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package de.lmu.ifi.dbs.elki.algorithm.clustering.correlation;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
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.DBSCAN;
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.database.Database;
import de.lmu.ifi.dbs.elki.math.linearalgebra.pca.LimitEigenPairFilter;
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;
/**
* Perform a full 4C run, and compare the result with a clustering derived from
* the data set labels. This test ensures that 4C performance doesn't
* unexpectedly drop on this data set (and also ensures that the algorithms
* work, as a side effect).
*
* @author Erich Schubert
* @author Katharina Rausch
* @since 0.3
*/
public class FourCTest extends AbstractSimpleAlgorithmTest implements JUnit4Test {
/**
* Run 4F with fixed parameters and compare the result to a golden standard.
*
* @throws ParameterException on errors.
*/
@Test
public void testFourCResults() {
Database db = makeSimpleDatabase(UNITTEST + "hierarchical-3d2d1d.csv", 600);
// Setup 4C
ListParameterization params = new ListParameterization();
params.addParameter(DBSCAN.Parameterizer.EPSILON_ID, 0.30);
params.addParameter(DBSCAN.Parameterizer.MINPTS_ID, 50);
params.addParameter(LimitEigenPairFilter.Parameterizer.EIGENPAIR_FILTER_DELTA, 0.5);
params.addParameter(FourC.Settings.Parameterizer.LAMBDA_ID, 1);
FourC<DoubleVector> fourc = ClassGenericsUtil.parameterizeOrAbort(FourC.class, params);
testParameterizationOk(params);
// run 4C on database
Clustering<Model> result = fourc.run(db);
testFMeasure(db, result, 0.7052);
testClusterSizes(result, new int[] { 218, 382 });
}
/**
* Run ERiC with fixed parameters and compare the result to a golden standard.
*
* @throws ParameterException on errors.
*/
@Test
public void testFourCOverlap() {
Database db = makeSimpleDatabase(UNITTEST + "correlation-overlap-3-5d.ascii", 650);
// Setup algorithm
ListParameterization params = new ListParameterization();
// 4C
params.addParameter(DBSCAN.Parameterizer.EPSILON_ID, 3);
params.addParameter(DBSCAN.Parameterizer.MINPTS_ID, 50);
params.addParameter(LimitEigenPairFilter.Parameterizer.EIGENPAIR_FILTER_DELTA, 0.5);
params.addParameter(FourC.Settings.Parameterizer.LAMBDA_ID, 3);
FourC<DoubleVector> fourc = ClassGenericsUtil.parameterizeOrAbort(FourC.class, params);
testParameterizationOk(params);
// run 4C on database
Clustering<Model> result = fourc.run(db);
testFMeasure(db, result, 0.9073744);
testClusterSizes(result, new int[] { 200, 202, 248 });
}
}
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