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ClassifierPanel.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.
 */

/*
 *    ClassifierPanel.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.gui.explorer;

import weka.classifiers.Classifier;
import weka.classifiers.CostMatrix;
import weka.classifiers.Evaluation;
import weka.classifiers.Sourcable;
import weka.classifiers.evaluation.CostCurve;
import weka.classifiers.evaluation.MarginCurve;
import weka.classifiers.evaluation.ThresholdCurve;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Drawable;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.OptionHandler;
import weka.core.Range;
import weka.core.SerializedObject;
import weka.core.Utils;
import weka.core.Version;
import weka.core.converters.IncrementalConverter;
import weka.core.converters.Loader;
import weka.core.converters.ConverterUtils.DataSource;
import weka.gui.CostMatrixEditor;
import weka.gui.ExtensionFileFilter;
import weka.gui.GenericObjectEditor;
import weka.gui.Logger;
import weka.gui.PropertyDialog;
import weka.gui.PropertyPanel;
import weka.gui.ResultHistoryPanel;
import weka.gui.SaveBuffer;
import weka.gui.SetInstancesPanel;
import weka.gui.SysErrLog;
import weka.gui.TaskLogger;
import weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent;
import weka.gui.explorer.Explorer.CapabilitiesFilterChangeListener;
import weka.gui.explorer.Explorer.ExplorerPanel;
import weka.gui.explorer.Explorer.LogHandler;
import weka.gui.graphvisualizer.BIFFormatException;
import weka.gui.graphvisualizer.GraphVisualizer;
import weka.gui.treevisualizer.PlaceNode2;
import weka.gui.treevisualizer.TreeVisualizer;
import weka.gui.visualize.Plot2D;
import weka.gui.visualize.PlotData2D;
import weka.gui.visualize.ThresholdVisualizePanel;
import weka.gui.visualize.VisualizePanel;
import weka.gui.visualize.plugins.VisualizePlugin;

import java.awt.BorderLayout;
import java.awt.Dimension;
import java.awt.FlowLayout;
import java.awt.Font;
import java.awt.GridBagConstraints;
import java.awt.GridBagLayout;
import java.awt.GridLayout;
import java.awt.Insets;
import java.awt.Point;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.awt.event.InputEvent;
import java.awt.event.MouseAdapter;
import java.awt.event.MouseEvent;
import java.beans.PropertyChangeEvent;
import java.beans.PropertyChangeListener;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.InputStream;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.OutputStream;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.Random;
import java.util.Vector;
import java.util.zip.GZIPInputStream;
import java.util.zip.GZIPOutputStream;

import javax.swing.BorderFactory;
import javax.swing.ButtonGroup;
import javax.swing.DefaultComboBoxModel;
import javax.swing.JButton;
import javax.swing.JCheckBox;
import javax.swing.JComboBox;
import javax.swing.JFileChooser;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JMenu;
import javax.swing.JMenuItem;
import javax.swing.JOptionPane;
import javax.swing.JPanel;
import javax.swing.JPopupMenu;
import javax.swing.JRadioButton;
import javax.swing.JScrollPane;
import javax.swing.JTextArea;
import javax.swing.JTextField;
import javax.swing.JViewport;
import javax.swing.SwingConstants;
import javax.swing.event.ChangeEvent;
import javax.swing.event.ChangeListener;
import javax.swing.filechooser.FileFilter;

/** 
 * This panel allows the user to select and configure a classifier, set the
 * attribute of the current dataset to be used as the class, and evaluate
 * the classifier using a number of testing modes (test on the training data,
 * train/test on a percentage split, n-fold cross-validation, test on a
 * separate split). The results of classification runs are stored in a result
 * history so that previous results are accessible.
 *
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @author Mark Hall (mhall@cs.waikato.ac.nz)
 * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
 * @version $Revision: 1.111 $
 */
00138 public class ClassifierPanel 
  extends JPanel
  implements CapabilitiesFilterChangeListener, ExplorerPanel, LogHandler {
   
  /** for serialization */
00143   static final long serialVersionUID = 6959973704963624003L;

  /** the parent frame */
00146   protected Explorer m_Explorer = null;

  /** The filename extension that should be used for model files */
00149   public static String MODEL_FILE_EXTENSION = ".model";

  /** Lets the user configure the classifier */
00152   protected GenericObjectEditor m_ClassifierEditor =
    new GenericObjectEditor();

  /** The panel showing the current classifier selection */
00156   protected PropertyPanel m_CEPanel = new PropertyPanel(m_ClassifierEditor);
  
  /** The output area for classification results */
00159   protected JTextArea m_OutText = new JTextArea(20, 40);

  /** The destination for log/status messages */
00162   protected Logger m_Log = new SysErrLog();

  /** The buffer saving object for saving output */
00165   SaveBuffer m_SaveOut = new SaveBuffer(m_Log, this);

  /** A panel controlling results viewing */
00168   protected ResultHistoryPanel m_History = new ResultHistoryPanel(m_OutText);

  /** Lets the user select the class column */
00171   protected JComboBox m_ClassCombo = new JComboBox();

  /** Click to set test mode to cross-validation */
00174   protected JRadioButton m_CVBut = new JRadioButton("Cross-validation");

  /** Click to set test mode to generate a % split */
00177   protected JRadioButton m_PercentBut = new JRadioButton("Percentage split");

  /** Click to set test mode to test on training data */
00180   protected JRadioButton m_TrainBut = new JRadioButton("Use training set");

  /** Click to set test mode to a user-specified test set */
00183   protected JRadioButton m_TestSplitBut =
    new JRadioButton("Supplied test set");

  /** Check to save the predictions in the results list for visualizing
      later on */
00188   protected JCheckBox m_StorePredictionsBut = 
    new JCheckBox("Store predictions for visualization");

  /** Check to output the model built from the training data */
00192   protected JCheckBox m_OutputModelBut = new JCheckBox("Output model");

  /** Check to output true/false positives, precision/recall for each class */
00195   protected JCheckBox m_OutputPerClassBut =
    new JCheckBox("Output per-class stats");

  /** Check to output a confusion matrix */
00199   protected JCheckBox m_OutputConfusionBut =
    new JCheckBox("Output confusion matrix");

  /** Check to output entropy statistics */
00203   protected JCheckBox m_OutputEntropyBut =
    new JCheckBox("Output entropy evaluation measures");

  /** Check to output text predictions */
00207   protected JCheckBox m_OutputPredictionsTextBut =
    new JCheckBox("Output predictions");
  
  /** Lists indices for additional attributes to output */
00211   protected JTextField m_OutputAdditionalAttributesText =
    new JTextField("", 10);

  /** Label for the text field with additional attributes in the output */
00215   protected JLabel m_OutputAdditionalAttributesLab = 
    new JLabel("Output additional attributes");
  
  /** the range of attributes to output */
00219   protected Range m_OutputAdditionalAttributesRange = null;
  
  /** Check to evaluate w.r.t a cost matrix */
00222   protected JCheckBox m_EvalWRTCostsBut =
    new JCheckBox("Cost-sensitive evaluation");

  /** for the cost matrix */
00226   protected JButton m_SetCostsBut = new JButton("Set...");

  /** Label by where the cv folds are entered */
00229   protected JLabel m_CVLab = new JLabel("Folds", SwingConstants.RIGHT);

  /** The field where the cv folds are entered */
00232   protected JTextField m_CVText = new JTextField("10", 3);

  /** Label by where the % split is entered */
00235   protected JLabel m_PercentLab = new JLabel("%", SwingConstants.RIGHT);

  /** The field where the % split is entered */
00238   protected JTextField m_PercentText = new JTextField("66", 3);

  /** The button used to open a separate test dataset */
00241   protected JButton m_SetTestBut = new JButton("Set...");

  /** The frame used to show the test set selection panel */
00244   protected JFrame m_SetTestFrame;

  /** The frame used to show the cost matrix editing panel */
00247   protected PropertyDialog m_SetCostsFrame;

  /**
   * Alters the enabled/disabled status of elements associated with each
   * radio button
   */
00253   ActionListener m_RadioListener = new ActionListener() {
    public void actionPerformed(ActionEvent e) {
      updateRadioLinks();
    }
  };

  /** Button for further output/visualize options */
00260   JButton m_MoreOptions = new JButton("More options...");

  /** User specified random seed for cross validation or % split */
00263   protected JTextField m_RandomSeedText = new JTextField("1", 3);
  
  /** the label for the random seed textfield */
00266   protected JLabel m_RandomLab = new JLabel("Random seed for XVal / % Split", 
                                  SwingConstants.RIGHT);

  /** Whether randomization is turned off to preserve order */
00270   protected JCheckBox m_PreserveOrderBut = new JCheckBox("Preserve order for % Split");

  /** Whether to output the source code (only for classifiers importing Sourcable) */
00273   protected JCheckBox m_OutputSourceCode = new JCheckBox("Output source code");

  /** The name of the generated class (only applicable to Sourcable schemes) */
00276   protected JTextField m_SourceCodeClass = new JTextField("WekaClassifier", 10);
  
  /** Click to start running the classifier */
00279   protected JButton m_StartBut = new JButton("Start");

  /** Click to stop a running classifier */
00282   protected JButton m_StopBut = new JButton("Stop");

  /** Stop the class combo from taking up to much space */
00285   private Dimension COMBO_SIZE = new Dimension(150, m_StartBut
                                     .getPreferredSize().height);

  /** The cost matrix editor for evaluation costs */
00289   protected CostMatrixEditor m_CostMatrixEditor = new CostMatrixEditor();

  /** The main set of instances we're playing with */
00292   protected Instances m_Instances;

  /** The loader used to load the user-supplied test set (if any) */
00295   protected Loader m_TestLoader;
  
  /** A thread that classification runs in */
00298   protected Thread m_RunThread;

  /** The current visualization object */
00301   protected VisualizePanel m_CurrentVis = null;

  /** Filter to ensure only model files are selected */  
00304   protected FileFilter m_ModelFilter =
    new ExtensionFileFilter(MODEL_FILE_EXTENSION, "Model object files");

  /** The file chooser for selecting model files */
  protected JFileChooser m_FileChooser 
00309     = new JFileChooser(new File(System.getProperty("user.dir")));

  /* Register the property editors we need */
  static {
     GenericObjectEditor.registerEditors();
  }
  
