AugustBetty能做什么
这个包是由微信公众号"屁屁和铭仔的数据之路"博主铭仔和屁屁一起开发的，目的是为了将逻辑回归模型开发流程规范化，
安装
pip install AugustBetty
导入
from AugustBetty.AugustBetty import ModelMain
一句话建模
data = pd.read_csv(r"F:\论文-风控\train.csv") #读取训练集
m=MakeModel(data,"id","isDefault",0.02,5,512254,drop_value=["issueDate","earliesCreditLine"])
m.model_main()
v1.0.0的功能如下：
数值变量的最优分组
输入指定的参数即可进行最优分组 分组结果包含以下四项： 1. 已经包含最优分组后的变量的数据集（在原始数据集上增加分组后的变量） 2. 最优分组后的变量名以及没有进行最优分组的变量（进行是组数较少，不进行最优分组） 3. 参与最优分组的变量的映射口径以及转换之后的变量名 4. 计算输入变量中转换了的变量的iv以及woe.
字符变量的最优分组
1. 已经包含最优分组后的变量的数据集（在原始数据集上增加分组后的变量）
2. 最优分组后的变量名以及没有进行最优分组的变量（进行是组数较少，不进行最优分组）
3. 参与最优分组的变量的映射口径以及转换之后的变量名
4. 计算输入变量中转换了的变量的iv以及woe.
逻辑回归模型的开发
- 训练集测试集的自动分区
- 自动识别数据集中的字符以及数值变量进行最优分组
- 自动转woe
- 自动根据iv设定阈值，筛选出高于iv值的变量进入模型中拟合
- 测试集根据分组映射woe,计算测试集的ks&auc,模型最终结果是模型的summary信息，以及测试集和训练集的ks&auc结果。
V1.1.0的功能规划如下：
拆分出细致的单独功能： 1、单独的函数转原始数据 2、增加验证集（在原始数据集上使用时间参数，划分验证集，跑模型得出评估指标） 3、在输入的路径下，输出模型文件
V1.2.0的功能规划如下：
开发自动部署模型的函数，输入模型位置，以及数据转换文件，输出模型api,以支持部分公司微服务的需求
如有报错，或者其他需求，请发邮件到：watchmans@qq.com 希望能不断的完善这个包，这个包现阶段功能相对单一。 欢迎大家关注公众号：屁屁和铭仔的数据之路
数值变量分组调用
执行命令：gg=num_group_chi_main(train_data,id_name,y,initial_group_num,group_num)
train_data:原始数据集
id_name:主键名字，例如"idno"
y:y变量名,例如"isbad"
initial_group_num:粗分组组数，这里的逻辑是将数值变量先粗分组再细分组的方式进行最优分组，一般设定为20-50，看数据颗粒度
group_num：最优分组组数，一般设定为4-6组
dict_keys(['data', 'var_dict_total', 'var_bin_list', 'iv_dict'])data:增加新变量之后的数据集。原始变量没有改动。只对数值变量做分组。
{'data': id loanAmnt term ... n13_group_Bin n14_group n14_group_Bin
531009 531009 18000.0 5 ... Bin 0 1 Bin 0
370965 370965 3000.0 5 ... Bin 0 1 Bin 0
212592 212592 23600.0 3 ... Bin 0 1 Bin 0
610540 610540 20000.0 3 ... Bin 0 1 Bin 0
400590 400590 24250.0 3 ... Bin 0 1 Bin 0
... ... ... ... ... ... ...
603509 603509 5600.0 3 ... Bin 0 7 Bin 4
157628 157628 7200.0 3 ... Bin 0 7 Bin 4
147487 147487 10000.0 3 ... Bin 0 7 Bin 4
719781 719781 16000.0 3 ... Bin 0 7 Bin 4
735444 735444 20000.0 3 ... Bin 0 7 Bin 4
[800000 rows x 119 columns],var_dict_total:保存的是变量的分组口径
'var_dict_total': {'loanAmnt': {'bin': {'Bin 0': [-9999.0, 8000.0],
'Bin 1': [8000.0, 10000.0],
'Bin 2': [10000.0, 15000.0],
'Bin 3': [15000.0, 28000.0],
'Bin 4': [28000.0, 10000000000]},
'value_name': 'loanAmnt_group_Bin'},
'interestRate': {'bin': {'Bin 0': [-9999.0, 7.97],
'Bin 1': [7.97, 12.29],
'Bin 2': [12.29, 15.99],
'Bin 3': [15.99, 22.15],
'Bin 4': [22.15, 10000000000]},
'value_name': 'interestRate_group_Bin'},
'installment': {'bin': {'Bin 0': [-9999.0, 248.45],
'Bin 1': [248.45, 324.3],
'Bin 2': [324.3, 10000000000]},
'value_name': 'installment_group_Bin'},
'employmentTitle': {'bin': {'Bin 0': [-9999.0, 54.0],
'Bin 1': [54.0, 169034.0],
'Bin 2': [169034.0, 10000000000]},
'value_name': 'employmentTitle_group_Bin'},
'homeOwnership': {'bin': {'Bin 0': [-9999.0, 1], 'Bin 1': [1, 10000000000]},
'value_name': 'homeOwnership_group_Bin'},
'annualIncome': {'bin': {'Bin 0': [-9999.0, 45600.0],
'Bin 1': [45600.0, 60000.0],
'Bin 2': [60000.0, 85000.0],
'Bin 3': [85000.0, 125000.0],
'Bin 4': [125000.0, 10000000000]},
'value_name': 'annualIncome_group_Bin'},
'purpose': {'bin': {'Bin 0': [-9999.0, 2], 'Bin 1': [2, 10000000000]},
'value_name': 'purpose_group_Bin'},
'postCode': {'bin': {'Bin 0': [-9999.0, 19.0],
'Bin 1': [19.0, 262.0],
'Bin 2': [262.0, 10000000000]},
'value_name': 'postCode_group_Bin'},
'regionCode': {'bin': {'Bin 0': [-9999.0, 13], 'Bin 1': [13, 10000000000]},
'value_name': 'regionCode_group_Bin'},
'dti': {'bin': {'Bin 0': [-9999.0, 14.18],
'Bin 1': [14.18, 21.25],
'Bin 2': [21.25, 25.69],
'Bin 3': [25.69, 29.78],
'Bin 4': [29.78, 10000000000]},
'value_name': 'dti_group_Bin'},
'delinquency_2years': {'bin': {'Bin 0': [-9999.0, 1.0],
'Bin 1': [1.0, 2.0],
'Bin 2': [2.0, 10000000000]},
'value_name': 'delinquency_2years_group_Bin'},
'ficoRangeLow': {'bin': {'Bin 0': [-9999.0, 670.0],
'Bin 1': [670.0, 690.0],
'Bin 2': [690.0, 720.0],
'Bin 3': [720.0, 760.0],
'Bin 4': [760.0, 10000000000]},
'value_name': 'ficoRangeLow_group_Bin'},
'ficoRangeHigh': {'bin': {'Bin 0': [-9999.0, 674.0],
'Bin 1': [674.0, 694.0],
'Bin 2': [694.0, 724.0],
'Bin 3': [724.0, 764.0],
'Bin 4': [764.0, 10000000000]},
'value_name': 'ficoRangeHigh_group_Bin'},
'openAcc': {'bin': {'Bin 0': [-9999.0, 7.0],
'Bin 1': [7.0, 10.0],
'Bin 2': [10.0, 17.0],
'Bin 3': [17.0, 22.0],
'Bin 4': [22.0, 10000000000]},
'value_name': 'openAcc_group_Bin'},
'pubRec': {'bin': {'Bin 0': [-9999.0, 1.0], 'Bin 1': [1.0, 10000000000]},
'value_name': 'pubRec_group_Bin'},
'pubRecBankruptcies': {'bin': {'Bin 0': [-9999.0, 0.0],
'Bin 1': [0.0, 1.0],
'Bin 2': [1.0, 10000000000]},
'value_name': 'pubRecBankruptcies_group_Bin'},
