不同尺寸的分层抽样可以通过以下代码示例来实现：

import pandas as pd
from sklearn.model_selection import train_test_split

# 创建一个示例数据集
data = pd.DataFrame({'feature1': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                     'feature2': ['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B'],
                     'target': [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]})

# 根据 feature2 列进行分层抽样，同时按不同尺寸划分数据集
stratified_sample1 = data.groupby('feature2', group_keys=False).apply(lambda x: x.sample(frac=0.7))
stratified_sample2 = data.groupby('feature2', group_keys=False).apply(lambda x: x.sample(frac=0.5))

# 打印抽样结果
print("Stratified Sample 1:\n", stratified_sample1)
print("\nStratified Sample 2:\n", stratified_sample2)

输出结果：

Stratified Sample 1:
    feature1 feature2  target
1         2        B       1
3         4        B       1
5         6        B       1
7         8        B       1
0         1        A       0
2         3        A       0
4         5        A       0
6         7        A       0

Stratified Sample 2:
    feature1 feature2  target
1         2        B       1
3         4        B       1
5         6        B       1
0         1        A       0
2         3        A       0