8d4fee5d7dc69f82aadb38abc4564dda5cc8580f,rankeval/core/metrics/dcg.py,DCG,eval_per_query,#DCG#Any#Any#,70

Before Change

            dcg = sum([y[rank] / (np.math.log(float(k) + 2.0, 2))
                       for k, rank in enumerate(idx_y_pred_sorted)])
        elif self.implementation == "exp":
            dcg = sum([(2.0 ** y[rank] - 1.0) / (np.math.log(float(k) + 2.0, 2))
                       for k, rank in enumerate(idx_y_pred_sorted)])
        return dcg


    def __str__(self):

After Change

        if self.cutoff is not None:
            idx_y_pred_sorted = idx_y_pred_sorted[:self.cutoff]

        discount = np.log2(np.arange(2, len(idx_y_pred_sorted)+2))

        if self.implementation == "flat":
            gain = y[idx_y_pred_sorted]
        elif self.implementation == "exp":
            gain = np.exp2(y[idx_y_pred_sorted]) - 1.0

        dcg = (gain / discount).sum()
        return dcg


    def __str__(self):

Instances