Example usage for weka.classifiers.trees Id3 buildClassifier

List of usage examples for weka.classifiers.trees Id3 buildClassifier

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Introduction

In this page you can find the example usage for weka.classifiers.trees Id3 buildClassifier.

Prototype

@Override
public void buildClassifier(Instances data) throws Exception

Source Link

Document

Builds Id3 decision tree classifier.

Usage

From source file:Control.Classificador.java

public ArrayList<Resultado> classificar(Plano plano, Arquivo arq) {
    try {//  w  ww  .j a v a2  s .  c o 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;
}

From source file:tubes.ml.pkg1.TubesML1.java

public void akses() throws Exception {
    Discretize filter;/*from w ww. jav  a 2  s.c  o  m*/
    int fold = 10;
    int fold3 = 3;
    int trainNum, testNum;
    PrintWriter file = new PrintWriter("model.txt");

    /***dataset 1***/
    file.println("***DATASET 1***");
    fileReader tets = new fileReader("./src/data/iris.arff");
    try {
        tets.read();
    } catch (IOException ex) {
        Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
    }
    Instances data = tets.getData();
    filter = new Discretize();
    try {
        filter.setInputFormat(data);
    } catch (Exception ex) {
        Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
    }

    /*ID3*/
    Instances discreteData;
    discreteData = Filter.useFilter(data, filter);
    trainNum = discreteData.numInstances() * 3 / 4;
    testNum = discreteData.numInstances() / 4;

    for (int i = 0; i < fold; i++) {
        try {

            Instances train = discreteData.trainCV(fold, i);
            Instances test = discreteData.testCV(fold, i);

            Id3 iTiga = new Id3();
            Evaluation validation = new Evaluation(train);
            try {
                iTiga.buildClassifier(train);
                System.out.println(iTiga.toString());
                file.println(iTiga.toString());
            } catch (Exception ex) {
                Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
            }
            validation.evaluateModel(iTiga, test);
            System.out.println(validation.toSummaryString());
            file.println("Validation " + (i + 1));
            file.println(validation.toSummaryString());
        } catch (Exception ex) {
            Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    /*J48*/
    trainNum = data.numInstances() * 3 / 4;
    testNum = data.numInstances() / 4;
    J48 jKT = new J48();
    for (int i = 0; i < fold; i++) {
        Instances train = data.trainCV(fold, i);
        Instances test = data.testCV(fold, i);
        try {
            Evaluation validation = new Evaluation(train);
            try {
                jKT.buildClassifier(data);
            } catch (Exception ex) {
                Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
            }
            validation.evaluateModel(jKT, test);
            System.out.println(validation.toSummaryString());
            file.println("Validation " + (i + 1));
            file.println(validation.toSummaryString());
            // System.out.println(jKT.toString());
        } catch (Exception ex) {
            Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    /*dataset 2*/
    file.println("***DATASET 2***");
    tets.setFilepath("./src/data/weather.arff");
    try {
        tets.read();
    } catch (IOException ex) {
        Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
    }
    data = new Instances(tets.getData());

    /*ID3*/
    discreteData = Filter.useFilter(data, filter);
    trainNum = discreteData.numInstances() * 3 / 4;
    testNum = discreteData.numInstances() / 4;

    for (int i = 0; i < fold3; i++) {
        try {
            Instances train = discreteData.trainCV(trainNum, i);
            Instances test = discreteData.testCV(testNum, i);

            Id3 iTiga = new Id3();
            Evaluation validation = new Evaluation(train);
            try {
                iTiga.buildClassifier(train);
                System.out.println(iTiga.toString());
                //file.println(iTiga.toString());
            } catch (Exception ex) {
                Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
            }
            validation.evaluateModel(iTiga, test);
            System.out.println(validation.toSummaryString());
            file.println("Validation " + (i + 1));
            file.println(validation.toSummaryString());
        } catch (Exception ex) {
            Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
        }
    }
    System.out.println(testNum);
    file.println("Test Number");
    file.println(testNum);

    /*J48*/
    trainNum = data.numInstances() * 3 / 4;
    testNum = data.numInstances() / 4;

    for (int i = 0; i < fold; i++) {
        Instances train = data.trainCV(fold, i);
        Instances test = data.testCV(fold, i);
        try {
            Evaluation validation = new Evaluation(train);
            try {
                jKT.buildClassifier(data);
            } catch (Exception ex) {
                Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
            }
            validation.evaluateModel(jKT, test);
            System.out.println(validation.toSummaryString());
            file.println(validation.toSummaryString());
            System.out.println(jKT.toString());
            file.println(jKT.toString());
        } catch (Exception ex) {
            Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    /*dataset 3*/
    file.println("***DATASET 3***");
    tets.setFilepath("./src/data/weather.nominal.arff");
    try {
        tets.read();
    } catch (IOException ex) {
        Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
    }
    data = new Instances(tets.getData());

    /*ID3*/
    discreteData = Filter.useFilter(data, filter);
    trainNum = discreteData.numInstances() * 3 / 4;
    testNum = discreteData.numInstances() / 4;

    for (int i = 0; i < fold3; i++) {
        try {
            Instances train = discreteData.trainCV(fold, i);
            Instances test = discreteData.testCV(fold, i);

            Id3 iTiga = new Id3();
            Evaluation validation = new Evaluation(train);
            try {
                iTiga.buildClassifier(train);
                System.out.println(iTiga.toString());
                file.println(iTiga.toString());
            } catch (Exception ex) {
                Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
            }
            validation.evaluateModel(iTiga, test);
            System.out.println(validation.toSummaryString());
            file.println(validation.toSummaryString());
        } catch (Exception ex) {
            Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
        }
    }
    System.out.println(testNum);
    file.println("Test Number");
    file.println(testNum);

    /*J48*/
    trainNum = data.numInstances() * 3 / 4;
    testNum = data.numInstances() / 4;

    for (int i = 0; i < fold; i++) {
        Instances train = data.trainCV(fold, i);
        Instances test = data.testCV(fold, i);
        try {
            Evaluation validation = new Evaluation(train);
            try {
                jKT.buildClassifier(data);
            } catch (Exception ex) {
                Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
            }
            validation.evaluateModel(jKT, test);
            System.out.println(validation.toSummaryString());
            file.println(validation.toSummaryString());
            System.out.println(jKT.toString());
            file.println(jKT.toString());
        } catch (Exception ex) {
            Logger.getLogger(TubesML1.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    /*RESULTT*/
    System.out.println(jKT.toString());
    file.println("RESULT");
    file.println(jKT.toString());
    file.close();
}