python风控工具_Toad | Pyhon评分卡工具轻松实现风控模型开发

前言

介绍一个超级、超级、超级强大的评分卡模型开发库 。很多从业者都知道信贷风控界有一个库叫做Scorecardpy。作者是谢士晨博士。今天为读者介绍另一个同样用于开发评分卡的库，名为toad。

⭐️toad是由厚本金融风控团队内部孵化，后开源并坚持维护的标准化评分卡库。其功能全面、性能稳健、运行速度快、问题反馈后维护迅速、深受同行喜爱。如果有些小伙伴没有一些标准化的信用评分开发工具或者企业级的定制化脚本，toad应该会极大的节省大家的时间。

本文以一个不能分享的数据为例，演示一下toad包的功能，同时为读者讲解一下评分卡的构建方法。没有真实数据又对此感兴趣的胖友，其实可以随便从网上找个二分类项目，或者使用一些风控竞赛的开源数据。

正文

⭐️首先加载本文所需的库。

import pandas as pd

from sklearn.metrics import roc_auc_score,roc_curve,auc

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import GridSearchCV as gscv

from sklearn.neighbors import KNeighborsClassifier

import numpy as np

import glob

import math

import xgboost as xgb

import toad

⭐️加载数据。

#加载数据path = "D:/风控模型/data/"

data_all = pd.read_csv(path+"data.txt",engine='python',index_col=False)

data_all_woe = pd.read_csv(path+"ccard_all_woe.txt",engine='python',index_col=False)

#指定不参与训练列名

ex_lis = ['uid','obs_mth','ovd_dt','samp_type','weight',

'af30_status','submit_time','bad_ind']

#参与训练列名

ft_lis = list(data_all.columns)

for i in ex_lis:

ft_lis.remove(i)

⭐️划分训练集与测试集。

#训练集与跨时间验证集合

dev = data_all[(data_all['samp_type'] == 'dev') |

(data_all['samp_type'] == 'val') |

(data_all['samp_type'] == 'off1') ]

off = data_all[data_all['samp_type'] == 'off2']

⭐️EDA，探索性数据分析 同时处理数值型和字符型。

a = toad.detector.detect(data_all)

a.head(8)

⭐️特征筛选empty：缺失率上限

iv：信息量

corr：相关系数大于阈值，则删除IV小的特征

return_drop：返回删除特征

exclude：不参与筛选的变量名

dev_slct1, drop_lst= toad.selection.select(dev,dev['bad_ind'], empty = 0.7,

iv = 0.02, corr = 0.7, return_drop=True, exclude=ex_lis)

print("keep:",dev_slct1.shape[1],

"drop empty:",len(drop_lst['empty']),

"drop iv:",len(drop_lst['iv']),

"drop corr:",len(drop_lst['corr']))

keep: 584

drop empty: 637

drop iv: 1961

drop corr: 2043

dev_slct2, drop_lst= toad.selection.select(dev_slct1,dev_slct1['bad_ind'], empty = 0.6,

iv = 0.02, corr = 0.7, return_drop=True, exclude=ex_lis)

print("keep:",dev_slct2.shape[1],

"drop empty:",len(drop_lst['empty']),

"drop iv:",len(drop_lst['iv']),

"drop corr:",len(drop_lst['corr']))

keep: 560

drop empty: 24

drop iv: 0

drop corr: 0

分箱，先找到分箱的阈值

分箱阈值的方法(method) 包括：'chi','dt','quantile','step','kmeans'

然后利用分箱阈值进行粗分箱。

#得到切分节点

combiner = toad.transform.Combiner()

combiner.fit(dev_slct2,dev_slct2['bad_ind'],method='chi',min_samples = 0.05,

exclude=ex_lis)

#导出箱的节点

bins = combiner.export()

#根据节点实施分箱

dev_slct3 = combiner.transform(dev_slct2)

off3 = combiner.transform(off[dev_slct2.columns])

#分箱后通过画图观察

from toad.plot import bin_plot,badrate_plot

bin_plot(dev_slct3,x='p_ovpromise_6mth',target='bad_ind')

bin_plot(off3,x='p_ovpromise_6mth',target='bad_ind')

⭐️后2箱不单调。

#查看单箱节点

bins['p_ovpromise_6mth']

[0.0, 24.0, 60.0, 100.0]

⭐️合并最后两箱。

adj_bin = {'p_ovpromise_6mth': [0.0, 24.0, 60.0]}

combiner.set_rules(adj_bin)

dev_slct3 = combiner.transform(dev_slct2)

off3 = combiner.transform(off[dev_slct2.columns])

bin_plot(dev_slct3,x='p_ovpromise_6mth',target='bad_ind')

bin_plot(off3,x='p_ovpromise_6mth',target='bad_ind')

⭐️对比不同数据集上特征的badrate图是否有交叉。

data = pd.concat([dev_slct3,off3],join='inner')

badrate_plot(data, x='samp_type', target='bad_ind', by='p_ovpromise_6mth')

⭐️没有交叉，因此该特征的分组不需要再进行合并。篇幅有限，不对所有特征的精细化调整做展示。接下来进行WOE映射。

t=toad.transform.WOETransformer()

dev_slct2_woe = t.fit_transform(dev_slct3,dev_slct3['bad_ind'], exclude=ex_lis)

off_woe = t.transform(off3[dev_slct3.columns])

data = pd.concat([dev_slct2_woe,off_woe])

⭐️通过稳定性筛选特征。计算训练集与跨时间验证集的PSI。删除PSI大于0.05的特征。

psi_df = toad.metrics.PSI(dev_slct2_woe, off_woe).sort_values(0)

psi_df = psi_df.reset_index()

psi_df = psi_df.rename(columns = {'index' : 'feature',0:'psi'})

psi005 = list(psi_df[psi_df.psi<0.05].feature)

for i in ex_lis:

if i in psi005:

pass

else:

psi005.append(i)

data = data[psi005]

dev_woe_psi = dev_slct2_woe[psi005]

off_woe_psi = off_woe[psi005]

print(data.shape)

(41199, 476)

⭐️由于分箱后变量之间的共线性会变强，通过相关性再次筛选特征。

dev_woe_psi2, drop_lst= toad.selection.select(dev_woe_psi,dev_woe_psi['bad_ind'], empty = 0.6,

iv = 0.02, corr = 0.5, return_drop=True, exclude=ex_lis)

print("keep:",dev_woe_psi2.shape[1],

"drop empty:",len(drop_lst['empty']),

"drop iv:",len(drop_lst['iv']),

"drop corr:",len(drop_lst['corr']))

keep: 85

drop empty: 0

drop iv: 56

drop corr: 335

⭐️接下来通过逐步回归进行最终的特征筛选。检验方法(criterion)：'aic'

'bic'

⭐️检验模型(estimator)：'ols': LinearRegression,

'lr': LogisticRegression,

'lasso': Lasso,

'ridge': Ridge,

dev_woe_psi_stp = toad.selection.stepwise(dev_woe_psi2,

dev_woe_psi2['bad_ind'],

exclude = ex_lis,

direction = 'both',

criterion = 'aic',

estimator = 'ols',

intercept = False)

off_woe_psi_stp = off_woe_psi[dev_woe_psi_stp.columns]

data = pd.concat([dev_woe_psi_stp,off_woe_psi_stp])

data.shape

(41199, 33)

⭐️接下来定义双向逻辑回归和检验模型XGBoost。

#定义逻辑回归

def lr_model(x,y,offx,offy,C):

model = LogisticRegression(C=C,class_weight='balanced')

model.fit(x,y)

y_pred = model.predict_proba(x)[:,1]

fpr_dev,tpr_dev,_ = roc_curve(y,y_pred)

train_ks = abs(fpr_dev - tpr_dev).max()

print('train_ks : ',train_ks)

y_pred = model.predict_proba(offx)[:,1]

fpr_off,tpr_off,_ = roc_curve(offy,y_pred)

off_ks = abs(fpr_off - tpr_off).max()

print('off_ks : ',off_ks)

from matplotlib import pyplot as plt

plt.plot(fpr_dev,tpr_dev,label = 'train')

plt.plot(fpr_off,tpr_off,label = 'off')

plt.plot([0,1],[0,1],'k--')

plt.xlabel('False positive rate')

plt.ylabel('True positive rate')

plt.title('ROC Curve')

plt.legend(loc = 'best')

plt.show()

#定义xgboost辅助判断盘牙鞥特征交叉是否有必要

def xgb_model(x,y,offx,offy):

model = xgb.XGBClassifier(learning_rate=0.05,

n_estimators=400,

max_depth=3,

class_weight='balanced',

min_child_weight=1,

subsample=1,

objective="binary:logistic",

nthread=-1,

scale_pos_weight=1,

random_state=1,

n_jobs=-1,

reg_lambda=300)

model.fit(x,y)

print('>>>>>>>>>')

y_pred = model.predict_proba(x)[:,1]

fpr_dev,tpr_dev,_ = roc_curve(y,y_pred)

train_ks = abs(fpr_dev - tpr_dev).max()

print('train_ks : ',train_ks)

y_pred = model.predict_proba(offx)[:,1]

fpr_off,tpr_off,_ = roc_curve(offy,y_pred)

off_ks = abs(fpr_off - tpr_off).max()

print('off_ks : ',off_ks)

from matplotlib import pyplot as plt

plt.plot(fpr_dev,tpr_dev,label = 'train')

plt.plot(fpr_off,tpr_off,label = 'off')

plt.plot([0,1],[0,1],'k--')

plt.xlabel('False positive rate')

plt.ylabel('True positive rate')

plt.title('ROC Curve')

plt.legend(loc = 'best')

plt.show()

#模型训练

def c_train(data,dep='bg_result_compensate',exclude=None):

from sklearn.preprocessing import StandardScaler

std_scaler = StandardScaler()

#变量名

lis = list(data.columns)

for i in exclude:

lis.remove(i)

data[lis] = std_scaler.fit_transform(data[lis])

devv = data[(data['samp_type']=='dev') | (data['samp_type']=='val')]

offf = data[(data['samp_type']=='off1') | (data['samp_type']=='off2') ]

x,y = devv[lis],devv[dep]

offx,offy = offf[lis],offf[dep]

#逻辑回归正向

lr_model(x,y,offx,offy,0.1)

#逻辑回归反向

lr_model(offx,offy,x,y,0.1)

#XGBoost正向

xgb_model(x,y,offx,offy)

#XGBoost反向

xgb_model(offx,offy,x,y)

⭐️在特征精细化分箱后，xgboost模型的KS明显高于LR，则特征交叉是有必要的。需要返回特征工程过程进行特征交叉衍生。两模型KS接近代表特征交叉对模型没有明显提升。反向模型KS代表模型最高可能达到的结果。如果反向训练集效果较差，说明跨时间验证集本身分布较为特殊，应当重新划分数据。

c_train(data,dep='bad_ind',exclude=ex_lis)

⭐️评分卡模型训练。

#模型训练

dep = 'bad_ind'

lis = list(data.columns)

for i in ex_lis:

lis.remove(i)

devv = data[(data['samp_type']=='dev') | (data['samp_type']=='val')]

offf = data[(data['samp_type']=='off1') | (data['samp_type']=='off2') ]

x,y = devv[lis],devv[dep]

offx,offy = offf[lis],offf[dep]

lr = LogisticRegression()

lr.fit(x,y)

⭐️分别计算：F1分数 KS值 AUC值。

from toad.metrics import KS, F1, AUC

prob_dev = lr.predict_proba(x)[:,1]

print('训练集')

print('F1:', F1(prob_dev,y))

print('KS:', KS(prob_dev,y))

print('AUC:', AUC(prob_dev,y))

prob_off = lr.predict_proba(offx)[:,1]

print('跨时间')

print('F1:', F1(prob_off,offy))

print('KS:', KS(prob_off,offy))

print('AUC:', AUC(prob_off,offy))

⭐️训练集

F1: 0.30815569972196477

KS: 0.2819389063516508

AUC: 0.6908879633467695

⭐️跨时间

F1: 0.2848354792560801

KS: 0.23181102640650808

AUC: 0.6522823050763138

⭐️计算模型PSI和变量PSI，两个角度衡量稳定性。

print('模型PSI:',toad.metrics.PSI(prob_dev,prob_off))

print('特征PSI:','\n',toad.metrics.PSI(x,offx).sort_values(0))

模型PSI: 0.022260098554531284

特征PSI:

⭐️生产模型KS报告。

off_bucket = toad.metrics.KS_bucket(prob_off,offy,bucket=10,method='quantile')

off_bucket

⭐️生产评分卡。支持传入所有的模型参数，以及Fico分数校准的基础分与pdo(point of double odds)，我一直管pdo叫步长...orz。

from toad.scorecard import ScoreCard

card = ScoreCard(combiner = combiner, transer = t,class_weight = 'balanced',C=0.1,base_score = 600,base_odds = 35 ,pdo = 60,rate = 2)

card.fit(x,y)

final_card = card.export(to_frame = True)

final_card.head(8)

github主页:amphibian-dev/toad​github.com

文档：Welcome to toad’s documentation!​toad.readthedocs.io

演示：Basic Tutorial for Toad​toad.readthedocs.io

whl下载地址：Links for toad​pypi.org

最后，TOAD是个好开源工具，希望能被大家看见。

感谢阅读 。