原油画图边界判断20改为60
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@ -172,25 +172,25 @@ def predict_main():
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row, col = df.shape
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now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
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# ex_Model(df,
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# horizon=horizon,
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# input_size=input_size,
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# train_steps=train_steps,
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# val_check_steps=val_check_steps,
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# early_stop_patience_steps=early_stop_patience_steps,
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# is_debug=is_debug,
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# dataset=dataset,
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# is_train=is_train,
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# is_fivemodels=is_fivemodels,
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# val_size=val_size,
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# test_size=test_size,
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# settings=settings,
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# now=now,
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# etadata=etadata,
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# modelsindex=modelsindex,
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# data=data,
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# is_eta=is_eta,
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# )
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ex_Model(df,
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horizon=horizon,
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input_size=input_size,
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train_steps=train_steps,
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val_check_steps=val_check_steps,
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early_stop_patience_steps=early_stop_patience_steps,
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is_debug=is_debug,
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dataset=dataset,
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is_train=is_train,
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is_fivemodels=is_fivemodels,
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val_size=val_size,
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test_size=test_size,
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settings=settings,
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now=now,
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etadata=etadata,
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modelsindex=modelsindex,
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data=data,
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is_eta=is_eta,
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)
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logger.info('模型训练完成')
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@ -353,11 +353,11 @@ def model_losss(sqlitedb):
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def find_most_common_model():
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# 最多频率的模型名称
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min_model_max_frequency_model = df_combined3['min_model'].tail(20).value_counts().idxmax()
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max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().idxmax()
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min_model_max_frequency_model = df_combined3['min_model'].tail(60).value_counts().idxmax()
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max_model_max_frequency_model = df_combined3['max_model'].tail(60).value_counts().idxmax()
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if min_model_max_frequency_model == max_model_max_frequency_model:
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# 取20天第二多的模型
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max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().nlargest(2).index[1]
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max_model_max_frequency_model = df_combined3['max_model'].tail(60).value_counts().nlargest(2).index[1]
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df_predict['min_model'] = min_model_max_frequency_model
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df_predict['max_model'] = max_model_max_frequency_model
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