skfeaturellm 0.0.4

LLM-powered feature engineering with scikit-learn API compatibility

SKFeatureLLM

SKFeatureLLM is a Python library that brings the power of Large Language Models (LLMs) to feature engineering for tabular data, wrapped in a familiar scikit-learn–style API. The library aims to leverage LLMs' capabilities to automatically generate and implement meaningful features for your machine learning tasks.

📑 Table of Contents

• SKFeatureLLM
  • 📑 Table of Contents
  • 🌟 Key Features
  • 📦 Installation
  • 📄 License
  • 🤝 Contributing
    • Development Setup
    • Code Style
  • 👤 Author
  • 📚 Documentation

🌟 Key Features

• 🤖 LLM-powered feature engineering
• 🔌 Model-agnostic: works with any LLM provider (OpenAI, Anthropic, etc.)
• 🛠 Scikit-learn compatible API
• 📊 Comprehensive feature evaluation and reporting
• 🎯 Support for both supervised and unsupervised feature engineering

📦 Installation

To install the latest release of SKFeatureLLM from PyPI:

pip install skfeaturellm

This will install the library and its core dependencies for end users.

✅ Running Tests

To run the test suite, ensure pytest is installed and execute:

poetry run pytest

Tests are located in the tests/ directory and cover the core functionality of SKFeatureLLM.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

We welcome contributions! Here's how you can help:

1. Report Bugs: If you find a bug, please open an issue with a detailed description.
2. Suggest Features: Have an idea for a new feature? Open an issue to discuss it.
3. Submit Pull Requests: We love PRs! Here's how to submit one:
   • Fork the repository
   • Create a new branch for your feature
   • Make your changes
   • Submit a pull request

Development Setup

1. Clone the repository:

git clone https://github.com/yourusername/skfeaturellm.git
cd skfeaturellm

2. Install development dependencies:

pip install -e ".[dev]"

3. Run tests:

pytest

4. Format code:

black .
isort .

Code Style

We use:

• Black for code formatting
• isort for import sorting
• pylint for linting
• mypy for type checking

Please ensure your code passes all checks before submitting a PR.

👤 Author

• Roberto Corti - GitHub
• Stefano Polo - GitHub

📚 Documentation

Our documentation is hosted on Read the Docs and includes:

• 📖 User Guide - Learn how to use SKFeatureLLM effectively
• 🔍 API Reference - Detailed documentation of all classes and methods
• 🎯 Examples - Practical examples for different use cases
• 📊 Feature Engineering Guide - Best practices for feature engineering with LLMs

For any questions or issues, please open an issue on our GitHub repository.