def compute_attribute(user_weibo_dict):
# test
uid_list = user_weibo_dict.keys()
times = len(uid_list)/1000
bulk_action = []
count = 0
count_list = set()
for i in range(times+1):
flow_result = get_flow_information(uid_list[1000*i:1000*(i+1)]) # 流数据更新
register_result = get_profile_information(uid_list) # 背景信息数据更新
for user in uid_list:
weibo_list = user_weibo_dict[user]
results = compute_text_attribute(user, weibo_list) # 文本属性计算
results['uid'] = str(user)
flow_dict = flow_result[str(user)]
results.update(flow_dict)
user_info = {'uid':str(user), 'domain':results['domain'], 'topic':results['topic'], 'activity_geo':results['geo_string']}
evaluation_index = get_evaluate_index(user_info, status='insert')
results.update(evaluation_index)
register_dict = register_result[user]
results.update(register_dict)
action = {'index':{'_id':str(user)}}
bulk_action.extend([action, results])
count_list.add(user)
count += 1
if count % 200 == 0:
es.bulk(bulk_action, index='sensitive_user_portrait_0103', doc_type="user", timeout=60)
bulk_action = []
print count
if bulk_action:
es.bulk(bulk_action, index='sensitive_user_portrait_0103', doc_type="user", timeout=60)
return "1"
def compute_attribute(uid_list=[]):
# test
user_weibo_dict = read_user_weibo(uid_list)
uid_list = user_weibo_dict.keys()
flow_result = get_flow_information(uid_list)
register_result = get_profile_information(uid_list)
bulk_action = []
count = 0
count_list = set()
for user in uid_list:
weibo_list = user_weibo_dict[user]
uname = weibo_list[0]['uname']
results = compute_text_attribute(user, weibo_list)
results['uname'] = uname
results['uid'] = str(user)
flow_dict = flow_result[str(user)]
results.update(flow_dict)
user_info = {'uid':str(user), 'domain':results['domain'], 'topic':results['topic'], 'activity_geo':results['geo_string']}
evaluation_index = get_evaluate_index(user_info, status='insert')
results.update(evaluation_index)
register_dict = register_result[user]
results.update(register_dict)
action = {'index':{'_id':str(user)}}
bulk_action.extend([action, results])
count_list.add(user)
count += 1
if count % 200 == 0:
es.bulk(bulk_action, index=index_name, doc_type="user", timeout=60)
bulk_action = []
print count
if bulk_action:
status = save_user_results(bulk_action)
return "1"
def compute2in(uid_list, user_weibo_dict):
#get user flow information: hashtag, activity_geo, keywords
flow_result = get_flow_information(uid_list)
#get user topic information
topic_results_dict, topic_results_label = topic_classfiy(user_weibo_list)
#get user domain information
domain_results = domain_classfiy(user_weibo_dict)
domain_results_dict = domain_results[0]
domain_results_label = domain_results[1]
#get user psy information
#psy_results_dict = psychology_classfiy(user_weibo_dict)
#get user profile information
register_result = get_profile_information(uid_list)
#get user fansnum max
fansnum_max = get_fansnum_max()
#get user activeness by bulk_action
activeness_results = get_activity_time(uid_list)
#get user inlfuence by bulk action
influence_results = get_influence(uid_list)
#deal bulk action
for user in user_weibo_dict:
weibo_list = user_weibo_dict[user]
uname = weibo_list[0]['uname']
#compute text attribute: online_pattern
results = compute_text_attribute(user, weibo_list)
results['uname'] = uname
results['uid'] = str(user)
#add flow information: hashtag, activity_geo, keywords
flow_dict = flow_result[str(user)]
results = dict(results, **flow_dict)
#add topic attribute
topic_dict = topic_results_dict[user]
results['topic'] = json.dumps(topic_dic) #{topic1_en:pro1, topic2_en:pro, ...}
topic_label = topic_results_label[user]
results['topic_string'] = topic_en2ch(topic_label) #topic1_ch&topic2_ch&topic3_ch
#add domain attribute
user_domain_dict = domain_results_dict[user]
user_domain_label = domain_results_label[user]
results['domain_v3'] = json.dumps(user_domain_dict) #[domain_en1, domain_en2, domain_en3]
results['domain_string'] = domain_en2ch(user_domain_label) #domain_ch
