On 08/31/2015 12:09 AM, Jaime Fernández del Río wrote:
> There are three ways of fixing this that I see:
>
> 1. Arbitrarily choose a value to set the return to. This is equivalent
> to choosing a default return for `cmp` for comparisons. This
> preserves behavior, but feels wrong.
> 2. Similarly to how np.sign of a floating point array with nans returns
> nan for those values, return e,g, None for these cases. This is my
> preferred option.
> 3. Raise an error, along the lines of the TypeError: unorderable types
> that 3.x produces for some comparisons.
I think np.sign on nan object arrays should raise the error
AttributeError: 'float' object has no attribute 'sign'
If I've understood correctly, currently object arrays work like this:
If a ufunc has an equivalent pure-python func (eg, PyNumber_Add for
np.add, PyNumber_Absolute for np.abs, < for np.greater_than) then numpy
calls that for objects. Otherwise, if the object defines a method with
the same name as the ufunc, numpy calls that method. For example, arccos
is a ufunc that has no pure python equivalent, so you get the following
behavior
>>> a = np.array([-1], dtype='O')
>>> np.abs(a)
array([1], dtype=object)
>>> np.arccos(a)
AttributeError: 'int' object has no attribute 'arccos'
>>> class MyClass:
... def arccos(self):
... return 1
>>> b = np.array([MyClass()], dtype='O')
>>> np.arccos(b)
array([1], dtype=object)
Now, most comparison operators (eg, greater_than) are treated a little
specially in loops.c. For some reason, sign is treated just like the
other comparison operators, even through technically there is no
pure-python equivalent to sign.
I think that because there is no pure-python 'sign', numpy should
attempt to call obj.sign, and in most cases this should fail with the
error above. See also
http://stackoverflow.com/questions/1986152/why-doesnt-python-have-a-sign-function
I think the fix for sign is that the 'sign' ufunc in generate_umath.py
should look more like the arccos one, and we should get rid of
OBJECT_sign in loops.c. I'm not 100% sure about this since I haven't
followed all of how generate_umath.py works yet.
-------
By the way, based on some comments I saw somewhere (apologies, I forget
who by!) I wrote up a vision for how ufuncs could work for objects,
here: https://gist.github.com/ahaldane/c3f9bcf1f62d898be7c7
I'm a little unsure the ideas there are a good idea since they might be
made obsolete by the big dtype subclassing improvements being discussed
in the numpy roadmap thread.
Allan
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