Source code for botorch.acquisition.acquisition

#!/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"""
Abstract base module for all botorch acquisition functions.
"""

from __future__ import annotations

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

from botorch.exceptions import BotorchWarning
from botorch.models.model import Model
from torch import Tensor
from torch.nn import Module


[docs]class AcquisitionFunction(Module, ABC):
    r"""Abstract base class for acquisition functions."""

    def __init__(self, model: Model) -> None:
        r"""Constructor for the AcquisitionFunction base class.

        Args:
            model: A fitted model.
        """
        super().__init__()
        self.add_module("model", model)

[docs]    def set_X_pending(self, X_pending: Optional[Tensor] = None) -> None:
        r"""Informs the acquisition function about pending design points.

        Args:
            X_pending: `n x d` Tensor with `n` `d`-dim design points that have
                been submitted for evaluation but have not yet been evaluated.
        """
        if X_pending is not None:
            if X_pending.requires_grad:
                warnings.warn(
                    "Pending points require a gradient but the acquisition function"
                    " will not provide a gradient to these points.",
                    BotorchWarning,
                )
            self.X_pending = X_pending.detach().clone()
        else:
            self.X_pending = X_pending

[docs]    @abstractmethod
    def forward(self, X: Tensor) -> Tensor:
        r"""Evaluate the acquisition function on the candidate set X.

        Args:
            X: A `(b) x q x d`-dim Tensor of `(b)` t-batches with `q` `d`-dim
                design points each.

        Returns:
            A `(b)`-dim Tensor of acquisition function values at the given
            design points `X`.
        """
        pass  # pragma: no cover


[docs]class OneShotAcquisitionFunction(AcquisitionFunction, ABC):
    r"""Abstract base class for acquisition functions using one-shot optimization"""

[docs]    @abstractmethod
    def get_augmented_q_batch_size(self, q: int) -> int:
        r"""Get augmented q batch size for one-shot optimziation.

        Args:
            q: The number of candidates to consider jointly.

        Returns:
            The augmented size for one-shot optimization (including variables
            parameterizing the fantasy solutions).
        """
        pass  # pragma: no cover

[docs]    @abstractmethod
    def extract_candidates(self, X_full: Tensor) -> Tensor:
        r"""Extract the candidates from a full "one-shot" parameterization.

        Args:
            X_full: A `b x q_aug x d`-dim Tensor with `b` t-batches of `q_aug`
                design points each.

        Returns:
            A `b x q x d`-dim Tensor with `b` t-batches of `q` design points each.
        """
        pass  # pragma: no cover