def search_influence_detail(uid_list, index_name, doctype):
result = es.mget(index=index_name,
doc_type=doctype,
body={"ids": uid_list},
_source=True)["docs"]
return result[0]['_source']
def get_user_detail(date, input_result, status):
results = []
if status=='show_in':
uid_list = input_result
if status=='show_compute':
uid_list = input_result.keys()
if status=='show_in_history':
uid_list = input_result.keys()
if date!='all':
index_name = 'bci_' + ''.join(date.split('-'))
else:
now_ts = time.time()
now_date = ts2datetime(now_ts)
index_name = 'bci_' + ''.join(now_date.split('-'))
index_type = 'bci'
user_bci_result = es_cluster.mget(index=index_name, doc_type=index_type, body={'ids':uid_list}, _source=True)['docs']
user_profile_result = es_user_profile.mget(index='weibo_user', doc_type='user', body={'ids':uid_list}, _source=True)['docs']
max_evaluate_influ = get_evaluate_max(index_name)
for i in range(0, len(uid_list)):
uid = uid_list[i]
bci_dict = user_bci_result[i]
profile_dict = user_profile_result[i]
try:
bci_source = bci_dict['_source']
except:
bci_source = None
if bci_source:
influence = bci_source['user_index']
influence = math.log(influence/max_evaluate_influ['user_index'] * 9 + 1 ,10)
influence = influence * 100
else:
influence = ''
try:
profile_source = profile_dict['_source']
except:
profile_source = None
if profile_source:
uname = profile_source['nick_name']
location = profile_source['user_location']
fansnum = profile_source['fansnum']
statusnum = profile_source['statusnum']
else:
uname = ''
location = ''
fansnum = ''
statusnum = ''
if status == 'show_in':
results.append([uid, uname, location, fansnum, statusnum, influence])
if status == 'show_compute':
in_date = json.loads(input_result[uid])[0]
compute_status = json.loads(input_result[uid])[1]
if compute_status == '1':
compute_status = '3'
results.append([uid, uname, location, fansnum, statusnum, influence, in_date, compute_status])
if status == 'show_in_history':
in_status = input_result[uid]
results.append([uid, uname, location, fansnum, statusnum, influence, in_status])
return results
def get_recommentation(submit_user):
if RUN_TYPE:
now_ts = time.time()
else:
now_ts = datetime2ts(RUN_TEST_TIME)
in_portrait_set = set(r.hkeys("compute"))
result = []
for i in range(7):
iter_ts = now_ts - i*DAY
iter_date = ts2datetime(iter_ts)
submit_user_recomment = "recomment_" + submit_user + "_" + str(iter_date)
bci_date = ts2datetime(iter_ts - DAY)
submit_user_recomment = r.hkeys(submit_user_recomment)
bci_index_name = "bci_" + bci_date.replace('-', '')
exist_bool = es_cluster.indices.exists(index=bci_index_name)
if not exist_bool:
continue
if submit_user_recomment:
user_bci_result = es_cluster.mget(index=bci_index_name, doc_type="bci", body={'ids':submit_user_recomment}, _source=True)['docs']
user_profile_result = es_user_profile.mget(index='weibo_user', doc_type='user', body={'ids':submit_user_recomment}, _source=True)['docs']
max_evaluate_influ = get_evaluate_max(bci_index_name)
for i in range(len(submit_user_recomment)):
uid = submit_user_recomment[i]
bci_dict = user_bci_result[i]
profile_dict = user_profile_result[i]
try:
bci_source = bci_dict['_source']
except:
bci_source = None
if bci_source:
influence = bci_source['user_index']
influence = math.log(influence/max_evaluate_influ['user_index'] * 9 + 1 ,10)
influence = influence * 100
else:
influence = ''
try:
profile_source = profile_dict['_source']
except:
profile_source = None
if profile_source:
uname = profile_source['nick_name']
location = profile_source['user_location']
fansnum = profile_source['fansnum']
statusnum = profile_source['statusnum']
else:
uname = ''
location = ''
fansnum = ''
statusnum = ''
if uid in in_portrait_set:
in_portrait = "1"
else:
in_portrait = "0"
recomment_day = iter_date
result.append([iter_date, uid, uname, location, fansnum, statusnum, influence, in_portrait])
return result
