import numpy as np import os import pandas as pd from tqdm import tqdm from model_data.BaseUtility import BaseUtility def list_subdirectories(directory_path): return [d for d in os.listdir(directory_path) if os.path.isdir(os.path.join(directory_path, d))] DATA_DIRECTORY = os.getcwd() + '/model_data/simulation_system/data/all-domestic-certificates' FIXED_FEATURES = [ 'PROPERTY_TYPE', 'BUILT_FORM', 'CONSTRUCTION_AGE_BAND', 'NUMBER_HABITABLE_ROOMS', 'CONSTITUENCY', 'NUMBER_HEATED_ROOMS', 'FIXED_LIGHTING_OUTLETS_COUNT', 'FLOOR_HEIGHT', 'FLOOR_LEVEL', 'TOTAL_FLOOR_AREA', ] COMPONENT_FEATURES = [ 'TRANSACTION_TYPE', 'WALLS_DESCRIPTION', 'FLOOR_DESCRIPTION', 'LIGHTING_DESCRIPTION', 'ROOF_DESCRIPTION', 'MAINHEAT_DESCRIPTION', 'HOTWATER_DESCRIPTION', 'MAIN_FUEL', 'MECHANICAL_VENTILATION', 'SECONDHEAT_DESCRIPTION', 'ENERGY_TARIFF', # Not sure if this is relevant 'SOLAR_WATER_HEATING_FLAG', 'PHOTO_SUPPLY', 'WINDOWS_DESCRIPTION', 'GLAZED_TYPE', 'MULTI_GLAZE_PROPORTION', 'LIGHTING_DESCRIPTION', 'LOW_ENERGY_LIGHTING', 'NUMBER_OPEN_FIREPLACES', 'MAINHEATCONT_DESCRIPTION', 'EXTENSION_COUNT', # 'GLAZED_AREA', # May not need this since we have MULTI_GLAZE_PROPORTION ] # For these fields, we take an average if we have multiple values AVERAGE_FIXED_FEATURES = [ "TOTAL_FLOOR_AREA", "FLOOR_HEIGHT" ] # For these fields, we take the latest value if we have multiple values # Since more recent EPCs have been conducted with more rigour, we assume that the latest value is # the most accurate LATEST_FIELD = [ "NUMBER_HABITABLE_ROOMS", "NUMBER_HEATED_ROOMS", "FIXED_LIGHTING_OUTLETS_COUNT", "CONSTRUCTION_AGE_BAND", "FLOOR_LEVEL", "CONSTRUCTION_AGE_BAND", # This is a field we're probably want to use verisk data for ] # If we see thee features changing, we don't use the EPC, since deem it not to be reliable MANDATORY_FIXED_FEATURES = [ "PROPERTY_TYPE", "BUILT_FORM", "CONSTITUENCY" ] # 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 EARLIEST_EPC_DATE = "2014-08-01" RDSAP_RESPONSE = "CURRENT_ENERGY_EFFICIENCY" HEAT_DEMAND_RESPONSE = "ENERGY_CONSUMPTION_CURRENT" def make_cleaning_averages(df): # Define a custom function to calculate the median, excluding missing values def median_without_missing(group): return group[AVERAGE_FIXED_FEATURES].dropna().median() cleaning_averages = df.groupby( ["PROPERTY_TYPE", "BUILT_FORM", "CONSTRUCTION_AGE_BAND", "NUMBER_HABITABLE_ROOMS", "NUMBER_HEATED_ROOMS"], observed=True ).apply(median_without_missing).reset_index() general_averages = df.groupby(["PROPERTY_TYPE", "BUILT_FORM"], observed=True).apply( median_without_missing).reset_index() return cleaning_averages, general_averages def iterative_filtering(cleaning_averages, property_data): # Define the columns to filter on columns_to_filter = ["PROPERTY_TYPE", "BUILT_FORM", "CONSTRUCTION_AGE_BAND", "NUMBER_HABITABLE_ROOMS", "NUMBER_HEATED_ROOMS"] # Start with the entire cleaning_averages DataFrame filtered_data = cleaning_averages.copy() # Iterate through the columns and apply filters one by one for column in columns_to_filter: # Apply the filter using the value from property_data new_filtered_data = filtered_data[filtered_data[column] == property_data[column].iloc[0]] # If the filter results in no data, return the previous result if new_filtered_data.empty: continue # If the filter is successful, update the filtered data filtered_data = new_filtered_data return filtered_data def clean_multi_glaze_proportion(df): fully_glazed_descriptions = [ "Fully double glazed", "High performance glazing", "Fully triple glazed", "Full secondary glazing", "Multiple glazing throughout", ] df["MULTI_GLAZE_PROPORTION"] = np.where( pd.isnull(df["MULTI_GLAZE_PROPORTION"]) & (df["WINDOWS_DESCRIPTION"].isin(fully_glazed_descriptions)), 100, df["MULTI_GLAZE_PROPORTION"], ) return df def ordinal(n): if 10 <= n % 100 <= 20: suffix = 'th' else: suffix = {1: 'st', 2: 'nd', 3: 'rd'}.get(n % 10, 'th') return str(n) + suffix FLOOR_LEVEL_MAP = { "Basement": -1, "Ground": 0, "ground floor": 0, "20+": 20, "21st or above": 21, **{str(i).zfill(2): i for i in range(0, 21)}, **{ordinal(i): i for i in range(-1, 21)}, **{str(i): i for i in range(-1, 21)}, **{i: i for i in range(-1, 21)}, } BUILT_FORM_REMAP = { "Enclosed End-Terrace": "End-Terrace", "Enclosed Mid-Terrace": "Mid-Terrace", } def app(): # Get all the files in the directory # Data glossary: # https://epc.opendatacommunities.org/docs/guidance#glossary