def ajax_revise_task():
task_name = request.args.get('task_name', '') # must
finish = request.args.get("finish", "10")
stop_time = request.args.get('stop_time', '') # timestamp
user = request.args.get('user', '')
#now_ts = datetime2ts("2013-09-06")
_id = user + '-' + task_name
now_ts = time.time()
if stop_time and stop_time < now_ts:
return json.dumps([])
if task_name and user:
task_detail = es.get(index=index_manage_sensing_task,
doc_type=task_doc_type,
id=_id)['_source']
if stop_time:
task_detail['stop_time'] = stop_time
if int(finish) == 0:
task_detail['finish'] = finish
task_detail['processing_status'] = "1" # 重启时将处理状态改为
if stop_time or int(finish) == 0:
es.index(index=index_manage_sensing_task,
doc_type=task_doc_type,
id=_id,
body=task_detail)
return json.dumps(['1'])
return json.dumps([])
def get_text_detail(task_name, ts, text_type, user, order, size=100):
results = []
_id = user + '-' + task_name
task_detail = es.get(index=index_manage_sensing_task, doc_type=task_doc_type, id=_id)["_source"]
if int(text_type) == 0: # 热门原创微博
results = get_origin_weibo_detail(ts, user, task_name, size, order, 1)
elif int(text_type) == 1: # 热门转发微博
results = get_origin_weibo_detail(ts, user, task_name, size, order, 2)
elif int(text_type) == 2: # 普通转发微博
results = get_retweet_weibo_detail(ts, user, task_name, size, "message_type", 3)
elif int(text_type) == 3: # 普通评论微博
results = get_retweet_weibo_detail(ts, user, task_name, size, "message_type", 2)
elif int(text_type) == 4: # 积极微博
results = get_retweet_weibo_detail(ts, user, task_name, size, "sentiment", "1")
elif int(text_type) == 5: # 中性微博
results = get_retweet_weibo_detail(ts, user, task_name, size, "sentiment", "0")
elif int(text_type) == 6: # 消极微博
results = get_retweet_weibo_detail(ts, user, task_name, size, "sentiment", ["2", "3", "4", "5", "6"])
elif int(text_type) == 7: # 敏感微博
results = get_origin_weibo_detail(ts, user, task_name, size, order, 3)
else:
print "error"
return results
def get_group_user_track(uid):
results = []
#step1:get user_portrait activity_geo_dict
try:
portrait_result = es_user_portrait.get(index=portrait_index_name, doc_type=portrait_index_type,\
id=uid, _source=False, fields=['activity_geo_dict'])
except:
portrait_result = {}
if portrait_result == {}:
return 'uid is not in user_portrait'
activity_geo_dict = json.loads(
portrait_result['fields']['activity_geo_dict'][0])
now_date_ts = datetime2ts(ts2datetime(int(time.time())))
start_ts = now_date_ts - DAY * len(activity_geo_dict)
#step2: iter date to get month track
for geo_item in activity_geo_dict:
iter_date = ts2datetime(start_ts)
sort_day_dict = sorted(geo_item.items(),
key=lambda x: x[1],
reverse=True)
if sort_day_dict:
results.append([iter_date, sort_day_dict[0][0]])
else:
results.append([iter_date, ''])
start_ts = start_ts + DAY
return results
def ajax_get_group_detail():
task_name = request.args.get('task_name','') # task_name
user = request.args.get('user', '')
_id = user + '-' + task_name
portrait_detail = []
top_activeness = get_top_influence("activeness")
top_influence = get_top_influence("influence")
top_importance = get_top_influence("importance")
search_result = es.get(index=index_group_manage, doc_type=doc_type_group, id=_id).get('_source', {})
if search_result:
try:
uid_list = json.loads(search_result['uid_list'])
except:
uid_list = search_result['uid_list']
if uid_list:
search_results = es.mget(index=portrait_index_name, doc_type=portrait_index_type, body={"ids":uid_list}, fields=SOCIAL_SENSOR_INFO)['docs']
for item in search_results:
temp = []
if item['found']:
for iter_item in SOCIAL_SENSOR_INFO:
if iter_item == "topic_string":
temp.append(item["fields"][iter_item][0].split('&'))
temp.append(item["fields"][iter_item][0].split('&'))
elif iter_item == "activeness":
temp.append(math.log(item['fields']['activeness'][0]/float(top_activeness)*9+1, 10)*100)
elif iter_item == "importance":
temp.append(math.log(item['fields']['importance'][0]/float(top_importance)*9+1, 10)*100)
elif iter_item == "influence":
temp.append(math.log(item['fields']['influence'][0]/float(top_influence)*9+1, 10)*100)
else:
temp.append(item["fields"][iter_item][0])
portrait_detail.append(temp)
return json.dumps(portrait_detail)
def get_group_user_track(uid):
results = []
# step1:get user_portrait activity_geo_dict
try:
portrait_result = es_user_portrait.get(
index=portrait_index_name, doc_type=portrait_index_type, id=uid, _source=False, fields=["activity_geo_dict"]
)
except:
portrait_result = {}
if portrait_result == {}:
return "uid is not in user_portrait"
activity_geo_dict = json.loads(portrait_result["fields"]["activity_geo_dict"][0])
now_date_ts = datetime2ts(ts2datetime(int(time.time())))
start_ts = now_date_ts - DAY * len(activity_geo_dict)
# step2: iter date to get month track
for geo_item in activity_geo_dict:
iter_date = ts2datetime(start_ts)
sort_day_dict = sorted(geo_item.items(), key=lambda x: x[1], reverse=True)
if sort_day_dict:
results.append([iter_date, sort_day_dict[0][0]])
else:
results.append([iter_date, ""])
start_ts = start_ts + DAY
return results
def ajax_stop_task():
task_name = request.args.get('task_name','') # must
user = request.args.get('user', '')
if task_name and user:
_id = user + "-" + task_name
task_detail = es.get(index=index_manage_sensing_task, doc_type=task_doc_type, id=_id)['_source']
#task_detail["finish"] = finish_signal
task_detail['processing_status'] = '0'
es.index(index=index_manage_sensing_task, doc_type=task_doc_type, id=_id, body=task_detail)
return json.dumps(['1'])
else:
return json.dumps([])
def ajax_get_group_detail():
task_name = request.args.get('task_name', '') # task_name
user = request.args.get('user', '')
_id = user + '-' + task_name
portrait_detail = []
top_activeness = get_top_influence("activeness")
top_influence = get_top_influence("influence")
top_importance = get_top_influence("importance")
