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 (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)

0.6.4 (2017-06-22)

0.6.3 (2017-06-02)

0.6.2 (2017-05-17)

0.6.1 (2017-05-10)

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 Transformation pipelines for more.
  • Inverting of HashingVectorizer is now supported inside FeatureUnion via eli5.sklearn.unhashing.invert_hashing_and_fit(). See Reversing hashing trick.
  • 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)

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)

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);
  • 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

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 .
  • 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 class allows to explain predictions of black-box text classification pipelines using LIME algorithm; many improvements in 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 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 ( 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)