rth
diff --git a/‎doc/whats_new/v1.0.rst
Lines changed: 5 additions & 1 deletion b/‎doc/whats_new/v1.0.rst
Lines changed: 5 additions & 1 deletion
diff --git a/‎sklearn/feature_extraction/_dict_vectorizer.py
Lines changed: 7 additions & 7 deletions b/‎sklearn/feature_extraction/_dict_vectorizer.py
Lines changed: 7 additions & 7 deletions
diff --git a/‎sklearn/feature_extraction/tests/test_dict_vectorizer.py
Lines changed: 18 additions & 0 deletions b/‎sklearn/feature_extraction/tests/test_dict_vectorizer.py
Lines changed: 18 additions & 0 deletions
@@ -340,10 +340,14 @@ Changelog
 :mod:`sklearn.feature_extraction`
 .................................
 
-- |Fix| Fixed a bug in class:`feature_extraction.HashingVectorizer` where some
+- |Fix| Fixed a bug in :class:`feature_extraction.HashingVectorizer` where some
   input strings would result in negative indices in the transformed data.
   :pr:`19035` by :user:`Liu Yu <ly648499246>`.
 
+- |Fix| Fixed a bug in :class:`feature_extraction.DictVectorizer` by raising an
+  error with unsupported value type.
+  :pr:`19520` by :user:`Jeff Zhao <kamiyaa>`.
+
 :mod:`sklearn.feature_selection`
 ................................
 
 
@@ -226,13 +226,7 @@ def _transform(self, X, fitting):
                     v = 1
                 elif isinstance(v, Number) or (v is None):
                     feature_name = f
-                elif isinstance(v, Mapping):
-                    raise TypeError(
-                        f"Unsupported value Type {type(v)} "
-                        f"for {f}: {v}.\n"
-                        "Mapping objects are not supported."
-                    )
-                elif isinstance(v, Iterable):
+                elif not isinstance(v, Mapping) and isinstance(v, Iterable):
                     feature_name = None
                     self._add_iterable_element(
                         f,
@@ -244,6 +238,12 @@ def _transform(self, X, fitting):
                         indices=indices,
                         values=values,
                     )
+                else:
+                    raise TypeError(
+                        f"Unsupported value Type {type(v)} "
+                        f"for {f}: {v}.\n"
+                        f"{type(v)} objects are not supported."
+                    )
 
                 if feature_name is not None:
                     if fitting and feature_name not in vocab:
 
@@ -209,3 +209,21 @@ def test_dictvectorizer_dense_sparse_equivalence():
     expected_inverse = [{"category=thriller": 1.0}]
     assert dense_inverse_transform == expected_inverse
     assert sparse_inverse_transform == expected_inverse
+
+
+def test_dict_vectorizer_unsupported_value_type():
+    """Check that we raise an error when the value associated to a feature
+    is not supported.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/19489
+    """
+
+    class A:
+        pass
+
+    vectorizer = DictVectorizer(sparse=True)
+    X = [{"foo": A()}]
+    err_msg = "Unsupported value Type"
+    with pytest.raises(TypeError, match=err_msg):
+        vectorizer.fit_transform(X)