diff --git a/sklearn/metrics/tests/test_common.py b/sklearn/metrics/tests/test_common.py
index 00db32e1ef389..5b9f6d2b69027 100644
--- a/sklearn/metrics/tests/test_common.py
+++ b/sklearn/metrics/tests/test_common.py
@@ -369,11 +369,11 @@
 
 # No Sample weight support
 METRICS_WITHOUT_SAMPLE_WEIGHT = [
-    "confusion_matrix", # Left this one here because the tests in this file do
-                        # not work for confusion_matrix, as its output is a
-                        # matrix instead of a number. Testing of
-                        # confusion_matrix with sample_weight is in
-                        # test_classification.py
+    "confusion_matrix",  # Left this one here because the tests in this file do
+                         # not work for confusion_matrix, as its output is a
+                         # matrix instead of a number. Testing of
+                         # confusion_matrix with sample_weight is in
+                         # test_classification.py
     "median_absolute_error",
 ]
 
@@ -619,9 +619,9 @@ def test_invariance_string_vs_numbers_labels():
 
 
 def test_inf_nan_input():
-    invalids =[([0, 1], [np.inf, np.inf]),
-               ([0, 1], [np.nan, np.nan]),
-               ([0, 1], [np.nan, np.inf])]
+    invalids = [([0, 1], [np.inf, np.inf]),
+                ([0, 1], [np.nan, np.nan]),
+                ([0, 1], [np.nan, np.inf])]
 
     METRICS = dict()
     METRICS.update(THRESHOLDED_METRICS)
@@ -1011,7 +1011,8 @@ def check_sample_weight_invariance(name, metric, y1, y2):
                   sample_weight=np.hstack([sample_weight, sample_weight]))
 
 
-def test_sample_weight_invariance(n_samples=50):
+def generate_sample_weight_invariance(n_samples=50):
+    # create generative function to iterate through each relevant metric
     random_state = check_random_state(0)
     # regression
     y_true = random_state.random_sample(size=(n_samples,))
@@ -1088,6 +1089,12 @@ def test_sample_weight_invariance(n_samples=50):
                    metric, y_true, y_pred)
 
 
+def test_sample_weight_invariance(n_samples=50):
+    # iterate through each metric testing each case
+    for metrics in generate_sample_weight_invariance(n_samples):
+        pass
+
+
 @ignore_warnings
 def test_no_averaging_labels():
     # test labels argument when not using averaging