Skip to main content

Debug machine learning classifiers and explain their predictions

Project description

PyPI Version Build Status Code Coverage Documentation

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. 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 and XGBRegressor.
  • 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 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.


0.6.1 (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

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)


Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for eli5, version 0.6.1
Filename, size File type Python version Upload date Hashes
Filename, size eli5-0.6.1-py2.py3-none-any.whl (84.8 kB) File type Wheel Python version 3.5 Upload date Hashes View
Filename, size eli5-0.6.1.tar.gz (172.5 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page