wurenzhi / Constrained-Labeling-for-Weakly-Supervised-Learning

This repo contains code for Constrained Labeling for Weakly Supervised Learning

Constrained-Labeling-for-Weakly-Supervised-Learning

This repo contains code for Constrained Labeling for Weakly Supervised Learning

If you use this work in an academic study, please cite our paper

@article{arachie2020constrained,
  title={Constrained Labeling for Weakly Supervised Learning},
  author={Arachie, Chidubem and Huang, Bert},
  journal={arXiv preprint arXiv:2009.07360},
  year={2020}
}

Requirements

The library is tested in Python 3.6 and 3.7. Its main requirement is numpy, Tensorflow is also required to train generated labels

Algorithm

The most important script is the train_CLL.py script that contains implementation of the algorithm. The other scripts are secondary classes for running experiments

Examples

The file run_experiments creates synthetic example for the experiment in the paper. It also runs the real data experiments.

To run examples on real datasets from the paper; 1. download the datasets, 2. run generate_weak_signals, and 3. use run_experiment script in the run_experiments file