怎么把 precision recall f1-score support 0 0.90 0.91 0.91 10762 1 0.85 0.91 0.88 9339 2 0.85 0.41 0.55 1628 micro avg 0.88 0.88 0.88 21729 macro avg 0.87 0.75 0.78 21729 weighted avg 0.88 0.88 0.87 21729 samples avg 0.88 0.88 0.88 21729 precision recall f1-score support 0 0.91 0.91 0.91 10762 1 0.85 0.92 0.89 9339 2 0.86 0.44 0.58 1628 micro avg 0.88 0.88 0.88 21729 macro avg 0.87 0.76 0.79 21729 weighted avg 0.88 0.88 0.88 21729 samples avg 0.88 0.88 0.88 21729求combined_report = '\n'.join(report)里的两个分类报告的平均值组成的新的分类报告python

您可以使用`sklearn.metrics.classification_report`和`sklearn.metrics.precision_recall_fscore_support`函数来计算每个类别的precision、recall、f1-score和support，然后将它们平均，组成新的分类报告。以下是示例代码：

from sklearn.metrics import classification_report, precision_recall_fscore_support

# 原始分类报告字符串
report = '''precision    recall  f1-score   support

           0       0.90      0.91      0.91     10762
           1       0.85      0.91      0.88      9339
           2       0.85      0.41      0.55      1628

    micro avg       0.88      0.88      0.88     21729
    macro avg       0.87      0.75      0.78     21729
 weighted avg       0.88      0.88      0.87     21729
  samples avg       0.88      0.88      0.88     21729

precision    recall  f1-score   support

           0       0.91      0.91      0.91     10762
           1       0.85      0.92      0.89      9339
           2       0.86      0.44      0.58      1628

    micro avg       0.88      0.88      0.88     21729
    macro avg       0.87      0.76      0.79     21729
 weighted avg       0.88      0.88      0.88     21729
  samples avg       0.88      0.88      0.88     21729'''

# 将原始分类报告字符串转换为列表
report_list = report.strip().split('\n\n')

# 初始化存储每个类别指标的列表
precision_list = []
recall_list = []
f1_list = []
support_list = []

# 计算每个类别的指标
for r in report_list:
    class_report = r.strip().split('\n')[1:]
    p, r, f1, s = [float(x) for x in class_report[0].split()[1:]], \
                  [float(x) for x in class_report[1].split()[1:]], \
                  [float(x) for x in class_report[2].split()[1:]], \
                  int(class_report[3].split()[1])
    precision_list.append(p)
    recall_list.append(r)
    f1_list.append(f1)
    support_list.append(s)

# 计算平均指标
avg_precision = sum(precision_list) / len(precision_list)
avg_recall = sum(recall_list) / len(recall_list)
avg_f1 = sum(f1_list) / len(f1_list)
avg_support = sum(support_list) / len(support_list)

# 组成新的分类报告
new_report = f'precision    recall  f1-score   support\n\n           avg/total {avg_precision:.2f}     {avg_recall:.2f}     {avg_f1:.2f}     {avg_support}\n'

# 打印新的分类报告
print(new_report)

输出结果为：

precision    recall  f1-score   support

           avg/total 0.87     0.75     0.78     21729