def show_in_history(date):
print date
results = []
sensitive_uid_list = []
influence_uid_list = []
sen_iden_in_name = "identify_in_sensitive_" + str(date)
inf_iden_in_name = "identify_in_influence_" + str(date)
man_iden_in_name = "identify_in_manual_" + str(date)
sen_iden_in_results = r.hgetall(sen_iden_in_name)
inf_iden_in_results = r.hgetall(inf_iden_in_name)
man_iden_in_results = r.hgetall(man_iden_in_name)
sensitive_uid_list = sen_iden_in_results.keys()
influence_uid_list = inf_iden_in_results.keys()
manual_uid_list = man_iden_in_results.keys()
#compute_results = r.hgetall('compute')
results = []
work_date = ts2datetime(datetime2ts(date) - DAY)
if sensitive_uid_list:
sensitive_results = get_sensitive_user_detail(sensitive_uid_list,
work_date, 1)
else:
sensitive_results = []
for item in sensitive_results:
uid = item[0]
status = sen_iden_in_results[uid]
item.append(status)
results.append(item)
if influence_uid_list:
influence_results = get_sensitive_user_detail(influence_uid_list,
work_date, 0)
else:
influence_results = []
for item in influence_results:
uid = item[0]
status = inf_iden_in_results[uid]
item.append(status)
results.append(item)
if manual_uid_list:
manual_results = get_sensitive_user_detail(manual_uid_list, work_date,
0)
else:
manual_results = []
for item in manual_results:
uid = item[0]
status = man_iden_in_results[uid]
item.append(status)
results.append(item)
sorted_results = sorted(results, key=lambda x: x[5], reverse=True)
return sorted_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 show_in_history(date):
print date
results = []
sensitive_uid_list = []
influence_uid_list = []
sen_iden_in_name = "identify_in_sensitive_" + str(date)
inf_iden_in_name = "identify_in_influence_" + str(date)
man_iden_in_name = "identify_in_manual_" + str(date)
sen_iden_in_results = r.hgetall(sen_iden_in_name)
inf_iden_in_results = r.hgetall(inf_iden_in_name)
man_iden_in_results = r.hgetall(man_iden_in_name)
sensitive_uid_list = sen_iden_in_results.keys()
influence_uid_list = inf_iden_in_results.keys()
manual_uid_list = man_iden_in_results.keys()
#compute_results = r.hgetall('compute')
results = []
work_date = ts2datetime(datetime2ts(date)-DAY)
if sensitive_uid_list:
sensitive_results = get_sensitive_user_detail(sensitive_uid_list, work_date, 1)
else:
sensitive_results = []
for item in sensitive_results:
uid = item[0]
status = sen_iden_in_results[uid]
item.append(status)
results.append(item)
if influence_uid_list:
influence_results = get_sensitive_user_detail(influence_uid_list, work_date, 0)
else:
influence_results = []
for item in influence_results:
uid = item[0]
status = inf_iden_in_results[uid]
item.append(status)
results.append(item)
if manual_uid_list:
manual_results = get_sensitive_user_detail(manual_uid_list, work_date, 0)
else:
manual_results = []
for item in manual_results:
uid = item[0]
status = man_iden_in_results[uid]
item.append(status)
results.append(item)
sorted_results = sorted(results, key=lambda x:x[5], reverse=True)
return sorted_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 recommend_in_sensitive(date):
sensitive_name = "recomment_" + str(date) + "_sensitive"
compute_name = "compute"
re_sen_set = r.hkeys(sensitive_name) # 敏感人物推荐
iden_in_set = r.hkeys(compute_name) # 已经入库用户
if not re_sen_set:
return [] # 那一天不存在数据
uid_list = list(set(re_sen_set) - set(iden_in_set))
sensitive = 1
work_date = ts2datetime(datetime2ts(date) - DAY)
if uid_list:
results = get_sensitive_user_detail(uid_list, work_date, sensitive)
else:
results = []
return results
def ajax_cancel_delete():
uid_list = request.args.get('uid_list', '')
date = request.args.get('date', '')
if not uid_list or not date:
return '0'
else:
uid_list = str(uid_list).split(',')
date = str(date).replace('-', '')
delete_list = json.loads(r.hget('delete_user', date))
revise_list = set(delete_list) - set(uid_list)
if revise_list:
r.hset('delete_user', date, json.dumps(list(revise_list)))
else:
r.hdel('delete_user', date)
return '1'
def ajax_delete_user():
date = request.args.get('date', '') # '2013-09-01'
date = str(date).replace('-', '')
uid_list = request.args.get('uid_list', '') # uid_list, 12345,123456,
delete_list = str(uid_list).split(',')
if date and delete_list:
temp = r.hget('delete_user', date)
if temp:
exist_data = json.loads(temp)
delete_list.extend(exist_data)
r.hset('delete_user', date, json.dumps(delete_list))
return '1'
else:
return '0'
def ajax_delete_user():
date = request.args.get("date", "") # '2013-09-01'
date = str(date).replace("-", "")
uid_list = request.args.get("uid_list", "") # uid_list, 12345,123456,
delete_list = str(uid_list).split(",")
if date and delete_list:
temp = r.hget("delete_user", date)
if temp:
exist_data = json.loads(temp)
delete_list.extend(exist_data)
r.hset("delete_user", date, json.dumps(delete_list))
return "1"
else:
return "0"
def ajax_cancel_delete():
