Fix single update in pearson corrcoef (#2019)

* fix
* changelog

CHANGELOG.md

@@ -26,6 +26,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
+- Fixed bug in `PearsonCorrCoef` is updated on single samples at a time ([#2019](https://github.com/Lightning-AI/torchmetrics/pull/2019)
+
+
 - Fixed support for pixelwise MSE ([#2017](https://github.com/Lightning-AI/torchmetrics/pull/2017)
 
 

src/torchmetrics/functional/regression/pearson.py

@@ -52,9 +52,9 @@ def _pearson_corrcoef_update(
     # Data checking
     _check_same_shape(preds, target)
     _check_data_shape_to_num_outputs(preds, target, num_outputs)
-    cond = n_prior.mean() > 0
-
     n_obs = preds.shape[0]
+    cond = n_prior.mean() > 0 or n_obs == 1
+
     if cond:
         mx_new = (n_prior * mean_x + preds.sum(0)) / (n_prior + n_obs)
         my_new = (n_prior * mean_y + target.sum(0)) / (n_prior + n_obs)
@@ -67,7 +67,6 @@ def _pearson_corrcoef_update(
     if cond:
         var_x += ((preds - mx_new) * (preds - mean_x)).sum(0)
         var_y += ((target - my_new) * (target - mean_y)).sum(0)
-
     else:
         var_x += preds.var(0) * (n_obs - 1)
         var_y += target.var(0) * (n_obs - 1)

tests/unittests/regression/test_pearson.py

@@ -149,3 +149,20 @@ def test_pearsons_warning_on_small_input(dtype, scale):
     target = scale * torch.randn(100, dtype=dtype)
     with pytest.warns(UserWarning, match="The variance of predictions or target is close to zero.*"):
         pearson_corrcoef(preds, target)
+
+
+def test_single_sample_update():
+    """See issue: https://github.com/Lightning-AI/torchmetrics/issues/2014."""
+    metric = PearsonCorrCoef()
+
+    # Works
+    metric(torch.tensor([3.0, -0.5, 2.0, 7.0]), torch.tensor([2.5, 0.0, 2.0, 8.0]))
+    res1 = metric.compute()
+    metric.reset()
+
+    metric(torch.tensor([3.0]), torch.tensor([2.5]))
+    metric(torch.tensor([-0.5]), torch.tensor([0.0]))
+    metric(torch.tensor([2.0]), torch.tensor([2.0]))
+    metric(torch.tensor([7.0]), torch.tensor([8.0]))
+    res2 = metric.compute()
+    assert torch.allclose(res1, res2)