根据类别分布在训练和测试之间划分数据集[重复]

Question

我想在具有以下分布的给定数据集中运行 10 次机器学习算法

np.unique(x[:,24], return_counts=True)
(array([1., 2.]), array([700, 300]))

这意味着我 70% 的数据来自第 1 类，30% 来自第 2 类。

下面是我的数据的快照。最后一列通知类标签（1 或 2）：

1,6,4,12,5,5,3,4,1,67,3,2,1,2,1,0,0,1,0,0,1,0,0,1,1
2,48,2,60,1,3,2,2,1,22,3,1,1,1,1,0,0,1,0,0,1,0,0,1,2
4,12,4,21,1,4,3,3,1,49,3,1,2,1,1,0,0,1,0,0,1,0,1,0,1
1,42,2,79,1,4,3,4,2,45,3,1,2,1,1,0,0,0,0,0,0,0,0,1,1
1,24,3,49,1,3,3,4,4,53,3,2,2,1,1,1,0,1,0,0,0,0,0,1,2
4,36,2,91,5,3,3,4,4,35,3,1,2,2,1,0,0,1,0,0,0,0,1,0,1
4,24,2,28,3,5,3,4,2,53,3,1,1,1,1,0,0,1,0,0,1,0,0,1,1
2,36,2,69,1,3,3,2,3,35,3,1,1,2,1,0,1,1,0,1,0,0,0,0,1
4,12,2,31,4,4,1,4,1,61,3,1,1,1,1,0,0,1,0,0,1,0,1,0,1
2,30,4,52,1,1,4,2,3,28,3,2,1,1,1,1,0,1,0,0,1,0,0,0,2
2,12,2,13,1,2,2,1,3,25,3,1,1,1,1,1,0,1,0,1,0,0,0,1,2
1,48,2,43,1,2,2,4,2,24,3,1,1,1,1,0,0,1,0,1,0,0,0,1,2
2,12,2,16,1,3,2,1,3,22,3,1,1,2,1,0,0,1,0,0,1,0,0,1,1
1,24,4,12,1,5,3,4,3,60,3,2,1,1,1,1,0,1,0,0,1,0,1,0,2
1,15,2,14,1,3,2,4,3,28,3,1,1,1,1,1,0,1,0,1,0,0,0,1,1
1,24,2,13,2,3,2,2,3,32,3,1,1,1,1,0,0,1,0,0,1,0,1,0,2
4,24,4,24,5,5,3,4,2,53,3,2,1,1,1,0,0,1,0,0,1,0,0,1,1
1,30,0,81,5,2,3,3,3,25,1,3,1,1,1,0,0,1,0,0,1,0,0,1,1
2,24,2,126,1,5,2,2,4,44,3,1,1,2,1,0,1,1,0,0,0,0,0,0,2
4,24,2,34,3,5,3,2,3,31,3,1,2,2,1,0,0,1,0,0,1,0,0,1,1
4,9,4,21,1,3,3,4,3,48,3,3,1,2,1,1,0,1,0,0,1,0,0,1,1
1,6,2,26,3,3,3,3,1,44,3,1,2,1,1,0,0,1,0,1,0,0,0,1,1
1,10,4,22,1,2,3,3,1,48,3,2,2,1,2,1,0,1,0,1,0,0,1,0,1
2,12,4,18,2,2,3,4,2,44,3,1,1,1,1,0,1,1,0,0,1,0,0,1,1
4,10,4,21,5,3,4,1,3,26,3,2,1,1,2,0,0,1,0,0,1,0,0,1,1
1,6,2,14,1,3,3,2,1,36,1,1,1,2,1,0,0,1,0,0,1,0,1,0,1
4,6,0,4,1,5,4,4,3,39,3,1,1,1,1,0,0,1,0,0,1,0,1,0,1
3,12,1,4,4,3,2,3,1,42,3,2,1,1,1,0,0,1,0,1,0,0,0,1,1
2,7,2,24,1,3,3,2,1,34,3,1,1,1,1,0,0,0,0,0,1,0,0,1,1
1,60,3,68,1,5,3,4,4,63,3,2,1,2,1,0,0,1,0,0,1,0,0,1,2
2,18,2,19,4,2,4,3,1,36,1,1,1,2,1,0,0,1,0,0,1,0,0,1,1
1,24,2,40,1,3,3,2,3,27,2,1,1,1,1,0,0,1,0,0,1,0,0,1,1
2,18,2,59,2,3,3,2,3,30,3,2,1,2,1,1,0,1,0,0,1,0,0,1,1
4,12,4,13,5,5,3,4,4,57,3,1,1,1,1,0,0,1,0,1,0,0,1,0,1
3,12,2,15,1,2,2,1,2,33,1,1,1,2,1,0,0,1,0,0,1,0,0,0,1
2,45,4,47,1,2,3,2,2,25,3,2,1,1,1,0,0,1,0,0,1,0,1,0,2
4,48,4,61,1,3,3,3,4,31,1,1,1,2,1,0,0,1,0,0,0,0,0,1,1

完整的数据集可以在here找到

我想将数据分成 90% 用于训练和 10% 用于测试。但是，对于每个拆分，我必须保持数据的比例（例如，在训练和验证拆分中，70% 的数据必须属于 1 类，30% 属于 2 类）

我知道如何简单地将数据划分为训练和测试，但我不知道如何使这种划分服从我上面引用的类分布。如何在 Python 中做到这一点？

Answer 1

一种众所周知的将数据拆分为训练和测试的方法是 scikit-learn train_test_split。

model_selection.train_test_split 的 API 文档。

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=42)

您可以使用random_state 变量（种子），直到您的类之间的比例正确为止。虽然train_test_split 不会强制执行比例，但它通常遵循人口比例。

Answer 2

您可以使用RepeatedStratifiedKFold，顾名思义，它会重复K-Fold 交叉验证器n 次。要重复这个过程10 次，设置n_repeats，并在train/test 大小中大约有9:1 的比例，我们可以设置n_splits=10：

from sklearn.model_selection import RepeatedStratifiedKFold

X = a[:,:-1]
y = a[:,-1]

rskf = RepeatedStratifiedKFold(n_splits=10, n_repeats=10, random_state=2)

for train_index, test_index in rskf.split(X, y):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    print(f'\nClass 1: {((y_train==1).sum()/len(y_train))*100:.0f}%') 
    print(f'\nShape of train: {X_train.shape[0]}')
    print(f'Shape of test: {X_test.shape[0]}')

---

Class 1: 73%

Shape of train: 33
Shape of test: 4

Class 1: 73%

Shape of train: 33
Shape of test: 4

Class 1: 73%

Shape of train: 33
Shape of test: 4

Class 1: 73%

Shape of train: 33
Shape of test: 4
...