project design

bot-detector-ML

this repository is responsible for the machine learning model. We are currently using two models, a binary classifier and multi label classifier, the binary classifier is responsible for the Real_Player & Unkown_bot classification, if the Real_Player classification is less then 50% the predictions of the multi class classifier are used.

design

building this repository

Install:

• Docker
• Github desktop
  • Git windows, Git unix will also work.
• An integrated development environment (IDE).
  • We recommend VSCode, but any IDE will work.

Setup:

1. Open a terminal cmd
2. Navigate cd to where you want to save our code.
3. The command below will Create a folder bot-detector with two sub folders remote & local & download the remote repositories in the remote folder.
   • To add the repositories in github desktop, select File on the top left than click Add local repository, and navigate to the cloned repositories.

Windows

mkdir bot-detector\remote bot-detector\local && cd bot-detector\remote
git clone https://github.com/Bot-detector/Bot-Detector-Core-Files.git
git clone https://github.com/Bot-detector/bot-detector-mysql.git
git clone https://github.com/Bot-detector/bot-detector-ML.git

Linux

mkdir -p bot-detector/{remote,local}
git clone https://github.com/Bot-detector/Bot-Detector-Core-Files.git
git clone https://github.com/Bot-detector/bot-detector-mysql.git
git clone https://github.com/Bot-detector/bot-detector-ML.git

4. Now you can start the project. Make sure docker desktop is running!

Creating the database. (you can skip this step if the database is already created) (this might take a while)

cd bot-detector-mysql
docker-compose up --build

In the terminal you will now see /usr/sbin/mysqld: ready for connections. The database has been created, you can now stop the database

ctrl+c

You can now create the necessary containers: if you just want to run the mysql + api

cd Bot-Detector-Core-Files
docker-compose up --build

if you want to run the mysql + api + ml

cd Bot-Detector-Core-ML
docker-compose up --build

6. Test the api's:
   • Core api: http://localhost:5000/
   • Machine learning: http://localhost:8000/

adding /docs at the end will give return the swagger documentation for the components /docs

contributing

To contribute you must fork a repository, if you followed the setup step, fork the repository in the bot-detector\local folder.