Example usage for weka.classifiers Evaluation toMatrixString

Introduction

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

Prototype

public String toMatrixString() throws Exception 

Source Link

Document

Calls toMatrixString() with a default title.

Usage

From source file:myclassifier.naiveBayes.java

public void CrossValidation() throws Exception {
    if (data != null) {
        Instances train = data;//from  w  w  w .ja v a2s. co  m
        // train classifier
        NBClassifier.buildClassifier(train);
        // evaluate classifier and print some statistics
        Evaluation eval = new Evaluation(train);
        eval.crossValidateModel(NBClassifier, train, 10, new Random(1));
        System.out.println(eval.toSummaryString("\nResults\n======\n", false));
        System.out.println(eval.toClassDetailsString("\n=== Detailed Accuracy By Class ===\n"));
        System.out.println(eval.toMatrixString());
    } else {
        System.out.println("Data is null");
    }
}

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From source file:myclassifier.naiveBayes.java

public void PercentageSplit(double percent) throws Exception {
    // Percent split
    int trainSize = (int) Math.round(data.numInstances() * percent / 100);
    int testSize = data.numInstances() - trainSize;
    Instances train = new Instances(data, 0, trainSize);
    Instances test = new Instances(data, trainSize, testSize);
    // train classifier
    NBClassifier.buildClassifier(train);
    // evaluate classifier and print some statistics
    Evaluation eval = new Evaluation(train);
    eval.evaluateModel(NBClassifier, test);
    System.out.println(eval.toSummaryString("\nResults\n======\n", false));
    System.out.println(eval.toClassDetailsString("\n=== Detailed Accuracy By Class ===\n"));
    System.out.println(eval.toMatrixString());
}

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From source file:NaiveBayes.NaiveBayes.java

/**
 * @param args the command line arguments
 * @throws java.io.IOException//  w w w.  j a v  a2s .  co 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:naivebayes.NBRun.java

public static void main(String[] args) throws Exception {
    System.out.println("Naive Bayes Classifier");
    Instances data = TucilWeka.readDataSet("C:\\Program Files\\Weka-3-8\\data\\mush_test.arff");
    Scanner scan = new Scanner(System.in);
    Classifier cls;//from w  w  w .j  a  va2  s.  c o  m
    Instances train = TucilWeka.readDataSet("C:\\Program Files\\Weka-3-8\\data\\mush.arff");

    System.out.println("Read or create model? r/c");
    if (scan.next().equals("c")) {
        cls = new NBTubesAI();
        cls.buildClassifier(train);
        TucilWeka.saveModel(train);
    } else {
        cls = (NBTubesAI) TucilWeka.readModel();
    }
    Evaluation eval = new Evaluation(data);
    System.out.println("10 fold cross validate or Full train? c/f");
    if (scan.next().equals("c")) {
        int fold = 10;
        for (int i = 0; i < data.numAttributes(); i++) {
            System.out.println(i + ". " + data.attribute(i));
        }
        eval.crossValidateModel(cls, data, fold, new Random(1));
    } else {
        for (int i = 0; i < data.numAttributes(); i++) {
            System.out.println(i + ". " + data.attribute(i));
        }
        data.deleteWithMissingClass();
        try {

            eval.evaluateModel(cls, data);
        } catch (java.lang.Exception ex) {
            eval.crossValidateModel(cls, data, 11, new Random(1));
        }
    }
    //        Classifier cls=new NBTubesAI();
    //        cls.buildClassifier(data);

    System.out.println("Hasil evaluasi: ");
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toMatrixString());
    System.out.println(eval.toClassDetailsString());
}

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From source file:NaiveBayesPckge.NaiveBayesMain.java

public static void printEvaluationSplit(Instances instance) throws Exception {
    Evaluation eval = new Evaluation(instance);

    System.out.println("Split Test Result :");
    eval.evaluateModel(naive, instance);
    System.out.println(eval.toSummaryString()); // Summary of Training
    //System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());

}

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From source file:NaiveBayesPckge.NaiveBayesMain.java

public static void printEvaluation(Instances instance) throws Exception {
    Evaluation eval = new Evaluation(instance);
    Evaluation eval2 = new Evaluation(instance);

