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BOSSVSClassifierPyts

API referenceSource
Predict probaMissing values

Bag-of-SFA Symbols in Vector Space, from pyts.

Quickstart

python
from sktime.classification.dictionary_based import BOSSVSClassifierPyts

estimator = BOSSVSClassifierPyts(word_size=4, n_bins=4, window_size=10, window_step=1, anova=False, drop_sum=False, norm_mean=False, norm_std=False, strategy='quantile', alphabet=None, numerosity_reduction=True, use_idf=True, smooth_idf=False, sublinear_tf=True)

Parameters(14)

word_sizeint (default = 4)
Size of each word.
n_binsint (default = 4)
The number of bins to produce. It must be between 2 and 26.
window_sizeint or float (default = 10)

Size of the sliding window. If float, it represents the percentage of the size of each time series and must be between 0 and 1. The window size will be computed as ceil(window_size * n_timestamps).

window_stepint or float (default = 1)

Step of the sliding window. If float, it represents the percentage of the size of each time series and must be between 0 and 1. The window size will be computed as ceil(window_step * n_timestamps).

anovabool (default = False)
If True, the Fourier coefficient selection is done via a one-way ANOVA test. If False, the first Fourier coefficients are selected.
drop_sumbool (default = False)
If True, the first Fourier coefficient (i.e. the sum of the subseries) is dropped. Otherwise, it is kept.
norm_meanbool (default = False)
If True, center each subseries before scaling.
norm_stdbool (default = False)
If True, scale each subseries to unit variance.
strategystr (default = ‘quantile’)

Strategy used to define the widths of the bins:

  • ‘uniform’: All bins in each sample have identical widths

  • ‘quantile’: All bins in each sample have the same number of points

  • ‘normal’: Bin edges are quantiles from a standard normal distribution

  • ‘entropy’: Bin edges are computed using information gain

alphabetNone, ‘ordinal’ or array-like, shape = (n_bins,)
Alphabet to use. If None, the first n_bins letters of the Latin alphabet are used.
numerosity_reductionbool (default = True)
If True, delete sample-wise all but one occurrence of back to back identical occurrences of the same words.
use_idfbool (default = True)
Enable inverse-document-frequency reweighting.
smooth_idfbool (default = False)
Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions.
sublinear_tfbool (default = True)
Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).

References

  1. [1 ] P. Schäfer, “Scalable Time Series Classification”. Data Mining and Knowledge Discovery, 30(5), 1273-1298 (2016).