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Debug machine learning classifiers and explain their predictions

Project Description

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.
  • xgboost - show feature importances and explain predictions of XGBClassifier, XGBRegressor and xgboost.Booster.
  • LightGBM - show feature importances and explain predictions of LGBMClassifier and LGBMRegressor.
  • 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.

Changelog

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.

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