MatrixProfileClassifier

class MatrixProfileClassifier(subsequence_length=10, estimator=None, n_jobs=1, random_state=None)[source]

Martrix Profile (MP) classifier.

This classifier simply transforms the input data using the MatrixProfile [1] transformer and builds a provided estimator using the transformed data.

Parameters:

subsequence_lengthint, default=10: The subsequence length for the MatrixProfile transformer.
estimatorsklearn classifier, default=None: An sklearn estimator to be built using the transformed data. Defaults to a 1-nearest neighbour classifier.
n_jobsint, default=1: The number of jobs to run in parallel for both fit and predict. -1 means using all processors. Currently available for the classifier portion only.
random_stateint or None, default=None: Seed for random, integer.

Attributes:

n_classes_int: Number of classes. Extracted from the data.
classes_ndarray of shape (n_classes_): Holds the label for each class.

References

[1]

Yeh, Chin-Chia Michael, et al. “Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile.” Data Mining and Knowledge Discovery 32.1 (2018): 83-123. https://link.springer.com/article/10.1007/s10618-017-0519-9

Examples

>>> from sktime.classification.feature_based import MatrixProfileClassifier
>>> 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 = MatrixProfileClassifier()
>>> clf.fit(X_train, y_train)
MatrixProfileClassifier(...)
>>> 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.