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LUCENE-5795: MoreLikeThisQuery now only collects the top N terms 

git-svn-id: https://svn.apache.org/repos/asf/lucene/dev/trunk@1609474 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Simon Willnauer 2014-07-10 15:15:44 +00:00
parent 8f7dc8d07b
commit 173a44e67c
3 changed files with 135 additions and 48 deletions
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4
lucene/CHANGES.txt
View File

@ -143,6 +143,10 @@ Optimizations
to another analyzer, e.g. per field name: PerFieldAnalyzerWrapper and
Solr's schema support. (Shay Banon, Uwe Schindler, Robert Muir)
* LUCENE-5795: MoreLikeThisQuery now only collects the top N terms instead
of collecting all terms from the like text when building the query.
(Alex Ksikes, Simon Willnauer)
Bug Fixes
* LUCENE-5796: Fixes the Scorer.getChildren() method for two combinations

112
lucene/queries/src/java/org/apache/lucene/queries/mlt/MoreLikeThis.java
View File

@ -604,22 +604,19 @@ public final class MoreLikeThis {
/**
* Create the More like query from a PriorityQueue
*/
private Query createQuery(PriorityQueue<Object[]> q) {
private Query createQuery(PriorityQueue<ScoreTerm> q) {
BooleanQuery query = new BooleanQuery();
Object cur;
int qterms = 0;
float bestScore = 0;
ScoreTerm scoreTerm;
float bestScore = -1;
while ((cur = q.pop()) != null) {
Object[] ar = (Object[]) cur;
TermQuery tq = new TermQuery(new Term((String) ar[1], (String) ar[0]));
while ((scoreTerm = q.pop()) != null) {
TermQuery tq = new TermQuery(new Term(scoreTerm.topField, scoreTerm.word));
if (boost) {
if (qterms == 0) {
bestScore = ((Float) ar[2]);
if (bestScore == -1) {
bestScore = (scoreTerm.score);
}
float myScore = ((Float) ar[2]);
float myScore = (scoreTerm.score);
tq.setBoost(boostFactor * myScore / bestScore);
}
@ -629,13 +626,7 @@ public final class MoreLikeThis {
catch (BooleanQuery.TooManyClauses ignore) {
break;
}
qterms++;
if (maxQueryTerms > 0 && qterms >= maxQueryTerms) {
break;
}
}
return query;
}
@ -644,10 +635,11 @@ public final class MoreLikeThis {
*
* @param words a map of words keyed on the word(String) with Int objects as the values.
*/
private PriorityQueue<Object[]> createQueue(Map<String, Int> words) throws IOException {
private PriorityQueue<ScoreTerm> createQueue(Map<String, Int> 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
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
@ -679,16 +671,18 @@ public final class MoreLikeThis {
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
});
if (queue.size() < limit) {
// there is still space in the queue
queue.add(new ScoreTerm(word, topField, score, idf, docFreq, tf));
} 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, idf, docFreq, tf);
queue.updateTop();
}
}
}
return res;
return queue;
}
/**
@ -717,7 +711,7 @@ public final class MoreLikeThis {
*
* @param docNum the id of the lucene document from which to find terms
*/
public PriorityQueue<Object[]> retrieveTerms(int docNum) throws IOException {
private PriorityQueue<ScoreTerm> retrieveTerms(int docNum) throws IOException {
Map<String, Int> termFreqMap = new HashMap<>();
for (String fieldName : fieldNames) {
final Fields vectors = ir.getTermVectors(docNum);
@ -857,7 +851,7 @@ public final class MoreLikeThis {
* @return the most interesting words in the document ordered by score, with the highest scoring, or best entry, first
* @see #retrieveInterestingTerms
*/
public PriorityQueue<Object[]> retrieveTerms(Reader r, String fieldName) throws IOException {
private PriorityQueue<ScoreTerm> retrieveTerms(Reader r, String fieldName) throws IOException {
Map<String, Int> words = new HashMap<>();
addTermFrequencies(r, words, fieldName);
return createQueue(words);
@ -868,13 +862,12 @@ public final class MoreLikeThis {
*/
public String[] retrieveInterestingTerms(int docNum) throws IOException {
ArrayList<Object> al = new ArrayList<>(maxQueryTerms);
PriorityQueue<Object[]> pq = retrieveTerms(docNum);
Object cur;
PriorityQueue<ScoreTerm> pq = retrieveTerms(docNum);
ScoreTerm scoreTerm;
int lim = maxQueryTerms; // have to be careful, retrieveTerms returns all words but that's probably not useful to our caller...
// we just want to return the top words
while (((cur = pq.pop()) != null) && lim-- > 0) {
Object[] ar = (Object[]) cur;
al.add(ar[0]); // the 1st entry is the interesting word
while (((scoreTerm = pq.pop()) != null) && lim-- > 0) {
al.add(scoreTerm.word); // the 1st entry is the interesting word
}
String[] res = new String[al.size()];
return al.toArray(res);
@ -892,13 +885,12 @@ public final class MoreLikeThis {
*/
public String[] retrieveInterestingTerms(Reader r, String fieldName) throws IOException {
ArrayList<Object> al = new ArrayList<>(maxQueryTerms);
PriorityQueue<Object[]> pq = retrieveTerms(r, fieldName);
Object cur;
PriorityQueue<ScoreTerm> pq = retrieveTerms(r, fieldName);
ScoreTerm scoreTerm;
int lim = maxQueryTerms; // have to be careful, retrieveTerms returns all words but that's probably not useful to our caller...
// we just want to return the top words
while (((cur = pq.pop()) != null) && lim-- > 0) {
Object[] ar = (Object[]) cur;
al.add(ar[0]); // the 1st entry is the interesting word
while (((scoreTerm = pq.pop()) != null) && lim-- > 0) {
al.add(scoreTerm.word); // the 1st entry is the interesting word
}
String[] res = new String[al.size()];
return al.toArray(res);
@ -907,16 +899,42 @@ public final class MoreLikeThis {
/**
* PriorityQueue that orders words by score.
*/
private static class FreqQ extends PriorityQueue<Object[]> {
FreqQ(int s) {
super(s);
private static class FreqQ extends PriorityQueue<ScoreTerm> {
FreqQ(int maxSize) {
super(maxSize);
}
@Override
protected boolean lessThan(Object[] aa, Object[] bb) {
Float fa = (Float) aa[2];
Float fb = (Float) bb[2];
return fa > fb;
protected boolean lessThan(ScoreTerm a, ScoreTerm b) {
return a.score < b.score;
}
}
private static class ScoreTerm {
// only really need 1st 3 entries, other ones are for troubleshooting
String word;
String topField;
float score;
float idf;
int docFreq;
int tf;
ScoreTerm(String word, String topField, float score, float idf, int docFreq, int tf) {
this.word = word;
this.topField = topField;
this.score = score;
this.idf = idf;
this.docFreq = docFreq;
this.tf = tf;
}
void update(String word, String topField, float score, float idf, int docFreq, int tf) {
this.word = word;
this.topField = topField;
this.score = score;
this.idf = idf;
this.docFreq = docFreq;
this.tf = tf;
}
}

67
lucene/queries/src/test/org/apache/lucene/queries/mlt/TestMoreLikeThis.java
View File

@ -74,7 +74,15 @@ public class TestMoreLikeThis extends LuceneTestCase {
doc.add(newTextField("text", text, Field.Store.YES));
writer.addDocument(doc);
}
private void addDoc(RandomIndexWriter writer, String[] texts) throws IOException {
Document doc = new Document();
for (String text : texts) {
doc.add(newTextField("text", text, Field.Store.YES));
}
writer.addDocument(doc);
}
public void testBoostFactor() throws Throwable {
Map<String,Float> originalValues = getOriginalValues();
@ -166,5 +174,62 @@ public class TestMoreLikeThis extends LuceneTestCase {
Query query = new MoreLikeThisQuery("this is a test", new String[] { "text" }, new MockAnalyzer(random()), "text");
QueryUtils.check(random(), query, searcher);
}
public void testTopN() throws Exception {
int numDocs = 100;
int topN = 25;
// add series of docs with terms of decreasing df
Directory dir = newDirectory();
RandomIndexWriter writer = new RandomIndexWriter(random(), dir);
for (int i = 0; i < numDocs; i++) {
addDoc(writer, generateStrSeq(0, i + 1));
}
IndexReader reader = writer.getReader();
writer.shutdown();
// setup MLT query
MoreLikeThis mlt = new MoreLikeThis(reader);
mlt.setAnalyzer(new MockAnalyzer(random(), MockTokenizer.WHITESPACE, false));
mlt.setMaxQueryTerms(topN);
mlt.setMinDocFreq(1);
mlt.setMinTermFreq(1);
mlt.setMinWordLen(1);
mlt.setFieldNames(new String[]{"text"});
// perform MLT query
String likeText = "";
for (String text : generateStrSeq(0, numDocs)) {
likeText += text + " ";
}
BooleanQuery query = (BooleanQuery) mlt.like("text", new StringReader(likeText));
// check best terms are topN of highest idf
List<BooleanClause> clauses = query.clauses();
assertEquals("Expected" + topN + "clauses only!", topN, clauses.size());
Term[] expectedTerms = new Term[topN];
int idx = 0;
for (String text : generateStrSeq(numDocs - topN, topN)) {
expectedTerms[idx++] = new Term("text", text);
}
for (BooleanClause clause : clauses) {
Term term = ((TermQuery) clause.getQuery()).getTerm();
assertTrue(Arrays.asList(expectedTerms).contains(term));
}
// clean up
reader.close();
dir.close();
}
private String[] generateStrSeq(int from, int size) {
String[] generatedStrings = new String[size];
for (int i = 0; i < generatedStrings.length; i++) {
generatedStrings[i] = String.valueOf(from + i);
}
return generatedStrings;
}
// TODO: add tests for the MoreLikeThisQuery
}