我在软件包glmnet(版本2.0.10和2.0.13,至少)中获得随机崩溃,尝试使用ridge逻辑回归运行cv.glmnet . 下面提供了可再现的例子 . 正如您将看到的,行为取决于所选的随机种子 .
该错误发生在 cv.lognet()
中,因为有时 nlami==0
. 这是因为全局(非交叉验证)λ序列的范围(即下例中的[14.3; 20.7])完全小于其中一个折叠上的λ范围(即折叠4, [32.5; 22.4])
可能的解决方法是通过更改 which_lam
的定义强制 nlami>=1
,如下所示:
which_lam = lambda >= min(mlami, max(lambda))
这样可以避免崩溃,但不能确定结果是否正确 . 有人可以确认或提出另一个修复方案吗?
NB:似乎与未解决的问题有关cv.glmnet fails for ridge, not lasso, for simulated data with coder error
可重复的例子
library(glmnet)
x=structure(c(0.294819653005975, -0.755878041644385, -0.460947383309942,
-1.25359210780316, -0.643969512320233, -0.146301489038128, -0.190235360501265,
-0.778418128295596, -0.659228201713315, -0.589987067456389, 1.33064976036166,
-0.232480434360983, -0.374383490492533, -0.504817187501063, -0.558531620483801,
2.16732105550181, 0.238948891919474, -0.857229316573454, -0.673919980092841,
1.17924306872964, 0.831719897152008, -1.15770770325374, 2.54984789196214,
-0.970167597835476, -0.557900637238063, -0.432268012373971, 1.15479761345536,
1.72197312745038, -0.460658453148444, -1.17746101934592, 0.411060691690596,
0.172735774511478, 0.328416881299735, 2.13514661730084, -0.498720272451663,
0.290967756655844, -0.87284566376257, -0.652533179632676, -0.89323787137697,
-0.566883371886824, -1.1794485033936, 0.821276174960557, -0.396480750015741,
-0.121609740429242, -0.464060359619162, 0.0396628676584573, -0.942871230138644,
0.160331360905244, -0.369955203694528, -0.192318421900764, -1.39309898491775,
-0.264395753844046, 2.25142560078458, -0.897873918532094, -0.159680604037913,
-0.918027468751383, 0.43181753901048, 1.56060286954228, -0.617456504201816,
1.73106033616784, -0.97099289786049, -1.09325650121771, -0.0407358272757967,
0.553103582991963, 1.15479545417553, 0.36144086171342, -1.35507249278068,
1.37684903500442, 0.755599287825675, 0.820363089698391, 1.65541232241803,
-0.692008406375665, 1.65484854848556, -1.14659093945895), .Dim = c(37L, 2L))
# NB: x is already standardized
print(apply(x,2,mean))
print(apply(x,2,sd))
y=c(TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE,
FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE,
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE,
TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE)
# NB: y is moderately unbalanced
print(table(y))
# This works OK (with a warning):
set.seed(3)
m = cv.glmnet(x, y, family = "binomial", alpha = 0, standardize = FALSE, type.measure = "class", nfolds = 5)
# This crashes:
set.seed(1)
m = cv.glmnet(x, y, family = "binomial", alpha = 0, standardize = FALSE, type.measure = "class", nfolds = 5)
# Error in predmat[which, seq(nlami)] <- preds :
# replacement has length zero
编辑:数据的可视化显示没有特定的模式 . 预计线性分离器的性能较低:
1 回答
我认为问题在于,在交叉验证期间,有一个数据样本只有一个响应变量(y全部为TRUE,或全部为FALSE),因为您的观察结果很少 . 使用一些随机种子你很幸运,但这不会发生,但种子等于1 . 我对这么少观察的建议是跳过交叉验证并适合模型,然后观察变化的lambda如何改变系数:
请注意,这适用于任何种子(我测试过)而没有错误 .
关于你对预测进行评估的愿望,我就是这样做的,请注意,对于glmnet软件包,请忽略一个交叉验证,因此必须在此处手动完成 .