Migrate MultiObjectiveOptimizationConfig callers from objective= to objectives=[]

ax/adapter/tests/test_adapter_utils.py

@@ -56,9 +56,9 @@ def test_feasible_hypervolume(self) -> None:
         mb = Metric(name="b", lower_is_better=True)
         mc = Metric(name="c", lower_is_better=False)
         optimization_config = MultiObjectiveOptimizationConfig(
-            objective=MultiObjective(
-                objectives=[Objective(metric=ma), Objective(metric=mb)]
-            ),
+            objectives=[
+                MultiObjective(objectives=[Objective(metric=ma), Objective(metric=mb)])
+            ],
             outcome_constraints=[
                 OutcomeConstraint(
                     metric=mc,

ax/adapter/tests/test_base_adapter.py

@@ -214,7 +214,7 @@ def test_fit_tracking_metrics(self) -> None:
             fit_tracking_metrics=False,
         )
         new_oc = OptimizationConfig(
-            objective=Objective(metric=Metric(name="test_metric2"), minimize=False),
+            objectives=[Objective(metric=Metric(name="test_metric2"), minimize=False)],
         )
         with self.assertRaisesRegex(UnsupportedError, "fit_tracking_metrics"):
             adapter.gen(n=1, optimization_config=new_oc)
@@ -301,7 +301,7 @@ def test_gen_base(self, mock_fit: Mock, mock_gen_arms: Mock) -> None:
 
         # Gen with a different optimization config.
         oc2 = OptimizationConfig(
-            objective=Objective(metric=Metric(name="branin"), minimize=True)
+            objectives=[Objective(metric=Metric(name="branin"), minimize=True)]
         )
         with mock.patch(ADAPTER__GEN_PATH, return_value=mock_return_value) as mock_gen:
             adapter.gen(n=1, search_space=search_space, optimization_config=oc2)
@@ -1369,10 +1369,12 @@ def test_untransform_observation_features_derived_parameter_with_digits(
             is_test=True,
             tracking_metrics=[metric],
             optimization_config=OptimizationConfig(
-                objective=Objective(
-                    metric=metric,
-                    minimize=True,
-                )
+                objectives=[
+                    Objective(
+                        metric=metric,
+                        minimize=True,
+                    )
+                ]
             ),
             runner=SyntheticRunner(),
         )

ax/adapter/tests/test_cross_validation.py

@@ -475,7 +475,7 @@ def test_has_good_opt_config_model_fit(self) -> None:
 
         # Test single objective
         optimization_config = OptimizationConfig(
-            objective=Objective(metric=Metric("m1"), minimize=True)
+            objectives=[Objective(metric=Metric("m1"), minimize=True)]
         )
         has_good_fit = has_good_opt_config_model_fit(
             optimization_config=optimization_config,
@@ -485,12 +485,14 @@ def test_has_good_opt_config_model_fit(self) -> None:
 
         # Test multi objective
         optimization_config = MultiObjectiveOptimizationConfig(
-            objective=MultiObjective(
-                objectives=[
-                    Objective(metric=Metric("m1"), minimize=False),
-                    Objective(metric=Metric("m2"), minimize=False),
-                ]
-            )
+            objectives=[
+                MultiObjective(
+                    objectives=[
+                        Objective(metric=Metric("m1"), minimize=False),
+                        Objective(metric=Metric("m2"), minimize=False),
+                    ]
+                )
+            ]
         )
         has_good_fit = has_good_opt_config_model_fit(
             optimization_config=optimization_config,
@@ -500,7 +502,7 @@ def test_has_good_opt_config_model_fit(self) -> None:
 
