ROCKETClassifier

class sktime.classification.shapelet_based.ROCKETClassifier(num_kernels=10000, ensemble=False, ensemble_size=25, random_state=None)[source]

Classifier wrapped for the ROCKET transformer using RidgeClassifierCV as the base classifier. Allows the creation of an ensemble of ROCKET classifiers to allow for generation of probabilities as the expense of scalability.

Parameters
  • num_kernels (int, number of kernels for ROCKET transform) –

  • (default=10

  • 000)

  • ensemble (boolean, create ensemble of ROCKET's (default=False)) –

  • ensemble_size (int, size of the ensemble (default=25)) –

  • random_state (int or None, seed for random, integer,) –

  • (default to no seed) (optional) –

classifiers[source]
Type

array of IndividualTDE classifiers

weights[source]
Type

weight of each classifier in the ensemble

weight_sum[source]
Type

sum of all weights

n_classes[source]
Type

extracted from the data

Notes

@article{dempster_etal_2019,

author = {Dempster, Angus and Petitjean, Francois and Webb, Geoffrey I}, title = {ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels}, year = {2019}, journal = {arXiv:1910.13051}

}

Java version https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/ tsml/classifiers/hybrids/ROCKETClassifier.java

__init__(num_kernels=10000, ensemble=False, ensemble_size=25, random_state=None)[source]

Initialize self. See help(type(self)) for accurate signature.