Write Clusters To JSON Report

jplag/src/main/java/de/jplag/clustering/Cluster.java

@@ -4,7 +4,6 @@
 import java.util.Collection;
 import java.util.List;
 import java.util.function.BiFunction;
-import java.util.stream.Collectors;
 
 /**
  * Cluster part of a {@link ClusteringResult}.
@@ -15,17 +14,28 @@ public class Cluster<T> {
     private final float communityStrength;
     private final Collection<T> members;
     private ClusteringResult<T> clusteringResult = null;
+    private final float averageSimilarity;
 
-    public Cluster(Collection<T> members, float communityStrength) {
+    /**
+     * @param members Members of the cluster.
+     * @param communityStrength A metric of how strongly the members of this cluster are connected.
+     * @param averageSimilarity The average similarity between all tuple comparisons of the members in this cluster.
+     */
+    public Cluster(Collection<T> members, float communityStrength, float averageSimilarity) {
         this.members = new ArrayList<>(members);
         this.communityStrength = communityStrength;
+        this.averageSimilarity = averageSimilarity;
     }
 
     public Collection<T> getMembers() {
         // TODO Check why access to local attribute.
         return members;
     }
 
+    public float getAverageSimilarity() {
+        return averageSimilarity;
+    }
+
     /**
      * See {@link ClusteringResult#getCommunityStrength}
      * @return community strength of the cluster
@@ -60,8 +70,7 @@ public float getCommunityStrengthPerConnection() {
      * @return normalized community strength per connection
      */
     public float getNormalizedCommunityStrengthPerConnection() {
-        List<Cluster<T>> goodClusters = clusteringResult.getClusters().stream().filter(cluster -> cluster.getCommunityStrength() > 0)
-                .collect(Collectors.toList());
+        List<Cluster<T>> goodClusters = clusteringResult.getClusters().stream().filter(cluster -> cluster.getCommunityStrength() > 0).toList();
         float posCommunityStrengthSum = (float) goodClusters.stream().mapToDouble(Cluster::getCommunityStrengthPerConnection).sum();
 
         int size = clusteringResult.getClusters().size();
@@ -87,7 +96,7 @@ public double getWorth(BiFunction<T, T, Float> similarity) {
      * @param similarity function that supplies the similarity of two cluster members.
      * @return average similarity
      */
-    public float averageSimilarity(BiFunction<T, T, Float> similarity) {
+    private float averageSimilarity(BiFunction<T, T, Float> similarity) {
         List<T> members = new ArrayList<>(getMembers());
         if (members.size() < 2) {
             return 1;

jplag/src/main/java/de/jplag/clustering/ClusteringAdapter.java

@@ -60,7 +60,7 @@ public ClusteringResult<Submission> doClustering(GenericClusteringAlgorithm algo
         ClusteringResult<Integer> modularityClusterResult = ClusteringResult.fromIntegerCollections(new ArrayList<>(intResult), similarityMatrix);
         List<Cluster<Submission>> mappedClusters = modularityClusterResult.getClusters().stream()
                 .map(unmappedCluster -> new Cluster<>(unmappedCluster.getMembers().stream().map(mapping::unmap).collect(Collectors.toList()),
-                        unmappedCluster.getCommunityStrength()))
+                        unmappedCluster.getCommunityStrength(), unmappedCluster.getAverageSimilarity()))
                 .collect(Collectors.toList());
         return new ClusteringResult<>(mappedClusters, modularityClusterResult.getCommunityStrength());
     }

jplag/src/main/java/de/jplag/clustering/ClusteringResult.java

@@ -1,12 +1,6 @@
 package de.jplag.clustering;
 
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.Collection;
-import java.util.Collections;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
+import java.util.*;
 import java.util.function.BiFunction;
 import java.util.stream.DoubleStream;
 
@@ -20,7 +14,7 @@
 public class ClusteringResult<T> {
 
     private final List<Cluster<T>> clusters;
-    private float communityStrength = 0;
+    private final float communityStrength;
 
     public ClusteringResult(Collection<Cluster<T>> clusters, float communityStrength) {
         this.clusters = List.copyOf(clusters);
@@ -92,11 +86,36 @@ public static ClusteringResult<Integer> fromIntegerCollections(List<Collection<I
             for (int i = 0; i < clustering.size(); i++) {
                 double outWeightSum = percentagesOfSimilaritySums.getRowVector(i).getL1Norm();
                 double clusterCommunityStrength = percentagesOfSimilaritySums.getEntry(i, i) - outWeightSum * outWeightSum;
-                clusters.add(new Cluster<>(clustering.get(i), (float) clusterCommunityStrength));
+                float averageSimilarity = calculateAverageSimilarityFor(clustering.get(i), similarity);
+                clusters.add(new Cluster<>(clustering.get(i), (float) clusterCommunityStrength, averageSimilarity));
                 communityStrength += clusterCommunityStrength;
             }
         }
         return new ClusteringResult<>(clusters, communityStrength);
     }
 
+    private static float calculateAverageSimilarityFor(Collection<Integer> cluster, RealMatrix similarityMatrix) {
+        var sumOfSimilarities = 0f;
+        List<Integer> indices = List.copyOf(cluster);
+        for (int i = 1; i < cluster.size(); i++) {
+            int indexOfSubmission1 = indices.get(i);
+            for (int j = 0; j < i; j++) { // as the similarity matrix is symmetrical we need only iterate over one half of it
+                int indexOfSubmission2 = indices.get(j);
+                sumOfSimilarities += similarityMatrix.getEntry(indexOfSubmission1, indexOfSubmission2);
+            }
+        }
+        int nMinusOne = cluster.size() - 1;
+        float numberOfComparisons = (nMinusOne * (nMinusOne + 1))
+                / 2f; /*
+                       * Use Gauss sum to calculate number of comparisons in cluster: Given cluster of size n we need Gauss sum of n-1
+                       * comparisons: compare first element of cluster to all other except itself: n-1 comparisons. compare second element to
+                       * all other except itself and first element (as these two were already compared when we processed the first element),
+                       * n-2 comparisons. compare third element to all other but itself and all previously compared: n-3 comparisons and so
+                       * on. when we reach the second to last element we have n-(n-1)=1 comparisons left. when we reach the last element it
+                       * has already been compared to all other. adding up all comparisons we get: (n-1) + (n-2) + (n-3) + ... + (n-(n-1)) =
+                       * Gauss sum of (n-1)
+                       */
+        return sumOfSimilarities / numberOfComparisons;
+    }
+
 }

jplag/src/main/java/de/jplag/reporting/reportobject/ReportObjectFactory.java

@@ -12,6 +12,7 @@
 import org.slf4j.LoggerFactory;
 
 import de.jplag.*;
+import de.jplag.reporting.reportobject.mapper.ClusteringResultMapper;
 import de.jplag.reporting.reportobject.model.*;
 import de.jplag.reporting.reportobject.model.Match;
 
@@ -21,6 +22,7 @@
 public class ReportObjectFactory {
 
     private static final Logger logger = LoggerFactory.getLogger(ReportObjectFactory.class);
+    private static final ClusteringResultMapper clusteringResultMapper = new ClusteringResultMapper();
 
     /**
      * Converts a JPlagResult to a JPlagReport.
@@ -58,7 +60,7 @@ private static OverviewReport generateOverviewReport(JPlagResult result) {
         overviewReport.setExecutionTime(result.getDuration());
         overviewReport.setComparisonNames(getComparisonNames(comparisons));
         overviewReport.setMetrics(getMetrics(result));
-        overviewReport.setClusters(getClusters(result));
+        overviewReport.setClusters(clusteringResultMapper.map(result));
 
         return overviewReport;
     }
@@ -166,13 +168,6 @@ private static Match convertMatchToReportMatch(JPlagComparison comparison, de.jp
         return new Match(startTokenFirst.getFile(), startTokenSecond.getFile(), startFirst, endFirst, startSecond, endSecond, tokens);
     }
 
-    // TODO implement after PR Read clustering #281
-    private static List<Cluster> getClusters(JPlagResult result) {
-        // List<ClusteringResult<Submission>> clusters = result.getClusteringResult();
-        // return clusters.map( c -> new Cluster(getAvgSimilarity, getStrength, c.getMembers().map(Submission::getName)))
-        return List.of();
-    }
-
     private static List<String> readFileLines(File file) {
         List<String> lines = new ArrayList<>();
         try (BufferedReader bufferedReader = new BufferedReader(new FileReader(file))) {

jplag/src/main/java/de/jplag/reporting/reportobject/mapper/ClusteringResultMapper.java