    System.out.println("Full training Result :");
    eval.evaluateModel(naive, instance);
    System.out.println(eval.toSummaryString()); // Summary of Training
    //System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());

    System.out.println("10 cross validation Result :");
    Random rand = new Random(1);
    eval2.crossValidateModel(naive, instance, 10, rand);
    System.out.println(eval2.toSummaryString()); // Summary of Training
    //System.out.println(eval2.toClassDetailsString());
    System.out.println(eval2.toMatrixString());

    double errorRates = eval.incorrect() / eval.numInstances() * 100;
    double accuracy = eval.correct() / eval.numInstances() * 100;

    //        System.out.println("Accuracy: " + df.format(accuracy) + " %");
    //        System.out.println("Error rate: " + df.format(errorRates) + " %"); // Printing Training Mean root squared error
}

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From source file:newclassifier.NewClassifier.java

public void crossValidation() throws Exception {
    cls.buildClassifier(data);//from   w  w w  .ja v  a  2s .  c  om
    Evaluation eval = new Evaluation(data);
    eval.crossValidateModel(cls, data, 10, new Random(1));
    System.out.println(eval.toSummaryString("\nResults\n======\n", false));
    System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());
}

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From source file:newclassifier.NewClassifier.java

public void givenTestSet(String path) throws Exception {
    Instances test = DataSource.read(path);
    test.setClassIndex(test.numAttributes() - 1);

    cls.buildClassifier(data);//from w w  w. ja va  2  s.co  m

    Evaluation eval = new Evaluation(data);
    eval.evaluateModel(cls, test);
    System.out.println(eval.toSummaryString("\nResults\n======\n", false));
    System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());
}

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From source file:org.dkpro.similarity.algorithms.ml.ClassifierSimilarityMeasure.java

License:Open Source License

public ClassifierSimilarityMeasure(WekaClassifier classifier, File trainArff, File testArff) throws Exception {
    CLASSIFIER = getClassifier(classifier);

    // Get all instances
    Instances train = getTrainInstances(trainArff);
    test = getTestInstances(testArff);//from ww  w.ja  va2 s.  c om

    // Apply log filter
    Filter logFilter = new LogFilter();
    logFilter.setInputFormat(train);
    train = Filter.useFilter(train, logFilter);
    logFilter.setInputFormat(test);
    test = Filter.useFilter(test, logFilter);

    Classifier clsCopy;
    try {
        // Copy the classifier
        clsCopy = AbstractClassifier.makeCopy(CLASSIFIER);

        // Build the classifier
        filteredClassifier = clsCopy;
        filteredClassifier.buildClassifier(train);

        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(filteredClassifier, test);

        System.out.println(eval.toSummaryString());
        System.out.println(eval.toMatrixString());
    } catch (Exception e) {
        throw new SimilarityException(e);
    }
}

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From source file:org.processmining.analysis.clusteranalysis.DecisionAnalyzer.java

License:Open Source License

/**
 * Creates an evaluation overview of the built classifier.
 * //w  ww.j av a  2  s. c  o  m
 * @return the panel to be displayed as result evaluation view for the
 *         current decision point
 */
protected JPanel createEvaluationVisualization(Instances data) {
    // build text field to display evaluation statistics
    JTextPane statistic = new JTextPane();

    try {
        // build evaluation statistics
        Evaluation evaluation = new Evaluation(data);
        evaluation.evaluateModel(myClassifier, data);
        statistic.setText(evaluation.toSummaryString() + "\n\n" + evaluation.toClassDetailsString() + "\n\n"
                + evaluation.toMatrixString());

    } catch (Exception ex) {
        ex.printStackTrace();
        return createMessagePanel("Error while creating the decision tree evaluation view");
    }

    statistic.setFont(new Font("Courier", Font.PLAIN, 14));
    statistic.setEditable(false);
    statistic.setCaretPosition(0);

    JPanel resultViewPanel = new JPanel();
    resultViewPanel.setLayout(new BoxLayout(resultViewPanel, BoxLayout.PAGE_AXIS));
    resultViewPanel.add(new JScrollPane(statistic));

    return resultViewPanel;
}

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