Example usage for weka.classifiers Evaluation Evaluation

List of usage examples for weka.classifiers Evaluation Evaluation

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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:com.Machine_learning.model.MyNaiveBayes.java

public MyNaiveBayes(Instances data) {
    dataInstances = data;// w  w  w.ja  v a2 s .  c o  m
    try {
        classifier = new NaiveBayes();
        classifier.buildClassifier(dataInstances);

        eval = new Evaluation(dataInstances);
    } catch (Exception ex) {
        Logger.getLogger(MyNaiveBayes.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:com.Machine_learning.model.MyNaiveBayes.java

public void applyMethod(String method) {
    try {//from   w  ww .  j  ava  2 s .co  m
        List<Instances> datasets = new ArrayList<>();
        if (method.equals("cross-validation")) {
            eval.crossValidateModel(classifier, dataInstances, 4, new Random(1));
            return;
        } else if (method.equals("test-set")) {
            Preprocessing preprocessTestSet = new Preprocessing(null);
            datasets = preprocessTestSet.getDataSets(
                    MyNaiveBayes.class.getResource("/data/categories-per-train.arff").getPath(),
                    MyNaiveBayes.class.getResource("/data/2017-articles-correct.arff").getPath());

        } else if (method.equals("percentage")) {
            Preprocessing preprocessTestSet = new Preprocessing(null);
            datasets = preprocessTestSet.getDataSets(
                    MyNaiveBayes.class.getResource("/data/categories-per-train.arff").getPath(),
                    MyNaiveBayes.class.getResource("/data/categories-per-test.arff").getPath());

        } else {
            return;
        }

        classifier = new NaiveBayes();
        classifier.buildClassifier(datasets.get(0));
        eval = new Evaluation(datasets.get(0));
        eval.evaluateModel(classifier, datasets.get(1));
    } catch (Exception ex) {
        Logger.getLogger(MyNaiveBayes.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:com.Machine_learning.model.MySupportVectorMachine.java

public MySupportVectorMachine(Instances data) {
    dataInstances = data;/* w  w w .j  a v  a2s  . c  o m*/
    try {
        classifier = new LibSVM();
        classifier.setOptions(splitOptions(options));
        classifier.buildClassifier(data);
        eval = new Evaluation(dataInstances);
    } catch (Exception ex) {
        System.out.println("THROWN " + ex.getMessage());
        Logger.getLogger(MySupportVectorMachine.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:com.Machine_learning.model.MySupportVectorMachine.java

public void applyMethod(String method) {
    try {//from   w  w w.ja va  2 s.co  m

        List<Instances> datasets = new ArrayList<>();
        if (method.equals("cross-validation")) {
            eval.crossValidateModel(classifier, dataInstances, 4, new Random(1));
            return;
        } else if (method.equals("test-set")) {
            Preprocessing preprocessTestSet = new Preprocessing(null);
            datasets = preprocessTestSet.getDataSets(
                    MyNaiveBayes.class.getResource("/data/categories-per-train.arff").getPath(),
                    MyNaiveBayes.class.getResource("/data/2017-articles-correct.arff").getPath());

        } else if (method.equals("percentage")) {
            Preprocessing preprocessTestSet = new Preprocessing(null);
            datasets = preprocessTestSet.getDataSets(
                    MyNaiveBayes.class.getResource("/data/categories-per-train.arff").getPath(),
                    MyNaiveBayes.class.getResource("/data/categories-per-test.arff").getPath());

        } else {
            return;
        }

        classifier = new LibSVM();
        classifier.setOptions(splitOptions(options));
        classifier.buildClassifier(datasets.get(0));
        eval = new Evaluation(datasets.get(0));
        eval.evaluateModel(classifier, datasets.get(1));
    } catch (Exception ex) {
        Logger.getLogger(MyNaiveBayes.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:com.mycompany.id3classifier.ID3Shell.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource("lensesData.csv");
    Instances dataSet = source.getDataSet();

    Discretize filter = new Discretize();
    filter.setInputFormat(dataSet);//from   w w w  .  j av  a 2  s .co m
    dataSet = Filter.useFilter(dataSet, filter);

    Standardize standardize = new Standardize();
    standardize.setInputFormat(dataSet);
    dataSet = Filter.useFilter(dataSet, standardize);

    dataSet.setClassIndex(dataSet.numAttributes() - 1);
    dataSet.randomize(new Random(9001)); //It's over 9000!!

