Source code for botorch.acquisition.preference

#!/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.

Preference acquisition functions. This includes:
Analytical EUBO acquisition function as introduced in [Lin2022preference]_.

.. [Lin2022preference]
    Lin, Z.J., Astudillo, R., Frazier, P.I. and Bakshy, E. Preference Exploration
    for Efficient Bayesian Optimization with Multiple Outcomes. International
    Conference on Artificial Intelligence and Statistics (AISTATS), 2022.

from __future__ import annotations

from typing import Optional

import torch
from botorch.acquisition import AnalyticAcquisitionFunction
from botorch.exceptions.errors import UnsupportedError
from botorch.models.deterministic import DeterministicModel
from botorch.models.model import Model
from botorch.utils.transforms import match_batch_shape, t_batch_mode_transform
from torch import Tensor

[docs]class AnalyticExpectedUtilityOfBestOption(AnalyticAcquisitionFunction): r"""Analytic Prefential Expected Utility of Best Options, i.e., Analytical EUBO""" def __init__( self, pref_model: Model, outcome_model: Optional[DeterministicModel] = None, previous_winner: Optional[Tensor] = None, ) -> None: r"""Analytic implementation of Expected Utility of the Best Option under the Laplace model (assumes a PairwiseGP is used as the preference model) as proposed in [Lin2022preference]_. Args: pref_model: The preference model that maps the outcomes (i.e., Y) to scalar-valued utility. model: A deterministic model that maps parameters (i.e., X) to outcomes (i.e., Y). The outcome model f defines the search space of Y = f(X). If model is None, we are directly calculating EUBO on the parameter space. When used with `OneSamplePosteriorDrawModel`, we are obtaining EUBO-zeta as described in [Lin2022preference]. previous_winner: Tensor representing the previous winner in the Y space. """ pref_model.eval() super().__init__(model=pref_model) # ensure the model is in eval mode self.add_module("outcome_model", outcome_model) self.register_buffer("previous_winner", previous_winner) tkwargs = { "dtype": pref_model.datapoints.dtype, "device": pref_model.datapoints.device, } std_norm = torch.distributions.normal.Normal( torch.zeros(1, **tkwargs), torch.ones(1, **tkwargs), ) self.std_norm = std_norm
[docs] @t_batch_mode_transform() def forward(self, X: Tensor) -> Tensor: r"""Evaluate analytical EUBO on the candidate set X. Args: X: A `batch_shape x q x d`-dim Tensor, where `q = 2` if `previous_winner` is not `None`, and `q = 1` otherwise. Returns: The acquisition value for each batch as a tensor of shape `batch_shape`. """ if not ( (X.shape[-2] == 2) or ((X.shape[-2] == 1) and (self.previous_winner is not None)) ): raise UnsupportedError( f"{self.__class__.__name__} only support q=2 or q=1" "with a previous winner specified" ) Y = X if self.outcome_model is None else self.outcome_model(X) if self.previous_winner is not None: Y =[Y, match_batch_shape(self.previous_winner, Y)], dim=-2) # Calling forward directly instead of posterior here to # obtain the full covariance matrix pref_posterior = self.model(Y) pref_mean = pref_posterior.mean pref_cov = pref_posterior.covariance_matrix delta = pref_mean[..., 0] - pref_mean[..., 1] sigma = torch.sqrt( pref_cov[..., 0, 0] + pref_cov[..., 1, 1] - pref_cov[..., 0, 1] - pref_cov[..., 1, 0] ) u = delta / sigma ucdf = self.std_norm.cdf(u) updf = torch.exp(self.std_norm.log_prob(u)) acqf_val = sigma * (updf + u * ucdf) if self.previous_winner is None: acqf_val = acqf_val + pref_mean[..., 1] return acqf_val