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https://github.com/Hestia-Homes/Model.git
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added SapModel to app
This commit is contained in:
Khalim Conn-Kowlessar
parent
8d4e0c956b
commit
3e5d6cc4b4
2 changed files with 5 additions and 351 deletions
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@ -2,8 +2,7 @@ import numpy as np
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import pandas as pd
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import statsmodels.api as sm
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import matplotlib.pyplot as plt
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import pickle
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from typing import Any, Dict, Tuple, Optional, List
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from typing import Dict, Optional, List
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, explained_variance_score, \
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median_absolute_error, mean_absolute_percentage_error
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@ -18,10 +17,6 @@ from utils.logger import setup_logger
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logger = setup_logger()
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# with open("all_data.pkl", "rb") as f:
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# all_data = pickle.load(f)
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class SapModel:
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# We want to estimate for making improvements on different property components
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RESPONSE = "current-energy-efficiency"
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@ -1,16 +1,12 @@
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from tqdm import tqdm
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import os
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from model_data.BoreholeClient import BoreholeClient
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from model_data.LandRegistryClient import LandRegistryClient
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from model_data.temp_inputs import input_data
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from model_data.Property import Property
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from model_data.config import EPC_AUTH_TOKEN
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from epc_api.client import EpcClient
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from model_data.downloader import pagenated_epc_download
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from model_data.EpcClean import EpcClean
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from open_uprn.OpenUprnClient import OpenUprnClient
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from model_data.analysis.UvalueEstimations import UvalueEstimations
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from model_data.analysis.SapModel import SapModel
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LAND_REGISTRY_PATHS = [
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os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-monthly-update-new-version.csv",
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@ -24,345 +20,6 @@ LAND_REGISTRY_PATHS = [
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]
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def handler():
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# To begin with, the input data is a list of dictionaries, however we would read this file in
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epc_client = EpcClient(auth_token=EPC_AUTH_TOKEN)
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input_properties = [
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Property(postcode=config['postcode'], address1=config['address1'], epc_client=epc_client)
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for config in input_data
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]
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for p in input_properties:
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p.search_address_epc()
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p.set_year_built()
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uprns = [p.data['uprn'] for p in input_properties]
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open_uprn_client = OpenUprnClient(
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path=os.path.abspath(
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os.path.dirname(__file__)
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) + "/model_data/local_data/osopenuprn_202306_csv/osopenuprn_202305.csv",
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uprns=uprns
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)
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open_uprn_client.read()
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# We're using Ordinance Survey Open Uprn data
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# to find the coordinates of each address, which we will then be able to use at a later stage
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for p in input_properties:
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p.get_coordinates(open_uprn_client)
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conservation_area_client = ConservationAreaClient(
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historic_england_path=os.path.abspath(
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os.path.dirname(__file__)
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) + "/model_data/local_data/Historic_Eng_Conservation_Areas/Conservation_Areas.shp",
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gov_path=os.path.abspath(
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os.path.dirname(__file__)
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) + "/model_data/local_data/gov-conservation-area.geojson"
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)
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conservation_area_client.read()
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# Check if the property is in a conversation area
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for p in input_properties:
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in_conservation_area = conservation_area_client.is_in_conservation_area(p.coordinates)
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p.set_is_in_conservation_area(in_conservation_area)
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local_authorities = {p.data['local-authority'] for p in input_properties}
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# TODO: Do this at a constituency level
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constituencies = {p.data["constituency"] for p in input_properties}
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property_types = ["bungalow", "flat", "house", "maisonette", "park home"]
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floor_areas = ["unknown", "s", "m", "l", "xl", "xxl", "xxxl"]
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# We pull properties from local authorities, by property type. This will allow us to build
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# a dataset of up to 10k properties per local authority/property type combination
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# For particularly old EPC data, we have inconsistent records so we'll only include EPCS that were
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# conducted after 2010, since SAP09 was introduced in 2009 an later SAP12 was introduced in England
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# and Wales from 31 July 2014
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# Download data from August 2014 onwards
