sbibm-jax 0.1.1

Simulators and datasets for simulation-based inference benchmarks in JAX/NumPyro

sbibm-jax

A JAX/NumPyro library including several SBI benchmarks. Includes a port of sbibm

Overview

sbibm-jax ports the Simulation-Based Inference Benchmark (sbibm) from PyTorch/Pyro to JAX, NumPyro, and diffrax. Each task defines a prior, a simulator, reference observations, and reference posterior samples for benchmarking SBI methods. Tasks can be used directly, or consumed as pre-generated / on-the-fly HuggingFace datasets. It is the benchmark companion to GenSBI.

Installation

Using uv (recommended):

uv add sbibm-jax

Or using pip:

pip install sbibm-jax

The default JAX dependency is the CUDA 12 build (jax[cuda12]) for GPU support; on a CPU-only machine install a CPU build of JAX instead.

Optional extras:

pip install sbibm-jax[hf]       # build/export HuggingFace datasets
pip install sbibm-jax[loader]   # consume datasets via grain: TaskDataset / OnlineTaskDataset
pip install sbibm-jax[pypesto]  # the beer_molbiosystems PEtab task (compiles AMICI)

Usage

1. Benchmark tasks

Use a task's prior, simulator, and reference data directly:

import jax
from sbibm_jax import get_task, get_available_tasks

print(get_available_tasks())                      # every task name

task = get_task("two_moons")
key = jax.random.PRNGKey(0)

theta = task.get_prior(key, num_samples=1000)     # (1000, dim_theta)
simulator = task.get_simulator(key)
x = simulator(key, theta)                          # (1000, dim_x)

# Reference data for observation #1
x_o = task.get_observation(num_observation=1)                        # (1, dim_x)
theta_o = task.get_true_parameters(num_observation=1)                # (1, dim_theta)
posterior = task.get_reference_posterior_samples(num_observation=1)  # (N, dim_theta)

2. Offline datasets (pre-generated)

TaskDataset streams the pre-generated benchmark splits from the Hub with grain. Requires the [loader] extra. Loaders yield (theta, x) already tokenized to shape (batch, dim, 1):

from sbibm_jax.data import TaskDataset

ds = TaskDataset(
    "two_moons",  
    normalize=True,                          # apply gen-time mean/std from metadata.json
)

train = ds.get_train_loader(batch_size=256)   # infinite: shuffle -> repeat -> batch
theta, x = next(iter(train))                  # theta: (256, dim_theta, 1), x: (256, dim_x, 1)

posterior = ds.get_reference(num_observation=1)

kind="joint" concatenates (theta, x) along the feature axis; get_val_loader / get_test_loader serve the validation and test splits.

3. Online datasets (simulate on the fly)

OnlineTaskDataset reads the same metadata.json (shapes + normalization stats) but draws fresh (theta, x) from the task's prior and simulator each batch — the splits are never downloaded. Finite-simulator, vector-theta tasks only:

from sbibm_jax.data import OnlineTaskDataset

ds = OnlineTaskDataset(
    "two_moons",
    repo="aurelio-amerio/SBI-benchmarks",
    normalize=True,
)

loader = ds.get_online_train_loader(batch_size=256, seed=0, num_workers=4)  # num_workers=0 disables prefetch workers
theta, x = next(iter(loader))   # a fresh draw every batch

Available tasks

Call get_available_tasks() for the full list — analytical, ODE, image, and time-series tasks. Each lives under src/sbibm_jax/tasks/<name>/.

License

MIT — see LICENSE. If you use sbibm-jax, please also consider citing the original sbibm benchmark.