CODAC

Instructions for installing the software and running reproducing experiments for CODAC (under review).

Installing

# set up a conda enviroment
conda create -n codac python=3.13
conda activate codac
# install numpy if not installed yet (required by ClustPy)
pip install numpy
# clone the repository
git clone https://github.com/edahelsinki/codac.git
cd codac
# clone ClustPy
git clone https://github.com/collinleiber/ClustPy.git
cd ClustPy
# install the development version accordingly to the instructions in https://github.com/collinleiber/ClustPy
python setup.py install --prefix ~/.local
python setup.py build_ext --inplace

cd ..
# install additional requirements
pip install scikit-learn-extra, wandb, ucimlrepo, active-semi-supervised-clustering, nltk

# install project as a package
pip install -e .

# DONE

Running experiments in HPC

Utilizing slurm for managing jobs, one can use existing scripts. The directory experiments contain the Python code for running experiment loading datasets and creating figures.

Loading datasets

By running the following commands the data are dowloaded and cached automatically.

cd experiments
python data.py

Running experiments

The following commands reproduce the experiments, note that the scripts are designed to be run on high performance cluster. One can see the .sh script for a reference how to run the experiments. The results are created in experiments/results.

Reproducing Fig. 3:

# in experiments
cd scripts

# Figure 3
bash start_experiments_deep.sh
bash start_experiments_shallow.sh
bash start_experiments_a3s.sh
bash start_experiments_a3s_deep.sh
bash start_experiments_ffqs.sh
bash start_experiments_ffqs_deep.sh