Source code for ax.benchmark.problems.registry

# 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.

# pyre-strict

import copy
from dataclasses import dataclass
from typing import Any, Callable, Dict

from ax.benchmark.benchmark_problem import (
    BenchmarkProblem,
    MultiObjectiveBenchmarkProblem,
    SingleObjectiveBenchmarkProblem,
)
from ax.benchmark.problems.hd_embedding import embed_higher_dimension
from ax.benchmark.problems.hpo.torchvision import PyTorchCNNTorchvisionBenchmarkProblem
from ax.benchmark.problems.synthetic.hss.jenatton import get_jenatton_benchmark_problem
from botorch.test_functions import synthetic
from botorch.test_functions.multi_objective import BraninCurrin


[docs]@dataclass class BenchmarkProblemRegistryEntry: factory_fn: Callable[..., BenchmarkProblem] factory_kwargs: Dict[str, Any]
BENCHMARK_PROBLEM_REGISTRY = { "ackley4": BenchmarkProblemRegistryEntry( factory_fn=SingleObjectiveBenchmarkProblem.from_botorch_synthetic, factory_kwargs={ "test_problem_class": synthetic.Ackley, "test_problem_kwargs": {"dim": 4}, "lower_is_better": True, "num_trials": 40, "observe_noise_sd": False, }, ), "branin": BenchmarkProblemRegistryEntry( factory_fn=SingleObjectiveBenchmarkProblem.from_botorch_synthetic, factory_kwargs={ "test_problem_class": synthetic.Branin, "test_problem_kwargs": {}, "lower_is_better": True, "num_trials": 30, "observe_noise_sd": False, }, ), "branin_currin": BenchmarkProblemRegistryEntry( factory_fn=MultiObjectiveBenchmarkProblem.from_botorch_multi_objective, factory_kwargs={ "test_problem_class": BraninCurrin, "test_problem_kwargs": {}, "num_trials": 30, "observe_noise_sd": False, }, ), "branin_currin30": BenchmarkProblemRegistryEntry( factory_fn=lambda n, num_trials: embed_higher_dimension( problem=MultiObjectiveBenchmarkProblem.from_botorch_multi_objective( test_problem_class=BraninCurrin, test_problem_kwargs={}, num_trials=num_trials, observe_noise_sd=False, ), total_dimensionality=n, ), factory_kwargs={"n": 30, "num_trials": 30}, ), "griewank4": BenchmarkProblemRegistryEntry( factory_fn=SingleObjectiveBenchmarkProblem.from_botorch_synthetic, factory_kwargs={ "test_problem_class": synthetic.Griewank, "test_problem_kwargs": {"dim": 4}, "lower_is_better": True, "num_trials": 40, "observe_noise_sd": False, }, ), "hartmann3": BenchmarkProblemRegistryEntry( factory_fn=SingleObjectiveBenchmarkProblem.from_botorch_synthetic, factory_kwargs={ "test_problem_class": synthetic.Hartmann, "test_problem_kwargs": {"dim": 3}, "lower_is_better": True, "num_trials": 30, "observe_noise_sd": False, }, ), "hartmann6": BenchmarkProblemRegistryEntry( factory_fn=SingleObjectiveBenchmarkProblem.from_botorch_synthetic, factory_kwargs={ "test_problem_class": synthetic.Hartmann, "test_problem_kwargs": {"dim": 6}, "lower_is_better": True, "num_trials": 35, "observe_noise_sd": False, }, ), "hartmann30": BenchmarkProblemRegistryEntry( factory_fn=lambda n, num_trials: embed_higher_dimension( problem=SingleObjectiveBenchmarkProblem.from_botorch_synthetic( test_problem_class=synthetic.Hartmann, test_problem_kwargs={"dim": 6}, lower_is_better=True, num_trials=num_trials, observe_noise_sd=False, ), total_dimensionality=n, ), factory_kwargs={"n": 30, "num_trials": 25}, ), "hpo_pytorch_cnn_MNIST": BenchmarkProblemRegistryEntry( factory_fn=PyTorchCNNTorchvisionBenchmarkProblem.from_dataset_name, factory_kwargs={ "name": "MNIST", "num_trials": 20, }, ), "hpo_pytorch_cnn_FashionMNIST": BenchmarkProblemRegistryEntry( factory_fn=PyTorchCNNTorchvisionBenchmarkProblem.from_dataset_name, factory_kwargs={ "name": "FashionMNIST", "num_trials": 50, }, ), "jenatton": BenchmarkProblemRegistryEntry( factory_fn=get_jenatton_benchmark_problem, factory_kwargs={"num_trials": 