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• MIBoost.java

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weka.classifiers.mi.MIBoost Maven / Gradle / Ivy

/*
 *   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 3 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, see .
 */

/*
 * MIBoost.java
 * Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.mi;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.classifiers.SingleClassifierEnhancer;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.MultiInstanceCapabilitiesHandler;
import weka.core.Optimization;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Discretize;
import weka.filters.unsupervised.attribute.MultiInstanceToPropositional;

/**
 *  MI AdaBoost method, considers the geometric mean of
 * posterior of instances inside a bag (arithmatic mean of log-posterior) and
 * the expectation for a bag is taken inside the loss function.

 * 

 * For more information about Adaboost, see:

 * 

 * Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm.
 * In: Thirteenth International Conference on Machine Learning, San Francisco,
 * 148-156, 1996.
 * 

 * 
 * 
 *  BibTeX:
 * 
 * 
 * @inproceedings{Freund1996,
 *    address = {San Francisco},
 *    author = {Yoav Freund and Robert E. Schapire},
 *    booktitle = {Thirteenth International Conference on Machine Learning},
 *    pages = {148-156},
 *    publisher = {Morgan Kaufmann},
 *    title = {Experiments with a new boosting algorithm},
 *    year = {1996}
 * }
 * 
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -B <num>
 *  The number of bins in discretization
 *  (default 0, no discretization)
 * 
 * 
 * 
 * -R <num>
 *  Maximum number of boost iterations.
 *  (default 10)
 * 
 * 
 * 
 * -W <class name>
 *  Full name of classifier to boost.
 *  eg: weka.classifiers.bayes.NaiveBayes
 * 
 * 
 * 
 * 
 * @author Eibe Frank ([email protected])
 * @author Xin Xu ([email protected])
 * @version $Revision: 10369 $
 */
public class MIBoost extends SingleClassifierEnhancer implements OptionHandler,
  MultiInstanceCapabilitiesHandler, TechnicalInformationHandler {

  /** for serialization */
  static final long serialVersionUID = -3808427225599279539L;

  /** the models for the iterations */
  protected Classifier[] m_Models;

  /** The number of the class labels */
  protected int m_NumClasses;

  /** Class labels for each bag */
  protected int[] m_Classes;

  /** attributes name for the new dataset used to build the model */
  protected Instances m_Attributes;

  /** Number of iterations */
  private int m_NumIterations = 100;

  /** Voting weights of models */
  protected double[] m_Beta;

  /** the maximum number of boost iterations */
  protected int m_MaxIterations = 10;

  /** the number of discretization bins */
  protected int m_DiscretizeBin = 0;

  /** filter used for discretization */
  protected Discretize m_Filter = null;

  /** filter used to convert the MI dataset into single-instance dataset */
  protected MultiInstanceToPropositional m_ConvertToSI = new MultiInstanceToPropositional();

  /**
   * Returns a string describing this filter
   * 
   * @return a description of the filter suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String globalInfo() {
    return "MI AdaBoost method, considers the geometric mean of posterior "
      + "of instances inside a bag (arithmatic mean of log-posterior) and "
      + "the expectation for a bag is taken inside the loss function.\n\n"
      + "For more information about Adaboost, 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
   */
  @Override
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation result;

    result = new TechnicalInformation(Type.INPROCEEDINGS);
    result.setValue(Field.AUTHOR, "Yoav Freund and Robert E. Schapire");
    result.setValue(Field.TITLE, "Experiments with a new boosting algorithm");
    result.setValue(Field.BOOKTITLE,
      "Thirteenth International Conference on Machine Learning");
    result.setValue(Field.YEAR, "1996");
    result.setValue(Field.PAGES, "148-156");
    result.setValue(Field.PUBLISHER, "Morgan Kaufmann");
    result.setValue(Field.ADDRESS, "San Francisco");

    return result;
  }

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

    Vector result = new Vector();

    result.addElement(new Option("\tThe number of bins in discretization\n"
      + "\t(default 0, no discretization)", "B", 1, "-B "));

    result.addElement(new Option("\tMaximum number of boost iterations.\n"
      + "\t(default 10)", "R", 1, "-R "));

    result.addAll(Collections.list(super.listOptions()));

    return result.elements();
  }

  /**
   * Parses a given list of options.
   * 

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -B <num>
   *  The number of bins in discretization
   *  (default 0, no discretization)
   * 
   * 
   * 
   * -R <num>
   *  Maximum number of boost iterations.
   *  (default 10)
   * 
   * 
   * 
   * -W <class name>
   *  Full name of classifier to boost.
   *  eg: weka.classifiers.bayes.NaiveBayes
   * 
   * 
   * 
   * 
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  @Override
  public void setOptions(String[] options) throws Exception {
    setDebug(Utils.getFlag('D', options));

