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After this issue is solved, we should be ready to enforce data constraints on sampled data.
In order to implement them, they should be checked after data is sampled and reverse_transformed but before is returned. It should be on sdv.Sampler.sample_rows as it's the common access to the process of sampling for the three public methods. The roadmap should be as follows:
1-. Create a method sdv.Sampler.check_constraints that gets a dataframe sampled and reverse transformed and return an array of indices corresponding to rows that fulfill constraints.
2-. Modify the method sdv.Sampler.sample_rows that handles the process of sampling, but before returning the result, checks the data fullfill the constraints, discard the rows that fail and samples again until it gets to the desired number of rows.
The text was updated successfully, but these errors were encountered:
After this issue is solved, we should be ready to enforce data constraints on sampled data.
In order to implement them, they should be checked after data is sampled and reverse_transformed but before is returned. It should be on
sdv.Sampler.sample_rows
as it's the common access to the process of sampling for the three public methods. The roadmap should be as follows:1-. Create a method
sdv.Sampler.check_constraints
that gets a dataframe sampled and reverse transformed and return an array of indices corresponding to rows that fulfill constraints.2-. Modify the method
sdv.Sampler.sample_rows
that handles the process of sampling, but before returning the result, checks the data fullfill the constraints, discard the rows that fail and samples again until it gets to the desired number of rows.The text was updated successfully, but these errors were encountered: