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Nathan Coad
2026-04-06 18:41:16 +10:00
parent 3a7309b2cf
commit 1ef300d25e
6 changed files with 52 additions and 24 deletions
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docs/rain_data_issues.md
+1 -1
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@@ -19,7 +19,7 @@ This document captures known data-quality issues observed in the rain-model pipe
Run this regularly and retain JSON reports for comparison:
```sh
python scripts/audit_rain_data.py \
scripts/rainml_py.sh scripts/audit_rain_data.py \
--site home \
--start "2026-02-01T00:00:00Z" \
--end "2026-03-03T23:55:00Z" \
docs/rain_model_runbook.md
+10 -10
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@@ -25,7 +25,7 @@ docker compose exec -T timescaledb \
Recommended evaluation run (includes validation-only tuning, calibration comparison, naive baselines, and walk-forward folds):
```sh
python scripts/train_rain_model.py \
scripts/rainml_py.sh scripts/train_rain_model.py \
--site "home" \
--start "2026-02-01T00:00:00Z" \
--end "2026-03-03T23:55:00Z" \
@@ -59,7 +59,7 @@ Review in report:
2. Run one dry-run inference:
```sh
python scripts/predict_rain_model.py \
scripts/rainml_py.sh scripts/predict_rain_model.py \
--site home \
--model-path "models/rain_model.pkl" \
--model-name "rain_next_4h" \
@@ -70,7 +70,7 @@ python scripts/predict_rain_model.py \
3. Run live inference:
```sh
python scripts/predict_rain_model.py \
scripts/rainml_py.sh scripts/predict_rain_model.py \
--site home \
--model-path "models/rain_model.pkl" \
--model-name "rain_next_4h" \
@@ -128,7 +128,7 @@ Alert heuristic: sustained Brier-score increase > 25% from trailing 30-day avera
Use the health-check script in cron, systemd timer, or your alerting scheduler:
```sh
python scripts/check_rain_pipeline_health.py \
scripts/rainml_py.sh scripts/check_rain_pipeline_health.py \
--site home \
--model-name rain_next_4h \
--horizon-hours 4 \
@@ -164,7 +164,7 @@ Recommended production defaults:
To compare saved training reports and pick a deployment candidate automatically:
```sh
python scripts/recommend_rain_model.py \
scripts/rainml_py.sh scripts/recommend_rain_model.py \
--reports-glob "models/rain_model_report*.json" \
--require-walk-forward \
--top-k 5 \
@@ -194,7 +194,7 @@ docker compose exec -T timescaledb \
3. Run a full 4h training/evaluation cycle and save report:
```sh
python scripts/train_rain_model.py \
scripts/rainml_py.sh scripts/train_rain_model.py \
--site "home" \
--start "2026-02-01T00:00:00Z" \
--end "2026-03-03T23:55:00Z" \
@@ -213,21 +213,21 @@ python scripts/train_rain_model.py \
4. Compare 4h metrics against the latest 1h benchmark report before switching dashboard defaults:
```sh
python scripts/compare_rain_reports.py \
scripts/rainml_py.sh scripts/compare_rain_reports.py \
--baseline "models/rain_model_report_1h.json" \
--candidate "models/rain_model_report_4h.json"
```
5. Run dry-run inference, then live inference with 4h model name/horizon:
```sh
python scripts/predict_rain_model.py \
scripts/rainml_py.sh scripts/predict_rain_model.py \
--site home \
--model-path "models/rain_model_4h.pkl" \
--model-name "rain_next_4h" \
--horizon-hours 4 \
--dry-run
python scripts/predict_rain_model.py \
scripts/rainml_py.sh scripts/predict_rain_model.py \
--site home \
--model-path "models/rain_model_4h.pkl" \
