原油日度调试完成
This commit is contained in:
jingboyitiji
parent
25cdefe8aa
commit
fe0aaf72fe
@ -163,7 +163,7 @@ table_name = 'v_tbl_crude_oil_warning'
|
||||
### 开关
|
||||
is_train = False # 是否训练
|
||||
is_debug = True # 是否调试
|
||||
is_eta = False # 是否使用eta接口
|
||||
is_eta = True # 是否使用eta接口
|
||||
is_market = True # 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_fivemodels = False # 是否使用之前保存的最佳的5个模型
|
||||
|
||||
279
main_yuanyou.py
279
main_yuanyou.py
@ -1,12 +1,76 @@
|
||||
# 读取配置
|
||||
from lib.dataread import *
|
||||
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
|
||||
|
||||
import glob
|
||||
from lib.dataread import *
|
||||
from config_jingbo import *
|
||||
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
|
||||
import datetime
|
||||
import torch
|
||||
torch.set_float32_matmul_precision("high")
|
||||
|
||||
global_config.update({
|
||||
# 核心参数
|
||||
'logger': logger,
|
||||
'dataset': dataset,
|
||||
'y': y,
|
||||
'is_debug': is_debug,
|
||||
'is_train': is_train,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'settings': settings,
|
||||
|
||||
|
||||
# 模型参数
|
||||
'data_set': data_set,
|
||||
'input_size': input_size,
|
||||
'horizon': horizon,
|
||||
'train_steps': train_steps,
|
||||
'val_check_steps': val_check_steps,
|
||||
'val_size': val_size,
|
||||
'test_size': test_size,
|
||||
'modelsindex': modelsindex,
|
||||
'rote': rote,
|
||||
|
||||
# 特征工程开关
|
||||
'is_del_corr': is_del_corr,
|
||||
'is_del_tow_month': is_del_tow_month,
|
||||
'is_eta': is_eta,
|
||||
'is_update_eta': is_update_eta,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'early_stop_patience_steps': early_stop_patience_steps,
|
||||
|
||||
# 时间参数
|
||||
'start_year': start_year,
|
||||
'end_time': end_time or datetime.datetime.now().strftime("%Y-%m-%d"),
|
||||
'freq': freq, # 保持列表结构
|
||||
|
||||
# 接口配置
|
||||
'login_pushreport_url': login_pushreport_url,
|
||||
'login_data': login_data,
|
||||
'upload_url': upload_url,
|
||||
'upload_warning_url': upload_warning_url,
|
||||
'warning_data': warning_data,
|
||||
|
||||
# 查询接口
|
||||
'query_data_list_item_nos_url': query_data_list_item_nos_url,
|
||||
'query_data_list_item_nos_data': query_data_list_item_nos_data,
|
||||
|
||||
# eta 配置
|
||||
'APPID': APPID,
|
||||
'SECRET': SECRET,
|
||||
'etadata': data,
|
||||
'edbcodelist': edbcodelist,
|
||||
'ClassifyId': ClassifyId,
|
||||
'edbcodedataurl': edbcodedataurl,
|
||||
'classifyidlisturl': classifyidlisturl,
|
||||
'edbdatapushurl': edbdatapushurl,
|
||||
'edbdeleteurl': edbdeleteurl,
|
||||
'edbbusinessurl': edbbusinessurl,
|
||||
'ClassifyId': ClassifyId,
|
||||
'classifylisturl': classifylisturl,
|
||||
|
||||
# 数据库配置
|
||||
'sqlitedb': sqlitedb,
|
||||
})
|
||||
|
||||
|
||||
def predict_main():
|
||||
@ -48,31 +112,23 @@ def predict_main():
|
||||
返回:
|
||||
None
|
||||
"""
|
||||
global end_time
|
||||
signature = BinanceAPI(APPID, SECRET)
|
||||
etadata = EtaReader(signature=signature,
|
||||
classifylisturl=classifylisturl,
|
||||
classifyidlisturl=classifyidlisturl,
|
||||
edbcodedataurl=edbcodedataurl,
|
||||
edbcodelist=edbcodelist,
|
||||
edbdatapushurl=edbdatapushurl,
|
||||
edbdeleteurl=edbdeleteurl,
|
||||
edbbusinessurl=edbbusinessurl
|
||||
)
|
||||
end_time = global_config['end_time']
|
||||
# 获取数据
|
||||
if is_eta:
|
||||
logger.info('从eta获取数据...')
