[Numpy-discussion] Catching and dealing with floating point errors
Bruce Southey
bsouthey@gmail....
Mon Nov 8 15:04:57 CST 2010
On 11/08/2010 02:52 PM, Skipper Seabold wrote:
> On Mon, Nov 8, 2010 at 3:42 PM, Bruce Southey<bsouthey@gmail.com> wrote:
>> On 11/08/2010 02:17 PM, Skipper Seabold 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?
>>>
>>>> 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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>> What do you mean by 'floating point error'?
>> For example, log of zero is not what I would consider a 'floating point
>> error'.
>>
>> In this case, if you are after a log distribution, then you should be
>> ensuring that the lower bound to the np.random.uniform() is always
>> greater than zero. That is, if lb<= zero then you *know* you have a
>> problem at the very start.
>>
>>
> Just a toy example to get a similar error. I call x<= 0 on purpose here.
>
>
I was aware of that.
Messing about warnings is not what I consider Pythonic because you
should be fixing the source of the problem. In this case, your sampling
must be greater than zero. If you are sampling from a distribution, then
that should be built into the call otherwise your samples will not be
from the requested distribution.
Bruce
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