def Export_Model(self, model_path):
data = dict(word_dict=self.word_dict,
huffman=self.huffman,
vec_len=self.vec_len,
learn_rate=self.learn_rate,
win_len=self.win_len,
model=self.model)
FI.save_pickle(data, model_path)
def Export_Model(self,model_path):
data=dict(
word_dict = self.word_dict,
huffman = self.huffman,
vec_len = self.vec_len,
learn_rate = self.learn_rate,
win_len = self.win_len,
model = self.model
)
FI.save_pickle(data,model_path)
def generate_data_for_gelphi():
generate_node_and_edge()
nodes = FI.load_pickle('.\\temp_res\\node.pkl')
edges = FI.load_pickle('.\\temp_res\\edge.pkl')
nodes = list(nodes.values())
edges = list(edges.values())
nodes_mail = [x['mail'] for x in nodes]
# edges = [tuple(x) for x in edges]
G = nx.Graph()
G.add_nodes_from(nodes_mail)
G.add_weighted_edges_from(edges)
nx.write_gexf(G,'.\\temp_res\\data.gexf')
def generate_data_for_gelphi():
generate_node_and_edge()
nodes = FI.load_pickle('.\\temp_res\\node.pkl')
edges = FI.load_pickle('.\\temp_res\\edge.pkl')
nodes = list(nodes.values())
edges = list(edges.values())
nodes_mail = [x['mail'] for x in nodes]
# edges = [tuple(x) for x in edges]
G = nx.Graph()
G.add_nodes_from(nodes_mail)
G.add_weighted_edges_from(edges)
nx.write_gexf(G, '.\\temp_res\\data.gexf')
def Load_Word_Freq(self,word_freq_path):
# load the info of word frequence
# will generate a word dict
if self.word_dict is not None:
raise RuntimeError('the word dict is not empty')
word_freq = FI.load_pickle(word_freq_path)
self.__Gnerate_Word_Dict(word_freq)
def Load_Word_Freq(self, word_freq_path):
# 加载词频数据
# 加载词频后输出词典
if self.word_dict is not None:
raise RuntimeError('the word dict is not empty')
word_freq = FI.load_pickle(word_freq_path)
self.__Gnerate_Word_Dict(word_freq)
def Load_Word_Freq(self, word_freq_path):
# load the info of word frequence
# will generate a word dict
if self.word_dict is not None:
raise RuntimeError('the word dict is not empty')
word_freq = FI.load_pickle(word_freq_path)
self.__Gnerate_Word_Dict(word_freq)
def Import_Model(self,model_path):
model = FI.load_pickle(model_path) # a dict, {'word_dict','huffman','vec_len'}
self.word_dict = model.word_dict
self.huffman = model.huffman
self.vec_len = model.vec_len
self.learn_rate = model.learn_rate
self.win_len = model.win_len
self.model = model.model
def Import_Model(self,model_path):
model = FI.load_pickle(model_path) # a dict, {'word_dict','huffman','vec_len'}
self.word_dict = model.word_dict
self.huffman = model.huffman
self.vec_len = model.vec_len
self.learn_rate = model.learn_rate
self.win_len = model.win_len
self.model = model.model
def Import_Model(self,model_path): #直接读入word2vec模型
model = FI.load_pickle(model_path) # 以字典形式储存, {'word_dict','huffman','vec_len'}
self.word_dict = model['word_dict']
self.huffman = model['huffman']
self.vec_len = model['vec_len']
self.learn_rate = model['learn_rate']
self.win_len = model['win_len']
self.model = model['model']
return e
def __Sigmoid(self, value):
return 1 / (1 + math.exp(-value))
if __name__ == '__main__':
