package de.lmu.ifi.dbs.elki.algorithm.clustering.subspace;
/*
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 org.junit.Test;
import de.lmu.ifi.dbs.elki.JUnit4Test;
import de.lmu.ifi.dbs.elki.algorithm.AbstractSimpleAlgorithmTest;
import de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByLabelClustering;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.DoubleVector;
import de.lmu.ifi.dbs.elki.data.model.Model;
import de.lmu.ifi.dbs.elki.data.model.SubspaceModel;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.evaluation.paircounting.PairCountingFMeasure;
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;
/**
* Performs a full CLIQUE run, and compares the result with a clustering derived
* from the data set labels. This test ensures that CLIQUE performance doesn't
* unexpectedly drop on this data set (and also ensures that the algorithms
* work, as a side effect).
*
* @author Elke Achtert
* @author Katharina Rausch
* @author Erich Schubert
*/
public class TestCLIQUEResults extends AbstractSimpleAlgorithmTest implements JUnit4Test {
/**
* Run CLIQUE with fixed parameters and compare the result to a golden
* standard.
*
* @throws ParameterException
*/
@Test
public void testCLIQUEResults() {
Database db = makeSimpleDatabase(UNITTEST + "subspace-simple.csv", 600);
ListParameterization params = new ListParameterization();
params.addParameter(CLIQUE.TAU_ID, "0.1");
params.addParameter(CLIQUE.XSI_ID, 20);
// setup algorithm
CLIQUE<DoubleVector> clique = ClassGenericsUtil.parameterizeOrAbort(CLIQUE.class, params);
testParameterizationOk(params);
// run CLIQUE on database
Clustering<SubspaceModel<DoubleVector>> result = clique.run(db);
// Run by-label as reference
ByLabelClustering bylabel = new ByLabelClustering(true, null);
Clustering<Model> rbl = bylabel.run(db);
double score = PairCountingFMeasure.compareClusterings(result, rbl, 1.0);
org.junit.Assert.assertEquals(this.getClass().getSimpleName() + ": Score does not match.", 0.9882, score, 0.0001);
testClusterSizes(result, new int[] { 200, 200, 216, 400 });
}
/**
* Run CLIQUE with fixed parameters and compare the result to a golden
* standard.
*
* @throws ParameterException
*/
@Test
public void testCLIQUESubspaceOverlapping() {
Database db = makeSimpleDatabase(UNITTEST + "subspace-overlapping-3-4d.ascii", 850);
// Setup algorithm
ListParameterization params = new ListParameterization();
params.addParameter(CLIQUE.TAU_ID, 0.2);
params.addParameter(CLIQUE.XSI_ID, 6);
CLIQUE<DoubleVector> clique = ClassGenericsUtil.parameterizeOrAbort(CLIQUE.class, params);
testParameterizationOk(params);
// run CLIQUE on database
Clustering<SubspaceModel<DoubleVector>> result = clique.run(db);
testFMeasure(db, result, 0.433661);
testClusterSizes(result, new int[] { 255, 409, 458, 458, 480 });
}
}