Example usage for weka.classifiers Evaluation crossValidateModel

Introduction

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

Prototype

public void crossValidateModel(Classifier classifier, Instances data, int numFolds, Random random)
        throws Exception 

Source Link

Document

Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.

Usage

From source file:DocClassifier.java

public Evaluation cvClassify(Classifier classifier, int k) throws Exception {
    docPredList.clear();/*from  ww  w.j  a v  a 2  s  .  c  o  m*/
    Instances trainInstances = createInstances(trainFiles);
    Evaluation ev = new Evaluation(trainInstances);
    ev.crossValidateModel(classifier, trainInstances, k, new Random(1));
    return ev;
}

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From source file:PreparingSteps.java

public double getSourceFitnessValue(int foldnumber, int N, Debug.Random rand, Instances data,
        double[] food_source, Evaluation eval, Classifier classifier) {
    double fitness = 0;
    int girildi = 0;
    Instances data1 = data;//  w w w .j  a va  2 s.  com

    for (int j = 0; j < N - 1; j++) {
        if (food_source[j] == 0) {
            data1.deleteAttributeAt(j - girildi);
            girildi += 1;
        }

    }

    try {
        eval.crossValidateModel(classifier, data1, foldnumber, rand);
        fitness = eval.weightedFMeasure();
    } catch (Exception ex) {
        ex.printStackTrace();
    }

    return fitness;
}

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From source file:homemadeWEKA.java

public static void treeLearning_crossVal(Instances data) throws Exception {
    Evaluation eval = new Evaluation(data);
    J48 tree = new J48();
    eval.crossValidateModel(tree, data, 10, new Random(1));
    save_modelWithEval(tree, eval);//from   w w  w . j a  va 2  s .  c  om
    //        System.out.println(eval.toSummaryString("\nResult of tree learning with cross validation 10 folds\n \n",false));
}

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From source file:GrowTree.java

public void crossvalidaion() throws Exception {
    DataSource source = new DataSource(
            "F:\\backup\\BTH\\#6DV2542 Machine Learning\\WEKA experiments\\UCI\\iris.arff");
    Instances newData = source.getDataSet();
    Evaluation eval = new Evaluation(newData);
    eval.crossValidateModel(tree, newData, 10, new Random(1));
    System.out.println(eval.toSummaryString("\nResults\n======\n", false));
}

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From source file:adams.opt.cso.AbstractClassifierBasedSimpleCatSwarmOptimization.java

License:Open Source License

/**
 * Evaluates the classifier on the dataset and returns the metric.
 *
 * @param cls      the classifier to evaluate
 * @param data      the data to use for evaluation
 * @param folds      the number of folds to use
 * @param seed      the seed for the randomization
 * @return         the metric/*from   w ww  .j a  v  a2 s . c o  m*/
 * @throws Exception   if the evaluation fails
 */
protected double evaluateClassifier(Classifier cls, Instances data, int folds, int seed) throws Exception {
    Evaluation evaluation;

    evaluation = new Evaluation(data);
    evaluation.crossValidateModel(cls, data, folds, new Random(seed));

    return getMeasure().extract(evaluation, true);
}

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From source file:adams.opt.optimise.genetic.fitnessfunctions.AttributeSelection.java

License:Open Source License

public double evaluate(OptData opd) {
    init();/*from   ww  w.j  a v a2 s  . c  o m*/
    int cnt = 0;
    int[] weights = getWeights(opd);
    Instances newInstances = new Instances(getInstances());
    for (int i = 0; i < getInstances().numInstances(); i++) {
        Instance in = newInstances.instance(i);
        cnt = 0;
        for (int a = 0; a < getInstances().numAttributes(); a++) {
            if (a == getInstances().classIndex())
                continue;
            if (weights[cnt++] == 0) {
                in.setValue(a, 0);
            } else {
                in.setValue(a, in.value(a));
            }
        }
    }
    Classifier newClassifier = null;

    try {
        newClassifier = (Classifier) OptionUtils.shallowCopy(getClassifier());
        // evaluate classifier on data
        Evaluation evaluation = new Evaluation(newInstances);
        evaluation.crossValidateModel(newClassifier, newInstances, getFolds(),
                new Random(getCrossValidationSeed()));

