diff --git a/src/mhtree/benchmarking/PerformanceMeasures.java b/src/mhtree/benchmarking/PerformanceMeasures.java
index dee978afb2663777e0f7b16954dfaf8e30a87a92..8d61f34222b7bc3d1feb578fba10932b72e16373 100644
--- a/src/mhtree/benchmarking/PerformanceMeasures.java
+++ b/src/mhtree/benchmarking/PerformanceMeasures.java
@@ -4,7 +4,6 @@ import messif.algorithms.Algorithm;
import messif.algorithms.AlgorithmMethodException;
import messif.objects.util.DistanceRankedObject;
import messif.objects.util.RankedAbstractObject;
-import messif.operations.query.ApproxKNNQueryOperation;
import messif.operations.query.KNNQueryOperation;
import java.util.ArrayList;
@@ -16,7 +15,7 @@ import java.util.stream.Collectors;
public class PerformanceMeasures {
- public static double measureErrorOnThePosition(ApproxKNNQueryOperation approxKNNQueryOperation, Algorithm tree) throws NoSuchMethodException, AlgorithmMethodException {
+ public static double measureErrorOnThePosition(KNNQueryOperation approxKNNQueryOperation, Algorithm tree) throws NoSuchMethodException, AlgorithmMethodException {
KNNQueryOperation rankedObjects = new KNNQueryOperation(approxKNNQueryOperation.getQueryObject(), tree.getObjectCount());
tree.executeOperation(rankedObjects);
@@ -40,7 +39,7 @@ public class PerformanceMeasures {
}
// 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, Algorithm tree) throws NoSuchMethodException, AlgorithmMethodException {
+ public static double measureRecall(KNNQueryOperation approxKNNQueryOperation, Algorithm tree) throws NoSuchMethodException, AlgorithmMethodException {
if (approxKNNQueryOperation.getAnswerCount() == 0) return 0d;
KNNQueryOperation knnQueryOperation = new KNNQueryOperation(approxKNNQueryOperation.getQueryObject(), approxKNNQueryOperation.getK());
@@ -73,10 +72,11 @@ public class PerformanceMeasures {
return trueKNNFoundCount / (double) knnQueryOperation.getAnswerCount();
}
- public static double measureRecall(ApproxKNNQueryOperation approxKNNQueryOperation, Map<String, List<RankedAbstractObject>> trueKNN) {
+ public static double measureRecall(KNNQueryOperation approxKNNQueryOperation, Map<String, List<RankedAbstractObject>> trueKNN) {
if (approxKNNQueryOperation.getAnswerCount() == 0) return 0d;
- List<RankedAbstractObject> kNNObjects = trueKNN.get(approxKNNQueryOperation.getQueryObject().getLocatorURI()).subList(0, approxKNNQueryOperation.getK());
+ List<RankedAbstractObject> kNNObjects = trueKNN.get(approxKNNQueryOperation.getQueryObject().getLocatorURI())
+ .subList(0, approxKNNQueryOperation.getK());
Map<Float, Long> frequencyMap = kNNObjects
.stream()
diff --git a/src/mhtree/benchmarking/RunBenchmark.java b/src/mhtree/benchmarking/RunBenchmark.java
index 9f1bfa9092ad8605995346637a15c178c3a72752..72fe19179de5155dd635a1552f452f02d664429b 100644
--- a/src/mhtree/benchmarking/RunBenchmark.java
+++ b/src/mhtree/benchmarking/RunBenchmark.java
@@ -4,7 +4,6 @@ import cz.muni.fi.disa.similarityoperators.cover.AbstractRepresentation;
import messif.algorithms.AlgorithmMethodException;
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;
@@ -24,19 +23,19 @@ import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
+import java.util.Locale;
import java.util.Map;
-import java.util.logging.Level;
-import java.util.logging.Logger;
import java.util.stream.Collectors;
import messif.algorithms.Algorithm;
+import messif.objects.keys.AbstractObjectKey;
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 != 7 && args.length != 10) {
+ public static void main(String[] args) throws IOException, AlgorithmMethodException, InstantiationException, NoSuchMethodException, BucketStorageException, ClassNotFoundException {
+ if (args.length != 8 && args.length != 11) {
throw new IllegalArgumentException("Unexpected number of params");
}
@@ -45,18 +44,21 @@ public class RunBenchmark {
default -> false;
};
+ // e.g. messif.objects.impl.ObjectFloatVectorNeuralNetworkL2
+ Class<? extends LocalAbstractObject> objClass = (Class<? extends LocalAbstractObject>) Class.forName(args[1]);
+
//Statistics.enableGlobally();
Statistics.disableGlobally();
AbstractRepresentation.PrecomputedDistances.COMPUTATION_THREADS = 16;
- List<LocalAbstractObject> objects = loadDataset(args[1]);
