# 读取配置
from config_jingbo import *
from lib.tools import *
from lib.dataread import *
from models.nerulforcastmodels import ex_Model,model_losss,brent_export_pdf
from models.lstmmodels import ex_Lstm_M,ex_Lstm
from models.grumodels import ex_GRU
import glob
import torch
torch.set_float32_matmul_precision("high")

if __name__ == '__main__':
    signature = BinanceAPI(APPID, SECRET)

    # 遍历参数训练模型
    input_size_list = [14]
    horizon_list = [7]
    train_steps_list = [500,1000,1500,2000]
    k_list = [10,18,25,50,100]
    end_time_list = ['2024-07-03']
    is_debug = False
    is_fivemodels = False # 是否使用之前保存的最佳的5个模型
    delweekenday = True
    # 组合上面三个参数
    for i in range(len(input_size_list)):
        for h in range(len(horizon_list)):
            for j in range(len(train_steps_list)):
                for k in range(len(k_list)):
                    for end_time in end_time_list:
                        input_size = input_size_list[i]
                        horizons = horizon_list[h]
                        train_steps = train_steps_list[j]
                        K = k_list[k]
                        settings = f'{input_size}-{horizon_list[h]}-{train_steps}-{K}-{data_set}-{end_time}-{y}' 
                        logger.info(f'当前配置：{settings}')
                                        
                        # 获取数据
                        if is_eta:
                            etadata = EtaReader(signature=signature,
                                                classifylisturl = classifylisturl,
                                                classifyidlisturl=classifyidlisturl,
                                                edbcodedataurl=edbcodedataurl,
                                                edbcodelist=edbcodelist
                                                )
                            df = etadata.get_eta_api_data(data_set=data_set,dataset=dataset)  # 原始数据，未处理
                        else:
                            filename = os.path.join(dataset,data_set)
                            logger.info(f'未启用Eta数据,将读取本地数据{filename}')
                            df = pd.read_excel(filename,sheet_name='指标数据')
                        
                        # 数据处理
                        df = datachuli(df=df,dataset=dataset,end_time=end_time,y=y,delweekenday=delweekenday)
                        
                        if is_timefurture:
                            df = addtimecharacteristics(df=df,dataset=dataset)
                        
                        # 更改预测列名称
                        df.rename(columns={y:'y'},inplace=True)
                        
                        logger.info(f"开始训练模型...")
                        row,col = df.shape
                        logger.info(f'当前配置：{settings}')
                        # 获取日期时间 计算今天日期 %Y-%m-%d-%H-%M-%S
                        from datetime import datetime
                        now = 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,
                                    now=now
                                    )

                        # 模型评估
                        model_results3 = model_losss(df,dataset=dataset,horizon=horizon)
                        # 模型报告

                        reportname = f'{settings}--{now}-预测报告.pdf' # 报告文件名
                        reportname = reportname.replace(':', '-') # 替换冒号
                        title = f'{settings}--{now}-预测报告' # 报告标题
                        brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
                                    reportname=reportname),
                        
                        # 发送邮件
                        m = SendMail(
                            username=username,
                            passwd=passwd,
                            recv=recv,
                            title=title,
                            content=content,
                            file=max(glob.glob(os.path.join(dataset,reportname)), key=os.path.getctime),
                            ssl=ssl,
                        )
                        # m.send_mail()   
    
    # # LSTM 单变量模型
    # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
    
    # # lstm 多变量模型
    # ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
    
    # # GRU 模型
    # # 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.send_mail()