The flexible interface¶
In the previous tutorial, we have demonstrated how
sbi can be used to run simulation-based inference with just a single line of code.
In addition to this simple interface,
sbi also provides a flexible interface which provides several additional features implemented in
Note, you can find the original version of this notebook at https://github.com/mackelab/sbi/blob/main/tutorials/02_flexible_interface.ipynb in the
The flexible interface offers the following features (and many more):
- performing sequential posterior estimation by focusing on a particular observation over multiple rounds. This can decrease the number of simulations one has to run, but the inference procedure is no longer amortized (tutorial).
- specify your own density estimator, or change hyperparameters of existing ones (e.g. number of hidden units for NSF) (tutorial).
- use an
embedding_netto learn summary features from high-dimensional simulation outputs (tutorial).
- provide presimulated data
- choose between different methods to sample from the posterior.
- use calibration kernels as proposed by Lueckmann, Goncalves et al. 2017.
from sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi simulator, prior = prepare_for_sbi(simulator, prior) inference = SNPE(prior) theta, x = simulate_for_sbi(simulator, proposal=prior, num_simulations=1000) density_estimator = inference.append_simulations(theta, x).train() posterior = inference.build_posterior(density_estimator)
Linear Gaussian example¶
We will show an example of how we can use the flexible interface to infer the posterior for an example with a Gaussian likelihood (same example as before). First, we import the inference method we want to use (
SNRE) and other helper functions.
import torch from sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi from sbi.utils.get_nn_models import posterior_nn from sbi import utils as utils from sbi import analysis as analysis
Next, we define the prior and simulator:
num_dim = 3 prior = utils.BoxUniform(low=-2 * torch.ones(num_dim), high=2 * torch.ones(num_dim))
def linear_gaussian(theta): return theta + 1.0 + torch.randn_like(theta) * 0.1
In the flexible interface, you have to ensure that your simulator and prior adhere the requirements of
sbi. You can do so with the
simulator, prior = prepare_for_sbi(linear_gaussian, prior)
Then, we instantiate the inference object:
inference = SNPE(prior=prior)
Next, we run simulations. You can do so either by yourself by sampling from the prior and running the simulator (e.g. on a compute cluster), or you can use a helper function provided by
simulate_for_sbi. This function allows to parallelize your code with
theta, x = simulate_for_sbi(simulator, proposal=prior, num_simulations=2000)
Running 2000 simulations.: 0%| | 0/2000 [00:00<?, ?it/s]
We then pass the simulated data to the inference object.
x should both be a
torch.Tensor of type
inference = inference.append_simulations(theta, x)
Next, we train the neural density estimator.
density_estimator = inference.train()
Neural network successfully converged after 73 epochs.
Lastly, we can use this density estimator to build the posterior:
posterior = inference.build_posterior(density_estimator)
Once we have obtained the posterior, we can
.pairplot() in the same way as for the simple interface.
x_o = torch.zeros( 3, )
posterior_samples = posterior.sample((10000,), x=x_o) # plot posterior samples _ = analysis.pairplot( posterior_samples, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(5, 5) )
Drawing 10000 posterior samples: 0%| | 0/10000 [00:00<?, ?it/s]
We can always print the posterior to know how it was trained:
Posterior conditional density p(θ|x) of type DirectPosterior. It samples the posterior network and rejects samples that lie outside of the prior bounds.