/*
* 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.
*/
package wekaexamples.gui.visualize.plugins;
import weka.classifiers.evaluation.NominalPrediction;
import weka.classifiers.evaluation.NumericPrediction;
import weka.core.Attribute;
import weka.core.FastVector;
import weka.gui.visualize.plugins.VisualizePlugin;
import java.awt.BorderLayout;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.util.Vector;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JMenuItem;
import javax.swing.JOptionPane;
import org.math.plot.Plot2DPanel;
/**
* A panel that displays the prediction errors.
*
* @author peter (peter at waikato dot ac dot nz)
* @version $Revision$
*/
public class PredictionError
implements VisualizePlugin {
/**
* Get a JMenu or JMenuItem which contain action listeners
* that perform the visualization, using some but not
* necessarily all of the data. Exceptions thrown because of
* changes in Weka since compilation need to be caught by
* the implementer.
*
* @see NoClassDefFoundError
* @see IncompatibleClassChangeError
*
* @param preds predictions
* @param classAtt class attribute
* @return menuitem for opening visualization(s), or null
* to indicate no visualization is applicable for the input
*/
public JMenuItem getVisualizeMenuItem(FastVector preds, Attribute classAtt) {
final FastVector finalPreds = preds;
final Attribute finalClassAtt = classAtt;
JMenuItem result = new JMenuItem("Prediction error");
result.addActionListener(new ActionListener() {
public void actionPerformed(ActionEvent arg0) {
display(finalPreds, finalClassAtt);
}
});
return result;
}
/**
* Get the minimum version of Weka, inclusive, the class
* is designed to work with. eg: <code>3.5.0</code>
*
* @return the minimum version
*/
public String getMinVersion() {
return "3.5.3";
}
/**
* Get the maximum version of Weka, exclusive, the class
* is designed to work with. eg: <code>3.6.0</code>
*
* @return the maximum version
*/
public String getMaxVersion() {
return "3.6.0";
}
/**
* Get the specific version of Weka the class is designed for.
* eg: <code>3.5.1</code>
*
* @return the version it was designed for
*/
public String getDesignVersion() {
return "3.5.7";
}
/**
* Displays the prediction error.
*
* @param preds the predictions to display
* @param classAtt the class attribute
*/
protected void display(FastVector preds, Attribute classAtt) {
double[] x;
double[] y;
Vector<Double> xVals;
Vector<Double> yVals;
Plot2DPanel plot;
JFrame frame;
NominalPrediction nom;
NumericPrediction num;
int i;
int n;
if (preds == null) {
JOptionPane.showMessageDialog(null, "No predictions to display!");
return;
}
// setup plot
plot = new Plot2DPanel();
plot.addLegend("SOUTH");
if (preds.size() > 0) {
if (preds.elementAt(0) instanceof NominalPrediction) {
for (n = 1; n <= 2; n++) {
// collect data: 1=correct, 2=incorrect predictions
xVals = new Vector<Double>();
yVals = new Vector<Double>();
for (i = 0; i < preds.size(); i++) {
nom = (NominalPrediction) preds.elementAt(i);
if (n == 1) {
if (nom.actual() == nom.predicted()) {
xVals.add((double) i);
yVals.add(nom.distribution()[(int) nom.actual()]);
}
}
else {
if (nom.actual() != nom.predicted()) {
xVals.add((double) i);
yVals.add(nom.distribution()[(int) nom.actual()]);
}
}
}
// transfer into arrays
x = new double[xVals.size()];
y = new double[yVals.size()];
for (i = 0; i < x.length; i++) {
x[i] = xVals.get(i);
y[i] = yVals.get(i);
}
// add plot
if (n == 1)
plot.addBarPlot("Correct", x, y);
else
plot.addBarPlot("Incorrect", x, y);
}
}
else {
xVals = new Vector<Double>();
yVals = new Vector<Double>();
for (i = 0; i < preds.size(); i++) {
num = (NumericPrediction) preds.elementAt(i);
xVals.add((double) i);
yVals.add(num.actual() - num.predicted());
}
// transfer into arrays
x = new double[xVals.size()];
y = new double[yVals.size()];
for (i = 0; i < x.length; i++) {
x[i] = xVals.get(i);
y[i] = yVals.get(i);
}
// add plot
plot.addBarPlot("Error", x, y);
}
}
// setup frame
frame = new JFrame("Prediction error");
frame.setSize(600, 600);
frame.setVisible(true);
frame.getContentPane().setLayout(new BorderLayout());
frame.getContentPane().add(plot, BorderLayout.CENTER);
frame.getContentPane().add(new JLabel("Displays the probability the classifier returns for the actual class label."), BorderLayout.SOUTH);
}
}