MultiRocket

Multi RandOm Convolutional KErnel Transform (MultiRocket).

Quickstart

from sktime.transformations.rocket import MultiRocket

estimator = MultiRocket(num_kernels=6250, max_dilations_per_kernel=32, n_features_per_kernel=4, normalise=False, n_jobs=1, random_state=None)

Parameters(6)

num_kernelsint, default = 6,250

number of random convolutional kernels. This should be a multiple of 84. If it is lower than 84, it will be set to 84. If it is higher than 84 and not a multiple of 84, the number of kernels used to transform the data will rounded down to the next positive multiple of 84.

max_dilations_per_kernelint, default = 32

maximum number of dilations per kernel.

n_features_per_kernelint, default = 4

number of features per kernel.

normalisebool, default False

n_jobsint, default=1

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

random_stateNone or int, default = None

Examples

>>> from sktime.transformations.rocket import Rocket
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train")
>>> X_test, y_test = load_unit_test (split = "test")
>>> trf = MultiRocket (num_kernels = 512)
>>> trf. fit (X_train) MultiRocket(
... )
>>> X_train = trf. transform (X_train)
>>> X_test = trf. transform (X_test)

References

1. [1 ] Tan, Chang Wei and Dempster, Angus and Bergmeir, Christoph and Webb, Geoffrey I, “MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification”,2022, https://link.springer.com/article/10.1007/s10618-022-00844-1 https://arxiv.org/abs/2102.00457