Cookiecutter Data Science

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

Original Project homepage

Requirements to use the cookiecutter template:

Python 2.7 or 3.5
Cookiecutter Python package >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:

$ pip install cookiecutter

$ conda config --add channels conda-forge
$ conda install cookiecutter

To start a new project, run:

cookiecutter https://github.com/indigo-dc/cookiecutter-data-science

The resulting directories

Once you answer all the questions, two directories will be created:

DEEP-OC-<your_project>
<your_project>

each directory is a git repository and has two branches: master and test.

The directory structure of <your_project> looks like this:

├── LICENSE
├── README.md              <- The top-level README for developers using this project.
├── data
│   └── 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 (if many user development), 
│                             and a short `_` delimited description, e.g.
│                             `1.0-jqp-initial_data_exploration.ipynb`.
│
├── 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`
├── test-requirements.txt  <- The requirements file for the test environment
│
├── setup.py               <- makes project pip installable (pip install -e .) so {{cookiecutter.repo_name}} can be imported
├── {{cookiecutter.repo_name}}    <- Source code for use in this project.
│   ├── __init__.py        <- Makes {{cookiecutter.repo_name}} a Python module
│   │
│   ├── dataset            <- 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 make predictions
│   │   └── deep_api.py    <- Main script for the integration with DEEP API
│   │
│   ├── tests              <- Scripts to perfrom code testing
│   │
│   └── 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 directory structure of DEEP-OC-<your_project> looks like this:

├─ Dockerfile             Describes main steps on integrationg DEEPaaS API and
│                         <your_project> application in one Docker image
│
├─ Jenkinsfile            Describes basic Jenkins CI/CD pipeline
│
├─ LICENSE                License file
│
├─ README.md              README for developers and users.
│
├─ docker-compose.yml     Allows running the application with various configurations via docker-compose
│
├─ metadata.json          Defines information propagated to the [DEEP Open Catalog](https://marketplace.deep-hybrid-datacloud.eu)

Documentation

More extended documentation can be found here

Installing development requirements

pip install -r requirements.txt

Running the tests

py.test tests

rahulmparab / cookiecutter-data-science