multiclassclassifier.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.
 */

/*
 *    MultiClassClassifier.java
 *    Copyright (C) 1999 Eibe Frank,Len Trigg
 *
 */

package weka.classifiers.meta;

import weka.classifiers.DistributionClassifier;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.rules.ZeroR;
import java.io.Serializable;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
import weka.core.Attribute;
import weka.core.AttributeStats;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.Utils;
import weka.core.FastVector;
import weka.core.Range;
import weka.filters.unsupervised.attribute.MakeIndicator;
import weka.filters.unsupervised.instance.RemoveWithValues;
import weka.filters.Filter;

/**
 * Class for handling multi-class datasets with 2-class distribution
 * classifiers.<p>
 *
 * Valid options are:<p>
 *
 * -M num <br>
 * Sets the method to use. Valid values are 0 (1-against-all),
 * 1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0) <p>
 *
 * -R num <br>
 * Sets the multiplier when using random codes. (default 2.0)<p>
 *
 * -W classname <br>
 * Specify the full class name of a classifier as the basis for 
 * the multi-class classifier (required).<p>
 *
 * -Q seed <br>
 * Random number seed (default 1).<p>
 *
 * @author Eibe Frank ([e-mail])
 * @author Len Trigg ([e-mail])
 * @author Richard Kirkby ([e-mail])
 * @version $Revision: 1.1.1.1 $
 */
public class MultiClassClassifier extends DistributionClassifier 
  implements OptionHandler {

  /** The classifiers. */
  private Classifier [] m_Classifiers;

  /** The filters used to transform the class. */
  private Filter[] m_ClassFilters;

  /** The class name of the base classifier. */
  private DistributionClassifier m_Classifier = new weka.classifiers.rules.ZeroR();

  /** ZeroR classifier for when all base classifier return zero probability. */
  private ZeroR m_ZeroR;

  /** Internal copy of the class attribute for output purposes */
  private Attribute m_ClassAttribute;
  
  /** A transformed dataset header used by the  1-against-1 method */
  private Instances m_TwoClassDataset;

  /** Random number seed */
  protected int m_Seed = 1;

  /** 
   * The multiplier when generating random codes. Will generate
   * numClasses * m_RandomWidthFactor codes
   */
  private double m_RandomWidthFactor = 2.0;

  /** The multiclass method to use */
  private int m_Method = METHOD_1_AGAINST_ALL;

  /** The error correction modes */
  public static final int METHOD_1_AGAINST_ALL    = 0;
  public static final int METHOD_ERROR_RANDOM     = 1;
  public static final int METHOD_ERROR_EXHAUSTIVE = 2;
  public static final int METHOD_1_AGAINST_1      = 3;
  public static final Tag [] TAGS_METHOD = {
    new Tag(METHOD_1_AGAINST_ALL, "1-against-all"),
    new Tag(METHOD_ERROR_RANDOM, "Random correction code"),
    new Tag(METHOD_ERROR_EXHAUSTIVE, "Exhaustive correction code"),
    new Tag(METHOD_1_AGAINST_1, "1-against-1")
  };

  /** Interface for the code constructors */
  private abstract class Code implements Serializable {

    /**
     * Subclasses must allocate and fill these. 
     * First dimension is number of codes.
     * Second dimension is number of classes.
     */
    protected boolean [][]m_Codebits;

    /** Returns the number of codes. */
    public int size() {
      return m_Codebits.length;
    }

    /** 
     * Returns the indices of the values set to true for this code, 
     * using 1-based indexing (for input to Range).
     */
    public String getIndices(int which) {
      StringBuffer sb = new StringBuffer();
      for (int i = 0; i < m_Codebits[which].length; i++) {
        if (m_Codebits[which][i]) {
          if (sb.length() != 0) {
            sb.append(',');
          }
          sb.append(i + 1);
        }
      }
      return sb.toString();
    }

    /** Returns a human-readable representation of the codes. */
    public String toString() {
      StringBuffer sb = new StringBuffer();
      for(int i = 0; i < m_Codebits[0].length; i++) {
        for (int j = 0; j < m_Codebits.length; j++) {
          sb.append(m_Codebits[j][i] ? " 1" : " 0");
        }
        sb.append('\n');
      }
      return sb.toString();
    }
  }