def search_influence_detail(uid_list, index_name, doctype):
result = es.mget(index=index_name, doc_type=doctype, body={"ids": uid_list}, _source=True)["docs"]
return result[0]['_source']
def get_user_detail(date, input_result, status, user_type="influence", auth=""):
bci_date = ts2datetime(datetime2ts(date) - DAY)
results = []
if status=='show_in':
uid_list = input_result
if status=='show_compute':
uid_list = input_result.keys()
if status=='show_in_history':
uid_list = input_result.keys()
if date!='all':
index_name = 'bci_' + ''.join(bci_date.split('-'))
else:
now_ts = time.time()
now_date = ts2datetime(now_ts)
index_name = 'bci_' + ''.join(now_date.split('-'))
index_type = 'bci'
user_bci_result = es_cluster.mget(index=index_name, doc_type=index_type, body={'ids':uid_list}, _source=True)['docs']
user_profile_result = es_user_profile.mget(index='weibo_user', doc_type='user', body={'ids':uid_list}, _source=True)['docs']
max_evaluate_influ = get_evaluate_max(index_name)
for i in range(0, len(uid_list)):
uid = uid_list[i]
bci_dict = user_bci_result[i]
profile_dict = user_profile_result[i]
try:
bci_source = bci_dict['_source']
except:
bci_source = None
if bci_source:
influence = bci_source['user_index']
influence = math.log(influence/max_evaluate_influ['user_index'] * 9 + 1 ,10)
influence = influence * 100
else:
influence = ''
try:
profile_source = profile_dict['_source']
except:
profile_source = None
if profile_source:
uname = profile_source['nick_name']
location = profile_source['user_location']
fansnum = profile_source['fansnum']
statusnum = profile_source['statusnum']
else:
uname = ''
location = ''
fansnum = ''
statusnum = ''
if status == 'show_in':
if user_type == "sensitive":
tmp_ts = datetime2ts(date) - DAY
tmp_data = r_cluster.hget("sensitive_"+str(tmp_ts), uid)
if tmp_data:
sensitive_dict = json.loads(tmp_data)
sensitive_words = sensitive_dict.keys()
else:
sensitive_words = []
results.append([uid, uname, location, fansnum, statusnum, influence, sensitive_words])
else:
results.append([uid, uname, location, fansnum, statusnum, influence])
if auth:
hashname_submit = "submit_recomment_" + date
tmp_data = json.loads(r.hget(hashname_submit, uid))
recommend_list = (tmp_data['operation']).split('&')
admin_list = []
admin_list.append(tmp_data['system'])
admin_list.append(list(set(recommend_list)))
admin_list.append(len(recommend_list))
results[-1].extend(admin_list)
if status == 'show_compute':
in_date = json.loads(input_result[uid])[0]
compute_status = json.loads(input_result[uid])[1]
if compute_status == '1':
compute_status = '3'
results.append([uid, uname, location, fansnum, statusnum, influence, in_date, compute_status])
if status == 'show_in_history':
in_status = input_result[uid]
if user_type == "sensitive":
tmp_ts = datetime2ts(date) - DAY
tmp_data = r_cluster.hget("sensitive_"+str(tmp_ts), uid)
if tmp_data:
sensitive_dict = json.loads(tmp_data)
sensitive_words = sensitive_dict.keys()
results.append([uid, uname, location, fansnum, statusnum, influence, in_status, sensitive_words])
else:
results.append([uid, uname, location, fansnum, statusnum, influence, in_status])
return results
sensitive_string = "sensitive_score_" + tmp_ts
query_sensitive_body = {
"query":{
"match_all":{}
},
"size":1,
"sort":{sensitive_string:{"order":"desc"}}
}
try:
top_sensitive_result = es_bci_history.search(index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX, body=query_sensitive_body, _source=False, fields=[sensitive_string])['hits']['hits']
top_sensitive = top_sensitive_result[0]['fields'][sensitive_string][0]
except Exception, reason:
print Exception, reason
top_sensitive = 400
index_type = 'bci'
user_bci_result = es_cluster.mget(index=index_name, doc_type=index_type, body={'ids':uid_list}, _source=True)['docs']
user_profile_result = es_user_profile.mget(index='weibo_user', doc_type='user', body={'ids':uid_list}, _source=True)['docs']
