A Python package for fitting Quinlan's Cubist regression model
Inspired by the R wrapper for cubist https://github.com/topepo/Cubist
Taking inspiration from the R wrapper this is what needs to be done
- Get cubist compiled on own machine so we have binary available
- Understand the inputs the binary require. The training dataset needs to Conform to an expected format. May need to write python code that converts Pandas dataframe to this.
- Write python code that forks off cubist process with correct arguments And files
- Write interpreter that translates cubists model definition to executable Python code.
- Write pypi package that bundles all this
- Enhance package to compile cubist on users machine
- Make python translation performant by using scipy or numpy
- Adapt api to conform to scikit learn
- Submit package to scikit learn
links: https://www.linkedin.com/pulse/machine-learning-example-r-using-cubist-kirk-mettler http://rulequest.com/cubist-info.html
Help welcome