decisiontable.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.
 */

/*
 *    DecisionTable.java
 *    Copyright (C) 1999 Mark Hall
 *
 */

package weka.classifiers.rules;

import java.io.Serializable;
import java.util.BitSet;
import java.util.Enumeration;
import java.util.Hashtable;
import java.util.Random;
import java.util.Vector;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.lazy.IBk;
import weka.core.AdditionalMeasureProducer;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.UnsupportedAttributeTypeException;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;

/**
 * Class for building and using a simple decision table majority classifier.
 * For more information see: <p>
 *
 * Kohavi R. (1995).<i> The Power of Decision Tables.</i> In Proc
 * European Conference on Machine Learning.<p>
 *
 * Valid options are: <p>
 *
 * -S num <br>
 * Number of fully expanded non improving subsets to consider
 * before terminating a best first search.
 * (Default = 5) <p>
 *
 * -X num <br>
 * Use cross validation to evaluate features. Use number of folds = 1 for
 * leave one out CV. (Default = leave one out CV) <p>
 * 
 * -I <br>
 * Use nearest neighbour instead of global table majority. <p>
 *
 * -R <br>
 * Prints the decision table. <p>
 *
 * @author Mark Hall ([e-mail])
 * @version $Revision$ 
 */
public class DecisionTable extends Classifier 
  implements OptionHandler, WeightedInstancesHandler, 
	     AdditionalMeasureProducer {
  
  /** The hashtable used to hold training instances */
  private Hashtable m_entries;

  /** Holds the final feature set */
  private int [] m_decisionFeatures;

  /** Discretization filter */
  private Filter m_disTransform;

  /** Filter used to remove columns discarded by feature selection */
  private Remove m_delTransform;

  /** IB1 used to classify non matching instances rather than majority class */
  private IBk m_ibk;
  
  /** Holds the training instances */
  private Instances m_theInstances;
  
  /** The number of attributes in the dataset */
  private int m_numAttributes;

  /** The number of instances in the dataset */
  private int m_numInstances;

  /** Class is nominal */
  private boolean m_classIsNominal;

  /** Output debug info */
  private boolean m_debug;

  /** Use the IBk classifier rather than majority class */
  private boolean m_useIBk;

  /** Display Rules */
  private boolean m_displayRules;

  /** 
   * Maximum number of fully expanded non improving subsets for a best 
   * first search. 
   */
  private int m_maxStale;

  /** Number of folds for cross validating feature sets */
  private int m_CVFolds;

  /** Random numbers for use in cross validation */
  private Random m_rr;

  /** Holds the majority class */
  private double m_majority;

  /**
   * Returns a string describing classifier
   * @return a description suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {

    return  "Class for building and using a simple decision table majority "
      + "classifier. For more information see: \n\n"
      + "Kohavi R. (1995). \"The Power of Decision Tables.\" In Proc "
      + "European Conference on Machine Learning.";
  }

  /**
   * Class for a node in a linked list. Used in best first search.
   */
  public class Link {

    /** The group */
    BitSet m_group;

    /** The merit */
    double m_merit;

    /**
     * The constructor.
     *
     * @param gr the group
     * @param mer the merit
     */
    public Link (BitSet gr, double mer) {

      m_group = (BitSet)gr.clone();
      m_merit = mer;
    }
  
    /**
     * Gets the group.
     */
    public BitSet getGroup() {

      return m_group;
    }
  
    /**
     * Gets the merit.
     */
    public double getMerit() {

      return m_merit;
    }

    /**
     * Returns string representation.
     */
    public String toString() {

      return ("Node: "+m_group.toString()+"  "+m_merit);
    }
  }
  
  /**
   * Class for handling a linked list. Used in best first search.
   * Extends the Vector class.
   */
  public class LinkedList extends FastVector {

    /**
     * Removes an element (Link) at a specific index from the list.
     *
     * @param index the index of the element to be removed.
     */
    public void removeLinkAt(int index) throws Exception {

      if ((index >= 0) && (index < size())) {
	removeElementAt(index);
      } else {
	throw new Exception("index out of range (removeLinkAt)");
      }
    }

    /**
     * Returns the element (Link) at a specific index from the list.
     *
     * @param index the index of the element to be returned.
     */
    public Link getLinkAt(int index) throws Exception {

      if (size()==0) {
	throw new Exception("List is empty (getLinkAt)");
      } else if ((index >= 0) && (index < size())) {
	return ((Link)(elementAt(index)));
      } else {
	throw new Exception("index out of range (getLinkAt)");
      }
    }

    /**
     * Aadds an element (Link) to the list.
     *
     * @param gr the feature set specification
     * @param mer the "merit" of this feature set
     */
    public void addToList(BitSet gr, double mer) {

      Link newL = new Link(gr, mer);
	
      if (size()==0) {
	addElement(newL);
      }
      else if (mer > ((Link)(firstElement())).getMerit()) {
	insertElementAt(newL,0);
      } else {
	int i = 0;
	int size = size();
	boolean done = false;
	while ((!done) && (i < size)) {
	  if (mer > ((Link)(elementAt(i))).getMerit()) {
	    insertElementAt(newL,i);
	    done = true;
	  } else if (i == size-1) {
	    addElement(newL);
	    done = true;
	  } else {
	    i++;
	  }
	}
      }
    }
  }

