Example usage for weka.classifiers.functions SGD updateClassifier

Introduction

In this page you can find the example usage for weka.classifiers.functions SGD updateClassifier.

Prototype

@Override
public void updateClassifier(Instance instance) throws Exception 

Source Link

Document

Updates the classifier with the given instance.

Usage

From source file:sentinets.Prediction.java

License:Open Source License

public String updateModel(String inputFile, ArrayList<Double[]> metrics) {
    String output = "";
    this.setInstances(inputFile);
    FilteredClassifier fcls = (FilteredClassifier) this.cls;
    SGD cls = (SGD) fcls.getClassifier();
    Filter filter = fcls.getFilter();
    Instances insAll;//from  ww w  .j a v  a 2s  .  c  om
    try {
        insAll = Filter.useFilter(this.unlabled, filter);
        if (insAll.size() > 0) {
            Random rand = new Random(10);
            int folds = 10 > insAll.size() ? 2 : 10;
            Instances randData = new Instances(insAll);
            randData.randomize(rand);
            if (randData.classAttribute().isNominal()) {
                randData.stratify(folds);
            }
            Evaluation eval = new Evaluation(randData);
            eval.evaluateModel(cls, insAll);
            System.out.println("Initial Evaluation");
            System.out.println(eval.toSummaryString());
            System.out.println(eval.toClassDetailsString());
            metrics.add(new Double[] { eval.fMeasure(0), eval.fMeasure(1), eval.weightedFMeasure() });
            output += "\n====" + "Initial Evaluation" + "====\n";
            output += "\n" + eval.toSummaryString();
            output += "\n" + eval.toClassDetailsString();
            System.out.println("Cross Validated Evaluation");
            output += "\n====" + "Cross Validated Evaluation" + "====\n";
            for (int n = 0; n < folds; n++) {
                Instances train = randData.trainCV(folds, n);
                Instances test = randData.testCV(folds, n);

                for (int i = 0; i < train.numInstances(); i++) {
                    cls.updateClassifier(train.instance(i));
                }

                eval.evaluateModel(cls, test);
                System.out.println("Cross Validated Evaluation fold: " + n);
                output += "\n====" + "Cross Validated Evaluation fold (" + n + ")====\n";
                System.out.println(eval.toSummaryString());
                System.out.println(eval.toClassDetailsString());
                output += "\n" + eval.toSummaryString();
                output += "\n" + eval.toClassDetailsString();
                metrics.add(new Double[] { eval.fMeasure(0), eval.fMeasure(1), eval.weightedFMeasure() });
            }
            for (int i = 0; i < insAll.numInstances(); i++) {
                cls.updateClassifier(insAll.instance(i));
            }
            eval.evaluateModel(cls, insAll);
            System.out.println("Final Evaluation");
            System.out.println(eval.toSummaryString());
            System.out.println(eval.toClassDetailsString());
            output += "\n====" + "Final Evaluation" + "====\n";
            output += "\n" + eval.toSummaryString();
            output += "\n" + eval.toClassDetailsString();
            metrics.add(new Double[] { eval.fMeasure(0), eval.fMeasure(1), eval.weightedFMeasure() });
            fcls.setClassifier(cls);
            String modelFilePath = outputDir + "/" + Utils.getOutDir(Utils.OutDirIndex.MODELS)
                    + "/updatedClassifier.model";
            weka.core.SerializationHelper.write(modelFilePath, fcls);
            output += "\n" + "Updated Model saved at: " + modelFilePath;
        } else {
            output += "No new instances for training the model.";
        }
    } catch (Exception e) {
        e.printStackTrace();
    }
    return output;
}

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