37 lines
1.5 KiB
Python
37 lines
1.5 KiB
Python
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import StandardScaler
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import sys
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sensor_data_files = dict(snakemake.input)
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provider = snakemake.params["provider"]
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provider_key = snakemake.params["provider_key"]
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sensor_key = snakemake.params["sensor_key"]
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pd.set_option('display.max_columns', None)
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if provider_key == "cr":
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sys.path.append('/rapids/src/features/')
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from cr_features_helper_methods import extract_second_order_features
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provider_main = snakemake.params["provider_main"]
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prefix = sensor_key + "_" + provider_key + "_"
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windows_features_data = pd.read_csv(sensor_data_files["windows_features_data"])
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excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime', prefix + "level_1"]
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windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)] = \
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StandardScaler().fit_transform(windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)])
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windows_features_data.to_csv(snakemake.output[1], index=False)
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if provider_main["WINDOWS"]["COMPUTE"] and "SECOND_ORDER_FEATURES" in provider_main["WINDOWS"]:
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so_features_names = provider_main["WINDOWS"]["SECOND_ORDER_FEATURES"]
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windows_so_features_data = extract_second_order_features(windows_features_data, so_features_names, prefix)
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windows_so_features_data.to_csv(snakemake.output[0], index=False)
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else:
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pd.DataFrame().to_csv(snakemake.output[0], index=False)
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else:
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pass #To-Do for the rest of the sensors.
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