def _createNoTermsFrequencyFieldType():
f = FieldType()
f.setIndexed(True)
f.setTokenized(True)
f.setOmitNorms(True)
f.setIndexOptions(FieldInfo.IndexOptions.DOCS_ONLY)
f.freeze()
return f
def _createNoTermsFrequencyFieldType():
f = FieldType()
f.setIndexed(True)
f.setTokenized(True)
f.setOmitNorms(True)
f.setIndexOptions(FieldInfo.IndexOptions.DOCS_ONLY)
f.freeze()
return f
def main():
INDEX_DIR = "full_index1"
DOCUMENTS_DIR = "/media/joseph/Windows8_OS/Users/Joseph/AppData/Local/lxss/home/jwymbs23/data_science_projects/french_pamphlets/frc-data-master/OCR_text/"
# Initialize lucene and JVM
lucene.initVM(vmargs=['-Djava.awt.headless=true'])
print("lucene version is:", lucene.VERSION, '\n')
store = getStore(INDEX_DIR)
analyzer = getAnalyzer()
writer = getWriter(store=store, analyzer=analyzer, create=True)
#get list of documents
doc_list = getDoclist(DOCUMENTS_DIR)
ftype = FieldType()
ftype.setIndexOptions(IndexOptions.DOCS_AND_FREQS_AND_POSITIONS)
ftype.setTokenized(True)
ftype.setStoreTermVectors(True)
ftype.freeze()
for cd, doc_name in enumerate(doc_list):
if not cd % 1000:
print(cd, '--', len(doc_list))
with open(doc_name, 'r') as d:
doc_lines = d.readlines()
full_text = ''.join([i.strip() for i in doc_lines]).lower()
try:
# create a document that would we added to the index
doc = Document()
# Add fields to this document
#could process fname here instead of in the dataframe later
doc.add(
Field("identifier",
doc_name.split('/')[-1], TextField.TYPE_STORED)
) #Store.YES))#, Field.Index.ANALYZED))
doc.add(
Field("vectext", full_text, ftype)
) #TextField.TYPE_STORED, TermVector.YES, ))#Store.YES))#, Field.Index.ANALYZED))
doc.add(Field("text", full_text, TextField.TYPE_STORED))
# Add the document to the index
writer.addDocument(doc)
except:
print("Failed in indexDocs: ", doc_name)
#writer.optimize()
writer.commit()
def setUp(self):
super(Test_Bug1842, self).setUp()
self.analyzer = StandardAnalyzer()
w1 = self.getWriter(analyzer=self.analyzer)
doc1 = Document()
ftype = FieldType()
ftype.setIndexOptions(IndexOptions.DOCS_AND_FREQS_AND_POSITIONS)
ftype.setTokenized(True)
ftype.setStoreTermVectors(True)
ftype.freeze()
doc1.add(Field("all", "blah blah blah Gesundheit", ftype))
doc1.add(Field('id', '1', StringField.TYPE_NOT_STORED))
w1.addDocument(doc1)
w1.close()
def setUp(self):
super(Test_Bug1842, self).setUp()
self.analyzer = StandardAnalyzer()
w1 = self.getWriter(analyzer=self.analyzer)
doc1 = Document()
ftype = FieldType()
ftype.setIndexOptions(IndexOptions.DOCS_AND_FREQS_AND_POSITIONS)
ftype.setTokenized(True)
ftype.setStoreTermVectors(True)
ftype.freeze()
doc1.add(Field("all", "blah blah blah Gesundheit", ftype))
doc1.add(Field('id', '1', StringField.TYPE_NOT_STORED))
w1.addDocument(doc1)
w1.close()
class Indexer:
"""
Indexer Class
"""
(NAME, CONTENT, DATE, URL, TAGS, TIMESTAMP) = ("name", "content", "date",
"url", "tags", "timestamp")
def __init__(self, indexDir="", debug=False, verbose=False):
"""
:Parameters:
- `indexDir`: Path where the Index will be saved. (Str)
- `debug`: Create the Index in RAM Memory (indexDir will be ignored). (Boolean)
- `verbose`: Provide additional information about the initialization process. (Boolean)
"""
self.__verbose = verbose
if indexDir != "":
INDEX_DIR = indexDir
else:
INDEX_DIR = os.path.dirname(
os.path.realpath(__file__)) + "/luceneIndex"
if not os.path.exists(INDEX_DIR):
os.makedirs(INDEX_DIR)
self.__boAppend = False
else:
self.__boAppend = True
# Initialize lucene and JVM
lucene.initVM()
# Get index storage
if debug:
# Store the index in memory
self.__indexDir = RAMDirectory()
