Package: halite-agent
haliteCreated by Two Sigma in 2016, more than 15,000 people around the world have participated in a Halite challenge. Players apply advanced algorithms in a dynamic, open source game setting. The strategic depth and immersive, interactive nature of Halite games make each challenge a unique learning environment.The challenge is to create an agent that can succeed in the game of Halite IV.  (Kaggle Proj)https://www.kaggle.com/c/haliteHow-to Pull Episode ReplaysThis can module can be run to scrape episode replays from Kaggle.
These episode replays will be exported to "data/raw" as JSON.
This scraper respects the 60 request/minute rate-limit.
It can be configured further by "# How-to Configure" section.
Source code is available in./halite_agent/data/fetch_dataset.py.
Notebook used to develop the code is in./notebooks/1-2-iwong-scraper.ipynb.$condacreate-ntestenvpython=3.6
$condaactivatetestenv
$pipinstallhalite-agent
$python3-mhalite_agent.data.fetch_datasetdata/rawHow-to ConfigureHALITE_AGENT_FETCH_DATA_EPISODE_WATERMARK=1100 # pull games with avg. score > 1100
HALITE_AGENT_FETCH_DATA_TEAM_WATERMARK=25 # pull teams with rank > 25
HALITE_AGENT_FETCH_DATA_REQUEST_LIMIT=10 # run scraper that can make only 10 requests
HALITE_AGENT_FETCH_DATA_DISCOVERY_BUDGET=0.1 # used 10% of requests to query for new team metadata
HALITE_AGENT_FETCH_DATA_SCRAPER_METADATA_FILEPATH='./scraper_metadata' # store scraper metadata files here
HALITE_AGENT_FETCH_DATA_ARBITRARY_TEAM_ID='5118174' # use this team id to bootstrap the scraperHow-to Develop$gitclonehttps://github.com/iainwo/kaggle.git
$cdhalite/
$makecreate_environment
$condaactivatehalite
$makerequirements
$vim...Other Commands(my-kaggle-project)talisman-2:my-kaggle-projectiainwong$make
Availablerules:

buildBuildpythonpackagecleanDeleteallcompiledPythonfilescreate_environmentSetuppythoninterpreterenvironmentdataMakeDatasetdata_finalMakeDatasetforKaggleSubmissionedaGeneratevisualsforfeatureEDAlintLintusingflake8modelMakeModelpredictionsMakePredictionspublishPublishpythonpackagetoPyPirequirementsInstallPythonDependenciesrequirements_devInstallDevelopmentDepssync_data_from_s3DownloadDatafromS3sync_data_to_s3UploadDatatoS3testRununitteststest_environmentTestpythonenvironmentissetupcorrectlyProject 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`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── 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.orgProject based on thecookiecutter data science project template. #cookiecutterdatascience