decisiontreedataview.java

一个数据挖掘软件ALPHAMINERR的整个过程的JAVA版源代码

/*
 *    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.
 */

/*
 * Created on 2005-1-27
 *
 * TODO To change the template for this generated file go to
 * Window - Preferences - Java - Code Style - Code Templates
 */
package eti.bi.alphaminer.patch.standard.operation.result.view;


import java.awt.BorderLayout;
import java.awt.Color;
import java.awt.Component;
import java.awt.Dimension;
import java.io.File;
import java.util.ArrayList;
import java.util.Vector;

import javax.swing.BorderFactory;
import javax.swing.JFileChooser;
import javax.swing.JOptionPane;
import javax.swing.JScrollPane;
import javax.swing.JTable;
import javax.swing.table.TableColumnModel;

import weka.classifiers.trees.J48;
import weka.core.Instance;
import weka.core.Instances;

import com.prudsys.pdm.Adapters.Weka.WekaCoreAdapter;
import com.prudsys.pdm.Core.CategoricalAttribute;
import com.prudsys.pdm.Core.MiningAttribute;
import com.prudsys.pdm.Core.MiningException;
import com.prudsys.pdm.Core.NumericAttribute;
import com.prudsys.pdm.Input.MiningStoredData;
import com.prudsys.pdm.Input.MiningVector;

import eti.bi.alphaminer.operation.result.ResultView;
import eti.bi.alphaminer.operation.result.datamodel.SortingDataGridModel;
import eti.bi.alphaminer.operation.result.export.ExcelExporter;
import eti.bi.alphaminer.operation.result.renderer.DataCellRenderer;
import eti.bi.common.Locale.Resource;
import eti.bi.exception.AppException;
import eti.bi.exception.SysException;

/**
 * 
 * Take J48 classifier, MiningStoredData, MiningAttribute and the target 
 * MiningAttribute as inputs. Generate the predicted class and the 
 * probability for each intance in the stored data by using the J48 classifier.
 * Output a JPanel (JTable) shown these infor.
 *  
 * @author TWang On Jan 27, 2005.
 * 
 */

public class DecisionTreeDataView extends ResultView {
	 
	/**
	 * 
	 */
	private static final long serialVersionUID = 1L;
	// JTable that shows the data
	private JTable m_DataTable;
	private String[] m_DataTableHeader;
	private Object[][] m_DataTableContent;
	private Class[] m_DataTableType; 
	
	// JScrollPane that contains JTable
	private JScrollPane m_ScrollPane;
	
	private J48 m_J48Classifier;  
	private MiningAttribute[] m_MiningAttributes;
	private MiningAttribute m_TargetMiningAttribute;
	private MiningStoredData m_MiningStoredData;
	private Instances m_Instances; 
	
	// The target attribute's classes.
	private ArrayList m_ClassValues;

	 
	/**
	 * Might need to check all the input parameters before usage. 
	 * Throw exceptions as necessary. 
	 * 
	 * @param a_Cassifier
	 * @param a_MiningStoredData
	 * @param a_MiningAttributes
	 * @param a_TargetMiningAttribute
	 * @throws Exception
	 */
	public DecisionTreeDataView(J48 a_Cassifier, MiningStoredData a_MiningStoredData, MiningAttribute[] a_MiningAttributes, MiningAttribute a_TargetMiningAttribute) throws Exception{
		super(Resource.srcStr("DataView")); 
		m_ViewType = ResultView.TYPE_DATA;
		
		if (a_Cassifier == null) {  
			throw new SysException("The J48 Classifier in the DecisionOperator is NULL."); 
		}		
		m_J48Classifier = a_Cassifier; 
	
		if ( !(a_TargetMiningAttribute instanceof CategoricalAttribute) ) {  
			throw new SysException("The target attribute is not categorical."); 
		}	  
		m_TargetMiningAttribute = a_TargetMiningAttribute;
		m_ClassValues = ((CategoricalAttribute) a_TargetMiningAttribute).getValues();
		m_MiningAttributes = a_MiningAttributes;
		m_MiningStoredData = a_MiningStoredData;
		
		m_ScrollPane = new JScrollPane();  
	
		this.setLayout(new BorderLayout());
		this.setBorder(BorderFactory.createEmptyBorder(0, 0, 0, 0)); 
		this.add(m_ScrollPane, BorderLayout.CENTER);
	 
		createDataTable(); 
				
		m_ScrollPane.setPreferredSize(new Dimension(200, 70));
		m_ScrollPane.getViewport().add(m_DataTable);
		m_ScrollPane.getViewport().setBackground(Color.WHITE);   
	} 
	
