多分类ROCpython

在Python中，可以使用scikit-learn库的`roc_curve`函数来绘制多分类的ROC曲线。以下是一个示例代码：

from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import numpy as np

# y_true是真实的类别标签，y_pred是预测的概率值
# 例如，y_true为[0, 1, 2, 1, 0, 2]，y_pred为[[0.2, 0.5, 0.3], [0.1, 0.8, 0.1], [0.3, 0.2, 0.5], [0.4, 0.5, 0.1], [0.6, 0.2, 0.2], [0.1, 0.3, 0.6]]
fpr = dict()
tpr = dict()
roc_auc = dict()
n_classes = 3 # 类别数
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_true[:, i], y_pred[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])

# micro-average ROC curve
fpr["micro"], tpr["micro"], _ = roc_curve(y_true.ravel(), y_pred.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

# macro-average ROC curve
# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))
# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
    mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])
# Finally average it and compute AUC
mean_tpr /= n_classes
fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

# Plot ROC curves
plt.figure()
lw = 2
plt.plot(fpr["micro"], tpr["micro"],
         label='micro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["micro"]),
         color='deeppink', linestyle=':', linewidth=4)

plt.plot(fpr["macro"], tpr["macro"],
         label='macro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["macro"]),
         color='navy', linestyle=':', linewidth=4)

colors = ['aqua', 'darkorange', 'cornflowerblue']
for i, color in zip(range(n_classes), colors):
    plt.plot(fpr[i], tpr[i], color=color, lw=lw,
             label='ROC curve of class {0} (area = {1:0.2f})'
             ''.format(i, roc_auc[i]))

plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Multi-class ROC curve')
plt.legend(loc="lower right")
plt.show()

在这个示例中，我们使用了一个包含3个类别的数据集，并计算了每个类别的ROC曲线和AUC值。此外，我们还计算了micro-average和macro-average ROC曲线。最后，我们使用matplotlib库绘制了多分类ROC曲线。