Example usage for weka.classifiers Evaluation fMeasure

List of usage examples for weka.classifiers Evaluation fMeasure

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Introduction

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

Prototype

public double fMeasure(int classIndex)

Source Link

Document

Calculate the F-Measure with respect to a particular class.

Usage

From source file:adams.flow.core.EvaluationHelper.java

License:Open Source License

/**
 * Returns a statistical value from the evaluation object.
 *
 * @param eval   the evaluation object to get the value from
 * @param statistic   the type of value to return
 * @param classIndex   the class label index, for statistics like AUC
 * @return      the determined value, Double.NaN if not found
 * @throws Exception   if evaluation fails
 *///from  w w w.j a v a 2s .c om
public static double getValue(Evaluation eval, EvaluationStatistic statistic, int classIndex) throws Exception {
    switch (statistic) {
    case NUMBER_CORRECT:
        return eval.correct();
    case NUMBER_INCORRECT:
        return eval.incorrect();
    case NUMBER_UNCLASSIFIED:
        return eval.unclassified();
    case PERCENT_CORRECT:
        return eval.pctCorrect();
    case PERCENT_INCORRECT:
        return eval.pctIncorrect();
    case PERCENT_UNCLASSIFIED:
        return eval.pctUnclassified();
    case KAPPA_STATISTIC:
        return eval.kappa();
    case MEAN_ABSOLUTE_ERROR:
        return eval.meanAbsoluteError();
    case ROOT_MEAN_SQUARED_ERROR:
        return eval.rootMeanSquaredError();
    case RELATIVE_ABSOLUTE_ERROR:
        return eval.relativeAbsoluteError();
    case ROOT_RELATIVE_SQUARED_ERROR:
        return eval.rootRelativeSquaredError();
    case CORRELATION_COEFFICIENT:
        return eval.correlationCoefficient();
    case SF_PRIOR_ENTROPY:
        return eval.SFPriorEntropy();
    case SF_SCHEME_ENTROPY:
        return eval.SFSchemeEntropy();
    case SF_ENTROPY_GAIN:
        return eval.SFEntropyGain();
    case SF_MEAN_PRIOR_ENTROPY:
        return eval.SFMeanPriorEntropy();
    case SF_MEAN_SCHEME_ENTROPY:
        return eval.SFMeanSchemeEntropy();
    case SF_MEAN_ENTROPY_GAIN:
        return eval.SFMeanEntropyGain();
    case KB_INFORMATION:
        return eval.KBInformation();
    case KB_MEAN_INFORMATION:
        return eval.KBMeanInformation();
    case KB_RELATIVE_INFORMATION:
        return eval.KBRelativeInformation();
    case TRUE_POSITIVE_RATE:
        return eval.truePositiveRate(classIndex);
    case NUM_TRUE_POSITIVES:
        return eval.numTruePositives(classIndex);
    case FALSE_POSITIVE_RATE:
        return eval.falsePositiveRate(classIndex);
    case NUM_FALSE_POSITIVES:
        return eval.numFalsePositives(classIndex);
    case TRUE_NEGATIVE_RATE:
        return eval.trueNegativeRate(classIndex);
    case NUM_TRUE_NEGATIVES:
        return eval.numTrueNegatives(classIndex);
    case FALSE_NEGATIVE_RATE:
        return eval.falseNegativeRate(classIndex);
    case NUM_FALSE_NEGATIVES:
        return eval.numFalseNegatives(classIndex);
    case IR_PRECISION:
        return eval.precision(classIndex);
    case IR_RECALL:
        return eval.recall(classIndex);
    case F_MEASURE:
        return eval.fMeasure(classIndex);
    case MATTHEWS_CORRELATION_COEFFICIENT:
        return eval.matthewsCorrelationCoefficient(classIndex);
    case AREA_UNDER_ROC:
        return eval.areaUnderROC(classIndex);
    case AREA_UNDER_PRC:
        return eval.areaUnderPRC(classIndex);
    case WEIGHTED_TRUE_POSITIVE_RATE:
        return eval.weightedTruePositiveRate();
    case WEIGHTED_FALSE_POSITIVE_RATE:
        return eval.weightedFalsePositiveRate();
    case WEIGHTED_TRUE_NEGATIVE_RATE:
        return eval.weightedTrueNegativeRate();
    case WEIGHTED_FALSE_NEGATIVE_RATE:
        return eval.weightedFalseNegativeRate();
    case WEIGHTED_IR_PRECISION:
        return eval.weightedPrecision();
    case WEIGHTED_IR_RECALL:
        return eval.weightedRecall();
    case WEIGHTED_F_MEASURE:
        return eval.weightedFMeasure();
    case WEIGHTED_MATTHEWS_CORRELATION_COEFFICIENT:
        return eval.weightedMatthewsCorrelation();
    case WEIGHTED_AREA_UNDER_ROC:
        return eval.weightedAreaUnderROC();
    case WEIGHTED_AREA_UNDER_PRC:
        return eval.weightedAreaUnderPRC();
    case UNWEIGHTED_MACRO_F_MEASURE:
        return eval.unweightedMacroFmeasure();
    case UNWEIGHTED_MICRO_F_MEASURE:
        return eval.unweightedMicroFmeasure();
    case BIAS:
        return eval.getPluginMetric(Bias.class.getName()).getStatistic(Bias.NAME);
    case RSQUARED:
        return eval.getPluginMetric(RSquared.class.getName()).getStatistic(RSquared.NAME);
    case SDR:
        return eval.getPluginMetric(SDR.class.getName()).getStatistic(SDR.NAME);
    case RPD:
        return eval.getPluginMetric(RPD.class.getName()).getStatistic(RPD.NAME);
    default:
        throw new IllegalArgumentException("Unhandled statistic field: " + statistic);
    }
}

