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.
   */
  
  /*
   *    GainRatioAttributeEval.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.attributeSelection;
  
  import weka.core.Capabilities;
  import weka.core.ContingencyTables;
  import weka.core.Instance;
  import weka.core.Instances;
  import weka.core.Option;
  import weka.core.OptionHandler;
  import weka.core.RevisionUtils;
  import weka.core.Utils;
  import weka.core.Capabilities.Capability;
  import weka.filters.Filter;
  import weka.filters.supervised.attribute.Discretize;
  
  import java.util.Enumeration;
  import java.util.Vector;
  
  /** 
   <!-- globalinfo-start -->
   * GainRatioAttributeEval :<br/>
   * <br/>
   * Evaluates the worth of an attribute by measuring the gain ratio with respect to the class.<br/>
   * <br/>
   * GainR(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute).<br/>
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -M
   *  treat missing values as a seperate value.</pre>
   * 
   <!-- options-end -->
   *
   * @author Mark Hall (mhall@cs.waikato.ac.nz)
   * @version $Revision: 5447 $
   * @see Discretize
   */
  public class GainRatioAttributeEval
    extends ASEvaluation
    implements AttributeEvaluator, OptionHandler {
    
    /** for serialization */
    static final long serialVersionUID = -8504656625598579926L;
  
    /** The training instances */
    private Instances m_trainInstances;
  
    /** The class index */
    private int m_classIndex;
  
    /** The number of attributes */
    private int m_numAttribs;
  
    /** The number of instances */
    private int m_numInstances;
  
    /** The number of classes */
    private int m_numClasses;
  
    /** Merge missing values */
    private boolean m_missing_merge;
  
    /**
     * Returns a string describing this attribute evaluator
     * @return a description of the evaluator suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
      return "GainRatioAttributeEval :\n\nEvaluates the worth of an attribute "
        +"by measuring the gain ratio with respect to the class.\n\n"
        +"GainR(Class, Attribute) = (H(Class) - H(Class | Attribute)) / "
        +"H(Attribute).\n";
    }
  
    /**
     * Constructor
     */
    public GainRatioAttributeEval () {
      resetOptions();
    }
  
  
    /**
     * Returns an enumeration describing the available options.
     * @return an enumeration of all the available options.
     **/
    public Enumeration listOptions () {
      Vector newVector = new Vector(1);
      newVector.addElement(new Option("\ttreat missing values as a seperate " 
  				    + "value.", "M", 0, "-M"));
      return  newVector.elements();
    }
  
  
    /**
     * Parses a given list of options. <p/>
     *
     <!-- options-start -->
     * Valid options are: <p/>
     * 
     * <pre> -M
     *  treat missing values as a seperate value.</pre>
     * 
     <!-- options-end -->
     *
     * @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 {
      resetOptions();
      setMissingMerge(!(Utils.getFlag('M', options)));
    }
    
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String missingMergeTipText() {
      return "Distribute counts for missing values. Counts are distributed "
        +"across other values in proportion to their frequency. Otherwise, "
        +"missing is treated as a separate value.";
    }
  
    /**
     * distribute the counts for missing values across observed values
     *
     * @param b true=distribute missing values.
     */
    public void setMissingMerge (boolean b) {
      m_missing_merge = b;
    }
  
  
    /**
     * get whether missing values are being distributed or not
     *
     * @return true if missing values are being distributed.
     */
    public boolean getMissingMerge () {
      return  m_missing_merge;
    }
  
  
    /**
     * Gets the current settings of WrapperSubsetEval.
     * @return an array of strings suitable for passing to setOptions()
     */
    public String[] getOptions () {
      String[] options = new String[1];
      int current = 0;
  
      if (!getMissingMerge()) {
        options[current++] = "-M";
      }
  
      while (current < options.length) {
        options[current++] = "";
      }
  
      return  options;
    }
  
    /**
     * Returns the capabilities of this evaluator.
     *
     * @return            the capabilities of this evaluator
     * @see               Capabilities
     */
    public Capabilities getCapabilities() {
      Capabilities result = super.getCapabilities();
      result.disableAll();
      
      // attributes
      result.enable(Capability.NOMINAL_ATTRIBUTES);
      result.enable(Capability.NUMERIC_ATTRIBUTES);
      result.enable(Capability.DATE_ATTRIBUTES);
      result.enable(Capability.MISSING_VALUES);
      
      // class
      result.enable(Capability.NOMINAL_CLASS);
      result.enable(Capability.MISSING_CLASS_VALUES);
      
      return result;
    }
  
    /**
     * Initializes a gain ratio attribute evaluator.
     * Discretizes all attributes that are numeric.
     *
     * @param data set of instances serving as training data 
     * @throws Exception if the evaluator has not been 
     * generated successfully
     */
    public void buildEvaluator (Instances data)
      throws Exception {
      
      // can evaluator handle data?
      getCapabilities().testWithFail(data);
  
      m_trainInstances = data;
      m_classIndex = m_trainInstances.classIndex();
      m_numAttribs = m_trainInstances.numAttributes();
      m_numInstances = m_trainInstances.numInstances();
      Discretize disTransform = new Discretize();
      disTransform.setUseBetterEncoding(true);
      disTransform.setInputFormat(m_trainInstances);
      m_trainInstances = Filter.useFilter(m_trainInstances, disTransform);
      m_numClasses = m_trainInstances.attribute(m_classIndex).numValues();
    }
  
