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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.
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/*
 *   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 3 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, see .
 */

/*
 *    CostBenefitAnalysisPanel.java
 *    Copyright (C) 2009-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.gui;

import weka.classifiers.evaluation.ThresholdCurve;
import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Utils;
import weka.gui.beans.CostBenefitAnalysis;
import weka.gui.visualize.PlotData2D;
import weka.gui.visualize.VisualizePanel;

import javax.swing.BorderFactory;
import javax.swing.ButtonGroup;
import javax.swing.JButton;
import javax.swing.JLabel;
import javax.swing.JPanel;
import javax.swing.JRadioButton;
import javax.swing.JSlider;
import javax.swing.JTextField;
import javax.swing.SwingConstants;
import javax.swing.event.ChangeEvent;
import javax.swing.event.ChangeListener;
import java.awt.BorderLayout;
import java.awt.Color;
import java.awt.Dimension;
import java.awt.FlowLayout;
import java.awt.Graphics;
import java.awt.GridLayout;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.awt.event.FocusEvent;
import java.awt.event.FocusListener;
import java.util.ArrayList;

/**
 * Panel for displaying the cost-benefit plots and all control widgets.
 *
 * @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
 * @version $Revision: $
 */
public class CostBenefitAnalysisPanel extends JPanel {

  /** For serialization */
  private static final long serialVersionUID = 5364871945448769003L;

  /** Displays the performance graphs(s) */
  protected VisualizePanel m_performancePanel = new VisualizePanel();

  /** Displays the cost/benefit (profit/loss) graph */
  protected VisualizePanel m_costBenefitPanel = new VisualizePanel();

  /**
   * The class attribute from the data that was used to generate the threshold
   * curve
   */
  protected Attribute m_classAttribute;

  /** Data for the threshold curve */
  protected PlotData2D m_masterPlot;

  /** Data for the cost/benefit curve */
  protected PlotData2D m_costBenefit;

  /** The size of the points being plotted */
  protected int[] m_shapeSizes;

  /** The index of the previous plotted point that was highlighted */
  protected int m_previousShapeIndex = -1;

  /** The slider for adjusting the threshold */
  protected JSlider m_thresholdSlider = new JSlider(0, 100, 0);

  protected JRadioButton m_percPop = new JRadioButton("% of Population");
  protected JRadioButton m_percOfTarget = new JRadioButton(
    "% of Target (recall)");
  protected JRadioButton m_threshold = new JRadioButton("Score Threshold");

  protected JLabel m_percPopLab = new JLabel();
  protected JLabel m_percOfTargetLab = new JLabel();
  protected JLabel m_thresholdLab = new JLabel();

  // Confusion matrix stuff
  protected JLabel m_conf_predictedA = new JLabel("Predicted (a)",
    SwingConstants.RIGHT);
  protected JLabel m_conf_predictedB = new JLabel("Predicted (b)",
    SwingConstants.RIGHT);
  protected JLabel m_conf_actualA = new JLabel(" Actual (a):");
  protected JLabel m_conf_actualB = new JLabel(" Actual (b):");
  protected ConfusionCell m_conf_aa = new ConfusionCell();
  protected ConfusionCell m_conf_ab = new ConfusionCell();
  protected ConfusionCell m_conf_ba = new ConfusionCell();
  protected ConfusionCell m_conf_bb = new ConfusionCell();

  // Cost matrix stuff
  protected JLabel m_cost_predictedA = new JLabel("Predicted (a)",
    SwingConstants.RIGHT);
  protected JLabel m_cost_predictedB = new JLabel("Predicted (b)",
    SwingConstants.RIGHT);
  protected JLabel m_cost_actualA = new JLabel(" Actual (a)");
  protected JLabel m_cost_actualB = new JLabel(" Actual (b)");
  protected JTextField m_cost_aa = new JTextField("0.0", 5);
  protected JTextField m_cost_ab = new JTextField("1.0", 5);
  protected JTextField m_cost_ba = new JTextField("1.0", 5);
  protected JTextField m_cost_bb = new JTextField("0.0", 5);
  protected JButton m_maximizeCB = new JButton("Maximize Cost/Benefit");
  protected JButton m_minimizeCB = new JButton("Minimize Cost/Benefit");
  protected JRadioButton m_costR = new JRadioButton("Cost");
  protected JRadioButton m_benefitR = new JRadioButton("Benefit");
  protected JLabel m_costBenefitL = new JLabel("Cost: ", SwingConstants.RIGHT);
  protected JLabel m_costBenefitV = new JLabel("0");
  protected JLabel m_randomV = new JLabel("0");
  protected JLabel m_gainV = new JLabel("0");

  protected int m_originalPopSize;

