[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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