StratifiedKFold与KFold

概述：
StratifiedKFold用法类似Kfold，但是他是分层采样，确保训练集，测试集中各类别样本的比例与原始数据集中相同。

import numpy as np
from sklearn.model_selection import KFold,StratifiedKFold

X=np.array([
    [1,2,3,4],
    [11,12,13,14],
    [21,22,23,24],
    [31,32,33,34],
    [41,42,43,44],
    [51,52,53,54],
    [61,62,63,64],
    [71,72,73,74]
])

y=np.array([1,1,0,0,1,1,0,0])

sfolder=StratifiedKFold(n_splits=4,random_state=0,shuffle=False)

for train, test in sfolder.split(X,y):
    print(train, test)

OUT:
[1 3 4 5 6 7] [0 2]
[0 2 4 5 6 7] [1 3]
[0 1 2 3 5 7] [4 6]
[0 1 2 3 4 6] [5 7]

floder = KFold(n_splits=4,random_state=0,shuffle=False)
for train, test in floder.split(X,y):
    print('Train: %s | test: %s' % (train, test))

OUT:
Train: [2 3 4 5 6 7] | test: [0 1]
Train: [0 1 4 5 6 7] | test: [2 3]
Train: [0 1 2 3 6 7] | test: [4 5]
Train: [0 1 2 3 4 5] | test: [6 7]

 注意返回的仅仅是索引号，可以看到上图中StratifiedKFold 分层采样交叉切分，确保训练集，测试集中各类别样本的比例与原始数据集中相同。比如原数据中，0,1两类比例是1:1，通过观察StratifiedKFold切分的每个测试集可以发现，0,1两类的占比也为1:1，这就是分层采样。确定了测试集后，测试集的补集就是训练集。同时应注意到每个测试集均是互斥的。