search_result = es.get(index=index_group_manage,
doc_type=doc_type_group,
id=_id).get('_source', {})
if search_result:
try:
uid_list = json.loads(search_result['uid_list'])
except:
uid_list = search_result['uid_list']
if uid_list:
search_results = es.mget(index=portrait_index_name,
doc_type=portrait_index_type,
body={"ids": uid_list},
fields=SOCIAL_SENSOR_INFO)['docs']
for item in search_results:
temp = []
if item['found']:
for iter_item in SOCIAL_SENSOR_INFO:
if iter_item == "topic_string":
temp.append(
item["fields"][iter_item][0].split('&'))
temp.append(
item["fields"][iter_item][0].split('&'))
elif iter_item == "activeness":
temp.append(
math.log(
item['fields']['activeness'][0] /
float(top_activeness) * 9 + 1, 10) * 100)
elif iter_item == "importance":
temp.append(
math.log(
item['fields']['importance'][0] /
float(top_importance) * 9 + 1, 10) * 100)
elif iter_item == "influence":
temp.append(
math.log(
item['fields']['influence'][0] /
float(top_influence) * 9 + 1, 10) * 100)
else:
temp.append(item["fields"][iter_item][0])
portrait_detail.append(temp)
return json.dumps(portrait_detail)
def ajax_stop_task():
task_name = request.args.get('task_name', '') # must
user = request.args.get('user', '')
if task_name and user:
_id = user + "-" + task_name
task_detail = es.get(index=index_manage_sensing_task,
doc_type=task_doc_type,
id=_id)['_source']
#task_detail["finish"] = finish_signal
task_detail['processing_status'] = '0'
es.index(index=index_manage_sensing_task,
doc_type=task_doc_type,
id=_id,
body=task_detail)
return json.dumps(['1'])
else:
return json.dumps([])
def ajax_get_task_detail_info():
task_name = request.args.get('task_name','') # task_name
user = request.args.get('user', 'admin')
_id = user + "-" + task_name
task_detail = es.get(index=index_manage_sensing_task, doc_type=task_doc_type, id=_id)['_source']
task_detail["social_sensors"] = json.loads(task_detail["social_sensors"])
#task_detail['keywords'] = json.loads(task_detail['keywords'])
#task_detail["sensitive_words"]= json.loads(task_detail["sensitive_words"])
history_status = json.loads(task_detail['history_status'])
if history_status:
temp_list = []
"""
temp_list.append(history_status[-1])
print history_status
for item in history_status[:-1]:
temp_list.append(item)
"""
sorted_list = sorted(history_status, key=lambda x:x, reverse=True)
task_detail['history_status'] = sorted_list
else:
task_detail['history_status'] = []
task_detail['social_sensors_portrait'] = []
portrait_detail = []
if task_detail["social_sensors"]:
search_results = es.mget(index=portrait_index_name, doc_type=portrait_index_type, body={"ids": task_detail["social_sensors"]})['docs']
if search_results:
for item in search_results:
temp = []
if item['found']:
for iter_item in SOCIAL_SENSOR_INFO:
if iter_item == "topic_string":
temp.append(item["_source"][iter_item].split('&'))
else:
temp.append(item["_source"][iter_item])
portrait_detail.append(temp)
if portrait_detail:
portrait_detail = sorted(portrait_detail, key=lambda x:x[5], reverse=True)
task_detail['social_sensors_portrait'] = portrait_detail
return json.dumps(task_detail)
def ajax_get_task_detail_info():
task_name = request.args.get('task_name', '') # task_name
user = request.args.get('user', 'admin')
_id = user + "-" + task_name
task_detail = es.get(index=index_manage_sensing_task,
doc_type=task_doc_type,
id=_id)['_source']
history_status = json.loads(task_detail['history_status'])
if history_status:
temp_list = []
"""
temp_list.append(history_status[-1])
print history_status
for item in history_status[:-1]:
temp_list.append(item)
"""
sorted_list = sorted(history_status, key=lambda x: x, reverse=True)
task_detail['history_status'] = sorted_list
else:
task_detail['history_status'] = []
task_detail['social_sensors_portrait'] = []
portrait_detail = []
"""
if task_detail["social_sensors"]:
search_results = es.mget(index=portrait_index_name, doc_type=portrait_index_type, body={"ids": task_detail["social_sensors"]})['docs']
if search_results:
for item in search_results:
temp = []
if item['found']:
for iter_item in SOCIAL_SENSOR_INFO:
if iter_item == "topic_string":
temp.append(item["_source"][iter_item].split('&'))
else:
temp.append(item["_source"][iter_item])
portrait_detail.append(temp)
if portrait_detail:
portrait_detail = sorted(portrait_detail, key=lambda x:x[5], reverse=True)
task_detail['social_sensors_portrait'] = portrait_detail
"""
return json.dumps(task_detail)
def get_text_detail(task_name, ts, text_type, user, order, size=100):
results = []
_id = user + '-' + task_name
task_detail = es.get(index=index_manage_sensing_task,
doc_type=task_doc_type,
id=_id)["_source"]
if int(text_type) == 0: # 热门原创微博
results = get_origin_weibo_detail(ts, user, task_name, size, order, 1)
elif int(text_type) == 1: # 热门转发微博
results = get_origin_weibo_detail(ts, user, task_name, size, order, 2)
elif int(text_type) == 2: # 普通转发微博
results = get_retweet_weibo_detail(ts, user, task_name, size,
"message_type", 3)
elif int(text_type) == 3: # 普通评论微博
results = get_retweet_weibo_detail(ts, user, task_name, size,
"message_type", 2)
elif int(text_type) == 4: # 积极微博
results = get_retweet_weibo_detail(ts, user, task_name, size,
"sentiment", "1")
elif int(text_type) == 5: # 中性微博
results = get_retweet_weibo_detail(ts, user, task_name, size,
"sentiment", "0")
elif int(text_type) == 6: # 消极微博
results = get_retweet_weibo_detail(ts, user, task_name, size,
"sentiment",
["2", "3", "4", "5", "6"])
elif int(text_type) == 7: # 敏感微博
results = get_origin_weibo_detail(ts, user, task_name, size, order, 3)
else:
print "error"
return results
def ajax_get_clustering_topic():
task_name = request.args.get('task_name','') # task_name
user = request.args.get('user', '')
ts = int(request.args.get('ts', '')) # timestamp: 123456789
topic_list = []
_id = user + '-' + task_name
task_detail = es.get(index=index_sensing_task, doc_type=_id, id=ts)['_source']
