def preprocess(fin_path):
fin = open(fin_path, 'r')
manager_groups = {}
for line in fin:
splits = line.split(',')
aa = re.findall('\d+', splits[0])[0]
pid = int(aa)
try:
ll = splits[3].replace('\n', '')
if pid in manager_groups:
manager_groups[pid].add_node(SentenceNode(splits[4], extra=int(ll)))
else:
snm = SentenceNodeManager()
snm.add_node(SentenceNode(splits[4], extra=int(ll)))
manager_groups[pid] = snm
except Exception, e:
print e
print line
def text_rank(sentences, num=10, sim_func=get_similarity, pagerank_config={'alpha': 0.85, }):
"""将句子按照关键程度从大到小排序
Keyword arguments:
sentences -- 列表，元素是句子
words -- 二维列表，子列表和sentences中的句子对应，子列表由单词组成
sim_func -- 计算两个句子的相似性，参数是两个由单词组成的列表
pagerank_config -- pagerank的设置
"""
def cal_cos(vec1, vec2):
def __cal_vecmod(vec):
mod_result = 0.0
for i in vec:
mod_result += i ** 2
return math.sqrt(mod_result)
# 首先是分子的计算
fenzi = 0.0
for i in xrange(len(vec1)):
fenzi += vec1[i] * vec2[i]
# 计算分母
fenmu = __cal_vecmod(vec1) * __cal_vecmod(vec2)
if fenmu == 0:
return 0
else:
return fenzi / fenmu
sorted_sentences = []
snm = SentenceNodeManager()
for sent in sentences:
snode = SentenceNode(sent)
snm.add_node(snode)
snm.normalize_all_sentnodes()
vlist = snm.get_vec_list()
# _source = words
sentences_num = len(vlist)
graph = np.zeros((sentences_num, sentences_num))
for x in xrange(sentences_num):
for y in xrange(x, sentences_num):
similarity = cal_cos(vlist[x], vlist[y])
graph[x, y] = similarity
graph[y, x] = similarity
nx_graph = nx.from_numpy_matrix(graph)
scores = nx.pagerank(nx_graph, **pagerank_config) # this is a dict
sorted_scores = sorted(scores.items(), key=lambda item: item[1], reverse=True)
for index, score in sorted_scores:
item = AttrDict(sentence=sentences[index], weight=score)
sorted_sentences.append(item)
return sorted_sentences[:num]
def save_csv(jd_result, fout_path=None):
if not fout_path:
fout_path = jd_result['title'][:10]
with open('../data/csv/%s-%s.csv' % (arrow.utcnow().timestamp, fout_path), 'w') as fout:
fout.write(codecs.BOM_UTF8)
for item in jd_result['comments']:
fout.write('%s,%s,%s,0,%s\n' % (
jd_result['pid'], item['creationTime'], item['nickname'], item['content'].replace('\n', '')))
print 'saved! path = %s' % fout_path
if __name__ == '__main__':
ITEM_ID = 411082
MAXPAGE = 20
# RES = get_jd_rate(ITEM_ID,MAXPAGE)
jd_res = jd_parser(ITEM_ID, MAXPAGE)
open('%s-%s-parser_result.json' % (arrow.utcnow().timestamp, jd_res['title'][:10]), 'w').write(
json.dumps(jd_res, ensure_ascii=False))
save_csv(jd_res)
# add sentence
# st = open('parser_result.json', 'r').read()
# jd_res = json.loads(st)
snm = SentenceNodeManager()
for item in jd_res['comments']:
snm.add_node(SentenceNode(item['content']))
import utils.node_vec_utils.node_cluster_utils as CU
CU.APcluster(snm, '../data/clusters/%s-APresult.json' % jd_res['title'][:10])
CU.DBSCANcluster(snm, '../data/clusters/%s-DBSCANresult.json' % jd_res['title'][:10])
def amazon_preprocess(start=0, end=10, label_rate=0.65, min_vote=0):
"""
:param start:
:param end:
:param label_rate:
:return:
"""
# prepare train set
db_inst = get_db_inst('AmazonReviews', 'AndroidAPP')
# print len(db_inst.distinct('asin'))
manager_groups = {}
asin_file = open('%s/process/data/asin.list' % PROJECT_PATH, 'r')
# for asin in db_inst.distinct('asin'):
# asin_file.write('%s\n' % asin)
lines = asin_file.readlines()
shuffle(lines)
# for asin in db_inst.distinct('asin'):
tlines = lines[start:end]
review_dicts = {}
asin_list = []
for asin in tlines:
asin = asin.replace('\n', '')
asin_list.append(asin)
print 'loading %s' % asin
# snm.add_node(SentenceNode(splits[4], extra=int(ll)))
# 计算每个APP下的评论
a_reviews = []
max_vote = 0 # 常量
for find_item in db_inst.find({"asin": asin, 'total_vote': {"$gt": min_vote}}):
max_vote = max(find_item['total_vote'], max_vote)
a_reviews.append(find_item)
# process item reviews VOTE RANK
review_rank = []
print '%s has %s reviews' % (asin, len(a_reviews))
snm = SentenceNodeManager()
for review in a_reviews:
alpha_const = 0
T = float(review['total_vote']) / max_vote
V = 1 / (1.0 + math.exp(-0.01 * (2 * review['up_vote'] - review['total_vote'])))
# V = float(review['up_vote']) / review['total_vote']
vote_rank_value = 2 * (T + alpha_const) * (V + alpha_const) / (T + V + 2 * alpha_const)
if vote_rank_value >= label_rate:
snm.add_node(
SentenceNode(review['reviewText'].lower(), extra=(int(1), vote_rank_value, review['reviewerID']),
get_pos_func=tag_sents,
get_keywords_func=cal_en_tfidf))
elif vote_rank_value < label_rate:
snm.add_node(
SentenceNode(review['reviewText'].lower(), extra=(int(0), vote_rank_value, review['reviewerID']),
get_pos_func=tag_sents,
get_keywords_func=cal_en_tfidf))
review_rank.append((review, vote_rank_value))
manager_groups[asin] = snm
review_dicts[asin] = review_rank
# else:
# break
veclist = []
sentlist = []
labellist = []
tokenlist = []
nodelist = []
group_nodelist = []
print 'start normalizing vecs'
for pid in manager_groups.keys():
manager = manager_groups[pid]
# DBSCANcluster(manager, '%s_DBSCANcluster.json' % pid)
# APcluster(manager, '%s_APcluster.json' % pid)
manager.normalize_all_sentnodes(tfidf_func=tag_sents)
veclist.extend(manager.get_vec_list())
sentlist.extend(manager.get_sent_list())
gnodelist = []
for node in manager.node_list:
labellist.append(node.extra[0])
tokenlist.append(node.feature2token())
nodelist.append(node)
gnodelist.append(node)
group_nodelist.append(gnodelist)
print 'end normalizing vecs'
return veclist, sentlist, labellist, tokenlist, nodelist, manager_groups