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data = []
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for c in tqdm(constituencies):
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for pt in property_types:
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for fa in floor_areas:
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data.extend(
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pagenated_epc_download(
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client=epc_client,
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params={
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"constituency": c,
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"property-type": pt,
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"from-month": 8,
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"from-year": 2014,
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"floor-area": fa,
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},
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page_size=5000,
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n_pages=10,
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)
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)
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# Incorporate input data into cleaning
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cleaner = EpcClean(data + [p.data for p in input_properties])
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cleaner.clean()
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z = [x for x in data if x["floor-description"] == "(anheddiad arall islaw)"]
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address_meta = [
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{
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"postcode": x["postcode"].upper(),
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"address1": x["address1"].upper(),
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"address2": x["address2"].upper(),
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"address3": x["address3"].upper(),
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"address": x["address"],
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"uprn": x["uprn"]
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} for x in data
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]
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import pickle
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with open("sample_addresses.pkl", "wb") as f:
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pickle.dump(address_meta, f)
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# Land registry
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land_registry_client = LandRegistryClient(
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paths=LAND_REGISTRY_PATHS,
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addresses=address_meta
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)
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lr_data = land_registry_client.read()
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# Borehole
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borehole_client = BoreholeClient(
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path=os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/borehole/borehole.dbf"
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)
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borehole_client.read()
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# Now, for our input properties, we need to identify the components of the building, based
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# on the cleaning we've done
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for p in input_properties:
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p.get_components(cleaner)
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# TODO: Add property age band into this
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uvalue_estimates = UvalueEstimations(data=data)
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uvalue_estimates.get_estimates(cleaner=cleaner)
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x = {'low-energy-fixed-light-count': '', 'address': 'Flat 28, 22, Adelina Grove', 'uprn-source': 'Address Matched',
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'floor-height': '', 'heating-cost-potential': '668', 'unheated-corridor-length': '7.73',
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'hot-water-cost-potential': '190', 'construction-age-band': 'England and Wales: 1991-1995',
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'potential-energy-rating': 'D', 'mainheat-energy-eff': 'Very Poor', 'windows-env-eff': 'Average',
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'lighting-energy-eff': 'Average', 'environment-impact-potential': '46',
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'glazed-type': 'double glazing, unknown install date', 'heating-cost-current': '1081', 'address3': '',
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'mainheatcont-description': 'No time or thermostatic control of room temperature',
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'sheating-energy-eff': 'N/A', 'property-type': 'Flat', 'local-authority-label': 'Tower Hamlets',
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'fixed-lighting-outlets-count': '', 'energy-tariff': 'dual', 'mechanical-ventilation': 'natural',
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'hot-water-cost-current': '190', 'county': 'Greater London Authority', 'postcode': 'E1 3BX',
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'solar-water-heating-flag': 'N', 'constituency': 'E14000555', 'co2-emissions-potential': '5.2',
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'number-heated-rooms': '2', 'floor-description': '(another dwelling below)',
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'energy-consumption-potential': '301', 'local-authority': 'E09000030', 'built-form': 'Semi-Detached',
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'number-open-fireplaces': '0', 'windows-description': 'Fully double glazed', 'glazed-area': 'Normal',
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'inspection-date': '2018-09-05', 'mains-gas-flag': 'N', 'co2-emiss-curr-per-floor-area': '53',
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'address1': 'Flat 28', 'heat-loss-corridor': 'unheated corridor', 'flat-storey-count': '',
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'constituency-label': 'Bethnal Green and Bow', 'roof-energy-eff': 'Average', 'total-floor-area': '103.0',
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'building-reference-number': '4441803568', 'environment-impact-current': '44', 'co2-emissions-current': '5.5',
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'roof-description': 'Pitched, insulated (assumed)', 'floor-energy-eff': 'NO DATA!',
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'number-habitable-rooms': '2', 'address2': '22, Adelina Grove', 'hot-water-env-eff': 'Poor',
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'posttown': 'LONDON', 'mainheatc-energy-eff': 'Very Poor', 'main-fuel': 'electricity (not community)',
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'lighting-env-eff': 'Average', 'windows-energy-eff': 'Average', 'floor-env-eff': 'N/A',
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'sheating-env-eff': 'N/A', 'lighting-description': 'Low energy lighting in 25% of fixed outlets',
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'roof-env-eff': 'Average', 'walls-energy-eff': 'Good', 'photo-supply': '', 'lighting-cost-potential': '84',
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'mainheat-env-eff': 'Very Poor', 'multi-glaze-proportion': '100', 'main-heating-controls': '2701',
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'lodgement-datetime': '2018-09-06 17:25:59', 'flat-top-storey': 'Y', 'current-energy-rating': 'E',
