def recommentation_in_auto(seatch_date, submit_user):
results = []
#run type
if RUN_TYPE == 1:
now_date = ts2datetime(time.time() - DAY)
else:
now_date = ts2datetime(datetime2ts(RUN_TEST_TIME) - DAY)
recomment_hash_name = 'recomment_' + now_date + '_auto'
recomment_influence_hash_name = 'recomment_' + now_date + '_influence'
recomment_sensitive_hash_name = 'recomment_' + now_date + '_sensitive'
recomment_compute_hash_name = 'compute'
#step1: get auto
auto_result = r.hget(recomment_hash_name, 'auto')
if auto_result:
auto_user_list = json.loads(auto_result)
else:
auto_user_list = []
#step2: get admin user result
admin_result = r.hget(recomment_hash_name, submit_user)
if admin_result:
admin_user_list = json.loads(admin_result)
else:
admin_user_list = []
#step3: get union user and filter compute/influence/sensitive
union_user_auto_set = set(auto_user_list) | set(admin_user_list)
influence_user = set(r.hkeys(recomment_influence_hash_name))
sensitive_user = set(r.hkeys(recomment_sensitive_hash_name))
compute_user = set(r.hkeys(recomment_compute_hash_name))
filter_union_user = union_user_auto_set - (influence_user | sensitive_user | compute_user)
auto_user_list = list(filter_union_user)
#step4: get user detail
results = get_user_detail(now_date, auto_user_list, 'show_in', 'auto')
return results
def recommentation_in(input_ts, recomment_type, submit_user):
date = ts2datetime(input_ts)
recomment_results = []
# read from redis
results = []
hash_name = 'recomment_'+str(date) + "_" + recomment_type
identify_in_hashname = "identify_in_" + str(date)
submit_user_recomment = "recomment_" + submit_user + "_" + str(date) # 用户自推荐名单
results = r.hgetall(hash_name)
if not results:
return []
# search from user_profile to rich the show information
recommend_list = set(r.hkeys(hash_name))
identify_in_list = set(r.hkeys("compute"))
submit_user_recomment = set(r.hkeys(submit_user_recomment))
recomment_results = list(recommend_list - identify_in_list)
recomment_results = list(set(recomment_results) - submit_user_recomment)
if recomment_results:
results = get_user_detail(date, recomment_results, 'show_in', recomment_type)
else:
results = []
#test
'''
f = open('/home/user_portrait_0320/revised_user_portrait/user_portrait/user_portrait/recommentation/influence_recommend.csv', 'wb')
writer = csv.writer(f)
for item_result in results:
writer.writerow(item_result)
f.close()
'''
return results
def recommentation_in(input_ts, recomment_type, submit_user):
date = ts2datetime(input_ts)
recomment_results = []
# read from redis
results = []
hash_name = 'recomment_' + str(date) + "_" + recomment_type
identify_in_hashname = "identify_in_" + str(date)
submit_user_recomment = "recomment_" + submit_user + "_" + str(
date) # 用户自推荐名单
results = r.hgetall(hash_name)
if not results:
return []
# search from user_profile to rich the show information
recommend_list = set(r.hkeys(hash_name))
identify_in_list = set(r.hkeys("compute"))
submit_user_recomment = set(r.hkeys(submit_user_recomment))
recomment_results = list(recommend_list - identify_in_list)
recomment_results = list(set(recomment_results) - submit_user_recomment)
if recomment_results:
results = get_user_detail(date, recomment_results, 'show_in',
recomment_type)
else:
results = []
return results
def get_recommentation(submit_user):
if RUN_TYPE:
now_ts = time.time()
else:
now_ts = datetime2ts(RUN_TEST_TIME)
in_portrait_set = set(r.hkeys("compute"))
result = []
for i in range(7):
iter_ts = now_ts - i*DAY
iter_date = ts2datetime(iter_ts)
submit_user_recomment = "recomment_" + submit_user + "_" + str(iter_date)
bci_date = ts2datetime(iter_ts - DAY)
submit_user_recomment = r.hkeys(submit_user_recomment)
bci_index_name = "bci_" + bci_date.replace('-', '')
exist_bool = es_cluster.indices.exists(index=bci_index_name)
if not exist_bool:
continue
if submit_user_recomment:
user_bci_result = es_cluster.mget(index=bci_index_name, doc_type="bci", body={'ids':submit_user_recomment}, _source=True)['docs']
user_profile_result = es_user_profile.mget(index='weibo_user', doc_type='user', body={'ids':submit_user_recomment}, _source=True)['docs']
max_evaluate_influ = get_evaluate_max(bci_index_name)
for i in range(len(submit_user_recomment)):
uid = submit_user_recomment[i]
bci_dict = user_bci_result[i]
profile_dict = user_profile_result[i]
try:
bci_source = bci_dict['_source']
except:
bci_source = None
if bci_source:
influence = bci_source['user_index']
influence = math.log(influence/max_evaluate_influ['user_index'] * 9 + 1 ,10)
influence = influence * 100
else:
influence = ''
try:
profile_source = profile_dict['_source']
except:
profile_source = None
if profile_source:
uname = profile_source['nick_name']
location = profile_source['user_location']
fansnum = profile_source['fansnum']
statusnum = profile_source['statusnum']
else:
uname = ''
location = ''
fansnum = ''
statusnum = ''
if uid in in_portrait_set:
in_portrait = "1"
else:
in_portrait = "0"
recomment_day = iter_date
result.append([iter_date, uid, uname, location, fansnum, statusnum, influence, in_portrait])
return result
def identify_compute(data):
results = False
compute_status = 1
hash_name = 'compute'
uid2compute = r.hgetall(hash_name)
for item in data:
uid = item[1]
result = r.hget(hash_name, uid)
in_date = json.loads(result)[0]
r.hset(hash_name, uid, json.dumps([in_date, compute_status]))
return True
def identify_compute(data):
results = False
compute_status = 1
hash_name = 'compute'
uid2compute = r.hgetall(hash_name)
for item in data:
uid = item[1]
result = r.hget(hash_name, uid)
in_date = json.loads(result)[0]
r.hset(hash_name, uid, json.dumps([in_date, compute_status]))
return True
def submit_identify_in_uname(input_data):
date = input_data['date']
