mirror of
https://github.com/Hestia-Homes/Model.git
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set up template for heating recommendation testing
This commit is contained in:
Khalim Conn-Kowlessar
parent
2890ff13cd
commit
e2e9721605
3 changed files with 247 additions and 53 deletions
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@ -21,7 +21,7 @@ def clean_colnames(df):
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return df
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def main():
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def lesney_farms():
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"""
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Some rough and ready analysis to get a view of what the achetypes could be, ahead of a meeting with Wates
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on the 28th Aug 2024
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@ -150,16 +150,25 @@ def main():
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].drop_duplicates()
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system_build_data_comparison = system_builds.merge(
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epc_data[["Asset Reference", "walls-description", "roof-description", "current-energy-rating"]],
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epc_data[
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["Asset Reference", "walls-description", "roof-description", "current-energy-rating", "lodgement-date",
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"current-energy-efficiency"]],
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left_on='Asset Reference',
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right_on='Asset Reference',
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how="left"
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)
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system_build_data_comparison["PRE CALCULATED EPC"].value_counts()
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system_build_data_comparison["current-energy-rating"].value_counts()
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# Apply patches
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patches = {
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25847: {"Property Type": "Semi Detached House"},
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}
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epc_cs_system_builds = system_build_data_comparison[system_build_data_comparison["current-energy-rating"] == "C"]
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for asset_ref, patch in patches.items():
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for k, v in patch.items():
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system_build_data_comparison.loc[
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system_build_data_comparison["Asset Reference"] == asset_ref,
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k
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] = v
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archetype_columns = [
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["Asset Type", "Property Type", "Wall Type", "Location"],
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@ -194,53 +203,34 @@ def main():
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)
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counts = archetyped_data["archetype ID"].value_counts()
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# Archetype 0: Semi D, Uninsulated system built, Pre calculated EPC D, flat insulated roof, (Lesney-0)
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# Archetype 0: Semi D, As built system built, Pre calculated EPC D, flat insulated roof, (Lesney-0)
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# Archetype 1: Semi D, Externally insulated system built, Pre calculated EPC D, flat insulated roof (Lesney-1)
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# Archetype 5: Semi D, System built with unknown insulation, Pre calculated EPC D, flat roof insulated (Lesney-2)
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# Archetype 4: Semi D, System built with unknown insulation, Pre calculated EPC D, flat roof insulated (Lesney-2)
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# Archetype 3: Semi D, Externally insulated system built, Pre calculated EPC D, flat roof uninsulated (assumed) (
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# Lesney-3)
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# 0 21
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# 1 10
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# 5 10
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# 3 3
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# 2 1
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# 4 1
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# 6 1
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# 7 1
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# 8 1
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# 9 1
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# 10 1
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# 11 1
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# 0 21
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# 1 11
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# 4 11
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# 3 3
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# 2 1
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# 5 1
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# 6 1
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# 7 1
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# 8 1
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# 9 1
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# This archetype is the same as 0, apart from the pre calculate EPC being an E. The registry says this is a D
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# This has been added to additonal units
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eg1 = archetyped_data[archetyped_data["archetype ID"] == 2]
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# This archetype is the same as 3, apart from it having limited flat roof insulation.
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# TODO: The insulation status of this property should be confirmed
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eg2 = archetyped_data[archetyped_data["archetype ID"] == 4]
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eg2["roof-description"]
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z = epc_data[epc_data["Asset Reference"] == eg2["Asset Reference"].values[0]]
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# Semi D, System built with unknown insulation, Pre calculated EPC D, flat roof insulated
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# This looks like it would fit either in archetype
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eg2 = archetyped_data[archetyped_data["archetype ID"] == 5]
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# This is the one mid-terrace - the EPC data indicates that this is Semi-detached
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# Otherwise this is archetype 5
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# this should be semi-detached
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eg3 = archetyped_data[archetyped_data["archetype ID"] == 6]
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eg3_epc_data = epc_data[epc_data["Asset Reference"] == eg3["Asset Reference"].values[0]]
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# This warrants its own archetype
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# Semi D, System built with unknown insulation, Pre calculated EPC D, flat uninsulated roof
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eg4 = archetyped_data[archetyped_data["archetype ID"] == 7]
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# This property stands out due to the mixed cavity and system built wall, but besides that it's similar to
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# archetype 0
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# The latest EPC agrees that this is a mixed wall type but the EPC suggests solid and cavity, with an assumed
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# insulated cavity, as built
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eg5 = archetyped_data[archetyped_data["archetype ID"] == 8]
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# Archetypes 9, 10, 11 are all similar, Semi D, Uninsulated system built, with pitched lofts with up to 200mm
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# Archetypes 7, 8, 9 are all similar, Semi D, Uninsulated system built, with pitched lofts with up to 200mm
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# insulation in the lofts
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eg6 = archetyped_data[archetyped_data["archetype ID"] == 9]
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# It's just the three units
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# They're all labelled as
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@ -266,6 +256,164 @@ def main():
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}
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]
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patches = {
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25847: {"Property Type": "Semi Detached House", "archetype ID": 5},
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}
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# These are As Built, System Built
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system_built_streets = (
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archetyped_data["Address"].str.split(",").str[0].str.split(" ").str[1].unique()
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)
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all_assets_w_epcs = all_assets.merge(epc_data, on="Asset Reference", how="left")
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# Grab all of the properties on this street that aren't system built
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streets_not_system_builds = all_assets_w_epcs[
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all_assets_w_epcs["Address"].str.split(",").str[0].str.split(" ").str[1].isin(system_built_streets) &
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~all_assets_w_epcs["Wall Type"].str.contains("SystemBuilt")
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]
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system_builds = archetyped_data[
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archetyped_data["Wall Type"].str.contains("SystemBuilt")
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][["Asset Reference", "Address", "Wall Type", "walls-description"]].sort_values("Address")
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birling_street_system_builds = system_builds[system_builds["Address"].str.contains("Birling")]
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halstead_street_system_builds = system_builds[system_builds["Address"].str.contains("Halstead")]
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brasted_street_system_builds = system_builds[system_builds["Address"].str.contains("Brasted")]
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frinstead_street_system_builds = system_builds[
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system_builds["Address"].str.contains("Frinstead") | system_builds["Address"].str.contains("Frinsted")
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]
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pd.set_option('display.max_rows', 500)
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pd.set_option('display.max_columns', 500)
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pd.set_option('display.width', 1000)
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streets_not_system_builds[["Asset Reference", "Address", "Wall Type", "walls-description"]]
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system_builds[system_builds["Address"].str.contains("Birling")]
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# Possible System Builds
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# Create the proposed sample
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# lesney-0
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archetyped_data["lodgement-date"] = pd.to_datetime(archetyped_data["lodgement-date"])
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lesney_0 = archetyped_data[archetyped_data["archetype ID"] == 0].copy()
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# Get the oldest EPC per postcode
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lesney_0 = lesney_0.sort_values(["Address - Postcode", "lodgement-date"])
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lesney_0[["Address", "Address - Postcode", "lodgement-date"]]
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lesney_1 = archetyped_data[archetyped_data["archetype ID"] == 1].copy()
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lesney_1 = lesney_1.sort_values(["Address - Postcode", "lodgement-date"])
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lesney_1[["Address", "Address - Postcode", "lodgement-date"]]
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lesney_2 = archetyped_data[archetyped_data["archetype ID"] == 4].copy()
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lesney_2 = lesney_2.sort_values(["Address - Postcode", "lodgement-date"])
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lesney_2[["Address", "Address - Postcode", "lodgement-date"]]
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lesney_3 = archetyped_data[archetyped_data["archetype ID"] == 3].copy()
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lesney_3 = lesney_3.sort_values(["Address - Postcode", "lodgement-date"])
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lesney_3[["Address", "Address - Postcode", "lodgement-date", "roof-description"]]
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# Get the pitched roof properties, which are lesney-4
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lesney_4 = archetyped_data[archetyped_data["archetype ID"].isin([7, 8, 9])].copy()
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lesney_4 = lesney_4.sort_values(["Address - Postcode", "lodgement-date"])
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lesney_4[["Address", "Address - Postcode", "lodgement-date", "roof-description"]]
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assigned_archetypes = archetyped_data[
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["Asset Reference", "archetype ID", "Address"] + chosen_combination +
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["lodgement-date", "current-energy-rating", "current-energy-efficiency", "walls-description"]
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].copy()
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# Map the archetype ID to their string representation
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assigned_archetypes["archetype ID"] = assigned_archetypes["archetype ID"].replace(
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{
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0: "Lesney-0",
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1: "Lesney-1",
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4: "Lesney-2",
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3: "Lesney-3",
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7: "Lesney-4",
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8: "Lesney-4",
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9: "Lesney-4",
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2: "Lesney-0",
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5: "Lesney-2",
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6: "Lesney-0",
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}
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)
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assigned_archetypes["Asset Reference"] = assigned_archetypes["Asset Reference"].astype(int)
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assigned_archetypes.to_csv(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Orbit - Wates/assigned_archetypes.csv", index=False
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)
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def culworth_court():
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"""
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Some rough works on Cuthwork Court
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They're looking at an ASHP/GSHP
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:return:
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"""
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asset_list = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Orbit - Wates/001 - EPC CULWORTH COURT.xlsx",
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sheet_name="EPC C",
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header=1
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)
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asset_list = clean_colnames(asset_list)
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# Let's get the EPC data
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# Get the EPC data
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epc_data = []
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for _, home in tqdm(asset_list.iterrows(), total=len(asset_list)):
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address = home["Address"]
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# Spelling error
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if "Frinstead" in address:
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address = address.replace("Frinstead", "Frinsted")
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address1 = address.split(",")[0]
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asset_type_map = {
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"HOUSE": "House",
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"BUNGALOWS": "Bungalow",
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"FLATS": "Flat",
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"MAISONETTES": "Maisonette",
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}
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searcher = SearchEpc(
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address1=address1,
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postcode=home["Address - Postcode"],
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auth_token=EPC_AUTH_TOKEN,
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os_api_key="",
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full_address=address,
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)