         # Test constraints
         optimization_config = OptimizationConfig(
-            objective=Objective(metric=Metric("m1"), minimize=False),
+            objectives=[Objective(metric=Metric("m1"), minimize=False)],
             outcome_constraints=[
                 OutcomeConstraint(metric=Metric("m2"), op=ComparisonOp.GEQ, bound=0.1)
             ],

ax/adapter/tests/test_discrete_adapter.py

@@ -143,7 +143,7 @@ def test_predict(self) -> None:
     def test_gen(self) -> None:
         # Test with constraints
         optimization_config = OptimizationConfig(
-            objective=Objective(metric=Metric("m1"), minimize=True),
+            objectives=[Objective(metric=Metric("m1"), minimize=True)],
             outcome_constraints=[
                 OutcomeConstraint(
                     metric=Metric("m2"), op=ComparisonOp.GEQ, bound=2, relative=False
@@ -231,7 +231,7 @@ def test_gen(self) -> None:
 
         # Test validation
         optimization_config = OptimizationConfig(
-            objective=Objective(metric=Metric("m1"), minimize=False),
+            objectives=[Objective(metric=Metric("m1"), minimize=False)],
             outcome_constraints=[
                 OutcomeConstraint(
                     metric=Metric("m2"), op=ComparisonOp.GEQ, bound=2, relative=True

ax/adapter/tests/test_hierarchical_search_space.py

@@ -142,10 +142,12 @@ def _test_gen_base(
             search_space=hss,
             tracking_metrics=[metric],
             optimization_config=OptimizationConfig(
-                objective=Objective(
-                    metric=metric,
-                    minimize=True,
-                )
+                objectives=[
+                    Objective(
+                        metric=metric,
+                        minimize=True,
+                    )
+                ]
             ),
             runner=SyntheticRunner(),
         )

ax/adapter/tests/test_torch_adapter.py

@@ -95,7 +95,7 @@ def test_TorchAdapter(self, device: torch.device | None = None) -> None:
             min=0.0, max=5.0, parameter_names=feature_names
         )
         opt_config = OptimizationConfig(
-            objective=Objective(metric=Metric("y1"), minimize=True),
+            objectives=[Objective(metric=Metric("y1"), minimize=True)],
             outcome_constraints=[
                 OutcomeConstraint(
                     metric=Metric("y2"), op=ComparisonOp.GEQ, bound=0.0, relative=False
@@ -212,7 +212,7 @@ def test_TorchAdapter(self, device: torch.device | None = None) -> None:
         )
         best_point_return_value = torch.tensor([1.0, 2.0, 3.0], **tkwargs)
         opt_config = OptimizationConfig(
-            objective=Objective(metric=Metric("y1"), minimize=False),
+            objectives=[Objective(metric=Metric("y1"), minimize=False)],
         )
         pending_observations = {
             "y2": [ObservationFeatures(parameters={"x1": 1.0, "x2": 2.0, "x3": 3.0})]
@@ -406,7 +406,7 @@ def test_evaluate_acquisition_function(self) -> None:
     def test_best_point(self) -> None:
         search_space = get_search_space_for_range_value()
         oc = OptimizationConfig(
-            objective=Objective(metric=Metric("a"), minimize=False),
+            objectives=[Objective(metric=Metric("a"), minimize=False)],
             outcome_constraints=[],
         )
         exp = Experiment(search_space=search_space, optimization_config=oc, name="test")
@@ -469,7 +469,7 @@ def test_best_point(self) -> None:
             adapter.gen(
                 n=1,
                 optimization_config=OptimizationConfig(
-                    objective=Objective(metric=Metric("a"), minimize=False),
+                    objectives=[Objective(metric=Metric("a"), minimize=False)],
                     outcome_constraints=[
                         ScalarizedOutcomeConstraint(
                             metrics=[Metric("wrong_metric_name")],
@@ -738,7 +738,7 @@ def test_convert_contextual_observations(self) -> None:
         )
         # Make an optimization config that includes all metrics.
         opt_config = OptimizationConfig(
-            objective=Objective(metric=Metric("y"), minimize=True),
+            objectives=[Objective(metric=Metric("y"), minimize=True)],
             outcome_constraints=[
                 OutcomeConstraint(
                     metric=Metric(f"y:c{i}"), op=ComparisonOp.GEQ, bound=0
@@ -1205,10 +1205,12 @@ def test_pairwise_preference_generator(self) -> None:
                     surrogate=surrogate,
                 ),
                 optimization_config=OptimizationConfig(
-                    Objective(
-                        metric=Metric(Keys.PAIRWISE_PREFERENCE_QUERY.value),
-                        minimize=False,
-                    )
+                    objectives=[
+                        Objective(
+                            metric=Metric(Keys.PAIRWISE_PREFERENCE_QUERY.value),
+                            minimize=False,
+                        )
+                    ]
                 ),
                 fit_tracking_metrics=False,
             )

