BaseEarlyClassifier

class BaseEarlyClassifier[source]

Abstract base class for early time series classifiers.

The base classifier specifies the methods and method signatures that all early classifiers have to implement. Attributes with an underscore suffix are set in the method fit.

Parameters:

classes_ndarray of class labels, possibly strings
n_classes_integer, number of classes (length of classes_)
fit_time_integer, time (in milliseconds) for fit to run.
_class_dictionarydictionary mapping classes_ onto integers 0…n_classes_-1.
_threads_to_usenumber of threads to use in fit as determined by n_jobs.
state_infoAn array containing the state info for each decision in X.

Attributes:

is_fitted: Whether fit has been called.

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.
`filter_X`(X, decisions)	Remove True cases from X given a boolean array of decisions.
`filter_X_y`(X, y, decisions)	Remove True cases from X and y given a boolean array of decisions.
`fit`(X, y)	Fit time series classifier 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 a dict of parameters values for this object.
`get_state_info`()	Return the state information generated from the last predict/update call.
`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 skbase object.
`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.
`reset_state_info`()	Reset the state information used in update methods.
`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.
`split_indices`(indices, decisions)	Split a list of indices given a boolean array of decisions.
`split_indices_and_filter`(X, indices, decisions)	Remove True cases and split a list of indices given an array of decisions.
`update_predict`(X)	Update label prediction for sequences in X at a larger series length.
`update_predict_proba`(X)	Update label probabilities for sequences in X at a larger series length.