更新电压lstm模型

浙江电压等级电量/电压等级_输出为5.py

@@ -52,61 +52,61 @@ def data_preprocessing(data):
     return data
 
 # 拼接数据集
-# file_dir = r'C:\Users\user\Desktop\浙江各地市分电压日电量数据'
-# excel = os.listdir(file_dir)[0]
-# data = pd.read_excel(os.path.join(file_dir, excel), sheet_name=0, index_col='stat_date')
-# data.drop(columns='地市',inplace=True)
-# data = data_preprocessing(data)
-#
-# df = data[data.columns[0]]
-# df.dropna(inplace = True)
-# dataset_x, dataset_y = create_dataset(df, DAYS_FOR_TRAIN)
-#
-# for level in data.columns[1:]:
-#     df = data[level]
-#     df.dropna(inplace=True)
-#     x, y = create_dataset(df, DAYS_FOR_TRAIN)
-#     dataset_x = np.concatenate((dataset_x, x))
-#     dataset_y = np.concatenate((dataset_y, y))
-#
-#
-# for excel in os.listdir(file_dir)[1:]:
-#
-#     data = pd.read_excel(os.path.join(file_dir,excel), sheet_name=0,index_col='stat_date')
-#     data.drop(columns='地市', inplace=True)
-#     data = data_preprocessing(data)
-#
-#     for level in data.columns:
-#         df = data[level]
-#         df.dropna(inplace=True)
-#         x,y = create_dataset(df,DAYS_FOR_TRAIN)
-#         dataset_x = np.concatenate((dataset_x,x))
-#         dataset_y = np.concatenate((dataset_y,y))
-#
-#
-#
+file_dir = r'C:\Users\user\Desktop\浙江各地市分电压日电量数据'
+excel = os.listdir(file_dir)[0]
+data = pd.read_excel(os.path.join(file_dir, excel), sheet_name=0, index_col='stat_date')
+data.drop(columns='地市',inplace=True)
+data = data_preprocessing(data)
+
+df = data[data.columns[0]]
+df.dropna(inplace = True)
+dataset_x, dataset_y = create_dataset(df, DAYS_FOR_TRAIN)
+
+for level in data.columns[1:]:
+    df = data[level]
+    df.dropna(inplace=True)
+    x, y = create_dataset(df, DAYS_FOR_TRAIN)
+    dataset_x = np.concatenate((dataset_x, x))
+    dataset_y = np.concatenate((dataset_y, y))
+
+
+for excel in os.listdir(file_dir)[1:]:
+
+    data = pd.read_excel(os.path.join(file_dir,excel), sheet_name=0,index_col='stat_date')
+    data.drop(columns='地市', inplace=True)
+    data = data_preprocessing(data)
+
+    for level in data.columns:
+        df = data[level]
+        df.dropna(inplace=True)
+        x,y = create_dataset(df,DAYS_FOR_TRAIN)
+        dataset_x = np.concatenate((dataset_x,x))
+        dataset_y = np.concatenate((dataset_y,y))
+
+
+
 # # 训练
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 #
-# # 标准化到0~1
-# max_value = np.max(dataset_x)
-# min_value = np.min(dataset_x)
-# dataset_x = (dataset_x - min_value) / (max_value - min_value)
-# dataset_y = (dataset_y - min_value) / (max_value - min_value)
-#
-# print(max_value,min_value)
-# # 划分训练集和测试集
-# train_size = int(len(dataset_x)*0.7)
-# train_x = dataset_x[:train_size]
-# train_y = dataset_y[:train_size]
-#
-# # 将数据改变形状，RNN 读入的数据维度是 (seq_size, batch_size, feature_size)
-# train_x = train_x.reshape(-1, 1, DAYS_FOR_TRAIN)
-# train_y = train_y.reshape(-1, 1, 5)
-#
-# # 转为pytorch的tensor对象
-# train_x = torch.from_numpy(train_x).to(device).type(torch.float32)
-# train_y = torch.from_numpy(train_y).to(device).type(torch.float32)
+# 标准化到0~1
+max_value = np.max(dataset_x)
+min_value = np.min(dataset_x)
+dataset_x = (dataset_x - min_value) / (max_value - min_value)
+dataset_y = (dataset_y - min_value) / (max_value - min_value)
+
+print(max_value,min_value)
+# 划分训练集和测试集
+train_size = int(len(dataset_x)*0.7)
+train_x = dataset_x[:train_size]
+train_y = dataset_y[:train_size]
+
+# 将数据改变形状，RNN 读入的数据维度是 (seq_size, batch_size, feature_size)
+train_x = train_x.reshape(-1, 1, DAYS_FOR_TRAIN)
+train_y = train_y.reshape(-1, 1, 5)
+
+# 转为pytorch的tensor对象
+train_x = torch.from_numpy(train_x).to(device).type(torch.float32)
+train_y = torch.from_numpy(train_y).to(device).type(torch.float32)
 
 model = LSTM_Regression(DAYS_FOR_TRAIN, 32, output_size=5, num_layers=2).to(device)  # 导入模型并设置模型的参数输入输出层、隐藏层等