Example usage for weka.classifiers.bayes NaiveBayes NaiveBayes

List of usage examples for weka.classifiers.bayes NaiveBayes NaiveBayes

  1. HOME
  2. Java
  3. weka
  4. weka.classifiers.bayes.*
  5. NaiveBayes
  6. NaiveBayes

Introduction

In this page you can find the example usage for weka.classifiers.bayes NaiveBayes NaiveBayes.

Prototype

NaiveBayes

Source Link

Usage

From source file:ca.uqac.florentinth.speakerauthentication.Learning.Learning.java

License:Apache License

public void trainClassifier(Classifier classifier, FileReader trainingDataset, FileOutputStream trainingModel,
        Integer crossValidationFoldNumber) throws Exception {
    Instances instances = new Instances(new BufferedReader(trainingDataset));

    switch (classifier) {
    case KNN:/* w ww.  j ava  2s  .  com*/
        int K = (int) Math.ceil(Math.sqrt(instances.numInstances()));
        this.classifier = new IBk(K);
        break;
    case NB:
        this.classifier = new NaiveBayes();
    }

    if (instances.classIndex() == -1) {
        instances.setClassIndex(instances.numAttributes() - 1);
    }

    this.classifier.buildClassifier(instances);

    if (crossValidationFoldNumber > 0) {
        Evaluation evaluation = new Evaluation(instances);
        evaluation.crossValidateModel(this.classifier, instances, crossValidationFoldNumber, new Random(1));
        kappa = evaluation.kappa();
        fMeasure = evaluation.weightedFMeasure();
        confusionMatrix = evaluation.toMatrixString("Confusion matrix: ");
    }

    ObjectOutputStream outputStream = new ObjectOutputStream(trainingModel);
    outputStream.writeObject(this.classifier);
    outputStream.flush();
    outputStream.close();
}

From source file:clasificador.Perceptron.java

public void naive_Bayes() {
    try {// w w w .  j a v  a  2s .  com
        //INSTANCIAS PARA ENTRENAMIENTO DEL CLASIFICADOR
        ConverterUtils.DataSource converU = new ConverterUtils.DataSource(
                "C:\\Users\\Kathy\\Documents\\tutorial perl\\libro.arff");
        Instances instancias = converU.getDataSet();
        instancias.setClassIndex(instancias.numAttributes() - 1);

        //INSTANCIAS PARA EL TEST DEL MODELO 
        ConverterUtils.DataSource convertest = new ConverterUtils.DataSource(
                "C:\\Users\\Kathy\\Documents\\tutorial perl\\libro5.arff");
        Instances testInstance = convertest.getDataSet();
        testInstance.setClassIndex(testInstance.numAttributes() - 1);

        //CONTRUCCIN DEL CLASIFICADOR
        NaiveBayes perceptron = new NaiveBayes();
        perceptron.buildClassifier(instancias);
        //Evaluar las instancias
        Evaluation ev = new Evaluation(instancias);
        //EVALUAR MODELO DE ENTRENAMIENTO
        ev.evaluateModel(perceptron, instancias);
        //System.out.println(instancias);
        System.out.println("\n\nENTRENAMIENTO DEL MODELO NAIVE BAYES\n\n");
        System.out.println(ev.toSummaryString("_____RESULTADO_____", true));
        System.out.println(ev.toMatrixString("_____Matriz confusion___"));

        //EVALUACIN DEL MODELO
        ev.evaluateModel(perceptron, testInstance);
        //System.out.println(instancias);
        System.out.println("\n\nTEST DEL MODELO NAIVE BAYES\n\n");
        System.out.println(ev.toSummaryString("_____RESULTADO_____", true));
        System.out.println(ev.toMatrixString("_____Matriz confusion___"));

        //MOSTRAR VALORES 
        for (int i = 0; i < ev.evaluateModel(perceptron, testInstance).length; i++) {
            System.out.println("Se clasifica como:  " + ev.evaluateModel(perceptron, testInstance)[i]);
        }
    }

    catch (Exception ex) {
        Logger.getLogger(Perceptron.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:com.ivanrf.smsspam.SpamClassifier.java

