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287 lines
12 KiB
Python
287 lines
12 KiB
Python
# Using a simply python file as settings for now
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# TODO: migrate to dynaconf
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from pathlib import Path
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METRIC_FILENAME = "metrics.csv"
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OPTIMISE_METRIC = "mean_absolute_error"
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BEST_MODEL_COLUMN_NAME = "best_model"
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# TODO: remove these setting elsewhere for CML
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RESIDUAL_TRUE_LABEL = "true"
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RESIDUAL_PREDICTION_LABEL = "pred"
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RESIDUAL_FILE = "residual.png"
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SEABORN_RESIDUAL_AXIS_FONTSIZE = 12
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SEABORN_RESIDUAL_TITLE_FONTSIZE = 22
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SEABORN_RESIDUAL_STYLE = "whitegrid"
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SEABORN_RESIDUAL_ASPECT_RATIO = "equal"
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SEABORN_RESIDUAL_PLOT_DPI = 120
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SEABORN_RESIDUAL_RANGE = [-100, 100]
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SEABORN_RESIDUAL_LINE_COLOUR = "black"
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SEABORN_RESIDUAL_LINE_WIDTH = 1
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# Can move to a hyperparmeters file
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# If anything we might want to have a file that can be loaded and sent to this script
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MODEL_HYPERPARAMETERS = {
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"autogluon": {
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"problem_type": "regression",
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"eval_metric": "mean_absolute_error",
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"time_limit": 45,
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"presets": "medium_quality",
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"excluded_model_types": None,
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}
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}
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TIMESTAMP_FORMAT = "%Y_%m_%d_%H_%M_%S"
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RANDOM_SEED = 0
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SUBSAMPLE_FACTOR = 200
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TRAIN_AND_VALIDATION_DATA_NAME = "train_validation_data.parquet"
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TEST_DATA_NAME = "test_data.parquet"
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REGISTRY_FILE = "model_registry.csv"
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MODEL_DIRECTORY = "model_directory"
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BASE_REGISTRY_PATH = Path(__file__).parent.parent / MODEL_DIRECTORY
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PREDICTION_LOCATION = Path("predictions")
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PREDICTION_FILE = "prediction.json"
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METADATA_FILE = "metadata.json"
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MODEL_FOLDER = "model"
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METRICS_FOLDER = "metrics"
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DEPLOYMENT_FOLDER = "deployment"
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TOTAL_FLOOR_AREA_NATIONAL_AVERAGE = 70
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FLOOR_HEIGHT_NATIONAL_AVERAGE = 2.45
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AVERAGE_FIXED_FEATURES = [
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"TOTAL_FLOOR_AREA",
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"FLOOR_HEIGHT",
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"FIXED_LIGHTING_OUTLETS_COUNT",
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]
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COLUMNS_TO_MERGE_ON = [
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"PROPERTY_TYPE",
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"BUILT_FORM",
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"CONSTRUCTION_AGE_BAND",
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"NUMBER_HABITABLE_ROOMS",
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"NUMBER_HEATED_ROOMS",
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]
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FULLY_GLAZED_DESCRIPTIONS = [
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"Fully double glazed",
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"High performance glazing",
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"Fully triple glazed",
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"Full secondary glazing",
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"Multiple glazing throughout",
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]
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FIXED_FEATURES = [
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"PROPERTY_TYPE",
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"BUILT_FORM",
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"CONSTRUCTION_AGE_BAND",
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"NUMBER_HABITABLE_ROOMS",
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"CONSTITUENCY",
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"NUMBER_HEATED_ROOMS",
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"FIXED_LIGHTING_OUTLETS_COUNT",
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]
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CORE_COMPONENT_FEATURES = [
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"WALLS_DESCRIPTION",