#add psy attribute
user_psy_dict = psy_results_dict[user]
results['psycho_status'] = json.dumps(user_psy_dict)
#add user profile attribute
register_dict = register_result[str(user)]
results = dict(results, **register_dict)
#add user_evaluate attribute---importance
results['importance'] = get_importance(results['domain'], results['topic_string'], results['fansnum'], fansnum)
#add user_evaluate attribute---activeness
user_activeness_time = activeness_results[user]
user_activeness_geo = json.loads(results['activity_geo_dict'])[-1]
results['activeness'] = get_activeness(user_activeness_geo, user_activeness_time)
#add user_evaluate attribute---influence
results['influence'] = influence_results[user]
#bulk_action
action = {'index':{'_id':str(user)}}
bulk_action.extend([action, results])
status = save_user_results(bulk_action)
return True
def update_portrait():
user_weibo_dict = read_user_weibo()
uid_list = user_weibo_dict.keys()
flow_result = get_flow_information(uid_list)
bulk_action = []
count = 0
for user in uid_list:
action = {'update':{'_id': str(user)}}
result = {'doc':flow_result[user]}
bulk_action.extend([action, result])
count += 1
if count % 500 == 0:
es.bulk(bulk_action, index='sensitive_user_portrait', doc_type='user', timeout=60)
bulk_action = []
print count
if bulk_action:
es.bulk(bulk_action, index='sensitive_user_portrait', doc_type='user', timeout=60)
return '1'
def daily_update_portrait(user_weibo_dict): # {uid: [weibo_text]}
uid_list = user_weibo_dict.keys()
bulk_action = []
count = 0
for user in uid_list:
results = dict()
weibo_list = user_weibo_dict[user]
flow_result = get_flow_information(user)
text_result = temporary_text_update(user, weibo_list)
evaluate_result = evaluate_index(user, status='update')
results.update(flow_result)
results.update(text_result)
results.update(evaluate_result)
action = {'update':{'_id': str(user)}}
result = {'doc':results}
bulk_action.extend([action, result])
if count % 1000 == 0:
es.bulk(bulk_action, index='sensitive_user_portrait', doc_type='user', timeout=60)
bulk_action = []
print count
if bulk_action:
es.bulk(bulk_action, index='sensitive_user_portrait', doc_type='user', timeout=60)
return '1'
def test_cron_text_attribute(user_weibo_dict):
#get user weibo 7day {user:[weibos]}
print 'start cron_text_attribute'
uid_list = user_weibo_dict.keys()
print 'user count:', len(uid_list)
#get user flow information: hashtag, activity_geo, keywords
print 'get flow result'
flow_result = get_flow_information(uid_list)
print 'flow result len:', len(flow_result)
#get user profile information
print 'get register result'
register_result = get_profile_information(uid_list)
print 'register result len:', len(register_result)
#get topic and domain input data
user_weibo_string_dict = get_user_weibo_string(user_weibo_dict) # use as the tendency input data
user_keywords_dict = get_user_keywords_dict(user_weibo_string_dict)
#get user event results by bulk action
event_results_dict = event_classfiy(user_weibo_string_dict)
print 'event_result len:', len(event_results_dict)
#get user topic and domain by bulk action
print 'get topic and domain'
topic_results_dict, topic_results_label = topic_classfiy(user_keywords_dict)
domain_results = domain_classfiy(user_keywords_dict)
domain_results_dict = domain_results[0]
domain_results_label = domain_results[1]
print 'topic result len:', len(topic_results_dict)
print 'domain result len:', len(domain_results_dict)
#get user psy attribute
#print 'get psy result'
#psy_results_dict = psychology_classfiy(user_weibo_dict)
#print 'psy result len:', len(psy_results_dict)
#get user character attribute
print 'get character result'
#type_mark = 0/1 for identify the task input status---just sentiment or text
now_ts = time.time()
#test
now_ts = datetime2ts('2013-09-08')
character_end_time = ts2datetime(now_ts - DAY)
character_start_time = ts2datetime(now_ts - DAY * CHARACTER_TIME_GAP)
character_type_mark = 1
