def main():
RUN_TYPE = 0
if RUN_TYPE == 1:
now_ts = time.time()
else:
now_ts = datetime2ts(RUN_TEST_TIME)
now_ts = datetime2ts('2013-09-02')
date = ts2datetime(now_ts - DAY)
# auto recommendation: step 1:4
#step1: read from top es_daily_rank
top_user_set, user_dict = search_from_es(date)
#step2: filter black_uid
black_user_set = read_black_user()
subtract_user_set = top_user_set - black_user_set
#step3: filter users have been in
subtract_user_set = list(subtract_user_set)
candidate_results = filter_in(subtract_user_set)
#step4: filter rules about ip count& reposts/bereposts count&activity count
results = filter_rules(candidate_results)
#step5: get sensitive user
sensitive_user = list(get_sensitive_user(date))
results = results - set(sensitive_user) # influence user - sensitive user
new_date = ts2datetime(now_ts)
hashname_influence = "recomment_" + new_date + "_influence"
if results:
for uid in results:
#print uid
r.hset(hashname_influence, uid, "0")
hashname_sensitive = "recomment_" + new_date + "_sensitive"
if sensitive_user:
for uid in sensitive_user:
#print "sensitive"
r.hset(hashname_sensitive, uid, "0")
"""
def main():
RUN_TYPE = 0
if RUN_TYPE == 1:
now_ts = time.time()
else:
now_ts = datetime2ts(RUN_TEST_TIME)
now_ts = datetime2ts('2013-09-02')
date = ts2datetime(now_ts - DAY)
# auto recommendation: step 1:4
#step1: read from top es_daily_rank
top_user_set, user_dict = search_from_es(date)
#step2: filter black_uid
black_user_set = read_black_user()
subtract_user_set = top_user_set - black_user_set
#step3: filter users have been in
subtract_user_set = list(subtract_user_set)
candidate_results = filter_in(subtract_user_set)
#step4: filter rules about ip count& reposts/bereposts count&activity count
results = filter_rules(candidate_results)
#step5: get sensitive user
sensitive_user = list(get_sensitive_user(date))
results = results - set(sensitive_user) # influence user - sensitive user
new_date = ts2datetime(now_ts)
hashname_influence = "recomment_" + new_date + "_influence"
if results:
for uid in results:
#print uid
r.hset(hashname_influence, uid, "0")
hashname_sensitive = "recomment_" + new_date + "_sensitive"
if sensitive_user:
for uid in sensitive_user:
#print "sensitive"
r.hset(hashname_sensitive, uid, "0")
"""
def save_results(save_type, user, recomment_results):
save_mark = False
#run_type
if RUN_TYPE == 1:
now_date = ts2datetime(time.time())
else:
now_date = ts2datetime(datetime2ts(RUN_TEST_TIME))
recomment_hash_name = 'recomment_' + now_date + '_auto'
#print 'save operation results'
R_RECOMMENTATION.hset(recomment_hash_name, user, json.dumps(recomment_results))
save_mark = True
return save_mark
def save_results(save_type, recomment_results):
save_mark = False
#run_type
if RUN_TYPE == 1:
now_date = ts2datetime(time.time())
else:
now_date = ts2datetime(datetime2ts(RUN_TEST_TIME) - DAY)
recomment_hash_name = 'recomment_' + now_date + '_auto'
if save_type == 'hotspot':
#print 'save hotspot results'
R_RECOMMENTATION.hset(recomment_hash_name, 'auto', json.dumps(recomment_results))
save_mark = True
elif save_type == 'operation':
#print 'save operation results'
R_RECOMMENTATION.hmset(recomment_hash_name, recomment_results)
save_mark = True
return save_mark
def change_status_computed(mapping_dict):
hash_name = 'compute'
status = 4
new_mapping_dict = {}
for uid in mapping_dict:
user_list = json.loads(mapping_dict[uid])
user_list[1] = '4'
in_date = user_list[0]
new_mapping_dict[uid] = json.dumps(user_list)
#revise identify_in_date
influence_hashname = 'identify_in_influence_'+str(in_date)
sensitive_hashname = 'identify_in_sensitive_'+str(in_date)
tmp = r.hget(influence_hashname, uid)
if tmp:
r.hset(influence_hashname, uid, '4')
else:
r.hset(sensitive_hashname, uid, '4')
r.hmset(hash_name, new_mapping_dict)
