python绘制多分类ROC曲线

要绘制多分类ROC曲线，可以使用micro-averaging或macro-averaging方法，具体方法如下：

1. 计算每个类别的ROC曲线和AUC值。
2. 对于micro-averaging方法，将每个类别的预测结果合并为一个二元分类问题。即将所有真实值为正例和所有真实值为负例的样本合并为两个集合，分别计算它们的ROC曲线和AUC值。
3. 对于macro-averaging方法，对每个类别的ROC曲线和AUC值进行平均。

以下是一个使用sklearn库绘制多分类ROC曲线的示例代码：

import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
from sklearn.metrics import roc_curve, auc
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# 生成多分类数据集
X, y = make_classification(n_samples=1000, n_features=10, n_classes=5, n_informative=5, random_state=42)
y = label_binarize(y, classes=[0, 1, 2, 3, 4])
n_classes = y.shape[1]

# 分割训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 训练分类器
clf = OneVsRestClassifier(LogisticRegression())
clf.fit(X_train, y_train)

# 预测概率
y_score = clf.predict_proba(X_test)

# 计算每个类别的ROC曲线和AUC值
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])

# micro-averaging方法
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

# macro-averaging方法
# 计算所有ROC曲线的平均值
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
    mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])
mean_tpr /= n_classes
fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

# 绘制ROC曲线
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 = cycle(['aqua', 'darkorange', 'cornflowerblue', 'red', 'purple'])
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('Receiver operating characteristic for multi-class')
plt.legend(loc="lower right")
plt.show()

运行代码后，会得到一个多分类ROC曲线的图像。