def from_config(cls, cp, model, output_file=None, **kwds):
"""
Loads the sampler from the given config file.
"""
skeys = {}
opts = {
'update_interval_iter_fraction': float,
'update_interval_ncall': int,
'log_interval': int,
'show_status': bool,
'dlogz': float,
'dKL': float,
'frac_remain': float,
'Lepsilon': float,
'min_ess': int,
'max_iters': int,
'max_ncalls': int,
'log_dir': str,
'stepsampling': bool,
'enable_plots': bool,
'max_num_improvement_loops': int,
'min_num_live_points': int,
'cluster_num_live_points:': int
}
for opt_name in opts:
if cp.has_option('sampler', opt_name):
value = cp.get('sampler', opt_name)
skeys[opt_name] = opts[opt_name](value)
inst = cls(model, **skeys)
do_mpi, _, rank = use_mpi()
if not do_mpi or (rank == 0):
setup_output(inst, output_file)
return inst
def __init__(self, generator, variable_args=(), record_failures=False,
**frozen_params):
self.generator = generator
self.variable_args = tuple(variable_args)
self.frozen_params = frozen_params
# we'll keep a dictionary of the current parameters for fast
# generation
self.current_params = frozen_params.copy()
# keep a list of functions to call before waveform generation
self._pregenerate_functions = []
# If we are under mpi, then failed waveform will be stored by
# mpi rank to avoid file writing conflicts. We'll check for this
# upfront
self.record_failures = (record_failures or
('PYCBC_RECORD_FAILED_WAVEFORMS' in os.environ))
self.mpi_enabled, _, self.mpi_rank = use_mpi()
def __init__(self,
model,
log_dir=None,
stepsampling=False,
enable_plots=False,
**kwargs):
super(UltranestSampler, self).__init__(model)
import ultranest
log_likelihood_call, prior_call = setup_calls(model, copy_prior=True)
# Check for cyclic boundaries
periodic = []
cyclic = self.model.prior_distribution.cyclic
for param in self.variable_params:
if param in cyclic:
logging.info('Param: %s will be cyclic', param)
periodic.append(True)
else:
periodic.append(False)
self._sampler = ultranest.ReactiveNestedSampler(
list(self.model.variable_params),
log_likelihood_call,
prior_call,
log_dir=log_dir,
wrapped_params=periodic,
resume=True)
if stepsampling:
import ultranest.stepsampler
self._sampler.stepsampler = ultranest.stepsampler.RegionBallSliceSampler(
nsteps=100,
adaptive_nsteps='move-distance',
region_filter=True)
self.enable_plots = enable_plots
self.nlive = 0
self.ndim = len(self.model.variable_params)
self.result = None
self.kwargs = kwargs # Keywords for the run method of ultranest
do_mpi, _, rank = use_mpi()
self.main = (not do_mpi) or (rank == 0)
