def save_city(uid, ip, timestamp, sensitive):
ts = ts2datetime(timestamp).replace('-','')
key = str(uid)
try:
if sensitive:
ip_count_string = r_cluster.hget('sensitive_ip_'+str(ts), str(uid))
else:
ip_count_string = r_cluster.hget('ip_'+str(ts), str(uid))
ip_count_dict = json.loads(ip_count_string)
try:
ip_count_dict[str(ip)] += 1
except:
ip_count_dict[str(ip)] = 1
if sensitive:
r_cluster.hset('sensitive_ip_'+str(ts), str(uid), json.dumps(ip_count_dict))
else:
r_cluster.hset('ip_'+str(ts), str(uid), json.dumps(ip_count_dict))
except:
if sensitive:
r_cluster.hset('sensitive_ip_'+str(ts), str(uid), json.dumps({str(ip):1}))
else:
r_cluster.hset('ip_'+str(ts), str(uid), json.dumps({str(ip):1}))
def save_city(uid, ip, timestamp, sensitive):
ts = ts2datetime(timestamp).replace('-', '')
key = str(uid)
try:
if sensitive:
ip_count_string = r_cluster.hget('sensitive_ip_' + str(ts),
str(uid))
else:
ip_count_string = r_cluster.hget('ip_' + str(ts), str(uid))
ip_count_dict = json.loads(ip_count_string)
try:
ip_count_dict[str(ip)] += 1
except:
ip_count_dict[str(ip)] = 1
if sensitive:
r_cluster.hset('sensitive_ip_' + str(ts), str(uid),
json.dumps(ip_count_dict))
else:
r_cluster.hset('ip_' + str(ts), str(uid),
json.dumps(ip_count_dict))
except:
if sensitive:
r_cluster.hset('sensitive_ip_' + str(ts), str(uid),
json.dumps({str(ip): 1}))
else:
r_cluster.hset('ip_' + str(ts), str(uid), json.dumps({str(ip): 1}))
def save_activity(uid, ts, time_segment, sensitive):
key = str(ts)
try:
if sensitive:
activity_count_dict = r_cluster.hget('sensitive_activity_' + key,
str(uid))
else:
activity_count_dict = r_cluster.hget('activity_' + key, str(uid))
activity_count_dict = json.loads(activity_count_dict)
try:
activity_count_dict[str(time_segment)] += 1
except:
activity_count_dict[str(time_segment)] = 1
if sensitive:
r_cluster.hset('sensitive_activity_' + key, str(uid),
json.dumps(activity_count_dict))
else:
r_cluster.hset('activity_' + key, str(uid),
json.dumps(activity_count_dict))
except:
if sensitive:
r_cluster.hset('sensitive_activity_' + key, str(uid),
json.dumps({str(time_segment): 1}))
else:
r_cluster.hset('activity_' + key, str(uid),
json.dumps({str(time_segment): 1}))
def save_at(uid, at_uid, timestamp, sensitive):
ts = ts2datetime(timestamp).replace('-', '')
key = str(uid)
try:
if sensitive:
ruid_count_string = r_cluster.hget('sensitive_at_' + str(ts),
str(uid))
else:
ruid_count_string = r_cluster.hget('at_' + str(ts), str(uid))
ruid_count_dict = json.loads(ruid_count_string)
try:
ruid_count_dict[str(at_uid)] += 1
except:
ruid_count_dict[str(at_uid)] = 1
if sensitive:
r_cluster.hset('sensitive_at_' + str(ts), str(uid),
json.dumps(ruid_count_dict))
else:
r_cluster.hset('at_' + str(ts), str(uid),
json.dumps(ruid_count_dict))
except:
if sensitive:
r_cluster.hset('sensitive_at_' + str(ts), str(uid),
json.dumps({str(at_uid): 1}))
else:
r_cluster.hset('at_' + str(ts), str(uid),
json.dumps({str(at_uid): 1}))
def cal_sensitive_words_work(item, sw_list):
timestamp = item['timestamp']
uid = item['uid']
timestamp = ts2datetime(timestamp).replace('-','')
ts = timestamp
map = {}
for w in sw_list:
word = "".join([chr(x) for x in w])
word = word.decode('utf-8')
if not map.__contains__(word):
map[word] = 1
else:
map[word] += 1
try:
sensitive_count_string = r_cluster.hget('sensitive_'+str(ts), str(uid))
sensitive_count_dict = json.loads(sensitive_count_string)
for word in map:
count = map[word]
if sensitive_count_dict.__contains__(word):
sensitive_count_dict[word] += count
else:
sensitive_count_dict[word] = count
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(sensitive_count_dict))
except:
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(map))
def cal_class_ratio():
ratio_results = {}
date = '2013-09-07'
ts = datetime2ts(date)
scan_count = 0
scan_cursor = 0
all_count = 0
while 1:
if scan_count == 1000000:
break
results = r_cluster.hscan('activity_' + str(ts),
scan_cursor,
count=1000)
scan_cursor = results[0]
scan_count += 1000
for uid in results[1]:
activity_dict_string = r_cluster.hget('activity_' + str(ts), uid)
activity_dict = json.loads(activity_dict_string)
weibo_count = 0
for time_seg in activity_dict:
weibo_count += int(activity_dict[time_seg])
if weibo_count >= 6:
indic_3 = '1'
else:
indic_3 = '0'
retweet_results = r.hgetall('retweet_' + str(uid))
retweet_count = len(retweet_results)
if retweet_count >= 8:
indic_1 = '1'
else:
indic_1 = '0'
be_retweet_results = r.hgetall('be_retweet_' + str(uid))
be_retweet_count = len(be_retweet_results)
#print 'be_retweet_count:', be_retweet_count
if be_retweet_count >= 9:
indic_2 = '1'
else:
indic_2 = '0'
#print 'indic_2:', indic_2
key = indic_1 + indic_2 + indic_3
try:
ratio_results[key] += 1
except:
ratio_results[key] = 1
# write eight type users
'''
if key=='001':
writer1.writerow([uid, retweet_count, be_retweet_count, weibo_count])
elif key=='111':
writer2.writerow([uid, retweet_count, be_retweet_count, weibo_count])
elif key=='101':
writer3.writerow([uid, retweet_count, be_retweet_count, weibo_count])
elif key=='011':
writer4.writerow([uid, retweet_count, be_retweet_count, weibo_count])
elif key=='110':
writer5.writerow([uid, retweet_count, be_retweet_count, weibo_count])
if key=='010':
writer6.writerow([uid, retweet_count, be_retweet_count, weibo_count])
'''
print 'ratio_results:', ratio_results
def cal_ave_weibo():
date = '2013-09-07'
timestamp = datetime2ts(date)
scan_count = 0
scan_cursor = 0
all_count = 0
while 1:
if scan_count == 1000000:
break
results = r_cluster.hscan('activity_' + str(timestamp),
scan_cursor,
count=1000)
scan_cursor = results[0]
scan_count += 1000
for uid in results[1]:
for i in range(0, 1):
ts = timestamp - 24 * 3600 * i
activity_dict_string = r_cluster.hget('activity_' + str(ts),
uid)
if activity_dict_string:
activity_dict = json.loads(activity_dict_string)
weibo_count = 0
for time_seg in activity_dict:
weibo_count += int(activity_dict[time_seg])
all_count += weibo_count
ave_count = float(all_count) / scan_count
print 'ave_count:', ave_count
def cal_text_work(item):
uid = item['uid']
timestamp = item['timestamp']
date = ts2datetime(timestamp)
ts = datetime2ts(date)
text = item['text']
if isinstance(text, str):
text = text.decode('utf-8', 'ignore')
RE = re.compile(u'#([a-zA-Z-_⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]+)#', re.UNICODE)
hashtag_list = RE.findall(text)
if hashtag_list:
