junte/Model
1
0
Fork 0
mirror of https://github.com/Hestia-Homes/Model.git synced 2026-06-08 11:17:27 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

patched issue with cleaned lookup

Browse source
This commit is contained in:
Khalim Conn-Kowlessar 2024-01-24 14:16:33 +00:00
parent f5d780a1b0
commit d557653129
1 changed files with 86 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

86
etl/eligibility/ha_15_32/ha_analysis_batch_3.py
View file

@ -609,6 +609,75 @@ def get_epc_data(
}
)
scoring_df = pd.DataFrame(scoring_data)
scoring_df = scoring_df.drop(
columns=[
"rdsap_change", "heat_demand_change", "carbon_change", "sap_ending", "heat_demand_ending",
"carbon_ending"
]
)
model_api = ModelApi(portfolio_id="-".join([ha_name, "eligibility"]), timestamp=created_at)
all_predictions = model_api.predict_all(
df=scoring_df,
bucket="retrofit-data-dev",
prediction_buckets={
"sap_change_predictions": "retrofit-sap-predictions-dev",
"heat_demand_predictions": "retrofit-heat-predictions-dev",
"carbon_change_predictions": "retrofit-carbon-predictions-dev"
}
)
results_df = pd.DataFrame(results)
predictions = all_predictions["sap_change_predictions"].copy()
predictions = predictions.rename(columns={"property_id": "row_id"}).merge(
results_df[["row_id", "sap"]], how="left", on="row_id"
)
predictions["sap_uplift"] = predictions["predictions"] - predictions["sap"]
predictions = predictions.groupby("row_id")["sap_uplift"].sum().reset_index()
results_df = results_df.merge(
predictions[["sap_uplift", "row_id"]],
how="left",
on="row_id"
)
results_df["post_install_sap"] = results_df["sap"] + results_df["sap_uplift"]
eligibility_assessment = []
for _, row in results_df[results_df["eco4_eligible"] == True].iterrows():
# The upgrade requirements are dependent on the current SAP
# If the property is an F or G, it only needs to upgrade to an %
if row["sap"] <= 38:
if row["post_install_sap"] >= 57:
eligibility_classification = "highest confidence"
elif row["post_install_sap"] >= 55:
eligibility_classification = "high confidence"
elif row["post_install_sap"] >= 53:
eligibility_classification = "medium confidence"
else:
eligibility_classification = "unlikely"
else:
if row["post_install_sap"] >= 71:
eligibility_classification = "highest confidence"
elif row["post_install_sap"] >= 69:
eligibility_classification = "high confidence"
elif row["post_install_sap"] >= 67:
eligibility_classification = "medium confidence"
else:
eligibility_classification = "unlikely"
eligibility_assessment.append(
{
"row_id": row["row_id"],
"eligibility_classification": eligibility_classification
}
)
def analyse_ha_data():
"""
@ -706,6 +775,23 @@ def app():
]
)
# We treat unknown loft insulation as no insulation
cleaned["roof-description"].extend(
[
{'original_description': 'Pitched, Unknown loft insulation', 'clean_description': 'Pitched, no insulation',
'thermal_transmittance': None, 'thermal_transmittance_unit': None, 'is_pitched': True,
'is_roof_room': False,
'is_loft': False, 'is_flat': False, 'is_thatched': False, 'is_at_rafters': False, 'is_assumed': True,
'has_dwelling_above': False, 'is_valid': True, 'insulation_thickness': 'none'}
]
)
# We patch this record because there is another property below
for x in cleaned["floor-description"]:
if x["original_description"] == '(Same dwelling below) insulated (assumed)':
x["another_property_below"] = True
x["thermal_transmittance"] = 0
cleaning_data = read_dataframe_from_s3_parquet(
bucket_name="retrofit-data-dev", file_key="sap_change_model/cleaning_dataset.parquet",
)