/*
* 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.
*/
/*
* VisualizeClusterAssignments.java
* Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
*/
package wekaexamples.gui.visualize;
import weka.clusterers.AbstractClusterer;
import weka.clusterers.ClusterEvaluation;
import weka.clusterers.Clusterer;
import weka.core.Instances;
import weka.core.Utils;
import weka.core.converters.ConverterUtils.DataSource;
import weka.gui.explorer.ClustererAssignmentsPlotInstances;
import weka.gui.explorer.ExplorerDefaults;
import weka.gui.visualize.PlotData2D;
import weka.gui.visualize.VisualizePanel;
import java.awt.BorderLayout;
import java.text.SimpleDateFormat;
import java.util.Date;
import javax.swing.JFrame;
/**
* Runs a clusterer on a dataset and visualizes the cluster assignments,
* like with right-click menu in Explorer.
* <p/>
* Takes two arguments:
* <ol>
* <li>-t dataset</li>
* <li>-W cluster algorithm with options</li>
* </ol>
*
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision$
*/
public class VisualizeClusterAssignments {
public static void main(String[] args) throws Exception {
// load data
Instances train = DataSource.read(Utils.getOption('t', args));
// some data formats store the class attribute information as well
if (train.classIndex() != -1)
throw new IllegalArgumentException("Data cannot have class attribute!");
// instantiate clusterer
String[] options = Utils.splitOptions(Utils.getOption('W', args));
String classname = options[0];
options[0] = "";
Clusterer clusterer = AbstractClusterer.forName(classname, options);
// evaluate clusterer
clusterer.buildClusterer(train);
ClusterEvaluation eval = new ClusterEvaluation();
eval.setClusterer(clusterer);
eval.evaluateClusterer(train);
// setup visualization
// taken from: ClustererPanel.startClusterer()
ClustererAssignmentsPlotInstances plotInstances = ExplorerDefaults.getClustererAssignmentsPlotInstances();
plotInstances.setClusterer(clusterer);
plotInstances.setInstances(train);
plotInstances.setClusterEvaluation(eval);
plotInstances.setUp();
String name = (new SimpleDateFormat("HH:mm:ss - ")).format(new Date());
String cname = clusterer.getClass().getName();
if (cname.startsWith("weka.clusterers."))
name += cname.substring("weka.clusterers.".length());
else
name += cname;
PlotData2D predData = plotInstances.getPlotData(name);
VisualizePanel vp = new VisualizePanel();
vp.setName(predData.getPlotName());
vp.addPlot(predData);
// display data
// taken from: ClustererPanel.visualizeClusterAssignments(VisualizePanel)
String plotName = vp.getName();
JFrame jf = new JFrame("Weka Clusterer Visualize: " + plotName);
jf.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
jf.setSize(500,400);
jf.getContentPane().setLayout(new BorderLayout());
jf.getContentPane().add(vp, BorderLayout.CENTER);
jf.setVisible(true);
}
}