==== ELI5 ====
    .. image:: https://img.shields.io/pypi/v/eli5.svg
       :target: https://pypi.python.org/pypi/eli5
       :alt: PyPI Version

    .. image:: https://github.com/eli5-org/eli5/workflows/build/badge.svg?branch=master
       :target: https://github.com/eli5-org/eli5/actions
       :alt: Build Status

    .. image:: https://codecov.io/github/TeamHG-Memex/eli5/coverage.svg?branch=master
       :target: https://codecov.io/github/TeamHG-Memex/eli5?branch=master
       :alt: Code Coverage

    .. image:: https://readthedocs.org/projects/eli5/badge/?version=latest
       :target: https://eli5.readthedocs.io/en/latest/?badge=latest
       :alt: Documentation


    ELI5 is a Python package which helps to debug machine learning
    classifiers and explain their predictions.

    .. image:: https://raw.githubusercontent.com/TeamHG-Memex/eli5/master/docs/source/static/word-highlight.png
       :alt: explain_prediction for text data

    .. image:: https://raw.githubusercontent.com/TeamHG-Memex/eli5/master/docs/source/static/gradcam-catdog.png
       :alt: explain_prediction for image data

    It provides support for the following machine learning frameworks and packages:

    * scikit-learn_. Currently ELI5 allows to explain weights and predictions
      of scikit-learn linear classifiers and regressors, print decision trees
      as text or as SVG, show feature importances and explain predictions
      of decision trees and tree-based ensembles. ELI5 understands text
      processing utilities from scikit-learn and can highlight text data
      accordingly. Pipeline and FeatureUnion are supported.
      It also allows to debug scikit-learn pipelines which contain
      HashingVectorizer, by undoing hashing.

    * Keras_ - explain predictions of image classifiers via Grad-CAM visualizations.

    * xgboost_ - show feature importances and explain predictions of XGBClassifier,
      XGBRegressor and xgboost.Booster.

    * LightGBM_ - show feature importances and explain predictions of
      LGBMClassifier, LGBMRegressor and lightgbm.Booster.

    * CatBoost_ - show feature importances of CatBoostClassifier,
      CatBoostRegressor and catboost.CatBoost.

    * lightning_ - explain weights and predictions of lightning classifiers and
      regressors.

    * sklearn-crfsuite_. ELI5 allows to check weights of sklearn_crfsuite.CRF
      models.


    ELI5 also implements several algorithms for inspecting black-box models
    (see `Inspecting Black-Box Estimators`_):

    * TextExplainer_ allows to explain predictions
      of any text classifier using LIME_ algorithm (Ribeiro et al., 2016).
      There are utilities for using LIME with non-text data and arbitrary black-box
      classifiers as well, but this feature is currently experimental.
    * `Permutation importance`_ method can be used to compute feature importances
      for black box estimators.

    Explanation and formatting are separated; you can get text-based explanation
    to display in console, HTML version embeddable in an IPython notebook
    or web dashboards, a ``pandas.DataFrame`` object if you want to process
    results further, or JSON version which allows to implement custom rendering
    and formatting on a client.

    .. _lightning: https://github.com/scikit-learn-contrib/lightning
    .. _scikit-learn: https://github.com/scikit-learn/scikit-learn
    .. _sklearn-crfsuite: https://github.com/TeamHG-Memex/sklearn-crfsuite
    .. _LIME: https://eli5.readthedocs.io/en/latest/blackbox/lime.html
    .. _TextExplainer: https://eli5.readthedocs.io/en/latest/tutorials/black-box-text-classifiers.html
    .. _xgboost: https://github.com/dmlc/xgboost
    .. _LightGBM: https://github.com/Microsoft/LightGBM
    .. _Catboost: https://github.com/catboost/catboost
    .. _Keras: https://keras.io/
    .. _Permutation importance: https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html
    .. _Inspecting Black-Box Estimators: https://eli5.readthedocs.io/en/latest/blackbox/index.html

    License is MIT.

    Check `docs <https://eli5.readthedocs.io/>`_ for more.

    .. note::
        This is the same project as https://github.com/TeamHG-Memex/eli5/,
        but due to temporary github access issues, 0.11 release is prepared in
        https://github.com/eli5-org/eli5 (this repo).

