WordExtractor

class pyts.bag_of_words.WordExtractor(window_size: int | float = 0.1, window_step: int | float = 1, numerosity_reduction: bool = True)

Transform discretized time series into sequences of words.

Parameters:

window_sizeint or float (default = 0.1)

Size of the sliding window (i.e. the size of each word). 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).

numerosity_reductionbool (default = True)

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

Examples

>>> from pyts.bag_of_words import WordExtractor
>>> X = [['a', 'a', 'b', 'a', 'b', 'b', 'b', 'b', 'a'],
...      ['a', 'b', 'c', 'c', 'c', 'c', 'a', 'a', 'c']]
>>> word = WordExtractor(window_size=2)
>>> print(word.transform(X))
['aa ab ba ab bb ba' 'ab bc cc ca aa ac']
>>> word = WordExtractor(window_size=2, numerosity_reduction=False)
>>> print(word.transform(X))
['aa ab ba ab bb bb bb ba' 'ab bc cc cc cc ca aa ac']

Methods

__init__([window_size, window_step, ...])
fit([X, y])	Pass.
fit_transform(X[, y])	Fit to data, then transform it.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Transform time series into sequences of words.

__init__(window_size: int | float = 0.1, window_step: int | float = 1, numerosity_reduction: bool = True) → None

fit(X: ArrayLike | None = None, y: ArrayLike | None = None) → Self

Pass.

Parameters:

X

ignored

y

Ignored

Returns:

selfobject

transform(X: ArrayLike) → NDArray[str_]

Transform time series into sequences of words.

Parameters:

Xarray-like, shape = (n_samples, n_timestamps)

Returns:

X_newarray, shape = (n_samples,)

Transformed data. Each row is a string consisting of words separated by a whitespace.

Examples using pyts.bag_of_words.WordExtractor

Word Extractor

Word Extractor