Fix hitrate

CHANGELOG.md

@@ -29,6 +29,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - minimal requirements issue ([#1147](https://github.com/catalyst-team/catalyst/issues/1147))
 - nested dicts in `loaders_params`/`samplers_params` overriding fixed ([#1150](https://github.com/catalyst-team/catalyst/pull/1150))
+- fixed hitrate calculation issue ([#1155]) (https://github.com/catalyst-team/catalyst/issues/1155)
 
 ## [21.03.1] - 2021-03-28
 

catalyst/metrics/functional/_hitrate.py

@@ -7,7 +7,8 @@
 
 def hitrate(outputs: torch.Tensor, targets: torch.Tensor, topk: List[int]) -> List[torch.Tensor]:
     """
-    Calculate the hit rate score given model outputs and targets.
+    Calculate the hit rate (aka recall) score given
+    model outputs and targets.
     Hit-rate is a metric for evaluating ranking systems.
     Generate top-N recommendations and if one of the recommendation is
     actually what user has rated, you consider that a hit.
@@ -39,7 +40,10 @@ def hitrate(outputs: torch.Tensor, targets: torch.Tensor, topk: List[int]) -> Li
     targets_sort_by_outputs = process_recsys_components(outputs, targets)
     for k in topk:
         k = min(outputs.size(1), k)
-        hits_score = torch.sum(targets_sort_by_outputs[:, :k], dim=1) / k
+        if targets.sum(dim=1).data[0] != 0:
+            hits_score = torch.sum(targets_sort_by_outputs[:, :k], dim=1) / targets.sum(dim=1)
+        else:
+            hits_score = torch.tensor([0.0])
         results.append(torch.mean(hits_score))
 
     return results

catalyst/metrics/functional/tests/test_hitrate.py

@@ -9,13 +9,16 @@ def test_hitrate():
     """
     Tests for catalyst.metrics.hitrate metric.
     """
-    y_pred = [0.5, 0.2]
-    y_true = [1.0, 0.0]
-    k = [1, 2]
+    y_pred = [0.5, 0.2, 0.1]
+    y_true = [1.0, 0.0, 1.0]
+    k = [1, 2, 3]
 
-    hitrate_at1, hitrate_at2 = hitrate(torch.Tensor([y_pred]), torch.Tensor([y_true]), k)
-    assert hitrate_at1 == 1.0
+    hitrate_at1, hitrate_at2, hitrate_at3 = hitrate(
+        torch.Tensor([y_pred]), torch.Tensor([y_true]), k
+    )
+    assert hitrate_at1 == 0.5
     assert hitrate_at2 == 0.5
+    assert hitrate_at3 == 1.0
 
     # check 1 simple case
     y_pred = [0.5, 0.2]