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
 * //  w w  w  . j a va 2s .  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;
}

From source file:classif.ExperimentsLauncher.java

License:Open Source License

public void launchFSKMeans() {
    try {//from   ww  w  .  j  ava 2 s  .  com
        //         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();
    }
}

From source file:classif.ExperimentsLauncher.java

License:Open Source License

public void launchPU(double ratio) {
    try {//from w  ww  . j a v a 2 s  . c  o m
        //         out = new PrintStream(new FileOutputStream(rep + "/DT_" + dataName + "_results.csv", true));
        String algo = "PU";
        System.out.println(algo);
        double testError = 0.0;
        startTime = System.currentTimeMillis();
        //         POSC45 pu = new POSC45();
        DTWD pu = new DTWD();
        pu.setRatio(ratio);
        pu.buildClassifier(train);
        endTime = System.currentTimeMillis();
        duration = endTime - startTime;
        //         Duration traintime = Duration.ofMillis(duration);
        //         System.out.println(traintime);
        startTime = System.currentTimeMillis();
        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(pu, test);
        //         StringBuffer forPredictionsPrinting = new StringBuffer();
        //         eval.evaluateModel(pu, train, forPredictionsPrinting, null, false);
        //         System.out.println(eval.toClassDetailsString());
        //         System.out.println(eval.toSummaryString("\nResults\n======\n", false));
        //         System.out.println(eval.toMatrixString());
        //         System.out.println(forPredictionsPrinting);
        System.out.println(eval.fMeasure(0));
        testError = eval.errorRate();
        endTime = System.currentTimeMillis();
        duration = endTime - startTime;
        //         Duration testtime = Duration.ofMillis(duration);
        //         System.out.println(testtime);
        System.out.println("TestError:" + testError + "\n");
        //         System.out.println(eval.toSummaryString());
        //         out.format("%s,%.4f\n", dataName,  testError);
        //         out.flush();
    } catch (Exception e) {
        e.printStackTrace();
    }
}