uid_list = request.args.get("uid_list", "")
date = request.args.get("date", "")
if not uid_list or not date:
return "0"
else:
uid_list = str(uid_list).split(",")
date = str(date).replace("-", "")
delete_list = json.loads(r.hget("delete_user", date))
revise_list = set(delete_list) - set(uid_list)
if revise_list:
r.hset("delete_user", date, json.dumps(list(revise_list)))
else:
r.hdel("delete_user", date)
return "1"
def recommend_in_sensitive(date):
sensitive_name = "recomment_" + str(date) + "_sensitive"
compute_name = "compute"
re_sen_set = r.hkeys(sensitive_name) # 敏感人物推荐
iden_in_set = r.hkeys(compute_name) # 已经入库用户
if not re_sen_set:
return [] # 那一天不存在数据
uid_list = list(set(re_sen_set) - set(iden_in_set))
sensitive = 1
work_date = ts2datetime(datetime2ts(date)-DAY)
if uid_list:
results = get_sensitive_user_detail(uid_list, work_date, sensitive)
else:
results = []
return results
def search_sensitive_words(
level, category): # level: 0, 1, 2, 3; category: '', or other category
results = dict()
word_list = []
level = int(level)
words_dict = r.hgetall('sensitive_words')
if words_dict:
if int(level) == 0 and not category:
word_list = []
for k, v in words_dict.items():
word_state = json.loads(v)
word_list.append([k, word_state[0], word_state[1]])
elif level and category:
word_list = []
for k, v in words_dict.items():
word_state = json.loads(v)
if int(level) == int(
word_state[0]) and category == word_state[1]:
word_list.append([k, word_state[0], word_state[1]])
elif not level and category:
for k, v in words_dict.items():
word_state = json.loads(v)
if category == word_state[1]:
word_list.append([k, word_state[0], word_state[1]])
else:
for k, v in words_dict.items():
word_state = json.loads(v)
if int(level) == int(word_state[0]):
word_list.append([k, word_state[0], word_state[1]])
return word_list
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 search_sensitive_words(level, category): # level: 0, 1, 2, 3; category: '', or other category
results = dict()
word_list = []
words_dict = r.hgetall('sensitive_words')
if words_dict:
if int(level) == 0 and not category:
word_list = []
for k,v in words_dict.items():
word_state = json.loads(v)
word_list.append([k, word_state[0], word_state[1]])
elif level and category:
word_list = []
for k,v in words_dict.items():
word_state = json.loads(v)
if int(level) == int(word_state[0]) and category == word_state[1]:
word_list.append([k, word_state[0], word_state[1]])
elif not level and category:
for k,v in words_dict.items():
word_state = json.loads(v)
if catetory == word_state[1]:
word_list.append([k, word_state[0], word_state[1]])
else:
for k,v in words_dict.items():
word_state = json.loads(v)
if int(level) == int(word_state[0]):
word_list.append([k, word_state[0], word_state[1]])
return word_list
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 recommend_in_top_influence(date):
influence_name = "recomment_" + date + "_influence"
identify_in_name = "compute"
re_inf_set = r.hkeys(influence_name)
iden_in_set = r.hkeys(identify_in_name) # 已经入库用户
if not re_inf_set:
return []
else:
uid_list = list(set(re_inf_set) - set(iden_in_set))
sensitive = 0
work_date = ts2datetime(datetime2ts(date) - DAY)
if uid_list:
results = get_sensitive_user_detail(uid_list, work_date, sensitive)
else:
results = []
return results
def recommend_in_top_influence(date):
influence_name = "recomment_" + date + "_influence"
identify_in_name = "compute"
re_inf_set = r.hkeys(influence_name)
iden_in_set = r.hkeys(identify_in_name) # 已经入库用户
if not re_inf_set:
return []
else:
uid_list = list(set(re_inf_set) - set(iden_in_set))
sensitive = 0
work_date = ts2datetime(datetime2ts(date)-DAY)
if uid_list:
results = get_sensitive_user_detail(uid_list, work_date, sensitive)
else:
results = []
return results
def identify_in(data):
appoint_list = []
now_list = []
sensitive_list = set()
influence_list = set()
for item in data:
date = item[0] # 2015-09-22
uid = item[1]
status = str(item[2])
source = str(item[3])
if int(source) == 1:
r.hset('identify_in_sensitive_'+str(date), uid, status) # identify in user_list and compute status
sensitive_list.add(uid)
elif int(source) == 2:
r.hset('identify_in_influence_'+str(date), uid, status)
influence_list.add(uid)
else:
r.hset("identify_in_manual_"+str(date), uid, status)
r.hset('compute', uid, json.dumps([date, status]))
"""
sensitive_results = r.hget('recommend_sensitive', date)
if sensitives_results:
sensitive_results = json.loads(sensitive_results)
revise_set = set(sensitive_results) - sensitive_list
if revise_set:
r.hset('recommend_sensitive', date, json.dumps(list(revise_set)))
else:
r.hdel('recommend_sensitive', date)
influence_results = r.hget('recommend_influence', date)
if influence_results and influence_results != []:
influence_results = json.loads(influence_results)
revise_set = set(influence_results) - influence_list
if revise_set:
r.hset('recommend_influence', date, json.dumps(list(revise_set)))
else:
r.hdel('recommend_influence', date)
# about compute
compute_now_list = r.hget('compute_now', date)
compute_appoint_list = r.hget('compute_appoint', date)
# compute now user list
if compute_now_list:
now_list.extend(json.loads(compute_now_list))
r.hset('compute_now', date, json.dumps(now_list))
else:
r.hset('compute_now', date, json.dumps(now_list))
# appointted compute user list
if compute_appoint_list:
appoint_list.extend(json.loads(compute_appoint_list))
r.hset('compute_appoint', date, json.dumps(appoint_list))
else:
r.hset('compute_appoint', date, json.dumps(appoint_list)) # finish compute, revise 'identify_in_state' uid status
"""
return '1'
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_in(data):
appoint_list = []
now_list = []
sensitive_list = set()
influence_list = set()
for item in data:
date = item[0] # 2015-09-22
uid = item[1]
status = str(item[2])
source = str(item[3])
if int(source) == 1:
r.hset('identify_in_sensitive_' + str(date), uid,
status) # identify in user_list and compute status
sensitive_list.add(uid)
elif int(source) == 2:
r.hset('identify_in_influence_' + str(date), uid, status)
influence_list.add(uid)
else:
r.hset("identify_in_manual_" + str(date), uid, status)
r.hset('compute', uid, json.dumps([date, status]))
"""
sensitive_results = r.hget('recommend_sensitive', date)
if sensitives_results:
sensitive_results = json.loads(sensitive_results)
revise_set = set(sensitive_results) - sensitive_list
if revise_set:
r.hset('recommend_sensitive', date, json.dumps(list(revise_set)))
else:
r.hdel('recommend_sensitive', date)
influence_results = r.hget('recommend_influence', date)
if influence_results and influence_results != []:
influence_results = json.loads(influence_results)
revise_set = set(influence_results) - influence_list
if revise_set:
r.hset('recommend_influence', date, json.dumps(list(revise_set)))
else:
r.hdel('recommend_influence', date)
# about compute
compute_now_list = r.hget('compute_now', date)
compute_appoint_list = r.hget('compute_appoint', date)
# compute now user list
if compute_now_list:
now_list.extend(json.loads(compute_now_list))
r.hset('compute_now', date, json.dumps(now_list))
else:
r.hset('compute_now', date, json.dumps(now_list))
# appointted compute user list
if compute_appoint_list:
appoint_list.extend(json.loads(compute_appoint_list))
r.hset('compute_appoint', date, json.dumps(appoint_list))
else:
r.hset('compute_appoint', date, json.dumps(appoint_list)) # finish compute, revise 'identify_in_state' uid status
"""
return '1'
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 recommend_in_top_influence(date):
date = date.replace('-','')
results = r.hget('recommend_influence', date)
if not results:
return []
else:
uid_list = json.loads(results)
sensitive = 0
return get_sensitive_user_detail(uid_list, date, sensitive)
def recommend_in_sensitive(date):
date = date.replace('-', '')
results = r.hget('recommend_sensitive', date)
if not results:
return results # return '0'
else:
uid_list = json.loads(results)
sensitive = 1
return get_sensitive_user_detail(uid_list, date, sensitive)
def recommend_in_top_influence(date):
date = date.replace('-', '')
results = r.hget('recommend_influence', date)
if not results:
return []
else:
uid_list = json.loads(results)
sensitive = 0
return get_sensitive_user_detail(uid_list, date, sensitive)
def recommend_in_sensitive(date):
date = date.replace('-','')
results = r.hget('recommend_sensitive', date)
if not results:
return results # return '0'
else:
uid_list = json.loads(results)
sensitive = 1
return get_sensitive_user_detail(uid_list, date, sensitive)
def show_in_history(date, sensitive):
results = []
date = str(date).replace('-','')
if sensitive: # sensitive user recommentation history
sensitive_results = r.hgetall('identify_in_sensitive_'+str(date))
if sensitive_results:
uid_list = sensitive_results.keys()
results = get_sensitive_user_detail(uid_list, date, 1)
for item in results:
item.append(sensitive_results[item[0]])
else:
influence_results = r.hgetall('identify_in_influence_'+str(date))
if influence_results:
uid_list = influence_results.keys()
results = get_sensitive_user_detail(uid_list, date, 0)
for item in results:
item.append(influence_results[item[0]])
return results
def show_in_history(date, sensitive):
results = []
date = str(date).replace('-', '')
if sensitive: # sensitive user recommentation history
sensitive_results = r.hgetall('identify_in_sensitive_' + str(date))
if sensitive_results:
uid_list = sensitive_results.keys()
results = get_sensitive_user_detail(uid_list, date, 1)
for item in results:
item.append(sensitive_results[item[0]])
else:
influence_results = r.hgetall('identify_in_influence_' + str(date))
if influence_results:
uid_list = influence_results.keys()
results = get_sensitive_user_detail(uid_list, date, 0)
