bayesflow-testing

You can test BayesFlow adapter pipelines by implementing configuration roundtrip checks that validate data transformations. These tests ensure your adapter correctly processes simulator outputs and maintains expected shapes throughout the pipeline stages.

Mocking inference networks in BayesFlow involves replacing summary and inference networks with mock objects to isolate testing. This strategy allows you to verify the behavior of surrounding components without the computational overhead of running actual network forward passes.

Setting up deterministic RNG for BayesFlow tests in a CI matrix requires configuring fixed random seeds within your pytest conftest setup. This ensures reproducible test execution across different environments and prevents flaky tests caused by stochastic simulator behavior.

The best way to structure a BayesFlow extension package test directory is to mirror the source code layout directly. This clear directory organization ensures that every module has corresponding tests, making it easier to locate and maintain unit and integration tests.

Common pitfalls when testing scientific computing libraries with pytest include neglecting deterministic RNG setup and failing to validate simulator output shapes. Addressing these issues requires configuring fixed seeds and implementing strict data type checks to avoid flaky and misleading test results.