skfeaturellm 0.0.2

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.

🌟 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

🛠 Development

1. Clone the repository

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

2. Install dependencies

poetry install

3. Activate the virtual environment

poetry env use python3 && poetry install && source $(poetry env info --path)/bin/activate

✅ 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

Contributions are welcome! Please feel free to submit a Pull Request.

👤 Author

• Roberto Corti - GitHub
• Stefano Polo - GitHub

Example Usage

from skfeaturellm.feature_engineer import LLMFeatureEngineer

# Initialize the feature engineer
llm_feature_engineer = LLMFeatureEngineer()

# Fit and transform the data
llm_feature_engineer.fit(X)
X_transformed = llm_feature_engineer.transform(X)

print(X_transformed)

This snippet demonstrates how to initialize the LLMFeatureEngineer, fit it to a DataFrame X, and transform the data to include new features generated by the LLM.