R: Cross validation on a dataset with factors

Question

Often, I want to run a cross validation on a dataset which contains some factor variables and after running for a while, the cross validation routine fails with the error: factor x has new levels Y.

For example, using package boot:

library(boot)
d <- data.frame(x=c('A', 'A', 'B', 'B', 'C', 'C'), y=c(1, 2, 3, 4, 5, 6))
m <- glm(y ~ x, data=d)
m.cv <- cv.glm(d, m, K=2) # Sometimes succeeds
m.cv <- cv.glm(d, m, K=2)
# Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : 
#   factor x has new levels B

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Update: This is a toy example. The same problem occurs with larger datasets as well, where there are several occurrences of level C but none of them is present in the training partition.

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The function createDataPartition function from the package caret does stratified sampling for the outcome variables and correctly warns:

Also, for ‘createDataPartition’, very small class sizes (<= 3) the classes may not show up in both the training and test data.

There are two solutions which spring to mind:

1. First, create a subset of the data by selecting one random sample of each factor level first, starting from the rarest class (by frequency) and then greedily satisfying the next rare class and so on. Then using createDataPartition on the rest of the dataset and merging the results to create a new train dataset which contains all levels.
2. Using createDataPartitions and and doing rejection sampling.

So far, option 2 has worked for me because of the data sizes, but I cannot help but think that there must be a better solution than a hand rolled out one.

Ideally, I would want a solution which just works for creating partitions and fails early if there is no way to create such partitions.

Is there a fundamental theoretical reason why packages do not offer this? Do they offer it and I just haven't been able to spot them because of a blind spot? Is there a better way of doing this stratified sampling?

Please leave a comment if I should ask this question on stats.stackoverflow.com.

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Update:

This is what my hand rolled out solution (2) looks like:

get.cv.idx <- function(train.data, folds, factor.cols = NA) {

    if (is.na(factor.cols)) {
        all.cols        <- colnames(train.data)
        factor.cols     <- all.cols[laply(llply(train.data[1, ], class), function (x) 'factor' %in% x)]
    }

    n                   <- nrow(train.data)
    test.n              <- floor(1 / folds * n)

    cond.met            <- FALSE
    n.tries             <- 0

    while (!cond.met) {
        n.tries         <- n.tries + 1
        test.idx        <- sample(nrow(train.data), test.n)
        train.idx       <- setdiff(1:nrow(train.data), test.idx)

        cond.met        <- TRUE

        for(factor.col in factor.cols) {
            train.levels <- train.data[ train.idx, factor.col ]
            test.levels  <- train.data[ test.idx , factor.col ]
            if (length(unique(train.levels)) < length(unique(test.levels))) {
                cat('Factor level: ', factor.col, ' violated constraint, retrying.\n')
                cond.met <- FALSE
            }
        }
    }

    cat('Done in ', n.tries, ' trie(s).\n')

    list( train.idx = train.idx
        , test.idx  = test.idx
        )
}

Answer 1

Everyone agrees that there sure is an optimal solution. But personally, I would just try the cv.glm call until it works usingwhile.

m.cv<- try(cv.glm(d, m, K=2)) #First try
class(m.cv) #Sometimes error, sometimes list
while ( inherits(m.cv, "try-error") ) {
m.cv<- try(cv.glm(d, m, K=2))
}
class(m.cv) #always list

I've tried it with 100,000 rows in the data.fame and it only takes a few seconds.

library(boot)
n <-100000
d <- data.frame(x=c(rep('A',n), rep('B', n), 'C', 'C'), y=1:(n*2+2))
m <- glm(y ~ x, data=d)

m.cv<- try(cv.glm(d, m, K=2))
class(m.cv) #Sometimes error, sometimes list
while ( inherits(m.cv, "try-error") ) {
m.cv<- try(cv.glm(d, m, K=2))
}
class(m.cv) #always list