ITMO_FS.filters.univariate.f_ratio_measure

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

Calculate Fisher score for features. Bigger values mean more important features.

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

https()

//papers.nips.cc/paper/2909-laplacian-score-for-feature-selection.pdf

Examples

>>> from ITMO_FS.filters.univariate import f_ratio_measure
>>> import numpy as np
>>> 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])
>>> f_ratio_measure(x, y)
array([0.6 , 0.2 , 1.  , 0.12, 5.4 ])