Ramp is a python library for rapid prototyping of machine learning solutions. It's a light-weight pandas-based machine learning framework pluggable with existing python machine learning and statistics tools (scikit-learn, rpy2, etc.). Ramp provides a simple, declarative syntax for exploring features, algorithms and transformations quickly and efficiently.

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.

Information

• Website: http://ramp.readthedocs.org/
• GitHub: https://github.com/kvh/ramp
• Documentation: http://ramp.readthedocs.org/
• Getting started: http://ramp.readthedocs.org/en/latest/intro.html
• Example: http://www.kenvanharen.com/2012/11/getting-started-with-ramp-detecting.html