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396 lines
15 KiB
Python
396 lines
15 KiB
Python
import pickle
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import pytest
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from etl.epc.Record import EPCRecord
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from etl.epc.settings import DATA_ANOMALY_MATCHES
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class TestEpcRecord:
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@pytest.fixture
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def base_record(self):
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record = EPCRecord(run_mode="training")
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record._prepared_epc = {}
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return record
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@pytest.fixture()
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def cleaning_data(self):
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with open("recommendations/tests/test_data/cleaning_data.pkl", "rb") as f:
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data = pickle.load(f)
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return data
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@pytest.fixture()
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def epc_records_1(self):
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epc_records_1 = {
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"original_epc": {
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"fixed-lighting-outlets-count": "11",
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"property-type": "House",
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"built-form": "Semi-Detached",
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"construction-age-band": "England and Wales: 1900-1929",
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"local-authority": "E08000025",
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"number-habitable-rooms": "4",
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"number-heated-rooms": "4",
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},
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"full_sap_epc": {},
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"old_data": [],
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}
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return epc_records_1
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def test_clean_built_form_valid_remap(self, cleaning_data):
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record = EPCRecord(cleaning_data=cleaning_data)
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record._prepared_epc = {
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"built-form": "Semi-Detached",
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"property-type": "Flat"
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}
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record._clean_built_form()
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assert record._prepared_epc["built-form"] == "Semi-Detached"
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def test_clean_built_form_anomaly(self, cleaning_data):
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record = EPCRecord(cleaning_data=cleaning_data)
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record._prepared_epc = {
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"built-form": "",
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"property-type": "Flat"
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}
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record._clean_built_form()
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assert record._prepared_epc["built-form"] == "End-Terrace"
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def test_clean_floor_area_valid(self, cleaning_data):
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record = EPCRecord(cleaning_data=cleaning_data)
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record._prepared_epc = {
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"total-floor-area": "120.5"
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}
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record._clean_floor_area()
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assert record._prepared_epc["total-floor-area"] == 120.5
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def test_clean_floor_area_empty(self, cleaning_data):
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record = EPCRecord(cleaning_data=cleaning_data)
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record._prepared_epc = {
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"total-floor-area": ""
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}
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with pytest.raises(ValueError):
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record._clean_floor_area()
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def test_clean_heat_loss_corridor_valid(self, cleaning_data):
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record = EPCRecord(cleaning_data=cleaning_data)
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record._prepared_epc = {
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"heat-loss-corridor": "unheated corridor",
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"unheated-corridor-length": ""
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}
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record._clean_heat_loss_corridor()
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assert record._prepared_epc["heat-loss-corridor"] == "unheated corridor"
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record = EPCRecord(cleaning_data=cleaning_data)
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record._prepared_epc = {
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"heat-loss-corridor": "unheated corridor",
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"unheated-corridor-length": None
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}
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record._clean_heat_loss_corridor()
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assert record._prepared_epc["heat-loss-corridor"] == "unheated corridor"
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assert record._prepared_epc["unheated-corridor-length"] is None
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def test_clean_heat_loss_corridor_anomaly(self, cleaning_data):
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record = EPCRecord(cleaning_data=cleaning_data)
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record._prepared_epc = {
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"heat-loss-corridor": "InvalidCorridor",
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"unheated-corridor-length": ""
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}
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record._clean_heat_loss_corridor()
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assert record._prepared_epc["heat-loss-corridor"] == "no corridor"
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def test_clean_solar_hot_water_valid(self, cleaning_data):
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record = EPCRecord(cleaning_data=cleaning_data)
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record._prepared_epc = {
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"solar-water-heating-flag": "Y"
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}
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record._clean_solar_hot_water()
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assert record._prepared_epc["solar-water-heating-flag"] == "Y"
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assert record.solar_water_heating_flag_bool is True
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def test_clean_solar_hot_water_empty(self, cleaning_data):
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record = EPCRecord(cleaning_data=cleaning_data)
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record._prepared_epc = {
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"solar-water-heating-flag": ""
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}
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record._clean_solar_hot_water()
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assert record._prepared_epc["solar-water-heating-flag"] == "N"
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assert record.solar_water_heating_flag_bool is False
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def test_clean_number_lighting_outlets_valid(self, cleaning_data, epc_records_1):
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record = EPCRecord(cleaning_data=cleaning_data, epc_records=epc_records_1)
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record._prepared_epc = {
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"fixed-lighting-outlets-count": "5"
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}
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record._clean_number_lighting_outlets()
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assert record._prepared_epc["fixed-lighting-outlets-count"] == 5.0
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def test_clean_number_lighting_outlets_empty(self, cleaning_data):
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record = EPCRecord(cleaning_data=cleaning_data)
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record.run_mode = "newdata"
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record._prepared_epc = {
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"fixed-lighting-outlets-count": "",
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"property-type": "Flat",
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"built-form": "Semi-Detached",
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"construction-age-band": "England and Wales: 1900-1929",
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"local-authority": "E08000025",
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"number-habitable-rooms": "4",
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"number-heated-rooms": "4",
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}
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record.old_data = []
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record.full_sap_epc = {}
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record._clean_number_lighting_outlets()
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assert record._prepared_epc["fixed-lighting-outlets-count"] == 10
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def test_clean_floor_level(self, cleaning_data):
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record = EPCRecord(cleaning_data=cleaning_data)
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record._prepared_epc = {"floor-level": "1"}
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record._clean_floor_level()
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assert record._prepared_epc["floor-level"] == 1.0
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record = EPCRecord(cleaning_data=cleaning_data)
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record._prepared_epc = {"floor-level": ""}
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record._clean_floor_level()
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assert record._prepared_epc["floor-level"] is None
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def test_year_built(self, cleaning_data):
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# This test handles a specific test case
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# Mock the property object
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epc_records = {
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"original_epc": {
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'low-energy-fixed-light-count': '', 'address': '19 Waterloo Road, Shoeburyness',
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'uprn-source': 'Energy Assessor', 'floor-height': '2.65', 'heating-cost-potential': '436',
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'unheated-corridor-length': '', 'hot-water-cost-potential': '100',
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'construction-age-band': 'England and Wales: 1900-1929', 'potential-energy-rating': 'B',
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'mainheat-energy-eff': 'Good', 'windows-env-eff': 'Good', 'lighting-energy-eff': 'Very Good',
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'environment-impact-potential': '89', 'glazed-type': 'double glazing installed during or after 2002',
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'heating-cost-current': '888', 'address3': '',
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'mainheatcont-description': 'Programmer and room thermostat',
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'sheating-energy-eff': 'N/A', 'report-type': '100', 'property-type': 'House',
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'local-authority-label': 'Southend-on-Sea', 'fixed-lighting-outlets-count': '9',
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'energy-tariff': 'Single',
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'mechanical-ventilation': 'natural', 'hot-water-cost-current': '386', 'county': '',
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'postcode': 'SS3 9EQ',
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'solar-water-heating-flag': 'N', 'constituency': 'E14001501', 'co2-emissions-potential': '0.7',
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'number-heated-rooms': '4', 'floor-description': 'Suspended, no insulation (assumed)',
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'energy-consumption-potential': '49', 'local-authority': 'E06000033', 'built-form': 'Mid-Terrace',
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'number-open-fireplaces': '0', 'windows-description': 'Fully double glazed', 'glazed-area': 'Normal',
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'inspection-date': '2025-03-17', 'mains-gas-flag': 'Y', 'co2-emiss-curr-per-floor-area': '58',
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'address1': '19 Waterloo Road', 'heat-loss-corridor': '', 'flat-storey-count': '',
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'constituency-label': '',
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'roof-energy-eff': 'Average', 'total-floor-area': '78.0', 'building-reference-number': '10007286268',
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'environment-impact-current': '48', 'co2-emissions-current': '4.5',
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'roof-description': 'Pitched, 100 mm loft insulation', 'floor-energy-eff': 'N/A',
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'number-habitable-rooms': '4', 'address2': 'Shoeburyness', 'hot-water-env-eff': 'Average',
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'posttown': 'SOUTHEND-ON-SEA', 'mainheatc-energy-eff': 'Average',
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'main-fuel': 'mains gas (not community)',
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'lighting-env-eff': 'Very Good', 'windows-energy-eff': 'Good', 'floor-env-eff': 'N/A',
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'sheating-env-eff': 'N/A', 'lighting-description': 'Low energy lighting in 78% of fixed outlets',
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'roof-env-eff': 'Average', 'walls-energy-eff': 'Very Poor', 'photo-supply': '0.0',
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'lighting-cost-potential': '101', 'mainheat-env-eff': 'Good', 'multi-glaze-proportion': '100',
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'main-heating-controls': '', 'lodgement-datetime': '2025-03-25 16:59:15', 'flat-top-storey': '',
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'current-energy-rating': 'D', 'secondheat-description': 'None', 'walls-env-eff': 'Very Poor',
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'transaction-type': 'marketed sale', 'uprn': 100090702270, 'current-energy-efficiency': '56',
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'energy-consumption-current': '329', 'mainheat-description': 'Boiler and radiators, mains gas',
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'lighting-cost-current': '101', 'lodgement-date': '2025-03-25', 'extension-count': '1',
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'mainheatc-env-eff': 'Average',
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'lmk-key': 'ff00a1e150063f7bbcac1644be57fdcf05b6c9c60053f80c5d218bf2863fea93',
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'wind-turbine-count': '0',
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'tenure': 'Owner-occupied', 'floor-level': '', 'potential-energy-efficiency': '89',
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'hot-water-energy-eff': 'Average', 'low-energy-lighting': '78',
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'walls-description': 'Solid brick, as built, no insulation (assumed)',
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'hotwater-description': 'From main system'
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},
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"full_sap_epc": {},
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"old_data": []
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}
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prepared_epc = EPCRecord(
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epc_records=epc_records,
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run_mode="newdata",
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cleaning_data=cleaning_data
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)
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assert prepared_epc.get("year_built") == 1900
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def test_cleaning_rules_energy(self, base_record):
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base_record._prepared_epc = {
