Deprecate OptimizationConfig.__init__(objective=), remove setter & clone_with_args(objective=)

Summary:
Goal: We added `objectives` input, we're now migrating all usage to `objectives` and eliminating the `objective` input. This will make it easier to eliminate `MultiObjective` and `MultiObjectiveOptimizationConfig`.

Phase 1 of the OptimizationConfig simplification migration:
- Add DeprecationWarning when passing objective= to OptimizationConfig.__init__
- Remove the objective setter on OptimizationConfig (MOOC keeps its own)
- Remove objective= param from OptimizationConfig.clone_with_args (MOOC/PreferenceOC keep theirs)
- Migrate all callers of the removed setter and clone_with_args(objective=)

Differential Revision: D99491494

Author: saitcakmak

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

@@ -180,7 +180,8 @@ def transform_optimization_config(
             )
         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/core/optimization_config.py

@@ -8,6 +8,7 @@
 
 from __future__ import annotations
 
+import warnings
 from collections.abc import Mapping
 from itertools import groupby
 from typing import Self
@@ -80,11 +81,17 @@ def __init__(
                 consideration, and if not, the parameter value will be replaced with
                 the corresponding value in the target arm.
         """
+        if objective is not None:
+            warnings.warn(
+                "Passing `objective` to OptimizationConfig is deprecated. "
+                "Use `objectives=[objective]` instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
         if objective is not None and objectives is not None:
             raise UserInputError(
                 "Cannot specify both `objective` and `objectives`. "
-                "Use `objective` for single-objective optimization or "
-                "`objectives` for multi-objective optimization."
+                "Use `objectives=[objective]` instead."
             )
         if objective is None and objectives is None:
             raise UserInputError("Must specify either `objective` or `objectives`.")
@@ -113,7 +120,6 @@ def clone(self) -> Self:
     def clone_with_args(
         self,
         *,
-        objective: Objective | None = None,
         objectives: list[Objective] | None = None,
         outcome_constraints: None | (list[OutcomeConstraint]) = _NO_OUTCOME_CONSTRAINTS,
         pruning_target_parameterization: Arm
@@ -122,21 +128,12 @@ def clone_with_args(
         """Make a copy of this optimization config.
 
         Args:
-            objective: Replace with a single objective. Mutually exclusive
-                with ``objectives``.
-            objectives: Replace with a list of objectives. Mutually exclusive
-                with ``objective``.
+            objectives: Replace with a list of objectives.
             outcome_constraints: Replace outcome constraints. Pass ``None``
                 to clear them.
             pruning_target_parameterization: Replace the pruning target.
         """
-        if objective is not None and objectives is not None:
-            raise UserInputError(
-                "Cannot specify both `objective` and `objectives` in clone_with_args."
-            )
-        if objective is not None:
-            cloned_objectives = [objective]
-        elif objectives is not None:
+        if objectives is not None:
             cloned_objectives = objectives
         else:
             cloned_objectives = [obj.clone() for obj in self._objectives]
@@ -182,15 +179,6 @@ def objective(self) -> Objective:
             )
         return self._objectives[0]
 
-    @objective.setter
-    def objective(self, objective: Objective) -> None:
-        """Set objective. Only valid for single-objective configs."""
-        self._validate_transformed_optimization_config(
-            objectives=[objective],
-            outcome_constraints=self.outcome_constraints,
-        )
-        self._objectives = [objective]
-
     @property
     def all_constraints(self) -> list[OutcomeConstraint]:
         """Get outcome constraints."""
@@ -449,7 +437,6 @@ def __init__(
     def clone_with_args(
         self,
         *,
-        objective: Objective | None = None,
         objectives: list[Objective] | None = None,
         outcome_constraints: None | (list[OutcomeConstraint]) = _NO_OUTCOME_CONSTRAINTS,
         objective_thresholds: None
@@ -458,13 +445,7 @@ def clone_with_args(
         | None = _NO_PRUNING_TARGET_PARAMETERIZATION,
     ) -> "MultiObjectiveOptimizationConfig":
         """Make a copy of this optimization config."""
-        if objective is not None and objectives is not None:
-            raise UserInputError(
-                "Cannot specify both `objective` and `objectives` in clone_with_args."
-            )
-        if objective is not None:
-            cloned_objectives = [objective]
-        elif objectives is not None:
+        if objectives is not None:
             cloned_objectives = objectives
         else:
             cloned_objectives = [obj.clone() for obj in self._objectives]
@@ -702,15 +683,19 @@ def is_bope_problem(self) -> bool:
     def clone_with_args(
         self,
         *,
-        objective: Objective | None = None,
+        objectives: list[Objective] | None = None,
         preference_profile_name: str | None = None,
         outcome_constraints: list[OutcomeConstraint] | None = _NO_OUTCOME_CONSTRAINTS,
         expect_relativized_outcomes: bool | None = None,
         pruning_target_parameterization: Arm
         | None = _NO_PRUNING_TARGET_PARAMETERIZATION,
     ) -> PreferenceOptimizationConfig:
         """Make a copy of this optimization config."""
-        objective = self._objectives[0].clone() if objective is None else objective
+        cloned_objectives = (
+            [obj.clone() for obj in self._objectives]
+            if objectives is None
+            else objectives
+        )
 