# there all use unicode·
hashtag_dict = dict()
for hashtag in hashtag_list:
try:
hashtag_dict[hashtag] += 1
except:
hashtag_dict[hashtag] = 1
try:
hashtag_count_string = r_cluster.hget('hashtag_'+str(ts), str(uid))
hashtag_count_dict = json.loads(hashtag_count_string)
for hashtag in hashtag_dict:
count = hashtag_dict[hashtag]
try:
hashtag_count_dict[hashtag] += count
except:
hashtag_count_dict[hashtag] = count
r_cluster.hset('hashtag_'+str(ts), str(uid), json.dumps(hashtag_count_dict))
except:
r_cluster.hset('hashtag_'+str(ts), str(uid), json.dumps(hashtag_dict))
def filter_activity(user_set):
results = []
#run_type
if RUN_TYPE == 1:
now_date = ts2datetime(time.time())
else:
now_date = RUN_TEST_TIME
ts = datetime2ts(now_date) - DAY
date = ts2datetime(ts)
timestamp = datetime2ts(date)
for user in user_set:
over_count = 0
for i in range(0,7):
ts = timestamp - DAY*i
result = r_cluster.hget('activity_'+str(ts), str(user))
if result:
items_dict = json.loads(result)
for item in items_dict:
weibo_count = items_dict[item]
if weibo_count > activity_threshold:
over_count += 1
if over_count == 0:
results.append(user)
else:
writer.writerow([user, 'activity'])
return results
def cal_text_sensitive(item):
text = item['text']
uid = item['uid']
timestamp = item['timestamp']
date = ts2datetime(timestamp)
ts = datetime2ts(date)
if isinstance(text, str):
text = text.decode('utf-8', 'ignore')
sensitive_result = [word for word in SENSITIVE_WORD if word in text]
if sensitive_result:
sensitive_dict = dict()
for word in sensitive_result:
try:
sensitive_dict[word] += 1
except:
sensitive_dict[word] = 1
try:
sensitive_count_string = r_cluster.hget('sensitive_'+str(ts), str(uid))
sensitive_count_dict = json.loads(sensitive_count_string)
for word in sensitive_dict:
count = sensitive_dict[word]
try:
sensitive_count_dict[word] += count
except:
sensitive_count_dict[word] = count
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(sensitive_count_dict))
except:
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(sensitive_dict))
def cal_text_work(item):
uid = item['uid']
timestamp = item['timestamp']
date = ts2datetime(timestamp)
ts = datetime2ts(date)
text = item['text']
if isinstance(text, str):
text = text.decode('utf-8', 'ignore')
RE = re.compile(u'#([a-zA-Z-_⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]+)#', re.UNICODE)
hashtag_list = RE.findall(text)
if hashtag_list:
# there all use unicode·
hashtag_dict = dict()
for hashtag in hashtag_list:
try:
hashtag_dict[hashtag] += 1
except:
hashtag_dict[hashtag] = 1
try:
hashtag_count_string = r_cluster.hget('hashtag_'+str(ts), str(uid))
hashtag_count_dict = json.loads(hashtag_count_string)
for hashtag in hashtag_dict:
count = hashtag_dict[hashtag]
try:
hashtag_count_dict[hashtag] += count
except:
hashtag_count_dict[hashtag] = count
r_cluster.hset('hashtag_'+str(ts), str(uid), json.dumps(hashtag_count_dict))
except:
r_cluster.hset('hashtag_'+str(ts), str(uid), json.dumps(hashtag_dict))
def filter_activity(user_set):
results = []
now_date = ts2datetime(time.time())
# test
now_date = '2013-09-08'
ts = datetime2ts(now_date) - 24*3600
date = ts2datetime(ts)
#print 'date:', date
timestamp = datetime2ts(date)
for user in user_set:
over_count = 0
for i in range(0,7):
ts = timestamp - 3600*24*i
result = r_cluster.hget('activity_'+str(ts), str(user))
if result:
items_dict = json.loads(result)
for item in items_dict:
weibo_count = items_dict[item]
if weibo_count > activity_threshold:
over_count += 1
if over_count == 0:
results.append(user)
else:
writer.writerow([user, 'activity'])
print 'after filter activity:', len(results)
return results
def filter_activity(user_set):
results = []
#run_type
if RUN_TYPE == 1:
now_date = ts2datetime(time.time())
else:
now_date = RUN_TEST_TIME
ts = datetime2ts(now_date) - DAY
date = ts2datetime(ts)
timestamp = datetime2ts(date)
for user in user_set:
over_count = 0
for i in range(0, 7):
ts = timestamp - DAY * i
result = r_cluster.hget('activity_' + str(ts), str(user))
if result:
items_dict = json.loads(result)
for item in items_dict:
weibo_count = items_dict[item]
if weibo_count > activity_threshold:
over_count += 1
if over_count == 0:
results.append(user)
else:
writer.writerow([user, 'activity'])
return results
def filter_activity(user_set):
results = []
now_date = ts2datetime(time.time())
now_date = '2013-09-08'
ts = datetime2ts(now_date) - 24 * 3600
date = ts2datetime(ts)
timestamp = datetime2ts(date)
ts = ts.replace('-', '')
for user in user_set:
over_count = 0
for i in range(0, 7):
ts = timestamp - 3600 * 24 * i
result = r_cluster.hget('activity_' + str(ts), str(user))
if result:
item_dict = json.loads(result)
sorted_dict = sorted(item_dict.iteritems(),
key=lambda asd: asd[1],
reverse=True)
if sorted_dict[0][1] > activity_threshold:
over_count = 1
if over_count == 0:
results.append(user)
else:
writer.writerow([user, 'activity'])
print 'after filter activity: ', len(results)
return results
def filter_activity(user_set):
results = []
now_date = ts2datetime(time.time())
now_date = '2013-09-08'
ts = datetime2ts(now_date) - 24*3600
date = ts2datetime(ts)
timestamp = datetime2ts(date)
ts = ts.replace('-','')
for user in user_set:
over_count = 0
for i in range(0,7):
ts = timestamp - 3600*24*i
result = r_cluster.hget('activity_'+str(ts), str(user))
if result:
item_dict = json.loads(result)
sorted_dict = sorted(item_dict.iteritems(), key=lambda asd:asd[1], reverse=True)
if sorted_dict[0][1] > activity_threshold:
over_count = 1
if over_count == 0:
results.append(user)
else:
writer.writerow([user, 'activity'])
print 'after filter activity: ', len(results)
return results
def cal_text_sensitive(item):
text = item['text']
uid = item['uid']
timestamp = item['timestamp']
date = ts2datetime(timestamp)
ts = datetime2ts(date)
if isinstance(text, str):
text = text.decode('utf-8', 'ignore')
sensitive_result = [word for word in SENSITIVE_WORD if word in text]
if sensitive_result:
sensitive_dict = dict()
for word in sensitive_result:
try:
sensitive_dict[word] += 1
except:
sensitive_dict[word] = 1
try:
sensitive_count_string = r_cluster.hget('sensitive_'+str(ts), str(uid))
sensitive_count_dict = json.loads(sensitive_count_string)
for word in sensitive_dict:
count = sensitive_dict[word]
try:
sensitive_count_dict[word] += count
except:
sensitive_count_dict[word] = count
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(sensitive_count_dict))