  /** Population text field */
  protected JTextField m_totalPopField = new JTextField(6);
  protected int m_totalPopPrevious;

  /** Classification accuracy */
  protected JLabel m_classificationAccV = new JLabel("-");

  // Only update curve & stats if values in cost matrix have changed
  protected double m_tpPrevious;
  protected double m_fpPrevious;
  protected double m_tnPrevious;
  protected double m_fnPrevious;

  /**
   * Inner class for handling a single cell in the confusion matrix. Displays
   * the value, value as a percentage of total population and graphical
   * depiction of percentage.
   *
   * @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
   */
  protected static class ConfusionCell extends JPanel {

    /** For serialization */
    private static final long serialVersionUID = 6148640235434494767L;

    private final JLabel m_conf_cell = new JLabel("-", SwingConstants.RIGHT);
    JLabel m_conf_perc = new JLabel("-", SwingConstants.RIGHT);

    private final JPanel m_percentageP;

    protected double m_percentage = 0;

    @SuppressWarnings("serial")
    public ConfusionCell() {
      setLayout(new BorderLayout());
      setBorder(BorderFactory.createEtchedBorder());

      add(m_conf_cell, BorderLayout.NORTH);

      m_percentageP = new JPanel() {
        @Override
        public void paintComponent(Graphics gx) {
          super.paintComponent(gx);

          if (m_percentage > 0) {
            gx.setColor(Color.BLUE);
            int height = this.getHeight();
            double width = this.getWidth();
            int barWidth = (int) (m_percentage * width);
            gx.fillRect(0, 0, barWidth, height);
          }
        }
      };

      Dimension d = new Dimension(30, 5);
      m_percentageP.setMinimumSize(d);
      m_percentageP.setPreferredSize(d);
      JPanel percHolder = new JPanel();
      percHolder.setLayout(new BorderLayout());
      percHolder.add(m_percentageP, BorderLayout.CENTER);
      percHolder.add(m_conf_perc, BorderLayout.EAST);

      add(percHolder, BorderLayout.SOUTH);
    }

    /**
     * Set the value of a cell.
     *
     * @param cellValue the value of the cell
     * @param max the max (for setting value as a percentage)
     * @param scaleFactor scale the value by this amount
     * @param precision precision for the percentage value
     */
    public void setCellValue(double cellValue, double max,
      double scaleFactor, int precision) {
      if (!Utils.isMissingValue(cellValue)) {
        m_percentage = cellValue / max;
      } else {
        m_percentage = 0;
      }

      m_conf_cell.setText(Utils.doubleToString((cellValue * scaleFactor), 0));
      m_conf_perc.setText(Utils.doubleToString(m_percentage * 100.0,
        precision) + "%");

      // refresh the percentage bar
      m_percentageP.repaint();
    }
  }

  public CostBenefitAnalysisPanel() {
    setLayout(new BorderLayout());
    m_performancePanel.setShowAttBars(false);
    m_performancePanel.setShowClassPanel(false);
    m_costBenefitPanel.setShowAttBars(false);
    m_costBenefitPanel.setShowClassPanel(false);

    Dimension size = new Dimension(500, 400);
    m_performancePanel.setPreferredSize(size);
    m_performancePanel.setMinimumSize(size);

    size = new Dimension(500, 400);
    m_costBenefitPanel.setMinimumSize(size);
    m_costBenefitPanel.setPreferredSize(size);

    m_thresholdSlider.addChangeListener(new ChangeListener() {
      @Override
      public void stateChanged(ChangeEvent e) {
        updateInfoForSliderValue(m_thresholdSlider.getValue() / 100.0);
      }
    });

    JPanel plotHolder = new JPanel();
    plotHolder.setLayout(new GridLayout(1, 2));
    plotHolder.add(m_performancePanel);
    plotHolder.add(m_costBenefitPanel);
    add(plotHolder, BorderLayout.CENTER);