submit_user = input_data['user']
upload_data = input_data['upload_data']
# get uname list from upload data
uname_list = upload_data.split('\n')
uid_list = []
#step1: get uid list from uname
profile_exist_result = es_user_profile.search(
index=profile_index_name,
doc_type=profile_index_type,
body={'query': {
'terms': {
'nick_name': uname_list
}
}},
_source=False)['hits']['hits']
for profile_item in profile_exist_result:
uid = profile_item['_id']
uid_list.append(uid)
if not uid_list:
return 'uname list valid'
#step2: filter user not in user_portrait and compute
#step2.1: identify in user_portrait
new_uid_list = []
exist_portrait_result = es_user_portrait.mget(index=portrait_index_name,
doc_type=portrait_index_type,
body={'ids':
uid_list})['docs']
new_uid_list = [
exist_item['_id'] for exist_item in exist_portrait_result
if exist_item['found'] == False
]
if not new_uid_list:
return 'uname list all in'
#step2.2: identify in compute
new_uid_set = set(new_uid_list)
compute_set = set(r.hkeys('compute'))
in_uid_list = list(new_uid_set - compute_set)
if not in_uid_list:
return 'uname list all in'
#step3: save submit
hashname_submit = 'submit_recomment_' + date
hashname_influence = 'recomment_' + date + '_influence'
hashname_sensitive = 'recomment_' + date + '_sensitive'
submit_user_recomment = 'recomment_' + submit_user + '_' + str(date)
auto_recomment_set = set(r.hkeys(hashname_influence)) | set(
r.hkeys(hashname_sensitive))
for in_item in in_uid_list:
if in_item in auto_recomment_set:
tmp = json.loads(r.hget(hashname_submit, in_item))
recommentor_list = tmp['operation'].split('&')
recommentor_list.append(str(submit_user))
new_list = list(set(recommentor_list))
tmp['operation'] = '&'.join(new_list)
else:
tmp = {'system': '0', 'operation': submit_user}
r.hset(hashname_submit, in_item, json.dumps(tmp))
r.hset(submit_user_recomment, in_item, '0')
return True
def submit_identify_in_uname(input_data):
date = input_data['date']
submit_user = input_data['user']
operation_type = input_data['operation_type']
upload_data = input_data['upload_data']
# get uname list from upload data
uname_list_pre = upload_data.split('\n')
uname_list = [item.split('\r')[0] for item in uname_list_pre]
uid_list = []
have_in_user_list = []
invalid_user_list = []
valid_uname_list = []
#step1: get uid list from uname
profile_exist_result = es_user_profile.search(index=profile_index_name, doc_type=profile_index_type, body={'query':{'terms':{'nick_name': uname_list}}}, _source=False, fields=['nick_name'])['hits']['hits']
for profile_item in profile_exist_result:
uid = profile_item['_id']
uid_list.append(uid)
uname = profile_item['fields']['nick_name'][0]
valid_uname_list.append(uname)
invalid_user_list = list(set(uname_list) - set(valid_uname_list))
if len(invalid_user_list) != 0:
return False, 'invalid user info', invalid_user_list
#step2: filter user not in user_portrait and compute
#step2.1: identify in user_portrait
new_uid_list = []
exist_portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, body={'ids': uid_list})['docs']
new_uid_list = [exist_item['_id'] for exist_item in exist_portrait_result if exist_item['found']==False]
have_in_user_list = [exist_item['_id'] for exist_item in exist_portrait_result if exist_item['found']==True]
if not new_uid_list:
return False, 'all user in'
#step2.2: identify in compute
new_uid_set = set(new_uid_list)
compute_set = set(r.hkeys('compute'))
in_uid_list = list(new_uid_set - compute_set)
if not in_uid_list:
return False, 'all user in'
#step3: save submit
hashname_submit = 'submit_recomment_' + date
hashname_influence = 'recomment_' + date + '_influence'
hashname_sensitive = 'recomment_' + date + '_sensitive'
submit_user_recomment = 'recomment_' + submit_user + '_' + str(date)
auto_recomment_set = set(r.hkeys(hashname_influence)) | set(r.hkeys(hashname_sensitive))
#identify final submit user list
final_submit_user_list = []
for in_item in in_uid_list:
if in_item in auto_recomment_set:
tmp = json.loads(r.hget(hashname_submit, in_item))
recommentor_list = tmp['operation'].split('&')
recommentor_list.append(str(submit_user))
new_list = list(set(recommentor_list))
tmp['operation'] = '&'.join(new_list)
else:
tmp = {'system':'0', 'operation': submit_user}
if operation_type == 'submit':
r.hset(hashname_submit, in_item, json.dumps(tmp))
r.hset(submit_user_recomment, in_item, '0')
final_submit_user_list.append(in_item)
return True, invalid_user_list, have_in_user_list, final_submit_user_list
def show_in_history(date):
results = []
hash_name = 'recomment_'+str(date)
r_results = r.hgetall(hash_name)
if r_results:
results = get_user_detail(date, r_results, 'show_in_history')
return results
def show_in_history(date, user_type):
results = []
#hash_name = 'recomment_'+str(date)
identify_in_hashname = "identify_in_" + str(date)
r_results = r.hgetall(identify_in_hashname)
if r_results:
results = get_user_detail(date, r_results, 'show_in_history', user_type)
return results
def show_in_history(date, user_type):
results = []
#hash_name = 'recomment_'+str(date)
identify_in_hashname = "identify_in_" + str(date)
r_results = r.hgetall(identify_in_hashname)
if r_results:
results = get_user_detail(date, r_results, 'show_in_history', user_type)
return results
def submit_identify_in_url(input_data):
date = input_data['date']
submit_user = input_data['user']
operation_type = input_data['operation_type']
upload_data = input_data['upload_data']
#step1: get uid list from input_data url
url_list_pre = upload_data.split('\n')
url_list = [item.split('\r')[0] for item in url_list_pre]
uid_list = []
invalid_uid_list = []
have_in_uid_list = []
for url_item in url_list:
try:
#url_item = 'http://weibo.com/p/1002065727942146/album?.....'
url_list = url_item.split('/')
uid = url_list[4][-10:]
uid_list.append(uid)
except:
invalid_uid_list.append(url_item)
if len(invalid_uid_list)!=0:
return False, 'invalid user info', invalid_uid_list
#step2: identify uid list is not exist in user_portrait and compute
#step2.1: identify in user_portrait
new_uid_list = []
exist_portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, body={'ids':uid_list}, _source=True)['docs']
new_uid_list = [exist_item['_id'] for exist_item in exist_portrait_result if exist_item['found']==False]
have_in_uid_list = [exist_item['_id'] for exist_item in exist_portrait_result if exist_item['found']==True]
#step2.2: identify in compute
new_uid_set = set(new_uid_list)
compute_set = set(r.hkeys('compute'))
in_uid_list = list(new_uid_set - compute_set)
if len(in_uid_list)==0:
return False, 'all user in'
#step3: save
hashname_submit = 'submit_recomment_' + date
hashname_influence = 'recomment_' + date + '_influence'
hashname_sensitive = 'recomment_' + date + '_sensitive'
submit_user_recomment = 'recomment_' + submit_user + '_' + str(date)
auto_recomment_set = set(r.hkeys(hashname_influence)) | set(r.hkeys(hashname_sensitive))
#identify the final submit user
final_submit_user_list = []
for in_item in in_uid_list:
if in_item in auto_recomment_set:
tmp = json.loads(r.hget(hashname_submit, in_item))
recommentor_list = tmp['operation'].split('&')
recommentor_list.append(str(submit_user))
new_list = list(set(recommentor_list))
tmp['operation'] = '&'.join(new_list)
else:
tmp = {'system': '0', 'operation': submit_user}
if operation_type == 'submit':
r.hset(hashname_submit, in_item, json.dumps(tmp))
r.hset(submit_user_recomment, in_item, '0')
final_submit_user_list.append(in_item)
return True, invalid_uid_list, have_in_uid_list, final_submit_user_list
def new_identify_in(data, date, submit_user):
in_status = 1
compute_status = 0
hashname_submit = "submit_recomment_" + date
hashname_influence = "recomment_" + date + "_influence"
hashname_sensitive = "recomment_" + date + "_sensitive"
submit_user_recomment = "recomment_" + submit_user + "_" + str(
date) # 用户自推荐名单
auto_recomment_set = set(r.hkeys(hashname_influence)) | set(
r.hkeys(hashname_sensitive)) # 系统自动推荐名单
for item in data:
date = item[0] # identify the date form '2013-09-01' with web
uid = item[1]
#status = item[2]
if uid in auto_recomment_set:
tmp = json.loads(r.hget(hashname_submit, uid))
recommentor_list = (tmp['operation']).split('&')
recommentor_list.append(str(submit_user))
new_list = list(set(recommentor_list))
tmp['operation'] = "&".join(new_list)
else:
tmp = {"system": "0", "operation": submit_user}
r.hset(hashname_submit, uid, json.dumps(tmp))
r.hset(submit_user_recomment, uid, "0")
return True
def submit_identify_in_url(input_data):
date = input_data['date']
submit_user = input_data['user']
operation_type = input_data['operation_type']
upload_data = input_data['upload_data']
#step1: get uid list from input_data url
url_list_pre = upload_data.split('\n')
url_list = [item.split('\r')[0] for item in url_list_pre]
uid_list = []
invalid_uid_list = []
have_in_uid_list = []
for url_item in url_list:
try:
#url_item = 'http://weibo.com/p/1002065727942146/album?.....'
url_list = url_item.split('/')
uid = url_list[4][-10:]
uid_list.append(uid)
except:
invalid_uid_list.append(url_item)
if len(invalid_uid_list)!=0:
return False, 'invalid user info', invalid_uid_list
#step2: identify uid list is not exist in user_portrait and compute
#step2.1: identify in user_portrait
new_uid_list = []
exist_portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, body={'ids':uid_list}, _source=True)['docs']
new_uid_list = [exist_item['_id'] for exist_item in exist_portrait_result if exist_item['found']==False]
have_in_uid_list = [exist_item['_id'] for exist_item in exist_portrait_result if exist_item['found']==True]
#step2.2: identify in compute
new_uid_set = set(new_uid_list)
compute_set = set(r.hkeys('compute'))
in_uid_list = list(new_uid_set - compute_set)
if len(in_uid_list)==0:
return False, 'all user in'
#step3: save
hashname_submit = 'submit_recomment_' + date
hashname_influence = 'recomment_' + date + '_influence'
hashname_sensitive = 'recomment_' + date + '_sensitive'
submit_user_recomment = 'recomment_' + submit_user + '_' + str(date)
auto_recomment_set = set(r.hkeys(hashname_influence)) | set(r.hkeys(hashname_sensitive))
#identify the final submit user
final_submit_user_list = []
for in_item in in_uid_list:
if in_item in auto_recomment_set:
tmp = json.loads(r.hget(hashname_submit, in_item))
recommentor_list = tmp['operation'].split('&')
recommentor_list.append(str(submit_user))
new_list = list(set(recommentor_list))
tmp['operation'] = '&'.join(new_list)
else:
tmp = {'system': '0', 'operation': submit_user}
if operation_type == 'submit':
r.hset(hashname_submit, in_item, json.dumps(tmp))
r.hset(submit_user_recomment, in_item, '0')
final_submit_user_list.append(in_item)
return True, invalid_uid_list, have_in_uid_list, final_submit_user_list
def get_attr(date):
results = {}
overview_result = r.hgetall(hash_name)
for item in overview_result:
value = overview_result[item]
if isinstance(value, str):
value = json.loads(value)
results[item] = value
return results
def identify_in(data):
in_status = 1
compute_status = 0
compute_hash_name = 'compute'
for item in data:
date = item[0] # identify the date form '2013-09-01' with web
uid = item[1]
status = item[2]
value_string = []
identify_in_hashname = "identify_in_" + str(date)
r.hset(identify_in_hashname, uid, in_status)
if status == '1':
in_date = date
compute_status = '1'
elif status == '2':
in_date = date
compute_status = '2'
r.hset(compute_hash_name, uid, json.dumps([in_date, compute_status]))
return True
def identify_in(data):
in_status = 1
compute_status = 0
compute_hash_name = 'compute'
for item in data:
date = item[0] # identify the date form '2013-09-01' with web
uid = item[1]
status = item[2]
value_string = []
identify_in_hashname = "identify_in_" + str(date)
r.hset(identify_in_hashname, uid, in_status)
