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pasted in reslts with 100 nearest homes

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Khalim Conn-Kowlessar 2024-01-03 21:34:01 +00:00
parent 0d154abc3d
commit a42cb555d2
2 changed files with 35 additions and 1 deletions
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2
backend/SearchEpc.py
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@ -440,6 +440,8 @@ class SearchEpc:
params = {"postcode": postcode}
if property_type:
params["property-type"] = property_type_api_map[property_type]
# We take the 20 nearest homes of the relevant type, so not to pull in too many irrelevant homes
epc_response = self.get_epc(params=params, size=100)
if epc_response["status"] == 200:

34
etl/testing_data/estimate_epc.py
View file

@ -142,12 +142,44 @@ def app():
avg_numeric_succes = results_df["numeric_success"].median()
avg_categorical_sucess = results_df["categorical_success"].median()
# Before changing the search methodology: 0.7963985988175015, 0.5348837209302325
# With 20 nearest homes
# 0.7718100840549558
# 0.5116279069767442
# 100 nearest homes
# 0.7859617377809409
# 0.5348837209302325
# Group by tenure
by_tenure = results_df.groupby("tenure").agg(
{"numeric_success": "median", "categorical_success": "median", "uprn": "count"}
)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
# With 20 nearest homes
# numeric_success categorical_success uprn
# tenure
# NO DATA! 0.847840 0.581395 278
# Not defined - use in the case of a new dwelling... 0.930282 0.651163 617
# Owner-occupied 0.770330 0.511628 2588
# Rented (private) 0.791885 0.558140 1232
# owner-occupied 0.741088 0.488372 10912
# rental (private) 0.749064 0.488372 3252
# rental (social) 0.822109 0.581395 3878
# unknown 0.895840 0.627907 1820
# 100 nearest homes
# tenure
# NO DATA! 0.899566 0.604651 233
# Not defined - use in the case of a new dwelling... 0.927518 0.674419 608
# Owner-occupied 0.777026 0.511628 3167
# Rented (private) 0.805646 0.534884 1316
# owner-occupied 0.762180 0.488372 10835
# rental (private) 0.760503 0.511628 3181
# rental (social) 0.830057 0.604651 3705
# unknown 0.899948 0.627907 1571
# By property type - we also want to see how many properties we have for each property type
by_property_type = results_df.groupby("property_type").agg(
{"numeric_success": "median", "categorical_success": "median", "uprn": "count"}