Add safenet when features dataframe is empty.
parent
bfd637eb9c
commit
8acac50125
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@ -14,7 +14,7 @@ def straw_cleaning(sensor_data_files, provider, target):
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features = pd.read_csv(sensor_data_files["sensor_data"][0])
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features = pd.read_csv(sensor_data_files["sensor_data"][0])
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features = features[features['local_segment_label'] == 'working_day'] # Filtriranje ustreznih časovnih segmentov
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# features = features[features['local_segment_label'] == 'working_day'] # Filtriranje ustreznih časovnih segmentov
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# print(features)
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# print(features)
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# sys.exit()
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# sys.exit()
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@ -36,7 +36,6 @@ def straw_cleaning(sensor_data_files, provider, target):
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return pd.DataFrame(columns=excluded_columns)
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return pd.DataFrame(columns=excluded_columns)
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graph_bf_af(features, "2target_rows_after")
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graph_bf_af(features, "2target_rows_after")
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print("HERE1", target, features["pid"])
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# (2) QUALITY CHECK (DATA YIELD COLUMN) drops the rows where E4 or phone data is low quality
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# (2) QUALITY CHECK (DATA YIELD COLUMN) drops the rows where E4 or phone data is low quality
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phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()
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phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()
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@ -66,6 +65,8 @@ def straw_cleaning(sensor_data_files, provider, target):
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# print(features["empatica_data_yield"].sort_values())
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# print(features["empatica_data_yield"].sort_values())
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features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
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features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
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if features.empty:
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return pd.DataFrame(columns=excluded_columns)
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graph_bf_af(features, "3data_yield_drop_rows")
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graph_bf_af(features, "3data_yield_drop_rows")
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