https://github.com/dr-saad-la/ruod

Rust Outlier detection library with Rust as the backend and with Python API
https://github.com/dr-saad-la/ruod

Last synced: 4 months ago
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Rust Outlier detection library with Rust as the backend and with Python API

• Host: GitHub
• URL: https://github.com/dr-saad-la/ruod
• Owner: dr-saad-la
• License: mit
• Created: 2024-08-20T00:19:30.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-08-20T05:22:40.000Z (about 1 year ago)
• Last Synced: 2025-05-19T00:38:36.724Z (5 months ago)
• Language: Rust
• Size: 14.6 KB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Rust Outlier Detection (Ruod)

Rust Outlier detection library with Rust as the backend and with Python API. 

**RuOD** is a planned high-performance library for outlier detection, implemented in Rust with a Python API. The goal of this library is to provide a fast, lightweight solution for detecting outliers in large datasets without any external dependencies. RuOD will be designed to cater to real-time and large-scale applications, offering a suite of state-of-the-art algorithms accessible through both Rust and Python.

## Vision

- **High Performance**: Leverage Rust’s speed and memory safety to create a library that can handle large-scale outlier detection tasks efficiently.

- **No Dependencies**: Implement the core functionality purely in Rust to avoid external dependencies, ensuring a lightweight and portable solution.

- **Python Integration**: Provide a seamless Python API, making the library accessible to the Python community without sacrificing performance.

- **Scalability**: Design the library to scale effectively across different data sizes and types, making it suitable for diverse applications.

## Planned Features

- **Isolation Forest (IForest)**: Efficiently isolates observations by randomly selecting features and split values, providing a robust method for outlier detection.

- **Local Outlier Factor (LOF)**: Identifies anomalies by comparing the local density of a point to the densities of its neighbors, making it suitable for datasets with varying density.

- **Principal Component Analysis (PCA)**: Detects outliers by reducing the dimensionality of data, highlighting points that deviate significantly from principal components.

- **Robust Principal Component Analysis (RPCA)**: Decomposes the data into low-rank and sparse components, with the sparse matrix highlighting outliers.

## Future Additions

- **Additional Algorithms**: Plan to include other outlier detection methods like DBSCAN, One-Class SVM, and more to provide a comprehensive toolkit.

- **Extensive Documentation**: Develop thorough documentation, including a detailed API reference, tutorials, and examples to help users understand and effectively use the library.

- **Benchmarking**: Integrate benchmarking tools to compare the performance of RuOD with other libraries, ensuring that it meets the highest standards of efficiency.

- **CI/CD Integration**: Implement continuous integration and deployment pipelines to ensure that every update is stable, well-tested, and easy to deploy.

## Roadmap

1. **Initial Development**:

    - Implement core outlier detection algorithms in Rust.

    - Set up basic Python bindings using PyO3.

    - Create a simple command-line interface for testing and demonstration.

2. **Enhancements**:

    - Optimize the performance of the initial algorithms.

    - Expand the library with additional detection methods.

    - Develop comprehensive documentation and usage guides.

3. **Release**:

    - Package the library for release on crates.io (Rust) and PyPI (Python).

    - Promote community contributions and feedback.

4. **Maintenance and Growth**:

    - Continuously improve the library based on user feedback.

    - Add new features and algorithms as needed.

    - Maintain high standards of performance and code quality.

## Contribution Plans

Contributions will be welcome once the initial version of the library is released. We plan to encourage community involvement to help shape RuOD into a robust and versatile tool. Contributions could include:

- **New Algorithms**: Implementing new outlier detection methods.

- **Optimization**: Enhancing the performance of existing algorithms.

- **Documentation**: Improving the clarity and comprehensiveness of the documentation.

- **Testing**: Adding tests and improving coverage to ensure the reliability of the library.

## License

RuOD will be licensed under the MIT License, making it open and accessible for both personal and commercial use. See the [LICENSE](LICENSE) file for more details. 

## To Do

- [ ] Implement core algorithms (IForest, LOF, PCA, RPCA).

- [ ] Develop Python API using PyO3.

- [ ] Create initial documentation and usage examples.

- [ ] Set up continuous integration for testing.

- [ ] Prepare for initial release on crates.io and PyPI.

- [ ] Begin community engagement for contributions and feedback.

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**Note:** This README outlines the plan and vision for the RuOD library. The project is currently in the planning stages, with development yet to begin.