BOSSEnsemble

Ensemble of Bag of Symbolic Fourier Approximation Symbols (BOSS).

Quickstart

from sktime.classification.dictionary_based import BOSSEnsemble

estimator = BOSSEnsemble(threshold=0.92, max_ensemble_size=500, max_win_len_prop=1, min_window=10, save_train_predictions=False, feature_selection='none', use_boss_distance=True, alphabet_size=2, n_jobs=1, random_state=None)

Parameters(10)

thresholdfloat, default=0.92

Threshold used to determine which classifiers to retain. All classifiers within percentage threshold of the best one are retained.

max_ensemble_sizeint or None, default=500

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.

save_train_predictionsbool, default=False

Save the ensemble member train predictions in fit for use in _get_train_probs leave-one-out cross-validation.

alphabet_sizedefault = 2

Number of possible letters (values) for each word.

n_jobsint, default=1

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

use_boss_distanceboolean, default=True

The Boss-distance is an asymmetric distance measure. It provides higher accuracy, yet is signifaicantly slower to compute.

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.

Examples

>>> from sktime.classification.dictionary_based import BOSSEnsemble
>>> 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 = BOSSEnsemble (max_ensemble_size = 3)
>>> clf. fit (X_train, y_train) BOSSEnsemble(
... )
>>> y_pred = clf. predict (X_test)

References

1. [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