Metadata-Version: 1.0
Name: ramp
Version: 0.1
Summary: Rapid machine learning prototyping
Home-page: http://github.com/kvh/ramp
Author: Ken Van Haren
Author-email: kvh@science.io
License: BSD
Description: Ramp - Rapid Machine Learning Prototyping
        ========
        
        Ramp is a python module for rapid prototyping of machine learning
        solutions. It is essentially a [pandas](http://pandas.pydata.org)
        wrapper around various python machine learning and statistics libraries
        ([scikit-learn](http://scikit-learn.org), [rpy2](http://rpy.sourceforge.net/rpy2.html), etc.),
        providing a simple, declarative syntax for
        exploring features, algorithms and transformations quickly and
        efficiently.
        
        Documentation: http://ramp.readthedocs.org
        
        ## Complex feature transformations
        Chain and combine features:
        
            Normalize(Log('x'))
            Interactions([Log('x1'), (F('x2') + F('x3')) / 2])
        
        Reduce feature dimension:
        
            SVDDimensionReduction([F('x%d'%i) for i in range(100)], n_keep=20)
        
        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.
        
        
        ## Quick example
        [Getting started with Ramp: Classifying insults](http://www.kenvanharen.com/2012/11/getting-started-with-ramp-detecting.html)
        
        ## Status
        Ramp is very alpha currently, so expect bugs, bug fixes and API changes.
        
        ## Requirements
         * Numpy
         * Scipy    
         * Pandas
         * PyTables
         * Sci-kit Learn
        
Keywords: machine learning data analysis statistics mining
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