|
||||
signature = BinanceAPI(APPID, SECRET)
|
||||
etadata = EtaReader(signature=signature,
|
||||
classifylisturl=classifylisturl,
|
||||
classifyidlisturl=classifyidlisturl,
|
||||
edbcodedataurl=edbcodedataurl,
|
||||
edbcodelist=edbcodelist,
|
||||
edbdatapushurl=edbdatapushurl,
|
||||
edbdeleteurl=edbdeleteurl,
|
||||
edbbusinessurl=edbbusinessurl,
|
||||
)
|
||||
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||
classifylisturl=global_config['classifylisturl'],
|
||||
classifyidlisturl=global_config['classifyidlisturl'],
|
||||
edbcodedataurl=global_config['edbcodedataurl'],
|
||||
edbcodelist=global_config['edbcodelist'],
|
||||
edbdatapushurl=global_config['edbdatapushurl'],
|
||||
edbdeleteurl=global_config['edbdeleteurl'],
|
||||
edbbusinessurl=global_config['edbbusinessurl'],
|
||||
classifyId=global_config['ClassifyId'],
|
||||
)
|
||||
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
|
||||
data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||
|
||||
if is_market:
|
||||
logger.info('从市场信息平台获取数据...')
|
||||
@ -83,26 +139,26 @@ def predict_main():
|
||||
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||
else:
|
||||
logger.info('从市场信息平台获取数据')
|
||||
df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
|
||||
df_zhibiaoshuju = get_market_data(
|
||||
end_time, df_zhibiaoshuju)
|
||||
|
||||
except :
|
||||
except:
|
||||
logger.info('最高最低价拼接失败')
|
||||
|
||||
# 保存到xlsx文件的sheet表
|
||||
with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
|
||||
with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
|
||||
df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
||||
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||
|
||||
|
||||
# 数据处理
|
||||
df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||
end_time=end_time)
|
||||
end_time=end_time)
|
||||
|
||||
else:
|
||||
# 读取数据
|
||||
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,
|
||||
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||
df, df_zhibiaoliebiao = getdata(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
|
||||
|
||||
# 更改预测列名称
|
||||
df.rename(columns={y: 'y'}, inplace=True)
|
||||
@ -124,47 +180,65 @@ def predict_main():
|
||||
else:
|
||||
for row in first_row.itertuples(index=False):
|
||||
row_dict = row._asdict()
|
||||
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
||||
check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'")
|
||||
config.logger.info(f'要保存的真实值：{row_dict}')
|
||||
# 判断ds是否为字符串类型,如果不是则转换为字符串类型
|
||||
if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
|
||||
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||
elif not isinstance(row_dict['ds'], str):
|
||||
try:
|
||||
row_dict['ds'] = pd.to_datetime(
|
||||
row_dict['ds']).strftime('%Y-%m-%d')
|
||||
except:
|
||||
logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
|
||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
||||
check_query = sqlitedb.select_data(
|
||||
'trueandpredict', where_condition=f"ds = '{row.ds}'")
|
||||
if len(check_query) > 0:
|
||||
set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||
sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||
set_clause = ", ".join(
|
||||
[f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||
sqlitedb.update_data(
|
||||
'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||
continue
|
||||
sqlitedb.insert_data('trueandpredict', tuple(row_dict.values()), columns=row_dict.keys())
|
||||
sqlitedb.insert_data('trueandpredict', tuple(
|
||||
