ais-gloss 0.1.0

AI-Scientist GLOSS Recommendation System

AIS-GLOSS

AI-Scientist GLOSS Recommendation System

🚀 Features

• Automated Model Training: Train and compare multiple ML models automatically
• GLOSS Algorithm: Find optimal points using intelligent search
• Comprehensive Visualization: Beautiful plots and detailed logging
• Easy to Use: Simple API for complex ML tasks
• Extensible: Add your own models and metrics

📦 Installation

Using pip

pip install ais_gloss

Using conda

conda env create -f environment.yml
conda activate ais_gloss

From source

git clone https://github.com/zbc0315/ais_gloss.git
cd ais_gloss
pip install -e .

🎯 Quick Start

Automated Model Training

from ais_gloss import AutoTrainer

# Initialize trainer
trainer = AutoTrainer(task='regression', metric='R2')

# Load data
trainer.load_data('data.csv', 
                 x_columns=['feature1', 'feature2'],
                 y_columns='target')

# Train all models
trainer.train_all_models()

# Optimize and get best model
trainer.optimize_hyperparameters()
trainer.train_final_model()

# Make predictions
predictions = trainer.predict(X_new)

GLOSS Recommendation

from ais_gloss import GLOSSRecommender

# Define model and comparator
gloss = GLOSSRecommender(
    model_func=your_model,
    y_comparator=lambda y1, y2: y1 > y2
)

# Find optimal points
recommendations = gloss.run_gloss(
    x_ranges={'x1': (0, 10, 0.5), 'x2': (0, 10, 0.5)},
    n_global=5,
    n_local=5,
    visualize=True
)

📚 Documentation

Full documentation is available at: https://zbc0315.github.io/ais_gloss/

• Installation Guide
• Quick Start
• Tutorials
• API Reference
• Examples

🎓 Examples

Check out the examples directory for complete working examples:

• Basic Regression
• Classification
• GLOSS Optimization

🛠️ Requirements

• Python >= 3.8
• numpy >= 1.21.0
• pandas >= 1.3.0
• scikit-learn >= 1.0.0
• torch >= 1.10.0
• matplotlib >= 3.4.0
• seaborn >= 0.11.0

🤝 Contributing

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

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

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

📧 Contact

Your Name - [e-mail]

Project Link: https://github.com/zbc0315/ais_gloss

🙏 Acknowledgments

• scikit-learn team for the excellent ML library
• PyTorch team for the deep learning framework
• All contributors to this project

📊 Citation

If you use AIS-GLOSS in your research, please cite:

XXX