palette = sns.color_palette("bright", 2)
sns.scatterplot(DA_embedded[:, 0],
DA_embedded[:, 1],
hue=y_DA_list,
legend='full',
palette=palette)
plt.savefig(figure_folder + '/DA.png')
plt.clf()
sns.set(rc={'figure.figsize': (11.7, 8.27)})
palette = sns.color_palette("bright", 2)
sns.scatterplot(TO_embedded[:, 0],
TO_embedded[:, 1],
hue=y_TO_list,
legend='full',
palette=palette,
markers='s')
plt.savefig(figure_folder + '/TO.png')
from helper_function import plot_feature_dist, plot_feature_pair_dist
plot_feature_dist(figure_folder + '/SO.png', SO_features, yt_test, 'SO')
plot_feature_dist(figure_folder + '/DA.png', DA_features, yt_test, 'DA')
plot_feature_dist(figure_folder + '/TO.png', target_features, yt_test, 'TO')
plot_feature_pair_dist(figure_folder + '/DA_TO.png',
DA_features,
target_features,
yt_test,
yt_test,
label=['DA', 'TO'])
test_auc_list, val_auc_list, src_test_list,
DA_model_folder + '/AUC_src_{}.png'.format(DA_model_name))
plot_auc_iterations(
test_auc_list, val_auc_list,
DA_model_folder + '/AUC_Final_{}.png'.format(DA_model_name))
if best_val_auc < val_target_AUC:
best_val_auc = val_target_AUC
target_saver.save(sess, DA_model_folder + '/target_best')
np.savetxt(os.path.join(DA_model_folder, 'test_stat.txt'),
test_target_stat)
np.savetxt(os.path.join(DA_model_folder, 'test_best_auc.txt'),
[test_target_AUC])
print_red('Update best:' + DA_model_folder)
if iteration % 1000 == 0:
indices = np.random.randint(0, Xs_tst.shape[0], 100)
source_feat = h_src.eval(session=sess,
feed_dict={
xs: Xs_tst[indices, ],
g_training: False
})
target_feat = h_trg.eval(session=sess,
feed_dict={
xt: Xt_tst[indices, ],
g_training: False
})
plot_feature_pair_dist(
DA_model_folder +
'/feat_{}_iter_{}.png'.format(DA_model_name, iteration),
np.squeeze(source_feat), np.squeeze(target_feat),
ys_tst[indices], yt_tst[indices], ['source', 'target'])
gc.collect()
fig_size=(10, 10))
# save models
if iteration % 100 == 0:
target_saver.save(sess,
DA_model_folder + '/target',
global_step=iteration)
if best_val_auc < val_target_AUC:
best_val_auc = val_target_AUC
target_saver.save(sess, DA_model_folder + '/target_best')
np.savetxt(os.path.join(DA_model_folder, 'test_stat.txt'),
test_target_stat)
np.savetxt(os.path.join(DA_model_folder, 'test_best_auc.txt'),
[test_target_AUC])
print_red('Update best:' + DA_model_folder)
if iteration % 10000 == 0:
source_feat = h_src.eval(session=sess,
feed_dict={
xs: Xs_tst,
is_training: False,
dis_training: False
})
target_feat = h_trg.eval(session=sess,
feed_dict={
xt: Xt_tst,
is_training: False,
dis_training: False
})
plot_feature_pair_dist(
DA_model_folder + '/feat_{}.png'.format(DA_model_name),
np.squeeze(source_feat), np.squeeze(target_feat), ys_tst,
yt_tst, ['source', 'target'])
