273 error required amr has no initials

notebook/april_ensemble/syndata_generate_and_test.ipynb

@@ -19,9 +19,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'torch' has no attribute 'fx'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyciemss\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpetri_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m deterministic\n",
+      "File \u001b[0;32m~/Projects/pyciemss/src/pyciemss/utils/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpetri_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (seq_id_suffix,\n\u001b[1;32m      2\u001b[0m                           reparameterize,\n\u001b[1;32m      3\u001b[0m            load_sim_result,\n\u001b[1;32m      4\u001b[0m            load,\n\u001b[1;32m      5\u001b[0m            draw_petri,\n\u001b[1;32m      6\u001b[0m            natural_order,\n\u001b[1;32m      7\u001b[0m            add_state_indicies,\n\u001b[1;32m      8\u001b[0m            register_template,\n\u001b[1;32m      9\u001b[0m            natural_conversion,\n\u001b[1;32m     10\u001b[0m            natural_degradation,\n\u001b[1;32m     11\u001b[0m            natural_order,\n\u001b[1;32m     12\u001b[0m            controlled_conversion,\n\u001b[1;32m     13\u001b[0m            grouped_controlled_conversion,\n\u001b[1;32m     14\u001b[0m            deterministic,\n\u001b[1;32m     15\u001b[0m            petri_to_ode,\n\u001b[1;32m     16\u001b[0m            order_state,\n\u001b[1;32m     17\u001b[0m            reparameterize,\n\u001b[1;32m     18\u001b[0m            unorder_state,\n\u001b[1;32m     19\u001b[0m            duplicate_petri_net,\n\u001b[1;32m     20\u001b[0m            intervene_petri_net)\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minference_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (get_tspan,\n\u001b[1;32m     22\u001b[0m                               state_flux_constraint,\n\u001b[1;32m     23\u001b[0m                               run_inference,\n\u001b[1;32m     24\u001b[0m                               is_density_equal,\n\u001b[1;32m     25\u001b[0m                               is_intervention_density_equal)\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mplot_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (setup_ax,\n\u001b[1;32m     27\u001b[0m            plot_predictive,\n\u001b[1;32m     28\u001b[0m            plot_trajectory,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     31\u001b[0m            sideaxis,\n\u001b[1;32m     32\u001b[0m            sideaxishist)\n",
+      "File \u001b[0;32m~/Projects/pyciemss/src/pyciemss/utils/petri_utils.py:264\u001b[0m\n\u001b[1;32m    259\u001b[0m \u001b[38;5;129m@register_template\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGroupedControlledConversion\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    260\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgrouped_controlled_conversion\u001b[39m(params: Dict[\u001b[38;5;28mstr\u001b[39m, T], t: T, states: Tuple[T, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[T, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m]:\n\u001b[1;32m    261\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m  \u001b[38;5;66;03m# TODO\u001b[39;00m\n\u001b[0;32m--> 264\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfx\u001b[49m\u001b[38;5;241m.\u001b[39mwrap\n\u001b[1;32m    265\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdeterministic\u001b[39m(name, value, \u001b[38;5;241m*\u001b[39m, event_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    266\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mdeterministic(name, value, event_dim\u001b[38;5;241m=\u001b[39mevent_dim)\n\u001b[1;32m    269\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpetri_to_ode\u001b[39m(\n\u001b[1;32m    270\u001b[0m     G: nx\u001b[38;5;241m.\u001b[39mMultiDiGraph,\n\u001b[1;32m    271\u001b[0m     funcs: Optional[Dict[\u001b[38;5;28mstr\u001b[39m, Callable[[Tuple[T, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m], T], Tuple[T, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m]]]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    272\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Callable[[T, Tuple[T, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m]], Tuple[T, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m]]:\n",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'torch' has no attribute 'fx'"
+     ]
+    }
+   ],
    "source": [
     "from pyciemss.utils.synth_data_utils import *"
    ]

notebook/april_ensemble/synthetic_data_with_custom_models.ipynb

@@ -26,7 +26,19 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pyro'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyro\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyro\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdistributions\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mdist\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyro'"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import torch\n",
@@ -1787,7 +1799,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

src/pyciemss/PetriNetODE/interfaces.py

@@ -26,6 +26,7 @@
 from pyciemss.visuals import plots
 
 import mira
+import json
 
 # Load base interfaces
 from pyciemss.interfaces import (
@@ -53,6 +54,46 @@
 PetriSolution = dict  # NOTE: [str, torch.tensor] type argument removed because of issues with type-based dispatch.
 PetriInferredParameters = pyro.nn.PyroModule
 