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'secondheat-description': 'None', 'walls-env-eff': 'Good', 'transaction-type': 'rental (private)',
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'uprn': '6032920', 'current-energy-efficiency': '48', 'energy-consumption-current': '316',
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'mainheat-description': 'Electric ceiling heating', 'lighting-cost-current': '147',
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'lodgement-date': '2018-09-06', 'extension-count': '1', 'mainheatc-env-eff': 'Very Poor',
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'lmk-key': '175926409402018090617255958380158', 'wind-turbine-count': '0', 'tenure': 'rental (private)',
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'floor-level': '4th', 'potential-energy-efficiency': '67', 'hot-water-energy-eff': 'Average',
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'low-energy-lighting': '25', 'walls-description': 'Solid brick, as built, insulated (assumed)',
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'hotwater-description': 'Electric immersion, off-peak'}
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from utils.uvalue_estimates import classify_decile_newvalues
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total_floor_area_group_decile = UvalueEstimations.classify_decile_newvalues(
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decile_boundaries=uvalue_estimates.walls_decile_data["decile_boundaries"],
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decile_labels=uvalue_estimates.walls_decile_data["decile_labels"],
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new_values=[float(x["total-floor-area"])],
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)[0]
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u_value_estimate = uvalue_estimates.walls[
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(uvalue_estimates.walls["local-authority"] == x["local-authority"]) &
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(uvalue_estimates.walls["property-type"] == x["property-type"]) &
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(uvalue_estimates.walls["built-form"] == x["built-form"]) &
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(uvalue_estimates.walls["walls-energy-eff"] == x["walls-energy-eff"]) &
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(uvalue_estimates.walls["walls-env-eff"] == x["walls-env-eff"]) &
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(uvalue_estimates.walls["total-floor-area_group"] == total_floor_area_group_decile)
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]
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uvalue_estimates.walls[
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uvalue_estimates.walls
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]
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# all_data = {
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# "input_properties": input_properties,
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# "cleaner": cleaner,
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# "uvalue_estimates": uvalue_estimates,
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# "land_registry_client": land_registry_client,
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# "borehole_client": borehole_client,
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# "conservation_area_client": conservation_area_client,
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# "open_uprn_client": open_uprn_client,
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# "data": data
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# }
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# import pickle
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# with open("all_data.pkl", "wb") as f:
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# pickle.dump(all_data, f)
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# input_properties[4].data["address1"]
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# input_properties[4].data["postcode"]
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# floors_df["address1"].values[4]
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# floors_df["original_description"].values[4]
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#
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# df = pd.DataFrame(
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# [
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# x.data for x in input_properties
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# ]
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# )
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# df["property-type"].unique()
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#
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# from model_data.recommendations.WallRecommendations import WallRecommendations
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# all_res = []
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# for p in input_properties:
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# inst = WallRecommendations(property_instance=p, uvalue_estimates=uvalue_estimates)
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# inst.recommend()
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# n_recs = len(inst.recommendations)
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# all_res.append(n_recs)
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#
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# self = WallRecommendations(property_instance=input_properties[2], uvalue_estimates=uvalue_estimates)
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# input_properties[6].walls
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# self.recommend()
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# df = pd.DataFrame(self.recommendations[0]["parts"])
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# recommendations = pd.DataFrame(self.recommendations)
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#
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# from model_data.recommendations.FloorRecommendations import FloorRecommendations
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# self = FloorRecommendations(property_instance=input_properties[4], uvalue_estimates=uvalue_estimates)
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# self.recommendations
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# self.recommend()
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# self.recommendations
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#
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# # We need to deduce a U-value for "Good" energy effieciency
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#
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# mainheating = pd.DataFrame(
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# [{"address1": p.address1, "postcode": p.postcode, **p.main_heating} for p in input_properties])
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# hotwater = pd.DataFrame([{"address1": p.address1, **p.hotwater} for p in input_properties])
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#
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# mainheating[["address1", "postcode"]]
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#
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# # TODO: I want to knwo what "Good" efficiency means for the description
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# # 'Flat 28, 22 Adelina Grove' 'Solid brick, as built, insulated (assumed)'
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# # so to do this, filter on the local authority code and property type, where we have U
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# # values for the wall and take a median!
