Hellinger Dinstance Criterion

Hellinger Distance criterion for sklearn Random Forest and Decision Tree classifiers

I'm working on adding this to scikit-learn-contrib/imbalanced-learn PR #437

Build

You will need a cython "header" file (.pxd) from sklearn.

In case you've installed sklearn from source code package, you've already got it.

In case you've installed sklearn using pip install sklearn then you need to get it.

• Got to https://github.com/scikit-learn/scikit-learn/tree/master/sklearn/tree
• Download _criterion.pxd and place it your local sklearn installation folder
  • Linux usually at /usr/local/lib/python3.5/dist-packages/sklearn/tree
  • Windows usually at C:\Users\[user name]\AppData\Local\Continuum\Anaconda3\Lib\site-packages\sklearn\tree\

python setup.py build_ext --inplace

Example

>>> import numpy as np
>>> from hellinger_distance_criterion import HellingerDistanceCriterion
>>> from sklearn.ensemble import RandomForestClassifier
>>>
>>> hdc = HellingerDistanceCriterion(1, np.array([2],dtype='int64'))
>>> clf = RandomForestClassifier(criterion=hdc, max_depth=4, n_estimators=100)
>>> clf.fit(X_train, y_train)
>>> print('hellinger distance score: ', clf.score(X_test, y_test))