SeattleFluStudy

Code pertaining to the UW Behavioral Data Science Lab's contributions to the Seattle Flu Study.

How to contibute:

1. Clone this repo: git clone https://github.com/behavioral-data/RobustDataScience.git
2. cd into it: cd RobustDataScience
3. Build the conda environment: make create_environment
4. Follow the project structure outlined below.

Why is this project set up like this?

Great question. For a more satisfying answer than can be provided here, look to the original cookiecutter page:

One thing that I really like about using the setup is that it's really easy to modularize code. Say that you wrote some really handy function in src/visualization.py that you wanted to use in a notebook. One option might have been to write your notebook in the main directory, and use src as a module. This is all well and good for notebook, but what if you have several? A more heinous (and common!) idea might have been to copy and paste over the code to your notebook. However, since we turned src into a module and added it to the PATH in make create_environment, we can just do something like this in our notebook, no matter where it is in the project:

from src.visualization import handy_viz
handy_viz(df)

Project Organization

├── 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

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Project based on the cookiecutter data science project template. #cookiecutterdatascience