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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 of random forests. 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.
  • lightning - explain weights and predictions of lightning classifiers and regressors.
  • sklearn-crfsuite. ELI5 allows to check weights of sklearn_crfsuite.CRF models.
  • xgboost - show feature importances using the same interface.

ELI5 also provides an alternative implementation of LIME algorithm, which allows to explain predictions of any black-box classifier. 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.

Changelog

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.

Release History

Release History

0.2

This version

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0.1.1

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0.1

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0.0.6

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0.0.5

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0.0.4

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0.0.3

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0.0.2

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0.0.1

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Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
eli5-0.2-py2.py3-none-any.whl (54.7 kB) Copy SHA256 Checksum SHA256 3.5 Wheel Dec 2, 2016
eli5-0.2.tar.gz (112.4 kB) Copy SHA256 Checksum SHA256 Source Dec 2, 2016

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