self.__boAppend = False
INDEX_DIR = "RAM Memory"
else:
# Store an index on disk
self.__indexDir = SimpleFSDirectory(Paths.get(INDEX_DIR))
# Create Content FieldType
self.__contentType = FieldType()
self.__contentType.setIndexOptions(IndexOptions.DOCS_AND_FREQS)
self.__contentType.setTokenized(True)
self.__contentType.setStored(True)
self.__contentType.setStoreTermVectors(True)
self.__contentType.setStoreTermVectorPositions(True)
self.__contentType.freeze()
# Get the Analyzer
self.__analyzer = StandardAnalyzer(
StandardAnalyzer.ENGLISH_STOP_WORDS_SET)
# Print Indexer Information
print("Lucene version is: ", lucene.VERSION)
print("Index Directory: ", INDEX_DIR)
def __del__(self):
self.__indexDir.close()
##################################################
#Private Methods
##################################################
@staticmethod
def __getTimestamp(dateTime):
"""
Converts the document's date to an integer timestamp
:Parameters:
- `dateTime`: Document's date (Str)
:Returns:
- Date timestamp (Int)
"""
tm = time.strptime(dateTime, '%Y-%m-%dT%H:%M:%SZ')
sTime = "{0:0>4}{1:0>2}{2:0>2}{3:0>2}{4:0>2}{5:0>2}".format(
tm.tm_year, tm.tm_mon, tm.tm_mday, tm.tm_hour, tm.tm_min,
tm.tm_sec)
return int(sTime)
@staticmethod
def __getDateTime(timeStamp):
"""
Converts the document's timestamp to date
:Parameters:
- `timeStamp`: Document's timestamp
:Returns:
- Date (Str)
"""
date = datetime.datetime(year=int(timeStamp[0:4]),
month=int(timeStamp[4:6]),
day=int(timeStamp[6:8]),
hour=int(timeStamp[8:10]),
minute=int(timeStamp[10:12]),
second=int(timeStamp[12:14]))
return date.strftime('%Y-%m-%d %H:%M:%S')
@staticmethod
def __qualifyTags(tags):
"""
Creates the qualify string for tags
:Parameters:
- `tags`: List of document's tags
:Return:
- Qualify Tags (Str)
"""
sTags = ""
for tag in tags:
sTags += tag + '|'
return sTags[:-1]
@staticmethod
def __scatterMatrix(numDocs, freqMtx):
print("Scattering Frequency Matrix...")
pB = ProgressBar(len(freqMtx), prefix='Progress:')
matrix = []
innerMatrix = ['Term']
#Generate Document Columns
for docIdx in range(numDocs):
innerMatrix.append("D{0:0>4}".format(docIdx))
matrix.append(innerMatrix)
#Generate Word Rows and Columns
for word in sorted(freqMtx):
innerMatrix = []
innerMatrix.append(word)
for docIdx in range(numDocs):
try:
termCount = round(freqMtx[word][str(docIdx)], 3)
innerMatrix.append(termCount)
except KeyError:
innerMatrix.append(0)
matrix.append(innerMatrix)
pB.updateProgress()
return matrix
@staticmethod
def __saveMatrix(numDocs, freqMtx):
pathMatrix = os.path.dirname(
os.path.realpath(__file__)) + "/freqMtx.txt"
fMatrix = open(pathMatrix, 'w')
print("Saving Frequency Matrix File: ", pathMatrix)
pB = ProgressBar(len(freqMtx), prefix='Progress:')
# File Generation Start
print("+========= Frequency Matrix =========+", file=fMatrix)
print("%20s" % (' '), end=' ', file=fMatrix)
for docIdx in range(numDocs):
print("D{0:0>4}".format(docIdx), end=' ', file=fMatrix)
print(file=fMatrix)
for word in sorted(freqMtx):
print("%20s" % (word), end=' ', file=fMatrix)
for docIdx in range(numDocs):
try:
termCount = freqMtx[word][str(docIdx)]
print("%02.03f" % (termCount), end=' ', file=fMatrix)
except KeyError:
print(" 0 ", end=' ', file=fMatrix)
print(file=fMatrix)
pB.updateProgress()
# Close File
fMatrix.close()
def __stemString(self, stringToStem):
stemmedTerms = []
tknStream = self.__analyzer.tokenStream('STEM', stringToStem)
stemmed = SnowballFilter(tknStream, "English")
stemmed.reset()
while stemmed.incrementToken():
stemmedTerms.append(