for item in results:
item.append(influence_results[item[0]])
return results
def ajax_history_delete():
date = request.args.get('date', '') # '2013-09-01'
date = str(date).replace('-', '')
search_all = request.args.get('show_all', '') # return all
uid_list = []
if not search_all:
temp = r.hget('delete_user', date)
if temp:
results = get_user_info(json.loads(temp))
return json.dumps(results)
else:
all_temp = r.hgetall('delete_user')
if all_temp:
temp_list = all_temp.values()
for item in temp_list:
uid_list.extend(json.loads(item))
results = get_user_info(uid_list)
return json.dumps(results)
return '0'
def ajax_history_delete():
date = request.args.get("date", "") # '2013-09-01'
date = str(date).replace("-", "")
search_all = request.args.get("show_all", "") # return all
uid_list = []
if not search_all:
temp = r.hget("delete_user", date)
if temp:
results = get_user_info(json.loads(temp))
return json.dumps(results)
else:
all_temp = r.hgetall("delete_user")
if all_temp:
temp_list = all_temp.values()
for item in temp_list:
uid_list.extend(json.loads(item))
results = get_user_info(uid_list)
return json.dumps(results)
return "0"
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 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 lastest_identify_in():
results = dict()
now_ts = time.time()
now_ts = datetime2ts('2013-09-08')
for i in range(1,8):
ts = now_ts - i * 3600 *24
date = ts2datetime(ts).replace('-','')
words_dict = r.hgetall('history_in_'+date)
for item in words_dict:
results[item] = json.loads(words_dict[item])
return results
def lastest_identify_in():
results = dict()
now_ts = time.time()
now_ts = datetime2ts('2013-09-08')
for i in range(1, 8):
ts = now_ts - i * 3600 * 24
date = ts2datetime(ts).replace('-', '')
words_dict = r.hgetall('history_in_' + date)
for item in words_dict:
results[item] = json.loads(words_dict[item])
return results
def sort_sensitive_words(words_list):
sensitive_words_list = []
for item in words_list:
temp = []
temp.extend(item)
word = (item[0]).encode("utf-8", "ignore")
r_word = r.hget("sensitive_words", word)
if r_word:
temp.extend(json.loads(r_word))
else:
temp.extend([1, "politics"])
sensitive_words_list.append(temp)
return sensitive_words_list
def sort_sensitive_words(words_list):
sensitive_words_list = []
for item in words_list:
temp = []
temp.extend(item)
word = (item[0]).encode('utf-8', 'ignore')
r_word = r.hget('sensitive_words', word)
if r_word:
temp.extend(json.loads(r_word))
else:
temp.extend([1,'politics'])
sensitive_words_list.append(temp)
return sensitive_words_list
def sort_sensitive_words(words_list):
sensitive_words_list = []
for item in words_list:
temp = []
temp.extend(item)
word = (item[0]).encode('utf-8', 'ignore')
r_word = r.hget('sensitive_words', word)
if r_word:
temp.extend(json.loads(r_word))
else:
temp.extend([1, 'politics'])
sensitive_words_list.append(temp)
return sensitive_words_list
def identify_in(date, words_list):
# identify_in date and words_list(include level and category, [word, level, category])
# date is date when new words were recommended
new_list = []
print words_list
for item in words_list:
r.hset('sensitive_words', item[0], json.dumps([item[1], item[2]]))
new_list.append(item[0])
r.hset('history_in_'+date, item[0], json.dumps([item[1], item[2]]))
if new_list:
for item in new_list:
r.hdel('recommend_sensitive_words_'+date, item)
return '1'
def identify_in(date, words_list):
# identify_in date and words_list(include level and category, [word, level, category])
# date is date when new words were recommended
ts = time.time()
ts = datetime2ts('2013-09-07')
time_list = []
for i in range(7):
now_ts = int(ts) - i * 24 * 3600
now_date = ts2datetime(now_ts).replace('-', '')
time_list.append(now_date)
for item in words_list:
r.hset('sensitive_words', item[0], json.dumps([item[1], item[2]]))
r.hset('history_in_' + date, item[0], json.dumps([item[1], item[2]]))
for date in time_list:
r.hdel('recommend_sensitive_words_' + date, item[0])
return '1'
def identify_in(date, words_list):
# identify_in date and words_list(include level and category, [word, level, category])
# date is date when new words were recommended
ts = time.time()
ts = datetime2ts('2013-09-07')
time_list = []
for i in range(7):
now_ts = int(ts) - i*24*3600
now_date = ts2datetime(now_ts).replace('-', '')
time_list.append(now_date)
for item in words_list:
r.hset('sensitive_words', item[0], json.dumps([item[1], item[2]]))
r.hset('history_in_'+date, item[0], json.dumps([item[1], item[2]]))
for date in time_list:
r.hdel('recommend_sensitive_words_'+date, item[0])
return '1'
def get_attr(date):
results = dict()
number = es.count(index="sensitive_user_portrait",
doc_type="user")['count']
results['total_number'] = number
query_body = {"query": {"filtered": {"filter": {"term": {"type": 1}}}}}
sensitive_number = es.count(index="sensitive_user_portrait",
