/*
* Carrot2 project.
*
* Copyright (C) 2002-2014, Dawid Weiss, Stanisław Osiński.
* All rights reserved.
*
* Refer to the full license file "carrot2.LICENSE"
* in the root folder of the repository checkout or at:
* http://www.carrot2.org/carrot2.LICENSE
*/
package org.carrot2.clustering.stc;
import java.util.Collection;
import java.util.List;
import java.util.Set;
import org.carrot2.core.Cluster;
import org.carrot2.core.Document;
import org.carrot2.core.ProcessingResult;
import org.carrot2.core.test.ClusteringAlgorithmTestBase;
import org.carrot2.core.test.SampleDocumentData;
import org.carrot2.text.preprocessing.CaseNormalizer;
import org.carrot2.util.attribute.AttributeUtils;
import org.junit.Test;
import com.google.common.collect.Lists;
import com.google.common.io.Resources;
/**
* Test cases for the {@link STCClusteringAlgorithm}.
*/
public class STCClusteringAlgorithmTest extends
ClusteringAlgorithmTestBase<STCClusteringAlgorithm>
{
@Override
public Class<STCClusteringAlgorithm> getComponentClass()
{
return STCClusteringAlgorithm.class;
}
@Test
public void testClusteringWithDfThreshold()
{
processingAttributes.put(
AttributeUtils.getKey(CaseNormalizer.class, "dfThreshold"), 20);
final Collection<Cluster> clustersWithThreshold = cluster(
SampleDocumentData.DOCUMENTS_DATA_MINING).getClusters();
// Clustering with df threshold must not fail
assertThat(clustersWithThreshold.size()).isGreaterThan(0);
}
@Test
public void testMaxClusters()
{
processingAttributes.put(
AttributeUtils.getKey(STCClusteringAlgorithm.class, "maxClusters"), 9);
final Collection<Cluster> clusters =
cluster(SampleDocumentData.DOCUMENTS_DATA_MINING).getClusters();
// 9 + others
assertThat(clusters.size()).isEqualTo(9 + 1);
}
@Test
public void testComputeIntersection()
{
int [] t1;
t1 = new int [] {0, 1, 2, 1, 2, 3};
assertEquals(2, STCClusteringAlgorithm.computeIntersection(t1, 0, 3, t1, 3, 3));
t1 = new int [] {0, 1, 2, 3, 5, 6};
assertEquals(0, STCClusteringAlgorithm.computeIntersection(t1, 0, 3, t1, 3, 3));
t1 = new int [] {0, 1, 2, -1, 2, 6};
assertEquals(1, STCClusteringAlgorithm.computeIntersection(t1, 0, 3, t1, 3, 3));
t1 = new int [] {0, 1, 2, 0};
assertEquals(1, STCClusteringAlgorithm.computeIntersection(t1, 0, 3, t1, 3, 1));
}
@Test
public void testMergingBaseClustersWithStemEquivalentPhrases()
{
List<Document> documents = Lists.newArrayList();
documents.add(new Document("good programs . foo1"));
documents.add(new Document("foo2 good programs . foo2"));
documents.add(new Document("good programs taste good"));
documents.add(new Document("good programs are good"));
documents.add(new Document("good programming . foo3"));
documents.add(new Document("foo4 good programming . foo4"));
documents.add(new Document("good programming makes you feel better"));
// Lower base cluster score.
STCClusteringAlgorithmDescriptor.attributeBuilder(processingAttributes)
.minBaseClusterScore(0);
ProcessingResult pr = cluster(documents);
Set<String> clusterLabels = collectClusterLabels(pr);
assertThat("Good Programs").isIn(clusterLabels);
assertThat("Good Programming").isNotIn(clusterLabels);
}
/**
* CARROT-1008: STC is not using term stems.
*/
@Test
public void testCarrot1008() throws Exception
{
ProcessingResult pr = ProcessingResult.deserialize(
Resources.newInputStreamSupplier(
Resources.getResource(this.getClass(), "CARROT-1008.xml")).getInput());
STCClusteringAlgorithmDescriptor.attributeBuilder(processingAttributes)
.maxClusters(30);
pr = cluster(pr.getDocuments());
dumpClusterLabels(pr);
Set<String> clusterLabels = ClusteringAlgorithmTestBase.collectClusterLabels(pr);
assertThat(
clusterLabels.contains("Guns") &&
clusterLabels.contains("Gun")).isFalse();
}
}