TSFreshClassifier

class TSFreshClassifier(default_fc_parameters='efficient', relevant_feature_extractor=True, estimator=None, verbose=0, n_jobs=1, chunksize=None, random_state=None)[source]

Time Series Feature Extraction based on Scalable Hypothesis Tests classifier.

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

Parameters:

default_fc_parametersstr, default=”efficient”: Set of TSFresh features to be extracted, options are “minimal”, “efficient” or “comprehensive”.
relevant_feature_extractorbool, default=False: Remove irrelevant features using the FRESH algorithm.
estimatorsklearn classifier, default=None: An sklearn estimator to be built using the transformed data. Defaults to a Random Forest with 200 trees.
verboseint, default=0: level of output printed to the console (for information only)
n_jobsint, default=1: The number of jobs to run in parallel for both fit and predict. -1 means using all processors.
chunksizeint or None, default=None: Number of series processed in each parallel TSFresh job, should be optimised for efficient parallelisation.
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]

Christ, Maximilian, et al. “Time series feature extraction on basis of scalable hypothesis tests (tsfresh-a python package).” Neurocomputing 307 (2018): 72-77. https://www.sciencedirect.com/science/article/pii/S0925231218304843

Examples

>>> from sktime.classification.feature_based import TSFreshClassifier
>>> 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 = TSFreshClassifier(
...     estimator=RandomForestClassifier(n_estimators=5),
...     default_fc_parameters="efficient",
... )
>>> clf.fit(X_train, y_train)
TSFreshClassifier(...)
>>> 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.