character_sentiment_result_dict = classify_sentiment(uid_list, character_start_time, character_end_time, character_type_mark)
character_type_mark = 1
character_text_result_dict = classify_topic(uid_list, character_start_time, character_end_time, character_type_mark)
print 'character result len:', len(character_sentiment_result_dict), len(character_text_result_dict)
print 'character_sentiment_result:', character_sentiment_result_dict
print 'character_text_result:', character_text_result_dict
#get user fansnum max
fansnum_max = get_fansnum_max()
#get user activeness by bulk_action
print 'get activeness results'
activeness_results = get_activity_time(uid_list)
print 'activeness result len:', len(activeness_results)
#get user inlfuence by bulk action
print 'get influence'
influence_results = get_influence(uid_list)
print 'influence results len:', len(influence_results)
# compute text attribute
user_set = set()
bulk_action = []
count = 0
for user in user_weibo_dict:
count += 1
results = {}
user_set.add(user)
weibo_list = user_weibo_dict[user]
uname = weibo_list[0]['uname']
#get user text attribute: online_pattern
results = compute_text_attribute(user, weibo_list)
results['uid'] = str(user)
#add user flow information: hashtag, activity_geo, keywords
flow_dict = flow_result[str(user)]
results = dict(results, **flow_dict)
#add user topic attribute
user_topic_dict = topic_results_dict[user]
user_label_dict = topic_results_label[user]
results['topic'] = json.dumps(user_topic_dict) # {'topic1_en':pro1, 'topic2_en':pro2...}
results['topic_string'] = topic_en2ch(user_label_dict) # 'topic1_ch&topic2_ch&topic3_ch'
#add user event attribute
results['tendency'] = event_results_dict[user]
#add user domain attribute
user_domain_dict = domain_results_dict[user]
user_label_dict = domain_results_label[user]
results['domain_v3'] = json.dumps(user_domain_dict) # [label1_en, label2_en, label3_en]
results['domain'] = domain_en2ch(user_label_dict) # label_ch
#add user character_sentiment attribute
character_sentiment = character_sentiment_result_dict[user]
results['character_sentiment'] = character_sentiment
#add user character_text attribtue
character_text = character_text_result_dict[user]
results['character_text'] = character_text
#add user psy attribute
user_psy_dict = [psy_results_dict[user]]
results['psycho_status'] = json.dumps(user_psy_dict)
#add user profile attribute
register_dict = register_result[str(user)]
results = dict(results, **register_dict)
#add user_evaluate attribute---importance
results['importance'] = get_importance(results['domain'], results['topic_string'], results['fansnum'], fansnum_max)
#add user_evaluate attribute---activeness
user_activeness_time = activeness_results[user]
user_activeness_geo = json.loads(results['activity_geo_dict'])[-1]
results['activeness'] = get_activeness(user_activeness_geo, user_activeness_time)
#add user_evaluate attribute---influence
results['influence'] = influence_results[user]
#bulk_action
action = {'index':{'_id': str(user)}}
bulk_action.extend([action, results])
if count >= 20:
mark = save_user_results(bulk_action)
print 'bulk_action:', bulk_action
bulk_action = []
count = 0
end_ts = time.time()
print 'user_set len:', len(user_set)
print 'count:', count
print 'bulk_action count:', len(bulk_action)
print 'bulk_action:', bulk_action
if bulk_action:
status = save_user_results(bulk_action)
#status = False
return status # save by bulk
def test_cron_text_attribute_v2(user_keywords_dict, user_weibo_dict, online_pattern_dict, character_start_ts):
status = False
print 'start cron_text_attribute'
uid_list = user_keywords_dict.keys()
#get user flow information: hashtag, activity_geo, keywords
print 'get flow result'
flow_result = get_flow_information(uid_list)
print 'flow result len:', len(flow_result)
#get user profile information
print 'get register result'
register_result = get_profile_information(uid_list)
print 'register result len:', len(register_result)