# text = FI.load_pickle('./static/demo.pkl')
# text =[ x['dealed_text']['left_content'][0] for x in text]
# # data = ['Merge multiple sorted inputs into a single sorted output','The API below differs from textbook heap algorithms in two aspects']
# wv = Word2Vec(vec_len=500)
# wv.Train_Model(text)
# FI.save_pickle(wv.word_dict,'./static/wv.pkl')
#
# data = FI.load_pickle('./static/wv.pkl')
# x = {}
# for key in data:
# temp = data[key]['vector']
# temp = preprocessing.normalize(temp)
# x[key] = temp
# FI.save_pickle(x,'./static/normal_wv.pkl')
x = FI.load_pickle('./static/normal_wv.pkl')
def cal_simi(data, key1, key2):
return data[key1].dot(data[key2].T)[0][0]
keys = list(x.keys())
for key in keys:
print(key, '\t', cal_simi(x, '小说', key))
else:
return temp[:high]
if __name__ == '__main__':
# text = FI.load_pickle('./static/demo.pkl')
# text =[ x['dealed_text']['left_content'][0] for x in text]
# data = ['Merge multiple sorted inputs into a single sorted output','The API below differs from textbook heap algorithms in two aspects']
# 输入文本
data = [
"小说”一词最早出现于《庄子·外物》：「饰小说以干县令，其于大达亦远矣。」庄子所谓的「小说」，是指琐碎的言论，与今日小说观念相差甚远。直至东汉桓谭《新论》：「小说家合残丛小语，近取譬喻，以作短书，治身理家，有可观之辞。」班固《汉书．艺文志》将「小说家」列为十家之后，其下的定义为：「小说家者流，盖出于稗官，街谈巷语，道听途说[4]之所造也。」才稍与今日小说的意义相近。而中国小说最大的特色，便自宋代开始具有文言小说与白话小说两种不同的小说系统。文言小说起源于先秦的街谈巷语，是一种小知小道的纪录。在历经魏晋南北朝及隋唐长期的发展，无论是题材或人物的描写，文言小说都有明显的进步，形成笔记与传奇两种小说类型。而白话小说则起源于唐宋时期说话人的话本，故事的取材来自民间，主要表现了百姓的生活及思想意识。但不管文言小说或白话小说都源远流长，呈现各自不同的艺术特色。"
]
wv = Word2Vec(vec_len=500) # vec 长度为500
wv.Train_Model(data)
FI.save_pickle(wv.word_dict, './static/wv.pkl')
# 保存以后归一化
data = FI.load_pickle('./static/wv.pkl')
x = {}
for key in data:
temp = data[key]['vector']
temp = preprocessing.normalize(temp)
x[key] = temp
FI.save_pickle(x, './static/normal_wv.pkl')
x = FI.load_pickle('./static/normal_wv.pkl')
# 计算两个词之间的相似度
def cal_simi(data, key1, key2):
return data[key1].dot(data[key2].T)[0][0]
# 从wikileaks下载邮件内容并生成结构化数据
# =============== ATTENTION =============== :
# 由于邮件格式较繁杂，因此无法全部解析（5%左右）
base_url = 'https://wikileaks.org/dnc-emails/get/{page}'
base_path = '.\static\{page}.pkl'
gotten_id = os.listdir('.\static')
gotten_id = [int((x.split('.'))[0]) for x in gotten_id]
task_pool = list(range(1,2000)) # wikileak中的邮件编号
while True :
if task_pool.__len__()==0:
break
task_id = task_pool.pop(0)
if task_id in gotten_id:
print('{id} skip'.format(id=task_id))
continue
url = base_url.format(page = task_id)
path = base_path.format(page = task_id)
try:
info = getStructedData(url)
FI.save_pickle(info,path)
print('{t} succeed'.format(t=task_id))
except Exception as e:
# task_pool.append(task_id)
print('{t} failed <--<--<--<--'.format(t=task_id))
print(e)
print('文件已下载完毕')
# 生成社交网络数据(需要networkx包)
generate_data_for_gelphi()
print('gexf文件已经生成,存放路径: {path}\\temp_res'.format(path=os.getcwd()))
if ret=='dict':
ret_data = {}
for ele,count in temp[:high]:
ret_data[ele]=count
return ret_data
else:
return temp[:high]
if __name__ == '__main__':
"""
stop_words = open(u"./static/中文停用词表（比较全面，有1208个停用词）.txt")
list1 = []
for line in stop_words:
list1.append(line.strip())
print len(list1)
pickle.dump(list1,open("./static/stop_words.pkl","wb"),protocol = 2)
"""
text = FI.load_pickle("./static/stop_words.pkl") #list
for x in text:
print x.decode("gbk")
break
data = ['Merge multiple sorted inputs into a single sorted output','The API below differs from textbook heap algorithms in two aspects']
wc = WordCounter(data)
print(wc.count_res.larger_than(16))
"""
c=MulCounter('abcdeabcdaffbcabag')
print(sorted(c.items(),key=_itemgetter(1),reverse=True))#operator.itemgetter(1)
print(c.larger_than(3))
