Metadata-Version: 1.0
Name: skll
Version: 0.9.4
Summary: SciKit-Learn Laboratory provides a number of utilities to make it simpler to run common scikit-learn experiments with pre-generated features.
Home-page: http://github.com/EducationalTestingService/skll
Author: Daniel Blanchard
Author-email: dblanchard@ets.org
License: GPL
Description: SciKit-Learn Laboratory
        -----------------------
        
        .. image:: https://api.travis-ci.org/EducationalTestingService/skll.png
           :alt: Build status
           :target: https://travis-ci.org/EducationalTestingService/skll
        
        .. image:: https://coveralls.io/repos/EducationalTestingService/skll/badge.png?branch=master
            :target: https://coveralls.io/r/EducationalTestingService/skll
        
        .. image:: https://pypip.in/d/skll/badge.png
           :target: https://crate.io/packages/skll
           :alt: PyPI downloads
        
        .. image:: https://pypip.in/v/skll/badge.png
           :target: https://crate.io/packages/skll
           :alt: Latest version on PyPI
        
        .. image:: https://d2weczhvl823v0.cloudfront.net/EducationalTestingService/skll/trend.png
           :alt: Bitdeli badge
           :target: https://bitdeli.com/free
        
        This Python package provides utilities to make it easier to run
        machine learning experiments with scikit-learn.
        
        Command-line Interface
        ~~~~~~~~~~~~~~~~~~~~~~
        
        ``run_experiment`` is a command-line utility for running a series of learners on
        datasets specified in a configuration file. For more information about using
        run_experiment (including a quick example), go
        `here <https://skll.readthedocs.org/en/latest/run_experiment.html>`__.
        
        Python API
        ~~~~~~~~~~
        
        If you just want to avoid writing a lot of boilerplate learning code, you can
        use our simple Python API. The main way you'll want to use the API is through
        the ``load_examples`` function and the ``Learner`` class. For more details on
        how to simply train, test, cross-validate, and run grid search on a variety of
        scikit-learn models see
        `the documentation <https://skll.readthedocs.org/en/latest/index.html>`__.
        
        A Note on Pronunciation
        ~~~~~~~~~~~~~~~~~~~~~~~
        
        SciKit-Learn Laboratory (SKLL) is pronounced "skull": that's where the learning
        happens.
        
        Requirements
        ~~~~~~~~~~~~
        
        -  Python 2.7+
        -  `scikit-learn <http://scikit-learn.org/stable/>`__
        -  `six <https://pypi.python.org/pypi/six>`__
        -  `PrettyTable <http://pypi.python.org/pypi/PrettyTable>`__
        -  `Grid Map <http://pypi.python.org/pypi/gridmap>`__ (only required if you plan
           to run things in parallel on a DRMAA-compatible cluster)
        
        Changelog
        ~~~~~~~~~
        
        -  v0.9.4
        
           +  Documentation fixes
           +  Added requirements.txt to manifest to fix broken PyPI release tarball.
        
        -  v0.9.3
        
           +  Fixed bug with merging feature sets that used to cause a crash.
           +  If you're running scikit-learn 0.14+, we use their StandardScaler, since
              the bug fix we include in FixedStandardScaler is in there.
           +  Unit tests all pass again
           +  Lots of little things related to using travis (which do not affect users)
        
        -  v0.9.2
        
           +  Fixed example.cfg path issue. Updated some documentation.
           +  Made path in make_example_iris_data.py consistent with the updated one
              in example.cfg
        
        -  v0.9.1
        
           +  Fixed bug where classification experiments would raise an error about class
              labels not being floats
           +  Updated documentation to include quick example for run_experiment.
        
Keywords: learning scikit-learn
Platform: UNKNOWN
