(self, labels, pred_scores)
Compute the recall
        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
        ----------
        dict
            The key is threshold and the value is another dic, which key is label in parameter labels, and value is the
            label's recall.
Compute the recall
        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
        ----------
        dict
            The key is threshold and the value is another dic, which key is label in parameter labels, and value is the
            label's recall.
Compute the recall 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 ---------- dict The key is threshold and the value is another dic which key is label in parameter labels and value is the label s recall .
def recall(self, labels, pred_scores):
        """
        Compute the recall
        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
        ----------
        dict
            The key is threshold and the value is another dic, which key is label in parameter labels, and value is the
            label's recall.
        """
        if self.eval_type == consts.BINARY:
            recall_operator = classification_metric.BiClassRecall()
            recall_res, thresholds, cuts = recall_operator.compute(labels, pred_scores)
            return recall_res, cuts, thresholds
        elif self.eval_type == consts.MULTY:
            recall_operator = classification_metric.MultiClassRecall()
            return recall_operator.compute(labels, pred_scores)
        else:
            logging.warning("error:can not find classification type:{}".format(self.eval_type))
def recall ( self , labels , pred_scores ) : if self . eval_type == consts . BINARY : recall_operator = classification_metric . BiClassRecall ( ) recall_res , thresholds , cuts = recall_operator . compute ( labels , pred_scores ) return recall_res , cuts , thresholds elif self . eval_type == consts . MULTY : recall_operator = classification_metric . MultiClassRecall ( ) return recall_operator . compute ( labels , pred_scores ) else : logging . warning ( "error:can not find classification type:{}" . format ( self . eval_type ) )