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package de.lmu.ifi.dbs.elki.index;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2011
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
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/>.
*/
import static junit.framework.Assert.assertEquals;
import static junit.framework.Assert.assertTrue;
import java.util.List;
import org.junit.Test;
import de.lmu.ifi.dbs.elki.JUnit4Test;
import de.lmu.ifi.dbs.elki.data.DoubleVector;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.StaticArrayDatabase;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
import de.lmu.ifi.dbs.elki.database.query.DistanceResultPair;
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.query.knn.LinearScanKNNQuery;
import de.lmu.ifi.dbs.elki.database.query.range.LinearScanRangeQuery;
import de.lmu.ifi.dbs.elki.database.query.range.RangeQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.datasource.FileBasedDatabaseConnection;
import de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
import de.lmu.ifi.dbs.elki.index.tree.TreeIndexFactory;
import de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree.MTree;
import de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree.MTreeFactory;
import de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query.MetricalIndexKNNQuery;
import de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.query.MetricalIndexRangeQuery;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.AbstractRStarTreeFactory;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.DoubleDistanceRStarTreeKNNQuery;
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.DoubleDistanceRStarTreeRangeQuery;
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.util.ApproximateLeastOverlapInsertionStrategy;
import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.ParameterException;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
/**
* Test case to validate some index structures for accuracy. For a known data
* set and query point, the top 10 nearest neighbors are queried and verified.
*
* Note that the internal operation of the index structure is not tested this
* way, only whether the database object with the index still returns reasonable
* results.
*
* @author Erich Schubert
*/
public class TestIndexStructures implements JUnit4Test {
// the following values depend on the data set used!
String dataset = "data/testdata/unittests/hierarchical-3d2d1d.csv";
// size of the data set
int shoulds = 600;
// query point
double[] querypoint = new double[] { 0.5, 0.5, 0.5 };
// number of kNN to query
int k = 10;
// the 10 next neighbors of the query point
double[][] shouldc = new double[][] { { 0.45000428746088883, 0.484504234161508, 0.5538595167151342 }, { 0.4111050036231091, 0.429204794352013, 0.4689430202460606 }, { 0.4758477631164003, 0.6021538103067177, 0.5556807408692025 }, { 0.4163288957164025, 0.49604545242979536, 0.4054361013566713 }, { 0.5819940640461848, 0.48586944418231115, 0.6289592025558619 }, { 0.4373568207802466, 0.3468650110814596, 0.49566951629699485 }, { 0.40283109564192643, 0.6301433694690401, 0.44313571161129883 }, { 0.6545840114867083, 0.4919617658889418, 0.5905461546078652 }, { 0.6011097673869055, 0.6562921241634017, 0.44830647520493694 }, { 0.5127485678175534, 0.29708449200895504, 0.561722374659424 }, };
// and their distances
double[] shouldd = new double[] { 0.07510351238126374, 0.11780839322826206, 0.11882371989803064, 0.1263282354232315, 0.15347043712184602, 0.1655090505771259, 0.17208323533934652, 0.17933052146586306, 0.19319066655063877, 0.21247795391113142 };
DoubleDistance eps = new DoubleDistance(0.21247795391113142);
/**
* Test exact query, also to validate the test is correct.
*
* @throws ParameterException on errors.
*/
@Test
public void testExact() {
ListParameterization params = new ListParameterization();
testFileBasedDatabaseConnection(params, LinearScanKNNQuery.class, LinearScanRangeQuery.class);
}
/**
* Test {@link MTree} using a file based database connection.
*
* @throws ParameterException on errors.
*/
@Test
public void testMetrical() {
ListParameterization metparams = new ListParameterization();
metparams.addParameter(StaticArrayDatabase.INDEX_ID, MTreeFactory.class);
metparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 100);
testFileBasedDatabaseConnection(metparams, MetricalIndexKNNQuery.class, MetricalIndexRangeQuery.class);
}
/**
* Test {@link RStarTree} using a file based database connection.
*
* @throws ParameterException on errors.
*/
@Test
public void testRStarTree() {
ListParameterization spatparams = new ListParameterization();
spatparams.addParameter(StaticArrayDatabase.INDEX_ID, RStarTreeFactory.class);
spatparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 300);
testFileBasedDatabaseConnection(spatparams, DoubleDistanceRStarTreeKNNQuery.class, DoubleDistanceRStarTreeRangeQuery.class);
}
/**
* Test {@link RStarTree} using a file based database connection. With "fast"
* mode enabled on an extreme level (since this should only reduce
* performance, not accuracy!)
