Skip to main content

A module for read and write ARFF files in Python.

Project description

https://travis-ci.org/renatopp/liac-arff.svg

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 repository for a large set of ARFF files.

Features

  • Read and write ARFF files using python built-in structures, such dictionaries and lists;

  • Supports scipy.sparse.coo and lists of dictionaries as used by SVMLight

  • 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 2.7+ and Python 3.3+;

  • Under MIT License

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/docs/liac-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

Project Page

https://github.com/renatopp/liac-arff

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

liac-arff-2.2.0.tar.gz (12.8 kB view details)

Uploaded Source

File details

Details for the file liac-arff-2.2.0.tar.gz.

File metadata

  • Download URL: liac-arff-2.2.0.tar.gz
  • Upload date:
  • Size: 12.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for liac-arff-2.2.0.tar.gz
Algorithm Hash digest
SHA256 2f850665f595a1065fc3ad950f222dbeb623f6fe0cc85437d50d189ac3ca5261
MD5 30b321f50e5198646cf10fad431b617b
BLAKE2b-256 ba850313183b90769cf11a812c633e2296d7d4a26f1571d7fc4694fbfc2cab96

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page