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 and LGBMRegressor.
- 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.
- 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.
- 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> 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.
- Fixed eli5.explain_prediction for recent LightGBM versions;
- fixed Python 3 deprecation warning in formatters.html;
- testing improvements.
- 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.
- 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 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.
- 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.
- 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 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.
- 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 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.
- 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 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.
- 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.
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