Example usage for weka.classifiers.trees RandomForest RandomForest

List of usage examples for weka.classifiers.trees RandomForest RandomForest

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Introduction

In this page you can find the example usage for weka.classifiers.trees RandomForest RandomForest.

Prototype

public RandomForest()

Source Link

Document

Constructor that sets base classifier for bagging to RandomTre and default number of iterations to 100.

Usage

From source file:ClassifierBuilder.java

public static MyClassifier buildClassifier(String name) {
    MyClassifier toReturn = new MyClassifier(name);
    switch (name) {
    case "Decision Table Majority":
        toReturn.setClassifier(new DecisionTable());
        break;//from w ww  . ja v  a2s. c om
    case "Logistic Regression":
        toReturn.setClassifier(new Logistic());
        break;
    case "Multi Layer Perceptron":
        toReturn.setClassifier(new MultilayerPerceptron());
        break;
    case "Naive Baesian":
        toReturn.setClassifier(new NaiveBayes());
        break;
    case "Random Forest":
        toReturn.setClassifier(new RandomForest());
        break;
    default:
        break;
    }
    return toReturn;
}

From source file:at.aictopic1.sentimentanalysis.machinelearning.impl.RandomForestClassifier.java

/**
 * sets classifier/*from   ww w.  j ava2s. co  m*/
 */
@Override
protected void setClassifier() {

    //classifier
    this.usedClassifier = new RandomForest();
    //.. other options
    this.fcClassifier.setClassifier(this.usedClassifier);
}

From source file:au.edu.usyd.it.yangpy.sampling.BPSO.java

License:Open Source License

/**
 * the target function in fitness form/*  w w w  .j a v a2 s  .  c om*/
 * 
 * @return   classification accuracy
 */
public double ensembleClassify() {
    double fitnessValue = 0.0;
    double classifiersScore = 0.0;

    /* load in the modified data set */
    try {
        Instances reducedSet = new Instances(new BufferedReader(new FileReader("reduced.arff")));
        reducedSet.setClassIndex(reducedSet.numAttributes() - 1);

        // calculating the evaluation values using each classifier respectively
        if (verbose == true) {
            System.out.println();
            System.out.println(" |----------J4.8-----------|");
            System.out.println(" |            |            |");
        }
        J48 tree = new J48();
        classifiersScore = classify(tree, reducedSet, internalTest);
        fitnessValue += classifiersScore;

        if (verbose == true) {
            System.out.println();
            System.out.println(" |-----3NearestNeighbor----|");
            System.out.println(" |            |            |");
        }
        IBk nn3 = new IBk(3);
        classifiersScore = classify(nn3, reducedSet, internalTest);
        fitnessValue += classifiersScore;

        if (verbose == true) {
            System.out.println();
            System.out.println(" |--------NaiveBayes-------|");
            System.out.println(" |            |            |");
        }
        NaiveBayes nb = new NaiveBayes();
        classifiersScore = classify(nb, reducedSet, internalTest);
        fitnessValue += classifiersScore;

        if (verbose == true) {
            System.out.println();
            System.out.println(" |-------RandomForest------|");
            System.out.println(" |            |            |");
        }
        RandomForest rf5 = new RandomForest();
        rf5.setNumTrees(5);
        classifiersScore = classify(rf5, reducedSet, internalTest);
        fitnessValue += classifiersScore;

        if (verbose == true) {
            System.out.println();
            System.out.println(" |---------Logistic--------|");
            System.out.println(" |            |            |");
        }
        Logistic log = new Logistic();
        classifiersScore = classify(log, reducedSet, internalTest);
        fitnessValue += classifiersScore;

    } catch (IOException ioe) {
        ioe.printStackTrace();
    }

    fitnessValue /= 5;

    if (verbose == true) {
        System.out.println();
        System.out.println("Fitness: " + fitnessValue);
        System.out.println("---------------------------------------------------");
    }

    return fitnessValue;
}

From source file:boa.aggregators.RandomForestAggregator.java

License:Apache License

/** {@inheritDoc} */
@Override/*from   ww  w.  j  a  v  a  2 s.c  o  m*/
public void finish() throws IOException, InterruptedException {
    int NumOfAttributes = this.getVectorSize();
    List<Attribute> attributes = new ArrayList<Attribute>();
    FastVector fvAttributes = new FastVector(NumOfAttributes);

    for (int i = 0; i < NumOfAttributes; i++) {
        attributes.add(new Attribute("Attribute" + i));
        fvAttributes.addElement(attributes.get(i));
    }

