def influenced_detail(uid, date, style):
date1 = str(date).replace("-", "")
index_name = pre_index + date1
# detail_text = {}
style = int(style)
try:
user_info = es_cluster.get(index=index_name, doc_type=influence_doctype, id=uid)["_source"]
except:
result = {}
return result
origin_retweetd = json.loads(user_info["origin_weibo_retweeted_top"])
origin_comment = json.loads(user_info["origin_weibo_comment_top"])
retweeted_retweeted = json.loads(user_info["retweeted_weibo_retweeted_top"])
retweeted_comment = json.loads(user_info["retweeted_weibo_comment_top"])
if style == 0:
detail_text = get_text(origin_retweetd, date, user_info, style)
elif style == 1:
detail_text = get_text(origin_comment, date, user_info, style)
elif style == 2:
detail_text = get_text(retweeted_retweeted, date, user_info, style)
else:
detail_text = get_text(retweeted_comment, date, user_info, style)
# detail_text["origin_retweeted"] = get_text(origin_retweetd, date)
# detail_text["origin_comment"] = get_text(origin_comment, date)
# detail_text["retweeted_retweeted"] = get_text(retweeted_retweeted, date)
# detail_text["retweeted_comment"] = get_text(retweeted_comment, date)
return detail_text
def tag_vector(uid, date):
date1 = str(date).replace('-', '')
index_name = pre_index + date1
index_flow_text = pre_text_index + date
result = []
try:
bci_result = es_cluster.get(index=index_name, doc_type=influence_doctype, id=uid)["_source"]
except:
tag = influence_tag["0"]
result.append(tag)
return result
origin_retweeted = json.loads(bci_result["origin_weibo_retweeted_detail"])
retweeted_retweeted = json.loads(bci_result["retweeted_weibo_retweeted_detail"])
origin_comment = json.loads(bci_result["origin_weibo_comment_detail"])
retweeted_comment = json.loads(bci_result["retweeted_weibo_comment_detail"])
sum_retweeted = sum(origin_retweeted.values()) + sum(origin_comment.values())
sum_comment = sum(retweeted_retweeted.values()) + sum(retweeted_comment.values())
if sum_retweeted >= retweeted_threshold:
if sum_comment >= comment_threshold:
tag = influence_tag['3']
else:
tag = influence_tag['1']
else:
if sum_comment >= comment_threshold:
tag = influence_tag['2']
else:
tag = influence_tag['4']
result.append(tag)
return result
def get_user_influence(uid, date):
date = str(date).replace("-","")
index_name = pre_index + date
try:
bci_info = es_cluster.get(index=index_name, doc_type=influence_doctype, id=uid)["_source"]
except:
bci_info = {}
result = {}
for key in BCI_LIST:
result[key] = bci_info.get(key, 0)
user_index = result["user_index"]
query_body = {
"query":{
"filtered":{
"filter":{
"range":{
"user_index":{
"gt": user_index
}
}
}
}
}
}
total_count = es_cluster.count(index=index_name, doc_type=influence_doctype)['count']
order_count = es_cluster.count(index=index_name, doc_type=influence_doctype, body=query_body)['count']
result["total_count"] = total_count
result["order_count"] = order_count + 1
return result
def influenced_detail(uid, date, style):
date1 = str(date).replace('-', '')
index_name = pre_index + date1
#detail_text = {}
style = int(style)
try:
user_info = es_cluster.get(index=index_name,
doc_type=influence_doctype,
id=uid)["_source"]
except:
result = {}
return result
origin_retweetd = json.loads(user_info["origin_weibo_retweeted_top"])
origin_comment = json.loads(user_info['origin_weibo_comment_top'])
retweeted_retweeted = json.loads(
user_info["retweeted_weibo_retweeted_top"])
retweeted_comment = json.loads(user_info["retweeted_weibo_comment_top"])
if style == 0:
detail_text = get_text(origin_retweetd, date, user_info, style)