    String bin = Utils.getOption('B', options);
    if (bin.length() != 0) {
      setDiscretizeBin(Integer.parseInt(bin));
    } else {
      setDiscretizeBin(0);
    }

    String boostIterations = Utils.getOption('R', options);
    if (boostIterations.length() != 0) {
      setMaxIterations(Integer.parseInt(boostIterations));
    } else {
      setMaxIterations(10);
    }

    super.setOptions(options);

    Utils.checkForRemainingOptions(options);
  }

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

    Vector result = new Vector(4);

    result.add("-R");
    result.add("" + getMaxIterations());

    result.add("-B");
    result.add("" + getDiscretizeBin());

    Collections.addAll(result, super.getOptions());

    return result.toArray(new String[result.size()]);
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String maxIterationsTipText() {
    return "The maximum number of boost iterations.";
  }

  /**
   * Set the maximum number of boost iterations
   * 
   * @param maxIterations the maximum number of boost iterations
   */
  public void setMaxIterations(int maxIterations) {
    m_MaxIterations = maxIterations;
  }

  /**
   * Get the maximum number of boost iterations
   * 
   * @return the maximum number of boost iterations
   */
  public int getMaxIterations() {

    return m_MaxIterations;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String discretizeBinTipText() {
    return "The number of bins in discretization.";
  }

  /**
   * Set the number of bins in discretization
   * 
   * @param bin the number of bins in discretization
   */
  public void setDiscretizeBin(int bin) {
    m_DiscretizeBin = bin;
  }

  /**
   * Get the number of bins in discretization
   * 
   * @return the number of bins in discretization
   */
  public int getDiscretizeBin() {
    return m_DiscretizeBin;
  }

  private class OptEng extends Optimization {

    private double[] weights, errs;

    public void setWeights(double[] w) {
      weights = w;
    }

    public void setErrs(double[] e) {
      errs = e;
    }

    /**
     * Evaluate objective function
     * 
     * @param x the current values of variables
     * @return the value of the objective function
     * @throws Exception if result is NaN
     */
    @Override
    protected double objectiveFunction(double[] x) throws Exception {
      double obj = 0;
      for (int i = 0; i < weights.length; i++) {
        obj += weights[i] * Math.exp(x[0] * (2.0 * errs[i] - 1.0));
        if (Double.isNaN(obj)) {
          throw new Exception("Objective function value is NaN!");
        }

      }
      return obj;
    }

    /**
     * Evaluate Jacobian vector
     * 
     * @param x the current values of variables
     * @return the gradient vector
     * @throws Exception if gradient is NaN
     */
    @Override
    protected double[] evaluateGradient(double[] x) throws Exception {
      double[] grad = new double[1];
      for (int i = 0; i < weights.length; i++) {
        grad[0] += weights[i] * (2.0 * errs[i] - 1.0)
          * Math.exp(x[0] * (2.0 * errs[i] - 1.0));
        if (Double.isNaN(grad[0])) {
          throw new Exception("Gradient is NaN!");
        }

      }
      return grad;
    }

    /**
     * Returns the revision string.
     * 
     * @return the revision
     */
    @Override
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 10369 $");
    }
  }

  /**
   * Returns default capabilities of the classifier.
   * 
   * @return the capabilities of this classifier
   */
  @Override
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.RELATIONAL_ATTRIBUTES);
    result.disable(Capability.MISSING_VALUES);

    // class
    result.disableAllClasses();
    result.disableAllClassDependencies();
    if (super.getCapabilities().handles(Capability.BINARY_CLASS)) {
      result.enable(Capability.BINARY_CLASS);
    }
    result.enable(Capability.MISSING_CLASS_VALUES);

    // other
    result.enable(Capability.ONLY_MULTIINSTANCE);

    return result;
  }

  /**
   * Returns the capabilities of this multi-instance classifier for the
   * relational data.
   * 
   * @return the capabilities of this object
   * @see Capabilities
   */
  @Override
  public Capabilities getMultiInstanceCapabilities() {
    Capabilities result = super.getCapabilities();

