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feature_selection.GenericUnivariateSelect()

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

Univariate feature selector with configurable strategy.

Read more in the User Guide.

Parameters:

score_func : callable

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.

param : float 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.

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=, mode='percentile', param=1e-05) [source]
fit(X, y) [source]

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) [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:

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) [source]

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) [source]

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) [source]

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) [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(X) [source]

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.
doc_scikit_learn
doc_scikit_learn
2017-01-15 04:22:13
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