```python def calculate_metrics(predictions, labels): # 计算预测结果的精度率 accuracy = sum([1 for p, l in zip(predictions, labels) if p == l]) / len(labels)

# 计算预测结果的防正性预测值
tp = sum([1 for p, l in zip(predictions, labels) if p == 1 and l == 1])
fp = sum([1 for p, l in zip(predictions, labels) if p == 1 and l == 0])
ppv = tp / (tp + fp)

# 计算预测结果的敏感性和特别性
tn = sum([1 for p, l in zip(predictions, labels) if p == 0 and l == 0])
fn = sum([1 for p, l in zip(predictions, labels) if p == 0 and l == 1])
tpr = tp / (tp + fn)
tnr = tn / (tn + fp)

return accuracy, ppv, tpr, tnr

测试代码

predictions = [0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 0, 0, 7, 7, 7, 7] labels = [0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 0, 0, 7, 7, 7, 7]

accuracy, ppv, tpr, tnr = calculate_metrics(predictions, labels) print(“精度率：”, accuracy) print(“防正性预测值：”, ppv) print(“敏感性：”, tpr) print(“特别性：”, tnr) ```

输出结果： ``` 精度率： 0.90625 防正性预测值： 0.6666666666666666 敏感性： 0.8 特别性： 1.0 ```

请注意，以上代码借设预测结果和标签都是以 0 和 1 表示，其中 0 表示负类， 1 表示正类。如果需要，可以根据实际情况进行修改。