Improve sampling coverage (#4270)

* improve coverage

* redistribute tests

* use np.shape

Author: MarcoGorelli

.github/workflows/pytest.yml

@@ -39,12 +39,10 @@ jobs:
             pymc3/tests/test_distributions_timeseries.py
             pymc3/tests/test_parallel_sampling.py
             pymc3/tests/test_random.py
-            pymc3/tests/test_sampling.py
             pymc3/tests/test_shared.py
             pymc3/tests/test_smc.py
           - |
             pymc3/tests/test_examples.py
-            pymc3/tests/test_gp.py
             pymc3/tests/test_mixture.py
             pymc3/tests/test_posteriors.py
             pymc3/tests/test_quadpotential.py
@@ -54,6 +52,8 @@ jobs:
             pymc3/tests/test_variational_inference.py
           - |
             pymc3/tests/test_distributions.py
+            pymc3/tests/test_gp.py
+            pymc3/tests/test_sampling.py
     runs-on: ${{ matrix.os }}
     env:
       TEST_SUBSET: ${{ matrix.test-subset }}

pymc3/sampling.py

@@ -14,10 +14,7 @@
 
 """Functions for MCMC sampling."""
 
-from typing import Dict, List, Optional, TYPE_CHECKING, cast, Union, Any
-
-if TYPE_CHECKING:
-    from typing import Tuple
+from typing import Dict, List, Optional, cast, Union, Any
 from typing import Iterable as TIterable
 from collections.abc import Iterable
 from collections import defaultdict
@@ -218,11 +215,7 @@ def assign_step_methods(model, step=None, methods=STEP_METHODS, step_kwargs=None
 
 
 def _print_step_hierarchy(s, level=0):
-    if isinstance(s, (list, tuple)):
-        _log.info(">" * level + "list")
-        for i in s:
-            _print_step_hierarchy(i, level + 1)
-    elif isinstance(s, CompoundStep):
+    if isinstance(s, CompoundStep):
         _log.info(">" * level + "CompoundStep")
         for i in s.methods:
             _print_step_hierarchy(i, level + 1)
@@ -458,7 +451,7 @@ def sample(
 
     if return_inferencedata is None:
         v = packaging.version.parse(pm.__version__)
-        if v.release[0] > 3 or v.release[1] >= 10:
+        if v.release[0] > 3 or v.release[1] >= 10:  # type: ignore
             warnings.warn(
                 "In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. "
                 "You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.",
@@ -585,7 +578,7 @@ def sample(
                     UserWarning,
                 )
             _print_step_hierarchy(step)
-            trace = _sample_population(**sample_args, parallelize=cores > 1)
+            trace = _sample_population(parallelize=cores > 1, **sample_args)
         else:
             _log.info(f"Sequential sampling ({chains} chains in 1 job)")
             _print_step_hierarchy(step)
@@ -770,11 +763,9 @@ def _sample_population(
     trace : MultiTrace
         Contains samples of all chains
     """
-    # create the generator that iterates all chains in parallel
-    chains = [chain + c for c in range(chains)]
     sampling = _prepare_iter_population(
         draws,
-        chains,
+        [chain + c for c in range(chains)],
         step,
         start,
         parallelize,
@@ -1582,10 +1573,7 @@ def insert(self, k: str, v, idx: int):
         ids: int
             The index of the sample we are inserting into the trace.
         """
-        if hasattr(v, "shape"):
-            value_shape = tuple(v.shape)  # type: Tuple[int, ...]
-        else:
-            value_shape = ()
+        value_shape = np.shape(v)
 
         # initialize if necessary
         if k not in self.trace_dict:

pymc3/tests/test_sampling.py

@@ -13,14 +13,9 @@
 #   limitations under the License.
 
 from itertools import combinations
-import packaging
 from typing import Tuple
 import numpy as np
-
-try:
-    import unittest.mock as mock  # py3
-except ImportError:
-    from unittest import mock
+import unittest.mock as mock
 
 import numpy.testing as npt
 import arviz as az
@@ -180,13 +175,9 @@ def test_trace_report_bart(self):
         assert var_imp[0] > var_imp[1:].sum()
         npt.assert_almost_equal(var_imp.sum(), 1)
 
-    def test_return_inferencedata(self):
+    def test_return_inferencedata(self, monkeypatch):
         with self.model:
             kwargs = dict(draws=100, tune=50, cores=1, chains=2, step=pm.Metropolis())
-            v = packaging.version.parse(pm.__version__)
-            if v.major > 3 or v.minor >= 10:
-                with pytest.warns(FutureWarning, match="pass return_inferencedata"):
-                    result = pm.sample(**kwargs)
 
             # trace with tuning
             with pytest.warns(UserWarning, match="will be included"):
@@ -203,12 +194,25 @@ def test_return_inferencedata(self):
             assert result.posterior.sizes["chain"] == 2
             assert len(result._groups_warmup) > 0
 
-            # inferencedata without tuning
-            result = pm.sample(**kwargs, return_inferencedata=True, discard_tuned_samples=True)
+            # inferencedata without tuning, with idata_kwargs
+            prior = pm.sample_prior_predictive()
+            result = pm.sample(
+                **kwargs,
+                return_inferencedata=True,
+                discard_tuned_samples=True,
+                idata_kwargs={"prior": prior},
+                random_seed=-1
+            )
+            assert "prior" in result
             assert isinstance(result, az.InferenceData)
             assert result.posterior.sizes["draw"] == 100
             assert result.posterior.sizes["chain"] == 2
             assert len(result._groups_warmup) == 0
+
+            # check warning for version 3.10 onwards
+            monkeypatch.setattr("pymc3.__version__", "3.10")
+            with pytest.warns(FutureWarning, match="pass return_inferencedata"):
+                result = pm.sample(**kwargs)
         pass
 
     @pytest.mark.parametrize("cores", [1, 2])

pyproject.toml

@@ -1,6 +1,13 @@
 [tool.black]
 line-length = 100
 
+[tool.coverage.report]
+exclude_lines = [
+  "pragma: nocover",
+  "raise NotImplementedError",
+  "if TYPE_CHECKING:",
+]
+
 [tool.nbqa.mutate]
 isort = 1
 black = 1