weka.classifiers.rules.ZeroR Java Examples

/**
 * reset to defaults
 */
protected void resetOptions () {
  m_trainingInstances = null;
  m_Evaluation = null;
  m_Classifier = new ZeroR();
  m_holdOutFile = new File("Click to set hold out or test instances");
  m_holdOutInstances = null;
  m_useTraining = false;
}
 

protected void resetOptions () {
  m_trainInstances = null;
  m_Evaluation = null;
  m_BaseClassifier = new ZeroR();
  m_folds = 5;
  m_seed = 1;
  m_threshold = 0.01;
}
 

@Test
public void testTrainingAndPrediction() throws DatasetDeserializationFailedException, InterruptedException, TrainingException, SplitFailedException, PredictionException {
	ILabeledDataset<?> dataset = this.problemSet.getDataset();
	List<ILabeledDataset<?>> split = SplitterUtil.getSimpleTrainTestSplit(dataset, 0, .7);
	WekaClassifier c = new WekaClassifier(new ZeroR());
	ISingleLabelClassificationPredictionBatch batch = c.fitAndPredict(split.get(0), split.get(1));
	assertEquals(split.get(1).size(), batch.size());
}
 

/**
 * Parses a given list of options. <p/>
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -B &lt;classifier&gt;
 *  class name of the classifier to use for accuracy estimation.
 *  Place any classifier options LAST on the command line
 *  following a "--". eg.:
 *   -B weka.classifiers.bayes.NaiveBayes ... -- -K
 *  (default: weka.classifiers.rules.ZeroR)</pre>
 * 
 * <pre> -T
 *  Use the training data to estimate accuracy.</pre>
 * 
 * <pre> -H &lt;filename&gt;
 *  Name of the hold out/test set to 
 *  estimate accuracy on.</pre>
 * 
 * <pre> 
 * Options specific to scheme weka.classifiers.rules.ZeroR:
 * </pre>
 * 
 * <pre> -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console</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 {
  String optionString;
  resetOptions();

  optionString = Utils.getOption('B', options);
  if (optionString.length() == 0)
    optionString = ZeroR.class.getName();
  setClassifier(AbstractClassifier.forName(optionString,
		     Utils.partitionOptions(options)));

  optionString = Utils.getOption('H',options);
  if (optionString.length() != 0) {
    setHoldOutFile(new File(optionString));
  }

  setUseTraining(Utils.getFlag('T',options));
}
 

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

   super.buildClassifier(data);

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

   // remove instances with missing class
   Instances newData = new Instances(data);
   newData.deleteWithMissingClass();

   double sum = 0;
   double temp_sum = 0;
   // Add the model for the mean first
   m_zeroR = new ZeroR();
   m_zeroR.buildClassifier(newData);
   
   // only class? -> use only ZeroR model
   if (newData.numAttributes() == 1) {
     System.err.println(
  "Cannot build model (only class attribute present in data!), "
  + "using ZeroR model instead!");
     m_SuitableData = false;
     return;
   }
   else {
     m_SuitableData = true;
   }
   
   newData = residualReplace(newData, m_zeroR, false);
   for (int i = 0; i < newData.numInstances(); i++) {
     sum += newData.instance(i).weight() *
newData.instance(i).classValue() * newData.instance(i).classValue();
   }
   if (m_Debug) {
     System.err.println("Sum of squared residuals "
		 +"(predicting the mean) : " + sum);
   }

   m_NumIterationsPerformed = 0;
   do {
     temp_sum = sum;

     // Build the classifier
     m_Classifiers[m_NumIterationsPerformed].buildClassifier(newData);

     newData = residualReplace(newData, m_Classifiers[m_NumIterationsPerformed], true);
     sum = 0;
     for (int i = 0; i < newData.numInstances(); i++) {
sum += newData.instance(i).weight() *
  newData.instance(i).classValue() * newData.instance(i).classValue();
     }
     if (m_Debug) {
System.err.println("Sum of squared residuals : "+sum);
     }
     m_NumIterationsPerformed++;
   } while (((temp_sum - sum) > Utils.SMALL) && 
     (m_NumIterationsPerformed < m_Classifiers.length));
 }