Example usage for weka.classifiers Evaluation Evaluation

List of usage examples for weka.classifiers Evaluation Evaluation

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

Introduction

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

Prototype

public Evaluation(Instances data) throws Exception

Source Link

Usage

From source file:tcc.FeatureExtraction.java

public void rbf() throws IOException {
    //parsing CSV to Arff
    CSVLoader loader = new CSVLoader();
    loader.setSource(new File("/root/TCC/Resultados/Parte 4 - Novos Casos/TamuraHaralickMomentos.csv"));
    Instances inst = loader.getDataSet();

    ArffSaver saver = new ArffSaver();
    saver.setInstances(inst);/* w  ww .  j a va2  s  .c om*/
    saver.setFile(new File("/root/TCC/Resultados/Parte 4 - Novos Casos/TamuraHaralickMomentos.arff"));
    saver.setDestination(new File("/root/TCC/Resultados/Parte 4 - Novos Casos/TamuraHaralickMomentos.arff"));
    saver.writeBatch();

    BufferedReader reader = new BufferedReader(
            new FileReader("/root/TCC/Resultados/Parte 4 - Novos Casos/TamuraHaralickMomentos.arff"));
    Instances data = new Instances(reader);
    reader.close();
    data.setClassIndex(data.numAttributes() - 1);

    //Normalizando
    try {
        Normalize norm = new Normalize();
        norm.setInputFormat(data);
        data = Filter.useFilter(data, norm);

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

    File csv = new File("/root/TCC/Resultados/rbf.csv");
    FileWriter fw = new FileWriter(csv);
    BufferedWriter bw = new BufferedWriter(fw);

    for (int i = 1; i < 51; i++) {
        //instanciando o classificador
        RBFNetwork rbf = new RBFNetwork();
        rbf.setNumClusters(i);

        try {

            rbf.buildClassifier(data);
            Evaluation eval = new Evaluation(data);
            //System.out.println(eval.toSummaryString("\nResults\n======\n", false));
            eval.crossValidateModel(rbf, data, 10, new Random(1), new Object[] {});
            double auc = eval.areaUnderROC(1);
            System.out.println(auc);
            bw.write(Double.toString(auc));
            bw.newLine();

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

}

From source file:textmining.TextMining.java

/**
 * Decision Table/* ww  w  .  j  a  v  a  2  s  . c o m*/
 *
 * @param instances
 * @return string
 * @throws Exception
 */
private static String C_DecisionTable(Instances instances) throws Exception {
    Classifier decisionTable = (Classifier) new DecisionTable();
    String[] options = weka.core.Utils.splitOptions("-X 1 -S \"weka.attributeSelection.BestFirst -D 1 -N 5\"");
    decisionTable.setOptions(options);
    decisionTable.buildClassifier(instances);
    Evaluation eval = new Evaluation(instances);
    //        eval.evaluateModel(decisionTable, instances);
    eval.crossValidateModel(decisionTable, instances, 5, new Random(1));
    String resume = eval.toSummaryString();

    return eval.toMatrixString(resume);
}

From source file:textmining.TextMining.java

private static String setOptions(Classifier classifier, Instances instances, String[] options)
        throws Exception {
    classifier.setOptions(options);/*from w  ww  .j av a 2  s  . co m*/
    classifier.buildClassifier(instances);
    Evaluation eval = new Evaluation(instances);
    eval.crossValidateModel(classifier, instances, 5, new Random(1));
    eval.evaluateModel(classifier, instances);
    String resume = eval.toSummaryString();
    return eval.toMatrixString(resume);
}

From source file:trabfs.machineLeaningFrameWork.core.AvaliadordeSolucao.java

public double avalia(Solucao s) {
    double precision = 0.0, c;
    try {/*  w  w w.  j  a  va 2 s  . co  m*/
        // cria vetor de indices dos atributos selecionados            
        int[] toremove = makeIndex(s);

        //remove atributos nao selecionados
        Remove remove = new Remove();
        remove.setAttributeIndicesArray(toremove);
        remove.setInvertSelection(true);
        remove.setInputFormat(p.getInstances());
        Instances subproblema = Filter.useFilter(p.getInstances(), remove);
        subproblema.setClassIndex(subproblema.numAttributes() - 1);
        // classifica e pega o resultado
        Random rand = new Random(1); // create seeded number generator

