Example usage for weka.classifiers Evaluation toSummaryString

List of usage examples for weka.classifiers Evaluation toSummaryString

Introduction

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

Prototype

@Override
public String toSummaryString() 

Source Link

Document

Calls toSummaryString() with no title and no complexity stats.

Usage

From source file:function.CrossValidation.java

public static void crossValidation(Instances data, AbstractClassifier cls) throws Exception {
    Evaluation evaluation = new Evaluation(data);
    evaluation.crossValidateModel(cls, data, 10, new Random(1));
    System.out.println(evaluation.toSummaryString());
}

From source file:function.PercentageSplit.java

public static void percentageSplit(Instances data, Classifier cls) throws Exception {
    int trainSize = (int) Math.round(data.numInstances() * 0.8);
    int testSize = data.numInstances() - trainSize;
    Instances train = new Instances(data, 0, trainSize);
    Instances test = new Instances(data, trainSize, testSize);

    Evaluation eval = new Evaluation(train);
    eval.evaluateModel(cls, test);/*from   w w w.j a  v a2  s.c  o m*/
    System.out.println(eval.toSummaryString());
}

From source file:gr.uoc.nlp.opinion.analysis.suggestion.AnalyzeSuggestions.java

/**
 *
 * @param classifier//www.  ja v  a2s  .  c o m
 */
public void crossValidationTrainSet(Classifier classifier) {

    Evaluation eval;
    try {
        //initialize cross validation
        eval = new Evaluation(this.trainset);
        //validate
        eval.crossValidateModel(classifier, this.trainset, 10, new Random(1));

        System.out.println(eval.toSummaryString());
        System.out.println(eval.toClassDetailsString());
        System.out.println(eval.toMatrixString());

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

From source file:gr.uoc.nlp.opinion.analysis.suggestion.AnalyzeSuggestions.java

/**
 *
 * @param classifier/*from w w w. j a v  a2  s.  c om*/
 * @param testset
 */
public void valuateSet(Classifier classifier, Instances testset) {

    Evaluation eval;
    try {
        eval = new Evaluation(this.trainset);
        eval.evaluateModel(classifier, testset);

        System.out.println(eval.toSummaryString());
        System.out.println(eval.toClassDetailsString());
        System.out.println(eval.toMatrixString());
    } catch (Exception ex) {
        Logger.getLogger(AnalyzeSuggestions.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:GroupProject.DMChartUI.java

/**
* Action for the generate button//w ww  .  j  a va 2  s  . com
* It reads the user input from the table and the selected options and performs
* a classifiecation of the user input
* the user can choose linear regression, naive bayes classifier, or j48 trees to classify 
*
*/
private void generateButtonActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_generateButtonActionPerformed
    // TODO add your handling code here:                                              
    // TODO add your handling code here:
    //File file = new File("studentTemp.csv");
    CSVtoArff converter = new CSVtoArff();
    Instances students = null;
    Instances students2 = null;
    try {
        converter.convert("studentTemp.csv", "studentTemp.arff");
    } catch (IOException ex) {
        Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
    }

    try {
        students = new Instances(new BufferedReader(new FileReader("studentTemp.arff")));
        students2 = new Instances(new BufferedReader(new FileReader("studentTemp.arff")));
    } catch (IOException ex) {
        Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
    }

    //get column to predict values for 
    //int target=students.numAttributes()-1; 
    int target = dataSelector.getSelectedIndex() + 1;
    System.out.printf("this is the target: %d\n", target);
    //set target 
    students.setClassIndex(target);
    students2.setClassIndex(target);

    //case on which radio button is selected 
    //Linear Regressions
    if (LRB.isSelected()) {

        LinearRegression model = null;
        if (Lmodel != null) {
            model = Lmodel;
        } else {
            buildLinearModel();
            model = Lmodel;
        }

        System.out.println("im doing linear regression");

        equationDisplayArea.setText(model.toString());

        System.out.println("im going to get the instance");

        Instance prediction2 = getInstance(true);