  /**
   * Creates the classifier panel
   */
00319   public ClassifierPanel() {

    // Connect / configure the components
    m_OutText.setEditable(false);
    m_OutText.setFont(new Font("Monospaced", Font.PLAIN, 12));
    m_OutText.setBorder(BorderFactory.createEmptyBorder(5, 5, 5, 5));
    m_OutText.addMouseListener(new MouseAdapter() {
      public void mouseClicked(MouseEvent e) {
      if ((e.getModifiers() & InputEvent.BUTTON1_MASK)
          != InputEvent.BUTTON1_MASK) {
        m_OutText.selectAll();
      }
      }
    });
    m_History.setBorder(BorderFactory.createTitledBorder("Result list (right-click for options)"));
    m_ClassifierEditor.setClassType(Classifier.class);
    m_ClassifierEditor.setValue(ExplorerDefaults.getClassifier());
    m_ClassifierEditor.addPropertyChangeListener(new PropertyChangeListener() {
      public void propertyChange(PropertyChangeEvent e) {
      repaint();
      }
    });

    m_ClassCombo.setToolTipText("Select the attribute to use as the class");
    m_TrainBut.setToolTipText("Test on the same set that the classifier"
                        + " is trained on");
    m_CVBut.setToolTipText("Perform a n-fold cross-validation");
    m_PercentBut.setToolTipText("Train on a percentage of the data and"
                        + " test on the remainder");
    m_TestSplitBut.setToolTipText("Test on a user-specified dataset");
    m_StartBut.setToolTipText("Starts the classification");
    m_StopBut.setToolTipText("Stops a running classification");
    m_StorePredictionsBut.
      setToolTipText("Store predictions in the result list for later "
                 +"visualization");
    m_OutputModelBut
      .setToolTipText("Output the model obtained from the full training set");
    m_OutputPerClassBut.setToolTipText("Output precision/recall & true/false"
                            + " positives for each class");
    m_OutputConfusionBut
      .setToolTipText("Output the matrix displaying class confusions");
    m_OutputEntropyBut
      .setToolTipText("Output entropy-based evaluation measures");
    m_EvalWRTCostsBut
      .setToolTipText("Evaluate errors with respect to a cost matrix");
    m_OutputPredictionsTextBut
      .setToolTipText("Include the predictions in the output buffer");
    m_OutputAdditionalAttributesText.setToolTipText(
      "Outputs additional attributes for the predictions, 'first' and 'last' are valid indices.");
    m_RandomLab.setToolTipText("The seed value for randomization");
    m_RandomSeedText.setToolTipText(m_RandomLab.getToolTipText());
    m_PreserveOrderBut.setToolTipText("Preserves the order in a percentage split");
    m_OutputSourceCode.setToolTipText(
      "Whether to output the built classifier as Java source code");
    m_SourceCodeClass.setToolTipText("The classname of the built classifier");

    m_FileChooser.setFileFilter(m_ModelFilter);
    m_FileChooser.setFileSelectionMode(JFileChooser.FILES_ONLY);

    m_StorePredictionsBut.setSelected(ExplorerDefaults.getClassifierStorePredictionsForVis());
    m_OutputModelBut.setSelected(ExplorerDefaults.getClassifierOutputModel());
    m_OutputPerClassBut.setSelected(ExplorerDefaults.getClassifierOutputPerClassStats());
    m_OutputConfusionBut.setSelected(ExplorerDefaults.getClassifierOutputConfusionMatrix());
    m_EvalWRTCostsBut.setSelected(ExplorerDefaults.getClassifierCostSensitiveEval());
    m_OutputEntropyBut.setSelected(ExplorerDefaults.getClassifierOutputEntropyEvalMeasures());
    m_OutputPredictionsTextBut.setSelected(ExplorerDefaults.getClassifierOutputPredictions());
    m_OutputPredictionsTextBut.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
      m_OutputAdditionalAttributesText.setEnabled(m_OutputPredictionsTextBut.isSelected());
      }
    });
    m_OutputAdditionalAttributesText.setText(ExplorerDefaults.getClassifierOutputAdditionalAttributes());
    m_OutputAdditionalAttributesText.setEnabled(m_OutputPredictionsTextBut.isSelected());
    m_RandomSeedText.setText("" + ExplorerDefaults.getClassifierRandomSeed());
    m_PreserveOrderBut.setSelected(ExplorerDefaults.getClassifierPreserveOrder());
    m_OutputSourceCode.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
        m_SourceCodeClass.setEnabled(m_OutputSourceCode.isSelected());
      }
    });
    m_OutputSourceCode.setSelected(ExplorerDefaults.getClassifierOutputSourceCode());
    m_SourceCodeClass.setText(ExplorerDefaults.getClassifierSourceCodeClass());
    m_SourceCodeClass.setEnabled(m_OutputSourceCode.isSelected());
    m_ClassCombo.setEnabled(false);
    m_ClassCombo.setPreferredSize(COMBO_SIZE);
    m_ClassCombo.setMaximumSize(COMBO_SIZE);
    m_ClassCombo.setMinimumSize(COMBO_SIZE);

    m_CVBut.setSelected(true);
    // see "testMode" variable in startClassifier
    m_CVBut.setSelected(ExplorerDefaults.getClassifierTestMode() == 1);
    m_PercentBut.setSelected(ExplorerDefaults.getClassifierTestMode() == 2);
    m_TrainBut.setSelected(ExplorerDefaults.getClassifierTestMode() == 3);
    m_TestSplitBut.setSelected(ExplorerDefaults.getClassifierTestMode() == 4);
    m_PercentText.setText("" + ExplorerDefaults.getClassifierPercentageSplit());
    m_CVText.setText("" + ExplorerDefaults.getClassifierCrossvalidationFolds());
    updateRadioLinks();
    ButtonGroup bg = new ButtonGroup();
    bg.add(m_TrainBut);
    bg.add(m_CVBut);
    bg.add(m_PercentBut);
    bg.add(m_TestSplitBut);
    m_TrainBut.addActionListener(m_RadioListener);
    m_CVBut.addActionListener(m_RadioListener);
    m_PercentBut.addActionListener(m_RadioListener);
    m_TestSplitBut.addActionListener(m_RadioListener);
    m_SetTestBut.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
      setTestSet();
      }
    });
    m_EvalWRTCostsBut.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
      m_SetCostsBut.setEnabled(m_EvalWRTCostsBut.isSelected());
      if ((m_SetCostsFrame != null) 
          && (!m_EvalWRTCostsBut.isSelected())) {
        m_SetCostsFrame.setVisible(false);
      }
      }
    });
    m_CostMatrixEditor.setValue(new CostMatrix(1));
    m_SetCostsBut.setEnabled(m_EvalWRTCostsBut.isSelected());
    m_SetCostsBut.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
      m_SetCostsBut.setEnabled(false);
      if (m_SetCostsFrame == null) {
        m_SetCostsFrame = new PropertyDialog(m_CostMatrixEditor, 100, 100);
        m_SetCostsFrame.setTitle("Cost Matrix Editor");
        //  pd.setSize(250,150);
        m_SetCostsFrame.addWindowListener(new java.awt.event.WindowAdapter() {
          public void windowClosing(java.awt.event.WindowEvent p) {
            m_SetCostsBut.setEnabled(m_EvalWRTCostsBut.isSelected());
            if ((m_SetCostsFrame != null) 
              && (!m_EvalWRTCostsBut.isSelected())) {
            m_SetCostsFrame.setVisible(false);
            }
          }
        });
      }
      
      // do we need to change the size of the matrix?
      int classIndex = m_ClassCombo.getSelectedIndex();
      int numClasses = m_Instances.attribute(classIndex).numValues();
      if (numClasses != ((CostMatrix) m_CostMatrixEditor.getValue()).numColumns())
        m_CostMatrixEditor.setValue(new CostMatrix(numClasses));
      
      m_SetCostsFrame.setVisible(true);
      }
    });

    m_StartBut.setEnabled(false);
    m_StopBut.setEnabled(false);
    m_StartBut.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
      startClassifier();
      }
    });
    m_StopBut.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
      stopClassifier();
      }
    });
   
    m_ClassCombo.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
      int selected = m_ClassCombo.getSelectedIndex();
      if (selected != -1) {
        boolean isNominal = m_Instances.attribute(selected).isNominal();
        m_OutputPerClassBut.setEnabled(isNominal);
        m_OutputConfusionBut.setEnabled(isNominal);   
      }
      updateCapabilitiesFilter(m_ClassifierEditor.getCapabilitiesFilter());
      }
    });

    m_History.setHandleRightClicks(false);
    // see if we can popup a menu for the selected result
    m_History.getList().addMouseListener(new MouseAdapter() {
      public void mouseClicked(MouseEvent e) {
        if (((e.getModifiers() & InputEvent.BUTTON1_MASK)
             != InputEvent.BUTTON1_MASK) || e.isAltDown()) {
          int index = m_History.getList().locationToIndex(e.getPoint());
          if (index != -1) {
            String name = m_History.getNameAtIndex(index);
            visualize(name, e.getX(), e.getY());
          } else {
            visualize(null, e.getX(), e.getY());
          }
        }
      }
      });

    m_MoreOptions.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
      m_MoreOptions.setEnabled(false);
      JPanel moreOptionsPanel = new JPanel();
      moreOptionsPanel.setBorder(BorderFactory.createEmptyBorder(0, 5, 5, 5));
      moreOptionsPanel.setLayout(new GridLayout(11, 1));
      moreOptionsPanel.add(m_OutputModelBut);
      moreOptionsPanel.add(m_OutputPerClassBut);        
      moreOptionsPanel.add(m_OutputEntropyBut);   
      moreOptionsPanel.add(m_OutputConfusionBut);       
      moreOptionsPanel.add(m_StorePredictionsBut);
      moreOptionsPanel.add(m_OutputPredictionsTextBut);
      JPanel additionalAttsPanel = new JPanel(new FlowLayout(FlowLayout.LEFT));
      additionalAttsPanel.add(m_OutputAdditionalAttributesLab);
      additionalAttsPanel.add(m_OutputAdditionalAttributesText);
      moreOptionsPanel.add(additionalAttsPanel);
      JPanel costMatrixOption = new JPanel(new FlowLayout(FlowLayout.LEFT));
      costMatrixOption.add(m_EvalWRTCostsBut);
      costMatrixOption.add(m_SetCostsBut);
      moreOptionsPanel.add(costMatrixOption);
      JPanel seedPanel = new JPanel(new FlowLayout(FlowLayout.LEFT));
      seedPanel.add(m_RandomLab);
      seedPanel.add(m_RandomSeedText);
      moreOptionsPanel.add(seedPanel);
      moreOptionsPanel.add(m_PreserveOrderBut);
        JPanel sourcePanel = new JPanel(new FlowLayout(FlowLayout.LEFT));
        m_OutputSourceCode.setEnabled(m_ClassifierEditor.getValue() instanceof Sourcable);
        m_SourceCodeClass.setEnabled(m_OutputSourceCode.isEnabled() && m_OutputSourceCode.isSelected());
        sourcePanel.add(m_OutputSourceCode);
        sourcePanel.add(m_SourceCodeClass);
        moreOptionsPanel.add(sourcePanel);

      JPanel all = new JPanel();
      all.setLayout(new BorderLayout());  

