def build_index():
lucene.initVM()
# post_dir = current_app.config['LOCAL_REPO_PATH'] + '/_posts/'
post_dir = '/Users/w3/data/github/codeif_backup'
index_store_dir = current_app.config['INDEX_STORE_DIR']
print post_dir
print index_store_dir
analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
store = SimpleFSDirectory(File(index_store_dir))
analyzer = LimitTokenCountAnalyzer(analyzer, 1048576)
config = IndexWriterConfig(Version.LUCENE_CURRENT, analyzer)
config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
writer = IndexWriter(store, config)
indexDocs(post_dir, writer)
ticker = Ticker()
print 'commit index',
threading.Thread(target=ticker.run).start()
writer.commit()
writer.close()
ticker.tick = False
print 'done'
def __init__(self, root, store_dir):
if not os.path.exists(store_dir):
os.mkdir(store_dir, 0777)
# NOTE: Hardcoded the analyzer instead of passing it
lucene.initVM()
'''
vm_env = lucene.getVMEnv()
vm_env.attachCurrentThread()
'''
analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
store = SimpleFSDirectory(File(store_dir))
analyzer = LimitTokenCountAnalyzer(analyzer, 1048576)
config = IndexWriterConfig(Version.LUCENE_CURRENT, analyzer)
# Set the permissions to 777 for the index directory and the write.lock file
chmod_indexdir_cmd = "chmod 0777 " + store_dir
writelock_file = store_dir + "/" + "write.lock"
chmod_writelock_cmd = "chmod 0777 " + writelock_file
if os.path.exists(store_dir):
cicmd=os.popen("sudo -S %s"%(chmod_indexdir_cmd), 'w').write('vagrant')
if os.path.exists(writelock_file):
cwcmd=os.popen("sudo -S %s"%(chmod_writelock_cmd), 'w').write('vagrant')
# setting CREATE will rewrite over the existing indexes.
###config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
writer = IndexWriter(store, config)
self.indexDocs(root, writer)
writer.close()
def __init__(self,root,storeDir,analyzer): # Create the index dir if it does not exist if not os.path.exists(storeDir): os.mkdir(storeDir) # the SimpleFSDirectory which the index will be written in store = SimpleFSDirectory(File(storeDir)) analyzer = LimitTokenCountAnalyzer(analyzer,1048576) config = IndexWriterConfig(Version.LUCENE_CURRENT,analyzer) config.setOpenMode(IndexWriterConfig.OpenMode.CREATE) # create a index writer # atach the index dir and config info to it writer = IndexWriter(store,config) # call the indexing procedure # indexing all the files in the directory specified by root # write the index with writer self.indexDocs(root,writer) # start a ticker ticker = Ticker() print 'commit index' threading.Thread(target=ticker.run).start() writer.commit() writer.close() # stop the ticker when the indexing procedure completes ticker.tick = False print 'Done'
def index(indexdir):
lucene.initVM()
indexDir = SimpleFSDirectory(File(indexdir))
writerConfig = IndexWriterConfig(Version.LUCENE_4_10_1, EnglishAnalyzer())
writer = IndexWriter(indexDir, writerConfig)
f = open('data/docid.documento-xml.txt')
st = PorterStemmer()
for i, line in enumerate(f.readlines()):
id, xmltext = line.split('\t')
xmltext = xmltext.rstrip('\n')
xmldoc = minidom.parseString(xmltext)
title = xmldoc.getElementsByTagName("TITLE")
title = "" if len(title) == 0 else title[0].childNodes[0].nodeValue
authors = xmldoc.getElementsByTagName("AUTHORS")
authors = "" if len(authors) == 0 else authors[0].childNodes[0].nodeValue
abstract = xmldoc.getElementsByTagName("ABSTRACT")
abstract = "" if len(abstract) == 0 else abstract[0].childNodes[0].nodeValue
doc = Document()
doc.add(Field("title", title, Field.Store.YES, Field.Index.ANALYZED))
doc.add(Field("authors", authors, Field.Store.YES, Field.Index.ANALYZED))
doc.add(Field("abstract", abstract, Field.Store.YES, Field.Index.ANALYZED))
doc.add(Field("id", id, Field.Store.YES, Field.Index.NOT_ANALYZED))
writer.addDocument(doc)
print "indexed %s docs" % (i+1)
writer.close()
def reindex(self):
writer = IndexWriter(SimpleFSDirectory(File(self.corpus.path)), self.corpus.analyzer, False, IndexWriter.MaxFieldLength.LIMITED)
indexutils.reindex_all(self.reader, writer, self.corpus.analyzer)
writer.optimize()
writer.close()
self.parent.write({'message': "Reindex successful. Corpus analyzer is now set to %s." % (self.corpus.analyzer_str,)})
self.parent.write({'status': "Ready!"})
class LuceneIndexer:
def __init__(self, path_to_save):
self.path_to_save = path_to_save
self.num_docs = 0
lucene.initVM()
self.indexDir = SimpleFSDirectory(File(self.path_to_save))
self.analyzer = StandardAnalyzer(Version.LUCENE_4_10_1)
self.analyzer2 = WhitespaceAnalyzer(Version.LUCENE_4_10_1)
self.writerConfig = IndexWriterConfig(Version.LUCENE_4_10_1, self.analyzer2)
self.writer = IndexWriter(self.indexDir, self.writerConfig)
def add_document(self, fields, header, id_):
doc = Document()
if len(fields) > len(header):
sys.stderr.write('SKIPPED_DOC\tunexpected_num_lines\t%s\n' % str(id_))
for field in fields:
sys.stderr.write('%s\n' % field)
return
for idx, field in enumerate(fields):
fname, fieldtype = header[idx]
if fieldtype is IntField:
field = int(field)
doc.add(fieldtype(fname, field, Field.Store.YES))
self.writer.addDocument(doc)
self.num_docs += 1
def close(self):
print 'Indexed %d lines from stdin (%d docs in index)' % (self.num_docs, self.writer.numDocs())
self.writer.close()
def _getLucene(self, path):
directory = FSDirectory.open(Paths.get(path))
config = IndexWriterConfig(None)
config.setRAMBufferSizeMB(256.0) # faster
config.setUseCompoundFile(False) # faster, for Lucene 4.4 and later
writer = IndexWriter(directory, config)
reader = writer.getReader()
searcher = IndexSearcher(reader)
return writer, reader, searcher
def getLucene(path):
directory = FSDirectory.open(Paths.get(path))
analyzer = WhitespaceAnalyzer()
config = IndexWriterConfig(analyzer)
config.setIndexSort(Sort(SortField(NUMERIC_STAMP_FIELD, SortField.Type.LONG)))
writer = IndexWriter(directory, config)
reader = writer.getReader()
searcher = IndexSearcher(reader)
return writer, reader, searcher
def wikipedia_indexer(storage, wikipedia_file) :
lucene.initVM()
indexDir = SimpleFSDirectory(File(storage))
stops = CharArraySet(Version.LUCENE_4_10_1, 0, True)
for s in stopwords :
stops.add(s)
analyzer = StandardAnalyzer(Version.LUCENE_4_10_1, stops)
writerConfig = IndexWriterConfig(Version.LUCENE_4_10_1, analyzer)
writer = IndexWriter(indexDir, writerConfig)
print "%d docs in index" % writer.numDocs()
print "Reading Documents"
f = open(wikipedia_file)
for i, line in enumerate(f) :
text = line.strip().decode('utf-8').split('\t')
title = text[0]
if 'disambigu' in text[0] or len(text) < 2:
continue
text = text[1]
doc = Document()
doc.add(Field("num", str(i), Field.Store.YES, Field.Index.NO))
doc.add(Field("title", title, Field.Store.YES, Field.Index.ANALYZED))
doc.add(Field("text", text, Field.Store.NO, Field.Index.ANALYZED))
writer.addDocument(doc)
if writer.numDocs() % 1000 == 0 :
print "Indexed (%d docs in index) Last %d" % (writer.numDocs(), i)
print "Closing index of %d docs..." % writer.numDocs()
writer.close()
def getLucene(path):
directory = FSDirectory.open(File(path))
analyzer = WhitespaceAnalyzer()
config = IndexWriterConfig(Version.LATEST, analyzer)
mergePolicy = config.getMergePolicy()
sortingMergePolicy = SortingMergePolicy(mergePolicy, Sort(SortField(NUMERIC_STAMP_FIELD, SortField.Type.LONG)))
config.setMergePolicy(sortingMergePolicy)
writer = IndexWriter(directory, config)
reader = writer.getReader()
searcher = IndexSearcher(reader)
return writer, reader, searcher
def make_index(indexed_data, index_destination, source='directory'):
#index wiki articles based on ck 12 topics
#analyzer = StandardAnalyzer(Version.LUCENE_30)
analyzer = SnowballAnalyzer(Version.LUCENE_30, "English", StandardAnalyzer.STOP_WORDS_SET)
indexWriterConfig = IndexWriterConfig(Version.LUCENE_30, analyzer)
writer = IndexWriter(SimpleFSDirectory(File(index_destination)), indexWriterConfig)
if source == 'directory':
indexDirectory(indexed_data, writer)
else:
indexDictionary(indexed_data, writer)
writer.close()
class Indexer(object):
def __init__(self, **kwargs):
""" Initialize a new instance of the Indexer
:param output: The output directory of the underlying index
:param anaylzer: The overloaded analyzer to work with
"""
self.output = kwargs.get("root", "index")
if not os.path.exists(self.output):
os.mkdir(self.output)
self.analyzer = kwargs.get("analyzer", StandardAnalyzer(Version.LUCENE_CURRENT))
self.analyzer = LimitTokenCountAnalyzer(self.analyzer, 1048576)
self.config = IndexWriterConfig(Version.LUCENE_CURRENT, self.analyzer)
self.config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
self.store = SimpleFSDirectory(File(self.output))
self.writer = IndexWriter(self.store, self.config)
self.create_field_types()
def index(self, document):
""" Given a new document, add it to the index.