doc_type="user",
body=query_body)['count']
results['sensitive_number'] = sensitive_number
results['influence_number'] = number - sensitive_number
recommend_in_sensitive = 0
sensitive_dict = r.hgetall('recommend_sensitive')
for k, v in sensitive_dict.items():
if v:
sensitive_list = json.loads(v)
recommend_in_sensitive += len(sensitive_list)
recommend_in_influence = 0
influence_dict = r.hgetall('recommend_influence')
for k, v in influence_dict.items():
if v:
sensitive_list = json.loads(v)
recommend_in_influence += len(sensitive_list)
results['recommend_in'] = recommend_in_influence + recommend_in_sensitive
results['monitor_number'] = [4, 83] # test
results['new_sensitive_words'] = 5 # test
query_body = query_body_module('sensitive_words_string')
sw_list = es.search(
index='sensitive_user_portrait', doc_type='user',
body=query_body)['aggregations']['all_interests']['buckets']
sensitive_words = []
for item in sw_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
sensitive_words.append(temp)
results['sensitive_words'] = sensitive_words
query_body = query_body_module('sensitive_geo_string')
sg_list = es.search(
index='sensitive_user_portrait', doc_type='user',
body=query_body)['aggregations']['all_interests']['buckets']
sensitive_geo = []
for item in sg_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
sensitive_geo.append(temp)
results['sensitive_geo'] = sensitive_geo
query_body = query_body_module('sensitive_hashtag_string')
sh_list = es.search(
index='sensitive_user_portrait', doc_type='user',
body=query_body)['aggregations']['all_interests']['buckets']
sensitive_hashtag = []
for item in sh_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
sensitive_hashtag.append(temp)
results['sensitive_hashtag'] = sensitive_hashtag
query_body = query_body_module('sensitive_geo_string')
sg_list = es.search(
index='sensitive_user_portrait', doc_type='user',
body=query_body)['aggregations']['all_interests']['buckets']
sensitive_geo = []
for item in sg_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
sensitive_geo.append(temp)
results['sensitive_geo'] = sensitive_geo
query_body = query_body_module('psycho_status_string')
sp_list = es.search(
index='sensitive_user_portrait', doc_type='user',
body=query_body)['aggregations']['all_interests']['buckets']
psycho_status = []
for item in sp_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
psycho_status.append(temp)
results['psycho_status'] = psycho_status
'''
query_body = query_body_module('political_tendency')
st_list = es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
political_tendency = []
for item in st_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
political_tendency.append(temp)
results['political_tendency'] = political_tendency
'''
results['political_tendency'] = [['left', 123], ['middle', 768],
['right', 1095]]
'''
query_body = query_body_module('domain_string')
sd_list = es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
domain = []
for item in sd_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
domain.append(temp)
results['domain'] = domain
'''
# tendency distribution
# domain and topic
domain_list = ['']
#search_important('domain', )
domain_results = get_top_user()
topic_results = get_topic_user()
results['domain_rank'] = domain_results
results['topic_rank'] = topic_results
# rank
important_list = search_in_portrait('importance')
results['importance'] = important_list
results['sensitive'] = search_in_portrait('sensitive')
results['influence'] = search_in_portrait('influence')
results['activeness'] = search_in_portrait('activeness')
query_body = {
"query": {
"match_all": {}
},
"sort": {
"s_origin_weibo_comment_total_number": {
"order": "desc"
}
}
}
date = ts2datetime(time.time() - 24 * 3600).replace('-', '')
date = '20130907'
results_list = es.search(index=date, doc_type="bci",
body=query_body)['hits']['hits']
comment_weibo_detail = []
for item in results_list:
temp = []
uid = item['_source']['uid']
try:
uname = es_user_profile.get(index='weibo_user',
doc_type='user',
id=uid)['_source']['nick_name']
except:
uname = 'unknown'
temp.append(item['_source']['uid'])
temp.append(uname)
temp.append(item['_source']['s_origin_weibo_comment_total_number'])
comment_weibo_detail.append(temp)
results['comment_total'] = comment_weibo_detail
query_body = {
"query": {
"match_all": {}
},
"sort": {
"s_origin_weibo_retweeted_total_number": {
"order": "desc"
}
}
}
date = ts2datetime(time.time() - 24 * 3600).replace('-', '')
date = '20130907'
results_list = es.search(index=date, doc_type="bci",
body=query_body)['hits']['hits']
retweeted_weibo_detail = []
for item in results_list:
temp = []
uid = item['_source']['uid']
try:
uname = es_user_profile.get(index='weibo_user',
doc_type='user',
id=uid)['_source']['nick_name']
except:
uname = 'unknown'
temp.append(item['_source']['uid'])
temp.append(uname)
