import logging
from time import process_time
from typing import List, Union
import numpy as np
from ..problem import Problem
from ..result import Result
from ..store import autosave
from .adaptive_metropolis import AdaptiveMetropolisSampler
from .sampler import Sampler
from .util import bound_n_samples_from_env
logger = logging.getLogger(__name__)
[docs]def sample(
problem: Problem,
n_samples: int,
sampler: Sampler = None,
x0: Union[np.ndarray, List[np.ndarray]] = None,
result: Result = None,
filename: Union[str, None] = "Auto"
) -> Result:
"""
Call to do parameter sampling.
Parameters
----------
problem:
The problem to be solved. If None is provided, a
:class:`pypesto.AdaptiveMetropolisSampler` is used.
n_samples:
Number of samples to generate.
sampler:
The sampler to perform the actual sampling.
x0:
Initial parameter for the Markov chain. If None, the best parameter
found in optimization is used. Note that some samplers require an
initial parameter, some may ignore it. x0 can also be a list,
to have separate starting points for parallel tempering chains.
result:
A result to write to. If None provided, one is created from the
problem.
filename:
Name of the hdf5 file, where the result will be saved. Default is
"Auto", in which case it will automatically generate a file named
`year_month_day_sampling_result.hdf5`. Deactivate saving by
setting filename to `None`.
Returns
-------
result:
A result with filled in sample_options part.
"""
# prepare result object
if result is None:
result = Result(problem)
# number of samples
n_samples = bound_n_samples_from_env(n_samples)
# try to find initial parameters
if x0 is None:
result.optimize_result.sort()
if len(result.optimize_result.list) > 0:
x0 = problem.get_reduced_vector(
result.optimize_result.list[0]['x'])
# TODO multiple x0 for PT, #269
# set sampler
if sampler is None:
sampler = AdaptiveMetropolisSampler()
# initialize sampler to problem
sampler.initialize(problem=problem, x0=x0)
# perform the sampling and track time
t_start = process_time()
sampler.sample(n_samples=n_samples)
t_elapsed = process_time() - t_start
logger.info("Elapsed time: "+str(t_elapsed))
# extract results
sampler_result = sampler.get_samples()
# record time
sampler_result.time = t_elapsed
# record results
result.sample_result = sampler_result
autosave(filename=filename,
result=result,
store_type="sample")
return result