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working auto differentiation, enables the hmc on test cases. #53

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working auto differentiation, enables the hmc on test cases. #53

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@wdpozzo wdpozzo commented Oct 14, 2019

whenever autodiff fails reverts to finite differencing with obvious performance degradation. Also fixes a bug in the differential evolution proposal.

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working auto differentiation, enables the hmc on test cases. whenever…
37f566d 
… autodiff fails reverts to finite differencing with obvious performance degradation
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pep8speaks commented Oct 14, 2019 •
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Hello @wdpozzo! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:

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Comment last updated at 2020-02-27 21:03:06 UTC

@wdpozzo wdpozzo requested a review from johnveitch October 18, 2019 05:51
johnveitch
johnveitch reviewed Nov 2, 2019
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Quite a lot of changes to digest here but there's some stuff missing that you'll need to add to the repo before I can test more.

cpnest/NestedSampling.py Outdated Show resolved Hide resolved
cpnest/cpnest.py Outdated Show resolved Hide resolved
cpnest/proposal.py Outdated Show resolved Hide resolved
cpnest/proposal.py Outdated Show resolved Hide resolved
cpnest/proposal.py
directional derivatives of the likelihood
at x.

It uses jax.grad for automatic differentiation, but reverts to
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Should jax be added to the requirements in setup.py then?

examples/eggbox.py Outdated Show resolved Hide resolved
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wdpozzo commented Nov 4, 2019

I pushed the changes you requested

johnveitch
johnveitch requested changes Nov 13, 2019
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cpnest/proposal.py Outdated
try:
from jax import grad, jit
FINITEDIFFERENCING = False
except:
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Don't use a bare except. Use except ImportError if that is what you want to catch

examples/test_50d.py
self.analytic_log_Z=0.0 - sum([np.log(self.bounds[i][1]-self.bounds[i][0]) for i in range(self.dim)])

def log_likelihood(self,p):
return np.sum([-0.5*p[n]**2-0.5*np.log(2.0*np.pi) for n in p.names])##np.sum([self.distr.logpdf(p[n]
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Why did you change the way of getting the parameter? Isn't this going to be slower?

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Hi @wdpozzo am seeing this on the branch but I can't work out why.

john@virgo:~/src/cpnest$ python3 setup.py test
running test
WARNING: Testing via this command is deprecated and will be removed in a future version. Users looking for a generic test entry point independent of test runner are encouraged to use tox.
running egg_info
writing cpnest.egg-info/PKG-INFO
writing dependency_links to cpnest.egg-info/dependency_links.txt
writing requirements to cpnest.egg-info/requires.txt
writing top-level names to cpnest.egg-info/top_level.txt
reading manifest file 'cpnest.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'cpnest.egg-info/SOURCES.txt'
running build_ext
skipping 'cpnest/parameter.c' Cython extension (up-to-date)
copying build/lib.linux-x86_64-3.6/cpnest/parameter.cpython-36m-x86_64-linux-gnu.so -> cpnest
Checkpoint exception raise
test_run (tests.test_gaussian.GaussianTestCase) ... 2019-12-20, 10:42:34 - CPNest  : Running with 8 parallel threads
Process Process-8:
Traceback (most recent call last):
  File "/usr/lib/python3.6/multiprocessing/process.py", line 258, in _bootstrap
    self.run()
  File "/usr/lib/python3.6/multiprocessing/process.py", line 93, in run
    self._target(*self._args, **self._kwargs)
  File "/home/john/src/cpnest/cpnest/sampler.py", line 157, in produce_sample
    self._produce_sample()
  File "/home/john/src/cpnest/cpnest/sampler.py", line 191, in _produce_sample
    (Nmcmc, outParam) = next(self.yield_sample(self.logLmin.value))
  File "/home/john/src/cpnest/cpnest/sampler.py", line 288, in yield_sample
    self.estimate_nmcmc()
  File "/home/john/src/cpnest/cpnest/sampler.py", line 148, in estimate_nmcmc
    self.Nmcmc_exact = (1.0 - 1.0/tau)*self.Nmcmc_exact + (safety/tau)*(2.0/self.sub_acceptance - 1.0)
ZeroDivisionError: float division by zero

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@wdpozzo @pep8speaks @johnveitch