From 1618ae1c87d74ca3ee079e9e95386bfc8a58b534 Mon Sep 17 00:00:00 2001 From: Fabiana <30911746+fabclmnt@users.noreply.github.com> Date: Tue, 28 Feb 2023 19:58:29 +0000 Subject: [PATCH] feat: streamlit app for ydata-synthetic (#236) * feat: improve example streamlit app to serve as the package interface * chore: remove from the examples as now it is a feature * feat: add input path to save model and save synthetic data samples * docs: add new streamlit example and update readme * docs: update readme with a video * fix: udapte the setup file and readme * fix: fix typo * fix: remove comments * fix: codacy code quality issues --- README.md | 49 +++++++- assets/streamlit_app.png | Bin 0 -> 104186 bytes .../streamlit app/.streamlit/config.toml | 5 - examples/regular/streamlit app/README.md | 23 ---- examples/regular/streamlit app/YData_logo.svg | 15 --- examples/regular/streamlit app/app.JPG | Bin 110598 -> 0 bytes examples/regular/streamlit app/app.gif | Bin 8785653 -> 0 bytes examples/regular/streamlit app/app.py | 113 ------------------ .../regular/streamlit app/requirements.txt | 6 - examples/streamlit_app.py | 7 ++ requirements.txt | 2 +- setup.py | 11 +- .../streamlit_app/.streamlit/config.toml | 3 + src/ydata_synthetic/streamlit_app/About.py | 83 +++++++++++++ src/ydata_synthetic/streamlit_app/__init__.py | 3 + .../pages/1_Train_a_synthesizer.py | 82 +++++++++++++ .../pages/2_Generate_synthetic_data.py | 41 +++++++ .../streamlit_app/pages/functions/__init__.py | 0 .../streamlit_app/pages/functions/generate.py | 22 ++++ .../pages/functions/load_data.py | 25 ++++ .../streamlit_app/pages/functions/train.py | 50 ++++++++ src/ydata_synthetic/streamlit_app/run.py | 15 +++ 22 files changed, 387 insertions(+), 168 deletions(-) create mode 100644 assets/streamlit_app.png delete mode 100644 examples/regular/streamlit app/.streamlit/config.toml delete mode 100644 examples/regular/streamlit app/README.md delete mode 100644 examples/regular/streamlit app/YData_logo.svg delete mode 100644 examples/regular/streamlit app/app.JPG delete mode 100644 examples/regular/streamlit app/app.gif delete mode 100644 examples/regular/streamlit app/app.py delete mode 100644 examples/regular/streamlit app/requirements.txt create mode 100644 examples/streamlit_app.py create mode 100644 src/ydata_synthetic/streamlit_app/.streamlit/config.toml create mode 100644 src/ydata_synthetic/streamlit_app/About.py create mode 100644 src/ydata_synthetic/streamlit_app/__init__.py create mode 100644 src/ydata_synthetic/streamlit_app/pages/1_Train_a_synthesizer.py create mode 100644 src/ydata_synthetic/streamlit_app/pages/2_Generate_synthetic_data.py create mode 100644 src/ydata_synthetic/streamlit_app/pages/functions/__init__.py create mode 100644 src/ydata_synthetic/streamlit_app/pages/functions/generate.py create mode 100644 src/ydata_synthetic/streamlit_app/pages/functions/load_data.py create mode 100644 src/ydata_synthetic/streamlit_app/pages/functions/train.py create mode 100644 src/ydata_synthetic/streamlit_app/run.py diff --git a/README.md b/README.md index 543c1302..0d8843b2 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,12 @@ Join us on [![Discord](https://img.shields.io/badge/Discord-7289DA?style=for-the # YData Synthetic A package to generate synthetic tabular and time-series data leveraging the state of the art generative models. + +## 🎊 We have **big news**: v1.0.0 is here +> We have exciting news for you. The new version of `ydata-synthetic` include new and exciting features: + > - A conditional architecture for tabular data: CTGAN, which will make the process of synthetic data generation easier and with higher quality! + > - A new streamlit app that delivers the synthetic data generation experience with a UI interface + ## 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. @@ -27,19 +33,54 @@ This repository contains material related with Generative Adversarial Networks f It consists a set of different GANs architectures developed using Tensorflow 2.0. Several example Jupyter Notebooks and Python scripts are included, to show how to use the different architectures. ## Quickstart - The source code is currently hosted on GitHub at: https://github.com/ydataai/ydata-synthetic Binary installers for the latest released version are available at the [Python Package Index (PyPI).](https://pypi.org/project/ydata-synthetic/) -``` +```commandline pip install ydata-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 ydata-syntehtic[streamlit] +``` +#### Quickstart +Use the code snippet below in a python file (Jupyter Notebooks are not supported): +```python +from ydata_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/ydataai/ydata-synthetic/tree/master/examples/streamlit_app.py). + +```commandline +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. - Synthesizing the minority class with VanillaGAN on credit fraud dataset [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ydataai/ydata-synthetic/blob/master/examples/regular/gan_example.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/ydataai/ydata-synthetic/blob/master/examples/timeseries/TimeGAN_Synthetic_stock_data.ipynb) - - More examples are continously added and can be found in `/examples` directory. + - 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: @@ -51,7 +92,6 @@ Here are some example datasets for you to try with the synthesizers: #### Sequential datasets - [Stock data](https://github.com/ydataai/ydata-synthetic/tree/master/data) - ## Project Resources In this repository you can find the several GAN architectures that are used to create synthesizers: @@ -64,6 +104,7 @@ In this repository you can find the several GAN architectures that are used to c - [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) ### Sequential data - [TimeGAN](https://papers.nips.cc/paper/2019/file/c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf) diff --git a/assets/streamlit_app.png b/assets/streamlit_app.png new file mode 100644 index 0000000000000000000000000000000000000000..fd283c32a212e4f9666e305db029e8d563da8dd0 GIT binary patch literal 104186 zcmeFZby!th69>8h3F%NkI#du45b17I8l+oVy1TnYMMN4zK%_xHx>3424k#UmZn*2b zpZb2^{r|@E+{<$wW$&~1UTe*o`OVC4W)q>LAcKQRh6zCs&eJE7DiDMUenlR@Km-5v z`^>6?g9nz+HJvr(<@k;5Y*>v<>|U6%y4%=;?;%J~#NFP=*vizI=7p)brL7RdPHiIt zjireYg9ev8yS%-GsfFbePe)T#Ple~ko>sw z|2&r;{Ej%y#z6DWCC*kt3}T1@X{6lk9Zj8_7{JYrCT9F9lG49#1xG>*7S7K0{A_G) zZf>k@k6G;;&DkFD@$s>-bFgu6JOo!fbn>uuHgbPx>qHNpK=aQdBu$-+9WCvhE$wV+ 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a/examples/regular/streamlit app/.streamlit/config.toml +++ /dev/null @@ -1,5 +0,0 @@ -[theme] -primaryColor="#040000" -backgroundColor="#770303" -secondaryBackgroundColor="#000000" -textColor="#f2f2f3" \ No newline at end of file diff --git a/examples/regular/streamlit app/README.md b/examples/regular/streamlit app/README.md deleted file mode 100644 index d5b4a2ab..00000000 --- a/examples/regular/streamlit app/README.md +++ /dev/null @@ -1,23 +0,0 @@ -# Streamlit application to generate synthetic data using ydata-synthetic - -streamlit app to generate synthetic data - -This application takes a pre-processed dataset as input and outputs a synthetic dataset based on the given input parameters. This is made with open source libraries streamlit, ydata-synthetic and deployed on the streamlit cloud. - -## How to use - -1. Upload a pre-processed dataset. -2. Choose the numerical features and categorical features. -3. Choose all the training parameters appropriately. -4. Click the 'click here to start the training process' button. - -streamlit app to generate synthetic data - -Wait for the training to end. You will see a graph comparing the original data and synthetic data after training. -Please use less number of epochs to complete the training process quickly as this application is deployed on the community cloud of streamlit which has computational limits. - -## Contributing - -Find the application here in this link [![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/rajeshai/ydata-synthetic-streamlit/main/app.py) - -Feel free to contribute to this app by adding more features and optimizing its performance further. diff --git a/examples/regular/streamlit app/YData_logo.svg b/examples/regular/streamlit app/YData_logo.svg deleted file mode 100644 index c17ecdba..00000000 --- a/examples/regular/streamlit app/YData_logo.svg +++ /dev/null @@ -1,15 +0,0 @@ - - - - - - - - - - - - - - - diff --git a/examples/regular/streamlit app/app.JPG b/examples/regular/streamlit app/app.JPG deleted file mode 100644 index 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