pytorch/浙江电压等级电量/电压等级_输出为3.py

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()