sklearn.model_selection.GroupKFold
分组K折交叉验证：sklearn.model_selection.GroupKFold(n_splits=3)
参数说明：
n_splits：折数，默认为3，至少为2
注意点：同一组的样本不可能同时出现在同一折的测试集和训练集中。
①数据集均等份，且每组中的样本也是均等
In [11]: from sklearn.model_selection import GroupKFold
...: import numpy as np
...: X = np.arange(24).reshape(12,2)
...: y = np.array([1,1,2,3,1,2,3,2,2,3,3,1])
...: groups = np.array([1,2,3,4,5,6,1,2,3,4,5,6])
...: kf = GroupKFold(n_splits=6)
...: for train_index , test_index in kf.split(X,y,groups):
...: print('train_index:%s , test_index: %s ' %(train_index,test_index)
...: )
...: print('train_groups:%s , test_groups: %s ' %(groups[train_index],g
...: roups[test_index]))
...:
train_index:[ 0 1 2 3 4 6 7 8 9 10] , test_index: [ 5 11]
train_groups:[1 2 3 4 5 1 2 3 4 5] , test_groups: [6 6]
train_index:[ 0 1 2 3 5 6 7 8 9 11] , test_index: [ 4 10]
train_groups:[1 2 3 4 6 1 2 3 4 6] , test_groups: [5 5]
train_index:[ 0 1 2 4 5 6 7 8 10 11] , test_index: [3 9]
train_groups:[1 2 3 5 6 1 2 3 5 6] , test_groups: [4 4]
train_index:[ 0 1 3 4 5 6 7 9 10 11] , test_index: [2 8]
train_groups:[1 2 4 5 6 1 2 4 5 6] , test_groups: [3 3]
train_index:[ 0 2 3 4 5 6 8 9 10 11] , test_index: [1 7]
train_groups:[1 3 4 5 6 1 3 4 5 6] , test_groups: [2 2]
train_index:[ 1 2 3 4 5 7 8 9 10 11] , test_index: [0 6]
train_groups:[2 3 4 5 6 2 3 4 5 6] , test_groups: [1 1]In [13]: #sklearn.model_selection.GroupKFold(n_splits=3)
...: from sklearn.model_selection import GroupKFold
...: import numpy as np
...: X = np.arange(24).reshape(12,2)
...: y = np.array([1,1,2,3,1,2,3,2,2,3,3,1])
...: groups = np.array([1,2,3,4,5,6,1,2,3,4,5,7])
...: kf = GroupKFold(n_splits=4)
...: for train_index , test_index in kf.split(X,y,groups):
...: print('train_index:%s , test_index: %s ' %(train_index,test_index)
...: )
...: print('train_groups:%s , test_groups: %s ' %(groups[train_index],g
...: roups[test_index]))
...:
train_index:[ 1 2 3 5 7 8 9 11] , test_index: [ 0 4 6 10]
train_groups:[2 3 4 6 2 3 4 7] , test_groups: [1 5 1 5]
train_index:[ 0 1 2 4 5 6 7 8 10] , test_index: [ 3 9 11]
train_groups:[1 2 3 5 6 1 2 3 5] , test_groups: [4 4 7]
train_index:[ 0 1 3 4 6 7 9 10 11] , test_index: [2 5 8]
train_groups:[1 2 4 5 1 2 4 5 7] , test_groups: [3 6 3]
train_index:[ 0 2 3 4 5 6 8 9 10 11] , test_index: [1 7]
train_groups:[1 3 4 5 6 1 3 4 5 7] , test_groups: [2 2]In [14]: from sklearn.model_selection import GroupKFold
...: import numpy as np
...: X = np.arange(24).reshape(12,2)
...: y = np.array([1,1,2,3,1,2,3,2,2,3,3,1])
...: groups = np.array([1,2,3,4,5,6,1,2,3,4,5,3])
...: kf = GroupKFold(n_splits=5)
...: for train_index , test_index in kf.split(X,y,groups):
...: print('train_index:%s , test_index: %s ' %(train_index,test_index)
...: )
...: print('train_groups:%s , test_groups: %s ' %(groups[train_index],g
...: roups[test_index]))
...:
train_index:[ 0 1 3 4 5 6 7 9 10] , test_index: [ 2 8 11]
train_groups:[1 2 4 5 6 1 2 4 5] , test_groups: [3 3 3]
train_index:[ 0 1 2 3 6 7 8 9 11] , test_index: [ 4 5 10]
train_groups:[1 2 3 4 1 2 3 4 3] , test_groups: [5 6 5]
train_index:[ 0 1 2 4 5 6 7 8 10 11] , test_index: [3 9]
train_groups:[1 2 3 5 6 1 2 3 5 3] , test_groups: [4 4]
train_index:[ 0 2 3 4 5 6 8 9 10 11] , test_index: [1 7]
train_groups:[1 3 4 5 6 1 3 4 5 3] , test_groups: [2 2]
train_index:[ 1 2 3 4 5 7 8 9 10 11] , test_index: [0 6]
train_groups:[2 3 4 5 6 2 3 4 5 3] , test_groups: [1 1]