Java tutorial
/** * 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. * * Copyright (c) 2003-2006 TU/e Eindhoven * by Eindhoven University of Technology * Department of Information Systems * http://is.tm.tue.nl * ************************************************************************/ package org.processmining.analysis.clusteranalysis; import java.awt.BorderLayout; import java.awt.GridLayout; import java.awt.event.ActionEvent; import java.awt.event.ActionListener; import javax.swing.JButton; import javax.swing.JCheckBox; import javax.swing.JPanel; import javax.swing.JScrollPane; import javax.swing.JSplitPane; import javax.swing.JTabbedPane; import org.processmining.analysis.petrinet.cpnexport.CpnUtils; import org.processmining.analysis.petrinet.cpnexport.ManagerLayout; import org.processmining.analysis.traceclustering.model.Cluster; import org.processmining.analysis.traceclustering.model.ClusterSet; import org.processmining.analysis.traceclustering.profile.AggregateProfile; import weka.classifiers.Classifier; import weka.core.Attribute; import weka.core.FastVector; import weka.core.Instance; import weka.core.Instances; /** * Class holding the machine learning settings and performing the actual data * mining tasks (with the help of the weka library). * * @author arozinat (a.rozinat@tm.tue.nl) */ public class ClusterDecisionAnalyzer { // for gui protected JPanel resultPanel = null; protected JSplitPane resultSplitPanel; protected JPanel left = null; protected JTabbedPane tabbedPane = null; protected JPanel treeViewPanel = null; protected JPanel algorithmViewPanel = null; protected JPanel evaluationViewPanel = null; protected JCheckBox[] checks; /** * The data mining classifier to be used for analysis. */ protected DecisionAnalyzer dAnalyzer; protected Classifier myClassifier; protected ClusterSet clusters; protected AggregateProfile agProfiles; private void initPanel() { left = new JPanel(new BorderLayout()); tabbedPane = new JTabbedPane(3); treeViewPanel = new JPanel(new BorderLayout()); algorithmViewPanel = new JPanel(new BorderLayout()); evaluationViewPanel = new JPanel(new BorderLayout()); tabbedPane.add(treeViewPanel, "Result", 0); tabbedPane.add(algorithmViewPanel, "Algorithm", 1); tabbedPane.add(evaluationViewPanel, "Evaluation", 2); resultSplitPanel = new JSplitPane(JSplitPane.HORIZONTAL_SPLIT, left, tabbedPane); resultSplitPanel.setDividerLocation(150); resultSplitPanel.setResizeWeight(0.5); resultSplitPanel.setOneTouchExpandable(true); resultPanel = new JPanel(new BorderLayout()); resultPanel.add(resultSplitPanel); // left panel // button JPanel p_top = new JPanel(new BorderLayout()); JButton startButton = new JButton("analyze"); startButton.addActionListener(new ActionListener() { public void actionPerformed(ActionEvent e) { exec(); } }); p_top.add(startButton); // check box JPanel p = new JPanel(new GridLayout(agProfiles.numberOfItems(), 1)); checks = new JCheckBox[agProfiles.numberOfItems()]; for (int i = 0; i < agProfiles.numberOfItems(); i++) { String name = CpnUtils.replaceSpecialCharacters(agProfiles.getItemKey(i)); checks[i] = new JCheckBox(name); checks[i].setSelected(true); p.add(checks[i]); } JScrollPane leftScrollPane = new JScrollPane(p); left.add(p_top, BorderLayout.NORTH); left.add(leftScrollPane, BorderLayout.CENTER); } public JCheckBox[] getChecks() { return checks; } public void exec() { dAnalyzer.analyse(this); } public JPanel analyse(ClusterSet aClusters) { this.clusters = aClusters; agProfiles = clusters.getAGProfiles(); dAnalyzer = new ClusterJ48Analyzer(); // dAnalyzer = new DecisionStumpAnalyzer(); dAnalyzer.initClassifier(); initPanel(); setAlgorithmVisualization(dAnalyzer.getParametersPanel()); return resultPanel; } /** * Creates a decision tree visualization for the current classification * problem. * * @return the panel to be displayed as analysis result for the current * decision point */ protected void setResultVisualization(JPanel result) { treeViewPanel.removeAll(); treeViewPanel.add(result); treeViewPanel.revalidate(); } /** * Creates a decision tree visualization for the current classification * problem. * * @return the panel to be displayed as analysis result for the current * decision point */ protected void setAlgorithmVisualization(JPanel result) { algorithmViewPanel.removeAll(); algorithmViewPanel.add(result); algorithmViewPanel.revalidate(); } /** * Creates a decision tree visualization for the current classification * problem. * * @return the panel to be displayed as analysis result for the current * decision point */ protected void setEvaluationVisualization(JPanel result) { evaluationViewPanel.removeAll(); evaluationViewPanel.add(result); evaluationViewPanel.revalidate(); } public Instances getDataInfo() { // create attribute information FastVector attributeInfo = new FastVector(); // make attribute // clean the relevant attribute list and re-fill based on new selection // scope for (int i = 0; i < agProfiles.numberOfItems(); i++) { if (checks[i].isSelected()) { String name = CpnUtils.replaceSpecialCharacters(agProfiles.getItemKey(i)); Attribute wekaAtt = new Attribute(name); attributeInfo.addElement(wekaAtt); } } // for target concept FastVector my_nominal_values = new FastVector(clusters.getClusters().size()); Attribute targetConcept = null; for (Cluster aCluster : clusters.getClusters()) { my_nominal_values.addElement(aCluster.getName()); } targetConcept = new Attribute("Cluster", my_nominal_values); attributeInfo.addElement(targetConcept); attributeInfo.trimToSize(); // learning Instances data = new Instances("Clustering", attributeInfo, 0); data.setClassIndex(data.numAttributes() - 1); for (Cluster aCluster : clusters.getClusters()) { String clusterName = aCluster.getName(); for (Integer i : aCluster.getTraceIndices()) { Instance instance0 = new Instance(attributeInfo.size()); for (int j = 0; j < agProfiles.numberOfItems(); j++) { if (checks[j].isSelected()) { String name = CpnUtils.replaceSpecialCharacters(agProfiles.getItemKey(j)); Attribute wekaAtt = data.attribute(name); if (wekaAtt != null) { double doubleAttValue = (new Double(agProfiles.getValue(i, j))).doubleValue(); instance0.setValue(wekaAtt, doubleAttValue); } else { System.out.println("fail to add"); } } } instance0.setDataset(data); instance0.setClassValue(clusterName); data.add(instance0); } } return data; } }