Example usage for weka.filters.supervised.instance StratifiedRemoveFolds setInvertSelection

Introduction

In this page you can find the example usage for weka.filters.supervised.instance StratifiedRemoveFolds setInvertSelection.

Prototype

public void setInvertSelection(boolean inverse) 

Source Link

Document

Sets if selection is to be inverted.

Usage

From source file:com.sliit.neuralnetwork.RecurrentNN.java

public String trainModel(String modelName, String filePath, int outputs, int inputsTot) throws NeuralException {
    System.out.println("calling trainModel");
    try {/*from   ww  w  . j a va  2s  .c o  m*/

        System.out.println("Neural Network Training start");
        loadSaveNN(modelName, false);
        if (model == null) {

            buildModel();
        }

        File fileGeneral = new File(filePath);
        CSVLoader loader = new CSVLoader();
        loader.setSource(fileGeneral);
        Instances instances = loader.getDataSet();
        instances.setClassIndex(instances.numAttributes() - 1);
        StratifiedRemoveFolds stratified = new StratifiedRemoveFolds();
        String[] options = new String[6];
        options[0] = "-N";
        options[1] = Integer.toString(5);
        options[2] = "-F";
        options[3] = Integer.toString(1);
        options[4] = "-S";
        options[5] = Integer.toString(1);
        stratified.setOptions(options);
        stratified.setInputFormat(instances);
        stratified.setInvertSelection(false);
        Instances testInstances = Filter.useFilter(instances, stratified);
        stratified.setInvertSelection(true);
        Instances trainInstances = Filter.useFilter(instances, stratified);
        String directory = fileGeneral.getParent();
        CSVSaver saver = new CSVSaver();
        File trainFile = new File(directory + "/" + "normtrainadded.csv");
        File testFile = new File(directory + "/" + "normtestadded.csv");
        if (trainFile.exists()) {

            trainFile.delete();
        }
        trainFile.createNewFile();
        if (testFile.exists()) {

            testFile.delete();
        }
        testFile.createNewFile();
        saver.setFile(trainFile);
        saver.setInstances(trainInstances);
        saver.writeBatch();
        saver = new CSVSaver();
        saver.setFile(testFile);
        saver.setInstances(testInstances);
        saver.writeBatch();
        SequenceRecordReader recordReader = new CSVSequenceRecordReader(0, ",");
        recordReader.initialize(new org.datavec.api.split.FileSplit(trainFile));
        SequenceRecordReader testReader = new CSVSequenceRecordReader(0, ",");
        testReader.initialize(new org.datavec.api.split.FileSplit(testFile));
        DataSetIterator iterator = new org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator(
                recordReader, 2, outputs, inputsTot, false);
        DataSetIterator testIterator = new org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator(
                testReader, 2, outputs, inputsTot, false);
        roc = new ArrayList<Map<String, Double>>();
        String statMsg = "";
        Evaluation evaluation;

        for (int i = 0; i < 100; i++) {
            if (i % 2 == 0) {

                model.fit(iterator);
                evaluation = model.evaluate(testIterator);
            } else {

                model.fit(testIterator);
                evaluation = model.evaluate(iterator);
            }
            Map<String, Double> map = new HashMap<String, Double>();
            Map<Integer, Integer> falsePositives = evaluation.falsePositives();
            Map<Integer, Integer> trueNegatives = evaluation.trueNegatives();
            Map<Integer, Integer> truePositives = evaluation.truePositives();
            Map<Integer, Integer> falseNegatives = evaluation.falseNegatives();
            double fpr = falsePositives.get(1) / (falsePositives.get(1) + trueNegatives.get(1));
            double tpr = truePositives.get(1) / (truePositives.get(1) + falseNegatives.get(1));
            map.put("FPR", fpr);
            map.put("TPR", tpr);
            roc.add(map);
            statMsg = evaluation.stats();
            iterator.reset();
            testIterator.reset();
        }
        loadSaveNN(modelName, true);
        System.out.println("ROC " + roc);

        return statMsg;

    } catch (Exception e) {
        e.printStackTrace();
        System.out.println("Error ocuured while building neural netowrk :" + e.getMessage());
        throw new NeuralException(e.getLocalizedMessage(), e);
    }
}

---