pytorch/各地级市日电量模型/lstm单日预测.py

import numpy as np
import pandas as pd
import torch
from torch import nn
import os
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from torch.utils.data import DataLoader,TensorDataset

class LSTM_Regression(nn.Module):
    def __init__(self, input_size, hidden_size, output_size=1, num_layers=2):
        super().__init__()

        self.lstm = nn.LSTM(input_size, hidden_size, num_layers)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, _x):
        x, _ = self.lstm(_x)  # _x is input, size (seq_len, batch, input_size)
        s, b, h = x.shape  # x is output, size (seq_len, batch, hidden_size)
        x = x.view(s * b, h)
        x = self.fc(x)
        x = x.view(s, b, -1)  # 把形状改回来
        return x


def create_dataset(data, days_for_train=5) -> (np.array, np.array):
    dataset_x, dataset_y = [], []
    for i in range(len(data) - days_for_train):
        _x = data[i:(i + days_for_train)]
        dataset_x.append(_x)
        dataset_y.append(data[i + days_for_train])
    return (np.array(dataset_x), np.array(dataset_y))


def inverse_transform_col(scaler,y,n_col):
    '''scaler是对包含多个feature的X拟合的,y对应其中一个feature,n_col为y在X中对应的列编号.返回y的反归一化结果'''
    y = y.copy()
    y -= scaler.min_[n_col]
    y /= scaler.scale_[n_col]
    return y


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
data = pd.read_excel(r'C:\Users\user\PycharmProjects\pytorch2\入模数据\杭州数据.xlsx',index_col='dtdate')
print(data.columns)
data.columns = data.columns.map(lambda x: x.strip())
data.drop(columns='city_name',inplace=True)
# 标准化到0~1
scaler = MinMaxScaler()
df_normalized = pd.DataFrame(scaler.fit_transform(data), columns=data.columns,index=data.index).astype(float)

# 划分训练集和测试集
x_all = df_normalized.drop(columns='售电量').loc['2021-1':'2023-9']
y_all = df_normalized['售电量'].loc['2021-1':'2023-9']

x_train = df_normalized.drop(columns='售电量').loc['2021-1':'2023-8']
y_train = df_normalized['售电量'].loc['2021-1':'2023-8']

eval = df_normalized.loc['2023-9']
x_eval = eval.drop(columns='售电量')
y_eval = eval['售电量']



# 将数据改变形状，RNN 读入的数据维度是 (seq_size, batch_size, feature_size)

x_train = np.array(x_train.values).reshape(-1, 1, 13)
y_train = np.array(y_train.values).reshape(-1, 1, 1)

# 转为pytorch的tensor对象
x_train = torch.from_numpy(x_train).to(device).type(torch.float32)
y_train = torch.from_numpy(y_train).to(device).type(torch.float32)

x_eval = torch.from_numpy(x_eval.values).to(device).type(torch.float32)

model = LSTM_Regression(13, 16, output_size=1, 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=0.005, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)

ds = TensorDataset(x_train,y_train)
dl = DataLoader(ds,batch_size=3,shuffle=True)


for i in range(300):
    for j,(x,y) in enumerate(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%100 == 0:
            print(f'epoch:{i+1} 第{j}次loss:{loss}')

# 保存模型
torch.save(model.state_dict(),'lstm.pth')
# torch.save(model.state_dict(),os.path.join(model_save_dir,model_file))

model.load_state_dict(torch.load('lstm.pth'))
# for test
model = model.eval()  # 转换成测试模式
# model.load_state_dict(torch.load(os.path.join(model_save_dir,model_file)))  # 读取参数

x_eval = x_eval.reshape(-1, 1, 13)  # (seq_size, batch_size, feature_size)


pred_test = model(x_eval)  # 全量训练集

# 模型输出 (seq_size, batch_size, output_size)
pred_test = pred_test.view(-1).cpu().data.numpy()
print(x_eval.shape,pred_test.shape)

# 反归一化

pred_test = inverse_transform_col(scaler,pred_test,-1)
pred_test = pred_test.reshape(-1)

y_eval = inverse_transform_col(scaler,y_eval,-1)
# 打印指标
print(abs(pred_test - y_eval).mean() /y_eval.mean())
result_eight = pd.DataFrame({'pred_test': pred_test, 'real': y_eval})
target = (result_eight['pred_test'][-3:].sum() - result_eight['real'][-3:].sum()) / result_eight[
    'real'].sum()
print(result_eight)
print(target)