MultiplexClassifier

MultiplexClassifier

class MultiplexClassifier(classifiers: list, selected_classifier=None)[source]

MultiplexClassifier for selecting among different models.

MultiplexClassifier facilitates a framework for performing model selection process over different model classes. It should be used in conjunction with GridSearchCV to get full utilization. It can be used with univariate and multivariate classifiers, single-output and multi-output classifiers.

MultiplexClassifier is specified with a (named) list of classifiers and a selected_classifier hyper-parameter, which is one of the classifier names. The MultiplexClassifier then behaves precisely as the classifier with name selected_classifier, ignoring functionality in the other classifiers.

When used with GridSearchCV, MultiplexClassifier provides an ability to tune across multiple estimators, i.e., to perform AutoML, by tuning the selected_classifier hyper-parameter. This combination will then select one of the passed classifiers via the tuning algorithm.

Parameters:

classifierslist of sktime classifiers, or

list of tuples (str, estimator) of sktime classifiers MultiplexClassifier can switch (“multiplex”) between these classifiers. These are “blueprint” classifiers, states do not change when fit is called.

selected_classifier: str or None, optional, Default=None.

If str, must be one of the classifier names.

If no names are provided, must coincide with auto-generated name strings. To inspect auto-generated name strings, call get_params.

If None, behaves as if the first classifier in the list is selected. Selects the classifier as which MultiplexClassifier behaves.

Attributes:

classifier_sktime classifier

clone of the selected classifier used for fitting and classification.

_classifierslist of (str, classifier) tuples

Classifiers turned into name/est tuples.

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 parameters of estimator.
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 composite.
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(**kwargs)	Set the parameters of estimator.
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.