BUG: Fix typecasting problem in scipy.sparse.lil_matrix truediv #19408
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j-bowhay
added
defect
Co-authored-by: Dan Schult <dschult@colgate.edu>
scipy/sparse/_lil.py
Outdated
| @@ -287,6 +287,9 @@ def _mul_scalar(self, other): | |||
| def __truediv__(self, other): # self / other | |||
| if isscalarlike(other): | |||
| new = self.copy() | |||
| new_dtype = np.result_type(self.dtype, np.array(other).dtype) | |||
| if new_dtype != self.dtype: | |||
| new.data = new.data.astype(new_dtype) | |||
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This won't work. The data member of LIL-format objects is a numpy array of Python lists, with dtype=object.
To change the dtype, we need to do something like this:
for i, rowvals in enumerate(new.data):
new.data[i] = rowvals.astype(new_dtype)
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Sorry, I should have noticed this. Isn't just the following what we really want?
new.dtype = np.result_type(self, other)The list data is actually modified correctly. Things only start breaking when trying to convert to a different storage format, for example.
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Hmmm... sorry that my solution is actually quite wrong in a number of ways.
@vetschn is correct that the values in new.data are correct (complex values).
It is the dtype that is not updated. The values are actually python objects. So they morph to what they need to become to hold the correct value.
It is definitely true that we need a test. That will show us what will work. :)
I think @vetschn 's solution should work. I have tried it at an interactive interpreter.
But there are many subtleties with dtype that I haven't experienced. :)
|
Can someone restart the failing test? It looks like it timed out during the build phase of the test. |
tylerjereddy
added
the
backport-candidate
…y#19408) * Fix typecasting problem in lil_matrix truedriv * Added test --------- Co-authored-by: Dan Schult <dschult@colgate.edu>
…y#19408) * Fix typecasting problem in lil_matrix truedriv * Added test --------- Co-authored-by: Dan Schult <dschult@colgate.edu>
tylerjereddy
removed
the
backport-candidate
Reference issue
Closes #19403
What does this implement/fix?
This fixes the inconsistent behavior I observed when dividing a
scipy.sparse.lil_matrixby a scalar of differing type. Now the resultingdtypeis checked and adjusted if necessary.