scikit-learn · thomasjpfan · Apr 15, 2020
diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
@@ -831,7 +831,11 @@ def matthews_corrcoef(y_true, y_pred, sample_weight=None):
     cov_ytyp = n_correct * n_samples - np.dot(t_sum, p_sum)
     cov_ypyp = n_samples ** 2 - np.dot(p_sum, p_sum)
     cov_ytyt = n_samples ** 2 - np.dot(t_sum, t_sum)
-    mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)
+
+    with warnings.catch_warnings():
+        # catches run time warning when dividing by 0.0
+        warnings.simplefilter('ignore', RuntimeWarning)
+        mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)
 
     if np.isnan(mcc):
         return 0.

diff --git a/sklearn/metrics/tests/test_classification.py b/sklearn/metrics/tests/test_classification.py
@@ -597,10 +597,13 @@ def test_cohen_kappa():
                         weights="quadratic"), 0.9541, decimal=4)
 
 
-@ignore_warnings
-def test_matthews_corrcoef_nan():
-    assert matthews_corrcoef([0], [1]) == 0.0
-    assert matthews_corrcoef([0, 0], [0, 1]) == 0.0
+@pytest.mark.parametrize(
+    "y_true, y_pred", [([0], [1]), ([0, 0], [0, 1])])
+def test_matthews_corrcoef_nan(y_true, y_pred):
+    # Test to make sure warnings are not raised and the metric is zero
+    with pytest.warns(None) as rec:
+        assert matthews_corrcoef(y_true, y_pred) == pytest.approx(0.0)
+    assert not rec
 
 
 def test_matthews_corrcoef_against_numpy_corrcoef():