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Carrot2 search results clustering framework. Minimal functional subset
(core algorithms and infrastructure, no document sources).
/*
* Carrot2 project.
*
* Copyright (C) 2002-2019, 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.text.vsm;
import org.carrot2.core.Document;
import org.carrot2.core.attribute.Internal;
import org.carrot2.core.attribute.Processing;
import org.carrot2.mahout.math.matrix.DoubleMatrix2D;
import org.carrot2.mahout.math.matrix.impl.DenseDoubleMatrix2D;
import org.carrot2.mahout.math.matrix.impl.SparseDoubleMatrix2D;
import org.carrot2.matrix.MatrixUtils;
import org.carrot2.text.analysis.TokenTypeUtils;
import org.carrot2.text.preprocessing.PreprocessingContext;
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.Level;
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 com.carrotsearch.hppc.IntIntHashMap;
import com.carrotsearch.hppc.sorting.IndirectComparator;
import com.carrotsearch.hppc.sorting.IndirectSort;
/**
* Builds a term document matrix based on the provided {@link PreprocessingContext}.
*/
@Bindable(prefix = "TermDocumentMatrixBuilder")
public class TermDocumentMatrixBuilder
{
/** {@link Group} name. */
public static final String MATRIX_MODEL = "Matrix model";
/**
* Title word boost. Gives more weight to words that appeared in
* {@link org.carrot2.core.Document#TITLE} fields.
*/
@Input
@Processing
@Attribute
@DoubleRange(min = 0, max = 10)
@Level(AttributeLevel.MEDIUM)
@Group(DefaultGroups.LABELS)
public double titleWordsBoost = 2.0;
/**
* Maximum matrix size. The maximum number of the term-document matrix elements. The
* larger the size, the more accurate, time- and memory-consuming clustering.
*/
@Input
@Processing
@Attribute
@IntRange(min = 50 * 100)
@Internal(configuration = true)
@Level(AttributeLevel.ADVANCED)
@Group(MATRIX_MODEL)
public int maximumMatrixSize = 250 * 150;
/**
* Maximum word document frequency. The maximum document frequency allowed for words
* as a fraction of all documents. Words with document frequency larger than
* maxWordDf will be ignored. For example, when maxWordDf is
* 0.4, words appearing in more than 40% of documents will be be ignored.
* A value of 1.0 means that all words will be taken into
* account, no matter in how many documents they appear.
*
* This attribute may be useful when certain words appear in most of the input
* documents (e.g. company name from header or footer) and such words dominate the
* cluster labels. In such case, setting maxWordDf to a value lower than
* 1.0, e.g. 0.9 may improve the clusters.
*
*
* Another useful application of this attribute is when there is a need to generate
* only very specific clusters, i.e. clusters containing small numbers of documents.
* This can be achieved by setting maxWordDf to extremely low values,
* e.g. 0.1 or 0.05.
*
*/
@Input
@Processing
@Attribute
@DoubleRange(min = 0.00, max = 1.0)
@Level(AttributeLevel.ADVANCED)
@Group(MATRIX_MODEL)
public double maxWordDf = 0.9;
/**
* Term weighting. The method for calculating weight of words in the term-document
* matrices.
*/
@Input
@Processing
@Attribute
@Required
@ImplementingClasses(classes =
{
LogTfIdfTermWeighting.class, LinearTfIdfTermWeighting.class,
TfTermWeighting.class
}, strict = false)
@Level(AttributeLevel.ADVANCED)
@Group(MATRIX_MODEL)
public ITermWeighting termWeighting = new LogTfIdfTermWeighting();
/**
* Builds a term document matrix from data provided in the context,
* stores the result in there.
*/
public void buildTermDocumentMatrix(VectorSpaceModelContext vsmContext)
{
final PreprocessingContext preprocessingContext = vsmContext.preprocessingContext;
final int documentCount = preprocessingContext.documents.size();
final int [] stemsTf = preprocessingContext.allStems.tf;
final int [][] stemsTfByDocument = preprocessingContext.allStems.tfByDocument;
final byte [] stemsFieldIndices = preprocessingContext.allStems.fieldIndices;
if (documentCount == 0)
{
vsmContext.termDocumentMatrix = new DenseDoubleMatrix2D(0, 0);
vsmContext.stemToRowIndex = new IntIntHashMap();
return;
}
// Determine the index of the title field
int titleFieldIndex = -1;
final String [] fieldsName = preprocessingContext.allFields.name;
for (int i = 0; i < fieldsName.length; i++)
{
if (Document.TITLE.equals(fieldsName[i]))
{
titleFieldIndex = i;
break;
}
}
// Determine the stems we, ideally, should include in the matrix
int [] stemsToInclude = computeRequiredStemIndices(preprocessingContext);
// Sort stems by weight, so that stems get included in the matrix in the order
// of frequency
final double [] stemsWeight = new double [stemsToInclude.length];
for (int i = 0; i < stemsToInclude.length; i++)
{
final int stemIndex = stemsToInclude[i];
stemsWeight[i] = termWeighting.calculateTermWeight(stemsTf[stemIndex],
stemsTfByDocument[stemIndex].length / 2, documentCount)
* getWeightBoost(titleFieldIndex, stemsFieldIndices[stemIndex]);
}
final int [] stemWeightOrder = IndirectSort.mergesort(0, stemsWeight.length,
new IndirectComparator.DescendingDoubleComparator(stemsWeight));
// Calculate the number of terms we can include to fulfill the max matrix size
final int maxRows = maximumMatrixSize / documentCount;
final DoubleMatrix2D tdMatrix = new DenseDoubleMatrix2D(Math.min(maxRows,
stemsToInclude.length), documentCount);
for (int i = 0; i < stemWeightOrder.length && i < maxRows; i++)
{
final int stemIndex = stemsToInclude[stemWeightOrder[i]];
final int [] tfByDocument = stemsTfByDocument[stemIndex];
final int df = tfByDocument.length / 2;
final byte fieldIndices = stemsFieldIndices[stemIndex];
for (int j = 0; j < df; j++) {
double weight = termWeighting.calculateTermWeight(
tfByDocument[j * 2 + 1], df, documentCount);
weight *= getWeightBoost(titleFieldIndex, fieldIndices);
tdMatrix.set(i, tfByDocument[j * 2], weight);
}
}
// Convert stemsToInclude into tdMatrixStemIndices
final IntIntHashMap stemToRowIndex = new IntIntHashMap();
for (int i = 0; i < stemWeightOrder.length && i < tdMatrix.rows(); i++)
{
stemToRowIndex.put(stemsToInclude[stemWeightOrder[i]], i);
}
// Store the results
vsmContext.termDocumentMatrix = tdMatrix;
vsmContext.stemToRowIndex = stemToRowIndex;
}
/**
* Builds a term-phrase matrix in the same space as the main term-document matrix. If
* the processing context contains no phrases,
* {@link VectorSpaceModelContext#termPhraseMatrix} will remain null.
*/
public void buildTermPhraseMatrix(VectorSpaceModelContext context)
{
final PreprocessingContext preprocessingContext = context.preprocessingContext;
final IntIntHashMap stemToRowIndex = context.stemToRowIndex;
final int [] labelsFeatureIndex = preprocessingContext.allLabels.featureIndex;
final int firstPhraseIndex = preprocessingContext.allLabels.firstPhraseIndex;
if (firstPhraseIndex >= 0 && stemToRowIndex.size() > 0)
{
// Build phrase matrix
int [] phraseFeatureIndices = new int [labelsFeatureIndex.length
- firstPhraseIndex];
for (int featureIndex = 0; featureIndex < phraseFeatureIndices.length; featureIndex++)
{
phraseFeatureIndices[featureIndex] = labelsFeatureIndex[featureIndex
+ firstPhraseIndex];
}
final DoubleMatrix2D phraseMatrix = TermDocumentMatrixBuilder
.buildAlignedMatrix(context, phraseFeatureIndices, termWeighting);
MatrixUtils.normalizeColumnL2(phraseMatrix, null);
context.termPhraseMatrix = phraseMatrix.viewDice();
}
}
/**
* Calculates the boost we should apply to a stem, based on the field indices array.
