pyts.classification.BOSSVS

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_metadata_routing() Get metadata routing of this object.
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.
set_score_request(*, sample_weight, None, str] =) Request metadata passed to the score method.
__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.

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.
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
get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routing : MetadataRequest

A MetadataRequest encapsulating routing information.
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.
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.
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.
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.
set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → pyts.classification.bossvs.BOSSVS

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
  • False: metadata is not requested and the meta-estimator will not pass it to score.
  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.
Returns:
self : object

The updated object.

Examples using pyts.classification.BOSSVS

Bag-of-SFA Symbols in Vector Space (BOSSVS)

Bag-of-SFA Symbols in Vector Space (BOSSVS)

Bag-of-SFA Symbols in Vector Space (BOSSVS)