import torch import pandas as pd 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=3): super().__init__() self.lstm = nn.LSTM(input_size,hidden_size,num_layers) self.fc1 = nn.Linear(hidden_size,64) self.fc2 = nn.Linear(64,128) self.fc3 = nn.Linear(128, output_size) self.ReLu = nn.ReLU() self.dropout = nn.Dropout() 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.ReLu(self.fc2(output)) output = self.fc3(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) print(np.max(dataset_x),np.min(dataset_x),np.max(dataset_y),np.min(dataset_y)) # 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, 3) # # 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) eval_y = torch.from_numpy(eval_y).to(device).type(torch.float32) train_ds = TensorDataset(train_x,train_y) train_dl = DataLoader(train_ds,batch_size=2,shuffle=True, drop_last=True) eval_ds = TensorDataset(eval_x,eval_y) eval_dl = DataLoader(eval_ds,batch_size=4,drop_last=True) 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 train_loss = [] loss_function = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.005) min_loss = 1 # for i in range(200): # model.train() # 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) % 10 == 0: # print(f'epoch {i+1}/200 step {j+1}/{len(train_dl)} loss:{loss}' ) # test_running_loss = 0 # model.eval() # with torch.no_grad(): # for x,y in eval_dl: # pred = model(eval_x) # loss = loss_function(pred,y) # test_running_loss += loss.item() # test_loss = test_running_loss/len(eval_dl) # if test_loss < min_loss: # min_loss = test_loss # best_model_weight = model.state_dict() # print(f'epoch {i+1} test_loss:{test_loss}') # # total_params = sum(p.numel() for p in model.parameters() if p.requires_grad) # print(f"Total parameters in the LSTM model: {total_params}") # # 保存模型 # torch.save(best_model_weight,'dy3.pth') # 读取模型 model = LSTM(27, 16, output_size=3, num_layers=3).to(device) model.load_state_dict(torch.load('dy3.pth')) # for test 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()