	/**
	 * 
	 * Create the JTable and attach it in the ScrollPane.   
	 *  
	 * @param a_ScrollPane
	 * @throws Exception
	 */
	@SuppressWarnings("unchecked")
	private void createDataTable() throws Exception { 
		
		// Three columns more to display index, predicted class, and rule confidence.
		int column = m_MiningAttributes.length + 3;
		m_DataTableType = new Class[column];
		m_DataTableHeader = new String[column]; 
		MiningAttribute attribute = null; 
 
		// Create JTable header and JTable class type.
		for (int coloumIndex = 0; coloumIndex < column; coloumIndex++) {
			if (coloumIndex == 0){ 
				m_DataTableType[coloumIndex] = Integer.class;
				m_DataTableHeader[coloumIndex] = Resource.srcStr("DATA_INDEX");
				continue;  
			}
			if (coloumIndex == column - 1) {
				m_DataTableType[coloumIndex] = Double.class;
				m_DataTableHeader[coloumIndex] = Resource.srcStr("DECISIONTREE_CONFIDENCE");
				continue;
			}
			if (coloumIndex == column - 2) {
				m_DataTableType[coloumIndex] = String.class;
				m_DataTableHeader[coloumIndex] = Resource.srcStr("DATA_PREDICT");
				continue;
			}
			attribute = m_MiningAttributes[coloumIndex-1];
			m_DataTableHeader[coloumIndex] = attribute.getName();
			m_DataTableType[coloumIndex] = String.class; 
			
			if (attribute instanceof NumericAttribute) {
				
				int dataType = ((NumericAttribute) attribute).getDataType();
				
				if (dataType == NumericAttribute.DOUBLE)
					m_DataTableType[coloumIndex] = Double.class;
				else if (dataType == NumericAttribute.FLOAT)
					m_DataTableType[coloumIndex] = Float.class;
				else if (dataType == NumericAttribute.INTEGER)
					m_DataTableType[coloumIndex] = Integer.class;
				else if (dataType == NumericAttribute.BOOLEAN)
					m_DataTableType[coloumIndex] = Boolean.class; 
			} 
			else if (attribute instanceof CategoricalAttribute) {
				
				int dataType = ((CategoricalAttribute) attribute).getDataType();

				if (dataType == CategoricalAttribute.BOOLEAN)
					m_DataTableType[coloumIndex] = Boolean.class; 
				else 
					m_DataTableType[coloumIndex] = String.class; 
			}
		}
		 
		// Fill the JTable content.
		if (m_MiningAttributes != null) {
			m_Instances = transform(m_MiningStoredData);
			
			// Must set the target attribute before execute the .classifyInstance() method.
			m_Instances.setClass(m_Instances.attribute(m_TargetMiningAttribute.getName()));
			
			ArrayList list = m_MiningStoredData.getMiningVectors();
			Object[][] content = new Object[list.size()][column];
			Vector allVec = null; 
			MiningVector vec = null; 
			
			for (int i = 0; i < list.size(); i++) {
				vec = (MiningVector) list.get(i);
 				// Convert a mining vector into a vector, handle the MISSING value.
//				for(int in=0; in<vec.getMetaData().getAttributesArray().length; in++){
// 					MiningAttribute attr = vec.getMetaData().getMiningAttribute(in);
// 					
// 					if (vec.isMissing(in)){
// 						allVec.add(MissingValue.DISPLAY_VALUE);
// 					}else if(attr instanceof CategoricalAttribute){
// 						allVec.add( ((CategoricalAttribute)attr).getCategory(vec.getValue(in)) );
// 					}else {
// 						allVec.add( new Double(vec.getValue(in)) );
// 					}			        	 
// 				}  
				allVec = vec.toVector();
				allVec.insertElementAt(new Integer(i+1), 0);
				Instance instance = m_Instances.instance(i);
				
				
				int classIndex =  (int)m_J48Classifier.classifyInstance(instance);
				allVec.addElement( m_ClassValues.get(classIndex));
				
				
				// Must call .getProbabity() only after calling .classifyInstance.
				allVec.addElement(new Double(100*m_J48Classifier.getProbability())); 
				//allVec.addElement( new Double( weka.core.Utils.roundDouble(((Double)m_Distances.elementAt(i)).doubleValue(), 3) ) );
				content[i] =allVec.toArray(); 
			}
			m_DataTableContent = content; 
		}

