[Numpy-discussion] Catching and dealing with floating point errors
Skipper Seabold
jsseabold@gmail....
Mon Nov 8 14:52:56 CST 2010
On Mon, Nov 8, 2010 at 3:45 PM, Warren Weckesser
<warren.weckesser@enthought.com> wrote:
>
>
> On Mon, Nov 8, 2010 at 2:17 PM, Skipper Seabold <jsseabold@gmail.com> wrote:
>>
>> On Mon, Nov 8, 2010 at 3:14 PM, Skipper Seabold <jsseabold@gmail.com>
>> wrote:
>> > I am doing some optimizations on random samples. In a small number of
>> > cases, the objective is not well-defined for a given sample (it's not
>> > possible to tell beforehand and hopefully won't happen much in
>> > practice). What is the most numpythonic way to handle this? It
>> > doesn't look like I can use np.seterrcall in this case (without
>> > ignoring its actual intent). Here's a toy example of the method I
>> > have come up with.
>> >
>> > import numpy as np
>> >
>> > def reset_seterr(d):
>> > """
>> > Helper function to reset FP error-handling to user's original
>> > settings
>> > """
>> > for action in [i+'='+"'"+d[i]+"'" for i in d]:
>> > exec(action)
>> > np.seterr(over=over, divide=divide, invalid=invalid, under=under)
>> >
>>
>> It just occurred to me that this is unsafe. Better options for
>> resetting seterr?
>
>
> Hey Skipper,
>
> I don't understand why you need your helper function. Why not just pass the
> saved dictionary back to seterr()? E.g.
>
> saved = np.seterr('raise')
> try:
> # Do something dangerous...
> result = whatever...
> except Exception:
> # Handle the problems...
> result = better result...
> np.seterr(**saved)
> return result
>
Ha. I knew I was forgetting something. Thanks.
>
> Warren
>
>
>
>>
>> > def log_random_sample(X):
>> > """
>> > Toy example to catch a FP error, re-sample, and return objective
>> > """
>> > d = np.seterr() # get original values to reset
>> > np.seterr('raise') # set to raise on fp error in order to catch
>> > try:
>> > ret = np.log(X)
>> > reset_seterr(d)
>> > return ret
>> > except:
>> > lb,ub = -1,1 # includes bad domain to test recursion
>> > X = np.random.uniform(lb,ub)
>> > reset_seterr(d)
>> > return log_random_sample(X)
>> >
>> > lb,ub = 0,0
>> > orig_setting = np.seterr()
>> > X = np.random.uniform(lb,ub)
>> > log_random_sample(X)
>> > assert(orig_setting == np.seterr())
>> >
>> > This seems to work, but I'm not sure it's as transparent as it could
>> > be. If it is, then maybe it will be useful to others.
>> >
>> > Skipper
>> >
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>
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