import tornado.httpserver
import tornado.ioloop
import tornado.web
from tornado.options import define
from tornado.options import options
import json
import numpy as np
class BaseHandler(tornado.web.RequestHandler):
def post(self, *args, **kwargs):
idcard = self.get_argument('idcard', '未传入身份证', True)
point = self.get_argument('point', '未传入逾期探针', True)
xinyan = self.get_argument('xinyan', '未传入新颜数据', True)
br = self.get_argument('br', '未传入百融数据', True)
#这里写你自己的跑分逻辑
import pandas as pd
def get_data_from_json(json_obj, key='max_performance_amt'):
if isinstance(json_obj, dict):
for k, v in json_obj.items():
if k == key:
return v
elif isinstance(v, (dict, list)):
result = get_data_from_json(v, key)
if result is not None:
return result
elif isinstance(json_obj, list):
for item in json_obj:
result = get_data_from_json(item, key)
if result is not None:
return result
return None
sj = pd.DataFrame({
'idcard': [idcard],
'point': [point],
'xinyan': [xinyan],
'br': [br]})
sj['消金贷款类机构最大授信额度'] = sj['xinyan'].apply(lambda x: get_data_from_json(json.loads(x),key="C22180009")
if isinstance(x, str) and x else None)
sj['近1个月履约贷款总金额'] = sj['xinyan'].apply(lambda x: get_data_from_json(json.loads(x),key="B22170040")
if isinstance(x, str) and x else None)
sj['近3个月履约贷款总金额'] = sj['xinyan'].apply(lambda x: get_data_from_json(json.loads(x),key="B22170041")
if isinstance(x, str) and x else None)
sj['最大逾期金额'] = sj['point'].apply(lambda x: get_data_from_json(json.loads(x),key="max_overdue_amt")
if isinstance(x, str) and x else None)
sj['按身份证号查询，近12个月在银行机构-网络零售银行申请次数'] = sj['br'].apply(lambda x: get_data_from_json(json.loads(x),key="als_m12_id_bank_ret_allnum")
if isinstance(x, str) and x else None)
sj['按手机号查询，近6个月在银行机构申请次数'] = sj['br'].apply(lambda x: get_data_from_json(json.loads(x),key="als_m6_cell_bank_allnum")
if isinstance(x, str) and x else None)
sj['按手机号查询，近12个月在银行机构申请机构数'] = sj['br'].apply(lambda x: get_data_from_json(json.loads(x),key="als_m12_cell_bank_orgnum")
if isinstance(x, str) and x else None)
sj['按身份证号查询，近12个月在银行机构申请机构数'] = sj['br'].apply(lambda x: get_data_from_json(json.loads(x),key="als_m12_id_bank_orgnum")
if isinstance(x, str) and x else None)
nn=sj.copy()
for i in ['消金贷款类机构最大授信额度', '按身份证号查询，近12个月在银行机构-网络零售银行申请次数',
'按手机号查询，近6个月在银行机构申请次数','按手机号查询，近12个月在银行机构申请机构数','按身份证号查询，近12个月在银行机构申请机构数']:
nn[i]=nn[i].apply(lambda x:np.nan if x"" else np.nan if x"其他" else np.nan if x"np.nan" else x)
nn[i]=nn[i].astype('float')
def qj(x):
values=[0,10000,20000,50000,np.inf]
