#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Objective Modules to be used with acquisition functions.
"""
from abc import ABC, abstractmethod
from typing import Callable, List
import torch
from botorch.utils import apply_constraints
from torch import Tensor
from torch.nn import Module
from ..posteriors.gpytorch import GPyTorchPosterior, scalarize_posterior
[docs]class AcquisitionObjective(Module, ABC):
r"""Abstract base class for objectives."""
...
[docs]class ScalarizedObjective(AcquisitionObjective):
r"""Affine objective to be used with analytic acquisition functions.
For a Gaussian posterior at a single point (`q=1`) with mean `mu` and
covariance matrix `Sigma`, this yields a single-output posterior with mean
`weights^T * mu` and variance `weights^T Sigma w`.
Example:
Example for a model with two outcomes:
>>> weights = torch.tensor([0.5, 0.25])
>>> objective = ScalarizedObjective(weights)
>>> EI = ExpectedImprovement(model, best_f=0.1, objective=objective)
"""
def __init__(self, weights: Tensor, offset: float = 0.0) -> None:
r"""Affine objective.
Args:
weights: A one-dimensional tensor with `m` elements representing the
linear weights on the outputs.
offset: An offset to be added to posterior mean.
"""
if weights.dim() != 1:
raise ValueError("weights must be a one-dimensional tensor.")
super().__init__()
self.register_buffer("weights", weights)
self.offset = offset
[docs] def forward(self, posterior: GPyTorchPosterior) -> GPyTorchPosterior:
r"""Compute the posterior of the affine transformation.
Args:
posterior: A posterior with the same number of outputs as the
elements in `self.weights`.
Returns:
A single-output posterior.
"""
return scalarize_posterior(
posterior=posterior, weights=self.weights, offset=self.offset
)
[docs]class MCAcquisitionObjective(AcquisitionObjective):
r"""Abstract base class for MC-based objectives."""
[docs] @abstractmethod
def forward(self, samples: Tensor) -> Tensor:
r"""Evaluate the objective on the samples.
Args:
samples: A `sample_shape x batch_shape x q x m`-dim Tensors of
samples from a model posterior.
Returns:
Tensor: A `sample_shape x batch_shape x q`-dim Tensor of objective
values (assuming maximization).
This method is usually not called directly, but via the objectives
Example:
>>> # `__call__` method:
>>> samples = sampler(posterior)
>>> outcome = mc_obj(samples)
"""
pass # pragma: no cover
[docs]class IdentityMCObjective(MCAcquisitionObjective):
r"""Trivial objective extracting the last dimension.
Example:
>>> identity_objective = IdentityMCObjective()
>>> samples = sampler(posterior)
>>> objective = identity_objective(samples)
"""
[docs] def forward(self, samples: Tensor) -> Tensor:
return samples.squeeze(-1)
[docs]class LinearMCObjective(MCAcquisitionObjective):
r"""Linear objective constructed from a weight tensor.
For input `samples` and `mc_obj = LinearMCObjective(weights)`, this produces
`mc_obj(samples) = sum_{i} weights[i] * samples[..., i]`
Example:
Example for a model with two outcomes:
>>> weights = torch.tensor([0.75, 0.25])
>>> linear_objective = LinearMCObjective(weights)
>>> samples = sampler(posterior)
>>> objective = linear_objective(samples)
"""
def __init__(self, weights: Tensor) -> None:
r"""Linear Objective.
Args:
weights: A one-dimensional tensor with `m` elements representing the
linear weights on the outputs.
"""
super().__init__()
if weights.dim() != 1:
raise ValueError("weights must be a one-dimensional tensor.")
self.register_buffer("weights", weights)
[docs] def forward(self, samples: Tensor) -> Tensor:
r"""Evaluate the linear objective on the samples.
Args:
samples: A `sample_shape x batch_shape x q x m`-dim tensors of
samples from a model posterior.
Returns:
A `sample_shape x batch_shape x q`-dim tensor of objective values.
"""
if samples.shape[-1] != self.weights.shape[-1]:
raise RuntimeError("Output shape of samples not equal to that of weights")
return torch.einsum("...m, m", [samples, self.weights])
[docs]class GenericMCObjective(MCAcquisitionObjective):
r"""Objective generated from a generic callable.
Allows to construct arbitrary MC-objective functions from a generic
callable. In order to be able to use gradient-based acquisition function
optimization it should be possible to backpropagate through the callable.
Example:
>>> generic_objective = GenericMCObjective(lambda Y: torch.sqrt(Y).sum(dim=-1))
>>> samples = sampler(posterior)
>>> objective = generic_objective(samples)
"""
def __init__(self, objective: Callable[[Tensor], Tensor]) -> None:
r"""Objective generated from a generic callable.
Args:
objective: A callable mapping a `sample_shape x batch-shape x q x m`-
dim Tensor to a `sample_shape x batch-shape x q`-dim Tensor of
objective values.
"""
super().__init__()
self.objective = objective
[docs] def forward(self, samples: Tensor) -> Tensor:
r"""Evaluate the feasibility-weigthed objective on the samples.
Args:
samples: A `sample_shape x batch_shape x q x m`-dim Tensors of
samples from a model posterior.
Returns:
A `sample_shape x batch_shape x q`-dim Tensor of objective values
weighted by feasibility (assuming maximization).
"""
return self.objective(samples)
[docs]class ConstrainedMCObjective(GenericMCObjective):
r"""Feasibility-weighted objective.
An Objective allowing to maximize some scalable objective on the model
outputs subject to a number of constraints. Constraint feasibilty is
approximated by a sigmoid function.
`mc_acq(X) = objective(X) * prod_i (1 - sigmoid(constraint_i(X)))`
TODO: Document functional form exactly.
See `botorch.utils.objective.apply_constraints` for details on the constarint
handling.
Example:
>>> bound = 0.0
>>> objective = lambda Y: Y[..., 0]
>>> # apply non-negativity constraint on f(x)[1]
>>> constraint = lambda Y: bound - Y[..., 1]
>>> constrained_objective = ConstrainedMCObjective(objective, [constraint])
>>> samples = sampler(posterior)
>>> objective = constrained_objective(samples)
"""
def __init__(
self,
objective: Callable[[Tensor], Tensor],
constraints: List[Callable[[Tensor], Tensor]],
infeasible_cost: float = 0.0,
eta: float = 1e-3,
) -> None:
r"""Feasibility-weighted objective.
Args:
objective: A callable mapping a `sample_shape x batch-shape x q x m`-
dim Tensor to a `sample_shape x batch-shape x q`-dim Tensor of
objective values.
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.
infeasible_cost: The cost of a design if all associated samples are
infeasible.
eta: The temperature parameter of the sigmoid function approximating
the constraint.
"""
super().__init__(objective=objective)
self.constraints = constraints
self.eta = eta
self.register_buffer("infeasible_cost", torch.tensor(infeasible_cost))
[docs] def forward(self, samples: Tensor) -> Tensor:
r"""Evaluate the feasibility-weighted objective on the samples.
Args:
samples: A `sample_shape x batch_shape x q x m`-dim Tensors of
samples from a model posterior.
Returns:
A `sample_shape x batch_shape x q`-dim Tensor of objective values
weighted by feasibility (assuming maximization).
"""
obj = super().forward(samples=samples)
return apply_constraints(
obj=obj,
constraints=self.constraints,
samples=samples,
infeasible_cost=self.infeasible_cost,
eta=self.eta,
)
