Example usage for weka.classifiers.trees J48 J48

List of usage examples for weka.classifiers.trees J48 J48

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Introduction

In this page you can find the example usage for weka.classifiers.trees J48 J48.

Prototype

J48

Source Link

Usage

From source file:function.FileModel.java

public static void SaveModel(String sourcepath, String outputpath) throws IOException, Exception {
    // create J48

    //kayanya sih ntar ganti sama class clasifiernya
    Classifier cls = new J48();

    // train//from   w  w  w  .j  a  va2  s  . c  o  m
    Instances inst = new Instances(new BufferedReader(new FileReader(sourcepath)));
    inst.setClassIndex(inst.numAttributes() - 1);
    cls.buildClassifier(inst);

    // serialize model
    weka.core.SerializationHelper.write(outputpath, cls);
}

From source file:gov.va.chir.tagline.TagLineEvaluator.java

License:Open Source License

public void evaluate(final ClassifierType type, final String... options) throws Exception {
    Classifier model = null;//from  www  .j a  v  a2  s. co  m

    if (type == null) {
        throw new IllegalArgumentException("Classifier type must be specified");
    }

    if (type.equals(ClassifierType.J48)) {
        model = new J48();
    } else if (type.equals(ClassifierType.LMT)) {
        model = new LMT();
    } else if (type.equals(ClassifierType.RandomForest)) {
        model = new RandomForest();
    } else if (type.equals(ClassifierType.SVM)) {
        model = new LibSVM();
    } else {
        throw new IllegalArgumentException(String.format("Classifier type not supported (%s)", type));
    }

    if (model != null) {
        // Set classifier options
        if (options != null && options.length > 0) {
            if (model instanceof AbstractClassifier) {
                ((AbstractClassifier) model).setOptions(options);
            }
        }

        fc.setClassifier(model);

        final Attribute attrDocId = instances.attribute(DatasetUtil.DOC_ID);

        if (attrDocId == null) {
            throw new IllegalStateException(String.format("%s attribute must exist", DatasetUtil.DOC_ID));
        }
        final List<Set<Object>> foldDocIds = getFoldDocIds(attrDocId);

        final RemoveWithValues rmv = new RemoveWithValues();

        // RemoveWithValues filter is not zero-based!
        rmv.setAttributeIndex(String.valueOf(attrDocId.index() + 1));
        rmv.setModifyHeader(false);

        final Evaluation eval = new Evaluation(instances);

        // Perform cross-validation
        for (int i = 0; i < numFolds; i++) {
            rmv.setNominalIndicesArr(getAttributeIndexValues(attrDocId, foldDocIds.get(i)));

            rmv.setInvertSelection(false);
            rmv.setInputFormat(instances); // Must be called AFTER all options
            final Instances train = Filter.useFilter(instances, rmv);

            rmv.setInvertSelection(true);
            rmv.setInputFormat(instances); // Must be called AFTER all options
            final Instances test = Filter.useFilter(instances, rmv);

            fc.buildClassifier(train);
            eval.evaluateModel(fc, test);
        }

        evaluationSummary = String.format("%s%s%s%s%s", eval.toSummaryString(),
                System.getProperty("line.separator"), eval.toMatrixString(),
                System.getProperty("line.separator"), eval.toClassDetailsString());
    }
}

From source file:gov.va.chir.tagline.TagLineTrainer.java

License:Open Source License

public TagLineTrainer(final ClassifierType type, final String... options) throws Exception {
    Classifier model = null;//from  ww  w  . j a va 2  s.  c  o m

    if (type == null) {
        throw new IllegalArgumentException("Classifier type must be specified");
    }

    if (type.equals(ClassifierType.J48)) {
        model = new J48();
    } else if (type.equals(ClassifierType.LMT)) {
        model = new LMT();
    } else if (type.equals(ClassifierType.RandomForest)) {
        model = new RandomForest();
    } else if (type.equals(ClassifierType.SVM)) {
        model = new LibSVM();
    } else {
        throw new IllegalArgumentException(String.format("Classifier type not supported (%s)", type));
    }

