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
🌟 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:
- Report Bugs: If you find a bug, please open an issue with a detailed description.
- Suggest Features: Have an idea for a new feature? Open an issue to discuss it.
- 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
- Clone the repository:
git clone https://github.com/yourusername/skfeaturellm.git
cd skfeaturellm
- Install development dependencies:
pip install -e ".[dev]"
- Run tests:
pytest
- 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
📚 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.
1 maintainer
Metadata
1 maintainer
Release files for skfeaturellm 0.0.15
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