Transformer

MiniRocketMultivariate

MINImally RandOm Convolutional KErnel Transform (MiniRocket) multivariate.

Quickstart

python

from sktime.transformations.rocket import MiniRocketMultivariate

estimator = MiniRocketMultivariate(num_kernels=10000, max_dilations_per_kernel=32, n_jobs=1, random_state=None)

Parameters(4)

num_kernelsint, default=10,000: 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_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 MiniRocketMultivariate
>>> from sktime.datasets import load_basic_motions
>>> X_train, y_train = load_basic_motions (split = "train")
>>> X_test, y_test = load_basic_motions (split = "test")
>>> trf = MiniRocketMultivariate (num_kernels = 512)
>>> trf. fit (X_train) MiniRocketMultivariate(
... )
>>> X_train = trf. transform (X_train)
>>> X_test = trf. transform (X_test)

References

[1 ] Dempster, Angus and Schmidt, Daniel F and Webb, Geoffrey I, “MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification”,2020, https://dl.acm.org/doi/abs/10.1145/3447548.3467231, https://arxiv.org/abs/2012.08791