在训练和测试之间分配有关类分布的数据集

Question

我想在给定的数据集中以以下分布进行10次机器学习算法的运行

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

这意味着我的数据中有70％来自第1类，而30％来自第2类。

下面有我的数据快照。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

可以找到完整的数据集here

我想将数据分成90％进行训练和10％进行测试。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，而30％的属于2类）

我知道如何将数据简单地分为训练和测试，但是我不知道如何进行这种划分以服从上面引用的类分布。如何在Python中做到这一点？

Answer 1

您可以使用RepeatedStratifiedKFold，顾名思义，将K-fold交叉验证器重复RepeatedStratifiedKFold次。要重复此过程n次，请设置10，并使n_repeats的比例大约为9:1 / train大小，我们可以设置test：

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]}')