sklearn.pipeline.FeatureUnion

class sklearn.pipeline.FeatureUnion(transformer_list, n_jobs=None, transformer_weights=None) [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’ or None.

Read more in the User Guide.

Parameters:

transformer_list : list 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. n_jobs : int or None, optional (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. transformer_weights : dict, optional Multiplicative weights for features per transformer. Keys are transformer names, values the weights.

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()	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) [source]

fit(X, y=None) [source]

Fit all transformers using X.

Parameters:	X : iterable or array-like, depending on transformers Input data, used to fit transformers. y : array-like, shape (n_samples, …), optional Targets for supervised learning.
Returns:	self : FeatureUnion This estimator

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

Fit all transformers, transform the data and concatenate results.

Parameters:	X : iterable or array-like, depending on transformers Input data to be transformed. y : array-like, shape (n_samples, …), optional Targets for supervised learning.
Returns:	X_t : array-like or sparse matrix, 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_names : list of strings Names of the features produced by transform.

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping 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:	X : iterable or array-like, depending on transformers Input data to be transformed.
Returns:	X_t : array-like or sparse matrix, shape (n_samples, sum_n_components) hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.

Examples using sklearn.pipeline.FeatureUnion