diff --git a/model_data/simulation_system/DataProcessor.py b/model_data/simulation_system/DataProcessor.py new file mode 100644 index 00000000..1f09a2aa --- /dev/null +++ b/model_data/simulation_system/DataProcessor.py @@ -0,0 +1,142 @@ +from pathlib import Path +import pandas as pd +from settings import ( + DATA_PROCESSOR_SETTINGS, + EARLIEST_EPC_DATE, + FULLY_GLAZED_DESCRIPTIONS, + AVERAGE_FIXED_FEATURES, + FLOOR_HEIGHT_NATIONAL_AVERAGE, + TOTAL_FLOOR_AREA_NATIONAL_AVERAGE + ) + + +class DataProcessor: + """ + Handle data loading and data preprocessing + """ + + def __init__(self, filepath: Path) -> None: + self.filepath = filepath + + def load_data(self, low_memory=False) -> None: + self.data = pd.read_csv(self.filepath, low_memory=low_memory) + + def pre_process(self) -> pd.DataFrame: + """ + Load data and begin initial cleaning + """ + self.load_data(low_memory=DATA_PROCESSOR_SETTINGS['low_memory']) + self.confine_data() + self.recast_df_columns(column_mappings=DATA_PROCESSOR_SETTINGS['column_mappings']) + self.clean_multi_glaze_proportion() + self.retain_multiple_epc_properties(epc_minimum_count=DATA_PROCESSOR_SETTINGS['epc_minimum_count']) + + self.data = self.data.sort_values(["UPRN", "LODGEMENT_DATE"], ascending=True) + + return self.data + + def make_cleaning_averages(self) -> pd.DataFrame: + # Define a custom function to calculate the median, excluding missing values + def median_without_missing(group): + return group[AVERAGE_FIXED_FEATURES].median(skipna=True) + + cleaning_averages = self.data.groupby( + ["PROPERTY_TYPE", "BUILT_FORM", "CONSTRUCTION_AGE_BAND", "NUMBER_HABITABLE_ROOMS", "NUMBER_HEATED_ROOMS"], + observed=True + ).apply(median_without_missing).reset_index() + + general_averages = self.data.groupby(["PROPERTY_TYPE", "BUILT_FORM"], observed=True).apply( + median_without_missing).reset_index() + + property_averages = self.data.groupby(["PROPERTY_TYPE"], observed=True).apply( + median_without_missing).reset_index() + + built_form_averages = self.data.groupby(["BUILT_FORM"], observed=True).apply( + median_without_missing).reset_index() + + # We can clean up any NA's in the cleaning averages with the general averages here + cleaning_averages_filled = pd.merge(cleaning_averages, general_averages, on=['PROPERTY_TYPE', 'BUILT_FORM'], suffixes=['', '_AVERAGE']) + cleaning_averages_filled = pd.merge(cleaning_averages_filled, property_averages, on=['PROPERTY_TYPE'], suffixes=['', '_PROPERTY_AVERAGE']) + cleaning_averages_filled = pd.merge(cleaning_averages_filled, built_form_averages, on=['BUILT_FORM'], suffixes=['', '_BUILT_FORM_AVERAGE']) + + # Replace any missing NAN values with averages for the same Property type and built form + cleaning_averages_filled['TOTAL_FLOOR_AREA'] = cleaning_averages_filled['TOTAL_FLOOR_AREA'].fillna(cleaning_averages_filled['TOTAL_FLOOR_AREA_AVERAGE']) + cleaning_averages_filled['FLOOR_HEIGHT'] = cleaning_averages_filled['FLOOR_HEIGHT'].fillna(cleaning_averages_filled['FLOOR_HEIGHT_AVERAGE']) + cleaning_averages_filled = cleaning_averages_filled.drop(columns=['TOTAL_FLOOR_AREA_AVERAGE', 'FLOOR_HEIGHT_AVERAGE']) + + # If there are still NA values i.e. the averages do not have values for a speicifc group of property tyope and built form + # We can use just the property type average and replace + cleaning_averages_filled['TOTAL_FLOOR_AREA'] = cleaning_averages_filled['TOTAL_FLOOR_AREA'].fillna(cleaning_averages_filled['TOTAL_FLOOR_AREA_PROPERTY_AVERAGE']) + cleaning_averages_filled['FLOOR_HEIGHT'] = cleaning_averages_filled['FLOOR_HEIGHT'].fillna(cleaning_averages_filled['FLOOR_HEIGHT_PROPERTY_AVERAGE']) + cleaning_averages_filled = cleaning_averages_filled.drop(columns=['TOTAL_FLOOR_AREA_PROPERTY_AVERAGE', 'FLOOR_HEIGHT_PROPERTY_AVERAGE']) + + # If there are still NA values, use BUILT FORM averages + cleaning_averages_filled['TOTAL_FLOOR_AREA'] = cleaning_averages_filled['TOTAL_FLOOR_AREA'].fillna(cleaning_averages_filled['TOTAL_FLOOR_AREA_BUILT_FORM_AVERAGE']) + cleaning_averages_filled['FLOOR_HEIGHT'] = cleaning_averages_filled['FLOOR_HEIGHT'].fillna(cleaning_averages_filled['FLOOR_HEIGHT_BUILT_FORM_AVERAGE']) + cleaning_averages_filled = cleaning_averages_filled.drop(columns=['TOTAL_FLOOR_AREA_BUILT_FORM_AVERAGE', 'FLOOR_HEIGHT_BUILT_FORM_AVERAGE']) + + # If there still is na values, use average across all properties in consituecy + cleaning_averages_filled['TOTAL_FLOOR_AREA'] = cleaning_averages_filled['TOTAL_FLOOR_AREA'].fillna(cleaning_averages_filled['TOTAL_FLOOR_AREA'].mean()) + cleaning_averages_filled['FLOOR_HEIGHT'] = cleaning_averages_filled['FLOOR_HEIGHT'].fillna(cleaning_averages_filled['FLOOR_HEIGHT'].mean()) + + # If the consituency is all NA values, then take UK AVERAGE VALUES + cleaning_averages_filled['TOTAL_FLOOR_AREA'] = cleaning_averages_filled['TOTAL_FLOOR_AREA'].fillna(TOTAL_FLOOR_AREA_NATIONAL_AVERAGE) + cleaning_averages_filled['FLOOR_HEIGHT'] = cleaning_averages_filled['FLOOR_HEIGHT'].fillna(FLOOR_HEIGHT_NATIONAL_AVERAGE) + + return cleaning_averages_filled + + def retain_multiple_epc_properties(self, epc_minimum_count: int = 1) -> None: + ''' + Reduce the data futher by keeping only datasets with multiple epcs + ''' + + counts = self.data.groupby("UPRN").size().reset_index() + counts.columns = ["UPRN", "count"] + + # take UPRNS with multiple EPCs + counts = counts[counts["count"] > epc_minimum_count] + self.data = pd.merge(self.data, counts, on='UPRN') + + + def recast_df_columns(self, column_mappings: dict) -> None: + """ + Recast columns from the dataframe to ensure the behaviour we want + """ + + for key, values in column_mappings.items(): + if key not in self.data.columns: + print('Column mapping incorrectly specified') + exit(1) + for value in values: + self.data[key] = self.data[key].astype(value) + + + def confine_data(self) -> None: + """ + Include all step to reduce down the data based on assumptions + """ + + # Filter 1: UPRN is a unique identifier for a property, so we remove any EPCs that don't have one + + # Filter 2: Lodgement date is the date the EPC was lodged, so we remove any EPCs that were lodged + # before the introduction of SAP09 + + # Filter 3: 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 + + # Filter 4: We remove floor level in top floor or mid floor since this is ambiguous + + self.data = self.data[~pd.isnull(self.data["UPRN"])] + self.data = self.data[self.data["LODGEMENT_DATE"] >= EARLIEST_EPC_DATE] + self.data = self.data[self.data["TRANSACTION_TYPE"] != "new dwelling"] + self.data = self.data[~self.data["FLOOR_LEVEL"].isin(["top floor", "mid floor"])] + + + def clean_multi_glaze_proportion(self) -> None: + """ + If there is no multi-glaze proportion but the windows are fully glazed, then we should assume a score of 100 + """ + + no_multi_glaze_proportion_index = pd.isnull(self.data["MULTI_GLAZE_PROPORTION"]) & (self.data["WINDOWS_DESCRIPTION"].isin(FULLY_GLAZED_DESCRIPTIONS)) + self.data.loc[no_multi_glaze_proportion_index, 'MULTI_GLAZE_PROPORTION'] = 100 + diff --git a/model_data/simulation_system/app.py b/model_data/simulation_system/app.py index 688d9cce..cdd50227 100644 --- a/model_data/simulation_system/app.py +++ b/model_data/simulation_system/app.py @@ -1,269 +1,42 @@ import numpy as np -import os import pandas as pd from tqdm import tqdm from model_data.BaseUtility import BaseUtility from pathlib import Path -from typing import Tuple - -def list_subdirectories(directory_path): - return [entry for entry in directory_path.iterdir() if entry.is_dir()] +from settings import ( + MANDATORY_FIXED_FEATURES, + AVERAGE_FIXED_FEATURES, + LATEST_FIELD, + COMPONENT_FEATURES, + RDSAP_RESPONSE, + HEAT_DEMAND_RESPONSE, + FLOOR_LEVEL_MAP, + BUILT_FORM_REMAP +) +from DataProcessor import DataProcessor DATA_DIRECTORY = Path(__file__).parent / 'data' / 'all-domestic-certificates' -FULLY_GLAZED_DESCRIPTIONS = [ - "Fully double glazed", - "High performance glazing", - "Fully triple glazed", - "Full secondary glazing", - "Multiple glazing throughout", -] - -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", - "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 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"] - - # Merge datasets together on columns - filtered_data = pd.merge(cleaning_averages, property_data.iloc[[-1]], on=columns_to_filter) - - # # 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 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", -} - -DATA_PROCESSOR_SETTINGS = { - 'low_memory': False, - 'epc_minimum_count': 1, - 'column_mappings': {'UPRN': [int, str]} -} - -class DataProcessor: - """ - Handle data loading and data preprocessing - """ - - def __init__(self, filepath: Path) -> None: - self.filepath = filepath - - def load_data(self, low_memory=False) -> None: - self.data = pd.read_csv(self.filepath, low_memory=low_memory) - - def process(self) -> pd.DataFrame: - """ - Load all data adnd process data via composition - """ - self.load_data(low_memory=DATA_PROCESSOR_SETTINGS['low_memory']) - self.confine_data() - self.recast_df_columns(column_mappings=DATA_PROCESSOR_SETTINGS['column_mappings']) - self.clean_multi_glaze_proportion() - self.retain_multiple_epc_properties(epc_minimum_count=DATA_PROCESSOR_SETTINGS['epc_minimum_count']) - - self.data = self.data.sort_values(["UPRN", "LODGEMENT_DATE"], ascending=True) - - return self.data - - def make_cleaning_averages(self) -> Tuple[pd.DataFrame, pd.DataFrame]: - # Define a custom function to calculate the median, excluding missing values - def median_without_missing(group): - return group[AVERAGE_FIXED_FEATURES].median(skipna=True) - - cleaning_averages = self.data.groupby( - ["PROPERTY_TYPE", "BUILT_FORM", "CONSTRUCTION_AGE_BAND", "NUMBER_HABITABLE_ROOMS", "NUMBER_HEATED_ROOMS"], - observed=True - ).apply(median_without_missing).reset_index() - - general_averages = self.data.groupby(["PROPERTY_TYPE", "BUILT_FORM"], observed=True).apply( - median_without_missing).reset_index() - - return cleaning_averages, general_averages - - def retain_multiple_epc_properties(self, epc_minimum_count: int = 1) -> None: - ''' - Reduce the data futher by keeping only datasets with multiple epcs - ''' - - counts = self.data.groupby("UPRN").size().reset_index() - counts.columns = ["UPRN", "count"] - - # take UPRNS with multiple EPCs - counts = counts[counts["count"] > epc_minimum_count] - self.data = pd.merge(self.data, counts, on='UPRN') - - - def recast_df_columns(self, column_mappings: dict) -> None: - """ - Recast