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原油预测，推送次日，本周数据到北京测试环境

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jingboyitiji 2025-03-12 11:25:58 +08:00
parent 7e71c8e1c8
commit c46709cef9
4 changed files with 342 additions and 218 deletions
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29
config_jingbo.py
View File

@ -98,6 +98,7 @@ login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave" # 上传报告 upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave" # 上传报告
upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save" # 停更预警 upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save" # 停更预警
query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos" # 查询数据项编码 query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos" # 查询数据项编码
push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList" # 上传数据项值
login_data = { login_data = {
"data": { "data": {
@ -150,6 +151,33 @@ query_data_list_item_nos_data = {
} }
} }
push_data_value_list_data = {
"funcModule": "数据表信息列表",
"funcOperation": "新增",
"data": [
{"dataItemNo":"91230600716676129",
"dataDate":"20230113",
"dataStatus":"add",
"dataValue":100.11
},
{"dataItemNo":"91230600716676129P|ETHYL_BEN|CAPACITY",
"dataDate":"20230113",
"dataStatus":"add",
"dataValue":100.55
},
{"dataItemNo":"91230600716676129P|ETHYL_BEN|CAPACITY",
"dataDate":"20230113",
"dataStatus":"add",
"dataValue":100.55
}
]
}
# 八大维度数据项编码
bdwd_items = {
'ciri' : 'yyycbdwdcr',
'benzhou': 'yyycbdwdbz',
}
# 北京环境数据库 # 北京环境数据库
host = '192.168.101.27' host = '192.168.101.27'
@ -172,6 +200,7 @@ 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 = False # 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_update_predict_value = True # 是否上传预测值到市场信息平台
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

40
lib/dataread.py
View File

@ -94,6 +94,7 @@ global_config = {
'offsite': None, # 站点名称 'offsite': None, # 站点名称
'edbcodenamedict': None, # EDB编码映射 'edbcodenamedict': None, # EDB编码映射
'rote': None, # 绘图上下界阈值 'rote': None, # 绘图上下界阈值
'bdwd_items':None,
# 接口配置（原有配置） # 接口配置（原有配置）
'login_pushreport_url': None, 'login_pushreport_url': None,
@ -1162,6 +1163,11 @@ class Config:
@property @property
def query_data_list_item_nos_data( def query_data_list_item_nos_data(
self): return global_config['query_data_list_item_nos_data'] self): return global_config['query_data_list_item_nos_data']
@property
def push_data_value_list_url(self): return global_config['push_data_value_list_url']
@property
def push_data_value_list_data(self): return global_config['push_data_value_list_data']
# 字段映射 # 字段映射
@property @property
@ -2030,6 +2036,40 @@ def get_market_data(end_time, df):
df = pd.merge(df, df2, how='left', on='date') df = pd.merge(df, df2, how='left', on='date')
return df return df
def push_market_data(data):
'''
上传预测价格到市场信息平台
data: 预测价格数据,示例：
[
{"dataItemNo":"91230600716676129",
"dataDate":"20230113",
"dataStatus":"add",
"dataValue":100.11
},
{"dataItemNo":"91230600716676129P|ETHYL_BEN|CAPACITY",
"dataDate":"20230113",
"dataStatus":"add",
"dataValue":100.55
},
{"dataItemNo":"91230600716676129P|ETHYL_BEN|CAPACITY",
"dataDate":"20230113",
"dataStatus":"add",
"dataValue":100.55
}
]
'''
# 获取token
token = get_head_auth_report()
# 定义请求参数
config.push_data_value_list_data['data'] = data
# 发送请求
headers = {"Authorization": token}
config.logger.info('上传数据中...')
items_res = requests.post(url=config.push_data_value_list_url, headers=headers,
json=config.push_data_value_list_data, timeout=(3, 35))
json_data = json.loads(items_res.text)
config.logger.info(f"上传结果:{json_data}")
return json_data
def get_high_low_data(df): def get_high_low_data(df):
# 读取excel 从第五行开始 # 读取excel 从第五行开始