:param document: The document to add to the indexer
"""
try:
self.writer.addDocument(document)
except Exception:
logger.exception("Failed to index the supplied document")
def shutdown(self):
""" Shutdown the currently processing indexer.
"""
try:
# self.writer.optimize()
self.writer.close()
except Exception:
logger.exception("Failed to shutdown the indexer correctly")
def create_field_types(self):
""" Create the field types that will be used to specify
what actions lucene should take on the various fields
supplied to index.
"""
self.field_clean = FieldType()
self.field_clean.setIndexed(True)
self.field_clean.setStored(True)
self.field_clean.setTokenized(False)
self.field_clean.setIndexOptions(FieldInfo.IndexOptions.DOCS_AND_FREQS)
self.field_dirty = FieldType()
self.field_dirty.setIndexed(True)
self.field_dirty.setStored(False)
self.field_dirty.setTokenized(True)
self.field_dirty.setIndexOptions(FieldInfo.IndexOptions.DOCS_AND_FREQS_AND_POSITIONS)
def __init__(self, root, storeDir, analyzer):
if not os.path.exists(storeDir):
os.mkdir(storeDir)
store = SimpleFSDirectory(File(storeDir))
config = IndexWriterConfig(Version.LUCENE_CURRENT, analyzer)
config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
writer = IndexWriter(store, config)
self.indexDocs(root, writer)
writer.commit()
writer.close()
def import_csv_with_content(self, csv_file, content_field):
try:
writer = IndexWriter(SimpleFSDirectory(File(self.corpus.path)), self.analyzer, False, IndexWriter.MaxFieldLength.LIMITED)
changed_rows = addmetadata.add_metadata_and_content_from_csv(self.searcher, self.reader, writer, csv_file, content_field, self.args_dir)
writer.close()
except UnicodeDecodeError:
try:
writer.close()
except:
pass
self.parent.write({'error': 'CSV import failed: file contained non-unicode characters. Please save the file with UTF-8 encoding and try again!'})
return
self.parent.write({'message': "CSV import complete: %s rows added." % (changed_rows,)})
def __init__(self, indexPath):
"""Instantiate the handler object."""
self.indexPath = indexPath
self.analyzer = StopAnalyzer()
# Make sure the path exists
if not os.path.exists(self.indexPath):
os.mkdir(self.indexPath)
if not os.path.exists(os.path.join(self.indexPath, 'segments.gen')):
log('Creating new index.')
writer = IndexWriter(self.indexPath, self.analyzer, 1)
writer.close()
def create_index(storage, paths) :
lucene.initVM()
indexDir = SimpleFSDirectory(File(storage))
stops = CharArraySet(Version.LUCENE_4_10_1, 0, True)
for s in stopwords :
stops.add(s)
analyzer = StandardAnalyzer(Version.LUCENE_4_10_1, stops)
writerConfig = IndexWriterConfig(Version.LUCENE_4_10_1, analyzer)
writer = IndexWriter(indexDir, writerConfig)
print "%d docs in index" % writer.numDocs()
print "Reading Documents"
import os
for path in paths :
for filen in os.listdir(path) :
text = sent_tokenize(get_data_from_file(path + filen))
total_sent = len(text)
for i in range(0, total_sent, 3) :
doc = Document()
a = i-5 if i-5 > 0 else 0
sentence = ' '.join(text[a:i+5])
doc.add(Field("text", sentence, Field.Store.YES, Field.Index.ANALYZED))
writer.addDocument(doc)
print("Done %s" % (path+filen))
print "Indexed (%d docs in index)" % (writer.numDocs())
print "Closing index of %d docs..." % writer.numDocs()
writer.close()
def lucene_index(texts):
"""
:param corpus_file_path:
:param f_type:
:return:
"""
index = set_lucene_index['ind'] # nonlocal variable index
config = IndexWriterConfig(version, analyzer)
writer = IndexWriter(index, config)
for t in texts:
addDoc(writer, t)
writer.close()
def create_index():
lucene.initVM()
if os.path.exists(prm.index_folder):
shutil.rmtree(prm.index_folder)
indexDir = SimpleFSDirectory(File(prm.index_folder))
writerConfig = IndexWriterConfig(Version.LUCENE_4_10_1, StandardAnalyzer())
writer = IndexWriter(indexDir, writerConfig)
wk = wiki.Wiki(prm.pages_path)
print "%d docs in index" % writer.numDocs()
print "Reading files from wikipedia..."
n = 0
for l in wk.get_text_iter():
doc = Document()
doc.add(Field("text", l, Field.Store.YES, Field.Index.ANALYZED))
doc.add(Field("id", str(n), Field.Store.YES, Field.Index.ANALYZED))
writer.addDocument(doc)
n += 1
if n % 100000 == 0:
print 'indexing article', n
print "Indexed %d docs from wikipedia (%d docs in index)" % (n, writer.numDocs())
print "Closing index of %d docs..." % writer.numDocs()
writer.close()
def index(analyzer, index_dest_dir, documents):
""" Builds Lucene index from provided documents using given analyzer
:param analyzer:
:param index_dest_dir:
:param list[Document] documents:
:return:
"""
if not all([isinstance(d, Document) for d in documents]):
raise TypeError("documents should be iterable of type Document! Given: %s" % type(documents[0]))
writer_config = IndexWriterConfig(Version.LUCENE_30, analyzer)
writer = IndexWriter(SimpleFSDirectory(File(index_dest_dir)), writer_config)
for doc in documents:
writer.addDocument(doc)
writer.close()
def __init__(self, path, settings):
self._settings = settings
self._multithreaded = settings.multithreaded
self._checker = DirectSpellChecker()
indexDirectory = MMapDirectory(File(join(path, 'index')))
indexDirectory.setUseUnmap(False)
taxoDirectory = MMapDirectory(File(join(path, 'taxo')))
taxoDirectory.setUseUnmap(False)
conf = IndexWriterConfig(Version.LUCENE_4_10_0, settings.analyzer)
conf.setSimilarity(settings.similarity)
mergePolicy = TieredMergePolicy()
mergePolicy.setMaxMergeAtOnce(settings.maxMergeAtOnce)
mergePolicy.setSegmentsPerTier(settings.segmentsPerTier)
conf.setMergePolicy(mergePolicy)
if not settings.readonly:
self._indexWriter = IndexWriter(indexDirectory, conf)
self._indexWriter.commit()
self._taxoWriter = DirectoryTaxonomyWriter(taxoDirectory, IndexWriterConfig.OpenMode.CREATE_OR_APPEND, LruTaxonomyWriterCache(settings.lruTaxonomyWriterCacheSize))
self._taxoWriter.commit()
self._indexAndTaxonomy = IndexAndTaxonomy(settings, indexDirectory, taxoDirectory)
self._readerSettingsWrapper = self._indexAndTaxonomy._readerSettingsWrapper
self._facetsConfig = settings.fieldRegistry.facetsConfig
self._ordinalsReader = CachedOrdinalsReader(DocValuesOrdinalsReader())
def __init__(self, root, storeDir, analyzer):
if not os.path.exists(storeDir):
os.mkdir(storeDir)
store = SimpleFSDirectory(File(storeDir))
analyzer = LimitTokenCountAnalyzer(analyzer, 1000)#1048576
config = IndexWriterConfig(Version.LUCENE_CURRENT, analyzer)
config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
writer = IndexWriter(store, config)
self.indexDocs(root, writer)
ticker = Ticker()
print 'commit index',
threading.Thread(target=ticker.run).start()
writer.commit()
writer.close()
ticker.tick = False
print 'done'
def rollback(collection_name): if collection_name!="DEFAULT": INDEX_DIR=collection_name else: INDEX_DIR=INDEX_DIR_DEFAULT direc=SimpleFSDirectory(File(INDEX_DIR)) analyzer=StandardAnalyzer(Version.LUCENE_CURRENT) #setting writer configurations config=IndexWriterConfig(Version.LUCENE_CURRENT,analyzer) config.setOpenMode(IndexWriterConfig.OpenMode.CREATE_OR_APPEND) writer=IndexWriter(direc,config) writer.rollback() writer.close()
def __init__(self, writerConfig, indexDir): lucene.initVM() self.mIndexDir = SimpleFSDirectory(File(indexDir)) self.mConfig = writerConfig self.mWriter = IndexWriter(self.mIndexDir, self.mConfig)
def open_writer(self):
"""Open IndexWriter."""
