This is the Python implementation belonging to the paper Learning Exceptional Subgroups by End-to-End Maximizing KL-divergence. The repository also includes the code required to replicate the results from the paper e.g. the implementation of Robust Subgroup Discovery. We additionally provide a demo in a jupyter notebook to provide an easy starting point. All our experiments are run on a machine equipped with a DGX-A100, AMD EPYC 7742 and 256GB of memory.
- PyTorch
- Numpy
- Pandas
- Matplotlib
- flowtorch
- pysubgroup
- sklearn
- tqdm
- data: We provide the the insurance dataset used in the paper and the demo.
- experiments: Python scripts used to run all the experiments
- scripts: Bash scripts to run the experiments with various sets of parameters
- src: Contains the source code of Syflow and RSD, as well as further utils to replicate the results
In the jupyter notebook demo.ipynb, we provide a demo to show how to use
Syflow. As an example, we use the Insurance
dataset provided in data/ and replicate the plot below. The parameters for
Syflow can be set using the SyflowConfig class.
To ease the readability, the majority of the code is implemented in src/demo_utils.py.
To run the experiments from the paper, either the Python scripts in experiments. These
scripts can be called individually or via the bash scripts provided in scripts.
In both cases, the scripts generate a folder results with subfolders corresponding
to the specific experiment suite. For real-world data, we also output the description for
the discovered subgroups.
If you find our work useful for your research, please consider citing:
@inproceedings{xu2024learningicml,
title = {Learning Exceptional Subgroups by End-to-End Maximizing KL-divergence},
author = {Xu, Sascha and Walter, Nils Philipp and Kalofolias, Janis and Vreeken, Jilles},
booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},
year = {2024},
organization = {PMLR},
}
This work is licensed under CC BY-NC-SA 4.0
