Example usage for weka.classifiers Evaluation toMatrixString

List of usage examples for weka.classifiers Evaluation toMatrixString

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Introduction

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

Prototype

public String toMatrixString() throws Exception

Source Link

Document

Calls toMatrixString() with a default title.

Usage

From source file:ffnn.FFNN.java

/**
 * @param args the command line arguments
 *//*www  .  ja  v  a  2s . c  o  m*/
public static void main(String[] args) throws Exception {
    FFNNTubesAI cls;
    Scanner scan = new Scanner(System.in);
    System.out.print("new / read? (n/r)");
    if (scan.next().equals("n")) {
        cls = new FFNNTubesAI();
    } else {
        cls = (FFNNTubesAI) TucilWeka.readModel();
    }
    int temp;
    Instances data = TucilWeka.readDataSet("C:\\Program Files\\Weka-3-8\\data\\Team.arff");
    //Tampilkan opsi
    for (int i = 0; i < data.numAttributes(); i++) {
        System.out.println(i + ". " + data.attribute(i));
    }
    System.out.print("Class Index : ");
    temp = scan.nextInt();
    data.setClassIndex(temp);
    data = preprocess(data);
    System.out.println(data);

    System.out.print("full train? (y/n)");
    if (scan.next().equals("y")) {
        try {
            cls.buildClassifier(data);
        } catch (Exception ex) {
            Logger.getLogger(FFNNTubesAI.class.getName()).log(Level.SEVERE, null, ex);
        }
    }
    int fold = 10;

    //FFNNTubesAI.printMatrix(cls.weight1, cls.input_layer+1, cls.hidden_layer);
    //FFNNTubesAI.printMatrix(cls.weight2, cls.hidden_layer, cls.output_layer);
    //FFNNTubesAI.printMatrix(cls.bias2, 1, cls.output_layer);
    Evaluation eval = new Evaluation(data);
    System.out.print("eval/10-fold? (e/f)");
    if (scan.next().equals("e")) {
        eval.evaluateModel(cls, data);
    } else {
        eval.crossValidateModel(cls, data, fold, new Random(1));
    }
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toMatrixString());
    System.out.println(eval.toClassDetailsString());
}

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

/**
 *
 * @param classifier//from   www  .ja  va  2s . co 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/* www  .jav  a 2 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:ia02classificacao.IA02Classificacao.java

/**
 * @param args the command line arguments
 *///from www. j  ava 2 s  . c o  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:machinelearningproject.MachineLearningProject.java

/**
 * @param args the command line arguments
 *///from   w  ww.j a  v  a2 s  .  com
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  ww. j a v  a  2s.c o  m
    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 {/* w w w.  ja v  a2  s. c  om*/
            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();
        }
    }

}

From source file:ml.ann.MainPTR.java

public static void main(String[] args) throws FileNotFoundException, IOException, Exception {
    boolean randomWeight;
    double weightawal = 0.0;
    double learningRate = 0.0001;
    double threshold = 0.00;
    double momentum = 0.00;
    int maxEpoch = 100000;
    int nCrossValidate = 2;

    m_nominalToBinaryFilter = new NominalToBinary();
    m_normalize = new Normalize();

    Scanner in = new Scanner(System.in);
    System.out.println("Lokasi file: ");

    String filepath = in.nextLine();
    filepath = "test-arffs/iris.arff";
    System.out.println("--- Algoritma ---");
    System.out.println("1. Perceptron Training Rule");
    System.out.println("2. Delta Rule Incremental");
    System.out.println("3. Delta Rule Batch");
    System.out.println("Pilihan Algoritma (1/2/3) : ");
    int choice = in.nextInt();
    String temp = in.nextLine();//from   w w  w  . j av a  2 s  .  c  o m

    System.out.println("Apakah Anda ingin memasukkan nilai weight awal? (YES/NO)");
    String isRandom = in.nextLine();
    System.out.println("Apakah Anda ingin memasukkan konfigurasi? (YES/NO)");
    String config = in.nextLine();

    if (config.equalsIgnoreCase("yes")) {
        System.out.print("Masukkan nilai learning rate: ");
        learningRate = in.nextDouble();
        System.out.print("Masukkan nilai threshold: ");
        threshold = in.nextDouble();
        System.out.print("Masukkan nilai momentum: ");
        momentum = in.nextDouble();
        System.out.print("Masukkan jumlah epoch: ");
        threshold = in.nextInt();
        System.out.print("Masukkan jumlah folds untuk crossvalidate: ");
        nCrossValidate = in.nextInt();
    }

    randomWeight = isRandom.equalsIgnoreCase("yes");

    if (randomWeight) {
        System.out.print("Masukkan nilai weight awal: ");
        weightawal = Double.valueOf(in.nextLine());
    }

    //print config
    if (isRandom.equalsIgnoreCase("yes")) {
        System.out.print("isRandom | ");
    } else {
        System.out.print("Weight " + weightawal + " | ");
    }

    System.out.print("L.rate " + learningRate + " | ");
    System.out.print("Max Epoch " + maxEpoch + " | ");
    System.out.print("Threshold " + threshold + " | ");
    System.out.print("Momentum " + momentum + " | ");
    System.out.print("Folds " + nCrossValidate + " | ");
    System.out.println();

    FileReader trainreader = new FileReader(filepath);
    Instances train = new Instances(trainreader);
    train.setClassIndex(train.numAttributes() - 1);

    m_nominalToBinaryFilter.setInputFormat(train);
    train = new Instances(Filter.useFilter(train, m_nominalToBinaryFilter));

    m_normalize.setInputFormat(train);
    train = new Instances(Filter.useFilter(train, m_normalize));

    MultiClassPTR tempMulti = new MultiClassPTR(choice, randomWeight, learningRate, maxEpoch, threshold);
    tempMulti.buildClassifier(train);

    Evaluation eval = new Evaluation(new Instances(train));
    eval.evaluateModel(tempMulti, train);
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());
}

From source file:model.clasification.klasifikacijaIstanca.java

public static void main(String[] args) throws Exception {

    // load data//from  w  w  w. j a  v a2  s  .  c  o  m
    DataSource loader = new DataSource(fileName);
    Instances data = loader.getDataSet();
    data.setClassIndex(data.numAttributes() - 1);

    // Create the Naive Bayes Classifier
    NaiveBayes bayesClsf = new NaiveBayes();
    bayesClsf.buildClassifier(data);

    // output generated model
    //      System.out.println(bayesClsf);

    // Test the model with the original set
    Evaluation eval = new Evaluation(data);
    eval.evaluateModel(bayesClsf, data);

    // Print the result as in Weka explorer
    String strSummary = eval.toSummaryString();

    //      System.out.println("=== Evaluation on training set ===");
    //      System.out.println("=== Summary ===");
    //      System.out.println(strSummary);

    // Get the confusion matrix
    System.out.println(eval.toMatrixString());

}

From source file:myclassifier.naiveBayes.java

public void TestData(Instances dataTest) throws Exception {
    if (data != null) {
        Instances train = data;//w  ww .j  a va  2  s  .c om
        // train classifier
        NBClassifier.buildClassifier(train);
        // evaluate classifier and print some statistics
        Evaluation eval = new Evaluation(dataTest);
        System.out.println(eval.toSummaryString("\nResults\n======\n", false));
        System.out.println(eval.toClassDetailsString("\n=== Detailed Accuracy By Class ===\n"));
        System.out.println(eval.toMatrixString());
    } else {
        System.out.println("Data is null");
    }
}