Synthetic Data Vault: A Powerful Tool for Data Generation #

Synthetic data is data that is artificially created to mimic the characteristics and patterns of real data. Synthetic data can be used for various purposes, such as testing, training, privacy protection, data augmentation, and more. However, generating high-quality synthetic data that preserves the complex relationships and distributions of real data is not an easy task. That’s where Synthetic Data Vault (SDV) comes in.

Please note that this article was written with the assistance of GPT. While I did provide all of the points that I intended to convey, the article is not as tight knit and polished as I’d like. I do believe, however, that this is a somewhat appropriate usecase for GPT and that the major ideas have been imparted. I would essentially like to employ this article as a placeholder until I get the time to do said polishing.

What is Synthetic Data Vault? #

Synthetic Data Vault (SDV) is an open-source Python library that allows users to easily model and sample synthetic data from multiple sources of real data. SDV can handle different types of data, such as tabular, relational, time series, and multi-modal. SDV uses deep learning models to learn the structure and dependencies of the real data and generate realistic synthetic data that can be used for various applications.

Why do we need synthetic data? #

Synthetic data has many advantages over real data, especially in scenarios where real data is scarce, sensitive, or expensive to obtain. Some of the benefits of synthetic data are:

• Privacy protection: Synthetic data can be used to anonymize sensitive information and comply with data protection regulations, such as GDPR. Synthetic data does not contain any personal or identifiable information, so it can be shared and used without risking privacy breaches.
• Data augmentation: Synthetic data can be used to increase the size and diversity of the available data, which can improve the performance and robustness of machine learning models. Synthetic data can also be used to create new scenarios and edge cases that are not present in the real data, which can enhance the testing and validation of the models.
• Data accessibility: Synthetic data can be used to overcome the limitations and challenges of obtaining real data, such as high costs, legal issues, ethical concerns, or technical difficulties. Synthetic data can be generated on-demand and customized according to the user’s needs and preferences.

Using SDV in projects to mimic the Faker library #

Faker is a popular Python library that can generate fake data for various purposes, such as testing, prototyping, or filling databases. Faker can create fake data for different categories, such as names, addresses, dates, phone numbers, emails, etc. However, Faker has some limitations compared to SDV:

• Faker generates fake data randomly and independently, without considering the relationships and distributions of the real data. This can result in unrealistic or inconsistent fake data that does not reflect the properties of the real data.
• Faker can only generate fake data for predefined categories and formats, which may not match the user’s specific requirements or expectations. Faker does not allow users to customize or fine-tune the fake data generation process.

SDV can overcome these limitations by using deep learning models to learn from the real data and generate synthetic data that preserves its structure and dependencies. SDV can also handle different types of data, such as tabular, relational, time series, and multi-modal. SDV allows users to control and adjust various aspects of the synthetic data generation process, such as sampling methods, constraints, conditions, etc.

Advantages compared to using Faker #

Compared to using Faker, SDV has several advantages for generating synthetic data:

• Realism: SDV generates synthetic data that mimics the characteristics and patterns of the real data. SDV ensures that the synthetic data has similar statistical properties and correlations as the real data. SDV also respects the constraints and conditions of the real data, such as uniqueness, validity, etc.
• Flexibility: SDV can generate synthetic data for different types of data sources and formats. SDV can also generate synthetic data according to user-defined criteria and specifications. SDV allows users to modify and manipulate the synthetic data as they wish.
• Scalability: SDV can generate synthetic data efficiently and effectively using deep learning models. SDV can handle large and complex datasets with multiple tables and variables. SDV can also leverage GPU acceleration to speed up the synthetic data generation process.

Available Models (Synthesizers) #

SDV provides different models (synthesizers) for generating synthetic data from different types of real data sources. Some of the available synthesizers are:

• GaussianCopula: This synthesizer uses a Gaussian copula model to capture the marginal distributions and correlations of tabular data. It can handle numerical, categorical, ordinal, datetime, and boolean variables. It also supports conditional sampling and rejection sampling methods.
• CTGAN: This synthesizer uses a conditional generative adversarial network (GAN) to model the joint distribution of tabular data. It can handle numerical and categorical variables, as well as missing values. It also supports conditional sampling and data transformations.
• SDV: This synthesizer uses a combination of deep neural networks and copulas to model the joint distribution of relational data. It can handle multiple tables with primary and foreign keys, as well as numerical and categorical variables. It also supports conditional sampling and hierarchical sampling methods.
• PAR: This synthesizer uses a recurrent neural network (RNN) to model the temporal dependencies of time series data. It can handle univariate or multivariate time series, as well as irregular or missing timestamps. It also supports conditional sampling and forecasting methods.
• MultiMod: This synthesizer uses a variational autoencoder (VAE) to model the joint distribution of multi-modal data. It can handle multiple modalities, such as images, text, audio, etc. It also supports conditional sampling and data fusion methods.

Steps to create a synthesizer #

To create a synthesizer using SDV, the following steps are required:

• Import SDV: Import the SDV library and the desired synthesizer class from the corresponding module. For example, to use the GaussianCopula synthesizer, the following code can be used:

import sdv
from sdv.tabular import GaussianCopula

• Load real data: Load the real data that will be used to train the synthesizer. The real data can be loaded from various sources, such as CSV files, pandas dataframes, SQL databases, etc. For example, to load a CSV file containing tabular data, the following code can be used:

import pandas as pd
real_data = pd.read_csv("real_data.csv")

• Create synthesizer instance: Create an instance of the synthesizer class and pass any optional parameters that are needed to customize the synthetic data generation process. For example, to create a GaussianCopula instance with a specified random seed, the following code can be used:

synthesizer = GaussianCopula(random_state=42)

• Fit synthesizer: Fit the synthesizer to the real data using the fit method. This will train the synthesizer to learn the structure and dependencies of the real data. For example, to fit the GaussianCopula instance to the real data, the following code can be used:

synthesizer.fit(real_data)

• Sample synthetic data: Sample synthetic data from the synthesizer using the sample method. This will generate synthetic data that mimics the characteristics and patterns of the real data. The sample method can take optional parameters that can control the size and conditions of the synthetic data. For example, to sample 100 rows of synthetic data from the GaussianCopula instance, the following code can be used:

synthetic_data = synthesizer.sample(100)

• Save or use synthetic data: Save or use the synthetic data for the desired purpose. The synthetic data can be saved to various formats, such as CSV files, pandas dataframes, SQL databases, etc. The synthetic data can also be used for various applications, such as testing, training, privacy protection, data augmentation, etc. For example, to save the synthetic data to a CSV file, the following code can be used:

synthetic_data.to_csv("synthetic_data.csv")

Conclusion #

Synthetic Data Vault (SDV) is a powerful and versatile tool for generating synthetic data from multiple sources of real data. SDV can handle different types of data, such as tabular, relational, time series, and multi-modal. SDV uses deep learning models to learn the structure and dependencies of the real data and generate realistic synthetic data that preserves its characteristics and patterns. SDV has many advantages over traditional fake data generation methods, such as Faker, in terms of realism, flexibility, and scalability. SDV allows users to easily create and customize synthesizers for their specific needs and preferences. SDV can be used for various applications, such as testing, training, privacy protection, data augmentation, and more. To learn more about SDV and how to use it, you can visit the official documentation or the GitHub repository.