# Source code for botorch.acquisition.multi_objective.base

```
#!/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.
r"""
Base classes for multi-objective acquisition functions.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Callable, Optional, Union
import torch
from botorch.acquisition.acquisition import AcquisitionFunction, MCSamplerMixin
from botorch.acquisition.multi_objective.objective import (
IdentityMCMultiOutputObjective,
MCMultiOutputObjective,
)
from botorch.acquisition.objective import PosteriorTransform
from botorch.exceptions.errors import UnsupportedError
from botorch.models.model import Model
from botorch.models.transforms.input import InputPerturbation
from botorch.sampling.base import MCSampler
from torch import Tensor
[docs]
class MultiObjectiveAnalyticAcquisitionFunction(AcquisitionFunction):
r"""Abstract base class for Multi-Objective batch acquisition functions."""
def __init__(
self,
model: Model,
posterior_transform: Optional[PosteriorTransform] = None,
) -> None:
r"""Constructor for the MultiObjectiveAnalyticAcquisitionFunction base class.
Args:
model: A fitted model.
posterior_transform: A PosteriorTransform (optional).
"""
super().__init__(model=model)
if posterior_transform is None or isinstance(
posterior_transform, PosteriorTransform
):
self.posterior_transform = posterior_transform
else:
raise UnsupportedError(
"Only a posterior_transform of type PosteriorTransform is "
"supported for Multi-Objective analytic acquisition functions."
)
[docs]
@abstractmethod
def forward(self, X: Tensor) -> Tensor:
r"""Takes in a `batch_shape x 1 x d` X Tensor of t-batches with `1` `d`-dim
design point each, and returns a Tensor with shape `batch_shape'`, where
`batch_shape'` is the broadcasted batch shape of model and input `X`.
"""
pass # pragma: no cover
[docs]
def set_X_pending(self, X_pending: Optional[Tensor] = None) -> None:
raise UnsupportedError(
"Analytic acquisition functions do not account for X_pending yet."
)
[docs]
class MultiObjectiveMCAcquisitionFunction(AcquisitionFunction, MCSamplerMixin, ABC):
r"""Abstract base class for Multi-Objective batch acquisition functions.
NOTE: This does not inherit from `MCAcquisitionFunction` to avoid circular imports.
Args:
_default_sample_shape: The `sample_shape` for the default sampler.
"""
_default_sample_shape = torch.Size([128])
def __init__(
self,
model: Model,
sampler: Optional[MCSampler] = None,
objective: Optional[MCMultiOutputObjective] = None,
constraints: Optional[list[Callable[[Tensor], Tensor]]] = None,
eta: Union[Tensor, float] = 1e-3,
X_pending: Optional[Tensor] = None,
) -> None:
r"""Constructor for the `MultiObjectiveMCAcquisitionFunction` base class.
Args:
model: A fitted model.
sampler: The sampler used to draw base samples. If not given,
a sampler is generated using `get_sampler`.
NOTE: For posteriors that do not support base samples,
a sampler compatible with intended use case must be provided.
See `ForkedRNGSampler` and `StochasticSampler` as examples.
objective: The MCMultiOutputObjective under which the samples are
evaluated. Defaults to `IdentityMCMultiOutputObjective()`.
constraints: A list of callables, each mapping a Tensor of dimension
`sample_shape x batch-shape x q x m` to a Tensor of dimension
`sample_shape x batch-shape x q`, where negative values imply
feasibility.
eta: The temperature parameter for the sigmoid function used for the
differentiable approximation of the constraints. In case of a float the
same eta is used for every constraint in constraints. In case of a
tensor the length of the tensor must match the number of provided
constraints. The i-th constraint is then estimated with the i-th
eta value.
X_pending: A `m x d`-dim Tensor of `m` design points that have
points that have been submitted for function evaluation
but have not yet been evaluated.
"""
super().__init__(model=model)
MCSamplerMixin.__init__(self, sampler=sampler)
if objective is None:
objective = IdentityMCMultiOutputObjective()
elif not isinstance(objective, MCMultiOutputObjective):
raise UnsupportedError(
"Only objectives of type MCMultiOutputObjective are supported for "
"Multi-Objective MC acquisition functions."
)
if (
hasattr(model, "input_transform")
and isinstance(model.input_transform, InputPerturbation)
and constraints is not None
):
raise UnsupportedError(
"Constraints are not supported with input perturbations, due to"
"sample q-batch shape being different than that of the inputs."
"Use a composite objective that applies feasibility weighting to"
"samples before calculating the risk measure."
)
self.add_module("objective", objective)
self.constraints = constraints
if constraints:
if type(eta) is not Tensor:
eta = torch.full((len(constraints),), eta)
self.register_buffer("eta", eta)
self.X_pending = None
if X_pending is not None:
self.set_X_pending(X_pending)
[docs]
@abstractmethod
def forward(self, X: Tensor) -> Tensor:
r"""Takes in a `batch_shape x q x d` X Tensor of t-batches with `q` `d`-dim
design points each, and returns a Tensor with shape `batch_shape'`, where
`batch_shape'` is the broadcasted batch shape of model and input `X`. Should
utilize the result of `set_X_pending` as needed to account for pending function
evaluations.
"""
pass # pragma: no cover
```