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#
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# p = input_properties[6]
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# df = pd.DataFrame(data)
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#
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# res = []
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# for p in input_properties:
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# distances = []
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# for borehole in tqdm(borehole_client.data, total=len(borehole_client.data)):
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# dist_meeters, _ = borehole_client.distance_between_bng_coords(
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# x1_bng=p.coordinates['x_coordinate'],
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# y1_bng=p.coordinates['y_coordinate'],
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# x2_bng=float(borehole['EASTING']),
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# y2_bng=float(borehole['NORTHING'])
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# )
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# distances.append(dist_meeters)
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#
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# res.append(
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# {
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# "uprn": int(p.data["uprn"]),
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# "meters_to_nearest_borehole": min(distances)
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# }
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#
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# )
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# res = pd.DataFrame(res)
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#
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# properties_dataset = [
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# {
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# **p.data,
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# "in_conservation_area": p.in_conservation_area,
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# **p.coordinates,
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#
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# } for p in input_properties
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# ]
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#
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# properties_dataset = pd.DataFrame(properties_dataset)
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# properties_dataset = properties_dataset.merge(res, on="uprn", how="left")
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#
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# properties_dataset.to_csv("properties_dataset.csv")
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# We test estimating gain
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import pandas as pd
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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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df = pd.DataFrame(data)
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# We need to split the data into a train and test set for model build
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# If these categorical variables are not of type 'category', convert them
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print(results.summary())
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grouped_error = []
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groupby = ["mainheat-description"]
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for group, data in model_data.groupby(groupby, observed=True):
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group_fit_error, _ = calculate_regression_metrics(y_true=data[response].astype(float), y_pred=data["fit"])
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# plot_regression(pd.DataFrame({"fit": data["fit"].values, "actual": data[response].astype(float).values}))
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grouped_error.append(
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{
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**dict(zip(groupby, group)),
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"n_samples": data.shape[0],
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**group_fit_error,
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}
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)
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grouped_error = pd.DataFrame(grouped_error)
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grouped_error = grouped_error.sort_values("R2 Score", ascending=True)
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plot_regression(fit_df)
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model_data[["thermal_transmittance", response]].corr()
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summary = model_data.groupby(["property-type", "built-form"], observed=True)[
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["thermal_transmittance", response]
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].corr()
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summary = (
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model_data
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.groupby(component_features + base_features)
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.agg({response: 'median', "idx": 'size'})
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.reset_index()
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)
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summary = summary.sort_values("walls-description")
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example = summary[
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(summary["walls-description"].isin(
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[
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"Solid brick, as built, no insulation (assumed)",
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"Solid brick, as built, partial insulation (assumed)",
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"Solid brick, as built, insulated (assumed)",
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]
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)) &
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(summary["property-type"] == "House") &
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(summary["built-form"] == "Detached") &
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# (summary["construction-age-band"] == "England and Wales: 1976-1982")
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(summary["number-habitable-rooms"] == "4")
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]
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from textblob import TextBlob
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converter = TextBlob("excelent lighting in this hosehold")
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from model_data.utils import correct_spelling
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result = correct_spelling("excelent lighting in this hosehold")
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print(result)
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'excellent lighting in this household'
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def app():
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"""
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For a pre-defined list of constituencies and property types, we'll download EPC data from the API
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@ -425,4 +82,6 @@ def app():
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uvalue_estimates.get_estimates(cleaner=cleaner)
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# TODO: Store these to a db
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uvalue_estimates.floors_decile_data
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sap_model = SapModel(data=data, cleaner=cleaner)
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sap_model.run()
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# TODO: Store outputs to db
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