Metadata-Version: 2.4
Name: fg-data-synthetic
Version: 2.1.0
Summary: Synthetic data generation methods with different synthetization methods.
Home-page: https://github.com/Data-Centric-AI-Community/fg-data-synthetic
Author: YData
Author-email: community@ydata.ai
License: https://github.com/Data-Centric-AI-Community/fg-data-synthetic/blob/master/LICENSE
Keywords: data science ydata
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
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Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: Implementation
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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License-File: LICENSE
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<p align="center"><img width="300" src="https://assets.ydata.ai/oss/ydata-synthetic_black.png" alt="Data Synthetic Logo"></p>

Join us on [![Discord](https://img.shields.io/badge/Discord-7289DA?style=for-the-badge&logo=discord&logoColor=white)](https://tiny.ydata.ai/dcai-ydata-synthetic)

# Data Synthetic
A package to generate synthetic tabular and time-series data leveraging the state of the art generative models.

## 🎊 The exciting features:
> These are must try features when it comes to synthetic data generation:
  > - A new streamlit app that delivers the synthetic data generation experience with a UI interface. A low code experience for the quick generation of synthetic data
  > - A new fast synthetic data generation model based on Gaussian Mixture. So you can quickstart in the world of synthetic data generation without the need for a GPU.
  > - A conditional architecture for tabular data: CTGAN, which will make the process of synthetic data generation easier and with higher quality!


## Migration Guide
 
### 1. Uninstall the old package
 
```bash
pip uninstall ydata-synthetic
```
 
### 2. Install the new package
 
```bash
pip install fg-data-synthetic
```
 
### 3. Update your imports
 
Find and replace all occurrences of the old import in your codebase:
 
```python
# Before
import ydata_synthetic
from ydata_synthetic.synthesizers.regular import RegularSynthesizer

# After
import data_synthetic
from data_synthetic.synthesizers.regular import RegularSynthesizer
```
 
You can use this one-liner to find all affected files:
 
```bash
grep -r "ydata_synthetic" . --include="*.py"
  
## Synthetic data
### What is synthetic data?
Synthetic data is artificially generated data that is not collected from real world events. It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy.

### Why Synthetic Data?
Synthetic data can be used for many applications:
  - Privacy compliance for data-sharing and Machine Learning development
  - Remove bias
  - Balance datasets
  - Augment datasets

> **Looking for an end-to-end solution to Synthetic Data Generation?**<br>
> [YData Fabric](https://ydata.ai/products/synthetic_data) enables the generation of high-quality datasets within a full UI experience, from data preparation to synthetic data generation and evaluation.<br>
> Check out the [Community Version](https://ydata.ai/ydata-fabric-free-trial).


# fg-data-synthetic
This repository contains material related with architectures and models for synthetic data, from Generative Adversarial Networks (GANs) to Gaussian Mixtures.
The repo includes a full ecosystem for synthetic data generation, that includes different models for the generation of synthetic structure data and time-series.
All the Deep Learning models are implemented leveraging Tensorflow 2.0.
Several example Jupyter Notebooks and Python scripts are included, to show how to use the different architectures.

Are you ready to learn more about synthetic data and the bext-practices for synthetic data generation?

## Quickstart
The source code is currently hosted on GitHub at: https://github.com/Data-Centric-AI-Community/fg-data-synthetic

Binary installers for the latest released version are available at the [Python Package Index (PyPI).](https://pypi.org/project/fg-data-synthetic/)
```bash
pip install fg-data-synthetic
```

### The UI guide for synthetic data generation

YData synthetic has now a UI interface to guide you through the steps and inputs to generate structure tabular data.
The streamlit app is available form *v1.0.0* onwards, and supports the following flows:
- Train a synthesizer model
- Generate & profile synthetic data samples

#### Installation

```commandline
pip install fg-data-synthetic[streamlit]
```
#### Quickstart
Use the code snippet below in a python file (Jupyter Notebooks are not supported):
```python
from data_synthetic import streamlit_app

streamlit_app.run()
```

Or use the file streamlit_app.py that can be found in the [examples folder](https://github.com/Data-Centric-AI-Community/fg-data-synthetic/tree/master/examples/streamlit_app.py).

