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

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);
}
 

/**
 * 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);
    }
 

@Override
    public void buildClassifier(Instances train) throws Exception {
        res =new ClassifierResults();
        long t=System.currentTimeMillis();
//        if(kernelOptimise)
//            selectKernel(train);
        if(buildFromPartial){
            if(paraSpace1==null)
                setStandardParaSearchSpace();  
            if(kernel==KernelType.RBF)
                setRBFParasFromPartiallyCompleteSearch();            
//            else if(kernel==KernelType.LINEAR || kernel==KernelType.QUADRATIC)
//                setFixedPolynomialParasFromPartiallyCompleteSearch();            
                else if(kernel==KernelType.POLYNOMIAL)
                    setPolynomialParasFromPartiallyCompleteSearch();            
        }
        else if(tuneParameters){
            if(paraSpace1==null)
                setStandardParaSearchSpace();
            if(buildFromFile){
                throw new Exception("Build from file in TunedSVM Not implemented yet");
            }else{
                if(kernel==KernelType.RBF)
                    tuneRBF(train); //Tunes two parameters
                else if(kernel==KernelType.LINEAR || kernel==KernelType.QUADRATIC)
                    tuneCForFixedPolynomial(train);//Tunes one parameter
                else if(kernel==KernelType.POLYNOMIAL)
                    tunePolynomial(train);
            }
        }
/*If there is no parameter search, then there is no train CV available.        
this gives the option of finding one using 10xCV  
*/        
        else if(findTrainAcc){
            int folds=10;
            if(folds>train.numInstances())
                folds=train.numInstances();
            SMO model = new SMO();
            model.setKernel(this.m_kernel);
            model.setC(this.getC());
            model.setBuildLogisticModels(true);
            model.setRandomSeed(seed);
            CrossValidationEvaluator cv = new CrossValidationEvaluator();
            cv.setSeed(seed); //trying to mimick old seeding behaviour below
            cv.setNumFolds(folds);
            cv.buildFolds(train);
            res = cv.crossValidateWithStats(model, train);
      }        
        
        
        
//If both kernelOptimise and tuneParameters are false, it just builds and SVM        
//With whatever the parameters are set to        
        super.buildClassifier(train);
        
        if(saveEachParaAcc)
            res.setBuildTime(combinedBuildTime);
        else
            res.setBuildTime(System.currentTimeMillis()-t);
        if(trainPath!=null && trainPath!=""){  //Save basic train results
            res.setClassifierName("TunedSVM"+kernel);
            res.setDatasetName(train.relationName());
            res.setFoldID(seed);
            res.setSplit("train");
            
            res.setParas(getParameters());
            res.writeFullResultsToFile(trainPath);
            File x=new File(trainPath);
            x.setWritable(true, false);
            
        }        
    }
 

/**
 * 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);
}