def test_stepsampler_cubeslice(plot=False):
np.random.seed(3)
sampler = ReactiveNestedSampler(paramnames, loglike, transform=transform1)
sampler.stepsampler = CubeSliceSampler(nsteps=len(paramnames))
r = sampler.run(log_interval=50, min_num_live_points=400)
sampler.print_results()
a = (np.abs(r['samples'] - 0.7) < 0.1).all(axis=1)
b = (np.abs(r['samples'] - 0.3) < 0.1).all(axis=1)
assert a.sum() > 1
assert b.sum() > 1
def test_stepsampler_adapt_when_stuck(plot=False):
# check that a stuck sampler can free itself
np.random.seed(1)
us = np.random.normal(0.7, 0.001, size=(1000, len(paramnames)))
region = make_region(len(paramnames), us=us)
Ls = loglike(us)
Lmin = Ls.min()
print('CubeMHSampler')
stepsampler = CubeMHSampler(nsteps=1, region_filter=True)
np.random.seed(23)
old_scale = stepsampler.scale
for i in range(1000):
if i > 100:
assert False, i
unew, pnew, Lnew, nc = stepsampler.__next__(region,
Lmin,
us,
Ls,
transform,
loglike,
ndraw=10)
if unew is not None:
break
new_scale = stepsampler.scale
assert new_scale != old_scale
assert new_scale < 0.01, new_scale
print('CubeSliceSampler')
stepsampler = CubeSliceSampler(nsteps=1, region_filter=True)
np.random.seed(23)
old_scale = stepsampler.scale
for j in range(100):
for i in range(1000):
if i > 100:
assert False, i
unew, pnew, Lnew, nc = stepsampler.__next__(region,
Lmin,
us,
Ls,
transform,
loglike,
ndraw=10)
if unew is not None:
break
new_scale = stepsampler.scale
assert new_scale != old_scale
assert new_scale < 0.01, new_scale
def main(args):
nlive = args.num_live_points
ndim = args.x_dim
nsteps = args.nsteps
problemname = args.problem
samplers = [
#CubeMHSampler(nsteps=16), #CubeMHSampler(nsteps=4), CubeMHSampler(nsteps=1),
#RegionMHSampler(nsteps=16), #RegionMHSampler(nsteps=4), RegionMHSampler(nsteps=1),
##DESampler(nsteps=16), DESampler(nsteps=4), #DESampler(nsteps=1),
CubeSliceSampler(
nsteps=2 * ndim
), #CubeSliceSampler(nsteps=ndim), CubeSliceSampler(nsteps=max(1, ndim//2)),
#RegionSliceSampler(nsteps=ndim), RegionSliceSampler(nsteps=max(1, ndim//2)),
#RegionSliceSampler(nsteps=2), RegionSliceSampler(nsteps=4),
#RegionSliceSampler(nsteps=ndim), RegionSliceSampler(nsteps=4*ndim),
#RegionBallSliceSampler(nsteps=2*ndim), RegionBallSliceSampler(nsteps=ndim), RegionBallSliceSampler(nsteps=max(1, ndim//2)),
# RegionSequentialSliceSampler(nsteps=2*ndim), RegionSequentialSliceSampler(nsteps=ndim), RegionSequentialSliceSampler(nsteps=max(1, ndim//2)),
#SpeedVariableRegionSliceSampler([Ellipsis]*ndim), SpeedVariableRegionSliceSampler([slice(i, ndim) for i in range(ndim)]),
#SpeedVariableRegionSliceSampler([Ellipsis]*ndim + [slice(1 + ndim//2, None)]*ndim),
]
if ndim < 14:
samplers.insert(0, MLFriendsSampler())
colors = {}
linestyles = {1: ':', 2: ':', 4: '--', 16: '-', 32: '-', 64: '-', -1: '-'}
markers = {1: 'x', 2: 'x', 4: '^', 16: 'o', 32: 's', 64: 's', -1: 'o'}
for isteps, ls, m in (max(1, ndim // 2), ':',
'x'), (ndim, '--',
'^'), (ndim + 1, '--',
'^'), (ndim * 2, '-',
'o'), (ndim * 4, '-.',
'^'), (ndim * 8, '-',
'v'), (ndim * 16,
'-', '>'):
if isteps not in markers:
markers[isteps] = m
if isteps not in linestyles:
linestyles[isteps] = ls
Lsequence_ref = None
label_ref = None
axL = plt.figure('Lseq').gca()
axS = plt.figure('shrinkage').gca()
axspeed = plt.figure('speed').gca()
plt.figure('stepsize', figsize=(14, 6))
axstep1 = plt.subplot(1, 3, 1)
axstep2 = plt.subplot(1, 3, 2)
axstep3 = plt.subplot(1, 3, 3)
lastspeed = None, None, None
for sampler in samplers:
print("evaluating sampler: %s" % sampler)
Lsequence, ncalls, steps = evaluate_warmed_sampler(
problemname, ndim, nlive, nsteps, sampler)
loglike, grad, volume, warmup = get_problem(problemname, ndim=ndim)
assert np.isfinite(Lsequence).all(), Lsequence
vol = np.asarray([volume(Li, ndim) for Li in Lsequence])
assert np.isfinite(vol).any(), (
"Sampler has not reached interesting likelihoods", vol, Lsequence)
shrinkage = 1 - (vol[np.isfinite(vol)][1:] /
vol[np.isfinite(vol)][:-1])**(1. / ndim)
fullsamplername = str(sampler)
samplername = fullsamplername.split('(')[0]
label = fullsamplername + ' %d evals' % ncalls
