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.add(auto_class(poutine.block(model, expose=["x"]), prefix="auto_x"))
guide.add(auto_class(poutine.block(model, expose=["y"]), prefix="auto_y"))
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.add(guide_x)
guide.add(auto_class(poutine.block(model, expose=["y"]), prefix="auto_y"))
values = guide()
assert set(values) == set(["y"])
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(2.),
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.add(AutoCallable(model, guide_x, median_x))
guide.add(AutoDiagonalNormal(poutine.block(model, hide=["x"])))
return guide
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.add(continuous_class(poutine.block(model, hide=["assignment"])))
guide.add(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.add(AutoDelta(poutine.block(model, expose=["x"])))
guide.add(AutoDiagonalNormal(poutine.block(model, hide=["x"])))
return guide
'beta_resp_loc', torch.randn(num_resp,
len(mix_params),
device=x.device))
beta_scale = pyro.param(
'beta_resp_scale',
torch.tril(
1. * torch.eye(len(mix_params), len(mix_params), device=x.device)),
constraint=constraints.lower_cholesky)
pyro.sample(
"beta_resp",
dist.MultivariateNormal(beta_loc, scale_tril=beta_scale).to_event(1))
# In[11]:
guide = AutoGuideList(model)
guide.add(AutoDiagonalNormal(poutine.block(model, expose=['theta',
'L_omega'])))
guide.add(my_local_guide) # automatically wrapped in an AutoCallable
# # Run variational inference
# In[12]:
# prepare data for running inference
train_x = torch.tensor(alt_attributes, dtype=torch.float)
train_x = train_x.cuda()
train_y = torch.tensor(true_choices, dtype=torch.int)
train_y = train_y.cuda()
alt_av_cuda = torch.from_numpy(alt_availability)
alt_av_cuda = alt_av_cuda.cuda()