pymc-testing

What problem does it solve?

This Skill addresses the challenge of efficiently testing PyMC models by providing optimized mocking and fixture utilities, allowing users to quickly verify model structure and functionality without the need for full MCMC sampling.

Core Features & Use Cases

• Mock Sampling: Accelerates model testing by replacing MCMC sampling with prior predictive sampling, reducing inference time.
• Test Fixtures: Provides pytest fixtures for convenient setup and teardown of model testing environments.
• Use Case: When developing Bayesian models, use this Skill to quickly write tests for model specification and downstream code, ensuring that your model behaves as expected without the need for extensive computational resources.

Quick Start

Execute the following command to test a PyMC model using the mock sampling feature:

from pymc.testing import mock_sample
pm.sample = mock_sample
# Your model code here