ax/adapter/tests/test_torch_moo_adapter.py

@@ -333,7 +333,6 @@ def test_hypervolume(self, _, cuda: bool = False) -> None:
             )
             for trial in exp.trials.values():
                 trial.mark_running(no_runner_required=True).mark_completed()
-            # pyre-fixme[16]: Optional type has no attribute `metrics`.
             metrics_dict = exp.metrics
             # Objective thresholds and synthetic observations chosen to have closed-form
             # hypervolumes to test.
@@ -464,9 +463,15 @@ def test_infer_objective_thresholds(self, _, cuda: bool = False) -> None:
         first = sub_exprs[0]
         if not first.startswith("-"):
             sub_exprs[0] = f"-{first}"
-        oc.objective = Objective(
-            expression=", ".join(sub_exprs),
-            metric_name_to_signature={s.lstrip("-"): s.lstrip("-") for s in sub_exprs},
+        oc = oc.clone_with_args(
+            objectives=[
+                Objective(
+                    expression=", ".join(sub_exprs),
+                    metric_name_to_signature={
+                        s.lstrip("-"): s.lstrip("-") for s in sub_exprs
+                    },
+                )
+            ]
         )
 
         for use_partial_thresholds in (False, True):

ax/adapter/transforms/relativize.py

@@ -157,7 +157,7 @@ def transform_optimization_config(
                 "Expected multi-objective, got single-objective"
             )
             new_optimization_config = optimization_config.clone_with_args(
-                objective=objective,
+                objectives=[objective],
                 outcome_constraints=constraints,
             )
         elif isinstance(optimization_config, MultiObjectiveOptimizationConfig):
@@ -174,13 +174,14 @@ def transform_optimization_config(
                 )
 
             new_optimization_config = optimization_config.clone_with_args(
-                objective=optimization_config.objective,
+                objectives=[optimization_config.objective],
                 outcome_constraints=constraints,
                 objective_thresholds=obj_thresholds,
             )
         else:
             new_optimization_config = optimization_config.clone_with_args(
-                objective=optimization_config.objective, outcome_constraints=constraints
+                objectives=[optimization_config.objective],
+                outcome_constraints=constraints,
             )
 
         return new_optimization_config

ax/adapter/transforms/standardize_y.py

@@ -133,10 +133,12 @@ def transform_optimization_config(
                 (name, new_w)
                 for (name, _), new_w in zip(objective.metric_weights, new_weights)
             ]
-            optimization_config.objective = _build_objective_from_metric_weights(
-                new_metric_weights,
-                metric_name_to_signature=objective.metric_name_to_signature,
-            )
+            optimization_config._objectives = [
+                _build_objective_from_metric_weights(
+                    new_metric_weights,
+                    metric_name_to_signature=objective.metric_name_to_signature,
+                )
+            ]
 
         new_constraints = self._transform_constraints(
             optimization_config.outcome_constraints, adapter

ax/adapter/transforms/stratified_standardize_y.py

@@ -196,10 +196,12 @@ def transform_optimization_config(
                 (name, new_w)
                 for (name, _), new_w in zip(objective.metric_weights, new_weights)
             ]
-            optimization_config.objective = _build_objective_from_metric_weights(
-                new_metric_weights,
-                metric_name_to_signature=objective.metric_name_to_signature,
-            )
+            optimization_config._objectives = [
+                _build_objective_from_metric_weights(
+                    new_metric_weights,
+                    metric_name_to_signature=objective.metric_name_to_signature,
+                )
+            ]
 
