CTGAN | Conditional GAN for generating synthetic tabular data | Machine Learning library

by sdv-dev Python Version: 0.10.1.dev0 License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | CTGAN Summary

CTGAN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. CTGAN has no vulnerabilities, it has build file available and it has medium support. However CTGAN has 1 bugs and it has a Non-SPDX License. You can install using 'pip install CTGAN' or download it from GitHub, PyPI.

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 clones with high fidelity.

Support

Quality

Security

License

Reuse

Support

CTGAN has a medium active ecosystem.

It has 899 star(s) with 234 fork(s). There are 21 watchers for this library.

There were 8 major release(s) in the last 12 months.

There are 33 open issues and 138 have been closed. On average issues are closed in 138 days. There are 8 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of CTGAN is 0.10.1.dev0

Quality

CTGAN has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 9 code smells.

Security

CTGAN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CTGAN code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

CTGAN has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

CTGAN releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

CTGAN saves you 489 person hours of effort in developing the same functionality from scratch.

It has 1151 lines of code, 66 functions and 15 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed CTGAN and discovered the below as its top functions. This is intended to give you an instant insight into CTGAN implemented functionality, and help decide if they suit your requirements.

Fit the transformer model
Gaussian softmax function
Apply activation function
Compute conditional loss
Sample from the model
Generate condition matrix
Sample the original condition
Convert a value to a discrete column id
Install minimum dependencies
Install minimum requirement
Validate the python version
Write data to a tsv file
Saves the device to disk
Run lint
Read a csv file
Read a TSV file
Parse command line arguments

Get all kandi verified functions for this library.

CTGAN Key Features

No Key Features are available at this moment for CTGAN.

CTGAN Examples and Code Snippets

No Code Snippets are available at this moment for CTGAN.

Community Discussions

Trending Discussions on CTGAN

Segmentation fault (core dumped) with optuna

Usage of LSTM/GRU and Flatten throws dimensional incompatibility error

QUESTION

Segmentation fault (core dumped) with optuna

Asked 2021-Aug-31 at 06:02

I know this question has already been asked multiple times, but with the answers of the other threads, I couldn't solve my problem. I'm running a python script in the command line with python3 CTGAN_noscale.py --database_name CTGAN_noshift and receive the following error (with faulthandler):

...

ANSWER

Answered 2021-Aug-31 at 06:02

I solved the problem by rebuilding a new image and derived a container from this image. In this container somehow the error didn't appear anymore.

Source https://stackoverflow.com/questions/68950349

QUESTION

Usage of LSTM/GRU and Flatten throws dimensional incompatibility error

Asked 2020-Sep-15 at 20:26

I want to make use of a promising NN I found at towardsdatascience for my case study.

The data shapes I have are:

...

ANSWER

Answered 2020-Aug-17 at 18:14

I cannot reproduce your error, check if the following code works for you:

Source https://stackoverflow.com/questions/63455257

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install CTGAN

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

Support

Please have a look at the Contributing Guide to see how you can contribute to the project.If you have any doubts, feature requests or detect an error, please open an issue on github or join our Slack Workspace.Also, do not forget to check the project documentation site!

Find more information at: