def search_location(now_ts, uid):
date = ts2datetime(now_ts)
#print 'date:', date
ts = datetime2ts(date)
#print 'date-ts:', ts
stat_results = dict()
results = dict()
for i in range(1, 8):
ts = ts - 24 * 3600
#print 'for-ts:', ts
result_string = r_cluster.hget('ip_' + str(ts), str(uid))
if not result_string:
continue
result_dict = json.loads(result_string)
for ip in result_dict:
try:
stat_results[ip] += result_dict[ip]
except:
stat_results[ip] = result_dict[ip]
for ip in stat_results:
city = ip2city(ip)
if city:
try:
results[city][ip] = stat_results[ip]
except:
results[city] = {ip: stat_results[ip]}
description = active_geo_description(results)
results['description'] = description
#print 'location results:', results
return results
def search_attribute_portrait(uid):
return_results = {}
index_name = "sensitive_user_portrait"
index_type = "user"
try:
search_result = es.get(index=index_name, doc_type=index_type, id=uid)
except:
return None
results = search_result['_source']
#return_results = results
user_sensitive = user_type(uid)
if user_sensitive:
#return_results.update(sensitive_attribute(uid))
return_results['user_type'] = 1
return_results['sensitive'] = 1
else:
return_results['user_type'] = 0
return_results['sensitive'] = 0
if results['photo_url'] == 0:
results['photo_url'] = 'unknown'
if results['location'] == 0:
results['location'] = 'unknown'
return_results['photo_url'] = results['photo_url']
return_results['uid'] = results['uid']
return_results['uname'] = results['uname']
if return_results['uname'] == 0:
return_results['uname'] = 'unknown'
return_results['location'] = results['location']
return_results['fansnum'] = results['fansnum']
return_results['friendsnum'] = results['friendsnum']
return_results['gender'] = results['gender']
return_results['psycho_status'] = json.loads(results['psycho_status'])
keyword_list = []
if results['keywords']:
keywords_dict = json.loads(results['keywords'])
sort_word_list = sorted(keywords_dict.items(),
key=lambda x: x[1],
reverse=True)
return_results['keywords'] = sort_word_list
else:
return_results['keywords'] = []
return_results['retweet'] = search_retweet(uid, 0)
return_results['follow'] = search_follower(uid, 0)
return_results['at'] = search_mention(uid, 0)
if results['ip'] and results['geo_activity']:
ip_dict = json.loads(results['ip'])
geo_dict = json.loads(results['geo_activity'])
geo_description = active_geo_description(ip_dict, geo_dict)
return_results['geo_description'] = geo_description
else:
return_results['geo_description'] = ''
geo_top = []
temp_geo = {}
if results['geo_activity']:
geo_dict = json.loads(results['geo_activity'])
if len(geo_dict) < 7:
ts = time.time()
ts = datetime2ts('2013-09-08') - 8 * 24 * 3600
for i in range(7):
ts = ts + 24 * 3600
date = ts2datetime(ts).replace('-', '')
if geo_dict.has_key(date):
pass
else:
geo_dict[date] = {}
activity_geo_list = sorted(geo_dict.items(),
key=lambda x: x[0],
reverse=False)
geo_list = geo_dict.values()
for k, v in activity_geo_list:
sort_v = sorted(v.items(), key=lambda x: x[1], reverse=True)
top_geo = [item[0] for item in sort_v]
geo_top.append([k, top_geo[0:2]])
for iter_key in v.keys():
if temp_geo.has_key(iter_key):
temp_geo[iter_key] += v[iter_key]
else:
temp_geo[iter_key] = v[iter_key]
sort_geo_dict = sorted(temp_geo.items(),
key=lambda x: x[1],
reverse=True)
return_results['top_activity_geo'] = sort_geo_dict
return_results['activity_geo_distribute'] = geo_top
else:
return_results['top_activity_geo'] = []
return_results['activity_geo_distribute'] = geo_top
hashtag_dict = get_user_hashtag(uid)[0]
return_results['hashtag'] = hashtag_dict
'''
emotion_result = {}