--model-name "rain_next_4h" \
@@ -237,7 +237,7 @@ python scripts/predict_rain_model.py \
6. Validate health checks and dashboard data path for 4h:
```sh
python scripts/check_rain_pipeline_health.py \
scripts/rainml_py.sh scripts/check_rain_pipeline_health.py \
--site home \
--model-name rain_next_4h \
--horizon-hours 4 \
docs/rain_prediction.md
+10 -10
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@@ -90,7 +90,7 @@ All examples below assume a 4-hour horizon (`--horizon-hours 4`) and `model-name
```sh
export DATABASE_URL="postgres://postgres:postgres@localhost:5432/micrometeo?sslmode=disable"
python scripts/audit_rain_data.py \
scripts/rainml_py.sh scripts/audit_rain_data.py \
--site home \
--start "2026-02-01T00:00:00Z" \
--end "2026-03-03T23:55:00Z" \
@@ -100,7 +100,7 @@ python scripts/audit_rain_data.py \
### 3) Train baseline model
```sh
python scripts/train_rain_model.py \
scripts/rainml_py.sh scripts/train_rain_model.py \
--site "home" \
--start "2026-02-01T00:00:00Z" \
--end "2026-03-03T23:55:00Z" \
@@ -117,7 +117,7 @@ python scripts/train_rain_model.py \
### 3b) Train expanded (P1) feature-set model
```sh
python scripts/train_rain_model.py \
scripts/rainml_py.sh scripts/train_rain_model.py \
--site "home" \
--start "2026-02-01T00:00:00Z" \
--end "2026-03-03T23:55:00Z" \
@@ -132,7 +132,7 @@ python scripts/train_rain_model.py \
### 3b.1) Train expanded + calendar (P2) feature-set model
```sh
python scripts/train_rain_model.py \
scripts/rainml_py.sh scripts/train_rain_model.py \
--site "home" \
--start "2026-02-01T00:00:00Z" \
--end "2026-03-03T23:55:00Z" \
@@ -146,7 +146,7 @@ python scripts/train_rain_model.py \
### 3c) Train tree-based baseline (P1)
```sh
python scripts/train_rain_model.py \
scripts/rainml_py.sh scripts/train_rain_model.py \
--site "home" \
--start "2026-02-01T00:00:00Z" \
--end "2026-03-03T23:55:00Z" \
@@ -161,7 +161,7 @@ python scripts/train_rain_model.py \
### 3d) Auto-compare logistic vs tree baseline
```sh
python scripts/train_rain_model.py \
scripts/rainml_py.sh scripts/train_rain_model.py \
--site "home" \
--start "2026-02-01T00:00:00Z" \
--end "2026-03-03T23:55:00Z" \
@@ -175,7 +175,7 @@ python scripts/train_rain_model.py \
### 3e) Full P1 evaluation (tuning + calibration + walk-forward)
```sh
python scripts/train_rain_model.py \
scripts/rainml_py.sh scripts/train_rain_model.py \
--site "home" \
--start "2026-02-01T00:00:00Z" \
--end "2026-03-03T23:55:00Z" \
@@ -194,7 +194,7 @@ python scripts/train_rain_model.py \
### 3f) Walk-forward threshold policy (more temporally robust alert threshold)
```sh
python scripts/train_rain_model.py \
scripts/rainml_py.sh scripts/train_rain_model.py \
--site "home" \
--start "2026-02-01T00:00:00Z" \
--end "2026-03-03T23:55:00Z" \
@@ -210,7 +210,7 @@ python scripts/train_rain_model.py \
### 4) Run inference and store prediction
```sh
python scripts/predict_rain_model.py \
scripts/rainml_py.sh scripts/predict_rain_model.py \
--site home \
--model-path "models/rain_model.pkl" \
--model-name "rain_next_4h" \
@@ -252,7 +252,7 @@ docker compose logs -f rainml
### 7) Recommend deploy candidate from saved reports
```sh
python scripts/recommend_rain_model.py \
scripts/rainml_py.sh scripts/recommend_rain_model.py \
--reports-glob "models/rain_model_report*.json" \
--require-walk-forward \
--top-k 5 \