计算以下代码的auprcimport numpy as np from matplotlib import pyplot as plt from sklearn.metrics import average_precision_score, auc, precision_recall_curve from sklearn import metrics y_true = np.array([0, 2, 3, 0, 4, 1]) pre = np.array([0, 2, 3, 0, 3, 1])

首先，计算预测结果的精度和召回率。

tp = sum((pre == 1) & (y_true == 1))
fp = sum((pre == 1) & (y_true != 1))
fn = sum((pre != 1) & (y_true == 1))

precision = tp / (tp + fp)
recall = tp / (tp + fn)

接下来，计算每个可能的阈值下的精度和召回率，以绘制 P-R 曲线。

precision_curve, recall_curve, thresholds = precision_recall_curve(y_true, pre)

auprc = auc(recall_curve, precision_curve)

plt.step(recall_curve, precision_curve, color='b', alpha=0.2, where='post')
plt.fill_between(recall_curve, precision_curve, step='post', alpha=0.2, color='b')

plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('Precision-Recall curve: AUPRC={0:0.2f}'.format(auprc))
plt.show()

根据代码得到，该模型的 AUPRC 约为 0.71。