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免费可下载原始参考数据 预测结果图#xff1a;
根据测试数据test_data的真实值real_flow#xff0c;与模型根据测试数据得到的输出结果pre_flow 完整源码#xff1a;
#!/usr/bin/env python
# _*_ coding: utf-8 _*_import pandas as pd
import nu…原始数据
免费可下载原始参考数据 预测结果图
根据测试数据test_data的真实值real_flow与模型根据测试数据得到的输出结果pre_flow 完整源码
#!/usr/bin/env python
# _*_ coding: utf-8 _*_import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as Data
from torchsummary import summary
import math
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
import time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error, mean_absolute_error
import math
from matplotlib.font_manager import FontProperties # 画图时可以使用中文# 加载数据
f pd.read_csv(rC:\Users\14600\Desktop\AE86.csv)# 从新设置列标
def set_columns():columns []for i in f.loc[2]:columns.append(i.strip())return columnsf.columns set_columns()
f.drop([0, 1, 2], inplaceTrue)
# 读取数据
data f[Total Carriageway Flow].astype(np.float64).values[:, np.newaxis]class LoadData(Dataset):def __init__(self, data, time_step, divide_days, train_mode):self.train_mode train_modeself.time_step time_stepself.train_days divide_days[0]self.test_days divide_days[1]self.one_day_length int(24 * 4)# flow_norm (max_data. min_data)self.flow_norm, self.flow_data LoadData.pre_process_data(data)# 不进行标准化# self.flow_data datadef __len__(self, ):if self.train_mode train:return self.train_days * self.one_day_length - self.time_stepelif self.train_mode test:return self.test_days * self.one_day_lengthelse:raise ValueError( train mode error)def __getitem__(self, index):if self.train_mode train:index indexelif self.train_mode test:index self.train_days * self.one_day_lengthelse:raise ValueError( train mode error)data_x, data_y LoadData.slice_data(self.flow_data, self.time_step, index,self.train_mode)data_x LoadData.to_tensor(data_x)data_y LoadData.to_tensor(data_y)return {flow_x: data_x, flow_y: data_y}# 这一步就是划分数据staticmethoddef slice_data(data, time_step, index, train_mode):if train_mode train:start_index indexend_index index time_stepelif train_mode test:start_index index - time_stepend_index indexelse:raise ValueError(train mode error)data_x data[start_index: end_index, :]data_y data[end_index]return data_x, data_y# 数据与处理staticmethoddef pre_process_data(data, ):norm_base LoadData.normalized_base(data)normalized_data LoadData.normalized_data(data, norm_base[0], norm_base[1])return norm_base, normalized_data# 生成原始数据中最大值与最小值staticmethoddef normalized_base(data):max_data np.max(data, keepdimsTrue) # keepdims保持维度不变min_data np.min(data, keepdimsTrue)# max_data.shape ---(1, 1)return max_data, min_data# 对数据进行标准化staticmethoddef normalized_data(data, max_data, min_data):data_base max_data - min_datanormalized_data (data - min_data) / data_basereturn normalized_datastaticmethod# 反标准化 在评价指标误差以及画图的使用使用def recoverd_data(data, max_data, min_data):data_base max_data - min_datarecoverd_data data * data_base - min_datareturn recoverd_datastaticmethoddef to_tensor(data):return torch.tensor(data, dtypetorch.float)# 划分数据
divide_days [25, 5]
time_step 5
batch_size 48
train_data LoadData(data, time_step, divide_days, train)
test_data LoadData(data, time_step, divide_days, test)
train_loader DataLoader(train_data, batch_sizebatch_size, shuffleTrue, )
test_loader DataLoader(test_data, batch_sizebatch_size, shuffleFalse, )# LSTM构建网络
class LSTM(nn.Module):def __init__(self, input_num, hid_num, layers_num, out_num, batch_firstTrue):super().__init__()self.l1 nn.LSTM(input_sizeinput_num,hidden_sizehid_num,num_layerslayers_num,batch_firstbatch_first)self.out nn.Linear(hid_num, out_num)def forward(self, data):flow_x data[flow_x] # B * T * Dl_out, (h_n, c_n) self.l1(flow_x, None) # None表示第一次 hidden_state是0# print(l_out[:, -1, :].shape)out self.out(l_out[:, -1, :])return out# 定义模型参数
input_num 1 # 输入的特征维度
hid_num 50 # 隐藏层个数
layers_num 3 # LSTM层个数
out_num 1
lstm LSTM(input_num, hid_num, layers_num, out_num)
loss_func nn.MSELoss()
optimizer torch.optim.Adam(lstm.parameters())# 训练模型
lstm.train()
epoch_loss_change []
for epoch in range(30):epoch_loss 0.0start_time time.time()for data_ in train_loader:lstm.zero_grad()predict lstm(data_)loss loss_func(predict, data_[flow_y])epoch_loss loss.item()loss.backward()optimizer.step()epoch_loss_change.append(1000 * epoch_loss / len(train_data))end_time time.time()print(Epoch: {:04d}, Loss: {:02.4f}, Time: {:02.2f} mins.format(epoch, 1000 * epoch_loss / len(train_data),(end_time - start_time) / 60))
plt.plot(epoch_loss_change)# 评价模型
lstm.eval()
with torch.no_grad(): # 关闭梯度total_loss 0.0pre_flow np.zeros([batch_size, 1]) # [B, D],T1 目标数据的维度用填充real_flow np.zeros_like(pre_flow)for data_ in test_loader:pre_value lstm(data_)loss loss_func(pre_value, data_[flow_y])total_loss loss.item()# 反归一化pre_value LoadData.recoverd_data(pre_value.detach().numpy(),test_data.flow_norm[0].squeeze(1), # max_datatest_data.flow_norm[1].squeeze(1), # min_data)target_value LoadData.recoverd_data(data_[flow_y].detach().numpy(),test_data.flow_norm[0].squeeze(1),test_data.flow_norm[1].squeeze(1),)pre_flow np.concatenate([pre_flow, pre_value])real_flow np.concatenate([real_flow, target_value])pre_flow pre_flow[batch_size:]real_flow real_flow[batch_size:]
# # 计算误差
mse mean_squared_error(pre_flow, real_flow)
rmse math.sqrt(mean_squared_error(pre_flow, real_flow))
mae mean_absolute_error(pre_flow, real_flow)
print(均方误差---, mse)
print(均方根误差---, rmse)
print(平均绝对误差--, mae)# 画出预测结果图
font_set FontProperties(fnamerC:\Windows\Fonts\simsun.ttc, size15) # 中文字体使用宋体15号
plt.figure(figsize(15, 10))
plt.plot(real_flow, labelReal_Flow, colorr, )
plt.plot(pre_flow, labelPre_Flow)
plt.xlabel(测试序列, fontpropertiesfont_set)
plt.ylabel(交通流量/辆, fontpropertiesfont_set)
plt.legend()
# 预测储存图片
# plt.savefig(...\Desktop\123.jpg)plt.show()
