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K折交叉验证：sklearn.model_selection.KFold(n_splits=3, shuffle=False, random_state=None)

思路：将训练/测试数据集划分n_splits个互斥子集，每次用其中一个子集当作验证集，剩下的n_splits-1个作为训练集，进行n_splits次训练和测试，得到n_splits个结果

注意点：对于不能均等份的数据集，其前n_samples % n_splits子集拥有n_samples // n_splits + 1个样本，其余子集都只有n_samples // n_splits样本

参数说明：

n_splits：表示划分几等份

shuffle：在每次划分时，是否进行洗牌

①若为Falses时，其效果等同于random_state等于整数，每次划分的结果相同

②若为True时，每次划分的结果都不一样，表示经过洗牌，随机取样的

random_state：随机种子数

属性：

①get_n_splits(X=None, y=None, groups=None)：获取参数n_splits的值

②split(X, y=None, groups=None)：将数据集划分成训练集和测试集，返回索引生成器

通过一个不能均等划分的栗子，设置不同参数值，观察其结果

①设置shuffle=False，运行两次，发现两次结果相同

In [     
     1]:      
     from sklearn.model_selection      
     import KFold    
       
   
     
       
       
      
       
    
             ...:      
     import numpy      
     as np    
       
   
     
       
       
      
       
    
             ...: X = np.arange(     
     24).reshape(     
     12,     
     2)    
       
   
     
       
       
      
       
    
             ...: y = np.random.choice([     
     1,     
     2],     
     12,p=[     
     0.4,     
     0.6])    
       
   
     
       
       
      
       
    
             ...: kf = KFold(n_splits=     
     5,shuffle=     
     False)    
       
   
     
       
       
      
       
    
             ...:      
     for train_index , test_index      
     in kf.split(X):    
       
   
     
       
       
      
       
    
             ...:     print(     
     'train_index:%s , test_index: %s ' %(train_index,test_index))    
       
   
     
       
       
      
       
    
             ...:    
       
   
     
       
       
      
       
    
             ...:    
       
   
     
       
       
      
       
    
          train_index:[      
     3       
     4       
     5       
     6       
     7       
     8       
     9      
     10      
     11] , test_index: [     
     0      
     1      
     2]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     6       
     7       
     8       
     9      
     10      
     11] , test_index: [     
     3      
     4      
     5]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     3       
     4       
     5       
     8       
     9      
     10      
     11] , test_index: [     
     6      
     7]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     3       
     4       
     5       
     6       
     7      
     10      
     11] , test_index: [     
     8      
     9]    
       
   
     
       
       
      
       
    
          train_index:[     
     0      
     1      
     2      
     3      
     4      
     5      
     6      
     7      
     8      
     9] , test_index: [     
     10      
     11]    
       
   
     
       
       
      
       
         
       
   
     
       
       
      
       
    
          In [     
     2]:      
     from sklearn.model_selection      
     import KFold    
       
   
     
       
       
      
       
    
             ...:      
     import numpy      
     as np    
       
   
     
       
       
      
       
    
             ...: X = np.arange(     
     24).reshape(     
     12,     
     2)    
       
   
     
       
       
      
       
    
             ...: y = np.random.choice([     
     1,     
     2],     
     12,p=[     
     0.4,     
     0.6])    
       
   
     
       
       
      
       
    
             ...: kf = KFold(n_splits=     
     5,shuffle=     
     False)    
       
   
     
       
       
      
       
    
             ...:      
     for train_index , test_index      
     in kf.split(X):    
       
   
     
       
       
      
       
    
             ...:     print(     
     'train_index:%s , test_index: %s ' %(train_index,test_index))    
       
   
     
       
       
      
       
    
             ...:    
       
   
     
       
       
      
       
    
             ...:    
       
   
     
       
       