'revolBal': {'bin': {'Bin 0': [-9999.0, 22682.0],
'Bin 1': [22682.0, 43290.0],
'Bin 2': [43290.0, 10000000000]},
'value_name': 'revolBal_group_Bin'},
'revolUtil': {'bin': {'Bin 0': [-9999.0, 18.1],
'Bin 1': [18.1, 33.4],
'Bin 2': [33.4, 52.1],
'Bin 3': [52.1, 74.9],
'Bin 4': [74.9, 10000000000]},
'value_name': 'revolUtil_group_Bin'},
'totalAcc': {'bin': {'Bin 0': [-9999.0, 16.0],
'Bin 1': [16.0, 22.0],
'Bin 2': [22.0, 10000000000]},
'value_name': 'totalAcc_group_Bin'},
'title': {'bin': {'Bin 0': [-9999.0, 1.0],
'Bin 1': [1.0, 10.0],
'Bin 2': [10.0, 10000000000]},
'value_name': 'title_group_Bin'},
'n0': {'bin': {'Bin 0': [-9999.0, 0.0], 'Bin 1': [0.0, 10000000000]},
'value_name': 'n0_group_Bin'},
'n1': {'bin': {'Bin 0': [-9999.0, 2.0],
'Bin 1': [2.0, 3.0],
'Bin 2': [3.0, 4.0],
'Bin 3': [4.0, 6.0],
'Bin 4': [6.0, 10000000000]},
'value_name': 'n1_group_Bin'},
'n2': {'bin': {'Bin 0': [-9999.0, 2.0],
'Bin 1': [2.0, 4.0],
'Bin 2': [4.0, 7.0],
'Bin 3': [7.0, 10.0],
'Bin 4': [10.0, 10000000000]},
'value_name': 'n2_group_Bin'},
'n3': {'bin': {'Bin 0': [-9999.0, 2.0],
'Bin 1': [2.0, 4.0],
'Bin 2': [4.0, 7.0],
'Bin 3': [7.0, 10.0],
'Bin 4': [10.0, 10000000000]},
'value_name': 'n3_group_Bin'},
'n4': {'bin': {'Bin 0': [-9999.0, 0.0],
'Bin 1': [0.0, 4.0],
'Bin 2': [4.0, 6.0],
'Bin 3': [6.0, 10.0],
'Bin 4': [10.0, 10000000000]},
'value_name': 'n4_group_Bin'},
'n5': {'bin': {'Bin 0': [-9999.0, 4.0], 'Bin 1': [4.0, 10000000000]},
'value_name': 'n5_group_Bin'},
'n6': {'bin': {'Bin 0': [-9999.0, 1.0],
'Bin 1': [1.0, 23.0],
'Bin 2': [23.0, 10000000000]},
'value_name': 'n6_group_Bin'},
'n7': {'bin': {'Bin 0': [-9999.0, 4.0],
'Bin 1': [4.0, 7.0],
'Bin 2': [7.0, 10.0],
'Bin 3': [10.0, 14.0],
'Bin 4': [14.0, 10000000000]},
'value_name': 'n7_group_Bin'},
'n8': {'bin': {'Bin 0': [-9999.0, 5.0], 'Bin 1': [5.0, 10000000000]},
'value_name': 'n8_group_Bin'},
'n9': {'bin': {'Bin 0': [-9999.0, 3.0],
'Bin 1': [3.0, 5.0],
'Bin 2': [5.0, 7.0],
'Bin 3': [7.0, 10.0],
'Bin 4': [10.0, 10000000000]},
'value_name': 'n9_group_Bin'},
'n10': {'bin': {'Bin 0': [-9999.0, 3.0],
'Bin 1': [3.0, 8.0],
'Bin 2': [8.0, 10.0],
'Bin 3': [10.0, 22.0],
'Bin 4': [22.0, 10000000000]},
'value_name': 'n10_group_Bin'},
'n11': {'bin': {'Bin 0': [-9999.0, 0.0], 'Bin 1': [0.0, 10000000000]},
'value_name': 'n11_group_Bin'},
'n12': {'bin': {'Bin 0': [-9999.0, 0.0], 'Bin 1': [0.0, 10000000000]},
'value_name': 'n12_group_Bin'},
'n13': {'bin': {'Bin 0': [-9999.0, 0.0], 'Bin 1': [0.0, 10000000000]},
'value_name': 'n13_group_Bin'},
'n14': {'bin': {'Bin 0': [-9999.0, 0.0],
'Bin 1': [0.0, 1.0],
'Bin 2': [1.0, 2.0],
'Bin 3': [2.0, 3.0],
'Bin 4': [3.0, 10000000000]},
'value_name': 'n14_group_Bin'}},var_bin_list：保存可用于如模型拟合的变量（逻辑回归本质上入模的变量都应该是数值变量，这里的list保存的时候分组好的变量以及原本的分组组已经满足组数（一般设定5组）的变量list,输出这个的原因是后续好循环转换woe,无需手工把变量敲出来。）
'var_bin_list': ['loanAmnt_group_Bin',
'interestRate_group_Bin',
'installment_group_Bin',
'employmentTitle_group_Bin',
'homeOwnership_group_Bin',
'annualIncome_group_Bin',
'purpose_group_Bin',
'postCode_group_Bin',
'regionCode_group_Bin',
'dti_group_Bin',
'delinquency_2years_group_Bin',
'ficoRangeLow_group_Bin',
'ficoRangeHigh_group_Bin',
'openAcc_group_Bin',
'pubRec_group_Bin',
'pubRecBankruptcies_group_Bin',
'revolBal_group_Bin',
'revolUtil_group_Bin',
'totalAcc_group_Bin',
'title_group_Bin',
'n0_group_Bin',
'n1_group_Bin',
'n2_group_Bin',
'n3_group_Bin',
'n4_group_Bin',
'n5_group_Bin',
'n6_group_Bin',
'n7_group_Bin',
'n8_group_Bin',
'n9_group_Bin',
'n10_group_Bin',
'n11_group_Bin',
'n12_group_Bin',
'n13_group_Bin',
'n14_group_Bin',
'term',
'verificationStatus',
'initialListStatus',
'applicationType',
'policyCode'],iv_dict：保存是的输入的数据集中可最优分组的变量，转换后的iv,woe,以及已经是足够组数的变量的iv,woe
'iv_dict': {'term': {'iv': 0.17263507884380802,
'woe': {3: -0.2686182828015269, 5: 0.6520081414240493}},
'verificationStatus': {'iv': 0.05451891242377009,
'woe': {0: -0.3672196958578996,
1: 0.06087198832351855,
2: 0.22488431630897235}},
'initialListStatus': {'iv': 0.0003419546491836722,
'woe': {0: 0.015549994613464051, 1: -0.021991286981396775}},
'applicationType': {'iv': 0.001906255272074922,
'woe': {0: -0.006394473785834787, 1: 0.2981570883254602}},
'policyCode': {'iv': 0.0, 'woe': {1.0: 0.0}},
'loanAmnt_group_Bin': {'iv': 0.03450643699626254,
'woe': {'Bin 0': -0.26698353031170635,
'Bin 1': -0.10585087483552284,
'Bin 2': 0.05207841143835551,
'Bin 3': 0.15704067216706727,
'Bin 4': 0.2587985257714207}},
'interestRate_group_Bin': {'iv': 0.432844128756394,
'woe': {'Bin 0': -1.4139767754271722,
'Bin 1': -0.47444483191986947,
'Bin 2': 0.12600617373518758,
