Example usage for weka.classifiers Evaluation toClassDetailsString

List of usage examples for weka.classifiers Evaluation toClassDetailsString

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Introduction

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

Prototype

public String toClassDetailsString() throws Exception

Source Link

Document

Generates a breakdown of the accuracy for each class (with default title), incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.

Usage

From source file:miRdup.WekaModule.java

License:Open Source License

public static String testModel(File testarff, String classifier) {
    // System.out.println("Testing model on "+testarff+". Submitted to model "+classifier);
    try {/*ww w.java  2 s  . com*/

        // load data
        DataSource source = new DataSource(testarff.toString());
        Instances data = source.getDataSet();
        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        //load model
        Classifier model = (Classifier) weka.core.SerializationHelper.read(classifier);

        // evaluate dataset on the model
        Evaluation eval = new Evaluation(data);

        eval.evaluateModel(model, data);
        FastVector fv = eval.predictions();

        //calculate importants values
        String ev[] = eval.toClassDetailsString().split("\n");

        String p = ev[3].trim();
        String n = ev[4].trim();

        double tp = Double.parseDouble(p.substring(0, 6).trim());
        double fp = 0;
        try {
            fp = Double.parseDouble(p.substring(11, 16).trim());
        } catch (Exception exception) {
            fp = Double.parseDouble(p.substring(7, 16).trim());
        }
        double tn = Double.parseDouble(n.substring(0, 6).trim());
        double fn = 0;
        try {
            fn = Double.parseDouble(n.substring(11, 16).trim());
        } catch (Exception exception) {
            fn = Double.parseDouble(n.substring(7, 16).trim());
        }

        //System.out.println("\nTP="+tp+"\nFP="+fp+"\nTN="+tn+"\nFN="+fn);
        //specificity, sensitivity, Mathew's correlation, Prediction accuracy
        double sp = ((tn) / (tn + fp));
        double se = ((tp) / (tp + fn));
        double acc = ((tp + tn) / (tp + tn + fp + fn));
        double mcc = ((tp * tn) - (fp * fn)) / Math.sqrt((tp + fp) * (tn + fn) * (tp + fn) * tn + fp);
        //            System.out.println("\nse="+se+"\nsp="+sp+"\nACC="+dec.format(acc).replace(",", ".")+"\nMCC="+dec.format(mcc).replace(",", "."));
        //            System.out.println(eval.toMatrixString());

        String out = dec.format(acc).replace(",", ".");
        System.out.println(out);
        return out;
    } catch (Exception e) {
        e.printStackTrace();
        return "";
    }

}

From source file:ml.ann.MainDriver.java

public static void testModel() {
    System.out.println("## Pilih bahan testing");
    System.out.println("## 1. Uji dengan data dari masukan training");
    System.out.println("## 2. Uji dengan data data masukan baru");
    System.out.print("## > ");

    int choice = (new Scanner(System.in)).nextInt();
    if (choice == 1) {
        try {/*from  w w w.ja va2  s .  c  o m*/
            Evaluation eval = new Evaluation(train);

            if (cv10) {
                eval.crossValidateModel(model, test, 10, new Random(1));
            } else {
                eval.evaluateModel(model, test);
            }

            System.out.println(eval.toSummaryString());
            System.out.println(eval.toClassDetailsString());
            System.out.println(eval.toMatrixString());

        } catch (Exception E) {
            E.printStackTrace();
        }
    } else if (choice == 2) {
        try {
            loadTestData();
            Evaluation eval = new Evaluation(train);
            if (cv10) {
                eval.crossValidateModel(model, test, 10, new Random(1));
            } else {
                eval.evaluateModel(model, test);
            }

            System.out.println(eval.toSummaryString());
            System.out.println(eval.toClassDetailsString());
            System.out.println(eval.toMatrixString());
        } catch (Exception E) {
            E.printStackTrace();
        }
    }

