2 Commits

Include renames

Author	SHA1	Message	Date
bvandeusenandClaude Opus 5.5	ff70f837d0	refactor: test row factories and the fetch stub have one copy each (3109) ... tests/factories.py holds image_row/make_image/make_image_async/make_tag. The 17 byte-identical _img/_tag helpers (15 modules) now import them under their old names, so no call site changed. frontend/test/support/stubFetch.js replaces 15 copies that differed only in formatting. Copies whose bodies differ (other defaults, other columns, a url-only stub) are left as they are; folding those needs a look at each caller. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LVjrnpQjRgHdvq95rASoiR	2026-09-24 16:39:38 -04:00
bvandeusenandClaude Opus 4.8	48c8811d69	feat(heads): auto-apply observability + on by default (#114 auto-apply B) ... Auto-apply is now ON by default (operator-asked: opt-OUT, not opt-in) — migration 0059 + model default flipped. The support (>=30) + measured-precision gates keep it safe and every auto-tag is reversible. Observability so the operator can tune from real data: - MISFIRE = an auto-applied (source='head_auto') tag the operator later removes. UNDER-FIRE = a tag with a head the operator adds by hand (the head missed it). Both captured at correction time in TagService.add_to_image/remove_from_image (source is lost on delete) into durable per-tag counters (head_metric), keyed by tag so they survive head retrain/prune. - Daily snapshot_head_metrics writes a per-concept time-series point (head_metrics_snapshot): auto-applied volume + cumulative misfires/under-fires + head quality; 180-day retention; daily beat. - GET /api/heads/metrics: per-concept current counts + realized misfire rate + head quality, plus the snapshot time-series — the report to tune the precision target + support floor. Migration 0060. Tests: misfire/under-fire counting (and the negatives — manual removal isn't a misfire, headless manual add isn't an under-fire), snapshot time-series, metrics API. What's the autofire threshold? There's no single number — each graduated head derives its OWN probability cutoff from its PR curve: the operating point that holds precision >= head_auto_apply_precision (0.97) at max recall. The global knobs are that target + the >=30 support floor. NEXT (slice 3): UI — enable toggle, dry-run preview, per-concept trends. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa	2026-06-29 00:36:58 -04:00