c['dataset'] = dataset
c = best_lstm(
c) # get best hyperparams from standard LSTM for this dataset
c['channelwise'] = True
hidden_size_choice = list(
int(x) for x in np.logspace(np.log2(4), np.log2(16), base=2, num=6))
c['hidden_size'] = random.choice(hidden_size_choice)
return c
if __name__ == '__main__':
for i in range(10):
try:
c = get_hyperparam_config('eICU')
log_folder_path = create_folder(
'models/experiments/hyperparameters/eICU', c.exp_name)
channel_wise_lstm = BaselineLSTM(config=c,
n_epochs=c.n_epochs,
name=c.exp_name,
base_dir=log_folder_path,
explogger_kwargs={
'folder_format':
'%Y-%m-%d_%H%M%S{run_number}'
})
channel_wise_lstm.run()
except RuntimeError:
continue
from eICU_preprocessing.split_train_test import create_folder
from models.run_lstm import BaselineLSTM
from models.initialise_arguments import initialise_lstm_arguments
from models.final_experiment_scripts.best_hyperparameters import best_lstm
if __name__ == '__main__':
c = initialise_lstm_arguments()
c['exp_name'] = 'StandardLSTM'
c['dataset'] = 'MIMIC'
c['task'] = 'mortality'
c = best_lstm(c)
log_folder_path = create_folder('models/experiments/final/MIMIC/mortality',
c.exp_name)
baseline_lstm = BaselineLSTM(
config=c,
n_epochs=c.n_epochs,
name=c.exp_name,
base_dir=log_folder_path,
explogger_kwargs={'folder_format': '%Y-%m-%d_%H%M%S{run_number}'})
baseline_lstm.run()
from eICU_preprocessing.split_train_test import create_folder
from models.run_lstm import BaselineLSTM
from models.hyperparameter_scripts.eICU.standard_lstm import get_hyperparam_config
if __name__ == '__main__':
for i in range(50):
try:
c = get_hyperparam_config('MIMIC')
log_folder_path = create_folder(
'models/experiments/hyperparameters/MIMIC', c.exp_name)
standard_lstm = BaselineLSTM(config=c,
n_epochs=c.n_epochs,
name=c.exp_name,
base_dir=log_folder_path,
explogger_kwargs={
'folder_format':
'%Y-%m-%d_%H%M%S{run_number}'
})
standard_lstm.run()
except RuntimeError:
continue