`ITMO_FS.filters.multivariate`.TraceRatioFisher¶

class ITMO_FS.filters.multivariate.TraceRatioFisher(n_selected_features)¶

Creates TraceRatio(similarity based) feature selection filter performed in supervised way, i.e fisher version

Parameters:	n_selected_features (int) – Amount of features to filter

Notes

For more details see this paper.

Examples

>>> from ITMO_FS.filters.multivariate import TraceRatioFisher
>>> from sklearn.datasets import make_classification
>>> x, y = make_classification(1000, 100, n_informative = 10,n_redundant = 30, n_repeated = 10, shuffle = False)
>>> tracer = TraceRatioFisher(10)
>>> print(tracer.fit_transform(x, y))

__init__(n_selected_features)¶: Initialize self. See help(type(self)) for accurate signature.

fit(X, y, feature_names=None)¶

Fits filter

Parameters:	X (numpy array, shape (n_samples, n_features)) – The training input samples y (numpy array, shape (n_samples, )) – The target values feature_names (list of strings, optional) – In case you want to define feature names
Returns:
Return type:	None

Examples

fit_transform(X, y, feature_names=None)¶

Fits the filter and transforms given dataset X.

Parameters:	X (array-like, shape (n_features, n_samples)) – The training input samples. y (array-like, shape (n_samples, )) – The target values. feature_names (list of strings, optional) – In case you want to define feature names
Returns:
Return type:	X dataset sliced with features selected by the filter

transform(X)¶

Transform given data by slicing it with selected features.

Parameters:	X (array-like, shape (n_samples, n_features)) – The training input samples.
Returns:
Return type:	Transformed 2D numpy array

ITMO_FS.filters.multivariate.TraceRatioFisher¶

`ITMO_FS.filters.multivariate`.TraceRatioFisher¶