weighted interventions

service/models/operations/optimize.py

@@ -59,13 +59,52 @@ def gen_risk_bound(self):
  return -self.risk_bound
 
 
-def objfun(x, initial_guess, objective_function_option):
- if objective_function_option == "lower_bound":
- return np.sum(np.abs(x))
- if objective_function_option == "upper_bound":
- return -np.sum(np.abs(x))
- if objective_function_option == "initial_guess":
- return np.sum(np.abs(x - initial_guess))
+def objfun(x, initial_guess, objective_function_option, relative_importance):
+ """
+ Calculate the weighted sum of objective functions based on the given parameters.
+
+ Parameters:
+ x (list or array): The current values of the variables.
+ initial_guess (list or array): The initial guess values of the variables.
+ objective_function_option (list): List of options specifying the type of objective function for each variable.
+ relative_importance (list): List of weights indicating the relative importance of each variable.
+
+ Returns:
+ float: The weighted sum of the objective functions.
+ """
+
+ # Initialize the total sum to zero
+ total_sum = 0
+
+ # Calculate the sum of all weights, fallback to 1 if the sum is 0
+ sum_of_all_weights = np.sum(relative_importance) or 1
+
+ # Check if any of the required parameters is None and raise an error if so
+ if (
+ x is None
+ or initial_guess is None
+ or objective_function_option is None
+ or relative_importance is None
+ ):
+ raise ValueError(
+ "There was an issue creating the objective function. None of the parameters x, initial_guess, objective_function_option, or relative_importance can be None"
+ )
+
+ # Iterate over each variable
+ for i in range(len(x)):
+ # Calculate the weight for the current variable
+ weight = relative_importance[i] / sum_of_all_weights
+
+ # Apply the corresponding objective function based on the option provided
+ if objective_function_option[i] == "lower_bound":
+ total_sum += weight * np.abs(x[i])
+ elif objective_function_option[i] == "upper_bound":
+ total_sum += weight * -np.abs(x[i])
+ elif objective_function_option[i] == "initial_guess":
+ total_sum += weight * np.abs(x[i] - initial_guess[i])
+
+ # Return the total weighted sum of the objective functions
+ return total_sum
 
 
 class InterventionObjective(BaseModel):
@@ -79,6 +118,7 @@ class InterventionObjective(BaseModel):
  start_time: Optional[list[float]] = None
  objective_function_option: Optional[list[str]] = None
  initial_guess: Optional[list[float]] = None
+ relative_importance: Optional[list[float]] = None
 
 
 class OptimizeExtra(BaseModel):
@@ -198,8 +238,9 @@ def progress_hook(current_results):
  "end_time": self.timespan.end,
  "objfun": lambda x: objfun(
  x,
- self.optimize_interventions.initial_guess[0],
- self.optimize_interventions.objective_function_option[0],
+ self.optimize_interventions.initial_guess,
+ self.optimize_interventions.objective_function_option,
+ self.optimize_interventions.relative_importance,
  ),
  "qoi": qoi_methods,
  "risk_bound": risk_bounds,

tests/examples/optimize/input/request.json

@@ -8,7 +8,8 @@
  "start_time": [2],
  "param_names": ["beta"],
  "param_values": [0.02],
- "initial_guess": [0]
+ "initial_guess": [0],
+ "relative_importance": [1]
  },
  "timespan": {
  "start": 0,