"""
pass
__author__ = 'multiangle'
import File_Interface as FI
import json
import os
import time
paths = os.listdir('.\static')
date_counter = {}
for path in paths:
data = FI.load_pickle('.\static\{x}'.format(x=path))
date_ori = data.get('Date')
if date_ori:
tag = time.strftime('%Y/%m/%d', date_ori)
c = date_counter.get(tag)
print(tag)
if c:
date_counter[tag]['count'] += 1
else:
date_counter[tag] = dict(date=tag, count=1)
# date_counter = list(date_counter.values())
# date_counter = sorted(date_counter,key=lambda x:x['date'])
# for date in date_counter:
# print('{a}\t{b}'.format(a=date['date'],b=date['count']))
def __GoAlong_Huffman(self, word_huffman, input_vector, root):
node = root
e = np.zeros([1, self.vec_len])
for level in range(word_huffman.__len__()):
huffman_charat = word_huffman[level]
q = self.__Sigmoid(input_vector.dot(node.value.T))
grad = self.learn_rate * (1 - int(huffman_charat) - q)
e += grad * node.value
node.value += grad * input_vector
node.value = preprocessing.normalize(node.value)
if huffman_charat == '0':
node = node.right
else:
node = node.left
return e
def __Sigmoid(self, value):
return 1 / (1 + math.exp(-value))
if __name__ == '__main__':
text = FI.load_pickle('./static/stop_words.pkl')
text = [x for x in text]
data = [
'Merge multiple sorted inputs into a single sorted output',
'The API below differs from textbook heap algorithms in two aspects'
]
wv = Word2Vec(vec_len=500)
wv.Train_Model(text)
FI.save_pickle(wv.word_dict, './static/wv.pkl')
pass
# pre_node = SNA_node.build_Head_Node(item.data['retweeted_status'])
# pre_node.add_Next(item)
# info_dict[pre_node.id] = pre_node
# info_id_list.append(pre_node.id)
# head_id_list.append(pre_node.id)
# head_id_list.remove(id)
# print(ite_times)
# ite_times += 1
# print(head_id_list.__len__())
# FI.save_pickle(info_dict,'./static/dealed_info_list.pkl')
# # 统计
# data = FI.load_pickle('./static/dealed_info_list.pkl')
# id_list = list(data.keys())
# for id in id_list:
# data[id].analyse()
# FI.save_pickle(data,'./static/analyse_info_list.pkl')
data = FI.load_pickle('./static/analyse_info_list.pkl')
id_list = list(data.keys())
for id in id_list:
if data[id].result['retweet_num']>1 :
print(data[id])
break
if list[mid]['time'] < value :
low = mid + 1
else:
high = mid - 1
if i<0:
i = low
if i==list.__len__() :
list.append({'time':value,'freq':1})
elif list[i]['time']==value :
list[i]['freq'] += 1
else:
list.insert(i,{'time':value,'freq':1})
if __name__=='__main__':
stop_words = FI.load_pickle('./static/stop_words.pkl')
wfts = FI.load_pickle('./static/wfts_continue.pkl')
client = MongoClient('localhost',27017)
db = client['microblog_spider']
latest_history = db.latest_history
count = 0
data = []
batch_size = 100
gone_size = 40000
while count<100 :
t1 = time.time()
data = latest_history.find().skip(gone_size+count*batch_size).limit(batch_size)
data = [x for x in data]
t2 = time.time()
count += 1
def generate_node_and_edge():
pkl_list = os.listdir('.\static')
node_list = {}
edge_list = {}
for path in pkl_list:
info = FI.load_pickle('.\static\{x}'.format(x=path))
# print(info)
# 处理 From情况
try:
sender_mail = info['From']['mail']
ori_mail = sender_mail
sender_mail = sender_mail.lower()
except:
continue # 如果其中没有From 则该邮件作废
sender_node = node_list.get(sender_mail)
if sender_node:
