FairTreeExperiments

This repository contains the codebase for the FairTree experiments. To ensure the project works seamlessly, you need to set the PYTHONPATH environment variable properly.

Critical: Set up constants.py

This repository uses constants.py for local configuration. This file is ignored by Git, so changes remain local to your system.

To set it up:

1. Copy the template file:

   cp constants.py.template constants.py

2. Edit constants.py and replace placeholder values with your local path information:

   LOCAL_PATH = "/your/local/path"

Setting Up PYTHONPATH

The PYTHONPATH environment variable must point to the root directory of this repository and the FairTree subdirectory. This allows Python to locate the necessary modules when running scripts or importing them.

Steps to Set PYTHONPATH

After cloning the repository, follow these steps:

1. Determine the Full Path to the Repository
   Replace PATH_TO_REPO in the instructions below with the absolute path to where you cloned this repository.

   Example: If you cloned the repository to /home/user/projects/FairTreeExperiments, use /home/user/projects/FairTreeExperiments as the PATH_TO_REPO.

2. Set PYTHONPATH

For Bash Users

To temporarily set PYTHONPATH for the current session:

export PYTHONPATH=PATH_TO_REPO:PATH_TO_REPO/FairTree

To make it permanent (persisting across sessions), add the following line to your ~/.bashrc (or ~/.bash_profile on macOS):

export PYTHONPATH=PATH_TO_REPO:PATH_TO_REPO/FairTree

After adding the line, reload your shell configuration:

source ~/.bashrc

For Fish Users

set -x PYTHONPATH PATH_TO_REPO PATH_TO_REPO/FairTree

To make it permanent (persisting across sessions), edit your Fish configuration file (~/.config/fish/config.fish) and add the following line:

set -x PYTHONPATH PATH_TO_REPO PATH_TO_REPO/FairTree

After editing, reload the configuration:

source ~/.config/fish/config.fish

Verify PYTHONPATH

To ensure PYTHONPATH is set correctly, run the following command in your shell:

echo $PYTHONPATH

You should see output similar to:

/home/user/projects/FairTreeExperiments:/home/user/projects/FairTreeExperiments/FairTree

Running the methods, hyperparamter optimization and evaluation

For running a single run of the one-tree or dual-tree approach, you have to use algos_one_tree/method.py or algos_two_trees/method.py.

The hyperparameter search can be performed using the script run_hp_opt.sh in the corresponding directory.

In the directory evaluation, you can find the python files that retrain the models for the best found hyperparameters, i.e., one_tree_opt.py, two_tree_opt.py, and rel_threshold_optimizer_opt.py.

Be careful to pass the correct paths as arguments.