source: branches/MetisMQI/src/main/java/weka/classifiers/meta/Bagging.java

/*
 *    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.
 */

/*
 *    Bagging.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.meta;

import weka.classifiers.RandomizableIteratedSingleClassifierEnhancer;
import weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer;
import weka.core.AdditionalMeasureProducer;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.Randomizable;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;

import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

/**
 <!-- globalinfo-start -->
 * Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner. <br/>
 * <br/>
 * For more information, see<br/>
 * <br/>
 * Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;article{Breiman1996,
 *    author = {Leo Breiman},
 *    journal = {Machine Learning},
 *    number = {2},
 *    pages = {123-140},
 *    title = {Bagging predictors},
 *    volume = {24},
 *    year = {1996}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -P
 *  Size of each bag, as a percentage of the
 *  training set size. (default 100)</pre>
 * 
 * <pre> -O
 *  Calculate the out of bag error.</pre>
 * 
 * <pre> -S &lt;num&gt;
 *  Random number seed.
 *  (default 1)</pre>
 * 
 * <pre> -I &lt;num&gt;
 *  Number of iterations.
 *  (default 10)</pre>
 * 
 * <pre> -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console</pre>
 * 
 * <pre> -W
 *  Full name of base classifier.
 *  (default: weka.classifiers.trees.REPTree)</pre>
 * 
 * <pre> 
 * Options specific to classifier weka.classifiers.trees.REPTree:
 * </pre>
 * 
 * <pre> -M &lt;minimum number of instances&gt;
 *  Set minimum number of instances per leaf (default 2).</pre>
 * 
 * <pre> -V &lt;minimum variance for split&gt;
 *  Set minimum numeric class variance proportion
 *  of train variance for split (default 1e-3).</pre>
 * 
 * <pre> -N &lt;number of folds&gt;
 *  Number of folds for reduced error pruning (default 3).</pre>
 * 
 * <pre> -S &lt;seed&gt;
 *  Seed for random data shuffling (default 1).</pre>
 * 
 * <pre> -P
 *  No pruning.</pre>
 * 
 * <pre> -L
 *  Maximum tree depth (default -1, no maximum)</pre>
 * 
 <!-- options-end -->
 *
 * Options after -- are passed to the designated classifier.<p>
 *
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @author Len Trigg (len@reeltwo.com)
 * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
 * @version $Revision: 5801 $
 */
public class Bagging
  extends RandomizableParallelIteratedSingleClassifierEnhancer 
  implements WeightedInstancesHandler, AdditionalMeasureProducer,
             TechnicalInformationHandler {

  /** for serialization */
  static final long serialVersionUID = -505879962237199703L;
  
  /** The size of each bag sample, as a percentage of the training size */
  protected int m_BagSizePercent = 100;

  /** Whether to calculate the out of bag error */
  protected boolean m_CalcOutOfBag = false;

  /** The out of bag error that has been calculated */
  protected double m_OutOfBagError;  
    
  /**
   * Constructor.
   */
  public Bagging() {
    
    m_Classifier = new weka.classifiers.trees.REPTree();
  }
  
  /**
   * Returns a string describing classifier
   * @return a description suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
 
    return "Class for bagging a classifier to reduce variance. Can do classification "
      + "and regression depending on the base learner. \n\n"
      + "For more information, see\n\n"
      + getTechnicalInformation().toString();
  }

  /**
   * Returns an instance of a TechnicalInformation object, containing 
   * detailed information about the technical background of this class,
   * e.g., paper reference or book this class is based on.
   * 
   * @return the technical information about this class
   */
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation        result;
    
    result = new TechnicalInformation(Type.ARTICLE);
    result.setValue(Field.AUTHOR, "Leo Breiman");
    result.setValue(Field.YEAR, "1996");
    result.setValue(Field.TITLE, "Bagging predictors");
    result.setValue(Field.JOURNAL, "Machine Learning");
    result.setValue(Field.VOLUME, "24");
    result.setValue(Field.NUMBER, "2");
    result.setValue(Field.PAGES, "123-140");
    
    return result;
  }

  /**
   * String describing default classifier.
   * 
   * @return the default classifier classname
   */
  protected String defaultClassifierString() {
    
    return "weka.classifiers.trees.REPTree";
  }

  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {

    Vector newVector = new Vector(2);

    newVector.addElement(new Option(
              "\tSize of each bag, as a percentage of the\n" 
              + "\ttraining set size. (default 100)",
              "P", 1, "-P"));
    newVector.addElement(new Option(
              "\tCalculate the out of bag error.",
              "O", 0, "-O"));

