Transformer

FeatureUnion

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 transformations 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’ or None.

Quickstart

python

from sktime.transformations.compose import FeatureUnion

estimator = FeatureUnion(transformer_list, n_jobs=None, transformer_weights=None, flatten_transform_index=True)

Parameters(4)

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.
n_jobsint or None, optional (default=None): Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.
transformer_weightsdict, optional: Multiplicative weights for features per transformer. Keys are transformer names, values the weights.
flatten_transform_indexbool, optional (default=True): if True, columns of return DataFrame are flat, by “transformer__variablename” if False, columns are MultiIndex (transformer, variablename) has no effect if return mtype is one without column names