         preference_profile_name = (
             self.preference_profile_name
@@ -734,7 +719,7 @@ def clone_with_args(
         )
 
         return PreferenceOptimizationConfig(
-            objective=objective,
+            objectives=cloned_objectives,
             preference_profile_name=preference_profile_name,
             outcome_constraints=outcome_constraints,
             expect_relativized_outcomes=expect_relativized_outcomes,

ax/core/tests/test_optimization_config.py

@@ -84,8 +84,6 @@ def test_Init(self) -> None:
             objective=self.objective, outcome_constraints=self.outcome_constraints
         )
         self.assertEqual(str(config1), OC_STR)
-        with self.assertRaises(ValueError):
-            config1.objective = self.alt_objective  # constrained Objective.
         # updating constraints is fine.
         config1.outcome_constraints = [self.outcome_constraint]
         self.assertEqual(len(config1.metric_names), 2)
@@ -94,10 +92,6 @@ def test_Init(self) -> None:
         config2 = OptimizationConfig(objective=self.objective)
         self.assertEqual(config2.outcome_constraints, [])
 
-        # setting objective is fine too, if it's compatible with constraints..
-        config2.objective = self.m2_objective
-        # setting constraints on objectives is fine for MultiObjective components.
-
         config2.outcome_constraints = self.outcome_constraints
         self.assertEqual(config2.outcome_constraints, self.outcome_constraints)
 
@@ -355,8 +349,8 @@ def test_objectives_kwarg_clone_and_repr(self) -> None:
         self.assertEqual(cloned.objectives[1].expression, "-m2")
         self.assertTrue(cloned.is_moo_problem)
 
-        # clone_with_args(objective=) replaces the list with a single objective
-        cloned = config.clone_with_args(objective=self.obj1)
+        # clone_with_args(objectives=) replaces the list with a single objective
+        cloned = config.clone_with_args(objectives=[self.obj1])
         self.assertEqual(len(cloned.objectives), 1)
         self.assertFalse(cloned.is_moo_problem)
 
@@ -366,10 +360,6 @@ def test_objectives_kwarg_clone_and_repr(self) -> None:
         self.assertEqual(len(cloned.objectives), 2)
         self.assertEqual(cloned.objectives[1].expression, "m3")
 
-        # objective= and objectives= are mutually exclusive in clone_with_args
-        with self.assertRaisesRegex(UserInputError, "Cannot specify both"):
-            config.clone_with_args(objective=self.obj1, objectives=[self.obj1])
-
         # repr always uses "objectives="
         self.assertIn("objectives=", repr(config))
         single_config = OptimizationConfig(objectives=[self.obj1])

ax/service/tests/test_best_point.py

@@ -60,12 +60,14 @@ def test_get_trace(self) -> None:
         self.assertEqual(get_trace(exp), [11, 10, 9, 9, 5])
 
         # Same experiment with maximize via new optimization config.
-        opt_conf = none_throws(exp.optimization_config).clone()
-        opt_conf.objective = Objective(
-            expression=opt_conf.objective.metric_names[0],
-            metric_name_to_signature={
-                opt_conf.objective.metric_names[0]: opt_conf.objective.metric_names[0]
-            },
+        metric_name = none_throws(exp.optimization_config).objective.metric_names[0]
+        opt_conf = none_throws(exp.optimization_config).clone_with_args(
+            objectives=[
+                Objective(
+                    expression=metric_name,
+                    metric_name_to_signature={metric_name: metric_name},
+                )
+            ],
         )
         self.assertEqual(get_trace(exp, opt_conf), [11, 11, 11, 15, 15])
 
