The challenge is to create an agent that can succeed in the game of Halite IV. (Kaggle Proj) https://www.kaggle.com/c/halite
Project description
halite
Created 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/halite
How-to Perform Inference
$ conda create -n testenv python=3.6
$ conda activate testenv
$ pip install halite
$ # TODO
How-to Develop
$ git clone https://github.com/iainwo/kaggle.git
$ cd halite/
$ make create_environment
$ conda activate halite
$ make requirements
$ vim my_changes.py
$ make data
$ make model
$ make predictions
Other Commands
(my-kaggle-project) talisman-2:my-kaggle-project iainwong$ make
Available rules:
build Build python package
clean Delete all compiled Python files
create_environment Set up python interpreter environment
data Make Dataset
data_final Make Dataset for Kaggle Submission
eda Generate visuals for feature EDA
lint Lint using flake8
model Make Model
predictions Make Predictions
publish Publish python package to PyPi
requirements Install Python Dependencies
requirements_dev Install Development Deps
sync_data_from_s3 Download Data from S3
sync_data_to_s3 Upload Data to S3
test Run unit tests
test_environment Test python environment is setup correctly
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`
│
├── 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.org
Project based on the cookiecutter data science project template. #cookiecutterdatascience
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