Support baseline-referenced SGES for single-trial experiments - #5267
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Support baseline-referenced SGES for single-trial experiments#5267umutgunduzfb wants to merge 1 commit into
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Summary: Stability-gated early stopping requires at least two comparable curves. Add an explicit baseline-reference hook so integrations can provide a comparison curve when an experiment has one candidate. Inject the cleaned raw baseline as a pseudo-trial before progression normalization. Ax already aligns curves sampled at different progressions and SGES interpolates them at decision checkpoints, so no integration-specific resampling is needed. Preserve unknown baseline uncertainty as NaN. Existing multi-candidate behavior remains unchanged. Expose a reference-needed query based on the same attached-data condition used for injection. It returns true only when the selected metric has exactly one candidate curve and no reference is cached, allowing integrations to avoid unused and repeated baseline fetches. Context: A one-candidate experiment has no peer curve, so SGES cannot satisfy its comparison requirements even when GAIN has a completed baseline. The reference hook supplies only the missing comparator. Restricting injection and fetching to exactly one attached candidate curve avoids changing leader selection, top-k protection, and inferred stability thresholds for existing multi-candidate experiments. Reviewed By: saitcakmak Differential Revision: D114376941
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This pull request has been merged in d389183. |
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Additional details and impacted files@@ Coverage Diff @@
## main #5267 +/- ##
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- Coverage 96.59% 96.58% -0.01%
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Files 621 621
Lines 70745 70802 +57
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+ Hits 68335 68384 +49
- Misses 2410 2418 +8 ☔ View full report in Codecov by Harness. 🚀 New features to boost your workflow:
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Summary:
Stability-gated early stopping requires at least two comparable curves. Add an explicit baseline-reference hook so integrations can provide a comparison curve when an experiment has one candidate.
Inject the cleaned raw baseline as a pseudo-trial before progression normalization. Ax already aligns curves sampled at different progressions and SGES interpolates them at decision checkpoints, so no integration-specific resampling is needed. Preserve unknown baseline uncertainty as NaN. Existing multi-candidate behavior remains unchanged.
Expose a reference-needed query based on the same attached-data condition used for injection. It returns true only when the selected metric has exactly one candidate curve and no reference is cached, allowing integrations to avoid unused and repeated baseline fetches.
Context:
A one-candidate experiment has no peer curve, so SGES cannot satisfy its comparison requirements even when GAIN has a completed baseline. The reference hook supplies only the missing comparator. Restricting injection and fetching to exactly one attached candidate curve avoids changing leader selection, top-k protection, and inferred stability thresholds for existing multi-candidate experiments.
Reviewed By: saitcakmak
Differential Revision: D114376941