def run(constrainer, termination):
print('configuring NestedSampler for constrainer', constrainer)
starttime = time.time()
if hasattr(constrainer, 'get_Lmax'):
constrainer_get_Lmax = constrainer.get_Lmax
else:
constrainer_get_Lmax = None
sampler = NestedSampler(nlive_points = nlive_points,
priortransform=priortransform, loglikelihood=loglikelihood,
draw_constrained = constrainer.draw_constrained, ndim=ndim,
constrainer_get_Lmax = constrainer_get_Lmax)
constrainer.sampler = sampler
print('running nested_integrator to termination', termination)
result = nested_integrator(sampler=sampler, max_samples=max_samples, terminationcriterion=termination)
endtime = time.time()
if hasattr(constrainer, 'stats'):
constrainer.stats()
print("RESULTS:")
print('lnZ = %s +- %s' % (result['logZ'], result['logZerr']))
print('niter: %d, duration: %.1fs' % (result['niterations'], endtime - starttime))
assert -1 < result['logZ'] < 1 # should be ~0
assert 0 < result['logZerr'] < 2 # should be ~0.5
print()
ndims = 2 # two parameters
tol = 0.5 # the stopping criterion
# set "constrainer" for sampling the constrained prior
constrainer = MetricLearningFriendsConstrainer(metriclearner='truncatedscaling', force_shrink=True, rebuild_every=50, verbose=False)
# set termination condition
termination = TerminationCriterion(tolerance=tol, maxRemainderFraction=0.001)
# set the sampler
sampler = NestedSampler(nlive_points=nlive, priortransform=prior_transform, loglikelihood=loglikelihood_ultranest, draw_constrained=constrainer.draw_constrained, ndim=ndims, constrainer_get_Lmax=constrainer.get_Lmax)
constrainer.sampler = sampler
# run the nested sampling algorithm
result = nested_integrator(sampler, termination)
logZultranest = result['logZ'] # value of logZ
logZerrultranest = result['logZerr'] # estimate of the statistcal uncertainty on logZ
# output marginal likelihood
print('Marginalised evidence is {} ± {}'.format(logZultranest, logZerrultranest))
# get the posterior samples (first output is samples in the unit hypercube, so ignore that)
nsamples = np.array([xi for ui, xi, Li, logwidth in result['weights']])
probs = np.array([Li + logwidth for ui, xi, Li, logwidth in result['weights']])
probs = np.exp(probs - probs.max())
keepidx = np.where(np.random.rand(len(probs)) < probs)[0]
postsamples = nsamples[keepidx,:]
def run_nested(**config):
ndim = config['ndim']
def priortransform(u):
assert len(u) == ndim, u
return u
if 'seed' in config:
numpy.random.seed(config['seed'])
# can use directly
loglikelihood = config['loglikelihood']
nlive_points = config['nlive_points']
method = config['draw_method']
if method.startswith('naive'):
constrainer = RejectionConstrainer()
elif method.startswith('maxfriends'): # maximum distance
constrainer = FriendsConstrainer(rebuild_every=nlive_points, radial=False, force_shrink=config['force_shrink'], verbose=False)
elif method.startswith('radfriends'): # radial distance
constrainer = FriendsConstrainer(rebuild_every=nlive_points, radial=True, metric = 'euclidean', jackknife=config['jackknife'], force_shrink=config['force_shrink'], verbose=False)
elif method.startswith('supfriends'): # supreme distance
constrainer = FriendsConstrainer(rebuild_every=nlive_points, radial=True, metric = 'chebyshev', jackknife=config['jackknife'], force_shrink=config['force_shrink'], verbose=False)
elif method.startswith('optimize'):
constrainer = OptimizeConstrainer()
elif method.startswith('galilean'):
velocity_scale = config['velocity_scale']
constrainer = GalileanConstrainer(nlive_points = nlive_points, ndim = ndim, velocity_scale = velocity_scale)
elif method.startswith('mcmc'):
adapt = config['adapt']
scale = config['scale']
if config['proposer'] == 'gauss':
proposer = GaussProposal(adapt=adapt, scale = scale)