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

    ButtonGroup bGroup = new ButtonGroup();
    bGroup.add(m_percPop);
    bGroup.add(m_percOfTarget);
    bGroup.add(m_threshold);

    ButtonGroup bGroup2 = new ButtonGroup();
    bGroup2.add(m_costR);
    bGroup2.add(m_benefitR);
    ActionListener rl = new ActionListener() {
      @Override
      public void actionPerformed(ActionEvent e) {
        if (m_costR.isSelected()) {
          m_costBenefitL.setText("Cost: ");
        } else {
          m_costBenefitL.setText("Benefit: ");
        }

        double gain = Double.parseDouble(m_gainV.getText());
        gain = -gain;
        m_gainV.setText(Utils.doubleToString(gain, 2));
      }
    };
    m_costR.addActionListener(rl);
    m_benefitR.addActionListener(rl);
    m_costR.setSelected(true);

    m_percPop.setSelected(true);
    JPanel threshPanel = new JPanel();
    threshPanel.setLayout(new BorderLayout());
    JPanel radioHolder = new JPanel();
    radioHolder.setLayout(new FlowLayout());
    radioHolder.add(m_percPop);
    radioHolder.add(m_percOfTarget);
    radioHolder.add(m_threshold);
    threshPanel.add(radioHolder, BorderLayout.NORTH);
    threshPanel.add(m_thresholdSlider, BorderLayout.SOUTH);

    JPanel threshInfoPanel = new JPanel();
    threshInfoPanel.setLayout(new GridLayout(3, 2));
    threshInfoPanel
      .add(new JLabel("% of Population: ", SwingConstants.RIGHT));
    threshInfoPanel.add(m_percPopLab);
    threshInfoPanel.add(new JLabel("% of Target: ", SwingConstants.RIGHT));
    threshInfoPanel.add(m_percOfTargetLab);
    threshInfoPanel
      .add(new JLabel("Score Threshold: ", SwingConstants.RIGHT));
    threshInfoPanel.add(m_thresholdLab);

    JPanel threshHolder = new JPanel();
    threshHolder.setBorder(BorderFactory.createTitledBorder("Threshold"));
    threshHolder.setLayout(new BorderLayout());
    threshHolder.add(threshPanel, BorderLayout.CENTER);
    threshHolder.add(threshInfoPanel, BorderLayout.EAST);

    lowerPanel.add(threshHolder, BorderLayout.NORTH);

    // holder for the two matrixes
    JPanel matrixHolder = new JPanel();
    matrixHolder.setLayout(new GridLayout(1, 2));

    // confusion matrix
    JPanel confusionPanel = new JPanel();
    confusionPanel.setLayout(new GridLayout(3, 3));
    confusionPanel.add(m_conf_predictedA);
    confusionPanel.add(m_conf_predictedB);
    confusionPanel.add(new JLabel()); // dummy
    confusionPanel.add(m_conf_aa);
    confusionPanel.add(m_conf_ab);
    confusionPanel.add(m_conf_actualA);
    confusionPanel.add(m_conf_ba);
    confusionPanel.add(m_conf_bb);
    confusionPanel.add(m_conf_actualB);
    JPanel tempHolderCA = new JPanel();
    tempHolderCA.setLayout(new BorderLayout());
    tempHolderCA.setBorder(BorderFactory
      .createTitledBorder("Confusion Matrix"));
    tempHolderCA.add(confusionPanel, BorderLayout.CENTER);

    JPanel accHolder = new JPanel();
    accHolder.setLayout(new FlowLayout(FlowLayout.LEFT));
    accHolder.add(new JLabel("Classification Accuracy: "));
    accHolder.add(m_classificationAccV);
    tempHolderCA.add(accHolder, BorderLayout.SOUTH);

    matrixHolder.add(tempHolderCA);

    // cost matrix
    JPanel costPanel = new JPanel();
    costPanel.setBorder(BorderFactory.createTitledBorder("Cost Matrix"));
    costPanel.setLayout(new BorderLayout());

