Java Code Examples for weka.classifiers.functions.SMO#turnChecksOff()

public void configureEnsemble(){
    ensemble.setWeightingScheme(new TrainAcc(4));
    ensemble.setVotingScheme(new MajorityConfidence());
    
    Classifier[] classifiers = new Classifier[3];
    String[] classifierNames = new String[3];
    
    SMO smo = new SMO();
    smo.turnChecksOff();
    smo.setBuildLogisticModels(true);
    PolyKernel kl = new PolyKernel();
    kl.setExponent(2);
    smo.setKernel(kl);
    if (seedClassifier)
        smo.setRandomSeed((int)seed);
    classifiers[0] = smo;
    classifierNames[0] = "SVMQ";

    RandomForest r=new RandomForest();
    r.setNumTrees(500);
    if(seedClassifier)
       r.setSeed((int)seed);            
    classifiers[1] = r;
    classifierNames[1] = "RandF";
        
        
    RotationForest rf=new RotationForest();
    rf.setNumIterations(100);
    if(seedClassifier)
       rf.setSeed((int)seed);
    classifiers[2] = rf;
    classifierNames[2] = "RotF";
    
    
   ensemble.setClassifiers(classifiers, classifierNames, null);        
    
}
 

/**
 * Uses the 'basic UCI' set up:
 * Comps: SVML, MLP, NN, Logistic, C4.5
 * Weight: TrainAcc(4) (train accuracies to the power 4)
 * Vote: MajorityConfidence (summing probability distributions)
 */
public final void setupDefaultSettings_NoLogistic() {
    this.ensembleName = "CAWPE-NoLogistic";
    
    this.weightingScheme = new TrainAcc(4);
    this.votingScheme = new MajorityConfidence();
    
    CrossValidationEvaluator cv = new CrossValidationEvaluator(seed, false, false, false, false); 
    cv.setNumFolds(10);
    this.trainEstimator = cv; 
    
    Classifier[] classifiers = new Classifier[4];
    String[] classifierNames = new String[4];

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

    kNN k=new kNN(100);
    k.setCrossValidate(true);
    k.normalise(false);
    k.setDistanceFunction(new EuclideanDistance());
    classifiers[1] = k;
    classifierNames[1] = "NN";

    classifiers[2] = new J48();
    classifierNames[2] = "C4.5";

    classifiers[3] = new MultilayerPerceptron();
    classifierNames[3] = "MLP";

    setClassifiers(classifiers, classifierNames, null);
}
 

public final void setupAdvancedSettings() {
    this.ensembleName = "CAWPE-A";
    
    this.weightingScheme = new TrainAcc(4);
    this.votingScheme = new MajorityConfidence();
    
    CrossValidationEvaluator cv = new CrossValidationEvaluator(seed, false, false, false, false); 
    cv.setNumFolds(10);
    this.trainEstimator = cv; 

    Classifier[] classifiers = new Classifier[3];
    String[] classifierNames = new String[3];

    SMO smo = new SMO();
    smo.turnChecksOff();
    smo.setBuildLogisticModels(true);
    PolyKernel kl = new PolyKernel();
    kl.setExponent(2);
    smo.setKernel(kl);
    smo.setRandomSeed(seed);
    classifiers[0] = smo;
    classifierNames[0] = "SVMQ";
    RandomForest rf= new RandomForest();
    rf.setNumTrees(500);
    classifiers[1] = rf;
    classifierNames[1] = "RandF";
    RotationForest rotf=new RotationForest();
    rotf.setNumIterations(200);
    classifiers[2] = rotf;
    classifierNames[2] = "RotF";

    setClassifiers(classifiers, classifierNames, null);
}
 

/**
 * 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;
}
 

/**
 * Classifiers used in the HIVE COTE paper
 */    
    public void configureDefaultEnsemble(){
//HIVE_SHAPELET_SVMQ    HIVE_SHAPELET_RandF    HIVE_SHAPELET_RotF    
//HIVE_SHAPELET_NN    HIVE_SHAPELET_NB    HIVE_SHAPELET_C45    HIVE_SHAPELET_SVML   
        ensemble=new CAWPE();
        ensemble.setWeightingScheme(new TrainAcc(4));
        ensemble.setVotingScheme(new MajorityConfidence());
        Classifier[] classifiers = new Classifier[7];
        String[] classifierNames = new String[7];
        
