content: /*
   *    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.
   */
  
  /*
   *    BVDecompose.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers;
  
  import weka.core.Attribute;
  import weka.core.Instance;
  import weka.core.Instances;
  import weka.core.Option;
  import weka.core.OptionHandler;
  import weka.core.RevisionHandler;
  import weka.core.RevisionUtils;
  import weka.core.TechnicalInformation;
  import weka.core.TechnicalInformationHandler;
  import weka.core.Utils;
  import weka.core.TechnicalInformation.Field;
  import weka.core.TechnicalInformation.Type;
  
  import java.io.BufferedReader;
  import java.io.FileReader;
  import java.io.Reader;
  import java.util.Enumeration;
  import java.util.Random;
  import java.util.Vector;
  
  /**
   <!-- globalinfo-start -->
   * Class for performing a Bias-Variance decomposition on any classifier using the method specified in:<br/>
   * <br/>
   * Ron Kohavi, David H. Wolpert: Bias Plus Variance Decomposition for Zero-One Loss Functions. In: Machine Learning: Proceedings of the Thirteenth International Conference, 275-283, 1996.
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- technical-bibtex-start -->
   * BibTeX:
   * <pre>
   * &#64;inproceedings{Kohavi1996,
   *    author = {Ron Kohavi and David H. Wolpert},
   *    booktitle = {Machine Learning: Proceedings of the Thirteenth International Conference},
   *    editor = {Lorenza Saitta},
   *    pages = {275-283},
   *    publisher = {Morgan Kaufmann},
   *    title = {Bias Plus Variance Decomposition for Zero-One Loss Functions},
   *    year = {1996},
   *    PS = {http://robotics.stanford.edu/\~ronnyk/biasVar.ps}
   * }
   * </pre>
   * <p/>
   <!-- technical-bibtex-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   *
   * <pre> -c &lt;class index&gt;
   *  The index of the class attribute.
   *  (default last)</pre>
   *
   * <pre> -t &lt;name of arff file&gt;
   *  The name of the arff file used for the decomposition.</pre>
   *
   * <pre> -T &lt;training pool size&gt;
   *  The number of instances placed in the training pool.
   *  The remainder will be used for testing. (default 100)</pre>
   *
   * <pre> -s &lt;seed&gt;
   *  The random number seed used.</pre>
   *
   * <pre> -x &lt;num&gt;
   *  The number of training repetitions used.
   *  (default 50)</pre>
   *
   * <pre> -D
   *  Turn on debugging output.</pre>
   *
   * <pre> -W &lt;classifier class name&gt;
   *  Full class name of the learner used in the decomposition.
   *  eg: weka.classifiers.bayes.NaiveBayes</pre>
   *
   * <pre>
   * Options specific to learner weka.classifiers.rules.ZeroR:
   * </pre>
   *
   * <pre> -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console</pre>
   *
   <!-- options-end -->
   *
   * Options after -- are passed to the designated sub-learner. <p>
   *
   * @author Len Trigg (trigg@cs.waikato.ac.nz)
   * @version $Revision: 6041 $
   */
  public class BVDecompose
    implements OptionHandler, TechnicalInformationHandler, RevisionHandler {
  
    /** Debugging mode, gives extra output if true */
    protected boolean m_Debug;
  
    /** An instantiated base classifier used for getting and testing options. */
    protected Classifier m_Classifier = new weka.classifiers.rules.ZeroR();
  
    /** The options to be passed to the base classifier. */
    protected String [] m_ClassifierOptions;
  
    /** The number of train iterations */
    protected int m_TrainIterations = 50;
  
    /** The name of the data file used for the decomposition */
    protected String m_DataFileName;
  
    /** The index of the class attribute */
    protected int m_ClassIndex = -1;
  
    /** The random number seed */
    protected int m_Seed = 1;
  
    /** The calculated bias (squared) */
    protected double m_Bias;
  
    /** The calculated variance */
    protected double m_Variance;
  
    /** The calculated sigma (squared) */
    protected double m_Sigma;
  
    /** The error rate */
    protected double m_Error;
  
    /** The number of instances used in the training pool */
    protected int m_TrainPoolSize = 100;
  
