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messing around with street match
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Khalim Conn-Kowlessar
parent
1b38832e27
commit
67f97feb18
1 changed files with 74 additions and 31 deletions
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@ -1637,7 +1637,7 @@ def propsed_wave_3_sample():
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# TODO: We drop 302 properties that are not priority postcodes - confirm w/ Stonewater and 7 properties missing
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# UPRN
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asset_list = asset_list[~asset_list["Archetype ID"].isin(["NOT PRIORITY POSTCODE", "MISSING UPRN"])]
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asset_list = asset_list[~asset_list["Archetype ID"].isin(["MISSING UPRN"])]
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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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@ -1645,12 +1645,13 @@ def propsed_wave_3_sample():
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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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asset_list["Street and Region"] = asset_list["Street name"] + " " + asset_list["Postal Region"]
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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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["UPRN", "Address ID", "Archetype ID", "Postal Region", "Postcode", "Property Type", "Wall Type", "Roof Type",
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"Heating"]
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["UPRN", "Address ID", "Archetype ID", "Postal Region", "Postcode", "Street and Region",
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"Property Type", "Wall Type", "Roof Type", "Heating"]
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]
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survey_results = pd.read_excel(
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@ -1853,7 +1854,6 @@ def propsed_wave_3_sample():
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suffixes=("", "_method2")
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)
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else:
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region_assets = region_assets.merge(
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archetype_surveyed,
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on="Archetype ID",
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@ -1903,20 +1903,20 @@ def propsed_wave_3_sample():
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surveyed = survey_results_with_original_features[
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(
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survey_results_with_original_features["Property Type"].str.split(":").str[0] ==
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property["Property Type"].split(":")[0]
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survey_results_with_original_features["Property Type"] ==
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property["Property Type"]
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) &
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(
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survey_results_with_original_features["Wall Type"].str.split(":").str[0] ==
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property["Wall Type"].split(":")[0]
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survey_results_with_original_features["Wall Type"] ==
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property["Wall Type"]
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) &
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(
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survey_results_with_original_features["Roof Type"].str.split(":").str[0] ==
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property["Roof Type"].split(":")[0]
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survey_results_with_original_features["Roof Type"] ==
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property["Roof Type"]
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) &
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(
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survey_results_with_original_features["Heating"].str.split(":").str[0] ==
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property["Heating"].split(":")[0]
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survey_results_with_original_features["Heating"] ==
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property["Heating"]
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)
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].copy()
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@ -1962,7 +1962,10 @@ def propsed_wave_3_sample():
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if "Electric" in property["Heating"]:
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# Take other electric heating systems
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surveyed = surveyed[surveyed["Heating"].str.contains("Electric")]
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elif property["Heating"] == "Community Heating Systems: Community boilers only (RdSAP)":
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elif property["Heating"] in [
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"Community Heating Systems: Community boilers only (RdSAP)",
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"Community Heating Systems: Community CHP and boilers (RdSAP)"
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]:
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# Take other community heating systems
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surveyed = surveyed[surveyed["Heating"].str.contains("Community")]
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elif property["Heating"] == 'Heat Pump: (from database)':
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@ -2001,8 +2004,8 @@ def propsed_wave_3_sample():
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if any(surveyed["Postal Region"] == property["Postal Region"]):
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surveyed = surveyed[surveyed["Postal Region"] == property["Postal Region"]]
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# Take the 5 nearest
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surveyed = surveyed.head(5)
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# Take the 3 nearest
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surveyed = surveyed.head(3)
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# # We allow a max distance of 10km
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# surveyed = surveyed[surveyed["distance_meters"] < 10000]
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@ -2176,6 +2179,9 @@ def propsed_wave_3_sample():
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results = pd.concat(results)
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# home = results[results["Confidence Tier"] == "5 - EPC C or above"].sample(1)
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# region = home["Postal Region"].values[0]
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# Create a pivot table for counts of Confidence Tier by Postal Region
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geographic_summary = results.pivot_table(
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index='Postal Region',
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@ -2192,7 +2198,9 @@ def propsed_wave_3_sample():
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# '3 - similar property, weighted on distance'
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gain_columns = [
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'1 - Archetype surveyed', '1 - property was surveyed', '2 - same archetype',
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'1 - Archetype surveyed',
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'1 - property was surveyed',
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'2 - same archetype',
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'3 - similar property, weighted on distance'
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]
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#
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@ -2200,8 +2208,11 @@ def propsed_wave_3_sample():
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# '4 - no similar property, needs survey to confirm',
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# '5 - EPC C or above', '5 - property was surveyed'
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loss_columns = ['4 - no similar property, needs survey to confirm', '5 - EPC C or above',
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'5 - property was surveyed']
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loss_columns = [
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'4 - no similar property, needs survey to confirm',
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'5 - EPC C or above',
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'5 - property was surveyed'
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]
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geographic_summary["Gain"] = geographic_summary[gain_columns].sum(axis=1)
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geographic_summary["Loss"] = geographic_summary[loss_columns].sum(axis=1)
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@ -2249,26 +2260,30 @@ def propsed_wave_3_sample():
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# We now see if there are any postcodes that have no loss that can be added
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unselected_regions = geographic_summary[~geographic_summary["Selected"]]["Postal Region"].values
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# TODO: Try on street
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postcode_summary = results.pivot_table(
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index='Postcode',
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index='Street and Region',
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columns='Confidence Tier',
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aggfunc='size',
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fill_value=0
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).reset_index()
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postcode_summary = postcode_summary.merge(
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results[["Postcode", "Postal Region"]].drop_duplicates(),
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how="left", on="Postcode"
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)
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postcode_summary_unselected_regions = postcode_summary[
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postcode_summary["Postcode"].str.split(" ").str[0].isin(unselected_regions)
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].copy()
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# postcode_summary = postcode_summary.merge(
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# results[["Postcode", "Postal Region"]].drop_duplicates(),
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# how="left", on="Postcode"
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# )
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#
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postcode_summary_unselected_regions = postcode_summary.copy()
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# postcode_summary_unselected_regions = postcode_summary[
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# postcode_summary["Postcode"].str.split(" ").str[0].isin(unselected_regions)
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# ].copy()
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postcode_summary_unselected_regions["Gain"] = postcode_summary_unselected_regions[gain_columns].sum(axis=1)
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postcode_summary_unselected_regions["Loss"] = postcode_summary_unselected_regions[loss_columns].sum(axis=1)
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# Remaining loss allowed
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remaining_loss_constraint = 250 - region_totals["Loss"]
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# remaining_loss_constraint = 230 - region_totals["Loss"]
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remaining_loss_constraint = 250
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postcode_selected_rows, _ = optimise(
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gain=postcode_summary_unselected_regions["Gain"].values,
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loss=postcode_summary_unselected_regions["Loss"].values,
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@ -2284,12 +2299,40 @@ def propsed_wave_3_sample():
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postcode_totals = postcode_optimised_additional_properties[["Gain", "Loss"]].sum()
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bid_size = region_totals.sum() + postcode_totals.sum()
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bid_size = postcode_totals.sum()
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print("Bid Size:", bid_size)
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total_epc_d_or_below = region_totals["Gain"] + postcode_totals["Gain"]
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total_epc_d_or_below = postcode_totals["Gain"]
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print("Total EPC D or below:", total_epc_d_or_below)
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total_epc_c = region_totals["Loss"] + postcode_totals["Loss"]
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total_epc_c = postcode_totals["Loss"]
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print("Total EPC C or above:", total_epc_c)
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# Total needing a survey
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total_needing_survey = postcode_optimised_additional_properties[
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"4 - no similar property, needs survey to confirm"
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].sum()
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print("Total needing survey:", total_needing_survey)
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# Look for postcodes that have no loss
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unselected_streets = postcode_summary_unselected_regions[
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~postcode_summary_unselected_regions["Selected"]
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]["Street and Region"].values
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postcode_summary2 = results[
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results["Street and Region"].isin(unselected_streets)
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].pivot_table(
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index='Postcode',
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columns='Confidence Tier',
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aggfunc='size',
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fill_value=0
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).reset_index()
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postcode_summary2["Gain"] = postcode_summary2[gain_columns].sum(axis=1)
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postcode_summary2["Loss"] = postcode_summary2[loss_columns].sum(axis=1)
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no_loss_postcodes = postcode_summary2[postcode_summary2["Loss"] == 0].sort_values("Gain", ascending=False)
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total_bid_size = bid_size + no_loss_postcodes["Gain"].sum()
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print(total_bid_size)
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z = results[results["Confidence Tier"] == "5 - EPC C or above"]
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# if __name__ == "__main__":
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# main()
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