if status == '1':
in_date = date
compute_status = '1'
elif status == '2':
in_date = date
compute_status = '2'
r.hset(compute_hash_name, uid, json.dumps([in_date, compute_status]))
return True
def recommentation_in(input_ts, recomment_type):
date = ts2datetime(input_ts)
recomment_results = []
# read from redis
results = []
hash_name = 'recomment_'+str(date) + "_" + recomment_type
identify_in_hashname = "identify_in_" + str(date)
results = r.hgetall(hash_name)
if not results:
return []
# search from user_profile to rich the show information
recommend_list = set(r.hkeys(hash_name))
identify_in_list = set(r.hkeys("compute"))
recomment_results = list(recommend_list - identify_in_list)
if recomment_results:
results = get_user_detail(date, recomment_results, 'show_in', recomment_type)
else:
results = []
return results
def identify_in(data):
#in_status = 1
compute_status = 0
in_hash_name = 'recomment_'
compute_hash_name = 'compute'
for item in data:
date = item[0] # identify the date form '2013-09-01' with web
in_hash_key = in_hash_name + str(date)
uid = item[1]
status = item[2]
value_string = []
r.hset(in_hash_key, uid, status)
if status == '1':
in_date = date
compute_status = '1'
elif status == '2':
in_date = date
compute_status = '2'
r.hset(compute_hash_name, uid, json.dumps([in_date, compute_status]))
return True
def new_identify_in(data, date, submit_user):
in_status = 1
compute_status = 0
hashname_submit = "submit_recomment_" + date
hashname_influence = "recomment_" + date + "_influence"
hashname_sensitive = "recomment_" + date + "_sensitive"
auto_recomment_set = set(r.hkeys(hashname_influence)) | set(r.hkeys(hashname_sensitive)) # 系统自动推荐名单
for item in data:
date = item[0] # identify the date form '2013-09-01' with web
uid = item[1]
#status = item[2]
if uid in auto_recomment_set:
tmp = json.loads(r.hget(hashname_submit, uid))
recommentor_list = (tmp['operation']).split('&')
recommentor_list.append(str(submit_user))
new_list = list(set(recommentor_list))
tmp['operation'] = "&".join(new_list)
else:
tmp = {"system":"0", "operation":submit_user}
r.hset(hashname_submit, uid, json.dumps(tmp))
return True
def admin_recommentation_in(input_ts):
date = ts2datetime(input_ts)
recomment_results = []
identify_in_hashname = "identify_in_" + str(date)
# read from redis
results = []
hashname_submit = "submit_recomment_" + date
results = r.hgetall(hashname_submit)
if not results:
return []
# search from user_profile to rich the show information
submit_set = set(r.hkeys(hashname_submit))
idntify_in_set = set(r.hkeys("compute")) # 已入库用户名单
recomment_results = list(submit_set - idntify_in_set) #过滤一下
if recomment_results:
results = get_user_detail(date, recomment_results, 'show_in', "sensitive", "admin")
sorted_results = sorted(results, key=lambda x:x[-1], reverse=True)
results = sorted_results
else:
results = []
return results
def admin_recommentation_in(input_ts):
date = ts2datetime(input_ts)
recomment_results = []
identify_in_hashname = "identify_in_" + str(date)
# read from redis
results = []
hashname_submit = "submit_recomment_" + date
results = r.hgetall(hashname_submit)
if not results:
return []
# search from user_profile to rich the show information
submit_set = set(r.hkeys(hashname_submit))
idntify_in_set = set(r.hkeys("compute")) # 已入库用户名单
recomment_results = list(submit_set - idntify_in_set) #过滤一下
if recomment_results:
results = get_user_detail(date, recomment_results, 'show_in', "sensitive", "admin")
sorted_results = sorted(results, key=lambda x:x[-1], reverse=True)
results = sorted_results
else:
results = []
return results
def recommentation_in_auto(search_date, submit_user):
results = []
#run type
'''
if RUN_TYPE == 1:
now_date = ts2datetime(time.time() - DAY)
else:
now_date = ts2datetime(datetime2ts(RUN_TEST_TIME) - DAY)
'''
now_date = search_date
recomment_hash_name = 'recomment_' + now_date + '_auto'
recomment_influence_hash_name = 'recomment_' + now_date + '_influence'
recomment_sensitive_hash_name = 'recomment_' + now_date + '_sensitive'
recomment_compute_hash_name = 'compute'
#step1: get auto
auto_result = r.hget(recomment_hash_name, 'auto')
if auto_result:
auto_user_list = json.loads(auto_result)
else:
auto_user_list = []
#step2: get admin user result
admin_result = r.hget(recomment_hash_name, submit_user)
if admin_result:
admin_user_list = json.loads(admin_result)
else:
admin_user_list = []
#step3: get union user and filter compute/influence/sensitive
union_user_auto_set = set(auto_user_list) | set(admin_user_list)
influence_user = set(r.hkeys(recomment_influence_hash_name))
sensitive_user = set(r.hkeys(recomment_sensitive_hash_name))
compute_user = set(r.hkeys(recomment_compute_hash_name))
filter_union_user = union_user_auto_set - (influence_user | sensitive_user
| compute_user)
auto_user_list = list(filter_union_user)
#step4: get user detail
results = get_user_detail(now_date, auto_user_list, 'show_in', 'auto')
return results
def get_attr(date):
results = {}
overview_result = r.hgetall(hash_name)
#print 'overview_result:', overview_result
for item in overview_result:
value = overview_result[item]
if isinstance(value, str):
value = json.loads(value)
'''
if item in rank_field:
value = value[:5]
'''
results[item] = value
#print 'overview result:', results
return results
def submit_identify_in_uname(input_data):
date = input_data['date']
submit_user = input_data['user']
upload_data = input_data['upload_data']
# get uname list from upload data
uname_list = upload_data.split('\n')
uid_list = []
#step1: get uid list from uname
profile_exist_result = es_user_profile.search(index=profile_index_name, doc_type=profile_index_type, body={'query':{'terms':{'nick_name': uname_list}}}, _source=False)['hits']['hits']
for profile_item in profile_exist_result:
uid = profile_item['_id']
uid_list.append(uid)
if not uid_list:
return 'uname list valid'
#step2: filter user not in user_portrait and compute
#step2.1: identify in user_portrait
new_uid_list = []
exist_portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, body={'ids': uid_list})['docs']
