from tqdm import tqdm import os from model_data.BoreholeClient import BoreholeClient from model_data.LandRegistryClient import LandRegistryClient from model_data.temp_inputs import input_data from model_data.Property import Property from model_data.config import EPC_AUTH_TOKEN from epc_api.client import EpcClient from model_data.downloader import pagenated_epc_download from model_data.EpcClean import EpcClean from open_uprn.OpenUprnClient import OpenUprnClient from model_data.analysis.UvalueEstimations import UvalueEstimations LAND_REGISTRY_PATHS = [ os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-monthly-update-new-version.csv", os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2022 (1).csv", os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2021.csv", os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2020.csv", os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2019.csv", os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2018.csv", os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2017-part1.csv", os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2017-part2.csv", ] def handler(): # To begin with, the input data is a list of dictionaries, however we would read this file in epc_client = EpcClient(auth_token=EPC_AUTH_TOKEN) input_properties = [ Property(postcode=config['postcode'], address1=config['address1'], epc_client=epc_client) for config in input_data ] for p in input_properties: p.search_address_epc() p.set_year_built() uprns = [p.data['uprn'] for p in input_properties] open_uprn_client = OpenUprnClient( path=os.path.abspath( os.path.dirname(__file__) ) + "/model_data/local_data/osopenuprn_202306_csv/osopenuprn_202305.csv", uprns=uprns ) open_uprn_client.read() # We're using Ordinance Survey Open Uprn data # to find the coordinates of each address, which we will then be able to use at a later stage for p in input_properties: p.get_coordinates(open_uprn_client) conservation_area_client = ConservationAreaClient( historic_england_path=os.path.abspath( os.path.dirname(__file__) ) + "/model_data/local_data/Historic_Eng_Conservation_Areas/Conservation_Areas.shp", gov_path=os.path.abspath( os.path.dirname(__file__) ) + "/model_data/local_data/gov-conservation-area.geojson" ) conservation_area_client.read() # Check if the property is in a conversation area for p in input_properties: in_conservation_area = conservation_area_client.is_in_conservation_area(p.coordinates) p.set_is_in_conservation_area(in_conservation_area) 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"] floor_areas = ["unknown", "s", "m", "l", "xl", "xxl", "xxxl"] # 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 # For particularly old EPC data, we have inconsistent records so we'll only include EPCS that were # conducted after 2010, since SAP09 was introduced in 2009 an later SAP12 was introduced in England # and Wales from 31 July 2014 # Download data from August 2014 onwards data = [] for c in tqdm(constituencies): for pt in property_types: for fa in floor_areas: data.extend( pagenated_epc_download( client=epc_client, params={ "constituency": c, "property-type": pt, "from-month": 8, "from-year": 2014, "floor-area": fa, }, page_size=5000, n_pages=10, ) ) # Incorporate input data into cleaning cleaner = EpcClean(data + [p.data for p in input_properties]) cleaner.clean() z = [x for x in data if x["floor-description"] == "(anheddiad arall islaw)"] address_meta = [ { "postcode": x["postcode"].upper(), "address1": x["address1"].upper(), "address2": x["address2"].upper(), "address3": x["address3"].upper(), "address": x["address"], "uprn": x["uprn"] } for x in data ] import pickle with open("sample_addresses.pkl", "wb") as f: pickle.dump(address_meta, f) # Land registry land_registry_client = LandRegistryClient( paths=LAND_REGISTRY_PATHS, addresses=address_meta ) lr_data = land_registry_client.read() # Borehole