def _init_samples(self,
mcmc_steps=5000,
mcmc_batch_size=5,
ignore_rows=0.3):
u = 2 * (np.random.uniform(size=(mcmc_batch_size, self.x_dim)) - 0.5)
v = self.transform(u)
logl = self.loglike(v)
samples = []
likes = []
for i in range(mcmc_steps):
du = np.random.standard_normal(u.shape) * 0.1
u_prime = u + du
v_prime = self.transform(u_prime)
log_ratio_1 = np.zeros(mcmc_batch_size)
prior = np.logical_or(np.abs(u) > 1, np.abs(u_prime) > 1)
idx = np.where([np.any(p) for p in prior])
log_ratio_1[idx] = -np.inf
rnd_u = np.random.rand(mcmc_batch_size)
ratio = np.clip(np.exp(log_ratio_1), 0, 1)
mask = (rnd_u < ratio).astype(int)
logl_prime = np.full(mcmc_batch_size, logl)
for idx, im in enumerate(mask):
if im:
lp = self.loglike(v_prime[idx])
if lp >= logl[idx]:
logl_prime[idx] = lp
elif rnd_u[idx] < np.clip(np.exp(lp - logl[idx]), 0, 1):
logl_prime[idx] = lp
else:
mask[idx] = 0
m = mask[:, None]
u = u_prime * m + u * (1 - m)
v = v_prime * m + v * (1 - m)
logl = logl_prime * mask + logl * (1 - mask)
samples.append(v)
likes.append(logl)
samples = np.transpose(np.array(samples), axes=[1, 0, 2])
loglikes = -np.transpose(np.array(likes), axes=[1, 0])
weights = np.ones(loglikes.shape)
self._chain_stats(samples)
self._save_samples(samples, weights, loglikes)
names = ['p%i' % i for i in range(int(self.x_dim))]
labels = [r'x_%i' % i for i in range(int(self.x_dim))]
files = chainFiles(os.path.join(self.logs['chains'], 'chain'),
first_chain=1,
last_chain=mcmc_batch_size)
mc = MCSamples(self.logs['chains'],
names=names,
labels=labels,
ignore_rows=ignore_rows)
mc.readChains(files)
return mc
def _read_samples(self, fileroot, match='', ignore_rows=0.3):
names = ['p%i' % i for i in range(int(self.num_params))]
labels = [r'x_%i' % i for i in range(int(self.num_params))]
if match:
files = glob.glob(os.path.join(fileroot, match))
else:
files = chainFiles(fileroot)
mc = MCSamples(fileroot,
names=names,
labels=labels,
ignore_rows=ignore_rows)
mc.readChains(files)
return mc
class NestedBackend(Run):
"""
Container for nested samples generated by a single run, and backends
for analysis of the run.
The other keyword arguments are generic properties passed to the parent
class, such as the identification (ID) string of the run.
:param str root:
The root filename of the sample file collection.
:param str base_dir:
The directly containing the sample file collection.
:param bool use_nestcheck:
Invoke :mod:`nestcheck` for nested sampling error analysis?
:param callable transform:
A function to transform the parameter vector to another space.
"""
def __init__(self,
root,
base_dir,
use_nestcheck,
transform=None,
overwrite_transformed=False,
**kwargs):
filerootpath = _os.path.join(base_dir, root)
_filerootpath = filerootpath
if transform is not None:
samples = _np.loadtxt(filerootpath + '.txt')
ndims = samples.shape[1] - 2
temp = transform(samples[0, 2:], old_API=True)
ntransform = len(temp) - ndims
_exists = _os.path.isfile(filerootpath + '_transformed.txt')
if not _exists or overwrite_transformed:
transformed = _np.zeros(
(samples.shape[0], samples.shape[1] + ntransform))
transformed[:, :2] = samples[:, :2]
for i in range(samples.shape[0]):
transformed[i, 2:] = transform(samples[i, 2:],
old_API=True)
_np.savetxt(filerootpath + '_transformed.txt', transformed)
filerootpath += '_transformed'
root += '_transformed'
super(NestedBackend, self).__init__(filepath=filerootpath + '.txt',
**kwargs)
if getdist is not None:
# getdist backend
self._gd_bcknd = MCSamples(
root=filerootpath,
settings=self.kde_settings,
sampler='nested',
names=self.names,
ranges=self.bounds,
labels=[self.labels[name] for name in self.names])
self._gd_bcknd.readChains(getdist.chains.chainFiles(filerootpath))
self.use_nestcheck = use_nestcheck
if self.use_nestcheck: # nestcheck backend
if transform is not None:
for ext in ['dead-birth.txt', 'phys_live-birth.txt']:
_exists = _os.path.isfile(filerootpath + ext)
if not _exists or overwrite_transformed:
samples = _np.loadtxt(_filerootpath + ext)
transformed = _np.zeros(
(samples.shape[0], samples.shape[1] + ntransform))
transformed[:, ndims + ntransform:] = samples[:,
ndims:]
for i in range(samples.shape[0]):
transformed[i,:ndims+ntransform] =\
transform(samples[i,:ndims],
old_API=True)
_np.savetxt(filerootpath + ext, transformed)
# .stats file with same root needed, but do not need to modify
# the .stats file contents
if not _os.path.isfile(filerootpath + '.stats'):
if _os.path.isfile(_filerootpath + '.stats'):
try:
from shutil import copyfile as _copyfile
except ImportError:
pass
else:
_copyfile(_filerootpath + '.stats',
filerootpath + '.stats')
try:
kwargs['implementation']
except KeyError:
print('Root %r sampling implementation not specified... '
'assuming MultiNest for nestcheck...')
self._nc_bcknd = process_multinest_run(root, base_dir=base_dir)
else:
if kwargs['implementation'] == 'multinest':
self._nc_bcknd = process_multinest_run(root,
base_dir=base_dir)
elif kwargs['implementation'] == 'polychord':
self._nc_bcknd = process_polychord_run(root,
base_dir=base_dir)
else:
raise ValueError('Cannot process with nestcheck.')
@property
def getdist_backend(self):
""" Get the :class:`getdist.mcsamples.MCSamples` instance. """
return self._gd_bcknd
@property
def nestcheck_backend(self):
""" Get the :mod:`nestcheck` backend for the nested samples. """
return self._nc_bcknd
@property
def margeStats(self):
""" Return the marginal statistics using :mod:`getdist`. """
return self._mcsamples.getMargeStats()
