statmodels OLS在python中给出了一个TypeError

我正在尝试将一组特性应用到statsmodel的OLS线性回归模型中。

我一次添加几个功能。第一个功能很好。但是,当我不断添加新功能时,它给了我一个错误。

Traceback (most recent call last):
  File "read_xml.py", line 337, in <module>
    model = sm.OLS(Y, X).fit()
...
  File "D:\pythonprojects\testproj\test_env\lib\site-packages\statsmodels\base\data.py", line 132, in _handle_constant
    if not np.isfinite(ptp_).all():
TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

所以我用

X = X.astype(float)

然后出现了另一个错误。

Traceback (most recent call last):
  File "read_xml.py", line 339, in <module>
    print(model.summary())
...
File "D:\pythonprojects\testproj\test_env\lib\site-packages\scipy\stats\_distn_infrastructure.py", line 1824, in sf
    place(output, (1-cond0)+np.isnan(x), self.badvalue)
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

new_df0 = pd.concat([df_lex[0], summary_df[0]], axis = 0, join = 'inner')
new_df1 = pd.concat([df_lex[1], summary_df[1]], axis = 0, join = 'inner')
data = pd.concat([new_df0, new_df1], axis = 1)
print(data.shape)
X = data.values[0:6,:]
Y = data.values[6,:]
Y = Y.reshape(1,88)
X = X.T
Y = Y.T
X = X.astype(float)
model = sm.OLS(Y, X).fit()
predictions = model.predict(X)
print(model.summary())

在中触发的第一个错误 model = sm.OLS(Y,X).fit() 第二个错误触发 model.summary()

但是在其他一些功能上,没有错误。

new_df0 = pd.concat([df_len[0], summary_df[0]], axis = 0, join = 'inner')
new_df1 = pd.concat([df_len[1], summary_df[1]], axis = 0, join = 'inner')

data = pd.concat([new_df0, new_df1], axis = 1)
print(data.shape)
X = data.values[0:2,:]
Y = data.values[2,:]
Y = Y.reshape(1,88)
X = X.T
Y = Y.T
X = X.astype(float)
print(X.shape)
print(Y.shape)

model = sm.OLS(Y, X).fit()
predictions = model.predict(X)
print(model.summary())