Remove reference_point from multi-objective benchmark problems (#2619)

Summary:
Pull Request resolved: #2619

It is not used.

Reviewed By: sdaulton

Differential Revision: D60474602

fbshipit-source-id: 68367847d2639373cfa9657eaa5e21b21be1f60d

ax/benchmark/benchmark_problem.py

@@ -206,13 +206,12 @@ class MultiObjectiveBenchmarkProblem(BenchmarkProblem):
     A `BenchmarkProblem` that supports multiple objectives.
 
     For multi-objective problems, `optimal_value` indicates the maximum
-    hypervolume attainable with the given `reference_point`.
+    hypervolume attainable with the objective thresholds provided on the
+    `optimization_config`.
 
-    For argument descriptions, see `BenchmarkProblem`; it additionally takes a `runner`
-    and a `reference_point`.
+    For argument descriptions, see `BenchmarkProblem`.
     """
 
-    reference_point: List[float]
     optimization_config: MultiObjectiveOptimizationConfig
 
 
@@ -289,5 +288,4 @@ def create_multi_objective_problem_from_botorch(
         observe_noise_stds=observe_noise_sd,
         has_ground_truth=problem.has_ground_truth,
         optimal_value=test_problem.max_hv,
-        reference_point=test_problem._ref_point,
     )

ax/benchmark/problems/surrogate.py

@@ -4,12 +4,19 @@
 # LICENSE file in the root directory of this source tree.
 
 # pyre-strict
+"""
+Benchmark problems based on surrogates.
+
+These problems might appear to function identically to their non-surrogate
+counterparts, `BenchmarkProblem` and `MultiObjectiveBenchmarkProblem`, aside
+from the restriction that their runners are of type `SurrogateRunner`. However,
+they are treated specially within JSON storage because surrogates cannot be
+easily serialized.
+"""
 
 from dataclasses import dataclass, field
-from typing import List
 
 from ax.benchmark.benchmark_problem import BenchmarkProblem
-
 from ax.benchmark.runners.surrogate import SurrogateRunner
 from ax.core.optimization_config import MultiObjectiveOptimizationConfig
 
@@ -21,6 +28,8 @@ class SurrogateBenchmarkProblemBase(BenchmarkProblem):
 
     Its `runner` is a `SurrogateRunner`, which allows for the surrogate to be
     constructed lazily and datasets to be downloaded lazily.
+
+    For argument descriptions, see `BenchmarkProblem`.
     """
 
     runner: SurrogateRunner = field(repr=False)
@@ -34,9 +43,10 @@ class SOOSurrogateBenchmarkProblem(SurrogateBenchmarkProblemBase):
 class MOOSurrogateBenchmarkProblem(SurrogateBenchmarkProblemBase):
     """
     Has the same attributes/properties as a `MultiObjectiveBenchmarkProblem`,
-    but its runner is not constructed until needed, to allow for deferring
-    constructing the surrogate and downloading data.
+    but its `runner` is a `SurrogateRunner`, which allows for the surrogate to be
+    constructed lazily and datasets to be downloaded lazily.
+
+    For argument descriptions, see `BenchmarkProblem`.
     """
 
     optimization_config: MultiObjectiveOptimizationConfig
-    reference_point: List[float]

ax/benchmark/tests/test_benchmark_problem.py

@@ -203,7 +203,11 @@ def test_moo_from_botorch(self) -> None:
 
         # Test hypervolume
         self.assertEqual(branin_currin_problem.optimal_value, test_problem._max_hv)
-        self.assertEqual(branin_currin_problem.reference_point, test_problem._ref_point)
+        opt_config = branin_currin_problem.optimization_config
+        reference_point = [
+            threshold.bound for threshold in opt_config.objective_thresholds
+        ]
+        self.assertEqual(reference_point, test_problem._ref_point)
 
     def test_moo_from_botorch_constrained(self) -> None:
         with self.assertRaisesRegex(

ax/utils/testing/benchmark_stubs.py

@@ -148,7 +148,6 @@ def get_moo_surrogate() -> MOOSurrogateBenchmarkProblem:
         num_trials=10,
         observe_noise_stds=True,
         optimal_value=1.0,
-        reference_point=[],
         runner=runner,
         is_noiseless=runner.is_noiseless,
     )