weka.core.AdditionalMeasureProducer Java Examples

/**
  * Returns an enumeration of the additional measure names
  * @return an enumeration of the measure names
  */
 public Enumeration enumerateMeasures() {
   Vector newVector = new Vector(3);
   newVector.addElement("measureNumAttributesSelected");
   newVector.addElement("measureSelectionTime");
   newVector.addElement("measureTime");
   if (m_Classifier instanceof AdditionalMeasureProducer) {
     Enumeration en = ((AdditionalMeasureProducer)m_Classifier).
enumerateMeasures();
     while (en.hasMoreElements()) {
String mname = (String)en.nextElement();
newVector.addElement(mname);
     }
   }
   return newVector.elements();
 }
 

/**
  * Returns the value of the named measure
  * @param additionalMeasureName the name of the measure to query for its value
  * @return the value of the named measure
  * @throws IllegalArgumentException if the named measure is not supported
  */
 public double getMeasure(String additionalMeasureName) {
   if (additionalMeasureName.compareToIgnoreCase("measureNumAttributesSelected") == 0) {
     return measureNumAttributesSelected();
   } else if (additionalMeasureName.compareToIgnoreCase("measureSelectionTime") == 0) {
     return measureSelectionTime();
   } else if (additionalMeasureName.compareToIgnoreCase("measureTime") == 0) {
     return measureTime();
   } else if (m_Classifier instanceof AdditionalMeasureProducer) {
     return ((AdditionalMeasureProducer)m_Classifier).
getMeasure(additionalMeasureName);
   } else {
     throw new IllegalArgumentException(additionalMeasureName 
		  + " not supported (AttributeSelectedClassifier)");
   }
 }
 

/**
 * Returns an enumeration of the additional measure names
 * @return an enumeration of the measure names
 */
public Enumeration enumerateMeasures() {
  Vector newVector = new Vector();
  
  if (m_Classifier instanceof AdditionalMeasureProducer) {
    Enumeration en = ((AdditionalMeasureProducer)m_Classifier).
      enumerateMeasures();
    while (en.hasMoreElements()) {
      String mname = (String)en.nextElement();
      newVector.addElement(mname);
    }
  }
  return newVector.elements();
}
 

/**
 * Returns the value of the named measure
 * @param additionalMeasureName the name of the measure to query for its value
 * @return the value of the named measure
 * @throws IllegalArgumentException if the named measure is not supported
 */
public double getMeasure(String additionalMeasureName) {
   if (m_Classifier instanceof AdditionalMeasureProducer) {
    return ((AdditionalMeasureProducer)m_Classifier).
      getMeasure(additionalMeasureName);
  } else {
    throw new IllegalArgumentException(additionalMeasureName 
                        + " not supported (InputMappedClassifier)");
  }
}
 

/**
 * performs the actual building of the classifier
 * 
 * @throws Exception if building fails
 */
@Override
protected void buildClassifier() throws Exception {
  Classifier        tree;
  int               i;
  int               n;
  int               nextSeed;
  double[]          dist;
  Instances         bagData;
  boolean[]         inBag;
  double            outOfBagCount;
  double            errorSum;
  Instance          outOfBagInst;

  m_PureTrainNodes = 0;
  m_PureTestNodes  = 0;
  
  for (i = 0; i < getNumTrees(); i++) {
    // info
    if (getVerbose())
      System.out.print(".");

    // get next seed number
    nextSeed = m_Random.nextInt();

    // bagging?
    if (getUseBagging()) {
      // inBag-dataset/array
      inBag   = new boolean[m_TrainsetNew.numInstances()];
      bagData = resample(m_TrainsetNew, nextSeed, inBag);
      
      // build i.th tree
      tree = initClassifier(nextSeed);

      // determine and store distributions
      for (n = 0; n < m_TestsetNew.numInstances(); n++) {
        dist = tree.distributionForInstance(m_TestsetNew.instance(n));
        m_List.addDistribution(m_TestsetNew.instance(n), dist);
      }

      // determine out-of-bag-error
      outOfBagCount = 0;
      errorSum      = 0;

      for (n = 0; n < inBag.length; n++) {  
        if (!inBag[n]) {
          outOfBagInst = m_TrainsetNew.instance(n);
          outOfBagCount += outOfBagInst.weight();
          if (m_TrainsetNew.classAttribute().isNumeric()) {
            errorSum += outOfBagInst.weight() *
              StrictMath.abs(tree.classifyInstance(outOfBagInst)
                       - outOfBagInst.classValue());
          } 
          else {
            if (tree.classifyInstance(outOfBagInst) 
                  != outOfBagInst.classValue()) {
              errorSum += outOfBagInst.weight();
            }
          }
        }
      }

      m_OutOfBagError = errorSum / outOfBagCount;
    }
    else {
      // build i.th tree
      tree = initClassifier(nextSeed);

      // determine and store distributions
      for (n = 0; n < m_TestsetNew.numInstances(); n++) {
        dist = tree.distributionForInstance(m_TestsetNew.instance(n));
        m_List.addDistribution(m_TestsetNew.instance(n), dist);
      }
    }

    // get information about pure nodes
    try {
      if (tree instanceof AdditionalMeasureProducer) {
        m_PureTrainNodes += ((AdditionalMeasureProducer) tree).getMeasure(
                                "measurePureTrainNodes");
        m_PureTestNodes  += ((AdditionalMeasureProducer) tree).getMeasure(
                                "measurePureTestNodes");
      }
    }
    catch (Exception e) {
      e.printStackTrace();
    }

    tree = null;
  }
    
  if (getVerbose())
    System.out.println();
}