This project aims to use a neural net to classify galaxies in the Radio Galaxy Dataset [1] created by Griese et al. The goal is to find one of four classes, FR-I, FR-II, compact, or bent, for each galaxy image.
The dataset is a collection of several catalogues using the FIRST radio galaxy survey [2]. To these images, the following license applies:
"Provenance: The FIRST project team: R.J. Becker, D.H. Helfand, R.L. White M.D. Gregg. S.A. Laurent-Muehleisen. Copyright: 1994, University of California. Permission is granted for publication and reproduction of this material for scholarly, educational, and private non-commercial use. Inquiries for potential commercial uses should be addressed to: Robert Becker, Physics Dept, University of California, Davis, CA 95616"
To use the code in this repository, install the conda virtual environment found in
environment.yml. An installation of Miniforge3 is recommended since
it provides a minimal installation of Python together with the fast and reliable mamba package
manager.
The environment can be installed via
$ mamba env create -f environment.yml
and can be activated by calling
$ mamba activate rolf
A dev version of the environment can be installed via the file environment_dev.yml
After you installed and activated the environment, please install the rolf package
using pip
$ pip install -e .
Additionally, you will need an installation of cuda >= 12.1 to fully utilize PyTorch.
Versions lower than 12.1 may work too, but have not been tested. Change the version
in the environment file depending on the version installed on your system.
Make sure you have installed the environment and rolf and have activated it
call
$ rolf-info
to print an overview of available commands.
$ rolf-info --tools
will print an overview of all available command-line interface (CLI) tools.
Before any model can be trained, please call
$ rolf-data -n -o build
in the root directory of the repository. This will download the data from the
list of URLs in urls.toml. Then call
$ rolf-unpack build/galaxy_data_h5.zip -o data
and
$ rolf-unpack build/galaxy_data.zip -o data
to unpack the data to the data directory. The directories are created automatically.
Config files for both the training and the hyperparameter optimization are provided in the configs directory
and are loaded per default when calling any of the following CLI tools.
- The training of ROLF can be started using
$ rolf-train
- The training of the random forest classifier ROMF can be started using
$ romf-train
- The hyperparameter optimization for ROLF can be started via
$ rolf-optim
- The hyperparameter optimization of the random forest classifier can be started by calling
$ romf-optim
All optional arguments of the CLI tools can be printed by adding the --help flag.
We have provided pre-trained models in the trained_models directory.
The ROLF model can be loaded and evaluated using the classification_viewer.ipynb notebook
found in the notebooks directory. This notebook can also be used to evaluate checkpoints
of models trained via rolf-train.
The training progress of ROLF can be monitored using TensorBoard:
$ tensorboard --logdir <path/to/checkpoints/directory>
The hyperparameter optimization can be monitored using Optuna Dashboard:
$ optuna-dashboard <path/to/database.sqlite3>
[1] Griese, F., Kummer, J., & Rustige, L., "Radio Galaxy Dataset (v0.1.3)", Zenodo (2022). https://zenodo.org/doi/10.5281/zenodo.7113623
[2] R. H. Becker, R. L. White, D. J. Helfand, "The FIRST Survey: Faint Images of the Radio Sky at Twenty Centimeters", The Astrophysical Journal, Vol. 450, p. 559 (1995).
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