Update various binary classification counts for a single (label, pred)
        pair.

        Parameters
        ----------
        label : `NDArray`
            The labels of the data.

        pred : `NDArray`
            Predicted values.

def update_binary_stats(self, label, pred):
        """
        Update various binary classification counts for a single (label, pred)
        pair.

        Parameters
        ----------
        label : `NDArray`
            The labels of the data.

        pred : `NDArray`
            Predicted values.
        """
        pred = pred.asnumpy()
        label = label.asnumpy().astype('int32')
        pred_label = numpy.argmax(pred, axis=1)

        check_label_shapes(label, pred)
        if len(numpy.unique(label)) > 2:
            raise ValueError("%s currently only supports binary classification."
                             % self.__class__.__name__)
        pred_true = (pred_label == 1)
        pred_false = 1 - pred_true
        label_true = (label == 1)
        label_false = 1 - label_true

        true_pos = (pred_true * label_true).sum()
        false_pos = (pred_true * label_false).sum()
        false_neg = (pred_false * label_true).sum()
        true_neg = (pred_false * label_false).sum()
        self.true_positives += true_pos
        self.global_true_positives += true_pos
        self.false_positives += false_pos
        self.global_false_positives += false_pos
        self.false_negatives += false_neg
        self.global_false_negatives += false_neg
        self.true_negatives += true_neg
        self.global_true_negatives += true_neg