index=['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T']
for i in range(len(values)-1):
if values[i]<=x<values[i+1]:
return '{0}[{1},{2})'.format(index[i],values[i],values[i+1])
return '空值'
nn['消金贷款类机构最大授信额度']=nn['消金贷款类机构最大授信额度'].astype('float')
nn['消金贷款类机构最大授信额度']=nn['消金贷款类机构最大授信额度'].apply(qj)
def qj(x):
values=[1,2,7,15,17,np.inf]
index=['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T']
for i in range(len(values)-1):
if values[i]<=x<values[i+1]:
return '{0}[{1},{2})'.format(index[i],values[i],values[i+1])
return '空值'
nn['按身份证号查询，近12个月在银行机构-网络零售银行申请次数']=nn['按身份证号查询，近12个月在银行机构-网络零售银行申请次数'].astype('float')
nn['按身份证号查询，近12个月在银行机构-网络零售银行申请次数']=nn['按身份证号查询，近12个月在银行机构-网络零售银行申请次数'].apply(qj)
def qj(x):
values=[0,3,5,9,24,26,np.inf]
index=['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T']
for i in range(len(values)-1):
if values[i]<=x<values[i+1]:
return '{0}[{1},{2})'.format(index[i],values[i],values[i+1])
return '空值'
nn['按手机号查询，近6个月在银行机构申请次数']=nn['按手机号查询，近6个月在银行机构申请次数'].astype('float')
nn['按手机号查询，近6个月在银行机构申请次数']=nn['按手机号查询，近6个月在银行机构申请次数'].apply(qj)
def qj(x):
values=[0,5,7,10,18,24,np.inf]
index=['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T']
for i in range(len(values)-1):
if values[i]<=x<values[i+1]:
return '{0}[{1},{2})'.format(index[i],values[i],values[i+1])
return '空值'
nn['按身份证号查询，近12个月在银行机构申请机构数']=nn['按身份证号查询，近12个月在银行机构申请机构数'].astype('float')
nn['按身份证号查询，近12个月在银行机构申请机构数']=nn['按身份证号查询，近12个月在银行机构申请机构数'].apply(qj)
def qj(x):
values=[0,4,5,17,21,23,np.inf]
index=['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T']
for i in range(len(values)-1):
if values[i]<=x<values[i+1]:
return '{0}_[{1},{2})'.format(index[i],values[i],values[i+1])
return '空值'
nn['按手机号查询，近12个月在银行机构申请机构数']=nn['按手机号查询，近12个月在银行机构申请机构数'].astype('float')
nn['按手机号查询，近12个月在银行机构申请机构数']=nn['按手机号查询，近12个月在银行机构申请机构数'].apply(qj)
def qj4(x):
if x'0':
return 'A_0'
if x'1-1000':
return 'B_1-1000'
elif x'1000-2000':
return 'C_1000-2000'
elif x'[1000,2000)':
return 'D_[1000,2000)'
elif x'2000-3000':
return 'E_2000-3000'
elif x'3000-4000':
return 'F_3000-4000'
elif x'4000-6000':
return 'G_4000-6000'
elif x'6000-8000':
return 'H_6000-8000'
elif x'8000-10000':
return 'I_8000-10000'
elif x'10000-20000':
return 'J_10000-20000'
elif x'20000-40000':
return 'K_20000-40000'
else: return "空值"
nn['最大逾期金额']=nn['最大逾期金额'].apply(qj4)
def qj1(x):
if x'0':
return 'A_0'
if x'(0,500)':
return 'B_(0,500)'
elif x'[500,1000)':
return 'C_[500,1000)'
elif x'[1000,2000)':