*
* @throws ParameterException on errors.
*/
@Test
public void testRStarTreeFast() {
ListParameterization spatparams = new ListParameterization();
spatparams.addParameter(StaticArrayDatabase.INDEX_ID, RStarTreeFactory.class);
spatparams.addParameter(AbstractRStarTreeFactory.INSERTION_STRATEGY_ID, ApproximateLeastOverlapInsertionStrategy.class);
spatparams.addParameter(ApproximateLeastOverlapInsertionStrategy.Parameterizer.INSERTION_CANDIDATES_ID, 1);
spatparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 300);
testFileBasedDatabaseConnection(spatparams, DoubleDistanceRStarTreeKNNQuery.class, DoubleDistanceRStarTreeRangeQuery.class);
}
/**
* Test {@link XTree} using a file based database connection.
*
* @throws ParameterException
*/
// @Test
// public void testXTree() {
// ListParameterization xtreeparams = new ListParameterization();
// xtreeparams.addParameter(StaticArrayDatabase.INDEX_ID,
// experimentalcode.shared.index.xtree.XTreeFactory.class);
// xtreeparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 300);
// testFileBasedDatabaseConnection(xtreeparams,
// DoubleDistanceRStarTreeKNNQuery.class,
// DoubleDistanceRStarTreeRangeQuery.class);
// }
/**
* Actual test routine.
*
* @param inputparams
* @throws ParameterException
*/
void testFileBasedDatabaseConnection(ListParameterization inputparams, Class<?> expectKNNQuery, Class<?> expectRangeQuery) {
inputparams.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);
// get database
Database db = ClassGenericsUtil.parameterizeOrAbort(StaticArrayDatabase.class, inputparams);
db.initialize();
Relation<DoubleVector> rep = db.getRelation(TypeUtil.DOUBLE_VECTOR_FIELD);
DistanceQuery<DoubleVector, DoubleDistance> dist = db.getDistanceQuery(rep, EuclideanDistanceFunction.STATIC);
// verify data set size.
assertTrue(rep.size() == shoulds);
{
// get the 10 next neighbors
DoubleVector dv = new DoubleVector(querypoint);
KNNQuery<DoubleVector, DoubleDistance> knnq = db.getKNNQuery(dist, k);
assertTrue("Returned knn query is not of expected class.", expectKNNQuery.isAssignableFrom(knnq.getClass()));
List<DistanceResultPair<DoubleDistance>> ids = knnq.getKNNForObject(dv, k);
assertEquals("Result size does not match expectation!", shouldd.length, ids.size());
// verify that the neighbors match.
int i = 0;
for(DistanceResultPair<DoubleDistance> res : ids) {
// Verify distance
assertEquals("Expected distance doesn't match.", shouldd[i], res.getDistance().doubleValue());
// verify vector
DBID id = res.getDBID();
DoubleVector c = rep.get(id);
DoubleVector c2 = new DoubleVector(shouldc[i]);
assertEquals("Expected vector doesn't match: " + c.toString(), 0.0, dist.distance(c, c2).doubleValue(), 0.00001);
i++;
}
}
{
// Do a range query
DoubleVector dv = new DoubleVector(querypoint);
RangeQuery<DoubleVector, DoubleDistance> rangeq = db.getRangeQuery(dist, eps);
assertTrue("Returned range query is not of expected class.", expectRangeQuery.isAssignableFrom(rangeq.getClass()));
List<DistanceResultPair<DoubleDistance>> ids = rangeq.getRangeForObject(dv, eps);
assertEquals("Result size does not match expectation!", shouldd.length, ids.size());
// verify that the neighbors match.
int i = 0;
for(DistanceResultPair<DoubleDistance> res : ids) {
// Verify distance
assertEquals("Expected distance doesn't match.", shouldd[i], res.getDistance().doubleValue());
// verify vector
DBID id = res.getDBID();
DoubleVector c = rep.get(id);
DoubleVector c2 = new DoubleVector(shouldc[i]);
assertEquals("Expected vector doesn't match: " + c.toString(), 0.0, dist.distance(c, c2).doubleValue(), 0.00001);
i++;
}
}
}
}
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