Metadata-Version: 1.1
Name: eli5
Version: 0.0.5
Summary: Debug machine learning classifiers and explain their predictions
Home-page: https://github.com/TeamHG-Memex/eli5
Author: Mikhail Korobov
Author-email: kmike84@gmail.com
License: MIT license
Description: ====
        ELI5
        ====
        
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           :alt: PyPI Version
        
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           :target: http://travis-ci.org/TeamHG-Memex/eli5
           :alt: Build Status
        
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           :target: http://codecov.io/github/TeamHG-Memex/eli5?branch=master
           :alt: Code Coverage
        
        .. image:: https://readthedocs.org/projects/eli5/badge/?version=latest
           :target: http://eli5.readthedocs.org/en/latest/?badge=latest
           :alt: Documentation
        
        
        ELI5 is a Python package which helps to debug machine learning
        classifiers and explain their predictions.
        
        Currently it allows to:
        
        * explain weights and predictions of scikit-learn linear classifiers
          and regressors;
        * explain weights of scikit-learn decision trees and tree-based ensemble
          classifiers (via feature importances);
        * debug scikit-learn pipelines which contain HashingVectorizer,
          by undoing hashing;
        * explain predictions of any black-box classifier using LIME
          ( http://arxiv.org/abs/1602.04938 ) algorithm.
        
        TODO:
        
        * IPython and HTML support
        * https://github.com/TeamHG-Memex/sklearn-crfsuite
          and https://github.com/tpeng/python-crfsuite
        * https://github.com/scikit-learn-contrib/polylearn
        * https://github.com/scikit-learn-contrib/lightning
        * fasttext (?)
        * xgboost (?)
        * eli5.lime improvements
        * image input
        * built-in support for non-text data in eli5.lime
        * tensorflow, theano, lasagne, keras
        * Naive Bayes from scikit-learn
          (see https://github.com/scikit-learn/scikit-learn/issues/2237)
        * Reinforcement Learning support
        * explain predictions of decision trees and treee-based ensembles
        
        License is MIT.
        
        Check `docs <http://eli5.readthedocs.org/>`_ for more (sorry, also TODO).
        
        
        Changelog
        =========
        
        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.
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
