scikit-learn随机分层k-fold交叉验证

Question

是否有任何内置的方法让scikit-learn执行洗牌分层k-fold交叉验证?这是最常见的CV方法之一，我很惊讶我找不到一个内置的方法来做到这一点。

我看到cross_validation.KFold()有一个洗牌标志，但它不是分层的。不幸的是，cross_validation.StratifiedKFold()没有这样的选择，cross_validation.StratifiedShuffleSplit()不会产生不相交的褶皱。

我错过了什么吗?这是有计划的吗?

(显然我可以自己实现)

Answer 1

cross_validation.StratifiedKFold的洗牌标志已在当前版本0.15中引入:

http://scikit-learn.org/0.15/modules/generated/sklearn.cross_validation.StratifiedKFold.html

这可以在Changelog中找到:

http://scikit-learn.org/stable/whats_new.html新特性

cross_validation.StratifiedKFold的Shuffle选项。由杰弗里Blackburne .

Answer 2

我想我应该把我的解决方案贴出来，以防它对其他人有用。

from collections import defaultdict
import random
def strat_map(y):
    """
    Returns permuted indices that maintain class
    """
    smap = defaultdict(list)
    for i,v in enumerate(y):
        smap[v].append(i)
    for values in smap.values():
        random.shuffle(values)
    y_map = np.zeros_like(y)
    for i,v in enumerate(y):
        y_map[i] = smap[v].pop()
    return y_map
##########
#Example Use
##########
skf = StratifiedKFold(y, nfolds)
sm = strat_map(y)
for test, train in skf:
    test,train = sm[test], sm[train]
    #then cv as usual

#######
#tests#
#######
import numpy.random as rnd
for _ in range(100):
    y = np.array( [0]*10 + [1]*20 + [3] * 10)
    rnd.shuffle(y)
    sm = strat_map(y)
    shuffled = y[sm]
    assert (sm != range(len(y))).any() , "did not shuffle"
    assert (shuffled == y).all(), "classes not in right position"
    assert (set(sm) == set(range(len(y)))), "missing indices"

for _ in range(100):
    nfolds = 10
    skf = StratifiedKFold(y, nfolds)
    sm = strat_map(y)
    for test, train in skf:
        assert (sm[test] != test).any(), "did not shuffle"
        assert (y[sm[test]] == y[test]).all(), "classes not in right position"

Answer 3

这是我将分层洗牌分为训练集和测试集的实现:

import numpy as np
def get_train_test_inds(y,train_proportion=0.7):
    '''Generates indices, making random stratified split into training set and testing sets
    with proportions train_proportion and (1-train_proportion) of initial sample.
    y is any iterable indicating classes of each observation in the sample.
    Initial proportions of classes inside training and 
    test sets are preserved (stratified sampling).
    '''
    y=np.array(y)
    train_inds = np.zeros(len(y),dtype=bool)
    test_inds = np.zeros(len(y),dtype=bool)
    values = np.unique(y)
    for value in values:
        value_inds = np.nonzero(y==value)[0]
        np.random.shuffle(value_inds)
        n = int(train_proportion*len(value_inds))
        train_inds[value_inds[:n]]=True
        test_inds[value_inds[n:]]=True
    return train_inds,test_inds

y = np.array([1,1,2,2,3,3])
train_inds,test_inds = get_train_test_inds(y,train_proportion=0.5)
print y[train_inds]
print y[test_inds]

这段代码输出:

[1 2 3]
[1 2 3]

Answer 4

据我所知，这实际上是在scikit-learn中实现的。

" " "分层ShuffleSplit交叉验证迭代器

提供训练/测试索引来分割训练测试集中的数据。

这个交叉验证对象是StratifiedKFold和ShuffleSplit，它返回分层随机折叠。折叠的是通过保留每个类别的样本百分比来制作的。

注:像ShuffleSplit策略一样，分层随机分裂不保证所有的折叠都是不同的，虽然这是对于大数据集来说仍然是很有可能的。" " "