#!/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.
from typing import Any, Dict, Type
# Ax `Acquisition` imports
from ax.models.torch.botorch_modular.acquisition import Acquisition
# BoTorch `AcquisitionFunction` imports
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.acquisition.analytic import ExpectedImprovement
from botorch.acquisition.knowledge_gradient import (
qKnowledgeGradient,
qMultiFidelityKnowledgeGradient,
)
from botorch.acquisition.max_value_entropy_search import (
qMaxValueEntropy,
qMultiFidelityMaxValueEntropy,
)
from botorch.acquisition.monte_carlo import (
qExpectedImprovement,
qNoisyExpectedImprovement,
)
from botorch.acquisition.multi_objective.monte_carlo import (
qExpectedHypervolumeImprovement,
qNoisyExpectedHypervolumeImprovement,
)
# 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 FixedNoiseMultiTaskGP, MultiTaskGP
# 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
from torch.nn import Module
# 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] = {
FixedNoiseGP: "FixedNoiseGP",
FixedNoiseMultiFidelityGP: "FixedNoiseMultiFidelityGP",
FixedNoiseMultiTaskGP: "FixedNoiseMultiTaskGP",
MixedSingleTaskGP: "MixedSingleTaskGP",
ModelListGP: "ModelListGP",
MultiTaskGP: "MultiTaskGP",
SingleTaskGP: "SingleTaskGP",
SingleTaskMultiFidelityGP: "SingleTaskMultiFidelityGP",
}
"""
Mapping of Botorch `AcquisitionFunction` classes to class name strings.
"""
ACQUISITION_FUNCTION_REGISTRY: Dict[Type[AcquisitionFunction], str] = {
ExpectedImprovement: "ExpectedImprovement",
qExpectedHypervolumeImprovement: "qExpectedHypervolumeImprovement",
qNoisyExpectedHypervolumeImprovement: "qNoisyExpectedHypervolumeImprovement",
qExpectedImprovement: "qExpectedImprovement",
qKnowledgeGradient: "qKnowledgeGradient",
qMaxValueEntropy: "qMaxValueEntropy",
qMultiFidelityKnowledgeGradient: "qMultiFidelityKnowledgeGradient",
qMultiFidelityMaxValueEntropy: "qMultiFidelityMaxValueEntropy",
qNoisyExpectedImprovement: "qNoisyExpectedImprovement",
}
"""
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[Module], str] = {
Interval: "Interval",
GammaPrior: "GammaPrior",
}
"""
Overarching mapping from encoded classes to registry map.
"""
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,
}
"""
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]] = {
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[Module]] = {
v: k for k, v in GPYTORCH_COMPONENT_REGISTRY.items()
}
"""
Overarching mapping from encoded classes to reverse registry map.
"""
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,
}
[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})
