ValueError：未知标签类型：使用逻辑回归时“连续”

Question

我正在使用逻辑回归来绘制 ROC。我正在使用此代码来提取数据。

Diabetes=pd.read_csv('datasource/ScoringDatasheet.csv', sep=';')

然后我像这样使用iloc。

inputData=Diabetes.iloc[:,:60]
outputData=Diabetes.iloc[:,60]  

然后我使用logistical regression分析数据并绘制ROC

from sklearn.linear_model import LogisticRegression
logit1=LogisticRegression()
logit1.fit(inputData,outputData)

logit1.score(inputData,outputData)



np.mean(logit1.predict(inputData)==outputData)

trueInput=Diabetes.ix[Diabetes['Outcome']==1].iloc[:,:62]
trueOutput=Diabetes.ix[Diabetes['Outcome']==1].iloc[:,62]

np.mean(logit1.predict(trueInput)==trueOutput)

falseInput=Diabetes.ix[Diabetes['Outcome']==0].iloc[:,:62]
falseOutput=Diabetes.ix[Diabetes['Outcome']==0].iloc[:,62]

np.mean(logit1.predict(falseInput)==falseOutput)





from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score
confusion_matrix(logit1.predict(inputData),outputData)


fpr, tpr,_=roc_curve(logit1.predict(inputData),outputData,drop_intermediate=False)
import matplotlib.pyplot as plt
plt.figure()
plt.plot(fpr, tpr, color='red', lw=2, label='ROC curve')
plt.plot([0, 1], [0, 1], color='blue', lw=2, linestyle='--')
plt.xlabel('False Positive ')
plt.ylabel('True Positive ')
plt.title('ROC curve')
plt.show()


roc_auc_score(logit1.predict(inputData),outputData)

coef_DF=pd.DataFrame(data={'Variable':list(inputData),
'value':(logit1.coef_[0])})

coef_DF_standardised=pd.DataFrame(data={'Variable':list(inputData),
'value':(logit1.coef_[0])*np.std(inputData,axis=0)/np.std(outputData)})

import matplotlib.pyplot as plt
plt.figure()
plt.scatter(inputData.iloc[:,1],inputData.iloc[:,5],c=logit1.predict_proba(inputData)[:,1],alpha=0.4)
plt.xlabel('Glucose level ')
plt.ylabel('BMI ')
plt.show()

plt.figure()
plt.scatter(inputData.iloc[:,1],inputData.iloc[:,5],c=outputData,alpha=0.4)
plt.xlabel('Glucose level ')
plt.ylabel('BMI ')
plt.show()  

但是当我运行我的代码时，我得到以下错误：

Traceback (most recent call last):                                                                                                                                                                                                             File "index.py", line 13, in <module>                                                                                                                                                                                                          logit1.fit(inputData,outputData)                                                                                                                                                                                                           File "C:\Users\kulkaa\AppData\Local\Programs\Python\Python37-32\lib\site-packages\sklearn\linear_model\logistic.py", line 1221, in fit                                                                                                         check_classification_targets(y)                                                                                                                                                                                                            File "C:\Users\kulkaa\AppData\Local\Programs\Python\Python37-32\lib\site-packages\sklearn\utils\multiclass.py", line 171, in check_classification_targets                                                                                      raise ValueError("Unknown label type: %r" % y_type)                                                                                                                                                                                      ValueError: Unknown label type: 'continuous'  

根据this link，如果我使用分类器，我应该将floats 转换为categorical values。但是我在这里使用回归，我该如何解决这个错误？
我正在使用的部分数据集如下所示：

