From b0118655179e4c26c3ae523dd7264e0341f326a9 Mon Sep 17 00:00:00 2001
From: Khalim Conn-Kowlessar <kconnkowlessar@gmail.com>
Date: Tue, 8 Oct 2024 18:43:24 +0100
Subject: [PATCH] adding the ability to switch off optimisation

---
 backend/app/plan/router.py | 37 ++++++++++++++++++++++++-------------
 1 file changed, 24 insertions(+), 13 deletions(-)

diff --git a/backend/app/plan/router.py b/backend/app/plan/router.py
index 2dc03e60..f4924c71 100644
--- a/backend/app/plan/router.py
+++ b/backend/app/plan/router.py
@@ -631,27 +631,38 @@ async def trigger_plan(body: PlanTriggerRequest):
         for p in input_properties:
             if not recommendations.get(p.id):
                 continue
+
             input_measures = prepare_input_measures(recommendations[p.id], body.goal)
 
             current_sap_points = int(p.data["current-energy-efficiency"])
             target_sap_points = epc_to_sap_lower_bound(body.goal_value)
             sap_gain = CostOptimiser.calculate_sap_gain_with_slack(target_sap_points - current_sap_points)
 
-            if body.budget:
-                optimiser = GainOptimiser(
-                    input_measures, max_cost=body.budget, max_gain=sap_gain if sap_gain > 0 else 0
-                )
+            if not body.optimise:
+                if body.goal != "Increasing EPC":
+                    raise NotImplementedError("Only EPC optimisation is currently supported")
+                solution = []
+                for sub_list in input_measures:
+                    # Select the entry with the highest gain, and if tied, choose the one with the lowest cost
+                    best_measure = max(sub_list, key=lambda x: (x['gain'], -x['cost']))
+                    solution.append(best_measure)
             else:
-                # The minimum gain is the minimum number of SAP points required to get to the target SAP band
-                # If the gain is negative, the optimiser will return an empty solution
-                optimiser = CostOptimiser(
-                    input_measures,
-                    min_gain=sap_gain
-                )
 
-            optimiser.setup()
-            optimiser.solve()
-            solution = optimiser.solution
+                if body.budget:
+                    optimiser = GainOptimiser(
+                        input_measures, max_cost=body.budget, max_gain=sap_gain if sap_gain > 0 else 0
+                    )
+                else:
+                    # The minimum gain is the minimum number of SAP points required to get to the target SAP band
+                    # If the gain is negative, the optimiser will return an empty solution
+                    optimiser = CostOptimiser(
+                        input_measures,
+                        min_gain=sap_gain
+                    )
+
+                optimiser.setup()
+                optimiser.solve()
+                solution = optimiser.solution
 
             selected_recommendations = {r["id"] for r in solution}