聚烯烃日度预测调试
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				| @ -977,21 +977,21 @@ def datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_t | ||||
|     two_months_ago = current_date - timedelta(days=40) | ||||
|     # 检查两月不更新的特征 | ||||
| 
 | ||||
|     def check_column(col_name): | ||||
|         if 'ds' in col_name or 'y' in col_name: | ||||
|             return False | ||||
|         df_check_column = df[['ds', col_name]] | ||||
|         df_check_column = df_check_column.dropna() | ||||
|         if len(df_check_column) == 0: | ||||
|             return True | ||||
|         if df_check_column[(df_check_column['ds'] >= two_months_ago)].groupby(col_name).ngroups < 2: | ||||
|             return True | ||||
|         corresponding_date = df_check_column.iloc[-1]['ds'] | ||||
|         return corresponding_date < two_months_ago | ||||
|     columns_to_drop = df.columns[df.columns.map(check_column)].tolist() | ||||
|     df = df.drop(columns=columns_to_drop) | ||||
|     # def check_column(col_name): | ||||
|     #     if 'ds' in col_name or 'y' in col_name: | ||||
|     #         return False | ||||
|     #     df_check_column = df[['ds', col_name]] | ||||
|     #     df_check_column = df_check_column.dropna() | ||||
|     #     if len(df_check_column) == 0: | ||||
|     #         return True | ||||
|     #     if df_check_column[(df_check_column['ds'] >= two_months_ago)].groupby(col_name).ngroups < 2: | ||||
|     #         return True | ||||
|     #     corresponding_date = df_check_column.iloc[-1]['ds'] | ||||
|     #     return corresponding_date < two_months_ago | ||||
|     # columns_to_drop = df.columns[df.columns.map(check_column)].tolist() | ||||
|     # df = df.drop(columns=columns_to_drop) | ||||
| 
 | ||||
|     config.logger.info(f'删除两月不更新特征后数据量:{df.shape}') | ||||
|     # config.logger.info(f'删除两月不更新特征后数据量:{df.shape}') | ||||
| 
 | ||||
|     # 删除预测列空值的行 | ||||
|     df = df.dropna(subset=['y']) | ||||
|  | ||||
| @ -388,7 +388,7 @@ def predict_main(): | ||||
| if __name__ == '__main__': | ||||
|     # global end_time | ||||
|     # 遍历2024-11-25 到 2024-12-3 之间的工作日日期 | ||||
|     for i_time in pd.date_range('2025-2-1', '2025-3-18', freq='B'): | ||||
|     for i_time in pd.date_range('2025-2-10', '2025-3-18', freq='B'): | ||||
|         try: | ||||
|             global_config['end_time'] = i_time.strftime('%Y-%m-%d') | ||||
|             predict_main() | ||||
|  | ||||
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