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ctgan-0.7.2.dev0
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مشاهده بیشترتوضیحات
Create tabular synthetic data using a conditional GAN
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ctgan-0.7.2.dev0 |
| نام | ctgan |
| نسخه کتابخانه | 0.7.2.dev0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | DataCebo, Inc. |
| ایمیل نویسنده | info@sdv.dev |
| آدرس صفحه اصلی | https://github.com/sdv-dev/CTGAN |
| آدرس اینترنتی | https://pypi.org/project/ctgan/ |
| 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/ctgan)
[](https://github.com/sdv-dev/CTGAN/actions/workflows/unit.yml)
[](https://pepy.tech/project/ctgan)
[](https://codecov.io/gh/sdv-dev/CTGAN)
<div align="left">
<br/>
<p align="center">
<a href="https://github.com/sdv-dev/CTGAN">
<img align="center" width=40% src="https://github.com/sdv-dev/SDV/blob/master/docs/images/CTGAN-DataCebo.png"></img>
</a>
</p>
</div>
</div>
# Overview
CTGAN is a collection of Deep Learning based synthetic data generators for single table data, which are able to learn from real data and generate synthetic data with high fidelity.
| Important Links | |
| --------------------------------------------- | -------------------------------------------------------------------- |
| :computer: **[Website]** | Check out the SDV Website for more information about our overall synthetic data ecosystem.|
| :orange_book: **[Blog]** | A deeper look at open source, synthetic data creation and evaluation.|
| :book: **[Documentation]** | Quickstarts, User and Development Guides, and API Reference. |
| :octocat: **[Repository]** | The link to the Github Repository of this library. |
| :keyboard: **[Development Status]** | This software is in its Pre-Alpha stage. |
| [![][Slack Logo] **Community**][Community] | Join our Slack Workspace for announcements and discussions. |
[Website]: https://sdv.dev
[Blog]: https://datacebo.com/blog
[Documentation]: https://bit.ly/sdv-docs
[Repository]: https://github.com/sdv-dev/CTGAN
[License]: https://github.com/sdv-dev/CTGAN/blob/master/LICENSE
[Development Status]: https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha
[Slack Logo]: https://github.com/sdv-dev/SDV/blob/master/docs/images/slack.png
[Community]: https://bit.ly/sdv-slack-invite
Currently, this library implements the **CTGAN** and **TVAE** models described in the [Modeling Tabular data using Conditional GAN](https://arxiv.org/abs/1907.00503) paper, presented at the 2019 NeurIPS conference.
# Install
## Use CTGAN through the SDV library
:warning: If you're just getting started with synthetic data, we recommend installing the SDV library which provides user-friendly APIs for accessing CTGAN. :warning:
The SDV library provides wrappers for preprocessing your data as well as additional usability features like constraints. See the [SDV documentation](https://bit.ly/sdv-docs) to get started.
## Use the CTGAN standalone library
Alternatively, you can also install and use **CTGAN** directly, as a standalone library:
**Using `pip`:**
```bash
pip install ctgan
```
**Using `conda`:**
```bash
conda install -c pytorch -c conda-forge ctgan
```
When using the CTGAN library directly, you may need to manually preprocess your data into the correct format, for example:
* Continuous data must be represented as floats
* Discrete data must be represented as ints or strings
* The data should not contain any missing values
# Usage Example
In this example we load the [Adult Census Dataset](https://archive.ics.uci.edu/ml/datasets/adult)* which is a built-in demo dataset. We use CTGAN to learn from the real data and then generate some synthetic data.
```python3
from ctgan import CTGAN
from ctgan import load_demo
real_data = load_demo()
# Names of the columns that are discrete
discrete_columns = [
'workclass',
'education',
'marital-status',
'occupation',
'relationship',
'race',
'sex',
'native-country',
'income'
]
ctgan = CTGAN(epochs=10)
ctgan.fit(real_data, discrete_columns)
# Create synthetic data
synthetic_data = ctgan.sample(1000)
```
*For more information about the dataset see:
Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml].
Irvine, CA: University of California, School of Information and Computer Science.
# Join our community
Join our [Slack channel](https://bit.ly/sdv-slack-invite) to discuss more about CTGAN and synthetic data. If you find a bug or have a feature request, you can also [open an issue](https://github.com/sdv-dev/CTGAN/issues) on our GitHub.