#get user topic and domain by bulk action
print 'get topic and domain, politics'
topic_results_dict, topic_results_label = topic_classfiy(uid_list, user_keywords_dict)
domain_results = domain_classfiy(uid_list, user_keywords_dict)
politics_results = political_classify(uid_list, user_keywords_dict)
print "topic: ", topic_results_dict, topic_results_label
print "domain: ", domain_results
print "politics:", politics_results
#get user character attribute
#print 'get character result'
#type_mark = 0/1 for identify the task input status---just sentiment or text
#character_start_time = ts2datetime(character_start_ts)
#character_end_time = ts2datetime(character_start_ts + DAY * CHARACTER_TIME_GAP - DAY)
#print 'character_start_time:', character_start_time
#print 'character_end_time:', character_end_time
#character_sentiment_result_dict = classify_sentiment(uid_list, user_weibo_dict, character_start_time, character_end_time, WEIBO_API_INPUT_TYPE)
#character_text_result_dict = classify_topic(uid_list, user_keywords_dict)
#print 'character result len:', len(character_sentiment_result_dict), len(character_text_result_dict)
#get user fansnum max
fansnum_max = get_fansnum_max()
#get user activeness by bulk_action
print 'get activeness results'
activeness_results = get_activity_time(uid_list)
print 'activeness result len:', len(activeness_results)
#get user inlfuence by bulk action
print 'get influence'
influence_results = get_influence(uid_list)
print 'influence results len:', len(influence_results)
# compute text attribute
bulk_action = []
count = 0
for user in uid_list:
count += 1
results = {}
#get user text attribute: online_pattern
results['online_pattern'] = json.dumps(online_pattern_dict[user])
try:
results['online_pattern_aggs'] = '&'.join(online_pattern_dict[user].keys())
except:
results['online_pattern_aggs'] = ''
results['uid'] = str(user)
#add user flow information: hashtag, activity_geo, keywords
flow_dict = flow_result[str(user)]
results = dict(results, **flow_dict)
#add user topic attribute
user_topic_dict = topic_results_dict[user]
user_label_dict = topic_results_label[user]
results['topic'] = json.dumps(user_topic_dict) # {'topic1_en':pro1, 'topic2_en':pro2...}
results['topic_string'] = topic_en2ch(user_label_dict) # 'topic1_ch&topic2_ch&topic3_ch'
#add user domain attribute
user_domain_dict = domain_results[user]
domain_list = domain_en2ch(user_domain_dict)
if domain_list:
results['domain_list'] = json.dumps(domain_list) # [label1_en, label2_en, label3_en]
results['domain'] = domain_list[0] # label_ch
else:
results['domain'] = "其他"
results['domain_list'] = json.dumps(["其他"])
politics_label = politics_results[user]
results['politics'] = politics_en2ch(politics_label)
#add user character_sentiment attribute
#character_sentiment = character_sentiment_result_dict[user]
#results['character_sentiment'] = character_sentiment
#add user character_text attribtue
#character_text = character_text_result_dict[user]
#results['character_text'] = character_text
#add user profile attribute
register_dict = register_result[str(user)]
results = dict(results, **register_dict)
#add user_evaluate attribute---importance
results['importance'] = get_importance(results['domain'], results['topic_string'], results['fansnum'], fansnum_max)
#add user_evaluate attribute---activeness
user_activeness_time = activeness_results[user]
user_activeness_geo = json.loads(results['activity_geo_dict'])[-1]
results['activeness'] = get_activeness(user_activeness_geo, user_activeness_time)
#add user_evaluate attribute---influence
results['influence'] = influence_results[user]
#bulk_action
action = {'index':{'_id': str(user)}}
bulk_action.extend([action, results])
status = save_user_results(bulk_action)
print "status:", status
return status
def compute2in(uid_list, user_weibo_dict):
#get user flow information: hashtag, activity_geo, keywords
flow_result = get_flow_information(uid_list)
#get user topic information
