Code

Code to classify supernovae based on their lightcurves.

Running

First unzip the data files in the data directory by

tar -xvf SIMGEN_PUBLIC_DES.tar.gz

Next preprocess the data by

python preprocess.py

which will create 5 random augmentations of missing data. You can train the default model (host galaxy redshift, 50% training data, unidirectional 2 layer LSTM with 16 hidden units in each layer) by

python run.py -f test.ini

After 200 epochs this should have an AUC of around 0.986, an accuracy of 94.8% and an F1 score of 0.64. The training loss should be just below the test loss. To run with a GPU (note here it is better to run with a larger batch size)

THEANO_FLAGS=device=gpu,floatX=float32 python run.py -f test.ini

To run all the combinations of models in the paper

python arch.py