BaggingClassifier

class BaggingClassifier(estimator, n_estimators=10, n_samples=1.0, n_features=1.0, bootstrap=True, bootstrap_features=False, random_state=None)[source]

Bagging ensemble of time series classifiers.

Fits n_estimators clones of a classifier on datasets which are instance sub-samples and/or variable sub-samples.

On predict_proba, the mean average of probabilistic predictions is returned. For a deterministic classifier, this results in majority vote for predict.

The estimator allows to choose sample sizes for instances, variables, and whether sampling is with or without replacement.

Direct generalization of sklearn’s BaggingClassifier to the time series classification task.

Note: if n_features=1, BaggingClassifier turns a univariate classifier into a multivariate classifier, because slices seen by estimator are all univariate. This can be used to give a univariate classifier multivariate capabilities.

Parameters:

estimatorsktime classifier, descendant of BaseClassifier: classifier to use in the bagging estimator
n_estimatorsint, default=10: number of estimators in the sample for bagging
n_samplesint or float, default=1.0: The number of instances drawn from X in fit to train each clone If int, then indicates number of instances precisely If float, interpreted as a fraction, and rounded by ceil
n_featuresint or float, default=1.0: The number of features/variables drawn from X in fit to train each clone If int, then indicates number of instances precisely If float, interpreted as a fraction, and rounded by ceil Note: if n_features=1, BaggingClassifier turns a univariate classifier into a multivariate classifier (as slices seen by estimator are all univariate).
bootstrapboolean, default=True: whether samples/instances are drawn with replacement (True) or not (False)
bootstrap_featuresboolean, default=False: whether features/variables are drawn with replacement (True) or not (False)
random_stateint, RandomState instance or None, optional (default=None): If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes:

estimators_list of of sktime classifiers: clones of classifier in estimator fitted in the ensemble

Examples

>>> from sktime.classification.ensemble import BaggingClassifier
>>> from sktime.classification.kernel_based import RocketClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train")
>>> X_test, y_test = load_unit_test(split="test")
>>> clf = BaggingClassifier(
...     RocketClassifier(num_kernels=100),
...     n_estimators=10,
... )
>>> clf.fit(X_train, y_train)
BaggingClassifier(...)
>>> 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.