I thought I knew how to do error handling in python, but apparently I
dont. I have a bunch of code to calculate statistical likelihoods, and
use error handling to catch invalid parameters. For example, for the
bernoulli distribution, I have:
def bernoulli_like(self, x, p, name='bernoulli'):
"""Bernoulli log-likelihood"""
# Ensure proper dimensionality of parameters
dim = shape(x)
p = resize(p,dim)
# Ensure valid values of parameters
if sum(p>=1 or p<=0): raise LikelihoodError
... etc.
where LikelihoodError is simply a subclass of ValueError that I created:
class LikelihoodError(ValueError):
"Log-likelihood is invalid or negative infinite"
I catch these errors with the following:
try:
like = self.calculate_likelihood()
except LikelihoodError:
return 0
So far, so good. When I calculate_likelihood is called in the above,
which contains a call to bernoulli_like:
p = invlogit(beta0 + sum([b*h for b,h in zip(self.beta,hab)]))
like=self.bernoulli_like(x,p)
I get the following when an invalid parameter is passed:
Traceback (most recent call last):
File
"C:\Conroy\working\resource_selection_ms\analyses\IIbq\sampled\new_chris\model_000.py",
line 381, in ?
model.sample(iterations=iter, burn=burn,plot=False)
File "C:\Python23\Lib\site-packages\PyMC\MCMC.py", line 1691, in sample
self._like = self.calculate_likelihood()
File
"C:\Conroy\working\resource_selection_ms\analyses\IIbq\sampled\new_chris\model_000.py",
line 194, in calculate_likelihood
like+=self.bernoulli_like(x,p)
File "C:\Python23\Lib\site-packages\PyMC\MCMC.py", line 868, in bernoulli_like
if sum(p>=1 or p<=0): raise LikelihoodError
LikelihoodError
I have no idea how this can happen, given how I have coded this.
Anyone see what I must be missing?
Thanks,
C.
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