def nested_auto_guide_callable(model):
guide = AutoGuideList(model)
guide.append(AutoDelta(poutine.block(model, expose=['x'])))
guide_y = AutoGuideList(poutine.block(model, expose=['y']))
guide_y.z = AutoIAFNormal(poutine.block(model, expose=['y']))
guide.append(guide_y)
return guide
def test_guide_list(auto_class):
def model():
pyro.sample("x", dist.Normal(0., 1.).expand([2]))
pyro.sample("y", dist.MultivariateNormal(torch.zeros(5), torch.eye(5, 5)))
guide = AutoGuideList(model)
guide.append(auto_class(poutine.block(model, expose=["x"])))
guide.append(auto_class(poutine.block(model, expose=["y"])))
guide()
def auto_guide_callable(model):
def guide_x():
x_loc = pyro.param("x_loc", torch.tensor(1.))
x_scale = pyro.param("x_scale", torch.tensor(.1), constraint=constraints.positive)
pyro.sample("x", dist.Normal(x_loc, x_scale))
def median_x():
return {"x": pyro.param("x_loc", torch.tensor(1.))}
guide = AutoGuideList(model)
guide.append(AutoCallable(model, guide_x, median_x))
guide.append(AutoDiagonalNormal(poutine.block(model, hide=["x"])))
return guide
def AutoMixed(model_full, init_loc={}, delta=None):
guide = AutoGuideList(model_full)
marginalised_guide_block = poutine.block(model_full,
expose_all=True,
hide_all=False,
hide=['tau'])
if delta is None:
guide.append(
AutoNormal(marginalised_guide_block,
init_loc_fn=autoguide.init_to_value(values=init_loc),
init_scale=0.05))
elif delta == 'part' or delta == 'all':
guide.append(
AutoDelta(marginalised_guide_block,
init_loc_fn=autoguide.init_to_value(values=init_loc)))
full_rank_guide_block = poutine.block(model_full,
hide_all=True,
expose=['tau'])
if delta is None or delta == 'part':
guide.append(
AutoMultivariateNormal(
full_rank_guide_block,
init_loc_fn=autoguide.init_to_value(values=init_loc),
init_scale=0.05))
else:
guide.append(
AutoDelta(full_rank_guide_block,
init_loc_fn=autoguide.init_to_value(values=init_loc)))
return guide
def test_callable(auto_class):
def model():
pyro.sample("x", dist.Normal(0., 1.))
pyro.sample("y", dist.MultivariateNormal(torch.zeros(5), torch.eye(5, 5)))
def guide_x():
x_loc = pyro.param("x_loc", torch.tensor(0.))
pyro.sample("x", dist.Delta(x_loc))
guide = AutoGuideList(model)
guide.append(guide_x)
guide.append(auto_class(poutine.block(model, expose=["y"])))
values = guide()
assert set(values) == set(["y"])
def test_discrete_parallel(continuous_class):
K = 2
data = torch.tensor([0., 1., 10., 11., 12.])
def model(data):
weights = pyro.sample('weights', dist.Dirichlet(0.5 * torch.ones(K)))
locs = pyro.sample('locs', dist.Normal(0, 10).expand_by([K]).to_event(1))
scale = pyro.sample('scale', dist.LogNormal(0, 1))
with pyro.plate('data', len(data)):
weights = weights.expand(torch.Size((len(data),)) + weights.shape)
assignment = pyro.sample('assignment', dist.Categorical(weights))
pyro.sample('obs', dist.Normal(locs[assignment], scale), obs=data)
guide = AutoGuideList(model)
guide.append(continuous_class(poutine.block(model, hide=["assignment"])))
guide.append(AutoDiscreteParallel(poutine.block(model, expose=["assignment"])))
elbo = TraceEnum_ELBO(max_plate_nesting=1)
loss = elbo.loss_and_grads(model, guide, data)
assert np.isfinite(loss), loss
def auto_guide_list_x(model):
guide = AutoGuideList(model)
guide.append(AutoDelta(poutine.block(model, expose=["x"])))
guide.append(AutoDiagonalNormal(poutine.block(model, hide=["x"])))
return guide
