text: from qweNet.qweNet import *
  from data_util.generate_bc_feature import generate_bc_feature
  import networkx as nx
  import matplotlib.pyplot as plt
  import numpy as np
  import time
  from scipy.stats import kendalltau as kd
  import torch.optim as optim
  EMBEDDING_SIZE = 128
  REG_HIDDEN = (int)(EMBEDDING_SIZE / 2)
  MIN_SIZE = 100
  MAX_SIZE = 200
  MAX_EPOCH = 10000
  N_VALID = 10 # number of validation graphs
  N_TRAIN = 100
  BATCH_SIZE = 16
  LEARNING_RATE = 0.0002
  max_bp_iter = 5     # neighbor propagation steps
  
  def main():
      ########## Train
      btl = QweNet(max_bp_iter, 3, EMBEDDING_SIZE, EMBEDDING_SIZE, REG_HIDDEN, 1, 0, inTraining = False)
      System = QweTool()
      top001, top005, top01, kendal, run_time = System.evaluateRealData(btl, "/root/CS280_QWE/model/nrange_iter_100_200_5000.pkl", "/p300/data/GNN_Data/Real/amazon.txt", "/p300/data/GNN_Data/Real/exact_bc/amazon.txt")
      print("%f, %f, %f, %f, %f" % (top001, top005, top01, kendal, run_time))
  
  if __name__ == "__main__":
      main()
  