})
val_target_stat = np.exp(val_target_logit)
val_target_AUC = roc_auc_score(yt_val, val_target_stat)
test_auc_list.append(test_target_AUC)
val_auc_list.append(val_target_AUC)
np.savetxt(DA_model_folder + '/test_auc.txt', test_auc_list)
np.savetxt(DA_model_folder + '/val_auc.txt', val_auc_list)
# loss
M_loss_list.append(
M_loss
) # ;D_loss_list.append(D_loss);sC_loss_list.append(sC_loss)
np.savetxt(DA_model_folder + '/MMD_loss.txt', M_loss_list)
# np.savetxt(DA_model_folder+'/G_loss.txt',G_loss_list);
np.savetxt(DA_model_folder + '/src_clf_loss.txt', sC_loss_list)
# print and plot results
print_block(symbol='-', nb_sybl=60)
print_yellow(os.path.basename(DA_model_folder))
if nb_trg_labels > 0:
train_auc_list.append(train_target_AUC)
tC_loss_list.append(tC_loss)
np.savetxt(os.path.join(DA_model_folder, 'trg_train_auc.txt'),
train_auc_list)
np.savetxt(os.path.join(DA_model_folder, 'trg_clf_loss.txt'),
tC_loss_list)
print_green(
'AUC: T-test {0:.4f}, T-valid {1:.4f}, T-train {2:.4f}, S-test: {3:.4f}'
.format(test_target_AUC, val_target_AUC, train_target_AUC,
test_source_AUC))
print_yellow(
'Loss: D:{:.4f}, S:{:.4f}, T:{:.4f}, Iter:{:}'.format(
M_loss, sC_loss, tC_loss, iteration))
test_source_logit = model_test(sess, source_logit, xs, Xs_tst)
# test_source_logit=source_logit.eval(session=sess,feed_dict={xs:Xs_tst,g_training:False,d_training:False})
test_source_stat=np.exp(test_source_logit);test_source_AUC=roc_auc_score(ys_tst,test_source_stat);src_test_list.append(test_source_AUC)
# test_target_logit = target_logit.eval(session=sess,feed_dict={xt:Xt_tst, g_training: False, d_training: False})
test_target_logit = model_test(sess, target_logit, xt, Xt_tst)
test_target_stat = np.exp(test_target_logit);test_target_AUC = roc_auc_score(yt_tst, test_target_stat)
val_target_logit = target_logit.eval(session=sess,feed_dict={xt:Xt_val, g_training: False})
val_target_stat = np.exp(val_target_logit);val_target_AUC = roc_auc_score(yt_val, val_target_stat)
test_auc_list.append(test_target_AUC);val_auc_list.append(val_target_AUC)
np.savetxt(DA_model_folder+'/test_auc.txt', test_auc_list);np.savetxt(DA_model_folder+'/val_auc.txt', val_auc_list)
# loss
M_loss_list.append(M_loss)# ;D_loss_list.append(D_loss);sC_loss_list.append(sC_loss)
np.savetxt(DA_model_folder+'/MMD_loss.txt',M_loss_list);# np.savetxt(DA_model_folder+'/G_loss.txt',G_loss_list);
np.savetxt(DA_model_folder+'/src_clf_loss.txt',sC_loss_list)
# print and plot results
print_block(symbol = '-', nb_sybl = 60);print_yellow(os.path.basename(DA_model_folder))
if nb_trg_labels > 0:
train_auc_list.append(train_target_AUC);tC_loss_list.append(tC_loss)
np.savetxt(os.path.join(DA_model_folder,'trg_train_auc.txt'), train_auc_list)
np.savetxt(os.path.join(DA_model_folder,'trg_clf_loss.txt'),tC_loss_list)
print_green('AUC: T-test {0:.4f}, T-valid {1:.4f}, T-train {2:.4f}, S-test: {3:.4f}'.format(test_target_AUC, val_target_AUC, train_target_AUC, test_source_AUC))
print_yellow('Loss: D:{:.4f}, S:{:.4f}, T:{:.4f}, Iter:{:}'.format(M_loss, sC_loss, tC_loss, iteration))
plot_LOSS(DA_model_folder+'/loss_{}.png'.format(DA_model_name), M_loss_list, sC_loss_list, tC_loss_list)
plot_loss(DA_model_folder, M_loss_list, M_loss_list, DA_model_folder+'/MMD_loss_{}.png'.format(DA_model_name))
plot_src_trg_AUCs(DA_model_folder+'/AUC_src_{}.png'.format(DA_model_name), train_auc_list, val_auc_list, test_auc_list, src_test_list)
plot_AUCs(DA_model_folder+'/AUC_trg_{}.png'.format(DA_model_name), train_auc_list, val_auc_list, test_auc_list)
else:
print_green('AUC: T-test {0:.4f}, T-valid {1:.4f}, S-test: {2:.4f}'.format(test_target_AUC, val_target_AUC, test_source_AUC))
print_yellow('Loss: D:{:.4f}, S:{:.4f}, Iter:{:}'.format(M_loss, sC_loss, iteration))
plot_loss(DA_model_folder, M_loss_list, sC_loss_list, DA_model_folder+'/loss_{}.png'.format(DA_model_name))
plot_loss(DA_model_folder, M_loss_list, M_loss_list, DA_model_folder+'/MMD_lOSS_{}.png'.format(DA_model_name))