如何获得可重复但不同的GroupKFold实例

在GroupKFold源中,random_state设置为None

    def __init__(self, n_splits=3):
    super(GroupKFold, self).__init__(n_splits, shuffle=False,
                                     random_state=None)

因此,多次运行时(代码来自这里)

import numpy as np
from sklearn.model_selection import GroupKFold

for i in range(0,10):
    X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
    y = np.array([1, 2, 3, 4])
    groups = np.array([0, 0, 2, 2])
    group_kfold = GroupKFold(n_splits=2)
    group_kfold.get_n_splits(X, y, groups)

    print(group_kfold)

    for train_index, test_index in group_kfold.split(X, y, groups):
        print("TRAIN:", train_index, "TEST:", test_index)
        X_train, X_test = X[train_index], X[test_index]
        y_train, y_test = y[train_index], y[test_index]
        print(X_train, X_test, y_train, y_test)
    print 
    print 

O/P

GroupKFold(n_splits=2)
('TRAIN:', array([0, 1]), 'TEST:', array([2, 3]))
(array([[1, 2],
       [3, 4]]), array([[5, 6],
       [7, 8]]), array([1, 2]), array([3, 4]))
('TRAIN:', array([2, 3]), 'TEST:', array([0, 1]))
(array([[5, 6],
       [7, 8]]), array([[1, 2],
       [3, 4]]), array([3, 4]), array([1, 2]))


GroupKFold(n_splits=2)
('TRAIN:', array([0, 1]), 'TEST:', array([2, 3]))
(array([[1, 2],
       [3, 4]]), array([[5, 6],
       [7, 8]]), array([1, 2]), array([3, 4]))
('TRAIN:', array([2, 3]), 'TEST:', array([0, 1]))
(array([[5, 6],
       [7, 8]]), array([[1, 2],
       [3, 4]]), array([3, 4]), array([1, 2]))

等......

分裂是相同的.

如何设置random_statefor GroupKFold以便在交叉验证的几个不同试验中获得不同(但可重复)的分组集？

我想要

GroupKFold(n_splits=2, random_state=42)
('TRAIN:', array([0, 1]), 
  'TEST:', array([2, 3]))

('TRAIN:', array([2, 3]), 
'TEST:', array([0, 1]))


GroupKFold(n_splits=2, random_state=13)
('TRAIN:', array([0, 2]), 
 'TEST:', array([1, 3]))

('TRAIN:', array([1, 3]), 
'TEST:', array([0, 2]))

到目前为止,这似乎是一个策略可能是使用sklearn.utils.shuffle第一,在此建议后.然而,这实际上只是重新排列每个折叠的元素 - 它不会给我们新的分裂.

from sklearn.utils import shuffle
from sklearn.model_selection import GroupKFold
import numpy as np
import sys
import pdb

random_state = int(sys.argv[1])


X = np.arange(20).reshape((10,2))
y = np.arange(10)
groups = np.array([0,0,0,1,2,3,4,5,6,7])

def cv(X, y, groups, random_state):
    X_s, y_s, groups_s = shuffle(X,y, groups, random_state=random_state)
    cv_out = GroupKFold(n_splits=2)
    cv_out_splits = cv_out.split(X_s, y_s, groups_s)
    for train, test in cv_out_splits:
        print "---"
        print X_s[test]
        print y_s[test]
        print "test groups", groups_s[test]
        print "train groups", groups_s[train]
    pdb.set_trace()
print "***"
cv(X, y, groups, random_state)

输出:

>python sshuf.py 32

***
---
[[ 2  3]
 [ 4  5]
 [ 0  1]
 [ 8  9]
 [12 13]]
[1 2 0 4 6]
test groups [0 0 0 2 4]
train groups [7 6 1 3 5]
---
[[18 19]
 [16 17]
 [ 6  7]
 [10 11]
 [14 15]]
[9 8 3 5 7]
test groups [7 6 1 3 5]
train groups [0 0 0 2 4]

>python sshuf.py 234

***
---
[[12 13]
 [ 4  5]
 [ 0  1]
 [ 2  3]
 [ 8  9]]
[6 2 0 1 4]
test groups [4 0 0 0 2]
train groups [7 3 1 5 6]
---
[[18 19]
 [10 11]
 [ 6  7]
 [14 15]
 [16 17]]
[9 5 3 7 8]
test groups [7 3 1 5 6]
train groups [4 0 0 0 2]

joeln.. 8

KFold只是随机的shuffle=True.一些数据集不应该被洗牌.

GroupKFold根本不是随机的.因此random_state=None.

GroupShuffleSplit 可能更接近你正在寻找的东西.

基于组的分离器的比较:

在GroupKFold,测试集形成所有数据的完整分区.

LeavePGroupsOut将所有可能的P组子集组合出来,组合起来; 对于P> 1,测试集将重叠.因为这意味着P ** n_groups完全分裂,通常你想要一个小的P,并且最常想要的LeaveOneGroupOut是GroupKFold与它基本相同的k=1.

GroupShuffleSplit没有说明连续测试集之间的关系; 每个列车/测试拆分是独立执行的.

另外,Dmytro Lituiev 提出了一种替代GroupShuffleSplit算法,它可以更好地在指定的测试集中获得正确数量的样本(不仅仅是正确数量的组)test_size.

KFold只是随机的shuffle=True.一些数据集不应该被洗牌.

GroupKFold根本不是随机的.因此random_state=None.

GroupShuffleSplit 可能更接近你正在寻找的东西.

基于组的分离器的比较:

在GroupKFold,测试集形成所有数据的完整分区.

LeavePGroupsOut将所有可能的P组子集组合出来,组合起来; 对于P> 1,测试集将重叠.因为这意味着P ** n_groups完全分裂,通常你想要一个小的P,并且最常想要的LeaveOneGroupOut是GroupKFold与它基本相同的k=1.

GroupShuffleSplit没有说明连续测试集之间的关系; 每个列车/测试拆分是独立执行的.

另外,Dmytro Lituiev 提出了一种替代GroupShuffleSplit算法,它可以更好地在指定的测试集中获得正确数量的样本(不仅仅是正确数量的组)test_size.