return 'D_[1000,2000)'
elif x'[2000,3000)':
return 'E_[2000,3000)'
elif x'[3000,5000)':
return 'F_[3000,5000)'
elif x'[5000,10000)':
return 'G_[5000,10000)'
elif x'[10000,20000)':
return 'H_[10000,20000)'
elif x'[20000,30000)':
return 'I_[20000,30000)'
elif x'[30000,50000)':
return 'J_[30000,50000)'
elif x'[50000,+)':
return 'K_[50000,+)'
else: return "空值"
nn['近1个月履约贷款总金额']=nn['近1个月履约贷款总金额'].apply(qj1)
nn['近3个月履约贷款总金额']=nn['近3个月履约贷款总金额'].apply(qj1)
nn1=nn.copy()
import pandas as pd
data = {"变量":["最大逾期金额",
"最大逾期金额",
"最大逾期金额",
"最大逾期金额",
"最大逾期金额",
"最大逾期金额",
"最大逾期金额",
"最大逾期金额",
"最大逾期金额",
"最大逾期金额",
"消金贷款类机构最大授信额度",
"消金贷款类机构最大授信额度",
"消金贷款类机构最大授信额度",
"消金贷款类机构最大授信额度",
"消金贷款类机构最大授信额度",
"近1个月履约贷款总金额",
"近1个月履约贷款总金额",
"近1个月履约贷款总金额",
"近1个月履约贷款总金额",
"近1个月履约贷款总金额",
"近1个月履约贷款总金额",
"近1个月履约贷款总金额",
"近1个月履约贷款总金额",
"近1个月履约贷款总金额",
"近1个月履约贷款总金额",
"近1个月履约贷款总金额",
"近3个月履约贷款总金额",
"近3个月履约贷款总金额",
"近3个月履约贷款总金额",
"近3个月履约贷款总金额",
"近3个月履约贷款总金额",
"近3个月履约贷款总金额",
"近3个月履约贷款总金额",
"近3个月履约贷款总金额",
"近3个月履约贷款总金额",
"近3个月履约贷款总金额",
"近3个月履约贷款总金额",
"近3个月履约贷款总金额",
"按身份证号查询，近12个月在银行机构-网络零售银行申请次数",
"按身份证号查询，近12个月在银行机构-网络零售银行申请次数",
"按身份证号查询，近12个月在银行机构-网络零售银行申请次数",
"按身份证号查询，近12个月在银行机构-网络零售银行申请次数",
"按身份证号查询，近12个月在银行机构-网络零售银行申请次数",
"按身份证号查询，近12个月在银行机构-网络零售银行申请次数",
"按手机号查询，近6个月在银行机构申请次数",
"按手机号查询，近6个月在银行机构申请次数",
"按手机号查询，近6个月在银行机构申请次数",
"按手机号查询，近6个月在银行机构申请次数",
"按手机号查询，近6个月在银行机构申请次数",
"按手机号查询，近6个月在银行机构申请次数",
"按手机号查询，近6个月在银行机构申请次数",
"按手机号查询，近12个月在银行机构申请机构数",
"按手机号查询，近12个月在银行机构申请机构数",
"按手机号查询，近12个月在银行机构申请机构数",
"按手机号查询，近12个月在银行机构申请机构数",
"按手机号查询，近12个月在银行机构申请机构数",
"按手机号查询，近12个月在银行机构申请机构数",
"按手机号查询，近12个月在银行机构申请机构数",
"按身份证号查询，近12个月在银行机构申请机构数",
"按身份证号查询，近12个月在银行机构申请机构数",
"按身份证号查询，近12个月在银行机构申请机构数",
"按身份证号查询，近12个月在银行机构申请机构数",
"按身份证号查询，近12个月在银行机构申请机构数",
"按身份证号查询，近12个月在银行机构申请机构数",
"按身份证号查询，近12个月在银行机构申请机构数"],"区间":["B_1-1000",
"C_1000-2000",
"E_2000-3000",
"F_3000-4000",
"G_4000-6000",
"H_6000-8000",
"I_8000-10000",
"J_10000-20000",
"K_20000-40000",
"空值",
"A_[0,10000)",
"B_[10000,20000)",
"C_[20000,50000)",
"D_[50000,inf)",
"空值",
"A_0",
"B_(0,500)",
"C_[500,1000)",
"D_[1000,2000)",
"E_[2000,3000)",
"F_[3000,5000)",
"G_[5000,10000)",
"H_[10000,20000)",
"I_[20000,30000)",
"J_[30000,50000)",
"空值",
"A_0",
"B_(0,500)",
"C_[500,1000)",
"D_[1000,2000)",
"E_[2000,3000)",
"F_[3000,5000)",
"G_[5000,10000)",
"H_[10000,20000)",
"I_[20000,30000)",
"J_[30000,50000)",
"K_[50000,+)",
"空值",
"A_[1,2)",
"B_[2,7)",
"C_[7,15)",
"D_[15,17)",
"E_[17,inf)",
"空值",
"A_[0,3)",
"B_[3,5)",
"C_[5,9)",
"D_[9,24)",
"E_[24,26)",
