DrCIF

class DrCIF(n_estimators=200, n_intervals=None, att_subsample_size=10, min_interval=4, max_interval=None, base_estimator='CIT', time_limit_in_minutes=0.0, contract_max_n_estimators=500, save_transformed_data=False, n_jobs=1, random_state=None)[source]

Diverse Representation Canonical Interval Forest Classifier (DrCIF).

Extension of the CIF algorithm using multiple representations. Implementation of the interval based forest making use of the catch22 feature set on randomly selected intervals on the base series, periodogram representation and differences representation described in the HIVE-COTE 2.0 paper Middlehurst et al (2021). [1]

Overview: Input “n” series with “d” dimensions of length “m”. For each tree

Sample n_intervals intervals per representation of random position and length

Subsample att_subsample_size catch22 or summary statistic attributes randomly

Randomly select dimension for each interval

Calculate attributes for each interval from its representation, concatenate to form new data set

Build decision tree on new data set

Ensemble the trees with averaged probability estimates

Parameters:

n_estimatorsint, default=200: Number of estimators to build for the ensemble.
n_intervalsint, length 3 list of int or None, default=None: Number of intervals to extract per representation per tree as an int for all representations or list for individual settings, if None extracts (4 + (sqrt(representation_length) * sqrt(n_dims)) / 3) intervals.
att_subsample_sizeint, default=10: Number of catch22 or summary statistic attributes to subsample per tree.
min_intervalint or length 3 list of int, default=4: Minimum length of an interval per representation as an int for all representations or list for individual settings.
max_intervalint, length 3 list of int or None, default=None: Maximum length of an interval per representation as an int for all representations or list for individual settings, if None set to (representation_length / 2).
base_estimatorBaseEstimator or str, default=”CIT”: Base estimator for the ensemble, can be supplied a sklearn BaseEstimator or a string for suggested options. “DTC” uses the sklearn DecisionTreeClassifier using entropy as a splitting measure (sklearn.tree.DecisionTreeClassifier). “CIT” uses the sktime ContinuousIntervalTree, an implementation of the original tree used with embedded attribute processing for faster predictions (sktime.classification.interval_based.ContinuousIntervalTree). In order to pass parameters to estimators, pass a BaseEstimator instance.
time_limit_in_minutesint, default=0: Time contract to limit build time in minutes, overriding n_estimators. Default of 0 means n_estimators is used.
contract_max_n_estimatorsint, default=500: Max number of estimators when time_limit_in_minutes is set.
save_transformed_databool, default=False: Save the data transformed in fit for use in _get_train_probs.
n_jobsint, default=1: The number of jobs to run in parallel for both fit and predict. -1 means using all processors.
random_stateint or None, default=None: Seed for random number generation.

Attributes:

n_classes_int: The number of classes.
n_instances_int: The number of train cases.
n_dims_int: The number of dimensions per case.
series_length_int: The length of each series.
classes_list: The classes labels.
total_intervals_int: Total number of intervals per tree from all representations.
estimators_list of shape (n_estimators) of BaseEstimator: The collections of estimators trained in fit.
intervals_list of shape (n_estimators) of ndarray with shape (total_intervals,2): Stores indexes of each intervals start and end points for all classifiers.
atts_list of shape (n_estimators) of array with shape (att_subsample_size): Attribute indexes of the subsampled catch22 or summary statistic for all classifiers.
dims_list of shape (n_estimators) of array with shape (total_intervals): The dimension to extract attributes from each interval for all classifiers.
transformed_data_list of shape (n_estimators) of ndarray with shape
(n_instances,total_intervals * att_subsample_size): The transformed dataset for all classifiers. Only saved when save_transformed_data is true.

Notes

For the Java version, see TSML.

References

[1]

Middlehurst, Matthew, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, and Anthony Bagnall. “HIVE-COTE 2.0: a new meta ensemble for time series classification.” arXiv preprint arXiv:2104.07551 (2021).

Examples

>>> from sktime.classification.interval_based import DrCIF
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
>>> X_test, y_test = load_unit_test(split="test", return_X_y=True)
>>> clf = DrCIF(
...     n_estimators=3, n_intervals=2, att_subsample_size=2
... )
>>> clf.fit(X_train, y_train)
DrCIF(...)
>>> 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.