topic_results_dict, topic_results_label = topic_classfiy(user_weibo_list)
#get user domain information
domain_results = domain_classfiy(user_weibo_dict)
domain_results_dict = domain_results[0]
domain_results_label = domain_results[1]
#get user psy information
#psy_results_dict = psychology_classfiy(user_weibo_dict)
#get user profile information
register_result = get_profile_information(uid_list)
#get user fansnum max
fansnum_max = get_fansnum_max()
#get user activeness by bulk_action
activeness_results = get_activity_time(uid_list)
#get user inlfuence by bulk action
influence_results = get_influence(uid_list)
#deal bulk action
for user in user_weibo_dict:
weibo_list = user_weibo_dict[user]
uname = weibo_list[0]['uname']
#compute text attribute: online_pattern
results = compute_text_attribute(user, weibo_list)
results['uname'] = uname
results['uid'] = str(user)
#add flow information: hashtag, activity_geo, keywords
flow_dict = flow_result[str(user)]
results = dict(results, **flow_dict)
#add topic attribute
topic_dict = topic_results_dict[user]
results['topic'] = json.dumps(
topic_dic) #{topic1_en:pro1, topic2_en:pro, ...}
topic_label = topic_results_label[user]
results['topic_string'] = topic_en2ch(
topic_label) #topic1_ch&topic2_ch&topic3_ch
#add domain attribute
user_domain_dict = domain_results_dict[user]
user_domain_label = domain_results_label[user]
results['domain_v3'] = json.dumps(
user_domain_dict) #[domain_en1, domain_en2, domain_en3]
results['domain_string'] = domain_en2ch(user_domain_label) #domain_ch
#add psy attribute
user_psy_dict = psy_results_dict[user]
results['psycho_status'] = json.dumps(user_psy_dict)
#add user profile attribute
register_dict = register_result[str(user)]
results = dict(results, **register_dict)
#add user_evaluate attribute---importance
results['importance'] = get_importance(results['domain'],
results['topic_string'],
results['fansnum'], fansnum)
#add user_evaluate attribute---activeness
user_activeness_time = activeness_results[user]
user_activeness_geo = json.loads(results['activity_geo_dict'])[-1]
results['activeness'] = get_activeness(user_activeness_geo,
user_activeness_time)
#add user_evaluate attribute---influence
results['influence'] = influence_results[user]
#bulk_action
action = {'index': {'_id': str(user)}}
bulk_action.extend([action, results])
status = save_user_results(bulk_action)
return True
def test_cron_text_attribute(user_weibo_dict):
#get user weibo 7day {user:[weibos]}
print 'start cron_text_attribute'
uid_list = user_weibo_dict.keys()
print 'user count:', len(uid_list)
#get user flow information: hashtag, activity_geo, keywords
print 'get flow result'
flow_result = get_flow_information(uid_list)
print 'flow result len:', len(flow_result)
#get user profile information
print 'get register result'
register_result = get_profile_information(uid_list)
print 'register result len:', len(register_result)
#get topic and domain input data
user_weibo_string_dict = get_user_weibo_string(
user_weibo_dict) # use as the tendency input data
user_keywords_dict = get_user_keywords_dict(user_weibo_string_dict)
#get user event results by bulk action
event_results_dict = event_classfiy(user_weibo_string_dict)
print 'event_result len:', len(event_results_dict)
#get user topic and domain by bulk action
print 'get topic and domain'
topic_results_dict, topic_results_label = topic_classfiy(
user_keywords_dict)
domain_results = domain_classfiy(user_keywords_dict)
domain_results_dict = domain_results[0]
domain_results_label = domain_results[1]
print 'topic result len:', len(topic_results_dict)
print 'domain result len:', len(domain_results_dict)
#get user psy attribute
#print 'get psy result'
#psy_results_dict = psychology_classfiy(user_weibo_dict)
#print 'psy result len:', len(psy_results_dict)
#get user character attribute
print 'get character result'
#type_mark = 0/1 for identify the task input status---just sentiment or text
now_ts = time.time()
#test
