(self, labels, pred_scores)
Compute Kolmogorov-Smirnov
        Parameters
        ----------
        labels: value list. The labels of data set.
        pred_scores: pred_scores: value list. The predict results of model. It should be corresponding to labels each data.
        Returns
        ----------
        max_ks_interval: float max value of each tpr - fpt
        fpr:
Compute Kolmogorov-Smirnov
        Parameters
        ----------
        labels: value list. The labels of data set.
        pred_scores: pred_scores: value list. The predict results of model. It should be corresponding to labels each data.
        Returns
        ----------
        max_ks_interval: float max value of each tpr - fpt
        fpr:
Compute Kolmogorov - Smirnov Parameters ---------- labels : value list . The labels of data set . pred_scores : pred_scores : value list . The predict results of model . It should be corresponding to labels each data . Returns ---------- max_ks_interval : float max value of each tpr - fpt fpr :
def ks(self, labels, pred_scores):

        """
        Compute Kolmogorov-Smirnov
        Parameters
        ----------
        labels: value list. The labels of data set.
        pred_scores: pred_scores: value list. The predict results of model. It should be corresponding to labels each data.
        Returns
        ----------
        max_ks_interval: float max value of each tpr - fpt
        fpr:
        """

        if self.eval_type == consts.ONE_VS_REST:
            try:
                rs = classification_metric.KS().compute(labels, pred_scores)
            except:
                rs = [0, [0], [0], [0], [0]]   # in case all labels are 0 or 1
                logging.warning("all true labels are 0/1 when running ovr KS")
            return rs
        else:
            return classification_metric.KS().compute(labels, pred_scores)
def ks ( self , labels , pred_scores ) : if self . eval_type == consts . ONE_VS_REST : try : rs = classification_metric . KS ( ) . compute ( labels , pred_scores ) except : rs = [ 0 , [ 0 ] , [ 0 ] , [ 0 ] , [ 0 ] ] # in case all labels are 0 or 1 logging . warning ( "all true labels are 0/1 when running ovr KS" ) return rs else : return classification_metric . KS ( ) . compute ( labels , pred_scores )