sklearn.feature_selection.SelectFwe

class sklearn.feature_selection.SelectFwe(score_func=<function f_classif at 0x2fd4f50>, alpha=0.05)

Filter: Select the p-values corresponding to Family-wise error rate

Parameters :

score_func : callable

Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues).

alpha : float, optional

The highest uncorrected p-value for features to keep.

Attributes

scores_	array-like, shape=(n_features,)	Scores of features.
pvalues_	array-like, shape=(n_features,)	p-values of feature scores.

Methods

fit(X, y)	Evaluate the score function on samples X with outputs y.
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
get_support([indices])	Get a mask, or integer index, of the features selected
inverse_transform(X)	Reverse the transformation operation
set_params(**params)	Set the parameters of this estimator.
transform(X)	Reduce X to the selected features.

__init__(score_func=<function f_classif at 0x2fd4f50>, alpha=0.05)

fit(X, y)

Evaluate the score function on samples X with outputs y.

Records and selects features according to the p-values output by the score function.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters :

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns :

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters :

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns :

params : mapping of string to any

Parameter names mapped to their values.

get_support(indices=False)

Get a mask, or integer index, of the features selected

Parameters :

indices : boolean (default False)

If True, the return value will be an array of integers, rather than a boolean mask.

Returns :

support : array

An index that selects the retained features from a feature vector. If indices is False, this is a boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention. If indices is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.

inverse_transform(X)

Reverse the transformation operation

Parameters :

X : array of shape [n_samples, n_selected_features]

The input samples.

Returns :

X_r : array of shape [n_samples, n_original_features]

X with columns of zeros inserted where features would have been removed by transform.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :

transform(X)

Reduce X to the selected features.

Parameters :

X : array of shape [n_samples, n_features]

The input samples.

Returns :

X_r : array of shape [n_samples, n_selected_features]

The input samples with only the selected features.