6 (2016-10-12)\n------------------\n\n* Candidate features in eli5.sklearn.InvertableHashingVectorizer\n are ordered by their frequency, first candidate is always positive.\n\n0.0.5 (2016-09-27)\n------------------\n\n* HashingVectorizer support in explain_prediction;\n* add an option to pass coefficient scaling array; it is useful\n if you want to compare coefficients for features which scale or sign\n is different in the input;\n* bug fix: classifier weights are no longer changed by eli5 functions.\n\n0.0.4 (2016-09-24)\n------------------\n\n* eli5.sklearn.InvertableHashingVectorizer and\n eli5.sklearn.FeatureUnhasher allow to recover feature names for\n pipelines which use HashingVectorizer or FeatureHasher;\n* added support for scikit-learn linear regression models (ElasticNet,\n Lars, Lasso, LinearRegression, LinearSVR, Ridge, SGDRegressor);\n* doc and vec arguments are swapped in explain_prediction function;\n vec can now be omitted if an example is already vectorized;\n* fixed issue with dense feature vectors;\n* all class_names arguments are renamed to target_names;\n* feature name guessing is fixed for scikit-learn ensemble estimators;\n* testing improvements.\n\n0.0.3 (2016-09-21)\n------------------\n\n* support any black-box classifier using LIME (http://arxiv.org/abs/1602.04938)\n algorithm; 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