MultiplexRegressor

class MultiplexRegressor(regressors: list, selected_regressor=None)[source]

MultiplexRegressor for selecting among different models.

MultiplexRegressor 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 regressors, single-output and multi-output regressors.

MultiplexRegressor is specified with a (named) list of regressors and a selected_regressor hyper-parameter, which is one of the regressor names. The MultiplexRegressor then behaves precisely as the regressor with name selected_regressor, ignoring functionality in the other regressors.

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

Parameters:

regressorslist of sktime regressors, or

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

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

If str, must be one of the regressor 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 regressor in the list is selected. Selects the regressor as which MultiplexRegressor behaves.

Attributes:

regressor_sktime regressor: clone of the selected regressor used for fitting and regression.
_regressorslist of (str, regressor) tuples: Regressors 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 regressor to training data.
`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.
`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[, multioutput])	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.