#!/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 Dict, List, Optional, Tuple, Type
import torch
from ax.core.search_space import SearchSpaceDigest
from ax.models.types import TConfig
from ax.utils.common.constants import Keys
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast
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.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.gpytorch import BatchedMultiOutputGPyTorchModel
from botorch.models.model import Model
from botorch.models.multitask import FixedNoiseMultiTaskGP, MultiTaskGP
from torch import Tensor
MIN_OBSERVED_NOISE_LEVEL = 1e-7
logger = get_logger(__name__)
[docs]def use_model_list(Xs: List[Tensor], botorch_model_class: Type[Model]) -> bool:
if issubclass(botorch_model_class, MultiTaskGP):
# We currently always wrap multi-task models into `ModelListGP`.
return True
if len(Xs) == 1:
# Just one outcome, can use single model.
return False
if issubclass(botorch_model_class, BatchedMultiOutputGPyTorchModel) and all(
torch.equal(Xs[0], X) for X in Xs[1:]
):
# Single model, batched multi-output case.
return False
# If there are multiple Xs and they are not all equal, we
# use `ListSurrogate` and `ModelListGP`.
return True
[docs]def choose_model_class(
Yvars: List[Tensor],
search_space_digest: SearchSpaceDigest,
) -> Type[Model]:
"""Chooses a BoTorch `Model` using the given data (currently just Yvars)
and its properties (information about task and fidelity features).
Args:
Yvars: List of tensors, each representing observation noise for a
given outcome, where outcomes are in the same order as in Xs.
task_features: List of columns of X that are tasks.
fidelity_features: List of columns of X that are fidelity parameters.
Returns:
A BoTorch `Model` class.
"""
if len(search_space_digest.fidelity_features) > 1:
raise NotImplementedError(
"Only a single fidelity feature supported "
f"(got: {search_space_digest.fidelity_features})."
)
if len(search_space_digest.task_features) > 1:
raise NotImplementedError(
f"Only a single task feature supported "
f"(got: {search_space_digest.task_features})."
)
if search_space_digest.task_features and search_space_digest.fidelity_features:
raise NotImplementedError(
"Multi-task multi-fidelity optimization not yet supported."
)
Yvars_cat = torch.cat(Yvars).clamp_min_(MIN_OBSERVED_NOISE_LEVEL)
is_nan = torch.isnan(Yvars_cat)
all_nan_Yvar = torch.all(is_nan)
if torch.any(is_nan) and not all_nan_Yvar:
raise ValueError(
"Mix of known and unknown variances indicates valuation function "
"errors. Variances should all be specified, or none should be."
)
# Multi-task cases (when `task_features` specified).
if search_space_digest.task_features and all_nan_Yvar:
model_class = MultiTaskGP # Unknown observation noise.
elif search_space_digest.task_features:
model_class = FixedNoiseMultiTaskGP # Known observation noise.
# Single-task multi-fidelity cases.
elif search_space_digest.fidelity_features and all_nan_Yvar:
model_class = SingleTaskMultiFidelityGP # Unknown observation noise.
elif search_space_digest.fidelity_features:
model_class = FixedNoiseMultiFidelityGP # Known observation noise.
# Mixed optimization case. Note that presence of categorical
# features in search space digest indicates that downstream in the
# stack we chose not to perform continuous relaxation on those
# features.
elif search_space_digest.categorical_features:
if not all_nan_Yvar:
logger.warning(
"Using `MixedSingleTaskGP` despire the known `Yvar` values. This "
"is a temporary measure while fixed-noise mixed BO is in the works."
)
model_class = MixedSingleTaskGP
# Single-task single-fidelity cases.
elif all_nan_Yvar: # Unknown observation noise.
model_class = SingleTaskGP
else:
model_class = FixedNoiseGP # Known observation noise.
logger.debug(f"Chose BoTorch model class: {model_class}.")
return model_class
[docs]def choose_botorch_acqf_class(
pending_observations: Optional[List[Tensor]] = None,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
objective_thresholds: Optional[Tensor] = None,
objective_weights: Optional[Tensor] = None,
) -> Type[AcquisitionFunction]:
"""Chooses a BoTorch `AcquisitionFunction` class."""
if objective_thresholds is not None or (
# using objective_weights is a less-than-ideal fix given its ambiguity,
# the real fix would be to revisit the infomration passed down via
# the modelbridge (and be explicit about whether we scalarize or perform MOO)
objective_weights is not None
and objective_weights.nonzero().numel() > 1
):
acqf_class = qNoisyExpectedHypervolumeImprovement
else:
acqf_class = qNoisyExpectedImprovement
logger.debug(f"Chose BoTorch acquisition function class: {acqf_class}.")
return acqf_class
[docs]def construct_acquisition_and_optimizer_options(
acqf_options: TConfig, model_gen_options: Optional[TConfig] = None
) -> Tuple[TConfig, TConfig]:
"""Extract acquisition and optimizer options from `model_gen_options`."""
acq_options = acqf_options.copy()
opt_options = {}
if model_gen_options:
acq_options.update(
checked_cast(dict, model_gen_options.get(Keys.ACQF_KWARGS, {}))
)
# TODO: Add this if all acq. functions accept the `subset_model`
# kwarg or opt for kwarg filtering.
# acq_options[SUBSET_MODEL] = model_gen_options.get(SUBSET_MODEL)
opt_options = checked_cast(
dict, model_gen_options.get(Keys.OPTIMIZER_KWARGS, {})
).copy()
return acq_options, opt_options
