Source code for botorch.acquisition.prior_guided

#!/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.

Prior-Guided Acquisition Functions


.. [Hvarfner2022]
    C. Hvarfner, D. Stoll, A. Souza, M. Lindauer, F. Hutter, L. Nardi. PiBO:
    Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization.
    ICLR 2022.
from __future__ import annotations

from typing import Optional

from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.acquisition.monte_carlo import SampleReducingMCAcquisitionFunction
from botorch.utils.transforms import concatenate_pending_points, t_batch_mode_transform
from torch import Tensor

from torch.nn import Module

[docs]class PriorGuidedAcquisitionFunction(AcquisitionFunction): r"""Class for weighting acquisition functions by a prior distribution. Supports MC and batch acquisition functions via SampleReducingAcquisitionFunction. See [Hvarfner2022]_ for details. """ def __init__( self, acq_function: AcquisitionFunction, prior_module: Module, log: bool = False, prior_exponent: float = 1.0, X_pending: Optional[Tensor] = None, ) -> None: r"""Initialize the prior-guided acquisition function. Args: acq_function: The base acquisition function. prior_module: A Module that computes the probability (or log probability) for the provided inputs. `prior_module.forward` should take a `batch_shape x q`-dim tensor of inputs and return a `batch_shape x q`-dim tensor of probabilities. log: A boolean that should be true if the acquisition function emits a log-transformed value and the prior module emits a log probability. prior_exponent: The exponent applied to the prior. This can be used for example to decay the effect the prior over time as in [Hvarfner2022]_. X_pending: `n x d` Tensor with `n` `d`-dim design points that have been submitted for evaluation but have not yet been evaluated. """ super().__init__(model=acq_function.model) self.acq_func = acq_function self.prior_module = prior_module self._log = log self._prior_exponent = prior_exponent self._is_sample_reducing_af = isinstance( acq_function, SampleReducingMCAcquisitionFunction ) self.set_X_pending(X_pending=X_pending)
[docs] @concatenate_pending_points @t_batch_mode_transform() def forward(self, X: Tensor) -> Tensor: r"""Compute the acquisition function weighted by the prior.""" # batch_shape x q prior = self.prior_module(X) if self._is_sample_reducing_af: # sample_shape x batch_shape x q af_val = self.acq_func._non_reduced_forward(X) else: if prior.shape[-1] > 1: raise NotImplementedError( "q-batches with q>1 are only supported using " "SampleReducingMCAcquisitionFunction." ) # batch_shape x q af_val = self.acq_func(X).unsqueeze(-1) if self._log: weighted_af_val = af_val + prior * self._prior_exponent else: weighted_af_val = af_val * prior.pow(self._prior_exponent) if self._is_sample_reducing_af: return self.acq_func._sample_reduction( self.acq_func._q_reduction(weighted_af_val) ) return weighted_af_val.squeeze(-1) # squeeze q-dim