Example usage for weka.classifiers Evaluation errorRate

List of usage examples for weka.classifiers Evaluation errorRate

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Introduction

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

Prototype

public final double errorRate()

Source Link

Document

Returns the estimated error rate or the root mean squared error (if the class is numeric).

Usage

From source file:Pair.java

License:Open Source License

private double getTestError() throws Exception {

    Evaluation evaluation;
    evaluation = new Evaluation(testData);
    evaluation.evaluateModel(this, testData);
    return evaluation.errorRate();

}

From source file:binarizer.LayoutAnalysis.java

public double crossValidation(String arffFile) throws Exception {
    DataSource source = new DataSource(arffFile);
    Instances trainingData = source.getDataSet();
    if (trainingData.classIndex() == -1)
        trainingData.setClassIndex(trainingData.numAttributes() - 1);
    NaiveBayes nb = new NaiveBayes();
    nb.setUseSupervisedDiscretization(true);
    Evaluation evaluation = new Evaluation(trainingData);
    evaluation.crossValidateModel(nb, trainingData, 10, new Random(1));
    System.out.println(evaluation.toSummaryString());
    return evaluation.errorRate();
}

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();
}

From source file:classif.ExperimentsLauncher.java

License:Open Source License

private void runDropsSteped(String algo, Prototyper prototype) {
    try {/*from   www. j  a  va 2  s.com*/
        nbPrototypesMax = this.train.numInstances() / this.train.numClasses();

        for (int i = 1; i <= nbPrototypesMax; i++) {
            prototype.setNbPrototypesPerClass(i);
            prototype.setFillPrototypes(false);

            startTime = System.currentTimeMillis();
            prototype.buildClassifier(train);
            endTime = System.currentTimeMillis();
            duration = endTime - startTime;

            int[] classDistrib = PrototyperUtil.getPrototypesPerClassDistribution(prototype.prototypes, train);

            Evaluation eval = new Evaluation(train);
            eval.evaluateModel(prototype, test);

            double testError = eval.errorRate();
            double trainError = Double.NaN;

            out.format("%s;%s;%d;%d;%.4f;%.4f;%s\n", dataName, algo, (i * train.numClasses()), duration,
                    trainError, testError, Arrays.toString(classDistrib));
            out.flush();
        }
    } catch (Exception e) {
        e.printStackTrace();
    }
}

From source file:classif.ExperimentsLauncher.java

License:Open Source License

private void runDrops(String algo, Prototyper prototype) {
    try {//from   w ww  . j  a v  a2 s  . c o  m
        for (int i = 1; i <= this.train.numInstances(); i++) {
            prototype.setNbPrototypesPerClass(i);
            prototype.setFillPrototypes(false);

            startTime = System.currentTimeMillis();
            prototype.buildClassifier(train);
            endTime = System.currentTimeMillis();
            duration = endTime - startTime;

            int[] classDistrib = PrototyperUtil.getPrototypesPerClassDistribution(prototype.prototypes, train);

            Evaluation eval = new Evaluation(train);
            eval.evaluateModel(prototype, test);

            double testError = eval.errorRate();
            // double trainError = prototype.predictAccuracyXVal(10);
            double trainError = Double.NaN;

            out.format("%s;%s;%d;%d;%.4f;%.4f;%s\n", dataName, algo, i, duration, trainError, testError,
                    Arrays.toString(classDistrib));
            out.flush();
        }

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

From source file:classif.ExperimentsLauncher.java

License:Open Source License

/**
 * //from   w ww .  ja v a  2s . com
 */
public void launchKMeans() {
    try {
        //         File f = new File(rep + "/" + dataName + "_results.csv");
        //         // if somebody is processing it
        //         if (f.exists()) {
        //            return;
        //         }
        //
        //         out = new PrintStream(new FileOutputStream(rep + "/KMeansDTW_" + "all" + "_results.csv", true));
        //         out.println("dataset,algorithm,nbPrototypes,testErrorRate,trainErrorRate");
        String algo = "KMEANS";
        System.out.println(algo);
        //         PrintStream outProto = new PrintStream(new FileOutputStream(rep + "/" + dataName + "_KMEANS.proto", append));

        nbPrototypesMax = this.train.numInstances() / this.train.numClasses();
        if (nbPrototypesMax > 10)
            nbPrototypesMax = 10;
        int tmp;
        tmp = nbExp;

        for (int j = 1; j <= nbPrototypesMax; j++) {
            if (j == 1)
                nbExp = 1;
            else
                nbExp = tmp;
            System.out.println("nbPrototypes=" + j);
            for (int n = 0; n < nbExp; n++) {
                System.out.println("This is the " + n + " time.");
                DTWKNNClassifierKMeans classifierKMeans = new DTWKNNClassifierKMeans();
                classifierKMeans.setNbPrototypesPerClass(j);
                classifierKMeans.setFillPrototypes(true);

                startTime = System.currentTimeMillis();
                classifierKMeans.buildClassifier(train);
                endTime = System.currentTimeMillis();
                duration = endTime - startTime;
                //               Duration traintime = Duration.ofMillis(duration);
                //               System.out.println(traintime);

                int[] classDistrib = PrototyperUtil
                        .getPrototypesPerClassDistribution(classifierKMeans.prototypes, train);