'Bin 3': 0.6329403904111698,
'Bin 4': 1.1364715170302837}},
'installment_group_Bin': {'iv': 0.028446931068475675,
'woe': {'Bin 0': -0.2965362458671355,
'Bin 1': -0.027032933155407588,
'Bin 2': 0.11545123400239903}},
'employmentTitle_group_Bin': {'iv': 0.01199262426711711,
'woe': {'Bin 0': 0.1974127496581969,
'Bin 1': 0.015794613325623683,
'Bin 2': -0.19061653548410487}},
'homeOwnership_group_Bin': {'iv': 0.0003224362597742428,
'woe': {'Bin 0': -0.006310542058851011, 'Bin 1': 0.051096232036222884}},
'annualIncome_group_Bin': {'iv': 0.028755712221678248,
'woe': {'Bin 0': 0.19386504291971357,
'Bin 1': 0.09133649436575257,
'Bin 2': -0.026827665423123023,
'Bin 3': -0.19469768671127013,
'Bin 4': -0.339204930328438}},
'purpose_group_Bin': {'iv': 0.007295208182839682,
'woe': {'Bin 0': 0.060104555523002035, 'Bin 1': -0.12144911490483187}},
'postCode_group_Bin': {'iv': 0.0008119510164983021,
'woe': {'Bin 0': -0.10613844771754728,
'Bin 1': -0.006970955095481508,
'Bin 2': 0.02290230125349964}},
'regionCode_group_Bin': {'iv': 1.3326503587742891e-05,
'woe': {'Bin 0': 0.0037306864251708577, 'Bin 1': -0.003572135757512189}},
'dti_group_Bin': {'iv': 0.0691444505823585,
'woe': {'Bin 0': -0.29639426975460115,
'Bin 1': -0.05303861299797819,
'Bin 2': 0.14116987973826364,
'Bin 3': 0.2922661709950033,
'Bin 4': 0.49293116504622203}},
'delinquency_2years_group_Bin': {'iv': 0.002139337579274896,
'woe': {'Bin 0': -0.012315786189818509,
'Bin 1': 0.14386566036838583,
'Bin 2': 0.20305465903034065}},
'ficoRangeLow_group_Bin': {'iv': 0.11849558508830776,
'woe': {'Bin 0': 0.33499130235102315,
'Bin 1': 0.14036402817126167,
'Bin 2': -0.16102580213328785,
'Bin 3': -0.582451594346089,
'Bin 4': -1.0396374414819025}},
'ficoRangeHigh_group_Bin': {'iv': 0.11849558508830776,
'woe': {'Bin 0': 0.33499130235102315,
'Bin 1': 0.14036402817126167,
'Bin 2': -0.16102580213328785,
'Bin 3': -0.582451594346089,
'Bin 4': -1.0396374414819025}},
'openAcc_group_Bin': {'iv': 0.004573486625874178,
'woe': {'Bin 0': -0.0890448653651065,
'Bin 1': -0.030985128792218982,
'Bin 2': 0.03156043510552112,
'Bin 3': 0.08247580996942927,
'Bin 4': 0.18573680489483868}},
'pubRec_group_Bin': {'iv': 0.001300898810070593,
'woe': {'Bin 0': -0.006265183515565402, 'Bin 1': 0.20766187334039157}},
'pubRecBankruptcies_group_Bin': {'iv': 0.004275157313198444,
'woe': {'Bin 0': -0.025198889500429856,
'Bin 1': 0.16242870100858983,
'Bin 2': 0.2476354559457032}},
'revolBal_group_Bin': {'iv': 0.003766992022044162,
'woe': {'Bin 0': 0.02404226891591574,
'Bin 1': -0.05292092268354001,
'Bin 2': -0.24978853781535482}},
'revolUtil_group_Bin': {'iv': 0.02397767300459437,
'woe': {'Bin 0': -0.358979455056802,
'Bin 1': -0.17312304317097293,
'Bin 2': -0.021333642551765307,
'Bin 3': 0.09298419081740512,
'Bin 4': 0.1599708320355574}},
'totalAcc_group_Bin': {'iv': 0.001774565948034374,
'woe': {'Bin 0': 0.0654454812926487,
'Bin 1': 0.0046315344580754,
'Bin 2': -0.03502643818846728}},
'title_group_Bin': {'iv': 0.02138620287983934,
'woe': {'Bin 0': 0.12076691332729704,
'Bin 1': -0.06234571640663273,
'Bin 2': -0.303010040073495}},
'n0_group_Bin': {'iv': 0.0036482088372045843,
'woe': {'Bin 0': -0.03328497196626471, 'Bin 1': 0.10963861162794214}},
'n1_group_Bin': {'iv': 0.012776531508686802,
'woe': {'Bin 0': -0.1139367854446227,
'Bin 1': -0.03145428250504604,
'Bin 2': 0.029134607317330145,
'Bin 3': 0.10471618734510002,
'Bin 4': 0.24003074689416587}},
'n2_group_Bin': {'iv': 0.03305847723307784,
'woe': {'Bin 0': -0.2599793507860163,
'Bin 1': -0.1159979609788405,
'Bin 2': 0.03678331487568212,
'Bin 3': 0.19935833147539536,
'Bin 4': 0.3818852049766906}},
'n3_group_Bin': {'iv': 0.03305847723307784,
'woe': {'Bin 0': -0.2599793507860163,
'Bin 1': -0.1159979609788405,
'Bin 2': 0.03678331487568212,
'Bin 3': 0.19935833147539536,
'Bin 4': 0.3818852049766906}},
'n4_group_Bin': {'iv': 0.005517283907591153,
'woe': {'Bin 0': -0.2889421330249432,
'Bin 1': -0.013202349900056868,
'Bin 2': 0.019088384219396928,
'Bin 3': 0.06247232677779756,
'Bin 4': 0.13402280957203358}},
'n5_group_Bin': {'iv': 0.00020945793402011507,
'woe': {'Bin 0': 0.023494043351489774, 'Bin 1': -0.008915518979970272}},
'n6_group_Bin': {'iv': 0.0014692680358088994,
'woe': {'Bin 0': -0.0765558185257892,
'Bin 1': 0.0053247020248672955,
'Bin 2': 0.1210950403409298}},
'n7_group_Bin': {'iv': 0.009675687051079466,
'woe': {'Bin 0': -0.14429755533030605,
'Bin 1': -0.027727108030571924,
'Bin 2': 0.041131450162872625,
'Bin 3': 0.08806082871714123,
'Bin 4': 0.18795745190250174}},
'n8_group_Bin': {'iv': 0.0007184262698178514,
