GenerateROC.java

/*
 *    This program 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.
 *
 *    This program 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 this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 * GenerateROC.java
 * Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
 */

package wekaexamples.gui.visualize;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.evaluation.ThresholdCurve;
import weka.core.Instances;
import weka.core.Utils;
import weka.core.converters.ConverterUtils.DataSource;
import weka.gui.visualize.PlotData2D;
import weka.gui.visualize.ThresholdVisualizePanel;

import java.awt.BorderLayout;
import java.util.Random;

import javax.swing.JFrame;

/**
 * Generates and displays a ROC curve from a dataset. Uses a default 
 * NaiveBayes to generate the ROC data.
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision$
 */
public class GenerateROC {

  /**
   * Takes one argument: dataset in ARFF format (expects class to 
   * be last attribute).
   * 
   * @param args	the commandline arguments
   * @throws Exception	if something goes wrong
   */
  public static void main(String[] args) throws Exception {
    // load data
    Instances data = DataSource.read(args[0]);
    data.setClassIndex(data.numAttributes() - 1);

    // evaluate classifier
    Classifier cl = new NaiveBayes();
    Evaluation eval = new Evaluation(data);
    eval.crossValidateModel(cl, data, 10, new Random(1));

    // generate curve
    ThresholdCurve tc = new ThresholdCurve();
    int classIndex = 0;
    Instances curve = tc.getCurve(eval.predictions(), classIndex);

    // plot curve
    ThresholdVisualizePanel tvp = new ThresholdVisualizePanel();
    tvp.setROCString("(Area under ROC = " + 
	Utils.doubleToString(ThresholdCurve.getROCArea(curve), 4) + ")");
    tvp.setName(curve.relationName());
    PlotData2D plotdata = new PlotData2D(curve);
    plotdata.setPlotName(curve.relationName());
    plotdata.addInstanceNumberAttribute();
    // specify which points are connected
    boolean[] cp = new boolean[curve.numInstances()];
    for (int n = 1; n < cp.length; n++)
      cp[n] = true;
    plotdata.setConnectPoints(cp);
    // add plot
    tvp.addPlot(plotdata);

    // display curve
    final JFrame jf = new JFrame("WEKA ROC: " + tvp.getName());
    jf.setSize(500,400);
    jf.getContentPane().setLayout(new BorderLayout());
    jf.getContentPane().add(tvp, BorderLayout.CENTER);
    jf.setDefaultCloseOperation(JFrame.DISPOSE_ON_CLOSE);
    jf.setVisible(true);
  }
}