tutorials.featureselection.TutorialWekaAttributeSelection.java Source code

Java tutorial

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Here is the source code for tutorials.featureselection.TutorialWekaAttributeSelection.java

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package tutorials.featureselection;

/**
 * This file is part of the Java Machine Learning Library
 * 
 * The Java Machine Learning Library is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 * 
 * The Java Machine Learning Library is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 * 
 * You should have received a copy of the GNU General Public License
 * along with the Java Machine Learning Library; if not, write to the Free Software
 * Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
 * 
 * Copyright (c) 2006-2010, Thomas Abeel
 * 
 * Project: http://java-ml.sourceforge.net/
 * 
 */
import java.io.File;
import java.io.IOException;

import net.sf.javaml.core.Dataset;
import net.sf.javaml.tools.data.FileHandler;
import net.sf.javaml.tools.weka.WekaAttributeSelection;
import weka.attributeSelection.ASEvaluation;
import weka.attributeSelection.ASSearch;
import weka.attributeSelection.GainRatioAttributeEval;
import weka.attributeSelection.Ranker;

/**
 * Tutorial how to use the Bridge to WEKA AS Evaluation , AS Search and
 * Evaluator algorithms in Java-ML
 * 
 * 
 * @author Irwan Krisna
 */
public class TutorialWekaAttributeSelection {

    public static void main(String[] args) throws IOException {
        /* Load data */
        Dataset data = FileHandler.loadDataset(new File("devtools/data/iris.data"), 4, ",");
        /* Create a AS Evaluation algorithm */
        ASEvaluation eval = new GainRatioAttributeEval();
        /* Create a Weka's AS Search algorithm */
        ASSearch search = new Ranker();
        /* Wrap Wekas' Algorithms in bridge */
        WekaAttributeSelection wekaattrsel = new WekaAttributeSelection(eval, search);
        /*
         * to apply algorithm to the data set and generate the new data based on
         * the given parameters
         */
        wekaattrsel.build(data);
        /* to retrieve the number of attributes */
        System.out.println("Total number of attributes:  " + wekaattrsel.noAttributes());
        /* to display all the rank and score for each attribute */
        for (int i = 0; i < wekaattrsel.noAttributes() - 1; i++) {
            System.out.println(
                    "Attribute  " + i + "  Ranks  " + wekaattrsel.rank(i) + " and Scores " + wekaattrsel.score(i));
        }

    }

}