"sort": [{"user_index": {"order": "desc"}}]
}
result = es_cluster.search(index=index_name,doc_type="bci", body=query_body)["hits"]["hits"]
sensitive_uid = []
for item in result:
sensitive_uid.append(item['_source']['uid'])
return sensitive_uid
if __name__ == "__main__":
now_date = ts2datetime(time.time()).replace('-','')
former_date = ts2datetime(time.time()-7*24*3600).replace('-','')
r_recommend.hdel('recommend_sensitive', former_date) # delete 7 days ago recommentation uid_list
r_recommend.hdel('recommend_influence', former_date) # delete 7 days ago recommentation uid_list
now_date = '20130901' # test
sensitive_weibo_uid = search_sensitive_weibo(now_date) # sensitive words uid list, direct recommend in
top_influence_uid = search_top_k(now_date, 10000) # top influence uid list, filter
# step 1: no sensitive user in top influence
revise_influence_uid_list = set(top_influence_uid) - set(sensitive_weibo_uid)
black_uid_list = read_black_user_list()
revise_influence_uid_list = set(revise_influence_uid_list) - set(black_uid_list)
print 'filter black list: ', len(revise_influence_uid_list)
#total = set(sensitive_weibo_uid) | set(top_influence_uid)
# step 2: no recommending
sensitive_uid_recommending_filter = filter_recommend(sensitive_weibo_uid)
top_influence_recommending_filter = filter_recommend(revise_influence_uid_list)
# step 3: no one in portrait
}
result = es_cluster.search(index=index_name,
doc_type="bci",
body=query_body)["hits"]["hits"]
sensitive_uid = []
for item in result:
sensitive_uid.append(item['_source']['uid'])
return sensitive_uid
if __name__ == "__main__":
now_date = ts2datetime(time.time()).replace('-', '')
former_date = ts2datetime(time.time() - 7 * 24 * 3600).replace('-', '')
r_recommend.hdel('recommend_sensitive',
former_date) # delete 7 days ago recommentation uid_list
r_recommend.hdel('recommend_influence',
former_date) # delete 7 days ago recommentation uid_list
now_date = '20130901' # test
sensitive_weibo_uid = search_sensitive_weibo(
now_date) # sensitive words uid list, direct recommend in
top_influence_uid = search_top_k(now_date,
10000) # top influence uid list, filter
# step 1: no sensitive user in top influence
revise_influence_uid_list = set(top_influence_uid) - set(
sensitive_weibo_uid)
black_uid_list = read_black_user_list()
revise_influence_uid_list = set(revise_influence_uid_list) - set(
black_uid_list)
print 'filter black list: ', len(revise_influence_uid_list)
from text_attribute import compute_attribute
reload(sys)
sys.path.append('./../../')
from global_utils import R_RECOMMENTATION as r
from global_utils import es_sensitive_user_text as es_text
from time_utils import datetime2ts, ts2datetime
date = ts2datetime(time.time()-24*3600).replace('-', '')
temp = r.hget('compute_appoint', date)
if temp:
now_list = json.loads(temp)
uid_list = []
count = 0
for item in now_list:
uid_list.append(item[0])
user_weibo_dict = dict()
# extract user weibo text
compute_attribute(user_weibo_dict)
for i in range(now_list):
uid = now_list[i][0]
source = now_list[i][1]
if source == '1':
r.hset('identify_in_sensitive_'+str(date), uid, '3') # finish comoute
else:
r.hset('identify_in_influence_'+str(date), uid, '3')
r.hdel('compute_appoint', date)
from global_utils import es_sensitive_user_text as es_text
from time_utils import datetime2ts, ts2datetime
date = ts2datetime(time.time()).replace('-', '')
temp = r.hget('compute_now', date)
if temp:
now_list = json.loads(temp)
uid_list = []
count = 0
for item in now_list:
uid_list.append(item[0])
user_weibo_dict = dict()
# extract user weibo text
compute_attribute(user_weibo_dict)
for i in range(now_list):
uid = now_list[i][0]
source = now_list[i][1]
if source == '1':
r.hset('identify_in_sensitive_'+str(date), uid, '3') # finish comoute
else:
r.hset('identify_in_influence_'+str(date), uid, '3')
renow_list = json.loads(r.hget('compute_now', date))
revise_set = set(renow_list) - set(now_list)
if revise_set:
r.hset('compute_now', date)
else:
r.hdel('compute_now', date)
from global_utils import R_RECOMMENTATION as r
from global_utils import es_sensitive_user_text as es_text
from time_utils import datetime2ts, ts2datetime
date = ts2datetime(time.time()).replace('-', '')
temp = r.hget('compute_now', date)
if temp:
now_list = json.loads(temp)
uid_list = []
count = 0
for item in now_list:
uid_list.append(item[0])
user_weibo_dict = dict()
# extract user weibo text
compute_attribute(user_weibo_dict)
for i in range(now_list):
uid = now_list[i][0]
source = now_list[i][1]
if source == '1':
r.hset('identify_in_sensitive_' + str(date), uid,
'3') # finish comoute
else:
r.hset('identify_in_influence_' + str(date), uid, '3')
renow_list = json.loads(r.hget('compute_now', date))
revise_set = set(renow_list) - set(now_list)
if revise_set:
r.hset('compute_now', date)
else:
r.hdel('compute_now', date)
