resultspanel.java

wekaUT是 university texas austin 开发的基于weka的半指导学习(semi supervised learning)的分类器

    JLabel lab = new JLabel("Run field", SwingConstants.RIGHT);
    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.EAST;
    gbC.gridy = 1;     gbC.gridx = 0;
    gbC.insets = new Insets(2, 10, 2, 10);
    gbL.setConstraints(lab, gbC);
    p3.add(lab);
    gbC = new GridBagConstraints();
    gbC.gridy = 1;     gbC.gridx = 1;  gbC.weightx = 100;
    gbC.insets = new Insets(5,0,5,0);
    gbL.setConstraints(m_RunCombo, gbC);
    p3.add(m_RunCombo);
    
    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.EAST;
    gbC.gridy = 2;     gbC.gridx = 0;
    gbC.insets = new Insets(2, 10, 2, 10);
    gbL.setConstraints(m_ResultKeyLabel, gbC);
    p3.add(m_ResultKeyLabel);
    gbC = new GridBagConstraints();
    gbC.gridy = 2;     gbC.gridx = 1;  gbC.weightx = 100;
    gbC.insets = new Insets(5,0,5,0);
    gbL.setConstraints(m_ResultKeyBut, gbC);
    p3.add(m_ResultKeyBut);
    
    lab = new JLabel("Comparison field", SwingConstants.RIGHT);
    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.EAST;
    gbC.gridy = 3;     gbC.gridx = 0;
    gbC.insets = new Insets(2, 10, 2, 10);
    gbL.setConstraints(lab, gbC);
    p3.add(lab);
    gbC = new GridBagConstraints();
    gbC.gridy = 3;     gbC.gridx = 1;  gbC.weightx = 100;
    gbC.insets = new Insets(5,0,5,0);
    gbL.setConstraints(m_CompareCombo, gbC);
    p3.add(m_CompareCombo);
    
    lab = new JLabel("Significance", SwingConstants.RIGHT);
    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.EAST;
    gbC.gridy = 4;     gbC.gridx = 0;
    gbC.insets = new Insets(2, 10, 2, 10);
    gbL.setConstraints(lab, gbC);
    p3.add(lab);
    gbC = new GridBagConstraints();
    gbC.gridy = 4;     gbC.gridx = 1;  gbC.weightx = 100;
    gbL.setConstraints(m_SigTex, gbC);
    p3.add(m_SigTex);
    
    lab = new JLabel("Test base", SwingConstants.RIGHT);
    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.EAST;
    gbC.gridy = 5;     gbC.gridx = 0;
    gbC.insets = new Insets(2, 10, 2, 10);
    gbL.setConstraints(lab, gbC);
    p3.add(lab);
    gbC = new GridBagConstraints();
    gbC.fill = GridBagConstraints.HORIZONTAL;
    gbC.gridy = 5;     gbC.gridx = 1;  gbC.weightx = 100;
    gbC.insets = new Insets(5,0,5,0);
    gbL.setConstraints(m_TestsButton, gbC);
    p3.add(m_TestsButton);

    //=============== BEGIN EDIT melville ===============
    lab = new JLabel("Significant digits", SwingConstants.RIGHT);
    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.EAST;
    gbC.gridy = 6;     gbC.gridx = 0;
    gbC.insets = new Insets(2, 10, 2, 10);
    gbL.setConstraints(lab, gbC);
    p3.add(lab);
    gbC = new GridBagConstraints();
    gbC.gridy = 6;     gbC.gridx = 1;  gbC.weightx = 100;
    gbL.setConstraints(m_PrecTex, gbC);
    p3.add(m_PrecTex);
    //=============== END EDIT melville ===============

    lab = new JLabel("Show std. deviations", SwingConstants.RIGHT);
    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.EAST;
    gbC.gridy = 7;     gbC.gridx = 0;
    gbC.insets = new Insets(2, 10, 2, 10);
    gbL.setConstraints(lab, gbC);
    p3.add(lab);
    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.WEST;
    gbC.gridy = 7;     gbC.gridx = 1;  gbC.weightx = 100;
    gbC.insets = new Insets(5,0,5,0);
    gbL.setConstraints(m_ShowStdDevs, gbC);
    p3.add(m_ShowStdDevs);

    JPanel output = new JPanel();
    output.setLayout(new BorderLayout());
    output.setBorder(BorderFactory.createTitledBorder("Test output"));
    output.add(new JScrollPane(m_OutText), BorderLayout.CENTER);

    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(p3, gbC);
    mondo.add(p3);

