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copulas-0.9.0.dev0
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مشاهده بیشتر
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مشاهده بیشترتوضیحات
Create tabular synthetic data using copulas-based modeling.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | copulas-0.9.0.dev0 |
| نام | copulas |
| نسخه کتابخانه | 0.9.0.dev0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | DataCebo, Inc. |
| ایمیل نویسنده | info@sdv.dev |
| آدرس صفحه اصلی | https://github.com/sdv-dev/Copulas |
| آدرس اینترنتی | https://pypi.org/project/copulas/ |
| مجوز | BSL-1.1 |
<div align="center">
<br/>
<p align="center">
<i>This repository is part of <a href="https://sdv.dev">The Synthetic Data Vault Project</a>, a project from <a href="https://datacebo.com">DataCebo</a>.</i>
</p>
[](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)
[](https://pypi.python.org/pypi/copulas)
[](https://pepy.tech/project/copulas)
[](https://github.com/sdv-dev/Copulas/actions/workflows/unit.yml)
[](https://codecov.io/gh/sdv-dev/Copulas)
[](https://bit.ly/sdv-slack-invite)
<div align="left">
<br/>
<p align="center">
<a href="https://github.com/sdv-dev/Copulas">
<img align="center" width=40% src="https://github.com/sdv-dev/SDV/blob/master/docs/images/Copulas-DataCebo.png"></img>
</a>
</p>
</div>
</div>
# Overview
**Copulas** is a Python library for modeling multivariate distributions and sampling from them
using copula functions.
Given a table of numerical data, use Copulas to learn the distribution and
generate new synthetic data following the same statistical properties.
**Key Features:**
* **Model multivariate data.** Choose from a variety of univariate
distributions and copulas – including Archimedian Copulas, Gaussian Copulas and Vine Copulas.
* **Compare real and synthetic data visually** after building your model. Visualizations
are available as 1D histograms, 2D scatterplots and 3D scatterplots.
* **Access & manipulate learned parameters.** With complete access to the internals
of the model, set or tune parameters to your choosing.
# Install
Install the Copulas library using pip or conda.
```bash
pip install copulas
```
```bash
conda install -c conda-forge copulas
```
# Usage
Get started using a demo dataset. This dataset contains 3 numerical columns.
```python
from copulas.datasets import sample_trivariate_xyz
real_data = sample_trivariate_xyz()
real_data.head()
```
<img src="docs/images/copulas_sample_dataset.png" width="300">
Model the data using a copula and use it to create synthetic data.
The Copulas library offers many options including Gaussian Copula,
Vine Copulas and Archimedian Copulas.
```python
from copulas.multivariate import GaussianMultivariate
copula = GaussianMultivariate()
copula.fit(real_data)
synthetic_data = copula.sample(len(real_data))
```
Visualize the real and synthetic data side-by-side. Let's do this in 3D so see our full dataset.
```python
from copulas.visualization import compare_3d
compare_3d(real_data, synthetic_data)
```
# Tutorials
Click below to run the code yourself on a Colab Notebook and discover new features.
[](https://bit.ly/copulas-demo)
# Community & Support
Learn more about Copulas library from our [documentation](https://sdv.dev/Copulas/) site.
**Questions or issues?** Join our [Slack channel](https://bit.ly/sdv-slack-invite)
to discuss more about Copulas and synthetic data.
If you find a bug or have a feature request, you can also
[open an issue](https://github.com/sdv-dev/Copulas/issues/new/choose) on our GitHub.
**Interested in contributing to Copulas?** Read our
[Contribution Guide](https://sdv.dev/Copulas/contributing.html) to get started.
# Credits
The Copulas open source project first started at the Data to AI Lab at MIT in 2018.
Thank you to our team of contributors who have built and maintained the library over the years!
