Arsenal

class 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)[source]

Arsenal ensemble.

Overview: an ensemble of ROCKET transformers using RidgeClassifierCV base classifier. Weights each classifier using the accuracy from the ridge cross-validation. Allows for generation of probability estimates at the expense of scalability compared to RocketClassifier.

Parameters:

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.

Attributes:

n_classesint: The number of classes.
n_instances_int: The number of train cases.
n_dims_int: The number of dimensions per case.
series_length_int: The length of each series.
classes_list: The classes labels.
estimators_list of shape (n_estimators) of BaseEstimator: The collections of estimators trained in fit.
weights_list of shape (n_estimators) of float: Weight of each estimator in the ensemble.
transformed_data_list of shape (n_estimators) of ndarray with shape
(n_instances,total_intervals * att_subsample_size): The transformed dataset for all classifiers. Only saved when save_transformed_data is true.

Notes

For the Java version, see TSML.

References

[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).

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)

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 classifier to training data.
`fit_predict`(X, y[, cv, change_state])	Fit and predict labels for sequences in X.
`fit_predict_proba`(X, y[, cv, change_state])	Fit and predict labels probabilities for sequences in X.
`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.
`predict_proba`(X)	Predicts labels probabilities 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)	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.