}

From source file:ml.ann.MainPTR.java

public static void main(String[] args) throws FileNotFoundException, IOException, Exception {
    boolean randomWeight;
    double weightawal = 0.0;
    double learningRate = 0.0001;
    double threshold = 0.00;
    double momentum = 0.00;
    int maxEpoch = 100000;
    int nCrossValidate = 2;

    m_nominalToBinaryFilter = new NominalToBinary();
    m_normalize = new Normalize();

    Scanner in = new Scanner(System.in);
    System.out.println("Lokasi file: ");

    String filepath = in.nextLine();
    filepath = "test-arffs/iris.arff";
    System.out.println("--- Algoritma ---");
    System.out.println("1. Perceptron Training Rule");
    System.out.println("2. Delta Rule Incremental");
    System.out.println("3. Delta Rule Batch");
    System.out.println("Pilihan Algoritma (1/2/3) : ");
    int choice = in.nextInt();
    String temp = in.nextLine();//from  w ww.  j  a v a  2 s .  co m

    System.out.println("Apakah Anda ingin memasukkan nilai weight awal? (YES/NO)");
    String isRandom = in.nextLine();
    System.out.println("Apakah Anda ingin memasukkan konfigurasi? (YES/NO)");
    String config = in.nextLine();

    if (config.equalsIgnoreCase("yes")) {
        System.out.print("Masukkan nilai learning rate: ");
        learningRate = in.nextDouble();
        System.out.print("Masukkan nilai threshold: ");
        threshold = in.nextDouble();
        System.out.print("Masukkan nilai momentum: ");
        momentum = in.nextDouble();
        System.out.print("Masukkan jumlah epoch: ");
        threshold = in.nextInt();
        System.out.print("Masukkan jumlah folds untuk crossvalidate: ");
        nCrossValidate = in.nextInt();
    }

    randomWeight = isRandom.equalsIgnoreCase("yes");

    if (randomWeight) {
        System.out.print("Masukkan nilai weight awal: ");
        weightawal = Double.valueOf(in.nextLine());
    }

    //print config
    if (isRandom.equalsIgnoreCase("yes")) {
        System.out.print("isRandom | ");
    } else {
        System.out.print("Weight " + weightawal + " | ");
    }

    System.out.print("L.rate " + learningRate + " | ");
    System.out.print("Max Epoch " + maxEpoch + " | ");
    System.out.print("Threshold " + threshold + " | ");
    System.out.print("Momentum " + momentum + " | ");
    System.out.print("Folds " + nCrossValidate + " | ");
    System.out.println();

    FileReader trainreader = new FileReader(filepath);
    Instances train = new Instances(trainreader);
    train.setClassIndex(train.numAttributes() - 1);

    m_nominalToBinaryFilter.setInputFormat(train);
    train = new Instances(Filter.useFilter(train, m_nominalToBinaryFilter));

    m_normalize.setInputFormat(train);
    train = new Instances(Filter.useFilter(train, m_normalize));

    MultiClassPTR tempMulti = new MultiClassPTR(choice, randomWeight, learningRate, maxEpoch, threshold);
    tempMulti.buildClassifier(train);

    Evaluation eval = new Evaluation(new Instances(train));
    eval.evaluateModel(tempMulti, train);
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());
}

From source file:myclassifier.wekaCode.java

public static void testingTestSet(Instances dataSet, Classifier classifiers, Instances testSet)
        throws Exception {
    Evaluation evaluation = new Evaluation(dataSet);
    evaluation.evaluateModel(classifiers, testSet); //Evaluates the classifier on a given set of instances.
    System.out.println(evaluation.toSummaryString("\n Testing Model given Test Set ", false));
    System.out.println(evaluation.toClassDetailsString());

}

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 ww w  .ja  v a  2 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());
}

From source file:newclassifier.NewClassifier.java

public void crossValidation() throws Exception {
    cls.buildClassifier(data);/*www.  j  av  a  2  s  .  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());
}

