ITMO_FS.filters.multivariate.IWFS

ITMO_FS.filters.multivariate.IWFS(selected_features, free_features, X, y)

Interaction Weight base feature scoring criteria. IWFS is good at identifyng Given set of already selected features and set of remaining features on dataset X with labels y selects next feature.

Parameters:

• selected_features (list of ints,) – already selected features
• free_features (list of ints) – free features
• X (array-like, shape (n_samples, n_features)) – The training input samples.
• y (array-like, shape (n_samples, )) – The target values.

Notes

For more details see this paper.

Examples

>>> from ITMO_FS.filters.multivariate import IWFS
>>> from sklearn.datasets import make_classification
>>> from sklearn.preprocessing import KBinsDiscretizer
>>> import numpy as np
>>> dataset = make_classification(n_samples=100, n_features=20, n_informative=4, n_redundant=0, shuffle=False)
>>> est = KBinsDiscretizer(n_bins=10, encode='ordinal')
>>> data, target = np.array(dataset[0]), np.array(dataset[1])
>>> est.fit(data)
>>> data = est.transform(data)
>>> selected_features = [1, 2]
>>> other_features = [i for i in range(0, data.shape[1]) if i not in selected_features]
>>> print(IWFS(np.array(selected_features), np.array(other_features), data, target))