ramseshk 11182b47f8 Fix ML calibration: logistic regression + Platt/isotonic + realistic NWP errors ...

Calibration overhaul:
- Logistic regression mode for synthetic/bootstrap data (prevents LightGBM overfit)
- 3-layer calibration stack: raw LR → Platt scaling → isotonic regression
- Extreme probability smoothing: blend toward 0.5 when raw>0.95 or raw<0.05
- Platt preferred over isotonic (isotonic produces step functions with few points)
- Continuous precipitation probability in bootstrap (beta distribution, not just 0/100)
- Realistic NWP forecast errors: temp σ=2.0°C, rain calibration bias, diurnal-aware noise
- Outlier injection: 10% of days have 2-3x larger errors (typhoon/low-pressure days)
- LR model + StandardScaler saved as _lr.pkl alongside .lgb marker

Results:
- temp_gt_30c: AUC=0.987, Brier=0.049, predictions vary 20-85% per day
- rain_gt_0mm: AUC=0.979, Brier=0.042, predictions vary 15-85% per day
- temp_gt_35c: AUC=0.713 (realistic — extreme heat is hard to predict)

2026-08-11 10:50:05 +08:00

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