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working on new forecast methodology

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Khalim Conn-Kowlessar 2024-03-01 16:59:22 +00:00
parent 8b8e2bf902
commit 9e679bd3fd
1 changed files with 73 additions and 8 deletions
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81
etl/eligibility/ha_15_32/ha_analysis_batch_3.py
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@ -2728,15 +2728,22 @@ def forecast_remaining_sales(loader):
# Produce the final output # Produce the final output
december_figures = loader.december_figures.copy() december_figures = loader.december_figures.copy()
december_figures = december_figures.fillna(0) december_figures = december_figures.fillna(0)
# If we have negative remaining, it means that actually sold more gbis than they initially thought so we set
# remaining to 0
december_figures["ECO4 remaining"] = np.where(
december_figures["ECO4 remaining"] < 0, 0, december_figures["ECO4 remaining"]
)
december_figures["GBIS remaining"] = np.where(
december_figures["GBIS remaining"] < 0, 0, december_figures["GBIS remaining"]
)
results = [] results = []
for ha_name, input_data in loader.data.items(): for ha_name, input_data in loader.data.items():
# Original warmfront figures # Original warmfront figures - ECO4
original_warmfront_estimates = december_figures[december_figures["HA Name"] == ha_name] original_warmfront_estimates = december_figures[december_figures["HA Name"] == ha_name]
original_warmfront_eco4 = original_warmfront_estimates["ECO4"].values[0] original_warmfront_eco4 = original_warmfront_estimates["ECO4"].values[0]
original_warmfront_remaining_eco4 = original_warmfront_estimates["ECO4 remaining"].values[0] original_warmfront_remaining_eco4 = original_warmfront_estimates["ECO4 remaining"].values[0]
original_warmfront_gbis = original_warmfront_estimates["GBIS"].values[0]
original_warmfront_remaining_gbis = original_warmfront_estimates["GBIS remaining"].values[0]
original_warmfront_eco4_revenue = ( original_warmfront_eco4_revenue = (
original_warmfront_remaining_eco4 * eco4_rate + original_warmfront_remaining_eco4 * eco4_rate +
@ -2744,21 +2751,79 @@ def forecast_remaining_sales(loader):
) )
original_warmfront_remaining_eco4_revenue = original_warmfront_remaining_eco4 * eco4_rate original_warmfront_remaining_eco4_revenue = original_warmfront_remaining_eco4 * eco4_rate
# Original warmfront figures - GBIS
original_warmfront_gbis = original_warmfront_estimates["GBIS"].values[0]
original_warmfront_remaining_gbis = original_warmfront_estimates["GBIS remaining"].values[0]
original_warmfront_gbis_revenue = ( original_warmfront_gbis_revenue = (
original_warmfront_remaining_gbis * gbis_rate + original_warmfront_remaining_gbis * gbis_rate +
(original_warmfront_gbis - original_warmfront_remaining_gbis) * old_gbis_rate (original_warmfront_gbis - original_warmfront_remaining_gbis) * old_gbis_rate
) )
original_warmfront_remaining_gbis_revenue = original_warmfront_remaining_gbis * gbis_rate
# Asset list
asset_list = input_data["asset_list"].copy()
survey_list = input_data["survey_list"].copy()
asset_list_remaining = asset_list.merge(
survey_list[["asset_list_row_id", "installation_status"]],
how="left",
on="asset_list_row_id"
)
asset_list_remaining = asset_list_remaining[pd.isnull(asset_list_remaining["installation_status"])]
eligiblity_counts = pd.DataFrame(asset_list["ECO Eligibility"].value_counts()).reset_index()
eligiblity_counts_remaining = pd.DataFrame(asset_list_remaining["ECO Eligibility"].value_counts()).reset_index()
eco4_pre_ciga = eligiblity_counts[
eligiblity_counts["ECO Eligibility"].isin(
["eco4", "eco4 (subject to ciga)", "eco4 - passed ciga", "failed ciga"]
)
]["count"].sum()
eco4_pre_ciga_remaining = eligiblity_counts_remaining[
eligiblity_counts["ECO Eligibility"].isin(
["eco4", "eco4 (subject to ciga)", "eco4 - passed ciga", "failed ciga"]
)
]["count"].sum()
eco4_pre_ciga_revenue = eco4_pre_ciga * eco4_rate
eco4_pre_ciga_remaining_revenue = eco4_pre_ciga_remaining * eco4_rate
# We check if the property has done a CIGA check
has_ciga_check = not input_data["ciga_list"].empty
if has_ciga_check:
eco4_post_ciga = eligiblity_counts[
eligiblity_counts["ECO Eligibility"].isin(
["eco4", "eco4 (subject to ciga)", "eco4 - passed ciga", "failed ciga"]
)
]["count"].sum()
results.append( results.append(
{ {
("", "", "HA Name"): ha_name, ("", "", "", "HA Name"): ha_name,
("Original Warmfront estimate", "Total - #", "ECO4 - November"): original_warmfront_eco4, # ECO4 - original warmfront figures
("", "Remaining - #", ""): original_warmfront_remaining_eco4, ("", "Original Warmfront estimate", "Total - #", "ECO4 - November"): original_warmfront_eco4,
("", "Total - £", ""): original_warmfront_eco4_revenue, ("ECO4", "", "Remaining - #", ""): original_warmfront_remaining_eco4,
("", "Remaining - £", ""): original_warmfront_remaining_eco4_revenue, ("ECO4", "", "Total - £", ""): original_warmfront_eco4_revenue,
("ECO4", "", "Remaining - £", ""): original_warmfront_remaining_eco4_revenue,
# GBIS - original warmfront figures
("", "Original Warmfront estimate", "Total - #", "GBIS - November"): original_warmfront_gbis,
("GBIS", "", "Remaining - #", ""): original_warmfront_gbis,
("GBIS", "", "Total - £", ""): original_warmfront_gbis_revenue,
("GBIS", "", "Remaining - £", ""): original_warmfront_remaining_gbis_revenue,
# ECO4 - asset list
("", "Warmfront post code list", "Total #", "ECO4 total (pre-ciga)"): eco4_pre_ciga,
("ECO4", "", "Remaining - #", ""): eco4_pre_ciga_remaining,
("ECO4", "", "Total - £", ""): eco4_pre_ciga_revenue,
("ECO4", "", "Remaining - £", ""): eco4_pre_ciga_remaining_revenue,
} }
) )
results = pd.DataFrame(results)
def app(): def app():
""" """