Ramp is a python module for rapid prototyping of machine learning solutions. It is essentially a pandas wrapper around various python machine learning and statistics libraries (scikit-learn, rpy2, etc.), providing a simple, declarative syntax for exploring features, algorithms and transformations quickly and efficiently.

Documentation: http://ramp.readthedocs.org

Why Ramp?

• Complex feature transformations

  Chain and combine features:

  Normalize(Log('x'))
  Interactions([Log('x1'), (F('x2') + F('x3')) / 2])
  

  Reduce feature dimension:

  DimensionReduction([F('x%d'%i) for i in range(100)], decomposer=PCA(n_components=3))
  

  Incorporate residuals or predictions to blend with other models:

  Residuals(config_model1) + Predictions(config_model2)
  

  Any feature that uses the target ("y") variable will automatically respect the current training and test sets.

• Caching

  Ramp caches and stores on disk in fast HDF5 format (or elsewhere if you want) all features and models it computes, so nothing is recomputed unnecessarily. Results are stored and can be retrieved, compared, blended, and reused between runs.

• Easy extensibility

  Ramp has a simple API, allowing you to plug in estimators from scikit-learn, rpy2 and elsewhere, or easily build your own feature transformations, metrics, feature selectors, reporters, or estimators.

Getting started with Ramp: Classifying insults

Ramp is very alpha currently, so expect bugs, bug fixes and API changes.

• Numpy
• Scipy
• Pandas
• PyTables
• Sci-kit Learn