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Metrics to evaluate quality and efficacy of synthetic datasets.
| Date | Stars |
|---|---|
| 2026-07-24 | 261 |
| 2026-07-25 | 260 |
| 2026-07-28 | 260 |
| 2026-07-30 | 260 |
| 2026-08-06 | 260 |
Today
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Momentum
35.0
growth rate 0.00%/day
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<p align="center">
<i>This repository is part of <a href="https://sdv.dev">The Synthetic Data Vault Project</a>, a project from <a href="https://datacebo.com">DataCebo</a>.</i>
</p>
[](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)
[](https://pypi.python.org/pypi/sdmetrics)
[](https://pepy.tech/project/sdmetrics)
[](https://github.com/sdv-dev/SDMetrics/actions?query=workflow%3A%22Run+Tests%22+branch%3Amain)
[](https://codecov.io/gh/sdv-dev/SDMetrics)
[](https://forum.datacebo.com)
[](https://bit.ly/sdmetrics-demo)
[](https://doi.org/10.5281/zenodo.14279167)
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<p align="center">
<a href="https://github.com/sdv-dev/SDV">
<img align="center" width=40% src="https://github.com/sdv-dev/SDV/blob/stable/docs/images/SDMetrics-DataCebo.png"></img>
</a>
</p>
</div>
</div>
# Overview
The SDMetrics library evaluates synthetic data by comparing it to the real data that you're trying to mimic. It includes a variety of metrics to capture different aspects of the data, for example **quality and privacy**. It also includes reports that you can run to generate insights, visualize data and share with your team.
The SDMetrics library is **model-agnostic**, meaning you can use any synthetic data. The library does not need to know how you created the data.
<img align="center" src="docs/images/column_comparison.png"></img>
# Install
Install SDMetrics using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device.
```bash
pip install sdmetrics
```
```bash
conda install -c conda-forge sdmetrics
```
For more information about using SDMetrics, visit the [SDMetrics Documentation](https://docs.sdv.dev/sdmetrics).
# Usage
Get started with **SDMetrics Reports** using some demo data,
```python
from sdmetrics import load_demo
from sdmetrics.reports.single_table import QualityReport
real_data, synthetic_data, metadata = load_demo(modality='single_table')
my_report = QualityReport()
my_report.generate(real_data, synthetic_data, metadata)
```
```
Creating report: 100%|██████████| 4/4 [00:00<00:00, 5.22it/s]
Overall Quality Score: 82.84%
Properties:
Column Shapes: 82.78%
Column Pair Trends: 82.9%
```
Once you generate the report, you can drill down on the details and visualize the results.
```python
my_report.get_visualization(property_name='Column Pair Trends')
```
<img align="center" src="docs/images/column_pairs.png"></img>
Save the report and share it with your team.
```python
my_report.save(filepath='demo_data_quality_report.pkl')
# load it at any point in the future
my_report = QualityReport.load(filepath='demo_data_quality_report.pkl')
```
**Want more metrics?** You can also manually apply any of the metrics in this library to your data.
```python
# calculate whether the synthetic data respects the min/max bounds
# set by the real data
from sdmetrics.single_column import BoundaryAdherence
BoundaryAdherence.compute(real_data['start_date'], synthetic_data['start_date'])
```
```
0.8503937007874016
```
```python
# calculate whether the synthetic data is new or whether it's an exact copy of the real data
from sdmetrics.single_table import NewRowSynthesis
NewRowSynthesis.compute(real_data, synthetic_data, metadata)
```
```
1.0
```
# What's next?
To learn more about the reports and metrics, visit the [SDMetrics DocumentationExcerpt of 5,251 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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