f4beaac559e00a3676d942dc7e8fea69efc01cfe,catalyst/metrics/mrr.py,,mrr,#,8

Before Change

    values, indices = torch.max(true_sorted_by_pred_shrink, dim=1)
    indices = indices.type_as(values).unsqueeze(dim=0).t()
    result = torch.tensor(1.0) / (indices + torch.tensor(1.0))

    zero_sum_mask = values == 0.0
    result[zero_sum_mask] = 0.0

After Change

            The mrr score for each batch.
    
    results = []
    for k in topk:
        results.append(torch.mean(reciprocal_rank(outputs, targets, k)))

    return results


__all__ = ["reciprocal_rank", "mrr"]

Instances