Welcome to the repository for our collaborative project, focusing on implementing various uncertainty estimation models tailored for a retail use-case. The project includes models like LightGBM, LightGBM Quantile Regressor, Bootstrapping, NGBoost, Probabilistic Gradient Boosting Machines (PGBM), Level Set Forecaster (LSF), Conformalized Regression, Temporal Fusion Transformer (TFT), and MQ-CNN.
Under the notebooks folder, you'll find Python notebooks utilized to train and evaluate the models on Kaggle datasets. The Kaggle datasets used for evaluation include:
- Blue Book For Bulldozers (bulldozer)
- Rossmann Store Sales (rossmann)
- Corporación Favorita Grocery Sales Forecasting (favorita)
Ensure you have poetry installed on your local machine. After cloning the project, create a virtual environment and install packages from the pyproject.toml file:
poetry update
poetry install
poetry build
pip install uncertainty_estimation_models-0.1.0-py3-none-any.whl
Make sure to separately install the various dependencies listed below.
For Windows users encountering issues installing the PGBM package, install Build Tools for Visual Studio and ensure you add the compiler cl to your PATH environment variable (see here). Verify Windows can find cl by executing where cl in a Windows command line terminal.
Navigate to the desired directory for the git repo and run:
git clone https://github.com/daroczisandor/uncertainty-estimation.git
cd uncertainty_estimation
Obtain the required datasets from Google Drive and store them in a folder called "datasets" at the top level of this repo.
Contributions are welcome! Feel free to create issues or submit pull requests to enhance and extend the project. 🚀