# 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 typing import Any, Dict, Optional, Type
from ax.benchmark.benchmark_method import (
BenchmarkMethod,
get_sequential_optimization_scheduler_options,
)
from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
from ax.modelbridge.registry import Models
from ax.models.torch.botorch_modular.list_surrogate import ListSurrogate
from ax.models.torch.botorch_modular.surrogate import Surrogate
from ax.utils.common.constants import Keys
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement
from botorch.acquisition.multi_objective.monte_carlo import (
qNoisyExpectedHypervolumeImprovement,
)
from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
from botorch.models.gp_regression import FixedNoiseGP
[docs]def get_sobol_botorch_modular_fixed_noise_gp_qnei() -> BenchmarkMethod:
model_gen_kwargs = {
"model_gen_options": {
Keys.OPTIMIZER_KWARGS: {
"num_restarts": 50,
"raw_samples": 1024,
},
Keys.ACQF_KWARGS: {
"prune_baseline": True,
"qmc": True,
"mc_samples": 512,
},
}
}
generation_strategy = GenerationStrategy(
name="SOBOL+BOTORCH_MODULAR::FixedNoiseGP_qNoisyExpectedImprovement",
steps=[
GenerationStep(model=Models.SOBOL, num_trials=5, min_trials_observed=5),
GenerationStep(
model=Models.BOTORCH_MODULAR,
num_trials=-1,
max_parallelism=1,
model_kwargs={
"surrogate": Surrogate(FixedNoiseGP),
"botorch_acqf_class": qNoisyExpectedImprovement,
},
model_gen_kwargs=model_gen_kwargs,
),
],
)
return BenchmarkMethod(
name=generation_strategy.name,
generation_strategy=generation_strategy,
scheduler_options=get_sequential_optimization_scheduler_options(),
)
[docs]def get_sobol_botorch_modular_fixed_noise_gp_qnehvi() -> BenchmarkMethod:
model_gen_kwargs = {
"model_gen_options": {
Keys.OPTIMIZER_KWARGS: {
"num_restarts": 50,
"raw_samples": 1024,
},
Keys.ACQF_KWARGS: {
"prune_baseline": True,
"qmc": True,
"mc_samples": 512,
},
}
}
generation_strategy = GenerationStrategy(
name="SOBOL+BOTORCH_MODULAR::FixedNoiseGP_qNoisyExpectedHypervolumeImprovement",
steps=[
GenerationStep(
model=Models.SOBOL,
num_trials=5,
min_trials_observed=5,
),
GenerationStep(
model=Models.BOTORCH_MODULAR,
num_trials=-1,
max_parallelism=1,
model_kwargs={
"surrogate": Surrogate(FixedNoiseGP),
"botorch_acqf_class": qNoisyExpectedHypervolumeImprovement,
},
model_gen_kwargs=model_gen_kwargs,
),
],
)
return BenchmarkMethod(
name=generation_strategy.name,
generation_strategy=generation_strategy,
scheduler_options=get_sequential_optimization_scheduler_options(),
)
[docs]def get_sobol_botorch_modular_saas_fully_bayesian_single_task_gp_qnei() -> BenchmarkMethod: # noqa
generation_strategy = GenerationStrategy(
name="SOBOL+BOTORCH_MODULAR::SaasFullyBayesianSingleTaskGP_qNoisyExpectedImprovement", # noqa
steps=[
GenerationStep(model=Models.SOBOL, num_trials=5, min_trials_observed=5),
GenerationStep(
model=Models.BOTORCH_MODULAR,
num_trials=-1,
max_parallelism=1,
model_kwargs={
"surrogate": ListSurrogate(
botorch_submodel_class=SaasFullyBayesianSingleTaskGP
),
"botorch_acqf_class": qNoisyExpectedImprovement,
},
),
],
)
return BenchmarkMethod(
name=generation_strategy.name,
generation_strategy=generation_strategy,
scheduler_options=get_sequential_optimization_scheduler_options(),
)
[docs]def get_sobol_botorch_modular_saas_fully_bayesian_single_task_gp_qnehvi() -> BenchmarkMethod: # noqa
generation_strategy = GenerationStrategy(
name="SOBOL+BOTORCH_MODULAR::SaasFullyBayesianSingleTaskGP_qNoisyExpectedHypervolumeImprovement", # noqa
steps=[
GenerationStep(
model=Models.SOBOL,
num_trials=5,
min_trials_observed=5,
),
GenerationStep(
model=Models.BOTORCH_MODULAR,
num_trials=-1,
max_parallelism=1,
model_kwargs={
"surrogate": ListSurrogate(
botorch_submodel_class=SaasFullyBayesianSingleTaskGP
),
"botorch_acqf_class": qNoisyExpectedHypervolumeImprovement,
},
),
],
)
return BenchmarkMethod(
name=generation_strategy.name,
generation_strategy=generation_strategy,
scheduler_options=get_sequential_optimization_scheduler_options(),
)
[docs]def get_sobol_botorch_modular_default() -> BenchmarkMethod:
generation_strategy = GenerationStrategy(
name="SOBOL+BOTORCH_MODULAR::default",
steps=[
GenerationStep(model=Models.SOBOL, num_trials=5, min_trials_observed=5),
GenerationStep(
model=Models.BOTORCH_MODULAR,
num_trials=-1,
max_parallelism=1,
),
],
)
return BenchmarkMethod(
name=generation_strategy.name,
generation_strategy=generation_strategy,
scheduler_options=get_sequential_optimization_scheduler_options(),
)
[docs]def get_sobol_botorch_modular_acquisition(
acquisition_cls: Type[AcquisitionFunction],
acquisition_options: Optional[Dict[str, Any]] = None,
) -> BenchmarkMethod:
generation_strategy = GenerationStrategy(
name=f"SOBOL+BOTORCH_MODULAR::{acquisition_cls.__name__}",
steps=[
GenerationStep(
model=Models.SOBOL,
num_trials=5,
min_trials_observed=5,
),
GenerationStep(
model=Models.BOTORCH_MODULAR,
num_trials=-1,
max_parallelism=1,
model_kwargs={
"botorch_acqf_class": acquisition_cls,
"acquisition_options": acquisition_options,
},
),
],
)
return BenchmarkMethod(
name=generation_strategy.name,
generation_strategy=generation_strategy,
scheduler_options=get_sequential_optimization_scheduler_options(),
)
