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:asap.NLPSystem.java

private void evaluateModel(boolean printEvaluation) {
    //        checkInstancesFeatures(evaluationSet);
    PerformanceCounters.startTimer("evaluateModel");
    System.out.println("Evaluating model...");
    AbstractClassifier abstractClassifier = (AbstractClassifier) classifier;
    try {//from  w w w  .j a  v  a  2s. co m
        // evaluate classifier and print some statistics
        Evaluation eval = new Evaluation(evaluationSet);

        evaluationPredictions = eval.evaluateModel(abstractClassifier, evaluationSet);

        if (printEvaluation) {
            System.out.println("\tstats for model:" + abstractClassifier.getClass().getName() + " "
                    + Utils.joinOptions(abstractClassifier.getOptions()));
            System.out.println(eval.toSummaryString());
        }

        evaluationPearsonsCorrelation = eval.correlationCoefficient();
        evaluated = true;
    } catch (Exception ex) {
        Logger.getLogger(PostProcess.class.getName()).log(Level.SEVERE, null, ex);
    }

    System.out.println("\tevaluation done.");
    PerformanceCounters.stopTimer("evaluateModel");
}

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From source file:asap.PostProcess.java

private static double[] evaluateModel(AbstractClassifier cl, Instances data, boolean printEvaluation) {
    PerformanceCounters.startTimer("evaluateModel");
    System.out.println("Evaluating model...");
    double[] predictions = null;

    try {/*from   w  ww . j  a  v  a 2  s . c om*/
        // evaluate classifier and print some statistics
        Evaluation eval = new Evaluation(data);

        predictions = eval.evaluateModel(cl, data);

        if (printEvaluation) {
            System.out.println(
                    "\tstats for model:" + cl.getClass().getName() + " " + Utils.joinOptions(cl.getOptions()));
            System.out.println(eval.toSummaryString());
        }
    } catch (Exception ex) {
        Logger.getLogger(PostProcess.class.getName()).log(Level.SEVERE, null, ex);
    }

    System.out.println("\tevaluation done.");
    PerformanceCounters.stopTimer("evaluateModel");
    return predictions;
}

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From source file:assign00.ExperimentShell.java

/**
 * @param args the command line arguments
 */// w ww .j a  va2 s  . co m
public static void main(String[] args) throws Exception {
    DataSource source = new DataSource(file);
    Instances dataSet = source.getDataSet();

    //Set up data
    dataSet.setClassIndex(dataSet.numAttributes() - 1);
    dataSet.randomize(new Random(1));

    //determine sizes
    int trainingSize = (int) Math.round(dataSet.numInstances() * .7);
    int testSize = dataSet.numInstances() - trainingSize;

    Instances training = new Instances(dataSet, 0, trainingSize);

    Instances test = new Instances(dataSet, trainingSize, testSize);

    Standardize standardizedData = new Standardize();
    standardizedData.setInputFormat(training);

    Instances newTest = Filter.useFilter(test, standardizedData);
    Instances newTraining = Filter.useFilter(training, standardizedData);

    NeuralNetworkClassifier NWC = new NeuralNetworkClassifier();
    NWC.buildClassifier(newTraining);

    Evaluation eval = new Evaluation(newTraining);
    eval.evaluateModel(NWC, newTest);

    System.out.println(eval.toSummaryString("\nResults\n======\n", false));
}

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From source file:at.aictopic1.sentimentanalysis.machinelearning.impl.TwitterClassifer.java

public void trainModel() {
    Instances trainingData = loadTrainingData();

    System.out.println("Class attribute: " + trainingData.classAttribute().toString());

    // Partition dataset into training and test sets
    RemovePercentage filter = new RemovePercentage();

    filter.setPercentage(10);// w  ww  .j av  a  2s.  co m

    Instances testData = null;

    // Split in training and testdata
    try {
        filter.setInputFormat(trainingData);

        testData = Filter.useFilter(trainingData, filter);
    } catch (Exception ex) {
        //Logger.getLogger(Trainer.class.getName()).log(Level.SEVERE, null, ex);
        System.out.println("Error getting testData: " + ex.toString());
    }

    // Train the classifier
    Classifier model = (Classifier) new NaiveBayes();

    try {
        // Save the model to fil
        // serialize model
        weka.core.SerializationHelper.write(modelDir + algorithm + ".model", model);
    } catch (Exception ex) {
        Logger.getLogger(TwitterClassifer.class.getName()).log(Level.SEVERE, null, ex);
    }
    // Set the local model 
    this.trainedModel = model;

    try {
        model.buildClassifier(trainingData);
    } catch (Exception ex) {
        //Logger.getLogger(Trainer.class.getName()).log(Level.SEVERE, null, ex);
        System.out.println("Error training model: " + ex.toString());
    }

    try {
        // Evaluate model
        Evaluation test = new Evaluation(trainingData);
        test.evaluateModel(model, testData);

        System.out.println(test.toSummaryString());

