def prepare_problem(problemname, ndim, nlive, sampler):
loglike, grad, volume, warmup = get_problem(problemname, ndim=ndim)
if hasattr(sampler, 'set_gradient'):
sampler.set_gradient(grad)
np.random.seed(1)
us = np.random.uniform(size=(nlive, ndim))
if ndim > 1:
transformLayer = AffineLayer()
else:
transformLayer = ScalingLayer()
transformLayer.optimize(us, us)
region = MLFriends(us, transformLayer)
region.maxradiussq, region.enlarge = region.compute_enlargement(
nbootstraps=30)
region.create_ellipsoid(minvol=1.0)
Ls = np.array([loglike(u) for u in us])
ncalls = 0
nok = 0
i = 0
while True:
if i % int(nlive * 0.2) == 0:
minvol = (1 - 1. / nlive)**i
nextTransformLayer = transformLayer.create_new(us,
region.maxradiussq,
minvol=minvol)
nextregion = MLFriends(us, nextTransformLayer)
nextregion.maxradiussq, nextregion.enlarge = nextregion.compute_enlargement(
nbootstraps=30)
if nextregion.estimate_volume() <= region.estimate_volume():
region = nextregion
transformLayer = region.transformLayer
region.create_ellipsoid(minvol=minvol)
# replace lowest likelihood point
j = np.argmin(Ls)
Lmin = float(Ls[j])
while True:
u, v, logl, nc = sampler.__next__(region, Lmin, us, Ls, transform,
loglike)
ncalls += nc
if logl is not None:
break
us[j, :] = u
region.u[j, :] = u
region.unormed[j, :] = region.transformLayer.transform(u)
Ls[j] = logl
i = i + 1
#print(i, Lmin, volume(Lmin, ndim))
if np.isfinite(volume(Lmin, ndim)):
nok += 1
if nok > 2 * nlive + 1000:
break
return region, i, Lmin, us, Ls, transform, loglike
def evaluate_warmed_sampler(problemname,
ndim,
nlive,
nsteps,
sampler,
seed=1,
region_class=RobustEllipsoidRegion):
loglike, grad, volume, warmup = get_problem(problemname, ndim=ndim)
if hasattr(sampler, 'set_gradient'):
sampler.set_gradient(grad)
np.random.seed(seed)
def multi_loglike(xs):
return np.asarray([loglike(x) for x in xs])
us = np.array([warmup(ndim) for i in range(nlive)])
Ls = np.array([loglike(u) for u in us])
vol0 = volume(Ls.min(), ndim)
nwarmup = 3 * nlive
if ndim > 1:
transformLayer = AffineLayer()
else:
transformLayer = ScalingLayer()
transformLayer.optimize(us, us)
region = region_class(us, transformLayer)
region.maxradiussq, region.enlarge = region.compute_enlargement(
nbootstraps=30)
region.create_ellipsoid(minvol=vol0)
assert region.ellipsoid_center is not None
sampler.region_changed(Ls, region)
Lsequence = []
stepsequence = []
ncalls = 0
for i in tqdm.trange(nsteps + nwarmup):
if i % int(nlive * 0.2) == 0:
minvol = (1 - 1. / nlive)**i * vol0
with warnings.catch_warnings(), np.errstate(all='raise'):
try:
nextTransformLayer = transformLayer.create_new(
us, region.maxradiussq, minvol=minvol)
nextregion = region_class(us, nextTransformLayer)
nextregion.maxradiussq, nextregion.enlarge = nextregion.compute_enlargement(
nbootstraps=30)
if isinstance(nextregion, RobustEllipsoidRegion
) or nextregion.estimate_volume(
) <= region.estimate_volume():
nextregion.create_ellipsoid(minvol=minvol)
region = nextregion
transformLayer = region.transformLayer
assert region.ellipsoid_center is not None
sampler.region_changed(Ls, region)
except Warning as w:
print("not updating region because: %s" % w)
except FloatingPointError as e:
print("not updating region because: %s" % e)
except np.linalg.LinAlgError as e:
print("not updating region because: %s" % e)
# replace lowest likelihood point
j = np.argmin(Ls)
Lmin = float(Ls[j])
while True:
u, v, logl, nc = sampler.__next__(region, Lmin, us, Ls, transform,
multi_loglike)
if i > nwarmup:
ncalls += nc
if logl is not None:
assert np.isfinite(u).all(), u
assert np.isfinite(v).all(), v
assert np.isfinite(logl), logl
break
if i > nwarmup:
Lsequence.append(Lmin)
stepsequence.append(quantify_step(us[sampler.starti, :], u))
us[j, :] = u
Ls[j] = logl
Lsequence = np.asarray(Lsequence)
return Lsequence, ncalls, np.array(stepsequence)