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IndividualBOSS

API referenceSource

Single bag of Symbolic Fourier Approximation Symbols (IndividualBOSS).

Quickstart

python
from sktime.classification.dictionary_based import IndividualBOSS

estimator = IndividualBOSS(window_size=10, word_length=8, norm=False, alphabet_size=2, save_words=False, typed_dict='deprecated', use_boss_distance=True, feature_selection='none', store_histogram=False, n_jobs=1, random_state=None)

Parameters(8)

window_sizeint
Size of the window to use in BOSS algorithm.
word_lengthint
Length of word to use to use in BOSS algorithm.
normbool, default = False
Whether to normalize words by dropping the first Fourier coefficient.
alphabet_sizedefault = 2
Number of possible letters (values) for each word.
save_wordsbool, default = True

Whether to keep NumPy array of words in SFA transformation even after the dictionary of words is returned. If True, the array is saved, which can shorten the time to calculate dictionaries using a shorter word_length (since the last “n” letters can be removed).

store_histogrambool, default = False

Whether to store the histograms of words in fit. If False, avoids storing the histograms in memory, which can be large for some datasets.

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, integer.

Examples

>>> from sktime.classification.dictionary_based import IndividualBOSS
>>> 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 = IndividualBOSS ()
>>> clf. fit (X_train, y_train) IndividualBOSS(
... )
>>> 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