svi = pyro.infer.SVI(model, guide, opt, loss=pyro.infer.Trace_ELBO())
X = 35 # Predict variable 35.
for batch in data.batches(btchsz=256):
b = batch.shape[0] # Batch size.
d = batch.shape[1] # Dimension of the observations.
# Pre-process the data to avoid NaN on 0s and 1.
batch = torch.clamp(batch, min=.01, max=.99).to(device)
# Remove variable to predict.
idx = torch.tensor(np.delete(np.arange(d), X)).to(device)
obs = batch[:, idx]
loss = 0
for step in range(2500):
loss += svi.step(obs, idx)
if (step + 1) % 10 == 0:
print(loss)
loss = 0
# Inferred parameters.
infmu = pyro.param('mu')
infsd = pyro.param('sd')
import pdb
pdb.set_trace()
# Sample latent variables with approximate posterior.
z = Normal(infmu, infsd).sample([100]).view(100 * b, -1)
# Propagate forward and sample observable 'x'.
a, b = genr.detfwd(z)
xx = Beta(a[:, X:X + 1], b[:, X:X + 1]).sample().view(100, -1, 1)
x = xx.sum(dim=0) / 100.
# Compare.
torch.cat([x, batch[:, X:X + 1]], dim=1)
nbatch = 0
btot = 0
for batch in data.batches(btchsz=128):
bsz = batch.shape[0] # Batch size.
d = batch.shape[1] # Dimension of the observations.
# Pre-process the data to avoid NaN on 0s and 1.
batch = torch.clamp(batch, min=.01, max=.99).to(device)
# Remove variable to predict.
#idx = torch.tensor(np.delete(np.arange(d), X)).to(device)
idx = X
obs = batch[:, idx:idx + 1]
pyro.clear_param_store() # Important on every restart.
for step in range(2000):
svi.step(obs, idx)
# Inferred parameters.
infmu = pyro.param('mu')
infsd = pyro.param('sd')
# Sample latent variables with approximate posterior.
z = Normal(infmu, infsd).sample([1000]).view(1000 * bsz, -1)
# Propagate forward and sample observable 'x'.
with torch.no_grad():
a, b = genr.detfwd(z)
xx = Beta(a, b).sample().view(1000, -1, d)
#xx = Beta(a,b).sample().view(1000,-1,1)
tot += float(torch.sum((xx.sum(dim=0) / 1000 - batch)**2))
nbatch += 1
btot += float(bsz)
if nbatch >= 20:
break
print(X, tot / btot)
