sklearn's PLSRegression: “ValueError: array must not contain infs or NaNs”

问题:

When using sklearn.cross_decomposition.PLSRegression:

import numpy as np import sklearn.cross_decomposition  pls2 = sklearn.cross_decomposition.PLSRegression() xx = np.random.random((5,5)) yy = np.zeros((5,5) )   yy[0,:] = [0,1,0,0,0] yy[1,:] = [0,0,0,1,0] yy[2,:] = [0,0,0,0,1] #yy[3,:] = [1,0,0,0,0] # Uncommenting this line solves the issue  pls2.fit(xx, yy) 

I get:

C:\Anaconda\lib\site-packages\sklearn\cross_decomposition\pls_.py:44: RuntimeWarning: invalid value encountered in divide   x_weights = np.dot(X.T, y_score) / np.dot(y_score.T, y_score) C:\Anaconda\lib\site-packages\sklearn\cross_decomposition\pls_.py:64: RuntimeWarning: invalid value encountered in less   if np.dot(x_weights_diff.T, x_weights_diff) < tol or Y.shape[1] == 1: C:\Anaconda\lib\site-packages\sklearn\cross_decomposition\pls_.py:67: UserWarning: Maximum number of iterations reached   warnings.warn('Maximum number of iterations reached') C:\Anaconda\lib\site-packages\sklearn\cross_decomposition\pls_.py:297: RuntimeWarning: invalid value encountered in less   if np.dot(x_scores.T, x_scores) < np.finfo(np.double).eps: C:\Anaconda\lib\site-packages\sklearn\cross_decomposition\pls_.py:275: RuntimeWarning: invalid value encountered in less   if np.all(np.dot(Yk.T, Yk) < np.finfo(np.double).eps): Traceback (most recent call last):   File "C:\svn\hw4\code\test_plsr2.py", line 8, in <module>     pls2.fit(xx, yy)   File "C:\Anaconda\lib\site-packages\sklearn\cross_decomposition\pls_.py", line 335, in fit     linalg.pinv(np.dot(self.x_loadings_.T, self.x_weights_)))   File "C:\Anaconda\lib\site-packages\scipy\linalg\basic.py", line 889, in pinv     a = _asarray_validated(a, check_finite=check_finite)   File "C:\Anaconda\lib\site-packages\scipy\_lib\_util.py", line 135, in _asarray_validated     a = np.asarray_chkfinite(a)   File "C:\Anaconda\lib\site-packages\numpy\lib\function_base.py", line 613, in asarray_chkfinite     "array must not contain infs or NaNs") ValueError: array must not contain infs or NaNs 

What could be the issue?

I am aware of scikit-learn GitHub issue #2089, but since I use scikit-learn 0.16.1 (with Python 2.7.10 x64) this problem should be solved (the code snippets mentioned in the GitHub issue work fine).

回答1:

Please check if any of your values being passed in are NaN or inf:

np.isnan(xx).any() np.isnan(yy).any()  np.isinf(xx).any() np.isinf(yy).any() 

If any of those yields true. Remove the nan entries or inf entries. E.g. you can set them to 0 with:

xx = np.nan_to_num(xx) yy = np.nan_to_num(yy) 

It's also possible for numpy to be fed such large positive and negative and zeroed values, that the equations deep down in the library are producing zeros, Nan's or Inf's. One workaround, oddly enough, is to send in smaller numbers (say representative numbers between -1 and 1. One way to do this is by standardization, see: https://stackoverflow.com/a/36390482/445131

If none of that solves the problem, then you may be dealing with a low level bug in the library your using, or some sort of singularity in your data. Create an sscce and post it to stackoverflow or create a new bug report on the library maintaining your software.

回答2:

The issue is caused by a bug in scikit-learn. I reported it on GitHub: https://github.com/scikit-learn/scikit-learn/issues/2089#issuecomment-152753095

回答3:

I can reproduce the same bug, I silenced this bug by filtering all 0s away

threshold_for_bug = 0.00000001 # could be any value, ex numpy.min xx[xx < threshold_for_bug] = threshold_for_bug 

This silences the bug (i never check the precision difference)

My system info:

numpy-1.11.2 python-3.5 macOS Sierra 

文章来源: sklearn's PLSRegression: “ValueError: array must not contain infs or NaNs”