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. The tranformer can be ‘drop’ for it to be ignored.
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. 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
- n_features_in_int
Number of features seen during fit. Only defined if the underlying first transformer in
transformer_list
exposes such an attribute when fit.New in version 0.24.
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 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.
- 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.
- Returns
- selfFeatureUnion
This estimator
- fit_transform(X, y=None, **fit_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.
- Returns
- X_tarray-like 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.
Returns the parameters given in the constructor as well as the estimators contained within the
transformer_list
of theFeatureUnion
.- 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()
. Note that you can directly set the parameters of the estimators contained intranformer_list
.- Returns
- self
- transform(X)[source]¶
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)
hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.