new_uid_list = [exist_item['_id'] for exist_item in exist_portrait_result if exist_item['found']==False]
if not new_uid_list:
return 'uname list all in'
#step2.2: identify in compute
new_uid_set = set(new_uid_list)
compute_set = r.hkeys('compute')
in_uid_list = list(new_uid_set - compute_set)
if not in_uid_list:
return 'uname list all in'
#step3: save submit
hashname_submit = 'submit_recomment_' + date
hashname_influence = 'recomment_' + date + '_influence'
hashname_sensitive = 'recomment_' + date + '_sensitive'
submit_user_recomment = 'recomment_' + submit_user + '_' + str(date)
auto_recomment_set = set(r.hkeys(hashname_influence)) | set(r.hkeys(hashname_sensitive))
for in_item in in_uid_list:
if in_item in auto_recomment_set:
tmp = json.loads(r.hget(hashname_submit, uid))
recommentor_list = tmp['operation'].split('&')
recommentor_list.append(str(submit_user))
new_list = list(set(recommentor_list))
tmp['operation'] = '&'.join(new_list)
else:
tmp = {'system':'0', 'operation': submit_user}
r.hset(hashname_submit, uid, json.dumps(tmp))
r.hset(submit_user_recomment, uid, '0')
return True
def submit_identify_in_uid(input_data):
date = input_data['date']
submit_user = input_data['user']
hashname_submit = 'submit_recomment_' + date
hashname_influence = 'recomment_' + date + '_influence'
hashname_sensitive = 'recomment_' + date + '_sensitive'
submit_user_recomment = 'recomment_' + submit_user + '_' + str(date)
auto_recomment_set = set(r.hkeys(hashname_influence)) | set(
r.hkeys(hashname_sensitive))
upload_data = input_data['upload_data']
line_list = upload_data.split('\n')
uid_list = []
for line in line_list:
uid = line[:10]
if len(uid) == 10:
uid_list.append(uid)
#identify the uid is not exist in user_portrait and compute
#step1: filter in user_portrait
new_uid_list = []
exist_portrait_result = es_user_portrait.mget(index=portrait_index_name,
doc_type=portrait_index_type,
body={'ids': uid_list},
_source=False)['docs']
for exist_item in exist_portrait_result:
if exist_item['found'] == False:
new_uid_list.append(exist_item['_id'])
#step2: filter in compute
new_uid_set = set(new_uid_list)
compute_set = set(r.hkeys('compute'))
in_uid_set = list(new_uid_set - compute_set)
for in_item in in_uid_set:
if in_item in auto_recomment_set:
tmp = json.loads(r.hget(hashtname_submit, in_item))
recommentor_list = tmp['operation'].split('&')
recommentor_list.append(str(submit_user))
new_list = list(set(recommentor_list))
tmp['operation'] = '&'.join(new_list)
else:
tmp = {'system': '0', 'operation': submit_user}
r.hset(hashname_submit, in_item, json.dumps(tmp))
r.hset(submit_user_recomment, in_item, '0')
return True
def recommentation_in(input_ts):
date = ts2datetime(input_ts)
recomment_results = []
# read from redis
results = []
hash_name = 'recomment_'+str(date)
results = r.hgetall(hash_name)
if not results:
return results
# search from user_profile to rich the show information
for item in results:
status = results[item]
if status=='0':
recomment_results.append(item)
if recomment_results:
results = get_user_detail(date, recomment_results, 'show_in')
else:
results = []
return results
def submit_identify_in_url(input_data):
date = input_data['date']
submit_user = input_data['user']
upload_data = input_data['upload_data']
#step1: get uid list from input_data url
url_list = upload_data.split('\n')
uid_list = []
for url_item in url_list:
#url_item = 'weibo.com/p/1002065727942146/album?.....'
url_list = url_item.split('/')
uid = url_list[2][-10:]
uid_list.append(uid)
#step2: identify uid list is not exist in user_portrait and compute
#step2.1: identify in user_portrait
new_uid_list = []
exist_portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, body={'ids':uid_list}, _source=True)['docs']
new_uid_list = [exist_item['_id'] for exist_item in exist_portrait_result if exist_item['found']==False]
#step2.2: identify in compute
new_uid_set = set(new_uid_list)
compute_set = r.hkeys('compute')
in_uid_list = list(new_uid_set - compute_set)
#step3: save
hashname_submit = 'submit_recomment_' + date
hashname_influence = 'recomment_' + date + '_influence'
hashname_sensitive = 'recomment_' + date + '_sensitive'
submit_user_recomment = 'recomment_' + submit_user + '_' + str(date)
auto_recomment_set = set(r.hkeys(hashname_influence)) | set(r.hkeys(hashname_sensitive))
for in_item in in_uid_list:
if in_item in auto_recomment_set:
tmp = json.loads(r.hget(hashname_submit, uid))
recommentor_list = tmp['operation'].split('&')
recommentor_list.append(str(submit_user))
new_list = list(set(recommentor_list))
tmp['operation'] = '&'.join(new_list)
else:
tmp = {'system': '0', 'operation': submit_user}
r.hset(hashname_submit, uid, json.dumps(tmp))
r.hset(submit_user_recomment, uid, '0')
return True
def submit_identify_in_uid(input_data):
date = input_data['date']
submit_user = input_data['user']
hashname_submit = 'submit_recomment_' + date
hashname_influence = 'recomment_' + date + '_influence'
hashname_sensitive = 'recomment_' + date + '_sensitive'
submit_user_recomment = 'recomment_' + submit_user + '_' + str(date)
auto_recomment_set = set(r.hkeys(hashname_influence)) | set(r.hkeys(hashname_sensitive))
upload_data = input_data['upload_data']
line_list = upload_data.split('\n')
uid_list = []
for line in line_list:
uid = line[:10]
if len(uid)==10:
uid_list.append(uid)
#identify the uid is not exist in user_portrait and compute
#step1: filter in user_portrait
new_uid_list = []
exist_portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, body={'ids':uid_list}, _source=False)['docs']
for exist_item in exist_portrait_result:
if exist_item['found'] == False:
new_uid_list.append(exist_item['_id'])
#step2: filter in compute
new_uid_set = set(new_uid_list)
compute_set = set(r.hkeys('compute'))
in_uid_set = list(new_uid_set - compute_set)
for in_item in in_uid_set:
if in_item in auto_recomment_set:
tmp = json.loads(r.hget(hashtname_submit, in_item))
recommentor_list = tmp['operation'].split('&')
recommentor_list.append(str(submit_user))
new_list = list(set(recommentor_list))
