grabowski
4358d52d55
Security & Dependency Updates / Dependency Security Scan (push) Successful in 1m8s
Security & Dependency Updates / License Compliance (push) Successful in 25s
Security & Dependency Updates / Check for Dependency Updates (push) Successful in 20s
Security & Dependency Updates / Code Quality Metrics (push) Successful in 17s
Security & Dependency Updates / Security Summary (push) Successful in 9s
Add src/ml/ package predicting, per station and per 6/12/24 h horizon, the probability of exceeding warning (3.0 m) and danger (4.5 m) levels plus expected peak level, trained on the 592k-row PostgreSQL history: - features.py: hourly grid with coverage gating and no future leakage; upstream stations enter at empirically measured travel-time lags (P.20 +17h ... P.103 +1h vs P.1); hour-of-day deliberately excluded (it encodes the scrape schedule, not hydrology) - train.py: HistGradientBoosting regression + warn/danger classifier heads per station x horizon, >=30-positives gate with calibrated sigmoid-on-regression fallback, strict temporal splits, per-event lead-time evaluation; guards against sklearn 1.9.0 crash on degenerate feature columns - predict.py: bundle loading with feature-name checks, heuristic fallback tier, get_latest_forecasts() for the API; raises when no models are trained so the endpoint 503s instead of serving persistence output as forecasts - data.py: Postgres-first loader (FLOOD_ML_DB_URL override), HTTP API fallback (flagged: that path backfills synthetic discharge), csv.gz cache - /forecast endpoint (15-min TTL cache) + dashboard flood-risk panel (hidden until models exist) - docs/FLOOD_FORECASTING.md: full system doc with measured deployment numbers (~335 MB RSS, CPU negligible, ~6 min full retrain) and retraining policy Validation: out-of-sample backtest of the record 2024 flood season (train <= Aug 2024) alerted 24-48 h ahead of the Oct 5 peak; 2025-26 test split: P.1 6h PR-AUC 0.974, recall 98.3% at 1% false-alarm rate. Also: fix P.81 station coordinates (was Ban Pong/Ratchaburi, 493 km out of basin; now 18.6936 N 99.0819 E per RID station page), pin scikit-learn==1.9.0 and numpy<2, gitignore model artifacts (~100 MB, train on the server via scripts/train_flood_model.py).
151 lines
1.7 KiB
Plaintext
151 lines
1.7 KiB
Plaintext
# Python
|
|
__pycache__/
|
|
*.py[cod]
|
|
*$py.class
|
|
*.so
|
|
.Python
|
|
build/
|
|
develop-eggs/
|
|
dist/
|
|
downloads/
|
|
eggs/
|
|
.eggs/
|
|
lib/
|
|
lib64/
|
|
parts/
|
|
sdist/
|
|
var/
|
|
wheels/
|
|
share/python-wheels/
|
|
*.egg-info/
|
|
.installed.cfg
|
|
*.egg
|
|
PIPFILE.lock
|
|
|
|
# Virtual environments
|
|
.env
|
|
.venv
|
|
env/
|
|
venv/
|
|
ENV/
|
|
env.bak/
|
|
venv.bak/
|
|
|
|
# IDE
|
|
.vscode/
|
|
.idea/
|
|
*.swp
|
|
*.swo
|
|
*~
|
|
|
|
# Kiro IDE (keep local only)
|
|
.kiro/
|
|
|
|
# Logs
|
|
*.log
|
|
logs/
|
|
water_monitor.log
|
|
water_monitor_performance.log
|
|
|
|
# Database files
|
|
*.db
|
|
*.sqlite
|
|
*.sqlite3
|
|
water_levels.db
|
|
demo_water_sqlite.db
|
|
|
|
# Configuration files with secrets
|
|
.env.local
|
|
.env.production
|
|
config.local.py
|
|
|
|
# Docker
|
|
.dockerignore
|
|
|
|
# OS
|
|
.DS_Store
|
|
.DS_Store?
|
|
._*
|
|
.Spotlight-V100
|
|
.Trashes
|
|
ehthumbs.db
|
|
Thumbs.db
|
|
|
|
# Temporary files
|
|
*.tmp
|
|
*.temp
|
|
temp/
|
|
tmp/
|
|
|
|
# Coverage reports
|
|
htmlcov/
|
|
.tox/
|
|
.nox/
|
|
.coverage
|
|
.coverage.*
|
|
.cache
|
|
nosetests.xml
|
|
coverage.xml
|
|
*.cover
|
|
*.py,cover
|
|
.hypothesis/
|
|
.pytest_cache/
|
|
cover/
|
|
|
|
# Jupyter Notebook
|
|
.ipynb_checkpoints
|
|
|
|
# pyenv
|
|
.python-version
|
|
|
|
# pipenv
|
|
Pipfile.lock
|
|
|
|
# PEP 582
|
|
__pypackages__/
|
|
|
|
# Celery
|
|
celerybeat-schedule
|
|
celerybeat.pid
|
|
|
|
# SageMath parsed files
|
|
*.sage.py
|
|
|
|
# Environments
|
|
.env
|
|
.env.local
|
|
.env.development.local
|
|
.env.test.local
|
|
.env.production.local
|
|
|
|
# mypy
|
|
.mypy_cache/
|
|
.dmypy.json
|
|
dmypy.json
|
|
|
|
# Pyre type checker
|
|
.pyre/
|
|
|
|
# pytype static type analyzer
|
|
.pytype/
|
|
|
|
# Cython debug symbols
|
|
cython_debug/
|
|
|
|
# Docker volumes
|
|
vm_data/
|
|
grafana_data/
|
|
# Runtime station config (persisted CRUD); bundled default lives in src/data/
|
|
/stations.json
|
|
|
|
# Ruflo local secrets and runtime data
|
|
.env.*.local
|
|
.claude-flow/data/
|
|
.claude-flow/logs/
|
|
.claude-flow/sessions/
|
|
|
|
# Trained flood-forecast model artifacts (produced on the server, ~100 MB; see docs/FLOOD_FORECASTING.md)
|
|
models/*.joblib
|
|
models/cache/
|
|
models/metrics.json
|