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handling the case of there not being any suitable recommendations for a property

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Khalim Conn-Kowlessar 2025-11-30 20:40:07 +00:00
parent 852420a8fa
commit 110f461d49
2 changed files with 45 additions and 28 deletions
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65
backend/engine/engine.py
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@ -1043,38 +1043,47 @@ async def model_engine(body: PlanTriggerRequest):
work_package=eco_packages[p.id][2]
)
# If the solution isn't eligible, we can't really consider it
solutions = solutions[
(solutions["is_eligible"] & (solutions["scheme"] != "none")) | (solutions["scheme"] == "none")
]
if solutions["meets_upgrade_target"].any():
# If we have a solution that meets the upgrade target, we select that one
optimal_solution = solutions[solutions["meets_upgrade_target"]].iloc[0]
# if handle the empty case
if solutions.empty:
scheme = "none"
funded_measures, solution = [], []
(
project_funding, total_uplift, full_project_score, partial_project_score, uplift_project_score
) = 0, 0, 0, 0, 0
else:
# Pick the cheapest
optimal_solution = solutions.iloc[0]
# This is the list of measures that we will recommend
scheme = optimal_solution["scheme"]
# If the solution isn't eligible, we can't really consider it
solutions = solutions[
(solutions["is_eligible"] & (solutions["scheme"] != "none")) | (solutions["scheme"] == "none")
]
# We create this full list of selected measures, which is used in the next section for setting
# default measures
solution = deepcopy(optimal_solution["items"]) + deepcopy(optimal_solution["unfunded_items"])
funded_measures = deepcopy(optimal_solution["items"]) if scheme != "none" else []
if solutions["meets_upgrade_target"].any():
# If we have a solution that meets the upgrade target, we select that one
optimal_solution = solutions[solutions["meets_upgrade_target"]].iloc[0]
else:
# Pick the cheapest
optimal_solution = solutions.iloc[0]
# This is the total amount of funding that the project will produce (EXCLUDING uplifts) (£)
project_funding = optimal_solution["full_project_funding"] if scheme == "eco4" else \
optimal_solution["partial_project_funding"]
# This is the total amount of funding associated to the uplift (£)
total_uplift = optimal_solution["total_uplift"]
# This is the funding scheme selected
# This is the full project ABS
full_project_score = optimal_solution["project_score"]
# This is the partial project ABS
partial_project_score = optimal_solution["partial_project_score"]
# This is the uplift score ABS
uplift_project_score = optimal_solution["total_uplift_score"]
# This is the list of measures that we will recommend
scheme = optimal_solution["scheme"]
# We create this full list of selected measures, which is used in the next section for setting
# default measures
solution = deepcopy(optimal_solution["items"]) + deepcopy(optimal_solution["unfunded_items"])
funded_measures = deepcopy(optimal_solution["items"]) if scheme != "none" else []
# This is the total amount of funding that the project will produce (EXCLUDING uplifts) (£)
project_funding = optimal_solution["full_project_funding"] if scheme == "eco4" else \
optimal_solution["partial_project_funding"]
# This is the total amount of funding associated to the uplift (£)
total_uplift = optimal_solution["total_uplift"]
# This is the funding scheme selected
# This is the full project ABS
full_project_score = optimal_solution["project_score"]
# This is the partial project ABS
partial_project_score = optimal_solution["partial_project_score"]
# This is the uplift score ABS
uplift_project_score = optimal_solution["total_uplift_score"]
else:
# We optimise and then we determine eligibility for funding, based on the measures selected
optimiser = (

8
recommendations/optimiser/funding_optimiser.py
View file

@ -502,6 +502,10 @@ def optimise_with_funding_paths(
solutions = pd.DataFrame(solutions)
if solutions.empty:
# We return a blank dataframe
return solutions
# Given the scheme, we now check if the packages are eligible. If they *are* eligible, but they don't meet the
# final upgrade target, we then look to perform a final optimisation pass to meet the target gain.
solutions["meets_upgrade_target"] = solutions["total_gain"] >= target_gain - 0.1
@ -779,6 +783,10 @@ def run_optimizer(input_measures, budget=None, sub_target_gain=None, allow_slack
Thin wrapper over your optimisers.
Returns: list[dict] selected_options
"""
if not input_measures:
return None, 0.0, 0.0
if budget is not None:
opt = GainOptimiser(
input_measures, max_cost=budget, max_gain=(sub_target_gain or float("inf")),