/*
* This file is part of ELKI:
* Environment for Developing KDD-Applications Supported by Index-Structures
*
* Copyright (C) 2017
* ELKI Development Team
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
package de.lmu.ifi.dbs.elki.algorithm;
import org.junit.Test;
import de.lmu.ifi.dbs.elki.data.DoubleVector;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.QueryUtil;
import de.lmu.ifi.dbs.elki.database.StaticArrayDatabase;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.KNNList;
import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
import de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.ManhattanDistanceFunction;
import de.lmu.ifi.dbs.elki.index.tree.spatial.SpatialEntry;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu.DeLiCluTree;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.deliclu.DeLiCluTreeFactory;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTree;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTreeFactory;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTreeNode;
import de.lmu.ifi.dbs.elki.math.MeanVariance;
import de.lmu.ifi.dbs.elki.persistent.AbstractPageFileFactory;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.ParameterException;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
/**
* Unit test for kNN joins.
*
* @author Erich Schubert
* @since 0.4.0
*/
public class KNNJoinTest {
// the following values depend on the data set used!
String dataset = "elki/testdata/unittests/uebungsblatt-2d-mini.csv";
// size of the data set
int shoulds = 20;
// mean number of 2NN
double mean2nnEuclid = 2.85;
// variance
double var2nnEuclid = 0.87105;
// mean number of 2NN
double mean2nnManhattan = 2.9;
// variance
double var2nnManhattan = 0.83157894;
@Test
public void testLinearScan() {
Database db = AbstractSimpleAlgorithmTest.makeSimpleDatabase(dataset, shoulds);
Relation<NumberVector> relation = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
// Euclidean
{
DistanceQuery<NumberVector> dq = db.getDistanceQuery(relation, EuclideanDistanceFunction.STATIC);
KNNQuery<NumberVector> knnq = QueryUtil.getLinearScanKNNQuery(dq);
MeanVariance meansize = new MeanVariance();
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
KNNList knnlist = knnq.getKNNForDBID(iditer, 2);
meansize.put(knnlist.size());
}
org.junit.Assert.assertEquals("Euclidean mean 2NN", mean2nnEuclid, meansize.getMean(), 0.00001);
org.junit.Assert.assertEquals("Euclidean variance 2NN", var2nnEuclid, meansize.getSampleVariance(), 0.00001);
}
// Manhattan
{
DistanceQuery<NumberVector> dq = db.getDistanceQuery(relation, ManhattanDistanceFunction.STATIC);
KNNQuery<NumberVector> knnq = QueryUtil.getLinearScanKNNQuery(dq);
MeanVariance meansize = new MeanVariance();
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
KNNList knnlist = knnq.getKNNForDBID(iditer, 2);
meansize.put(knnlist.size());
}
org.junit.Assert.assertEquals("Manhattan mean 2NN", mean2nnManhattan, meansize.getMean(), 0.00001);
org.junit.Assert.assertEquals("Manhattan variance 2NN", var2nnManhattan, meansize.getSampleVariance(), 0.00001);
}
}
/**
* Test {@link RStarTree} using a file based database connection.
*
* @throws ParameterException on errors.
*/
@Test
public void testKNNJoinRtreeMini() {
ListParameterization spatparams = new ListParameterization();
spatparams.addParameter(StaticArrayDatabase.Parameterizer.INDEX_ID, RStarTreeFactory.class);
spatparams.addParameter(AbstractPageFileFactory.Parameterizer.PAGE_SIZE_ID, 200);
doKNNJoin(spatparams);
}
/**
* Test {@link RStarTree} using a file based database connection.
*
* @throws ParameterException on errors.
*/
@Test
public void testKNNJoinRtreeMaxi() {
ListParameterization spatparams = new ListParameterization();
spatparams.addParameter(StaticArrayDatabase.Parameterizer.INDEX_ID, RStarTreeFactory.class);
spatparams.addParameter(AbstractPageFileFactory.Parameterizer.PAGE_SIZE_ID, 2000);
doKNNJoin(spatparams);
}
/**
* Test {@link DeLiCluTree} using a file based database connection.
*
* @throws ParameterException on errors.
*/
@Test
public void testKNNJoinDeLiCluTreeMini() {
ListParameterization spatparams = new ListParameterization();
spatparams.addParameter(StaticArrayDatabase.Parameterizer.INDEX_ID, DeLiCluTreeFactory.class);
spatparams.addParameter(AbstractPageFileFactory.Parameterizer.PAGE_SIZE_ID, 200);
doKNNJoin(spatparams);
}
/**
* Actual test routine.
*
* @param inputparams
* @throws ParameterException
*/
void doKNNJoin(ListParameterization inputparams) {
Database db = AbstractSimpleAlgorithmTest.makeSimpleDatabase(dataset, shoulds, inputparams);
Relation<NumberVector> relation = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
// Euclidean
{
KNNJoin<DoubleVector, ?, ?> knnjoin = new KNNJoin<DoubleVector, RStarTreeNode, SpatialEntry>(EuclideanDistanceFunction.STATIC, 2);
Relation<KNNList> result = knnjoin.run(db);
MeanVariance meansize = new MeanVariance();
for(DBIDIter id = relation.getDBIDs().iter(); id.valid(); id.advance()) {
KNNList knnlist = result.get(id);
meansize.put(knnlist.size());
}
org.junit.Assert.assertEquals("Euclidean mean 2NN set size", mean2nnEuclid, meansize.getMean(), 0.00001);
org.junit.Assert.assertEquals("Euclidean variance 2NN", var2nnEuclid, meansize.getSampleVariance(), 0.00001);
}
// Manhattan
{
KNNJoin<DoubleVector, ?, ?> knnjoin = new KNNJoin<DoubleVector, RStarTreeNode, SpatialEntry>(ManhattanDistanceFunction.STATIC, 2);
Relation<KNNList> result = knnjoin.run(db);
MeanVariance meansize = new MeanVariance();
for(DBIDIter id = relation.getDBIDs().iter(); id.valid(); id.advance()) {
KNNList knnlist = result.get(id);
meansize.put(knnlist.size());
}
org.junit.Assert.assertEquals("Manhattan mean 2NN", mean2nnManhattan, meansize.getMean(), 0.00001);
org.junit.Assert.assertEquals("Manhattan variance 2NN", var2nnManhattan, meansize.getSampleVariance(), 0.00001);
}
}
}