      JButton oK = new JButton("OK");
      JPanel okP = new JPanel();
      okP.setBorder(BorderFactory.createEmptyBorder(5, 5, 5, 5));
      okP.setLayout(new GridLayout(1,1,5,5));
      okP.add(oK);

      all.add(moreOptionsPanel, BorderLayout.CENTER);
      all.add(okP, BorderLayout.SOUTH);
      
      final javax.swing.JFrame jf = 
        new javax.swing.JFrame("Classifier evaluation options");
      jf.getContentPane().setLayout(new BorderLayout());
      jf.getContentPane().add(all, BorderLayout.CENTER);
      jf.addWindowListener(new java.awt.event.WindowAdapter() {
        public void windowClosing(java.awt.event.WindowEvent w) {
          jf.dispose();
          m_MoreOptions.setEnabled(true);
        }
      });
      oK.addActionListener(new ActionListener() {
        public void actionPerformed(ActionEvent a) {
          m_MoreOptions.setEnabled(true);
          jf.dispose();
        }
      });
      jf.pack();
      jf.setLocation(m_MoreOptions.getLocationOnScreen());
      jf.setVisible(true);
      }
    });

    // Layout the GUI
    JPanel p1 = new JPanel();
    p1.setBorder(BorderFactory.createCompoundBorder(
             BorderFactory.createTitledBorder("Classifier"),
             BorderFactory.createEmptyBorder(0, 5, 5, 5)
             ));
    p1.setLayout(new BorderLayout());
    p1.add(m_CEPanel, BorderLayout.NORTH);

    JPanel p2 = new JPanel();
    GridBagLayout gbL = new GridBagLayout();
    p2.setLayout(gbL);
    p2.setBorder(BorderFactory.createCompoundBorder(
             BorderFactory.createTitledBorder("Test options"),
             BorderFactory.createEmptyBorder(0, 5, 5, 5)
             ));
    GridBagConstraints gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.WEST;
    gbC.gridy = 0;     gbC.gridx = 0;
    gbL.setConstraints(m_TrainBut, gbC);
    p2.add(m_TrainBut);

    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.WEST;
    gbC.gridy = 1;     gbC.gridx = 0;
    gbL.setConstraints(m_TestSplitBut, gbC);
    p2.add(m_TestSplitBut);

    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.EAST;
    gbC.fill = GridBagConstraints.HORIZONTAL;
    gbC.gridy = 1;     gbC.gridx = 1;    gbC.gridwidth = 2;
    gbC.insets = new Insets(2, 10, 2, 0);
    gbL.setConstraints(m_SetTestBut, gbC);
    p2.add(m_SetTestBut);

    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.WEST;
    gbC.gridy = 2;     gbC.gridx = 0;
    gbL.setConstraints(m_CVBut, gbC);
    p2.add(m_CVBut);

    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.EAST;
    gbC.fill = GridBagConstraints.HORIZONTAL;
    gbC.gridy = 2;     gbC.gridx = 1;
    gbC.insets = new Insets(2, 10, 2, 10);
    gbL.setConstraints(m_CVLab, gbC);
    p2.add(m_CVLab);

    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.EAST;
    gbC.fill = GridBagConstraints.HORIZONTAL;
    gbC.gridy = 2;     gbC.gridx = 2;  gbC.weightx = 100;
    gbC.ipadx = 20;
    gbL.setConstraints(m_CVText, gbC);
    p2.add(m_CVText);

    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.WEST;
    gbC.gridy = 3;     gbC.gridx = 0;
    gbL.setConstraints(m_PercentBut, gbC);
    p2.add(m_PercentBut);

    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.EAST;
    gbC.fill = GridBagConstraints.HORIZONTAL;
    gbC.gridy = 3;     gbC.gridx = 1;
    gbC.insets = new Insets(2, 10, 2, 10);
    gbL.setConstraints(m_PercentLab, gbC);
    p2.add(m_PercentLab);

    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.EAST;
    gbC.fill = GridBagConstraints.HORIZONTAL;
    gbC.gridy = 3;     gbC.gridx = 2;  gbC.weightx = 100;
    gbC.ipadx = 20;
    gbL.setConstraints(m_PercentText, gbC);
    p2.add(m_PercentText);


    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.WEST;
    gbC.fill = GridBagConstraints.HORIZONTAL;
    gbC.gridy = 4;     gbC.gridx = 0;  gbC.weightx = 100;
    gbC.gridwidth = 3;

    gbC.insets = new Insets(3, 0, 1, 0);
    gbL.setConstraints(m_MoreOptions, gbC);
    p2.add(m_MoreOptions);

    JPanel buttons = new JPanel();
    buttons.setLayout(new GridLayout(2, 2));
    buttons.add(m_ClassCombo);
    m_ClassCombo.setBorder(BorderFactory.createEmptyBorder(5, 5, 5, 5));
    JPanel ssButs = new JPanel();
    ssButs.setBorder(BorderFactory.createEmptyBorder(5, 5, 5, 5));
    ssButs.setLayout(new GridLayout(1, 2, 5, 5));
    ssButs.add(m_StartBut);
    ssButs.add(m_StopBut);

    buttons.add(ssButs);
    
    JPanel p3 = new JPanel();
    p3.setBorder(BorderFactory.createTitledBorder("Classifier output"));
    p3.setLayout(new BorderLayout());
    final JScrollPane js = new JScrollPane(m_OutText);
    p3.add(js, BorderLayout.CENTER);
    js.getViewport().addChangeListener(new ChangeListener() {
      private int lastHeight;
      public void stateChanged(ChangeEvent e) {
      JViewport vp = (JViewport)e.getSource();
      int h = vp.getViewSize().height; 
      if (h != lastHeight) { // i.e. an addition not just a user scrolling
        lastHeight = h;
        int x = h - vp.getExtentSize().height;
        vp.setViewPosition(new Point(0, x));
      }
      }
    });
    
    JPanel mondo = new JPanel();
    gbL = new GridBagLayout();
    mondo.setLayout(gbL);
    gbC = new GridBagConstraints();
    //    gbC.anchor = GridBagConstraints.WEST;
    gbC.fill = GridBagConstraints.HORIZONTAL;
    gbC.gridy = 0;     gbC.gridx = 0;
    gbL.setConstraints(p2, gbC);
    mondo.add(p2);
    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.NORTH;
    gbC.fill = GridBagConstraints.HORIZONTAL;
    gbC.gridy = 1;     gbC.gridx = 0;
    gbL.setConstraints(buttons, gbC);
    mondo.add(buttons);
    gbC = new GridBagConstraints();
    //gbC.anchor = GridBagConstraints.NORTH;
    gbC.fill = GridBagConstraints.BOTH;
    gbC.gridy = 2;     gbC.gridx = 0; gbC.weightx = 0;
    gbL.setConstraints(m_History, gbC);
    mondo.add(m_History);
    gbC = new GridBagConstraints();
    gbC.fill = GridBagConstraints.BOTH;
    gbC.gridy = 0;     gbC.gridx = 1;
    gbC.gridheight = 3;
    gbC.weightx = 100; gbC.weighty = 100;
    gbL.setConstraints(p3, gbC);
    mondo.add(p3);

    setLayout(new BorderLayout());
    add(p1, BorderLayout.NORTH);
    add(mondo, BorderLayout.CENTER);
  }

  
  /**
   * Updates the enabled status of the input fields and labels.
   */
00736   protected void updateRadioLinks() {
    
    m_SetTestBut.setEnabled(m_TestSplitBut.isSelected());
    if ((m_SetTestFrame != null) && (!m_TestSplitBut.isSelected())) {
      m_SetTestFrame.setVisible(false);
    }
    m_CVText.setEnabled(m_CVBut.isSelected());
    m_CVLab.setEnabled(m_CVBut.isSelected());
    m_PercentText.setEnabled(m_PercentBut.isSelected());
    m_PercentLab.setEnabled(m_PercentBut.isSelected());
  }

  /**
   * Sets the Logger to receive informational messages
   *
   * @param newLog the Logger that will now get info messages
   */
00753   public void setLog(Logger newLog) {

    m_Log = newLog;
  }

  /**
   * Tells the panel to use a new set of instances.
   *
   * @param inst a set of Instances
   */
00763   public void setInstances(Instances inst) {
    m_Instances = inst;

    String [] attribNames = new String [m_Instances.numAttributes()];
    for (int i = 0; i < attribNames.length; i++) {
      String type = "";
      switch (m_Instances.attribute(i).type()) {
      case Attribute.NOMINAL:
      type = "(Nom) ";
      break;
      case Attribute.NUMERIC:
      type = "(Num) ";
      break;
      case Attribute.STRING:
      type = "(Str) ";
      break;
      case Attribute.DATE:
      type = "(Dat) ";
      break;
      case Attribute.RELATIONAL:
      type = "(Rel) ";
      break;
      default:
      type = "(???) ";
      }
      attribNames[i] = type + m_Instances.attribute(i).name();
    }
    m_ClassCombo.setModel(new DefaultComboBoxModel(attribNames));
    if (attribNames.length > 0) {
      if (inst.classIndex() == -1)
      m_ClassCombo.setSelectedIndex(attribNames.length - 1);
      else
      m_ClassCombo.setSelectedIndex(inst.classIndex());
      m_ClassCombo.setEnabled(true);
      m_StartBut.setEnabled(m_RunThread == null);
      m_StopBut.setEnabled(m_RunThread != null);
    } else {
      m_StartBut.setEnabled(false);
      m_StopBut.setEnabled(false);
    }
  }

  /**
   * Sets the user test set. Information about the current test set
   * is displayed in an InstanceSummaryPanel and the user is given the
   * ability to load another set from a file or url.
   *
   */
00811   protected void setTestSet() {

    if (m_SetTestFrame == null) {
      final SetInstancesPanel sp = new SetInstancesPanel();

      if (m_TestLoader != null) {
        try {
          if (m_TestLoader.getStructure() != null)
            sp.setInstances(m_TestLoader.getStructure());
        } catch (Exception ex) {
          ex.printStackTrace();
        }
      }
      sp.addPropertyChangeListener(new PropertyChangeListener() {
      public void propertyChange(PropertyChangeEvent e) {
        m_TestLoader = sp.getLoader();
      }
      });
      // Add propertychangelistener to update m_TestLoader whenever
      // it changes in the settestframe
      m_SetTestFrame = new JFrame("Test Instances");
      sp.setParentFrame(m_SetTestFrame);   // enable Close-Button
      m_SetTestFrame.getContentPane().setLayout(new BorderLayout());
      m_SetTestFrame.getContentPane().add(sp, BorderLayout.CENTER);
      m_SetTestFrame.pack();
    }
    m_SetTestFrame.setVisible(true);
  }

  /**
   * Process a classifier's prediction for an instance and update a
   * set of plotting instances and additional plotting info. plotInfo
   * for nominal class datasets holds shape types (actual data points have
   * automatic shape type assignment; classifier error data points have
   * box shape type). For numeric class datasets, the actual data points
   * are stored in plotInstances and plotInfo stores the error (which is
   * later converted to shape size values)
   * @param toPredict the actual data point
   * @param classifier the classifier
   * @param eval the evaluation object to use for evaluating the classifier on
   * the instance to predict
   * @param plotInstances a set of plottable instances
   * @param plotShape additional plotting information (shape)
   * @param plotSize additional plotting information (size)
   */
00856   public static void processClassifierPrediction(Instance toPredict,
                                           Classifier classifier,
                                 Evaluation eval,
                                 Instances plotInstances,
                                 FastVector plotShape,
                                 FastVector plotSize) {
    try {
      double pred = eval.evaluateModelOnceAndRecordPrediction(classifier, 
                                                toPredict);

      if (plotInstances != null) {
        double [] values = new double[plotInstances.numAttributes()];
        for (int i = 0; i < plotInstances.numAttributes(); i++) {
          if (i < toPredict.classIndex()) {
            values[i] = toPredict.value(i);
          } else if (i == toPredict.classIndex()) {
            values[i] = pred;
            values[i+1] = toPredict.value(i);
            /* // if the class value of the instances to predict is missing then
            // set it to the predicted value
            if (toPredict.isMissing(i)) {
          values[i+1] = pred;
          } */
            i++;
          } else {
            values[i] = toPredict.value(i-1);
          }
        }

        plotInstances.add(new Instance(1.0, values));
        if (toPredict.classAttribute().isNominal()) {
          if (toPredict.isMissing(toPredict.classIndex()) 
              || Instance.isMissingValue(pred)) {
            plotShape.addElement(new Integer(Plot2D.MISSING_SHAPE));
          } else if (pred != toPredict.classValue()) {
            // set to default error point shape
            plotShape.addElement(new Integer(Plot2D.ERROR_SHAPE));
          } else {
            // otherwise set to constant (automatically assigned) point shape
            plotShape.addElement(new Integer(Plot2D.CONST_AUTOMATIC_SHAPE));
          }
          plotSize.addElement(new Integer(Plot2D.DEFAULT_SHAPE_SIZE));
        } else {
          // store the error (to be converted to a point size later)
          Double errd = null;
          if (!toPredict.isMissing(toPredict.classIndex()) && 
              !Instance.isMissingValue(pred)) {
            errd = new Double(pred - toPredict.classValue());
            plotShape.addElement(new Integer(Plot2D.CONST_AUTOMATIC_SHAPE));
          } else {
            // missing shape if actual class not present or prediction is missing
            plotShape.addElement(new Integer(Plot2D.MISSING_SHAPE));
          }
          plotSize.addElement(errd);
        }
      }
    } catch (Exception ex) {
      ex.printStackTrace();
    }
  }

  /**
   * Post processes numeric class errors into shape sizes for plotting
   * in the visualize panel
   * @param plotSize a FastVector of numeric class errors
   */
00922   private void postProcessPlotInfo(FastVector plotSize) {
    int maxpSize = 20;
    double maxErr = Double.NEGATIVE_INFINITY;
    double minErr = Double.POSITIVE_INFINITY;
    double err;
    
    for (int i = 0; i < plotSize.size(); i++) {
      Double errd = (Double)plotSize.elementAt(i);
      if (errd != null) {
      err = Math.abs(errd.doubleValue());
        if (err < minErr) {
        minErr = err;
      }
      if (err > maxErr) {
        maxErr = err;
      }
      }
    }
    
    for (int i = 0; i < plotSize.size(); i++) {
      Double errd = (Double)plotSize.elementAt(i);
      if (errd != null) {
      err = Math.abs(errd.doubleValue());
      if (maxErr - minErr > 0) {
        double temp = (((err - minErr) / (maxErr - minErr)) 
                   * maxpSize);
        plotSize.setElementAt(new Integer((int)temp), i);
      } else {
        plotSize.setElementAt(new Integer(1), i);
      }
      } else {
      plotSize.setElementAt(new Integer(1), i);
      }
    }
  }

  /**
   * Sets up the structure for the visualizable instances. This dataset
   * contains the original attributes plus the classifier's predictions
   * for the class as an attribute called "predicted+WhateverTheClassIsCalled".
   * @param trainInstances the instances that the classifier is trained on
   * @return a new set of instances containing one more attribute (predicted
   * class) than the trainInstances
   */
00966   public static Instances setUpVisualizableInstances(Instances trainInstances) {
    FastVector hv = new FastVector();
    Attribute predictedClass;

    Attribute classAt = trainInstances.attribute(trainInstances.classIndex());
    if (classAt.isNominal()) {
      FastVector attVals = new FastVector();
      for (int i = 0; i < classAt.numValues(); i++) {
      attVals.addElement(classAt.value(i));
      }
      predictedClass = new Attribute("predicted"+classAt.name(), attVals);
    } else {
      predictedClass = new Attribute("predicted"+classAt.name());
    }

    for (int i = 0; i < trainInstances.numAttributes(); i++) {
      if (i == trainInstances.classIndex()) {
      hv.addElement(predictedClass);
      }
      hv.addElement(trainInstances.attribute(i).copy());
    }
    return new Instances(trainInstances.relationName()+"_predicted", hv, 
                   trainInstances.numInstances());
  }

  /**
   * outputs the header for the predictions on the data
   * 
   * @param outBuff     the buffer to add the output to
   * @param inst  the data header
   * @param title the title to print
   */
00998   protected void printPredictionsHeader(StringBuffer outBuff, Instances inst, String title) {
    outBuff.append("=== Predictions on " + title + " ===\n\n");
    outBuff.append(" inst#,    actual, predicted, error");
    if (inst.classAttribute().isNominal()) {
      outBuff.append(", probability distribution");
    }
    if (m_OutputAdditionalAttributesRange != null) {
      outBuff.append(" (");
      boolean first = true;
      for (int i = 0; i < inst.numAttributes() - 1; i++) {
      if (m_OutputAdditionalAttributesRange.isInRange(i)) {
        if (!first)
          outBuff.append(",");
        else
          first = false;
        outBuff.append(inst.attribute(i).name());
      }
      }
      outBuff.append(")");
    }
    outBuff.append("\n");
  }
  
  /**
   * Starts running the currently configured classifier with the current
   * settings. This is run in a separate thread, and will only start if
   * there is no classifier already running. The classifier output is sent
   * to the results history panel.
   */
01027   protected void startClassifier() {

    if (m_RunThread == null) {
      synchronized (this) {
      m_StartBut.setEnabled(false);
      m_StopBut.setEnabled(true);
      }
      m_RunThread = new Thread() {
      public void run() {
        // Copy the current state of things
        m_Log.statusMessage("Setting up...");
        CostMatrix costMatrix = null;
        Instances inst = new Instances(m_Instances);
        DataSource source = null;
          Instances userTestStructure = null;
        // additional vis info (either shape type or point size)
        FastVector plotShape = new FastVector();
        FastVector plotSize = new FastVector();
        Instances predInstances = null;
       
        // for timing
        long trainTimeStart = 0, trainTimeElapsed = 0;

          try {
            if (m_TestLoader != null && m_TestLoader.getStructure() != null) {
              m_TestLoader.reset();
              source = new DataSource(m_TestLoader);
              userTestStructure = source.getStructure();
            }
          } catch (Exception ex) {
            ex.printStackTrace();
          }
        if (m_EvalWRTCostsBut.isSelected()) {
          costMatrix = new CostMatrix((CostMatrix) m_CostMatrixEditor
                              .getValue());
        }
        boolean outputModel = m_OutputModelBut.isSelected();
        boolean outputConfusion = m_OutputConfusionBut.isSelected();
        boolean outputPerClass = m_OutputPerClassBut.isSelected();
        boolean outputSummary = true;
          boolean outputEntropy = m_OutputEntropyBut.isSelected();
        boolean saveVis = m_StorePredictionsBut.isSelected();
        boolean outputPredictionsText = m_OutputPredictionsTextBut.isSelected();
        if (m_OutputAdditionalAttributesText.getText().equals("")) {
          m_OutputAdditionalAttributesRange = null;
        }
        else {
          m_OutputAdditionalAttributesRange = new Range(m_OutputAdditionalAttributesText.getText());
          m_OutputAdditionalAttributesRange.setUpper(inst.numAttributes() - 1);
        }

        String grph = null;

        int testMode = 0;
        int numFolds = 10;
          double percent = 66;
        int classIndex = m_ClassCombo.getSelectedIndex();
        Classifier classifier = (Classifier) m_ClassifierEditor.getValue();
        Classifier template = null;
        try {
          template = Classifier.makeCopy(classifier);
        } catch (Exception ex) {
          m_Log.logMessage("Problem copying classifier: " + ex.getMessage());
        }
        Classifier fullClassifier = null;
        StringBuffer outBuff = new StringBuffer();
        String name = (new SimpleDateFormat("HH:mm:ss - "))
        .format(new Date());
        String cname = classifier.getClass().getName();
        if (cname.startsWith("weka.classifiers.")) {
          name += cname.substring("weka.classifiers.".length());
        } else {
          name += cname;
        }
          String cmd = m_ClassifierEditor.getValue().getClass().getName();
          if (m_ClassifierEditor.getValue() instanceof OptionHandler)
            cmd += " " + Utils.joinOptions(((OptionHandler) m_ClassifierEditor.getValue()).getOptions());
        Evaluation eval = null;
        try {
          if (m_CVBut.isSelected()) {
            testMode = 1;
            numFolds = Integer.parseInt(m_CVText.getText());
            if (numFolds <= 1) {
            throw new Exception("Number of folds must be greater than 1");
            }
          } else if (m_PercentBut.isSelected()) {
            testMode = 2;
            percent = Double.parseDouble(m_PercentText.getText());
            if ((percent <= 0) || (percent >= 100)) {
            throw new Exception("Percentage must be between 0 and 100");
            }
          } else if (m_TrainBut.isSelected()) {
            testMode = 3;
          } else if (m_TestSplitBut.isSelected()) {
            testMode = 4;
            // Check the test instance compatibility
            if (source == null) {
            throw new Exception("No user test set has been specified");
            }
            if (!inst.equalHeaders(userTestStructure)) {
            throw new Exception("Train and test set are not compatible");
            }
              userTestStructure.setClassIndex(classIndex);
          } else {
            throw new Exception("Unknown test mode");
          }
          inst.setClassIndex(classIndex);

          // set up the structure of the plottable instances for 
          // visualization
            if (saveVis) {
              predInstances = setUpVisualizableInstances(inst);
              predInstances.setClassIndex(inst.classIndex()+1);
            } 

          // Output some header information
          m_Log.logMessage("Started " + cname);
          m_Log.logMessage("Command: " + cmd);
          if (m_Log instanceof TaskLogger) {
            ((TaskLogger)m_Log).taskStarted();
          }
          outBuff.append("=== Run information ===\n\n");
          outBuff.append("Scheme:       " + cname);
          if (classifier instanceof OptionHandler) {
            String [] o = ((OptionHandler) classifier).getOptions();
            outBuff.append(" " + Utils.joinOptions(o));
          }
          outBuff.append("\n");
          outBuff.append("Relation:     " + inst.relationName() + '\n');
          outBuff.append("Instances:    " + inst.numInstances() + '\n');
          outBuff.append("Attributes:   " + inst.numAttributes() + '\n');
          if (inst.numAttributes() < 100) {
            for (int i = 0; i < inst.numAttributes(); i++) {
            outBuff.append("              " + inst.attribute(i).name()
                         + '\n');
            }
          } else {
            outBuff.append("              [list of attributes omitted]\n");
          }

          outBuff.append("Test mode:    ");
          switch (testMode) {
            case 3: // Test on training
            outBuff.append("evaluate on training data\n");
            break;
            case 1: // CV mode
            outBuff.append("" + numFolds + "-fold cross-validation\n");
            break;
            case 2: // Percent split
            outBuff.append("split " + percent
                + "% train, remainder test\n");
            break;
            case 4: // Test on user split
            if (source.isIncremental())
              outBuff.append("user supplied test set: "
                  + " size unknown (reading incrementally)\n");
            else
              outBuff.append("user supplied test set: "
                  + source.getDataSet().numInstances() + " instances\n");
            break;
          }
            if (costMatrix != null) {
               outBuff.append("Evaluation cost matrix:\n")
               .append(costMatrix.toString()).append("\n");
            }
          outBuff.append("\n");
          m_History.addResult(name, outBuff);
          m_History.setSingle(name);
          
          // Build the model and output it.
          if (outputModel || (testMode == 3) || (testMode == 4)) {
            m_Log.statusMessage("Building model on training data...");

            trainTimeStart = System.currentTimeMillis();
            classifier.buildClassifier(inst);
            trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
          }

          if (outputModel) {
            outBuff.append("=== Classifier model (full training set) ===\n\n");
            outBuff.append(classifier.toString() + "\n");
            outBuff.append("\nTime taken to build model: " +
                       Utils.doubleToString(trainTimeElapsed / 1000.0,2)
                       + " seconds\n\n");
            m_History.updateResult(name);
            if (classifier instanceof Drawable) {
            grph = null;
            try {
              grph = ((Drawable)classifier).graph();
            } catch (Exception ex) {
            }
            }
            // copy full model for output
            SerializedObject so = new SerializedObject(classifier);
            fullClassifier = (Classifier) so.getObject();
          }
          
          switch (testMode) {
            case 3: // Test on training
            m_Log.statusMessage("Evaluating on training data...");
            eval = new Evaluation(inst, costMatrix);
            
            if (outputPredictionsText) {
            printPredictionsHeader(outBuff, inst, "training set");
            }

            for (int jj=0;jj<inst.numInstances();jj++) {
            processClassifierPrediction(inst.instance(jj), classifier,
                                  eval, predInstances, plotShape, 
                                  plotSize);
            
            if (outputPredictionsText) { 
              outBuff.append(predictionText(classifier, inst.instance(jj), jj+1));
            }
            if ((jj % 100) == 0) {
              m_Log.statusMessage("Evaluating on training data. Processed "
                              +jj+" instances...");
            }
            }
            if (outputPredictionsText) {
            outBuff.append("\n");
            } 
            outBuff.append("=== Evaluation on training set ===\n");
            break;

            case 1: // CV mode
            m_Log.statusMessage("Randomizing instances...");
            int rnd = 1;
            try {
            rnd = Integer.parseInt(m_RandomSeedText.getText().trim());
            // System.err.println("Using random seed "+rnd);
            } catch (Exception ex) {
            m_Log.logMessage("Trouble parsing random seed value");
            rnd = 1;
            }
            Random random = new Random(rnd);
            inst.randomize(random);
            if (inst.attribute(classIndex).isNominal()) {
            m_Log.statusMessage("Stratifying instances...");
            inst.stratify(numFolds);
            }
            eval = new Evaluation(inst, costMatrix);
      
            if (outputPredictionsText) {
            printPredictionsHeader(outBuff, inst, "test data");
            }

            // Make some splits and do a CV
            for (int fold = 0; fold < numFolds; fold++) {
            m_Log.statusMessage("Creating splits for fold "
                            + (fold + 1) + "...");
            Instances train = inst.trainCV(numFolds, fold, random);
            eval.setPriors(train);
            m_Log.statusMessage("Building model for fold "
                            + (fold + 1) + "...");
            Classifier current = null;
            try {
              current = Classifier.makeCopy(template);
            } catch (Exception ex) {
              m_Log.logMessage("Problem copying classifier: " + ex.getMessage());
            }
            current.buildClassifier(train);
            Instances test = inst.testCV(numFolds, fold);
            m_Log.statusMessage("Evaluating model for fold "
                            + (fold + 1) + "...");
            for (int jj=0;jj<test.numInstances();jj++) {
              processClassifierPrediction(test.instance(jj), current,
                                    eval, predInstances, plotShape,
                                    plotSize);
              if (outputPredictionsText) { 
                outBuff.append(predictionText(current, test.instance(jj), jj+1));
              }
            }
            }
            if (outputPredictionsText) {
            outBuff.append("\n");
            } 
            if (inst.attribute(classIndex).isNominal()) {
            outBuff.append("=== Stratified cross-validation ===\n");
            } else {
            outBuff.append("=== Cross-validation ===\n");
            }
            break;
            
            case 2: // Percent split
            if (!m_PreserveOrderBut.isSelected()) {
            m_Log.statusMessage("Randomizing instances...");
            try {
              rnd = Integer.parseInt(m_RandomSeedText.getText().trim());
            } catch (Exception ex) {
              m_Log.logMessage("Trouble parsing random seed value");
              rnd = 1;
            }
            inst.randomize(new Random(rnd));
            }
            int trainSize = (int) Math.round(inst.numInstances() * percent / 100);
            int testSize = inst.numInstances() - trainSize;
            Instances train = new Instances(inst, 0, trainSize);
            Instances test = new Instances(inst, trainSize, testSize);
            m_Log.statusMessage("Building model on training split ("+trainSize+" instances)...");
            Classifier current = null;
            try {
            current = Classifier.makeCopy(template);
            } catch (Exception ex) {
            m_Log.logMessage("Problem copying classifier: " + ex.getMessage());
            }
            current.buildClassifier(train);
            eval = new Evaluation(train, costMatrix);
            m_Log.statusMessage("Evaluating on test split...");
           
            if (outputPredictionsText) {
            printPredictionsHeader(outBuff, inst, "test split");
            }
     
            for (int jj=0;jj<test.numInstances();jj++) {
            processClassifierPrediction(test.instance(jj), current,
                                  eval, predInstances, plotShape,
                                  plotSize);
            if (outputPredictionsText) { 
                outBuff.append(predictionText(current, test.instance(jj), jj+1));
            }
            if ((jj % 100) == 0) {
              m_Log.statusMessage("Evaluating on test split. Processed "
                              +jj+" instances...");
            }
            }
            if (outputPredictionsText) {
            outBuff.append("\n");
            } 
            outBuff.append("=== Evaluation on test split ===\n");
            break;
            
            case 4: // Test on user split
            m_Log.statusMessage("Evaluating on test data...");
            eval = new Evaluation(inst, costMatrix);
            
            if (outputPredictionsText) {
            printPredictionsHeader(outBuff, inst, "test set");
            }

            Instance instance;
            int jj = 0;
            while (source.hasMoreElements(userTestStructure)) {
            instance = source.nextElement(userTestStructure);
            processClassifierPrediction(instance, classifier,
                eval, predInstances, plotShape,
                plotSize);
            if (outputPredictionsText) { 
              outBuff.append(predictionText(classifier, instance, jj+1));
            }
            if ((++jj % 100) == 0) {
              m_Log.statusMessage("Evaluating on test data. Processed "
                  +jj+" instances...");
            }
            }

            if (outputPredictionsText) {
            outBuff.append("\n");
            } 
            outBuff.append("=== Evaluation on test set ===\n");
            break;

            default:
            throw new Exception("Test mode not implemented");
          }
          
          if (outputSummary) {
            outBuff.append(eval.toSummaryString(outputEntropy) + "\n");
          }

          if (inst.attribute(classIndex).isNominal()) {

            if (outputPerClass) {
            outBuff.append(eval.toClassDetailsString() + "\n");
            }

            if (outputConfusion) {
            outBuff.append(eval.toMatrixString() + "\n");
            }
          }

            if (   (fullClassifier instanceof Sourcable) 
                 && m_OutputSourceCode.isSelected()) {
              outBuff.append("=== Source code ===\n\n");
              outBuff.append(
                Evaluation.wekaStaticWrapper(
                    ((Sourcable) fullClassifier),
                    m_SourceCodeClass.getText()));
            }

          m_History.updateResult(name);
          m_Log.logMessage("Finished " + cname);
          m_Log.statusMessage("OK");
        } catch (Exception ex) {
          ex.printStackTrace();
          m_Log.logMessage(ex.getMessage());
          JOptionPane.showMessageDialog(ClassifierPanel.this,
                                "Problem evaluating classifier:\n"
                                + ex.getMessage(),
                                "Evaluate classifier",
                                JOptionPane.ERROR_MESSAGE);
          m_Log.statusMessage("Problem evaluating classifier");
        } finally {
          try {
              if (!saveVis && outputModel) {
              FastVector vv = new FastVector();
              vv.addElement(fullClassifier);
              Instances trainHeader = new Instances(m_Instances, 0);
              trainHeader.setClassIndex(classIndex);
              vv.addElement(trainHeader);
                  if (grph != null) {
                vv.addElement(grph);
              }
              m_History.addObject(name, vv);
              } else if (saveVis && predInstances != null && 
                  predInstances.numInstances() > 0) {
            if (predInstances.attribute(predInstances.classIndex())
                .isNumeric()) {
              postProcessPlotInfo(plotSize);
            }
            m_CurrentVis = new VisualizePanel();
            m_CurrentVis.setName(name+" ("+inst.relationName()+")");
            m_CurrentVis.setLog(m_Log);
            PlotData2D tempd = new PlotData2D(predInstances);
            tempd.setShapeSize(plotSize);
            tempd.setShapeType(plotShape);
            tempd.setPlotName(name+" ("+inst.relationName()+")");
            tempd.addInstanceNumberAttribute();
            
            m_CurrentVis.addPlot(tempd);
            m_CurrentVis.setColourIndex(predInstances.classIndex()+1);
          
                FastVector vv = new FastVector();
                if (outputModel) {
                  vv.addElement(fullClassifier);
                  Instances trainHeader = new Instances(m_Instances, 0);
                  trainHeader.setClassIndex(classIndex);
                  vv.addElement(trainHeader);
                  if (grph != null) {
                    vv.addElement(grph);
                  }
                }
                vv.addElement(m_CurrentVis);
                
                if ((eval != null) && (eval.predictions() != null)) {
                  vv.addElement(eval.predictions());
                  vv.addElement(inst.classAttribute());
                }
                m_History.addObject(name, vv);
            }
          } catch (Exception ex) {
            ex.printStackTrace();
          }
          
          if (isInterrupted()) {
            m_Log.logMessage("Interrupted " + cname);
            m_Log.statusMessage("Interrupted");
          }

          synchronized (this) {
            m_StartBut.setEnabled(true);
            m_StopBut.setEnabled(false);
            m_RunThread = null;
          }
          if (m_Log instanceof TaskLogger) {
              ((TaskLogger)m_Log).taskFinished();
            }
          }
      }
      };
      m_RunThread.setPriority(Thread.MIN_PRIORITY);
      m_RunThread.start();
    }
  }

