ElasticEnsemble¶

class ElasticEnsemble(distance_measures: str | list[str] = 'all', proportion_of_param_options: float = 1.0, proportion_train_in_param_finding: float = 1.0, proportion_train_for_test: float = 1.0, n_jobs: int = 1, random_state: int = 0, verbose: int = 0, majority_vote: bool = False)[source]¶

Bases: BaseClassifier

The Elastic Ensemble (EE) of time series distance measures.

The Elastic Ensemble [1] is an ensemble of 1-NN classifiers using elastic distances (as defined in aeon.distances). By default, each 1-NN classifier is tuned over 100 parameter values and the ensemble vote is weighted by an estimate of accuracy formed on the train set.

Parameters:

distance_measuresstr or list of str, default=”all”: A list of strings identifying which distance measures to include. Valid values are one or more of: euclidean, dtw, wdtw, ddtw, wddtw, lcss, erp, msm, twe. The default value all means that all the previously listed distances are used. The special value ts-quad can be used to select the distance measures for the TS-QUAD ensemble: WDTW, DDTW, LCSS, and MSM.
proportion_of_param_optionsfloat, default=1: The proportion of the parameter grid space to search optional.
proportion_train_in_param_findingfloat, default=1: The proportion of the train set to use in the parameter search optional.
proportion_train_for_testfloat, default=1: The proportion of the train set to use in classifying new cases optional.
n_jobsint, default=1: The number of jobs to run in parallel for both fit and predict. -1 means using all processors.
random_stateint, default=0: If int, random_state is the seed used by the random number generator;
verboseint, default=0: If >0, then prints out debug information.
majority_vote: boolean, default = False: Whether to use majority vote or weighted vote.

Attributes:

estimators_list: A list storing all classifiers.
train_accs_by_classifier_np.ndarray: Store the train accuracies of the classifiers.
constituent_build_times_array of float: build time for each member of the ensemble.

Notes

Capabilities ¶
Missing Values	No
Multithreading	Yes
Univariate	Yes
Multivariate	Yes
Unequal Length	Yes
Train Estimate	No
Contractable	No

References

[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 aeon.classification.distance_based import ElasticEnsemble
>>> from aeon.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

`clone`([random_state])	Obtain a clone of the object with the same hyperparameters.
`fit`(X, y)	Fit time series classifier to training data.
`fit_predict`(X, y, **kwargs)	Fits the classifier and predicts class labels for X.
`fit_predict_proba`(X, y, **kwargs)	Fits the classifier and predicts class label probabilities for X.
`get_class_tag`(tag_name[, raise_error, ...])	Get tag value from estimator class (only class tags).
`get_class_tags`()	Get class tags from estimator class and all its parent classes.
`get_fitted_params`([deep])	Get fitted parameters.
`get_metric_params`()	Return the parameters for the distance metrics used.
`get_params`([deep])	Get parameters for this estimator.
`get_tag`(tag_name[, raise_error, ...])	Get tag value from estimator class.
`get_tags`()	Get tags from estimator.
`predict`(X)	Predicts class labels for time series in X.
`predict_proba`(X)	Predicts class label probabilities for time series in X.
`reset`([keep])	Reset the object to a clean post-init state.
`score`(X, y[, metric, use_proba, metric_params])	Scores predicted labels against ground truth labels on X.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, metric, ...])	Configure whether metadata should be requested to be passed to the `score` method.
`set_tags`(**tag_dict)	Set dynamic tags to given values.

clone(random_state=None)[source]¶

Obtain a clone of the object with the same hyperparameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Parameters:

random_stateint, RandomState instance, or None, default=None: Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.

Returns:

estimatorobject: Instance of type(self), clone of self (see above)

fit(X, y) → BaseCollectionEstimator[source]¶

Fit time series classifier to training data.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

Returns:

selfBaseClassifier: Reference to self.

Raises:

ValueError: If X has multivariate series but the estimator does not have capability:multivariate tag set to True.
ValueError: If X has unequal length series but the estimator does not have capability:unequal_length tag set to True.
TypeError: If y is a pd.DataFrame with more than one column.

Notes

Changes state by creating a fitted model that updates attributes ending in “_” and sets is_fitted flag to True.

fit_predict(X, y, **kwargs) → ndarray[source]¶

Fits the classifier and predicts class labels for X.

fit_predict produces prediction estimates using just the train data. By default, this is through 10x cross validation, although some estimators may utilise specialist techniques such as out-of-bag estimates or leave-one-out cross-validation.

Classifiers which override _fit_predict will have the capability:train_estimate tag set to True.

Generally, this will not be the same as fitting on the whole train data then making train predictions. To do this, you should call fit(X,y).predict(X)

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

kwargsdict

key word arguments to configure the default cross validation if the base class default fit_predict is used (i.e. if function _fit_predict is not overridden. If _fit_predict is overridden, kwargs may not function as expected. If _fit_predict is not overridden, valid input is cv_size integer, which is the number of cross validation folds to use to estimate train data. If cv_size is not passed, the default is 10. If cv_size is greater than the minimum number of samples in any class, it is set to this minimum.

Returns:

predictionsnp.ndarray: shape [n_cases] - predicted class labels indices correspond to instance indices in

Raises:

ValueError: If X has multivariate series but the estimator does not have capability:multivariate tag set to True.
ValueError: If X has unequal length series but the estimator does not have capability:unequal_length tag set to True.
ValueError: If cv_size is not an integer greater than 0.
ValueError: If all classes have fewer than 2 samples, making cross-validation impossible.

fit_predict_proba(X, y, **kwargs) → ndarray[source]¶

Fits the classifier and predicts class label probabilities for X.

fit_predict_proba produces probability estimates using just the train data. By default, this is through 10x cross validation, although some estimators may utilise specialist techniques such as out-of-bag estimates or leave-one-out cross-validation.