    /**
     * Returns a string describing this object
     * @return a description of the classifier suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
  
      return
          "Class for performing a Bias-Variance decomposition on any classifier "
        + "using the method specified in:\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.INPROCEEDINGS);
      result.setValue(Field.AUTHOR, "Ron Kohavi and David H. Wolpert");
      result.setValue(Field.YEAR, "1996");
      result.setValue(Field.TITLE, "Bias Plus Variance Decomposition for Zero-One Loss Functions");
      result.setValue(Field.BOOKTITLE, "Machine Learning: Proceedings of the Thirteenth International Conference");
      result.setValue(Field.PUBLISHER, "Morgan Kaufmann");
      result.setValue(Field.EDITOR, "Lorenza Saitta");
      result.setValue(Field.PAGES, "275-283");
      result.setValue(Field.PS, "http://robotics.stanford.edu/~ronnyk/biasVar.ps");
  
      return result;
    }
  
    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration listOptions() {
  
      Vector newVector = new Vector(7);
  
      newVector.addElement(new Option(
            "\tThe index of the class attribute.\n"+
            "\t(default last)",
            "c", 1, "-c <class index>"));
      newVector.addElement(new Option(
            "\tThe name of the arff file used for the decomposition.",
            "t", 1, "-t <name of arff file>"));
      newVector.addElement(new Option(
            "\tThe number of instances placed in the training pool.\n"
            + "\tThe remainder will be used for testing. (default 100)",
            "T", 1, "-T <training pool size>"));
      newVector.addElement(new Option(
            "\tThe random number seed used.",
            "s", 1, "-s <seed>"));
      newVector.addElement(new Option(
            "\tThe number of training repetitions used.\n"
            +"\t(default 50)",
            "x", 1, "-x <num>"));
      newVector.addElement(new Option(
            "\tTurn on debugging output.",
            "D", 0, "-D"));
      newVector.addElement(new Option(
            "\tFull class name of the learner used in the decomposition.\n"
            +"\teg: weka.classifiers.bayes.NaiveBayes",
            "W", 1, "-W <classifier class name>"));
  
      if ((m_Classifier != null) &&
          (m_Classifier instanceof OptionHandler)) {
        newVector.addElement(new Option(
              "",
              "", 0, "\nOptions specific to learner "
              + m_Classifier.getClass().getName()
              + ":"));
        Enumeration enu = ((OptionHandler)m_Classifier).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> -c &lt;class index&gt;
     *  The index of the class attribute.
     *  (default last)</pre>
     *
     * <pre> -t &lt;name of arff file&gt;
     *  The name of the arff file used for the decomposition.</pre>
     *
     * <pre> -T &lt;training pool size&gt;
     *  The number of instances placed in the training pool.
     *  The remainder will be used for testing. (default 100)</pre>
     *
     * <pre> -s &lt;seed&gt;
     *  The random number seed used.</pre>
     *
     * <pre> -x &lt;num&gt;
     *  The number of training repetitions used.
     *  (default 50)</pre>
     *
     * <pre> -D
     *  Turn on debugging output.</pre>
     *
     * <pre> -W &lt;classifier class name&gt;
     *  Full class name of the learner used in the decomposition.
     *  eg: weka.classifiers.bayes.NaiveBayes</pre>
     *
     * <pre>
     * Options specific to learner weka.classifiers.rules.ZeroR:
     * </pre>
     *
     * <pre> -D
     *  If set, classifier is run in debug mode and
     *  may output additional info to the console</pre>
     *
     <!-- options-end -->
     *
     * Options after -- are passed to the designated sub-learner. <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 {
  
      setDebug(Utils.getFlag('D', options));
  
      String classIndex = Utils.getOption('c', options);
      if (classIndex.length() != 0) {
        if (classIndex.toLowerCase().equals("last")) {
          setClassIndex(0);
        } else if (classIndex.toLowerCase().equals("first")) {
          setClassIndex(1);
        } else {
          setClassIndex(Integer.parseInt(classIndex));
        }
      } else {
        setClassIndex(0);
      }
  
      String trainIterations = Utils.getOption('x', options);
      if (trainIterations.length() != 0) {
        setTrainIterations(Integer.parseInt(trainIterations));
      } else {
        setTrainIterations(50);
      }
  
      String trainPoolSize = Utils.getOption('T', options);
      if (trainPoolSize.length() != 0) {
        setTrainPoolSize(Integer.parseInt(trainPoolSize));
      } else {
        setTrainPoolSize(100);
      }
  
      String seedString = Utils.getOption('s', options);
      if (seedString.length() != 0) {
        setSeed(Integer.parseInt(seedString));
      } else {
        setSeed(1);
      }
  
      String dataFile = Utils.getOption('t', options);
      if (dataFile.length() == 0) {
        throw new Exception("An arff file must be specified"
            + " with the -t option.");
      }
      setDataFileName(dataFile);
  
      String classifierName = Utils.getOption('W', options);
      if (classifierName.length() == 0) {
        throw new Exception("A learner must be specified with the -W option.");
      }
      setClassifier(AbstractClassifier.forName(classifierName,
            Utils.partitionOptions(options)));
    }
  
    /**
     * Gets the current settings of the CheckClassifier.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String [] getOptions() {
  
