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The Adobe Experience Manager SDK
/**
* Copyright 2004-2005 The Apache Software Foundation.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.lucene.queries.mlt;
import java.io.*;
import java.util.*;
import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
import org.apache.lucene.document.Document;
import org.apache.lucene.index.Fields;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.IndexableField;
import org.apache.lucene.index.MultiFields;
import org.apache.lucene.index.Term;
import org.apache.lucene.index.Terms;
import org.apache.lucene.index.TermsEnum;
import org.apache.lucene.search.*;
import org.apache.lucene.search.similarities.DefaultSimilarity;
import org.apache.lucene.search.similarities.TFIDFSimilarity;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.CharsRef;
import org.apache.lucene.util.IOUtils;
import org.apache.lucene.util.PriorityQueue;
import org.apache.lucene.util.UnicodeUtil;
/**
* Generate "more like this" similarity queries.
* Based on this mail:
*
* Lucene does let you access the document frequency of terms, with IndexReader.docFreq().
* Term frequencies can be computed by re-tokenizing the text, which, for a single document,
* is usually fast enough. But looking up the docFreq() of every term in the document is
* probably too slow.
*
* You can use some heuristics to prune the set of terms, to avoid calling docFreq() too much,
* or at all. Since you're trying to maximize a tf*idf score, you're probably most interested
* in terms with a high tf. Choosing a tf threshold even as low as two or three will radically
* reduce the number of terms under consideration. Another heuristic is that terms with a
* high idf (i.e., a low df) tend to be longer. So you could threshold the terms by the
* number of characters, not selecting anything less than, e.g., six or seven characters.
* With these sorts of heuristics you can usually find small set of, e.g., ten or fewer terms
* that do a pretty good job of characterizing a document.
*
* It all depends on what you're trying to do. If you're trying to eek out that last percent
* of precision and recall regardless of computational difficulty so that you can win a TREC
* competition, then the techniques I mention above are useless. But if you're trying to
* provide a "more like this" button on a search results page that does a decent job and has
* good performance, such techniques might be useful.
*
* An efficient, effective "more-like-this" query generator would be a great contribution, if
* anyone's interested. I'd imagine that it would take a Reader or a String (the document's
* text), analyzer Analyzer, and return a set of representative terms using heuristics like those
* above. The frequency and length thresholds could be parameters, etc.
*
* Doug
*
Initial Usage
*
* This class has lots of options to try to make it efficient and flexible.
* The simplest possible usage is as follows. The bold
* fragment is specific to this class.
*
*
*
* IndexReader ir = ...
* IndexSearcher is = ...
*
* MoreLikeThis mlt = new MoreLikeThis(ir);
* Reader target = ... // orig source of doc you want to find similarities to
* Query query = mlt.like( target);
*
* Hits hits = is.search(query);
* // now the usual iteration thru 'hits' - the only thing to watch for is to make sure
* //you ignore the doc if it matches your 'target' document, as it should be similar to itself
*
*
*
* Thus you:
*
* - do your normal, Lucene setup for searching,
*
- create a MoreLikeThis,
*
- get the text of the doc you want to find similarities to
*
- then call one of the like() calls to generate a similarity query
*
- call the searcher to find the similar docs
*
*
* More Advanced Usage
*
* You may want to use {@link #setFieldNames setFieldNames(...)} so you can examine
* multiple fields (e.g. body and title) for similarity.
*
*
* Depending on the size of your index and the size and makeup of your documents you
* may want to call the other set methods to control how the similarity queries are
* generated:
*
* - {@link #setMinTermFreq setMinTermFreq(...)}
*
- {@link #setMinDocFreq setMinDocFreq(...)}
*
- {@link #setMaxDocFreq setMaxDocFreq(...)}
*
- {@link #setMaxDocFreqPct setMaxDocFreqPct(...)}
*
- {@link #setMinWordLen setMinWordLen(...)}
*
- {@link #setMaxWordLen setMaxWordLen(...)}
*
- {@link #setMaxQueryTerms setMaxQueryTerms(...)}
*
- {@link #setMaxNumTokensParsed setMaxNumTokensParsed(...)}
*
- {@link #setStopWords setStopWord(...)}
*
*
*
*
* Changes: Mark Harwood 29/02/04
* Some bugfixing, some refactoring, some optimisation.