emotion_conclusion_dict = {}
if results['emotion_words']:
emotion_words_dict = json.loads(results['emotion_words'])
for word_type in emotion_mark_dict:
try:
word_dict = emotion_words_dict[word_type]
if word_type=='126' or word_type=='127':
emotion_conclusion_dict[word_type] = word_dict
sort_word_dict = sorted(word_dict.items(), key=lambda x:x[1], reverse=True)
word_list = sort_word_dict[:5]
except:
results['emotion_words'] = emotion_result
emotion_result[emotion_mark_dict[word_type]] = word_list
return_results['emotion_words'] = emotion_result
'''
# topic
if results['topic']:
topic_dict = json.loads(results['topic'])
sort_topic_dict = sorted(topic_dict.items(),
key=lambda x: x[1],
reverse=True)
return_results['topic'] = sort_topic_dict[:5]
else:
return_results['topic'] = []
# domain
if results['domain']:
domain_string = results['domain']
domain_list = domain_string.split('_')
return_results['domain'] = domain_list
else:
return_results['domain'] = []
'''
# emoticon
if results['emotion']:
emotion_dict = json.loads(results['emotion'])
sort_emotion_dict = sorted(emotion_dict.items(), key=lambda x:x[1], reverse=True)
return_results['emotion'] = sort_emotion_dict[:5]
else:
return_results['emotion'] = []
'''
# on_line pattern
if results['online_pattern']:
online_pattern_dict = json.loads(results['online_pattern'])
sort_online_pattern_dict = sorted(online_pattern_dict.items(),
key=lambda x: x[1],
reverse=True)
return_results['online_pattern'] = sort_online_pattern_dict[:5]
else:
return_results['online_pattern'] = []
'''
#psycho_feature
if results['psycho_feature']:
psycho_feature_list = results['psycho_feature'].split('_')
return_results['psycho_feature'] = psycho_feature_list
else:
return_results['psycho_feature'] = []
'''
# self_state
try:
profile_result = es_user_profile.get(index='weibo_user',
doc_type='user',
id=uid)
self_state = profile_result['_source'].get('description', '')
return_results['description'] = self_state
except:
return_results['description'] = ''
if results['importance']:
query_body = {
'query': {
'range': {
'importance': {
'from': results['importance'],
'to': 100000
}
}
}
}
importance_rank = es.count(index='sensitive_user_portrait',
doc_type='user',
body=query_body)
if importance_rank['_shards']['successful'] != 0:
return_results['importance_rank'] = importance_rank['count']
else:
return_results['importance_rank'] = 0
else:
return_results['importance_rank'] = 0
return_results['importance'] = results['importance']
if results['activeness']:
query_body = {
'query': {
'range': {
'activeness': {
'from': results['activeness'],
'to': 10000
}
}
}
}
activeness_rank = es.count(index='sensitive_user_portrait',
doc_type='user',
body=query_body)
print activeness_rank
if activeness_rank['_shards']['successful'] != 0:
return_results['activeness_rank'] = activeness_rank['count']
else:
return_results['activeness_rank'] = 0
else:
return_results['activeness_rank'] = 0
return_results['activeness'] = results['activeness']
if results['influence']:
query_body = {
'query': {
'range': {
'influence': {
'from': results['influence'],
'to': 100000
}
}
}
}
influence_rank = es.count(index='sensitive_user_portrait',
doc_type='user',
body=query_body)
if influence_rank['_shards']['successful'] != 0:
return_results['influence_rank'] = influence_rank['count']
else:
return_results['influence_rank'] = 0
else:
return_results['influence_rank'] = 0
return_results['influence'] = results['influence']
if results['sensitive']:
query_body = {
'query': {
'range': {
'sensitive': {
'from': results['sensitive'],
'to': 100000
}
}
}
}
influence_rank = es.count(index='sensitive_user_portrait',
doc_type='user',
body=query_body)
if influence_rank['_shards']['successful'] != 0:
return_results['sensitive_rank'] = influence_rank['count']
else:
return_results['sensitive_rank'] = 0
else:
return_results['sensitive_rank'] = 0
return_results['sensitive'] = results['sensitive']
query_body = {'query': {"match_all": {}}}
all_count = es.count(index='sensitive_user_portrait',
doc_type='user',
body=query_body)
if all_count['_shards']['successful'] != 0:
return_results['all_count'] = all_count['count']
else:
print 'es_sensitive_user_portrait error'
return_results['all_count'] = 0
# link
link_ratio = results['link']
return_results['link'] = link_ratio
weibo_trend = get_user_trend(uid)[0]
return_results['time_description'] = active_time_description(weibo_trend)
return_results['time_trend'] = weibo_trend
# user influence trend
influence_detail = []
influence_value = []
attention_value = []
ts = time.time()
ts = datetime2ts('2013-09-08') - 8 * 24 * 3600
for i in range(1, 8):
date = ts2datetime(ts + i * 24 * 3600).replace('-', '')
detail = [0] * 10
try:
item = es.get(index=date, doc_type='bci', id=uid)['_source']
'''
if return_results['utype']:
detail[0] = item.get('s_origin_weibo_number', 0)
detail[1] = item.get('s_retweeted_weibo_number', 0)
detail[2] = item.get('s_origin_weibo_retweeted_total_number', 0) + item.get('s_retweeted_weibo_retweeted_total_number', 0)
detail[3] = item.get('s_origin_weibo_comment_total_number', 0) + item.get('s_retweeted_weibo_comment_total_number', 0)
else:
'''
if 1:
detail[0] = item.get('origin_weibo_number', 0)
detail[1] = item.get('retweeted_weibo_number', 0)
detail[2] = item.get(
'origin_weibo_retweeted_total_number', 0) + item.get(
'retweeted_weibo_retweeted_total_number', 0)
detail[3] = item.get(
'origin_weibo_comment_total_number', 0) + item.get(
'retweeted_weibo_comment_total_number', 0)
retweeted_id = item.get('origin_weibo_top_retweeted_id', '0')
detail[4] = retweeted_id
if retweeted_id:
try:
detail[5] = es.get(index='sensitive_user_text',
doc_type='user',
id=retweeted_id)['_source']['text']
except:
detail[5] = ''
else:
detail[5] = ''
detail[6] = item.get('origin_weibo_retweeted_top_number', 0)
detail[7] = item.get('origin_weibo_top_comment_id', '0')
if detail[7]:
try:
detail[8] = es.get(index='sensitive_user_text',
doc_type='user',
id=detail[7])['_source']['text']
except:
detail[8] = ''
else:
detail[8] = ''
detail[9] = item.get('origin_weibo_comment_top_number', 0)
attention_number = detail[2] + detail[3]
attention = 2 / (1 + math.exp(-0.005 * attention_number)) - 1
influence_value.append([date, item['user_index']])
influence_detail.append([date, detail])
attention_value.append(attention)
except:
influence_value.append([date, 0])
influence_detail.append([date, detail])
attention_value.append(0)
return_results['influence_trend'] = influence_value
return_results['common_influence_detail'] = influence_detail
return_results['attention_degree'] = attention_value
return return_results
def search_attribute_portrait(uid):
return_results = {}
index_name = "sensitive_user_portrait"
index_type = "user"
try:
search_result = es.get(index=index_name, doc_type=index_type, id=uid)
except:
return None
results = search_result['_source']
#return_results = results
user_sensitive = user_type(uid)
if user_sensitive:
#return_results.update(sensitive_attribute(uid))
return_results['user_type'] = 1
return_results['sensitive'] = 1
else:
return_results['user_type'] = 0
return_results['sensitive'] = 0
if results['photo_url'] == 0:
results['photo_url'] = 'unknown'
if results['location'] == 0:
results['location'] = 'unknown'
return_results['photo_url'] = results['photo_url']