columns from the dataframe to ensure the behaviour we want - """ - - for key, values in column_mappings.items(): - if key not in self.data.columns: - print('Column mapping incorrectly specified') - exit(1) - for value in values: - self.data[key] = self.data[key].astype(value) - - - def confine_data(self) -> None: - """ - Include all step to reduce down the data based on assumptions - """ - - # Filter 1: UPRN is a unique identifier for a property, so we remove any EPCs that don't have one - - # Filter 2: Lodgement date is the date the EPC was lodged, so we remove any EPCs that were lodged - # before the introduction of SAP09 - - # Filter 3: 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 - - # Filter 4: We remove floor level in top floor or mid floor since this is ambiguous - - self.data = self.data[~pd.isnull(self.data["UPRN"])] - self.data = self.data[self.data["LODGEMENT_DATE"] >= EARLIEST_EPC_DATE] - self.data = self.data[self.data["TRANSACTION_TYPE"] != "new dwelling"] - self.data = self.data[~self.data["FLOOR_LEVEL"].isin(["top floor", "mid floor"])] - - - def clean_multi_glaze_proportion(self) -> None: - """ - If there is no multi-glaze proportion but the windows are fully glazed, then we should assume a score of 100 - """ - - no_multi_glaze_proportion_index = pd.isnull(self.data["MULTI_GLAZE_PROPORTION"]) & (self.data["WINDOWS_DESCRIPTION"].isin(FULLY_GLAZED_DESCRIPTIONS)) - self.data.loc[no_multi_glaze_proportion_index, 'MULTI_GLAZE_PROPORTION'] = 100 - - - def app(): # Get all the files in the directory # Data glossary: # https://epc.opendatacommunities.org/docs/guidance#glossary - directories = list_subdirectories(DATA_DIRECTORY) + # List all subdirectories + directories = [entry for entry in DATA_DIRECTORY.iterdir() if entry.is_dir()] dataset = [] + + for directory in tqdm(directories): filepath = directory / "certificates.csv" data_processor = DataProcessor(filepath=filepath) - df = data_processor.process() - cleaning_averages, general_averages = data_processor.make_cleaning_averages() + df = data_processor.pre_process() + cleaning_averages = data_processor.make_cleaning_averages() for uprn, property_data in df.groupby("UPRN", observed=True): @@ -280,44 +53,51 @@ def app(): # If a property has changed building type, we can ignore the epc rating i.e. this should be 1 unique row if max(modified_property_data[MANDATORY_FIXED_FEATURES].nunique()) > 1: continue - - mandatory_field_data = modified_property_data[MANDATORY_FIXED_FEATURES].iloc[-1].to_dict() # Remap certain columns modified_property_data['FLOOR_LEVEL'] = modified_property_data['FLOOR_LEVEL'].replace(FLOOR_LEVEL_MAP) modified_property_data['BUILT_FROM'] = modified_property_data['BUILT_FORM'].replace(BUILT_FORM_REMAP) + # Take the latest row for both the LATEST_FEILDS and MANDATORY FIELDS latest_field_data = modified_property_data[LATEST_FIELD].iloc[-1].to_dict() + mandatory_field_data = modified_property_data[MANDATORY_FIXED_FEATURES].iloc[-1].to_dict() # Taking just the last row, which is the percentage change from the latest to previous one only # modified_property_data[AVERAGE_FIXED_FEATURES].fillna(value=0).pct_change().iloc[-1] > 0.1 + # We can replace any NA values for Average fixed features + # We have columns that we want to merge on, but some of these columns are all NA values + # So we determine which columns to merge on, and get the equivalent grouping in the averages + columns_to_merge_on = ["PROPERTY_TYPE", "BUILT_FORM", "CONSTRUCTION_AGE_BAND", "NUMBER_HABITABLE_ROOMS", + "NUMBER_HEATED_ROOMS"] + + if any(modified_property_data[columns_to_merge_on].isna()): + # If there are any NA value, back fill first (i.e most recent), then forward fill if needed + modified_property_data[columns_to_merge_on] = modified_property_data[columns_to_merge_on].fillna(method='bfill').fillna(method='ffill') + + # Extract the columns that are non all None + na_columns = modified_property_data[columns_to_merge_on].isna().all() + columns_to_merge_on = na_columns.index[~na_columns].to_list() + # Get the corresponding groupby and merge, and fill in NA values + cleaning_averages_to_merge = cleaning_averages.groupby(columns_to_merge_on)[['TOTAL_FLOOR_AREA', 'FLOOR_HEIGHT']].mean() + modified_property_data = pd.merge(modified_property_data, cleaning_averages_to_merge, on=columns_to_merge_on, suffixes=['', '_AVERAGE']) + modified_property_data['TOTAL_FLOOR_AREA'] = modified_property_data['TOTAL_FLOOR_AREA'].fillna(modified_property_data['TOTAL_FLOOR_AREA_AVERAGE']) + modified_property_data['FLOOR_HEIGHT'] = modified_property_data['FLOOR_HEIGHT'].fillna(modified_property_data['FLOOR_HEIGHT_AVERAGE']) + modified_property_data = modified_property_data.drop(columns=['TOTAL_FLOOR_AREA_AVERAGE', 'FLOOR_HEIGHT_AVERAGE']) for field in AVERAGE_FIXED_FEATURES: vals = list(modified_property_data[field].dropna().unique()) if len(vals) > 1: # Check the values are too far apart + # TODO: we could have multiple values here, why only use the first two? 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, modified_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"] == modified_property_data["PROPERTY_TYPE"].iloc[0]) & - (general_averages["BUILT_FORM"] == modified_property_data["BUILT_FORM"].iloc[0]) - ][field].iloc[0] - + fixed_data[field] = field_value #Combine all fields together @@ -369,6 +149,9 @@ def app(): dataset.extend(property_model_data) + output = pd.DataFrame(dataset) + output.to_parquet('./dataset.parquet') + if __name__ == "__main__": app() \ No newline at end of file diff --git a/model_data/simulation_system/settings.py b/model_data/simulation_system/settings.py new file mode 100644 index 00000000..04e11c25 --- /dev/null +++ b/model_data/simulation_system/settings.py @@ -0,0 +1,114 @@ +# Using a simply python file as settings for now +# TODO: migrate to dynaconf + +TOTAL_FLOOR_AREA_NATIONAL_AVERAGE = 70 +FLOOR_HEIGHT_NATIONAL_AVERAGE = 2.45 + +FULLY_GLAZED_DESCRIPTIONS = [ + "Fully double glazed", + "High performance glazing", + "Fully triple glazed", + "Full secondary glazing", + "Multiple glazing throughout", +] + +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", + "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 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", +} + +DATA_PROCESSOR_SETTINGS = { + 'low_memory': False, + 'epc_minimum_count': 1, + 'column_mappings': {'UPRN': [int, str]} +} \ No newline at end of file diff --git a/model_data/simulation_system/training.py b/model_data/simulation_system/training.py new file mode 100644 index 00000000..72b7aba7 --- /dev/null +++ b/model_data/simulation_system/training.py @@ -0,0 +1,20 @@ +import os +from logging import Logger + +logger = Logger(__name__) + +def training(): + """ + Pipeline to run training on the dataset + """ + + logger.info('Loading data') + + logger.info('Feature selection') + + logger.info('Build Model') + + logger.info('Evaluate matrics') + +if __name__ == "__main__": + training() \ No newline at end of file