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 The svm.py generates semi-synthetic data, when base ranker is naive-svm. To run this code, use python2, along with the follwing arguments
 
 argument 1: 99% of train data in set1 of Yahoo! learning-to-rank dataset
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 argument 2: prediction file that contains base ranker scores for 99% of train data
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 argument 3: output file address in which naive-svm data will be written in
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 argument 4: output file address in which propensity-svm data will be written in
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 argument 5: output file address in which heckmanRank data will be written in
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 argument 6: number of passes or sampling times, which is 5 in our results
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 argument 7: cut off (k), which is 0-29
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 argument 8: eta or severity of position bias (0, 0.5, 1, 1.5 and 2 in our results)