def get_topic_user():
result = {}
index_name = 'weibo_user'
index_type = 'user'
'''
test_user_list = [['1499104401', '1265965213', '3270699555', '2073915493', '1686474312'],\
['2803301701', '2105426467', '1665372775', '3716504593', '2892376557'],\
['1457530250', '1698513182', '2793591492', '2218894100', '1737961042'],\
['1656818110', '1660127070', '1890124610', '1182391230', '1243861100'],\
['1680430844', '2998045524', '2202896360', '1639498782', '3494698730'],\
['2587093162', '1677675054', '1871767009', '1193111400', '1672418622'],\
['1730726640', '1752502540', '1868725480', '1262486750', '1235733080'],\
['1250041100', '2275231150', '1268642530', '1658606270', '1857599860'],\
['1929496477', '2167425990', '1164667670', '2417139911', '1708853044'],\
['1993292930', '1645823930', '1890926610', '1641561810', '2023833990'],\
['2005471590', '1233628160', '2074684140', '1396715380', '1236762250'],\
['1423592890', '2612799560', '1926127090', '2684951180', '1760607220']]
'''
for topic in search_dict:
result[topic] = []
user_list = search_dict[topic]
profile_result = es_user_profile.mget(index=index_name, doc_type=index_type, body={'ids':user_list})['docs']
for profile in profile_result:
uid = profile['_id']
try:
uname = profile['_source']['nick_name']
photo_url = profile['_source']['photo_url']
except:
uname = 'unknown'
photo_url = 'unknown'
result[topic].append([uid, uname, photo_url])
return result
def recommend_new_words(date_list):
results = []
for date in date_list:
date = date.replace('-', '')
words_dict = r.hgetall('recommend_sensitive_words_' + date)
if words_dict:
for key, value in words_dict.items():
detail = []
detail.append(key)
value = json.loads(value)
uid_list = value[0]
uname = []
try:
search_results = es_user_profile.mget(
index='weibo_user',
doc_type='user',
body={'ids': uid_list})['docs']
for item in search_results:
if item['found']:
uname.append(item['_source']['nick_name'])
else:
uname.append('unknown')
except:
uname = uid_list
detail.extend([uname, value[1]])
results.append(detail)
sorted_results = sorted(results, key=lambda x: x[2], reverse=True)
return sorted_results
def get_sensitive_user_detail(uid_list, date, sensitive):
results = []
index_name = str(date).replace('-','') # index_name:20130901
user_bci_results = es_cluster.mget(index=index_name, doc_type='bci', body={'ids':uid_list}, _source=True)['docs']
user_profile_results = es_user_profile.mget(index="weibo_user", doc_type="user", body={"ids":uid_list}, _source=True)['docs']
for i in range(0, len(uid_list)):
personal_info = ['']*6
uid = uid_list[i]
personal_info[0] = uid_list[i]
if user_profile_results[i]['found']:
profile_dict = user_profile_results[i]['_source']
personal_info[1] = profile_dict['nick_name']
personal_info[2] = profile_dict['user_location']
personal_info[3] = profile_dict['fansnum']
personal_info[4] = profile_dict['statusnum']
if user_bci_results[i]['found']:
personal_info[5] = user_bci_results[i]['_source'].get('user_index', 0)
else:
personal_info[5] = 0
if sensitive:
sensitive_words = r_cluster.hget('sensitive_' + index_name, str(uid))
if sensitive_words:
sensitive_dict = json.loads(sensitive_words)
personal_info.append(sensitive_dict.keys())
else:
personal_info.append([])
results.append(personal_info)
return results
def recommend_new_words(date_list):
results = []
for date in date_list:
date = date.replace('-', '')
words_dict = r.hgetall('recommend_sensitive_words_'+date)
if words_dict:
for key, value in words_dict.items():
detail = []
detail.append(key)
value = json.loads(value)
uid_list = value[0]
uname = []
try:
search_results = es_user_profile.mget(index='weibo_user', doc_type='user', body={'ids': uid_list})['docs']
for item in search_results:
if item['found']:
uname.append(item['_source']['nick_name'])
else:
uname.append('unknown')
except:
uname = uid_list
detail.extend([uname,value[1]])
results.append(detail)
sorted_results = sorted(results, key=lambda x:x[2], reverse=True)
return sorted_results
def get_sensitive_user_detail(uid_list, date, sensitive):
es_cluster = es_user_profile
ts = datetime2ts(date)
results = []
index_name = pre_influence_index + str(date).replace(
'-', '') # index_name:20130901
user_bci_results = es_bci.mget(index=index_name,
doc_type='bci',
body={'ids': uid_list},
_source=False,
fields=['user_index'])['docs']
user_profile_results = es_user_profile.mget(index="weibo_user",
doc_type="user",
body={"ids": uid_list},
_source=True)['docs']
top_influnce_value = get_top_value("user_index", es_bci, index_name, "bci")
for i in range(0, len(uid_list)):
personal_info = [''] * 6
uid = uid_list[i]
personal_info[0] = uid_list[i]
personal_info[1] = uid_list[i]
if user_profile_results[i]['found']:
profile_dict = user_profile_results[i]['_source']
uname = profile_dict['nick_name']
if uname:
personal_info[1] = uname
personal_info[2] = profile_dict['user_location']
personal_info[3] = profile_dict['fansnum']
personal_info[4] = profile_dict['statusnum']
if user_bci_results[i]['found']:
try:
tmp_bci = user_bci_results[i]['fields']['user_index'][0]
influence = math.log(
tmp_bci / float(top_influnce_value) * 9 + 1, 10) * 100
personal_info[5] = influence
except:
personal_info[5] = 0
else:
personal_info[5] = 0
if sensitive:
sensitive_words = redis_cluster.hget('sensitive_' + str(ts),
str(uid))
if sensitive_words:
sensitive_dict = json.loads(sensitive_words)
personal_info.append(sensitive_dict.keys())
else:
personal_info.append([])
else:
personal_info.append([])
results.append(personal_info)
return results
def search_follower(uid, sensitive):
results = dict()
stat_results = dict()
for db_num in R_DICT:
r = R_DICT[db_num]
if sensitive:
br_uid_results = r.hgetall('sensitive_be_retweet_' + str(uid))
else:
br_uid_results = r.hgetall('be_retweet_' + str(uid))
if br_uid_results:
for br_uid in br_uid_results:
if br_uid != uid:
try:
stat_results[br_uid] += br_uid_results[br_uid]
except:
stat_results[br_uid] = br_uid_results[br_uid]
if not stat_results:
return [None, 0]
try:
sort_stat_results = sorted(stat_results.items(),
key=lambda x: x[1],
reverse=True)[:20]
except:
return [None, 0]
uid_list = [item[0] for item in sort_stat_results]
es_profile_results = es_user_profile.mget(index='weibo_user',
doc_type='user',
body={'ids': uid_list})['docs']
es_portrait_results = es.mget(index='sensitive_user_portrait',
doc_type='user',
body={'ids': uid_list})['docs']
result_list = []
for i in range(len(es_profile_results)):
item = es_profile_results[i]
uid = item['_id']
try:
source = item['_source']
uname = source['nick_name']
except:
uname = u'unknown'
portrait_item = es_portrait_results[i]
try:
source = portrait_item['_source']
in_status = 1
except:
in_status = 0
result_list.append([uid, [uname, stat_results[uid], in_status]])
return [result_list[:20], len(stat_results)]
def identify_user_out(input_uid_list):
out_user_list = []
in_user_list = []
input_len = len(input_uid_list)
iter_count = 0
print 'identify user out'
#get user list who is out user_portrait
while iter_count < input_len:
iter_user_list = input_uid_list[iter_count: iter_count+DETECT_ITER_COUNT]
try:
portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, body={'ids':iter_user_list}, _source=False)['docs']
except:
portrait_result = []
for item in portrait_result:
uid = item['_id']
if item['found'] != True:
out_user_list.append(uid)
else:
in_user_list.append(uid)
iter_count += DETECT_ITER_COUNT
print 'get out user portrait information'
#get user profile information for out user_portrait
iter_count = 0
out_user_count = len(out_user_list)
out_user_result = []
while iter_count < out_user_count:
iter_user_list = out_user_list[iter_count: iter_count+DETECT_ITER_COUNT]
try:
profile_result = es_user_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={'ids':iter_user_list}, _source=True)['docs']
except:
profile_result = []
for item in profile_result:
uid = item['_id']
if item['found']==True:
source = item['_source']
uname = source['nick_name']
fansnum = source['fansnum']
statusnum = source['statusnum']
friendsnum = source['friendsnum']
else:
uname = u'未知'
fansnum = u'未知'
statusnum = u'未知'
friendsnum = u'未知'
out_user_result.append([uid, uname, fansnum, statusnum, friendsnum])
iter_count += DETECT_ITER_COUNT
sort_out_user_result = sorted(out_user_result, key=lambda x:x[2], reverse=True)
return in_user_list, sort_out_user_result
def search_retweet(uid, sensitive):
stat_results = dict()
results = dict()
for db_num in R_DICT:
r = R_DICT[db_num]
if not sensitive:
ruid_results = r.hgetall('retweet_' + str(uid))
else:
ruid_results = r.hgetall('sensitive_retweet_' +
str(uid)) # because of sensitive weibo
if ruid_results:
for ruid in ruid_results:
if ruid != uid:
if stat_results.has_key(ruid):
stat_results[ruid] += ruid_results[ruid]
else:
stat_results[ruid] = ruid_results[ruid]
if stat_results:
sort_stat_results = sorted(stat_results.items(),
key=lambda x: x[1],
reverse=True)[:20]
else:
return [None, 0]
uid_list = [item[0] for item in sort_stat_results]
es_profile_results = es_user_profile.mget(index='weibo_user',
doc_type='user',
body={'ids': uid_list})['docs']
es_portrait_results = es.mget(index='sensitive_user_portrait',
doc_type='user',
body={'ids': uid_list})['docs']
result_list = []
for i in range(len(es_profile_results)):
item = es_profile_results[i]
uid = item['_id']
if item['found']:
uname = item['_source']['nick_name']
else:
uname = u'unknown'
portrait_item = es_portrait_results[i]
if portrait_item['found']:
in_status = 1
else:
in_status = 0
result_list.append([uid, [uname, stat_results[uid], in_status]])
return [result_list[:20], len(stat_results)]
def get_top_user():
results = dict()
for domain in domain_dict:
results[domain] = []
user_list = domain_dict[domain]
profile_result = es_user_profile.mget(index=index_name, doc_type=index_type, body={'ids':user_list})['docs']
for profile in profile_result:
uid = profile['_id']
try:
uname = profile['_source']['nick_name']
photo_url = profile['_source']['photo_url']
except:
uname = 'unknown'
photo_url = 'unknown'
results[domain].append([uid, uname, photo_url])
return results
def search_follower(uid, sensitive):
results = dict()
stat_results = dict()
for db_num in R_DICT:
r = R_DICT[db_num]
if sensitive:
br_uid_results = r.hgetall("sensitive_be_retweet_" + str(uid))
else:
br_uid_results = r.hgetall("be_retweet_" + str(uid))
if br_uid_results:
for br_uid in br_uid_results:
if br_uid != uid:
try:
stat_results[br_uid] += br_uid_results[br_uid]
except:
stat_results[br_uid] = br_uid_results[br_uid]
if not stat_results:
return [None, 0]
try:
sort_stat_results = sorted(stat_results.items(), key=lambda x: x[1], reverse=True)[:20]
except:
return [None, 0]
uid_list = [item[0] for item in sort_stat_results]
es_profile_results = es_user_profile.mget(index="weibo_user", doc_type="user", body={"ids": uid_list})["docs"]
es_portrait_results = es.mget(index="sensitive_user_portrait", doc_type="user", body={"ids": uid_list})["docs"]
result_list = []
for i in range(len(es_profile_results)):
item = es_profile_results[i]
uid = item["_id"]
try:
source = item["_source"]
uname = source["nick_name"]
except:
uname = u"unknown"
portrait_item = es_portrait_results[i]
try:
source = portrait_item["_source"]
in_status = 1
except:
in_status = 0
result_list.append([uid, [uname, stat_results[uid], in_status]])
return [result_list[:20], len(stat_results)]
def search_follower(uid, sensitive):
results = dict()
stat_results = dict()
if 1:
r = r_cluster
