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.). 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. Contents: .. toctree:: :maxdepth: 2 intro Data contexts configurations features stores estimators reporters Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search`