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https://github.com/Hestia-Homes/Model.git
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Added HA25
This commit is contained in:
Khalim Conn-Kowlessar
parent
46f5ee8ea4
commit
d9e9be4389
1 changed files with 51 additions and 28 deletions
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@ -159,19 +159,18 @@ class DataLoader:
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}
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UNMATCHED_CIGA = {
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# We expect 4 unmatched addresses, which have been validated manually as being in the ciga file but not
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# the asset list
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"HA6": 117,
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"HA14": 3,
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"HA16": 7,
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# There's just too many unmatched here
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"HA6": 117,
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"HA24": 12,
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"HA107": 51,
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}
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def __init__(self, directories, december_figures_filepath, use_cache):
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def __init__(self, directories, december_figures_filepath, use_cache, rebuild):
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self.directories = directories
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self.use_cache = use_cache
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self.december_figures_filepath = december_figures_filepath
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self.rebuild = rebuild
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self.data = {}
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self.december_figures = None
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@ -312,23 +311,20 @@ class DataLoader:
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return asset_list
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@staticmethod
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def create_ciga_list_house_no(ha_name, ciga_list):
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def create_ciga_list_house_no(ciga_list):
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"""
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This function will append the House number onto the asset list
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:return:
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"""
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if ha_name in ["HA6", "HA14", "HA107", "HA16"]:
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split_addresses = ciga_list['Matched Address'].str.split(',', expand=True)
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house_numbers = split_addresses[0].str.split(' ', expand=True)
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# THe first column should be HouseNo - we aren't interested in the other columns, but we don't know how
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# many columns there might be
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house_numbers = house_numbers.iloc[:, 0:1]
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house_numbers.columns = ['HouseNo']
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split_addresses = ciga_list['Matched Address'].str.split(',', expand=True)
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house_numbers = split_addresses[0].str.split(' ', expand=True)
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# THe first column should be HouseNo - we aren't interested in the other columns, but we don't know how
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# many columns there might be
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house_numbers = house_numbers.iloc[:, 0:1]
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house_numbers.columns = ['HouseNo']
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ciga_list = pd.concat([ciga_list, house_numbers[["HouseNo"]]], axis=1)
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else:
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raise NotImplementedError("Implement me")
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ciga_list = pd.concat([ciga_list, house_numbers[["HouseNo"]]], axis=1)
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return ciga_list
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@ -447,7 +443,7 @@ class DataLoader:
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# Remove rows with missing postcode which happens in a small number of cases
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ciga_list = ciga_list[~pd.isnull(ciga_list["Matched Postcode"])]
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ciga_list["ciga_list_row_id"] = [ha_name + "_ciga_" + str(i) for i in range(0, len(ciga_list))]
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ciga_list = self.create_ciga_list_house_no(ha_name, ciga_list)
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ciga_list = self.create_ciga_list_house_no(ciga_list)
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ciga_list = self.dedupe_ciga_list(ciga_list)
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ciga_list = self.merge_ciga_to_assets(asset_list, ciga_list, ha_name)
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@ -800,6 +796,10 @@ class DataLoader:
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"st. leodegars close", "st leodegars close"
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)
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survey_list["Street / Block Name"] = survey_list["Street / Block Name"].str.replace(
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"montgomery crescent", "montgomery road"
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)
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return survey_list
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@staticmethod
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@ -1102,16 +1102,18 @@ class DataLoader:
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for col in ["ECO4", "GBIS", "ECO4 remaining", "GBIS remaining"]:
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self.december_figures[col] = self.december_figures[col].astype("Int64")
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if self.use_cache:
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self.data = read_pickle_from_s3(
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if self.use_cache and not self.rebuild:
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data = read_pickle_from_s3(
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bucket_name="retrofit-datalake-dev",
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s3_file_name="ha-analysis/batch3-inputs.pickle",
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)
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return
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else:
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data = {}
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data = {}
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for filepath in self.directories:
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ha_name = filepath.split("/")[2]
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if ha_name in data:
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continue
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# Load asset list
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logger.info("Loading data for {}".format(ha_name))
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asset_list, survey_list, ciga_list = self.load_asset_list(
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@ -2635,6 +2637,10 @@ def forecast_remaining_sales(loader):
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# and I don't want the numbers to change too much, depenent on the CIGA conversation rate
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maximum_ciga_conversion = 0.75
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# This is a hard limit to the allowed conversion rates to final sale. These are typically very
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# high but there are some anomalies, amongst surveys that are early on
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sales_conversion_lower_bound = 0.8
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gbis_rate = 600
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eco4_rate = 1710
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# old_gbis_rate = 432
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@ -2796,14 +2802,30 @@ def forecast_remaining_sales(loader):
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eco4_ciga_independent_passrates = pd.DataFrame(eco4_ciga_independent_passrates)
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gbis_ciga_independent_passrates = pd.DataFrame(gbis_ciga_independent_passrates)
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eco4_ciga_independent_passrates["conversion"] = (
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eco4_ciga_independent_passrates["# ECO4 successfully installed"] /
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eco4_ciga_independent_passrates["# ECO4 at install stage"]
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)
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eco4_ciga_independent_passrates_clipped = eco4_ciga_independent_passrates[
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eco4_ciga_independent_passrates["conversion"] >= sales_conversion_lower_bound
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]
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gbis_ciga_independent_passrates["conversion"] = (
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gbis_ciga_independent_passrates["# GBIS successfully installed"] /
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gbis_ciga_independent_passrates["# GBIS at install stage"]
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)
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gbis_ciga_independent_passrates_clipped = gbis_ciga_independent_passrates[
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gbis_ciga_independent_passrates["conversion"] >= sales_conversion_lower_bound
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]
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median_eco4_to_install = (
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eco4_ciga_independent_passrates["# ECO4 successfully installed"].sum() /
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eco4_ciga_independent_passrates["# ECO4 at install stage"].sum()
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eco4_ciga_independent_passrates_clipped["# ECO4 successfully installed"].sum() /
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eco4_ciga_independent_passrates_clipped["# ECO4 at install stage"].sum()
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)
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median_gbis_to_install = (
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gbis_ciga_independent_passrates["# GBIS successfully installed"].sum() /
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gbis_ciga_independent_passrates["# GBIS at install stage"].sum()
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gbis_ciga_independent_passrates_clipped["# GBIS successfully installed"].sum() /
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gbis_ciga_independent_passrates_clipped["# GBIS at install stage"].sum()
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)
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# Produce the final output
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@ -3270,6 +3292,8 @@ def app():
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use_cache = True
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# Determines if we want to perform the data pull
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pull_data = False
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# Override to re-build all inputs
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rebuild_inputs = False
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# List all of the data in the folder
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@ -3278,12 +3302,11 @@ def app():
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# Grab the December HA figures filepath
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december_figures_filepath = "local_data/ha_data/HA_December_figures.csv"
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# priority_has = ["HA1", "HA6", "HA7", "HA14", "HA16", "HA24", "HA39", "HA107"]
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priority_has = ["HA1", "HA6", "HA7", "HA14", "HA16", "HA39", "HA107"]
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priority_has = ["HA1", "HA6", "HA7", "HA14", "HA16", "HA24", "HA25", "HA39", "HA107"]
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# Filter down the directories to only the priority HAs
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directories = [d for d in directories if d.split("/")[2] in priority_has]
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loader = DataLoader(directories, december_figures_filepath, use_cache)
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loader = DataLoader(directories, december_figures_filepath, use_cache, rebuild_inputs)
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loader.load()
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loader.ha_facts_and_figures()
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