userclassifier.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.
 */

/*
 *    UserClassifier.java
 *    Copyright (C) 1999 Malcolm Ware
 *
 */

package weka.classifiers.trees;

import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.awt.event.WindowAdapter;
import java.awt.event.WindowEvent;
import java.io.Serializable;

import javax.swing.JFrame;
import javax.swing.JOptionPane;
import javax.swing.JTabbedPane;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.functions.LinearRegression;
import weka.core.Drawable;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;
import weka.gui.GenericObjectEditor;
import weka.gui.PropertyDialog;
import weka.gui.treevisualizer.PlaceNode1;
import weka.gui.treevisualizer.PlaceNode2;
import weka.gui.treevisualizer.TreeDisplayEvent;
import weka.gui.treevisualizer.TreeDisplayListener;
import weka.gui.treevisualizer.TreeVisualizer;
import weka.gui.visualize.VisualizePanel;
import weka.gui.visualize.VisualizePanelEvent;
import weka.gui.visualize.VisualizePanelListener;




/**
 * Class for generating an user defined decision tree. For more info see <p>
 *
 * Ware M., Frank E., Holmes G., Hall M. and Witten I.H. (2000).
 * <i>interactive machine learning - letting users build classifiers</i>,
 * Working Paper 00/4, Department of Computer Science, 
 * University of Waikato; March. Also available online at
 * <a href="http://www.cs.waikato.ac.nz/~ml/publications/2000/
 * 00MW-etal-Interactive-ML.ps">
 * http://www.cs.waikato.ac.nz/~ml/publications/2000/
 * 00MW-etal-Interactive-ML.ps</a>. <p>
 *
 * @author Malcolm Ware ([e-mail])
 * @version $Revision$
 */
public class UserClassifier extends Classifier implements Drawable,
TreeDisplayListener, VisualizePanelListener {
  

  /** I am not sure if these are strictly adhered to in visualizepanel
   * so I am making them private to avoid confusion, (note that they will
   * be correct in this class, VLINE and HLINE aren't used).
   */
  private static final int LEAF = 0;
  private static final int RECTANGLE = 1;
  private static final int POLYGON = 2;
  private static final int POLYLINE = 3;
  private static final int VLINE = 5;
  private static final int HLINE =6;
  

  /** The tree display panel. */
  private TreeVisualizer m_tView = null;
  /** The instances display. */
  private VisualizePanel m_iView = null;
  /** Two references to the structure of the decision tree. */
  private TreeClass m_top, m_focus;
  /** The next number that can be used as a unique id for a node. */
  private int m_nextId;
  /** These two frames aren't used anymore. */
  private JFrame m_treeFrame;
  private JFrame m_visFrame;
  
  /** The tabbed window for the tree and instances view. */
  private JTabbedPane m_reps;
  /** The window. */
  private JFrame m_mainWin;
  /** The status of whether there is a decision tree ready or not. */
  private boolean m_built=false;
  /** A list of other m_classifiers. */
  private GenericObjectEditor m_classifiers;
  /** A window for selecting other classifiers. */
  private PropertyDialog m_propertyDialog;

  /* Register the property editors we need */
  static {
    java.beans.PropertyEditorManager
      .registerEditor(weka.core.SelectedTag.class,
		      weka.gui.SelectedTagEditor.class);
    java.beans.PropertyEditorManager
      .registerEditor(weka.filters.Filter.class,
		      weka.gui.GenericObjectEditor.class);
    java.beans.PropertyEditorManager
      .registerEditor(weka.classifiers.Classifier [].class,
		      weka.gui.GenericArrayEditor.class);
    java.beans.PropertyEditorManager
      .registerEditor(Object [].class,
		      weka.gui.GenericArrayEditor.class);
    java.beans.PropertyEditorManager
      .registerEditor(weka.classifiers.Classifier.class,
		      weka.gui.GenericObjectEditor.class);
    java.beans.PropertyEditorManager
      .registerEditor(weka.classifiers.CostMatrix.class,
		      weka.gui.CostMatrixEditor.class);
  }

  /**
   * Main method for testing this class.
   *
   * @param argv should contain command line options (see setOptions)
   */
  public static void main(String [] argv) {

    try {
      System.out.println(Evaluation.evaluateModel(new UserClassifier(), argv));
    } catch (Exception e) {
      System.err.println(e.getMessage());
      e.printStackTrace();
    }
    System.exit(0);
    //System.out.println("im done");
  }

  /**
   * @return a string that represents this objects tree.
   */
  public String toString() {
    if (!m_built) {

      return "Tree Not Built";
    }
    StringBuffer text = new StringBuffer();
    try {
      m_top.toString(0, text);
      
      m_top.objectStrings(text);

    } catch(Exception e) {
      System.out.println("error: " + e.getMessage());
    }
    
    return text.toString();


