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c78c6fc3ef
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fa210fa6a8
@ -397,7 +397,7 @@ def predict_main():
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end_time=global_config['end_time'],
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end_time=global_config['end_time'],
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)
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)
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logger.info('模型训练完成')
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# logger.info('模型训练完成')
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logger.info('训练数据绘图ing')
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logger.info('训练数据绘图ing')
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model_results3 = model_losss(sqlitedb, end_time=end_time)
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model_results3 = model_losss(sqlitedb, end_time=end_time)
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@ -415,6 +415,14 @@ def predict_main():
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logger.info('模型训练完成')
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logger.info('模型训练完成')
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push_market_value()
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push_market_value()
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# # LSTM 单变量模型
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# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
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# # lstm 多变量模型
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# ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
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# # GRU 模型
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# # ex_GRU(df)
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# 发送邮件
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# 发送邮件
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# m = SendMail(
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# m = SendMail(
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@ -948,8 +948,6 @@ def model_losss(sqlitedb, end_time):
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'accuracy', where_condition=f"created_dt <= '{end_time}'")
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'accuracy', where_condition=f"created_dt <= '{end_time}'")
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if len(df_combined) < 100:
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if len(df_combined) < 100:
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len(df_combined) + ''
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len(df_combined) + ''
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if df_combined['y'].isnull().sum() / len(df_combined) > 0.8:
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len(df_combined) + ''
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except:
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except:
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df_combined = loadcsv(os.path.join(
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df_combined = loadcsv(os.path.join(
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config.dataset, "cross_validation.csv"))
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config.dataset, "cross_validation.csv"))
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