                Evaluation eval = new Evaluation(train);
                eval.evaluateModel(classifierKMeans, test);

                double testError = eval.errorRate();
                System.out.println("TestError:" + testError + "\n");

                //               PrototyperUtil.savePrototypes(classifierKMeans.prototypes, rep + "/" + dataName + "_KMEANS[" + j + "]_XP" + n + ".proto");

                //               out.format("%s,%s,%d,%.4f\n", dataName, algo, (j * train.numClasses()), testError);
                //               out.flush();
            }

        }
        //         outProto.close();
    } catch (Exception e) {
        e.printStackTrace();
    }
}

From source file:classif.ExperimentsLauncher.java

License:Open Source License

public void launchKMeansEUC() {
    try {// w ww  .j a  v a 2  s.  c  om
        out = new PrintStream(new FileOutputStream(rep + "/KEUC_All_results.csv", true));
        //         out.println("dataset;algorithm;nbPrototypes;execTime;trainErrorRate;testErrorRate;prototypesPerClassDistribution");
        String algo = "KMEANSEUC";
        System.out.println(algo);

        //         PrintStream outProto = new PrintStream(new FileOutputStream(rep + "/" + dataName + "_KMEANS.proto", append));

        nbPrototypesMax = this.train.numInstances() / this.train.numClasses();
        //         if(nbPrototypesMax>20)
        nbPrototypesMax = 10;
        //         if (nbPrototypesMax > 100)
        //            nbPrototypesMax = 100;
        int tmp;
        tmp = nbExp;

        for (int j = 1; j <= nbPrototypesMax; j++) {
            if (j == 1)
                nbExp = 1;
            else
                nbExp = tmp;
            System.out.println("nbPrototypes=" + j);
            for (int n = 0; n < nbExp; n++) {
                EUCKNNClassifierKMeans classifierKMeansEUC = new EUCKNNClassifierKMeans();
                classifierKMeansEUC.setNbPrototypesPerClass(j);
                classifierKMeansEUC.setFillPrototypes(true);

                startTime = System.currentTimeMillis();
                classifierKMeansEUC.buildClassifier(train);
                endTime = System.currentTimeMillis();
                duration = endTime - startTime;

                //               int[] classDistrib = PrototyperUtil.getPrototypesPerClassDistribution(classifierKMeansEUC.prototypes, train);

                Evaluation eval = new Evaluation(train);
                eval.evaluateModel(classifierKMeansEUC, test);
                System.out.println(eval.toSummaryString());
                //               Evaluation evaltrain = new Evaluation(train);
                //               evaltrain.evaluateModel(classifierKMeansEUC, train);

                double testError = eval.errorRate();
                //               double trainError = evaltrain.errorRate();
                //               System.out.println("TrainError:"+trainError+"\n");
                System.out.println("TestError:" + testError + "\n");

                //               PrototyperUtil.savePrototypes(classifierKMeansEUC.prototypes, rep + "/" + dataName + "_KMEANSEUC[" + j + "]_XP" + n + ".proto");

                out.format("%s,%s,%d,%.4f\n", dataName, algo, (j * train.numClasses()), testError);
                out.flush();
            }

        }
        //         outProto.close();
    } catch (Exception e) {
        e.printStackTrace();
    }
}

From source file:classif.ExperimentsLauncher.java

License:Open Source License

public void launchKMeansProbabilistic() {
    try {/*  w  w w. j a  v  a  2  s.com*/
        out = new PrintStream(new FileOutputStream(rep + "/All_results.csv", true));
        //         out.println("dataset;algorithm;nbPrototypes;trainErrorRate_Now;testErrorRate_Now;trainErrorRate_Before;testErrorRate_Before");
        String algo = "KMEANS";

        nbPrototypesMax = this.train.numInstances() / this.train.numClasses();
        //         if(nbPrototypesMax>2)
        //         nbPrototypesMax=2;
        int tmp;
        tmp = nbExp;

        for (int j = 1; j <= nbPrototypesMax; j++) {
            if (j == 1)
                nbExp = 1;
            else
                nbExp = tmp;
            System.out.println("nbPrototypes=" + j);
            for (int n = 0; n < nbExp; n++) {
                System.out.println("This is the " + n + " time.");
                DTWProbabilisticClassifierKMeans classifierKMeansPro = new DTWProbabilisticClassifierKMeans();
                classifierKMeansPro.setNClustersPerClass(j);

                startTime = System.currentTimeMillis();
                classifierKMeansPro.buildClassifier(train);
                endTime = System.currentTimeMillis();
                duration = endTime - startTime;

                int[] classDistrib = PrototyperUtil
                        .getPrototypesPerClassDistribution(classifierKMeansPro.getPrototypes(), train);

                Evaluation eval = new Evaluation(train);
                eval.evaluateModel(classifierKMeansPro, test);
                double testError = eval.errorRate();
                double trainError = Double.NaN;

