使用scikit绘制ROC曲线-使用图像数据集学习人脸识别导致错误。( fetch_olivetti_faces)

from sklearn.datasets import fetch_olivetti_faces
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt

# 加载数据集
data = fetch_olivetti_faces()

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

# 构建分类模型
model = SVC(probability=True)
model.fit(X_train, y_train)

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

# 计算ROC曲线的参数
fpr, tpr, thresholds = roc_curve(y_test, y_score[:, 1])
roc_auc = auc(fpr, tpr)

# 绘制ROC曲线
plt.figure()
plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
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')
plt.legend(loc="lower right")
plt.show()