#burst_reason = task_detail['burst_reason']
burst_reason = 1
filter_list = []
if burst_reason:
topic_list = task_detail.get("clustering_topic", [])
if topic_list:
topic_list = json.loads(topic_list)
for item in topic_list:
tmp = []
for word in item:
if len(word) > 1:
tmp.append(word)
filter_list.append(tmp)
return json.dumps(filter_list[:5])
def ajax_get_clustering_topic():
task_name = request.args.get('task_name', '') # task_name
user = request.args.get('user', '')
ts = int(request.args.get('ts', '')) # timestamp: 123456789
topic_list = []
_id = user + '-' + task_name
task_detail = es.get(index=index_sensing_task, doc_type=_id,
id=ts)['_source']
#burst_reason = task_detail['burst_reason']
burst_reason = 1
filter_list = []
if burst_reason:
topic_list = task_detail.get("clustering_topic", [])
if topic_list:
topic_list = json.loads(topic_list)
for item in topic_list:
tmp = []
for word in item:
if len(word) > 1:
tmp.append(word)
filter_list.append(tmp)
return json.dumps(filter_list[:5])
def ajax_revise_task():
task_name = request.args.get('task_name','') # must
finish = request.args.get("finish", "10")
stop_time = request.args.get('stop_time', '') # timestamp
user = request.args.get('user', '')
#now_ts = datetime2ts("2013-09-06")
_id = user + '-' + task_name
now_ts = time.time()
if stop_time and stop_time < now_ts:
return json.dumps([])
if task_name and user:
task_detail = es.get(index=index_manage_sensing_task, doc_type=task_doc_type, id=_id)['_source']
if stop_time:
task_detail['stop_time'] = stop_time
if int(finish) == 0:
task_detail['finish'] = finish
task_detail['processing_status'] = "1" # 重启时将处理状态改为
if stop_time or int(finish) == 0:
es.index(index=index_manage_sensing_task, doc_type=task_doc_type, id=_id, body=task_detail)
return json.dumps(['1'])
return json.dumps([])
def get_sensitive_text_detail(task_name, ts, user, order):
_id = user + '-' + task_name
task_detail = es.get(index=index_sensing_task, doc_type=_id, id=ts)['_source']
weibo_detail = json.loads(task_detail['sensitive_weibo_detail'])
weibo_detail_list = []
if weibo_detail:
for iter_mid, item in weibo_detail.iteritems():
tmp = []
tmp.append(iter_mid)
tmp.append(item[iter_mid])
tmp.append(item['retweeted'])
tmp.append(item['comment'])
weibo_detail_list.append(tmp)
mid_list = weibo_detail.keys()
results = []
query_body = {
"query":{
"filtered":{
"filter":{
"terms":{"mid": mid_list}
}
}
}
}
index_list = []
datetime = ts2datetime(ts)
datetime_1 = ts2datetime(ts-DAY)
index_name = flow_text_index_name_pre + datetime
exist_es = es_text.indices.exists(index_name)
if exist_es:
index_list.append(index_name)
index_name_1 = flow_text_index_name_pre + datetime_1
exist_es_1 = es_text.indices.exists(index_name_1)
if exist_es_1:
index_list.append(index_name_1)
if index_list and mid_list:
search_results = es_text.search(index=index_list, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"]
else:
search_results = []
uid_list = []
text_dict = dict() # 文本信息
portrait_dict = dict() # 背景信息
if search_results:
for item in search_results:
uid_list.append(item["_source"]['uid'])
text_dict[item['_id']] = item['_source'] # _id是mid
if uid_list:
portrait_result = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":uid_list}, fields=['nick_name', 'photo_url'])["docs"]
for item in portrait_result:
if item['found']:
portrait_dict[item['_id']] = {"nick_name": item["fields"]["nick_name"][0], "photo_url": item["fields"]["photo_url"][0]}
else:
portrait_dict[item['_id']] = {"nick_name": item['_id'], "photo_url":""}
if order == "total":
sorted_list = sorted(weibo_detail_list, key=lambda x:x[1], reverse=True)
elif order == "retweeted":
sorted_list = sorted(weibo_detail_list, key=lambda x:x[2], reverse=True)
elif order == "comment":
sorted_list = sorted(weibo_detail_list, key=lambda x:x[3], reverse=True)
else:
sorted_list = weibo_detail_list
count_n = 0
for item in sorted_list:
mid = item[0]
iter_text = text_dict.get(mid, {})
temp = []
# uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type
if iter_text:
uid = iter_text['uid']
temp.append(uid)
iter_portrait = portrait_dict.get(uid, {})
if iter_portrait:
temp.append(iter_portrait['nick_name'])
temp.append(iter_portrait['photo_url'])
else:
temp.extend([uid,''])
temp.append(iter_text["text"])
temp.append(iter_text["sentiment"])
temp.append(ts2date(iter_text['timestamp']))
temp.append(iter_text['geo'])
temp.append(iter_text['message_type'])
temp.append(item[2])
temp.append(item[3])
temp.append(iter_text.get('sensitive', 0))
count_n += 1
results.append(temp)
if results and order == "ts":
results = sorted(results, key=lambda x:x[5], reverse=True)
if results and order == "sensitive":
results = sorted(results, key=lambda x:x[-1], reverse=True)
return results
def get_sensitive_text_detail(task_name, ts, user, order):
_id = user + '-' + task_name
task_detail = es.get(index=index_sensing_task, doc_type=_id,
id=ts)['_source']
weibo_detail = json.loads(task_detail['sensitive_weibo_detail'])
weibo_detail_list = []
if weibo_detail:
for iter_mid, item in weibo_detail.iteritems():
tmp = []
tmp.append(iter_mid)
tmp.append(item[iter_mid])
tmp.append(item['retweeted'])
tmp.append(item['comment'])
weibo_detail_list.append(tmp)
mid_list = weibo_detail.keys()
results = []
query_body = {
"query": {
"filtered": {
"filter": {
"terms": {
"mid": mid_list
}
}
}
}
}
index_list = []
datetime = ts2datetime(ts)
datetime_1 = ts2datetime(ts - DAY)
index_name = flow_text_index_name_pre + datetime
exist_es = es_text.indices.exists(index_name)
if exist_es:
index_list.append(index_name)
index_name_1 = flow_text_index_name_pre + datetime_1
exist_es_1 = es_text.indices.exists(index_name_1)
if exist_es_1:
index_list.append(index_name_1)
if index_list and mid_list:
search_results = es_text.search(index=index_list,
doc_type=flow_text_index_type,
body=query_body)["hits"]["hits"]
else:
search_results = []