bci_history_result = es_bci_history.mget(index=bci_history_index_name, doc_type=bci_history_index_type, body={"ids":uid_list}, fields=['user_fansnum', 'weibo_month_sum'])['docs']
sensitive_history_result = es_bci_history.mget(index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX, body={'ids':uid_list}, fields=[sensitive_string], _source=False)['docs']
max_evaluate_influ = get_evaluate_max(index_name)
for i in range(0, len(uid_list)):
uid = uid_list[i]
bci_dict = user_bci_result[i]
profile_dict = user_profile_result[i]
bci_history_dict = bci_history_result[i]
sensitive_history_dict = sensitive_history_result[i]
#print sensitive_history_dict
try:
bci_source = bci_dict['_source']
except:
bci_source = None
def get_recommentation(submit_user):
if RUN_TYPE:
now_ts = time.time()
else:
now_ts = datetime2ts(RUN_TEST_TIME)
in_portrait_set = set(r.hkeys("compute"))
result = []
for i in range(7):
iter_ts = now_ts - i * DAY
iter_date = ts2datetime(iter_ts)
submit_user_recomment = "recomment_" + submit_user + "_" + str(
iter_date)
bci_date = ts2datetime(iter_ts - DAY)
submit_user_recomment = r.hkeys(submit_user_recomment)
bci_index_name = "bci_" + bci_date.replace('-', '')
exist_bool = es_cluster.indices.exists(index=bci_index_name)
if not exist_bool:
continue
if submit_user_recomment:
user_bci_result = es_cluster.mget(
index=bci_index_name,
doc_type="bci",
body={'ids': submit_user_recomment},
_source=True)['docs']
user_profile_result = es_user_profile.mget(
index='weibo_user',
doc_type='user',
body={'ids': submit_user_recomment},
_source=True)['docs']
max_evaluate_influ = get_evaluate_max(bci_index_name)
for i in range(len(submit_user_recomment)):
uid = submit_user_recomment[i]
bci_dict = user_bci_result[i]
profile_dict = user_profile_result[i]
try:
bci_source = bci_dict['_source']
except:
bci_source = None
if bci_source:
influence = bci_source['user_index']
influence = math.log(
influence / max_evaluate_influ['user_index'] * 9 + 1,
10)
influence = influence * 100
else:
influence = ''
try:
profile_source = profile_dict['_source']
except:
profile_source = None
if profile_source:
uname = profile_source['nick_name']
location = profile_source['user_location']
fansnum = profile_source['fansnum']
statusnum = profile_source['statusnum']
else:
uname = ''
location = ''
fansnum = ''
statusnum = ''
if uid in in_portrait_set:
in_portrait = "1"
else:
in_portrait = "0"
recomment_day = iter_date
result.append([
iter_date, uid, uname, location, fansnum, statusnum,
influence, in_portrait
])
return result
def get_user_detail(date, input_result, status, user_type="influence", auth=""):
results = []
if status=='show_in':
uid_list = input_result
if status=='show_compute':
uid_list = input_result.keys()
if status=='show_in_history':
uid_list = input_result.keys()
if date!='all':
index_name = 'bci_' + ''.join(date.split('-'))
else:
now_ts = time.time()
now_date = ts2datetime(now_ts)
index_name = 'bci_' + ''.join(now_date.split('-'))
index_type = 'bci'
user_bci_result = es_cluster.mget(index=index_name, doc_type=index_type, body={'ids':uid_list}, _source=True)['docs']
user_profile_result = es_user_profile.mget(index='weibo_user', doc_type='user', body={'ids':uid_list}, _source=True)['docs']
max_evaluate_influ = get_evaluate_max(index_name)
for i in range(0, len(uid_list)):
uid = uid_list[i]
bci_dict = user_bci_result[i]
profile_dict = user_profile_result[i]
try:
bci_source = bci_dict['_source']
except:
bci_source = None
if bci_source:
influence = bci_source['user_index']
influence = math.log(influence/max_evaluate_influ['user_index'] * 9 + 1 ,10)
influence = influence * 100
else:
influence = ''
try:
profile_source = profile_dict['_source']
except:
profile_source = None
if profile_source:
uname = profile_source['nick_name']
location = profile_source['user_location']
fansnum = profile_source['fansnum']
