# Source code for botorch.models.cost

```
#!/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.
r"""
Cost models to be used with multi-fidelity optimization.
Cost are useful for defining known cost functions when the cost of an evaluation
is heterogeneous in fidelity. For a full worked example, see the
`tutorial <https://botorch.org/tutorials/multi_fidelity_bo>`_ on continuous
multi-fidelity Bayesian Optimization.
"""
from __future__ import annotations
from typing import Dict, Optional
import torch
from botorch.models.deterministic import DeterministicModel
from torch import Tensor
[docs]class AffineFidelityCostModel(DeterministicModel):
r"""Deterministic, affine cost model operating on fidelity parameters.
For each (q-batch) element of a candidate set `X`, this module computes a
cost of the form
cost = fixed_cost + sum_j weights[j] * X[fidelity_dims[j]]
For a full worked example, see the
`tutorial <https://botorch.org/tutorials/multi_fidelity_bo>`_ on continuous
multi-fidelity Bayesian Optimization.
Example:
>>> from botorch.models import AffineFidelityCostModel
>>> from botorch.acquisition.cost_aware import InverseCostWeightedUtility
>>> cost_model = AffineFidelityCostModel(
>>> fidelity_weights={6: 1.0}, fixed_cost=5.0
>>> )
>>> cost_aware_utility = InverseCostWeightedUtility(cost_model=cost_model)
"""
def __init__(
self,
fidelity_weights: Optional[Dict[int, float]] = None,
fixed_cost: float = 0.01,
) -> None:
r"""
Args:
fidelity_weights: A dictionary mapping a subset of columns of `X`
(the fidelity parameters) to its associated weight in the
affine cost expression. If omitted, assumes that the last
column of `X` is the fidelity parameter with a weight of 1.0.
fixed_cost: The fixed cost of running a single candidate point (i.e.
an element of a q-batch).
"""
if fidelity_weights is None:
fidelity_weights = {-1: 1.0}
super().__init__()
self.fidelity_dims = sorted(fidelity_weights)
self.fixed_cost = fixed_cost
weights = torch.tensor([fidelity_weights[i] for i in self.fidelity_dims])
self.register_buffer("weights", weights)
self._num_outputs = 1
[docs] def forward(self, X: Tensor) -> Tensor:
r"""Evaluate the cost on a candidate set X.
Computes a cost of the form
cost = fixed_cost + sum_j weights[j] * X[fidelity_dims[j]]
for each element of the q-batch
Args:
X: A `batch_shape x q x d'`-dim tensor of candidate points.
Returns:
A `batch_shape x q x 1`-dim tensor of costs.
"""
# TODO: Consider different aggregation (i.e. max) across q-batch
lin_cost = torch.einsum(
"...f,f", X[..., self.fidelity_dims], self.weights.to(X)
)
return self.fixed_cost + lin_cost.unsqueeze(-1)
[docs]class FixedCostModel(DeterministicModel):
r"""Deterministic, fixed cost model.
For each (q-batch) element of a candidate set `X`, this module computes a
fixed cost per objective.
"""
def __init__(
self,
fixed_cost: Tensor,
) -> None:
r"""
Args:
fixed_cost: A `m`-dim tensor containing the fixed cost of evaluating each
objective.
"""
super().__init__()
self.register_buffer("fixed_cost", fixed_cost)
self._num_outputs = fixed_cost.shape[-1]
[docs] def forward(self, X: Tensor) -> Tensor:
r"""Evaluate the cost on a candidate set X.
Computes the fixed cost of evaluating each objective for each element
of the q-batch.
Args:
X: A `batch_shape x q x d'`-dim tensor of candidate points.
Returns:
A `batch_shape x q x m`-dim tensor of costs.
"""
view_shape = [1] * (X.ndim - 1) + [self._num_outputs]
expand_shape = X.shape[:-1] + torch.Size([self._num_outputs])
return self.fixed_cost.view(view_shape).expand(expand_shape)
```