Java Code Examples for weka.classifiers.lazy.IBk#setCrossValidate()

/**
 * performs initialization of members
 */
@Override
protected void initializeMembers() {
  super.initializeMembers();

  m_KNNdetermined    = -1;
  m_NeighborsTestset = null;
  m_TrainsetNew      = null;
  m_TestsetNew       = null;
  m_UseNaiveSearch   = false;
  m_LabeledTestset   = null;
  m_Missing          = new ReplaceMissingValues();
  
  m_Classifier = new IBk();
  m_Classifier.setKNN(10);
  m_Classifier.setCrossValidate(true);
  m_Classifier.setWindowSize(0);
  m_Classifier.setMeanSquared(false);
  
  m_KNN = m_Classifier.getKNN();
  
  m_AdditionalMeasures.add("measureDeterminedKNN");
}
 

public static void testkNNvsIBk(boolean norm, boolean crossValidate){
        System.out.println("FIRST BASIC SANITY TEST FOR THIS WRAPPER");
        System.out.print("Compare 1-NN with IB1, normalisation turned");
        String str=norm?" on":" off";
        System.out.println(str);
        System.out.print("Cross validation turned");
        str=crossValidate?" on":" off";
        System.out.println(str);
        System.out.println("Compare on the UCI data sets");
        System.out.print("If normalisation is off, then there may be differences");
        kNN knn = new kNN(100);
        IBk ibk=new IBk(100);
        knn.normalise(norm);
        knn.setCrossValidate(crossValidate);
        ibk.setCrossValidate(crossValidate);
        int diff=0;
        DecimalFormat df = new DecimalFormat("####.###");
        for(String s:DatasetLists.uciFileNames){
            Instances train=DatasetLoading.loadDataNullable("Z:/ArchiveData/Uci_arff/"+s+"\\"+s+"-train");
            Instances test=DatasetLoading.loadDataNullable("Z:/ArchiveData/Uci_arff/"+s+"\\"+s+"-test");
            try{
                knn.buildClassifier(train);
//                ib1.buildClassifier(train);
                ibk.buildClassifier(train);
                double a1=ClassifierTools.accuracy(test, knn);
                double a2=ClassifierTools.accuracy(test, ibk);
                if(a1!=a2){
                    diff++;
                    System.out.println(s+": 1-NN ="+df.format(a1)+" ibk="+df.format(a2));
                }
            }catch(Exception e){
                System.out.println(" Exception builing a classifier");
                System.exit(0);
            }
        }
         System.out.println("Total problems ="+DatasetLists.uciFileNames.length+" different on "+diff);
    }
 

/**
 * Initializes the CAWPE ensemble model consisting of five classifiers (SMO,
 * KNN, J48, Logistic and MLP) using a majority voting strategy. The ensemble
 * uses Weka classifiers. It refers to "Heterogeneous ensemble of standard
 * classification algorithms" (HESCA) as described in Lines, Jason & Taylor,
 * Sarah & Bagnall, Anthony. (2018). Time Series Classification with HIVE-COTE:
 * The Hierarchical Vote Collective of Transformation-Based Ensembles. ACM
 * Transactions on Knowledge Discovery from Data. 12. 1-35. 10.1145/3182382.
 *
 * @param seed
 *            Seed used within the classifiers and the majority confidence
 *            voting scheme
 * @param numFolds
 *            Number of folds used within the determination of the classifier
 *            weights for the {@link MajorityConfidenceVote}
 * @return Returns an initialized (but untrained) ensemble model.
 * @throws Exception
 *             Thrown when the initialization has failed
 */
public static Classifier provideCAWPEEnsembleModel(final int seed, final int numFolds) throws Exception {
	Classifier[] classifiers = new Classifier[5];

	Vote voter = new MajorityConfidenceVote(numFolds, seed);

	SMO smo = new SMO();
	smo.turnChecksOff();
	smo.setBuildCalibrationModels(true);
	PolyKernel kl = new PolyKernel();
	kl.setExponent(1);
	smo.setKernel(kl);
	smo.setRandomSeed(seed);
	classifiers[0] = smo;

	IBk k = new IBk(100);
	k.setCrossValidate(true);
	EuclideanDistance ed = new EuclideanDistance();
	ed.setDontNormalize(true);
	k.getNearestNeighbourSearchAlgorithm().setDistanceFunction(ed);
	classifiers[1] = k;

	J48 c45 = new J48();
	c45.setSeed(seed);
	classifiers[2] = c45;

	classifiers[3] = new Logistic();

	classifiers[4] = new MultilayerPerceptron();

	voter.setClassifiers(classifiers);
	return voter;
}