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 of shape (n_features,)

Scores of features.

pvalues_array-like of 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 0x7ffa31b6a8b0>, 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 of shape (n_samples, n_features)

The training input samples.

yarray-like of 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.

**fit_paramsdict

Additional fit parameters.

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

deepbool, default=True

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.

Parameters

**paramsdict

Estimator parameters.

Returns

selfobject

Estimator instance.

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.