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

It seems it only happens when you print the figure using matplotlib, else you can run the fit algorithm as many times as you like.

However if you change the data type from float64 to float32 (Grzesik answer), strangely enough the error disappears. Feels like a bug to me Why would changing the data type affect the interaction between matplotlib and the lapack_function within sklearn?

More a question than an answer, but it is a bit scary to find these unexpected interactions across functions and data types.

import numpy as npimport sklearnfrom sklearn.linear_model import LinearRegressionimport matplotlib.pyplot as pltdef 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()

No plotting

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)      passfloat640.5957165420019624 [0.91960601]float640.5957165420019624 [0.91960601]

Plotting 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)    passfloat640.5957165420019624 [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

Plotting 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

As of numpy 1.19.1 and sklearn v0.23.2, I found that polyfit(deg=1) and LinearRegression().fit() gave unexpected errors without any good reason. No, data didn't have any NaN or Inf value. I eventually used scipy.stats.linregress().

slope, intercept, r_value, p_value, std_err = stats.linregress(x.astype(np.float32), y.astype(np.float32))

First check for nan,inf values. and also try normalize=True

lreg=LinearRegression(fit_intercept=True, normalize=True, copy_X=True).fit() 

But these didn't work for me. Also, my data didn't have any nan or inf values.But while experimenting, I found that running the same code second time works.hence I did this

try:     lreg=LinearRegression(fit_intercept=True, normalize=True, copy_X=True).fit()except:    lreg=LinearRegression(fit_intercept=True, normalize=True, copy_X=True).fit()

I don't know why this work, but this solved the problem for me.So trying to run the same code twice did the trick for me.