def loadterms():
ireader = IndexReader.open(STORE_DIR)
tpv = TermPositionVector.cast_(ireader.getTermFreqVector(0, 'title'))
a = ireader.terms()
rownames = []
# 列名为term的中英文表示
colnames = []
# term-freq矩阵
data = []
ireader.document(- 1)
i = 0
while a.next():
term = a.term()
if term.field() == 'summary':
colnames.append(term.text())
if term.text() == '':
print 'ok'
break
i = i+1
if i == 1000:
break
docs = ireader.termDocs(term)
vector = []
lastdoc = 0
while docs.next():
# 填补那些不包含当前term的document的词频为0
if lastdoc < docs.doc():
id = docs.doc()
for j in range(id - lastdoc):
vector.append(0)
vector.append(docs.freq())
data.append(vector)
ireader.close()
return colnames, data
def loadterms():
ireader = IndexReader.open(STORE_DIR)
tpv = TermPositionVector.cast_(ireader.getTermFreqVector(0, 'title'))
a = ireader.terms()
rownames = []
colnames = []
data = []
print dir(a)
print dir(ireader)
ireader.document(- 1)
while 0 and a.next():
term = a.term()
if term.field() == 'summary':
colnames.append(term.text())
docs = ireader.termDocs(term)
vector = []
lastdoc = 0
while docs.next():
if lastdoc < docs.doc():
id = docs.doc()
for j in range(id - lastdoc):
vector.append(0)
vector.append(docs.freq())
data.append(vector)
return colnames, data
def label_assign(self, docs, labels, lucene_ids):
term_row = {}
all = []
ireader = IndexReader.open(STORE_DIR)
total_terms = 0
for i in range(len(lucene_ids)):
tpv = TermPositionVector.cast_(ireader.getTermFreqVector(lucene_ids[i], 'summary'))
self.add2matrix(tpv, all, term_row, lucene_ids, i)
tpv = TermPositionVector.cast_(ireader.getTermFreqVector(lucene_ids[i], 'title'))
self.add2matrix(tpv, all, term_row, lucene_ids, i)
# 对label进行分词
analyzer = CJKAnalyzer()
labelmatrix = zeros((len(all), len(labels)))
label_term = []
for i in range(len(labels)):
if not labels[i].is_candicate_label and len(labels[i].text) >= 3:
label_term.append([])
continue
#print labels[i].text,labels[i].id
stream = analyzer.tokenStream('', StringReader(labels[i].text))
terms = []
for token in stream:
if term_row.has_key(token.term()):
# weighting
termdocs = ireader.termDocs(Term('summary', token.term()))
count = 0
span = 0
terms.append(token.term())
while termdocs.next():
count += termdocs.freq()
span += 1
weight = labels[i].label_weight
#if float(span)/ireader.numDocs() >= 0.18 and not re.search('a-zA-z', token.term()):
#weight = 0
labelmatrix[term_row[token.term()]][i] = weight
label_term.append(terms)
termmatrix = array(all)
termmatrix = transpose(termmatrix)
#for i in range(len(labelmatrix[0])):
#for j in range(len(termmatrix[0])):
# row是doc,col是label
#p = self.product(termmatrix,labelmatrix)
d = dot(termmatrix, labelmatrix)
result = d / (norm(labelmatrix) * norm(termmatrix))
doc_label = []
for i in range(len(result)):
m = - 1
index = - 1
group = []
for j in range(len(result[i])):
if result[i][j] > 0:
labels[j].id = result[i][j]
group.append(labels[j])
# substring是按照id来排序的，这里正好用到
group.sort()
group.reverse()
max_label = group[0]
# i:doc number(just occur position in the docs)
# label id
# label score
# 如果label自身并没有出现在当前doc中
if not max_label.doc_freq.has_key(i):
#print 'oringial:',labels[index].text
count = 0
overlap = ''
for k in label_term[index]:
if term_row.has_key(k) and termmatrix[i][term_row[k]] != 0:
overlap = k
print k
count += 1
# 至少有一个交集，并且长度大于等于2
if count == 1 and len(overlap) >= 2 :
new_label = pextractor.Substring()
new_label.text = overlap
new_label.id = m
doc_label.append(group[0])
continue
#labels[index].id = m
doc_label.append(group[0])
return doc_label
def assign(self, docs, labels, lucene_ids):
term_row = {}
all = []
ireader = IndexReader.open(STORE_DIR)
total_terms = 0
term_doc_freq = {}
for i in range(len(lucene_ids)):
tpv = TermPositionVector.cast_(ireader.getTermFreqVector(lucene_ids[i], 'summary'))
self.add2matrix(tpv, all, term_row, lucene_ids, i,term_doc_freq)
"""
TODO:给属于标题的term加权
"""
tpv = TermPositionVector.cast_(ireader.getTermFreqVector(lucene_ids[i], 'title'))
