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A template for VIP analytics module projects

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VIP-ALIEN analytics project template

A template for the projects of the analytics module in VIP-ALIEN Consortium

Project Organization


├── LICENSE
├── .pylintrc          <- Python style guidance
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── env                <- CI configuration
├── 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               <- Documentation of the project design, engineering method, etc...
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering)
│                         and a short `-` delimited description, e.g.
│                         `1.0-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
│   │   ├── base_models.py         <- Abtract classes that implemented models should inherit from. 
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualizer.py
│   │
│   └── api  <- Scripts to connect to frontend and send interactive plots 
│       └── endpoints.py <- Flask app defining endpoints to which frontend could make requests
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

Setup

conda create -n vip-env python=3.7
conda activate vip-env
pip install -r requirements.txt

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  • Makefile 45.9%
  • Python 27.7%
  • Jupyter Notebook 18.6%
  • Dockerfile 7.8%