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Active Learning for SN photometric classification
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crp4 Paper DOI arXiv


Active Learning for Supernova Photometric Classification

This repository holds the code and data used in Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning, by Ishida, Beck, Gonzalez-Gaitan, de Souza, Krone-Martins, Barrett, Kennamer, Vilalta, Burgess, Quint, Vitorelli, Mahabal and Gangler, 2018.

This is one of the products of COIN Residence Program #4, which took place in August/2017 in Clermont-Ferrand (France).

We kindly ask you to include the full citation if you use this material in your research: Ishida et al, 2019, MNRAS, 483 (1), 2–18.

Full documentation can be found at readthedocs.


For code:

  • Python>=3.7
  • argparse>=1.1
  • matplotlib>=3.1.1
  • numpy>=1.17.0
  • pandas>=0.25.0
  • setuptools>=41.0.1
  • scipy>=1.3.0
  • scikit-learn>=0.20.3
  • seaborn>=0.9.0

For documentation:

  • sphinx>=2.1.2


The current version runs in Python-3.7.

We recommend you use anaconda to create a suitable environment.

To set up the enviroment clone this repository, navigate to its location in the terminal and do::

>> conda env create -f environment.yml

Once the environment is created, activate it using::

>> conda activate ActSNClass

You will notice a (ActSNCLass) to the left of your terminal line. This means everything is ok!

In order to install this code you should clone this repository and do::

(ActSNClass) >> python install
You can’t perform that action at this time.