Fix bugs in metrics

benchmarks/yambda/evaluation/metrics.py → Fix bugs in metrics

@@ -47,7 +47,10 @@ class Recall(Metric):
             num_positives = targets.lengths.to(torch.float32)
             num_positives[num_positives == 0] = torch.inf
 
-            values[k] = target_mask[:, :k].to(torch.float32).sum(dim=-1) / num_positives
+            # there was a bug: we divided by num_positives instead of max(num_positives, k)
+            # this may have slightly affected the absolute metric values,
+            # but as far as we can judge it didn't change the ranking of the models reported in the paper.
+            values[k] = target_mask[:, :k].to(torch.float32).sum(dim=-1) / torch.clamp(num_positives, max=k)
 
             values[k] = torch.mean(values[k]).item()
 
@@ -134,16 +137,41 @@ class NDCG(Metric):
     def __call__(
         self, ranked: Ranked | None, targets: Targets, target_mask: torch.Tensor, ks: Iterable[int]
     ) -> dict[int, float]:
-        actual_dcg = DCG()(ranked, targets, target_mask, ks)
+
+        # there was a bug: we computed (dcg_1 + ... + dcg_n) / (idcg_1 + ... + idcg_n)
+        # instead of (1 / n) * (dcg_1 / idcg_1 + ... + dcg_n / idcg_n)
+        # this may have affected the absolute metric values,
+        # but as far as we can judge it didn't change the ranking of the models reported in the paper.
+
+        assert all(0 < k <= target_mask.shape[1] for k in ks)
+
+        def calc_dcg(target_mask: torch.Tensor) -> dict[int, torch.Tensor]:
+            values = {}
+
+            discounts = 1.0 / torch.log2(
+                torch.arange(2, target_mask.shape[1] + 2, device=target_mask.device, dtype=torch.float32)
+            )
+
+            for k in ks:
+                dcg_k = torch.sum(target_mask[:, :k] * discounts[:k], dim=1)
+                values[k] = dcg_k
+            return values
+
+        actual_dcg = calc_dcg(target_mask)
 
         ideal_target_mask = (
             torch.arange(target_mask.shape[1], device=targets.device)[None, :] < targets.lengths[:, None]
         ).to(torch.float32)
         assert target_mask.shape == ideal_target_mask.shape
 
-        ideal_dcg = DCG()(ranked, targets, ideal_target_mask, ks)
+        ideal_dcg = calc_dcg(target_mask)
+
+        def divide(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+            assert x.shape == y.shape
+            assert x.shape[0] == target_mask.shape[0]
+            return torch.where(y == 0, 0, x / y).mean()
 
-        ndcg_values = {k: (actual_dcg[k] / ideal_dcg[k] if ideal_dcg[k] != 0 else 0.0) for k in ks}
+        ndcg_values = {k: divide(actual_dcg[k], ideal_dcg[k]).item() for k in ks}
 
         return ndcg_values
 
@@ -201,4 +229,4 @@ def calc_metrics(ranked: Ranked, targets: Targets, metrics: list[str]) -> dict[s
     for name, ks in grouped_metrics.items():
         result[name] = REGISTERED_METRIC_FN[name](ranked, targets, target_mask, ks=ks)
 
-    return result
+    return result