stemmed.getAttribute(CharTermAttribute.class_).toString())
tknStream.close()
return stemmedTerms
@staticmethod
def __normalize(qVector, freqMtx):
for term in qVector:
for docId in freqMtx:
if (term in freqMtx[docId]) and (freqMtx[docId][term] >
qVector[term]):
qVector[term] = freqMtx[docId][term]
@staticmethod
def __dotProduct(aVector, bVector):
"""
Calculate Dot Product
:Parameters:
- `aVector`: A Vector. (Dict)
- `bVector`: B Vector. (Dict)
:Returns:
- Dot Product. (Int)
"""
dotProduct = 0
for term in aVector:
if term in bVector:
product = aVector[term] * bVector[term]
dotProduct += product
return dotProduct
@staticmethod
def __magnitude(vector):
"""
Calculate Dot Product
:Parameters:
- `vector`: Query Vector. (Dict)
:Returns:
- Vector Magnitude. (Int)
"""
# Magnitude of the vector is the square root of the dot product of the vector with itself.
vectorMagnitude = Indexer.__dotProduct(vector, vector)
vectorMagnitude = math.sqrt(vectorMagnitude)
return vectorMagnitude
##################################################
#Public Methods
##################################################
def IndexDocs(self, documents):
"""
Index documents under the directory
:Parameters:
- `documents`: Documents to be indexed (List)
"""
# Get the Writer Configuration
writerConfig = IndexWriterConfig(self.__analyzer)
# Get index writer
writer = IndexWriter(self.__indexDir, writerConfig)
for document in documents:
# Create a document that would we added to the index
doc = Document()
# Add a field to this document
doc.add(TextField(Indexer.NAME, document['name'], Field.Store.YES))
doc.add(
Field(Indexer.CONTENT, document['content'],
self.__contentType))
doc.add(
StringField(Indexer.DATE, document['date'], Field.Store.YES))
doc.add(StringField(Indexer.URL, document['url'], Field.Store.YES))
doc.add(
TextField(Indexer.TAGS, self.__qualifyTags(document['tags']),
Field.Store.YES))
doc.add(
LongPoint(Indexer.TIMESTAMP,
self.__getTimestamp(document['date'])))
# Add or update the document to the index
if not self.__boAppend:
# New index, so we just add the document (no old document can be there):
if self.__verbose:
print("Adding " + document['name'])
writer.addDocument(doc)
else:
# Existing index (an old copy of this document may have been indexed) so
# we use updateDocument instead to replace the old one matching the exact
# path, if present:
if self.__verbose:
print("Updating " + document['name'])
writer.updateDocument(Term(Indexer.NAME, document['name']),
doc)
# Print index information and close writer
print("Indexed %d documents (%d docs in index)" %
(len(documents), writer.numDocs()))
writer.close()
def Search(self, query, field=NAME, maxResult=1000):
"""
Search for a document into the Lucene's Index
:Parameters:
- `query`: Request to be made to the Index (Str).
- `field`: Field to be consulted by the query (NAME, CONTENT, DATE, URL, TAGS).
- `maxResult`: Maximum number of results.
"""
# Get the Index Directory
reader = DirectoryReader.open(self.__indexDir)
searcher = IndexSearcher(reader)
# Create a query
queryParser = QueryParser(field, self.__analyzer).parse(query)
# Do a search
hits = searcher.search(queryParser, maxResult)
print("Found %d document(s) that matched query '%s':" %
(hits.totalHits, queryParser))
for hit in hits.scoreDocs:
doc = searcher.doc(hit.doc)
print("Document Nº: %d - Score: %.5f" % (hit.doc, hit.score))
print("Name: " + doc.get('name'))
print("Tags: " + doc.get('tags') + "\n")
reader.close()
def StemDocument(self, docIdx):
"""
Return an array of the document's stemmed terms
:Parameters:
- `docIdx`: Document's index ID (Int).
