# 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.
from ax.benchmark.benchmark_problem import SingleObjectiveBenchmarkProblem
from ax.core.objective import Objective
from ax.core.optimization_config import OptimizationConfig
from ax.core.parameter import ChoiceParameter, ParameterType, RangeParameter
from ax.core.search_space import HierarchicalSearchSpace
from ax.metrics.jenatton import JenattonMetric
from ax.runners.synthetic import SyntheticRunner
[docs]def get_jenatton_benchmark_problem(
num_trials: int = 50,
infer_noise: bool = True,
) -> SingleObjectiveBenchmarkProblem:
search_space = HierarchicalSearchSpace(
parameters=[
ChoiceParameter(
name="x1",
parameter_type=ParameterType.INT,
values=[0, 1],
dependents={0: ["x2", "r8"], 1: ["x3", "r9"]},
),
ChoiceParameter(
name="x2",
parameter_type=ParameterType.INT,
values=[0, 1],
dependents={0: ["x4"], 1: ["x5"]},
),
ChoiceParameter(
name="x3",
parameter_type=ParameterType.INT,
values=[0, 1],
dependents={0: ["x6"], 1: ["x7"]},
),
*[
RangeParameter(
name=f"x{i}",
parameter_type=ParameterType.FLOAT,
lower=0.0,
upper=1.0,
)
for i in range(4, 8)
],
RangeParameter(
name="r8", parameter_type=ParameterType.FLOAT, lower=0.0, upper=1.0
),
RangeParameter(
name="r9", parameter_type=ParameterType.FLOAT, lower=0.0, upper=1.0
),
]
)
optimization_config = OptimizationConfig(
objective=Objective(
metric=JenattonMetric(infer_noise=infer_noise), minimize=True
)
)
name = "Jenatton" + ("" if infer_noise else "_fixed_noise")
return SingleObjectiveBenchmarkProblem(
name=name,
search_space=search_space,
optimization_config=optimization_config,
runner=SyntheticRunner(),
num_trials=num_trials,
infer_noise=infer_noise,
optimal_value=0.1,
)
