Testing a Boosted Decision Tree Impementation of Omnifold

Overview

This project compares a boosted decision tree implementation of omnifold to DNN based omnifold and Iterative Bayesian Unfolding

Prerequisites

Before you begin, ensure you have the following software installed on your system:

• Git
• Conda (Miniconda/Anaconda)
• CMake (for building RooUnfold)

Installation Instructions

Follow these steps to set up the environment and dependencies required for this project.

1. Clone Dependency Repositories

First, clone the following repositories into your home directory as they are required dependencies:

git clone https://github.com/rymilton/unfolding.git ~/unfolding
git clone https://github.com/ericmetodiev/OmniFold.git ~/OmniFold
git clone https://github.com/ViniciusMikuni/OmniLearn.git ~/OmniLearn
git clone https://gitlab.cern.ch/RooUnfold/RooUnfold.git ~/RooUnfold

2. Build RooUnfold

Once you've cloned the RooUnfold repository, navigate to the directory and build it using the following commands:

cd ~/RooUnfold
mkdir build
cd build
cmake ..
make -j4
cd ..
source build/setup.sh

3. Set Up Conda Environment

To ensure all required Python dependencies are installed, create the conda environment using the provided environment.yml file.

conda env create -f environment.yml
conda activate <environment_name>