mirror of
https://github.com/Hestia-Homes/Model.git
synced 2026-06-08 11:17:27 +00:00
minor changes to add propety type as a breakdown in downloader
This commit is contained in:
Khalim Conn-Kowlessar
parent
abed2ce2de
commit
b922d5a9b7
1 changed files with 150 additions and 83 deletions
|
|
@ -1,3 +1,4 @@
|
|||
import pandas as pd
|
||||
from tqdm import tqdm
|
||||
import os
|
||||
from model_data.BoreholeClient import BoreholeClient
|
||||
|
|
@ -69,19 +70,23 @@ def handler():
|
|||
p.set_is_in_conservation_area(conservation_area_client)
|
||||
|
||||
local_authorities = {p.data['local-authority'] for p in input_properties}
|
||||
# TODO: Do this at a constituency level
|
||||
constituencies = {p.data["constituency"] for p in input_properties}
|
||||
property_types = ["bungalow", "flat", "house", "maisonette", "park home"]
|
||||
|
||||
# TODO: Create a more balanced sample where we grab more properties across different properties
|
||||
# types, as e.g. we're pulling many more flats than houses
|
||||
# We pull properties from local authorities, by property type. This will allow us to build
|
||||
# a dataset of up to 10k properties per local authority/property type combination
|
||||
data = []
|
||||
for la in tqdm(local_authorities):
|
||||
data.extend(
|
||||
pagenated_epc_download(
|
||||
client=epc_client,
|
||||
params={"local-authority": la},
|
||||
page_size=5000,
|
||||
n_pages=10,
|
||||
for pt in property_types:
|
||||
data.extend(
|
||||
pagenated_epc_download(
|
||||
client=epc_client,
|
||||
params={"local-authority": la, "property-type": pt},
|
||||
page_size=5000,
|
||||
n_pages=10,
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
# Incorporate input data into cleaning
|
||||
cleaner = EpcClean(data + [p.data for p in input_properties])
|
||||
|
|
@ -120,85 +125,147 @@ def handler():
|
|||
uvalue_estimates = UvalueEstimations(data=data)
|
||||
uvalue_estimates.get_estimates(cleaner=cleaner)
|
||||
|
||||
input_properties[4].data["address1"]
|
||||
input_properties[4].data["postcode"]
|
||||
floors_df["address1"].values[4]
|
||||
floors_df["original_description"].values[4]
|
||||
# all_data = {
|
||||
# "input_properties": input_properties,
|
||||
# "cleaner": cleaner,
|
||||
# "uvalue_estimates": uvalue_estimates,
|
||||
# "land_registry_client": land_registry_client,
|
||||
# "borehole_client": borehole_client,
|
||||
# "conservation_area_client": conservation_area_client,
|
||||
# "open_uprn_client": open_uprn_client,
|
||||
# "data": data
|
||||
# }
|
||||
|
||||
df = pd.DataFrame(
|
||||
[
|
||||
x.data for x in input_properties
|
||||
]
|
||||
)
|
||||
df["property-type"].unique()
|
||||
# import pickle
|
||||
# with open("all_data.pkl", "wb") as f:
|
||||
# pickle.dump(all_data, f)
|
||||
|
||||
from model_data.recommendations.WallRecommendations import WallRecommendations
|
||||
all_res = []
|
||||
for p in input_properties:
|
||||
inst = WallRecommendations(property_instance=p, uvalue_estimates=uvalue_estimates)
|
||||
inst.recommend()
|
||||
n_recs = len(inst.recommendations)
|
||||
all_res.append(n_recs)
|
||||
# input_properties[4].data["address1"]
|
||||
# input_properties[4].data["postcode"]
|
||||
# floors_df["address1"].values[4]
|
||||
# floors_df["original_description"].values[4]
|
||||
#
|
||||
# df = pd.DataFrame(
|
||||
# [
|
||||
# x.data for x in input_properties
|
||||
# ]
|
||||
# )
|
||||
# df["property-type"].unique()
|
||||
#
|
||||
# from model_data.recommendations.WallRecommendations import WallRecommendations
|
||||
# all_res = []
|
||||
# for p in input_properties:
|
||||
# inst = WallRecommendations(property_instance=p, uvalue_estimates=uvalue_estimates)
|
||||
# inst.recommend()
|
||||
# n_recs = len(inst.recommendations)
|
||||
# all_res.append(n_recs)
|
||||
#
|
||||
# self = WallRecommendations(property_instance=input_properties[2], uvalue_estimates=uvalue_estimates)
|
||||
# input_properties[6].walls
|
||||
# self.recommend()
|
||||
# df = pd.DataFrame(self.recommendations[0]["parts"])
|
||||
# recommendations = pd.DataFrame(self.recommendations)
|
||||
#
|
||||
# from model_data.recommendations.FloorRecommendations import FloorRecommendations
|
||||
# self = FloorRecommendations(property_instance=input_properties[4], uvalue_estimates=uvalue_estimates)
|
||||
# self.recommendations
|
||||
# self.recommend()
|
||||
# self.recommendations
|
||||
#
|
||||
# # We need to deduce a U-value for "Good" energy effieciency
|
||||
#
|
||||
# mainheating = pd.DataFrame(
|
||||
# [{"address1": p.address1, "postcode": p.postcode, **p.main_heating} for p in input_properties])
|
||||
# hotwater = pd.DataFrame([{"address1": p.address1, **p.hotwater} for p in input_properties])
|
||||
#
|
||||
# mainheating[["address1", "postcode"]]
|
||||
#
|
||||
# # TODO: I want to knwo what "Good" efficiency means for the description
|
||||
# # 'Flat 28, 22 Adelina Grove' 'Solid brick, as built, insulated (assumed)'
|
||||
# # so to do this, filter on the local authority code and property type, where we have U
|
||||
# # values for the wall and take a median!
|
||||
#
|
||||
# p = input_properties[6]
|
||||
# df = pd.DataFrame(data)
|
||||
#
|
||||
# res = []
|
||||
# for p in input_properties:
|
||||
# distances = []
|
||||
# for borehole in tqdm(borehole_client.data, total=len(borehole_client.data)):
|
||||
# dist_meeters, _ = borehole_client.distance_between_bng_coords(
|
||||
# x1_bng=p.coordinates['x_coordinate'],
|
||||
# y1_bng=p.coordinates['y_coordinate'],
|
||||
# x2_bng=float(borehole['EASTING']),
|
||||
# y2_bng=float(borehole['NORTHING'])
|
||||
# )
|
||||
# distances.append(dist_meeters)
|
||||
#
|
||||
# res.append(
|
||||
# {
|
||||
# "uprn": int(p.data["uprn"]),
|
||||
# "meters_to_nearest_borehole": min(distances)
|
||||
# }
|
||||
#
|
||||
# )
|
||||
# res = pd.DataFrame(res)
|
||||
#
|
||||
# properties_dataset = [
|
||||
# {
|
||||
# **p.data,
|
||||
# "in_conservation_area": p.in_conservation_area,
|
||||
# **p.coordinates,
|
||||
#
|
||||
# } for p in input_properties
|
||||
# ]
|
||||
#
|
||||
# properties_dataset = pd.DataFrame(properties_dataset)
|
||||
# properties_dataset = properties_dataset.merge(res, on="uprn", how="left")
|
||||
#
|
||||
# properties_dataset.to_csv("properties_dataset.csv")
|
||||
|
||||
self = WallRecommendations(property_instance=input_properties[2], uvalue_estimates=uvalue_estimates)
|
||||
input_properties[6].walls
|
||||
self.recommend()
|
||||
df = pd.DataFrame(self.recommendations[0]["parts"])
|
||||
recommendations = pd.DataFrame(self.recommendations)
|
||||
|
||||
from model_data.recommendations.FloorRecommendations import FloorRecommendations
|
||||
self = FloorRecommendations(property_instance=input_properties[4], uvalue_estimates=uvalue_estimates)
|
||||
self.recommendations
|
||||
self.recommend()
|
||||
self.recommendations
|
||||
|
||||
# We need to deduce a U-value for "Good" energy effieciency
|
||||
|
||||
mainheating = pd.DataFrame(
|
||||
[{"address1": p.address1, "postcode": p.postcode, **p.main_heating} for p in input_properties])
|
||||
hotwater = pd.DataFrame([{"address1": p.address1, **p.hotwater} for p in input_properties])
|
||||
|
||||
mainheating[["address1", "postcode"]]
|
||||
|
||||
# TODO: I want to knwo what "Good" efficiency means for the description
|
||||
# 'Flat 28, 22 Adelina Grove' 'Solid brick, as built, insulated (assumed)'
|
||||
# so to do this, filter on the local authority code and property type, where we have U
|
||||
# values for the wall and take a median!
|
||||
|
||||
p = input_properties[6]
|
||||
# We test estimating gain
|
||||
import pandas as pd
|
||||
pd.set_option('display.max_rows', 500)
|
||||
pd.set_option('display.max_columns', 500)
|
||||
pd.set_option('display.width', 1000)
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
res = []
|
||||
for p in input_properties:
|
||||
distances = []
|
||||
for borehole in tqdm(borehole_client.data, total=len(borehole_client.data)):
|
||||
dist_meeters, _ = borehole_client.distance_between_bng_coords(
|
||||
x1_bng=p.coordinates['x_coordinate'],
|
||||
y1_bng=p.coordinates['y_coordinate'],
|
||||
x2_bng=float(borehole['EASTING']),
|
||||
y2_bng=float(borehole['NORTHING'])
|
||||
)
|
||||
distances.append(dist_meeters)
|
||||
|
||||
res.append(
|
||||
{
|
||||
"uprn": int(p.data["uprn"]),
|
||||
"meters_to_nearest_borehole": min(distances)
|
||||
}
|
||||
|
||||
)
|
||||
res = pd.DataFrame(res)
|
||||
|
||||
properties_dataset = [
|
||||
{
|
||||
**p.data,
|
||||
"in_conservation_area": p.in_conservation_area,
|
||||
**p.coordinates,
|
||||
|
||||
} for p in input_properties
|
||||
# We want to estimate for making improvements on different property components
|
||||
response = "environment-impact-current"
|
||||
base_features = [
|
||||
"property-type",
|
||||
"built-form",
|
||||
# "construction-age-band",
|
||||
"number-habitable-rooms",
|
||||
]
|
||||
|
||||
properties_dataset = pd.DataFrame(properties_dataset)
|
||||
properties_dataset = properties_dataset.merge(res, on="uprn", how="left")
|
||||
component_features = [
|
||||
"walls-description",
|
||||
"floor-description",
|
||||
]
|
||||
|
||||
properties_dataset.to_csv("properties_dataset.csv")
|
||||
model_data = df[[response] + component_features + base_features]
|
||||
model_data = model_data.reset_index()
|
||||
model_data["idx"] = model_data.index.copy()
|
||||
summary = (
|
||||
model_data
|
||||
.groupby(component_features + base_features)
|
||||
.agg({response: 'median', "idx": 'size'})
|
||||
.reset_index()
|
||||
)
|
||||
|
||||
summary = summary.sort_values("walls-description")
|
||||
|
||||
example = summary[
|
||||
(summary["walls-description"].isin(
|
||||
[
|
||||
"Solid brick, as built, no insulation (assumed)",
|
||||
"Solid brick, as built, partial insulation (assumed)",
|
||||
"Solid brick, as built, insulated (assumed)",
|
||||
]
|
||||
)) &
|
||||
(summary["property-type"] == "House") &
|
||||
(summary["built-form"] == "Detached") &
|
||||
# (summary["construction-age-band"] == "England and Wales: 1976-1982")
|
||||
(summary["number-habitable-rooms"] == "4")
|
||||
]
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue