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added gbis pps

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This commit is contained in:
Khalim Conn-Kowlessar 2025-08-19 21:48:16 +01:00
parent 0b6bc95881
commit 2595d3a2de
3 changed files with 34 additions and 6 deletions
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12
backend/engine/engine.py
View file

@ -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)

25
recommendations/optimiser/funding_optimiser.py
View file

@ -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"]])

3
recommendations/optimiser/optimiser_functions.py
View file

@ -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"]
}
)