`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
>>> X = np.array([[1, 2, 3, 3, 1],[2, 2, 3, 3, 2], [1, 3, 3, 1, 3],[3, 1, 3, 1, 4],[4, 4, 3, 1, 5]], dtype = np.integer)
>>> y = np.array([1, 2, 3, 4, 5], dtype=np.integer)
>>> tracer = TraceRatioFisher(3)
>>> tracer.fit_transform(X, y)
array([[1, 1, 2],
       [2, 2, 2],
       [3, 1, 3],
       [4, 3, 1],
       [5, 4, 4]])

__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¶