  /**
   * generates a prediction row for an instance
   * 
   * @param classifier the classifier to use for making the prediction
   * @param inst the instance to predict
   * @param instNum the index of the instance
   * @throws Exception if something goes wrong
   * @return the generated row
   */
01511   protected String predictionText(Classifier classifier, Instance inst, int instNum) throws Exception {

    //> inst#   actual   predicted   error  probability distribution

    StringBuffer text = new StringBuffer();
    // inst #
    text.append(Utils.padLeft("" + instNum, 6) + " ");
    if (inst.classAttribute().isNominal()) {

      // actual
      if (inst.classIsMissing()) text.append(Utils.padLeft("?", 10) + " ");
      else text.append(Utils.padLeft("" + ((int) inst.classValue()+1) + ":"
                        + inst.stringValue(inst.classAttribute()), 10) + " ");

      // predicted
      double[] probdist = null;
      double pred;
      if (inst.classAttribute().isNominal()) {
      probdist = classifier.distributionForInstance(inst);
      pred = (double) Utils.maxIndex(probdist);
      if (probdist[(int) pred] <= 0.0) pred = Instance.missingValue();
      } else {
      pred = classifier.classifyInstance(inst);
      }
      text.append(Utils.padLeft((Instance.isMissingValue(pred) ? "?" :
                         (((int) pred+1) + ":"
                         + inst.classAttribute().value((int) pred))), 10) + " ");
      // error
      if (pred == inst.classValue()) text.append(Utils.padLeft(" ", 6) + " ");
      else text.append(Utils.padLeft("+", 6) + " ");

      // prob dist
      if (inst.classAttribute().type() == Attribute.NOMINAL) {
      for (int i=0; i<probdist.length; i++) {
        if (i == (int) pred) text.append(" *");
        else text.append("  ");
        text.append(Utils.doubleToString(probdist[i], 5, 3));
      }
      }
    } else {

      // actual
      if (inst.classIsMissing()) text.append(Utils.padLeft("?", 10) + " ");
      else text.append(Utils.doubleToString(inst.classValue(), 10, 3) + " ");

      // predicted
      double pred = classifier.classifyInstance(inst);
      if (Instance.isMissingValue(pred)) text.append(Utils.padLeft("?", 10) + " ");
      else text.append(Utils.doubleToString(pred, 10, 3) + " ");

      // err
      if (!inst.classIsMissing() && !Instance.isMissingValue(pred))
      text.append(Utils.doubleToString(pred - inst.classValue(), 10, 3));
    }
    
    // additional Attributes
    if (m_OutputAdditionalAttributesRange != null) {
      text.append(" (");
      boolean first = true;
      for (int i = 0; i < inst.numAttributes() - 1; i++) {
      if (m_OutputAdditionalAttributesRange.isInRange(i)) {
        if (!first)
          text.append(",");
        else
          first = false;
        text.append(inst.toString(i));
      }
      }
      text.append(")");
    }
    
    text.append("\n");
    return text.toString();
  }

  /**
   * Handles constructing a popup menu with visualization options.
   * @param name the name of the result history list entry clicked on by
   * the user
   * @param x the x coordinate for popping up the menu
   * @param y the y coordinate for popping up the menu
   */
01593   protected void visualize(String name, int x, int y) {
    final String selectedName = name;
    JPopupMenu resultListMenu = new JPopupMenu();
    
    JMenuItem visMainBuffer = new JMenuItem("View in main window");
    if (selectedName != null) {
      visMainBuffer.addActionListener(new ActionListener() {
        public void actionPerformed(ActionEvent e) {
          m_History.setSingle(selectedName);
        }
      });
    } else {
      visMainBuffer.setEnabled(false);
    }
    resultListMenu.add(visMainBuffer);
    
    JMenuItem visSepBuffer = new JMenuItem("View in separate window");
    if (selectedName != null) {
      visSepBuffer.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
        m_History.openFrame(selectedName);
      }
      });
    } else {
      visSepBuffer.setEnabled(false);
    }
    resultListMenu.add(visSepBuffer);
    
    JMenuItem saveOutput = new JMenuItem("Save result buffer");
    if (selectedName != null) {
      saveOutput.addActionListener(new ActionListener() {
        public void actionPerformed(ActionEvent e) {
          saveBuffer(selectedName);
        }
      });
    } else {
      saveOutput.setEnabled(false);
    }
    resultListMenu.add(saveOutput);
    
    JMenuItem deleteOutput = new JMenuItem("Delete result buffer");
    if (selectedName != null) {
      deleteOutput.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
        m_History.removeResult(selectedName);
      }
      });
    } else {
      deleteOutput.setEnabled(false);
    }
    resultListMenu.add(deleteOutput);

    resultListMenu.addSeparator();
    
    JMenuItem loadModel = new JMenuItem("Load model");
    loadModel.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
        loadClassifier();
      }
      });
    resultListMenu.add(loadModel);

    FastVector o = null;
    if (selectedName != null) {
      o = (FastVector)m_History.getNamedObject(selectedName);
    }

    VisualizePanel temp_vp = null;
    String temp_grph = null;
    FastVector temp_preds = null;
    Attribute temp_classAtt = null;
    Classifier temp_classifier = null;
    Instances temp_trainHeader = null;
      
    if (o != null) { 
      for (int i = 0; i < o.size(); i++) {
      Object temp = o.elementAt(i);
      if (temp instanceof Classifier) {
        temp_classifier = (Classifier)temp;
      } else if (temp instanceof Instances) { // training header
        temp_trainHeader = (Instances)temp;
      } else if (temp instanceof VisualizePanel) { // normal errors
        temp_vp = (VisualizePanel)temp;
      } else if (temp instanceof String) { // graphable output
        temp_grph = (String)temp;
      } else if (temp instanceof FastVector) { // predictions
        temp_preds = (FastVector)temp;
      } else if (temp instanceof Attribute) { // class attribute
        temp_classAtt = (Attribute)temp;
      }
      }
    }

    final VisualizePanel vp = temp_vp;
    final String grph = temp_grph;
    final FastVector preds = temp_preds;
    final Attribute classAtt = temp_classAtt;
    final Classifier classifier = temp_classifier;
    final Instances trainHeader = temp_trainHeader;
    
    JMenuItem saveModel = new JMenuItem("Save model");
    if (classifier != null) {
      saveModel.addActionListener(new ActionListener() {
        public void actionPerformed(ActionEvent e) {
          saveClassifier(selectedName, classifier, trainHeader);
        }
      });
    } else {
      saveModel.setEnabled(false);
    }
    resultListMenu.add(saveModel);

    JMenuItem reEvaluate =
      new JMenuItem("Re-evaluate model on current test set");
    if (classifier != null && m_TestLoader != null) {
      reEvaluate.addActionListener(new ActionListener() {
        public void actionPerformed(ActionEvent e) {
          reevaluateModel(selectedName, classifier, trainHeader);
        }
      });
    } else {
      reEvaluate.setEnabled(false);
    }
    resultListMenu.add(reEvaluate);
    
    resultListMenu.addSeparator();
    
    JMenuItem visErrors = new JMenuItem("Visualize classifier errors");
    if (vp != null) {
      visErrors.addActionListener(new ActionListener() {
        public void actionPerformed(ActionEvent e) {
          visualizeClassifierErrors(vp);
        }
      });
    } else {
      visErrors.setEnabled(false);
    }
    resultListMenu.add(visErrors);

    JMenuItem visGrph = new JMenuItem("Visualize tree");
    if (grph != null) {
      if(((Drawable)temp_classifier).graphType()==Drawable.TREE) {
          visGrph.addActionListener(new ActionListener() {
                public void actionPerformed(ActionEvent e) {
                  String title;
                  if (vp != null) title = vp.getName();
                  else title = selectedName;
                  visualizeTree(grph, title);
                }
            });
      }
      else if(((Drawable)temp_classifier).graphType()==Drawable.BayesNet) {
          visGrph.setText("Visualize graph");
          visGrph.addActionListener(new ActionListener() {
                public void actionPerformed(ActionEvent e) {
                  Thread th = new Thread() {
                        public void run() {
                        visualizeBayesNet(grph, selectedName);
                        }
                      };
                  th.start();
                }
            });
      }
      else
          visGrph.setEnabled(false);
    } else {
      visGrph.setEnabled(false);
    }
    resultListMenu.add(visGrph);

    JMenuItem visMargin = new JMenuItem("Visualize margin curve");
    if (preds != null) {
      visMargin.addActionListener(new ActionListener() {
        public void actionPerformed(ActionEvent e) {
          try {
            MarginCurve tc = new MarginCurve();
            Instances result = tc.getCurve(preds);
            VisualizePanel vmc = new VisualizePanel();
            vmc.setName(result.relationName());
            vmc.setLog(m_Log);
            PlotData2D tempd = new PlotData2D(result);
            tempd.setPlotName(result.relationName());
            tempd.addInstanceNumberAttribute();
            vmc.addPlot(tempd);
            visualizeClassifierErrors(vmc);
          } catch (Exception ex) {
            ex.printStackTrace();
          }
        }
      });
    } else {
      visMargin.setEnabled(false);
    }
    resultListMenu.add(visMargin);

    JMenu visThreshold = new JMenu("Visualize threshold curve");
    if (preds != null && classAtt != null) {
      for (int i = 0; i < classAtt.numValues(); i++) {
      JMenuItem clv = new JMenuItem(classAtt.value(i));
      final int classValue = i;
      clv.addActionListener(new ActionListener() {
          public void actionPerformed(ActionEvent e) {
            try {
            ThresholdCurve tc = new ThresholdCurve();
            Instances result = tc.getCurve(preds, classValue);
            //VisualizePanel vmc = new VisualizePanel();
            ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
            vmc.setROCString("(Area under ROC = " + 
                         Utils.doubleToString(ThresholdCurve.getROCArea(result), 4) + ")");
            vmc.setLog(m_Log);
            vmc.setName(result.relationName()+". (Class value "+
                      classAtt.value(classValue)+")");
            PlotData2D tempd = new PlotData2D(result);
            tempd.setPlotName(result.relationName());
            tempd.addInstanceNumberAttribute();
            vmc.addPlot(tempd);
            visualizeClassifierErrors(vmc);
            } catch (Exception ex) {
            ex.printStackTrace();
            }
            }
        });
        visThreshold.add(clv);
      }
    } else {
      visThreshold.setEnabled(false);
    }
    resultListMenu.add(visThreshold);

