python - ValueError : illegal value in 4-th argument of internal None when running sklearn LinearRegression(). fit()

Question

出于某种原因，我无法再让这段代码正常运行:

import numpy as np
from sklearn.linear_model import LinearRegression

# Create linear data with some noise
x = np.random.uniform(0, 100, 1000)
y = 2. * x + 3. + np.random.normal(0, 10, len(x))

# Fit linear data with sklearn LinearRegression
lm = LinearRegression()
lm.fit(x.reshape(-1, 1), y)

Traceback (most recent call last):
  File "<input>", line 2, in <module>
  File "C:\Python37\lib\site-packages\sklearn\linear_model\_base.py", line 547, in fit
    linalg.lstsq(X, y)
  File "C:\Python37\lib\site-packages\scipy\linalg\basic.py", line 1224, in lstsq
    % (-info, lapack_driver))
ValueError: illegal value in 4-th argument of internal None

我不确定为什么我会在这样一个简单的示例中收到此错误。这是我当前的版本:

scipy.__version__
'1.5.0'
sklearn.__version__
'0.23.1'

我在 64 位 Windows 10 Enterprise 和 Python 3.7.3 上运行它。我试过卸载并重新安装 scipy 和 scikit-learn。我已经尝试过早期版本的 scipy。我试过卸载并重新安装 Python，但这些都没有解决问题。
更新 :
所以它似乎也与 matplotlib 相关。我之前在 Pycharm 中运行过这个示例，但我已经转而直接从 PowerShell 中运行它。因此，如果我在 Pycharm 之外运行此代码，则不会出现错误

import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# Create linear data with some noise
x = np.random.uniform(0, 100, 1000)
y = 2. * x + 3. + np.random.normal(0, 10, len(x))

# Fit linear data with sklearn LinearRegression
lm = LinearRegression()
lm.fit(x.reshape(-1, 1), y)

但是，如果我在其中绘制数据，则会出现错误:

import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# Create linear data with some noise
x = np.random.uniform(0, 100, 1000)
y = 2. * x + 3. + np.random.normal(0, 10, len(x))

# Plot data
plt.scatter(x, y)
plt.plot(np.linspace(0, 100, 10), 2. * np.linspace(0, 100, 10) + 3., ls='--', c='red')

# Fit linear data with sklearn LinearRegression
lm = LinearRegression()
lm.fit(x.reshape(-1, 1), y)

 ** On entry to DLASCLS parameter number  4 had an illegal value
Traceback (most recent call last):
  File ".\run.py", line 18, in <module>
    lm.fit(x.reshape(-1, 1), y)
  File "C:\Python37\lib\site-packages\sklearn\linear_model\_base.py", line 547, in fit
    linalg.lstsq(X, y)
  File "C:\Python37\lib\site-packages\scipy\linalg\basic.py", line 1224, in lstsq
    % (-info, lapack_driver))
ValueError: illegal value in 4-th argument of internal None

但是如果我注释掉 plt.plot(np.linspace(0, 100, 10), 2. * np.linspace(0, 100, 10) + 3., ls='--', c='red')它工作正常。

Answer 1

似乎只有在使用 matplotlib 打印图形时才会发生这种情况，否则您可以根据需要多次运行拟合算法。
但是，如果您将数据类型从 float64 更改为 float32(Grzesik 答案)，那么奇怪的是错误消失了。 对我来说感觉像个错误 为什么更改数据类型会影响 matplotlib 和 sklearn 中的 lapack_function 之间的交互？
与其说是一个答案，不如说是一个问题，但发现这些跨函数和数据类型的意外交互有点可怕。

import numpy as np
import sklearn
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt


def main(print_matplotlib=False,dtype=np.float64):
    x = np.linspace(-3,3,100).astype(dtype)
    print(x.dtype)
    y = 2*np.random.rand(x.shape[0])*x + np.random.rand(x.shape[0])
    x = x.reshape((-1,1))

    reg=LinearRegression().fit(x,y)
    print(reg.intercept_,reg.coef_)
    
    yh = reg.predict(x)
    
    if print_matplotlib:
        plt.scatter(x,y)
        plt.plot(x,yh)
        plt.show()

没有绘图

if __name__ == "__main__":
    np.random.seed(64)
    main(print_matplotlib = False, dtype=np.float64)
    np.random.seed(64)
    main(print_matplotlib = False, dtype=np.float64)  
    pass

float64
0.5957165420019624 [0.91960601]
float64
0.5957165420019624 [0.91960601]

绘制 dtype = np.float64

if __name__ == "__main__":
    np.random.seed(64)
    main(print_matplotlib = True, dtype=np.float64)
    np.random.seed(64)
    main(print_matplotlib = True, dtype=np.float64)
    pass

float64
0.5957165420019624 [0.91960601]

Plot 1

float64
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-3-52593a548324> in <module>
      3     main(print_matplotlib = True)
      4     np.random.seed(64)
----> 5     main(print_matplotlib = True)
      6 
      7     pass

<ipython-input-1-11139051f2d3> in main(print_matplotlib, dtype)
     11     x = x.reshape((-1,1))
     12 
---> 13     reg=LinearRegression().fit(x,y)
     14     print(reg.intercept_,reg.coef_)
     15 

~\Anaconda3\lib\site-packages\sklearn\linear_model\_base.py in fit(self, X, y, sample_weight)
    545         else:
    546             self.coef_, self._residues, self.rank_, self.singular_ = \
--> 547                 linalg.lstsq(X, y)
    548             self.coef_ = self.coef_.T
    549 

~\AppData\Roaming\Python\Python37\site-packages\scipy\linalg\basic.py in lstsq(a, b, cond, overwrite_a, overwrite_b, check_finite, lapack_driver)
   1249         if info < 0:
   1250             raise ValueError('illegal value in %d-th argument of internal %s'
-> 1251                              % (-info, lapack_driver))
   1252         resids = np.asarray([], dtype=x.dtype)
   1253         if m > n:

ValueError: illegal value in 4-th argument of internal None

绘制 dtype=np.float32

if __name__ == "__main__":
    np.random.seed(64)
    main(print_matplotlib = True, dtype=np.float32)
    np.random.seed(64)
    main(print_matplotlib = True, dtype=np.float32)
    pass

Output 2