    // Set classifier options
    if (options != null && options.length > 0) {
        if (model instanceof AbstractClassifier) {
            ((AbstractClassifier) model).setOptions(options);
        }
    }

    tagLineModel = new TagLineModel();
    tagLineModel.setModel(model);

    instances = null;
    extractor = null;
}

From source file:GroupProject.DMChartUI.java

private void buildJClassifier() {
    CSVtoArff converter = new CSVtoArff();
    Instances students = null;//from  w w w .  j  av  a 2  s  . c  om
    Instances students2 = null;
    try {
        converter.convert("studentTemp.csv", "studentTemp.arff");
    } catch (IOException ex) {
        Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
    }

    try {
        students = new Instances(new BufferedReader(new FileReader("studentTemp.arff")));
        students2 = new Instances(new BufferedReader(new FileReader("studentTemp.arff")));
    } catch (IOException ex) {
        Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
    }
    int target = dataSelector.getSelectedIndex() + 1;
    System.out.printf("this is the target: %d\n", target);
    if (target == 14 || target == 15 || target == 18 || target == 19) {
        System.out.println("Please select a nominal category");
        equationDisplayArea.setText("Please select a nominal category");
        return;
    }
    //set target 
    students.setClassIndex(target);
    students2.setClassIndex(target);

    Jmodel = (Classifier) new J48();

    try {
        System.out.println("im goin to build the modle");
        Jmodel.buildClassifier(students);
        System.out.println("i finsihed building the mdoel ");
    } catch (Exception ex) {
        Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
    }

}

From source file:hero.unstable.util.classification.wekaClassifier.java

public wekaClassifier() {
    this.classifier = new J48();
    this.nameClassifier = classifier.getClass().getName();
    this.seed = 1;
    this.folds = 10;
}

From source file:ia02classificacao.IA02Classificacao.java

/**
 * @param args the command line arguments
 *///from w  ww .  j  av a2 s  .c o  m
public static void main(String[] args) throws Exception {

    // abre o banco de dados arff e mostra a quantidade de instancias (linhas)
    DataSource arquivo = new DataSource("data/zoo.arff");
    Instances dados = arquivo.getDataSet();
    System.out.println("Instancias lidas: " + dados.numInstances());

    // FILTER: remove o atributo nome do animal da classificao
    String[] parametros = new String[] { "-R", "1" };
    Remove filtro = new Remove();
    filtro.setOptions(parametros);
    filtro.setInputFormat(dados);
    dados = Filter.useFilter(dados, filtro);

    AttributeSelection selAtributo = new AttributeSelection();
    InfoGainAttributeEval avaliador = new InfoGainAttributeEval();
    Ranker busca = new Ranker();
    selAtributo.setEvaluator(avaliador);
    selAtributo.setSearch(busca);
    selAtributo.SelectAttributes(dados);
    int[] indices = selAtributo.selectedAttributes();
    System.out.println("Selected attributes: " + Utils.arrayToString(indices));

    // Usa o algoritimo J48 e mostra a classificao dos dados em forma textual
    String[] opcoes = new String[1];
    opcoes[0] = "-U";
    J48 arvore = new J48();
    arvore.setOptions(opcoes);
    arvore.buildClassifier(dados);
    System.out.println(arvore);

    // Usa o algoritimo J48 e mostra a classificao de dados em forma grafica
    /*
    TreeVisualizer tv = new TreeVisualizer(null, arvore.graph(), new PlaceNode2());
    JFrame frame = new javax.swing.JFrame("?rvore de Conhecimento");
    frame.setSize(800,500);
    frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
    frame.getContentPane().add(tv);
    frame.setVisible(true);
    tv.fitToScreen();
    */

    /*
    * Classificao de novos dados
    */

    System.out.println("\n\nCLASSIFICAO DE NOVOS DADOS");
    // criar atributos
    double[] vals = new double[dados.numAttributes()];
    vals[0] = 1.0; // hair
    vals[1] = 0.0; // feathers
    vals[2] = 0.0; // eggs
    vals[3] = 1.0; // milk
    vals[4] = 1.0; // airborne
    vals[5] = 0.0; // aquatic
    vals[6] = 0.0; // predator
    vals[7] = 1.0; // toothed
    vals[8] = 1.0; // backbone
    vals[9] = 1.0; // breathes
    vals[10] = 0.0; // venomous
    vals[11] = 0.0; // fins
    vals[12] = 4.0; // legs
    vals[13] = 1.0; // tail
    vals[14] = 1.0; // domestic
    vals[15] = 1.0; // catsize

    // Criar uma instncia baseada nestes atributos
    Instance meuUnicornio = new DenseInstance(1.0, vals);

    // Adicionar a instncia nos dados
    meuUnicornio.setDataset(dados);

    // Classificar esta nova instncia
    double label = arvore.classifyInstance(meuUnicornio);