elif style == 1:
detail_text = get_text(origin_comment, date, user_info, style)
elif style == 2:
detail_text = get_text(retweeted_retweeted, date, user_info, style)
else:
detail_text = get_text(retweeted_comment, date, user_info, style)
#detail_text["origin_retweeted"] = get_text(origin_retweetd, date)
#detail_text["origin_comment"] = get_text(origin_comment, date)
#detail_text["retweeted_retweeted"] = get_text(retweeted_retweeted, date)
#detail_text["retweeted_comment"] = get_text(retweeted_comment, date)
return detail_text
def search_portrait_history_active_info(uid, date, index_name="copy_user_portrait", doctype="user"):
# date.formate: 20130901
date_list = time_series(date)
try:
result = es.get(index=index_name, doc_type=doctype, id=uid, _source=True)['_source']
except NotFoundError:
return "NotFound"
except:
return None
date_max = {}
for date_str in date_list:
query_body = {
'query':{
'match_all':{}
},
'size': 1,
'sort': [{date_str: {'order': 'desc'}}]
}
try:
max_item = es.search(index=index_name, doc_type=doctype, body=query_body)['hits']['hits']
except Exception, e:
raise e
date_max[date_str] = max_item[0]['_source'][date_str]
def get_user_influence(uid, date):
date = str(date).replace("-","")
index_name = pre_index + date
try:
bci_info = es_cluster.get(index=index_name, doc_type=influence_doctype, id=uid)["_source"]
except:
bci_info = {}
result = {}
for key in BCI_LIST:
result[key] = bci_info.get(key, 0)
user_index = result["user_index"]
query_body = {
"query":{
"filtered":{
"filter":{
"range":{
"user_index":{
"gt": user_index
}
}
}
}
}
}
total_count = es_cluster.count(index=index_name, doc_type=influence_doctype)['count']
order_count = es_cluster.count(index=index_name, doc_type=influence_doctype, body=query_body)['count']
result["total_count"] = total_count
result["order_count"] = order_count + 1
return result
def tag_vector(uid, date):
date1 = str(date).replace('-', '')
index_name = pre_index + date1
index_flow_text = pre_text_index + date
result = []
try:
bci_result = es_cluster.get(index=index_name, doc_type=influence_doctype, id=uid)["_source"]
except:
tag = influence_tag["0"]
result.append(tag)
return result
origin_retweeted = json.loads(bci_result["origin_weibo_retweeted_detail"])
retweeted_retweeted = json.loads(bci_result["retweeted_weibo_retweeted_detail"])
origin_comment = json.loads(bci_result["origin_weibo_comment_detail"])
retweeted_comment = json.loads(bci_result["retweeted_weibo_comment_detail"])
sum_retweeted = sum(origin_retweeted.values()) + sum(origin_comment.values())
sum_comment = sum(retweeted_retweeted.values()) + sum(retweeted_comment.values())
if sum_retweeted >= retweeted_threshold:
if sum_comment >= comment_threshold:
tag = influence_tag['3']
else:
tag = influence_tag['1']
else:
if sum_comment >= comment_threshold:
tag = influence_tag['2']
else:
tag = influence_tag['4']
result.append(tag)
return result
def statistics_influence_people(uid, date, style):
# output: different retweeted and comment, uids' domain distribution, topic distribution, registeration geo distribution
results = {} # retwweted weibo people and comment weibo people
date1 = str(date).replace("-", "")
index_name = pre_index + date1
index_flow_text = pre_text_index + date
try:
bci_result = es_cluster.get(index=index_name, doc_type=influence_doctype, id=uid)["_source"]
except:
bci_result = []
return results
origin_retweeted_mid = [] # origin weibo mid
retweeted_retweeted_mid = [] # retweeted weibo mid
origin_comment_mid = []
retweeted_comment_mid = []
origin_retweeted = json.loads(bci_result["origin_weibo_retweeted_detail"])
