Metadata-Version: 2.1
Name: dp-cgans
Version: 0.0.4
Summary: A library to generate synthetic tabular data using Conditional Generative Adversary Networks (GANs) combined with Differential Privacy techniques.
Home-page: https://github.com/sunchang0124/dp_cgans
License: MIT
Keywords: CGAN,synthetic data,DP,Differential Privacy,GAN
Author: Chang Sun
Author-email: sunchang0124@gmail.com
Requires-Python: >=3.8,<3.10
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: copulas
Requires-Dist: faker
Requires-Dist: graphviz
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pyreadstat
Requires-Dist: rdt (==0.6.4)
Requires-Dist: scipy
Requires-Dist: sdv (==0.14.0)
Requires-Dist: sklearn
Requires-Dist: torch
Requires-Dist: typer
Requires-Dist: wheel
Project-URL: Bug Tracker, https://github.com/sunchang0124/dp_cgans/issues
Project-URL: Repository, https://github.com/sunchang0124/dp_cgans
Description-Content-Type: text/markdown

# 👯 DP-CGANS (Differentially Private - Conditional Generative Adversarial NetworkS)

[![PyPi Shield](https://img.shields.io/pypi/v/dp-cgans)](https://pypi.org/project/dp-cgans/) [![Py versions](https://img.shields.io/pypi/pyversions/dp-cgans)](https://pypi.org/project/dp-cgans/) [![Test package](https://github.com/sunchang0124/dp_cgans/actions/workflows/test.yml/badge.svg)](https://github.com/sunchang0124/dp_cgans/actions/workflows/test.yml) [![Publish package](https://github.com/sunchang0124/dp_cgans/actions/workflows/publish.yml/badge.svg)](https://github.com/sunchang0124/dp_cgans/actions/workflows/publish.yml)



<!-- [![Development Status](https://img.shields.io/badge/Development%20Status-2%20--%20Pre--Alpha-yellow)](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha) -->
<!-- [![PyPi Shield](https://img.shields.io/badge/pypi-v0.0.2-blue)](https://pypi.org/project/dp-cgans/) -->
<!-- [![Tests](https://github.com/sdv-dev/SDV/workflows/Run%20Tests/badge.svg)](https://github.com/sdv-dev/SDV/actions?query=workflow%3A%22Run+Tests%22+branch%3Amaster) -->

**Abstract**: This repository presents a Conditional Generative Adversary Networks (GANs) on tabular data (and RDF data) combining with Differential Privacy techniques. Our pre-print publication: [Improving Correlation Capture in Generating Imbalanced Data using Differentially Private Conditional GANs](https://arxiv.org/abs/2206.13787).

**Author**: Chang Sun, Institute of Data Science, Maastricht University
**Start date**: Nov-2021
**Status**: Under development

**Note**: "Standing on the shoulders of giants". This repository is inspired by the excellent work of [CTGAN](https://github.com/sdv-dev/CTGAN) from [Synthetic Data Vault (SDV)](https://github.com/sdv-dev/SDV), [Tensorflow Privacy](https://github.com/tensorflow/privacy), and [RdfPdans](https://github.com/cadmiumkitty/rdfpandas). Highly appreciate they shared the ideas and implementations, made code publicly available, well-written documentation. More related work can be found in the References below.  

This package is extended from SDV (https://github.com/sdv-dev/SDV), CTGAN (https://github.com/sdv-dev/CTGAN), and Differential Privacy in GANs (https://github.com/civisanalytics/dpwgan). The author modified the conditional matrix and cost functions to emphasize on the relations between variables. The main changes are in ctgan/synthesizers/ctgan.py ../data_sampler.py ../data_transformer.py


## 📥️ Installation

You will need Python >=3.8+ and <3.10

```shell
pip install dp-cgans
```

## 🪄 Usage

### ⌨️ Use as a command-line interface

You can easily generate synthetic data for a file using your terminal after installing `dp-cgans` with pip.

To quickly run our example, you can download the [example data](https://raw.githubusercontent.com/sunchang0124/dp_cgans/main/resources/example_tabular_data_UCIAdult.csv):

```bash
wget https://raw.githubusercontent.com/sunchang0124/dp_cgans/main/resources/example_tabular_data_UCIAdult.csv
```

Then run `dp-cgans`:

```bash
dp-cgans gen example_tabular_data_UCIAdult.csv --epochs 2 --output out.csv --gen-size 100
```

Get a full rundown of the available options for generating synthetic data with:

```bash
dp-cgans gen --help
```

### 🐍 Use with python 

This library can also be used directly in python scripts

If your input is tabular data (e.g., csv):

 ```python
from dp_cgans import DP_CGAN
import pandas as pd

tabular_data=pd.read_csv("../resources/example_tabular_data_UCIAdult.csv")

# We adjusted the original CTGAN model from SDV. Instead of looking at the distribution of individual variable, we extended to two variables and keep their corrll
model = DP_CGAN(
    epochs=100, # number of training epochs
    batch_size=1000, # the size of each batch
    log_frequency=True,
    verbose=True,
    generator_dim=(128, 128, 128),
    discriminator_dim=(128, 128, 128),
    generator_lr=2e-4, 
    discriminator_lr=2e-4,
    discriminator_steps=1, 
    private=False,
)

print("Start training model")
model.fit(tabular_data)
model.save("generator.pkl")

# Generate 100 synthetic rows
syn_data = model.sample(100)
syn_data.to_csv("syn_data_file.csv")
 ```

<!-- 
2. If your input data is in RDF format:

  ```python
from dp_cgans import DP_CGAN
from dp_cgans import RDF_to_Tabular

# Step 1. Load RDF to a plain table (dataframe)
plain_tabular=RDF_to_Tabular(file_path="../resources/example_rdf_data.ttl")

# Step 2. Convert plain table to a structured table 
# After step 1, RDF data will be converted a plain tabular dataset (all the nodes/entities will be presented as rows. Step 2 will structure the table by recognizing and sorting the types of the entities, replacing the URI with actual value which is attached to that URI. Users can decide how many levels they want to unfold their RDF models to tabular datasets.)
tabular_data,rel_pred_obj=plain_tabular.fit_convert(user_define_data_instance="http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C16960", 
                                                    user_define_is_a=["rdf:type{URIRef}"], 
                                                    user_define_has_value=["http://www.cancerdata.org/roo/P100042"], 
                                                    set_level="full", 
                                                    as_column='object', 
                                                    remove_columns_unique_values=True)

# Step 3. Define your GANS model
model = DP_CGAN(
    epochs=100, # number of training epochs
    batch_size=1000, # the size of each batch
    log_frequency=True,
    verbose=True,
    generator_dim=(128, 128, 128),
    discriminator_dim=(128, 128, 128),
    generator_lr=2e-4, 
    discriminator_lr=2e-4,
    discriminator_steps=1, 
    private=False,
)

print("Start training model")
model.fit(tabular_data)

# Sample the generated synthetic data
model.sample(100)
  ```
-->


## 🧑‍💻 Development setup

<details>
<summary>You will need to <a href="https://python-poetry.org/docs">install Poetry</a></summary><br/>

Be careful as poetry sometime uses a weird python version by default, you can check for the environment used by poetry for the current folder by running:

```bash
poetry env list
```

You can easily tell `poetry` to use your current version of python for this folder by running the following command:

```bash
poetry env use $(which python)
```
</details>


### Install

Clone the repository:

```bash
git clone https://github.com/sunchang0124/dp_cgans
cd dp_cgans
```

Install the dependencies:

```bash
poetry install
```

### Run

Run the library with the CLI:

```bash
poetry run dp-cgans gen --help
```

Run the tests locally:

```bash
poetry run pytest -s
```

### Add a new dependency

You can change the `pyproject.toml` file and run:

```bash
poetry update
```

Or you can do it directly with the CLI (e.g. for `pandas` here):

```bash
poetry add pandas
```

### Build and publish

Build:

```bash
poetry build
```

Publishing a new release is automatically done by a GitHub Action workflow when a release is created on GitHub:

```bash
poetry publish
```

## 📦️ New release process

The deployment of new releases is done automatically by a GitHub Action workflow when a new release is created on GitHub. To release a new version:

1. Make sure the `PYPI_API_TOKEN` secret has been defined in the GitHub repository (in Settings > Secrets > Actions). You can get an API token from PyPI [here](https://pypi.org/manage/account/).
2. Increment the `version` number in the `pyproject.toml` file in the root folder of the repository.
3. Create a new release on GitHub, which will automatically trigger the publish workflow, and publish the new release to PyPI.

You can also manually trigger the workflow from the Actions tab in your GitHub repository webpage.

## 📚️ References / Further reading 

There are many excellent work on generating synthetic data using GANS and other methods. We list the studies that made great conbributions for the field and inspiring for our work.

##### GANS

   1. Synthetic Data Vault (SDV) [[Paper](https://dai.lids.mit.edu/wp-content/uploads/2018/03/SDV.pdf)] [[Github](https://github.com/sdv-dev/SDV)]
   2. Modeling Tabular Data using Conditional GAN (a part of SDV) [[Paper](https://arxiv.org/abs/1907.00503)] [[Github](https://github.com/sdv-dev/CTGAN)]
   3. Wasserstein GAN [[Paper](https://arxiv.org/pdf/1701.07875.pdf)]
   4. Improved Training of Wasserstein GANs [[Paper](https://papers.nips.cc/paper/2017/file/892c3b1c6dccd52936e27cbd0ff683d6-Paper.pdf)]
   5. Synthesising Tabular Data using Wasserstein Conditional GANs with Gradient Penalty (WCGAN-GP) [[Paper](http://ceur-ws.org/Vol-2771/AICS2020_paper_57.pdf)]
   6. PacGAN: The power of two samples in generative adversarial networks [[Paper](https://proceedings.neurips.cc/paper/2018/file/288cc0ff022877bd3df94bc9360b9c5d-Paper.pdf)]
   7. CTAB-GAN: Effective Table Data Synthesizing [[Paper](https://arxiv.org/pdf/2102.08369.pdf)]
   8. Conditional Tabular GAN-Based Two-Stage Data Generation Scheme for Short-Term Load Forecasting [[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9253644)]
   9. TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks [[Paper](https://arxiv.org/pdf/2109.00666.pdf)]
   10. Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning [[Paper](https://arxiv.org/pdf/2008.09202.pdf)]

   ##### Differential Privacy

   1. Tensorflow Privacy [[Github](https://github.com/tensorflow/privacy)]
   2. Renyi Differential Privacy [[Paper](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46029.pdf)]
   3. DP-CGAN : Differentially Private Synthetic Data and Label Generation [[Paper](https://arxiv.org/pdf/2001.09700.pdf)]
   4. Differentially Private Generative Adversarial Network [[Paper](https://arxiv.org/pdf/1802.06739.pdf)] [[Github](https://github.com/illidanlab/dpgan)] Another implementation [[Github](https://github.com/civisanalytics/dpwgan)]
   5. Private Data Generation Toolbox [[Github](https://github.com/BorealisAI/private-data-generation)]
   6. autodp: Automating differential privacy computation [[Github](https://github.com/yuxiangw/autodp)]
   7. Differentially Private Synthetic Medical Data Generation using Convolutional GANs [[Paper](https://arxiv.org/pdf/2012.11774.pdf)]
   8. DTGAN: Differential Private Training for Tabular GANs [[Paper](https://arxiv.org/pdf/2107.02521.pdf)]
   9. DIFFERENTIALLY PRIVATE SYNTHETIC DATA: APPLIED EVALUATIONS AND ENHANCEMENTS [[Paper](https://arxiv.org/pdf/2011.05537.pdf)]
   10. FFPDG: FAST, FAIR AND PRIVATE DATA GENERATION [[Paper](https://sdg-quality-privacy-bias.github.io/papers/SDG_paper_19.pdf)]

##### Others

   1. EvoGen: a Generator for Synthetic Versioned RDF [[Paper](http://ceur-ws.org/Vol-1558/paper9.pdf)]
   2. Generation and evaluation of synthetic patient data [[Paper](https://bmcmedresmethodol.biomedcentral.com/track/pdf/10.1186/s12874-020-00977-1.pdf)]
   3. Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation [[Paper](https://www.mdpi.com/2076-3417/11/5/2158)]
   4. Generating and evaluating cross-sectional synthetic electronic healthcare data: Preserving data utility and patient privacy [[Paper](https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.12427)]
   5. Synthetic data for open and reproducible methodological research in social sciences and official statistics [[Paper](https://link.springer.com/article/10.1007/s11943-017-0214-8#Sec2)]
   6. A Study of the Impact of Synthetic Data Generation Techniques on Data Utility using the 1991 UK Samples of Anonymised Records [[Paper](https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2017/4_utility_paper.pdf)]

