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recommendation api working locally

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Khalim Conn-Kowlessar 2023-09-12 17:27:28 +01:00
parent 28bdc119fd
commit cda0ceb009
2 changed files with 38 additions and 45 deletions
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80
backend/app/plan/router.py
View file

@ -304,11 +304,6 @@ async def trigger_plan(body: PlanTriggerRequest):
if not property_recommendations: if not property_recommendations:
continue continue
# We'll unlist the recommendations so they're a bit easier to handle from here onwards
property_recommendations = [
rec for recommendations_by_type in property_recommendations for rec in recommendations_by_type
]
recommendations[p.id] = property_recommendations recommendations[p.id] = property_recommendations
# Finally, we'll prepare data for predicting the impact on SAP # Finally, we'll prepare data for predicting the impact on SAP
@ -330,25 +325,26 @@ async def trigger_plan(body: PlanTriggerRequest):
'Suspended, no insulation (assumed)': 'Suspended, insulated (assumed)', 'Suspended, no insulation (assumed)': 'Suspended, insulated (assumed)',
'Solid, no insulation (assumed)': 'Solid, insulated (assumed)', 'Solid, no insulation (assumed)': 'Solid, insulated (assumed)',
} }
for rec in property_recommendations: for recommendations_by_type in property_recommendations:
scoring_dict = { for rec in recommendations_by_type:
"UPRN": p.data["uprn"], scoring_dict = {
"id": "+".join([str(p.id), str(rec["recommendation_id"])]), "UPRN": p.data["uprn"],
"LOCAL_AUTHORITY": p.data["local-authority"], "id": "+".join([str(p.id), str(rec["recommendation_id"])]),
**starting_epc_data.to_dict("records")[0], "LOCAL_AUTHORITY": p.data["local-authority"],
**ending_epc_data.to_dict("records")[0], **starting_epc_data.to_dict("records")[0],
**fixed_data.to_dict("records")[0] **ending_epc_data.to_dict("records")[0],
} **fixed_data.to_dict("records")[0]
}
# We update the description to indicate it's insulated # We update the description to indicate it's insulated
if rec["type"] == "wall_insulation": if rec["type"] == "wall_insulation":
scoring_dict["WALLS_DESCRIPTION_ENDING"] = scoring_map[p.walls["clean_description"]] scoring_dict["WALLS_DESCRIPTION_ENDING"] = scoring_map[p.walls["clean_description"]]
elif rec["type"] == "floor_insulation": elif rec["type"] == "floor_insulation":
scoring_dict["FLOOR_DESCRIPTION_ENDING"] = scoring_map[p.floor["clean_description"]] scoring_dict["FLOOR_DESCRIPTION_ENDING"] = scoring_map[p.floor["clean_description"]]
else: else:
raise NotImplementedError("Implement me") raise NotImplementedError("Implement me")
recommendations_scoring_data.append(scoring_dict) recommendations_scoring_data.append(scoring_dict)
recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data) recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data)
@ -416,10 +412,14 @@ async def trigger_plan(body: PlanTriggerRequest):
property = [p for p in input_properties if p.id == property_id][0] property = [p for p in input_properties if p.id == property_id][0]
property_predictions = predictions[predictions["property_id"] == str(property_id)] property_predictions = predictions[predictions["property_id"] == str(property_id)]
for rec in recommendations[property_id]: for recommendations_by_type in recommendations[property_id]:
rec["sap_points"] = property_predictions[property_predictions["recommendation_id"] == str( for rec in recommendations_by_type:
rec["recommendation_id"] rec["sap_points"] = property_predictions[property_predictions["recommendation_id"] == str(
)]["RDSAP_CHANGE"].values[0] rec["recommendation_id"]
)]["RDSAP_CHANGE"].values[0]
if not rec["sap_points"]:
raise ValueError("Sap points missing")
input_measures = prepare_input_measures(recommendations[property_id], body.goal) input_measures = prepare_input_measures(recommendations[property_id], body.goal)
@ -441,20 +441,22 @@ async def trigger_plan(body: PlanTriggerRequest):
selected_recommendations = {r["id"] for r in solution} selected_recommendations = {r["id"] for r in solution}
# For selected recommendations, mark them as default # We'll use the set of selected recommendations to filter the recommendations to upload
for rec in recommendations[property_id]: final_recommendations = [
rec["default"] = rec["recommendation_id"] in selected_recommendations
for p in input_properties:
property_recommendations = [
[ [
{**rec, "default": True if rec["recommendation_id"] in selected_recommendations else False} {**rec, "default": True if rec["recommendation_id"] in selected_recommendations else False}
for rec in recommendations_by_type for rec in recommendations_by_type
] ]
for recommendations_by_type in property_recommendations for recommendations_by_type in recommendations[property_id]
] ]
input_measures = prepare_input_measures(property_recommendations, body.goal) # 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
]
# We update recommendations[property_id]
recommendations[property_id] = final_recommendations
# 1) the property data # 1) the property data
# 2) the property details (epc) # 2) the property details (epc)
@ -476,16 +478,6 @@ async def trigger_plan(body: PlanTriggerRequest):
if not recommendations_to_upload: if not recommendations_to_upload:
continue continue
property_predictions = predictions[predictions["property_id"] == str(p.id)]
for rec in recommendations_to_upload:
# Insert the prediction for sap points
rec["sap_points"] = property_predictions[property_predictions["recommendation_id"] == str(
rec["recommendation_id"]
)]["RDSAP_CHANGE"].values[0]
if not rec["sap_points"]:
raise ValueError("Sap points missing")
# Create a plan # Create a plan
new_plan_id = create_plan( new_plan_id = create_plan(
session, session,

3
model_data/optimiser/optimiser_functions.py
View file

@ -17,7 +17,7 @@ def prepare_input_measures(property_recommendations, goal):
raise NotImplementedError("Not implemented this gain type - investigate me") raise NotImplementedError("Not implemented this gain type - investigate me")
input_measures = [] input_measures = []
for rec in property_recommendations: for recs in property_recommendations:
input_measures.append( input_measures.append(
[ [
{ {
@ -26,6 +26,7 @@ def prepare_input_measures(property_recommendations, goal):
"gain": rec[goal_key], "gain": rec[goal_key],
"type": rec["type"] "type": rec["type"]
} }
for rec in recs
] ]
) )