A short description of the project.
├── 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
-
make setup
- sets up the whole project, automatically trigger by cookiecutter upon project creation- sets up
git
repository if it doesnt exist - downloads submodules and installs DSNs system-wide to access DBs
- sets up
conda env
by creating one if it doesn't exist, or updating it - runs jupyter lab
- sets up
-
make setup-git
- sets up
git
repository if it doesnt exist - downloads submodules and installs DSNs system-wide to access DBs
- sets up
-
make environment
- create or update
conda env
, and then lock the environment by creatingenvironment.yml.lock
file
- create or update
-
make run
- source the
conda env
and runjupyter lab
- source the
-
make pkg-install <name_of_your_pip_or_conda_package>
- adds the package to
environment.yml
file and re-runsmake environment
- adds the package to
Project based on the cookiecutter data science project template. #cookiecutterdatascience