Fix Auroc metric when max_fpr is set and a class is missing

CHANGELOG.md

@@ -26,6 +26,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
+- Fixed the use of `max_fpr` in `AUROC` metric when only one class is present ([#1895](https://github.com/Lightning-AI/torchmetrics/pull/1895))
+
+
 - Fixed bug related to empty predictions for `IntersectionOverUnion` metric ([#1892](https://github.com/Lightning-AI/torchmetrics/pull/1892))
 
 

src/torchmetrics/functional/classification/auroc.py

@@ -86,7 +86,7 @@ def _binary_auroc_compute(
  pos_label: int = 1,
 ) -> Tensor:
  fpr, tpr, _ = _binary_roc_compute(state, thresholds, pos_label)
- if max_fpr is None or max_fpr == 1:
+ if max_fpr is None or max_fpr == 1 or fpr.sum() == 0 or tpr.sum() == 0:
  return _auc_compute_without_check(fpr, tpr, 1.0)
 
  _device = fpr.device if isinstance(fpr, Tensor) else fpr[0].device

tests/unittests/classification/test_auroc.py

@@ -393,3 +393,17 @@ def test_valid_input_thresholds(metric, thresholds):
  with pytest.warns(None) as record:
  metric(thresholds=thresholds)
  assert len(record) == 0
+
+
+@pytest.mark.parametrize("max_fpr", [None, 0.8, 0.5])
+def test_corner_case_max_fpr(max_fpr):
+ """Check that metric returns 0 when one class is missing and `max_fpr` is set."""
+ preds = torch.tensor([0.1, 0.2, 0.3, 0.4])
+ target = torch.tensor([0, 0, 0, 0])
+ metric = BinaryAUROC(max_fpr=max_fpr)
+ assert metric(preds, target) == 0.0
+
+ preds = torch.tensor([0.5, 0.6, 0.7, 0.8])
+ target = torch.tensor([1, 1, 1, 1])
+ metric = BinaryAUROC(max_fpr=max_fpr)
+ assert metric(preds, target) == 0.0