On Fri, Jun 11, 2010 at 1:07 PM, <josef.pktd@gmail.com> wrote:
On Fri, Jun 11, 2010 at 12:45 PM, Skipper Seabold <jsseabold@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?
Numpy '2.0.0.dev8417' Scipy '0.9.0.dev6447'
I don't get any warning with the first example. (numpy 1.4.0) (I cannot run the second example because I have a scipy revision with a broken fit() method)
I don't think wrapping functions/methods to turn off warnings is a good option. (many of them are in inner loops for example for random number generation)
Granted I haven't looked too much into the details of the warnings context manager other than using some toy examples once or twice, but if you could just suppress them for when the solver is called within a function/method then this would do the trick (at least for the ones I have been running into, mostly to do with fitting like this or with maximum likelihood). Skipper