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m_new

MNew

Bases: MultiClassMetric

Metric M_new, which represents the percentage of novel class instances misclassified as known.

Parameters:

Name Type Description Default
known_classes list of int

List of known labels, the labels the algorithm knows prior to the online phase

 required
cm ConfusionMatrixNovelty

Optional, can specify an already existing confusion matrix instead of creating a new one for the metric

 None

Attributes:

Name Type Description
cm ConfusionMatrixNovelty

Confusion matrix
Source code in streamndr/metrics/m_new.py 
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class MNew(metrics.base.MultiClassMetric):
    """Metric M_new, which represents the percentage of novel class instances misclassified as known.

    Parameters
    ----------
    known_classes : list of int
        List of known labels, the labels the algorithm knows prior to the online phase
    cm : ConfusionMatrixNovelty
        Optional, can specify an already existing confusion matrix instead of creating a new one for the metric

    Attributes
    ----------
    cm : ConfusionMatrixNovelty
        Confusion matrix
    """
    def __init__(self, known_classes, cm: ConfusionMatrixNovelty = None):
        if cm is None:
            cm = ConfusionMatrixNovelty(known_classes)

        super(metrics.base.MultiClassMetric, self).__init__(cm)

    def get(self):
        fn = self.cm.false_negatives_novelty() #Number of novelties wrongly classified as known

        try:
            return fn / self.cm.nc_samples
        except ZeroDivisionError:
            return 0.0