PCA within cross validation; however, only with a subset of variables

Question

This question is very similar to preprocess within cross-validation in caret; however, in a project that i'm working on I would only like to do PCA on three predictors out of 19 in my case. Here is the example from preprocess within cross-validation in caret and I'll use this data (PimaIndiansDiabetes) for ease (this is not my project data but concept should be the same). I would then like to do the preProcess only on a subset of variables i.e. PimaIndiansDiabetes[, c(4,5,6)]. Is there a way to do this?

library(caret)
library(mlbench)
data(PimaIndiansDiabetes)

control <- trainControl(method="cv", 
                        number=5)
p <- preProcess(PimaIndiansDiabetes[, c(4,5,6)], #only do these columns!
                     method = c("center", "scale", "pca"))
p
grid=expand.grid(mtry=c(1,2,3))

model <- train(diabetes~., data=PimaIndiansDiabetes, method="rf", 
               preProcess= p, 
               trControl=control,
               tuneGrid=grid)

But I get this error:

Error: pre-processing methods are limited to: BoxCox, YeoJohnson, expoTrans, invHyperbolicSine, center, scale, range, knnImpute, bagImpute, medianImpute, pca, ica, spatialSign, ignore, keep, remove, zv, nzv, conditionalX, corr

The reason I'm trying to do this is so I can reduce three variables to one PCA1 and use for predicting. In the project I'm doing all three variables are correlated above 90% but would like to incorporate them as other studies have used them as well. Thanks. Trying to avoid data leakage!

Answer 1

As far as I know this is not possible with caret. This might be possible using recipes. However I do not use recipes but I do use mlr3 so I will show how to do it with this package:

library(mlr3)
library(mlr3pipelines)
library(mlr3learners)
library(paradox)
library(mlr3tuning)
library(mlbench)

create a task from the data:

data("PimaIndiansDiabetes")

pima_tsk <- TaskClassif$new(id = "Pima",
                            backend = PimaIndiansDiabetes,
                            target = "diabetes")

define a pre process selector named "slct1":

pos1 <- po("select", id = "slct1")

and define the selector function within it:

pos1$param_set$values$selector <- selector_name(colnames(PimaIndiansDiabetes[, 4:6]))

now define what should happen to the selected features: scaling -> pca with 1st PC selected (param_vals = list(rank. = 1))

pos1 %>>%
  po("scale", id = "scale1") %>>%
  po("pca", id = "pca1", param_vals = list(rank. = 1)) -> pr1

now define an invert selector:

pos2 <- po("select", id = "slct2")

pos2$param_set$values$selector <- selector_invert(pos1$param_set$values$selector)

define the learner:

rf_lrn <- po("learner", lrn("classif.ranger")) #ranger is a faster version of rf

combine them:

gunion(list(pr1, pos2)) %>>%
  po("featureunion") %>>%
  rf_lrn -> graph

check if it looks ok:

graph$plot(html = TRUE)

convert graph to a learner:

glrn <- GraphLearner$new(graph)

define parameters you want tuned:

ps <-  ParamSet$new(list(
  ParamInt$new("classif.ranger.mtry", lower = 1, upper = 6),
  ParamInt$new("classif.ranger.num.trees", lower = 100, upper = 1000)))

define resampling:

cv10 <- rsmp("cv", folds = 10)

define tuning:

instance <- TuningInstance$new(
  task = pima_tsk,
  learner = glrn,
  resampling = cv10,
  measures = msr("classif.ce"),
  param_set = ps,
  terminator = term("evals", n_evals = 20)
)

set.seed(1)
tuner <- TunerRandomSearch$new()
tuner$tune(instance)
instance$result

For additional details on how to tune the number of PC components to keep check this answer: R caret: How do I apply separate pca to different dataframes before training?

If you find this interesting check out the mlr3book

Also

cor(PimaIndiansDiabetes[, 4:6])
          triceps   insulin      mass
triceps 1.0000000 0.4367826 0.3925732
insulin 0.4367826 1.0000000 0.1978591
mass    0.3925732 0.1978591 1.0000000

does not produce what you mention in the question.