487
main_yuanyou.py
View File

@ -29,6 +29,7 @@ global_config.update({
'test_size': test_size, 'test_size': test_size,
'modelsindex': modelsindex, 'modelsindex': modelsindex,
'rote': rote, 'rote': rote,
'bdwd_items':bdwd_items,
# 特征工程开关 # 特征工程开关
'is_del_corr': is_del_corr, 'is_del_corr': is_del_corr,
@ -36,6 +37,7 @@ global_config.update({
'is_eta': is_eta, 'is_eta': is_eta,
'is_update_eta': is_update_eta, 'is_update_eta': is_update_eta,
'is_fivemodels': is_fivemodels, 'is_fivemodels': is_fivemodels,
'is_update_predict_value': is_update_predict_value,
'early_stop_patience_steps': early_stop_patience_steps, 'early_stop_patience_steps': early_stop_patience_steps,
# 时间参数 # 时间参数
@ -112,243 +114,296 @@ def predict_main():
返回: 返回:
None None
""" """
end_time = global_config['end_time'] # end_time = global_config['end_time']
# 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=global_config['classifylisturl'], # classifylisturl=global_config['classifylisturl'],
classifyidlisturl=global_config['classifyidlisturl'], # classifyidlisturl=global_config['classifyidlisturl'],
edbcodedataurl=global_config['edbcodedataurl'], # edbcodedataurl=global_config['edbcodedataurl'],
edbcodelist=global_config['edbcodelist'], # edbcodelist=global_config['edbcodelist'],
edbdatapushurl=global_config['edbdatapushurl'], # edbdatapushurl=global_config['edbdatapushurl'],
edbdeleteurl=global_config['edbdeleteurl'], # edbdeleteurl=global_config['edbdeleteurl'],
edbbusinessurl=global_config['edbbusinessurl'], # edbbusinessurl=global_config['edbbusinessurl'],
classifyId=global_config['ClassifyId'], # classifyId=global_config['ClassifyId'],
) # )
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data( # df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
data_set=data_set, dataset=dataset) # 原始数据，未处理 # 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...')
try: # try:
# 如果是测试环境，最高价最低价取excel文档 # # 如果是测试环境，最高价最低价取excel文档
if server_host == '192.168.100.53': # if server_host == '192.168.100.53':
logger.info('从excel文档获取最高价最低价') # logger.info('从excel文档获取最高价最低价')
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( # df_zhibiaoshuju = get_market_data(
end_time, df_zhibiaoshuju) # 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(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture, # 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: # 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(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)
if is_edbnamelist: # if is_edbnamelist:
df = df[edbnamelist] # df = df[edbnamelist]
df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False) # df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
# 保存最新日期的y值到数据库 # # 保存最新日期的y值到数据库
# 取第一行数据存储到数据库中 # # 取第一行数据存储到数据库中
first_row = df[['ds', 'y']].tail(1) # first_row = df[['ds', 'y']].tail(1)
# 判断y的类型是否为float # # 判断y的类型是否为float
if not isinstance(first_row['y'].values[0], float): # if not isinstance(first_row['y'].values[0], float):
logger.info(f'{end_time}预测目标数据为空，跳过') # logger.info(f'{end_time}预测目标数据为空，跳过')
return None # return None
# 将最新真实值保存到数据库 # # 将最新真实值保存到数据库
if not sqlitedb.check_table_exists('trueandpredict'): # if not sqlitedb.check_table_exists('trueandpredict'):
first_row.to_sql('trueandpredict', sqlitedb.connection, index=False) # first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
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()
config.logger.info(f'要保存的真实值：{row_dict}') # 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 # # 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)): # if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d') # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
elif not isinstance(row_dict['ds'], str): # elif not isinstance(row_dict['ds'], str):
try: # try:
row_dict['ds'] = pd.to_datetime( # row_dict['ds'] = pd.to_datetime(
row_dict['ds']).strftime('%Y-%m-%d') # row_dict['ds']).strftime('%Y-%m-%d')
except: # except:
logger.warning(f"无法解析的时间格式: {row_dict['ds']}") # 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')
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S') # # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
check_query = sqlitedb.select_data( # check_query = sqlitedb.select_data(
'trueandpredict', where_condition=f"ds = '{row.ds}'") # 'trueandpredict', where_condition=f"ds = '{row.ds}'")
if len(check_query) > 0: # if len(check_query) > 0:
set_clause = ", ".join( # set_clause = ", ".join(
[f"{key} = '{value}'" for key, value in row_dict.items()]) # [f"{key} = '{value}'" for key, value in row_dict.items()])
sqlitedb.update_data( # sqlitedb.update_data(
'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'") # 'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
continue # continue
sqlitedb.insert_data('trueandpredict', tuple( # sqlitedb.insert_data('trueandpredict', tuple(
row_dict.values()), columns=row_dict.keys()) # 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( # update_y = sqlitedb.select_data(
'accuracy', where_condition="y is null") # '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( # sqlitedb.update_data(
'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'") # '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}')
# 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( # model_results = sqlitedb.select_data(
'trueandpredict', order_by="ds DESC", limit="60") # '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( # model_results = model_results.dropna(
thresh=len(model_results)*0.1, axis=1) # 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]
for col in model_results[modelnames].select_dtypes(include=['object']).columns: # for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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[f'{model}_abs_error_rate'] = abs(
