randomtree.java

wekaUT是 university texas austin 开发的基于weka的半指导学习(semi supervised learning)的分类器

/*
 *    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.
 */

/*
 *    RandomTree.java
 *    Copyright (C) 2001 Richard Kirkby, Eibe Frank
 *
 */

package weka.classifiers.trees;

import weka.classifiers.*;
import weka.core.*;
import java.util.*;

/**
 * Class for constructing a tree that considers K random features at each node.
 * Performs no pruning.
 *
 * @author Eibe Frank ([e-mail])
 * @author Richard Kirkby ([e-mail])
 * @version $Revision: 1.1.1.1 $
 */
public class RandomTree extends DistributionClassifier 
  implements OptionHandler, WeightedInstancesHandler, Randomizable {

  /** The subtrees appended to this tree. */ 
  protected RandomTree[] m_Successors;
    
  /** The attribute to split on. */
  protected int m_Attribute = -1;
    
  /** The split point. */
  protected double m_SplitPoint = Double.NaN;
    
  /** The class distribution from the training data. */
  protected double[][] m_Distribution = null;
    
  /** The header information. */
  protected Instances m_Info = null;
    
  /** The proportions of training instances going down each branch. */
  protected double[] m_Prop = null;
    
  /** Class probabilities from the training data. */
  protected double[] m_ClassProbs = null;
    
  /** Minimum number of instances for leaf. */
  protected double m_MinNum = 1.0;
    
  /** Debug info */
  protected boolean m_Debug = false;
  
  /** The number of attributes considered for a split. */
  protected int m_KValue = 1;

  /** Random number generator. */
  protected Random m_random;

  /** The random seed to use. */
  protected int m_randomSeed = 1;

  /**
   * Get the value of MinNum.
   *
   * @return Value of MinNum.
   */
  public double getMinNum() {
    
    return m_MinNum;
  }
  
  /**
   * Set the value of MinNum.
   *
   * @param newMinNum Value to assign to MinNum.
   */
  public void setMinNum(double newMinNum) {
    
    m_MinNum = newMinNum;
  }
  
  /**
   * Get the value of K.
   *
   * @return Value of K.
   */
  public int getKValue() {
    
    return m_KValue;
  }
  
  /**
   * Set the value of K.
   *
   * @param k Value to assign to K.
   */
  public void setKValue(int k) {
    
    m_KValue = k;
  }

  /**
   * Get the value of Debug.
   *
   * @return Value of Debug.
   */
  public boolean getDebug() {
    
    return m_Debug;
  }
  
  /**
   * Set the value of Debug.
   *
   * @param newDebug Value to assign to Debug.
   */
  public void setDebug(boolean newDebug) {
    
    m_Debug = newDebug;
  }

  /**
   * Set the seed for random number generation.
   *
   * @param seed the seed 
   */
  public void setSeed(int seed) {

    m_randomSeed = seed;
  }
  
  /**
   * Gets the seed for the random number generations
   *
   * @return the seed for the random number generation
   */
  public int getSeed() {

    return m_randomSeed;
  }
  
  /**
   * Lists the command-line options for this classifier.
   */
  public Enumeration listOptions() {
    
    Vector newVector = new Vector(6);

    newVector.
      addElement(new Option("\tNumber of attributes to randomly investigate.",
			    "K", 1, "-K <number of attributes>"));

    newVector.
      addElement(new Option("\tSet minimum number of instances per leaf.",
			    "M", 1, "-M <minimum number of instances>"));

    newVector.
      addElement(new Option("\tTurns debugging info on.",
			    "D", 0, "-D"));

    newVector
      .addElement(new Option("\tSeed for random number generator.\n"
			     + "\t(default 1)",
			     "S", 1, "-S"));

    return newVector.elements();
  } 

  /**
   * Gets options from this classifier.
   */
  public String[] getOptions() {
    
    String [] options = new String [10];
    int current = 0;
    options[current++] = "-K"; 
    options[current++] = "" + getKValue();
    options[current++] = "-M"; 
    options[current++] = "" + getMinNum();
    options[current++] = "-S";
    options[current++] = "" + getSeed();
    if (getDebug()) {
      options[current++] = "-D";
    }
    while (current < options.length) {
      options[current++] = "";
    }
    return options;
  }

  /**
   * Parses a given list of options.
   * @param options the list of options as an array of strings
   * @exception Exception if an option is not supported
   */
  public void setOptions(String[] options) throws Exception{
    
    String kValueString = Utils.getOption('K', options);
    if (kValueString.length() != 0) {
      m_KValue = Integer.parseInt(kValueString);
    } else {
      m_KValue = 1;
    }
    String minNumString = Utils.getOption('M', options);
    if (minNumString.length() != 0) {
      m_MinNum = (double)Integer.parseInt(minNumString);
    } else {
      m_MinNum = 1;
    }
    String seed = Utils.getOption('S', options);
    if (seed.length() != 0) {
      setSeed(Integer.parseInt(seed));
    } else {
      setSeed(1);
    }
    m_Debug = Utils.getFlag('D', options);
    Utils.checkForRemainingOptions(options);
  }

  /**
   * Builds classifier.
   */
  public void buildClassifier(Instances data) throws Exception {

    // Make sure K value is in range
    if (m_KValue > data.numAttributes()-1) m_KValue = data.numAttributes()-1;

    // Delete instances with missing class
    data = new Instances(data);
    data.deleteWithMissingClass();

    Instances train = data;

