ClaSPTransformer

class ClaSPTransformer(window_length=10, scoring_metric='ROC_AUC', exclusion_radius=0.05)[source]

ClaSP (Classification Score Profile) Transformer.

Implementation of the Classification Score Profile of a time series. ClaSP hierarchically splits a TS into two parts, where each split point is determined by training a binary TS classifier for each possible split point and selecting the one with highest accuracy, i.e., the one that is best at identifying subsequences to be from either of the partitions.

Parameters:

window_lengthint, default = 10: size of window for sliding.
scoring_metricstring, default = ROC_AUC: the scoring metric to use in ClaSP - choose from ROC_AUC or F1
exclusion_radiusint: Exclusion Radius for change points to be non-trivial matches

Attributes:

is_fitted: Whether fit has been called.

Notes

As described in @inproceedings{clasp2021,

title={ClaSP - Time Series Segmentation}, author={Sch”afer, Patrick and Ermshaus, Arik and Leser, Ulf}, booktitle={CIKM}, year={2021}

}

Examples

>>> from sktime.transformations.clasp import ClaSPTransformer
>>> from sktime.detection.clasp import find_dominant_window_sizes
>>> from sktime.datasets import load_electric_devices_segmentation
>>> X, true_period_size, true_cps = load_electric_devices_segmentation()
>>> dominant_period_size = find_dominant_window_sizes(X)
>>> clasp = ClaSPTransformer(window_length=dominant_period_size)
>>> clasp.fit(X)
>>> profile = clasp.transform(X)

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.