问题描述
我正在使用 glmnet 在插入符号中运行弹性网络正则化.
I am running elastic net regularization in caret using glmnet.
我将值序列传递给 trainControl 用于 alpha 和 lambda，然后我执行 repeatedcv 以获得 alpha 和 lambda 的最佳调整.
I pass sequence of values to trainControl for alpha and lambda, then I perform repeatedcv to get the optimal tunings of alpha and lambda.
这是一个示例，其中 alpha 和 lambda 的最佳调整分别为 0.7 和 0.5:
Here is an example where the optimal tunings for alpha and lambda are 0.7 and 0.5 respectively:
age <- c(4, 8, 7, 12, 6, 9, 10, 14, 7, 6, 8, 11, 11, 6, 2, 10, 14, 7, 12, 6, 9, 10, 14, 7)
gender <- make.names(as.factor(c(1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1)))
bmi_p <- c(0.86, 0.45, 0.99, 0.84, 0.85, 0.67, 0.91, 0.29, 0.88, 0.83, 0.48, 0.99, 0.80, 0.85,
0.50, 0.91, 0.29, 0.88, 0.99, 0.84, 0.80, 0.85, 0.88, 0.99)
m_edu <- make.names(as.factor(c(0, 1, 1, 2, 2, 3, 2, 0, 1, 1, 0, 1, 2, 2, 1, 2, 0, 1, 1, 2, 2, 0 , 1, 0)))
p_edu <- make.names(as.factor(c(0, 2, 2, 2, 2, 3, 2, 0, 0, 0, 1, 2, 2, 1, 3, 2, 3, 0, 0, 2, 0, 1, 0, 1)))
f_color <- make.names(as.factor(c("blue", "blue", "yellow", "red", "red", "yellow",
"yellow", "red", "yellow","blue", "blue", "yellow", "red", "red", "yellow",
"yellow", "red", "yellow", "yellow", "red", "blue", "yellow", "yellow", "red")))
asthma <- make.names(as.factor(c(1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1)))
x <- data.frame(age, gender, bmi_p, m_edu, p_edu, f_color, asthma)
tuneGrid <- expand.grid(alpha = seq(0, 1, 0.05), lambda = seq(0, 0.5, 0.05))
fitControl <- trainControl(method = 'repeatedcv', number = 3, repeats = 5, classProbs = TRUE, summaryFunction = twoClassSummary)
set.seed(1352)
model.test <- caret::train(asthma ~ age + gender + bmi_p + m_edu + p_edu + f_color, data = x, method = "glmnet",
family = "binomial", trControl = fitControl, tuneGrid = tuneGrid,
metric = "ROC")
model.test$bestTune
我的问题?
当我运行 as.matrix(coef(model.test$finalModel)) 时，我假设它会给我对应于最佳模型的系数，我得到 100 组不同的系数.
When I run as.matrix(coef(model.test$finalModel)) which I would assume give me the coefficients corresponding to the best model, I get 100 different sets of coefficients.
那么我如何获得与最佳调谐相对应的系数?
So how do I get the coefficients corresponding to the best tuning?
我已经看到了获得最佳模型的建议 coef(model.test$finalModel, model.test$bestTune$lambda) 但是，这会返回 NULL 系数，并且在任何情况下，都会只返回与 lambda 相关的最佳调整，而不是另外返回 alpha.
I've seen this recommendation to get the best model coef(model.test$finalModel, model.test$bestTune$lambda) However, this returns NULL coefficients, and In any case, would only be returning the best tunings related to lambda, and not to alpha in addition.
在互联网上到处搜索后，我现在能找到的所有指向正确答案的方向是 this 博客文章，其中说 model.test$finalModel 返回对应于最佳 alpha 调整的模型，并且 coef(model.test$finalModel, model.caret$bestTune$lambda) 返回对应于最佳 lambda 值的系数集.如果这是真的，那么这就是我问题的答案.然而，由于这是一篇单独的博客文章，我找不到其他任何东西来支持这一说法，我仍然持怀疑态度.任何人都可以验证 model.test$finalModel 返回与最佳 alpha 相对应的模型的说法吗?如果是这样，那么这个问题就迎刃而解了.谢谢！
After searching everywhere on the internet, all I can find now which points me in the direction of the correct answer is this blog post, which says that model.test$finalModel returns the model corresponding to the best alpha tuning, and coef(model.test$finalModel, model.caret$bestTune$lambda) returns the set of coefficients corresponding to the best values of lambda. If this is true then this is the answer to my question. However, as this is a single blog post, and I can't find anything else to back up this claim, I am still skeptical. Can anyone validate this claim that model.test$finalModel returns the model corresponding to the best alpha?? If so then this question would be solved. Thanks!
