MultiRocketMultivariate

MultiRocketMultivariate

class MultiRocketMultivariate(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 the multivariate version.

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_.

See also

MultiRocketMultivariate, MiniRocket, MiniRocketMultivariate, Rocket

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_basic_motions
>>> X_train, y_train = load_basic_motions(split="train")
>>> X_test, y_test = load_basic_motions(split="test")
>>> trf = MultiRocketMultivariate(num_kernels=512)
>>> trf.fit(X_train)
MultiRocketMultivariate(...)
>>> 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()	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.