        Remove remove = new Remove();
        int[] toremove = { 0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17 };
        remove.setAttributeIndicesArray(toremove);

        try {
            remove.setInputFormat(students);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        Instances instNew = null;
        try {
            instNew = Filter.useFilter(students, remove);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        prediction2.setDataset(instNew);
        System.err.print("i got the instance");
        double result = 0;
        try {
            result = model.classifyInstance(prediction2);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        System.out.printf("the result : %f \n ", result);
        predictValue.setText(Double.toString(result));
        System.out.println("I'm done with Linear Regression");
    }

    //Naive Bayes
    else if (NBB.isSelected()) {
        Classifier cModel = null;

        if (NBmodel != null) {
            cModel = NBmodel;
        } else {
            buildNBClassifier();
            cModel = NBmodel;
        }

        System.out.println("im doing NB");

        //build test 
        Evaluation eTest = null;
        try {
            eTest = new Evaluation(students);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }
        System.out.println("Using NB");

        try {
            eTest.evaluateModel(cModel, students);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        //display the test results to console 
        String strSummary = eTest.toSummaryString();
        System.out.println(strSummary);

        //build instance to predict 
        System.out.println("im going to get the instance");

        Instance prediction2 = getInstance(false);

        prediction2.setDataset(students);
        System.err.print("i got the instance");

        //replace with loop stating the class names 
        //fit text based on name of categories 
        double pred = 0;
        try {
            pred = cModel.classifyInstance(prediction2);
            prediction2.setClassValue(pred);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }
        //get the predicted value and set predictValue to it 
        predictValue.setText(prediction2.classAttribute().value((int) pred));

        System.out.println("I'm done with Naive Bayes");

        double[] fDistribution2 = null;
        try {
            fDistribution2 = cModel.distributionForInstance(prediction2);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        double max = 0;
        int maxindex = 0;
        max = fDistribution2[0];
        for (int i = 0; i < fDistribution2.length; i++) {
            if (fDistribution2[i] > max) {
                maxindex = i;
                max = fDistribution2[i];
            }
            System.out.println("the value at " + i + " : " + fDistribution2[i]);
            System.out.println("the label at " + i + prediction2.classAttribute().value(i));
        }
        prediction2.setClassValue(maxindex);
        predictValue.setText(prediction2.classAttribute().value(maxindex));

    }
    //J48 Tree
    else if (JB.isSelected()) {

        System.out.println("im doing j48 ");

        Classifier jModel = null;
        if (Jmodel != null) {
            jModel = Jmodel;
        } else {
            buildJClassifier();
            jModel = Jmodel;
        }
        //test model 
        Evaluation eTest2 = null;
        try {
            eTest2 = new Evaluation(students);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }
        System.out.println("Using J48 test");
        try {
            eTest2.evaluateModel(jModel, students);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }
        String strSummary2 = eTest2.toSummaryString();
        System.out.println(strSummary2);

        System.out.println("im going to get the instance");

        Instance prediction2 = getInstance(false);

        prediction2.setDataset(students);
        System.err.print("i got the instance\n");

        double pred = 0;
        try {
            pred = jModel.classifyInstance(prediction2);
            prediction2.setClassValue(pred);
            System.out.println("i did a prediction");
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        //get the predicted value and set predictValue to it 
        System.out.println("this was pred:" + pred);
        predictValue.setText(prediction2.classAttribute().value((int) pred));

        System.out.println("I'm done with J48");
        //replace with loop stating the class names 
        //fit text based on name of categories 

        double[] fDistribution2 = null;
        try {
            fDistribution2 = jModel.distributionForInstance(prediction2);
        } catch (Exception ex) {
            Logger.getLogger(DMChartUI.class.getName()).log(Level.SEVERE, null, ex);
        }

        double max = 0;
        int maxindex = 0;
        max = fDistribution2[0];
        for (int i = 0; i < fDistribution2.length; i++) {
            if (fDistribution2[i] > max) {
                maxindex = i;
                max = fDistribution2[i];
            }
            System.out.println("the value at " + i + " : " + fDistribution2[i]);
            System.out.println("the label at " + i + " " + prediction2.classAttribute().value(i));
        }
        prediction2.setClassValue(maxindex);
        predictValue.setText(prediction2.classAttribute().value(maxindex));