    ----

    .. image:: https://hyperiongray.s3.amazonaws.com/define-hg.svg
        :target: https://www.hyperiongray.com/?pk_campaign=github&pk_kwd=eli5
        :alt: define hyperiongray


    Changelog
    =========

    0.13.0 (2022-05-11)
    -------------------

    * drop python2.7 support
    * fix newer xgboost with unnamed features

    0.12.0 (2022-05-11)
    -------------------

    * use Jinja2 >= 3.0.0, please use eli5 0.11 if you'd prefer to use
      an older version of Jinja2
    * support lightgbm.Booster

    0.11.0 (2021-01-23)
    -------------------

    * fixed scikit-learn 0.22+ and 0.24+ support.
    * allow nan inputs in permutation importance (if model supports them).
    * fix for permutation importance with sample_weight and cross-validation.
    * doc fixes (typos, keras and TF versions clarified).
    * don't use deprecated getargspec function.
    * less type ignores, mypy updated to 0.750.
    * python 3.8 and 3.9 tested on GI, python 3.4 not tested any more.
    * tests moved to github actions.

    0.10.1 (2019-08-29)
    -------------------

    * Don't include typing dependency on Python 3.5+
      to fix installation on Python 3.7

    0.10.0 (2019-08-21)
    -------------------

    * Keras image classifiers: explaining predictions with Grad-CAM
      (GSoC-2019 project by @teabolt).

    0.9.0 (2019-07-05)
    ------------------

    * CatBoost support: show feature importances of CatBoostClassifier,
      CatBoostRegressor and catboost.CatBoost.
    * Test fixes: fixes for scikit-learn 0.21+, use xenial base on Travis
    * Catch exceptions from improperly installed LightGBM

    0.8.2 (2019-04-04)
    ------------------

    * fixed scikit-learn 0.21+ support (randomized linear models are removed
      from scikit-learn);
    * fixed pandas.DataFrame + xgboost support for PermutationImportance;
    * fixed tests with recent numpy;
    * added conda install instructions (conda package is maintained by community);
    * tutorial is updated to use xgboost 0.81;
    * update docs to use pandoc 2.x.

    0.8.1 (2018-11-19)
    ------------------

    * fixed Python 3.7 support;
    * added support for XGBoost > 0.6a2;
    * fixed deprecation warnings in numpy >= 1.14;
    * documentation, type annotation and test improvements.

    0.8 (2017-08-25)
    ----------------

    * **backwards incompatible**: DataFrame objects with explanations no longer
      use indexes and pivot tables, they are now just plain DataFrames;
    * new method for inspection black-box models is added
      (`eli5-permutation-importance`);
    * transfor_feature_names is implemented for sklearn's MinMaxScaler,
      StandardScaler, MaxAbsScaler and RobustScaler;
    * zero and negative feature importances are no longer hidden;
    * fixed compatibility with scikit-learn 0.19;
    * fixed compatibility with LightGBM master (2.0.5 and 2.0.6 are still
      unsupported - there are bugs in LightGBM);
    * documentation, testing and type annotation improvements.

    0.7 (2017-07-03)
    ----------------

    * better pandas.DataFrame integration: `eli5.explain_weights_df`,
      `eli5.explain_weights_dfs`, `eli5.explain_prediction_df`,
      `eli5.explain_prediction_dfs`,
      `eli5.format_as_dataframe <eli5.formatters.as_dataframe.format_as_dataframe>`
      and `eli5.format_as_dataframes <eli5.formatters.as_dataframe.format_as_dataframes>`
      functions allow to export explanations to pandas.DataFrames;
    * `eli5.explain_prediction` now shows predicted class for binary
      classifiers (previously it was always showing positive class);
    * `eli5.explain_prediction` supports ``targets=[<class>]`` now
      for binary classifiers; e.g. to show result as seen for negative class,
      you can use ``eli5.explain_prediction(..., targets=[False])``;
    * support `eli5.explain_prediction` and `eli5.explain_weights`
      for libsvm-based linear estimators from sklearn.svm: ``SVC(kernel='linear')``
      (only binary classification), ``NuSVC(kernel='linear')`` (only
      binary classification), ``SVR(kernel='linear')``, ``NuSVR(kernel='linear')``,
      ``OneClassSVM(kernel='linear')``;
    * fixed `eli5.explain_weights` for LightGBM_ estimators in Python 2 when
      ``importance_type`` is 'split' or 'weight';
    * testing improvements.

    0.6.4 (2017-06-22)
    ------------------

    * Fixed `eli5.explain_prediction` for recent LightGBM_ versions;
    * fixed Python 3 deprecation warning in formatters.html;
    * testing improvements.

    0.6.3 (2017-06-02)
    ------------------

    * `eli5.explain_weights` and `eli5.explain_prediction`
      works with xgboost.Booster, not only with sklearn-like APIs;
    * `eli5.formatters.as_dict.format_as_dict` is now available as
      ``eli5.format_as_dict``;
    * testing and documentation fixes.

