run_case_直接.py 2.4 KB

1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162
  1. # -*- coding: UTF-8 -*-
  2. import numpy as np
  3. np.random.seed(42)
  4. import os
  5. from data_process import data_process
  6. from data_features import data_features
  7. from logger import load_logger
  8. from config import myargparse
  9. from data_analyse import data_analyse
  10. frame = "keras"
  11. if frame == "keras":
  12. from model.model_keras_base import train, predict
  13. os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3'
  14. else:
  15. raise Exception("Wrong frame seletion")
  16. def main():
  17. parse = myargparse(discription="training config", add_help=False)
  18. opt = parse.parse_args_and_yaml()
  19. logger = load_logger(opt)
  20. try:
  21. process = data_process(opt=opt)
  22. features = data_features(opt=opt)
  23. if opt.do_train:
  24. data_train = process.get_train_data()
  25. train_X, valid_X, train_Y, valid_Y = features.get_train_data([data_train])
  26. print("训练的数据集有{}个点".format(len(train_X)))
  27. train_Y = np.array([y[:, 2] for y in train_Y])
  28. valid_Y = np.array([y[:, 2] for y in valid_Y])
  29. train(opt, [train_X, train_Y, valid_X, valid_Y])
  30. if opt.do_predict:
  31. data_test = process.get_test_data()
  32. # dfs = [group for name, group in data_test.groupby('label')]
  33. test_X, test_Y, df_Y = features.get_test_data([data_test])
  34. print("测试集有{}个点".format(len(test_X)))
  35. analyse = data_analyse(opt, logger)
  36. result = predict(opt, test_X)
  37. analyse.predict_acc(result, df_Y, predict_all=True)
  38. except Exception:
  39. logger.error("Run Error", exc_info=True)
  40. if __name__ == "__main__":
  41. import argparse
  42. # argparse方便于命令行下输入参数,可以根据需要增加更多
  43. # parser = argparse.ArgumentParser()
  44. # parser.add_argument("-t", "--do_train", default=False, type=bool, help="whether to train")
  45. # parser.add_argument("-p", "--do_predict", default=True, type=bool, help="whether to train")
  46. # parser.add_argument("-b", "--batch_size", default=64, type=int, help="batch size")
  47. # parser.add_argument("-e", "--epoch", default=20, type=int, help="epochs num")
  48. # args = parser.parse_args()
  49. # con = Config()
  50. # for key in dir(args): # dir(args) 函数获得args所有的属性
  51. # if not key.startswith("_"): # 去掉 args 自带属性,比如__name__等
  52. # setattr(con, key, getattr(args, key)) # 将属性值赋给Config
  53. main()