if sensitive:
br_uid_results = r.hgetall('sensitive_be_retweet_'+str(uid))
else:
br_uid_results = r.hgetall('be_retweet_'+str(uid))
if br_uid_results:
for br_uid in br_uid_results:
if br_uid != uid:
try:
stat_results[br_uid] += br_uid_results[br_uid]
except:
stat_results[br_uid] = br_uid_results[br_uid]
if not stat_results:
return [None, 0]
try:
sort_stat_results = sorted(stat_results.items(), key=lambda x:x[1], reverse=True)[:20]
except:
return [None, 0]
uid_list = [item[0] for item in sort_stat_results]
es_profile_results = es_user_profile.mget(index='weibo_user', doc_type='user', body={'ids':uid_list})['docs']
es_portrait_results = es.mget(index='sensitive_user_portrait', doc_type='user', body={'ids':uid_list})['docs']
result_list = []
for i in range(len(es_profile_results)):
item = es_profile_results[i]
uid = item['_id']
try:
source = item['_source']
uname = source['nick_name']
except:
uname = u'unknown'
portrait_item = es_portrait_results[i]
try:
source = portrait_item['_source']
in_status = 1
except:
in_status = 0
result_list.append([uid,[uname, stat_results[uid], in_status]])
return [result_list[:20], len(stat_results)]
def get_user_url(uid_list):
results = []
try:
es_results = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids": uid_list})['docs']
except:
es_results = {}
for item in es_results:
temp = []
if item['found']:
temp.append(item['_source']["photo_url"])
temp.append(item['_source']['nick_name'])
temp.append(item['_id'])
else:
temp.append("unknown")
temp.append(item['_id'])
temp.append(item['_id'])
results.append(temp)
return results
def search_retweet(uid, sensitive):
stat_results = dict()
results = dict()
if 1:
r = r_cluster
if not sensitive:
ruid_results = r.hgetall('retweet_'+str(uid))
else:
ruid_results = r.hgetall('sensitive_retweet_'+str(uid)) # because of sensitive weibo
if ruid_results:
for ruid in ruid_results:
if ruid != uid:
if stat_results.has_key(ruid):
stat_results[ruid] += ruid_results[ruid]
else:
stat_results[ruid] = ruid_results[ruid]
if stat_results:
sort_stat_results = sorted(stat_results.items(), key=lambda x:x[1], reverse=True)[:20]
else:
return [None, 0]
uid_list = [item[0] for item in sort_stat_results]
es_profile_results = es_user_profile.mget(index='weibo_user', doc_type='user', body={'ids':uid_list})['docs']
es_portrait_results = es.mget(index='sensitive_user_portrait', doc_type='user', body={'ids':uid_list})['docs']
result_list = []
for i in range(len(es_profile_results)):
item = es_profile_results[i]
uid = item['_id']
if item['found']:
uname = item['_source']['nick_name']
else:
uname = u'unknown'
portrait_item = es_portrait_results[i]
if portrait_item['found']:
in_status = 1
else:
in_status = 0
result_list.append([uid,[uname, stat_results[uid], in_status]])
return [result_list[:20], len(stat_results)]
def search_retweet(uid, sensitive):
stat_results = dict()
results = dict()
for db_num in R_DICT:
r = R_DICT[db_num]
if not sensitive:
ruid_results = r.hgetall("retweet_" + str(uid))
else:
ruid_results = r.hgetall("sensitive_retweet_" + str(uid)) # because of sensitive weibo
if ruid_results:
for ruid in ruid_results:
if ruid != uid:
if stat_results.has_key(ruid):
stat_results[ruid] += ruid_results[ruid]
else:
stat_results[ruid] = ruid_results[ruid]
if stat_results:
sort_stat_results = sorted(stat_results.items(), key=lambda x: x[1], reverse=True)[:20]
else:
return [None, 0]
uid_list = [item[0] for item in sort_stat_results]
es_profile_results = es_user_profile.mget(index="weibo_user", doc_type="user", body={"ids": uid_list})["docs"]
es_portrait_results = es.mget(index="sensitive_user_portrait", doc_type="user", body={"ids": uid_list})["docs"]
result_list = []
for i in range(len(es_profile_results)):
item = es_profile_results[i]
uid = item["_id"]
if item["found"]:
uname = item["_source"]["nick_name"]
else:
uname = u"unknown"
portrait_item = es_portrait_results[i]
if portrait_item["found"]:
in_status = 1
else:
in_status = 0
result_list.append([uid, [uname, stat_results[uid], in_status]])
return [result_list[:20], len(stat_results)]
def get_sensitive_user_detail(uid_list, date, sensitive):
es_cluster = es_user_profile
ts = datetime2ts(date)
results = []
index_name = pre_influence_index + str(date).replace('-','') # index_name:20130901
user_bci_results = es_bci.mget(index=index_name, doc_type='bci', body={'ids':uid_list}, _source=False, fields=['user_index'])['docs']
user_profile_results = es_user_profile.mget(index="weibo_user", doc_type="user", body={"ids":uid_list}, _source=True)['docs']
top_influnce_value = get_top_value("user_index", es_bci, index_name, "bci")
for i in range(0, len(uid_list)):
personal_info = ['']*6
uid = uid_list[i]
personal_info[0] = uid_list[i]
personal_info[1] = uid_list[i]
if user_profile_results[i]['found']:
profile_dict = user_profile_results[i]['_source']
uname = profile_dict['nick_name']
if uname:
personal_info[1] = uname
personal_info[2] = profile_dict['user_location']
personal_info[3] = profile_dict['fansnum']
personal_info[4] = profile_dict['statusnum']
if user_bci_results[i]['found']:
try:
tmp_bci = user_bci_results[i]['fields']['user_index'][0]
influence = math.log(tmp_bci/float(top_influnce_value)*9+1, 10)*100
personal_info[5] = influence
except:
personal_info[5] = 0
else:
personal_info[5] = 0
if sensitive:
sensitive_words = redis_cluster.hget('sensitive_' + str(ts), str(uid))
if sensitive_words:
sensitive_dict = json.loads(sensitive_words)
personal_info.append(sensitive_dict.keys())
else:
personal_info.append([])
else:
personal_info.append([])
results.append(personal_info)
return results
def get_sensitive_user_detail(uid_list, date, sensitive):
results = []
index_name = str(date).replace('-', '') # index_name:20130901
