def polychord_sampler(self):
import pypolychord
from pypolychord.settings import PolyChordSettings
from pypolychord.priors import UniformPrior, GaussianPrior
ndim = len(self.params.p0)
nder = 0
# Log-likelihood compliant with PolyChord's input
def likelihood(theta):
return self.lnlike(theta), [0]
def prior(hypercube):
prior = []
for h, pr in zip(hypercube, self.params.p_free_priors):
if pr[1] == 'Gaussian':
prior.append(
GaussianPrior(float(pr[2][0]), float(pr[2][1]))(h))
else:
prior.append(
UniformPrior(float(pr[2][0]), float(pr[2][2]))(h))
return prior
# Optional dumper function giving run-time read access to
# the live points, dead points, weights and evidences
def dumper(live, dead, logweights, logZ, logZerr):
print("Last dead point:", dead[-1])
settings = PolyChordSettings(ndim, nder)
settings.base_dir = self.get_output(
'param_chains')[:-4] # Remove ".npz"
settings.file_root = 'pch'
settings.nlive = self.config['nlive']
settings.num_repeats = self.config['nrepeat']
settings.do_clustering = False # Assume unimodal posterior
settings.boost_posterior = 10 # Increase number of posterior samples
settings.nprior = 200 # Draw nprior initial prior samples
settings.maximise = True # Maximize posterior at the end
settings.read_resume = False # Read from resume file of earlier run
settings.feedback = 2 # Verbosity {0,1,2,3}
output = pypolychord.run_polychord(likelihood, ndim, nder, settings,
prior, dumper)
return output
def run(self):
"""
Executes the inference
"""
if self.prepare():
# Setup the inference
ndim = np.sum(self.pstep > 0)
settings = PolyChordSettings(ndim, 0)
settings.base_dir = self.outputdir
settings.file_root = self.fprefix
settings.nlive = self.nlive
settings.read_resume = self.resume
if self.nrepeat is not None:
settings.num_repeat = self.nrepeat
settings.precision_criterion = self.dlogz
settings.grade_dims = [int(ndim)]
settings.read_resume = False
settings.feedback = self.verb
# Run it
if self.dumper is not None:
out = pypolychord.run_polychord(self.loglike, ndim, 0,
settings, self.prior,
self.dumper)
else:
out = pypolychord.run_polychord(self.loglike, ndim, 0,
settings, self.prior)
outp = np.loadtxt(os.path.join(self.outputdir, self.fprefix) +\
'_equal_weights.txt')
self.outp = outp[:, 2:].T
ibest = np.argmin(outp[:, 1])
self.bestp = self.outp[:, ibest]
# Save posterior and bestfit params
if self.fsavefile is not None:
np.save(self.fsavefile, self.outp)
if self.fbestp is not None:
np.save(self.fbestp, self.bestp)
return self.outp, self.bestp
else:
if self.verb:
print("Sampler is not fully prepared to run. " + \
"Correct the above errors and try again.")
def PC_fit(self,nlive_const='auto', dynamic=True,dynamic_goal=1.0, ninit=100,
basename='dypc_chains', verbose=True, plot=False):
'''
Parameters
----------
dynamic_goal : float, opt
Parameter in [0,1] determining whether algorithm prioritizes accuracy in
evidence accuracy (goal near 0) or parameter estimation (goal near 1).
ninit : int, opt
Number of live points to use in initial exploratory run.
nlive_const : int, opt
Total computational budget, equivalent to non-dynamic nested sampling with nlive_const live points.
dynamic : bool, opt
If True, use dynamic nested sampling via dyPolyChord. Otherwise, use the
standard PolyChord.
basename : str, opt
Location in which chains will be stored.
verbose : bool, opt
If True, text will be output on the terminal to check how run is proceeding.
plot : bool, opt
Display some sample plots to check result of dynamic slice nested sampling.
'''
if dynamic:
print('Dynamic slice nested sampling')
else:
print('Slice nested sampling')
# obtain maximum likelihood fits
theta0 = self.lineModel.guessFit()
self.result_ml = self.optimizeFit(theta0)
self.theta_ml = self.result_ml['x']
# save theta_ml also in the lineModel object,
# so that constraints may be set based on ML result
self.lineModel.theta_ml = self.theta_ml
# save moments obtained from maximum likelihood optimization
self.m_ml = self.lineModel.modelMoments(self.theta_ml)
# dimensionality of the problem
self.ndim = int(self.lineModel.thetaLength())
if dynamic:
# dyPolyChord (dynamic slice nested sampling)
# ------------------------------
try:
import dyPolyChord.pypolychord_utils
import dyPolyChord
except:
print("********************")
print("Could not import dyPolyChord! Make sure that this is in your PYTHONPATH.")
print("PolyChord must also be on your LD_LIBRARY_PATH")
raise ValueError("Abort BSFC fit")
#Make a callable for running dyPolyChord
my_callable = dyPolyChord.pypolychord_utils.RunPyPolyChord(
self.PC_loglike,
self.lineModel.hypercube_lnprior_generalized_simplex,
self.ndim
)
# Specify sampler settings (see run_dynamic_ns.py documentation for more details)
settings_dict = {'file_root': 'bsfc',
'base_dir': basename,
'seed': 1}
# Run dyPolyChord
MPI_parallel=True
if MPI_parallel:
from mpi4py import MPI
comm = MPI.COMM_WORLD
dyPolyChord.run_dypolychord(my_callable, dynamic_goal, settings_dict,
ninit=ninit,
nlive_const=int(25*self.ndim) if nlive_const=='auto' else nlive_const,
comm=comm)
else:
dyPolyChord.run_dypolychord(my_callable, dynamic_goal, settings_dict,
ninit=ninit,
nlive_const=int(25*self.ndim) if nlive_const=='auto' else nlive_const)
else:
# PolyChord (slice nested sampling)
# ------------------------------
try:
import pypolychord
from pypolychord.settings import PolyChordSettings
except:
print("********************")
print("Could not import pypolychord! Make sure that this is in your PYTHONPATH.")
raise ValueError("Abort BSFC fit")
nDerived=0
settings = PolyChordSettings(self.ndim, nDerived)
settings.file_root = 'bsfc'
settings.base_dir = basename
settings.nlive = int(25*self.ndim) if nlive_const=='auto' else int(nlive_const)
#settings.do_clustering = True
#settings.read_resume = False
settings.feedback = 3
def dumper(live, dead, logweights, logZ, logZerr):
#print("Last dead point:", dead[-1])
print("logZ = "+str(logZ)+"+/-"+str(logZerr))
self.polychord_output = pypolychord.run_polychord(self.PC_loglike,
self.ndim,
nDerived,
settings,
self.lineModel.hypercube_lnprior_generalized_simplex,
dumper)
self.good=True
# In this module I'm going to investigate the effect of the so-called
# Lasenby parameter as well as the effects of proper prior
# repartitioning, and benchmark to see a speedup.
# Vanilla
mu = numpy.array([1.0, 2.5])
cov = numpy.array([[1.0, 0.6], [0.6, 1.0]])
nDims = mu.size
settings = PolyChordSettings(nDims, 0)
settings.file_root = 'vanilla'
settings.nlive = 10**3
settings.read_resume = False
settings.do_clustering = True
settings.feedback = 0
def gaussian_likelihood(theta):
invSig = numpy.linalg.inv(cov)
norm = numpy.linalg.slogdet(2 * numpy.pi * cov)[1] / 2
logL = -norm - (theta - mu) @ invSig @ (theta - mu) / 2
return logL, []
def uniform_prior(point_in_hypercube):
return pypolychord.priors.UniformPrior(-20, 20)(point_in_hypercube)
# try:
# samples = anesthetic.NestedSamples(root='./chains/vanilla')
def pc(test_statistic,
transform,
n_dim,
observed,
n_live=100,
base_dir="chains/",
file_root="pc_",
do_clustering=False,
resume=False,
ev_data=False,
feedback=0,
**kwargs):
"""
Nested sampling with PC
"""
# copy key word arguments to settings object
settings = PolyChordSettings(n_dim, 0, **kwargs)
settings.nfail = n_live
settings.precision_criterion = 0.
settings.read_resume = resume
settings.base_dir = base_dir
settings.file_root = file_root
settings.nlive = n_live
settings.logLstop = observed
settings.do_clustering = do_clustering
settings.feedback = feedback
loglike = pc_wrap(test_statistic)
output = pypolychord.run_polychord(loglike, n_dim, 0, settings, transform,
dumper(0, observed))
# get number of calls directly
calls = output.nlike
# get log X from resume file
label = "=== local volume -- log(<X_p>) ==="
log_xp = None
res_name = "{}/{}.resume".format(base_dir, file_root)
with open(res_name) as res_file:
for line in res_file:
if line.strip() == label:
next_line = res_file.readline()
log_xp = np.array([float(e) for e in next_line.split()])
break
else:
raise RuntimeError("didn't find {}".format(label))
log_x = logsumexp(log_xp)
n_iter = -log_x * n_live
if not ev_data:
return Result.from_ns(n_iter, n_live, calls)
# get ev data
ev_name = "{}/{}_dead.txt".format(base_dir, file_root)
ev_data = np.genfromtxt(ev_name)
test_statistic = ev_data[:, 0]
log_x = -np.arange(0, len(test_statistic), 1.) / n_live
log_x_delta = np.sqrt(-log_x / n_live)
return Result.from_ns(n_iter, n_live,
calls), [test_statistic, log_x, log_x_delta]
# | The derived parameter is the squared radius
nDims = 4
nDerived = 0
sigma = 0.1
mu = 0
nlive = 72
thetamin, thetamax = -1000, 1000
betamin, betamax = 0, 1
sigmaLL = ((thetamax - thetamin) / sqrt(2 * pi))
# outputs = {}
# samples = {}
# colors = {}
normal_settings = PolyChordSettings(nDims, nDerived)
normal_settings.read_resume = False
normal_settings.feedback = 0
normal_settings.nlive = nlive
normal_settings.nDims = nDims
normal_settings.nDerived = nDerived
repart_settings = PolyChordSettings(nDims + 1, nDerived)
repart_settings.read_resume = False
repart_settings.feedback = 0
repart_settings.nlive = nlive
repart_settings.nDims = nDims + 1
repart_settings.nDerived = nDerived
def rank_nlike_calls(outs):
for k in sorted(outputs, key=(lambda x: outputs[x].nlike)):
print('{} {:.2E}'.format(k, outs[k].nlike))