Classifiers which override _fit_predict_proba will have the capability:train_estimate tag set to True.

Generally, this will not be the same as fitting on the whole train data then making train predictions. To do this, you should call fit(X,y).predict_proba(X)

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

kwargsdict

Returns:

probabilitiesnp.ndarray: 2D array of shape (n_cases, n_classes) - predicted class probabilities First dimension indices correspond to instance indices in X, second dimension indices correspond to class labels, (i, j)-th entry is estimated probability that i-th instance is of class j

Raises:

ValueError: If X has multivariate series but the estimator does not have capability:multivariate tag set to True.
ValueError: If X has unequal length series but the estimator does not have capability:unequal_length tag set to True.
ValueError: If cv_size is not an integer greater than 0.
ValueError: If all classes have fewer than 2 samples, making cross-validation impossible.

classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)[source]¶

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr: Name of tag value.
raise_errorbool, default=True: Whether a ValueError is raised when the tag is not found.
tag_value_defaultany type, default=None: Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value: Value of the tag_name tag in cls. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError: if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True

classmethod get_class_tags()[source]¶

Get class tags from estimator class and all its parent classes.

Returns:

collected_tagsdict: Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance. These are not overridden by dynamic tags set by set_tags or class __init__ calls.

get_fitted_params(deep=True)[source]¶

Get fitted parameters.

State required:: Requires state to be “fitted”.

Parameters:

deepbool, default=True: If True, will return the fitted parameters for this estimator and contained subobjects that are estimators.

Returns:

fitted_paramsdict: Fitted parameter names mapped to their values.

get_metric_params() → dict[source]¶

Return the parameters for the distance metrics used.

Returns:

paramsdict: The distance measures and the list of their parameter values.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

get_tag(tag_name, raise_error=True, tag_value_default=None)[source]¶

Get tag value from estimator class.

Includes dynamic and overridden tags.

Parameters:

tag_namestr: Name of tag to be retrieved.
raise_errorbool, default=True: Whether a ValueError is raised when the tag is not found.
tag_value_defaultany type, default=None: Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value: Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError: if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True

get_tags()[source]¶

Get tags from estimator.

Includes dynamic and overridden tags.

Returns:

collected_tagsdict: Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance and then any overridden and new tags from __init__ or set_tags.

predict(X) → ndarray[source]¶

Predicts class labels for time series in X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i other types are allowed and converted into one of the above.

Returns:

predictionsnp.ndarray: 1D np.array of float, of shape (n_cases) - predicted class labels indices correspond to instance indices in X

Raises:

ValueError: If X has multivariate series but the estimator does not have capability:multivariate tag set to True.
ValueError: If X has unequal length series but the estimator does not have capability:unequal_length tag set to True.
ValueError: If the time series length of X does not match the length seen during fit.
ValueError: If the number of channels in X does not match the number of channels seen during fit (for multivariate estimators).
NotFittedError: If the classifier has not been fitted yet (fit not called).

predict_proba(X) → ndarray[source]¶

Predicts class label probabilities for time series in X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

Returns:

probabilitiesnp.ndarray: 2D array of shape (n_cases, n_classes) - predicted class probabilities First dimension indices correspond to instance indices in X, second dimension indices correspond to class labels, (i, j)-th entry is estimated probability that i-th instance is of class j

Raises:

ValueError: If X has multivariate series but the estimator does not have capability:multivariate tag set to True.
ValueError: If X has unequal length series but the estimator does not have capability:unequal_length tag set to True.
ValueError: If the time series length of X does not match the length seen during fit.
ValueError: If the number of channels in X does not match the number of channels seen during fit (for multivariate estimators).
NotFittedError: If the classifier has not been fitted yet (fit not called).

reset(keep=None)[source]¶

Reset the object to a clean post-init state.

After a self.reset() call, self is equal or similar in value to type(self)(**self.get_params(deep=False)), assuming no other attributes were kept using keep.

Detailed behaviour:

removes any object attributes, except:: hyper-parameters (arguments of __init__) object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyperparameters (result of get_params)

Not affected by the reset are:

object attributes containing double-underscores class and object methods, class attributes any attributes specified in the keep argument

Parameters:

keepNone, str, or list of str, default=None: If None, all attributes are removed except hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:

selfobject: Reference to self.

Raises:

TypeError: If ‘keep’ is not a string or a list of strings.

score(X, y, metric='accuracy', use_proba=False, metric_params=None) → float[source]¶

Scores predicted labels against ground truth labels on X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

metricUnion[str, callable], default=”accuracy”,

Defines the scoring metric to test the fit of the model. For supported strings arguments, check sklearn.metrics.get_scorer_names.

use_probabool, default=False,

Argument to check if scorer works on probability estimates or not.

metric_paramsdict, default=None,

Contains parameters to be passed to the scoring function. If None, no parameters are passed.

Returns:

scorefloat: Accuracy score of predict(X) vs y.

Raises:

ValueError: If metric is a string not found in sklearn.metrics.get_scorer_names().
ValueError: If metric is not a string or callable.
NotFittedError: If the classifier has not been fitted yet (fit not called).

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

metricstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for metric parameter in score.
metric_paramsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for metric_params parameter in score.
use_probastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for use_proba parameter in score.

Returns:

selfobject: The updated object.

set_tags(**tag_dict)[source]¶

Set dynamic tags to given values.

Parameters:

**tag_dictdict: Dictionary of tag name and tag value pairs.

Returns:

selfobject: Reference to self.