return_results['uid'] = results['uid']
return_results['uname'] = results['uname']
if return_results['uname'] == 0:
return_results['uname'] = 'unknown'
return_results['location'] = results['location']
return_results['fansnum'] = results['fansnum']
return_results['friendsnum'] = results['friendsnum']
return_results['gender'] = results['gender']
return_results['psycho_status'] = json.loads(results['psycho_status'])
keyword_list = []
if results['keywords']:
keywords_dict = json.loads(results['keywords'])
sort_word_list = sorted(keywords_dict.items(), key=lambda x:x[1], reverse=True)
return_results['keywords'] = sort_word_list
else:
return_results['keywords'] = []
return_results['retweet'] = search_retweet(uid, 0)
return_results['follow'] = search_follower(uid, 0)
return_results['at'] = search_mention(uid, 0)
if results['ip'] and results['geo_activity']:
ip_dict = json.loads(results['ip'])
geo_dict = json.loads(results['geo_activity'])
geo_description = active_geo_description(ip_dict, geo_dict)
return_results['geo_description'] = geo_description
else:
return_results['geo_description'] = ''
geo_top = []
temp_geo = {}
if results['geo_activity']:
geo_dict = json.loads(results['geo_activity'])
if len(geo_dict) < 7:
ts = time.time()
ts = datetime2ts('2013-09-08') - 8*24*3600
for i in range(7):
ts = ts + 24*3600
date = ts2datetime(ts).replace('-', '')
if geo_dict.has_key(date):
pass
else:
geo_dict[date] = {}
activity_geo_list = sorted(geo_dict.items(), key=lambda x:x[0], reverse=False)
geo_list = geo_dict.values()
for k,v in activity_geo_list:
sort_v = sorted(v.items(), key=lambda x:x[1], reverse=True)
top_geo = [item[0] for item in sort_v]
geo_top.append([k, top_geo[0:2]])
for iter_key in v.keys():
if temp_geo.has_key(iter_key):
temp_geo[iter_key] += v[iter_key]
else:
temp_geo[iter_key] = v[iter_key]
sort_geo_dict = sorted(temp_geo.items(), key=lambda x:x[1], reverse=True)
return_results['top_activity_geo'] = sort_geo_dict
return_results['activity_geo_distribute'] = geo_top
else:
return_results['top_activity_geo'] = []
return_results['activity_geo_distribute'] = geo_top
hashtag_dict = get_user_hashtag(uid)[0]
return_results['hashtag'] = hashtag_dict
'''
emotion_result = {}
emotion_conclusion_dict = {}
if results['emotion_words']:
emotion_words_dict = json.loads(results['emotion_words'])
for word_type in emotion_mark_dict:
try:
word_dict = emotion_words_dict[word_type]
if word_type=='126' or word_type=='127':
emotion_conclusion_dict[word_type] = word_dict
sort_word_dict = sorted(word_dict.items(), key=lambda x:x[1], reverse=True)
word_list = sort_word_dict[:5]
except:
results['emotion_words'] = emotion_result
emotion_result[emotion_mark_dict[word_type]] = word_list
return_results['emotion_words'] = emotion_result
'''
# topic
if results['topic']:
topic_dict = json.loads(results['topic'])
sort_topic_dict = sorted(topic_dict.items(), key=lambda x:x[1], reverse=True)
return_results['topic'] = sort_topic_dict[:5]
else:
return_results['topic'] = []
# domain
if results['domain']:
domain_string = results['domain']
domain_list = domain_string.split('_')
return_results['domain'] = domain_list
else:
return_results['domain'] = []
'''
# emoticon
if results['emotion']:
emotion_dict = json.loads(results['emotion'])
sort_emotion_dict = sorted(emotion_dict.items(), key=lambda x:x[1], reverse=True)
return_results['emotion'] = sort_emotion_dict[:5]
else:
return_results['emotion'] = []
'''
# on_line pattern
if results['online_pattern']:
online_pattern_dict = json.loads(results['online_pattern'])
sort_online_pattern_dict = sorted(online_pattern_dict.items(), key=lambda x:x[1], reverse=True)
