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Debug machine learning classifiers and explain their predictions

Project description

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

explain_prediction for text data

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. It also allows to debug scikit-learn pipelines which contain HashingVectorizer, by undoing hashing.

  • xgboost - show feature importances and explain predictions of XGBClassifier and XGBRegressor.

  • lightning - explain weights and predictions of lightning classifiers and regressors.

  • sklearn-crfsuite. ELI5 allows to check weights of sklearn_crfsuite.CRF models.

ELI5 also provides TextExplainer which 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.

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, or JSON version which allows to implement custom rendering and formatting on a client.

License is MIT.

Check docs for more.


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 <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 ( 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)


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