将数据分解为测试和训练，建立大熊猫的逻辑回归模型

Question

我正在尝试运行以下代码：（归功于Greg）

import pandas as pd
from sklearn.model_selection import train_test_split
import statsmodels.api as sm
quality = pd.read_csv("https://courses.edx.org/c4x/MITx/15.071x/asset/quality.csv")
train, test = train_test_split(quality, train_size=0.75, random_state=1)
qualityTrain = pd.DataFrame(train, columns=quality.columns)
qualityTest = pd.DataFrame(test, columns=quality.columns)
qualityTrain['PoorCare'] = qualityTrain['PoorCare'].astype(int)
cols = ['OfficeVisits', 'Narcotics']
x = qualityTrain[cols]
x = sm.add_constant(x)
y = qualityTrain['PoorCare']
model = sm.Logit(y, x).fit()
model.summary()

但我得到了：

AttributeError: 'int' object has no attribute 'exp'

在倒数第二行。这显然是通过对数据进行采样（train_testrongplit）引入的，因为该模型非常适合整个未修改的数据集。

如何解决此问题？

Answer 1

只需将x变量转换为浮点：

model = sm.Logit(y, x.astype(float)).fit()

我得到以下结果：

<class 'statsmodels.iolib.summary.Summary'>
"""
                           Logit Regression Results                           
==============================================================================
Dep. Variable:               PoorCare   No. Observations:                   98
Model:                          Logit   Df Residuals:                       95
Method:                           MLE   Df Model:                            2
Date:                Mon, 23 Mar 2015   Pseudo R-squ.:                  0.2390
Time:                        16:45:51   Log-Likelihood:                -39.714
converged:                       True   LL-Null:                       -52.188
                                        LLR p-value:                 3.823e-06
================================================================================
                   coef    std err          z      P>|z|      [95.0% Conf. Int.]
--------------------------------------------------------------------------------
const           -2.7718      0.561     -4.940      0.000        -3.872    -1.672
OfficeVisits     0.0680      0.031      2.211      0.027         0.008     0.128
Narcotics        0.1223      0.041      2.991      0.003         0.042     0.203
================================================================================
"""