- int leafCapacity = Integer.parseInt(args[2]);
- int nodeDegree = Integer.parseInt(args[3]);
- List<LocalAbstractObject> queries = loadDataset(args[4]);
+ List<LocalAbstractObject> objects = loadDataset(args[2], objClass);
+ int leafCapacity = Integer.parseInt(args[3]);
+ int nodeDegree = Integer.parseInt(args[4]);
+ List<LocalAbstractObject> queries = loadDataset(args[5], objClass);
- InsertType insertType = args[5].equals("INCREMENTAL") ? InsertType.INCREMENTAL : InsertType.GREEDY;
+ InsertType insertType = args[6].equals("INCREMENTAL") ? InsertType.INCREMENTAL : InsertType.GREEDY;
- ObjectToNodeDistance objectToNodeDistance = switch (args[6]) {
+ ObjectToNodeDistance objectToNodeDistance = switch (args[7]) {
case "FURTHEST" -> FURTHEST;
case "AVERAGE" -> AVERAGE;
default -> NEAREST;
@@ -75,17 +77,17 @@ public class RunBenchmark {
Arrays.asList(NEAREST),
//Arrays.asList(NEAREST, FURTHEST, AVERAGE),
queries, ks);
- } else if (args.length == 7) { // Original M-tree
+ } else if (args.length == 8) { // Original M-tree
percentageToRecallMTree(cfg,
objects,
queries, ks,
0, 0, null);
} else { // Pivoting M-tree
- List<LocalAbstractObject> pivots = loadDataset(args[9]);
+ List<LocalAbstractObject> pivots = loadDataset(args[10], objClass);
percentageToRecallMTree(cfg,
objects,
queries, ks,
- Integer.parseInt(args[7]), Integer.parseInt(args[8]), pivots);
+ Integer.parseInt(args[8]), Integer.parseInt(args[9]), pivots);
}
}
@@ -202,8 +204,10 @@ public class RunBenchmark {
mTree.insert(opIns);
long buildingTime = System.currentTimeMillis() - buildingStartTimeStamp;
+ mTree.markObjectsWithBucketIds();
mTree.printStatistics();
- System.out.println("Fat factor: " + mTree.getFatFactor());
+ //mTree.checkConsistency();
+ //System.out.println("Fat factor: " + mTree.getFatFactor());
System.out.println("Building time: " + buildingTime + " msec");
System.out.println("kNN queries will be executed for k=" + Arrays.toString(ks));
@@ -257,16 +261,16 @@ public class RunBenchmark {
"",
String.valueOf(k),
String.valueOf(percentage),
- String.format("%.2f,%.2f,%.2f,%.2f",
+ String.format(Locale.ENGLISH, "%.2f,%.2f,%.2f,%.2f",
recallStats.getMin(),
recallStats.getAverage(),
recallStats.getMedian(),
recallStats.getMax()),
- String.format("%.2f", timeStats.getAverage())));
+ String.format(Locale.ENGLISH, "%.2f", timeStats.getAverage())));
minimalRecall = recallStats.getMin();
- if (minimalRecall == 1.0)
- break;
+ //if (minimalRecall == 1.0)
+ // break;
}
}
}
@@ -285,17 +289,21 @@ public class RunBenchmark {
alg.executeOperation(op);
} catch (AlgorithmMethodException | NoSuchMethodException ex) { }
KnnResultPair pair = new KnnResultPair();
+ if (!AbstractObjectKey.class.equals(op.getQueryObject().getObjectKey().getClass()))
+ System.out.println("ERROR: Ground truth query has a non-expected abstract key! " + op.getQueryObject().getObjectKey());
pair.id = op.getQueryObject().getLocatorURI();
pair.kNNObjects = new ArrayList<>(maxK);
for (RankedAbstractObject o : op)
pair.kNNObjects.add(o);
+ if (pair.kNNObjects.size() != maxK)
+ System.out.println("ERROR: Ground truth for query " + pair.id + " constains " + pair.kNNObjects.size() + " objects, Expected " + maxK);
return pair;
})
.collect(Collectors.toMap(entry -> entry.id, entry -> entry.kNNObjects));
return kNNResults;
}
- private static List<LocalAbstractObject> loadDataset(String path) throws IOException {
- return new AbstractObjectList<>(new StreamGenericAbstractObjectIterator<>(ObjectFloatVectorNeuralNetworkL2.class, path));
+ private static List<LocalAbstractObject> loadDataset(String path, Class<? extends LocalAbstractObject> objCls) throws IOException {
+ return new AbstractObjectList<>(new StreamGenericAbstractObjectIterator<>(objCls, path));
}
}
diff --git a/src/mhtree/benchmarking/Stats.java b/src/mhtree/benchmarking/Stats.java
index f9d793fe2d8d9d4c83df648123bd707f2e6ad748..8b760a4300851e45bae97517de685ca23a42e5f0 100644
--- a/src/mhtree/benchmarking/Stats.java
+++ b/src/mhtree/benchmarking/Stats.java
@@ -6,7 +6,7 @@ import java.util.List;
public class Stats {
List<Double> sortedData;
- Stats(List<Double> data) {
+ public Stats(List<Double> data) {
sortedData = data;
Collections.sort(sortedData);
}