ClassNamePrefixFeaturesOutMixin#

class sklearn.base.ClassNamePrefixFeaturesOutMixin[source]#

Mixin class for transformers that generate their own names by prefixing.

This mixin is useful when the transformer needs to generate its own feature names out, such as PCA. For example, if PCA outputs 3 features, then the generated feature names out are: ["pca0", "pca1", "pca2"].

This mixin assumes that a _n_features_out attribute is defined when the transformer is fitted. _n_features_out is the number of output features that the transformer will return in transform of fit_transform.

Examples

>>> import numpy as np
>>> from sklearn.base import ClassNamePrefixFeaturesOutMixin
>>> class MyEstimator(ClassNamePrefixFeaturesOutMixin):
...     def fit(self, X, y=None):
...         self._n_features_out = X.shape[1]
...         return self
>>> X = np.array([[1, 2], [3, 4]])
>>> MyEstimator().fit(X).get_feature_names_out()
array(['myestimator0', 'myestimator1'], dtype=object)

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: ["class_name0", "class_name1", "class_name2"].

Parameters:

input_featuresarray-like of str or None, default=None: Only used to validate feature names with the names seen in fit.

Returns:

feature_names_outndarray of str objects: Transformed feature names.