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聚烯烃图片报告预测保留整数，相关性保留两位小数

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jingboyitiji 2025-08-06 19:47:37 +08:00
parent ff7b9bf235
commit fc05840cf8
5 changed files with 14 additions and 13 deletions
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8
lib/tools.py
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@ -835,7 +835,7 @@ def convert_df_to_pydantic_pp(df_predict, model_id_name_dict, global_config):
if c not in ['ds', 'created_dt']:
data['model_id'] = reverse_model_id_name_dict[c]
data['predicted_price'] = Decimal(
round(df_predict[c].values[0], 2))
int(df_predict[c].values[0]))
result = PpPredictionResult(**data)
results.append(result)
return results
@ -959,7 +959,7 @@ def find_best_models(date='', global_config=None):
ciridate = last_trading_day_str
global_config['logger'].info(f'计算预测{last_trading_day}的次日{last_trading_day}最佳模型')
global_config['logger'].info(
f'{date}真实价格：{true_price[true_price["ds"] == date]["y"].values[0]}')
f'{date}真实价格：{true_price[true_price["ds"] == last_trading_day_str]["y"].values[0]}')
price = df[['data_date', wd, 'model_id']]
price = price[(price['data_date'] == ciridate)
| (price['data_date'] == date)]
@ -1098,7 +1098,7 @@ def plot_pp_predict_result(y_hat, global_config,wd='yuedu'):
y = pd.read_csv('juxitingdataset/指标数据.csv')[['ds', 'y']][-30:]
xgx_df = pd.read_csv('juxitingdataset/相关系数.csv')
xgx_df = xgx_df.rename(columns={xgx_df.columns[0]: '指标', xgx_df.columns[1]: '系数'})
top_10_correlations = xgx_df.sort_values(by='系数',ascending=False)[1:11]
top_10_correlations = xgx_df.sort_values(by='系数', ascending=False)[1:11].round().astype({'系数': int})
y['ds'] = pd.to_datetime(y['ds'])
y = y[y['ds'] < y_hat['ds'].iloc[0]]
@ -1138,7 +1138,7 @@ def plot_pp_predict_result(y_hat, global_config,wd='yuedu'):
'next_march_price': '次三月', 'next_april_price': '次四月',
}, inplace=True)
columns = y_hat.columns.tolist()
data = y_hat.values.tolist()
data = [round(num) for num in y_hat.values.ravel().tolist()]
# 将日期转换为字符串格式
for row in data:

2
main_juxiting.py
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@ -557,7 +557,7 @@ if __name__ == '__main__':
# except Exception as e:
# logger.info(f'预测失败：{e}')
# continue
global_config['end_time'] = '2025-08-04'
# global_config['end_time'] = '2025-08-05'
predict_main()
# global_config['end_time'] = '2025-08-01'

13
main_juxiting_yuedu.py
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@ -530,12 +530,13 @@ if __name__ == '__main__':
# logger.info(f'预测失败：{e}')
# continue
global_config['end_time'] = '2025-08-04'
predict_main()
# global_config['end_time'] = '2025-08-05'
# predict_main()
# push_market_value()
# sql_inset_predict(global_config)
# 图片报告
# global_config['end_time'] = '2025-07-31'
# logger.info('图片报告ing')
# pp_bdwd_png(global_config=global_config)
# logger.info('图片报告end')
global_config['end_time'] = '2025-08-05'
logger.info('图片报告ing')
pp_bdwd_png(global_config=global_config)
logger.info('图片报告end')

2
main_juxiting_zhoudu.py
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@ -513,7 +513,7 @@ if __name__ == '__main__':
# continue
global_config['end_time'] = '2025-08-04'
global_config['end_time'] = '2025-08-05'
predict_main()

2
models/nerulforcastmodels.py
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@ -396,7 +396,7 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear
# VanillaTransformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
# Autoformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
# NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
# NBEATSx (h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ), //报错
# HINT(h=horizon),