source: src/main/java/weka/gui/visualize/ThresholdVisualizePanel.java @ 17

/*
 *    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.
 */

/*
 *    ThresholdVisualizePanel.java
 *    Copyright (C) 2003 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.gui.visualize;

import weka.classifiers.Classifier;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.evaluation.EvaluationUtils;
import weka.classifiers.evaluation.ThresholdCurve;
import weka.core.FastVector;
import weka.core.Instances;
import weka.core.SingleIndex;
import weka.core.Utils;

import java.awt.BorderLayout;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.awt.event.WindowAdapter;
import java.awt.event.WindowEvent;
import java.io.BufferedReader;
import java.io.FileReader;

import javax.swing.BorderFactory;
import javax.swing.JFrame;
import javax.swing.border.TitledBorder;

/** 
 * This panel is a VisualizePanel, with the added ablility to display the
 * area under the ROC curve if an ROC curve is chosen.
 *
 * @author Dale Fletcher (dale@cs.waikato.ac.nz)
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision: 5928 $
 */
public class ThresholdVisualizePanel 
  extends VisualizePanel {

  /** for serialization */
  private static final long serialVersionUID = 3070002211779443890L;

  /** The string to add to the Plot Border. */
  private String m_ROCString="";
 
  /** Original border text */
  private String m_savePanelBorderText;

  /**
   * default constructor
   */
  public ThresholdVisualizePanel() {
    super();

    // Save the current border text
    TitledBorder tb=(TitledBorder) m_plotSurround.getBorder();
    m_savePanelBorderText = tb.getTitle();
  }
  
  /**
   * Set the string with ROC area
   * @param str ROC area string to add to border
   */  
  public void setROCString(String str) {
    m_ROCString=str;
  }

  /**
   * This extracts the ROC area string 
   * @return ROC area string 
   */
  public String getROCString() {
    return m_ROCString;
  }

  /**
   * This overloads VisualizePanel's setUpComboBoxes to add 
   * ActionListeners to watch for when the X/Y Axis comboboxes
   * are changed. 
   * @param inst a set of instances with data for plotting
   */
  public void setUpComboBoxes(Instances inst) {
    super.setUpComboBoxes(inst);

    m_XCombo.addActionListener(new ActionListener() {
        public void actionPerformed(ActionEvent e) {
          setBorderText();
        }
    });
    m_YCombo.addActionListener(new ActionListener() {
        public void actionPerformed(ActionEvent e) {
          setBorderText();
        }
    });

    // Just in case the default is ROC
    setBorderText();
  }

  /**
   * This checks the current selected X/Y Axis comboBoxes to see if 
   * an ROC graph is selected. If so, add the ROC area string to the
   * plot border, otherwise display the original border text.
   */
  private void setBorderText() {

    String xs = m_XCombo.getSelectedItem().toString();
    String ys = m_YCombo.getSelectedItem().toString();

    if (xs.equals("X: False Positive Rate (Num)") && ys.equals("Y: True Positive Rate (Num)"))   {
        m_plotSurround.setBorder((BorderFactory.createTitledBorder(m_savePanelBorderText+" "+m_ROCString)));
    } else
        m_plotSurround.setBorder((BorderFactory.createTitledBorder(m_savePanelBorderText))); 
  }

  /**
   * displays the previously saved instances
   * 
   * @param insts       the instances to display
   * @throws Exception  if display is not possible
   */
  protected void openVisibleInstances(Instances insts) throws Exception {
    super.openVisibleInstances(insts);

    setROCString(
        "(Area under ROC = " 
        + Utils.doubleToString(ThresholdCurve.getROCArea(insts), 4) + ")");
    
    setBorderText();
  }
  
  /**
   * Starts the ThresholdVisualizationPanel with parameters from the command line. <p/>
   * 
   * Valid options are: <p/>
   *  -h <br/>
   *  lists all the commandline parameters <p/>
   *  
   *  -t file <br/>
   *  Dataset to process with given classifier. <p/>
   *  
   *  -W classname <br/>
   *  Full classname of classifier to run.<br/>
   *  Options after '--' are passed to the classifier. <br/>
   *  (default weka.classifiers.functions.Logistic) <p/>
   *  
   *  -r number <br/>
   *  The number of runs to perform (default 2). <p/>
   *  
   *  -x number <br/>
   *  The number of Cross-validation folds (default 10). <p/>
   *  
   *  -l file <br/>
   *  Previously saved threshold curve ARFF file. <p/>
   *
   * @param args optional commandline parameters
   */
  public static void main(String [] args) {
    Instances           inst;
    Classifier          classifier;
    int                 runs;
    int                 folds;
    String              tmpStr;
    boolean             compute;
    Instances           result;
    String[]            options;
    SingleIndex         classIndex;
    SingleIndex         valueIndex;
    int                 seed;
    
