{
    "info": {
        "author": "Pan Fu",
        "author_email": "panfu0207@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "\n\n```python\nimport pandas as pd\nimport numpy as np\nimport scoring as sc\n\nfrom sklearn.model_selection import train_test_split as tts\nfrom sklearn.linear_model import LogisticRegression as lr\nimport sklearn.metrics as metrics\n```\n\n\n```python\ndf=pd.read_csv('gc.csv')\nvardict=pd.read_csv('dict.csv')\ndf['Risk']=df['Risk'].apply(lambda x: 1 if x=='bad' else 0)\ndf=sc.renameCols(df,vardict,False)\nlabel,disc,cont=sc.getVarTypes(vardict)\n# sc.discSummary(df)\n\n# ### No row needs to be removed from this example in this stage ###\n# vardict.loc[vardict['new'].isin(['Age','Sex']),'isDel']=1\n# df,vardict=cl.delFromVardict(df,vardict)\n```\n\n\n```python\ndf1=sc.binData(df,vardict)\n```\n\n    #########################################\n    ####It's using Chi-Merge algorithm...####\n    #########################################\n\n    Doing continous feature: Age\n\n    Doing continous feature: Credit amount\n    Equal Depth Binning is required, number of bins is: 100\n\n    Doing continous feature: Duration\n\n    Doing discrete feature: Sex\n\n    Doing discrete feature: Job\n\n    Doing discrete feature: Housing\n\n    Doing discrete feature: Saving accounts\n\n    Doing discrete feature: Checking account\n\n    Doing discrete feature: Purpose\n\n    Finished\n\n\n\n```python\nbidict=sc.getBiDict(df1,label)\n```\n\n\n```python\nbidict['Credit amount']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Credit amount</th>\n      <th>total</th>\n      <th>good</th>\n      <th>bad</th>\n      <th>totalDist</th>\n      <th>goodDist</th>\n      <th>badDist</th>\n      <th>goodRate</th>\n      <th>badRate</th>\n      <th>woe</th>\n      <th>iv</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>(-inf, 1282.0]</td>\n      <td>211</td>\n      <td>144</td>\n      <td>67</td>\n      <td>0.211</td>\n      <td>0.223</td>\n      <td>0.206</td>\n      <td>0.682</td>\n      <td>0.318</td>\n      <td>-0.082</td>\n      <td>0.001</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>(1282.0, 3446.32]</td>\n      <td>469</td>\n      <td>352</td>\n      <td>117</td>\n      <td>0.469</td>\n      <td>0.390</td>\n      <td>0.503</td>\n      <td>0.751</td>\n      <td>0.249</td>\n      <td>0.254</td>\n      <td>0.029</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>(3446.32, 3913.26]</td>\n      <td>60</td>\n      <td>55</td>\n      <td>5</td>\n      <td>0.060</td>\n      <td>0.017</td>\n      <td>0.079</td>\n      <td>0.917</td>\n      <td>0.083</td>\n      <td>1.551</td>\n      <td>0.096</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>(3913.26, inf]</td>\n      <td>260</td>\n      <td>149</td>\n      <td>111</td>\n      <td>0.260</td>\n      <td>0.370</td>\n      <td>0.213</td>\n      <td>0.573</td>\n      <td>0.427</td>\n      <td>-0.553</td>\n      <td>0.087</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\n# modified credit amount\nsc.bivariate(pd.DataFrame({'y':df['y'],\n                           'Credit amount':sc.manuallyBin(df,\n                                                          'Credit amount',\n                                                          'cont',\n                                                          [-np.inf,1300,3500,4000,np.inf])}\n                         ),'Credit amount','y')[0]\ndf1['Credit amount']=sc.manuallyBin(df,'Credit amount','cont',[-np.inf,1300,3500,4000,np.inf])\n```\n\n\n```python\nbidict=sc.getBiDict(df1,label)\nivtable=sc.ivTable(bidict)\n```\n\n\n```python\ndf1,vardict,bidict=sc.featureFilter(df1,vardict,bidict,ivtable)\n```\n\n\n```python\ndf=sc.mapWOE(df1,bidict,label)\n```\n\n\n```python\n### Modelling ###\n#################\ntrainx,testx,trainy,testy=tts(df.iloc[:,1:],df[label],test_size=0.3)\nm=lr(penalty='l1', C=0.9, solver='saga', n_jobs=-1)\nm.fit(trainx,trainy)\npred=m.predict(testx)\npred_prob=m.predict_proba(testx)[:,1]\n\n# \u93cc\u30e7\u6e45\u5a34\u5b2d\u762f\u7f01\u64b4\u7049\ncm=metrics.confusion_matrix(testy, pred)\nprint('**Precision is:',(cm[0][0]+cm[1][1])/(sum(cm[0])+sum(cm[1])))\nprint('\\n**Confusion matrix is:\\n',cm)\nprint('\\n**Classification report is:\\n',metrics.classification_report(testy, pred))\n```\n\n    **Precision is: 0.7233333333333334\n\n    **Confusion matrix is:\n     [[179  18]\n     [ 65  38]]\n\n    **Classification report is:\n                   precision    recall  f1-score   support\n\n               0       0.73      0.91      0.81       197\n               1       0.68      0.37      0.48       103\n\n       micro avg       0.72      0.72      0.72       300\n       macro avg       0.71      0.64      0.64       300\n    weighted avg       0.71      0.72      0.70       300\n\n\n\n\n```python\n### Evaluation ###\n##################\nsc.plotROC(testy,pred_prob)\nsc.plotKS(testy,pred_prob)\nsc.plotCM(metrics.confusion_matrix(testy,pred), classes=df[label].unique(),\n          title='Confusion matrix, without normalization')\n```\n\n\n![png](output_10_0.png)\n\n\n\n![png](output_10_1.png)\n\n\n    Confusion matrix, without normalization\n    [[179  18]\n     [ 65  38]]\n\n\n\n![png](output_10_3.png)\n\n\n\n```python\n### Scoring ###\n###############\nscored,basescore=sc.scoring(trainx.reset_index(drop=True),\n                            trainy.reset_index(drop=True),\n                            'y',\n                         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