def id_shenfen(self, id_no):
id_yinshe = lujings().id_yinshe()
id_city = lujings().id_city()
ids = id_no[0:4]
city = list(id_yinshe[id_yinshe['ID_4_digit'] == int(ids)]['City'])[0]
##print(city)
idss = list(id_city[id_city['id_city'] == city]['ids'])[0]
return idss
def datas(self, results_dicts):
###xgb的输出
# results_dicts['id_city'] = 'M_B'
#results_dicts = self.join()
df_all = pd.DataFrame([results_dicts], dtype='float')
#print('pppppppppppppppp',df_all)
df_all['frg_group_num'] = df_all['frg_group_num'].apply(
lambda x: 1 if x == 'a' else
(2 if x == 'b' else (3 if x == 'c' else (4 if x == 'd' else 4))))
#print('qqqqqqqqqqq',df_all['frg_group_num'])
#df_all['id_city'] = 1
df_all['ir_id_x_cell_notmat_days'] = 0
df_all['a86'] = -999
df_all['b14'] = -999
df_all['b2'] = -999
df_all['b26'] = -999
df_all['d40'] = -999
df_all['b20'] = -999
df_all['d15'] = -999
# xgb = lujins.xgb()
# feature = lujins.feature()
#print('wwwwwwwwwwwwwwwwwwwwwww',list(df_all.columns))
feature = lujings().feature()
td_in = [x for x in feature if x not in list(df_all.columns)]
#print('ttttttttttttttttttttttttttttttttttttttttttt', td_in)
for ff in td_in:
df_all[ff] = -99
#print('eeeeeeeeeeeeeeee', df_all[feature])
xgb = lujings().xgb()
p = xgb.predict_proba(df_all[feature])[:, 1]
s = (0.1 / 0.9) / (0.05 / 0.95)
p_1 = 1 / (1 + s * np.exp(-np.log(p / (1 - p))))
score = 652 - 95 * np.log((p_1 / (1 - p_1)) / (0.05 / 0.95))
if score < 300:
score = 300
elif score > 1000:
score = 1000
else:
score = score
return score
def join(self):
cell_province = lujings().cell_province()
cell_province['cell_province'] = cell_province[
'cell_province'].str.strip()
province = list(self.cellphone['cell_province'])[0]
#print('111111', province)
cell_city = lujings().cell_city()[['cell_city', 'ids']]
cell_city['cell_city'] = cell_city['cell_city'].str.strip()
city = list(self.cellphone['cell_city'])[0]
#print('222222', city)
self.data['cell_province'] = list(cell_province[
cell_province['cell_province'] == province]['ids'])[0]
#print('333333333333333333', cell_province[cell_province['cell_province'] == province]['ids'])
# #print('qqqqqq',results_dicts)
self.data['cell_city'] = list(
(cell_city[cell_city['cell_city'] == city]['ids']))[0]
self.data.update(self.dict_sex)
#print('dddddddddddddddddddddddd', self.data)
return self.data
import json
import pandas as pd
from django.shortcuts import render, HttpResponse
from akamx.bairong import Bairong
import json
from bs4 import BeautifulSoup
import requests
from akamx.lujing import lujings
from akamx.tongdun import Tongdun
from akamx.ir import Score
from rest_framework.response import Response
from akamx.bairongcelv import Strategy
from akamx.pboccl import People
from akamx.models import jsonss
score = Score()
lujins = lujings()
www = lujins.shenfen()
import logging
logger = logging.getLogger('django')
class akmoxing:
##print("kaishi")
# 定义请求方法为post，
def gets(self, parameter):
a = time.clock()
# logger.info("开始时间{a}".format(a=a))
# parameter_json = request.body
#
# # json转字典
class Score:
def __init__(self):
