ElasticEnsemble

class sktime.classification.distance_based.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=None, random_state=0, verbose=0)[source]

The Elastic Ensemble (EE) as described in Jason Lines and Anthony Bagnall, “Time Series Classification with Ensembles of Elastic Distance Measures”, Data Mining and Knowledge Discovery, 29(3), 2015.

https://link.springer.com/article/10.1007/s10618-014-0361-2

Overview:

  • Input n series length m

  • EE is an ensemble of elastic nearest neighbor classifiers

Note

For the original Java version, see ElasticEnsemble.

Parameters
  • distance_measures (list of strings, optional (default="all")) – A list of strings identifying which distance measures to include.

  • proportion_of_param_option (float, optional (default=1)) – The proportion of the parameter grid space to search optional.

  • proportion_train_in_param_finding (float, optional (default=1)) – The proportion of the train set to use in the parameter search optional.

  • proportion_train_for_test (float, optional (default=1)) – The proportion of the train set to use in classifying new cases optional.

  • n_jobs (int or None, optional (default=None)) – The number of jobs to run in parallel for both fit and predict. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

  • random_state (int, default=0) – The random seed.

  • verbose (int, default=0) – If >0, then prints out debug information.

estimators_[source]

A list storing all classifiers

Type

list

train_accs_by_classifier[source]

Store the train accuracies of the classifiers

Type

ndarray

train_preds_by_classifier[source]

Store the train predictions of each classifier

Type

list

__init__(distance_measures='all', proportion_of_param_options=1.0, proportion_train_in_param_finding=1.0, proportion_train_for_test=1.0, n_jobs=None, random_state=0, verbose=0)[source]

Initialize self. See help(type(self)) for accurate signature.