#!/usr/bin/env python3
# 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, Dict, Type
import torch
# Ax `Acquisition` imports
from ax.models.torch.botorch_modular.acquisition import Acquisition
from ax.models.torch.botorch_modular.sebo import SEBOAcquisition
# BoTorch `AcquisitionFunction` imports
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.acquisition.analytic import (
ExpectedImprovement,
LogExpectedImprovement,
LogNoisyExpectedImprovement,
NoisyExpectedImprovement,
)
from botorch.acquisition.knowledge_gradient import (
qKnowledgeGradient,
qMultiFidelityKnowledgeGradient,
)
from botorch.acquisition.logei import (
qLogExpectedImprovement,
qLogNoisyExpectedImprovement,
)
from botorch.acquisition.max_value_entropy_search import (
qMaxValueEntropy,
qMultiFidelityMaxValueEntropy,
)
from botorch.acquisition.monte_carlo import (
qExpectedImprovement,
qNoisyExpectedImprovement,
)
from botorch.acquisition.multi_objective.logei import (
qLogExpectedHypervolumeImprovement,
qLogNoisyExpectedHypervolumeImprovement,
)
from botorch.acquisition.multi_objective.monte_carlo import (
qExpectedHypervolumeImprovement,
qNoisyExpectedHypervolumeImprovement,
)
from botorch.acquisition.preference import AnalyticExpectedUtilityOfBestOption
from botorch.models import SaasFullyBayesianSingleTaskGP
from botorch.models.contextual import LCEAGP
from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP
# BoTorch `Model` imports
from botorch.models.gp_regression import FixedNoiseGP, SingleTaskGP
from botorch.models.gp_regression_fidelity import (
FixedNoiseMultiFidelityGP,
SingleTaskMultiFidelityGP,
)
from botorch.models.gp_regression_mixed import MixedSingleTaskGP
from botorch.models.model import Model
from botorch.models.model_list_gp_regression import ModelListGP
from botorch.models.multitask import MultiTaskGP
from botorch.models.transforms.input import (
ChainedInputTransform,
InputPerturbation,
InputTransform,
Normalize,
Round,
Warp,
)
from botorch.models.transforms.outcome import (
ChainedOutcomeTransform,
OutcomeTransform,
Standardize,
)
# Miscellaneous BoTorch imports
from gpytorch.constraints import Interval
from gpytorch.likelihoods.gaussian_likelihood import GaussianLikelihood
from gpytorch.likelihoods.likelihood import Likelihood
# BoTorch `MarginalLogLikelihood` imports
from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
from gpytorch.mlls.leave_one_out_pseudo_likelihood import LeaveOneOutPseudoLikelihood
from gpytorch.mlls.marginal_log_likelihood import MarginalLogLikelihood
from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood
from gpytorch.priors.torch_priors import GammaPrior
# NOTE: When adding a new registry for a class, make sure to make changes
# to `CLASS_TO_REGISTRY` and `CLASS_TO_REVERSE_REGISTRY` in this file.
"""
Mapping of modular Ax `Acquisition` classes to class name strings.
"""
ACQUISITION_REGISTRY: Dict[Type[Acquisition], str] = {
Acquisition: "Acquisition",
}
"""
Mapping of BoTorch `Model` classes to class name strings.
"""
MODEL_REGISTRY: Dict[Type[Model], str] = {
# NOTE: Fixed noise models are deprecated. They point to their
# supported parent classes, so that we can reap them with minimal
# concern for backwards compatibility when the time comes.
FixedNoiseGP: "SingleTaskGP",
FixedNoiseMultiFidelityGP: "SingleTaskMultiFidelityGP",
MixedSingleTaskGP: "MixedSingleTaskGP",
ModelListGP: "ModelListGP",
MultiTaskGP: "MultiTaskGP",
SingleTaskGP: "SingleTaskGP",
SingleTaskMultiFidelityGP: "SingleTaskMultiFidelityGP",
SaasFullyBayesianSingleTaskGP: "SaasFullyBayesianSingleTaskGP",
SaasFullyBayesianMultiTaskGP: "SaasFullyBayesianMultiTaskGP",
LCEAGP: "LCEAGP",
}
"""
Mapping of Botorch `AcquisitionFunction` classes to class name strings.
