gt:     return new SelectedTag(m_MatrixSource, TAGS_MATRIX_SOURCE);
context: /*
   *    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.
   */
  
  /*
   *    MetaCost.java
   *    Copyright (C) 2002 University of Waikato
   *
   */
  
  package weka.classifiers.meta;
  
  import weka.classifiers.*;
  import weka.classifiers.rules.ZeroR;
  import java.io.*;
  import java.util.*;
  import weka.core.*;
  import weka.filters.Filter;
  
  
  /**
   * This metaclassifier makes its base classifier cost-sensitive using the
   * method specified in <p>
   *
   * Pedro Domingos (1999). <i>MetaCost: A general method for making classifiers
   * cost-sensitive</i>, Proceedings of the Fifth International Conference on 
   * Knowledge Discovery and Data Mining, pp. 155-164. Also available online at
   * <a href="http://www.cs.washington.edu/homes/pedrod/kdd99.ps.gz">
   * http://www.cs.washington.edu/homes/pedrod/kdd99.ps.gz</a>. <p>
   *
   * This classifier should produce similar results to one created by
   * passing the base learner to Bagging, which is in turn passed to a
   * CostSensitiveClassifier operating on minimum expected cost. The difference
   * is that MetaCost produces a single cost-sensitive classifier of the
   * base learner, giving the benefits of fast classification and interpretable
   * output (if the base learner itself is interpretable). This implementation 
   * uses all bagging iterations when reclassifying training data (the MetaCost
   * paper reports a marginal improvement when only those iterations containing
   * each training instance are used in reclassifying that instance). <p>
   *
   * Valid options are:<p>
   *
   * -W classname <br>
   * Specify the full class name of a classifier (required).<p>
   *
   * -C cost file <br>
   * File name of a cost matrix to use. If this is not supplied, a cost
   * matrix will be loaded on demand. The name of the on-demand file
   * is the relation name of the training data plus ".cost", and the
   * path to the on-demand file is specified with the -N option.<p>
   *
   * -N directory <br>
   * Name of a directory to search for cost files when loading costs on demand
   * (default current directory). <p>
   *
   * -I num <br>
   * Set the number of bagging iterations (default 10). <p>
   *
   * -S seed <br>
   * Random number seed used when reweighting by resampling (default 1).<p>
   *
   * -P num <br>
   * Size of each bag, as a percentage of the training size (default 100). <p>
   *
   * -cost-matrix matrix<br>
   * The cost matrix, specified in Matlab single line format.<p>
   *
   * Options after -- are passed to the designated classifier.<p>
   *
   * @author Len Trigg (len@reeltwo.com)
   * @version $Revision: 1.15.2.1 $ 
   */
  public class MetaCost extends RandomizableSingleClassifierEnhancer {
  
    /* Specify possible sources of the cost matrix */
    public static final int MATRIX_ON_DEMAND = 1;
    public static final int MATRIX_SUPPLIED = 2;
    public static final Tag [] TAGS_MATRIX_SOURCE = {
      new Tag(MATRIX_ON_DEMAND, "Load cost matrix on demand"),
      new Tag(MATRIX_SUPPLIED, "Use explicit cost matrix")
    };
  
    /** Indicates the current cost matrix source */
    protected int m_MatrixSource = MATRIX_ON_DEMAND;
  
    /** 
     * The directory used when loading cost files on demand, null indicates
     * current directory 
     */
    protected File m_OnDemandDirectory = new File(System.getProperty("user.dir"));
  
    /** The name of the cost file, for command line options */
    protected String m_CostFile;
  
    /** The cost matrix */
    protected CostMatrix m_CostMatrix = new CostMatrix(1);
  
    /** The number of iterations. */
    protected int m_NumIterations = 10;
  
    /** The size of each bag sample, as a percentage of the training size */
    protected int m_BagSizePercent = 100;
      
    /**
     * Returns a string describing classifier
     * @return a description suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
   
      return  "This metaclassifier makes its base classifier cost-sensitive using the "
        + "method specified in\n\n"
        + "Pedro Domingos (1999) \"MetaCost: A general method for making classifiers "
        + "cost-sensitive\", Proceedings of the Fifth International Conference on "
        + "Knowledge Discovery and Data Mining, pp 155-164.\n\n"
        + "This classifier should produce similar results to one created by "
        + "passing the base learner to Bagging, which is in turn passed to a "
        + "CostSensitiveClassifier operating on minimum expected cost. The difference "
        + "is that MetaCost produces a single cost-sensitive classifier of the "
        + "base learner, giving the benefits of fast classification and interpretable "
        + "output (if the base learner itself is interpretable). This implementation  "
        + "uses all bagging iterations when reclassifying training data (the MetaCost "
        + "paper reports a marginal improvement when only those iterations containing "
        + "each training instance are used in reclassifying that instance).";
   
