[Numpy-discussion] Catching and dealing with floating point errors

Bruce Southey bsouthey@gmail....
Mon Nov 8 14:42:12 CST 2010


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.


Bruce




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