v0.2.0 commit

.github/workflows/main.yaml

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+name: Build
+
+on:
+  workflow_dispatch:
+  release:
+    types: [published]
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - "*"
+
+env:
+  PROJECT_NAME: foo
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.7, 3.8, 3.9, "3.10", "3.11"]
+
+    steps:
+      - uses: actions/checkout@v1
+        with:
+          fetch-depth: 9
+          submodules: false
+
+      - name: Use Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v1
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - uses: actions/cache@v1
+        id: depcache
+        with:
+          path: deps
+          key: requirements-pip-${{ matrix.python-version }}-${{ hashFiles('requirements.txt') }}
+
+      - name: Download dependencies
+        if: steps.depcache.outputs.cache-hit != 'true'
+        run: |
+          pip download --dest=deps -r requirements.txt
+
+      - name: Install dependencies
+        run: |
+          pip install -U --no-index --find-links=deps deps/*
+
+      - name: Run tests
+        run: |
+          pytest --doctest-modules --junitxml=junit/pytest-results-${{ matrix.python-version }}.xml --cov=$PROJECT_NAME --cov-report=xml tests/
+
+      - name: Upload pytest test results
+        uses: actions/upload-artifact@master
+        with:
+          name: pytest-results-${{ matrix.python-version }}
+          path: junit/pytest-results-${{ matrix.python-version }}.xml
+        if: always()
+
+      - name: Install distribution dependencies
+        run: pip install --upgrade twine setuptools wheel
+        if: matrix.python-version == 3.9
+
+      - name: Create distribution package
+        run: python setup.py sdist bdist_wheel
+        if: matrix.python-version == 3.9
+
+      - name: Upload distribution package
+        uses: actions/upload-artifact@master
+        with:
+          name: dist-package-${{ matrix.python-version }}
+          path: dist
+        if: matrix.python-version == 3.9
+
+  publish:
+    runs-on: ubuntu-latest
+    needs: build
+    if: github.event_name == 'release'
+    steps:
+      - name: Download a distribution artifact
+        uses: actions/download-artifact@v2
+        with:
+          name: dist-package-3.9
+          path: dist
+      - name: Publish distribution 📦 to Test PyPI
+        uses: pypa/gh-action-pypi-publish@master
+        with:
+          skip_existing: true
+          user: __token__
+          password: ${{ secrets.test_pypi_password }}
+          repository_url: https://test.pypi.org/legacy/
+
+      - name: Publish distribution 📦 to PyPI
+        uses: pypa/gh-action-pypi-publish@master
+        with:
+          user: __token__
+          password: ${{ secrets.pypi_password }}

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

LICENSE

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+MIT License
+
+Copyright (c) 2024 Sartaj Bhuvaji
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,2 +1,61 @@
-# SDGnE
-Synthetic Data Generation and Evaluation
+<br />
+<div align="center">
+ <a href="https://github.com/SartajBhuvaji/SDGnE">
+    <img src="img/logo.png" alt="logo" width="80" height="80">
+  </a>
+
+  <h3 align="center">SDGnE</h3>
+  <h4 align="center">Synthetic Data Generation and Evaluation</h3><br >
+
+  <p align="center">
+    Seattle University: Data Science Research
+    <br />
+    <br />
+    Guide    <br /> 
+    <a href="https://github.com/baew-seattleu">Dr. Wan Bae</a> <br /><br />
+    Students
+    <br /> 
+    <a href="https://github.com/SartajBhuvaji">Sartaj Bhuvaji</a><br>
+    <a href="https://github.com/Siddheshwari19">Siddheshwari Bankar</a> 
+
+        SDGNE - Synthetic Data Generation and Evaluation
+  </p>
+</div>
+
+  [![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/drive/1tfihhB8TeC47EYbNgB-uU-3p2KJLmkmC?usp=sharing)
+
+  ### 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)
+
+  ### 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)
+
+ ## 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.
+
+

docs/README.md

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+# GITHUB PAGE