由于“完美分离错误”，无法运行逻辑回归

我是 Python 数据分析的初学者，并且在完成这项特定任务时遇到了麻烦。我进行了广泛的搜索，但无法找出问题所在。

我导入了一个文件并将其设置为数据框。清理了文件中的数据。然而，当我尝试将我的模型拟合到数据时，我得到了

检测到完美分离，结果不可用

这是代码：

from scipy import stats
import numpy as np
import pandas as pd 
import collections
import matplotlib.pyplot as plt
import statsmodels.api as sm

loansData = pd.read_csv('https://spark-   public.s3.amazonaws.com/dataanalysis/loansData.csv')

loansData = loansData.to_csv('loansData_clean.csv', header=True, index=False)

## cleaning the file
loansData['Interest.Rate'] = loansData['Interest.Rate'].map(lambda x:  round(float(x.rstrip('%')) / 100, 4))
loanlength = loansData['Loan.Length'].map(lambda x: x.strip('months'))
loansData['FICO.Range'] = loansData['FICO.Range'].map(lambda x: x.split('-'))
loansData['FICO.Range'] = loansData['FICO.Range'].map(lambda x: int(x[0]))
loansData['FICO.Score'] = loansData['FICO.Range']

#add interest rate less than column and populate
## we only care about interest rates less than 12%
loansData['IR_TF'] = pd.Series('', index=loansData.index)
loansData['IR_TF'] = loansData['Interest.Rate'].map(lambda x: True if x < 12 else False)

#create intercept column
loansData['Intercept'] = pd.Series(1.0, index=loansData.index)

# create list of ind var col names
ind_vars = ['FICO.Score', 'Amount.Requested', 'Intercept'] 

#define logistic regression
logit = sm.Logit(loansData['IR_TF'], loansData[ind_vars])

#fit the model
result = logit.fit()

#get fitted coef
coeff = result.params

print coeff

任何帮助将非常感激！

Thx, A

你有PerfectSeparationError因为你的贷款数据['IR_TF']只有一个值True（或 1）。您首先将利率从 % 转换为小数，因此您应该将 IR_TF 定义为

loansData['IR_TF'] = loansData['Interest.Rate'] < 0.12 #not 12, and you don't need .map 

然后你的回归将成功运行：

Optimization terminated successfully.
         Current function value: 0.319503
         Iterations 8
FICO.Score           0.087423
Amount.Requested    -0.000174
Intercept          -60.125045
dtype: float64

另外，我注意到各个地方都可以变得更容易阅读和/或获得一些性能改进，特别是.map可能不如矢量化计算快。以下是我的建议：

from scipy import stats
import numpy as np
import pandas as pd 
import collections
import matplotlib.pyplot as plt
import statsmodels.api as sm

loansData = pd.read_csv('https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')

## cleaning the file
loansData['Interest.Rate'] = loansData['Interest.Rate'].str.rstrip('%').astype(float).round(2) / 100.0

loanlength = loansData['Loan.Length'].str.strip('months')#.astype(int)  --> loanlength not used below

loansData['FICO.Score'] = loansData['FICO.Range'].str.split('-', expand=True)[0].astype(int)

#add interest rate less than column and populate
## we only care about interest rates less than 12%
loansData['IR_TF'] = loansData['Interest.Rate'] < 0.12

#create intercept column
loansData['Intercept'] = 1.0

# create list of ind var col names
ind_vars = ['FICO.Score', 'Amount.Requested', 'Intercept'] 

#define logistic regression
logit = sm.Logit(loansData['IR_TF'], loansData[ind_vars])

#fit the model
result = logit.fit()

#get fitted coef
coeff = result.params

#print coeff
print result.summary() #result has more information


Logit Regression Results                           
==============================================================================
Dep. Variable:                  IR_TF   No. Observations:                 2500
Model:                          Logit   Df Residuals:                     2497
Method:                           MLE   Df Model:                            2
Date:                Thu, 07 Jan 2016   Pseudo R-squ.:                  0.5243
Time:                        23:15:54   Log-Likelihood:                -798.76
converged:                       True   LL-Null:                       -1679.2
                                        LLR p-value:                     0.000
====================================================================================
                       coef    std err          z      P>|z|      [95.0% Conf. Int.]
------------------------------------------------------------------------------------
FICO.Score           0.0874      0.004     24.779      0.000         0.081     0.094
Amount.Requested    -0.0002    1.1e-05    -15.815      0.000        -0.000    -0.000
Intercept          -60.1250      2.420    -24.840      0.000       -64.869   -55.381
====================================================================================

对了——这是P2P借贷数据吗？