TEASER

class TEASER(estimator=None, one_class_classifier=None, one_class_param_grid=None, classification_points=None, n_jobs=1, random_state=None)[source]

Two-tier Early and Accurate Series Classifier (TEASER).

An early classifier which uses one class SVM’s trained on prediction probabilities to determine whether an early prediction is safe or not.

Overview:

Build n classifiers, where n is the number of classification_points. For each classifier, train a one class svm used to determine prediction safety at that series length. Tune the number of consecutive safe svm predictions required to consider the prediction safe.

While a prediction is still deemed unsafe:: Make a prediction using the series length at classification point i. Decide whether the predcition is safe or not using decide_prediction_safety.

Parameters:

estimator: sktime classifier, default=None: An sktime estimator to be built at each of the classification_points time stamps. Defaults to a WEASEL classifier.
one_class_classifier: one-class sklearn classifier, default=None: An sklearn one-class classifier used to determine whether an early decision is safe. Defaults to a tuned one-class SVM classifier.
one_class_param_grid: dict or list of dict, default=None: The hyper-parameters for the one-class classifier to learn using grid-search. Dictionary with parameters names (str) as keys and lists of parameter settings to try as values, or a list of such dictionaries.
classification_pointsList or None, default=None: List of integer time series time stamps to build classifiers and allow predictions at. Early predictions must have a series length that matches a value in the _classification_points List. Duplicate values will be removed, and the full series length will be appended if not present. If None, will use 20 thresholds linearly spaces from 0 to the series length.
n_jobsint, default=1: The number of jobs to run in parallel for both fit and predict. -1 means using all processors.
random_stateint or None, default=None: Seed for random number generation.

Attributes:

n_classes_int: The number of classes.
n_instances_int: The number of train cases.
n_dims_int: The number of dimensions per case.
series_length_int: The full length of each series.
classes_list: The unique class labels.
state_info2d np.ndarray (4 columns): Information stored about input instances after the decision-making process in update/predict methods. Used in update methods to make decisions based on the results of previous method calls. Records in order: the time stamp index, the number of consecutive decisions made, the predicted class and the series length.

References

[1]

Schäfer, Patrick, and Ulf Leser. “TEASER: early and accurate time series classification.” Data mining and knowledge discovery 34, no. 5 (2020)

Examples

>>> from sktime.classification.early_classification import TEASER
>>> from sktime.classification.interval_based import TimeSeriesForestClassifier
>>> 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 = TEASER(
...     classification_points=[6, 16, 24],
...     estimator=TimeSeriesForestClassifier(n_estimators=5),
... )
>>> clf.fit(X_train, y_train)
TEASER(...)
>>> y_pred, decisions = 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.
`filter_X`(X, decisions)	Remove True cases from X given a boolean array of decisions.
`filter_X_y`(X, y, decisions)	Remove True cases from X and y given a boolean array of decisions.
`fit`(X, y)	Fit time series classifier to training data.
`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_state_info`()	Return the state information generated from the last predict/update call.
`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.
`reset_state_info`()	Reset the state information used in update methods.
`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.
`split_indices`(indices, decisions)	Split a list of indices given a boolean array of decisions.
`split_indices_and_filter`(X, indices, decisions)	Remove True cases and split a list of indices given an array of decisions.
`update_predict`(X)	Update label prediction for sequences in X at a larger series length.
`update_predict_proba`(X)	Update label probabilities for sequences in X at a larger series length.