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grabowski 5c4f464383 feat(forecast): Rust forecast crate, bit-for-bit with the Python serving path
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Phase 2a of docs/RUST_REWRITE.md. rust/forecast (ping-forecast) ports the
serving half of src/ml: the HII mirror gap fill, the hourly grid, every
feature (pandas' Kahan rolling sums included), the Mae Ngat dam frame,
HistGradientBoosting tree inference, the heuristic fallback, stale-threshold
handling and the P.1 flood-stage probabilities.

Hand-over from Python (src/ml/export.py):
- models/flood_<station>.json.gz next to each joblib bundle; train_all() writes
  it, retrain.sh promotes it, `python -m src.ml.export` exports existing ones.
- rust/forecast/flood_config.json: thresholds, stages, leads and notify
  constants, compiled in. tests/test_model_export.py fails while it differs
  from the Python source.

Parity (rust/forecast/tests/parity.rs, fixtures from
testdata/gen_forecast_fixtures.py on the production code path): four
scenarios incl. the 2024 flood with RID lagging behind HII. Merged readings,
all features and all levels identical; with all 15 local models 377/384
probabilities bit-identical, the rest within 2 ulp (exp).

Python refactor without behaviour change: predict.forecasts_from_long, and
get_latest_forecasts/load_latest/serving_frame take now/rain for
reproducible comparisons; the HII query orders its rows.
2026-10-18 14:21:56 +07:00
grabowski 764764e07e feat: refuse silent v3->v2 downgrade; monthly retrain timer with staged promote
train_all() now raises RainUnavailableError when use_rain=True and the
Open-Meteo history cannot be loaded, instead of logging a warning and
writing gauge-only (v2) bundles over the deployed v3 set -- which is what
the 2026-09-01 server retrain did unnoticed. --no-rain remains the explicit
way to get v2. CLI exits 2 with a one-line error. Three tests cover the
guard, the opt-out, and the v3 happy path.

scripts/retrain.sh trains into models/.staging, refuses to promote unless
metrics.json shows hgb-v3+ and >=14 trained stations, then renames bundles
into place (previous generation kept in models/.previous). No API restart:
predict.py reloads by mtime on the hourly precompute.

water-monitor-retrain.{service,timer}: 1st of each month 03:30, Persistent,
OMP_NUM_THREADS=4, Nice=15, same sandbox as the API unit. install.sh now
does `uv sync` into .venv (one env rule; removes a stale venv/) and enables
the timer. water-monitor.service in the repo matched neither the deployed
unit nor the uv env; it now does (run.py --web-api, .venv, EnvironmentFile).
2026-09-11 21:37:11 +02:00