pymc-testing
Load when writing or modifying pytest tests that touch pymc.Model, pm.sample, or any PyMC model code. Covers pymc.testing.mock_sample, pytest fixtures for Bayesian models, and the distinction between fast structure-only tests (mocking) and slow posterior inference tests. Triggers include: testing PyMC, pytest with pymc, unit tests for Bayesian models, mock sampling, test fixtures for models, CI/CD for PyMC.
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- pymc-labs/python-analytics-skills
- Letzte Quellaktivität
- 22. Juni 2026 um 14:35
- Erkannte Sprache von SKILL.md
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Quellanweisungen · Schreibgeschützte Vorschau- name
- pymc-testing
- description
- Load when writing or modifying pytest tests that touch pymc.Model, pm.sample, or any PyMC model code. Covers pymc.testing.mock_sample, pytest fixtures for Bayesian models, and the distinction between fast structure-only tests (mocking) and slow posterior inference tests. Triggers include: testing PyMC, pytest with pymc, unit tests for Bayesian models, mock sampling, test fixtures for models, CI/CD for PyMC.
# 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
| Aspect | Mock Sampling | Real Sampling |
|--------|---------------|---------------|
| Speed | Fast (seconds) | Slow (minutes) |
| Use case | Model structure, downstream code | Posterior values, convergence |
| Output | `prior`, `prior_predictive` | Full `posterior`, `sample_stats`, warmup groups |
| Divergences | Mocked (configurable) | Real diagnostics |
**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](../pymc-modeling/SKILL.md) 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:
```python
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:
```python
# 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
### Distribution testing helpers
`pymc.testing` also exposes helpers for validating custom distributions against their analytic log-CDFs. In PyMC 6, both forms are available:
```python
from pymc.testing import check_logcdf, check_logccdf
check_logcdf(MyDist, domain, params) # log CDF
check_logccdf(MyDist, domain, params) # log complementary CDF (log survival)
```
## Mocking Sample Stats
By default, no sample_stats are created. Pass a dictionary to mock specific stats:
```python
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](https://github.com/pymc-labs/pymc-marketing/blob/main/scripts/run_notebooks/injected.py):
```python
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.
## DataTree 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 DataTree structures.
## Common Testing Patterns
See [references/patterns.md](references/patterns.md) for:
- Basic model structure tests
- Testing with multiple chains
- Testing downstream code (plotting, serialization)
- CI/CD integration
Auf GitHub ansehen