def sample(step, problem):
"""
Sample solution space with the previously initalised algorithm.
Parameters
----------
step : :class:`SMC` or :class:`pymc3.metropolis.Metropolis`
from problem.init_sampler()
problem : :class:`Problem` with characteristics of problem to solve
"""
sc = problem.config.sampler_config
pa = sc.parameters
if hasattr(pa, 'update_covariances'):
if pa.update_covariances:
update = problem
else:
update = None
if sc.name == 'Metropolis':
logger.info('... Starting Metropolis ...\n')
ensuredir(problem.outfolder)
sampler.metropolis_sample(
n_steps=pa.n_steps,
step=step,
progressbar=sc.progressbar,
buffer_size=sc.buffer_size,
homepath=problem.outfolder,
burn=pa.burn,
thin=pa.thin,
model=problem.model,
n_jobs=pa.n_jobs,
rm_flag=pa.rm_flag)
elif sc.name == 'SMC':
logger.info('... Starting SMC ...\n')
sampler.smc_sample(
pa.n_steps,
step=step,
progressbar=sc.progressbar,
model=problem.model,
n_jobs=pa.n_jobs,
stage=pa.stage,
update=update,
homepath=problem.outfolder,
buffer_size=sc.buffer_size,
rm_flag=pa.rm_flag)
elif sc.name == 'PT':
logger.info('... Starting Parallel Tempering ...\n')
sampler.pt_sample(
step=step,
n_chains=pa.n_chains,
n_samples=pa.n_samples,
swap_interval=pa.swap_interval,
beta_tune_interval=pa.beta_tune_interval,
n_workers_posterior=pa.n_chains_posterior,
homepath=problem.outfolder,
progressbar=sc.progressbar,
buffer_size=sc.buffer_size,
model=problem.model,
rm_flag=pa.rm_flag)
else:
logger.error('Sampler "%s" not implemented.' % sc.name)
def sample(step, problem):
"""
Sample solution space with the previously initalised algorithm.
Parameters
----------
step : :class:`SMC` or :class:`pymc3.metropolis.Metropolis`
from problem.init_sampler()
problem : :class:`Problem` with characteristics of problem to solve
"""
pc = problem.config.problem_config
sc = problem.config.sampler_config
pa = sc.parameters
if hasattr(pa, 'update_covariances'):
if pa.update_covariances:
update = problem
else:
update = None
if pc.mode == bconfig.ffi_mode_str:
logger.info('Chain initialization with:')
if pc.mode_config.initialization == 'random':
logger.info('Random starting point.\n')
start = None
elif pc.mode_config.initialization == 'lsq':
from tqdm import tqdm
logger.info('Least-squares-solution \n')
if 'seismic' in pc.datatypes:
logger.warning('Least-squares initialization is not'
' supported (yet) for seismic data, only!')
start = []
for i in tqdm(range(step.n_chains)):
point = problem.get_random_point()
start.append(problem.lsq_solution(point))
else:
start = None
if sc.name == 'Metropolis':
logger.info('... Starting Metropolis ...\n')
ensuredir(problem.outfolder)
sampler.metropolis_sample(n_steps=pa.n_steps,
step=step,
progressbar=sc.progressbar,
buffer_size=sc.buffer_size,
buffer_thinning=sc.buffer_thinning,
homepath=problem.outfolder,
start=start,
burn=pa.burn,
thin=pa.thin,
model=problem.model,
n_jobs=pa.n_jobs,
rm_flag=pa.rm_flag)
elif sc.name == 'SMC':
logger.info('... Starting SMC ...\n')
sampler.smc_sample(pa.n_steps,
step=step,
progressbar=sc.progressbar,
model=problem.model,
start=start,
n_jobs=pa.n_jobs,
stage=pa.stage,
update=update,
buffer_thinning=sc.buffer_thinning,
homepath=problem.outfolder,
buffer_size=sc.buffer_size,
rm_flag=pa.rm_flag)
elif sc.name == 'PT':
logger.info('... Starting Parallel Tempering ...\n')
sampler.pt_sample(
step=step,
n_chains=pa.n_chains + 1, # add master
n_samples=pa.n_samples,
start=start,
swap_interval=pa.swap_interval,
beta_tune_interval=pa.beta_tune_interval,
n_workers_posterior=pa.n_chains_posterior,
homepath=problem.outfolder,
progressbar=sc.progressbar,
buffer_size=sc.buffer_size,
buffer_thinning=sc.buffer_thinning,
model=problem.model,
resample=pa.resample,
rm_flag=pa.rm_flag,
record_worker_chains=pa.record_worker_chains)
else:
logger.error('Sampler "%s" not implemented.' % sc.name)