OneToOneFeatureMixin (original) (raw)

class sklearn.base.OneToOneFeatureMixin[source]#

Provides get_feature_names_out for simple transformers.

This mixin assumes there’s a 1-to-1 correspondence between input features and output features, such as StandardScaler.

Examples

import numpy as np from sklearn.base import OneToOneFeatureMixin, BaseEstimator class MyEstimator(OneToOneFeatureMixin, BaseEstimator): ... def fit(self, X, y=None): ... self.n_features_in_ = X.shape[1] ... return self X = np.array([[1, 2], [3, 4]]) MyEstimator().fit(X).get_feature_names_out() array(['x0', 'x1'], dtype=object)

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

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

Same as input features.