sklearn.pipeline.FeatureUnion

class sklearn.pipeline.FeatureUnion(transformer_list, n_jobs=1, transformer_weights=None)

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 :

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, optional :

Number of jobs to run in parallel (default 1).

transformer_weights: dict, optional :

Multiplicative weights for features per transformer. Keys are transformer names, values the weights.

Methods

fit(X[, y])	Fit all transformers using X.
fit_transform(X[, y])	Fit all transformers using X, transform the data and concatenate
get_feature_names()	Get feature names from all transformers.
get_params([deep])
set_params(**params)	Set the parameters of this estimator.
transform(X)	Transform X separately by each transformer, concatenate results.

__init__(transformer_list, n_jobs=1, transformer_weights=None)

fit(X, y=None)

Fit all transformers using X.

Parameters :

X : array-like or sparse matrix, shape (n_samples, n_features)

Input data, used to fit transformers.

fit_transform(X, y=None, **fit_params)

Fit all transformers using X, transform the data and concatenate results.

Parameters :

X : array-like or sparse matrix, shape (n_samples, n_features)

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.

get_feature_names()

Get feature names from all transformers.

Returns :

feature_names : list of strings

Names of the features produced by transform.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :

transform(X)

Transform X separately by each transformer, concatenate results.

Parameters :

X : array-like or sparse matrix, shape (n_samples, n_features)

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.