RocketRegressor

class RocketRegressor(num_kernels=10000, rocket_transform='rocket', max_dilations_per_kernel=32, n_features_per_kernel=4, use_multivariate='auto', n_jobs=1, random_state=None)[source]

Regressor wrapped for the Rocket transformer using RidgeCV regressor.

This regressor simply transforms the input data using the Rocket [1] transformer and builds a RidgeCV estimator using the transformed data.

Shorthand for the pipeline rocket * StandardScaler(with_mean=False) * RidgeCV(alphas) where alphas = np.logspace(-3, 3, 10), and where rocket depends on params rocket_transform, use_multivariate as follows:

rocket_transform | use_multivariate | rocket (class) |

|------------------|——————–|-------------------------| | “rocket” | any | Rocket | | “minirocket” | “yes | MiniRocketMultivariate | | “minirocket” | “no” | MiniRocket | | “multirocket” | “yes” | MultiRocketMultivariate | | “multirocket” | “no” | MultiRocket |

classes are sktime classes, other parameters are passed on to the rocket class.

To build other regressors with rocket transformers, use make_pipeline or the pipeline dunder *, and different transformers/regressors in combination.

Parameters:

num_kernelsint, optional, default=10,000: The number of kernels the for Rocket transform.
rocket_transformstr, optional, default=”rocket”: The type of Rocket transformer to use. Valid inputs = [“rocket”, “minirocket”, “multirocket”]
max_dilations_per_kernelint, optional, default=32: MiniRocket and MultiRocket only. The maximum number of dilations per kernel.
n_features_per_kernelint, optional, default=4: MultiRocket only. The number of features per kernel.
use_multivariatestr, [“auto”, “yes”, “no”], optional, default=”auto”: whether to use multivariate rocket transforms or univariate ones “auto” = multivariate iff data seen in fit is multivariate, otherwise univariate “yes” = always uses multivariate transformers, native multi/univariate “no” = always univariate transformers, multivariate by framework vectorization
n_jobsint, default=1: The number of jobs to run in parallel for both fit and predict. -1 means using all processors.
random_stateint or None, default=None: Seed for random number generation.

Attributes:

n_classesint: The number of classes.
classes_list: The classes labels.
estimator_RegressorPipeline: Shorthand for the internal estimator that is fitted.
num_kernels_int: The true number of kernels used in the rocket transform. When rocket_transform=”rocket”, this is num_kernels. When rocket_transform is either “minirocket” or “multirocket”, 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, François Petitjean, and Geoffrey I. Webb. “Rocket: exceptionally fast and accurate time series classification using random convolutional kernels.” Data Mining and Knowledge Discovery 34.5 (2020)

Examples

>>> from sktime.regression.kernel_based import RocketRegressor
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
>>> X_test, y_test = load_unit_test(split="test", return_X_y=True)
>>> reg = RocketRegressor(num_kernels=500)
>>> reg.fit(X_train, y_train)
RocketRegressor(...)
>>> y_pred = reg.predict(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 time series regressor to training data.
`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.
`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.
`predict`(X)	Predicts labels for sequences in X.
`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.
`score`(X, y[, multioutput])	Scores predicted labels against ground truth labels on X.
`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.