def get_hyperparam_config(dataset):
c = initialise_tpc_arguments()
c['mode'] = 'train'
c['exp_name'] = 'TPC'
if dataset == 'MIMIC':
c['no_diag'] = True
c['dataset'] = dataset
c['model_type'] = 'tpc'
# hyper-parameter grid
param_grid = {
#'batchnorm': ['mybatchnorm', 'pointonly', 'temponly', 'low_momentum', 'none', 'default'],
'main_dropout_rate':
[0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5],
'last_linear_size':
list(
int(x)
for x in np.logspace(np.log2(16), np.log2(64), base=2, num=16)),
'diagnosis_size':
list(
int(x)
for x in np.logspace(np.log2(16), np.log2(64), base=2, num=16)),
}
#c['batchnorm'] = random.choice(param_grid['batchnorm'])
c['main_dropout_rate'] = random.choice(param_grid['main_dropout_rate'])
c['last_linear_size'] = random.choice(param_grid['last_linear_size'])
c['diagnosis_size'] = random.choice(param_grid['diagnosis_size'])
return c
def get_hyperparam_config(dataset):
c = initialise_tpc_arguments()
c['mode'] = 'train'
c['exp_name'] = 'TPC'
if dataset == 'MIMIC':
c['no_diag'] = True
c['dataset'] = dataset
c['model_type'] = 'tpc'
c = best_global(c)
# hyper-parameter grid
param_grid = {
'n_layers': [5, 6, 7, 8, 9, 10, 11,
12], # most promising range is 5-12 layers
'temp_kernels':
list(
int(x)
for x in np.logspace(np.log2(4), np.log2(16), base=2, num=16)),
'point_sizes':
list(
int(x)
for x in np.logspace(np.log2(4), np.log2(16), base=2, num=16)),
'learning_rate':
list(np.logspace(np.log10(0.001), np.log10(0.01), base=10, num=100)),
'batch_size':
list(
int(x)
for x in np.logspace(np.log2(4), np.log2(512), base=2, num=8)
), # might have to search between 4 and 32 to avoid memory issues
'temp_dropout_rate':
[0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5],
'kernel_size': { #1: list(range(4, 25)),
#2: [5, 6, 7, 8, 9, 10],
#3: [3, 4, 5, 6, 7],
#4: [2, 3, 4, 5, 6],
5: [2, 3, 4, 5],
6: [2, 3, 4, 5],
7: [2, 3, 4, 5],
8: [2, 3, 4, 5],
9: [3, 4],
10: [3, 4],
11: [3, 4],
12: [3, 4]
}
}
c['n_layers'] = random.choice(param_grid['n_layers'])
c['kernel_size'] = random.choice(param_grid['kernel_size'][c['n_layers']])
c['temp_kernels'] = [random.choice(param_grid['temp_kernels'])
] * c['n_layers']
c['point_sizes'] = [random.choice(param_grid['point_sizes'])
] * c['n_layers']
c['learning_rate'] = round(random.choice(param_grid['learning_rate']), 5)
c['batch_size'] = random.choice(param_grid['batch_size'])
c['temp_dropout_rate'] = random.choice(param_grid['temp_dropout_rate'])
return c
def get_hyperparam_config(dataset):
c = initialise_tpc_arguments()
c['mode'] = 'train'
c['exp_name'] = 'TempWeightShare'
if dataset == 'MIMIC':
c['no_diag'] = True
c['dataset'] = dataset
c['model_type'] = 'temp_only'
c['share_weights'] = True
c = best_global(c)
temp_kernels_choice = list(
int(x) for x in np.logspace(np.log2(16), np.log2(64), base=2, num=9))
c['temp_kernels'] = [random.choice(temp_kernels_choice)] * c['n_layers']
return c
from eICU_preprocessing.split_train_test import create_folder
from models.run_tpc import TPC
from models.initialise_arguments import initialise_tpc_arguments
if __name__ == '__main__':
c = initialise_tpc_arguments()
c['mode'] = 'test'
c['exp_name'] = 'TempWeightShare'
c['model_type'] = 'temp_only'
c['share_weights'] = True
c['temp_kernels'] = [32] * c['n_layers']
log_folder_path = create_folder('models/experiments/final', c.exp_name)
temp_weight_share = TPC(
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}'})
temp_weight_share.run()
