3. Neural ratio estimation#
This tutorial demonstrates how to perform neural ratio estimation (NRE) with lampe
.
%matplotlib inline
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import zuko
from itertools import islice
from tqdm import tqdm
from lampe.data import JointLoader
from lampe.inference import NRE, NRELoss, MetropolisHastings
from lampe.plots import nice_rc, corner, mark_point
from lampe.utils import GDStep
3.1. Simulator#
LABELS = [r'$\theta_1$', r'$\theta_2$', r'$\theta_3$']
LOWER = -torch.ones(3)
UPPER = torch.ones(3)
prior = zuko.distributions.BoxUniform(LOWER, UPPER)
def simulator(theta: torch.Tensor) -> torch.Tensor:
x = torch.stack([
theta[..., 0] + theta[..., 1] * theta[..., 2],
theta[..., 0] * theta[..., 1] + theta[..., 2],
], dim=-1)
return x + 0.05 * torch.randn_like(x)
theta = prior.sample()
x = simulator(theta)
print(theta, x, sep='\n')
tensor([ 0.2592, -0.2490, 0.0877])
tensor([0.1855, 0.0274])
loader = JointLoader(prior, simulator, batch_size=256, vectorized=True)
3.2. Training#
The principle of neural ratio estimation (NRE) is to train a classifier network \(d_\phi(\theta, x)\) to distinguish between pairs \((\theta, x)\) equally sampled from the joint distribution \(p(\theta, x)\) or from the product of the marginals \(p(\theta) p(x)\). We use the NRE
class provided by the lampe.inference
module to create a classifier network adapted to the simulator’s input and output sizes. For numerical stability reasons, the created network returns the logit of the class prediction \(\text{logit}(d_\phi(\theta, x)) = \log r_\phi(\theta, x)\).
estimator = NRE(3, 2, hidden_features=[64] * 5, activation=nn.ELU)
estimator
NRE(
(net): MLP(
(0): Linear(in_features=5, out_features=64, bias=True)
(1): ELU(alpha=1.0)
(2): Linear(in_features=64, out_features=64, bias=True)
(3): ELU(alpha=1.0)
(4): Linear(in_features=64, out_features=64, bias=True)
(5): ELU(alpha=1.0)
(6): Linear(in_features=64, out_features=64, bias=True)
(7): ELU(alpha=1.0)
(8): Linear(in_features=64, out_features=64, bias=True)
(9): ELU(alpha=1.0)
(10): Linear(in_features=64, out_features=1, bias=True)
)
)
Then, we train our classifier using a standard neural network training routine.
loss = NRELoss(estimator)
optimizer = optim.AdamW(estimator.parameters(), lr=1e-3)
step = GDStep(optimizer, clip=1.0) # gradient descent step with gradient clipping
estimator.train()
with tqdm(range(128), unit='epoch') as tq:
for epoch in tq:
losses = torch.stack([
step(loss(theta, x))
for theta, x in islice(loader, 256) # 256 batches per epoch
])
tq.set_postfix(loss=losses.mean().item())
100%|██████████| 128/128 [01:08<00:00, 1.87epoch/s, loss=0.0447]
3.3. Inference#
Now that we have an estimator of the likelihood-to-evidence (LTE) ratio \(r(\theta, x) = \frac{p(\theta | x)}{p(\theta)}\), we can sample from the posterior distribution of an observation \(x^*\) via MCMC or nested sampling. In our case, we use the MetropolisHastings
sampler provided by the lampe.inference
module.
theta_star = prior.sample()
x_star = simulator(theta_star)
estimator.eval()
with torch.no_grad():
theta_0 = prior.sample((1024,)) # 1024 concurrent Markov chains
log_p = lambda theta: estimator(theta, x_star) + prior.log_prob(theta) # p(theta | x) = r(theta, x) p(theta)
sampler = MetropolisHastings(theta_0, log_f=log_p, sigma=0.5)
samples = torch.cat(list(sampler(2048, burn=1024, step=4)))
plt.rcParams.update(nice_rc(latex=True)) # nicer plot settings
fig = corner(
samples,
smooth=2,
domain=(LOWER, UPPER),
labels=LABELS,
legend=r'$p_\phi(\theta | x^*)$',
figsize=(4.8, 4.8),
)
mark_point(fig, theta_star)