revert formatting

pymc/smc/kernels.py

@@ -193,10 +193,13 @@ def initialize_population(self) -> dict[str, np.ndarray]:
             )
 
             model = self.model
+
             prior_expression = make_initial_point_expression(
                 free_rvs=model.free_RVs,
                 rvs_to_transforms=model.rvs_to_transforms,
-                initval_strategies={},
+                initval_strategies={
+                    **model.rvs_to_initial_values,
+                },
                 default_strategy="prior",
                 return_transformed=True,
             )

tests/smc/test_smc.py

@@ -25,6 +25,7 @@
 import pymc as pm
 
 from pymc.backends.base import MultiTrace
+from pymc.distributions.transforms import Ordered
 from pymc.pytensorf import floatX
 from pymc.smc.kernels import IMH, systematic_resampling
 from tests.helpers import assert_random_state_equal
@@ -79,7 +80,9 @@ def test_sample(self):
         initial_rng_state = np.random.get_state()
         with self.SMC_test:
             mtrace = pm.sample_smc(
-                draws=self.samples, return_inferencedata=False, progressbar=not _IS_WINDOWS
+                draws=self.samples,
+                return_inferencedata=False,
+                progressbar=not _IS_WINDOWS,
             )
 
         # Verify sampling was done with a non-global random generator
@@ -148,7 +151,10 @@ def test_marginal_likelihood(self):
                 a = pm.Beta("a", alpha, beta)
                 y = pm.Bernoulli("y", a, observed=data)
                 trace = pm.sample_smc(
-                    2000, chains=2, return_inferencedata=False, progressbar=not _IS_WINDOWS
+                    2000,
+                    chains=2,
+                    return_inferencedata=False,
+                    progressbar=not _IS_WINDOWS,
                 )
             # log_marginal_likelihood is found in the last value of each chain
             lml = np.mean([chain[-1] for chain in trace.report.log_marginal_likelihood])
@@ -211,7 +217,9 @@ def test_return_datatype(self, chains):
                 warnings.filterwarnings("ignore", ".*number of samples.*", UserWarning)
                 warnings.filterwarnings("ignore", "More chains .* than draws .*", UserWarning)
                 idata = pm.sample_smc(
-                    chains=chains, draws=draws, progressbar=not (chains > 1 and _IS_WINDOWS)
+                    chains=chains,
+                    draws=draws,
+                    progressbar=not (chains > 1 and _IS_WINDOWS),
                 )
                 mt = pm.sample_smc(
                     chains=chains,
@@ -269,6 +277,30 @@ def test_deprecated_abc_args(self):
             ):
                 pm.sample_smc(draws=10, chains=1, save_log_pseudolikelihood=True)
 
+    def test_ordered(self):
+        """
+        Test that initial population respects custom initval, especially when applied
+        to the Ordered transformation. Regression test for #7438.
+        """
+        with pm.Model() as m:
+            pm.Normal(
+                "a",
+                mu=0.0,
+                sigma=1.0,
+                size=(2,),
+                transform=Ordered(),
+                initval=[-1.0, 1.0],
+            )
+
+        smc = IMH(model=m)
+        out = smc.initialize_population()
+
+        # initial point should not include NaNs
+        assert not np.any(np.isnan(out["a_ordered__"]))
+
+        # initial point should match for all particles
+        assert np.all(out["a_ordered__"][0] == out["a_ordered__"])
+
 
 class TestMHKernel:
     def test_normal_model(self):