Migrate core/adapter test callers 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/adapter/transforms/tests/test_log_y_transform.py

@@ -138,14 +138,14 @@ def test_TransformOptimizationConfig(self) -> None:
         # basic test
         m1 = Metric(name="m1")
         objective_m1 = Objective(metric=m1, minimize=False)
-        oc = OptimizationConfig(objective=objective_m1, outcome_constraints=[])
+        oc = OptimizationConfig(objectives=[objective_m1], outcome_constraints=[])
         tf = LogY(search_space=None, config={"metrics": ["m1"]})
         oc_tf = tf.transform_optimization_config(deepcopy(oc), None, None)
         self.assertEqual(oc_tf, oc)
         # output constraint on a different metric should work
         m2 = Metric(name="m2")
         oc = OptimizationConfig(
-            objective=objective_m1,
+            objectives=[objective_m1],
             outcome_constraints=[
                 get_outcome_constraint(metric=m2, bound=-1, relative=False)
             ],
@@ -155,7 +155,7 @@ def test_TransformOptimizationConfig(self) -> None:
         # output constraint with a negative bound should fail
         objective_m2 = Objective(metric=m2, minimize=False)
         oc = OptimizationConfig(
-            objective=objective_m2,
+            objectives=[objective_m2],
             outcome_constraints=[
                 get_outcome_constraint(metric=m1, bound=-1.234, relative=False)
             ],
@@ -170,7 +170,7 @@ def test_TransformOptimizationConfig(self) -> None:
         )
         # output constraint with a zero bound should also fail
         oc = OptimizationConfig(
-            objective=objective_m2,
+            objectives=[objective_m2],
             outcome_constraints=[
                 get_outcome_constraint(metric=m1, bound=0, relative=False)
             ],
@@ -185,7 +185,7 @@ def test_TransformOptimizationConfig(self) -> None:
         )
         # output constraint with a positive bound should work
         oc = OptimizationConfig(
-            objective=objective_m2,
+            objectives=[objective_m2],
             outcome_constraints=[
                 get_outcome_constraint(metric=m1, bound=2.345, relative=False)
             ],
@@ -200,7 +200,7 @@ def test_TransformOptimizationConfig(self) -> None:
         self.assertEqual(oc_tf, oc)
         # output constraint with a relative bound should fail
         oc = OptimizationConfig(
-            objective=objective_m2,
+            objectives=[objective_m2],
             outcome_constraints=[
                 get_outcome_constraint(metric=m1, bound=2.345, relative=True)
             ],

ax/adapter/transforms/tests/test_power_y_transform.py

@@ -207,7 +207,7 @@ def test_transform_optimization_config(self) -> None:
         # basic test
         m1 = Metric(name="m1")
         objective_m1 = Objective(metric=m1, minimize=False)
-        oc = OptimizationConfig(objective=objective_m1, outcome_constraints=[])
+        oc = OptimizationConfig(objectives=[objective_m1], outcome_constraints=[])
         tf = PowerTransformY(
             search_space=None,
             experiment_data=self.experiment_data,
@@ -219,7 +219,7 @@ def test_transform_optimization_config(self) -> None:
         m2 = Metric(name="m2")
         for bound in [-1.234, 0, 2.345]:
             oc = OptimizationConfig(
-                objective=objective_m1,
+                objectives=[objective_m1],
                 outcome_constraints=get_constraint(
                     metric=m2, bound=bound, relative=False
                 ),
@@ -230,7 +230,7 @@ def test_transform_optimization_config(self) -> None:
         objective_m2 = Objective(metric=m2, minimize=False)
         for bound in [-1.234, 0, 2.345]:
             oc = OptimizationConfig(
-                objective=objective_m2,
+                objectives=[objective_m2],
                 outcome_constraints=get_constraint(
                     metric=m1, bound=bound, relative=False
                 ),
@@ -259,7 +259,7 @@ def test_transform_optimization_config(self) -> None:
             self.assertAlmostEqual(c_actual.bound, c_expected.bound, places=5)
         # Relative constraints aren't supported
         oc = OptimizationConfig(
-            objective=objective_m2,
+            objectives=[objective_m2],
             outcome_constraints=get_constraint(metric=m1, bound=2.345, relative=True),
         )
         with self.assertRaisesRegex(
@@ -277,7 +277,7 @@ def test_transform_optimization_config(self) -> None:
         # Support for scalarized outcome constraints isn't implemented
         m3 = Metric(name="m3")
         oc = OptimizationConfig(
-            objective=objective_m2,
+            objectives=[objective_m2],
             outcome_constraints=[
                 ScalarizedOutcomeConstraint(
                     metrics=[m1, m3], op=ComparisonOp.GEQ, bound=2.345, relative=False
@@ -295,7 +295,9 @@ def test_transform_optimization_config(self) -> None:
         scalarized_objective = ScalarizedObjective(
             metrics=[m1, m3], weights=[1.0, 2.0], minimize=False
         )
-        oc = OptimizationConfig(objective=scalarized_objective, outcome_constraints=[])
+        oc = OptimizationConfig(
+            objectives=[scalarized_objective], outcome_constraints=[]
+        )
         with self.assertRaisesRegex(NotImplementedError, "ScalarizedObjective"):
             tf.transform_optimization_config(oc, None, None)
 