"""
ACQUISITION_FUNCTION_REGISTRY: Dict[Type[AcquisitionFunction], str] = {
ExpectedImprovement: "ExpectedImprovement",
AnalyticExpectedUtilityOfBestOption: "AnalyticExpectedUtilityOfBestOption",
NoisyExpectedImprovement: "NoisyExpectedImprovement",
qExpectedHypervolumeImprovement: "qExpectedHypervolumeImprovement",
qNoisyExpectedHypervolumeImprovement: "qNoisyExpectedHypervolumeImprovement",
qExpectedImprovement: "qExpectedImprovement",
qKnowledgeGradient: "qKnowledgeGradient",
qMaxValueEntropy: "qMaxValueEntropy",
qMultiFidelityKnowledgeGradient: "qMultiFidelityKnowledgeGradient",
qMultiFidelityMaxValueEntropy: "qMultiFidelityMaxValueEntropy",
qNoisyExpectedImprovement: "qNoisyExpectedImprovement",
# LogEI family below:
LogExpectedImprovement: "LogExpectedImprovement",
LogNoisyExpectedImprovement: "LogNoisyExpectedImprovement",
qLogExpectedImprovement: "qLogExpectedImprovement",
qLogNoisyExpectedImprovement: "qLogNoisyExpectedImprovement",
qLogExpectedHypervolumeImprovement: "qLogExpectedHypervolumeImprovement",
qLogNoisyExpectedHypervolumeImprovement: "qLogNoisyExpectedHypervolumeImprovement",
}
"""
Mapping of BoTorch `MarginalLogLikelihood` classes to class name strings.
"""
MLL_REGISTRY: Dict[Type[MarginalLogLikelihood], str] = {
ExactMarginalLogLikelihood: "ExactMarginalLogLikelihood",
LeaveOneOutPseudoLikelihood: "LeaveOneOutPseudoLikelihood",
SumMarginalLogLikelihood: "SumMarginalLogLikelihood",
}
LIKELIHOOD_REGISTRY: Dict[Type[GaussianLikelihood], str] = {
GaussianLikelihood: "GaussianLikelihood"
}
GPYTORCH_COMPONENT_REGISTRY: Dict[Type[torch.nn.Module], str] = {
Interval: "Interval",
GammaPrior: "GammaPrior",
}
"""
Mapping of BoTorch `InputTransform` classes to class name strings.
"""
INPUT_TRANSFORM_REGISTRY: Dict[Type[InputTransform], str] = {
ChainedInputTransform: "ChainedInputTransform",
Normalize: "Normalize",
Round: "Round",
Warp: "Warp",
InputPerturbation: "InputPerturbation",
}
"""
Mapping of BoTorch `OutcomeTransform` classes to class name strings.
"""
OUTCOME_TRANSFORM_REGISTRY: Dict[Type[OutcomeTransform], str] = {
ChainedOutcomeTransform: "ChainedOutcomeTransform",
Standardize: "Standardize",
}
"""
Overarching mapping from encoded classes to registry map.
"""
# pyre-fixme[5]: Global annotation cannot contain `Any`.
CLASS_TO_REGISTRY: Dict[Any, Dict[Type[Any], str]] = {
Acquisition: ACQUISITION_REGISTRY,
AcquisitionFunction: ACQUISITION_FUNCTION_REGISTRY,
Likelihood: LIKELIHOOD_REGISTRY,
MarginalLogLikelihood: MLL_REGISTRY,
Model: MODEL_REGISTRY,
Interval: GPYTORCH_COMPONENT_REGISTRY,
GammaPrior: GPYTORCH_COMPONENT_REGISTRY,
InputTransform: INPUT_TRANSFORM_REGISTRY,
OutcomeTransform: OUTCOME_TRANSFORM_REGISTRY,
}
"""
Reverse registries for decoding.