model_results['y'] - model_results[model]) / model_results['y'] # 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( # min_abs_error_rate_values = model_results.apply(
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1) # 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( # min_abs_error_rate_column_name = model_results.apply(
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1) # 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( # min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
lambda x: x.split('_')[0]) # 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( # sqlitedb.create_table(
'most_model', columns="ds datetime, most_common_model TEXT") # 'most_model', columns="ds datetime, most_common_model TEXT")
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime( # sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',)) # '%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][[ # 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']] # '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', # 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'}) # '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( # engine = create_engine(
f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}') # f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime( # warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
"%Y-%m-%d %H:%M:%S") # "%Y-%m-%d %H:%M:%S")
warning_data_df['TENANT_CODE'] = 'T0004' # 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( # warning_data_df['ID'] = range(
max_id + 1, max_id + 1 + len(warning_data_df)) # 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( # warning_data_df.to_sql(
table_name, con=engine, if_exists='append', index=False) # 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:
logger.info('上传预警信息到数据库失败') # logger.info('上传预警信息到数据库失败')
if is_corr: # if is_corr:
df = corr_feature(df=df) # df = corr_feature(df=df)
df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用 # df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
logger.info(f"开始训练模型...") # logger.info(f"开始训练模型...")
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(df, # ex_Model(df,
horizon=global_config['horizon'], # horizon=global_config['horizon'],
input_size=global_config['input_size'], # input_size=global_config['input_size'],
train_steps=global_config['train_steps'], # train_steps=global_config['train_steps'],
val_check_steps=global_config['val_check_steps'], # val_check_steps=global_config['val_check_steps'],
early_stop_patience_steps=global_config['early_stop_patience_steps'], # early_stop_patience_steps=global_config['early_stop_patience_steps'],
is_debug=global_config['is_debug'], # is_debug=global_config['is_debug'],
dataset=global_config['dataset'], # dataset=global_config['dataset'],
is_train=global_config['is_train'], # is_train=global_config['is_train'],
is_fivemodels=global_config['is_fivemodels'], # is_fivemodels=global_config['is_fivemodels'],
val_size=global_config['val_size'], # val_size=global_config['val_size'],
test_size=global_config['test_size'], # test_size=global_config['test_size'],
settings=global_config['settings'], # settings=global_config['settings'],
now=now, # now=now,
etadata=global_config['etadata'], # etadata=global_config['etadata'],
modelsindex=global_config['modelsindex'], # modelsindex=global_config['modelsindex'],
data=data, # data=data,
is_eta=global_config['is_eta'], # is_eta=global_config['is_eta'],
end_time=global_config['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')
model_results3 = model_losss(sqlitedb, end_time=end_time)
logger.info('训练数据绘图end') # logger.info('训练数据绘图ing')
# 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') # 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原油大模型日度预测--{end_time}.pdf' # 报告文件名 # 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 # reportname = reportname.replace(':', '-') # 替换冒号
brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time, # brent_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')
logger.info('发送预测结果到市场信息平台')
# 读取预测数据和模型评估数据
predict_file_path = os.path.join(config.dataset, 'predict.csv')
model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
try:
predictdata_df = pd.read_csv(predict_file_path)
top_models_df = pd.read_csv(model_eval_file_path)
except FileNotFoundError as e:
logger.error(f"文件未找到: {e}")
return
predictdata = predictdata_df.copy()
# 取模型前十
top_models = top_models_df['模型(Model)'].head(10).tolist()
# 计算前十模型的均值
predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
# 打印日期和前十模型均值
print(predictdata_df[['ds', 'top_models_mean']])
# 准备要推送的数据
first_date = predictdata_df['ds'].iloc[0].replace('-', '')
last_date = predictdata_df['ds'].iloc[-1].replace('-', '')
first_mean = predictdata_df['top_models_mean'].iloc[0]
last_mean = predictdata_df['top_models_mean'].iloc[-1]
predictdata = [
{
"dataItemNo": global_config['bdwd_items']['ciri'],
"dataDate": first_date,
"dataStatus": "add",
"dataValue": first_mean
},
{
"dataItemNo": global_config['bdwd_items']['benzhou'],
"dataDate": last_date,
"dataStatus": "add",
"dataValue": last_mean
}
]
print(predictdata)
# 推送数据到市场信息平台
try:
push_market_data(predictdata)
except Exception as e:
logger.error(f"推送数据失败: {e}")
# # 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)

4
测试环境获取市场信息平台数据项.ipynb
View File

@ -280,7 +280,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "base", "display_name": "predict",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -294,7 +294,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.11.7" "version": "3.11.9"
} }
}, },
"nbformat": 4, "nbformat": 4,