uid_list = []
text_dict = dict() # 文本信息
portrait_dict = dict() # 背景信息
if search_results:
for item in search_results:
uid_list.append(item["_source"]['uid'])
text_dict[item['_id']] = item['_source'] # _id是mid
if uid_list:
portrait_result = es_profile.mget(
index=profile_index_name,
doc_type=profile_index_type,
body={"ids": uid_list},
fields=['nick_name', 'photo_url'])["docs"]
for item in portrait_result:
if item['found']:
portrait_dict[item['_id']] = {
"nick_name": item["fields"]["nick_name"][0],
"photo_url": item["fields"]["photo_url"][0]
}
else:
portrait_dict[item['_id']] = {
"nick_name": item['_id'],
"photo_url": ""
}
if order == "total":
sorted_list = sorted(weibo_detail_list,
key=lambda x: x[1],
reverse=True)
elif order == "retweeted":
sorted_list = sorted(weibo_detail_list,
key=lambda x: x[2],
reverse=True)
elif order == "comment":
sorted_list = sorted(weibo_detail_list,
key=lambda x: x[3],
reverse=True)
else:
sorted_list = weibo_detail_list
count_n = 0
for item in sorted_list:
mid = item[0]
iter_text = text_dict.get(mid, {})
temp = []
# uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type
if iter_text:
uid = iter_text['uid']
temp.append(uid)
iter_portrait = portrait_dict.get(uid, {})
if iter_portrait:
temp.append(iter_portrait['nick_name'])
temp.append(iter_portrait['photo_url'])
else:
temp.extend([uid, ''])
temp.append(iter_text["text"])
temp.append(iter_text["sentiment"])
temp.append(ts2date(iter_text['timestamp']))
temp.append(iter_text['geo'])
temp.append(iter_text['message_type'])
temp.append(item[2])
temp.append(item[3])
temp.append(iter_text.get('sensitive', 0))
count_n += 1
results.append(temp)
if results and order == "ts":
results = sorted(results, key=lambda x: x[5], reverse=True)
if results and order == "sensitive":
results = sorted(results, key=lambda x: x[-1], reverse=True)
return results
def imagine(uid, query_fields_dict,index_name=portrait_index_name, doctype=portrait_index_type):
default_setting_dict = query_fields_dict
print query_fields_dict
personal_info = es.get(index=portrait_index_name, doc_type=portrait_index_type, id=uid, _source=True)['_source']
# tag
tag_dict = dict()
tag_dict_value = 0
if "tag" in query_fields_dict:
tag_dict_value = query_fields_dict["tag"]
query_fields_dict.pop("tag")
for key,value in personal_info.iteritems():
if "tag-" in key:
tag_dict[key] = value
print tag_dict, tag_dict_value
# size
sort_size = query_fields_dict["size"]
query_fields_dict.pop("size")
keys_list = []
for k, v in query_fields_dict.iteritems():
if v:
keys_list.append(k) #需要进行关联的键
search_dict = {} # 检索的属性字典
iter_list = []
tag_attri_vaule = []
# 对搜索的键值进行过滤，去掉无用的键
for iter_key in keys_list:
if iter_key in personal_info:
if not personal_info[iter_key] or not query_fields_dict[iter_key]:
query_fields_dict.pop(iter_key)
continue
else:
iter_list.append(iter_key)
temp = personal_info[iter_key]
search_dict[iter_key] = temp.split('&')
"""
query_fields_dict.pop(iter_key)
if tag_dict.get(iter_key,''):
tag_attri_vaule.append(iter_key+"-"+tag_dict[iter_key])
"""
if len(iter_list) == 0 and len(tag_dict) == 0:
return []
query_body = {
'query':{
'function_score':{
'query':{
'bool':{
'must':[
]
}
}
}
}
}
number = es.count(index=index_name, doc_type=doctype, body=query_body)['count']
query_body["size"] = sort_size+100
for (k,v) in query_fields_dict.items():
temp = {}
temp_list = []
if k in personal_info and v != 0:
for iter_key in search_dict[k]:
temp_list.append({'wildcard':{k:{'wildcard':'*'+iter_key+'*', 'boost': v}}})
query_body['query']['function_score']['query']['bool']['must'].append({'bool':{'should':temp_list}})
if tag_dict and tag_dict_value:
temp_list = []
for k,v in tag_dict.iteritems():
temp = {"term":{k:v}}
temp_list.append(temp)
query_body['query']['function_score']['query']['bool']['must'].append({'bool':{'should':temp_list}})
result = es.search(index=index_name, doc_type=doctype, body=query_body)['hits']['hits']
field_list = ['uid','uname', 'activeness','importance', 'influence', 'sensitive']
evaluate_index_list = ['activeness', 'importance', 'influence', 'sensitive']
return_list = []
count = 0
if len(result) > 1 and result:
if result[0]['_id'] != uid:
top_score = result[0]['_score']
else:
top_score = result[1]['_score']
#get evaluate max to normal
evaluate_max_dict = get_evaluate_max()
for item in result:
if uid == item['_id']:
score = item['_score']
continue
info = []
for field in field_list:
if field in evaluate_index_list:
value = item['_source'][field]
normal_value = math.log(value / float(evaluate_max_dict[field] )* 9 + 1, 10) * 100
else:
normal_value = item['_source'][field]
if not normal_value:
normal_value = item['_id']
info.append(normal_value)
info.append(item['_score']/float(top_score)*100)
return_list.append(info)
count += 1
if count == sort_size:
break
return_list.append(number)
temp_list = []
for field in field_list:
if field in evaluate_index_list:
value = personal_info[field]
normal_value = math.log(value / float(evaluate_max_dict[field]) * 9 + 1, 10) * 100
else:
normal_value = personal_info[field]
temp_list.append(normal_value)
results = []
results.append(temp_list)
results.extend(return_list)
return results
def show_important_users(task_name):
return_results = dict() # 返回字典
top_influence = get_top_influence()
task_detail = es.get(index=index_manage_social_task, doc_type=task_doc_type, id=task_name)["_source"]
portrait_detail = []
important_user_set = set() # 重要人物列表
history_status = json.loads(task_detail['history_status'])
start_time = int(task_detail['create_at'])
stop_time = int(task_detail['stop_time'])
time_series = []
keywords_list = json.loads(task_detail['keywords'])
return_results['keywords'] = keywords_list
return_results['remark'] = task_detail['remark']
social_sensors = json.loads(task_detail['social_sensors'])
for item in history_status:
time_series.append(item[0])
# return social sensors details
if social_sensors:
search_results = es.mget(index=portrait_index_name, doc_type=portrait_index_type, body={"ids":social_sensors},fields=['uid', 'uname', 'domain', 'topic_string', "photo_url", 'importance', 'influence', 'activeness'])["docs"]
for item in search_results:
temp = []
if item['found']:
for iter_item in SOCIAL_SENSOR_INFO:
if iter_item == "topic_string":
temp.append(item["fields"][iter_item][0].split('&'))
else:
temp.append(item["fields"][iter_item][0])
portrait_detail.append(temp)
return_results['social_sensors_detail'] = portrait_detail
if time_series:
flow_detail = es.mget(index=index_sensing_task, doc_type=task_name, body={"ids": time_series})['docs']
else:
flow_detail = {}
if flow_detail:
for item in flow_detail:
item = item['_source']
temp_user_list = json.loads(item['important_users'])
important_user_set = important_user_set | set(temp_user_list)
top_importance = get_top_influence('importance')
top_activeness = get_top_influence('activeness')
top_influence = get_top_influence('influence')
important_uid_list = list(important_user_set)
user_detail_info = [] #
if important_uid_list:
user_results = es.mget(index=portrait_index_name, doc_type=portrait_index_type, body={"ids":important_uid_list},fields=['uid', 'uname', 'domain', 'topic_string', "photo_url", 'importance', 'influence','activeness'])['docs']
for item in user_results:
if item['found']:
temp = []
if int(item['fields']['importance'][0]) < IMPORTANT_USER_THRESHOULD:
continue
temp.append(item['fields']['uid'][0])
temp.append(item['fields']['uname'][0])
temp.append(item['fields']['photo_url'][0])
temp.append(item['fields']['domain'][0])
temp.append(item['fields']['topic_string'][0].split('&'))
hot_count = count_hot_uid(item['fields']['uid'][0], start_time, stop_time, keywords_list)
temp.append(hot_count)
importance = math.log(item['fields']['importance'][0]/top_importance*9+1, 10)*100
temp.append("%.2f" %importance)
temp.append(math.log(item['fields']['influence'][0]/top_influence*9+1, 10)*100)
temp.append(math.log(item['fields']['activeness'][0]/top_activeness*9+1, 10)*100)
user_detail_info.append(temp)
return_results['group_list'] = user_detail_info
return return_results
def get_retweet_weibo_detail(ts, user, task_name, size, text_type, type_value):
_id = user + '-' + task_name
task_detail = es_user_portrait.get(index=index_sensing_task, doc_type=_id, id=ts)['_source']
origin_weibo_detail = json.loads(task_detail['origin_weibo_detail'])
retweeted_weibo_detail = json.loads(task_detail['retweeted_weibo_detail'])
mid_list = []
mid_list.extend(origin_weibo_detail.keys())
mid_list.extend(retweeted_weibo_detail.keys())
query_body = {
"query":{
"filtered":{
"filter":{
"bool":{
"must":[
{"range":{
"timestamp":{
"gte": ts - time_interval,
"lt": ts
}
}},
{"terms": {"root_mid": mid_list}}
]
}
}
}
},
"sort": {"timestamp": {"order": "desc"}},
"size": 100
}
if text_type == "message_type":
query_body['query']['filtered']['filter']['bool']['must'].append({"term":{text_type: type_value}})
if text_type == "sentiment":
#if isinstance(type_value, str):
if len(type_value) == 1:
query_body['query']['filtered']['filter']['bool']['must'].append({"term":{text_type: type_value}})
else:
query_body['query']['filtered']['filter']['bool']['must'].append({"terms":{text_type: type_value}})
datetime = ts2datetime(ts)
datetime_1 = ts2datetime(ts-time_interval)
index_name = flow_text_index_name_pre + datetime
exist_es = es_text.indices.exists(index_name)
index_name_1 = flow_text_index_name_pre + datetime_1
exist_es_1 = es_text.indices.exists(index_name_1)
# 1. 查询微博
if datetime == datetime_1 and exist_es:
search_results = es_text.search(index=index_name, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"]
elif datetime != datetime_1 and exist_es_1:
search_results = es_text.search(index=index_name_1, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"]
else:
search_results = []
#print search_results
# 2. 获取微博相关信息
results = []
uid_list = []
if search_results:
for item in search_results:
uid_list.append(item["_source"]['uid'])
if uid_list:
portrait_result = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":uid_list}, fields=['nick_name', 'photo_url'])["docs"]
for i in range(len(uid_list)):
item = search_results[i]['_source']
temp = []
# uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type
temp.append(item['uid'])
if portrait_result[i]['found']:
temp.append(portrait_result[i]["fields"]["nick_name"][0])
temp.append(portrait_result[i]["fields"]["photo_url"][0])
else:
temp.append(item['uid'])
temp.append("")
temp.append(item["text"])
#print item['text']
temp.append(item["sentiment"])
temp.append(ts2date(item['timestamp']))
temp.append(item['geo'])
temp.append(item["message_type"])
results.append(temp)
return results
def get_origin_weibo_detail(ts, user, task_name, size, order, message_type=1):
_id = user + '-' + task_name
task_detail = es_user_portrait.get(index=index_sensing_task, doc_type=_id, id=ts)['_source']
mid_value = json.loads(task_detail['mid_topic_value'])
duplicate_dict = json.loads(task_detail['duplicate_dict'])
tmp_duplicate_dict = dict()
for k,v in duplicate_dict.iteritems():
try:
tmp_duplicate_dict[v].append(k)
except:
tmp_duplicate_dict[v] = [k, v]
if message_type == 1:
weibo_detail = json.loads(task_detail['origin_weibo_detail'])
elif message_type == 2:
weibo_detail = json.loads(task_detail['retweeted_weibo_detail'])
else:
weibo_detail = json.loads(task_detail['sensitive_weibo_detail'])
weibo_detail_list = []
if weibo_detail:
for iter_mid, item in weibo_detail.iteritems():
tmp = []
tmp.append(iter_mid)
tmp.append(item[iter_mid])
tmp.append(item['retweeted'])
tmp.append(item['comment'])
weibo_detail_list.append(tmp)
mid_list = weibo_detail.keys()
results = []
query_body = {
"query":{
"filtered":{
"filter":{
"terms":{"mid": mid_list}
}
}
},
"size": 1000,
"sort": {"timestamp": {"order": "desc"}}
}
index_list = []
