[SciPy-Dev] Warnings raised (from fit in scipy.stats)

[Skipper Seabold]

On Fri, Jun 11, 2010 at 2:17 PM, Vincent Davis <vincent at vincentdavis.net> wrote:
> On Fri, Jun 11, 2010 at 11:34 AM, Skipper Seabold <jsseabold at gmail.com> wrote:
>> On Fri, Jun 11, 2010 at 1:07 PM,  <josef.pktd at gmail.com> wrote:
>>> On Fri, Jun 11, 2010 at 12:45 PM, Skipper Seabold <jsseabold at gmail.com> wrote:
>>>> Since the raising of warning behavior has been changed (I believe), I
>>>> have been running into a lot of warnings in my code when say I do
>>>> something like
>>>>
>>>> In [120]: from scipy import stats
>>>>
>>>> In [121]: y = [-45, -3, 1, 0, 1, 3]
>>>>
>>>> In [122]: v = stats.norm.pdf(y)/stats.norm.cdf(y)
>>>> Warning: invalid value encountered in divide
>>>>
>>>> Sometimes, this is useful to know.  Sometimes, though, it's very
>>>> disturbing when it's encountered in some kind of iteration or
>>>> optimization.  I have been using numpy.clip to get around this in my
>>>> own code, but when it's buried a bit deeper, it's not quite so simple.
>>>>
>>>> Take this example.
>>>>
>>>> In [123]: import numpy as np
>>>>
>>>> In [124]: np.random.seed(12345)
>>>>
>>>> In [125]: B = 6.0
>>>>
>>>> In [126]: x = np.random.exponential(scale=B, size=5000)
>>>>
>>>> In [127]: from scipy.stats import expon
>>>>
>>>> In [128]: expon.fit(x)
>>>>
>>>> <dozens of warnings clipped>
>>>>
>>>> Out[128]: (0.21874043533906118, 5.7122829778172939)
>>>>
>>>> The fit is achieved by fmin (as far as I know, since disp=0 in the
>>>> rv_continuous.fit...), but there are a number of warnings emitted.  Is
>>>> there any middle ground to be had in these type of situations via
>>>> context management perhaps?
>>>>
>>>> Should I file a ticket?
>>>
>>> Which numpy scipy versions are you using?
>
> I get know warnings
>>>> import numpy as np
>>>> np.random.seed(12345)
>>>> B = 6.0
>>>> x = np.random.exponential(scale=B, size=5000)
>>>> from scipy.stats import expon
>>>> expon.fit(x)
> array([  6.43573559e-04,   5.93058867e+00])
>

You also get different values than I do, which shouldn't be the case.

I just discovered that my expon.fit(x) just returns the first and
second moments of the data (even when I set floc = 0, I still get the
second moment), and I am trying to figure out why.  It looks like
something is amiss.

Skipper