SplitterBootstrapTransformer

class SplitterBootstrapTransformer(splitter=None, fold='train', shuffle=False, subsample=None, replace=True, random_state=None)[source]

Splitter based Bootstrapping method for synthetic time series generation.

A generalized form of bootstrap based on an sktime splitter.

Any sktime splitter can be passed as a component to this transformer, which will then produce for each series in the input of transform a panel of time series with the train and/or test sub-series.

The output of transform will have additional levels:

all levels of the transform input
an additional integer indexed top level, indicating the number of the sample note: this is in general the number of the sample and corresponds to the number of the fold only in the deterministic, exhaustive case
if split="train" or split="test", no further levels
if split="both", the second top level contains strings "train" and

"test": to indicate train or test fold from the split

For instance, if split="train", and there is a single original series X, the output of transform will have a top level (level 0) with integer index ranging from 0 to splitter.get_n_splits(X)-1.

The splitter can be exhaustive and deterministic, or random. By default, exhaustive ordered samples are returned (deterministic). Randomness is controlled by the following parameters:

shuffle (by default off) applies random uniform shuffling to the instances
subsample (by default off) applies sub-sampling with or without replacement
replace (by default False) selects sub-sampling with or without

replacement

Caution: the instance index of the transform output will correspond to the split index only if shuffle=False and subsample=None (unless by coincidence)

Parameters:

splitteroptional, sktime splitter, BaseSplitter descendant: default = SlidingWindowSplitter(window_length=3, step_length=1) The splitter used for the bootstrap splitting.
foldstr, one of “train” (default), “test”, and “both”: Determines which fold is returned as new instances in the panel. “train” - the training folds; “test” - the test folds; “both” - both training and test folds, and an additional string level with possible values "train" and "test" is present
shufflebool, default=False: whether to shuffle the order of folds uniformly at random before returning if not, folds will be returned in the ordering defined by the splitter
subsampleoptional, int or float, default = None: if provided, subsamples the folds returned uniformly at random int = subsample of that size will be returned (or full sample if smaller) float, must be between 0 and 1 = subsample of that fraction is returned Note: integer 1 selects one series; float 1 selects number in splitter many
replacebool, default=True; only used if subsample=True: whether sampling, if subsample is provided is with or without replacement True = with replacement, False = without replacement
random_stateint, np.random.RandomState or None (default): Random seed for the estimator if None, numpy environment random seed is used if int, passed to numpy RandomState as seed if RandomState, will be used as random generator

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> from sktime.transformations.bootstrap import SplitterBootstrapTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = SplitterBootstrapTransformer(fold="both")
>>> y_hat = transformer.fit_transform(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 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.