        // obtain measure
        double measure = 0;
        if (getMeasure() == Measure.ACC)
            measure = evaluation.pctCorrect();
        else if (getMeasure() == Measure.CC)
            measure = evaluation.correlationCoefficient();
        else if (getMeasure() == Measure.MAE)
            measure = evaluation.meanAbsoluteError();
        else if (getMeasure() == Measure.RAE)
            measure = evaluation.relativeAbsoluteError();
        else if (getMeasure() == Measure.RMSE)
            measure = evaluation.rootMeanSquaredError();
        else if (getMeasure() == Measure.RRSE)
            measure = evaluation.rootRelativeSquaredError();
        else
            throw new IllegalStateException("Unhandled measure '" + getMeasure() + "'!");
        measure = getMeasure().adjust(measure);

        return (measure);
        // process fitness

    } catch (Exception e) {
        getLogger().log(Level.SEVERE, "Error evaluating", e);
    }

    return 0;
}

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From source file:algoritmogeneticocluster.Cromossomo.java

private void classifica() {
    //SMO classifier = new SMO();
    //HyperPipes classifier = new HyperPipes();
    IBk classifier = new IBk(5);
    BufferedReader datafile = readDataFile(inId + ".arff");

    Instances data;/*from w ww.  j  a v a 2  s . co m*/
    Evaluation eval;
    try {
        data = new Instances(datafile);
        data.setClassIndex(data.numAttributes() - 1);
        eval = new Evaluation(data);
        Random rand = new Random(1); // usando semente = 1
        int folds = 10;
        eval.crossValidateModel(classifier, data, folds, rand);
        //this.fitness = eval.pctCorrect();
        //fitness = new BigDecimal(fitness).setScale(2, RoundingMode.HALF_UP).doubleValue();//arredondamento para duas casas
        pctAcerto = eval.pctCorrect();
        pctAcerto = new BigDecimal(pctAcerto).setScale(2, RoundingMode.HALF_UP).doubleValue();
        microAverage = getMicroAverage(eval, data);
        microAverage = new BigDecimal(microAverage).setScale(2, RoundingMode.HALF_UP).doubleValue();
        macroAverage = getMacroAverage(eval, data);
        macroAverage = new BigDecimal(macroAverage).setScale(2, RoundingMode.HALF_UP).doubleValue();

    } catch (Exception ex) {
        System.out.println("Erro ao tentar fazer a classificacao");
        Logger.getLogger(WekaSimulation.class.getName()).log(Level.SEVERE, null, ex);
    }

    switch (metodoFitness) {
    case 1:
        fitness = pctAcerto;
        break;
    case 2:
        fitness = microAverage;
        break;
    case 3:
        fitness = macroAverage;
        break;
    default:
        break;
    }

}

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From source file:algoritmogeneticocluster.WekaSimulation.java

/**
 * @param args the command line arguments
 *///from   w w  w.  ja  va 2 s  .  co  m
public static void main(String[] args) {
    SMO classifier = new SMO();
    HyperPipes hy = new HyperPipes();
    //        classifier.buildClassifier(trainset);

    BufferedReader datafile = readDataFile("tabela10.arff");

    Instances data;
    Evaluation eval;
    try {
        data = new Instances(datafile);
        data.setClassIndex(data.numAttributes() - 1);
        eval = new Evaluation(data);
        Random rand = new Random(1); // using seed = 1
        int folds = 10;
        eval.crossValidateModel(classifier, data, folds, rand);
        System.out.println(eval.toString());
        System.out.println(eval.numInstances());
        System.out.println(eval.correct());
        System.out.println(eval.incorrect());
        System.out.println(eval.pctCorrect());
        System.out.println(eval.pctIncorrect());

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

}

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From source file:ann.ANN.java

public static void crossValidation(Classifier model, Instances data) {
    try {/*  w  w  w  .j ava 2 s .  co  m*/
        Evaluation eval = new Evaluation(data);
        eval.crossValidateModel(model, data, 10, new Random(1));
        System.out.println("================================");
        System.out.println("========Cross Validation========");
        System.out.println("================================");
        System.out.println(eval.toSummaryString("\n=== Summary ===\n", false));
        System.out.println(eval.toClassDetailsString("=== Detailed Accuracy By Class ===\n"));
        System.out.println(eval.toMatrixString("=== Confusion Matrix ===\n"));
    } catch (Exception ex) {
        System.out.println(ex.toString());
    }
}

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From source file:ANN_single2.MultilayerPerceptron.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource(
            ("D:\\Program Files\\Weka-3-8\\data\\Team.arff"));
    Instances train = source.getDataSet();
    Normalize nm = new Normalize();
    nm.setInputFormat(train);/*from   w ww  . j  a v a  2  s.  c o  m*/
    train = Filter.useFilter(train, nm);
    train.setClassIndex(train.numAttributes() - 1);
    MultilayerPerceptron slp = new MultilayerPerceptron(train, 13, 0.1, 0.5);
    //        slp.buildClassifier(train);
    Evaluation eval = new Evaluation(train);
    eval.crossValidateModel(slp, train, 10, new Random(1));
    //        eval.evaluateModel(slp, train);
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toMatrixString());
}

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