Source code for ax.benchmark.problems.synthetic.hss.jenatton

# 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, ) -> 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(), minimize=True) ) return SingleObjectiveBenchmarkProblem( name="Jenatton", search_space=search_space, optimization_config=optimization_config, runner=SyntheticRunner(), num_trials=num_trials, optimal_value=0.1, )