"min" method in cal_ranks function: scoring ties in RSN about rsn HOT 1 CLOSED

Hi, thanks for your attention to our paper.

As you can see in the comment of cal_ranks, the results are usually identical if the model is correctly trained. You can replace the policy with 'average' and find that the result is almost same.

On the other hand, the 'min' policy is implemented by ourselves, and it is much faster compared with others (because we actually do not sort the probs). When we conducted the experiments, we did use the 'average' policy to obtain results only on testing data. But it seems we overlook this in the revision, and we have fixed it.

from rsn.