Based off this original cookiecutter, I have changed:
- Folder structure
- Use pdoc instead of Sphinx. This documentation will only be internal
- Simplified some of the make commands
- added pre-commit
- using conda instead of virualenv
NOTE: I have not yet figured out how to use s3 and aws_profile options but plan to include in the future
Also borrowed some ideas from this R cookiecutter
cookiecutter cookiecutter-data-science
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.
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ ├── exploratory <- Quick exploration of data
| └── reports <- More mature documents that create a repeated output
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ ├── understand <- Finalised analysis pieces on a certain area
│ ├── presentations <- Presentation files with the date at the start, e.g. 24_02_06_title
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── environment.yaml <- Conda environment file for reproducing the analysis environment.
├── requirements.txt <- Installs the local package in -e mode and any non-conda packages
│
├── pyproject.toml <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ └── mypkg/
│ __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
│
└── docs <- Documentation from either make docs_md or docs_html