《Quality meets Diversity:A Model-Agnostic Framework for Computerized Adaptive Testing》论文笔记

def expected_model_change(self, sid: int, qid: int, adaptest_data: AdapTestDataset):"""期望模型改变Args:sid: 学生idqid: 问题idadaptest_data: 测试集数据Returns:"""# 训练epoch数epochs = self.config['num_epochs']# 学习率lr = self.config['learning_rate']# 设备device = self.config['device']# 优化器optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)# 只将知识状态theta设为可更新for name, param in self.model.named_parameters():if 'theta' not in name:param.requires_grad = False# 克隆theta初始权重值original_weights = self.model.theta.weight.data.clone()# 设置数据张量student_id = torch.LongTensor([sid]).to(device)question_id = torch.LongTensor([qid]).to(device)correct = torch.LongTensor([1]).to(device).float()wrong = torch.LongTensor([0]).to(device).float()# 根据sid做的题qid一条数据进行训练(以答对为目标)for ep in range(epochs):optimizer.zero_grad() # 梯度清零# 计算模型输出pred = self.model(student_id, question_id)# 计算lossloss = self._loss_function(pred, correct)loss.backward() # 反向传播optimizer.step() # 梯度下降# 记录正方向theta的值pos_weights = self.model.theta.weight.data.clone()# 将theta用初始权重替代self.model.theta.weight.data.copy_(original_weights)# 训练数据(以答错为目标)for ep in range(epochs):optimizer.zero_grad()pred = self.model(student_id, question_id)loss = self._loss_function(pred, wrong)loss.backward()optimizer.step()# 记录负方向theta的值neg_weights = self.model.theta.weight.data.clone()# 使用原始权重替换self.model.theta.weight.data.copy_(original_weights)# 模型参数回复记录梯度for param in self.model.parameters():param.requires_grad = True# 使用初始模型预测概率pred = self.model(student_id, question_id).item()# 返回概率(正负加权)return pred * torch.norm(pos_weights - original_weights).item() + (1 - pred) * torch.norm(neg_weights - original_weights).item()