now_ts = datetime2ts('2013-09-08')
character_end_time = ts2datetime(now_ts - DAY)
character_start_time = ts2datetime(now_ts - DAY * CHARACTER_TIME_GAP)
character_type_mark = 1
character_sentiment_result_dict = classify_sentiment(
uid_list, character_start_time, character_end_time,
character_type_mark)
character_type_mark = 1
character_text_result_dict = classify_topic(uid_list, character_start_time,
character_end_time,
character_type_mark)
print 'character result len:', len(character_sentiment_result_dict), len(
character_text_result_dict)
print 'character_sentiment_result:', character_sentiment_result_dict
print 'character_text_result:', character_text_result_dict
#get user fansnum max
fansnum_max = get_fansnum_max()
#get user activeness by bulk_action
print 'get activeness results'
activeness_results = get_activity_time(uid_list)
print 'activeness result len:', len(activeness_results)
#get user inlfuence by bulk action
print 'get influence'
influence_results = get_influence(uid_list)
print 'influence results len:', len(influence_results)
# compute text attribute
user_set = set()
bulk_action = []
count = 0
for user in user_weibo_dict:
count += 1
results = {}
user_set.add(user)
weibo_list = user_weibo_dict[user]
uname = weibo_list[0]['uname']
#get user text attribute: online_pattern
results = compute_text_attribute(user, weibo_list)
results['uid'] = str(user)
#add user flow information: hashtag, activity_geo, keywords
flow_dict = flow_result[str(user)]
results = dict(results, **flow_dict)
#add user topic attribute
user_topic_dict = topic_results_dict[user]
user_label_dict = topic_results_label[user]
results['topic'] = json.dumps(
user_topic_dict) # {'topic1_en':pro1, 'topic2_en':pro2...}
results['topic_string'] = topic_en2ch(
user_label_dict) # 'topic1_ch&topic2_ch&topic3_ch'
#add user event attribute
results['tendency'] = event_results_dict[user]
#add user domain attribute
user_domain_dict = domain_results_dict[user]
user_label_dict = domain_results_label[user]
results['domain_v3'] = json.dumps(
user_domain_dict) # [label1_en, label2_en, label3_en]
results['domain'] = domain_en2ch(user_label_dict) # label_ch
#add user character_sentiment attribute
character_sentiment = character_sentiment_result_dict[user]
results['character_sentiment'] = character_sentiment
#add user character_text attribtue
character_text = character_text_result_dict[user]
results['character_text'] = character_text
#add user psy attribute
user_psy_dict = [psy_results_dict[user]]
results['psycho_status'] = json.dumps(user_psy_dict)
#add user profile attribute
register_dict = register_result[str(user)]
results = dict(results, **register_dict)
#add user_evaluate attribute---importance
results['importance'] = get_importance(results['domain'],
results['topic_string'],
results['fansnum'], fansnum_max)
#add user_evaluate attribute---activeness
user_activeness_time = activeness_results[user]
user_activeness_geo = json.loads(results['activity_geo_dict'])[-1]
results['activeness'] = get_activeness(user_activeness_geo,
user_activeness_time)
#add user_evaluate attribute---influence
results['influence'] = influence_results[user]
#bulk_action
action = {'index': {'_id': str(user)}}
bulk_action.extend([action, results])
if count >= 20:
mark = save_user_results(bulk_action)
print 'bulk_action:', bulk_action
bulk_action = []
count = 0
end_ts = time.time()
print 'user_set len:', len(user_set)
print 'count:', count
print 'bulk_action count:', len(bulk_action)
print 'bulk_action:', bulk_action
if bulk_action:
status = save_user_results(bulk_action)
#status = False
return status # save by bulk
def test_cron_text_attribute_v2(user_keywords_dict, user_weibo_dict,
online_pattern_dict, character_start_ts):
status = False
print 'start cron_text_attribute'
uid_list = user_keywords_dict.keys()
#get user flow information: hashtag, activity_geo, keywords
print 'get flow result'
flow_result = get_flow_information(uid_list)
print 'flow result len:', len(flow_result)
