'''
num_Category = max event/censoring time * 1.2 (to make enough time horizon)
num_Event = number of evetns i.e. len(np.unique(label))-1
max_length = maximum number of measurements
x_dim = data dimension including delta (num_features)
mask1, mask2 = used for cause-specific network (FCNet structure)
'''
# pdb.set_trace()
if data_mode == 'SYNTHETIC':
(x_dim), (data, time, label), (mask1,
mask2) = impt.import_dataset_SYNTHETIC(
norm_mode='standard')
EVAL_TIMES = [12, 24, 36]
elif data_mode == 'METABRIC':
(x_dim), (data, time, label), (mask1,
mask2) = impt.import_dataset_METABRIC(
norm_mode='standard')
EVAL_TIMES = [144, 288, 432]
elif data_mode == 'MEC':
(x_dim), (data, time,
label), (mask1,
mask2) = impt.import_dataset_MEC(norm_mode='standard')
EVAL_TIMES = [5, 10, 15]
else:
print('ERROR: DATA_MODE NOT FOUND !!!')
_, num_Event, num_Category = np.shape(
mask1) # dim of mask1: [subj, Num_Event, Num_Category]
in_path = data_mode + '/results/'
if not os.path.exists(in_path):
def run_experiment(data_mode):
if data_mode == 'SYNTHETIC':
(x_dim), (data, time, label), (mask1,
mask2) = impt.import_dataset_SYNTHETIC(
norm_mode='standard')
EVAL_TIMES = [12, 24, 36]
elif data_mode == 'METABRIC':
(x_dim), (data, time, label), (mask1,
mask2) = impt.import_dataset_METABRIC(
norm_mode='standard')
EVAL_TIMES = [144, 288, 432]
elif data_mode[0:3] == 'MZZ':
first_ = data_mode.find("_")
second_ = data_mode[first_ + 1:].find("_") + first_ + 1
num_samples = data_mode[first_ + 1:second_]
num_features = data_mode[second_ + 1:]
(x_dim), (data, time, label), (mask1,
mask2) = impt.import_mzz_SYNTHETIC(
num_samples=num_samples,
num_features=num_features,
norm_mode='standard')
EVAL_TIMES = [50, 100, int(max(time))]
else:
print('ERROR: DATA_MODE NOT FOUND !!!')
DATA = (data, time, label)
MASK = (
mask1, mask2
) # masks are required to calculate loss functions without for-loops.
out_path = os.path.join('experiments', data_mode, 'results')
#out_path = data_mode + '/results/'
for itr in range(OUT_ITERATION):
if not os.path.exists(out_path + '/itr_' + str(itr) + '/'):
os.makedirs(out_path + '/itr_' + str(itr) + '/')
max_valid = 0.
max_valid_list = []
log_name = out_path + '/itr_' + str(itr) + '/hyperparameters_log.txt'
for r_itr in range(RS_ITERATION):
print('OUTER_ITERATION: ' + str(itr))
print('Random search... itr: ' + str(r_itr))
new_parser = get_random_hyperparameters(out_path)
print(new_parser)
# get validation performance given the hyper - parameters
tmp_max = get_main.get_valid_performance(DATA,
MASK,
new_parser,
itr,
EVAL_TIMES,
MAX_VALUE=max_valid)
if tmp_max > max_valid:
max_valid = tmp_max
max_parser = new_parser
save_logging(
max_parser, log_name
) # save the hyperparameters if this provides the maximum validation performance
print('Current best: ' + str(max_valid))
max_valid_list.append(max_valid)
result_fpath = os.path.join(out_path, 'itr_' + str(itr),
'performance.txt')
with open(result_fpath, 'w') as f:
f.write('Max:{}\n'.format(np.max(max_valid)))
f.write('Std:{}\n'.format(np.std(max_valid_list)))
print(np.max(max_valid))
print(np.std(max_valid_list))
'''
num_Category = typically, max event/censoring time * 1.2 (to make enough time horizon)
num_Event = number of evetns i.e. len(np.unique(label))-1
max_length = maximum number of measurements
x_dim = data dimension including delta (num_features)
mask1, mask2 = used for cause-specific network (FCNet structure)
EVAL_TIMES = set specific evaluation time horizons at which the validatoin performance is maximized.
(This must be selected based on the dataset)
'''
if data_mode == 'SYNTHETIC':
(x_dim), (data, time, label), (mask1, mask2) = impt.import_dataset_SYNTHETIC(norm_mode = 'standard')
EVAL_TIMES = [12, 24, 36]
elif data_mode == 'METABRIC':
(x_dim), (data, time, label), (mask1, mask2) = impt.import_dataset_METABRIC(norm_mode = 'standard')
EVAL_TIMES = [144, 288, 432]
else:
print('ERROR: DATA_MODE NOT FOUND !!!')
DATA = (data, time, label)
MASK = (mask1, mask2) #masks are required to calculate loss functions without for-loops.
out_path = data_mode + '/results/'
for itr in range(OUT_ITERATION):
if not os.path.exists(out_path + '/itr_' + str(itr) + '/'):
os.makedirs(out_path + '/itr_' + str(itr) + '/')