# 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.
"""
Mixed integer extensions of some common synthetic test functions.
These are adapted from [Daulton2022bopr]_.
References
.. [Daulton2022bopr]
S. Daulton, X. Wan, D. Eriksson, M. Balandat, M. A. Osborne, E. Bakshy.
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic
Reparameterization. Advances in Neural Information Processing Systems
35, 2022.
"""
from typing import Any, Dict, List, Optional, Tuple, Type
from ax.benchmark.benchmark_problem import BenchmarkProblem
from ax.core.objective import Objective
from ax.core.optimization_config import OptimizationConfig
from ax.core.parameter import ParameterType, RangeParameter
from ax.core.search_space import SearchSpace
from ax.metrics.botorch_test_problem import BotorchTestProblemMetric
from ax.runners.botorch_test_problem import BotorchTestProblemRunner
from botorch.test_functions.synthetic import (
Ackley,
Hartmann,
Rosenbrock,
SyntheticTestFunction,
)
def _get_problem_from_common_inputs(
bounds: List[Tuple[float, float]],
dim_int: int,
metric_name: str,
infer_noise: bool,
test_problem_class: Type[SyntheticTestFunction],
benchmark_name: str,
num_trials: int,
test_problem_bounds: Optional[List[Tuple[float, float]]] = None,
) -> BenchmarkProblem:
"""This is a helper that deduplicates common bits of the below problems.
Args:
bounds: The parameter bounds.
dim_int: The number of integer dimensions. First `dim_int` parameters
are assumed to be integers.
metric_name: The name of the metric.
infer_noise: Whether to infer noise or assume noise-free objective.
test_problem_class: The BoTorch test problem class.
benchmark_name: The name of the benchmark problem.
num_trials: The number of trials.
test_problem_bounds: Optional bounds to evaluate the base test problem on.
These are passed in as `bounds` while initializing the test problem.
Returns:
A mixed-integer BenchmarkProblem constructed from the given inputs.
"""
dim = len(bounds)
search_space = SearchSpace(
parameters=[
RangeParameter(
name=f"x{i + 1}",
parameter_type=ParameterType.INT
if i < dim_int
else ParameterType.FLOAT,
lower=bounds[i][0],
upper=bounds[i][1],
)
for i in range(dim)
]
)
optimization_config = OptimizationConfig(
objective=Objective(
metric=BotorchTestProblemMetric(
name=metric_name,
noise_sd=None if infer_noise else 0.0,
),
minimize=True,
)
)
test_problem_kwargs: Dict[str, Any] = {"dim": dim}
if test_problem_bounds is not None:
test_problem_kwargs["bounds"] = test_problem_bounds
runner = BotorchTestProblemRunner(
test_problem_class=test_problem_class,
test_problem_kwargs=test_problem_kwargs,
modified_bounds=bounds,
)
return BenchmarkProblem(
name=benchmark_name,
search_space=search_space,
optimization_config=optimization_config,
runner=runner,
num_trials=num_trials,
infer_noise=infer_noise,
)
[docs]def get_discrete_hartmann(
num_trials: int = 50,
infer_noise: bool = True,
bounds: Optional[List[Tuple[float, float]]] = None,
) -> BenchmarkProblem:
"""6D Hartmann problem where first 4 dimensions are discretized."""
dim_int = 4
if bounds is None:
bounds = [
(0, 3),
(0, 3),
(0, 19),
(0, 19),
(0.0, 1.0),
(0.0, 1.0),
]
return _get_problem_from_common_inputs(
bounds=bounds,
dim_int=dim_int,
metric_name="Hartmann",
infer_noise=infer_noise,
test_problem_class=Hartmann,
benchmark_name="Discrete Hartmann",
num_trials=num_trials,
)
[docs]def get_discrete_ackley(
num_trials: int = 50,
infer_noise: bool = True,
bounds: Optional[List[Tuple[float, float]]] = None,
) -> BenchmarkProblem:
"""13D Ackley problem where first 10 dimensions are discretized.
This also restricts Ackley evaluation bounds to [0, 1].
"""
dim = 13
dim_int = 10
if bounds is None:
bounds = [
*[(0, 2)] * 5,
*[(0, 4)] * 5,
*[(0.0, 1.0)] * 3,
]
return _get_problem_from_common_inputs(
bounds=bounds,
dim_int=dim_int,
metric_name="Ackley",
infer_noise=infer_noise,
test_problem_class=Ackley,
benchmark_name="Discrete Ackley",
num_trials=num_trials,
test_problem_bounds=[(0.0, 1.0)] * dim,
)
[docs]def get_discrete_rosenbrock(
num_trials: int = 50,
infer_noise: bool = True,
bounds: Optional[List[Tuple[float, float]]] = None,
) -> BenchmarkProblem:
"""10D Rosenbrock problem where first 6 dimensions are discretized."""
dim_int = 6
if bounds is None:
bounds = [
*[(0, 3)] * 6,
*[(0.0, 1.0)] * 4,
]
return _get_problem_from_common_inputs(
bounds=bounds,
dim_int=dim_int,
metric_name="Rosenbrock",
infer_noise=infer_noise,
test_problem_class=Rosenbrock,
benchmark_name="Discrete Rosenbrock",
num_trials=num_trials,
)