statusnum = profile_source['statusnum']
else:
uname = ''
location = ''
fansnum = ''
statusnum = ''
if status == 'show_in':
if user_type == "sensitive":
tmp_ts = datetime2ts(date) - DAY
tmp_data = r_cluster.hget("sensitive_"+str(tmp_ts), uid)
if tmp_data:
sensitive_dict = json.loads(tmp_data)
sensitive_words = sensitive_dict.keys()
else:
senstive_words = []
results.append([uid, uname, location, fansnum, statusnum, influence, sensitive_words])
else:
results.append([uid, uname, location, fansnum, statusnum, influence])
if auth:
hashname_submit = "submit_recomment_" + date
tmp_data = json.loads(r.hget(hashname_submit, uid))
recommend_list = (tmp_data['operation']).split('&')
admin_list = []
admin_list.append(tmp_data['system'])
admin_list.append(list(set(recommend_list)))
admin_list.append(len(recommend_list))
results[-1].extend(admin_list)
if status == 'show_compute':
in_date = json.loads(input_result[uid])[0]
compute_status = json.loads(input_result[uid])[1]
if compute_status == '1':
compute_status = '3'
results.append([uid, uname, location, fansnum, statusnum, influence, in_date, compute_status])
if status == 'show_in_history':
in_status = input_result[uid]
if user_type == "sensitive":
tmp_ts = datetime2ts(date) - DAY
tmp_data = r_cluster.hget("sensitive_"+str(tmp_ts), uid)
if tmp_data:
sensitive_dict = json.loads(tmp_data)
sensitive_words = sensitive_dict.keys()
results.append([uid, uname, location, fansnum, statusnum, influence, in_status, sensitive_words])
else:
results.append([uid, uname, location, fansnum, statusnum, influence, in_status])
return results
sensitive_string = "sensitive_score_" + tmp_ts
query_sensitive_body = {
"query":{
"match_all":{}
},
"size":1,
"sort":{sensitive_string:{"order":"desc"}}
}
try:
top_sensitive_result = es_bci_history.search(index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX, body=query_sensitive_body, _source=False, fields=[sensitive_string])['hits']['hits']
top_sensitive = top_sensitive_result[0]['fields'][sensitive_string][0]
except Exception, reason:
print Exception, reason
top_sensitive = 400
index_type = 'bci'
user_bci_result = es_cluster.mget(index=index_name, doc_type=index_type, body={'ids':uid_list}, _source=True)['docs']
user_profile_result = es_user_profile.mget(index='weibo_user', doc_type='user', body={'ids':uid_list}, _source=True)['docs']
bci_history_result = es_bci_history.mget(index=bci_history_index_name, doc_type=bci_history_index_type, body={"ids":uid_list}, fields=['user_fansnum', 'weibo_month_sum'])['docs']
sensitive_history_result = es_bci_history.mget(index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX, body={'ids':uid_list}, fields=[sensitive_string], _source=False)['docs']
max_evaluate_influ = get_evaluate_max(index_name)
for i in range(0, len(uid_list)):
uid = uid_list[i]
bci_dict = user_bci_result[i]
profile_dict = user_profile_result[i]
bci_history_dict = bci_history_result[i]
sensitive_history_dict = sensitive_history_result[i]
#print sensitive_history_dict
try:
bci_source = bci_dict['_source']
except:
bci_source = None
def influenced_user_detail(uid, date, origin_retweeted_mid, retweeted_retweeted_mid, message_type, default_number=20):
query_body = {
"query":{
"filtered":{
"filter":{
"bool":{
"must": [
]
}
}
}
},
"size":100000,
"sort":{"user_fansnum":{"order":"desc"}}
}
#详细影响到的人
date1 = str(date).replace('-', '')
index_name = pre_index + date1
index_flow_text = pre_text_index + date
origin_retweeted_uid = [] # influenced user uid_list
retweeted_retweeted_uid = []
origin_comment_uid = []
retweeted_comment_uid = []
query_origin = copy.deepcopy(query_body)
query_retweeted = copy.deepcopy(query_body)
if origin_retweeted_mid: # 所有转发该条原创微博的用户
query_origin["query"]["filtered"]["filter"]["bool"]["must"].append({"terms": {"root_mid": origin_retweeted_mid}})
query_origin["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term":{"message_type": message_type}}, {"term":{"root_uid": uid}}])