def save_results(save_type, recomment_results):
save_mark = False
#run_type
if RUN_TYPE == 1:
now_date = ts2datetime(time.time())
else:
now_date = ts2datetime(datetime2ts(RUN_TEST_TIME) - DAY)
recomment_hash_name = 'recomment_' + now_date + '_auto'
if save_type == 'hotspot':
#print 'save hotspot results'
R_RECOMMENTATION.hset(recomment_hash_name, 'auto',
json.dumps(recomment_results))
save_mark = True
elif save_type == 'operation':
#print 'save operation results'
R_RECOMMENTATION.hmset(recomment_hash_name, recomment_results)
save_mark = True
return save_mark
def main():
#run_type
if RUN_TYPE == 1:
now_ts = time.time()
else:
now_ts = datetime2ts(RUN_TEST_TIME)
date = ts2datetime(now_ts - DAY)
print date
# auto recommendation: step 1:4
#step1: read from top es_daily_rank
top_user_set, user_dict = search_from_es(date)
#step2: filter black_uid
black_user_set = read_black_user()
subtract_user_set = top_user_set - black_user_set
#step3: filter users have been in
subtract_user_set = list(subtract_user_set)
candidate_results = filter_in(subtract_user_set)
#step4: filter rules about ip count& reposts/bereposts count&activity count
results = filter_rules(candidate_results)
new_date = ts2datetime(now_ts)
hashname_influence = "recomment_" + new_date + "_influence"
if results:
for uid in results:
r.hset(hashname_influence, uid, "0")
#step5: get sensitive user
print date,'date'
sensitive_user = list(get_sensitive_user(date))
hashname_sensitive = "recomment_" + new_date + "_sensitive"
if sensitive_user:
for uid in sensitive_user:
print uid, hashname_sensitive
r.hset(hashname_sensitive, uid, "0")
results.extend(sensitive_user)
results = set(results)
# step6: write to recommentation csv/redis ++for super admin
hashname_submit = "submit_recomment_" + new_date
if results:
for uid in results:
r.hset(hashname_submit, uid, json.dumps({"system":1, "operation":"system"}))
def change_status_computed(mapping_dict):
hash_name = 'compute'
status = 4
new_mapping_dict = {}
for uid in mapping_dict:
user_list = json.loads(mapping_dict[uid])
user_list[1] = '4'
in_date = user_list[0]
new_mapping_dict[uid] = json.dumps(user_list)
#revise identify_in_date
influence_hashname = 'identify_in_influence_' + str(in_date)
sensitive_hashname = 'identify_in_sensitive_' + str(in_date)
manual_hashname = "identify_in_manual_" + str(in_date)
tmp = r.hget(influence_hashname, uid)
tmp1 = r.hget(sensitive_hashname, uid)
if tmp:
r.hset(influence_hashname, uid, '4')
elif tmp1:
r.hset(sensitive_hashname, uid, '4')
else:
r.hset(manual_hashname, uid, '4')
r.hmset(hash_name, new_mapping_dict)
def change_status_compute_fail(mapping_dict):
hash_name = 'compute'
status = 1
new_mapping_dict = {}
for uid in mapping_dict:
user_list = json.loads(mapping_dict[uid])
user_list[1] = '1'
new_mapping_dict[uid] = json.dumps(user_list)
in_date = user_list[0]
#revise identify_in_date
influence_hashname = 'identify_in_influence_'+str(in_date)
sensitive_hashname = 'identify_in_sensitive_'+str(in_date)
manual_hashname = "identify_in_manual_"+str(in_date)
tmp = r.hget(influence_hashname, uid)
tmp1 = r.hget(sensitive_hashname, uid)
if tmp:
r.hset(influence_hashname, uid, '1')
elif tmp1:
r.hset(sensitive_hashname, uid, '1')
else:
r.hset(manual_hashname, uid, '1')
r.hmset(hash_name, new_mapping_dict)
def main():
#run_type
if RUN_TYPE == 1:
now_ts = time.time()
else:
now_ts = datetime2ts(RUN_TEST_TIME)
date = ts2datetime(now_ts - DAY)
# auto recommendation: step 1:4
#step1: read from top es_daily_rank
top_user_set, user_dict = search_from_es(date)
#step2: filter black_uid
black_user_set = read_black_user()
subtract_user_set = top_user_set - black_user_set
#step3: filter users have been in
subtract_user_set = list(subtract_user_set)
candidate_results = filter_in(subtract_user_set)
