FLAML/.github/copilot-instructions.md

GitHub Copilot Instructions for FLAML

Project Overview

FLAML (Fast Library for Automated Machine Learning & Tuning) is a lightweight Python library for efficient automation of machine learning and AI operations. It automates workflow based on large language models, machine learning models, etc. and optimizes their performance.

Key Components:

• flaml/automl/: AutoML functionality for classification and regression
• flaml/tune/: Generic hyperparameter tuning
• flaml/default/: Zero-shot AutoML with default configurations
• flaml/autogen/: Legacy autogen code (note: AutoGen has moved to a separate repository)
• flaml/fabric/: Microsoft Fabric integration
• test/: Comprehensive test suite

Build and Test Commands

Installation

# Basic installation
pip install -e .

# Install with test dependencies
pip install -e .[test]

# Install with automl dependencies
pip install -e .[automl]

# Install with forecast dependencies (Linux only)
pip install -e .[forecast]

Running Tests

# Run all tests (excluding autogen)
pytest test/ --ignore=test/autogen --reruns 2 --reruns-delay 10

# Run tests with coverage
coverage run -a -m pytest test --ignore=test/autogen --reruns 2 --reruns-delay 10
coverage xml

# Check dependencies
python test/check_dependency.py

Linting and Formatting

# Run pre-commit hooks
pre-commit run --all-files

# Format with black (line length: 120)
black . --line-length 120

# Run ruff for linting and auto-fix
ruff check . --fix

Code Style and Formatting

Python Style

• Line length: 120 characters (configured in both Black and Ruff)
• Formatter: Black (v23.3.0+)
• Linter: Ruff with Pyflakes and pycodestyle rules
• Import sorting: Use isort (via Ruff)
• Python version: Supports Python >= 3.10 (full support for 3.10, 3.11, 3.12 and 3.13)

Code Quality Rules

• Follow Black formatting conventions
• Keep imports sorted and organized
• Avoid unused imports (F401) - these are flagged but not auto-fixed
• Avoid wildcard imports (F403) where possible
• Complexity: Max McCabe complexity of 10
• Use type hints where appropriate
• Write clear docstrings for public APIs

Pre-commit Hooks

The repository uses pre-commit hooks for:

• Checking for large files, AST syntax, YAML/TOML/JSON validity
• Detecting merge conflicts and private keys
• Trailing whitespace and end-of-file fixes
• pyupgrade for Python 3.8+ syntax
• Black formatting
• Markdown formatting (mdformat with GFM and frontmatter support)
• Ruff linting with auto-fix

Testing Strategy

Test Organization

• Tests are in the test/ directory, organized by module
• test/automl/: AutoML feature tests
• test/tune/: Hyperparameter tuning tests
• test/default/: Zero-shot AutoML tests
• test/nlp/: NLP-related tests
• test/spark/: Spark integration tests

Test Requirements

• Write tests for new functionality
• Ensure tests pass on multiple Python versions (3.10, 3.11, 3.12 and 3.13)
• Tests should work on both Ubuntu and Windows
• Use pytest markers for platform-specific tests (e.g., @pytest.mark.spark)
• Tests should be idempotent and not depend on external state
• Use --reruns 2 --reruns-delay 10 for flaky tests

Coverage

• Aim for good test coverage on new code
• Coverage reports are generated for Python 3.11 builds
• Coverage reports are uploaded to Codecov

Git Workflow and Best Practices

Branching

• Main branch: main
• Create feature branches from main
• PR reviews are required before merging

Commit Messages

• Use clear, descriptive commit messages
• Reference issue numbers when applicable
• ALWAYS run pre-commit run --all-files before each commit to avoid formatting issues

Pull Requests

• Ensure all tests pass before requesting review
• Update documentation if adding new features
• Follow the PR template in .github/PULL_REQUEST_TEMPLATE.md

Project Structure

flaml/
├── automl/         # AutoML functionality
├── tune/           # Hyperparameter tuning
├── default/        # Zero-shot AutoML
├── autogen/        # Legacy autogen (deprecated, moved to separate repo)
├── fabric/         # Microsoft Fabric integration
├── onlineml/       # Online learning
└── version.py      # Version information

test/               # Test suite
├── automl/
├── tune/
├── default/
├── nlp/
└── spark/

notebook/           # Example notebooks
website/            # Documentation website

Dependencies and Package Management

Core Dependencies

• NumPy >= 1.17
• Python >= 3.10 (officially supported: 3.10, 3.11, 3.12 and 3.13)

Optional Dependencies

• [automl]: lightgbm, xgboost, scipy, pandas, scikit-learn
• [test]: Full test suite dependencies
• [spark]: PySpark and joblib dependencies
• [forecast]: holidays, prophet, statsmodels, hcrystalball, pytorch-forecasting, pytorch-lightning, tensorboardX
• [hf]: Hugging Face transformers and datasets
• See setup.py for complete list

Version Constraints

• Be mindful of Python version-specific dependencies (check setup.py)
• XGBoost versions differ based on Python version
• NumPy 2.0+ only for Python >= 3.13
• Some features (like vowpalwabbit) only work with older Python versions

Boundaries and Restrictions

Do NOT Modify

• .git/ directory and Git configuration
• LICENSE file
• Version information in flaml/version.py (unless explicitly updating version)
• GitHub Actions workflows without careful consideration
• Existing test files unless fixing bugs or adding coverage

Be Cautious With

• setup.py: Changes to dependencies should be carefully reviewed
• pyproject.toml: Linting and testing configuration
• .pre-commit-config.yaml: Pre-commit hook configuration
• Backward compatibility: FLAML is a library with external users

Security Considerations

• Never commit secrets or API keys
• Be careful with external data sources in tests
• Validate user inputs in public APIs
• Follow secure coding practices for ML operations

Special Notes

AutoGen Migration

• AutoGen has moved to a separate repository: https://github.com/microsoft/autogen
• The flaml/autogen/ directory contains legacy code
• Tests in test/autogen/ are ignored in the main test suite
• Direct users to the new AutoGen repository for AutoGen-related issues

Platform-Specific Considerations

• Some tests only run on Linux (e.g., forecast tests with prophet)
• Windows and Ubuntu are the primary supported platforms
• macOS support exists but requires special libomp setup for lgbm/xgboost

Performance

• FLAML focuses on efficient automation and tuning
• Consider computational cost when adding new features
• Optimize for low resource usage where possible

Documentation

• Main documentation: https://microsoft.github.io/FLAML/
• Update documentation when adding new features
• Provide clear examples in docstrings
• Add notebook examples for significant new features

Contributing

• Follow the contributing guide: https://microsoft.github.io/FLAML/docs/Contribute
• Sign the Microsoft CLA when making your first contribution
• Be respectful and follow the Microsoft Open Source Code of Conduct
• Join the Discord community for discussions: https://discord.gg/Cppx2vSPVP