Eli5

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* readable :func:`eli5.explain_weights` for XGBoost models trained on
pandas.DataFrame;
* readable :func:`eli5.explain_weights` for LightGBM models trained on
pandas.DataFrame;
* fixed an issue with :func:`eli5.explain_prediction` for XGBoost
models trained on pandas.DataFrame when feature names contain dots;
* testing improvements.

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* Better pandas support in :func:`eli5.explain_prediction` for
xgboost, sklearn, LightGBM and lightning.

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* Better scikit-learn Pipeline support in :func:`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 :ref:`sklearn-pipelines` for more.
* Inverting of HashingVectorizer is now supported inside FeatureUnion
via :func:`eli5.sklearn.unhashing.invert_hashing_and_fit`.
See :ref:`sklearn-unhashing`.
* Fixed compatibility with Jupyter Notebook >= 5.0.0.
* Fixed :func:`eli5.explain_weights` for Lasso regression with a single
feature and no intercept.
* Fixed unhashing support in Python 2.x.
* Documentation and testing improvements.

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* LightGBM_ support: :func:`eli5.explain_prediction` and
:func:`eli5.explain_weights` are now supported for
``LGBMClassifier`` and ``LGBMRegressor``
(see :ref:`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.

.. _LightGBM: https://github.com/Microsoft/LightGBM

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* bug fix: eli5 should remain importable if xgboost is available, but
not installed correctly.

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* feature contribution calculation fixed
for :func:`eli5.xgboost.explain_prediction_xgboost`