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- # -*- coding: UTF-8 -*-
- import numpy as np
- import os
- from data_process import data_process
- from data_features import data_features
- from logger import load_logger
- from config import myargparse
- from data_analyse import data_analyse
- frame = "keras"
- if frame == "keras":
- from model.model_keras import train, predict
- os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3'
- else:
- raise Exception("Wrong frame seletion")
- def main():
- parse = myargparse(discription="training config", add_help=False)
- opt = parse.parse_args_and_yaml()
- logger = load_logger(opt)
- try:
- np.random.seed(opt.Model["random_seed"])
- process = data_process(opt=opt)
- dfs = process.get_processed_data()
- features = data_features(opt=opt, mean=process.mean, std=process.std)
- if opt.do_train:
- train_X, valid_X, train_Y, valid_Y = features.get_train_data([dfs[0][:'2021/8/1'], dfs[1][:'2022/3/1']])
- train(opt, [train_X, train_Y, valid_X, valid_Y])
- if opt.do_predict:
- test_X, test_Y, df_Y = features.get_test_data([dfs[0]['2021/8/1':'2021/9/6'], dfs[1]['2022/3/1':'2022/4/4']])
- result = predict(opt, test_X) # 这里输出的是未还原的归一化预测数据
- analyse = data_analyse(opt, logger, process)
- analyse.predict_acc(result, df_Y)
- except Exception:
- logger.error("Run Error", exc_info=True)
- if __name__ == "__main__":
- import argparse
- # argparse方便于命令行下输入参数，可以根据需要增加更多
- # parser = argparse.ArgumentParser()
- # parser.add_argument("-t", "--do_train", default=False, type=bool, help="whether to train")
- # parser.add_argument("-p", "--do_predict", default=True, type=bool, help="whether to train")
- # parser.add_argument("-b", "--batch_size", default=64, type=int, help="batch size")
- # parser.add_argument("-e", "--epoch", default=20, type=int, help="epochs num")
- # args = parser.parse_args()
- # con = Config()
- # for key in dir(args): # dir(args) 函数获得args所有的属性
- # if not key.startswith("_"): # 去掉 args 自带属性，比如__name__等
- # setattr(con, key, getattr(args, key)) # 将属性值赋给Config
- main()
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