Corrected the missing zero_division argument

doc/whats_new/v0.22.rst

@@ -46,6 +46,10 @@ Changelog
   correctly to maximize contrast with its background. :pr:`15936` by
   `Thomas Fan`_ and :user:`DizietAsahi`.
 
+- |Fix| :func:`metrics.classification_report` does no longer ignore the
+  value of the ``zero_division`` keyword argument. :pr:`15879`
+  by :user:`Bibhash Chandra Mitra <Bibyutatsu>`.
+
 :mod:`sklearn.utils`
 ....................
 

sklearn/metrics/_classification.py

@@ -1964,7 +1964,8 @@ class 2       1.00      0.67      0.80         3
         # compute averages with specified averaging method
         avg_p, avg_r, avg_f1, _ = precision_recall_fscore_support(
             y_true, y_pred, labels=labels,
-            average=average, sample_weight=sample_weight)
+            average=average, sample_weight=sample_weight,
+            zero_division=zero_division)
         avg = [avg_p, avg_r, avg_f1, np.sum(s)]
 
         if output_dict:

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
 from sklearn.utils._testing import assert_warns_div0
 from sklearn.utils._testing import assert_no_warnings
 from sklearn.utils._testing import assert_warns_message
@@ -154,6 +153,22 @@ def test_classification_report_dictionary_output():
     assert type(expected_report['macro avg']['support']) == int
 
 
+@pytest.mark.parametrize('zero_division', ["warn", 0, 1])
+def test_classification_report_zero_division_warning(zero_division):
+    y_true, y_pred = ["a", "b", "c"], ["a", "b", "d"]
+    with warnings.catch_warnings(record=True) as record:
+        classification_report(
+            y_true, y_pred, zero_division=zero_division, output_dict=True)
+        if zero_division == "warn":
+            assert len(record) > 1
+            for item in record:
+                msg = ("Use `zero_division` parameter to control this "
+                       "behavior.")
+                assert msg in str(item.message)
+        else:
+            assert not record
+
+
 def test_multilabel_accuracy_score_subset_accuracy():
     # Dense label indicator matrix format
     y1 = np.array([[0, 1, 1], [1, 0, 1]])