SelectorMixin

class sklearn.feature_selection.SelectorMixin

Transformer mixin that performs feature selection given a support mask

This mixin provides a feature selector implementation with transform and inverse_transform functionality given an implementation of _get_support_mask.

Examples

>>> import numpy as np
>>> from sklearn.datasets import load_iris
>>> from sklearn.base import BaseEstimator
>>> from sklearn.feature_selection import SelectorMixin
>>> class FeatureSelector(SelectorMixin, BaseEstimator):
...    def fit(self, X, y=None):
...        self.n_features_in_ = X.shape[1]
...        return self
...    def _get_support_mask(self):
...        mask = np.zeros(self.n_features_in_, dtype=bool)
...        mask[:2] = True  # select the first two features
...        return mask
>>> X, y = load_iris(return_X_y=True)
>>> FeatureSelector().fit_transform(X, y).shape
(150, 2)

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:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)

Mask feature names according to selected features.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

• If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].

• If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:

feature_names_outndarray of str objects

Transformed feature names.

get_support(indices=False)

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

Parameters:

indicesbool, 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(X)

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_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

• "default": Default output format of a transformer

• "pandas": DataFrame output

• "polars": Polars output

• None: Transform configuration is unchanged

Added in version 1.4: "polars" option was added.

Returns:

selfestimator instance

Estimator instance.

transform(X)

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.