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Carrot2 search results clustering framework. Minimal functional subset
(core algorithms and infrastructure, no document sources).
/*
* Carrot2 project.
*
* Copyright (C) 2002-2016, 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.lingo;
import java.util.Collections;
import java.util.List;
import org.carrot2.core.Cluster;
import org.carrot2.core.Document;
import org.carrot2.core.IClusteringAlgorithm;
import org.carrot2.core.LanguageCode;
import org.carrot2.core.ProcessingComponentBase;
import org.carrot2.core.ProcessingException;
import org.carrot2.core.attribute.AttributeNames;
import org.carrot2.core.attribute.CommonAttributes;
import org.carrot2.core.attribute.Init;
import org.carrot2.core.attribute.Internal;
import org.carrot2.core.attribute.Processing;
import org.carrot2.text.clustering.IMonolingualClusteringAlgorithm;
import org.carrot2.text.clustering.MultilingualClustering;
import org.carrot2.text.preprocessing.LabelFormatter;
import org.carrot2.text.preprocessing.PreprocessingContext;
import org.carrot2.text.preprocessing.pipeline.CompletePreprocessingPipeline;
import org.carrot2.text.preprocessing.pipeline.IPreprocessingPipeline;
import org.carrot2.text.vsm.ReducedVectorSpaceModelContext;
import org.carrot2.text.vsm.TermDocumentMatrixBuilder;
import org.carrot2.text.vsm.TermDocumentMatrixReducer;
import org.carrot2.text.vsm.VectorSpaceModelContext;
import org.carrot2.util.attribute.Attribute;
import org.carrot2.util.attribute.AttributeLevel;
import org.carrot2.util.attribute.Bindable;
import org.carrot2.util.attribute.DefaultGroups;
import org.carrot2.util.attribute.Group;
import org.carrot2.util.attribute.Input;
import org.carrot2.util.attribute.Label;
import org.carrot2.util.attribute.Level;
import org.carrot2.util.attribute.Output;
import org.carrot2.util.attribute.Required;
import org.carrot2.util.attribute.constraint.DoubleRange;
import org.carrot2.util.attribute.constraint.ImplementingClasses;
import org.carrot2.util.attribute.constraint.IntRange;
import com.carrotsearch.hppc.BitSet;
import org.carrot2.shaded.guava.common.collect.Lists;
/**
* Lingo clustering algorithm. Implementation as described in: "Stanisław Osiński,
* Dawid Weiss: A Concept-Driven Algorithm for Clustering Search Results. IEEE Intelligent
* Systems, May/June, 3 (vol. 20), 2005, pp. 48—54.".
*/
@Bindable(prefix = "LingoClusteringAlgorithm", inherit = CommonAttributes.class)
@Label("Lingo Clustering")
public class LingoClusteringAlgorithm extends ProcessingComponentBase implements
IClusteringAlgorithm
{
/**
* Query that produced the documents. The query will help the algorithm to create
* better clusters. Therefore, providing the query is optional but desirable.
*/
@Processing
@Input
@Internal
@Attribute(key = AttributeNames.QUERY, inherit = true)
public String query = null;
/**
* Documents to cluster.
*/
@Processing
@Input
@Required
@Internal
@Attribute(key = AttributeNames.DOCUMENTS, inherit = true)
public List documents;
@Processing
@Output
@Internal
@Attribute(key = AttributeNames.CLUSTERS, inherit = true)
public List clusters = null;
/**
* Balance between cluster score and size during cluster sorting. Value equal to 0.0
* will cause Lingo to sort clusters based only on cluster size. Value equal to 1.0
* will cause Lingo to sort clusters based only on cluster score.
*/
@Input
@Processing
@Attribute
@DoubleRange(min = 0.0, max = 1.0)
@Label("Size-Score sorting ratio")
@Level(AttributeLevel.MEDIUM)
@Group(DefaultGroups.CLUSTERS)
public double scoreWeight = 0.0;
/**
* Desired cluster count base. Base factor used to calculate the number of clusters
* based on the number of documents on input. The larger the value, the more clusters
* will be created. The number of clusters created by the algorithm will be
* proportional to the cluster count base, but not in a linear way.
*/
@Input
@Processing
@Attribute
@IntRange(min = 2, max = 100)
@Label("Cluster count base")
@Level(AttributeLevel.BASIC)
@Group(DefaultGroups.CLUSTERS)
public int desiredClusterCountBase = 30;
/**
* Common preprocessing tasks handler, contains bindable attributes.
