Example usage for weka.classifiers Evaluation evaluateModel

Introduction

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

Prototype

public static String evaluateModel(Classifier classifier, String[] options) throws Exception 

Source Link

Document

Evaluates a classifier with the options given in an array of strings.

Usage

From source file:smo2.SMO.java

License:Open Source License

/**
 * Main method for testing this class.//from   ww w . java  2  s  . c  o  m
 */
public static void main(String[] argv) {

    // Classifier scheme;
    SMO scheme;

    try {
        scheme = new SMO();

        // String m_inpStr =
        // "-C 1.0 -E 1.0 -G 0.01 -A 250007 -L 0.0010 -P 1.0E-12 -M -N 0 -V 10 -W 1 -t ../EXPERIMENT/filtered/2new/plasm-new-infogain50-dataset.arff -T ./plasm-overlap50-dataset.arff -p 0";
        String m_inpStr = "-C 1.0 -E 1.0 -G 0.01 -A 250007 -L 0.0010 -P 1.0E-12 -M -N 0 -V 10 -W 1 -i -t ./plasm-train184newinfo21-dataset.arff -T ./plasm-gametinfo21-dataset.arff";
        // String m_inpStr =
        // "-C 1.0 -E 1.0 -G 0.01 -A 250007 -L 0.0010 -P 1.0E-12 -M -N 0 -R -V 10 -W 1 -t ../EXPERIMENT/filtered/2class/plasm-infogainranker-t60.arff -d ./smo.model";
        // String m_inpStr = "-T ./1test.arff -l ./smo.model";
        String[] m_res_inpStr = m_inpStr.split("\\s");
        System.out.println(Evaluation.evaluateModel(scheme, m_res_inpStr));

        m_res_inpStr = m_inpStr.split("\\s");
        String m_trainFileName = Utils.getOption('t', m_res_inpStr);
        String m_testFileName = Utils.getOption('T', m_res_inpStr);

        BufferedReader m_trainReader = null;
        BufferedReader m_testReader = null;

        Instances m_train = null;
        Instances m_test = null;

        if (m_trainFileName.length() != 0) {
            System.out.println("trainfile = " + m_trainFileName);
            m_trainReader = new BufferedReader(new FileReader(m_trainFileName));
            m_train = new Instances(m_trainReader);
            m_train.setClassIndex(m_train.numAttributes() - 1);
        }

        if (m_testFileName.length() != 0) {
            System.out.println("testfile = " + m_testFileName);
            m_testReader = new BufferedReader(new FileReader(m_testFileName));
            m_test = new Instances(m_testReader);
            m_test.setClassIndex(m_test.numAttributes() - 1);
        }

        // if(m_trainFileName.length() != 0)
        // scheme.mygenstat(m_train);

        System.out.println("#################");

        if (m_testFileName.length() != 0)
            scheme.mygenstat(m_test);

        // System.out.println(Evaluation.evaluateModel(scheme, argv));

    } catch (Exception e) {
        e.printStackTrace();
        System.err.println(e.getMessage());
    }
}

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From source file:SpamDetector.SpamDetector.java

/**
 * @param args the command line arguments
 *///w  ww . j  a v a  2s.  c  om
public static void main(String[] args) throws IOException, Exception {
    ArrayList<ArrayList<String>> notSpam = processCSV("notspam.csv");
    ArrayList<ArrayList<String>> spam = processCSV("spam.csv");

    // Cobain generate attribute & data
    FeatureExtraction fe = new FeatureExtraction();
    fe.generateArff(spam, notSpam);

    // Cobain CART
    BufferedReader br = new BufferedReader(new FileReader("data.arff"));

    ArffReader arff = new ArffReader(br);
    Instances data = arff.getData();
    data.setClassIndex(data.numAttributes() - 1);

    SimpleCart tree = new SimpleCart();
    tree.buildClassifier(data);
    System.out.println(tree.toString());

    Evaluation eval = new Evaluation(data);
    eval.evaluateModel(tree, data);
    System.out.println(eval.toSummaryString("\n\n\n\nResults\n======\n", false));
    eval.crossValidateModel(tree, data, 10, new Random());
    System.out.println(eval.toSummaryString("\n\n\n\n10-Fold\n======\n", false));

}

---

From source file:statistics.BinaryStatisticsEvaluator.java

@Override
public double[][] getConfusionMatrix(Instances Training_Instances, Instances Testing_Instances,
        String classifier) {/*from w ww  . ja  v a  2  s  .  co m*/

    Classifier cModel = null;
    if ("NB".equals(classifier)) {
        cModel = (Classifier) new NaiveBayes();
        try {
            cModel.buildClassifier(Training_Instances);
        } catch (Exception ex) {
            Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
        }
    } else if ("DT".equals(classifier)) {
        cModel = (Classifier) new J48();
        try {
            cModel.buildClassifier(Training_Instances);
        } catch (Exception ex) {
            Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
        }
    } else if ("SVM".equals(classifier)) {
        cModel = (Classifier) new SMO();

        try {
            cModel.buildClassifier(Training_Instances);
        } catch (Exception ex) {
            Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
        }
    } else if ("KNN".equals(classifier)) {
        cModel = (Classifier) new IBk();
        try {
            cModel.buildClassifier(Training_Instances);
        } catch (Exception ex) {
            Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
        }
    }
    //Test the model
    Evaluation eTest;
    try {
        eTest = new Evaluation(Training_Instances);
        eTest.evaluateModel(cModel, Testing_Instances);
        //Print the result
        String strSummary = eTest.toSummaryString();
        System.out.println(strSummary);
        String strSummary1 = eTest.toMatrixString();
        System.out.println(strSummary1);
        String strSummary2 = eTest.toClassDetailsString();
        System.out.println(strSummary2);

        //Get the confusion matrix
        double[][] cmMatrix = eTest.confusionMatrix();
        return cmMatrix;
    } catch (Exception ex) {
        Logger.getLogger(BinaryStatisticsEvaluator.class.getName()).log(Level.SEVERE, null, ex);
    }
    return null;
}

---

From source file:Statistics.WekaFunctions.java

public void buildLRcls() {
    try {//from ww  w  .  j  a  v a 2 s.co  m
        System.out.println("building classifier");
        model.setOptions(weka.core.Utils.splitOptions("-S 0 -D")); // set options
        model.buildClassifier(trainParameters);

        // evaluate classifier and print some statistics 
        Evaluation eval = new Evaluation(trainParameters);
        eval.evaluateModel(model, trainParameters);
        System.out.println(eval.toSummaryString());

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

---

From source file:textmining.TextMining.java

private static String setOptions(Classifier classifier, Instances instances, String[] options)
        throws Exception {
    classifier.setOptions(options);//from   w w w  .  j a va2 s . com
    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);
}

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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.
 *///from  ww w. ja va  2 s.  c  o  m
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//from w w w  .  j a  v a  2  s.  c o  m
 * @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 ww  w .  java2  s  .c o  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 {//from  w  w  w  . j a va2  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;/*from ww w  . ja va2  s.c  om*/
    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();
}

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