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Working on adding new Recommendations class for updating recommendations with impact

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This commit is contained in:
Khalim Conn-Kowlessar 2023-11-27 17:10:47 +00:00
parent 3166ce5521
commit 5d54226378
3 changed files with 64 additions and 16 deletions
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2
backend/app/db/functions/recommendations_functions.py
View file

@ -80,6 +80,8 @@ def upload_recommendations(session: Session, recommendations_to_upload, property
"starting_u_value": rec.get("starting_u_value"),
"new_u_value": rec.get("new_u_value"),
"sap_points": rec["sap_points"],
"heat_demand": rec["heat_demand"],
"co2_equivalent_savings": rec["co2_equivalent_savings"],
"total_work_hours": rec["labour_hours"],
}
for rec in recommendations_to_upload

26
backend/app/plan/router.py
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@ -37,6 +37,7 @@ from recommendations.optimiser.CostOptimiser import CostOptimiser
from recommendations.optimiser.GainOptimiser import GainOptimiser
from recommendations.optimiser.optimiser_functions import prepare_input_measures
from recommendations.WallRecommendations import WallRecommendations
from recommendations.Recommendations import Recommendations
from utils.logger import setup_logger
from utils.s3 import read_dataframe_from_s3_parquet
@ -245,28 +246,21 @@ async def trigger_plan(body: PlanTriggerRequest):
logger.info("Optimising recommendations")
for property_id in recommendations.keys():
property = [p for p in input_properties if p.id == property_id][0]
predictions = all_predictions["sap_change_predictions"]
property_predictions = predictions[predictions["property_id"] == str(property_id)]
property_instance = [p for p in input_properties if p.id == property_id][0]
for recommendations_by_type in recommendations[property_id]:
for rec in recommendations_by_type:
new_sap = property_predictions[property_predictions["recommendation_id"] == str(
rec["recommendation_id"]
)]["predictions"].values[0]
recommendations_with_impact = Recommendations.calculate_recommendation_impact(
property_instance=property_instance,
all_predictions=all_predictions,
recommendations=recommendations
)
rec["sap_points"] = new_sap - float(property.data["current-energy-efficiency"])
if rec["sap_points"] is None:
raise ValueError("Sap points missing")
input_measures = prepare_input_measures(recommendations[property_id], body.goal)
input_measures = prepare_input_measures(recommendations_with_impact, body.goal)
if body.budget:
optimiser = GainOptimiser(input_measures, max_cost=body.budget)
else:
# The minimum gain is the minimum number of SAP points required to get to the target SAP band
current_sap_points = int(property.data["current-energy-efficiency"])
current_sap_points = int(property_instance.data["current-energy-efficiency"])
target_sap_points = epc_to_sap_lower_bound(body.goal_value)
# If the gain is negative, the optimiser will return an empty solution
@ -286,7 +280,7 @@ 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[property_id]
for recommendations_by_type in recommendations_with_impact
]
# We'll also unlist the recommendations so they're a bit easier to handle from here onwards

52
recommendations/Recommendations.py Normal file
View file

@ -0,0 +1,52 @@
class Recommendations:
"""
High level recommendations class, which sits above the measure specific recommendation classes
"""
@classmethod
def calculate_recommendation_impact(cls, property_instance, all_predictions, recommendations):
"""
Given predictions from the model apis, with method will update the recommendations with the predicted
impact of the recommendation on the property
:param property_instance: Instance of the Property class, for the home associated to property_id
:param all_predictions: dictionary of predictions from the model apis
:param recommendations: dictionary of recommendations for the property
:return:
"""
property_sap_predictions = all_predictions["sap_change_predictions"][
all_predictions["sap_change_predictions"]["property_id"] == str(property_instance.id)
]
property_heat_predictions = all_predictions["heat_demand_predictions"][
all_predictions["heat_demand_predictions"]["property_id"] == str(property_instance.id)
]
property_carbon_predictions = all_predictions["carbon_change_predictions"][
all_predictions["carbon_change_predictions"]["property_id"] == str(property_instance.id)
]
property_recommendations = recommendations[property_instance.id].copy()
for recommendations_by_type in property_recommendations:
for rec in recommendations_by_type:
new_sap = property_sap_predictions[property_sap_predictions["recommendation_id"] == str(
rec["recommendation_id"]
)]["predictions"].values[0]
new_heat_demand = property_heat_predictions[property_heat_predictions["recommendation_id"] == str(
rec["recommendation_id"]
)]["predictions"].values[0]
new_carbon = property_carbon_predictions[property_carbon_predictions["recommendation_id"] == str(
rec["recommendation_id"]
)]["predictions"].values[0]
rec["sap_points"] = new_sap - float(property_instance.data["current-energy-efficiency"])
rec["co2_equivalent_savings"] = float(property_instance.data["co2-emissions-current"]) - new_carbon
rec["heat_demand"] = float(property_instance.data["co2-emissions-current"]) - new_heat_demand
if rec["sap_points"] is None:
raise ValueError("Sap points missing")
return property_recommendations