diff --git a/main_yuanyou_zhoudu.py b/main_yuanyou_zhoudu.py
index af5492e..3b880da 100644
--- a/main_yuanyou_zhoudu.py
+++ b/main_yuanyou_zhoudu.py
@@ -174,228 +174,228 @@ def predict_main():
返回:
None
"""
- end_time = global_config['end_time']
+ # end_time = global_config['end_time']
- signature = BinanceAPI(APPID, SECRET)
- etadata = EtaReader(signature=signature,
- 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'],
- )
- # 获取数据
- if is_eta:
- logger.info('从eta获取数据...')
+ # signature = BinanceAPI(APPID, SECRET)
+ # etadata = EtaReader(signature=signature,
+ # 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'],
+ # )
+ # # 获取数据
+ # if is_eta:
+ # logger.info('从eta获取数据...')
- df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
- data_set=data_set, dataset=dataset) # 原始数据，未处理
+ # 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...')
- try:
- # 如果是测试环境，最高价最低价取excel文档
- if server_host == '192.168.100.53':
- logger.info('从excel文档获取最高价最低价')
- 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)
+ # if is_market:
+ # logger.info('从市场信息平台获取数据...')
+ # try:
+ # # 如果是测试环境，最高价最低价取excel文档
+ # if server_host == '192.168.100.53':
+ # logger.info('从excel文档获取最高价最低价')
+ # 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)
- except:
- logger.info('最高最低价拼接失败')
+ # 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:
- 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)
+ # # 保存到xlsx文件的sheet表
+ # 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)
+ # # 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)
- 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
+ # 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
- # 更改预测列名称
- df.rename(columns={y: 'y'}, inplace=True)
+ # # 更改预测列名称
+ # df.rename(columns={y: 'y'}, inplace=True)
- if is_edbnamelist:
- df = df[edbnamelist]
- 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
- # 取第一行数据存储到数据库中
- first_row = df[['ds', 'y']].tail(1)
- # 判断y的类型是否为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')
- return None
+ # if is_edbnamelist:
+ # df = df[edbnamelist]
+ # 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
+ # # 取第一行数据存储到数据库中
+ # first_row = df[['ds', 'y']].tail(1)
+ # # 判断y的类型是否为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')
+ # return None
- # 将最新真实值保存到数据库
- if not sqlitedb.check_table_exists('trueandpredict'):
- first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
- else:
- for row in first_row.itertuples(index=False):
- row_dict = row._asdict()
- 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}'")
- continue
- sqlitedb.insert_data('trueandpredict', tuple(
- row_dict.values()), columns=row_dict.keys())
+ # # 将最新真实值保存到数据库
+ # if not sqlitedb.check_table_exists('trueandpredict'):
+ # first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
+ # else:
+ # for row in first_row.itertuples(index=False):
+ # row_dict = row._asdict()
+ # 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}'")
+ # continue
+ # 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")
- 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]
- 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']}'")
- 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}')
+ # # 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")
+ # 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]
+ # 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']}'")
+ # 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}')
- # 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")
- # 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)
- # 删除空行
- model_results = model_results.dropna()
- modelnames = model_results.columns.to_list()[2:-2]
- for col in model_results[modelnames].select_dtypes(include=['object']).columns:
- 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']
- # 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)
- # 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)
- # 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])
- # 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',))
+ # # 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")
+ # # 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)
+ # # 删除空行
+ # model_results = model_results.dropna()
+ # modelnames = model_results.columns.to_list()[2:-2]
+ # for col in model_results[modelnames].select_dtypes(include=['object']).columns:
+ # 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']
+ # # 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)
+ # # 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)
+ # # 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])
+ # # 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',))
- try:
- if is_weekday:
- # 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']]
- # 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'})
- from sqlalchemy import create_engine
- import urllib
- global password
- if '@' in password:
- password = urllib.parse.quote_plus(password)
+ # try:
+ # if is_weekday:
+ # # 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']]
+ # # 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'})
+ # 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'
- # 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))
- 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)
- if is_update_warning_data:
- upload_warning_info(len(warning_data_df))
- except:
- logger.info('上传预警信息到数据库失败')
+ # 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))
+ # 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)
+ # if is_update_warning_data:
+ # upload_warning_info(len(warning_data_df))
+ # except:
+ # logger.info('上传预警信息到数据库失败')
- if is_corr:
- df = corr_feature(df=df)
+ # if is_corr:
+ # 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
- logger.info(f"开始训练模型...")
- row, col = df.shape
+ # 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...")
+ # row, col = df.shape
- now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
- ex_Model(df,
- 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=global_config['modelsindex'],
- data=data,
- is_eta=global_config['is_eta'],
- end_time=global_config['end_time'],
- )
+ # now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
+ # ex_Model(df,
+ # 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=global_config['modelsindex'],
+ # data=data,
+ # is_eta=global_config['is_eta'],
+ # end_time=global_config['end_time'],
+ # )
# logger.info('模型训练完成')
diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py
index df12d19..81902e8 100644
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@@ -948,6 +948,8 @@ def model_losss(sqlitedb, end_time):
'accuracy', where_condition=f"created_dt <= '{end_time}'")
if len(df_combined) < 100:
len(df_combined) + ''
+ if df_combined['y'].isnull().sum() / len(df_combined) > 0.8:
+ len(df_combined) + ''
except:
df_combined = loadcsv(os.path.join(
config.dataset, "cross_validation.csv"))