sklearn.feature_selection.GenericUnivariateSelect¶

class sklearn.feature_selection.GenericUnivariateSelect(score_func=<function f_classif>, mode='percentile', param=1e-05)[source]¶

Univariate feature selector with configurable strategy.

Read more in the User Guide.

Parameters

score_funccallable

Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues). For modes ‘percentile’ or ‘kbest’ it can return a single array scores.

mode{‘percentile’, ‘k_best’, ‘fpr’, ‘fdr’, ‘fwe’}

Feature selection mode.

paramfloat or int depending on the feature selection mode

Parameter of the corresponding mode.

Attributes

scores_array-like, shape=(n_features,)

Scores of features.

pvalues_array-like, shape=(n_features,)

p-values of feature scores, None if score_func returned scores only.

See also

f_classif

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

mutual_info_classif

Mutual information for a discrete target.

chi2

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

f_regression

F-value between label/feature for regression tasks.

mutual_info_regression

Mutual information for a continuous target.

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.

SelectFwe

Select features based on family-wise error rate.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.feature_selection import GenericUnivariateSelect, chi2
>>> X, y = load_breast_cancer(return_X_y=True)
>>> X.shape
(569, 30)
>>> transformer = GenericUnivariateSelect(chi2, 'k_best', param=20)
>>> X_new = transformer.fit_transform(X, y)
>>> X_new.shape
(569, 20)

Methods

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

__init__(self, score_func=<function f_classif at 0x1a20fd2cb0>, mode='percentile', param=1e-05)[source]¶

Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y)[source]¶

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

Parameters

Xarray-like, shape = [n_samples, n_features]

The training input samples.

yarray-like, shape = [n_samples]

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

Returns

selfobject

fit_transform(self, X, y=None, **fit_params)[source]¶

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

Xnumpy array of shape [n_samples, n_features]

Training set.

ynumpy array of shape [n_samples]

Target values.

Returns

X_newnumpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(self, deep=True)[source]¶

Get parameters for this estimator.

Parameters

deepboolean, optional

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

Returns

paramsmapping of string to any

Parameter names mapped to their values.

get_support(self, indices=False)[source]¶

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

Parameters

indicesboolean (default False)

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

Returns

supportarray

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(self, X)[source]¶

Reverse the transformation operation

Parameters

Xarray of shape [n_samples, n_selected_features]

The input samples.

Returns

X_rarray of shape [n_samples, n_original_features]

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

set_params(self, **params)[source]¶

Set the parameters of this estimator.

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

Returns

self

transform(self, X)[source]¶

Reduce X to the selected features.

Parameters

Xarray of shape [n_samples, n_features]

The input samples.

Returns

X_rarray of shape [n_samples, n_selected_features]

The input samples with only the selected features.