es.jarias.FMC.ClassCompoundTransformation.java Source code

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Here is the source code for es.jarias.FMC.ClassCompoundTransformation.java

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/**
    
Copyright 2015 Jacinto Arias  - www.jarias.es
    
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
    
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.
    
You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
    
    
    
FMC multilabel classifier. This source code is an experimental
implementation of the method presented in:
    
J. Arias, J.A. Gamez, T.D. Nielsen and J.M. Puerta 
    
A scalable pairwise class interaction framework for multidimensional classification.
    
**/

package es.jarias.FMC;

import java.util.ArrayList;
import mulan.data.LabelSet;
import mulan.data.MultiLabelInstances;
import mulan.transformations.RemoveAllLabels;
import weka.core.Attribute;
import weka.core.Instance;
import weka.core.Instances;

/**
 * 
 * @author jarias
 *
 */
public class ClassCompoundTransformation {

    private Instances transformedFormat;

    /**
     * Returns the format of the transformed instances
     * 
     * @return the format of the transformed instances
     */
    public Instances getTransformedFormat() {
        return transformedFormat;
    }

    Instances data;
    int numLabels;
    int[] labelIndices;
    Attribute newClass;

    /**
     * 
     * @param mlData
     * @return the transformed instances
     * @throws Exception
     */
    public Instances transformInstances(MultiLabelInstances mlData) throws Exception {
        data = mlData.getDataSet();
        numLabels = mlData.getNumLabels();
        labelIndices = mlData.getLabelIndices();

        Instances newData = null;

        // This must be different in order to combine ALL class states, not only existing ones.
        // gather distinct label combinations
        // ASSUME CLASSES ARE BINARY

        ArrayList<LabelSet> labelSets = new ArrayList<LabelSet>();

        double[] dblLabels = new double[numLabels];
        double nCombinations = Math.pow(2, numLabels);

        for (int i = 0; i < nCombinations; i++) {
            for (int l = 0; l < numLabels; l++) {
                int digit = (int) Math.pow(2, numLabels - 1 - l);
                dblLabels[l] = (digit & i) / digit;
            }

            LabelSet labelSet = new LabelSet(dblLabels);
            labelSets.add(labelSet);
        }

        //        for (int i = 0; i < numInstances; i++) {
        //            // construct labelset
        //            double[] dblLabels = new double[numLabels];
        //            for (int j = 0; j < numLabels; j++) {
        //                int index = labelIndices[j];
        //                dblLabels[j] = Double.parseDouble(data.attribute(index).value((int) data.instance(i).value(index)));
        //            }
        //            LabelSet labelSet = new LabelSet(dblLabels);
        //
        //            // add labelset if not already present
        //            labelSets.add(labelSet);
        //        }

        // create class attribute
        ArrayList<String> classValues = new ArrayList<String>(labelSets.size());
        for (LabelSet subset : labelSets) {
            classValues.add(subset.toBitString());
        }
        newClass = new Attribute("class", classValues);

        //        for (String s : classValues)
        //        {
        //           System.out.print(s+", ");
        //           
        //        }
        //        System.out.println();

        // remove all labels
        newData = RemoveAllLabels.transformInstances(data, labelIndices);

        // add new class attribute
        newData.insertAttributeAt(newClass, newData.numAttributes());
        newData.setClassIndex(newData.numAttributes() - 1);

        // add class values
        for (int i = 0; i < newData.numInstances(); i++) {
            //System.out.println(newData.instance(i).toString());
            String strClass = "";
            for (int j = 0; j < numLabels; j++) {
                int index = labelIndices[j];
                strClass = strClass + data.attribute(index).value((int) data.instance(i).value(index));
            }
            //System.out.println(strClass);
            newData.instance(i).setClassValue(strClass);
        }
        transformedFormat = new Instances(newData, 0);
        return newData;
    }

    /**
     * 
     * @param instance
     * @param labelIndices
     * @return tranformed instance
     * @throws Exception
     */
    public Instance transformInstance(Instance instance, int[] labelIndices) throws Exception {
        Instance transformedInstance = RemoveAllLabels.transformInstance(instance, labelIndices);
        transformedInstance.setDataset(null);
        transformedInstance.insertAttributeAt(transformedInstance.numAttributes());
        transformedInstance.setDataset(transformedFormat);
        return transformedInstance;
    }

}