bayesflow-irt

A BayesFlow add-on for amortized Bayesian inference in Item Response Theory (IRT) models: train a network once on simulated response matrices, then draw posteriors for new data sets without refitting, for any number of items and persons within the trained range.

What is included

• Variable-size simulators and adapters for the 1PL, 2PL, PCM, and GPCM models
• MatrixEquivariantIRTSummary, a summary network that is exchangeable over both items and persons (Hartford-style matrix blocks)
• EquivariantIRTApproximator for per-item posterior inference
• Person scoring, metadata sidecars for trained models, and memory controls for large n_persons
• Optional: calibration-aware training (CalibratedIRTApproximator) and hyperparameter-optimization hooks (bayesflow_irt.hpo)
• Planned: pretrained checkpoints with model cards, and example notebooks

Installation

The package is not on PyPI yet. Install it from a clone:

git clone https://github.com/matthiaskloft/bayesflow-irt.git
cd bayesflow-irt
pip install -e ".[torch]"

Extras: dev (tests), examples (plotting and Jupyter), card (model-card plots), hpo (bayesflow-hpo, from PyPI), numba (faster response sampling), and calibration (bayesflow-calibration-loss, which will be released separately; until then this extra cannot be installed). The tensorflow and jax backend extras are also available.

Set the Keras backend before importing keras, bayesflow, or bayesflow_irt:

export KERAS_BACKEND=torch          # PowerShell: $env:KERAS_BACKEND = "torch"

Quick start

import bayesflow as bf
from bayesflow_irt import (
    EquivariantIRTApproximator,
    MatrixEquivariantIRTSummary,
    Prior,
    make_2pl_adapter,
    make_2pl_simulator,
)

simulator = make_2pl_simulator(
    n_items=(10, 25),
    n_persons=(100, 250),
    prior_a=Prior("lognormal", loc=0.0, scale=0.3),
    prior_b=Prior("normal", loc=0.0, scale=2.0),
)
adapter = make_2pl_adapter(simulator, internal_parameterization="loading")

approximator = EquivariantIRTApproximator(
    adapter=adapter,
    inference_network=bf.networks.FlowMatching(),
    summary_network=MatrixEquivariantIRTSummary(embed_dim=64),
)
approximator.compile(optimizer="adam")
approximator.fit(simulator=simulator, epochs=10, num_batches=32, batch_size=64)

batch = simulator.sample((4,))
posterior = approximator.sample(num_samples=200, conditions=batch)

The public functions and classes are documented in their docstrings.

Examples

examples/train_1pl_minimal.py is a minimal end-to-end script (simulate, adapt, train, sample, save). Pretrained checkpoints are not distributed yet; example notebooks will follow with them.

For longer runs, scripts/train_irt.py runs the same training pipeline from a JSON config and writes only files:

python scripts/train_irt.py --config scripts/configs/smoke_1pl.json

scripts/configs/ has a quick smoke test (smoke_1pl.json) and a fixed-size 2PL run (2pl_fixed.json).

On Windows, run Jupyter with a kernel that imports torch first (scripts/ipykernel_torch_preload.py); otherwise loading pyzmq before torch can crash with WinError 1114.

Development

pip install -e ".[dev,torch]"
pytest tests/
ruff check src/

See CONTRIBUTING.md and CHANGELOG.md.

License and citation

MIT, see LICENSE. To cite the software, see CITATION.cff. The methods the package implements are cited in references.md.