![]() ![]() It also has a GUI (a Web app based on Django) that enables you to test it directly without coding. Or you could also use our State tool to install this runtime environment.įor Windows users, run the following at a CMD prompt to automatically download and install our CLI, the State Tool along with the Synthetic Data runtime into a virtual environment: powershell -Command "& $(::Create((New-Object Net.WebClient).DownloadString(''))) -activate-default Pizza-Team/Synthetic-Data"įor Linux users, run the following to automatically download and install our CLI, the State Tool along with the Synthetic Data runtime into a virtual environment: sh <(curl -q ) -activate-default Pizza-Team/Synthetic-Data 1–DataSynthesizerĭataSynthesizer is a tool that provides three modules (DataDescriber, DataGenerator, and ModelInspector) for generating synthetic data. Signing up is easy and it unlocks the ActiveState Platform’s many benefits for you! Just use your GitHub credentials or your email address to register. In order to download this ready-to-use Python environment, you will need to create an ActiveState Platform account. To try out some of the packages in this article, you can download and install our pre-built Synthetic Data environment, which contains a version of Python 3.9 and the packages used in this post, along with all their dependencies. ![]() Some focus on providing only the synthetic data itself, but others provide a full set of tools that aim to achieve the synthetically-augmented replica described above.īefore You Start: Install The Synthetic Data Environment Performing disclosure control evaluation on a case-by-case basis is critical.Įach of the following libraries take different approaches to generating synthetic data. Synthetically-augmented replica : provides the closest possible replication.For this one, you must perform disclosure control evaluation on a case-by-case basis. Synthetically-augmented multivariate detailed : replicates detailed relationships.Synthetically-augmented multivariate plausible : replicates high-level relationships with plausible distributions (multivariate).Synthetically-augmented plausible : replicates the distributions of each data sample where possible without accounting for the relationship between different columns (univariate).You should introduce missing value codes, errors, and inconsistencies to replicate the original data. Synthetic valid : not only preserves the structure, but also returns values that are plausible in the context of the dataset.Synthetic structural : preserves the structure of the original data, which is useful for testing code.This scale considers how closely the synthetic data resembles the original data, its purpose, and the disclosure risk. The ONS methodology also provides a scale for evaluating the maturity of a synthetic dataset. The statistical properties of synthetic data should be similar to those of the original data. ![]()
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