def map(prob, modulename='model'):
try:
mystr = "from " + modulename + " import model"
exec(mystr)
except:
print('cannot import', modulename)
M = MAP(model(prob))
M.fit()
return M
def sample(self,
model,
mapstart=False,
step_methods=None,
iters=80000,
burn=20000,
num_chains=4,
doplot=True,
showplots=False,
force=False,
progress_bar=False):
print('MCMC for %s' % self.name)
if self.is_done():
if not force:
print('\tAlready done, skipping...')
return self.db_file
else:
print(
'\tWARNING: recomputing, there might be spurious files from previous runs...'
) # Not a good idea
# Let's graph the model
graph = pymc.graph.dag(pymc.Model(model),
name=self.name,
path=self.model_dir)
graph.write_png(op.join(self.model_dir, self.name + '.png'))
start = time.time()
if mapstart:
# See http://stronginference.com/post/burn-in-and-other-mcmc-folklore
# BUT WARNING, WOULD THIS MAKE MULTIPLE CHAIN START BE OVERLY CORRELATED?
try:
from pymc import MAP
print('\tFinding MAP estimates...')
M = MAP(model)
M.fit()
model = M.variables
print('\tMAP estimates found...')
except Exception, e:
print('\tMAP Failed...', str(e))
