├── LICENSE            <- MIT License
├── Makefile           <- Makefile with targets to 'load', 'evaluate', and 'plot' ('make all' runs all three analysis steps)
├── poetry.lock        <- Details of used package versions
├── pyproject.toml     <- Lists all dependencies
├── README.md          <- This README file.
├── docs/              
|    └──               <- Slides of the practical tutorial
├── data/
|    └──               <- A copy of the handwritten digit dataset provided by scikit-learn
|
├── results/
|    ├── estimates/
|    │    └──          <- Generated estimates of classifier performance
|    └── figures/
|         └──          <- Generated figures
|
├── scrips/
|    ├── load_data.py                       <- Downloads the dataset to specified 'data-path'
|    ├── evaluate_hyper_params_effect.py    <- Runs cross-validated hyper-parameter evaluation
|    ├── plot_hyper_params_effect.py        <- Summarizes results of evaluation in a figure
|    └── run_analysis.sh                    <- Runs all analysis steps
|
└── src/
    ├── hyper/
    │    ├──  __init__.py                   <- Makes 'hyper' a Python module
    │    ├── grid.py                        <- Functionality to sample hyper-parameter grid
    │    ├── evaluation.py                  <- Functionality to evaluate classifier performance, given hyper-parameters
    │    └── plotting.py                    <- Functionality to visualize results
    └── setup.py                            <- Makes 'hyper' pip-installable (pip install -e .)  

git clone https://github.com/athms/reproducible-modelling

cd reproducible-modelling/
poetry install

cd src/
poetry run pip install -e .

cd scripts
poetry run bash run_analysis.sh

poetry run make <ANALYSIS TARGET>