Welcome to ramp’s documentation!
Ramp is a python package for rapid machine learning prototyping.
It provides a simple, declarative syntax
for exploring features, algorithms and transformations quickly and efficiently.
At its core it’s a unified pandas-based framework for working with
existing python machine learning and statistics libraries (scikit-learn, rpy2, etc.).
- 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.