    int folds = 10;
    //Perform crossvalidation
    Evaluation eval = new Evaluation(dataSet);
    for (int n = 0; n < folds; n++) {
        int trainingSize = (int) Math.round(dataSet.numInstances() * .7);
        int testSize = dataSet.numInstances() - trainingSize;

        Instances trainingData = dataSet.trainCV(folds, n);
        Instances testData = dataSet.testCV(folds, n);

        ID3Classifier classifier = new ID3Classifier();
        // Id3 classifier = new Id3();
        classifier.buildClassifier(trainingData);

        eval.evaluateModel(classifier, testData);
    }
    System.out.println(eval.toSummaryString("\nResults:\n", false));
}

From source file:com.mycompany.knnclassifier.kNNShell.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource("carData.csv");
    Instances dataSet = source.getDataSet();

    Standardize standardize = new Standardize();
    standardize.setInputFormat(dataSet);
    dataSet = Filter.useFilter(dataSet, standardize);

    dataSet.setClassIndex(dataSet.numAttributes() - 1);
    dataSet.randomize(new Random(9001)); //It's over 9000!!

    int trainingSize = (int) Math.round(dataSet.numInstances() * .7);
    int testSize = dataSet.numInstances() - trainingSize;

    Instances trainingData = new Instances(dataSet, 0, trainingSize);
    Instances testData = new Instances(dataSet, trainingSize, testSize);

    kNNClassifier classifier = new kNNClassifier(3);
    classifier.buildClassifier(trainingData);

    //Used to compare to Weka's built in KNN algorithm
    //Classifier classifier = new IBk(1);
    //classifier.buildClassifier(trainingData);

    Evaluation eval = new Evaluation(trainingData);
    eval.evaluateModel(classifier, testData);

    System.out.println(eval.toSummaryString("\nResults:\n", false));
}

From source file:com.mycompany.neuralnetwork.NeuralNetworkShell.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource("irisData.csv");
    Instances dataSet = source.getDataSet();

    Standardize standardize = new Standardize();
    standardize.setInputFormat(dataSet);
    dataSet = Filter.useFilter(dataSet, standardize);
    dataSet.setClassIndex(dataSet.numAttributes() - 1);
    dataSet.randomize(new Random(9001)); //It's over 9000!!

    int trainingSize = (int) Math.round(dataSet.numInstances() * .7);
    int testSize = dataSet.numInstances() - trainingSize;

    Instances trainingData = new Instances(dataSet, 0, trainingSize);
    Instances testData = new Instances(dataSet, trainingSize, testSize);

    //MultilayerPerceptron classifier = new MultilayerPerceptron();
    NeuralNetworkClassifier classifier = new NeuralNetworkClassifier(3, 20000, 0.1);
    classifier.buildClassifier(trainingData);

    Evaluation eval = new Evaluation(trainingData);
    eval.evaluateModel(classifier, testData);

    System.out.println(eval.toSummaryString("\nResults:\n", false));
}

From source file:com.reactivetechnologies.analytics.core.eval.AdaBoostM1WithBuiltClassifiers.java

License:Open Source License

@Override
protected void buildClassifierWithWeights(Instances data) throws Exception {

    Instances training;//from  w w  w. j av a2s.  c  o  m
    double epsilon, reweight;
    Evaluation evaluation;
    int numInstances = data.numInstances();

    // Initialize data
    m_Betas = new double[m_Classifiers.length];
    m_NumIterationsPerformed = 0;

    // Create a copy of the data so that when the weights are diddled
    // with it doesn't mess up the weights for anyone else
    training = new Instances(data, 0, numInstances);

    // Do boostrap iterations
    for (m_NumIterationsPerformed = 0; m_NumIterationsPerformed < m_Classifiers.length; m_NumIterationsPerformed++) {
        if (m_Debug) {
            System.err.println("Training classifier " + (m_NumIterationsPerformed + 1));
        }
        // Select instances to train the classifier on
        if (m_WeightThreshold < 100) {
            selectWeightQuantile(training, (double) m_WeightThreshold / 100);
        } else {
            new Instances(training, 0, numInstances);
        }

        /** Changed here: DO NOT Build the classifier! */
        /*if (m_Classifiers[m_NumIterationsPerformed] instanceof Randomizable)
          ((Randomizable) m_Classifiers[m_NumIterationsPerformed]).setSeed(randomInstance.nextInt());
                
        m_Classifiers[m_NumIterationsPerformed].buildClassifier(trainData);*/
        /** End change */

        // Evaluate the classifier
        evaluation = new Evaluation(data);
        evaluation.evaluateModel(m_Classifiers[m_NumIterationsPerformed], training);
        epsilon = evaluation.errorRate();

