#Pre-processing script that can run with SageMaker SKLearn training container in script mode import warnings from datetime import datetime from dateutil.relativedelta import relativedelta import numpy as np import datetime import pandas as pd from datetime import datetime import sys def legacy_postprocessing(inputdf): #Post-process your data outputdf=inputdf return(outputdf) #main function to start the execution if __name__ == "__main__": print("Started Postprocessing Script Run") #make the script usable for local testing if len(sys.argv) > 1: inputtype = sys.argv[1] localpath = sys.argv[2] else: inputtype = "" localpath = "" if inputtype == "local": inputfilepath = localpath outputfilepath = localpath else: #SM processing container's default input/output paths inputfilepath = '/opt/ml/processing/input/data/' outputfilepath = '/opt/ml/processing/output/' #read Input file filename = inputfilepath + 'predictions_output.csv' inputdata = pd.read_csv(str(filename)) #Plug-in your legacy code here or call any functions #Postprocess your data outputdata=legacy_postprocessing(inputdata) #write Output back outputfilename = outputfilepath+"final_output.csv" outputdata.to_csv(outputfilename, index=False) print("Completed Postprocessing Script Run")