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In numpy typically columns are numbered using integers. Currently our implementation converts these into strings (i.e., '0', '1', etc.) in the returned generalizations. This is due to the bahavior of scikitlearn's OneHotEncoder which differs between integers and strings. But it would be better to leave these as integer keys to be consistent with numpy.
One workaround is to store in the beginning of the code a boolean stating whether the column indexes were received as integers, and at the end cast the strings back to integers in the return value.
The text was updated successfully, but these errors were encountered:
In numpy typically columns are numbered using integers. Currently our implementation converts these into strings (i.e., '0', '1', etc.) in the returned generalizations. This is due to the bahavior of scikitlearn's OneHotEncoder which differs between integers and strings. But it would be better to leave these as integer keys to be consistent with numpy.
One workaround is to store in the beginning of the code a boolean stating whether the column indexes were received as integers, and at the end cast the strings back to integers in the return value.
The text was updated successfully, but these errors were encountered: