X_train_std.shape[1],
batch_norm,
dropout,
output_bias=output_bias)
net = nn.Sequential(ResidualBlock(X_train_std.shape[1], mlp),
Scaler(X_train_std.shape[1]))
optimizer = tt.optim.Adam(lr=lr)
if num_durations > 0:
labtrans = NKSDiscrete.label_transform(num_durations)
y_train_discrete = labtrans.fit_transform(*y_train.T)
surv_model = NKSDiscrete(net, optimizer,
duration_index=labtrans.cuts)
else:
surv_model = NKS(net, optimizer)
model_filename = \
os.path.join(output_dir, 'models',
'%s_%s_exp%d_bs%d_nep%d_nla%d_nno%d_lr%f_nd%d_test.pt'
% (survival_estimator_name, dataset, experiment_idx,
batch_size, n_epochs, n_layers, n_nodes, lr, num_durations))
assert os.path.isfile(model_filename)
if not os.path.isfile(model_filename):
print('*** Fitting with hyperparam:', hyperparam, flush=True)
if num_durations > 0:
surv_model.fit(X_train_std, y_train_discrete,
batch_size, n_epochs, verbose=True)
else:
surv_model.fit(X_train_std, (y_train[:, 0], y_train[:, 1]),
batch_size, n_epochs, verbose=True)
emb_model.save_net(emb_model_filename)
else:
emb_model.load_net(emb_model_filename)
emb_model.net.train()
print('*** Fine-tuning with DKSA...')
torch.manual_seed(fine_tune_random_seed + 1)
np.random.seed(fine_tune_random_seed + 1)
optimizer = tt.optim.Adam(lr=lr)
if num_durations > 0:
labtrans = NKSDiscrete.label_transform(num_durations)
y_train_discrete = labtrans.fit_transform(*y_train.T)
surv_model = NKSDiscrete(emb_model.net, optimizer,
duration_index=labtrans.cuts)
else:
surv_model = NKS(emb_model.net, optimizer)
model_filename = \
os.path.join(output_dir, 'models',
'%s_%s_exp%d_mf%d_msl%d_km%d_'
% (survival_estimator_name, dataset,
experiment_idx, max_features,
min_samples_leaf, use_km)
+
'bs%d_nep%d_nla%d_nno%d_'
% (batch_size, n_epochs, n_layers, n_nodes)
+
'lr%f_nd%d_test.pt'
% (lr, num_durations))
if not os.path.isfile(model_filename):
print('*** Fitting with hyperparam:', hyperparam, flush=True)
