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tidying up optimisation process
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Khalim Conn-Kowlessar
parent
5d5001fec3
commit
d65c99f62a
1 changed files with 24 additions and 81 deletions
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@ -2297,39 +2297,9 @@ def propsed_wave_3_sample():
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if pd.isnull(results[c]).sum():
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raise Exception("Something went wrong")
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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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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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# We create the gain and loss columns
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# Gain is the sum of these columns:
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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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gain_columns = sorted([x for x in results["Confidence Tier"].unique() if "1 - " in x or "2 - " in x or "3 - " in x])
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loss_columns = sorted([x for x in results["Confidence Tier"].unique() if "4 - " in x or "5 - " in x])
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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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print(geographic_summary.sum())
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geographic_summary = geographic_summary.sort_values("Loss", ascending=True)
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geographic_summary["Loss Cumulative Sum"] = geographic_summary["Loss"].cumsum()
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geographic_summary[geographic_summary["Loss Cumulative Sum"] <= 250]["Gain"].sum()
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loss = geographic_summary["Loss"].values
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gain = geographic_summary["Gain"].values
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def optimise(gain, loss, max_loss=250):
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# Define the coefficients for the objective function (negative because we maximize Gain)
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@ -2352,76 +2322,51 @@ def propsed_wave_3_sample():
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return selected_rows, optimal_gain
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selected_rows, _ = optimise(gain, loss, 250)
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# Select the rows that are selected
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geographic_summary["Selected"] = selected_rows == 1
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geographic_summary[geographic_summary["Selected"]].sum()
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region_totals = geographic_summary[
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geographic_summary["Selected"]
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][["Gain", "Loss"]].sum()
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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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street_summary = results.pivot_table(
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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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#
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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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street_summary["Gain"] = street_summary[gain_columns].sum(axis=1)
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street_summary["Loss"] = street_summary[loss_columns].sum(axis=1)
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# Remaining loss allowed
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# remaining_loss_constraint = 230 - region_totals["Loss"]
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remaining_loss_constraint = 220
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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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max_loss=int(remaining_loss_constraint)
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print(street_summary.sum())
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selected_rows, _ = optimise(
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gain=street_summary["Gain"].values,
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loss=street_summary["Loss"].values,
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max_loss=250
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)
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postcode_summary_unselected_regions["Selected"] = postcode_selected_rows == 1
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postcode_summary_unselected_regions[postcode_summary_unselected_regions["Selected"]][["Gain", "Loss"]].sum()
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street_summary["Selected"] = selected_rows == 1
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print(street_summary[street_summary["Selected"]][["Gain", "Loss"]].sum())
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postcode_optimised_additional_properties = postcode_summary_unselected_regions[
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postcode_summary_unselected_regions["Selected"]
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selected_streets = street_summary[
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street_summary["Selected"]
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]
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postcode_totals = postcode_optimised_additional_properties[["Gain", "Loss"]].sum()
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totals = selected_streets[["Gain", "Loss"]].sum()
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bid_size = postcode_totals.sum()
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bid_size = totals.sum()
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print("Bid Size:", bid_size)
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total_epc_d_or_below = postcode_totals["Gain"]
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total_epc_d_or_below = totals["Gain"]
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print("Total EPC D or below:", total_epc_d_or_below)
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total_epc_c = postcode_totals["Loss"]
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total_epc_c = 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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total_needing_survey = selected_streets[
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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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unselected_streets = street_summary[
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~street_summary["Selected"]
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]["Street and Region"].values
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postcode_summary2 = results[
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postcode_summary = 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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@ -2430,14 +2375,12 @@ def propsed_wave_3_sample():
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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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postcode_summary["Gain"] = postcode_summary[gain_columns].sum(axis=1)
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postcode_summary["Loss"] = postcode_summary[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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no_loss_postcodes = postcode_summary[postcode_summary["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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