      
       
    
          train_index:[      
     3       
     4       
     5       
     6       
     7       
     8       
     9      
     10      
     11] , test_index: [     
     0      
     1      
     2]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     6       
     7       
     8       
     9      
     10      
     11] , test_index: [     
     3      
     4      
     5]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     3       
     4       
     5       
     8       
     9      
     10      
     11] , test_index: [     
     6      
     7]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     3       
     4       
     5       
     6       
     7      
     10      
     11] , test_index: [     
     8      
     9]    
       
   
     
       
       
      
       
    
          train_index:[     
     0      
     1      
     2      
     3      
     4      
     5      
     6      
     7      
     8      
     9] , test_index: [     
     10      
     11]

In [     
     3]:      
     from sklearn.model_selection      
     import KFold    
       
   
     
       
       
      
       
    
             ...:      
     import numpy      
     as np    
       
   
     
       
       
      
       
    
             ...: X = np.arange(     
     24).reshape(     
     12,     
     2)    
       
   
     
       
       
      
       
    
             ...: y = np.random.choice([     
     1,     
     2],     
     12,p=[     
     0.4,     
     0.6])    
       
   
     
       
       
      
       
    
             ...: kf = KFold(n_splits=     
     5,shuffle=     
     True)    
       
   
     
       
       
      
       
    
             ...:      
     for train_index , test_index      
     in kf.split(X):    
       
   
     
       
       
      
       
    
             ...:     print(     
     'train_index:%s , test_index: %s ' %(train_index,test_index))    
       
   
     
       
       
      
       
    
             ...:    
       
   
     
       
       
      
       
    
             ...:    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     4       
     5       
     6       
     7       
     8      
     10] , test_index: [      
     3       
     9      
     11]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     3       
     4       
     5       
     9      
     10      
     11] , test_index: [     
     6      
     7      
     8]    
       
   
     
       
       
      
       
    
          train_index:[      
     2       
     3       
     4       
     5       
     6       
     7       
     8       
     9      
     10      
     11] , test_index: [     
     0      
     1]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     3       
     4       
     5       
     6       
     7       
     8       
     9      
     11] , test_index: [      
     2      
     10]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     3       
     6       
     7       
     8       
     9      
     10      
     11] , test_index: [     
     4      
     5]    
       
   
     
       
       
      
       
         
       
   
     
       
       
      
       
    
          In [     
     4]:      
     from sklearn.model_selection      
     import KFold    
       
   
     
       
       
      
       
    
             ...:      
     import numpy      
     as np    
       
   
     
       
       
      
       
    
             ...: X = np.arange(     
     24).reshape(     
     12,     
     2)    
       
   
     
       
       
      
       
    
             ...: y = np.random.choice([     
     1,     
     2],     
     12,p=[     
     0.4,     
     0.6])    
       
   
     
       
       
      
       
    
             ...: kf = KFold(n_splits=     
     5,shuffle=     
     True)    
       
   
     
       
       
      
       
    
             ...:      
     for train_index , test_index      
     in kf.split(X):    
       
   
     
       
       
      
       
    
             ...:     print(     
     'train_index:%s , test_index: %s ' %(train_index,test_index))    
       
   
     
       
       
      
       
    
             ...:    
       
   
     
       
       
      
       
    
             ...:    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     3       
     4       
     5       
     7       
     8      
     11] , test_index: [      
     6       
     9      
     10]    
       
   
     
       
       
      
       
    
          train_index:[      
     2       
     3       
     4       
     5       
     6       
     8       
     9      
     10      
     11] , test_index: [     
     0      
     1      
     7]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     3       
     5       
     6       
     7       
     8       
     9      
     10      
     11] , test_index: [     
     2      
     4]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     3       
     4       
     6       
     7       
     9      
     10      
     11] , test_index: [     
     5      
     8]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     4       
     5       
     6       
     7       
     8       
     9      
     10] , test_index: [      
     3      
     11]

In [     
     5]:      
     from sklearn.model_selection      
     import KFold    
       
