sklearn.pipeline.FeatureUnion¶
- class sklearn.pipeline.FeatureUnion(transformer_list, n_jobs=1, 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 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, 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, 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)[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.