ClaSPSegmentation

class ClaSPSegmentation(period_length=10, n_cps=1, exclusion_radius=0.05)[source]

ClaSP (Classification Score Profile) Segmentation.

Using ClaSP for the CPD problem is straightforward: We first compute the profile and then choose its global maximum as the change point. The following CPDs are obtained using a bespoke recursive split segmentation algorithm.

Parameters:

period_lengthint, default = 10: size of window for sliding, based on the period length of the data
n_cpsint, default = 1: the number of change points to search
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.detection.clasp import ClaSPSegmentation
>>> from sktime.detection.clasp import find_dominant_window_sizes
>>> from sktime.datasets import load_gun_point_segmentation
>>> X, true_period_size, cps = load_gun_point_segmentation()
>>> dominant_period_size = find_dominant_window_sizes(X)
>>> clasp = ClaSPSegmentation(dominant_period_size, n_cps=1)
>>> found_cps = clasp.fit_predict(X)
>>> profiles = clasp.profiles
>>> scores = clasp.scores

Methods

`change_points_to_segments`(y_sparse[, start, end])	Convert an series of change point indexes to segments.
`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.
`dense_to_sparse`(y_dense)	Convert the dense output from an detector to a sparse format.
`fit`(X[, y])	Fit to training data.
`fit_predict`(X[, y])	Fit to data, then predict it.
`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`()	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.
`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.
`predict`(X)	Create labels on test/deployment data.
`predict_points`(X)	Predict changepoints/anomalies on test/deployment data.
`predict_scores`(X)	Return scores for predicted labels on test/deployment data.
`predict_segments`(X)	Predict segments on test/deployment data.
`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.
`segments_to_change_points`(y_sparse)	Convert segments to change points.
`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.
`sparse_to_dense`(y_sparse, index)	Convert the sparse output from an detector to a dense format.
`transform`(X)	Create labels on test/deployment data.
`transform_scores`(X)	Return scores for predicted labels on test/deployment data.
`update`(X[, y])	Update model with new data and optional ground truth labels.
`update_predict`(X[, y])	Update model with new data and create labels for it.