def perform_search(self, searchterm, results_per_page, page):
# if there is a field in the searchterm
"""if ":" in searchterm:
# processing a query
parser = QueryParser(Version.LUCENE_CURRENT, "content", self.analyzer)
parser.setDefaultOperator(QueryParser.Operator.AND)
query = parser.parse(searchterm)
else:
query = BooleanQuery()
query_title = TermQuery(Term("title", searchterm))
query_description = TermQuery(Term("description", searchterm))
query_content = TermQuery(Term("content", searchterm))
# BooleanClause.Occur.MUST for AND queries
query.add(query_title, BooleanClause.Occur.SHOULD)
query.add(query_description, BooleanClause.Occur.SHOULD)
query.add(query_content, BooleanClause.Occur.SHOULD)"""
# create QueryParser for each field to be searched
parser_title = QueryParser(Version.LUCENE_CURRENT, "title", self.analyzer)
parser_description = QueryParser(Version.LUCENE_CURRENT, "description", self.analyzer)
parser_content = QueryParser(Version.LUCENE_CURRENT, "content", self.analyzer)
# put fields together
query = BooleanQuery()
query.add(parser_title.parse(searchterm), BooleanClause.Occur.SHOULD)
query.add(parser_description.parse(searchterm), BooleanClause.Occur.SHOULD)
query.add(parser_content.parse(searchterm), BooleanClause.Occur.SHOULD)
# conducting search
searcher = IndexSearcher(DirectoryReader.open(self.store))
start = datetime.now()
hits = searcher.search(query, results_per_page + (results_per_page * page))
score_docs = hits.scoreDocs
count_results = hits.totalHits
duration = datetime.now() - start
# results to return
results = []
count = 0
for scoreDoc in score_docs:
# skip offset
if count < results_per_page * page:
count += 1
continue
count += 1
doc = searcher.doc(scoreDoc.doc)
table = dict((field.name(), field.stringValue()) for field in doc.getFields())
results.append(table)
return results, duration, count_results
def findPropertyURIs(self, propertyType, max):
uris = list() # list()
try:
analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
parser = QueryParser(Version.LUCENE_CURRENT, FreyaConstants.CLASS_FEATURE_LKB, analyzer)
query = parser.parse("\"" + QueryParser.escape(propertyType) + "\"")
result = self._searcher.search(query, 1)
freq = result.totalHits
if max != None:
freq = max.intValue()
if freq > 0:
result = self._searcher.search(query, freq)
hits = pyJava.JArray2List(result.scoreDocs)
logging.debug("For " + str(query) + " : " + str(result.totalHits) + " max:" + str(max))
print "For " + str(query) + " : " + str(result.totalHits) + " max:" + str(max)
# for (ScoreDoc hit : hits) {
indexus = 0
while indexus < len(hits):
hit = hits[indexus]
doc = self._searcher.doc(hit.doc)
self._searcher.explain(query, hit.doc)
uris.append(doc.get(FreyaConstants.INST_FEATURE_LKB))
indexus += 1
except Exception as e:#CorruptIndexException(e):
print e.message
logging.error("Error")
return uris
def findDirectTypes(self, instanceUri, max):
dTypes = list()
try:
analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
parser = QueryParser(Version.LUCENE_CURRENT, "inst", analyzer)
query = parser.parse("\"" + QueryParser.escape(instanceUri) + "\"")
result = self._searcher.search(query, 1)
logging.debug("For " + str(query) + " : " + str(result.totalHits))
freq = 0
if max != None:
freq = max
else:
freq = result.totalHits
if freq > 0:
result = self._searcher.search(query, freq)
hits = pyJava.JArray2List(result.scoreDocs)
# for (ScoreDoc hit : hits) {
indexus = 0
while indexus < len(hits):
hit = hits[indexus]
doc = self._searcher.doc(hit.doc)
self._searcher.explain(query, hit.doc)
dTypes.append(doc.get(FreyaConstants.CLASS_FEATURE_LKB))
indexus += 1
except Exception as e:#CorruptIndexException(e):
print e.message
logging.error("Error")
logging.debug("there are " + str(len(dTypes)) + " unique direct types")
return dTypes
def buscar(indexDir, args,options = None):
#lucene.initVM(vmargs=['-Djava.awt.headless=true'])
fsDir = SimpleFSDirectory(File(indexDir))
#print fsDir
#Criando buscador baseado no diretorio dos indices passados pelo usuario
searcher = IndexSearcher(DirectoryReader.open(fsDir))
#Analizador para filtro dos tokens
analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
#print analyzer
#Criando um QueryParser usando por padrao contents
#Variavel com as restricoes da busca
parser = QueryParser(Version.LUCENE_CURRENT, "keywords", analyzer)
#print parser
parser.setDefaultOperator(QueryParser.Operator.AND)
#print args
#Juntando parametros passados com o valor do mesmo
command = ' +'.join(args)
#print command
query = parser.parse(command)
print query
#Criando um JArray com resultado da consulta
return searcher.search(query, 200).scoreDocs
def more_like_this(self, result_num, query):
result = []
queryparser = QueryParser(Version.LUCENE_CURRENT, "methods_called",
self.porter_analyzer)
if query:
try:
query = arranging_query_regex(query=query)
# print '4. Right after the regex handling : ', query
like_query = queryparser.parse(query)
# print '5. Right after the Lucene parser : ', like_query
hits = self.searcher.search(like_query, result_num).scoreDocs
# filterScoreDosArray = hits.topDocs().scoreDocs;
for i, hit in enumerate(hits):
doc = self.searcher.doc(hit.doc)
# matched_terms = self.get_matched_keywords(like_query, hit.doc)
result.append(doc.get("answer_id"))
except Exception as e:
print "AnswerSearcher: Error: %s" % e
print(traceback.format_exc())
# self.searchermgr.decRef(self.searcher)
self.searchermgr.release(self.searcher)
self.searcher = None
self.directory.close()
self.directory = None
return result
def get_image_pmcid(pmcid, classes = ""):
fields = ["pmcid", "class"]
docs = []
location = web.__path__[0] + "/static/web/files/index/index.figures"
#lucene.initVM()
vm_env = lucene.getVMEnv()
vm_env.attachCurrentThread()
analyzer = StandardAnalyzer(Version.LUCENE_4_10_1)
reader = IndexReader.open(SimpleFSDirectory(File(location)))
searcher = IndexSearcher(reader)
# multi field query: http://stackoverflow.com/questions/2005084/how-to-specify-two-fields-in-lucene-queryparser
#query = MultiFieldQueryParser(Version.LUCENE_4_10_1, fields, analyzer)
# query.setDefaultOperator(QueryParserBase.AND_OPERATOR)
#query = query.parse(query, ('4175339','1'))
# query.parse(queryString)#"Shigella sonnei"
# query = QueryParser(Version.LUCENE_4_10_1, "abstract", analyzer).parse(queryString)#"Shigella sonnei"
MAX = 10000
#hits = searcher.search(query, MAX)
if classes == "all":
queryStr = "pmcid:(" + ' '.join(pmcid) +")"
else:
queryStr = "pmcid:(" + ' '.join(pmcid) +")" + " AND class:" + classes
query = QueryParser(Version.LUCENE_4_10_1, "pmcid",analyzer)#needed to build a custom query
q = query.parse(queryStr)
hits = searcher.search(q, MAX)
for hit in hits.scoreDocs:#should only be one
#print hit.score, hit.doc, hit.toString()
docs.append(searcher.doc(hit.doc))
return docs #This will return the image documents that belong to a pmcid(article)
def more_like_this2(self, item_doc, result_num):
similar_questions = []
if not item_doc:
item_doc.append(ResultItem(None, 1.0, "No Title", 0))
query = ""
if item_doc.doc:
query += self.document_to_query(item_doc.doc)
query = remove_unified_stop_lists(query)
queryparser = QueryParser(Version.LUCENE_CURRENT, "term",
self.analyzer)
if query:
try:
like_query = queryparser.parse(query)
hits = self.searcher.search(like_query, result_num).scoreDocs
for i, hit in enumerate(hits):
doc = self.searcher.doc(hit.doc)
similar_questions.append(doc.get("question_id"))
except Exception as e:
print "Question Searcher: Error: %s" % e
# write_search_log("Question Searcher: Error: %s" % e + "\n")
print(traceback.format_exc())
# self.searchermgr.decRef(self.searcher)
# self.searchermgr.release(self.searcher)
# self.searcher = None
# self.directory.close()
# self.directory = None
return similar_questions
# def release(self, searcher):
def Qsearch(self,query):
words = seg.segment(query.strip())
#words = self.segmentor.segment(query.strip())
#print ' '.join(words)
vm_env = lucene.getVMEnv()
vm_env.attachCurrentThread()
result = QueryParser(Version.LUCENE_CURRENT, "contents",self.analyzer)
result.setPhraseSlop(0)
# "\""+' '.join(words)+"\"~0" means words should be continuous
query = result.parse("\""+' '.join(words)+"\"~0")
totalHits = self.searcher.search(query, 50)
#print "%s total matching documents." % totalHits.totalHits
#return totalHits.totalHits
for hit in totalHits.scoreDocs:
#print"Hit Score: ",hit.score, "Hit Doc:",hit.doc, "HitString:",hit.toString()
doc= self.searcher.doc(hit.doc)
#print doc.get("name").encode("utf-8")
#print "----------------------------------------"
t = Term('contents',' '.join(words))
#termDocs = ireader.termDocs(t)
#for tt in termDocs:
# print ireader.document(termDocs.docs).getFeildable('neme'),termDocs.freq()
#print self.reader.totalTermFreq(t)
return self.reader.totalTermFreq(t)
def search(self, index_dir): # Get handle to index directory directory = SimpleFSDirectory(File(index_dir)) # Creates a searcher searching the provided index. ireader = DirectoryReader.open(directory) # Implements search over a single IndexReader. # Use a single instance and use it across queries # to improve performance. searcher = IndexSearcher(ireader) # Get the analyzer analyzer = StandardAnalyzer(Version.LUCENE_CURRENT) # Constructs a query parser. We specify what field to search into. queryParser = QueryParser(Version.LUCENE_CURRENT, FIELD_CONTENTS, analyzer) # Create the query query = queryParser.parse(self.query) # Run the query and get top 50 results topDocs = searcher.search(query, self.retrieve_count) # Get top hits scoreDocs = topDocs.scoreDocs doc_ids = [] for scoreDoc in scoreDocs: doc = searcher.doc(scoreDoc.doc) doc_ids.append(doc.get(FIELD_PATH)) return [int(item) for item in doc_ids]
