eli5 0.13.0

Debug machine learning classifiers and explain their predictions

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

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.

License is MIT.

Check docs 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).

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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.

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 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 support;

• 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.