    inst       = null;
    classifier = null;
    runs       = 2;
    folds      = 10;
    compute    = true;
    result     = null;
    classIndex = null;
    valueIndex = null;
    seed       = 1;
    
    try {
      // help?
      if (Utils.getFlag('h', args)) {
        System.out.println("\nOptions for " + ThresholdVisualizePanel.class.getName() + ":\n");
        System.out.println("-h\n\tThis help.");
        System.out.println("-t <file>\n\tDataset to process with given classifier.");
        System.out.println("-c <num>\n\tThe class index. first and last are valid, too (default: last).");
        System.out.println("-C <num>\n\tThe index of the class value to get the the curve for (default: first).");
        System.out.println("-W <classname>\n\tFull classname of classifier to run.\n\tOptions after '--' are passed to the classifier.\n\t(default: weka.classifiers.functions.Logistic)");
        System.out.println("-r <number>\n\tThe number of runs to perform (default: 1).");
        System.out.println("-x <number>\n\tThe number of Cross-validation folds (default: 10).");
        System.out.println("-S <number>\n\tThe seed value for randomizing the data (default: 1).");
        System.out.println("-l <file>\n\tPreviously saved threshold curve ARFF file.");
        return;
      }
      
      // regular options
      tmpStr = Utils.getOption('l', args);
      if (tmpStr.length() != 0) {
        result = new Instances(new BufferedReader(new FileReader(tmpStr)));
        compute = false;
      }
      
      if (compute) {
        tmpStr = Utils.getOption('r', args);
        if (tmpStr.length() != 0)
          runs = Integer.parseInt(tmpStr);
        else
          runs = 1;
        
        tmpStr = Utils.getOption('x', args);
        if (tmpStr.length() != 0)
          folds = Integer.parseInt(tmpStr);
        else
          folds = 10;
        
        tmpStr = Utils.getOption('S', args);
        if (tmpStr.length() != 0)
          seed = Integer.parseInt(tmpStr);
        else
          seed = 1;
        
        tmpStr = Utils.getOption('t', args);
        if (tmpStr.length() != 0) {
          inst = new Instances(new BufferedReader(new FileReader(tmpStr)));
          inst.setClassIndex(inst.numAttributes() - 1);
        }
        
        tmpStr = Utils.getOption('W', args);
        if (tmpStr.length() != 0) {
          options = Utils.partitionOptions(args);
        }
        else {
          tmpStr = weka.classifiers.functions.Logistic.class.getName();
          options = new String[0];
        }
        classifier = AbstractClassifier.forName(tmpStr, options);
        
        tmpStr = Utils.getOption('c', args);
        if (tmpStr.length() != 0)
          classIndex = new SingleIndex(tmpStr);
        else
          classIndex = new SingleIndex("last");
        
        tmpStr = Utils.getOption('C', args);
        if (tmpStr.length() != 0)
          valueIndex = new SingleIndex(tmpStr);
        else
          valueIndex = new SingleIndex("first");
      }
      
      // compute if necessary
      if (compute) {
        if (classIndex != null) {
          classIndex.setUpper(inst.numAttributes() - 1);
          inst.setClassIndex(classIndex.getIndex());
        }
        else {
          inst.setClassIndex(inst.numAttributes() - 1);
        }
        
        if (valueIndex != null) {
          valueIndex.setUpper(inst.classAttribute().numValues() - 1);
        }
        
        ThresholdCurve tc = new ThresholdCurve();
        EvaluationUtils eu = new EvaluationUtils();
        FastVector predictions = new FastVector();
        for (int i = 0; i < runs; i++) {
          eu.setSeed(seed + i);
          predictions.appendElements(eu.getCVPredictions(classifier, inst, folds));
        }
        
        if (valueIndex != null)
          result = tc.getCurve(predictions, valueIndex.getIndex());
        else
          result = tc.getCurve(predictions);
      }
      
      // setup GUI
      ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
      vmc.setROCString("(Area under ROC = " + 
          Utils.doubleToString(ThresholdCurve.getROCArea(result), 4) + ")");
      if (compute)     
        vmc.setName(
            result.relationName() 
            + ". (Class value " + inst.classAttribute().value(valueIndex.getIndex()) + ")");
      else
        vmc.setName(
            result.relationName()
            + " (display only)");
      PlotData2D tempd = new PlotData2D(result);
      tempd.setPlotName(result.relationName());
      tempd.addInstanceNumberAttribute();
      vmc.addPlot(tempd);
      
      String plotName = vmc.getName(); 
      final JFrame jf = new JFrame("Weka Classifier Visualize: "+plotName);
      jf.setSize(500,400);
      jf.getContentPane().setLayout(new BorderLayout());
      
      jf.getContentPane().add(vmc, BorderLayout.CENTER);
      jf.addWindowListener(new WindowAdapter() {
        public void windowClosing(WindowEvent e) {
          jf.dispose();
        }
      });
      
      jf.setVisible(true);
    }
    catch (Exception e) {
      e.printStackTrace();
    }
  }
}