chuwyler/irismil

Using Multiple Instance Learning for Explainable Solar Flare Prediction

Using Multiple Instance Learning for Explainable Solar Flare Prediction

This github repository provides data, code and some results for the paper:

• Official version on Astronomy & Computing: https://authors.elsevier.com/sd/article/S2213-1337(22)00082-8
• Pre-print on arXiv: https://arxiv.org/abs/2203.13896

Getting the data

The dataset is available via Zenodo:

IRIS Multiple Instance Learning Dataset (9.4 Gb, Numpy npz-Format)

Running the code

The code was run on Python 3.6.9 and with the package versions listed in the requirements.txt file. To run it, adhere to the following steps:

1. Create a virtual environment and install the required packages, e.g. with virtualenv:

virtualenv -p python3 irismil_env
source irismil_venv/bin/activate
pip install -r requirements.txt

2. Run the model_runner.py script with

python model_runner.py <model_name> <parameter_value> <runs_per_fold>

e.g. to run an ibMIL model with r=3 and ten runs for each of the three CV-folds:

python model_runner.py ibMIL 3 10

Videos (10 September 2014 Flare)

Pre-flare phase only:

pf0910_sji_overplot.mp4

Whole observation:

fl0910_sji_overplot.mp4