推荐答案
在对您的代码进行了一些尝试后，我发现 glmnet train 根据种子选择不同的 lambda 范围非常奇怪.下面是一个例子:
After a bit of playing with your code I find it very odd that glmnet train chooses different lambda ranges depending on the seed. Here is an example:
library(caret)
library(glmnet)
set.seed(13)
model.test <- caret::train(asthma ~ age + gender + bmi_p + m_edu + p_edu + f_color, data = x, method = "glmnet",
family = "binomial", trControl = fitControl, tuneGrid = tuneGrid,
metric = "ROC")
c(head(model.test$finalModel$lambda, 5), tail(model.test$finalModel$lambda, 5))
#output
[1] 3.7796447301 3.4438715094 3.1379274562 2.8591626295 2.6051625017 0.0005483617 0.0004996468 0.0004552595 0.0004148155
[10] 0.0003779645
最佳 lambda 是:
optimum lambda is:
model.test$finalModel$lambdaOpt
#output
#[1] 0.05
这有效:
coef(model.test$finalModel, model.test$finalModel$lambdaOpt)
#12 x 1 sparse Matrix of class "dgCMatrix"
1
(Intercept) -0.03158974
age 0.03329806
genderX1 -1.24093677
bmi_p 1.65156913
m_eduX1 0.45314106
m_eduX2 -0.09934991
m_eduX3 -0.72360297
p_eduX1 -0.51949828
p_eduX2 -0.80063642
p_eduX3 -2.18231433
f_colorred 0.87618211
f_coloryellow -1.52699254
给出最好的 alpha 和 lambda 系数
giving the coefficients at best alpha and lambda
当使用这个模型来预测一些y被预测为X1而一些被预测为X2
when using this model to predict some y are predicted as X1 and some as X2
[1] X1 X1 X0 X1 X1 X0 X0 X1 X1 X1 X0 X1 X1 X1 X0 X0 X0 X1 X1 X1 X1 X0 X1 X1
Levels: X0 X1
现在使用您使用的种子
set.seed(1352)
model.test <- caret::train(asthma ~ age + gender + bmi_p + m_edu + p_edu + f_color, data = x, method = "glmnet",
family = "binomial", trControl = fitControl, tuneGrid = tuneGrid,
metric = "ROC")
c(head(model.test$finalModel$lambda, 5), tail(model.test$finalModel$lambda, 5))
#output
[1] 2.699746e-01 2.459908e-01 2.241377e-01 2.042259e-01 1.860830e-01 3.916870e-05 3.568906e-05 3.251854e-05 2.962968e-05
[10] 2.699746e-05
lambda 值小 10 倍，这给出了空系数，因为 lambdaOpt 不在测试的 lambda 范围内:
lambda values are 10 times smaller and this gives empty coefficients since lambdaOpt is not in the range of tested lambda:
coef(model.test$finalModel, model.test$finalModel$lambdaOpt)
#output
12 x 1 sparse Matrix of class "dgCMatrix"
1
(Intercept) .
age .
genderX1 .
bmi_p .
m_eduX1 .
m_eduX2 .
m_eduX3 .
p_eduX1 .
p_eduX2 .
p_eduX3 .
f_colorred .
f_coloryellow .
model.test$finalModel$lambdaOpt
#output
0.5
现在在此模型上进行预测时仅预测 X0(第一级):
now when predicting upon this model only X0 is predicted (the first level):
predict(model.test, x)
#output
[1] X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0 X0
Levels: X0 X1
很奇怪的行为，可能值得报告
quite odd behavior, probably worth reporting
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