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working on stonewater matching algorithm
This commit is contained in:
Khalim Conn-Kowlessar
parent
31c5935577
commit
dc1cf6d604
3 changed files with 171 additions and 9 deletions
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@ -229,7 +229,3 @@ def app():
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filename = ("/Users/khalimconn-kowlessar/Documents/hestia/Customers/southend/southend EPC Data pull - 14 Nov "
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"2024.xlsx")
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asset_list.to_excel(filename, index=False)
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asset_list["% of the Roof with PV"].value_counts()
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asset_list[asset_list["% of the Roof with PV"] == "50.0"][["Address", "Postcode"]]
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@ -117,7 +117,7 @@ def extract_summary_report(pdf_path):
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- Fuel Bill
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- Address
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"""
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data = {
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"Address": None,
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"Postcode": None,
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@ -1618,5 +1618,136 @@ def append_stonewater_id():
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index=False
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)
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def propsed_wave_3_sample():
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"""
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Stonewater want to ensure that the properties that when selecting properties for wave 3, they choose properties
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such that most of the properties within a geographical area are treatable within the bid.
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Name, if we take a geographical area (which could be postal region) they want the most, and ideally all, of the
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properties within that geographical area to be included within the bid
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:return:
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"""
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asset_list = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Stonewater/Stonewater SHDF_3_0_Board Triage 22.05.24 "
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"- Archetyped V3.1.xlsx",
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header=4
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)
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# Clean address ids
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asset_list = asset_list[~pd.isnull(asset_list["Address ID"])]
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asset_list = asset_list[asset_list["Address ID"] != "Address ID"]
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asset_list["Address ID"] = asset_list["Address ID"].astype(int)
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# Create the postal region, taking the first part of the postcode
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asset_list["Postal Region"] = asset_list["Postcode"].str.split(" ").str[0]
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unique_postal_regions = asset_list["Postal Region"].unique()
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# Keep just the columns we need
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asset_list = asset_list[
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["Address ID", "Archetype ID", "Postal Region", "Postcode", "Property Type", "Wall Type", "Roof Type",
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"Heating"]
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]
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survey_results = pd.read_excel(
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os.path.join(CUSTOMER_FOLDER_PATH, "Stonewater - Bid Packages WIP 14.11.24.xlsx"),
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header=13,
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sheet_name="Modelled Packages"
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)
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# TOOD: We probably want the actual surveyed wall, roof, heating type
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survey_results = survey_results[
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["Address ID", "Archetype ID", "Current SAP Rating", "Current EPC Band", "Postcode"]
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]
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survey_results["Postal Region"] = survey_results["Postcode"].str.split(" ").str[0]
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survey_results_with_original_features = survey_results.merge(
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asset_list[["Address ID", "Property Type", "Wall Type", "Roof Type", "Heating"]],
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on="Address ID",
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how="left"
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)
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if survey_results_with_original_features.shape[0] != survey_results.shape[0]:
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raise ValueError("Something went wrong")
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# Tier definitions
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# Tier 1: We have a property in the same postal region and same archetype that was surveyed and is below EPC D
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# Tier 2: We have a property in the same archetype that was surveyed and is below EPC D
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#
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for region in unique_postal_regions:
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# Take all of the properties in that region
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region_assets = asset_list[asset_list["Postal Region"] == region].copy()
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archetypes = region_assets["Archetype ID"].unique()
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# We get the properties that have been surveyed
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region_surveyed = survey_results[
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survey_results["Archetype ID"].isin(archetypes) &
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(survey_results["Postal Region"] == region)
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][["Archetype ID", "Current EPC Band"]].drop_duplicates()
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if region_surveyed["Archetype ID"].duplicated().sum():
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raise NotImplementedError("Fix me")
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region_assets = region_assets.merge(
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region_surveyed,
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on="Archetype ID",
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how="left"
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)
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# Label the tier 1 properties
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region_assets["Confidence Tier"] = None
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region_assets["Confidence Tier"] = np.where(
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region_assets["Current EPC Band"].isin(["D", "E", "F", "G"]),
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"1", region_assets["Confidence Tier"]
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)
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# TODO: Turn into a function
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missed_archetypes = set(archetypes) - set(region_surveyed["Archetype ID"])
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region_surveyed = survey_results[
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survey_results["Archetype ID"].isin(missed_archetypes)
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][["Archetype ID", "Current EPC Band"]].drop_duplicates()
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if region_surveyed["Archetype ID"].duplicated().sum():
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raise NotImplementedError("Fix me 2")
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region_assets = region_assets.merge(
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region_surveyed,
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on="Archetype ID",
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how="left",