*/
@Init
@Input
@Attribute
@Internal
@ImplementingClasses(classes = {}, strict = false)
@Level(AttributeLevel.ADVANCED)
public IPreprocessingPipeline preprocessingPipeline = new CompletePreprocessingPipeline();
/**
* Term-document matrix builder for the algorithm, contains bindable attributes.
*/
public final TermDocumentMatrixBuilder matrixBuilder = new TermDocumentMatrixBuilder();
/**
* Term-document matrix reducer for the algorithm, contains bindable attributes.
*/
public final TermDocumentMatrixReducer matrixReducer = new TermDocumentMatrixReducer();
/**
* Cluster label builder, contains bindable attributes.
*/
public final ClusterBuilder clusterBuilder = new ClusterBuilder();
/**
* Cluster label formatter, contains bindable attributes.
*/
public final LabelFormatter labelFormatter = new LabelFormatter();
/**
* A helper for performing multilingual clustering.
*/
public final MultilingualClustering multilingualClustering = new MultilingualClustering();
/**
* Performs Lingo clustering of {@link #documents}.
*/
@Override
public void process() throws ProcessingException
{
// There is a tiny trick here to support multilingual clustering without
// refactoring the whole component: we remember the original list of documents
// and invoke clustering for each language separately within the
// IMonolingualClusteringAlgorithm implementation below. This is safe because
// processing components are not thread-safe by definition and
// IMonolingualClusteringAlgorithm forbids concurrent execution by contract.
final List originalDocuments = documents;
clusters = multilingualClustering.process(documents,
new IMonolingualClusteringAlgorithm()
{
public List process(List documents,
LanguageCode language)
{
LingoClusteringAlgorithm.this.documents = documents;
LingoClusteringAlgorithm.this.cluster(language);
return LingoClusteringAlgorithm.this.clusters;
}
});
documents = originalDocuments;
}
/**
* Performs the actual clustering with an assumption that all documents are written in
* one language.
*/
private void cluster(LanguageCode language)
{
// Preprocessing of documents
final PreprocessingContext context = preprocessingPipeline.preprocess(documents,
query, language);
// Further processing only if there are words to process
clusters = Lists.newArrayList();
if (context.hasLabels())
{
// Term-document matrix building and reduction
final VectorSpaceModelContext vsmContext = new VectorSpaceModelContext(
context);
final ReducedVectorSpaceModelContext reducedVsmContext = new ReducedVectorSpaceModelContext(
vsmContext);
LingoProcessingContext lingoContext = new LingoProcessingContext(
reducedVsmContext);
matrixBuilder.buildTermDocumentMatrix(vsmContext);
matrixBuilder.buildTermPhraseMatrix(vsmContext);
matrixReducer.reduce(reducedVsmContext,
computeClusterCount(desiredClusterCountBase, documents.size()));
// Cluster label building
clusterBuilder.buildLabels(lingoContext, matrixBuilder.termWeighting);
// Document assignment
clusterBuilder.assignDocuments(lingoContext);
// Cluster merging
clusterBuilder.merge(lingoContext);
// Format final clusters
final int [] clusterLabelIndex = lingoContext.clusterLabelFeatureIndex;
final BitSet [] clusterDocuments = lingoContext.clusterDocuments;
final double [] clusterLabelScore = lingoContext.clusterLabelScore;
for (int i = 0; i < clusterLabelIndex.length; i++)
{
final Cluster cluster = new Cluster();
final int labelFeature = clusterLabelIndex[i];
if (labelFeature < 0)
{
// Cluster removed during merging
continue;
}
// Add label and score
cluster.addPhrases(labelFormatter.format(context, labelFeature));
cluster.setAttribute(Cluster.SCORE, clusterLabelScore[i]);
// Add documents
final BitSet bs = clusterDocuments[i];
for (int bit = bs.nextSetBit(0); bit >= 0; bit = bs.nextSetBit(bit + 1))
{
cluster.addDocuments(documents.get(bit));
}
// Add cluster
clusters.add(cluster);
}
Collections.sort(clusters,
Cluster.byReversedWeightedScoreAndSizeComparator(scoreWeight));
}
Cluster.appendOtherTopics(documents, clusters);
}
/**
* Computes the number of clusters to create based on a very simple heuristic based on
* the number of documents on input.
*/
static int computeClusterCount(int desiredClusterCountBase, int documentCount)
{
return Math.min(
(int) ((desiredClusterCountBase / 10.0) * Math.sqrt(documentCount)),
documentCount);
}
}