FitInTransform

class FitInTransform(transformer, skip_inverse_transform=True)[source]

Transformer wrapper to delay fit to the transform phase.

In panel settings, e.g., time series classification, it can be preferable (or, necessary) to fit and transform on the test set, e.g., interpolate within the same series that interpolation parameters are being fitted on. FitInTransform can be used to wrap any transformer to ensure that fit and transform happen always on the same series, by delaying the fit to the transform batch.

Warning: The use of FitInTransform will typically not be useful, or can constitute a mistake (data leakage) when naively used in a forecasting setting.

Parameters:

transformerEstimator: scikit-learn-like or sktime-like transformer to fit and apply to series.
skip_inverse_transformbool: The FitInTransform will skip inverse_transform by default, of the param skip_inverse_transform=False, then the inverse_transform is calculated by means of transformer.fit(X=X, y=y).inverse_transform(X=X, y=y) where transformer is the inner transformer. So the inner transformer is fitted on the inverse_transform data. This is required to have a non- state changing transform() method of FitInTransform.

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> from sktime.datasets import load_longley
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.forecasting.base import ForecastingHorizon
>>> from sktime.forecasting.compose import ForecastingPipeline
>>> from sktime.split import temporal_train_test_split
>>> from sktime.transformations.compose import FitInTransform
>>> from sktime.transformations.impute import Imputer
>>> y, X = load_longley()
>>> y_train, y_test, X_train, X_test = temporal_train_test_split(y, X)
>>> fh = ForecastingHorizon(y_test.index, is_relative=False)
>>> # we want to fit the Imputer only on the predict (=transform) data.
>>> # note that NaiveForecaster can't use X data, this is just a show case.
>>> pipe = ForecastingPipeline(
...     steps=[
...         ("imputer", FitInTransform(Imputer(method="mean"))),
...         ("forecaster", NaiveForecaster()),
...     ]
... )
>>> pipe.fit(y_train, X_train)
ForecastingPipeline(...)
>>> y_pred = pipe.predict(fh=fh, X=X_test)

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.