added gbis pps

backend/engine/engine.py

@@ -874,7 +874,9 @@ async def model_engine(body: PlanTriggerRequest):
                 for group in measures_to_optimise_with_uplift:
                     for r in group:
                         if r["type"] in ["mechanical_ventilation", "low_energy_lighting", "secondary_heating"]:
-                            r["innovation_uplift"] = 0
+                            r["partial_project_score"], r["partial_project_funding"], r["innovation_uplift"] = (
+                                0, 0, 0
+                            )
                             continue
 
                          (
@@ -906,8 +908,14 @@ async def model_engine(body: PlanTriggerRequest):
                 )
 
                 # Given the solutions we select the optimal one
+                solutions["cost_less_full_project_funding"] = np.where(
+                    solutions["scheme"] == "eco4",
+                    solutions["total_cost"] - solutions["full_project_funding"] - solutions["total_uplift"],
+                    solutions["total_cost"] - solutions["partial_project_funding"] - solutions["total_uplift"]
+                )
+
                 solutions["cost_less_full_project_funding"] = (
-                    solutions["total_cost"] - solutions["eco4_full_project_funding"] - solutions["total_uplift"]
+                    solutions["total_cost"] - solutions["full_project_funding"] - solutions["total_uplift"]
                 )
                 solutions = solutions.sort_values("cost_less_full_project_funding", ascending=True)
 

recommendations/optimiser/funding_optimiser.py

@@ -272,6 +272,12 @@ def optimise_with_funding_paths(p, input_measures, housing_type, funding: Fundin
 
             # 3) compute fixed cost/gain, and strip those groups from subproblem
             fixed_items = [opt for (_, _, opt) in fixed]
+
+            if scheme == "gbis":
+                # Re-set costs as the only funding we get is the PPS
+                for x in fixed_items:
+                    x["cost"] = x["raw_cost"]
+
             fixed_ids = [opt['id'] for opt in fixed_items]
             fixed_cost, fixed_gain = sum_cost_gain(fixed_items)
             fixed_groups = {gi for (gi, _, _) in fixed}
@@ -285,7 +291,7 @@ def optimise_with_funding_paths(p, input_measures, housing_type, funding: Fundin
                 # do this by adding innovation back onto the cost
                 for grp in sub_measures:
                     for opt in grp:
-                        opt["cost"] = opt["cost_minus_uplift"] + opt.get("innovation_uplift", 0.0)
+                        opt["cost"] = x["raw_cost"]
 
             if scheme == "eco4":
                 # Need to strip out any measure types that are not eligible for ECO4 funding (e.g. secondary heating)
@@ -391,9 +397,13 @@ def optimise_with_funding_paths(p, input_measures, housing_type, funding: Fundin
         axis=1
     )
     rate = funding.get_abs_rate(is_cavity=p.walls["is_cavity_wall"])
-    solutions["eco4_full_project_funding"] = solutions["project_score"] * rate
+    solutions["full_project_funding"] = solutions["project_score"] * rate
     # if the scheme is not ECO4, we set the funding to 0 with iloc
-    solutions.loc[solutions["scheme"] != "eco4", "eco4_full_project_funding"] = 0.0
+    solutions.loc[solutions["scheme"] != "eco4", "full_project_funding"] = 0.0
+    solutions["partial_project_funding"] = solutions.apply(
+        lambda x: get_gbis_pps(x),
+        axis=1
+    )
 
     # We pull out uplifts
     solutions["total_uplift"] = solutions.apply(lambda x: get_total_uplift(x), axis=1)
@@ -404,6 +414,15 @@ def optimise_with_funding_paths(p, input_measures, housing_type, funding: Fundin
 # ---- helpers -------------------------------------------------------------
 
 
+def get_gbis_pps(x):
+    if x["scheme"] != "gbis":
+        return 0
+    fixed_ids = row["fixed_ids"]
+    if len(fixed_ids) != 1:
+        raise ValueError("More than one fixed ID for GBIS")
+    return [x for x in row["items"] if x["id"] in fixed_ids][0]["partial_project_funding"]
+
+
 def get_total_uplift(x):
     return sum([y["innovation_uplift"] for y in x["items"]])
 

recommendations/optimiser/optimiser_functions.py

@@ -116,7 +116,8 @@ def prepare_input_measures(property_recommendations, goal, needs_ventilation, fu
                     "gain": gain, "type": rec_type,
                     "innovation_uplift": rec["innovation_uplift"] if funding else 0,
                     "cost_minus_uplift": total,
-                    "raw_cost": raw_cost
+                    "raw_cost": raw_cost,
+                    "partial_project_funding": rec["partial_project_funding"]
                 }
             )