news.classifier.WekaLearner.java Source code

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Here is the source code for news.classifier.WekaLearner.java

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package news.classifier;

import java.io.Serializable;
import java.util.List;
import java.util.Random;

import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.evaluation.Prediction;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.SerializationHelper;
import weka.core.converters.ConverterUtils;

/**
 *
 * @author Winson
 */
public class WekaLearner implements Serializable {
    /**
    * 
    */
    private static final long serialVersionUID = 2183403483098061244L;

    public static final String[] CLASSIFIER_AVAILABLE = { "J48", "NaiveBayes", "IBk", "MultilayerPerceptron" };
    private Classifier wClassifier;
    private Instances wTrainingSet;
    private int wClassIndex;
    private Evaluation wEvaluation;

    public void setClassifier(String name, String[] options) throws Exception {
        wClassifier = AbstractClassifier.forName(name, options);
    }

    public void setClassifier(Classifier classifier) {
        wClassifier = classifier;
    }

    public void setTrainingData(String fileLocation) throws Exception {
        wTrainingSet = ConverterUtils.DataSource.read(fileLocation);
        wClassIndex = wTrainingSet.numAttributes() - 1;
        wTrainingSet.setClassIndex(wClassIndex);
        wEvaluation = new Evaluation(wTrainingSet);
    }

    public void setTrainingData(Instances trainingData) {
        wTrainingSet = trainingData;
    }

    public void setClassIndex(int index) {
        wClassIndex = index;
        wTrainingSet.setClassIndex(wClassIndex);
    }

    public void buildClassifier() throws Exception {
        wClassifier.buildClassifier(wTrainingSet);
    }

    public double classifyInstance(double[] instance) throws Exception {
        wClassifier.buildClassifier(wTrainingSet);
        Instances ins = new Instances(wTrainingSet, 0);
        Instance row = new DenseInstance(1.0, instance);
        ins.add(row);
        return wClassifier.classifyInstance(ins.lastInstance());
    }

    public String fullTrainingEvaluation() throws Exception {
        wClassifier.buildClassifier(wTrainingSet);

        wEvaluation = new Evaluation(wTrainingSet);
        wEvaluation.evaluateModel(wClassifier, wTrainingSet);

        return wClassifier.toString()
                + wEvaluation.toSummaryString("\nHasil evaluasi dengan full-trainning:\n", false);
    }

    public List<Prediction> fullTrainingEvaluation(Instances testData) throws Exception {
        wClassifier.buildClassifier(wTrainingSet);

        wEvaluation = new Evaluation(wTrainingSet);
        wEvaluation.evaluateModel(wClassifier, testData);

        return wEvaluation.predictions();
    }

    public String crossValidationEvaluation(int fold) throws Exception {
        wEvaluation = new Evaluation(wTrainingSet);
        wEvaluation.crossValidateModel(wClassifier, wTrainingSet, fold, new Random(1));

        return wClassifier.toString() + wEvaluation.toSummaryString(
                "\nHasil evaluasi dengan cross-validation " + Integer.toString(fold) + "-fold:\n", false);
    }

    public void loadModel(String fileLocation) throws Exception {
        wClassifier = (Classifier) SerializationHelper.read(fileLocation);
    }

    public void saveModel(String fileLocation) throws Exception {
        SerializationHelper.write(fileLocation, wClassifier);
    }

    public final Instances getInstances() {
        return wTrainingSet;
    }

}