def retrieve_sents(self):
indexDir = self.indexDir
query = self.query
sent_ind_list = []
# template = CustomTemplate(format)
fsDir = SimpleFSDirectory(Paths.get(indexDir))
# print indexDir
searcher = IndexSearcher(DirectoryReader.open(fsDir))
analyzer = StandardAnalyzer()
parser = QueryParser("contents", analyzer)
parser.setDefaultOperator(QueryParser.Operator.OR)
query = parser.parse(query)
# print query
start = datetime.now()
scoreDocs = searcher.search(query, 50).scoreDocs
duration = datetime.now() - start
# print query
if self.stats:
print("Found %d sentences (in %s) that matched query '%s':" % (len(scoreDocs), duration, query),
file=sys.stderr)
for scoreDoc in scoreDocs:
# print scoreDoc.doc
# doc = searcher.doc(scoreDoc.doc)
sent_ind_list.append(scoreDoc.doc)
return sent_ind_list
def more_like_this2(self, so_items):
github_result = []
if not so_items:
so_items.append(SOResultItem(None, 1.0, "No Title", 0, ""))
query = ""
for so_item in so_items:
queryparser = QueryParser(Version.LUCENE_CURRENT,
"typed_method_call", self.analyzer)
if so_item.doc:
query += self.document_to_query(so_item.doc)
query += self.code_as_text()
if query:
print "-" * 30
print "UNified Query: %s" % query
print "-" * 30
try:
like_query = queryparser.parse(query)
hits = self.searcher.search(like_query, 10).scoreDocs
for i, hit in enumerate(hits):
doc = self.searcher.doc(hit.doc)
matched_terms = self.get_matched_keywords2(
like_query, hit.doc)
# apis = [d.stringValue() for d in doc.getFields("typed_method_call")]
print("file__", doc.get("file"), "file_content",
doc.get("file_content"), "line_numbers",
doc.get("line_numbers"))
file_path = "/extdsk/FaCoY/Git_data/G" + doc.get(
"file")[24:]
print(file_path)
content = None
try:
with open(file_path) as f:
content = f.read()
except:
pass
if content:
item = GithubResultItem(doc.get("file"), content,
matched_terms,
hit.score, so_item,
doc.get("line_numbers"),
hit.doc) # code
github_result.append(item)
#print("%d. File: %s, Matched: %s, Score: %s" % (i + 1, doc.get("file"), matched_terms, hit.score))
except Exception as e:
print "GitSearcher: Error: %s" % e
print(traceback.format_exc())
# print Counter(files).most_common(5)
return github_result
class SearchImgs(object):
def __init__(self, store_dir, analyzer, preprocess = lambda x: x):
'''
Input: `store_dir`: directory storing the Lucene index
`analyzer`: analyzer required to split the query
`preprocess`: user-defined preprocess function
'''
# Initialize `IndexSearcher`
self.dir = SimpleFSDirectory(File(store_dir).toPath())
self.searcher = IndexSearcher(DirectoryReader.open(self.dir))
self.preprocess = preprocess
# Initialize `QueryParser`
self.parser = QueryParser("description", analyzer)
def search_command(self, command):
'''
Interface for other programs to search in a particular index.
Input: `command`: raw query in the str format
Output: list of documents found in the index
'''
command = self.preprocess(command)
score_docs = self.search(command)
return self.output(score_docs)
def search(self, command):
'''
Search for the query in the Lucene index.
Input: `command`: keyword to be searched
Output: score_docs satisfying the requirement
'''
query = self.parser.parse(command)
return self.searcher.search(query, 50).scoreDocs
def output(self, score_docs):
'''
Highlight and return the search results.
Input: `score_docs`: search results from the index
Output: list of documents info found in the index,
details includes `title`, `url` and `description` and `action_url`
'''
results = []
for score_doc in score_docs:
doc = self.searcher.doc(score_doc.doc)
result = {
'title': doc.get('url_title'),
'url': doc.get('img_url'),
'description': doc.get('description').replace(' ', ''),
'action_url': doc.get('url')
}
results.append(result)
return results
def more_like_this2(self, limit, item_doc, score_logs_for_each, user_query,
flag): #flag = UQ(1) or not(0)
bench_result = []
query = ""
if flag == 1:
query += user_query
# item_doc = ResultItem(None, 0.0, "No Title", 'None','None', None)
if flag == 0 and item_doc.doc:
query += self.document_to_query(item_doc.doc)
query = remove_unified_stop_lists(query)
queryparser = QueryParser(Version.LUCENE_CURRENT, "typed_method_call",
self.analyzer)
if query:
try:
parsed_query = queryparser.parse(query)
hits = self.searcher.search(parsed_query, limit).scoreDocs
temp = 1
for i, hit in enumerate(hits):
doc = self.searcher.doc(hit.doc)
matched = doc.get('file').split('/')[9].split('.')[0]
score_logs_for_each += str(matched) + '\t' + str(
round(hit.score, 2)) + '\n'
matched_terms = self.get_matched_keywords2(
parsed_query, hit.doc)
# print "Matched Terms : ", matched_terms
# print("File %s" % temp, doc.get("file"), "//", doc.get("file_content"))
temp += 1
file_path = doc.get("file")
content = None
try:
with open(file_path) as f:
content = f.read()
except:
pass
if content:
item = BenchResultItem(doc.get("file"), content,
matched_terms,
hit.score, item_doc,
doc.get("line_numbers"),
hit.doc)
bench_result.append(item)
except Exception as e:
print "BenchSearcher Error: %s" % e
print(traceback.format_exc())
# self.searchermgr.release()
# self.searcher = None
# self.directory.close()
# self.directory = None
return bench_result, score_logs_for_each
def pairSearch(self, pair, sim):
"""
Method that searches through documents using only content_section Field
searchDir : the path to the folder that contains the index.
"""
# Now search the index:
title = pair[0].replace('_', ' ')
content = pair[1]
parser = QueryParser("content_section", self.analyzer)
query1 = parser.parse(QueryParser.escape(title))
query2 = parser.parse(QueryParser.escape(content))
bq = BooleanQuery.Builder()
bq.add(query1, BooleanClause.Occur.FILTER)
bq.add(query2, BooleanClause.Occur.SHOULD)
self.searcher.setSimilarity(sim)
hits = self.searcher.search(bq.build(), 6).scoreDocs
return hits
class SearchIndex(object):
def __init__(self):
vm_env = lucene.getVMEnv()
vm_env.attachCurrentThread()
indexDir = SimpleFSDirectory(File(app.config['INDEX_PATH']))
self.searcher = IndexSearcher(DirectoryReader.open(indexDir))
self.analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
self.parser = QueryParser(Version.LUCENE_CURRENT, "contents",
self.analyzer)
def search(self, q, page=1, duplicates=False):
query = self.parser.parse(q)
if not duplicates:
query = self.addDuplicatesQuery(query)
perPage = 10
start = (page - 1) * perPage
results = TopScoreDocCollector.create(1000, True)
self.searcher.search(query, results)
highlighter = Highlighter(QueryScorer(query))
highlighter.setTextFragmenter(SimpleFragmenter(40))
docs = []
for scoreDoc in results.topDocs(start, perPage).scoreDocs:
doc = self.searcher.doc(scoreDoc.doc)
tokenStream = self.analyzer.tokenStream(
"contents", StringReader(doc['contents']))
highlight = highlighter.getBestFragments(tokenStream,
doc['contents'], 3, "...")
docs.append({
'title': doc['title'],
'url': doc['url'],
'duplicate': doc['duplicate'],
'highlight': highlight
})
del self.searcher
totalPages = int(math.ceil(results.getTotalHits() / float(perPage)))
return totalPages, docs
def addDuplicatesQuery(self, query):
not_duplicate = TermQuery(Term('duplicate', 'false'))
booleanQuery = BooleanQuery()
booleanQuery.add(not_duplicate, BooleanClause.Occur.MUST)
booleanQuery.add(query, BooleanClause.Occur.MUST)
return booleanQuery
class SearchIndex(object):
def __init__(self):
vm_env = lucene.getVMEnv()
vm_env.attachCurrentThread()
indexDir = SimpleFSDirectory(File(app.config['INDEX_PATH']))
self.searcher = IndexSearcher(DirectoryReader.open(indexDir))
self.analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
self.parser = QueryParser(Version.LUCENE_CURRENT, "contents", self.analyzer)
def search(self, q, page = 1, duplicates = False):
query = self.parser.parse(q)
if not duplicates:
query = self.addDuplicatesQuery(query)
perPage = 10
start = (page - 1) * perPage
results = TopScoreDocCollector.create(1000, True)
self.searcher.search(query, results)
highlighter = Highlighter(QueryScorer(query))
highlighter.setTextFragmenter(SimpleFragmenter(40))
docs = []
for scoreDoc in results.topDocs(start, perPage).scoreDocs:
doc = self.searcher.doc(scoreDoc.doc)
tokenStream = self.analyzer.tokenStream("contents", StringReader(doc['contents']))
highlight = highlighter.getBestFragments(tokenStream, doc['contents'], 3, "...")
docs.append({
'title': doc['title'],
'url': doc['url'],
'duplicate': doc['duplicate'],
'highlight': highlight}
)
del self.searcher
totalPages = int(math.ceil(results.getTotalHits()/float(perPage)))
return totalPages, docs
def addDuplicatesQuery(self, query):
not_duplicate = TermQuery(Term('duplicate', 'false'))
booleanQuery = BooleanQuery()
booleanQuery.add(not_duplicate, BooleanClause.Occur.MUST)
booleanQuery.add(query, BooleanClause.Occur.MUST)
return booleanQuery
def simpleSearchID(self, query, sim):
"""
Method that searches through documents using only content_section Field
searchDir : the path to the folder that contains the index.
