Changeset 41 for trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/SetWiseEMContextSelection.java

Timestamp:

09/24/15 10:59:05 (9 years ago)

Author:

sherbold

Message:

• formatted code and added copyrights

File:

• trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/SetWiseEMContextSelection.java (modified) (2 diffs)

trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/SetWiseEMContextSelection.java

@@ +1 @@
+// Copyright 2015 Georg-August-Universität Göttingen, Germany
+//
+//   Licensed under the Apache License, Version 2.0 (the "License");
+//   you may not use this file except in compliance with the License.
+//   You may obtain a copy of the License at
+//
+//       http://www.apache.org/licenses/LICENSE-2.0
+//
+//   Unless required by applicable law or agreed to in writing, software
+//   distributed under the License is distributed on an "AS IS" BASIS,
+//   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+//   See the License for the specific language governing permissions and
+//   limitations under the License.
+
 package de.ugoe.cs.cpdp.dataselection;
 
@@ -22 +36 @@
  * 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" />
+ * 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[] 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;
-        }
+
+    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;
+    }
 }