KeyUniqueness
Last updated
Last updated
This metric measures whether the keys in a particular dataset are unique. We expect that certain types of keys, such as primary keys, are always unique in order to be valid.
ID : This metric is meant for ID data
Other : This metric can work with any other type of semantic data that is used in place of an ID, such as a natural key like email
(best) 1.0: All of the key values in the synthetic data are unique
(worst) 0.0: None of the key values in the synthetic data are unique
This metric measures how many values in the synthetic data, s, are duplicates, meaning that there is another value that is exactly the same. Call this set Ds. The score is the proportion of values that are not duplicates.
To manually run this metric, access the single_column
module and use the compute
method.
Parameters
(required) real_data
: A pandas.Series object with the column of real data
(required) synthetic_data
: A pandas.Series object with the column of synthetic data
Recommended Usage: The applies this metric to applicable keys (primary and alternate keys).
If you are running this score on a foreign key, then the score may not be 1, as foreign keys are allowed to repeat. For foreign keys, we recommend using the metric instead.