Classifiers module
|
Accumulated samples classifier. |
|
Sample Weighted Meta Estimator. |
- class strlearn.classifiers.ASC(base_clf=None)
Bases:
sklearn.ensemble._base.BaseEnsemble
,sklearn.base.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:
sklearn.base.BaseEstimator
,sklearn.base.ClassifierMixin
Sample Weighted Meta Estimator.