Delete some unneeded tests (#2766)

Summary:
Pull Request resolved: #2766

Context: There used to be more `BenchmarkProblem` subclasses, and they used to implement their own '__repr__' methods, so there were tests for the custom repr methods. Now `BenchmarkProblem` and its subclass `SurrogateBenchmarkProblem` get their `__repr__` methods from being data classes. These tests have become annoying because they break with any change to `BenchmarkProblem, even if just changing the order of arguments.

This PR:
* Removes two `test_repr` methods.

Reviewed By: Balandat

Differential Revision: D62518032

fbshipit-source-id: aa3374645196e7ff922bc13205cbd1095460acef

ax/benchmark/tests/problems/test_surrogate_problems.py

@@ -16,8 +16,6 @@
 class TestSurrogateProblems(TestCase):
     def setUp(self) -> None:
         super().setUp()
-        # print max output so errors in 'repr' can be fully shown
-        self.maxDiff = None
 
     def test_conforms_to_api(self) -> None:
         sbp = get_soo_surrogate()
@@ -26,21 +24,6 @@ def test_conforms_to_api(self) -> None:
         mbp = get_moo_surrogate()
         self.assertIsInstance(mbp, BenchmarkProblem)
 
-    def test_repr(self) -> None:
-
-        sbp = get_soo_surrogate()
-
-        expected_repr = (
-            "SurrogateBenchmarkProblem(name='test', "
-            "optimization_config=OptimizationConfig(objective=Objective(metric_name="
-            '"branin", '
-            "minimize=True), "
-            "outcome_constraints=[]), num_trials=6, "
-            "observe_noise_stds=True, "
-            "optimal_value=0.0)"
-        )
-        self.assertEqual(repr(sbp), expected_repr)
-
     def test_compute_score_trace(self) -> None:
         soo_problem = get_soo_surrogate()
         score_trace = compute_score_trace(

ax/benchmark/tests/test_benchmark_problem.py

@@ -149,15 +149,6 @@ def test_single_objective_from_botorch(self) -> None:
                 self.assertEqual(
                     test_problem.optimization_config.outcome_constraints, []
                 )
-                expected_repr = (
-                    "BenchmarkProblem(name='Ackley', "
-                    "optimization_config=OptimizationConfig(objective=Objective("
-                    'metric_name="Ackley", '
-                    "minimize=True), outcome_constraints=[]), "
-                    "num_trials=1, "
-                    "observe_noise_stds=False, "
-                    "optimal_value=0.0)"
-                )
             else:
                 outcome_constraint = (
                     test_problem.optimization_config.outcome_constraints[0]
@@ -166,18 +157,6 @@ def test_single_objective_from_botorch(self) -> None:
                 self.assertEqual(outcome_constraint.op, ComparisonOp.GEQ)
                 self.assertFalse(outcome_constraint.relative)
                 self.assertEqual(outcome_constraint.bound, 0.0)
-                expected_repr = (
-                    "BenchmarkProblem(name='ConstrainedHartmann', "
-                    "optimization_config=OptimizationConfig(objective=Objective("
-                    'metric_name="ConstrainedHartmann", minimize=True), '
-                    "outcome_constraints=[OutcomeConstraint(constraint_slack_0"
-                    " >= 0.0)]), "
-                    "num_trials=1, "
-                    "observe_noise_stds=False, "
-                    "optimal_value=-3.32237)"
-                )
-
-            self.assertEqual(repr(test_problem), expected_repr)
 
     def _test_constrained_from_botorch(
         self,

ax/service/utils/best_point.py

@@ -237,7 +237,7 @@ def get_best_parameters_from_model_predictions_with_trial_index(
             except ValueError:
                 return _gr_to_prediction_with_trial_index(idx, gr)
 
-            # If model is not TorchModelBridge, just use the best arm frmo the
+            # If model is not TorchModelBridge, just use the best arm from the
             # last good generator run
             if not isinstance(model, TorchModelBridge):
                 return _gr_to_prediction_with_trial_index(idx, gr)