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added tests for clean_ventilation
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Khalim Conn-Kowlessar
parent
0c1ce64789
commit
1699102cd9
3 changed files with 129 additions and 2 deletions
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@ -164,6 +164,32 @@ async def trigger_plan(body: PlanTriggerRequest):
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model_api = ModelApi(portfolio_id=body.portfolio_id, timestamp=created_at)
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recommendations_scoring_data.head()
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z = recommendations_scoring_data[recommendations_scoring_data["uprn"] == 100070505235].copy()
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z = z[z["roof_thermal_transmittance"] != z["roof_thermal_transmittance_ending"]]
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z["roof_thermal_transmittance_ending"] = 0.4
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z["roof_energy_eff_ending"] = "Average"
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now = model_api.predict_all(
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df=z,
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bucket=get_settings().DATA_BUCKET,
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prediction_buckets={
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"sap_change_predictions": get_settings().SAP_PREDICTIONS_BUCKET,
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"heat_demand_predictions": get_settings().HEAT_PREDICTIONS_BUCKET,
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"carbon_change_predictions": get_settings().CARBON_PREDICTIONS_BUCKET
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}
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)
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now["sap_change_predictions"]
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input_properties[1].data["mechanical-ventilation"]
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# id predictions property_id recommendation_id
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# 0 3696+9 56.3 3696 9
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# 1 3696+10 56.8 3696 10
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# 2 3696+11 56.3 3696 11
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# 3 3696+12 56.8 3696 12
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# With good rather than very good
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now["sap_change_predictions"]
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all_predictions = model_api.predict_all(
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df=recommendations_scoring_data,
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bucket=get_settings().DATA_BUCKET,
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@ -635,8 +635,11 @@ class EPCRecord:
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This method will clean the ventilation, if empty or invalid
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"""
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self.prepared_epc['mechanical-ventilation'] = None if (
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self.mechanical_ventilation == "" or self.mechanical_ventilation in DATA_ANOMALY_MATCHES) else (
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self.mechanical_ventilation)
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(self.prepared_epc['mechanical-ventilation'] == "") or
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(self.prepared_epc['mechanical-ventilation'] in DATA_ANOMALY_MATCHES)
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) else (
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self.prepared_epc['mechanical-ventilation']
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)
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def _field_validation(self):
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"""
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98
etl/epc/tests/test_epcrecord.py
Normal file
98
etl/epc/tests/test_epcrecord.py
Normal file
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@ -0,0 +1,98 @@
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import pytest
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from utils.s3 import read_dataframe_from_s3_parquet
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from etl.epc.Record import EPCRecord
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from unittest.mock import Mock
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class TestEpcRecord:
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@pytest.fixture()
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def cleaning_data(self):
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cleaning_data = read_dataframe_from_s3_parquet(
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bucket_name="retrofit-data-dev", file_key="sap_change_model/cleaning_dataset.parquet",
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)
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return cleaning_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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'low-energy-fixed-light-count': '', 'address': '139 School Road, Hall Green',
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'uprn-source': 'Energy Assessor', 'floor-height': '2.6', 'heating-cost-potential': '1138',
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'unheated-corridor-length': '', 'hot-water-cost-potential': '175',
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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': 'Average', 'lighting-energy-eff': 'Very Good',
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'environment-impact-potential': '82', 'glazed-type': 'double glazing, unknown install date',
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'heating-cost-current': '2711', 'address3': '',
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'mainheatcont-description': 'Programmer, TRVs and bypass',
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'sheating-energy-eff': 'N/A', 'property-type': 'House', 'local-authority-label': 'Birmingham',
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'fixed-lighting-outlets-count': '11', 'energy-tariff': 'Single', 'mechanical-ventilation': 'natural',
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'hot-water-cost-current': '310', 'county': '', 'postcode': 'B28 8JF', 'solar-water-heating-flag': 'N',
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'constituency': 'E14000562', 'co2-emissions-potential': '2.0', 'number-heated-rooms': '4',
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'floor-description': 'Suspended, no insulation (assumed)', 'energy-consumption-potential': '107',
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'local-authority': 'E08000025', 'built-form': 'Semi-Detached', 'number-open-fireplaces': '0',
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'windows-description': 'Fully double glazed', 'glazed-area': 'Normal', 'inspection-date': '2023-07-05',
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'mains-gas-flag': 'Y', 'co2-emiss-curr-per-floor-area': '65', 'address1': '139 School Road',
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'heat-loss-corridor': '', 'flat-storey-count': '', 'constituency-label': 'Birmingham, Hall Green',
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'roof-energy-eff': 'Average', 'total-floor-area': '103.0', 'building-reference-number': '10004697322',
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'environment-impact-current': '43', 'co2-emissions-current': '6.7',
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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': 'Hall Green', 'hot-water-env-eff': 'Good',
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'posttown': 'BIRMINGHAM', 'mainheatc-energy-eff': 'Average', 'main-fuel': 'mains gas (not community)',
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'lighting-env-eff': 'Very Good', 'windows-energy-eff': 'Average', 'floor-env-eff': 'N/A',
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'sheating-env-eff': 'N/A', 'lighting-description': 'Low energy lighting in 82% 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': '182', 'mainheat-env-eff': 'Good', 'multi-glaze-proportion': '100',
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'main-heating-controls': '', 'lodgement-datetime': '2023-07-13 08:23:07', 'flat-top-storey': '',
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'current-energy-rating': 'E', 'secondheat-description': 'None', 'walls-env-eff': 'Very Poor',
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'transaction-type': 'rental', 'uprn': '100070505235', 'current-energy-efficiency': '51',
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'energy-consumption-current': '366', 'mainheat-description': 'Boiler and radiators, mains gas',
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'lighting-cost-current': '182', 'lodgement-date': '2023-07-13', 'extension-count': '0',
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'mainheatc-env-eff': 'Average',
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'lmk-key': 'c1d137711da433fb3cced74b1a6848da8bbc1159d076455d26d7b4668982601e',
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'wind-turbine-count': '0',
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'tenure': 'Rented (social)', 'floor-level': '', 'potential-energy-efficiency': '84',
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'hot-water-energy-eff': 'Good', 'low-energy-lighting': '82',
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'walls-description': 'Solid brick, as built, no insulation (assumed)',
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'hotwater-description': 'From main system'}, 'full_sap_epc': {}, 'old_data': []
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}
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return epc_records_1
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def test_clean_mechanical_ventilation(self, cleaning_data, epc_records_1):
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# We have an epc with Natural ventilation - the resulting epc should also have natural ventulation
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record = EPCRecord(cleaning_data=cleaning_data)
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record.prepared_epc = {
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"mechanical-ventilation": "natural"
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}
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record._clean_ventilation()
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assert record.prepared_epc["mechanical-ventilation"] == "natural"
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record2 = EPCRecord(cleaning_data=cleaning_data)
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record2.prepared_epc = {
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"mechanical-ventilation": ""
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}
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record2._clean_ventilation()
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assert record2.prepared_epc["mechanical-ventilation"] is None
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record3 = EPCRecord(cleaning_data=cleaning_data)
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record3.prepared_epc = {
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"mechanical-ventilation": None
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}
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record3._clean_ventilation()
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assert record3.prepared_epc["mechanical-ventilation"] is None
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record4 = EPCRecord(cleaning_data=cleaning_data)
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record4.prepared_epc = {
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"mechanical-ventilation": "INVALID"
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}
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record4._clean_ventilation()
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assert record4.prepared_epc["mechanical-ventilation"] is None
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