27.09.2013 22:15, Nathaniel Smith kirjoitti: [clip]
3) The issue of how to make an in-place like ndarray += sparse continue to work in the brave new __numpy_ufunc__ world.
For this last issue, I think we disagree. It seems to me that the right answer is that csc_matrix.__numpy_ufunc__ needs to step up and start supporting out=! If I have a large dense ndarray and I try to += a sparse array to it, this operation should take no temporary memory and nnz time. Right now it sounds like it actually copies the large dense ndarray, which takes time and space proportional to its size. AFAICT the only way to avoid that is for scipy.sparse to implement out=. It shouldn't be that hard...?
Sure, scipy.sparse can easily support also the output argument. But I still maintain that the implementation of __iadd__ in Numpy is wrong. What it does now is: def __iadd__(self, other): return np.add(self, other, out=self) But since we know this raises a TypeError if the input is of a type that cannot be dealt with, it should be: def __iadd__(self, other): try: return np.add(self, other, out=self) except TypeError: return NotImplemented Of course, it's written in C so it's a bit more painful to write this. I think this will have little performance impact, since the check would be only a NULL check in the inplace op methods + subsequent handling. I can take a look at some point at this... -- Pauli Virtanen