    Enumeration enu = super.listOptions();
    while (enu.hasMoreElements()) {
      newVector.addElement(enu.nextElement());
    }
    return newVector.elements();
  }


  /**
   * Parses a given list of options. <p/>
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -P
   *  Size of each bag, as a percentage of the
   *  training set size. (default 100)</pre>
   * 
   * <pre> -O
   *  Calculate the out of bag error.</pre>
   * 
   * <pre> -S &lt;num&gt;
   *  Random number seed.
   *  (default 1)</pre>
   * 
   * <pre> -I &lt;num&gt;
   *  Number of iterations.
   *  (default 10)</pre>
   * 
   * <pre> -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console</pre>
   * 
   * <pre> -W
   *  Full name of base classifier.
   *  (default: weka.classifiers.trees.REPTree)</pre>
   * 
   * <pre> 
   * Options specific to classifier weka.classifiers.trees.REPTree:
   * </pre>
   * 
   * <pre> -M &lt;minimum number of instances&gt;
   *  Set minimum number of instances per leaf (default 2).</pre>
   * 
   * <pre> -V &lt;minimum variance for split&gt;
   *  Set minimum numeric class variance proportion
   *  of train variance for split (default 1e-3).</pre>
   * 
   * <pre> -N &lt;number of folds&gt;
   *  Number of folds for reduced error pruning (default 3).</pre>
   * 
   * <pre> -S &lt;seed&gt;
   *  Seed for random data shuffling (default 1).</pre>
   * 
   * <pre> -P
   *  No pruning.</pre>
   * 
   * <pre> -L
   *  Maximum tree depth (default -1, no maximum)</pre>
   * 
   <!-- options-end -->
   *
   * Options after -- are passed to the designated classifier.<p>
   *
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  public void setOptions(String[] options) throws Exception {

    String bagSize = Utils.getOption('P', options);
    if (bagSize.length() != 0) {
      setBagSizePercent(Integer.parseInt(bagSize));
    } else {
      setBagSizePercent(100);
    }

    setCalcOutOfBag(Utils.getFlag('O', options));

    super.setOptions(options);
  }

  /**
   * Gets the current settings of the Classifier.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String [] getOptions() {


    String [] superOptions = super.getOptions();
    String [] options = new String [superOptions.length + 3];

    int current = 0;
    options[current++] = "-P"; 
    options[current++] = "" + getBagSizePercent();

    if (getCalcOutOfBag()) { 
      options[current++] = "-O";
    }

    System.arraycopy(superOptions, 0, options, current, 
                     superOptions.length);

    current += superOptions.length;
    while (current < options.length) {
      options[current++] = "";
    }
    return options;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String bagSizePercentTipText() {
    return "Size of each bag, as a percentage of the training set size.";
  }

  /**
   * Gets the size of each bag, as a percentage of the training set size.
   *
   * @return the bag size, as a percentage.
   */
  public int getBagSizePercent() {

    return m_BagSizePercent;
  }
  
  /**
   * Sets the size of each bag, as a percentage of the training set size.
   *
   * @param newBagSizePercent the bag size, as a percentage.
   */
  public void setBagSizePercent(int newBagSizePercent) {

    m_BagSizePercent = newBagSizePercent;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String calcOutOfBagTipText() {
    return "Whether the out-of-bag error is calculated.";
  }

  /**
   * Set whether the out of bag error is calculated.
   *
   * @param calcOutOfBag whether to calculate the out of bag error
   */
  public void setCalcOutOfBag(boolean calcOutOfBag) {

    m_CalcOutOfBag = calcOutOfBag;
  }

  /**
   * Get whether the out of bag error is calculated.
   *
   * @return whether the out of bag error is calculated
   */
  public boolean getCalcOutOfBag() {

    return m_CalcOutOfBag;
  }

  /**
   * Gets the out of bag error that was calculated as the classifier
   * was built.
   *
   * @return the out of bag error 
   */
  public double measureOutOfBagError() {
    
    return m_OutOfBagError;
  }
  
  /**
   * Returns an enumeration of the additional measure names.
   *
   * @return an enumeration of the measure names
   */
  public Enumeration enumerateMeasures() {
    
