##一个简单的2折交叉验证

from sklearn.model_selection import KFold

import numpy as np

X=np.array([[1,2],[3,4],[1,3],[3,5]])

Y=np.array([1,2,3,4])

KF=KFold(n_splits=2) #建立4折交叉验证方法 查一下KFold函数的参数

for train_index,test_index in KF.split(X):

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)

print(Y_train,Y_test)

#小结：KFold这个包 划分k折交叉验证的时候，是以TEST集的顺序为主的，举例来说，如果划分4折交叉验证，那么TEST选取的顺序为[0].[1],[2],[3]。

#提升

import numpy as np

from sklearn.model_selection import KFold

#Sample=np.random.rand(50,15) #建立一个50行12列的随机数组

Sam=np.array(np.random.randn(1000)) #1000个随机数

New_sam=KFold(n_splits=5)

for train_index,test_index in New_sam.split(Sam): #对Sam数据建立5折交叉验证的划分

#for test_index,train_index in New_sam.split(Sam): #默认第一个参数是训练集，第二个参数是测试集

#print(train_index,test_index)

Sam_train,Sam_test=Sam[train_index],Sam[test_index] print('训练集数量:',Sam_train.shape,'测试集数量:',Sam_test.shape) #结果表明每次划分的数量

#Stratified k-fold 按照百分比划分数据

from sklearn.model_selection import StratifiedKFold

import numpy as np

m=np.array([[1,2],[3,5],[2,4],[5,7],[3,4],[2,7]])

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

skf=StratifiedKFold(n_splits=3)

for train_index,test_index in skf.split(m,n):

print("train",train_index,"test",test_index)

x_train,x_test=m[train_index],m[test_index]#Stratified k-fold 按照百分比划分数据

from sklearn.model_selection import StratifiedKFold

import numpy as np

y1=np.array(range(10))

y2=np.array(range(20,30))

y3=np.array(np.random.randn(10))

m=np.append(y1,y2) #生成1000个随机数

m1=np.append(m,y3)

n=[i//10 for i in range(30)] #生成25个重复数据

skf=StratifiedKFold(n_splits=5)

for train_index,test_index in skf.split(m1,n):

print("train",train_index,"test",test_index)

x_train,x_test=m1[train_index],m1[test_index]