* - bugfix: retrieveTerms(int docNum) was not working for indexes without a termvector -added missing code
* - bugfix: No significant terms being created for fields with a termvector - because
* was only counting one occurrence per term/field pair in calculations(ie not including frequency info from TermVector)
* - refactor: moved common code into isNoiseWord()
* - optimise: when no termvector support available - used maxNumTermsParsed to limit amount of tokenization
*
*/
public final class MoreLikeThis {
/**
* Default maximum number of tokens to parse in each example doc field that is not stored with TermVector support.
*
* @see #getMaxNumTokensParsed
*/
public static final int DEFAULT_MAX_NUM_TOKENS_PARSED = 5000;
/**
* Ignore terms with less than this frequency in the source doc.
*
* @see #getMinTermFreq
* @see #setMinTermFreq
*/
public static final int DEFAULT_MIN_TERM_FREQ = 2;
/**
* Ignore words which do not occur in at least this many docs.
*
* @see #getMinDocFreq
* @see #setMinDocFreq
*/
public static final int DEFAULT_MIN_DOC_FREQ = 5;
/**
* Ignore words which occur in more than this many docs.
*
* @see #getMaxDocFreq
* @see #setMaxDocFreq
* @see #setMaxDocFreqPct
*/
public static final int DEFAULT_MAX_DOC_FREQ = Integer.MAX_VALUE;
/**
* Boost terms in query based on score.
*
* @see #isBoost
* @see #setBoost
*/
public static final boolean DEFAULT_BOOST = false;
/**
* Default field names. Null is used to specify that the field names should be looked
* up at runtime from the provided reader.
*/
public static final String[] DEFAULT_FIELD_NAMES = new String[]{"contents"};
/**
* Ignore words less than this length or if 0 then this has no effect.
*
* @see #getMinWordLen
* @see #setMinWordLen
*/
public static final int DEFAULT_MIN_WORD_LENGTH = 0;
/**
* Ignore words greater than this length or if 0 then this has no effect.
*
* @see #getMaxWordLen
* @see #setMaxWordLen
*/
public static final int DEFAULT_MAX_WORD_LENGTH = 0;
/**
* Default set of stopwords.
* If null means to allow stop words.
*
* @see #setStopWords
* @see #getStopWords
*/
public static final Set DEFAULT_STOP_WORDS = null;
/**
* Current set of stop words.
*/
private Set stopWords = DEFAULT_STOP_WORDS;
/**
* Return a Query with no more than this many terms.
*
* @see BooleanQuery#getMaxClauseCount
* @see #getMaxQueryTerms
* @see #setMaxQueryTerms
*/
public static final int DEFAULT_MAX_QUERY_TERMS = 25;
/**
* Analyzer that will be used to parse the doc.
*/
private Analyzer analyzer = null;
/**
* Ignore words less frequent that this.
*/
private int minTermFreq = DEFAULT_MIN_TERM_FREQ;
/**
* Ignore words which do not occur in at least this many docs.
*/
private int minDocFreq = DEFAULT_MIN_DOC_FREQ;
/**
* Ignore words which occur in more than this many docs.
*/
private int maxDocFreq = DEFAULT_MAX_DOC_FREQ;
/**
* Should we apply a boost to the Query based on the scores?
*/
private boolean boost = DEFAULT_BOOST;
/**
* Field name we'll analyze.
*/
private String[] fieldNames = DEFAULT_FIELD_NAMES;
/**
* The maximum number of tokens to parse in each example doc field that is not stored with TermVector support
*/
private int maxNumTokensParsed = DEFAULT_MAX_NUM_TOKENS_PARSED;
/**
* Ignore words if less than this len.
*/
private int minWordLen = DEFAULT_MIN_WORD_LENGTH;
/**
* Ignore words if greater than this len.
*/
private int maxWordLen = DEFAULT_MAX_WORD_LENGTH;
/**
* Don't return a query longer than this.
*/
private int maxQueryTerms = DEFAULT_MAX_QUERY_TERMS;
/**
* For idf() calculations.
*/
private TFIDFSimilarity similarity;// = new DefaultSimilarity();
/**
* IndexReader to use
*/
private final IndexReader ir;
/**
* Boost factor to use when boosting the terms
*/
private float boostFactor = 1;
/**
* Returns the boost factor used when boosting terms
*
* @return the boost factor used when boosting terms
* @see #setBoostFactor(float)
*/
public float getBoostFactor() {
return boostFactor;
}
/**
* Sets the boost factor to use when boosting terms
*
* @see #getBoostFactor()
*/
public void setBoostFactor(float boostFactor) {
this.boostFactor = boostFactor;
}
/**
* Constructor requiring an IndexReader.
*/
public MoreLikeThis(IndexReader ir) {
this(ir, new DefaultSimilarity());
}
public MoreLikeThis(IndexReader ir, TFIDFSimilarity sim) {
this.ir = ir;
this.similarity = sim;
}
public TFIDFSimilarity getSimilarity() {
return similarity;
}
public void setSimilarity(TFIDFSimilarity similarity) {
this.similarity = similarity;
}
/**
* Returns an analyzer that will be used to parse source doc with. The default analyzer
* is not set.
*
* @return the analyzer that will be used to parse source doc with.
*/
public Analyzer getAnalyzer() {
return analyzer;
}
/**
* Sets the analyzer to use. An analyzer is not required for generating a query with the
* {@link #like(int)} method, all other 'like' methods require an analyzer.
*
* @param analyzer the analyzer to use to tokenize text.
*/
public void setAnalyzer(Analyzer analyzer) {
this.analyzer = analyzer;
}
/**
* Returns the frequency below which terms will be ignored in the source doc. The default
* frequency is the {@link #DEFAULT_MIN_TERM_FREQ}.
*
* @return the frequency below which terms will be ignored in the source doc.
*/
public int getMinTermFreq() {
return minTermFreq;
}
/**
* Sets the frequency below which terms will be ignored in the source doc.
*
* @param minTermFreq the frequency below which terms will be ignored in the source doc.
*/
public void setMinTermFreq(int minTermFreq) {
this.minTermFreq = minTermFreq;
}
/**
* Returns the frequency at which words will be ignored which do not occur in at least this
* many docs. The default frequency is {@link #DEFAULT_MIN_DOC_FREQ}.
*
* @return the frequency at which words will be ignored which do not occur in at least this
* many docs.
*/
public int getMinDocFreq() {
return minDocFreq;
}
/**
* Sets the frequency at which words will be ignored which do not occur in at least this
* many docs.
*
* @param minDocFreq the frequency at which words will be ignored which do not occur in at
* least this many docs.
*/
public void setMinDocFreq(int minDocFreq) {
this.minDocFreq = minDocFreq;
}
/**
* Returns the maximum frequency in which words may still appear.
* Words that appear in more than this many docs will be ignored. The default frequency is
* {@link #DEFAULT_MAX_DOC_FREQ}.
*
* @return get the maximum frequency at which words are still allowed,
* words which occur in more docs than this are ignored.
*/
public int getMaxDocFreq() {
return maxDocFreq;
}
/**
* Set the maximum frequency in which words may still appear. Words that appear
* in more than this many docs will be ignored.
*
* @param maxFreq the maximum count of documents that a term may appear
* in to be still considered relevant
*/
public void setMaxDocFreq(int maxFreq) {
this.maxDocFreq = maxFreq;
}
/**
* Set the maximum percentage in which words may still appear. Words that appear
* in more than this many percent of all docs will be ignored.
*
* @param maxPercentage the maximum percentage of documents (0-100) that a term may appear
* in to be still considered relevant
*/
public void setMaxDocFreqPct(int maxPercentage) {
this.maxDocFreq = maxPercentage * ir.numDocs() / 100;
}
/**
* Returns whether to boost terms in query based on "score" or not. The default is
* {@link #DEFAULT_BOOST}.
*
* @return whether to boost terms in query based on "score" or not.
* @see #setBoost
*/
public boolean isBoost() {
return boost;
}
/**
* Sets whether to boost terms in query based on "score" or not.
*
* @param boost true to boost terms in query based on "score", false otherwise.
* @see #isBoost
*/
public void setBoost(boolean boost) {
this.boost = boost;
}
/**
* Returns the field names that will be used when generating the 'More Like This' query.
* The default field names that will be used is {@link #DEFAULT_FIELD_NAMES}.
*
* @return the field names that will be used when generating the 'More Like This' query.
*/
public String[] getFieldNames() {
return fieldNames;
}
/**
* Sets the field names that will be used when generating the 'More Like This' query.
* Set this to null for the field names to be determined at runtime from the IndexReader
* provided in the constructor.
*
* @param fieldNames the field names that will be used when generating the 'More Like This'
* query.
*/
public void setFieldNames(String[] fieldNames) {
this.fieldNames = fieldNames;
}
/**
* Returns the minimum word length below which words will be ignored. Set this to 0 for no
* minimum word length. The default is {@link #DEFAULT_MIN_WORD_LENGTH}.
*
* @return the minimum word length below which words will be ignored.
*/
public int getMinWordLen() {
return minWordLen;
}
/**
* Sets the minimum word length below which words will be ignored.
*
* @param minWordLen the minimum word length below which words will be ignored.
*/
public void setMinWordLen(int minWordLen) {
this.minWordLen = minWordLen;
}
/**
* Returns the maximum word length above which words will be ignored. Set this to 0 for no
* maximum word length. The default is {@link #DEFAULT_MAX_WORD_LENGTH}.
*
* @return the maximum word length above which words will be ignored.
*/
public int getMaxWordLen() {
return maxWordLen;
}
/**
* Sets the maximum word length above which words will be ignored.
*
* @param maxWordLen the maximum word length above which words will be ignored.
*/
public void setMaxWordLen(int maxWordLen) {
this.maxWordLen = maxWordLen;
}
/**
* Set the set of stopwords.
* Any word in this set is considered "uninteresting" and ignored.
* Even if your Analyzer allows stopwords, you might want to tell the MoreLikeThis code to ignore them, as
* for the purposes of document similarity it seems reasonable to assume that "a stop word is never interesting".
*
* @param stopWords set of stopwords, if null it means to allow stop words
* @see #getStopWords
*/
public void setStopWords(Set stopWords) {
this.stopWords = stopWords;
}
/**
* Get the current stop words being used.
*
* @see #setStopWords
*/
public Set getStopWords() {
return stopWords;
}
/**
* Returns the maximum number of query terms that will be included in any generated query.
* The default is {@link #DEFAULT_MAX_QUERY_TERMS}.
*
* @return the maximum number of query terms that will be included in any generated query.
*/
public int getMaxQueryTerms() {
return maxQueryTerms;
}
/**
* Sets the maximum number of query terms that will be included in any generated query.
*
* @param maxQueryTerms the maximum number of query terms that will be included in any
* generated query.
*/
public void setMaxQueryTerms(int maxQueryTerms) {
this.maxQueryTerms = maxQueryTerms;
}
/**
* @return The maximum number of tokens to parse in each example doc field that is not stored with TermVector support
* @see #DEFAULT_MAX_NUM_TOKENS_PARSED
*/
public int getMaxNumTokensParsed() {
return maxNumTokensParsed;
}
/**
* @param i The maximum number of tokens to parse in each example doc field that is not stored with TermVector support
*/
public void setMaxNumTokensParsed(int i) {
maxNumTokensParsed = i;
}
/**
* Return a query that will return docs like the passed lucene document ID.
*
* @param docNum the documentID of the lucene doc to generate the 'More Like This" query for.
* @return a query that will return docs like the passed lucene document ID.
*/
public Query like(int docNum) throws IOException {
if (fieldNames == null) {
// gather list of valid fields from lucene
Collection fields = MultiFields.getIndexedFields(ir);
fieldNames = fields.toArray(new String[fields.size()]);
}
return createQuery(retrieveTerms(docNum));
}
/**
* Return a query that will return docs like the passed Reader.
*
* @return a query that will return docs like the passed Reader.
*/
public Query like(Reader r, String fieldName) throws IOException {
return createQuery(retrieveTerms(r, fieldName));
}
/**
* Create the More like query from a PriorityQueue
*/
private Query createQuery(PriorityQueue q) {
BooleanQuery query = new BooleanQuery();
Object cur;
int qterms = 0;
float bestScore = 0;
while ((cur = q.pop()) != null) {
Object[] ar = (Object[]) cur;
TermQuery tq = new TermQuery(new Term((String) ar[1], (String) ar[0]));
if (boost) {
if (qterms == 0) {
bestScore = ((Float) ar[2]);
}
float myScore = ((Float) ar[2]);
tq.setBoost(boostFactor * myScore / bestScore);
}
try {
query.add(tq, BooleanClause.Occur.SHOULD);
}
catch (BooleanQuery.TooManyClauses ignore) {
break;
}
qterms++;
if (maxQueryTerms > 0 && qterms >= maxQueryTerms) {
break;
}
}
return query;
}
/**
* Create a PriorityQueue from a word->tf map.
*
* @param words a map of words keyed on the word(String) with Int objects as the values.
*/
private PriorityQueue createQueue(Map words) throws IOException {
// have collected all words in doc and their freqs
int numDocs = ir.numDocs();
FreqQ res = new FreqQ(words.size()); // will order words by score
for (String word : words.keySet()) { // for every word
int tf = words.get(word).x; // term freq in the source doc
if (minTermFreq > 0 && tf < minTermFreq) {
continue; // filter out words that don't occur enough times in the source
}
// go through all the fields and find the largest document frequency
String topField = fieldNames[0];
int docFreq = 0;
for (String fieldName : fieldNames) {
int freq = ir.docFreq(new Term(fieldName, word));
topField = (freq > docFreq) ? fieldName : topField;
docFreq = (freq > docFreq) ? freq : docFreq;
}
if (minDocFreq > 0 && docFreq < minDocFreq) {
continue; // filter out words that don't occur in enough docs
}
if (docFreq > maxDocFreq) {
continue; // filter out words that occur in too many docs
}
if (docFreq == 0) {
continue; // index update problem?
}
float idf = similarity.idf(docFreq, numDocs);
float score = tf * idf;
// only really need 1st 3 entries, other ones are for troubleshooting
res.insertWithOverflow(new Object[]{word, // the word
topField, // the top field
score, // overall score
idf, // idf
docFreq, // freq in all docs
tf
});
}
return res;
}
/**
* Describe the parameters that control how the "more like this" query is formed.
*/
public String describeParams() {
StringBuilder sb = new StringBuilder();
sb.append("\t").append("maxQueryTerms : ").append(maxQueryTerms).append("\n");
sb.append("\t").append("minWordLen : ").append(minWordLen).append("\n");
sb.append("\t").append("maxWordLen : ").append(maxWordLen).append("\n");
sb.append("\t").append("fieldNames : ");
String delim = "";
for (String fieldName : fieldNames) {
sb.append(delim).append(fieldName);
delim = ", ";
}
sb.append("\n");
sb.append("\t").append("boost : ").append(boost).append("\n");
sb.append("\t").append("minTermFreq : ").append(minTermFreq).append("\n");
sb.append("\t").append("minDocFreq : ").append(minDocFreq).append("\n");
return sb.toString();
}
/**
* Find words for a more-like-this query former.
