PyMC Testing

PyMC provides testing utilities to speed up test suites by mocking MCMC sampling with prior predictive sampling. This is useful for checking model structure without running expensive inference.

Mock Sampling vs Real Sampling

Use mocking when: Testing model specification, CI/CD pipelines, plotting code, API integration, serialization.

Use real sampling when: Checking posterior values, ESS/r_hat diagnostics, LOO-CV, model comparison. See pymc-modeling skill for real inference.

PyMC Testing Utilities

See: https://www.pymc.io/projects/docs/en/latest/api/testing.html

mock_sample

Replaces pm.sample() with prior predictive sampling:

from functools import partial
import numpy as np
import pymc as pm
from pymc.testing import mock_sample

# Basic usage - replaces pm.sample
pm.sample = mock_sample

with pm.Model() as model:
    pm.Normal("x", 0, 1)
    idata = pm.sample()  # Uses prior predictive, not MCMC

mock_sample_setup_and_teardown

Pytest fixture helper for setup/tear-down:

# conftest.py
import pytest
from pymc.testing import mock_sample_setup_and_teardown

mock_pymc_sample = pytest.fixture(scope="function")(mock_sample_setup_and_teardown)

# test_model.py
def test_model_runs(mock_pymc_sample):
    with pm.Model() as model:
        pm.Normal("x", 0, 1)
        idata = pm.sample()
        assert "x" in idata.posterior

A production-ready example from pymc-marketing:

• conftest.py: https://github.com/pymc-labs/pymc-marketing/blob/main/tests/conftest.py
• Also configures pytest markers for slow tests with --run-slow / --only-slow CLI options

Mocking Sample Stats

By default, no sample_stats are created. Pass a dictionary to mock specific stats:

from functools import partial
import numpy as np
import pymc as pm
from pymc.testing import mock_sample

def mock_diverging(size):
    return np.zeros(size, dtype=int)

def mock_tree_depth(size):
    return np.random.choice(range(2, 10), size=size)

mock_sample_with_stats = partial(
    mock_sample,
    sample_stats={
        "diverging": mock_diverging,
        "tree_depth": mock_tree_depth,
    },
)

pm.sample = mock_sample_with_stats

Example from pymc-marketing:

from functools import partial
import numpy as np
import pymc as pm
import pymc.testing

def mock_diverging(size):
    return np.zeros(size, dtype=int)

pm.sample = partial(
    pymc.testing.mock_sample,
    sample_stats={"diverging": mock_diverging},
)
pm.HalfFlat = pm.HalfNormal
pm.Flat = pm.Normal

What Gets Mocked

The fixture automatically replaces:

• pm.Flat → pm.Normal
• pm.HalfFlat → pm.HalfNormal

This ensures prior predictive sampling works without invalid starting values.

InferenceData Structure Comparison

Mock sampling output (from mock_sample):

• posterior (derived from prior predictive)
• observed_data

Note: mock_sample uses prior predictive internally but returns it as posterior to mimic the pm.sample() API. By default there is no prior, prior_predictive, posterior_predictive, or sample_stats group. However, you can pass a sample_stats dictionary to mock specific stats (see Mocking Sample Stats section).

Real sampling output (from pm.sample):

• posterior
• sample_stats
• observed_data

Note: posterior_predictive is NOT included by default - you must call pm.sample_posterior_predictive(idata, model=model) separately. Warmup groups are sampler-dependent (nutpie includes them, default NUTS does not).

Gotcha: Code that expects posterior_predictive, warmup groups, or sample_stats will fail with mock sampling. Different samplers produce different InferenceData structures.

Common Testing Patterns

See references/patterns.md for:

• Basic model structure tests
• Testing with multiple chains
• Testing downstream code (plotting, serialization)
• CI/CD integration