MultiRocket

class MultiRocket(num_kernels=6250, max_dilations_per_kernel=32, n_features_per_kernel=4, normalise=False, n_jobs=1, random_state=None)[source]

Multi RandOm Convolutional KErnel Transform (MultiRocket).

MultiRocket [1] is uses the same set of kernels as MiniRocket on both the raw series and the first order differenced series representation. It uses a different set of dilations and used for each representation. In addition to percentage of positive values (PPV) MultiRocket adds 3 pooling operators: Mean of Positive Values (MPV); Mean of Indices of Positive Values (MIPV); and Longest Stretch of Positive Values (LSPV). This version is for univariate time series only. Use class MultiRocketMultivariate for multivariate input.

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

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

Parameters:

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

Attributes:

parametertuple: parameter (dilations, num_features_per_dilation, biases) for transformation of input X
parameter1tuple: parameter (dilations, num_features_per_dilation, biases) for transformation of input X1 = np.diff(X, 1)
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. The calculated number of features is given as 2*n_features_per_kernel*num_kernels_.

References

[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

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)

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.