"""
REVERSE_ACQUISITION_REGISTRY: Dict[str, Type[Acquisition]] = {
v: k for k, v in ACQUISITION_REGISTRY.items()
}
REVERSE_MODEL_REGISTRY: Dict[str, Type[Model]] = {
# NOTE: These ensure backwards compatibility. Keep them around.
"FixedNoiseGP": SingleTaskGP,
"FixedNoiseMultiFidelityGP": SingleTaskMultiFidelityGP,
"FixedNoiseMultiTaskGP": MultiTaskGP,
**{v: k for k, v in MODEL_REGISTRY.items()},
}
REVERSE_ACQUISITION_FUNCTION_REGISTRY: Dict[str, Type[AcquisitionFunction]] = {
v: k for k, v in ACQUISITION_FUNCTION_REGISTRY.items()
}
REVERSE_MLL_REGISTRY: Dict[str, Type[MarginalLogLikelihood]] = {
v: k for k, v in MLL_REGISTRY.items()
}
REVERSE_LIKELIHOOD_REGISTRY: Dict[str, Type[Likelihood]] = {
v: k for k, v in LIKELIHOOD_REGISTRY.items()
}
REVERSE_GPYTORCH_COMPONENT_REGISTRY: Dict[str, Type[torch.nn.Module]] = {
v: k for k, v in GPYTORCH_COMPONENT_REGISTRY.items()
}
REVERSE_INPUT_TRANSFORM_REGISTRY: Dict[str, Type[InputTransform]] = {
v: k for k, v in INPUT_TRANSFORM_REGISTRY.items()
}
REVERSE_OUTCOME_TRANSFORM_REGISTRY: Dict[str, Type[OutcomeTransform]] = {
v: k for k, v in OUTCOME_TRANSFORM_REGISTRY.items()
}
"""
Overarching mapping from encoded classes to reverse registry map.
"""
# pyre-fixme[5]: Global annotation cannot contain `Any`.
CLASS_TO_REVERSE_REGISTRY: Dict[Any, Dict[str, Type[Any]]] = {
Acquisition: REVERSE_ACQUISITION_REGISTRY,
AcquisitionFunction: REVERSE_ACQUISITION_FUNCTION_REGISTRY,
Likelihood: REVERSE_LIKELIHOOD_REGISTRY,
MarginalLogLikelihood: REVERSE_MLL_REGISTRY,
Model: REVERSE_MODEL_REGISTRY,
Interval: REVERSE_GPYTORCH_COMPONENT_REGISTRY,
GammaPrior: REVERSE_GPYTORCH_COMPONENT_REGISTRY,
InputTransform: REVERSE_INPUT_TRANSFORM_REGISTRY,
OutcomeTransform: REVERSE_OUTCOME_TRANSFORM_REGISTRY,
}
[docs]def register_acquisition(acq_class: Type[Acquisition]) -> None:
"""Add a custom acquisition class to the SQA and JSON registries."""
class_name = acq_class.__name__
CLASS_TO_REGISTRY[Acquisition].update({acq_class: class_name})
CLASS_TO_REVERSE_REGISTRY[Acquisition].update({class_name: acq_class})
[docs]def register_acquisition_function(acqf_class: Type[AcquisitionFunction]) -> None:
"""Add a custom acquisition class to the SQA and JSON registries."""
class_name = acqf_class.__name__
CLASS_TO_REGISTRY[AcquisitionFunction].update({acqf_class: class_name})
CLASS_TO_REVERSE_REGISTRY[AcquisitionFunction].update({class_name: acqf_class})
[docs]def register_model(model_class: Type[Model]) -> None:
"""Add a custom model class to the SQA and JSON registries."""
class_name = model_class.__name__
CLASS_TO_REGISTRY[Model].update({model_class: class_name})
CLASS_TO_REVERSE_REGISTRY[Model].update({class_name: model_class})
register_acquisition(SEBOAcquisition)
