ElasticEnsemble

class 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)[source]

The Elastic Ensemble (EE).

EE as described in [1].

Overview:

Input n series length m
EE is an ensemble of elastic nearest neighbor classifiers

Parameters:

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.

Attributes:

estimators_list: A list storing all classifiers
train_accs_by_classifierndarray: Store the train accuracies of the classifiers

Notes

..[1] 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

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)

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_metric_params`()	Return the parameters for the distance metrics used.
`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.