datetime = ts2datetime(ts)
datetime_1 = ts2datetime(ts-DAY)
index_name = flow_text_index_name_pre + datetime
exist_es = es_text.indices.exists(index_name)
if exist_es:
index_list.append(index_name)
index_name_1 = flow_text_index_name_pre + datetime_1
exist_es_1 = es_text.indices.exists(index_name_1)
if exist_es_1:
index_list.append(index_name_1)
if index_list and mid_list:
search_results = es_text.search(index=index_list, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"]
else:
search_results = []
uid_list = []
text_dict = dict() # 文本信息
portrait_dict = dict() # 背景信息
sort_results = []
if search_results:
for item in search_results:
uid_list.append(item["_source"]['uid'])
text_dict[item['_id']] = item['_source'] # _id是mid
if uid_list:
portrait_result = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":uid_list}, fields=['nick_name', 'photo_url'])["docs"]
for item in portrait_result:
if item['found']:
portrait_dict[item['_id']] = {"nick_name": item["fields"]["nick_name"][0], "photo_url": item["fields"]["photo_url"][0]}
else:
portrait_dict[item['_id']] = {"nick_name": item['_id'], "photo_url":""}
if order == "total":
sorted_list = sorted(weibo_detail_list, key=lambda x:x[1], reverse=True)
elif order == "retweeted":
sorted_list = sorted(weibo_detail_list, key=lambda x:x[2], reverse=True)
elif order == "comment":
sorted_list = sorted(weibo_detail_list, key=lambda x:x[3], reverse=True)
else:
sorted_list = weibo_detail_list
count_n = 0
results_dict = dict()
mid_index_dict = dict()
for item in sorted_list: # size
mid = item[0]
iter_text = text_dict.get(mid, {})
temp = []
# uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type
if iter_text:
uid = iter_text['uid']
temp.append(uid)
iter_portrait = portrait_dict.get(uid, {})
if iter_portrait:
temp.append(iter_portrait['nick_name'])
temp.append(iter_portrait['photo_url'])
else:
temp.extend([uid,''])
temp.append(iter_text["text"])
temp.append(iter_text["sentiment"])
temp.append(ts2date(iter_text['timestamp']))
temp.append(iter_text['geo'])
if message_type == 1:
temp.append(1)
elif message_type == 2:
temp.append(3)
else:
temp.append(iter_text['message_type'])
temp.append(item[2])
temp.append(item[3])
temp.append(iter_text.get('sensitive', 0))
temp.append(iter_text['timestamp'])
temp.append(mid_value[mid])
temp.append(mid)
results.append(temp)
count_n += 1
results = sorted(results, key=operator.itemgetter(-4, -2, -6), reverse=True) # -4 -2 -3
sort_results = []
count = 0
for item in results:
sort_results.append([item])
mid_index_dict[item[-1]] = count
count += 1
if tmp_duplicate_dict:
remove_list = []
value_list = tmp_duplicate_dict.values() # [[mid, mid], ]
for item in value_list:
tmp = []
for mid in item:
if mid_index_dict.get(mid, 0):
tmp.append(mid_index_dict[mid])
if len(tmp) > 1:
tmp_min = min(tmp)
else:
continue
tmp.remove(tmp_min)
for iter_count in tmp:
sort_results[tmp_min].extend(sort_results[iter_count])
remove_list.append(sort_results[iter_count])
if remove_list:
for item in remove_list:
sort_results.remove(item)
return sort_results
def show_important_users(task_name):
return_results = dict() # 返回字典
top_influence = get_top_influence()
task_detail = es.get(index=index_manage_social_task,
doc_type=task_doc_type,
id=task_name)["_source"]
portrait_detail = []
important_user_set = set() # 重要人物列表
history_status = json.loads(task_detail['history_status'])
start_time = int(task_detail['create_at'])
stop_time = int(task_detail['stop_time'])
time_series = []
keywords_list = json.loads(task_detail['keywords'])
return_results['keywords'] = keywords_list
return_results['remark'] = task_detail['remark']
social_sensors = json.loads(task_detail['social_sensors'])
for item in history_status:
time_series.append(item[0])
# return social sensors details
if social_sensors:
search_results = es.mget(index=portrait_index_name,
doc_type=portrait_index_type,
body={"ids": social_sensors},
fields=[
'uid', 'uname', 'domain', 'topic_string',
"photo_url", 'importance', 'influence',
'activeness'
])["docs"]
for item in search_results:
temp = []
if item['found']:
for iter_item in SOCIAL_SENSOR_INFO:
if iter_item == "topic_string":
temp.append(item["fields"][iter_item][0].split('&'))
else:
temp.append(item["fields"][iter_item][0])
portrait_detail.append(temp)
return_results['social_sensors_detail'] = portrait_detail
if time_series:
flow_detail = es.mget(index=index_sensing_task,
doc_type=task_name,
body={"ids": time_series})['docs']
else:
flow_detail = {}
if flow_detail:
for item in flow_detail:
item = item['_source']
temp_user_list = json.loads(item['important_users'])
important_user_set = important_user_set | set(temp_user_list)
top_importance = get_top_influence('importance')
top_activeness = get_top_influence('activeness')
top_influence = get_top_influence('influence')
important_uid_list = list(important_user_set)
user_detail_info = [] #
if important_uid_list:
user_results = es.mget(index=portrait_index_name,
doc_type=portrait_index_type,
body={"ids": important_uid_list},
fields=[
'uid', 'uname', 'domain', 'topic_string',
"photo_url", 'importance', 'influence',
'activeness'
])['docs']
for item in user_results:
if item['found']:
temp = []
if int(item['fields']['importance']
[0]) < IMPORTANT_USER_THRESHOULD:
continue
temp.append(item['fields']['uid'][0])
temp.append(item['fields']['uname'][0])
temp.append(item['fields']['photo_url'][0])
temp.append(item['fields']['domain'][0])
temp.append(item['fields']['topic_string'][0].split('&'))
hot_count = count_hot_uid(item['fields']['uid'][0], start_time,
stop_time, keywords_list)
temp.append(hot_count)
importance = math.log(
item['fields']['importance'][0] / top_importance * 9 + 1,
10) * 100
temp.append("%.2f" % importance)
temp.append(
math.log(
item['fields']['influence'][0] / top_influence * 9 + 1,
10) * 100)
temp.append(
math.log(
item['fields']['activeness'][0] / top_activeness * 9 +
1, 10) * 100)
