CROPS

class CROPS(cost, min_penalty, max_penalty, selection_method='bic', min_segment_length=1, step_size=1, split_cost=0.0, prune=True, pruning_margin=0.0, middle_penalty_nudge=1e-05)[source]

CROPS algorithm for path solutions to the PELT algorithm.

Solves for all penalised optimal partitionings within [min_penalty, max_penalty] using CROPS [1], then selects the best segmentation via BIC or elbow criterion.

Parameters:

costBaseIntervalScorer: Cost function.
min_penaltyfloat: Lower bound of penalty range.
max_penaltyfloat: Upper bound of penalty range.
selection_methodstr, default=”bic”: "bic" or "elbow".
min_segment_lengthint, default=1: Minimum segment length.
step_sizeint, default=1: JumpPELT step size.
split_costfloat, default=0.0: Additive split cost.
prunebool, default=True: Enable pruning.
pruning_marginfloat, default=0.0: Pruning margin.
middle_penalty_nudgefloat, default=1e-5: Nudge factor for threshold penalties.

Attributes:

is_fitted: Whether fit has been called.

References

[1]

Haynes, K., Eckley, I. A. & Fearnhead, P. (2017). Computationally efficient changepoint detection for a range of penalties. JCGS, 26(1), 134-143.

Examples

>>> from sktime.detection.crops import CROPS
>>> from sktime.detection.costs._l2_cost import L2Cost
>>> import numpy as np
>>> X = np.concatenate([np.zeros(50), 10*np.ones(50)])
>>> det = CROPS(cost=L2Cost(), min_penalty=0.5, max_penalty=50.0)
>>> det.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.