"""
# Now search the index:
parser = QueryParser("id_section", self.analyzer)
query = parser.parse(QueryParser.escape(query))
self.searcher.setSimilarity(sim)
hits = self.searcher.search(query, 6).scoreDocs
return hits
def query_parser_filter(self, field_values, field_filter=['Vector']):
"""
Filtering queries according to field values
:param field_values: values of the fields
:param field_filter: fields to filter
"""
assert len(field_filter) == len(
field_values), "Number of fields different from number of values"
for i in range(len(field_filter)):
query_parser = QueryParser(field_filter[i], self.analyzer)
query = query_parser.parse(field_values[i])
self.constrained_query.add(query, BooleanClause.Occur.FILTER)
def more_like_this3(self, limit, score_logs_for_each, user_query):
query = ""
bench_result = []
# if not item_doc:
# item_doc.append(ResultItem(None, 1.0, "No Title", 0, 0))
# if item_doc.doc:
# query += self.document_to_query(item_doc.doc)
query += user_query
query = remove_unified_stop_lists(query)
queryparser = QueryParser(Version.LUCENE_CURRENT, "typed_method_call",
self.analyzer)
if query:
try:
parsed_query = queryparser.parse(query)
hits = self.searcher.search(parsed_query, limit).scoreDocs
temp = 1
for i, hit in enumerate(hits):
score_logs_for_each += str(round(hit.score, 2)) + '\n'
doc = self.searcher.doc(hit.doc)
matched_terms = self.get_matched_keywords2(
parsed_query, hit.doc)
# print "Matched Terms : ", matched_terms
# print("File %s" % temp, doc.get("file"), "//", doc.get("file_content"))
temp += 1
file_path = doc.get("file")
content = None
try:
with open(file_path) as f:
content = f.read()
except:
pass
if content:
item = BenchResultItem_UQ(doc.get("file"), content,
matched_terms, hit.score,
doc.get("line_numbers"),
hit.doc)
bench_result.append(item)
except Exception as e:
print "BenchSearcher Error: %s" % e
print(traceback.format_exc())
# self.searchermgr.decRef(self.searcher)
# self.searchermgr.release(self.searcher)
# self.searcher = None
# self.directory.close()
# self.directory = None
return bench_result, score_logs_for_each
def query_parser_must(self, field_values, field_must=['Text']):
"""
The values that the fields must match
:param field_values: values of the fields
:param field_must: fields that must match
"""
assert len(field_must) == len(
field_values), "Number of fields different from number of values"
for i in range(len(field_must)):
query_parser = QueryParser(field_must[i], self.analyzer)
query = query_parser.parse(field_values[i])
self.constrained_query.add(query, BooleanClause.Occur.MUST)
class LuceneRanker(object):
def __init__(self, tfidf_path, strict=True):
lucene.initVM()
analyzer = StandardAnalyzer()
reader = DirectoryReader.open(SimpleFSDirectory(Paths.get(tfidf_path)))
self.searcher = IndexSearcher(reader)
self.parser = QueryParser("text", analyzer)
self.parser.setDefaultOperator(QueryParser.Operator.OR)
def closest_docs(self, query, k=1):
"""Closest docs by dot product between query and documents
in tfidf weighted word vector space.
"""
query = self.parser.parse(
query.replace('/', '//').replace('?', '').replace('"', ''))
hits = self.searcher.search(query, k)
docids = []
docs = []
for i, hit in enumerate(hits.scoreDocs):
doc = self.searcher.doc(hit.doc)
docs.append(unicode(doc['text']))
docids.append(unicode(doc['title']))
return docids, docs
def batch_closest_docs(self, queries, k=1, num_workers=None):
"""Process a batch of closest_docs requests multithreaded."""
# get highest scoring document for multiple queries
batch = []
for i, q in enumerate(queries):
if i % 100 == 0:
print(i)
t0 = time.time()
docids, docs = self.closest_docs(q, k)
batch.append((docids, docs))
return batch
def parse(self, query):
return None
def text2spvec(self, query):
return None
def get_doc_index(self, doc_id):
return 0
def get_doc_id(self, doc_index):
return 0
def __exit__(self, *args):
pass
def define_search_params(STORE_DIR, FIELD_CONTENTS, TERM):
store = SimpleFSDirectory(Paths.get(STORE_DIR))
reader = DirectoryReader.open(store)
searcher = IndexSearcher(reader)
# Get the analyzer
analyzer = WhitespaceAnalyzer()
# Constructs a query parser. We specify what field to search into.
queryParser = QueryParser(FIELD_CONTENTS, analyzer)
# Create the query
query = queryParser.parse(TERM)
return searcher, reader, query
def getQueryBuiler():
# builder = QueryBuilder(analyzer)
boolean_query = BooleanQuery.Builder()
# print(args.search)
if len(args.search) == 0:
boolean_query.add(MatchAllDocsQuery(), BooleanClause.Occur.MUST)
return boolean_query
for i in range(len(args.search)):
curSearch = args.search[i].split(' ')
if curSearch[1] == 'query':
parser = QueryParser(curSearch[2], analyzer)
query = parser.parse(curSearch[3])
elif curSearch[1] == 'intrange':
query = IntPoint.newRangeQuery(curSearch[2], curSearch[3], curSearch[4])
elif curSearch[1] == 'termrange':
lowerDate = handleDate(curSearch[3], '%d/%b/%Y:%H:%M:%S')
upperDate = handleDate(curSearch[4], '%d/%b/%Y:%H:%M:%S')
query = TermRangeQuery.newStringRange(curSearch[2], lowerDate, upperDate, True, True)
if curSearch[0] == 'must':
boolean_query.add(query, BooleanClause.Occur.MUST)
elif curSearch[0] == 'should':
boolean_query.add(query, BooleanClause.Occur.SHOULD)
elif curSearch[0] == 'filter':
boolean_query.add(query, BooleanClause.Occur.FILTER)
elif curSearch[0] == 'must_not':
boolean_query.add(query, BooleanClause.Occur.MUST_NOT)
else:
print('raise exception')
# raise Exception
# exit()
# parser = QueryParser('method1', analyzer)
# query = parser.parse('options')
# boolean_query.add(query, BooleanClause.Occur.MUST)
# parser = QueryParser('response_code', analyzer)
# query = IntPoint.newRangeQuery('response_code', 200, 300)
# boolean_query.add(query, BooleanClause.Occur.MUST)
# lowerDate = handleDate("19/Jul/2020:05:40:00 +0000")
# upperDate = handleDate("19/Jul/2020:06:45:04 +0000")
# query = TermRangeQuery.newStringRange("date_time", lowerDate, upperDate, True, True)
# boolean_query.add(query, BooleanClause.Occur.MUST)
return boolean_query
def retrieve_wiki(text_query, searcher, analyzer):
txt =text_query
try:
query = QueryParser(Version.LUCENE_CURRENT, "contents",
analyzer).parse(txt)
except:
qp = QueryParser(Version.LUCENE_CURRENT, "contents", analyzer)
txt = qp.escape(txt)
query = qp.parse(txt)
scoreDocs = searcher.search(query, 1000).scoreDocs
for scoreDoc in scoreDocs:
doc = searcher.doc(scoreDoc.doc)
yield doc.get('title'), doc.get('contents')
def similarityOfSynopsis(self):
directory = SimpleFSDirectory(File(settings.SYNOPSIS_INDEX))
ireader = DirectoryReader.open(directory)
searcher = IndexSearcher(ireader)
analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
queryParser = QueryParser(Version.LUCENE_CURRENT, FIELD_CONTENTS,
analyzer)
for root, dirnames, filenames in os.walk(settings.SYNOPSIS):
filenames = [int(item) for item in filenames]
filenames.sort()
filenames = [str(item) for item in filenames]
for filename in filenames:
path = os.path.join(root, filename)
major_movie = models.Movie.objects.get(pk=filename)
with open(path, 'r') as moviedoc:
content = moviedoc.read().replace('\n', ' ')
content = re.sub('[^A-Za-z0-9 ]+', '', content)
while True:
try:
query = queryParser.parse(
QueryParser.escape(content))
except Exception as e:
self.boolean_query.setMaxClauseCount(
self.boolean_query.maxClauseCount * 2)
print self.boolean_query.maxClauseCount
continue
break
topDocs = searcher.search(query, len(filenames))
scoreDocs = topDocs.scoreDocs
for scoreDoc in scoreDocs:
doc = searcher.doc(scoreDoc.doc)
movie_id = int(doc.get(FIELD_PATH))
if movie_id <= major_movie.id:
continue
minor_movie = models.Movie.objects.get(pk=movie_id)
try:
similarity = models.Similarities.objects.filter(
first_movie=major_movie,
second_movie=minor_movie).first()
if not similarity:
similarity = models.Similarities.objects.filter(
first_movie=minor_movie,
second_movie=major_movie).first()
similarity.synopsis = scoreDoc.score
similarity.save()
except Exception as e:
print major_movie.id, minor_movie.id
raise e
print u"{0} completed.".format(major_movie.id)
def retrieve_wiki(text_query, searcher, analyzer):
txt = text_query
try:
query = QueryParser(Version.LUCENE_CURRENT, "contents",
analyzer).parse(txt)
except:
qp = QueryParser(Version.LUCENE_CURRENT, "contents", analyzer)
txt = qp.escape(txt)
query = qp.parse(txt)
scoreDocs = searcher.search(query, 1000).scoreDocs
for scoreDoc in scoreDocs:
doc = searcher.doc(scoreDoc.doc)
yield doc.get('title'), doc.get('contents')
class Searcher:
def __init__(self, indexDir):
self.directory = SimpleFSDirectory(Paths.get(indexDir))
self.reader = DirectoryReader.open(self.directory)
self.searcher = IndexSearcher(self.reader)
self.nameQueryParser = QueryParser('name', StandardAnalyzer())
self.nameQueryParser.setDefaultOperator(QueryParser.Operator.AND)
self.idQueryParser = QueryParser('id', StandardAnalyzer())
self.idQueryParser.setDefaultOperator(QueryParser.Operator.AND)
def find_by_name(self, name):
query = self.nameQueryParser.parse(name)
docs = self.searcher.search(query, 100).scoreDocs
tables = []
for scoreDoc in docs:
doc = self.searcher.doc(scoreDoc.doc)
table = dict((field.name(), field.stringValue())
for field in doc.getFields())
tables.append(table)
return tables
def find_by_id(self, id):
query = self.idQueryParser.parse(id)
docs = self.searcher.search(query, 100).scoreDocs
tables = []
for scoreDoc in docs:
doc = self.searcher.doc(scoreDoc.doc)
table = dict((field.name(), field.stringValue())
for field in doc.getFields())
tables.append(table)
return tables
def close(self):
self.directory.close()
self.reader.close()
def doc_search(self, keywords):
analyzer = StandardAnalyzer()
parser = QueryParser('Title', analyzer)
query = parser.parse(keywords)
try:
collector = TopScoreDocCollector.create(3000)
self.lSearcher.search(query, collector)
hits = collector.topDocs().scoreDocs
except RuntimeError:
print "Score docoment run fail"
self.hits = hits
return hits
def author_search(qry, limit):
helper.initPyLucene()
RNLP_ctxt = _get_rnlp_ctxt()
