ClusterSegmenter

ClusterSegmenter

class ClusterSegmenter(clusterer=None)[source]

Cluster-based Time Series Segmentation.

time series segmentation using clustering is simple task. This detector segments time series data into distinct segments based on similarity, identified using the chosen clustering algorithm.

Parameters:

clusterersklearn.cluster

The instance of clustering algorithm used for segmentation.

Attributes:

is_fitted

Whether fit has been called.

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.