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import torch
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from torch import nn
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import os
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from torch.utils.data import TensorDataset,DataLoader
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import datetime
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torch.manual_seed(42)
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os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" # 解决OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
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pd.set_option('display.width',None)
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class LSTM(nn.Module):
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def __init__(self,input_size,hidden_size,output_size,num_layers=3):
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super().__init__()
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self.lstm = nn.LSTM(input_size,hidden_size,num_layers)
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self.fc1 = nn.Linear(hidden_size,64)
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self.fc2 = nn.Linear(64,128)
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self.fc3 = nn.Linear(128, output_size)
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self.ReLu = nn.ReLU()
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self.dropout = nn.Dropout()
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def forward(self,x):
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output,_ = self.lstm(x)
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s,b,h = output.shape
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output = output.reshape(-1,h)
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output = self.ReLu(self.fc1(output))
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output = self.ReLu(self.fc2(output))
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output = self.fc3(output)
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return output
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def create_data(df_level,volt_level):
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dataset_x = []
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dataset_y = []
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# 按月份分组
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grouped = df_level.groupby(df_level['stat_date'].dt.to_period('M'))
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# 遍历每个月的数据
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for name, group in grouped:
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if len(group) == 31:
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dataset_x.append(list(group[volt_level].values[1:28]))
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dataset_y.append(list(group[volt_level].values[-3:]))
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if len(group) == 30:
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dataset_x.append(list(group[volt_level].values[:27]))
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dataset_y.append(list(group[volt_level].values[-3:]))
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if len(group) == 28:
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fst = group[volt_level].values[0]
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dataset_x.append([fst,fst,fst]+list(group[volt_level].values[1:25]))
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dataset_y.append(list(group[volt_level].values[-3:]))
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else:
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fst = group[volt_level].values[0]
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if len([fst, fst]+list(group[volt_level].values[1:26])) != 27:
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break
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dataset_x.append([fst, fst]+list(group[volt_level].values[1:26]))
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dataset_y.append(list(group[volt_level].values[-3:]))
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return np.array(dataset_x),np.array(dataset_y)
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# 创建数据集
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file_dir = './浙江各地市分电压日电量数据'
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print(os.listdir(file_dir))
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city1 = os.listdir(file_dir)[0]
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df_city = pd.read_excel(os.path.join(file_dir,city1)).drop(columns='地市')
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df_city = df_city[['stat_date','1-10kv','110kv(含66kv)','35kv']]
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df_city[['1-10kv','110kv(含66kv)','35kv']] /= 10000
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df_city.stat_date = pd.to_datetime(df_city.stat_date)
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volt_level = '1-10kv'
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df_level = df_city[['stat_date',volt_level]]
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dataset_x,dataset_y = create_data(df_level,volt_level)
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for volt_level in df_city.columns[2:]:
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df_level = df_city[['stat_date',volt_level]]
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x,y = create_data(df_level,volt_level)
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dataset_x = np.concatenate([dataset_x,x])
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dataset_y = np.concatenate([dataset_y,y])
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for excel in os.listdir(file_dir)[1:]:
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df_city = pd.read_excel(os.path.join(file_dir, excel)).drop(columns='地市')
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df_city = df_city[['stat_date', '1-10kv', '110kv(含66kv)', '35kv']]
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df_city[['1-10kv', '110kv(含66kv)', '35kv']] /= 10000
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df_city.stat_date = pd.to_datetime(df_city.stat_date)
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for volt_level in df_city.columns[1:]:
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df_level = df_city[['stat_date', volt_level]]
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x, y = create_data(df_level, volt_level)
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dataset_x = np.concatenate([dataset_x, x])
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dataset_y = np.concatenate([dataset_y, y])
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print(dataset_x.shape,dataset_y.shape)
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# 标准化到0~1
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max_value = np.max(dataset_x)
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min_value = np.min(dataset_x)
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dataset_x = (dataset_x - min_value) / (max_value - min_value)
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dataset_y = (dataset_y - min_value) / (max_value - min_value)
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print(max_value,min_value)
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print(np.max(dataset_x),np.min(dataset_x),np.max(dataset_y),np.min(dataset_y))
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# 划分训练集和测试集
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train_size = int(len(dataset_x)*0.8)
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train_x = dataset_x[:train_size]
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train_y = dataset_y[:train_size]
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eval_x = dataset_x[train_size:]
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eval_y = dataset_y[train_size:]
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# # 将数据改变形状，RNN 读入的数据维度是 (seq_size, batch_size, feature_size)
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train_x = train_x.reshape(-1, 1, 27)
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train_y = train_y.reshape(-1, 1, 3)
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eval_x = eval_x.reshape(-1, 1, 27)
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eval_y = eval_y.reshape(-1, 1, 3)
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# # 转为pytorch的tensor对象
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train_x = torch.from_numpy(train_x).to(device).type(torch.float32)
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train_y = torch.from_numpy(train_y).to(device).type(torch.float32)
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eval_x = torch.from_numpy(eval_x).to(device).type(torch.float32)
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eval_y = torch.from_numpy(eval_y).to(device).type(torch.float32)
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train_ds = TensorDataset(train_x,train_y)
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train_dl = DataLoader(train_ds,batch_size=2,shuffle=True, drop_last=True)
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eval_ds = TensorDataset(eval_x,eval_y)
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eval_dl = DataLoader(eval_ds,batch_size=4,drop_last=True)
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model = LSTM(27, 16, output_size=3, num_layers=3).to(device) # 导入模型并设置模型的参数输入输出层、隐藏层等
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train_loss = []
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loss_function = nn.MSELoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=0.005)
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min_loss = 1
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# for i in range(200):
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# model.train()
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# for j,(x,y) in enumerate(train_dl):
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# x,y = x.to(device),y.to(device)
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# out = model(x)
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# loss = loss_function(out, y)
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# loss.backward()
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# optimizer.step()
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# optimizer.zero_grad()
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# train_loss.append(loss.item())
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# # if (i+1) % 100 == 0:
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# # print(f'epoch {i+1}/1500 loss:{round(loss.item(),5)}')
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# if (j + 1) % 10 == 0:
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# print(f'epoch {i+1}/200 step {j+1}/{len(train_dl)} loss:{loss}' )
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# test_running_loss = 0
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# model.eval()
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# with torch.no_grad():
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# for x,y in eval_dl:
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# pred = model(eval_x)
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# loss = loss_function(pred,y)
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# test_running_loss += loss.item()
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# test_loss = test_running_loss/len(eval_dl)
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# if test_loss < min_loss:
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# min_loss = test_loss
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# best_model_weight = model.state_dict()
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# print(f'epoch {i+1} test_loss:{test_loss}')
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#
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# total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
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# print(f"Total parameters in the LSTM model: {total_params}")
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# # 保存模型
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# torch.save(best_model_weight,'dy3.pth')
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# 读取模型
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model = LSTM(27, 16, output_size=3, num_layers=3).to(device)
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model.load_state_dict(torch.load('dy3.pth'))
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# for test
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dataset_x = dataset_x.reshape(-1, 1, 27) # (seq_size, batch_size, feature_size)
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dataset_x = torch.from_numpy(dataset_x).to(device).type(torch.float32)
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pred_test = model(dataset_x) # 全量训练集
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# 模型输出 (seq_size, batch_size, output_size)
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pred_test = pred_test.view(-1).cpu().detach().numpy()
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plt.plot(pred_test.reshape(-1), 'r', label='prediction')
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plt.plot(dataset_y.reshape(-1), 'b', label='real')
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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
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plt.legend(loc='best')
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plt.show()
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