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聚烯烃周度预测调试完成

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jingboyitiji 2025-03-11 14:20:56 +08:00
parent 3b0011ceeb
commit 95da926b3a
3 changed files with 225 additions and 120 deletions
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22
config_juxiting_zhoudu.py
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@ -202,15 +202,15 @@ table_name = 'v_tbl_crude_oil_warning'
### 开关 ### 开关
is_train = False # 是否训练 is_train = False # 是否训练
is_debug = False # 是否调试 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 = 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 = True # 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 = False # 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
@ -224,9 +224,9 @@ print("数据库连接成功",host,dbname,dbusername)
# 数据截取日期 # 数据截取日期
start_year = 2020 # 数据开始年份 start_year = 2015 # 数据开始年份
end_time = '2025-01-27' # 数据截取日期 end_time = '' # 数据截取日期
freq = 'W' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日 freq = 'WW' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
delweekenday = True if freq == 'B' else False # 是否删除周末数据 delweekenday = True if freq == 'B' else False # 是否删除周末数据
is_corr = False # 特征是否参与滞后领先提升相关系数 is_corr = False # 特征是否参与滞后领先提升相关系数
add_kdj = False # 是否添加kdj指标 add_kdj = False # 是否添加kdj指标
@ -243,8 +243,8 @@ avg_cols = [
] ]
offsite = 80 offsite = 80
offsite_col = ['PP：拉丝：HP550J：市场价：青岛：金能化学（日）'] offsite_col = ['PP：拉丝：HP550J：市场价：青岛：金能化学（日）']
horizon =1 # 预测的步长 horizon =2 # 预测的步长
input_size = 7 # 输入序列长度 input_size = 14 # 输入序列长度
train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数 train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数
val_check_steps = 30 # 评估频率 val_check_steps = 30 # 评估频率
early_stop_patience_steps = 5 # 早停的耐心步数 early_stop_patience_steps = 5 # 早停的耐心步数
@ -263,10 +263,10 @@ weight_dict = [0.4,0.15,0.1,0.1,0.25] # 权重
### 文件 ### 文件
data_set = 'PP指标数据.xlsx' # 数据集文件 data_set = 'PP指标数据.xlsx' # 数据集文件
dataset = 'juxitingzhududataset' # 数据集文件夹 dataset = 'juxitingzhoududataset' # 数据集文件夹
# 数据库名称 # 数据库名称
db_name = os.path.join(dataset,'jbsh_juxiting.db') db_name = os.path.join(dataset,'jbsh_juxiting_zhoudu.db')
sqlitedb = SQLiteHandler(db_name) sqlitedb = SQLiteHandler(db_name)
sqlitedb.connect() sqlitedb.connect()

16
lib/dataread.py
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@ -1063,6 +1063,22 @@ def getdata_juxiting(filename, datecol='date', y='y', dataset='', add_kdj=False,
return df, df_zhibiaoliebiao return df, df_zhibiaoliebiao
def getdata_zhoudu_juxiting(filename, datecol='date', y='y', dataset='', add_kdj=False, is_timefurture=False, end_time=''):
config.logger.info('getdata接收：'+filename+' '+datecol+' '+end_time)
# 判断后缀名 csv或excel
if filename.endswith('.csv'):
df = loadcsv(filename)
else:
# 读取excel 指标数据
df_zhibiaoshuju = pd.read_excel(filename, sheet_name='指标数据')
df_zhibiaoliebiao = pd.read_excel(filename, sheet_name='指标列表')
# 日期字符串转为datatime
df = zhoududatachuli(df_zhibiaoshuju, df_zhibiaoliebiao, datecol, y=y, dataset=dataset,
add_kdj=add_kdj, is_timefurture=is_timefurture, end_time=end_time)
return df, df_zhibiaoliebiao
def sanitize_filename(filename): def sanitize_filename(filename):
# 使用正则表达式替换不合规的字符 # 使用正则表达式替换不合规的字符

307
main_juxiting_zhoudu.py
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@ -1,12 +1,80 @@
# 读取配置 # 读取配置
from lib.dataread import *
from lib.tools import SendMail,exception_logger
from models.nerulforcastmodels import ex_Model_Juxiting,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_juxiting_zhoudu import *
from lib.tools import SendMail, exception_logger
from models.nerulforcastmodels import ex_Model, model_losss_juxiting, tansuanli_export_pdf, pp_export_pdf
import datetime
import torch import torch
torch.set_float32_matmul_precision("high") torch.set_float32_matmul_precision("high")
global_config.update({
# 核心参数
'logger': logger,
'dataset': dataset,
'y': y,
'offsite_col': offsite_col,
'avg_cols': avg_cols,
'offsite': offsite,
'edbcodenamedict': edbcodenamedict,
'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(): def predict_main():
@ -48,31 +116,23 @@ def predict_main():
返回: 返回:
None None
""" """
global end_time end_time = global_config['end_time']
signature = BinanceAPI(APPID, SECRET)
etadata = EtaReader(signature=signature,
classifylisturl=classifylisturl,
classifyidlisturl=classifyidlisturl,
edbcodedataurl=edbcodedataurl,
edbcodelist=edbcodelist,
edbdatapushurl=edbdatapushurl,
edbdeleteurl=edbdeleteurl,
edbbusinessurl=edbbusinessurl
)
# 获取数据 # 获取数据
if is_eta: if is_eta:
logger.info('从eta获取数据...') logger.info('从eta获取数据...')
