We'd like to be able to get a transformer into a fitted state using known parameters instead of existing data. This would allow us to set up a transformer to be able to reverse transform without first having to fit it on real data.
Expected behavior
Add the method _set_fitted_parameters to the AnonymizedFaker. When called, the provided arguments should be set on the transformer to get it into a 'fitted' state so that it can be used to reverse transform. After being called, a user should be able to call reverse_transform and have it work as expected.
column_name [str]: The name of the column to use for the transformer. Should be used to set self.columns.
cardinality [int]: The number of unique values to generate. Must be set if self.cardinality_rule is set to match
nan_frequency [float]: The fraction of values that should be replaced with nan values if self.missing_value_generation is 'random'. Should be used to set self._nan_frequency
Problem Description
We'd like to be able to get a transformer into a fitted state using known parameters instead of existing data. This would allow us to set up a transformer to be able to reverse transform without first having to fit it on real data.
Expected behavior
Add the method
_set_fitted_parametersto theAnonymizedFaker. When called, the provided arguments should be set on the transformer to get it into a 'fitted' state so that it can be used to reverse transform. After being called, a user should be able to callreverse_transformand have it work as expected.def _set_fitted_parameters(self, column_name, cardinality, nan_frequency=0.0):
self.columns.self.cardinality_ruleis set tomatchself.missing_value_generationis 'random'. Should be used to setself._nan_frequencyThis method should not return anything.