statsmodels引发TypeError:优化输入中的输入类型不支持ufunc“isfinite”

Question

我需要运行代码的帮助，它显示错误：-“TypeError:ufunc'isfinite'不支持输入类型，并且无法根据强制转换规则“安全”将输入安全强制为任何支持的类型”

我发现了一些解决方案（statsmodels raises TypeError: ufunc 'isfinite' not supported for the input types将数据类型更改为float或int仍然不起作用。请任何人告诉我我在这段代码中犯了什么错误，如下所示：

import statsmodels.api as sm

X = np.append(arr = np.ones((50,1)).astype(int),values=X,axis=1)

X.astype('float64')

X_opt = X[:,[0,1,2,3,4,5]]

regressor_ols = sm.OLS(endog=y,exog=X_opt).fit()

或

import statsmodels.regression.linear_model as lm

X = np.append(arr = np.ones((50,1)).astype(int),values=X,axis=1)

X.astype('float64')

X_opt = X[:,[0,1,2,3,4,5]]

regressor_ols = lm.OLS(endog=y,exog=X_opt).fit()


regressor_ols = lm.OLS(endog=y,exog=X_opt).fit()
Traceback (most recent call last):
  File "<input>", line 1, in <module>
  File "C:\Users\hp\PycharmProjects\DataScientist\venv\lib\site-packages\statsmodels\regression\linear_model.py", line 858, in __init__
    super(OLS, self).__init__(endog, exog, missing=missing,
  File "C:\Users\hp\PycharmProjects\DataScientist\venv\lib\site-packages\statsmodels\regression\linear_model.py", line 701, in __init__
    super(WLS, self).__init__(endog, exog, missing=missing,
  File "C:\Users\hp\PycharmProjects\DataScientist\venv\lib\site-packages\statsmodels\regression\linear_model.py", line 190, in __init__
    super(RegressionModel, self).__init__(endog, exog, **kwargs)
  File "C:\Users\hp\PycharmProjects\DataScientist\venv\lib\site-packages\statsmodels\base\model.py", line 236, in __init__
    super(LikelihoodModel, self).__init__(endog, exog, **kwargs)
  File "C:\Users\hp\PycharmProjects\DataScientist\venv\lib\site-packages\statsmodels\base\model.py", line 76, in __init__
    self.data = self._handle_data(endog, exog, missing, hasconst,
  File "C:\Users\hp\PycharmProjects\DataScientist\venv\lib\site-packages\statsmodels\base\model.py", line 100, in _handle_data
    data = handle_data(endog, exog, missing, hasconst, **kwargs)
  File "C:\Users\hp\PycharmProjects\DataScientist\venv\lib\site-packages\statsmodels\base\data.py", line 671, in handle_data
    return klass(endog, exog=exog, missing=missing, hasconst=hasconst,
  File "C:\Users\hp\PycharmProjects\DataScientist\venv\lib\site-packages\statsmodels\base\data.py", line 87, in __init__
    self._handle_constant(hasconst)
  File "C:\Users\hp\PycharmProjects\DataScientist\venv\lib\site-packages\statsmodels\base\data.py", line 132, in _handle_constant
    if not np.isfinite(exog_max).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''

Answer 1

这：

X = np.append(arr = np.ones((50,1)).astype(int),values=X,axis=1)

创建数据类型int的数组，但分类器需要浮点值。看起来您正试图通过以下方式纠正此问题：

X.astype('float64')

但这并没有任何作用，因为您从未分配给它（正确的是X = X.astype('float64')）

我建议您从阵列创建中删除astype(int)：

X = np.append(arr=np.ones((50,1)), values=X, axis=1)