⭐Quality Report

The SDMetrics Quality Report captures the statistical similarity between real data and synthetic data. If the synthetic and real data are statistically similar, we refer to the synthetic data as being high quality (aka high fidelity). The report runs select metrics to measure these properties and summarizes the results.

from sdmetrics.reports.single_table import QualityReport

report = QualityReport()
report.generate(real_data, synthetic_data, metadata)

Generating report ...

(1/2) Evaluating Column Shapes: |██████████| 9/9 [00:00<00:00, 273.13it/s]|
Column Shapes Score: 89.11%

(2/2) Evaluating Column Pair Trends: |██████████| 36/36 [00:00<00:00, 57.42it/s]|
Column Pair Trends Score: 88.3%

Overall Score (Average): 88.7%

Once you have generated the report you can get more details to explain the results, visualize the scores and save the report to share it.

How does it work?

The quality report captures the Column Shapes, Column Pair Trends and Cardinality. This guide contains some technical details about each property.

Column Shapes

Does the synthetic data capture the shape of each column?

The shape of a column describes its overall distribution. The higher the score, the more similar the distributions of real and synthetic data.

Methodology

This property applies metrics based on the column types.

This yields a separate score for every column. The final Column Shapes score is the average of all columns.

Column Pair Trends

Does the synthetic data capture trends between pairs of columns?

The trend between two columns describes how they vary in relation to each other, for example the correlation. The higher the score, the more the trends are alike.

Methodology

This property applies a different metric metric based on the type of data

This yields a score between every pair of columns. The Column Pair Trends score is the average of all the scores.

Cardinality

Does the synthetic data capture the number of connections between parent and child tables? This is also known as the cardinality of the tables.

Methodology

This property applies the CardinalityShapeSimilarity metric for every set of connected tables: parent table and child table.

Intertable Trends

Does the synthetic data capture trends between columns across different tables?

This is similar to the Column Pair Trends property, but it is applied across parent/child tables. For example, a column in a parent table might be correlated with a column in the child.

Methodology

This property denormalizes the parent and child table into a single, flat table. Then, it applies the same metrics as the Column Pair Trends property.

This yields a score between every pair of columns. The Intertable Trends score is the average of all the scores.

Usage

If you have a single-table dataset, refer to the Single Table API. Pass in a DataFrame containing the real data and synthetic data.

from sdmetrics.reports.single_table import QualityReport

report = QualityReport()
report.generate(real_data, synthetic_data, metadata)

If you have a multi-table dataset, use the Multi Table API instead. This allows you to pass in a dictionary containing multiple, connected DataFrame objects.

from sdmetrics.reports.multi_table import QualityReport

report = QualityReport()
report.generate(real_data, synthetic_data, metadata)

The Quality Report is not optimized to compute the quality of ordered, sequential information. However, you can still apply the single table report for some basic analysis.