Skip to content

CONTRIBUTING.md

Latest commit

 

History

History
70 lines (52 loc) · 2.12 KB

CONTRIBUTING.md

File metadata and controls

70 lines (52 loc) · 2.12 KB
Raw

Contributing to agentune

Thanks for your interest in contributing!

Setup

git clone https://github.com/huijokim/agentune.git
cd agentune
docker compose up -d   # Postgres + MLflow
uv sync                # install all dependencies including dev

Running Tests

# Create test database (once)
PGPASSWORD=agentune psql -h localhost -U agentune -c "CREATE DATABASE agentune_test;"

# Run all tests
uv run pytest tests/ -v

# Run a specific test file
uv run pytest tests/test_report.py -v

Tests that need Postgres will use AGENTUNE_TEST_DB_URL (defaults to postgresql://agentune:agentune@localhost:5432/agentune_test).

Project Structure

src/agentune/
  backends/       # XGBoost, LightGBM, CatBoost objective functions + tuning guides
  core/           # Campaign service, DB, state machines, data models
  cli.py          # Click CLI commands
  datasets.py     # Dataset loaders (tabular + time-series via mlforecast)
  mcp_server.py   # MCP tools for Claude Code integration
  runner.py       # Round orchestration + MLflow logging
  report.py       # HTML report generation
  scheduler.py    # Budget clipping + stop conditions
  summarizer.py   # Signal extraction from Optuna studies

Making Changes

  1. Create a branch: git checkout -b my-feature
  2. Write tests first when possible
  3. Run the full test suite before submitting
  4. Keep commits focused -- one logical change per commit

Adding a Backend

  1. Create src/agentune/backends/my_backend.py following the XGBoostBackend pattern
  2. Implement the ObjectiveBackend protocol (see backends/base.py)
  3. Register in backends/__init__.py
  4. Add tests in tests/test_backend_my_backend.py

Adding a Dataset

  1. Add an entry to DATASETS in src/agentune/datasets.py
  2. For time-series: add "temporal": True, "file": "my_dataset.parquet" and create a prep script in scripts/
  3. Add tests in tests/test_datasets_*.py
  4. Update the dataset table in README.md

Pull Requests

  • Keep PRs focused on a single feature or fix
  • Include tests for new functionality
  • Update README.md if adding user-facing features
  • CI must pass before merge