		// Create Table			
		m_DataTable = new JTable();
// 		m_DataTable.setModel(new DataGridModel(m_DataTableContent, m_DataTableHeader, m_DataTableType));
		SortingDataGridModel model = new SortingDataGridModel(m_DataTableContent, m_DataTableHeader, m_DataTableType);
		m_DataTable.setModel(model);
		m_DataTable.setDefaultRenderer(String.class, new DataCellRenderer(true));
		m_DataTable.setDefaultRenderer(Short.class, new DataCellRenderer(true));
		m_DataTable.setDefaultRenderer(Long.class, new DataCellRenderer(true));
		m_DataTable.setDefaultRenderer(Integer.class, new DataCellRenderer(true));
		m_DataTable.setDefaultRenderer(Double.class, new DataCellRenderer(true));
		m_DataTable.setDefaultRenderer(Float.class, new DataCellRenderer(true));
		
		model.addMouseListenerToHeader(m_DataTable);
		setColumnWidth();
	}
	
	/**
	 * Helper calss to transform Xelopes MingStoredData into WEKA Instances.
	 * 
	 * @param a_InputMiningStoredData
	 * @return
	 * @throws MiningException
	 */
	public Instances transform(MiningStoredData a_InputMiningStoredData) throws MiningException
	{ 
		Instances wekaInstances;
		try {
			// Reset the cursor of the MiningStoredData set, so the transform starts from
			// the first reord. Otherwise, the returned object might be NULL.
			// By TWang. Jan 25, 2005.
			a_InputMiningStoredData.reset();
			
			wekaInstances = (Instances) WekaCoreAdapter.PDMMiningInputStream2WekaInstances(a_InputMiningStoredData); 
		} catch (Exception e) { 
			e.printStackTrace();
			throw new MiningException("Can not transform from MiningStoredData to Instances.");
		}
		return wekaInstances;
	}
	
	
	public void setColumnWidth() {
			TableColumnModel tcm = m_DataTable.getColumnModel();
		 
			for (int i=1; i<tcm.getColumnCount(); i++)
				tcm.getColumn(i).setMinWidth(60);
			
			m_DataTable.setAutoResizeMode(JTable.AUTO_RESIZE_OFF);
			 
			tcm.getColumn(0).setPreferredWidth(60);
			tcm.getColumn(0).setCellRenderer(m_DataTable.getTableHeader().getDefaultRenderer()); 
	}
	
	/**
	 * Exort the data table into an excel file.
	 * 
	 * Called by the OperatorResult class. The subclass of OperatorResult must 
	 * call m_SelectedView.export() explictly if it overwirte the export() function 
	 * of OperatorResult.
	 */
	public void export() throws AppException, SysException {
		// Use user home directory 
		File directory = new File(System.getProperty("user.dir"));

		// Create and initialize file chooser for excel
		JFileChooser chooser = new JFileChooser(directory);
		chooser.setDialogTitle(Resource.srcStr("FileExport"));
		chooser.setFileFilter(ExcelExporter.FILTER);
		chooser.setSelectedFile(ExcelExporter.DEFAULT_FILE);

  		// pop up the file chooser dialog and return the file value
		int returnVal = chooser.showSaveDialog(this);	  
		if (returnVal == JFileChooser.APPROVE_OPTION) 
		{
			File exportFile = chooser.getSelectedFile();
			
			//<<tyleung 20/4/2005
			if (exportFile.exists()) {
				int option = JOptionPane
						.showConfirmDialog(
								(Component) this,
								"The file  \""
										+ exportFile.getName()
										+ "\""
										+ " already exists. Do you want to replace the existing file?",//						
								"AlphaMiner", JOptionPane.YES_NO_OPTION,
								JOptionPane.QUESTION_MESSAGE);
				if (option != JOptionPane.CANCEL_OPTION) {
					if (option == JOptionPane.YES_OPTION) {
//					  Create the excel exporter with the excel file extension enforced to be .xls
					  	ExcelExporter aExporter = new ExcelExporter(m_DataTable, exportFile, true);
			 			
						aExporter.export();
						
					}else{
						returnVal = chooser.showSaveDialog(this);
					}
				}
			}else {

				// Create the excel exporter with the excel file extension enforced to be .xls
			  	ExcelExporter aExporter = new ExcelExporter(m_DataTable, exportFile, true);
	 			
				aExporter.export();
			}
			//tyleung 20/4/2005>>
		}
	}
}