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searcher.ordnance_survey_client.property_type = asset_type_map[home["Asset Type"]]
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searcher.ordnance_survey_client.built_form = None
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searcher.find_property(skip_os=True)
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if searcher.newest_epc is None:
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raise Exception("Couldn't find")
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epc_data.append(
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{
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"Asset Reference": home["Asset Reference"],
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**searcher.newest_epc.copy()
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}
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)
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epc_data = pd.DataFrame(epc_data)
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asset_list = asset_list.merge(epc_data, on="Asset Reference", how="left")
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asset_list["floor-level"] = np.where(
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asset_list["floor-level"] == "NODATA!",
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"",
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asset_list["floor-level"]
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)
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asset_list["built-form"] = np.where(
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asset_list["built-form"] == "Enclosed End-Terrace",
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"End-Terrace",
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asset_list["built-form"]
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)
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archetype_combinations = asset_list[
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["Asset Type", "Property Type", "built-form", "floor-level"]
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].drop_duplicates()
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z = asset_list[asset_list["built-form"] == "Enclosed End-Terrace"]
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@ -55,11 +55,17 @@ testing_examples = [
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'fixed-lighting-outlets-count': 10.0, 'low-energy-fixed-light-count': 7.0, 'uprn': 100110195416.0,
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'uprn-source': 'Address Matched'
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},
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"kwh": {
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},
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"recommendation_descripptions": [
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]
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"heating_recommendation_descriptions": [
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"Install an air source heat pump, and upgrade heating controls to Smart Thermostats, room sensors and "
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"smart radiator valves (time & temperature zone control). The cost includes the £7500 boiler upgrade "
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"scheme grant",
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],
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"heating_controls_recommendation_descriptions": [
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"Upgrade heating controls to Smart Thermostats, room sensors and smart radiator valves (time & "
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"temperature zone control)"
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],
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"notes": "This property has a boiler, radiators & mains gas with good efficiency so the only recommendation"
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"we expect here is for an air source heat pump. The heating controls are a programmer, room thermostat"
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"and TRVs and so we should expect a TTZC recommendation"
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}
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]
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@ -1,3 +1,4 @@
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from datetime import datetime
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import pandas as pd
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import msgpack
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from utils.s3 import read_dataframe_from_s3_parquet, read_from_s3
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@ -29,7 +30,18 @@ class TestHeatingRecommendations:
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@pytest.fixture
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def kwh_client(self):
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return KwhData(bucket="retrofit-data-dev", read_consumption_data=True)
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client = KwhData(bucket="retrofit-data-dev", read_consumption_data=False)
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# We fix this pricing table for these tests
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client.retail_price_comparison = pd.DataFrame(
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[
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{
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"Date": datetime.today().strftime("%Y-%m-%d"),
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'Average standard variable tariff (Large legacy suppliers)': 1
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}
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]
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)
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client.retail_price_comparison["Date"] = pd.to_datetime(client.retail_price_comparison["Date"])
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return client
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@pytest.mark.parametrize(
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"test_case",
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@ -60,8 +72,21 @@ class TestHeatingRecommendations:
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"energy_assessment_is_newer": False
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}
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)
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# TODO: Implement me
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kwh_predictions = test_case["kwhs"]
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# For these tests, this can be fixed
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kwh_predictions = {
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"heating_kwh_predictions": pd.DataFrame(
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[
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{"id": p.uprn, "predictions": 12000}
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]
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),
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"hotwater_kwh_predictions": pd.DataFrame(
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[
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{"id": p.uprn, "predictions": 3000}
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]
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),
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}
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p.set_features(cleaned=cleaned, kwh_client=kwh_client, kwh_predictions=kwh_predictions)
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recommender = HeatingRecommender(property_instance=p)
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@ -71,4 +96,19 @@ class TestHeatingRecommendations:
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recommender.recommend(has_cavity_or_loft_recommendations=False)
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# TODO: We check results against expected behaviour
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assert len(recommender.heating_recommendations) == len(test_case["heating_recommendation_descriptions"])
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assert (
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len(recommender.heating_control_recommendations) ==
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len(test_case["heating_controls_recommendation_descriptions"])
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)
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# Check the exact descriptions
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assert (
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{x["description"] for x in recommender.heating_recommendations} ==
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set(test_case["heating_recommendation_descriptions"])
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)
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assert (
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{x["description"] for x in recommender.heating_control_recommendations} ==
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set(test_case["heating_controls_recommendation_descriptions"])
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)
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