user_bci_results = es_cluster.mget(index=index_name,
doc_type='bci',
body={'ids': uid_list},
_source=True)['docs']
user_profile_results = es_user_profile.mget(index="weibo_user",
doc_type="user",
body={"ids": uid_list},
_source=True)['docs']
for i in range(0, len(uid_list)):
personal_info = [''] * 6
uid = uid_list[i]
personal_info[0] = uid_list[i]
if user_profile_results[i]['found']:
profile_dict = user_profile_results[i]['_source']
personal_info[1] = profile_dict['nick_name']
personal_info[2] = profile_dict['user_location']
personal_info[3] = profile_dict['fansnum']
personal_info[4] = profile_dict['statusnum']
if user_bci_results[i]['found']:
personal_info[5] = user_bci_results[i]['_source'].get(
'user_index', 0)
else:
personal_info[5] = 0
if sensitive:
sensitive_words = r_cluster.hget('sensitive_' + index_name,
str(uid))
if sensitive_words:
sensitive_dict = json.loads(sensitive_words)
personal_info.append(sensitive_dict.keys())
else:
personal_info.append([])
results.append(personal_info)
return results
def get_task_detail_2(task_name, ts, user):
results = dict()
index_name = task_name
_id = user + "-" + task_name
task_detail = es.get(index=index_manage_sensing_task, doc_type=task_doc_type, id=_id)["_source"]
task_name = task_detail['task_name']
history_status = json.loads(task_detail['history_status'])
start_time = task_detail['create_at']
create_by = task_detail['create_by']
stop_time = task_detail['stop_time']
remark = task_detail.get('remark', '')
portrait_detail = []
count = 0 # 计数
top_influence = get_top_influence("influence")
top_activeness = get_top_influence("activeness")
top_importance = get_top_influence("importance")
time_series = [] # 时间
#positive_sentiment_list = [] # 情绪列表
#neutral_sentiment_list = []
#negetive_sentiment_list = []
all_weibo_list = []
origin_weibo_list = [] # 微博列表
retweeted_weibo_list = []
#retweeted_weibo_count = [] # 别人转发他的数量
#comment_weibo_count = []
#total_number_count = []
#burst_time_list = [] # 爆发时间列表
important_user_set = set() # 重要人物列表
out_portrait_users = set() # 未入库
ts = int(ts)
time_series = history_status
#for item in history_status:
# if int(item[0]) <= ts:
# time_series.append(item[0]) # 到目前为止的所有的时间戳
# get detail task information from es
if time_series:
flow_detail = es.mget(index=index_sensing_task, doc_type=_id, body={"ids": time_series})['docs']
else:
flow_detail = {}
if flow_detail:
for item in flow_detail:
item = item['_source']
timestamp = item['timestamp']
#sentiment_distribution = json.loads(item["sentiment_distribution"])
#positive_sentiment_list.append(int(sentiment_distribution['1']))
#negetive_sentiment_list.append(int(sentiment_distribution['2'])+int(sentiment_distribution['3']) \
# +int(sentiment_distribution['4'])+int(sentiment_distribution['5'])+int(sentiment_distribution['6']))
#neutral_sentiment_list.append(int(sentiment_distribution['0']))
origin_weibo_list.append(item["origin_weibo_number"]) # real
retweeted_weibo_list.append(item['retweeted_weibo_number']) # real
all_weibo_list.append(item["origin_weibo_number"]+item['retweeted_weibo_number'])
#retweeted_weibo_count.append(item['retweeted_weibo_count'])
#comment_weibo_count.append(item['comment_weibo_count'])
#total_number_count.append(item['weibo_total_number'])
temp_important_user_list = json.loads(item['important_users'])
unfiltered_users = json.loads(item['unfilter_users'])
temp_out_portrait_users = set(unfiltered_users) - set(temp_important_user_list) # 未入库
important_user_set = important_user_set | set(temp_important_user_list)
out_portrait_users = out_portrait_users | set(temp_out_portrait_users)
#burst_reason = item.get("burst_reason", "")
#if burst_reason:
# burst_time_list.append([timestamp, count, burst_reason])
count += 1
####################################################################################
# 统计爆发原因，下相应的结论
"""
weibo_variation_count = 0
weibo_variation_time = []
sentiment_variation_count = 0
sentiment_variation_time = []
sensitive_variation_count = 0 # sensitive
sensitive_variation_time = [] # sensitive
common_variation_count = 0
common_variation_time = []
if burst_time_list:
for item in burst_time_list:
tmp_common = 0
x1 = 0
x2 = 0
x3 = 0
if signal_count_varition in item[2]:
weibo_variation_count += 1
weibo_variation_time.append([ts2date_min(item[0]), total_number_count[item[1]]])
x1 = total_number_count[item[1]]
tmp_common += 1
if signal_sentiment_varition in item[2]:
tmp_common += 1
sentiment_variation_count += 1
x2 = negetive_sentiment_list[item[1]]
sentiment_variation_time.append([ts2date_min(item[0]), negetive_sentiment_list[item[1]]])
if signal_sensitive_variation in item[2]:
tmp_common += 1
sensitive_variation_count += 1
x3 = sensitive_total_number_list[item[1]]
sensitive_variation_time.append([ts2date_min(item[0]), all_weibo_list[item[1]]])
if tmp_common >= 2:
common_variation_count += 1
common_variation_time.append([ts2date_min(item[0]), x1, x2, x3])
warning_conclusion = remark
variation_distribution = []
if weibo_variation_count:
variation_distribution.append(weibo_variation_time)
else:
variation_distribution.append([])
if sentiment_variation_count:
variation_distribution.append(sentiment_variation_time)
else:
variation_distribution.append([])
if sensitive_variation_count:
variation_distribution.append(sensitive_variation_time)
else:
variation_distribution.append([])
if common_variation_count:
variation_distribution.append(common_variation_time)
else:
variation_distribution.append([])
results['warning_conclusion'] = warning_conclusion
results['variation_distribution'] = variation_distribution
# 每个用户的热度
"""
# 获取重要用户的个人信息
important_uid_list = list(important_user_set)
out_portrait_users_list = list(out_portrait_users)
user_detail_info = [] #
