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updating methdology for matching
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Khalim Conn-Kowlessar
parent
6eb52a509e
commit
ac9b7b3730
1 changed files with 114 additions and 79 deletions
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@ -1867,6 +1867,19 @@ def propsed_wave_3_sample():
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return surveyed
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def fill_survey_columns(region_assets, suffix):
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for col in [
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'Current EPC Band', 'Current SAP Rating',
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'Survey: Main Wall Type', 'Survey: Main Alternative Wall',
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'Survey: Main Roof Type', 'Survey: Primary Heating System',
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'Survey: Matching Address ID', 'Distance to Closest Match (m)'
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]:
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region_assets[col] = np.where(
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pd.isnull(region_assets[col]) & pd.notnull(region_assets[col + suffix]),
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region_assets[col + suffix], region_assets[col]
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)
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return region_assets
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survey_attribute_columns = [
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"Survey: Main Wall Type", 'Survey: Main Alternative Wall', 'Survey: Main Roof Type',
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'Survey: Primary Heating System'
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@ -1920,6 +1933,14 @@ def propsed_wave_3_sample():
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].copy()
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if archetype_data.empty:
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continue
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match_type = "2 - same archetype"
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if any(archetype_data["Postal Region"] == property["Postal Region"]):
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match_type = "1 - same archetype, same postal region"
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archetype_data = archetype_data[
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archetype_data["Postal Region"] == property["Postal Region"]
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]
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if archetype_data.shape[0] > 1:
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# Look for an exact match, or as close as possible
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archetype_data_filtered = match_property_to_surveyed(property, archetype_data)
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@ -1949,11 +1970,21 @@ def propsed_wave_3_sample():
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'Survey: Main Roof Type': closest_match["Survey: Main Roof Type"],
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'Survey: Primary Heating System': closest_match["Survey: Primary Heating System"],
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"Survey: Matching Address ID": closest_match["Address ID"],
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'Distance to Closest Match (m)': closest_match["distance_meters"]
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'Distance to Closest Match (m)': closest_match["distance_meters"],
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"Match Type": match_type
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}
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)
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region_surveyed = pd.DataFrame(region_surveyed)
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if region_surveyed.empty:
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region_surveyed = pd.DataFrame(
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columns=[
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"Archetype ID", "Address ID", "Current EPC Band", "Current SAP Rating",
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'Survey: Main Wall Type', 'Survey: Main Alternative Wall', 'Survey: Main Roof Type',
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'Survey: Primary Heating System', "Survey: Matching Address ID", 'Distance to Closest Match (m)'
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]
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)
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starting_shape = region_assets.shape[0]
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region_assets = region_assets.merge(
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region_surveyed,
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@ -1968,95 +1999,99 @@ def propsed_wave_3_sample():
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region_assets["Confidence Tier"] = np.where(
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region_assets["Current EPC Band_method1"].isin(["D", "E", "F", "G"]) &
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pd.isnull(region_assets["Confidence Tier"]),
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"1 - Archetype surveyed", region_assets["Confidence Tier"]
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"1 - Archetype surveyed in region", region_assets["Confidence Tier"]
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)
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region_assets["Current EPC Band"] = np.where(
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pd.isnull(region_assets["Current EPC Band"]) & pd.notnull(region_assets["Current EPC Band_method1"]),
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region_assets["Current EPC Band_method1"], region_assets["Current EPC Band"]
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)
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# Handle EPC C
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region_assets["Confidence Tier"] = np.where(
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region_assets["Current EPC Band"].isin(["C", "B", "A"]) & pd.isnull(region_assets["Confidence Tier"]),
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region_assets["Current EPC Band_method1"].isin(["C", "B", "F", "G"]) &
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pd.isnull(region_assets["Confidence Tier"]),
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"5 - EPC C or above", region_assets["Confidence Tier"]
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)
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region_assets = region_assets.drop(columns=["Current EPC Band_method1"])
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# TODO: Turn into a function
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missed_archetypes = set(archetypes) - set(region_surveyed["Archetype ID"])
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region_assets = fill_survey_columns(region_assets, suffix="_method1")
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archetype_surveyed = survey_results[
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survey_results["Archetype ID"].isin(missed_archetypes)
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][["Archetype ID", "Current EPC Band"]].drop_duplicates()
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method_1_columns = [c for c in region_assets.columns if c.endswith("_method1")]
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region_assets = region_assets.drop(columns=method_1_columns)
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if archetype_surveyed["Archetype ID"].duplicated().sum():
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missed_archetypes = set(archetype_ids) - set(region_surveyed["Archetype ID"])
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archetype_surveyed = []
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for arch_id in missed_archetypes:
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for _, property in region_assets[region_assets["Archetype ID"] == arch_id].iterrows():
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archetype_data = survey_results_with_original_features[
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survey_results["Archetype ID"] == arch_id
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].copy()
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if archetype_data.empty:
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continue
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archetype_data["distance_meters"] = haversine(
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lat1=property.latitude, lon1=property.longitude,
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lat2=archetype_data["latitude"].values, lon2=archetype_data["longitude"].values
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)
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expected_sap = np.average(
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archetype_data["Current SAP Rating"], weights=1 / (archetype_data["distance_meters"] + 1)
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)
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expected_epc = sap_to_epc(expected_sap)
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archetype_surveyed.append(
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{
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"Archetype ID": arch_id,
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"Address ID": property["Address ID"],
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"Current EPC Band": expected_epc
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}
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)
