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Revert D60472381 #2627

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92 changes: 1 addition & 91 deletions ax/benchmark/tests/test_benchmark.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,11 +6,9 @@
# pyre-strict

import tempfile
from typing import List
from unittest.mock import patch

import numpy as np
import torch
from ax.benchmark.benchmark import (
_create_benchmark_experiment,
benchmark_multiple_problems_methods,
Expand All @@ -23,10 +21,7 @@
BenchmarkMethod,
get_benchmark_scheduler_options,
)
from ax.benchmark.benchmark_problem import (
BenchmarkProblem,
create_single_objective_problem_from_botorch,
)
from ax.benchmark.benchmark_problem import create_single_objective_problem_from_botorch
from ax.benchmark.benchmark_result import BenchmarkResult
from ax.benchmark.methods.modular_botorch import get_sobol_botorch_modular_acquisition
from ax.benchmark.metrics.base import GroundTruthMetricMixin
Expand Down Expand Up @@ -58,13 +53,6 @@
from botorch.models.gp_regression import SingleTaskGP
from botorch.optim.optimize import optimize_acqf
from botorch.test_functions.synthetic import Branin
from pyre_extensions import none_throws


# Branin is subclassed here for convenience.
class ThreeProblem(Branin):
def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:
return torch.tensor(3.0, dtype=torch.float64)


class TestBenchmark(TestCase):
Expand Down Expand Up @@ -102,84 +90,6 @@ def test_batch(self) -> None:
)
self.assertEqual(mock_optimize_acqf.call_args.kwargs["q"], batch_size)

def test_noisy_property_test(self) -> None:
problem = create_single_objective_problem_from_botorch(
test_problem_class=ThreeProblem,
test_problem_kwargs={"noise_std": 1.0},
lower_is_better=True,
num_trials=5,
)

res = benchmark_replication(
problem=problem,
method=get_sobol_benchmark_method(),
seed=0,
)
self.assertTrue((res.optimization_trace == 3).all())
df = none_throws(res.experiment).fetch_data().df
self.assertTrue(
(df.loc[df["metric_name"] == "ThreeProblem", "mean"] != 3).all()
)

def test_characterization_noisy_soo(self) -> None:
soo_problem = get_single_objective_benchmark_problem(
test_problem_kwargs={"noise_std": 1.0}
)
expected = [
104.36542659448965,
90.20076516036951,
56.18382601460175,
7.5867050504430775,
]
self._noisy_characterization_test(problem=soo_problem, expected_result=expected)

def test_characterization_noisy_moo(self) -> None:
moo_problem = get_multi_objective_benchmark_problem(
test_problem_kwargs={"noise_std": 1.0}
)
expected = [0.0, 0.0, 6.952682254445629, 6.952682254445629]
self._noisy_characterization_test(problem=moo_problem, expected_result=expected)

def test_characterization_noisy_soo_surrogate(self) -> None:
# Surrogate construction shouldn't be random
torch.manual_seed(0)
soo_surrogate_problem = get_soo_surrogate(noise_stds=1.0)
expected = [
104.36542659448779,
104.36542659448779,
104.36542659448779,
104.36542659448779,
]
self._noisy_characterization_test(
problem=soo_surrogate_problem,
expected_result=expected,
)

def test_characterization_noisy_moo_surrogate(self) -> None:
# Surrogate construction shouldn't be random
torch.manual_seed(0)
moo_surrogate_problem = get_moo_surrogate(noise_stds=1.0)
expected = [
24.999999999997655,
24.999999999997655,
24.999999999997655,
24.999999999997655,
]
self._noisy_characterization_test(
problem=moo_surrogate_problem,
expected_result=expected,
)

def _noisy_characterization_test(
self, problem: BenchmarkProblem, expected_result: List[float]
) -> None:
res = benchmark_replication(
problem=problem,
method=get_sobol_benchmark_method(),
seed=0,
)
self.assertTrue((res.optimization_trace == expected_result).all())

def test_storage(self) -> None:
problem = get_single_objective_benchmark_problem()
res = benchmark_replication(
Expand Down
84 changes: 22 additions & 62 deletions ax/utils/testing/benchmark_stubs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,26 +17,24 @@
MultiObjectiveBenchmarkProblem,
)
from ax.benchmark.benchmark_result import AggregatedBenchmarkResult, BenchmarkResult
from ax.benchmark.metrics.benchmark import BenchmarkMetric
from ax.benchmark.problems.surrogate import (
MOOSurrogateBenchmarkProblem,
SOOSurrogateBenchmarkProblem,
)
from ax.benchmark.runners.surrogate import SurrogateRunner
from ax.core.experiment import Experiment
from ax.core.objective import MultiObjective, Objective
from ax.core.optimization_config import (
MultiObjectiveOptimizationConfig,
OptimizationConfig,
)
from ax.core.outcome_constraint import ObjectiveThreshold
from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
from ax.modelbridge.registry import Models
from ax.modelbridge.torch import TorchModelBridge
from ax.models.torch.botorch_modular.model import BoTorchModel
from ax.models.torch.botorch_modular.surrogate import Surrogate
from ax.service.scheduler import SchedulerOptions
from ax.utils.common.constants import Keys
from ax.utils.common.typeutils import checked_cast
from ax.utils.testing.core_stubs import (
get_branin_experiment,
get_branin_experiment_with_multi_objective,
Expand All @@ -62,13 +60,11 @@ def get_single_objective_benchmark_problem(


