Bag-Graph-MIL

Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks

This repository contains the code to replicate the results reported in our AAAI 2022 paper: Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks.

Contributors:

Antonios Valkanas & Soumyasundar Pal (equal first authors), Florence Regol, Mark Coates (Prof. McGill University)

Getting Started

Install the dependencies from the command line with pip:

pip install -r requirements.txt --progress-bar off

Note: When the datasets are small enough we include them in the repository, otherwise we point to the original source where they can be downloaded. We provide all pre-processing code and the plotting code for the diagrams in the paper. Google Drive secondary repo with full datasets for experiments 1, 2, 4.

Training

All our experiments occupy separate folders. To run a specific experiment go to the appropriate directory and follow the instructions below. Unless otherwise specified, the datasets are included in the experiment subfolders.

Running Experiments & Reproducing Results

We perform classification experiments on 5 benchmark MIL datasets, 20 text datasets from the 20 Newsgroups corpus, and the 2016 US election data. In addition, we also consider a distribution regression task of predicting neighborhood property rental prices in New York City.

1. Benchmark MIL Datasets

Go to mil_benchamark folder and run:

python run_trials.py

2. Text Categorization

Navigate to mil_text folder and run:

python run_trials.py

3. Electoral Results Prediction

To get the data go to the source repository and download centroids_cartesian_10.csv and results-2016-election.csv. Create a data subdirectory and copy these files there. Continue by downloading the set_features and cleaned_set_features subdirectories from our drive link and saving them under the same data directory.

In mil_election_classification folder run:

python run_trials.py

After the runs have completed you can get the figure from the paper by running:

python visualization.py

You should get the following figure (top 2 methods are baselines, bottom left is ours, bottom right is ground truth):

4. Rental Price Prediction

Locate mil_rental_data folder and run:

python deepset_main.py              # runs DeepSet backbone trials
python set_transformer_main.py      # runs Set Transformer backbone trials
python evaluate.py                  # statistical significance tests

Cite

Please cite our paper if you use this code in your own work:

@inproceedings{pal_valkanas2022, 
author={S. Pal and A. Valkanas and F. Regol and M. Coates}, 
title = {Bag graph: {M}ultiple instance learning using {B}ayesian graph neural networks},
booktitle={Proc. AAAI Conf. Artificial Intell.}, 
month = {Feb.},
year = {2022},
address = {Online Conference}
}