def infer(
simulator: Callable,
prior: Distribution,
method: str,
num_simulations: int,
num_workers: int = 1,
) -> NeuralPosterior:
r"""Runs simulation-based inference and returns the posterior.
This function provides a simple interface to run sbi. Inference is run for a single
round and hence the returned posterior $p(\theta|x)$ can be sampled and evaluated
for any $x$ (i.e. it is amortized).
The scope of this function is limited to the most essential features of sbi. For
more flexibility (e.g. multi-round inference, different density estimators) please
use the flexible interface described here:
https://www.mackelab.org/sbi/tutorial/02_flexible_interface/
Args:
simulator: A function that takes parameters $\theta$ and maps them to
simulations, or observations, `x`, $\mathrm{sim}(\theta)\to x$. Any
regular Python callable (i.e. function or class with `__call__` method)
can be used.
prior: A probability distribution that expresses prior knowledge about the
parameters, e.g. which ranges are meaningful for them. Any
object with `.log_prob()`and `.sample()` (for example, a PyTorch
distribution) can be used.
method: What inference method to use. Either of SNPE, SNLE or SNRE.
num_simulations: Number of simulation calls. More simulations means a longer
runtime, but a better posterior estimate.
num_workers: Number of parallel workers to use for simulations.
Returns: Posterior over parameters conditional on observations (amortized).
"""
try:
method_fun: Callable = getattr(sbi.inference, method.upper())
except AttributeError:
raise NameError(
"Method not available. `method` must be one of 'SNPE', 'SNLE', 'SNRE'."
)
simulator, prior = prepare_for_sbi(simulator, prior)
inference = method_fun(prior=prior)
theta, x = simulate_for_sbi(
simulator=simulator,
proposal=prior,
num_simulations=num_simulations,
num_workers=num_workers,
)
_ = inference.append_simulations(theta, x).train()
posterior = inference.build_posterior()
return posterior
prior = sbi.utils.torchutils.BoxUniform(low=torch.as_tensor(prior_min),
high=torch.as_tensor(prior_max))
# Simulate samples from the prior distribution
theta = prior.sample((10_000, ))
print('Simulating samples from prior simulation... ', end='')
stats = simulation_wrapper(theta.numpy())
print('done.')
# Train inference network
density_estimator_build_fun = sbi.utils.posterior_nn(model='mdn')
inference = sbi.inference.SNPE(
prior, density_estimator=density_estimator_build_fun)
print('Training inference network... ')
inference.append_simulations(theta, stats).train()
posterior = inference.build_posterior()
# true parameters for real ground truth data
true_params = np.array([[32., 1.]])
true_data = simulate(true_params)
t = true_data['t']
I_inj = true_data['I_inj']
v = true_data['v']
xo = calculate_summary_statistics(true_data)
print("The true summary statistics are: ", xo)
# Plot estimated posterior distribution
samples = posterior.sample((1000, ), x=xo, show_progress_bars=False)
labels_params = [r'$\overline{g}_{Na}$', r'$\overline{g}_{K}$']
sbi.analysis.pairplot(samples,
limits=[[.5, 80], [1e-4, 15.]],