def get_hyperparam_config(dataset): c = initialise_lstm_arguments() c['mode'] = 'train' c['exp_name'] = 'ChannelwiseLSTM' if dataset == 'MIMIC': c['no_diag'] = True 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
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()