MiniRocketMultivariateCython

class MiniRocketMultivariateCython(num_kernels=10000, max_dilations_per_kernel=32, n_jobs=1, random_state=None)[source]

MiniRocket multivariate transform, Cython backend (no numba).

Numerically equivalent to MiniRocketMultivariate but uses ahead-of-time compiled Cython kernels instead of numba, eliminating JIT “warmup” latency (which can exceed 30s on the first transform of larger series). The numba MiniRocketMultivariate is retained as the reference/groundtruth.

MiniRocketMultivariate [1] is an almost deterministic version of Rocket. It creates convolutions of length 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. Works with univariate and multivariate time series.

This transformer fits one set of parameters per individual series, and applies the transform with fitted parameter i to the i-th series in transform. Vanilla use requires the same number of series in fit and transform.

Parameters:

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 be 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: Number of threads used in transform (the GIL-releasing Cython kernel is run over disjoint instance chunks). -1 uses all processors.
random_stateNone or int, default = None

Attributes:

num_kernels_int: The true number of kernels used in the rocket transform. This is num_kernels rounded down to the nearest multiple of 84. It is 84 if num_kernels is less than 84.

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

Examples

>>> from sktime.transformations.rocket import MiniRocketMultivariateCython
>>> from sktime.datasets import load_basic_motions
>>> X_train, y_train = load_basic_motions(split="train")
>>> trf = MiniRocketMultivariateCython(num_kernels=512)
>>> trf.fit(X_train)
MiniRocketMultivariateCython(...)
>>> X_train = trf.transform(X_train)

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X[, y])	Fit transformer to X, optionally to y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter sets for the estimator.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.