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https://github.com/Hestia-Homes/Model.git
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created template of code to create the ha analysis results
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Khalim Conn-Kowlessar
parent
5c6bac1f8a
commit
b6c57c7253
2 changed files with 207 additions and 41 deletions
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@ -177,15 +177,13 @@ class Eligibility:
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is_empty = (not self.walls["is_filled_cavity"]) or (
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self.walls["is_as_built"] and self.walls["insulation_thickness"] not in ["average", "above average"]
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)
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is_partial_filled = (
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self.walls["is_as_built"] and self.walls["insulation_thickness"] not in ["below average"]
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)
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is_partial_filled = "partial" in self.walls["clean_description"].lower()
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# We look for potentially under performing cavities - anything that is assumed, as built and insulated
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is_underperforming = (
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self.walls["is_as_built"] and self.walls["insulation_thickness"] in ["average"] and self.walls["is_assumed"]
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)
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is_unfilled_cavity = is_cavity and is_empty
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is_unfilled_cavity = is_cavity and (is_empty and not is_partial_filled)
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is_partial_filled_cavity = is_cavity and is_partial_filled
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is_underperforming_cavity = is_cavity and is_underperforming
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@ -901,6 +901,7 @@ def analyse_ha_data(outputs, loader):
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:return:
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"""
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ha_analysis_results = []
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for ha_name, datasets in outputs.items():
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inputs = [x for k, x in loader.data.items() if k == ha_name][0]
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@ -917,9 +918,20 @@ def analyse_ha_data(outputs, loader):
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inputs["asset_list"]["funding_scheme"]
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)
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# TODO: Also temp, just for HA 6
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if ha_name == "ha_6":
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inputs["survey_list"]["funding_scheme"] = None
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inputs["survey_list"]["funding_scheme"] = np.where(
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inputs["survey_list"][
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'AFFORDABLE WARMTH OR EPC FOR HOUSING ASSOCIATION '] == "AFFORDABLE WARMTH",
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"ECO4",
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"GBIS"
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)
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# End placholder
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results_df = datasets["results_df"].copy()
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analysis_data = inputs["asset_list"][['asset_list_row_id', "row_meaning", "funding_scheme"]].rename(
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columns={"row_meaning": "asset_identification_status"}
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).merge(
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@ -929,23 +941,6 @@ def analyse_ha_data(outputs, loader):
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left_on="asset_list_row_id"
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)
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# If we have a survey list, we merge this onto the results
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n_properties_in_asset_list = analysis_data["asset_list_row_id"].nunique()
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properties_sold = (
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inputs["survey_list"].groupby("funding_scheme")["survey_list_row_id"].nunique().reset_index() if
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inputs["survey_list"] is not None else 0
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)
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properties_sold_eco4 = (
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properties_sold[properties_sold["funding_scheme"] == "ECO4"]["survey_list_row_id"].values[0] if
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properties_sold != 0 else 0
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)
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properties_sold_gbis = (
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properties_sold[properties_sold["funding_scheme"] == "GBIS"]["survey_list_row_id"].values[0] if
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properties_sold != 0 else 0
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)
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# We now merge the survey list onto the analysis data and remove anything that is sold, to give us just what is
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# remaining
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@ -956,8 +951,23 @@ def analyse_ha_data(outputs, loader):
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# Drop any rows that have a survey_list_row_id
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analysis_data = analysis_data[pd.isnull(analysis_data["survey_list_row_id"])]
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# If we have a survey list, we merge this onto the results
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n_properties_in_asset_list = analysis_data["asset_list_row_id"].nunique()
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properties_sold = (
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inputs["survey_list"].groupby("funding_scheme")["survey_list_row_id"].nunique().reset_index() if
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inputs["survey_list"] is not None else pd.DataFrame(columns=["funding_scheme"])
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)
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properties_sold_eco4 = (
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properties_sold[properties_sold["funding_scheme"] == "ECO4"]["survey_list_row_id"].values[0] if
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(not properties_sold.empty) and ("ECO4" in properties_sold["funding_scheme"].values) else 0
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)
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properties_sold_gbis = (
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properties_sold[properties_sold["funding_scheme"] == "GBIS"]["survey_list_row_id"].values[0] if
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(not properties_sold.empty) and ("GBIS" in properties_sold["funding_scheme"].values) else 0
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)
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# We now calculate the number of remaining properties, by scheme
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# TODO: We might need to tweak a bit of the logic
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remaining_properties = analysis_data[
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analysis_data["asset_identification_status"] == "identified potential eco works (CWI)"
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].copy()
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@ -966,6 +976,7 @@ def analyse_ha_data(outputs, loader):
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remaining_properties_by_scheme = (
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remaining_properties.groupby("funding_scheme")["asset_list_row_id"].nunique().reset_index()
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)
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n_remaining_properties_eco4 = remaining_properties_by_scheme[
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remaining_properties_by_scheme["funding_scheme"] == "ECO4"
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]["asset_list_row_id"].values[0]
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@ -983,13 +994,17 @@ def analyse_ha_data(outputs, loader):
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# - This is not the very strictest definition of ECO4 eligible, but we aim to characterise the properties
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# here and re-surveying is a common practicce by Warmfront. Additionally, many of the social homes have
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# very old EPCs which may score lower when re-done
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# 2) Subject to CIGA check - Meets loft conditions but shows a filled cavity.
