基于机器学习实现的气温预测python源码+数据集+训练好的模型(课程设计).zip

import re import numpy as np import pandas as pd import torch import torch.nn as nn from matplotlib import pyplot as plt from sklearn.model_selection import train_test_split from torch import optim from torch.utils.data import DataLoader, TensorDataset from torchsummary import summary def load_data(data_path): # 读取数据 data = pd.read_csv(data_path) data['前一天'] = data['前一天'].astype(str).apply( lambda r: np.mean(list(map(float, re.sub(r'[\[\]]', '', r).split(','))))) data['上一年同一天'] = data['上一年同一天'].astype(str).apply( lambda r: np.mean(list(map(float, re.sub(r'[\[\]]', '', r).split(','))))) data['当天'] = data['当天'].astype(str).apply( lambda r: np.mean(list(map(float, re.sub(r'[\[\]]', '', r).split(','))))) # 数据预处理，防止异常数据出现，导致loss:nan出现 data['前一天'].fillna(data['当天'], inplace=True) data['上一年同一天'].fillna(data['当天'], inplace=True) plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] data.plot(x='前一天', y='当天', kind='scatter') plt.ylabel("前一天ºC") plt.xlabel("当天ºC") plt.title('前一天/当天【气温】') data.plot(x='上一年同一天', y='当天', kind='scatter') plt.title('上一年同一天/当天【气温】') plt.ylabel("上一年同一天ºC") plt.xlabel("当天ºC") plt.show() # 提取特征和目标 features = data[['前一天', '上一年同一天']] target = data['当天'] # 划分训练集、验证集和测试集 x_train, x_temp, y_train, y_temp = train_test_split(features, target, test_size=0.2, random_state=42) x_val, x_test, y_val, y_test = train_test_split(x_temp, y_temp, test_size=0.5, random_state=42) return x_train, x_val, x_test, y_train, y_val, y_test def train(train_loader, val_loader, model, criterion, optimizer, num_epochs=100): best_val_loss = float('inf') for epoch in range(num_epochs): # Training model.train() for batch_idx, (inputs, targets) in enumerate(train_loader): optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, targets.view(-1, 1)) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1) optimizer.step() # print('Input:', inputs) # print('Target:', targets) # print('Gradients:', [p.grad.data for p in model.parameters()]) # print('Weights:', [p.data for p in model.parameters()]) # print('Outputs:', outputs) if batch_idx % 10 == 0: print(f'Training - Epoch {epoch + 1}/{num_epochs}, Batch {batch_idx}, Loss: {loss.item()}') # Validation model.eval() val_loss = 0.0 with torch.no_grad(): for inputs, targets in val_loader: outputs = model(inputs) val_loss += criterion(outputs, targets.view(-1, 1)).item() val_loss /= len(val_loader) print(f'Validation - Epoch {epoch + 1}/{num_epochs}, Loss: {val_loss}') # Save the best model if val_loss < best_val_loss: best_val_loss = val_loss torch.save(model.state_dict(), 'model.pth') def visualize_model(model, data_loader): model.eval() inputs, targets = next(iter(data_loader)) with torch.no_grad(): outputs = model(inputs) if inputs.ndim > 1: num_features = inputs.shape[1] # 创建子图，每个特征对应一个子图 fig, axs = plt.subplots(1, num_features, figsize=(15, 4)) # 遍历每个特征，绘制散点图 for i in range(num_features): axs[i].scatter(inputs[:, i].numpy(), targets.numpy(), label=f'特征 {i + 1}真实数据') axs[i].scatter(inputs[:, i].numpy(), outputs.numpy(), label=f'特征 {i + 1}预测结果') axs[i].set_xlabel(f'特征 {i + 1}') axs[i].set_ylabel('目标') axs[i].legend() plt.show() else: plt.scatter(inputs.numpy(), targets.numpy(), label='真实数据') plt.scatter(inputs.numpy(), outputs.numpy(), label='预测结果') plt.xlabel('Input') plt.ylabel('Target') plt.legend() plt.show() # 定义神经网络模型 class TemperaturePredictor(nn.Module): def __init__(self, input_size=365, hidden_size=64, output_size=1): super(TemperaturePredictor, self).__init__() # 非线性关系 self.model = nn.Sequential( nn.Linear(input_size, hidden_size), nn.ReLU(), nn.Linear(hidden_size, output_size) ) # 线性关系 # self.model = nn.Linear(input_size, output_size) self.criterion = nn.L1Loss() self.optimizer = optim.SGD(self.model.parameters(), lr=0.0001, momentum=0.9) def forward(self, x): return self.model(x) if __name__ == '__main__': x_train, x_val, x_test, y_train, y_val, y_test = load_data('weather-2022_2021.csv') # 转换为 PyTorch 张量 x_train_tensor = torch.tensor(x_train.values, dtype=torch.float32) y_train_tensor = torch.tensor(y_train.values, dtype=torch.float32) x_val_tensor = torch.tensor(x_train.values, dtype=torch.float32) y_val_tensor = torch.tensor(y_train.values, dtype=torch.float32) x_test_tensor = torch.tensor(x_test.values, dtype=torch.float32) y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32) # 创建数据加载器 train_dataset = TensorDataset(x_train_tensor, y_train_tensor) val_dataset = TensorDataset(x_val_tensor, y_val_tensor) test_dataset = TensorDataset(x_test_tensor, y_test_tensor) train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False) test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) # 初始化模型 input_size = len(x_train.columns) hidden_size = 64 output_size = 1 model = TemperaturePredictor(input_size, hidden_size, output_size) # 打印模型结构 summary(model, input_size=(input_size,)) # 训练模型 train(train_loader, val_loader, model, model.criterion, model.optimizer, num_epochs=1000) # 加载模型参数 model.load_state_dict(torch.load('model.pth')) # 可视化模型预测结果 visualize_model(model, test_loader) # 模型信息 print('模型信息：', model.state_dict())