pyts.classification.BOSSVS¶
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class
pyts.classification.BOSSVS(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)[source]¶ Bag-of-SFA Symbols in Vector Space.
Each time series is transformed into an histogram using the Bag-of-SFA Symbols (BOSS) algorithm. Then, for each class, the histograms are added up and a tf-idf vector is computed. The predicted class for a new sample is the class giving the highest cosine similarity between its tf vector and the tf-idf vectors of each class.
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.
- 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.
- 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
- 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.
- numerosity_reduction : bool (default = True)
If True, delete sample-wise all but one occurence of back to back identical occurences of the same words.
- use_idf : bool (default = True)
Enable inverse-document-frequency reweighting.
- smooth_idf : bool (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_tf : bool (default = True)
Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
References
[1] P. Schäfer, “Scalable Time Series Classification”. Data Mining and Knowledge Discovery, 30(5), 1273-1298 (2016). Examples
>>> from pyts.classification import BOSSVS >>> from pyts.datasets import load_gunpoint >>> X_train, X_test, y_train, y_test = load_gunpoint(return_X_y=True) >>> clf = BOSSVS(window_size=28) >>> clf.fit(X_train, y_train) BOSSVS(...) >>> clf.score(X_test, y_test) 0.98
Attributes: - classes_ : array, shape = (n_classes,)
An array of class labels known to the classifier.
- idf_ : array, shape = (n_features,) , or None
The learned idf vector (global term weights) when
use_idf=True, None otherwise.- tfidf_ : array, shape = (n_classes, n_words)
Term-document matrix.
- vocabulary_ : dict
A mapping of feature indices to terms.
Methods
__init__([word_size, n_bins, window_size, …])Initialize self. decision_function(X)Evaluate the cosine similarity between document-term matrix and X. fit(X, y)Compute the document-term matrix. get_params([deep])Get parameters for this estimator. predict(X)Predict the class labels for the provided data. score(X, y[, sample_weight])Return the mean accuracy on the given test data and labels. set_params(**params)Set the parameters of this estimator. -
__init__(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)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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decision_function(X)[source]¶ Evaluate the cosine similarity between document-term matrix and X.
Parameters: - X : array-like, shape (n_samples, n_timestamps)
Test samples.
Returns: - X : array, shape (n_samples, n_classes)
Cosine similarity between the document-term matrix and X.
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fit(X, y)[source]¶ Compute the document-term matrix.
Parameters: - X : array-like, shape = (n_samples, n_timestamps)
Training vector.
- y : array-like, shape = (n_samples,)
Class labels for each data sample.
Returns: - self : object
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get_params(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 : dict
Parameter names mapped to their values.
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predict(X)[source]¶ Predict the class labels for the provided data.
Parameters: - X : array-like, shape = (n_samples, n_timestamps)
Test samples.
Returns: - y_pred : array, shape = (n_samples,)
Class labels for each data sample.
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score(X, y, sample_weight=None)¶ Return the mean accuracy on the given test data and labels.
Parameters: - X : array-like, shape = (n_samples, n_timestamps)
Univariate time series.
- y : array-like, shape = (n_samples,)
True labels for X.
- sample_weight : None or array-like, shape = (n_samples,) (default = None)
Sample weights.
Returns: - score : float
Mean accuracy of
self.predict(X)with regards to y.
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set_params(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). 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 : estimator instance
Estimator instance.
