Feature/batch size dependent book keeping gp bo#140
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Signed-off-by: Grossberger Lukas (CR/AIR3) <Lukas.Grossberger@de.bosch.com>
Signed-off-by: Grossberger Lukas (CR/AIR3) <Lukas.Grossberger@de.bosch.com>
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Pull request overview
This PR updates the BoTorch-based Bayesian optimization implementation so that when max_pending_evaluations=None (no limit), the optimizer no longer tracks pending evaluation specifications / IDs, avoiding unbounded pending-spec bookkeeping.
Changes:
- Disable assigning/storing
evaluation_idand pending-spec tracking whenmax_pending_evaluationsisNone. - Add targeted tests covering “no bookkeeping” behavior and reporting of custom evaluations under
max_pending_evaluations=None. - Bump project version to
5.4.0and update packaging/lock metadata (including addingurllib3>=2.7.0to theallextra).
Reviewed changes
Copilot reviewed 4 out of 5 changed files in this pull request and generated 2 comments.
Show a summary per file
| File | Description |
|---|---|
blackboxopt/optimizers/botorch_base.py |
Makes pending-spec bookkeeping conditional on max_pending_evaluations being set. |
tests/optimizers/botorch_base_test.py |
Adds tests ensuring behavior is correct when pending bookkeeping is disabled/enabled. |
pyproject.toml |
Bumps version; adds urllib3>=2.7.0 to the all optional extra. |
blackboxopt/__init__.py |
Bumps __version__ to 5.4.0. |
uv.lock |
Updates locked metadata for the version bump and dependency set (incl. urllib3). |
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| else: | ||
| eval_spec = self._generate_evaluation_specification() | ||
| eval_spec.optimizer_info["model_based_pick"] = True | ||
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| eval_id = self.X.size(-2) + len(self.pending_specifications) | ||
| eval_spec.optimizer_info["evaluation_id"] = eval_id | ||
| self.pending_specifications[eval_id] = eval_spec | ||
| if self.max_pending_evaluations is not None: |
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| "torch>=2.3", | ||
| "botorch>=0.7.1", | ||
| "pymoo>=0.5,<1.0", | ||
| "scikit-learn", | ||
| "urllib3>=2.7.0", | ||
| ] |
14e6
marked this pull request as draft
June 8, 2026 11:35
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Deactivate book-keeping of evaluation specifications for GP based Bayesian Optimization when no limit on pending specifications specified.