        SMO smo = new SMO();
        smo.turnChecksOff();
        smo.setBuildLogisticModels(true);
        PolyKernel kl = new PolyKernel();
        kl.setExponent(2);
        smo.setKernel(kl);
        if (seedClassifier)
            smo.setRandomSeed((int)seed);
        classifiers[0] = smo;
        classifierNames[0] = "SVMQ";

        RandomForest r=new RandomForest();
        r.setNumTrees(500);
        if(seedClassifier)
           r.setSeed((int)seed);            
        classifiers[1] = r;
        classifierNames[1] = "RandF";
            
            
        RotationForest rf=new RotationForest();
        rf.setNumIterations(100);
        if(seedClassifier)
           rf.setSeed((int)seed);
        classifiers[2] = rf;
        classifierNames[2] = "RotF";
        IBk nn=new IBk();
        classifiers[3] = nn;
        classifierNames[3] = "NN";
        NaiveBayes nb=new NaiveBayes();
        classifiers[4] = nb;
        classifierNames[4] = "NB";
        J48 c45=new J48();
        classifiers[5] = c45;
        classifierNames[5] = "C45";
        SMO svml = new SMO();
        svml.turnChecksOff();
        svml.setBuildLogisticModels(true);
        PolyKernel k2 = new PolyKernel();
        k2.setExponent(1);
        smo.setKernel(k2);
        classifiers[6] = svml;
        classifierNames[6] = "SVML";
        ensemble.setClassifiers(classifiers, classifierNames, null);
    }
 

/**
 * Uses the 'basic UCI' set up:
 * Comps: SVML, MLP, NN, Logistic, C4.5
 * Weight: TrainAcc(4) (train accuracies to the power 4)
 * Vote: MajorityConfidence (summing probability distributions)
 */
@Override //Abstract Ensemble 
public final void setupDefaultEnsembleSettings() {
    this.ensembleName = "CAWPE";
    
    this.weightingScheme = new TrainAcc(4);
    this.votingScheme = new MajorityConfidence();
    this.transform = null;
    
    CrossValidationEvaluator cv = new CrossValidationEvaluator(seed, false, false, false, false); 
    cv.setNumFolds(10);
    this.trainEstimator = cv; 

    Classifier[] classifiers = new Classifier[5];
    String[] classifierNames = new String[5];

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

    kNN k=new kNN(100);
    k.setCrossValidate(true);
    k.normalise(false);
    k.setDistanceFunction(new EuclideanDistance());
    classifiers[1] = k;
    classifierNames[1] = "NN";

    classifiers[2] = new J48();
    classifierNames[2] = "C4.5";

    classifiers[3] = new Logistic();
    classifierNames[3] = "Logistic";

    classifiers[4] = new MultilayerPerceptron();
    classifierNames[4] = "MLP";
    
    setClassifiers(classifiers, classifierNames, null);
}
 

/**
     * Comps: NN, SVML, SVMQ, C4.5, NB,  RotF, RandF, BN,
     * Weight: TrainAcc
     * Vote: MajorityVote
     *
     * As used originally in ST_HESCA, COTE.
     * NOTE the original also contained Bayes Net (BN). We have removed it because the classifier crashes
     * unpredictably when discretising features (due to lack of variance in the feature, but not easily detected and
     * dealt with
     *
     */
    public final void setupOriginalHESCASettings() {
        this.ensembleName = "HESCA";
        
        this.weightingScheme = new TrainAcc();
        this.votingScheme = new MajorityVote();
        
