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原油画图边界判断20改为60

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liurui 2024-12-06 11:18:45 +08:00
parent 9812e92f52
commit 11630bfcce
2 changed files with 22 additions and 22 deletions
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38
main_yuanyou.py
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@ -172,25 +172,25 @@ def predict_main():
row, col = df.shape row, col = df.shape
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
# ex_Model(df, ex_Model(df,
# horizon=horizon, horizon=horizon,
# input_size=input_size, input_size=input_size,
# train_steps=train_steps, train_steps=train_steps,
# val_check_steps=val_check_steps, val_check_steps=val_check_steps,
# early_stop_patience_steps=early_stop_patience_steps, early_stop_patience_steps=early_stop_patience_steps,
# is_debug=is_debug, is_debug=is_debug,
# dataset=dataset, dataset=dataset,
# is_train=is_train, is_train=is_train,
# is_fivemodels=is_fivemodels, is_fivemodels=is_fivemodels,
# val_size=val_size, val_size=val_size,
# test_size=test_size, test_size=test_size,
# settings=settings, settings=settings,
# now=now, now=now,
# etadata=etadata, etadata=etadata,
# modelsindex=modelsindex, modelsindex=modelsindex,
# data=data, data=data,
# is_eta=is_eta, is_eta=is_eta,
# ) )
logger.info('模型训练完成') logger.info('模型训练完成')

6
models/nerulforcastmodels.py
View File

@ -353,11 +353,11 @@ def model_losss(sqlitedb):
def find_most_common_model(): def find_most_common_model():
# 最多频率的模型名称 # 最多频率的模型名称
min_model_max_frequency_model = df_combined3['min_model'].tail(20).value_counts().idxmax() min_model_max_frequency_model = df_combined3['min_model'].tail(60).value_counts().idxmax()
max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().idxmax() max_model_max_frequency_model = df_combined3['max_model'].tail(60).value_counts().idxmax()
if min_model_max_frequency_model == max_model_max_frequency_model: if min_model_max_frequency_model == max_model_max_frequency_model:
# 取20天第二多的模型 # 取20天第二多的模型
max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().nlargest(2).index[1] max_model_max_frequency_model = df_combined3['max_model'].tail(60).value_counts().nlargest(2).index[1]
df_predict['min_model'] = min_model_max_frequency_model df_predict['min_model'] = min_model_max_frequency_model
df_predict['max_model'] = max_model_max_frequency_model df_predict['max_model'] = max_model_max_frequency_model