RandomIntervalSpectralEnsemble

RandomIntervalSpectralEnsemble

class RandomIntervalSpectralEnsemble(n_estimators=500, max_interval=0, min_interval=16, acf_lag=100, acf_min_values=4, n_jobs=1, random_state=None)[source]

Random Interval Spectral Ensemble (RISE).

Input: n series length m For each tree

• sample a random intervals

• take the ACF and PS over this interval, and concatenate features

• build tree on new features

Ensemble the trees through averaging probabilities.

Parameters:

n_estimatorsint, default=200

The number of trees in the forest.

min_intervalint, default=16

The minimum width of an interval.

acf_lagint, default=100

The maximum number of autocorrelation terms to use.

acf_min_valuesint, default=4

Never use fewer than this number of terms to find a correlation.

n_jobsint, default=1

The number of jobs to run in parallel for both fit and predict. -1 means using all processors.

random_stateint, RandomState instance or None, 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:

n_classes_int

The number of classes.

classes_list

The classes labels.

intervals_array of shape = [n_estimators][2]

Stores indexes of start and end points for all classifiers.

Notes

For the Java version, see TSML.

References

[1]

Jason Lines, Sarah Taylor and Anthony Bagnall, “Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles”, ACM Transactions on Knowledge and Data Engineering, 12(5): 2018

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.