def LoadData(filename):
xml = filename + ".xml"
trace = "Data\\" + filename + "\\"
title, labels, jconf, authors, FullName, organization = ProcessData.ProcessingRawData(
trace + xml)
title = ProcessData.Wipe_off_Punctuation(title)
title_vocab, title_split = ProcessData.Split_Title(title)
title_one_hot, Max_Sequence_Len, vocab_size = ProcessData.One_hot_encoding(
title_vocab, title_split)
title_one_hot_padding = ProcessData.Padding_One_hot(
title_one_hot, Max_Sequence_Len)
author_vocab, authors_split = ProcessData.Split_Authors(authors)
edge_type = np.load(trace + "edge_type.npy")
edge_list_src = np.load(trace + "edge_list_src.npy")
edge_list_dst = np.load(trace + "edge_list_dst.npy")
num_nodes = len(authors_split)
edge_norm = [1 for i in range(len(edge_type))]
print("Number of edges: ", len(edge_list_src))
print("Number of nodes: ", len(authors_split))
print("Number of class: ", max(labels) + 1)
train_idx = random.sample(range(len(authors_split)),
int(len(authors_split) * 0.8))
test_idx = []
for i in range(len(authors_split)):
if i not in train_idx:
test_idx.append(i)
inputs = title_one_hot_padding
labels = labels
return edge_type, edge_list_src, edge_list_dst, num_nodes, edge_norm, vocab_size, train_idx, test_idx, inputs, labels