Pat_ID  Demo1   Demo2   Demo3   Demo4   Demo5   Demo6   DisHis1 DisHis1Times    DisHis2 DisHis2Times    DisHis3 DisHis3Times    DisHis4 DisHis5 DisHis6 DisHis7 DisStage1   DisStage2   LungFun1    LungFun2    LungFun3    LungFun4    LungFun5    LungFun6    LungFun7    LungFun8    LungFun9    LungFun10   LungFun11   LungFun12   LungFun13   LungFun14   LungFun15   LungFun16   LungFun17   LungFun18   LungFun19   LungFun20   Dis1    Dis1Treat   Dis2    Dis2Times   Dis3    Dis3Times   Dis4    Dis4Treat   Dis5    Dis5Treat   Dis6    Dis6Treat   Dis7    RespQues1   ResQues1a   ResQues1b   ResQues1c   ResQues2a   SmokHis1    SmokHis2    SmokHis3    SmokHis4
6   0   0.430159833 0.596541787 0.323296661 0   0.867768595 0   0   0   0   0   0   0   0   0   0   0.8 0.714285714 0.447443182 0.280725319 0.392405063 0.315347722 0.442765731 0.35344 0.306497788 0.078249895 0.230895645 0   0.175430575 0.776595745 0.194322248 0.123935854 0.792696843 0.873987854 0.803933254 0.528064786 1   0.1 0   0   0   0   0.333333333 0.15    0   0   0   0   0.333333333 1   0   0.273565574 0.1074  0.7282  0.0469  0.3 0.082352941 0.085237258 0.724137931 0.145833333
9   0   0.218902015 0.484149856 0.177957923 0   0.225895317 0   0   0   0   0   0   0   0   0   0   0.6 0.142857143 0.899147727 0.441235729 0.620253165 0.708333333 0.69303235  0.55904 0.532922703 0.263357173 0.718707204 0.729159016 0.65096784  0.64893617  0.385594463 0.234804989 0.613921643 0.409665992 0.483313468 0.115610165 0   0.5 0   0   0   0   1   0   1   0   1   0   0.333333333 1   0   0.456557377 0.1791  0.7896  0.3212  0.2 0.176470588 0.144991213 0.620689655 0
15  0   0.628908965 0.433717579 0.594093804 1   0.363636364 0   0   0   0   0   0   0   0   0   0   0   0.142857143 0.970170455 0.396910678 0.746835443 0.575239808 0.478848205 0.36944 0.565368266 0.309002945 0.569433032 0.463643041 0.425392471 0.787234043 0.427004516 0.290833498 0.652339293 0.484311741 0.511323004 0.138788048 0   0.6 0   0   0   0   1   0   0   0   0   0   0.333333333 1   0   0.396413934 0.2596  0.8032  0.1836  0.2 0.058823529 0.052724077 0.637931034 0.0625
25  1   0.236275191 0.268011527 0.280254777 0   0.388429752 0   0   0   0   0   0   0   0   0   0   0.6 0   0.721590909 0.39758227  0.53164557  0.363309353 0.394063278 0.31088 0.224863364 0.096339924 0.321007943 0.351817848 0.361377839 0.521276596 0.213208986 0.059196199 0.728413846 0.497975709 0.62932062  0.147165596 1   0.6 0   0   1   0   0.333333333 0.05    0   0   0   0   0   0   0   0   0   0   0   0.1 0.176470588 0.118629174 0.517241379 0.104166667
27  1   0.397498263 0.327089337 0.425786528 0   0.063360882 0   0   0   0   0   0   0   0   0   0   0   0   0.950284091 0.358629953 0.82278481  0.580035971 0.462851049 0.33696 0.40426824  0.508834666 0.594631608 0.491737055 0.431489102 0.819148936 0.372514517 0.373589388 0.623430962 0.422823887 0.489272944 0.114493158 0   0.9 0   0   0.333333333 0.020833333 0.333333333 0.05    0   0   0   0   0   0   0   0.058709016 0   0.1847  0   0   0.176470588 0.087873462 0.396551724 0.0625
28  1   0.510771369 0.452449568 0.468249373 0   0.027548209 0   0   0   0   0   0   1   0   0   0   0   0.142857143 0.928977273 0.392209537 0.746835443 0.648081535 0.547813722 0.4232  0.46777132  0.379259571 0.675431389 0.581894969 0.502362445 0.79787234  0.351398909 0.388437933 0.597565614 0.441548583 0.472586412 0.122591455 0   0.9 0   0   0   0   0   0   0   0   0   0   0   0   1   0.480840164 0.5239  0.5354  0.4146  0.1 0.411764706 0.156414763 0.272413793 0
36  1   0.385684503 0.341498559 0.405134144 0   0.195592287 0   0   0   0   0   0   0   0   0   0   0.6 0.142857143 0.737215909 0.36937542  0.594936709 0.43735012  0.455563455 0.33952 0.259651254 0.165124106 0.447274719 0.432611091 0.384545039 0.691489362 0.212387823 0.159176401 0.647014074 0.504807692 0.511918951 0.148841106 0   0.8 1   0   0.333333333 0.041666667 0.333333333 0.1 0.333333333 0   1   0   1   0   1   0.453790984 0.5014  0.5946  0.3379  0.2 0.117647059 0.077768014 0.515517241 0

Answer 1

sklearn.linear_model.LogisticRegression 是根据http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html 的分类器（不是回归器）。