Official implementation of Exact Reformulation and Optimization for Binary Imbalanced Classification

Quick Start

Installation

Pull Git Repo

git clone git@github.com:PL97/DMO.git

Prepare Environment

conda env update -n dmo --file env.yml
conda activate dmo

Prepare Datasets

Download Dataset

Dataset Name	Download Link
UCI dataset	Download
Fire dataset	Download
eyepacs	Download
ADE-corpus-V2	Download

mkdir data/
mv [dataset] data/

Examples

# Fix precision at real, using wilt dataset, with a prefix threshold 0f 0.8, using a random seed 0
python FPOR.py --ds wilt --alpha 0.8 --seed 0

# Fix recall at precision, using wilt dataset, with a prefix threshold 0f 0.8, using a random seed 0
python FROP.py --ds wilt --alpha 0.8 --seed 0

# Optimize F-beta score, using wilt dataset, with a prefix threshold 0f 0.8, using a random seed 0
python OFBS.py --ds wilt --seed 0

=========99/100===============
lambda: 3.226607916197264e+23, 4.729280783717073e+25, [3.226608e+23]
violation: 3.321038093417883e-05, 3.321038093417883e-05, [0.0181669]
real obj: [[0.5915493]]                  const: [[0.75]]
estimated obj: [[0.616051]]              const: [[0.7818332]]

=========================final evaluation===============================
Train: real obj: [0.61971831]            const: [0.76300578]
Test: real obj: [0.70833333]             const: [0.77272727]

How to cite this work

If you find this gitrepo useful, please consider citing the associated paper using the snippet below:

@inproceedings{travadi2023direct,
  title={Direct Metric Optimization for Imbalanced Classification},
  author={Travadi, Yash and Peng, Le and Cui, Ying and Sun, Ju},
  booktitle={2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)},
  pages={698--700},
  year={2023},
  organization={IEEE}
}