numpy.maximum / numpy.minimum fail quietly on sparse matrices

As I understand, the numpy.maximum and numpy.minimum functions do not support sparse arrays. However, they fail quietly, producing erroneous output when given a scipy.sparse.dok_matrix:

>>> import numpy
>>> from scipy.sparse import dok_matrix
>>> numpy.maximum(dok_matrix([[1,0]]), dok_matrix([[0,1]])).toarray()
75: array([[0, 1]])

When given a scipy.sparse.csr_matrix, these functions fail with an exception, but the error is not clear:

>>> import numpy
>>> from scipy.sparse import csr_matrix
>>> numpy.maximum(csr_matrix([[1,0]]), csr_matrix([[0,1]])).toarray()
Traceback (most recent call last):
  File "<pyshell#169>", line 1, in <module>
    numpy.maximum(csr_matrix([[1,0]]), csr_matrix([[0,1]])).toarray()
  File "/home/amitmo/virtualenv/local/lib/python2.7/site-packages/scipy/sparse/base.py", line 183, in __bool__
    raise ValueError("The truth value of an array with more than one "
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all().
>>> 

I think an exception should be raised if the data type is not supported.

[SimZhou]

It is 2020 now and I still got this issue.

scipy.version: 1.4.1 numpy.version: 1.18.1

[thirschbuechler]

the outputs seem to be the same now, still non-working however python 3.8.10 scipy 1.8.0 numpy 1.22.3

/home/user/.local/lib/python3.8/site-packages/scipy/sparse/_compressed.py:311: SparseEfficiencyWarning: Comparing sparse matrices using >= and <= is inefficient, using <, >, or !=, instead.
  warn("Comparing sparse matrices using >= and <= is inefficient, "
Traceback (most recent call last):
  File "/home/user/test2.py", line 3, in <module>
    numpy.maximum(dok_matrix([[1,0]]), dok_matrix([[0,1]])).toarray()
  File "/home/user/.local/lib/python3.8/site-packages/scipy/sparse/_base.py", line 337, in __bool__
    raise ValueError("The truth value of an array with more than one "
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all().

/home/user/.local/lib/python3.8/site-packages/scipy/sparse/_compressed.py:311: SparseEfficiencyWarning: Comparing sparse matrices using >= and <= is inefficient, using <, >, or !=, instead.
  warn("Comparing sparse matrices using >= and <= is inefficient, "
Traceback (most recent call last):
  File "/home/user/test.py", line 3, in <module>
    numpy.maximum(csr_matrix([[1,0]]), csr_matrix([[0,1]])).toarray()
  File "/home/user/.local/lib/python3.8/site-packages/scipy/sparse/_base.py", line 337, in __bool__
    raise ValueError("The truth value of an array with more than one "
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all().