return_results['online_pattern'] = sort_online_pattern_dict[:5]
else:
return_results['online_pattern'] = []
'''
#psycho_feature
if results['psycho_feature']:
psycho_feature_list = results['psycho_feature'].split('_')
return_results['psycho_feature'] = psycho_feature_list
else:
return_results['psycho_feature'] = []
'''
# self_state
try:
profile_result = es_user_profile.get(index='weibo_user', doc_type='user', id=uid)
self_state = profile_result['_source'].get('description', '')
return_results['description'] = self_state
except:
return_results['description'] = ''
if results['importance']:
query_body = {
'query':{
'range':{
'importance':{
'from':results['importance'],
'to': 100000
}
}
}
}
importance_rank = es.count(index='sensitive_user_portrait', doc_type='user', body=query_body)
if importance_rank['_shards']['successful'] != 0:
return_results['importance_rank'] = importance_rank['count']
else:
return_results['importance_rank'] = 0
else:
return_results['importance_rank'] = 0
return_results['importance'] = results['importance']
if results['activeness']:
query_body = {
'query':{
'range':{
'activeness':{
'from':results['activeness'],
'to': 10000
}
}
}
}
activeness_rank = es.count(index='sensitive_user_portrait', doc_type='user', body=query_body)
if activeness_rank['_shards']['successful'] != 0:
return_results['activeness_rank'] = activeness_rank['count']
else:
return_results['activeness_rank'] = 0
else:
return_results['activeness_rank'] = 0
return_results['activeness'] = results['activeness']
if results['influence']:
query_body = {
'query':{
'range':{
'influence':{
'from':results['influence'],
'to': 100000
}
}
}
}
influence_rank = es.count(index='sensitive_user_portrait', doc_type='user', body=query_body)
if influence_rank['_shards']['successful'] != 0:
return_results['influence_rank'] = influence_rank['count']
else:
return_results['influence_rank'] = 0
else:
return_results['influence_rank'] = 0
return_results['influence'] = results['influence']
if results['sensitive']:
query_body = {
'query':{
'range':{
'sensitive':{
'from':results['sensitive'],
'to': 100000
}
}
}
}
influence_rank = es.count(index='sensitive_user_portrait', doc_type='user', body=query_body)
if influence_rank['_shards']['successful'] != 0:
return_results['sensitive_rank'] = influence_rank['count']
else:
return_results['sensitive_rank'] = 0
else:
return_results['sensitive_rank'] = 0
return_results['sensitive'] = results['sensitive']
query_body = {
'query':{
"match_all":{}
}
}
all_count = es.count(index='sensitive_user_portrait', doc_type='user', body=query_body)
if all_count['_shards']['successful'] != 0:
return_results['all_count'] = all_count['count']
else:
print 'es_sensitive_user_portrait error'
return_results['all_count'] = 0
# link
link_ratio = results['link']
return_results['link'] = link_ratio
weibo_trend = get_user_trend(uid)[0]
return_results['time_description'] = active_time_description(weibo_trend)
return_results['time_trend'] = weibo_trend
# user influence trend
influence_detail = []
influence_value = []
attention_value = []
ts = time.time()
ts = datetime2ts('2013-09-08') - 8*24*3600
for i in range(1,8):
date = ts2datetime(ts + i*24*3600).replace('-', '')
detail = [0]*10
try:
item = es.get(index=date, doc_type='bci', id=uid)['_source']
'''
if return_results['utype']:
detail[0] = item.get('s_origin_weibo_number', 0)
detail[1] = item.get('s_retweeted_weibo_number', 0)
detail[2] = item.get('s_origin_weibo_retweeted_total_number', 0) + item.get('s_retweeted_weibo_retweeted_total_number', 0)
detail[3] = item.get('s_origin_weibo_comment_total_number', 0) + item.get('s_retweeted_weibo_comment_total_number', 0)
else:
'''
if 1:
detail[0] = item.get('origin_weibo_number', 0)
detail[1] = item.get('retweeted_weibo_number', 0)
detail[2] = item.get('origin_weibo_retweeted_total_number', 0) + item.get('retweeted_weibo_retweeted_total_number', 0)
detail[3] = item.get('origin_weibo_comment_total_number', 0) + item.get('retweeted_weibo_comment_total_number', 0)
retweeted_id = item.get('origin_weibo_top_retweeted_id', '0')
detail[4] = retweeted_id
if retweeted_id:
try:
detail[5] = es.get(index='sensitive_user_text', doc_type='user', id=retweeted_id)['_source']['text']
except:
detail[5] = ''
else:
detail[5] = ''
detail[6] = item.get('origin_weibo_retweeted_top_number', 0)
detail[7] = item.get('origin_weibo_top_comment_id', '0')
if detail[7]:
try:
detail[8] = es.get(index='sensitive_user_text', doc_type='user', id=detail[7])['_source']['text']
except:
detail[8] = ''
else:
detail[8] = ''
detail[9] = item.get('origin_weibo_comment_top_number', 0)
attention_number = detail[2] + detail[3]
attention = 2/(1+math.exp(-0.005*attention_number)) - 1
influence_value.append([date, item['user_index']])
influence_detail.append([date, detail])
attention_value.append(attention)
except:
influence_value.append([date, 0])
influence_detail.append([date, detail])
attention_value.append(0)
return_results['influence_trend'] = influence_value
return_results['common_influence_detail'] = influence_detail
return_results['attention_degree'] = attention_value
return return_results
def search_attribute_portrait(uid):
return_results = {}
index_name = "sensitive_user_portrait"
index_type = "user"
try:
search_result = es.get(index=index_name, doc_type=index_type, id=uid)
except:
return None
results = search_result["_source"]
# return_results = results
user_sensitive = user_type(uid)
if user_sensitive:
# return_results.update(sensitive_attribute(uid))
return_results["user_type"] = 1
return_results["sensitive"] = 1
else:
return_results["user_type"] = 0
return_results["sensitive"] = 0
if results["photo_url"] == 0:
results["photo_url"] = "unknown"
if results["location"] == 0:
results["location"] = "unknown"
return_results["photo_url"] = results["photo_url"]
return_results["uid"] = results["uid"]
return_results["uname"] = results["uname"]
if return_results["uname"] == 0:
return_results["uname"] = "unknown"
return_results["location"] = results["location"]
return_results["fansnum"] = results["fansnum"]
return_results["friendsnum"] = results["friendsnum"]
return_results["gender"] = results["gender"]
return_results["psycho_status"] = json.loads(results["psycho_status"])
keyword_list = []
if results["keywords"]:
keywords_dict = json.loads(results["keywords"])
sort_word_list = sorted(keywords_dict.items(), key=lambda x: x[1], reverse=True)
return_results["keywords"] = sort_word_list
else:
return_results["keywords"] = []
return_results["retweet"] = search_retweet(uid, 0)
return_results["follow"] = search_follower(uid, 0)
return_results["at"] = search_mention(uid, 0)
if results["ip"] and results["geo_activity"]:
ip_dict = json.loads(results["ip"])
geo_dict = json.loads(results["geo_activity"])
geo_description = active_geo_description(ip_dict, geo_dict)
return_results["geo_description"] = geo_description
else:
return_results["geo_description"] = ""
geo_top = []
temp_geo = {}
if results["geo_activity"]:
geo_dict = json.loads(results["geo_activity"])
if len(geo_dict) < 7:
ts = time.time()
ts = datetime2ts("2013-09-08") - 8 * 24 * 3600
for i in range(7):
ts = ts + 24 * 3600
date = ts2datetime(ts).replace("-", "")
if geo_dict.has_key(date):
pass
else:
geo_dict[date] = {}
activity_geo_list = sorted(geo_dict.items(), key=lambda x: x[0], reverse=False)
