diff --git a/src/mhtree/benchmarking/RunBenchmark.java b/src/mhtree/benchmarking/RunBenchmark.java
index aa723ffad6df86409c775fb3d247b8ab6ae8ca7e..ebf9d50c576942cb8fb233576beadb3ae6b7fd23 100644
--- a/src/mhtree/benchmarking/RunBenchmark.java
+++ b/src/mhtree/benchmarking/RunBenchmark.java
@@ -15,7 +15,7 @@ import messif.operations.query.KNNQueryOperation;
import messif.statistics.Statistics;
import mhtree.InsertType;
import mhtree.MHTree;
-import mhtree.MergeType;
+import mhtree.MergingMethod;
import mhtree.ObjectToNodeDistance;
import mtree.MTree;
@@ -27,6 +27,10 @@ import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;
+import static mhtree.ObjectToNodeDistance.AVERAGE;
+import static mhtree.ObjectToNodeDistance.FURTHEST;
+import static mhtree.ObjectToNodeDistance.NEAREST;
+
public class RunBenchmark {
public static void main(String[] args) throws IOException, AlgorithmMethodException, InstantiationException, NoSuchMethodException, BucketStorageException {
if (args.length != 5) {
@@ -44,13 +48,13 @@ public class RunBenchmark {
switch (args[4]) {
case "FURTHEST":
- objectToNodeDistance = ObjectToNodeDistance.FURTHEST;
+ objectToNodeDistance = FURTHEST;
break;
case "AVERAGE":
- objectToNodeDistance = ObjectToNodeDistance.AVERAGE;
+ objectToNodeDistance = AVERAGE;
break;
default:
- objectToNodeDistance = ObjectToNodeDistance.NEAREST;
+ objectToNodeDistance = NEAREST;
break;
}
@@ -61,90 +65,96 @@ public class RunBenchmark {
objectToNodeDistance
),
objects,
- new int[]{1, 10, 50, 100}
+ new int[]{1, 50}
);
}
private static void percentageToRecallMHTree(MHTreeConfig config, List<LocalAbstractObject> objects, int[] ks) throws BucketStorageException, RuntimeException {
- MHTree mTree = new MHTree.Builder(objects, config.leafCapacity, config.nodeDegree)
+ MHTree mhTree = new MHTree.MHTreeBuilder(objects, config.leafCapacity, config.nodeDegree)
.objectToNodeDistance(config.objectToNodeDistance)
- .mergeType(MergeType.REPRESENTATION_BASED)
+ .mergingMethod(MergingMethod.HULL_BASED_MERGE)
.build();
- mTree.printStatistics();
+ mhTree.printStatistics();
- System.out.println("leafCapacity,nodeDegree,objectToNodeDistance,k,percentage,recall (min),recall (avg),recall (med),recall (max)");
+ for (ObjectToNodeDistance dist : Arrays.asList(NEAREST, FURTHEST, AVERAGE)) {
+ System.gc();
- int maxK = Arrays.stream(ks).max().getAsInt();
+ mhTree.getNodes().forEach(node -> node.objectToNodeDistance = dist);
- List<KNNQueryOperation> kNNOperations = objects
- .parallelStream()
- .map(object -> new KNNQueryOperation(object, maxK))
- .collect(Collectors.toList());
+ System.out.println("leafCapacity,nodeDegree,objectToNodeDistance,k,percentage,recall (min),recall (avg),recall (med),recall (max)");
- class Pair {
- public List<RankedAbstractObject> kNNObjects;
- public String id;
- }
+ int maxK = Arrays.stream(ks).max().getAsInt();
- Map<String, List<RankedAbstractObject>> kNNResults = kNNOperations
- .parallelStream()
- .map(op -> {
- mTree.kNN(op);
- Pair pair = new Pair();
- pair.id = op.getQueryObject().getLocatorURI();
- pair.kNNObjects = new ArrayList<>(maxK);
- for (RankedAbstractObject o : op)
- pair.kNNObjects.add(o);
- return pair;
- })
- .collect(Collectors.toMap(entry -> entry.id, entry -> entry.kNNObjects));
-
- for (int k : ks) {
- List<ApproxKNNQueryOperation> approxOperations = objects
+ List<KNNQueryOperation> kNNOperations = objects
.parallelStream()
