RocketRegressor
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.
Quickstart
from sktime.regression.kernel_based import RocketRegressor
estimator = 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)Parameters(7)
- 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
fitandpredict.-1means using all processors.- random_stateint or None, default=None
- Seed for random number generation.
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)References
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)