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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.AbstractProjectedDBSCAN;
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.datasource.filter.ClassLabelFilter;
import de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj.PreDeConSubspaceIndex.Factory;
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 PreDeCon run, and compare the result with a clustering derived
* from the data set labels. This test ensures that PreDeCon 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
*/
public class TestPreDeConResults extends AbstractSimpleAlgorithmTest implements JUnit4Test {
/**
* Run PreDeCon with fixed parameters and compare the result to a golden
* standard.
*
* @throws ParameterException
*/
@Test
public void testPreDeConResults() {
// Additional input parameters
ListParameterization inp = new ListParameterization();
inp.addParameter(ClassLabelFilter.CLASS_LABEL_INDEX_ID, 1);
Class<?>[] filters = new Class<?>[] { ClassLabelFilter.class };
// FIXME: makeSimpleDatabase currently does also add FILTERS, this doesn't
// work.
Database db = makeSimpleDatabase(UNITTEST + "axis-parallel-subspace-clusters-6d.csv.gz", 2500, inp, filters);
ListParameterization params = new ListParameterization();
// PreDeCon
// FIXME: These parameters do NOT work...
params.addParameter(AbstractProjectedDBSCAN.EPSILON_ID, 50);
params.addParameter(AbstractProjectedDBSCAN.MINPTS_ID, 50);
params.addParameter(AbstractProjectedDBSCAN.LAMBDA_ID, 2);
// setup algorithm
PreDeCon<DoubleVector> predecon = ClassGenericsUtil.parameterizeOrAbort(PreDeCon.class, params);
testParameterizationOk(params);
// run PredeCon on database
Clustering<Model> result = predecon.run(db);
// FIXME: find working parameters...
testFMeasure(db, result, 0.40153);
testClusterSizes(result, new int[] { 2500 });
}
/**
* Run PreDeCon with fixed parameters and compare the result to a golden
* standard.
*
* @throws ParameterException
*/
@Test
public void testPreDeConSubspaceOverlapping() {
Database db = makeSimpleDatabase(UNITTEST + "subspace-overlapping-3-4d.ascii", 850);
// Setup algorithm
ListParameterization params = new ListParameterization();
// PreDeCon
params.addParameter(AbstractProjectedDBSCAN.EPSILON_ID, 2.0);
params.addParameter(AbstractProjectedDBSCAN.MINPTS_ID, 7);
params.addParameter(AbstractProjectedDBSCAN.LAMBDA_ID, 4);
params.addParameter(Factory.DELTA_ID, 0.04);
PreDeCon<DoubleVector> predecon = ClassGenericsUtil.parameterizeOrAbort(PreDeCon.class, params);
testParameterizationOk(params);
// run PredeCon on database
Clustering<Model> result = predecon.run(db);
testFMeasure(db, result, 0.6470817);
testClusterSizes(result, new int[] { 7, 10, 10, 13, 15, 16, 16, 18, 28, 131, 586 });
}
}
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