def testOutOfOrderDocsScoringSort(self):
"""
Two Sort criteria to instantiate the multi/single comparators.
"""
sorts = [Sort(SortField.FIELD_DOC), Sort()]
tfcOptions = [[False, False, False],
[False, False, True],
[False, True, False],
[False, True, True],
[True, False, False],
[True, False, True],
[True, True, False],
[True, True, True]]
actualTFCClasses = [
"OutOfOrderOneComparatorNonScoringCollector",
"OutOfOrderOneComparatorScoringMaxScoreCollector",
"OutOfOrderOneComparatorScoringNoMaxScoreCollector",
"OutOfOrderOneComparatorScoringMaxScoreCollector",
"OutOfOrderOneComparatorNonScoringCollector",
"OutOfOrderOneComparatorScoringMaxScoreCollector",
"OutOfOrderOneComparatorScoringNoMaxScoreCollector",
"OutOfOrderOneComparatorScoringMaxScoreCollector"
]
bq = BooleanQuery()
# Add a Query with SHOULD, since bw.scorer() returns BooleanScorer2
# which delegates to BS if there are no mandatory clauses.
bq.add(MatchAllDocsQuery(), BooleanClause.Occur.SHOULD)
# Set minNrShouldMatch to 1 so that BQ will not optimize rewrite to
# return the clause instead of BQ.
bq.setMinimumNumberShouldMatch(1)
for sort in sorts:
for tfcOption, actualTFCClass in izip(tfcOptions,
actualTFCClasses):
tdc = TopFieldCollector.create(sort, 10, tfcOption[0],
tfcOption[1], tfcOption[2],
False)
self.assert_(tdc.getClass().getName().endswith("$" + actualTFCClass))
self.full.search(bq, tdc)
tds = tdc.topDocs()
sds = tds.scoreDocs
self.assertEqual(10, len(sds))
def searcher_text(text):
vm_env = lucene.getVMEnv()
vm_env.attachCurrentThread()
tags_count = {}
sentences = tokenize.sent_tokenize(text)
t_phrases = []
for sentence in sentences:
sentence = sentence.replace('\n', ' ')
#print sentence
t_phrases = sentence.split()
t_phrases = [p.lower() for p in t_phrases]
query = BooleanQuery()
query.setMinimumNumberShouldMatch(1)
i = 0
phrases = getPhrases(sentence)
t_phrases = t_phrases + phrases
for k in t_phrases:
for t in k.split():
if (i < 1000):
query.add(TermQuery(Term("Body", t)),
BooleanClause.Occur.SHOULD)
i = i + 1
MAX = 5
hits = searcher.search(query, MAX)
for hit in hits.scoreDocs:
if (hit.score > 0.0):
doc = searcher.doc(hit.doc)
tgs = doc.get("Tags")
score = hit.score
addTags(tgs, score, tags_count)
for tg in tags_count:
tags_count[tg] = tags_count[tg] / float(1 +
math.log(stack_tags_count[tg]))
sorted_words = sorted(tags_count.items(), key=lambda x: x[1], reverse=True)
k = 25
ret = []
for word, score in sorted_words[:k]:
ret.append(word)
return ret