  /** Constructs a code with no error correction */
  private class StandardCode extends Code {
    public StandardCode(int numClasses) {
      m_Codebits = new boolean[numClasses][numClasses];
      for (int i = 0; i < numClasses; i++) {
        m_Codebits[i][i] = true;
      }
      System.err.println("Code:\n" + this);
    }
  }

  /** Constructs a random code assignment */
  private class RandomCode extends Code {
    Random r = new Random(m_Seed);
    public RandomCode(int numClasses, int numCodes) {
      numCodes = Math.max(numClasses, numCodes);
      m_Codebits = new boolean[numCodes][numClasses];
      int i = 0;
      do {
        randomize();
        //System.err.println(this);
      } while (!good() && (i++ < 100));
      System.err.println("Code:\n" + this);
    }

    private boolean good() {
      boolean [] ninClass = new boolean[m_Codebits[0].length];
      boolean [] ainClass = new boolean[m_Codebits[0].length];
      for (int i = 0; i < ainClass.length; i++) {
	ainClass[i] = true;
      }

      for (int i = 0; i < m_Codebits.length; i++) {
        boolean ninCode = false;
        boolean ainCode = true;
        for (int j = 0; j < m_Codebits[i].length; j++) {
          boolean current = m_Codebits[i][j];
          ninCode = ninCode || current;
          ainCode = ainCode && current;
          ninClass[j] = ninClass[j] || current;
          ainClass[j] = ainClass[j] && current;
        }
        if (!ninCode || ainCode) {
          return false;
        }
      }
      for (int j = 0; j < ninClass.length; j++) {
        if (!ninClass[j] || ainClass[j]) {
          return false;
        }
      }
      return true;
    }

    private void randomize() {
      for (int i = 0; i < m_Codebits.length; i++) {
        for (int j = 0; j < m_Codebits[i].length; j++) {
	  double temp = r.nextDouble();
          m_Codebits[i][j] = (temp < 0.5) ? false : true;
        }
      }
    }
  }

  /**
   * TODO: Constructs codes as per:
   * Bose, R.C., Ray Chaudhuri (1960), On a class of error-correcting
   * binary group codes, Information and Control, 3, 68-79.
   * Hocquenghem, A. (1959) Codes corecteurs d'erreurs, Chiffres, 2, 147-156. 
   */
  //private class BCHCode extends Code {...}

  /** Constructs an exhaustive code assignment */
  private class ExhaustiveCode extends Code {
    public ExhaustiveCode(int numClasses) {
      int width = (int)Math.pow(2, numClasses - 1) - 1;
      m_Codebits = new boolean[width][numClasses];
      for (int j = 0; j < width; j++) {
        m_Codebits[j][0] = true;
      }
      for (int i = 1; i < numClasses; i++) {
        int skip = (int) Math.pow(2, numClasses - (i + 1));
        for(int j = 0; j < width; j++) {
          m_Codebits[j][i] = ((j / skip) % 2 != 0);
        }
      }
      System.err.println("Code:\n" + this);
    }
  }



  /**
   * Builds the classifiers.
   *
   * @param insts the training data.
   * @exception Exception if a classifier can't be built
   */
  public void buildClassifier(Instances insts) throws Exception {

    Instances newInsts;

    if (m_Classifier == null) {
      throw new Exception("No base classifier has been set!");
    }
    m_ZeroR = new ZeroR();
    m_ZeroR.buildClassifier(insts);

    m_TwoClassDataset = null;

    int numClassifiers = insts.numClasses();
    if (numClassifiers <= 2) {

      m_Classifiers = Classifier.makeCopies(m_Classifier, 1);
      m_Classifiers[0].buildClassifier(insts);

      m_ClassFilters = null;