   
     
       
       
      
       
    
             ...:      
     import numpy      
     as np    
       
   
     
       
       
      
       
    
             ...: X = np.arange(     
     24).reshape(     
     12,     
     2)    
       
   
     
       
       
      
       
    
             ...: y = np.random.choice([     
     1,     
     2],     
     12,p=[     
     0.4,     
     0.6])    
       
   
     
       
       
      
       
    
             ...: kf = KFold(n_splits=     
     5,shuffle=     
     True,random_state=     
     0)    
       
   
     
       
       
      
       
    
             ...:      
     for train_index , test_index      
     in kf.split(X):    
       
   
     
       
       
      
       
    
             ...:     print(     
     'train_index:%s , test_index: %s ' %(train_index,test_index))    
       
   
     
       
       
      
       
    
             ...:    
       
   
     
       
       
      
       
    
             ...:    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     3       
     5       
     7       
     8       
     9      
     10] , test_index: [      
     4       
     6      
     11]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     3       
     4       
     5       
     6       
     7       
     9      
     11] , test_index: [      
     2       
     8      
     10]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     2       
     3       
     4       
     5       
     6       
     8       
     9      
     10      
     11] , test_index: [     
     1      
     7]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     4       
     5       
     6       
     7       
     8      
     10      
     11] , test_index: [     
     3      
     9]    
       
   
     
       
       
      
       
    
          train_index:[      
     1       
     2       
     3       
     4       
     6       
     7       
     8       
     9      
     10      
     11] , test_index: [     
     0      
     5]    
       
   
     
       
       
      
       
         
       
   
     
       
       
      
       
    
          In [     
     6]:      
     from sklearn.model_selection      
     import KFold    
       
   
     
       
       
      
       
    
             ...:      
     import numpy      
     as np    
       
   
     
       
       
      
       
    
             ...: X = np.arange(     
     24).reshape(     
     12,     
     2)    
       
   
     
       
       
      
       
    
             ...: y = np.random.choice([     
     1,     
     2],     
     12,p=[     
     0.4,     
     0.6])    
       
   
     
       
       
      
       
    
             ...: kf = KFold(n_splits=     
     5,shuffle=     
     True,random_state=     
     0)    
       
   
     
       
       
      
       
    
             ...:      
     for train_index , test_index      
     in kf.split(X):    
       
   
     
       
       
      
       
    
             ...:     print(     
     'train_index:%s , test_index: %s ' %(train_index,test_index))    
       
   
     
       
       
      
       
    
             ...:    
       
   
     
       
       
      
       
    
             ...:    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     3       
     5       
     7       
     8       
     9      
     10] , test_index: [      
     4       
     6      
     11]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     3       
     4       
     5       
     6       
     7       
     9      
     11] , test_index: [      
     2       
     8      
     10]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     2       
     3       
     4       
     5       
     6       
     8       
     9      
     10      
     11] , test_index: [     
     1      
     7]    
       
   
     
       
       
      
       
    
          train_index:[      
     0       
     1       
     2       
     4       
     5       
     6       
     7       
     8      
     10      
     11] , test_index: [     
     3      
     9]    
       
   
     
       
       
      
       
    
          train_index:[      
     1       
     2       
     3       
     4       
     6       
     7       
     8       
     9      
     10      
     11] , test_index: [     
     0      
     5]

In [     
     8]: kf.split(X)    
       
   
     
       
       
      
       
    
          Out[     
     8]: 
   
        
       
   
     
       
       
      
       
         
       
   
     
       
       
      
       
    
          In [     
     9]: kf.get_n_splits()    
       
   
     
       
       
      
       
    
          Out[     
     9]:      
     5    
       
   
     
       
       
      
       
         
       
   
     
       
       
      
       
    
          In [     
     10]: kf.n_splits    
       
   
     
       
       
      
       
    
          Out[     
     10]:      
     5