基于pytorch框架写一个deepsurv的模型代码来进行生存分析

以下是基于PyTorch框架写的一个Deepsurv模型代码，用于进行生存分析：

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

class Deepsurv(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(Deepsurv, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, output_dim)
        self.activation = nn.ReLU()
        
    def forward(self, x):
        x = self.fc1(x)
        x = self.activation(x)
        x = self.fc2(x)
        return x

def negative_log_likelihood(preds, events):
    hazard_ratio = torch.exp(preds)
    log_risk = torch.log(torch.cumsum(hazard_ratio, dim=1))
    uncensored_likelihood = (preds.T - log_risk.T).T
    censored_likelihood = uncensored_likelihood * events
    neg_likelihood = -torch.sum(censored_likelihood)
    return neg_likelihood

def train(model, optimizer, train_loader, epochs):
    for epoch in range(epochs):
        running_loss = 0.0
        for i, data in enumerate(train_loader, 0):
            inputs, events = data
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = negative_log_likelihood(outputs, events)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
        print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(train_loader)))

input_dim = 10
hidden_dim = 20
output_dim = 1
lr = 0.001
epochs = 10
batch_size = 32

model = Deepsurv(input_dim, hidden_dim, output_dim)
optimizer = optim.Adam(model.parameters(), lr=lr)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

train(model, optimizer, train_loader, epochs)

其中，Deepsurv类是我们定义的模型，包含两个全连接层和一个ReLU激活函数。negative_log_likelihood函数是用来计算负对数似然损失的，它首先计算每个样本的hazard ratio，然后计算log risk和uncensored likelihood，最后根据事件状态计算censored likelihood，并将其相加并取负作为损失函数。train函数是训练模型的函数，它使用Adam优化器对模型进行训练。

在使用这个代码之前，需要将数据集转换为PyTorch张量或数据集。具体而言，需要将生存时间和事件状态分别转换为张量，并使用torch.utils.data.TensorDataset或torch.utils.data.Dataset将它们组合成数据集。然后，可以使用torch.utils.data.DataLoader将数据集加载到模型中进行训练。