tmp['operation'] = '&'.join(new_list)
else:
tmp = {'system':'0', 'operation':submit_user}
r.hset(hashname_submit, in_item, json.dumps(tmp))
r.hset(submit_user_recomment, in_item, '0')
return True
def show_compute(date):
results = []
hash_name = 'compute'
r_results = r.hgetall(hash_name)
input_data = {}
#search user profile to inrich information
if r_results and date!='all':
for user in r_results:
item = r_results[user]
in_date = json.loads(item)[0]
if in_date == date:
input_data[user] = item
if input_data:
results = get_user_detail(date, input_data, 'show_compute')
else:
results = []
return results
elif r_results and date=='all':
results = get_user_detail(date, r_results, 'show_compute')
return results
else:
return results
def show_compute(date):
results = []
hash_name = 'compute'
r_results = r.hgetall(hash_name)
input_data = {}
#search user profile to inrich information
if r_results and date!='all':
for user in r_results:
item = r_results[user]
in_date = json.loads(item)[0]
if in_date == date:
input_data[user] = item
if input_data:
results = get_user_detail(date, input_data, 'show_compute')
else:
results = []
return results
elif r_results and date=='all':
results = get_user_detail(date, r_results, 'show_compute')
return results
else:
return results
def get_user_detail(date, input_result, status, user_type="influence", auth=""):
results = []
if status=='show_in':
uid_list = input_result
if status=='show_compute':
uid_list = input_result.keys()
if status=='show_in_history':
uid_list = input_result.keys()
if date!='all':
index_name = 'bci_' + ''.join(date.split('-'))
else:
now_ts = time.time()
now_date = ts2datetime(now_ts)
index_name = 'bci_' + ''.join(now_date.split('-'))
index_type = 'bci'
user_bci_result = es_cluster.mget(index=index_name, doc_type=index_type, body={'ids':uid_list}, _source=True)['docs']
user_profile_result = es_user_profile.mget(index='weibo_user', doc_type='user', body={'ids':uid_list}, _source=True)['docs']
max_evaluate_influ = get_evaluate_max(index_name)
for i in range(0, len(uid_list)):
uid = uid_list[i]
bci_dict = user_bci_result[i]
profile_dict = user_profile_result[i]
try:
bci_source = bci_dict['_source']
except:
bci_source = None
if bci_source:
influence = bci_source['user_index']
influence = math.log(influence/max_evaluate_influ['user_index'] * 9 + 1 ,10)
influence = influence * 100
else:
influence = ''
try:
profile_source = profile_dict['_source']
except:
profile_source = None
if profile_source:
uname = profile_source['nick_name']
location = profile_source['user_location']
fansnum = profile_source['fansnum']
statusnum = profile_source['statusnum']
else:
uname = ''
location = ''
fansnum = ''
statusnum = ''
if status == 'show_in':
if user_type == "sensitive":
tmp_ts = datetime2ts(date) - DAY
tmp_data = r_cluster.hget("sensitive_"+str(tmp_ts), uid)
if tmp_data:
sensitive_dict = json.loads(tmp_data)
sensitive_words = sensitive_dict.keys()
else:
senstive_words = []
results.append([uid, uname, location, fansnum, statusnum, influence, sensitive_words])
else:
results.append([uid, uname, location, fansnum, statusnum, influence])
if auth:
hashname_submit = "submit_recomment_" + date
tmp_data = json.loads(r.hget(hashname_submit, uid))
recommend_list = (tmp_data['operation']).split('&')
admin_list = []
admin_list.append(tmp_data['system'])
admin_list.append(list(set(recommend_list)))
admin_list.append(len(recommend_list))
results[-1].extend(admin_list)
if status == 'show_compute':
in_date = json.loads(input_result[uid])[0]
compute_status = json.loads(input_result[uid])[1]
if compute_status == '1':
compute_status = '3'
results.append([uid, uname, location, fansnum, statusnum, influence, in_date, compute_status])
if status == 'show_in_history':
in_status = input_result[uid]
if user_type == "sensitive":
tmp_ts = datetime2ts(date) - DAY
tmp_data = r_cluster.hget("sensitive_"+str(tmp_ts), uid)
if tmp_data:
sensitive_dict = json.loads(tmp_data)
sensitive_words = sensitive_dict.keys()
results.append([uid, uname, location, fansnum, statusnum, influence, in_status, sensitive_words])
else:
results.append([uid, uname, location, fansnum, statusnum, influence, in_status])
return results
def admin_delete_submit_in(date, uid_list):
status = True
identify_in_hashname = 'identify_in_' + str(date)
for uid in uid_list:
r.hdel(identify_in_hashname, uid)
return status
def get_retweeted_top():
overview_result = r.hgetall(hash_name)
retweeted_top = json.loads(overview_result['top_retweeted_user'])
return retweeted_top
def get_comment_top():
overview_result = r.hgetall(hash_name)
comment_top = json.loads(overview_result['top_comment_user'])
return comment_top
def submit_identify_in_uid(input_data):
date = input_data['date']
submit_user = input_data['user']
operation_type = input_data['operation_type']
hashname_submit = 'submit_recomment_' + date
hashname_influence = 'recomment_' + date + '_influence'
hashname_sensitive = 'recomment_' + date + '_sensitive'
submit_user_recomment = 'recomment_' + submit_user + '_' + str(date)
auto_recomment_set = set(r.hkeys(hashname_influence)) | set(r.hkeys(hashname_sensitive))
upload_data = input_data['upload_data']
line_list = upload_data.split('\n')
uid_list = []
invalid_uid_list = []
for line in line_list:
uid = line.split('\r')[0]
#if len(uid)==10:
# uid_list.append(uid)
if uid != '':
uid_list.append(uid)
if len(invalid_uid_list)!=0:
return False, 'invalid user info', invalid_uid_list
#identify the uid is not exist in user_portrait and compute
#step1: filter in user_portrait
new_uid_list = []
have_in_uid_list = []
try:
exist_portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, body={'ids':uid_list}, _source=False)['docs']
except:
exist_portrait_result = []
if exist_portrait_result:
for exist_item in exist_portrait_result:
if exist_item['found'] == False:
new_uid_list.append(exist_item['_id'])
else:
have_in_uid_list.append(exist_item['_id'])
else:
new_uid_list = uid_list
#step2: filter in compute
new_uid_set = set(new_uid_list)
compute_set = set(r.hkeys('compute'))
in_uid_set = list(new_uid_set - compute_set)
