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/toadgithub.com
文档:Welcome to toad’s documentation!toad.readthedocs.io
演示:Basic Tutorial for Toadtoad.readthedocs.io
whl下载地址:Links for toadpypi.org
最后,TOAD是个好开源工具,希望能被大家看见。
感谢阅读 。