[Numpy-discussion] Catching and dealing with floating point errors
Skipper Seabold
jsseabold@gmail....
Mon Nov 8 14:17:45 CST 2010
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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