'woe': {'Bin 0': -0.06987676998104353, 'Bin 1': 0.010281947543667881}},
'n9_group_Bin': {'iv': 0.031249289588835223,
'woe': {'Bin 0': -0.2096436342241601,
'Bin 1': -0.0429081760617028,
'Bin 2': 0.07105221873661946,
'Bin 3': 0.1970894208174422,
'Bin 4': 0.3774005738856138}},
'n10_group_Bin': {'iv': 0.008444830436101544,
'woe': {'Bin 0': -0.293226157535438,
'Bin 1': -0.06274924779747353,
'Bin 2': -0.00328536625477653,
'Bin 3': 0.05386221291998551,
'Bin 4': 0.19061556079370015}},
'n11_group_Bin': {'iv': 7.421631321053656e-08,
'woe': {'Bin 0': 7.237673677632179e-06, 'Bin 1': -0.010254233468312956}},
'n12_group_Bin': {'iv': 7.759942856943026e-05,
'woe': {'Bin 0': -0.0004960486167429998, 'Bin 1': 0.15643612383430344}},
'n13_group_Bin': {'iv': 0.0015580211455861362,
'woe': {'Bin 0': -0.009717341239876488, 'Bin 1': 0.1603549088517746}},
'n14_group_Bin': {'iv': 0.048440899304046665,
'woe': {'Bin 0': -0.3045306871267572,
'Bin 1': -0.14851272009664643,
'Bin 2': 0.0023652073777263066,
'Bin 3': 0.14618635044605022,
'Bin 4': 0.3310966221715067}}}}字符变量分组调用
char_group_chi_main(data_total,id_name,y,group_num,drop_value=[])
data_total:原始数据集
id_name:主键名字，例如"idno"
y:y变量名,例如"isbad"
group_num：最优分组组数，一般设定为4-6组
drop_value:不参与最优分组的变量，例如字符型的日期这一类变量。
例如以下填写参数：
char_group_chi_main(train_data,"id","isDefault",5,drop_value=["issueDate","earliesCreditLine"])返回值：与数值的变量的一样，不再赘述：
{'data': id loanAmnt ... employmentLength_group employmentLength_group_Bin
0 0 35000.0 ... 8 Bin 2
1 1 18000.0 ... 4 Bin 1
2 2 12000.0 ... 5 Bin 2
3 3 11000.0 ... 1 Bin 0
4 4 3000.0 ... 12 Bin 4
... ... ... ... ...
799995 799995 25000.0 ... 3 Bin 1
799996 799996 17000.0 ... 1 Bin 0
799997 799997 6000.0 ... 1 Bin 0
799998 799998 19200.0 ... 1 Bin 0
799999 799999 9000.0 ... 4 Bin 1
[800000 rows x 54 columns],
'var_dict_total': {'grade': {'bin': {'F': 'Bin 4',
'G': 'Bin 4',
'D': 'Bin 3',
'E': 'Bin 4',
'C': 'Bin 2',
'A': 'Bin 0',
'B': 'Bin 1'},
'var_name': 'grade_group_Bin'},
'subGrade': {'bin': {'G1': 'Bin 4',
'G2': 'Bin 4',
'G3': 'Bin 4',
'F4': 'Bin 4',
'F3': 'Bin 4',
'G4': 'Bin 4',
'E3': 'Bin 4',
'F5': 'Bin 4',
'E4': 'Bin 4',
'D3': 'Bin 3',
'D2': 'Bin 3',
'F1': 'Bin 4',
'A2': 'Bin 0',
'F2': 'Bin 4',
'A5': 'Bin 0',
'A3': 'Bin 0',
'B5': 'Bin 1',
'B4': 'Bin 1',
'E5': 'Bin 4',
'G5': 'Bin 4',
'D1': 'Bin 3',
'E1': 'Bin 4',
'C1': 'Bin 2',
'D4': 'Bin 3',
'B1': 'Bin 1',
'D5': 'Bin 3',
'C2': 'Bin 2',
'B2': 'Bin 1',
'C5': 'Bin 3',
'A1': 'Bin 0',
'E2': 'Bin 4',
'C4': 'Bin 3',
'C3': 'Bin 2',
'A4': 'Bin 0',
'B3': 'Bin 1'},
'var_name': 'subGrade_group_Bin'},
'employmentLength': {'bin': {'6 years': 'Bin 1',
'3 years': 'Bin 2',
'9 years': 'Bin 2',
'4 years': 'Bin 2',
'< 1 year': 'Bin 3',
'8 years': 'Bin 2',
'1 year': 'Bin 3',
'null': 'Bin 4',
'2 years': 'Bin 2',
'7 years': 'Bin 1',
'10+ years': 'Bin 0',
'5 years': 'Bin 1'},
'var_name': 'employmentLength_group_Bin'}},
'var_bin_list': ['grade_group_Bin',
'subGrade_group_Bin',
'employmentLength_group_Bin'],
'iv_dict': {'grade_group_Bin': {'iv': 0.46008926359723423,
'woe': {'Bin 0': -1.355565869742944,
'Bin 1': -0.4854115082757125,
'Bin 2': 0.15269375877204816,
'Bin 3': 0.560321181232565,
'Bin 4': 1.0184060522289387}},
'subGrade_group_Bin': {'iv': 0.46133308496995795,
'woe': {'Bin 0': -1.355565869742944,
'Bin 1': -0.4854115082757125,
'Bin 2': 0.04570655110165618,
'Bin 3': 0.46384538567636546,
'Bin 4': 1.0184060522289387}},
'employmentLength_group_Bin': {'iv': 0.012227612557761728,
'woe': {'Bin 0': -0.08002342137428835,
'Bin 1': -0.03072040532440133,
'Bin 2': -0.00026093587689965793,
'Bin 3': 0.03412595689559949,
'Bin 4': 0.38856251491409827}}}}一个语句建模
刚才的最优分组都是可以单独使用的，一个语句建模，即只需要执行一个语句，则能将原始的数据集从分组、转换woe、筛选变量，拟合模型、最终得到测试集以及训练集的结果。
定义模型参数：
m=MakeModel(data,"id","isDefault",0.02,5,512254,drop_value=["issueDate","earliesCreditLine"])
def __init__(self,data,id_name,y_name,iv_num,group_num,random_state,drop_value=[]):data:原始数据集，最好是你处理过的数据集，具体的衍生变量你自己做啦~~~
id_name：主键的变量名 例如 "idno"
iv_num：设定iv大于多少就可以进入模型拟合 建议是0.01-0.05之间
y_name:y变量名 例如"isbad"
group_num:最优分组最终组数，最好的4-6之间
random_state：划分数据集的随机码
drop_value：不参与建模的变量，你可以自己在原始数据集里面删掉，也可以在这写上。
###执行语句
m=MakeModel(data,"id","isDefault",0.02,5,512254,drop_value=["issueDate","earliesCreditLine"])
m.model_main()最终m包含以下信息： bin_woe 这是类中定义的一个用户转换woe的函数，不管！！
char_object：这个是最终字符变量最优分组输出的信息，上面贴过了，我再贴一次，因为我知道你懒得翻回去
{'data': id loanAmnt ... employmentLength_group employmentLength_group_Bin
742562 742562 10000.0 ... 1 Bin 0
762848 762848 14950.0 ... 6 Bin 2
644240 644240 20000.0 ... 12 Bin 4
451072 451072 12000.0 ... 9 Bin 3
93411 93411 2500.0 ... 11 Bin 3
... ... ... ... ...
245245 245245 4450.0 ... 9 Bin 3
30730 30730 5700.0 ... 1 Bin 0
476922 476922 7500.0 ... 2 Bin 1
476992 476992 15000.0 ... 10 Bin 3
39730 39730 40000.0 ... 2 Bin 1
[560000 rows x 56 columns],
'var_dict_total': {'grade': {'bin': {'G': 'Bin 4',
'E': 'Bin 4',
'F': 'Bin 4',
'D': 'Bin 3',
'C': 'Bin 2',
'B': 'Bin 1',
'A': 'Bin 0'},
'var_name': 'grade_group_Bin'},
'subGrade': {'bin': {'G5': 'Bin 4',
'G4': 'Bin 4',
'F2': 'Bin 4',
'G3': 'Bin 4',
'G2': 'Bin 4',
'F5': 'Bin 4',
'F4': 'Bin 4',
'E3': 'Bin 4',
'G1': 'Bin 4',
'E4': 'Bin 4',
'A2': 'Bin 0',
'A1': 'Bin 0',
'A3': 'Bin 0',
'E1': 'Bin 3',
'D1': 'Bin 3',
'C4': 'Bin 3',
'D2': 'Bin 3',
'E5': 'Bin 4',
'C3': 'Bin 3',
'D5': 'Bin 3',
'E2': 'Bin 4',
'B4': 'Bin 2',
'F1': 'Bin 4',
'C5': 'Bin 3',
'B1': 'Bin 1',
'C1': 'Bin 2',
'D4': 'Bin 3',
'A4': 'Bin 0',
'B2': 'Bin 1',
'F3': 'Bin 4',
'D3': 'Bin 3',
'C2': 'Bin 2',
'B5': 'Bin 2',
'B3': 'Bin 1',
'A5': 'Bin 1'},
'var_name': 'subGrade_group_Bin'},
'employmentLength': {'bin': {'9 years': 'Bin 2',
'4 years': 'Bin 2',
'8 years': 'Bin 2',
'7 years': 'Bin 1',
'5 years': 'Bin 1',
'null': 'Bin 4',
'1 year': 'Bin 3',
'2 years': 'Bin 2',
'3 years': 'Bin 3',
'10+ years': 'Bin 0',
'< 1 year': 'Bin 3',
'6 years': 'Bin 1'},
'var_name': 'employmentLength_group_Bin'}},
'var_bin_list': ['grade_group_Bin',
'subGrade_group_Bin',
'employmentLength_group_Bin'],
'iv_dict': {'grade_group_Bin': {'iv': 0.46497204120261115,
'woe': {'Bin 0': -1.366114993583636,
'Bin 1': -0.4872500245227714,
'Bin 2': 0.15134427847202556,
'Bin 3': 0.5673843591805174,
'Bin 4': 1.0199366732850919}},
'subGrade_group_Bin': {'iv': 0.46933032966767474,
'woe': {'Bin 0': -1.5499290796800491,
'Bin 1': -0.7155043413125765,
'Bin 2': -0.14179350218267067,
'Bin 3': 0.43440570785349064,
'Bin 4': 1.0693024724239095}},
'employmentLength_group_Bin': {'iv': 0.011729759860744725,
'woe': {'Bin 0': -0.07860890366978489,
'Bin 1': -0.030637664745026258,
'Bin 2': -0.005312943616562383,
'Bin 3': 0.02768067382373055,
'Bin 4': 0.3796614757205021}}}}corr_x:定义的一个计算相关系数矩阵的函数，不管！！！
deal_raw_data：处理映射的函数，不管！！
drop_value： 你输入的删掉的变量的参数
feature_list：进入模型拟合的变量列表，如果你对选出来的变量不满意，可以自己选变量，这样子跑就可以了
m=MakeModel(data,"id","isDefault",0.02,5,512254,drop_value=["issueDate","earliesCreditLine"])
m.model_main(feature_list=["aa","bb","dd","cc"])group_num:你输入的分组数
id_name：你输入的主键的变量名
iv_num：你输入的iv阈值
ks_calc_auc：一个计算ks的函数，不用管。你想用，我之后给你放出来，这个版本不放
model_main：建模主函数，不用管
num_object：这个是最终数值变量最优分组输出的信息，上面贴过了，我再贴一次，因为我知道你懒得翻回去
{'data': id loanAmnt term ... n13_group_Bin n14_group n14_group_Bin
384065 384065 7500.0 3 ... Bin 0 1 Bin 0
416852 416852 35000.0 5 ... Bin 0 1 Bin 0
794591 794591 30000.0 5 ... Bin 0 1 Bin 0
686008 686008 5000.0 3 ... Bin 0 1 Bin 0
449392 449392 12000.0 3 ... Bin 0 1 Bin 0
... ... ... ... ... ... ...