- .map(object -> new ApproxKNNQueryOperation(object, k, 0, Approximate.LocalSearchType.PERCENTAGE, LocalAbstractObject.UNKNOWN_DISTANCE))
+ .map(object -> new KNNQueryOperation(object, maxK))
.collect(Collectors.toList());
- approxOperations
- .parallelStream()
- .forEach(op -> op.suppData = new SearchState(mTree, op));
- double minimalRecall = 0;
- int percentage = 0;
- int percentageStep = 5;
+ class Pair {
+ public List<RankedAbstractObject> kNNObjects;
+ public String id;
+ }
- while (minimalRecall != 1.0) {
+ Map<String, List<RankedAbstractObject>> kNNResults = kNNOperations
+ .parallelStream()
+ .map(op -> {
+ mhTree.kNN(op);
+ Pair pair = new Pair();
+ pair.id = op.getQueryObject().getLocatorURI();
+ pair.kNNObjects = new ArrayList<>(maxK);
+ for (RankedAbstractObject o : op)
+ pair.kNNObjects.add(o);
+ return pair;
+ })
+ .collect(Collectors.toMap(entry -> entry.id, entry -> entry.kNNObjects));
+
+ for (int k : ks) {
+ List<ApproxKNNQueryOperation> approxOperations = objects
+ .parallelStream()
+ .map(object -> new ApproxKNNQueryOperation(object, k, 0, Approximate.LocalSearchType.PERCENTAGE, LocalAbstractObject.UNKNOWN_DISTANCE))
+ .collect(Collectors.toList());
approxOperations
.parallelStream()
- .filter(op -> ((SearchState) op.suppData).recall != 1d)
- .forEach(op -> {
- SearchState searchState = (SearchState) op.suppData;
- mTree.approxKNN(op);
- searchState.recall = PerformanceMeasures.measureRecall(op, kNNResults);
- searchState.approxState.limit += Math.round((float) objects.size() * (float) percentageStep / 100f);
- });
-
- Stats recallStats = new Stats(
- approxOperations
- .stream()
- .map(op -> ((SearchState) op.suppData).recall)
- .collect(Collectors.toList())
- );
-
- System.out.println(String.join(",",
- String.valueOf(config.leafCapacity),
- String.valueOf(config.nodeDegree),
- String.valueOf(config.objectToNodeDistance),
- String.valueOf(k),
- String.valueOf(percentage),
- String.format("%.2f,%.2f,%.2f,%.2f",
- recallStats.getMin(),
- recallStats.getAverage(),
- recallStats.getMedian(),
- recallStats.getMax())));
-
- minimalRecall = recallStats.getMin();
- percentage += percentageStep;
+ .forEach(op -> op.suppData = new SearchState(mhTree, op));
+
+ double minimalRecall = 0;
+ int percentage = 0;
+ int percentageStep = 5;
+
+ while (minimalRecall != 1.0) {
+ approxOperations
+ .parallelStream()
+ .filter(op -> ((SearchState) op.suppData).recall != 1d)
+ .forEach(op -> {
+ SearchState searchState = (SearchState) op.suppData;
+ mhTree.approxKNN(op);
+ searchState.recall = PerformanceMeasures.measureRecall(op, kNNResults);
+ searchState.approximateState.limit += Math.round((float) objects.size() * (float) percentageStep / 100f);
+ });
+
+ Stats recallStats = new Stats(
+ approxOperations
+ .stream()
+ .map(op -> ((SearchState) op.suppData).recall)
+ .collect(Collectors.toList())
+ );
+
+ System.out.println(String.join(",",
+ String.valueOf(config.leafCapacity),
+ String.valueOf(config.nodeDegree),
+ String.valueOf(dist),
+ String.valueOf(k),
+ String.valueOf(percentage),
+ String.format("%.2f,%.2f,%.2f,%.2f",
+ recallStats.getMin(),
+ recallStats.getAverage(),
+ recallStats.getMedian(),
+ recallStats.getMax())));
+
+ minimalRecall = recallStats.getMin();
+ percentage += percentageStep;
+ }
}
}
}
@@ -214,7 +224,6 @@ public class RunBenchmark {
}
}
-
private static List<LocalAbstractObject> loadDataset(String path) throws IOException {
return new AbstractObjectList<>(new StreamGenericAbstractObjectIterator<>(ObjectFloatVectorNeuralNetworkL2.class, path));
}