QuinielaML

Example about a project based on InterSystems IRIS and IntegratedML capabilities as back-end to get predictions about football matches of spanish league and an Angular project as front-end

You can find more in-depth information in https://learning.intersystems.com.

What do you need to install?

Git
Docker (if you are using Windows, make sure you set your Docker installation to use “Linux containers”).
Docker Compose
Visual Studio Code + InterSystems ObjectScript VSCode Extension

Setup

Build the image we will use during the workshop:

$ git clone https://github.com/intersystems-ib/workshop-quiniela
$ cd workshop-quiniela
$ docker-compose build

Introduction

What is “Quiniela”?

The Quiniela is a popular game in Spain, during many year sport bets were forbidden in Spain and Quiniela was the only game allowed. This game is based on Spanish Football/Soccer league (First and Second division). The Quiniela ticket has 15 matches (10 of First Division and 5 of Second Division) and the player has to check one of three options, selecting which team is going to be the winner of the match. 1 for the local team 2 for the visitor team or X for a draw (also known as “The 1X2 game”).

How does this project work?

This project is designed as a common web application with a backend developed on InterSystems IRIS Community edition and a frontend developed on Angular.

Backend

As we said before, our backend is developed on InterSystems IRIS with IntegratedML technologies. The backend is responsible for:

Get historic results of Spanish League from an external web using webscrapping with Embedded Python capabilities.
Prepare the data get from the external web to be used by the prediction model.
Create model and train with the prepared data using the IntegratedML capabilities.
Receive and manage REST calls from the front-end.
Generate predictions for the matches.
Provide a JWT in order to securize the communication between frontend and backend.

Frontend

Developed on Angular provides an easy to use user interface sending REST calls to the backend and receiving and managing the responses.

Testing the project

Run the containers to deploy the backend and the frontend:

docker-compose up -d

Automatically an IRIS instance will be deployed and a production will be configured and run available to import data to create the prediction model and train it.

Open the Management Portal.
Login using the default superuser/ SYS account.
Click on Production to access the production that we are going to use. You can access also through Interoperability > User > Configure > Production.

Now you can check the frontend:

Open the main page from this URL.
Login using superuser / SYS account.
Click on the icon on the upper left of the screen and check the options of the menu.
Click on Data management and follow the arrows: Launch import -> Launch preparation -> Launch training. Wait for the end of each step.
Now open the Menu again and click on Result prediction.
You can add all the matches and see the prediction.
You can keep the data updated adding the real result clicking on the match and introducing the result: