This is the code and a little demodata as well as a guide to go for the extra mile 🏃♀️ to test the code with demofiles at a large scale, for a research paper on supporting team level decisions in Counter-Strike: Global Offensive
This is an overview of all functions in this project
❯ make help
Available rules:
clean Delete all compiled Python files
create_environment Set up python interpreter environment
data Make Dataset
lint Lint using flake8
parse Parse raw demofiles
requirements Install Python Dependencies
test_environment Test python environment is setup correctly
train Train Model
visuals Prepare all reports and figures
(END)Although in this repository for illustration some demofiles are included as sample data or the parsed datasets are provided you have to download cs:go demofiles first to be able to use the pipeline to its full extent. The Python package GoScrape is suitable for this.
Start by installing the package using pip
pip install goscrapeTo learn more about other functions of the package it makes sense to read the documentation. In the following only the functions relevant for this project will be explained.
First a json lookup needs to be constructed to store all relevant information for matches and games. I used CS:GO matches hosted by htlv.org that took place between 2021/04/01 and 2022/04/01. All of those matches were online. This lookup can be achieved by the following command:
goscrape events -s 2021-04-01 -e 2022-04-01 -m -t ONLINE -f data/lookups/
Afterwards the demofiles have to be downloaded.
goscrape fetch -f event_lookup__2021_04_01__2022_04_01__ONLINE.json -o data/raw/They can either be directly put into the /raw subdirectory of the data folder or all manually extracted and put there.
make create_environmentmake test_environmentIf conda is installed it will be preferred
pip install -r requirements.txtmake parseThis will parse the raw files under /data/raw and put the structured json files in /data/interim
make dataThis will concatenate the parsed json files into a single feather file stored under /data/processed
make trainBuild the Training Dataset and build all relevant Models. These will be saved under /models
make visualsRun the command to get all relevant plots, charts and figures in the LaTeX compatible .pgf format
The project is structured in the following way:
.
├── LICENSE
├── Makefile
├── README.md
├── data
│ ├── interim
│ │ ├── 9z-vs-meta-py-nuke.json
│ │ ├── b8-vs-run-or-die-m4-vertigo.json
│ │ ├── bears-vs-isurus-m1-ancient_68896.json
│ │ ├── black-dragons-vs-intz-m2-vertigo.json
│ │ ├── bth-vs-boca-juniors-m2-dust2.json
│ │ ├── coscu-army-vs-isurus-nuke.json
│ │ ├── croatia-vs-czech-republic-mirage.json
│ │ ├── enterprise-vs-kappab-m2-overpass.json
│ │ ├── exploit-vs-souldazz-nuke.json
│ │ ├── furious-vs-paqueta-m3-vertigo.json
│ │ ├── gaijin-vs-prifu-m1-mirage.json
│ │ ├── gaijin-vs-prifu-m2-overpass.json
│ │ ├── iberian-family-vs-offset-m2-vertigo.json
│ │ ├── isurus-vs-eqole-inferno.json
│ │ ├── isurus-vs-intz-dust2.json
│ │ ├── isurus-vs-leviatan-m2-nuke.json
│ │ ├── kappab-vs-biiceps-m2-mirage_66795.json
│ │ ├── nexus-vs-portugal-m1-dust2.json
│ │ ├── order-vs-paradox-m1-dust2.json
│ │ ├── sestri-vs-ec-kyiv-m2-inferno.json
│ │ └── virtus-pro-vs-gambit-vertigo.json
│ ├── lookups
│ │ └── event_lookup__2021_04_01__2022_04_01__ONLINE.json
│ ├── processed
│ │ ├── team_score_and_buy__dataset.feather
│ │ └── team_score_and_buy__dataset__training.feather
│ └── raw
│ ├── 9z-vs-meta-py-nuke.dem
│ ├── b8-vs-run-or-die-m4-vertigo.dem
│ ├── bears-vs-isurus-m1-ancient_68896.dem
│ ├── black-dragons-vs-intz-m2-vertigo.dem
│ ├── bth-vs-boca-juniors-m2-dust2.dem
│ ├── coscu-army-vs-isurus-nuke.dem
│ ├── croatia-vs-czech-republic-mirage.dem
│ ├── enterprise-vs-kappab-m2-overpass.dem
│ ├── exploit-vs-souldazz-nuke.dem
│ ├── furious-vs-paqueta-m3-vertigo.dem
│ ├── gaijin-vs-prifu-m1-mirage.dem
│ ├── gaijin-vs-prifu-m2-overpass.dem
│ ├── iberian-family-vs-offset-m2-vertigo.dem
│ ├── isurus-vs-eqole-inferno.dem
│ ├── isurus-vs-intz-dust2.dem
│ ├── isurus-vs-leviatan-m2-nuke.dem
│ ├── kappab-vs-biiceps-m2-mirage_66795.dem
│ ├── nexus-vs-portugal-m1-dust2.dem
│ ├── order-vs-paradox-m1-dust2.dem
│ ├── sestri-vs-ec-kyiv-m2-inferno.dem
│ └── virtus-pro-vs-gambit-vertigo.dem
├── models
│ ├── decision_tree_classifier.pkl
│ ├── logistic_regression.pkl
│ ├── mlp_classifier.pkl
│ └── mlp_classifier_with_maps.pkl
├── notebooks
│ ├── AdvisingPlayerDecisions.ipynb
│ ├── DataExploration.ipynb
│ └── HyperparameterTuning.ipynb
├── reports
│ └── figures
│ ├── auroc_curves_model_comparison.pgf
│ ├── auroc_curves_model_maps.pgf
│ ├── buy_type_count-img0.png
│ ├── buy_type_count.pgf
│ ├── buy_type_probability-img0.png
│ ├── buy_type_probability.pgf
│ ├── comparing_optimal_and_actual.pgf
│ ├── feature_importance.pgf
│ ├── game_win_probability_by_rounds-img0.png
│ ├── game_win_probability_by_rounds.pgf
│ └── pairplot.pgf
├── requirements.txt
├── setup.py
├── src
│ ├── __init__.py
│ ├── data
│ │ ├── __init__.py
│ │ ├── constants.py
│ │ ├── make_dataset.py
│ │ └── parse_data.py
│ ├── models
│ │ ├── __init__.py
│ │ └── train_model.py
│ └── visualization
│ ├── __init__.py
│ ├── constants.py
│ └── visualize.py
├── test_environment.py
└── tox.ini
13 directories, 80 files
The source code for the site is licensed under the MIT license, which you can find in the MIT-LICENSE.txt file.
I am not affiliated with hltv.org in any way and all rights and ownership for the provided demofiles are belong to hltv.org.