PELT

class PELT(cost=None, penalty=None, min_segment_length=1, step_size=1, split_cost=0.0, prune=True, pruning_margin=0.0)[source]

Pruned Exact Linear Time (PELT) changepoint detection.

PELT [1] solves the penalised optimal-partitioning problem with pruning of admissible start points, giving exact solutions in expected linear time for well-behaved cost functions.

Parameters:

costBaseIntervalScorer, optional, default=L2Cost(): Cost scorer used for changepoint detection.
penaltyfloat or None, default=None: Penalty for adding a changepoint. None uses a BIC penalty computed from the data at prediction time.
min_segment_lengthint, default=1: Minimum number of observations in a segment.
step_sizeint, default=1: Only multiples of step_size are considered as changepoints (JumpPELT).
split_costfloat, default=0.0: Additive cost of splitting a segment.
prunebool, default=True: If False, disables pruning (optimal partitioning).
pruning_marginfloat, default=0.0: Margin added to the pruning criterion.

Attributes:

is_fitted: Whether fit has been called.

References

[1]

Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590-1598.

[2]

Bakka, K. B. (2018). Changepoint model selection in Gaussian data. Master’s thesis, NTNU.

Examples

>>> from sktime.detection.pelt import PELT
>>> from sktime.detection.costs._l2_cost import L2Cost
>>> import numpy as np
>>> X = np.concatenate([np.zeros(50), 10 * np.ones(50)])
>>> detector = PELT(cost=L2Cost(), penalty=15)
>>> detector.fit(X).predict(X)

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.