implement feedback

Author: Goose

pymc/sampling/forward.py

@@ -473,9 +473,7 @@ def sample_prior_predictive(
     )
 
     # All model variables have a name, but mypy does not know this
-    _log.info(
-        f"Sampling: {sorted(volatile_basic_rvs.intersection(vars_to_sample), key=lambda var: var.name)}"  # type: ignore[arg-type, return-value]
-    )
+    _log.info(f"Sampling: {sorted(volatile_basic_rvs, key=lambda var: var.name)}")  # type: ignore[arg-type, return-value]
     values = zip(*(sampler_fn() for i in range(draws)))
 
     data = {k: np.stack(v) for k, v in zip(names, values)}

tests/sampling/test_forward.py

@@ -857,21 +857,22 @@ def test_logging_sampled_basic_rvs_prior(self, caplog):
             y = pm.Deterministic("y", x + 1)
             z = pm.Normal("z", y, observed=0)
 
+        # all volatile RVs in model
         with m:
             pm.sample_prior_predictive(draws=1)
         assert caplog.record_tuples == [("pymc.sampling.forward", logging.INFO, "Sampling: [x, z]")]
         caplog.clear()
 
-        # RV only
+        # `x` has no dependencies so will be sampled by itself
         with m:
             pm.sample_prior_predictive(draws=1, var_names=["x"])
         assert caplog.record_tuples == [("pymc.sampling.forward", logging.INFO, "Sampling: [x]")]
         caplog.clear()
 
-        # observed only
+        # `z` depends on `x`
         with m:
             pm.sample_prior_predictive(draws=1, var_names=["z"])
-        assert caplog.record_tuples == [("pymc.sampling.forward", logging.INFO, "Sampling: [z]")]
+        assert caplog.record_tuples == [("pymc.sampling.forward", logging.INFO, "Sampling: [x, z]")]
         caplog.clear()
 
     def test_logging_sampled_basic_rvs_posterior(self, caplog):