      String [] classifierOptions = new String [0];
      if ((m_Classifier != null) &&
          (m_Classifier instanceof OptionHandler)) {
        classifierOptions = ((OptionHandler)m_Classifier).getOptions();
          }
      String [] options = new String [classifierOptions.length + 14];
      int current = 0;
      if (getDebug()) {
        options[current++] = "-D";
      }
      options[current++] = "-c"; options[current++] = "" + getClassIndex();
      options[current++] = "-x"; options[current++] = "" + getTrainIterations();
      options[current++] = "-T"; options[current++] = "" + getTrainPoolSize();
      options[current++] = "-s"; options[current++] = "" + getSeed();
      if (getDataFileName() != null) {
        options[current++] = "-t"; options[current++] = "" + getDataFileName();
      }
      if (getClassifier() != null) {
        options[current++] = "-W";
        options[current++] = getClassifier().getClass().getName();
      }
      options[current++] = "--";
      System.arraycopy(classifierOptions, 0, options, current,
          classifierOptions.length);
      current += classifierOptions.length;
      while (current < options.length) {
        options[current++] = "";
      }
      return options;
    }
  
    /**
     * Get the number of instances in the training pool.
     *
     * @return number of instances in the training pool.
     */
    public int getTrainPoolSize() {
  
      return m_TrainPoolSize;
    }
  
    /**
     * Set the number of instances in the training pool.
     *
     * @param numTrain number of instances in the training pool.
     */
    public void setTrainPoolSize(int numTrain) {
  
      m_TrainPoolSize = numTrain;
    }
  
    /**
     * Set the classifiers being analysed
     *
     * @param newClassifier the Classifier to use.
     */
    public void setClassifier(Classifier newClassifier) {
  
      m_Classifier = newClassifier;
    }
  
    /**
     * Gets the name of the classifier being analysed
     *
     * @return the classifier being analysed.
     */
    public Classifier getClassifier() {
  
      return m_Classifier;
    }
  
    /**
     * Sets debugging mode
     *
     * @param debug true if debug output should be printed
     */
    public void setDebug(boolean debug) {
  
      m_Debug = debug;
    }
  
    /**
     * Gets whether debugging is turned on
     *
     * @return true if debugging output is on
     */
    public boolean getDebug() {
  
      return m_Debug;
    }
  
    /**
     * Sets the random number seed
     *
     * @param seed the random number seed
     */
    public void setSeed(int seed) {
  
      m_Seed = seed;
    }
  
    /**
     * Gets the random number seed
     *
     * @return the random number seed
     */
    public int getSeed() {
  
      return m_Seed;
    }
  
    /**
     * Sets the maximum number of boost iterations
     *
     * @param trainIterations the number of boost iterations
     */
    public void setTrainIterations(int trainIterations) {
  
      m_TrainIterations = trainIterations;
    }
  
    /**
     * Gets the maximum number of boost iterations
     *
     * @return the maximum number of boost iterations
     */
    public int getTrainIterations() {
  
      return m_TrainIterations;
    }
  
    /**
     * Sets the name of the data file used for the decomposition
     *
     * @param dataFileName the data file to use
     */
    public void setDataFileName(String dataFileName) {
  
      m_DataFileName = dataFileName;
    }
  
    /**
     * Get the name of the data file used for the decomposition
     *
     * @return the name of the data file
     */
    public String getDataFileName() {
  
      return m_DataFileName;
    }
  
    /**
     * Get the index (starting from 1) of the attribute used as the class.
     *
     * @return the index of the class attribute
     */
    public int getClassIndex() {
  
      return m_ClassIndex + 1;
    }
  
    /**
     * Sets index of attribute to discretize on
     *
     * @param classIndex the index (starting from 1) of the class attribute
     */
    public void setClassIndex(int classIndex) {
  
      m_ClassIndex = classIndex - 1;
    }
  
    /**
     * Get the calculated bias squared
     *
     * @return the bias squared
     */
    public double getBias() {
  
      return m_Bias;
    }
  
    /**
     * Get the calculated variance
     *
     * @return the variance
     */
    public double getVariance() {
  
      return m_Variance;
    }
  
    /**
     * Get the calculated sigma squared
     *
     * @return the sigma squared
     */
    public double getSigma() {
  
      return m_Sigma;
    }
  
    /**
     * Get the calculated error rate
     *
     * @return the error rate
     */
    public double getError() {
  
      return m_Error;
    }
  
    /**
     * Carry out the bias-variance decomposition
     *
     * @throws Exception if the decomposition couldn't be carried out
     */
    public void decompose() throws Exception {
  
