def test_non_blocked():
"""Test that samplers correctly create non-blocked compound steps.
"""
start, model = simple_2model()
with model:
# Metropolis and Slice are non-blocked by default
mh = Metropolis()
assert isinstance(mh, CompoundStep)
slicer = Slice()
assert isinstance(slicer, CompoundStep)
hmc = HamiltonianMC(blocked=False)
assert isinstance(hmc, CompoundStep)
nuts = NUTS(blocked=False)
assert isinstance(nuts, CompoundStep)
mh_blocked = Metropolis(blocked=True)
assert isinstance(mh_blocked, Metropolis)
slicer_blocked = Slice(blocked=True)
assert isinstance(slicer_blocked, Slice)
hmc_blocked = HamiltonianMC()
assert isinstance(hmc_blocked, HamiltonianMC)
nuts_blocked = NUTS()
assert isinstance(nuts_blocked, NUTS)
compound = CompoundStep([hmc_blocked, mh_blocked])
def test_step_continuous():
start, model, (mu, C) = mv_simple()
with model:
mh = Metropolis()
slicer = Slice()
hmc = HamiltonianMC(scaling=C, is_cov=True, blocked=False)
nuts = NUTS(scaling=C, is_cov=True, blocked=False)
mh_blocked = Metropolis(S=C,
proposal_dist=MultivariateNormalProposal,
blocked=True)
slicer_blocked = Slice(blocked=True)
hmc_blocked = HamiltonianMC(scaling=C, is_cov=True)
nuts_blocked = NUTS(scaling=C, is_cov=True)
compound = CompoundStep([hmc_blocked, mh_blocked])
steps = [
slicer, hmc, nuts, mh_blocked, hmc_blocked, slicer_blocked,
nuts_blocked, compound
]
unc = np.diag(C)**.5
check = [('x', np.mean, mu, unc / 10.), ('x', np.std, unc, unc / 10.)]
for st in steps:
h = sample(8000, st, start, model=model, random_seed=1)
for (var, stat, val, bound) in check:
yield check_stat, repr(st), h, var, stat, val, bound
def test_proposal_choice(self):
_, model, _ = mv_simple()
with model:
s = np.ones(model.ndim)
sampler = Metropolis(S=s)
assert isinstance(sampler.proposal_dist, NormalProposal)
s = np.diag(s)
sampler = Metropolis(S=s)
assert isinstance(sampler.proposal_dist, MultivariateNormalProposal)
s[0, 0] = -s[0, 0]
with self.assertRaises(np.linalg.LinAlgError):
sampler = Metropolis(S=s)
def test_step_continuous(self):
start, model, (mu, C) = mv_simple()
unc = np.diag(C)**.5
check = (('x', np.mean, mu, unc / 10.), ('x', np.std, unc, unc / 10.))
with model:
steps = (
Slice(),
HamiltonianMC(scaling=C, is_cov=True, blocked=False),
NUTS(scaling=C, is_cov=True, blocked=False),
Metropolis(S=C,
proposal_dist=MultivariateNormalProposal,
blocked=True),
Slice(blocked=True),
HamiltonianMC(scaling=C, is_cov=True),
NUTS(scaling=C, is_cov=True),
CompoundStep([
HamiltonianMC(scaling=C, is_cov=True),
HamiltonianMC(scaling=C, is_cov=True, blocked=False)
]),
)
for step in steps:
trace = sample(8000,
step=step,
start=start,
model=model,
random_seed=1)
yield self.check_stat, check, trace, step.__class__.__name__
def test_step_continuous(self):
start, model, (mu, C) = mv_simple()
unc = np.diag(C)**0.5
check = (("x", np.mean, mu, unc / 10.0), ("x", np.std, unc,
unc / 10.0))
with model:
steps = (
Slice(),
HamiltonianMC(scaling=C, is_cov=True, blocked=False),
NUTS(scaling=C, is_cov=True, blocked=False),
Metropolis(S=C,
proposal_dist=MultivariateNormalProposal,
blocked=True),
Slice(blocked=True),
HamiltonianMC(scaling=C, is_cov=True),
NUTS(scaling=C, is_cov=True),
CompoundStep([
HamiltonianMC(scaling=C, is_cov=True),
HamiltonianMC(scaling=C, is_cov=True, blocked=False),
]),
)
for step in steps:
trace = sample(
0,
tune=8000,
chains=1,
discard_tuned_samples=False,
step=step,
start=start,
model=model,
#random_seed=1,
cores=1)
self.check_stat(check, trace, step.__class__.__name__)
def test_step_discrete(self):
start, model, (mu, C) = mv_simple_discrete()
unc = np.diag(C) ** .5
check = (('x', np.mean, mu, unc / 10.),
('x', np.std, unc, unc / 10.))
with model:
steps = (
Metropolis(S=C, proposal_dist=MultivariateNormalProposal),
)
for step in steps:
trace = sample(20000, step=step, start=start, model=model, random_seed=1)
yield self.check_stat, check, trace, step.__class__.__name__
def test_step_discrete(self):
if theano.config.floatX == "float32":
return # Cannot use @skip because it only skips one iteration of the yield
start, model, (mu, C) = mv_simple_discrete()
unc = np.diag(C) ** 0.5
check = (("x", np.mean, mu, unc / 10.0), ("x", np.std, unc, unc / 10.0))
with model:
steps = (Metropolis(S=C, proposal_dist=MultivariateNormalProposal),)
for step in steps:
trace = sample(
20000, tune=0, step=step, start=start, model=model, random_seed=1, chains=1
)
self.check_stat(check, trace, step.__class__.__name__)
def test_tuning_reset(self):
"""Re-use of the step method instance with cores=1 must not leak tuning information between chains."""
with Model() as pmodel:
D = 3
Normal('n', 0, 2, shape=(D, ))
trace = sample(tune=600,
draws=500,
step=Metropolis(tune=True, scaling=0.1),
cores=1,
chains=3,
discard_tuned_samples=False)
for c in range(trace.nchains):
# check that the tuned settings changed and were reset
assert trace.get_sampler_stats('scaling', chains=c)[0] == 0.1
assert trace.get_sampler_stats('scaling', chains=c)[-1] != 0.1
pass
def test_step_discrete():
start, model, (mu, C) = mv_simple_discrete()
with model:
mh = Metropolis(S=C, proposal_dist=MultivariateNormalProposal)
slicer = Slice()
steps = [mh]
unc = np.diag(C)**.5
check = [('x', np.mean, mu, unc / 10.), ('x', np.std, unc, unc / 10.)]
for st in steps:
h = sample(20000, st, start, model=model, random_seed=1)
for (var, stat, val, bound) in check:
yield check_stat, repr(st), h, var, stat, val, bound