MultiplexTransformer

class MultiplexTransformer(transformers: list, selected_transformer=None)[source]

Facilitate an AutoML based selection of the best transformer.

When used in combination with either TransformedTargetForecaster or ForecastingPipeline in combination with ForecastingGridSearchCV MultiplexTransformer provides a framework for transformer selection. Through selection of the appropriate pipeline (ie TransformedTargetForecaster vs ForecastingPipeline) the transformers in MultiplexTransformer will either be applied to exogenous data, or to the target data.

MultiplexTransformer delegates all transforming tasks (ie, calls to fit, transform, inverse_transform, and update) to a copy of the transformer in transformers whose name matches selected_transformer. All other transformers in transformers will be ignored.

Parameters:

transformerslist of sktime transformers, or

list of tuples (str, estimator) of named sktime transformers MultiplexTransformer can switch (“multiplex”) between these transformers. Note - all the transformers passed in “transformers” should be thought of as blueprints. Calling transformation functions on MultiplexTransformer will not change their state at all. - Rather a copy of each is created and this is what is updated.

selected_transformer: str or None, optional, Default=None.

If str, must be one of the transformer names.: If passed in transformers were unnamed then selected_transformer must coincide with auto-generated name strings. To inspect auto-generated name strings, call get_params.
If None, selected_transformer defaults to the name of the first transformer: in transformers.
selected_transformer represents the name of the transformer MultiplexTransformer: should behave as (ie delegate all relevant transformation functionality to)

Attributes:

transformer_sktime transformer: clone of the transformer named by selected_transformer to which all the transformation functionality is delegated to.
_transformerslist of (name, est) tuples, where est are direct references to: Forecasters turned into name/est tuples.

Examples

>>> from sktime.datasets import load_shampoo_sales
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.transformations.compose import MultiplexTransformer
>>> from sktime.transformations.impute import Imputer
>>> from sktime.forecasting.compose import TransformedTargetForecaster
>>> from sktime.forecasting.model_selection import ForecastingGridSearchCV
>>> from sktime.split import ExpandingWindowSplitter
>>> # create MultiplexTransformer:
>>> multiplexer = MultiplexTransformer(transformers=[
...     ("impute_mean", Imputer(method="mean", missing_values = -1)),
...     ("impute_near", Imputer(method="nearest", missing_values = -1)),
...     ("impute_rand", Imputer(method="random", missing_values = -1))])
>>> cv = ExpandingWindowSplitter(
...     initial_window=24,
...     step_length=12,
...     fh=[1,2,3])
>>> pipe = TransformedTargetForecaster(steps = [
...     ("multiplex", multiplexer),
...     ("forecaster", NaiveForecaster())
...     ])
>>> gscv = ForecastingGridSearchCV(
...     cv=cv,
...     param_grid={"multiplex__selected_transformer":
...     ["impute_mean", "impute_near", "impute_rand"]},
...     forecaster=pipe,
...     )
>>> y = load_shampoo_sales()
>>> # randomly make some of the values nans:
>>> y.loc[y.sample(frac=0.1).index] = -1
>>> gscv = gscv.fit(y)

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X[, y])	Fit transformer to X, optionally to y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get parameters of estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`is_composite`()	Check if the object is composite.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**kwargs)	Set the parameters of estimator.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.