Source code for botorch.acquisition.multi_objective.max_value_entropy_search

#!/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.

Acquisition functions for max-value entropy search for multi-objective
Bayesian optimization (MESMO).


.. [Belakaria2019]
    S. Belakaria, A. Deshwal, J. R. Doppa. Max-value Entropy Search
    for Multi-Objective Bayesian Optimization. Advances in Neural
    Information Processing Systems, 32. 2019.

from __future__ import annotations

from typing import Any, Callable, Optional

import torch
from botorch.acquisition.max_value_entropy_search import qMaxValueEntropy
from botorch.acquisition.multi_objective.monte_carlo import (
from botorch.models.converter import (
from botorch.models.model import Model
from botorch.models.model_list_gp_regression import ModelListGP
from botorch.sampling.samplers import MCSampler, SobolQMCNormalSampler
from botorch.utils.transforms import t_batch_mode_transform
from torch import Tensor

[docs]class qMultiObjectiveMaxValueEntropy( qMaxValueEntropy, MultiObjectiveMCAcquisitionFunction ): r"""The acquisition function for MESMO. This acquisition function computes the mutual information of Pareto frontier and a candidate point. See [Belakaria2019]_ for a detailed discussion. q > 1 is supported through cyclic optimization and fantasies. Noisy observations are support by computing information gain with observation noise as in Appendix C in [Takeno2020mfmves]_. Note: this only supports maximization. Example: >>> model = SingleTaskGP(train_X, train_Y) >>> MESMO = qMultiObjectiveMaxValueEntropy(model, sample_pfs) >>> mesmo = MESMO(test_X) """ def __init__( self, model: Model, sample_pareto_frontiers: Callable[[Model], Tensor], num_fantasies: int = 16, X_pending: Optional[Tensor] = None, sampler: Optional[MCSampler] = None, **kwargs: Any, ) -> None: r"""Multi-objective max-value entropy search acquisition function. Args: model: A fitted multi-output model. sample_pareto_frontiers: A callable that takes a model and returns a `num_samples x n' x m`-dim tensor of outcomes to use for constructing `num_samples` sampled Pareto frontiers. num_fantasies: Number of fantasies to generate. The higher this number the more accurate the model (at the expense of model complexity, wall time and memory). Ignored if `X_pending` is `None`. X_pending: A `m x d`-dim Tensor of `m` design points that have been submitted for function evaluation but have not yet been evaluated. """ MultiObjectiveMCAcquisitionFunction.__init__(self, model=model, sampler=sampler) # Batch GP models (e.g. fantasized models) are not currently supported if isinstance(model, ModelListGP): train_X = model.models[0].train_inputs[0] else: train_X = model.train_inputs[0] if train_X.ndim > 3: raise NotImplementedError( "Batch GP models (e.g. fantasized models) " "are not yet supported by qMultiObjectiveMaxValueEntropy" ) # convert to batched MO model batched_mo_model = ( model_list_to_batched(model) if isinstance(model, ModelListGP) else model ) self._init_model = batched_mo_model self.mo_model = batched_mo_model self.model = batched_multi_output_to_single_output( batch_mo_model=batched_mo_model ) self.fantasies_sampler = SobolQMCNormalSampler(num_fantasies) self.num_fantasies = num_fantasies # weight is used in _compute_information_gain self.maximize = True self.weight = 1.0 self.sample_pareto_frontiers = sample_pareto_frontiers # this avoids unnecessary model conversion if X_pending is None if X_pending is None: self._sample_max_values() else: self.set_X_pending(X_pending)
[docs] def set_X_pending(self, X_pending: Optional[Tensor] = None) -> None: r"""Set pending points. Informs the acquisition function about pending design points, fantasizes the model on the pending points and draws max-value samples from the fantasized model posterior. Args: X_pending: `m x d` Tensor with `m` `d`-dim design points that have been submitted for evaluation but have not yet been evaluated. """ MultiObjectiveMCAcquisitionFunction.set_X_pending(self, X_pending=X_pending) if X_pending is not None: # fantasize the model fantasy_model = self._init_model.fantasize( X=X_pending, sampler=self.fantasies_sampler, observation_noise=True ) self.mo_model = fantasy_model # convert model to batched single outcome model. self.model = batched_multi_output_to_single_output( batch_mo_model=self.mo_model ) self._sample_max_values() else: # This is mainly for setting the model to the original model # after the sequential optimization at q > 1 self.mo_model = self._init_model self.model = batched_multi_output_to_single_output( batch_mo_model=self.mo_model ) self._sample_max_values()
def _sample_max_values(self) -> None: r"""Sample max values for MC approximation of the expectation in MES""" with torch.no_grad(): # num_samples x (num_fantasies) x n_pareto_points x m sampled_pfs = self.sample_pareto_frontiers(self.mo_model) if sampled_pfs.ndim == 3: # add fantasy dim sampled_pfs = sampled_pfs.unsqueeze(-3) # take component-wise max value self.posterior_max_values = sampled_pfs.max(dim=-2).values
[docs] @t_batch_mode_transform(expected_q=1) def forward(self, X: Tensor) -> Tensor: r"""Compute max-value entropy at the design points `X`. Args: X: A `batch_shape x 1 x d`-dim Tensor of `batch_shape` t-batches with `1` `d`-dim design points each. Returns: A `batch_shape`-dim Tensor of MVE values at the given design points `X`. """ # `m` dim tensor of information gains # unsqueeze X to add a batch-dim for the batched model igs = qMaxValueEntropy.forward(self, X=X.unsqueeze(-3)) # sum over objectives return igs.sum(dim=-1)