A python library for building different types of copulas and using them for sampling.
An open source project from Data to AI Lab at MIT.
- License: MIT
- Development Status: Pre-Alpha
- Documentation: https://sdv-dev.github.io/Copulas
- Homepage: https://github.com/sdv-dev/Copulas
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.
- Student T
- Gaussian KDE
- Truncated Gaussian
Archimedean Copulas (Bivariate)
Copulas has been developed and tested on Python 3.5, 3.6 and 3.7
Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid interfering with other software installed in the system where Copulas is run.
Install with pip
The easiest and recommended way to install Copulas is using pip:
pip install copulas
This will pull and install the latest stable release from PyPi.
If you want to install from source or contribute to the project please read the Contributing Guide.
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:
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.
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 email@example.com
- Carles Sala firstname.lastname@example.org
- José David Pérez email@example.com
- (Alicia)Yi Sun firstname.lastname@example.org
- Andrew Montanez email@example.com
- Kalyan Veeramachaneni firstname.lastname@example.org
- paulolimac email@example.com
- Kevin Alex Zhang firstname.lastname@example.org
- Gabriele Bonomi email@example.com
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.
Log Laplace Distribution - Issue #188 by @rollervan
- Add Uniform Univariate - Issue #179 by @rollervan
- Raise numpy version upper bound to 2 - Issue #178 by @csala
- Add Student T Univariate - Issue #172 by @gbonomib
- Error in Quickstarts : Unknown projection '3d' - Issue #174 by @csala
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
- 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.
- Convert import_object to get_instance - Issue #114 by @JDTheRipperPC
- Allow creating copula classes directly - Issue #117 by @csala
Bivariate- Issue #118 by @csala
Rename TruncNorm to TruncGaussian and make it non standard - Issue #102 by @csala @JDTheRipperPC
- Error on Frank and Gumble sampling - Issue #112 by @csala
- Add support to Python 3.7 - Issue #53 by @JDTheRipperPC
Document RELEASE workflow - Issue #105 by @JDTheRipperPC
Improve serialization of univariate distributions - Issue #99 by @ManuelAlvarezC and @JDTheRipperPC
- The method 'select_copula' of Bivariate return wrong CopulaType - Issue #101 by @JDTheRipperPC
truncnormdistribution and a generic wrapper for
scipy.rv_continousdistributions - Issue #27 by @amontanez, @csala and @ManuelAlvarezC
Independencebivariate 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
- 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
- Fix bug on Vine copulas, that made it crash during the bivariate copula selection - Issue #64 by @echo66 and @ManuelAlvarezC
0.2.1 - Vine serialization
- Add serialization to Vine copulas.
distributionas 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.
0.2.0 - Unified API
- New API for stats methods.
- Standarize input and output to
- Increase unittest coverage to 90%.
- Add methods to load/save copulas.
- Improve Gaussian copula sampling accuracy.
0.1.1 - 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.
0.1.0 - First Release
- First release on PyPI.
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