if self.writer is None:
config = IndexWriterConfig(self.get_version(), self.get_analyzer())
config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
self.writer = IndexWriter(self.dir, config)
else:
raise Exception("IndexWriter is already open")
def buildIndex(self, inputFile):
analyzer = self.getAnalyzer()
iwconf = IndexWriterConfig(Version.LUCENE_CURRENT, analyzer)
iwconf.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
writer = IndexWriter( SimpleFSDirectory( File(self.luceneDir) ), iwconf)
# read through input file and write out to lucene
counter = 0
linesReadCounter = 0
with open(inputFile, 'r') as lines:
linesRead = 0
for line in lines:
try:
linesRead+=1
if linesRead % 1000 == 0:
print "%d lines read" % linesRead
cui, concept = line.replace("\",\"", "\t").replace("\"", "").split("\t")
concept = concept.strip()
cui = cui.strip()
strNorm = self.normalizeCasePunct(concept)
strSorted = self.sortWords(strNorm)
strStemmed = self.stemWords(strNorm)
strStemmedSorted = self.stemWords(strSorted)
fdoc = Document()
counter +=1
fid = counter
fdoc.add( Field("id", unicode(fid), Field.Store.YES, Field.Index.NOT_ANALYZED))
fdoc.add( Field("cui", cui, Field.Store.YES, Field.Index.NOT_ANALYZED))
fdoc.add( Field("str", concept, Field.Store.YES, Field.Index.NOT_ANALYZED))
fdoc.add( Field("str_norm", strNorm, Field.Store.YES, Field.Index.NOT_ANALYZED))
fdoc.add( Field("str_sorted", strSorted, Field.Store.YES, Field.Index.NOT_ANALYZED))
fdoc.add( Field("str_stemmed", strStemmed, Field.Store.YES, Field.Index.NOT_ANALYZED))
fdoc.add( Field("str_stemmedSorted", strStemmedSorted, Field.Store.YES, Field.Index.NOT_ANALYZED))
writer.addDocument(fdoc)
if fid % 1000 == 0:
writer.commit()
except:
"Skipping line: %s" % line
writer.commit()
writer.close()
def __init__(self, destination_directory, analyzer):
if not os.path.exists(destination_directory):
os.mkdir(destination_directory)
store = SimpleFSDirectory(File(destination_directory))
analyzer = LimitTokenCountAnalyzer(analyzer, 1048576)
config = IndexWriterConfig(Version.LUCENE_CURRENT, analyzer)
config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
writer = IndexWriter(store, config)
self.tweetIndexer(writer)
ticker = Ticker()
threading.Thread(target=ticker.run).start()
writer.commit()
writer.close()
ticker.tick = False
print 'done'
def __init__(self, path_to_save):
self.path_to_save = path_to_save
self.num_docs = 0
lucene.initVM()
self.indexDir = SimpleFSDirectory(File(self.path_to_save))
self.analyzer = StandardAnalyzer(Version.LUCENE_4_10_1)
self.analyzer2 = WhitespaceAnalyzer(Version.LUCENE_4_10_1)
self.writerConfig = IndexWriterConfig(Version.LUCENE_4_10_1, self.analyzer2)
self.writer = IndexWriter(self.indexDir, self.writerConfig)
def __init__(self, store_dir):
self.store_dir = store_dir
if not os.path.exists(store_dir):
os.mkdir(store_dir, 0777)
self.store = SimpleFSDirectory(Paths.get(store_dir))
self.analyzer = StandardAnalyzer()
self.analyzer = LimitTokenCountAnalyzer(self.analyzer, 1048576)
self.config = IndexWriterConfig(self.analyzer)
self.writer = IndexWriter(self.store, self.config)
def __init__(self, fileRoot, storeDir, analyzer):
if not os.path.exists(storeDir):
os.mkdir(storeDir)
store = SimpleFSDirectory(File(storeDir))
analyzer = LimitTokenCountAnalyzer(analyzer, 1048576)
config = IndexWriterConfig(Version.LUCENE_CURRENT, analyzer)
config.setSimilarity(similarities.BM25Similarity())
#Available similarity: BM25Similarity, MultiSimilarity, PerFieldSimilarityWrapper, SimilarityBase, TFIDFSimilarity
config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
writer = IndexWriter(store, config)
self.indexDocs(fileRoot, writer)
print 'commit index',
writer.commit()
writer.close()
print 'done'
def createIndexWriter(self, actual_dir, max_field_length=20000000):
"""
Returns an IndexWriter object created for the actual_dir specified
"""
ensureDirExists(actual_dir)
index = SimpleFSDirectory(File(actual_dir))
analyzer = StandardAnalyzer(LuceneVersion.LUCENE_CURRENT)
writerConfig=IndexWriterConfig(LuceneVersion.LUCENE_CURRENT, analyzer)
similarity=FieldAgnosticSimilarity()
writerConfig.setSimilarity(similarity)
writerConfig.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
## res= IndexWriter(index, analyzer, True, IndexWriter.MaxFieldLength(max_field_length))
res= IndexWriter(index, writerConfig)
res.deleteAll()
return res
sheet1 = wb.sheet_by_index(1)
print('initializing Lucene VM')
lucene.initVM()
print('lucene version ', lucene.VERSION)
index_path = Paths.get('./lucene.index')
question_field = 'question'
answer_field = 'answer'
index_store = SimpleFSDirectory(index_path)
# analyzer = StandardAnalyzer()
analyzer = PersianAnalyzer()
config = IndexWriterConfig(analyzer)
config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
writer = IndexWriter(index_store, config)
TokenizeFields = True
# Question field type
qft = FieldType()
# qft.setIndexed(True) # todo
qft.setStored(True)
qft.setTokenized(TokenizeFields)
qft.setIndexOptions(IndexOptions.DOCS_AND_FREQS)
# Answer field type
aft = FieldType()
# aft.setIndexed(False) # todo
aft.setStored(True)
doc.add(
Field("keywords", ' '.join((command, name, synopsis, description)),
TextField.TYPE_NOT_STORED))
doc.add(Field("filename", os.path.abspath(path), StringField.TYPE_STORED))
writer.addDocument(doc)
if __name__ == '__main__':
if len(sys.argv) != 2:
print("Usage: python manindex.py <index dir>")
else:
lucene.initVM(vmargs=['-Djava.awt.headless=true'])
directory = SimpleFSDirectory(Paths.get(sys.argv[1]))
analyzer = StandardAnalyzer()
analyzer = LimitTokenCountAnalyzer(analyzer, 10000)
config = IndexWriterConfig(analyzer)
writer = IndexWriter(directory, config)
manpath = os.environ.get('MANPATH', '/usr/share/man').split(os.pathsep)
for dir in manpath:
print("Crawling", dir)
for name in os.listdir(dir):
path = os.path.join(dir, name)
if os.path.isdir(path):
indexDirectory(path)
writer.commit()
writer.close()
# indexWriterConfig = IndexWriterConfig(Version.LUCENE_30, analyzer)
# writer = IndexWriter(SimpleFSDirectory(File("data/index/ck12_books_paragraphs")), indexWriterConfig)
# indexDictionary(docs, writer)
#
# writer.close()
dir_name = 'data/ck12_book/OEBPS'
docs = {}
html_paths = [os.path.join(dir_name, str(i + 1) + '.html') for i in range(124)]
#
for f_name in html_paths:
docs.update(book_processing.get_h_all_text(open(f_name).read()))
for fname in os.listdir("data/allwiki"):
content = open(os.path.join("data", "allwiki", fname)).read()
i = 0
for doc in content.split("\n"):
if len(doc.strip()) > 0:
docs["%s%d" % (fname, i)] = doc
i += 1
#analyzer = StandardAnalyzer(Version.LUCENE_30)
analyzer = SnowballAnalyzer(Version.LUCENE_30, "English",
StandardAnalyzer.STOP_WORDS_SET)
indexWriterConfig = IndexWriterConfig(Version.LUCENE_30, analyzer)
writer = IndexWriter(SimpleFSDirectory(File("data/index/combo4")),
indexWriterConfig)
indexDictionary(docs, writer)
writer.close()
luceneIndexPath = '/home/tarun/PE/lucene/luceneIndexDirectoryNewCorpus60/'
corpus = '/home/tarun/PE/newCorpus60/'
trainingFilePath = '/home/tarun/PE/Dataset/training_set.tsv'
lucene.initVM()
# ANALYZER
analyzer = StandardAnalyzer(util.Version.LUCENE_CURRENT)
# DIRECTORY
directory = SimpleFSDirectory(File(luceneIndexPath))
# INDEX WRITER
writerConfig = IndexWriterConfig(util.Version.LUCENE_CURRENT, analyzer)
writer = IndexWriter(directory, writerConfig)
print writer.numDocs()
# INDEXING ALL DOCUMENTS/ARTICLES IN THE CORPUS
for fileName in os.listdir(corpus):
#print fileName
document = Document()
article = os.path.join(corpus, fileName)
content = open(article, 'r').read()
document.add(Field("text", content, Field.Store.YES, Field.Index.ANALYZED))
writer.addDocument(document)
print writer.numDocs()
writer.close()
# INDEX READER
reader = IndexReader.open(directory)
def __init__(self, indexDir):
self.directory = SimpleFSDirectory(Paths.get(indexDir))
self.analyzer = StandardAnalyzer()
# analyzer = LimitTokenCountAnalyzer(analyzer, 10000)
self.config = IndexWriterConfig(self.analyzer)
self.writer = IndexWriter(self.directory, self.config)
class Index(object):
def __init__(self, path, settings):
self._settings = settings
self._multithreaded = settings.multithreaded
self._checker = DirectSpellChecker()
indexDirectory = MMapDirectory(File(join(path, 'index')))
indexDirectory.setUseUnmap(False)
taxoDirectory = MMapDirectory(File(join(path, 'taxo')))
taxoDirectory.setUseUnmap(False)
conf = IndexWriterConfig(Version.LUCENE_4_10_0, settings.analyzer)
conf.setSimilarity(settings.similarity)
mergePolicy = TieredMergePolicy()
mergePolicy.setMaxMergeAtOnce(settings.maxMergeAtOnce)
mergePolicy.setSegmentsPerTier(settings.segmentsPerTier)
conf.setMergePolicy(mergePolicy)
if not settings.readonly:
self._indexWriter = IndexWriter(indexDirectory, conf)
self._indexWriter.commit()
self._taxoWriter = DirectoryTaxonomyWriter(taxoDirectory, IndexWriterConfig.OpenMode.CREATE_OR_APPEND, LruTaxonomyWriterCache(settings.lruTaxonomyWriterCacheSize))
self._taxoWriter.commit()
self._indexAndTaxonomy = IndexAndTaxonomy(settings, indexDirectory, taxoDirectory)
self._readerSettingsWrapper = self._indexAndTaxonomy._readerSettingsWrapper
self._facetsConfig = settings.fieldRegistry.facetsConfig
self._ordinalsReader = CachedOrdinalsReader(DocValuesOrdinalsReader())
def addDocument(self, term, document):
document = self._facetsConfig.build(self._taxoWriter, document)
self._indexWriter.updateDocument(term, document)
def deleteDocument(self, term):
self._indexWriter.deleteDocuments(term)
def search(self, query, filter, collector):
self._indexAndTaxonomy.searcher.search(query, filter, collector)
def suggest(self, query, count, field):
suggestions = {}
for token, startOffset, endOffset in self._analyzeToken(query):
suggestWords = self._checker.suggestSimilar(Term(field, token), count, self._indexAndTaxonomy.searcher.getIndexReader())
if suggestWords:
suggestions[token] = (startOffset, endOffset, [suggestWord.string for suggestWord in suggestWords])
return suggestions
def termsForField(self, field, prefix=None, limit=10, **kwargs):
convert = lambda term: term.utf8ToString()
terms = []
termsEnum = MultiFields.getTerms(self._indexAndTaxonomy.searcher.getIndexReader(), field)
if termsEnum is None:
return terms
iterator = termsEnum.iterator(None)
if prefix:
iterator.seekCeil(BytesRef(prefix))
terms.append((iterator.docFreq(), convert(iterator.term())))
bytesIterator = BytesRefIterator.cast_(iterator)
try:
while len(terms) < limit:
term = convert(bytesIterator.next())
if prefix and not term.startswith(prefix):
break
terms.append((iterator.docFreq(), term))
except StopIteration:
pass
return terms
def fieldnames(self):
indexAndTaxonomy = self._indexAndTaxonomy
fieldnames = []
fields = MultiFields.getFields(indexAndTaxonomy.searcher.getIndexReader())
if fields is None:
return fieldnames
iterator = fields.iterator()
while iterator.hasNext():
fieldnames.append(iterator.next())
return fieldnames
def drilldownFieldnames(self, path=None, limit=50):
taxoReader = self._indexAndTaxonomy.taxoReader
parentOrdinal = TaxonomyReader.ROOT_ORDINAL if path is None else taxoReader.getOrdinal(path[0], path[1:])
childrenIter = taxoReader.getChildren(parentOrdinal)
names = []
while True:
ordinal = childrenIter.next()
if ordinal == TaxonomyReader.INVALID_ORDINAL:
break
names.append(taxoReader.getPath(ordinal).components[-1])
if len(names) >= limit:
break
return names
def numDocs(self):
return self._indexAndTaxonomy.searcher.getIndexReader().numDocs()
def commit(self):
if not self._settings.readonly:
self._taxoWriter.commit()
self._indexWriter.commit()
self._indexAndTaxonomy.reopen()
def getDocument(self, docId):
return self._indexAndTaxonomy.searcher.doc(docId)
def createFacetCollector(self):
if not self._multithreaded:
return FacetsCollector()
return FacetSuperCollector(self._indexAndTaxonomy.taxoReader, self._facetsConfig, self._ordinalsReader)
def facetResult(self, facetCollector):
facetResult = TaxonomyFacetCounts(self._ordinalsReader, self._indexAndTaxonomy.taxoReader, self._facetsConfig, facetCollector)
return Facets.cast_(facetResult)
def close(self):
self._indexAndTaxonomy.close()
if not self._settings.readonly:
self._taxoWriter.close()
self._indexWriter.close()
def _analyzeToken(self, token):
result = []
reader = StringReader(unicode(token))
stda = self._settings.analyzer
ts = stda.tokenStream("dummy field name", reader)
termAtt = ts.addAttribute(CharTermAttribute.class_)
offsetAtt = ts.addAttribute(OffsetAttribute.class_)
try:
ts.reset()
while ts.incrementToken():
result.append((termAtt.toString(), offsetAtt.startOffset(), offsetAtt.endOffset()))
ts.end()
finally:
ts.close()
return result
lucene.initVM(vmargs=['-Djava.awt.headless=true'])
# indexing directory
indexDir = FSDirectory.open(File("lucene_index.Index"))
# input which will be indexed with Lucene
title1 = "text of title1"
title2 = "title2"
abstract1 = "abstract1 has many words, e.g. hellow world can be the text"
abstract2 = "text of abstract2"
# configure indexing
config = IndexWriterConfig(Version.LUCENE_CURRENT,
WhitespaceAnalyzer(Version.LUCENE_CURRENT))
config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
iw = IndexWriter(indexDir, config)
# count number of documents processed
nDocsAdded = 0
# create first document
doc = Document()
doc.add(TextField("Title", title1, Field.Store.YES))
doc.add(TextField("Abstract", abstract1, Field.Store.YES))
iw.addDocument(doc)
nDocsAdded += 1
# create second document
doc = Document()
doc.add(TextField("Title", title2, Field.Store.YES))
doc.add(TextField("Abstract", abstract2, Field.Store.YES))
class LuceneSearch():
def __init__(self):
self.env = lucene.initVM(initialheap='28g',
maxheap='28g',
vmargs=['-Djava.awt.headless=true'])
self.vocab = None
BooleanQuery.setMaxClauseCount(2048)
if not os.path.exists(prm.index_folder):
print 'Creating index at', prm.index_folder
if prm.docs_path == prm.docs_path_term:
add_terms = True
else:
add_terms = False
self.create_index(prm.index_folder, prm.docs_path, add_terms)
if prm.local_index_folder:
print 'copying index from', prm.index_folder, 'to', prm.local_index_folder
if os.path.exists(prm.local_index_folder):
print 'Folder', prm.local_index_folder, 'already exists! Doing nothing.'
else:
shutil.copytree(prm.index_folder, prm.local_index_folder)
self.index_folder = prm.local_index_folder
else:
self.index_folder = prm.index_folder
fsDir = MMapDirectory(Paths.get(prm.index_folder))
self.searcher = IndexSearcher(DirectoryReader.open(fsDir))
if prm.docs_path != prm.docs_path_term:
if not os.path.exists(prm.index_folder_term):
print 'Creating index at', prm.index_folder_term
self.create_index(prm.index_folder_term,
prm.docs_path_term,
add_terms=True)
if prm.local_index_folder_term:
print 'copying index from', prm.index_folder_term, 'to', prm.local_index_folder_term
if os.path.exists(prm.local_index_folder_term):
print 'Folder', prm.local_index_folder_term, 'already exists! Doing nothing.'