retweeted_retweeted = json.loads(bci_result["retweeted_weibo_retweeted_detail"])
origin_comment = json.loads(bci_result["origin_weibo_comment_detail"])
retweeted_comment = json.loads(bci_result["retweeted_weibo_comment_detail"])
retweeted_total_number = sum(origin_retweeted.values()) + sum(retweeted_retweeted.values())
comment_total_number = sum(origin_comment.values()) + sum(retweeted_comment.values())
if origin_retweeted:
origin_retweeted_mid = filter_mid(origin_retweeted)
if retweeted_retweeted:
retweeted_retweeted_mid = filter_mid(retweeted_retweeted)
if origin_comment:
origin_comment_mid = filter_mid(origin_comment)
if retweeted_comment:
retweeted_comment_mid = filter_mid(retweeted_comment)
query_body = {"query": {"filtered": {"filter": {"bool": {"should": [], "must": []}}}}, "size": 10000}
if int(style) == 0: # retweeted
retweeted_origin = []
if retweeted_retweeted_mid:
text_result = es.mget(
index=index_flow_text, doc_type=flow_text_index_type, body={"ids": retweeted_retweeted_mid}
)["docs"]
for item in text_result:
mid = item.get("source", {}).get("root_mid", "0")
retweeted_origin.append(mid)
retweeted_results = influenced_user_detail(uid, date, origin_retweeted_mid, retweeted_origin, 3)
retweeted_results["total_number"] = retweeted_total_number
results = retweeted_results
else:
retweeted_origin = []
if retweeted_comment_mid:
text_result = es.mget(
index=index_flow_text, doc_type=flow_text_index_type, body={"ids": retweeted_comment_mid}
)["docs"]
for item in text_result:
mid = item.get("source", {}).get("root_mid", "0")
retweeted_origin.append(mid)
comment_results = influenced_user_detail(uid, date, origin_comment_mid, retweeted_origin, 2)
comment_results["total_number"] = comment_total_number
results = comment_results
return results
def statistics_influence_people(uid, date, style):
# output: different retweeted and comment, uids' domain distribution, topic distribution, registeration geo distribution
results = {} # retwweted weibo people and comment weibo people
date1 = str(date).replace('-', '')
index_name = pre_index + date1
index_flow_text = pre_text_index + date
try:
bci_result = es_cluster.get(index=index_name, doc_type=influence_doctype, id=uid)["_source"]
except:
bci_result = []
return results
origin_mid = [] # origin weibo mid
retweeted_mid = [] # retweeted weibo mid
query_body = {
"query":{
"filtered":{
"filter":{
"bool":{
"must":[
]
}
}
}
},
"size":1000
}
body_1 = copy.deepcopy(query_body)
body_2 = copy.deepcopy(query_body)
body_1["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term":{"message_type": 1}}, {"term":{"uid": uid}}])
result_1 = es.search(index=index_flow_text, doc_type=flow_text_index_type, body=body_1)["hits"]["hits"]
if result_1:
for item in result_1:
origin_mid.append(item['_id'])
body_1["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term":{"message_type": 3}}, {"term":{"uid": uid}}])
result_2 = es.search(index=index_flow_text, doc_type=flow_text_index_type, body=body_2)["hits"]["hits"]
if result_2:
for item in result_2:
if item['_source'].get('root_mid', ''):
retweeted_mid.append(item['_source']['root_mid'])
if int(style) == 0: # retweeted
retweeted_results = influenced_user_detail(uid, date, origin_mid, retweeted_mid, 3)
results = retweeted_results
else:
comment_results = influenced_user_detail(uid, date, origin_mid, retweeted_mid, 2)
results = comment_results
return results
def comment_on_influence(uid, date):
date1 = str(date).replace('-', '')
index_name = pre_index + date1
index_flow_text = pre_text_index + date
result = []
underline = []
try:
bci_result = es_cluster.get(index=index_name,
doc_type=influence_doctype,
id=uid)["_source"]
except:
description = CURRENT_INFLUENCE_CONCLUSION['0']
result.append(description)
return ([result, underline])
user_index = bci_result['user_index']
if user_index < CURRNET_INFLUENCE_THRESHOULD[0]:
description = CURRENT_INFLUENCE_CONCLUSION['0']
elif user_index >= CURRNET_INFLUENCE_THRESHOULD[
0] and user_index < CURRNET_INFLUENCE_THRESHOULD[1]:
description = CURRENT_INFLUENCE_CONCLUSION['1']
elif user_index >= CURRNET_INFLUENCE_THRESHOULD[
1] and user_index < CURRNET_INFLUENCE_THRESHOULD[2]:
description = CURRENT_INFLUENCE_CONCLUSION['2']
elif user_index >= CURRNET_INFLUENCE_THRESHOULD[
2] and user_index < CURRNET_INFLUENCE_THRESHOULD[3]:
description = CURRENT_INFLUENCE_CONCLUSION['3']
elif user_index >= CURRNET_INFLUENCE_THRESHOULD[
3] and user_index < CURRNET_INFLUENCE_THRESHOULD[4]:
description = CURRENT_INFLUENCE_CONCLUSION['4']
else:
description = CURRENT_INFLUENCE_CONCLUSION['5']
result.append(description)
for i in range(4):
if bci_result[INFLUENCE_TOTAL_LIST[i]] > INFLUENCE_TOTAL_THRESHOULD[i]:
result.append(INFLUENCE_TOTAL_CONCLUSION[i])
if bci_result[
INFLUENCE_BRUST_LIST[i]] > INFLUENCE_BRUST_THRESHOULD[i]:
result.append(INFLUENCE_BRUST_CONCLUSION[i])
underline.append(UNDERLINE_CONCLUSION[i])
else:
result.append('')
underline.append('')
else:
result.extend(['', ''])
underline.append('')
return [result, underline]
def comment_on_influence(uid, date):
date1 = str(date).replace('-', '')
index_name = pre_index + date1
index_flow_text = pre_text_index + date
result = []
underline = []
try:
bci_result = es_cluster.get(index=index_name, doc_type=influence_doctype, id=uid)["_source"]
except:
description = CURRENT_INFLUENCE_CONCLUSION['0']
result.append(description)
return ([result, underline])
user_index = bci_result['user_index']
if user_index < CURRNET_INFLUENCE_THRESHOULD[0]:
description = CURRENT_INFLUENCE_CONCLUSION['0']
elif user_index >= CURRNET_INFLUENCE_THRESHOULD[0] and user_index < CURRNET_INFLUENCE_THRESHOULD[1]:
description = CURRENT_INFLUENCE_CONCLUSION['1']
elif user_index >= CURRNET_INFLUENCE_THRESHOULD[1] and user_index < CURRNET_INFLUENCE_THRESHOULD[2]:
description = CURRENT_INFLUENCE_CONCLUSION['2']
elif user_index >= CURRNET_INFLUENCE_THRESHOULD[2] and user_index < CURRNET_INFLUENCE_THRESHOULD[3]:
description = CURRENT_INFLUENCE_CONCLUSION['3']
elif user_index >= CURRNET_INFLUENCE_THRESHOULD[3] and user_index < CURRNET_INFLUENCE_THRESHOULD[4]:
description = CURRENT_INFLUENCE_CONCLUSION['4']
else:
description = CURRENT_INFLUENCE_CONCLUSION['5']
result.append(description)
for i in range(4):
if bci_result[INFLUENCE_TOTAL_LIST[i]] > INFLUENCE_TOTAL_THRESHOULD[i]:
result.append(INFLUENCE_TOTAL_CONCLUSION[i])
if bci_result[INFLUENCE_BRUST_LIST[i]] > INFLUENCE_BRUST_THRESHOULD[i]:
result.append(INFLUENCE_BRUST_CONCLUSION[i])
underline.append(UNDERLINE_CONCLUSION[i])
else:
result.append('')
underline.append('')
else:
result.extend(['',''])
underline.append('')
return [result, underline]
def comment_on_influence(uid, date):
date1 = str(date).replace("-", "")
index_name = pre_index + date1
index_flow_text = pre_text_index + date
result = []
underline = []
try:
bci_result = es_cluster.get(index=index_name, doc_type=influence_doctype, id=uid)["_source"]
except:
description = CURRENT_INFLUENCE_CONCLUSION["0"]
result.append(description)
return [result, underline]
user_index = bci_result["user_index"]
if user_index < CURRNET_INFLUENCE_THRESHOULD[0]:
description = CURRENT_INFLUENCE_CONCLUSION["0"]
elif user_index >= CURRNET_INFLUENCE_THRESHOULD[0] and user_index < CURRNET_INFLUENCE_THRESHOULD[1]:
description = CURRENT_INFLUENCE_CONCLUSION["1"]
elif user_index >= CURRNET_INFLUENCE_THRESHOULD[1] and user_index < CURRNET_INFLUENCE_THRESHOULD[2]:
description = CURRENT_INFLUENCE_CONCLUSION["2"]
elif user_index >= CURRNET_INFLUENCE_THRESHOULD[2] and user_index < CURRNET_INFLUENCE_THRESHOULD[3]:
description = CURRENT_INFLUENCE_CONCLUSION["3"]
elif user_index >= CURRNET_INFLUENCE_THRESHOULD[3] and user_index < CURRNET_INFLUENCE_THRESHOULD[4]:
description = CURRENT_INFLUENCE_CONCLUSION["4"]
else:
description = CURRENT_INFLUENCE_CONCLUSION["5"]
result.append(description)
for i in range(4):
if bci_result[INFLUENCE_TOTAL_LIST[i]] > INFLUENCE_TOTAL_THRESHOULD[i]:
result.append(INFLUENCE_TOTAL_CONCLUSION[i])
if bci_result[INFLUENCE_BRUST_LIST[i]] > INFLUENCE_BRUST_THRESHOULD[i]:
result.append(INFLUENCE_BRUST_CONCLUSION[i])
underline.append(UNDERLINE_CONCLUSION[i])
else:
result.append("")
underline.append("")
else:
result.extend(["", ""])
underline.append("")
return [result, underline]
def search_portrait_history_active_info(uid,
date,
index_name=copy_portrait_index_name,
doctype=copy_portrait_index_name):
# date.formate: 20130901
date_list = time_series(date)
try:
result = es.get(index=index_name,
doc_type=doctype,
id=uid,
_source=True)['_source']
except NotFoundError:
return "NotFound"
except:
return None
date_max = {}
for date_str in date_list:
query_body = {
'query': {
'match_all': {}
},
'size': 1,
'sort': [{
date_str: {
'order': 'desc'
}
}]
}
try:
max_item = es.search(index=index_name,
doc_type=doctype,
body=query_body)['hits']['hits']
except Exception, e:
raise e
date_max[date_str] = max_item[0]['_source'][date_str]
def search_portrait_history_active_info(uid, date, index_name="copy_user_portrait", doctype="user"):
# date.formate: 20130901
date_list = time_series(date)
try:
result = es.get(index=index_name, doc_type=doctype, id=uid, _source=True)['_source']
except NotFoundError:
return "NotFound"
except:
return None
return_dict = {}
for item in date_list:
return_dict[item] = result.get(item, 0)
in_list = []
for item in sorted(date_list):
in_list.append(return_dict[item])
#print 'in_list:', in_list
max_influence = max(in_list)
ave_influence = sum(in_list) / float(7)
min_influence = min(in_list)
if max_influence - min_influence <= 400 and ave_influence >= 900:
mark = u'平稳高影响力'
elif max_influence - min_influence > 400 and ave_influence >= 900:
mark = u'波动高影响力'
elif max_influence - min_influence <= 400 and ave_influence < 900 and ave_influence >= 500:
mark = u'平稳一般影响力'
elif max_influence - min_influence > 400 and ave_influence < 900 and ave_influence >= 500:
mark = u'波动一般影响力'
elif max_influence - min_influence <= 400 and ave_influence < 500:
mark = u'平稳低影响力'
else:
mark = u'波动低影响力'
description = [u'该用户为', mark]
return [in_list, description]
def statistics_influence_people(uid, date, style):
# output: different retweeted and comment, uids' domain distribution, topic distribution, registeration geo distribution
results = {} # retwweted weibo people and comment weibo people
date1 = str(date).replace('-', '')
index_name = pre_index + date1
index_flow_text = pre_text_index + date
try:
bci_result = es_cluster.get(index=index_name,
doc_type=influence_doctype,
id=uid)["_source"]
except:
bci_result = []
return results
origin_retweeted_mid = [] # origin weibo mid
retweeted_retweeted_mid = [] # retweeted weibo mid
origin_comment_mid = []
retweeted_comment_mid = []
