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adding in archetype check process to model
This commit is contained in:
Khalim Conn-Kowlessar
parent
c1a15052f2
commit
b46da0f6c0
1 changed files with 82 additions and 17 deletions
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@ -3658,19 +3658,47 @@ def patch_cleaned(cleaned):
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def calculate_eco4_post_ciga(
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eligiblity_counts, input_data, ha_ciga_conversion_rate, ha_ciga_pass_to_sale_rate, ha_eco4_to_sale_rate,
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eco4_rate
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eco4_rate, archetype_conversion_rate
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):
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remaining_needing_ciga_check = eligiblity_counts[
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eligiblity_counts["ECO Eligibility"] == "eco4 (subject to ciga)"
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eligiblity_counts["ECO Eligibility"].str.contains("subject to ciga") &
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~eligiblity_counts["ECO Eligibility"].str.contains("subject to archetype")
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]["count"].sum()
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remaining_needing_ciga_and_archetype_check = eligiblity_counts[
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eligiblity_counts["ECO Eligibility"].str.contains("subject to ciga") &
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eligiblity_counts["ECO Eligibility"].str.contains("subject to archetype")
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]["count"].sum()
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# We scale this down by the archetype_conversion_rate, and add this on to the remaining_needing_ciga_check
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remaining_needing_ciga_and_archetype_check_passed = np.round(
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remaining_needing_ciga_and_archetype_check * archetype_conversion_rate
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)
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remaining_needing_ciga_check += remaining_needing_ciga_and_archetype_check_passed
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eco4_no_ciga_needed = eligiblity_counts[
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eligiblity_counts["ECO Eligibility"] == "eco4"
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]["count"].sum()
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eco4_no_ciga_archetype_needed = eligiblity_counts[
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eligiblity_counts["ECO Eligibility"] == "eco4 (subject to archetype)"
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]["count"].sum()
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eco4_no_ciga_archetype_needed_passed = np.round(
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eco4_no_ciga_archetype_needed * archetype_conversion_rate
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)
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eco4_no_ciga_needed += eco4_no_ciga_archetype_needed_passed
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failed_archetype_check = int(
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remaining_needing_ciga_and_archetype_check +
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eco4_no_ciga_archetype_needed -
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remaining_needing_ciga_and_archetype_check_passed -
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eco4_no_ciga_archetype_needed_passed
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)
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has_ciga_check = not input_data["ciga_list"].empty
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if has_ciga_check:
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eco4_no_ciga_needed = eligiblity_counts[
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eligiblity_counts["ECO Eligibility"] == "eco4"
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]["count"].sum()
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eco4_ciga_passed = eligiblity_counts[
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eligiblity_counts["ECO Eligibility"] == "eco4 - passed ciga"
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]["count"].sum()
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@ -3681,8 +3709,10 @@ def calculate_eco4_post_ciga(
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eco4_no_ciga_needed_or_ciga_passed = eco4_no_ciga_needed + eco4_ciga_passed
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eco4_confirmed = (eco4_no_ciga_needed * ha_eco4_to_sale_rate) + (eco4_ciga_passed * ha_ciga_pass_to_sale_rate)
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eco4_confirmed = np.round(eco4_confirmed)
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eco4_confirmed = np.round(
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(eco4_no_ciga_needed * ha_eco4_to_sale_rate) +
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(eco4_ciga_passed * ha_ciga_pass_to_sale_rate)
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)
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eco4_no_ciga_needed_cancellations = int(eco4_no_ciga_needed_or_ciga_passed - eco4_confirmed)
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@ -3704,9 +3734,7 @@ def calculate_eco4_post_ciga(
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eco4_expected_cancellations = eco4_no_ciga_needed_cancellations + eco4_ciga_needed_cancellations
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else:
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eco4_no_ciga_needed = eligiblity_counts[
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eligiblity_counts["ECO Eligibility"] == "eco4"
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]["count"].sum()
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eco4_confirmed_ciga_failures = 0
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# Multiply by sale conversion
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eco4_confirmed = np.round(eco4_no_ciga_needed * ha_eco4_to_sale_rate)
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@ -3735,6 +3763,9 @@ def calculate_eco4_post_ciga(
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"ECO4 - post CIGA - £": eco4_post_ciga * eco4_rate,
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"Of which confirmed - £": eco4_confirmed * eco4_rate,
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"Of which forecast - £": eco4_remaining_forecast * eco4_rate,
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# Archetype check failures
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"Estimated total - failed archetype check - #": failed_archetype_check,
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"Estimated total - failed archetype check - £": failed_archetype_check * eco4_rate,
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# Ciga failures
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"Estimated total - failed CIGA": int(eco4_confirmed_ciga_failures + eco4_estimated_ciga_failures),
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"Confirmed CIGA failures": eco4_confirmed_ciga_failures,
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@ -3766,6 +3797,14 @@ def forecast_remaining_sales(loader):
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gbis_rate = 600
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eco4_rate = 1710
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# Based on ONS https://www.ons.gov.uk/peoplepopulationandcommunity/housing/bulletins/housingenglandandwales
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# /census2021
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# there are 5.7 million terraced properties in the UK, of the 19.3 million houses or bungalows. We therefore apply
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# a 30% discount to homes that are dependent on an archetype check, since around 30% of them will be mid terraced
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# This 30% is slightly harsh but we be conservative
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# Therefore, the archetype check conversion rate is 70%
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archetype_conversion_rate = 0.7
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# 1) Calculate the conversion rate from passed CIGA to actual sale
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converted_ciga_jobs = []
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for ha_name, input_data in loader.data.items():
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@ -4010,13 +4049,27 @@ def forecast_remaining_sales(loader):
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eco4_pre_ciga = eligiblity_counts[
