Metadata-Version: 1.1
Name: featureforge
Version: 0.1.3
Summary: A library to build and test machine learning features
Home-page: https://github.com/machinalis/featureforge
Author: Rafael Carrascosa, Daniel Moisset, Javier Mansilla
Author-email: rcarrascosa@machinalis.com
License: UNKNOWN
Description: Feature Forge
        =============
        
        This library provides a set of tools that can be useful in many machine
        learning applications (classification, clustering, regression, etc.), and
        particularly helpful if you use scikit-learn (although this can work if
        you have a different algorithm).
        
        Most machine learning problems involve an step of feature definition and
        preprocessing. Feature Forge helps you with:
        
         * Defining and documenting features
         * Testing your features against specified cases and against randomly generated
           cases (stress-testing). This helps you making your application more robust
           against invalid/misformatted input data. This also helps you checking that
           low-relevance results when doing feature analysis is actually because the
           feature is bad, and not because there's a slight bug in your feature code.
         * Evaluating your features on a data set, producing a feature evaluation
           matrix. The evaluator has a robust mode that allows you some tolerance both
           for invalid data and buggy features.
        
        Installation
        ------------
        
        Just `pip install featureforge`. On pip 1.5.x you will need to put the --process-dependecy-links flag
        
        Documentation
        -------------
        
        Documentation is available at http://feature-forge.readthedocs.org/en/latest/
        
        Contact information
        -------------------
        
        Feature Forge is © 2014 Machinalis (http://www.machinalis.com/). Its primary
        authors are:
        
         * Javier Mansilla <jmansilla@machinalis.com> (jmansilla at github)
         * Daniel Moisset <dmoisset@machinalis.com> (dmoisset at github)
         * Rafael Carrascosa <rcarrascosa@machinalis.com> (rafacarrascosa at github)
        
        Any contributions or suggestions are welcome, the official channel for this is
        submitting github pull requests or issues.
        
        Changelog
        ---------
        0.1.3:
            - Added support for running and generating stats for experiments
        
        0.1.2:
            - Fixing installer dependencies
        
        0.1.1:
            - Added support for python 3
            - Added support for bag-of-words features
        
        0.1:
            - Initial release
        
Keywords: machine learning,scikit,scikit-learn,sklearn,features,testing,vectorization,preprocessing
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Intended Audience :: Developers
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Testing