user_detail_info.append(temp)
return_results['group_list'] = user_detail_info
return return_results
def get_task_detail_2(task_name, ts, user):
results = dict()
index_name = task_name
_id = user + "-" + task_name
task_detail = es.get(index=index_manage_sensing_task, doc_type=task_doc_type, id=_id)["_source"]
task_name = task_detail['task_name']
history_status = json.loads(task_detail['history_status'])
start_time = task_detail['create_at']
create_by = task_detail['create_by']
stop_time = task_detail['stop_time']
remark = task_detail.get('remark', '')
portrait_detail = []
count = 0 # 计数
top_influence = get_top_influence("influence")
top_activeness = get_top_influence("activeness")
top_importance = get_top_influence("importance")
time_series = [] # 时间
#positive_sentiment_list = [] # 情绪列表
#neutral_sentiment_list = []
#negetive_sentiment_list = []
all_weibo_list = []
origin_weibo_list = [] # 微博列表
retweeted_weibo_list = []
#retweeted_weibo_count = [] # 别人转发他的数量
#comment_weibo_count = []
#total_number_count = []
#burst_time_list = [] # 爆发时间列表
important_user_set = set() # 重要人物列表
out_portrait_users = set() # 未入库
ts = int(ts)
time_series = history_status
#for item in history_status:
# if int(item[0]) <= ts:
# time_series.append(item[0]) # 到目前为止的所有的时间戳
# get detail task information from es
if time_series:
flow_detail = es.mget(index=index_sensing_task, doc_type=_id, body={"ids": time_series})['docs']
else:
flow_detail = {}
if flow_detail:
for item in flow_detail:
item = item['_source']
timestamp = item['timestamp']
#sentiment_distribution = json.loads(item["sentiment_distribution"])
#positive_sentiment_list.append(int(sentiment_distribution['1']))
#negetive_sentiment_list.append(int(sentiment_distribution['2'])+int(sentiment_distribution['3']) \
# +int(sentiment_distribution['4'])+int(sentiment_distribution['5'])+int(sentiment_distribution['6']))
#neutral_sentiment_list.append(int(sentiment_distribution['0']))
origin_weibo_list.append(item["origin_weibo_number"]) # real
retweeted_weibo_list.append(item['retweeted_weibo_number']) # real
all_weibo_list.append(item["origin_weibo_number"]+item['retweeted_weibo_number'])
#retweeted_weibo_count.append(item['retweeted_weibo_count'])
#comment_weibo_count.append(item['comment_weibo_count'])
#total_number_count.append(item['weibo_total_number'])
temp_important_user_list = json.loads(item['important_users'])
unfiltered_users = json.loads(item['unfilter_users'])
temp_out_portrait_users = set(unfiltered_users) - set(temp_important_user_list) # 未入库
important_user_set = important_user_set | set(temp_important_user_list)
out_portrait_users = out_portrait_users | set(temp_out_portrait_users)
#burst_reason = item.get("burst_reason", "")
#if burst_reason:
# burst_time_list.append([timestamp, count, burst_reason])
count += 1
####################################################################################
# 统计爆发原因，下相应的结论
"""
weibo_variation_count = 0
weibo_variation_time = []
sentiment_variation_count = 0
sentiment_variation_time = []
sensitive_variation_count = 0 # sensitive
sensitive_variation_time = [] # sensitive
common_variation_count = 0
common_variation_time = []
if burst_time_list:
for item in burst_time_list:
tmp_common = 0
x1 = 0
x2 = 0
x3 = 0
if signal_count_varition in item[2]:
weibo_variation_count += 1
weibo_variation_time.append([ts2date_min(item[0]), total_number_count[item[1]]])
x1 = total_number_count[item[1]]
tmp_common += 1
if signal_sentiment_varition in item[2]:
tmp_common += 1
sentiment_variation_count += 1
x2 = negetive_sentiment_list[item[1]]
sentiment_variation_time.append([ts2date_min(item[0]), negetive_sentiment_list[item[1]]])
if signal_sensitive_variation in item[2]:
tmp_common += 1
sensitive_variation_count += 1
x3 = sensitive_total_number_list[item[1]]
sensitive_variation_time.append([ts2date_min(item[0]), all_weibo_list[item[1]]])
if tmp_common >= 2:
common_variation_count += 1
common_variation_time.append([ts2date_min(item[0]), x1, x2, x3])
warning_conclusion = remark
variation_distribution = []
if weibo_variation_count:
variation_distribution.append(weibo_variation_time)
else:
variation_distribution.append([])
if sentiment_variation_count:
variation_distribution.append(sentiment_variation_time)
else:
variation_distribution.append([])
if sensitive_variation_count:
variation_distribution.append(sensitive_variation_time)
else:
variation_distribution.append([])
if common_variation_count:
variation_distribution.append(common_variation_time)
else:
variation_distribution.append([])
results['warning_conclusion'] = warning_conclusion
results['variation_distribution'] = variation_distribution
# 每个用户的热度
"""
# 获取重要用户的个人信息
important_uid_list = list(important_user_set)
out_portrait_users_list = list(out_portrait_users)
user_detail_info = [] #
out_user_detail_info = []
if important_uid_list:
user_results = es.mget(index=portrait_index_name, doc_type=portrait_index_type, body={"ids":important_uid_list}, fields=['uid', 'uname', 'domain', 'topic_string', "photo_url", 'importance', 'influence', 'activeness'])['docs']
for item in user_results:
if item['found']:
temp = []
#if int(item['fields']['importance'][0]) < IMPORTANT_USER_THRESHOULD:
# continue
temp.append(item['fields']['uid'][0])
uname = item['fields']['uname'][0]
if not uname or uname == "未知":
uname = item['fields']['uid'][0]
temp.append(uname)
temp.append(item['fields']['photo_url'][0])
temp.append(item['fields']['domain'][0])
temp.append(item['fields']['topic_string'][0].split('&'))
#hot_count = count_hot_uid(item['fields']['uid'][0], start_time, stop_time)
#temp.append(hot_count)
temp.append(math.log(item['fields']['importance'][0]/float(top_importance)*9+1, 10)*100)
temp.append(math.log(item['fields']['influence'][0]/float(top_influence)*9+1, 10)*100)
temp.append(math.log(item['fields']['activeness'][0]/float(top_activeness)*9+1, 10)*100)