elif config['proposer'] == 'multiscale':
proposer = MultiScaleProposal(adapt=adapt, scale=scale)
constrainer = MCMCConstrainer(proposer = proposer)
else:
raise NotImplementedError('draw_method "%s" not implemented' % method)
print( 'configuring NestedSampler')
starttime = time.time()
sampler = NestedSampler(nlive_points = nlive_points,
priortransform=priortransform, loglikelihood=loglikelihood,
draw_constrained = constrainer.draw_constrained, ndim=ndim)
constrainer.sampler = sampler
print('running nested_integrator to tolerance 0.5')
assert config['integrator'] == 'normal', config['integrator']
result = nested_integrator(tolerance=0.5, sampler=sampler, max_samples=2000000)
endtime = time.time()
if hasattr(constrainer, 'stats'):
constrainer.stats()
output_basename = config['output_basename']
if config.get('seed', 0) == 0:
x = numpy.array([x for _, x, _ in sampler.samples])
y = exp([l for _, _, l in sampler.samples])
plt.plot(x[:,0], y, 'x', color='blue', ms=1)
plt.savefig(output_basename + 'nested_samples.pdf', bbox_inches='tight')
plt.close()
L = numpy.array([L for _, _, L, _ in result['weights']])
width = numpy.array([w for _, _, _, w in result['weights']])
plt.plot(width, L, 'x-', color='blue', ms=1, label='Z=%.2f (%.2f)' % (
result['logZ'], log(exp(L + width).sum())))
fromleft = exp(L + width)[::-1].cumsum()
fromleft /= fromleft.max()
mask = (fromleft < 0.99)[::-1]
if mask.any():
i = width[mask].argmax()
plt.ylim(L.max() - log(1000), L.max())
plt.fill_between(width[mask], L[mask], L.max() - log(1000), color='grey', alpha=0.3)
plt.xlabel('prior mass')
plt.ylabel('likelihood')
plt.legend(loc='best')
plt.savefig(output_basename + 'nested_integral.pdf', bbox_inches='tight')
plt.close()
posterioru, posteriorx = equal_weighted_posterior(result['weights'])
plt.figure(figsize=(ndim*2, ndim*2))
marginal_plots(weights=result['weights'], ndim=ndim)
plt.savefig(output_basename + 'posterior.pdf', bbox_inches='tight')
plt.close()
return dict(
Z_computed = float(result['logZ']),
Z_computed_err = float(result['logZerr']),
niterations = result['niterations'],
duration = endtime - starttime,
)
radial=True,
metric='euclidean',
jackknife=False,
force_shrink=False,
verbose=False)
print('preparing sampler')
sampler = NestedSampler(nlive_points=400,
priortransform=priortransform,
loglikelihood=loglikelihood,
draw_constrained=constrainer.draw_constrained,
ndim=2)
# tell constrainer about sampler so they can interact
constrainer.sampler = sampler
print('running sampler')
result = nested_integrator(tolerance=0.2, sampler=sampler)
x = numpy.array([x for _, x, _ in sampler.samples])
y = numpy.exp([l for _, _, l in sampler.samples])
plt.plot(x, y, 'x', color='blue', ms=1)
plt.savefig('nested_samples.pdf', bbox_inches='tight')
plt.close()
weights = result['weights']
L = numpy.array([Li for ui, xi, Li, logwidth in weights])
widths = numpy.array([logwidth for ui, xi, Li, logwidth in weights])
plt.plot(exp(widths), exp(L), 'x-', color='blue', ms=1)
plt.xlabel('prior mass')
plt.ylabel('likelihood')
plt.xscale('log')
plt.yscale('log')
from nested_sampling.nested_integrator import nested_integrator
from nested_sampling.nested_sampler import NestedSampler
from nested_sampling.samplers.rejection import RejectionConstrainer
from nested_sampling.samplers.friends import FriendsConstrainer
import nested_sampling.postprocess as post
#constrainer = RejectionConstrainer()
constrainer = FriendsConstrainer(radial=True,
metric='euclidean',
jackknife=True)
sampler = NestedSampler(nlive_points=400,
priortransform=priortransform,
loglikelihood=loglikelihood,
draw_constrained=constrainer.draw_constrained,
ndim=2)
constrainer.sampler = sampler
results = nested_integrator(tolerance=0.5, sampler=sampler)
