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script_2_pre_train.py
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script_2_pre_train.py
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import numpy as np
import torch
from code.DatasetLoader import DatasetLoader
from code.MethodBertComp import GraphBertConfig
from code.MethodGraphBertNodeConstruct import MethodGraphBertNodeConstruct
from code.MethodGraphBertGraphRecovery import MethodGraphBertGraphRecovery
from code.ResultSaving import ResultSaving
from code.Settings import Settings
#---- 'cora' , 'citeseer', 'pubmed' ----
dataset_name = 'cora'
np.random.seed(1)
torch.manual_seed(1)
#---- cora-small is for debuging only ----
if dataset_name == 'cora-small':
nclass = 7
nfeature = 1433
ngraph = 10
elif dataset_name == 'cora':
nclass = 7
nfeature = 1433
ngraph = 2708
elif dataset_name == 'citeseer':
nclass = 6
nfeature = 3703
ngraph = 3312
elif dataset_name == 'pubmed':
nclass = 3
nfeature = 500
ngraph = 19717
#---- Pre-Training Task #1: Graph Bert Node Attribute Reconstruction (Cora, Citeseer, and Pubmed) ----
if 0:
#---- hyper-parameters ----
if dataset_name == 'pubmed':
lr = 0.001
k = 30
max_epoch = 200 # ---- do an early stop when necessary ----
elif dataset_name == 'cora':
lr = 0.001
k = 7
max_epoch = 200 # ---- do an early stop when necessary ----
elif dataset_name == 'citeseer':
k = 5
lr = 0.001
max_epoch = 200 # it takes a long epochs to converge, probably more than 2000
x_size = nfeature
hidden_size = intermediate_size = 32
num_attention_heads = 2
num_hidden_layers = 2
y_size = nclass
graph_size = ngraph
residual_type = 'graph_raw'
# --------------------------
print('************ Start ************')
print('GrapBert, dataset: ' + dataset_name + ', Pre-training, Node Attribute Reconstruction.')
# ---- objection initialization setction ---------------
data_obj = DatasetLoader()
data_obj.dataset_source_folder_path = './data/' + dataset_name + '/'
data_obj.dataset_name = dataset_name
data_obj.k = k
data_obj.load_all_tag = True
bert_config = GraphBertConfig(residual_type = residual_type, k=k, x_size=nfeature, y_size=y_size, hidden_size=hidden_size, intermediate_size=intermediate_size, num_attention_heads=num_attention_heads, num_hidden_layers=num_hidden_layers)
method_obj = MethodGraphBertNodeConstruct(bert_config)
method_obj.max_epoch = max_epoch
method_obj.lr = lr
result_obj = ResultSaving()
result_obj.result_destination_folder_path = './result/GraphBert/'
result_obj.result_destination_file_name = dataset_name + '_' + str(k) + '_node_reconstruction'
setting_obj = Settings()
evaluate_obj = None
# ------------------------------------------------------
# ---- running section ---------------------------------
setting_obj.prepare(data_obj, method_obj, result_obj, evaluate_obj)
setting_obj.load_run_save_evaluate()
# ------------------------------------------------------
print('************ Finish ************')
#------------------------------------
#---- Pre-Training Task #2: Graph Bert Network Structure Recovery (Cora, Citeseer, and Pubmed) ----
if 0:
#---- hyper-parameters ----
if dataset_name == 'pubmed':
lr = 0.001
k = 30
max_epoch = 200 # ---- do an early stop when necessary ----
elif dataset_name == 'cora':
lr = 0.001
k = 7
max_epoch = 200 # ---- do an early stop when necessary ----
elif dataset_name == 'citeseer':
k = 5
lr = 0.001
max_epoch = 200 # it takes a long epochs to converge, probably more than 2000
else:
k=5
lr = 0.01
max_epoch = 200
x_size = nfeature
hidden_size = intermediate_size = 32
num_attention_heads = 2
num_hidden_layers = 2
y_size = nclass
graph_size = ngraph
residual_type = 'graph_raw'
# --------------------------
print('************ Start ************')
print('GrapBert, dataset: ' + dataset_name + ', Pre-training, Graph Structure Recovery.')
# ---- objection initialization setction ---------------
data_obj = DatasetLoader()
data_obj.dataset_source_folder_path = './data/' + dataset_name + '/'
data_obj.dataset_name = dataset_name
data_obj.k = k
data_obj.load_all_tag = True
bert_config = GraphBertConfig(residual_type = residual_type, k=k, x_size=nfeature, y_size=y_size, hidden_size=hidden_size, intermediate_size=intermediate_size, num_attention_heads=num_attention_heads, num_hidden_layers=num_hidden_layers)
method_obj = MethodGraphBertGraphRecovery(bert_config)
method_obj.max_epoch = max_epoch
method_obj.lr = lr
result_obj = ResultSaving()
result_obj.result_destination_folder_path = './result/GraphBert/'
result_obj.result_destination_file_name = dataset_name + '_' + str(k) + '_graph_recovery'
setting_obj = Settings()
evaluate_obj = None
# ------------------------------------------------------
# ---- running section ---------------------------------
setting_obj.prepare(data_obj, method_obj, result_obj, evaluate_obj)
setting_obj.load_run_save_evaluate()
# ------------------------------------------------------
print('************ Finish ************')
#------------------------------------