Added def instead of lambda function. Still getting recursive call

docs/source/hierarchical_sir_model.ipynb

@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -18,7 +18,7 @@
        "['gamma_mean', 'gamma', 'beta_mean', 'beta']"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }

pyciemss/compiled_dynamics.py

@@ -21,7 +21,7 @@ class CompiledDynamics(pyro.nn.PyroModule):
     def __init__(self, src, **kwargs):
         super().__init__()
         self.src = src
-
+        params = _compile_param_values(self.src)
         try:
             params = _compile_param_values(self.src)
         except Exception as e:

pyciemss/mira_integration/compiled_dynamics.py

@@ -3,6 +3,7 @@
 import numbers
 from typing import Callable, Dict, Optional, Tuple, TypeVar, Union
 
+import networkx as nx
 import mira
 import mira.metamodel
 import mira.modeling
@@ -27,13 +28,44 @@
     eval_observables,
     get_name,
 )
-from pyciemss.mira_integration.distributions import (
-    mira_distribution_to_pyro
-)
+
+from pyciemss.mira_integration.distributions import mira_distribution_to_pyro
 
 S = TypeVar("S")
 T = TypeVar("T")
 
+@_sort_dependencies.register(mira.modeling.Model)
+def sort_mira_dependencies(src: mira.modeling.Model) -> list:
+    """
+    Sort the model parameters of a MIRA TemplateModel by their distribution parameter dependencies.
+
+    Parameters
+    ----------
+    src : mira.modeling.Model
+        The MIRA Model to sort.
+
+    Returns
+    -------
+    list
+        A list of parameter names in the order in which they must be evaluated.
+    """
+    dependencies = nx.DiGraph()
+    for param_name, param_info in src.parameters.items():
+        param_name = get_name(param_info)
+        #if param_info.placeholder:
+        #    continue
+        param_dist = getattr(param_info, "distribution", None)
+
+        if param_dist is None:
+            dependencies.add_node(param_name)
+        else:
+            for k, v in param_dist.parameters.items():
+                # Check to see if the distribution parameters are sympy expressions
+                # and add their free symbols to the dependency graph
+                if isinstance(v, mira.metamodel.utils.SympyExprStr):
+                    for free_symbol in v.free_symbols:
+                        dependencies.add_edge(str(free_symbol), str(param_name))
+    return list(nx.topological_sort(dependencies))
 
 @_compile_deriv.register(mira.modeling.Model)
 def _compile_deriv_mira(src: mira.modeling.Model) -> Callable[..., Tuple[torch.Tensor]]:
@@ -94,20 +126,24 @@ def _compile_param_values_mira(
         param_info = src.parameters[param_name]
         if param_info.placeholder:
             continue
-
+            
         param_dist = getattr(param_info, "distribution", None)
         if param_dist is None:
             param_value = float(param_info.value)
         else:
             idx = sorted_dependencies.index(param_name)
-            param_value = lambda self: mira_distribution_to_pyro(param_dist, {
-                k: getattr(self, f"persistent_{k}") for k in sorted_dependencies[:idx]
-                }
-            )
+            upstream_dependencies = sorted_dependencies[:idx]
+
+            def param_value(model: pyro.nn.PyroModule) -> torch.Tensor:
+                return mira_distribution_to_pyro(
+                    param_dist, {
+                        k: getattr(model, f"persistent_{k}")
+                        for k in upstream_dependencies
+                    })
 
         if isinstance(param_value, torch.nn.Parameter):
             param_values[param_name] = pyro.nn.PyroParam(param_value)
-        elif isinstance(param_value, pyro.distributions.distribution.Distribution):
+        elif isinstance(param_value, (pyro.distributions.distribution.Distribution, Callable)):
             param_values[param_name] = pyro.nn.PyroSample(param_value)
         elif isinstance(param_value, (numbers.Number, numpy.ndarray, torch.Tensor)):
             param_values[param_name] = torch.as_tensor(param_value, dtype=torch.float32)
@@ -208,6 +244,9 @@ def _get_name_mira_transition(trans: mira.modeling.Transition) -> str:
 def _get_name_mira_modelparameter(param: mira.modeling.ModelParameter) -> str:
     return str(param.key)
 
+@get_name.register
+def _get_name_mira_metamodel_parameter(param: mira.metamodel.Parameter) -> str:
+    return str(param.name)
 
 @get_name.register
 def _get_name_mira_model_observable(obs: mira.modeling.ModelObservable) -> str:

tests/test_compiled_dynamics.py

@@ -10,8 +10,12 @@
 from mira.sources.amr import model_from_url
 from pyro.infer.inspect import get_dependencies
 
-from pyciemss.compiled_dynamics import CompiledDynamics
-from pyciemss.mira_integration.distributions import sort_mira_dependencies
+from pyciemss.compiled_dynamics import (
+    CompiledDynamics,
+)
+
+from pyciemss.mira_integration.compiled_dynamics import sort_mira_dependencies
+
 
 from .fixtures import (
     ACYCLIC_MODELS,