self.col = [
"woe_als_m12_id_nbank_orgnum", "woe_cell_city_ec_level",
"woe_i_max_cnt_partner_daily_Loan_finance_365day",
"woe_i_get_node_rank_value_Loan_all_all", "woe_融资金额",
'woe_age_frg', "woe_ir_m6_cell_x_linkman_cell_cnt",
"woe_sl_gender", "woe_cell_province", 'woe_cell_city', "flagy"
]
#
diqu = lujings().diqu()
#
theProv1 = diqu['theProv1']
theProv2 = diqu['theProv2']
theProv3 = diqu['theProv3']
theProv4 = diqu['theProv4']
theCity1 = diqu['theCity1']
theCity2 = diqu['theCity2']
theCity3 = diqu['theCity3']
# theProv1 = {'上海', '江西', '湖北', '河北', 'M_B'}
# theProv2 = {'广东', '湖南', '重庆', '浙江', '内蒙古', '河南', '山东', '天津', '黑龙江'}
# theProv3 = {'贵州', '青海', '陕西', '山西', '四川', '辽宁', '福建', '新疆', '广西', '江苏', '西藏'}
# theProv4 = {'云南', '甘肃', '安徽', '海南', '宁夏', '北京', '吉林'}
#
# theCity1 = {'莆田', '衡水', '烟台', '吉安', '承德', '秦皇岛', '南充', '永州', '宁波', '龙岩', '开封',
# '万州', '邵阳', '邯郸', '赤峰', '奎屯', '临沂', '淮安', '威海', '三门峡', 'M_B', '乌海', '湘潭', '赣州',
# '玉树', '大庆', '连云港', '韶关', '清远', '九江', '咸宁', '江门', '日照', '衢州', '安阳', '泰州', '甘孜',
# '抚顺', '揭阳', '钦州', '松原', '通化', '贺州', '阿坝'}
#
# theCity2 = {'潍坊', '深圳', '东莞', '乐山', '南宁', '武汉', '盘锦', '临汾', '成都', '兴义', '岳阳', '廊坊',
# '惠州', '常德', '崇左', '厦门', '荆州', '包头', '沧州', '青岛', '柳州', '大连', '苏州', '海西', '南通', '襄阳',
# '桂林', '广安', '邢台', '驻马店', '台州', '绍兴', '渭南', '金华', '德州', '娄底', '阿拉善盟', '湘西', '眉山',
# '鸡西', '枣庄', '无锡', '凯里', '保定', '海南', '武威', '亳州', '白城', '莱芜', '泰安', '盐城', '吐鲁番', '营口',
# '汕尾', '海北', '茂名', '阳江', '博乐', '仙桃', '克拉玛依', '海拉尔', '云浮', '珲春', '鹰潭', '黑河', '西昌', '延边'}
#
# theCity3 = {'重庆', '宝鸡', '丽水', '北京', '昆明', '萍乡', '宜宾', '平凉', '天津', '曲靖', '漳州', '西安', '遵义',
# '泸州', '太原', '铜仁', '定西', '忻州', '锡林浩特', '巴中', '石家庄', '延安', '嘉兴', '恩施', '广元', '达州', '白银',
# '银川', '海口', '许昌', '庆阳', '酒泉', '内江', '西双版纳', '吉首', '广州', '凉山', '吕梁', '西宁', '长治', '陇南',
# '天水', '玉溪', '株洲', '淮南', '济南', '大同', '洛阳', '汕头', '温州', '文山', '南京', '济宁', '珠海', '南阳', '果洛',
# '张掖', '咸阳', '格尔木', '唐山', '晋城', '大理', '红河', '阜阳', '海东', '保山', '宜昌', '镇江', '贵阳', '楚雄', '固原',
# '合肥', '绵阳', '泉州', '巴彦淖尔', '贵港', '菏泽', '佛山', '杭州', '兰州', '郑州', '上海', '上饶', '阿克苏', '益阳', '石河子',
# '嘉峪关', '汉中', '景德镇', '运城', '郴州', '毕节', '涪陵', '芜湖', '丽江', '玉林', '六安', '乌鲁木齐', '安顺', '临沧', '聊城',
# '扬州', '中卫', '六盘水', '福州', '长沙', '葫芦岛', '三明', '共和', '怀化', '黔江', '信阳', '淄博', '迪庆', '晋中', '中山',
# '吴忠', '乌兰察布', '沈阳', '昭通', '张家口', '都匀', '徐州', '新乡', '金昌', '黄南', '通辽', '思茅', '黔东南', '黄冈', '德阳',
# '随州', '德令哈', '南昌', '伊犁', '安庆', '衡阳', '百色', '舟山', '鄂尔多斯', '抚州', '资阳', '常州', '河池', '海晏', '濮阳', '朔州',
# '黔西南', '攀枝花', '淮北', '四平', '肇庆', '宜春', '滨州', '周口', '甘南', '铁岭', '安康', '焦作', '梅州', '平顶山', '黔南',
# '池州', '呼和浩特', '自贡', '朝阳', '滁州', '本溪', '怒江', '宁德', '哈尔滨', '荆门', '牡丹江', '临夏', '石嘴山', '雅安', '鞍山',
# '那曲', '辽阳', '白山', '昌吉', '湛江', '梧州', '商丘', '孝感', '哈密', '克孜勒苏', '德宏', '东营', '榆林', '防城港', '宿迁', '乌兰浩特',
# '长春', '锦州', '湖州', '蚌埠', '北海', '十堰', '江汉', '宿州', '集宁', '林芝', '大兴安岭', '吉林', '宣城', '漯河', '商洛', '遂宁', '丹东',
# '普洱', '张家界', '河源', '铜川', '喀什', '阿勒泰', '阜新', '南平', '伊春', '拉萨', '阳泉', '阿里', '锡林郭勒盟', '潮州', '鄂州', '新余',
# '来宾', '铜陵', '库尔勒', '黄石'}
@staticmethod
def WOE_als(df: pd.DataFrame) -> float:
temp = df['als_m12_id_nbank_orgnum']
if temp <= -99:
return -0.9905
elif temp <= -1:
return -0.6534
elif temp <= 1:
return -0.2642
elif temp <= 2:
return 0.0430
elif temp <= 5:
return 0.4120
elif temp <= 10:
return 1.1025
else:
return 1.3919
@staticmethod
def WOE_city_ec(df: pd.DataFrame) -> float:
temp = df['cell_city_ec_level']
if temp <= -1:
return -0.1563
elif temp <= 1200:
return -0.4482
elif temp <= 1350:
return -0.3093
elif temp <= 1900:
return 0.0475
elif temp <= 3000:
return 0.0622
else:
return 0.6009
@staticmethod
def WOE_i_max_cnt_partner_daily_Loan_finance_365day(
df: pd.DataFrame) -> float:
temp = df['i_max_cnt_partner_daily_Loan_finance_365day']
if temp <= -99:
return -1.0218
elif temp <= 1:
return -0.3546
elif temp <= 2:
return 0.5453
else:
return 1.1982
@staticmethod
def WOE_i_get_node_rank_value_Loan_all_all(df: pd.DataFrame) -> float:
temp = df['i_get_node_rank_value_Loan_all_all']
if temp <= -1111:
return -0.1196
elif temp <= -999:
return -0.3269
elif temp == 0:
return 0.5136
else:
return 0.5265
@staticmethod
def WOE_RZJE(df: pd.DataFrame) -> float:
temp = df['融资金额']
if temp <= 70000:
return 0.0199
elif temp <= 80000:
return -0.4453
elif temp <= 95000:
return -0.3649
elif temp <= 110000:
return 0.2451
else:
return 0.5391
@staticmethod
def WOE_age_frg(df: pd.DataFrame) -> float:
if df['frg_list_level'] <= -99 and df['apply_age'] < 25:
return -0.2532
elif df['frg_list_level'] <= -99 and df['apply_age'] < 45:
return -0.5545
elif df['frg_list_level'] <= -99 and df['apply_age'] >= 45:
return -0.4625
elif df['frg_list_level'] < 5:
return 0.7118
else:
return 1.2803
@staticmethod
def WOE_ir(df: pd.DataFrame) -> float:
temp = df['ir_m6_cell_x_linkman_cell_cnt']
if temp <= -99:
return -1.1601
elif temp <= -1:
return -0.5295
else:
return 0.4640
@staticmethod
def WOE_SS(df: pd.DataFrame) -> float:
theSp = df['flag_specialList_c']
thegen = df['gender']
if thegen == 0 and theSp == 0:
return -0.1126
elif thegen == 1 and theSp == 0:
return -0.0585
else:
return 0.8694
@staticmethod
def WOE_cell_province(df: pd.DataFrame) -> float:
temp = df['cell_province']
if temp in Score.theProv1:
return 0.5578
elif temp in Score.theProv2:
return 0.4508
elif temp in Score.theProv3:
return 0.0003
else:
return -0.4442
@staticmethod
def WOE_cell_city(df: pd.DataFrame) -> float:
temp = df['cell_city']
if temp in Score.theCity1:
return 0.7762
elif temp in Score.theCity2:
return 0.5512
else:
return -0.1598
@staticmethod
def get_odds(df: pd.DataFrame) -> pd.DataFrame:
df["odds"] = df["woe_als_m12_id_nbank_orgnum"] * 0.38929019234372647 \
+ df["woe_cell_city_ec_level"] * 0.4256609883834146 \
+ df["woe_i_max_cnt_partner_daily_Loan_finance_365day"] * 0.3376071667460782 \
+ df["woe_i_get_node_rank_value_Loan_all_all"] * 0.2793197568224026 \
+ df["woe_融资金额"] * 0.8403668316875686 \
+ df["woe_age_frg"] * 0.2150064685794368 \
+ df["woe_ir_m6_cell_x_linkman_cell_cnt"] * 0.2794698572885128 \
+ df["woe_sl_gender"] * 0.49792835781599715 \
+ df["woe_cell_province"] * 0.5784174302105042 \
+ df["woe_cell_city"] * 0.5417858499432695 \
