basic optimisation framework working

recommendations/optimiser/CostOptimiser.py

@@ -12,7 +12,7 @@ class CostOptimiser:
     # We add an optional buffer to the minimum gain to allow for slack in the optimisation
     BUFFER = 0.2
 
-    def __init__(self, components, min_gain):
+    def __init__(self, components, min_gain, verbose=False):
         self.components = components
         self.min_gain = min_gain
         self.gain_constraint = None
@@ -22,6 +22,7 @@ class CostOptimiser:
 
         self.solution_cost = None
         self.solution_gain = None
+        self.verbose = verbose
 
     @classmethod
     def calculate_sap_gain_with_slack(cls, min_gain: int | float):
@@ -42,6 +43,8 @@ class CostOptimiser:
     def setup(self):
         # Initialize Model
         self.m = Model("knapsack")
+        # Set the verbosity level
+        self.m.verbose = 1 if self.verbose else 0
 
         # Create variables
         self.variables = [

recommendations/optimiser/GainOptimiser.py

@@ -9,7 +9,7 @@ class GainOptimiser:
     This class is used to maximise gain, given a constrained cost
     """
 
-    def __init__(self, components, max_cost, max_gain, allow_slack=True):
+    def __init__(self, components, max_cost, max_gain, allow_slack=True, verbose=False):
         """
         This function will try and maximise the gain, given a constrained cost. If we specific a max_gain, then the
         optimisation routine is constained to try not to exceed a maximum increase
@@ -23,6 +23,7 @@ class GainOptimiser:
         :param max_gain: Maximum gain constraint
         :param allow_slack: If True, allows the model to use slack variables to relax the cost constraint if the model
                             is infeasible. Defaults to True.
+        :param verbose: If True, enables verbose logging
         """
         self.components = components
         self.max_cost = max_cost
@@ -35,11 +36,15 @@ class GainOptimiser:
         self.solution_gain = None
         self.solution_cost = None
         self.allow_slack = allow_slack
+        self.verbose = verbose
 
     def setup(self):
         # Initialize Model
         self.m = Model("knapsack")
 
+        # Set the verbosity level
+        self.m.verbose = 1 if self.verbose else 0
+
         # Create variables
         self.variables = [
             [self.m.add_var(var_type=BINARY, name=str(component["id"])) for component in group] for group in

recommendations/tests/test_optimisers.py

@@ -782,10 +782,6 @@ def optimise_with_funding_paths(input_measures, budget=None, target_gain=None, s
             total_gain = fixed_gain + sub_gain
             total_picks = fixed_items + picked
 
-            # you can change the objective here; I’ll use max gain under budget
-            if budget is not None and total_cost > budget + 1e-9:
-                continue
-
             solutions.append({
                 "fixed_ids": fixed_ids,
                 "items": total_picks,