From source file:classif.ExperimentsLauncher.java

License:Open Source License

public void launchPUKMeans() {
    try {/*from w  w w .j  a v a 2s. c o  m*/
        //         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 = "PUKMEANS";
        System.out.println(algo);
        //         PrintStream outProto = new PrintStream(new FileOutputStream(rep + "/" + dataName + "_KMEANS.proto", append));

        nbPrototypesMax = this.train.numInstances() / this.train.numClasses();
        if (nbPrototypesMax > 100)
            nbPrototypesMax = 50;
        int tmp;
        tmp = nbExp;
        double[] avgerror = new double[5];
        double[] avgf1 = new double[5];

        for (int j = 1; j <= nbPrototypesMax; j += 1) {
            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.");
                DTWPUKMeans classifierKMeans = new DTWPUKMeans();
                classifierKMeans.setNbClustersinUNL(j);
                startTime = System.currentTimeMillis();
                classifierKMeans.buildClassifier(train);
                endTime = System.currentTimeMillis();
                duration = endTime - startTime;
                //               Duration traintime = Duration.ofMillis(duration);
                //               System.out.println(traintime);
                Evaluation eval = new Evaluation(train);
                eval.evaluateModel(classifierKMeans, test);
                avgerror[n] = eval.errorRate();
                avgf1[n] = eval.fMeasure(0);

                //               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();
            }
            System.out
                    .println("TestError:" + Utils.mean(avgerror) + "\tF-Measures:" + Utils.mean(avgf1) + "\n");
        }
        //         outProto.close();
    } catch (Exception e) {
        e.printStackTrace();
    }
}

From source file:de.ugoe.cs.cpdp.dataselection.DecisionTreeSelection.java

License:Apache License

@Override
public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
    final Instances data = characteristicInstances(testdata, traindataSet);

    final ArrayList<String> attVals = new ArrayList<String>();
    attVals.add("same");
    attVals.add("more");
    attVals.add("less");
    final ArrayList<Attribute> atts = new ArrayList<Attribute>();
    for (int j = 0; j < data.numAttributes(); j++) {
        atts.add(new Attribute(data.attribute(j).name(), attVals));
    }//from  w  ww .  j a  v a2  s.  c o m
    atts.add(new Attribute("score"));
    Instances similarityData = new Instances("similarity", atts, 0);
    similarityData.setClassIndex(similarityData.numAttributes() - 1);

    try {
        Classifier classifier = new J48();
        for (int i = 0; i < traindataSet.size(); i++) {
            classifier.buildClassifier(traindataSet.get(i));
            for (int j = 0; j < traindataSet.size(); j++) {
                if (i != j) {
                    double[] similarity = new double[data.numAttributes() + 1];
                    for (int k = 0; k < data.numAttributes(); k++) {
                        if (0.9 * data.get(i + 1).value(k) > data.get(j + 1).value(k)) {
                            similarity[k] = 2.0;
                        } else if (1.1 * data.get(i + 1).value(k) < data.get(j + 1).value(k)) {
                            similarity[k] = 1.0;
                        } else {
                            similarity[k] = 0.0;
                        }
                    }

                    Evaluation eval = new Evaluation(traindataSet.get(j));
                    eval.evaluateModel(classifier, traindataSet.get(j));
                    similarity[data.numAttributes()] = eval.fMeasure(1);
                    similarityData.add(new DenseInstance(1.0, similarity));
                }
            }
        }
        REPTree repTree = new REPTree();
        if (repTree.getNumFolds() > similarityData.size()) {
            repTree.setNumFolds(similarityData.size());
        }
        repTree.setNumFolds(2);
        repTree.buildClassifier(similarityData);

        Instances testTrainSimilarity = new Instances(similarityData);
        testTrainSimilarity.clear();
        for (int i = 0; i < traindataSet.size(); i++) {
            double[] similarity = new double[data.numAttributes() + 1];
            for (int k = 0; k < data.numAttributes(); k++) {
                if (0.9 * data.get(0).value(k) > data.get(i + 1).value(k)) {
                    similarity[k] = 2.0;
                } else if (1.1 * data.get(0).value(k) < data.get(i + 1).value(k)) {
                    similarity[k] = 1.0;
                } else {
                    similarity[k] = 0.0;
                }
            }
            testTrainSimilarity.add(new DenseInstance(1.0, similarity));
        }

        int bestScoringProductIndex = -1;
        double maxScore = Double.MIN_VALUE;
        for (int i = 0; i < traindataSet.size(); i++) {
            double score = repTree.classifyInstance(testTrainSimilarity.get(i));
            if (score > maxScore) {
                maxScore = score;
                bestScoringProductIndex = i;
            }
        }
        Instances bestScoringProduct = traindataSet.get(bestScoringProductIndex);
        traindataSet.clear();
        traindataSet.add(bestScoringProduct);
    } catch (Exception e) {
        Console.printerr("failure during DecisionTreeSelection: " + e.getMessage());
        throw new RuntimeException(e);
    }
}

