sklearn.pipeline
.FeatureUnion¶

class
sklearn.pipeline.
FeatureUnion
(transformer_list, *, n_jobs=None, transformer_weights=None, verbose=False)[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, or removed by setting to ‘drop’.
Read more in the User Guide.
New in version 0.13.
 Parameters
 transformer_listlist of (string, transformer) tuples
List of transformer objects to be applied to the data. The first half of each tuple is the name of the transformer.
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 ajoblib.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.
 verbosebool, default=False
If True, the time elapsed while fitting each transformer will be printed as it is completed.
 Attributes
 n_features_in_
See also
sklearn.pipeline.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 from all transformers.
get_params
([deep])Get parameters for this estimator.
set_params
(**kwargs)Set the parameters of this estimator.
transform
(X)Transform X separately by each transformer, concatenate results.

__init__
(transformer_list, *, n_jobs=None, transformer_weights=None, verbose=False)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(X, y=None, **fit_params)[source]¶ Fit all transformers using X.
 Parameters
 Xiterable or arraylike, depending on transformers
Input data, used to fit transformers.
 yarraylike of shape (n_samples, n_outputs), default=None
Targets for supervised learning.
 Returns
 selfFeatureUnion
This estimator

fit_transform
(X, y=None, **fit_params)[source]¶ Fit all transformers, transform the data and concatenate results.
 Parameters
 Xiterable or arraylike, depending on transformers
Input data to be transformed.
 yarraylike of shape (n_samples, n_outputs), default=None
Targets for supervised learning.
 Returns
 X_tarraylike or sparse matrix of shape (n_samples, sum_n_components)
hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.

get_feature_names
()[source]¶ Get feature names from all transformers.
 Returns
 feature_nameslist of strings
Names of the features produced by transform.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 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.

set_params
(**kwargs)[source]¶ Set the parameters of this estimator.
Valid parameter keys can be listed with
get_params()
. Returns
 self

transform
(X)[source]¶ Transform X separately by each transformer, concatenate results.
 Parameters
 Xiterable or arraylike, depending on transformers
Input data to be transformed.
 Returns
 X_tarraylike or sparse matrix of shape (n_samples, sum_n_components)
hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.