out_user_detail_info = []
if important_uid_list:
user_results = es.mget(index=portrait_index_name, doc_type=portrait_index_type, body={"ids":important_uid_list}, fields=['uid', 'uname', 'domain', 'topic_string', "photo_url", 'importance', 'influence', 'activeness'])['docs']
for item in user_results:
if item['found']:
temp = []
#if int(item['fields']['importance'][0]) < IMPORTANT_USER_THRESHOULD:
# continue
temp.append(item['fields']['uid'][0])
uname = item['fields']['uname'][0]
if not uname or uname == "未知":
uname = item['fields']['uid'][0]
temp.append(uname)
temp.append(item['fields']['photo_url'][0])
temp.append(item['fields']['domain'][0])
temp.append(item['fields']['topic_string'][0].split('&'))
#hot_count = count_hot_uid(item['fields']['uid'][0], start_time, stop_time)
#temp.append(hot_count)
temp.append(math.log(item['fields']['importance'][0]/float(top_importance)*9+1, 10)*100)
temp.append(math.log(item['fields']['influence'][0]/float(top_influence)*9+1, 10)*100)
temp.append(math.log(item['fields']['activeness'][0]/float(top_activeness)*9+1, 10)*100)
user_detail_info.append(temp)
# 排序
if user_detail_info:
user_detail_info = sorted(user_detail_info, key=lambda x:x[6], reverse=True)
else:
user_detail_info = []
if out_portrait_users_list:
profile_results = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":out_portrait_users_list})["docs"]
bci_index = "bci_" + ts2datetime(ts-DAY).replace('-','')
influence_results = es.mget(index=bci_index, doc_type="bci", body={"ids":out_portrait_users_list}, fields=["user_index"])['docs']
bci_results = es_profile.mget(index="bci_history", doc_type="bci", body={"ids":out_portrait_users_list}, fields=['user_fansnum'])['docs']
top_influence = get_top_all_influence("user_index", ts)
count = 0
if profile_results:
for item in profile_results:
temp = []
if item['found']:
uid = item['_source']['uid']
temp = get_user_profile([uid], ['nick_name', 'user_location', 'statusnum', 'fansnum'])[0]
else:
temp = [item['_id'], item['_id'], '', '', '']
"""
if item['found']:
temp.append(item['_source']['uid'])
if item['_source']['nick_name']:
temp.append(item['_source']['nick_name'])
else:
temp.append(item['_source']['uid'])
temp.append(item['_source']['user_location'])
temp.append(item['_source']['statusnum'])
temp.append(item['_source']['friendsnum'])
else:
temp.append(item['_id'])
temp.append(item['_id'])
temp.extend([''])
temp.append('--')
temp.append('--')
try:
user_fansnum = bci_results[count]["fields"]["user_fansnum"][0]
except:
user_fansnum = 0
temp.append(user_fansnum)
temp_influ = influence_results[count]
if temp_influ.get('found', 0):
user_index = temp_influ['fields']['user_index'][0]
temp.append(math.log(user_index/float(top_influence)*9+1, 10)*100)
else:
temp.append(0)
"""
count += 1
out_user_detail_info.append(temp)
revise_time_series = []
for item in time_series:
revise_time_series.append(ts2date_min(item))
results['important_user_detail'] = user_detail_info
results['out_portrait_user_detail'] = out_user_detail_info
#results['burst_time'] = burst_time_list # 爆发时间点，以及爆发原因
results['time_series'] = revise_time_series
#results['positive_sentiment_list'] = positive_sentiment_list
#esults['negetive_sentiment_list'] = negetive_sentiment_list
#results['neutral_sentiment_list'] = neutral_sentiment_list
results['all_weibo_list'] = all_weibo_list
results['origin_weibo_list'] = origin_weibo_list
results['retweeted_weibo_list'] = retweeted_weibo_list
#results['comment_weibo_count'] = comment_weibo_count
#results['retweeted_weibo_count'] = retweeted_weibo_count
#results['total_number_list'] = total_number_count
return results
def get_sensitive_text_detail(task_name, ts, user, order):
_id = user + '-' + task_name
task_detail = es.get(index=index_sensing_task, doc_type=_id,
id=ts)['_source']
weibo_detail = json.loads(task_detail['sensitive_weibo_detail'])
weibo_detail_list = []
if weibo_detail:
for iter_mid, item in weibo_detail.iteritems():
tmp = []
tmp.append(iter_mid)
tmp.append(item[iter_mid])
tmp.append(item['retweeted'])
tmp.append(item['comment'])
weibo_detail_list.append(tmp)
mid_list = weibo_detail.keys()
results = []
query_body = {
"query": {
"filtered": {
"filter": {
"terms": {
"mid": mid_list
}
}
}
}
}
index_list = []
datetime = ts2datetime(ts)
datetime_1 = ts2datetime(ts - DAY)
index_name = flow_text_index_name_pre + datetime
exist_es = es_text.indices.exists(index_name)
if exist_es:
index_list.append(index_name)
index_name_1 = flow_text_index_name_pre + datetime_1
exist_es_1 = es_text.indices.exists(index_name_1)
if exist_es_1:
index_list.append(index_name_1)
if index_list and mid_list:
search_results = es_text.search(index=index_list,
doc_type=flow_text_index_type,
body=query_body)["hits"]["hits"]
else:
search_results = []
uid_list = []
text_dict = dict() # 文本信息
portrait_dict = dict() # 背景信息
if search_results:
for item in search_results:
uid_list.append(item["_source"]['uid'])
text_dict[item['_id']] = item['_source'] # _id是mid
if uid_list:
portrait_result = es_profile.mget(
index=profile_index_name,
doc_type=profile_index_type,
body={"ids": uid_list},
fields=['nick_name', 'photo_url'])["docs"]
for item in portrait_result:
if item['found']:
portrait_dict[item['_id']] = {
"nick_name": item["fields"]["nick_name"][0],
"photo_url": item["fields"]["photo_url"][0]
}
else:
portrait_dict[item['_id']] = {
"nick_name": item['_id'],
"photo_url": ""
}
if order == "total":
sorted_list = sorted(weibo_detail_list,
key=lambda x: x[1],
reverse=True)
elif order == "retweeted":
sorted_list = sorted(weibo_detail_list,
key=lambda x: x[2],
reverse=True)
elif order == "comment":
sorted_list = sorted(weibo_detail_list,
key=lambda x: x[3],
reverse=True)
else:
sorted_list = weibo_detail_list