user_detail_info.append(temp)
# 排序
if user_detail_info:
user_detail_info = sorted(user_detail_info, key=lambda x:x[6], reverse=True)
else:
user_detail_info = []
if out_portrait_users_list:
profile_results = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":out_portrait_users_list})["docs"]
bci_index = "bci_" + ts2datetime(ts-DAY).replace('-','')
influence_results = es.mget(index=bci_index, doc_type="bci", body={"ids":out_portrait_users_list}, fields=["user_index"])['docs']
bci_results = es_profile.mget(index="bci_history", doc_type="bci", body={"ids":out_portrait_users_list}, fields=['user_fansnum'])['docs']
top_influence = get_top_all_influence("user_index", ts)
count = 0
if profile_results:
for item in profile_results:
temp = []
if item['found']:
uid = item['_source']['uid']
temp = get_user_profile([uid], ['nick_name', 'user_location', 'statusnum', 'fansnum'])[0]
else:
temp = [item['_id'], item['_id'], '', '', '']
"""
if item['found']:
temp.append(item['_source']['uid'])
if item['_source']['nick_name']:
temp.append(item['_source']['nick_name'])
else:
temp.append(item['_source']['uid'])
temp.append(item['_source']['user_location'])
temp.append(item['_source']['statusnum'])
temp.append(item['_source']['friendsnum'])
else:
temp.append(item['_id'])
temp.append(item['_id'])
temp.extend([''])
temp.append('--')
temp.append('--')
try:
user_fansnum = bci_results[count]["fields"]["user_fansnum"][0]
except:
user_fansnum = 0
temp.append(user_fansnum)
temp_influ = influence_results[count]
if temp_influ.get('found', 0):
user_index = temp_influ['fields']['user_index'][0]
temp.append(math.log(user_index/float(top_influence)*9+1, 10)*100)
else:
temp.append(0)
"""
count += 1
out_user_detail_info.append(temp)
revise_time_series = []
for item in time_series:
revise_time_series.append(ts2date_min(item))
results['important_user_detail'] = user_detail_info
results['out_portrait_user_detail'] = out_user_detail_info
#results['burst_time'] = burst_time_list # 爆发时间点，以及爆发原因
results['time_series'] = revise_time_series
#results['positive_sentiment_list'] = positive_sentiment_list
#esults['negetive_sentiment_list'] = negetive_sentiment_list
#results['neutral_sentiment_list'] = neutral_sentiment_list
results['all_weibo_list'] = all_weibo_list
results['origin_weibo_list'] = origin_weibo_list
results['retweeted_weibo_list'] = retweeted_weibo_list
#results['comment_weibo_count'] = comment_weibo_count
#results['retweeted_weibo_count'] = retweeted_weibo_count
#results['total_number_list'] = total_number_count
return results
def imagine(uid,
query_fields_dict,
index_name=portrait_index_name,
doctype=portrait_index_type):
default_setting_dict = query_fields_dict
print query_fields_dict
personal_info = es.get(index=portrait_index_name,
doc_type=portrait_index_type,
id=uid,
_source=True)['_source']
# tag
tag_dict = dict()
tag_dict_value = 0
if "tag" in query_fields_dict:
tag_dict_value = query_fields_dict["tag"]
query_fields_dict.pop("tag")
for key, value in personal_info.iteritems():
if "tag-" in key:
tag_dict[key] = value
print tag_dict, tag_dict_value
# size
sort_size = query_fields_dict["size"]
query_fields_dict.pop("size")
keys_list = []
for k, v in query_fields_dict.iteritems():
if v:
keys_list.append(k) #需要进行关联的键
search_dict = {} # 检索的属性字典
iter_list = []
tag_attri_vaule = []
# 对搜索的键值进行过滤，去掉无用的键
for iter_key in keys_list:
if iter_key in personal_info:
if not personal_info[iter_key] or not query_fields_dict[iter_key]:
query_fields_dict.pop(iter_key)
continue
else:
iter_list.append(iter_key)
temp = personal_info[iter_key]
search_dict[iter_key] = temp.split('&')
"""
query_fields_dict.pop(iter_key)
if tag_dict.get(iter_key,''):
tag_attri_vaule.append(iter_key+"-"+tag_dict[iter_key])
"""
if len(iter_list) == 0 and len(tag_dict) == 0:
return []
query_body = {
'query': {
'function_score': {
'query': {
'bool': {
'must': []
}
}
}
}
}
number = es.count(index=index_name, doc_type=doctype,
body=query_body)['count']
query_body["size"] = sort_size + 100
for (k, v) in query_fields_dict.items():
temp = {}
temp_list = []
if k in personal_info and v != 0:
for iter_key in search_dict[k]:
temp_list.append({
'wildcard': {
k: {
'wildcard': '*' + iter_key + '*',
'boost': v
}
}
})
query_body['query']['function_score']['query']['bool'][
'must'].append({'bool': {
'should': temp_list
}})
if tag_dict and tag_dict_value:
temp_list = []
for k, v in tag_dict.iteritems():
temp = {"term": {k: v}}
temp_list.append(temp)
query_body['query']['function_score']['query']['bool']['must'].append(
{'bool': {
'should': temp_list
}})
result = es.search(index=index_name, doc_type=doctype,
body=query_body)['hits']['hits']
field_list = [
'uid', 'uname', 'activeness', 'importance', 'influence', 'sensitive'
]
evaluate_index_list = [
'activeness', 'importance', 'influence', 'sensitive'
]
return_list = []
count = 0
if len(result) > 1 and result:
if result[0]['_id'] != uid:
top_score = result[0]['_score']
else:
top_score = result[1]['_score']
#get evaluate max to normal
evaluate_max_dict = get_evaluate_max()
for item in result:
if uid == item['_id']:
score = item['_score']
continue
info = []
for field in field_list:
if field in evaluate_index_list:
value = item['_source'][field]
normal_value = math.log(
value / float(evaluate_max_dict[field]) * 9 + 1, 10) * 100
else:
normal_value = item['_source'][field]
if not normal_value:
normal_value = item['_id']
info.append(normal_value)
info.append(item['_score'] / float(top_score) * 100)
return_list.append(info)
count += 1
if count == sort_size:
break
return_list.append(number)
temp_list = []
for field in field_list:
if field in evaluate_index_list:
value = personal_info[field]
normal_value = math.log(
value / float(evaluate_max_dict[field]) * 9 + 1, 10) * 100
else:
normal_value = personal_info[field]
temp_list.append(normal_value)
results = []
results.append(temp_list)
results.extend(return_list)
return results