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"FLOOR_DESCRIPTION",
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"LIGHTING_DESCRIPTION",
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"ROOF_DESCRIPTION",
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"MAINHEAT_DESCRIPTION",
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"HOTWATER_DESCRIPTION",
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"MAIN_FUEL",
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"MECHANICAL_VENTILATION",
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"SECONDHEAT_DESCRIPTION",
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"WINDOWS_DESCRIPTION",
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"GLAZED_TYPE",
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"MULTI_GLAZE_PROPORTION",
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"LOW_ENERGY_LIGHTING",
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"NUMBER_OPEN_FIREPLACES",
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"MAINHEATCONT_DESCRIPTION",
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"SOLAR_WATER_HEATING_FLAG",
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"PHOTO_SUPPLY",
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]
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EFFICIENCY_FEATURES = [
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'HOT_WATER_ENERGY_EFF',
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'FLOOR_ENERGY_EFF',
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'WINDOWS_ENERGY_EFF',
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'WALLS_ENERGY_EFF',
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'SHEATING_ENERGY_EFF',
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'ROOF_ENERGY_EFF',
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'MAINHEAT_ENERGY_EFF',
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'MAINHEATC_ENERGY_EFF',
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'LIGHTING_ENERGY_EFF'
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]
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COMPONENT_FEATURES = CORE_COMPONENT_FEATURES + [
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"TRANSACTION_TYPE",
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"ENERGY_TARIFF", # Not sure if this is relevant
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"EXTENSION_COUNT",
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"TOTAL_FLOOR_AREA",
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"FLOOR_HEIGHT",
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# 'GLAZED_AREA', # May not need this since we have MULTI_GLAZE_PROPORTION
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]
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POTENTIAL_COLUMNS = [
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'POTENTIAL_ENERGY_EFFICIENCY',
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'ENVIRONMENT_IMPACT_POTENTIAL',
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'ENERGY_CONSUMPTION_POTENTIAL',
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'CO2_EMISSIONS_POTENTIAL',
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# We don't include cost features for the moment
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# 'LIGHTING_COST_POTENTIAL',
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# 'HEATING_COST_POTENTIAL',
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# 'HOT_WATER_COST_POTENTIAL'
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]
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# For these fields, we take the latest value if we have multiple values
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# Since more recent EPCs have been conducted with more rigour, we assume that the latest value is
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# the most accurate
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LATEST_FIELD = [
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"NUMBER_HABITABLE_ROOMS",
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"NUMBER_HEATED_ROOMS",
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"FIXED_LIGHTING_OUTLETS_COUNT",
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"CONSTRUCTION_AGE_BAND", # This is a field we're probably want to use verisk data for
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]
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# If we see thee features changing, we don't use the EPC, since deem it not to be reliable
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MANDATORY_FIXED_FEATURES = ["PROPERTY_TYPE", "BUILT_FORM", "CONSTITUENCY"]
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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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EARLIEST_EPC_DATE = "2014-08-01"
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RDSAP_RESPONSE = "CURRENT_ENERGY_EFFICIENCY"
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HEAT_DEMAND_RESPONSE = "ENERGY_CONSUMPTION_CURRENT"
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CARBON_RESPONSE = "CO2_EMISSIONS_CURRENT"
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BUILT_FORM_REMAP = {
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"Enclosed End-Terrace": "End-Terrace",
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"Enclosed Mid-Terrace": "Mid-Terrace",
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}
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DATA_PROCESSOR_SETTINGS = {
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"low_memory": False,
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"epc_minimum_count": 1,
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"column_mappings": {"UPRN": [int, str]},
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}
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# This has a manual mapping of the column types required
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COLUMNTYPES = {
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'UPRN': 'object', 'TOTAL_FLOOR_AREA': 'float64', 'FLOOR_HEIGHT': 'float64', 'PROPERTY_TYPE': 'object',