    Vector newVector = new Vector(1);
    newVector.addElement("measureOutOfBagError");
    return newVector.elements();
  }
  
  /**
   * Returns the value of the named measure.
   *
   * @param additionalMeasureName the name of the measure to query for its value
   * @return the value of the named measure
   * @throws IllegalArgumentException if the named measure is not supported
   */
  public double getMeasure(String additionalMeasureName) {
    
    if (additionalMeasureName.equalsIgnoreCase("measureOutOfBagError")) {
      return measureOutOfBagError();
    }
    else {throw new IllegalArgumentException(additionalMeasureName 
                                             + " not supported (Bagging)");
    }
  }

  /**
   * Creates a new dataset of the same size using random sampling
   * with replacement according to the given weight vector. The
   * weights of the instances in the new dataset are set to one.
   * The length of the weight vector has to be the same as the
   * number of instances in the dataset, and all weights have to
   * be positive.
   *
   * @param data the data to be sampled from
   * @param random a random number generator
   * @param sampled indicating which instance has been sampled
   * @return the new dataset
   * @throws IllegalArgumentException if the weights array is of the wrong
   * length or contains negative weights.
   */
  public final Instances resampleWithWeights(Instances data,
                                             Random random, 
                                             boolean[] sampled) {

    double[] weights = new double[data.numInstances()];
    for (int i = 0; i < weights.length; i++) {
      weights[i] = data.instance(i).weight();
    }
    Instances newData = new Instances(data, data.numInstances());
    if (data.numInstances() == 0) {
      return newData;
    }
    double[] probabilities = new double[data.numInstances()];
    double sumProbs = 0, sumOfWeights = Utils.sum(weights);
    for (int i = 0; i < data.numInstances(); i++) {
      sumProbs += random.nextDouble();
      probabilities[i] = sumProbs;
    }
    Utils.normalize(probabilities, sumProbs / sumOfWeights);

    // Make sure that rounding errors don't mess things up
    probabilities[data.numInstances() - 1] = sumOfWeights;
    int k = 0; int l = 0;
    sumProbs = 0;
    while ((k < data.numInstances() && (l < data.numInstances()))) {
      if (weights[l] < 0) {
        throw new IllegalArgumentException("Weights have to be positive.");
      }
      sumProbs += weights[l];
      while ((k < data.numInstances()) &&
             (probabilities[k] <= sumProbs)) { 
        newData.add(data.instance(l));
        sampled[l] = true;
        newData.instance(k).setWeight(1);
        k++;
      }
      l++;
    }
    return newData;
  }
  
  protected Random m_random;
  protected boolean[][] m_inBag;
  protected Instances m_data;
  
  /**
   * Returns a training set for a particular iteration.
   * 
   * @param iteration the number of the iteration for the requested training set.
   * @return the training set for the supplied iteration number
   * @throws Exception if something goes wrong when generating a training set.
   */
  protected synchronized Instances getTrainingSet(int iteration) throws Exception {
    int bagSize = m_data.numInstances() * m_BagSizePercent / 100;
    Instances bagData = null;

    // create the in-bag dataset
    if (m_CalcOutOfBag) {
      m_inBag[iteration] = new boolean[m_data.numInstances()];
      bagData = resampleWithWeights(m_data, m_random, m_inBag[iteration]);
    } else {
      bagData = m_data.resampleWithWeights(m_random);
      if (bagSize < m_data.numInstances()) {
        bagData.randomize(m_random);
        Instances newBagData = new Instances(bagData, 0, bagSize);
        bagData = newBagData;
      }
    }
    
    return bagData;
  }
  
  /**
   * Bagging method.
   *
   * @param data the training data to be used for generating the
   * bagged classifier.
   * @throws Exception if the classifier could not be built successfully
   */
  public void buildClassifier(Instances data) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(data);

