[Numpy-discussion] Odd numerical difference between Numpy 1.5.1 and Numpy > 1.5.1
Charles R Harris
charlesr.harris at gmail.com
Mon Apr 11 16:55:22 EDT 2011
On Mon, Apr 11, 2011 at 2:31 PM, Mark Wiebe <mwwiebe at gmail.com> wrote:
> On Mon, Apr 11, 2011 at 12:37 PM, Robert Kern <robert.kern at gmail.com>wrote:
>
>> On Mon, Apr 11, 2011 at 13:54, Skipper Seabold <jsseabold at gmail.com>
>> wrote:
>> > All,
>> >
>> > We noticed some failing tests for statsmodels between numpy 1.5.1 and
>> > numpy >= 1.6.0. These are the versions where I noticed the change. It
>> > seems that when you divide a float array and multiply by a boolean
>> > array the answers are different (unless the others are also off by
>> > some floating point). Can anyone replicate the following using this
>> > script or point out what I'm missing?
>> >
>> > import numpy as np
>> > print np.__version__
>> > np.random.seed(12345)
>> >
>> > test = np.random.randint(0,2,size=10).astype(bool)
>> > t = 1.345
>> > arr = np.random.random(size=10)
>> >
>> > arr # okay
>> > t/arr # okay
>> > (1-test)*arr # okay
>> > (1-test)*t/arr # huh?
>>
>> [~]
>> |12> 1-test
>> array([1, 0, 0, 0, 1, 0, 1, 1, 0, 1], dtype=int8)
>>
>> [~]
>> |13> (1-test)*t
>> array([ 1.34472656, 0. , 0. , 0. ,
>> 1.34472656, 0. ,
>> 1.34472656, 1.34472656, 0. , 1.34472656], dtype=float16)
>>
>> Some of the recent ufunc changes or the introduction of the float16
>> type must have changed the way the dtype is chosen for the
>> "int8-array*float64-scalar" case. Previously, the result was a float64
>> array, as expected.
>>
>> Mark, I expect this is a result of one of your changes. Can you take a
>> look at this?
>>
>
> It's the type promotion rules change that is causing this, and it's
> definitely a 1.6.0 release blocker. Good catch!
>
> I can think of an easy, reasonable way to fix it in the result_type
> function, but since the ufuncs themselves use a poor method of resolving the
> types, it will take some thought to apply the same idea there. Maybe
> detecting that the ufunc is a binary op at its creation time, setting a
> flag, and using the result_type function in that case. This would have the
> added bonus of being faster, too.
>
> Going back to the 1.5 code isn't an option, since adding the new data types
> while maintaining ABI compatibility required major surgery to this part of
> the system.
>
>
There isn't a big rush here, so take the time work it through.
Chuck
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