# add contours?
usamples, xsamples = post.equal_weighted_posterior(results['weights'])
u, x, L, width = list(zip(*results['weights']))
x, y = numpy.array(x).T
weight = numpy.add(L, width)
#plt.plot(xsamples[:,0], xsamples[:,1], '.', color='green', alpha=0.1)
#plt.hexbin(x, y, exp(weight - weight.max()), gridsize=40, cmap=plt.cm.RdBu_r,
# vmin=0, vmax=1)
#x, y = numpy.array(xsamples).T
#plt.hexbin(x, y, gridsize=40, cmap=plt.cm.RdBu_r, vmax=len(x)/(40.), vmin=0)
def priortransform(u): return numpy.array([u[0] * 4 - 2, u[1] * 2]) # run nested sampling from nested_sampling.nested_integrator import nested_integrator from nested_sampling.nested_sampler import NestedSampler from nested_sampling.samplers.rejection import RejectionConstrainer from nested_sampling.samplers.friends import FriendsConstrainer import nested_sampling.postprocess as post #constrainer = RejectionConstrainer() constrainer = FriendsConstrainer(radial = True, metric = 'euclidean', jackknife=True) sampler = NestedSampler(nlive_points = 400, priortransform=priortransform, loglikelihood=loglikelihood, draw_constrained = constrainer.draw_constrained, ndim=2) constrainer.sampler = sampler results = nested_integrator(tolerance=0.1, sampler=sampler) # add contours? usamples, xsamples = post.equal_weighted_posterior(results['weights']) u, x, L, width = zip(*results['weights']) x, y = numpy.array(x).T weight = numpy.add(L, width) #plt.plot(xsamples[:,0], xsamples[:,1], '.', color='green', alpha=0.1) #plt.hexbin(x, y, exp(weight - weight.max()), gridsize=40, cmap=plt.cm.RdBu_r, # vmin=0, vmax=1) #x, y = numpy.array(xsamples).T #plt.hexbin(x, y, gridsize=40, cmap=plt.cm.RdBu_r, vmax=len(x)/(40.), vmin=0)
from nested_sampling.termination_criteria import TerminationCriterion
from nested_sampling.nested_sampler import NestedSampler
from nested_sampling.samplers.rejection import RejectionConstrainer
from nested_sampling.samplers.friends import FriendsConstrainer
import nested_sampling.postprocess as post
constrainer = FriendsConstrainer(radial=True,
metric='euclidean',
jackknife=True)
sampler = NestedSampler(nlive_points=400,
priortransform=priortransform,
loglikelihood=loglikelihood,
draw_constrained=constrainer.draw_constrained,
ndim=2)
constrainer.sampler = sampler
results = nested_integrator(
sampler=sampler, terminationcriterion=TerminationCriterion(tolerance=0.5))
# extract contours:
usamples, xsamples = post.equal_weighted_posterior(results['weights'])
u, x, L, width = list(zip(*results['weights']))
x, y = numpy.array(x).T
weight = numpy.add(L, width)
#plt.plot(xsamples[:,0], xsamples[:,1], '.', color='green', alpha=0.1)
#plt.hexbin(x, y, exp(weight - weight.max()), gridsize=40, cmap=plt.cm.RdBu_r,
# vmin=0, vmax=1)
#x, y = numpy.array(xsamples).T
#plt.hexbin(x, y, gridsize=40, cmap=plt.cm.RdBu_r, vmax=len(x)/(40.), vmin=0)
# create contours using the lowest values, always summing up until 1%, 10%, 50%
def run_nested(**config):
ndim = config['ndim']
def priortransform(u):
assert len(u) == ndim, u
return u
if 'seed' in config:
numpy.random.seed(config['seed'])
print('Configuring for %s, with seed=%s ...' %
(config.get('output_basename'), config.get('seed')))