"""
reader = DirectoryReader.open(self.__indexDir)
doc = reader.document(docIdx).get(Indexer.CONTENT)
reader.close()
return self.__stemString(doc)
def FreqMatrix(self, scattered=False, byTerms=True, saveMtx=False):
"""
Generates a Frequency Matrix of the current Index
:Parameters:
- `saveMtx`: Save the Frequency Matrix to a .txt file. (Boolean)
"""
freqMtx = {} # Terms - DocumentID Matrix
reader = DirectoryReader.open(self.__indexDir)
numDocs = reader.numDocs()
print("Generating Frequency Matrix...")
pB = ProgressBar(numDocs - 1, prefix='Progress:')
for docIdx in range(numDocs):
termItr = self.StemDocument(docIdx)
termSize = len(termItr)
docStr = '{0}'.format(docIdx)
termDict = {}
for termText in termItr:
if byTerms:
# Check if the term exists
if termText in freqMtx:
# Check if the document exists
if docStr in freqMtx[termText]:
termCount = int(
math.ceil(
((freqMtx[termText][docStr] * termSize) /
100)))
freqMtx[termText].update(
{docStr: ((termCount + 1) / termSize) * 100})
else:
freqMtx[termText].update(
{docStr: (1 / termSize) * 100})
else:
termIdx = {termText: {docStr: (1 / termSize) * 100}}
freqMtx.update(termIdx)
else:
# Check if the term exists
termText = termText.replace('.', '_')
if termText in termDict:
termCount = int(
math.ceil((termDict[termText] * termSize) / 100))
termDict[termText] = ((termCount + 1) / termSize) * 100
else:
termIdx = {termText: (1 / termSize) * 100}
termDict.update(termIdx)
if not byTerms:
freqMtx.update({docStr: termDict})
pB.updateProgress()
if saveMtx and byTerms:
self.__saveMatrix(numDocs, freqMtx)
if scattered and byTerms:
freqMtx = self.__scatterMatrix(numDocs, freqMtx)
# Close IndexReader
reader.close()
return freqMtx
def GetSimilarity(self, query, freqMtx):
"""
Cosine Similarity
"""
qVector = {}
qList = self.__stemString(query)
for stem in qList:
qVector.update({stem: 0})
self.__normalize(qVector, freqMtx)
qList = []
#Get similarity between query and doc[n]
for docIdx, dVector in freqMtx.items():
dP = self.__dotProduct(qVector, dVector)
qM = self.__magnitude(qVector)
dM = self.__magnitude(dVector)
cosSimilarity = dP / (qM * dM)
qList.append((docIdx, cosSimilarity))
return sorted(qList,
key=lambda similarity: similarity[1],
reverse=True)
def AnalyzeDocument(self, docIdx):
"""
Generates a list of (entity, relation, entity) tuples as its output.
:Parameters:
- `docIdx`: Document's index ID (Int).
"""
gpeList = {}
geolocator = Geocode()
reader = DirectoryReader.open(self.__indexDir)
doc = reader.document(docIdx)
# Load NLTK Data
nltkPath = os.path.dirname(
os.path.realpath(__file__)) + '/../tools/nltk_data'
nltk.data.path.append(nltkPath)
# Named Entity Recognition
content = doc.get(Indexer.CONTENT)
sentences = nltk.sent_tokenize(content)
#ProgressBar
print("Analazing Document {0}".format(docIdx))
pB = ProgressBar(len(sentences), prefix='Progress:')
# Loop over each sentence and tokenize it separately
for sentence in sentences:
ner = nltk.word_tokenize(sentence)
ner = nltk.pos_tag(ner)
ner = nltk.ne_chunk(ner)
# Get all the Geo-Political Entities
for subtrees in list(
ner.subtrees(
filter=lambda subtree: subtree.label() == 'GPE')):
entityName = ' '.join([child[0] for child in subtrees])
if entityName not in gpeList:
location = geolocator.GetGPE(entityName)
if location:
gpeList.update(location)
pB.updateProgress()
gpeList = geolocator.GetFeatureCollection(gpeList)
return gpeList
def GetDocField(self, docIdx, field=CONTENT):
"""
Get the document's field
:Parameters:
- `docIdx`: Document's index ID (Int).
- `field`: Field to retrieve (Str).
:Returns:
- Document's field. (Str)
"""
reader = DirectoryReader.open(self.__indexDir)
doc = reader.document(docIdx)
content = doc.get(field)
reader.close()
return content
