Fix Matthews correlation coefficient when the denominator is 0

CHANGELOG.md

@@ -30,6 +30,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed check for available modules ([#772](https://github.com/PyTorchLightning/metrics/pull/772))
 
 
+- Fixed Matthews correlation coefficient when the denominator is 0 ([#781](https://github.com/PyTorchLightning/metrics/pull/781))
+
+
 ## [0.7.0] - 2022-01-17
 
 ### Added

tests/classification/test_matthews_corrcoef.py

@@ -140,3 +140,10 @@ def test_matthews_corrcoef_differentiability(self, preds, target, sk_metric, num
                 "threshold": THRESHOLD,
             },
         )
+
+
+def test_zero_case():
+    """Cases where the denominator in the matthews corrcoef is 0, the score should return 0."""
+    # Example where neither 1 or 2 is present in the target tensor
+    out = matthews_corrcoef(torch.tensor([0, 1, 2]), torch.tensor([0, 0, 0]), 3)
+    assert out == 0.0

torchmetrics/functional/classification/matthews_corrcoef.py

@@ -37,7 +37,15 @@ def _matthews_corrcoef_compute(confmat: Tensor) -> Tensor:
     pk = confmat.sum(dim=0).float()
     c = torch.trace(confmat).float()
     s = confmat.sum().float()
-    return (c * s - sum(tk * pk)) / (torch.sqrt(s ** 2 - sum(pk * pk)) * torch.sqrt(s ** 2 - sum(tk * tk)))
+
+    cov_ytyp = c * s - sum(tk * pk)
+    cov_ypyp = s ** 2 - sum(pk * pk)
+    cov_ytyt = s ** 2 - sum(tk * tk)
+
+    if cov_ypyp * cov_ytyt == 0:
+        return 0.0
+    else:
+        return cov_ytyp / torch.sqrt(cov_ytyt * cov_ypyp)
 
 
 def matthews_corrcoef(