        IBk clf = new IBk(K);
        //SimpleNaiveBayes clf = new SimpleNaiveBayes();
        //NaiveBayesSimple clf = new NaiveBayesSimple();

        //clf.buildClassifier(subproblema);
        Evaluation eval = new Evaluation(subproblema);
        eval.crossValidateModel(clf, subproblema, nfolds, rand);
        precision = (double) eval.correct() / subproblema.numInstances();

        calls++;

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

    s.setQuality(precision);

    if (precision > this.best) {
        this.best = precision;
    }

    evolucao.add(this.best);
    return s.getQuality();
}

From source file:trainableSegmentation.WekaSegmentation.java

License:GNU General Public License

/**
 * Get training error (from loaded data).
 *
 * @param verbose option to display evaluation information in the log window
 * @return classifier error on the training data set.
 */// w w w . j  a  v a 2 s. c  om
public double getTrainingError(boolean verbose) {
    if (null == this.trainHeader)
        return -1;

    double error = -1;
    try {
        final Evaluation evaluation = new Evaluation(this.loadedTrainingData);
        evaluation.evaluateModel(classifier, this.loadedTrainingData);
        if (verbose)
            IJ.log(evaluation.toSummaryString("\n=== Training set evaluation ===\n", false));
        error = evaluation.errorRate();
    } catch (Exception e) {

        e.printStackTrace();
    }

    return error;
}

From source file:trainableSegmentation.WekaSegmentation.java

License:GNU General Public License

/**
 * Get test error of current classifier on a specific image and its binary labels
 *
 * @param image input image//w  ww. j  av a 2s  .  c  om
 * @param labels binary labels
 * @param whiteClassIndex index of the white class
 * @param blackClassIndex index of the black class
 * @param verbose option to display evaluation information in the log window
 * @return pixel classification error
 */
public double getTestError(ImagePlus image, ImagePlus labels, int whiteClassIndex, int blackClassIndex,
        boolean verbose) {
    IJ.showStatus("Creating features for test image...");
    if (verbose)
        IJ.log("Creating features for test image " + image.getTitle() + "...");

    // Set proper class names (skip empty list ones)
    ArrayList<String> classNames = new ArrayList<String>();
    if (null == loadedClassNames) {
        for (int i = 0; i < numOfClasses; i++)
            if (examples[0].get(i).size() > 0)
                classNames.add(getClassLabels()[i]);
    } else
        classNames = loadedClassNames;

    // Apply labels
    final int height = image.getHeight();
    final int width = image.getWidth();
    final int depth = image.getStackSize();

    Instances testData = null;

    for (int z = 1; z <= depth; z++) {
        final ImagePlus testSlice = new ImagePlus(image.getImageStack().getSliceLabel(z),
                image.getImageStack().getProcessor(z));
        // Create feature stack for test image
        IJ.showStatus("Creating features for test image (slice " + z + ")...");
        if (verbose)
            IJ.log("Creating features for test image (slice " + z + ")...");
        final FeatureStack testImageFeatures = new FeatureStack(testSlice);
        // Use the same features as the current classifier
        testImageFeatures.setEnabledFeatures(featureStackArray.getEnabledFeatures());
        testImageFeatures.setMaximumSigma(maximumSigma);
        testImageFeatures.setMinimumSigma(minimumSigma);
        testImageFeatures.setMembranePatchSize(membranePatchSize);
        testImageFeatures.setMembraneSize(membraneThickness);
        testImageFeatures.updateFeaturesMT();
        testImageFeatures.setUseNeighbors(featureStackArray.useNeighborhood());
        filterFeatureStackByList(this.featureNames, testImageFeatures);

        final Instances data = testImageFeatures.createInstances(classNames);
        data.setClassIndex(data.numAttributes() - 1);
        if (verbose)
            IJ.log("Assigning classes based on the labels...");

        final ImageProcessor slice = labels.getImageStack().getProcessor(z);
        for (int n = 0, y = 0; y < height; y++)
            for (int x = 0; x < width; x++, n++) {
                final double newValue = slice.getPixel(x, y) > 0 ? whiteClassIndex : blackClassIndex;
                data.get(n).setClassValue(newValue);
            }

        if (null == testData)
            testData = data;
        else {
            for (int i = 0; i < data.numInstances(); i++)
                testData.add(data.get(i));
        }
    }
    if (verbose)
        IJ.log("Evaluating test data...");