@@ -0,0 +1,26 @@
+package de.jplag.reporting.reportobject.mapper;
+
+import java.util.Collection;
+import java.util.List;
+
+import de.jplag.JPlagResult;
+import de.jplag.Submission;
+import de.jplag.clustering.ClusteringResult;
+import de.jplag.reporting.reportobject.model.Cluster;
+
+/**
+ * Extracts and maps the clusters from the JPlagResult to the corresponding JSON DTO
+ */
+public class ClusteringResultMapper {
+    public List<Cluster> map(JPlagResult result) {
+        var clusteringResult = result.getClusteringResult();
+        return clusteringResult.stream().map(ClusteringResult::getClusters).flatMap(Collection::stream).map(this::convertCluster).toList();
+    }
+
+    private Cluster convertCluster(de.jplag.clustering.Cluster<Submission> from) {
+        var strength = from.getCommunityStrength();
+        var avgSimilarity = from.getAverageSimilarity();
+        var member = from.getMembers().stream().map(Submission::getName).toList();
+        return new Cluster(avgSimilarity, strength, member);
+    }
+}

jplag/src/main/java/de/jplag/reporting/reportobject/model/Cluster.java

@@ -4,32 +4,6 @@
 
 import com.fasterxml.jackson.annotation.JsonProperty;
 
-public class Cluster {
-
-    @JsonProperty("average_similarity")
-    private final float averageSimilarity;
-
-    @JsonProperty("strength")
-    private final float strength;
-
-    @JsonProperty("members")
-    private final List<String> members;
-
-    public Cluster(float averageSimilarity, float strength, List<String> members) {
-        this.averageSimilarity = averageSimilarity;
-        this.strength = strength;
-        this.members = List.copyOf(members);
-    }
-
-    public float getAverageSimilarity() {
-        return averageSimilarity;
-    }
-
-    public float getStrength() {
-        return strength;
-    }
-
-    public List<String> getMembers() {
-        return members;
-    }
+public record Cluster(@JsonProperty("average_similarity") float averageSimilarity, @JsonProperty("strength") float strength,
+        @JsonProperty("members") List<String> members) {
 }

jplag/src/test/java/de/jplag/clustering/ClusterTest.java

@@ -13,36 +13,27 @@ public class ClusterTest {
     private static final double EPSILON = 0.00001;
     Cluster<Character> cluster;
 
-    @Test
-    public void testAverageSimilarity() {
-        cluster = new Cluster<>(List.of('a', 'b', 'c'), 0);
-        float averageSimilarity = cluster.averageSimilarity((a, b) -> {
-            return Math.abs((float) (((int) a) - ((int) b)));
-        });
-        assertEquals((1.f + 2.f + 1.f + 1.f + 2.f + 1.f) / 6, averageSimilarity, EPSILON);
-    }
-
     @Test
     public void testCommunityStrengthPerConnectionOneMember() {
-        cluster = new Cluster<>(List.of('a'), 10);
+        cluster = new Cluster<>(List.of('a'), 10, 0);
         assertEquals(0.0, cluster.getCommunityStrengthPerConnection(), EPSILON);
     }
 
     @Test
     public void testCommunityStrengthPerConnectionTwoMembers() {
-        cluster = new Cluster<>(List.of('a', 'b'), 10);
+        cluster = new Cluster<>(List.of('a', 'b'), 10, 0);
         assertEquals(10.0, cluster.getCommunityStrengthPerConnection(), EPSILON);
     }
 
     @Test
     public void testCommunityStrengthPerConnectionThreeMembers() {
-        cluster = new Cluster<>(List.of('a', 'b', 'c'), 10);
+        cluster = new Cluster<>(List.of('a', 'b', 'c'), 10, 0);
         assertEquals(10.0 / 3, cluster.getCommunityStrengthPerConnection(), EPSILON);
     }
 
     @Test
     public void testNormalizedCommunityStrength() {
-        cluster = new Cluster<>(List.of('a', 'b', 'c'), 10);
+        cluster = new Cluster<>(List.of('a', 'b', 'c'), 10, 0);
         @SuppressWarnings("unchecked")
         ClusteringResult<Character> clusteringResult = mock(ClusteringResult.class);
         when(clusteringResult.getClusters()).thenReturn(List.of(cluster, cluster));