RandomShapeletTransform

Random Shapelet Transform.

Quickstart

from sktime.transformations.shapelet_transform import RandomShapeletTransform

estimator = RandomShapeletTransform(n_shapelet_samples=10000, max_shapelets=None, min_shapelet_length=3, max_shapelet_length=None, remove_self_similar=True, time_limit_in_minutes=0.0, contract_max_n_shapelet_samples=inf, n_jobs=1, parallel_backend=None, batch_size=100, random_state=None)

Parameters(11)

n_shapelet_samplesint, default=10000

The number of candidate shapelets to be considered for the final transform. Filtered down to <= max_shapelets, keeping the shapelets with the most information gain.

max_shapeletsint or None, default=None

Max number of shapelets to keep for the final transform. Each class value will have its own max, set to n_classes / max_shapelets. If None uses the min between 10 * n_instances and 1000

min_shapelet_lengthint, default=3

Lower bound on candidate shapelet lengths.

max_shapelet_lengthint or None, default= None

Upper bound on candidate shapelet lengths. If None no max length is used.

remove_self_similarboolean, default=True

Remove overlapping “self-similar” shapelets when merging candidate shapelets.

time_limit_in_minutesint, default=0

Time contract to limit build time in minutes, overriding n_shapelet_samples. Default of 0 means n_shapelet_samples is used.

contract_max_n_shapelet_samplesint, default=np.inf

Max number of shapelets to extract when time_limit_in_minutes is set.

n_jobsint, default=1

The number of jobs to run in parallel for both fit and transform. -1 means using all processors.

parallel_backendstr, ParallelBackendBase instance or None, default=None

Specify the parallelisation backend implementation in joblib, if None a ‘prefer’ value of “threads” is used by default. Valid options are “loky”, “multiprocessing”, “threading” or a custom backend. See the joblib Parallel documentation for more details.

batch_sizeint or None, default=100

Number of shapelet candidates processed before being merged into the set of best shapelets.

random_stateint or None, default=None

Seed for random number generation.

Examples

>>> from sktime.transformations.shapelet_transform import (
... RandomShapeletTransform
... )
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train", return_X_y = True)
>>> t = RandomShapeletTransform (
... n_shapelet_samples = 500,
... max_shapelets = 10,
... batch_size = 100,
... )
>>> t. fit (X_train, y_train) RandomShapeletTransform(
... )
>>> X_t = t. transform (X_train)

References

1. [1 ] Jon Hills et al., “Classification of time series by shapelet transformation”, Data Mining and Knowledge Discovery, 28(4), 851–881, 2014. [2 ] A. Bostrom and A. Bagnall, “Binary Shapelet Transform for Multiclass Time Series Classification”, Transactions on Large-Scale Data and Knowledge Centered Systems, 32, 2017.