Predictive Uncertainty in Gradient-Boosted Regression Trees : A Muon Energy Reconstruction Case Study
This repository hosts the code base for the report that I wrote during my internship at INPP, at NCSR Demokritos with Evangelia Drakopoulou at the ANNIE experiment of Fermilab.
To create a conda environment with the needed dependecies run:
conda env create -f env.yml
Then:
conda activate reco_env and
pip install ibug
To reproduce table 1 and train uncertainty models for tables 2 and 3 run bash reproduce_train.sh. The results for the table 1 will be in the 'results' directory.
To reproduce the metrics of each uncertainty model run one-by-one:
python ibug_catb_test.py
python ibug_xgb_test.py
python CBU_pred.py
python ibug_cbu.py