ContractableBOSS

class ContractableBOSS(n_parameter_samples=250, max_ensemble_size=50, max_win_len_prop=1, min_window=10, time_limit_in_minutes=0.0, contract_max_n_parameter_samples=inf, save_train_predictions=False, feature_selection='none', n_jobs=1, random_state=None)[source]

Contractable Bag of Symbolic Fourier Approximation Symbols (cBOSS).

Implementation of BOSS Ensemble from Schäfer (2015) with refinements described in Middlehurst, Vickers and Bagnall (2019). [1, 2]_

Overview: Input “n” series of length “m” and cBOSS randomly samples n_parameter_samples parameter sets, evaluating each with LOOCV. It then retains max_ensemble_size classifiers with the highest accuracy There are three primary parameters:

alpha: alphabet size

w: window length

l: word length.

For any combination, a single BOSS slides a window length “w” along the series. The “w” length window is shortened to an “l” length word by taking a Fourier transform and keeping the first l/2 complex coefficients. These “l” coefficients are then discretised into “alpha” possible values, to form a word length “l”. A histogram of words for each series is formed and stored.

Fit involves finding “n” histograms.

Predict uses 1 nearest neighbor with a bespoke BOSS distance function.

Parameters:

n_parameter_samplesint, default = 250: If search is randomised, number of parameter combos to try.
max_ensemble_sizeint or None, default = 50: Maximum number of classifiers to retain. Will limit number of retained classifiers even if more than max_ensemble_size are within threshold.
max_win_len_propint or float, default = 1: Maximum window length as a proportion of the series length.
min_windowint, default = 10: Minimum window size.
time_limit_in_minutesint, default = 0: Time contract to limit build time in minutes. Default of 0 means no limit.
contract_max_n_parameter_samplesint, default=np.inf: Max number of parameter combinations to consider when time_limit_in_minutes is set.
save_train_predictionsbool, default=False: Save the ensemble member train predictions in fit for use in _get_train_probs leave-one-out cross-validation.
n_jobsint, default = 1: The number of jobs to run in parallel for both fit and predict. -1 means using all processors.
feature_selection: {“chi2”, “none”, “random”}, default: none: Sets the feature selections strategy to be used. Chi2 reduces the number of words significantly and is thus much faster (preferred). Random also reduces the number significantly. None applies not feature selectiona and yields large bag of words, e.g. much memory may be needed.
random_stateint or None, default=None: Seed for random integer.

Attributes:

n_classes_int: Number of classes. Extracted from the data.
classes_list: The classes labels.
n_instances_int: Number of instances. Extracted from the data.
n_estimators_int: The final number of classifiers used. Will be <= max_ensemble_size if max_ensemble_size has been specified.
series_length_int: Length of all series (assumed equal).
estimators_list: List of DecisionTree classifiers.
weights_: Weight of each classifier in the ensemble.

Notes

For the Java version, see TSML.

References

[1]

Patrick Schäfer, “The BOSS is concerned with time series classification in the presence of noise”, Data Mining and Knowledge Discovery, 29(6): 2015 https://link.springer.com/article/10.1007/s10618-014-0377-7

[2]

Matthew Middlehurst, William Vickers and Anthony Bagnall “Scalable Dictionary Classifiers for Time Series Classification”, in proc 20th International Conference on Intelligent Data Engineering and Automated Learning,LNCS, volume 11871 https://link.springer.com/chapter/10.1007/978-3-030-33607-3_2

Examples

>>> from sktime.classification.dictionary_based import ContractableBOSS
>>> 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 = ContractableBOSS(
...     n_parameter_samples=10, max_ensemble_size=3
... )
>>> clf.fit(X_train, y_train)
ContractableBOSS(...)
>>> 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.