    }

}

From source file:ia02classificacao.IA02Classificacao.java

/**
 * @param args the command line arguments
 *//* www. j a v a 2  s  . co m*/
public static void main(String[] args) throws Exception {

    // abre o banco de dados arff e mostra a quantidade de instancias (linhas)
    DataSource arquivo = new DataSource("data/zoo.arff");
    Instances dados = arquivo.getDataSet();
    System.out.println("Instancias lidas: " + dados.numInstances());

    // FILTER: remove o atributo nome do animal da classificao
    String[] parametros = new String[] { "-R", "1" };
    Remove filtro = new Remove();
    filtro.setOptions(parametros);
    filtro.setInputFormat(dados);
    dados = Filter.useFilter(dados, filtro);

    AttributeSelection selAtributo = new AttributeSelection();
    InfoGainAttributeEval avaliador = new InfoGainAttributeEval();
    Ranker busca = new Ranker();
    selAtributo.setEvaluator(avaliador);
    selAtributo.setSearch(busca);
    selAtributo.SelectAttributes(dados);
    int[] indices = selAtributo.selectedAttributes();
    System.out.println("Selected attributes: " + Utils.arrayToString(indices));

    // Usa o algoritimo J48 e mostra a classificao dos dados em forma textual
    String[] opcoes = new String[1];
    opcoes[0] = "-U";
    J48 arvore = new J48();
    arvore.setOptions(opcoes);
    arvore.buildClassifier(dados);
    System.out.println(arvore);

    // Usa o algoritimo J48 e mostra a classificao de dados em forma grafica
    /*
    TreeVisualizer tv = new TreeVisualizer(null, arvore.graph(), new PlaceNode2());
    JFrame frame = new javax.swing.JFrame("?rvore de Conhecimento");
    frame.setSize(800,500);
    frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
    frame.getContentPane().add(tv);
    frame.setVisible(true);
    tv.fitToScreen();
    */

    /*
    * Classificao de novos dados
    */

    System.out.println("\n\nCLASSIFICAO DE NOVOS DADOS");
    // criar atributos
    double[] vals = new double[dados.numAttributes()];
    vals[0] = 1.0; // hair
    vals[1] = 0.0; // feathers
    vals[2] = 0.0; // eggs
    vals[3] = 1.0; // milk
    vals[4] = 1.0; // airborne
    vals[5] = 0.0; // aquatic
    vals[6] = 0.0; // predator
    vals[7] = 1.0; // toothed
    vals[8] = 1.0; // backbone
    vals[9] = 1.0; // breathes
    vals[10] = 0.0; // venomous
    vals[11] = 0.0; // fins
    vals[12] = 4.0; // legs
    vals[13] = 1.0; // tail
    vals[14] = 1.0; // domestic
    vals[15] = 1.0; // catsize

    // Criar uma instncia baseada nestes atributos
    Instance meuUnicornio = new DenseInstance(1.0, vals);

    // Adicionar a instncia nos dados
    meuUnicornio.setDataset(dados);

    // Classificar esta nova instncia
    double label = arvore.classifyInstance(meuUnicornio);

    // Imprimir o resultado da classificao
    System.out.println("Novo Animal: Unicrnio");
    System.out.println("classificacao: " + dados.classAttribute().value((int) label));

    /*
    * Avaliao e predio de erros de mtrica
    */
    System.out.println("\n\nAVALIAO E PREDIO DE ERROS DE MTRICA");
    Classifier cl = new J48();
    Evaluation eval_roc = new Evaluation(dados);
    eval_roc.crossValidateModel(cl, dados, 10, new Random(1), new Object[] {});
    System.out.println(eval_roc.toSummaryString());

