176 lines
7.5 KiB
Python
176 lines
7.5 KiB
Python
# 读取配置
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from lib.dataread import *
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from lib.tools import *
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from models.nerulforcastmodels import ex_Model,model_losss,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting
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import glob
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import torch
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torch.set_float32_matmul_precision("high")
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sqlitedb = SQLiteHandler(db_name)
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sqlitedb.connect()
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def predict_main():
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signature = BinanceAPI(APPID, SECRET)
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etadata = EtaReader(signature=signature,
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classifylisturl = classifylisturl,
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classifyidlisturl=classifyidlisturl,
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edbcodedataurl=edbcodedataurl,
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edbcodelist=edbcodelist,
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edbdatapushurl=edbdatapushurl,
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edbdeleteurl=edbdeleteurl,
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edbbusinessurl=edbbusinessurl
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)
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# 获取数据
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if is_eta:
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# eta数据
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logger.info('从eta获取数据...')
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signature = BinanceAPI(APPID, SECRET)
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etadata = EtaReader(signature=signature,
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classifylisturl = classifylisturl,
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classifyidlisturl=classifyidlisturl,
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edbcodedataurl=edbcodedataurl,
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edbcodelist=edbcodelist,
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edbdatapushurl=edbdatapushurl,
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edbdeleteurl=edbdeleteurl,
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edbbusinessurl=edbbusinessurl,
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)
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df_zhibiaoshuju,df_zhibiaoliebiao = etadata.get_eta_api_pp_data(data_set=data_set,dataset=dataset) # 原始数据，未处理
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# 数据处理
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df = datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,y = y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time)
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else:
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logger.info('读取本地数据：'+os.path.join(dataset,data_set))
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df = 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
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# 更改预测列名称
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df.rename(columns={y:'y'},inplace=True)
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if is_edbnamelist:
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df = df[edbnamelist]
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df.to_csv(os.path.join(dataset,'指标数据.csv'), index=False)
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# 保存最新日期的y值到数据库
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# 取第一行数据存储到数据库中
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first_row = df[['ds','y']].tail(1)
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# 将最新真实值保存到数据库
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if not sqlitedb.check_table_exists('trueandpredict'):
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first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)
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else:
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for row in first_row.itertuples(index=False):
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row_dict = row._asdict()
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row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
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check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'")
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if len(check_query) > 0:
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set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
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sqlitedb.update_data('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'")
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continue
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sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=row_dict.keys())
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import datetime
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# 判断当前日期是不是周一
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is_weekday = datetime.datetime.now().weekday() == 1
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if is_weekday:
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logger.info('今天是周一，更新预测模型')
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# 计算最近20天预测残差最低的模型名称
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model_results = sqlitedb.select_data('trueandpredict',order_by = "ds DESC",limit = "20")
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# 删除空值率为40%以上的列,删除空行
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model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)
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model_results = model_results.dropna()
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modelnames = model_results.columns.to_list()[2:]
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for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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model_results[col] = model_results[col].astype(np.float32)
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# 计算每个预测值与真实值之间的偏差率
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for model in modelnames:
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model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
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# 获取每行对应的最小偏差率值
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min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
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# 获取每行对应的最小偏差率值对应的列名
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min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
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# 将列名索引转换为列名
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min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
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# 取出现次数最多的模型名称
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most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
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logger.info(f"最近20天预测残差最低的模型名称：{most_common_model}")
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# 保存结果到数据库
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if not sqlitedb.check_table_exists('most_model'):
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sqlitedb.create_table('most_model',columns="ds datetime, most_common_model TEXT")
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sqlitedb.insert_data('most_model',(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),most_common_model,),columns=('ds','most_common_model',))
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if is_corr:
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df = corr_feature(df=df)
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df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
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logger.info(f"开始训练模型...")
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row,col = df.shape
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now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
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# ex_Model(df,
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# horizon=horizon,
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# input_size=input_size,
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# train_steps=train_steps,
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# val_check_steps=val_check_steps,
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# early_stop_patience_steps=early_stop_patience_steps,
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# is_debug=is_debug,
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# dataset=dataset,
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# is_train=is_train,
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# is_fivemodels=is_fivemodels,
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# val_size=val_size,
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# test_size=test_size,
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# settings=settings,
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# now=now,
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# etadata = etadata,
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# modelsindex = modelsindex,
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# data = data,
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# is_eta=is_eta,
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# )
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logger.info('模型训练完成')
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# # 模型评估
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logger.info('训练数据绘图ing')
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model_results3 = model_losss_juxiting(sqlitedb)
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logger.info('训练数据绘图end')
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# 模型报告
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logger.info('制作报告ing')
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title = f'{settings}--{now}-预测报告' # 报告标题
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pp_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
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reportname=reportname,sqlitedb=sqlitedb),
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logger.info('制作报告end')
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logger.info('模型训练完成')
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# tansuanli_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,end_time=end_time,reportname=reportname)
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# # LSTM 单变量模型
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# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
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# # lstm 多变量模型
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# ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
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# # GRU 模型
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# # ex_GRU(df)
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# 发送邮件
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m = SendMail(
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username=username,
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passwd=passwd,
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recv=recv,
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title=title,
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content=content,
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file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
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ssl=ssl,
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)
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# m.send_mail()
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if __name__ == '__main__':
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predict_main() |