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.
   */
  
  /*
   *    InfoGainAttributeEval.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 weka.filters.unsupervised.attribute.NumericToBinary;
  
  import java.util.Enumeration;
  import java.util.Vector;
  
  /** 
   <!-- globalinfo-start -->
   * InfoGainAttributeEval :<br/>
   * <br/>
   * Evaluates the worth of an attribute by measuring the information gain with respect to the class.<br/>
   * <br/>
   * InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute).<br/>
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -M
   *  treat missing values as a seperate value.</pre>
   * 
   * <pre> -B
   *  just binarize numeric attributes instead 
   *  of properly discretizing them.</pre>
   * 
   <!-- options-end -->
   *
   * @author Mark Hall (mhall@cs.waikato.ac.nz)
   * @version $Revision: 5447 $
   * @see Discretize
   * @see NumericToBinary
   */
  public class InfoGainAttributeEval
    extends ASEvaluation
    implements AttributeEvaluator, OptionHandler {
    
    /** for serialization */
    static final long serialVersionUID = -1949849512589218930L;
  
    /** Treat missing values as a seperate value */
    private boolean m_missing_merge;
  
    /** Just binarize numeric attributes */
    private boolean m_Binarize;
  
    /** The info gain for each attribute */
    private double[] m_InfoGains;
  
    /**
     * 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 "InfoGainAttributeEval :\n\nEvaluates the worth of an attribute "
        +"by measuring the information gain with respect to the class.\n\n"
        +"InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute).\n";
    }
  
    /**
     * Constructor
     */
    public InfoGainAttributeEval () {
      resetOptions();
    }
  
    /**
     * 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("\ttreat missing values as a seperate " 
                                      + "value.", "M", 0, "-M"));
      newVector.addElement(new Option("\tjust binarize numeric attributes instead \n" 
                                      +"\tof properly discretizing them.", "B", 0, 
                                      "-B"));
      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>
     * 
     * <pre> -B
     *  just binarize numeric attributes instead 
     *  of properly discretizing them.</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)));
      setBinarizeNumericAttributes(Utils.getFlag('B', options));
    }
  
  
    /**
     * Gets the current settings of WrapperSubsetEval.
     *
     * @return an array of strings suitable for passing to setOptions()
     */
    public String[] getOptions () {
      String[] options = new String[2];
      int current = 0;
  
      if (!getMissingMerge()) {
        options[current++] = "-M";
      }
      if (getBinarizeNumericAttributes()) {
        options[current++] = "-B";
      }
  
      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 binarizeNumericAttributesTipText() {
      return "Just binarize numeric attributes instead of properly discretizing them.";
    }
  
    /**
     * Binarize numeric attributes.
     *
     * @param b true=binarize numeric attributes
     */
    public void setBinarizeNumericAttributes (boolean b) {
      m_Binarize = b;
    }
  
  
    /**
     * get whether numeric attributes are just being binarized.
     *
     * @return true if missing values are being distributed.
     */
    public boolean getBinarizeNumericAttributes () {
      return  m_Binarize;
    }
  
    /**
     * 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;
    }
  
    /**
     * 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 an information gain 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);
  
      int classIndex = data.classIndex();
      int numInstances = data.numInstances();
      
      if (!m_Binarize) {
        Discretize disTransform = new Discretize();
        disTransform.setUseBetterEncoding(true);
        disTransform.setInputFormat(data);
        data = Filter.useFilter(data, disTransform);
      } else {
        NumericToBinary binTransform = new NumericToBinary();
        binTransform.setInputFormat(data);
        data = Filter.useFilter(data, binTransform);
      }      
      int numClasses = data.attribute(classIndex).numValues();
  
      // Reserve space and initialize counters
      double[][][] counts = new double[data.numAttributes()][][];
      for (int k = 0; k < data.numAttributes(); k++) {
        if (k != classIndex) {
          int numValues = data.attribute(k).numValues();
          counts[k] = new double[numValues + 1][numClasses + 1];
        }
      }
  
      // Initialize counters
      double[] temp = new double[numClasses + 1];
      for (int k = 0; k < numInstances; k++) {
        Instance inst = data.instance(k);
        if (inst.classIsMissing()) {
          temp[numClasses] += inst.weight();
        } else {
          temp[(int)inst.classValue()] += inst.weight();
        }
      }
      for (int k = 0; k < counts.length; k++) {
        if (k != classIndex) {
          for (int i = 0; i < temp.length; i++) {
            counts[k][0][i] = temp[i];
          }
        }
      }
  
      // Get counts
      for (int k = 0; k < numInstances; k++) {
        Instance inst = data.instance(k);
        for (int i = 0; i < inst.numValues(); i++) {
          if (inst.index(i) != classIndex) {
            if (inst.isMissingSparse(i) || inst.classIsMissing()) {
              if (!inst.isMissingSparse(i)) {
                counts[inst.index(i)][(int)inst.valueSparse(i)][numClasses] += 
                  inst.weight();
                counts[inst.index(i)][0][numClasses] -= inst.weight();
              } else if (!inst.classIsMissing()) {
                counts[inst.index(i)][data.attribute(inst.index(i)).numValues()]
                  [(int)inst.classValue()] += inst.weight();
                counts[inst.index(i)][0][(int)inst.classValue()] -= 
                  inst.weight();
              } else {
                counts[inst.index(i)][data.attribute(inst.index(i)).numValues()]
                  [numClasses] += inst.weight();
                counts[inst.index(i)][0][numClasses] -= inst.weight();
              }
            } else {
              counts[inst.index(i)][(int)inst.valueSparse(i)]
                [(int)inst.classValue()] += inst.weight();
              counts[inst.index(i)][0][(int)inst.classValue()] -= inst.weight();
            }
          }
        }
      }
  
      // distribute missing counts if required
      if (m_missing_merge) {
        
        for (int k = 0; k < data.numAttributes(); k++) {
          if (k != classIndex) {
            int numValues = data.attribute(k).numValues();
  
            // Compute marginals
            double[] rowSums = new double[numValues];
            double[] columnSums = new double[numClasses];
            double sum = 0;
            for (int i = 0; i < numValues; i++) {
              for (int j = 0; j < numClasses; j++) {
                rowSums[i] += counts[k][i][j];
                columnSums[j] += counts[k][i][j];
              }
              sum += rowSums[i];
            }
  
            if (Utils.gr(sum, 0)) {
              double[][] additions = new double[numValues][numClasses];
  
              // Compute what needs to be added to each row
              for (int i = 0; i < numValues; i++) {
                for (int j = 0; j  < numClasses; j++) {
                  additions[i][j] = (rowSums[i] / sum) * counts[k][numValues][j];
                }
              }
              
              // Compute what needs to be added to each column
              for (int i = 0; i < numClasses; i++) {
                for (int j = 0; j  < numValues; j++) {
                  additions[j][i] += (columnSums[i] / sum) * 
                    counts[k][j][numClasses];
                }
              }
              
              // Compute what needs to be added to each cell
              for (int i = 0; i < numClasses; i++) {
                for (int j = 0; j  < numValues; j++) {
                  additions[j][i] += (counts[k][j][i] / sum) * 
                    counts[k][numValues][numClasses];
                }
              }
              
              // Make new contingency table
              double[][] newTable = new double[numValues][numClasses];
              for (int i = 0; i < numValues; i++) {
                for (int j = 0; j < numClasses; j++) {
                  newTable[i][j] = counts[k][i][j] + additions[i][j];
                }
              }
              counts[k] = newTable;
            }
          }
        }
      }
  
      // Compute info gains
      m_InfoGains = new double[data.numAttributes()];
      for (int i = 0; i < data.numAttributes(); i++) {
        if (i != classIndex) {
          m_InfoGains[i] = 
            (ContingencyTables.entropyOverColumns(counts[i]) 
             - ContingencyTables.entropyConditionedOnRows(counts[i]));
        }
      }
    }
  
    /**
     * Reset options to their default values
     */
    protected void resetOptions () {
      m_InfoGains = null;
      m_missing_merge = true;
      m_Binarize = false;
    }
  
  
    /**
     * evaluates an individual attribute by measuring the amount
     * of information gained about the class given the attribute.
     *
     * @param attribute the index of the attribute to be evaluated
     * @return the info gain
     * @throws Exception if the attribute could not be evaluated
     */
    public double evaluateAttribute (int attribute)
      throws Exception {
  
      return m_InfoGains[attribute];
    }
  
    /**
     * Describe the attribute evaluator
     * @return a description of the attribute evaluator as a string
     */
    public String toString () {
      StringBuffer text = new StringBuffer();
  
      if (m_InfoGains == null) {
        text.append("Information Gain attribute evaluator has not been built");
      }
      else {
        text.append("\tInformation Gain Ranking Filter");
        if (!m_missing_merge) {
          text.append("\n\tMissing values treated as seperate");
        }
        if (m_Binarize) {
          text.append("\n\tNumeric attributes are just binarized");
        }
      }
      
      text.append("\n");
      return  text.toString();
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5447 $");
    }
    
    // ============
    // Test method.
    // ============
    /**
     * Main method for testing this class.
     *
     * @param args the options
     */
    public static void main (String[] args) {
      runEvaluator(new InfoGainAttributeEval(), args);
    }
  }
  