Reduce exhaustive optimizer per-candidate dataframe overhead

Objective

Reduce exhaustive optimizer per-candidate dataframe overhead.

Notes: Precomputed the ordered numeric training matrix and enumerated integer column combinations, eliminating repeated label-based DataFrame slicing while preserving exhaustive selection and deterministic ties; 19 focused tests pass. Latency gate remains open pending compliant live evidence.

Approach

[reconstructed — uncertain] No dedicated findings Markdown located for t312. Summary reconstructed from research/findings/task-history.json entry and git history. Uncertain fields: detailed approach, exact file list, and metrics beyond the task note.

Files / code / data changed

• No commit SHA confidently associated with t312 in sampled log [reconstructed — uncertain].
• Task-history entry: research/findings/task-history.json#t312

Results

Task note: Precomputed the ordered numeric training matrix and enumerated integer column combinations, eliminating repeated label-based DataFrame slicing while preserving exhaustive selection and deterministic ties; 19 focused tests pass. Latency gate remains open pending compliant live evidence. [reconstructed — uncertain] No quantitative tables recovered for this entry; see git diff and task-history for grounds.

Conclusions

[reconstructed — uncertain] Outcome inferred from status done and task note.

Problems / follow-ups

• See source report or PR discussion for follow-ups. No unsupported follow-ups fabricated.

Links

• Task-history: research/findings/task-history.json id t312