AddFeasibility transform#4547
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Summary:
Transform that adds failure-awareness capability to Ax optimization.
This transform enables Ax to learn from deterministic trial failures (ABANDONED trials) and avoid sampling similar parameter configurations that are likely to fail. It achieves this by:
1. Adding a "is_feasible" metric to experiment data based on trial status
- ABANDONED trials get feasibility value of 0.0 (infeasible)
- Other trials get feasibility value of 1.0 (feasible)
2. Adding a feasibility constraint to the optimization config
- The constraint enforces P(is_feasible) >= threshold
- This guides the acquisition function to avoid infeasible regions
NOTE: We should maybe pick a different word than "feasibility" to not be confused with feasibility in the sense of not violating user-specified outcome constraints.
Differential Revision: D85185246
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Summary: Pull Request resolved: facebook#4547 Transform that adds failure-awareness capability to Ax optimization. This transform enables Ax to learn from deterministic trial failures (ABANDONED trials) and avoid sampling similar parameter configurations that are likely to fail. It achieves this by: 1. Adding a "execution_viable" metric to experiment data based on trial status - ABANDONED trials get feasibility value of 0.0 (not viable) - Other trials get feasibility value of 1.0 (viable) 2. Adding the execution_viable constraint to the optimization config - The constraint enforces P(execution_viable) >= threshold - This guides the acquisition function to avoid non-viable regions Differential Revision: D85185246
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## main #4547 +/- ##
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- Coverage 96.83% 96.83% -0.01%
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Files 599 601 +2
Lines 64584 64800 +216
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+ Hits 62540 62748 +208
- Misses 2044 2052 +8 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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Summary:
Transform that adds failure-awareness capability to Ax optimization.
This transform enables Ax to learn from deterministic trial failures (ABANDONED trials) and avoid sampling similar parameter configurations that are likely to fail. It achieves this by:
1. Adding a "execution_viable" metric to experiment data based on trial status
- ABANDONED trials get feasibility value of 0.0 (not viable)
- Other trials get feasibility value of 1.0 (viable)
2. Adding the execution_viable constraint to the optimization config
- The constraint enforces P(execution_viable) >= threshold
- This guides the acquisition function to avoid non-viable regions
Reviewed By: saitcakmak
Differential Revision: D85185246
added 2 commits
March 12, 2026 10:11
Summary: Allow custom tolerated_trial_failure_rate for failure aware benchmark problems where Standard BO without failure awareness is expected to have a high trial failure rate Reviewed By: Balandat Differential Revision: D87092423
Summary:
Transform that adds failure-awareness capability to Ax optimization.
This transform enables Ax to learn from deterministic trial failures (ABANDONED trials) and avoid sampling similar parameter configurations that are likely to fail. It achieves this by:
1. Adding a "execution_viable" metric to experiment data based on trial status
- ABANDONED trials get feasibility value of 0.0 (not viable)
- Other trials get feasibility value of 1.0 (viable)
2. Adding the execution_viable constraint to the optimization config
- The constraint enforces P(execution_viable) >= threshold
- This guides the acquisition function to avoid non-viable regions
Reviewed By: saitcakmak
Differential Revision: D85185246
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Summary:
Transform that adds failure-awareness capability to Ax optimization.
This transform enables Ax to learn from deterministic trial failures (ABANDONED trials) and avoid sampling similar parameter configurations that are likely to fail. It achieves this by:
Adding a "is_feasible" metric to experiment data based on trial status
- ABANDONED trials get feasibility value of 0.0 (infeasible)
- Other trials get feasibility value of 1.0 (feasible)
Adding a feasibility constraint to the optimization config
- The constraint enforces P(is_feasible) >= threshold
- This guides the acquisition function to avoid infeasible regions
NOTE: We should maybe pick a different word than "feasibility" to not be confused with feasibility in the sense of not violating user-specified outcome constraints.
Differential Revision: D85185246