怎么求得分层抽样的每一层的classfication_report对应分类指标的平均值的非标类报告python代码

可以使用 scikit-learn 库中的 `classification_report` 和 `StratifiedKFold` 方法实现分层抽样，并计算每一层的分类指标的平均值。具体代码如下：

from sklearn.metrics import classification_report
from sklearn.model_selection import StratifiedKFold

# 定义数据集和标签
X = ...
y = ...

# 定义分层抽样的 k 折交叉验证器
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

# 定义空列表存储每一层的分类指标
precisions = []
recalls = []
f1_scores = []
supports = []

# 遍历每一层，计算分类指标并存储到列表中
for train_index, test_index in skf.split(X, y):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    clf = ...  # 定义模型
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    report = classification_report(y_test, y_pred, output_dict=True)
    precisions.append(report['weighted avg']['precision'])
    recalls.append(report['weighted avg']['recall'])
    f1_scores.append(report['weighted avg']['f1-score'])
    supports.append(report['weighted avg']['support'])

# 计算每一层的分类指标的平均值
precision_mean = sum(precisions) / len(precisions)
recall_mean = sum(recalls) / len(recalls)
f1_score_mean = sum(f1_scores) / len(f1_scores)
support_mean = sum(supports) / len(supports)

# 输出结果
print('Precision:', precision_mean)
print('Recall:', recall_mean)
print('F1-score:', f1_score_mean)
print('Support:', support_mean)

需要根据实际情况修改模型定义和数据集的载入方式。