#get user profile information
print 'get register result'
register_result = get_profile_information(uid_list)
print 'register result len:', len(register_result)
#get user topic and domain by bulk action
print 'get topic and domain, politics'
topic_results_dict, topic_results_label = topic_classfiy(
uid_list, user_keywords_dict)
domain_results = domain_classfiy(uid_list, user_keywords_dict)
politics_results = political_classify(uid_list, user_keywords_dict)
print "topic: ", topic_results_dict, topic_results_label
print "domain: ", domain_results
print "politics:", politics_results
#get user character attribute
#print 'get character result'
#type_mark = 0/1 for identify the task input status---just sentiment or text
#character_start_time = ts2datetime(character_start_ts)
#character_end_time = ts2datetime(character_start_ts + DAY * CHARACTER_TIME_GAP - DAY)
#print 'character_start_time:', character_start_time
#print 'character_end_time:', character_end_time
#character_sentiment_result_dict = classify_sentiment(uid_list, user_weibo_dict, character_start_time, character_end_time, WEIBO_API_INPUT_TYPE)
#character_text_result_dict = classify_topic(uid_list, user_keywords_dict)
#print 'character result len:', len(character_sentiment_result_dict), len(character_text_result_dict)
#get user fansnum max
fansnum_max = get_fansnum_max()
#get user activeness by bulk_action
print 'get activeness results'
activeness_results = get_activity_time(uid_list)
print 'activeness result len:', len(activeness_results)
#get user inlfuence by bulk action
print 'get influence'
influence_results = get_influence(uid_list)
print 'influence results len:', len(influence_results)
# compute text attribute
bulk_action = []
count = 0
for user in uid_list:
count += 1
results = {}
#get user text attribute: online_pattern
results['online_pattern'] = json.dumps(online_pattern_dict[user])
try:
results['online_pattern_aggs'] = '&'.join(
online_pattern_dict[user].keys())
except:
results['online_pattern_aggs'] = ''
results['uid'] = str(user)
#add user flow information: hashtag, activity_geo, keywords
flow_dict = flow_result[str(user)]
results = dict(results, **flow_dict)
#add user topic attribute
user_topic_dict = topic_results_dict[user]
user_label_dict = topic_results_label[user]
results['topic'] = json.dumps(
user_topic_dict) # {'topic1_en':pro1, 'topic2_en':pro2...}
results['topic_string'] = topic_en2ch(
user_label_dict) # 'topic1_ch&topic2_ch&topic3_ch'
#add user domain attribute
user_domain_dict = domain_results[user]
domain_list = domain_en2ch(user_domain_dict)
if domain_list:
results['domain_list'] = json.dumps(
domain_list) # [label1_en, label2_en, label3_en]
results['domain'] = domain_list[0] # label_ch
else:
results['domain'] = "其他"
results['domain_list'] = json.dumps(["其他"])
politics_label = politics_results[user]
results['politics'] = politics_en2ch(politics_label)
#add user character_sentiment attribute
#character_sentiment = character_sentiment_result_dict[user]
#results['character_sentiment'] = character_sentiment
#add user character_text attribtue
#character_text = character_text_result_dict[user]
#results['character_text'] = character_text
#add user profile attribute
register_dict = register_result[str(user)]
results = dict(results, **register_dict)
#add user_evaluate attribute---importance
results['importance'] = get_importance(results['domain'],
results['topic_string'],
results['fansnum'], fansnum_max)
#add user_evaluate attribute---activeness
user_activeness_time = activeness_results[user]
user_activeness_geo = json.loads(results['activity_geo_dict'])[-1]
results['activeness'] = get_activeness(user_activeness_geo,
user_activeness_time)
#add user_evaluate attribute---influence
results['influence'] = influence_results[user]
#bulk_action
action = {'index': {'_id': str(user)}}
bulk_action.extend([action, results])
status = save_user_results(bulk_action)
print "status:", status
return status
