from sklearn.tree import DecisionTreeRegressor model2 = DecisionTreeRegressor(random_state=0) model2.fit(x_train, Y_train) y_pred2=model2.predict(x_test) assessing(Y_test,y_pred2)，对以上代码调用的模型换成PyTorch模型

使用PyTorch构建回归模型的步骤如下：

1. 导入PyTorch库和相关模块：

import torch
import torch.nn as nn
import torch.optim as optim

2. 定义模型类：

class Regressor(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(Regressor, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        out = torch.relu(self.fc1(x))
        out = self.fc2(out)
        return out

3. 定义损失函数和优化器：

criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)

4. 训练模型：

inputs = torch.from_numpy(x_train).float()
labels = torch.from_numpy(y_train).float()

for epoch in range(num_epochs):
    # Forward pass
    outputs = model(inputs)
    loss = criterion(outputs, labels)

    # Backward and optimize
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (epoch+1) % 100 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

5. 预测并评估模型：

with torch.no_grad():
    inputs = torch.from_numpy(x_test).float()
    labels = torch.from_numpy(y_test).float()
    outputs = model(inputs)
    loss = criterion(outputs, labels)
    print('Test Loss: {:.4f}'.format(loss.item()))

完整代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# Define model class
class Regressor(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(Regressor, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        out = torch.relu(self.fc1(x))
        out = self.fc2(out)
        return out

# Define hyperparameters
input_dim = 13
hidden_dim = 10
output_dim = 1
learning_rate = 0.01
num_epochs = 500

# Load data
data = np.loadtxt('data/housing.csv', delimiter=',', skiprows=1)
x_train = data[:400,:-1]
y_train = data[:400,-1]
x_test = data[400:,:-1]
y_test = data[400:,-1]

# Define model, loss function, and optimizer
model = Regressor(input_dim, hidden_dim, output_dim)
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)

# Train model
inputs = torch.from_numpy(x_train).float()
labels = torch.from_numpy(y_train).float()

for epoch in range(num_epochs):
    # Forward pass
    outputs = model(inputs)
    loss = criterion(outputs, labels)

    # Backward and optimize
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (epoch+1) % 100 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# Test model
with torch.no_grad():
    inputs = torch.from_numpy(x_test).float()
    labels = torch.from_numpy(y_test).float()
    outputs = model(inputs)
    loss = criterion(outputs, labels)
    print('Test Loss: {:.4f}'.format(loss.item()))

需要注意的是，PyTorch中使用的数据类型是Tensor而不是numpy数组，因此需要使用`torch.from_numpy()`将numpy数组转换为Tensor。同时，在训练模型时需要将inputs和labels都转换为Tensor并调用`backward()`方法进行反向传播，最后使用`optimizer.step()`方法更新模型参数。在预测时需要使用`torch.no_grad()`上下文管理器来关闭梯度计算，以加快运行速度。