single_column

Single Column Metrics

The metrics found on this folder operate on individual columns (or univariate random variables), passed as two 1 dimensional arrays.

Implemented metrics:

• Statistical: Metrics that compare the arrays using statistical tests
  • CSTest: Chi-Squared test to compare the distributions of two categorical columns.
  • KSTest: Kolmogorov-Smirnov test to compare the distributions of two numerical columns using their empirical CDF.

SingleColumnMetric

All the single column metrics are subclasses form the sdmetrics.single_column.SingleColumnMetric class, which can be used to locate all of them:

In [1]: from sdmetrics.single_column import SingleColumnMetric

In [2]: SingleColumnMetric.get_subclasses()
Out[2]:
{'CSTest': sdmetrics.single_column.statistical.cstest.CSTest,
 'KSTest': sdmetrics.single_column.statistical.kstest.KSTest}

Single Column Inputs and Outputs

All the single column metrics operate on just two inputs:

• real_data: A 1d numpy array, coming from the real dataset.
• synthetic_data: A 1d numpy array, coming from the synthetic dataset.

For example, this how the KSTest metric can be computed for the age column from the demo data:

In [3]: from sdmetrics import load_demo

In [4]: real_data, synthetic_data, metadata = load_demo()

In [5]: from sdmetrics.single_column import KSTest

In [6]: real_column = real_data['users']['age'].to_numpy()

In [7]: synthetic_column = synthetic_data['users']['age'].to_numpy()

In [8]: KSTest.compute(real_column, synthetic_column)
Out[8]: 0.8