    }
  
    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration listOptions() {
  
      Vector newVector = new Vector(6);
  
      newVector.addElement(new Option(
  	      "\tNumber of bagging iterations.\n"
  	      + "\t(default 10)",
  	      "I", 1, "-I <num>"));
      newVector.addElement(new Option(
  	      "\tFile name of a cost matrix to use. If this is not supplied,\n"
                +"\ta cost matrix will be loaded on demand. The name of the\n"
                +"\ton-demand file is the relation name of the training data\n"
                +"\tplus \".cost\", and the path to the on-demand file is\n"
                +"\tspecified with the -N option.",
  	      "C", 1, "-C <cost file name>"));
      newVector.addElement(new Option(
                "\tName of a directory to search for cost files when loading\n"
                +"\tcosts on demand (default current directory).",
                "N", 1, "-N <directory>"));
      newVector.addElement(new Option(
                "\tThe cost matrix in Matlab single line format.",
                "cost-matrix", 1, "-cost-matrix <matrix>"));
      newVector.addElement(new Option(
                "\tSize of each bag, as a percentage of the\n" 
                + "\ttraining set size. (default 100)",
                "P", 1, "-P"));
  
      Enumeration enu = super.listOptions();
      while (enu.hasMoreElements()) {
        newVector.addElement(enu.nextElement());
      }
      return newVector.elements();
    }
  
    /**
     * Parses a given list of options. Valid options are:<p>
     *
     * -W classname <br>
     * Specify the full class name of a classifier (required).<p>
     *
     * -C cost file <br>
     * File name of a cost matrix to use. If this is not supplied, a cost
     * matrix will be loaded on demand. The name of the on-demand file
     * is the relation name of the training data plus ".cost", and the
     * path to the on-demand file is specified with the -N option.<p>
     *
     * -N directory <br>
     * Name of a directory to search for cost files when loading costs on demand
     * (default current directory). <p>
     *
     * -I num <br>
     * Set the number of bagging iterations (default 10). <p>
     *
     * -S seed <br>
     * Random number seed used when reweighting by resampling (default 1).<p>
     *
     * -P num <br>
     * Size of each bag, as a percentage of the training size (default 100). <p>
     *
     * -cost-matrix matrix<br>
     * The cost matrix, specified in Matlab single line format.<p>
     *
     * Options after -- are passed to the designated classifier.<p>
     *
     * @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 bagIterations = Utils.getOption('I', options);
      if (bagIterations.length() != 0) {
        setNumIterations(Integer.parseInt(bagIterations));
      } else {
        setNumIterations(10);
      }
  
      String bagSize = Utils.getOption('P', options);
      if (bagSize.length() != 0) {
        setBagSizePercent(Integer.parseInt(bagSize));
      } else {
        setBagSizePercent(100);
      }
  
      String costFile = Utils.getOption('C', options);
      if (costFile.length() != 0) {
        setCostMatrix(new CostMatrix(new BufferedReader(
                                     new FileReader(costFile))));
        setCostMatrixSource(new SelectedTag(MATRIX_SUPPLIED,
                                            TAGS_MATRIX_SOURCE));
        m_CostFile = costFile;
      } else {
        setCostMatrixSource(new SelectedTag(MATRIX_ON_DEMAND, 
                                            TAGS_MATRIX_SOURCE));
      }
      
      String demandDir = Utils.getOption('N', options);
      if (demandDir.length() != 0) {
        setOnDemandDirectory(new File(demandDir));
      }
  
      String cost_matrix = Utils.getOption("cost-matrix", options);
      if (cost_matrix.length() != 0) {
        StringWriter writer = new StringWriter();
        CostMatrix.parseMatlab(cost_matrix).write(writer);
        setCostMatrix(new CostMatrix(new StringReader(writer.toString())));
        setCostMatrixSource(new SelectedTag(MATRIX_SUPPLIED,
                                            TAGS_MATRIX_SOURCE));
      }
      
      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;
  
      options = new String [superOptions.length + 6];
      int current = 0;
  
      if (m_MatrixSource == MATRIX_SUPPLIED) {
        if (m_CostFile != null) {
          options[current++] = "-C";
          options[current++] = "" + m_CostFile;
        }
        else {
          options[current++] = "-cost-matrix";
          options[current++] = getCostMatrix().toMatlab();
        }
      } else {
        options[current++] = "-N";
        options[current++] = "" + getOnDemandDirectory();
      }
      options[current++] = "-I"; options[current++] = "" + getNumIterations();
      options[current++] = "-P"; options[current++] = "" + getBagSizePercent();
  
      System.arraycopy(superOptions, 0, options, current, 
  		     superOptions.length);
      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 costMatrixSourceTipText() {
      return "Gets the source location method of the cost matrix. Will "
        + "be one of MATRIX_ON_DEMAND or MATRIX_SUPPLIED.";
    }
  
    /**
     * Gets the source location method of the cost matrix. Will be one of
     * MATRIX_ON_DEMAND or MATRIX_SUPPLIED.
     *
     * @return the cost matrix source.
     */
    public SelectedTag getCostMatrixSource() {
  
  