This README provides instructions for running experiments on structured and text-based datasets using the provided Python script.
## Overview
## Getting started
The script allows for running experiments on two types of datasets: structured and text-based. Users can specify which type of experiment to run and optionally target a specific dataset within that type.
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
## Requirements
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
- Python 3.6 or higher
- Dependencies are listed in the `requirements.txt` file.
## Add your files
### Installing Dependencies
-[ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-[ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
Before running the experiments, install the necessary Python packages using the following command:
-[ ] [Set up project integrations](https://gitlab.com/thesis8480028/Thesis/-/settings/integrations)
## Collaborate with your team
-[ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-[ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-[ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
Use the built-in continuous integration in GitLab.
-[ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-[ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-[ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-[ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-[ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
***
# Editing this README
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
## Suggestions for a good README
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
## Name
Choose a self-explaining name for your project.
Ensure that you have Python and pip installed on your system. If you encounter any issues during the installation, please check that your Python environment is correctly set up.
## Configuration
The script uses a JSON configuration file to determine which datasets are available for experiments. The structure of the configuration file should be as follows:
```json
{
"structured":[
{
"datasetName":"BA2Motif"
},
{
"datasetName":"MultiShape"
},
{
"datasetName":"MUTAG"
}
],
"text":[
{
"datasetName":"dblp",
"grouped_keyword_dir":"rawData/dblp/groups",
"entity_name":"author"
},
{
"datasetName":"imdb",
"grouped_keyword_dir":"rawData/imdb/groups",
"entity_name":"movie"
}
]
}
```
## Description
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
## How to Run
## Badges
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
1.**Setup the Configuration File**: Ensure your `config.json` file is set up as described in the Configuration section and is located in the same directory as your script, or provide the path to it when running the script.
## Visuals
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
2.**Running Experiments**:
- Use the command-line interface to specify the type of dataset and optionally target a specific dataset within that type.
- You can specify the dataset type (`structured` or `text`) and optionally target a specific dataset.
## Installation
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
### Command-Line Arguments
## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-`-c, --config`: Path to the configuration file. Defaults to `config.json`.
-`-t, --type`: Type of dataset to run the experiments on. Choices are `structured` or `text`. Defaults to `structured`.
-`-d, --dataset`: Specific dataset name to run. Optional.
## Support
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
### Examples
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
-**Run All Structured Datasets**:
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
```bash
python main.py --type structured
```
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-**Run a Specific Structured Dataset**:
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
Show your appreciation to those who have contributed to the project.
-**Run a Specific Text Dataset**:
## License
For open source projects, say how it is licensed.
```bash
python main.py --type text --dataset dblp
```
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