# can use these directly
loglikelihood = config['loglikelihood']
nlive_points = config['nlive_points']
method = config['draw_method']
if method.startswith('naive'):
constrainer = RejectionConstrainer()
elif method.startswith('maxfriends'): # maximum distance
constrainer = FriendsConstrainer(rebuild_every=nlive_points,
radial=False,
force_shrink=config['force_shrink'],
verbose=False)
elif method.startswith('radfriends2'): # radial distance
constrainer = FriendsConstrainer2(rebuild_every=nlive_points,
radial=True,
metric='euclidean',
jackknife=config['jackknife'],
force_shrink=config['force_shrink'],
verbose=False)
elif method.startswith('supfriends2'): # supreme distance
constrainer = FriendsConstrainer2(rebuild_every=nlive_points,
radial=True,
metric='chebyshev',
jackknife=config['jackknife'],
force_shrink=config['force_shrink'],
verbose=False)
elif method.startswith('radfriends'): # radial distance
constrainer = FriendsConstrainer(
rebuild_every=nlive_points,
radial=True,
metric='euclidean',
jackknife=config['jackknife'],
force_shrink=config['force_shrink'],
keep_phantom_points=config.get('keep_phantom_points', False),
optimize_phantom_points=config.get('optimize_phantom_points',
False),
verbose=False)
elif method.startswith('mlfriends'): # metric-learning distance
constrainer = MetricLearningFriendsConstrainer(
metriclearner=config['metriclearner'],
keep_phantom_points=config.get('keep_phantom_points', False),
optimize_phantom_points=config.get('optimize_phantom_points',
False),
force_shrink=config['force_shrink'],
rebuild_every=config.get('rebuild_every', nlive_points),
verbose=False)
elif method.startswith('hradfriends'): # radial distance
friends_filter = FriendsConstrainer(
rebuild_every=nlive_points,
radial=True,
metric='euclidean',
jackknife=config['jackknife'],
force_shrink=config['force_shrink'],
keep_phantom_points=config.get('keep_phantom_points', False),
optimize_phantom_points=config.get('optimize_phantom_points',
False),
verbose=False)
if config['proposer'] == 'gauss':
proposer = nested_sampling.samplers.hybrid.FilteredGaussProposal(
adapt=True, scale=0.1)
elif config['proposer'] == 'svargauss':
proposer = nested_sampling.samplers.hybrid.FilteredSVarGaussProposal(
adapt=True, scale=0.1)
elif config['proposer'] == 'mahgauss':
proposer = nested_sampling.samplers.hybrid.FilteredMahalanobisGaussProposal(
adapt=True, scale=0.1)
elif config['proposer'] == 'harm':
proposer = nested_sampling.samplers.hybrid.FilteredUnitHARMProposal(
adapt=False, scale=1)
elif config['proposer'] == 'mahharm':
proposer = nested_sampling.samplers.hybrid.FilteredMahalanobisHARMProposal(
adapt=False, scale=1)
elif config['proposer'] == 'ptharm':
proposer = nested_sampling.samplers.hybrid.FilteredPointHARMProposal(
adapt=False, scale=10)
elif config['proposer'] == 'ess':
proposer = nested_sampling.samplers.hybrid.FilteredEllipticalSliceProposal(
)
else:
assert False, config['proposer']
if config['nsteps'] < 0:
filtered_mcmc = nested_sampling.samplers.hybrid.FilteredVarlengthMCMCConstrainer(
proposer=proposer, nsteps_initial=-config['nsteps'])
else:
filtered_mcmc = nested_sampling.samplers.hybrid.FilteredMCMCConstrainer(
proposer=proposer,
nsteps=config['nsteps'],
nminaccepts=config.get('nminaccepts', 0))
constrainer = nested_sampling.samplers.hybrid.HybridFriendsConstrainer(
friends_filter,
filtered_mcmc,
switchover_efficiency=config.get('switchover_efficiency', 0))
elif method.startswith('hmlfriends'): # radial distance
friends_filter = MetricLearningFriendsConstrainer(
rebuild_every=nlive_points,
metriclearner=config['metriclearner'],
keep_phantom_points=config.get('keep_phantom_points', False),
optimize_phantom_points=config.get('optimize_phantom_points',
False),
force_shrink=config['force_shrink'],
verbose=False)
if config['proposer'] == 'gauss':
proposer = nested_sampling.samplers.hybrid.FilteredGaussProposal(
adapt=True, scale=0.1)
elif config['proposer'] == 'harm':
proposer = nested_sampling.samplers.hybrid.FilteredUnitHARMProposal(
adapt=False, scale=1)
elif config['proposer'] == 'mahharm':
proposer = nested_sampling.samplers.hybrid.FilteredMahalanobisHARMProposal(
adapt=False, scale=1)
elif config['proposer'] == 'ptharm':
proposer = nested_sampling.samplers.hybrid.FilteredPointHARMProposal(
adapt=False, scale=10)
elif config['proposer'] == 'diffptharm':
proposer = nested_sampling.samplers.hybrid.FilteredDeltaPointHARMProposal(
adapt=False, scale=10)
elif config['proposer'] == 'ess':
proposer = nested_sampling.samplers.hybrid.FilteredEllipticalSliceProposal(
)
else:
assert False, config['proposer']
if config['nsteps'] < 0:
filtered_mcmc = nested_sampling.samplers.hybrid.FilteredVarlengthMCMCConstrainer(
proposer=proposer, nsteps_initial=-config['nsteps'])
else:
filtered_mcmc = nested_sampling.samplers.hybrid.FilteredMCMCConstrainer(
proposer=proposer,
nsteps=config['nsteps'],
nminaccepts=config.get('nminaccepts', 0))
constrainer = nested_sampling.samplers.hybrid.HybridMLFriendsConstrainer(
friends_filter,
filtered_mcmc,
switchover_efficiency=config.get('switchover_efficiency', 0),
unfiltered=config.get('unfiltered', False))
elif method.startswith('hmultiellipsoid'): # multi-ellipsoid
if config['proposer'] == 'gauss':
proposer = nested_sampling.samplers.hybrid.FilteredGaussProposal(
adapt=True, scale=0.1)
elif config['proposer'] == 'svargauss':
proposer = nested_sampling.samplers.hybrid.FilteredSVarGaussProposal(
adapt=True, scale=0.1)
elif config['proposer'] == 'mahgauss':
proposer = nested_sampling.samplers.hybrid.FilteredMahalanobisGaussProposal(
adapt=True, scale=0.1)
elif config['proposer'] == 'harm':
proposer = nested_sampling.samplers.hybrid.FilteredUnitHARMProposal(
adapt=False, scale=1)
elif config['proposer'] == 'mahharm':
proposer = nested_sampling.samplers.hybrid.FilteredMahalanobisHARMProposal(
adapt=False, scale=1)
elif config['proposer'] == 'ptharm':
proposer = nested_sampling.samplers.hybrid.FilteredPointHARMProposal(
adapt=False, scale=10)
elif config['proposer'] == 'diffptharm':
proposer = nested_sampling.samplers.hybrid.FilteredDeltaPointHARMProposal(
adapt=False, scale=10)
elif config['proposer'] == 'ess':
proposer = nested_sampling.samplers.hybrid.FilteredEllipticalSliceProposal(
)
else:
assert False, config['proposer']
if config['nsteps'] < 0:
filtered_mcmc = nested_sampling.samplers.hybrid.FilteredVarlengthMCMCConstrainer(
proposer=proposer, nsteps_initial=-config['nsteps'])
else:
filtered_mcmc = nested_sampling.samplers.hybrid.FilteredMCMCConstrainer(
proposer=proposer,
nsteps=config['nsteps'],
nminaccepts=config.get('nminaccepts', 0))
constrainer = nested_sampling.samplers.hybrid.HybridMultiEllipsoidConstrainer(
filtered_mcmc,
enlarge=config.get('enlarge', 1.2),
switchover_efficiency=config.get('switchover_efficiency', 0))
elif method.startswith('hmlmultiellipsoid'): # multi-ellipsoid
if config['proposer'] == 'gauss':
proposer = nested_sampling.samplers.hybrid.FilteredGaussProposal(
adapt=True, scale=0.1)
elif config['proposer'] == 'svargauss':
proposer = nested_sampling.samplers.hybrid.FilteredSVarGaussProposal(
adapt=True, scale=0.1)
elif config['proposer'] == 'mahgauss':
proposer = nested_sampling.samplers.hybrid.FilteredMahalanobisGaussProposal(
adapt=True, scale=0.1)
elif config['proposer'] == 'harm':
proposer = nested_sampling.samplers.hybrid.FilteredUnitHARMProposal(
adapt=False, scale=1)
elif config['proposer'] == 'mahharm':
proposer = nested_sampling.samplers.hybrid.FilteredMahalanobisHARMProposal(
adapt=False, scale=1)
elif config['proposer'] == 'ptharm':
proposer = nested_sampling.samplers.hybrid.FilteredPointHARMProposal(
adapt=False, scale=10)
elif config['proposer'] == 'diffptharm':
proposer = nested_sampling.samplers.hybrid.FilteredDeltaPointHARMProposal(
adapt=False, scale=10)
elif config['proposer'] == 'ess':
proposer = nested_sampling.samplers.hybrid.FilteredEllipticalSliceProposal(
)
else:
assert False, config['proposer']
if config['nsteps'] < 0:
filtered_mcmc = nested_sampling.samplers.hybrid.FilteredVarlengthMCMCConstrainer(
proposer=proposer, nsteps_initial=-config['nsteps'])
else:
filtered_mcmc = nested_sampling.samplers.hybrid.FilteredMCMCConstrainer(
proposer=proposer,
nsteps=config['nsteps'],
nminaccepts=config.get('nminaccepts', 0))
constrainer = nested_sampling.samplers.hybrid.HybridMLMultiEllipsoidConstrainer(
filtered_mcmc,
metriclearner=config['metriclearner'],
switchover_efficiency=config.get('switchover_efficiency', 0),
enlarge=config.get('enlarge', 1.2),
bs_enabled=config.get('bs_enabled', False),
)
elif method.startswith('supfriends'): # supreme distance
constrainer = FriendsConstrainer(rebuild_every=nlive_points,
radial=True,
metric='chebyshev',
jackknife=config['jackknife'],
force_shrink=config['force_shrink'],
verbose=False)
# These two do not work
# Because after an update, at a later time, the distances computed are rescaled based on new points
# we would need to store the metric at update time
#elif method.startswith('sradfriends'):
# constrainer = FriendsConstrainer(rebuild_every=nlive_points, radial=True, metric = 'seuclidean', jackknife=config['jackknife'], force_shrink=config['force_shrink'], verbose=False)
#elif method.startswith('mahfriends'):
# constrainer = FriendsConstrainer(rebuild_every=nlive_points, radial=True, metric = 'mahalanobis', jackknife=config['jackknife'], force_shrink=config['force_shrink'], verbose=False)
elif method.startswith('optimize'):
constrainer = OptimizeConstrainer()
elif method.startswith('ellipsoid'):
constrainer = EllipsoidConstrainer()
elif method.startswith('multiellipsoid'):
constrainer = MultiEllipsoidConstrainer()
elif method.startswith('galilean'):
velocity_scale = config['velocity_scale']
constrainer = GalileanConstrainer(nlive_points=nlive_points,
ndim=ndim,
velocity_scale=velocity_scale)
elif method.startswith('mcmc'):
adapt = config['adapt']
scale = config['scale']
if config['proposer'] == 'gauss':
proposer = GaussProposal(adapt=adapt, scale=scale)
elif config['proposer'] == 'multiscale':
proposer = MultiScaleProposal(adapt=adapt, scale=scale)
constrainer = MCMCConstrainer(proposer=proposer,
nsteps=config['nsteps'],
nminaccepts=config.get('nminaccepts', 0))
else:
raise NotImplementedError('draw_method "%s" not implemented' % method)
print('configuring TerminationCriterion')
if config.get('unlimited_sampling', False):
max_samples = None
else:
max_samples = 2000000
if config['integrator'] == 'normal':
termination = TerminationCriterion(tolerance=0.5)
elif config['integrator'] == 'normal-max':
termination = MaxErrorCriterion(tolerance=0.5)
elif config['integrator'] == 'normal-verysmall':
termination = TerminationCriterion(tolerance=0.5,
maxRemainderFraction=0.001)
elif config['integrator'] == 'normal-bs':
termination = BootstrappedCriterion(tolerance=0.5)
#result = nested_integrator(tolerance=0.5, sampler=sampler, max_samples=max_samples, need_small_remainder=False, need_robust_remainder_error=True)
elif config['integrator'] == 'normal+bs2':
termination = BootstrappedCriterion(tolerance=0.5,
maxRemainderFraction=0.5)
elif config['integrator'] == 'normal+bs3':
termination = BootstrappedCriterion(tolerance=0.5,
maxRemainderFraction=1 / 3.)
elif config['integrator'] == 'normal+bs10':
termination = BootstrappedCriterion(tolerance=0.5,
maxRemainderFraction=1 / 10.)
elif config['integrator'] == 'normal-rbs3':
termination = RememberingBootstrappedCriterion(tolerance=0.5,
memory_length=3)
elif config['integrator'] == 'normal-rbs5':
termination = RememberingBootstrappedCriterion(tolerance=0.5,
memory_length=5)
elif config['integrator'] == 'normal+rbs32':
termination = RememberingBootstrappedCriterion(
tolerance=0.5, memory_length=3, maxRemainderFraction=0.5)
elif config['integrator'] == 'normal-dbs11':
termination = DecliningBootstrappedCriterion(
tolerance=0.5, required_decrease=1., required_decrease_scatter=1.)
elif config['integrator'] == 'normal-dbs22':
termination = DecliningBootstrappedCriterion(
tolerance=0.5,
required_decrease=0.5,
required_decrease_scatter=0.5)
#elif config['integrator'] == 'normal-dbs31':
# termination = DecliningBootstrappedCriterion(tolerance=0.5, required_decrease=1./3., required_decrease_scatter=1.)
elif config['integrator'] == 'normal-dbs33':
termination = DecliningBootstrappedCriterion(
tolerance=0.5,
required_decrease=1. / 3.,
required_decrease_scatter=1. / 3.)
elif config['integrator'] == 'normal-dbs03':
termination = DecliningBootstrappedCriterion(
tolerance=0.5,
required_decrease=0.,
required_decrease_scatter=1. / 3.)
elif config['integrator'] == 'normal-dbs01':
termination = DecliningBootstrappedCriterion(
tolerance=0.5, required_decrease=0., required_decrease_scatter=1.)
elif config['integrator'] == 'normal-dbs10':
termination = DecliningBootstrappedCriterion(
tolerance=0.5, required_decrease=1., required_decrease_scatter=0.)