    // class
    result.disableAllClasses();
    result.enable(Capability.NO_CLASS);

    return result;
  }

  /**
   * Builds the classifier
   * 
   * @param exps the training data to be used for generating the boosted
   *          classifier.
   * @throws Exception if the classifier could not be built successfully
   */
  @Override
  public void buildClassifier(Instances exps) throws Exception {

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

    // remove instances with missing class
    Instances train = new Instances(exps);
    train.deleteWithMissingClass();

    m_NumClasses = train.numClasses();
    m_NumIterations = m_MaxIterations;

    if (m_Classifier == null) {
      throw new Exception("A base classifier has not been specified!");
    }
    if (!(m_Classifier instanceof WeightedInstancesHandler)) {
      throw new Exception("Base classifier cannot handle weighted instances!");
    }

    m_Models = AbstractClassifier.makeCopies(m_Classifier, getMaxIterations());
    if (m_Debug) {
      System.err.println("Base classifier: "
        + m_Classifier.getClass().getName());
    }

    m_Beta = new double[m_NumIterations];

    /*
     * modified by Lin Dong. (use MIToSingleInstance filter to convert the MI
     * datasets)
     */

    // Initialize the bags' weights
    double N = train.numInstances(), sumNi = 0;
    for (int i = 0; i < N; i++) {
      int nn = train.instance(i).relationalValue(1).numInstances();
      sumNi += nn;
    }
    for (int i = 0; i < N; i++) {
      train.instance(i).setWeight(sumNi / N);
    }

    // convert the training dataset into single-instance dataset
    m_ConvertToSI.setInputFormat(train);
    Instances data = Filter.useFilter(train, m_ConvertToSI);

    data.deleteAttributeAt(0); // remove the bagIndex attribute;

    // Assume the order of the instances are preserved in the Discretize filter
    if (m_DiscretizeBin > 0) {
      m_Filter = new Discretize();
      m_Filter.setInputFormat(new Instances(data, 0));
      m_Filter.setBins(m_DiscretizeBin);
      data = Filter.useFilter(data, m_Filter);
    }

    // Main algorithm
    int dataIdx;
    iterations: for (int m = 0; m < m_MaxIterations; m++) {

      // Build a model
      m_Models[m].buildClassifier(data);

      // Prediction of each bag
      double[] err = new double[(int) N], weights = new double[(int) N];
      boolean perfect = true, tooWrong = true;
      dataIdx = 0;
      for (int n = 0; n < N; n++) {
        Instance exn = train.instance(n);
        // Prediction of each instance and the predicted class distribution
        // of the bag
        double nn = exn.relationalValue(1).numInstances();
        for (int p = 0; p < nn; p++) {
          Instance testIns = data.instance(dataIdx++);
          if ((int) m_Models[m].classifyInstance(testIns) != (int) exn
            .classValue()) {
            err[n]++;
          }
        }
        weights[n] = exn.weight();
        err[n] /= nn;
        if (err[n] > 0.5) {
          perfect = false;
        }
        if (err[n] < 0.5) {
          tooWrong = false;
        }
      }

      if (perfect || tooWrong) { // No or 100% classification error, cannot find
                                 // beta
        if (m == 0) {
          m_Beta[m] = 1.0;
        } else {
          m_Beta[m] = 0;
        }
        m_NumIterations = m + 1;
        if (m_Debug) {
          System.err.println("No errors");
        }
        break iterations;
      }

      double[] x = new double[1];
      x[0] = 0;
      double[][] b = new double[2][x.length];
      b[0][0] = Double.NaN;
      b[1][0] = Double.NaN;

      OptEng opt = new OptEng();
      opt.setWeights(weights);
      opt.setErrs(err);
      // opt.setDebug(m_Debug);
      if (m_Debug) {
        System.out.println("Start searching for c... ");
      }
      x = opt.findArgmin(x, b);
      while (x == null) {
        x = opt.getVarbValues();
        if (m_Debug) {
          System.out.println("200 iterations finished, not enough!");
        }
        x = opt.findArgmin(x, b);
      }
      if (m_Debug) {
        System.out.println("Finished.");
      }
      m_Beta[m] = x[0];

      if (m_Debug) {
        System.err.println("c = " + m_Beta[m]);
      }

      // Stop if error too small or error too big and ignore this model
      if (Double.isInfinite(m_Beta[m]) || Utils.smOrEq(m_Beta[m], 0)) {
        if (m == 0) {
          m_Beta[m] = 1.0;
        } else {
          m_Beta[m] = 0;
        }
        m_NumIterations = m + 1;
        if (m_Debug) {
          System.err.println("Errors out of range!");
        }
        break iterations;
      }

