Commonly used functions in feature engineering in the domain of anomaly detection are listed.
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Sample API implementation of missingValue - https://mqidentity-my.sharepoint.com/:u:/g/personal/nkhadri_atomiton_com/EaCRIMfh5qRNt6ebuSgCxfkBF_n-tcymL1GCMptKMH2JXA?e=oFwHRi
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Threshold
Threshold (fileLocation=None, indexColumn=None, thresholdColumns=None, thresholdLevels=None)
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Eg: continuing with sample data-
if parameterValueDict = {Flatness:50,Symetry:18}, thresholdColumns - [Thr-1, Thr-2, Thr-3]
output - {Flatness:3,Symetry:2}
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HistoricalStats
HistoricalStats(HistoricalStatFileLocation=None, indexColumn=None, statsColumns=None)
Reads the file from a location and load the content in DataFrame with index as indexColumn. It exposes data frame attribute, from which stats can be fetched for required parameters.
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Note: If the parameter fetched does not exist in the input file, the exception is raised.
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MissingValue
missingValue (inputDataFrame, subset=None, valueForMissing=0, missingValueMag=1, initDurationForFirstEpisode=None, incrementalEpisodes=False)
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Note: Input data should be continuous in time, which means The data should have the constant difference between consecutive rows.
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SuddenValueChange
SuddenValueChange (inputDataFrame, subset=None, rollingWIndowMins=3, HistoricalStats=None, zScoreThresholdForSVC=3, maxTimeDifferenceBetweenSignificantChange = 5, maxSignChange = 1, initDurationForFirstEpisode=None, incrementalEpisodes=False)
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