/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
*/
package weka.filters.unsupervised.attribute;
import weka.classifiers.meta.FilteredClassifier;
import weka.core.Instances;
import weka.core.TestInstances;
import weka.filters.AbstractFilterTest;
import weka.filters.Filter;
import junit.framework.Test;
import junit.framework.TestSuite;
/**
* Tests ClusterMembership. Run from the command line with: <p/>
* java weka.filters.unsupervised.attribute.ClusterMembershipTest
*
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision: 1.4 $
*/
public class ClusterMembershipTest
extends AbstractFilterTest {
public ClusterMembershipTest(String name) {
super(name);
}
/** Need to remove non-nominal/numeric attributes, set class index */
protected void setUp() throws Exception {
super.setUp();
// remove attributes that are not nominal/numeric
int i = 0;
while (i < m_Instances.numAttributes()) {
if ( ( !m_Instances.attribute(i).isNominal()
&& !m_Instances.attribute(i).isNumeric() )
|| m_Instances.attribute(i).isDate() )
m_Instances.deleteAttributeAt(i);
else
i++;
}
// class index
m_Instances.setClassIndex(1);
}
/** Creates a default ClusterMembership */
public Filter getFilter() {
ClusterMembership f = new ClusterMembership();
return f;
}
/**
* returns the configured FilteredClassifier. Since the base classifier is
* determined heuristically, derived tests might need to adjust it.
*
* @return the configured FilteredClassifier
*/
protected FilteredClassifier getFilteredClassifier() {
FilteredClassifier result;
result = new FilteredClassifier();
result.setFilter(getFilter());
result.setClassifier(new weka.classifiers.trees.J48());
return result;
}
/**
* returns data generated for the FilteredClassifier test
*
* @return the dataset for the FilteredClassifier
* @throws Exception if generation of data fails
*/
protected Instances getFilteredClassifierData() throws Exception{
TestInstances test;
Instances result;
test = TestInstances.forCapabilities(m_FilteredClassifier.getCapabilities());
test.setClassIndex(TestInstances.CLASS_IS_LAST);
result = test.generate();
return result;
}
public void testNominal() {
m_Filter = getFilter();
m_Instances.setClassIndex(1);
Instances result = useFilter();
// classes must be still the same
assertEquals(m_Instances.numClasses(), result.numClasses());
// at least one cluster per label besides class
assertTrue(result.numAttributes() >= m_Instances.numClasses() + 1);
}
public void testNumeric() {
m_Filter = getFilter();
m_Instances.setClassIndex(2);
Instances result = useFilter();
// at least one cluster (only one clusterer is generateed) besides class
assertTrue(result.numAttributes() >= 1 + 1);
}
public static Test suite() {
return new TestSuite(ClusterMembershipTest.class);
}
public static void main(String[] args){
junit.textui.TestRunner.run(suite());
}
}