pyts.multivariate.classification.MultivariateClassifier

class pyts.multivariate.classification.MultivariateClassifier(estimator, weights=None)

Classifier for multivariate time series.

It provides a convenient class to classify multivariate time series with classifier that can only deal with univariate time series. The labels are predicted in a hard voting fashion using the predictions for each feature.

Parameters:

estimator : estimator object or list thereof

Classifier. If one estimator is provided, it is cloned and each clone performs prediction for one feature. If a list of estimators is provided, each estimator performs prediction for one feature.

weights : array-like, shape = (n_classifiers,) or None (default=None)

Sequence of weights (float or int) to weight the occurrences of predicted class labels. Uses uniform weights if None.

Examples

>>> from pyts.classification import BOSSVS
>>> from pyts.datasets import load_basic_motions
>>> from pyts.multivariate.classification import MultivariateClassifier
>>> X_train, X_test, y_train, y_test = load_basic_motions(return_X_y=True)
>>> clf = MultivariateClassifier(BOSSVS())
>>> clf.fit(X_train, y_train)
MultivariateClassifier(...)
>>> clf.score(X_test, y_test)
1.0

Attributes:

classes_ : array, shape = (n_classes,)

An array of class labels known to the classifier.

estimators_ : list of estimator objects

The collection of fitted classifiers.

Methods

__init__(estimator[, weights])	Initialize self.
fit(X, y)	Fit each classifier.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict class labels using hard voting.
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__(estimator, weights=None)

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

fit(X, y)

Fit each classifier.

Parameters:

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

Multivariate time series.

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

Class labels.

Returns:

self : object

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)

Predict class labels using hard voting.

Parameters:

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

Multivariate time series.

Returns:

y_pred : array, shape = (n_samples,)

Predicted class labels.

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_features, n_timestamps)

Multivariate 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.