Cookiecutter DataLab

A modification of https://github.com/drivendata/cookiecutter-data-science

Version 1.1.1

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

How to use?

1. pipx install cookiecutter or pip install cookiecutter
2. cookiecutter https://src.cinimex.ru/DLR/cookiecutter-datalab.git

Features

• Gitlab CI config
• Cinimex Artifactory config
• mypy and pre-commit config
• Useful shortcuts script
• Source formatting with black
• nbqa runs black and mypy on notebooks

Shortcuts

This template provides quick commands for common actions.

project --help     
usage: project [init | c (commit) | p (push) | push-master]

Useful shortcuts for your project

positional arguments:
  {init,commit,c,push-dev,p,push-master}
    init                initialize git, conda environment and precommit
    commit (c)          create conventional commit message, bump version,
                        update changelog and make git commit
    push-dev (p)        push to develop
    push-master         push to master

optional arguments:
  -h, --help            show this help message and exit

The resulting directory structure

---

The directory structure of your new project looks like this:

├── 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.readthedocs.io

Installing development requirements

---

pip install -r requirements.txt

Running the tests

---

py.test tests