__author__ = 'aymgal'
import os
import json
import shutil
import numpy as np
from lenstronomy.Sampling.Samplers.base_nested_sampler import NestedSampler
import lenstronomy.Util.sampling_util as utils
__all__ = ['MultiNestSampler']
[docs]class MultiNestSampler(NestedSampler):
"""
Wrapper for nested sampling algorithm MultInest by F. Feroz & M. Hobson
papers : arXiv:0704.3704, arXiv:0809.3437, arXiv:1306.2144
pymultinest doc : https://johannesbuchner.github.io/PyMultiNest/pymultinest.html
"""
def __init__(self, likelihood_module, prior_type='uniform',
prior_means=None, prior_sigmas=None, width_scale=1, sigma_scale=1,
output_dir=None, output_basename='-',
remove_output_dir=False, use_mpi=False):
"""
:param likelihood_module: likelihood_module like in likelihood.py (should be callable)
:param prior_type: 'uniform' of 'gaussian', for converting the unit hypercube to param cube
:param prior_means: if prior_type is 'gaussian', mean for each param
:param prior_sigmas: if prior_type is 'gaussian', std dev for each param
:param width_scale: scale the widths of the parameters space by this factor
:param sigma_scale: if prior_type is 'gaussian', scale the gaussian sigma by this factor
:param output_dir: name of the folder that will contain output files
:param output_basename: prefix for output files
:param remove_output_dir: remove the output_dir folder after completion
:param use_mpi: flag directly passed to MultInest sampler (NOT TESTED)
"""
self._check_install()
super(MultiNestSampler, self).__init__(likelihood_module, prior_type,
prior_means, prior_sigmas,
width_scale, sigma_scale)
# here we assume number of dimensons = number of parameters
self.n_params = self.n_dims
if output_dir is None:
self._output_dir = 'multinest_out_default'
else:
self._output_dir = output_dir
self._is_master = True
self._use_mpi = use_mpi
if self._use_mpi:
from mpi4py import MPI
self._comm = MPI.COMM_WORLD
if self._comm.Get_rank() != 0:
self._is_master = False
else:
self._comm = None
if self._is_master:
if os.path.exists(self._output_dir):
shutil.rmtree(self._output_dir, ignore_errors=True)
os.mkdir(self._output_dir)
self.files_basename = os.path.join(self._output_dir, output_basename)
# required for analysis : save parameter names in json file
if self._is_master:
with open(self.files_basename + 'params.json', 'w') as file:
json.dump(self.param_names, file, indent=2)
self._rm_output = remove_output_dir
self._has_warned = False
[docs] def prior(self, cube, ndim, nparams):
"""
compute the mapping between the unit cube and parameter cube (in-place)
:param cube: unit hypercube, sampled by the algorithm
:param ndim: number of sampled parameters
:param nparams: total number of parameters
"""
cube_py = self._multinest2python(cube, ndim)
if self.prior_type == 'gaussian':
utils.cube2args_gaussian(cube_py, self.lowers, self.uppers,
self.means, self.sigmas, self.n_dims)
elif self.prior_type == 'uniform':
utils.cube2args_uniform(cube_py, self.lowers, self.uppers, self.n_dims)
for i in range(self.n_dims):
cube[i] = cube_py[i]
[docs] def log_likelihood(self, args, ndim, nparams):
"""
compute the log-likelihood given list of parameters
:param args: parameter values
:param ndim: number of sampled parameters
:param nparams: total number of parameters
:return: log-likelihood (from the likelihood module)
"""
args_py = self._multinest2python(args, ndim)
logL = self._ll(args_py)
if not np.isfinite(logL):
if not self._has_warned:
print("WARNING : logL is not finite : return very low value instead")
logL = -1e15
self._has_warned = True
return float(logL)
[docs] def run(self, kwargs_run):
"""
run the MultiNest nested sampler
see https://johannesbuchner.github.io/PyMultiNest/pymultinest.html for content of kwargs_run
:param kwargs_run: kwargs directly passed to pymultinest.run
:return: samples, means, logZ, logZ_err, logL, stats
"""
print("prior type :", self.prior_type)
print("parameter names :", self.param_names)
if self._pymultinest_installed:
self._pymultinest.run(self.log_likelihood, self.prior, self.n_dims,
outputfiles_basename=self.files_basename,
resume=False, verbose=True,
init_MPI=self._use_mpi, **kwargs_run)
analyzer = self._Analyzer(self.n_dims, outputfiles_basename=self.files_basename)
samples = analyzer.get_equal_weighted_posterior()[:, :-1]
data = analyzer.get_data() # gets data from the *.txt output file
stats = analyzer.get_stats()
else:
# in case MultiNest was not compiled properly, for unit tests
samples = np.zeros((1, self.n_dims))
data = np.zeros((self.n_dims, 3))
stats = {
'global evidence': np.zeros(self.n_dims),
'global evidence error': np.zeros(self.n_dims),
'modes': [{'mean': np.zeros(self.n_dims)}]
}
logL = -0.5 * data[:, 1] # since the second data column is -2*logL
logZ = stats['global evidence']
logZ_err = stats['global evidence error']
means = stats['modes'][0]['mean'] # or better to use stats['marginals'][:]['median'] ???
print("MultiNest output files have been saved to {}*"
.format(self.files_basename))
if self._rm_output and self._is_master:
shutil.rmtree(self._output_dir, ignore_errors=True)
print("MultiNest output directory removed")
return samples, means, logZ, logZ_err, logL, stats
def _multinest2python(self, multinest_list, num_dims):
"""convert ctypes list to standard python list"""
python_list = []
for i in range(num_dims):
python_list.append(multinest_list[i])
return python_list
def _check_install(self):
try:
import pymultinest
from pymultinest.analyse import Analyzer
except:
print("Warning : MultiNest/pymultinest not properly installed (results might be unexpected). \
You can get it from : https://johannesbuchner.github.io/PyMultiNest/pymultinest.html")
self._pymultinest_installed = False
else:
self._pymultinest_installed = True
self._pymultinest = pymultinest
self._Analyzer = Analyzer