print 'new_uid_set:', new_uid_set
print 'in_uid_set:', in_uid_set
if len(in_uid_set)==0:
return False, 'all user in'
#identify the final add user
final_submit_user_list = []
for in_item in in_uid_set:
if in_item in auto_recomment_set:
tmp = json.loads(r.hget(hashname_submit, in_item))
recommentor_list = tmp['operation'].split('&')
recommentor_list.append(str(submit_user))
new_list = list(set(recommentor_list))
tmp['operation'] = '&'.join(new_list)
else:
tmp = {'system':'0', 'operation':submit_user}
if operation_type == 'submit':
r.hset(hashname_submit, in_item, json.dumps(tmp))
r.hset(submit_user_recomment, in_item, '0')
final_submit_user_list.append(in_item)
return True, invalid_uid_list, have_in_uid_list, final_submit_user_list
def get_importance_top():
overview_result = r.hgetall(hash_name)
importance_top = json.loads(overview_result['top_importance'])
return importance_top
sensitive_words = sensitive_dict.keys()
else:
sensitive_words = []
if sensitive_history_dict.get('fields',0):
#print sensitive_history_dict['fields'][sensitive_string][0]
#print top_sensitive
sensitive_value = math.log(sensitive_history_dict['fields'][sensitive_string][0]/float(top_sensitive)*9+1, 10)*100
#print "sensitive_value", sensitive_value
else:
sensitive_value = 0
results.append([uid, uname, location, fansnum, statusnum, influence, sensitive_words, sensitive_value])
else:
results.append([uid, uname, location, fansnum, statusnum, influence])
if auth:
hashname_submit = "submit_recomment_" + date
tmp_data = json.loads(r.hget(hashname_submit, uid))
recommend_list = (tmp_data['operation']).split('&')
admin_list = []
admin_list.append(tmp_data['system'])
admin_list.append(list(set(recommend_list)))
admin_list.append(len(recommend_list))
results[-1].extend(admin_list)
if status == 'show_compute':
in_date = json.loads(input_result[uid])[0]
compute_status = json.loads(input_result[uid])[1]
if compute_status == '1':
compute_status = '3'
results.append([uid, uname, location, fansnum, statusnum, influence, in_date, compute_status])
if status == 'show_in_history':
in_status = input_result[uid]
if user_type == "sensitive":
def get_recommentation(submit_user):
if RUN_TYPE:
now_ts = time.time()
else:
now_ts = datetime2ts(RUN_TEST_TIME)
in_portrait_set = set(r.hkeys("compute"))
result = []
for i in range(7):
iter_ts = now_ts - i * DAY
iter_date = ts2datetime(iter_ts)
submit_user_recomment = "recomment_" + submit_user + "_" + str(
iter_date)
bci_date = ts2datetime(iter_ts - DAY)
submit_user_recomment = r.hkeys(submit_user_recomment)
bci_index_name = "bci_" + bci_date.replace('-', '')
exist_bool = es_cluster.indices.exists(index=bci_index_name)
if not exist_bool:
continue
if submit_user_recomment:
user_bci_result = es_cluster.mget(
index=bci_index_name,
doc_type="bci",
body={'ids': submit_user_recomment},
_source=True)['docs']
user_profile_result = es_user_profile.mget(
index='weibo_user',
doc_type='user',
body={'ids': submit_user_recomment},
_source=True)['docs']
max_evaluate_influ = get_evaluate_max(bci_index_name)
for i in range(len(submit_user_recomment)):
uid = submit_user_recomment[i]
bci_dict = user_bci_result[i]
profile_dict = user_profile_result[i]
try:
bci_source = bci_dict['_source']
except:
bci_source = None
if bci_source:
influence = bci_source['user_index']
influence = math.log(
influence / max_evaluate_influ['user_index'] * 9 + 1,
10)
influence = influence * 100
else:
influence = ''
try:
profile_source = profile_dict['_source']
except:
profile_source = None
if profile_source:
uname = profile_source['nick_name']
location = profile_source['user_location']
fansnum = profile_source['fansnum']
statusnum = profile_source['statusnum']
else:
uname = ''
location = ''
fansnum = ''
statusnum = ''
if uid in in_portrait_set:
in_portrait = "1"
else:
in_portrait = "0"
recomment_day = iter_date
result.append([
iter_date, uid, uname, location, fansnum, statusnum,
influence, in_portrait
])
return result
def get_online_pattern():
overview_result = r.hgetall(hash_name)
online_pattern = json.loads(overview_result['online_pattern_top'])
return online_pattern
def get_geo_top():
overview_result = r.hgetall(hash_name)
geo_top = json.loads(overview_result['activity_geo_top'])
return geo_top
def get_hashtag_top():
overview_result = r.hgetall(hash_name)
hashtag_top = json.loads(overview_result['hashtag_top'])
return hashtag_top
def get_geo_top():
overview_result = r.hgetall(hash_name)
geo_top = json.loads(overview_result['activity_geo_top'])
return geo_top
def submit_identify_in_uid(input_data):
date = input_data['date']
submit_user = input_data['user']
operation_type = input_data['operation_type']
hashname_submit = 'submit_recomment_' + date
hashname_influence = 'recomment_' + date + '_influence'
hashname_sensitive = 'recomment_' + date + '_sensitive'
submit_user_recomment = 'recomment_' + submit_user + '_' + str(date)
auto_recomment_set = set(r.hkeys(hashname_influence)) | set(r.hkeys(hashname_sensitive))
upload_data = input_data['upload_data']
line_list = upload_data.split('\n')
uid_list = []
invalid_uid_list = []
for line in line_list:
uid = line.split('\r')[0]
#if len(uid)==10:
# uid_list.append(uid)
if uid != '':
uid_list.append(uid)
if len(invalid_uid_list)!=0:
return False, 'invalid user info', invalid_uid_list
#identify the uid is not exist in user_portrait and compute
#step1: filter in user_portrait
new_uid_list = []
have_in_uid_list = []
try:
exist_portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, body={'ids':uid_list}, _source=False)['docs']
except:
exist_portrait_result = []
if exist_portrait_result:
for exist_item in exist_portrait_result:
if exist_item['found'] == False:
new_uid_list.append(exist_item['_id'])
else:
have_in_uid_list.append(exist_item['_id'])
else:
new_uid_list = uid_list
#step2: filter in compute
new_uid_set = set(new_uid_list)
compute_set = set(r.hkeys('compute'))
in_uid_set = list(new_uid_set - compute_set)
print 'new_uid_set:', new_uid_set
print 'in_uid_set:', in_uid_set
if len(in_uid_set)==0:
return False, 'all user in'
#identify the final add user
final_submit_user_list = []
for in_item in in_uid_set:
if in_item in auto_recomment_set:
tmp = json.loads(r.hget(hashname_submit, in_item))
recommentor_list = tmp['operation'].split('&')
recommentor_list.append(str(submit_user))
new_list = list(set(recommentor_list))
tmp['operation'] = '&'.join(new_list)
else:
tmp = {'system':'0', 'operation':submit_user}
if operation_type == 'submit':
r.hset(hashname_submit, in_item, json.dumps(tmp))
r.hset(submit_user_recomment, in_item, '0')
final_submit_user_list.append(in_item)
return True, invalid_uid_list, have_in_uid_list, final_submit_user_list
def get_activeness_top():
overview_result = r.hgetall(hash_name)
activeness_top = json.loads(overview_result['top_activeness'])
return activeness_top
def get_online_pattern():
overview_result = r.hgetall(hash_name)
online_pattern = json.loads(overview_result['online_pattern_top'])
return online_pattern
sensitive_words = sensitive_dict.keys()
else:
sensitive_words = []
if sensitive_history_dict.get('fields',0):
#print sensitive_history_dict['fields'][sensitive_string][0]
#print top_sensitive
sensitive_value = math.log(sensitive_history_dict['fields'][sensitive_string][0]/float(top_sensitive)*9+1, 10)*100
#print "sensitive_value", sensitive_value
else:
sensitive_value = 0
results.append([uid, uname, location, fansnum, statusnum, influence, sensitive_words, sensitive_value])
else:
results.append([uid, uname, location, fansnum, statusnum, influence])
if auth:
hashname_submit = "submit_recomment_" + date
tmp_data = json.loads(r.hget(hashname_submit, uid))
recommend_list = (tmp_data['operation']).split('&')
admin_list = []
admin_list.append(tmp_data['system'])
admin_list.append(list(set(recommend_list)))
admin_list.append(len(recommend_list))
results[-1].extend(admin_list)
if status == 'show_compute':
in_date = json.loads(input_result[uid])[0]
compute_status = json.loads(input_result[uid])[1]
if compute_status == '1':
compute_status = '3'
results.append([uid, uname, location, fansnum, statusnum, influence, in_date, compute_status])
if status == 'show_in_history':
in_status = input_result[uid]
if user_type == "sensitive":
def get_importance_top():
overview_result = r.hgetall(hash_name)
importance_top = json.loads(overview_result['top_importance'])
return importance_top
def get_user_detail(date, input_result, status, user_type="influence", auth=""):
bci_date = ts2datetime(datetime2ts(date) - DAY)
results = []
if status=='show_in':
uid_list = input_result
if status=='show_compute':
uid_list = input_result.keys()
if status=='show_in_history':
uid_list = input_result.keys()
if date!='all':
index_name = 'bci_' + ''.join(bci_date.split('-'))
else:
now_ts = time.time()
now_date = ts2datetime(now_ts)
index_name = 'bci_' + ''.join(now_date.split('-'))
index_type = 'bci'
user_bci_result = es_cluster.mget(index=index_name, doc_type=index_type, body={'ids':uid_list}, _source=True)['docs']
user_profile_result = es_user_profile.mget(index='weibo_user', doc_type='user', body={'ids':uid_list}, _source=True)['docs']
max_evaluate_influ = get_evaluate_max(index_name)
for i in range(0, len(uid_list)):
uid = uid_list[i]
bci_dict = user_bci_result[i]
profile_dict = user_profile_result[i]
try:
bci_source = bci_dict['_source']
except:
bci_source = None
if bci_source:
influence = bci_source['user_index']
influence = math.log(influence/max_evaluate_influ['user_index'] * 9 + 1 ,10)
influence = influence * 100
else:
influence = ''
try:
profile_source = profile_dict['_source']
except:
profile_source = None
if profile_source:
uname = profile_source['nick_name']
location = profile_source['user_location']
fansnum = profile_source['fansnum']
statusnum = profile_source['statusnum']
else:
uname = ''
location = ''
fansnum = ''
statusnum = ''
if status == 'show_in':
if user_type == "sensitive":
tmp_ts = datetime2ts(date) - DAY
tmp_data = r_cluster.hget("sensitive_"+str(tmp_ts), uid)
if tmp_data:
sensitive_dict = json.loads(tmp_data)
sensitive_words = sensitive_dict.keys()
else:
sensitive_words = []
results.append([uid, uname, location, fansnum, statusnum, influence, sensitive_words])
else:
results.append([uid, uname, location, fansnum, statusnum, influence])
if auth:
hashname_submit = "submit_recomment_" + date
tmp_data = json.loads(r.hget(hashname_submit, uid))
recommend_list = (tmp_data['operation']).split('&')
admin_list = []
admin_list.append(tmp_data['system'])
admin_list.append(list(set(recommend_list)))
admin_list.append(len(recommend_list))
results[-1].extend(admin_list)
if status == 'show_compute':
in_date = json.loads(input_result[uid])[0]
compute_status = json.loads(input_result[uid])[1]
if compute_status == '1':
compute_status = '3'
results.append([uid, uname, location, fansnum, statusnum, influence, in_date, compute_status])
if status == 'show_in_history':
in_status = input_result[uid]
if user_type == "sensitive":
tmp_ts = datetime2ts(date) - DAY
tmp_data = r_cluster.hget("sensitive_"+str(tmp_ts), uid)
if tmp_data:
sensitive_dict = json.loads(tmp_data)
sensitive_words = sensitive_dict.keys()
results.append([uid, uname, location, fansnum, statusnum, influence, in_status, sensitive_words])
else:
results.append([uid, uname, location, fansnum, statusnum, influence, in_status])
return results
def get_hashtag_top():
overview_result = r.hgetall(hash_name)
hashtag_top = json.loads(overview_result['hashtag_top'])
return hashtag_top
def get_comment_top():
overview_result = r.hgetall(hash_name)
comment_top = json.loads(overview_result['top_comment_user'])
return comment_top
def admin_delete_submit_in(date, uid_list):
status = True
identify_in_hashname = 'identify_in_' + str(date)
for uid in uid_list:
r.hdel(identify_in_hashname, uid)
return status
def get_retweeted_top():
overview_result = r.hgetall(hash_name)
retweeted_top = json.loads(overview_result['top_retweeted_user'])
return retweeted_top
def get_activeness_top():
overview_result = r.hgetall(hash_name)
activeness_top = json.loads(overview_result['top_activeness'])
return activeness_top