Ramp is a python library for rapid machine learning prototyping. It provides a simple, declarative syntax for exploring features, algorithms and transformations quickly and efficiently. The library leverages pandas, which provides high-performance, easy-to-use data structures and data analysis tools, as well as various python machine learning and statistics libraries (scikit-learn, rpy2, etc.). Some features:
- Fast caching and persistence of all intermediate and final calculations – nothing is recomputed unnecessarily.
- Advanced training and preparation logic. Ramp respects the current training set, even when using complex trained features and blended predictions, and also tracks the given preparation set (the x values used in feature preparation – e.g. the mean and stdev used for feature normalization.)
- A growing library of feature transformations, metrics and estimators. Ramp’s simple API allows for easy extension.
kvh has a nice blog post introducing Ramp. I’m looking forward to experimenting with it.