SupervisedTimeSeriesForest

SupervisedTimeSeriesForest

class SupervisedTimeSeriesForest(n_estimators=200, n_jobs=1, random_state=None)[source]

Supervised Time Series Forest (STSF).

An ensemble of decision trees built on intervals selected through a supervised process as described in _[1]. Overview: Input n series length m For each tree

• sample X using class-balanced bagging

• sample intervals for all 3 representations and 7 features using supervised

• method

• find mean, median, std, slope, iqr, min and max using their corresponding

• interval for each rperesentation, concatenate to form new data set

• build decision tree on new data set

Ensemble the trees with averaged probability estimates.

Parameters:

n_estimatorsint, default=200

Number of estimators to build for the ensemble.

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.

series_length_int

The length of each series.

classes_list

The classes labels.

intervalsarray-like of shape [n_estimators][3][7][n_intervals][2]

Stores indexes of all start and end points for all estimators. Each estimator contains indexes for each representation and feature combination.

estimators_list of shape (n_estimators) of DecisionTreeClassifier

The collections of estimators trained in fit.

Notes

For the Java version, see `TSML <https://github.com/uea-machine-learning/tsml/blob/master/src/main/

java/tsml/classifiers/interval_based/STSF.java>`_.

References

[1]

Cabello, Nestor, et al. “Fast and Accurate Time Series Classification Through Supervised Interval Search.” IEEE ICDM 2020

Examples

>>> from sktime.classification.interval_based import SupervisedTimeSeriesForest
>>> 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 = SupervisedTimeSeriesForest(n_estimators=5)
>>> clf.fit(X_train, y_train)
SupervisedTimeSeriesForest(n_estimators=5)
>>> 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.