764543 764543 12000.0 3 ... Bin 0 7 Bin 4
230131 230131 5550.0 3 ... Bin 1 7 Bin 4
289950 289950 2200.0 3 ... Bin 0 7 Bin 4
95878 95878 5500.0 3 ... Bin 1 7 Bin 4
157628 157628 7200.0 3 ... Bin 0 7 Bin 4
[560000 rows x 117 columns],
'var_dict_total': {'loanAmnt': {'bin': {'Bin 0': [-9999.0, 8625.0],
'Bin 1': [8625.0, 10000.0],
'Bin 2': [10000.0, 15000.0],
'Bin 3': [15000.0, 28000.0],
'Bin 4': [28000.0, 10000000000]},
'value_name': 'loanAmnt_group_Bin'},
'interestRate': {'bin': {'Bin 0': [-9999.0, 7.97],
'Bin 1': [7.97, 12.29],
'Bin 2': [12.29, 15.99],
'Bin 3': [15.99, 22.15],
'Bin 4': [22.15, 10000000000]},
'value_name': 'interestRate_group_Bin'},
'installment': {'bin': {'Bin 0': [-9999.0, 248.25],
'Bin 1': [248.25, 324.07],
'Bin 2': [324.07, 10000000000]},
'value_name': 'installment_group_Bin'},
'employmentTitle': {'bin': {'Bin 0': [-9999.0, 54.0],
'Bin 1': [54.0, 169060.0],
'Bin 2': [169060.0, 10000000000]},
'value_name': 'employmentTitle_group_Bin'},
'homeOwnership': {'bin': {'Bin 0': [-9999.0, 1],
'Bin 1': [1, 2],
'Bin 2': [2, 10000000000]},
'value_name': 'homeOwnership_group_Bin'},
'annualIncome': {'bin': {'Bin 0': [-9999.0, 42000.0],
'Bin 1': [42000.0, 60000.0],
'Bin 2': [60000.0, 85000.0],
'Bin 3': [85000.0, 125000.0],
'Bin 4': [125000.0, 10000000000]},
'value_name': 'annualIncome_group_Bin'},
'purpose': {'bin': {'Bin 0': [-9999.0, 2], 'Bin 1': [2, 10000000000]},
'value_name': 'purpose_group_Bin'},
'postCode': {'bin': {'Bin 0': [-9999.0, 19.0],
'Bin 1': [19.0, 300.0],
'Bin 2': [300.0, 10000000000]},
'value_name': 'postCode_group_Bin'},
'regionCode': {'bin': {'Bin 0': [-9999.0, 21], 'Bin 1': [21, 10000000000]},
'value_name': 'regionCode_group_Bin'},
'dti': {'bin': {'Bin 0': [-9999.0, 15.31],
'Bin 1': [15.31, 21.25],
'Bin 2': [21.25, 25.69],
'Bin 3': [25.69, 29.78],
'Bin 4': [29.78, 10000000000]},
'value_name': 'dti_group_Bin'},
'delinquency_2years': {'bin': {'Bin 0': [-9999.0, 1.0],
'Bin 1': [1.0, 2.0],
'Bin 2': [2.0, 10000000000]},
'value_name': 'delinquency_2years_group_Bin'},
'ficoRangeLow': {'bin': {'Bin 0': [-9999.0, 680.0],
'Bin 1': [680.0, 705.0],
'Bin 2': [705.0, 720.0],
'Bin 3': [720.0, 760.0],
'Bin 4': [760.0, 10000000000]},
'value_name': 'ficoRangeLow_group_Bin'},
'ficoRangeHigh': {'bin': {'Bin 0': [-9999.0, 684.0],
'Bin 1': [684.0, 709.0],
'Bin 2': [709.0, 724.0],
'Bin 3': [724.0, 764.0],
'Bin 4': [764.0, 10000000000]},
'value_name': 'ficoRangeHigh_group_Bin'},
'openAcc': {'bin': {'Bin 0': [-9999.0, 8.0],
'Bin 1': [8.0, 10.0],
'Bin 2': [10.0, 19.0],
'Bin 3': [19.0, 22.0],
'Bin 4': [22.0, 10000000000]},
'value_name': 'openAcc_group_Bin'},
'pubRec': {'bin': {'Bin 0': [-9999.0, 1.0], 'Bin 1': [1.0, 10000000000]},
'value_name': 'pubRec_group_Bin'},
'pubRecBankruptcies': {'bin': {'Bin 0': [-9999.0, 0.0],
'Bin 1': [0.0, 1.0],
'Bin 2': [1.0, 10000000000]},
'value_name': 'pubRecBankruptcies_group_Bin'},
'revolBal': {'bin': {'Bin 0': [-9999.0, 22687.0],
'Bin 1': [22687.0, 43329.0],
'Bin 2': [43329.0, 10000000000]},
'value_name': 'revolBal_group_Bin'},
'revolUtil': {'bin': {'Bin 0': [-9999.0, 18.1],
'Bin 1': [18.1, 37.4],
'Bin 2': [37.4, 55.6],
'Bin 3': [55.6, 74.9],
'Bin 4': [74.9, 10000000000]},
'value_name': 'revolUtil_group_Bin'},
'totalAcc': {'bin': {'Bin 0': [-9999.0, 16.0],
'Bin 1': [16.0, 22.0],
'Bin 2': [22.0, 26.0],
'Bin 3': [26.0, 10000000000]},
'value_name': 'totalAcc_group_Bin'},
'title': {'bin': {'Bin 0': [-9999.0, 1.0],
'Bin 1': [1.0, 10.0],
'Bin 2': [10.0, 10000000000]},
'value_name': 'title_group_Bin'},
'n0': {'bin': {'Bin 0': [-9999.0, 0.0], 'Bin 1': [0.0, 10000000000]},
'value_name': 'n0_group_Bin'},
'n1': {'bin': {'Bin 0': [-9999.0, 2.0],
'Bin 1': [2.0, 3.0],
'Bin 2': [3.0, 4.0],
'Bin 3': [4.0, 6.0],
'Bin 4': [6.0, 10000000000]},
'value_name': 'n1_group_Bin'},
'n2': {'bin': {'Bin 0': [-9999.0, 2.0],
'Bin 1': [2.0, 4.0],
'Bin 2': [4.0, 6.0],
'Bin 3': [6.0, 8.0],
'Bin 4': [8.0, 10000000000]},
'value_name': 'n2_group_Bin'},
'n3': {'bin': {'Bin 0': [-9999.0, 2.0],
'Bin 1': [2.0, 4.0],
'Bin 2': [4.0, 6.0],
'Bin 3': [6.0, 8.0],
'Bin 4': [8.0, 10000000000]},
'value_name': 'n3_group_Bin'},
'n4': {'bin': {'Bin 0': [-9999.0, 0.0],
'Bin 1': [0.0, 4.0],
'Bin 2': [4.0, 6.0],
'Bin 3': [6.0, 10000000000]},
'value_name': 'n4_group_Bin'},
'n5': {'bin': {'Bin 0': [-9999.0, 4.0], 'Bin 1': [4.0, 10000000000]},
'value_name': 'n5_group_Bin'},
'n6': {'bin': {'Bin 0': [-9999.0, 1.0],
'Bin 1': [1.0, 23.0],
'Bin 2': [23.0, 10000000000]},
'value_name': 'n6_group_Bin'},
'n7': {'bin': {'Bin 0': [-9999.0, 4.0],
'Bin 1': [4.0, 7.0],
'Bin 2': [7.0, 10.0],
'Bin 3': [10.0, 14.0],
'Bin 4': [14.0, 10000000000]},
'value_name': 'n7_group_Bin'},
'n8': {'bin': {'Bin 0': [-9999.0, 5.0], 'Bin 1': [5.0, 10000000000]},
'value_name': 'n8_group_Bin'},
'n9': {'bin': {'Bin 0': [-9999.0, 3.0],
'Bin 1': [3.0, 5.0],
'Bin 2': [5.0, 7.0],
'Bin 3': [7.0, 10.0],
'Bin 4': [10.0, 10000000000]},
'value_name': 'n9_group_Bin'},