    JPanel bts = new JPanel();
    bts.setLayout(new GridLayout(1,2,5,5));
    bts.add(m_PerformBut);
    bts.add(m_SaveOutBut);
    //=============== BEGIN EDIT mbilenko ===============
    bts.add(m_PlotBut);
    //=============== END EDIT mbilenko ===============

    gbC = new GridBagConstraints();
    gbC.anchor = GridBagConstraints.NORTH;
    gbC.fill = GridBagConstraints.HORIZONTAL;
    gbC.gridy = 1;     gbC.gridx = 0;
    gbC.insets = new Insets(5,5,5,5);
    gbL.setConstraints(bts, gbC);
    mondo.add(bts);
    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(output, gbC);
    mondo.add(output);

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

  /**
   * Sets the combo-boxes to a fixed size so they don't take up too much room
   * that would be better devoted to the test output box
   */
  protected void setComboSizes() {
    
    m_DatasetKeyBut.setPreferredSize(COMBO_SIZE);
    m_RunCombo.setPreferredSize(COMBO_SIZE);
    m_ResultKeyBut.setPreferredSize(COMBO_SIZE);
    m_CompareCombo.setPreferredSize(COMBO_SIZE);
    m_SigTex.setPreferredSize(COMBO_SIZE);
    
    m_DatasetKeyBut.setMaximumSize(COMBO_SIZE);
    m_RunCombo.setMaximumSize(COMBO_SIZE);
    m_ResultKeyBut.setMaximumSize(COMBO_SIZE);
    m_CompareCombo.setMaximumSize(COMBO_SIZE);
    m_SigTex.setMaximumSize(COMBO_SIZE);
    
    m_DatasetKeyBut.setMinimumSize(COMBO_SIZE);
    m_RunCombo.setMinimumSize(COMBO_SIZE);
    m_ResultKeyBut.setMinimumSize(COMBO_SIZE);
    m_CompareCombo.setMinimumSize(COMBO_SIZE);
    m_SigTex.setMinimumSize(COMBO_SIZE);
    
    //=============== BEGIN EDIT melville ===============
    m_PrecTex.setPreferredSize(COMBO_SIZE);
    m_PrecTex.setMaximumSize(COMBO_SIZE);
    m_PrecTex.setMinimumSize(COMBO_SIZE);
    //=============== END EDIT melville ===============
  }
  
  /**
   * Tells the panel to use a new experiment.
   *
   * @param exp a value of type 'Experiment'
   */
  public void setExperiment(Experiment exp) {
    
    m_Exp = exp;
    setFromExpEnabled();
  }

  /**
   * Updates whether the current experiment is of a type that we can
   * determine the results destination.
   */
  protected void setFromExpEnabled() {

    if ((m_Exp.getResultListener() instanceof InstancesResultListener)
	|| (m_Exp.getResultListener() instanceof DatabaseResultListener)) {
      m_FromExpBut.setEnabled(true);
    } else {
      m_FromExpBut.setEnabled(false);
    }
  }

  /**
   * Queries the user enough to make a database query to retrieve experiment
   * results.
   */
  protected void setInstancesFromDBaseQuery() {

    try {
      if (m_InstanceQuery == null) {
	m_InstanceQuery = new InstanceQuery();
      }
      String dbaseURL = m_InstanceQuery.getDatabaseURL();
      dbaseURL = (String) JOptionPane.showInputDialog(this,
					     "Enter the database URL",
					     "Query Database",
					     JOptionPane.PLAIN_MESSAGE,
					     null,
					     null,
					     dbaseURL);
      if (dbaseURL == null) {
	m_FromLab.setText("Cancelled");
	return;
      }
      m_InstanceQuery.setDatabaseURL(dbaseURL);
      m_InstanceQuery.connectToDatabase();
      if (!m_InstanceQuery.experimentIndexExists()) {
	m_FromLab.setText("No experiment index");
	return;
      }
      m_FromLab.setText("Getting experiment index");
      Instances index = m_InstanceQuery.retrieveInstances("SELECT * FROM "
				       + InstanceQuery.EXP_INDEX_TABLE);
      if (index.numInstances() == 0) {
	m_FromLab.setText("No experiments available");
	return;	
      }
      m_FromLab.setText("Got experiment index");

      DefaultListModel lm = new DefaultListModel();
      for (int i = 0; i < index.numInstances(); i++) {
	lm.addElement(index.instance(i).toString());
      }
      JList jl = new JList(lm);
      ListSelectorDialog jd = new ListSelectorDialog(null, jl);
      int result = jd.showDialog();
      if (result != ListSelectorDialog.APPROVE_OPTION) {
	m_FromLab.setText("Cancelled");
	return;
      }
      Instance selInst = index.instance(jl.getSelectedIndex());
      Attribute tableAttr = index.attribute(InstanceQuery.EXP_RESULT_COL);
      String table = InstanceQuery.EXP_RESULT_PREFIX
	+ selInst.toString(tableAttr);

      setInstancesFromDatabaseTable(table);
    } catch (Exception ex) {
      m_FromLab.setText("Problem reading database");
    }
  }
  
