This repository contains all the code (in the form of Jupyter Notebooks) to reproduce the results in our paper, "Light Curve Classification with DistClassiPy: a new distance-based classifier".
The accompanying package, DistClassiPy can be found on GitHub at sidchaini/DistClassiPy and can be installed from PyPI by the command pip install distclassipy.
The Jupyter Notebooks are in the notebooks/ directory. There are a total of 10 base notebooks with subvariants for each classification depending on the notebook.
- Download Data
- Distance Metrics
- Preprocess Data (a,b,c)
- Classification (a,b,c)
- Analysis (a,b,c)
- RFC Comparison (a,b,c)
- Hidden Set Results (a,b,c)
- Computational Complexity
- Confidence Comparison (a,b,c)
- Robustness (a,b,c)
Note that a denotes the one-vs-rest classification problem (EA vs notEA), b denotes the binary classification problem (RSCVn vs BYDra) and c denotes the multi-class classification problem (CEP vs DSCT vs RR vs RRc).
If you use DistClassiPy in your research or project, please consider citing the paper:
Chaini, S., Mahabal, A., Kembhavi, A., & Bianco, F. B. (2024). Light Curve Classification with DistClassiPy: a new distance-based classifier. arXiv. https://doi.org/10.48550/arXiv.2403.12120
@ARTICLE{chaini2024light,
author = {{Chaini}, Siddharth and {Mahabal}, Ashish and {Kembhavi}, Ajit and {Bianco}, Federica B.},
title = "{Light Curve Classification with DistClassiPy: a new distance-based classifier}",
journal = {arXiv e-prints},
keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics, Computer Science - Machine Learning},
year = 2024,
month = mar,
eid = {arXiv:2403.12120},
pages = {arXiv:2403.12120},
archivePrefix = {arXiv},
eprint = {2403.12120},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240312120C},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}Siddharth Chaini, Ashish Mahabal, Ajit Kembhavi and Federica B. Bianco.