    } catch (Exception ex) {
        //Logger.getLogger(Trainer.class.getName()).log(Level.SEVERE, null, ex);
        System.out.println("Error evaluating model: " + ex.toString());
    }
}

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From source file:au.edu.usyd.it.yangpy.sampling.BPSO.java

License:Open Source License

/**
 * this method evaluate a classifier with 
 * the sampled data and internal test data
 * //ww  w. j  a va2s .c o m
 * @param c   classifier
 * @param train   sampled set
 * @param test   internal test set
 * @return   evaluation results
 */
public double classify(Classifier c, Instances train, Instances test) {
    double AUC = 0;
    double FM = 0;
    double GM = 0;

    try {
        c.buildClassifier(train);

        // evaluate classifier
        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(c, test);

        AUC = eval.areaUnderROC(1);
        FM = eval.fMeasure(1);
        GM = eval.truePositiveRate(0);
        GM *= eval.truePositiveRate(1);
        GM = Math.sqrt(GM);

    } catch (IOException ioe) {
        ioe.printStackTrace();
    } catch (Exception e) {
        e.printStackTrace();
    }

    double mean = (AUC + FM + GM) / 3;

    if (verbose == true) {
        System.out.print("AUC: " + dec.format(AUC) + " ");
        System.out.print("FM: " + dec.format(FM) + " ");
        System.out.println("GM: " + dec.format(GM));
        System.out.println("      \\       |       /  ");
        System.out.println("        Mean: " + dec.format(mean));
    }

    return mean;
}

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From source file:au.edu.usyd.it.yangpy.snp.Ensemble.java

License:Open Source License

public double classify(Classifier c, int cId) throws Exception {

    // train the classifier with training data
    c.buildClassifier(train);/* ww w.j a v a 2s .  co m*/

    // get the predict value and predict distribution from each test instances
    for (int i = 0; i < test.numInstances(); i++) {
        predictDistribution[cId][i] = c.distributionForInstance(test.instance(i));
        predictValue[cId][i] = c.classifyInstance(test.instance(i));
    }

    // of course, get the AUC for each classifier
    Evaluation eval = new Evaluation(train);
    eval.evaluateModel(c, test);
    return eval.areaUnderROC(1) * 100;
}

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From source file:boostingPL.boosting.AdaBoost.java

License:Open Source License

private double weightError(int t) throws Exception {
    // evaluate all instances
    Evaluation eval = new Evaluation(insts);
    eval.evaluateModel(classifiers[t], insts);
    return eval.errorRate();
}

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From source file:br.unicamp.ic.recod.gpsi.gp.gpsiJGAPRoiFitnessFunction.java

@Override
protected double evaluate(IGPProgram igpp) {

    double mean_accuracy = 0.0;
    Object[] noargs = new Object[0];

    gpsiRoiBandCombiner roiBandCombinator = new gpsiRoiBandCombiner(new gpsiJGAPVoxelCombiner(super.b, igpp));
    // TODO: The ROI descriptors must combine the images first
    //roiBandCombinator.combineEntity(this.dataset.getTrainingEntities());

    gpsiMLDataset mlDataset = new gpsiMLDataset(this.descriptor);
    try {/* ww  w .  j  a v  a  2 s. c  o m*/
        mlDataset.loadWholeDataset(this.dataset, true);
    } catch (Exception ex) {
        Logger.getLogger(gpsiJGAPRoiFitnessFunction.class.getName()).log(Level.SEVERE, null, ex);
    }

    int dimensionality = mlDataset.getDimensionality();
    int n_classes = mlDataset.getTrainingEntities().keySet().size();
    int n_entities = mlDataset.getNumberOfTrainingEntities();
    ArrayList<Byte> listOfClasses = new ArrayList<>(mlDataset.getTrainingEntities().keySet());

    Attribute[] attributes = new Attribute[dimensionality];
    FastVector fvClassVal = new FastVector(n_classes);

    int i, j;
    for (i = 0; i < dimensionality; i++)
        attributes[i] = new Attribute("f" + Integer.toString(i));
    for (i = 0; i < n_classes; i++)
        fvClassVal.addElement(Integer.toString(listOfClasses.get(i)));

    Attribute classes = new Attribute("class", fvClassVal);

    FastVector fvWekaAttributes = new FastVector(dimensionality + 1);

    for (i = 0; i < dimensionality; i++)
        fvWekaAttributes.addElement(attributes[i]);
    fvWekaAttributes.addElement(classes);

    Instances instances = new Instances("Rel", fvWekaAttributes, n_entities);
    instances.setClassIndex(dimensionality);

    Instance iExample;
    for (byte label : mlDataset.getTrainingEntities().keySet()) {
        for (double[] featureVector : mlDataset.getTrainingEntities().get(label)) {
            iExample = new Instance(dimensionality + 1);
            for (j = 0; j < dimensionality; j++)
                iExample.setValue(i, featureVector[i]);
            iExample.setValue(dimensionality, label);
            instances.add(iExample);
        }
    }

    int folds = 5;
    Random rand = new Random();
    Instances randData = new Instances(instances);
    randData.randomize(rand);

