以下是使用Python和`scikit-learn`库对包含500个样本的.csv数据进行Transformer五折交叉验证的代码示例。假设使用的是`pytorch`框架的`torch.nn.Transformer`模块，并且将生存分析问题转换为分类问题（这里简单假设根据生存时间和事件状态进行分类）。 ```python import numpy as np import pandas as pd from sklearn.model_selection import KFold import torch import torch.nn as nn import torch.optim as optim # 读取数据 data = pd.read_csv('your_data.csv') # 提取特征 features = data.iloc[:, 3:].values # 提取标签（这里简单将event作为标签） labels = data['event'].values # 定义Transformer模型 class TransformerModel(nn.Module): def __init__(self, input_dim, d_model=512, nhead=8, num_encoder_layers=6, num_decoder_layers=6): super(TransformerModel, self).__init__() self.transformer = nn.Transformer(d_model=d_model, nhead=nhead, num_encoder_layers=num_encoder_layers, num_decoder_layers=num_decoder_layers, dim_feedforward=2048, dropout=0.1, activation='relu', layer_norm_eps=1e-05, batch_first=False, norm_first=False, bias=True) self.fc = nn.Linear(d_model, 2) # 假设是二分类问题 def forward(self, x): x = self.transformer(x.unsqueeze(1)) x = x.squeeze(1) x = self.fc(x) return x # 五折交叉验证 kf = KFold(n_splits=5, shuffle=True, random_state=42) for train_index, test_index in kf.split(features): train_features, test_features = features[train_index], features[test_index] train_labels, test_labels = labels[train_index], labels[test_index] # 转换为torch张量 train_features = torch.tensor(train_features, dtype=torch.float32) train_labels = torch.tensor(train_labels, dtype=torch.long) test_features = torch.tensor(test_features, dtype=torch.float32) test_labels = torch.tensor(test_labels, dtype=torch.long) # 初始化模型、损失函数和优化器 model = TransformerModel(input_dim=512) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # 训练模型 num_epochs = 10 for epoch in range(num_epochs): model.train() optimizer.zero_grad() outputs = model(train_features) loss = criterion(outputs, train_labels) loss.backward() optimizer.step() # 评估模型 model.eval() with torch.no_grad(): outputs = model(test_features) _, predicted = torch.max(outputs.data, 1) accuracy = (predicted == test_labels).sum().item() / test_labels.size(0) print(f'Fold accuracy: {accuracy}') ``` ###