sklearn.KFold「建议收藏」

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简介

K折交叉验证：将样本切成K份，每次取其中一份做为测试集，剩余的K-1份做为训练集。根据训练训练出模型或者假设函数。 把这个模型放到测试集上，得到分类率。计算k次求得的分类率的平均值，作为该模型或者假设函数的真实分类率。

在sklearn.model_selection 中提供了几种K折交叉验证。

生成样本

>>> from sklearn.datasets import make_classification
>>> data,target=make_classification(n_samples=10)
>>> print(target)
[1 1 0 1 1 0 0 1 0 0]

sklearn.model_selection.KFold

KFold按数据原有的顺序对数据进行分割。可以通过定义shuffle来打乱顺序。

>>> from sklearn.model_selection import KFold
>>> kfold= KFold(n_splits=5,random_state =None)
>>> for train_index,test_index in kfold.split(data,target):
...   print("TRAIN:", train_index, "TEST:", test_index)
...   print("TRAIN_target:", target[train_index].mean(), "TEST_target:", target[test_index].mean())
TRAIN: [2 3 4 5 6 7 8 9] TEST: [0 1]
TRAIN_target: 0.375 TEST_target: 1.0
TRAIN: [0 1 4 5 6 7 8 9] TEST: [2 3]
TRAIN_target: 0.5 TEST_target: 0.5
TRAIN: [0 1 2 3 6 7 8 9] TEST: [4 5]
TRAIN_target: 0.5 TEST_target: 0.5
TRAIN: [0 1 2 3 4 5 8 9] TEST: [6 7]
TRAIN_target: 0.5 TEST_target: 0.5
TRAIN: [0 1 2 3 4 5 6 7] TEST: [8 9]
TRAIN_target: 0.625 TEST_target: 0.0

sklearn.model_selection.StratifiedKFold

StratifiedKFold是KFold的一个变种，目的是保证每一个分层标签的比例和原始样本一致。

>>> from sklearn.model_selection import StratifiedKFold    
>>> stkfold= StratifiedKFold(n_splits=5,random_state =None)
>>> for train_index,test_index in stkfold.split(data,target):
...    print("TRAIN:", train_index, "TEST:", test_index)
...    print("TRAIN_target:", target[train_index].mean(), "TEST_target:", target[test_index].mean())
TRAIN: [1 3 4 5 6 7 8 9] TEST: [0 2]
TRAIN_target: 0.5 TEST_target: 0.5
TRAIN: [0 2 3 4 6 7 8 9] TEST: [1 5]
TRAIN_target: 0.5 TEST_target: 0.5
TRAIN: [0 1 2 4 5 7 8 9] TEST: [3 6]
TRAIN_target: 0.5 TEST_target: 0.5
TRAIN: [0 1 2 3 5 6 7 9] TEST: [4 8]
TRAIN_target: 0.5 TEST_target: 0.5
TRAIN: [0 1 2 3 4 5 6 8] TEST: [7 9]
TRAIN_target: 0.5 TEST_target: 0.5

sklearn.model_selection.RepeatedKFold

重复n次K-Fold ，每次重复有不同的随机性。

>>> from sklearn.model_selection import RepeatedKFold    
>>> rpkfold= RepeatedKFold(n_splits=5,n_repeats=2,random_state =2652124)
>>> for train_index,test_index in rpkfold.split(data,target):
...    print("TRAIN:", train_index, "TEST:", test_index)
...    print("TRAIN_target:", target[train_index].mean(), "TEST_target:", target[test_index].mean())
TRAIN: [0 1 3 4 5 6 7 9] TEST: [2 8]
TRAIN_target: 0.625 TEST_target: 0.0
TRAIN: [0 2 3 4 5 6 8 9] TEST: [1 7]
TRAIN_target: 0.375 TEST_target: 1.0
TRAIN: [0 1 2 4 5 6 7 8] TEST: [3 9]
TRAIN_target: 0.5 TEST_target: 0.5
TRAIN: [1 2 3 4 5 7 8 9] TEST: [0 6]
TRAIN_target: 0.5 TEST_target: 0.5
TRAIN: [0 1 2 3 6 7 8 9] TEST: [4 5]
TRAIN_target: 0.5 TEST_target: 0.5
TRAIN: [0 1 3 4 6 7 8 9] TEST: [2 5]
TRAIN_target: 0.625 TEST_target: 0.0
TRAIN: [0 1 2 3 4 5 8 9] TEST: [6 7]
TRAIN_target: 0.5 TEST_target: 0.5
TRAIN: [0 1 2 5 6 7 8 9] TEST: [3 4]
TRAIN_target: 0.375 TEST_target: 1.0
TRAIN: [0 2 3 4 5 6 7 9] TEST: [1 8]
TRAIN_target: 0.5 TEST_target: 0.5
TRAIN: [1 2 3 4 5 6 7 8] TEST: [0 9]
TRAIN_target: 0.5 TEST_target: 0.5

sklearn.model_selection.GroupKFold

按组对样本进行分层。
同一组不会出现在两个不同的分层中(不同组的数量必须至少等于折的数量)。

>>> import numpy as np
>>> from sklearn.model_selection import GroupKFold    
>>> gpkfold= GroupKFold(n_splits=5)
>>> groups = np.array([0, 0, 1 ,1 ,3 ,4 ,1 ,1 ,2 , 2])
>>> for train_index,test_index in gpkfold.split(data,target,groups):
...    print("TRAIN:", train_index, "TEST:", test_index)
...    print("TRAIN_target:", target[train_index].mean(), "TEST_target:", target[test_index].mean())
TRAIN: [0 1 4 5 8 9] TEST: [2 3 6 7]
TRAIN_target: 0.5 TEST_target: 0.5
TRAIN: [0 1 2 3 4 5 6 7] TEST: [8 9]
TRAIN_target: 0.625 TEST_target: 0.0
TRAIN: [2 3 4 5 6 7 8 9] TEST: [0 1]
TRAIN_target: 0.375 TEST_target: 1.0
TRAIN: [0 1 2 3 4 6 7 8 9] TEST: [5]
TRAIN_target: 0.555555555556 TEST_target: 0.0
TRAIN: [0 1 2 3 5 6 7 8 9] TEST: [4]
TRAIN_target: 0.444444444444 TEST_target: 1.0

结论

建模时，一般是使用KFold和StratifiedKFold。需要完成特殊分群的时，比如按月份划分数据，可以使用GroupKFold 。