Merge pull request #2 from SartajBhuvaji/main

Updated workflows

.github/workflows/main.yaml

@@ -95,4 +95,5 @@ jobs:
         uses: pypa/gh-action-pypi-publish@master
         with:
           user: __token__
-          password: ${{ secrets.pypi_password }}
+          password: ${{ secrets.pypi_password }}
+          repository_url: https://upload.pypi.org/legacy/

PYPI_README.md

@@ -0,0 +1,39 @@
+
+  [![Build](https://github.com/SartajBhuvaji/SDGnE/actions/workflows/main.yaml/badge.svg)](https://github.com/SartajBhuvaji/SDGnE/actions/workflows/main.yaml)
+
+
+## About
+- SDGnE (Synthetic Data Generation and Evaluation) is a Python package designed to generate synthetic data and evaluate its quality using neural network models. 
+- This tool is intended for developers and researchers who require synthetic datasets for testing and development.
+- The current dittto version `v0.2.0` uses <i>Autoencoders</i> and <i>SMOTE</i> to generate synthetic data.
+
+## Getting Started
+`pip install sdgne`
+
+ ## Notebooks
+ To get started, we have created notebook for the Autoencoder and SMOTE algorithm.
+
+  ### Auto Encoder
+  Autoencoders are a class of neural networks designed for unsupervised learning and representing features in a smaller space. They consist of an encoder and a decoder, intending to learn the input data's compressed representation (encoding).  We leverage this architecture to generate synthetic data.
+
+  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SartajBhuvaji/SDGnE/blob/main/notebooks/SDGnE_Autoencoder_Notebook.ipynb)
+
+  ### SMOTE
+  SMOTE, abbreviated as Synthetic Minority Oversampling Technique, is used to generate synthetic data from the original dataset. Over the years, several variants of SMOTE have been developed, each tailored to specific scenarios and requirements. These variants employ distinct methodologies and innovations to enhance the generation of synthetic data, thereby improving model performance by ensuring a more balanced distribution of classes. We provide a few SMOTE variants for synthetic data generation.
+
+  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SartajBhuvaji/SDGnE/blob/main/notebooks/SDGnE_SMOTE_Notebook.ipynb)
+
+  ### Comparison
+  In this notebook, we will compare the `Single Encoder Autoencoder` and the `SMOTE Algorithm` for synthetic data generation. We will generate synthetic data using both the algorithms and perform statistical evaluation.
+
+  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SartajBhuvaji/SDGnE/blob/main/notebooks/SDGnE_Comparison_Notebook.ipynb)
+
+ ## Features
+
+- **Data Generation**: Create synthetic datasets that mimic the statistical properties of real-world data.
+- **Neural Autoencoders**: Utilize various autoencoder architectures to learn data representations.
+- **Evaluation Metrics**: Assess the quality of synthetic data using built-in evaluation metrics.
+- **Extensibility**: Easily extend the package with custom data generators and evaluators.
+
+ ## Links
+ - **Documentation**: https://seattle-university.gitbook.io/sdgne/

README.md

@@ -24,6 +24,8 @@
 
   [![Build](https://github.com/SartajBhuvaji/SDGnE/actions/workflows/main.yaml/badge.svg)](https://github.com/SartajBhuvaji/SDGnE/actions/workflows/main.yaml)
 
+  ![PyPI](https://img.shields.io/pypi/v/sdgne?label=sdgne)
+
 
 ## About
 - SDGnE (Synthetic Data Generation and Evaluation) is a Python package designed to generate synthetic data and evaluate its quality using neural network models. 
@@ -39,17 +41,17 @@
   ### Auto Encoder
   Autoencoders are a class of neural networks designed for unsupervised learning and representing features in a smaller space. They consist of an encoder and a decoder, intending to learn the input data's compressed representation (encoding).  We leverage this architecture to generate synthetic data.
 
-  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tfihhB8TeC47EYbNgB-uU-3p2KJLmkmC?usp=sharing)
+  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SartajBhuvaji/SDGnE/blob/main/notebooks/SDGnE_Autoencoder_Notebook.ipynb)
 
   ### SMOTE
   SMOTE, abbreviated as Synthetic Minority Oversampling Technique, is used to generate synthetic data from the original dataset. Over the years, several variants of SMOTE have been developed, each tailored to specific scenarios and requirements. These variants employ distinct methodologies and innovations to enhance the generation of synthetic data, thereby improving model performance by ensuring a more balanced distribution of classes. We provide a few SMOTE variants for synthetic data generation.
 
-  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/18kAMJR0VBtfC3swIkbuTPWd0Sb0mDVCo?usp=sharing)
+  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SartajBhuvaji/SDGnE/blob/main/notebooks/SDGnE_SMOTE_Notebook.ipynb)
 
   ### Comparison
   In this notebook, we will compare the `Single Encoder Autoencoder` and the `SMOTE Algorithm` for synthetic data generation. We will generate synthetic data using both the algorithms and perform statistical evaluation.
 
-  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1VAM6hGRPvoLfS8x_WYDbexAc7V4I00_h?usp=sharing)
+  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SartajBhuvaji/SDGnE/blob/main/notebooks/SDGnE_Comparison_Notebook.ipynb)
 
  ## Features
 
@@ -58,4 +60,6 @@
 - **Evaluation Metrics**: Assess the quality of synthetic data using built-in evaluation metrics.
 - **Extensibility**: Easily extend the package with custom data generators and evaluators.
 
-
+ ## Links
+ - **Documentation**: https://seattle-university.gitbook.io/sdgne/
+ - **PyPI**: 

setup.py

@@ -4,12 +4,12 @@
 
 here = os.path.abspath(os.path.dirname(__file__))
 
-with codecs.open(os.path.join(here, "README.md"), encoding="utf-8") as fh:
+with codecs.open(os.path.join(here, "PYPI_README.md"), encoding="utf-8") as fh:
     long_description = "\n" + fh.read()
 
-VERSION = '0.2.0'
+VERSION = '0.2.2'
 DESCRIPTION = 'Generates synthetic data'
-LONG_DESCRIPTION = 'Generates synthetic data'
+LONG_DESCRIPTION = long_description
 URL = "https://github.com/SartajBhuvaji"
 
 # Setting up
@@ -33,7 +33,7 @@
     classifiers=[
         "Development Status :: 5 - Production/Stable",
         "Intended Audience :: Developers",
-        "Programming Language :: Python :: 3.7, 3.8, 3.9, 3.10, 3.11",
+        "Programming Language :: Python :: 3.7",
         "Operating System :: Unix",
         "Operating System :: MacOS :: MacOS X",
         "Operating System :: Microsoft :: Windows",