#step4: filter rules about ip count& reposts/bereposts count&activity count
results = filter_rules(candidate_results)
new_date = ts2datetime(now_ts)
hashname_influence = "recomment_" + new_date + "_influence"
if results:
for uid in results:
r.hset(hashname_influence, uid, "0")
#step5: get sensitive user
sensitive_user = list(get_sensitive_user(date))
hashname_sensitive = "recomment_" + new_date + "_sensitive"
if sensitive_user:
for uid in sensitive_user:
r.hset(hashname_sensitive, uid, "0")
results.extend(sensitive_user)
results = set(results)
#step6: write to recommentation csv/redis
hashname_submit = "submit_recomment_" + new_date
if results:
for uid in results:
r.hset(hashname_submit, uid, json.dumps({"system":1, "operation":"system"}))
def scan_compute_redis():
hash_name = 'compute'
results = r.hgetall('compute')
iter_user_list = []
mapping_dict = dict()
for uid in results:
user_list = json.loads(results[uid])
in_date = user_list[0]
status = user_list[1]
if status == '2':
iter_user_list.append(uid)
mapping_dict[uid] = json.dumps([in_date, '3']) # mark status:3 computing
#revise identify_in_date
influence_hashname = 'identify_in_influence_'+str(in_date)
sensitive_hashname = 'identify_in_sensitive_'+str(in_date)
tmp = r.hget(influence_hashname, uid)
if tmp:
r.hset(influence_hashname, uid, '3')
else:
r.hset(sensitive_hashname, uid, '3')
if len(iter_user_list) % 100 == 0 and len(iter_user_list) != 0:
#mark status from 1 to 3 as identify_compute to computing
r.hmset('compute', mapping_dict)
#acquire bulk user weibo data
if WEIBO_API_INPUT_TYPE == 0:
user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts = read_flow_text_sentiment(iter_user_list)
else:
user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts = read_flow_text(iter_user_list)
#compute text attribute
compute_status = test_cron_text_attribute_v2(user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts)
if compute_status==True:
change_status_computed(mapping_dict)
else:
change_status_compute_fail(mapping_dict)
#deal user no weibo to compute portrait attribute
if len(user_keywords_dict) != len(iter_user_list):
change_mapping_dict = dict()
change_user_list = set(iter_user_list) - set(user_keywords_dict.keys())
for change_user in change_user_list:
change_mapping_dict[change_user] = json.dumps([in_date, '2'])
r.hmset(change_mapping_dict)
iter_user_list = []
mapping_dict = {}
if iter_user_list != [] and mapping_dict != {}:
r.hmset('compute', mapping_dict)
#acquire bulk user weibo date
if WEIBO_API_INPUT_TYPE == 0:
user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts = read_flow_text_sentiment(iter_user_list)
else:
user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts = read_flow_text(iter_user_list)
#compute text attribute
compute_status = test_cron_text_attribute_v2(user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts)
if compute_status==True:
change_status_computed(mapping_dict)
else:
change_status_compute_fail(mapping_dict)
#deal user no weibo to compute portrait attribute
if len(user_keywords_dict) != len(iter_user_list):
change_mapping_dict = dict()
change_user_list = set(iter_user_list) - set(user_keywords_dict.keys())
for change_user in change_user_list:
change_mapping_dict[change_user] = json.dumps([in_date, '2'])
r.hmset(change_mapping_dict)
def scan_compute_redis():
hash_name = 'compute'
results = r.hgetall('compute')
iter_user_list = []
mapping_dict = dict()
for uid in results:
user_list = json.loads(results[uid])
in_date = user_list[0]
status = user_list[1]
if status == '2':
iter_user_list.append(uid)
mapping_dict[uid] = json.dumps([in_date,
'3']) # mark status:3 computing
#revise identify_in_date
influence_hashname = 'identify_in_influence_' + str(in_date)
sensitive_hashname = 'identify_in_sensitive_' + str(in_date)