    Instances trainingSet, testingSet;
    Classifier cModel;
    Evaluation eTest;
    try {
        for (i = 0; i < folds; i++) {
            cModel = (Classifier) new SimpleLogistic();
            trainingSet = randData.trainCV(folds, i);
            testingSet = randData.testCV(folds, i);

            cModel.buildClassifier(trainingSet);

            eTest = new Evaluation(trainingSet);
            eTest.evaluateModel(cModel, testingSet);

            mean_accuracy += eTest.pctCorrect();

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

    mean_accuracy /= (folds * 100);

    return mean_accuracy;

}

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From source file:ca.uottawa.balie.WekaLearner.java

License:Open Source License

/**
 * Test the learned model.//from  w  w w  . j a  va 2  s  . c o  m
 * 
 * @return A summary string of the performance of the classifier
 */
public String TestModel() {
    if (DEBUG)
        DebugInfo.Out("Testing on " + m_TestingSet.numInstances() + " instances");
    Evaluation evaluation = null;
    try {
        evaluation = new Evaluation(m_TrainingSet);
        evaluation.evaluateModel(m_Scheme, m_TestingSet);
    } catch (Exception e) {
        System.out.println(e.getMessage());
    }
    String strSummary = evaluation.toSummaryString();
    strSummary += "\n\nConfusion Matrix: \n\n";
    m_ConfusionMatrix = evaluation.confusionMatrix();
    for (int i = 0; i != m_ConfusionMatrix.length; ++i) {
        for (int j = 0; j != m_ConfusionMatrix[i].length; ++j) {
            strSummary += String.valueOf(m_ConfusionMatrix[i][j]) + "\t";
        }
        strSummary += "\n";
    }
    return strSummary;
}

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From source file:cezeri.evaluater.FactoryEvaluation.java

public static Evaluation performCrossValidate(Classifier model, Instances datax, int folds, boolean show_text,
        boolean show_plot, TFigureAttribute attr) {
    Random rand = new Random(1);
    Instances randData = new Instances(datax);
    randData.randomize(rand);//from   ww w. j a  v a2s . c  om
    if (randData.classAttribute().isNominal()) {
        randData.stratify(folds);
    }
    Evaluation eval = null;
    try {
        // perform cross-validation
        eval = new Evaluation(randData);
        //            double[] simulated = new double[0];
        //            double[] observed = new double[0];
        //            double[] sim = new double[0];
        //            double[] obs = new double[0];
        for (int n = 0; n < folds; n++) {
            Instances train = randData.trainCV(folds, n, rand);
            Instances validation = randData.testCV(folds, n);
            // build and evaluate classifier
            Classifier clsCopy = Classifier.makeCopy(model);
            clsCopy.buildClassifier(train);

            //                sim = eval.evaluateModel(clsCopy, validation);
            //                obs = validation.attributeToDoubleArray(validation.classIndex());
            //                if (show_plot) {
            //                    double[][] d = new double[2][sim.length];
            //                    d[0] = obs;
            //                    d[1] = sim;
            //                    CMatrix f1 = CMatrix.getInstance(d);
            //                    f1.transpose().plot(attr);
            //                }
            //                if (show_text) {
            //                    // output evaluation
            //                    System.out.println();
            //                    System.out.println("=== Setup for each Cross Validation fold===");
            //                    System.out.println("Classifier: " + model.getClass().getName() + " " + Utils.joinOptions(model.getOptions()));
            //                    System.out.println("Dataset: " + randData.relationName());
            //                    System.out.println("Folds: " + folds);
            //                    System.out.println("Seed: " + 1);
            //                    System.out.println();
            //                    System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false));
            //                }
            simulated = FactoryUtils.concatenate(simulated, eval.evaluateModel(clsCopy, validation));
            observed = FactoryUtils.concatenate(observed,
                    validation.attributeToDoubleArray(validation.classIndex()));
            //                simulated = FactoryUtils.mean(simulated,eval.evaluateModel(clsCopy, validation));
            //                observed = FactoryUtils.mean(observed,validation.attributeToDoubleArray(validation.classIndex()));
        }

        if (show_plot) {
            double[][] d = new double[2][simulated.length];
            d[0] = observed;
            d[1] = simulated;
            CMatrix f1 = CMatrix.getInstance(d);
            attr.figureCaption = "overall performance";
            f1.transpose().plot(attr);
        }
        if (show_text) {
            // output evaluation
            System.out.println();
            System.out.println("=== Setup for Overall Cross Validation===");
            System.out.println(
                    "Classifier: " + model.getClass().getName() + " " + Utils.joinOptions(model.getOptions()));
            System.out.println("Dataset: " + randData.relationName());
            System.out.println("Folds: " + folds);
            System.out.println("Seed: " + 1);
            System.out.println();
            System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false));
        }
    } catch (Exception ex) {
        Logger.getLogger(FactoryEvaluation.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}

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