make_dataset.make_dataset(settings)
lu.print_blue("Finished constructing dataset")
################
# TRAINING
################
if settings.train_rnn:
# Train
if settings.cyclic:
train_rnn.train_cyclic(settings)
else:
train_rnn.train(settings)
# Obtain predictions
validate_rnn.get_predictions(settings)
# Compute metrics
metrics.get_metrics_singlemodel(settings, model_type="rnn")
# Plot some lightcurves
early_prediction.make_early_prediction(settings)
lu.print_blue(
"Finished rnn training, validating, testing and plotting lcs")
if settings.train_rf:
train_randomforest.train(settings)
# Obtain predictions
validate_randomforest.get_predictions(settings)
# Compute metrics
metrics.get_metrics_singlemodel(settings, model_type="rf")
if installed by "pip install supernnova" you can run this code in the parent folder (where run.py is) """ # get config args args = conf.get_args() # create database args.data = True # conf: making new dataset args.dump_dir = "tests/dump" # conf: where the dataset will be saved args.raw_dir = "tests/raw" # conf: where raw photometry files are saved args.fits_dir = "tests/fits" # conf: where salt2fits are saved settings = conf.get_settings(args) # conf: set settings make_dataset.make_dataset(settings) # make dataset # train model args.data = False # conf: no database creation args.train_rnn = True # conf: train rnn args.dump_dir = "tests/dump" # conf: where the dataset is saved args.nb_epoch = 2 # conf: training epochs settings = conf.get_settings(args) # conf: set settings train_rnn.train(settings) # train rnn # validate (test set classificatio) args.data = False # conf: no database creation args.train_rnn = False # conf: no train rnn args.validate_rnn = False # conf: validate rnn args.dump_dir = "tests/dump" # conf: where the dataset is saved settings = conf.get_settings(args) # conf: set settings validate_rnn.get_predictions(settings) # classify test set