"F_[26,inf)",
"空值",
"A_[0,4)",
"B_[4,5)",
"C_[5,17)",
"D_[17,21)",
"E_[21,23)",
"F_[23,inf)",
"空值",
"A_[0,5)",
"B_[5,7)",
"C_[7,10)",
"D_[10,18)",
"E_[18,24)",
"F_[24,inf)",
"空值"],"得分":[-61,
-79,
-79,
-119,
-119,
-119,
-119,
-119,
-119,
14,
-6,
0,
5,
18,
-26,
-3,
0,
-1,
1,
3,
1,
1,
4,
4,
6,
-6,
-12,
-9,
-6,
-2,
-4,
-10,
3,
6,
10,
18,
37,
-26,
-32,
-3,
6,
12,
24,
-46,
-17,
-7,
-1,
6,
20,
34,
-19,
-8,
-3,
0,
2,
5,
8,
-5,
-58,
-26,
-10,
0,
17,
42,
-68]}
pfk = pd.DataFrame(data)
pfk
coll=['最大逾期金额', '消金贷款类机构最大授信额度', '近1个月履约贷款总金额', '近3个月履约贷款总金额',
'按身份证号查询，近12个月在银行机构-网络零售银行申请次数', '按手机号查询，近6个月在银行机构申请次数',
'按手机号查询，近12个月在银行机构申请机构数', '按身份证号查询，近12个月在银行机构申请机构数']
ys=pfk
for i in coll:
dictys = dict(zip(ys[ys['变量']i]['区间'],ys[ys['变量']==i]['得分']))
nn[i]=nn[i].map(dictys)
nn['fenshu'] = nn[coll].sum(axis=1)
nn['fenshu'] = 383+nn['fenshu']
nn.rename(columns={'fenshu':"tw_v200"},inplace=True)
nn.drop_duplicates(subset="idcard",inplace=True)
nn1.drop_duplicates(subset="idcard",inplace=True)
zt=nn1.merge(nn[['idcard','tw_v200']],on="idcard",how="left")
zt.drop_duplicates(subset='idcard',inplace=True)
def qj(x):
values=[0,277,318,350,375,389,400,410,425,444,np.inf]
index=['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R']
for i in range(len(values)-1):
if values[i]<=x<values[i+1]:
return '{0}[{1},{2})'.format(index[i],values[i],values[i+1])
return '空值'
zt['tw_v200']=zt['tw_v200'].astype('float')
zt['tw_v200_bin']=zt['tw_v200'].apply(qj)
zd={"A[0,277)":"1",
"B_[277,318)":"1",
"C_[318,350)":"1",
"D_[350,375)":"2",
"E_[375,389)":"2",
"F_[389,400)":"2",
"G_[400,410)":"2",
"H_[410,425)":"2",
"I_[425,444)":"3",
"J_[444,inf)":"3"}
zt['status']=zt['tw_v200_bin'].map(zd)
import pymysql
from sqlalchemy import create_engine
conn=pymysql.connect(host="112.124.40.41",user="score",passwd="jxy7RwPaLBryzFF8",db='score',port=9806)
conn = create_engine('mysql+pymysql://score:[email protected]:9806/score')
zt.to_sql("scorev2", conn, if_exists='append',index=False)
self.finish({'idcard': zt.at[0,'idcard'], 'score':zt.at[0,'tw_v200_bin'],'status':zt.at[0,'status']}) # 返回给前端,status 表示通过情况，0表示拒绝，1表示建议人审，2表示通过
if name == 'main':
define('port', default=8668, help='设置启动服务的端口', type=int) # 定义端口, 可以全局使用options.port获取,可通过命令行更改
application = tornado.web.Application(handlers=[('/score', BaseHandler)]) # 设置路由
server = tornado.httpserver.HTTPServer(application)
server.listen(options.port) # 监听端口8080,可以通过命令行改变,python xxx.py --port=8090
tornado.ioloop.IOLoop.instance().start() # 启动web服务