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Diffstat (limited to 'elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/subspace/PROCLUSTest.java')
-rw-r--r-- | elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/subspace/PROCLUSTest.java | 100 |
1 files changed, 100 insertions, 0 deletions
diff --git a/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/subspace/PROCLUSTest.java b/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/subspace/PROCLUSTest.java new file mode 100644 index 00000000..75d42b07 --- /dev/null +++ b/elki/src/test/java/de/lmu/ifi/dbs/elki/algorithm/clustering/subspace/PROCLUSTest.java @@ -0,0 +1,100 @@ +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) 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.data.Clustering; +import de.lmu.ifi.dbs.elki.data.DoubleVector; +import de.lmu.ifi.dbs.elki.database.Database; +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 PROCLUS run, and compares the result with a clustering + * derived from the data set labels. This test ensures that PROCLUS 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 + * @since 0.3 + */ +public class PROCLUSTest extends AbstractSimpleAlgorithmTest implements JUnit4Test { + /** + * Run PROCLUS with fixed parameters and compare the result to a golden + * standard. + * + * @throws ParameterException + */ + @Test + public void testPROCLUSResults() { + Database db = makeSimpleDatabase(UNITTEST + "subspace-simple.csv", 600); + + ListParameterization params = new ListParameterization(); + params.addParameter(PROCLUS.Parameterizer.L_ID, 1); + params.addParameter(PROCLUS.Parameterizer.K_ID, 4); + // NOTE: PROCLUS quality heavily depends on random... + params.addParameter(PROCLUS.Parameterizer.SEED_ID, 0); + + // setup algorithm + PROCLUS<DoubleVector> proclus = ClassGenericsUtil.parameterizeOrAbort(PROCLUS.class, params); + testParameterizationOk(params); + + // run PROCLUS on database + Clustering<?> result = proclus.run(db); + + testFMeasure(db, result, 0.75156834); + testClusterSizes(result, new int[] { 41, 95, 200, 264 }); + } + + /** + * Run PROCLUS with fixed parameters and compare the result to a golden + * standard. + * + * @throws ParameterException + */ + @Test + public void testPROCLUSSubspaceOverlapping() { + Database db = makeSimpleDatabase(UNITTEST + "subspace-overlapping-3-4d.ascii", 850); + + // Setup algorithm + ListParameterization params = new ListParameterization(); + params.addParameter(PROCLUS.Parameterizer.L_ID, 2); + params.addParameter(PROCLUS.Parameterizer.K_ID, 3); + // NOTE: PROCLUS quality heavily depends on random... + params.addParameter(PROCLUS.Parameterizer.SEED_ID, 1); + PROCLUS<DoubleVector> proclus = ClassGenericsUtil.parameterizeOrAbort(PROCLUS.class, params); + testParameterizationOk(params); + + // run PROCLUS on database + Clustering<?> result = proclus.run(db); + testFMeasure(db, result, 0.95856963); + testClusterSizes(result, new int[] { 151, 286, 413 }); + } +}
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