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 -------- <p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p> 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.