    } else if (m_Method == METHOD_1_AGAINST_1) {
 
      // generate fastvector of pairs
      FastVector pairs = new FastVector();
      for (int i=0; i<insts.numClasses(); i++) {
	for (int j=0; j<insts.numClasses(); j++) {
	  if (j<=i) continue;
	  int[] pair = new int[2];
	  pair[0] = i; pair[1] = j;
	  pairs.addElement(pair);
	}
      }

      numClassifiers = pairs.size();
      m_Classifiers = Classifier.makeCopies(m_Classifier, numClassifiers);
      m_ClassFilters = new Filter[numClassifiers];

      // generate the classifiers
      for (int i=0; i<numClassifiers; i++) {
	RemoveWithValues classFilter = new RemoveWithValues();
	classFilter.setAttributeIndex(insts.classIndex());
	classFilter.setModifyHeader(true);
	classFilter.setInvertSelection(false);
	classFilter.setNominalIndicesArr((int[])pairs.elementAt(i));
	int[] pair = (int[])pairs.elementAt(i);
	Instances tempInstances = new Instances(insts, 0);
	tempInstances.setClassIndex(-1);
	classFilter.setInputFormat(tempInstances);
	newInsts = Filter.useFilter(insts, classFilter);
	newInsts.setClassIndex(insts.classIndex());
	m_Classifiers[i].buildClassifier(newInsts);
	m_ClassFilters[i] = classFilter;
      }

      // construct a two-class header version of the dataset
      m_TwoClassDataset = new Instances(insts, 0);
      int classIndex = m_TwoClassDataset.classIndex();
      m_TwoClassDataset.setClassIndex(-1);
      m_TwoClassDataset.deleteAttributeAt(classIndex);
      FastVector classLabels = new FastVector();
      classLabels.addElement("class0");
      classLabels.addElement("class1");
      m_TwoClassDataset.insertAttributeAt(new Attribute("class", classLabels),
					  classIndex);
      m_TwoClassDataset.setClassIndex(classIndex);

    } else { // use error correcting code style methods
      Code code = null;
      switch (m_Method) {
      case METHOD_ERROR_EXHAUSTIVE:
        code = new ExhaustiveCode(numClassifiers);
        break;
      case METHOD_ERROR_RANDOM:
        code = new RandomCode(numClassifiers, 
                              (int)(numClassifiers * m_RandomWidthFactor));
        break;
      case METHOD_1_AGAINST_ALL:
        code = new StandardCode(numClassifiers);
        break;
      default:
        throw new Exception("Unrecognized correction code type");
      }
      numClassifiers = code.size();
      m_Classifiers = Classifier.makeCopies(m_Classifier, numClassifiers);
      m_ClassFilters = new MakeIndicator[numClassifiers];
      AttributeStats classStats = insts.attributeStats(insts.classIndex());
      for (int i = 0; i < m_Classifiers.length; i++) {
        if ((m_Method == METHOD_1_AGAINST_ALL) && 
            (classStats.nominalCounts[i] == 0)) {
          m_Classifiers[i] = null;
        } else {
          m_ClassFilters[i] = new MakeIndicator();
	  MakeIndicator classFilter = (MakeIndicator) m_ClassFilters[i];
          classFilter.setAttributeIndex(insts.classIndex());
          classFilter.setValueIndices(code.getIndices(i));
          classFilter.setNumeric(false);
          classFilter.setInputFormat(insts);
          newInsts = Filter.useFilter(insts, m_ClassFilters[i]);
          m_Classifiers[i].buildClassifier(newInsts);
        }
      }
    }
    m_ClassAttribute = insts.classAttribute();
  }

  /**
   * Returns the individual predictions of the base classifiers
   * for an instance. Used by StackedMultiClassClassifier.
   * Returns the probability for the second "class" predicted
   * by each base classifier.
   *
   * @exception Exception if the predictions can't be computed successfully
   */
  public double[] individualPredictions(Instance inst) throws Exception {
    
    double[] result = null;

    if (m_Classifiers.length == 1) {
      result = new double[1];
      result[0] = ((DistributionClassifier)m_Classifiers[0])
        .distributionForInstance(inst)[1];
    } else {
      result = new double[m_ClassFilters.length];
      for(int i = 0; i < m_ClassFilters.length; i++) {
	if (m_Classifiers[i] != null) {