ITMO_FS.filters.univariate.gini_index

ITMO_FS.filters.univariate.gini_index(x, y)

Calculate Gini index for features. Bigger values mean more important features. This measure works best with discrete features due to being based on information theory.

Parameters:

• x (array-like, shape (n_samples, n_features)) – The training input samples.
• y (array-like, shape (n_samples,)) – The target values.

Returns:

array-like, shape (n_features,)

Return type:

feature scores

See also

http()

//lkm.fri.uni-lj.si/xaigor/slo/clanki/ijcai95z.pdf

Examples

>>> from ITMO_FS.filters.univariate import gini_index
>>> from sklearn.preprocessing import KBinsDiscretizer
>>> x = np.array([[3, 3, 3, 2, 2], [3, 3, 1, 2, 3], [1, 3, 5, 1, 1],
... [3, 1, 4, 3, 1], [3, 1, 2, 3, 1]])
>>> y = np.array([1, 3, 2, 1, 2])
>>> est = KBinsDiscretizer(n_bins=10, encode='ordinal')
>>> x = est.fit_transform(x)
>>> gini_index(x, y)
array([0.14      , 0.04      , 0.64      , 0.24      , 0.37333333])