sender_node['send'] += 1
else:
node_list[sender_mail] = dict(
mail = sender_mail,
send = 1,
receive = 0,
cc = 0,
ori_mail = ori_mail
)
if info.get('name'):
node_list[sender_mail]['name'] = info['name']
# 处理To情况
receiver_list = info.get('To')
if not receiver_list:
continue
for receiver in receiver_list:
mail = receiver['mail']
ori_mail = mail
mail = mail.lower()
node = node_list.get(mail)
if node:
node['receive'] += 1
else:
node_list[mail] = dict(
mail = mail,
send = 0 ,
receive = 1,
cc = 0,
ori_mail = ori_mail
)
if receiver.get('name'):
node_list[mail]['name'] = receiver['name']
edge_key = sender_mail + '|----->' + mail
edge = edge_list.get(edge_key)
if edge:
edge[2] += 1 # 如果该条边已经存在
else:
edge_list[edge_key] = [sender_mail,mail,1] #如果该条边不存在，则新加一条边
# 处理CC的情况
cc_list = info.get('CC')
if cc_list:
for cc in cc_list:
mail = cc['mail']
ori_mail = mail
mail = mail.lower()
node = node_list.get(mail)
if node:
node['cc'] += 1
else:
node_list[mail] = dict(
mail = mail,
send = 0 ,
receive = 0 ,
cc = 1,
ori_mail = ori_mail
)
if cc.get('name'):
node_list[mail]['name'] = cc['name']
edge_key = sender_mail + '|----->' + mail
edge = edge_list.get(edge_key)
if edge:
edge[2] += 1 # 如果该条边已经存在
else:
edge_list[edge_key] = [sender_mail,mail,1] #如果该条边不存在，则新加一条边
print('{id} is dealed'.format(id=path))
FI.save_pickle(node_list,'.\\temp_res\\node.pkl')
FI.save_pickle(edge_list,'.\\temp_res\\edge.pkl')
__author__ = 'multiangle'
import jieba
import File_Interface as FI
data = FI.load_pickle('demo.pkl')
user_list = [x['user_name'] for x in data]
text_list = [x['dealed_text']['left_content'] for x in data]
for line in text_list:
print(line)
res = jieba.cut(line[0], cut_all=False)
# print(list(seg_list))
res = list(res)
print(res)
if list[mid]['time'] < value:
low = mid + 1
else:
high = mid - 1
if i < 0:
i = low
if i == list.__len__():
list.append({'time': value, 'freq': 1})
elif list[i]['time'] == value:
list[i]['freq'] += 1
else:
list.insert(i, {'time': value, 'freq': 1})
if __name__ == '__main__':
stop_words = FI.load_pickle('./static/stop_words.pkl')
wfts = FI.load_pickle('./static/wfts_continue.pkl')
client = MongoClient('localhost', 27017)
db = client['microblog_spider']
latest_history = db.latest_history
count = 0
data = []
batch_size = 100
gone_size = 40000
while count < 100:
t1 = time.time()
data = latest_history.find().skip(gone_size +
count * batch_size).limit(batch_size)
data = [x for x in data]
t2 = time.time()
high = mid
if temp[low][1]>maxvalue:
if ret=='dict':
return {}
else:
return []
if ret=='dict':
ret_data = {}
for ele,count in temp[:high]:
ret_data[ele]=count
return ret_data
else:
return temp[:high]
if __name__ == '__main__':
text = FI.load_pickle('./static/demo.pkl')
text =[ x['dealed_text']['left_content'][0] for x in text]
# data = ['Merge multiple sorted inputs into a single sorted output','The API below differs from textbook heap algorithms in two aspects']
wv = Word2Vec(vec_len=500)
wv.Train_Model(text)
FI.save_pickle(wv.word_dict,'./static/wv.pkl')
#
# data = FI.load_pickle('./static/wv.pkl')
# x = {}
# for key in data:
# temp = data[key]['vector']
# temp = preprocessing.normalize(temp)
# x[key] = temp
# FI.save_pickle(x,'./static/normal_wv.pkl')
# x = FI.load_pickle('./static/normal_wv.pkl')
if sort_type == 'up':
res = [
pop_id(x)
