Arsenal

Arsenal ensemble.

Quickstart

from sktime.classification.kernel_based import Arsenal

estimator = Arsenal(num_kernels=2000, n_estimators=25, rocket_transform='rocket', max_dilations_per_kernel=32, n_features_per_kernel=4, time_limit_in_minutes=0.0, contract_max_n_estimators=100, save_transformed_data=False, n_jobs=1, random_state=None)

Parameters(10)

num_kernelsint, default=2,000

Number of kernels for each ROCKET transform.

n_estimatorsint, default=25

Number of estimators to build for the ensemble.

rocket_transformstr, default=”rocket”

The type of Rocket transformer to use. Valid inputs = [“rocket”,”minirocket”,”multirocket”]

max_dilations_per_kernelint, default=32

MiniRocket and MultiRocket only. The maximum number of dilations per kernel.

n_features_per_kernelint, default=4

MultiRocket only. The number of features per kernel.

time_limit_in_minutesint, default=0

Time contract to limit build time in minutes, overriding n_estimators. Default of 0 means n_estimators is used.

contract_max_n_estimatorsint, default=100

Max number of estimators when time_limit_in_minutes is set.

save_transformed_databool, default=False

Save the data transformed in fit for use in _get_train_probs.

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.

Examples

>>> from sktime.classification.kernel_based import Arsenal
>>> 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)
>>> clf = Arsenal (num_kernels = 100, n_estimators = 5)
>>> clf. fit (X_train, y_train) Arsenal(
... )
>>> y_pred = clf. predict (X_test)

References

1. [1 ] Middlehurst, Matthew, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, and Anthony Bagnall. “HIVE-COTE 2.0: a new meta ensemble for time series classification.” arXiv preprint arXiv:2104.07551 (2021).