text: import pickle
  import time
  
  from sklearn.metrics import f1_score, mean_absolute_error
  
  import lightgbm as lgb
  import cv2
  import numpy as np
  import pandas as pd
  
  
  if __name__ == '__main__':
      csvpath = r"datasets/datasets.csv"
      csvdf = pd.read_csv(csvpath, header=0)
  
      alldataX = csvdf[["calctype","col","row","convert_rate","iscoincidence","sobeld","jlxd","a1","a2","a3","a4","a5","a6","a7","a8",
                        "s1","s2","s3","s4","s5","s6","s7","s8","s9","s10","s11","s12","s13","s14","s15","s16","s17","s18","s19","s20"]]
      alldataY = csvdf["Y"]
  
      # 模型加载
      # gbm = lgb.Booster(model_file=config.model_file[0])
      with open('lgb.pickle', 'rb') as f:
          gbm = pickle.load(f)
  
      # 模型预测
      start = time.time()
      y_pred = gbm.predict(alldataX[:1].values)
      print(time.time()-start)
  
      score = mean_absolute_error(y_pred, alldataY[:1])
      print(score)
  