多分类AUCpython实战代码
时间: 2023-08-08 14:09:58 浏览: 103
以下是一个多分类AUC的Python实现代码:
```python
import numpy as np
def multiclass_roc_auc_score(y_true, y_score, average="macro"):
# Check that we have a valid 'average' parameter
if average != "macro" and average != "micro" and average != "weighted":
raise ValueError("Unsupported averaging method: %s" % average)
# Create one-vs-all ROC curves and calculate AUC for each
n_classes = len(np.unique(y_true))
roc_auc = np.zeros(n_classes)
for i in range(n_classes):
# Create one-vs-all binary labels
y_true_binary = np.zeros(len(y_true))
y_true_binary[y_true == i] = 1
# Calculate ROC curve and AUC for this class
roc_auc[i] = roc_auc_score(y_true_binary, y_score[:, i])
# Calculate average ROC AUC
if average == "macro":
return np.mean(roc_auc)
elif average == "micro":
# Flatten all true and predicted labels
y_true = y_true.ravel()
y_score = y_score.ravel()
return roc_auc_score(y_true, y_score, average="macro")
elif average == "weighted":
# Compute class weights as the frequency of each class
class_counts = np.bincount(y_true)
class_weights = class_counts / float(len(y_true))
# Calculate weighted average ROC AUC
return np.average(roc_auc, weights=class_weights)
```
这个函数使用了numpy和sklearn中的roc_auc_score函数,可以计算多分类情况下的平均ROC AUC。其中,y_true是真实标签,y_score是预测概率矩阵,每一行代表一个样本,每一列代表一个类别。参数average可以指定计算平均ROC AUC的方法,可以是"macro"、"micro"或"weighted"。