comments about bugs in metric calculation

benchmarks/yambda/evaluation/metrics.py

@@ -47,6 +47,9 @@ class Recall(Metric):
             num_positives = targets.lengths.to(torch.float32)
             num_positives[num_positives == 0] = torch.inf
 
+            # there is a bug: we divide by num_positives instead of max(num_positives, k)
+            # this may slightly affect the absolute metric values, 
+            # but as far as we can judge it does not change the ranking of the models reported in the paper.
             values[k] = target_mask[:, :k].to(torch.float32).sum(dim=-1) / num_positives
 
             values[k] = torch.mean(values[k]).item()
@@ -134,6 +137,12 @@ class NDCG(Metric):
     def __call__(
         self, ranked: Ranked | None, targets: Targets, target_mask: torch.Tensor, ks: Iterable[int]
     ) -> dict[int, float]:
+
+        # there is a bug: we compute (dcg_1 + ... + dcg_n) / (idcg_1 + ... + idcg_n)
+        # instead of (1 / n) * (dcg_1 / idcg_1 + ... + dcg_n / idcg_n)
+        # this may affect the absolute metric values,
+        # but as far as we can judge it does not change the ranking of the models reported in the paper.
+        
         actual_dcg = DCG()(ranked, targets, target_mask, ks)
 
         ideal_target_mask = (