Source code for botorch.acquisition.fixed_feature

#!/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.

A wrapper around AquisitionFunctions to fix certain features for optimization.
This is useful e.g. for performing contextual optimization.

from __future__ import annotations

from typing import List, Union

import torch
from botorch.acquisition.acquisition import AcquisitionFunction
from torch import Tensor
from torch.nn import Module

[docs]class FixedFeatureAcquisitionFunction(AcquisitionFunction): """A wrapper around AquisitionFunctions to fix a subset of features. Example: >>> model = SingleTaskGP(train_X, train_Y) # d = 5 >>> qEI = qExpectedImprovement(model, best_f=0.0) >>> columns = [2, 4] >>> values = X[..., columns] >>> qEI_FF = FixedFeatureAcquisitionFunction(qEI, 5, columns, values) >>> qei = qEI_FF(test_X) # d' = 3 """ def __init__( self, acq_function: AcquisitionFunction, d: int, columns: List[int], values: Union[Tensor, List[float]], ) -> None: r"""Derived Acquisition Function by fixing a subset of input features. Args: acq_function: The base acquisition function, operating on input tensors `X_full` of feature dimension `d`. d: The feature dimension expected by `acq_function`. columns: `d_f < d` indices of columns in `X_full` that are to be fixed to the provided values. values: The values to which to fix the columns in `columns`. Either a full `batch_shape x q x d_f` tensor of values (if values are different for each of the `q` input points), or an array-like of values that is broadcastable to the input across `t`-batch and `q`-batch dimensions, e.g. a list of length `d_f` if values are the same across all `t` and `q`-batch dimensions. """ Module.__init__(self) self.acq_func = acq_function self.d = d values = torch.as_tensor(values).detach().clone() self.register_buffer("values", values) # build selector for _construct_X_full self._selector = [] idx_X, idx_f = 0, d - values.shape[-1] for i in range(self.d): if i in columns: self._selector.append(idx_f) idx_f += 1 else: self._selector.append(idx_X) idx_X += 1
[docs] def forward(self, X: Tensor): r"""Evaluate base acquisition function under the fixed features. Args: X: Input tensor of feature dimension `d' < d` such that `d' + d_f = d`. Returns: Base acquisition function evaluated on tensor `X_full` constructed by adding `values` in the appropriate places (see `_construct_X_full`). """ X_full = self._construct_X_full(X) return self.acq_func(X_full)
def _construct_X_full(self, X: Tensor) -> Tensor: r"""Constructs the full input for the base acquisition function. Args: X: Input tensor with shape `batch_shape x q x d'` such that `d' + d_f = d`. Returns: Tensor `X_full` of shape `batch_shape x q x d`, where `X_full[..., i] = values[..., i]` if `i in columns`, and `X_full[..., i] = X[..., j]`, with `j = i - sum_{l<=i} 1_{l in fixed_colunns}`. """ d_prime, d_f = X.shape[-1], self.values.shape[-1] if d_prime + d_f != self.d: raise ValueError( f"Feature dimension d' ({d_prime}) of input must be " f"d - d_f ({self.d - d_f})." ) # concatenate values to the end values =*X.shape[:-1], d_f) X_perm =[X, values], dim=-1) # now select the appropriate column order return X_perm[..., self._selector]