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'BUILT_FORM': 'object', 'CONSTITUENCY': 'object', 'NUMBER_HABITABLE_ROOMS': 'float64',
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'NUMBER_HEATED_ROOMS': 'float64', 'FIXED_LIGHTING_OUTLETS_COUNT': 'float64',
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'CONSTRUCTION_AGE_BAND': 'object',
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'TRANSACTION_TYPE': 'object',
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'WALLS_DESCRIPTION': 'object',
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'FLOOR_DESCRIPTION': 'object',
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'LIGHTING_DESCRIPTION': 'object',
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'ROOF_DESCRIPTION': 'object',
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'MAINHEAT_DESCRIPTION': 'object',
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'HOTWATER_DESCRIPTION': 'object', 'MAIN_FUEL': 'object',
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'MECHANICAL_VENTILATION': 'object',
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'SECONDHEAT_DESCRIPTION': 'object', 'ENERGY_TARIFF': 'object',
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'SOLAR_WATER_HEATING_FLAG': 'object', 'PHOTO_SUPPLY': 'float64',
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'WINDOWS_DESCRIPTION': 'object',
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'GLAZED_TYPE': 'object',
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'MULTI_GLAZE_PROPORTION': 'float64',
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'LOW_ENERGY_LIGHTING': 'float64',
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'NUMBER_OPEN_FIREPLACES': 'float64',
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'MAINHEATCONT_DESCRIPTION': 'object',
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'EXTENSION_COUNT': 'float64',
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'LODGEMENT_DATE': 'object',
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**dict(zip(EFFICIENCY_FEATURES, ['object', ] * len(EFFICIENCY_FEATURES))),
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**dict(zip(POTENTIAL_COLUMNS, ['float64', ] * len(POTENTIAL_COLUMNS)))
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}
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# For modelling, we don't allow records with more than 100 SAP points
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MAX_SAP_SCORE = 100
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fill_na_map = {
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# There are some descriptions, such as "To be used only when there is no heating/hot-water system or data is from
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# a community network" that could be clustered with unknown fuel
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"MAIN_FUEL": "UNKNOWN",
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"MECHANICAL_VENTILATION": "Unknown",
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"SECONDHEAT_DESCRIPTION": "None",
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"ENERGY_TARIFF": "Unknown",
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# We set solar water heating flag to N - we could investigate using a different category entirely
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"SOLAR_WATER_HEATING_FLAG": "N",
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"GLAZED_TYPE": "not defined",
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"MULTI_GLAZE_PROPORTION": 0,
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"LOW_ENERGY_LIGHTING": 0,
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"MAINHEATCONT_DESCRIPTION": "Unknown",
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"EXTENSION_COUNT": 0,
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"NUMBER_OPEN_FIREPLACES": 0
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}
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################################################################################################
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# These are the features we need for scoring
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# We'll likely change how we do this in the future
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################################################################################################
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STARTING_SUFFIX_COMPONENT_COLS = [
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"SAP", "HEAT_DEMAND", "CARBON", "TRANSACTION_TYPE", "MECHANICAL_VENTILATION",
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"SECONDHEAT_DESCRIPTION", "ENERGY_TARIFF", "SOLAR_WATER_HEATING_FLAG", "PHOTO_SUPPLY",
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"GLAZED_TYPE", "MULTI_GLAZE_PROPORTION", "LOW_ENERGY_LIGHTING", "NUMBER_OPEN_FIREPLACES",
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"EXTENSION_COUNT", "TOTAL_FLOOR_AREA", "FLOOR_HEIGHT", "DAYS_TO", "estimated_perimeter"
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]
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NO_SUFFIX_COMPONENT_COLS = ['walls_thermal_transmittance', 'is_cavity_wall',
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'is_filled_cavity', 'is_solid_brick', 'is_system_built', 'is_timber_frame',
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'is_granite_or_whinstone', 'is_as_built', 'is_cob', 'is_sandstone_or_limestone',
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'is_park_home', 'walls_insulation_thickness', 'external_insulation', 'internal_insulation',
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'floor_thermal_transmittance', 'is_to_unheated_space', 'is_to_external_air', 'is_suspended',
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'is_solid', 'another_property_below', 'floor_insulation_thickness',
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'roof_thermal_transmittance', 'is_pitched', 'is_roof_room', 'is_loft', 'is_flat',
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'is_thatched', 'is_at_rafters', 'has_dwelling_above', 'roof_insulation_thickness',