    0.6.2 (2017-05-17)
    ------------------

    * readable `eli5.explain_weights` for XGBoost models trained on
      pandas.DataFrame;
    * readable `eli5.explain_weights` for LightGBM models trained on
      pandas.DataFrame;
    * fixed an issue with `eli5.explain_prediction` for XGBoost
      models trained on pandas.DataFrame when feature names contain dots;
    * testing improvements.

    0.6.1 (2017-05-10)
    ------------------

    * Better pandas support in `eli5.explain_prediction` for
      xgboost, sklearn, LightGBM and lightning.

    0.6 (2017-05-03)
    ----------------

    * Better scikit-learn Pipeline support in `eli5.explain_weights`:
      it is now possible to pass a Pipeline object directly. Curently only
      SelectorMixin-based transformers, FeatureUnion and transformers
      with ``get_feature_names`` are supported, but users can register other
      transformers; built-in list of supported transformers will be expanded
      in future. See `sklearn-pipelines` for more.
    * Inverting of HashingVectorizer is now supported inside FeatureUnion
      via `eli5.sklearn.unhashing.invert_hashing_and_fit`.
      See `sklearn-unhashing`.
    * Fixed compatibility with Jupyter Notebook >= 5.0.0.
    * Fixed `eli5.explain_weights` for Lasso regression with a single
      feature and no intercept.
    * Fixed unhashing support in Python 2.x.
    * Documentation and testing improvements.


    0.5 (2017-04-27)
    ----------------

    * LightGBM_ support: `eli5.explain_prediction` and
      `eli5.explain_weights` are now supported for
      ``LGBMClassifier`` and ``LGBMRegressor``
      (see `eli5 LightGBM support <library-lightgbm>`).
    * fixed text formatting if all weights are zero;
    * type checks now use latest mypy;
    * testing setup improvements: Travis CI now uses Ubuntu 14.04.

    .. _LightGBM: https://github.com/Microsoft/LightGBM

    0.4.2 (2017-03-03)
    ------------------

    * bug fix: eli5 should remain importable if xgboost is available, but
      not installed correctly.

    0.4.1 (2017-01-25)
    ------------------

    * feature contribution calculation fixed
      for `eli5.xgboost.explain_prediction_xgboost`


    0.4 (2017-01-20)
    ----------------

    * `eli5.explain_prediction`: new 'top_targets' argument allows
      to display only predictions with highest or lowest scores;
    * `eli5.explain_weights` allows to customize the way feature importances
      are computed for XGBClassifier and XGBRegressor using ``importance_type``
      argument (see docs for the `eli5 XGBoost support <library-xgboost>`);
    * `eli5.explain_weights` uses gain for XGBClassifier and XGBRegressor
      feature importances by default; this method is a better indication of
      what's going, and it makes results more compatible with feature importances
      displayed for scikit-learn gradient boosting methods.

    0.3.1 (2017-01-16)
    ------------------

    * packaging fix: scikit-learn is added to install_requires in setup.py.

    0.3 (2017-01-13)
    ----------------

    * `eli5.explain_prediction` works for XGBClassifier, XGBRegressor
      from XGBoost and for ExtraTreesClassifier, ExtraTreesRegressor,
      GradientBoostingClassifier, GradientBoostingRegressor,
      RandomForestClassifier, RandomForestRegressor, DecisionTreeClassifier
      and DecisionTreeRegressor from scikit-learn.
      Explanation method is based on
      http://blog.datadive.net/interpreting-random-forests/ .
    * `eli5.explain_weights` now supports tree-based regressors from
      scikit-learn: DecisionTreeRegressor, AdaBoostRegressor,
      GradientBoostingRegressor, RandomForestRegressor and ExtraTreesRegressor.
    * `eli5.explain_weights` works for XGBRegressor;
    * new `TextExplainer <lime-tutorial>` class allows to explain predictions
      of black-box text classification pipelines using LIME algorithm;
      many improvements in `eli5.lime <eli5-lime>`.
    * better ``sklearn.pipeline.FeatureUnion`` support in
      `eli5.explain_prediction`;
    * rendering performance is improved;
    * a number of remaining feature importances is shown when the feature
      importance table is truncated;
    * styling of feature importances tables is fixed;
    * `eli5.explain_weights` and `eli5.explain_prediction` support
      more linear estimators from scikit-learn: HuberRegressor, LarsCV, LassoCV,
      LassoLars, LassoLarsCV, LassoLarsIC, OrthogonalMatchingPursuit,
      OrthogonalMatchingPursuitCV, PassiveAggressiveRegressor,
      RidgeClassifier, RidgeClassifierCV, TheilSenRegressor.
    * text-based formatting of decision trees is changed: for binary
      classification trees only a probability of "true" class is printed,
      not both probabilities as it was before.
    * `eli5.explain_weights` supports ``feature_filter`` in addition
      to ``feature_re`` for filtering features, and `eli5.explain_prediction`
      now also supports both of these arguments;
    * 'Weight' column is renamed to 'Contribution' in the output of
      `eli5.explain_prediction`;
    * new ``show_feature_values=True`` formatter argument allows to display
      input feature values;
    * fixed an issue with analyzer='char_wb' highlighting at the start of the
      text.

