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started basic works

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Khalim Conn-Kowlessar 2026-02-11 10:57:55 +00:00
parent d4064da365
commit 8078f32fd4
2 changed files with 31 additions and 1 deletions
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12
backend/Property.py
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@ -868,7 +868,7 @@ class Property:
lodgement_date = self.data["lodgement-date"] lodgement_date = self.data["lodgement-date"]
# We check if the lodgement date is more than 10 years old # We check if the lodgement date is more than 10 years old
is_expired = (datetime.now() - pd.to_datetime(lodgement_date)) > timedelta(days=3650) is_expired = self.epc_is_expired
# Handle re-baselining # Handle re-baselining
co2_emissions = self.energy["co2_emissions"] co2_emissions = self.energy["co2_emissions"]
@ -1499,3 +1499,13 @@ class Property:
] ]
return self.data.get("mechanical-ventilation") in ventilation_descriptions return self.data.get("mechanical-ventilation") in ventilation_descriptions
@property
def epc_is_expired(self) -> bool:
"""
This property indicates that the EPC is expired. This is based on the lodgement date, where an EPC is
valid for 10 years.
:return: boolean indicating whether the EPC is expired
"""
lodgement_date = self.data["lodgement-date"]
return (datetime.now() - pd.to_datetime(lodgement_date)) > timedelta(days=3650)

20
backend/engine/engine.py
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@ -943,6 +943,26 @@ async def model_engine(body: PlanTriggerRequest):
# We also make a tweak - if the property has been flagged for solar but doesn't contain # We also make a tweak - if the property has been flagged for solar but doesn't contain
# any panel performance, we ensure that we have a 3kWp and 4kWp option for the property # any panel performance, we ensure that we have a 3kWp and 4kWp option for the property
# TODO: Temp - test re-baselining
p = input_properties[0]
p.create_base_difference_epc_record(cleaned_lookup=cleaned)
scoring_data = p.base_difference_record.df
# We just need a recent date to trigger the right models,
# as we are only interested in the deltas
scoring_data["is_post_sap10_starting"] = True
# Score model - SAP re-baselining model
model_api.MODEL_URLS["retrofit-sap-baseline-predictions"] = "sapbaselinemodel"
model_api.prediction_buckets["retrofit-sap-baseline-predictions"] = "retrofit-sap-baseline-predictions-dev"
example_response = model_api.predict_all(
df=scoring_data,
bucket=get_settings().DATA_BUCKET,
model_prefixes=["retrofit-sap-baseline-predictions"],
extract_ids=False
)
input_properties[0].data["current-energy-efficiency"] = 58.8
input_properties[0].data["current-energy-rating"] = "D"
logger.info("Identifying property recommendations") logger.info("Identifying property recommendations")
recommendations, recommendations_scoring_data, representative_recommendations = {}, [], {} recommendations, recommendations_scoring_data, representative_recommendations = {}, [], {}
for p in tqdm(input_properties): for p in tqdm(input_properties):