Example usage for weka.classifiers Evaluation toSummaryString

Introduction

In this page you can find the example usage for weka.classifiers Evaluation toSummaryString.

Prototype

public String toSummaryString(boolean printComplexityStatistics) 

Source Link

Document

Calls toSummaryString() with a default title.

Usage

From source file:adams.flow.transformer.WekaEvaluationSummary.java

License:Open Source License

/**
 * Executes the flow item./*from  w  w w  . j  a  va 2s. c  o  m*/
 *
 * @return      null if everything is fine, otherwise error message
 */
@Override
protected String doExecute() {
    String result;
    Evaluation eval;
    StringBuilder buffer;
    boolean prolog;
    String[] comment;

    result = null;

    if (m_InputToken.getPayload() instanceof WekaEvaluationContainer)
        eval = (Evaluation) ((WekaEvaluationContainer) m_InputToken.getPayload())
                .getValue(WekaEvaluationContainer.VALUE_EVALUATION);
    else
        eval = (Evaluation) m_InputToken.getPayload();
    buffer = new StringBuilder();
    prolog = false;

    // comments
    if (m_Comment.getValue().length() > 0) {
        comment = m_Comment.getValue().split("\n");
        if (comment.length == 1) {
            buffer.append("Comment: " + m_Comment + "\n");
        } else {
            buffer.append("Comment:\n");
            for (String line : comment)
                buffer.append(line + "\n");
        }
        prolog = true;
    }

    // relation name
    if (m_OutputRelationName) {
        buffer.append("Relation: " + eval.getHeader().relationName() + "\n");
        prolog = true;
    }

    // separator
    if (prolog)
        buffer.append("\n");

    // summary
    if (m_TitleSummary.isEmpty())
        buffer.append(eval.toSummaryString(m_ComplexityStatistics));
    else
        buffer.append(eval.toSummaryString(Utils.unbackQuoteChars(m_TitleSummary), m_ComplexityStatistics));

    // confusion matrix
    if (m_ConfusionMatrix) {
        try {
            buffer.append("\n\n");
            if (m_TitleMatrix.isEmpty())
                buffer.append(eval.toMatrixString());
            else
                buffer.append(eval.toMatrixString(Utils.unbackQuoteChars(m_TitleMatrix)));
        } catch (Exception e) {
            result = handleException("Failed to generate confusion matrix: ", e);
        }
    }

    // class details
    if (m_ClassDetails) {
        try {
            buffer.append("\n\n");
            if (m_TitleClassDetails.isEmpty())
                buffer.append(eval.toClassDetailsString());
            else
                buffer.append(eval.toClassDetailsString(Utils.unbackQuoteChars(m_TitleClassDetails)));
        } catch (Exception e) {
            result = handleException("Failed to generate class details: ", e);
        }
    }

    m_OutputToken = new Token(buffer.toString());

    return result;
}

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From source file:NaiveBayes.NaiveBayes.java

/**
 * @param args the command line arguments
 * @throws java.io.IOException/*from   www . jav  a  2  s  . c o  m*/
 */
public static void main(String[] args) throws IOException, Exception {
    System.out.print("1. Buat Model \n");
    System.out.print("2. Load Model\n");
    System.out.print("Masukkan pilihan : ");
    Scanner sc = new Scanner(System.in);
    int pil = sc.nextInt();
    System.out.print("Masukkan nama file : ");
    String filename = sc.next();
    DataSource source = new DataSource(("D:\\Program Files\\Weka-3-8\\data\\" + filename));
    Instances train = source.getDataSet();
    for (int i = 0; i < train.numAttributes(); i++)
        System.out.println(i + ". " + train.attribute(i).name());
    System.out.print("Masukkan indeks kelas : ");
    int classIdx = sc.nextInt();
    train.setClassIndex(classIdx);
    //        MultilayerPerceptron mlp = new MultilayerPerceptron(train, 0.1, 10000, 14);
    //        mlp.buildClassifier(train);
    //        Evaluation eval = new Evaluation (train);
    ////        eval.evaluateModel(mlp, train);
    //        System.out.println(eval.toSummaryString());
    NaiveBayes tb = new NaiveBayes();
    Evaluation eval = new Evaluation(train);
    switch (pil) {
    case 1:
        //                tb.buildClassifier(train);
        //                tb.toSummaryString();
        //                eval.evaluateModel(tb, train);
        eval.crossValidateModel(tb, train, 10, new Random(1));
        System.out.println(eval.toSummaryString(true));
        System.out.println(eval.toMatrixString());
        //saveModel(tb);
        break;
    default:
        tb = loadModel();
        tb.toSummaryString();
        eval.crossValidateModel(tb, train, 10, new Random(1));
        System.out.println(eval.toSummaryString());
        //                System.out.println(eval.toMatrixString());
    }
}

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From source file:nl.detoren.ijc.neural.Voorspeller.java

License:Open Source License

/**
 * Evalueer trainingsdata//from  w ww.  j a v  a 2  s  .  c o  m
 *
 * @param data
 * @return
 * @throws Exception
 */
private Evaluation evaluateTrainingData(Instances data) throws Exception {
    mlp.buildClassifier(data);
    Evaluation eval = new Evaluation(data);
    eval.evaluateModel(mlp, data);
    logger.log(Level.INFO, eval.toSummaryString(true));
    return eval;
}

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