eli5fix 0.13.0
Debug machine learning classifiers and explain their predictions
License: MIT license
Requires Python: >=3.6

====
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.