Transformer

MiniRocket

MINImally RandOm Convolutional KErnel Transform (MiniRocket).

MiniRocket [1] is an almost deterministic version of Rocket. If creates convolutions of length of 9 with weights restricted to two values, and uses 84 fixed convolutions with six of one weight, three of the second weight to seed dilations. MiniRocket is for unviariate time series only. Use class MiniRocketMultivariate for multivariate time series.

This transformer fits one set of paramereters per individual series, and applies them to series of the same number in the test set.

To fit and transform at the same time, without an identification of fit/transform instances, wrap this transformer in FitInTransform, from sktime.transformations.compose.

Schnellstart

python

from sktime.transformations.rocket import MiniRocket

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

Parameter(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

Beispiele

>>> from sktime.transformations.rocket import MiniRocket
>>> 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 = MiniRocket (num_kernels = 512)
>>> trf. fit (X_train) MiniRocket(
... )
>>> X_train = trf. transform (X_train)
>>> X_test = trf. transform (X_test)

Referenzen

[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