```bash
python -m streamlit_app
```

The below models are supported:
  - CGAN
  - WGAN
  - WGANGP
  - DRAGAN
  - CRAMER
  - CTGAN

[![Watch the video](assets/streamlit_app.png)](https://youtu.be/ep0PhwsFx0A)

### Examples
Here you can find usage examples of the package and models to synthesize tabular data.
  - Fast tabular data synthesis on adult census income dataset [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Data-Centric-AI-Community/fg-data-synthetic/blob/master/examples/regular/models/Fast_Adult_Census_Income_Data.ipynb)
  - Tabular synthetic data generation with CTGAN on adult census income dataset [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Data-Centric-AI-Community/fg-data-synthetic/blob/master/examples/regular/models/CTGAN_Adult_Census_Income_Data.ipynb)
  - Time Series synthetic data generation with TimeGAN on stock dataset [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Data-Centric-AI-Community/fg-data-synthetic/blob/master/examples/timeseries/TimeGAN_Synthetic_stock_data.ipynb)
  - Time Series synthetic data generation with DoppelGANger on FCC MBA dataset [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Data-Centric-AI-Community/fg-data-synthetic/blob/master/examples/timeseries/DoppelGANger_FCC_MBA_Dataset.ipynb)
  - More examples are continuously added and can be found in `/examples` directory.

### Datasets for you to experiment
Here are some example datasets for you to try with the synthesizers:
#### Tabular datasets
- [Adult Census Income](https://www.kaggle.com/datasets/uciml/adult-census-income)
- [Credit card fraud](https://www.kaggle.com/mlg-ulb/creditcardfraud)
- [Cardiovascular Disease dataset](https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset)

#### Sequential datasets
- [Stock data](https://github.com/Data-Centric-AI-Community/fg-data-synthetic/tree/master/data)
- [FCC MBA data](https://github.com/Data-Centric-AI-Community/fg-data-synthetic/tree/master/data)

## Project Resources

In this repository you can find the several GAN architectures that are used to create synthesizers:

### Tabular data
  - [GAN](https://arxiv.org/abs/1406.2661)
  - [CGAN (Conditional GAN)](https://arxiv.org/abs/1411.1784)
  - [WGAN (Wasserstein GAN)](https://arxiv.org/abs/1701.07875)
  - [WGAN-GP (Wassertein GAN with Gradient Penalty)](https://arxiv.org/abs/1704.00028)
  - [DRAGAN (On Convergence and stability of GANS)](https://arxiv.org/pdf/1705.07215.pdf)
  - [Cramer GAN (The Cramer Distance as a Solution to Biased Wasserstein Gradients)](https://arxiv.org/abs/1705.10743)
  - [CWGAN-GP (Conditional Wassertein GAN with Gradient Penalty)](https://cameronfabbri.github.io/papers/conditionalWGAN.pdf)
  - [CTGAN (Conditional Tabular GAN)](https://arxiv.org/pdf/1907.00503.pdf)
  - [Gaussian Mixture](https://towardsdatascience.com/gaussian-mixture-models-explained-6986aaf5a95)

### Sequential data
  - [TimeGAN](https://papers.nips.cc/paper/2019/file/c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf)
  - [DoppelGANger](https://dl.acm.org/doi/pdf/10.1145/3419394.3423643)

## Contributing
We are open to collaboration! If you want to start contributing you only need to:
  1. Search for an issue in which you would like to work. Issues for newcomers are labeled with good first issue.
  2. Create a PR solving the issue.
  3. We would review every PRs and either accept or ask for revisions.

## Support
For support in using this library, please join our Discord server. Our Discord community is very friendly and great about quickly answering questions about the use and development of the library. [Click here to join our Discord community!](https://tiny.ydata.ai/dcai-ydata-synthetic)

## FAQs
Have a question? Check out the [Frequently Asked Questions](https://ydata.ai/resources/10-most-asked-questions-on-ydata-synthetic) about `fg-data-synthetic`. If you feel something is missing, feel free to [book a beary informal chat with us](https://meetings.hubspot.com/fabiana-clemente).

## License
[MIT License](https://github.com/Data-Centric-AI-Community/fg-data-synthetic/blob/master/LICENSE)