elif config['integrator'] == 'normal-nbs':
termination = NoisyBootstrappedCriterion(tolerance=0.5)
elif config['integrator'] == 'normal-cnbs':
termination = NoisyBootstrappedCriterion(tolerance=0.5,
conservative=True)
elif config['integrator'] == 'normal-ndbs10':
termination = NoiseDetectingBootstrappedCriterion(
tolerance=0.5, maxNoisyRemainder=0.1)
elif config['integrator'] == 'normal-ndbs100':
termination = NoiseDetectingBootstrappedCriterion(
tolerance=0.5, maxNoisyRemainder=0.01)
else:
assert config['integrator'] == 'normal', config['integrator']
# only record for the first seed
termination.plot = config.get('seed', 0) == 0
print('configuring NestedSampler')
starttime = time.time()
if hasattr(constrainer, 'get_Lmax'):
constrainer_get_Lmax = constrainer.get_Lmax
else:
constrainer_get_Lmax = None
sampler = NestedSampler(nlive_points=nlive_points,
priortransform=priortransform,
loglikelihood=loglikelihood,
draw_constrained=constrainer.draw_constrained,
ndim=ndim,
constrainer_get_Lmax=constrainer_get_Lmax)
constrainer.sampler = sampler
print('running nested_integrator to tolerance 0.5')
result = nested_integrator(sampler=sampler,
max_samples=max_samples,
terminationcriterion=termination)
endtime = time.time()
if hasattr(constrainer, 'stats'):
constrainer.stats()
output_basename = config['output_basename']
#numpy.savetxt(output_basename + 'convergencetests.txt.gz', result['convergence_tests'])
if config.get('seed', 0) == 0:
# drawn samples
print('plotting drawn samples...')
x = numpy.array([x for _, x, _ in sampler.samples])
y = exp([l for _, _, l in sampler.samples])
plt.plot(x[:, 0], y, 'x', color='blue', ms=1)
plt.savefig(output_basename + 'nested_samples.pdf',
bbox_inches='tight')
plt.close()
# L vs V
print('plotting V-L...')
L = numpy.array([L for _, _, L, _ in result['weights']])
width = numpy.array([w for _, _, _, w in result['weights']])
plt.plot(width,
L,
'x-',
color='blue',
ms=1,
label='Z=%.2f (%.2f)' %
(result['logZ'], log(exp(L + width).sum())))
fromleft = exp(L + width)[::-1].cumsum()
fromleft /= fromleft.max()
mask = (fromleft < 0.99)[::-1]
if mask.any():
i = width[mask].argmax()
plt.ylim(L.max() - log(1000), L.max())
plt.fill_between(width[mask],
L[mask],
L.max() - log(1000),
color='grey',
alpha=0.3)
plt.xlabel('prior mass')
plt.ylabel('likelihood')
plt.legend(loc='best')
plt.savefig(output_basename + 'nested_integral.pdf',
bbox_inches='tight')
plt.close()
# posteriors
print('plotting posteriors...')
posterioru, posteriorx = equal_weighted_posterior(result['weights'])
plt.figure(figsize=(ndim * 2, ndim * 2))
marginal_plots(weights=result['weights'], ndim=ndim)
plt.savefig(output_basename + 'posterior.pdf', bbox_inches='tight')
plt.close()
# plot convergence history
print('plotting Z history...')
plt.figure()
plt.plot(termination.plotdata['normalZ'], label='NS')
plt.plot(termination.plotdata['remainderZ'], label='remainder')
plt.plot(termination.plotdata['totalZ'], label='total')
hi = max(termination.plotdata['totalZ'])
plt.ylim(hi - 10, hi + 0.1)
plt.legend(loc='best', prop=dict(size=8))
plt.savefig(output_basename + 'convergence_Z.pdf', bbox_inches='tight')
plt.close()
print('plotting convergence history...')
plt.figure()
plt.plot(termination.plotdata['normalZerr'], label='NS')
plt.plot(termination.plotdata['remainderZerr'], label='remainder')
plt.plot(termination.plotdata['totalZerr'], label='total')
if 'memory_sigma' in termination.plotdata:
plt.plot(termination.plotdata['memory_sigma'],
label='memory_sigma')
if 'classic_totalZerr' in termination.plotdata:
plt.plot(termination.plotdata['classic_totalZerr'],
label='classic_totalZerr')
plt.ylim(0, 2)
plt.legend(loc='best', prop=dict(size=8))
plt.savefig(output_basename + 'convergence_Zerr.pdf',
bbox_inches='tight')
plt.close()
return dict(
Z_computed=float(result['logZ']),
Z_computed_err=float(result['logZerr']),
niterations=result['niterations'],
duration=endtime - starttime,
)