BinarySegmentation

class BinarySegmentation(threshold, min_cp_distance=0, max_iter=10000)[source]

Binary segmentation change point detector.

This method finds change points by fitting piecewise constant functions to a timeseries. Change points are selected according to the CUMSUM statistic.

Parameters:

thresholdfloat: Threshold for the CUMSUM statistic. Change points that do not increase the CUMSUM statistic above this threshold are ignored.
min_cp_distanceint: Minimum distance between change points.
max_iterint: Maximum number of interactions before the found change points are returned.

Attributes:

is_fitted: Whether fit has been called.

Notes

This is based on the implementation of binary segmentation described in [1].

References

[1]

Fryzlewicz, Piotr. “WILD BINARY SEGMENTATION FOR MULTIPLE CHANGE-POINT DETECTION.” The Annals of Statistics, vol. 42, no. 6, 2014, pp. 2243-81. JSTOR, http://www.jstor.org/stable/43556493. Accessed 4 July 2024.

Examples

>>> import pandas as pd
>>> from sktime.detection.bs import BinarySegmentation
>>> model = BinarySegmentation(threshold=1)
>>> X = pd.Series([1, 1, 1, 1, 5, 5, 5, 5])
>>> model.fit_predict(X)
   ilocs
0      3
>>> X = pd.Series([1.1, 1.3, -1.4, -1.4, 5.5, 5.6])
>>> model.fit_predict(X)
   ilocs
0      1
1      3

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`([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.
`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.