borehole_client = BoreholeClient( path=os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/borehole/borehole.dbf" ) borehole_client.read() # Now, for our input properties, we need to identify the components of the building, based # on the cleaning we've done for p in input_properties: p.get_components(cleaner) # TODO: Add property age band into this uvalue_estimates = UvalueEstimations(data=data) uvalue_estimates.get_estimates(cleaner=cleaner) x = {'low-energy-fixed-light-count': '', 'address': 'Flat 28, 22, Adelina Grove', 'uprn-source': 'Address Matched', 'floor-height': '', 'heating-cost-potential': '668', 'unheated-corridor-length': '7.73', 'hot-water-cost-potential': '190', 'construction-age-band': 'England and Wales: 1991-1995', 'potential-energy-rating': 'D', 'mainheat-energy-eff': 'Very Poor', 'windows-env-eff': 'Average', 'lighting-energy-eff': 'Average', 'environment-impact-potential': '46', 'glazed-type': 'double glazing, unknown install date', 'heating-cost-current': '1081', 'address3': '', 'mainheatcont-description': 'No time or thermostatic control of room temperature', 'sheating-energy-eff': 'N/A', 'property-type': 'Flat', 'local-authority-label': 'Tower Hamlets', 'fixed-lighting-outlets-count': '', 'energy-tariff': 'dual', 'mechanical-ventilation': 'natural', 'hot-water-cost-current': '190', 'county': 'Greater London Authority', 'postcode': 'E1 3BX', 'solar-water-heating-flag': 'N', 'constituency': 'E14000555', 'co2-emissions-potential': '5.2', 'number-heated-rooms': '2', 'floor-description': '(another dwelling below)', 'energy-consumption-potential': '301', 'local-authority': 'E09000030', 'built-form': 'Semi-Detached', 'number-open-fireplaces': '0', 'windows-description': 'Fully double glazed', 'glazed-area': 'Normal', 'inspection-date': '2018-09-05', 'mains-gas-flag': 'N', 'co2-emiss-curr-per-floor-area': '53', 'address1': 'Flat 28', 'heat-loss-corridor': 'unheated corridor', 'flat-storey-count': '', 'constituency-label': 'Bethnal Green and Bow', 'roof-energy-eff': 'Average', 'total-floor-area': '103.0', 'building-reference-number': '4441803568', 'environment-impact-current': '44', 'co2-emissions-current': '5.5', 'roof-description': 'Pitched, insulated (assumed)', 'floor-energy-eff': 'NO DATA!', 'number-habitable-rooms': '2', 'address2': '22, Adelina Grove', 'hot-water-env-eff': 'Poor', 'posttown': 'LONDON', 'mainheatc-energy-eff': 'Very Poor', 'main-fuel': 'electricity (not community)', 'lighting-env-eff': 'Average', 'windows-energy-eff': 'Average', 'floor-env-eff': 'N/A', 'sheating-env-eff': 'N/A', 'lighting-description': 'Low energy lighting in 25% of fixed outlets', 'roof-env-eff': 'Average', 'walls-energy-eff': 'Good', 'photo-supply': '', 'lighting-cost-potential': '84', 'mainheat-env-eff': 'Very Poor', 'multi-glaze-proportion': '100', 'main-heating-controls': '2701', 'lodgement-datetime': '2018-09-06 17:25:59', 'flat-top-storey': 'Y', 'current-energy-rating': 'E', 'secondheat-description': 'None', 'walls-env-eff': 'Good', 'transaction-type': 'rental (private)', 'uprn': '6032920', 'current-energy-efficiency': '48', 'energy-consumption-current': '316', 'mainheat-description': 'Electric ceiling heating', 'lighting-cost-current': '147', 'lodgement-date': '2018-09-06', 'extension-count': '1', 'mainheatc-env-eff': 'Very Poor', 'lmk-key': '175926409402018090617255958380158', 'wind-turbine-count': '0', 'tenure': 'rental (private)', 'floor-level': '4th', 'potential-energy-efficiency': '67', 'hot-water-energy-eff': 'Average', 'low-energy-lighting': '25', 'walls-description': 'Solid brick, as built, insulated (assumed)', 'hotwater-description': 'Electric immersion, off-peak'} from utils.uvalue_estimates import classify_decile_newvalues total_floor_area_group_decile = UvalueEstimations.classify_decile_newvalues( decile_boundaries=uvalue_estimates.walls_decile_data["decile_boundaries"], decile_labels=uvalue_estimates.walls_decile_data["decile_labels"], new_values=[float(x["total-floor-area"])], )[0] u_value_estimate = uvalue_estimates.walls[ (uvalue_estimates.walls["local-authority"] == x["local-authority"]) & (uvalue_estimates.walls["property-type"] == x["property-type"]) & (uvalue_estimates.walls["built-form"] == x["built-form"]) & (uvalue_estimates.walls["walls-energy-eff"] == x["walls-energy-eff"]) & (uvalue_estimates.walls["walls-env-eff"] == x["walls-env-eff"]) & (uvalue_estimates.walls["total-floor-area_group"] == total_floor_area_group_decile) ] uvalue_estimates.walls[ uvalue_estimates.walls ] # 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 # } # import pickle # with open("all_data.pkl", "wb") as f: # pickle.dump(all_data, f) # 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") # 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) # We need to split the data into a train and test set for model build # If these categorical variables are not of type 'category', convert them print(results.summary()) grouped_error = [] groupby = ["mainheat-description"] for group, data in model_data.groupby(groupby, observed=True): group_fit_error, _ = calculate_regression_metrics(y_true=data[response].astype(float), y_pred=data["fit"]) # plot_regression(pd.DataFrame({"fit": data["fit"].values, "actual": data[response].astype(float).values})) grouped_error.append( { **dict(zip(groupby, group)), "n_samples": data.shape[0], **group_fit_error, } ) grouped_error = pd.DataFrame(grouped_error) grouped_error = grouped_error.sort_values("R2 Score", ascending=True) plot_regression(fit_df) model_data[["thermal_transmittance", response]].corr() summary = model_data.groupby(["property-type", "built-form"], observed=True)[ ["thermal_transmittance", response] ].corr() 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") ] from textblob import TextBlob converter = TextBlob("excelent lighting in this hosehold") from model_data.utils import correct_spelling result = correct_spelling("excelent lighting in this hosehold") print(result) 'excellent lighting in this household' def app(): """ For a pre-defined list of constituencies and property types, we'll download EPC data from the API and produce a dataset of cleaned fields so that when we get new properties, we can quickly sanitise any description data :return: """ epc_client = EpcClient(auth_token=EPC_AUTH_TOKEN) constituencies = {'E14000555', 'E14000726', 'E14000720', 'E14000721', 'E14000553', 'E14000752'} property_types = ["bungalow", "flat", "house", "maisonette", "park home"] # 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 # For particularly old EPC data, we have inconsistent records so we'll only include EPCS that were # conducted after 2010, since SAP09 was introduced in 2009 an later SAP12 was introduced in England # and Wales from 31 July 2014 # Download data from August 2014 onwards data = [] for c in tqdm(constituencies): for pt in property_types: data.extend( pagenated_epc_download( client=epc_client, params={ "constituency": c, "property-type": pt, "from-month": 8, "from-year": 2014, }, page_size=5000, n_pages=10, ) ) # Production of sample data for land registry # address_meta = [ # { # "postcode": x["postcode"].upper(), # "address1": x["address1"].upper(), # "address2": x["address2"].upper(), # "address3": x["address3"].upper(), # "address": x["address"], # "uprn": x["uprn"] # } for x in data # ] # # import pickle # with open("sample_addresses.pkl", "wb") as f: # pickle.dump(address_meta, f) # Incorporate input data into cleaning cleaner = EpcClean(data) cleaner.clean() # TODO: cleaner.cleaned datasets to a db # TODO: Add property age band into this uvalue_estimates = UvalueEstimations(data=data) uvalue_estimates.get_estimates(cleaner=cleaner) # TODO: Store these to a db uvalue_estimates.floors_decile_data