+def run_petri_model_checks(petri_model_or_path: Union[str, mira.metamodel.TemplateModel, mira.modeling.Model]) -> bool:
+    """
+    Check that the model AMR (a) is readable/formatted correctly, (b) that the number of transitions matches the number of rate laws, and (c) includes initial conditions for each state variable.
+    """
+    amr_dict = {}
+    # Read the AMR as a dictionary and alert the user if the model path is incorrect, or the AMR in not properly formatted
+    try:
+        with open(petri_model_or_path, 'r') as file:
+            amr_dict = json.load(file)
+    except FileNotFoundError:
+        print("File not found. Make sure you have the correct model path.")
+    except json.JSONDecodeError:
+        print("Error decoding JSON data. Check that the AMR is formatted correctly.")
+
+    # Check that the AMR contains the required header, model, semantics, and metadata keys: 
+    required_keys = ['header', 'model', 'semantics', 'metadata']
+    missing_keys = [key for key in required_keys if key not in amr_dict]
+
+    # Get the number of transitions and rate laws for comparison 
+    num_transitions = len(amr_dict["model"]["transitions"])
+    num_rate_laws = len(amr_dict["semantics"]["ode"]["rates"])
+
+    # Check that every state variable is assigned an initial condition
+    missing_ids = [state['id'] for state in amr_dict["model"]["states"] if state['id'] not in set(item['target'] for item in amr_dict["semantics"]["ode"]["initials"])]
+
+    # Check that the AMR contains the required header, model, semantics, and metadata keys: 
+    if missing_keys:
+        missing_keys_str = ', '.join(missing_keys)
+        raise ValueError(f"The AMR is missing: {missing_keys_str}")
+    # Check that the number of transitions matches the number of rate laws
+    elif num_transitions < num_rate_laws:
+        raise ValueError("At least one transition is missing. The number of transitions must equal the number of rate laws.")
+    elif num_transitions > num_rate_laws:
+        raise ValueError("There are more transitions than rate laws. The number of transitions must equal the number of rate laws.")
+    # Check that every state variable is assigned an initial condition
+    elif missing_ids:
+        missing_ids_str = ', '.join(missing_ids)
+        raise ValueError(f"The following state variables do not have corresponding initials: {missing_ids_str}")
+    else:
+        return True
 
 def load_petri_model(
     petri_model_or_path: Union[str, mira.metamodel.TemplateModel, mira.modeling.Model],
@@ -65,11 +106,11 @@ def load_petri_model(
     """
     Load a petri net from a file and compile it into a probabilistic program.
     """
-    if noise_model == "scaled_beta":
+    if (noise_model == "scaled_beta") and run_petri_model_checks(petri_model_or_path):
         return ScaledBetaNoisePetriNetODESystem.from_askenet(
             petri_model_or_path, noise_scale=noise_scale, compile_rate_law_p=compile_rate_law_p, compile_observables_p=compile_observables_p, add_uncertainty=add_uncertainty
         )
-    elif noise_model == "scaled_normal":
+    elif (noise_model == "scaled_normal") and run_petri_model_checks(petri_model_or_path):
         return ScaledNormalNoisePetriNetODESystem.from_askenet(
             petri_model_or_path, noise_scale=noise_scale, compile_rate_law_p=compile_rate_law_p, compile_observables_p=compile_observables_p, add_uncertainty=add_uncertainty
         )

src/pyciemss/utils/__init__.py

@@ -11,7 +11,7 @@
            natural_order,
            controlled_conversion,
            grouped_controlled_conversion,
-           deterministic,
+#            deterministic,
            petri_to_ode,
            order_state,
            reparameterize,

src/pyciemss/utils/petri_utils.py

@@ -261,9 +261,9 @@ def grouped_controlled_conversion(params: Dict[str, T], t: T, states: Tuple[T, .
     raise NotImplementedError  # TODO
 
 
-@torch.fx.wrap
-def deterministic(name, value, *, event_dim=None):
-    return pyro.deterministic(name, value, event_dim=event_dim)
+# @torch.fx.wrap
+# def deterministic(name, value, *, event_dim=None):
+#     return pyro.deterministic(name, value, event_dim=event_dim)
 
 
 def petri_to_ode(