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https://github.com/baoliay2008/lccn_predictor

LeetCode Contest Rating Prediction
https://github.com/baoliay2008/lccn_predictor

beanie competitive-programming contest-programming daisyui elo fastapi fft leetcode mongodb prediciton pydantic python3 react

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LeetCode Contest Rating Prediction

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README 

        

# Introduction



This is a LeetCode weekly and biweekly contest rating predictor. The APP is available online at [🔗 lccn.lbao.site](https://lccn.lbao.site/)



Hopefully, you can get the predicted result within **15-30 minutes** after the contest has finished.



## Features



* ⚡️ Fast

  * The core Elo rating algorithm is significantly enhanced by a **Just-In-Time (JIT) compiler** through [Numba](https://numba.pydata.org), reducing execution time to approximately 20 seconds on a dual-core *Intel(R) Xeon(R) Platinum 8255C CPU* (@ 2.50GHz).

  * In addition to the JIT implementation, this project incorporates a [Fast Fourier Transform (FFT) implementation](https://github.com/baoliay2008/lccn_predictor/pull/43). The Elo rating system employed by LeetCode benefits significantly from the FFT algorithm, achieving speedups ranging from **65 to 1,000 times** for individual contest predictions. The most efficient FFT implementation (`EXPAND_SIZE=1`) completes predictions in under **0.25 seconds**, maintaining an impressively low Mean Squared Error (MSE) of approximately 0.027.

  * **Caching** the user's latest rating before initiating the prediction process leads to a substantial reduction in the time required for data retrieval.

* 🎯 Accurate

  * If there were no significant rejudges (assuming everyone's global ranking remains unchanged), it is ensured that the prediction error for rating deltas for **EACH** participant is within the precision limit of 0.05. As a result, the rating difference should be negligible.

  * Please note that a normal case is that there would be some misconduct detection, so your global ranking will be slightly higher even if your submissions are not rejudged, which results in a slightly higher rating :)

* 📱 Responsive web page

  * Tested on phones and tablets.



## More Information



* [🔗 refined-leetcode](https://github.com/XYShaoKang/refined-leetcode): A Chrome extension for leetcode.cn, created by [@XYShaoKang](https://github.com/XYShaoKang)



# Underlying Mechanism



## Algorithm



* [🔗 English official illustration on leetcode.com](https://leetcode.com/discuss/general-discussion/468851/New-Contest-Rating-Algorithm-(Coming-Soon))

* [🔗 Chinese official illustration on leetcode.cn](https://leetcode.cn/circle/article/neTUV4/)

* 🔗 Detailed post about FFT acceleration

  - ❤️ Special thanks to [@tiger2005](https://github.com/tiger2005) for proposing this idea in [issue #8](https://github.com/baoliay2008/lccn_predictor/issues/8)



## Database



* [MongoDB](https://www.mongodb.com/): NoSQL database

* [Beanie](https://beanie-odm.dev/): ODM for MongoDB



## Backend



* [APScheduler](https://apscheduler.readthedocs.io/en/3.x/): background tasks

* [Numpy](https://numpy.org/) and [Numba](https://numba.pydata.org/): core prediction algorithm implementation and acceleration

* [FastAPI](https://fastapi.tiangolo.com/): restful API

* 🚮 ~~[Jinja](https://jinja.palletsprojects.com/): HTML templates for server-side rendering~~



## Frontend



* [React](https://reactjs.org/): most popular front-end library

* [TailwindCSS](https://tailwindcss.com/) and [DaisyUI](https://daisyui.com/): modern CSS framework and its component library

* 🚮 ~~[Materialize](https://materializecss.com/): responsive front-end framework~~

* [Echarts](https://echarts.apache.org/en/index.html): data visualization



# Development



## Backend Deployment



### virtualenv



```shell

git clone [email protected]:baoliay2008/lccn_predictor.git

cd lccn_predictor



# write your mongodb environment config

cp config.yaml.template config.yaml

vi config.yaml



python3.10 -m virtualenv venv/

source venv/bin/activate



pip3 install -r requirements.txt



python main.py

uvicorn api.entry:app --host 0.0.0.0 --port 55555

```



### Docker



```shell

git clone [email protected]:baoliay2008/lccn_predictor.git

cd lccn_predictor



# write production environment mongodb config

cp config.yaml.template config.yaml

vi config.yaml



# build docker image

docker image build -t lccn_predictor:0.1.2 .



# create docker volume

docker volume create lccn_predictor



# run container

docker run -d -v lccn_predictor:/lccn_predictor -p 55555:55555 --name lp lccn_predictor:0.1.2



docker exec -it lp bash



docker container stop lp



docker container start lp



```



## Frontend Deployment



```shell

cd client



# install dependencies

npm install



# change `baseUrl` to your local backend process

vi src/data/constants.js

# if you followed instruction above

# it should be "http://localhost:55555/api/v1"



# local test

npm run dev



# publish

npm run build



```



# License



[MIT License](LICENSE)



# Changelog



* v0.0.1(2022/11/14)

  > make this repo public, first release.

* v0.0.2(2022/11/25)

  > first version in production

* v0.1.1(2023/02/14)

  > change frontend from server-side rendering([Jinja](https://jinja.palletsprojects.com/) + [Materialize](https://materializecss.com/)) to client-side rendering([React](https://reactjs.org/)).

* v0.1.2(2023/10/04)

  > refine backend logic to enhance robustness and clean up deprecated static site rendering code

* v0.1.3(2023/12/28)

  > last version prior to the rewrite of the Elo rating algorithm

---



# Supported by



[![JetBrains Logo (Main) logo](https://resources.jetbrains.com/storage/products/company/brand/logos/jb_beam.svg)](https://jb.gg/OpenSourceSupport)