// LUCENE-5725
public void testMultiValues() throws Exception {
MoreLikeThis mlt = new MoreLikeThis(reader);
Analyzer analyzer = new MockAnalyzer(random(), MockTokenizer.KEYWORD, false);
mlt.setAnalyzer(analyzer);
mlt.setMinDocFreq(1);
mlt.setMinTermFreq(1);
mlt.setMinWordLen(1);
mlt.setFieldNames(new String[] {"text"});
BooleanQuery query =
(BooleanQuery)
mlt.like(
"text",
new StringReader("lucene"),
new StringReader("lucene release"),
new StringReader("apache"),
new StringReader("apache lucene"));
Collection<BooleanClause> clauses = query.clauses();
assertEquals("Expected 2 clauses only!", 2, clauses.size());
for (BooleanClause clause : clauses) {
Term term = ((TermQuery) clause.getQuery()).getTerm();
assertTrue(
Arrays.asList(new Term("text", "lucene"), new Term("text", "apache")).contains(term));
}
analyzer.close();
}
private MoreLikeThis getMoreLikeThis() {
MoreLikeThis mlt = new MoreLikeThis(reader); // Pass the reader reader
mlt.setMaxDocFreqPct(maxPercentage);
mlt.setMaxQueryTerms(maxQueryTerms);
mlt.setMinDocFreq(minDocFreq);
mlt.setMinTermFreq(minTermFreq);
mlt.setAnalyzer(analyzer);
mlt.setFieldNames(new String[] {"text"}); // specify the fields for similiarity
return mlt;
}
// LUCENE-3326
public void testMultiFields() throws Exception {
MoreLikeThis mlt = new MoreLikeThis(reader);
Analyzer analyzer = new MockAnalyzer(random(), MockTokenizer.WHITESPACE, false);
mlt.setAnalyzer(analyzer);
mlt.setMinDocFreq(1);
mlt.setMinTermFreq(1);
mlt.setMinWordLen(1);
mlt.setFieldNames(new String[] {"text", "foobar"});
mlt.like("foobar", new StringReader("this is a test"));
analyzer.close();
}
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.close();
// setup MLT query
MoreLikeThis mlt = new MoreLikeThis(reader);
Analyzer analyzer = new MockAnalyzer(random(), MockTokenizer.WHITESPACE, false);
mlt.setAnalyzer(analyzer);
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
Collection<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();
analyzer.close();
}
public void testBoostFactor() throws Throwable {
Map<String, Float> originalValues = getOriginalValues();
MoreLikeThis mlt = new MoreLikeThis(reader);
Analyzer analyzer = new MockAnalyzer(random(), MockTokenizer.WHITESPACE, false);
mlt.setAnalyzer(analyzer);
mlt.setMinDocFreq(1);
mlt.setMinTermFreq(1);
mlt.setMinWordLen(1);
mlt.setFieldNames(new String[] {"text"});
mlt.setBoost(true);
// this mean that every term boost factor will be multiplied by this
// number
float boostFactor = 5;
mlt.setBoostFactor(boostFactor);
BooleanQuery query = (BooleanQuery) mlt.like("text", new StringReader("lucene release"));
Collection<BooleanClause> clauses = query.clauses();
assertEquals(
"Expected " + originalValues.size() + " clauses.", originalValues.size(), clauses.size());
for (BooleanClause clause : clauses) {
BoostQuery bq = (BoostQuery) clause.getQuery();
TermQuery tq = (TermQuery) bq.getQuery();
Float termBoost = originalValues.get(tq.getTerm().text());
assertNotNull("Expected term " + tq.getTerm().text(), termBoost);
float totalBoost = termBoost * boostFactor;
assertEquals(
"Expected boost of "
+ totalBoost
+ " for term '"
+ tq.getTerm().text()
+ "' got "
+ bq.getBoost(),
totalBoost,
bq.getBoost(),
0.0001);
}
