OptionalPassthrough

OptionalPassthrough

class OptionalPassthrough(transformer, passthrough=False)[source]

Wrap an existing transformer to tune whether to include it in a pipeline.

Allows tuning the implicit hyperparameter whether or not to use a particular transformer inside a pipeline (e.g. TransformedTargetForecaster) or not. This is achieved by the hyperparameter passthrough which can be added to a tuning grid then (see example).

Parameters:

transformerEstimator

scikit-learn-like or sktime-like transformer to fit and apply to series. this is a “blueprint” transformer, state does not change when fit is called

passthroughbool, default=False

Whether to apply the given transformer or to just

passthrough the data (identity transformation). If, True the transformer is not applied and the OptionalPassthrough uses the identity transformation.

Attributes:

transformer_: transformer,

this clone is fitted when fit is called and provides transform and inverse if passthrough = False, a clone of transformer``passed if passthrough = True, the identity transformer ``Id

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.transformations.compose import OptionalPassthrough
>>> from sktime.transformations.detrend import Deseasonalizer
>>> from sktime.transformations.adapt import TabularToSeriesAdaptor
>>> from sktime.forecasting.compose import TransformedTargetForecaster
>>> from sktime.forecasting.model_selection import ForecastingGridSearchCV
>>> from sktime.split import SlidingWindowSplitter
>>> from sklearn.preprocessing import StandardScaler
>>> # create pipeline
>>> pipe = TransformedTargetForecaster(steps=[
...     ("deseasonalizer", OptionalPassthrough(Deseasonalizer())),
...     ("scaler", OptionalPassthrough(TabularToSeriesAdaptor(StandardScaler()))),
...     ("forecaster", NaiveForecaster())])
>>> # putting it all together in a grid search
>>> cv = SlidingWindowSplitter(
...     initial_window=60,
...     window_length=24,
...     start_with_window=True,
...     step_length=48)
>>> param_grid = {
...     "deseasonalizer__passthrough" : [True, False],
...     "scaler__transformer__transformer__with_mean": [True, False],
...     "scaler__passthrough" : [True, False],
...     "forecaster__strategy": ["drift", "mean", "last"]}
>>> gscv = ForecastingGridSearchCV(
...     forecaster=pipe,
...     param_grid=param_grid,
...     cv=cv,
...     n_jobs=-1)
>>> gscv_fitted = gscv.fit(load_airline())

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 a dict of parameters values for this object.
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 composed of other BaseObjects.
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(**params)	Set the parameters of this object.
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.