liurui/PriceForecast
Template
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

聚烯烃日度配置调整

Browse Source
This commit is contained in:
workpc 2025-03-11 10:10:20 +08:00
parent d4187e8c1e
commit 24525e227d
3 changed files with 29 additions and 54 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
config_juxiting.py
View File

@ -201,17 +201,17 @@ table_name = 'v_tbl_crude_oil_warning'
# 开关 # 开关
is_train = False # 是否训练 is_train = True # 是否训练
is_debug = True # 是否调试 is_debug = True # 是否调试
is_eta = True # 是否使用eta接口 is_eta = False # 是否使用eta接口
is_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效 is_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
is_timefurture = True # 是否使用时间特征 is_timefurture = True # 是否使用时间特征
is_fivemodels = False # 是否使用之前保存的最佳的5个模型 is_fivemodels = False # 是否使用之前保存的最佳的5个模型
is_edbcode = False # 特征使用edbcoding列表中的 is_edbcode = False # 特征使用edbcoding列表中的
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
is_update_eta = False # 预测结果上传到eta is_update_eta = False # 预测结果上传到eta
is_update_report = True # 是否上传报告 is_update_report = False # 是否上传报告
is_update_warning_data = True # 是否上传预警数据 is_update_warning_data = False # 是否上传预警数据
is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征 is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
is_del_tow_month = True # 是否删除两个月不更新的特征 is_del_tow_month = True # 是否删除两个月不更新的特征
@ -306,7 +306,7 @@ logger.setLevel(logging.INFO)
file_handler = logging.handlers.RotatingFileHandler(os.path.join( file_handler = logging.handlers.RotatingFileHandler(os.path.join(
log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5) log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
file_handler.setFormatter(logging.Formatter( file_handler.setFormatter(logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')) '%(asctime)s - %(name)s - %(levelname)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S'))
# 配置控制台处理器，将日志打印到控制台 # 配置控制台处理器，将日志打印到控制台
console_handler = logging.StreamHandler() console_handler = logging.StreamHandler()

41
lib/dataread.py
View File

@ -115,37 +115,6 @@ global_config = {
# 数据库配置 # 数据库配置
'sqlitedb': None, 'sqlitedb': None,
} }
# logger = global_config['logger']
# dataset = global_config['dataset']
# y = global_config['y']
# data_set = global_config['data_set']
# input_size = global_config['input_size']
# horizon = global_config['horizon']
# train_steps = global_config['train_steps']
# val_check_steps = global_config['val_check_steps']
# is_del_corr = global_config['is_del_corr']
# is_del_tow_month = global_config['is_del_tow_month']
# is_eta = global_config['is_eta']
# is_update_eta = global_config['is_update_eta']
# is_update_eta_data = global_config['is_update_eta_data']
# start_year = global_config['start_year']
# end_time = global_config['end_time']
# freq = global_config['freq'][0]
# offsite_col = global_config['offsite_col']
# avg_cols = global_config['avg_col']
# offsite = global_config['offsite']
# edbcodenamedict = global_config['edbcodenamedict']
# query_data_list_item_nos_url = global_config['query_data_list_item_nos_url']
# query_data_list_item_nos_data = global_config['query_data_list_item_nos_data']
# config.login_pushreport_url = global_config['config.login_pushreport_url']
# login_data = global_config['login_data']
# upload_url = global_config['upload_url']
# upload_warning_url = global_config['upload_warning_url']
# upload_warning_data = global_config['upload_warning_data']
# warning_data = global_config['upload_warning_data']
# APPID = global_config['APPID']
# SECRET = global_config['SECRET']
# 定义函数 # 定义函数
@ -973,14 +942,16 @@ def datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_t
df.rename(columns={datecol: 'ds'}, inplace=True) df.rename(columns={datecol: 'ds'}, inplace=True)
# 指定列统一减少数值 # 指定列统一减少数值
df[config.offsite_col] = df[config.offsite_col]-config.offsite print(global_config.keys())
df[global_config['offsite_col']] = df[global_config['offsite_col']] - \
global_config['offsite']
# 预测列为avg_cols的均值 # 预测列为avg_cols的均值
df[y] = df[config.avg_cols].mean(axis=1) df[global_config['y']] = df[global_config['avg_cols']].mean(axis=1)
# 去掉多余的列avg_cols # 去掉多余的列avg_cols
df = df.drop(columns=config.avg_cols) df = df.drop(columns=global_config['avg_cols'])
# 重命名预测列 # 重命名预测列
df.rename(columns={y: 'y'}, inplace=True) df.rename(columns={global_config['y']: 'y'}, inplace=True)
# 按时间顺序排列 # 按时间顺序排列
df.sort_values(by='ds', inplace=True) df.sort_values(by='ds', inplace=True)
df['ds'] = pd.to_datetime(df['ds']) df['ds'] = pd.to_datetime(df['ds'])

32
main_juxiting.py
View File

@ -3,7 +3,7 @@
from lib.dataread import * from lib.dataread import *
from config_juxiting import * from config_juxiting import *
from lib.tools import SendMail, exception_logger from lib.tools import SendMail, exception_logger
from models.nerulforcastmodels import ex_Model, model_losss, model_losss_juxiting, brent_export_pdf, tansuanli_export_pdf, pp_export_pdf, model_losss_juxiting from models.nerulforcastmodels import ex_Model, model_losss_juxiting, tansuanli_export_pdf, pp_export_pdf
import datetime import datetime
import torch import torch
torch.set_float32_matmul_precision("high") torch.set_float32_matmul_precision("high")
@ -13,6 +13,10 @@ global_config.update({
'logger': logger, 'logger': logger,
'dataset': dataset, 'dataset': dataset,
'y': y, 'y': y,
'offsite_col': offsite_col,
'avg_cols': avg_cols,
'offsite': offsite,
'edbcodenamedict': edbcodenamedict,
'is_debug': is_debug, 'is_debug': is_debug,
'is_train': is_train, 'is_train': is_train,
'is_fivemodels': is_fivemodels, 'is_fivemodels': is_fivemodels,
@ -150,14 +154,14 @@ def predict_main():
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False) df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
# 数据处理 # 数据处理
df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture, df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
end_time=end_time) end_time=end_time)
else: else:
# 读取数据 # 读取数据
logger.info('读取本地数据：' + os.path.join(dataset, data_set)) logger.info('读取本地数据：' + os.path.join(dataset, data_set))
df, df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj, df, df_zhibiaoliebiao = getdata_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理 is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
# 更改预测列名称 # 更改预测列名称
df.rename(columns={y: 'y'}, inplace=True) df.rename(columns={y: 'y'}, inplace=True)
@ -335,19 +339,19 @@ def predict_main():
logger.info('模型训练完成') logger.info('模型训练完成')
logger.info('训练数据绘图ing') logger.info('训练数据绘图ing')
model_results3 = model_losss(sqlitedb, end_time=end_time) model_results3 = model_losss_juxiting(sqlitedb, end_time=end_time)
logger.info('训练数据绘图end') logger.info('训练数据绘图end')
# # 模型报告 # # 模型报告
# logger.info('制作报告ing') logger.info('制作报告ing')
# title = f'{settings}--{end_time}-预测报告' # 报告标题 title = f'{settings}--{end_time}-预测报告' # 报告标题
# reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名 reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名
# reportname = reportname.replace(':', '-') # 替换冒号 reportname = reportname.replace(':', '-') # 替换冒号
# brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time, pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
# reportname=reportname, sqlitedb=sqlitedb), reportname=reportname, sqlitedb=sqlitedb),
# logger.info('制作报告end') logger.info('制作报告end')
# logger.info('模型训练完成') logger.info('模型训练完成')
# # LSTM 单变量模型 # # LSTM 单变量模型
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset) # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)