batch_norm = True
dropout = 0.
output_bias = True
optimizer = tt.optim.Adam(lr=lr)
mlp = tt.practical.MLPVanilla(
fold_X_train_std.shape[1],
[n_nodes for layer_idx in range(n_layers)],
fold_X_train_std.shape[1],
batch_norm,
dropout,
output_bias=output_bias)
net = nn.Sequential(
ResidualBlock(fold_X_train_std.shape[1], mlp),
DiagonalScaler(fold_X_train_std.shape[1]))
if num_durations > 0:
labtrans = NKSDiscrete.label_transform(num_durations)
fold_y_train_discrete \
= labtrans.fit_transform(*fold_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_'
% (survival_estimator_name, dataset,
fold_X_val_std = transform_features(
fold_X_val, transformer)
fold_X_train_std = fold_X_train_std.astype('float32')
fold_X_val_std = fold_X_val_std.astype('float32')
tic = time.time()
torch.manual_seed(method_random_seed)
np.random.seed(method_random_seed)
batch_norm = True
dropout = 0.
output_bias = False
optimizer = tt.optim.Adam(lr=lr)
net = nn.Sequential(
DiagonalScaler(fold_X_train_std.shape[1]))
if num_durations > 0:
labtrans = NKSDiscrete.label_transform(num_durations)
fold_y_train_discrete \
= labtrans.fit_transform(*fold_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_lr%f_nd%d_cv%d.pt'
% (survival_estimator_name, dataset,
torch.manual_seed(method_random_seed)
np.random.seed(method_random_seed)
batch_norm = True
dropout = 0.0
output_bias = True
mlp = tt.practical.MLPVanilla(X_train_std.shape[1],
[n_nodes for layer_idx in range(n_layers)],
X_train_std.shape[1],
batch_norm,
dropout,
output_bias=output_bias)
net = nn.Sequential(ResidualBlock(X_train_std.shape[1], mlp),
DiagonalScaler(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))