  
    /**
     * reset options to default values
     */
    protected void resetOptions () {
      m_trainInstances = null;
      m_missing_merge = true;
    }
  
  
    /**
     * evaluates an individual attribute by measuring the gain ratio
     * of the class given the attribute.
     *
     * @param attribute the index of the attribute to be evaluated
     * @return the gain ratio
     * @throws Exception if the attribute could not be evaluated
     */
    public double evaluateAttribute (int attribute)
      throws Exception {
      int i, j, ii, jj;
      int ni, nj;
      double sum = 0.0;
      ni = m_trainInstances.attribute(attribute).numValues() + 1;
      nj = m_numClasses + 1;
      double[] sumi, sumj;
      Instance inst;
      double temp = 0.0;
      sumi = new double[ni];
      sumj = new double[nj];
      double[][] counts = new double[ni][nj];
      sumi = new double[ni];
      sumj = new double[nj];
  
      for (i = 0; i < ni; i++) {
        sumi[i] = 0.0;
  
        for (j = 0; j < nj; j++) {
          sumj[j] = 0.0;
          counts[i][j] = 0.0;
        }
      }
  
      // Fill the contingency table
      for (i = 0; i < m_numInstances; i++) {
        inst = m_trainInstances.instance(i);
  
        if (inst.isMissing(attribute)) {
          ii = ni - 1;
        }
        else {
          ii = (int)inst.value(attribute);
        }
  
        if (inst.isMissing(m_classIndex)) {
          jj = nj - 1;
        }
        else {
          jj = (int)inst.value(m_classIndex);
        }
  
        counts[ii][jj]++;
      }
  
      // get the row totals
      for (i = 0; i < ni; i++) {
        sumi[i] = 0.0;
  
        for (j = 0; j < nj; j++) {
          sumi[i] += counts[i][j];
          sum += counts[i][j];
        }
      }
  
      // get the column totals
      for (j = 0; j < nj; j++) {
        sumj[j] = 0.0;
  
        for (i = 0; i < ni; i++) {
          sumj[j] += counts[i][j];
        }
      }
  
      // distribute missing counts
      if (m_missing_merge && 
  	(sumi[ni-1] < m_numInstances) && 
  	(sumj[nj-1] < m_numInstances)) {
        double[] i_copy = new double[sumi.length];
        double[] j_copy = new double[sumj.length];
        double[][] counts_copy = new double[sumi.length][sumj.length];
  
        for (i = 0; i < ni; i++) {
          System.arraycopy(counts[i], 0, counts_copy[i], 0, sumj.length);
        }
  
        System.arraycopy(sumi, 0, i_copy, 0, sumi.length);
        System.arraycopy(sumj, 0, j_copy, 0, sumj.length);
        double total_missing = (sumi[ni - 1] + sumj[nj - 1] - 
  			      counts[ni - 1][nj - 1]);
  
        // do the missing i's
        if (sumi[ni - 1] > 0.0) {
          for (j = 0; j < nj - 1; j++) {
            if (counts[ni - 1][j] > 0.0) {
              for (i = 0; i < ni - 1; i++) {
                temp = ((i_copy[i]/(sum - i_copy[ni - 1]))*counts[ni - 1][j]);
                counts[i][j] += temp;
                sumi[i] += temp;
              }
  
              counts[ni - 1][j] = 0.0;
            }
          }
        }
  
        sumi[ni - 1] = 0.0;
  
        // do the missing j's
        if (sumj[nj - 1] > 0.0) {
          for (i = 0; i < ni - 1; i++) {
            if (counts[i][nj - 1] > 0.0) {
              for (j = 0; j < nj - 1; j++) {
                temp = ((j_copy[j]/(sum - j_copy[nj - 1]))*counts[i][nj - 1]);
                counts[i][j] += temp;
                sumj[j] += temp;
              }
  
              counts[i][nj - 1] = 0.0;
            }
          }
        }
  
        sumj[nj - 1] = 0.0;
  
        // do the both missing
        if (counts[ni - 1][nj - 1] > 0.0  && total_missing != sum) {
          for (i = 0; i < ni - 1; i++) {
            for (j = 0; j < nj - 1; j++) {
              temp = (counts_copy[i][j]/(sum - total_missing)) * 
  	      counts_copy[ni - 1][nj - 1];
              counts[i][j] += temp;
              sumi[i] += temp;
              sumj[j] += temp;
            }
          }
  
          counts[ni - 1][nj - 1] = 0.0;
        }
      }
  
      return  ContingencyTables.gainRatio(counts);
    }
  
  
    /**
     * Return a description of the evaluator
     * @return description as a string
     */
    public String toString () {
      StringBuffer text = new StringBuffer();
  
      if (m_trainInstances == null) {
        text.append("\tGain Ratio evaluator has not been built");
      }
      else {
        text.append("\tGain Ratio feature evaluator");
  
        if (!m_missing_merge) {
          text.append("\n\tMissing values treated as seperate");
        }
      }
  
      text.append("\n");
      return  text.toString();
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5447 $");
    }
  
    /**
     * Main method.
     *
     * @param args the options
     * -t training file
     */
    public static void main (String[] args) {
      runEvaluator(new GainRatioAttributeEval(), args);
    }
  }
  