amaiya / eli5-tf

A library for debugging/inspecting machine learning classifiers and explaining their predictions slightly modified for tf.keras.

ELI5

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, LGBMRegressor and lightgbm.Booster.
• 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.

Note

This is the same project as https://github.com/TeamHG-Memex/eli5/, but due to temporary github access issues, 0.11 release is prepared in https://github.com/eli5-org/eli5 (this repo).

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