[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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