2 Commits

Include renames

Author	SHA1	Message	Date
grabowski	21e9d2e114	feat: hgb-v2 — regression heads predict rise, recovering flood warning lead ... Rolling-origin evaluation (5 monsoon folds x 4 variants, P.1 + P.103; results in models/eval_variants.json) showed the absolute-level target alerting AT the crossing on essentially every event, while the rise target (future max - current level, level added back at serving) gives +6h on the hard 2024 crossings, +45h in 2025, fewer false alarms than weighted/quantile variants, and ~11% better MAE. Weighted and quantile variants rejected: more false alarms, no Brier-score calibration gain. Ported to production: train.py fits rise in both eval and refit passes (sigma/metrics computed in absolute space), bundles stamped hgb-v2 with regression_target='rise', predict.py adds the level back for v2 and stays compatible with v1 bundles, backtest_render.py mirrors the same math. Regenerated backtest charts: 2024 first alert 11:00 24 Sep (6h BEFORE the 17:00 crossing, was 18h after), 2025 alert 45h ahead, and the record-peak underprediction is gone (rise models can exceed the training max). The >=12h acceptance gate still fails honestly at +6h — closing that needs rainfall inputs. New P.1 MAE 5.0/7.2/9.4 cm at 6/12/24h; docs updated throughout.	2026-08-12 15:43:29 +07:00
grabowski	0005f7dce1	feat: codified backtests, honest docs, belt-and-braces serving, perf fixes ... Retrained on the gap-filled DB (592k -> 976k rows) and re-examined the flood backtests, now reproducible via scripts/backtest_render.py (renders the three docs/img charts and gates on a >=12h 2024 first-alert lead — currently failing by design and documented as such). Findings, all documented in FLOOD_FORECASTING.md: the true 2024 crossing was 24 Sep 17:00 (8h earlier than recorded; confirmed against the independent HII sensor), the historical 24h-warning claim was partly a missing-data artifact, and retrained warn classifiers collapse on the filled grid (P.1 24h PR-AUC 0.900 -> 0.288) while regression MAE improves (11.3 -> 10.5 cm). Serving therefore becomes max(classifier, sigmoid(regression)) so alerting is never worse than the regression path; metrics table, head-gating tiers, honest-limits and runbook expectations all updated to the current model (hgb-v1+d2d0e65). Perf, from Locust load testing (scripts/locustfile.py + load_test.py): single-flight lock around /forecast inference (concurrent cache misses previously each ran ~18s inference and starved the shared thread pool; 200-user run after: 105 rps, 0.01% errors), and /measurements/latest + /health moved off the event loop (synchronous DB/network calls in async handlers were stalling every request under load).	2026-08-12 10:46:00 +07:00