[Numpy-discussion] Allow == and != to raise errors
josef.pktd at gmail.com
josef.pktd at gmail.com
Fri Jul 12 19:29:07 EDT 2013
On Fri, Jul 12, 2013 at 3:35 PM, Frédéric Bastien <nouiz at nouiz.org> wrote:
> I also don't like that idea, but I'm not able to come to a good reasoning
> like Benjamin.
>
> I don't see advantage to this change and the reason isn't good enough to
> justify breaking the interface I think.
>
> But I don't think we rely on this, so if the change goes in, it probably
> won't break stuff or they will be easily seen and repared.
>
> Fred
>
>
> On Fri, Jul 12, 2013 at 9:13 AM, Benjamin Root <ben.root at ou.edu> wrote:
>>
>> I can see where you are getting at, but I would have to disagree. First
>> of all, when a comparison between two mis-shaped arrays occur, you get back
>> a bone fide python boolean, not a numpy array of bools. So if any action was
>> taken on the result of such a comparison assumed that the result was some
>> sort of an array, it would fail (yes, this does make it a bit difficult to
>> trace back the source of the problem, but not impossible).
>>
>> Second, no semantics are broken with this. Are the arrays equal or not? If
>> they weren't broadcastible, then returning False for == and True for !=
>> makes perfect sense to me. At least, that is my take on it.
>>
>> Cheers!
>> Ben Root
>>
>>
>>
>> On Fri, Jul 12, 2013 at 8:38 AM, Sebastian Berg
>> <sebastian at sipsolutions.net> wrote:
>>>
>>> Hey,
>>>
>>> the array comparisons == and != never raise errors but instead simply
>>> return False for invalid comparisons.
>>>
>>> The main example are arrays of non-matching dimensions, and object
>>> arrays with invalid element-wise comparisons:
>>>
>>> In [1]: np.array([1,2,3]) == np.array([1,2])
>>> Out[1]: False
>>>
>>> In [2]: np.array([1, np.array([2, 3])], dtype=object) == [1, 2]
>>> Out[2]: False
>>>
>>> This seems wrong to me, and I am sure not just me. I doubt any large
>>> projects makes use of such comparisons and assume that most would prefer
>>> the shape mismatch to raise an error, so I would like to change it. But
>>> I am a bit unsure especially about smaller projects. So to keep the
>>> transition a bit safer could imagine implementing a FutureWarning for
>>> these cases (and that would at least notify new users that what they are
>>> doing doesn't seem like the right thing).
>>>
>>> So the question is: Is such a change safe enough, or is there some good
>>> reason for the current behavior that I am missing?
>>>
>>> Regards,
>>>
>>> Sebastian
>>>
>>> (There may be other issues with structured types that would continue
>>> returning False I think, because neither side knows how to compare)
>>>
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>>> NumPy-Discussion at scipy.org
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>>
>>
>>
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>
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I thought Benjamin sounds pretty convincing, and since I never use
this, I don't care.
However, I (and I'm pretty convinced all statsmodels code) uses
equality comparison only element wise. Getting a boolean back is an
indicator for a bug, which is most of the time easy to trace back.
There is an inconsistency in the behavior with the inequalities.
>>> np.array([1,2,3]) < np.array([1,2])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: shape mismatch: objects cannot be broadcast to a single shape
>>> np.array([1,2,3]) <= np.array([1,2])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: shape mismatch: objects cannot be broadcast to a single shape
>>> (np.array([1,2,3]) == np.array([1,2])).any()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'bool' object has no attribute 'any'
The last one could be misleading and difficult to catch.
>>> np.any(np.array([1,2,3]) == np.array([1,2]))
False
numpy 1.5.1 since I'm playing rear guard
Josef
Josef
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