else:
train_percent = 20
ablation = "all"
shuffle = False
metapath_length = 3
mlp_settings = {'layer_list': [256], 'dropout_list': [0.5], 'activation': 'sigmoid'}
info_section = 40 # total embedding dim = info_section *3 = 120
learning_rate = 0.01 #
select_method = "all_node" # Only used in end-node study
single_path_limit = 5 # lambda = 5
# Automatically calculated parameters
num_batch_per_epoch = 5 # 每个epoch循环的批次数
batch_size = train_percent // 20 * 96
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
data = HeteData(dataset=dataset, train_percent=train_percent, shuffle=shuffle)
graph_list = data.get_dict_of_list()
homo_graph = nx.to_dict_of_lists(data.homo_graph)
input_dim = data.x.shape[1]
pre_embed_dim = data.type_num * info_section
output_dim = max(data.train_list[:, 1].tolist()) + 1 # 隐藏单元节点数 两层
DEVICE = "cuda" if torch.cuda.is_available() else "cpu" # 获取预处理数据
assert select_method in ["end_node", "all_node"]
x = data.x
train_list = data.train_list # 训练节点/数据对应的标签
test_list = data.test_list # 测试节点/数据对应的索引
val_list = data.val_list # 验证节点/数据对应的索引
'activation': 'sigmoid'
}
info_section = 40 # total embedding dim = info_section *3 = 120
learning_rate = 0.01 #
select_method = "all_node" # Only used in end-node study
single_path_limit = 8 # lambda = 5
metapath_name = {'P': ['PA', 'PS']}
metapath_list = [[0], [2]]
print("Selected Path: PA, PS")
# Automatically calculated parameters
num_batch_per_epoch = 5 # 每个epoch循环的批次数
batch_size = train_percent // 20 * 96
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
data = HeteData(dataset=dataset,
train_percent=train_percent,
shuffle=shuffle)
graph_list = data.get_dict_of_list()
homo_graph = nx.to_dict_of_lists(data.homo_graph)
input_dim = data.x.shape[1]
pre_embed_dim = data.type_num * info_section
output_dim = max(data.train_list[:, 1].tolist()) + 1 # 隐藏单元节点数 两层
DEVICE = "cuda" if torch.cuda.is_available() else "cpu" # 获取预处理数据
assert select_method in ["end_node", "all_node"]
x = data.x
train_list = data.train_list # 训练节点/数据对应的标签
test_list = data.test_list # 测试节点/数据对应的索引