Add LogExpectedImprovementPerCost acquisition function#3304
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Pull request overview
Adds a new cost-aware analytic acquisition function to botorch_community implementing log expected improvement per (spatially varying) evaluation cost, intended to support cost-aware stopping / selection rules.
Changes:
- Introduces
LogExpectedImprovementPerCost(analytic) computingLogEI(x) - alpha * log(c(x)). - Adds unit tests covering constant cost, varying
alpha, maximize/minimize, and batch shapes. - Exports the new acquisition function from
botorch_community.acquisition.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 5 comments.
| File | Description |
|---|---|
botorch_community/acquisition/log_expected_improvement_per_cost.py |
New analytic acquisition function implementing cost-adjusted LogEI. |
test_community/acquisition/test_log_expected_improvement_per_cost.py |
New unit tests validating behavior against LogExpectedImprovement. |
botorch_community/acquisition/__init__.py |
Re-exports LogExpectedImprovementPerCost in the community acquisition package. |
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Hi all,
I'm working on implementation for BO termination conditions methods in BoFire, and to implement one of them (by Xie et al., arXiv:2507.12453, 2025), we need this cost-aware logEI acquisition function (logEIC).
logEIC is analytic single-outcome acquisition function computing LogEI(x) - alpha * log(c(x)), supporting spatially varying costs.
Motivation
Cost-aware Bayesian optimization is important when function evaluations have heterogeneous costs (e.g. reaction time, compute budget, experimental reagent cost). This PR adds
LogExpectedImprovementPerCosttobotorch_community, which computes:where$c(x)$ is a user-supplied cost callable and $\alpha$ controls the cost influence.
Have you read the Contributing Guidelines on pull requests?
Yes.
Test Plan
Unit tests are added in
test_community/acquisition/test_log_expected_improvement_per_cost.py.