License:Apache License

private static FilteredClassifier initFilterClassifier(int wordsToKeep, String tokenizerOp,
        boolean useAttributeSelection, String classifierOp, boolean boosting) throws Exception {
    StringToWordVector filter = new StringToWordVector();
    filter.setDoNotOperateOnPerClassBasis(true);
    filter.setLowerCaseTokens(true);/*www .  j  av  a  2  s.c  om*/
    filter.setWordsToKeep(wordsToKeep);

    if (!tokenizerOp.equals(TOKENIZER_DEFAULT)) {
        //Make a tokenizer
        WordTokenizer wt = new WordTokenizer();
        if (tokenizerOp.equals(TOKENIZER_COMPLETE))
            wt.setDelimiters(" \r\n\t.,;:\'\"()?!-+*&#$%/=<>[]_`@\\^{}");
        else //TOKENIZER_COMPLETE_NUMBERS)
            wt.setDelimiters(" \r\n\t.,;:\'\"()?!-+*&#$%/=<>[]_`@\\^{}|~0123456789");
        filter.setTokenizer(wt);
    }

    FilteredClassifier classifier = new FilteredClassifier();
    classifier.setFilter(filter);

    if (useAttributeSelection) {
        AttributeSelection as = new AttributeSelection();
        as.setEvaluator(new InfoGainAttributeEval());
        Ranker r = new Ranker();
        r.setThreshold(0);
        as.setSearch(r);

        MultiFilter mf = new MultiFilter();
        mf.setFilters(new Filter[] { filter, as });

        classifier.setFilter(mf);
    }

    if (classifierOp.equals(CLASSIFIER_SMO))
        classifier.setClassifier(new SMO());
    else if (classifierOp.equals(CLASSIFIER_NB))
        classifier.setClassifier(new NaiveBayes());
    else if (classifierOp.equals(CLASSIFIER_IB1))
        classifier.setClassifier(new IBk(1));
    else if (classifierOp.equals(CLASSIFIER_IB3))
        classifier.setClassifier(new IBk(3));
    else if (classifierOp.equals(CLASSIFIER_IB5))
        classifier.setClassifier(new IBk(5));
    else if (classifierOp.equals(CLASSIFIER_PART))
        classifier.setClassifier(new PART()); //Tarda mucho

    if (boosting) {
        AdaBoostM1 boost = new AdaBoostM1();
        boost.setClassifier(classifier.getClassifier());
        classifier.setClassifier(boost); //Con NB tarda mucho
    }

    return classifier;
}

From source file:com.Machine_learning.model.MyNaiveBayes.java

public MyNaiveBayes(Instances data) {
    dataInstances = data;//from   w  w  w.  ja  v a 2  s .  co  m
    try {
        classifier = new NaiveBayes();
        classifier.buildClassifier(dataInstances);

        eval = new Evaluation(dataInstances);
    } catch (Exception ex) {
        Logger.getLogger(MyNaiveBayes.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:com.Machine_learning.model.MyNaiveBayes.java

public void applyMethod(String method) {
    try {/*from   ww w .  ja  v  a  2s.  c  o  m*/
        List<Instances> datasets = new ArrayList<>();
        if (method.equals("cross-validation")) {
            eval.crossValidateModel(classifier, dataInstances, 4, new Random(1));
            return;
        } else if (method.equals("test-set")) {
            Preprocessing preprocessTestSet = new Preprocessing(null);
            datasets = preprocessTestSet.getDataSets(
                    MyNaiveBayes.class.getResource("/data/categories-per-train.arff").getPath(),
                    MyNaiveBayes.class.getResource("/data/2017-articles-correct.arff").getPath());