    JPanel cmHolder = new JPanel();
    cmHolder.setLayout(new GridLayout(3, 3));
    cmHolder.add(m_cost_predictedA);
    cmHolder.add(m_cost_predictedB);
    cmHolder.add(new JLabel()); // dummy
    cmHolder.add(m_cost_aa);
    cmHolder.add(m_cost_ab);
    cmHolder.add(m_cost_actualA);
    cmHolder.add(m_cost_ba);
    cmHolder.add(m_cost_bb);
    cmHolder.add(m_cost_actualB);
    costPanel.add(cmHolder, BorderLayout.CENTER);

    FocusListener fl = new FocusListener() {
      @Override
      public void focusGained(FocusEvent e) {

      }

      @Override
      public void focusLost(FocusEvent e) {
        if (constructCostBenefitData()) {
          try {
            m_costBenefitPanel.setMasterPlot(m_costBenefit);
            m_costBenefitPanel.validate();
            m_costBenefitPanel.repaint();
          } catch (Exception ex) {
            ex.printStackTrace();
          }
          updateCostBenefit();
        }
      }
    };

    ActionListener al = new ActionListener() {
      @Override
      public void actionPerformed(ActionEvent e) {
        if (constructCostBenefitData()) {
          try {
            m_costBenefitPanel.setMasterPlot(m_costBenefit);
            m_costBenefitPanel.validate();
            m_costBenefitPanel.repaint();
          } catch (Exception ex) {
            ex.printStackTrace();
          }
          updateCostBenefit();
        }
      }
    };

    m_cost_aa.addFocusListener(fl);
    m_cost_aa.addActionListener(al);
    m_cost_ab.addFocusListener(fl);
    m_cost_ab.addActionListener(al);
    m_cost_ba.addFocusListener(fl);
    m_cost_ba.addActionListener(al);
    m_cost_bb.addFocusListener(fl);
    m_cost_bb.addActionListener(al);

    m_totalPopField.addFocusListener(fl);
    m_totalPopField.addActionListener(al);

    JPanel cbHolder = new JPanel();
    cbHolder.setLayout(new BorderLayout());
    JPanel tempP = new JPanel();
    tempP.setLayout(new GridLayout(3, 2));
    tempP.add(m_costBenefitL);
    tempP.add(m_costBenefitV);
    tempP.add(new JLabel("Random: ", SwingConstants.RIGHT));
    tempP.add(m_randomV);
    tempP.add(new JLabel("Gain: ", SwingConstants.RIGHT));
    tempP.add(m_gainV);
    cbHolder.add(tempP, BorderLayout.NORTH);
    JPanel butHolder = new JPanel();
    butHolder.setLayout(new GridLayout(2, 1));
    butHolder.add(m_maximizeCB);
    butHolder.add(m_minimizeCB);
    m_maximizeCB.addActionListener(new ActionListener() {
      @Override
      public void actionPerformed(ActionEvent e) {
        findMaxMinCB(true);
      }
    });

    m_minimizeCB.addActionListener(new ActionListener() {
      @Override
      public void actionPerformed(ActionEvent e) {
        findMaxMinCB(false);
      }
    });

    cbHolder.add(butHolder, BorderLayout.SOUTH);
    costPanel.add(cbHolder, BorderLayout.EAST);

    JPanel popCBR = new JPanel();
    popCBR.setLayout(new GridLayout(1, 2));
    JPanel popHolder = new JPanel();
    popHolder.setLayout(new FlowLayout(FlowLayout.LEFT));
    popHolder.add(new JLabel("Total Population: "));
    popHolder.add(m_totalPopField);

    JPanel radioHolder2 = new JPanel();
    radioHolder2.setLayout(new FlowLayout(FlowLayout.RIGHT));
    radioHolder2.add(m_costR);
    radioHolder2.add(m_benefitR);
    popCBR.add(popHolder);
    popCBR.add(radioHolder2);

    costPanel.add(popCBR, BorderLayout.SOUTH);

    matrixHolder.add(costPanel);

    lowerPanel.add(matrixHolder, BorderLayout.SOUTH);

    // popAccHolder.add(popHolder);

    // popAccHolder.add(accHolder);

    /*
     * JPanel lowerPanel2 = new JPanel(); lowerPanel2.setLayout(new
     * BorderLayout()); lowerPanel2.add(lowerPanel, BorderLayout.NORTH);
     * lowerPanel2.add(popAccHolder, BorderLayout.SOUTH);
     */

    add(lowerPanel, BorderLayout.SOUTH);

