- Don't include typing dependency on Python 3.5+ to fix installation on Python 3.7
- Keras image classifiers: explaining predictions with Grad-CAM (GSoC-2019 project by @teabolt).
- 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
- 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.
- fixed Python 3.7 support;
- added support for XGBoost > 0.6a2;
- fixed deprecation warnings in numpy >= 1.14;
- documentation, type annotation and test improvements.
- 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.
- 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>
andeli5.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
supportstargets=[<class>]
now for binary classifiers; e.g. to show result as seen for negative class, you can useeli5.explain_prediction(..., targets=[False])
;- support
eli5.explain_prediction
andeli5.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 whenimportance_type
is 'split' or 'weight'; - testing improvements.
- Fixed
eli5.explain_prediction
for recent LightGBM versions; - fixed Python 3 deprecation warning in formatters.html;
- testing improvements.
eli5.explain_weights
andeli5.explain_prediction
works with xgboost.Booster, not only with sklearn-like APIs;eli5.formatters.as_dict.format_as_dict
is now available aseli5.format_as_dict
;- testing and documentation fixes.
- 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.
- Better pandas support in
eli5.explain_prediction
for xgboost, sklearn, LightGBM and lightning.
- 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 withget_feature_names
are supported, but users can register other transformers; built-in list of supported transformers will be expanded in future. Seesklearn-pipelines
for more. - Inverting of HashingVectorizer is now supported inside FeatureUnion via
eli5.sklearn.unhashing.invert_hashing_and_fit
. Seesklearn-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.
- LightGBM support:
eli5.explain_prediction
andeli5.explain_weights
are now supported forLGBMClassifier
andLGBMRegressor
(seeeli5 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.
- bug fix: eli5 should remain importable if xgboost is available, but not installed correctly.
- feature contribution calculation fixed for
eli5.xgboost.explain_prediction_xgboost
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 usingimportance_type
argument (see docs for theeli5 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.
- packaging fix: scikit-learn is added to install_requires in setup.py.
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 ineli5.lime <eli5-lime>
. - better
sklearn.pipeline.FeatureUnion
support ineli5.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
andeli5.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
supportsfeature_filter
in addition tofeature_re
for filtering features, andeli5.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.
- 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.
- packaging fixes: require attrs > 16.0.0, fixed README rendering
- HTML output;
- IPython integration;
- JSON output;
- visualization of scikit-learn text vectorizers;
- sklearn-crfsuite support;
- lightning support;
eli5.show_weights
andeli5.show_prediction
functions;eli5.explain_weights
andeli5.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.
- Candidate features in eli5.sklearn.InvertableHashingVectorizer are ordered by their frequency, first candidate is always positive.
- 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.
- 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.
- 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.
- '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.
Pre-release.