Final fixes to failing tests

Author: lucianopaz

homepy/blocks/gp.py

@@ -140,7 +140,7 @@ def make_covariance_kernel(kernel_spec: str) -> pm.gp.cov.Covariance:
 
     def eval_(node):
         if isinstance(node, ast.Constant):
-            return node.n
+            return node.value
         elif isinstance(node, ast.BinOp):
             return operators[type(node.op)](eval_(node.left), eval_(node.right))
         elif isinstance(node, ast.UnaryOp):

homepy/models/base.py

@@ -261,21 +261,26 @@ def batched_sample_posterior_predictive(
         compile_kwargs.setdefault("accept_inplace", True)
 
         constant_data: Dict[str, np.ndarray] = {}
-        trace_coords: Dict[str, np.ndarray] = {}
         _constant_data = getattr(idata, "constant_data", None)
         if _constant_data is not None:
-            trace_coords.update(
-                {str(k): v.data for k, v in _constant_data.coords.items()}
-            )
             constant_data.update({str(k): v.data for k, v in _constant_data.items()})
+        idata_coords = {key: v.data for key, v in idata.posterior.coords.items()}
+        if "observed_data" in idata.groups():
+            idata_coords.update(
+                {key: v.data for key, v in idata.observed_data.coords.items()}
+            )
+        if "constant_data" in idata.groups():
+            idata_coords.update(
+                {key: v.data for key, v in idata.constant_data.coords.items()}
+            )
 
         constant_coords = set()
-        for dim, coord in trace_coords.items():
+        for dim, coord in idata_coords.items():
             current_coord = self.model.coords.get(dim, None)
             if (
                 current_coord is not None
                 and len(coord) == len(current_coord)
-                and np.all(coord == current_coord)
+                and np.all(coord == np.asarray(current_coord))
             ):
                 constant_coords.add(dim)
 
@@ -1094,10 +1099,28 @@ def compile_between_and_within_subjects(
         # Compile the forward sampling function that computes the expected values
         # The second argument that is returned by compile_forward_sampling_function
         # is the resampled variables, so we ignore it
+        idata_coords = {key: v.data for key, v in idata.posterior.coords.items()}
+        if "observed_data" in idata.groups():
+            idata_coords.update(
+                {key: v.data for key, v in idata.observed_data.coords.items()}
+            )
+        if "constant_data" in idata.groups():
+            idata_coords.update(
+                {key: v.data for key, v in idata.constant_data.coords.items()}
+            )
+        constant_coords = {
+            dim
+            for dim, coord in self.model.coords.items()
+            if dim not in idata_coords
+            or np.array_equal(np.asarray(coord), idata_coords[dim])
+        }
+
         return compile_forward_sampling_function(
             expected_values,
             vars_in_trace=vars_in_trace,
             basic_rvs=basic_RVs,
+            constant_data=getattr(idata, "constant_data", None),
+            constant_coords=constant_coords,
             **kwargs,
         )[0]
 

homepy/pytensorf.py

@@ -21,6 +21,7 @@
 from pymc.pytensorf import find_rng_nodes
 from pytensor.graph import FunctionGraph
 from pytensor.graph.basic import clone_get_equiv
+from pytensor.tensor.exceptions import NotScalarConstantError
 from pytensor.tensor.random.basic import NormalRV
 
 try:
@@ -46,16 +47,16 @@ def make_normal_not_centered(fgraph, node):
         nonlocal resampled_vars
         nonlocal free_RVs
         nonlocal free_RV_names
-        rng, size, dtype, loc, scale = node.inputs
+        rng, size, loc, scale = node.inputs
 
         try:
             loc_is_zero = get_underlying_scalar_constant_value(loc) == 0
-        except ValueError:
+        except NotScalarConstantError:
             loc_is_zero = False
 
         name = getattr(node.outputs[1], "name", None)
         if not loc_is_zero and name in resampled_vars_mapping:
-            raw = pt.random.normal(0, 1, rng=rng, size=size, dtype=dtype)
+            raw = pt.random.normal(0, 1, rng=rng, size=size)
             raw.name = name + "_raw"
             og_index = resampled_vars_mapping[name]
             resampled_vars[og_index[0]][og_index[1]] = raw

homepy/tests/blocks/test_likelihoods.py

@@ -205,7 +205,7 @@ def test_parse_observed_features_and_dims_error():
                 zero_rate_name="zero_rate",
                 zero_rate_prior=None,
             ),
-            ("add", "sigmoid", "halfnormal_rv", "uniform_rv", "MarginalMixtureRV"),
+            ("add", "sigmoid", "halfnormal_rv", "uniform_rv", "MixtureRV"),
             ("exp", "sqr", "exponential_rv", "log", "beta_rv"),
         ),
         (