    JMenu visCost = new JMenu("Visualize cost curve");
    if (preds != null && classAtt != null) {
      for (int i = 0; i < classAtt.numValues(); i++) {
      JMenuItem clv = new JMenuItem(classAtt.value(i));
      final int classValue = i;
      clv.addActionListener(new ActionListener() {
          public void actionPerformed(ActionEvent e) {
            try {
            CostCurve cc = new CostCurve();
            Instances result = cc.getCurve(preds, classValue);
            VisualizePanel vmc = new VisualizePanel();
            vmc.setLog(m_Log);
            vmc.setName(result.relationName()+". (Class value "+
                      classAtt.value(classValue)+")");
            PlotData2D tempd = new PlotData2D(result);
            tempd.m_displayAllPoints = true;
            tempd.setPlotName(result.relationName());
            boolean [] connectPoints = 
              new boolean [result.numInstances()];
            for (int jj = 1; jj < connectPoints.length; jj+=2) {
              connectPoints[jj] = true;
            }
            tempd.setConnectPoints(connectPoints);
            //            tempd.addInstanceNumberAttribute();
            vmc.addPlot(tempd);
            visualizeClassifierErrors(vmc);
            } catch (Exception ex) {
            ex.printStackTrace();
            }
          }
        });
      visCost.add(clv);
      }
    } else {
      visCost.setEnabled(false);
    }
    resultListMenu.add(visCost);
    
    JMenu visPlugins = new JMenu("Plugins");
    Vector pluginsVector = GenericObjectEditor.getClassnames(VisualizePlugin.class.getName());
    boolean availablePlugins = false;
    for (int i=0; i<pluginsVector.size(); i++) {
      String className = (String)(pluginsVector.elementAt(i));
      try {
        VisualizePlugin plugin = (VisualizePlugin) Class.forName(className).newInstance();
        if (plugin == null)
          continue;
        availablePlugins = true;
        JMenuItem pluginMenuItem = plugin.getVisualizeMenuItem(preds, classAtt);
        Version version = new Version();
        if (pluginMenuItem != null) {
          if (version.compareTo(plugin.getMinVersion()) < 0)
            pluginMenuItem.setText(pluginMenuItem.getText() + " (weka outdated)");
          if (version.compareTo(plugin.getMaxVersion()) >= 0)
            pluginMenuItem.setText(pluginMenuItem.getText() + " (plugin outdated)");
          visPlugins.add(pluginMenuItem);
        }
      }
      catch (ClassNotFoundException cnfe) {
        //System.out.println("Visualize plugin ClassNotFoundException " + cnfe.getMessage());
      }
      catch (InstantiationException ie) {
        //System.out.println("Visualize plugin InstantiationException " + ie.getMessage());
      }
      catch (IllegalAccessException iae) {
        //System.out.println("Visualize plugin IllegalAccessException " + iae.getMessage());
      }
    }
    if (availablePlugins)
      resultListMenu.add(visPlugins);

    resultListMenu.show(m_History.getList(), x, y);
  }

  /**
   * Pops up a TreeVisualizer for the classifier from the currently
   * selected item in the results list
   * @param dottyString the description of the tree in dotty format
   * @param treeName the title to assign to the display
   */
01903   protected void visualizeTree(String dottyString, String treeName) {
    final javax.swing.JFrame jf = 
      new javax.swing.JFrame("Weka Classifier Tree Visualizer: "+treeName);
    jf.setSize(500,400);
    jf.getContentPane().setLayout(new BorderLayout());
    TreeVisualizer tv = new TreeVisualizer(null,
                                 dottyString,
                                 new PlaceNode2());
    jf.getContentPane().add(tv, BorderLayout.CENTER);
    jf.addWindowListener(new java.awt.event.WindowAdapter() {
      public void windowClosing(java.awt.event.WindowEvent e) {
        jf.dispose();
      }
      });
    
    jf.setVisible(true);
    tv.fitToScreen();
  }

  /**
   * Pops up a GraphVisualizer for the BayesNet classifier from the currently
   * selected item in the results list
   * 
   * @param XMLBIF the description of the graph in XMLBIF ver. 0.3
   * @param graphName the name of the graph
   */
01929   protected void visualizeBayesNet(String XMLBIF, String graphName) {
    final javax.swing.JFrame jf = 
      new javax.swing.JFrame("Weka Classifier Graph Visualizer: "+graphName);
    jf.setSize(500,400);
    jf.getContentPane().setLayout(new BorderLayout());
    GraphVisualizer gv = new GraphVisualizer();
    try { gv.readBIF(XMLBIF);
    }
    catch(BIFFormatException be) { System.err.println("unable to visualize BayesNet"); be.printStackTrace(); }
    gv.layoutGraph();

    jf.getContentPane().add(gv, BorderLayout.CENTER);
    jf.addWindowListener(new java.awt.event.WindowAdapter() {
      public void windowClosing(java.awt.event.WindowEvent e) {
        jf.dispose();
      }
      });
    
    jf.setVisible(true);
  }


  /**
   * Pops up a VisualizePanel for visualizing the data and errors for 
   * the classifier from the currently selected item in the results list
   * @param sp the VisualizePanel to pop up.
   */
01956   protected void visualizeClassifierErrors(VisualizePanel sp) {
   
    if (sp != null) {
      String plotName = sp.getName(); 
      final javax.swing.JFrame jf = 
      new javax.swing.JFrame("Weka Classifier Visualize: "+plotName);
      jf.setSize(600,400);
      jf.getContentPane().setLayout(new BorderLayout());

      jf.getContentPane().add(sp, BorderLayout.CENTER);
      jf.addWindowListener(new java.awt.event.WindowAdapter() {
        public void windowClosing(java.awt.event.WindowEvent e) {
          jf.dispose();
        }
      });

    jf.setVisible(true);
    }
  }

  /**
   * Save the currently selected classifier output to a file.
   * @param name the name of the buffer to save
   */
01980   protected void saveBuffer(String name) {
    StringBuffer sb = m_History.getNamedBuffer(name);
    if (sb != null) {
      if (m_SaveOut.save(sb)) {
      m_Log.logMessage("Save successful.");
      }
    }
  }
  

  /**
   * Stops the currently running classifier (if any).
   */
01993   protected void stopClassifier() {

    if (m_RunThread != null) {
      m_RunThread.interrupt();
      
      // This is deprecated (and theoretically the interrupt should do).
      m_RunThread.stop();
    }
  }

  /**
   * Saves the currently selected classifier
   * 
   * @param name the name of the run
   * @param classifier the classifier to save
   * @param trainHeader the header of the training instances
   */
02010   protected void saveClassifier(String name, Classifier classifier,
                        Instances trainHeader) {

    File sFile = null;
    boolean saveOK = true;
 
    int returnVal = m_FileChooser.showSaveDialog(this);
    if (returnVal == JFileChooser.APPROVE_OPTION) {
      sFile = m_FileChooser.getSelectedFile();
      if (!sFile.getName().toLowerCase().endsWith(MODEL_FILE_EXTENSION)) {
      sFile = new File(sFile.getParent(), sFile.getName() 
                   + MODEL_FILE_EXTENSION);
      }
      m_Log.statusMessage("Saving model to file...");
      
      try {
      OutputStream os = new FileOutputStream(sFile);
      if (sFile.getName().endsWith(".gz")) {
        os = new GZIPOutputStream(os);
      }
      ObjectOutputStream objectOutputStream = new ObjectOutputStream(os);
      objectOutputStream.writeObject(classifier);
      if (trainHeader != null) objectOutputStream.writeObject(trainHeader);
      objectOutputStream.flush();
      objectOutputStream.close();
      } catch (Exception e) {
      
      JOptionPane.showMessageDialog(null, e, "Save Failed",
                              JOptionPane.ERROR_MESSAGE);
      saveOK = false;
      }
      if (saveOK)
      m_Log.logMessage("Saved model (" + name
                   + ") to file '" + sFile.getName() + "'");
      m_Log.statusMessage("OK");
    }
  }

  /**
   * Loads a classifier
   */
02051   protected void loadClassifier() {

    int returnVal = m_FileChooser.showOpenDialog(this);
    if (returnVal == JFileChooser.APPROVE_OPTION) {
      File selected = m_FileChooser.getSelectedFile();
      Classifier classifier = null;
      Instances trainHeader = null;

      m_Log.statusMessage("Loading model from file...");

      try {
      InputStream is = new FileInputStream(selected);
      if (selected.getName().endsWith(".gz")) {
        is = new GZIPInputStream(is);
      }
      ObjectInputStream objectInputStream = new ObjectInputStream(is);
      classifier = (Classifier) objectInputStream.readObject();
      try { // see if we can load the header
        trainHeader = (Instances) objectInputStream.readObject();
      } catch (Exception e) {} // don't fuss if we can't
      objectInputStream.close();
      } catch (Exception e) {
      
      JOptionPane.showMessageDialog(null, e, "Load Failed",
                              JOptionPane.ERROR_MESSAGE);
      }     

      m_Log.statusMessage("OK");
      
      if (classifier != null) {
      m_Log.logMessage("Loaded model from file '" + selected.getName()+ "'");
      String name = (new SimpleDateFormat("HH:mm:ss - ")).format(new Date());
      String cname = classifier.getClass().getName();
      if (cname.startsWith("weka.classifiers."))
        cname = cname.substring("weka.classifiers.".length());
      name += cname + " from file '" + selected.getName() + "'";
      StringBuffer outBuff = new StringBuffer();

      outBuff.append("=== Model information ===\n\n");
      outBuff.append("Filename:     " + selected.getName() + "\n");
      outBuff.append("Scheme:       " + classifier.getClass().getName());
      if (classifier instanceof OptionHandler) {
        String [] o = ((OptionHandler) classifier).getOptions();
        outBuff.append(" " + Utils.joinOptions(o));
      }
      outBuff.append("\n");
      if (trainHeader != null) {
        outBuff.append("Relation:     " + trainHeader.relationName() + '\n');
        outBuff.append("Attributes:   " + trainHeader.numAttributes() + '\n');
        if (trainHeader.numAttributes() < 100) {
          for (int i = 0; i < trainHeader.numAttributes(); i++) {
            outBuff.append("              " + trainHeader.attribute(i).name()
                       + '\n');
          }
        } else {
          outBuff.append("              [list of attributes omitted]\n");
        }
      } else {
        outBuff.append("\nTraining data unknown\n");
      } 

      outBuff.append("\n=== Classifier model ===\n\n");
      outBuff.append(classifier.toString() + "\n");
      
      m_History.addResult(name, outBuff);
      m_History.setSingle(name);
      FastVector vv = new FastVector();
      vv.addElement(classifier);
      if (trainHeader != null) vv.addElement(trainHeader);
      // allow visualization of graphable classifiers
      String grph = null;
      if (classifier instanceof Drawable) {
        try {
          grph = ((Drawable)classifier).graph();
        } catch (Exception ex) {
        }
      }
      if (grph != null) vv.addElement(grph);
      
      m_History.addObject(name, vv);
      }
    }
  }
  
  /**
   * Re-evaluates the named classifier with the current test set. Unpredictable
   * things will happen if the data set is not compatible with the classifier.
   *
   * @param name the name of the classifier entry
   * @param classifier the classifier to evaluate
   * @param trainHeader the header of the training set
   */
02143   protected void reevaluateModel(final String name, 
                                 final Classifier classifier, 
                                 final Instances trainHeader) {

    if (m_RunThread == null) {
      synchronized (this) {
      m_StartBut.setEnabled(false);
      m_StopBut.setEnabled(true);
      }
      m_RunThread = new Thread() {
          public void run() {
            // Copy the current state of things
            m_Log.statusMessage("Setting up...");

