#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from typing import NamedTuple
from ax.core.objective import Objective
from ax.core.optimization_config import OptimizationConfig
from ax.core.outcome_constraint import OutcomeConstraint
from ax.core.parameter import ParameterType, RangeParameter
from ax.core.search_space import SearchSpace
from ax.core.types import ComparisonOp
from ax.metrics.branin import BraninMetric, NegativeBraninMetric
from ax.metrics.hartmann6 import Hartmann6Metric
from ax.metrics.l2norm import L2NormMetric
from ax.utils.measurement.synthetic_functions import (
branin as branin_function,
hartmann6 as hartmann6_function,
)
from ax.utils.testing.fake import get_branin_search_space
[docs]class BenchmarkProblem(NamedTuple):
"""Contains features that describe a benchmarking problem: its name, its
global optimum (maximum for maximization problems, minimum for minimization),
its optimization configuration, and its search space.
Args:
name: name of this problem
fbest: global optimum
optimization_config: optimization configuration
search_space: search space, on which this problem is defined
"""
name: str
fbest: float
optimization_config: OptimizationConfig
search_space: SearchSpace
# Branin problems
branin = BenchmarkProblem(
name=branin_function.name,
fbest=branin_function.fmin,
optimization_config=OptimizationConfig(
objective=Objective(
metric=BraninMetric(
name="branin_objective", param_names=["x1", "x2"], noise_sd=5.0
),
minimize=True,
)
),
search_space=get_branin_search_space(),
)
branin_max = BenchmarkProblem(
name=branin_function.name,
fbest=branin_function.fmax,
optimization_config=OptimizationConfig(
objective=Objective(
metric=NegativeBraninMetric(
name="neg_branin", param_names=["x1", "x2"], noise_sd=5.0
),
minimize=False,
)
),
search_space=get_branin_search_space(),
)
# Hartmann 6 problems
hartmann6 = BenchmarkProblem(
name=hartmann6_function.name,
fbest=hartmann6_function.fmin,
optimization_config=OptimizationConfig(
objective=Objective(
metric=Hartmann6Metric(
name=hartmann6_function.name,
param_names=[f"x{i}" for i in range(6)],
noise_sd=0.01,
),
minimize=True,
)
),
search_space=SearchSpace(
parameters=[
RangeParameter(
name=f"x{i}",
parameter_type=ParameterType.FLOAT,
lower=param_domain[0],
upper=param_domain[1],
)
for i, param_domain in enumerate(hartmann6_function.domain)
]
),
)
hartmann6_constrained = BenchmarkProblem(
name=hartmann6_function.name,
fbest=hartmann6_function.fmin,
optimization_config=OptimizationConfig(
objective=Objective(
metric=Hartmann6Metric(
name="hartmann6", param_names=[f"x{i}" for i in range(6)], noise_sd=0.2
),
minimize=True,
),
outcome_constraints=[
OutcomeConstraint(
metric=L2NormMetric(
name="l2norm", param_names=[f"x{i}" for i in range(6)], noise_sd=0.2
),
op=ComparisonOp.LEQ,
bound=1.25,
relative=False,
)
],
),
search_space=SearchSpace(
parameters=[
RangeParameter(
name=f"x{i}",
parameter_type=ParameterType.FLOAT,
lower=param_domain[0],
upper=param_domain[1],
)
for i, param_domain in enumerate(hartmann6_function.domain)
]
),
)
