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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.

Schnellstart

python
from sktime.transformations.compose import FeatureUnion

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

Tags

Fähigkeiten

  • Multivariat
  • Reihen ungleicher Länge
  • Inverse Transformation: Nicht unterstützt
  • Fehlende Werte: Nicht unterstützt
  • Entfernt fehlende Werte: Nicht unterstützt
  • Gleicht Reihenlängen an: Nicht unterstützt

Eigenschaften

Eingabetypscitype:transform-input
Series
Ausgabetypscitype:transform-output
Series
Label-Typscitype:transform-labels
None
Fit ist leerfit_is_empty
Nein
Behält den Zeitindextransform-returns-same-time-index
Nein
Benötigt Xrequires_X
Ja
Benötigt yrequires_y
Nein
X und y brauchen denselben IndexX-y-must-have-same-index
Nein

Parameter(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