            StringBuffer outBuff = m_History.getNamedBuffer(name);
            DataSource source = null;
            Instances userTestStructure = null;
            // additional vis info (either shape type or point size)
            FastVector plotShape = new FastVector();
            FastVector plotSize = new FastVector();
            Instances predInstances = null;

            CostMatrix costMatrix = null;
            if (m_EvalWRTCostsBut.isSelected()) {
              costMatrix = new CostMatrix((CostMatrix) m_CostMatrixEditor
                                          .getValue());
            }    
            boolean outputConfusion = m_OutputConfusionBut.isSelected();
            boolean outputPerClass = m_OutputPerClassBut.isSelected();
            boolean outputSummary = true;
            boolean outputEntropy = m_OutputEntropyBut.isSelected();
            boolean saveVis = m_StorePredictionsBut.isSelected();
            boolean outputPredictionsText = 
              m_OutputPredictionsTextBut.isSelected();
            String grph = null;    
            Evaluation eval = null;

            try {

              boolean incrementalLoader = (m_TestLoader instanceof IncrementalConverter);
              if (m_TestLoader != null && m_TestLoader.getStructure() != null) {
                m_TestLoader.reset();
                source = new DataSource(m_TestLoader);
                userTestStructure = source.getStructure();
              }
              // Check the test instance compatibility
              if (source == null) {
                throw new Exception("No user test set has been specified");
              }
              if (trainHeader != null) {
                if (trainHeader.classIndex() > 
                    userTestStructure.numAttributes()-1)
                  throw new Exception("Train and test set are not compatible");
                userTestStructure.setClassIndex(trainHeader.classIndex());
                if (!trainHeader.equalHeaders(userTestStructure)) {
                  throw new Exception("Train and test set are not compatible");
                }
              } else {
                userTestStructure.
                  setClassIndex(userTestStructure.numAttributes()-1);
              }
              if (m_Log instanceof TaskLogger) {
                ((TaskLogger)m_Log).taskStarted();
              }
              m_Log.statusMessage("Evaluating on test data...");
              m_Log.logMessage("Re-evaluating classifier (" + name 
                               + ") on test set");
              eval = new Evaluation(userTestStructure, costMatrix);
              eval.useNoPriors();
      
              // set up the structure of the plottable instances for 
              // visualization if selected
              if (saveVis) {
                predInstances = setUpVisualizableInstances(userTestStructure);
                predInstances.setClassIndex(userTestStructure.classIndex()+1);
              }
      
              outBuff.append("\n=== Re-evaluation on test set ===\n\n");
              outBuff.append("User supplied test set\n");  
              outBuff.append("Relation:     " 
                             + userTestStructure.relationName() + '\n');
              if (incrementalLoader)
            outBuff.append("Instances:     unknown (yet). Reading incrementally\n");
              else
            outBuff.append("Instances:    " + source.getDataSet().numInstances() + "\n");
              outBuff.append("Attributes:   " 
              + userTestStructure.numAttributes() 
              + "\n\n");
              if (trainHeader == null)
                outBuff.append("NOTE - if test set is not compatible then results are "
                               + "unpredictable\n\n");

              if (outputPredictionsText) {
                outBuff.append("=== Predictions on test set ===\n\n");
                outBuff.append(" inst#,    actual, predicted, error");
                if (userTestStructure.classAttribute().isNominal()) {
                  outBuff.append(", probability distribution");
                }
                outBuff.append("\n");
              }

            Instance instance;
            int jj = 0;
            while (source.hasMoreElements(userTestStructure)) {
            instance = source.nextElement(userTestStructure);
            processClassifierPrediction(instance, classifier,
                eval, predInstances, plotShape,
                plotSize);
            if (outputPredictionsText) { 
              outBuff.append(predictionText(classifier, instance, jj+1));
            }
            if ((++jj % 100) == 0) {
              m_Log.statusMessage("Evaluating on test data. Processed "
                  +jj+" instances...");
            }
            }

              if (outputPredictionsText) {
                outBuff.append("\n");
              } 
      
              if (outputSummary) {
                outBuff.append(eval.toSummaryString(outputEntropy) + "\n");
              }
      
              if (userTestStructure.classAttribute().isNominal()) {
      
                if (outputPerClass) {
                  outBuff.append(eval.toClassDetailsString() + "\n");
                }
      
                if (outputConfusion) {
                  outBuff.append(eval.toMatrixString() + "\n");
                }
              }
      
              m_History.updateResult(name);
              m_Log.logMessage("Finished re-evaluation");
              m_Log.statusMessage("OK");
            } catch (Exception ex) {
              ex.printStackTrace();
              m_Log.logMessage(ex.getMessage());
              m_Log.statusMessage("See error log");

              ex.printStackTrace();
              m_Log.logMessage(ex.getMessage());
              JOptionPane.showMessageDialog(ClassifierPanel.this,
                                            "Problem evaluationg classifier:\n"
                                            + ex.getMessage(),
                                            "Evaluate classifier",
                                            JOptionPane.ERROR_MESSAGE);
              m_Log.statusMessage("Problem evaluating classifier");
            } finally {
              try {
                if (predInstances != null && predInstances.numInstances() > 0) {
                  if (predInstances.attribute(predInstances.classIndex())
                      .isNumeric()) {
                    postProcessPlotInfo(plotSize);
                  }
                  m_CurrentVis = new VisualizePanel();
                  m_CurrentVis.setName(name+" ("
                                       +userTestStructure.relationName()+")");
                  m_CurrentVis.setLog(m_Log);
                  PlotData2D tempd = new PlotData2D(predInstances);
                  tempd.setShapeSize(plotSize);
                  tempd.setShapeType(plotShape);
                  tempd.setPlotName(name+" ("+userTestStructure.relationName()
                                    +")");
                  tempd.addInstanceNumberAttribute();
        
                  m_CurrentVis.addPlot(tempd);
                  m_CurrentVis.setColourIndex(predInstances.classIndex()+1);
        
                  if (classifier instanceof Drawable) {
                    try {
                      grph = ((Drawable)classifier).graph();
                    } catch (Exception ex) {
                    }
                  }

                  if (saveVis) {
                    FastVector vv = new FastVector();
                    vv.addElement(classifier);
                    if (trainHeader != null) vv.addElement(trainHeader);
                    vv.addElement(m_CurrentVis);
                    if (grph != null) {
                      vv.addElement(grph);
                    }
                    if ((eval != null) && (eval.predictions() != null)) {
                      vv.addElement(eval.predictions());
                      vv.addElement(userTestStructure.classAttribute());
                    }
                    m_History.addObject(name, vv);
                  } else {
                    FastVector vv = new FastVector();
                    vv.addElement(classifier);
                    if (trainHeader != null) vv.addElement(trainHeader);
                    m_History.addObject(name, vv);
                  }
                }
              } catch (Exception ex) {
                ex.printStackTrace();
              }
              if (isInterrupted()) {
                m_Log.logMessage("Interrupted reevaluate model");
                m_Log.statusMessage("Interrupted");
              }

              synchronized (this) {
                m_StartBut.setEnabled(true);
                m_StopBut.setEnabled(false);
                m_RunThread = null;
              }

              if (m_Log instanceof TaskLogger) {
                ((TaskLogger)m_Log).taskFinished();
              }
            }
          }
        };

      m_RunThread.setPriority(Thread.MIN_PRIORITY);
      m_RunThread.start();
    }
  }
  
  /**
   * updates the capabilities filter of the GOE
   * 
   * @param filter      the new filter to use
   */
02374   protected void updateCapabilitiesFilter(Capabilities filter) {
    Instances           tempInst;
    Capabilities  filterClass;

    if (filter == null) {
      m_ClassifierEditor.setCapabilitiesFilter(new Capabilities(null));
      return;
    }
    
    if (!ExplorerDefaults.getInitGenericObjectEditorFilter())
      tempInst = new Instances(m_Instances, 0);
    else
      tempInst = new Instances(m_Instances);
    tempInst.setClassIndex(m_ClassCombo.getSelectedIndex());

    try {
      filterClass = Capabilities.forInstances(tempInst);
    }
    catch (Exception e) {
      filterClass = new Capabilities(null);
    }
    
    // set new filter
    m_ClassifierEditor.setCapabilitiesFilter(filterClass);
  }
  
  /**
   * method gets called in case of a change event
   * 
   * @param e           the associated change event
   */
02405   public void capabilitiesFilterChanged(CapabilitiesFilterChangeEvent e) {
    if (e.getFilter() == null)
      updateCapabilitiesFilter(null);
    else
      updateCapabilitiesFilter((Capabilities) e.getFilter().clone());
  }

  /**
   * Sets the Explorer to use as parent frame (used for sending notifications
   * about changes in the data)
   * 
   * @param parent      the parent frame
   */
02418   public void setExplorer(Explorer parent) {
    m_Explorer = parent;
  }
  
  /**
   * returns the parent Explorer frame
   * 
   * @return            the parent
   */
02427   public Explorer getExplorer() {
    return m_Explorer;
  }
  
  /**
   * Returns the title for the tab in the Explorer
   * 
   * @return            the title of this tab
   */
02436   public String getTabTitle() {
    return "Classify";
  }
  
  /**
   * Returns the tooltip for the tab in the Explorer
   * 
   * @return            the tooltip of this tab
   */
02445   public String getTabTitleToolTip() {
    return "Classify instances";
  }
  
  /**
   * Tests out the classifier panel from the command line.
   *
   * @param args may optionally contain the name of a dataset to load.
   */
02454   public static void main(String [] args) {

    try {
      final javax.swing.JFrame jf =
      new javax.swing.JFrame("Weka Explorer: Classifier");
      jf.getContentPane().setLayout(new BorderLayout());
      final ClassifierPanel sp = new ClassifierPanel();
      jf.getContentPane().add(sp, BorderLayout.CENTER);
      weka.gui.LogPanel lp = new weka.gui.LogPanel();
      sp.setLog(lp);
      jf.getContentPane().add(lp, BorderLayout.SOUTH);
      jf.addWindowListener(new java.awt.event.WindowAdapter() {
      public void windowClosing(java.awt.event.WindowEvent e) {
        jf.dispose();
        System.exit(0);
      }
      });
      jf.pack();
      jf.setSize(800, 600);
      jf.setVisible(true);
      if (args.length == 1) {
      System.err.println("Loading instances from " + args[0]);
      java.io.Reader r = new java.io.BufferedReader(
                     new java.io.FileReader(args[0]));
      Instances i = new Instances(r);
      sp.setInstances(i);
      }
    } catch (Exception ex) {
      ex.printStackTrace();
      System.err.println(ex.getMessage());
    }
  }
}

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