RocketPyts

RocketPyts

class RocketPyts(n_kernels=10000, kernel_sizes=(7, 9, 11), random_state=None)[source]

RandOm Convolutional KErnel Transform (ROCKET), from pyts.

Direct interface to pyts.transformation.rocket.

ROCKET [1] generates random convolutional kernels, including random length and dilation. It transforms the time series with two features per kernel. The first feature is global max pooling and the second is proportion of positive values.

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:

n_kernelsint (default = 10000)

Number of kernels.

kernel_sizesarray-like (default = (7, 9, 11))

The possible sizes of the kernels.

random_stateNone, int or RandomState instance (default = None)

The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator. If None, the random number generator is the RandomState instance used by np.random.

Attributes:

weights_array, shape = (n_kernels, max(kernel_sizes))

Weights of the kernels. Zero padding values are added.

length_array, shape = (n_kernels,)

Length of each kernel.

bias_array, shape = (n_kernels,)

Bias of each kernel.

dilation_array, shape = (n_kernels,)

Dilation of each kernel.

padding_array, shape = (n_kernels,)

Padding of each kernel.

See also

MultiRocketMultivariate, MiniRocket, MiniRocketMultivariate, Rocket

References

[1]

Tan, Chang Wei and Dempster, Angus and Bergmeir, Christoph

and Webb, Geoffrey I, “ROCKET: Exceptionally fast and accurate time series

classification using random convolutional kernels”,2020, https://link.springer.com/article/10.1007/s10618-020-00701-z, https://arxiv.org/abs/1910.13051

Examples

>>> from sktime.transformations.rocket import RocketPyts
>>> 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 = RocketPyts(num_kernels=512)
>>> trf.fit(X_train)
Rocket(...)
>>> 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 settings 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.