Example usage for weka.classifiers Evaluation evaluateModel

List of usage examples for weka.classifiers Evaluation evaluateModel

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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:classif.ExperimentsLauncher.java

License:Open Source License

public void launchKMeansEUC() {
    try {/*from w w w  .j  a  v a2s  .co  m*/
        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 {/*from www  . j  ava2  s.c om*/
        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 {//from w w w .j  ava  2 s . 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 a2  s  . co  m
        //         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();
    }
}

From source file:classif.ExperimentsLauncher.java

License:Open Source License

public void launchGmm() {
    try {/*from w  ww  .j a v a2  s  .co  m*/
        //         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.");
                DTWKNNClassifierGmm classifierGmm = new DTWKNNClassifierGmm();
                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();
    }
}

From source file:classif.ExperimentsLauncher.java

License:Open Source License

public void launchGmmEUC() {
    try {/* w  w  w .j  a  v  a2s . co  m*/
        //         out = new PrintStream(new FileOutputStream(rep + "/GMMEUC_"+dataName+"_results.csv", true));
        //         out.println("dataset;algorithm;nbPrototypes;trainErrorRate_Now;testErrorRate_Now;trainErrorRate_Before;testErrorRate_Before");
        String algo = "GMMEUC";
        System.out.println(algo);
        //         nbPrototypesMax = this.train.numInstances() / this.train.numClasses();
        //         if (nbPrototypesMax > 10)
        nbPrototypesMax = 3;
        //         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.");
                EUCKNNClassifierGmm classifierGmmEUC = new EUCKNNClassifierGmm();
                classifierGmmEUC.setNClustersPerClass(j);

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

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

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

                double testError = eval.errorRate();
                //               double trainError = evaltrain.errorRate();
                //               System.out.println("TrainError:"+trainError+"\n");
                System.out.println("TestError:" + testError + "\n");
                //               PrototyperUtil.savePrototypes(classifierGmmEUC.getPrototypes(), rep + "/" + dataName + "_GMMEUC[" + 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 launchFCM() {
    try {//from www  .j  a  va 2  s . co  m
        out = new PrintStream(new FileOutputStream(rep + "/FCMDTW_" + dataName + "_results.csv", true));
        //         out.println("dataset,algorithm,nbPrototypes,testErrorRate");
        String algo = "FCM";
        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.");
                DTWKNNClassifierFCM classifierFCM = new DTWKNNClassifierFCM();
                classifierFCM.setNClustersPerClass(j);

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

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

                Evaluation eval = new Evaluation(train);
                eval.evaluateModel(classifierFCM, 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();
    }
}

From source file:classif.ExperimentsLauncher.java

License:Open Source License

/**
 * /*ww  w  .  j av  a2s  . c o  m*/
 */
public void launchKMedoids() {
    try {
        File f = new File(rep + "/" + dataName + "_results.csv");
        // if somebody is processing it
        if (f.exists()) {
            return;
        }

        out = new PrintStream(new FileOutputStream(rep + "/" + dataName + "_results.csv", append));
        out.println(
                "dataset;algorithm;nbPrototypes;execTime;trainErrorRate;testErrorRate;prototypesPerClassDistribution");
        String algo = "KMEDOIDS";

        nbPrototypesMax = this.train.numInstances() / this.train.numClasses();

        for (int j = 1; j <= nbPrototypesMax; j++) {
            System.out.println("nbPrototypes=" + j);
            for (int n = 0; n < nbExp; n++) {
                DTWKNNClassifierKMedoids classifier = new DTWKNNClassifierKMedoids();
                classifier.setNbPrototypesPerClass(j);
                classifier.setFillPrototypes(true);

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

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

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

                double testError = eval.errorRate();
                double trainError = classifier.predictAccuracyXVal(10);

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

                out.format("%s;%s;%d;%d;%.4f;%.4f;%s\n", dataName, algo, (j * train.numClasses()), duration,
                        trainError, testError, Arrays.toString(classDistrib));
                out.flush();
                // deterministic
                if (j == 1) {
                    break;
                }
            }