manual_hashname = "identify_in_manual_" + str(in_date)
tmp = r.hget(influence_hashname, uid)
tmp1 = r.hget(sensitive_hashname, uid)
if tmp:
r.hset(influence_hashname, uid, '3')
elif tmp1:
r.hset(sensitive_hashname, uid, '3')
else:
r.hset(manual_hashname, uid, '3')
if len(iter_user_list) % 100 == 0 and len(iter_user_list) != 0:
#mark status from 1 to 3 as identify_compute to computing
r.hmset('compute', mapping_dict)
#acquire bulk user weibo data
if WEIBO_API_INPUT_TYPE == 0:
user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts = read_flow_text_sentiment(
iter_user_list)
else:
user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts = read_flow_text(
iter_user_list)
#compute text attribute
compute_status = test_cron_text_attribute_v2(
user_keywords_dict, user_weibo_dict, online_pattern_dict,
character_start_ts)
if compute_status == True:
change_status_computed(mapping_dict)
else:
change_status_compute_fail(mapping_dict)
#deal user no weibo to compute portrait attribute
if len(user_keywords_dict) != len(iter_user_list):
change_mapping_dict = dict()
change_user_list = set(iter_user_list) - set(
user_keywords_dict.keys())
for change_user in change_user_list:
change_mapping_dict[change_user] = json.dumps(
[in_date, '2'])
r.hmset(change_mapping_dict)
iter_user_list = []
mapping_dict = {}
if iter_user_list != [] and mapping_dict != {}:
r.hmset('compute', mapping_dict)
#acquire bulk user weibo date
if WEIBO_API_INPUT_TYPE == 0:
user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts = read_flow_text_sentiment(
iter_user_list)
else:
user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts = read_flow_text(
iter_user_list)
#compute text attribute
compute_status = test_cron_text_attribute_v2(user_keywords_dict,
user_weibo_dict,
online_pattern_dict,
character_start_ts)
if compute_status == True:
change_status_computed(mapping_dict)
else:
change_status_compute_fail(mapping_dict)
#deal user no weibo to compute portrait attribute
if len(user_keywords_dict) != len(iter_user_list):
change_mapping_dict = dict()
change_user_list = set(iter_user_list) - set(
user_keywords_dict.keys())
for change_user in change_user_list:
change_mapping_dict[change_user] = json.dumps([in_date, '2'])
r.hmset(change_mapping_dict)
def save_recommentation2redis(date, user_set):
hash_name = 'recomment_' + str(date)
status = 0
for uid in user_set:
r.hset(hash_name, uid, status)
return True
def save_recommentation2redis(date, user_set):
hash_name = 'recomment_'+str(date)
status = 0
for uid in user_set:
r.hset(hash_name, uid, status)
return True
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)
def save_result(results):
hash_name = 'overview'
for item in results:
r_recomment.hset(hash_name, item, results[item])
return True
# 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
sensitive_uid_in_filter = filter_in(sensitive_uid_recommending_filter)
top_influence_in_filter = filter_in(top_influence_recommending_filter)
print len(sensitive_uid_in_filter)
print len(top_influence_in_filter)
top_influence_filter_result = filter_rules(top_influence_in_filter)
if sensitive_uid_in_filter:
r_recommend.hset('recommend_sensitive', now_date, json.dumps(sensitive_uid_in_filter))
else:
r_recommend.hset('recommend_sensitive', now_date, '0')
if top_influence_filter_result:
r_recommend.hset('recommend_influence', now_date, json.dumps(top_influence_in_filter))
else:
r_recommend.hset('recommend_influence', now_date, '0')
def save_result(results):
hash_name = 'overview'
for item in results:
r_recomment.hset(hash_name, item, results[item])
return True
def save_recommentation2redis(date, user_set):
hash_name = 'recommend'
date = date.replace('-','')
if user_set:
r.hset(hash_name, date, json.dumps(list(user_set)))
return 1
