sklearn.feature_selection.SelectFwe

class sklearn.feature_selection.SelectFwe(score_func=<function f_classif at 0x2aed2b04de60>, 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.

See also

f_classif

ANOVA F-value between labe/feature for classification tasks.

chi2

Chi-squared stats of non-negative features for classification tasks.

f_regression

F-value between label/feature for regression tasks.

SelectPercentile

Select features based on percentile of the highest scores.

SelectKBest

Select features based on the k highest scores.

SelectFpr

Select features based on a false positive rate test.

SelectFdr

Select features based on an estimated false discovery rate.

GenericUnivariateSelect

Univariate feature selector with configurable mode.

Methods

fit(X, y)	Run score function on (X, y) and get the appropriate features.
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 0x2aed2b04de60>, alpha=0.05)

fit(X, y)

Run score function on (X, y) and get the appropriate features.

Parameters:

X : array-like, shape = [n_samples, n_features]

The training input samples.

y : array-like, shape = [n_samples]

The target values (class labels in classification, real numbers in regression).

Returns:

self : object

Returns self.

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.