GreedyGaussianSegmentation

GreedyGaussianSegmentation

class GreedyGaussianSegmentation(k_max: int = 10, lamb: float = 1.0, max_shuffles: int = 250, verbose: bool = False, random_state: int = None)[source]

Greedy Gaussian Segmentation Estimator.

Implementation based on [1].

• source code adapted based on: https://github.com/cvxgrp/GGS

• paper available at: https://stanford.edu/~boyd/papers/pdf/ggs.pdf

The method approximates solutions for the problem of breaking a multivariate time series into segments, where the data in each segment could be modeled as independent samples from a multivariate Gaussian distribution. It uses a dynamic programming search algorithm with a heuristic that allows finding approximate solutions in linear time with respect to the data length and always yields a locally optimal choice.

Greedy Gaussian Segmentation (GGS) fits a segmented gaussian model (SGM) to the data by computing the approximate solution to the combinatorial problem of finding the approximate covariance-regularized maximum log-likelihood for fixed number of change points and a reagularization strength. It follows an interactive procedure where a new breakpoint is added and then adjusting all breakpoints to (approximately) maximize the objective. It is similar to the top-down search used in other change point detection problems.

Parameters:

k_maxint, default=10

Maximum number of change points to find. The number of segments is thus k+1.

lambfloat, default=1.0

Regularization parameter lambda (>= 0), which controls the amount of (inverse) covariance regularization. A higher lambda favors simpler models.

max_shufflesint, default=250

Maximum number of shuffles

verbosebool, default=False

If True, verbose output is enabled.

random_stateint or np.random.RandomState, default=None

Either random seed or an instance of np.random.RandomState

Attributes:

change_points_: array_like, default=[]

Locations of change points as integer indexes.

References

[1]

Hallac, D., Nystrup, P. & Boyd, S., “Greedy Gaussian segmentation of multivariate time series.”, Adv Data Anal Classif 13, 727-751 (2019). https://doi.org/10.1007/s11634-018-0335-0

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)	Perform segmentation.
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.