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"energy-consumption-current": "150",
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"co2-emissions-current": "32.5"
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}
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base_record._apply_cleaning_rules()
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assert base_record._prepared_epc["energy-consumption-current"] == 150.0
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assert base_record._prepared_epc["co2-emissions-current"] == 32.5
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def test_cleaning_rules_energy_anomaly(self, base_record):
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base_record._prepared_epc = {
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"energy-consumption-current": "INVALID",
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"co2-emissions-current": "INVALID"
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}
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base_record._apply_cleaning_rules()
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assert base_record._prepared_epc["energy-consumption-current"] == "INVALID"
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assert base_record._prepared_epc["co2-emissions-current"] == "INVALID"
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def test_cleaning_rules_mains_gas(self, base_record):
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base_record._prepared_epc = {
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"mains-gas-flag": "Y"
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}
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base_record._apply_cleaning_rules()
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assert base_record._prepared_epc["mains-gas-flag"] is True
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def test_cleaning_rules_mains_gas_anomaly(self, base_record):
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base_record._prepared_epc = {
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"mains-gas-flag": "INVALID"
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}
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base_record._apply_cleaning_rules()
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assert base_record._prepared_epc["mains-gas-flag"] is None
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def test_cleaning_rules_wind_turbine(self, base_record):
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base_record._prepared_epc = {
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"wind-turbine-count": "3"
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}
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base_record._apply_cleaning_rules()
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assert base_record._prepared_epc["wind-turbine-count"] == 3
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def test_cleaning_rules_extension_count(self, base_record):
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base_record._prepared_epc = {
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"extension-count": "2"
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}
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base_record._apply_cleaning_rules()
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assert base_record._prepared_epc["extension-count"] == 2
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def test_apply_averages_cleaning_fills_missing_values(self, cleaning_data):
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record = EPCRecord(run_mode="training", cleaning_data=cleaning_data)
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record._prepared_epc = {
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"property-type": cleaning_data["property_type"].iloc[0],
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"local-authority": cleaning_data["local_authority"].iloc[0],
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"total-floor-area": float(cleaning_data["total_floor_area"].iloc[0]),
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"number-habitable-rooms": None,
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"number-heated-rooms": None,
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"floor-height": None,
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}
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record._apply_averages_cleaning()
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assert record._prepared_epc["number-habitable-rooms"] is not None
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assert record._prepared_epc["number-heated-rooms"] is not None
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assert record._prepared_epc["floor-height"] is not None
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def test_apply_averages_cleaning_no_missing(self, cleaning_data):
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record = EPCRecord(run_mode="training", cleaning_data=cleaning_data)
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record._prepared_epc = {
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"property-type": cleaning_data["property_type"].iloc[0],
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"local-authority": cleaning_data["local_authority"].iloc[0],
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"total-floor-area": float(cleaning_data["total_floor_area"].iloc[0]),
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"number-habitable-rooms": 5,
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"number-heated-rooms": 5,
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"floor-height": 2.5,
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}
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original = record._prepared_epc.copy()
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record._apply_averages_cleaning()
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assert record._prepared_epc == original
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def test_apply_averages_cleaning_caps_heated_rooms(self, cleaning_data):
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record = EPCRecord(run_mode="training", cleaning_data=cleaning_data)
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record._prepared_epc = {
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"property-type": cleaning_data["property_type"].iloc[0],
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"local-authority": cleaning_data["local_authority"].iloc[0],
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"total-floor-area": float(cleaning_data["total_floor_area"].iloc[0]),
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"number-habitable-rooms": None,
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"number-heated-rooms": None,
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"floor-height": None,
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}
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record._apply_averages_cleaning()
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assert (
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record._prepared_epc["number-heated-rooms"]
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<= record._prepared_epc["number-habitable-rooms"]
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)
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def test_apply_averages_cleaning_floor_area_filter(self, cleaning_data):
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record = EPCRecord(run_mode="training", cleaning_data=cleaning_data)
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floor_area = float(cleaning_data["total_floor_area"].median())
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record._prepared_epc = {
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"property-type": cleaning_data["property_type"].iloc[0],
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"local-authority": cleaning_data["local_authority"].iloc[0],
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"total-floor-area": floor_area,
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"number-habitable-rooms": None,
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"number-heated-rooms": None,
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"floor-height": None,
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}
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record._apply_averages_cleaning()
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assert record._prepared_epc["floor-height"] > 0
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def test_apply_averages_cleaning_requires_cleaning_data(self):
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record = EPCRecord(run_mode="training", cleaning_data=None)
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record._prepared_epc = {}
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with pytest.raises(ValueError):
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record._apply_averages_cleaning()
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