Classifiers module

`ASC`([base_clf])	Accumulated samples classifier.
`SampleWeightedMetaEstimator`([base_classifier])	Sample Weighted Meta Estimator.

class strlearn.classifiers.ASC(base_clf=None)

Bases: BaseEnsemble, ClassifierMixin

Accumulated samples classifier.

Classifier fitted on accumulated samples from all data chunks.

Variables:: classes (array-like, shape (n_classes, )) – The class labels.
Example:

>>> import strlearn as sl
>>> stream = sl.streams.StreamGenerator()
>>> clf = sl.classifiers.AccumulatedSamplesClassifier()
>>> evaluator = sl.evaluators.TestThenTrainEvaluator()
>>> evaluator.process(clf, stream)
>>> print(evaluator.scores_)
...
[[0.92       0.91879699 0.91848191 0.91879699 0.92523364]
[0.945      0.94648779 0.94624912 0.94648779 0.94240838]
[0.92       0.91936979 0.91936231 0.91936979 0.9047619 ]
...
[0.92       0.91907051 0.91877671 0.91907051 0.9245283 ]
[0.885      0.8854889  0.88546135 0.8854889  0.87830688]
[0.935      0.93569212 0.93540766 0.93569212 0.93467337]]

fit(X, y): Fitting.

partial_fit(X, y, classes=None): Partial fitting.

class strlearn.classifiers.SampleWeightedMetaEstimator(base_classifier=GaussianNB())

Bases: BaseEstimator, ClassifierMixin

Sample Weighted Meta Estimator.