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 a joblib.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_
    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() 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 the FeatureUnion.  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 in tranformer_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.     
 
 Examples using sklearn.pipeline.FeatureUnion
 
  Concatenating multiple feature extraction methods