source: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/SetWiseEMContextSelection.java @ 37

package de.ugoe.cs.cpdp.dataselection;

import java.util.ArrayList;
import java.util.HashSet;
import java.util.LinkedList;
import java.util.List;
import java.util.Set;
import java.util.logging.Level;

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

import de.ugoe.cs.util.console.Console;
import weka.clusterers.EM;
import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Normalize;

/**
 * Selects training data by clustering project context factors.
 * 
 * The project context factors used for the clustering are configured in 
 * the XML param attribute, Example:
 * <setwiseselector name="SetWiseEMContextSelection" param="AFS TND TNC" />
 */
public class SetWiseEMContextSelection implements ISetWiseDataselectionStrategy {
        
        private String parameters;
        private String[] project_context_factors; // = new String[]{"TND", "TNC", "TNF", "TLOC"};
        
        @Override
        public void setParameter(String parameters) {
                if( parameters!=null ) {
                        project_context_factors = parameters.split(" ");
                }
        }
        
        /**
         * Uses the Weka EM-Clustering algorithm to cluster the projects
         * by their project context factors. 
         * The project context factors are first normalized and then used for clustering.
         * They can be configured in the configuration param.
         *  
         * @param testdata
         * @param traindataSet
         */
        protected void cluster(Instances testdata, SetUniqueList<Instances> traindataSet) {
                // now do the clustering, normalizedCharacteristicInstances ruft getContextFactors auf
                final Instances data = this.normalizedCharacteristicInstances(testdata, traindataSet); 
                
                final Instance targetInstance = data.instance(0);
                final List<Instance> candidateInstances = new LinkedList<Instance>();
                for( int i=1; i<data.numInstances(); i++ ) {
                        candidateInstances.add(data.instance(i));
                }
                
                // cluster and select
                try {
                        final EM emeans = new EM();
                        boolean onlyTarget = true;
                        int targetCluster;
                        int maxNumClusters = candidateInstances.size();
                        
                        do { // while(onlyTarget)
                                emeans.setMaximumNumberOfClusters(maxNumClusters);
                                emeans.buildClusterer(data);
                                                        
                                targetCluster = emeans.clusterInstance(targetInstance);
                                
                                // check if cluster only contains target project
                                for( int i=0 ; i<candidateInstances.size() && onlyTarget; i++ ) {
                                        onlyTarget &= !(emeans.clusterInstance(candidateInstances.get(i))==targetCluster);
                                }
                                maxNumClusters = emeans.numberOfClusters()-1;
                                
                                //Console.traceln(Level.INFO, "number of clusters: " + emeans.numberOfClusters());
                        } while(onlyTarget);
                        
                        Console.traceln(Level.INFO, "clusters: " + maxNumClusters);
                        Console.traceln(Level.INFO, "instances vor dem clustern: " + traindataSet.size());
                        int numRemoved = 0;
                        for( int i=0 ; i<candidateInstances.size() ; i++ ) {
                                if( emeans.clusterInstance(candidateInstances.get(i))!=targetCluster ) {
                                        traindataSet.remove(i-numRemoved++);
                                }
                        }
                        Console.traceln(Level.INFO, "instances nach dem clustern: " + traindataSet.size());
                } catch(Exception e) {
                        throw new RuntimeException("error applying setwise EM clustering training data selection", e);
                }
        }

        @Override
        public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
                // issuetracking und pl muss passen
                /*
                int s = traindataSet.size();
                Console.traceln(Level.INFO, "remove non matching PL and IssueTracking projects, size now: " + s);
                this.removeWrongContext(testdata, traindataSet, "PL");
                this.removeWrongContext(testdata, traindataSet, "IssueTracking");
                s = traindataSet.size();
                Console.traceln(Level.INFO, "size after removal: " + s);
                */
                // now cluster
                this.cluster(testdata, traindataSet);
        }

        /**
         * Returns test- and training data with only the project context factors
         * which were chosen in the configuration.
         * This is later used for clustering.
         * 
         * @param testdata
         * @param traindataSet
         * @return
         */
        protected Instances getContextFactors(Instances testdata, SetUniqueList<Instances> traindataSet) {
                // setup weka Instances for clustering
                final ArrayList<Attribute> atts = new ArrayList<Attribute>();
                
                // we only want the project context factors
                for( String pcf : this.project_context_factors ) {
                        atts.add(new Attribute(pcf));
                }
                
                // set up the data
                final Instances data = new Instances("project_context_factors", atts, 0);
                double[] instanceValues = new double[atts.size()];
                
                // only project context factors + only one instance per project needed 
                int i = 0;
                for( String pcf : this.project_context_factors ) {
                        instanceValues[i] = testdata.instance(0).value(testdata.attribute(pcf));
                        //Console.traceln(Level.INFO, "adding attribute: " + pcf + " value: " + instanceValues[i]);
                        i++;
                }
                data.add(new DenseInstance(1.0, instanceValues));
                
                // now for the projects of the training stet
                for( Instances traindata : traindataSet ) {
                        instanceValues = new double[atts.size()];  // ohne das hier immer dieselben werte?!
                        i = 0;
                        for( String pcf : this.project_context_factors ) {
                                instanceValues[i] = traindata.instance(0).value(traindata.attribute(pcf));
                                //Console.traceln(Level.INFO, "adding attribute: " + pcf + " value: " + instanceValues[i]);
                                i++;
                        }

                        data.add(new DenseInstance(1.0, instanceValues));
                }

                return data;
        }

        /**
         * Delete projects where the project context does not match the training project
         * 
         * @param testdata
         * @param traindataSet
         * @param attribute
         */
        protected void removeWrongContext(Instances testdata, SetUniqueList<Instances> traindataSet, String attribute) {
                Set<Instances> remove = new HashSet<Instances>();       
                for( Instances traindata : traindataSet ) {
                        if( traindata.firstInstance().value(traindata.attribute(attribute)) != testdata.firstInstance().value(testdata.attribute(attribute)) ) {
                                remove.add(traindata);
                                //Console.traceln(Level.WARNING, "rmove attribute "+attribute+" test: "+testdata.firstInstance().value(testdata.attribute(attribute))+" train: "+traindata.firstInstance().value(traindata.attribute(attribute)));
                        }
                }
                
                // now delete the projects from set
                for( Instances i : remove ) {
                        traindataSet.remove(i);
                        //Console.traceln(Level.INFO, "removing training project from set");
                }
        }

        /**
         * Normalizes the data before it gets used for clustering
         * 
         * @param testdata
         * @param traindataSet
         * @return
         */
        protected Instances normalizedCharacteristicInstances(Instances testdata, SetUniqueList<Instances> traindataSet) {
                Instances data = this.getContextFactors(testdata, traindataSet);
                try {
                        final Normalize normalizer = new Normalize();
                        normalizer.setInputFormat(data);
                        data = Filter.useFilter(data, normalizer);
                } catch (Exception e) {
                        throw new RuntimeException("Unexpected exception during normalization of distributional characteristics.", e);
                }
                return data;
        }
}