  /**
   * Examines the supplied experiment to determine the results destination
   * and attempts to load the results.
   *
   * @param exp a value of type 'Experiment'
   */
  protected void setInstancesFromExp(Experiment exp) {

    if (exp.getResultListener() instanceof InstancesResultListener) {
      File resultFile = ((InstancesResultListener) exp.getResultListener())
	.getOutputFile();
      if ((resultFile == null) || (resultFile.getName().equals("-"))) {
	m_FromLab.setText("No result file");
      } else {
	setInstancesFromFile(resultFile);
      }
    } else if (exp.getResultListener() instanceof DatabaseResultListener) {
      String dbaseURL = ((DatabaseResultListener) exp.getResultListener())
	.getDatabaseURL();
      try {
	if (m_InstanceQuery == null) {
	  m_InstanceQuery = new InstanceQuery();
	}
	m_InstanceQuery.setDatabaseURL(dbaseURL);
	m_InstanceQuery.connectToDatabase();
	String tableName = m_InstanceQuery
	  .getResultsTableName(exp.getResultProducer());
	setInstancesFromDatabaseTable(tableName);
      } catch (Exception ex) {
	m_FromLab.setText("Problem reading database");
      }
    } else {
      m_FromLab.setText("Can't get results from experiment");
    }
  }

  
  /**
   * Queries a database to load results from the specified table name. The
   * database connection must have already made by m_InstanceQuery.
   *
   * @param tableName the name of the table containing results to retrieve.
   */
  protected void setInstancesFromDatabaseTable(String tableName) {

    try {
      m_FromLab.setText("Reading from database, please wait...");
      final Instances i = m_InstanceQuery.retrieveInstances("SELECT * FROM "
						      + tableName);
      SwingUtilities.invokeAndWait(new Runnable() {
	public void run() {
	  setInstances(i);
	}
      });
      m_InstanceQuery.disconnectFromDatabase();
    } catch (Exception ex) {
      m_FromLab.setText(ex.getMessage());
    }
  }
  
  /**
   * Loads results from a set of instances contained in the supplied
   * file.
   *
   * @param f a value of type 'File'
   */
  protected void setInstancesFromFile(File f) {
      
    try {
      m_FromLab.setText("Reading from file...");
      Reader r = new BufferedReader(new FileReader(f));
      setInstances(new Instances(r));
    } catch (Exception ex) {
      ex.printStackTrace();
      m_FromLab.setText(ex.getMessage());
    }
  }

  /**
   * Sets up the panel with a new set of instances, attempting
   * to guess the correct settings for various columns.
   *
   * @param newInstances the new set of results.
   */
  public void setInstances(Instances newInstances) {

    m_Instances = newInstances;
    m_TTester.setInstances(m_Instances);
    m_FromLab.setText("Got " + m_Instances.numInstances() + " results");

    // Temporarily remove the configuration listener
    m_RunCombo.removeActionListener(m_ConfigureListener);
    
    // Do other stuff
    m_DatasetKeyModel.removeAllElements();
    m_RunModel.removeAllElements();
    m_ResultKeyModel.removeAllElements();
    m_CompareModel.removeAllElements();
    int datasetCol = -1;
    int runCol = -1;
    String selectedList = "";
    String selectedListDataset = "";
      //=============== BEGIN EDIT melville ===============
    boolean noise = false;//keep track of whether noise levels eval is required
    boolean learning = false;//keep track of whether learning curve eval is required
    boolean fraction = false;//keep track of whether fractions of datasets are provided for learning
    //the key on which to base the learning curves (either total instances or fraction) 
    int learning_key = -1;
    boolean classificationTask = false;//used to determine if regression measures should be selected
      //=============== END EDIT melville ===============
    for (int i = 0; i < m_Instances.numAttributes(); i++) {
      String name = m_Instances.attribute(i).name();
      m_DatasetKeyModel.addElement(name);
      m_RunModel.addElement(name);
      m_ResultKeyModel.addElement(name);
      m_CompareModel.addElement(name);

            //=============== BEGIN EDIT melville ===============
      //If learning curves were generated then treat each
      //dataset + pt combination as a different dataset
      if(name.toLowerCase().equals("key_noise_levels")){
	  //noise overrides learning curves - but treat noise levels
	  //like pts on learning curve
	  learning_key = i;
	  learning = true;
	  noise = true;
	  //fraction = true;
      }else if(name.toLowerCase().equals("key_fraction_instances") && !noise){
	  //fraction overrides total_instances
	  learning_key = i;
	  learning = true;
	  fraction = true;
      }else if(name.toLowerCase().equals("key_total_instances") && !learning){
	  learning_key = i;
	  learning = true;
      }else
            //=============== END EDIT melville ===============	
      if (name.toLowerCase().equals("key_dataset")) {
	m_DatasetKeyList.addSelectionInterval(i, i);
	selectedListDataset += "," + (i + 1);
      } else if ((runCol == -1)