下面是一个使用Python的示例代码，用于比较模型之间的AUC、对数损失和准确率得分：

# 导入所需的库
from sklearn.metrics import roc_auc_score, log_loss, accuracy_score

# 模型预测结果
model1_predictions = [0.2, 0.4, 0.6, 0.8]
model2_predictions = [0.3, 0.5, 0.7, 0.9]
true_labels = [0, 1, 1, 0]

# 计算AUC
model1_auc = roc_auc_score(true_labels, model1_predictions)
model2_auc = roc_auc_score(true_labels, model2_predictions)
print("Model 1 AUC:", model1_auc)
print("Model 2 AUC:", model2_auc)

# 计算对数损失
model1_log_loss = log_loss(true_labels, model1_predictions)
model2_log_loss = log_loss(true_labels, model2_predictions)
print("Model 1 Log Loss:", model1_log_loss)
print("Model 2 Log Loss:", model2_log_loss)

# 计算准确率得分
model1_accuracy = accuracy_score(true_labels, [1 if p >= 0.5 else 0 for p in model1_predictions])
model2_accuracy = accuracy_score(true_labels, [1 if p >= 0.5 else 0 for p in model2_predictions])
print("Model 1 Accuracy:", model1_accuracy)
print("Model 2 Accuracy:", model2_accuracy)

在上述示例中，model1_predictions和model2_predictions分别表示两个模型的预测结果。true_labels表示真实的标签。通过调用roc_auc_score、log_loss和accuracy_score函数，可以分别计算AUC、对数损失和准确率得分。然后将结果打印出来进行比较。