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league-ml2

Machine learning, data collection, and data analysis of League of Legends games via Riot API

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

The entrypoint for the code in this project is run_process.py.

run_process.py modes:

  • crawl
  • history
  • train
  • score

crawl

To build a dataset of matches, one must query for a summoner's match history. Using that history, one can then acquire more summoner IDs, and continue crawling.

Parameters
  • prime_data_dir # directory of data that has already been crawled
  • summoner_name # if no prime_data_dir supplied, supply a summoner_name with which to begin the crawl
  • crawl_time # time in hours to crawl

history

Look up a summoner name or encrypted id, and aggregate their history on all champions played

Parameters
  • summoner_name # plaintext of summoner name
  • summoner_id # encrypted summoner id, will be used over summoner name if available
  • queue # what type of queue
  • season # specific season

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2nd pass at a League of Legends machine learning project

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