sklearn Kfold 访问单折而不是 for 循环

Question

使用 cross_validation.KFold(n, n_folds=folds) 后，我想访问索引以训练和测试单折，而不是遍历所有折。

那么让我们以示例代码为例：

from sklearn import cross_validation
X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
y = np.array([1, 2, 3, 4])
kf = cross_validation.KFold(4, n_folds=2)

>>> print(kf)  
sklearn.cross_validation.KFold(n=4, n_folds=2, shuffle=False,
                           random_state=None)
>>> for train_index, test_index in kf:

我想像这样访问 kf 中的第一个折叠（而不是 for 循环）：

train_index, test_index in kf[0]

这应该只返回第一个折叠，但我得到了错误：“TypeError: 'KFold' object does not support indexing”

我想要的输出：

>>> train_index, test_index in kf[0]
>>> print("TRAIN:", train_index, "TEST:", test_index)
TRAIN: [2 3] TEST: [0 1]

链接：http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.KFold.html

问题

如何在不遍历整个 for 循环的情况下仅检索一次训练和测试的索引？

Answer 1

# We saved all the K Fold samples in different list  then we access to this throught [i]
from sklearn.model_selection import KFold
import numpy as np
import pandas as pd

kf = KFold(n_splits=4)

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

Y = np.array([0,0,0,1])
Y=Y.reshape(4,1)

X=pd.DataFrame(X)
Y=pd.DataFrame(Y)


X_train_base=[]
X_test_base=[]
Y_train_base=[]
Y_test_base=[]

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

    X_train, X_test = X.iloc[train_index,:], X.iloc[test_index,:]
    Y_train, Y_test = Y.iloc[train_index,:], Y.iloc[test_index,:]
    X_train_base.append(X_train)
    X_test_base.append(X_test)
    Y_train_base.append(Y_train)
    Y_test_base.append(Y_test)

print(X_train_base[0])
print(Y_train_base[0])
print(X_train_base[1])
print(Y_train_base[1])

Answer 2

你在正确的轨道上。您现在需要做的就是：

kf = cross_validation.KFold(4, n_folds=2)
mylist = list(kf)
train, test = mylist[0]

kf 实际上是一个生成器，它在需要之前不会计算训练测试拆分。这可以提高内存使用率，因为您不会存储不需要的项目。列出KFold 对象会强制它使所有值都可用。

这里有两个很好的 SO 问题来解释什么是生成器：one 和 two

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2018 年 11 月编辑

自 sklearn 0.20 以来，API 发生了变化。更新示例（针对 py3.6）：

from sklearn.model_selection import KFold
import numpy as np

kf = KFold(n_splits=4)

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


X_train, X_test = next(kf.split(X))

In [12]: X_train
Out[12]: array([2, 3])

In [13]: X_test
Out[13]: array([0, 1])