'n10': {'bin': {'Bin 0': [-9999.0, 3.0],
'Bin 1': [3.0, 8.0],
'Bin 2': [8.0, 10.0],
'Bin 3': [10.0, 19.0],
'Bin 4': [19.0, 10000000000]},
'value_name': 'n10_group_Bin'},
'n12': {'bin': {'Bin 0': [-9999.0, 0.0], 'Bin 1': [0.0, 10000000000]},
'value_name': 'n12_group_Bin'},
'n13': {'bin': {'Bin 0': [-9999.0, 0.0], 'Bin 1': [0.0, 10000000000]},
'value_name': 'n13_group_Bin'},
'n14': {'bin': {'Bin 0': [-9999.0, 0.0],
'Bin 1': [0.0, 1.0],
'Bin 2': [1.0, 2.0],
'Bin 3': [2.0, 3.0],
'Bin 4': [3.0, 10000000000]},
'value_name': 'n14_group_Bin'}},
'var_bin_list': ['loanAmnt_group_Bin',
'interestRate_group_Bin',
'installment_group_Bin',
'employmentTitle_group_Bin',
'homeOwnership_group_Bin',
'annualIncome_group_Bin',
'purpose_group_Bin',
'postCode_group_Bin',
'regionCode_group_Bin',
'dti_group_Bin',
'delinquency_2years_group_Bin',
'ficoRangeLow_group_Bin',
'ficoRangeHigh_group_Bin',
'openAcc_group_Bin',
'pubRec_group_Bin',
'pubRecBankruptcies_group_Bin',
'revolBal_group_Bin',
'revolUtil_group_Bin',
'totalAcc_group_Bin',
'title_group_Bin',
'n0_group_Bin',
'n1_group_Bin',
'n2_group_Bin',
'n3_group_Bin',
'n4_group_Bin',
'n5_group_Bin',
'n6_group_Bin',
'n7_group_Bin',
'n8_group_Bin',
'n9_group_Bin',
'n10_group_Bin',
'n12_group_Bin',
'n13_group_Bin',
'n14_group_Bin',
'term',
'verificationStatus',
'initialListStatus',
'applicationType',
'policyCode',
'n11'],
'iv_dict': {'term': {'iv': 0.17475272449362428,
'woe': {3: -0.2703191679368651, 5: 0.6559694111951718}},
'verificationStatus': {'iv': 0.053778743185793496,
'woe': {0: -0.36484279075332327,
1: 0.06141693489701235,
2: 0.22266031298696004}},
'initialListStatus': {'iv': 0.00038571827775202977,
'woe': {0: 0.01649700561049484, 1: -0.023381860067453963}},
'applicationType': {'iv': 0.001984746493023151,
'woe': {0: -0.006543082114183424, 1: 0.3033852042307132}},
'policyCode': {'iv': 0.0, 'woe': {1.0: 0.0}},
'n11': {'iv': 5.669110692040425e-05,
'woe': {0.0: -0.0013462832120869654,
1.0: 0.0003796115927952374,
2.0: -1.321366999272626,
4.0: -10.126261596374201}},
'loanAmnt_group_Bin': {'iv': 0.03343117392609739,
'woe': {'Bin 0': -0.25565091759789366,
'Bin 1': -0.10181829737829946,
'Bin 2': 0.048523004375282064,
'Bin 3': 0.15719425693473638,
'Bin 4': 0.2554982959104215}},
'interestRate_group_Bin': {'iv': 0.43944475156239415,
'woe': {'Bin 0': -1.4336529379346452,
'Bin 1': -0.47629575281922726,
'Bin 2': 0.1260287233070282,
'Bin 3': 0.638424679929549,
'Bin 4': 1.1369940841694457}},
'installment_group_Bin': {'iv': 0.027688805426894836,
'woe': {'Bin 0': -0.29335432895221053,
'Bin 1': -0.02248530913230762,
'Bin 2': 0.11333264072431821}},
'employmentTitle_group_Bin': {'iv': 0.011673058473026044,
'woe': {'Bin 0': 0.19241479983416104,
'Bin 1': 0.01650461957193068,
'Bin 2': -0.18949121362830715}},
'homeOwnership_group_Bin': {'iv': 0.0002916379612195541,
'woe': {'Bin 0': -0.00598818429937241,
'Bin 1': 0.04866764184572045,
'Bin 2': 0.0582736696198456}},
'annualIncome_group_Bin': {'iv': 0.028734272942132155,
'woe': {'Bin 0': 0.20550314786386936,
'Bin 1': 0.1013731532894062,
'Bin 2': -0.029439154506365764,
'Bin 3': -0.19611334622465693,
'Bin 4': -0.3341722425856623}},
'purpose_group_Bin': {'iv': 0.007398348262121109,
'woe': {'Bin 0': 0.06057157985102101, 'Bin 1': -0.12221757368584851}},
'postCode_group_Bin': {'iv': 0.0007570488920220754,
'woe': {'Bin 0': -0.10132004266079044,
'Bin 1': -0.005811282866672897,
'Bin 2': 0.02460536621769406}},
'regionCode_group_Bin': {'iv': 0.0003170244903853317,
'woe': {'Bin 0': 0.010672734282877688, 'Bin 1': -0.029704933969456917}},
'dti_group_Bin': {'iv': 0.06736335316502741,
'woe': {'Bin 0': -0.2732685235815132,
'Bin 1': -0.03476878794313418,
'Bin 2': 0.13763524255784043,
'Bin 3': 0.28987300233823476,
'Bin 4': 0.4903505432780388}},
'delinquency_2years_group_Bin': {'iv': 0.0022623712300652897,
'woe': {'Bin 0': -0.012708363930036529,
'Bin 1': 0.1475062082023903,
'Bin 2': 0.20801887412532466}},
'ficoRangeLow_group_Bin': {'iv': 0.12055544422234395,
'woe': {'Bin 0': 0.28837473031500915,
'Bin 1': -0.008500511432484521,
'Bin 2': -0.30104870120279137,
'Bin 3': -0.5875041637976371,
'Bin 4': -1.0326430545002714}},
'ficoRangeHigh_group_Bin': {'iv': 0.12055544422234395,
'woe': {'Bin 0': 0.28837473031500915,
'Bin 1': -0.008500511432484521,
'Bin 2': -0.30104870120279137,
'Bin 3': -0.5875041637976371,
'Bin 4': -1.0326430545002714}},
'openAcc_group_Bin': {'iv': 0.004306029382523432,
'woe': {'Bin 0': -0.07821428563153432,
'Bin 1': -0.019151061460234813,
'Bin 2': 0.034864790449546074,
'Bin 3': 0.10529612262224165,
'Bin 4': 0.17443385861739738}},
'pubRec_group_Bin': {'iv': 0.0012341393260821526,
'woe': {'Bin 0': -0.0061018199179689245, 'Bin 1': 0.20227836670835725}},
'pubRecBankruptcies_group_Bin': {'iv': 0.004386867849714986,
'woe': {'Bin 0': -0.025551658775713103,
'Bin 1': 0.16462934316291766,
'Bin 2': 0.2489311629377492}},
'revolBal_group_Bin': {'iv': 0.003941432411932272,
'woe': {'Bin 0': 0.025043463019543088,
