def time_glm_hierarchical_init(self, init):
"""How long does it take to run the initialization."""
with glm_hierarchical_model():
pm.init_nuts(init=init,
chains=self.chains,
progressbar=False,
seeds=np.arange(self.chains))
def check_exec_nuts_init(method):
with pm.Model() as model:
pm.Normal("a", mu=0, sigma=1, size=2)
pm.HalfNormal("b", sigma=1)
with model:
start, _ = pm.init_nuts(init=method, n_init=10, seeds=[1])
assert isinstance(start, list)
assert len(start) == 1
assert isinstance(start[0], dict)
assert model.a.tag.value_var.name in start[0]
assert model.b.tag.value_var.name in start[0]
start, _ = pm.init_nuts(init=method, n_init=10, chains=2, seeds=[1, 2])
assert isinstance(start, list)
assert len(start) == 2
assert isinstance(start[0], dict)
assert model.a.tag.value_var.name in start[0]
assert model.b.tag.value_var.name in start[0]
def track_glm_hierarchical_ess(self, init):
with glm_hierarchical_model():
start, step = pm.init_nuts(init=init,
chains=self.chains,
progressbar=False,
seeds=np.arange(self.chains))
t0 = time.time()
idata = pm.sample(
draws=self.draws,
step=step,
cores=4,
chains=self.chains,
start=start,
seeds=np.arange(self.chains),
progressbar=False,
compute_convergence_checks=False,
)
tot = time.time() - t0
ess = float(az.ess(idata, var_names=["mu_a"])["mu_a"].values)
return ess / tot
def track_marginal_mixture_model_ess(self, init):
model, start = mixture_model()
with model:
_, step = pm.init_nuts(init=init,
chains=self.chains,
progressbar=False,
seeds=np.arange(self.chains))
start = [{k: v
for k, v in start.items()} for _ in range(self.chains)]
t0 = time.time()
idata = pm.sample(
draws=self.draws,
step=step,
cores=4,
chains=self.chains,
start=start,
seeds=np.arange(self.chains),
progressbar=False,
compute_convergence_checks=False,
)
tot = time.time() - t0
ess = az.ess(idata, var_names=["mu"])["mu"].values.min() # worst case
return ess / tot