#!/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 __future__ import annotations
import inspect
from typing import Any, Dict, List, Optional, Type
from ax.models.torch.botorch_modular.surrogate import Surrogate
from ax.utils.common.constants import Keys
from ax.utils.common.logger import get_logger
from botorch.models.model import Model
from botorch.models.model_list_gp_regression import ModelListGP
from botorch.models.transforms.input import InputTransform
from botorch.models.transforms.outcome import OutcomeTransform
from botorch.utils.datasets import SupervisedDataset
from gpytorch.kernels import Kernel
from gpytorch.likelihoods.likelihood import Likelihood
from gpytorch.mlls import ExactMarginalLogLikelihood
from gpytorch.mlls.marginal_log_likelihood import MarginalLogLikelihood
# pyre-fixme[5]: Global expression must be annotated.
logger = get_logger(__name__)
[docs]class ListSurrogate(Surrogate):
"""Special type of ``Surrogate`` that wraps a set of submodels into
``ModelListGP`` under the hood for multi-outcome or multi-task
models.
Args:
botorch_submodel_class_per_outcome: Mapping from metric name to
BoTorch model class that should be used as surrogate model for
that metric. Use instead of ``botorch_submodel_class``.
botorch_submodel_class: BoTorch ``Model`` class, shortcut for when
all submodels of this surrogate's underlying ``ModelListGP`` are
of the same type.
Use instead of ``botorch_submodel_class_per_outcome``.
submodel_options_per_outcome: Optional mapping from metric name to
dictionary of kwargs for the submodel for that outcome.
submodel_options: Optional dictionary of kwargs, shared between all
submodels.
NOTE: kwargs for submodel are ``submodel_options`` (shared) +
``submodel_outions_per_outcome[submodel_outcome]`` (individual).
mll_class: ``MarginalLogLikelihood`` class to use for model-fitting.
mll_options: Dictionary of options / kwargs for the MLL.
submodel_outcome_transforms: A dictionary mapping each outcome to a
BoTorch outcome transform. Gets passed down to the BoTorch ``Model``s.
To use multiple outcome transforms on a submodel, chain them
together using ``ChainedOutcomeTransform``.
submodel_input_transforms: A dictionary mapping each outcome to a
BoTorch input transform. Gets passed down to the BoTorch ``Model``.
If sharing a single ``InputTransform`` object across submodels is
preferred, pass in a dictionary where each outcome key references the
same ``InputTransform`` object. To use multiple input transfroms on
a submodel, chain them together using ``ChainedInputTransform``.
"""
botorch_submodel_class_per_outcome: Dict[str, Type[Model]]
botorch_submodel_class: Optional[Type[Model]]
submodel_options_per_outcome: Dict[str, Dict[str, Any]]
submodel_options: Dict[str, Any]
mll_class: Type[MarginalLogLikelihood]
mll_options: Dict[str, Any]
submodel_outcome_transforms: Dict[str, OutcomeTransform]
submodel_input_transforms: Dict[str, InputTransform]
submodel_covar_module_class: Dict[str, Type[Kernel]]
submodel_covar_module_options: Dict[str, Dict[str, Any]]
submodel_likelihood_class: Dict[str, Type[Likelihood]]
submodel_likelihood_options: Dict[str, Dict[str, Any]]
_model: Optional[Model] = None
# Special setting for surrogates instantiated via `Surrogate.from_botorch`,
# to avoid re-constructing the underlying BoTorch model on `Surrogate.fit`
# when set to `False`.
_should_reconstruct: bool = True
def __init__(
self,
botorch_submodel_class_per_outcome: Optional[Dict[str, Type[Model]]] = None,
botorch_submodel_class: Optional[Type[Model]] = None,
submodel_options_per_outcome: Optional[Dict[str, Dict[str, Any]]] = None,
submodel_options: Optional[Dict[str, Any]] = None,
mll_class: Type[MarginalLogLikelihood] = ExactMarginalLogLikelihood,
mll_options: Optional[Dict[str, Any]] = None,
submodel_outcome_transforms: Optional[Dict[str, OutcomeTransform]] = None,
submodel_input_transforms: Optional[Dict[str, InputTransform]] = None,
submodel_covar_module_class: Optional[Dict[str, Type[Kernel]]] = None,
submodel_covar_module_options: Optional[Dict[str, Dict[str, Any]]] = None,
submodel_likelihood_class: Optional[Dict[str, Type[Likelihood]]] = None,
submodel_likelihood_options: Optional[Dict[str, Dict[str, Any]]] = None,
) -> None:
if not bool(botorch_submodel_class_per_outcome) ^ bool(botorch_submodel_class):
raise ValueError( # pragma: no cover
"Please specify either `botorch_submodel_class_per_outcome` or "
"`botorch_model_class`. In the latter case, the same submodel "
"class will be used for all outcomes."
