eli5 0.1

====
ELI5
====

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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.

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.

.. _lightning: https://github.com/scikit-learn-contrib/lightning
.. _scikit-learn: https://github.com/scikit-learn/scikit-learn
.. _sklearn-crfsuite: https://github.com/TeamHG-Memex/sklearn-crfsuite
.. _LIME: http://arxiv.org/abs/1602.04938

License is MIT.

Check `docs <http://eli5.readthedocs.io/>`_ for more.


Changelog
=========

0.1 (2016-11-24)
----------------

* HTML output;
* IPython integration;
* JSON output;
* visualization of scikit-learn text vectorizers;
* `sklearn-crfsuite <https://github.com/TeamHG-Memex/sklearn-crfsuite>`_
support;
* `lightning <https://github.com/scikit-learn-contrib/lightning>`_ support;
* :func:`eli5.show_weights` and :func:`eli5.show_prediction` functions;
* :func:`eli5.explain_weights` and :func:`eli5.explain_prediction`
functions;
* :ref:`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.