Skip to main content

Incremental learning written in C++ exposed in Python

Project description

ml-rapids: Incremental learning written in C++ exposed in Python and NodeJS

ml-rapids implements incremental learning methods in C++ and exposes them via SWIG in Python and NodeJS.

Incremental learning methods:

  • Classification
    • Majority Class
    • Naive Bayes
    • Logistic Regression
    • Perceptron
    • VFDT (Very Fast Decision Trees) aka Hoeffding Trees
    • HAT (Hoeffding Adaptive Trees)
    • Bagging
  • Regression
    • /

All the methods implement sklearn incremantal learner interface (includes fit, partial_fit and predict methods).

Future plans

Streaming random forest on top of Hoeffding trees will be implemented.

The library will be exposed via pypi and npm packages.

Python:

  • pip install ml-rapids

NodeJS:

  • npm install ml-rapids

Development

Development notes can be read here.

Acknowledgements

ml-rapids is developed by AILab at Jozef Stefan Institute.

This repository is based strongly on streamDM-cpp.

Project has received funding from European Union's Horizon 2020 Research and Innovation Programme under the Grant Agreement 776115 (PerceptiveSentinel).

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ml-rapids-0.0.1.2.tar.gz (152.6 kB view hashes)

Uploaded Source

Built Distribution

ml_rapids-0.0.1.2-cp38-cp38-macosx_10_9_x86_64.whl (301.0 kB view hashes)

Uploaded CPython 3.8 macOS 10.9+ x86-64

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page