row_dict.values()), columns=row_dict.keys())
|
||||
|
||||
# 更新accuracy表的y值
|
||||
if not sqlitedb.check_table_exists('accuracy'):
|
||||
pass
|
||||
else:
|
||||
update_y = sqlitedb.select_data('accuracy',where_condition="y is null")
|
||||
update_y = sqlitedb.select_data(
|
||||
'accuracy', where_condition="y is null")
|
||||
if len(update_y) > 0:
|
||||
logger.info('更新accuracy表的y值')
|
||||
# 找到update_y 中ds且df中的y的行
|
||||
update_y = update_y[update_y['ds']<=end_time]
|
||||
update_y = update_y[update_y['ds'] <= end_time]
|
||||
logger.info(f'要更新y的信息：{update_y}')
|
||||
# try:
|
||||
for row in update_y.itertuples(index=False):
|
||||
try:
|
||||
row_dict = row._asdict()
|
||||
yy = df[df['ds']==row_dict['ds']]['y'].values[0]
|
||||
LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
|
||||
HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].values[0]
|
||||
sqlitedb.update_data('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||
yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
|
||||
LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
|
||||
HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
|
||||
sqlitedb.update_data(
|
||||
'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||
except:
|
||||
logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||
# except Exception as e:
|
||||
# logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
|
||||
import datetime
|
||||
# 判断当前日期是不是周一
|
||||
is_weekday = datetime.datetime.now().weekday() == 0
|
||||
if is_weekday:
|
||||
logger.info('今天是周一，更新预测模型')
|
||||
# 计算最近60天预测残差最低的模型名称
|
||||
model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
|
||||
model_results = sqlitedb.select_data(
|
||||
'trueandpredict', order_by="ds DESC", limit="60")
|
||||
# 删除空值率为90%以上的列
|
||||
if len(model_results) > 10:
|
||||
model_results = model_results.dropna(thresh=len(model_results)*0.1,axis=1)
|
||||
model_results = model_results.dropna(
|
||||
thresh=len(model_results)*0.1, axis=1)
|
||||
# 删除空行
|
||||
model_results = model_results.dropna()
|
||||
modelnames = model_results.columns.to_list()[2:-1]
|
||||
@ -172,54 +246,61 @@ def predict_main():
|
||||
model_results[col] = model_results[col].astype(np.float32)
|
||||
# 计算每个预测值与真实值之间的偏差率
|
||||
for model in modelnames:
|
||||
model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
|
||||
model_results[f'{model}_abs_error_rate'] = abs(
|
||||
model_results['y'] - model_results[model]) / model_results['y']
|
||||
# 获取每行对应的最小偏差率值
|
||||
min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
min_abs_error_rate_values = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
# 获取每行对应的最小偏差率值对应的列名
|
||||
min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
min_abs_error_rate_column_name = model_results.apply(
|
||||
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
# 将列名索引转换为列名
|
||||
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
|
||||
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
||||
lambda x: x.split('_')[0])
|
||||
# 取出现次数最多的模型名称
|
||||
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# 保存结果到数据库
|
||||
if not sqlitedb.check_table_exists('most_model'):
|
||||
sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
|
||||
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
if is_update_warning_data:
|
||||
upload_warning_info(len(warning_data_df))
|
||||
sqlitedb.create_table(
|
||||
'most_model', columns="ds datetime, most_common_model TEXT")
|
||||
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||