except:
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(sensitive_dict))
def cal_sensitive_words_work(item, sw_list):
timestamp = item['timestamp']
uid = item['uid']
timestamp = ts2datetime(timestamp).replace('-', '')
ts = timestamp
map = {}
for w in sw_list:
word = "".join([chr(x) for x in w])
word = word.decode('utf-8')
if not map.__contains__(word):
map[word] = 1
else:
map[word] += 1
try:
sensitive_count_string = r_cluster.hget('sensitive_' + str(ts),
str(uid))
sensitive_count_dict = json.loads(sensitive_count_string)
for word in map:
count = map[word]
if sensitive_count_dict.__contains__(word):
sensitive_count_dict[word] += count
else:
sensitive_count_dict[word] = count
r_cluster.hset('sensitive_' + str(ts), str(uid),
json.dumps(sensitive_count_dict))
except:
r_cluster.hset('sensitive_' + str(ts), str(uid), json.dumps(map))
def cal_hashtag_work(item, sensitive):
text = item['text']
uid = item['uid']
timestamp = item['timestamp']
ts = ts2datetime(timestamp).replace('-', '')
if isinstance(text, str):
text = text.decode('utf-8', 'ignore')
RE = re.compile(u'#([a-zA-Z-_⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]+)#',
re.UNICODE)
hashtag_list = RE.findall(text)
if hashtag_list:
hashtag_dict = {}
for hashtag in hashtag_list:
try:
hashtag_dict[hashtag] += 1
except:
hashtag_dict[hashtag] = 1
try:
if sensitive:
hashtag_count_string = r_cluster.hget(
'sensitive_hashtag_' + str(ts), str(uid))
else:
hashtag_count_string = r_cluster.hget('hashtag_' + str(ts),
str(uid))
hashtag_count_dict = json.loads(hashtag_count_string)
for hashtag in hashtag_dict:
count = hashtag_dict[hashtag]
try:
hashtag_count_dict[hashtag] += count
except:
hashtag_count_dict[hashtag] = count
if sensitive:
r_cluster.hset('sensitive_hashtag_' + str(ts), str(uid),
json.dumps(hashtag_count_dict))
else:
r_cluster.hset('hashtag_' + str(ts), str(uid),
json.dumps(hashtag_count_dict))
except:
if sensitive:
r_cluster.hset('sensitive_hashtag_' + str(ts), str(uid),
json.dumps(hashtag_dict))
else:
r_cluster.hset('hashtag_' + str(ts), str(uid),
json.dumps(hashtag_dict))
def cal_class_ratio():
ratio_results = {}
date = '2013-09-07'
ts = datetime2ts(date)
scan_count = 0
scan_cursor = 0
all_count = 0
while 1:
if scan_count == 1000000:
break
results = r_cluster.hscan('activity_'+str(ts), scan_cursor, count=1000)
scan_cursor = results[0]
scan_count += 1000
for uid in results[1]:
activity_dict_string = r_cluster.hget('activity_'+str(ts), uid)
activity_dict = json.loads(activity_dict_string)
weibo_count = 0
for time_seg in activity_dict:
weibo_count += int(activity_dict[time_seg])
if weibo_count >= 6:
indic_3 = '1'
else:
indic_3 = '0'
retweet_results = r.hgetall('retweet_'+str(uid))
retweet_count = len(retweet_results)
if retweet_count >= 8:
indic_1 = '1'
else:
indic_1 = '0'
be_retweet_results = r.hgetall('be_retweet_'+str(uid))
be_retweet_count = len(be_retweet_results)
#print 'be_retweet_count:', be_retweet_count
if be_retweet_count >= 9:
indic_2 = '1'
else:
indic_2 = '0'
#print 'indic_2:', indic_2
key = indic_1 + indic_2 + indic_3
try:
ratio_results[key] += 1
except:
ratio_results[key] = 1
# write eight type users
'''
if key=='001':
writer1.writerow([uid, retweet_count, be_retweet_count, weibo_count])
elif key=='111':
writer2.writerow([uid, retweet_count, be_retweet_count, weibo_count])
elif key=='101':
writer3.writerow([uid, retweet_count, be_retweet_count, weibo_count])
elif key=='011':
writer4.writerow([uid, retweet_count, be_retweet_count, weibo_count])
elif key=='110':
writer5.writerow([uid, retweet_count, be_retweet_count, weibo_count])
if key=='010':
writer6.writerow([uid, retweet_count, be_retweet_count, weibo_count])
'''
print 'ratio_results:', ratio_results
def cal_core_class():
date = '2013-09-07'
timestamp = datetime2ts(date)
f_r = open(
'/home/ubuntu8/huxiaoqian/user_portrait/user_portrait/cron/recommentation_in/core_list.csv',
'rb')
reader = csv.reader(f_r)
f_w = open(
'/home/ubuntu8/huxiaoqian/user_portrait/user_portrait/cron/recommentation_in/core_class.csv',
'wb')
writer = csv.writer(f_w)
result_list = []
count011 = 0
for line in reader:
uid = line[0]
retweet_results = r.hgetall('retweet_' + str(uid))
retweet_count = len(retweet_results)
be_retweet_results = r.hgetall('be_retweet_' + str(uid))
be_retweet_count = len(be_retweet_results)
weibo_count = 0
for i in range(0, 7):
ts = timestamp - 24 * 3600 * i
activity_string = r_cluster.hget('activity_' + str(ts), str(uid))
if activity_string:
activity_dict = json.loads(activity_string)
else:
activity_dict = {}
for time_seg in activity_dict:
count = activity_dict[time_seg]
weibo_count += count
ave_weibo_count = float(weibo_count) / 7
if retweet_count >= 8:
indic_1 = '1'
else:
indic_1 = '0'
if be_retweet_count >= 9:
indic_2 = '1'
else:
indic_2 = '0'
if ave_weibo_count >= 6:
indic_3 = '1'
else:
indic_3 = '0'
key = indic_1 + indic_2 + indic_3
if key == '011':
count011 += 1
result_list.append(
[uid, key, retweet_count, be_retweet_count, ave_weibo_count])
f_r.close()
sort_result = sorted(result_list, key=lambda x: x[3], reverse=True)
for item in sort_result:
writer.writerow(list(item))
f_w.close()
print 'count011:', count011
def save_activity(uid, ts, time_segment):
key = str(ts)
try:
activity_count_dict = r_cluster.hget('activity_' + key, str(uid))
activity_count_dict = json.loads(activity_count_dict)
try:
activity_count_dict[str(time_segment)] += 1
except:
activity_count_dict[str(time_segment)] = 1
r_cluster.hset('activity_' + key, str(uid), json.dumps(activity_count_dict))
except:
r_cluster.hset('activity_' + key, str(uid), json.dumps({str(time_segment): 1}))
def cal_hashtag_work(item, sensitive):
text = item['text']
uid = item['uid']
timestamp = item['timestamp']
ts = ts2datetime(timestamp).replace('-','')
if isinstance(text, str):
text = text.decode('utf-8', 'ignore')
RE = re.compile(u'#([a-zA-Z-_⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]+)#', re.UNICODE)
hashtag_list = RE.findall(text)
if hashtag_list:
hashtag_dict = {}
for hashtag in hashtag_list:
try:
hashtag_dict[hashtag] += 1
except:
hashtag_dict[hashtag] = 1
try:
if sensitive:
hashtag_count_string = r_cluster.hget('sensitive_hashtag_'+str(ts), str(uid))
else:
hashtag_count_string = r_cluster.hget('hashtag_'+str(ts), str(uid))
hashtag_count_dict = json.loads(hashtag_count_string)
for hashtag in hashtag_dict:
count = hashtag_dict[hashtag]
try:
hashtag_count_dict[hashtag] += count
except:
hashtag_count_dict[hashtag] = count
if sensitive:
r_cluster.hset('sensitive_hashtag_'+str(ts), str(uid), json.dumps(hashtag_count_dict))
else:
r_cluster.hset('hashtag_'+str(ts), str(uid), json.dumps(hashtag_count_dict))
except:
if sensitive:
r_cluster.hset('sensitive_hashtag_'+str(ts), str(uid), json.dumps(hashtag_dict))
else:
r_cluster.hset('hashtag_'+str(ts), str(uid), json.dumps(hashtag_dict))
def save_at(uid, at_uid, timestamp):
date = ts2datetime(timestamp)
ts = datetime2ts(date)
key = str(uid)
try:
ruid_count_string = r_cluster.hget('at_' + str(ts), str(uid))
ruid_count_dict = json.loads(ruid_count_string)
try:
ruid_count_dict[str(at_uid)] += 1
except:
ruid_count_dict[str(at_uid)] = 1
r_cluster.hset('at_' + str(ts), str(uid), json.dumps(ruid_count_dict))
except:
r_cluster.hset('at_' + str(ts), str(uid), json.dumps({str(at_uid): 1}))
def save_at(uid, at_uid, timestamp):
date = ts2datetime(timestamp)
ts = datetime2ts(date)
key = str(uid)
try:
ruid_count_string = r_cluster.hget('at_'+str(ts), str(uid))
ruid_count_dict = json.loads(ruid_count_string)
try:
ruid_count_dict[str(at_uid)] += 1
except:
ruid_count_dict[str(at_uid)] = 1
r_cluster.hset('at_'+str(ts), str(uid), json.dumps(ruid_count_dict))
except:
r_cluster.hset('at_'+str(ts), str(uid), json.dumps({str(at_uid):1}))
def save_city(uid, ip, timestamp):
date = ts2datetime(timestamp)
ts = datetime2ts(date)
key = str(uid)
try:
ip_count_string = r_cluster.hget('ip_'+str(ts), str(uid))
ip_count_dict = json.loads(ip_count_string)
try:
ip_count_dict[str(ip)] += 1
except:
ip_count_dict[str(ip)] = 1
r_cluster.hset('ip_'+str(ts), str(uid), json.dumps(ip_count_dict))
except:
r_cluster.hset('ip_'+str(ts), str(uid), json.dumps({str(ip):1}))
def cal_core_class():
date = '2013-09-07'
timestamp = datetime2ts(date)
f_r = open('/home/ubuntu8/huxiaoqian/user_portrait/user_portrait/cron/recommentation_in/core_list.csv', 'rb')
reader = csv.reader(f_r)
f_w = open('/home/ubuntu8/huxiaoqian/user_portrait/user_portrait/cron/recommentation_in/core_class.csv', 'wb')
writer = csv.writer(f_w)
result_list = []
count011 = 0
for line in reader:
uid = line[0]
retweet_results = r.hgetall('retweet_'+str(uid))
retweet_count = len(retweet_results)
be_retweet_results = r.hgetall('be_retweet_'+str(uid))
be_retweet_count = len(be_retweet_results)
weibo_count = 0
for i in range(0,7):
ts = timestamp - 24*3600*i
activity_string = r_cluster.hget('activity_'+str(ts), str(uid))
if activity_string:
activity_dict = json.loads(activity_string)
else:
activity_dict = {}
for time_seg in activity_dict:
count = activity_dict[time_seg]
weibo_count += count
ave_weibo_count = float(weibo_count) / 7
if retweet_count >= 8:
indic_1 = '1'
else:
indic_1 = '0'
if be_retweet_count >= 9:
indic_2 = '1'
else:
indic_2 = '0'
if ave_weibo_count >= 6:
indic_3 = '1'
else:
indic_3 = '0'
key = indic_1 + indic_2 + indic_3
if key=='011':
count011 += 1
result_list.append([uid, key, retweet_count, be_retweet_count, ave_weibo_count])
f_r.close()
sort_result = sorted(result_list, key=lambda x:x[3], reverse=True)
for item in sort_result:
writer.writerow(list(item))
f_w.close()
print 'count011:', count011
def save_city_timestamp(uid, ip, timestamp):
date = ts2datetime(timestamp)
ts = datetime2ts(date)
try:
ip_timestamp_string = r_cluster.hget('new_ip_'+str(ts), str(uid))
ip_timestamp_string_dict = json.loads(ip_timestamp_string)
try:
add_string = '&'+str(timestamp)
ip_timestamp_string_dict[str(ip)] += add_string
except:
ip_timestamp_string_dict[str(ip)] = str(timestamp)
r_cluster.hset('new_ip_'+str(ts), str(uid), json.dumps(ip_timestamp_string_dict))
except:
r_cluster.hset('new_ip_'+str(ts), str(uid), json.dumps({str(ip): str(timestamp)}))
def save_at(uid, at_uid, timestamp, sensitive):
ts = ts2datetime(timestamp).replace('-','')
key = str(uid)
try:
if sensitive:
ruid_count_string = r_cluster.hget('sensitive_at_'+str(ts), str(uid))
else:
ruid_count_string = r_cluster.hget('at_'+str(ts), str(uid))
ruid_count_dict = json.loads(ruid_count_string)
try:
ruid_count_dict[str(at_uid)] += 1
except:
ruid_count_dict[str(at_uid)] = 1
if sensitive:
r_cluster.hset('sensitive_at_'+str(ts), str(uid), json.dumps(ruid_count_dict))
else:
r_cluster.hset('at_'+str(ts), str(uid), json.dumps(ruid_count_dict))
except:
if sensitive:
r_cluster.hset('sensitive_at_'+str(ts), str(uid), json.dumps({str(at_uid):1}))
else:
r_cluster.hset('at_'+str(ts), str(uid), json.dumps({str(at_uid):1}))
def save_city_timestamp(uid, ip, timestamp):
date = ts2datetime(timestamp)
ts = datetime2ts(date)
try:
ip_timestamp_string = r_cluster.hget('new_ip_' + str(ts), str(uid))
ip_timestamp_string_dict = json.loads(ip_timestamp_string)
try:
add_string = '&' + str(timestamp)
ip_timestamp_string_dict[str(ip)] += add_string
except:
ip_timestamp_string_dict[str(ip)] = str(timestamp)
r_cluster.hset('new_ip_' + str(ts), str(uid),
json.dumps(ip_timestamp_string_dict))
except:
r_cluster.hset('new_ip_' + str(ts), str(uid),
json.dumps({str(ip): str(timestamp)}))
def get_activeness(uid, activity_geo):
result = 0
now_ts = time.time()
now_date = ts2datetime(now_ts)
timestamp = datetime2ts(now_date)
#test
#timestamp = datetime2ts('2013-09-08')
# deal activity_time fft and statusnum
activity_list = []
statusnum = 0
for i in range(1,8):
ts = timestamp - 24*3600*i
r_result = r_cluster.hget('activity_'+str(ts), uid)
if r_result:
r_result = json.loads(r_result)
#print 'r_result:', r_result
for i in range(0,96):
try:
count = r_result[str(i)]
except:
count = 0
activity_list.append(float(count))
#print 'activity_list:', activity_list
statusnum = sum(activity_list)
signal = np.array(activity_list)
fftResult = np.abs(np.fft.fft(signal)) ** 2
n = signal.size
freq = np.fft.fftfreq(n, d=1)
i = 0
max_val = 0
max_freq = 0
for val in fftResult:
#print str(1/freq[i]) + ',' + str(val)
if val>max_val and freq[i]>0:
max_val = val
max_freq = freq[i]
i = i + 1
#print 'i:', i
#print 'max_freq, max_val:', max_freq, max_val
# deal avtivity_geo input: 'geo&geo'
activity_geo_count = len(activity_geo.split('&'))
result = activeness_weight_dict['activity_time'] * math.log(max_freq + 1) + \
activeness_weight_dict['activity_geo'] * math.log(activity_geo_count + 1) +\
activeness_weight_dict['statusnum'] * math.log(statusnum + 1)
#print 'activeness:', result
return result
def get_user_at():
#step1: get_uid_list