temp.append(item['_source']['s_origin_weibo_retweeted_total_number'])
retweeted_weibo_detail.append(temp)
results['retweeted_total'] = retweeted_weibo_detail
query_body = {
"query": {
"match_all": {}
},
"sort": {
"s_origin_weibo_number": {
"order": "desc"
}
}
}
date = ts2datetime(time.time() - 24 * 3600).replace('-', '')
date = '20130907'
results_list = es.search(index=date, doc_type="bci",
body=query_body)['hits']['hits']
weibo_detail = []
for item in results_list:
temp = []
uid = item['_source']['uid']
try:
uname = es_user_profile.get(index='weibo_user',
doc_type='user',
id=uid)['_source']['nick_name']
except:
uname = 'unknown'
temp.append(item['_source']['uid'])
temp.append(uname)
temp.append(item['_source']['s_origin_weibo_number'])
weibo_detail.append(temp)
results['top_weibo_number'] = weibo_detail
return results
def self_delete(word):
print word
r.hdel('sensitive_words', word)
r.sadd('black_sensitive_words', word)
return '1'
def self_add_in(date, word, level, category):
r.hset('sensitive_words', word, json.dumps([level, category]))
r.hset('history_in_'+date, word, json.dumps([level, category]))
return '1'
def self_add_in(date, word, level, category):
r.hset('sensitive_words', word, json.dumps([level, category]))
r.hset('history_in_' + date, word, json.dumps([level, category]))
return '1'
def get_attr(date):
results = dict()
number = es.count(index="sensitive_user_portrait", doc_type="user")['count']
results['total_number'] = number
query_body={
"query":{
"filtered":{
"filter":{
"term":{
"type": 1
}
}
}
}
}
sensitive_number = es.count(index="sensitive_user_portrait", doc_type="user", body=query_body)['count']
results['sensitive_number'] = sensitive_number
results['influence_number'] = number - sensitive_number
recommend_in_sensitive = 0
sensitive_dict = r.hgetall('recommend_sensitive')
for k,v in sensitive_dict.items():
if v:
sensitive_list = json.loads(v)
recommend_in_sensitive += len(sensitive_list)
recommend_in_influence = 0
influence_dict = r.hgetall('recommend_influence')
for k,v in influence_dict.items():
if v:
sensitive_list = json.loads(v)
recommend_in_influence += len(sensitive_list)
results['recommend_in'] = recommend_in_influence + recommend_in_sensitive
results['monitor_number'] = [4, 83] # test
results['new_sensitive_words'] = 5 # test
query_body = query_body_module('sensitive_words_string')
sw_list = es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
sensitive_words = []
for item in sw_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
sensitive_words.append(temp)
results['sensitive_words'] = sensitive_words
query_body = query_body_module('sensitive_geo_string')
sg_list = es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
sensitive_geo = []
for item in sg_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
sensitive_geo.append(temp)
results['sensitive_geo'] = sensitive_geo
query_body = query_body_module('sensitive_hashtag_string')
sh_list = es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
sensitive_hashtag = []
for item in sh_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
sensitive_hashtag.append(temp)
results['sensitive_hashtag'] = sensitive_hashtag
query_body = query_body_module('sensitive_geo_string')
sg_list = es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
sensitive_geo = []
for item in sg_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
sensitive_geo.append(temp)
results['sensitive_geo'] = sensitive_geo
query_body = query_body_module('psycho_status_string')
sp_list = es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
psycho_status = []
for item in sp_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
psycho_status.append(temp)
results['psycho_status'] = psycho_status
'''
query_body = query_body_module('political_tendency')
st_list = es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
political_tendency = []
for item in st_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
political_tendency.append(temp)
results['political_tendency'] = political_tendency
'''
results['political_tendency'] = [['left', 123], ['middle', 768], ['right', 1095]]
'''
query_body = query_body_module('domain_string')
sd_list = es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
domain = []
for item in sd_list:
temp = []
temp.append(item['key'])
temp.append(item['doc_count'])
domain.append(temp)
results['domain'] = domain
'''
# tendency distribution
# domain and topic
domain_list = ['']
#search_important('domain', )
domain_results = get_top_user()
topic_results = get_topic_user()
results['domain_rank'] = domain_results
results['topic_rank'] = topic_results
# rank
important_list = search_in_portrait('importance')
results['importance'] = important_list
results['sensitive'] = search_in_portrait('sensitive')
results['influence'] = search_in_portrait('influence')
results['activeness'] = search_in_portrait('activeness')
query_body={
"query":{