entry_map = RNLP_ctxt.get_entry_map()
rootdir = OUT_RAW_DIR
from org.apache.lucene.index import DirectoryReader
from org.apache.lucene.search import IndexSearcher
from org.apache.lucene.queryparser.classic import QueryParser
from org.apache.lucene.analysis.standard import StandardAnalyzer
from org.apache.lucene.store import FSDirectory
from org.apache.lucene.util import Version
from java.io import File
reader = DirectoryReader.open(FSDirectory.open(File(IDX_DIR)))
searcher = IndexSearcher(reader)
analyzer = StandardAnalyzer(Version.LUCENE_40)
field = 'contents'
parser = QueryParser(Version.LUCENE_40, field, analyzer);
query = parser.parse(qry);
print 'Searching for:', query.toString(field)
raw_results = searcher.search(query, limit)
hits = raw_results.scoreDocs
numTotalHits = raw_results.totalHits
print numTotalHits, 'total matching documents'
results = {}
for hit in hits:
doc = searcher.doc(hit.doc)
entry_id = doc.get('entry_id')
entry = entry_map.get(entry_id)
short_title = entry['short_title']
print(entry['prim_author'])
if qry in entry['prim_author'].lower():
fname = short_title + CONTENT_EXT
results[entry_id] = {'title': short_title, 'file': fname }
f = open ('/Users/Nelle/Documents/coding/text_analysis/newsvn/RenaissanceNLP/data/dataResults/authorPaths/' + qry + '.json', 'w')
f.write(json.dumps(results))
f.close()
return json.dumps(results)
def perform_search(self, searchterm):
# processing a query
parser = QueryParser(Version.LUCENE_CURRENT, "content", self.analyzer)
parser.setDefaultOperator(QueryParser.Operator.AND)
query = parser.parse(searchterm)
# conducting search
searcher = IndexSearcher(DirectoryReader.open(self.store))
start = datetime.now()
scoreDocs = searcher.search(query, 50).scoreDocs
duration = datetime.now() - start
print scoreDocs
print duration
def more_like_this(self, so_items):
github_result = []
if not so_items:
so_items.append(SOResultItem(None, 1.0, "No Title", 0, ""))
for so_item in so_items:
queryparser = QueryParser(Version.LUCENE_CURRENT,
"typed_method_call", self.analyzer)
query = ""
if so_item.doc:
query = self.document_to_query(so_item.doc)
query += self.code_as_text()
if query:
print "-" * 30
print "Query: %s" % query
print "-" * 30
try:
like_query = queryparser.parse(query)
hits = self.searcher.search(like_query, 10).scoreDocs
for i, hit in enumerate(hits):
doc = self.searcher.doc(hit.doc)
matched_terms = self.get_matched_keywords2(
like_query, hit.doc)
# apis = [d.stringValue() for d in doc.getFields("typed_method_call")]
item = GithubResultItem(doc.get("file"),
decompress(
doc.get("file_content")),
matched_terms,
hit.score, so_item,
doc.get("line_numbers"),
hit.doc) # code
github_result.append(item)
#print("%d. File: %s, Matched: %s, Score: %s" % (i + 1, doc.get("file"), matched_terms, hit.score))
except Exception as e:
print "Error: %s" % e
# print Counter(files).most_common(5)
return github_result
def more_like_this2(self, limit, item_doc, user_query,
flag): #flag = UQ(1) or not(0)
results = []
query = ""
if flag == 1:
query += user_query
# item_doc = ResultItem(None, 0.0, "No Title", 'None','None', None)
if flag == 0 and item_doc.doc:
query += self.document_to_query(item_doc.doc)
query = remove_unified_stop_lists(query)
queryparser = QueryParser(Version.LUCENE_CURRENT, "typed_method_call",
self.analyzer)
if query:
try:
parsed_query = queryparser.parse(query)
hits = self.searcher.search(parsed_query, limit).scoreDocs
temp = 1
for i, hit in enumerate(hits):
doc = self.searcher.doc(hit.doc)
matched_terms = self.get_matched_keywords2(
parsed_query, hit.doc)
temp += 1
file_path = doc.get("file")
content = None
try:
with open(file_path) as f:
content = f.read()
except:
pass
if content:
item = GithubResultItem(doc.get("file"), content,
matched_terms, hit.score,
item_doc,
doc.get("line_numbers"),
hit.doc)
results.append(item)
except Exception as e:
print "GitHub Searcher Error: %s" % e
print(traceback.format_exc())
return results
def search_phrase(self, term, phrase):
print('Phrase search')
self.hits = []
index_list = []
parser = QueryParser('text', self.analyzer)
query = parser.parse(term)
hits = self.searcher.search(query, 1000).scoreDocs
if hits is None:
return
for hit in hits:
index = []
doc = self.searcher.doc(hit.doc)
text = doc.get("text")
phrases = doc.get("phrase")
# processing with saved text and phrase
terms = text.split()
phrases = phrases.split()
flag = 1 # this flag is judging for phrase in every target term in text
index = [] # index number for searched term, maybe many terms
for i in range(len(terms)):
if term == terms[i]:
index.append(i)
if not phrase == phrases[i]:
flag = 0
break
if flag == 1:
self.hits.append(text)
index_list.append(index)
self.recover_sentence(index_list)
hits_copy = self.hits
self.hits = []
# add font tags for terms
for hit in hits_copy:
simpleHTMLFormatter = SimpleHTMLFormatter(prefixHTML, suffixHTML)
highlighter = Highlighter(simpleHTMLFormatter, QueryScorer(query))
highLightText = highlighter.getBestFragment(
self.analyzer, 'text', hit)
if highLightText is not None:
self.hits.append(highLightText)
return self.hits[:40]
def similarityOfSynopsis(self):
directory = SimpleFSDirectory(File(settings.SYNOPSIS_INDEX))
ireader = DirectoryReader.open(directory)
searcher = IndexSearcher(ireader)
analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
queryParser = QueryParser(Version.LUCENE_CURRENT, FIELD_CONTENTS, analyzer)
for root, dirnames, filenames in os.walk(settings.SYNOPSIS):
filenames = [int(item) for item in filenames]
filenames.sort()
filenames = [str(item) for item in filenames]
for filename in filenames:
path = os.path.join(root, filename)
major_movie = models.Movie.objects.get(pk=filename)
with open(path, 'r') as moviedoc:
content = moviedoc.read().replace('\n', ' ')
content = re.sub('[^A-Za-z0-9 ]+', '', content)
while True:
try:
query = queryParser.parse(QueryParser.escape(content))
except Exception as e:
self.boolean_query.setMaxClauseCount(self.boolean_query.maxClauseCount * 2)
print self.boolean_query.maxClauseCount
continue
break
topDocs = searcher.search(query, len(filenames))
scoreDocs = topDocs.scoreDocs
for scoreDoc in scoreDocs:
doc = searcher.doc(scoreDoc.doc)
movie_id = int(doc.get(FIELD_PATH))
if movie_id <= major_movie.id:
continue
minor_movie = models.Movie.objects.get(pk=movie_id)
try:
similarity = models.Similarities.objects.filter(first_movie=major_movie, second_movie=minor_movie).first()
if not similarity:
similarity = models.Similarities.objects.filter(first_movie=minor_movie, second_movie=major_movie).first()
similarity.synopsis = scoreDoc.score
similarity.save()
except Exception as e:
print major_movie.id, minor_movie.id
raise e
print u"{0} completed.".format(major_movie.id)
def custom_search(qry, limit):
helper.initPyLucene()
RNLP_ctxt = _get_rnlp_ctxt()
entry_map = RNLP_ctxt.get_entry_map()
rootdir = OUT_RAW_DIR
from org.apache.lucene.index import DirectoryReader
from org.apache.lucene.search import IndexSearcher
from org.apache.lucene.queryparser.classic import QueryParser
from org.apache.lucene.analysis.standard import StandardAnalyzer
from org.apache.lucene.store import FSDirectory
from org.apache.lucene.util import Version
from java.io import File
reader = DirectoryReader.open(FSDirectory.open(File(IDX_DIR)))
searcher = IndexSearcher(reader)
analyzer = StandardAnalyzer(Version.LUCENE_40)
field = 'contents'
parser = QueryParser(Version.LUCENE_40, field, analyzer);
query = parser.parse(qry);
print 'Searching for:', query.toString(field)
raw_results = searcher.search(query, limit)
hits = raw_results.scoreDocs
numTotalHits = raw_results.totalHits
print numTotalHits, 'total matching documents'
print rootdir
results = {}
for hit in hits:
doc = searcher.doc(hit.doc)
entry_id = doc.get('entry_id')
entry = entry_map.get(entry_id)
short_title = entry['short_title']
year = entry['publ_year']
fname = short_title + CONTENT_EXT
results[fname] = year;
def do_search(qry, limit):
helper.initPyLucene()
RNLP_ctxt = _get_rnlp_ctxt()
entry_map = RNLP_ctxt.get_entry_map()
from org.apache.lucene.index import DirectoryReader
from org.apache.lucene.search import IndexSearcher
from org.apache.lucene.queryparser.classic import QueryParser
from org.apache.lucene.analysis.standard import StandardAnalyzer
from org.apache.lucene.store import FSDirectory
from org.apache.lucene.util import Version
from java.io import File
print os.path.abspath(os.path.pardir)
reader = DirectoryReader.open(FSDirectory.open(File(IDX_DIR)))
searcher = IndexSearcher(reader)
analyzer = StandardAnalyzer(Version.LUCENE_40)
field = 'contents'
parser = QueryParser(Version.LUCENE_40, field, analyzer);
query = parser.parse(qry);
print 'Searching for:', query.toString(field)
raw_results = searcher.search(query, limit)
hits = raw_results.scoreDocs
numTotalHits = raw_results.totalHits
print numTotalHits, 'total matching documents'
results = []
for hit in hits:
doc = searcher.doc(hit.doc);
entry_id = doc.get('entry_id')
entry = entry_map.get(entry_id)
#print 'entry:', entry
score = hit.score
#print 'Hit:', entry['short_title'], score
results.append((score, doc, entry))
return results
def findTopClasses(self):
propertyURI = RDFS.SUBCLASSOF
allClasses = list()
topClasses = list()
try:
analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
parser = QueryParser(Version.LUCENE_CURRENT, FreyaConstants.PROPERTY_FEATURE_LKB, analyzer)
query = parser.parse("\"" + QueryParser.escape(propertyURI) + "\"")
result = self._searcher.search(query, 1)
logging.debug("For " + str(query) + " : " + str(result.totalHits))
freq = result.totalHits
if freq > 0:
result = self._searcher.search(query, freq)
hits = pyJava.JArray2List(result.scoreDocs)
# for (ScoreDoc hit : hits) {
indexus = 0
while indexus < len(hits):
hit = hits[indexus]
doc = self._searcher.doc(hit.doc)
allClasses.append(doc.get(FreyaConstants.CLASS_FEATURE_LKB))
indexus += 1
# for (String classUri : allClasses) {
indexus = 0
while indexus < len(allClasses):
classUri = allClasses[indexus]
logging.info("Checking whether " + classUri + " is a top class.")