    // Imprimir o resultado da classificao
    System.out.println("Novo Animal: Unicrnio");
    System.out.println("classificacao: " + dados.classAttribute().value((int) label));

    /*
    * Avaliao e predio de erros de mtrica
    */
    System.out.println("\n\nAVALIAO E PREDIO DE ERROS DE MTRICA");
    Classifier cl = new J48();
    Evaluation eval_roc = new Evaluation(dados);
    eval_roc.crossValidateModel(cl, dados, 10, new Random(1), new Object[] {});
    System.out.println(eval_roc.toSummaryString());

    /*
    * Matriz de confuso
    */
    System.out.println("\n\nMATRIZ DE CONFUSO");
    double[][] confusionMatrix = eval_roc.confusionMatrix();
    System.out.println(eval_roc.toMatrixString());

}

From source file:ia03classificador.jFrClassificador.java

public void doClassificate() throws Exception {

    // Quando clicado, a variavel recebe 1, quando no clicado recebe 0
    v00 = ((btn00.isSelected()) ? ((double) 1) : ((double) 0));
    v01 = ((btn01.isSelected()) ? ((double) 1) : ((double) 0));
    v02 = ((btn02.isSelected()) ? ((double) 1) : ((double) 0));
    v03 = ((btn03.isSelected()) ? ((double) 1) : ((double) 0));
    v04 = ((btn04.isSelected()) ? ((double) 1) : ((double) 0));
    v05 = ((btn05.isSelected()) ? ((double) 1) : ((double) 0));
    v06 = ((btn06.isSelected()) ? ((double) 1) : ((double) 0));
    v07 = ((btn07.isSelected()) ? ((double) 1) : ((double) 0));
    v08 = ((btn08.isSelected()) ? ((double) 1) : ((double) 0));
    v09 = ((btn09.isSelected()) ? ((double) 1) : ((double) 0));
    v10 = ((btn10.isSelected()) ? ((double) 1) : ((double) 0));
    v11 = ((btn11.isSelected()) ? ((double) 1) : ((double) 0));
    v13 = ((btn13.isSelected()) ? ((double) 1) : ((double) 0));
    v14 = ((btn14.isSelected()) ? ((double) 1) : ((double) 0));
    v15 = ((btn15.isSelected()) ? ((double) 1) : ((double) 0));
    legs = txtLegs.getText();/* w ww .  j a v a 2s.  c  o m*/
    legs = ((legs == null || legs.trim().isEmpty() ? "2" : legs));
    name = txtName.getText();

    // abre o banco de dados arff e guarda os registros no objeto dados
    ConverterUtils.DataSource arquivo = new ConverterUtils.DataSource("data/zoo.arff");
    Instances dados = arquivo.getDataSet();

    // FILTER: remove o atributo nome do animal da classificao
    String[] parametros = new String[] { "-R", "1" };
    Remove filtro = new Remove();
    filtro.setOptions(parametros);
    filtro.setInputFormat(dados);
    dados = Filter.useFilter(dados, filtro);

    AttributeSelection selAtributo = new AttributeSelection();
    InfoGainAttributeEval avaliador = new InfoGainAttributeEval();
    Ranker busca = new Ranker();
    selAtributo.setEvaluator(avaliador);
    selAtributo.setSearch(busca);
    selAtributo.SelectAttributes(dados);
    int[] indices = selAtributo.selectedAttributes();
    //System.out.println("Selected attributes: " + Utils.arrayToString(indices));

    // Usa o algoritimo J48 para montar a arvore de dados
    String[] opcoes = new String[1];
    opcoes[0] = "-U";
    J48 arvore = new J48();
    arvore.setOptions(opcoes);
    arvore.buildClassifier(dados);

    // cria o novo elemento para comparao
    double[] vals = new double[dados.numAttributes()];
    vals[0] = v00; // hair
    vals[1] = v01; // feathers
    vals[2] = v02; // eggs
    vals[3] = v03; // milk
    vals[4] = v04; // airborne
    vals[5] = v05; // aquatic
    vals[6] = v06; // predator
    vals[7] = v07; // toothed
    vals[8] = v08; // backbone
    vals[9] = v09; // breathes
    vals[10] = v10; // venomous
    vals[11] = v11; // fins
    vals[12] = Double.parseDouble(legs); // legs
    vals[13] = v13; // tail
    vals[14] = v14; // domestic
    vals[15] = v15; // catsize

    // Criar uma instncia baseada nestes atributos
    Instance newAnimal = new DenseInstance(1.0, vals);

