Example usage for weka.classifiers.meta FilteredClassifier getFilter

Introduction

In this page you can find the example usage for weka.classifiers.meta FilteredClassifier getFilter.

Prototype

public Filter getFilter() 

Source Link

Document

Gets the filter used.

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 a2s . c o  m
    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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