signature = BinanceAPI(APPID, SECRET) signature = BinanceAPI(APPID, SECRET)
etadata = EtaReader(signature=signature, etadata = EtaReader(signature=signature,
classifylisturl=classifylisturl, classifylisturl=global_config['classifylisturl'],
classifyidlisturl=classifyidlisturl, classifyidlisturl=global_config['classifyidlisturl'],
edbcodedataurl=edbcodedataurl, edbcodedataurl=global_config['edbcodedataurl'],
edbcodelist=edbcodelist, edbcodelist=global_config['edbcodelist'],
edbdatapushurl=edbdatapushurl, edbdatapushurl=global_config['edbdatapushurl'],
edbdeleteurl=edbdeleteurl, edbdeleteurl=global_config['edbdeleteurl'],
edbbusinessurl=edbbusinessurl, edbbusinessurl=global_config['edbbusinessurl'],
classifyId=global_config['ClassifyId'],
) )
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(data_set=data_set, dataset=dataset) # 原始数据，未处理 df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(
data_set=data_set, dataset=dataset) # 原始数据，未处理
if is_market: if is_market:
logger.info('从市场信息平台获取数据...') logger.info('从市场信息平台获取数据...')
@ -83,26 +143,26 @@ def predict_main():
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju) df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
else: else:
logger.info('从市场信息平台获取数据') 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') logger.info('最高最低价拼接失败')
# 保存到xlsx文件的sheet表 # 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_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) df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
# 数据处理 # 数据处理
df = datachuli_juxiting(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_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj, df, df_zhibiaoliebiao = getdata_zhoudu_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)
@ -124,47 +184,65 @@ def predict_main():
else: else:
for row in first_row.itertuples(index=False): for row in first_row.itertuples(index=False):
row_dict = row._asdict() row_dict = row._asdict()
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S') config.logger.info(f'要保存的真实值：{row_dict}')
check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'") # 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: if len(check_query) > 0:
set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()]) set_clause = ", ".join(
sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'") [f"{key} = '{value}'" for key, value in row_dict.items()])
sqlitedb.update_data(
'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
continue 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 # 更新accuracy表的y值
if not sqlitedb.check_table_exists('accuracy'): if not sqlitedb.check_table_exists('accuracy'):
pass pass
else: 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: if len(update_y) > 0:
logger.info('更新accuracy表的y值') logger.info('更新accuracy表的y值')
# 找到update_y 中ds且df中的y的行 # 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}') logger.info(f'要更新y的信息：{update_y}')
# try: # try:
for row in update_y.itertuples(index=False): for row in update_y.itertuples(index=False):
try: try:
row_dict = row._asdict() row_dict = row._asdict()
yy = df[df['ds']==row_dict['ds']]['y'].values[0] yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0] LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].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']}'") sqlitedb.update_data(
'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
except: except:
logger.info(f'更新accuracy表的y值失败：{row_dict}') logger.info(f'更新accuracy表的y值失败：{row_dict}')
# except Exception as e: # except Exception as e:
# logger.info(f'更新accuracy表的y值失败：{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 # 判断当前日期是不是周一
is_weekday = datetime.datetime.now().weekday() == 0 is_weekday = datetime.datetime.now().weekday() == 0
if is_weekday: if is_weekday:
logger.info('今天是周一，更新预测模型') 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 # 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 # 删除空值率为90%以上的列
if len(model_results) > 10: 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 # 删除空行
model_results = model_results.dropna() model_results = model_results.dropna()
modelnames = model_results.columns.to_list()[2:-1] modelnames = model_results.columns.to_list()[2:-1]
@ -172,47 +250,59 @@ def predict_main():
model_results[col] = model_results[col].astype(np.float32) 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 # 计算每个预测值与真实值之间的偏差率
for model in modelnames: 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 # 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 # 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 # 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 # 取出现次数最多的模型名称
