The goal of this repository is to make a fairly simple file structure that will allow the user to streamline project creation and create standards across groups. Noteably, this is an extremely minimal version of what can be done both with cookiecutter and the cookiecutter-data-science fork.
$ conda config --add channels conda-forge
$ conda install cookiecuttercookiecutter https://github.com/anpatton/cookiecutter-data-science
After you follow the prompts create a conda environment with the same name as the repo name from the propmts
conda create --name repo_name_goes_here
Once our environment is active and has the packages you need (this can be done multiple times as you add/remove dependencies)
conda env export --name env_name_goes_here > env_name_goes_here.yml
The directory structure of your new project looks like this:
The directory structure of your new project looks like this:
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── 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
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── results <- Generated analysis, final results, predictions, etc.
│ └── figures <- Generated graphics and figures
│
├── env_name_goes_here.yml <- The requirements file for reproducing the analysis environment (user created after the fact)
│
├── 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
│ │
│ ├── 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