Java Code Examples for weka.classifiers.Evaluation#errorRate()

public void findCVWeights() throws Exception {
	cvWeights=new double[nosTransforms];
	int folds=numInstances;
	if(folds>THRESHOLD1){
			folds=10;
	}
	System.out.print("\n Finding CV Accuracy: ");
	for(int i=0;i<nosTransforms;i++){
		 Evaluation evaluation = new Evaluation(train.get(i));
                        if(i==0)
                           evaluation.crossValidateModel(AbstractClassifier.makeCopy(baseTime), train.get(i), folds, new Random());
                        else
                            evaluation.crossValidateModel(AbstractClassifier.makeCopy(base), train.get(i), folds, new Random());
		 cvWeights[i]=1-evaluation.errorRate();
		 System.out.print(","+cvWeights[i]);
	}
	 System.out.print("\n");
}
 

/**
  * Finds the best parameter combination. (recursive for each parameter
  * being optimised).
  * 
  * @param depth the index of the parameter to be optimised at this level
  * @param trainData the data the search is based on
  * @param random a random number generator
  * @throws Exception if an error occurs
  */
 protected void findParamsByCrossValidation(int depth, Instances trainData,
				     Random random)
   throws Exception {

   if (depth < m_CVParams.size()) {
     CVParameter cvParam = (CVParameter)m_CVParams.elementAt(depth);

     double upper;
     switch ((int)(cvParam.m_Lower - cvParam.m_Upper + 0.5)) {
     case 1:
upper = m_NumAttributes;
break;
     case 2:
upper = m_TrainFoldSize;
break;
     default:
upper = cvParam.m_Upper;
break;
     }
     double increment = (upper - cvParam.m_Lower) / (cvParam.m_Steps - 1);
     for(cvParam.m_ParamValue = cvParam.m_Lower; 
  cvParam.m_ParamValue <= upper; 
  cvParam.m_ParamValue += increment) {
findParamsByCrossValidation(depth + 1, trainData, random);
     }
   } else {
     
     Evaluation evaluation = new Evaluation(trainData);

     // Set the classifier options
     String [] options = createOptions();
     if (m_Debug) {
System.err.print("Setting options for " 
		 + m_Classifier.getClass().getName() + ":");
for (int i = 0; i < options.length; i++) {
  System.err.print(" " + options[i]);
}
System.err.println("");
     }
     ((OptionHandler)m_Classifier).setOptions(options);
     for (int j = 0; j < m_NumFolds; j++) {

       // We want to randomize the data the same way for every 
       // learning scheme.
Instances train = trainData.trainCV(m_NumFolds, j, new Random(1));
Instances test = trainData.testCV(m_NumFolds, j);
m_Classifier.buildClassifier(train);
evaluation.setPriors(train);
evaluation.evaluateModel(m_Classifier, test);
     }
     double error = evaluation.errorRate();
     if (m_Debug) {
System.err.println("Cross-validated error rate: " 
		   + Utils.doubleToString(error, 6, 4));
     }
     if ((m_BestPerformance == -99) || (error < m_BestPerformance)) {

m_BestPerformance = error;
m_BestClassifierOptions = createOptions();
     }
   }
 }
 

/**
    * Returns the misclassification error of the current model on a set of instances.
    * @param data the set of instances
    * @return the error rate
    * @throws Exception if something goes wrong
    */
   protected double getErrorRate(Instances data) throws Exception {
Evaluation eval = new Evaluation(data);
eval.evaluateModel(this,data);
return eval.errorRate();
   }