)
self.botorch_submodel_class_per_outcome = (
botorch_submodel_class_per_outcome or {}
)
self.botorch_submodel_class = botorch_submodel_class
self.submodel_options_per_outcome = submodel_options_per_outcome or {}
self.submodel_options = submodel_options or {}
self.submodel_outcome_transforms = submodel_outcome_transforms or {}
self.submodel_input_transforms = submodel_input_transforms or {}
self.submodel_covar_module_class = submodel_covar_module_class or {}
self.submodel_covar_module_options = submodel_covar_module_options or {}
self.submodel_likelihood_class = submodel_likelihood_class or {}
self.submodel_likelihood_options = submodel_likelihood_options or {}
super().__init__(
botorch_model_class=ModelListGP,
mll_class=mll_class,
mll_options=mll_options,
)
[docs] def construct(
self, datasets: List[SupervisedDataset], metric_names: List[str], **kwargs: Any
) -> None:
"""Constructs the underlying BoTorch ``Model`` using the training data.
Args:
datasets: List of ``SupervisedDataset`` for the submodels of
``ModelListGP``. Each training data is for one outcome, and the order
of outcomes should match the order of metrics in ``metric_names``
argument.
metric_names: Names of metrics, in the same order as datasets (so if
datasets is ``[ds_A, ds_B]``, the metrics are ``["A" and "B"]``).
These are used to match training data with correct submodels of
``ModelListGP``.
**kwargs: Keyword arguments, accepts:
- ``fidelity_features``: Indices of columns in X that represent
fidelity
- ``task_features``: Indices of columns in X that represent tasks
"""
fidelity_features = kwargs.get(Keys.FIDELITY_FEATURES, [])
task_features = kwargs.get(Keys.TASK_FEATURES, [])
if len(fidelity_features) > 0 and len(task_features) > 0:
raise NotImplementedError(
"Multi-Fidelity GP models with task_features are "
"currently not supported."
)
# TODO: Allow each metric having different task_features or fidelity_features
# TODO: Need upstream change in the modelbrdige
if len(task_features) > 1:
raise NotImplementedError("This model only supports 1 task feature!")
elif len(task_features) == 1:
task_feature = task_features[0]
else:
task_feature = None
self._training_data = datasets
submodels = []
for m, dataset in zip(metric_names, datasets):
model_cls = self.botorch_submodel_class_per_outcome.get(
m, self.botorch_submodel_class
)
if not model_cls:
raise ValueError(f"No model class specified for outcome {m}.")
if self._outcomes is not None and m not in self._outcomes:
logger.info(f"Metric {m} not in training data.")
continue
# NOTE: here we do a shallow copy of `self.submodel_options`, to
# protect from accidental modification of shared options. As it is
# a shallow copy, it does not protect the objects in the dictionary,
# just the dictionary itself.
submodel_options = {
**self.submodel_options,
**self.submodel_options_per_outcome.get(m, {}),
}
formatted_model_inputs = model_cls.construct_inputs(
training_data=dataset,
fidelity_features=fidelity_features,
task_feature=task_feature,
**submodel_options,
)
# Add input / outcome transforms.
# TODO: The use of `inspect` here is not ideal. We should find a better
# way to filter the arguments. See the comment in `Surrogate.construct`
# regarding potential use of a `ModelFactory` in the future.
model_cls_args = inspect.getfullargspec(model_cls).args
covar_module_class = self.submodel_covar_module_class.get(m)
covar_module_options = self.submodel_covar_module_options.get(m)
likelihood_class = self.submodel_likelihood_class.get(m)
likelihood_options = self.submodel_likelihood_options.get(m)
outcome_transform = self.submodel_outcome_transforms.get(m)
input_transform = self.submodel_input_transforms.get(m)
self._set_formatted_inputs(
formatted_model_inputs=formatted_model_inputs,
inputs=[
["covar_module", covar_module_class, covar_module_options, None],
["likelihood", likelihood_class, likelihood_options, None],
["outcome_transform", None, None, outcome_transform],
["input_transform", None, None, input_transform],
],
dataset=dataset,
botorch_model_class_args=model_cls_args,
)
# pyre-ignore[45]: Py raises informative error if model is abstract.
submodels.append(model_cls(**formatted_model_inputs))
self._model = ModelListGP(*submodels)
def _serialize_attributes_as_kwargs(self) -> Dict[str, Any]:
"""Serialize attributes of this surrogate, to be passed back to it
as kwargs on reinstantiation.
"""
submodel_classes = self.botorch_submodel_class_per_outcome
return {
"botorch_submodel_class_per_outcome": submodel_classes,
"botorch_submodel_class": self.botorch_submodel_class,
"submodel_options_per_outcome": self.submodel_options_per_outcome,
"submodel_options": self.submodel_options,
"mll_class": self.mll_class,
"mll_options": self.mll_options,
"submodel_outcome_transforms": self.submodel_outcome_transforms,
"submodel_input_transforms": self.submodel_input_transforms,
"submodel_covar_module_class": self.submodel_covar_module_class,
"submodel_covar_module_options": self.submodel_covar_module_options,
"submodel_likelihood_class": self.submodel_likelihood_class,
"submodel_likelihood_options": self.submodel_likelihood_options,
}