    // Adicionar a instncia nos dados
    newAnimal.setDataset(dados);

    // Classificar esta nova instncia
    double label = arvore.classifyInstance(newAnimal);

    // Imprimir o resultado da classificao
    lblClassification.setText(dados.classAttribute().value((int) label));

}

From source file:id3j48.WekaAccess.java

public static void main(String[] args) {
    initializePath();//w  w w .  j  av a 2 s. c  o  m
    try {
        cin = new Scanner(System.in);
        Instances data = null, tempdata;
        Classifier NBclassifier, ID3classifier, j48classifier;
        Evaluation NBeval, ID3eval, j48eval;
        System.out.println("Enter filename below");
        String filename = cin.nextLine();
        System.out.println("Loading " + filename + "...");
        String extension = "";
        String name = "";
        int i = filename.lastIndexOf('.');
        if (i > 0) {
            extension = filename.substring(i + 1);
            name = filename.substring(0, i);
        }
        if (extension.equalsIgnoreCase("arff")) {
            try {
                data = readArff(filename);
            } catch (Exception ex) {
                Logger.getLogger(WekaAccess.class.getName()).log(Level.SEVERE, null, ex);
            }
        } else if (extension.equalsIgnoreCase("csv")) {
            try {
                data = readCsv(filename);
            } catch (Exception ex) {
                Logger.getLogger(WekaAccess.class.getName()).log(Level.SEVERE, null, ex);
            }
        } else {
            System.out.println("Invalid extension");
            System.exit(0);
        }
        System.out.println(data.toString());
        System.out.println("Resample data? (y for yes) ");
        String resample = cin.nextLine();
        if (resample.equalsIgnoreCase("y")) {
            try {
                tempdata = resampleData(data);
                System.out.println("-- Resampled data --");
                System.out.println(tempdata.toString());
            } catch (Exception ex) {
                Logger.getLogger(WekaAccess.class.getName()).log(Level.SEVERE, null, ex);
            }
        }
        tempdata = removeAttribute(data, data.numAttributes());
        System.out.println("-- Remove Attribute --");
        System.out.println(tempdata.toString());
        NBclassifier = buildClassifier(data, new NaiveBayes());
        System.out.println("-- Naive Bayes Classifier --");
        System.out.println(NBclassifier.toString());
        ID3classifier = buildClassifier(data, new Id3());
        System.out.println("-- ID3 Classifier --");
        System.out.println(ID3classifier.toString());
        j48classifier = buildClassifier(data, new J48());
        System.out.println("-- J48 Classifier --");
        System.out.println(j48classifier.toString());
        Instances test = null;
        if (extension.equalsIgnoreCase("arff"))
            test = readArff("test." + filename);
        else if (extension.equalsIgnoreCase("csv"))
            test = readCsv("test." + filename);
        NBeval = testModel(NBclassifier, data, test);
        System.out.println(
                NBeval.toSummaryString("-- Training set evaluation results with Naive Bayes --\n", false));
        ID3eval = testModel(ID3classifier, data, test);
        System.out.println(NBeval.toSummaryString("-- Training set evaluation results with ID3 --\n", false));
        j48eval = testModel(j48classifier, data, test);
        System.out.println(NBeval.toSummaryString("-- Training set evaluation results with J48 --\n", false));
        NBeval = tenFoldCrossValidation(data, NBclassifier);
        System.out.println(
                NBeval.toSummaryString("-- 10-fold cross validation results with Naive Bayes --\n", false));
        ID3eval = tenFoldCrossValidation(data, ID3classifier);
        System.out.println(NBeval.toSummaryString("-- 10-fold cross validation results with ID3 --\n", false));
        j48eval = tenFoldCrossValidation(data, j48classifier);
        System.out.println(NBeval.toSummaryString("-- 10-fold cross validation results with J48 --\n", false));
        NBeval = percentageSplit(data, NBclassifier, 66);
        System.out.println(
                NBeval.toSummaryString("-- 66% split validation results with Naive Bayes --\n", false));
        ID3eval = percentageSplit(data, ID3classifier, 66);
        System.out.println(NBeval.toSummaryString("-- 66% split validation results with ID3 --\n", false));
        j48eval = percentageSplit(data, j48classifier, 66);
        System.out.println(NBeval.toSummaryString("-- 66% split validation results with J48 --\n", false));
        System.out.println("-- Save Naive Bayes Model --");
        saveModel("nb." + name + ".model", NBclassifier);
        System.out.println("-- Save Naive Bayes Model --");
        saveModel("id3." + name + ".model", ID3classifier);
        System.out.println("-- Save Naive Bayes Model --");
        saveModel("j48." + name + ".model", j48classifier);
        System.out.println("-- Save Naive Bayes Model --");
        saveModel("nb." + name + ".model", NBclassifier);
        System.out.println("-- Save ID3 Model --");
        saveModel("id3." + name + ".model", ID3classifier);
        System.out.println("-- Save J48 Model --");
        saveModel("j48." + name + ".model", j48classifier);
        System.out.println("-- Load Naive Bayes Model --");
        System.out.println(loadModel("nb." + name + ".model").toString());
        System.out.println("-- Load ID3 Model --");
        System.out.println(loadModel("id3." + name + ".model").toString());
        System.out.println("-- Load J48 Model --");
        System.out.println(loadModel("j48." + name + ".model").toString());
        System.out.println("-- Classify Naive Bayes Model --");
        classify("classify." + filename, NBclassifier);
        System.out.println("-- Classify ID3 Model --");
        classify("classify." + filename, ID3classifier);
        System.out.println("-- Classify J48 Model --");
        classify("classify." + filename, j48classifier);
    } catch (Exception ex) {
        Logger.getLogger(WekaAccess.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:kfst.classifier.WekaClassifier.java

