SameLocSplitter

class SameLocSplitter(cv, y_template=None)[source]

Splitter that replicates loc indices from another splitter.

Takes a splitter cv and a time series y_template. Splits y in split and split_loc such that loc indices of splits are identical to loc indices of cv applied to y_template.

This splitter is useful when you need to replicate train-test splits across multiple time series with consistent loc-based indexing.

Mathematically, let \(y_{template}\) represent the reference time series, corresponding to the input y_template, and let \(I_{train, 1}, I_{test, 1}, \ldots, I_{train, K}, I_{test, K}\) be the loc-based train-test splits generated by cv on y_template.

The SameLocSplitter returns the corresponding positional indices in y, such that the loc-based splits are identical to the splits generated by cv, namely, \(I_{train, 1}, I_{test, 1}, \ldots, I_{train, K}, I_{test, K}\).

Parameters:

cvBaseSplitter: splitter for which to replicate splits by loc index
y_templatetime series container of Series scitype, optional: template used in cv to determine loc indices if None, y_template=y will be used in methods

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.split import (
...    ExpandingWindowSplitter,
...    SameLocSplitter,
... )

>>> y = load_airline()
>>> y_template = y[:60]
>>> cv_tpl = ExpandingWindowSplitter(fh=[2, 4], initial_window=24, step_length=12)

>>> splitter = SameLocSplitter(cv_tpl, y_template)

these two are the same:

>>> list(cv_tpl.split(y_template))
>>> list(splitter.split(y))

Methods

`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.
`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_cutoffs`([y])	Return the cutoff points in .iloc[] context.
`get_fh`()	Return the forecasting horizon.
`get_n_splits`([y])	Return the number of splits.
`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 splitter.
`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.
`split`(y)	Get iloc references to train/test splits of y.
`split_loc`(y)	Get loc references to train/test splits of y.
`split_series`(y)	Split y into training and test windows.