         optimization_config.outcome_constraints = self._transform_constraints(
             optimization_config.outcome_constraints, strata, adapter

ax/adapter/transforms/tests/test_base_transform.py

@@ -116,7 +116,7 @@ def test_transform_optimization_config_with_pruning_target_parameterization(
         # modifies parameters
         pruning_target_parameterization = Arm(parameters={"x1": 2.5, "x2": 7.5})
         optimization_config = OptimizationConfig(
-            objective=Objective(metric=Metric("m1"), minimize=False),
+            objectives=[Objective(metric=Metric("m1"), minimize=False)],
             pruning_target_parameterization=pruning_target_parameterization,
         )
 
@@ -142,7 +142,7 @@ def test_transform_optimization_config_without_pruning_target_parameterization(
     ) -> None:
         # Setup: create optimization config without target arm
         optimization_config = OptimizationConfig(
-            objective=Objective(metric=Metric("m1"), minimize=False),
+            objectives=[Objective(metric=Metric("m1"), minimize=False)],
             pruning_target_parameterization=None,
         )
 
@@ -169,7 +169,7 @@ def test_transform_optimization_config_preserves_other_fields(self) -> None:
             )
         ]
         optimization_config = OptimizationConfig(
-            objective=Objective(metric=Metric("m1"), minimize=True),
+            objectives=[Objective(metric=Metric("m1"), minimize=True)],
             outcome_constraints=outcome_constraints,
             pruning_target_parameterization=pruning_target_parameterization,
         )

ax/adapter/transforms/tests/test_derelativize_transform.py

@@ -106,7 +106,7 @@ def _test_DerelativizeTransform(
         # Test with no relative constraints
         objective = Objective(metric=Metric("c"), minimize=True)
         oc = OptimizationConfig(
-            objective=objective,
+            objectives=[objective],
             outcome_constraints=[
                 OutcomeConstraint(
                     metric=Metric("m1"), op=ComparisonOp.LEQ, bound=2, relative=False
@@ -126,7 +126,7 @@ def _test_DerelativizeTransform(
         # Test with relative constraint, in-design status quo
         relative_bound = -10
         oc = OptimizationConfig(
-            objective=objective,
+            objectives=[objective],
             outcome_constraints=[
                 OutcomeConstraint(
                     metric=Metric("m1"), op=ComparisonOp.LEQ, bound=2, relative=False
@@ -192,7 +192,7 @@ def _test_DerelativizeTransform(
         )
         g = Adapter(experiment=experiment_2, generator=Generator())
         oc = OptimizationConfig(
-            objective=objective,
+            objectives=[objective],
             outcome_constraints=[
                 OutcomeConstraint(
                     metric=Metric("m1"), op=ComparisonOp.LEQ, bound=2, relative=False
@@ -250,7 +250,7 @@ def _test_DerelativizeTransform(
         )
         g = Adapter(experiment=experiment_3, generator=Generator())
         oc = OptimizationConfig(
-            objective=objective,
+            objectives=[objective],
             outcome_constraints=[
                 OutcomeConstraint(
                     metric=Metric("m1"), op=ComparisonOp.LEQ, bound=2, relative=False
@@ -282,7 +282,7 @@ def _test_DerelativizeTransform(
 
         # Same for scalarized constraint only.
         oc_scalarized_only = OptimizationConfig(
-            objective=objective,
+            objectives=[objective],
             outcome_constraints=[
                 ScalarizedOutcomeConstraint(
                     metrics=[Metric("m1"), Metric("m2")],
@@ -319,7 +319,7 @@ def _test_DerelativizeTransform(
     def test_errors(self) -> None:
         t = Derelativize(search_space=None)
         oc = OptimizationConfig(
-            objective=Objective(metric=Metric("c"), minimize=False),
+            objectives=[Objective(metric=Metric("c"), minimize=False)],
             outcome_constraints=[
                 OutcomeConstraint(
                     metric=Metric("m1"), op=ComparisonOp.LEQ, bound=2, relative=True