for x in collection.find(select, f).limit(limit).sort(
sort, pymongo.ASCENDING)
]
else:
res = [
pop_id(x)
for x in collection.find(select, f).limit(limit).sort(
sort, pymongo.DESCENDING)
]
return res
def pop_id(data):
data.pop('_id')
return data
res = read_content_in_mongo(
'latest_history', {'user_id': '1681029540'},
['dealed_text.left_content', 'created_at', 'user_name'], -1, 'id', 'down')
data = FI.load_pickle('demo.pkl')
data = data + res
FI.save_pickle(data, 'demo.pkl')
for line in res:
print(line)
print(res.__len__())
if temp[low][1] > maxvalue:
if ret == 'dict':
return {}
else:
return []
if ret == 'dict':
ret_data = {}
for ele, count in temp[:high]:
ret_data[ele] = count
return ret_data
else:
return temp[:high]
if __name__ == '__main__':
text = FI.load_pickle('./static/demo.pkl')
text = [x['dealed_text']['left_content'][0] for x in text]
# data = ['Merge multiple sorted inputs into a single sorted output','The API below differs from textbook heap algorithms in two aspects']
wv = Word2Vec(vec_len=500)
wv.Train_Model(text)
FI.save_pickle(wv.word_dict, './static/wv.pkl')
#
# data = FI.load_pickle('./static/wv.pkl')
# x = {}
# for key in data:
# temp = data[key]['vector']
# temp = preprocessing.normalize(temp)
# x[key] = temp
# FI.save_pickle(x,'./static/normal_wv.pkl')
# x = FI.load_pickle('./static/normal_wv.pkl')
for level in range(word_huffman.__len__()):
huffman_charat = word_huffman[level]
q = self.__Sigmoid(input_vector.dot(node.value.T))
grad = self.learn_rate * (1 - int(huffman_charat) - q)
e += grad * node.value
node.value += grad * input_vector
node.value = preprocessing.normalize(node.value)
if huffman_charat == '0':
node = node.right
else:
node = node.left
return e
def __Sigmoid(self, value):
return 1 / (1 + math.exp(-value))
if __name__ == '__main__':
import WordCount
# text = WordCount.readfile('./static/text8_mini')
# text = WordCount.readfile('./static/text8')
# mv = Word2Vec(vec_len=500)
# mv.Train_Model(text)
# FI.save_picle(mv.word_dict, './model/model_h.pkl')
model_h = FI.load_pickle('./model/model_h.pkl')
keys = list(model_h.keys())
print(keys.__len__())
print(keys[0])
def Get_Stop_Words(self):
ret = []
ret = FI.load_pickle('./static/stop_words.pkl')
return ret
def generate_node_and_edge():
pkl_list = os.listdir('.\static')
node_list = {}
edge_list = {}
for path in pkl_list:
info = FI.load_pickle('.\static\{x}'.format(x=path))
# print(info)
# 处理 From情况
try:
sender_mail = info['From']['mail']
ori_mail = sender_mail
sender_mail = sender_mail.lower()
except:
continue # 如果其中没有From 则该邮件作废
sender_node = node_list.get(sender_mail)
if sender_node:
sender_node['send'] += 1
else:
node_list[sender_mail] = dict(mail=sender_mail,
send=1,
receive=0,
cc=0,
ori_mail=ori_mail)
if info.get('name'):
node_list[sender_mail]['name'] = info['name']
# 处理To情况
receiver_list = info.get('To')
if not receiver_list:
continue
for receiver in receiver_list:
mail = receiver['mail']
ori_mail = mail
mail = mail.lower()
node = node_list.get(mail)
if node:
node['receive'] += 1
else:
node_list[mail] = dict(mail=mail,
send=0,
receive=1,
cc=0,
ori_mail=ori_mail)
if receiver.get('name'):
node_list[mail]['name'] = receiver['name']
edge_key = sender_mail + '|----->' + mail
edge = edge_list.get(edge_key)
if edge:
edge[2] += 1 # 如果该条边已经存在
else:
edge_list[edge_key] = [sender_mail, mail, 1] #如果该条边不存在，则新加一条边
# 处理CC的情况
cc_list = info.get('CC')
if cc_list:
for cc in cc_list:
mail = cc['mail']
ori_mail = mail
mail = mail.lower()
node = node_list.get(mail)
if node:
node['cc'] += 1
else:
node_list[mail] = dict(mail=mail,
send=0,
receive=0,
cc=1,
ori_mail=ori_mail)
if cc.get('name'):
node_list[mail]['name'] = cc['name']
edge_key = sender_mail + '|----->' + mail
edge = edge_list.get(edge_key)
if edge:
edge[2] += 1 # 如果该条边已经存在
else:
edge_list[edge_key] = [sender_mail, mail,
1] #如果该条边不存在，则新加一条边
print('{id} is dealed'.format(id=path))
FI.save_pickle(node_list, '.\\temp_res\\node.pkl')
FI.save_pickle(edge_list, '.\\temp_res\\edge.pkl')
else:
node = node.left
return e
def __Sigmoid(self,value):
return 1/(1+math.exp(-value))
if __name__ == '__main__':
# text = FI.load_pickle('./static/demo.pkl')
# text =[ x['dealed_text']['left_content'][0] for x in text]
# # data = ['Merge multiple sorted inputs into a single sorted output','The API below differs from textbook heap algorithms in two aspects']
# wv = Word2Vec(vec_len=500)
# wv.Train_Model(text)
# FI.save_pickle(wv.word_dict,'./static/wv.pkl')
#
# data = FI.load_pickle('./static/wv.pkl')
# x = {}
# for key in data:
# temp = data[key]['vector']
# temp = preprocessing.normalize(temp)
# x[key] = temp
# FI.save_pickle(x,'./static/normal_wv.pkl')
x = FI.load_pickle('./static/normal_wv.pkl')
def cal_simi(data,key1,key2):
return data[key1].dot(data[key2].T)[0][0]
keys=list(x.keys())
for key in keys:
print(key,'\t',cal_simi(x,'姚明',key))
__author__ = 'multiangle'
import jieba
import File_Interface as FI
data = FI.load_pickle('demo.pkl')
user_list = [x['user_name'] for x in data]
text_list = [x['dealed_text']['left_content'] for x in data]
for line in text_list:
print(line)
res = jieba.cut(line[0],cut_all=False)
# print(list(seg_list))
res = list(res)
print(res)
# item = info_dict[id]
# if item.hasPre:
# changed = True
# if item.data['retweeted_status']['id'] in info_id_list:
# info_dict[item.data['retweeted_status']['id']].add_Next(item)
# head_id_list.remove(id)
# else:
# pre_node = SNA_node.build_Head_Node(item.data['retweeted_status'])
# pre_node.add_Next(item)
# info_dict[pre_node.id] = pre_node
# info_id_list.append(pre_node.id)
# head_id_list.append(pre_node.id)
# head_id_list.remove(id)
# print(ite_times)
# ite_times += 1
# print(head_id_list.__len__())
# FI.save_pickle(info_dict,'./static/dealed_info_list.pkl')
# # 统计
# data = FI.load_pickle('./static/dealed_info_list.pkl')
# id_list = list(data.keys())
# for id in id_list:
# data[id].analyse()
# FI.save_pickle(data,'./static/analyse_info_list.pkl')
data = FI.load_pickle('./static/analyse_info_list.pkl')
id_list = list(data.keys())
for id in id_list:
if data[id].result['retweet_num'] > 1:
print(data[id])
def post(self):
try:
user_basic_info = self.get_argument('user_basic_info')
attends = self.get_argument('user_attends')
user_basic_info = eval(user_basic_info)
attends = eval(attends)
self.write('success to return user info')
self.finish()
except:
self.write('fail to return user info')
self.finish()
return
try:
dbi = MySQL_Interface()
except:
print('unable to connect to MySql DB')
try:
if attends.__len__() > 0: #store attends info
table_name = 'cache_attends'
attends_col_info = dbi.get_col_name(table_name)
