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pass averages to lighting cleaner
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parent
2acf5c3534
commit
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3 changed files with 8 additions and 6 deletions
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@ -9,6 +9,7 @@ from etl.epc.Dataset import TrainingDataset
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from etl.epc.Record import EPCRecord
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from etl.epc.settings import LATEST_FIELD, MANDATORY_FIXED_FEATURES
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from etl.epc_clean.epc_attributes.all_cleaners import all_cleaner_map
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from etl.epc_clean.tests.test_lighting_attributes import averages
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from utils.logger import setup_logger
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from utils.s3 import read_dataframe_from_s3_parquet
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from etl.epc.settings import DATA_ANOMALY_MATCHES
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@ -624,7 +625,10 @@ class Property:
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if len(attributes) == 0:
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# We attempt to perform the clean on the fly
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cleaner_cls = all_cleaner_map[description]
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cleaner_cls = cleaner_cls(self.data[description])
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if description == "lighting-description":
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cleaner_cls = cleaner_cls(self.data[description], averages=None)
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else:
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cleaner_cls = cleaner_cls(self.data[description])
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processed = {
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"original_description": self.data[description],
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"clean_description": cleaner_cls.description.replace(
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@ -174,11 +174,6 @@ for p in input_properties:
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input_properties
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]
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for p in tqdm(input_properties):
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# TEMP
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p.DATA_ANOMALY_MATCHES = DATA_ANOMALY_MATCHES
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p.set_features(cleaned=cleaned, kwh_client=kwh_client, kwh_predictions=mocked_kwh_predictions)
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# Run the recommendations
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recommendations = {}
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recommendations_scoring_data = []
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@ -28,6 +28,9 @@ class LightingAttributes(Definitions):
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self.nodata = (not description) or (description in self.DATA_ANOMALY_MATCHES) or (
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description in self.OBSERVED_ERRORS) or (description == "SAP05:Lighting")
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if description == "SAP05:Lighting":
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self.description = description # Reset self.description
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def welsh_translation_search(self):
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"""
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For welsh text describing the percentage of low energy lighting, we match the regular
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