ax/adapter/transforms/tests/test_standardize_y_transform.py

@@ -111,7 +111,7 @@ def test_TransformOptimizationConfig(self) -> None:
             ),
         ]
 
-        oc = OptimizationConfig(objective=objective, outcome_constraints=cons)
+        oc = OptimizationConfig(objectives=[objective], outcome_constraints=cons)
         with self.assertRaisesRegex(
             DataRequiredError, "`StandardizeY` transform requires constraint metric"
         ):
@@ -125,7 +125,7 @@ def test_TransformOptimizationConfig(self) -> None:
                 relative=False,
             ),
         ]
-        oc = OptimizationConfig(objective=objective, outcome_constraints=cons)
+        oc = OptimizationConfig(objectives=[objective], outcome_constraints=cons)
         with self.assertRaisesRegex(
             DataRequiredError, "`StandardizeY` transform requires constraint metric"
         ):
@@ -145,7 +145,7 @@ def test_TransformOptimizationConfig(self) -> None:
                 relative=False,
             ),
         ]
-        oc = OptimizationConfig(objective=objective, outcome_constraints=cons)
+        oc = OptimizationConfig(objectives=[objective], outcome_constraints=cons)
         oc = self.t.transform_optimization_config(oc, None, None)
         # Verify the transformed constraints have the expected values.
         # We compare properties individually to avoid floating-point string
@@ -174,7 +174,7 @@ def test_TransformOptimizationConfig(self) -> None:
         con = OutcomeConstraint(
             metric=m1, op=ComparisonOp.GEQ, bound=2.0, relative=True
         )
-        oc = OptimizationConfig(objective=objective, outcome_constraints=[con])
+        oc = OptimizationConfig(objectives=[objective], outcome_constraints=[con])
         with self.assertRaises(ValueError):
             oc = self.t.transform_optimization_config(oc, None, None)
 
@@ -229,7 +229,7 @@ def test_TransformOptimizationConfigWithScalarizedObjective(self) -> None:
         objective = ScalarizedObjective(
             metrics=[m1, m2], weights=[0.5, 0.5], minimize=False
         )
-        oc = OptimizationConfig(objective=objective)
+        oc = OptimizationConfig(objectives=[objective])
         oc_transformed = self.t.transform_optimization_config(oc, None, None)
 
         # Check that weights are scaled by standard deviations
@@ -245,7 +245,7 @@ def test_TransformOptimizationConfigWithScalarizedObjective(self) -> None:
         objective_missing = ScalarizedObjective(
             metrics=[m1, m3], weights=[0.5, 0.5], minimize=False
         )
-        oc_missing = OptimizationConfig(objective=objective_missing)
+        oc_missing = OptimizationConfig(objectives=[objective_missing])
         with self.assertRaisesRegex(
             DataRequiredError, "`StandardizeY` transform requires objective metric"
         ):
@@ -255,7 +255,7 @@ def test_TransformOptimizationConfigWithScalarizedObjective(self) -> None:
         objective_minimize = ScalarizedObjective(
             metrics=[m1, m2], weights=[1.0, -2.0], minimize=True
         )
-        oc_minimize = OptimizationConfig(objective=objective_minimize)
+        oc_minimize = OptimizationConfig(objectives=[objective_minimize])
         oc_minimize_transformed = self.t.transform_optimization_config(
             oc_minimize, None, None
         )

