deepbridge 0.1.3

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DeepBridge

DeepBridge is a Python library for streamlining machine learning model validation and distillation processes. It provides tools to manage experiments, validate models, and create more efficient versions of complex models.

Installation

You can install DeepBridge using pip:

pip install deepbridge

Or install from source:

git clone https://github.com/DeepBridge-Validation/DeepBridge.git
cd deepbridge
pip install -e .

Quick Start

Model Validation

from deepbridge.model_validation import ModelValidation

# Create experiment
experiment = ModelValidation("my_experiment")

# Add data
experiment.add_data(X_train, y_train, X_test, y_test)

# Add and save model
experiment.add_model(model, "model_v1")
experiment.save_model("model_v1")

Model Distillation

from deepbridge.model_distiller import ModelDistiller

# Create and train distilled model
distiller = ModelDistiller(model_type="gbm")
distiller.fit(X=features, probas=predictions)

# Make predictions
predictions = distiller.predict(X_new)

Using the CLI

# Create experiment
deepbridge validation create my_experiment --path ./experiments

# Train distilled model
deepbridge distill train gbm predictions.csv features.csv -s ./models

Features

• Model Validation

  • Experiment management
  • Metric tracking
  • Model versioning
  • Surrogate model support

• Model Distillation

  • Multiple model types (GBM, XGBoost, MLP)
  • Performance metrics
  • Optimization options
  • Easy model persistence

• Command Line Interface

  • Intuitive commands
  • Rich output formatting
  • Multiple data format support

Requirements

• Python 3.8+
• Dependencies:

  numpy>=1.24.0
  pandas>=2.0.0
  scikit-learn>=1.2.0
  xgboost>=1.7.0
  scipy>=1.10.0
  typer[all]>=0.9.0
  rich>=13.0.0
  

Documentation

For detailed documentation, visit our documentation page.

Example Notebooks

Check out our example notebooks for detailed usage scenarios:

• Basic Model Validation
• Model Distillation Techniques
• CLI Usage Examples

Contributing

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

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

Development Setup

# Clone the repository
git clone https://github.com/DeepBridge-Validation/DeepBridge.git
cd deepbridge

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate     # Windows

# Install development dependencies
pip install -r requirements-dev.txt

Running Tests

pytest tests/

License

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

Citation

If you use DeepBridge in your research, please cite:

@software{deepbridge2024,
  title = {DeepBridge: A Python Library for Model Validation and Distillation},
  author = {Team DeepBridge},
  year = {2025},
  url = {https://github.com/DeepBridge-Validation/DeepBridge}
}

Acknowledgments

• Thanks to all contributors
• Inspired by best practices in model optimization
• Built with modern Python tools and libraries

Contact

• GitHub Issues: For bugs and feature requests
• Email: [e-mail]