    Instances trainingSet = new Instances("RandomForest", fvAttributes, 1);
    trainingSet.setClassIndex(NumOfAttributes - 1);

    for (List<Double> vector : this.vectors.values()) {
        Instance instance = new Instance(NumOfAttributes);
        for (int i = 0; i < vector.size(); i++) {
            instance.setValue((Attribute) fvAttributes.elementAt(i), vector.get(i));
        }
        trainingSet.add(instance);
    }

    try {
        this.model = new RandomForest();
        this.model.setOptions(options);
        this.model.buildClassifier(trainingSet);
    } catch (Exception ex) {
    }

    this.saveModel(this.model);
}

From source file:classifier.SellerClassifier.java

public void rebuildModel(String dataset) {
    try {// w  w w . j  a  va  2s .  com
        myInstances = startFeatureExtraction(loadData(dataset));
        myClassifier = new RandomForest();

        // build the model
        myClassifier.buildClassifier(myInstances);
        SerializationHelper.write(modelPath, myClassifier);
        SerializationHelper.write(refPath, myFilter);
    } catch (Exception ex) {
        Logger.getLogger(SellerClassifier.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:com.edwardraff.WekaMNIST.java

License:Open Source License

public static void main(String[] args) throws IOException, Exception {
    String folder = args[0];//w ww .  j av  a  2 s .  com
    String trainPath = folder + "MNISTtrain.arff";
    String testPath = folder + "MNISTtest.arff";

    System.out.println("Weka Timings");
    Instances mnistTrainWeka = new Instances(new BufferedReader(new FileReader(new File(trainPath))));
    mnistTrainWeka.setClassIndex(mnistTrainWeka.numAttributes() - 1);
    Instances mnistTestWeka = new Instances(new BufferedReader(new FileReader(new File(testPath))));
    mnistTestWeka.setClassIndex(mnistTestWeka.numAttributes() - 1);

    //normalize range like into [0, 1]
    Normalize normalizeFilter = new Normalize();
    normalizeFilter.setInputFormat(mnistTrainWeka);

    mnistTestWeka = Normalize.useFilter(mnistTestWeka, normalizeFilter);
    mnistTrainWeka = Normalize.useFilter(mnistTrainWeka, normalizeFilter);

    long start, end;

    System.out.println("RBF SVM (Full Cache)");
    SMO smo = new SMO();
    smo.setKernel(new RBFKernel(mnistTrainWeka, 0/*0 causes Weka to cache the whole matrix...*/, 0.015625));
    smo.setC(8.0);
    smo.setBuildLogisticModels(false);
    evalModel(smo, mnistTrainWeka, mnistTestWeka);

    System.out.println("RBF SVM (No Cache)");
    smo = new SMO();
    smo.setKernel(new RBFKernel(mnistTrainWeka, 1, 0.015625));
    smo.setC(8.0);
    smo.setBuildLogisticModels(false);
    evalModel(smo, mnistTrainWeka, mnistTestWeka);

    System.out.println("Decision Tree C45");
    J48 wekaC45 = new J48();
    wekaC45.setUseLaplace(false);
    wekaC45.setCollapseTree(false);
    wekaC45.setUnpruned(true);
    wekaC45.setMinNumObj(2);
    wekaC45.setUseMDLcorrection(true);

    evalModel(wekaC45, mnistTrainWeka, mnistTestWeka);

    System.out.println("Random Forest 50 trees");
    int featuresToUse = (int) Math.sqrt(28 * 28);//Weka uses different defaults, so lets make sure they both use the published way

    RandomForest wekaRF = new RandomForest();
    wekaRF.setNumExecutionSlots(1);
    wekaRF.setMaxDepth(0/*0 for unlimited*/);
    wekaRF.setNumFeatures(featuresToUse);
    wekaRF.setNumTrees(50);

    evalModel(wekaRF, mnistTrainWeka, mnistTestWeka);

    System.out.println("1-NN (brute)");
    IBk wekaNN = new IBk(1);
    wekaNN.setNearestNeighbourSearchAlgorithm(new LinearNNSearch());
    wekaNN.setCrossValidate(false);

    evalModel(wekaNN, mnistTrainWeka, mnistTestWeka);

    System.out.println("1-NN (Ball Tree)");
    wekaNN = new IBk(1);
    wekaNN.setNearestNeighbourSearchAlgorithm(new BallTree());
    wekaNN.setCrossValidate(false);

    evalModel(wekaNN, mnistTrainWeka, mnistTestWeka);

    System.out.println("1-NN (Cover Tree)");
    wekaNN = new IBk(1);
    wekaNN.setNearestNeighbourSearchAlgorithm(new CoverTree());
    wekaNN.setCrossValidate(false);

    evalModel(wekaNN, mnistTrainWeka, mnistTestWeka);

