init_num_durations))
time_elapsed_filename = model_filename[:-3] + '_time.txt'
print('*** Pre-training...')
# assert os.path.isfile(model_filename)
if not os.path.isfile(model_filename):
# print('*** Fitting with hyperparam:', hyperparam, flush=True)
surv_model.fit(X_train_std,
y_train_discrete,
init_batch_size,
init_n_epochs,
verbose=False)
elapsed = time.time() - tic
# print('Time elapsed: %f second(s)' % elapsed)
np.savetxt(time_elapsed_filename,
np.array(elapsed).reshape(1, -1))
surv_model.save_net(model_filename)
else:
# print('*** Loading ***', flush=True)
surv_model.load_net(model_filename)
elapsed = float(np.loadtxt(time_elapsed_filename))
# print('Time elapsed (from previous fitting): %f second(s)'
# % elapsed)
surv_model.net.train()
pretrain_time = elapsed
print('*** Fine-tuning with DKSA...')
tic = time.time()
torch.manual_seed(fine_tune_random_seed)
np.random.seed(fine_tune_random_seed)
net = nn.Sequential(*surv_model.net.net, nn.Softmax(1))
def main():
parser = setup_parser()
args = parser.parse_args()
if args.which_gpu != 'none':
os.environ["CUDA_VISIBLE_DEVICES"] = args.which_gpu
# save setting
if not os.path.exists(os.path.join(args.save_path, args.model_name)):
os.mkdir(os.path.join(args.save_path, args.model_name))
# label transform
labtrans = DeepHitSingle.label_transform(args.durations)
# data reading seeting
singnal_data_path = args.signal_dataset_path
table_path = args.table_path
time_col = 'SurvivalDays'
event_col = 'Mortality'
# dataset
data_pathes, times, events = read_dataset(singnal_data_path, table_path,
time_col, event_col,
args.sample_ratio)
data_pathes_train, data_pathes_test, times_train, times_test, events_train, events_test = train_test_split(
data_pathes, times, events, test_size=0.3, random_state=369)
data_pathes_train, data_pathes_val, times_train, times_val, events_train, events_val = train_test_split(
data_pathes_train,
times_train,
events_train,
test_size=0.2,
random_state=369)
labels_train = label_transfer(times_train, events_train)
target_train = labtrans.fit_transform(*labels_train)
dataset_train = VsDatasetBatch(data_pathes_train, *target_train)
dl_train = tt.data.DataLoaderBatch(dataset_train,
args.train_batch_size,
shuffle=True)
labels_val = label_transfer(times_val, events_val)
target_val = labtrans.transform(*labels_val)
dataset_val = VsDatasetBatch(data_pathes_val, *target_val)
dl_val = tt.data.DataLoaderBatch(dataset_val,
args.train_batch_size,
shuffle=True)
labels_test = label_transfer(times_test, events_test)
dataset_test_x = VsTestInput(data_pathes_test)
dl_test_x = DataLoader(dataset_test_x, args.test_batch_size, shuffle=False)
net = resnet18(args)
model = DeepHitSingle(net,
tt.optim.Adam(lr=args.lr,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=5e-4,
amsgrad=False),
duration_index=labtrans.cuts)
# callbacks = [tt.cb.EarlyStopping(patience=15)]
callbacks = [
tt.cb.BestWeights(file_path=os.path.join(
args.save_path, args.model_name, args.model_name + '_bestWeight'),
rm_file=False)
]
verbose = True
model_log = model.fit_dataloader(dl_train,
args.epochs,
callbacks,
verbose,
val_dataloader=dl_val)
save_args(os.path.join(args.save_path, args.model_name), args)
model_log.to_pandas().to_csv(os.path.join(args.save_path, args.model_name,
'loss.csv'),
index=False)
model.save_net(
path=os.path.join(args.save_path, args.model_name, args.model_name +
'_final'))
surv = model.predict_surv_df(dl_test_x)
surv.to_csv(os.path.join(args.save_path, args.model_name,
'test_sur_df.csv'),
index=False)
ev = EvalSurv(surv, *labels_test, 'km')
print(ev.concordance_td())
save_cindex(os.path.join(args.save_path, args.model_name),
ev.concordance_td())
print('done')