feat(format): drift-detection-v1 4-gate PARTIAL discharge#1374
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feat(format): drift-detection-v1 4-gate PARTIAL discharge#1374noahgift wants to merge 2 commits into
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Binds FALSIFY-DRIFT-001..004 from drift-detection-v1 at PARTIAL_ALGORITHM_LEVEL via 4 verdict functions plus a pure-Rust reference classifier (univariate_drift_score + classify_drift). - DRIFT-001: drift_score >= 0 for all inputs - DRIFT-002: higher score → equal-or-higher severity (monotone) - DRIFT-003: |data| < min_samples → NoDrift - DRIFT-004: identical reference == current → score=0 → NoDrift ## Five Whys 1. Why does drift-detection-v1 list 4 falsification IDs without algorithm-level discharge? PMAT lints flagged FALSIFY-DRIFT-001..004 as unbound at PARTIAL_ALGORITHM_LEVEL. 2. Why does that block ship? Coverage % cannot move while peripheral ML monitoring contracts have no algorithm-level verdict module. 3. Why bind both verdicts AND a reference classifier? The monotone-classification gate (DRIFT-002) requires a deterministic threshold predicate; the identical-distribution gate (DRIFT-004) requires that mu_ref == mu_cur ⇒ score == 0. Both need the classifier in-module to avoid coupling the verdict to a specific downstream impl. 4. Why fail-closed (NoDrift) on invalid thresholds? Per Gama 2004, alarming on insufficient or malformed input is a worse failure mode than missing real drift; the spec lists `min_samples_guard` as an invariant for this reason. 5. Why a 7-section mutation-survey layout? The score-sweep over 81 pairs (9×9 probes around the threshold band) catches AND→OR classification mutations and off-by-epsilon boundary regressions that single-fixture tests would miss. Adds 29 unit tests. Realistic-healthy vector covers a passing production scenario; pre-fix vector encodes the 4 regression classes (sign-bug, inverted-threshold, premature-alarm, invalid-sigma). No runtime % shift; algorithm-level coverage advances by 4 gates.
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Summary
drift-detection-v1atPARTIAL_ALGORITHM_LEVELvia 4 verdict functions + in-module reference classifier.Gates bound
drift_score >= 0ANDis_finite()for all inputss_lo <= s_hi⇒status(s_lo) <= status(s_hi)(monotone)|data| < min_samples⇒status == NoDrift(fail-closed)mu_ref == mu_cur⇒score == 0ANDstatus == NoDriftReference impl
univariate_drift_score(mu_ref, mu_cur, sigma_ref) = |mu_ref - mu_cur| / sigma_ref(NaN onsigma_ref <= 0).classify_drift(score, warn, drift)— fail-closedNoDrifton invalid thresholds.Five Whys
See commit message — captures why fail-closed on invalid thresholds (Gama 2004 reasoning) and why the reference classifier ships in-module rather than coupling to a downstream impl.
Test plan
cargo test -p aprender-core --lib drift_001_004— 29 passed🤖 Generated with Claude Code