Bug fix: _filter_kwargs was erroring when provided a function without a __name__ attribute

botorch/optim/utils/common.py

@@ -23,9 +23,14 @@ def _filter_kwargs(function: Callable, **kwargs: Any) -> Any:
     allowed_params = signature(function).parameters
     removed = {k for k in kwargs.keys() if k not in allowed_params}
     if len(removed) > 0:
+        fn_descriptor = (
+            f" for function {function.__name__}"
+            if hasattr(function, "__name__")
+            else ""
+        )
         warn(
             f"Keyword arguments {list(removed)} will be ignored because they are"
-            f" not allowed parameters for function {function.__name__}. Allowed "
+            f" not allowed parameters{fn_descriptor}. Allowed "
             f"parameters are {list(allowed_params.keys())}."
         )
     return {k: v for k, v in kwargs.items() if k not in removed}

test/optim/test_optimize.py

@@ -115,13 +115,10 @@ def test_optimize_acqf_joint(
                 mock_gen_candidates_scipy,
                 mock_gen_candidates_torch,
             ):
-                # Mocks don't have a __name__ attribute.
-                # Set the attribute, since it is needed for testing _filter_kwargs
                 if mock_gen_candidates == mock_gen_candidates_torch:
                     mock_signature.return_value = signature(gen_candidates_torch)
                 else:
                     mock_signature.return_value = signature(gen_candidates_scipy)
-                mock_gen_candidates.__name__ = "gen_candidates"
 
                 mock_gen_batch_initial_conditions.return_value = torch.zeros(
                     num_restarts, q, 3, device=self.device, dtype=dtype
@@ -835,13 +832,10 @@ def nlc(x):
             mock_gen_candidates_torch,
             mock_gen_candidates_scipy,
         ):
-            # Mocks don't have a __name__ attribute.
-            # Set the attribute, since it is needed for testing _filter_kwargs
             if mock_gen_candidates == mock_gen_candidates_torch:
                 mock_signature.return_value = signature(gen_candidates_torch)
             else:
                 mock_signature.return_value = signature(gen_candidates_scipy)
-            mock_gen_candidates.__name__ = "gen_candidates"
             for dtype in (torch.float, torch.double):
 
                 mock_acq_function = MockAcquisitionFunction()

test/optim/utils/test_common.py

@@ -25,14 +25,23 @@ def mock_adam(params, lr: float = 0.001) -> None:
             return  # pragma: nocover
 
         kwargs = {"lr": 0.01, "maxiter": 3000}
-        with catch_warnings(record=True) as ws:
+        expected_msg = (
+            r"Keyword arguments \['maxiter'\] will be ignored because they are "
+            r"not allowed parameters for function mock_adam. Allowed parameters "
+            r"are \['params', 'lr'\]."
+        )
+
+        with self.assertWarnsRegex(Warning, expected_msg):
             valid_kwargs = _filter_kwargs(mock_adam, **kwargs)
+        self.assertEqual(set(valid_kwargs.keys()), {"lr"})
+
+        mock_partial = partial(mock_adam, lr=2.0)
         expected_msg = (
-            "Keyword arguments ['maxiter'] will be ignored because they are not"
-            " allowed parameters for function mock_adam. Allowed parameters are "
-            "['params', 'lr']."
+            r"Keyword arguments \['maxiter'\] will be ignored because they are "
+            r"not allowed parameters. Allowed parameters are \['params', 'lr'\]."
         )
-        self.assertEqual(expected_msg, str(ws[0].message))
+        with self.assertWarnsRegex(Warning, expected_msg):
+            valid_kwargs = _filter_kwargs(mock_partial, **kwargs)
         self.assertEqual(set(valid_kwargs.keys()), {"lr"})
 
     def test_handle_numerical_errors(self):