[View Contributors](https://github.com/sdv-dev/Copulas/graphs/contributors)
---
<div align="center">
<a href="https://datacebo.com"><img align="center" width=40% src="https://github.com/sdv-dev/SDV/blob/master/docs/images/DataCebo.png"></img></a>
</div>
<br/>
<br/>
[The Synthetic Data Vault Project](https://sdv.dev) was first created at MIT's [Data to AI Lab](
https://dai.lids.mit.edu/) in 2016. After 4 years of research and traction with enterprise, we
created [DataCebo](https://datacebo.com) 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](https://sdv.dev/SDV/getting_started/install.html) -- a fully
integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries
for specific needs.
# History
## v0.9.0 - 2023-04-26
This release adds support for pandas 2.0 and above. Additionally adds a functionality to find
version add-ons and renames ``covariance`` to ``correlation``.
### Maintenance
* Remove upper bound for pandas - Issue[#349](https://github.com/sdv-dev/Copulas/issues/349) by @pvk-developer
* Rename covariance to correlation - PR[#346](https://github.com/sdv-dev/Copulas/pull/346) by @frances-h
* Add functionality to find version add-on - Issue[#349](https://github.com/sdv-dev/Copulas/issues/349) by @frances-h
## v0.8.0 - 2023-01-06
This release adds support for python 3.10 and 3.11. Additionally, it drops support for python 3.6.
### Maintenance
* Support python 3.10 and above - PR[#338](https://github.com/sdv-dev/Copulas/pull/338) by @pvk-developer
* Copulas Package Maintenance Updates - Issue[#336](https://github.com/sdv-dev/Copulas/issues/336) by @pvk-developer
* Add support for python 3.10 - PR[#329](https://github.com/sdv-dev/Copulas/pull/329) by @katxiao
## v0.7.0 - 2022-05-10
This release adds `gaussian` as a fallback distribution in case the user specified one fails. It also improves the `fit` of the `beta` distribution by properly estimating the `loc` and `scale` parameters.
### General Improvements
* Add gaussian as fallback - Issue[#320](https://github.com/sdv-dev/Copulas/issues/320) by @fealho
* Improve the fit of the Beta distribution: Use the new loc and scale - Issue[#317](https://github.com/sdv-dev/Copulas/issues/317) by @pvk-developer
## v0.6.1 - 2022-02-25
This release improves the `random_state` functionality by taking in RandomState objects in addition to
random seeds.
### General Improvements
* Use random_state instead of random_seed - Issue[#113](https://github.com/sdv-dev/Copulas/issues/113) by @katxiao
## 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](https://github.com/sdv-dev/Copulas/issues/282) by @amontanez24
* Remove entry point in setup.py - Issue[#280](https://github.com/sdv-dev/Copulas/issues/280) by @amontanez24
* Update pandas dependency range - Issue[#266](https://github.com/sdv-dev/Copulas/issues/266) by @katxiao
* Fix repository language - Issue[#272](https://github.com/sdv-dev/Copulas/issues/272) by @pvk-developer
* Add pip check to CI workflows - Issue[#274](https://github.com/sdv-dev/Copulas/issues/274) by @pvk-developer
* Reorganize workflows and add codecov - PR[#267](https://github.com/sdv-dev/Copulas/pull/267) by @csala
* Constrain jinja2 versions - PR[#269](https://github.com/sdv-dev/Copulas/pull/269/files) 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](https://github.com/sdv-dev/Copulas/issues/264) by @csala
* Use scipy stats models directly without creating instances - Issue [#261](https://github.com/sdv-dev/Copulas/issues/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](https://github.com/sdv-dev/Copulas/issues/154) by @csala
### Bug Fixes
* ScipyModel subclasses fail to restore constant values when using `from_dict` - Issue [#212](https://github.com/sdv-dev/Copulas/issues/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](https://github.com/sdv-dev/Copulas/issues/200) by @k15z and @fealho
* Use sub-sampling in `select_univariate` - Issue [#183](https://github.com/sdv-dev/Copulas/issues/183) by @csala
## v0.3.3 - 2020-09-18
### General Improvements
* Use `corr` instead of `cov` in the GaussianMultivariate - Issue [#195](https://github.com/sdv-dev/Copulas/issues/195) by @rollervan
* Add arguments to GaussianKDE - Issue [#181](https://github.com/sdv-dev/Copulas/issues/181) by @rollervan
### New Features
* Log Laplace Distribution - Issue [#188](https://github.com/sdv-dev/Copulas/issues/188) by @rollervan
## v0.3.2 - 2020-08-08
### General Improvements
* Support Python 3.8 - Issue [#185](https://github.com/sdv-dev/Copulas/issues/185) by @csala
* Support scipy >1.3 - Issue [#180](https://github.com/sdv-dev/Copulas/issues/180) by @csala
### New Features
* Add Uniform Univariate - Issue [#179](https://github.com/sdv-dev/Copulas/issues/179) by @rollervan
## v0.3.1 - 2020-07-09
### General Improvements
* Raise numpy version upper bound to 2 - Issue [#178](https://github.com/sdv-dev/Copulas/issues/178) by @csala
### New Features
* Add Student T Univariate - Issue [#172](https://github.com/sdv-dev/Copulas/issues/172) by @gbonomib
### Bug Fixes
* Error in Quickstarts : Unknown projection '3d' - Issue [#174](https://github.com/sdv-dev/Copulas/issues/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](https://github.com/sdv-dev/Copulas/issues/114) by @JDTheRipperPC
## v0.2.4 - 2019-12-23
### New Features
* Allow creating copula classes directly - Issue [#117](https://github.com/sdv-dev/Copulas/issues/117) by @csala
### General Improvements
* Remove `select_copula` from `Bivariate` - Issue [#118](https://github.com/sdv-dev/Copulas/issues/118) by @csala
* Rename TruncNorm to TruncGaussian and make it non standard - Issue [#102](https://github.com/sdv-dev/Copulas/issues/102) by @csala @JDTheRipperPC
### Bugs fixed
* Error on Frank and Gumble sampling - Issue [#112](https://github.com/sdv-dev/Copulas/issues/112) by @csala
## v0.2.3 - 2019-09-17
### New Features
* Add support to Python 3.7 - Issue [#53](https://github.com/sdv-dev/Copulas/issues/53) by @JDTheRipperPC
### General Improvements
* Document RELEASE workflow - Issue [#105](https://github.com/sdv-dev/Copulas/issues/105) by @JDTheRipperPC
* Improve serialization of univariate distributions - Issue [#99](https://github.com/sdv-dev/Copulas/issues/99) by @ManuelAlvarezC and @JDTheRipperPC
### Bugs fixed
* The method 'select_copula' of Bivariate return wrong CopulaType - Issue [#101](https://github.com/sdv-dev/Copulas/issues/101) by @JDTheRipperPC
## v0.2.2 - 2019-07-31
### New Features
* `truncnorm` distribution and a generic wrapper for `scipy.rv_continous` distributions - Issue [#27](https://github.com/sdv-dev/Copulas/issues/27) by @amontanez, @csala and @ManuelAlvarezC
* `Independence` bivariate copulas - Issue [#46](https://github.com/sdv-dev/Copulas/issues/46) by @aliciasun, @csala and @ManuelAlvarezC
* Option to select seed on random number generator - Issue [#63](https://github.com/sdv-dev/Copulas/issues/63) by @echo66 and @ManuelAlvarezC
* Option on Vine copulas to select number of rows to sample - Issue [#77](https://github.com/sdv-dev/Copulas/issues/77) by @ManuelAlvarezC
* Make copulas accept both scalars and arrays as arguments - Issues [#85](https://github.com/sdv-dev/Copulas/issues/85) and [#90](https://github.com/sdv-dev/Copulas/issues/90) by @ManuelAlvarezC
### General Improvements
* Ability to properly handle constant data - Issues [#57](https://github.com/sdv-dev/Copulas/issues/57) and [#82](https://github.com/sdv-dev/Copulas/issues/82) by @csala and @ManuelAlvarezC
* Tests for analytics properties of copulas - Issue [#61](https://github.com/sdv-dev/Copulas/issues/61) by @ManuelAlvarezC
* Improved documentation - Issue [#96](https://github.com/sdv-dev/Copulas/issues/96) by @ManuelAlvarezC
### Bugs fixed
* Fix bug on Vine copulas, that made it crash during the bivariate copula selection - Issue [#64](https://github.com/sdv-dev/Copulas/issues/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.
نیازمندی
| مقدار | نام |
|---|---|
| <2,>=1.20.0 | numpy |
| >=1.1.3 | pandas |
| <2,>=1.5.4 | scipy |
| <4,>=3.6.0 | matplotlib |
| <2,>=1.23.3 | numpy |
| <2,>=1.9.2 | scipy |
| >=1.3.4 | pandas |
| >=1.5.0 | pandas |
| <4,>=3.4.0 | matplotlib |
| <7,>=6.2.5 | pytest |
| <3,>=2.6.0 | pytest-cov |
| <10,>=9.0.0 | pytest-rerunfailures |
| <0.5,>=0.4.3 | rundoc |
| >=9.0.1 | pip |
| <0.6,>=0.5.3 | bumpversion |
| <0.11,>=0.8.3 | watchdog |
| <0.3,>=0.2.0 | m2r |
| <0.7,>=0.5.0 | nbsphinx |
| <3,>=1.7.1 | Sphinx |
| <0.5,>=0.2.4 | sphinx-rtd-theme |
| <3,>=2 | Jinja2 |
| <4,>=3.7.7 | flake8 |
| <5,>=4.3.4 | isort |
| <4.1,>=4.0.0 | flake8-debugger |
| <0.4,>=0.3 | flake8-mock |
| <1.3,>=1.2.0 | flake8-mutable |
| <1.2,>=1.1.1 | flake8-fixme |
| <0.13,>=0.12.1 | pep8-naming |
| <0.12,>=0.11.0 | dlint |
| <2,>=1.5.0 | flake8-docstrings |
| <6.2,>=6.1.1 | pydocstyle |
| <2,>=1.5.0 | flake8-pytest-style |
| <3.7,>=3.6.1 | flake8-comprehensions |
| <4.1,>=4.0.0 | flake8-print |
| <0.1,>=0.0.9 | flake8-expression-complexity |
| <0.1,>=0.0.18 | flake8-multiline-containers |
| <0.3,>=0.2.2 | pandas-vet |
| <1.6,>=1.5.3 | flake8-builtins |
| <1.2,>=1.1.0 | flake8-eradicate |
| <4,>=3.3.0 | flake8-quotes |
| <0.1,>=0.0.4 | flake8-variables-names |
| <0.1,>=0.0.3 | flake8-sfs |
| <2,>=1.0 | flake8-absolute-import |
| <2,>=1.1 | autoflake |
| <1.6,>=1.4.3 | autopep8 |
| <4,>=1.10.0 | twine |
| >=0.30.0 | wheel |
| <6,>=4.5.1 | coverage |
| <4,>=2.9.1 | tox |
| - | invoke |
| <0.9,>=0.8.0 | doc8 |
| <1.26,>=1.20 | urllib3 |
| <0.9,>=0.8.3 | tabulate |
| <1.10,>=1.7.47 | boto3 |
| <0.15,>=0.10 | docutils |
| <=2.0.1 | markupsafe |
| <1.2,>=0.24 | scikit-learn |
| <2,>=1.0.0 | jupyter |
| <7,>=6.2.5 | pytest |
| <3,>=2.6.0 | pytest-cov |
| <10,>=9.0.0 | pytest-rerunfailures |
| <0.5,>=0.4.3 | rundoc |
| <=2.0.1 | markupsafe |
| <1.2,>=0.24 | scikit-learn |
| <2,>=1.0.0 | jupyter |
| <=2.0.1 | markupsafe |
| <1.2,>=0.24 | scikit-learn |
| <2,>=1.0.0 | jupyter |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7,<3.12 | Python |
نحوه نصب
نصب پکیج whl copulas-0.9.0.dev0:
pip install copulas-0.9.0.dev0.whl
نصب پکیج tar.gz copulas-0.9.0.dev0:
pip install copulas-0.9.0.dev0.tar.gz