*/
private double getWeightBoost(int titleFieldIndex, final byte fieldIndices)
{
if ((fieldIndices & (1 << titleFieldIndex)) != 0)
{
return titleWordsBoost;
}
return 1;
}
/**
* Computes stem indices of words that are one-word label candidates or are non-stop
* words from phrase label candidates.
*/
private int [] computeRequiredStemIndices(PreprocessingContext context)
{
final int [] labelsFeatureIndex = context.allLabels.featureIndex;
final int [] wordsStemIndex = context.allWords.stemIndex;
final short [] wordsTypes = context.allWords.type;
final int [][] phrasesWordIndices = context.allPhrases.wordIndices;
final int wordCount = wordsStemIndex.length;
final int [][] stemsTfByDocument = context.allStems.tfByDocument;
int documentCount = context.documents.size();
final BitSet requiredStemIndices = new BitSet(labelsFeatureIndex.length);
for (int i = 0; i < labelsFeatureIndex.length; i++)
{
final int featureIndex = labelsFeatureIndex[i];
if (featureIndex < wordCount)
{
addStemIndex(wordsStemIndex, documentCount, stemsTfByDocument,
requiredStemIndices, featureIndex);
}
else
{
final int [] wordIndices = phrasesWordIndices[featureIndex - wordCount];
for (int j = 0; j < wordIndices.length; j++)
{
final int wordIndex = wordIndices[j];
if (!TokenTypeUtils.isCommon(wordsTypes[wordIndex]))
{
addStemIndex(wordsStemIndex, documentCount, stemsTfByDocument,
requiredStemIndices, wordIndex);
}
}
}
}
return requiredStemIndices.asIntLookupContainer().toArray();
}
/**
* Adds stem index to the set with a check on the stem's document frequency.
*/
private void addStemIndex(final int [] wordsStemIndex, int documentCount,
int [][] stemsTfByDocument, final BitSet requiredStemIndices,
final int featureIndex)
{
final int stemIndex = wordsStemIndex[featureIndex];
final int df = stemsTfByDocument[stemIndex].length / 2;
if (((double) df / documentCount) <= maxWordDf)
{
requiredStemIndices.set(stemIndex);
}
}
/**
* Builds a sparse term-document-like matrix for the provided matrixWordIndices in the
* same term space as the original term-document matrix.
*/
static DoubleMatrix2D buildAlignedMatrix(VectorSpaceModelContext vsmContext,
int [] featureIndex, ITermWeighting termWeighting)
{
final IntIntHashMap stemToRowIndex = vsmContext.stemToRowIndex;
if (featureIndex.length == 0)
{
return new DenseDoubleMatrix2D(stemToRowIndex.size(), 0);
}
final DoubleMatrix2D phraseMatrix = new SparseDoubleMatrix2D(stemToRowIndex
.size(), featureIndex.length);
final PreprocessingContext preprocessingContext = vsmContext.preprocessingContext;
final int [] wordsStemIndex = preprocessingContext.allWords.stemIndex;
final int [] stemsTf = preprocessingContext.allStems.tf;
final int [][] stemsTfByDocument = preprocessingContext.allStems.tfByDocument;
final int [][] phrasesWordIndices = preprocessingContext.allPhrases.wordIndices;
final int documentCount = preprocessingContext.documents.size();
final int wordCount = wordsStemIndex.length;
for (int i = 0; i < featureIndex.length; i++)
{
final int feature = featureIndex[i];
final int [] wordIndices;
if (feature < wordCount)
{
wordIndices = new int []
{
feature
};
}
else
{
wordIndices = phrasesWordIndices[feature - wordCount];
}
for (int wordIndex = 0; wordIndex < wordIndices.length; wordIndex++)
{
final int stemIndex = wordsStemIndex[wordIndices[wordIndex]];
final int index = stemToRowIndex.indexOf(stemIndex);
if (stemToRowIndex.indexExists(index))
{
final int rowIndex = stemToRowIndex.indexGet(index);
double weight = termWeighting.calculateTermWeight(stemsTf[stemIndex],
stemsTfByDocument[stemIndex].length / 2, documentCount);
phraseMatrix.setQuick(rowIndex, i, weight);
}
}
}
return phraseMatrix;
}
}