Catch22Classifier

class Catch22Classifier(outlier_norm=False, replace_nans=True, estimator=None, n_jobs=1, random_state=None)[source]

Canonical Time-series Characteristics (catch22) classifier.

This classifier simply transforms the input data using the Catch22 [1] transformer and builds a provided estimator using the transformed data.

Shorthand for the pipeline Catch22(outlier_norm, replace_nans) * estimator

Parameters:

outlier_normbool, optional, default=False: Normalise each series during the two outlier Catch22 features, which can take a while to process for large values.
replace_nansbool, optional, default=True: Replace NaN or inf values from the Catch22 transform with 0.
estimatorsklearn classifier, optional, default=None: An sklearn estimator to be built using the transformed data. Defaults to sklearn RandomForestClassifier(n_estimators=200)
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 or None, optional, 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.
estimator_ClassifierPipeline: Catch22Classifier as a ClassifierPipeline, fitted to data internally

Notes

Authors catch22ForestClassifier.

For the Java version, see tsml.

References

[1]

Lubba, Carl H., et al. “catch22: Canonical time-series characteristics.” Data Mining and Knowledge Discovery 33.6 (2019): 1821-1852. https://link.springer.com/article/10.1007/s10618-019-00647-x

Examples

>>> from sktime.classification.feature_based import Catch22Classifier
>>> from sklearn.ensemble import RandomForestClassifier
>>> 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 = Catch22Classifier(
...     estimator=RandomForestClassifier(n_estimators=5),
...     outlier_norm=True,
... )
>>> clf.fit(X_train, y_train)
Catch22Classifier(...)
>>> 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.