RocketClassifier

Classifier wrapped for the Rocket transformer using RidgeClassifierCV.

Quickstart

from sktime.classification.kernel_based import RocketClassifier

estimator = RocketClassifier(num_kernels=10000, rocket_transform='rocket', max_dilations_per_kernel=32, n_features_per_kernel=4, use_multivariate='auto', n_jobs=1, random_state=None)

Parameters(7)

num_kernelsint, optional, default=10,000

The number of kernels for the Rocket transform.

rocket_transformstr, optional, default=”rocket”

The type of Rocket transformer to use. Valid inputs = [“rocket”, “minirocket”, “multirocket”]

max_dilations_per_kernelint, optional, default=32

MiniRocket and MultiRocket only. The maximum number of dilations per kernel.

n_features_per_kernelint, optional, default=4

MultiRocket only. The number of features per kernel.

use_multivariatestr, [“auto”, “yes”, “no”], optional, default=”auto”

whether to use multivariate rocket transforms or univariate ones “auto” = multivariate iff data seen in fit is multivariate, otherwise univariate “yes” = always uses multivariate transformers, native multi/univariate “no” = always univariate transformers, multivariate by framework vectorization

n_jobsint, default=1

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

random_stateint or None, default=None

Seed for random number generation.

Examples

>>> from sktime.classification.kernel_based import RocketClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train", return_X_y = True)
>>> X_test, y_test = load_unit_test (split = "test", return_X_y = True)
>>> clf = RocketClassifier (num_kernels = 500)
>>> clf. fit (X_train, y_train) RocketClassifier(
... )
>>> y_pred = clf. predict (X_test)

References

1. [1 ] Dempster, Angus, François Petitjean, and Geoffrey I. Webb. “Rocket: exceptionally fast and accurate time series classification using random convolutional kernels.” Data Mining and Knowledge Discovery 34.5 (2020)