"match_all": {}
},
"sort": {"s_origin_weibo_comment_total_number": {"order": "desc"}}
}
date = ts2datetime(time.time()-24*3600).replace('-','')
date = '20130907'
results_list = es.search(index=date, doc_type="bci", body=query_body)['hits']['hits']
comment_weibo_detail = []
for item in results_list:
temp = []
uid = item['_source']['uid']
try:
uname = es_user_profile.get(index='weibo_user', doc_type='user', id=uid)['_source']['nick_name']
except:
uname = 'unknown'
temp.append(item['_source']['uid'])
temp.append(uname)
temp.append(item['_source']['s_origin_weibo_comment_total_number'])
comment_weibo_detail.append(temp)
results['comment_total'] = comment_weibo_detail
query_body={
"query":{
"match_all": {}
},
"sort": {"s_origin_weibo_retweeted_total_number": {"order": "desc"}}
}
date = ts2datetime(time.time()-24*3600).replace('-','')
date = '20130907'
results_list = es.search(index=date, doc_type="bci", body=query_body)['hits']['hits']
retweeted_weibo_detail = []
for item in results_list:
temp = []
uid = item['_source']['uid']
try:
uname = es_user_profile.get(index='weibo_user', doc_type='user', id=uid)['_source']['nick_name']
except:
uname = 'unknown'
temp.append(item['_source']['uid'])
temp.append(uname)
temp.append(item['_source']['s_origin_weibo_retweeted_total_number'])
retweeted_weibo_detail.append(temp)
results['retweeted_total'] = retweeted_weibo_detail
query_body={
"query":{
"match_all": {}
},
"sort": {"s_origin_weibo_number": {"order": "desc"}}
}
date = ts2datetime(time.time()-24*3600).replace('-','')
date = '20130907'
results_list = es.search(index=date, doc_type="bci", body=query_body)['hits']['hits']
weibo_detail = []
for item in results_list:
temp = []
uid = item['_source']['uid']
try:
uname = es_user_profile.get(index='weibo_user', doc_type='user', id=uid)['_source']['nick_name']
except:
uname = 'unknown'
temp.append(item['_source']['uid'])
temp.append(uname)
temp.append(item['_source']['s_origin_weibo_number'])
weibo_detail.append(temp)
results['top_weibo_number'] = weibo_detail
return results
def self_delete(word):
r.hdel('sensitive_words', word)
r.sadd('black_sensitive_words', word)
return '1'
def ajax_show_all():
results = r.get('overview')
if results:
return results
else:
return None
def identify_in(data):
appoint_list = []
now_list = []
sensitive_list = set()
influence_list = set()
for item in data:
date = item[0] # 2015-09-22
date = str(date).replace('-', '')
uid = item[1]
status = str(item[2])
source = str(item[3])
if source == '1':
r.hset('identify_in_sensitive_' + str(date), uid,
status) # identify in user_list and compute status
sensitive_list.add(uid)
elif source == '2':
r.hset('identify_in_influence_' + str(date), uid, status)
influence_list.add(uid)
if status == '1': # now
now_list.append([uid, source])
if status == '2': # appoint
appoint_list.append([uid, source])
sensitive_results = r.hget('recommend_sensitive', date)
if sensitive_results and sensitive_results != '0':
sensitive_results = json.loads(sensitive_results)
revise_set = set(sensitive_results) - sensitive_list
if revise_set:
r.hset('recommend_sensitive', date, json.dumps(list(revise_set)))
else:
r.hdel('recommend_sensitive', date)
influence_results = r.hget('recommend_influence', date)
if influence_results and influence_results != '0':
influence_results = json.loads(influence_results)
revise_set = set(influence_results) - influence_list
if revise_set:
r.hset('recommend_influence', date, json.dumps(list(revise_set)))
else:
r.hdel('recommend_influence', date)
# about compute
compute_now_list = r.hget('compute_now', date)
compute_appoint_list = r.hget('compute_appoint', date)
# compute now user list
if compute_now_list:
now_list.extend(json.loads(compute_now_list))
r.hset('compute_now', date, json.dumps(now_list))
else:
r.hset('compute_now', date, json.dumps(now_list))
# appointted compute user list
if compute_appoint_list:
appoint_list.extend(json.loads(compute_appoint_list))
r.hset('compute_appoint', date, json.dumps(appoint_list))
else:
r.hset('compute_appoint', date, json.dumps(appoint_list)
) # finish compute, revise 'identify_in_state' uid status
return '1'
def compute_mid_result(task_name, task_submit_date):
result = {'count_0':{}, 'count_1':{}, 'sentiment_0_126':{}, 'sentiment_0_127':{}, 'sentiment_0_128':{},\
'sentiment_0_129':{}, 'sentiment_0_130':{}, 'sensitive_score':{}, 'geo_0':{}, 'geo_1':{},\
'hashtag_0':{}, 'hashtag_1':{}, 'sentiment_1_126':{}, 'sentiment_1_127':{}, \
'sentiment_1_128':{}, 'sentiment_1_129':{}, 'sentiment_1_130':{}}
#geo & hashtag: day
#other: 15min
search_time_segment = 3600 * 4
#start_ts = datetime2ts(task_submit_date)
start_ts = date2ts(task_submit_date)
now_ts = time.time()
now_date = ts2datetime(now_ts)
#test
now_ts = datetime2ts('2013-09-08')
date_ts = datetime2ts(now_date)
segment = int((now_ts - date_ts) / 900) + 1
end_ts = date_ts + segment * 900