  }

  /**
   * Get the master threshold plot data
   *
   * @return the master threshold data
   */
  public PlotData2D getMasterPlot() {
    return m_masterPlot;
  }

  private void findMaxMinCB(boolean max) {
    double maxMin = (max) ? Double.NEGATIVE_INFINITY
      : Double.POSITIVE_INFINITY;

    Instances cBCurve = m_costBenefit.getPlotInstances();
    int maxMinIndex = 0;

    for (int i = 0; i < cBCurve.numInstances(); i++) {
      Instance current = cBCurve.instance(i);
      if (max) {
        if (current.value(1) > maxMin) {
          maxMin = current.value(1);
          maxMinIndex = i;
        }
      } else {
        if (current.value(1) < maxMin) {
          maxMin = current.value(1);
          maxMinIndex = i;
        }
      }
    }

    // set the slider to the correct position
    int indexOfSampleSize = m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.SAMPLE_SIZE_NAME).index();
    int indexOfPercOfTarget = m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.RECALL_NAME).index();
    int indexOfThreshold = m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.THRESHOLD_NAME).index();
    int indexOfMetric;

    if (m_percPop.isSelected()) {
      indexOfMetric = indexOfSampleSize;
    } else if (m_percOfTarget.isSelected()) {
      indexOfMetric = indexOfPercOfTarget;
    } else {
      indexOfMetric = indexOfThreshold;
    }

    double valueOfMetric = m_masterPlot.getPlotInstances()
      .instance(maxMinIndex).value(indexOfMetric);
    valueOfMetric *= 100.0;

    // set the approximate location of the slider
    m_thresholdSlider.setValue((int) valueOfMetric);

    // make sure the actual values relate to the true min/max rather
    // than being off due to slider location error.
    updateInfoGivenIndex(maxMinIndex);
  }

  private void updateCostBenefit() {
    double value = m_thresholdSlider.getValue() / 100.0;
    Instances plotInstances = m_masterPlot.getPlotInstances();
    int indexOfSampleSize = m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.SAMPLE_SIZE_NAME).index();
    int indexOfPercOfTarget = m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.RECALL_NAME).index();
    int indexOfThreshold = m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.THRESHOLD_NAME).index();
    int indexOfMetric;

    if (m_percPop.isSelected()) {
      indexOfMetric = indexOfSampleSize;
    } else if (m_percOfTarget.isSelected()) {
      indexOfMetric = indexOfPercOfTarget;
    } else {
      indexOfMetric = indexOfThreshold;
    }

    int index = findIndexForValue(value, plotInstances, indexOfMetric);
    updateCBRandomGainInfo(index);
  }

  private void updateCBRandomGainInfo(int index) {
    double requestedPopSize = m_originalPopSize;
    try {
      requestedPopSize = Double.parseDouble(m_totalPopField.getText());
    } catch (NumberFormatException e) {
    }
    double scaleFactor = requestedPopSize / m_originalPopSize;

    double CB = m_costBenefit.getPlotInstances().instance(index).value(1);
    m_costBenefitV.setText(Utils.doubleToString(CB, 2));

    double totalRandomCB = 0.0;
    Instance first = m_masterPlot.getPlotInstances().instance(0);
    double totalPos = first.value(m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.TRUE_POS_NAME).index())
      * scaleFactor;
    double totalNeg = first.value(m_masterPlot.getPlotInstances().attribute(
      ThresholdCurve.FALSE_POS_NAME))
      * scaleFactor;

    double posInSample = (totalPos * (Double.parseDouble(m_percPopLab
      .getText()) / 100.0));
    double negInSample = (totalNeg * (Double.parseDouble(m_percPopLab
      .getText()) / 100.0));
    double posOutSample = totalPos - posInSample;
    double negOutSample = totalNeg - negInSample;

    double tpCost = 0.0;
    try {
      tpCost = Double.parseDouble(m_cost_aa.getText());
    } catch (NumberFormatException n) {
    }
    double fpCost = 0.0;
    try {
      fpCost = Double.parseDouble(m_cost_ba.getText());
    } catch (NumberFormatException n) {
    }
    double tnCost = 0.0;
    try {
      tnCost = Double.parseDouble(m_cost_bb.getText());
    } catch (NumberFormatException n) {
    }
    double fnCost = 0.0;
    try {
      fnCost = Double.parseDouble(m_cost_ab.getText());
    } catch (NumberFormatException n) {
    }