origin_retweeted = json.loads(bci_result["origin_weibo_retweeted_detail"])
retweeted_retweeted = json.loads(
bci_result["retweeted_weibo_retweeted_detail"])
origin_comment = json.loads(bci_result["origin_weibo_comment_detail"])
retweeted_comment = json.loads(
bci_result["retweeted_weibo_comment_detail"])
retweeted_total_number = sum(origin_retweeted.values()) + sum(
retweeted_retweeted.values())
comment_total_number = sum(origin_comment.values()) + sum(
retweeted_comment.values())
if origin_retweeted:
origin_retweeted_mid = filter_mid(origin_retweeted)
if retweeted_retweeted:
retweeted_retweeted_mid = filter_mid(retweeted_retweeted)
if origin_comment:
origin_comment_mid = filter_mid(origin_comment)
if retweeted_comment:
retweeted_comment_mid = filter_mid(retweeted_comment)
query_body = {
"query": {
"filtered": {
"filter": {
"bool": {
"should": [],
"must": []
}
}
}
},
"size": 10000
}
if int(style) == 0: # retweeted
retweeted_origin = []
if retweeted_retweeted_mid:
text_result = es.mget(index=index_flow_text,
doc_type=flow_text_index_type,
body={"ids":
retweeted_retweeted_mid})["docs"]
for item in text_result:
mid = item.get("source", {}).get("root_mid", '0')
retweeted_origin.append(mid)
retweeted_results = influenced_user_detail(uid, date,
origin_retweeted_mid,
retweeted_origin, 3)
retweeted_results["total_number"] = retweeted_total_number
results = retweeted_results
else:
retweeted_origin = []
if retweeted_comment_mid:
text_result = es.mget(index=index_flow_text,
doc_type=flow_text_index_type,
body={"ids": retweeted_comment_mid})["docs"]
for item in text_result:
mid = item.get("source", {}).get("root_mid", '0')
retweeted_origin.append(mid)
comment_results = influenced_user_detail(uid, date, origin_comment_mid,
retweeted_origin, 2)
comment_results["total_number"] = comment_total_number
results = comment_results
return results
def statistics_influence_people(uid, date, style):
# output: different retweeted and comment, uids' domain distribution, topic distribution, registeration geo distribution
results = {} # retwweted weibo people and comment weibo people
date1 = str(date).replace('-', '')
index_name = pre_index + date1
index_flow_text = pre_text_index + date
try:
bci_result = es_cluster.get(index=index_name, doc_type=influence_doctype, id=uid)["_source"]
except:
bci_result = []
return results
origin_mid = [] # origin weibo mid
retweeted_mid = [] # retweeted weibo mid
query_body = {
"query":{
"filtered":{
"filter":{
"bool":{
"must":[
]
}
}
}
},
"size":1000
}
body_1 = copy.deepcopy(query_body)
body_2 = copy.deepcopy(query_body)
body_1["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term":{"message_type": 1}}, {"term":{"uid": uid}}])
result_1 = es.search(index=index_flow_text, doc_type=flow_text_index_type, body=body_1)["hits"]["hits"]
if result_1:
for item in result_1:
origin_mid.append(item['_id'])
body_1["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term":{"message_type": 3}}, {"term":{"uid": uid}}])
result_2 = es.search(index=index_flow_text, doc_type=flow_text_index_type, body=body_2)["hits"]["hits"]
if result_2:
for item in result_2:
if item['_source'].get('root_mid', ''):
retweeted_mid.append(item['_source']['root_mid'])
origin_retweeted = json.loads(bci_result["origin_weibo_retweeted_detail"])
retweeted_retweeted = json.loads(bci_result["retweeted_weibo_retweeted_detail"])
origin_comment = json.loads(bci_result["origin_weibo_comment_detail"])
retweeted_comment = json.loads(bci_result["retweeted_weibo_comment_detail"])
"""
retweeted_total_number = sum(origin_retweeted.values()) + sum(retweeted_retweeted.values())
comment_total_number = sum(origin_comment.values()) + sum(retweeted_comment.values())
if origin_retweeted:
origin_retweeted_mid = filter_mid(origin_retweeted)
if retweeted_retweeted:
retweeted_retweeted_mid = filter_mid(retweeted_retweeted)
if origin_comment:
origin_comment_mid = filter_mid(origin_comment)
if retweeted_comment:
retweeted_comment_mid = filter_mid(retweeted_comment)
query_body = {
"query":{
"filtered":{
"filter":{
"bool":{
"should":[
],
"must": [
]
}
}
}
},
"size":10000
}
"""
if int(style) == 0: # retweeted
retweeted_results = influenced_user_detail(uid, date, origin_mid, retweeted_mid, 3)
results = retweeted_results
else:
comment_results = influenced_user_detail(uid, date, origin_mid, retweeted_mid, 2)
results = comment_results
return results
def influenced_detail(uid, date, style):
date1 = str(date).replace('-', '')
index_name = pre_index + date1
index_text = "flow_text_" + date
#detail_text = {}
style = int(style)
try:
user_info = es_cluster.get(index=index_name, doc_type=influence_doctype, id=uid)["_source"]
except:
result = {}
return result
origin_retweetd_dict = json.loads(user_info["origin_weibo_retweeted_detail"])
origin_comment_dict = json.loads(user_info['origin_weibo_comment_detail'])
retweeted_retweeted_dict = json.loads(user_info["retweeted_weibo_retweeted_detail"])
retweeted_comment_dict = json.loads(user_info["retweeted_weibo_comment_detail"])
origin_retweetd = sorted(origin_retweetd_dict.items(), key=lambda x:x[1], reverse=True)
origin_comment = sorted(origin_comment_dict.items(), key=lambda x:x[1], reverse=True)
retweeted_retweeted = sorted(retweeted_retweeted_dict.items(), key=lambda x:x[1], reverse=True)
retweeted_comment = sorted(retweeted_comment_dict.items(), key=lambda x:x[1], reverse=True)
query_body_origin = {
"query":{
"filtered":{
"filter":{
"bool":{
"must":[
{"term":{"message_type": 1}},
{"term":{"uid": uid}}
]
}
}
}
},
"size": 10000
}
result_1 = es.search(index=index_text, doc_type="text", body=query_body_origin)['hits']['hits']
origin_set = set()
if result_1:
for item in result_1:
origin_set.add(item['_id'])
query_body_retweeted = {
"query":{
"filtered":{
"filter":{
"bool":{
"must":[
{"term":{"message_type": 3}},
{"term":{"uid": uid}}
]
}
}
}
},
"size": 10000
}
result_2 = es.search(index=index_text, doc_type="text", body=query_body_retweeted)['hits']['hits']
retweeted_set = set()
if result_2:
for item in retweeted_set:
retweeted_set.add(item['_id'])
if origin_retweetd:
for item in origin_retweetd:
if item[0] not in origin_set:
origin_retweetd.remove(item)
if origin_comment:
for item in origin_comment:
if item[0] not in origin_set:
origin_comment.remove(item)
if retweeted_retweeted:
for item in retweeted_retweeted:
if item[0] not in retweeted_set:
retweeted_retweeted.remove(item)
if retweeted_comment:
for item in retweeted_comment:
if item[0] not in retweeted_set:
retweeted_comment.remove(item)
if style == 0:
detail_text = get_text(origin_retweetd[:20], date, user_info, style)
elif style == 1:
detail_text = get_text(origin_comment[:20], date, user_info, style)
elif style == 2:
detail_text = get_text(retweeted_retweeted[:20], date, user_info, style)
else:
detail_text = get_text(retweeted_comment[:20], date, user_info, style)
#detail_text["origin_retweeted"] = get_text(origin_retweetd, date)
#detail_text["origin_comment"] = get_text(origin_comment, date)
#detail_text["retweeted_retweeted"] = get_text(retweeted_retweeted, date)
#detail_text["retweeted_comment"] = get_text(retweeted_comment, date)
return detail_text