  }


  /**
   * Receives user choices from the tree view, and then deals with these 
   * choices. 
   * @param e The choice. 
   */
  public void userCommand(TreeDisplayEvent e) {
    
    if (m_propertyDialog != null) {
      m_propertyDialog.dispose();
      m_propertyDialog = null;
    }
    try {
      if (m_iView == null || m_tView == null) {
	//throw exception
      }
      if (e.getCommand() == e.NO_COMMAND) {
	//do nothing
      }
      else if (e.getCommand() == e.ADD_CHILDREN) {
	//highlight the particular node and reset the vis panel
	if (m_top == null) {
	  //this shouldn't happen , someone elses code would 
	  //have to have added a trigger to this listener.
	  System.out.println("Error : Received event from a TreeDisplayer"
			     + " that is unknown to the classifier.");
	}
	else {
	  m_tView.setHighlight(e.getID());
	  /*if (m_iView == null)
	    {
	    m_iView = new VisualizePanel(this);
	    m_iView.setSize(400, 300);
	    }*/
	  m_focus = m_top.getNode(e.getID());
	  m_iView.setInstances(m_focus.m_training);
	  if (m_focus.m_attrib1 >= 0) {
	    m_iView.setXIndex(m_focus.m_attrib1);
	  }
	  if (m_focus.m_attrib2 >= 0) {
	    m_iView.setYIndex(m_focus.m_attrib2);
	  }
	  m_iView.setColourIndex(m_focus.m_training.classIndex());
	  if (((Double)((FastVector)m_focus.m_ranges.elementAt(0)).
	       elementAt(0)).intValue() != LEAF) {
	    m_iView.setShapes(m_focus.m_ranges);
	  }
	  //m_iView.setSIndex(2);
	}
      }
      else if (e.getCommand() == e.REMOVE_CHILDREN) {
	/*if (m_iView == null)
	  {
	  m_iView = new VisualizePanel(this);
	  m_iView.setSize(400, 300);
	  }*/
	m_focus = m_top.getNode(e.getID());
	m_iView.setInstances(m_focus.m_training);
	if (m_focus.m_attrib1 >= 0) {
	  m_iView.setXIndex(m_focus.m_attrib1);
	}
	if (m_focus.m_attrib2 >= 0) {
	  m_iView.setYIndex(m_focus.m_attrib2);
	}
	m_iView.setColourIndex(m_focus.m_training.classIndex());
	if (((Double)((FastVector)m_focus.m_ranges.elementAt(0)).
	     elementAt(0)).intValue() != LEAF) {
	  m_iView.setShapes(m_focus.m_ranges);
	}
	//m_iView.setSIndex(2);
	//now to remove all the stuff
	m_focus.m_set1 = null;
	m_focus.m_set2 = null;
	m_focus.setInfo(m_focus.m_attrib1, m_focus.m_attrib2, null);
	//tree_frame.getContentPane().removeAll();
	m_tView = new TreeVisualizer(this, graph(), new PlaceNode2());
	//tree_frame.getContentPane().add(m_tView);
	m_reps.setComponentAt(0, m_tView);
	//tree_frame.getContentPane().doLayout();
	m_tView.setHighlight(m_focus.m_identity);
      }
      else if (e.getCommand() == e.CLASSIFY_CHILD) {
	/*if (m_iView == null)
	  {
	  m_iView = new VisualizePanel(this);
	  m_iView.setSize(400, 300);
	  }*/
	m_focus = m_top.getNode(e.getID());
	m_iView.setInstances(m_focus.m_training);
	if (m_focus.m_attrib1 >= 0) {
	  m_iView.setXIndex(m_focus.m_attrib1);
	}
	if (m_focus.m_attrib2 >= 0) {
	  m_iView.setYIndex(m_focus.m_attrib2);
	}
	m_iView.setColourIndex(m_focus.m_training.classIndex());
	if (((Double)((FastVector)m_focus.m_ranges.elementAt(0)).
	     elementAt(0)).intValue() != LEAF) {
	  m_iView.setShapes(m_focus.m_ranges);
	}
	
	
	m_propertyDialog = new PropertyDialog(m_classifiers, 
					      m_mainWin.getLocationOnScreen().x,
					      m_mainWin.getLocationOnScreen().y);
	
	//note property dialog may change all the time
	//but the generic editor which has the listeners does not
	//so at the construction of the editor is when I am going to add
	//the listeners.
	
	
	
	//focus.setClassifier(new IB1());
	//tree_frame.getContentPane().removeAll();
	//////m_tView = new Displayer(this, graph(), new PlaceNode2());
	//tree_frame.getContentPane().add(m_tView);
	//tree_frame.getContentPane().doLayout();
	/////////////reps.setComponentAt(0, m_tView);
	m_tView.setHighlight(m_focus.m_identity);
      }
      /*else if (e.getCommand() == e.SEND_INSTANCES) {
	TreeClass source = m_top.getNode(e.getID());
	m_iView.setExtInstances(source.m_training);
	}*/
      else if (e.getCommand() == e.ACCEPT) {
	
	int well = JOptionPane.showConfirmDialog(m_mainWin, 
						 "Are You Sure...\n"
						 + "Click Yes To Accept The"
						 + " Tree" 
						 + "\n Click No To Return",
						 "Accept Tree", 
						 JOptionPane.YES_NO_OPTION);
	
	if (well == 0) {
	  m_mainWin.setDefaultCloseOperation(JFrame.DISPOSE_ON_CLOSE);
	  m_mainWin.dispose();
	  blocker(false);  //release the thread waiting at blocker to 
	  //continue.
	}
	
