TransformerMixin (original) (raw)

class sklearn.base.TransformerMixin[source]#

Mixin class for all transformers in scikit-learn.

This mixin defines the following functionality:

If get_feature_names_out is defined, then BaseEstimator will automatically wrap transform and fit_transform to follow the set_outputAPI. See the Developer API for set_output for details.

OneToOneFeatureMixin andClassNamePrefixFeaturesOutMixin are helpful mixins for defining get_feature_names_out.

Examples

import numpy as np from sklearn.base import BaseEstimator, TransformerMixin class MyTransformer(TransformerMixin, BaseEstimator): ... def init(self, *, param=1): ... self.param = param ... def fit(self, X, y=None): ... return self ... def transform(self, X): ... return np.full(shape=len(X), fill_value=self.param) transformer = MyTransformer() X = [[1, 2], [2, 3], [3, 4]] transformer.fit_transform(X) array([1, 1, 1])

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_paramsand 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.

set_output(*, transform=None)[source]#

Set output container.

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

Parameters:

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

Configure output of transform and fit_transform.

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

Returns:

selfestimator instance

Estimator instance.