weka.core.WeightedInstancesHandler Java Examples

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the clusterer handles instance weights
 */
protected boolean[] weightedInstancesHandler() {
  
  boolean[] result = new boolean[2];
  
  print("weighted instances clusterer...");
  if (m_Clusterer instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }
  
  return result;
}
 

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the associator handles instance weights
 */
protected boolean[] weightedInstancesHandler() {
  
  boolean[] result = new boolean[2];
  
  print("weighted instances associator...");
  if (m_Associator instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }
  
  return result;
}
 

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the scheme handles instance weights
 */
protected boolean[] weightedInstancesHandler() {
  
  boolean[] result = new boolean[2];
  
  print("weighted instances scheme...");
  if (getTestObject() instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }
  
  return result;
}
 

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the classifier handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances classifier...");
  if (m_Classifier instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }

  return result;
}
 

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the kernel handles instance weights
 */
protected boolean[] weightedInstancesHandler() {
  
  boolean[] result = new boolean[2];
  
  print("weighted instances kernel...");
  if (m_Kernel instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }
  
  return result;
}
 

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the estimator handles instance weights
 */
protected boolean[] weightedInstancesHandler() {
  
  boolean[] result = new boolean[2];
  
  print("weighted instances estimator...");
  if (m_Estimator instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }
  
  return result;
}
 

/**
 * the standard collective classifier accepts only nominal, binary classes
 * otherwise an exception is thrown. Additionally, all classifiers must be
 * able to handle weighted instances.
 * @throws Exception if the data doesn't have a nominal, binary class
 */
@Override
protected void checkRestrictions() throws Exception {
  int         i;
  String      nonWeighted;
  
  super.checkRestrictions();

  // do all implement the WeightedInstancesHandler?
  nonWeighted = "";
  for (i = 0; i < getClassifiers().length; i++) {
    if (!(getClassifiers()[i] instanceof WeightedInstancesHandler)) {
      if (nonWeighted.length() > 0)
        nonWeighted += ", ";
      nonWeighted += getClassifiers()[i].getClass().getName();
    }
  }
  if (nonWeighted.length() > 0)
    throw new Exception(
        "The following classifier(s) cannot handle weighted instances:\n" 
        + nonWeighted);
}
 

/**
 * Build the classifier on the filtered data.
 *
 * @param data the training data
 * @throws Exception if the classifier could not be built successfully
 */
public void buildClassifier(Instances data) throws Exception {

  if (m_Classifier == null) {
    throw new Exception("No base classifier has been set!");
  }

  getCapabilities().testWithFail(data);

  Random r = (data.numInstances() > 0) ? data.getRandomNumberGenerator(getSeed()) : new Random(getSeed());
  data = setUp(data, r);
  if (!data.allInstanceWeightsIdentical() && !(m_Classifier instanceof WeightedInstancesHandler)) {
    data = data.resampleWithWeights(r); // The filter may have assigned weights.
  }
  if (!data.allAttributeWeightsIdentical() && !(m_Classifier instanceof WeightedAttributesHandler)) {
    data = resampleAttributes(data, false, r);
  }

  if (m_Classifier instanceof Randomizable) {
    ((Randomizable)m_Classifier).setSeed(r.nextInt());
  }

  m_Classifier.buildClassifier(data);
}
 

/**
   * Builds the committee of randomizable classifiers.
   *
   * @param data the training data to be used for generating the
   * bagged classifier.
   * @exception Exception if the classifier could not be built successfully
   */
  public void buildClassifier(Instances data) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(data);

    // remove instances with missing class
    m_data = new Instances(data);
    m_data.deleteWithMissingClass();
    super.buildClassifier(m_data);
    
    if (!(m_Classifier instanceof Randomizable)) {
      throw new IllegalArgumentException("Base learner must implement Randomizable!");
    }

    m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, m_NumIterations);

    Random random = m_data.getRandomNumberGenerator(m_Seed);

    // Resample data based on weights if base learner can't handle weights
    if (!(m_Classifier instanceof WeightedInstancesHandler)) {
      m_data = m_data.resampleWithWeights(random);
    }

    for (int j = 0; j < m_Classifiers.length; j++) {

      // Set the random number seed for the current classifier.
      ((Randomizable) m_Classifiers[j]).setSeed(random.nextInt());
      
      // Build the classifier.
//      m_Classifiers[j].buildClassifier(m_data);
    }
    
    buildClassifiers();
    
    // save memory
    m_data = null;
  }
 

/**
 * Build the classifier on the dimensionally reduced data.
 *
 * @param data the training data
 * @throws Exception if the classifier could not be built successfully
 */
public void buildClassifier(Instances data) throws Exception {
  if (m_Classifier == null) {
    throw new Exception("No base classifier has been set!");
  }

  if (m_Evaluator == null) {
    throw new Exception("No attribute evaluator has been set!");
  }

  if (m_Search == null) {
    throw new Exception("No search method has been set!");
  }
 
  // can classifier handle the data?
  getCapabilities().testWithFail(data);

  // remove instances with missing class
  Instances newData = new Instances(data);
  newData.deleteWithMissingClass();
  
  if (newData.numInstances() == 0) {
    m_Classifier.buildClassifier(newData);
    return;
  }
  if (newData.classAttribute().isNominal()) {
    m_numClasses = newData.classAttribute().numValues();
  } else {
    m_numClasses = 1;
  }

  Instances resampledData = null;
  // check to see if training data has all equal weights
  double weight = newData.instance(0).weight();
  boolean ok = false;
  for (int i = 1; i < newData.numInstances(); i++) {
    if (newData.instance(i).weight() != weight) {
      ok = true;
      break;
    }
  }
  
  if (ok) {
    if (!(m_Evaluator instanceof WeightedInstancesHandler) || 
        !(m_Classifier instanceof WeightedInstancesHandler)) {
      Random r = new Random(1);
      for (int i = 0; i < 10; i++) {
        r.nextDouble();
      }
      resampledData = newData.resampleWithWeights(r);
    }
  } else {
    // all equal weights in the training data so just use as is
    resampledData = newData;
  }

  m_AttributeSelection = new AttributeSelection();
  m_AttributeSelection.setEvaluator(m_Evaluator);
  m_AttributeSelection.setSearch(m_Search);
  long start = System.currentTimeMillis();
  m_AttributeSelection.
    SelectAttributes((m_Evaluator instanceof WeightedInstancesHandler) 
                     ? newData
                     : resampledData);
  long end = System.currentTimeMillis();
  if (m_Classifier instanceof WeightedInstancesHandler) {
    newData = m_AttributeSelection.reduceDimensionality(newData);
    m_Classifier.buildClassifier(newData);
  } else {
    resampledData = m_AttributeSelection.reduceDimensionality(resampledData);
    m_Classifier.buildClassifier(resampledData);
  }

  long end2 = System.currentTimeMillis();
  m_numAttributesSelected = m_AttributeSelection.numberAttributesSelected();
  m_ReducedHeader = 
    new Instances((m_Classifier instanceof WeightedInstancesHandler) ?
                  newData
                  : resampledData, 0);
  m_selectionTime = (double)(end - start);
  m_totalTime = (double)(end2 - start);
}