  /**
   * Class providing keys to the hash table
   */
  public static class hashKey implements Serializable {
    
    /** Array of attribute values for an instance */
    private double [] attributes;
    
    /** True for an index if the corresponding attribute value is missing. */
    private boolean [] missing;

    /** The values */
    private String [] values;

    /** The key */
    private int key;

    /**
     * Constructor for a hashKey
     *
     * @param t an instance from which to generate a key
     * @param numAtts the number of attributes
     */
    public hashKey(Instance t, int numAtts) throws Exception {

      int i;
      int cindex = t.classIndex();

      key = -999;
      attributes = new double [numAtts];
      missing = new boolean [numAtts];
      for (i=0;i<numAtts;i++) {
	if (i == cindex) {
	  missing[i] = true;
	} else {
	  if ((missing[i] = t.isMissing(i)) == false) {
	    attributes[i] = t.value(i);
	  }
	}
      }
    }

    /**
     * Convert a hash entry to a string
     *
     * @param t the set of instances
     * @param maxColWidth width to make the fields
     */
    public String toString(Instances t, int maxColWidth) {

      int i;
      int cindex = t.classIndex();
      StringBuffer text = new StringBuffer();
      
      for (i=0;i<attributes.length;i++) {
	if (i != cindex) {
	  if (missing[i]) {
	    text.append("?");
	    for (int j=0;j<maxColWidth;j++) {
	      text.append(" ");
	    }
	  } else {
	    String ss = t.attribute(i).value((int)attributes[i]);
	    StringBuffer sb = new StringBuffer(ss);
	    
	    for (int j=0;j < (maxColWidth-ss.length()+1); j++) {
		sb.append(" ");
	    }
	    text.append(sb);
	  }
	}
      }
      return text.toString();
    }

    /**
     * Constructor for a hashKey
     *
     * @param t an array of feature values
     */
    public hashKey(double [] t) {

      int i;
      int l = t.length;

      key = -999;
      attributes = new double [l];
      missing = new boolean [l];
      for (i=0;i<l;i++) {
	if (t[i] == Double.MAX_VALUE) {
	  missing[i] = true;
	} else {
	  missing[i] = false;
	  attributes[i] = t[i];
	}
      }
    }
    
    /**
     * Calculates a hash code
     *
     * @return the hash code as an integer
     */
    public int hashCode() {

      int hv = 0;
      
      if (key != -999)
	return key;
      for (int i=0;i<attributes.length;i++) {
	if (missing[i]) {
	  hv += (i*13);
	} else {
	  hv += (i * 5 * (attributes[i]+1));
	}
      }
      if (key == -999) {
	key = hv;
      }
      return hv;
    }

    /**
     * Tests if two instances are equal
     *
     * @param b a key to compare with
     */
    public boolean equals(Object b) {
      
      if ((b == null) || !(b.getClass().equals(this.getClass()))) {
        return false;
      }
      boolean ok = true;
      boolean l;
      if (b instanceof hashKey) {
	hashKey n = (hashKey)b;
	for (int i=0;i<attributes.length;i++) {
	  l = n.missing[i];
	  if (missing[i] || l) {
	    if ((missing[i] && !l) || (!missing[i] && l)) {
	      ok = false;
	      break;
	    }
	  } else {
	    if (attributes[i] != n.attributes[i]) {
	      ok = false;
	      break;
	    }
	  }
	}
      } else {
	return false;
      }
      return ok;
    }
    
    /**
     * Prints the hash code
     */
    public void print_hash_code() {
      
      System.out.println("Hash val: "+hashCode());
    }
  }

  /**
   * Inserts an instance into the hash table
   *
   * @param inst instance to be inserted
   * @exception Exception if the instance can't be inserted
   */
  private void insertIntoTable(Instance inst, double [] instA)
       throws Exception {

    double [] tempClassDist2;
    double [] newDist;
    hashKey thekey;

    if (instA != null) {
      thekey = new hashKey(instA);
    } else {
      thekey = new hashKey(inst, inst.numAttributes());
    }
      
    // see if this one is already in the table
    tempClassDist2 = (double []) m_entries.get(thekey);
    if (tempClassDist2 == null) {
      if (m_classIsNominal) {
	newDist = new double [m_theInstances.classAttribute().numValues()];
	newDist[(int)inst.classValue()] = inst.weight();

	// add to the table
	m_entries.put(thekey, newDist);
      } else {
	newDist = new double [2];
	newDist[0] = inst.classValue() * inst.weight();
	newDist[1] = inst.weight();

	// add to the table
	m_entries.put(thekey, newDist);
      }
    } else { 

      // update the distribution for this instance
      if (m_classIsNominal) {
	tempClassDist2[(int)inst.classValue()]+=inst.weight();
	
	// update the table
	m_entries.put(thekey, tempClassDist2);
      } else  {
	tempClassDist2[0] += (inst.classValue() * inst.weight());
	tempClassDist2[1] += inst.weight();

	// update the table
	m_entries.put(thekey, tempClassDist2);
      }
    }
  }