'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
|
||||
try:
|
||||
if is_weekday:
|
||||
# if True:
|
||||
# if True:
|
||||
logger.info('今天是周一，发送特征预警')
|
||||
# 上传预警信息到数据库
|
||||
warning_data_df = df_zhibiaoliebiao.copy()
|
||||
warning_data_df = warning_data_df[warning_data_df['停更周期']> 3 ][['指标名称', '指标id', '频度','更新周期','指标来源','最后更新时间','停更周期']]
|
||||
warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
|
||||
'指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
|
||||
# 重命名列名
|
||||
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
||||
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
|
||||
'更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
||||
from sqlalchemy import create_engine
|
||||
import urllib
|
||||
global password
|
||||
if '@' in password:
|
||||
password = urllib.parse.quote_plus(password)
|
||||
|
||||
engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
|
||||
warning_data_df['TENANT_CODE'] = 'T0004'
|
||||
engine = create_engine(
|
||||
f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
|
||||
"%Y-%m-%d %H:%M:%S")
|
||||
warning_data_df['TENANT_CODE'] = 'T0004'
|
||||
# 插入数据之前查询表数据然后新增id列
|
||||
existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||
if not existing_data.empty:
|
||||
max_id = existing_data['ID'].astype(int).max()
|
||||
warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
|
||||
warning_data_df['ID'] = range(
|
||||
max_id + 1, max_id + 1 + len(warning_data_df))
|
||||
else:
|
||||
warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
||||
warning_data_df.to_sql(table_name, con=engine, if_exists='append', index=False)
|
||||
warning_data_df.to_sql(
|
||||
table_name, con=engine, if_exists='append', index=False)
|
||||
if is_update_warning_data:
|
||||
upload_warning_info(len(warning_data_df))
|
||||
|
||||
# 发送钉钉消息
|
||||
upload_warning_info(len(warning_data_df))
|
||||
except:
|
||||
logger.info('上传预警信息到数据库失败')
|
||||
|
||||
@ -232,40 +313,39 @@ def predict_main():
|
||||
|
||||
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||
ex_Model(df,
|
||||
horizon=horizon,
|
||||
input_size=input_size,
|
||||
train_steps=train_steps,
|
||||
val_check_steps=val_check_steps,
|
||||
early_stop_patience_steps=early_stop_patience_steps,
|
||||
is_debug=is_debug,
|
||||
dataset=dataset,
|
||||
is_train=is_train,
|
||||
is_fivemodels=is_fivemodels,
|
||||
val_size=val_size,
|
||||
test_size=test_size,
|
||||
settings=settings,
|
||||
horizon=global_config['horizon'],
|
||||
input_size=global_config['input_size'],
|
||||
train_steps=global_config['train_steps'],
|
||||
val_check_steps=global_config['val_check_steps'],
|
||||
early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
||||
is_debug=global_config['is_debug'],
|
||||
dataset=global_config['dataset'],
|
||||
is_train=global_config['is_train'],
|
||||
is_fivemodels=global_config['is_fivemodels'],
|
||||
val_size=global_config['val_size'],
|
||||
test_size=global_config['test_size'],
|
||||
settings=global_config['settings'],
|
||||
now=now,
|
||||
etadata=etadata,
|
||||
modelsindex=modelsindex,
|
||||
etadata=global_config['etadata'],
|
||||
modelsindex=global_config['modelsindex'],
|
||||
data=data,
|
||||
is_eta=is_eta,
|
||||
end_time=end_time,
|
||||
is_eta=global_config['is_eta'],
|
||||
end_time=global_config['end_time'],
|
||||
)
|
||||
|
||||
|
||||
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')
|
||||
model_results3 = model_losss(sqlitedb,end_time=end_time)
|
||||
model_results3 = model_losss(sqlitedb, end_time=end_time)
|
||||
logger.info('训练数据绘图end')
|
||||
|
||||
# 模型报告
|
||||
# # 模型报告
|
||||
logger.info('制作报告ing')
|
||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
reportname = f'Brent原油大模型预测--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
|
||||
reportname=reportname,sqlitedb=sqlitedb),
|
||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
reportname = f'Brent原油大模型日度预测--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||
reportname=reportname, sqlitedb=sqlitedb),
|
||||
|
||||
logger.info('制作报告end')
|
||||
logger.info('模型训练完成')
|
||||
@ -280,23 +360,22 @@ def predict_main():
|
||||
# # ex_GRU(df)
|
||||
|
||||
# 发送邮件
|
||||
m = SendMail(
|
||||
username=username,
|
||||
passwd=passwd,
|
||||
recv=recv,
|
||||
title=title,
|
||||
content=content,
|
||||
file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
|
||||
ssl=ssl,
|
||||
)
|
||||
# m = SendMail(
|
||||
# username=username,
|
||||
# passwd=passwd,
|
||||
# recv=recv,
|
||||
# title=title,
|
||||
# content=content,
|
||||
# file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
|
||||
# ssl=ssl,
|
||||
# )
|
||||
# m.send_mail()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# global end_time
|
||||
# is_on = True
|
||||
# # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2025-1-20', '2025-2-6', freq='B'):
|
||||
# for i_time in pd.date_range('2024-12-1', '2025-2-26', freq='W'):
|
||||
# end_time = i_time.strftime('%Y-%m-%d')
|
||||
# predict_main()
|
||||
|
||||
|
||||
@ -35,6 +35,7 @@ global_config.update({
|
||||
'is_del_tow_month': is_del_tow_month,
|
||||
'is_eta': is_eta,
|
||||
'is_update_eta': is_update_eta,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'early_stop_patience_steps': early_stop_patience_steps,
|
||||
|
||||
# 时间参数
|
||||
@ -57,6 +58,15 @@ global_config.update({
|
||||
'APPID': APPID,
|
||||
'SECRET': SECRET,
|
||||
'etadata': data,
|
||||
'edbcodelist': edbcodelist,
|
||||
'ClassifyId': ClassifyId,
|
||||
'edbcodedataurl': edbcodedataurl,
|
||||
'classifyidlisturl': classifyidlisturl,
|
||||
'edbdatapushurl': edbdatapushurl,
|
||||
'edbdeleteurl': edbdeleteurl,
|
||||
'edbbusinessurl': edbbusinessurl,
|
||||
'ClassifyId': ClassifyId,
|
||||
'classifylisturl': classifylisturl,
|
||||
|
||||
# 数据库配置
|
||||
'sqlitedb': sqlitedb,
|
||||
|
||||
@ -35,11 +35,12 @@ global_config.update({
|
||||
'is_del_tow_month': is_del_tow_month,
|
||||
'is_eta': is_eta,
|
||||
'is_update_eta': is_update_eta,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'early_stop_patience_steps': early_stop_patience_steps,
|
||||
|
||||
# 时间参数
|
||||
'start_year': start_year,
|
||||
'end_time': end_time,
|
||||
'end_time': end_time or datetime.datetime.now().strftime("%Y-%m-%d"),
|
||||
'freq': freq, # 保持列表结构
|
||||
|
||||
# 接口配置
|
||||
@ -57,12 +58,20 @@ global_config.update({
|
||||
'APPID': APPID,
|
||||
'SECRET': SECRET,
|
||||
'etadata': data,
|
||||
'edbcodelist': edbcodelist,
|
||||
'ClassifyId': ClassifyId,
|
||||
'edbcodedataurl': edbcodedataurl,
|
||||
'classifyidlisturl': classifyidlisturl,
|
||||
'edbdatapushurl': edbdatapushurl,
|
||||
'edbdeleteurl': edbdeleteurl,
|
||||
'edbbusinessurl': edbbusinessurl,
|
||||
'ClassifyId': ClassifyId,
|
||||
'classifylisturl': classifylisturl,
|
||||
|
||||
# 数据库配置
|
||||
'sqlitedb': sqlitedb,
|
||||
})
|
||||
|
||||
|
||||
def predict_main():
|
||||
"""
|
||||
主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。
|
||||
|
||||
Loading…
Reference in New Issue
Block a user