    0.2 (2016-12-03)
    ----------------

    * XGBClassifier support (from `XGBoost <https://github.com/dmlc/xgboost>`__
      package);
    * `eli5.explain_weights` support for sklearn OneVsRestClassifier;
    * std deviation of feature importances is no longer printed as zero
      if it is not available.

    0.1.1 (2016-11-25)
    ------------------

    * packaging fixes: require attrs > 16.0.0, fixed README rendering

    0.1 (2016-11-24)
    ----------------

    * HTML output;
    * IPython integration;
    * JSON output;
    * visualization of scikit-learn text vectorizers;
    * `sklearn-crfsuite <https://github.com/TeamHG-Memex/sklearn-crfsuite>`__
      support;
    * `lightning <https://github.com/scikit-learn-contrib/lightning>`__ support;
    * `eli5.show_weights` and `eli5.show_prediction` functions;
    * `eli5.explain_weights` and `eli5.explain_prediction`
      functions;
    * `eli5.lime <eli5-lime>` improvements: samplers for non-text data,
      bug fixes, docs;
    * HashingVectorizer is supported for regression tasks;
    * performance improvements - feature names are lazy;
    * sklearn ElasticNetCV and RidgeCV support;
    * it is now possible to customize formatting output - show/hide sections,
      change layout;
    * sklearn OneVsRestClassifier support;
    * sklearn DecisionTreeClassifier visualization (text-based or svg-based);
    * dropped support for scikit-learn < 0.18;
    * basic mypy type annotations;
    * ``feature_re`` argument allows to show only a subset of features;
    * ``target_names`` argument allows to change display names of targets/classes;
    * ``targets`` argument allows to show a subset of targets/classes and
      change their display order;
    * documentation, more examples.


    0.0.6 (2016-10-12)
    ------------------

    * Candidate features in eli5.sklearn.InvertableHashingVectorizer
      are ordered by their frequency, first candidate is always positive.

    0.0.5 (2016-09-27)
    ------------------

    * HashingVectorizer support in explain_prediction;
    * add an option to pass coefficient scaling array; it is useful
      if you want to compare coefficients for features which scale or sign
      is different in the input;
    * bug fix: classifier weights are no longer changed by eli5 functions.

    0.0.4 (2016-09-24)
    ------------------

    * eli5.sklearn.InvertableHashingVectorizer and
      eli5.sklearn.FeatureUnhasher allow to recover feature names for
      pipelines which use HashingVectorizer or FeatureHasher;
    * added support for scikit-learn linear regression models (ElasticNet,
      Lars, Lasso, LinearRegression, LinearSVR, Ridge, SGDRegressor);
    * doc and vec arguments are swapped in explain_prediction function;
      vec can now be omitted if an example is already vectorized;
    * fixed issue with dense feature vectors;
    * all class_names arguments are renamed to target_names;
    * feature name guessing is fixed for scikit-learn ensemble estimators;
    * testing improvements.

    0.0.3 (2016-09-21)
    ------------------

    * support any black-box classifier using LIME (http://arxiv.org/abs/1602.04938)
      algorithm; text data support is built-in;
    * "vectorized" argument for sklearn.explain_prediction; it allows to pass
      example which is already vectorized;
    * allow to pass feature_names explicitly;
    * support classifiers without get_feature_names method using auto-generated
      feature names.

    0.0.2 (2016-09-19)
    ------------------

    * 'top' argument of ``explain_prediction``
      can be a tuple (num_positive, num_negative);
    * classifier name is no longer printed by default;
    * added eli5.sklearn.explain_prediction to explain individual examples;
    * fixed numpy warning.

    0.0.1 (2016-09-15)
    ------------------

    Pre-release.