Metadata-Version: 1.1
Name: EvoDAG
Version: 0.2.1
Summary: Evolving Directed Acyclic Graph
Home-page: https://github.com/mgraffg/EvoDAG
Author: Mario Graff
Author-email: mgraffg@ieee.org
License: UNKNOWN
Description: |Build Status|
        
        |PyPI version|
        
        |Coverage Status|
        
        EvoDAG
        ======
        
        Evolving Directed Acyclic Graph (EvoDAG) is a steady-state Genetic
        Programming system with tournament selection. The main characteristic of
        EvoDAG is that the genetic operation is performed at the root. EvoDAG
        was inspired by the geometric semantic crossover proposed by `Alberto
        Moraglio <https://scholar.google.com.mx/citations?user=0y4XRI0AAAAJ&hl=en&oi=ao>`__
        *et al.* and the implementation performed by `Leonardo
        Vanneschi <https://scholar.google.com.mx/citations?user=uR5K07QAAAAJ&hl=en&oi=ao>`__
        *et al*.
        
        Example using command line
        ==========================
        
        Let us assume one wants to create a classifier of iris dataset. The
        first step is to download the dataset from the UCI Machine Learning
        Repository
        
        .. code:: bash
        
            curl -O https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
        
        In order to train the EvoDAG using a population of 10 individuals, using
        early stopping to 11, sampling 100 different parameter configurations,
        creating an ensemble of 12, and using 4 cores then the following command
        is used:
        
        .. code:: bash
        
            ~/.local/bin/EvoDAG -e 10 -p 11 -r 100 -u 4 -n 12 iris.data
        
        The EvoDAG ensemble is stored in iris.evodag.gz.
        
        Now that the ensemble has been initialized one can predict a test set
        and store the output in file called output.csv using the following
        command.
        
        .. code:: bash
        
            ~/.local/bin/EvoDAG -m iris.evodag.gz -t iris.data -o output.csv
        
        Install EvoDAG
        --------------
        
        -  Install using pip
           ``pip install EvoDAG``
        
        Using the source code
        ~~~~~~~~~~~~~~~~~~~~~
        
        -  Clone the repository
           ``git clone  https://github.com/mgraffg/EvoDAG.git``
        -  Install the package as usual
           ``python setup.py install``
        -  To install only for the use then
           ``python setup.py install --user``
        
        .. |Build Status| image:: https://travis-ci.org/mgraffg/EvoDAG.svg?branch=master
           :target: https://travis-ci.org/mgraffg/EvoDAG
        .. |PyPI version| image:: https://badge.fury.io/py/EvoDAG.svg
           :target: https://badge.fury.io/py/EvoDAG
        .. |Coverage Status| image:: https://coveralls.io/repos/github/mgraffg/EvoDAG/badge.svg?branch=master
           :target: https://coveralls.io/github/mgraffg/EvoDAG?branch=master
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
