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)    # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
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]

Initialize self. See help(type(self)) for accurate signature.

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