'Bin 1': -0.057776412254312,
'Bin 2': -0.2522823282700653}},
'revolUtil_group_Bin': {'iv': 0.02315807262983133,
'woe': {'Bin 0': -0.34972229902847746,
'Bin 1': -0.15293900491846227,
'Bin 2': 0.012278839217558546,
'Bin 3': 0.09393480729600885,
'Bin 4': 0.16029349283335198}},
'totalAcc_group_Bin': {'iv': 0.001898851737310343,
'woe': {'Bin 0': 0.06652798510153149,
'Bin 1': 0.0063347829562498655,
'Bin 2': -0.022235005488180352,
'Bin 3': -0.04113034955049715}},
'title_group_Bin': {'iv': 0.021017346706830586,
'woe': {'Bin 0': 0.12026055634191922,
'Bin 1': -0.06297069710648713,
'Bin 2': -0.29878201272196026}},
'n0_group_Bin': {'iv': 0.0038659161878576197,
'woe': {'Bin 0': -0.03432653353796549, 'Bin 1': 0.11265809331135226}},
'n1_group_Bin': {'iv': 0.01251701221843841,
'woe': {'Bin 0': -0.11118731349483088,
'Bin 1': -0.030385344572322486,
'Bin 2': 0.024394768158194042,
'Bin 3': 0.10070490376937744,
'Bin 4': 0.24324257832089885}},
'n2_group_Bin': {'iv': 0.03182485856762544,
'woe': {'Bin 0': -0.25234529542718825,
'Bin 1': -0.12039210786755326,
'Bin 2': 0.01654240445119373,
'Bin 3': 0.12840398734442102,
'Bin 4': 0.30175137102433}},
'n3_group_Bin': {'iv': 0.03182485856762544,
'woe': {'Bin 0': -0.25234529542718825,
'Bin 1': -0.12039210786755326,
'Bin 2': 0.01654240445119373,
'Bin 3': 0.12840398734442102,
'Bin 4': 0.30175137102433}},
'n4_group_Bin': {'iv': 0.005267251015654909,
'woe': {'Bin 0': -0.28953419649589196,
'Bin 1': -0.011632803273824087,
'Bin 2': 0.016095261458987927,
'Bin 3': 0.07691228812277608}},
'n5_group_Bin': {'iv': 0.0002521958240056829,
'woe': {'Bin 0': 0.025796860662542026, 'Bin 1': -0.009776426926970056}},
'n6_group_Bin': {'iv': 0.0013155150687629844,
'woe': {'Bin 0': -0.07315867586330163,
'Bin 1': 0.005254489185691692,
'Bin 2': 0.11321926249888056}},
'n7_group_Bin': {'iv': 0.009545069117979268,
'woe': {'Bin 0': -0.14454735339316377,
'Bin 1': -0.024874826581771457,
'Bin 2': 0.03959338275756879,
'Bin 3': 0.08367884371104235,
'Bin 4': 0.18950818931946098}},
'n8_group_Bin': {'iv': 0.0006247355335945231,
'woe': {'Bin 0': -0.06522263650073688, 'Bin 1': 0.009579006538587252}},
'n9_group_Bin': {'iv': 0.030937050220452105,
'woe': {'Bin 0': -0.20889047134965136,
'Bin 1': -0.04280319854690665,
'Bin 2': 0.07380942227136413,
'Bin 3': 0.19161748950791213,
'Bin 4': 0.3782251363238346}},
'n10_group_Bin': {'iv': 0.008253500739303452,
'woe': {'Bin 0': -0.29328765643532173,
'Bin 1': -0.06153347064891405,
'Bin 2': -0.0017968871206732596,
'Bin 3': 0.047852298082086936,
'Bin 4': 0.1469804159216567}},
'n12_group_Bin': {'iv': 8.313258112458898e-05,
'woe': {'Bin 0': -0.0005117920937828983, 'Bin 1': 0.16243538529117849}},
'n13_group_Bin': {'iv': 0.0017296224617373412,
'woe': {'Bin 0': -0.010273646500446446, 'Bin 1': 0.16837952088281963}},
'n14_group_Bin': {'iv': 0.05013255836162971,
'woe': {'Bin 0': -0.31114470867320426,
'Bin 1': -0.15108234506634566,
'Bin 2': 0.0032456128370830016,
'Bin 3': 0.15067931120695519,
'Bin 4': 0.33510301297940864}}}}result：最终的结果，长这个样子：
{'model_summary': <class 'statsmodels.iolib.summary.Summary'>
"""
Logit Regression Results
==============================================================================
Dep. Variable: isDefault No. Observations: 560000
Model: Logit Df Residuals: 559984
Method: MLE Df Model: 15
Date: Thu, 04 Mar 2021 Pseudo R-squ.: 0.08853
Time: 14:28:07 Log-Likelihood: -2.5537e+05
converged: True LL-Null: -2.8018e+05
Covariance Type: nonrobust LLR p-value: 0.000
===========================================================================================
coef std err z P>|z| [0.025 0.975]
-------------------------------------------------------------------------------------------
term 0.4856 0.010 48.088 0.000 0.466 0.505
verificationStatus 0.2431 0.016 15.175 0.000 0.212 0.275
loanAmnt_group_Bin 0.2776 0.041 6.815 0.000 0.198 0.357
interestRate_group_Bin 0.1153 0.016 7.022 0.000 0.083 0.148
installment_group_Bin 0.2646 0.039 6.728 0.000 0.187 0.342
annualIncome_group_Bin 1.0481 0.025 42.018 0.000 0.999 1.097
dti_group_Bin 0.3618 0.014 25.125 0.000 0.334 0.390
ficoRangeLow_group_Bin 0.1894 nan nan nan nan nan
ficoRangeHigh_group_Bin 0.1894 nan nan nan nan nan
revolUtil_group_Bin -0.2620 0.028 -9.307 0.000 -0.317 -0.207
title_group_Bin 0.4616 0.025 18.401 0.000 0.412 0.511
n2_group_Bin 0.0472 nan nan nan nan nan
n3_group_Bin 0.0472 nan nan nan nan nan
n9_group_Bin 0.2948 0.004 74.254 0.000 0.287 0.303
n14_group_Bin 0.3586 0.018 20.174 0.000 0.324 0.393
grade_group_Bin 0.3026 0.017 17.876 0.000 0.269 0.336
subGrade_group_Bin 0.3198 0.014 22.258 0.000 0.292 0.348
intercept -1.3887 0.004 -387.449 0.000 -1.396 -1.382
===========================================================================================
""",
'ks': 0.29957454898668295,
'auc': 0.7067054639204269,
'val_ks': 0.29633281875616696,
'val_auc': 0.7043781162452405}select_value_iv：一个函数来的，不用管啦
train_data：训练集
val_data：测试集
y_name：你输入的y值的变量名