Optimize interface updates

service/models/operations/optimize.py

@@ -6,14 +6,14 @@
 import numpy as np
 import torch
 from pydantic import BaseModel, Field, Extra
-from models.base import OperationRequest, Timespan
+from models.base import OperationRequest, Timespan, InterventionObject
 from pyciemss.integration_utils.intervention_builder import (
  param_value_objective,
  start_time_objective,
 )
 
 from pyciemss.ouu.qoi import obs_nday_average_qoi, obs_max_qoi
-
+from models.converters import convert_to_static_interventions
 from utils.tds import fetch_model, fetch_inferred_parameters
 
 
@@ -80,7 +80,7 @@ class Optimize(OperationRequest):
  pyciemss_lib_function: ClassVar[str] = "optimize"
  model_config_id: str = Field(..., example="ba8da8d4-047d-11ee-be56")
  timespan: Timespan = Timespan(start=0, end=90)
- interventions: InterventionObjective
+ policy_interventions: InterventionObjective
  step_size: float = 1.0
  qoi: QOI
  risk_bound: float
@@ -90,29 +90,37 @@ class Optimize(OperationRequest):
  None,
  description="optional extra system specific arguments for advanced use cases",
  )
+ fixed_static_parameter_interventions: List[InterventionObject] = Field(
+ default_factory=list,
+ description="The interventions provided via the model config which are not being optimized",
+ )
 
  def gen_pyciemss_args(self, job_id):
  # Get model from TDS
  amr_path = fetch_model(self.model_config_id, job_id)
-
- intervention_type = self.interventions.selection
+ fixed_static_parameter_interventions = convert_to_static_interventions(
+ self.fixed_static_parameter_interventions
+ )
+ intervention_type = self.policy_interventions.selection
  if intervention_type == "param_value":
- assert self.interventions.start_time is not None
- start_time = [torch.tensor(time) for time in self.interventions.start_time]
- param_value = [None] * len(self.interventions.param_names)
+ assert self.policy_interventions.start_time is not None
+ start_time = [
+ torch.tensor(time) for time in self.policy_interventions.start_time
+ ]
+ param_value = [None] * len(self.policy_interventions.param_names)
 
- interventions = param_value_objective(
+ policy_interventions = param_value_objective(
  start_time=start_time,
- param_name=self.interventions.param_names,
+ param_name=self.policy_interventions.param_names,
  param_value=param_value,
  )
  else:
- assert self.interventions.param_values is not None
+ assert self.policy_interventions.param_values is not None
  param_value = [
- torch.tensor(value) for value in self.interventions.param_values
+ torch.tensor(value) for value in self.policy_interventions.param_values
  ]
- interventions = start_time_objective(
- param_name=self.interventions.param_names,
+ policy_interventions = start_time_objective(
+ param_name=self.policy_interventions.param_names,
  param_value=param_value,
  )
 
@@ -133,7 +141,8 @@ def gen_pyciemss_args(self, job_id):
  "risk_bound": self.risk_bound,
  "initial_guess_interventions": self.initial_guess_interventions,
  "bounds_interventions": self.bounds_interventions,
- "static_parameter_interventions": interventions,
+ "static_parameter_interventions": policy_interventions,
+ "fixed_static_parameter_interventions": fixed_static_parameter_interventions,
  "inferred_parameters": inferred_parameters,
  "n_samples_ouu": n_samples_ouu,
  **extra_options,

tests/examples/optimize/input/request.json

@@ -2,7 +2,7 @@
  "engine": "ciemss",
  "user_id": "not_provided",
  "model_config_id": "sidarthe",
- "interventions": {
+ "policy_interventions": {
  "selection": "param_value",
  "start_time": [2],
  "param_names": ["beta"],