*
* @param docNum the id of the lucene document from which to find terms
*/
public PriorityQueue retrieveTerms(int docNum) throws IOException {
Map termFreqMap = new HashMap();
for (String fieldName : fieldNames) {
final Fields vectors = ir.getTermVectors(docNum);
final Terms vector;
if (vectors != null) {
vector = vectors.terms(fieldName);
} else {
vector = null;
}
// field does not store term vector info
if (vector == null) {
Document d = ir.document(docNum);
IndexableField fields[] = d.getFields(fieldName);
for (IndexableField field : fields) {
final String stringValue = field.stringValue();
if (stringValue != null) {
addTermFrequencies(new StringReader(stringValue), termFreqMap, fieldName);
}
}
} else {
addTermFrequencies(termFreqMap, vector);
}
}
return createQueue(termFreqMap);
}
/**
* Adds terms and frequencies found in vector into the Map termFreqMap
*
* @param termFreqMap a Map of terms and their frequencies
* @param vector List of terms and their frequencies for a doc/field
*/
private void addTermFrequencies(Map termFreqMap, Terms vector) throws IOException {
final TermsEnum termsEnum = vector.iterator(null);
final CharsRef spare = new CharsRef();
BytesRef text;
while((text = termsEnum.next()) != null) {
UnicodeUtil.UTF8toUTF16(text, spare);
final String term = spare.toString();
if (isNoiseWord(term)) {
continue;
}
final int freq = (int) termsEnum.totalTermFreq();
// increment frequency
Int cnt = termFreqMap.get(term);
if (cnt == null) {
cnt = new Int();
termFreqMap.put(term, cnt);
cnt.x = freq;
} else {
cnt.x += freq;
}
}
}
/**
* Adds term frequencies found by tokenizing text from reader into the Map words
*
* @param r a source of text to be tokenized
* @param termFreqMap a Map of terms and their frequencies
* @param fieldName Used by analyzer for any special per-field analysis
*/
private void addTermFrequencies(Reader r, Map termFreqMap, String fieldName)
throws IOException {
if (analyzer == null) {
throw new UnsupportedOperationException("To use MoreLikeThis without " +
"term vectors, you must provide an Analyzer");
}
TokenStream ts = analyzer.tokenStream(fieldName, r);
try {
int tokenCount = 0;
// for every token
CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class);
ts.reset();
while (ts.incrementToken()) {
String word = termAtt.toString();
tokenCount++;
if (tokenCount > maxNumTokensParsed) {
break;
}
if (isNoiseWord(word)) {
continue;
}
// increment frequency
Int cnt = termFreqMap.get(word);
if (cnt == null) {
termFreqMap.put(word, new Int());
} else {
cnt.x++;
}
}
ts.end();
} finally {
IOUtils.closeWhileHandlingException(ts);
}
}
/**
* determines if the passed term is likely to be of interest in "more like" comparisons
*
* @param term The word being considered
* @return true if should be ignored, false if should be used in further analysis
*/
private boolean isNoiseWord(String term) {
int len = term.length();
if (minWordLen > 0 && len < minWordLen) {
return true;
}
if (maxWordLen > 0 && len > maxWordLen) {
return true;
}
return stopWords != null && stopWords.contains(term);
}
/**
* Find words for a more-like-this query former.
* The result is a priority queue of arrays with one entry for every word in the document.
* Each array has 6 elements.
* The elements are:
*
* - The word (String)
*
- The top field that this word comes from (String)
*
- The score for this word (Float)
*
- The IDF value (Float)
*
- The frequency of this word in the index (Integer)
*
- The frequency of this word in the source document (Integer)
*
* This is a somewhat "advanced" routine, and in general only the 1st entry in the array is of interest.
* This method is exposed so that you can identify the "interesting words" in a document.
* For an easier method to call see {@link #retrieveInterestingTerms retrieveInterestingTerms()}.
*
* @param r the reader that has the content of the document
* @param fieldName field passed to the analyzer to use when analyzing the content
* @return the most interesting words in the document ordered by score, with the highest scoring, or best entry, first
* @see #retrieveInterestingTerms
*/
public PriorityQueue retrieveTerms(Reader r, String fieldName) throws IOException {
Map words = new HashMap();
addTermFrequencies(r, words, fieldName);
return createQueue(words);
}
/**
* @see #retrieveInterestingTerms(java.io.Reader, String)
*/
public String[] retrieveInterestingTerms(int docNum) throws IOException {
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