Migrate core_stubs.py from objective= to objectives=[]

ax/adapter/tests/test_torch_adapter.py

@@ -1205,7 +1205,7 @@ def test_pairwise_preference_generator(self) -> None:
                     surrogate=surrogate,
                 ),
                 optimization_config=OptimizationConfig(
-                    Objective(
+                    objective=Objective(
                         metric=Metric(Keys.PAIRWISE_PREFERENCE_QUERY.value),
                         minimize=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/api/utils/instantiation/from_string.py

@@ -97,6 +97,6 @@ def optimization_config_from_string(
             )
 
     return OptimizationConfig(
-        objective=objective,
+        objectives=[objective],
         outcome_constraints=outcome_constraints,
     )

ax/benchmark/benchmark_problem.py

@@ -334,7 +334,7 @@ def get_soo_opt_config(
     )
 
     config = OptimizationConfig(
-        objective=objective, outcome_constraints=outcome_constraints
+        objectives=[objective], outcome_constraints=outcome_constraints
     )
     return config, [obj_metric] + constraint_metrics
 

ax/benchmark/problems/surrogate/hss/cifar10_surrogate.py

@@ -182,14 +182,16 @@ def get_cifar10_surrogate_benchmark(
     )
 
     optimization_config = OptimizationConfig(
-        objective=Objective(
-            metric=BenchmarkMetric(
-                name="CIFAR10 Test Accuracy",
-                lower_is_better=False,
-                observe_noise_sd=False,
-            ),
-            minimize=False,
-        )
+        objectives=[
+            Objective(
+                metric=BenchmarkMetric(
+                    name="CIFAR10 Test Accuracy",
+                    lower_is_better=False,
+                    observe_noise_sd=False,
+                ),
+                minimize=False,
+            )
+        ],
     )
 
     return BenchmarkProblem(

ax/benchmark/problems/surrogate/hss/fashion_mnist_surrogate.py

@@ -182,14 +182,16 @@ def get_fashion_mnist_surrogate_benchmark(
     )
 
     optimization_config = OptimizationConfig(
-        objective=Objective(
-            metric=BenchmarkMetric(
-                name="FashionMNIST Test Accuracy",
-                lower_is_better=False,
-                observe_noise_sd=False,
-            ),
-            minimize=False,
-        )
+        objectives=[
+            Objective(
+                metric=BenchmarkMetric(
+                    name="FashionMNIST Test Accuracy",
+                    lower_is_better=False,
+                    observe_noise_sd=False,
+                ),
+                minimize=False,
+            )
+        ],
     )
 
     return BenchmarkProblem(

ax/benchmark/problems/surrogate/hss/mnist_surrogate.py

@@ -150,14 +150,16 @@ def get_mnist_surrogate_benchmark(
     )
 
     optimization_config = OptimizationConfig(
-        objective=Objective(
-            metric=BenchmarkMetric(
-                name="MNIST Test Accuracy",
-                lower_is_better=False,
-                observe_noise_sd=False,
-            ),
-            minimize=False,
-        )
+        objectives=[
+            Objective(
+                metric=BenchmarkMetric(
+                    name="MNIST Test Accuracy",
+                    lower_is_better=False,
+                    observe_noise_sd=False,
+                ),
+                minimize=False,
+            )
+        ],
     )
 
     return BenchmarkProblem(