      // Update weights of data and class label of wfData
      dataIdx = 0;
      double totWeights = 0;
      for (int r = 0; r < N; r++) {
        Instance exr = train.instance(r);
        exr.setWeight(weights[r] * Math.exp(m_Beta[m] * (2.0 * err[r] - 1.0)));
        totWeights += exr.weight();
      }

      if (m_Debug) {
        System.err.println("Total weights = " + totWeights);
      }

      for (int r = 0; r < N; r++) {
        Instance exr = train.instance(r);
        double num = exr.relationalValue(1).numInstances();
        exr.setWeight(sumNi * exr.weight() / totWeights);
        // if(m_Debug)
        // System.err.print("\nExemplar "+r+"="+exr.weight()+": \t");
        for (int s = 0; s < num; s++) {
          Instance inss = data.instance(dataIdx);
          inss.setWeight(exr.weight() / num);
          // if(m_Debug)
          // System.err.print("instance "+s+"="+inss.weight()+
          // "|ew*iw*sumNi="+data.instance(dataIdx).weight()+"\t");
          if (Double.isNaN(inss.weight())) {
            throw new Exception("instance " + s + " in bag " + r
              + " has weight NaN!");
          }
          dataIdx++;
        }
        // if(m_Debug)
        // System.err.println();
      }
    }
  }

  /**
   * Computes the distribution for a given exemplar
   * 
   * @param exmp the exemplar for which distribution is computed
   * @return the classification
   * @throws Exception if the distribution can't be computed successfully
   */
  @Override
  public double[] distributionForInstance(Instance exmp) throws Exception {

    double[] rt = new double[m_NumClasses];

    Instances insts = new Instances(exmp.dataset(), 0);
    insts.add(exmp);

    // convert the dataset into single-instance dataset
    insts = Filter.useFilter(insts, m_ConvertToSI);
    insts.deleteAttributeAt(0); // remove the bagIndex attribute

    double n = insts.numInstances();

    if (m_DiscretizeBin > 0) {
      insts = Filter.useFilter(insts, m_Filter);
    }

    for (int y = 0; y < n; y++) {
      Instance ins = insts.instance(y);
      for (int x = 0; x < m_NumIterations; x++) {
        rt[(int) m_Models[x].classifyInstance(ins)] += m_Beta[x] / n;
      }
    }

    for (int i = 0; i < rt.length; i++) {
      rt[i] = Math.exp(rt[i]);
    }

    Utils.normalize(rt);
    return rt;
  }

  /**
   * Gets a string describing the classifier.
   * 
   * @return a string describing the classifer built.
   */
  @Override
  public String toString() {

    if (m_Models == null) {
      return "No model built yet!";
    }
    StringBuffer text = new StringBuffer();
    text.append("MIBoost: number of bins in discretization = "
      + m_DiscretizeBin + "\n");
    if (m_NumIterations == 0) {
      text.append("No model built yet.\n");
    } else if (m_NumIterations == 1) {
      text.append("No boosting possible, one classifier used: Weight = "
        + Utils.roundDouble(m_Beta[0], 2) + "\n");
      text.append("Base classifiers:\n" + m_Models[0].toString());
    } else {
      text.append("Base classifiers and their weights: \n");
      for (int i = 0; i < m_NumIterations; i++) {
        text.append("\n\n" + i + ": Weight = "
          + Utils.roundDouble(m_Beta[i], 2) + "\nBase classifier:\n"
          + m_Models[i].toString());
      }
    }

    text
      .append("\n\nNumber of performed Iterations: " + m_NumIterations + "\n");

    return text.toString();
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10369 $");
  }

  /**
   * Main method for testing this class.
   * 
   * @param argv should contain the command line arguments to the scheme (see
   *          Evaluation)
   */
  public static void main(String[] argv) {
    runClassifier(new MIBoost(), argv);
  }
}