        CrossValidationEvaluator cv = new CrossValidationEvaluator(seed, false, false, false, false); 
        cv.setNumFolds(10);
        this.trainEstimator = cv; 
        int numClassifiers=7;
        Classifier[] classifiers = new Classifier[numClassifiers];
        String[] classifierNames = new String[numClassifiers];

        kNN k=new kNN(100);
        k.setCrossValidate(true);
        k.normalise(false);
        k.setDistanceFunction(new EuclideanDistance());
        classifiers[0] = k;
        classifierNames[0] = "NN";

        classifiers[1] = new NaiveBayes();
        classifierNames[1] = "NB";

        classifiers[2] = new J48();
        classifierNames[2] = "C45";

        SMO svml = new SMO();
        svml.turnChecksOff();
        PolyKernel kl = new PolyKernel();
        kl.setExponent(1);
        svml.setKernel(kl);
        svml.setRandomSeed(seed);
        classifiers[3] = svml;
        classifierNames[3] = "SVML";

        SMO svmq =new SMO();
//Assumes no missing, all real valued and a discrete class variable
        svmq.turnChecksOff();
        PolyKernel kq = new PolyKernel();
        kq.setExponent(2);
        svmq.setKernel(kq);
        svmq.setRandomSeed(seed);
        classifiers[4] =svmq;
        classifierNames[4] = "SVMQ";

        RandomForest r=new RandomForest();
        r.setNumTrees(500);
        r.setSeed(seed);
        classifiers[5] = r;
        classifierNames[5] = "RandF";

        RotationForest rf=new RotationForest();
        rf.setNumIterations(50);
        rf.setSeed(seed);
        classifiers[6] = rf;
        classifierNames[6] = "RotF";

//        classifiers[7] = new BayesNet();
//        classifierNames[7] = "bayesNet";

        setClassifiers(classifiers, classifierNames, null);
    }
 

/**
 * Initializes the HIVE COTE ensemble consisting of 7 classifiers using a
 * majority voting strategy as described in J. Lines, S. Taylor and A. Bagnall,
 * "HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based
 * Ensembles for Time Series Classification," 2016 IEEE 16th International
 * Conference on Data Mining (ICDM), Barcelona, 2016, pp. 1041-1046. doi:
 * 10.1109/ICDM.2016.0133.
 *
 * @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 the initialized (but untrained) HIVE COTE ensemble model.
 */
public static Classifier provideHIVECOTEEnsembleModel(final long seed) {
	Classifier[] classifier = new Classifier[7];

	Vote voter = new MajorityConfidenceVote(5, seed);

	// SMO poly2
	SMO smop = new SMO();
	smop.turnChecksOff();
	smop.setBuildCalibrationModels(true);
	PolyKernel kernel = new PolyKernel();
	kernel.setExponent(2);
	smop.setKernel(kernel);
	smop.setRandomSeed((int)seed);
	classifier[0] = smop;

	// Random Forest
	RandomForest rf = new RandomForest();
	rf.setSeed((int)seed);
	rf.setNumIterations(500);
	classifier[1] = rf;

	// Rotation forest
	RotationForest rotF = new RotationForest();
	rotF.setSeed((int)seed);
	rotF.setNumIterations(100);
	classifier[2] = rotF;

	// NN
	IBk nn = new IBk();
	classifier[3] = nn;

	// Naive Bayes
	NaiveBayes nb = new NaiveBayes();
	classifier[4] = nb;

	// C45
	J48 c45 = new J48();
	c45.setSeed((int)seed);
	classifier[5] = c45;

	// SMO linear
	SMO smol = new SMO();
	smol.turnChecksOff();
	smol.setBuildCalibrationModels(true);
	PolyKernel linearKernel = new PolyKernel();
	linearKernel.setExponent(1);
	smol.setKernel(linearKernel);
	classifier[6] = smol;

	voter.setClassifiers(classifier);
	return voter;
}