diff --git a/src/mhtree/benchmarking/PerformanceMeasures.java b/src/mhtree/benchmarking/PerformanceMeasures.java
index 3e8d97bd5798ff112d5d38b59eb635faa60ff38d..767170dc64f7bd9c178fa39558da74c1ccb54d95 100644
--- a/src/mhtree/benchmarking/PerformanceMeasures.java
+++ b/src/mhtree/benchmarking/PerformanceMeasures.java
@@ -46,15 +46,16 @@ public class PerformanceMeasures {
KNNQueryOperation knnQueryOperation = new KNNQueryOperation(approxKNNQueryOperation.getQueryObject(), approxKNNQueryOperation.getK());
tree.executeOperation(knnQueryOperation);
- List<RankedAbstractObject> objects = new ArrayList<>(knnQueryOperation.getAnswerCount());
+ List<RankedAbstractObject> kNNObjects = new ArrayList<>(knnQueryOperation.getAnswerCount());
for (RankedAbstractObject object : knnQueryOperation)
- objects.add(object);
+ kNNObjects.add(object);
- Map<Float, Long> frequencyMap = objects.parallelStream()
+ Map<Float, Long> frequencyMap = kNNObjects
+ .stream()
.map(DistanceRankedObject::getDistance)
.collect(Collectors.groupingBy(Function.identity(), Collectors.counting()));
- long trueCount = 0;
+ long trueKNNFoundCount = 0;
for (RankedAbstractObject approxObject : approxKNNQueryOperation) {
float distance = approxObject.getDistance();
@@ -65,37 +66,10 @@ public class PerformanceMeasures {
} else {
frequencyMap.replace(distance, count - 1);
}
- trueCount++;
+ trueKNNFoundCount++;
}
}
- return trueCount / (double) knnQueryOperation.getAnswerCount();
- }
-
- // comparing done based on distances, counts how many of the same distances of KNNQueryOperation were presents in the answer of ApproxKNNQueryOperation
- public static double measureRecall(ApproxKNNQueryOperation approxKNNQueryOperation, HashMap<String, List<RankedAbstractObject>> kNNs) throws NoSuchMethodException, AlgorithmMethodException {
- if (approxKNNQueryOperation.getAnswerCount() == 0) return 0d;
-
- Map<Float, Long> frequencyMap = kNNs.get(approxKNNQueryOperation.getQueryObject().getLocatorURI()).subList(0, approxKNNQueryOperation.getK())
- .parallelStream()
- .map(DistanceRankedObject::getDistance)
- .collect(Collectors.groupingBy(Function.identity(), Collectors.counting()));
-
- long trueCount = 0;
-
- for (RankedAbstractObject approxObject : approxKNNQueryOperation) {
- float distance = approxObject.getDistance();
- if (frequencyMap.containsKey(distance)) {
- long count = frequencyMap.get(distance);
- if (count == 1) {
- frequencyMap.remove(distance);
- } else {
- frequencyMap.replace(distance, count - 1);
- }
- trueCount++;
- }
- }
-
- return trueCount / (double) approxKNNQueryOperation.getK();
+ return trueKNNFoundCount / (double) knnQueryOperation.getAnswerCount();
}
}
diff --git a/src/mhtree/benchmarking/RunBenchmark.java b/src/mhtree/benchmarking/RunBenchmark.java
index 1c59f2826d1b9d2c4b8080096d74a1d24cddb620..2ed5262099eb848a0bf2b15f1c51f71d36f39554 100644
--- a/src/mhtree/benchmarking/RunBenchmark.java
+++ b/src/mhtree/benchmarking/RunBenchmark.java
@@ -6,53 +6,28 @@ import messif.buckets.BucketStorageException;
import messif.objects.LocalAbstractObject;
import messif.objects.impl.ObjectFloatVectorNeuralNetworkL2;
import messif.objects.util.AbstractObjectList;
-import messif.objects.util.RankedAbstractObject;
import messif.objects.util.StreamGenericAbstractObjectIterator;
import messif.operations.Approximate;
import messif.operations.query.ApproxKNNQueryOperation;
-import messif.operations.query.KNNQueryOperation;
+import messif.statistics.Statistics;
import mhtree.InsertType;
import mhtree.MHTree;
import mhtree.ObjectToNodeDistance;
-import mhtree.SearchState;
import java.io.IOException;
import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.HashMap;
import java.util.List;
-import java.util.stream.Collectors;
public class RunBenchmark {
- private static final String dataset = "./data/middle_c.txt";
-
- public static void main(String[] args) throws IOException, NoSuchMethodException, AlgorithmMethodException, BucketStorageException, ClassNotFoundException {
- if (!(args.length == 0 || args.length == 5)) {
- throw new IllegalArgumentException("unexpected number of params");
+ public static void main(String[] args) throws IOException, NoSuchMethodException, AlgorithmMethodException, BucketStorageException, InstantiationException {
+ if (args.length != 5) {
+ throw new IllegalArgumentException("Unexpected number of params");
}
+ Statistics.enableGlobally();
AbstractRepresentation.PrecomputedDistances.COMPUTATION_THREADS = 16;
- List<LocalAbstractObject> objects;
-
- if (args.length != 5) {
- objects = loadDataset(RunBenchmark.dataset);
-
- DCtoRecall(
- new MHTreeConfig(
- 10,
- 2,
- InsertType.GREEDY,
- ObjectToNodeDistance.NEAREST
- ),
- objects,
- new int[]{1, 50, 100}
- );
-
- return;
- }
-
- objects = loadDataset(args[0]);
+ List<LocalAbstractObject> objects = loadDataset(args[0]);
int leafCapacity = Integer.parseInt(args[1]);
int nodeDegree = Integer.parseInt(args[2]);
InsertType insertType = args[3].equals("INCREMENTAL") ? InsertType.INCREMENTAL : InsertType.GREEDY;
@@ -70,90 +45,52 @@ public class RunBenchmark {
break;
}
-// Collections.shuffle(objects);
-
- DCtoRecall(new MHTreeConfig(
+ percentageToRecall(new MHTreeConfig(
leafCapacity,
nodeDegree,
insertType,
objectToNodeDistance
),
objects,
- new int[]{1, 50}
+ new int[]{1, 10, 25, 50, 100}
);
}
- private static void DCtoRecall(MHTreeConfig config, List<LocalAbstractObject> objects, int[] ks) throws NoSuchMethodException, AlgorithmMethodException, BucketStorageException, RuntimeException {
- MHTree mhTree = new MHTree.Builder(objects, config.leafCapacity, config.nodeDegree)
- .insertType(config.insertType)
+ private static void percentageToRecall(MHTreeConfig config, List<LocalAbstractObject> objects, int[] ks) throws NoSuchMethodException, AlgorithmMethodException, BucketStorageException, RuntimeException {
+ int numberOfObjects = objects.size();
+
+ MHTree mTree = new MHTree.Builder(objects, config.leafCapacity, config.nodeDegree)
.objectToNodeDistance(config.objectToNodeDistance)
.build();
- mhTree.printStatistics();
-
- int maxK = Arrays.stream(ks)
- .max()
- .orElseThrow(() -> new RuntimeException("bad max k"));
-
- // Precomputed NNs for each object
- HashMap<String, List<RankedAbstractObject>> URIToKNNs = new HashMap<>(objects.size());
-
- for (LocalAbstractObject object : objects) {
- KNNQueryOperation knnQueryOperation = new KNNQueryOperation(object, maxK);
- mhTree.executeOperation(knnQueryOperation);
-
- List<RankedAbstractObject> rankedKNNObjects = new ArrayList<>(knnQueryOperation.getAnswerCount());
- for (RankedAbstractObject knnObj : knnQueryOperation) {
- rankedKNNObjects.add(knnObj);
- }
-
- URIToKNNs.put(object.getLocatorURI(), rankedKNNObjects);
- }
+ mTree.printStatistics();
- System.out.println("leafCapacity,nodeDegree,objectToNodeDistance,k,distanceComputations,recall (min),recall (avg),recall (med),recall (max)");
+ System.out.println("leafCapacity,nodeDegree,objectToNodeDistance,k,percentage,recall (min),recall (avg),recall (med),recall (max)");
- double minRecall = 0;
- long distanceComputations = 0;
+ double minimalRecall = 0;
+ int percentage = 0;
+ int percentageStep = 5;
for (int k : ks) {
- List<ApproxKNNQueryOperation> operations = objects
- .stream()
- .map(object -> new ApproxKNNQueryOperation(
- object,
- k,
- 1000,
- Approximate.LocalSearchType.ABS_DC_COUNT,
- LocalAbstractObject.UNKNOWN_DISTANCE))
- .collect(Collectors.toList());
-
- operations.forEach(operation -> operation.suppData = new SearchState(null, null, false));
-
- System.out.println(
- String.join(",",
- String.valueOf(config.leafCapacity),
- String.valueOf(config.nodeDegree),
- String.valueOf(config.objectToNodeDistance),
- String.valueOf(k),
- String.valueOf(distanceComputations),
- String.format("%.2f,%.2f,%.2f,%.2f", 0f, 0f, 0f, 0f)
- )
- );
-
- distanceComputations += 1000;
-
- List<Double> recalls = new ArrayList<>(operations.size());
- for (int i = 0; i < operations.size(); i++) {
- recalls.add(0d);
+ List<Double> recalls = new ArrayList<>(numberOfObjects);
+ for (int i = 0; i < numberOfObjects; i++) {
+ recalls.add(0.0);
}
- while (minRecall != 1) {
- for (int i = 0; i < operations.size(); i++) {
- if (recalls.get(i) != 1d) {
- ApproxKNNQueryOperation operation = operations.get(i);
+ while (minimalRecall != 1.0) {
+ for (int i = 0; i < numberOfObjects; i++) {
+ if (recalls.get(i) != 1.0) {
+ ApproxKNNQueryOperation operation = new ApproxKNNQueryOperation(
+ objects.get(i),
+ k,
+ percentage,
+ Approximate.LocalSearchType.PERCENTAGE,
+ LocalAbstractObject.UNKNOWN_DISTANCE
+ );
- mhTree.executeOperation(operation);
+ mTree.executeOperation(operation);
- recalls.set(i, PerformanceMeasures.measureRecall(operation, URIToKNNs));
+ recalls.set(i, PerformanceMeasures.measureRecall(operation, mTree));
}
}
@@ -164,19 +101,19 @@ public class RunBenchmark {
String.valueOf(config.nodeDegree),
String.valueOf(config.objectToNodeDistance),
String.valueOf(k),
- String.valueOf(distanceComputations),
+ String.valueOf(percentage),
String.format("%.2f,%.2f,%.2f,%.2f",
recallStats.getMin(),
recallStats.getAverage(),
recallStats.getMedian(),
recallStats.getMax())));
- minRecall = recallStats.getMin();
- distanceComputations += 1000;
+ minimalRecall = recallStats.getMin();
+ percentage += percentageStep;
}
- minRecall = 0;
- distanceComputations = 0;
+ minimalRecall = 0;
+ percentage = 0;
}
}