From source file:entity.NfoldCrossValidationManager.java

License:Open Source License

/**
 * n fold cross validation without noise
 * /*from ww w.  ja v a2  s  .  c o  m*/
 * @param classifier
 * @param dataset
 * @param folds
 * @return
 */
public Stats crossValidate(Classifier classifier, Instances dataset, int folds) {

    // randomizes order of instances
    Instances randDataset = new Instances(dataset);
    randDataset.randomize(RandomizationManager.randomGenerator);

    // cross-validation
    Evaluation eval = null;
    try {
        eval = new Evaluation(randDataset);
    } catch (Exception e) {
        e.printStackTrace();
    }
    for (int n = 0; n < folds; n++) {
        Instances test = randDataset.testCV(folds, n);
        Instances train = randDataset.trainCV(folds, n, RandomizationManager.randomGenerator);

        // build and evaluate classifier
        Classifier clsCopy;
        try {
            clsCopy = Classifier.makeCopy(classifier);
            clsCopy.buildClassifier(train);
            eval.evaluateModel(clsCopy, test);
        } catch (Exception e) {
            e.printStackTrace();
        }

    }

    // output evaluation for the nfold cross validation
    Double precision = eval.precision(Settings.classificationChoice);
    Double recall = eval.recall(Settings.classificationChoice);
    Double fmeasure = eval.fMeasure(Settings.classificationChoice);
    Double classificationTP = eval.numTruePositives(Settings.classificationChoice);
    Double classificationTN = eval.numTrueNegatives(Settings.classificationChoice);
    Double classificationFP = eval.numFalsePositives(Settings.classificationChoice);
    Double classificationFN = eval.numFalseNegatives(Settings.classificationChoice);
    Double kappa = eval.kappa();

    return new Stats(classificationTP, classificationTN, classificationFP, classificationFN, kappa, precision,
            recall, fmeasure);
}

From source file:entity.NfoldCrossValidationManager.java

License:Open Source License

/**
 * n fold cross validation with noise (independent fp and fn)
 * /* w ww  .ja va  2 s  .  c o  m*/
 * @param classifier
 * @param dataset
 * @param folds
 * @return
 */
public Stats crossValidateWithNoise(Classifier classifier, Instances dataset, int folds,
        BigDecimal fpPercentage, BigDecimal fnPercentage) {

    // noise manager
    NoiseInjectionManager noiseInjectionManager = new NoiseInjectionManager();

    // randomizes order of instances
    Instances randDataset = new Instances(dataset);
    randDataset.randomize(RandomizationManager.randomGenerator);

    // cross-validation
    Evaluation eval = null;
    try {
        eval = new Evaluation(randDataset);
    } catch (Exception e) {
        e.printStackTrace();
    }
    for (int n = 0; n < folds; n++) {
        Instances test = randDataset.testCV(folds, n);
        Instances train = randDataset.trainCV(folds, n, RandomizationManager.randomGenerator);

        // copies instances of train set to not modify the original
        Instances noisyTrain = new Instances(train);
        // injects level of noise in the copied train set
        noiseInjectionManager.addNoiseToDataset(noisyTrain, fpPercentage, fnPercentage);

        // build and evaluate classifier
        Classifier clsCopy;
        try {
            clsCopy = Classifier.makeCopy(classifier);
            // trains the model using a noisy train set
            clsCopy.buildClassifier(noisyTrain);
            eval.evaluateModel(clsCopy, test);
        } catch (Exception e) {
            e.printStackTrace();
        }

