#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from ax.benchmark.benchmark_method import BenchmarkMethod
from ax.benchmark.benchmark_problem import (
BenchmarkProblem,
MultiObjectiveBenchmarkProblem,
SingleObjectiveBenchmarkProblem,
)
from ax.benchmark.benchmark_result import AggregatedBenchmarkResult, BenchmarkResult
from ax.benchmark.problems.surrogate import (
MOOSurrogateBenchmarkProblem,
SOOSurrogateBenchmarkProblem,
)
from ax.core.experiment import Experiment
from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
from ax.modelbridge.registry import Models
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.testing.core_stubs import (
get_branin_multi_objective_optimization_config,
get_branin_optimization_config,
get_branin_search_space,
)
from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement
from botorch.models.gp_regression import SingleTaskGP
from botorch.test_functions.multi_objective import BraninCurrin
from botorch.test_functions.synthetic import Branin
[docs]def get_benchmark_problem() -> BenchmarkProblem:
return BenchmarkProblem.from_botorch(
test_problem_class=Branin, test_problem_kwargs={}, num_trials=4
)
[docs]def get_single_objective_benchmark_problem(
infer_noise: bool = True,
num_trials: int = 4,
) -> SingleObjectiveBenchmarkProblem:
return SingleObjectiveBenchmarkProblem.from_botorch_synthetic(
test_problem_class=Branin,
test_problem_kwargs={},
num_trials=num_trials,
infer_noise=infer_noise,
)
[docs]def get_multi_objective_benchmark_problem(
infer_noise: bool = True, num_trials: int = 4
) -> MultiObjectiveBenchmarkProblem:
return MultiObjectiveBenchmarkProblem.from_botorch_multi_objective(
test_problem_class=BraninCurrin,
test_problem_kwargs={},
num_trials=num_trials,
infer_noise=infer_noise,
)
[docs]def get_sobol_benchmark_method() -> BenchmarkMethod:
return BenchmarkMethod(
name="SOBOL",
generation_strategy=GenerationStrategy(
steps=[GenerationStep(model=Models.SOBOL, num_trials=-1)],
name="SOBOL",
),
scheduler_options=SchedulerOptions(
total_trials=4, init_seconds_between_polls=0
),
)
[docs]def get_soo_surrogate() -> SOOSurrogateBenchmarkProblem:
surrogate = Surrogate(
botorch_model_class=SingleTaskGP,
)
return SOOSurrogateBenchmarkProblem(
name="test",
search_space=get_branin_search_space(),
optimization_config=get_branin_optimization_config(),
num_trials=6,
infer_noise=False,
metric_names=[],
get_surrogate_and_datasets=lambda: (surrogate, []),
optimal_value=0.0,
)
[docs]def get_moo_surrogate() -> MOOSurrogateBenchmarkProblem:
surrogate = Surrogate(botorch_model_class=SingleTaskGP)
return MOOSurrogateBenchmarkProblem(
name="test",
search_space=get_branin_search_space(),
optimization_config=get_branin_multi_objective_optimization_config(),
num_trials=10,
infer_noise=False,
metric_names=[],
get_surrogate_and_datasets=lambda: (surrogate, []),
maximum_hypervolume=1.0,
reference_point=[],
)
[docs]def get_sobol_gpei_benchmark_method() -> BenchmarkMethod:
return BenchmarkMethod(
name="MBO_SOBOL_GPEI",
generation_strategy=GenerationStrategy(
name="Modular::Sobol+GPEI",
steps=[
GenerationStep(model=Models.SOBOL, num_trials=3, min_trials_observed=3),
GenerationStep(
model=Models.BOTORCH_MODULAR,
num_trials=-1,
model_kwargs={
"surrogate": Surrogate(SingleTaskGP),
# TODO: tests should better reflect defaults and not
# re-implement this logic.
"botorch_acqf_class": qNoisyExpectedImprovement,
},
model_gen_kwargs={
"model_gen_options": {
Keys.OPTIMIZER_KWARGS: {
"num_restarts": 50,
"raw_samples": 1024,
},
Keys.ACQF_KWARGS: {
"prune_baseline": True,
},
}
},
),
],
),
scheduler_options=SchedulerOptions(
total_trials=4, init_seconds_between_polls=0
),
)
[docs]def get_benchmark_result() -> BenchmarkResult:
problem = get_single_objective_benchmark_problem()
return BenchmarkResult(
name="test_benchmarking_result",
seed=0,
experiment=Experiment(
name="test_benchmarking_experiment",
search_space=problem.search_space,
optimization_config=problem.optimization_config,
runner=problem.runner,
is_test=True,
),
optimization_trace=np.array([3, 2, 1, 0.1]),
score_trace=np.array([3, 2, 1, 0.1]),
fit_time=0.1,
gen_time=0.2,
)
[docs]def get_aggregated_benchmark_result() -> AggregatedBenchmarkResult:
result = get_benchmark_result()
return AggregatedBenchmarkResult.from_benchmark_results([result, result])
