建立信用风险分析记分卡
scoring的Python项目详细描述
importpandasaspdimportnumpyasnpimportscoringasscfromsklearn.model_selectionimporttrain_test_splitasttsfromsklearn.linear_modelimportLogisticRegressionaslrimportsklearn.metricsasmetrics
df=pd.read_csv('gc.csv')vardict=pd.read_csv('dict.csv')df['Risk']=df['Risk'].apply(lambdax:1ifx=='bad'else0)df=sc.renameCols(df,vardict,False)label,disc,cont=sc.getVarTypes(vardict)# sc.discSummary(df)# ### No row needs to be removed from this example in this stage #### vardict.loc[vardict['new'].isin(['Age','Sex']),'isDel']=1# df,vardict=cl.delFromVardict(df,vardict)
df1=sc.binData(df,vardict)
#########################################
####It's using Chi-Merge algorithm...####
#########################################
Doing continous feature: Age
Doing continous feature: Credit amount
Equal Depth Binning is required, number of bins is: 100
Doing continous feature: Duration
Doing discrete feature: Sex
Doing discrete feature: Job
Doing discrete feature: Housing
Doing discrete feature: Saving accounts
Doing discrete feature: Checking account
Doing discrete feature: Purpose
Finished
bidict=sc.getBiDict(df1,label)
bidict['Credit amount']
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| Credit amount | total | good | bad | totalDist | goodDist | badDist | goodRate | badRate | woe | iv | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | (-inf, 1282.0] | 211 | 144 | 67 | 0.211 | 0.223 | 0.206 | 0.682 | 0.318 | -0.082 | 0.001 |
| 1 | (1282.0, 3446.32] | 469 | 352 | 117 | 0.469 | 0.390 | 0.503 | 0.751 | 0.249 | 0.254 | 0.029 |
| 2 | (3446.32, 3913.26] | 60 | 55 | 5 | 0.060 | 0.017 | 0.079 | 0.917 | 0.083 | 1.551 | 0.096 |
| 3 | (3913.26, inf] | 260 | 149 | 111 | 0.260 | 0.370 | 0.213 | 0.573 | 0.427 | -0.553 | 0.087 |
# modified credit amountsc.bivariate(pd.DataFrame({'y':df['y'],'Credit amount':sc.manuallyBin(df,'Credit amount','cont',[-np.inf,1300,3500,4000,np.inf])}),'Credit amount','y')[0]df1['Credit amount']=sc.manuallyBin(df,'Credit amount','cont',[-np.inf,1300,3500,4000,np.inf])
bidict=sc.getBiDict(df1,label)ivtable=sc.ivTable(bidict)
df1,vardict,bidict=sc.featureFilter(df1,vardict,bidict,ivtable)
df=sc.mapWOE(df1,bidict,label)
### Modelling ####################trainx,testx,trainy,testy=tts(df.iloc[:,1:],df[label],test_size=0.3)m=lr(penalty='l1',C=0.9,solver='saga',n_jobs=-1)m.fit(trainx,trainy)pred=m.predict(testx)pred_prob=m.predict_proba(testx)[:,1]# 鏌ョ湅娴嬭瘯缁撴灉cm=metrics.confusion_matrix(testy,pred)print('**Precision is:',(cm[0][0]+cm[1][1])/(sum(cm[0])+sum(cm[1])))print('\n**Confusion matrix is:\n',cm)print('\n**Classification report is:\n',metrics.classification_report(testy,pred))
**Precision is: 0.7233333333333334
**Confusion matrix is:
[[179 18]
[ 65 38]]
**Classification report is:
precision recall f1-score support
0 0.73 0.91 0.81 197
1 0.68 0.37 0.48 103
micro avg 0.72 0.72 0.72 300
macro avg 0.71 0.64 0.64 300
weighted avg 0.71 0.72 0.70 300
### Evaluation #####################sc.plotROC(testy,pred_prob)sc.plotKS(testy,pred_prob)sc.plotCM(metrics.confusion_matrix(testy,pred),classes=df[label].unique(),title='Confusion matrix, without normalization')
Confusion matrix, without normalization
[[179 18]
[ 65 38]]
### Scoring ##################scored,basescore=sc.scoring(trainx.reset_index(drop=True),trainy.reset_index(drop=True),'y',m,bidict)
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