      Reader dataReader = new BufferedReader(new FileReader(m_DataFileName));
      Instances data = new Instances(dataReader);
  
      if (m_ClassIndex < 0) {
        data.setClassIndex(data.numAttributes() - 1);
      } else {
        data.setClassIndex(m_ClassIndex);
      }
      if (data.classAttribute().type() != Attribute.NOMINAL) {
        throw new Exception("Class attribute must be nominal");
      }
      int numClasses = data.numClasses();
  
      data.deleteWithMissingClass();
      if (data.checkForStringAttributes()) {
        throw new Exception("Can't handle string attributes!");
      }
  
      if (data.numInstances() < 2 * m_TrainPoolSize) {
        throw new Exception("The dataset must contain at least "
            + (2 * m_TrainPoolSize) + " instances");
      }
      Random random = new Random(m_Seed);
      data.randomize(random);
      Instances trainPool = new Instances(data, 0, m_TrainPoolSize);
      Instances test = new Instances(data, m_TrainPoolSize,
          data.numInstances() - m_TrainPoolSize);
      int numTest = test.numInstances();
      double [][] instanceProbs = new double [numTest][numClasses];
  
      m_Error = 0;
      for (int i = 0; i < m_TrainIterations; i++) {
        if (m_Debug) {
          System.err.println("Iteration " + (i + 1));
        }
        trainPool.randomize(random);
        Instances train = new Instances(trainPool, 0, m_TrainPoolSize / 2);
  
        Classifier current = AbstractClassifier.makeCopy(m_Classifier);
        current.buildClassifier(train);
  
        //// Evaluate the classifier on test, updating BVD stats
        for (int j = 0; j < numTest; j++) {
          int pred = (int)current.classifyInstance(test.instance(j));
          if (pred != test.instance(j).classValue()) {
            m_Error++;
          }
          instanceProbs[j][pred]++;
        }
      }
      m_Error /= (m_TrainIterations * numTest);
  
      // Average the BV over each instance in test.
      m_Bias = 0;
      m_Variance = 0;
      m_Sigma = 0;
      for (int i = 0; i < numTest; i++) {
        Instance current = test.instance(i);
        double [] predProbs = instanceProbs[i];
        double pActual, pPred;
        double bsum = 0, vsum = 0, ssum = 0;
        for (int j = 0; j < numClasses; j++) {
          pActual = (current.classValue() == j) ? 1 : 0; // Or via 1NN from test data?
          pPred = predProbs[j] / m_TrainIterations;
          bsum += (pActual - pPred) * (pActual - pPred)
            - pPred * (1 - pPred) / (m_TrainIterations - 1);
          vsum += pPred * pPred;
          ssum += pActual * pActual;
        }
        m_Bias += bsum;
        m_Variance += (1 - vsum);
        m_Sigma += (1 - ssum);
      }
      m_Bias /= (2 * numTest);
      m_Variance /= (2 * numTest);
      m_Sigma /= (2 * numTest);
  
      if (m_Debug) {
        System.err.println("Decomposition finished");
      }
    }
  
  
    /**
     * Returns description of the bias-variance decomposition results.
     *
     * @return the bias-variance decomposition results as a string
     */
    public String toString() {
  
      String result = "\nBias-Variance Decomposition\n";
  
      if (getClassifier() == null) {
        return "Invalid setup";
      }
  
      result += "\nClassifier   : " + getClassifier().getClass().getName();
      if (getClassifier() instanceof OptionHandler) {
        result += Utils.joinOptions(((OptionHandler)m_Classifier).getOptions());
      }
      result += "\nData File    : " + getDataFileName();
      result += "\nClass Index  : ";
      if (getClassIndex() == 0) {
        result += "last";
      } else {
        result += getClassIndex();
      }
      result += "\nTraining Pool: " + getTrainPoolSize();
      result += "\nIterations   : " + getTrainIterations();
      result += "\nSeed         : " + getSeed();
      result += "\nError        : " + Utils.doubleToString(getError(), 6, 4);
      result += "\nSigma^2      : " + Utils.doubleToString(getSigma(), 6, 4);
      result += "\nBias^2       : " + Utils.doubleToString(getBias(), 6, 4);
      result += "\nVariance     : " + Utils.doubleToString(getVariance(), 6, 4);
  
      return result + "\n";
    }
  
    /**
     * Returns the revision string.
     *
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 6041 $");
    }
  
    /**
     * Test method for this class
     *
     * @param args the command line arguments
     */
    public static void main(String [] args) {
  
      try {
        BVDecompose bvd = new BVDecompose();
  
        try {
          bvd.setOptions(args);
          Utils.checkForRemainingOptions(args);
        } catch (Exception ex) {
          String result = ex.getMessage() + "\nBVDecompose Options:\n\n";
          Enumeration enu = bvd.listOptions();
          while (enu.hasMoreElements()) {
            Option option = (Option) enu.nextElement();
            result += option.synopsis() + "\n" + option.description() + "\n";
          }
          throw new Exception(result);
        }
  
        bvd.decompose();
        System.out.println(bvd.toString());
      } catch (Exception ex) {
        System.err.println(ex.getMessage());
      }
    }
  }
  