(labels, pred_labels)
return clustering_metric.AdjustedRandScore().compute(labels, pred_labels)
Compute the adjusted-rand score
        Parameters
        ----------
        labels: value list. The labels of data set.
        pred_labels: value list. The predict results of model. It should be corresponding to labels each data.
        Return
        ----------
        float
            A positive floating point value
Compute the adjusted-rand score
        Parameters
        ----------
        labels: value list. The labels of data set.
        pred_labels: value list. The predict results of model. It should be corresponding to labels each data.
        Return
        ----------
        float
            A positive floating point value
Compute the adjusted - rand score Parameters ---------- labels : value list . The labels of data set . pred_labels : value list . The predict results of model . It should be corresponding to labels each data . Return ---------- float A positive floating point value
def adjusted_rand_score(labels, pred_labels):
        """
        Compute the adjusted-rand score
        Parameters
        ----------
        labels: value list. The labels of data set.
        pred_labels: value list. The predict results of model. It should be corresponding to labels each data.
        Return
        ----------
        float
            A positive floating point value
        """

        return clustering_metric.AdjustedRandScore().compute(labels, pred_labels)
def adjusted_rand_score ( labels , pred_labels ) : return clustering_metric . AdjustedRandScore ( ) . compute ( labels , pred_labels )