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# 2) Meets Fabric requirements, not SAP
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# Warmfront has identified the property as eligible, but the EPC is not D or below. We consider this but
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# label is separately as not a strict
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# 3) Subject to CIGA check - Meets loft conditions but shows a filled cavity.
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# - we don't have a SAP constraint here because the EPC is (currently) showing what the property might
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# actually look like after retrofit and so the EPC currently being a C or above means little, because
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# the updated EPC, showing an empty cavity, could bring the property within
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# 3) Loft insulation too thick - Meets empty cavity but shows a loft with between 101 and 270mm insulation.
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# 4) Loft insulation too thick - Meets empty cavity but shows a loft with between 101 and 270mm insulation.
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# - No SAP constraint, for the same reason as in category 2)
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# 4) Does not look like ECO4 candidate
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# 5) Looks like GBIS instead
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# 6) Does not look like ECO4 candidate
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#
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# For properties that have been identified as GBIS
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# 1) Strict GBIS candidates
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@ -1000,43 +1015,156 @@ def analyse_ha_data(outputs, loader):
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remaining_eco4_df = remaining_properties[
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remaining_properties["funding_scheme"] == "ECO4"
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].copy()
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####################################
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# ECO4
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####################################
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# 1) We identify this if:
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# - remaining_properties["eco4_eligible"] == True
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remaining_eco4_df["prospect_type"] = np.where(
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remaining_eco4_df["eco4_eligible"] == True,
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(remaining_eco4_df["eco4_eligible"] == True),
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"strict ECO4",
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remaining_eco4_df["prospect_type"]
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)
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# 2) We identify this if it has a filled cavity but meets the loft conditions
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# 2) Meets fabric requirements
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remaining_eco4_df["prospect_type"] = np.where(
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(
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(remaining_eco4_df["eco4_message"] == "sap too high") &
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remaining_eco4_df["eligibility_cavity_type"].isin(["partial", "empty"]) &
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remaining_eco4_df["eligibility_loft_type"].isin(["0-100mm"])
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),
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"ECO4 if SAP downgrade",
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remaining_eco4_df["prospect_type"]
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)
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remaining_eco4_df["prospect_type"]
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# 3) We identify this if it has a filled cavity but meets the loft conditions
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# TODO: Consider if we should also allow 100-270mm or if we should add some slight tolerance (e.g. 150mm)
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# to account for measurement error
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remaining_eco4_df["prospect_type"] = np.where(
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(
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remaining_eco4_df["eligibility_cavity_type"].isin(["full"]) &
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remaining_eco4_df["eligibility_loft_type"].isin(["0-100mm"])
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),
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"Filled cavity - subject to CIGA check",
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remaining_eco4_df["prospect_type"]
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)
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z = remaining_eco4_df[remaining_eco4_df["eco4_message"] == "sap too high"]
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# 4) We identify this by ensuring the cavity if empty or partial, and the loft has between 101 and 270mm
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remaining_eco4_df["prospect_type"] = np.where(
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(
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remaining_eco4_df["eligibility_cavity_type"].isin(["empty", "partial"]) &
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remaining_eco4_df["eligibility_loft_type"].isin(["100-270mm"])
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),
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"ECO4 prospect - empty cavity, loft insulation below regulation",
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remaining_eco4_df["prospect_type"]
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)
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remaining_properties[remaining_properties["eco4_eligible"] == True]["eco4_message"].value_counts()
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remaining_properties["eco4_message"].value_counts()
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z = remaining_properties[
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(remaining_properties["eco4_message"] == "Possibly eligible but property currently EPC D") &
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(remaining_properties["eco4_eligible"] == True)
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]
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# 5) Looks like GBIS instead
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remaining_eco4_df["prospect_type"] = np.where(
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(remaining_eco4_df["gbis_eligible"] == True),
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"Looks like GBIS",
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remaining_eco4_df["prospect_type"]
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)
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k = z[z["property_type"] == "Flat"]
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k["uprn"]
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# 6) This is everything else (i.e. both the cavity is full and the loft insulation is above 100mm)
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remaining_eco4_df["prospect_type"] = remaining_eco4_df["prospect_type"].fillna(
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"Does not look like ECO4 candidate"
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)
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ha_analysis_results = {
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####################################
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# GBIS
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####################################
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remaining_gbis = remaining_properties[
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remaining_properties["funding_scheme"] == "GBIS"
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].copy()
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# 1) Strict GBIS candidates
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remaining_gbis["prospect_type"] = np.where(
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(
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(remaining_gbis["gbis_eligible"] == True) & (remaining_gbis["eco4_eligible"] == False)
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),
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"strict GBIS",
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remaining_gbis["prospect_type"]
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)
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# 2) GBIS candidates that look like strict ECO4 candidates