                System.out.println(testError + "\n");
                PrototyperUtil.savePrototypes(classifierKMeansPro.getPrototypes(),
                        rep + "/" + dataName + "_KMEANSPro[" + j + "]_XP" + n + ".proto");

                out.format("%s;%s;%d;%.4f\n", dataName, algo, (j * train.numClasses()), testError);
                out.flush();
            }
        }
    } catch (Exception e) {
        e.printStackTrace();
    }
}

From source file:classif.ExperimentsLauncher.java

License:Open Source License

public void launchKMeansProbabilisticEUC() {
    try {//  ww w.j a v a2s  .  c o  m
        //         out = new PrintStream(new FileOutputStream(rep + "/KPEUC_All_results.csv", true));
        //         out.println("dataset;algorithm;nbPrototypes;trainErrorRate_Now;testErrorRate_Now;trainErrorRate_Before;testErrorRate_Before");
        String algo = "KMEANSProbabilisticEUC";
        System.out.println(algo);
        nbPrototypesMax = this.train.numInstances() / this.train.numClasses();
        //         if(nbPrototypesMax>20)
        nbPrototypesMax = 1;
        //         if (nbPrototypesMax > 100)
        //            nbPrototypesMax = 100;
        int tmp;
        tmp = nbExp;

        for (int j = 1; j <= nbPrototypesMax; j++) {
            if (j == 1)
                nbExp = 1;
            else
                nbExp = tmp;
            System.out.println("nbPrototypes=" + j);
            for (int n = 0; n < nbExp; n++) {
                System.out.println("This is the " + n + " time.");
                EUCProbabilisticClassifierKMeans classifierKMeansProEUC = new EUCProbabilisticClassifierKMeans();
                classifierKMeansProEUC.setNClustersPerClass(j);

                startTime = System.currentTimeMillis();
                classifierKMeansProEUC.buildClassifier(train);
                endTime = System.currentTimeMillis();
                duration = endTime - startTime;

                //               int[] classDistrib = PrototyperUtil.getPrototypesPerClassDistribution(classifierKMeansProEUC.getPrototypes(), train);

                Evaluation eval = new Evaluation(train);
                eval.evaluateModel(classifierKMeansProEUC, test);
                Evaluation evaltrain = new Evaluation(train);
                evaltrain.evaluateModel(classifierKMeansProEUC, train);

                double testError = eval.errorRate();
                double trainError = evaltrain.errorRate();

                System.out.println("TrainError:" + trainError + "\n");
                System.out.println("TestError:" + testError + "\n");
                //               PrototyperUtil.savePrototypes(classifierKMeansProEUC.getPrototypes(), rep + "/" + dataName + "_KMEANSProEUC[" + j + "]_XP" + n + ".proto");

                //               out.format("%s;%s;%d;%.4f;%.4f\n", dataName, algo, (j * train.numClasses()), trainError,testError);
                //               out.flush();
            }
        }
    } catch (Exception e) {
        e.printStackTrace();
    }
}

From source file:classif.ExperimentsLauncher.java

License:Open Source License

public void launchNewKMeans() {
    try {/*from  w  w  w .  j  av  a 2  s. c  om*/
        //         out = new PrintStream(new FileOutputStream(rep + "/GMMDTW_"+dataName+"_results.csv", true));
        //         out.println("dataset;algorithm;nbPrototypes;testErrorRate");
        String algo = "GMM";
        System.out.println(algo);

        //         nbPrototypesMax = this.train.numInstances() / this.train.numClasses();
        //         if(nbPrototypesMax>10)
        nbPrototypesMax = 10;
        int tmp;
        tmp = nbExp;

        for (int j = 1; j <= nbPrototypesMax; j++) {
            if (j == 1)
                nbExp = 1;
            else
                nbExp = tmp;
            System.out.println("nbPrototypes=" + j);
            for (int n = 0; n < nbExp; n++) {
                System.out.println("This is the " + n + " time.");
                DTWKNNClassifierNK classifierGmm = new DTWKNNClassifierNK();
                classifierGmm.setNClustersPerClass(j);

                startTime = System.currentTimeMillis();
                classifierGmm.buildClassifier(train);
                endTime = System.currentTimeMillis();
                duration = endTime - startTime;

                //               int[] classDistrib = PrototyperUtil.getPrototypesPerClassDistribution(classifierGmm.getPrototypes(), train);

                Evaluation eval = new Evaluation(train);
                eval.evaluateModel(classifierGmm, test);
                double testError = eval.errorRate();
                double trainError = Double.NaN;

                System.out.println("TestError:" + testError + "\n");
                //               PrototyperUtil.savePrototypes(classifierGmm.getPrototypes(), rep + "/" + dataName + "_GMM[" + j + "]_XP" + n + ".proto");

                //               out.format("%s,%s,%d,%.4f\n", dataName, algo, (j * train.numClasses()), testError);
                //               out.flush();
            }
        }
    } catch (Exception e) {
        e.printStackTrace();
    }
}