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archetype_surveyed = pd.DataFrame(archetype_surveyed)
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region_assets = region_assets.merge(
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archetype_surveyed,
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on=["Archetype ID", "Address ID"],
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how="left",
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suffixes=("", "_method2")
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)
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else:
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region_assets = region_assets.merge(
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archetype_surveyed,
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on="Archetype ID",
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how="left",
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suffixes=("", "_method2")
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)
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region_assets["Confidence Tier"] = np.where(
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region_assets["Current EPC Band_method2"].isin(["D", "E", "F", "G"]) & pd.isnull(
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region_assets["Confidence Tier"]),
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"2 - same archetype", region_assets["Confidence Tier"]
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)
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region_assets["Current EPC Band"] = np.where(
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pd.isnull(region_assets["Current EPC Band"]) & pd.notnull(region_assets["Current EPC Band_method2"]),
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region_assets["Current EPC Band_method2"], region_assets["Current EPC Band"]
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)
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region_assets = region_assets.drop(columns=["Current EPC Band_method2"])
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archetype_surveyed = []
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for arch_id in missed_archetypes:
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for _, property in region_assets[region_assets["Archetype ID"] == arch_id].iterrows():
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archetype_data = survey_results_with_original_features[
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survey_results["Archetype ID"] == arch_id
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].copy()
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if archetype_data.empty:
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continue
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raise Exception("IMPLEMENT ME")
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# archetype_data["distance_meters"] = haversine(
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# lat1=property.latitude, lon1=property.longitude,
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# lat2=archetype_data["latitude"].values, lon2=archetype_data["longitude"].values
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# )
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# expected_sap = np.average(
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# archetype_data["Current SAP Rating"], weights=1 / (archetype_data["distance_meters"] + 1)
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# )
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# expected_epc = sap_to_epc(expected_sap)
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# archetype_surveyed.append(
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# {
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# "Archetype ID": arch_id,
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# "Address ID": property["Address ID"],
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# "Current EPC Band": expected_epc
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# }
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# )
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# archetype_surveyed = pd.DataFrame(archetype_surveyed)
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# if archetype_surveyed.empty:
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# archetype_surveyed = pd.DataFrame(
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# columns=[
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# "Archetype ID", "Address ID", "Current EPC Band", "Current SAP Rating",
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# 'Survey: Main Wall Type', 'Survey: Main Alternative Wall', 'Survey: Main Roof Type',
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# 'Survey: Primary Heating System', "Survey: Matching Address ID", 'Distance to Closest Match (m)'
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# ]
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# )
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#
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# region_assets = region_assets.merge(
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# archetype_surveyed,
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# on=["Archetype ID", "Address ID"],
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# how="left",
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# suffixes=("", "_method2")
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# )
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#
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# region_assets["Confidence Tier"] = np.where(
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# region_assets["Current EPC Band_method2"].isin(["D", "E", "F", "G"]) & pd.isnull(
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# region_assets["Confidence Tier"]),
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# "2 - same archetype", region_assets["Confidence Tier"]
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# )
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#
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# for col in [
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# 'Current EPC Band', 'Current SAP Rating',
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# 'Survey: Main Wall Type', 'Survey: Main Alternative Wall',
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# 'Survey: Main Roof Type', 'Survey: Primary Heating System',
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# 'Survey: Matching Address ID', 'Distance to Closest Match (m)'
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# ]:
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# region_assets[col] = np.where(
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# pd.isnull(region_assets[col]) & pd.notnull(region_assets[col + "_method2"]),
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# region_assets[col + "_method2"], region_assets[col]
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# )
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#
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# method_2_columns = [c for c in region_assets.columns if c.endswith("_method2")]
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# region_assets = region_assets.drop(columns=method_2_columns)
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# We label EPC C properties
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region_assets["Confidence Tier"] = np.where(
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region_assets["Current EPC Band"].isin(["C", "B", "A"]) & pd.isnull(region_assets["Confidence Tier"]),
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"5 - EPC C or above", region_assets["Confidence Tier"]
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)
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region_assets["Confidence Tier"] = np.where(
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region_assets["Archetype ID"] == "EPC C OR ABOVE",
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"5 - EPC C or above", region_assets["Confidence Tier"]
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)
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region_assets["Current EPC Band"] = np.where(
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region_assets["Archetype ID"] == "EPC C OR ABOVE",
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"C", region_assets["Current EPC Band"]
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)
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# region_assets["Confidence Tier"] = np.where(
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# region_assets["Current EPC Band"].isin(["C", "B", "A"]) & pd.isnull(region_assets["Confidence Tier"]),
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# "5 - EPC C or above", region_assets["Confidence Tier"]
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# )
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#
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# region_assets["Confidence Tier"] = np.where(
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# region_assets["Archetype ID"] == "EPC C OR ABOVE",
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# "5 - EPC C or above", region_assets["Confidence Tier"]
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# )
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#
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# region_assets["Current EPC Band"] = np.where(
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# region_assets["Archetype ID"] == "EPC C OR ABOVE",
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# "C", region_assets["Current EPC Band"]
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# )
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missed_addressids = region_assets[pd.isnull(region_assets["Confidence Tier"])]["Address ID"].unique().tolist()
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