ShapeletTransform

ShapeletTransform

class ShapeletTransform(min_shapelet_length=3, max_shapelet_length=inf, max_shapelets_to_store_per_class=200, random_state=None, verbose=0, remove_self_similar=True)[source]

Shapelet Transform.

Original journal publication: @article{hills2014classification,

title={Classification of time series by shapelet transformation}, author={Hills, Jon and Lines, Jason and Baranauskas, Edgaras and Mapp, James and Bagnall, Anthony}, journal={Data Mining and Knowledge Discovery}, volume={28}, number={4}, pages={851–881}, year={2014}, publisher={Springer}

}

Parameters:

min_shapelet_lengthint, lower bound on candidate

shapelet lengths (default = 3)

max_shapelet_lengthint, upper bound on candidate

shapelet lengths (default = inf or series length)

max_shapelets_to_store_per_classint, upper bound on number of

shapelets to retain from each distinct class (default = 200)

random_stateRandomState, int, or none: to

control random state objects for deterministic results (default = None)

verboseint, level of output printed to

the console (for information only) (default = 0)

remove_self_similarboolean, remove overlapping

“self-similar” shapelets from the final transform (default = True)

Attributes:

predefined_ig_rejection_levelfloat, minimum information gain

required to keep a shapelet (default = 0.05)

self.shapeletslist of Shapelet objects,

the stored shapelets after a dataset has been processed

Methods

binary_entropy(num_this_class, num_other_class)	Binary entropy.
calc_binary_ig(orderline, ...)	Binary information gain.
calc_early_binary_ig(orderline, ...)	Early binary IG.
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.
euclidean_distance_early_abandon(u, v, min_dist)	Euclidean distance with early abandon.
fit(X[, y])	Fit transformer to X, optionally to y.
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_shapelets()	Accessor method to return the extracted shapelets.
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 skbase object.
inverse_transform(X[, y])	Inverse transform X and return an inverse transformed version.
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.
remove_self_similar_shapelets(shapelet_list)	Remove self-similar shapelets from an input list.
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.
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.
transform(X[, y])	Transform X and return a transformed version.
update(X[, y, update_params])	Update transformer with X, optionally y.
zscore(a[, axis, ddof])	Return the normalised version of series.