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put in get_epc_data
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Khalim Conn-Kowlessar
parent
8a27daf71b
commit
7878983dbe
1 changed files with 172 additions and 1 deletions
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@ -171,7 +171,178 @@ def load_data():
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def get_epc_data(data, cleaned, cleaning_data, created_at):
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pass
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scoring_data = []
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results = []
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nodata = []
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for _, property_meta in tqdm(data.iterrows(), total=len(data)):
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searcher = SearchEpc(
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address1=property_meta["HouseNo"],
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postcode=property_meta["Postcode"],
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size=1000
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)
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searcher.search()
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if searcher.data is None:
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nodata.append(property_meta)
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continue
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newest_epc, older_epcs, full_sap_epc = searcher.retrieve(address=property_meta["Address"])
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# We also want to get the penultimate epc
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penultimate_epc, _ = searcher.filter_newest_epc(older_epcs)
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if not penultimate_epc:
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penultimate_epc = newest_epc
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eligibility = Eligibility(epc=newest_epc, cleaned=cleaned)
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eligibility.check_gbis_warmfront()
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eligibility.check_eco4_warmfront()
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if (not eligibility.eco4_warmfront["eligible"]) and (not eligibility.gbis_warmfront) and (
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property_meta["warmfront_identified"]
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):
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eligibility = Eligibility(epc=penultimate_epc, cleaned=cleaned)
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eligibility.check_gbis_warmfront()
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eligibility.check_eco4_warmfront()
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# If this is the case, we need to update the older epcs
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older_epcs = [
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x for x in older_epcs if x["lmk-key"] not in [newest_epc["lmk-key"], penultimate_epc["lmk-key"]]
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]
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# Full checks
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eligibility.check_gbis()
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eligibility.check_eco4()
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if eligibility.eco4_warmfront["eligible"]:
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if eligibility.epc["uprn"] == "":
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eligibility.epc["uprn"] = int(property_meta["row_id"].split("_")[1])
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scoring_dictionary = prepare_model_data_row(
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property_id=property_meta["row_id"],
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modelling_epc=eligibility.epc,
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cleaned=cleaned,
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cleaning_data=cleaning_data,
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created_at=created_at,
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old_data=older_epcs,
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full_sap_epc=full_sap_epc
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)
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scoring_data.extend(scoring_dictionary)
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results.append(
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{
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"row_id": property_meta["row_id"],
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"uprn": eligibility.epc["uprn"],
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"Address": property_meta["Address"],
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"Postcode": property_meta["Postcode"],
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"property_type": eligibility.epc["property-type"],
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"gbis_eligible": eligibility.gbis_warmfront,
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"eco4_eligible": eligibility.eco4_warmfront["eligible"],
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"eco4_message": eligibility.eco4_warmfront["message"],
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"sap": float(eligibility.epc["current-energy-efficiency"]),
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"gbis_eligible_future": eligibility.gbis["eligible"],
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"gbis_eligible_future_message": eligibility.gbis["message"],
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"eco4_eligible_future": eligibility.eco4["eligible"],
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"eco4_eligible_future_message": eligibility.eco4["message"],
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# Property components
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"roof": eligibility.roof["clean_description"],
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"walls": eligibility.walls["clean_description"],
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"cavity_type": eligibility.cavity["type"],
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"heating": eligibility.epc["mainheat-description"],
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"tenure": eligibility.tenure,
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"date_epc": eligibility.epc["lodgement-date"],
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}
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)
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scoring_df = pd.DataFrame(scoring_data)
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# Perform the same cleaning as in the model - first clean number of room variables though
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scoring_df = DataProcessor.apply_averages_cleaning(
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data_to_clean=scoring_df,
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cleaning_data=cleaning_data,
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cols_to_merge_on=['PROPERTY_TYPE', 'BUILT_FORM', 'CONSTRUCTION_AGE_BAND', 'LOCAL_AUTHORITY'],
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colnames=["NUMBER_HABITABLE_ROOMS", "NUMBER_HEATED_ROOMS"],
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)
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scoring_df = DataProcessor.apply_averages_cleaning(
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data_to_clean=scoring_df,
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cleaning_data=cleaning_data,
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cols_to_merge_on=COLUMNS_TO_MERGE_ON + ["LOCAL_AUTHORITY"],
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).drop(columns=["LOCAL_AUTHORITY"])
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scoring_df = DataProcessor.clean_missings_after_description_process(
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scoring_df,
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ignore_cols=[c for c in scoring_df.columns if ("thermal_transmittance" in c) or (
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"insulation_thickness" in c) or ("ENERGY_EFF" in c)]
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)
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scoring_df = DataProcessor.clean_efficiency_variables(scoring_df)
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scoring_df["UPRN"] = scoring_df["UPRN"].astype(int)
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model_api = ModelApi(portfolio_id="ha33-eligibility", timestamp=created_at)
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all_predictions = model_api.predict_all(
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df=scoring_df,
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bucket="retrofit-data-dev",
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prediction_buckets={
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"sap_change_predictions": "retrofit-sap-predictions-dev",
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"heat_demand_predictions": "retrofit-heat-predictions-dev",
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"carbon_change_predictions": "retrofit-carbon-predictions-dev"
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}
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)
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predictions = all_predictions["sap_change_predictions"].copy()
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results_df = pd.DataFrame(results)
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predictions = predictions.rename(columns={"property_id": "row_id"}).merge(
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results_df[["row_id", "sap"]], how="left", on="row_id"
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)
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predictions["sap_uplift"] = predictions["predictions"] - predictions["sap"]
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predictions = predictions.groupby("row_id")["sap_uplift"].sum().reset_index()
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results_df = results_df.merge(
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predictions[["sap_uplift", "row_id"]],
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how="left",
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on="row_id"
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)
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results_df["post_install_sap"] = results_df["sap"] + results_df["sap_uplift"]
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eligibility_assessment = []
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for _, row in results_df[results_df["eco4_eligible"] == True].iterrows():
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# The upgrade requirements are dependent on the current SAP
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# If the property is an F or G, it only needs to upgrade to an %
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if row["sap"] <= 38:
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if row["post_install_sap"] >= 57:
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eligibility_classification = "highest confidence"
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elif row["post_install_sap"] >= 55:
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eligibility_classification = "high confidence"
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elif row["post_install_sap"] >= 53:
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eligibility_classification = "medium confidence"
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else:
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eligibility_classification = "unlikely"
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else:
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if row["post_install_sap"] >= 71:
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eligibility_classification = "highest confidence"
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elif row["post_install_sap"] >= 69:
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eligibility_classification = "high confidence"
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elif row["post_install_sap"] >= 67:
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eligibility_classification = "medium confidence"
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else:
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eligibility_classification = "unlikely"
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eligibility_assessment.append(
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{
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"row_id": row["row_id"],
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"eligibility_classification": eligibility_classification
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}
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)
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eligibility_assessment = pd.DataFrame(eligibility_assessment)
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results_df = results_df.merge(
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eligibility_assessment, how="left", on="row_id"
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)
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return results_df, scoring_data, nodata
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def app():
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