self.add2matrix(tpv, all, term_row, lucene_ids, i,term_doc_freq)
#for k,v in term_doc_freq.items():
# if v> 3:
# print k,v
# 对label进行分词
analyzer = CJKAnalyzer()
labelmatrix = zeros((len(all), len(labels)))
label_term = []
# doc -label:每个doc对应的label
all_weight_table = {}
#label -doc:每个label对应的doc
label_doc = []
label_doc_map = {}
for i in range(len(labels)):
nonzero_table = []
# 一个label对应和所有doc的权重之积
weight_table = []
stream = analyzer.tokenStream('', StringReader(labels[i].text))
terms = []
c = 0
weight_row = {}
nonzero_index = []
is_incomplete = False
for token in stream:
term = token.term()#token.decode('utf-8')#
#print term
if term_row.has_key(term):
row = term_row[term]
terms.append(term)
docs_with_current_term = all[row]
for j in range(len(docs_with_current_term)):
if docs_with_current_term[j] != 0:
if c == 0:
nonzero_index.append(j)
if c == 0 or j in nonzero_index:
weight_row[j] = weight_row.get(j, 0) + docs_with_current_term[j] * term_doc_freq[term] * labels[i].label_weight
else:
# 加1防止权重之积为0
# 针对第一次出现在nonzero_index中而在后面的过程中没有出现的doc ,乘以-100使得权重乘积最小表示当前label不适用于此doc
weight_row[j] = (1 + docs_with_current_term[j] * term_doc_freq[term] * labels[i].label_weight) * (- 100)
# 针对第一次没有在nonzero_index中而在后面的过程中出现的doc
elif docs_with_current_term[j] == 0 and j in nonzero_index:
# 加1防止权重之积为0
weight_row[j] = (1 + docs_with_current_term[j] * labels[i].label_weight) * (- 100)
c += 1
else:
is_incomplete = True
label_term.append(terms)
# bugfix:如果当前label经分词后，不是所有的term都在全部doc的term中，那么放弃当前label,舍之。
if is_incomplete:
weight_row = {}
for doc, weight in weight_row.items():
last = all_weight_table.get(doc)
if weight > 0:
if not label_doc_map.has_key(labels[i].text):
kc = dao.get_keyword_category_by_category(self.query, labels[i].text)
#label_doc.append([ 0,labels[i].text,[]])
label_doc.append([ 0,labels[i].text,0])
label_doc_map[labels[i].text] = len(label_doc) - 1
new_label = pextractor.Substring()
new_label.text = labels[i].text
new_label.id = weight
if last:
all_weight_table[doc].append(new_label)
else:
all_weight_table[doc] = [new_label]
#label_doc[label_doc_map[labels[i].text]][2].append(doc)
label_doc[label_doc_map[labels[i].text]][2] += 1
label_doc[label_doc_map[labels[i].text]][0] += weight
#try:
# category = dao.save_category(labels[i].text, weight, 'd')
# entry = self.entries[doc]
# ec = dao.save_entry_cat(entry, category, weight)
#except Exception,e:
# print e
#if last:
# all_weight_table[doc].append(ec)
#else:
# all_weight_table[doc] = [ec]
# 如果doc已经存在，那么用已经存在的doc-label权重比较当前的权重，如果当前的更大则替换已经存在的，即选择最大权重的label
#if last:
# if last.id < weight and weight > 0:
# labels[i].id = weight
# all_weight_table[doc] = labels[i]
#else:
# labels[i].id = weight
# all_weight_table[doc] = labels[i]
label_doc.sort(reverse=True)
for k, v in all_weight_table.items():
v.sort(reverse=True)
# 因为map中键为连续的整数值，哈希算法会把他按从小到大的位置排放,所以直接返回的values是已经排好序的了
thread = SaveLabelsThread(all_weight_table,label_doc,self.entries,self.query)
thread.start()
return all_weight_table,label_doc
if __name__ == '__main__':
analyzer = CJKAnalyzer()
directory = RAMDirectory()
ireader = IndexReader.open(STORE_DIR)
iwriter = IndexWriter(directory, StandardAnalyzer(), True)
ts = ["javasd。 $#＃open所大家教唆犯地方地方即可解放大家空间艰苦奋斗矿井口地方", "所看看对抗赛不久交会法觉得拮抗剂"]
for t in ts:
doc = Document()
doc.add(Field("fieldname", t,
Field.Store.YES, Field.Index.TOKENIZED,
Field.TermVector.WITH_POSITIONS_OFFSETS))
iwriter.addDocument(doc)
iwriter.optimize()
iwriter.close()
ireader = IndexReader.open(directory)
tpv = TermPositionVector.cast_(ireader.getTermFreqVector(0, 'fieldname'))
for (t, f, i) in zip(tpv.getTerms(), tpv.getTermFrequencies(), xrange(100000)):
print 'term %s' % t
print ' freq: %i' % f
try:
print ' pos: ' + str([p for p in tpv.getTermPositions(i)])
except:
print ' no pos'
try:
print ' off: ' + \
str(["%i-%i" % (o.getStartOffset(), o.getEndOffset())
for o in tpv.getOffsets(i)])
except:
print ' no offsets'
text = "地方库 fd### fd 反对 发"
