lu.lippmann.cdb.ext.hydviga.gaps.GapFillerClassifier.java Source code

Introduction

Here is the source code for lu.lippmann.cdb.ext.hydviga.gaps.GapFillerClassifier.java

Source

/**
 * Copyright 2014-2016 LIST (Luxembourg Institute of Science and Technology), all right reserved.
 * Authorship : Olivier PARISOT, Yoanne DIDRY
 * Licensed under GNU General Public License version 3
 */
package lu.lippmann.cdb.ext.hydviga.gaps;

import lu.lippmann.cdb.weka.*;
import weka.classifiers.Classifier;
import weka.core.*;

/**
 * TimeSeriesGapFillerClassifier.
 * 
 * @author Olivier PARISOT
 *
 */
public class GapFillerClassifier extends GapFiller {
    //
    // Instance fields
    //

    /** */
    private final Classifier classifier;

    //
    // Constructors
    //   

    /**
     * Constructor.    
     */
    GapFillerClassifier(final boolean wdt, final Classifier classifier) {
        super(wdt);
        this.classifier = classifier;
    }

    //
    // Instance methods
    //

    /**
     * {@inheritDoc}
     */
    @Override
    Instances fillGaps0(final Instances ds) throws Exception {
        final Instances newds = WekaDataProcessingUtil.buildDataSetWithoutConstantAttributes(ds);

        final int attrWithMissingIdx = WekaDataStatsUtil.getFirstAttributeWithMissingValue(newds);
        if (attrWithMissingIdx == -1)
            throw new IllegalStateException();

        final Instances trainingSet = new Instances(newds, 0);
        for (int i = 0; i < newds.numInstances(); i++) {
            if (!newds.instance(i).hasMissingValue())
                trainingSet.add(newds.instance(i));
        }
        //System.out.println(trainingSet);      
        trainingSet.setClassIndex(attrWithMissingIdx);

        //System.out.println("Training (size="+trainingSet.numInstances()+") ...");      
        this.classifier.buildClassifier(trainingSet);
        //System.out.println("... trained!");

        newds.setClassIndex(attrWithMissingIdx);
        for (int i = 0; i < newds.numInstances(); i++) {
            if (newds.instance(i).isMissing(attrWithMissingIdx)) {
                final Instance newrecord = new DenseInstance(newds.instance(i));
                newrecord.setDataset(newds);
                final double newval = this.classifier.classifyInstance(newrecord);
                newds.instance(i).setValue(attrWithMissingIdx, newval);
            }
        }

        //System.out.println("initial -> "+ds.toSummaryString());
        //System.out.println("corrected -> "+newds.toSummaryString());

        this.model = this.classifier.toString();

        return newds;
    }

    /**
     * {@inheritDoc}
     */
    @Override
    public boolean hasExplicitModel() {
        return true;
    }
}