    // remove instances with missing class
    m_data = new Instances(data);
    m_data.deleteWithMissingClass();
    
    super.buildClassifier(m_data);

    if (m_CalcOutOfBag && (m_BagSizePercent != 100)) {
      throw new IllegalArgumentException("Bag size needs to be 100% if " +
                                         "out-of-bag error is to be calculated!");
    }

    int bagSize = m_data.numInstances() * m_BagSizePercent / 100;
    m_random = new Random(m_Seed);
    
    m_inBag = null;
    if (m_CalcOutOfBag)
      m_inBag = new boolean[m_Classifiers.length][];
    
    for (int j = 0; j < m_Classifiers.length; j++) {      
      if (m_Classifier instanceof Randomizable) {
        ((Randomizable) m_Classifiers[j]).setSeed(m_random.nextInt());
      }
    }
    
    buildClassifiers();
    
    // calc OOB error?
    if (getCalcOutOfBag()) {
      double outOfBagCount = 0.0;
      double errorSum = 0.0;
      boolean numeric = m_data.classAttribute().isNumeric();
      
      for (int i = 0; i < m_data.numInstances(); i++) {
        double vote;
        double[] votes;
        if (numeric)
          votes = new double[1];
        else
          votes = new double[m_data.numClasses()];
        
        // determine predictions for instance
        int voteCount = 0;
        for (int j = 0; j < m_Classifiers.length; j++) {
          if (m_inBag[j][i])
            continue;
          
          voteCount++;
          double pred = m_Classifiers[j].classifyInstance(m_data.instance(i));
          if (numeric)
            votes[0] += pred;
          else
            votes[(int) pred]++;
        }
        
        // "vote"
        if (numeric) {
          vote = votes[0];
          if (voteCount > 0) {
            vote  /= voteCount;    // average
          }
        } else {
          vote = Utils.maxIndex(votes);   // majority vote
        }
        
        // error for instance
        outOfBagCount += m_data.instance(i).weight();
        if (numeric) {
          errorSum += StrictMath.abs(vote - m_data.instance(i).classValue()) 
          * m_data.instance(i).weight();
        }
        else {
          if (vote != m_data.instance(i).classValue())
            errorSum += m_data.instance(i).weight();
        }
      }
      
      m_OutOfBagError = errorSum / outOfBagCount;
    }
    else {
      m_OutOfBagError = 0;
    }
    
    // save memory
    m_data = null;
  }

  /**
   * Calculates the class membership probabilities for the given test
   * instance.
   *
   * @param instance the instance to be classified
   * @return preedicted class probability distribution
   * @throws Exception if distribution can't be computed successfully 
   */
  public double[] distributionForInstance(Instance instance) throws Exception {

    double [] sums = new double [instance.numClasses()], newProbs; 
    
    for (int i = 0; i < m_NumIterations; i++) {
      if (instance.classAttribute().isNumeric() == true) {
        sums[0] += m_Classifiers[i].classifyInstance(instance);
      } else {
        newProbs = m_Classifiers[i].distributionForInstance(instance);
        for (int j = 0; j < newProbs.length; j++)
          sums[j] += newProbs[j];
      }
    }
    if (instance.classAttribute().isNumeric() == true) {
      sums[0] /= (double)m_NumIterations;
      return sums;
    } else if (Utils.eq(Utils.sum(sums), 0)) {
      return sums;
    } else {
      Utils.normalize(sums);
      return sums;
    }
  }

  /**
   * Returns description of the bagged classifier.
   *
   * @return description of the bagged classifier as a string
   */
  public String toString() {
    
    if (m_Classifiers == null) {
      return "Bagging: No model built yet.";
    }
    StringBuffer text = new StringBuffer();
    text.append("All the base classifiers: \n\n");
    for (int i = 0; i < m_Classifiers.length; i++)
      text.append(m_Classifiers[i].toString() + "\n\n");
    
    if (m_CalcOutOfBag) {
      text.append("Out of bag error: "
                  + Utils.doubleToString(m_OutOfBagError, 4)
                  + "\n\n");
    }

    return text.toString();
  }
  
  /**
   * Returns the revision string.
   * 
   * @return            the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 5801 $");
  }

  /**
   * Main method for testing this class.
   *
   * @param argv the options
   */
  public static void main(String [] argv) {
    runClassifier(new Bagging(), argv);
  }
}