pyts.bag_of_words
.BagOfWords¶
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class
pyts.bag_of_words.
BagOfWords
(window_size=0.1, window_step=1, numerosity_reduction=True)[source]¶ Transform time series into bag of words.
Parameters: - window_size : int 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_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)
.- numerosity_reduction : bool (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 BagOfWords >>> X = [['a', 'a', 'b', 'a', 'b', 'b', 'b', 'b', 'a'], ... ['a', 'b', 'c', 'c', 'c', 'c', 'a', 'a', 'c']] >>> bow = BagOfWords(window_size=2) >>> print(bow.transform(X)) ['aa ab ba ab bb ba' 'ab bc cc ca aa ac'] >>> bow = BagOfWords(window_size=2, numerosity_reduction=False) >>> print(bow.transform(X)) ['aa ab ba ab bb bb bb ba' 'ab bc cc cc cc ca aa ac']
Methods
__init__
(self[, window_size, window_step, …])Initialize self. fit
(self[, X, y])Pass. fit_transform
(self, X[, y])Fit to 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 time series into sequences of words. -
__init__
(self, window_size=0.1, window_step=1, numerosity_reduction=True)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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fit_transform
(self, X, y=None, **fit_params)¶ Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters: - X : numpy array of shape [n_samples, n_features]
Training set.
- y : numpy array of shape [n_samples]
Target values.
- **fit_params : dict
Additional fit parameters.
Returns: - X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
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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.
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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.