scikit-learn · ogrisel · Jun 3, 2021 · Apr 25, 2021 · Apr 25, 2021 · Apr 25, 2021
diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
@@ -878,12 +878,11 @@ 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)
 
-    if np.isnan(mcc):
+    if cov_ypyp * cov_ytyt == 0:
         return 0.
     else:
-        return mcc
+        return cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)
 
 
 @_deprecate_positional_args

diff --git a/sklearn/metrics/tests/test_classification.py b/sklearn/metrics/tests/test_classification.py
@@ -20,7 +20,6 @@
 from sklearn.utils._testing import assert_array_equal
 from sklearn.utils._testing import assert_array_almost_equal
 from sklearn.utils._testing import assert_allclose
-from sklearn.utils._testing import assert_warns_div0
 from sklearn.utils._testing import assert_no_warnings
 from sklearn.utils._testing import assert_warns_message
 from sklearn.utils._testing import ignore_warnings
@@ -622,7 +621,6 @@ 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
@@ -684,17 +682,11 @@ def test_matthews_corrcoef():
     assert_almost_equal(matthews_corrcoef(y_true, y_true_inv2), -1)
 
     # For the zero vector case, the corrcoef cannot be calculated and should
-    # result in a RuntimeWarning
-    mcc = assert_warns_div0(matthews_corrcoef, [0, 0, 0, 0], [0, 0, 0, 0])
-
-    # But will output 0
-    assert_almost_equal(mcc, 0.)
+    # output 0
+    assert_almost_equal(matthews_corrcoef([0, 0, 0, 0], [0, 0, 0, 0]), 0.)
 
     # And also for any other vector with 0 variance
-    mcc = assert_warns_div0(matthews_corrcoef, y_true, ['a'] * len(y_true))
-
-    # But will output 0
-    assert_almost_equal(mcc, 0.)
+    assert_almost_equal(matthews_corrcoef(y_true, ['a'] * len(y_true)), 0.)
 
     # These two vectors have 0 correlation and hence mcc should be 0
     y_1 = [1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]
@@ -731,12 +723,15 @@ def test_matthews_corrcoef_multiclass():
     assert_almost_equal(matthews_corrcoef(y_true, y_pred_min),
                         -12 / np.sqrt(24 * 16))
 
-    # Zero variance will result in an mcc of zero and a Runtime Warning
+    # Zero variance will result in an mcc of zero
     y_true = [0, 1, 2]
     y_pred = [3, 3, 3]
-    mcc = assert_warns_message(RuntimeWarning, 'invalid value encountered',
-                               matthews_corrcoef, y_true, y_pred)
-    assert_almost_equal(mcc, 0.0)
+    assert_almost_equal(matthews_corrcoef(y_true, y_pred), 0.0)
+
+    # Also for ground truth with zero variance
+    y_true = [3, 3, 3]
+    y_pred = [0, 1, 2]
+    assert_almost_equal(matthews_corrcoef(y_true, y_pred), 0.0)
 
     # These two vectors have 0 correlation and hence mcc should be 0
     y_1 = [0, 1, 2, 0, 1, 2, 0, 1, 2]
@@ -754,16 +749,12 @@ def test_matthews_corrcoef_multiclass():
                                           sample_weight=sample_weight), -1)
 
     # For the zero vector case, the corrcoef cannot be calculated and should
-    # result in a RuntimeWarning
+    # output 0
     y_true = [0, 0, 1, 2]
     y_pred = [0, 0, 1, 2]
     sample_weight = [1, 1, 0, 0]
-    mcc = assert_warns_message(RuntimeWarning, 'invalid value encountered',
-                               matthews_corrcoef, y_true, y_pred,
-                               sample_weight=sample_weight)
-
-    # But will output 0
-    assert_almost_equal(mcc, 0.)
+    assert_almost_equal(matthews_corrcoef(y_true, y_pred,
+                                          sample_weight=sample_weight), 0.)
 
 
 @pytest.mark.parametrize('n_points', [100, 10000])

diff --git a/sklearn/utils/_testing.py b/sklearn/utils/_testing.py
@@ -187,30 +187,6 @@ def check_in_message(msg): return message in msg
     return result
 
 
-def assert_warns_div0(func, *args, **kw):
-    """Assume that numpy's warning for divide by zero is raised.
-
-    Handles the case of platforms that do not support warning on divide by
-    zero.
-
-    Parameters
-    ----------
-    func
-    *args
-    **kw
-    """
-
-    with np.errstate(divide='warn', invalid='warn'):
-        try:
-            assert_warns(RuntimeWarning, np.divide, 1, np.zeros(1))
-        except AssertionError:
-            # This platform does not report numpy divide by zeros
-            return func(*args, **kw)
-        return assert_warns_message(RuntimeWarning,
-                                    'invalid value encountered',
-                                    func, *args, **kw)
-
-
 # To remove when we support numpy 1.7
 def assert_no_warnings(func, *args, **kw):
     """