        // Stop if error too small or error too big and ignore this model
        if (Utils.grOrEq(epsilon, 0.5) || Utils.eq(epsilon, 0)) {
            if (m_NumIterationsPerformed == 0) {
                m_NumIterationsPerformed = 1; // If we're the first we have to to use it
            }
            break;
        }
        // Determine the weight to assign to this model
        m_Betas[m_NumIterationsPerformed] = Math.log((1 - epsilon) / epsilon);
        reweight = (1 - epsilon) / epsilon;
        if (m_Debug) {
            System.err.println("\terror rate = " + epsilon + "  beta = " + m_Betas[m_NumIterationsPerformed]);
        }

        // Update instance weights
        setWeights(training, reweight);
    }
}

From source file:com.reactivetechnologies.analytics.core.eval.AdaBoostM1WithBuiltClassifiers.java

License:Open Source License

@Override
protected void buildClassifierUsingResampling(Instances data) throws Exception {

    Instances trainData, training;/*from ww w .j a  v  a 2 s .c  o  m*/
    double epsilon, reweight, sumProbs;
    Evaluation evaluation;
    int numInstances = data.numInstances();
    int resamplingIterations = 0;

    // Initialize data
    m_Betas = new double[m_Classifiers.length];
    m_NumIterationsPerformed = 0;
    // Create a copy of the data so that when the weights are diddled
    // with it doesn't mess up the weights for anyone else
    training = new Instances(data, 0, numInstances);
    sumProbs = training.sumOfWeights();
    for (int i = 0; i < training.numInstances(); i++) {
        training.instance(i).setWeight(training.instance(i).weight() / sumProbs);
    }

    // Do boostrap iterations
    for (m_NumIterationsPerformed = 0; m_NumIterationsPerformed < m_Classifiers.length; m_NumIterationsPerformed++) {
        if (m_Debug) {
            System.err.println("Training classifier " + (m_NumIterationsPerformed + 1));
        }

        // Select instances to train the classifier on
        if (m_WeightThreshold < 100) {
            trainData = selectWeightQuantile(training, (double) m_WeightThreshold / 100);
        } else {
            trainData = new Instances(training);
        }

        // Resample
        resamplingIterations = 0;
        double[] weights = new double[trainData.numInstances()];
        for (int i = 0; i < weights.length; i++) {
            weights[i] = trainData.instance(i).weight();
        }
        do {

            /** Changed here: DO NOT build classifier*/
            // Build and evaluate classifier
            //m_Classifiers[m_NumIterationsPerformed].buildClassifier(sample);
            /** End change */

            evaluation = new Evaluation(data);
            evaluation.evaluateModel(m_Classifiers[m_NumIterationsPerformed], training);
            epsilon = evaluation.errorRate();
            resamplingIterations++;
        } while (Utils.eq(epsilon, 0) && (resamplingIterations < 10));

        // Stop if error too big or 0
        if (Utils.grOrEq(epsilon, 0.5) || Utils.eq(epsilon, 0)) {
            if (m_NumIterationsPerformed == 0) {
                m_NumIterationsPerformed = 1; // If we're the first we have to to use it
            }
            break;
        }

        // Determine the weight to assign to this model
        m_Betas[m_NumIterationsPerformed] = Math.log((1 - epsilon) / epsilon);
        reweight = (1 - epsilon) / epsilon;
        if (m_Debug) {
            System.err.println("\terror rate = " + epsilon + "  beta = " + m_Betas[m_NumIterationsPerformed]);
        }

        // Update instance weights
        setWeights(training, reweight);
    }
}

From source file:com.sliit.rules.RuleContainer.java

public Map<String, String> evaluateModel() {

    Map<String, String> evaluationSummary = new HashMap<String, String>();
    try {/*w  w  w .  ja  v a  2  s  .co  m*/

        instances.setClassIndex(instances.numAttributes() - 1);
        Evaluation evaluation = new Evaluation(instances);
        evaluation.evaluateModel(ruleMoldel, instances);
        ArrayList<Rule> rulesList = ruleMoldel.getRuleset();
        String rules = ruleMoldel.toString();
        evaluationSummary.put("rules", rules);
        evaluationSummary.put("summary", evaluation.toSummaryString());
        evaluationSummary.put("confusion_matrix", evaluation.toMatrixString());
    } catch (Exception e) {

        log.error("Error occurred:" + e.getLocalizedMessage());
    }
    return evaluationSummary;
}