I have tried to use emcee library to implement Monte Carlo Markov Chain inside a class and also make multiprocessing module works but after running such a test code:
import numpy as np
import emcee
import scipy.optimize as op
# Choose the "true" parameters.
m_true = -0.9594
b_true = 4.294
f_true = 0.534
# Generate some synthetic data from the model.
N = 50
x = np.sort(10*np.random.rand(N))
yerr = 0.1+0.5*np.random.rand(N)
y = m_true*x+b_true
y += np.abs(f_true*y) * np.random.randn(N)
y += yerr * np.random.randn(N)
class modelfit():
def __init__(self):
self.x=x
self.y=y
self.yerr=yerr
self.m=-0.6
self.b=2.0
self.f=0.9
def get_results(self):
def func(a):
model=a[0]*self.x+a[1]
inv_sigma2 = 1.0/(self.yerr**2 + model**2*np.exp(2*a[2]))
return 0.5*(np.sum((self.y-model)**2*inv_sigma2 + np.log(inv_sigma2)))
result = op.minimize(func, [self.m, self.b, np.log(self.f)],options={'gtol': 1e-6, 'disp': True})
m_ml, b_ml, lnf_ml = result["x"]
return result["x"]
def lnprior(self,theta):
m, b, lnf = theta
if -5.0 < m < 0.5 and 0.0 < b < 10.0 and -10.0 < lnf < 1.0:
return 0.0
return -np.inf
def lnprob(self,theta):
lp = self.lnprior(theta)
likelihood=self.lnlike(theta)
if not np.isfinite(lp):
return -np.inf
return lp + likelihood
def lnlike(self,theta):
m, b, lnf = theta
model = m * self.x + b
inv_sigma2 = 1.0/(self.yerr**2 + model**2*np.exp(2*lnf))
return -0.5*(np.sum((self.y-model)**2*inv_sigma2 - np.log(inv_sigma2)))
def run_mcmc(self,nstep):
ndim, nwalkers = 3, 100
pos = [self.get_results() + 1e-4*np.random.randn(ndim) for i in range(nwalkers)]
self.sampler = emcee.EnsembleSampler(nwalkers, ndim, self.lnprob,threads=10)
self.sampler.run_mcmc(pos, nstep)
test=modelfit()
test.x=x
test.y=y
test.yerr=yerr
test.get_results()
test.run_mcmc(5000)
I got this error message :
File "MCMC_model.py", line 157, in run_mcmc
self.sampler.run_mcmc(theta0, nstep)
File "build/bdist.linux-x86_64/egg/emcee/sampler.py", line 157, in run_mcmc
File "build/bdist.linux-x86_64/egg/emcee/ensemble.py", line 198, in sample
File "build/bdist.linux-x86_64/egg/emcee/ensemble.py", line 382, in _get_lnprob
File "build/bdist.linux-x86_64/egg/emcee/interruptible_pool.py", line 94, in map
File "/vol/aibn84/data2/zahra/anaconda/lib/python2.7/multiprocessing/pool.py", line 558, in get
raise self._value
cPickle.PicklingError: Can't pickle <type 'instancemethod'>: attribute lookup __builtin__.instancemethod failed
I reckon it has something to do with how I have used multiprocessing in the class but I could not figure out how I could keep the structure of my class the way it is and meanwhile use multiprocessing as well??!!
I will appreciate for any tips.
PS I have to mention the code works perfectly if I remove threads=10
from the last function.
There are a number of SO questions that discuss what's going on:
…including this one, which seems to be your response… to nearly the same question:
However, the difference here is that you are not using multiprocessing
directly -- but emcee
is. Therefore, the pathos.multiprocessing
solution (from the links above) is not available for you. Since emcee
uses cPickle
, you'll have to stick to things that pickle
knows how to serialize. You are out of luck for class instances. Typical workarounds are to either use copy_reg
to register the type of object you want to serialize, or to add a __reduce__
method to tell python how to serialize it. You can see several of the answers from the above links suggest similar things… but none enable you to keep the class the way you have written it.
For the record, you can now create a pathos.multiprocessing
pool, and pass it to emcee using the pool
argument. However, be aware that the overhead of multiprocessing can actually slow things down, unless your likelihood is particularly time-consuming to compute.
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