SDMetrics
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sdv-dev
Metrics to evaluate quality and efficacy of synthetic datasets.
### Environment Details Please indicate the following details about the environment in which you found the bug: * SDMetrics version: 0.3.2 * Python version: 3.8.6 * Operating System: CentOS ###...
### Problem Description Columns of type `id` should be dropped when computing metrics. ID columns are not synthetically generated, so should not contribute to the metric calculation. Currently they are...
We are calling pydocstyle for rdt, when it should be called for SDMetrics: https://github.com/sdv-dev/SDMetrics/blob/18baa701813770f2910847fabbb006e7d9adcbeb/tasks.py#L116
The GMLogLikelihood metric was added to cover the metrics that existed in the original SDGym iteration, which used GM Log Likelhood metric over datasets that were simulated using GaussianMixtures. However,...
### Description Having an easy way of measuring the privacy of synthesized data would be very useful for users of the tool. It could be added on top of the...
* SDMetrics version: 0.4.5 * Python version: 3.6.9 * Operating System: Linux RHEL ### Description As a end user we are finding it difficult to interpret the result of SDMetrics....
### Environment Details Please indicate the following details about the environment in which you found the bug: * SDMetrics version: 0.5.0 * Python version: 3.9 * Operating System: Windows ###...
Hi all! I am one of the [smartnoise-sdk](https://github.com/opendp/smartnoise-sdk) maintainers (part of the [OpenDP collaboration](https://github.com/opendp)). Specifically, I work on differentially private (DP) data synthesizers. ### Problem Description It would be nice...
The current `README` doesn't print the latest output. More specifically, the command `sdmetrics.compute_metrics(metrics, real_data, synthetic_data, metadata=metadata)` currently doesn't print the same as what the README prints (e.g. the current code...
### Problem Description The [Privacy Metrics](https://sdv.dev/SDV/user_guides/evaluation/single_table_metrics.html#privacy-metrics) assume an adversarial attack model where a user with access to a few `key_fields` might be able to predict `sensitive_fields`. I understand that we...
