# 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
from typing import Any
from ax.benchmark.benchmark_method import BenchmarkMethod
from ax.modelbridge.generation_node import GenerationStep
from ax.modelbridge.generation_strategy import GenerationStrategy
from ax.modelbridge.registry import Models
from ax.models.torch.botorch_modular.surrogate import SurrogateSpec
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.acquisition.analytic import LogExpectedImprovement
from botorch.acquisition.logei import qLogNoisyExpectedImprovement
from botorch.acquisition.multi_objective.logei import (
qLogNoisyExpectedHypervolumeImprovement,
)
from botorch.acquisition.multi_objective.monte_carlo import (
qNoisyExpectedHypervolumeImprovement,
)
from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
from botorch.models.model import Model
model_names_abbrevations: dict[str, str] = {
SaasFullyBayesianSingleTaskGP.__name__: "SAAS",
}
acqf_name_abbreviations: dict[str, str] = {
qLogNoisyExpectedImprovement.__name__: "qLogNEI",
qNoisyExpectedHypervolumeImprovement.__name__: "qNEHVI",
qLogNoisyExpectedHypervolumeImprovement.__name__: "qLogNEHVI",
LogExpectedImprovement.__name__: "LogEI",
}
[docs]
def get_sobol_mbm_generation_strategy(
model_cls: type[Model],
acquisition_cls: type[AcquisitionFunction],
name: str | None = None,
num_sobol_trials: int = 5,
model_gen_kwargs: dict[str, Any] | None = None,
batch_size: int = 1,
) -> GenerationStrategy:
"""Get a `BenchmarkMethod` that uses Sobol followed by MBM.
Args:
model_cls: BoTorch model class, e.g. SingleTaskGP
acquisition_cls: Acquisition function class, e.g.
`qLogNoisyExpectedImprovement`.
scheduler_options: Passed as-is to scheduler. Default:
`get_benchmark_scheduler_options()`.
name: Name that will be attached to the `GenerationStrategy`.
num_sobol_trials: Number of Sobol trials; if the scheduler_options
specify to use `BatchTrial`s, then this refers to the number of
`BatchTrial`s.
model_gen_kwargs: Passed to the BoTorch `GenerationStep` and ultimately
to the BoTorch `Model`.
Example:
>>> # A simple example
>>> from ax.benchmark.methods.sobol_botorch_modular import (
... get_sobol_mbm_generation_strategy
... )
>>> from ax.benchmark.benchmark_method import get_benchmark_scheduler_options
>>> gs = get_sobol_mbm_generation_strategy(
... model_cls=SingleTaskGP,
... acquisition_cls=qLogNoisyExpectedImprovement,
... distribute_replications=False,
... )
"""
model_kwargs: dict[str, type[AcquisitionFunction] | SurrogateSpec | bool] = {
"botorch_acqf_class": acquisition_cls,
"surrogate_spec": SurrogateSpec(botorch_model_class=model_cls),
}
model_name = model_names_abbrevations.get(model_cls.__name__, model_cls.__name__)
acqf_name = acqf_name_abbreviations.get(
acquisition_cls.__name__, acquisition_cls.__name__
)
# Historically all benchmarks were sequential, so sequential benchmarks
# don't get anything added to their name, for continuity
batch_suffix = f"_q{batch_size}" if batch_size > 1 else ""
name = name or f"MBM::{model_name}_{acqf_name}{batch_suffix}"
generation_strategy = GenerationStrategy(
name=name,
steps=[
GenerationStep(
model=Models.SOBOL,
num_trials=num_sobol_trials,
min_trials_observed=num_sobol_trials,
),
GenerationStep(
model=Models.BOTORCH_MODULAR,
num_trials=-1,
model_kwargs=model_kwargs,
model_gen_kwargs=model_gen_kwargs or {},
),
],
)
return generation_strategy
[docs]
def get_sobol_botorch_modular_acquisition(
model_cls: type[Model],
acquisition_cls: type[AcquisitionFunction],
distribute_replications: bool,
name: str | None = None,
num_sobol_trials: int = 5,
model_gen_kwargs: dict[str, Any] | None = None,
use_model_predictions_for_best_point: bool = False,
batch_size: int = 1,
) -> BenchmarkMethod:
"""Get a `BenchmarkMethod` that uses Sobol followed by MBM.
Args:
model_cls: BoTorch model class, e.g. SingleTaskGP
acquisition_cls: Acquisition function class, e.g.
`qLogNoisyExpectedImprovement`.
distribute_replications: Whether to use multiple machines
scheduler_options: Passed as-is to scheduler. Default:
`get_benchmark_scheduler_options()`.
name: Name that will be attached to the `GenerationStrategy`.
num_sobol_trials: Number of Sobol trials; if the scheduler_options
specify to use `BatchTrial`s, then this refers to the number of
`BatchTrial`s.
model_gen_kwargs: Passed to the BoTorch `GenerationStep` and ultimately
to the BoTorch `Model`.
use_model_predictions_for_best_point: Passed to the created `BenchmarkMethod`.
batch_size: Passed to the created ``BenchmarkMethod``.
Example:
>>> # A simple example
>>> from ax.benchmark.methods.sobol_botorch_modular import (
... get_sobol_botorch_modular_acquisition
... )
>>> from ax.benchmark.benchmark_method import get_benchmark_scheduler_options
>>>
>>> method = get_sobol_botorch_modular_acquisition(
... model_cls=SingleTaskGP,
... acquisition_cls=qLogNoisyExpectedImprovement,
... distribute_replications=False,
... )
>>> # Pass sequential=False to BoTorch's optimize_acqf
>>> batch_method = get_sobol_botorch_modular_acquisition(
... model_cls=SingleTaskGP,
... acquisition_cls=qLogNoisyExpectedImprovement,
... distribute_replications=False,
... batch_size=5,
... model_gen_kwargs={
... "model_gen_options": {
... "optimizer_kwargs": {"sequential": False}
... }
... },
... num_sobol_trials=1,
... )
"""
generation_strategy = get_sobol_mbm_generation_strategy(
model_cls=model_cls,
acquisition_cls=acquisition_cls,
name=name,
num_sobol_trials=num_sobol_trials,
model_gen_kwargs=model_gen_kwargs,
batch_size=batch_size,
)
return BenchmarkMethod(
generation_strategy=generation_strategy,
distribute_replications=distribute_replications,
use_model_predictions_for_best_point=use_model_predictions_for_best_point,
batch_size=batch_size,
)