analyzer.close();
}
private Map<String, Float> getOriginalValues() throws IOException {
Map<String, Float> originalValues = new HashMap<>();
MoreLikeThis mlt = new MoreLikeThis(reader);
Analyzer analyzer = new MockAnalyzer(random(), MockTokenizer.WHITESPACE, false);
mlt.setAnalyzer(analyzer);
mlt.setMinDocFreq(1);
mlt.setMinTermFreq(1);
mlt.setMinWordLen(1);
mlt.setFieldNames(new String[] {"text"});
mlt.setBoost(true);
BooleanQuery query = (BooleanQuery) mlt.like("text", new StringReader("lucene release"));
Collection<BooleanClause> clauses = query.clauses();
for (BooleanClause clause : clauses) {
BoostQuery bq = (BoostQuery) clause.getQuery();
TermQuery tq = (TermQuery) bq.getQuery();
originalValues.put(tq.getTerm().text(), bq.getBoost());
}
analyzer.close();
return originalValues;
}
@AwaitsFix(bugUrl = "https://issues.apache.org/jira/browse/LUCENE-7161")
public void testMultiFieldShouldReturnPerFieldBooleanQuery() throws Exception {
IndexReader reader = null;
Directory dir = newDirectory();
Analyzer analyzer = new MockAnalyzer(random(), MockTokenizer.WHITESPACE, false);
try {
int maxQueryTerms = 25;
String[] itShopItemForSale =
new String[] {
"watch",
"ipod",
"asrock",
"imac",
"macbookpro",
"monitor",
"keyboard",
"mouse",
"speakers"
};
String[] itShopItemNotForSale = new String[] {"tie", "trousers", "shoes", "skirt", "hat"};
String[] clothesShopItemForSale = new String[] {"tie", "trousers", "shoes", "skirt", "hat"};
String[] clothesShopItemNotForSale =
new String[] {
"watch",
"ipod",
"asrock",
"imac",
"macbookpro",
"monitor",
"keyboard",
"mouse",
"speakers"
};
// add series of shop docs
RandomIndexWriter writer = new RandomIndexWriter(random(), dir);
for (int i = 0; i < 300; i++) {
addShopDoc(writer, "it", itShopItemForSale, itShopItemNotForSale);
}
for (int i = 0; i < 300; i++) {
addShopDoc(writer, "clothes", clothesShopItemForSale, clothesShopItemNotForSale);
}
// Input Document is a clothes shop
int inputDocId =
addShopDoc(writer, "clothes", clothesShopItemForSale, clothesShopItemNotForSale);
reader = writer.getReader();
writer.close();
// setup MLT query
MoreLikeThis mlt = new MoreLikeThis(reader);
mlt.setAnalyzer(analyzer);
mlt.setMaxQueryTerms(maxQueryTerms);
mlt.setMinDocFreq(1);
mlt.setMinTermFreq(1);
mlt.setMinWordLen(1);
mlt.setFieldNames(new String[] {FOR_SALE, NOT_FOR_SALE});
// perform MLT query
BooleanQuery query = (BooleanQuery) mlt.like(inputDocId);
Collection<BooleanClause> clauses = query.clauses();
Collection<BooleanClause> expectedClothesShopClauses = new ArrayList<BooleanClause>();
for (String itemForSale : clothesShopItemForSale) {
BooleanClause booleanClause =
new BooleanClause(
new TermQuery(new Term(FOR_SALE, itemForSale)), BooleanClause.Occur.SHOULD);
expectedClothesShopClauses.add(booleanClause);
}
for (String itemNotForSale : clothesShopItemNotForSale) {
BooleanClause booleanClause =
new BooleanClause(
new TermQuery(new Term(NOT_FOR_SALE, itemNotForSale)), BooleanClause.Occur.SHOULD);
expectedClothesShopClauses.add(booleanClause);
}
for (BooleanClause expectedClause : expectedClothesShopClauses) {
assertTrue(clauses.contains(expectedClause));
}
} finally {
// clean up
if (reader != null) {
reader.close();
}
dir.close();
analyzer.close();
}
}