    }

    // output evaluation for the nfold cross validation
    Double precision = eval.precision(Settings.classificationChoice);
    Double recall = eval.recall(Settings.classificationChoice);
    Double fmeasure = eval.fMeasure(Settings.classificationChoice);
    Double classificationTP = eval.numTruePositives(Settings.classificationChoice);
    Double classificationTN = eval.numTrueNegatives(Settings.classificationChoice);
    Double classificationFP = eval.numFalsePositives(Settings.classificationChoice);
    Double classificationFN = eval.numFalseNegatives(Settings.classificationChoice);
    Double kappa = eval.kappa();

    return new Stats(classificationTP, classificationTN, classificationFP, classificationFN, kappa, precision,
            recall, fmeasure);
}

From source file:entity.NfoldCrossValidationManager.java

License:Open Source License

/**
 * n fold cross validation with noise (combined fp and fn)
 * //from  w w  w.  j  a  v  a  2  s  .com
 * @param classifier
 * @param dataset
 * @param folds
 * @return
 */

public Stats crossValidateWithNoise(Classifier classifier, Instances dataset, int folds,
        BigDecimal combinedFpFnPercentage) {

    // noise manager
    NoiseInjectionManager noiseInjectionManager = new NoiseInjectionManager();

    // randomizes order of instances
    Instances randDataset = new Instances(dataset);
    randDataset.randomize(RandomizationManager.randomGenerator);

    // cross-validation
    Evaluation eval = null;
    try {
        eval = new Evaluation(randDataset);
    } catch (Exception e) {
        e.printStackTrace();
    }
    for (int n = 0; n < folds; n++) {
        Instances test = randDataset.testCV(folds, n);
        Instances train = randDataset.trainCV(folds, n, RandomizationManager.randomGenerator);

        // copies instances of train set to not modify the original
        Instances noisyTrain = new Instances(train);
        // injects level of noise in the copied train set
        noiseInjectionManager.addNoiseToDataset(noisyTrain, combinedFpFnPercentage);

        // build and evaluate classifier
        Classifier clsCopy;
        try {
            clsCopy = Classifier.makeCopy(classifier);
            // trains the model using a noisy train set
            clsCopy.buildClassifier(noisyTrain);
            eval.evaluateModel(clsCopy, test);
        } catch (Exception e) {
            e.printStackTrace();
        }

    }

    // output evaluation for the nfold cross validation
    Double precision = eval.precision(Settings.classificationChoice);
    Double recall = eval.recall(Settings.classificationChoice);
    Double fmeasure = eval.fMeasure(Settings.classificationChoice);
    Double classificationTP = eval.numTruePositives(Settings.classificationChoice);
    Double classificationTN = eval.numTrueNegatives(Settings.classificationChoice);
    Double classificationFP = eval.numFalsePositives(Settings.classificationChoice);
    Double classificationFN = eval.numFalseNegatives(Settings.classificationChoice);
    Double kappa = eval.kappa();

    return new Stats(classificationTP, classificationTN, classificationFP, classificationFN, kappa, precision,
            recall, fmeasure);
}

From source file:hero.unstable.util.classification.wekaClassifier.java

/** Result as:
 * [correctClassified, TPR(class True), TPR(class False), avgTPR, PPV(class True), PPV(class False), avgPPV,  Fvalue(class True), Fvalue(class False), avgFvalue]   
 * @param result/*w  w  w .  j a v  a 2s .  c  o  m*/
 * @return 10 metrics
 */
public double[] getMetrics(Evaluation result) {
    double[] metrics = new double[10];

    metrics[0] = result.pctCorrect() / 100;

    metrics[1] = result.precision(0);
    metrics[2] = result.precision(1);
    metrics[3] = result.weightedPrecision();

    metrics[4] = result.recall(0);
    metrics[5] = result.recall(1);
    metrics[6] = result.weightedRecall();

    metrics[7] = result.fMeasure(0);
    metrics[8] = result.fMeasure(1);
    metrics[9] = result.weightedFMeasure();

    return metrics;
}