more work on model training

docs/rain_prediction.md

@@ -42,6 +42,11 @@ pip install -r scripts/requirements.txt
   `predictions_rain_1h`.
 - `scripts/run_rain_ml_worker.py`: long-running worker for periodic training + prediction.
 
+Feature-set options:
+- `baseline`: original 5 local observation features.
+- `extended`: adds wind-direction encoding, lag/rolling stats, recent rain accumulation,
+  and aligned forecast features from `forecast_openmeteo_hourly`.
+
 ## Usage
 ### 1) Apply schema update (existing DBs)
 `001_schema.sql` now includes `predictions_rain_1h`.
@@ -60,6 +65,7 @@ python scripts/audit_rain_data.py \
   --site home \
   --start "2026-02-01T00:00:00Z" \
   --end "2026-03-03T23:55:00Z" \
+  --feature-set "baseline" \
   --out "models/rain_data_audit.json"
 ```
 
@@ -72,9 +78,25 @@ python scripts/train_rain_model.py \
   --train-ratio 0.7 \
   --val-ratio 0.15 \
   --min-precision 0.70 \
+  --feature-set "baseline" \
   --model-version "rain-logreg-v1" \
   --out "models/rain_model.pkl" \
-  --report-out "models/rain_model_report.json"
+  --report-out "models/rain_model_report.json" \
+  --dataset-out "models/datasets/rain_dataset_{model_version}_{feature_set}.csv"
+```
+
+### 3b) Train expanded (P1) feature-set model
+```sh
+python scripts/train_rain_model.py \
+  --site "home" \
+  --start "2026-02-01T00:00:00Z" \
+  --end "2026-03-03T23:55:00Z" \
+  --feature-set "extended" \
+  --forecast-model "ecmwf" \
+  --model-version "rain-logreg-v1-extended" \
+  --out "models/rain_model_extended.pkl" \
+  --report-out "models/rain_model_report_extended.json" \
+  --dataset-out "models/datasets/rain_dataset_{model_version}_{feature_set}.csv"
 ```
 
 ### 4) Run inference and store prediction
@@ -107,16 +129,28 @@ docker compose logs -f rainml
 - Audit report: `models/rain_data_audit.json`
 - Training report: `models/rain_model_report.json`
 - Model artifact: `models/rain_model.pkl`
+- Dataset snapshot: `models/datasets/rain_dataset_<model_version>_<feature_set>.csv`
 - Prediction rows: `predictions_rain_1h` (probability + threshold decision + realized
   outcome fields once available)
 
-## Model Features (v1)
+## Model Features (v1 baseline)
 - `pressure_trend_1h`
 - `humidity`
 - `temperature_c`
 - `wind_avg_m_s`
 - `wind_max_m_s`
 
+## Model Features (extended set)
+- baseline features, plus:
+- `wind_dir_sin`, `wind_dir_cos`
+- `temp_lag_5m`, `temp_roll_1h_mean`, `temp_roll_1h_std`
+- `humidity_lag_5m`, `humidity_roll_1h_mean`, `humidity_roll_1h_std`
+- `wind_avg_lag_5m`, `wind_avg_roll_1h_mean`, `wind_gust_roll_1h_max`
+- `pressure_lag_5m`, `pressure_roll_1h_mean`, `pressure_roll_1h_std`
+- `rain_last_1h_mm`
+- `fc_temp_c`, `fc_rh`, `fc_pressure_msl_hpa`, `fc_wind_m_s`, `fc_wind_gust_m_s`,
+  `fc_precip_mm`, `fc_precip_prob`, `fc_cloud_cover`
+
 ## Notes
 - Data is resampled into 5-minute buckets.
 - Label is derived from incremental rain from WS90 cumulative `rain_mm`.