def prior_sampler(self,
nsamples,
seed=0,
test_lprob=False,
lks=None,
verbose=True,
debug=False,
**args):
"""Draw parameters from prior.
Parameters
----------
nsamples : int
Size of the prior sample
seed : int, optional
Set the random seed (0 by default)
test_lprob : bool, optional
Whether to ensure that drawn parameters have a finite likelihood (False by default)
verbose : bool, optional
debug : bool, optional
Returns
-------
array
Numpy array of parameters
"""
import tqdm
from grgrlib import map2arr, serializer
from .stats import get_prior
store_reduce_sys = np.copy(self.fdict['reduce_sys'])
l_max, k_max = lks or (None, None)
# if not store_reduce_sys:
# self.get_sys(reduce_sys=True, verbose=verbose > 1, **args)
if test_lprob and not hasattr(self, 'ndim'):
self.prep_estim(load_R=True, verbose=verbose > 2)
frozen_prior = get_prior(self.prior, verbose=verbose)[0]
self.debug |= debug
if hasattr(self, 'pool'):
from .estimation import create_pool
create_pool(self)
set_par = serializer(self.set_par)
get_par = serializer(self.get_par)
lprob = serializer(self.lprob) if test_lprob else None
def runner(locseed):
np.random.seed(seed + locseed)
done = False
no = 0
while not done:
no += 1
with np.warnings.catch_warnings(record=False):
try:
np.warnings.filterwarnings('error')
rst = np.random.randint(2**31) # win explodes with 2**32
pdraw = [
pl.rvs(random_state=rst + sn)
for sn, pl in enumerate(frozen_prior)
]
if test_lprob:
draw_prob = lprob(pdraw,
linear=None,
verbose=verbose > 1)
done = not np.isinf(draw_prob)
else:
set_par(pdraw)
done = True
except Exception as e:
if verbose > 1:
print(str(e) + '(%s) ' % no)
return pdraw, no
if verbose > 1:
print('[prior_sample:]'.ljust(15, ' ') + ' Sampling from the pior...')
wrapper = tqdm.tqdm if verbose < 2 else (lambda x, **kwarg: x)
pmap_sim = wrapper(self.mapper(runner, range(nsamples)), total=nsamples)
draws, nos = map2arr(pmap_sim)
# if not store_reduce_sys:
# self.get_sys(reduce_sys=False, verbose=verbose > 1, **args)
if verbose:
smess = ''
if test_lprob:
smess = 'of zero likelihood, '
print(
'[prior_sample:]'.ljust(15, ' ') +
' Sampling done. %2.2f%% of the prior is either %sindetermined or explosive.'
% (100 * (sum(nos) - nsamples) / sum(nos), smess))
return draws
def prior_sampler(self, nsample, seed=None, test_lprob=False, verbose=True):
"""Draw parameters from prior. Drawn parameters have a finite likelihood.
Parameters
----------
nsample : int
Size of the prior sample
Returns
-------
array
Numpy array of parameters
"""
import tqdm
from grgrlib import map2arr, serializer
if seed is None:
seed = 0
reduce_sys = np.copy(self.fdict['reduce_sys'])
if not reduce_sys:
self.get_sys(reduce_sys=True, verbose=verbose > 1)
if test_lprob and not hasattr(self, 'ndim'):
self.prep_estim(load_R=True, verbose=verbose > 2)
if not 'frozen_prior' in self.fdict.keys():
from .stats import get_prior
self.fdict['frozen_prior'] = get_prior(self.prior)[0]
frozen_prior = self.fdict['frozen_prior']
if not hasattr(self, 'mapper'):
self.mapper = map
if hasattr(self, 'pool'):
self.pool.clear()
get_par = serializer(self.get_par)
get_sys = serializer(self.get_sys)
def runner(locseed):
np.random.seed(seed + locseed)
done = False
no = 0
while not done:
no += 1
with np.warnings.catch_warnings(record=False):
try:
np.warnings.filterwarnings('error')
rst = np.random.randint(32**2 - 2)
pdraw = [
pl.rvs(random_state=rst + sn)
for sn, pl in enumerate(frozen_prior)
]
if test_lprob:
draw_prob = lprob(pdraw, None, verbose > 1)
done = not np.isinf(draw_prob)
else:
pdraw_full = get_par(pdraw, asdict=False, full=True)
get_sys(par=pdraw_full, reduce_sys=True)
done = True
except Exception as e:
if verbose > 1:
print(str(e) + '(%s) ' % no)
return pdraw, no
if verbose > 1:
print('[prior_sample:]'.ljust(15, ' ') + ' sampling from the pior...')
wrapper = tqdm.tqdm if verbose < 2 else (lambda x, **kwarg: x)
pmap_sim = wrapper(self.mapper(runner, range(nsample)), total=nsample)
draws, nos = map2arr(pmap_sim)
if not reduce_sys:
self.get_sys(reduce_sys=False, verbose=verbose > 1)
if verbose:
print(
'[prior_sample:]'.ljust(15, ' ') +
' sampling done. %2.2f%% of the prior are either indetermined or explosive.'
% (100 * (sum(nos) - nsample) / nsample))
return draws