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implementing funding optimiser
This commit is contained in:
Khalim Conn-Kowlessar
parent
ce362f5262
commit
0b6bc95881
5 changed files with 76 additions and 21 deletions
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@ -1056,17 +1056,20 @@ class Funding:
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if self.tenure == "Private":
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# We return ECO4 rates
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return innovation_uplift * (
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rate = (
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self.eco4_private_cavity_abs_rate if is_cavity
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else self.eco4_private_solid_abs_rate
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)
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return pps, pps * rate, innovation_uplift * rate
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if self.tenure == "Social":
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# We return ECO4 rates
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return innovation_uplift * (
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rate = (
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self.eco4_social_cavity_abs_rate if is_cavity
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else self.eco4_social_solid_abs_rate
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)
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return pps, pps * rate, innovation_uplift * rate
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raise ValueError("Invalid tenure type for innovation uplift calculation: {}".format(self.tenure))
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@ -107,7 +107,6 @@ class PlanTriggerRequest(BaseModel):
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scenario_name: Optional[str] = ""
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scenario_id: Optional[str | int] = None # Used to utilise and existing scenario for a engine run
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multi_plan: Optional[bool] = False
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optimise: Optional[bool] = True
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default_u_values: Optional[bool] = True
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ashp_cop: Optional[float] = 2.8
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@ -870,13 +870,16 @@ async def model_engine(body: PlanTriggerRequest):
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is_sandstone_or_limestone=p.walls["is_sandstone_or_limestone"],
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)
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# TODO: Turn this into a function and store the innovaiton uplift
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for group in measures_to_optimise_with_uplift:
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for r in group:
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if r["type"] in ["mechanical_ventilation", "low_energy_lighting", "secondary_heating"]:
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r["innovation_uplift"] = 0
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continue
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r["innovation_uplift"] = funding.get_innovation_uplift(
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(
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r["partial_project_score"], r["partial_project_funding"], r["innovation_uplift"]
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) = funding.get_innovation_uplift(
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measure=r,
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starting_sap=p.data["current-energy-efficiency"],
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floor_area=p.floor_area,
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@ -903,26 +906,26 @@ async def model_engine(body: PlanTriggerRequest):
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)
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# Given the solutions we select the optimal one
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solutions["cost_less_full_project_funding"] = solutions["total_cost"] - solutions[
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"eco4_full_project_funding"]
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solutions["cost_less_full_project_funding"] = (
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solutions["total_cost"] - solutions["eco4_full_project_funding"] - solutions["total_uplift"]
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)
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solutions = solutions.sort_values("cost_less_full_project_funding", ascending=True)
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if solutions["meets_upgrade_target"].any():
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# If we have a solution that meets the upgrade target, we select that one
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optimal_solution = solutions[solutions["meets_upgrade_target"]].iloc[0]
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else:
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optimal_solution = optimal_solution.iloc[0]
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optimal_solution = solutions.iloc[0]
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# optimal_solution =
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solution = optimal_solution["items"] + optimal_solution["unfunded_items"]
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full_project_funding = optimal_solution["eco4_full_project_funding"]
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scheme = optimal_solution["scheme"]
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if not body.optimise:
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if body.goal != "Increasing EPC":
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raise NotImplementedError("Only EPC optimisation is currently supported")
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solution = [max(sub_list, key=lambda x: (x['gain'], -x['cost'])) for sub_list in input_measures]
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else:
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# We optimise and then we determine eligibility for funding, based on the measures selected
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optimiser = (
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GainOptimiser(
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input_measures, max_cost=body.budget, max_gain=gain, allow_slack=body.goal == "Increasing EPC"
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input_measures, max_cost=body.budget, max_gain=gain, allow_slack=False
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) if body.budget else CostOptimiser(input_measures, min_gain=gain)
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)
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optimiser.setup()
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@ -172,10 +172,8 @@ def _ensure_unfunded_costs(groups):
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for grp in groups:
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for opt in grp:
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base = opt.get("cost_minus_uplift")
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upl = opt.get("innovation_uplift", 0.0)
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if base is not None:
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opt["cost"] = float(base) + float(upl)
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# else: assume opt["cost"] already includes uplift
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opt["cost"] = opt["raw_cost"]
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return groups
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@ -250,7 +248,8 @@ def optimise_with_funding_paths(p, input_measures, housing_type, funding: Fundin
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"total_gain": sub_gain,
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"path": path_spec,
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"scheme": scheme,
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"is_eligible": _is_eligible_funding_package(scheme, p.data["current-energy-efficiency"], sub_gain)
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"is_eligible": _is_eligible_funding_package(scheme, p.data["current-energy-efficiency"], sub_gain),
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"unfunded_items": []
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}
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)
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@ -318,6 +317,43 @@ def optimise_with_funding_paths(p, input_measures, housing_type, funding: Fundin
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scheme = _path_scheme(path_spec)
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unfunded_picked = []
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if total_gain - target_gain < -0.1:
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# In this case, we have a funded package that does not meet the target gain, so we look at the remaining
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# measures and see if we can include them
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picked_types = {opt["type"] for opt in total_picks}
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# We find the indexes of the picked types
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picked_group_index = {}
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for pt in picked_types:
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for gi, grp in enumerate(optimisation_input_measures):
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if any(pt in opt["type"] for opt in grp):
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picked_group_index[pt] = gi
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break
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# We get the remaining types
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# ECO4 case
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remaining = []
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for i, grp in enumerate(optimisation_input_measures):
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if i in picked_group_index.values():
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continue
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keep = [x for x in grp if x["type"] not in picked_types]
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if keep:
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for x in keep:
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# Adjust to raw cost (without funding)
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x["cost"] = x["raw_cost"]
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remaining.append(keep)
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if remaining:
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# If we have remaining measures we can optimise, we run them down an unfunded route
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unfunded_picked, unfunded_cost, unfunded_gain = run_optimizer(
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remaining,
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budget - total_cost if budget is not None else None,
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sub_target_gain=target_gain - total_gain if target_gain is not None else None
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)
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total_cost += unfunded_cost
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total_gain += unfunded_gain
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solutions.append({
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"fixed_ids": fixed_ids,
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"items": total_picks,
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@ -325,14 +361,15 @@ def optimise_with_funding_paths(p, input_measures, housing_type, funding: Fundin
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"total_gain": total_gain,
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"path": path_spec,
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"scheme": scheme,
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"is_eligible": _is_eligible_funding_package(scheme, p.data["current-energy-efficiency"], total_gain)
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"is_eligible": _is_eligible_funding_package(scheme, p.data["current-energy-efficiency"], total_gain),
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"unfunded_items": unfunded_picked,
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})
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solutions = pd.DataFrame(solutions)
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# Given the scheme, we now check if the packages are eligible. If they *are* eligible, but they don't meet the
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# final upgrade target, we then look to perform a final optimisation pass to meet the target gain.
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solutions["meets_upgrade_target"] = solutions["total_gain"] >= target_gain
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solutions["meets_upgrade_target"] = solutions["total_gain"] >= target_gain - 0.1
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# If we have packages that are fundable, but do not meet the upgrade target, we can run a final optimisation pass
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if not solutions[solutions["is_eligible"] & ~solutions["meets_upgrade_target"]].empty:
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@ -358,12 +395,19 @@ def optimise_with_funding_paths(p, input_measures, housing_type, funding: Fundin
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# if the scheme is not ECO4, we set the funding to 0 with iloc
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solutions.loc[solutions["scheme"] != "eco4", "eco4_full_project_funding"] = 0.0
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# We pull out uplifts
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solutions["total_uplift"] = solutions.apply(lambda x: get_total_uplift(x), axis=1)
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return solutions
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# ---- helpers -------------------------------------------------------------
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def get_total_uplift(x):
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return sum([y["innovation_uplift"] for y in x["items"]])
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def sum_cost_gain(items):
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c = sum(float(x['cost']) for x in items)
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g = sum(float(x['gain']) for x in items)
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@ -78,6 +78,9 @@ def prepare_input_measures(property_recommendations, goal, needs_ventilation, fu
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# Build enriched measure data
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to_append = []
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for rec in recs:
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raw_cost = rec["total"]
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if funding:
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total = (
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rec["total"] - rec["innovation_uplift"] + ventilation_recommendation["total"]
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@ -108,9 +111,12 @@ def prepare_input_measures(property_recommendations, goal, needs_ventilation, fu
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# We also include the innovation uplift
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to_append.append(
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{
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"id": rec["recommendation_id"], "cost": non_negative_total, "gain": gain, "type": rec_type,
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"id": rec["recommendation_id"],
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"cost": non_negative_total,
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"gain": gain, "type": rec_type,
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"innovation_uplift": rec["innovation_uplift"] if funding else 0,
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"cost_minus_uplift": total
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"cost_minus_uplift": total,
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"raw_cost": raw_cost
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}
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)
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