Skip to content
Snippets Groups Projects

bolsonaro

Bolsonaro project of QARMA non-permanents: deforesting random forest using OMP.

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.

├── notebooks          <- notebooks of prototypes etc

├── models             <- trained and serialized models, model predictions, or model summaries

├── 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 bolsonaro can be imported
├── bolsonaro          <- Source code for use in this project.
    ├── __init__.py    <- Makes bolsonaro a Python module

    ├── data           <- Scripts to download or generate data (to store under `/data/*relevant directory*`)
    │   └── make_dataset.py

    ├── models         <- Scripts to create base models (to store under `/models`)
    │   │                 
    │   └── create_model.py

    └── visualization  <- Scripts to create exploratory and results oriented visualizations (to store under `/reports/figures`)
        └── visualize.py

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

Instal project

First install the project pacakge:

pip install -r requirements.txt

Then create a file .env by copying the file .env.example:

cp .env.example .env

Then you must set the project directory in the .env file :

project_dir = "path/to/your/project/directory"	

This directory will be used for storing the model parameters.