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class sklearn.pipeline.FeatureUnion(transformer_list, n_jobs=1, transformer_weights=None)
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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. -
__init__(transformer_list, n_jobs=1, transformer_weights=None)
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fit(X, y=None)
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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
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fit_transform(X, y=None, **fit_params)
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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.
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get_feature_names()
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Get feature names from all transformers.
Returns: feature_names : list of strings
Names of the features produced by transform.
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get_params(deep=True)
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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.
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set_params(**kwargs)
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Set the parameters of this estimator.
Valid parameter keys can be listed with
get_params()
.Returns: self :
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transform(X)
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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.
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pipeline.FeatureUnion()
Examples using
2017-01-15 04:24:55
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