directories = list_subdirectories(DATA_DIRECTORY) dataset = [] for directory in tqdm(directories): filepath = os.path.join(DATA_DIRECTORY, directory, "certificates.csv") df = pd.read_csv(filepath, low_memory=False) # UPRN is a unique identifier for a property, so we remove any EPCs that don't have one df = df[~pd.isnull(df["UPRN"])] # Lodgement date is the date the EPC was lodged, so we remove any EPCs that were lodged # before the introduction of SAP09 df = df[df["LODGEMENT_DATE"] >= EARLIEST_EPC_DATE] cleaning_averages, general_averages = make_cleaning_averages(df) # We remove EPCS that were conducted for a new build, since these are performed with # full SAP, which produces different results to the RdSAP methodology df = df[df["TRANSACTION_TYPE"] != "new dwelling"] df = clean_multi_glaze_proportion(df) # We remove floor level in top floor or mid floor since this is ambiguous df = df[~df["FLOOR_LEVEL"].isin(["top floor", "mid floor"])] df["UPRN"] = df["UPRN"].astype(int).astype(str) counts = df.groupby("UPRN").size().reset_index() counts.columns = ["UPRN", "count"] counts = counts.sort_values("count", ascending=False) # take UPRNS with multiple EPCs counts = counts[counts["count"] > 1] df = df[df["UPRN"].isin(counts["UPRN"])] df = df.sort_values(["UPRN", "LODGEMENT_DATE"], ascending=True) for uprn, property_data in df.groupby("UPRN", observed=True): # Fixed features - these are property attributes that shouldn't change over time ignore_epc = False fixed_data = {} for field in FIXED_FEATURES: vals = property_data[field].dropna().unique() # Remove invalid values vals = [v for v in vals if v not in BaseUtility.DATA_ANOMALY_MATCHES] if field == "FLOOR_LEVEL": vals = list({FLOOR_LEVEL_MAP[v] for v in vals}) if field == "BUILT_FORM": vals = list({BUILT_FORM_REMAP.get(v, v) for v in vals}) if field in AVERAGE_FIXED_FEATURES: if len(vals) > 1: # Check the values are too far apart if abs(vals[0] - vals[1]) / vals[0] > 0.1: # Take the more recent value since it's likely to be more accurate vals = [vals[-1]] if vals: field_value = np.mean(vals) else: # Clean using averages avgs = iterative_filtering(cleaning_averages, property_data) # TODO: Should probably do a mean/median? field_value = avgs[field].iloc[0] if pd.isnull(field_value): # Just the use the general averages field_value = general_averages[ (general_averages["PROPERTY_TYPE"] == property_data["PROPERTY_TYPE"].iloc[0]) & (general_averages["BUILT_FORM"] == property_data["BUILT_FORM"].iloc[0]) ][field].iloc[0] elif field in LATEST_FIELD: field_value = vals[-1] if vals else None else: if len(vals) > 1: if field in MANDATORY_FIXED_FEATURES: ignore_epc = True else: raise ValueError("Fixed feature {} has more than one value - fix me".format(field)) field_value = vals[0] if vals else None fixed_data[field] = field_value if ignore_epc: continue # We include the lodgement date here as we probably need to factor time into the # model, since EPC standards and rigour have changed over time variable_data = property_data[ COMPONENT_FEATURES + ["LODGEMENT_DATE", RDSAP_RESPONSE, HEAT_DEMAND_RESPONSE] ] # Note: we look at changes between subsequent EPCS, however we could look at other permutations # e.g. first vs second, second vs third and also first vs third property_model_data = [] for idx in range(0, property_data.shape[0] - 1): if idx >= property_data.shape[0] - 1: break starting_record = variable_data.iloc[idx] ending_record = variable_data.iloc[idx + 1] rdsap_change = ending_record[RDSAP_RESPONSE] - starting_record[RDSAP_RESPONSE] heat_demand_change = ending_record[HEAT_DEMAND_RESPONSE] - starting_record[HEAT_DEMAND_RESPONSE] # TODO: Should this be <= 0? if rdsap_change == 0: # Assumption: We aren't interested in records that exhibit no change continue # TODO: We need to pre-process the data. For instance, rather than using static for roofs, walls and # floors, we may want to use the U-value. We may also want to handle the (assumed) tags # within descriptions starting_record = starting_record[COMPONENT_FEATURES + ["LODGEMENT_DATE"]].add_suffix("_STARTING") ending_record = ending_record[COMPONENT_FEATURES + ["LODGEMENT_DATE"]].add_suffix("_ENDING") features = pd.concat([starting_record, ending_record]) property_model_data.append( { "UPRN": uprn, "RDSAP_CHANGE": rdsap_change, "HEAT_DEMAND_CHANGE": heat_demand_change, **fixed_data, **features.to_dict() } ) dataset.extend(property_model_data)