删除重复数据集

浙江电压等级电量/电压等级_输出为3.py

@@ -4,24 +4,166 @@ import numpy as np
 import matplotlib.pyplot as plt
 from torch import nn
 import os
+from torch.utils.data import TensorDataset,DataLoader
+import datetime
 torch.manual_seed(42)
 os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"  # 解决OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
-
+pd.set_option('display.width',None)
 class LSTM(nn.Module):
     def __init__(self,input_size,hidden_size,output_size,num_layers=2):
         super().__init__()
-        self.lstm = nn.LSTM(input_size,hidden_size,num_layers=2)
-        self.fc1 = nn.Linear(hidden_size,64)
-        self.ReLu = nn.ReLU()
-        self.fc2 = nn.Linear(64,output_size)
-
+        self.lstm = nn.LSTM(input_size,hidden_size,num_layers)
+        self.fc1 = nn.Linear(hidden_size,output_size)
+        # self.ReLu = nn.ReLU()
+        # self.fc2 = nn.Linear(64,output_size)
     def forward(self,x):
         output,_ = self.lstm(x)
         s,b,h = output.shape
         output = output.reshape(-1,h)
-        output = self.ReLu(self.fc1(output))
-        output = self.fc2(output)
+        output = self.fc1(output)
+        # output = self.ReLu(self.fc1(output))
+        # output = self.fc2(output)
         return output
 
+
+def create_data(df_level,volt_level):
+    dataset_x = []
+    dataset_y = []
+    # 按月份分组
+    grouped = df_level.groupby(df_level['stat_date'].dt.to_period('M'))
+
+    # 遍历每个月的数据
+    for name, group in grouped:
+        if len(group) == 31:
+            dataset_x.append(list(group[volt_level].values[1:28]))
+            dataset_y.append(list(group[volt_level].values[-3:]))
+        if len(group) == 30:
+            dataset_x.append(list(group[volt_level].values[:27]))
+            dataset_y.append(list(group[volt_level].values[-3:]))
+        if len(group) == 28:
+            fst = group[volt_level].values[0]
+
+            dataset_x.append([fst,fst,fst]+list(group[volt_level].values[1:25]))
+            dataset_y.append(list(group[volt_level].values[-3:]))
+        else:
+            fst = group[volt_level].values[0]
+            if len([fst, fst]+list(group[volt_level].values[1:26])) != 27:
+                break
+            dataset_x.append([fst, fst]+list(group[volt_level].values[1:26]))
+            dataset_y.append(list(group[volt_level].values[-3:]))
+
+    return np.array(dataset_x),np.array(dataset_y)
+
 # 创建数据集
+file_dir = './浙江各地市分电压日电量数据'
+print(os.listdir(file_dir))
+city1 = os.listdir(file_dir)[0]
+df_city = pd.read_excel(os.path.join(file_dir,city1)).drop(columns='地市')
+df_city = df_city[['stat_date','1-10kv','110kv(含66kv)','35kv']]
+df_city[['1-10kv','110kv(含66kv)','35kv']] /= 10000
+df_city.stat_date = pd.to_datetime(df_city.stat_date)
+
+volt_level = '1-10kv'
+df_level = df_city[['stat_date',volt_level]]
+dataset_x,dataset_y = create_data(df_level,volt_level)
+
+for volt_level in df_city.columns[2:]:
+    df_level = df_city[['stat_date',volt_level]]
+    x,y = create_data(df_level,volt_level)
+    dataset_x = np.concatenate([dataset_x,x])
+    dataset_y = np.concatenate([dataset_y,y])
+
+
+for excel in os.listdir(file_dir)[1:]:
+    df_city = pd.read_excel(os.path.join(file_dir, excel)).drop(columns='地市')
+    df_city = df_city[['stat_date', '1-10kv', '110kv(含66kv)', '35kv']]
+    df_city[['1-10kv', '110kv(含66kv)', '35kv']] /= 10000
+    df_city.stat_date = pd.to_datetime(df_city.stat_date)
+    for volt_level in df_city.columns[1:]:
+        df_level = df_city[['stat_date', volt_level]]
+        x, y = create_data(df_level, volt_level)
+        dataset_x = np.concatenate([dataset_x, x])
+        dataset_y = np.concatenate([dataset_y, y])
+
+
+print(dataset_x.shape,dataset_y.shape)
+
+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)*0.8)
+train_x = dataset_x[:train_size]
+train_y = dataset_y[:train_size]
+eval_x = dataset_x[train_size:]
+eval_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, 27)
+train_y = train_y.reshape(-1, 1, 3)
+eval_x = eval_x.reshape(-1, 1, 27)
+eval_y = eval_y.reshape(-1, 1, 27)
+
+# # 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)
+eval_x = torch.from_numpy(eval_x).to(device).type(torch.float32)
+
+train_ds = TensorDataset(train_x,train_y)
+train_dl = DataLoader(train_ds,batch_size=32,drop_last=True)
+eval_ds = TensorDataset(eval_x,eval_y)
+eval_dl = DataLoader(eval_ds,batch_size=64,drop_last=True)
+
+
+model = LSTM(27, 16, output_size=3, 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
+
+train_loss = []
+loss_function = nn.MSELoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+min_loss = 1
+for i in range(200):
+    for j,(x,y) in enumerate(train_dl):
+        x,y = x.to(device),y.to(device)
+        out = model(x)
+        loss = loss_function(out, y)
+        loss.backward()
+        optimizer.step()
+        optimizer.zero_grad()
+        train_loss.append(loss.item())
+    # if (i+1) % 100 == 0:
+    #     print(f'epoch {i+1}/1500 loss:{round(loss.item(),5)}')
+        if (j + 1) % 50 == 0:
+            print(f'epoch {i+1}/200 step {j+1}/{len(train_dl)} loss:{loss}' )
+    model.eval()
+    for k,(x,y) in enumerate(eval_dl):
+        pred = model(eval_x)
+
+
+# 保存模型
+torch.save(model.state_dict(),'dy3.pth')
+
+# for test
+model = model.eval()
+
+dataset_x = dataset_x.reshape(-1, 1, 27)  # (seq_size, batch_size, feature_size)
+dataset_x = torch.from_numpy(dataset_x).to(device).type(torch.float32)
+
+pred_test = model(dataset_x)  # 全量训练集
+# 模型输出 (seq_size, batch_size, output_size)
+pred_test = pred_test.view(-1).cpu().detach().numpy()
+
+plt.plot(pred_test.reshape(-1), 'r', label='prediction')
+plt.plot(dataset_y.reshape(-1), 'b', label='real')
+plt.plot((train_size*3, train_size*3), (0, 1), 'g--')  # 分割线 左边是训练数据 右边是测试数据的输出
+plt.legend(loc='best')
+plt.show()
+
+
+