# search inst and pred retrieve class
# if class exists that means it is not top class otherwise add to
# topClasses
classes = self.searchForClass(classUri, propertyURI)
logging.info("top classes:" + str(len(classes)))
if classes != None or len(classes) > 0:
logging.info("This is not a top class...")
else:
topClasses.append(classUri)
logging.info("Adding " + classUri + " to top classes.")
indexus += 1
except Exception as e:#CorruptIndexException(e):
print e.message
logging.error("Error")
return topClasses
def closest_docs(self, question_, k=5):
"""Closest docs by dot product between query and documents
in tfidf weighted word vector space.
"""
doc_scores = []
doc_ids = []
doc_texts = []
words = self.parse(utils.normalize(question_))
query = ' '.join(words)
if not query:
logger.warning('has no query!')
return doc_ids, doc_scores, doc_texts
# bq_builder = BooleanQuery.Builder()
# title_query = TermQuery(Term("title", query))
# # boosted_title_query = BoostQuery(title_query, 2)
# bq_builder.add(TermQuery(Term("text", query)), BooleanClause.Occur.SHOULD)
# bq_builder.add(title_query, BooleanClause.Occur.SHOULD)
# lucene_query = bq_builder.build()
# lucene_query = self.query_parser.parse(query, ["title", "text"],
# [BooleanClause.Occur.SHOULD, BooleanClause.Occur.MUST],
# self.analyzer)
# lucene_query = 'title:"{0}"^2 OR "{0}"'.format(query)
self.env.attachCurrentThread()
query_parser = QueryParser("text", self.analyzer)
search_results = self.searcher.search(query_parser.parse(query),
k).scoreDocs
for search_result in search_results:
doc = self.searcher.doc(search_result.doc)
doc_id = doc["id"] + ", title=" + doc["title"]
doc_score = search_result.score
text = doc["text"]
doc_ids.append(doc_id)
doc_scores.append(doc_score)
doc_texts.append(text)
# print('id:', doc_id, 'ds:', doc_score, 'text:', text)
# logger.debug('question_d:%s, query:%s, doc_ids:%s, doc_scores:%s'
# % (question_, query, doc_ids, doc_scores))
return doc_ids, doc_scores, doc_texts
def search(termo, **args):
indexDir = os.environ.get('MANDEX') or '3iteracao'
fsDir = SimpleFSDirectory(File(indexDir))
searcher = IndexSearcher(DirectoryReader.open(fsDir))
analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
parser = QueryParser(Version.LUCENE_CURRENT, field, analyzer)
parser.setDefaultOperator(QueryParser.Operator.OR)
query = parser.parse(termo + ' '.join(args.values()))
start = datetime.now()
scoreDocs = searcher.search(query, 50).scoreDocs
duration = datetime.now() - start
politicos = []
for scoreDoc in scoreDocs:
doc = searcher.doc(scoreDoc.doc)
table = dict((field.name(), field.stringValue()) for field in doc.getFields())
politicos.append(table)
return politicos
def get_document_vector(searcher, reader, document_id, \
id_field, text_field):
'''
Given a document id, fetch the tf-idf vector of the document.
'''
tc_dict = {} # Counts of each term
dc_dict = {} # Number of docs associated with each term
tfidf_dict = {} # TF-IDF values of each term in the doc
# Get the document id.
query_parser = QueryParser(id_field, WhitespaceAnalyzer())
score_docs = searcher.search(query_parser.parse(str(document_id)),
1).scoreDocs
if len(score_docs) > 0:
# get the tf-idf vector.
termVector = reader.getTermVector(score_docs[0].doc, text_field)
termsEnumvar = termVector.iterator()
termsref = BytesRefIterator.cast_(termsEnumvar)
N_terms = 0
try:
while (termsref.next()):
termval = TermsEnum.cast_(termsref)
fg = termval.term().utf8ToString() # Term in unicode
if len(fg) > 3 and not fg.isdigit():
tc = termval.totalTermFreq() # Term count in the doc
# Number of docs having this term in the index
dc = reader.docFreq(Term(text_field, termval.term()))
N_terms = N_terms + 1
tc_dict[fg] = tc
dc_dict[fg] = dc
except:
print('error in term_dict')
# Compute TF-IDF for each term
for term in tc_dict:
tf = tc_dict[term] / N_terms
idf = 1 + math.log(reader.numDocs() / (dc_dict[term] + 1))
tfidf_dict[term] = tf * idf
return tfidf_dict
def search(self, term, window=2):
self.hits = []
index_list = []
sort_para = term
parser = QueryParser('text', self.analyzer)
query = parser.parse(term)
print(query)
# Jump to multi-terms search if there are several words
if self.multi_terms(query):
self.search_multi_terms(query)
return self.hits[:40]
hits = self.searcher.search(query, 1000).scoreDocs
for hit in hits:
index = []
doc = self.searcher.doc(hit.doc)
text = doc.get("text")
self.hits.append(text)
# save indexes of target term in each document
terms = text.split()
for i in range(len(terms)):
if term == terms[i]:
index.append(i)
index_list.append(index)
self.recover_sentence(index_list, window)
hits_copy = self.hits
self.hits = []
for hit in hits_copy:
simpleHTMLFormatter = SimpleHTMLFormatter(prefixHTML, suffixHTML)
highlighter = Highlighter(simpleHTMLFormatter, QueryScorer(query))
highLightText = highlighter.getBestFragment(
self.analyzer, 'text', hit)
if highLightText is not None:
self.hits.append(highLightText)
print('search over')
return self.hits[:40]
def doc_search(self, field, keywords, numHits):
if field != 'All':
analyzer = StandardAnalyzer()
parser = QueryParser(field, analyzer)
query = parser.parse(keywords)
# self.lReader.getDocCount("title");
try:
collector = TopScoreDocCollector.create(numHits)
self.lSearcher.search(query, collector)
hits = collector.topDocs().scoreDocs
except RuntimeError:
print "Score docoment run fail"
self.hits = hits
self.field = field
return hits
else:
analyzer = WhitespaceAnalyzer()
parser = MultiFieldQueryParser(['Title', 'Body'], analyzer)
query = MultiFieldQueryParser.parse(parser, keywords)
# self.lReader.getDocCount("title");
try:
collector = TopScoreDocCollector.create(numHits)
self.lSearcher.search(query, collector)
hits = collector.topDocs().scoreDocs
except RuntimeError:
print "Score docoment run fail"
self.hits = hits
self.field = field
return hits
self.hits = hits
self.field = field
return hits
def more_like_this2(self, limit, score_logs_for_each, user_query, flag):
bench_result = []
query = ""
if flag == 1:
query += user_query
query = remove_unified_stop_lists(query)
queryparser = QueryParser(Version.LUCENE_CURRENT, "typed_method_call", self.analyzer)
if query:
try:
parsed_query = queryparser.parse(query)
hits = self.searcher.search(parsed_query, limit).scoreDocs
temp = 1
for i, hit in enumerate(hits):
doc = self.searcher.doc(hit.doc)
matched = doc.get('file').split('/')[9].split('.')[0]
score_logs_for_each += str(matched) + '\t' + str(round(hit.score, 2)) + '\n'
matched_terms = self.get_matched_keywords2(parsed_query, hit.doc)
temp += 1
file_path = doc.get("file")
content = None
try:
with open(file_path) as f:
content = f.read()
except:
pass
if content:
item = BenchResultItem_UQ(doc.get("file"), content, matched_terms, hit.score, doc.get("line_numbers"), hit.doc)
bench_result.append(item)
except Exception as e:
print "BenchSearcher Error: %s" % e
print(traceback.format_exc())
return bench_result, score_logs_for_each
def extract_phrase_query(self, q, field, slop=0, boost=5):
phrases = re.findall(r'"([^"]*)"', q)
if len(phrases) == 0:
return None, q
q = re.sub(r'"([^"]*)"', "", q).strip() # query without phrases
if self.verbose:
print "Detected phrases: ", phrases
bq = BooleanQuery()
for phrase in phrases:
# pq = PhraseQuery()
# for term in filter(None, phrase.split(' ')):
# pq.add(Term(field, term))
qparser = QueryParser(Version.LUCENE_CURRENT, field, self.analyzer)
# parse phrase - this may or may not be desired
# pq = qparser.parse(field + ':"' + phrase + '"')
pq = qparser.parse('%s "%s"~%d^%.1f' %
(phrase, phrase, slop, boost))
# phrase queries have high priority
bq.add(pq, BooleanClause.Occur.MUST)
# bq.add(pq, BooleanClause.Occur.SHOULD)
return bq, q
def more_like_this2(
self, item_doc, result_num
): #들어온 질문 docs들에 대해 순회하면서 최종 query로 생성하고 Question Index에서 비슷한거 검색할 것.
