This repo uses Forest Cover Type Prediction dataset.
This package allows you to train model to predict an integer classification for the forest cover type.
- Clone this repository to your machine.
- Download Forest Cover Type Prediction dataset, save csv locally (default path is data/train.csv in repository's root).
- Make sure Python 3.9 and Poetry are installed on your machine (I use Poetry 1.1.13).
- Install the project dependencies (run this and following commands in a terminal, from the root of a cloned repository):
poetry install --no-dev- Run train with the following command:
poetry run train -d <path to csv with data> -s <path to save trained model>You can configure additional options (such as hyperparameters) in the CLI. Go to the commit before nested cross-validation (go to the commit 32e25fb48437250bc226450ac9230f2c96e1020d). To get a full list of them, use help:
poetry run train --help- Run MLflow UI to see the information about experiments you conducted:
poetry run mlflow uiExperiments with the models (MLflow):
Linting (flake8) and formatting (black):
TODO:
- error cases test(s)
- valid case test
- mypy (to type annotate the code)
- nox (to combine steps of testing and linting into a single command)
- github actions (to run tests and linters)