src/ml/skill.py joins forecast_history (what each deployed version
predicted for the 24 h peak, hourly) to water_measurements (what the
river did) and reports per version: verified hours, peak MAE, bias, the
persistence baseline (peak = current level), skill = 1 - MAE/persistence,
and the same MAE restricted to observed peaks >= 2 m. Only forecasts
whose window has elapsed with >= 75 % of hours observed count; a
version needs 24 verified hours before it is compared.
GET /api/forecast/skill?station_code=P.1&horizon=24 returns it (SWR
cached, 15 min). The dashboard's forecast card gains a panel with a
one-line verdict (current vs previous version), the per-version table,
and a caveat that quiet weeks measure quiet-river accuracy only: the
model is judged on flood-onset lead, which the backtests cover. EN + TH.
On today's production data: hgb-v3+28b62e5 (369 h, Aug 13 - Sep 1)
MAE 15.2 cm, skill -0.05; hgb-v2+f6570ac (224 h, Sep 1 - 11) MAE
12.3 cm, skill 0.36 - the "worse" v2 model scores better on a quieter
fortnight, which is exactly why the panel shows the >= 2 m column and
the caveat. Tests: 3, sqlite, synthetic.
scripts/dev_proxy.py: DEV_PROXY_LOCAL lets a not-yet-deployed endpoint be
answered from a local JSON file while everything else goes to prod.
