dev-7369: Make common metric tests look nicer (scikit-learn#7620)

aashil · TomDLT · commit 329b7ee204b8 · 2016-10-13T16:36:28.000+02:00
* Change the tests to yield after performing the _named_check.

* Tests affected:

  -test_averaging_multiclass
  -test_averaging_multilabel
  -test_averaging_multilabel_all_zeroes
  -test_averaging_multilabel_all_ones
  -test_sample_weight_invariance

* File updated: sklearn/metrics/tests/test_common.py
diff --git a/sklearn/metrics/tests/test_common.py b/sklearn/metrics/tests/test_common.py
@@ -22,6 +22,7 @@
 from sklearn.utils.testing import assert_raise_message
 from sklearn.utils.testing import assert_true
 from sklearn.utils.testing import ignore_warnings
+from sklearn.utils.testing import _named_check
 
 from sklearn.metrics import accuracy_score
 from sklearn.metrics import average_precision_score
@@ -891,8 +892,8 @@ def test_averaging_multiclass(n_samples=50, n_classes=3):
     y_pred_binarize = lb.transform(y_pred)
 
     for name in METRICS_WITH_AVERAGING:
-        yield (check_averaging, name, y_true, y_true_binarize, y_pred,
-               y_pred_binarize, y_score)
+        yield (_named_check(check_averaging, name), name, y_true,
+               y_true_binarize, y_pred, y_pred_binarize, y_score)
 
 
 def test_averaging_multilabel(n_classes=5, n_samples=40):
@@ -906,8 +907,8 @@ def test_averaging_multilabel(n_classes=5, n_samples=40):
     y_pred_binarize = y_pred
 
     for name in METRICS_WITH_AVERAGING + THRESHOLDED_METRICS_WITH_AVERAGING:
-        yield (check_averaging, name, y_true, y_true_binarize, y_pred,
-               y_pred_binarize, y_score)
+        yield (_named_check(check_averaging, name), name, y_true,
+               y_true_binarize, y_pred, y_pred_binarize, y_score)
 
 
 def test_averaging_multilabel_all_zeroes():
@@ -918,8 +919,8 @@ def test_averaging_multilabel_all_zeroes():
     y_pred_binarize = y_pred
 
     for name in METRICS_WITH_AVERAGING:
-        yield (check_averaging, name, y_true, y_true_binarize, y_pred,
-               y_pred_binarize, y_score)
+        yield (_named_check(check_averaging, name), name, y_true,
+               y_true_binarize, y_pred, y_pred_binarize, y_score)
 
     # Test _average_binary_score for weight.sum() == 0
     binary_metric = (lambda y_true, y_score, average="macro":
@@ -937,8 +938,8 @@ def test_averaging_multilabel_all_ones():
     y_pred_binarize = y_pred
 
     for name in METRICS_WITH_AVERAGING:
-        yield (check_averaging, name, y_true, y_true_binarize, y_pred,
-               y_pred_binarize, y_score)
+        yield (_named_check(check_averaging, name), name, y_true,
+               y_true_binarize, y_pred, y_pred_binarize, y_score)
 
 
 @ignore_warnings
@@ -1025,9 +1026,11 @@ def test_sample_weight_invariance(n_samples=50):
             continue
         metric = ALL_METRICS[name]
         if name in THRESHOLDED_METRICS:
-            yield check_sample_weight_invariance, name, metric, y_true, y_score
+            yield _named_check(check_sample_weight_invariance, name), name,\
+                  metric, y_true, y_score
         else:
-            yield check_sample_weight_invariance, name, metric, y_true, y_pred
+            yield _named_check(check_sample_weight_invariance, name), name,\
+                  metric, y_true, y_pred
 
     # multiclass
     random_state = check_random_state(0)
@@ -1040,9 +1043,11 @@ def test_sample_weight_invariance(n_samples=50):
             continue
         metric = ALL_METRICS[name]
         if name in THRESHOLDED_METRICS:
-            yield check_sample_weight_invariance, name, metric, y_true, y_score
+            yield _named_check(check_sample_weight_invariance, name), name,\
+                  metric, y_true, y_score
         else:
-            yield check_sample_weight_invariance, name, metric, y_true, y_pred
+            yield _named_check(check_sample_weight_invariance, name), name,\
+                  metric, y_true, y_pred
 
     # multilabel indicator
     _, ya = make_multilabel_classification(n_features=1, n_classes=20,
@@ -1062,11 +1067,11 @@ def test_sample_weight_invariance(n_samples=50):
 
         metric = ALL_METRICS[name]
         if name in THRESHOLDED_METRICS:
-            yield (check_sample_weight_invariance, name, metric, y_true,
-                   y_score)
+            yield (_named_check(check_sample_weight_invariance, name), name,
+                   metric, y_true, y_score)
         else:
-            yield (check_sample_weight_invariance, name, metric, y_true,
-                   y_pred)
+            yield (_named_check(check_sample_weight_invariance, name), name,
+                   metric, y_true, y_pred)
 
 
 @ignore_warnings