else:
shutil.copytree(prm.index_folder_term,
prm.local_index_folder_term)
self.index_folder_term = prm.local_index_folder_term
else:
self.index_folder_term = prm.index_folder_term
fsDir_term = MMapDirectory(Paths.get(prm.index_folder_term))
self.searcher_term = IndexSearcher(
DirectoryReader.open(fsDir_term))
self.analyzer = StandardAnalyzer()
self.pool = ThreadPool(processes=prm.n_threads)
self.cache = {}
print 'Loading Title-ID mapping...'
self.title_id_map, self.id_title_map = self.get_title_id_map()
if prm.idf_path:
print 'Loading IDF dictionary...'
self.idf = pkl.load(open(prm.idf_path))
def get_title_id_map(self):
# get number of docs
n_docs = self.searcher.getIndexReader().numDocs()
title_id = {}
id_title = {}
query = MatchAllDocsQuery()
hits = self.searcher.search(query, n_docs)
for hit in hits.scoreDocs:
doc = self.searcher.doc(hit.doc)
idd = int(doc['id'])
title = doc['title']
title_id[title] = idd
id_title[idd] = title
return title_id, id_title
def add_idf(self, txt):
txt = utils.clean(txt)
txt = txt.lower()
df = set()
for word in wordpunct_tokenize(txt):
if word not in df:
df.add(word)
self.idf[word] += 1.
def add_doc(self, doc_id, title, txt, add_terms):
doc = Document()
txt = utils.clean(txt)
if add_terms:
if prm.top_tfidf > 0:
words_idx = []
words, _ = utils.top_tfidf(txt.lower(), self.idf,
prm.top_tfidf, prm.min_term_freq)
if len(words) == 0:
words.append('unk')
for w in words:
if w in self.vocab:
words_idx.append(self.vocab[w])
else:
words_idx.append(-1) # unknown words.
else:
txt_ = txt.lower()
words_idx, words = utils.text2idx2([txt_], self.vocab,
prm.max_terms_per_doc)
words_idx = words_idx[0]
words = words[0]
doc.add(Field("id", str(doc_id), self.t1))
doc.add(Field("title", title, self.t1))
doc.add(Field("text", txt, self.t2))
if add_terms:
doc.add(Field("word_idx", ' '.join(map(str, words_idx)), self.t3))
doc.add(Field("word", '<&>'.join(words), self.t3))
self.writer.addDocument(doc)
def create_index(self, index_folder, docs_path, add_terms=False):
print 'Loading Vocab...'
if not self.vocab:
self.vocab = utils.load_vocab(prm.vocab_path, prm.n_words)
os.mkdir(index_folder)
self.t1 = FieldType()
self.t1.setStored(True)
self.t1.setIndexOptions(IndexOptions.DOCS)
self.t2 = FieldType()
self.t2.setStored(False)
self.t2.setIndexOptions(IndexOptions.DOCS_AND_FREQS)
self.t3 = FieldType()
self.t3.setStored(True)
self.t3.setIndexOptions(IndexOptions.NONE)
if add_terms:
if prm.top_tfidf > 0 or prm.idf_path:
print 'Creating IDF dictionary...'
self.idf = defaultdict(int)
doc_id = 0
if docs_path.lower().endswith('.hdf5'):
import corpus_hdf5
corpus = corpus_hdf5.CorpusHDF5(docs_path)
for txt in corpus.get_text_iter():
self.add_idf(txt)
if doc_id % 1000 == 0:
print 'Creating IDF, doc', doc_id
doc_id += 1
else:
# ClueWeb09
import warc
import gzip
from bs4 import BeautifulSoup
# list all files in the folder.
paths = []
for root, directories, filenames in os.walk(docs_path):
for filename in filenames:
paths.append(os.path.join(root, filename))
for path in paths:
with gzip.open(path, mode='rb') as gzf:
for record in warc.WARCFile(fileobj=gzf):
# remove html tags
txt = BeautifulSoup(
record.payload[:1000 * 1000],
"lxml").get_text()
# remove WARC headers.
txt = '\n'.join(txt.split('\n')[10:])
self.add_idf(txt)
if doc_id % 1000 == 0:
print 'Creating IDF, doc', doc_id
doc_id += 1
for key, val in self.idf.items():
self.idf[key] = math.log(float(doc_id) / val)
pkl.dump(self.idf, open(prm.idf_path, 'wb'))
fsDir = MMapDirectory(Paths.get(index_folder))
writerConfig = IndexWriterConfig(StandardAnalyzer())
self.writer = IndexWriter(fsDir, writerConfig)
print "%d docs in index" % self.writer.numDocs()
print "Indexing documents..."
doc_id = 0
if docs_path.lower().endswith('.hdf5'):
import corpus_hdf5
corpus = corpus_hdf5.CorpusHDF5(docs_path)
for txt in corpus.get_text_iter():
title = corpus.get_article_title(doc_id)
self.add_doc(doc_id, title, txt, add_terms)
if doc_id % 1000 == 0:
print 'indexing doc', doc_id
doc_id += 1
else:
# ClueWeb09
import warc
import gzip
from bs4 import BeautifulSoup
# list all files in the folder.
paths = []
for root, directories, filenames in os.walk(docs_path):
for filename in filenames:
paths.append(os.path.join(root, filename))
for path in paths:
with gzip.open(path, mode='rb') as gzf:
for record in warc.WARCFile(fileobj=gzf):
if 'warc-trec-id' in record:
title = record['warc-trec-id']
else:
title = record['warc-record-id']
# remove html tags
#txt = BeautifulSoup(record.payload[:1000*1000], "lxml").get_text()
txt = record.payload[:1000 * 1000]
# remove WARC headers.
txt = '\n'.join(txt.split('\n')[10:])
self.add_doc(doc_id, title, txt, add_terms)
if doc_id % 1000 == 0:
print 'indexing doc', doc_id
doc_id += 1
print "Index of %d docs..." % self.writer.numDocs()
self.writer.close()
def search_multithread(self, qs, max_cand, max_full_cand, searcher):
self.max_cand = max_cand
self.max_full_cand = max_full_cand
self.curr_searcher = searcher
out = self.pool.map(self.search_multithread_part, qs)
return out
def search_multithread_part(self, q):
if not self.env.isCurrentThreadAttached():
self.env.attachCurrentThread()
if q in self.cache:
return self.cache[q]
else:
try:
q = q.replace('AND',
'\\AND').replace('OR',
'\\OR').replace('NOT', '\\NOT')
query = QueryParser("text",
self.analyzer).parse(QueryParser.escape(q))
except:
print 'Unexpected error when processing query:', str(q)
print 'Using query "dummy".'
q = 'dummy'
query = QueryParser("text",
self.analyzer).parse(QueryParser.escape(q))
c = OrderedDict()
hits = self.curr_searcher.search(query, self.max_cand)
for i, hit in enumerate(hits.scoreDocs):
doc = self.curr_searcher.doc(hit.doc)
if i < self.max_full_cand:
word_idx = map(int, doc['word_idx'].split(' '))
word = doc['word'].split('<&>')
else:
word_idx = []
word = []
c[int(doc['id'])] = [word_idx, word]
return c
def search_singlethread(self, qs, max_cand, max_full_cand, curr_searcher):
out = []
for q in qs:
if q in self.cache:
out.append(self.cache[q])
else:
try:
q = q.replace('AND', '\\AND').replace('OR',
'\\OR').replace(
'NOT', '\\NOT')
query = QueryParser("text", self.analyzer).parse(
QueryParser.escape(q))
except:
print 'Unexpected error when processing query:', str(q)
print 'Using query "dummy".'
