请写代码：xgboost用于提升pytorch模型以学习波士顿房价，且数据划分为训练集和测试集

好的，以下是使用XGBoost提升PyTorch模型以学习波士顿房价，并将数据集划分为训练集和测试集的代码示例：

import torch
import xgboost as xgb
import numpy as np
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

# Load Boston Housing dataset
boston = load_boston()
X, y = boston.data, boston.target

# Split dataset into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define regression model using PyTorch
class RegressionModel(torch.nn.Module):
    def __init__(self):
        super(RegressionModel, self).__init__()
        self.layer1 = torch.nn.Linear(13, 64)
        self.layer2 = torch.nn.Linear(64, 32)
        self.layer3 = torch.nn.Linear(32, 1)
        
    def forward(self, x):
        x = torch.relu(self.layer1(x))
        x = torch.relu(self.layer2(x))
        x = self.layer3(x)
        return x

# Initialize PyTorch model and optimizer
model = RegressionModel()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Train PyTorch model without XGBoost
for epoch in range(1000):
    optimizer.zero_grad()
    y_pred = model(torch.from_numpy(X_train).float())
    loss = torch.nn.functional.mse_loss(y_pred, torch.from_numpy(y_train).float())
    loss.backward()
    optimizer.step()
    if epoch % 100 == 0:
        print('Epoch:', epoch, 'Loss:', loss.item())

# Use XGBoost to boost PyTorch model
xgb_model = xgb.XGBRegressor()
xgb_model.fit(X_train, y_train)
xgb_pred = xgb_model.predict(X_train)
xgb_pred_tensor = torch.from_numpy(xgb_pred).float()

for epoch in range(1000):
    optimizer.zero_grad()
    y_pred = model(xgb_pred_tensor)
    loss = torch.nn.functional.mse_loss(y_pred, torch.from_numpy(y_train).float())
    loss.backward()
    optimizer.step()
    if epoch % 100 == 0:
        print('Epoch:', epoch, 'Loss:', loss.item())

# Make predictions using trained PyTorch model with XGBoost
xgb_pred_test = xgb_model.predict(X_test)
xgb_pred_test_tensor = torch.from_numpy(xgb_pred_test).float()
y_pred = model(xgb_pred_test_tensor).detach().numpy()

# Compute mean squared error of PyTorch model with XGBoost
mse = np.mean((y_pred - y_test)**2)
print('Mean squared error with XGBoost:', mse)

在这个示例中，我们首先使用PyTorch训练一个回归模型，并在训练过程中输出每个epoch的损失。然后，我们使用XGBoost模型对训练数据进行预测，并将预测结果作为PyTorch模型的输入，以提升模型的性能。最后，我们使用训练好的PyTorch模型和XGBoost模型对测试数据进行预测，并计算模型的均方误差。