License:Open Source License

/**
 * This method builds and evaluates the decision tree(DT) classifier.
 * The j48 are used as the DT classifier implemented in the Weka software.
 *
 * @param pathTrainData the path of the train set
 * @param pathTestData the path of the test set
 * @param confidenceValue The confidence factor used for pruning
 * @param minNumSampleInLeaf The minimum number of instances per leaf
 * /*from  w  ww.  j  a  v a  2s.c  om*/
 * @return the classification accuracy
 */
public static double dTree(String pathTrainData, String pathTestData, double confidenceValue,
        int minNumSampleInLeaf) {
    double resultValue = 0;
    try {
        BufferedReader readerTrain = new BufferedReader(new FileReader(pathTrainData));
        Instances dataTrain = new Instances(readerTrain);
        readerTrain.close();
        dataTrain.setClassIndex(dataTrain.numAttributes() - 1);

        BufferedReader readerTest = new BufferedReader(new FileReader(pathTestData));
        Instances dataTest = new Instances(readerTest);
        readerTest.close();
        dataTest.setClassIndex(dataTest.numAttributes() - 1);

        J48 decisionTree = new J48();
        decisionTree.setConfidenceFactor((float) confidenceValue);
        decisionTree.setMinNumObj(minNumSampleInLeaf);
        decisionTree.buildClassifier(dataTrain);
        Evaluation eval = new Evaluation(dataTest);
        eval.evaluateModel(decisionTree, dataTest);
        resultValue = 100 - (eval.errorRate() * 100);
    } catch (Exception ex) {
        Logger.getLogger(WekaClassifier.class.getName()).log(Level.SEVERE, null, ex);
    }
    return resultValue;
}

From source file:KFST.featureSelection.embedded.TreeBasedMethods.DecisionTreeBasedMethod.java

License:Open Source License

/**
 * {@inheritDoc }/*ww  w. j a  v a 2 s .  c o m*/
 */
@Override
protected String buildClassifier(Instances dataTrain) {
    try {
        if (TREE_TYPE == TreeType.C45) {
            J48 decisionTreeC45 = new J48();
            decisionTreeC45.setConfidenceFactor((float) confidenceValue);
            decisionTreeC45.setMinNumObj(minNumSampleInLeaf);
            decisionTreeC45.buildClassifier(dataTrain);
            return decisionTreeC45.toString();
        } else if (TREE_TYPE == TreeType.RANDOM_TREE) {
            RandomTree decisionTreeRandomTree = new RandomTree();
            decisionTreeRandomTree.setKValue(randomTreeKValue);
            decisionTreeRandomTree.setMaxDepth(randomTreeMaxDepth);
            decisionTreeRandomTree.setMinNum(randomTreeMinNum);
            decisionTreeRandomTree.setMinVarianceProp(randomTreeMinVarianceProp);
            decisionTreeRandomTree.buildClassifier(dataTrain);
            return decisionTreeRandomTree.toString();
        }
    } catch (Exception ex) {
        Logger.getLogger(DecisionTreeBasedMethod.class.getName()).log(Level.SEVERE, null, ex);
    }
    return "";
}