Update interventions to have distinct param and state. (#115)

* updating intervention. distinct param and state.

* Please retest this Shawn

* adding comment for David to be happy

service/models/converters.py

@@ -9,7 +9,7 @@
 
 def fetch_and_convert_static_interventions(policy_intervention_id, job_id):
  if not (policy_intervention_id):
- return defaultdict(dict)
+ return defaultdict(dict), defaultdict(dict)
  policy_intervention = fetch_interventions(policy_intervention_id, job_id)
  interventionList: list[HMIIntervention] = []
  for inter in policy_intervention["interventions"]:
@@ -26,7 +26,7 @@ def fetch_and_convert_static_interventions(policy_intervention_id, job_id):
 
 def fetch_and_convert_dynamic_interventions(policy_intervention_id, job_id):
  if not (policy_intervention_id):
- return defaultdict(dict)
+ return defaultdict(dict), defaultdict(dict)
  policy_intervention = fetch_interventions(policy_intervention_id, job_id)
  interventionList: list[HMIIntervention] = []
  for inter in policy_intervention["interventions"]:
@@ -44,15 +44,19 @@ def fetch_and_convert_dynamic_interventions(policy_intervention_id, job_id):
 # Used to convert from HMI Intervention Policy -> pyciemss static interventions.
 def convert_static_interventions(interventions: list[HMIIntervention]):
  if not (interventions):
- return defaultdict(dict)
- static_interventions: Dict[torch.Tensor, Dict[str, any]] = defaultdict(dict)
+ return defaultdict(dict), defaultdict(dict)
+ static_param_interventions: Dict[torch.Tensor, Dict[str, any]] = defaultdict(dict)
+ static_state_interventions: Dict[torch.Tensor, Dict[str, any]] = defaultdict(dict)
  for inter in interventions:
  for static_inter in inter.static_interventions:
  time = torch.tensor(float(static_inter.timestep))
  parameter_name = inter.applied_to
  value = torch.tensor(float(static_inter.value))
- static_interventions[time][parameter_name] = value
- return static_interventions
+ if inter.type == "parameter":
+ static_param_interventions[time][parameter_name] = value
+ if inter.type == "state":
+ static_state_interventions[time][parameter_name] = value
+ return static_param_interventions, static_state_interventions
 
 
 # Define the threshold for when the intervention should be applied.
@@ -68,11 +72,15 @@ def var_threshold(time, state):
 # Used to convert from HMI Intervention Policy -> pyciemss dynamic interventions.
 def convert_dynamic_interventions(interventions: list[HMIIntervention]):
  if not (interventions):
- return defaultdict(dict)
+ return defaultdict(dict), defaultdict(dict)
  dynamic_parameter_interventions: Dict[
  Callable[[torch.Tensor, Dict[str, torch.Tensor]], torch.Tensor],
  Dict[str, any],
  ] = defaultdict(dict)
+ dynamic_state_interventions: Dict[
+ Callable[[torch.Tensor, Dict[str, torch.Tensor]], torch.Tensor],
+ Dict[str, any],
+ ] = defaultdict(dict)
  for inter in interventions:
  for dynamic_inter in inter.dynamic_interventions:
  parameter_name = inter.applied_to
@@ -81,11 +89,16 @@ def convert_dynamic_interventions(interventions: list[HMIIntervention]):
  threshold_func = make_var_threshold(
  dynamic_inter.parameter, threshold_value
  )
- dynamic_parameter_interventions[threshold_func].update(
- {parameter_name: to_value}
- )
-
- return dynamic_parameter_interventions
+ if inter.type == "parameter":
+ dynamic_parameter_interventions[threshold_func].update(
+ {parameter_name: to_value}
+ )
+ if inter.type == "state":
+ dynamic_state_interventions[threshold_func].update(
+ {parameter_name: to_value}
+ )
+
+ return dynamic_parameter_interventions, dynamic_state_interventions
 
 
 def convert_to_solution_mapping(config):

service/models/operations/calibrate.py

@@ -9,7 +9,10 @@
 
 
 from models.base import Dataset, OperationRequest, Timespan
-from models.converters import fetch_and_convert_static_interventions
+from models.converters import (
+ fetch_and_convert_static_interventions,
+ fetch_and_convert_dynamic_interventions,
+)
 from utils.rabbitmq import gen_rabbitmq_hook
 from utils.tds import fetch_dataset, fetch_model
 
@@ -62,9 +65,15 @@ def gen_pyciemss_args(self, job_id):
 
  dataset_path = fetch_dataset(self.dataset.dict(), job_id)
 
- static_interventions = fetch_and_convert_static_interventions(
- self.policy_intervention_id, job_id
- )
+ (
+ static_param_interventions,
+ static_state_interventions,
+ ) = fetch_and_convert_static_interventions(self.policy_intervention_id, job_id)
+
+ (
+ dynamic_param_interventions,
+ dynamic_state_interventions,
+ ) = fetch_and_convert_dynamic_interventions(self.policy_intervention_id, job_id)
 
  # TODO: Test RabbitMQ
  try:
@@ -95,7 +104,10 @@ def hook(progress, _loss):
  # TODO: Is this intentionally missing from `calibrate`?
  # "end_time": self.timespan.end,
  "data_path": dataset_path,
- "static_parameter_interventions": static_interventions,
+ "static_parameter_interventions": static_param_interventions,
+ "static_state_interventions": static_state_interventions,
+ "dynamic_parameter_interventions": dynamic_param_interventions,
+ "dynamic_state_interventions": dynamic_state_interventions,
  "progress_hook": hook,
  "solver_method": solver_method,
  "solver_options": solver_options,

service/models/operations/optimize.py

@@ -90,7 +90,7 @@ class Optimize(OperationRequest):
  model_config_id: str = Field(..., example="ba8da8d4-047d-11ee-be56")
  timespan: Timespan = Timespan(start=0, end=90)
  optimize_interventions: InterventionObjective # These are the interventions to be optimized.
- fixed_static_parameter_interventions: list[HMIIntervention] = Field(
+ fixed_interventions: list[HMIIntervention] = Field(
  None
  ) # Theses are interventions provided that will not be optimized
  logging_step_size: float = 1.0
@@ -105,9 +105,10 @@ class Optimize(OperationRequest):
  def gen_pyciemss_args(self, job_id):
  # Get model from TDS
  amr_path = fetch_model(self.model_config_id, job_id)
- fixed_static_parameter_interventions = convert_static_interventions(
- self.fixed_static_parameter_interventions
- )
+ (
+ fixed_static_parameter_interventions,
+ fixed_static_state_interventions,
+ ) = convert_static_interventions(self.fixed_interventions)
 
  intervention_type = self.optimize_interventions.intervention_type
  if intervention_type == "param_value":
@@ -166,6 +167,8 @@ def gen_pyciemss_args(self, job_id):
  "bounds_interventions": self.bounds_interventions,
  "static_parameter_interventions": optimize_interventions,
  "fixed_static_parameter_interventions": fixed_static_parameter_interventions,
+ # https://github.com/DARPA-ASKEM/terarium/issues/4612
+ # "fixed_static_state_interventions": fixed_static_state_interventions,
  "inferred_parameters": inferred_parameters,
  "n_samples_ouu": n_samples_ouu,
  "solver_method": solver_method,

service/models/operations/simulate.py

@@ -49,13 +49,15 @@ def gen_pyciemss_args(self, job_id):
  # Get model from TDS
  amr_path = fetch_model(self.model_config_id, job_id)
 
- static_interventions = fetch_and_convert_static_interventions(
- self.policy_intervention_id, job_id
- )
+ (
+ static_param_interventions,
+ static_state_interventions,
+ ) = fetch_and_convert_static_interventions(self.policy_intervention_id, job_id)
 
- dynamic_interventions = fetch_and_convert_dynamic_interventions(
- self.policy_intervention_id, job_id
- )
+ (
+ dynamic_param_interventions,
+ dynamic_state_interventions,
+ ) = fetch_and_convert_dynamic_interventions(self.policy_intervention_id, job_id)
 
  extra_options = self.extra.dict()
  inferred_parameters = fetch_inferred_parameters(
@@ -75,8 +77,10 @@ def gen_pyciemss_args(self, job_id):
  "logging_step_size": self.logging_step_size,
  "start_time": self.timespan.start,
  "end_time": self.timespan.end,
- "static_parameter_interventions": static_interventions,
- "dynamic_parameter_interventions": dynamic_interventions,
+ "static_parameter_interventions": static_param_interventions,
+ "static_state_interventions": static_state_interventions,
+ "dynamic_parameter_interventions": dynamic_param_interventions,
+ "dynamic_state_interventions": dynamic_state_interventions,
  "inferred_parameters": inferred_parameters,
  "solver_method": solver_method,
  "solver_options": solver_options,