This repository is the official implementation of Mixture Proportion Estimation and PU Learning: A Modern Approach. We also release implementation of relevant baselines for MPE and PU learning. If you find this repository useful or use this code in your research, please cite the following paper:
Garg, S., Wu, Y., Smola, A., Balakrishnan, S., Lipton, Z. (2021). Mixture Proportion Estimation and PU Learning: A Modern Approach. arxiv preprint arXiv:2111.00980.
@inproceedings{garg2021PUlearning,
title={Mixture Proportion Estimation and {PU} Learning: A Modern Approach},
author={Garg, Saurabh and Wu, Yifan and Smola, Alex and Balakrishnan, Sivaraman and Lipton, Zachary},
year={2021},
booktitle={Advances in Neural Information Processing Systems (NeurIPS)}
}
The code is written in Python and uses PyTorch. To install requirements, setup a conda enviornment using the following command:
conda create --file requirements.txt
train.py file is the main entry point for training the model and run the code with the following command:
python train_PU.py --data-type="cifar_DogCat" --train-method="TEDn" --net-type="ResNet" --epochs=1000 --warm-start --warm-start-epochs=100 --alpha=0.5
Change the parameters to your liking and run the experiment. For example, change dataset with varying --data-type and vary algorithm with varying --train-method. We implement the BBE estimator in estimator.py and CVIR algorithm in algorithm.py.
We provide a set of scripts to run experiments. See scripts folder for details. We provide paper results in paper_results folder and the corresponding code for plots in plot_helper.
This repository is licensed under the terms of the Apache-2.0 License.
For more details, refer to the accompanying NeurIPS 2021 paper (Spotlight): Mixture Proportion Estimation and PU Learning: A Modern Approach. If you have questions, please feel free to reach us at sgarg2@andrew.cmu.edu or open an issue.