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 ww.jav a  2  s. c om

    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());
}

From source file:nl.uva.expose.classification.WekaClassification.java

private void classifierTrainer(Instances trainData) throws Exception {
    trainData.setClassIndex(0);// ww  w  . ja  v  a 2s  .  co m
    //        classifier.setFilter(filter);
    classifier.setClassifier(new NaiveBayes());
    classifier.buildClassifier(trainData);
    Evaluation eval = new Evaluation(trainData);
    eval.crossValidateModel(classifier, trainData, 5, new Random(1));
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toClassDetailsString());
    System.out.println("===== Evaluating on filtered (training) dataset done =====");
    System.out.println("\n\nClassifier model:\n\n" + classifier);
}

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

License:Open Source License

/**
 * Creates an evaluation overview of the built classifier.
 * /*from   www .j  av a2 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;
}

From source file:PEBL.TwoStep.java

public static void main(String[] args) throws Exception {

    ConverterUtils.DataSource source = new ConverterUtils.DataSource(
            "Z:\\\\shared from vm\\\\fourthset\\\\mixed.csv");

    Instances data = source.getDataSet();

    // setting class attribute if the data format does not provide this information
    // For example, the XRFF format saves the class attribute information as well
    if (data.classIndex() == -1) {
        data.setClassIndex(data.numAttributes() - 1);
    }/*from  w  w w  . j av a2 s. co  m*/

    NumericToNominal nmf = new NumericToNominal();
    nmf.setInputFormat(data);
    data = Filter.useFilter(data, nmf);

    // build a c4.5 classifier
    String[] options = new String[1];
    // options[0] = "-C 0.25 -M 2";            // unpruned tree
    options[0] = "-K";
    NaiveBayes c = new NaiveBayes(); // new instance of tree
    c.setOptions(options); // set the options
    c.buildClassifier(data); // build classifier

    // eval
    Evaluation eval = new Evaluation(data);
    eval.crossValidateModel(c, data, 10, new Random(1));
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toMatrixString());
    System.out.println(eval.toClassDetailsString());
    System.out.println("--- model learned on mixed set ---");

    // load unlabeled data
    ConverterUtils.DataSource s = new ConverterUtils.DataSource(
            "Z:\\\\shared from vm\\\\fourthset\\\\unlabelled.csv");
    Instances unlabeled = s.getDataSet();
    // set class attribute
    unlabeled.setClassIndex(unlabeled.numAttributes() - 1);

    nmf = new NumericToNominal();
    nmf.setInputFormat(unlabeled);
    unlabeled = Filter.useFilter(unlabeled, nmf);

    // label instances
    for (int i = 0; i < unlabeled.numInstances(); i++) {
        double classZero = c.distributionForInstance(unlabeled.instance(i))[0];
        double classOne = c.distributionForInstance(unlabeled.instance(i))[1];
        System.out.print(
                "classifying: " + unlabeled.instance(i) + " : " + classZero + " - " + classOne + " == class: ");
        if (classZero > classOne) {
            System.out.print("0");
            unlabeled.instance(i).setClassValue("0");
        } else {
            System.out.print("1");
            unlabeled.instance(i).setClassValue("1");
        }
        System.out.println("");
    }

    // save labeled data
    // BufferedWriter writer = new BufferedWriter(
    //         new FileWriter("Z:\\\\shared from vm\\\\thirdset\\\\relabelled.arff"));
    // writer.write(labeled.toString());
    // writer.newLine();
    // writer.flush();
    // writer.close();
    ArffSaver saver = new ArffSaver();
    saver.setInstances(unlabeled);
    saver.setFile(new File("Z:\\shared from vm\\thirdset\\relabelled.arff"));
    //        saver.setDestination(new File("Z:\\shared from vm\\thirdset\\relabelled.arff"));   // **not** necessary in 3.5.4 and later
    saver.writeBatch();

}