- 2.9770612620547205
return df
@staticmethod
def get_score(odds: pd.Series) -> pd.Series:
p_1 = 1 / (1 + np.exp(-odds))
score = 655 - 95 * np.log2((p_1 / (1 - p_1)) / (0.05 / 0.95))
return score
def derive(self, df: pd.DataFrame) -> pd.DataFrame:
df["woe_als_m12_id_nbank_orgnum"] = df.apply(Score.WOE_als, axis=1)
df["woe_cell_city_ec_level"] = df.apply(Score.WOE_city_ec, axis=1)
df["woe_i_max_cnt_partner_daily_Loan_finance_365day"] = df.apply(
Score.WOE_i_max_cnt_partner_daily_Loan_finance_365day, axis=1)
df["woe_i_get_node_rank_value_Loan_all_all"] = df.apply(
Score.WOE_i_get_node_rank_value_Loan_all_all, axis=1)
df["woe_融资金额"] = df.apply(Score.WOE_RZJE, axis=1)
df["woe_age_frg"] = df.apply(Score.WOE_age_frg, axis=1)
df["woe_ir_m6_cell_x_linkman_cell_cnt"] = df.apply(Score.WOE_ir,
axis=1)
df["woe_sl_gender"] = df.apply(Score.WOE_SS, axis=1)
df["woe_cell_province"] = df.apply(Score.WOE_cell_province, axis=1)
df["woe_cell_city"] = df.apply(Score.WOE_cell_city, axis=1)
return df
def predict(self, df: pd.DataFrame) -> pd.DataFrame:
df = self.derive(df)
df = self.get_odds(df)
df.loc[:, "score"] = df["odds"].apply(self.get_score)
df.loc[(df["score"] < 300), "score"] = 300
df.loc[(df["score"] > 1000), "score"] = 1000
return df
def bar_moing(self, results_dicts):
www = lujings().shenfen()
# print('wwwwwwwwwwwwwwwwwwwwwwwwwww',www)
# print(results_dicts)
# www['号段'] = www['号段'].astype('str')
cell_province = lujings().cell_province()
cell_province['cell_province'] = cell_province[
'cell_province'].str.strip()
cell_city = lujings().cell_city()[['cell_city', 'ids']]
cell_city['cell_city'] = cell_city['cell_city'].str.strip()
#results_dicts = self.requestss()
if results_dicts != 4000:
try:
###print('results_dictsresults_dictsresults_dictsresults_dicts',results_dicts)
dict_sex = self.sex_gender()
### 获取百融数据的result_list
###取百融模型
bl_json = {}
list_binaliang = [
'als_m12_id_nbank_orgnum', 'frg_list_level',
'ir_m6_cell_x_linkman_cell_cnt', 'flag_specialList_c'
]
if results_dicts['flag_applyloanstr'] == '1':
for ii in list_binaliang:
if ii in results_dicts.keys():
bl_json[ii] = results_dicts[ii]
else:
bl_json[ii] = -1
else:
bl_json = {
'als_m12_id_nbank_orgnum': -99,
'frg_list_level': -99,
'ir_m6_cell_x_linkman_cell_cnt': -99,
'flag_specialList_c': -99
}
# ##print(bl_json)
# model_path = r"C:\Users\Administrator\shenfen.pickle"
www['号段'] = www['号段'].apply(lambda x: str(x))
###print('www',www)
phon_a = self.cellphone[0:7]
ss = www[www['号段'] == phon_a]
### LR的模型
ss_dict = ss.to_dict(orient='records')[0]
bl_json.update(ss_dict)
bl_json.update(dict_sex)
##print('ccccccccccccccccccccccccc')
##print(cell_province)
##print('cell_city',cell_city)
xgb = xgboost(results_dicts, ss, dict_sex)
datas = xgb.join()
id_city = xgb.id_shenfen(self.id_no)
datas['id_city'] = id_city
##print('dadadadaaaa',datas['id_city'])
xgb_score = xgb.datas(datas)
return bl_json, xgb_score
except:
bl_json = {'bairong': 4000}
xgb_score = [0]
return bl_json, xgb_score
else:
bl_json = {'bairong': 4000}
xgb_score = [0]
return bl_json, xgb_score