    /*
    * Matriz de confuso
    */
    System.out.println("\n\nMATRIZ DE CONFUSO");
    double[][] confusionMatrix = eval_roc.confusionMatrix();
    System.out.println(eval_roc.toMatrixString());

}

From source file:irisdata.IrisData.java

/**
 * @param args the command line arguments
 * @throws java.lang.Exception // ww  w .  j av  a  2 s.c o  m
 */
public static void main(String[] args) throws Exception {

    String file = "/Users/paul/Desktop/BYU-Idaho/Spring2015/CS450/iris.csv";

    DataSource source = new DataSource(file);
    Instances data = source.getDataSet();

    if (data.classIndex() == -1) {
        data.setClassIndex(data.numAttributes() - 1);
    }

    data.randomize(new Random(1));

    // set training set to 70%
    RemovePercentage remove = new RemovePercentage();
    remove.setPercentage(30);
    remove.setInputFormat(data);
    Instances trainingSet = Filter.useFilter(data, remove);

    // set the rest for the testing set
    remove.setInvertSelection(true);
    Instances testSet = Filter.useFilter(data, remove);

    // train classifier - kind of
    HardCodedClassifier classifier = new HardCodedClassifier();
    classifier.buildClassifier(trainingSet); // this does nothing right now

    // Evaluate classifier
    Evaluation eval = new Evaluation(trainingSet);
    eval.evaluateModel(classifier, testSet);
    //eval.crossValidateModel(classifier, data, 10, new Random(1));

    // Print some statistics
    System.out.println("Results: " + eval.toSummaryString());

}

From source file:machinelearningproject.MachineLearningProject.java

/**
 * @param args the command line arguments
 *//* w  ww.java 2s  .  c o m*/
public static void main(String[] args) throws Exception {
    // TODO code application logic here
    DataSource source = new DataSource("D:\\spambase.arff");
    //        DataSource source = new DataSource("D:\\weather-nominal.arff");
    Instances instances = source.getDataSet();
    int numAttr = instances.numAttributes();
    instances.setClassIndex(instances.numAttributes() - 1);

    int runs = 5;
    int seed = 15;
    for (int i = 0; i < runs; i++) {
        //randomize data
        seed = seed + 1; // the seed for randomizing the data
        Random rand = new Random(seed); // create seeded number generator
        Instances randData = new Instances(instances); // create copy of original data
        Collections.shuffle(randData);

        Evaluation evalDTree = new Evaluation(randData);
        Evaluation evalRF = new Evaluation(randData);
        Evaluation evalSVM = new Evaluation(randData);

        int folds = 10;
        for (int n = 0; n < folds; n++) {
            Instances train = randData.trainCV(folds, n, rand);
            Instances test = randData.testCV(folds, n);
            //instantiate classifiers
            DecisionTree dtree = new DecisionTree();
            RandomForest rf = new RandomForest(100);
            SMO svm = new SMO();
            RBFKernel rbfKernel = new RBFKernel();
            double gamma = 0.70;
            rbfKernel.setGamma(gamma);

            dtree.buildClassifier(train);
            rf.buildClassifier(train);
            svm.buildClassifier(train);

            evalDTree.evaluateModel(dtree, test);
            evalRF.evaluateModel(rf, test);
            evalSVM.evaluateModel(svm, test);
        }
        System.out.println("=== Decision Tree Evaluation ===");
        System.out.println(evalDTree.toSummaryString());
        System.out.println(evalDTree.toClassDetailsString());
        System.out.println(evalDTree.toMatrixString());

        System.out.println("=== Random Forest Evaluation ===");
        System.out.println(evalRF.toSummaryString());
        System.out.println(evalRF.toClassDetailsString());
        System.out.println(evalRF.toMatrixString());

        System.out.println("=== SVM Evaluation ===");
        System.out.println(evalSVM.toSummaryString());
        System.out.println(evalSVM.toClassDetailsString());
        System.out.println(evalSVM.toMatrixString());
    }
}

From source file:miRdup.WekaModule.java

License:Open Source License

public static void trainModel(File arff, String keyword) {
    dec.setMaximumFractionDigits(3);/*from   w  w  w.  j av a 2 s.com*/
    System.out.println("\nTraining model on file " + arff);
    try {
        // load data
        DataSource source = new DataSource(arff.toString());
        Instances data = source.getDataSet();
        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        PrintWriter pwout = new PrintWriter(new FileWriter(keyword + Main.modelExtension + "Output"));
        PrintWriter pwroc = new PrintWriter(new FileWriter(keyword + Main.modelExtension + "roc.arff"));

        //remove ID row
        Remove rm = new Remove();
        rm.setAttributeIndices("1");
        FilteredClassifier fc = new FilteredClassifier();
        fc.setFilter(rm);

        //            // train model svm
        //            weka.classifiers.functions.LibSVM model = new weka.classifiers.functions.LibSVM();
        //            model.setOptions(weka.core.Utils.splitOptions("-S 0 -K 2 -D 3 -G 0.0 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.0010 -P 0.1 -B"));
        // train model MultilayerPerceptron
        //            weka.classifiers.functions.MultilayerPerceptron model = new weka.classifiers.functions.MultilayerPerceptron();
        //            model.setOptions(weka.core.Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a"));
        // train model Adaboost on RIPPER
        //            weka.classifiers.meta.AdaBoostM1 model = new weka.classifiers.meta.AdaBoostM1();
        //            model.setOptions(weka.core.Utils.splitOptions("weka.classifiers.meta.AdaBoostM1 -P 100 -S 1 -I 10 -W weka.classifiers.rules.JRip -- -F 10 -N 2.0 -O 5 -S 1"));
        // train model Adaboost on FURIA
        //            weka.classifiers.meta.AdaBoostM1 model = new weka.classifiers.meta.AdaBoostM1();
        //            model.setOptions(weka.core.Utils.splitOptions("weka.classifiers.meta.AdaBoostM1 -P 100 -S 1 -I 10 -W weka.classifiers.rules.FURIA -- -F 10 -N 2.0 -O 5 -S 1 -p 0 -s 0"));
        //train model Adaboot on J48 trees
        //             weka.classifiers.meta.AdaBoostM1 model = new weka.classifiers.meta.AdaBoostM1();
        //             model.setOptions(
        //                     weka.core.Utils.splitOptions(
        //                     "-P 100 -S 1 -I 10 -W weka.classifiers.trees.J48 -- -C 0.25 -M 2"));
        //train model Adaboot on Random Forest trees
        weka.classifiers.meta.AdaBoostM1 model = new weka.classifiers.meta.AdaBoostM1();
        model.setOptions(weka.core.Utils
                .splitOptions("-P 100 -S 1 -I 10 -W weka.classifiers.trees.RandomForest -- -I 50 -K 0 -S 1"));

        if (Main.debug) {
            System.out.print("Model options: " + model.getClass().getName().trim() + " ");
        }
        System.out.print(model.getClass() + " ");
        for (String s : model.getOptions()) {
            System.out.print(s + " ");
        }

        pwout.print("Model options: " + model.getClass().getName().trim() + " ");
        for (String s : model.getOptions()) {
            pwout.print(s + " ");
        }

        //build model
        //            model.buildClassifier(data);
        fc.setClassifier(model);
        fc.buildClassifier(data);

        // cross validation 10 times on the model
        Evaluation eval = new Evaluation(data);
        //eval.crossValidateModel(model, data, 10, new Random(1));
        StringBuffer sb = new StringBuffer();
        eval.crossValidateModel(fc, data, 10, new Random(1), sb, new Range("first,last"), false);

        //System.out.println(sb);
        pwout.println(sb);
        pwout.flush();

        // output
        pwout.println("\n" + eval.toSummaryString());
        System.out.println(eval.toSummaryString());

        pwout.println(eval.toClassDetailsString());
        System.out.println(eval.toClassDetailsString());

        //calculate importants values
        String ev[] = eval.toClassDetailsString().split("\n");

        String ptmp[] = ev[3].trim().split(" ");
        String ntmp[] = ev[4].trim().split(" ");
        String avgtmp[] = ev[5].trim().split(" ");

        ArrayList<String> p = new ArrayList<String>();
        ArrayList<String> n = new ArrayList<String>();
        ArrayList<String> avg = new ArrayList<String>();

        for (String s : ptmp) {
            if (!s.trim().isEmpty()) {
                p.add(s);
            }
        }
        for (String s : ntmp) {
            if (!s.trim().isEmpty()) {
                n.add(s);
            }
        }
        for (String s : avgtmp) {
            if (!s.trim().isEmpty()) {
                avg.add(s);
            }
        }

        double tp = Double.parseDouble(p.get(0));
        double fp = Double.parseDouble(p.get(1));
        double tn = Double.parseDouble(n.get(0));
        double fn = Double.parseDouble(n.get(1));
        double auc = Double.parseDouble(avg.get(7));

        pwout.println("\nTP=" + tp + "\nFP=" + fp + "\nTN=" + tn + "\nFN=" + fn);
        System.out.println("\nTP=" + tp + "\nFP=" + fp + "\nTN=" + tn + "\nFN=" + fn);

        //specificity, sensitivity, Mathew's correlation, Prediction accuracy
        double sp = ((tn) / (tn + fp));
        double se = ((tp) / (tp + fn));
        double acc = ((tp + tn) / (tp + tn + fp + fn));
        double mcc = ((tp * tn) - (fp * fn)) / Math.sqrt((tp + fp) * (tn + fn) * (tp + fn) * tn + fp);

        String output = "\nse=" + dec.format(se).replace(",", ".") + "\nsp=" + dec.format(sp).replace(",", ".")
                + "\nACC=" + dec.format(acc).replace(",", ".") + "\nMCC=" + dec.format(mcc).replace(",", ".")
                + "\nAUC=" + dec.format(auc).replace(",", ".");

        pwout.println(output);
        System.out.println(output);

        pwout.println(eval.toMatrixString());
        System.out.println(eval.toMatrixString());

        pwout.flush();
        pwout.close();

        //Saving model
        System.out.println("Model saved: " + keyword + Main.modelExtension);
        weka.core.SerializationHelper.write(keyword + Main.modelExtension, fc.getClassifier() /*model*/);

        // get curve
        ThresholdCurve tc = new ThresholdCurve();
        int classIndex = 0;
        Instances result = tc.getCurve(eval.predictions(), classIndex);
        pwroc.print(result.toString());
        pwroc.flush();
        pwroc.close();

        // draw curve
        //rocCurve(eval);
    } catch (Exception e) {
        e.printStackTrace();
    }
}

From source file:ml.ann.MainDriver.java

public static void testModel() {
    System.out.println("## Pilih bahan testing");
    System.out.println("## 1. Uji dengan data dari masukan training");
    System.out.println("## 2. Uji dengan data data masukan baru");
    System.out.print("## > ");

    int choice = (new Scanner(System.in)).nextInt();
    if (choice == 1) {
        try {//from w  w w . ja  va2 s  .c o m
            Evaluation eval = new Evaluation(train);

            if (cv10) {
                eval.crossValidateModel(model, test, 10, new Random(1));
            } else {
                eval.evaluateModel(model, test);
            }

            System.out.println(eval.toSummaryString());
            System.out.println(eval.toClassDetailsString());
            System.out.println(eval.toMatrixString());

        } catch (Exception E) {
            E.printStackTrace();
        }
    } else if (choice == 2) {
        try {
            loadTestData();
            Evaluation eval = new Evaluation(train);
            if (cv10) {
                eval.crossValidateModel(model, test, 10, new Random(1));
            } else {
                eval.evaluateModel(model, test);
            }

            System.out.println(eval.toSummaryString());
            System.out.println(eval.toClassDetailsString());
            System.out.println(eval.toMatrixString());
        } catch (Exception E) {
            E.printStackTrace();
        }
    }

}