ax/adapter/transforms/tests/test_stratified_standardize_y_transform.py

@@ -314,7 +314,7 @@ def test_TransformObservations(self) -> None:
 
     def test_TransformOptimizationConfig(self) -> None:
         cons2 = deepcopy(self.cons)
-        oc = OptimizationConfig(objective=self.objective, outcome_constraints=cons2)
+        oc = OptimizationConfig(objectives=[self.objective], outcome_constraints=cons2)
         fixed_features = ObservationFeatures({"z": "a"})
         oc = self.t.transform_optimization_config(oc, None, fixed_features)
         # Verify constraint values approximately (expression-string equality
@@ -330,7 +330,7 @@ def test_TransformOptimizationConfig(self) -> None:
         self.assertTrue(oc.objective == self.objective)
 
         # No constraints
-        oc2 = OptimizationConfig(objective=self.objective)
+        oc2 = OptimizationConfig(objectives=[self.objective])
         oc3 = deepcopy(oc2)
         fixed_features = ObservationFeatures({"z": "a"})
         oc3 = self.t.transform_optimization_config(oc3, None, fixed_features)
@@ -340,7 +340,7 @@ def test_TransformOptimizationConfig(self) -> None:
         con = OutcomeConstraint(
             metric=self.m1, op=ComparisonOp.GEQ, bound=2.0, relative=True
         )
-        oc = OptimizationConfig(objective=self.objective, outcome_constraints=[con])
+        oc = OptimizationConfig(objectives=[self.objective], outcome_constraints=[con])
         with self.assertRaises(ValueError):
             oc = self.t.transform_optimization_config(oc, None, fixed_features)
         # Fail without strat param fixed
@@ -350,10 +350,10 @@ def test_TransformOptimizationConfig(self) -> None:
 
     def test_TransformOptimizationConfigWithStrataMapping(self) -> None:
         cons2 = deepcopy(self.cons)
-        oc = OptimizationConfig(objective=self.objective, outcome_constraints=cons2)
+        oc = OptimizationConfig(objectives=[self.objective], outcome_constraints=cons2)
         fixed_features = ObservationFeatures({"z": "a"})
         cons2 = deepcopy(self.cons)
-        oc = OptimizationConfig(objective=self.objective, outcome_constraints=cons2)
+        oc = OptimizationConfig(objectives=[self.objective], outcome_constraints=cons2)
         oc = self.t2.transform_optimization_config(oc, None, fixed_features)
         # Verify constraint values approximately.
         self.assertEqual(len(oc.outcome_constraints), 2)
@@ -367,7 +367,7 @@ def test_TransformOptimizationConfigWithStrataMapping(self) -> None:
         self.assertTrue(oc.objective == self.objective)
         fixed_features = ObservationFeatures({"z": "c"})
         cons2 = deepcopy(self.cons)
-        oc = OptimizationConfig(objective=self.objective, outcome_constraints=cons2)
+        oc = OptimizationConfig(objectives=[self.objective], outcome_constraints=cons2)
         oc = self.t2.transform_optimization_config(oc, None, fixed_features)
         # Verify constraint values approximately.
         self.assertEqual(len(oc.outcome_constraints), 2)
@@ -441,7 +441,7 @@ def test_TransformOptimizationConfigWithScalarizedObjective(self) -> None:
         objective = ScalarizedObjective(
             metrics=[self.m1, self.m2], weights=[0.5, 0.5], minimize=False
         )
-        oc = OptimizationConfig(objective=objective)
+        oc = OptimizationConfig(objectives=[objective])
         expected_weights = {
             "a": [0.5 * 1.0, 0.5 * sqrt(2) * 3],
             "b": [0.5 * 2 * sqrt(2), 0.5 * sqrt(2) * 0.5],
@@ -476,7 +476,7 @@ def test_TransformAndUntransformScalarizedOutcomeConstraint(self) -> None:
             relative=False,
         )
         oc = OptimizationConfig(
-            objective=self.objective, outcome_constraints=[scalarized_constraint]
+            objectives=[self.objective], outcome_constraints=[scalarized_constraint]
         )
         fixed_features = ObservationFeatures({"z": "a"})
         oc_transformed = self.t.transform_optimization_config(oc, None, fixed_features)

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(