Add KeyUniqueness metric

[frances-h]

Problem Description

As a user, I would like a metric that gives me information about the uniqueness of my primary key and alternate key columns.

Expected behavior

• Add a new single_column metric that calculates the percent of keys that are unique and not null.
• This metric takes in primary key or alternate key columns (either ID or PII sdtypes).

Attributes

The metric should have the following attributes:

• name: 'KeyUniqueness'
• goal: Goal.MAXIMIZE
• min_value: 0.0
• max_value: 1.0

Methods

The metric should also define the following methods

• compute(real_data, synthetic_data): Compute the score for the metric. The returned score should be the percent of keys that are unique and not null (eg. a score of 0.6 means 60% of the keys are unique and 40% are duplicates).
  • 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
  • Returns: The score for this metric
  • If the real data does not pass this test (eg. contains duplicate or null values), then the metric should Error.

>>> from sdmetrics.single_column import KeyUniqueness
>>> KeyUniqueness.compute(
	real_data=real_table['user_id'],
	synthetic_data=synthetic_table['user_id'])
1.0
>>> KeyUniqueness.compute_breakdown(
	real_data=real_table['ethnicity'],
	synthetic_data=synthetic_table['ethnicity'])
{ 'score': 1.0 }