uid_list = get_uid_list()
date = ts2datetime(time.time())
ts = datetime2ts(date)
f = open('/home/user_portrait_0320/revised_user_portrait/user_portrait/user_portrait/attribute/uid_at.txt', 'w')
for i in range(1, 8):
ts = ts - DAY
for uid in uid_list:
#try:
result_string = r_cluster.hget('at_' + str(ts), uid)
#except:
# result_string = ''
if result_string:
save_dict = {'ts': ts, 'result': result_string}
f.write('%s\n' % json.dumps(save_dict))
f.close()
def get_activeness(uid, activity_geo):
result = 0
now_ts = time.time()
now_date = ts2datetime(now_ts)
timestamp = datetime2ts(now_date)
#test
timestamp = datetime2ts('2013-09-08')
# deal activity_time fft and statusnum
activity_list = []
statusnum = 0
for i in range(1,8):
ts = timestamp - 24*3600*i
r_result = r_cluster.hget('activity_'+str(ts), uid)
if r_result:
r_result = json.loads(r_result)
#print 'r_result:', r_result
for i in range(0,96):
try:
count = r_result[str(i)]
except:
count = 0
activity_list.append(float(count))
#print 'activity_list:', activity_list
statusnum = sum(activity_list)
signal = np.array(activity_list)
fftResult = np.abs(np.fft.fft(signal)) ** 2
n = signal.size
freq = np.fft.fftfreq(n, d=1)
i = 0
max_val = 0
max_freq = 0
for val in fftResult:
#print str(1/freq[i]) + ',' + str(val)
if val>max_val and freq[i]>0:
max_val = val
max_freq = freq[i]
i = i + 1
#print 'i:', i
#print 'max_freq, max_val:', max_freq, max_val
# deal avtivity_geo input: 'geo&geo'
activity_geo_count = len(activity_geo.split('&'))
result = activeness_weight_dict['activity_time'] * math.log(max_freq + 1) + \
activeness_weight_dict['activity_geo'] * math.log(activity_geo_count + 1) +\
activeness_weight_dict['statusnum'] * math.log(statusnum + 1)
#print 'activeness:', result
return result
def get_activity_geo(uid):
ip_result = []
now_ts = time.time()
now_date = ts2datetime(now_ts)
ts = datetime2ts(now_date)
geo_result = {}
# test
ts = datetime2ts('2013-09-08')
for i in range(1,8):
ts = ts - 24*3600
r_result = r_cluster.hget('ip_'+str(ts), uid)
if r_result:
ip_list = json.loads(r_result).keys()
ip_result.extend(ip_list)
ip_list = set(ip_result)
geo_string = '&'.join(ip2geo(ip_list))
#print 'geo_string:', geo_string
return geo_string
def get_activity_geo(uid):
ip_result = []
now_ts = time.time()
now_date = ts2datetime(now_ts)
ts = datetime2ts(now_date)
geo_result = {}
# test
ts = datetime2ts('2013-09-08')
for i in range(1,8):
ts = ts - 24*3600
r_result = r_cluster.hget('ip_'+str(ts), uid)
if r_result:
ip_list = json.loads(r_result).keys()
ip_result.extend(ip_list)
ip_list = set(ip_result)
geo_string = '&'.join(ip2geo(ip_list))
#print 'geo_string:', geo_string
return geo_string
def get_user_at():
#step1: get_uid_list
uid_list = get_uid_list()
date = ts2datetime(time.time())
ts = datetime2ts(date)
f = open(
'/home/user_portrait_0320/revised_user_portrait/user_portrait/user_portrait/attribute/uid_at.txt',
'w')
for i in range(1, 8):
ts = ts - DAY
for uid in uid_list:
#try:
result_string = r_cluster.hget('at_' + str(ts), uid)
#except:
# result_string = ''
if result_string:
save_dict = {'ts': ts, 'result': result_string}
f.write('%s\n' % json.dumps(save_dict))
f.close()
def attr_hash(uid):
hashtag_results = {}
now_ts = time.time()
# test
now_ts = datetime2ts('2013-09-08')
date = ts2datetime(now_ts)
ts = datetime2ts(date)
for i in range(1, 8):
ts = ts - 24 * 3600
result_string = r_cluster.hget('hashtag_' + str(ts), str(uid))
if result_string:
result_dict = json.loads(result_string)
for hashtag in result_dict:
count = result_dict[hashtag]
try:
hashtag_results[hashtag] += count
except:
hashtag_results[hashtag] = count
return hashtag_results
def attr_hash(uid):
hashtag_results = {}
now_ts = time.time()
# test
now_ts = datetime2ts('2013-09-08')
date = ts2datetime(now_ts)
ts = datetime2ts(date)
for i in range(1,8):
ts = ts - 24*3600
result_string = r_cluster.hget('hashtag_'+str(ts), str(uid))
if result_string:
result_dict = json.loads(result_string)
for hashtag in result_dict:
count = result_dict[hashtag]
try:
hashtag_results[hashtag] += count
except:
hashtag_results[hashtag] = count
return hashtag_results
def filter_mention(user_set):
results = []
now_date = ts2datetime(time.time())
now_date = '2013-09-08'
timestamp = datetime2ts(now_date) - 24*3600
for user in user_set:
mention_set = set()
for i in range(0,7):
ts = timestamp - 3600*24*i
result = r_cluster.hget('at_'+str(ts), str(user))
if result:
item_dict = json.loads(result)
for at_user in item_dict:
mention_set.add(at_user)
if at_count < mention_threshold:
results.append(user)
else:
writer.writerow([user, 'mention'])
print 'after filter mention: ', len(results)
return results
def filter_mention(user_set):
results = []
now_date = ts2datetime(time.time())
now_date = '2013-09-08'
timestamp = datetime2ts(now_date) - 24 * 3600
for user in user_set:
mention_set = set()
for i in range(0, 7):
ts = timestamp - 3600 * 24 * i
result = r_cluster.hget('at_' + str(ts), str(user))
if result:
item_dict = json.loads(result)
for at_user in item_dict:
mention_set.add(at_user)
if at_count < mention_threshold:
results.append(user)
else:
writer.writerow([user, 'mention'])
print 'after filter mention: ', len(results)
return results
def filter_ip(user_set):
results = []
now_date = ts2datetime(time.time())
now_date = '2013-09-08'
ts = datetime2ts(now_date) - 24*3600
for user in user_set:
ip_set = set()
for i in range(0,7):
timestamp = ts - 3600*24*i
ip_result = r_cluster.hget('ip_'+str(ts), str(user))
if ip_result:
result_dict = json.loads(ip_result)
else:
result_dict = {}
for ip in result_dict:
ip_set.add(ip)
if len(result_dict) < ip_threshold:
results.append(user)
else:
writer.writerow([user, 'ip'])
print 'after filter ip: ', len(results)
return results
def filter_ip(user_set):
results = []
now_date = ts2datetime(time.time())
now_date = '2013-09-08'
ts = datetime2ts(now_date) - 24 * 3600
for user in user_set:
ip_set = set()
for i in range(0, 7):
timestamp = ts - 3600 * 24 * i
ip_result = r_cluster.hget('ip_' + str(ts), str(user))
if ip_result:
result_dict = json.loads(ip_result)
else:
result_dict = {}
for ip in result_dict:
ip_set.add(ip)
if len(result_dict) < ip_threshold:
results.append(user)
else:
writer.writerow([user, 'ip'])
print 'after filter ip: ', len(results)
return results
def filter_ip(user_set):
results = []
#run_type
if RUN_TYPE == 1:
now_date = ts2datetime(time.time())
else:
now_date = RUN_TEST_TIME
ts = datetime2ts(now_date) - DAY
for user in user_set:
ip_set = set()
for i in range(0,7):
timestamp = ts - DAY*i
ip_result = r_cluster.hget('ip_'+str(ts), str(user))
if ip_result:
result_dict = json.loads(ip_result)
else:
result_dict = {}
for ip in result_dict:
ip_set.add(ip)
if len(ip_set) < ip_threshold:
results.append(user)
else:
writer.writerow([user, 'ip'])
return results
def filter_ip(user_set):
results = []
#run_type
if RUN_TYPE == 1:
now_date = ts2datetime(time.time())
else:
now_date = RUN_TEST_TIME
ts = datetime2ts(now_date) - DAY
for user in user_set:
ip_set = set()
for i in range(0, 7):
timestamp = ts - DAY * i
ip_result = r_cluster.hget('ip_' + str(ts), str(user))
if ip_result:
result_dict = json.loads(ip_result)
else:
result_dict = {}
for ip in result_dict:
ip_set.add(ip)
if len(ip_set) < ip_threshold:
results.append(user)
else:
writer.writerow([user, 'ip'])
return results
def cal_ave_weibo():
date = '2013-09-07'
timestamp = datetime2ts(date)
scan_count = 0
scan_cursor = 0
all_count = 0
while 1:
if scan_count == 1000000:
break
results = r_cluster.hscan('activity_'+str(timestamp), scan_cursor, count=1000)
scan_cursor = results[0]
scan_count += 1000
for uid in results[1]:
for i in range(0,1):
ts = timestamp - 24*3600*i
activity_dict_string = r_cluster.hget('activity_'+str(ts), uid)
if activity_dict_string:
activity_dict = json.loads(activity_dict_string)
weibo_count = 0
for time_seg in activity_dict:
weibo_count += int(activity_dict[time_seg])
all_count += weibo_count
ave_count = float(all_count) / scan_count
print 'ave_count:', ave_count
def filter_mention(user_set):
results = []
#run_type
if RUN_TYPE == 1:
now_date = ts2datetime(time.time())
else:
now_date = RUN_TEST_TIME
timestamp = datetime2ts(now_date) - DAY
date = ts2datetime(timestamp)
for user in user_set:
mention_set = set()
for i in range(0, 7):
ts = timestamp - DAY * i
result = r_cluster.hget('at_' + str(ts), str(user))
if result:
item_dict = json.loads(result)
for at_user in item_dict:
mention_set.add(at_user)
at_count = len(mention_set)
if at_count < mention_threshold:
results.append(user)
else:
writer.writerow([user, 'mention'])
return results
def filter_mention(user_set):
results = []
#run_type
if RUN_TYPE == 1:
now_date = ts2datetime(time.time())
else:
now_date = RUN_TEST_TIME
timestamp = datetime2ts(now_date) - DAY
date = ts2datetime(timestamp)
for user in user_set:
mention_set = set()
for i in range(0,7):
ts = timestamp - DAY*i
result = r_cluster.hget('at_'+str(ts), str(user))
if result:
item_dict = json.loads(result)
for at_user in item_dict:
mention_set.add(at_user)
at_count = len(mention_set)
if at_count < mention_threshold:
results.append(user)
else:
writer.writerow([user, 'mention'])
return results
def test(ft_type):
print ft_type
if ft_type == 'facebook':
index_name_pre = facebook_flow_text_index_name_pre
index_type = facebook_flow_text_index_type
user_index_name = facebook_user_index_name
user_index_type = facebook_user_index_type
else:
index_name_pre = twitter_flow_text_index_name_pre
index_type = twitter_flow_text_index_type
user_index_name = twitter_user_index_name
user_index_type = twitter_user_index_type
# date_list = load_date_list(True)
date_list = load_date_list()
DFA = createWordTree()
query_body = {
'post_filter': {
'missing': {
'field': 'keywords_string'
}
},
'query': {
'filtered': {
'filter': {
'bool': {
'must': [{
'range': {
'flag_ch': {
'gte': -1
}
}
}]
}
}
}
}
}
for date in date_list:
count = 0
bulk_action = []
index_name = index_name_pre + date
try:
es_scan_results = scan(es,
query=query_body,
size=1000,
index=index_name,
doc_type=index_type)
while True:
try:
scan_data = es_scan_results.next()
item = scan_data['_source']
text = item['text_ch']
uid = item['uid']
if ft_type == 'facebook':
_id = item['fid']
else:
_id = item['tid']
ts = datetime2ts(date)
#add sentiment field to weibo
sentiment, keywords_list = triple_classifier(item)
#add key words to weibo
keywords_dict, keywords_string = get_weibo_keywords(
keywords_list)
#sensitive_words_dict
sensitive_words_dict = searchWord(
text.encode('utf-8', 'ignore'), DFA)
if sensitive_words_dict:
sensitive_words_string_data = "&".join(
sensitive_words_dict.keys())
sensitive_words_dict_data = json.dumps(
sensitive_words_dict)
else:
sensitive_words_string_data = ""
sensitive_words_dict_data = json.dumps({})
#redis
if sensitive_words_dict:
sensitive_count_string = r_cluster.hget(
'sensitive_' + str(ts), str(uid))
if sensitive_count_string: #redis取空
sensitive_count_dict = json.loads(
sensitive_count_string)
for word in sensitive_words_dict.keys():
if sensitive_count_dict.has_key(word):
sensitive_count_dict[
word] += sensitive_words_dict[word]
else:
sensitive_count_dict[
word] = sensitive_words_dict[word]
r_cluster.hset('sensitive_' + str(ts), str(uid),
json.dumps(sensitive_count_dict))
else:
r_cluster.hset('sensitive_' + str(ts), str(uid),
json.dumps(sensitive_words_dict))
#sensitive
sensitive_score = 0
if sensitive_words_dict:
for k, v in sensitive_words_dict.iteritems():
tmp_stage = r_sensitive.hget("sensitive_words", k)
if tmp_stage:
sensitive_score += v * sensitive_score_dict[
str(tmp_stage)]
#directed_uid
directed_uid_data = 0
directed_uid, directed_uname = get_root_retweet(
text, uid, ft_type)
if directed_uid:
directed_uid_data = long(directed_uid)
# hashtag
hashtag = ''
RE = re.compile(
u'#([0-9a-zA-Z-_⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]+)[ ," =.。： :、]'
)
hashtag_list = re.findall(RE, text)
if hashtag_list:
hashtag = '&'.join(hashtag_list)
#action
action = {'update': {'_id': _id}}
# action_data
action_data = {
'sentiment': str(sentiment),
'keywords_dict': json.dumps(keywords_dict),
'keywords_string': keywords_string,
'sensitive_words_string': sensitive_words_string_data,
'sensitive_words_dict': sensitive_words_dict_data,
'sensitive': sensitive_score,
'directed_uid': directed_uid_data,
'directed_uname': directed_uname,
'hashtag': hashtag,
}
bulk_action.extend([action, {'doc': action_data}])
count += 1
if count % 1000 == 0 and count != 0:
if bulk_action:
es.bulk(bulk_action,
index=index_name,
doc_type=facebook_flow_text_index_type,
timeout=600)
bulk_action = []
count = 0
except StopIteration:
break
if bulk_action:
es.bulk(bulk_action,
index=index_name,
doc_type=facebook_flow_text_index_type,
timeout=600)
except Exception, e: #es文档不存在
print e
def cal_propage_work(item, sensitive_words):
cluster_redis = R_CLUSTER_FLOW1
user = str(item['uid'])
uid = str(item['uid'])
followers_count = item['user_fansnum']
friends_count = item.get("user_friendsnum", 0)
cluster_redis.hset(user, 'user_fansnum', followers_count)
cluster_redis.hset(user, 'user_friendsnum', friends_count)
retweeted_uid = str(item['root_uid'])
retweeted_mid = str(item['root_mid'])
message_type = int(item['message_type'])
mid = str(item['mid'])
timestamp = item['timestamp']
text = item['text']
sw_list = searchWord(text.encode('utf-8'))
sensitive_result = len(sw_list)
if sensitive_result:
ts = ts2datetime(timestamp).replace('-','')
map = {}
for w in sw_list:
word = "".join([chr(x) for x in w])
word = word.decode('utf-8')
if not map.__contains__(word):
map[word] = 1
else:
map[word] += 1
try:
sensitive_count_string = r_cluster.hget('sensitive_'+str(ts), str(uid))
sensitive_count_dict = json.loads(sensitive_count_string)
for word in map:
count = map[word]
if sensitive_count_dict.__contains__(word):
sensitive_count_dict[word] += count
else:
sensitive_count_dict[word] = count
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(sensitive_count_dict))
except:
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(map))
if message_type == 1:
cluster_redis.sadd('user_set', user)
if sensitive_result:
cluster_redis.hset('s_'+user, mid + '_origin_weibo_timestamp', timestamp)
else:
cluster_redis.hset(user, mid + '_origin_weibo_timestamp', timestamp)
elif message_type == 2: # comment weibo
cluster_redis.sadd('user_set', user)
if cluster_redis.sismember(user + '_comment_weibo', retweeted_mid) or cluster_redis.sismember('s_' + user + '_comment_weibo', retweeted_mid):
return
#RE = re.compile(u'//@([a-zA-Z-_⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]+):', re.UNICODE)
#nicknames = RE.findall(text)
if not sensitive_result:
cluster_redis.sadd(user + '_comment_weibo', retweeted_mid)
queue_index = get_queue_index(timestamp)
cluster_redis.hincrby(user, 'comment_weibo', 1)
if 1:
#if len(nicknames) == 0:
cluster_redis.hincrby(retweeted_uid, retweeted_mid + '_origin_weibo_comment', 1)
cluster_redis.hincrby(retweeted_uid, 'origin_weibo_comment_timestamp_%s' % queue_index, 1)
cluster_redis.hset(retweeted_uid, retweeted_mid + '_origin_weibo_comment_timestamp', timestamp)
"""
else:
nick_id_ = nicknames[0]
_id = single_redis.hget(NICK_UID_NAMESPACE, nick_id_)
print _id
single_redis.hset(ACTIVE_NICK_UID_NAMESPACE, nick_id_, _id)
if _id:
cluster_redis.hincrby(str(_id), retweeted_mid + '_retweeted_weibo_comment', 1)
cluster_redis.hincrby(str(_id), 'retweeted_weibo_comment_timestamp_%s' % queue_index, 1)
cluster_redis.hset(str(_id), retweeted_mid + '_retweeted_weibo_comment_timestamp', timestamp)
"""
else:
cluster_redis.sadd('s_' + user + '_comment_weibo', retweeted_mid)
queue_index = get_queue_index(timestamp)
cluster_redis.hincrby('s_'+user, 'comment_weibo', 1)
if 1:
#if len(nicknames) == 0:
cluster_redis.hincrby('s_' + retweeted_uid, retweeted_mid + '_origin_weibo_comment', 1)
cluster_redis.hincrby('s_' + retweeted_uid, 'origin_weibo_comment_timestamp_%s' % queue_index, 1)
cluster_redis.hset('s_' + retweeted_uid, retweeted_mid + '_origin_weibo_comment_timestamp', timestamp)
"""
else:
nick_id_ = nicknames[0]
_id = single_redis.hget(NICK_UID_NAMESPACE, nick_id_)
print _id
single_redis.hset(ACTIVE_NICK_UID_NAMESPACE, nick_id_, _id)
if _id:
cluster_redis.hincrby('s_' + str(_id), retweeted_mid + '_retweeted_weibo_comment', 1)
cluster_redis.hincrby('s_' + str(_id), 'retweeted_weibo_comment_timestamp_%s' % queue_index, 1)
cluster_redis.hset('s_' + str(_id), retweeted_mid + '_retweeted_weibo_comment_timestamp', timestamp)
"""
elif message_type == 3:
cluster_redis.sadd('user_set', user)
if cluster_redis.sismember(user + '_retweeted_weibo', retweeted_mid) or cluster_redis.sismember('s_' + user + '_retweeted_weibo', retweeted_mid):
return
"""
RE = re.compile(u'//@([a-zA-Z-_⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]+):', re.UNICODE)
nicknames = RE.findall(text)
"""
if not sensitive_result:
cluster_redis.sadd(user + '_retweeted_weibo', retweeted_mid)
cluster_redis.hset(user, retweeted_mid + '_retweeted_weibo_timestamp', timestamp)
queue_index = get_queue_index(timestamp)
cluster_redis.hincrby(retweeted_uid, 'origin_weibo_retweeted_timestamp_%s' % queue_index, 1)
cluster_redis.hincrby(retweeted_uid, retweeted_mid + '_origin_weibo_retweeted', 1)
"""
if len(nicknames) != 0:
for nick_id in nicknames:
_id = single_redis.hget(NICK_UID_NAMESPACE, nick_id)
print _id
single_redis.hset(ACTIVE_NICK_UID_NAMESPACE, nick_id, _id)
if _id:
cluster_redis.hincrby(str(_id), retweeted_mid+'_retweeted_weibo_retweeted', 1)
cluster_redis.hset(str(_id), 'retweeted_weibo_retweeted_timestamp', timestamp)
cluster_redis.hincrby(str(_id), 'retweeted_weibo_retweeted_timestamp_%s' % queue_index, 1)
"""
else:
cluster_redis.sadd('s_' + user + '_retweeted_weibo', retweeted_mid)
cluster_redis.hset('s_' + user, retweeted_mid + '_retweeted_weibo_timestamp', timestamp)
queue_index = get_queue_index(timestamp)
cluster_redis.hincrby('s_' +retweeted_uid, 'origin_weibo_retweeted_timestamp_%s' %queue_index, 1)
cluster_redis.hincrby('s_' +retweeted_uid, retweeted_mid + '_origin_weibo_retweeted', 1)
"""
def cal_propage_work(item, sensitive_words):
cluster_redis = R_CLUSTER_FLOW1
user = str(item['uid'])
followers_count = item['user_fansnum']
friends_count = item.get("user_friendsnum", 0)
cluster_redis.hset(user, 'user_fansnum', followers_count)
cluster_redis.hset(user, 'user_friendsnum', friends_count)
retweeted_uid = str(item['root_uid'])
retweeted_mid = str(item['root_mid'])
message_type = int(item['message_type'])
mid = str(item['mid'])
timestamp = item['timestamp']
text = item['text']
sw_list = searchWord(text.encode('utf-8'))
sensitive_result = len(sw_list)
if sensitive_result:
date = ts2datetime(timestamp)
ts = datetime2ts(date)
map = {}
for w in sw_list:
word = "".join([chr(x) for x in w])
word = word.decode('utf-8')
print word
if not map.__contains__(word):
map[word] = 1
else:
map[word] += 1
try:
sensitive_count_string = r_cluster.hget('sensitive_'+str(ts), str(uid))
sensitive_count_dict = json.loads(sensitive_count_string)
for word in map:
count = map[word]
if sensitive_count_dict.__contains__(word):
sensitive_count_dict[word] += count
else:
sensitive_count_dict[word] = count