origin_retweeted_result = es.search(index=index_flow_text, doc_type=flow_text_index_type, body=query_origin, fields=["uid"])["hits"]["hits"]
if origin_retweeted_result:
for item in origin_retweeted_result:
origin_retweeted_uid.append(item["fields"]["uid"][0])
if retweeted_retweeted_mid: # 所有评论该条原创微博的用户
query_retweeted["query"]["filtered"]["filter"]["bool"]["must"].append({"terms": {"root_mid": retweeted_retweeted_mid}})
query_retweeted["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term":{"message_type": message_type}},{"term": {"directed_uid": uid}}])
retweeted_retweeted_result = es.search(index=index_flow_text, doc_type=flow_text_index_type, body=query_retweeted, fields=["uid"])["hits"]["hits"]
if retweeted_retweeted_result:
for item in retweeted_retweeted_result:
retweeted_retweeted_uid.append(item["fields"]["uid"][0])
retweeted_uid_list = [] # all retweeted user list
retweeted_results = {} # statistics of all retweeted uid information
retweeted_domain = {}
retweeted_topic = {}
retweeted_geo = {}
bci_results = {}
in_portrait = []
out_portrait = []
average_influence = 0
total_influence = 0
count = 0
all_uid_set = set(origin_retweeted_uid) | set(retweeted_retweeted_uid)
retweeted_uid_list.extend(origin_retweeted_uid)
retweeted_uid_list.extend(retweeted_retweeted_uid)
retweeted_uid_list = list(set(retweeted_uid_list) - set([uid])) # filter uids
if retweeted_uid_list:
user_portrait_result = es_user_portrait.mget(index=user_portrait, doc_type=portrait_index_type, body={"ids": retweeted_uid_list}, fields=["domain", "topic_string", "activity_geo_dict","importance", "influence"])["docs"]
bci_index = "bci_" + date.replace('-', '')
bci_results = es_cluster.mget(index=bci_index, doc_type="bci", body={"ids":retweeted_uid_list}, fields=['user_index'])["docs"]
for item in user_portrait_result:
if item["found"]:
temp = []
count += 1
temp.append(item['_id'])
temp.append(item["fields"]["importance"][0])
in_portrait.append(temp)
temp_domain = item["fields"]["domain"][0].split('&')
temp_topic = item["fields"]["topic_string"][0].split('&')
temp_geo = json.loads(item["fields"]["activity_geo_dict"][0])[-1].keys()
#total_influence += item["fields"]["influence"][0]
retweeted_domain = aggregation(temp_domain, retweeted_domain)
retweeted_topic = aggregation(temp_topic, retweeted_topic)
retweeted_geo = aggregation(temp_geo, retweeted_geo)
else:
out_portrait.append(item['_id'])
retweeted_domain = proportion(retweeted_domain)
retweeted_topic = proportion(retweeted_topic)
retweeted_geo = proportion(retweeted_geo)
if bci_results:
total_influence = 0
for item in bci_results:
if item['found']:
total_influence += item['fields']['user_index'][0]
try:
average_influence = total_influence/len(retweeted_uid_list)
except:
average_influence = 0
sorted_retweeted_domain = sorted(retweeted_domain.items(),key=lambda x:x[1], reverse=True)
sorted_retweeted_topic = sorted(retweeted_topic.items(),key=lambda x:x[1], reverse=True)
sorted_retweeted_geo = sorted(retweeted_geo.items(), key=lambda x:x[1], reverse=True)
retweeted_results["domian"] = sorted_retweeted_domain[:5]
retweeted_results["topic"] = sorted_retweeted_topic[:5]
retweeted_results["geo"] = sorted_retweeted_geo[:5]
retweeted_results["influence"] = average_influence
in_portrait = sorted(in_portrait, key=lambda x:x[1], reverse=True)
temp_list = []
for item in in_portrait:
temp_list.append(item[0])
retweeted_results['in_portrait_number'] = len(temp_list)
retweeted_results['out_portrait_number'] = len(out_portrait)
in_portrait_url = get_user_url(temp_list[:default_number])
out_portrait_url = get_user_url(out_portrait[:default_number])
retweeted_results["in_portrait"] = in_portrait_url
retweeted_results["out_portrait"] = out_portrait_url
retweeted_results["total_number"] = len(temp_list) + len(out_portrait)
return retweeted_results
