dtoolkit.pipeline.FeatureUnion#

class dtoolkit.pipeline.FeatureUnion(transformer_list, *, n_jobs=None, transformer_weights=None, verbose=False)[source]#

Concatenates results of multiple transformer objects.

See also

make_union: Convenience function for simplified feature union construction.

Notes

Different to sklearn.pipeline.FeatureUnion. This would let DataFrame in and DataFrame out.

Examples

>>> from dtoolkit.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...]])

Attributes:

feature_names_in_: Names of features seen during fit.
n_features_in_: Number of features seen during fit.
named_transformers

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_out`([input_features])	Get output feature names for transformation.
`get_metadata_routing`()	Raise NotImplementedError.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(X)	Undo transform to `X`.
`set_output`(*[, transform])	Set the output container when "transform" and "fit_transform" are called.
`set_params`(**kwargs)	Set the parameters of this estimator.
`transform`(X)	Transform X separately by each transformer, concatenate results.