50, "observe_noise_sd": False}, ), "levy4": BenchmarkProblemRegistryEntry( factory_fn=SingleObjectiveBenchmarkProblem.from_botorch_synthetic, factory_kwargs={ "test_problem_class": synthetic.Levy, "test_problem_kwargs": {"dim": 4}, "lower_is_better": True, "num_trials": 40, "observe_noise_sd": False, }, ), "powell4": BenchmarkProblemRegistryEntry( factory_fn=SingleObjectiveBenchmarkProblem.from_botorch_synthetic, factory_kwargs={ "test_problem_class": synthetic.Powell, "test_problem_kwargs": {"dim": 4}, "lower_is_better": True, "num_trials": 40, "observe_noise_sd": False, }, ), "rosenbrock4": BenchmarkProblemRegistryEntry( factory_fn=SingleObjectiveBenchmarkProblem.from_botorch_synthetic, factory_kwargs={ "test_problem_class": synthetic.Rosenbrock, "test_problem_kwargs": {"dim": 4}, "lower_is_better": True, "num_trials": 40, "observe_noise_sd": False, }, ), "six_hump_camel": BenchmarkProblemRegistryEntry( factory_fn=SingleObjectiveBenchmarkProblem.from_botorch_synthetic, factory_kwargs={ "test_problem_class": synthetic.SixHumpCamel, "test_problem_kwargs": {}, "lower_is_better": True, "num_trials": 30, "observe_noise_sd": False, }, ), "three_hump_camel": BenchmarkProblemRegistryEntry( factory_fn=SingleObjectiveBenchmarkProblem.from_botorch_synthetic, factory_kwargs={ "test_problem_class": synthetic.ThreeHumpCamel, "test_problem_kwargs": {}, "lower_is_better": True, "num_trials": 30, "observe_noise_sd": False, }, ), # Problems where we observe the noise level "branin_observed_noise": BenchmarkProblemRegistryEntry( factory_fn=SingleObjectiveBenchmarkProblem.from_botorch_synthetic, factory_kwargs={ "test_problem_class": synthetic.Branin, "test_problem_kwargs": {}, "lower_is_better": True, "num_trials": 20, "observe_noise_sd": True, }, ), "branin_currin_observed_noise": BenchmarkProblemRegistryEntry( factory_fn=MultiObjectiveBenchmarkProblem.from_botorch_multi_objective, factory_kwargs={ "test_problem_class": BraninCurrin, "test_problem_kwargs": {}, "num_trials": 30, "observe_noise_sd": True, }, ), "branin_currin30_observed_noise": BenchmarkProblemRegistryEntry( factory_fn=lambda n, num_trials: embed_higher_dimension( problem=MultiObjectiveBenchmarkProblem.from_botorch_multi_objective( test_problem_class=BraninCurrin, test_problem_kwargs={}, num_trials=num_trials, observe_noise_sd=True, ), total_dimensionality=n, ), factory_kwargs={"n": 30, "num_trials": 30}, ), "hartmann6_observed_noise": BenchmarkProblemRegistryEntry( factory_fn=SingleObjectiveBenchmarkProblem.from_botorch_synthetic, factory_kwargs={ "test_problem_class": synthetic.Hartmann, "test_problem_kwargs": {"dim": 6}, "lower_is_better": True, "num_trials": 50, "observe_noise_sd": True, }, ), "hartmann30_observed_noise": BenchmarkProblemRegistryEntry( factory_fn=lambda n, num_trials: embed_higher_dimension( problem=SingleObjectiveBenchmarkProblem.from_botorch_synthetic( test_problem_class=synthetic.Hartmann, test_problem_kwargs={"dim": 6}, lower_is_better=True, num_trials=num_trials, observe_noise_sd=True, ), total_dimensionality=n, ), factory_kwargs={"n": 30, "num_trials": 25}, ), "jenatton_observed_noise": BenchmarkProblemRegistryEntry( factory_fn=get_jenatton_benchmark_problem, factory_kwargs={"num_trials": 25, "observe_noise_sd": True}, ), "constrained_gramacy_observed_noise": BenchmarkProblemRegistryEntry( factory_fn=SingleObjectiveBenchmarkProblem.from_botorch_synthetic, factory_kwargs={ "test_problem_class": synthetic.ConstrainedGramacy, "test_problem_kwargs": {}, "lower_is_better": True, "num_trials": 50, "observe_noise_sd": True, }, ), }
[docs]def get_problem(problem_name: str, **additional_kwargs: Any) -> BenchmarkProblem: entry = BENCHMARK_PROBLEM_REGISTRY[problem_name] kwargs = copy.copy(entry.factory_kwargs) kwargs.update(additional_kwargs) return entry.factory_fn(**kwargs)