Add CategoryAdherence metric

[frances-h]

Problem Description

As a user, I would like a metric that verifies I do not have invalid discrete values.

Expected behavior

• Add a new single_column metric that calculates the percent of values that match at least 1 value from the real data.
• This metric takes in categorical and boolean sdtype columns.

Attributes

The metric should have the following attributes:

• name: 'CategoryAdherence'
• 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 synthetic values that match at least 1 value in the real data. Null values should be counted as a separate category.
  • 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

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