query = QueryParser("text", self.analyzer).parse(
QueryParser.escape('dummy'))
c = OrderedDict()
hits = curr_searcher.search(query, max_cand)
for i, hit in enumerate(hits.scoreDocs):
doc = curr_searcher.doc(hit.doc)
if i < max_full_cand:
word_idx = map(int, doc['word_idx'].split(' '))
word = doc['word'].split('<&>')
else:
word_idx = []
word = []
c[int(doc['id'])] = [word_idx, word]
out.append(c)
return out
def get_candidates(self,
qs,
max_cand,
max_full_cand=None,
save_cache=False,
extra_terms=True):
if not max_full_cand:
max_full_cand = max_cand
if prm.docs_path != prm.docs_path_term:
max_cand2 = 0
else:
max_cand2 = max_full_cand
if prm.n_threads > 1:
out = self.search_multithread(qs, max_cand, max_cand2,
self.searcher)
if (prm.docs_path != prm.docs_path_term) and extra_terms:
terms = self.search_multithread(qs, max_full_cand,
max_full_cand,
self.searcher_term)
else:
out = self.search_singlethread(qs, max_cand, max_cand2,
self.searcher)
if (prm.docs_path != prm.docs_path_term) and extra_terms:
terms = self.search_singlethread(qs, max_full_cand,
max_full_cand,
self.searcher_term)
if (prm.docs_path != prm.docs_path_term) and extra_terms:
for outt, termss in itertools.izip(out, terms):
for cand_id, term in itertools.izip(
outt.keys()[:max_full_cand], termss.values()):
outt[cand_id] = term
if save_cache:
for q, c in itertools.izip(qs, out):
if q not in self.cache:
self.cache[q] = c
return out
def testDelete(self, fieldName, searchString):
config = IndexWriterConfig(Version.LUCENE_CURRENT, self.Analyzer)
config.setOpenMode(IndexWriterConfig.OpenMode.APPEND)
writer = IndexWriter(self.store, config)
writer.deleteDocuments(Term(fieldName, searchString))
writer.close()
class LuceneHelper:
def __init__(self, index_dir):
self.index_dir = index_dir
self.indexDir = SimpleFSDirectory(File(self.index_dir).toPath())
self.q_parser = QueryParser("", WhitespaceAnalyzer())
self.commit_max = 500000
self.__get_writer_searcher()
def __get_writer_searcher(self):
writerConfig = IndexWriterConfig()
print(f"Codec : {writerConfig.getCodec()}")
self.writer = IndexWriter(self.indexDir, writerConfig)
self.reader = DirectoryReader.open(self.writer)
self.searcher = IndexSearcher(self.reader)
def __query(self, query_str, _max=10):
if self.searcher is None:
return None
query_cmd = self.q_parser.parse(query_str)
hits = self.searcher.search(query_cmd, _max)
print(
f"Found {hits.totalHits} document(s) that matched query :'{query_cmd}'"
)
return hits
def __count_docs(self, query_str):
if self.searcher is None:
return None
query_cmd = self.q_parser.parse(query_str)
total = self.searcher.count(query_cmd)
print(f"Found {total} document(s) that matched query :'{query_cmd}'")
return total
def refresh_searcher(self):
self.reader.close()
self.reader = DirectoryReader.open(self.indexDir)
self.searcher = IndexSearcher(self.reader)
def index_stats(self):
query_str = f"*:*"
total_docs = self.__count_docs(query_str)
if total_docs:
print(f"There is at least total [{total_docs}] docs.")
else:
print("There is no index right now.")
def delete_old_ttl(self):
now_time = int(time.time())
# check how many docs expired
ttl_query = LongPoint.newRangeQuery("ttl", 0, now_time - 1)
total_docs = self.searcher.count(ttl_query)
print(f"At least found {total_docs} document(s) are expired.")
# delete expired docs
self.writer.deleteDocuments(ttl_query)
self.writer.commit()
def add_doc(self, item_data):
item_id = item_data['item_id']
ttl = item_data['ttl']
version = item_data.get('version', 'default')
view_similar = json.dumps(item_data.get('view_similar', {}))
view_prospective = json.dumps(item_data.get('view_prospective', {}))
doc = Document()
_id = hashlib.md5(f"{item_id}_{version}".encode('utf-8')).hexdigest()
doc.add(StringField("id", _id, Field.Store.NO))
doc.add(LongPoint("ttl", ttl))
doc.add(StringField("version", version, Field.Store.YES))
doc.add(StringField("item_id", item_id, Field.Store.YES))
doc.add(StoredField("view_similar", view_similar))
doc.add(StoredField("view_prospective", view_prospective))
self.writer.updateDocument(Term("id", _id), doc)
def commit(self):
self.writer.commit()
def close(self):
self.writer.commit()
self.reader.close()
self.writer.close()
output_path = sys.argv[2]
lucene.initVM()
analyzer = StandardAnalyzer(Version.LUCENE_4_10_1)
analyzer_ws = WhitespaceAnalyzer(Version.LUCENE_4_10_1)
std_path = "%s/lucene_full_standard/" % (output_path)
ws_path = "%s/lucene_full_ws/" % (output_path)
if os.path.exists(std_path):
os.remove(std_path)
if os.path.exists(ws_path):
os.remove(ws_path)
indexDir1 = SimpleFSDirectory(File(std_path))
indexDir2 = SimpleFSDirectory(File(ws_path))
writerConfig1 = IndexWriterConfig(Version.LUCENE_4_10_1, analyzer)
writerConfig2 = IndexWriterConfig(Version.LUCENE_4_10_1, analyzer_ws)
writer1 = IndexWriter(indexDir1, writerConfig1)
writer2 = IndexWriter(indexDir2, writerConfig2)
print "%d docs in index1" % writer1.numDocs()
print "%d docs in index2" % writer2.numDocs()
print "Reading lines from sys.stdin..."
ftypes = open(LUCENE_TYPES_FILE, "w")
for n, l in enumerate(sys.stdin):
doc = Document()
doc_lc = Document()
fields = l.rstrip().split("\t")
all_ = []
if n == 0:
sys.stdout.write("TYPES_HEADER")
class Index:
def __init__(self, folder=None, fields=[], similarity="tfidf"):
self.jcc = lucene.initVM()
if folder:
self.directory = SimpleFSDirectory(File(folder))
else:
self.directory = RAMDirectory()
self.fields = {}
for field in fields:
ft = FieldType()
for pname, pvalue in field.props.items():
setter = getattr(ft, "set" + pname.capitalize())
setter(pvalue)
ft.setIndexOptions(FieldInfo.IndexOptions.DOCS_AND_FREQS)
# ft.setOmitNorms(True)
self.fields[field.name] = ft
self.similarity = similarity.lower()
self.analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
self.writer = None
self.searcher = None
def attach_thread(self):
self.jcc.attachCurrentThread()
def open_writer(self):
config = IndexWriterConfig(Version.LUCENE_CURRENT, self.analyzer)
config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
self.writer = IndexWriter(self.directory, config)
def add(self, **doc):
if not self.writer:
self.open_writer()
d = Document()
for field, value in doc.items():
# try :
d.add(Field(field, value, self.fields[field]))
# except Exception, e :
# print
# print "Fudeu"
# pass
self.writer.addDocument(d)
def commit(self):
self.writer.commit()
def close(self):
if self.writer:
self.writer.close()
def open_searcher(self):
self.reader = DirectoryReader.open(self.directory)
self.searcher = IndexSearcher(self.reader)
if (self.similarity == "bm25"):
self.searcher.setSimilarity(BM25Similarity())
def preprocess_query(self, query, fields, mode="ANY"):
'''
Fix query according to provided mode. If the value is not supported,
the query remains unchanged
'''
terms = query.lower().strip().split()
if mode == "ANY":
query = " OR ".join(terms)
elif mode == "ALL":
query = " AND ".join(terms)
else:
print "Invalid mode parameter '%s'." % mode
query = QueryParser.escape(query)
parser = MultiFieldQueryParser(Version.LUCENE_CURRENT, fields,
self.analyzer)
query = MultiFieldQueryParser.parse(parser, query)
return query
def search(self,
query,
search_fields,
return_fields,
filter=None,
ignore=set(),
mode="ANY",
return_scores=False,
limit=1000000):
'''
Search documents in the index using a standard analyzer (tokenizes and
removes top words). Supports two search modes: ANY and ALL
ANY: include documents that contain at least one term of the query.
ALL: include only documents that contain all terms of the query.
'''
if not self.searcher:
self.open_searcher()
# Return empty results if query is empty (Lucene can't handle it nicely)
if query.strip() == '':
if return_scores:
return [], []
else:
return []
query = self.preprocess_query(query, search_fields, mode)
# If limit is not provided, return all matched documents. A little hack is required
# to do that. We query for one document and get the count total matched documents.
# if not limit :
# hits = self.searcher.search(query, 1)
# limit = hits.totalHits
# Fetch more than asked in case we have to remove entries from the ignore set
if limit != None:
limit += len(ignore)
hits = self.searcher.search(query, filter, limit)
hits = hits.scoreDocs
docs = []
for hit in hits:
doc = self.searcher.doc(hit.doc)
if doc['id'] not in ignore:
docs.append([doc[f] for f in return_fields])
if return_scores:
scores = [hit.score for hit in hits]
return docs[:limit], scores[:limit]
return docs[:limit]
def explain(self, query, fields, doc):
if not self.searcher:
self.open_searcher()
query = QueryParser.escape(query)
parser = MultiFieldQueryParser(Version.LUCENE_CURRENT, fields,
self.analyzer)
query = MultiFieldQueryParser.parse(parser, query)
return self.searcher.explain(query, doc)
def get_documents(self, doc_ids, fields):
docs = []
for doc_id in doc_ids:
doc = self.reader.document(doc_id)
if isinstance(fields, basestring):
docs.append(doc.get(fields))
else:
docs.append({f: doc.get(f) for f in fields})
return docs
def get_query_scores(self, query, fields, doc_ids, mode="ANY"):
# Creates pre-filter to ignore all other documents
filter = TermsFilter([Term("id", id) for id in doc_ids])
query = self.preprocess_query(query, fields, mode)
hits = self.searcher.search(query, filter, len(doc_ids)).scoreDocs
# Creates scores' mapping using entity id instead of internal index id
scores = {
str(self.reader.document(hit.doc).get("id")): hit.score
for hit in hits
}
# Normalize to 0..1 interval
# n = 1.0/sum(scores.values())
# scores
# Adds to the mapping entries for the non-returned docs (no term found)
for doc_id in doc_ids:
if doc_id not in scores:
scores[doc_id] = 0.0
return scores
def main():
if len(sys.argv) < 2:
print('error: too few arguments')
print('command: python create_category_corpus.py NUMBER_TOP_CATEGORY')
quit()
NUMBER_TOP_CATEGORY = int(sys.argv[1])
print('NUMBER_TOP_CATEGORY=%d' % (NUMBER_TOP_CATEGORY))
print('loading category profiles')
profile = load_zipped_pickle('category_profiles_dbpedia_201510.gz')
print('finish loading category profiles')
system_flag = platform.system()
cwd = os.getcwd()
# initialize mongo client
if system_flag == 'Windows':
client = pymongo.MongoClient("localhost", 27017)
else:
client = pymongo.MongoClient("localhost", 58903)
db = client.wiki2015
wiki_article_categories = db['article_categories']
category_corpus = {}
pkl_filename = 'category_dbpedia_corpus_top%d_fsdm3.pkl.gz' % (
NUMBER_TOP_CATEGORY)
if system_flag == 'Windows':
lucene_dbpedia_fsdm = Lucene_Object('mmapDirectory\\dbpedia_v2_FSDM3',
'BM25', True)
else:
lucene_dbpedia_fsdm = Lucene_Object(
'%s/mmapDirectory/dbpedia_v2_FSDM3' % (cwd), 'BM25', True)
cnt = 0
if os.path.exists(pkl_filename) == True:
#if False==True:
print('loading category corpus')
category_corpus = load_zipped_pickle(pkl_filename)
else:
for item in wiki_article_categories.find():
list_category = item['categories'].strip().split('|')
uri_article = item['uri']
title = findTitle(uri_article)
entity_content_dict = {}
doc_entity = lucene_dbpedia_fsdm.findEntityDocFromIndex(
title, 'title', False)
if doc_entity is None:
continue
for f in [
'names', 'attributes', 'categories', 'similar_entities',
'related_entities', 'catchall'
]:
entity_content_dict[f] = doc_entity[f]
entity_content_dict['stemmed_' + f] = doc_entity['stemmed_' +
f]
if len(entity_content_dict['catchall'].strip()) == 0:
continue
for cat in list_category[:NUMBER_TOP_CATEGORY]:
if ('<http://dbpedia.org/resource/Category:' + cat +
'>') not in profile:
continue
if cat not in category_corpus:
category_corpus[cat] = []
if len(category_corpus[cat]) < 300:
category_corpus[cat].append(entity_content_dict)
#cnt+=1
#if cnt>20:
#break
print('saving corpus to pkl.gz')
save_zipped_pickle(category_corpus, pkl_filename)
client.close()
# begin write the data into index
print('begin write into index')
if system_flag == 'Windows':
LUCENE_INDEX_DIR = 'mmapDirectory\\category_corpus_dbpedia201510_top' + str(
NUMBER_TOP_CATEGORY) + '_fsdm3'
else:
LUCENE_INDEX_DIR = '%s/mmapDirectory/category_corpus_dbpedia201510_top' % (
cwd) + str(NUMBER_TOP_CATEGORY) + '_fsdm3'
# backup code files
cmd = 'robocopy %s %s\code_files *.py' % (
r'%cd%', LUCENE_INDEX_DIR
) if system_flag == 'Windows' else 'cp *.py %s/code_files' % (
LUCENE_INDEX_DIR)
os.system(cmd)
# specify index path
index_mm = MMapDirectory(Paths.get(LUCENE_INDEX_DIR))
# configure search engine
analyzer = SimpleAnalyzer()
config = IndexWriterConfig(analyzer)
# write data to index
w = IndexWriter(index_mm, config)
cnt = 0
data = {}
max_article_num = 0
stemmer = SnowballStemmer('english')
for cat, list_entity_dict in category_corpus.items():
cat_label = cleanSentence(cat, True)
data.clear()
data['category'] = (cat, 'StringField')
data['label'] = (cat_label, 'CUSTOM_FIELD_TEXT')
data['stemmed_label'] = (stemSentence(cat_label, stemmer,
True), 'CUSTOM_FIELD_TEXT')
data['num_articles'] = (len(list_entity_dict), 'INTEGER_STORED')
if data['num_articles'][0] > max_article_num:
max_article_num = data['num_articles'][0]
for f in [
'names', 'attributes', 'categories', 'similar_entities',
'related_entities', 'catchall'
]:
contents = cleanSentence(
' '.join([dic[f] for dic in list_entity_dict]), True, ' ')
data[f] = (contents, 'CUSTOM_FIELD_TEXT_NOT_STORED')
data['stemmed_' + f] = (stemSentence(contents, stemmer, False),
'CUSTOM_FIELD_TEXT_NOT_STORED')
#print ('--------------------')
# need to calculate corpus average length
addDoc(w, data)
#cnt+=1
#if cnt>20:
#break
w.close()
print('max article num=%d' % (max_article_num))
def create_index(self, index_folder, docs_path, add_terms=False):
print 'Loading Vocab...'
if not self.vocab:
self.vocab = utils.load_vocab(prm.vocab_path, prm.n_words)
os.mkdir(index_folder)
self.t1 = FieldType()
self.t1.setStored(True)
self.t1.setIndexOptions(IndexOptions.DOCS)
self.t2 = FieldType()
self.t2.setStored(False)
self.t2.setIndexOptions(IndexOptions.DOCS_AND_FREQS)
self.t3 = FieldType()
self.t3.setStored(True)
self.t3.setIndexOptions(IndexOptions.NONE)
if add_terms:
if prm.top_tfidf > 0 or prm.idf_path:
print 'Creating IDF dictionary...'
self.idf = defaultdict(int)
doc_id = 0
if docs_path.lower().endswith('.hdf5'):
import corpus_hdf5
corpus = corpus_hdf5.CorpusHDF5(docs_path)
for txt in corpus.get_text_iter():
self.add_idf(txt)
if doc_id % 1000 == 0:
print 'Creating IDF, doc', doc_id
doc_id += 1
else:
# ClueWeb09
import warc
import gzip
from bs4 import BeautifulSoup
# list all files in the folder.
paths = []
for root, directories, filenames in os.walk(docs_path):
for filename in filenames:
paths.append(os.path.join(root, filename))
for path in paths:
with gzip.open(path, mode='rb') as gzf:
for record in warc.WARCFile(fileobj=gzf):
# remove html tags
txt = BeautifulSoup(
record.payload[:1000 * 1000],
"lxml").get_text()
# remove WARC headers.
txt = '\n'.join(txt.split('\n')[10:])
self.add_idf(txt)
if doc_id % 1000 == 0:
print 'Creating IDF, doc', doc_id
doc_id += 1
for key, val in self.idf.items():
self.idf[key] = math.log(float(doc_id) / val)
pkl.dump(self.idf, open(prm.idf_path, 'wb'))
fsDir = MMapDirectory(Paths.get(index_folder))
writerConfig = IndexWriterConfig(StandardAnalyzer())
self.writer = IndexWriter(fsDir, writerConfig)
print "%d docs in index" % self.writer.numDocs()
print "Indexing documents..."
doc_id = 0
if docs_path.lower().endswith('.hdf5'):
import corpus_hdf5
corpus = corpus_hdf5.CorpusHDF5(docs_path)
for txt in corpus.get_text_iter():
title = corpus.get_article_title(doc_id)
self.add_doc(doc_id, title, txt, add_terms)
if doc_id % 1000 == 0:
print 'indexing doc', doc_id
doc_id += 1
else:
# ClueWeb09
import warc
import gzip
from bs4 import BeautifulSoup
# list all files in the folder.
paths = []
for root, directories, filenames in os.walk(docs_path):
for filename in filenames:
paths.append(os.path.join(root, filename))
for path in paths:
with gzip.open(path, mode='rb') as gzf:
for record in warc.WARCFile(fileobj=gzf):
if 'warc-trec-id' in record:
title = record['warc-trec-id']
else:
title = record['warc-record-id']
# remove html tags
#txt = BeautifulSoup(record.payload[:1000*1000], "lxml").get_text()
txt = record.payload[:1000 * 1000]
# remove WARC headers.
txt = '\n'.join(txt.split('\n')[10:])
self.add_doc(doc_id, title, txt, add_terms)
if doc_id % 1000 == 0:
print 'indexing doc', doc_id
doc_id += 1
print "Index of %d docs..." % self.writer.numDocs()
self.writer.close()
class Lucene(object):
# default fieldnames for id and contents
FIELDNAME_ID = "id"
FIELDNAME_CONTENTS = "contents"
# internal fieldtypes
# used as Enum, the actual values don't matter
FIELDTYPE_ID = "id"
FIELDTYPE_ID_TV = "id_tv"
FIELDTYPE_TEXT = "text"
FIELDTYPE_TEXT_TV = "text_tv"
FIELDTYPE_TEXT_TVP = "text_tvp"
def __init__(self, index_dir, use_ram=False, jvm_ram=None):
global lucene_vm_init
if not lucene_vm_init:
if jvm_ram:
# e.g. jvm_ram = "8g"
print "Increased JVM ram"
lucene.initVM(vmargs=['-Djava.awt.headless=true'], maxheap=jvm_ram)
else:
lucene.initVM(vmargs=['-Djava.awt.headless=true'])
lucene_vm_init = True
self.dir = SimpleFSDirectory(Paths.get(index_dir))
self.use_ram = use_ram
if use_ram:
print "Using ram directory..."
self.ram_dir = RAMDirectory(self.dir, IOContext.DEFAULT)
self.analyzer = None
self.reader = None
self.searcher = None
self.writer = None
self.ldf = None
print "Connected to index " + index_dir
def get_version(self):
"""Get Lucene version."""
#return Version.LUCENE_48
return lucene.VERSION
def get_analyzer(self):
"""Get analyzer."""
if self.analyzer is None:
self.analyzer = StandardAnalyzer()
#self.analyzer = SmartChineseAnalyzer()
return self.analyzer
def open_reader(self):
"""Open IndexReader."""
if self.reader is None:
if self.use_ram:
print "reading from ram directory(%s) ..."%self.ram_dir
self.reader = DirectoryReader.open(self.ram_dir)
else:
self.reader = DirectoryReader.open(self.dir)
def get_reader(self):
return self.reader
def close_reader(self):
"""Close IndexReader."""
if self.reader is not None:
self.reader.close()
self.reader = None
else:
raise Exception("There is no open IndexReader to close")
def open_searcher(self):
"""
Open IndexSearcher. Automatically opens an IndexReader too,
if it is not already open. There is no close method for the
searcher.
"""
if self.searcher is None:
self.open_reader()
self.searcher = IndexSearcher(self.reader)
def get_searcher(self):
"""Returns index searcher (opens it if needed)."""
self.open_searcher()
return self.searcher
def open_writer(self):
"""Open IndexWriter."""
if self.writer is None:
config = IndexWriterConfig(self.get_analyzer())
config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
self.writer = IndexWriter(self.dir, config)
else:
raise Exception("IndexWriter is already open")
def close_writer(self):
"""Close IndexWriter."""
if self.writer is not None:
self.writer.close()
self.writer = None
else:
raise Exception("There is no open IndexWriter to close")
def add_document(self, contents):
"""
Adds a Lucene document with the specified contents to the index.
See LuceneDocument.create_document() for the explanation of contents.
"""
if self.ldf is None: # create a single LuceneDocument object that will be reused
self.ldf = LuceneDocument()
self.writer.addDocument(self.ldf.create_document(contents))
def get_lucene_document_id(self, doc_id):
"""Loads a document from a Lucene index based on its id."""
self.open_searcher()
query = TermQuery(Term(self.FIELDNAME_ID, doc_id))
tophit = self.searcher.search(query, 1).scoreDocs
if len(tophit) == 1:
return tophit[0].doc
else:
return None
def get_document_id(self, lucene_doc_id):
"""Gets lucene document id and returns the document id."""
self.open_reader()
return self.reader.document(lucene_doc_id).get(self.FIELDNAME_ID)
def get_id_lookup_query(self, id, field=None):
"""Creates Lucene query for searching by (external) document id """
if field is None:
field = self.FIELDNAME_ID
return TermQuery(Term(field, id))
def get_and_query(self, queries):
"""Creates an AND Boolean query from multiple Lucene queries """
# empty boolean query with Similarity.coord() disabled
bq_builder = BooleanQuery.Builder()
for q in queries:
bq_builder.add(q, BooleanClause.Occur.MUST)
bq = bq_builder.build()
return bq
def get_or_query(self, queries):
"""Creates an OR Boolean query from multiple Lucene queries """
# empty boolean query with Similarity.coord() disabled
bq_builder = BooleanQuery.Builder()
for q in queries:
bq_builder.add(q, BooleanClause.Occur.SHOULD)
bq = bq_builder.build()
return bq
def get_phrase_query(self, query, field):
"""Creates phrase query for searching exact phrase."""
terms = []
trans_query = ''
#process Chinese query
for c in query:
if ord(c) >=256:
trans_query += '%s '%c
else:
trans_query += c
for t in trans_query.split():
#term = Term(field, t)
#terms.append(term)
terms.append(t)
#phq.add(Term(field, t))
phq = PhraseQuery(field, terms)
return phq
def num_docs(self):
"""Returns number of documents in the index."""
self.open_reader()
return self.reader.numDocs()
from org.apache.lucene.analysis.miscellaneous import LimitTokenCountAnalyzer
from org.apache.lucene.analysis.standard import StandardAnalyzer
from org.apache.lucene.store import RAMDirectory
from org.apache.lucene.document import Document, Field, FieldType
from org.apache.lucene.util import BytesRef, BytesRefIterator, Version
from org.apache.lucene.index import \
IndexWriterConfig, IndexWriter, DirectoryReader
if __name__ == '__main__':
lucene.initVM(vmargs=['-Djava.awt.headless=true'])
directory = RAMDirectory()
iconfig = IndexWriterConfig(
Version.LUCENE_CURRENT,
LimitTokenCountAnalyzer(StandardAnalyzer(Version.LUCENE_CURRENT), 100))
iwriter = IndexWriter(directory, iconfig)
ft = FieldType()
ft.setIndexed(True)
ft.setStored(True)
ft.setTokenized(True)
ft.setStoreTermVectors(True)
ft.setStoreTermVectorOffsets(True)
ft.setStoreTermVectorPositions(True)
ts = [
"this bernhard is the text to be index text",
"this claudia is the text to be indexed"
]
for t in ts:
doc = Document()
def open_writer(self):
config = IndexWriterConfig(Version.LUCENE_CURRENT, self.analyzer)
config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)
self.writer = IndexWriter(self.directory, config)
def main():
if len(sys.argv) < 2:
print('error: too few arguments')
print('command: python build_index_wikipedia.py FILENAME')
quit()
# create file object
filename = sys.argv[1]
print('processing ' + filename)
cnt = 0
stemmer = SnowballStemmer('english')
try:
lucene.initVM(vmargs=['-Djava.awt.headless=true'])
lucene_vm_init = True
except:
print('JavaVM already running')
LUCENE_INDEX_DIR = 'mmapDirectory\\index_wikipedia_2015'
is_index_Exist = os.path.exists(LUCENE_INDEX_DIR)
# specify index path
index_mm = MMapDirectory(Paths.get(LUCENE_INDEX_DIR))
# configure search engine
analyzer = StandardAnalyzer()
config = IndexWriterConfig(analyzer)
#config=config.setRAMBufferSizeMB(1024.0) # experimental setting !!
# write data to index
if not is_index_Exist:
print('begin backup code files')
system_flag = platform.system()
cmd = 'robocopy %s %s\code_files *.py' % (
r'%cd%', LUCENE_INDEX_DIR
) if system_flag == 'Windows' else 'cp *.py %s\code_files' % (
LUCENE_INDEX_DIR)
os.system(cmd)
w = IndexWriter(index_mm, config)
data = {}
with open(filename, 'r', encoding='utf-8') as src:
for page_pair in extract_pages(src):
label, content, page_id = page_pair[0], page_pair[1], page_pair[2]
pair_tokens = process_article((content, False, label, page_id))
content = remove_stopwords(' '.join(pair_tokens[0]), ' ')
if len(content.split()) < 10:
continue
stemmed_content = stemSentence(content, stemmer, False)
if DEBUG_MODE == True:
try:
print('%s\n%s\n%s\n%s' %
(label, page_id, content, stemmed_content))
except:
print('encoding error')
data.clear()
data['label'] = (label, 'StringField')
data['label_lower'] = (label.lower(), 'StringField')
data['label_lower_text'] = (label.lower(), 'TextField')
data['wiki_id'] = (page_id, 'StringField')
#data['content']=(content,'TextField')
data['stemmed_content'] = (stemmed_content, 'TextField')
addDoc(w, data)
cnt += 1
#if cnt>20:
#break
if cnt % 5000 == 0:
print('finish %d' % (cnt))
w.close()
def create_document(file_name):
path = './alldocs/' + file_name
file = open(path)
doc = Document()
doc.add(StringField("title", input_file, Field.Store.YES))
doc.add(TextField("text", file.read(), Field.Store.YES))
file.close()
return doc
# Initialize lucene and the JVM
lucene.initVM()
directory = RAMDirectory()
analyzer = StandardAnalyzer(Version.LUCENE_CURRENT)
analyzer = LimitTokenCountAnalyzer(analyzer, NoT)
config = IndexWriterConfig(Version.LUCENE_CURRENT, analyzer)
writer = IndexWriter(directory, config)
print "Number of indexed documents: %d\n" % writer.numDocs()
for input_file in listdir(INPUT_DIR):
print "Current file:", input_file
doc = create_document(input_file)
writer.addDocument(doc)
print "\nNumber of indexed documents: %d" % writer.numDocs()
writer.close()
print "Indexing done!\n"
print "------------------------------------------------------"