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Marius CiepluchandClaude Opus 5.5
f5180a14ec
learn/retrieve.py: stage 1 is a per-instrument Gower distance over the profile's allow-listed features with explicit missing (both ran: mean feature distance; differing states 1; same non-ran state 0; null in one 1; priors dropped with priors=off), n1 = 40 with seeded tie-breaks; stage 2 re-ranks by r = lam(1 - D) + (1 - lam)cos, signals_only without the target's vector; the balance rule takes k_ret = 6 (<= k/2 per label, <= 2 per group, <= 2 other families; `short` recorded). eligible() is the only way into a prompt: not the target's group, not an evaluated group, not the held-out source or framework, and outside deployment not a near-duplicate (cos >= 0.98). assert_boundary repeats the check on rendered ids; demonstrations of a held-out framework swap to their alternates; deployment_fold excludes a scanned repository that is a corpus group; seed_memory never hands a case its own group. learn/folds.py: compile split (25% of groups per primary family, 60/40 boot/val), outer grouped 5-fold stratified by family and label mix, leave-one-source-out, leave-one-framework-out (insufficient data below 10 groups), assert_disjoint and a folds digest; all seeded. Tests: test_learn_retrieve.py (hand-computed Gower, lam 0/0.5, balance, near-duplicates, test_boundary over outer/LOSO/LOFO folds with demonstrations), test_learn_folds.py. Co-Authored-By: Claude Opus 5.5 <[email protected]> Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo