FeatureUnion#

class sklearn.pipeline.FeatureUnion(transformer_list, *, n_jobs=None, transformer_weights=None, verbose=False, verbose_feature_names_out=True)[source]#

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).

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...]])
>>> # An estimator's parameter can be set using '__' syntax
>>> union.set_params(svd__n_components=1).fit_transform(X)
array([[-1.5       ,  3.0...],
       [ 1.5       ,  5.7...]])

For a more detailed example of usage, see Concatenating multiple feature extraction methods.

property feature_names_in_#: Names of features seen during fit.

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

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

If enable_metadata_routing=False (default): Parameters directly passed to the fit methods of the sub-transformers.
If enable_metadata_routing=True: Parameters safely routed to the fit methods of the sub-transformers. See Metadata Routing User Guide for more details.

Changed in version 1.5: **fit_params can be routed via metadata routing API.

Returns:

selfobject: FeatureUnion class instance.

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

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.

**paramsdict, default=None

If enable_metadata_routing=False (default): Parameters directly passed to the fit methods of the sub-transformers.
If enable_metadata_routing=True: Parameters safely routed to the fit methods of the sub-transformers. See Metadata Routing User Guide for more details.

Changed in version 1.5: **params can now be routed via metadata routing API.

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)[source]#

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_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Added in version 1.5.

Returns:

routingMetadataRouter: A MetadataRouter encapsulating routing information.

get_params(deep=True)[source]#

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)[source]#

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”, “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

Returns:

selfestimator instance: Estimator instance.

set_params(**kwargs)[source]#

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, **params)[source]#

Transform X separately by each transformer, concatenate results.

Parameters:

Xiterable or array-like, depending on transformers: Input data to be transformed.
**paramsdict, default=None: Parameters routed to the transform method of the sub-transformers via the metadata routing API. See Metadata Routing User Guide for more details.

Added in version 1.5.

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.

Gallery examples#

Time-related feature engineering

Concatenating multiple feature extraction methods