An open source project from Data to AI Lab at MIT.

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Copulas

• License: MIT

• Development Status: Pre-Alpha

• Documentation: https://sdv-dev.github.io/Copulas

• Homepage: https://github.com/sdv-dev/Copulas

Overview

Copulas is a Python library for modeling multivariate distributions and sampling from them using copula functions. Given a table containing numerical data, we can use Copulas to learn the distribution and later on generate new synthetic rows following the same statistical properties.

Some of the features provided by this library include:

• A variety of distributions for modeling univariate data.

• Multiple Archimedean copulas for modeling bivariate data.

• Gaussian and Vine copulas for modeling multivariate data.

• Automatic selection of univariate distributions and bivariate copulas.

Supported Distributions

Univariate

• Gaussian

• Beta

• Gamma

• Gaussian KDE

• Truncated Gaussian

• Student T

Archimedean Copulas (Bivariate)

• Clayton

• Frank

• Gumbel

Multivariate

• Gaussian

• D-Vine

• C-Vine

• R-Vine

Related Projects

SDV

SDV, for Synthetic Data Vault, is the end-user library for synthesizing data in development under the HDI Project. SDV allows you to easily model and sample relational datasets using Copulas thought a simple API. Other features include anonymization of Personal Identifiable Information (PII) and preserving relational integrity on sampled records.

CTGAN

CTGAN is a GAN based model for synthesizing tabular data. It’s also developed by the MIT’s Data to AI Lab and is under active development.

User Guides

• Introduction to Copulas
  • Probability Review
  • Copulas
• Univariate Distributions
  • Univariate Usage Example
  • Selecting the best Univariate
• Multivariate Distributions
  • Gaussian Multivariate
  • Vine Copulas

Advanced Usage

• Synthetic Data for Machine Learning
  • Loading the dataset
  • Generating synthetic data
  • Training a linear model

Indices and tables

• Index

• Module Index

• Search Page