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Showing content from https://scikit-learn.org/stable/modules/generated/sklearn.base.TransformerMixin.html below:

TransformerMixin — scikit-learn 1.7.0 documentation

class sklearn.base.TransformerMixin[source]#

Mixin class for all transformers in scikit-learn.

This mixin defines the following functionality:

• a fit_transform method that delegates to fit and transform;

• a set_output method to output X as a specific container type.

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

OneToOneFeatureMixin and ClassNamePrefixFeaturesOutMixin 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_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.

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

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.

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