count_n = 0
for item in sorted_list:
mid = item[0]
iter_text = text_dict.get(mid, {})
temp = []
# uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type
if iter_text:
uid = iter_text['uid']
temp.append(uid)
iter_portrait = portrait_dict.get(uid, {})
if iter_portrait:
temp.append(iter_portrait['nick_name'])
temp.append(iter_portrait['photo_url'])
else:
temp.extend([uid, ''])
temp.append(iter_text["text"])
temp.append(iter_text["sentiment"])
temp.append(ts2date(iter_text['timestamp']))
temp.append(iter_text['geo'])
temp.append(iter_text['message_type'])
temp.append(item[2])
temp.append(item[3])
temp.append(iter_text.get('sensitive', 0))
count_n += 1
results.append(temp)
if results and order == "ts":
results = sorted(results, key=lambda x: x[5], reverse=True)
if results and order == "sensitive":
results = sorted(results, key=lambda x: x[-1], reverse=True)
return results
def get_retweet_weibo_detail(ts, user, task_name, size, text_type, type_value):
_id = user + '-' + task_name
task_detail = es_user_portrait.get(index=index_sensing_task, doc_type=_id, id=ts)['_source']
origin_weibo_detail = json.loads(task_detail['origin_weibo_detail'])
retweeted_weibo_detail = json.loads(task_detail['retweeted_weibo_detail'])
mid_list = []
mid_list.extend(origin_weibo_detail.keys())
mid_list.extend(retweeted_weibo_detail.keys())
query_body = {
"query":{
"filtered":{
"filter":{
"bool":{
"must":[
{"range":{
"timestamp":{
"gte": ts - time_interval,
"lt": ts
}
}},
{"terms": {"root_mid": mid_list}}
]
}
}
}
},
"sort": {"timestamp": {"order": "desc"}},
"size": 100
}
if text_type == "message_type":
query_body['query']['filtered']['filter']['bool']['must'].append({"term":{text_type: type_value}})
if text_type == "sentiment":
#if isinstance(type_value, str):
if len(type_value) == 1:
query_body['query']['filtered']['filter']['bool']['must'].append({"term":{text_type: type_value}})
else:
query_body['query']['filtered']['filter']['bool']['must'].append({"terms":{text_type: type_value}})
datetime = ts2datetime(ts)
datetime_1 = ts2datetime(ts-time_interval)
index_name = flow_text_index_name_pre + datetime
exist_es = es_text.indices.exists(index_name)
index_name_1 = flow_text_index_name_pre + datetime_1
exist_es_1 = es_text.indices.exists(index_name_1)
# 1. 查询微博
if datetime == datetime_1 and exist_es:
search_results = es_text.search(index=index_name, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"]
elif datetime != datetime_1 and exist_es_1:
search_results = es_text.search(index=index_name_1, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"]
else:
search_results = []
#print search_results
# 2. 获取微博相关信息
results = []
uid_list = []
if search_results:
for item in search_results:
uid_list.append(item["_source"]['uid'])
if uid_list:
portrait_result = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":uid_list}, fields=['nick_name', 'photo_url'])["docs"]
for i in range(len(uid_list)):
item = search_results[i]['_source']
temp = []
# uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type
temp.append(item['uid'])
if portrait_result[i]['found']:
temp.append(portrait_result[i]["fields"]["nick_name"][0])
temp.append(portrait_result[i]["fields"]["photo_url"][0])
else:
temp.append(item['uid'])
temp.append("")
temp.append(item["text"])
#print item['text']
temp.append(item["sentiment"])
temp.append(ts2date(item['timestamp']))
temp.append(item['geo'])
temp.append(item["message_type"])
results.append(temp)
return results
def get_positive_weibo_detail(ts, social_sensors, keywords_list, size, sentiment_type=1):
former_mid_list = query_mid_list(ts-time_interval, keywords_list, time_segment, social_sensors) # 前一段时间内的微博mid list
current_mid_list = query_mid_list(ts, keywords_list, time_interval, social_sensors)
mid_list = []
mid_list.extend(former_mid_list)
mid_list.extend(current_mid_list)
query_body = {
"query":{
"filtered":{
"filter":{
"bool":{
"must":[
{"range":{
"timestamp":{
"gte": ts - time_interval,
"lt": ts
}
}},
],
"should":[
{"terms": {"root_mid": mid_list}},
{"terms": {"mid": mid_list}},
{"terms":{"keywords_string": keywords_list}}
]
}
}
}
},
"sort": {"timestamp": {"order": "desc"}},
"size": 100
}
#if social_sensors and int(sentiment_type) == 1:
# query_body["query"]["filtered"]["filter"]["bool"]["must"].append({"terms":{"uid": social_sensors}})
if int(sentiment_type) == 1 or int(sentiment_type) == 0:
query_body["query"]["filtered"]["filter"]["bool"]["must"].append({"term":{"sentiment":sentiment_type}})
else:
query_body["query"]["filtered"]["filter"]["bool"]["must"] = [{"terms":{"sentiment": ["2", "3"]}}]
# 判断当前ts和ts-time_interval是否属于同一天，确定查询哪个es
datetime = ts2datetime(ts)
datetime_1 = ts2datetime(ts-time_interval)
index_name = flow_text_index_name_pre + datetime
exist_es = es_text.indices.exists(index_name)
index_name_1 = flow_text_index_name_pre + datetime_1
exist_es_1 = es_text.indices.exists(index_name_1)
# 1. 聚合原创微博mid list
if datetime == datetime_1 and exist_es:
search_results = es_text.search(index=index_name, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"]
elif datetime != datetime_1 and exist_es_1:
search_results = es_text.search(index=index_name_1, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"]
else:
search_results = []
uid_list = []
results = []
if search_results:
for item in search_results:
uid_list.append(item["_source"]['uid'])
if uid_list:
portrait_result = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":uid_list}, fields=['nick_name', 'photo_url'])["docs"]
for i in range(len(uid_list)):
item = search_results[i]['_source']
temp = []
# uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type
temp.append(item['uid'])
if portrait_result[i]['found']:
temp.append(portrait_result[i]["fields"]["nick_name"][0])
temp.append(portrait_result[i]["fields"]["photo_url"][0])
else:
temp.append("unknown")
temp.append("")
temp.append(item["text"])
temp.append(item["sentiment"])
temp.append(ts2date(item['timestamp']))
temp.append(item['geo'])
keywords_set = set(item['keywords_string'].split('&'))
common_keywords = set(keywords_list) & keywords_set
temp.append(list(common_keywords))
temp.append(item['message_type'])
results.append(temp)
return results
def get_origin_weibo_detail(ts, user, task_name, size, order, message_type=1):
_id = user + '-' + task_name
task_detail = es_user_portrait.get(index=index_sensing_task, doc_type=_id, id=ts)['_source']
mid_value = json.loads(task_detail['mid_topic_value'])
duplicate_dict = json.loads(task_detail['duplicate_dict'])
tmp_duplicate_dict = dict()
for k,v in duplicate_dict.iteritems():
try:
tmp_duplicate_dict[v].append(k)
except:
tmp_duplicate_dict[v] = [k, v]
if message_type == 1:
weibo_detail = json.loads(task_detail['origin_weibo_detail'])
elif message_type == 2:
weibo_detail = json.loads(task_detail['retweeted_weibo_detail'])
else:
weibo_detail = json.loads(task_detail['sensitive_weibo_detail'])
weibo_detail_list = []
if weibo_detail:
for iter_mid, item in weibo_detail.iteritems():
tmp = []
tmp.append(iter_mid)
tmp.append(item[iter_mid])
tmp.append(item['retweeted'])
tmp.append(item['comment'])
weibo_detail_list.append(tmp)
mid_list = weibo_detail.keys()
results = []
query_body = {
"query":{
"filtered":{
"filter":{
"terms":{"mid": mid_list}
}
}
},
"size": 1000,
"sort": {"timestamp": {"order": "desc"}}
}
index_list = []
datetime = ts2datetime(ts)
datetime_1 = ts2datetime(ts-DAY)
index_name = flow_text_index_name_pre + datetime
exist_es = es_text.indices.exists(index_name)
if exist_es:
index_list.append(index_name)
index_name_1 = flow_text_index_name_pre + datetime_1
exist_es_1 = es_text.indices.exists(index_name_1)
if exist_es_1:
index_list.append(index_name_1)
if index_list and mid_list:
search_results = es_text.search(index=index_list, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"]
else:
search_results = []
uid_list = []
text_dict = dict() # 文本信息
portrait_dict = dict() # 背景信息
sort_results = []
if search_results:
for item in search_results:
uid_list.append(item["_source"]['uid'])
text_dict[item['_id']] = item['_source'] # _id是mid
if uid_list:
portrait_result = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":uid_list}, fields=['nick_name', 'photo_url'])["docs"]
for item in portrait_result:
if item['found']:
portrait_dict[item['_id']] = {"nick_name": item["fields"]["nick_name"][0], "photo_url": item["fields"]["photo_url"][0]}
else:
portrait_dict[item['_id']] = {"nick_name": item['_id'], "photo_url":""}
if order == "total":
sorted_list = sorted(weibo_detail_list, key=lambda x:x[1], reverse=True)
elif order == "retweeted":
sorted_list = sorted(weibo_detail_list, key=lambda x:x[2], reverse=True)
elif order == "comment":
sorted_list = sorted(weibo_detail_list, key=lambda x:x[3], reverse=True)
else:
sorted_list = weibo_detail_list
count_n = 0
results_dict = dict()
mid_index_dict = dict()
for item in sorted_list: # size
mid = item[0]
iter_text = text_dict.get(mid, {})
temp = []
# uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type
if iter_text:
uid = iter_text['uid']
temp.append(uid)
iter_portrait = portrait_dict.get(uid, {})
if iter_portrait:
temp.append(iter_portrait['nick_name'])
temp.append(iter_portrait['photo_url'])
else:
temp.extend([uid,''])
temp.append(iter_text["text"])
temp.append(iter_text["sentiment"])
temp.append(ts2date(iter_text['timestamp']))
temp.append(iter_text['geo'])
if message_type == 1:
temp.append(1)
elif message_type == 2:
temp.append(3)
else:
temp.append(iter_text['message_type'])
temp.append(item[2])
temp.append(item[3])
temp.append(iter_text.get('sensitive', 0))
temp.append(iter_text['timestamp'])
temp.append(mid_value[mid])
temp.append(mid)
results.append(temp)
count_n += 1
results = sorted(results, key=operator.itemgetter(-4, -2, -6), reverse=True) # -4 -2 -3
sort_results = []
count = 0
for item in results:
sort_results.append([item])
mid_index_dict[item[-1]] = count
count += 1
if tmp_duplicate_dict:
remove_list = []
value_list = tmp_duplicate_dict.values() # [[mid, mid], ]
for item in value_list:
tmp = []
for mid in item:
if mid_index_dict.get(mid, 0):
tmp.append(mid_index_dict[mid])
if len(tmp) > 1:
tmp_min = min(tmp)
else:
continue
tmp.remove(tmp_min)
for iter_count in tmp:
sort_results[tmp_min].extend(sort_results[iter_count])
remove_list.append(sort_results[iter_count])
if remove_list:
for item in remove_list:
sort_results.remove(item)
return sort_results
def get_sensitive_text_detail(task_name, ts, user, order):
_id = user + '-' + task_name
task_detail = es.get(index=index_sensing_task, doc_type=_id, id=ts)['_source']
weibo_detail = json.loads(task_detail['sensitive_weibo_detail'])
weibo_detail_list = []
if weibo_detail:
for iter_mid, item in weibo_detail.iteritems():
tmp = []
tmp.append(iter_mid)
tmp.append(item[iter_mid])
tmp.append(item['retweeted'])
tmp.append(item['comment'])
weibo_detail_list.append(tmp)
mid_list = weibo_detail.keys()
results = []
query_body = {
"query":{
"filtered":{
"filter":{
"terms":{"mid": mid_list}
}
}
}
}
index_list = []
datetime = ts2datetime(ts)
datetime_1 = ts2datetime(ts-DAY)
index_name = flow_text_index_name_pre + datetime
exist_es = es_text.indices.exists(index_name)
if exist_es:
index_list.append(index_name)
index_name_1 = flow_text_index_name_pre + datetime_1
exist_es_1 = es_text.indices.exists(index_name_1)
if exist_es_1:
index_list.append(index_name_1)
if index_list and mid_list:
search_results = es_text.search(index=index_list, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"]
