I have a real dataset of sequences of events and a "fake" dataset generated using an lstm model. The two datasets are made up of the same vocabulary but are of a different length. I'm putting together an evaluation script to assess how similar the two datasets are, and one of the metrics should be a comparison of the ranking of the top 500 most frequent n-grams in the real data and the fake data. Seeing as the top 500 most frequent n-grams in the real data might be different to the ones in the fake data, I don't know what ranking measure to use that would give me a clear idea of how similar the two datasets are. Does anyone know of a measure that would allow for such a difference? I need one which treats the real dataset as the gold truth and compares it to the fake dataset.

Any help would be much appreciated.


The best way to proceed is to get the top 500 most frequent n-grams in both datasets, then get the intersection and use the two rankings to get the Kendall's-W measure, which serves as an assessment for rank agreement between two different rankings:


Implementation in python :


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