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'heater_type', 'system_type', 'thermostat_characteristics', 'heating_scope',
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'energy_recovery',
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'hotwater_tariff_type', 'extra_features', 'chp_systems', 'distribution_system',
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'no_system_present', 'appliance', 'has_radiators', 'has_fan_coil_units',
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'has_pipes_in_screed_above_insulation', 'has_pipes_in_insulated_timber_floor',
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'has_pipes_in_concrete_slab', 'has_boiler', 'has_air_source_heat_pump', 'has_room_heaters',
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'has_electric_storage_heaters', 'has_warm_air', 'has_electric_underfloor_heating',
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'has_electric_ceiling_heating', 'has_community_scheme', 'has_ground_source_heat_pump',
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'has_no_system_present', 'has_portable_electric_heaters', 'has_water_source_heat_pump',
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'has_electric_heat_pump', 'has_micro-cogeneration', 'has_solar_assisted_heat_pump',
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'has_exhaust_source_heat_pump', 'has_community_heat_pump', 'has_electric', 'has_mains_gas',
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'has_wood_logs', 'has_coal', 'has_oil', 'has_wood_pellets', 'has_anthracite',
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'has_dual_fuel_mineral_and_wood', 'has_smokeless_fuel', 'has_lpg', 'has_b30k',
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'has_electricaire', 'has_assumed_for_most_rooms', 'has_underfloor_heating',
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'thermostatic_control', 'charging_system', 'switch_system', 'no_control', 'dhw_control',
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'community_heating', 'multiple_room_thermostats', 'auxiliary_systems', 'trvs',
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'rate_control',
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'glazing_type', 'fuel_type', 'main-fuel_tariff_type', 'is_community',
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'no_individual_heating_or_community_network', 'complex_fuel_type',
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]
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ENDING_SUFFIX_COMPONENT_COLS = [
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'SAP', 'HEAT_DEMAND', 'CARBON', 'TRANSACTION_TYPE', 'MECHANICAL_VENTILATION', 'SECONDHEAT_DESCRIPTION',
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'ENERGY_TARIFF', 'SOLAR_WATER_HEATING_FLAG', 'PHOTO_SUPPLY', 'GLAZED_TYPE', 'MULTI_GLAZE_PROPORTION',
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'LOW_ENERGY_LIGHTING', 'NUMBER_OPEN_FIREPLACES', 'EXTENSION_COUNT', 'TOTAL_FLOOR_AREA', 'FLOOR_HEIGHT',
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'DAYS_TO', 'walls_thermal_transmittance', 'is_park_home', 'walls_insulation_thickness',
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'external_insulation', 'internal_insulation', 'floor_thermal_transmittance', 'floor_insulation_thickness',
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'roof_thermal_transmittance', 'roof_insulation_thickness', 'heater_type', 'system_type',
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'thermostat_characteristics', 'heating_scope', 'energy_recovery', 'hotwater_tariff_type', 'extra_features',
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'chp_systems', 'distribution_system', 'no_system_present', 'appliance', 'has_radiators',
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'has_fan_coil_units', 'has_pipes_in_screed_above_insulation', 'has_pipes_in_insulated_timber_floor',
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'has_pipes_in_concrete_slab', 'has_boiler', 'has_air_source_heat_pump', 'has_room_heaters',
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'has_electric_storage_heaters', 'has_warm_air', 'has_electric_underfloor_heating',
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'has_electric_ceiling_heating', 'has_community_scheme', 'has_ground_source_heat_pump',
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'has_no_system_present', 'has_portable_electric_heaters', 'has_water_source_heat_pump',
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'has_electric_heat_pump', 'has_micro-cogeneration', 'has_solar_assisted_heat_pump',
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'has_exhaust_source_heat_pump', 'has_community_heat_pump', 'has_electric', 'has_mains_gas', 'has_wood_logs',
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'has_coal', 'has_oil', 'has_wood_pellets', 'has_anthracite', 'has_dual_fuel_mineral_and_wood',
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'has_smokeless_fuel', 'has_lpg', 'has_b30k', 'has_electricaire', 'has_assumed_for_most_rooms',
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'has_underfloor_heating', 'thermostatic_control', 'charging_system', 'switch_system', 'no_control',
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'dhw_control', 'community_heating', 'multiple_room_thermostats', 'auxiliary_systems', 'trvs',
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'rate_control', 'glazing_type', 'fuel_type', 'main-fuel_tariff_type', 'is_community',
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'no_individual_heating_or_community_network', 'complex_fuel_type', 'estimated_perimeter'
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]
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# We found that without performing any filtering, the bottom 0.5% of homes had a floor height of 1.65m. We'll therefore
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# filter out any homes with a floor height below this
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MINIMUM_FLOOR_HEIGHT = 1.65
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