else:
search_results = []
uid_list = []
text_dict = dict() # 文本信息
portrait_dict = dict() # 背景信息
if search_results:
for item in search_results:
uid_list.append(item["_source"]['uid'])
text_dict[item['_id']] = item['_source'] # _id是mid
if uid_list:
portrait_result = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":uid_list}, fields=['nick_name', 'photo_url'])["docs"]
for item in portrait_result:
if item['found']:
portrait_dict[item['_id']] = {"nick_name": item["fields"]["nick_name"][0], "photo_url": item["fields"]["photo_url"][0]}
else:
portrait_dict[item['_id']] = {"nick_name": item['_id'], "photo_url":""}
if order == "total":
sorted_list = sorted(weibo_detail_list, key=lambda x:x[1], reverse=True)
elif order == "retweeted":
sorted_list = sorted(weibo_detail_list, key=lambda x:x[2], reverse=True)
elif order == "comment":
sorted_list = sorted(weibo_detail_list, key=lambda x:x[3], reverse=True)
else:
sorted_list = weibo_detail_list
count_n = 0
for item in sorted_list:
mid = item[0]
iter_text = text_dict.get(mid, {})
temp = []
# uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type
if iter_text:
uid = iter_text['uid']
temp.append(uid)
iter_portrait = portrait_dict.get(uid, {})
if iter_portrait:
temp.append(iter_portrait['nick_name'])
temp.append(iter_portrait['photo_url'])
else:
temp.extend([uid,''])
temp.append(iter_text["text"])
temp.append(iter_text["sentiment"])
temp.append(ts2date(iter_text['timestamp']))
temp.append(iter_text['geo'])
temp.append(iter_text['message_type'])
temp.append(item[2])
temp.append(item[3])
temp.append(iter_text.get('sensitive', 0))
count_n += 1
results.append(temp)
if results and order == "ts":
results = sorted(results, key=lambda x:x[5], reverse=True)
if results and order == "sensitive":
results = sorted(results, key=lambda x:x[-1], reverse=True)
return results
def get_temporal_rank(task_type, sort="retweeted", number=100):
number = int(number) - 1
if int(task_type) == 0: # 到目前位置
sort_list = r.zrange("influence_%s" %sort, 0, number, withscores=True, desc=True)
elif int(task_type) == 1:
sort_list = r.zrange("influence_%s_1" %sort, 0, number, withscores=True, desc=True)
elif int(task_type) == 2:
sort_list = r.zrange("influence_%s_2" %sort, 0, number, withscores=True, desc=True)
elif int(task_type) == 3:
sort_list = r.zrange("influence_%s_3" %sort, 0, number, withscores=True, desc=True)
else:
sort_list = r.zrange("influence_%s_4" %sort, 0, number, withscores=True, desc=True)
uid_list = []
for item in sort_list:
uid_list.append(item[0])
if sort == "retweeted":
other = "comment"
else:
other = "retweeted"
results = []
# 查看背景信息
if uid_list:
profile_result = es_user_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":uid_list})["docs"]
bci_result = es_user_profile.mget(index="bci_history", doc_type="bci", body={"ids":uid_list}, fields=['user_fansnum',"weibo_month_sum" ])["docs"]
count = 0
for item in profile_result:
_id = item['_id']
index = profile_result.index(item)
tmp = []
tmp.append(item['_id'])
if item['found']:
item = item['_source']
tmp.append(item['nick_name'])
tmp.append(item['statusnum'])
tmp.append(item['user_location'])
tmp.append(item['fansnum'])
else:
tmp.extend(['',0,'',0])
try:
user_fansnum = bci_result[count]['fileds']['user_fansnum']
tmp[4] = user_fansnum
except:
pass
try:
weibo_number = bci_result[count]['fileds']["weibo_month_sum"]
tmp[2] = weibo_number
except:
pass
count_1 = int(sort_list[index][1])
if int(task_type) == 0:
tmp_count = r.zscore("influence_%s" %other, _id)
if tmp_count:
count_2 = int(tmp_count)
else:
count_2 = 0
else:
tmp_count = r.zscore("influence_%s_%s" %(other,task_type), _id)
if tmp_count:
count_2 = int(tmp_count)
else:
count_2 = 0
if sort == "retweeted":
tmp.append(count_1)
tmp.append(count_2)
else:
tmp.append(count_2)
tmp.append(count_1)
results.append(tmp)
count += 1
if uid_list:
count = 0
portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, body={"ids":uid_list})["docs"]
for item in portrait_result:
if item['found']:
results[count].append("1")
else:
results[count].append("0")
count += 1
return results
def identify_user_out(input_uid_list):
out_user_list = []
in_user_list = []
input_len = len(input_uid_list)
iter_count = 0
print 'identify user out'
#get user list who is out user_portrait
while iter_count < input_len:
iter_user_list = input_uid_list[iter_count:iter_count +
DETECT_ITER_COUNT]
try:
portrait_result = es_user_portrait.mget(
index=portrait_index_name,
doc_type=portrait_index_type,
body={'ids': iter_user_list},
_source=False)['docs']
except:
portrait_result = []
for item in portrait_result:
uid = item['_id']
if item['found'] != True:
out_user_list.append(uid)
else:
in_user_list.append(uid)
iter_count += DETECT_ITER_COUNT
print 'get out user portrait information'
#get user profile information for out user_portrait
iter_count = 0
out_user_count = len(out_user_list)
out_user_result = []
while iter_count < out_user_count:
iter_user_list = out_user_list[iter_count:iter_count +
DETECT_ITER_COUNT]
try:
profile_result = es_user_profile.mget(index=profile_index_name,
doc_type=profile_index_type,
body={'ids': iter_user_list},
_source=True)['docs']
except:
profile_result = []
for item in profile_result:
uid = item['_id']
if item['found'] == True:
source = item['_source']
uname = source['nick_name']
fansnum = source['fansnum']
statusnum = source['statusnum']
friendsnum = source['friendsnum']
else:
uname = u'未知'
fansnum = u'未知'
statusnum = u'未知'
friendsnum = u'未知'
out_user_result.append(
[uid, uname, fansnum, statusnum, friendsnum])
iter_count += DETECT_ITER_COUNT
sort_out_user_result = sorted(out_user_result,
key=lambda x: x[2],
reverse=True)
return in_user_list, sort_out_user_result