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handling GBIS in optimisation
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Khalim Conn-Kowlessar
parent
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commit
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2 changed files with 73 additions and 34 deletions
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@ -90,7 +90,10 @@ def prepare_input_measures(property_recommendations, goal, needs_ventilation, fu
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if rec["measure_type"] in assumptions.measures_needing_ventilation and needs_ventilation
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else rec["total"]
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)
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total = 0 if total < 0 else total
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# If the innovation uplift being removed make this negative, we keep the total so we can re-engineer
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# the original cost
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non_negative_total = 0 if total < 0 else total
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gain = (
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rec[goal_key] + ventilation_recommendation[goal_key]
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if rec["measure_type"] in assumptions.measures_needing_ventilation and needs_ventilation
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@ -105,8 +108,9 @@ 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": total, "gain": gain, "type": rec_type,
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"id": rec["recommendation_id"], "cost": non_negative_total, "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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}
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)
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@ -9,6 +9,9 @@ from recommendations.optimiser.CostOptimiser import CostOptimiser
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from recommendations.optimiser.GainOptimiser import GainOptimiser
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from backend.Funding import Funding
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# measures we DO NOT treat as fundable in the ECO4 'funded' pass
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_ECO4_EXCLUDE_TYPES = {"secondary_heating"}
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project_scores_matrix = pd.read_csv("/Users/khalimconn-kowlessar/Downloads/ECO4 Full Project Scores Matrix.csv")
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partial_project_scores_matrix = pd.read_csv("backend/tests/test_data/ECO4_Partial_Project_Scores_Matrix_v6.csv")
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partial_project_scores_matrix.columns = ['Measure category', 'Measure_Type', 'Pre_Main_Heating_Source',
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@ -460,30 +463,6 @@ input_measures = optimiser_functions.prepare_input_measures(
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measures_to_optimise, "Increasing EPC", needs_ventilation, True
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)
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# ---- rule definitions you can tweak -------------------------------------
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HEATING_TYPES = {"air_source_heat_pump", "high_heat_retention_storage_heater", "solar_pv"}
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MIN_INSULATION_OR = [{"loft_insulation"}, {"cavity_wall_insulation"}] # extend if needed
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# “Funding paths”: each is a list of elements; each element is:
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# - {"OR": {"types": {..}}} means choose one option from any group whose type is in that set
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# - {"AND": [{"types": {..}}, {"types": {..}}]} means choose one from each of those
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FUNDING_PATHS = [
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# Path A: IWI OR EWI
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[
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{
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"OR": {
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"types": {"internal_wall_insulation", "external_wall_insulation"}
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}
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}
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],
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# Path B: Solar PV AND HHRSH
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[{"AND": [{"types": {"solar_pv"}}, {"types": {"high_heat_retention_storage_heater"}}]}],
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# Path C: ASHP alone (may still trigger min insulation rule below)
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[{"OR": {"types": {"air_source_heat_pump"}}}],
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#
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]
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def _find_measure(input_measures, measure_type):
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for measures in input_measures:
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@ -695,8 +674,56 @@ def make_funding_paths(p, input_measures, tenure):
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# Run inputs:
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target_gain = 18.5
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from itertools import product
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import math
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def _path_scheme(path_spec):
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"""
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Infer scheme from any 'reference' tag in the path.
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Defaults to 'eco4' if not specified.
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"""
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for elem in path_spec or []:
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ref = elem.get("reference")
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if isinstance(ref, str):
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if ref.endswith(":gbis"):
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return "gbis"
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if ref.endswith(":eco4"):
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return "eco4"
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return "eco4"
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def _filter_fundable_subgroups(groups, scheme):
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"""
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Keep only options eligible for the funded pass of the given scheme.
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- ECO4: drop excluded types (e.g., secondary_heating)
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- GBIS: funded pass is the GBIS fixed measure only, so return empty sub-groups
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"""
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if scheme == "gbis":
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return [] # we won't optimise 'the rest' under GBIS here
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# ECO4 case
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filtered = []
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for grp in groups:
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kept = [opt for opt in grp
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if not any(ex in opt["type"] for ex in _ECO4_EXCLUDE_TYPES)]
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if kept:
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filtered.append(kept)
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return filtered
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def _sum_cost_gain_with_scheme(items, scheme):
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"""
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Sum cost/gain of fixed items, adjusting for scheme rules.
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- GBIS: strip innovation uplift from GBIS-funded fixed measures only.
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"""
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total_cost = 0.0
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total_gain = 0.0
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for it in items:
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cost = float(it["cost"])
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if scheme == "gbis":
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# innovation uplifts are not paid under GBIS
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cost -= float(it.get("innovation_uplift", 0.0))
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total_cost += cost
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total_gain += float(it["gain"])
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return total_cost, total_gain
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def violates_min_insulation(fixed):
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@ -740,11 +767,6 @@ def optimise_with_funding_paths(input_measures, budget=None, target_gain=None, s
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solutions = []
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for path_spec in funding_paths:
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# TODO: If the path spec is GBIS, need to handle this differently. There is no funding associated
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# with the other measures we're optimising. Instead, we fix the GBIS measure (which is funded)
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# and then run the optimiser on the remaining measures which are NOT funded. The key change is all
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# measures in input_measures right now have costs adjusted with innovation uplift, which we don't want
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# to apply to the GBIS measures. So we need to strip the innovation uplift from the GBIS measures
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# 1) expand fixed selections for this path
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fixed_selections = expand_funding_path(input_measures, path_spec) if path_spec else [[]]
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if not fixed_selections:
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@ -758,13 +780,26 @@ def optimise_with_funding_paths(input_measures, budget=None, target_gain=None, s
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logger.error("Skipping fixed selection due to minimum insulation violation: %s", fixed)
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continue
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scheme = _path_scheme(path_spec)
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# 3) compute fixed cost/gain, and strip those groups from subproblem
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fixed_items = [opt for (_, _, opt) in fixed]
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fixed_ids = [opt['id'] for opt in fixed_items]
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fixed_cost, fixed_gain = sum_cost_gain(fixed_items)
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fixed_groups = {gi for (gi, _, _) in fixed}
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sub_measures = [grp for gi, grp in enumerate(input_measures) if gi not in fixed_groups]
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sub_measures = deepcopy([grp for gi, grp in enumerate(input_measures) if gi not in fixed_groups])
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if scheme == "gbis":
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# Then for the sub-measures, we need to strip the innovation uplift from the GBIS fixed measures. We
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# do this by adding innovation back onto the cost
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for grp in sub_measures:
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for opt in grp:
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opt["cost"] = opt["cost_minus_uplift"] + opt.get("innovation_uplift", 0.0)
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if scheme == "eco4":
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# Need to strip out any measure types that are not eligible for ECO4 funding (e.g. secondary heating)
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raise ValueError()
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# 4) run your existing optimiser for the remaining groups
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# If we have a budget, we need to ensure the subproblem respects it so we remove the fixed cost (which
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