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suffixes=("", "_method2")
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)
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region_assets["Confidence Tier"] = np.where(
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region_assets["Current EPC Band_method2"].isin(["D", "E", "F", "G"]),
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"2 - same archetype", region_assets["Confidence Tier"]
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)
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region_assets["Current EPC Band"] = region_assets["Current EPC Band"].fillna(
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region_assets["Current EPC Band_method2"])
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region_assets = region_assets.drop(columns=["Current EPC Band_method2"])
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missed_addressids = region_assets[pd.isnull(region_assets["Confidence Tier"])]["Address ID"].unique().tolist()
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# This means that this archetype was never surveyed and so we need to find a sufficiently similar property
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for a_id in missed_addressids:
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property = asset_list[asset_list["Address ID"] == a_id].squeeze()
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surveyed_same_postcode = survey_results_with_original_features[
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(survey_results_with_original_features["Postcode"] == property["Postcode"]) &
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(survey_results_with_original_features["Property Type"] == property["Property Type"])
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]
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surveyed_same_region = survey_results_with_original_features[
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(survey_results_with_original_features["Postal Region"] == property["Postal Region"]) &
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(survey_results_with_original_features["Property Type"] == property["Property Type"])
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]
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same_postcode = survey_results[
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survey_results["Archetype ID"].isin(missed_archetypes) &
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(survey_results["Postal Region"] == region)
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][["Archetype ID", "Current EPC Band"]].drop_duplicates()
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pd.isnull(region_assets["Current EPC Band"]).sum()
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# if __name__ == "__main__":
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# main()
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@ -206,6 +206,14 @@ def app():
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# Drop the column that is ""
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transformed_df = transformed_df.drop(columns=[""])
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# Get the find my epc data
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find_my_epc_data = epc_df[["row_id", "find_my_epc_data"]].drop(columns=["find_my_epc_data"]).join(
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pd.json_normalize(epc_df["find_my_epc_data"])
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)
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# We check if we get the solar pv column:
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if "Solar photovoltaics" not in find_my_epc_data.columns:
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find_my_epc_data["Solar photovoltaics"] = False
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# Retrieve just the data we need
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epc_df = epc_df[
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[
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@ -228,6 +236,7 @@ def app():
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"mainheat-description",
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#
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"energy-consumption-current", # kwh/m2
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"photo-supply",
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]
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]
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@ -236,12 +245,25 @@ def app():
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how="left",
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on="row_id"
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).merge(
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transformed_df,
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find_my_epc_data[
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[
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"row_id", "heating_text", "hot_water_text", 'Assessor’s name',
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"Assessor's Telephone", "Assessor's Email", "Accreditation scheme",
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"Assessor’s ID", "Solar photovoltaics"
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]
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].rename(
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columns={
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"Solar photovoltaics": "Has Solar PV",
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"heating_text": "Heating Estimated kWh",
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"hot_water_text": "Hot Water Estimated kWh",
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}
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),
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how="left",
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on="row_id"
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)
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asset_list = asset_list.drop(columns=["row_id"])
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asset_list["Has Solar PV"] = asset_list["Has Solar PV"] | ~asset_list["photo-supply"].isin(["0.0", 0, None, ""])
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asset_list = asset_list.drop(columns=["photo-supply"])
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# Rename the columns
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asset_list = asset_list.rename(columns={
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@ -259,7 +281,7 @@ def app():
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"mainheat-description": "Heating Type",
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"secondheat-description": "Secondary Heating",
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"transaction-type": "Reason for last EPC",
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"energy-consumption-current": "Heat Demand (kWh/m2)"
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"energy-consumption-current": "Heat Demand (kWh/m2)",
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})
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asset_list["Estimated Number of Floors"] = asset_list.apply(
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@ -295,6 +317,19 @@ def app():
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axis=1
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)
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# For all of the columns in transformed_df, prefix with "Recommendation: "
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for col in transformed_df.columns:
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if col == "row_id":
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continue
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transformed_df = transformed_df.rename(columns={col: f"Recommendation: {col}"})
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asset_list = asset_list.merge(
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transformed_df,
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how="left",
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on="row_id"
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)
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asset_list = asset_list.drop(columns=["row_id"])
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# Store as an excel
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filename = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Settle/Settle EPC Data pull - 08 Nov 2024.xlsx"
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filename = os.path.join(DATA_FOLDER, ".".join(DATA_FILENAME.split(".")[:-1])) + " EPC Data Pull.xlsx"
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asset_list.to_excel(filename, index=False)
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