    totalRandomCB += posInSample * tpCost;
    totalRandomCB += negInSample * fpCost;
    totalRandomCB += posOutSample * fnCost;
    totalRandomCB += negOutSample * tnCost;

    m_randomV.setText(Utils.doubleToString(totalRandomCB, 2));
    double gain = (m_costR.isSelected()) ? totalRandomCB - CB : CB
      - totalRandomCB;
    m_gainV.setText(Utils.doubleToString(gain, 2));

    // update classification rate
    Instance currentInst = m_masterPlot.getPlotInstances().instance(index);
    double tp = currentInst.value(m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.TRUE_POS_NAME).index());
    double tn = currentInst.value(m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.TRUE_NEG_NAME).index());
    m_classificationAccV.setText(Utils.doubleToString((tp + tn)
      / (totalPos + totalNeg) * 100.0, 4)
      + "%");
  }

  private void updateInfoGivenIndex(int index) {
    Instances plotInstances = m_masterPlot.getPlotInstances();
    int indexOfSampleSize = m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.SAMPLE_SIZE_NAME).index();
    int indexOfPercOfTarget = m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.RECALL_NAME).index();
    int indexOfThreshold = m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.THRESHOLD_NAME).index();

    // update labels
    m_percPopLab.setText(Utils.doubleToString(
      100.0 * plotInstances.instance(index).value(indexOfSampleSize), 4));
    m_percOfTargetLab.setText(Utils.doubleToString(100.0 * plotInstances
      .instance(index).value(indexOfPercOfTarget), 4));
    m_thresholdLab.setText(Utils.doubleToString(plotInstances.instance(index)
      .value(indexOfThreshold), 4));
    /*
     * if (m_percPop.isSelected()) {
     * m_percPopLab.setText(Utils.doubleToString(100.0 * value, 4)); } else if
     * (m_percOfTarget.isSelected()) {
     * m_percOfTargetLab.setText(Utils.doubleToString(100.0 * value, 4)); }
     * else { m_thresholdLab.setText(Utils.doubleToString(value, 4)); }
     */

    // Update the highlighted point on the graphs */
    if (m_previousShapeIndex >= 0) {
      m_shapeSizes[m_previousShapeIndex] = 1;
    }

    m_shapeSizes[index] = 10;
    m_previousShapeIndex = index;

    // Update the confusion matrix
    // double totalInstances =
    int tp = plotInstances.attribute(ThresholdCurve.TRUE_POS_NAME).index();
    int fp = plotInstances.attribute(ThresholdCurve.FALSE_POS_NAME).index();
    int tn = plotInstances.attribute(ThresholdCurve.TRUE_NEG_NAME).index();
    int fn = plotInstances.attribute(ThresholdCurve.FALSE_NEG_NAME).index();
    Instance temp = plotInstances.instance(index);
    double totalInstances = temp.value(tp) + temp.value(fp) + temp.value(tn)
      + temp.value(fn);
    // get the value out of the total pop field (if possible)
    double requestedPopSize = totalInstances;
    try {
      requestedPopSize = Double.parseDouble(m_totalPopField.getText());
    } catch (NumberFormatException e) {
    }

    m_conf_aa.setCellValue(temp.value(tp), totalInstances, requestedPopSize
      / totalInstances, 2);
    m_conf_ab.setCellValue(temp.value(fn), totalInstances, requestedPopSize
      / totalInstances, 2);
    m_conf_ba.setCellValue(temp.value(fp), totalInstances, requestedPopSize
      / totalInstances, 2);
    m_conf_bb.setCellValue(temp.value(tn), totalInstances, requestedPopSize
      / totalInstances, 2);

    updateCBRandomGainInfo(index);

    repaint();
  }

  private void updateInfoForSliderValue(double value) {
    int indexOfSampleSize = m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.SAMPLE_SIZE_NAME).index();
    int indexOfPercOfTarget = m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.RECALL_NAME).index();
    int indexOfThreshold = m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.THRESHOLD_NAME).index();
    int indexOfMetric;

    if (m_percPop.isSelected()) {
      indexOfMetric = indexOfSampleSize;
    } else if (m_percOfTarget.isSelected()) {
      indexOfMetric = indexOfPercOfTarget;
    } else {
      indexOfMetric = indexOfThreshold;
    }