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(sensitive_count_dict))
except:
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(sensitive_dict))
if message_type == 1:
cluster_redis.sadd('user_set', user)
if sensitive_result:
cluster_redis.hset('s_'+user, mid + '_origin_weibo_timestamp', timestamp)
else:
cluster_redis.hset(user, mid + '_origin_weibo_timestamp', timestamp)
elif message_type == 2: # comment weibo
cluster_redis.sadd('user_set', user)
if cluster_redis.sismember(user + '_comment_weibo', retweeted_mid) or cluster_redis.sismember('s_' + user + '_comment_weibo', retweeted_mid):
return
#RE = re.compile(u'//@([a-zA-Z-_⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]+):', re.UNICODE)
#nicknames = RE.findall(text)
if not sensitive_result:
cluster_redis.sadd(user + '_comment_weibo', retweeted_mid)
queue_index = get_queue_index(timestamp)
cluster_redis.hincrby(user, 'comment_weibo', 1)
if 1:
#if len(nicknames) == 0:
cluster_redis.hincrby(retweeted_uid, retweeted_mid + '_origin_weibo_comment', 1)
cluster_redis.hincrby(retweeted_uid, 'origin_weibo_comment_timestamp_%s' % queue_index, 1)
cluster_redis.hset(retweeted_uid, retweeted_mid + '_origin_weibo_comment_timestamp', timestamp)
"""
else:
nick_id_ = nicknames[0]
_id = single_redis.hget(NICK_UID_NAMESPACE, nick_id_)
print _id
single_redis.hset(ACTIVE_NICK_UID_NAMESPACE, nick_id_, _id)
if _id:
cluster_redis.hincrby(str(_id), retweeted_mid + '_retweeted_weibo_comment', 1)
cluster_redis.hincrby(str(_id), 'retweeted_weibo_comment_timestamp_%s' % queue_index, 1)
cluster_redis.hset(str(_id), retweeted_mid + '_retweeted_weibo_comment_timestamp', timestamp)
"""
else:
cluster_redis.sadd('s_' + user + '_comment_weibo', retweeted_mid)
queue_index = get_queue_index(timestamp)
cluster_redis.hincrby('s_'+user, 'comment_weibo', 1)
if 1:
#if len(nicknames) == 0:
cluster_redis.hincrby('s_' + retweeted_uid, retweeted_mid + '_origin_weibo_comment', 1)
cluster_redis.hincrby('s_' + retweeted_uid, 'origin_weibo_comment_timestamp_%s' % queue_index, 1)
cluster_redis.hset('s_' + retweeted_uid, retweeted_mid + '_origin_weibo_comment_timestamp', timestamp)
"""
else:
nick_id_ = nicknames[0]
_id = single_redis.hget(NICK_UID_NAMESPACE, nick_id_)
print _id
single_redis.hset(ACTIVE_NICK_UID_NAMESPACE, nick_id_, _id)
if _id:
cluster_redis.hincrby('s_' + str(_id), retweeted_mid + '_retweeted_weibo_comment', 1)
cluster_redis.hincrby('s_' + str(_id), 'retweeted_weibo_comment_timestamp_%s' % queue_index, 1)
cluster_redis.hset('s_' + str(_id), retweeted_mid + '_retweeted_weibo_comment_timestamp', timestamp)
"""
elif message_type == 3:
cluster_redis.sadd('user_set', user)
if cluster_redis.sismember(user + '_retweeted_weibo', retweeted_mid) or cluster_redis.sismember('s_' + user + '_retweeted_weibo', retweeted_mid):
return
"""
RE = re.compile(u'//@([a-zA-Z-_⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]+):', re.UNICODE)
nicknames = RE.findall(text)
"""
if not sensitive_result:
cluster_redis.sadd(user + '_retweeted_weibo', retweeted_mid)
cluster_redis.hset(user, retweeted_mid + '_retweeted_weibo_timestamp', timestamp)
queue_index = get_queue_index(timestamp)
cluster_redis.hincrby(retweeted_uid, 'origin_weibo_retweeted_timestamp_%s' % queue_index, 1)
cluster_redis.hincrby(retweeted_uid, retweeted_mid + '_origin_weibo_retweeted', 1)
"""
if len(nicknames) != 0:
for nick_id in nicknames:
_id = single_redis.hget(NICK_UID_NAMESPACE, nick_id)
print _id
single_redis.hset(ACTIVE_NICK_UID_NAMESPACE, nick_id, _id)
if _id:
cluster_redis.hincrby(str(_id), retweeted_mid+'_retweeted_weibo_retweeted', 1)
cluster_redis.hset(str(_id), 'retweeted_weibo_retweeted_timestamp', timestamp)
cluster_redis.hincrby(str(_id), 'retweeted_weibo_retweeted_timestamp_%s' % queue_index, 1)
"""
else:
cluster_redis.sadd('s_' + user + '_retweeted_weibo', retweeted_mid)
cluster_redis.hset('s_' + user, retweeted_mid + '_retweeted_weibo_timestamp', timestamp)
queue_index = get_queue_index(timestamp)
cluster_redis.hincrby('s_' +retweeted_uid, 'origin_weibo_retweeted_timestamp_%s' %queue_index, 1)
cluster_redis.hincrby('s_' +retweeted_uid, retweeted_mid + '_origin_weibo_retweeted', 1)
"""
item['keywords_string'] = keywords_string # use to search
sensitive_words_dict = searchWord(text.encode('utf-8', 'ignore'), DFA)
if sensitive_words_dict:
item['sensitive_words_string'] = "&".join(sensitive_words_dict.keys())
item['sensitive_words_dict'] = json.dumps(sensitive_words_dict)
else:
item['sensitive_words_string'] = ""
item['sensitive_words_dict'] = json.dumps({})
timestamp = item['timestamp']
date = ts2datetime(timestamp)
ts = datetime2ts(date)
if sensitive_words_dict:
print sensitive_words_dict.keys()[0]
sensitive_count_string = r_cluster.hget('sensitive_'+str(ts), str(uid))
if sensitive_count_string: #redis取空
sensitive_count_dict = json.loads(sensitive_count_string)
for word in sensitive_words_dict.keys():
if sensitive_count_dict.has_key(word):
sensitive_count_dict[word] += sensitive_words_dict[word]
else:
sensitive_count_dict[word] = sensitive_words_dict[word]
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(sensitive_count_dict))
else:
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(sensitive_words_dict))
#identify whether to mapping new es
weibo_timestamp = item['timestamp']
should_index_name_date = ts2datetime(weibo_timestamp)
if should_index_name_date != now_index_name_date:
text.encode('utf-8', 'ignore'), DFA)
if sensitive_words_dict:
item['sensitive_words_string'] = "&".join(
sensitive_words_dict.keys())
item['sensitive_words_dict'] = json.dumps(
sensitive_words_dict)
else:
item['sensitive_words_string'] = ""
item['sensitive_words_dict'] = json.dumps({})
timestamp = item['timestamp']
date = ts2datetime(timestamp)
ts = datetime2ts(date)
if sensitive_words_dict:
#print 'sensitive_words_dict...keys[0]...',sensitive_words_dict.keys()[0]
sensitive_count_string = r_cluster.hget(
'sensitive_' + str(ts), str(uid))
if sensitive_count_string: #redis取空
sensitive_count_dict = json.loads(
sensitive_count_string)
for word in sensitive_words_dict.keys():
if sensitive_count_dict.has_key(word):
sensitive_count_dict[
word] += sensitive_words_dict[word]
else:
sensitive_count_dict[
word] = sensitive_words_dict[word]
r_cluster.hset('sensitive_' + str(ts), str(uid),
json.dumps(sensitive_count_dict))
else:
r_cluster.hset('sensitive_' + str(ts), str(uid),
json.dumps(sensitive_words_dict))
