pyts.transformation.BOSS

class pyts.transformation.BOSS(word_size=4, n_bins=4, strategy='quantile', window_size=10, window_step=1, anova=False, drop_sum=False, norm_mean=False, norm_std=False, numerosity_reduction=True, sparse=True, alphabet=None)[source]

Bag of Symbolic Fourier Approximation Symbols.

For each time series, subseries are extracted using a slidind window. Then the subseries are transformed into a word using the Symbolic Fourier Approximation (SFA) algorithm. For each time series, the words are grouped together and a histogram counting the occurences of each word is created.

Parameters:
word_size : int (default = 4)

Size of each word.

n_bins : int (default = 4)

The number of bins to produce. It must be between 2 and 26.

strategy : str (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
window_size : int 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_step : int 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).

anova : bool (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_sum : bool (default = False)

If True, the first Fourier coefficient (i.e. the sum of the subseries) is dropped. Otherwise, it is kept.

norm_mean : bool (default = False)

If True, center each subseries before scaling.

norm_std : bool (default = False)

If True, scale each subseries to unit variance.

numerosity_reduction : bool (default = True)

If True, delete sample-wise all but one occurence of back to back identical occurences of the same words.

sparse : bool (default = True)

Return a sparse matrix if True, else return an array.

alphabet : None, ‘ordinal’ or array-like, shape = (n_bins,)

Alphabet to use. If None, the first n_bins letters of the Latin alphabet are used.

References

[R1d415568a83d-1]P. Schäfer, “The BOSS is concerned with time series classification in the presence of noise”. Data Mining and Knowledge Discovery, 29(6), 1505-1530 (2015).

Examples

>>> from pyts.datasets import load_gunpoint
>>> from pyts.transformation import BOSS
>>> X_train, X_test, _, _ = load_gunpoint(return_X_y=True)
>>> boss = BOSS(word_size=2, n_bins=2, sparse=False)
>>> boss.fit(X_train)
BOSS(...)
>>> sorted(boss.vocabulary_.values())
['aa', 'ab', 'ba', 'bb']
>>> boss.transform(X_test)
array(...)
Attributes:
vocabulary_ : dict

A mapping of feature indices to terms.

Methods

__init__(self[, word_size, n_bins, …]) Initialize self.
fit(self, X[, y]) Fit the model according to the given training data.
fit_transform(self, X[, y]) Fit the data then transform it.
get_params(self[, deep]) Get parameters for this estimator.
set_params(self, \*\*params) Set the parameters of this estimator.
transform(self, X) Transform the provided data.
__init__(self, word_size=4, n_bins=4, strategy='quantile', window_size=10, window_step=1, anova=False, drop_sum=False, norm_mean=False, norm_std=False, numerosity_reduction=True, sparse=True, alphabet=None)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y=None)[source]

Fit the model according to the given training data.

Parameters:
X : array-like, shape = (n_samples, n_timestamps)

Training vector.

y : None or array-like, shape = (n_samples,)

Class labels for each data sample.

Returns:
self : object
fit_transform(self, X, y=None)[source]

Fit the data then transform it.

Parameters:
X : array-like, shape = (n_samples, n_timestamps)

Training vector.

y : None or array-like, shape = (n_samples,)

Class labels for each data sample.

Returns:
X_new : sparse matrix, shape = (n_samples, n_words)

Document-term matrix.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters:
deep : bool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
params : mapping of string to any

Parameter names mapped to their values.

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**params : dict

Estimator parameters.

Returns:
self : object

Estimator instance.

transform(self, X)[source]

Transform the provided data.

Parameters:
X : array-like, shape = (n_samples, n_timestamps)

Test samples.

Returns:
X_new : sparse matrix, shape = (n_samples, n_words)

Document-term matrix.

Examples using pyts.transformation.BOSS