if Lsequence_ref is None:
label_ref = label
Lsequence_ref = Lsequence
ls = '-'
color = 'pink'
else:
color = colors.get(samplername)
ls = '-' if sampler.adaptive_nsteps else linestyles[sampler.nsteps]
l, = axL.plot(Lsequence_ref,
Lsequence,
label=label,
color=color,
linestyle=ls,
lw=1)
colors[samplername] = l.get_color()
# convert to a uniformly distributed variable, according to expectations
cdf_expected = 1 - (1 - shrinkage)**(ndim * nlive)
axS.hist(cdf_expected,
cumulative=True,
density=True,
histtype='step',
bins=np.linspace(0, 1, 4000),
label=label,
color=color,
ls=ls)
print("%s shrunk %.4f, from %d shrinkage samples" %
(fullsamplername, cdf_expected.mean(), len(shrinkage)))
axspeed.plot(cdf_expected.mean(),
ncalls,
markers[1 if sampler.adaptive_nsteps else sampler.nsteps],
label=label,
color=color)
if lastspeed[0] == samplername:
axspeed.plot([lastspeed[1], cdf_expected.mean()],
[lastspeed[2], ncalls],
'-',
color=color)
lastspeed = [samplername, cdf_expected.mean(), ncalls]
stepsize, angular_step, radial_step = steps.transpose()
assert len(stepsize) == len(Lsequence), (len(stepsize), len(Lsequence))
# here we estimate the volume differently: from the expected shrinkage per iteration
it = np.arange(len(stepsizesq))
vol = (1 - 1. / nlive)**it
assert np.isfinite(vol).all(), vol
assert (vol > 0).all(), vol
assert (vol <= 1).all(), vol
relstepsize = stepsize / vol**(1. / ndim)
relradial_step = radial_step / vol**(1. / ndim)
axstep1.hist(relstepsize[np.isfinite(relstepsize)],
bins=1000,
cumulative=True,
density=True,
histtype='step',
label=label,
color=color,
ls=ls)
axstep2.hist(angular_step,
bins=1000,
cumulative=True,
density=True,
histtype='step',
label=label,
color=color,
ls=ls)
axstep3.hist(relradial_step,
bins=1000,
cumulative=True,
density=True,
histtype='step',
label=label,
color=color,
ls=ls)
sampler.plot(filename='evaluate_sampling_%s_%dd_N%d_%s.png' %
(args.problem, ndim, nlive, samplername))
print('range:', Lsequence_ref[0], Lsequence_ref[-1])
axL.plot([Lsequence_ref[0], Lsequence_ref[-1]],
[Lsequence_ref[0], Lsequence_ref[-1]],
'-',
color='k',
lw=1,
label=label_ref)
axL.set_xlabel('logL (reference)')
axL.set_ylabel('logL')
lo, hi = Lsequence_ref[int(len(Lsequence_ref) * 0.1)], Lsequence_ref[-1]
axL.set_xlim(lo, hi)
axL.set_ylim(lo, hi)
axL.legend(loc='best', prop=dict(size=6))
filename = 'evaluate_sampling_%s_%dd_N%d_L.pdf' % (args.problem, ndim,
nlive)
print("plotting to %s ..." % filename)
plt.figure('Lseq')
plt.savefig(filename, bbox_inches='tight')
plt.close()
plt.figure('shrinkage')
plt.xlabel('Shrinkage Volume')
plt.ylabel('Cumulative Distribution')
plt.xlim(0, 1)
plt.plot([0, 1], [0, 1], '--', color='k')
plt.legend(loc='upper left', prop=dict(size=6))
filename = 'evaluate_sampling_%s_%dd_N%d_shrinkage.pdf' % (args.problem,
ndim, nlive)
print("plotting to %s ..." % filename)
plt.savefig(filename, bbox_inches='tight')
plt.close()
plt.figure('speed')
plt.xlabel('Bias')
plt.ylabel('# of function evaluations')
plt.yscale('log')
lo, hi = plt.xlim()
hi = max(0.5 - lo, hi - 0.5, 0.04)
plt.xlim(0.5 - hi, 0.5 + hi)
lo, hi = plt.ylim()
plt.vlines(0.5, lo, hi)
plt.ylim(lo, hi)
plt.legend(loc='best', prop=dict(size=6), fancybox=True, framealpha=0.5)
filename = 'evaluate_sampling_%s_%dd_N%d_speed.pdf' % (args.problem, ndim,
nlive)
print("plotting to %s ..." % filename)
plt.savefig(filename, bbox_inches='tight')
plt.savefig(filename.replace('.pdf', '.png'), bbox_inches='tight')
plt.close()
plt.figure('stepsize')
axstep1.set_ylabel('Cumulative Distribution')
axstep1.set_xlabel('Euclidean distance')
axstep1.legend(loc='lower right', prop=dict(size=6))
axstep2.set_ylabel('Cumulative Distribution')
axstep2.set_xlabel('Angular distance')
#axstep2.legend(loc='best', prop=dict(size=6))
axstep3.set_ylabel('Cumulative Distribution')
axstep3.set_xlabel('Radial distance')
#axstep3.legend(loc='best', prop=dict(size=6))
filename = 'evaluate_sampling_%s_%dd_N%d_step.pdf' % (args.problem, ndim,
nlive)
print("plotting to %s ..." % filename)
plt.savefig(filename, bbox_inches='tight')
plt.close()