keys = attends[0].keys()
attends = [[
line[i] if i in keys else '' for i in attends_col_info
] for line in attends]
fans_col_pos = attends_col_info.index('fans_num')
insert_attends = []
for line in attends:
if line[fans_col_pos] > 1000:
insert_attends.append(line)
dbi.insert_asList(table_name, insert_attends, unique=True)
print('Success : attends of {uid} is stored in {tname}'.format(
uid=user_basic_info['uid'], tname=table_name))
else:
pass
except Exception as e:
print(e)
path = "temp" + os.sep + "{uid}_attends.pkl".format(
uid=user_basic_info['uid'])
print(
'unable to store attends of {uid}, it will be stored '.format(
uid=user_basic_info['uid']))
FI.save_pickle(attends, path)
try:
atten_num_real = user_basic_info['attends_num']
atten_num_get = attends.__len__()
user_basic_info['accuracy'] = atten_num_get # 实际获取到的关注数目
col_info = dbi.get_col_name(
'cache_user_info') # store user basic info
keys = user_basic_info.keys()
data = [user_basic_info[i] if i in keys else '' for i in col_info]
dbi.insert_asList('cache_user_info', [data], unique=True)
print('Success : basic info of {uid} is stored in cache_user_info'.
format(uid=user_basic_info['uid']))
except Exception as e:
print(e)
path = 'temp' + os.sep + '{uid}_basic_info.pkl'.format(
uid=user_basic_info['uid'])
print('unable to store basic info of {uid} , it will be stored'.
format(uid=user_basic_info['uid']))
FI.save_pickle(user_basic_info, path)
try:
if attends.__len__() > 0: # store atten connection web
from_uid = user_basic_info['uid']
from_fans_num = user_basic_info['fans_num']
from_blog_num = user_basic_info['blog_num']
data = [[
from_uid, from_fans_num, from_blog_num,
str(x[attends_col_info.index('uid')]),
str(x[attends_col_info.index('fans_num')]),
str(x[attends_col_info.index('blog_num')])
] for x in attends]
dbi.insert_asList('cache_atten_web', data)
print(
'Success : conn web of {uid} is stored in cache_atten_web'.
format(uid=user_basic_info['uid']))
else:
pass
except Exception as e:
print(e)
path = '{uid}_atten_web.pkl'.format(uid=user_basic_info['uid'])
print('unable to store atten web of {uid} , it will be stored'.
format(uid=user_basic_info['uid']))
FI.save_pickle(data, path)
def post(self):
try:
user_basic_info=self.get_argument('user_basic_info')
attends=self.get_argument('user_attends')
user_basic_info=eval(user_basic_info)
attends=eval(attends)
self.write('success to return user info')
self.finish()
except:
self.write('fail to return user info')
self.finish()
return
try:
dbi=MySQL_Interface()
except:
print('unable to connect to MySql DB')
try:
if attends.__len__()>0: #store attends info
table_name='cache_attends'
attends_col_info=dbi.get_col_name(table_name)
keys=attends[0].keys()
attends= [[line[i] if i in keys else '' for i in attends_col_info] for line in attends]
fans_col_pos=attends_col_info.index('fans_num')
insert_attends=[]
for line in attends:
if line[fans_col_pos]>1000:
insert_attends.append(line)
dbi.insert_asList(table_name,insert_attends,unique=True)
print('Success : attends of {uid} is stored in {tname}'
.format(uid=user_basic_info['uid'],tname=table_name))
else:
pass
except Exception as e:
print(e)
path="temp\\{uid}_attends.pkl".format(uid=user_basic_info['uid'])
print('unable to store attends of {uid}, it will be stored '
.format(uid=user_basic_info['uid']))
FI.save_pickle(attends,path)
try:
atten_num_real=user_basic_info['attends_num']
atten_num_get=attends.__len__()
user_basic_info['accuracy']=atten_num_get # 实际获取到的关注数目
col_info=dbi.get_col_name('cache_user_info') # store user basic info
keys=user_basic_info.keys()
data=[user_basic_info[i] if i in keys else '' for i in col_info]
dbi.insert_asList('cache_user_info',[data],unique=True)
print('Success : basic info of {uid} is stored in cache_user_info'
.format(uid=user_basic_info['uid']))
except Exception as e:
print(e)
path='temp\\{uid}_basic_info.pkl'.format(uid=user_basic_info['uid'])
print('unable to store basic info of {uid} , it will be stored'
.format(uid=user_basic_info['uid']))
FI.save_pickle(user_basic_info,path)
try:
if attends.__len__()>0: # store atten connection web
from_uid=user_basic_info['uid']
from_fans_num=user_basic_info['fans_num']
from_blog_num=user_basic_info['blog_num']
data=[[from_uid,from_fans_num,from_blog_num,str(x[attends_col_info.index('uid')]),str(x[attends_col_info.index('fans_num')]),str(x[attends_col_info.index('blog_num')])]for x in attends]
dbi.insert_asList('cache_atten_web',data)
print('Success : conn web of {uid} is stored in cache_atten_web'
.format(uid=user_basic_info['uid']))
else:
pass
except Exception as e:
print(e)
path='{uid}_atten_web.pkl'.format(uid=user_basic_info['uid'])
print('unable to store atten web of {uid} , it will be stored'
.format(uid=user_basic_info['uid']))
FI.save_pickle(data,path)
res=[pop_id(x) for x in collection.find(select).limit(limit).sort(sort,pymongo.ASCENDING)]
else:
res=[pop_id(x) for x in collection.find(select).limit(limit).sort(sort,pymongo.DESCENDING)]
else:
f={}
for item in field:
f[item]=1
if sort=='':
res=[pop_id(x) for x in collection.find(select,f).limit(limit)]
else:
if sort_type=='up':
res=[pop_id(x) for x in collection.find(select,f).limit(limit).sort(sort,pymongo.ASCENDING)]
else:
res=[pop_id(x) for x in collection.find(select,f).limit(limit).sort(sort,pymongo.DESCENDING)]
return res
def pop_id(data):
data.pop('_id')
return data
res=read_content_in_mongo('latest_history',{'user_id':'1681029540'},['dealed_text.left_content','created_at','user_name'],-1,'id','down')
data = FI.load_pickle('demo.pkl')
data = data + res
FI.save_pickle(data,'demo.pkl')
for line in res:
print(line)
print(res.__len__())
def Get_Stop_Words(self):
ret = []
ret = FI.load_pickle('./static/stop_words.pkl')
return ret
# 从wikileaks下载邮件内容并生成结构化数据
# =============== ATTENTION =============== :
# 由于邮件格式较繁杂，因此无法全部解析（5%左右）
base_url = 'https://wikileaks.org/dnc-emails/get/{page}'
base_path = '.\static\{page}.pkl'
gotten_id = os.listdir('.\static')
gotten_id = [int((x.split('.'))[0]) for x in gotten_id]
task_pool = list(range(1, 2000)) # wikileak中的邮件编号
while True:
if task_pool.__len__() == 0:
break
task_id = task_pool.pop(0)
if task_id in gotten_id:
print('{id} skip'.format(id=task_id))
continue
url = base_url.format(page=task_id)
path = base_path.format(page=task_id)
try:
info = getStructedData(url)
FI.save_pickle(info, path)
print('{t} succeed'.format(t=task_id))
except Exception as e:
# task_pool.append(task_id)
print('{t} failed <--<--<--<--'.format(t=task_id))
print(e)
print('文件已下载完毕')
# 生成社交网络数据(需要networkx包)
generate_data_for_gelphi()
print('gexf文件已经生成,存放路径: {path}\\temp_res'.format(path=os.getcwd()))