    System.out.println("Logistic Regression LBFGS lambda = 1e-4");
    Logistic logisticLBFGS = new Logistic();
    logisticLBFGS.setRidge(1e-4);
    logisticLBFGS.setMaxIts(500);

    evalModel(logisticLBFGS, mnistTrainWeka, mnistTestWeka);

    System.out.println("k-means (Loyd)");
    int origClassIndex = mnistTrainWeka.classIndex();
    mnistTrainWeka.setClassIndex(-1);
    mnistTrainWeka.deleteAttributeAt(origClassIndex);
    {
        long totalTime = 0;
        for (int i = 0; i < 10; i++) {
            SimpleKMeans wekaKMeans = new SimpleKMeans();
            wekaKMeans.setNumClusters(10);
            wekaKMeans.setNumExecutionSlots(1);
            wekaKMeans.setFastDistanceCalc(true);

            start = System.currentTimeMillis();
            wekaKMeans.buildClusterer(mnistTrainWeka);
            end = System.currentTimeMillis();
            totalTime += (end - start);
        }
        System.out.println("\tClustering took: " + (totalTime / 10.0) / 1000.0 + " on average");
    }
}

From source file:com.rokittech.ml.server.utils.MLUtils.java

License:Open Source License

public static Classifier getClassifier(String mlAlgorithm) {
    notEmpty(mlAlgorithm);//from   w  ww  .j  a  va  2  s .  com
    Classifier classifier;
    switch (mlAlgorithm.toUpperCase()) {
    case "J48": {
        classifier = new J48();
        break;
    }
    case "IBK": {
        classifier = new IBk();
        break;
    }
    case "NAIVE_BAYES": {
        classifier = new NaiveBayes();
        break;
    }
    case "RANDOM_TREE": {
        classifier = new RandomTree();
        break;
    }
    case "RANDOM_FOREST": {
        classifier = new RandomForest();
        break;
    }
    case "BOOSTING": {
        classifier = new DecisionStump();
        break;
    }
    case "BAGGING": {
        classifier = new Bagging();
        break;
    }
    default:
        throw new UnsupportedOperationException("Classifier " + mlAlgorithm + " is not supported.");
    }
    return classifier;
}

From source file:cz.vse.fis.keg.entityclassifier.core.salience.EntitySaliencer.java

License:Open Source License

private void trainModel() {

    BufferedReader reader = null;

    try {// w  w  w .  j a v  a2  s.c o  m

        URL fileURL = THDController.getInstance().getClass().getResource(Settings.SALIENCE_DATASET);
        File arrfFile = new File(fileURL.getFile());

        reader = new BufferedReader(new FileReader(arrfFile));
        Instances data = new Instances(reader);
        data.setClassIndex(data.numAttributes() - 1);

        //            classifier = new NaiveBayes();
        classifier = new RandomForest();

        // Train the classifer.
        classifier.buildClassifier(data);

    } catch (FileNotFoundException ex) {
        Logger.getLogger(EntitySaliencer.class.getName()).log(Level.SEVERE, null, ex);
    } catch (IOException ex) {
        Logger.getLogger(EntitySaliencer.class.getName()).log(Level.SEVERE, null, ex);
    } catch (Exception ex) {
        Logger.getLogger(EntitySaliencer.class.getName()).log(Level.SEVERE, null, ex);
    } finally {
        try {
            reader.close();
            System.out.println("Model was successfully trained.");
        } catch (IOException ex) {
            Logger.getLogger(EntitySaliencer.class.getName()).log(Level.SEVERE, null, ex);
        }
    }
}

From source file:de.fub.maps.project.detector.model.inference.impl.RandomForestInferenceModel.java

License:Open Source License

@Override
protected Classifier createClassifier() {
    classifier = new RandomForest();
    return classifier;
}

From source file:de.tudarmstadt.ukp.dkpro.spelling.experiments.hoo2012.featureextraction.AllFeaturesExtractor.java

License:Apache License

private Classifier getClassifier() throws Exception {
    Classifier cl = null;//from  ww  w  .j ava  2 s.co m
    // Build and evaluate classifier
    // The options given correspond to the default settings in the WEKA GUI
    if (classifier.equals("smo")) {
        SMO smo = new SMO();
        smo.setOptions(Utils.splitOptions(
                "-C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\""));
        cl = smo;
    } else if (classifier.equals("j48")) {
        J48 j48 = new J48();
        j48.setOptions(new String[] { "-C", "0.25", "-M", "2" });
        cl = j48;
    } else if (classifier.equals("naivebayes")) {
        cl = new NaiveBayes();
    } else if (classifier.equals("randomforest")) {
        RandomForest rf = new RandomForest();
        rf.setOptions(Utils.splitOptions("-I 10 -K 0 -S 1"));
        cl = rf;
    }
    return cl;
}