source: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/PointWiseEMClusterSelection.java @ 7

package de.ugoe.cs.cpdp.dataselection;

import java.util.LinkedList;
import java.util.List;
import java.util.logging.Level;

import org.apache.commons.collections4.list.SetUniqueList;

import weka.clusterers.EM;
import weka.core.Instances;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.AddCluster;
import weka.filters.unsupervised.attribute.Remove;
import de.ugoe.cs.util.console.Console;


/**
 * Use in Config:
 * 
 * Specify number of clusters
 * -N = Num Clusters
 * <pointwiseselector name="PointWiseEMClusterSelection" param="-N 10"/>
 *
 * Try to determine the number of clusters:
 * -I 10 = max iterations
 * -X 5 = 5 folds for cross evaluation
 * -max = max number of clusters
 * <pointwiseselector name="PointWiseEMClusterSelection" param="-I 10 -X 5 -max 300"/>
 * 
 * Don't forget to add:
 * <preprocessor name="Normalization" param=""/>
 */
public class PointWiseEMClusterSelection implements IPointWiseDataselectionStrategy {
        
        private String[] params; 
        
        @Override
        public void setParameter(String parameters) {
                params = parameters.split(" ");
        }

        
        /**
         * 1. Cluster the traindata
         * 2. for each instance in the testdata find the assigned cluster
         * 3. select only traindata from the clusters we found in our testdata
         * 
         * @returns the selected training data
         */
        @Override
        public Instances apply(Instances testdata, Instances traindata) {
                //final Attribute classAttribute = testdata.classAttribute();
                
                final List<Integer> selectedCluster = SetUniqueList.setUniqueList(new LinkedList<Integer>());

                // 1. copy train- and testdata
                Instances train = new Instances(traindata);
                Instances test = new Instances(testdata);
                
                Instances selected = null;
                
                try {
                        // remove class attribute from traindata
                        Remove filter = new Remove();
                        filter.setAttributeIndices("" + (train.classIndex() + 1));
                        filter.setInputFormat(train);
                        train = Filter.useFilter(train, filter);
                        
                        Console.traceln(Level.INFO, String.format("starting clustering"));
                        
                        // 3. cluster data
                        EM clusterer = new EM();
                        clusterer.setOptions(params);
                        clusterer.buildClusterer(train);
                        int numClusters = clusterer.getNumClusters();
                        if ( numClusters == -1) {
                                Console.traceln(Level.INFO, String.format("we have unlimited clusters"));
                        }else {
                                Console.traceln(Level.INFO, String.format("we have: "+numClusters+" clusters"));
                        }
                        
                        
                        // 4. classify testdata, save cluster int
                        
                        // remove class attribute from testdata?
                        Remove filter2 = new Remove();
                        filter2.setAttributeIndices("" + (test.classIndex() + 1));
                        filter2.setInputFormat(test);
                        test = Filter.useFilter(test, filter2);
                        
                        int cnum;
                        for( int i=0; i < test.numInstances(); i++ ) {
                                cnum = ((EM)clusterer).clusterInstance(test.get(i));

                                // we dont want doubles (maybe use a hashset instead of list?)
                                if ( !selectedCluster.contains(cnum) ) {
                                        selectedCluster.add(cnum);
                                        //Console.traceln(Level.INFO, String.format("assigned to cluster: "+cnum));
                                }
                        }
                        
                        Console.traceln(Level.INFO, String.format("our testdata is in: "+selectedCluster.size()+" different clusters"));
                        
                        // 5. get cluster membership of our traindata
                        AddCluster cfilter = new AddCluster();
                        cfilter.setClusterer(clusterer);
                        cfilter.setInputFormat(train);
                        Instances ctrain = Filter.useFilter(train, cfilter);
                        
                        
                        // 6. for all traindata get the cluster int, if it is in our list of testdata cluster int add the traindata
                        // of this cluster to our returned traindata
                        int cnumber;
                        selected = new Instances(traindata);
                        selected.delete();
                        
                        for ( int j=0; j < ctrain.numInstances(); j++ ) {
                                // get the cluster number from the attributes
                                cnumber = Integer.parseInt(ctrain.get(j).stringValue(ctrain.get(j).numAttributes()-1).replace("cluster", ""));
                                
                                //Console.traceln(Level.INFO, String.format("instance "+j+" is in cluster: "+cnumber));
                                if ( selectedCluster.contains(cnumber) ) {
                                        // this only works if the index does not change
                                        selected.add(traindata.get(j));
                                        // check for differences, just one attribute, we are pretty sure the index does not change
                                        if ( traindata.get(j).value(3) != ctrain.get(j).value(3) ) {
                                                Console.traceln(Level.WARNING, String.format("we have a difference between train an ctrain!"));
                                        }
                                }
                        }
                        
                        Console.traceln(Level.INFO, String.format("that leaves us with: "+selected.numInstances()+" traindata instances from "+traindata.numInstances()));
                }catch( Exception e ) {
                        Console.traceln(Level.WARNING, String.format("ERROR"));
                        throw new RuntimeException("error in pointwise em", e);
                }
        
                return selected;
        }

}