copulas 0.6.1.dev0

A python library for building different types of copulas and using them for sampling.

This repository is part of The Synthetic Data Vault Project, a project from DataCebo.

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.

Important Links
:computer: Website	Check out the SDV Website for more information about the project.
:orange_book: SDV Blog	Regular publshing of useful content about Synthetic Data Generation.
:book: Documentation	Quickstarts, User and Development Guides, and API Reference.
:octocat: Repository	The link to the Github Repository of this library.
:scroll: License	The entire ecosystem is published under the MIT License.
:keyboard: Development Status	This software is in its Pre-Alpha stage.
Community	Join our Slack Workspace for announcements and discussions.
Tutorials	Run the SDV Tutorials in a Binder environment.

Features

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

• Beta
• Gamma
• Gaussian
• Gaussian KDE
• Log-Laplace
• Student T
• Truncated Gaussian
• Uniform

Archimedean Copulas (Bivariate)

• Clayton
• Frank
• Gumbel

Multivariate

• Gaussian Copula
• D-Vine
• C-Vine
• R-Vine

Install

Copulas is part of the SDV project and is automatically installed alongside it. For details about this process please visit the SDV Installation Guide

Optionally, Copulas can also be installed as a standalone library using the following commands:

Using pip:

pip install copulas

Using conda:

conda install -c conda-forge copulas

For more installation options please visit the Copulas installation Guide

Quickstart

In this short quickstart, we show how to model a multivariate dataset and then generate synthetic data that resembles it.

import warnings
warnings.filterwarnings('ignore')

from copulas.datasets import sample_trivariate_xyz
from copulas.multivariate import GaussianMultivariate
from copulas.visualization import compare_3d

# Load a dataset with 3 columns that are not independent
real_data = sample_trivariate_xyz()

# Fit a gaussian copula to the data
copula = GaussianMultivariate()
copula.fit(real_data)

# Sample synthetic data
synthetic_data = copula.sample(len(real_data))

# Plot the real and the synthetic data to compare
compare_3d(real_data, synthetic_data)

The output will be a figure with two plots, showing what both the real and the synthetic data that you just generated look like:

What's next?

For more details about Copulas and all its possibilities and features, please check the documentation site.

There you can learn more about how to contribute to Copulas in order to help us developing new features or cool ideas.

Credits

Copulas is an open source project from the Data to AI Lab at MIT which has been built and maintained over the years by the following team:

• Manuel Alvarez [e-mail]
• Carles Sala [e-mail]
• (Alicia) Yi Sun [e-mail]
• José David Pérez [e-mail]
• Kevin Alex Zhang [e-mail]
• Andrew Montanez [e-mail]
• Gabriele Bonomi [e-mail]
• Kalyan Veeramachaneni [e-mail]
• Iván Ramírez [e-mail]
• Felipe Alex Hofmann [e-mail]
• paulolimac [e-mail]
• nazar-ivantsiv [e-mail]

---

The Synthetic Data Vault Project was first created at MIT's Data to AI Lab in 2016. After 4 years of research and traction with enterprise, we created DataCebo in 2020 with the goal of growing the project. Today, DataCebo is the proud developer of SDV, the largest ecosystem for synthetic data generation & evaluation. It is home to multiple libraries that support synthetic data, including:

• 🔄 Data discovery & transformation. Reverse the transforms to reproduce realistic data.
• 🧠 Multiple machine learning models -- ranging from Copulas to Deep Learning -- to create tabular, multi table and time series data.
• 📊 Measuring quality and privacy of synthetic data, and comparing different synthetic data generation models.

Get started using the SDV package -- a fully integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries for specific needs.

History

v0.6.0 - 2021-05-13

This release makes Copulas compatible with Python 3.9! It also improves library maintenance by updating dependencies, reorganizing the CI workflows, adding pip check to the workflows and removing unused files.

General Improvements

• Add support for Python 3.9 - Issue#282 by @amontanez24
• Remove entry point in setup.py - Issue#280 by @amontanez24
• Update pandas dependency range - Issue#266 by @katxiao
• Fix repository language - Issue#272 by @pvk-developer
• Add pip check to CI workflows - Issue#274 by @pvk-developer
• Reorganize workflows and add codecov - PR#267 by @csala
• Constrain jinja2 versions - PR#269 by @fealho

v0.5.1 - 2021-08-13

This release improves performance by changing the way scipy stats is used, calling their methods directly without creating intermediate instances.

It also fixes a bug introduced by the scipy 1.7.0 release where some distributions fail to fit because scipy validates the learned parameters.

Issues Closed

• Exception: Optimization converged to parameters that are outside the range allowed by the distribution. - Issue #264 by @csala
• Use scipy stats models directly without creating instances - Issue #261 by @csala

v0.5.0 - 2021-01-24

This release introduces conditional sampling for the GaussianMultivariate modeling. The new conditioning feature allows passing a dictionary with the values to use to condition the rest of the columns.

It also fixes a bug that prevented constant distributions to be restored from a dictionary and updates some dependencies.

New Features

• Conditional sampling from Gaussian copula - Issue #154 by @csala

Bug Fixes

• ScipyModel subclasses fail to restore constant values when using from_dict - Issue #212 by @csala

v0.4.0 - 2021-01-27

This release introduces a few changes to optimize processing speed by re-implementing the Gaussian KDE pdf to use vectorized root finding methods and also adding the option to subsample the data during univariate selection.

General Improvements

• Make gaussian_kde faster - Issue #200 by @k15z and @fealho
• Use sub-sampling in select_univariate - Issue #183 by @csala

v0.3.3 - 2020-09-18

General Improvements

• Use corr instead of cov in the GaussianMultivariate - Issue #195 by @rollervan
• Add arguments to GaussianKDE - Issue #181 by @rollervan

New Features

• Log Laplace Distribution - Issue #188 by @rollervan

v0.3.2 - 2020-08-08

General Improvements

• Support Python 3.8 - Issue #185 by @csala
• Support scipy >1.3 - Issue #180 by @csala

New Features

• Add Uniform Univariate - Issue #179 by @rollervan

v0.3.1 - 2020-07-09

General Improvements

• Raise numpy version upper bound to 2 - Issue #178 by @csala

New Features

• Add Student T Univariate - Issue #172 by @gbonomib

Bug Fixes

• Error in Quickstarts : Unknown projection '3d' - Issue #174 by @csala

v0.3.0 - 2020-03-27

Important revamp of the internal implementation of the project, the testing infrastructure and the documentation by Kevin Alex Zhang @k15z, Carles Sala @csala and Kalyan Veeramachaneni @kveerama

Enhancements

• Reimplementation of the existing Univariate distributions.
• Addition of new Beta and Gamma Univariates.
• New Univariate API with automatic selection of the optimal distribution.
• Several improvements and fixes on the Bivariate and Multivariate Copulas implementation.
• New visualization module with simple plotting patterns to visualize probability distributions.
• New datasets module with toy datasets sampling functions.
• New testing infrastructure with end-to-end, numerical and large scale testing.
• Improved tutorials and documentation.

v0.2.5 - 2020-01-17

General Improvements

• Convert import_object to get_instance - Issue #114 by @JDTheRipperPC

v0.2.4 - 2019-12-23

New Features

• Allow creating copula classes directly - Issue #117 by @csala

General Improvements

• Remove select_copula from Bivariate - Issue #118 by @csala
• Rename TruncNorm to TruncGaussian and make it non standard - Issue #102 by @csala @JDTheRipperPC

Bugs fixed

• Error on Frank and Gumble sampling - Issue #112 by @csala

v0.2.3 - 2019-09-17

New Features

• Add support to Python 3.7 - Issue #53 by @JDTheRipperPC

General Improvements

• Document RELEASE workflow - Issue #105 by @JDTheRipperPC
• Improve serialization of univariate distributions - Issue #99 by @ManuelAlvarezC and @JDTheRipperPC

Bugs fixed

• The method 'select_copula' of Bivariate return wrong CopulaType - Issue #101 by @JDTheRipperPC

v0.2.2 - 2019-07-31

New Features

• truncnorm distribution and a generic wrapper for scipy.rv_continous distributions - Issue #27 by @amontanez, @csala and @ManuelAlvarezC
• Independence bivariate copulas - Issue #46 by @aliciasun, @csala and @ManuelAlvarezC
• Option to select seed on random number generator - Issue #63 by @echo66 and @ManuelAlvarezC
• Option on Vine copulas to select number of rows to sample - Issue #77 by @ManuelAlvarezC
• Make copulas accept both scalars and arrays as arguments - Issues #85 and #90 by @ManuelAlvarezC

General Improvements

• Ability to properly handle constant data - Issues #57 and #82 by @csala and @ManuelAlvarezC
• Tests for analytics properties of copulas - Issue #61 by @ManuelAlvarezC
• Improved documentation - Issue #96 by @ManuelAlvarezC

Bugs fixed

• Fix bug on Vine copulas, that made it crash during the bivariate copula selection - Issue #64 by @echo66 and @ManuelAlvarezC

v0.2.1 - Vine serialization

• Add serialization to Vine copulas.
• Add distribution as argument for the Gaussian Copula.
• Improve Bivariate Copulas code structure to remove code duplication.
• Fix bug in Vine Copulas sampling: 'Edge' object has no attribute 'index'
• Improve code documentation.
• Improve code style and linting tools configuration.

v0.2.0 - Unified API

• New API for stats methods.
• Standarize input and output to numpy.ndarray.
• Increase unittest coverage to 90%.
• Add methods to load/save copulas.
• Improve Gaussian copula sampling accuracy.

v0.1.1 - Minor Improvements

• Different Copula types separated in subclasses
• Extensive Unit Testing
• More pythonic names in the public API.
• Stop using third party elements that will be deprected soon.
• Add methods to sample new data on bivariate copulas.
• New KDE Univariate copula
• Improved examples with additional demo data.

v0.1.0 - First Release

• First release on PyPI.