Example usage for weka.classifiers Evaluation Evaluation

List of usage examples for weka.classifiers Evaluation Evaluation

  1. HOME
  2. Java
  3. weka
  4. weka.classifiers.*
  5. Evaluation
  6. Evaluation

Introduction

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

Prototype

public Evaluation(Instances data) throws Exception

Source Link

Usage

From source file:myclassifier.naiveBayes.java

public void TestData(Instances dataTest) throws Exception {
    if (data != null) {
        Instances train = data;//from ww  w  .  j  a  v  a2 s. c  o  m
        // 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");
    }
}

From source file:myclassifier.naiveBayes.java

public void CrossValidation() throws Exception {
    if (data != null) {
        Instances train = data;/* ww  w  .  j ava 2s. c  o m*/
        // train classifier
        NBClassifier.buildClassifier(train);
        // evaluate classifier and print some statistics
        Evaluation eval = new Evaluation(train);
        eval.crossValidateModel(NBClassifier, train, 10, new Random(1));
        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");
    }
}

From source file:myclassifier.naiveBayes.java

public void PercentageSplit(double percent) throws Exception {
    // Percent split
    int trainSize = (int) Math.round(data.numInstances() * percent / 100);
    int testSize = data.numInstances() - trainSize;
    Instances train = new Instances(data, 0, trainSize);
    Instances test = new Instances(data, trainSize, testSize);
    // train classifier
    NBClassifier.buildClassifier(train);
    // evaluate classifier and print some statistics
    Evaluation eval = new Evaluation(train);
    eval.evaluateModel(NBClassifier, test);
    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());
}

From source file:myclassifier.Util.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  ava 2  s  .c  o  m*/
}

From source file:myclassifier.wekaCode.java

public static void testingTestSet(Instances dataSet, Classifier classifiers, Instances testSet)
        throws Exception {
    Evaluation evaluation = new Evaluation(dataSet);
    evaluation.evaluateModel(classifiers, testSet); //Evaluates the classifier on a given set of instances.
    System.out.println(evaluation.toSummaryString("\n Testing Model given Test Set ", false));
    System.out.println(evaluation.toClassDetailsString());

}

From source file:myclassifier.wekaCode.java

public static void foldValidation(Instances dataSet, Classifier classifiers) throws Exception {
    Evaluation evaluation = new Evaluation(dataSet);
    evaluation.crossValidateModel(classifiers, dataSet, 10, new Random(1)); //Evaluates the classifier on a given set of instances.
    System.out.println(evaluation.toSummaryString("\n 10-fold cross validation", false));
    System.out.println(evaluation.toMatrixString("\n Confusion Matrix"));

}

From source file:myid3andc45classifier.Main.java

/**
 * @param args the command line arguments
 *///from   w  w w.ja va 2 s  .  c  o m
public static void main(String[] args) throws Exception {
    // TODO code application logic here
    WekaAccessor accessor = new WekaAccessor();
    Instances trainset;
    trainset = accessor.readARFF(
            "D:\\Semester VII\\ML\\myID3andC45classifier\\myID3andC45classifier\\resources\\iris.arff");
    Classifier j48 = new J48();
    Classifier model = accessor.train(trainset, j48);
    //accessor.saveModel(model, "C:\\Users\\Julio Savigny\\Desktop\\myID3andC45classifier\\myID3andC45classifier\\some.model");
    //Classifier loadedModel = accessor.loadModel("C:\\Users\\Julio Savigny\\Desktop\\myID3andC45classifier\\myID3andC45classifier\\some.model");
    System.out.println(model);
    //System.out.println(loadedModel);

    // Coba ID3 Apoy
    Classifier customID3 = new MyC45();
    Classifier myId3Model = accessor.train(trainset, customID3);
    Instances resampledTrainset = accessor.resample(trainset);
    System.out.println("WOY");
    System.out.println(myId3Model);
    System.out.println(accessor.tenFoldCrossValidation(trainset, customID3).toSummaryString());
    Evaluation eval = new Evaluation(trainset);
    eval.evaluateModel(myId3Model, trainset);
    //System.out.println(eval.toSummaryString());

    //        System.out.println(trainset);
    //        System.out.println(resampledTrainset);

    // Coba C4.5 Bayu
    //        Classifier customC45 = new myC45();
    //        Classifier myC45Model = accessor.train(trainset, customC45);
    //        System.out.println(myC45Model);
}

From source file:NaiveBayes.NaiveBayes.java

/**
 * @param args the command line arguments
 * @throws java.io.IOException/*w  w w.  j  a va2  s. c om*/
 */
public static void main(String[] args) throws IOException, Exception {
    System.out.print("1. Buat Model \n");
    System.out.print("2. Load Model\n");
    System.out.print("Masukkan pilihan : ");
    Scanner sc = new Scanner(System.in);
    int pil = sc.nextInt();
    System.out.print("Masukkan nama file : ");
    String filename = sc.next();
    DataSource source = new DataSource(("D:\\Program Files\\Weka-3-8\\data\\" + filename));
    Instances train = source.getDataSet();
    for (int i = 0; i < train.numAttributes(); i++)
        System.out.println(i + ". " + train.attribute(i).name());
    System.out.print("Masukkan indeks kelas : ");
    int classIdx = sc.nextInt();
    train.setClassIndex(classIdx);
    //        MultilayerPerceptron mlp = new MultilayerPerceptron(train, 0.1, 10000, 14);
    //        mlp.buildClassifier(train);
    //        Evaluation eval = new Evaluation (train);
    ////        eval.evaluateModel(mlp, train);
    //        System.out.println(eval.toSummaryString());
    NaiveBayes tb = new NaiveBayes();
    Evaluation eval = new Evaluation(train);
    switch (pil) {
    case 1:
        //                tb.buildClassifier(train);
        //                tb.toSummaryString();
        //                eval.evaluateModel(tb, train);
        eval.crossValidateModel(tb, train, 10, new Random(1));
        System.out.println(eval.toSummaryString(true));
        System.out.println(eval.toMatrixString());
        //saveModel(tb);
        break;
    default:
        tb = loadModel();
        tb.toSummaryString();
        eval.crossValidateModel(tb, train, 10, new Random(1));
        System.out.println(eval.toSummaryString());
        //                System.out.println(eval.toMatrixString());
    }
}

From source file:naivebayes.NBRun.java

public static void main(String[] args) throws Exception {
    System.out.println("Naive Bayes Classifier");
    Instances data = TucilWeka.readDataSet("C:\\Program Files\\Weka-3-8\\data\\mush_test.arff");
    Scanner scan = new Scanner(System.in);
    Classifier cls;/*from   ww w  .j  a  v  a2  s  .  c  o  m*/
    Instances train = TucilWeka.readDataSet("C:\\Program Files\\Weka-3-8\\data\\mush.arff");

    System.out.println("Read or create model? r/c");
    if (scan.next().equals("c")) {
        cls = new NBTubesAI();
        cls.buildClassifier(train);
        TucilWeka.saveModel(train);
    } else {
        cls = (NBTubesAI) TucilWeka.readModel();
    }
    Evaluation eval = new Evaluation(data);
    System.out.println("10 fold cross validate or Full train? c/f");
    if (scan.next().equals("c")) {
        int fold = 10;
        for (int i = 0; i < data.numAttributes(); i++) {
            System.out.println(i + ". " + data.attribute(i));
        }
        eval.crossValidateModel(cls, data, fold, new Random(1));
    } else {
        for (int i = 0; i < data.numAttributes(); i++) {
            System.out.println(i + ". " + data.attribute(i));
        }
        data.deleteWithMissingClass();
        try {

            eval.evaluateModel(cls, data);
        } catch (java.lang.Exception ex) {
            eval.crossValidateModel(cls, data, 11, new Random(1));
        }
    }
    //        Classifier cls=new NBTubesAI();
    //        cls.buildClassifier(data);

    System.out.println("Hasil evaluasi: ");
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toMatrixString());
    System.out.println(eval.toClassDetailsString());
}

From source file:naivebayes.TucilWeka.java

public static Evaluation crossValidation(Instances data) {
    //10-fold cross validation
    Evaluation eval = null;/* w w w  .  j  a v a 2 s.  co  m*/
    try {

        eval = new Evaluation(data);
        Classifier cls = new NBTubesAI();
        if (cls == null) {
            System.out.println("MODEL CANNOT BE USED");
        } else {
            System.out.println("MODEL IS USED");
        }
        cls.buildClassifier(data);
        //crossValidateModel:
        //param 1 = tipe classifier (disini J48)
        //param 2 = Instances data
        //param 3 = jumlah fold
        //param 4 = Randomizer (seed)

        eval.crossValidateModel(cls, data, 10, new Random(1));
    } catch (Exception ex) {
        Logger.getLogger(TucilWeka.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}