聚烯烃配置上传eta

main_juxiting.py

@@ -1,7 +1,7 @@
 # 读取配置
 from lib.dataread import *
 from lib.tools import SendMail,exception_logger
-from models.nerulforcastmodels import ex_Model,model_losss,model_losss_juxiting,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting
+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
 import torch
@@ -226,7 +226,7 @@ def predict_main():
     row, col = df.shape
 
     now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
-    ex_Model(df,
+    ex_Model_Juxiting(df,
              horizon=horizon,
              input_size=input_size,
              train_steps=train_steps,

models/nerulforcastmodels.py

@@ -228,6 +228,197 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien
     # return nf_test_preds
     return 
 
+@exception_logger
+def ex_Model_Juxiting(df,horizon,input_size,train_steps,val_check_steps,early_stop_patience_steps,
+             is_debug,dataset,is_train,is_fivemodels,val_size,test_size,settings,now,
+             etadata,modelsindex,data,is_eta,end_time):
+    '''
+    模型训练与预测
+    :param df: 数据集
+            horizon # 预测的步长
+            input_size  # 输入序列长度
+            train_steps  # 训练步数,用来限定epoch次数
+            val_check_steps  # 评估频率
+            early_stop_patience_steps # 早停的耐心步数   
+        :return: 预测结果
+    '''
+
+    # 模型预测列表列名
+    # columns2 = [
+    #     'NHITS',
+    #     'Informer',
+    #     'LSTM',
+    #     'iTransformer',
+    #     'TSMixer',
+    #     'TSMixerx',
+    #     'PatchTST',
+    #     'RNN',
+    #     'GRU',
+    #     'TCN',
+    #     # 'DeepAR',
+    #     'DeepAR-median',
+    #     'DeepAR-lo-90',
+    #     'DeepAR-lo-80',
+    #     'DeepAR-hi-80',
+    #     'DeepAR-hi-90',
+    #     'BiTCN',
+    #     'DilatedRNN',
+    #     'MLP',
+    #     'DLinear',
+    #     'NLinear',
+    #     'TFT',
+    #     'FEDformer',
+    #     'StemGNN',
+    #     'MLPMultivariate',
+    #     'TiDE',
+    #     'DeepNPT',
+    # ]
+
+    df= df.replace(',', '', regex=True)
+    df = df.rename(columns={'date': 'ds'})
+    df['y'] = pd.to_numeric(df['y'], errors='coerce')
+    df['ds'] = pd.to_datetime(df['ds'], errors='coerce')  # 使用errors='coerce'来处理无效日期
+    # df 数值列转为 float32
+    for col in df.select_dtypes(include=['int']).columns:
+        df[col] = df[col].astype(np.float32)
+
+    # 设置中文字体
+    plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
+    plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
+
+
+
+    # 不筛选特征用下面的
+    df_reg = df
+    df_reg.sort_values('ds', inplace=True)
+    if is_debug:
+        df_reg = df_reg[-1000:-1]
+
+    # 计算训练集的结束索引，占总数据的90%
+    split_index = int(0.9* len(df_reg))
+
+    # 按照时间顺序划分训练集和测试集
+    df_train = df_reg[:split_index]
+    df_test = df_reg[-split_index:]
+    df_train['unique_id'] = 1
+    df_test['unique_id'] = 1
+
+    # 显示划分后的数据集的前几行
+    logger.info("Training set head:")
+    logger.info(df_train.head())
+
+    logger.info("\nTesting set head:")
+    logger.info(df_test.head())
+
+
+    models = [
+        NHITS (h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', activation='ReLU', early_stop_patience_steps=early_stop_patience_steps),
+        Informer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps ),
+        LSTM(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        iTransformer(h=horizon, input_size=input_size,n_series = 1, max_steps=train_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        TSMixer(h=horizon, input_size=input_size, n_series = 1,  max_steps=train_steps,   early_stop_patience_steps=early_stop_patience_steps),
+        TSMixerx(h=horizon, input_size=input_size,n_series = 1,  max_steps=train_steps,   early_stop_patience_steps=early_stop_patience_steps),
+        PatchTST(h=horizon, input_size=input_size, max_steps=train_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        RNN(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        GRU(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        TCN(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        # DeepAR(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        BiTCN(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        DilatedRNN(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        MLP(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        DLinear(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        NLinear(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        TFT(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        FEDformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        StemGNN(h=horizon, input_size=input_size,n_series = 1,  max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        MLPMultivariate(h=horizon, input_size=input_size,n_series = 1,  max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        TiDE(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        DeepNPTS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',  early_stop_patience_steps=early_stop_patience_steps),
+        
+        # VanillaTransformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),  //报错了
+        # Autoformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
+        # NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
+        # NBEATSx (h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ),   //报错
+        # HINT(h=horizon),
+        
+    ]
+
+    if is_fivemodels:
+        # 获取之前存好的最好的五个模型
+        with open(os.path.join(dataset,'best_modelnames.txt'), 'r',encoding='utf-8') as f:
+            best_modelnames = f.readlines()[0]
+            logger.info(f'获取本地最佳模型名称：{best_modelnames}')
+        
+        # 重新拼接models 
+        all_models = models
+        models = []
+        for model in all_models:
+            if model._get_name() in best_modelnames:
+                models.append(model)
+
+    # 创建NeuralForecast实例并训练模型
+    nf = NeuralForecast(models=models, freq=freq)
+
+    from joblib import dump, load
+    if is_train:
+        # 模型交叉验证
+        nf_preds = nf.cross_validation(df=df_train, val_size=val_size, test_size=test_size, n_windows=None)
+        nf_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False)    
+        
+        nf_preds = nf_preds.reset_index()
+        # 保存模型
+        # 生成文件名，按时间 精确到分
+        filename = f'{settings}--{now}.joblib'
+        #文件名去掉冒号
+        filename = filename.replace(':', '-') # 替换冒号
+        dump(nf, os.path.join(dataset,filename))
+    else:
+        # glob获取dataset下最新的joblib文件
+        import glob
+        filename = max(glob.glob(os.path.join(dataset,'*.joblib')), key=os.path.getctime)
+        logger.info('读取模型：'+ filename)
+        nf = load(filename)
+    # 测试集预测
+    nf_test_preds = nf.cross_validation(df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
+    # 测试集预测结果保存
+    nf_test_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False)
+
+    df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce')
+
+    #进行未来时间预测
+    df_predict=nf.predict(df_test).reset_index()
+    # 去掉index列
+    df_predict.drop(columns=['index'], inplace=True)
+    df_predict.astype({col: 'float32' for col in df_predict.columns if col not in ['ds'] })
+    
+    # 添加预测时间
+    df_predict['created_dt'] = end_time
+    
+    # 保存预测值
+    df_predict.to_csv(os.path.join(dataset,"predict.csv"),index=False)
+
+    # 将预测结果保存到数据库
+    save_to_database(sqlitedb,df_predict,'predict',end_time)
+    
+    # 把预测值上传到eta
+    if is_update_eta:
+        df_predict['ds'] = pd.to_datetime(df_predict['ds'])
+        dates = df_predict['ds'].dt.strftime('%Y-%m-%d')
+
+        for m in modelsindex.keys():
+            list = []
+            for date,value in zip(dates,df_predict[m].round(2)):
+                list.append({'Date':date,'Value':value})
+            data['DataList'] = list
+            data['IndexCode'] = modelsindex[m]
+            data['IndexName'] = f'价格预测{m}模型'
+            data['Remark'] = m
+            etadata.push_data(data) 
+
+
+    # return nf_test_preds
+    return 
+
 
 # 雍安环境预测评估指数
 @exception_logger