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refactoring router

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Khalim Conn-Kowlessar 2024-08-09 14:57:29 +01:00
parent a5e6dc3399
commit 4ebd516d2e
1 changed files with 29 additions and 29 deletions
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58
backend/app/plan/router.py
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@ -661,15 +661,7 @@ async def trigger_plan(body: PlanTriggerRequest):
for key, scored in predictions_dict.items():
all_predictions[key] = pd.concat([all_predictions[key], scored])
# We now produce predictions for the kwh models
# Insert the predictions into the recommendations and run the optimiser
# TODO: If a recommendation has a negative impact on SAP, we should remove it - this seems to have become a
# possibility with heating system
# TODO: After optimising, if there are any cheap, quick win measures (e.g. insulate water tank with hot water
# cylinder jacket), we should add these to the recommendations as default
raise Exception("Add the cost impacts into the cost model")
logger.info("Optimising recommendations")
# Insert the predictions into the recommendations, and get the impact summary
scoring_epcs = [] # For scoring the kwh models
for property_id in recommendations.keys():
property_instance = [p for p in input_properties if p.id == property_id][0]
@ -686,10 +678,33 @@ async def trigger_plan(body: PlanTriggerRequest):
# at each phase
property_scoring_epcs = property_instance.update_simulation_epcs(impact_summary)
scoring_epcs.extend(property_scoring_epcs)
recommendations[property_id] = recommendations_with_impact
input_measures = prepare_input_measures(recommendations_with_impact, body.goal)
# We call the API with the scoring epcs
scoring_epcs = pd.DataFrame(scoring_epcs)
scoring_epcs = kwh_client.transform(data=scoring_epcs, cleaned=cleaned)
current_sap_points = int(property_instance.data["current-energy-efficiency"])
kwh_simulation_predictions = model_api.predict_all(
df=scoring_epcs,
bucket=get_settings().DATA_BUCKET,
prediction_buckets=get_prediction_buckets(),
model_prefixes=["heating_kwh_predictions", "hotwater_kwh_predictions"],
)
# TODO: Costing model, which should include today's costs!
# Insert the predictions into the recommendations and run the optimiser
# TODO: If a recommendation has a negative impact on SAP, we should remove it - this seems to have become a
# possibility with heating system
# TODO: After optimising, if there are any cheap, quick win measures (e.g. insulate water tank with hot water
# cylinder jacket), we should add these to the recommendations as default
for p in input_properties.keys():
if not recommendations[p.id]:
continue
input_measures = prepare_input_measures(recommendations[p.id], body.goal)
current_sap_points = int(p.data["current-energy-efficiency"])
target_sap_points = epc_to_sap_lower_bound(body.goal_value)
sap_gain = CostOptimiser.calculate_sap_gain_with_slack(target_sap_points - current_sap_points)
@ -716,7 +731,7 @@ async def trigger_plan(body: PlanTriggerRequest):
"internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation"
]):
ventilation_rec = next(
(r[0] for r in recommendations_with_impact if r[0]["type"] == "mechanical_ventilation"),
(r[0] for r in recommendations[p.id] if r[0]["type"] == "mechanical_ventilation"),
None
)
@ -730,29 +745,14 @@ async def trigger_plan(body: PlanTriggerRequest):
{**rec, "default": True if rec["recommendation_id"] in selected_recommendations else False}
for rec in recommendations_by_type
]
for recommendations_by_type in recommendations_with_impact
for recommendations_by_type in recommendations[p.id]
]
# We'll also unlist the recommendations so they're a bit easier to handle from here onwards
final_recommendations = [
rec for recommendations_by_type in final_recommendations for rec in recommendations_by_type
]
recommendations[property_id] = final_recommendations
# We call the API with the scoring epcs
scoring_epcs = pd.DataFrame(scoring_epcs)
scoring_epcs = add_features_from_code(scoring_epcs)
scoring_epcs = add_estimate_annual_kwh(scoring_epcs)
# TODO: Drop all potential and env columns
kwh_simulation_predictions = model_api.predict_all(
df=scoring_epcs,
bucket=get_settings().DATA_BUCKET,
prediction_buckets=get_prediction_buckets(),
model_prefixes=["heating_kwh_predictions", "hotwater_kwh_predictions"],
extract_ids=True
)
# TODO: Costing model, which should include today's costs!
recommendations[p.id] = final_recommendations
# We now insert into the recommendations
for property_id in recommendations.keys():