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)
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
sensitive_uid_in_filter = filter_in(sensitive_uid_recommending_filter)
top_influence_in_filter = filter_in(top_influence_recommending_filter)
print len(sensitive_uid_in_filter)
print len(top_influence_in_filter)
top_influence_filter_result = filter_rules(top_influence_in_filter)
if sensitive_uid_in_filter:
r_recommend.hset('recommend_sensitive', now_date,
json.dumps(sensitive_uid_in_filter))
else:
r_recommend.hset('recommend_sensitive', now_date, '0')
if top_influence_filter_result:
r_recommend.hset('recommend_influence', now_date,
json.dumps(top_influence_in_filter))
else:
r_recommend.hset('recommend_influence', now_date, '0')
def save_recommentation2redis(date, user_set):
hash_name = 'recommend'
date = date.replace('-', '')
if user_set:
r.hset(hash_name, date, json.dumps(list(user_set)))
return 1
def scan_compute_redis():
hash_name = 'compute'
results = r.hgetall('compute')
iter_user_list = []
mapping_dict = dict()
identify_in_dict = dict()
#test
count = 0
for uid in results:
user_list = json.loads(results[uid])
in_date = user_list[0]
status = user_list[1]
if int(status) == 1: #imme
#test
count += 1
iter_user_list.append(uid)
mapping_dict[uid] = json.dumps([in_date, '3']) # mark status:3 computing
#revise identify_in_date
influence_hashname = 'identify_in_influence_'+str(in_date)
sensitive_hashname = 'identify_in_sensitive_'+str(in_date)
manual_hashname = "identify_in_manual_"+str(in_date)
tmp = r.hget(influence_hashname, uid)
tmp1 = r.hget(sensitive_hashname, uid)
if tmp:
r.hset(influence_hashname, uid, '3')
elif tmp1:
r.hset(sensitive_hashname, uid, '3')
else:
r.hset(manual_hashname, uid, '3')
if len(iter_user_list) % 100 == 0 and len(iter_user_list) != 0:
print iter_user_list
r.hmset('compute', mapping_dict)
#acquire bulk user weibo data
if WEIBO_API_INPUT_TYPE == 0:
user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts = read_flow_text_sentiment(iter_user_list)
else:
user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts = read_flow_text(iter_user_list)
#compute text attribute
compute_status = test_cron_text_attribute_v2(user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts)
if compute_status==True:
change_status_computed(mapping_dict)
else:
change_status_compute_fail(mapping_dict)
#when uid user no weibo at latest week to change compute status to 1
if len(user_keywords_dict) != len(iter_user_list):
change_mapping_dict = dict()
change_user_list = set(iter_user_list) - set(user_keywords_dict.keys())
for change_user in change_user_list:
change_mapping_dict[change_user] = json.dumps([in_date, '1'])
r.hmset('compute', change_mapping_dict)
iter_user_list = []
mapping_dict = {}
if iter_user_list != [] and mapping_dict != {}:
r.hmset('compute', mapping_dict)
#acquire bulk user weibo date
print 'iter_user_list:', iter_user_list
if WEIBO_API_INPUT_TYPE == 0:
user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts = read_flow_text_sentiment(iter_user_list)
else:
user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts = read_flow_text(iter_user_list)
#compute text attribute
print 'user_weibo_dict:', len(user_weibo_dict)
compute_status = test_cron_text_attribute_v2(user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts)
if compute_status==True:
change_status_computed(mapping_dict)
else:
change_status_compute_fail(mapping_dict)
#when uid user no weibo at latest week to change compute status to 1
if len(user_keywords_dict) != len(iter_user_list):
change_mapping_dict = dict()
change_user_list = set(iter_user_list) - set(user_keywords_dict.keys())
for change_user in change_user_list:
change_mapping_dict[change_user] = json.dumps([in_date, '1'])
r.hmset('compute', change_mapping_dict)