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

From source file:classif.ExperimentsLauncher.java

License:Open Source License

public void launchseq() {
    try {/*w ww.j  a v  a 2  s . c  o  m*/
        nbPrototypesMax = 10;
        int[] bestprototypes = new int[train.numClasses()];
        double lowerror = 1.0;
        for (int j = 1; j <= nbPrototypesMax; j++) {
            int[] nbPrototypesPerClass = new int[train.numClasses()];
            for (int i = 0; i < train.numClasses(); i++) {
                nbPrototypesPerClass[i] = j;
            }
            double errorBefore = 1;
            double errorNow = 1;
            int flag = 0;
            do {
                Unbalancecluster classifierseq = new Unbalancecluster();
                classifierseq.setNbPrototypesPerClass(nbPrototypesPerClass);
                System.out.println(Arrays.toString(nbPrototypesPerClass));
                //               classifierseq.buildClassifier(train);
                Evaluation evalcv = new Evaluation(train);
                Random rand = new Random(1);
                evalcv.crossValidateModel(classifierseq, train, 10, rand);
                //               errorNow = classifierseq.predictAccuracyXVal(10);
                errorNow = evalcv.errorRate();
                System.out.println("errorBefore " + errorBefore);
                System.out.println("errorNow " + errorNow);
                if (errorNow < errorBefore) {
                    nbPrototypesPerClass[flag]++;
                    errorBefore = errorNow;
                } else {
                    nbPrototypesPerClass[flag]--;
                    flag++;
                    if (flag >= nbPrototypesPerClass.length)
                        break;
                    nbPrototypesPerClass[flag]++;
                }
            } while (flag < nbPrototypesPerClass.length);
            //            System.out.println("\nbest nbPrototypesPerClass " + Arrays.toString(nbPrototypesPerClass));
            double testError = 0;
            for (int n = 0; n < nbExp; n++) {
                Unbalancecluster classifier = new Unbalancecluster();
                classifier.setNbPrototypesPerClass(nbPrototypesPerClass);
                classifier.buildClassifier(train);
                Evaluation evaltest = new Evaluation(train);
                evaltest.evaluateModel(classifier, test);
                testError += evaltest.errorRate();
            }
            double avgTestError = testError / nbExp;
            System.out.println(avgTestError);
            if (avgTestError < lowerror) {
                bestprototypes = nbPrototypesPerClass;
                lowerror = avgTestError;
            }
        }
        System.out.println("Best prototypes:" + Arrays.toString(bestprototypes) + "\n");
        System.out.println("Best errorRate:" + lowerror + "\n");
    } catch (Exception e) {
        e.printStackTrace();
    }
}

From source file:classif.ExperimentsLauncher.java

License:Open Source License

public void launchFSKMeans() {
    try {// w  w  w.j  ava2 s.  co m
        //         File f = new File(rep + "/" + dataName + "_results.csv");
        //         // if somebody is processing it
        //         if (f.exists()) {
        //            return;
        //         }
        //
        //         out = new PrintStream(new FileOutputStream(rep + "/FastKMeansDTW_" + dataName + "_results.csv", true));
        //         out.println("dataset,algorithm,nbPrototypes,testErrorRate,trainErrorRate");
        String algo = "FastKMEANS";
        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 = 50;
        int tmp;
        tmp = nbExp;
        double[] avgerror = new double[5];
        double[] avgf1 = new double[5];
        //         double[] trainrctmp = new double[5];
        //         double[] testrctmp = new double[5];
        //         double[] cvrctmp = new double[5];
        //         boolean stopflag=false;
        for (int j = 1; j <= nbPrototypesMax; j++) {
            //            double[] trainrc = new double[5];
            //            double[] testrc = new double[5];
            //            double[] cvrc = new double[5];
            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.");
                DTWKNNClassifierKMeansCached classifierKMeans = new DTWKNNClassifierKMeansCached();
                classifierKMeans.setNbPrototypesPerClass(j);
                classifierKMeans.setFillPrototypes(true);

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

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

                Evaluation evaltest = new Evaluation(train);
                evaltest.evaluateModel(classifierKMeans, test);
                avgerror[n] = evaltest.errorRate();
                avgf1[n] = evaltest.fMeasure(0);
                //               Evaluation evaltrain = new Evaluation(train);
                //               evaltrain.evaluateModel(classifierKMeans, train);

                /*DTWKNNClassifierKMeansCached KMeans = new DTWKNNClassifierKMeansCached();
                KMeans.setNbPrototypesPerClass(j);
                KMeans.setFillPrototypes(true);
                Evaluation evalcv = new Evaluation(train);
                Random rand = new Random(1);
                evalcv.crossValidateModel(KMeans, train, 10, rand);
                double CVError = evalcv.errorRate();
                System.out.println("CVError:"+CVError+"\n");*/

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

                //               out.format("%s,%s,%d,%.4f,%.4f,%.4f\n", dataName, algo, (j * train.numClasses()), testError,CVError,trainError);
                //               out.flush();
                //               trainrc[n]=trainError;
                //               testrc[n]=testError;
                //               cvrc[n]=CVError;
                //               if (n == 4) {
                //                  if (j == 1) {
                //                     trainrctmp = trainrc;
                //                     testrctmp = testrc;
                //                     cvrctmp = cvrc;
                //                  } else {
                //                     if (Arrays.equals(trainrc, trainrctmp) && Arrays.equals(testrc, testrctmp)
                //                           && Arrays.equals(cvrc, cvrctmp)) {
                //                        System.out.println("Stable at " + j);
                //                        stopflag=true;
                //                     } else {
                //                        trainrctmp = trainrc;
                //                        testrctmp = testrc;
                //                        cvrctmp = cvrc;
                //                     }
                //                  }
                //               }
            }
            System.out
                    .println("TestError:" + Utils.mean(avgerror) + "\tF-Measures:" + Utils.mean(avgf1) + "\n");
            //            if(stopflag==true)
            //               break;
        }
        //         outProto.close();
    } catch (Exception e) {
        e.printStackTrace();
    }
}