Metadata-Version: 1.1
Name: liac-arff
Version: 2.0.2
Summary: A module for read and write ARFF files in Python.
Home-page: http://inf.ufrgs.br/~rppereira/arff
Author: Renato de Pontes Pereira
Author-email: renato.ppontes@gmail.com
License: MIT License
Download-URL: https://github.com/renatopp/liac-arff
Description: =========
        LIAC-ARFF
        =========
        
        The liac-arff module implements functions to read and write ARFF files in
        Python. It was created in the Connectionist Artificial Intelligence Laboratory
        (LIAC), which takes place at the Federal University of Rio Grande do Sul 
        (UFRGS), in Brazil.
        
        ARFF (Attribute-Relation File Format) is an file format specially created for
        describe datasets which are used commonly for machine learning experiments and
        softwares. This file format was created to be used in Weka, the best 
        representative software for machine learning automated experiments.
        
        You can clone the `arff-datasets <https://github.com/renatopp/arff-datasets>`_ 
        repository for a large set of ARFF files.
        
        --------
        Features
        --------
        
        - Read and write ARFF files using python built-in structures, such dictionaries
          and lists;
        - Supports the following attribute types: NUMERIC, REAL, INTEGER, STRING, and
          NOMINAL;
        - Has an interface similar to other built-in modules such as ``json``, or 
          ``zipfile``;
        - Supports read and write the descriptions of files;
        - Supports missing values and names with spaces;
        - Supports unicode values and names;
        - Fully compatible with Python 2.6+ and Python 3.4+;
        - Under `MIT License <http://opensource.org/licenses/MIT>`_
        
        --------------
        How To Install
        --------------
        
        Via pip::
        
            $ pip install liac-arff
        
        Via easy_install::
        
            $ easy_install liac-arff
        
        Manually::
        
            $ python setup.py install
        
        
        -------------
        Documentation
        -------------
        
        For a complete description of the module, consult the official documentation at
        http://packages.python.org/liac-arff/ with mirror in
        http://inf.ufrgs.br/~rppereira/arff/index.html
        
        
        -----
        Usage
        -----
        
        You can read an ARFF file as follows::
        
            >>> import arff
            >>> data = arff.load(open('wheater.arff', 'rb'))
        
        Which results in::
        
            >>> data
            {
                u'attributes': [
                    (u'outlook', [u'sunny', u'overcast', u'rainy']),
                    (u'temperature', u'REAL'),
                    (u'humidity', u'REAL'),
                    (u'windy', [u'TRUE', u'FALSE']),
                    (u'play', [u'yes', u'no'])],
                u'data': [
                    [u'sunny', 85.0, 85.0, u'FALSE', u'no'],
                    [u'sunny', 80.0, 90.0, u'TRUE', u'no'],
                    [u'overcast', 83.0, 86.0, u'FALSE', u'yes'],
                    [u'rainy', 70.0, 96.0, u'FALSE', u'yes'],
                    [u'rainy', 68.0, 80.0, u'FALSE', u'yes'],
                    [u'rainy', 65.0, 70.0, u'TRUE', u'no'],
                    [u'overcast', 64.0, 65.0, u'TRUE', u'yes'],
                    [u'sunny', 72.0, 95.0, u'FALSE', u'no'],
                    [u'sunny', 69.0, 70.0, u'FALSE', u'yes'],
                    [u'rainy', 75.0, 80.0, u'FALSE', u'yes'],
                    [u'sunny', 75.0, 70.0, u'TRUE', u'yes'],
                    [u'overcast', 72.0, 90.0, u'TRUE', u'yes'],
                    [u'overcast', 81.0, 75.0, u'FALSE', u'yes'],
                    [u'rainy', 71.0, 91.0, u'TRUE', u'no']
                ],
                u'description': u'',
                u'relation': u'weather'
            }
        
        You can write an ARFF file with this structure::
        
            >>> print arff.dumps(data)
            @RELATION weather
        
            @ATTRIBUTE outlook {sunny, overcast, rainy}
            @ATTRIBUTE temperature REAL
            @ATTRIBUTE humidity REAL
            @ATTRIBUTE windy {TRUE, FALSE}
            @ATTRIBUTE play {yes, no}
        
            @DATA
            sunny,85.0,85.0,FALSE,no
            sunny,80.0,90.0,TRUE,no
            overcast,83.0,86.0,FALSE,yes
            rainy,70.0,96.0,FALSE,yes
            rainy,68.0,80.0,FALSE,yes
            rainy,65.0,70.0,TRUE,no
            overcast,64.0,65.0,TRUE,yes
            sunny,72.0,95.0,FALSE,no
            sunny,69.0,70.0,FALSE,yes
            rainy,75.0,80.0,FALSE,yes
            sunny,75.0,70.0,TRUE,yes
            overcast,72.0,90.0,TRUE,yes
            overcast,81.0,75.0,FALSE,yes
            rainy,71.0,91.0,TRUE,no
            %
            %
            %
        
        
        Contributors
        ------------
        
        - `Nate Moseley (FinalDoom) <https://github.com/FinalDoom>`_
        - `Tarek Amr (gr33ndata) <https://github.com/gr33ndata>`_
        - `Simon (M3t0r) <https://github.com/M3t0r>`_
        - `Gonzalo Almeida (flecox) <https://github.com/flecox>`_
        - `André Nordbø (AndyNor) <http://andynor.net>`_
        - `Niedakh <https://github.com/niedakh>`_
        - `Zichen Wang (wangz10) <https://github.com/wangz10>`_
        - `Matthias Feurer (mfeurer) <https://github.com/mfeurer>`_
        - `Hongjoo Lee (midnightradio) <https://github.com/midnightradio>`_
        
        Project Page
        ------------
        
        https://github.com/renatopp/liac-arff
        
Keywords: arff weka parser liac python
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: MacOS X
Classifier: Environment :: Win32 (MS Windows)
Classifier: Environment :: X11 Applications
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