    // Create array of sorted indices and weights
    int[][] sortedIndices = new int[train.numAttributes()][0];
    double[][] weights = new double[train.numAttributes()][0];
    double[] vals = new double[train.numInstances()];
    for (int j = 0; j < train.numAttributes(); j++) {
      if (j != train.classIndex()) {
	weights[j] = new double[train.numInstances()];
	if (train.attribute(j).isNominal()) {

	  // Handling nominal attributes. Putting indices of
	  // instances with missing values at the end.
	  sortedIndices[j] = new int[train.numInstances()];
	  int count = 0;
	  for (int i = 0; i < train.numInstances(); i++) {
	    Instance inst = train.instance(i);
	    if (!inst.isMissing(j)) {
	      sortedIndices[j][count] = i;
	      weights[j][count] = inst.weight();
	      count++;
	    }
	  }
	  for (int i = 0; i < train.numInstances(); i++) {
	    Instance inst = train.instance(i);
	    if (inst.isMissing(j)) {
	      sortedIndices[j][count] = i;
	      weights[j][count] = inst.weight();
	      count++;
	    }
	  }
	} else {
	  
	  // Sorted indices are computed for numeric attributes
	  for (int i = 0; i < train.numInstances(); i++) {
	    Instance inst = train.instance(i);
	    vals[i] = inst.value(j);
	  }
	  sortedIndices[j] = Utils.sort(vals);
	  for (int i = 0; i < train.numInstances(); i++) {
	    weights[j][i] = train.instance(sortedIndices[j][i]).weight();
	  }
	}
      }
    }

    // Compute initial class counts
    double[] classProbs = new double[train.numClasses()];
    for (int i = 0; i < train.numInstances(); i++) {
      Instance inst = train.instance(i);
      classProbs[(int)inst.classValue()] += inst.weight();
    }

    // Create the attribute indices window
    int[] attIndicesWindow = new int[data.numAttributes()-1];
    int j=0;
    for (int i=0; i<attIndicesWindow.length; i++) {
      if (j == data.classIndex()) j++; // do not include the class
      attIndicesWindow[i] = j++;
    }

    // Build tree
    buildTree(sortedIndices, weights, train, classProbs,
	      new Instances(train, 0), m_MinNum, m_Debug,
	      attIndicesWindow);
  }
  
  /**
   * Computes class distribution of an instance using the decision tree.
   */
  public double[] distributionForInstance(Instance instance) throws Exception {
    
    double[] returnedDist = null;
    
    if (m_Attribute > -1) {
      
      // Node is not a leaf
      if (instance.isMissing(m_Attribute)) {

	// Value is missing
	returnedDist = new double[m_Info.numClasses()];
	// Split instance up
	for (int i = 0; i < m_Successors.length; i++) {
	  double[] help = m_Successors[i].distributionForInstance(instance);
	  if (help != null) {
	    for (int j = 0; j < help.length; j++) {
	      returnedDist[j] += m_Prop[i] * help[j];
	    }
	  }
	}
      } else if (m_Info.attribute(m_Attribute).isNominal()) {
	  
	// For nominal attributes
	returnedDist =  m_Successors[(int)instance.value(m_Attribute)].
	  distributionForInstance(instance);
      } else {
	
	// For numeric attributes
	if (Utils.sm(instance.value(m_Attribute), m_SplitPoint)) {
	  returnedDist = m_Successors[0].distributionForInstance(instance);
	} else {
	  returnedDist = m_Successors[1].distributionForInstance(instance);
	}
      }
    }
    if ((m_Attribute == -1) || (returnedDist == null)) {

      // Node is a leaf or successor is empty
      return m_ClassProbs;
    } else {
      return returnedDist;
    }
  }

  /**
   * Outputs the decision tree as a graph
   */
  public String toGraph() {

    try {
      StringBuffer resultBuff = new StringBuffer();
      toGraph(resultBuff, 0);
      String result = "digraph Tree {\n" + "edge [style=bold]\n" + resultBuff.toString()
	+ "\n}\n";
      return result;
    } catch (Exception e) {
      return null;
    }
  }
  
  /**
   * Outputs one node for graph.
   */
  public int toGraph(StringBuffer text, int num) throws Exception {
    
    int maxIndex = Utils.maxIndex(m_ClassProbs);
    String classValue = m_Info.classAttribute().value(maxIndex);
    
    num++;
    if (m_Attribute == -1) {
      text.append("N" + Integer.toHexString(hashCode()) +
		  " [label=\"" + num + ": " + classValue + "\"" +
		  "shape=box]\n");
    }else {
      text.append("N" + Integer.toHexString(hashCode()) +
		  " [label=\"" + num + ": " + classValue + "\"]\n");
      for (int i = 0; i < m_Successors.length; i++) {
	text.append("N" + Integer.toHexString(hashCode()) 
		    + "->" + 
		    "N" + Integer.toHexString(m_Successors[i].hashCode())  +
		    " [label=\"" + m_Info.attribute(m_Attribute).name());
	if (m_Info.attribute(m_Attribute).isNumeric()) {
	  if (i == 0) {
	    text.append(" < " +
			Utils.doubleToString(m_SplitPoint, 2));
	  } else {
	    text.append(" >= " +
			Utils.doubleToString(m_SplitPoint, 2));
	  }
	} else {
	  text.append(" = " + m_Info.attribute(m_Attribute).value(i));
	}
	text.append("\"]\n");
	num = m_Successors[i].toGraph(text, num);
      }
    }
    
    return num;