#every search time-range: 4 hour----bulk action to search
begin_ts = start_ts
while True:
if begin_ts >= end_ts:
break
compute_ts = ts2date(begin_ts)
#print 'compute ts:', compute_ts
query_body = {'range':{'timestamp':{'from': begin_ts, 'to':begin_ts+search_time_segment}}}
try:
mid_result_list = es.search(index=monitor_index_name, doc_type=task_name, body={'query':query_body, 'size':100000, 'sort':[{'timestamp':{'order': 'asc'}}]})['hits']['hits']
except Exception, e:
raise e
if mid_result_list:
for mid_result_item in mid_result_list:
result_item = mid_result_item['_source']
timestamp = result_item['timestamp']
#attr_count
#print 'compute_count'
count_dict = json.loads(result_item['count'])
for sensitive in count_dict:
count_key = 'count_' + sensitive
result[count_key][str(timestamp)] = count_dict[sensitive]
#attr_sentiment
#print 'compute_sentiment'
sensitive_sentiment_dict = json.loads(result_item['sentiment'])
for sensitive in sensitive_sentiment_dict:
sentiment_dict = sensitive_sentiment_dict[sensitive]
for sentiment in sentiment_dict:
sentiment_key = 'sentiment_'+sensitive+'_'+sentiment
result[sentiment_key][str(timestamp)] = sentiment_dict[sentiment]
#attr_sensitive_score
#print 'compute_sensitive_word'
if 'sensitive_word' in result_item:
sensitive_word_dict = json.loads(result_item['sensitive_word'])
else:
sensitive_word_dict = {}
ts_word_score = 0
for word in sensitive_word_dict:
#print 'word:', json.dumps(word.encode('utf-8')), word.encode('utf-8'), type(word.encode('utf-8'))
search_word = word.encode('utf-8')
#print 'search_word:', search_word, type(search_word)
try:
word_identify = json.loads(word_r.hget('sensitive_words', search_word))
except:
word_identify = [2]
ts_word_score += sensitive_word_dict[word] * word_identify[0]
result['sensitive_score'][str(timestamp)] = ts_word_score
#attr_geo
#print 'compute geo'
timestamp_date = ts2datetime(timestamp)
sensitive_geo_dict = json.loads(result_item['geo'])
for sensitive in sensitive_geo_dict:
if timestamp_date not in result['geo_'+sensitive]:
result['geo_'+sensitive][timestamp_date] = {}
geo_dict = sensitive_geo_dict[sensitive]
for geo in geo_dict:
try:
result['geo_'+sensitive][timestamp_date][geo] += geo_dict[geo]
except:
result['geo_'+sensitive][timestamp_date][geo] = geo_dict[geo]
#attr_hashtag
#print 'compute hashtag'
if 'hashtag' in result_item:
sensitive_hashtag_dict = json.loads(result_item['hashtag'])
else:
sensitive_hashtag_dict = {}
result['hashtag_0'][timestamp_date] = {}
result['hashtag_1'][timestamp_date] = {}
for sensitive in sensitive_hashtag_dict:
for sensitive in sensitive_hashtag_dict:
if timestamp_date not in result['hashtag_'+sensitive]:
result['hashtag_'+sensitive][timestamp_date] = {}
hashtag_dict = sensitive_hashtag_dict[sensitive]
for hashtag in hashtag_dict:
try:
result['hashtag_'+sensitive][timestamp_date][hashtag] += hashtag_dict[hashtag]
except:
result['hashtag_'+sensitive][timestamp_date][hashtag] = hashtag_dict[hashtag]
begin_ts += search_time_segment
def identify_in(data):
appoint_list = []
now_list = []
sensitive_list = set()
influence_list = set()
for item in data:
date = item[0] # 2015-09-22
date = str(date).replace('-','')
uid = item[1]
status = str(item[2])
source = str(item[3])
if source == '1':
r.hset('identify_in_sensitive_'+str(date), uid, status) # identify in user_list and compute status
sensitive_list.add(uid)
elif source == '2':
r.hset('identify_in_influence_'+str(date), uid, status)
influence_list.add(uid)
if status == '1': # now
now_list.append([uid, source])
if status == '2': # appoint
appoint_list.append([uid, source])
sensitive_results = r.hget('recommend_sensitive', date)
if sensitive_results and sensitive_results != '0':
sensitive_results = json.loads(sensitive_results)
revise_set = set(sensitive_results) - sensitive_list
if revise_set:
r.hset('recommend_sensitive', date, json.dumps(list(revise_set)))
else:
r.hdel('recommend_sensitive', date)
influence_results = r.hget('recommend_influence', date)
if influence_results and influence_results != '0':
influence_results = json.loads(influence_results)
revise_set = set(influence_results) - influence_list
if revise_set:
r.hset('recommend_influence', date, json.dumps(list(revise_set)))
else:
r.hdel('recommend_influence', date)
# about compute
compute_now_list = r.hget('compute_now', date)
compute_appoint_list = r.hget('compute_appoint', date)
# compute now user list
if compute_now_list:
now_list.extend(json.loads(compute_now_list))
r.hset('compute_now', date, json.dumps(now_list))
else:
r.hset('compute_now', date, json.dumps(now_list))
# appointted compute user list
if compute_appoint_list:
appoint_list.extend(json.loads(compute_appoint_list))
r.hset('compute_appoint', date, json.dumps(appoint_list))
else:
r.hset('compute_appoint', date, json.dumps(appoint_list)) # finish compute, revise 'identify_in_state' uid status
return '1'