def get_multi_objective_benchmark_problem(
observe_noise_sd: bool = False,
num_trials: int = 4,
test_problem_kwargs: Optional[Dict[str, Any]] = None,
observe_noise_sd: bool = False, num_trials: int = 4
) -> MultiObjectiveBenchmarkProblem:
return create_multi_objective_problem_from_botorch(
test_problem_class=BraninCurrin,
test_problem_kwargs={} if test_problem_kwargs is None else test_problem_kwargs,
test_problem_kwargs={},
num_trials=num_trials,
observe_noise_sd=observe_noise_sd,
)
Expand Down Expand Up @@ -98,97 +94,61 @@ def get_sobol_benchmark_method() -> BenchmarkMethod:
)


def get_soo_surrogate(noise_stds: float = 0.0) -> SOOSurrogateBenchmarkProblem:
outcome_name = "branin"
observe_noise_stds = True
def get_soo_surrogate() -> SOOSurrogateBenchmarkProblem:
experiment = get_branin_experiment(with_completed_trial=True)

optimization_config = OptimizationConfig(
objective=Objective(
metric=BenchmarkMetric(
name=outcome_name,
lower_is_better=False,
observe_noise_sd=observe_noise_stds,
),
minimize=False,
)
)
surrogate = Surrogate(botorch_model_class=SingleTaskGP)
model_bridge = TorchModelBridge(
surrogate = TorchModelBridge(
experiment=experiment,
search_space=experiment.search_space,
model=BoTorchModel(surrogate=surrogate),
model=BoTorchModel(surrogate=Surrogate(botorch_model_class=SingleTaskGP)),
data=experiment.lookup_data(),
transforms=[],
)
datasets = surrogate.training_data
runner = SurrogateRunner(
name=outcome_name,
name="test",
search_space=experiment.search_space,
outcome_names=[outcome_name],
get_surrogate_and_datasets=lambda: (model_bridge, datasets),
noise_stds=noise_stds,
outcome_names=["branin"],
get_surrogate_and_datasets=lambda: (surrogate, []),
)
return SOOSurrogateBenchmarkProblem(
name="test",
search_space=experiment.search_space,
optimization_config=optimization_config,
optimization_config=checked_cast(
OptimizationConfig, experiment.optimization_config
),
num_trials=6,
observe_noise_stds=observe_noise_stds,
observe_noise_stds=True,
optimal_value=0.0,
runner=runner,
is_noiseless=runner.is_noiseless,
)


def get_moo_surrogate(noise_stds: float = 0.0) -> MOOSurrogateBenchmarkProblem:
observe_noise_stds = True
outcome_names = ["branin_a", "branin_b"]
# set this to be easy to beat, so hypervolume computations aren't all zero
ref_point = [10.0, 10.0]
surrogate = Surrogate(botorch_model_class=SingleTaskGP)
def get_moo_surrogate() -> MOOSurrogateBenchmarkProblem:
experiment = get_branin_experiment_with_multi_objective(with_completed_trial=True)
model_bridge = TorchModelBridge(
surrogate = TorchModelBridge(
experiment=experiment,
search_space=experiment.search_space,
model=BoTorchModel(surrogate=surrogate),
model=BoTorchModel(surrogate=Surrogate(botorch_model_class=SingleTaskGP)),
data=experiment.lookup_data(),
transforms=[],
)

runner = SurrogateRunner(
name="test",
search_space=experiment.search_space,
outcome_names=outcome_names,
get_surrogate_and_datasets=lambda: (model_bridge, surrogate.training_data),
noise_stds=noise_stds,
outcome_names=["branin_a", "branin_b"],
get_surrogate_and_datasets=lambda: (surrogate, []),
)
metrics = [
BenchmarkMetric(
name=name,
lower_is_better=True,
observe_noise_sd=observe_noise_stds,
)
for name in outcome_names
]
objectives = [Objective(metric=metric) for metric in metrics]
objective_thresholds = [
ObjectiveThreshold(metric=metric, bound=ref_p, relative=False)
for metric, ref_p in zip(metrics, ref_point)
]
optimization_config = MultiObjectiveOptimizationConfig(
objective=MultiObjective(objectives=objectives),
objective_thresholds=objective_thresholds,
)

return MOOSurrogateBenchmarkProblem(
name="test",
search_space=experiment.search_space,
optimization_config=optimization_config,
optimization_config=checked_cast(
MultiObjectiveOptimizationConfig, experiment.optimization_config
),
num_trials=10,
observe_noise_stds=True,
optimal_value=1.0,
reference_point=ref_point,
reference_point=[],
runner=runner,
is_noiseless=runner.is_noiseless,
)
Expand Down