        } else if (method.equals("percentage")) {
            Preprocessing preprocessTestSet = new Preprocessing(null);
            datasets = preprocessTestSet.getDataSets(
                    MyNaiveBayes.class.getResource("/data/categories-per-train.arff").getPath(),
                    MyNaiveBayes.class.getResource("/data/categories-per-test.arff").getPath());

        } else {
            return;
        }

        classifier = new NaiveBayes();
        classifier.buildClassifier(datasets.get(0));
        eval = new Evaluation(datasets.get(0));
        eval.evaluateModel(classifier, datasets.get(1));
    } catch (Exception ex) {
        Logger.getLogger(MyNaiveBayes.class.getName()).log(Level.SEVERE, null, ex);
    }
}

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

License:Open Source License

public static Classifier getClassifier(String mlAlgorithm) {
    notEmpty(mlAlgorithm);//from  ww  w. j  a  v  a  2s.co m
    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:com.sliit.views.DataVisualizerPanel.java

void getRocCurve() {
    try {/*w  ww  . j  a v  a  2  s.c o m*/
        Instances data;
        data = new Instances(new BufferedReader(new FileReader(datasetPathText.getText())));
        data.setClassIndex(data.numAttributes() - 1);

        // train classifier
        Classifier cl = new NaiveBayes();
        Evaluation eval = new Evaluation(data);
        eval.crossValidateModel(cl, data, 10, new Random(1));

        // generate curve
        ThresholdCurve tc = new ThresholdCurve();
        int classIndex = 0;
        Instances result = tc.getCurve(eval.predictions(), classIndex);

        // plot curve
        ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
        vmc.setROCString("(Area under ROC = " + Utils.doubleToString(tc.getROCArea(result), 4) + ")");
        vmc.setName(result.relationName());
        PlotData2D tempd = new PlotData2D(result);
        tempd.setPlotName(result.relationName());
        tempd.addInstanceNumberAttribute();
        // specify which points are connected
        boolean[] cp = new boolean[result.numInstances()];
        for (int n = 1; n < cp.length; n++) {
            cp[n] = true;
        }
        tempd.setConnectPoints(cp);
        // add plot
        vmc.addPlot(tempd);

        // display curve
        String plotName = vmc.getName();
        final javax.swing.JFrame jf = new javax.swing.JFrame("Weka Classifier Visualize: " + plotName);
        jf.setSize(500, 400);
        jf.getContentPane().setLayout(new BorderLayout());
        jf.getContentPane().add(vmc, BorderLayout.CENTER);
        jf.addWindowListener(new java.awt.event.WindowAdapter() {
            public void windowClosing(java.awt.event.WindowEvent e) {
                jf.dispose();
            }
        });
        jf.setVisible(true);
    } catch (IOException ex) {
        Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex);
    } catch (Exception ex) {
        Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:com.sliit.views.KNNView.java

void getRocCurve() {
    try {//from  ww w  .  j a va2s  .  com
        Instances data;
        data = new Instances(new BufferedReader(new java.io.FileReader(PredictorPanel.modalText.getText())));
        data.setClassIndex(data.numAttributes() - 1);

        // train classifier
        Classifier cl = new NaiveBayes();
        Evaluation eval = new Evaluation(data);
        eval.crossValidateModel(cl, data, 10, new Random(1));

        // generate curve
        ThresholdCurve tc = new ThresholdCurve();
        int classIndex = 0;
        Instances result = tc.getCurve(eval.predictions(), classIndex);

        // plot curve
        ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
        vmc.setROCString("(Area under ROC = " + Utils.doubleToString(tc.getROCArea(result), 4) + ")");
        vmc.setName(result.relationName());
        PlotData2D tempd = new PlotData2D(result);
        tempd.setPlotName(result.relationName());
        tempd.addInstanceNumberAttribute();
        // specify which points are connected
        boolean[] cp = new boolean[result.numInstances()];
        for (int n = 1; n < cp.length; n++) {
            cp[n] = true;
        }
        tempd.setConnectPoints(cp);
        // add plot
        vmc.addPlot(tempd);

        rocPanel.removeAll();
        rocPanel.add(vmc, "vmc", 0);
        rocPanel.revalidate();

    } catch (IOException ex) {
        Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex);
    } catch (Exception ex) {
        Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:com.sliit.views.SVMView.java

/**
 * draw ROC curve//from ww w  .  j a  v a  2  s.  co  m
 */
void getRocCurve() {
    try {
        Instances data;
        data = new Instances(new BufferedReader(new FileReader(PredictorPanel.modalText.getText())));
        data.setClassIndex(data.numAttributes() - 1);

        //train classifier
        Classifier cl = new NaiveBayes();
        Evaluation eval = new Evaluation(data);
        eval.crossValidateModel(cl, data, 10, new Random(1));

        // generate curve
        ThresholdCurve tc = new ThresholdCurve();
        int classIndex = 0;
        Instances result = tc.getCurve(eval.predictions(), classIndex);

        // plot curve
        ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
        vmc.setROCString("(Area under ROC = " + Utils.doubleToString(tc.getROCArea(result), 4) + ")");
        vmc.setName(result.relationName());
        PlotData2D tempd = new PlotData2D(result);
        tempd.setPlotName(result.relationName());
        tempd.addInstanceNumberAttribute();
        // specify which points are connected
        boolean[] cp = new boolean[result.numInstances()];
        for (int n = 1; n < cp.length; n++) {
            cp[n] = true;
        }
        tempd.setConnectPoints(cp);
        // add plot
        vmc.addPlot(tempd);

        //            rocPanel.removeAll();
        //            rocPanel.add(vmc, "vmc", 0);
        //            rocPanel.revalidate();
    } catch (IOException ex) {
        Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex);
    } catch (Exception ex) {
        Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:Control.Classificador.java

public ArrayList<Resultado> classificar(Plano plano, Arquivo arq) {
    try {//from  w w  w  . jav a  2s  .  co m
        FileReader leitor = new FileReader(arq.arquivo);
        Instances conjunto = new Instances(leitor);
        conjunto.setClassIndex(conjunto.numAttributes() - 1);
        Evaluation avaliacao = new Evaluation(conjunto);
        conjunto = conjunto.resample(new Random());

        Instances baseTreino = null, baseTeste = null;
        Random rand = new Random(1);

        if (plano.eHoldOut) {
            baseTeste = conjunto.testCV(3, 0);
            baseTreino = conjunto.trainCV(3, 0);
        } else {
            baseTeste = baseTreino = conjunto;
        }

        if (plano.IBK) {
            try {
                IB1 vizinho = new IB1();
                vizinho.buildClassifier(baseTeste);
                avaliacao.crossValidateModel(vizinho, baseTeste,
                        (plano.eHoldOut) ? 4 : baseTeste.numInstances(), rand);
                Resultado resultado = new Resultado("NN",
                        avaliacao.toMatrixString("Algortmo Vizinho Mais Prximo - Matriz de Confuso"),
                        avaliacao.toClassDetailsString("kNN"));
                resultado.setTaxaErro(avaliacao.errorRate());
                resultado.setTaxaAcerto(1 - avaliacao.errorRate());
                resultado.setRevocacao(recallToDouble(avaliacao, baseTeste));
                resultado.setPrecisao(precisionToDouble(avaliacao, baseTeste));
                this.resultados.add(resultado);
            } catch (UnsupportedAttributeTypeException ex) {
                Mensagem.erro("Algortmo IB1 no suporta atributos numricos!", "MTCS - ERRO");
            }
        }
        if (plano.J48) {
            try {
                J48 j48 = new J48();
                j48.buildClassifier(baseTeste);
                avaliacao.crossValidateModel(j48, baseTeste, (plano.eHoldOut) ? 4 : baseTeste.numInstances(),
                        rand);
                Resultado resultado = new Resultado("J48",
                        avaliacao.toMatrixString("Algortmo J48 - Matriz de Confuso"),
                        avaliacao.toClassDetailsString("J48"));
                resultado.setTaxaErro(avaliacao.errorRate());
                resultado.setTaxaAcerto(1 - avaliacao.errorRate());
                resultado.setRevocacao(recallToDouble(avaliacao, baseTeste));
                resultado.setPrecisao(precisionToDouble(avaliacao, baseTeste));
                this.resultados.add(resultado);
            } catch (UnsupportedAttributeTypeException ex) {
                Mensagem.erro("Algortmo J48 no suporta atributos nominais!", "MTCS - ERRO");
            }
        }
        if (plano.KNN) {
            try {
                IBk knn = new IBk(3);
                knn.buildClassifier(baseTeste);
                avaliacao.crossValidateModel(knn, baseTeste, (plano.eHoldOut) ? 4 : baseTeste.numInstances(),
                        rand);
                Resultado resultado = new Resultado("KNN",
                        avaliacao.toMatrixString("Algortmo KNN - Matriz de Confuso"),
                        avaliacao.toClassDetailsString("kNN"));
                resultado.setTaxaErro(avaliacao.errorRate());
                resultado.setTaxaAcerto(1 - avaliacao.errorRate());
                resultado.setRevocacao(recallToDouble(avaliacao, baseTeste));
                resultado.setPrecisao(precisionToDouble(avaliacao, baseTeste));
                this.resultados.add(resultado);
            } catch (UnsupportedAttributeTypeException ex) {
                Mensagem.erro("Algortmo KNN no suporta atributos numricos!", "MTCS - ERRO");
            }

        }
        if (plano.Naive) {
            NaiveBayes naive = new NaiveBayes();
            naive.buildClassifier(baseTeste);
            avaliacao.crossValidateModel(naive, baseTeste, (plano.eHoldOut) ? 4 : baseTeste.numInstances(),
                    rand);
            Resultado resultado = new Resultado("Naive",
                    avaliacao.toMatrixString("Algortmo NaiveBayes - Matriz de Confuso"),
                    avaliacao.toClassDetailsString("kNN"));
            resultado.setTaxaErro(avaliacao.errorRate());
            resultado.setTaxaAcerto(1 - avaliacao.errorRate());
            resultado.setRevocacao(recallToDouble(avaliacao, baseTeste));
            resultado.setPrecisao(precisionToDouble(avaliacao, baseTeste));
            this.resultados.add(resultado);
        }
        if (plano.Tree) {
            try {
                Id3 id3 = new Id3();
                id3.buildClassifier(baseTeste);
                avaliacao.crossValidateModel(id3, baseTeste, (plano.eHoldOut) ? 4 : baseTeste.numInstances(),
                        rand);
                Resultado resultado = new Resultado("ID3",
                        avaliacao.toMatrixString("Algortmo ID3 - Matriz de Confuso"),
                        avaliacao.toClassDetailsString("kNN"));
                resultado.setTaxaErro(avaliacao.errorRate());
                resultado.setTaxaAcerto(1 - avaliacao.errorRate());
                resultado.setRevocacao(recallToDouble(avaliacao, baseTeste));
                resultado.setPrecisao(precisionToDouble(avaliacao, baseTeste));
                this.resultados.add(resultado);
            } catch (UnsupportedAttributeTypeException ex) {
                Mensagem.erro("Algortmo Arvore de Deciso no suporta atributos numricos!",
                        "MTCS - ERRO");

            }
        }

    } catch (FileNotFoundException ex) {

        Logger.getLogger(Classificador.class.getName()).log(Level.SEVERE, null, ex);
    } catch (IOException ex) {
        Logger.getLogger(Classificador.class.getName()).log(Level.SEVERE, null, ex);
    } catch (NullPointerException ex) {
        Mensagem.erro("Selecione um arquivo para comear!", "MTCS - ERRO");
        Logger.getLogger(Classificador.class.getName()).log(Level.SEVERE, null, ex);
    } catch (Exception ex) {
        Logger.getLogger(Classificador.class.getName()).log(Level.SEVERE, null, ex);
    }

    return this.resultados;
}