    double error = -1;
    try {
        final Evaluation evaluation = new Evaluation(testData);
        evaluation.evaluateModel(classifier, testData);
        if (verbose) {
            IJ.log(evaluation.toSummaryString("\n=== Test data evaluation ===\n", false));
            IJ.log(evaluation.toClassDetailsString() + "\n");
            IJ.log(evaluation.toMatrixString());
        }
        error = evaluation.errorRate();
    } catch (Exception e) {

        e.printStackTrace();
    }

    return error;
}

From source file:trainableSegmentation.WekaSegmentation.java

License:GNU General Public License

/**
 * Get test error of current classifier on a specific image and its binary labels
 *
 * @param image input image/*from  w  w  w . ja  va 2  s .  co m*/
 * @param labels binary labels
 * @param filters list of filters to create features
 * @param whiteClassIndex index of the white class
 * @param blackClassIndex index of the black class
 * @param verbose option to display evaluation information in the log window
 * @return pixel classification error
 */
public double getTestError(ImagePlus image, ImagePlus labels, ImagePlus filters, int whiteClassIndex,
        int blackClassIndex, boolean verbose) {
    IJ.showStatus("Creating features for test image...");
    if (verbose)
        IJ.log("Creating features for test image " + image.getTitle() + "...");

    // Set proper class names (skip empty list ones)
    ArrayList<String> classNames = new ArrayList<String>();
    if (null == loadedClassNames) {
        for (int i = 0; i < numOfClasses; i++)
            if (examples[0].get(i).size() > 0)
                classNames.add(getClassLabels()[i]);
    } else
        classNames = loadedClassNames;

    // Apply labels
    final int height = image.getHeight();
    final int width = image.getWidth();
    final int depth = image.getStackSize();

    Instances testData = null;

    for (int z = 1; z <= depth; z++) {
        final ImagePlus testSlice = new ImagePlus(image.getImageStack().getSliceLabel(z),
                image.getImageStack().getProcessor(z));
        // Create feature stack for test image
        IJ.showStatus("Creating features for test image...");
        if (verbose)
            IJ.log("Creating features for test image " + z + "...");
        final FeatureStack testImageFeatures = new FeatureStack(testSlice);
        // Create features by applying the filters
        testImageFeatures.addFeaturesMT(filters);

        final Instances data = testImageFeatures.createInstances(classNames);
        data.setClassIndex(data.numAttributes() - 1);
        if (verbose)
            IJ.log("Assigning classes based on the labels...");

        final ImageProcessor slice = labels.getImageStack().getProcessor(z);
        for (int n = 0, y = 0; y < height; y++)
            for (int x = 0; x < width; x++, n++) {
                final double newValue = slice.getPixel(x, y) > 0 ? whiteClassIndex : blackClassIndex;
                data.get(n).setClassValue(newValue);
            }

        if (null == testData)
            testData = data;
        else {
            for (int i = 0; i < data.numInstances(); i++)
                testData.add(data.get(i));
        }
    }
    if (verbose)
        IJ.log("Evaluating test data...");

    double error = -1;
    try {
        final Evaluation evaluation = new Evaluation(testData);
        evaluation.evaluateModel(classifier, testData);
        if (verbose) {
            IJ.log(evaluation.toSummaryString("\n=== Test data evaluation ===\n", false));
            IJ.log(evaluation.toClassDetailsString() + "\n");
            IJ.log(evaluation.toMatrixString());
        }
        error = evaluation.errorRate();
    } catch (Exception e) {

        e.printStackTrace();
    }

    return error;
}

From source file:Tubes.Classification.java

public Evaluation runClassifier(Classifier model, Instances training, Instances testing) {
    try {/*www .j ava 2 s.c o  m*/
        Evaluation eval_train = new Evaluation(training);
        model.buildClassifier(training);

        eval_train.evaluateModel(model, testing);
        return eval_train;

    } catch (Exception e) {
        e.printStackTrace();
    }
    return null;
}

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

public void akses() throws Exception {
    Discretize filter;/*www . j a  v a2 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();
}

From source file:tubes1.Main.java

public static Evaluation classify(Classifier model, Instances trainingSet, Instances testingSet)
        throws Exception {
    Evaluation evaluation = new Evaluation(trainingSet);
    model.buildClassifier(trainingSet);//from   w ww  .j a va 2 s  .  co  m
    evaluation.evaluateModel(model, testingSet);
    return evaluation;
}