@@ -441,12 +443,14 @@ def test_get_best_observed_value(self) -> None:
         )
         self.assertEqual(get_best(exp), 5)
         # Same experiment with maximize via new optimization config.
-        opt_conf = none_throws(exp.optimization_config).clone()
-        opt_conf.objective = Objective(
-            expression=opt_conf.objective.metric_names[0],
-            metric_name_to_signature={
-                opt_conf.objective.metric_names[0]: opt_conf.objective.metric_names[0]
-            },
+        metric_name = none_throws(exp.optimization_config).objective.metric_names[0]
+        opt_conf = none_throws(exp.optimization_config).clone_with_args(
+            objectives=[
+                Objective(
+                    expression=metric_name,
+                    metric_name_to_signature={metric_name: metric_name},
+                )
+            ],
         )
         self.assertEqual(get_best(exp, opt_conf), 15)
 

ax/service/tests/test_best_point_utils.py

@@ -615,7 +615,9 @@ def test_best_raw_objective_point_scalarized(self) -> None:
         exp = get_branin_experiment()
         gs = choose_generation_strategy_legacy(search_space=exp.search_space)
         exp.optimization_config = OptimizationConfig(
-            objective=ScalarizedObjective(metrics=[get_branin_metric()], minimize=True)
+            objectives=[
+                ScalarizedObjective(metrics=[get_branin_metric()], minimize=True)
+            ],
         )
         with self.assertRaisesRegex(ValueError, "Cannot identify best "):
             get_best_raw_objective_point_with_trial_index(exp)
@@ -637,11 +639,16 @@ def test_best_raw_objective_point_scalarized_multi(self) -> None:
         exp = get_branin_experiment()
         gs = choose_generation_strategy_legacy(search_space=exp.search_space)
         exp.optimization_config = OptimizationConfig(
-            objective=ScalarizedObjective(
-                metrics=[get_branin_metric(), get_branin_metric(lower_is_better=False)],
-                weights=[0.1, -0.9],
-                minimize=True,
-            )
+            objectives=[
+                ScalarizedObjective(
+                    metrics=[
+                        get_branin_metric(),
+                        get_branin_metric(lower_is_better=False),
+                    ],
+                    weights=[0.1, -0.9],
+                    minimize=True,
+                )
+            ],
         )
         with self.assertRaisesRegex(ValueError, "Cannot identify best "):
             get_best_raw_objective_point_with_trial_index(experiment=exp)
@@ -1037,11 +1044,13 @@ def test_best_parameters_from_model_predictions_scalarized(self) -> None:
         )
         exp.add_tracking_metric(metric2)
         exp.optimization_config = OptimizationConfig(
-            objective=ScalarizedObjective(
-                metrics=[metric1, metric2],
-                weights=[0.5, 0.5],
-                minimize=True,
-            )
+            objectives=[
+                ScalarizedObjective(
+                    metrics=[metric1, metric2],
+                    weights=[0.5, 0.5],
+                    minimize=True,
+                )
+            ],
         )
 
         # Run trials and generate data

ax/storage/sqa_store/tests/test_sqa_store.py

@@ -1270,9 +1270,10 @@ def test_experiment_objective_updates(self) -> None:
 
         # update objective
         # (should perform update in place)
-        optimization_config = get_optimization_config()
         objective = get_objective(minimize=True)
-        optimization_config.objective = objective
+        optimization_config = get_optimization_config().clone_with_args(
+            objectives=[objective]
+        )
         experiment.optimization_config = optimization_config
         save_experiment(experiment)
         self.assertEqual(
@@ -1282,8 +1283,8 @@ def test_experiment_objective_updates(self) -> None:
         # replace objective
         # (old one should become tracking metric)
         experiment.add_tracking_metric(Metric(name="objective"))
-        optimization_config.objective = Objective(
-            metric=Metric(name="objective"), minimize=False
+        optimization_config = optimization_config.clone_with_args(
+            objectives=[Objective(metric=Metric(name="objective"), minimize=False)]
         )
         experiment.optimization_config = optimization_config
         save_experiment(experiment)