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remaining_gbis["prospect_type"] = np.where(
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(remaining_gbis["eco4_eligible"] == True),
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"Upgradable to ECO4",
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remaining_gbis["prospect_type"]
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)
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# 3) Subject to CIGA check - Filled cavity
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remaining_gbis["prospect_type"] = np.where(
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(
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remaining_gbis["eligibility_cavity_type"].isin(["full"])
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),
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"Filled cavity - subject to CIGA check",
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remaining_gbis["prospect_type"]
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)
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# 4) Everything else
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remaining_gbis["prospect_type"] = remaining_gbis["prospect_type"].fillna(
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"Does not look like GBIS candidate"
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)
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####################################
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# Surplus properties
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####################################
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# Take properties that were not identified by Warmfront and identify those that look like they would qualify
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# under the strictest criteria
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surplus_df = analysis_data[
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analysis_data["asset_identification_status"] != "identified potential eco works (CWI)"
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].copy()
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eco4_surplus = surplus_df[
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(
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(surplus_df["eco4_eligible"] == True) & (surplus_df["eco4_message"] == "subject to post retrofit sap") &
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(
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surplus_df["eligibility_classification"].isin(
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["high confidence", "highest confidence", "medium confidence"]
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)
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)
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)
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].copy()
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gbis_surplus = surplus_df[
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(
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(surplus_df["gbis_eligible"] == True) & (surplus_df["eco4_eligible"] == False) & (
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surplus_df["eligibility_cavity_type"].isin(["empty", "partial"])
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)
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)
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].copy()
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ha_analysis_results.append({
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"n_properties_in_asset_list": n_properties_in_asset_list,
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############
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# ECO4
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############
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"properties_sold_eco4": properties_sold_eco4,
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"n_remaining_properties_eco4": n_remaining_properties_eco4,
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**remaining_eco4_df["prospect_type"].value_counts().to_dict(),
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############
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# GBIS
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############
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"properties_sold_gbis": properties_sold_gbis,
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"n_remaining_properties_gbis": n_remaining_properties_gbis
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}
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"n_remaining_properties_gbis": n_remaining_properties_gbis,
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**remaining_gbis["prospect_type"].value_counts().to_dict(),
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############
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# GBIS
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############
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"n_eco4_surplus": eco4_surplus.shape[0],
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"n_gbis_surplus": gbis_surplus.shape[0],
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})
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pass
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ha_analysis_results = pd.DataFrame(ha_analysis_results)
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# Todo: create revenue figures and automate creation of excel
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def app():
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@ -1152,3 +1280,43 @@ def app():
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outputs = get_epc_data(
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loader, cleaned, cleaning_data, created_at, photo_supply_lookup, floor_area_decile_thresholds, pull_data=False
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)
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# for ha_name, datasets in outputs.items():
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# datasets["results_df"] = datasets["results_df"].drop(
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# columns=["eligibility_cavity_type", "eligibility_loft_type"]
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# )
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#
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# # Re-do
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# res = []
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# for _, row in tqdm(datasets["results_df"].iterrows(), total=datasets["results_df"].shape[0]):
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# epc = {
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# "walls-description": row["walls"],
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# "roof-description": row["roof"],
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# "floor-description": "",
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# "tenure": "",
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# "current-energy-efficiency": row["sap"],
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# }
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# eligibility = Eligibility(epc=epc, cleaned=cleaned)
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# eligibility.check_eco4_warmfront()
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# res.append(
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# {
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# "row_id": row["row_id"],
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# "eligibility_cavity_type": eligibility.eco4_warmfront["cavity_type"],
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# "eligibility_loft_type": eligibility.eco4_warmfront["loft_type"]
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# }
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# )
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#
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# # Merge back on
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# res = pd.DataFrame(res)
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# datasets["results_df"] = datasets["results_df"].merge(res, how="left", on="row_id")
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#
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# # Re-save in s3
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# save_pickle_to_s3(
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# data={
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# "results_df": datasets["results_df"],
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# "scoring_df": datasets["scoring_df"],
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# "nodata": datasets["nodata"]
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# },
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# bucket_name="retrofit-datalake-dev",
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# s3_file_name=f"ha-analysis/{ha_name}/processed_results.pickle"
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# )
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