Quickstart guide (GitHub)

Welcome to SuperNNova!

This is a quick start guide so you can start testing our framework. If you want to install SuperNNova as a module, please take a look at Quickstart guide (pip).


Clone the GitHub repository

git clone https://github.com/supernnova/supernnova.git

Setup your environment. 3 options

  1. Create a docker image: Docker .

  2. Create a conda virtual env Environment configuration .

  3. Install packages manually. Inspect conda_env.txt for the list of packages we use.


For quick tests, a database that contains a limited number of light-curves is provided. It is located in tests/raw. For more information on the available data, check Data walkthrough.

Using command line

Build the database .. code:

python run.py --data --dump_dir tests/dump --raw_dir tests/raw --fits_dir tests/fits

Train an RNN

python run.py --train_rnn --dump_dir tests/dump

With this command you are training and validating our Baseline RNN with the test database. The trained model will be saved in a newly created model folder inside tests/dump/models.

The model folder has been named as follows: vanilla_S_0_CLF_2_R_None_saltfit_DF_1.0_N_global_lstm_32x2_0.05_128_True_mean (See below for the naming conventions). This folder’s contents are:

  • saved model (*.pt): PyTorch RNN model.

  • statistics (METRICS*.pickle): pickled Pandas DataFrame with accuracy and other performance statistics for this model.

  • predictions (PRED*.pickle): pickled Pandas DataFrame with the predicitons of our model on the test set.

  • figures (train_and_val_*.png): figures showing the evolution of the chosen metric at each training step.

Remember that our data is split in training, validation and test sets.

You have trained, validated and tested your model. You can now inspect the test light-curves and their predictions in tests/dump/lightcurves.

Using Yaml

Build the database .. code:

python run_yml.py configs_yml/default.yml --mode data

Train an RNN

python run_yml.py configs_yml/default.yml --mode train_rnn

Available modes: data,``train_rnn``, validate_rnn, plot_lcs. Currently RF classification is not suppported with the yaml configurations. An example of classification using existing model is in configs_yml/classify.yml.

Reproduce SuperNNova paper

To reproduce the results of the paper please use the branch paper and run:

cd SuperNNova && python run_paper.py --debug --dump_dir tests/dump

--debug will train simplified models with a reduced number of epochs. Remove this flag for full reproducibility. With the --debug flag on, this should take between 15 and 30 minutes on the CPU.

Naming conventions

  • vanilla/variational/bayesian: The type of RNN to be trained. variational and bayesian are bayesian recurrent networks

  • S_0: seed used for training. Default is 0.

  • CLF_2: number of targets to be used in classification. This case uses two classes: type Ia supernovae vs. all others.

  • R_None: host-galaxy redshift provided. Options: zpho (photometric) or zspe (spectroscopic)

  • saltfit: data used. In our database we split light-curves that have a succesful SALT2 fit (saltfit) and the complete dataset (photometry).

  • DF_1.0: data fraction used in training. With large datasets it is usefult to test training with a fraction of the available training set. In this case we use the whole dataset (1.0).

  • N_global: normalization used. Default: global.

  • lstm: type of layer used. Default lstm.

  • 32x2: hidden layer dimension x number the layers.

  • 0.05: dropout value.

  • 128: batch size.

  • True: if this model is bidirectional.

  • mean: output option. mean is mean pooling.

The naming convention is defined in SuperNNova/conf.py.