org.elasticsearch.common.lucene.search.XMoreLikeThis Maven / Gradle / Ivy
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package org.elasticsearch.common.lucene.search;
import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
import org.apache.lucene.index.Fields;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.PostingsEnum;
import org.apache.lucene.index.Term;
import org.apache.lucene.index.Terms;
import org.apache.lucene.index.TermsEnum;
import org.apache.lucene.search.BooleanClause;
import org.apache.lucene.search.BooleanQuery;
import org.apache.lucene.search.BoostQuery;
import org.apache.lucene.search.DocIdSetIterator;
import org.apache.lucene.search.Query;
import org.apache.lucene.search.TermQuery;
import org.apache.lucene.search.similarities.TFIDFSimilarity;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.CharsRefBuilder;
import org.apache.lucene.util.PriorityQueue;
import org.elasticsearch.core.Nullable;
import java.io.IOException;
import java.io.Reader;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Set;
/**
* 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 #setMinWordLen setMinWordLen(...)}
*
- {@link #setMaxWordLen setMaxWordLen(...)}
*
- {@link #setMaxQueryTerms setMaxQueryTerms(...)}
*
- {@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 XMoreLikeThis {
// static {
// assert Version.CURRENT.luceneVersion == org.apache.lucene.util.Version.LUCENE_4_9:
// "Remove this class once we upgrade to Lucene 5.0";
// }
/**
* Default maximum number of tokens to parse in each example doc field that is not stored with TermVector support.
*/
public static final int DEFAULT_MAX_NUM_TOKENS_PARSED = 5000;
/**
* Ignore terms with less than this frequency in the source doc.
*
* @see #setMinTermFreq
*/
public static final int DEFAULT_MIN_TERM_FREQ = 2;
/**
* Ignore words which do not occur in at least this many docs.
*
* @see #setMinDocFreq
*/
public static final int DEFAULT_MIN_DOC_FREQ = 5;
/**
* Ignore words which occur in more than this many docs.
*
* @see #setMaxDocFreq
*/
public static final int DEFAULT_MAX_DOC_FREQ = Integer.MAX_VALUE;
/**
* Boost terms in query based on score.
*
* @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 #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 #setMaxWordLen
*/
public static final int DEFAULT_MAX_WORD_LENGTH = 0;
/**
* Default set of stopwords.
* If null means to allow stop words.
*
* @see #setStopWords
*/
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 #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;
/**
* Current set of skip terms.
*/
private Set skipTerms = null;
/**
* Field name we'll analyze.
*/
private String[] fieldNames = DEFAULT_FIELD_NAMES;
/**
* 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 final TFIDFSimilarity similarity;// = new ClassicSimilarity();
/**
* IndexReader to use
*/
private final IndexReader ir;
/**
* Boost factor to use when boosting the terms
*/
private float boostFactor = 1;
/**
* Sets the boost factor to use when boosting terms
*/
public void setBoostFactor(float boostFactor) {
this.boostFactor = boostFactor;
}
/**
* Sets a list of terms to never select from
*/
public void setSkipTerms(Set skipTerms) {
this.skipTerms = skipTerms;
}
public XMoreLikeThis(IndexReader ir, TFIDFSimilarity sim) {
this.ir = ir;
this.similarity = sim;
}
/**
* Sets the analyzer to use. All 'like' methods require an analyzer.
*
* @param analyzer the analyzer to use to tokenize text.
*/
public void setAnalyzer(Analyzer analyzer) {
this.analyzer = analyzer;
}
/**
* 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;
}
/**
* 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;
}
/**
* 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;
}
/**
* 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.
*/
public void setBoost(boolean boost) {
this.boost = boost;
}
/**
* 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;
}
/**
* 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;
}
/**
* 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
*/
public void setStopWords(Set stopWords) {
this.stopWords = stopWords;
}
/**
* 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 a query that will return docs like the passed Readers.
* This was added in order to treat multi-value fields.
*
* @return a query that will return docs like the passed Readers.
*/
public Query like(String fieldName, Reader... readers) throws IOException {
Map words = new HashMap<>();
for (Reader r : readers) {
addTermFrequencies(r, words, fieldName);
}
return createQuery(createQueue(words));
}
/**
* Return a query that will return docs like the passed Fields.
*
* @return a query that will return docs like the passed Fields.
*/
public Query like(Fields... likeFields) throws IOException {
// get all field names
Set fieldNames = new HashSet<>();
for (Fields fields : likeFields) {
for (String fieldName : fields) {
fieldNames.add(fieldName);
}
}
// term selection is per field, then appended to a single boolean query
BooleanQuery.Builder bq = new BooleanQuery.Builder();
for (String fieldName : fieldNames) {
Map termFreqMap = new HashMap<>();
for (Fields fields : likeFields) {
Terms vector = fields.terms(fieldName);
if (vector != null) {
addTermFrequencies(termFreqMap, vector, fieldName);
}
}
addToQuery(createQueue(termFreqMap, fieldName), bq);
}
return bq.build();
}
/**
* Create the More like query from a PriorityQueue
*/
private Query createQuery(PriorityQueue q) {
BooleanQuery.Builder query = new BooleanQuery.Builder();
addToQuery(q, query);
return query.build();
}
/**
* Add to an existing boolean query the More Like This query from this PriorityQueue
*/
private void addToQuery(PriorityQueue q, BooleanQuery.Builder query) {
ScoreTerm scoreTerm;
float bestScore = -1;
while ((scoreTerm = q.pop()) != null) {
Query tq = new TermQuery(new Term(scoreTerm.topField, scoreTerm.word));
if (boost) {
if (bestScore == -1) {
bestScore = (scoreTerm.score);
}
float myScore = (scoreTerm.score);
tq = new BoostQuery(tq, boostFactor * myScore / bestScore);
}
try {
query.add(tq, BooleanClause.Occur.SHOULD);
} catch (BooleanQuery.TooManyClauses ignore) {
break;
}
}
}
/**
* 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 {
return createQueue(words, this.fieldNames);
}
/**
* 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.
* @param fieldNames an array of field names to override defaults.
*/
private PriorityQueue createQueue(Map words, String... fieldNames) throws IOException {
// have collected all words in doc and their freqs
int numDocs = ir.numDocs();
final int limit = Math.min(maxQueryTerms, words.size());
FreqQ queue = new FreqQ(limit); // 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 = Math.max(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;
if (queue.size() < limit) {
// there is still space in the queue
queue.add(new ScoreTerm(word, topField, score));
} else {
ScoreTerm term = queue.top();
if (term.score < score) { // update the smallest in the queue in place and update the queue.
term.update(word, topField, score);
queue.updateTop();
}
}
}
return queue;
}
/**
* 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
* @param fieldName Optional field name of the terms for skip terms
*/
private void addTermFrequencies(Map termFreqMap, Terms vector, @Nullable String fieldName) throws IOException {
final TermsEnum termsEnum = vector.iterator();
final CharsRefBuilder spare = new CharsRefBuilder();
BytesRef text;
while ((text = termsEnum.next()) != null) {
spare.copyUTF8Bytes(text);
final String term = spare.toString();
if (isNoiseWord(term)) {
continue;
}
if (isSkipTerm(fieldName, term)) {
continue;
}
final PostingsEnum docs = termsEnum.postings(null);
int freq = 0;
while (docs != null && docs.nextDoc() != DocIdSetIterator.NO_MORE_DOCS) {
freq += docs.freq();
}
// 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");
}
try (TokenStream ts = analyzer.tokenStream(fieldName, r)) {
int tokenCount = 0;
// for every token
CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class);
ts.reset();
while (ts.incrementToken()) {
String word = termAtt.toString();
tokenCount++;
/**
* The maximum number of tokens to parse in each example doc field that is not stored with TermVector support
*/
if (tokenCount > DEFAULT_MAX_NUM_TOKENS_PARSED) {
break;
}
if (isNoiseWord(word)) {
continue;
}
if (isSkipTerm(fieldName, word)) {
continue;
}
// increment frequency
Int cnt = termFreqMap.get(word);
if (cnt == null) {
termFreqMap.put(word, new Int());
} else {
cnt.x++;
}
}
ts.end();
}
}
/**
* 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);
}
/**
* determines if the passed term is to be skipped all together
*/
private boolean isSkipTerm(@Nullable String field, String value) {
return field != null && skipTerms != null && skipTerms.contains(new Term(field, value));
}
/**
* PriorityQueue that orders words by score.
*/
private static class FreqQ extends PriorityQueue {
FreqQ(int maxSize) {
super(maxSize);
}
@Override
protected boolean lessThan(ScoreTerm a, ScoreTerm b) {
return a.score < b.score;
}
}
private static class ScoreTerm {
String word;
String topField;
float score;
ScoreTerm(String word, String topField, float score) {
this.word = word;
this.topField = topField;
this.score = score;
}
void update(String word, String topField, float score) {
this.word = word;
this.topField = topField;
this.score = score;
}
}
/**
* Use for frequencies and to avoid renewing Integers.
*/
private static class Int {
int x;
Int() {
x = 1;
}
}
}
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