similar_questions = []
if not item_doc:
item_doc.append(ResultItem(None, 1.0, "No Title", 0))
query = ""
if item_doc.doc:
query += self.document_to_query(item_doc.doc)
query = remove_unified_stop_lists(query)
queryparser = QueryParser(Version.LUCENE_CURRENT, "term",
self.analyzer)
if query: #########이 시점에서의 Unified Query는 Tokenization, Stemming 이 되어있음..########
try:
like_query = queryparser.parse(query)
hits = self.searcher.search(
like_query,
result_num).scoreDocs #Q와 비슷한 Q들 상위 3개씩의 결과 그럼 총 9개
for i, hit in enumerate(hits):
doc = self.searcher.doc(hit.doc)
similar_questions.append(doc.get("question_id"))
except Exception as e:
print "Question Searcher: Error: %s" % e
# write_search_log("Question Searcher: Error: %s" % e + "\n")
print(traceback.format_exc())
# self.searchermgr.decRef(self.searcher)
# self.searchermgr.release(self.searcher)
# self.searcher = None
# self.directory.close()
# self.directory = None
return similar_questions
def get_doc_list(TERM, searcher, reader):
FIELD_CONTENTS = "text"
DOC_NAME = "identifier"
STORE_DIR = "./full_index1"
# Get the analyzer
analyzer = WhitespaceAnalyzer()
# Constructs a query parser. We specify what field to search into.
queryParser = QueryParser(FIELD_CONTENTS, analyzer)
# Create the query
query = queryParser.parse(TERM)
#lucene.initVM()
#searcher, reader, query = define_search_params(STORE_DIR, FIELD_CONTENTS, TERM)
# fieldInfos = MultiFields.getMergedFieldInfos(reader)
# print(fieldInfos)
# for fieldInfo in fieldInfos.iterator():
# print(fieldInfo.name)
# Run the query and get documents that contain the term
return searcher.search(query, reader.numDocs())
class TASearcher():
def __init__(self, queries=[], criteria=[], conjunctions=[], orderby=["ta"], ascending=True, limit=10000):
vm.attachCurrentThread()
self.queries = [query for query in queries if len(query.strip()) > 0]
self.criteria = criteria
self.conjunctions = conjunctions
self.orderby = orderby
self.ascending = ascending
self.queryString = ""
self.limit = limit
self.fields = fields
self.analyzer = PorterStemmerAnalyzer()
self.queryParser = QueryParser(Version.LUCENE_30, "freetext", self.analyzer)
self.queryParser.setAllowLeadingWildcard(True)
self.queryParser.setDefaultOperator(QueryParser.Operator.AND)
indexDir = settings.LUCENE_INDEX_DIRECTORY
self.index = MMapDirectory(File(indexDir))
def createQueryString(self):
# Simple
if len(self.criteria) == 0:
self.queryString = "(%s) OR freetext-normalized:(%s)" % (self.queries[0], self.queries[0])
# Advanced
else:
queryPairs = []
criteriaQueries = zip(self.criteria, self.queries)
self.criteria = dict(criteriaQueries).keys()
for criterion, query in criteriaQueries:
if criterion in ("volume", "number", "category-label", "pubtype", "author-sort"):
queryPairs.append("%s:%s" % (criterion, query))
elif criterion == "year":
queryPairs.append("year-start:%s OR year-end:%s" % (query, query))
else:
queryPairs.append('%s:%s OR %s-normalized:%s' % (criterion, query, criterion, query))
# queryPairs = ["%s:%s"%(criterion,query.replace(" ", "+")) for criterion, query in zip(criteria, queries)]
try:
queryString = "%s %s" % (queryPairs[0], " ".join(
["%s (%s)" % (conj, pair) for conj, pair in zip(self.conjunctions, queryPairs[1:])]))
self.queryString = queryString
return queryString
except:
self.queryString = "freetext"
return self.queryString
def getQueryString(self):
return self.queryString
def _getHits(self):
reader = IndexReader.open(self.index)
searcher = IndexSearcher(reader)
# Sortierung nach Band- und Eintragsnummer (4: Wert als Integer behandeln)
sortDict = {
"ta": (("volume", SortField.Type.INT), ("number", SortField.Type.INT)),
"year": (("year-start", SortField.Type.INT), ("year-end", SortField.Type.INT)),
"author-title": (("author-sort", SortField.Type.STRING), ("title-sort", SortField.Type.STRING)),
"title": (("title-sort", Locale.GERMAN),),
"author": (("author-sort", Locale.GERMAN),),
}
sortFields = []
reverse = not self.ascending
for name in self.orderby:
for fieldName, typeNum in sortDict.get(name, []):
sortFields.append(SortField(fieldName, typeNum, reverse))
if len(sortFields) == 0:
sortFields = [SortField("volume", SortField.Type.INT), SortField("number", SortField.Type.INT)]
sort = Sort(sortFields)
topDocs = searcher.search(self.query, None, 80000, sort)
hits = topDocs.scoreDocs
self.hits = hits
self.searcher = searcher
lang = translation.get_language()
if lang != "de":
lang = "en"
facets = {"author": {}, "pubtype": {}, "category-%s" % lang: {}}
# Highlighting
highlighter = Highlighter(SimpleHTMLFormatter('<span class="highlight">', '</span>'), QueryScorer(self.query))
hitObjects = []
fields = {}
for hit in hits:
doc = searcher.doc(hit.doc)
# print unicode(doc)
fields["score"] = hit.score
fields["volume"] = doc["volume"]
fields["number"] = doc["number"]
fields["id"] = doc["id"]
fields["title"] = doc["title"]
fields["author"] = doc["author"]
fields["authors"] = [field.stringValue() for field in doc.getFields("author")]
for author in fields["authors"]: # XXX
facets["author"][author] = facets["author"].get(author, 0) + 1 # XXX
fields["categories"] = [field.stringValue() for field in doc.getFields("category-%s" % lang)]
for cat in fields["categories"]:
facets["category-%s" % lang][cat] = facets["category-%s" % lang].get(cat, 0) + 1
maxNumFragmentsRequired = 2
fragmentSeparator = "...";
pubtype = doc["pubtype"]
fields["pubtype"] = pubtype
facets["pubtype"][pubtype] = facets["pubtype"].get(pubtype, 0) + 1
fields["city"] = doc["city"]
fields["year"] = doc["year-start"]
if fields["year"] and doc["year-end"] and doc["year-end"] != fields["year"]:
fields["year"] += " - " + doc["year-end"]
highlightFields = ("title", "author", "city", "year", "category")
if "freetext" in self.criteria:
for fieldName in highlightFields:
try:
tokenStream = self.analyzer.tokenStream(fieldName, lucene.StringReader(fields[fieldName]))
newVal = highlighter.getBestFragments(tokenStream, fields[fieldName], maxNumFragmentsRequired,
fragmentSeparator)
if len(newVal) > 0:
# fields[fieldName] = re.sub(r'</span>\s*<span class="highlight">', ' ', newVal)
fields[fieldName] = newVal
except:
continue
for fieldName in highlightFields:
if fieldName in self.criteria or fieldName + "-de" in self.criteria or fieldName + "-en" in self.criteria:
try:
tokenStream = self.analyzer.tokenStream(fieldName, lucene.StringReader(fields[fieldName]))
newVal = highlighter.getBestFragments(tokenStream, fields[fieldName], maxNumFragmentsRequired,
fragmentSeparator)
if len(newVal) > 0:
# fields[fieldName] = re.sub(r'</span>\s*<span class="highlight">', ' ', newVal)
fields[fieldName] = newVal
except:
continue
"""if "author" in self.criteria:
try:
tokenStream = self.analyzer.tokenStream("author", lucene.StringReader(fields["author"]))
fields["author"] = highlighter.getBestFragments(tokenStream, fields["author"], maxNumFragmentsRequired, fragmentSeparator)
except:
pass"""
hitObjects.append(
Hit(fields["id"], fields["volume"], fields["number"], fields["title"], fields["author"], fields["city"],
fields["year"], fields["categories"], fields["pubtype"], fields["score"]))
facetsToDelete = []
for facet in facets:
if len(facets[facet]) < 2:
facetsToDelete.append(facet)
continue
values = sorted(facets[facet].items(), key=itemgetter(0))
values = sorted(values, key=itemgetter(1), reverse=True)
facets[facet] = values[:25]
for facet in facetsToDelete:
del facets[facet]
self.facets = facets
reader.close()
self.hitObjects = hitObjects
return hitObjects
def search(self):
self.createQueryString()
querystr = self.getQueryString()
self.query = self.queryParser.parse(querystr)
return self._getHits()
def getAll(self):
self.query = MatchAllDocsQuery()
return self._getHits()
class HighlighterTestCase(PyLuceneTestCase):
"""
Unit tests ported from Java Lucene.
2004 by Yura Smolsky ;)
"""
FIELD_NAME = "contents"
texts = [ "A wicked problem is one for which each attempt to create a solution changes the understanding of the problem. Wicked problems cannot be solved in a traditional linear fashion, because the problem definition evolves as new possible solutions are considered and/or implemented."
"Wicked problems always occur in a social context -- the wickedness of the problem reflects the diversity among the stakeholders in the problem."
"From http://cognexus.org/id42.htm"
"Most projects in organizations -- and virtually all technology-related projects these days -- are about wicked problems. Indeed, it is the social complexity of these problems, not their technical complexity, that overwhelms most current problem solving and project management approaches."
"This text has a typo in referring to whicked problems" ];
def __init__(self, *args):
super(HighlighterTestCase, self).__init__(*args)
self.parser = QueryParser(Version.LUCENE_CURRENT, self.FIELD_NAME,
StandardAnalyzer(Version.LUCENE_CURRENT))
def setUp(self):
super(HighlighterTestCase, self).setUp()
self.analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
writer = self.getWriter(analyzer=self.analyzer)
for text in self.texts:
self.addDoc(writer, text)
writer.commit()
writer.close()
self.reader = self.getReader()
self.numHighlights = 0;
def testSimpleHighlighter(self):
self.doSearching("Wicked")
highlighter = Highlighter(QueryScorer(self.query))
highlighter.setTextFragmenter(SimpleFragmenter(40))
maxNumFragmentsRequired = 2
for scoreDoc in self.scoreDocs:
text = self.searcher.doc(scoreDoc.doc).get(self.FIELD_NAME)
tokenStream = self.analyzer.tokenStream(self.FIELD_NAME,
StringReader(text))
result = highlighter.getBestFragments(tokenStream, text,
maxNumFragmentsRequired,
"...")
print "\t", result
# Not sure we can assert anything here - just running to check we don't
# throw any exceptions
def testGetBestFragmentsSimpleQuery(self):
self.doSearching("Wicked")
self.doStandardHighlights()
self.assert_(self.numHighlights == 3,
("Failed to find correct number of highlights, %d found"
%(self.numHighlights)))
def doSearching(self, queryString):
self.searcher = self.getSearcher()
self.query = self.parser.parse(queryString)
# for any multi-term queries to work (prefix, wildcard, range,
# fuzzy etc) you must use a rewritten query!
self.query = self.query.rewrite(self.reader)
print "Searching for:", self.query.toString(self.FIELD_NAME)
self.scoreDocs = self.searcher.search(self.query, 100).scoreDocs
self.numHighlights = 0
def doStandardHighlights(self):
formatter = TestFormatter(self)
highlighter = Highlighter(formatter, QueryScorer(self.query))
highlighter.setTextFragmenter(SimpleFragmenter(20))
for scoreDoc in self.scoreDocs:
text = self.searcher.doc(scoreDoc.doc).get(self.FIELD_NAME)
maxNumFragmentsRequired = 2
fragmentSeparator = "..."
tokenStream = self.analyzer.tokenStream(self.FIELD_NAME,
StringReader(text))
result = highlighter.getBestFragments(tokenStream,
text,
maxNumFragmentsRequired,
fragmentSeparator)
print "\t", result
def countHighlightTerm(self):
self.numHighlights += 1 # update stats used in assertions
def addDoc(self, writer, text):
d = Document()
f = Field(self.FIELD_NAME, text, TextField.TYPE_STORED)
d.add(f)
writer.addDocument(d)
def run(command,searcher, aWrapper):
print
if command == '':
return
#debug
#print "Searching for:"+command
#query = MultiFieldQueryParser(Version.LUCENE_CURRENT,['subject_id','summary'],analyzer).parse(command);
#query = MultiFieldQueryParser.parse(command,['subject_id','summary'],analyzer);
#'''
#MultiFieldQueryParser(Version matchVersion, String[] fields, Analyzer analyzer)
#'''
#parser = MultiFieldQueryParser(Version.LUCENE_CURRENT, JArray('string')(['subject_id','summary']),analyzer)
#query = MultiFieldQueryParser.parse(parser, command_jarr)
#创建QueryParser对象 默认的搜索域为title
parser = QueryParser(Version.LUCENE_CURRENT, "title", aWrapper)
#A PerFieldAnalyzerWrapper can be used like any other analyzer, for both indexing and query parsing.
query = parser.parse(command)
print query.toString()
#test the analyzerWrapper
#printTokens(aWrapper,command,'title')
#printWrappedAnalyzer(aWrapper)
#所有有相关度的doc，进行排序
#sortField = SortField('boost',SortField.Type.FLOAT,True) #True表示降序
#sort = Sort(sortField)
'''
Error with:
> query = lucene.MultiFieldQueryParser(lucene.Version.LUCENE_CURRENT,
> ["payload","subject"], analyzer).parse(command)
I think there's a bug with the method binding. MultiFieldQueryParser has several static parse
methods, plus the inherited regular method from QueryParser. It looks like all of them are
being resolved as if they were static. As a workaround, you can call it like this:
parser = lucene.MultiFieldQueryParser(lucene.Version.LUCENE_CURRENT, ["payload","subject"],
analyzer)
lucene.MultiFieldQueryParser.parse(parser, command)
'''
#occ=[BooleanClause.Occur.SHOULD , BooleanClause.Occur.SHOULD]
#query = MultiFieldQueryParser.parse(command_list,['subject_id','summary'],occ,analyzer)
#query = QueryParser(Version.LUCENE_CURRENT, FIELD,analyzer).parse(command)
#scoreDocs = searcher.search(query, 50,sort).scoreDocs
scoreDocs = searcher.search(query, 50).scoreDocs
# retList = []
# for scoreDoc in scoreDocs:
# doc = searcher.doc(scoreDoc.doc)
# score = scoreDoc.score
# #print 'subject_id:', doc.get('subject_id')
# #print 'title:', doc.get('title')
# tmpDict = {
# 'subject_id':doc.get('subject_id'),
# 'title':doc.get('title'),
# 'directors':doc.get('directors'),
# 'summary':doc.get('summary'),
# 'image_small':doc.get('image_small'),
# 'boost':doc.get('boost'),
# 'user_tags':doc.get('user_tags'),
# 'year':doc.get('year'),
# 'score':score
# }
# retList.append(tmpDict)
maxDict = utils.maxDict
movieDictList = utils.scoreDocs2dictList(scoreDocs,searcher)
retList = movieDictList
retList = utils.reRank(movieDictList,maxDict,command)
#人工排序
#retList = sorted(retList, key=operator.itemgetter('boost'), reverse=True)
del searcher
return retList
def searchStemFirst(self, annotation):
annotations = list()
pocString = QueryParser.escape(annotation.getText())
preparePocStringOriginal = "\"" + pocString + "\""
preparePocStringLowercase = "\"" + pocString.lower() + "\""
try:
maxSynonyms = 0
# Analyzer stemmedAnalyser =
# AnalyzerUtil.getSynonymAnalyzer(AnalyzerUtil
# .getPorterStemmerAnalyzer(new StandardAnalyzer(Version.LUCENE_CURRENT)),
# synonymMap, maxSynonyms);
stemmedAnalyser = EnglishAnalyzer(Version.LUCENE_CURRENT)
analyser = StandardAnalyzer(Version.LUCENE_CURRENT)
stemParser = QueryParser(Version.LUCENE_CURRENT, FreyaConstants.FIELD_STEMMED_CONTENT, stemmedAnalyser)
query = stemParser.parse(preparePocStringLowercase)
result = self._searcher.search(query, 1)
logging.info("For " + str(query) + " : " + str(result.totalHits))
freq = result.totalHits
if freq > 0:
result = self._searcher.search(query, freq)
stemHits = result.scoreDocs
allHits = stemHits
# if(stemHits.length == 0) {
# search lowercased exact
parser = QueryParser(Version.LUCENE_CURRENT, FreyaConstants.FIELD_EXACT_LOWERCASED_CONTENT, analyser)
query = parser.parse(preparePocStringLowercase)
result = self._searcher.search(query, 1)
freq = result.totalHits
if freq > 0:
result = self._searcher.search(query, freq)
lowHits = result.scoreDocs
allHits = pyJava.JArray2List(allHits) + pyJava.JArray2List(lowHits) # ArrayUtils.addAll(allHits, lowHits)
logging.info("For " + str(query) + " : " + str(result.totalHits))
# }
# if(allHits.length == 0) {
# search exact
exactParser = QueryParser(Version.LUCENE_CURRENT, FreyaConstants.FIELD_EXACT_CONTENT, analyser)
query = exactParser.parse(preparePocStringLowercase)
result = self._searcher.search(query, 1)
freq = result.totalHits
if freq > 0:
result = self._searcher.search(query, freq)
allHits = pyJava.JArray2List(allHits) + pyJava.JArray2List(result.scoreDocs) #ArrayUtils.addAll(allHits, result.scoreDocs)
logging.info("For " + str(query) + " : " + str(result.totalHits))
# }
# for (ScoreDoc hit : allHits) {
indexus = 0
while indexus < len(allHits):
hit = allHits[indexus]
doc = self._searcher.doc(hit.doc)
self._searcher.explain(query, hit.doc)
ann = Annotation()
features = dict()
features[FreyaConstants.CLASS_FEATURE_LKB] = doc.get(FreyaConstants.CLASS_FEATURE_LKB)
features[FreyaConstants.INST_FEATURE_LKB] = doc.get(FreyaConstants.INST_FEATURE_LKB)
features[FreyaConstants.PROPERTY_FEATURE_LKB] = doc.get(FreyaConstants.PROPERTY_FEATURE_LKB)
features["string"] = doc.get(FreyaConstants.FIELD_EXACT_CONTENT)
features["score"] = hit.score
ann.setFeatures(features)
ann.setEndOffset(annotation.getEndOffset())
ann.setStartOffset(annotation.getStartOffset())
ann.setSyntaxTree(annotation.getSyntaxTree())
ann.setText(annotation.getText())
annotations.append(ann)
indexus += 1
except Exception as e:#CorruptIndexException(e):
print e.message
logging.error("Error")
return annotations
format = a
elif o == "--index":
indexDir = a
elif o == "--stats":
stats = True
class CustomTemplate(Template):
delimiter = '#'
template = CustomTemplate(format)
fsDir = SimpleFSDirectory(Paths.get(indexDir))
searcher = IndexSearcher(DirectoryReader.open(fsDir))
analyzer = StandardAnalyzer()
parser = QueryParser("keywords", analyzer)
parser.setDefaultOperator(QueryParser.Operator.AND)
query = parser.parse(' '.join(args))
start = datetime.now()
scoreDocs = searcher.search(query, 50).scoreDocs
duration = datetime.now() - start
if stats:
print >>sys.stderr, "Found %d document(s) (in %s) that matched query '%s':" %(len(scoreDocs), duration, query)
for scoreDoc in scoreDocs:
doc = searcher.doc(scoreDoc.doc)
table = dict((field.name(), field.stringValue())
for field in doc.getFields())
print template.substitute(table)
from org.apache.lucene.store import SimpleFSDirectory
from org.apache.lucene.util import Version
# This script will prepare a CSV for Cassandra DB
if __name__ == '__main__':
lucene.initVM()
base_dir = os.path.abspath(os.path.curdir)
index_file = os.path.join(base_dir, INDEX_DIR)
store = SimpleFSDirectory(File(index_file))
analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
reader = IndexReader.open(store)
searcher = IndexSearcher(reader)
query_parser = QueryParser(Version.LUCENE_CURRENT, "netflix_id", analyzer)
with open(sys.argv[1], 'r') as ratings:
for line in ratings:
user_id, netflix_id, score = line.split(",")
query = query_parser.parse(netflix_id)
scoreDocs = searcher.search(query, 1).scoreDocs
if scoreDocs:
doc = searcher.doc(scoreDocs[0].doc)
film_id = doc.getField("id").stringValue()
print "{0},{1},{2}".format(user_id, film_id, score),
def search(request):
query = request.GET.get('q', None)
page = int(request.GET.get('page', 1))
perPage = 5
nodes = []
usage = {}
usage["time"] = time.time()
if not query:
count = 0
nodes = []
keywords = []
else:
#conn = ReplicaSetConnection('localhost', replicaSet='jlu')
conn = MongoClient('localhost')
db = conn.sina
#db.read_preference = ReadPreference.SECONDARY
CACHE = db.cache
keywords = query.split(' ')
cache = CACHE.find_one({"query":keywords,"page":page})
if cache == None:
print "query cache not found"
VM_ENV.attachCurrentThread()
fsDir = SimpleFSDirectory(File(settings.ROOT_DIR+'/index'))
searcher = IndexSearcher(DirectoryReader.open(fsDir))
analyzer = SmartChineseAnalyzer(Version.LUCENE_CURRENT)
parser = QueryParser(Version.LUCENE_CURRENT, 'text', analyzer)
parser.setDefaultOperator(QueryParser.Operator.AND)
lucene_query = parser.parse(query)
scoreDocs = searcher.search(lucene_query, 3000000).scoreDocs
ids = []
for scoreDoc in scoreDocs:
doc = searcher.doc(scoreDoc.doc)
for field in doc.getFields():
ids.append(field.stringValue())
print "got ids from lucene",len(ids)
ids = [int(x) for x in ids]
NODES = conn.sina.nodes
count = 0
for n in NODES.find({"node_id":{"$in":ids}}).sort("in_degree",-1).skip((page-1)*perPage):
count += 1
print "doing",n["node_id"],count,"/",perPage
n["js"] = similarity(n["node_id"],topk=10)
nodes.append(n)
if len(nodes) == perPage:
break
count = len(ids)
CACHE.insert({"query":keywords,"page":page,"cache":nodes,"count":len(ids)})
usage["isCache"] = False
else:
print "found query cache"
usage["isCache"] = True
nodes = cache["cache"]
count = cache["count"]
pagenav = {}
if page == 1:
pagenav["has_pre"] = None
else:
pagenav["has_pre"] = page - 1
if page > count/perPage:
pagenav["has_next"] = None
else:
pagenav["has_next"] = page + 1
pagenav["page"] = page
usage["time"] = time.time() - usage["time"]
return {
'q' : request.GET.get('q', ''),
'keywords' : keywords,
'nodes' : nodes,
'count' : count,
'page' : pagenav,
'usage' : usage,
}
def run(command, searcher, aWrapper, use_custom_parser=False, debug=False):
if not os.path.isdir(query_log_dir):
os.mkdir(query_log_dir)
search_start = time.time()
query_log_file = os.path.join(query_log_dir, 'query_log.%s' % datetime.now().strftime('%Y-%m-%d'))
fw = open(query_log_file, 'a+')
cur_time = datetime.now()
cur_time = cur_time + timedelta(hours=8)#解决时区不在中国的问题
fw.write('\n*********query-log,time=%s*************\n' % cur_time.strftime('%Y-%m-%d %H:%M:%S') )
fw.write('raw_str=%s\n' % unicode_to_str(command))
if command == '':
return
#debug
#print "Searching for:"+command
#query = MultiFieldQueryParser(Version.LUCENE_CURRENT,['subject_id','summary'],analyzer).parse(command);
#query = MultiFieldQueryParser.parse(command,['subject_id','summary'],analyzer);
#'''
#MultiFieldQueryParser(Version matchVersion, String[] fields, Analyzer analyzer)
#'''
#parser = MultiFieldQueryParser(Version.LUCENE_CURRENT, JArray('string')(['subject_id','summary']),analyzer)
#query = MultiFieldQueryParser.parse(parser, command_jarr)
if debug:
print 'before query parser: ', command
command = custom_parser.parse(command) if use_custom_parser else command
fw.write('parsed_str=%s\n' % unicode_to_str(command))
if debug:
print 'after query parser: ', command
#创建QueryParser对象 默认的搜索域为title
parser = QueryParser(Version.LUCENE_CURRENT, "title", aWrapper)
#A PerFieldAnalyzerWrapper can be used like any other analyzer, for both indexing and query parsing.
query = parser.parse(command)
if debug:
print 'after lucene QueryParser: ', query.toString().encode('utf8')
fw.write('lucene_str=%s\n' % unicode_to_str(query.toString()))
#test the analyzerWrapper
#printTokens(aWrapper,command,'title')
#printWrappedAnalyzer(aWrapper)
#所有有相关度的doc，进行排序
#sortField = SortField('boost',SortField.Type.FLOAT,True) #True表示降序
#sort = Sort(sortField)
'''
Error with:
> query = lucene.MultiFieldQueryParser(lucene.Version.LUCENE_CURRENT,
> ["payload","subject"], analyzer).parse(command)
I think there's a bug with the method binding. MultiFieldQueryParser has several static parse
methods, plus the inherited regular method from QueryParser. It looks like all of them are
being resolved as if they were static. As a workaround, you can call it like this:
parser = lucene.MultiFieldQueryParser(lucene.Version.LUCENE_CURRENT, ["payload","subject"],
analyzer)
lucene.MultiFieldQueryParser.parse(parser, command)
'''
#occ=[BooleanClause.Occur.SHOULD , BooleanClause.Occur.SHOULD]
#query = MultiFieldQueryParser.parse(command_list,['subject_id','summary'],occ,analyzer)
#query = QueryParser(Version.LUCENE_CURRENT, FIELD,analyzer).parse(command)
#scoreDocs = searcher.search(query, 50,sort).scoreDocs
retN = 50
start_time = time.time()
scoreDocs = searcher.search(query, retN).scoreDocs
cost_time = time.time() - start_time
# retList = []
# for scoreDoc in scoreDocs:
# doc = searcher.doc(scoreDoc.doc)
# score = scoreDoc.score
# #print 'subject_id:', doc.get('subject_id')
# #print 'title:', doc.get('title')
# tmpDict = {
# 'subject_id':doc.get('subject_id'),
# 'title':doc.get('title'),
# 'directors':doc.get('directors'),
# 'summary':doc.get('summary'),
# 'image_small':doc.get('image_small'),
# 'boost':doc.get('boost'),
# 'user_tags':doc.get('user_tags'),
# 'year':doc.get('year'),
# 'score':score
# }
# retList.append(tmpDict)
maxDict = utils.maxDict
movieDictList = utils.scoreDocs2dictList(scoreDocs,searcher)
retList = movieDictList
retList = utils.reRank(movieDictList,maxDict,command)
#人工排序
#retList = sorted(retList, key=operator.itemgetter('boost'), reverse=True)
fw.write('***********return list(search/total=%.2fs/%.2fs)***************\n' % (cost_time, time.time() - search_start))
for r in retList:
line = '%s: %s, boost->%s||score=%s\n' % (r['subject_id'], r['title'], r['boost'], r['score'])
fw.write(unicode_to_str(line))
fw.write('**************************************************************\n\n')
del searcher
return retList[:20] if debug else retList
def searchIndex(self, annotation, specialTreatment):
if specialTreatment:
return self.searchStemFirst(annotation)
annotations = list() #ArrayList[Annotation]()
try:
maxSynonyms = 0
stemAnalyser = EnglishAnalyzer(Version.LUCENE_CURRENT)
# Analyzer stemmedAnalyser = AnalyzerUtil.getSynonymAnalyzer(AnalyzerUtil
# .getPorterStemmerAnalyzer(new StandardAnalyzer(Version.LUCENE_CURRENT)),
# synonymMap, maxSynonyms);
analyser = StandardAnalyzer(Version.LUCENE_CURRENT)
parser = QueryParser(Version.LUCENE_CURRENT, FreyaConstants.FIELD_EXACT_CONTENT, analyser)
pocString = QueryParser.escape(annotation.getText())
preparePocString = "\"" + pocString + "\""
preparePocStringLowercase = "\"" + pocString.lower() + "\""
query = parser.parse(preparePocString)
result = self._searcher.search(query, 1)
logging.debug("For " + str(query) + " : " + str(result.totalHits))
freq = result.totalHits
if freq > 0:
result = self._searcher.search(query, freq)
hits = pyJava.JArray2List(result.scoreDocs)
logging.debug("For " + str(query) + " : " + str(result.totalHits))
if freq <= 0:
# search lowercased exact
lowerCasedParser = QueryParser(Version.LUCENE_CURRENT, FreyaConstants.FIELD_EXACT_LOWERCASED_CONTENT, analyser)
query = lowerCasedParser.parse(preparePocStringLowercase)
# logging.info("Searching for: " + query.toString());
result = self._searcher.search(query, 1)
freq = result.totalHits
if freq > 0:
result = self._searcher.search(query, freq)
hits = pyJava.JArray2List(result.scoreDocs)
logging.debug("For " + str(query) + " : " + str(result.totalHits))
if len(hits) == 0 and preparePocStringLowercase.index(" ") < 0:
# search stemmed
stemParser = QueryParser(Version.LUCENE_CURRENT, FreyaConstants.FIELD_STEMMED_CONTENT, stemAnalyser)
query = stemParser.parse(preparePocStringLowercase)
# logging.info("Searching for: " + query.toString());
result = self._searcher.search(query, 1)
freq = result.totalHits
if freq > 0:
result = self._searcher.search(query, freq)
hits = pyJava.JArray2List(result.scoreDocs)
logging.info("For " + str(query) + " : " + str(result.totalHits))
# for (ScoreDoc hit : hits) {
indexus = 0
while indexus < len(hits):
hit = hits[indexus]
doc = self._searcher.doc(hit.doc)
self._searcher.explain(query, hit.doc)
ann = Annotation()
features = dict()
features[FreyaConstants.CLASS_FEATURE_LKB]=doc.get(FreyaConstants.CLASS_FEATURE_LKB)
features[FreyaConstants.INST_FEATURE_LKB]=doc.get(FreyaConstants.INST_FEATURE_LKB)
features[FreyaConstants.PROPERTY_FEATURE_LKB]=doc.get(FreyaConstants.PROPERTY_FEATURE_LKB)
features["string"]=doc.get(FreyaConstants.FIELD_EXACT_CONTENT)
features[FreyaConstants.SCORE]=hit.score
ann.setFeatures(features)
ann.setEndOffset(annotation.getEndOffset())
ann.setStartOffset(annotation.getStartOffset())
ann.setSyntaxTree(annotation.getSyntaxTree())
ann.setText(annotation.getText())
annotations.append(ann)
indexus += 1
except Exception as e:#CorruptIndexException(e):
print e.message
logging.error("Error")
return annotations