geo_list = geo_dict.values()
for k, v in activity_geo_list:
sort_v = sorted(v.items(), key=lambda x: x[1], reverse=True)
top_geo = [item[0] for item in sort_v]
geo_top.append([k, top_geo[0:2]])
for iter_key in v.keys():
if temp_geo.has_key(iter_key):
temp_geo[iter_key] += v[iter_key]
else:
temp_geo[iter_key] = v[iter_key]
sort_geo_dict = sorted(temp_geo.items(), key=lambda x: x[1], reverse=True)
return_results["top_activity_geo"] = sort_geo_dict
return_results["activity_geo_distribute"] = geo_top
else:
return_results["top_activity_geo"] = []
return_results["activity_geo_distribute"] = geo_top
hashtag_dict = get_user_hashtag(uid)[0]
return_results["hashtag"] = hashtag_dict
"""
emotion_result = {}
emotion_conclusion_dict = {}
if results['emotion_words']:
emotion_words_dict = json.loads(results['emotion_words'])
for word_type in emotion_mark_dict:
try:
word_dict = emotion_words_dict[word_type]
if word_type=='126' or word_type=='127':
emotion_conclusion_dict[word_type] = word_dict
sort_word_dict = sorted(word_dict.items(), key=lambda x:x[1], reverse=True)
word_list = sort_word_dict[:5]
except:
results['emotion_words'] = emotion_result
emotion_result[emotion_mark_dict[word_type]] = word_list
return_results['emotion_words'] = emotion_result
"""
# topic
if results["topic"]:
topic_dict = json.loads(results["topic"])
sort_topic_dict = sorted(topic_dict.items(), key=lambda x: x[1], reverse=True)
return_results["topic"] = sort_topic_dict[:5]
else:
return_results["topic"] = []
# domain
if results["domain"]:
domain_string = results["domain"]
domain_list = domain_string.split("_")
return_results["domain"] = domain_list
else:
return_results["domain"] = []
"""
# emoticon
if results['emotion']:
emotion_dict = json.loads(results['emotion'])
sort_emotion_dict = sorted(emotion_dict.items(), key=lambda x:x[1], reverse=True)
return_results['emotion'] = sort_emotion_dict[:5]
else:
return_results['emotion'] = []
"""
# on_line pattern
if results["online_pattern"]:
online_pattern_dict = json.loads(results["online_pattern"])
sort_online_pattern_dict = sorted(online_pattern_dict.items(), key=lambda x: x[1], reverse=True)
return_results["online_pattern"] = sort_online_pattern_dict[:5]
else:
return_results["online_pattern"] = []
"""
#psycho_feature
if results['psycho_feature']:
psycho_feature_list = results['psycho_feature'].split('_')
return_results['psycho_feature'] = psycho_feature_list
else:
return_results['psycho_feature'] = []
"""
# self_state
try:
profile_result = es_user_profile.get(index="weibo_user", doc_type="user", id=uid)
self_state = profile_result["_source"].get("description", "")
return_results["description"] = self_state
except:
return_results["description"] = ""
if results["importance"]:
query_body = {"query": {"range": {"importance": {"from": results["importance"], "to": 100000}}}}
importance_rank = es.count(index="sensitive_user_portrait", doc_type="user", body=query_body)
if importance_rank["_shards"]["successful"] != 0:
return_results["importance_rank"] = importance_rank["count"]
else:
return_results["importance_rank"] = 0
else:
return_results["importance_rank"] = 0
return_results["importance"] = results["importance"]
if results["activeness"]:
query_body = {"query": {"range": {"activeness": {"from": results["activeness"], "to": 10000}}}}
activeness_rank = es.count(index="sensitive_user_portrait", doc_type="user", body=query_body)
print activeness_rank
if activeness_rank["_shards"]["successful"] != 0:
return_results["activeness_rank"] = activeness_rank["count"]
else:
return_results["activeness_rank"] = 0
else:
return_results["activeness_rank"] = 0
