ITMO_FS.wrappers.deterministic.qpfs_wrapper

ITMO_FS.wrappers.deterministic.qpfs_wrapper(X, y, alpha, r=None, sigma=None, solv='quadprog', fn=<function pearson_corr>)

Performs Quadratic Programming Feature Selection algorithm. Note that this realization requires labels to start from 1 and be numberical. This is function for wrapper based on qpfs so alpha parameter must be specified, in case you don’t know alpha parameter it is suggested to use qpfs_filter

Parameters:
  • X (array-like, shape (n_samples,n_features)) – The input samples.
  • y (array-like, shape (n_samples)) – The classes for the samples.
  • alpha (double value) – That represents balance between relevance and redundancy of features.
  • r (int, optional) – The number of samples to be used in Nystrom optimization.
  • sigma (double, optional) – The threshold for eigenvalues to be used in solving QP optimization.
  • solv (string, optional) – The name of qp solver according to qpsolvers(https://pypi.org/project/qpsolvers/) naming. Note quadprog is used by default.
  • fn (function(array, array), optional) – The function to count correlation, for example pearson correlation or mutual information. Note pearson_corr from ITMO_FS measures is used by default.
Returns:

array-like, shape (n_features)
Return type:

the ranks of features in dataset, with rank increase, feature relevance increases and redundancy decreases.

See also

http()
//www.jmlr.org/papers/volume11/rodriguez-lujan10a/rodriguez-lujan10a.pdf

Examples

>>> 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])
>>> alpha = 0.5
>>> ranks = qpfs_wrapper(x, y, alpha)
>>> print(ranks)