sktime.series_as_features.compose

class sktime.series_as_features.compose.FeatureUnion(transformer_list, n_jobs=None, transformer_weights=None, preserve_dataframe=True)[source]

Bases: sklearn.pipeline.FeatureUnion, sktime.transformations.base._PanelToPanelTransformer

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. :param transformer_list: List of transformer objects to be applied to the data. The first

half of each tuple is the name of the transformer.

Parameters
  • n_jobs (int 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_weights (dict, optional) – Multiplicative weights for features per transformer. Keys are transformer names, values the weights.

fit(X, y=None, **fit_params)[source]

Fit all transformers using X.

Parameters
  • X (iterable or array-like, depending on transformers) – Input data, used to fit transformers.

  • y (array-like of shape (n_samples, n_outputs), default=None) – Targets for supervised learning.

Returns

self – This estimator

Return type

FeatureUnion

fit_transform(X, y=None, **fit_params)[source]

Fit all transformations, transform the data and concatenate results. :param X: Input data to be transformed. :type X: pandas DataFrame :param y: Targets for supervised learning. :type y: pandas Series, shape (n_samples, …), optional

Returns

Xt – hstack of results of transformations. sum_n_components is the sum of n_components (output dimension) over transformations.

Return type

pandas DataFrame

steps: List[Any][source]
transform(X)[source]

Transform X separately by each transformer, concatenate results. :param X: Input data to be transformed. :type X: pandas DataFrame

Returns

Xt – hstack of results of transformations. sum_n_components is the sum of n_components (output dimension) over transformations.

Return type

pandas DataFrame