Source code for botorch.sampling.index_sampler

#!/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.

Sampler to be used with `EnsemblePosteriors` to enable
deterministic optimization of acquisition functions with ensemble models.

from __future__ import annotations

import torch
from botorch.posteriors.ensemble import EnsemblePosterior
from botorch.sampling.base import MCSampler
from torch import Tensor

[docs] class IndexSampler(MCSampler): r"""A sampler that calls `posterior.rsample_from_base_samples` to generate the samples via index base samples."""
[docs] def forward(self, posterior: EnsemblePosterior) -> Tensor: r"""Draws MC samples from the posterior. Args: posterior: The ensemble posterior to sample from. Returns: The samples drawn from the posterior. """ self._construct_base_samples(posterior=posterior) samples = posterior.rsample_from_base_samples( sample_shape=self.sample_shape, base_samples=self.base_samples ) return samples
def _construct_base_samples(self, posterior: EnsemblePosterior) -> None: r"""Constructs base samples as indices to sample with them from the Posterior. Args: posterior: The ensemble posterior to construct the base samples for. """ if self.base_samples is None or self.base_samples.shape != self.sample_shape: with torch.random.fork_rng(): torch.manual_seed(self.seed) base_samples = torch.multinomial( posterior.weights, num_samples=self.sample_shape.numel(), replacement=True, ).reshape(self.sample_shape) self.register_buffer("base_samples", base_samples) if self.base_samples.device != posterior.device: # pragma: nocover def _update_base_samples( self, posterior: EnsemblePosterior, base_sampler: IndexSampler ) -> None: r"""Null operation just needed for compatibility with `CachedCholeskyAcquisitionFunction`.""" pass