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eligiblity_counts["ECO Eligibility"].isin(
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["eco4", "eco4 (subject to ciga)", "eco4 - passed ciga", "failed ciga"]
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[
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"eco4",
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"eco4 (subject to ciga)",
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"eco4 - passed ciga",
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"failed ciga",
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"eco4 (subject to ciga) (subject to archetype)",
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"eco4 (subject to archetype)"
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]
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)
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]["count"].sum()
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eco4_pre_ciga_remaining = eligiblity_counts_remaining[
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eligiblity_counts_remaining["ECO Eligibility"].isin(
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["eco4", "eco4 (subject to ciga)", "eco4 - passed ciga", "failed ciga"]
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[
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"eco4",
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"eco4 (subject to ciga)",
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"eco4 - passed ciga",
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"failed ciga",
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"eco4 (subject to ciga) (subject to archetype)",
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"eco4 (subject to archetype)"
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]
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)
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]["count"].sum()
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@ -4065,7 +4118,8 @@ def forecast_remaining_sales(loader):
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ha_ciga_conversion_rate=ha_ciga_conversion_rate,
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ha_ciga_pass_to_sale_rate=ha_ciga_pass_to_sale_rate,
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ha_eco4_to_sale_rate=ha_eco4_to_sale_rate,
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eco4_rate=eco4_rate
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eco4_rate=eco4_rate,
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archetype_conversion_rate=archetype_conversion_rate
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)
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eco4_post_ciga_remaining_results = calculate_eco4_post_ciga(
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@ -4074,7 +4128,8 @@ def forecast_remaining_sales(loader):
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ha_ciga_conversion_rate=ha_ciga_conversion_rate,
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ha_ciga_pass_to_sale_rate=ha_ciga_pass_to_sale_rate,
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ha_eco4_to_sale_rate=ha_eco4_to_sale_rate,
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eco4_rate=eco4_rate
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eco4_rate=eco4_rate,
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archetype_conversion_rate=archetype_conversion_rate
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)
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# Calculate the delta compared to Warmfront's original remaining
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@ -4111,6 +4166,8 @@ def forecast_remaining_sales(loader):
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gbis_remaining = int(np.round(gbis_remaining * ha_gbis_sale_conversion))
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gbis_remaining_revenue = int(gbis_remaining * gbis_rate)
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survey_list["installation_status"].value_counts()
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# GBIS delta
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if original_warmfront_remaining_gbis == 0:
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gbis_delta_vs_original_estimate_remaining = "N/A"
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@ -4176,7 +4233,7 @@ def forecast_remaining_sales(loader):
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surveys_with_eligibility["installation_status"] == "GBIS - cancelled"
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].shape[0]
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expected_gbis_unconfirmed_sales = incomplete_gbis_sales * ha_gbis_sale_conversion
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expected_gbis_unconfirmed_sales = np.round(incomplete_gbis_sales * ha_gbis_sale_conversion)
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gbis_expected_cancellations = int(incomplete_gbis_sales - expected_gbis_unconfirmed_sales)
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@ -4187,10 +4244,12 @@ def forecast_remaining_sales(loader):
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# Add in the variance:
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# We should expect that the pre-ciga total is:
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# 1) The number of post CIGA successes +
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# 2) The number of archetype failures +
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# 2) the number of CIGA failures +
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# 3) The number of cancellations
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variance_total = eco4_pre_ciga - (
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eco4_post_ciga_total_results["ECO4 - post CIGA - #"] +
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eco4_post_ciga_total_results["Estimated total - failed archetype check - #"] +
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eco4_post_ciga_total_results['Estimated total - failed CIGA'] +
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eco4_post_ciga_total_results["Expected cancellations - #"]
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)
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@ -4199,6 +4258,7 @@ def forecast_remaining_sales(loader):
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variance_remaining = eco4_pre_ciga_remaining - (
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eco4_post_ciga_remaining_results["ECO4 - post CIGA - #"] +
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eco4_post_ciga_remaining_results["Estimated total - failed archetype check - #"] +
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eco4_post_ciga_remaining_results['Estimated total - failed CIGA'] +
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eco4_post_ciga_remaining_results["Expected cancellations - #"]
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)
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@ -4290,6 +4350,11 @@ def forecast_remaining_sales(loader):
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("ECO4 Cancellations", "", "Of which expected cancellations - £", ""): eco4_post_ciga_remaining_results[
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"Expected cancellations - £"
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],
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# Archetype check failures
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("ECO4 CIGA failures", "", "Estimated total - failed Archetype check - #", ""):
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eco4_post_ciga_remaining_results['Estimated total - failed archetype check - #'],
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("ECO4 CIGA failures", "", "Estimated total - failed Archetype check - £", ""):
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eco4_post_ciga_remaining_results['Estimated total - failed archetype check - £'],
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# CIGA failures
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("ECO4 CIGA failures", "", "Estimated total - failed CIGA - #", ""): eco4_post_ciga_remaining_results[
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'Estimated total - failed CIGA'
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@ -4324,7 +4389,7 @@ def forecast_remaining_sales(loader):
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}
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# Make sure nothing is forgotten due to duplicate multi-index keys
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if len(to_append) != 47:
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if len(to_append) != 49:
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raise ValueError("Something went wrong")
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results.append(to_append)
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