ElasticEnsemble

The Elastic Ensemble (EE).

Quickstart

from sktime.classification.distance_based import ElasticEnsemble

estimator = ElasticEnsemble(distance_measures='all', proportion_of_param_options=1.0, proportion_train_in_param_finding=1.0, proportion_train_for_test=1.0, n_jobs=1, random_state=0, verbose=0, majority_vote=False)

Parameters(7)

distance_measureslist of strings, optional (default=”all”)

A list of strings identifying which distance measures to include. Valid values are one or more of: euclidean, dtw, wdtw, ddtw, dwdtw, lcss, erp, msm

proportion_of_param_optionsfloat, optional (default=1)

The proportion of the parameter grid space to search optional.

proportion_train_in_param_findingfloat, optional (default=1)

The proportion of the train set to use in the parameter search optional.

proportion_train_for_testfloat, optional (default=1)

The proportion of the train set to use in classifying new cases optional.

n_jobsint, optional (default=1)

The number of jobs to run in parallel for both fit and predict. -1 means using all processors.

random_stateint, default=0

The random seed.

verboseint, default=0

If >0, then prints out debug information.

Examples

>>> from sktime.classification.distance_based import ElasticEnsemble
>>> 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")
>>> clf = ElasticEnsemble (
... proportion_of_param_options = 0.1,
... proportion_train_for_test = 0.1,
... distance_measures = ["dtw", "ddtw" ],
... majority_vote = True,
... )
>>> clf. fit (X_train, y_train) ElasticEnsemble(
... )
>>> y_pred = clf. predict (X_test)