Source code for botorch.acquisition.cost_aware

#!/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"""
Cost functions for cost-aware acquisition functions, e.g. multi-fidelity KG.
To be used in a context where there is an objective/cost tradeoff.
"""

from __future__ import annotations

import warnings
from abc import ABC, abstractmethod
from typing import Any, Callable, Optional

import torch
from botorch import settings
from botorch.acquisition.objective import IdentityMCObjective, MCAcquisitionObjective
from botorch.exceptions.warnings import CostAwareWarning
from botorch.models.model import Model
from botorch.sampling.samplers import MCSampler
from torch import Tensor
from torch.nn import Module


[docs]class CostAwareUtility(Module, ABC): r"""Abstract base class for cost-aware utilities."""
[docs] @abstractmethod def forward(self, X: Tensor, deltas: Tensor, **kwargs: Any) -> Tensor: r"""Evaluate the cost-aware utility on the candidates and improvements. Args: X: A `batch_shape x q x d`-dim Tensor of with `q` `d`-dim design points each for each t-batch. deltas: A `num_fantasies x batch_shape`-dim Tensor of `num_fantasy` samples from the marginal improvement in utility over the current state at `X` for each t-batch. Returns: A `num_fantasies x batch_shape`-dim Tensor of cost-transformed utilities. """ pass # pragma: no cover
[docs]class GenericCostAwareUtility(CostAwareUtility): r"""Generic cost-aware utility wrapping a callable.""" def __init__(self, cost: Callable[[Tensor, Tensor], Tensor]) -> None: r"""Generic cost-aware utility wrapping a callable. Args: cost: A callable mapping a `batch_shape x q x d'`-dim candidate set to a `batch_shape`-dim tensor of costs """ super().__init__() self._cost_callable: Callable[[Tensor, Tensor], Tensor] = cost
[docs] def forward(self, X: Tensor, deltas: Tensor, **kwargs: Any) -> Tensor: r"""Evaluate the cost function on the candidates and improvements. Args: X: A `batch_shape x q x d'`-dim Tensor of with `q` `d`-dim design points for each t-batch. deltas: A `num_fantasies x batch_shape`-dim Tensor of `num_fantasy` samples from the marginal improvement in utility over the current state at `X` for each t-batch. Returns: A `num_fantasies x batch_shape`-dim Tensor of cost-weighted utilities. """ return self._cost_callable(X, deltas)
[docs]class InverseCostWeightedUtility(CostAwareUtility): r"""A cost-aware utility using inverse cost weighting based on a model. Computes the cost-aware utility by inverse-weighting samples `U = (u_1, ..., u_N)` of the increase in utility. If `use_mean=True`, this uses the posterior mean `mean_cost` of the cost model, i.e. `weighted utility = mean(U) / mean_cost`. If `use_mean=False`, it uses samples `C = (c_1, ..., c_N)` from the posterior of the cost model and performs the inverse weighting on the sample level: `weighted utility = mean(u_1 / c_1, ..., u_N / c_N)`. The cost is additive across multiple elements of a q-batch. """ def __init__( self, cost_model: Model, use_mean: bool = True, cost_objective: Optional[MCAcquisitionObjective] = None, min_cost: float = 1e-2, ) -> None: r"""Cost-aware utility that weights increase in utiltiy by inverse cost. Args: cost_model: A Model modeling the cost of evaluating a candidate set `X`, where `X` are the same features as in the model for the acquisition function this is to be used with. If no cost_objective is specified, the outputs are required to be non-negative. use_mean: If True, use the posterior mean, otherwise use posterior samples from the cost model. cost_objective: If specified, transform the posterior mean / the posterior samples from the cost model. This can be used e.g. to un-transform predictions/samples of a cost model fit on the log-transformed cost (often done to ensure non-negativity). min_cost: A value used to clamp the cost samples so that they are not too close to zero, which may cause numerical issues. Returns: The inverse-cost-weighted utiltiy. """ super().__init__() if cost_objective is None: cost_objective = IdentityMCObjective() self.cost_model = cost_model self.cost_objective = cost_objective self._use_mean = use_mean self._min_cost = min_cost
[docs] def forward( self, X: Tensor, deltas: Tensor, sampler: Optional[MCSampler] = None, **kwargs: Any, ) -> Tensor: r"""Evaluate the cost function on the candidates and improvements. Args: X: A `batch_shape x q x d`-dim Tensor of with `q` `d`-dim design points each for each t-batch. deltas: A `num_fantasies x batch_shape`-dim Tensor of `num_fantasy` samples from the marginal improvement in utility over the current state at `X` for each t-batch. sampler: A sampler used for sampling from the posterior of the cost model (required if `use_mean=False`, ignored if `use_mean=True`). Returns: A `num_fantasies x batch_shape`-dim Tensor of cost-weighted utilities. """ if not self._use_mean and sampler is None: raise RuntimeError("Must provide `sampler` if `use_mean=False`") cost_posterior = self.cost_model.posterior(X) if self._use_mean: cost = cost_posterior.mean # batch_shape x q x m' else: # This will be of shape num_fantasies x batch_shape x q x m' cost = sampler(cost_posterior) # TODO: Make sure this doesn't change base samples in-place cost = self.cost_objective(cost) # Ensure non-negativity of the cost if settings.debug.on(): if torch.any(cost < -1e-7): warnings.warn( "Encountered negative cost values in InverseCostWeightedUtility", CostAwareWarning, ) # clamp (away from zero) and sum cost across elements of the q-batch - # this will be of shape `num_fantasies x batch_shape` or `batch_shape` cost = cost.clamp_min(self._min_cost).sum(dim=-1) # if we are doing inverse weighting on the sample level, clamp numerator. if not self._use_mean: deltas = deltas.clamp_min(0.0) # compute and return the ratio on the sample level - If `use_mean=True` # this operation involves broadcasting the cost across fantasies return deltas / cost