      }
    } catch(Exception er) {
      System.out.println("Error : " + er);
      System.out.println("Part of user input so had to catch here");
      er.printStackTrace();
    }
  }
  


  /**
   * This receives shapes from the data view. 
   * It then enters these shapes into the decision tree structure. 
   * @param e Contains the shapes, and other info.
   */
  public void userDataEvent(VisualizePanelEvent e) {
    
    if (m_propertyDialog != null) {
      m_propertyDialog.dispose();
      m_propertyDialog = null;
    }
    
    try {
      if (m_focus != null) {
	

	double wdom = e.getInstances1().numInstances() 
	  + e.getInstances2().numInstances();
	if (wdom == 0) {
	  wdom = 1;
	}
	
	TreeClass tmp = m_focus;
	m_focus.m_set1 = new TreeClass(null, e.getAttribute1(), 
				       e.getAttribute2(), m_nextId, 
				       e.getInstances1().numInstances() / wdom,
 				       e.getInstances1(), m_focus);
	
	m_focus.m_set2 = new TreeClass(null, e.getAttribute1(), 
				       e.getAttribute2(), m_nextId, 
				       e.getInstances2().numInstances() / wdom,
				       e.getInstances2(), m_focus); 
	//this needs the other instance
	
	
	//tree_frame.getContentPane().removeAll();  
	m_focus.setInfo(e.getAttribute1(), e.getAttribute2(), e.getValues());
	//System.out.println(graph());
	m_tView = new TreeVisualizer(this, graph(), new PlaceNode2());
	//tree_frame.getContentPane().add(m_tView);
	//tree_frame.getContentPane().doLayout();
	m_reps.setComponentAt(0, m_tView);
	
	m_focus = m_focus.m_set2;
	m_tView.setHighlight(m_focus.m_identity);
	m_iView.setInstances(m_focus.m_training);
	if (tmp.m_attrib1 >= 0) {
	  m_iView.setXIndex(tmp.m_attrib1);
	}
	if (tmp.m_attrib2 >= 0) {
	  m_iView.setYIndex(tmp.m_attrib2);
	}
	m_iView.setColourIndex(m_focus.m_training.classIndex());
	if (((Double)((FastVector)m_focus.m_ranges.elementAt(0)).
	     elementAt(0)).intValue() != LEAF) {
	  m_iView.setShapes(m_focus.m_ranges);
	}
	//m_iView.setSIndex(2);
      }
      else {
	System.out.println("Somehow the focus is null");
      }
    } catch(Exception er) {
      System.out.println("Error : " + er);
      System.out.println("Part of user input so had to catch here");
      //er.printStackTrace();
    }
    
  }
  
  /** 
   * Constructor
   */
  public UserClassifier() {
    //do nothing here except set alot of variables to default values
    m_top = null;
    m_tView = null;
    m_iView = null;
    m_nextId = 0; 
    
  }
  
 /**
   *  Returns the type of graph this classifier
   *  represents.
   *  @return Drawable.TREE
   */   
  public int graphType() {
      return Drawable.TREE;
  }

  /**
   * @return A string formatted with a dotty representation of the decision
   * tree.
   * @exception Exception if String can't be built properly.
   */
  public String graph() throws Exception {
    //create a dotty rep of the tree from here
    StringBuffer text = new StringBuffer();
    text.append("digraph UserClassifierTree {\n" +
		"node [fontsize=10]\n" +
		"edge [fontsize=10 style=bold]\n");
    
    m_top.toDotty(text);
    return text.toString() +"}\n";
    
    
  }
  
  /**
   * A function used to stop the code that called buildclassifier
   * from continuing on before the user has finished the decision tree.
   * @param tf True to stop the thread, False to release the thread that is
   * waiting there (if one).
   */
  private synchronized void blocker(boolean tf) {
    if (tf) {
      try {
	wait();
      } catch(InterruptedException e) {
      }
    }
    else {
      notifyAll();
    }
    
    //System.out.println("out");
  }

  /**
   * This will return a string describing the classifier.
   * @return The string.
   */
  public String globalInfo() {

    return "Interactively classify through visual means."
      + " You are Presented with a scatter graph of the data against two user"
      + " selectable attributes, as well as a view of the decision tree."
      + " You can create binary splits by creating polygons around data"
      + " plotted on the scatter graph, as well as by allowing another"
      + " classifier to take over at points in the decision tree should you"
      + " see fit.";
  }

  /**
   * Call this function to build a decision tree for the training
   * data provided.
   * @param i The training data.
   * @exception Exception if can't build classification properly.
   */
  public void buildClassifier(Instances i) throws Exception {
    //construct a visualizer
    //construct a tree displayer and feed both then do nothing