Remove BenchmarkProblemWithKnownOptimum and BenchmarkProtocol typ…

…e annotations (facebook#2602)

Summary:
Pull Request resolved: facebook#2602

These are no longer necessary. See previous PR for context. This change reaps them and updates type annotations.

Differential Revision: D60146081

Reviewed By: saitcakmak

ax/benchmark/benchmark.py

@@ -27,10 +27,7 @@
 import numpy as np
 
 from ax.benchmark.benchmark_method import BenchmarkMethod
-from ax.benchmark.benchmark_problem import (
-    BenchmarkProblemProtocol,
-    BenchmarkProblemWithKnownOptimum,
-)
+from ax.benchmark.benchmark_problem import BenchmarkProblem
 from ax.benchmark.benchmark_result import AggregatedBenchmarkResult, BenchmarkResult
 from ax.benchmark.metrics.base import BenchmarkMetricBase, GroundTruthMetricMixin
 from ax.core.experiment import Experiment
@@ -53,16 +50,14 @@
 def compute_score_trace(
     optimization_trace: np.ndarray,
     num_baseline_trials: int,
-    problem: BenchmarkProblemProtocol,
+    problem: BenchmarkProblem,
 ) -> np.ndarray:
     """Computes a score trace from the optimization trace."""
 
     # Use the first GenerationStep's best found point as baseline. Sometimes (ex. in
     # a timeout) the first GenerationStep will not have not completed and we will not
     # have enough trials; in this case we do not score.
-    if (len(optimization_trace) <= num_baseline_trials) or not isinstance(
-        problem, BenchmarkProblemWithKnownOptimum
-    ):
+    if len(optimization_trace) <= num_baseline_trials:
         return np.full(len(optimization_trace), np.nan)
     optimum = problem.optimal_value
     baseline = optimization_trace[num_baseline_trials - 1]
@@ -77,7 +72,7 @@ def compute_score_trace(
 
 
 def _create_benchmark_experiment(
-    problem: BenchmarkProblemProtocol, method_name: str
+    problem: BenchmarkProblem, method_name: str
 ) -> Experiment:
     """Creates an empty experiment for the given problem and method.
 
@@ -117,7 +112,7 @@ def _create_benchmark_experiment(
 
 
 def benchmark_replication(
-    problem: BenchmarkProblemProtocol,
+    problem: BenchmarkProblem,
     method: BenchmarkMethod,
     seed: int,
 ) -> BenchmarkResult:
@@ -192,7 +187,7 @@ def benchmark_replication(
 
 
 def benchmark_one_method_problem(
-    problem: BenchmarkProblemProtocol,
+    problem: BenchmarkProblem,
     method: BenchmarkMethod,
     seeds: Iterable[int],
 ) -> AggregatedBenchmarkResult:
@@ -205,7 +200,7 @@ def benchmark_one_method_problem(
 
 
 def benchmark_multiple_problems_methods(
-    problems: Iterable[BenchmarkProblemProtocol],
+    problems: Iterable[BenchmarkProblem],
     methods: Iterable[BenchmarkMethod],
     seeds: Iterable[int],
 ) -> List[AggregatedBenchmarkResult]:
@@ -222,7 +217,7 @@ def benchmark_multiple_problems_methods(
 
 
 def make_ground_truth_metrics(
-    problem: BenchmarkProblemProtocol,
+    problem: BenchmarkProblem,
     include_tracking_metrics: bool = True,
 ) -> Dict[str, Metric]:
     """Makes a ground truth version for each metric defined on the problem.

ax/benchmark/benchmark_problem.py

@@ -11,17 +11,7 @@
 # in the UI.
 
 from dataclasses import dataclass, field
-from typing import (
-    Any,
-    Dict,
-    List,
-    Optional,
-    Protocol,
-    runtime_checkable,
-    Type,
-    TypeVar,
-    Union,
-)
+from typing import Any, Dict, List, Optional, Type, TypeVar, Union
 
 from ax.benchmark.metrics.base import BenchmarkMetricBase
 
@@ -72,33 +62,6 @@ def _get_name(
     return f"{base_name}{observed_noise}{dim_str}"
 
 
-@runtime_checkable
-class BenchmarkProblemProtocol(Protocol):
-    """
-    Specifies the interface any benchmark problem must adhere to.
-
-    Classes implementing this interface include BenchmarkProblem,
-    SurrogateBenchmarkProblem, and MOOSurrogateBenchmarkProblem.
-    """
-
-    name: str
-    search_space: SearchSpace
-    optimization_config: OptimizationConfig
-    num_trials: int
-    tracking_metrics: List[BenchmarkMetricBase]
-    is_noiseless: bool  # If True, evaluations are deterministic
-    observe_noise_stds: Union[
-        bool, Dict[str, bool]
-    ]  # Whether we observe the observation noise level
-    has_ground_truth: bool  # if True, evals (w/o synthetic noise) are determinstic
-    runner: Runner
-
-
-@runtime_checkable
-class BenchmarkProblemWithKnownOptimum(Protocol):
-    optimal_value: float
-
-
 @dataclass(kw_only=True, repr=True)
 class BenchmarkProblem(Base):
     """

ax/benchmark/tests/problems/test_surrogate_problems.py

@@ -8,7 +8,7 @@
 
 import numpy as np
 from ax.benchmark.benchmark import compute_score_trace
-from ax.benchmark.benchmark_problem import BenchmarkProblemProtocol
+from ax.benchmark.benchmark_problem import BenchmarkProblem
 from ax.utils.common.testutils import TestCase
 from ax.utils.testing.benchmark_stubs import get_moo_surrogate, get_soo_surrogate
 
@@ -19,12 +19,12 @@ def setUp(self) -> None:
         # print max output so errors in 'repr' can be fully shown
         self.maxDiff = None
 
-    def test_conforms_to_protocol(self) -> None:
+    def test_conforms_to_api(self) -> None:
         sbp = get_soo_surrogate()
-        self.assertIsInstance(sbp, BenchmarkProblemProtocol)
+        self.assertIsInstance(sbp, BenchmarkProblem)
 
         mbp = get_moo_surrogate()
-        self.assertIsInstance(mbp, BenchmarkProblemProtocol)
+        self.assertIsInstance(mbp, BenchmarkProblem)
 
     def test_repr(self) -> None: