def load_model(model_file, data, clinical, surv_time, edge_index):
"""The function for loading a pytorch model
"""
#############
m = MyNet(edge_index).to(device)
model = CoxPH(m, tt.optim.Adam(0.0001))
#_, features = m(data)
#print(features)
model.load_net(model_file)
prediction = model.predict_surv_df(data)
#print(prediction)
fs = features(model.net, torch.from_numpy(data).to(device))
#print(fs)
#ev = EvalSurv(prediction, clinical, surv_time)
#prediction = ev.concordance_td()
return prediction, fs
batch_norm,
dropout,
output_bias=output_bias)
optimizer = tt.optim.Adam(lr=lr)
surv_model = CoxPH(net, optimizer)
model_filename = \
os.path.join(output_dir, 'models',
'%s_%s_exp%d_%s_bs%d_nep%d_nla%d_nno%d_lr%f_test.pt'
% (survival_estimator_name, dataset, experiment_idx,
val_string, batch_size, n_epochs, n_layers, n_nodes, lr))
assert os.path.isfile(model_filename)
print('*** Loading ***', flush=True)
surv_model.load_net(model_filename)
surv_df = surv_model.predict_surv_df(X_test_std)
surv = surv_df.to_numpy().T
print()
print('[Test data statistics]')
sorted_y_test_times = np.sort(y_test[:, 0])
print('Quartiles:')
print('- Min observed time:', np.min(y_test[:, 0]))
print('- Q1 observed time:',
sorted_y_test_times[int(0.25 * len(sorted_y_test_times))])
print('- Median observed time:', np.median(y_test[:, 0]))
print('- Q3 observed time:',
sorted_y_test_times[int(0.75 * len(sorted_y_test_times))])
print('- Max observed time:', np.max(y_test[:, 0]))