    Instances plotInstances = m_masterPlot.getPlotInstances();
    int index = findIndexForValue(value, plotInstances, indexOfMetric);
    updateInfoGivenIndex(index);
  }

  private int findIndexForValue(double value, Instances plotInstances,
    int indexOfMetric) {
    // binary search
    // threshold curve is sorted ascending in the threshold (thus
    // descending for recall and pop size)
    int index = -1;
    int lower = 0;
    int upper = plotInstances.numInstances() - 1;
    int mid = (upper - lower) / 2;
    boolean done = false;
    while (!done) {
      if (upper - lower <= 1) {

        // choose the one closest to the value
        double comp1 = plotInstances.instance(upper).value(indexOfMetric);
        double comp2 = plotInstances.instance(lower).value(indexOfMetric);
        if (Math.abs(comp1 - value) < Math.abs(comp2 - value)) {
          index = upper;
        } else {
          index = lower;
        }

        break;
      }
      double comparisonVal = plotInstances.instance(mid).value(indexOfMetric);
      if (value > comparisonVal) {
        if (m_threshold.isSelected()) {
          lower = mid;
          mid += (upper - lower) / 2;
        } else {
          upper = mid;
          mid -= (upper - lower) / 2;
        }
      } else if (value < comparisonVal) {
        if (m_threshold.isSelected()) {
          upper = mid;
          mid -= (upper - lower) / 2;
        } else {
          lower = mid;
          mid += (upper - lower) / 2;
        }
      } else {
        index = mid;
        done = true;
      }
    }

    // now check for ties in the appropriate direction
    if (!m_threshold.isSelected()) {
      while (index + 1 < plotInstances.numInstances()) {
        if (plotInstances.instance(index + 1).value(indexOfMetric) == plotInstances
          .instance(index).value(indexOfMetric)) {
          index++;
        } else {
          break;
        }
      }
    } else {
      while (index - 1 >= 0) {
        if (plotInstances.instance(index - 1).value(indexOfMetric) == plotInstances
          .instance(index).value(indexOfMetric)) {
          index--;
        } else {
          break;
        }
      }
    }
    return index;
  }

  /**
   * Set the threshold data for the panel to use.
   *
   * @param data PlotData2D object encapsulating the threshold data.
   * @param classAtt the class attribute from the original data used to
   *          generate the threshold data.
   * @throws Exception if something goes wrong.
   */
  public synchronized void setDataSet(PlotData2D data, Attribute classAtt)
    throws Exception {
    // make a copy of the PlotData2D object
    m_masterPlot = new PlotData2D(data.getPlotInstances());
    boolean[] connectPoints = new boolean[m_masterPlot.getPlotInstances()
      .numInstances()];
    for (int i = 1; i < connectPoints.length; i++) {
      connectPoints[i] = true;
    }
    m_masterPlot.setConnectPoints(connectPoints);

    m_masterPlot.m_alwaysDisplayPointsOfThisSize = 10;
    setClassForConfusionMatrix(classAtt);
    m_performancePanel.setMasterPlot(m_masterPlot);
    m_performancePanel.validate();
    m_performancePanel.repaint();

    m_shapeSizes = new int[m_masterPlot.getPlotInstances().numInstances()];
    for (int i = 0; i < m_shapeSizes.length; i++) {
      m_shapeSizes[i] = 1;
    }
    m_masterPlot.setShapeSize(m_shapeSizes);
    constructCostBenefitData();
    m_costBenefitPanel.setMasterPlot(m_costBenefit);
    m_costBenefitPanel.validate();
    m_costBenefitPanel.repaint();

    m_totalPopPrevious = 0;
    m_fpPrevious = 0;
    m_tpPrevious = 0;
    m_tnPrevious = 0;
    m_fnPrevious = 0;
    m_previousShapeIndex = -1;

    // set the total population size
    Instance first = m_masterPlot.getPlotInstances().instance(0);
    double totalPos = first.value(m_masterPlot.getPlotInstances()
      .attribute(ThresholdCurve.TRUE_POS_NAME).index());
    double totalNeg = first.value(m_masterPlot.getPlotInstances().attribute(
      ThresholdCurve.FALSE_POS_NAME));
    m_originalPopSize = (int) (totalPos + totalNeg);
    m_totalPopField.setText("" + m_originalPopSize);

    m_performancePanel.setYIndex(5);
    m_performancePanel.setXIndex(10);
    m_costBenefitPanel.setXIndex(0);
    m_costBenefitPanel.setYIndex(1);
    // System.err.println(m_masterPlot.getPlotInstances());
    updateInfoForSliderValue(m_thresholdSlider.getValue() / 100.0);
  }

  private void setClassForConfusionMatrix(Attribute classAtt) {
    m_classAttribute = classAtt;
    m_conf_actualA.setText(" Actual (a): " + classAtt.value(0));
    m_conf_actualA.setToolTipText(classAtt.value(0));
    String negClasses = "";
    for (int i = 1; i < classAtt.numValues(); i++) {
      negClasses += classAtt.value(i);
      if (i < classAtt.numValues() - 1) {
        negClasses += ",";
      }
    }
    m_conf_actualB.setText(" Actual (b): " + negClasses);
    m_conf_actualB.setToolTipText(negClasses);
  }

  private boolean constructCostBenefitData() {
    double tpCost = 0.0;
    try {
      tpCost = Double.parseDouble(m_cost_aa.getText());
    } catch (NumberFormatException n) {
    }
    double fpCost = 0.0;
    try {
      fpCost = Double.parseDouble(m_cost_ba.getText());
    } catch (NumberFormatException n) {
    }
    double tnCost = 0.0;
    try {
      tnCost = Double.parseDouble(m_cost_bb.getText());
    } catch (NumberFormatException n) {
    }
    double fnCost = 0.0;
    try {
      fnCost = Double.parseDouble(m_cost_ab.getText());
    } catch (NumberFormatException n) {
    }

    double requestedPopSize = m_originalPopSize;
    try {
      requestedPopSize = Double.parseDouble(m_totalPopField.getText());
    } catch (NumberFormatException e) {
    }

    double scaleFactor = 1.0;
    if (m_originalPopSize != 0) {
      scaleFactor = requestedPopSize / m_originalPopSize;
    }

    if (tpCost == m_tpPrevious && fpCost == m_fpPrevious
      && tnCost == m_tnPrevious && fnCost == m_fnPrevious
      && requestedPopSize == m_totalPopPrevious) {
      return false;
    }

    // First construct some Instances for the curve
    ArrayList fv = new ArrayList();
    fv.add(new Attribute("Sample Size"));
    fv.add(new Attribute("Cost/Benefit"));
    fv.add(new Attribute("Threshold"));
    Instances costBenefitI = new Instances("Cost/Benefit Curve", fv, 100);

    // process the performance data to make this curve
    Instances performanceI = m_masterPlot.getPlotInstances();

    for (int i = 0; i < performanceI.numInstances(); i++) {
      Instance current = performanceI.instance(i);

      double[] vals = new double[3];
      vals[0] = current.value(10); // sample size
      vals[1] = (current.value(0) * tpCost + current.value(1) * fnCost
        + current.value(2) * fpCost + current.value(3) * tnCost)
        * scaleFactor;
      vals[2] = current.value(current.numAttributes() - 1);
      Instance newInst = new DenseInstance(1.0, vals);
      costBenefitI.add(newInst);
    }

    costBenefitI.compactify();

    // now set up the plot data
    m_costBenefit = new PlotData2D(costBenefitI);
    m_costBenefit.m_alwaysDisplayPointsOfThisSize = 10;
    m_costBenefit.setPlotName("Cost/benefit curve");
    boolean[] connectPoints = new boolean[costBenefitI.numInstances()];

    for (int i = 0; i < connectPoints.length; i++) {
      connectPoints[i] = true;
    }
    try {
      m_costBenefit.setConnectPoints(connectPoints);
      m_costBenefit.setShapeSize(m_shapeSizes);
    } catch (Exception ex) {
      // ignore
    }

    m_tpPrevious = tpCost;
    m_fpPrevious = fpCost;
    m_tnPrevious = tnCost;
    m_fnPrevious = fnCost;

    return true;
  }
}