sklearn.pipeline.FeatureUnion

class sklearn.pipeline.FeatureUnion(transformer_list, *, n_jobs=None, transformer_weights=None, verbose=False)

Concatenates results of multiple transformer objects.

This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine several feature extraction mechanisms into a single transformer.

Parameters of the transformers may be set using its name and the parameter name separated by a ‘__’. A transformer may be replaced entirely by setting the parameter with its name to another transformer, removed by setting to ‘drop’ or disabled by setting to ‘passthrough’ (features are passed without transformation).

Read more in the User Guide.

New in version 0.13.

Parameters:

transformer_listlist of (str, transformer) tuples

List of transformer objects to be applied to the data. The first half of each tuple is the name of the transformer. The transformer can be ‘drop’ for it to be ignored or can be ‘passthrough’ for features to be passed unchanged.

New in version 1.1: Added the option "passthrough".

Changed in version 0.22: Deprecated None as a transformer in favor of ‘drop’.

n_jobsint, default=None

Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

Changed in version v0.20: n_jobs default changed from 1 to None

transformer_weightsdict, default=None

Multiplicative weights for features per transformer. Keys are transformer names, values the weights. Raises ValueError if key not present in transformer_list.

verbosebool, default=False

If True, the time elapsed while fitting each transformer will be printed as it is completed.

Attributes:

named_transformersBunch

Dictionary-like object, with the following attributes. Read-only attribute to access any transformer parameter by user given name. Keys are transformer names and values are transformer parameters.

New in version 1.2.

n_features_in_int

Number of features seen during fit.

See also

make_union

Convenience function for simplified feature union construction.

Examples

>>> from sklearn.pipeline import FeatureUnion
>>> from sklearn.decomposition import PCA, TruncatedSVD
>>> union = FeatureUnion([("pca", PCA(n_components=1)),
...                       ("svd", TruncatedSVD(n_components=2))])
>>> X = [[0., 1., 3], [2., 2., 5]]
>>> union.fit_transform(X)
array([[ 1.5       ,  3.0...,  0.8...],
       [-1.5       ,  5.7..., -0.4...]])

Methods

fit(X[, y])	Fit all transformers using X.
fit_transform(X[, y])	Fit all transformers, transform the data and concatenate results.
get_feature_names_out([input_features])	Get output feature names for transformation.
get_params([deep])	Get parameters for this estimator.
set_output(*[, transform])	Set the output container when "transform" and "fit_transform" are called.
set_params(**kwargs)	Set the parameters of this estimator.
transform(X)	Transform X separately by each transformer, concatenate results.

fit(X, y=None, **fit_params)

Fit all transformers using X.

Parameters:

Xiterable or array-like, depending on transformers

Input data, used to fit transformers.

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

Targets for supervised learning.

**fit_paramsdict, default=None

Parameters to pass to the fit method of the estimator.

Returns:

selfobject

FeatureUnion class instance.

fit_transform(X, y=None, **fit_params)

Fit all transformers, transform the data and concatenate results.

Parameters:

Xiterable or array-like, depending on transformers

Input data to be transformed.

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

Targets for supervised learning.

**fit_paramsdict, default=None

Parameters to pass to the fit method of the estimator.

Returns:

X_tarray-like or sparse matrix of shape (n_samples, sum_n_components)

The hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.

get_feature_names_out(input_features=None)

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

Returns:

feature_names_outndarray of str objects

Transformed feature names.

get_params(deep=True)

Get parameters for this estimator.

Returns the parameters given in the constructor as well as the estimators contained within the transformer_list of the FeatureUnion.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsmapping of string to any

Parameter names mapped to their values.

property n_features_in_

Number of features seen during fit.

set_output(*, transform=None)

Set the output container when "transform" and "fit_transform" are called.

set_output will set the output of all estimators in transformer_list.

Parameters:

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

Configure output of transform and fit_transform.

• "default": Default output format of a transformer

• "pandas": DataFrame output

• None: Transform configuration is unchanged

Returns:

selfestimator instance

Estimator instance.

set_params(**kwargs)

Set the parameters of this estimator.

Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in transformer_list.

Parameters:

**kwargsdict

Parameters of this estimator or parameters of estimators contained in transform_list. Parameters of the transformers may be set using its name and the parameter name separated by a ‘__’.

Returns:

selfobject

FeatureUnion class instance.

transform(X)

Transform X separately by each transformer, concatenate results.

Parameters:

Xiterable or array-like, depending on transformers

Input data to be transformed.

Returns:

X_tarray-like or sparse matrix of shape (n_samples, sum_n_components)

The hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.

Examples using sklearn.pipeline.FeatureUnion

Time-related feature engineering

Time-related feature engineering

Concatenating multiple feature extraction methods

Concatenating multiple feature extraction methods