This project is a graph convolutional network for the MUTAG dataset, which allows for experimentation and testing of different hyperparameters, comparing their performance.
This project is made alongside a semester project report for AAU, focusing on graph convolutional networks. It generates a GNN model for the MUTAG dataset, while providing tools to gather insightful information, and visualizations. The hyperparameters can easily be adjusted for experimentation.
- Can run Bayesian optimization and grid search over a given search space
- Prints out relevant information from dataset
- Visualization of graphs from dataset
- Trains a graph covolutional network
- Measures the accuracy and loss over time
- Plots the AUC - ROC and AUC - PR curve
This project utilises Python venv's to ensure an easy setup process. To install the required dependencies, complete the following steps:
Located in the top-level-directory, write the following:
- python3 -m venv .venv
- source .venv/bin/activate
- pip install -r requirements.txt
After this, the dependencies should be installed. To run the code, navigate to the Main file, which contains the essentials for the project. Here different flags are available, which modify the control flow of the program, thereby enabling different features.
To run this program it requires python, along with the following libraries.
- PyTorch
- networkX
- numpy
- pandas
- sklearn
- matplotlib
- Bayesian_Search
- csv
- seaborn
This project was made by Andreas W. Holt, Daniel H. Hansen, Frederik Melchiors, Karen M. Andersen, Mikkel D. Bjørn, and Theis R. Mathiassen.
MIT License
Copyright (c) 2023 Theis Mathiassen
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.