strategy = 'isolated_segment'
dataset_name = 'IMDBMULTI'
residual_type = 'none'
if 1:
epoch_number = 500
result_obj = ResultSaving('', '')
result_obj.result_destination_folder_path = './result/AuGBert/' + strategy + '/' + dataset_name + '/'
result_list = []
time_list = []
for fold in range(1, 11):
result_obj.result_destination_file_name = dataset_name + '_' + str(
fold) + '_' + str(
epoch_number) + '_' + residual_type + '_' + strategy
loaded_result = result_obj.load()
time_list.append(
sum([loaded_result[epoch]['time'] for epoch in loaded_result]))
result_list.append(
np.max(
[loaded_result[epoch]['acc_test'] for epoch in loaded_result]))
print('accuracy: {:.2f}$\pm${:.2f}'.format(100 * np.mean(result_list),
100 * np.std(result_list)))
print('time: {:.2f}$\pm${:.2f}'.format(np.mean(time_list),
np.std(time_list)))
dataset_name = 'PROTEINS'
strategy = 'padding_pruning'
if 0:
epoch_number = 500
import matplotlib.pyplot as plt
from code.ResultSaving import ResultSaving
#---------- clustering results evaluation -----------------
dataset_name = 'pubmed'
if 0:
pre_train_task = 'node_reconstruction+structure_recovery'
result_obj = ResultSaving('', '')
result_obj.result_destination_folder_path = './result/GraphBert/'
result_obj.result_destination_file_name = 'clustering_' + dataset_name + '_' + pre_train_task
loaded_result = result_obj.load()
eval_obj = EvaluateClustering()
eval_obj.data = loaded_result
eval_result = eval_obj.evaluate()
print(eval_result)
#--------------- Graph Bert Pre-Training Records Convergence --------------
dataset_name = 'cora'
if 0:
if dataset_name == 'cora':
k = 7
elif dataset_name == 'citeseer':
k = 5
elif dataset_name == 'pubmed':