return_results["activeness"] = results["activeness"]
if results["influence"]:
query_body = {"query": {"range": {"influence": {"from": results["influence"], "to": 100000}}}}
influence_rank = es.count(index="sensitive_user_portrait", doc_type="user", body=query_body)
if influence_rank["_shards"]["successful"] != 0:
return_results["influence_rank"] = influence_rank["count"]
else:
return_results["influence_rank"] = 0
else:
return_results["influence_rank"] = 0
return_results["influence"] = results["influence"]
if results["sensitive"]:
query_body = {"query": {"range": {"sensitive": {"from": results["sensitive"], "to": 100000}}}}
influence_rank = es.count(index="sensitive_user_portrait", doc_type="user", body=query_body)
if influence_rank["_shards"]["successful"] != 0:
return_results["sensitive_rank"] = influence_rank["count"]
else:
return_results["sensitive_rank"] = 0
else:
return_results["sensitive_rank"] = 0
return_results["sensitive"] = results["sensitive"]
query_body = {"query": {"match_all": {}}}
all_count = es.count(index="sensitive_user_portrait", doc_type="user", body=query_body)
if all_count["_shards"]["successful"] != 0:
return_results["all_count"] = all_count["count"]
else:
print "es_sensitive_user_portrait error"
return_results["all_count"] = 0
# link
link_ratio = results["link"]
return_results["link"] = link_ratio
weibo_trend = get_user_trend(uid)[0]
return_results["time_description"] = active_time_description(weibo_trend)
return_results["time_trend"] = weibo_trend
# user influence trend
influence_detail = []
influence_value = []
attention_value = []
ts = time.time()
ts = datetime2ts("2013-09-08") - 8 * 24 * 3600
for i in range(1, 8):
date = ts2datetime(ts + i * 24 * 3600).replace("-", "")
detail = [0] * 10
try:
item = es.get(index=date, doc_type="bci", id=uid)["_source"]
"""
if return_results['utype']:
detail[0] = item.get('s_origin_weibo_number', 0)
detail[1] = item.get('s_retweeted_weibo_number', 0)
detail[2] = item.get('s_origin_weibo_retweeted_total_number', 0) + item.get('s_retweeted_weibo_retweeted_total_number', 0)
detail[3] = item.get('s_origin_weibo_comment_total_number', 0) + item.get('s_retweeted_weibo_comment_total_number', 0)
else:
"""
if 1:
detail[0] = item.get("origin_weibo_number", 0)
detail[1] = item.get("retweeted_weibo_number", 0)
detail[2] = item.get("origin_weibo_retweeted_total_number", 0) + item.get(
"retweeted_weibo_retweeted_total_number", 0
)
detail[3] = item.get("origin_weibo_comment_total_number", 0) + item.get(
"retweeted_weibo_comment_total_number", 0
)
retweeted_id = item.get("origin_weibo_top_retweeted_id", "0")
detail[4] = retweeted_id
if retweeted_id:
try:
detail[5] = es.get(index="sensitive_user_text", doc_type="user", id=retweeted_id)["_source"][
"text"
]
except:
detail[5] = ""
else:
detail[5] = ""
detail[6] = item.get("origin_weibo_retweeted_top_number", 0)
detail[7] = item.get("origin_weibo_top_comment_id", "0")
if detail[7]:
try:
detail[8] = es.get(index="sensitive_user_text", doc_type="user", id=detail[7])["_source"][
"text"
]
except:
detail[8] = ""
else:
detail[8] = ""
detail[9] = item.get("origin_weibo_comment_top_number", 0)
attention_number = detail[2] + detail[3]
attention = 2 / (1 + math.exp(-0.005 * attention_number)) - 1
influence_value.append([date, item["user_index"]])
influence_detail.append([date, detail])
attention_value.append(attention)
except:
influence_value.append([date, 0])
influence_detail.append([date, detail])
attention_value.append(0)
return_results["influence_trend"] = influence_value
return_results["common_influence_detail"] = influence_detail
return_results["attention_degree"] = attention_value
return return_results