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax() 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}") 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 # 保存结果到数据库
if not sqlitedb.check_table_exists('most_model'): if not sqlitedb.check_table_exists('most_model'):
sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT") sqlitedb.create_table(
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',)) '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: try:
if is_weekday: if is_weekday:
# if True: # if True:
logger.info('今天是周一，发送特征预警') logger.info('今天是周一，发送特征预警')
# 上传预警信息到数据库 # 上传预警信息到数据库
warning_data_df = df_zhibiaoliebiao.copy() 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 # 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 from sqlalchemy import create_engine
import urllib import urllib
global password global password
if '@' in password: if '@' in password:
password = urllib.parse.quote_plus(password) password = urllib.parse.quote_plus(password)
engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}') engine = create_engine(
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime("%Y-%m-%d %H:%M:%S") f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
warning_data_df['TENANT_CODE'] = 'T0004' 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 # 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) existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
if not existing_data.empty: if not existing_data.empty:
max_id = existing_data['ID'].astype(int).max() 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: else:
warning_data_df['ID'] = range(1, 1 + len(warning_data_df)) 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: if is_update_warning_data:
upload_warning_info(len(warning_data_df)) upload_warning_info(len(warning_data_df))
except: except:
@ -226,76 +316,75 @@ 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_Juxiting(df, ex_Model(df,
horizon=horizon, horizon=global_config['horizon'],
input_size=input_size, input_size=global_config['input_size'],
train_steps=train_steps, train_steps=global_config['train_steps'],
val_check_steps=val_check_steps, val_check_steps=global_config['val_check_steps'],
early_stop_patience_steps=early_stop_patience_steps, early_stop_patience_steps=global_config['early_stop_patience_steps'],
is_debug=is_debug, is_debug=global_config['is_debug'],
dataset=dataset, dataset=global_config['dataset'],
is_train=is_train, is_train=global_config['is_train'],
is_fivemodels=is_fivemodels, is_fivemodels=global_config['is_fivemodels'],
val_size=val_size, val_size=global_config['val_size'],
test_size=test_size, test_size=global_config['test_size'],
settings=settings, settings=global_config['settings'],
now=now, now=now,
etadata=etadata, etadata=global_config['etadata'],
modelsindex=modelsindex, modelsindex=global_config['modelsindex'],
data=data, data=data,
is_eta=is_eta, is_eta=global_config['is_eta'],
end_time=end_time, 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') logger.info('训练数据绘图ing')
model_results3 = model_losss_juxiting(sqlitedb) model_results3 = model_losss_juxiting(sqlitedb, end_time=global_config['end_time'],is_fivemodels=global_config['is_fivemodels'])
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'PP大模型预测报告--{end_time}.pdf' # 报告文件名 reportname = f'Brent原油大模型周度预测--{end_time}.pdf' # 报告文件名
reportname = reportname.replace(':', '-') # 替换冒号 reportname = reportname.replace(':', '-') # 替换冒号
pp_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)
# # lstm 多变量模型 # # lstm 多变量模型
# ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset) # ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
# # GRU 模型 # # GRU 模型
# # ex_GRU(df) # # ex_GRU(df)
# 发送邮件 # 发送邮件
m = SendMail( # m = SendMail(
username=username, # username=username,
passwd=passwd, # passwd=passwd,
recv=recv, # recv=recv,
title=title, # title=title,
content=content, # content=content,
file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime), # file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
ssl=ssl, # ssl=ssl,
) # )
# m.send_mail() # m.send_mail()
if __name__ == '__main__': if __name__ == '__main__':
# global end_time # global end_time
# is_on = True # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
# # 遍历2024-11-25 到 2024-12-3 之间的工作日日期 # for i_time in pd.date_range('2022-1-1', '2025-3-26', freq='M'):
# for i_time in pd.date_range('2025-1-20', '2025-2-6', freq='B'):
# end_time = i_time.strftime('%Y-%m-%d')
# try: # try:
# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
# predict_main() # predict_main()
# except: # except Exception as e:
# pass # logger.info(f'预测失败：{e}')
# continue
predict_main() predict_main()