**Interested in contributing to CTGAN?** Read our [Contribution Guide](CONTRIBUTING.rst) to get started.
# Citing CTGAN
If you use CTGAN, please cite the following work:
*Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni.* **Modeling Tabular data using Conditional GAN**. NeurIPS, 2019.
```LaTeX
@inproceedings{ctgan,
title={Modeling Tabular data using Conditional GAN},
author={Xu, Lei and Skoularidou, Maria and Cuesta-Infante, Alfredo and Veeramachaneni, Kalyan},
booktitle={Advances in Neural Information Processing Systems},
year={2019}
}
```
# Related Projects
Please note that these projects are external to the SDV Ecosystem. They are not affiliated with or maintained by DataCebo.
* **R Interface for CTGAN**: A wrapper around **CTGAN** that brings the functionalities to **R** users.
More details can be found in the corresponding repository: https://github.com/kasaai/ctgan
* **CTGAN Server CLI**: A package to easily deploy CTGAN onto a remote server. Created by Timothy Pillow @oregonpillow at: https://github.com/oregonpillow/ctgan-server-cli
---
<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
## v0.7.0 - 2023-01-20
This release adds support for python 3.10 and drops support for python 3.6. It also fixes a couple of the most common warnings that were surfacing.
### New Features
* Support Python 3.10 and 3.11 - Issue [#259](https://github.com/sdv-dev/CTGAN/issues/259) by @pvk-developer
### Bugs Fixed
* Fix SettingWithCopyWarning (may be leading to a numerical calculation bug) - Issue [#215](https://github.com/sdv-dev/CTGAN/issues/215) by @amontanez24
* FutureWarning in data_transformer with pandas 1.5.0 - Issue [#246](https://github.com/sdv-dev/CTGAN/issues/246) by @amontanez24
### Maintenance
* CTGAN Package Maintenance Updates - Issue [#257](https://github.com/sdv-dev/CTGAN/issues/257) by @amontanez24
## v0.6.0 - 2022-10-07
This release renames the models in CTGAN. `CTGANSynthesizer` is now called `CTGAN` and `TVAESynthesizer` is now called `TVAE`.
### New Features
* Rename synthesizers - Issue [#243](https://github.com/sdv-dev/CTGAN/issues/243) by @amontanez24
## v0.5.2 - 2022-08-18
This release updates CTGAN to use the latest version of RDT. It also includes performance and robustness updates to the data transformer.
### Issues closed
* Bump rdt version - Issue [#242](https://github.com/sdv-dev/CTGAN/issues/242) by @katxiao
* Single thread data transform is slow for huge table - Issue [#151](https://github.com/sdv-dev/CTGAN/issues/151) by @mfhbree
* Fix RDT api - Issue [#232](https://github.com/sdv-dev/CTGAN/issues/232) by @pvk-developer
* Update macos to use latest version. - Issue [#237](https://github.com/sdv-dev/CTGAN/issues/237) by @pvk-developer
* Update the RDT version to 1.0 - Issue [#224](https://github.com/sdv-dev/CTGAN/issues/224) by @pvk-developer
* Update slack invite link. - Issue [#222](https://github.com/sdv-dev/CTGAN/issues/222) by @pvk-developer
* robustness fix, when data have less rows than the default number of cl… - Issue [#211](https://github.com/sdv-dev/CTGAN/issues/211) by @Deathn0t
## v0.5.1 - 2022-02-25
This release fixes a bug with the decoder instantiation, and also allows users to set a random state for the model
fitting and sampling.
### Issues closed
* Update self.decoder with correct variable name - Issue [#203](https://github.com/sdv-dev/CTGAN/issues/203) by @tejuafonja
* Add random state - Issue [#204](https://github.com/sdv-dev/CTGAN/issues/204) by @katxiao
## v0.5.0 - 2021-11-18
This release adds support for Python 3.9 and updates dependencies to ensure compatibility with the
rest of the SDV ecosystem, and upgrades to the latests [RDT](https://github.com/sdv-dev/RDT/releases/tag/v0.6.1)
release.
### Issues closed
* Add support for Python 3.9 - Issue [#177](https://github.com/sdv-dev/CTGAN/issues/177) by @pvk-developer
* Add pip check to CI workflows - Issue [#174](https://github.com/sdv-dev/CTGAN/issues/174) by @pvk-developer
* Typo in `CTGAN` code - Issue [#158](https://github.com/sdv-dev/CTGAN/issues/158) by @ori-katz100 and @fealho
## v0.4.3 - 2021-07-12
Dependency upgrades to ensure compatibility with the rest of the SDV ecosystem.
## v0.4.2 - 2021-04-27
In this release, the way in which the loss function of the TVAE model was computed has been fixed.
In addition, the default value of the `discriminator_decay` has been changed to a more optimal
value. Also some improvements to the tests were added.
### Issues closed
* `TVAE`: loss function - Issue [#143](https://github.com/sdv-dev/CTGAN/issues/143) by @fealho and @DingfanChen
* Set `discriminator_decay` to `1e-6` - Pull request [#145](https://github.com/sdv-dev/CTGAN/pull/145/) by @fealho
* Adds unit tests - Pull requests [#140](https://github.com/sdv-dev/CTGAN/pull/140) by @fealho
## v0.4.1 - 2021-03-30
This release exposes all the hyperparameters which the user may find useful for both `CTGAN`
and `TVAE`. Also `TVAE` can now be fitted on datasets that are shorter than the batch
size and drops the last batch only if the data size is not divisible by the batch size.
### Issues closed
* `TVAE`: Adapt `batch_size` to data size - Issue [#135](https://github.com/sdv-dev/CTGAN/issues/135) by @fealho and @csala
* `ValueError` from `validate_discre_columns` with `uniqueCombinationConstraint` - Issue [133](https://github.com/sdv-dev/CTGAN/issues/133) by @fealho and @MLjungg
## v0.4.0 - 2021-02-24
Maintenance relese to upgrade dependencies to ensure compatibility with the rest
of the SDV libraries.
Also add a validation on the CTGAN `condition_column` and `condition_value` inputs.
### Improvements
* Validate condition_column and condition_value - Issue [#124](https://github.com/sdv-dev/CTGAN/issues/124) by @fealho
## v0.3.1 - 2021-01-27
### Improvements
* Check discrete_columns valid before fitting - [Issue #35](https://github.com/sdv-dev/CTGAN/issues/35) by @fealho
## Bugs fixed
* ValueError: max() arg is an empty sequence - [Issue #115](https://github.com/sdv-dev/CTGAN/issues/115) by @fealho
## v0.3.0 - 2020-12-18
In this release we add a new TVAE model which was presented in the original CTGAN paper.
It also exposes more hyperparameters and moves epochs and log_frequency from fit to the constructor.
A new verbose argument has been added to optionally disable unnecessary printing, and a new hyperparameter
called `discriminator_steps` has been added to CTGAN to control the number of optimization steps performed
in the discriminator for each generator epoch.
The code has also been reorganized and cleaned up for better readability and interpretability.
Special thanks to @Baukebrenninkmeijer @fealho @leix28 @csala for the contributions!
### Improvements
* Add TVAE - [Issue #111](https://github.com/sdv-dev/CTGAN/issues/111) by @fealho
* Move `log_frequency` to `__init__` - [Issue #102](https://github.com/sdv-dev/CTGAN/issues/102) by @fealho
* Add discriminator steps hyperparameter - [Issue #101](https://github.com/sdv-dev/CTGAN/issues/101) by @Baukebrenninkmeijer
* Code cleanup / Expose hyperparameters - [Issue #59](https://github.com/sdv-dev/CTGAN/issues/59) by @fealho and @leix28
* Publish to conda repo - [Issue #54](https://github.com/sdv-dev/CTGAN/issues/54) by @fealho
### Bugs fixed
* Fixed NaN != NaN counting bug. - [Issue #100](https://github.com/sdv-dev/CTGAN/issues/100) by @fealho
* Update dependencies and testing - [Issue #90](https://github.com/sdv-dev/CTGAN/issues/90) by @csala
## v0.2.2 - 2020-11-13
In this release we introduce several minor improvements to make CTGAN more versatile and
propertly support new types of data, such as categorical NaN values, as well as conditional
sampling and features to save and load models.
Additionally, the dependency ranges and python versions have been updated to support up
to date runtimes.
Many thanks @fealho @leix28 @csala @oregonpillow and @lurosenb for working on making this release possible!
### Improvements
* Drop Python 3.5 support - [Issue #79](https://github.com/sdv-dev/CTGAN/issues/79) by @fealho
* Support NaN values in categorical variables - [Issue #78](https://github.com/sdv-dev/CTGAN/issues/78) by @fealho
* Sample synthetic data conditioning on a discrete column - [Issue #69](https://github.com/sdv-dev/CTGAN/issues/69) by @leix28
* Support recent versions of pandas - [Issue #57](https://github.com/sdv-dev/CTGAN/issues/57) by @csala
* Easy solution for restoring original dtypes - [Issue #26](https://github.com/sdv-dev/CTGAN/issues/26) by @oregonpillow
### Bugs fixed
* Loss to nan - [Issue #73](https://github.com/sdv-dev/CTGAN/issues/73) by @fealho
* Swapped the sklearn utils testing import statement - [Issue #53](https://github.com/sdv-dev/CTGAN/issues/53) by @lurosenb
## v0.2.1 - 2020-01-27
Minor version including changes to ensure the logs are properly printed and
the option to disable the log transformation to the discrete column frequencies.
Special thanks to @kevinykuo for the contributions!
### Issues Resolved:
* Option to sample from true data frequency instead of logged frequency - [Issue #16](https://github.com/sdv-dev/CTGAN/issues/16) by @kevinykuo
* Flush stdout buffer for epoch updates - [Issue #14](https://github.com/sdv-dev/CTGAN/issues/14) by @kevinykuo
## v0.2.0 - 2019-12-18
Reorganization of the project structure with a new Python API, new Command Line Interface
and increased data format support.
### Issues Resolved:
* Reorganize the project structure - [Issue #10](https://github.com/sdv-dev/CTGAN/issues/10) by @csala
* Move epochs to the fit method - [Issue #5](https://github.com/sdv-dev/CTGAN/issues/5) by @csala
## v0.1.0 - 2019-11-07
First Release - NeurIPS 2019 Version.
نیازمندی
| مقدار | نام |
|---|---|
| <22,>=20 | packaging |
| <2.0,>=1.3.0 | rdt |
| <2,>=1.20.0 | numpy |
| <2,>=1.1.3 | pandas |
| <2,>=1.8.0 | torch |
| <2,>=1.23.3 | numpy |
| <2,>=1.3.4 | pandas |
| <2,>=1.1.3 | scikit-learn |
| <2,>=1.11.0 | torch |
| >=9.0.1 | pip |
| <0.6,>=0.5.3 | bumpversion |
| <0.11,>=0.8.3 | watchdog |
| <4,>=3.7.7 | flake8 |
| <5,>=4.3.4 | isort |
| <0.12,>=0.11.0 | dlint |
| <4.1,>=4.0.0 | flake8-debugger |
| <0.4,>=0.3 | flake8-mock |
| <1.3,>=1.2.0 | flake8-mutable |
| <2,>=1.0 | flake8-absolute-import |
| <0.1,>=0.0.18 | flake8-multiline-containers |
| <4.1,>=4.0.0 | flake8-print |
| <4,>=3.3.0 | flake8-quotes |
| <1.2,>=1.1.1 | flake8-fixme |
| <0.1,>=0.0.9 | flake8-expression-complexity |
| <1.2,>=1.1.0 | flake8-eradicate |
| <1.6,>=1.5.3 | flake8-builtins |
| <0.1,>=0.0.4 | flake8-variables-names |
| <0.3,>=0.2.2 | pandas-vet |
| <3.7,>=3.6.1 | flake8-comprehensions |
| <2,>=1.5.0 | flake8-docstrings |
| <0.1,>=0.0.3 | flake8-sfs |
| <2,>=1.5.0 | flake8-pytest-style |
| <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 |
| >=3.4.2 | pytest |
| <10,>=9.1.1 | pytest-rerunfailures |
| >=2.6.0 | pytest-cov |
| <0.5,>=0.4.3 | rundoc |
| >=3.4.2 | pytest |
| <10,>=9.1.1 | pytest-rerunfailures |
| >=2.6.0 | pytest-cov |
| <0.5,>=0.4.3 | rundoc |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7,<3.11 | Python |
نحوه نصب
نصب پکیج whl ctgan-0.7.2.dev0:
pip install ctgan-0.7.2.dev0.whl
نصب پکیج tar.gz ctgan-0.7.2.dev0:
pip install ctgan-0.7.2.dev0.tar.gz